diff --git a/.gitattributes b/.gitattributes index 1204b95bf26b5de9c6d1b642a778b49ea6dbbfdd..9f20ad59ff5cadf1f0bc43aa7fa14279349d19e2 100644 --- a/.gitattributes +++ b/.gitattributes @@ -66,3 +66,5 @@ Enjoy[[:space:]]Python/18.[[:space:]]Python[[:space:]]libraries/1.[[:space:]]Int Enjoy[[:space:]]Python/7.[[:space:]]Databases[[:space:]]in[[:space:]]Python[[:space:]]_[[:space:]]Milestone[[:space:]]Project[[:space:]]2/9.[[:space:]]Using[[:space:]]SQLite[[:space:]]in[[:space:]]Python.mp4 filter=lfs diff=lfs merge=lfs -text Enjoy[[:space:]]Python/8.[[:space:]]Type[[:space:]]hinting[[:space:]]in[[:space:]]Python/1.[[:space:]]Introduction[[:space:]]to[[:space:]]this[[:space:]]section.mp4 filter=lfs diff=lfs merge=lfs -text Enjoy[[:space:]]Python/8.[[:space:]]Type[[:space:]]hinting[[:space:]]in[[:space:]]Python/4.[[:space:]]Conclusion[[:space:]]of[[:space:]]this[[:space:]]section.mp4 filter=lfs diff=lfs merge=lfs -text +200[[:space:]]solved[[:space:]]problems[[:space:]]in[[:space:]]Python/pandas/06_Stats/US_Baby_Names/US_Baby_Names_right.csv filter=lfs diff=lfs merge=lfs -text +200[[:space:]]solved[[:space:]]problems[[:space:]]in[[:space:]]Python/pandas/07_Visualization/Online_Retail/Online_Retail.csv filter=lfs diff=lfs merge=lfs -text diff --git a/200 solved problems in Python/array/.ipynb_checkpoints/array_longest_non_repeat-checkpoint.ipynb b/200 solved problems in Python/array/.ipynb_checkpoints/array_longest_non_repeat-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..91b9adec639fb01345dd02479acfc9b2f90c76b7 --- /dev/null +++ b/200 solved problems in Python/array/.ipynb_checkpoints/array_longest_non_repeat-checkpoint.ipynb @@ -0,0 +1,49 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Challenge\n", + "# \n", + "# Given a string, find the length of the longest substring\n", + "# without repeating characters.\n", + "\n", + "# Examples:\n", + "\n", + "# Given \"abcabcbb\", the answer is \"abc\", which the length is 3.\n", + "# Given \"bbbbb\", the answer is \"b\", with the length of 1.\n", + "# Given \"pwwkew\", the answer is \"wke\", with the length of 3.\n", + "# ---------------------------------------------------------------" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/array/.ipynb_checkpoints/array_longest_non_repeat_solution-checkpoint.ipynb b/200 solved problems in Python/array/.ipynb_checkpoints/array_longest_non_repeat_solution-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..e14581216f4b1425cd833b4fcc5ca03cdf26b2ec --- /dev/null +++ b/200 solved problems in Python/array/.ipynb_checkpoints/array_longest_non_repeat_solution-checkpoint.ipynb @@ -0,0 +1,128 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['a', 'b', 'c']\n", + "['b', 'c', 'a']\n", + "['c', 'a', 'b']\n", + "['a', 'b', 'c']\n", + "['b', 'c']\n", + "['c', 'b']\n", + "['b']\n", + "['b']\n" + ] + }, + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Challenge\n", + "# Given a string, find the length of the longest substring\n", + "# without repeating characters.\n", + "\n", + "# Examples:\n", + "# Given \"abcabcbb\", the answer is \"abc\", which the length is 3.\n", + "# Given \"bbbbb\", the answer is \"b\", with the length of 1.\n", + "# Given \"pwwkew\", the answer is \"wke\", with the length of 3.\n", + "# ---------------------------------------------------------------\n", + "# Algorithm\n", + "\n", + "# In summary : Form all posible sub_strings from original string, then return length of longest sub_string\n", + "\n", + "# - start from 1st character, form as long as posible sub string\n", + "\n", + "# - Add first character to sub string\n", + "# - Add second character to sub string if second character not exist in sub string\n", + "# ...\n", + "# - Repeate until got a character which already exist inside sub string or \n", + " \n", + " \n", + "# - start from 2nd character, form as long as posible sub string\n", + "\n", + "# - Add first character to sub string\n", + "# - Add second character to sub string if second character not exist in sub string\n", + "# ...\n", + "# - Repeate until got a character which already exist inside sub string\n", + "\n", + "\n", + "# ....\n", + "\n", + "\n", + "# - start from final character, form as long as posible sub string\n", + "# - Add first character to sub string\n", + "# - Add second character to sub string if second character not exist in sub string\n", + "# ...\n", + "# - Repeate until got a character which already exist inside sub string\n", + "# ---------------------------------------------------------------\n", + "\n", + "str = \"abcbb\"\n", + "\n", + "def longest_non_repeat(str):\n", + " \n", + " # init start position and max length \n", + " i=0\n", + " max_length = 1\n", + "\n", + " for i,c in enumerate(str):\n", + "\n", + " # init counter and sub string value \n", + " start_at = i\n", + " sub_str=[]\n", + "\n", + " # continue increase sub string if did not repeat character \n", + " while (start_at < len(str)) and (str[start_at] not in sub_str):\n", + " sub_str.append(str[start_at])\n", + " start_at = start_at + 1\n", + "\n", + " # update the max length \n", + " if len(sub_str) > max_length:\n", + " max_length = len(sub_str)\n", + "\n", + " print(sub_str)\n", + " \n", + " return max_length\n", + "\n", + "longest_non_repeat(str)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/array/.ipynb_checkpoints/array_merge_intervals-checkpoint.ipynb b/200 solved problems in Python/array/.ipynb_checkpoints/array_merge_intervals-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..cad93bbdba1af041fdc95e3858bc5a2178044f43 --- /dev/null +++ b/200 solved problems in Python/array/.ipynb_checkpoints/array_merge_intervals-checkpoint.ipynb @@ -0,0 +1,42 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Given a collection of intervals which are already sorted by start number, merge all overlapping intervals.\n", + "# For example,\n", + "# Given [[1,3],[2,6],[5,10],[11,16],[15,18],[19,22]],\n", + "# return [[1, 10], [11, 18], [19, 22]]\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/array/.ipynb_checkpoints/array_merge_intervals_solution-checkpoint.ipynb b/200 solved problems in Python/array/.ipynb_checkpoints/array_merge_intervals_solution-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..6db82699bcb43231a8d0cd0d70e4ee14594c9250 --- /dev/null +++ b/200 solved problems in Python/array/.ipynb_checkpoints/array_merge_intervals_solution-checkpoint.ipynb @@ -0,0 +1,64 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1, 10], [11, 18], [19, 22]]\n" + ] + } + ], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "\n", + "\"\"\"\n", + "Given a collection of intervals which are already sorted by start number, merge all overlapping intervals.\n", + "For example,\n", + "Given [[1,3],[2,6],[5,10],[11,16],[15,18],[19,22]],\n", + "return [[1, 10], [11, 18], [19, 22]]\n", + "\"\"\"\n", + "\n", + "org_intervals = [[1,3],[2,6],[5,10],[11,16],[15,18],[19,22]]\n", + "\n", + "i = 0\n", + "\n", + "while i < len(org_intervals)-1:\n", + "# print(org_intervals[i])\n", + " if org_intervals[i+1][0] < org_intervals[i][1]:\n", + " org_intervals[i][1]=org_intervals[i+1][1]\n", + " del org_intervals[i+1]\n", + " i = i - 1\n", + " i = i + 1\n", + "\n", + "print(org_intervals)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/array/array_longest_non_repeat.ipynb b/200 solved problems in Python/array/array_longest_non_repeat.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..90726bb61ab207774fdc0c5ae28e31c5252caea2 --- /dev/null +++ b/200 solved problems in Python/array/array_longest_non_repeat.ipynb @@ -0,0 +1,370 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Challenge\n", + "# \n", + "# Given a string, find the length of the longest substring\n", + "# without repeating characters.\n", + "\n", + "# Examples:\n", + "\n", + "# Given \"abcabcbb\", the answer is \"abc\", which the length is 3.\n", + "# Given \"bbbbb\", the answer is \"b\", with the length of 1.\n", + "# Given \"pwwkew\", the answer is \"wke\", with the length of 3.\n", + "# ---------------------------------------------------------------\n", + "\n", + "def mot(b):\n", + " a=[]\n", + " for i in range(len(b)-1):\n", + " while b[i] not in a:\n", + " a+=b[i]\n", + " i=i+1\n", + " else:\n", + " break\n", + " return a \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['k', 'j', 'h']" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mot('kjhj')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['h', 'g', 'd', 'j', 'u']" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mot('hgdjuhg')" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['b']" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mot('bbbbbb')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['a', 'b', 'c']" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mot('abcabcbb')" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['p', 'w']" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mot('pwwkew')" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "def mot(b):\n", + " a=np.zeros(len(b))\n", + " for j in range(0,len(b)-2):\n", + " for i in range(len(b)-1):\n", + " while b[i] not in a:\n", + " a[i,:]=b[i]\n", + " i=i+1\n", + " else:\n", + " break\n", + " j+=1 \n", + " return a " + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "F:\\Anaconda35\\lib\\site-packages\\ipykernel_launcher.py:6: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " \n" + ] + }, + { + "ename": "IndexError", + "evalue": "too many indices for array", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'fkqhcgs'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32m\u001b[0m in \u001b[0;36mmot\u001b[1;34m(b)\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;32mwhile\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0ma\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 8\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mIndexError\u001b[0m: too many indices for array" + ] + } + ], + "source": [ + "mot('fkqhcgs')" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "a=np.mat(8)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[8]])" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "def matrice(i,j): return [[0 for q in range(0,j)] for p in range(0,i)]" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "r=matrice(7,7)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 0, 0, 0, 0, 0, 0, '1', '1', '1']" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "r[1]+='1'\n", + "r[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "def mot(b):\n", + " v=[]\n", + " a=matrice(len(b)-1,len(b)-1)\n", + " for j in range(0,len(b)-1):\n", + " for i in range(len(b)-2):\n", + " while b[i] not in a:\n", + " v+=b[i]\n", + " i=i+1\n", + " else:\n", + " break\n", + " j+=1 \n", + " return v[i,j] " + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "ename": "IndexError", + "evalue": "string index out of range", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'khjkh'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32m\u001b[0m in \u001b[0;36mmot\u001b[1;34m(b)\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[1;32mwhile\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 8\u001b[0m \u001b[0mv\u001b[0m\u001b[1;33m+=\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mIndexError\u001b[0m: string index out of range" + ] + } + ], + "source": [ + "mot('khjkh')" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Twinkle, twinkle, little star, \n", + "\tHow I wonder what you are! \n", + "\t\tUp above the world so high, \n", + "\t\tLike a diamond in the sky. \n", + "Twinkle, twinkle, little star, \n", + "\tHow I wonder what you are!\n" + ] + } + ], + "source": [ + "print(\"Twinkle, twinkle, little star, \\n\\tHow I wonder what you are! \\n\\t\\tUp above the world so high, \\n\\t\\tLike a diamond in the sky. \\nTwinkle, twinkle, little star, \\n\\tHow I wonder what you are!\")" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "What is your name: Moad\n", + "How old are you: 23\n", + "Moad will be 100 years old in the year 2095\n" + ] + } + ], + "source": [ + "name = input(\"What is your name: \")\n", + "age = int(input(\"How old are you: \"))\n", + "year = str((2018 - age)+100)\n", + "print(name + \" will be 100 years old in the year \" + year)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/200 solved problems in Python/array/array_longest_non_repeat_solution.ipynb b/200 solved problems in Python/array/array_longest_non_repeat_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..85b68813db7e05ef41313c6ec8d3c75b3c2535ae --- /dev/null +++ b/200 solved problems in Python/array/array_longest_non_repeat_solution.ipynb @@ -0,0 +1,128 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['a', 'b', 'c']\n", + "['b', 'c', 'a']\n", + "['c', 'a', 'b']\n", + "['a', 'b', 'c']\n", + "['b', 'c']\n", + "['c', 'b']\n", + "['b']\n", + "['b']\n" + ] + }, + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Challenge\n", + "# Given a string, find the length of the longest substring\n", + "# without repeating characters.\n", + "\n", + "# Examples:\n", + "# Given \"abcabcbb\", the answer is \"abc\", which the length is 3.\n", + "# Given \"bbbbb\", the answer is \"b\", with the length of 1.\n", + "# Given \"pwwkew\", the answer is \"wke\", with the length of 3.\n", + "# ---------------------------------------------------------------\n", + "# Algorithm\n", + "\n", + "# In summary : Form all posible sub_strings from original string, then return length of longest sub_string\n", + "\n", + "# - start from 1st character, form as long as posible sub string\n", + "\n", + "# - Add first character to sub string\n", + "# - Add second character to sub string if second character not exist in sub string\n", + "# ...\n", + "# - Repeate until got a character which already exist inside sub string or \n", + " \n", + " \n", + "# - start from 2nd character, form as long as posible sub string\n", + "\n", + "# - Add first character to sub string\n", + "# - Add second character to sub string if second character not exist in sub string\n", + "# ...\n", + "# - Repeate until got a character which already exist inside sub string\n", + "\n", + "\n", + "# ....\n", + "\n", + "\n", + "# - start from final character, form as long as posible sub string\n", + "# - Add first character to sub string\n", + "# - Add second character to sub string if second character not exist in sub string\n", + "# ...\n", + "# - Repeate until got a character which already exist inside sub string\n", + "# ---------------------------------------------------------------\n", + "\n", + "str = \"abcbb\"\n", + "\n", + "def longest_non_repeat(str):\n", + " \n", + " # init start position and max length \n", + " i=0\n", + " max_length = 1\n", + "\n", + " for i,c in enumerate(str):\n", + "\n", + " # init counter and sub string value \n", + " start_at = i\n", + " sub_str=[]\n", + "\n", + " # continue increase sub string if did not repeat character \n", + " while (start_at < len(str)) and (str[start_at] not in sub_str):\n", + " sub_str.append(str[start_at])\n", + " start_at = start_at + 1\n", + "\n", + " # update the max length \n", + " if len(sub_str) > max_length:\n", + " max_length = len(sub_str)\n", + "\n", + " print(sub_str)\n", + " \n", + " return max_length\n", + "\n", + "longest_non_repeat(str)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/200 solved problems in Python/array/array_merge_intervals.ipynb b/200 solved problems in Python/array/array_merge_intervals.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..cad93bbdba1af041fdc95e3858bc5a2178044f43 --- /dev/null +++ b/200 solved problems in Python/array/array_merge_intervals.ipynb @@ -0,0 +1,42 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Given a collection of intervals which are already sorted by start number, merge all overlapping intervals.\n", + "# For example,\n", + "# Given [[1,3],[2,6],[5,10],[11,16],[15,18],[19,22]],\n", + "# return [[1, 10], [11, 18], [19, 22]]\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/array/array_merge_intervals_solution.ipynb b/200 solved problems in Python/array/array_merge_intervals_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..f35da95436ea456ad5598364bc1032c4cc8e7cb2 --- /dev/null +++ b/200 solved problems in Python/array/array_merge_intervals_solution.ipynb @@ -0,0 +1,64 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1, 10], [11, 18], [19, 22]]\n" + ] + } + ], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "\"\"\"\n", + "Given a collection of intervals which are already sorted by start number, merge all overlapping intervals.\n", + "For example,\n", + "Given [[1,3],[2,6],[5,10],[11,16],[15,18],[19,22]],\n", + "return [[1, 10], [11, 18], [19, 22]]\n", + "\"\"\"\n", + "\n", + "org_intervals = [[1,3],[2,6],[5,10],[11,16],[15,18],[19,22]]\n", + "\n", + "i = 0\n", + "\n", + "while i < len(org_intervals)-1:\n", + "# print(org_intervals[i])\n", + " if org_intervals[i+1][0] < org_intervals[i][1]:\n", + " org_intervals[i][1]=org_intervals[i+1][1]\n", + " del org_intervals[i+1]\n", + " i = i - 1\n", + " i = i + 1\n", + "\n", + "print(org_intervals)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/200 solved problems in Python/array/array_missing_element_challenge.ipynb b/200 solved problems in Python/array/array_missing_element_challenge.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..7c8679d3031581a13a464a6c3ab48fd0cac726e6 --- /dev/null +++ b/200 solved problems in Python/array/array_missing_element_challenge.ipynb @@ -0,0 +1,43 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Consider an array of non-negative integers.\n", + "# A second array is formed by shuffling the elements of the first array and deleting a random element. \n", + "# Given these two arrays, find which element is missing in the second array.\n", + "# Here is an example input, the first array is shuffled and the number 5 is removed to construct the second array.\n", + "# Input:\n", + "# finder([1,2,3,4,5,6,7],[3,7,2,1,4,6])\n", + "# Output:\n", + "# 5 is the missing number" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/array/array_missing_element_solution.ipynb b/200 solved problems in Python/array/array_missing_element_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..f924bfa0fe5cb351578291f8b1a68485a50027b0 --- /dev/null +++ b/200 solved problems in Python/array/array_missing_element_solution.ipynb @@ -0,0 +1,67 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5\n" + ] + } + ], + "source": [ + "# ----------------------------------------------------------------\n", + "# Consider an array of non-negative integers.\n", + "# A second array is formed by shuffling the elements of the first array and deleting a random element. \n", + "# Given these two arrays, find which element is missing in the second array.\n", + "# Here is an example input, the first array is shuffled and the number 5 is removed to construct the second array.\n", + "# Input:\n", + "# finder([1,2,3,4,5,6,7],[3,7,2,1,4,6])\n", + "# Output:\n", + "# 5 is the missing number\n", + "# ----------------------------------------------------------------\n", + "# Algorithm\n", + "# \n", + "# ----------------------------------------------------------------\n", + "\n", + "\n", + "first_array = [1,2,3,4,5,6,7]\n", + "second_array = [3,7,2,1,4,6]\n", + "\n", + "def finder(first_array, second_array):\n", + " return(sum(first_array) - sum(second_array))\n", + "\n", + "missing_number = finder(first_array, second_array)\n", + "\n", + "print(missing_number) " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/array/array_rotate.ipynb b/200 solved problems in Python/array/array_rotate.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..507217e7279a886b859009aed9e3b3d0bc1a7733 --- /dev/null +++ b/200 solved problems in Python/array/array_rotate.ipynb @@ -0,0 +1,39 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Rotate an array of n elements to the right by k steps.\n", + "# For example, with n = 7 and k = 3,\n", + "# the array [1,2,3,4,5,6,7] is rotated to [5,6,7,1,2,3,4].\n", + "# Note:\n", + "# Try to come up as many solutions as you can,\n", + "# there are at least 3 different ways to solve this problem." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/array/array_rotate_solution.ipynb b/200 solved problems in Python/array/array_rotate_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..aa9304792e373afb166f6c805bbfdfd21bc98d25 --- /dev/null +++ b/200 solved problems in Python/array/array_rotate_solution.ipynb @@ -0,0 +1,61 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[5, 6, 7, 1, 2, 3, 4]\n" + ] + } + ], + "source": [ + "\"\"\"\n", + "Rotate an array of n elements to the right by k steps.\n", + "For example, with n = 7 and k = 3,\n", + "the array [1,2,3,4,5,6,7] is rotated to [5,6,7,1,2,3,4].\n", + "Note:\n", + "Try to come up as many solutions as you can,\n", + "there are at least 3 different ways to solve this problem.\n", + "\"\"\"\n", + "\n", + "org = [1,2,3,4,5,6,7]\n", + "result = org[:]\n", + "steps = 3\n", + "\n", + "for idx,num in enumerate(org):\n", + " if idx+steps < len(org):\n", + " result[idx+steps] = org[idx]\n", + " else:\n", + " result[idx+steps-len(org)] = org[idx]\n", + "\n", + "print(result)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/array/array_summary_ranges.ipynb b/200 solved problems in Python/array/array_summary_ranges.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..f0cd9c6a64e19090cb4346fbc9b6af1020b8433e --- /dev/null +++ b/200 solved problems in Python/array/array_summary_ranges.ipynb @@ -0,0 +1,36 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Given a sorted integer array without duplicates,\n", + "# return the summary of its ranges.\n", + "# For example, given [0,1,2,4,5,7], return [\"0->2\",\"4->5\",\"7\"]." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/array/array_summary_ranges_solution.ipynb b/200 solved problems in Python/array/array_summary_ranges_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..6f53b81d3cb7c8bd2f2d744a72656ffe11e986b9 --- /dev/null +++ b/200 solved problems in Python/array/array_summary_ranges_solution.ipynb @@ -0,0 +1,87 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['0-->2']\n", + "['0-->2', '4-->5']\n", + "['0-->2', '4-->5', '7']\n", + "['0-->2', '4-->5', '7']\n" + ] + } + ], + "source": [ + "\"\"\"\n", + "Given a sorted integer array without duplicates,\n", + "return the summary of its ranges.\n", + "For example, given [0,1,2,4,5,7], return [\"0->2\",\"4->5\",\"7\"].\n", + "\"\"\"\n", + "\n", + "input_array = [0,1,2,4,5,7]\n", + "# input_array = [1,2,3,6,9,11,12,13,14,19,20]\n", + "\n", + "start=0\n", + "result = []\n", + "\n", + "# i=0\n", + "# while i <= len(input_array)-1:\n", + "# print(i, input_array[i])\n", + "# i = i+1\n", + "\n", + "\n", + "while start < len(input_array):\n", + " \n", + " # initial end at start position \n", + " end = start\n", + "\n", + " # continue to move the end pointer if the gap is 1 with beside number\n", + " # only continue to move if index end+1 is inside array \n", + " while end+1{1}\".format(input_array[start], input_array[end]))\n", + " print(result)\n", + " else:\n", + " result.append(\"{0}\".format(input_array[start]))\n", + " print(result)\n", + " \n", + " # change to next range\n", + " start = end+1\n", + "\n", + "print(result)\n", + "\n", + "\n", + " " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/array/array_two_sum.ipynb b/200 solved problems in Python/array/array_two_sum.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..c74c8735a2d427aa7c3fc9edf8f0975551bf84fb --- /dev/null +++ b/200 solved problems in Python/array/array_two_sum.ipynb @@ -0,0 +1,41 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Given an array of integers, return indices of the two numbers\n", + "# such that they add up to a specific target.\n", + "# You may assume that each input would have exactly one solution\n", + "\n", + "# Example:\n", + "# Given nums = [2, 7, 11, 15], target = 26,\n", + "# Because nums[2] + nums[3] = 11 + 15 = 26,\n", + "# return [2, 3]." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/array/array_two_sum_solution.ipynb b/200 solved problems in Python/array/array_two_sum_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..3191e7a92ad4d2de051fa92a1a30e5623a3ce9af --- /dev/null +++ b/200 solved problems in Python/array/array_two_sum_solution.ipynb @@ -0,0 +1,79 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 1\n", + "0 2\n", + "0 3\n", + "1 2\n", + "1 3\n", + "2 3\n" + ] + } + ], + "source": [ + "\"\"\"\n", + "Given an array of integers, return indices of the two numbers\n", + "such that they add up to a specific target.\n", + "You may assume that each input would have exactly one solution,\n", + "and you may not use the same element twice.\n", + "Example:\n", + " Given nums = [2, 7, 11, 15], target = 26,\n", + " Because nums[0] + nums[1] = 11 + 15 = 26,\n", + " return [2, 3].\n", + "\"\"\"\n", + "\n", + "# Solution 1\n", + "# Try all \n", + "\n", + "input_array = [2, 7, 11, 15]\n", + "target = 26\n", + "result = []\n", + "\n", + "for i, num in enumerate(input_array):\n", + " for j in range(i+1, len(input_array)):\n", + " print(i,j)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Solution 2\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/basic/.ipynb_checkpoints/calculate_with_input-checkpoint.ipynb b/200 solved problems in Python/basic/.ipynb_checkpoints/calculate_with_input-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..18b4b3e9c470cefc995f2459c7a5ca0899201aa1 --- /dev/null +++ b/200 solved problems in Python/basic/.ipynb_checkpoints/calculate_with_input-checkpoint.ipynb @@ -0,0 +1,41 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program that accepts an integer (n) and computes the value of n+nn+nnn. Go to the editor\n", + "# Sample value of n is 5 \n", + "# Expected Result : 615" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/basic/.ipynb_checkpoints/calculate_with_input_solution-checkpoint.ipynb b/200 solved problems in Python/basic/.ipynb_checkpoints/calculate_with_input_solution-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..83f0c8122745f6e703bc66e40fbefd7a6d2f1cb3 --- /dev/null +++ b/200 solved problems in Python/basic/.ipynb_checkpoints/calculate_with_input_solution-checkpoint.ipynb @@ -0,0 +1,54 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input an integer : 5\n", + "615\n" + ] + } + ], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program that accepts an integer (n) and computes the value of n+nn+nnn. Go to the editor\n", + "# Sample value of n is 5 \n", + "# Expected Result : 615\n", + "\n", + "a = int(input(\"Input an integer : \"))\n", + "n1 = int( \"%s\" % a )\n", + "n2 = int( \"%s%s\" % (a,a) )\n", + "n3 = int( \"%s%s%s\" % (a,a,a) )\n", + "print (n1+n2+n3)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/basic/.ipynb_checkpoints/input_age-checkpoint.ipynb b/200 solved problems in Python/basic/.ipynb_checkpoints/input_age-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..ec75aaed0c99d8f3e2fe2e1f29d47474e0c90886 --- /dev/null +++ b/200 solved problems in Python/basic/.ipynb_checkpoints/input_age-checkpoint.ipynb @@ -0,0 +1,40 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Create a program that asks the user to enter their name and their age.\n", + "# Print out a message addressed to them that tells them the year that they will turn 100 years old." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/basic/.ipynb_checkpoints/input_age_solution-checkpoint.ipynb b/200 solved problems in Python/basic/.ipynb_checkpoints/input_age_solution-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..f0ea3195570f77ad1216c56cc2dc25807f04c0a9 --- /dev/null +++ b/200 solved problems in Python/basic/.ipynb_checkpoints/input_age_solution-checkpoint.ipynb @@ -0,0 +1,53 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "What is your name: Tan\n", + "How old are you: 34\n", + "Tan will be 100 years old in the year 2080\n" + ] + } + ], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Create a program that asks the user to enter their name and their age.\n", + "# Print out a message addressed to them that tells them the year that they will turn 100 years old.\n", + "\n", + "name = input(\"What is your name: \")\n", + "age = int(input(\"How old are you: \"))\n", + "year = str((2014 - age)+100)\n", + "print(name + \" will be 100 years old in the year \" + year)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/basic/.ipynb_checkpoints/print_string-checkpoint.ipynb b/200 solved problems in Python/basic/.ipynb_checkpoints/print_string-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..711f8e8ee6f75768f4f129f8c7ab85f004a6f5c5 --- /dev/null +++ b/200 solved problems in Python/basic/.ipynb_checkpoints/print_string-checkpoint.ipynb @@ -0,0 +1,50 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program to print the following string in a specific format (see the output). Go to the editor\n", + "\n", + "# Sample String : \"Twinkle, twinkle, little star, How I wonder what you are! Up above the world so high, Like a diamond in the sky. Twinkle, twinkle, little star, How I wonder what you are\" \n", + "\n", + "# Output :\n", + "\n", + "# Twinkle, twinkle, little star,\n", + "# \tHow I wonder what you are! \n", + "# \t\tUp above the world so high,\n", + "# \t\tLike a diamond in the sky. \n", + "# Twinkle, twinkle, little star, \n", + "# \tHow I wonder what you are" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/basic/.ipynb_checkpoints/print_string_solution-checkpoint.ipynb b/200 solved problems in Python/basic/.ipynb_checkpoints/print_string_solution-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..4fc596b1566a9f779874a192fb2306c6f3b0b805 --- /dev/null +++ b/200 solved problems in Python/basic/.ipynb_checkpoints/print_string_solution-checkpoint.ipynb @@ -0,0 +1,69 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Twinkle, twinkle, little star, \n", + "\tHow I wonder what you are! \n", + "\t\tUp above the world so high, \n", + "\t\tLike a diamond in the sky. \n", + "Twinkle, twinkle, little star, \n", + "\tHow I wonder what you are!\n" + ] + } + ], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program to print the following string in a specific format (see the output). Go to the editor\n", + "\n", + "# Sample String : \"Twinkle, twinkle, little star, How I wonder what you are! Up above the world so high, Like a diamond in the sky. Twinkle, twinkle, little star, How I wonder what you are\" \n", + "\n", + "# Output :\n", + "\n", + "# Twinkle, twinkle, little star,\n", + "# \tHow I wonder what you are! \n", + "# \t\tUp above the world so high,\n", + "# \t\tLike a diamond in the sky. \n", + "# Twinkle, twinkle, little star, \n", + "# \tHow I wonder what you are\n", + "\n", + "# ----------------------------------------------------\n", + "\n", + "# Hints\n", + "# Using \\n (newline) and \\t (tab) to format the string\n", + "\n", + "print(\"Twinkle, twinkle, little star, \\n\\tHow I wonder what you are! \\n\\t\\tUp above the world so high, \\n\\t\\tLike a diamond in the sky. \\nTwinkle, twinkle, little star, \\n\\tHow I wonder what you are!\")\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/basic/calculate_with_input.ipynb b/200 solved problems in Python/basic/calculate_with_input.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..18b4b3e9c470cefc995f2459c7a5ca0899201aa1 --- /dev/null +++ b/200 solved problems in Python/basic/calculate_with_input.ipynb @@ -0,0 +1,41 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program that accepts an integer (n) and computes the value of n+nn+nnn. Go to the editor\n", + "# Sample value of n is 5 \n", + "# Expected Result : 615" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/basic/calculate_with_input_solution.ipynb b/200 solved problems in Python/basic/calculate_with_input_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..83f0c8122745f6e703bc66e40fbefd7a6d2f1cb3 --- /dev/null +++ b/200 solved problems in Python/basic/calculate_with_input_solution.ipynb @@ -0,0 +1,54 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input an integer : 5\n", + "615\n" + ] + } + ], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program that accepts an integer (n) and computes the value of n+nn+nnn. Go to the editor\n", + "# Sample value of n is 5 \n", + "# Expected Result : 615\n", + "\n", + "a = int(input(\"Input an integer : \"))\n", + "n1 = int( \"%s\" % a )\n", + "n2 = int( \"%s%s\" % (a,a) )\n", + "n3 = int( \"%s%s%s\" % (a,a,a) )\n", + "print (n1+n2+n3)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/basic/input_age.ipynb b/200 solved problems in Python/basic/input_age.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..ec75aaed0c99d8f3e2fe2e1f29d47474e0c90886 --- /dev/null +++ b/200 solved problems in Python/basic/input_age.ipynb @@ -0,0 +1,40 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Create a program that asks the user to enter their name and their age.\n", + "# Print out a message addressed to them that tells them the year that they will turn 100 years old." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/basic/input_age_solution.ipynb b/200 solved problems in Python/basic/input_age_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..f0ea3195570f77ad1216c56cc2dc25807f04c0a9 --- /dev/null +++ b/200 solved problems in Python/basic/input_age_solution.ipynb @@ -0,0 +1,53 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "What is your name: Tan\n", + "How old are you: 34\n", + "Tan will be 100 years old in the year 2080\n" + ] + } + ], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Create a program that asks the user to enter their name and their age.\n", + "# Print out a message addressed to them that tells them the year that they will turn 100 years old.\n", + "\n", + "name = input(\"What is your name: \")\n", + "age = int(input(\"How old are you: \"))\n", + "year = str((2014 - age)+100)\n", + "print(name + \" will be 100 years old in the year \" + year)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/basic/print_string.ipynb b/200 solved problems in Python/basic/print_string.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..711f8e8ee6f75768f4f129f8c7ab85f004a6f5c5 --- /dev/null +++ b/200 solved problems in Python/basic/print_string.ipynb @@ -0,0 +1,50 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program to print the following string in a specific format (see the output). Go to the editor\n", + "\n", + "# Sample String : \"Twinkle, twinkle, little star, How I wonder what you are! Up above the world so high, Like a diamond in the sky. Twinkle, twinkle, little star, How I wonder what you are\" \n", + "\n", + "# Output :\n", + "\n", + "# Twinkle, twinkle, little star,\n", + "# \tHow I wonder what you are! \n", + "# \t\tUp above the world so high,\n", + "# \t\tLike a diamond in the sky. \n", + "# Twinkle, twinkle, little star, \n", + "# \tHow I wonder what you are" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/basic/print_string_solution.ipynb b/200 solved problems in Python/basic/print_string_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..4fc596b1566a9f779874a192fb2306c6f3b0b805 --- /dev/null +++ b/200 solved problems in Python/basic/print_string_solution.ipynb @@ -0,0 +1,69 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Twinkle, twinkle, little star, \n", + "\tHow I wonder what you are! \n", + "\t\tUp above the world so high, \n", + "\t\tLike a diamond in the sky. \n", + "Twinkle, twinkle, little star, \n", + "\tHow I wonder what you are!\n" + ] + } + ], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program to print the following string in a specific format (see the output). Go to the editor\n", + "\n", + "# Sample String : \"Twinkle, twinkle, little star, How I wonder what you are! Up above the world so high, Like a diamond in the sky. Twinkle, twinkle, little star, How I wonder what you are\" \n", + "\n", + "# Output :\n", + "\n", + "# Twinkle, twinkle, little star,\n", + "# \tHow I wonder what you are! \n", + "# \t\tUp above the world so high,\n", + "# \t\tLike a diamond in the sky. \n", + "# Twinkle, twinkle, little star, \n", + "# \tHow I wonder what you are\n", + "\n", + "# ----------------------------------------------------\n", + "\n", + "# Hints\n", + "# Using \\n (newline) and \\t (tab) to format the string\n", + "\n", + "print(\"Twinkle, twinkle, little star, \\n\\tHow I wonder what you are! \\n\\t\\tUp above the world so high, \\n\\t\\tLike a diamond in the sky. \\nTwinkle, twinkle, little star, \\n\\tHow I wonder what you are!\")\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/class/.ipynb_checkpoints/check_parentheses-checkpoint.ipynb b/200 solved problems in Python/class/.ipynb_checkpoints/check_parentheses-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..2adbfee94a3d3789f8a387f7b039aa8df452cf53 --- /dev/null +++ b/200 solved problems in Python/class/.ipynb_checkpoints/check_parentheses-checkpoint.ipynb @@ -0,0 +1,41 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program to find validity of a string of parentheses, '(', ')', '{', '}', '[' and ']. \n", + "# These brackets must be close in the correct order, \n", + "# for example \"()\" and \"()[]{}\" are valid but \"[)\", \"({[)]\" and \"{{{\" are invalid." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/class/.ipynb_checkpoints/check_parentheses_solution-checkpoint.ipynb b/200 solved problems in Python/class/.ipynb_checkpoints/check_parentheses_solution-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..59ea35fde272573814ab9a0224d393aab00c958a --- /dev/null +++ b/200 solved problems in Python/class/.ipynb_checkpoints/check_parentheses_solution-checkpoint.ipynb @@ -0,0 +1,63 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "False\n", + "True\n" + ] + } + ], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program to find validity of a string of parentheses, '(', ')', '{', '}', '[' and ']. \n", + "# These brackets must be close in the correct order, \n", + "# for example \"()\" and \"()[]{}\" are valid but \"[)\", \"({[)]\" and \"{{{\" are invalid.\n", + "\n", + "class py_solution:\n", + " def is_valid_parenthese(self, str1):\n", + " stack, pchar = [], {\"(\": \")\", \"{\": \"}\", \"[\": \"]\"}\n", + " for parenthese in str1:\n", + " if parenthese in pchar:\n", + " stack.append(parenthese)\n", + " elif len(stack) == 0 or pchar[stack.pop()] != parenthese:\n", + " return False\n", + " return len(stack) == 0\n", + "\n", + "print(py_solution().is_valid_parenthese(\"(){}[]\"))\n", + "print(py_solution().is_valid_parenthese(\"()[{)}\"))\n", + "print(py_solution().is_valid_parenthese(\"()\"))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/class/.ipynb_checkpoints/revert_word-checkpoint.ipynb b/200 solved problems in Python/class/.ipynb_checkpoints/revert_word-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..074f95d5d6dda776f2364bcbfcbb1e48dbbeac36 --- /dev/null +++ b/200 solved problems in Python/class/.ipynb_checkpoints/revert_word-checkpoint.ipynb @@ -0,0 +1,38 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python class to reverse a string word by word.\n", + "# Input \"hello world\"\n", + "# Output \"world hello\"" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/class/check_parentheses.ipynb b/200 solved problems in Python/class/check_parentheses.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..2adbfee94a3d3789f8a387f7b039aa8df452cf53 --- /dev/null +++ b/200 solved problems in Python/class/check_parentheses.ipynb @@ -0,0 +1,41 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program to find validity of a string of parentheses, '(', ')', '{', '}', '[' and ']. \n", + "# These brackets must be close in the correct order, \n", + "# for example \"()\" and \"()[]{}\" are valid but \"[)\", \"({[)]\" and \"{{{\" are invalid." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/class/check_parentheses_solution.ipynb b/200 solved problems in Python/class/check_parentheses_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..59ea35fde272573814ab9a0224d393aab00c958a --- /dev/null +++ b/200 solved problems in Python/class/check_parentheses_solution.ipynb @@ -0,0 +1,63 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "False\n", + "True\n" + ] + } + ], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program to find validity of a string of parentheses, '(', ')', '{', '}', '[' and ']. \n", + "# These brackets must be close in the correct order, \n", + "# for example \"()\" and \"()[]{}\" are valid but \"[)\", \"({[)]\" and \"{{{\" are invalid.\n", + "\n", + "class py_solution:\n", + " def is_valid_parenthese(self, str1):\n", + " stack, pchar = [], {\"(\": \")\", \"{\": \"}\", \"[\": \"]\"}\n", + " for parenthese in str1:\n", + " if parenthese in pchar:\n", + " stack.append(parenthese)\n", + " elif len(stack) == 0 or pchar[stack.pop()] != parenthese:\n", + " return False\n", + " return len(stack) == 0\n", + "\n", + "print(py_solution().is_valid_parenthese(\"(){}[]\"))\n", + "print(py_solution().is_valid_parenthese(\"()[{)}\"))\n", + "print(py_solution().is_valid_parenthese(\"()\"))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/class/circle.ipynb b/200 solved problems in Python/class/circle.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..c4f6670ef519b0feaba048c0d09572a1b2fc472e --- /dev/null +++ b/200 solved problems in Python/class/circle.ipynb @@ -0,0 +1,37 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python class named Circle constructed by a radius and two methods which\n", + "# will compute the area and the perimeter of a circle." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/class/circle_solution.ipynb b/200 solved problems in Python/class/circle_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..ccda7a1def3aaec7fa3de0084682c1102cc7639f --- /dev/null +++ b/200 solved problems in Python/class/circle_solution.ipynb @@ -0,0 +1,58 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "200.96\n", + "50.24\n" + ] + } + ], + "source": [ + "# Write a Python class named Circle constructed by a radius and two methods which\n", + "# will compute the area and the perimeter of a circle.\n", + "\n", + "class Circle():\n", + " def __init__(self, r):\n", + " self.radius = r\n", + "\n", + " def area(self):\n", + " return self.radius**2*3.14\n", + " \n", + " def perimeter(self):\n", + " return 2*self.radius*3.14\n", + "\n", + "NewCircle = Circle(8)\n", + "print(NewCircle.area())\n", + "print(NewCircle.perimeter())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/class/convert_to_int.ipynb b/200 solved problems in Python/class/convert_to_int.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..ec68cffd5fb917e748172eec193b06143d70147c --- /dev/null +++ b/200 solved problems in Python/class/convert_to_int.ipynb @@ -0,0 +1,46 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python class to convert a roman numeral to an integer\n", + "\n", + "# Sample input\n", + "# 'MMMCMLXXXVI'\n", + "# 'MMMM'\n", + "# 'C'\n", + "\n", + "# Sample output\n", + "# 3986 \n", + "# 4000 \n", + "# 100" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/class/convert_to_int_solution.ipynb b/200 solved problems in Python/class/convert_to_int_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..f2e74e9f773fbc6dd9a7c7e4a9d6c82f2dea5e62 --- /dev/null +++ b/200 solved problems in Python/class/convert_to_int_solution.ipynb @@ -0,0 +1,61 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python class to convert a roman numeral to an integer\n", + "\n", + "# Sample input\n", + "# 'MMMCMLXXXVI'\n", + "# 'MMMM'\n", + "# 'C'\n", + "\n", + "# Sample output\n", + "# 3986 \n", + "# 4000 \n", + "# 100\n", + "\n", + "class py_solution:\n", + " def roman_to_int(self, s):\n", + " rom_val = {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000}\n", + " int_val = 0\n", + " for i in range(len(s)):\n", + " if i > 0 and rom_val[s[i]] > rom_val[s[i - 1]]:\n", + " int_val += rom_val[s[i]] - 2 * rom_val[s[i - 1]]\n", + " else:\n", + " int_val += rom_val[s[i]]\n", + " return int_val\n", + "\n", + "print(py_solution().roman_to_int('MMMCMLXXXVI'))\n", + "print(py_solution().roman_to_int('MMMM'))\n", + "print(py_solution().roman_to_int('C'))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/class/convert_to_roman.ipynb b/200 solved problems in Python/class/convert_to_roman.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..00e8bb8a958c2eb6d2663ed67561ca230525c64c --- /dev/null +++ b/200 solved problems in Python/class/convert_to_roman.ipynb @@ -0,0 +1,38 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to convert an integer to a roman numeral.\n", + "# Input 1, 4000\n", + "# Output I, MMMM" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/class/convert_to_roman_solution.ipynb b/200 solved problems in Python/class/convert_to_roman_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..16aaf854d3074263a73dc1cd147e823c25a2b7ae --- /dev/null +++ b/200 solved problems in Python/class/convert_to_roman_solution.ipynb @@ -0,0 +1,70 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I\n", + "MMMM\n" + ] + } + ], + "source": [ + "# Write a Python program to convert an integer to a roman numeral.\n", + "\n", + "class py_solution:\n", + " def int_to_Roman(self, num):\n", + " val = [\n", + " 1000, 900, 500, 400,\n", + " 100, 90, 50, 40,\n", + " 10, 9, 5, 4,\n", + " 1\n", + " ]\n", + " syb = [\n", + " \"M\", \"CM\", \"D\", \"CD\",\n", + " \"C\", \"XC\", \"L\", \"XL\",\n", + " \"X\", \"IX\", \"V\", \"IV\",\n", + " \"I\"\n", + " ]\n", + " roman_num = ''\n", + " i = 0\n", + " while num > 0:\n", + " for _ in range(num // val[i]):\n", + " roman_num += syb[i]\n", + " num -= val[i]\n", + " i += 1\n", + " return roman_num\n", + "\n", + "\n", + "print(py_solution().int_to_Roman(1))\n", + "print(py_solution().int_to_Roman(4000))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/class/get_and_print.ipynb b/200 solved problems in Python/class/get_and_print.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..70a2ed7b292379dcf4333952ebbb5c5c4c36cedf --- /dev/null +++ b/200 solved problems in Python/class/get_and_print.ipynb @@ -0,0 +1,37 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python class which has two methods get_String and print_String. \n", + "# get_String accept a string from the user and print_String print the string in upper case." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/class/get_and_print_solution.ipynb b/200 solved problems in Python/class/get_and_print_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..45fb3edebbb7349ea60b76728b11660f761686b5 --- /dev/null +++ b/200 solved problems in Python/class/get_and_print_solution.ipynb @@ -0,0 +1,58 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n" + ] + } + ], + "source": [ + "# Write a Python class which has two methods get_String and print_String. \n", + "# get_String accept a string from the user and print_String print the string in upper case.\n", + "\n", + "class IOString():\n", + " def __init__(self):\n", + " self.str1 = \"\"\n", + "\n", + " def get_String(self):\n", + " self.str1 = input()\n", + "\n", + " def print_String(self):\n", + " print(self.str1.upper())\n", + "\n", + "str1 = IOString()\n", + "str1.get_String()\n", + "str1.print_String()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/class/rectange.ipynb b/200 solved problems in Python/class/rectange.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..50b5d3b2a5ef0008c7b6bb6da1da8a42b7365b85 --- /dev/null +++ b/200 solved problems in Python/class/rectange.ipynb @@ -0,0 +1,36 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python class named Rectangle constructed by a length and width and a method which will compute the area of a rectangle." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/class/rectange_solution.ipynb b/200 solved problems in Python/class/rectange_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..a13f7ea3469107492e10d07e020654c38e1360f9 --- /dev/null +++ b/200 solved problems in Python/class/rectange_solution.ipynb @@ -0,0 +1,54 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "120\n" + ] + } + ], + "source": [ + "# Write a Python class named Rectangle constructed by a length and width and a method which will compute the area of a rectangle.\n", + "\n", + "class Rectangle():\n", + " def __init__(self, l, w):\n", + " self.length = l\n", + " self.width = w\n", + "\n", + " def rectangle_area(self):\n", + " return self.length*self.width\n", + "\n", + "newRectangle = Rectangle(12, 10)\n", + "\n", + "print(newRectangle.rectangle_area())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/class/revert_word.ipynb b/200 solved problems in Python/class/revert_word.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..074f95d5d6dda776f2364bcbfcbb1e48dbbeac36 --- /dev/null +++ b/200 solved problems in Python/class/revert_word.ipynb @@ -0,0 +1,38 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python class to reverse a string word by word.\n", + "# Input \"hello world\"\n", + "# Output \"world hello\"" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/class/revert_word_solution.ipynb b/200 solved problems in Python/class/revert_word_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..83a1b6121b0dd78dfce9353ebbf35119a70feb9e --- /dev/null +++ b/200 solved problems in Python/class/revert_word_solution.ipynb @@ -0,0 +1,51 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "world hello\n" + ] + } + ], + "source": [ + "# Write a Python class to reverse a string word by word.\n", + "# Input \"hello world\"\n", + "# Output \"world hello\"\n", + "\n", + "class py_solution:\n", + " def reverse_words(self, s):\n", + " return ' '.join(reversed(s.split()))\n", + "\n", + "\n", + "print(py_solution().reverse_words('hello world'))\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/class/sum_zero.ipynb b/200 solved problems in Python/class/sum_zero.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..145477da0639e6b3a4d843c9fbf9c4676ea8fe90 --- /dev/null +++ b/200 solved problems in Python/class/sum_zero.ipynb @@ -0,0 +1,40 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to find the three elements that sum to zero from a set (array) of n real numbers.\n", + "# Input\n", + "# [-25, -10, -7, -3, 2, 4, 8, 10]\n", + "# Output\n", + "# [[-10, 2, 8], [-7, -3, 10]] " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/class/sum_zero_solution.ipynb b/200 solved problems in Python/class/sum_zero_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..19d82c955ee94fc0bc20a26587be2b53db7a7733 --- /dev/null +++ b/200 solved problems in Python/class/sum_zero_solution.ipynb @@ -0,0 +1,70 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-10, 2, 8], [-7, -3, 10]]\n" + ] + } + ], + "source": [ + "# Write a Python program to find the three elements that sum to zero from a set (array) of n real numbers.\n", + "# Input\n", + "# [-25, -10, -7, -3, 2, 4, 8, 10]\n", + "# Output\n", + "# [[-10, 2, 8], [-7, -3, 10]]\n", + "\n", + "class py_solution:\n", + " def threeSum(self, nums):\n", + " nums, result, i = sorted(nums), [], 0\n", + " while i < len(nums) - 2:\n", + " j, k = i + 1, len(nums) - 1\n", + " while j < k:\n", + " if nums[i] + nums[j] + nums[k] < 0:\n", + " j += 1\n", + " elif nums[i] + nums[j] + nums[k] > 0:\n", + " k -= 1\n", + " else:\n", + " result.append([nums[i], nums[j], nums[k]])\n", + " j, k = j + 1, k - 1\n", + " while j < k and nums[j] == nums[j - 1]:\n", + " j += 1\n", + " while j < k and nums[k] == nums[k + 1]:\n", + " k -= 1\n", + " i += 1\n", + " while i < len(nums) - 2 and nums[i] == nums[i - 1]:\n", + " i += 1\n", + " return result\n", + "\n", + "print(py_solution().threeSum([-25, -10, -7, -3, 2, 4, 8, 10]))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/condition and loop/.ipynb_checkpoints/2_dimensional_array-checkpoint.ipynb b/200 solved problems in Python/condition and loop/.ipynb_checkpoints/2_dimensional_array-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..be49b473f9e9e631124a3d993f65fe47bf598fe0 --- /dev/null +++ b/200 solved problems in Python/condition and loop/.ipynb_checkpoints/2_dimensional_array-checkpoint.ipynb @@ -0,0 +1,50 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program which takes two digits m (row) and n (column) as input and generates a two-dimensional array. \n", + "# The element value in the i-th row and j-th column of the array should be i*j.\n", + "# Note :\n", + "# i = 0,1.., m-1 \n", + "# j = 0,1, n-1.\n", + "\n", + "# Input\n", + "# Input number of rows: 3 \n", + "# Input number of columns: 4 \n", + "\n", + "# Output\n", + "# [[0, 0, 0, 0], [0, 1, 2, 3], [0, 2, 4, 6]] " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/condition and loop/.ipynb_checkpoints/2_dimensional_array_solution-checkpoint.ipynb b/200 solved problems in Python/condition and loop/.ipynb_checkpoints/2_dimensional_array_solution-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..526030cdc94028cc50881f7d55438b682b360e79 --- /dev/null +++ b/200 solved problems in Python/condition and loop/.ipynb_checkpoints/2_dimensional_array_solution-checkpoint.ipynb @@ -0,0 +1,60 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program which takes two digits m (row) and n (column) as input and generates a two-dimensional array. \n", + "# The element value in the i-th row and j-th column of the array should be i*j.\n", + "# Note :\n", + "# i = 0,1.., m-1 \n", + "# j = 0,1, n-1.\n", + "\n", + "# Input\n", + "# Input number of rows: 3 \n", + "# Input number of columns: 4 \n", + "\n", + "# Output\n", + "# [[0, 0, 0, 0], [0, 1, 2, 3], [0, 2, 4, 6]]\n", + "\n", + "row_num = int(input(\"Input number of rows: \"))\n", + "col_num = int(input(\"Input number of columns: \"))\n", + "multi_list = [[0 for col in range(col_num)] for row in range(row_num)]\n", + "\n", + "for row in range(row_num):\n", + " for col in range(col_num):\n", + " multi_list[row][col]= row*col\n", + "\n", + "print(multi_list)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/condition and loop/.ipynb_checkpoints/check_triange-checkpoint.ipynb b/200 solved problems in Python/condition and loop/.ipynb_checkpoints/check_triange-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..362b49175e2b8278d050cd3dfbd6df391c98f8e0 --- /dev/null +++ b/200 solved problems in Python/condition and loop/.ipynb_checkpoints/check_triange-checkpoint.ipynb @@ -0,0 +1,39 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program to check a triangle is valid or not" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/condition and loop/.ipynb_checkpoints/check_triange_solution-checkpoint.ipynb b/200 solved problems in Python/condition and loop/.ipynb_checkpoints/check_triange_solution-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..5ecc481dd0dfe8252e3abd043eee770756a858c9 --- /dev/null +++ b/200 solved problems in Python/condition and loop/.ipynb_checkpoints/check_triange_solution-checkpoint.ipynb @@ -0,0 +1,65 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "enter side 1\n", + "1\n", + "enter side 2\n", + "2\n", + "enter side 3\n", + "3\n", + "yes, it can form a degenerated triangle\n" + ] + } + ], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program to check a triangle is valid or not\n", + "\n", + "def triangle_check(l1,l2,l3):\n", + " if (l1>l2+l3) or (l2>l1+l3) or (l3>l1+l2):\n", + " print('No, the lengths wont form a triangle')\n", + " elif (l1==l2+l3) or (l2==l1+l3) or (l3==l1+l2):\n", + " print('yes, it can form a degenerated triangle')\n", + " else:\n", + " print('Yes, a triangle can be formed out of it')\n", + "\n", + "length1 = int(input('enter side 1\\n'))\n", + "length2 = int(input('enter side 2\\n'))\n", + "length3 = int(input('enter side 3\\n'))\n", + "\n", + "triangle_check(length1,length2,length3)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/condition and loop/.ipynb_checkpoints/construct_number_partern-checkpoint.ipynb b/200 solved problems in Python/condition and loop/.ipynb_checkpoints/construct_number_partern-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..4b8b610b655f194af61c3aa20cc53f0bbb3b9a27 --- /dev/null +++ b/200 solved problems in Python/condition and loop/.ipynb_checkpoints/construct_number_partern-checkpoint.ipynb @@ -0,0 +1,48 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program to construct the following pattern, using a nested loop number.\n", + "# 1 \n", + "# 22 \n", + "# 333 \n", + "# 4444 \n", + "# 55555 \n", + "# 666666 \n", + "# 7777777 \n", + "# 88888888 \n", + "# 999999999 " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/condition and loop/.ipynb_checkpoints/construct_number_partern_solution-checkpoint.ipynb b/200 solved problems in Python/condition and loop/.ipynb_checkpoints/construct_number_partern_solution-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..9c93c43255b9705e5b39f710a7a50b7d091fb334 --- /dev/null +++ b/200 solved problems in Python/condition and loop/.ipynb_checkpoints/construct_number_partern_solution-checkpoint.ipynb @@ -0,0 +1,66 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "1\n", + "22\n", + "333\n", + "4444\n", + "55555\n", + "666666\n", + "7777777\n", + "88888888\n", + "999999999\n" + ] + } + ], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program to construct the following pattern, using a nested loop number.\n", + "# 1 \n", + "# 22 \n", + "# 333 \n", + "# 4444 \n", + "# 55555 \n", + "# 666666 \n", + "# 7777777 \n", + "# 88888888 \n", + "# 999999999\n", + "for i in range(10):\n", + " print(str(i) * i)\n", + "\t" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/condition and loop/.ipynb_checkpoints/construct_parttern-checkpoint.ipynb b/200 solved problems in Python/condition and loop/.ipynb_checkpoints/construct_parttern-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..3e4fa7095255ab290e643e83ae890d860eb56074 --- /dev/null +++ b/200 solved problems in Python/condition and loop/.ipynb_checkpoints/construct_parttern-checkpoint.ipynb @@ -0,0 +1,48 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program to construct the following pattern, using a nested for loop.\n", + "# * \n", + "# * * \n", + "# * * * \n", + "# * * * * \n", + "# * * * * * \n", + "# * * * * \n", + "# * * * \n", + "# * * \n", + "# *" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/condition and loop/.ipynb_checkpoints/construct_parttern_solution-checkpoint.ipynb b/200 solved problems in Python/condition and loop/.ipynb_checkpoints/construct_parttern_solution-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..42953c066de31da6ac4993b74225b5108996bf97 --- /dev/null +++ b/200 solved problems in Python/condition and loop/.ipynb_checkpoints/construct_parttern_solution-checkpoint.ipynb @@ -0,0 +1,75 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "* \n", + "* * \n", + "* * * \n", + "* * * * \n", + "* * * * * \n", + "* * * * \n", + "* * * \n", + "* * \n", + "* \n" + ] + } + ], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program to construct the following pattern, using a nested for loop.\n", + "# * \n", + "# * * \n", + "# * * * \n", + "# * * * * \n", + "# * * * * * \n", + "# * * * * \n", + "# * * * \n", + "# * * \n", + "# *\n", + "\n", + "n=5;\n", + "for i in range(n):\n", + " for j in range(i):\n", + " print ('* ', end=\"\")\n", + " print('')\n", + "\n", + "for i in range(n,0,-1):\n", + " for j in range(i):\n", + " print('* ', end=\"\")\n", + " print('')\n", + "\t" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/condition and loop/.ipynb_checkpoints/count_digit_letter-checkpoint.ipynb b/200 solved problems in Python/condition and loop/.ipynb_checkpoints/count_digit_letter-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..eed5424a2b32913337c876eb9315cec5a2185576 --- /dev/null +++ b/200 solved problems in Python/condition and loop/.ipynb_checkpoints/count_digit_letter-checkpoint.ipynb @@ -0,0 +1,43 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program that accepts a string and calculate the number of digits and letters\n", + "# Sample Data : \"Python 3.2\"\n", + "# Expected Output :\n", + "# Letters 6 \n", + "# Digits 2" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/condition and loop/.ipynb_checkpoints/count_digit_letter_solution-checkpoint.ipynb b/200 solved problems in Python/condition and loop/.ipynb_checkpoints/count_digit_letter_solution-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..5501cbb13f924d7d68d2e5e3bd77dbabab8d9b6f --- /dev/null +++ b/200 solved problems in Python/condition and loop/.ipynb_checkpoints/count_digit_letter_solution-checkpoint.ipynb @@ -0,0 +1,55 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program that accepts a string and calculate the number of digits and letters\n", + "# Sample Data : \"Python 3.2\"\n", + "# Expected Output :\n", + "# Letters 6 \n", + "# Digits 2\n", + "\n", + "s = input(\"Input a string\")\n", + "d=l=0\n", + "for c in s:\n", + " if c.isdigit():\n", + " d=d+1\n", + " elif c.isalpha():\n", + " l=l+1\n", + " else:\n", + " pass\n", + "print(\"Letters\", l)\n", + "print(\"Digits\", d)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/condition and loop/2_dimensional_array.ipynb b/200 solved problems in Python/condition and loop/2_dimensional_array.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..be49b473f9e9e631124a3d993f65fe47bf598fe0 --- /dev/null +++ b/200 solved problems in Python/condition and loop/2_dimensional_array.ipynb @@ -0,0 +1,50 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program which takes two digits m (row) and n (column) as input and generates a two-dimensional array. \n", + "# The element value in the i-th row and j-th column of the array should be i*j.\n", + "# Note :\n", + "# i = 0,1.., m-1 \n", + "# j = 0,1, n-1.\n", + "\n", + "# Input\n", + "# Input number of rows: 3 \n", + "# Input number of columns: 4 \n", + "\n", + "# Output\n", + "# [[0, 0, 0, 0], [0, 1, 2, 3], [0, 2, 4, 6]] " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/condition and loop/2_dimensional_array_solution.ipynb b/200 solved problems in Python/condition and loop/2_dimensional_array_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..526030cdc94028cc50881f7d55438b682b360e79 --- /dev/null +++ b/200 solved problems in Python/condition and loop/2_dimensional_array_solution.ipynb @@ -0,0 +1,60 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program which takes two digits m (row) and n (column) as input and generates a two-dimensional array. \n", + "# The element value in the i-th row and j-th column of the array should be i*j.\n", + "# Note :\n", + "# i = 0,1.., m-1 \n", + "# j = 0,1, n-1.\n", + "\n", + "# Input\n", + "# Input number of rows: 3 \n", + "# Input number of columns: 4 \n", + "\n", + "# Output\n", + "# [[0, 0, 0, 0], [0, 1, 2, 3], [0, 2, 4, 6]]\n", + "\n", + "row_num = int(input(\"Input number of rows: \"))\n", + "col_num = int(input(\"Input number of columns: \"))\n", + "multi_list = [[0 for col in range(col_num)] for row in range(row_num)]\n", + "\n", + "for row in range(row_num):\n", + " for col in range(col_num):\n", + " multi_list[row][col]= row*col\n", + "\n", + "print(multi_list)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/condition and loop/check_triange.ipynb b/200 solved problems in Python/condition and loop/check_triange.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..362b49175e2b8278d050cd3dfbd6df391c98f8e0 --- /dev/null +++ b/200 solved problems in Python/condition and loop/check_triange.ipynb @@ -0,0 +1,39 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program to check a triangle is valid or not" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/condition and loop/check_triange_solution.ipynb b/200 solved problems in Python/condition and loop/check_triange_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..5ecc481dd0dfe8252e3abd043eee770756a858c9 --- /dev/null +++ b/200 solved problems in Python/condition and loop/check_triange_solution.ipynb @@ -0,0 +1,65 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "enter side 1\n", + "1\n", + "enter side 2\n", + "2\n", + "enter side 3\n", + "3\n", + "yes, it can form a degenerated triangle\n" + ] + } + ], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program to check a triangle is valid or not\n", + "\n", + "def triangle_check(l1,l2,l3):\n", + " if (l1>l2+l3) or (l2>l1+l3) or (l3>l1+l2):\n", + " print('No, the lengths wont form a triangle')\n", + " elif (l1==l2+l3) or (l2==l1+l3) or (l3==l1+l2):\n", + " print('yes, it can form a degenerated triangle')\n", + " else:\n", + " print('Yes, a triangle can be formed out of it')\n", + "\n", + "length1 = int(input('enter side 1\\n'))\n", + "length2 = int(input('enter side 2\\n'))\n", + "length3 = int(input('enter side 3\\n'))\n", + "\n", + "triangle_check(length1,length2,length3)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/condition and loop/construct_number_partern.ipynb b/200 solved problems in Python/condition and loop/construct_number_partern.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..4b8b610b655f194af61c3aa20cc53f0bbb3b9a27 --- /dev/null +++ b/200 solved problems in Python/condition and loop/construct_number_partern.ipynb @@ -0,0 +1,48 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program to construct the following pattern, using a nested loop number.\n", + "# 1 \n", + "# 22 \n", + "# 333 \n", + "# 4444 \n", + "# 55555 \n", + "# 666666 \n", + "# 7777777 \n", + "# 88888888 \n", + "# 999999999 " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/condition and loop/construct_number_partern_solution.ipynb b/200 solved problems in Python/condition and loop/construct_number_partern_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..9c93c43255b9705e5b39f710a7a50b7d091fb334 --- /dev/null +++ b/200 solved problems in Python/condition and loop/construct_number_partern_solution.ipynb @@ -0,0 +1,66 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "1\n", + "22\n", + "333\n", + "4444\n", + "55555\n", + "666666\n", + "7777777\n", + "88888888\n", + "999999999\n" + ] + } + ], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program to construct the following pattern, using a nested loop number.\n", + "# 1 \n", + "# 22 \n", + "# 333 \n", + "# 4444 \n", + "# 55555 \n", + "# 666666 \n", + "# 7777777 \n", + "# 88888888 \n", + "# 999999999\n", + "for i in range(10):\n", + " print(str(i) * i)\n", + "\t" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/condition and loop/construct_parttern.ipynb b/200 solved problems in Python/condition and loop/construct_parttern.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..3e4fa7095255ab290e643e83ae890d860eb56074 --- /dev/null +++ b/200 solved problems in Python/condition and loop/construct_parttern.ipynb @@ -0,0 +1,48 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program to construct the following pattern, using a nested for loop.\n", + "# * \n", + "# * * \n", + "# * * * \n", + "# * * * * \n", + "# * * * * * \n", + "# * * * * \n", + "# * * * \n", + "# * * \n", + "# *" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/condition and loop/construct_parttern_solution.ipynb b/200 solved problems in Python/condition and loop/construct_parttern_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..42953c066de31da6ac4993b74225b5108996bf97 --- /dev/null +++ b/200 solved problems in Python/condition and loop/construct_parttern_solution.ipynb @@ -0,0 +1,75 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "* \n", + "* * \n", + "* * * \n", + "* * * * \n", + "* * * * * \n", + "* * * * \n", + "* * * \n", + "* * \n", + "* \n" + ] + } + ], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program to construct the following pattern, using a nested for loop.\n", + "# * \n", + "# * * \n", + "# * * * \n", + "# * * * * \n", + "# * * * * * \n", + "# * * * * \n", + "# * * * \n", + "# * * \n", + "# *\n", + "\n", + "n=5;\n", + "for i in range(n):\n", + " for j in range(i):\n", + " print ('* ', end=\"\")\n", + " print('')\n", + "\n", + "for i in range(n,0,-1):\n", + " for j in range(i):\n", + " print('* ', end=\"\")\n", + " print('')\n", + "\t" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/condition and loop/count_digit_letter.ipynb b/200 solved problems in Python/condition and loop/count_digit_letter.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..eed5424a2b32913337c876eb9315cec5a2185576 --- /dev/null +++ b/200 solved problems in Python/condition and loop/count_digit_letter.ipynb @@ -0,0 +1,43 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program that accepts a string and calculate the number of digits and letters\n", + "# Sample Data : \"Python 3.2\"\n", + "# Expected Output :\n", + "# Letters 6 \n", + "# Digits 2" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/condition and loop/count_digit_letter_solution.ipynb b/200 solved problems in Python/condition and loop/count_digit_letter_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..5501cbb13f924d7d68d2e5e3bd77dbabab8d9b6f --- /dev/null +++ b/200 solved problems in Python/condition and loop/count_digit_letter_solution.ipynb @@ -0,0 +1,55 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program that accepts a string and calculate the number of digits and letters\n", + "# Sample Data : \"Python 3.2\"\n", + "# Expected Output :\n", + "# Letters 6 \n", + "# Digits 2\n", + "\n", + "s = input(\"Input a string\")\n", + "d=l=0\n", + "for c in s:\n", + " if c.isdigit():\n", + " d=d+1\n", + " elif c.isalpha():\n", + " l=l+1\n", + " else:\n", + " pass\n", + "print(\"Letters\", l)\n", + "print(\"Digits\", d)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/condition and loop/count_even_odd.ipynb b/200 solved problems in Python/condition and loop/count_even_odd.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..13ab26f2ee32213637e2cbbca8fdb7cb167286cb --- /dev/null +++ b/200 solved problems in Python/condition and loop/count_even_odd.ipynb @@ -0,0 +1,41 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Count the number of even and odd numbers from a series of numbers\n", + "# Input \n", + "# numbers = (1, 2, 3, 4, 5, 6, 7, 8, 9) # Declaring the tuple\n", + "# Output\n", + "# Number of even numbers : 4 \n", + "# Number of odd numbers : 5 " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/condition and loop/count_even_odd_solution.ipynb b/200 solved problems in Python/condition and loop/count_even_odd_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..401627ece20b4ef31f7a28b0da99d3121e2d10a0 --- /dev/null +++ b/200 solved problems in Python/condition and loop/count_even_odd_solution.ipynb @@ -0,0 +1,53 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Count the number of even and odd numbers from a series of numbers\n", + "# Input \n", + "# numbers = (1, 2, 3, 4, 5, 6, 7, 8, 9) # Declaring the tuple\n", + "# Output\n", + "# Number of even numbers : 4 \n", + "# Number of odd numbers : 5\n", + "\n", + "numbers = (1, 2, 3, 4, 5, 6, 7, 8, 9) # Declaring the tuple\n", + "count_odd = 0\n", + "count_even = 0\n", + "for x in numbers:\n", + " if not x % 2:\n", + " \t count_even+=1\n", + " else:\n", + " \t count_odd+=1\n", + " \n", + "print(\"Number of even numbers :\",count_even)\n", + "print(\"Number of odd numbers :\",count_odd)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/condition and loop/fibonaci_generator.ipynb b/200 solved problems in Python/condition and loop/fibonaci_generator.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..8ad081ee2e916177ff2cddd4abb6b92665c0c312 --- /dev/null +++ b/200 solved problems in Python/condition and loop/fibonaci_generator.ipynb @@ -0,0 +1,36 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to get the Fibonacci series between 0 to 50" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/condition and loop/fibonaci_generator_solution.ipynb b/200 solved problems in Python/condition and loop/fibonaci_generator_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..549b3a2b2334d4ba0e8d37ddf1c4522964e55498 --- /dev/null +++ b/200 solved problems in Python/condition and loop/fibonaci_generator_solution.ipynb @@ -0,0 +1,57 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "1\n", + "2\n", + "3\n", + "5\n", + "8\n", + "13\n", + "21\n", + "34\n" + ] + } + ], + "source": [ + "# Write a Python program to get the Fibonacci series between 0 to 50\n", + "\n", + "x,y=0,1\n", + "\n", + "while y<50:\n", + " print(y)\n", + " x,y = y,x+y\n", + "\t" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/condition and loop/find_number.ipynb b/200 solved problems in Python/condition and loop/find_number.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..49789dd098250f5ef7f4ea84e38dd249ef396a1a --- /dev/null +++ b/200 solved problems in Python/condition and loop/find_number.ipynb @@ -0,0 +1,34 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Write a Python program to find those numbers which are divisible by 7 and multiple of 5, between 1500 and 2700\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/condition and loop/find_number_solution.ipynb b/200 solved problems in Python/condition and loop/find_number_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..e870e482887207955b96d43d52dc499fdf80d7b4 --- /dev/null +++ b/200 solved problems in Python/condition and loop/find_number_solution.ipynb @@ -0,0 +1,47 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1505,1540,1575,1610,1645,1680,1715,1750,1785,1820,1855,1890,1925,1960,1995,2030,2065,2100,2135,2170,2205,2240,2275,2310,2345,2380,2415,2450,2485,2520,2555,2590,2625,2660,2695\n" + ] + } + ], + "source": [ + "# Write a Python program to find those numbers which are divisible by 7 and multiple of 5, between 1500 and 2700\n", + "nl=[]\n", + "for x in range(1500, 2700):\n", + " if (x%7==0) and (x%5==0):\n", + " nl.append(str(x))\n", + "print (','.join(nl))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/condition and loop/guess_game.ipynb b/200 solved problems in Python/condition and loop/guess_game.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..c89f69d879135649179762abd0e3ce9e07905a97 --- /dev/null +++ b/200 solved problems in Python/condition and loop/guess_game.ipynb @@ -0,0 +1,37 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Generate a random number between 1 and 9 (including 1 and 9).\n", + "# Ask the user to guess the number, then tell them whether they guessed too low, too high, or exactly right. \n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/condition and loop/guess_game_solution.ipynb b/200 solved problems in Python/condition and loop/guess_game_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..6c8c197a57f6dfb312aaa826d72d69504c6e7b07 --- /dev/null +++ b/200 solved problems in Python/condition and loop/guess_game_solution.ipynb @@ -0,0 +1,71 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "What's your guess?3\n", + "Too high!\n", + "What's your guess?2\n", + "You got it!\n", + "And it only took you 2 tries!\n" + ] + } + ], + "source": [ + "# Generate a random number between 1 and 9 (including 1 and 9).\n", + "# Ask the user to guess the number, then tell them whether they guessed too low, too high, or exactly right.\n", + "\n", + "import random\n", + "\n", + "number = random.randint(1,9)\n", + "guess = 0\n", + "count = 0\n", + "\n", + "\n", + "while guess != number and guess != \"exit\":\n", + " guess = input(\"What's your guess?\")\n", + " \n", + " if guess == \"exit\":\n", + " break\n", + " \n", + " guess = int(guess)\n", + " count += 1\n", + " \n", + " if guess < number:\n", + " print(\"Too low!\")\n", + " elif guess > number:\n", + " print(\"Too high!\")\n", + " else:\n", + " print(\"You got it!\")\n", + " print(\"And it only took you\",count,\"tries!\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/condition and loop/password_validation.ipynb b/200 solved problems in Python/condition and loop/password_validation.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..2e4f9ec838b1ffedce9d39c7029ce1204ac0f606 --- /dev/null +++ b/200 solved problems in Python/condition and loop/password_validation.ipynb @@ -0,0 +1,49 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to check the validity of a password (input from users).\n", + "\n", + "# Validation :\n", + "\n", + "# At least 1 letter between [a-z] and 1 letter between [A-Z].\n", + "# At least 1 number between [0-9].\n", + "# At least 1 character from [$#@].\n", + "# Minimum length 6 characters.\n", + "# Maximum length 16 characters.\n", + "\n", + "# Input\n", + "# W3r@100a\n", + "# Output\n", + "# Valid password" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/condition and loop/password_validation_solution.ipynb b/200 solved problems in Python/condition and loop/password_validation_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..804c012bc8f569ca5ee157a988d28df6569441a8 --- /dev/null +++ b/200 solved problems in Python/condition and loop/password_validation_solution.ipynb @@ -0,0 +1,73 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to check the validity of a password (input from users).\n", + "\n", + "# Validation :\n", + "\n", + "# At least 1 letter between [a-z] and 1 letter between [A-Z].\n", + "# At least 1 number between [0-9].\n", + "# At least 1 character from [$#@].\n", + "# Minimum length 6 characters.\n", + "# Maximum length 16 characters.\n", + "\n", + "# Input\n", + "# W3r@100a\n", + "# Output\n", + "# Valid password\n", + "\n", + "import re\n", + "p= input(\"Input your password\")\n", + "x = True\n", + "while x: \n", + " if (len(p)<6 or len(p)>12):\n", + " break\n", + " elif not re.search(\"[a-z]\",p):\n", + " break\n", + " elif not re.search(\"[0-9]\",p):\n", + " break\n", + " elif not re.search(\"[A-Z]\",p):\n", + " break\n", + " elif not re.search(\"[$#@]\",p):\n", + " break\n", + " elif re.search(\"\\s\",p):\n", + " break\n", + " else:\n", + " print(\"Valid Password\")\n", + " x=False\n", + " break\n", + "\n", + "if x:\n", + " print(\"Not a Valid Password\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/condition and loop/triangle_types.ipynb b/200 solved problems in Python/condition and loop/triangle_types.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..22c29c58038168d76ee637c69df9288c89becdcd --- /dev/null +++ b/200 solved problems in Python/condition and loop/triangle_types.ipynb @@ -0,0 +1,40 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to check a triangle is equilateral, isosceles or scalene.\n", + "# Note :\n", + "# An equilateral triangle is a triangle in which all three sides are equal.\n", + "# A scalene triangle is a triangle that has three unequal sides.\n", + "# An isosceles triangle is a triangle with (at least) two equal sides." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/condition and loop/triangle_types_solution.ipynb b/200 solved problems in Python/condition and loop/triangle_types_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..2cda0221f68640ae27015190bc594cde4930984c --- /dev/null +++ b/200 solved problems in Python/condition and loop/triangle_types_solution.ipynb @@ -0,0 +1,63 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input lengths of the triangle sides: \n", + "x: 1\n", + "y: 2\n", + "z: 3\n", + "Scalene triangle\n" + ] + } + ], + "source": [ + "# Write a Python program to check a triangle is equilateral, isosceles or scalene.\n", + "# Note :\n", + "# An equilateral triangle is a triangle in which all three sides are equal.\n", + "# A scalene triangle is a triangle that has three unequal sides.\n", + "# An isosceles triangle is a triangle with (at least) two equal sides.\n", + "\n", + "print(\"Input lengths of the triangle sides: \")\n", + "x = int(input(\"x: \"))\n", + "y = int(input(\"y: \"))\n", + "z = int(input(\"z: \"))\n", + "\n", + "if x == y == z:\n", + "\tprint(\"Equilateral triangle\")\n", + "elif x != y != z:\n", + "\tprint(\"Scalene triangle\")\n", + "else:\n", + "\tprint(\"isosceles triangle\") \n", + " " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/condition and loop/vowel_or_consonal.ipynb b/200 solved problems in Python/condition and loop/vowel_or_consonal.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..426b00d2cf50d2e177ae79ad17c9a619ff983114 --- /dev/null +++ b/200 solved problems in Python/condition and loop/vowel_or_consonal.ipynb @@ -0,0 +1,36 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to check whether an alphabet is a vowel or consonant\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/condition and loop/vowel_or_consonal_solution.ipynb b/200 solved problems in Python/condition and loop/vowel_or_consonal_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..e86696f16314db06647e82fb11bd2466287f186c --- /dev/null +++ b/200 solved problems in Python/condition and loop/vowel_or_consonal_solution.ipynb @@ -0,0 +1,52 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input a letter of the alphabet: d\n", + "d is a consonant.\n" + ] + } + ], + "source": [ + "# Write a Python program to check whether an alphabet is a vowel or consonant\n", + "\n", + "l = input(\"Input a letter of the alphabet: \")\n", + "\n", + "if l in ('a', 'e', 'i', 'o', 'u'):\n", + "\tprint(\"%s is a vowel.\" % l)\n", + "elif l == 'y':\n", + "\tprint(\"Sometimes letter y stand for vowel, sometimes stand for consonant.\")\n", + "else:\n", + "\tprint(\"%s is a consonant.\" % l) " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/datetime/convert.ipynb b/200 solved problems in Python/datetime/convert.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..7063934ec26f00c64b28fce30659473c102e8c4b --- /dev/null +++ b/200 solved problems in Python/datetime/convert.ipynb @@ -0,0 +1,36 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to convert Year/Month/Day to Day of Year in Python" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/datetime/convert_solution.ipynb b/200 solved problems in Python/datetime/convert_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..0c2860def90b80b2bbda03476f2e22802c533944 --- /dev/null +++ b/200 solved problems in Python/datetime/convert_solution.ipynb @@ -0,0 +1,46 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "260\n" + ] + } + ], + "source": [ + "# Write a Python program to convert Year/Month/Day to Day of Year in Python\n", + "import datetime\n", + "today = datetime.datetime.now()\n", + "day_of_year = (today - datetime.datetime(today.year, 1, 1)).days + 1\n", + "print(day_of_year)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/datetime/current_time.ipynb b/200 solved problems in Python/datetime/current_time.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..179d010af788daf9159be152c4a68c2321236836 --- /dev/null +++ b/200 solved problems in Python/datetime/current_time.ipynb @@ -0,0 +1,36 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to get the current time in Python." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/datetime/current_time_solution.ipynb b/200 solved problems in Python/datetime/current_time_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..65e6aad4f3e73e91ba183b1fcd7a6f1e1a43a822 --- /dev/null +++ b/200 solved problems in Python/datetime/current_time_solution.ipynb @@ -0,0 +1,44 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "11:28:36.782666\n" + ] + } + ], + "source": [ + "# Write a Python program to get the current time in Python.\n", + "import datetime\n", + "print(datetime.datetime.now().time())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/datetime/date_time_format.ipynb b/200 solved problems in Python/datetime/date_time_format.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..f504c84a02d577afe69956de9a3b325f8905dea8 --- /dev/null +++ b/200 solved problems in Python/datetime/date_time_format.ipynb @@ -0,0 +1,45 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python script to display the various Date Time formats.\n", + "\n", + "# a) Current date and time\n", + "# b) Current year\n", + "# c) Month of year\n", + "# d) Week number of the year\n", + "# e) Weekday of the week\n", + "# f) Day of year\n", + "# g) Day of the month\n", + "# h) Day of week" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/datetime/date_time_format_solution.ipynb b/200 solved problems in Python/datetime/date_time_format_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..cfe29c243dbf839a4e3d053d4a8a769e47d56bfd --- /dev/null +++ b/200 solved problems in Python/datetime/date_time_format_solution.ipynb @@ -0,0 +1,69 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current date and time: 2017-09-17 11:25:22.307885\n", + "Current year: 2017\n", + "Month of year: September\n", + "Week number of the year: 37\n", + "Weekday of the week: 0\n", + "Day of year: 260\n", + "Day of the month : 17\n", + "Day of week: Sunday\n" + ] + } + ], + "source": [ + "# Write a Python script to display the various Date Time formats.\n", + "\n", + "# a) Current date and time\n", + "# b) Current year\n", + "# c) Month of year\n", + "# d) Week number of the year\n", + "# e) Weekday of the week\n", + "# f) Day of year\n", + "# g) Day of the month\n", + "# h) Day of week\n", + "\n", + "import time\n", + "import datetime\n", + "print(\"Current date and time: \" , datetime.datetime.now())\n", + "print(\"Current year: \", datetime.date.today().strftime(\"%Y\"))\n", + "print(\"Month of year: \", datetime.date.today().strftime(\"%B\"))\n", + "print(\"Week number of the year: \", datetime.date.today().strftime(\"%W\"))\n", + "print(\"Weekday of the week: \", datetime.date.today().strftime(\"%w\"))\n", + "print(\"Day of year: \", datetime.date.today().strftime(\"%j\"))\n", + "print(\"Day of the month : \", datetime.date.today().strftime(\"%d\"))\n", + "print(\"Day of week: \", datetime.date.today().strftime(\"%A\"))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/datetime/millisecond.ipynb b/200 solved problems in Python/datetime/millisecond.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..0de86a416d3a94fdd42cc7f9191da488f174ab10 --- /dev/null +++ b/200 solved problems in Python/datetime/millisecond.ipynb @@ -0,0 +1,36 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to get current time in milliseconds in Python" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/datetime/millisecond_solution.ipynb b/200 solved problems in Python/datetime/millisecond_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..d831f48a807ef094a47fd11d3ed0bc664b45b222 --- /dev/null +++ b/200 solved problems in Python/datetime/millisecond_solution.ipynb @@ -0,0 +1,46 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1505622895427\n" + ] + } + ], + "source": [ + "# Write a Python program to get current time in milliseconds in Python\n", + "\n", + "import time\n", + "milli_sec = int(round(time.time() * 1000))\n", + "print(milli_sec)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/datetime/subtract_days.ipynb b/200 solved problems in Python/datetime/subtract_days.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..bb71b41b8553e668858924560e4dace0b9ef051f --- /dev/null +++ b/200 solved problems in Python/datetime/subtract_days.ipynb @@ -0,0 +1,36 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to subtract five days from current date" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/datetime/subtract_days_solution.ipynb b/200 solved problems in Python/datetime/subtract_days_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..7cff97026a586101a02e790fcca929489e8b9a43 --- /dev/null +++ b/200 solved problems in Python/datetime/subtract_days_solution.ipynb @@ -0,0 +1,48 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Current Date : 2017-09-17\n", + "5 days before Current Date : 2017-09-12\n" + ] + } + ], + "source": [ + "# Write a Python program to subtract five days from current date\n", + "\n", + "from datetime import date, timedelta\n", + "dt = date.today() - timedelta(5)\n", + "print('Current Date :',date.today())\n", + "print('5 days before Current Date :',dt)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/dictionary/.ipynb_checkpoints/check_key_exist-checkpoint.ipynb b/200 solved problems in Python/dictionary/.ipynb_checkpoints/check_key_exist-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..2b4654ec3caaead7e271e3ea8e474d56d4944b4b --- /dev/null +++ b/200 solved problems in Python/dictionary/.ipynb_checkpoints/check_key_exist-checkpoint.ipynb @@ -0,0 +1,46 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Check if a given key already exists in a dictionary\n", + "# input\n", + "# d = {1: 10, 2: 20, 3: 30, 4: 40, 5: 50, 6: 60}\n", + "# is_key_present(5)\n", + "# is_key_present(9)\n", + "# output\n", + "# Key is present in the dictionary \n", + "# Key is not present in the dictionary" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/dictionary/.ipynb_checkpoints/check_key_exist_solution-checkpoint.ipynb b/200 solved problems in Python/dictionary/.ipynb_checkpoints/check_key_exist_solution-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..29ba6ac816313870455dd558bcf9920b65cd76cc --- /dev/null +++ b/200 solved problems in Python/dictionary/.ipynb_checkpoints/check_key_exist_solution-checkpoint.ipynb @@ -0,0 +1,62 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Key is present in the dictionary\n", + "Key is not present in the dictionary\n" + ] + } + ], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Check if a given key already exists in a dictionary\n", + "# input\n", + "# d = {1: 10, 2: 20, 3: 30, 4: 40, 5: 50, 6: 60}\n", + "# is_key_present(5)\n", + "# is_key_present(9)\n", + "# output\n", + "# Key is present in the dictionary \n", + "# Key is not present in the dictionary\n", + "\n", + "d = {1: 10, 2: 20, 3: 30, 4: 40, 5: 50, 6: 60}\n", + "def is_key_present(x):\n", + " if x in d:\n", + " print('Key is present in the dictionary')\n", + " else:\n", + " print('Key is not present in the dictionary')\n", + "is_key_present(5)\n", + "is_key_present(9)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/dictionary/check_key_exist.ipynb b/200 solved problems in Python/dictionary/check_key_exist.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..2b4654ec3caaead7e271e3ea8e474d56d4944b4b --- /dev/null +++ b/200 solved problems in Python/dictionary/check_key_exist.ipynb @@ -0,0 +1,46 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Check if a given key already exists in a dictionary\n", + "# input\n", + "# d = {1: 10, 2: 20, 3: 30, 4: 40, 5: 50, 6: 60}\n", + "# is_key_present(5)\n", + "# is_key_present(9)\n", + "# output\n", + "# Key is present in the dictionary \n", + "# Key is not present in the dictionary" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/dictionary/check_key_exist_solution.ipynb b/200 solved problems in Python/dictionary/check_key_exist_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..29ba6ac816313870455dd558bcf9920b65cd76cc --- /dev/null +++ b/200 solved problems in Python/dictionary/check_key_exist_solution.ipynb @@ -0,0 +1,62 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Key is present in the dictionary\n", + "Key is not present in the dictionary\n" + ] + } + ], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Check if a given key already exists in a dictionary\n", + "# input\n", + "# d = {1: 10, 2: 20, 3: 30, 4: 40, 5: 50, 6: 60}\n", + "# is_key_present(5)\n", + "# is_key_present(9)\n", + "# output\n", + "# Key is present in the dictionary \n", + "# Key is not present in the dictionary\n", + "\n", + "d = {1: 10, 2: 20, 3: 30, 4: 40, 5: 50, 6: 60}\n", + "def is_key_present(x):\n", + " if x in d:\n", + " print('Key is present in the dictionary')\n", + " else:\n", + " print('Key is not present in the dictionary')\n", + "is_key_present(5)\n", + "is_key_present(9)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/dictionary/concatenate.ipynb b/200 solved problems in Python/dictionary/concatenate.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..28054106bda6e20bcfa10dc3eb4edcf43a82e011 --- /dev/null +++ b/200 solved problems in Python/dictionary/concatenate.ipynb @@ -0,0 +1,42 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python script to concatenate following dictionaries to create a new one\n", + "# Input\n", + "# dic1={1:10, 2:20}\n", + "# dic2={3:30, 4:40}\n", + "# dic3={5:50,6:60}\n", + "# Output\n", + "# {1: 10, 2: 20, 3: 30, 4: 40, 5: 50, 6: 60}" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/dictionary/concatenate_solution.ipynb b/200 solved problems in Python/dictionary/concatenate_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..cf86be0f578c06c7b19ccfa907c93793d192911d --- /dev/null +++ b/200 solved problems in Python/dictionary/concatenate_solution.ipynb @@ -0,0 +1,55 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{1: 10, 2: 20, 3: 30, 4: 40, 5: 50, 6: 60}\n" + ] + } + ], + "source": [ + "# Write a Python script to concatenate following dictionaries to create a new one\n", + "# Input\n", + "# dic1={1:10, 2:20}\n", + "# dic2={3:30, 4:40}\n", + "# dic3={5:50,6:60}\n", + "# Output\n", + "# {1: 10, 2: 20, 3: 30, 4: 40, 5: 50, 6: 60}\n", + "\n", + "dic1={1:10, 2:20}\n", + "dic2={3:30, 4:40}\n", + "dic3={5:50,6:60}\n", + "dic4 = {}\n", + "for d in (dic1, dic2, dic3): dic4.update(d)\n", + "print(dic4)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/dictionary/iterate_over_dictionary.ipynb b/200 solved problems in Python/dictionary/iterate_over_dictionary.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..b5dae7ad9395e15eb1e06c58c1fe8e5de11540ea --- /dev/null +++ b/200 solved problems in Python/dictionary/iterate_over_dictionary.ipynb @@ -0,0 +1,36 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to iterate over dictionaries using for loops.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/dictionary/iterate_over_dictionary_solution.ipynb b/200 solved problems in Python/dictionary/iterate_over_dictionary_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..7557169c5219e90d1b870e283ef5cbc95daa5646 --- /dev/null +++ b/200 solved problems in Python/dictionary/iterate_over_dictionary_solution.ipynb @@ -0,0 +1,49 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x -> 10\n", + "y -> 20\n", + "z -> 30\n" + ] + } + ], + "source": [ + "# Write a Python program to iterate over dictionaries using for loops.\n", + "\n", + "d = {'x': 10, 'y': 20, 'z': 30} \n", + "for dict_key, dict_value in d.items():\n", + " print(dict_key,'->',dict_value)\n", + "\t\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/dictionary/sort.ipynb b/200 solved problems in Python/dictionary/sort.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..b817de9fce622da13996ae1d20c6a68b3e5adf7b --- /dev/null +++ b/200 solved problems in Python/dictionary/sort.ipynb @@ -0,0 +1,39 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to sort (ascending and descending) a dictionary by value.\n", + "# Original dictionary : {0: 0, 1: 2, 2: 1, 3: 4, 4: 3} \n", + "# Dictionary in ascending order by value : [(0, 0), (1, 2), (2, 1), (3, 4), (4, 3)] \n", + "# Dictionary in descending order by value : [(4, 3), (3, 4), (2, 1), (1, 2), (0, 0)]\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/dictionary/sort_solution.ipynb b/200 solved problems in Python/dictionary/sort_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..07bc6a6541a7e9c516ee5c07363e51e01b948521 --- /dev/null +++ b/200 solved problems in Python/dictionary/sort_solution.ipynb @@ -0,0 +1,56 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original dictionary : {1: 2, 3: 4, 4: 3, 2: 1, 0: 0}\n", + "Dictionary in ascending order by value : [(0, 0), (1, 2), (2, 1), (3, 4), (4, 3)]\n", + "Dictionary in descending order by value : [(4, 3), (3, 4), (2, 1), (1, 2), (0, 0)]\n" + ] + } + ], + "source": [ + "# Write a Python program to sort (ascending and descending) a dictionary by value.\n", + "# Original dictionary : {0: 0, 1: 2, 2: 1, 3: 4, 4: 3} \n", + "# Dictionary in ascending order by value : [(0, 0), (1, 2), (2, 1), (3, 4), (4, 3)] \n", + "# Dictionary in descending order by value : [(4, 3), (3, 4), (2, 1), (1, 2), (0, 0)]\n", + "\n", + "import operator\n", + "\n", + "d = {1: 2, 3: 4, 4: 3, 2: 1, 0: 0}\n", + "print('Original dictionary : ',d)\n", + "sorted_d = sorted(d.items(), key=operator.itemgetter(0))\n", + "print('Dictionary in ascending order by value : ',sorted_d)\n", + "sorted_d = sorted(d.items(), key=operator.itemgetter(0),reverse=True)\n", + "print('Dictionary in descending order by value : ',sorted_d)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/dictionary/square.ipynb b/200 solved problems in Python/dictionary/square.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..a91dbc1d7c7aac9c620b7026df5e445417d4dbcd --- /dev/null +++ b/200 solved problems in Python/dictionary/square.ipynb @@ -0,0 +1,36 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python script to print a dictionary where the keys are numbers between 1 and 15 (both included) and the values are square of keys.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/dictionary/square_solution.ipynb b/200 solved problems in Python/dictionary/square_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..9bd0a7096f734c40205b7ae0136331bc3fa83ded --- /dev/null +++ b/200 solved problems in Python/dictionary/square_solution.ipynb @@ -0,0 +1,47 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36, 7: 49, 8: 64, 9: 81, 10: 100, 11: 121, 12: 144, 13: 169, 14: 196, 15: 225}\n" + ] + } + ], + "source": [ + "# Write a Python script to print a dictionary where the keys are numbers between 1 and 15 (both included) and the values are square of keys.\n", + "\n", + "d=dict()\n", + "for x in range(1,16):\n", + " d[x]=x**2\n", + "print(d)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/dictionary/sum_all_items.ipynb b/200 solved problems in Python/dictionary/sum_all_items.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..7e9e4734375e61a1aa8069482fbaa58c00cc2920 --- /dev/null +++ b/200 solved problems in Python/dictionary/sum_all_items.ipynb @@ -0,0 +1,38 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Sum all the items in a dictionary\n", + "# Input {'data1':100,'data2':-54,'data3':247}\n", + "# Output 293" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/dictionary/sum_all_items_solution.ipynb b/200 solved problems in Python/dictionary/sum_all_items_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..f31aa8702d9a81c274609493d59a44c0fb9ace2f --- /dev/null +++ b/200 solved problems in Python/dictionary/sum_all_items_solution.ipynb @@ -0,0 +1,47 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "293\n" + ] + } + ], + "source": [ + "# Sum all the items in a dictionary\n", + "# Input {'data1':100,'data2':-54,'data3':247}\n", + "# Output 293\n", + "\n", + "my_dict = {'data1':100,'data2':-54,'data3':247}\n", + "print(sum(my_dict.values()))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/file/.ipynb_checkpoints/file_size-checkpoint.ipynb b/200 solved problems in Python/file/.ipynb_checkpoints/file_size-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..7641a51b6f08487a33b7035f22f3f935c4affea8 --- /dev/null +++ b/200 solved problems in Python/file/.ipynb_checkpoints/file_size-checkpoint.ipynb @@ -0,0 +1,40 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program to get the file size of a plain file.\n", + "# Use test.txt file at same folder" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/file/.ipynb_checkpoints/file_size_solution-checkpoint.ipynb b/200 solved problems in Python/file/.ipynb_checkpoints/file_size_solution-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..9c920800d2316f3b172c325a406ed97a88b1d8a0 --- /dev/null +++ b/200 solved problems in Python/file/.ipynb_checkpoints/file_size_solution-checkpoint.ipynb @@ -0,0 +1,54 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File size in bytes of a plain file: 763\n" + ] + } + ], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program to get the file size of a plain file.\n", + "# Use test.txt file at same folder\n", + "\n", + "\n", + "def file_size(fname):\n", + " import os\n", + " statinfo = os.stat(fname)\n", + " return statinfo.st_size\n", + "\n", + "print(\"File size in bytes of a plain file: \",file_size(\"test.txt\"))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/file/file_size.ipynb b/200 solved problems in Python/file/file_size.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..7641a51b6f08487a33b7035f22f3f935c4affea8 --- /dev/null +++ b/200 solved problems in Python/file/file_size.ipynb @@ -0,0 +1,40 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program to get the file size of a plain file.\n", + "# Use test.txt file at same folder" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/file/file_size_solution.ipynb b/200 solved problems in Python/file/file_size_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..9c920800d2316f3b172c325a406ed97a88b1d8a0 --- /dev/null +++ b/200 solved problems in Python/file/file_size_solution.ipynb @@ -0,0 +1,54 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File size in bytes of a plain file: 763\n" + ] + } + ], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program to get the file size of a plain file.\n", + "# Use test.txt file at same folder\n", + "\n", + "\n", + "def file_size(fname):\n", + " import os\n", + " statinfo = os.stat(fname)\n", + " return statinfo.st_size\n", + "\n", + "print(\"File size in bytes of a plain file: \",file_size(\"test.txt\"))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/file/first_n_lines.ipynb b/200 solved problems in Python/file/first_n_lines.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..f61e114cfd103e9deff047a0fd2f8a3d272433e5 --- /dev/null +++ b/200 solved problems in Python/file/first_n_lines.ipynb @@ -0,0 +1,37 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to read first n lines of a file.\n", + "# Use test.txt file" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/file/first_n_lines_solution.ipynb b/200 solved problems in Python/file/first_n_lines_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..5c0c296c1acc60895b462626fe771d4d52b0e5dd --- /dev/null +++ b/200 solved problems in Python/file/first_n_lines_solution.ipynb @@ -0,0 +1,54 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "What is Python language? \n", + "\n", + "Python is a widely used high-level, general-purpose, interpreted, dynamic programming language.Its design philosophy emphasizes code readability, and its syntax allows programmers to express concepts in fewer lines of code than possible in \n", + "\n" + ] + } + ], + "source": [ + "# Write a Python program to read first n lines of a file.\n", + "# Use test.txt file\n", + "\n", + "def file_read_from_head(fname, nlines):\n", + " from itertools import islice\n", + " with open(fname) as f:\n", + " for line in islice(f, nlines):\n", + " print(line)\n", + " \n", + "file_read_from_head('test.txt',2)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/file/longest_word.ipynb b/200 solved problems in Python/file/longest_word.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..902437fde22d28c228226b01f121fb1a018be629 --- /dev/null +++ b/200 solved problems in Python/file/longest_word.ipynb @@ -0,0 +1,37 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a python program to find the longest words in a file\n", + "# Using test.txt file in same folder" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/file/longest_word_solution.ipynb b/200 solved problems in Python/file/longest_word_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..dfcc4f5b524a5f3c3b4cfb35d2e7a06552594b2d --- /dev/null +++ b/200 solved problems in Python/file/longest_word_solution.ipynb @@ -0,0 +1,51 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['general-purpose,', 'object-oriented,']\n" + ] + } + ], + "source": [ + "# Write a python program to find the longest words in a file\n", + "# Using text.txt file in same folder\n", + "\n", + "def longest_word(filename):\n", + " with open(filename, 'r') as infile:\n", + " words = infile.read().split()\n", + " max_len = len(max(words, key=len))\n", + " return [word for word in words if len(word) == max_len]\n", + "\n", + "print(longest_word('test.txt'))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/file/random_line.ipynb b/200 solved problems in Python/file/random_line.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..f4ae5218c1beb5d6724ed0030af6a8d19b7011ee --- /dev/null +++ b/200 solved problems in Python/file/random_line.ipynb @@ -0,0 +1,37 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to read a random line from a file.\n", + "# Using test.txt" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/file/random_line_solution.ipynb b/200 solved problems in Python/file/random_line_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..508475069219c1ff719de4746141af529f61c9c9 --- /dev/null +++ b/200 solved problems in Python/file/random_line_solution.ipynb @@ -0,0 +1,50 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "What is Python language? \n" + ] + } + ], + "source": [ + "# Write a Python program to read a random line from a file.\n", + "# Using test.txt\n", + "\n", + "import random\n", + "\n", + "def random_line(fname):\n", + " lines = open(fname).read().splitlines()\n", + " return random.choice(lines)\n", + "print(random_line('test.txt'))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/file/test.txt b/200 solved problems in Python/file/test.txt new file mode 100644 index 0000000000000000000000000000000000000000..cb2a56c0c20766d5ad34ddf5993060dccd2e6342 --- /dev/null +++ b/200 solved problems in Python/file/test.txt @@ -0,0 +1,4 @@ +What is Python language? +Python is a widely used high-level, general-purpose, interpreted, dynamic programming language.Its design philosophy emphasizes code readability, and its syntax allows programmers to express concepts in fewer lines of code than possible in +languages such as C++ or Java. +Python supports multiple programming paradigms, including object-oriented, imperative and functional programming or procedural styles.It features a dynamic type system and automatic memory management and has a large and comprehensive standard library.The best way we learn anything is by practice and exercise questions. We have started this section for those (beginner to intermediate) who are familiar with Python. \ No newline at end of file diff --git a/200 solved problems in Python/list/.ipynb_checkpoints/characters_to_string-checkpoint.ipynb b/200 solved problems in Python/list/.ipynb_checkpoints/characters_to_string-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..567e2bed52808a69ec6de8d29e6649d992567f8e --- /dev/null +++ b/200 solved problems in Python/list/.ipynb_checkpoints/characters_to_string-checkpoint.ipynb @@ -0,0 +1,41 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Convert a list of characters into a string\n", + "# Input ['a', 'b', 'c', 'd']\n", + "# Output abcd" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/list/.ipynb_checkpoints/characters_to_string_solution-checkpoint.ipynb b/200 solved problems in Python/list/.ipynb_checkpoints/characters_to_string_solution-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..3926a78d7349db5ed39fba0e5d964251cd7cdf3f --- /dev/null +++ b/200 solved problems in Python/list/.ipynb_checkpoints/characters_to_string_solution-checkpoint.ipynb @@ -0,0 +1,51 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "abcd\n" + ] + } + ], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Convert a list of characters into a string\n", + "# Input ['a', 'b', 'c', 'd']\n", + "# Output abcd\n", + "\n", + "s = ['a', 'b', 'c', 'd']\n", + "str1 = ''.join(s)\n", + "print(str1)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/list/characters_to_string.ipynb b/200 solved problems in Python/list/characters_to_string.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..567e2bed52808a69ec6de8d29e6649d992567f8e --- /dev/null +++ b/200 solved problems in Python/list/characters_to_string.ipynb @@ -0,0 +1,41 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Convert a list of characters into a string\n", + "# Input ['a', 'b', 'c', 'd']\n", + "# Output abcd" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/list/characters_to_string_solution.ipynb b/200 solved problems in Python/list/characters_to_string_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..3926a78d7349db5ed39fba0e5d964251cd7cdf3f --- /dev/null +++ b/200 solved problems in Python/list/characters_to_string_solution.ipynb @@ -0,0 +1,51 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "abcd\n" + ] + } + ], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Convert a list of characters into a string\n", + "# Input ['a', 'b', 'c', 'd']\n", + "# Output abcd\n", + "\n", + "s = ['a', 'b', 'c', 'd']\n", + "str1 = ''.join(s)\n", + "print(str1)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/list/check_a_list_contains_sublist.ipynb b/200 solved problems in Python/list/check_a_list_contains_sublist.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..67a505198a190122b578053a632b03ad95a9d583 --- /dev/null +++ b/200 solved problems in Python/list/check_a_list_contains_sublist.ipynb @@ -0,0 +1,43 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to check whether a list contains a sublist.\n", + "# Input\n", + "# a = [2,4,3,5,7]\n", + "# b = [4,3]\n", + "# c = [3,7]\n", + "# print(is_Sublist(a, b))\n", + "# print(is_Sublist(a, c))\n", + "# Output\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/list/check_a_list_contains_sublist_solution.ipynb b/200 solved problems in Python/list/check_a_list_contains_sublist_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..fb0347a630161a48b239f83b808914c2be8d308f --- /dev/null +++ b/200 solved problems in Python/list/check_a_list_contains_sublist_solution.ipynb @@ -0,0 +1,77 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "False\n" + ] + } + ], + "source": [ + "# Write a Python program to check whether a list contains a sublist.\n", + "# Input\n", + "# a = [2,4,3,5,7]\n", + "# b = [4,3]\n", + "# c = [3,7]\n", + "# print(is_Sublist(a, b))\n", + "# print(is_Sublist(a, c))\n", + "# Output\n", + "\n", + "def is_Sublist(l, s):\n", + "\tsub_set = False\n", + "\tif s == []:\n", + "\t\tsub_set = True\n", + "\telif s == l:\n", + "\t\tsub_set = True\n", + "\telif len(s) > len(l):\n", + "\t\tsub_set = False\n", + "\n", + "\telse:\n", + "\t\tfor i in range(len(l)):\n", + "\t\t\tif l[i] == s[0]:\n", + "\t\t\t\tn = 1\n", + "\t\t\t\twhile (n < len(s)) and (l[i+n] == s[n]):\n", + "\t\t\t\t\tn += 1\n", + "\t\t\t\t\n", + "\t\t\t\tif n == len(s):\n", + "\t\t\t\t\tsub_set = True\n", + "\n", + "\treturn sub_set\n", + "\n", + "a = [2,4,3,5,7]\n", + "b = [4,3]\n", + "c = [3,7]\n", + "print(is_Sublist(a, b))\n", + "print(is_Sublist(a, c))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/list/common_items.ipynb b/200 solved problems in Python/list/common_items.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..f41ac5ece7ec848e15d9cbb6c92f22baf53c8f3b --- /dev/null +++ b/200 solved problems in Python/list/common_items.ipynb @@ -0,0 +1,41 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to find common items from two lists.\n", + "# input\n", + "# color1 = \"Red\", \"Green\", \"Orange\", \"White\"\n", + "# color2 = \"Black\", \"Green\", \"White\", \"Pink\"\n", + "# output\n", + "# {'Green', 'White'}" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/list/common_items_solution.ipynb b/200 solved problems in Python/list/common_items_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..54c1074231dc7706b080031470065bdb8c9b8807 --- /dev/null +++ b/200 solved problems in Python/list/common_items_solution.ipynb @@ -0,0 +1,51 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'Green', 'White'}\n" + ] + } + ], + "source": [ + "# Write a Python program to find common items from two lists.\n", + "# input\n", + "# color1 = \"Red\", \"Green\", \"Orange\", \"White\"\n", + "# color2 = \"Black\", \"Green\", \"White\", \"Pink\"\n", + "# output\n", + "# {'Green', 'White'}\n", + "\n", + "color1 = \"Red\", \"Green\", \"Orange\", \"White\"\n", + "color2 = \"Black\", \"Green\", \"White\", \"Pink\"\n", + "print(set(color1) & set(color2))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/list/diff_between_2.ipynb b/200 solved problems in Python/list/diff_between_2.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..f5c187abe220dc388ef65734e8991c6ecaf1c9b1 --- /dev/null +++ b/200 solved problems in Python/list/diff_between_2.ipynb @@ -0,0 +1,41 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to get the difference between the two lists\n", + "# Input \n", + "# list1 = [1, 2, 3, 4]\n", + "# list2 = [1, 2]\n", + "# Output\n", + "# [3,4]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/list/diff_between_2_solution.ipynb b/200 solved problems in Python/list/diff_between_2_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..b954f55ad9de0b6449c03e83b3e1f2431248f83b --- /dev/null +++ b/200 solved problems in Python/list/diff_between_2_solution.ipynb @@ -0,0 +1,45 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to get the difference between the two lists\n", + "# Input \n", + "# list1 = [1, 2, 3, 4]\n", + "# list2 = [1, 2]\n", + "# Output\n", + "# [3,4]\n", + "\n", + "list1 = [1, 2, 3, 4]\n", + "list2 = [1, 2]\n", + "print(list(set(list1) - set(list2)))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/list/find_max.ipynb b/200 solved problems in Python/list/find_max.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..54fd495a5cfad5c7e1b0bba92d9b19ba8ccb27d1 --- /dev/null +++ b/200 solved problems in Python/list/find_max.ipynb @@ -0,0 +1,38 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to get the smallest number from a list.\n", + "# max_num_in_list([1, 2, -8, 0])\n", + "# return 2" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/list/find_max_solution.ipynb b/200 solved problems in Python/list/find_max_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..d7dfa2de2632b1c8e0478c73cea59c5ead6a0c3d --- /dev/null +++ b/200 solved problems in Python/list/find_max_solution.ipynb @@ -0,0 +1,52 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2\n" + ] + } + ], + "source": [ + "# Write a Python program to get the smallest number from a list.\n", + "# max_num_in_list([1, 2, -8, 0])\n", + "# return 2\n", + "\n", + "def max_num_in_list( list ):\n", + " max = list[ 0 ]\n", + " for a in list:\n", + " if a > max:\n", + " max = a\n", + " return max\n", + "print(max_num_in_list([1, 2, -8, 0]))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/list/frequency_of_elements.ipynb b/200 solved problems in Python/list/frequency_of_elements.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..337cfc4756651c19d158976fc3204a35d3743bcc --- /dev/null +++ b/200 solved problems in Python/list/frequency_of_elements.ipynb @@ -0,0 +1,40 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to get the frequency of the elements in a list.\n", + "# input\n", + "# my_list = [10,10,10,10,20,20,20,20,40,40,50,50,30]\n", + "# outout\n", + "# {10: 4, 20: 4, 40: 2, 50: 2, 30: 1}\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/list/frequency_of_elements_solution.ipynb b/200 solved problems in Python/list/frequency_of_elements_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..0cae8dc7c5e1b751fa3bdb39822e7807b978808f --- /dev/null +++ b/200 solved problems in Python/list/frequency_of_elements_solution.ipynb @@ -0,0 +1,53 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original List : [10, 10, 10, 10, 20, 20, 20, 20, 40, 40, 50, 50, 30]\n", + "Frequency of the elements in the List : Counter({10: 4, 20: 4, 40: 2, 50: 2, 30: 1})\n" + ] + } + ], + "source": [ + "# Write a Python program to get the frequency of the elements in a list.\n", + "# input\n", + "# my_list = [10,10,10,10,20,20,20,20,40,40,50,50,30]\n", + "# outout\n", + "# {10: 4, 20: 4, 40: 2, 50: 2, 30: 1}\n", + "\n", + "import collections\n", + "my_list = [10,10,10,10,20,20,20,20,40,40,50,50,30]\n", + "print(\"Original List : \",my_list)\n", + "ctr = collections.Counter(my_list)\n", + "print(\"Frequency of the elements in the List : \",ctr)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/list/permutations.ipynb b/200 solved problems in Python/list/permutations.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..18a1a00bfd6c30d7287b0d9a76f155d50b5f8648 --- /dev/null +++ b/200 solved problems in Python/list/permutations.ipynb @@ -0,0 +1,38 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to generate all permutations of a list in Python\n", + "# Input [1,2,3]\n", + "# Output [(1, 2, 3), (1, 3, 2), (2, 1, 3), (2, 3, 1), (3, 1, 2), (3, 2, 1)]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/list/permutations_solution.ipynb b/200 solved problems in Python/list/permutations_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..e84ba3a5bff272d0525f0ec9cd53051556cbb4aa --- /dev/null +++ b/200 solved problems in Python/list/permutations_solution.ipynb @@ -0,0 +1,47 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(1, 2, 3), (1, 3, 2), (2, 1, 3), (2, 3, 1), (3, 1, 2), (3, 2, 1)]\n" + ] + } + ], + "source": [ + "# Write a Python program to generate all permutations of a list in Python\n", + "# Input [1,2,3]\n", + "# Output [(1, 2, 3), (1, 3, 2), (2, 1, 3), (2, 3, 1), (3, 1, 2), (3, 2, 1)]\n", + "\n", + "import itertools\n", + "print(list(itertools.permutations([1,2,3])))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/list/remove_duplicate.ipynb b/200 solved problems in Python/list/remove_duplicate.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..07745978437f84989555794e292982de50ab764e --- /dev/null +++ b/200 solved problems in Python/list/remove_duplicate.ipynb @@ -0,0 +1,38 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to remove duplicates from a list.\n", + "# Input a = [10,20,30,20,10,50,60,40,80,50,40]\n", + "# Output {40, 10, 80, 50, 20, 60, 30} " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/list/remove_duplicate_solution.ipynb b/200 solved problems in Python/list/remove_duplicate_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..6d7ead0acd251be5d0ae549696eccab407be9dcf --- /dev/null +++ b/200 solved problems in Python/list/remove_duplicate_solution.ipynb @@ -0,0 +1,55 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[10, 20, 30, 50, 60, 40, 80]\n" + ] + } + ], + "source": [ + "# Write a Python program to remove duplicates from a list.\n", + "# Input a = [10,20,30,20,10,50,60,40,80,50,40]\n", + "# Output [10, 20, 30, 50, 60, 40, 80]\n", + "\n", + "a = [10,20,30,20,10,50,60,40,80,50,40]\n", + "\n", + "dup_items = set()\n", + "uniq_items = []\n", + "for x in a:\n", + " if x not in dup_items:\n", + " uniq_items.append(x)\n", + " dup_items.add(x)\n", + "\n", + "print(uniq_items)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/list/second_smallest.ipynb b/200 solved problems in Python/list/second_smallest.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..8ea78c902c3d66d919af4d5a0fee108effd18910 --- /dev/null +++ b/200 solved problems in Python/list/second_smallest.ipynb @@ -0,0 +1,40 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to find the second smallest number in a list.\n", + "# input\n", + "# second_smallest([1, 2, -8, -2, 0])\n", + "# output\n", + "# -2" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/list/second_smallest_solution.ipynb b/200 solved problems in Python/list/second_smallest_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..06aa416e5e0a39cc57905e9dd6b0bab92f3c4e64 --- /dev/null +++ b/200 solved problems in Python/list/second_smallest_solution.ipynb @@ -0,0 +1,51 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to find the second smallest number in a list.\n", + "# input\n", + "# second_smallest([1, 2, -8, -2, 0])\n", + "# output\n", + "# -2\n", + "\n", + "def second_smallest(numbers):\n", + " a1, a2 = float('inf'), float('inf')\n", + " for x in numbers:\n", + " if x <= a1:\n", + " a1, a2 = x, a1\n", + " elif x < a2:\n", + " a2 = x\n", + " return a2\n", + "\n", + "print(second_smallest([1, 2, -8, -2, 0]))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/list/sum_list.ipynb b/200 solved problems in Python/list/sum_list.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..f44d1df5034fc625d7359101563747942a0304bf --- /dev/null +++ b/200 solved problems in Python/list/sum_list.ipynb @@ -0,0 +1,38 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to sum all the items in a list\n", + "# Example sum_list([1,2,-8])\n", + "# Return -5" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/list/sum_list_solution.ipynb b/200 solved problems in Python/list/sum_list_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..09a032ddf9e5aeebaa1f89e68ba22e611dbde5db --- /dev/null +++ b/200 solved problems in Python/list/sum_list_solution.ipynb @@ -0,0 +1,51 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-5\n" + ] + } + ], + "source": [ + "# Write a Python program to sum all the items in a list\n", + "# Example sum_list([1,2,-8])\n", + "# Return -5\n", + "\n", + "def sum_list(items):\n", + " sum_numbers = 0\n", + " for x in items:\n", + " sum_numbers += x\n", + " return sum_numbers\n", + "print(sum_list([1,2,-8]))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/math/binary_to_decimal.ipynb b/200 solved problems in Python/math/binary_to_decimal.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..dd06c20f77db93a94f3754f44034ba47fa65412b --- /dev/null +++ b/200 solved problems in Python/math/binary_to_decimal.ipynb @@ -0,0 +1,36 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to convert a binary number to decimal number." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/math/binary_to_decimal_solution.ipynb b/200 solved problems in Python/math/binary_to_decimal_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..43ea9b95de5d662dfd7ce35801432b707a6bd7d5 --- /dev/null +++ b/200 solved problems in Python/math/binary_to_decimal_solution.ipynb @@ -0,0 +1,53 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input a binary number: 10001\n", + "The decimal value of the number is 17\n" + ] + } + ], + "source": [ + "# Write a Python program to convert a binary number to decimal number.\n", + "\n", + "\n", + "b_num = list(input(\"Input a binary number: \"))\n", + "value = 0\n", + "\n", + "for i in range(len(b_num)):\n", + "\tdigit = b_num.pop()\n", + "\tif digit == '1':\n", + "\t\tvalue = value + pow(2, i)\n", + "print(\"The decimal value of the number is\", value)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/math/flip_a_coin.ipynb b/200 solved problems in Python/math/flip_a_coin.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..86cec020456c796b899069336def7dc786291e74 --- /dev/null +++ b/200 solved problems in Python/math/flip_a_coin.ipynb @@ -0,0 +1,36 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to flip a coin 1000 times and count heads and tails." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/math/flip_a_coin_solution.ipynb b/200 solved problems in Python/math/flip_a_coin_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..549898046ef30ba0edcf55059c078854d0ac0fa3 --- /dev/null +++ b/200 solved problems in Python/math/flip_a_coin_solution.ipynb @@ -0,0 +1,59 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Heads: 5068\n", + "Tails: 4932\n" + ] + } + ], + "source": [ + "# Write a Python program to flip a coin 1000 times and count heads and tails.\n", + "\n", + "import random\n", + "import itertools\n", + "\n", + "results = {\n", + " 'heads': 0,\n", + " 'tails': 0,\n", + "}\n", + "\n", + "sides = list(results.keys())\n", + "\n", + "for i in range(10000):\n", + " results[random.choice(sides)] += 1\n", + "\n", + "print('Heads:', results['heads'])\n", + "print('Tails:', results['tails'])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/math/random.ipynb b/200 solved problems in Python/math/random.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..22f29c0ae575d8d70935d24442da6120990019c9 --- /dev/null +++ b/200 solved problems in Python/math/random.ipynb @@ -0,0 +1,34 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Write a Python program to generate a series of unique random numbers" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/math/random_solution.ipynb b/200 solved problems in Python/math/random_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..e12e6a149fadb1a9762eda1ebb1c9b5ec5da5165 --- /dev/null +++ b/200 solved problems in Python/math/random_solution.ipynb @@ -0,0 +1,149 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "40\n", + "52\n", + "72\n", + "0\n", + "91\n", + "11\n", + "1\n", + "33\n", + "69\n", + "8\n", + "76\n", + "48\n", + "42\n", + "35\n", + "51\n", + "6\n", + "17\n", + "61\n", + "50\n", + "44\n", + "85\n", + "94\n", + "83\n", + "23\n", + "2\n", + "66\n", + "99\n", + "43\n", + "78\n", + "89\n", + "93\n", + "7\n", + "29\n", + "87\n", + "58\n", + "64\n", + "67\n", + "22\n", + "63\n", + "5\n", + "30\n", + "45\n", + "95\n", + "26\n", + "37\n", + "62\n", + "21\n", + "4\n", + "41\n", + "81\n", + "31\n", + "15\n", + "80\n", + "9\n", + "74\n", + "53\n", + "25\n", + "47\n", + "16\n", + "57\n", + "3\n", + "54\n", + "36\n", + "71\n", + "34\n", + "92\n", + "38\n", + "70\n", + "90\n", + "75\n", + "39\n", + "59\n", + "68\n", + "96\n", + "97\n", + "32\n", + "82\n", + "77\n", + "55\n", + "27\n", + "79\n", + "24\n", + "20\n", + "10\n", + "12\n", + "86\n", + "49\n", + "56\n", + "60\n", + "46\n", + "28\n", + "18\n", + "65\n", + "14\n", + "88\n", + "73\n", + "98\n", + "19\n", + "13\n", + "84\n" + ] + } + ], + "source": [ + "# Write a Python program to generate a series of unique random numbers\n", + "\n", + "import random\n", + "\n", + "choices = list(range(100))\n", + "random.shuffle(choices)\n", + "print(choices.pop())\n", + "while choices:\n", + " print(choices.pop())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/math/round_up.ipynb b/200 solved problems in Python/math/round_up.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..d576d51607f46ab7f4add84a745e4d4cd9843883 --- /dev/null +++ b/200 solved problems in Python/math/round_up.ipynb @@ -0,0 +1,36 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python function to round up a number to specified digits." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/math/round_up_solution.ipynb b/200 solved problems in Python/math/round_up_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..ab6fd13eb728c09dcbc381e5d19e58a9bb1524f0 --- /dev/null +++ b/200 solved problems in Python/math/round_up_solution.ipynb @@ -0,0 +1,59 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original Number: 123.01247\n", + "124\n", + "123.1\n", + "123.02\n", + "123.013\n" + ] + } + ], + "source": [ + "# Write a Python function to round up a number to specified digits.\n", + "\n", + "import math\n", + "\n", + "def roundup(a, digits=0):\n", + " n = 10**-digits\n", + " return round(math.ceil(a / n) * n, digits)\n", + "\n", + "x = 123.01247\n", + "print(\"Original Number: \",x)\n", + "print(roundup(x, 0))\n", + "print(roundup(x, 1))\n", + "print(roundup(x, 2))\n", + "print(roundup(x, 3))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/math/standard_deviation.ipynb b/200 solved problems in Python/math/standard_deviation.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..39e466e8970d7dff081e2d338c5894f84d7f87bc --- /dev/null +++ b/200 solved problems in Python/math/standard_deviation.ipynb @@ -0,0 +1,40 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to calculate the standard deviation of the following data.\n", + "# Input\n", + "# Sample Data: [4, 2, 5, 8, 6] \n", + "# Output\n", + "# Standard Deviation : 2.23606797749979" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/math/standard_deviation_solution.ipynb b/200 solved problems in Python/math/standard_deviation_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..d370a9af4ae7db9d30a123bc46fc36c6ba32d044 --- /dev/null +++ b/200 solved problems in Python/math/standard_deviation_solution.ipynb @@ -0,0 +1,83 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sample Data: [4, 2, 5, 8, 6]\n", + "Standard Deviation : 2.23606797749979\n" + ] + } + ], + "source": [ + "# Write a Python program to calculate the standard deviation of the following data.\n", + "# Input\n", + "# Sample Data: [4, 2, 5, 8, 6] \n", + "# Output\n", + "# Standard Deviation : 2.23606797749979\n", + "\n", + "import math\n", + "import sys\n", + "\n", + "def sd_calc(data):\n", + " n = len(data)\n", + "\n", + " if n <= 1:\n", + " return 0.0\n", + "\n", + " mean, sd = avg_calc(data), 0.0\n", + "\n", + " # calculate stan. dev.\n", + " for el in data:\n", + " sd += (float(el) - mean)**2\n", + " sd = math.sqrt(sd / float(n-1))\n", + "\n", + " return sd\n", + "\n", + "\n", + "def avg_calc(ls):\n", + " n, mean = len(ls), 0.0\n", + "\n", + " if n <= 1:\n", + " return ls[0]\n", + "\n", + " # calculate average\n", + " for el in ls:\n", + " mean = mean + float(el)\n", + " mean = mean / float(n)\n", + "\n", + " return mean\n", + "\n", + "data = [4, 2, 5, 8, 6]\n", + "print(\"Sample Data: \",data)\n", + "print(\"Standard Deviation : \",sd_calc(data))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/numpy/.ipynb_checkpoints/100 Numpy exercises-checkpoint.ipynb b/200 solved problems in Python/numpy/.ipynb_checkpoints/100 Numpy exercises-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..c38ccbc16eda6ba170b43bea7d121e99e71e2e01 --- /dev/null +++ b/200 solved problems in Python/numpy/.ipynb_checkpoints/100 Numpy exercises-checkpoint.ipynb @@ -0,0 +1,1710 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "\n", + "# 100 numpy exercises\n", + "\n", + "This is a collection of exercises that have been collected in the numpy mailing list, on stack overflow and in the numpy documentation. The goal of this collection is to offer a quick reference for both old and new users but also to provide a set of exercices for those who teach." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1. Import the numpy package under the name `np` (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2. Print the numpy version and the configuration (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3. Create a null vector of size 10 (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4. How to find the memory size of any array (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5. How to get the documentation of the numpy add function from the command line? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 6. Create a null vector of size 10 but the fifth value which is 1 (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 7. Create a vector with values ranging from 10 to 49 (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 8. Reverse a vector (first element becomes last) (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 9. Create a 3x3 matrix with values ranging from 0 to 8 (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 10. Find indices of non-zero elements from \\[1,2,0,0,4,0\\] (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 11. Create a 3x3 identity matrix (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 12. Create a 3x3x3 array with random values (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 13. Create a 10x10 array with random values and find the minimum and maximum values (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 14. Create a random vector of size 30 and find the mean value (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 15. Create a 2d array with 1 on the border and 0 inside (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 16. How to add a border (filled with 0's) around an existing array? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 17. What is the result of the following expression? (★☆☆)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "0 * np.nan\n", + "np.nan == np.nan\n", + "np.inf > np.nan\n", + "np.nan - np.nan\n", + "0.3 == 3 * 0.1\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 18. Create a 5x5 matrix with values 1,2,3,4 just below the diagonal (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 19. Create a 8x8 matrix and fill it with a checkerboard pattern (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 20. Consider a (6,7,8) shape array, what is the index (x,y,z) of the 100th element?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 21. Create a checkerboard 8x8 matrix using the tile function (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 22. Normalize a 5x5 random matrix (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 23. Create a custom dtype that describes a color as four unsigned bytes (RGBA) (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 24. Multiply a 5x3 matrix by a 3x2 matrix (real matrix product) (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 25. Given a 1D array, negate all elements which are between 3 and 8, in place. (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 26. What is the output of the following script? (★☆☆)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "# Author: Jake VanderPlas\n", + "\n", + "print(sum(range(5),-1))\n", + "from numpy import *\n", + "print(sum(range(5),-1))\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 27. Consider an integer vector Z, which of these expressions are legal? (★☆☆)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "Z**Z\n", + "2 << Z >> 2\n", + "Z <- Z\n", + "1j*Z\n", + "Z/1/1\n", + "ZZ\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 28. What are the result of the following expressions?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "np.array(0) / np.array(0)\n", + "np.array(0) // np.array(0)\n", + "np.array([np.nan]).astype(int).astype(float)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 29. How to round away from zero a float array ? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 30. How to find common values between two arrays? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 31. How to ignore all numpy warnings (not recommended)? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 32. Is the following expressions true? (★☆☆)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "np.sqrt(-1) == np.emath.sqrt(-1)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 33. How to get the dates of yesterday, today and tomorrow? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 34. How to get all the dates corresponding to the month of July 2016? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 35. How to compute ((A+B)\\*(-A/2)) in place (without copy)? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 36. Extract the integer part of a random array using 5 different methods (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 37. Create a 5x5 matrix with row values ranging from 0 to 4 (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 38. Consider a generator function that generates 10 integers and use it to build an array (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 39. Create a vector of size 10 with values ranging from 0 to 1, both excluded (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 40. Create a random vector of size 10 and sort it (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 41. How to sum a small array faster than np.sum? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 42. Consider two random array A and B, check if they are equal (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 43. Make an array immutable (read-only) (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 44. Consider a random 10x2 matrix representing cartesian coordinates, convert them to polar coordinates (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 45. Create random vector of size 10 and replace the maximum value by 0 (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 46. Create a structured array with `x` and `y` coordinates covering the \\[0,1\\]x\\[0,1\\] area (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 47. Given two arrays, X and Y, construct the Cauchy matrix C (Cij =1/(xi - yj))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 48. Print the minimum and maximum representable value for each numpy scalar type (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 49. How to print all the values of an array? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 50. How to find the closest value (to a given scalar) in a vector? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 51. Create a structured array representing a position (x,y) and a color (r,g,b) (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 52. Consider a random vector with shape (100,2) representing coordinates, find point by point distances (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 53. How to convert a float (32 bits) array into an integer (32 bits) in place?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 54. How to read the following file? (★★☆)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```\n", + "1, 2, 3, 4, 5\n", + "6, , , 7, 8\n", + " , , 9,10,11\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 55. What is the equivalent of enumerate for numpy arrays? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 56. Generate a generic 2D Gaussian-like array (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 57. How to randomly place p elements in a 2D array? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 58. Subtract the mean of each row of a matrix (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 59. How to sort an array by the nth column? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 60. How to tell if a given 2D array has null columns? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 61. Find the nearest value from a given value in an array (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 62. Considering two arrays with shape (1,3) and (3,1), how to compute their sum using an iterator? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 63. Create an array class that has a name attribute (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 64. Consider a given vector, how to add 1 to each element indexed by a second vector (be careful with repeated indices)? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 65. How to accumulate elements of a vector (X) to an array (F) based on an index list (I)? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 66. Considering a (w,h,3) image of (dtype=ubyte), compute the number of unique colors (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 67. Considering a four dimensions array, how to get sum over the last two axis at once? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 68. Considering a one-dimensional vector D, how to compute means of subsets of D using a vector S of same size describing subset indices? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 69. How to get the diagonal of a dot product? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 70. Consider the vector \\[1, 2, 3, 4, 5\\], how to build a new vector with 3 consecutive zeros interleaved between each value? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 71. Consider an array of dimension (5,5,3), how to mulitply it by an array with dimensions (5,5)? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 72. How to swap two rows of an array? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 73. Consider a set of 10 triplets describing 10 triangles (with shared vertices), find the set of unique line segments composing all the triangles (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 74. Given an array C that is a bincount, how to produce an array A such that np.bincount(A) == C? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 75. How to compute averages using a sliding window over an array? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 76. Consider a one-dimensional array Z, build a two-dimensional array whose first row is (Z\\[0\\],Z\\[1\\],Z\\[2\\]) and each subsequent row is shifted by 1 (last row should be (Z\\[-3\\],Z\\[-2\\],Z\\[-1\\]) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 77. How to negate a boolean, or to change the sign of a float inplace? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 78. Consider 2 sets of points P0,P1 describing lines (2d) and a point p, how to compute distance from p to each line i (P0\\[i\\],P1\\[i\\])? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 79. Consider 2 sets of points P0,P1 describing lines (2d) and a set of points P, how to compute distance from each point j (P\\[j\\]) to each line i (P0\\[i\\],P1\\[i\\])? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 80. Consider an arbitrary array, write a function that extract a subpart with a fixed shape and centered on a given element (pad with a `fill` value when necessary) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 81. Consider an array Z = \\[1,2,3,4,5,6,7,8,9,10,11,12,13,14\\], how to generate an array R = \\[\\[1,2,3,4\\], \\[2,3,4,5\\], \\[3,4,5,6\\], ..., \\[11,12,13,14\\]\\]? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 82. Compute a matrix rank (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 83. How to find the most frequent value in an array?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 84. Extract all the contiguous 3x3 blocks from a random 10x10 matrix (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 85. Create a 2D array subclass such that Z\\[i,j\\] == Z\\[j,i\\] (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 86. Consider a set of p matrices wich shape (n,n) and a set of p vectors with shape (n,1). How to compute the sum of of the p matrix products at once? (result has shape (n,1)) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 87. Consider a 16x16 array, how to get the block-sum (block size is 4x4)? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 88. How to implement the Game of Life using numpy arrays? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 89. How to get the n largest values of an array (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 90. Given an arbitrary number of vectors, build the cartesian product (every combinations of every item) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true, + "scrolled": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 91. How to create a record array from a regular array? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 92. Consider a large vector Z, compute Z to the power of 3 using 3 different methods (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 93. Consider two arrays A and B of shape (8,3) and (2,2). How to find rows of A that contain elements of each row of B regardless of the order of the elements in B? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 94. Considering a 10x3 matrix, extract rows with unequal values (e.g. \\[2,2,3\\]) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 95. Convert a vector of ints into a matrix binary representation (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 96. Given a two dimensional array, how to extract unique rows? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 97. Considering 2 vectors A & B, write the einsum equivalent of inner, outer, sum, and mul function (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 98. Considering a path described by two vectors (X,Y), how to sample it using equidistant samples (★★★)?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 99. Given an integer n and a 2D array X, select from X the rows which can be interpreted as draws from a multinomial distribution with n degrees, i.e., the rows which only contain integers and which sum to n. (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 100. Compute bootstrapped 95% confidence intervals for the mean of a 1D array X (i.e., resample the elements of an array with replacement N times, compute the mean of each sample, and then compute percentiles over the means). (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/200 solved problems in Python/numpy/.ipynb_checkpoints/100 Numpy exercises_solution-checkpoint.ipynb b/200 solved problems in Python/numpy/.ipynb_checkpoints/100 Numpy exercises_solution-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..7d6eb76d68c2e89cfba5941a30a53461139095b1 --- /dev/null +++ b/200 solved problems in Python/numpy/.ipynb_checkpoints/100 Numpy exercises_solution-checkpoint.ipynb @@ -0,0 +1,2351 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "\n", + "# 100 numpy exercises\n", + "\n", + "This is a collection of exercises that have been collected in the numpy mailing list, on stack overflow and in the numpy documentation. The goal of this collection is to offer a quick reference for both old and new users but also to provide a set of exercices for those who teach." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1. Import the numpy package under the name `np` (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2. Print the numpy version and the configuration (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "print(np.__version__)\n", + "np.show_config()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3. Create a null vector of size 10 (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.zeros(10)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4. How to find the memory size of any array (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.zeros((10,10))\n", + "print(\"%d bytes\" % (Z.size * Z.itemsize))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5. How to get the documentation of the numpy add function from the command line? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%run `python -c \"import numpy; numpy.info(numpy.add)\"`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 6. Create a null vector of size 10 but the fifth value which is 1 (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.zeros(10)\n", + "Z[4] = 1\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 7. Create a vector with values ranging from 10 to 49 (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.arange(10,50)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 8. Reverse a vector (first element becomes last) (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.arange(50)\n", + "Z = Z[::-1]\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 9. Create a 3x3 matrix with values ranging from 0 to 8 (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.arange(9).reshape(3,3)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 10. Find indices of non-zero elements from \\[1,2,0,0,4,0\\] (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "nz = np.nonzero([1,2,0,0,4,0])\n", + "print(nz)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 11. Create a 3x3 identity matrix (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.eye(3)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 12. Create a 3x3x3 array with random values (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.random.random((3,3,3))\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 13. Create a 10x10 array with random values and find the minimum and maximum values (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.random.random((10,10))\n", + "Zmin, Zmax = Z.min(), Z.max()\n", + "print(Zmin, Zmax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 14. Create a random vector of size 30 and find the mean value (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.random.random(30)\n", + "m = Z.mean()\n", + "print(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 15. Create a 2d array with 1 on the border and 0 inside (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.ones((10,10))\n", + "Z[1:-1,1:-1] = 0\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 16. How to add a border (filled with 0's) around an existing array? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.ones((5,5))\n", + "Z = np.pad(Z, pad_width=1, mode='constant', constant_values=0)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 17. What is the result of the following expression? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "print(0 * np.nan)\n", + "print(np.nan == np.nan)\n", + "print(np.inf > np.nan)\n", + "print(np.nan - np.nan)\n", + "print(0.3 == 3 * 0.1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 18. Create a 5x5 matrix with values 1,2,3,4 just below the diagonal (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.diag(1+np.arange(4),k=-1)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 19. Create a 8x8 matrix and fill it with a checkerboard pattern (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.zeros((8,8),dtype=int)\n", + "Z[1::2,::2] = 1\n", + "Z[::2,1::2] = 1\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 20. Consider a (6,7,8) shape array, what is the index (x,y,z) of the 100th element?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "print(np.unravel_index(100,(6,7,8)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 21. Create a checkerboard 8x8 matrix using the tile function (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.tile( np.array([[0,1],[1,0]]), (4,4))\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 22. Normalize a 5x5 random matrix (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.random.random((5,5))\n", + "Zmax, Zmin = Z.max(), Z.min()\n", + "Z = (Z - Zmin)/(Zmax - Zmin)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 23. Create a custom dtype that describes a color as four unsigned bytes (RGBA) (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "color = np.dtype([(\"r\", np.ubyte, 1),\n", + " (\"g\", np.ubyte, 1),\n", + " (\"b\", np.ubyte, 1),\n", + " (\"a\", np.ubyte, 1)])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 24. Multiply a 5x3 matrix by a 3x2 matrix (real matrix product) (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.dot(np.ones((5,3)), np.ones((3,2)))\n", + "print(Z)\n", + "\n", + "# Alternative solution, in Python 3.5 and above\n", + "Z = np.ones((5,3)) @ np.ones((3,2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 25. Given a 1D array, negate all elements which are between 3 and 8, in place. (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Evgeni Burovski\n", + "\n", + "Z = np.arange(11)\n", + "Z[(3 < Z) & (Z <= 8)] *= -1\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 26. What is the output of the following script? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Jake VanderPlas\n", + "\n", + "print(sum(range(5),-1))\n", + "from numpy import *\n", + "print(sum(range(5),-1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 27. Consider an integer vector Z, which of these expressions are legal? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z**Z\n", + "2 << Z >> 2\n", + "Z <- Z\n", + "1j*Z\n", + "Z/1/1\n", + "ZZ" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 28. What are the result of the following expressions?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "print(np.array(0) / np.array(0))\n", + "print(np.array(0) // np.array(0))\n", + "print(np.array([np.nan]).astype(int).astype(float))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 29. How to round away from zero a float array ? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Charles R Harris\n", + "\n", + "Z = np.random.uniform(-10,+10,10)\n", + "print (np.copysign(np.ceil(np.abs(Z)), Z))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 30. How to find common values between two arrays? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z1 = np.random.randint(0,10,10)\n", + "Z2 = np.random.randint(0,10,10)\n", + "print(np.intersect1d(Z1,Z2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 31. How to ignore all numpy warnings (not recommended)? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Suicide mode on\n", + "defaults = np.seterr(all=\"ignore\")\n", + "Z = np.ones(1) / 0\n", + "\n", + "# Back to sanity\n", + "_ = np.seterr(**defaults)\n", + "\n", + "An equivalent way, with a context manager:\n", + "\n", + "with np.errstate(divide='ignore'):\n", + " Z = np.ones(1) / 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 32. Is the following expressions true? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "np.sqrt(-1) == np.emath.sqrt(-1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 33. How to get the dates of yesterday, today and tomorrow? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "yesterday = np.datetime64('today', 'D') - np.timedelta64(1, 'D')\n", + "today = np.datetime64('today', 'D')\n", + "tomorrow = np.datetime64('today', 'D') + np.timedelta64(1, 'D')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 34. How to get all the dates corresponding to the month of July 2016? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.arange('2016-07', '2016-08', dtype='datetime64[D]')\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 35. How to compute ((A+B)\\*(-A/2)) in place (without copy)? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "A = np.ones(3)*1\n", + "B = np.ones(3)*2\n", + "C = np.ones(3)*3\n", + "np.add(A,B,out=B)\n", + "np.divide(A,2,out=A)\n", + "np.negative(A,out=A)\n", + "np.multiply(A,B,out=A)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 36. Extract the integer part of a random array using 5 different methods (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.random.uniform(0,10,10)\n", + "\n", + "print (Z - Z%1)\n", + "print (np.floor(Z))\n", + "print (np.ceil(Z)-1)\n", + "print (Z.astype(int))\n", + "print (np.trunc(Z))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 37. Create a 5x5 matrix with row values ranging from 0 to 4 (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.zeros((5,5))\n", + "Z += np.arange(5)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 38. Consider a generator function that generates 10 integers and use it to build an array (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def generate():\n", + " for x in range(10):\n", + " yield x\n", + "Z = np.fromiter(generate(),dtype=float,count=-1)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 39. Create a vector of size 10 with values ranging from 0 to 1, both excluded (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.linspace(0,1,11,endpoint=False)[1:]\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 40. Create a random vector of size 10 and sort it (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.random.random(10)\n", + "Z.sort()\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 41. How to sum a small array faster than np.sum? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Evgeni Burovski\n", + "\n", + "Z = np.arange(10)\n", + "np.add.reduce(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 42. Consider two random array A and B, check if they are equal (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "A = np.random.randint(0,2,5)\n", + "B = np.random.randint(0,2,5)\n", + "\n", + "# Assuming identical shape of the arrays and a tolerance for the comparison of values\n", + "equal = np.allclose(A,B)\n", + "print(equal)\n", + "\n", + "# Checking both the shape and the element values, no tolerance (values have to be exactly equal)\n", + "equal = np.array_equal(A,B)\n", + "print(equal)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 43. Make an array immutable (read-only) (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.zeros(10)\n", + "Z.flags.writeable = False\n", + "Z[0] = 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 44. Consider a random 10x2 matrix representing cartesian coordinates, convert them to polar coordinates (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.random.random((10,2))\n", + "X,Y = Z[:,0], Z[:,1]\n", + "R = np.sqrt(X**2+Y**2)\n", + "T = np.arctan2(Y,X)\n", + "print(R)\n", + "print(T)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 45. Create random vector of size 10 and replace the maximum value by 0 (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.random.random(10)\n", + "Z[Z.argmax()] = 0\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 46. Create a structured array with `x` and `y` coordinates covering the \\[0,1\\]x\\[0,1\\] area (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.zeros((5,5), [('x',float),('y',float)])\n", + "Z['x'], Z['y'] = np.meshgrid(np.linspace(0,1,5),\n", + " np.linspace(0,1,5))\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 47. Given two arrays, X and Y, construct the Cauchy matrix C (Cij =1/(xi - yj))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Evgeni Burovski\n", + "\n", + "X = np.arange(8)\n", + "Y = X + 0.5\n", + "C = 1.0 / np.subtract.outer(X, Y)\n", + "print(np.linalg.det(C))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 48. Print the minimum and maximum representable value for each numpy scalar type (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "for dtype in [np.int8, np.int32, np.int64]:\n", + " print(np.iinfo(dtype).min)\n", + " print(np.iinfo(dtype).max)\n", + "for dtype in [np.float32, np.float64]:\n", + " print(np.finfo(dtype).min)\n", + " print(np.finfo(dtype).max)\n", + " print(np.finfo(dtype).eps)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 49. How to print all the values of an array? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "np.set_printoptions(threshold=np.nan)\n", + "Z = np.zeros((16,16))\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 50. How to find the closest value (to a given scalar) in a vector? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.arange(100)\n", + "v = np.random.uniform(0,100)\n", + "index = (np.abs(Z-v)).argmin()\n", + "print(Z[index])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 51. Create a structured array representing a position (x,y) and a color (r,g,b) (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.zeros(10, [ ('position', [ ('x', float, 1),\n", + " ('y', float, 1)]),\n", + " ('color', [ ('r', float, 1),\n", + " ('g', float, 1),\n", + " ('b', float, 1)])])\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 52. Consider a random vector with shape (100,2) representing coordinates, find point by point distances (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.random.random((10,2))\n", + "X,Y = np.atleast_2d(Z[:,0], Z[:,1])\n", + "D = np.sqrt( (X-X.T)**2 + (Y-Y.T)**2)\n", + "print(D)\n", + "\n", + "# Much faster with scipy\n", + "import scipy\n", + "# Thanks Gavin Heverly-Coulson (#issue 1)\n", + "import scipy.spatial\n", + "\n", + "Z = np.random.random((10,2))\n", + "D = scipy.spatial.distance.cdist(Z,Z)\n", + "print(D)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 53. How to convert a float (32 bits) array into an integer (32 bits) in place?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.arange(10, dtype=np.int32)\n", + "Z = Z.astype(np.float32, copy=False)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 54. How to read the following file? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from io import StringIO\n", + "\n", + "# Fake file \n", + "s = StringIO(\"\"\"1, 2, 3, 4, 5\\n\n", + " 6, , , 7, 8\\n\n", + " , , 9,10,11\\n\"\"\")\n", + "Z = np.genfromtxt(s, delimiter=\",\", dtype=np.int)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 55. What is the equivalent of enumerate for numpy arrays? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.arange(9).reshape(3,3)\n", + "for index, value in np.ndenumerate(Z):\n", + " print(index, value)\n", + "for index in np.ndindex(Z.shape):\n", + " print(index, Z[index])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 56. Generate a generic 2D Gaussian-like array (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "X, Y = np.meshgrid(np.linspace(-1,1,10), np.linspace(-1,1,10))\n", + "D = np.sqrt(X*X+Y*Y)\n", + "sigma, mu = 1.0, 0.0\n", + "G = np.exp(-( (D-mu)**2 / ( 2.0 * sigma**2 ) ) )\n", + "print(G)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 57. How to randomly place p elements in a 2D array? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Divakar\n", + "\n", + "n = 10\n", + "p = 3\n", + "Z = np.zeros((n,n))\n", + "np.put(Z, np.random.choice(range(n*n), p, replace=False),1)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 58. Subtract the mean of each row of a matrix (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Warren Weckesser\n", + "\n", + "X = np.random.rand(5, 10)\n", + "\n", + "# Recent versions of numpy\n", + "Y = X - X.mean(axis=1, keepdims=True)\n", + "\n", + "# Older versions of numpy\n", + "Y = X - X.mean(axis=1).reshape(-1, 1)\n", + "\n", + "print(Y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 59. How to sort an array by the nth column? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Steve Tjoa\n", + "\n", + "Z = np.random.randint(0,10,(3,3))\n", + "print(Z)\n", + "print(Z[Z[:,1].argsort()])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 60. How to tell if a given 2D array has null columns? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Warren Weckesser\n", + "\n", + "Z = np.random.randint(0,3,(3,10))\n", + "print((~Z.any(axis=0)).any())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 61. Find the nearest value from a given value in an array (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.random.uniform(0,1,10)\n", + "z = 0.5\n", + "m = Z.flat[np.abs(Z - z).argmin()]\n", + "print(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 62. Considering two arrays with shape (1,3) and (3,1), how to compute their sum using an iterator? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "A = np.arange(3).reshape(3,1)\n", + "B = np.arange(3).reshape(1,3)\n", + "it = np.nditer([A,B,None])\n", + "for x,y,z in it: z[...] = x + y\n", + "print(it.operands[2])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 63. Create an array class that has a name attribute (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class NamedArray(np.ndarray):\n", + " def __new__(cls, array, name=\"no name\"):\n", + " obj = np.asarray(array).view(cls)\n", + " obj.name = name\n", + " return obj\n", + " def __array_finalize__(self, obj):\n", + " if obj is None: return\n", + " self.info = getattr(obj, 'name', \"no name\")\n", + "\n", + "Z = NamedArray(np.arange(10), \"range_10\")\n", + "print (Z.name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 64. Consider a given vector, how to add 1 to each element indexed by a second vector (be careful with repeated indices)? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Brett Olsen\n", + "\n", + "Z = np.ones(10)\n", + "I = np.random.randint(0,len(Z),20)\n", + "Z += np.bincount(I, minlength=len(Z))\n", + "print(Z)\n", + "\n", + "# Another solution\n", + "# Author: Bartosz Telenczuk\n", + "np.add.at(Z, I, 1)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 65. How to accumulate elements of a vector (X) to an array (F) based on an index list (I)? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Alan G Isaac\n", + "\n", + "X = [1,2,3,4,5,6]\n", + "I = [1,3,9,3,4,1]\n", + "F = np.bincount(I,X)\n", + "print(F)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 66. Considering a (w,h,3) image of (dtype=ubyte), compute the number of unique colors (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Nadav Horesh\n", + "\n", + "w,h = 16,16\n", + "I = np.random.randint(0,2,(h,w,3)).astype(np.ubyte)\n", + "#Note that we should compute 256*256 first. \n", + "#Otherwise numpy will only promote F.dtype to 'uint16' and overfolw will occur\n", + "F = I[...,0]*(256*256) + I[...,1]*256 +I[...,2]\n", + "n = len(np.unique(F))\n", + "print(n)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 67. Considering a four dimensions array, how to get sum over the last two axis at once? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "A = np.random.randint(0,10,(3,4,3,4))\n", + "# solution by passing a tuple of axes (introduced in numpy 1.7.0)\n", + "sum = A.sum(axis=(-2,-1))\n", + "print(sum)\n", + "# solution by flattening the last two dimensions into one\n", + "# (useful for functions that don't accept tuples for axis argument)\n", + "sum = A.reshape(A.shape[:-2] + (-1,)).sum(axis=-1)\n", + "print(sum)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 68. Considering a one-dimensional vector D, how to compute means of subsets of D using a vector S of same size describing subset indices? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Jaime Fernández del Río\n", + "\n", + "D = np.random.uniform(0,1,100)\n", + "S = np.random.randint(0,10,100)\n", + "D_sums = np.bincount(S, weights=D)\n", + "D_counts = np.bincount(S)\n", + "D_means = D_sums / D_counts\n", + "print(D_means)\n", + "\n", + "# Pandas solution as a reference due to more intuitive code\n", + "import pandas as pd\n", + "print(pd.Series(D).groupby(S).mean())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 69. How to get the diagonal of a dot product? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Mathieu Blondel\n", + "\n", + "A = np.random.uniform(0,1,(5,5))\n", + "B = np.random.uniform(0,1,(5,5))\n", + "\n", + "# Slow version \n", + "np.diag(np.dot(A, B))\n", + "\n", + "# Fast version\n", + "np.sum(A * B.T, axis=1)\n", + "\n", + "# Faster version\n", + "np.einsum(\"ij,ji->i\", A, B)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 70. Consider the vector \\[1, 2, 3, 4, 5\\], how to build a new vector with 3 consecutive zeros interleaved between each value? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Warren Weckesser\n", + "\n", + "Z = np.array([1,2,3,4,5])\n", + "nz = 3\n", + "Z0 = np.zeros(len(Z) + (len(Z)-1)*(nz))\n", + "Z0[::nz+1] = Z\n", + "print(Z0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 71. Consider an array of dimension (5,5,3), how to mulitply it by an array with dimensions (5,5)? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "A = np.ones((5,5,3))\n", + "B = 2*np.ones((5,5))\n", + "print(A * B[:,:,None])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 72. How to swap two rows of an array? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Eelco Hoogendoorn\n", + "\n", + "A = np.arange(25).reshape(5,5)\n", + "A[[0,1]] = A[[1,0]]\n", + "print(A)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 73. Consider a set of 10 triplets describing 10 triangles (with shared vertices), find the set of unique line segments composing all the triangles (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Nicolas P. Rougier\n", + "\n", + "faces = np.random.randint(0,100,(10,3))\n", + "F = np.roll(faces.repeat(2,axis=1),-1,axis=1)\n", + "F = F.reshape(len(F)*3,2)\n", + "F = np.sort(F,axis=1)\n", + "G = F.view( dtype=[('p0',F.dtype),('p1',F.dtype)] )\n", + "G = np.unique(G)\n", + "print(G)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 74. Given an array C that is a bincount, how to produce an array A such that np.bincount(A) == C? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Jaime Fernández del Río\n", + "\n", + "C = np.bincount([1,1,2,3,4,4,6])\n", + "A = np.repeat(np.arange(len(C)), C)\n", + "print(A)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 75. How to compute averages using a sliding window over an array? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Jaime Fernández del Río\n", + "\n", + "def moving_average(a, n=3) :\n", + " ret = np.cumsum(a, dtype=float)\n", + " ret[n:] = ret[n:] - ret[:-n]\n", + " return ret[n - 1:] / n\n", + "Z = np.arange(20)\n", + "print(moving_average(Z, n=3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 76. Consider a one-dimensional array Z, build a two-dimensional array whose first row is (Z\\[0\\],Z\\[1\\],Z\\[2\\]) and each subsequent row is shifted by 1 (last row should be (Z\\[-3\\],Z\\[-2\\],Z\\[-1\\]) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Joe Kington / Erik Rigtorp\n", + "from numpy.lib import stride_tricks\n", + "\n", + "def rolling(a, window):\n", + " shape = (a.size - window + 1, window)\n", + " strides = (a.itemsize, a.itemsize)\n", + " return stride_tricks.as_strided(a, shape=shape, strides=strides)\n", + "Z = rolling(np.arange(10), 3)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 77. How to negate a boolean, or to change the sign of a float inplace? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Nathaniel J. Smith\n", + "\n", + "Z = np.random.randint(0,2,100)\n", + "np.logical_not(Z, out=Z)\n", + "\n", + "Z = np.random.uniform(-1.0,1.0,100)\n", + "np.negative(Z, out=Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 78. Consider 2 sets of points P0,P1 describing lines (2d) and a point p, how to compute distance from p to each line i (P0\\[i\\],P1\\[i\\])? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def distance(P0, P1, p):\n", + " T = P1 - P0\n", + " L = (T**2).sum(axis=1)\n", + " U = -((P0[:,0]-p[...,0])*T[:,0] + (P0[:,1]-p[...,1])*T[:,1]) / L\n", + " U = U.reshape(len(U),1)\n", + " D = P0 + U*T - p\n", + " return np.sqrt((D**2).sum(axis=1))\n", + "\n", + "P0 = np.random.uniform(-10,10,(10,2))\n", + "P1 = np.random.uniform(-10,10,(10,2))\n", + "p = np.random.uniform(-10,10,( 1,2))\n", + "print(distance(P0, P1, p))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 79. Consider 2 sets of points P0,P1 describing lines (2d) and a set of points P, how to compute distance from each point j (P\\[j\\]) to each line i (P0\\[i\\],P1\\[i\\])? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Italmassov Kuanysh\n", + "\n", + "# based on distance function from previous question\n", + "P0 = np.random.uniform(-10, 10, (10,2))\n", + "P1 = np.random.uniform(-10,10,(10,2))\n", + "p = np.random.uniform(-10, 10, (10,2))\n", + "print(np.array([distance(P0,P1,p_i) for p_i in p]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 80. Consider an arbitrary array, write a function that extract a subpart with a fixed shape and centered on a given element (pad with a `fill` value when necessary) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Nicolas Rougier\n", + "\n", + "Z = np.random.randint(0,10,(10,10))\n", + "shape = (5,5)\n", + "fill = 0\n", + "position = (1,1)\n", + "\n", + "R = np.ones(shape, dtype=Z.dtype)*fill\n", + "P = np.array(list(position)).astype(int)\n", + "Rs = np.array(list(R.shape)).astype(int)\n", + "Zs = np.array(list(Z.shape)).astype(int)\n", + "\n", + "R_start = np.zeros((len(shape),)).astype(int)\n", + "R_stop = np.array(list(shape)).astype(int)\n", + "Z_start = (P-Rs//2)\n", + "Z_stop = (P+Rs//2)+Rs%2\n", + "\n", + "R_start = (R_start - np.minimum(Z_start,0)).tolist()\n", + "Z_start = (np.maximum(Z_start,0)).tolist()\n", + "R_stop = np.maximum(R_start, (R_stop - np.maximum(Z_stop-Zs,0))).tolist()\n", + "Z_stop = (np.minimum(Z_stop,Zs)).tolist()\n", + "\n", + "r = [slice(start,stop) for start,stop in zip(R_start,R_stop)]\n", + "z = [slice(start,stop) for start,stop in zip(Z_start,Z_stop)]\n", + "R[r] = Z[z]\n", + "print(Z)\n", + "print(R)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 81. Consider an array Z = \\[1,2,3,4,5,6,7,8,9,10,11,12,13,14\\], how to generate an array R = \\[\\[1,2,3,4\\], \\[2,3,4,5\\], \\[3,4,5,6\\], ..., \\[11,12,13,14\\]\\]? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Stefan van der Walt\n", + "\n", + "Z = np.arange(1,15,dtype=np.uint32)\n", + "R = stride_tricks.as_strided(Z,(11,4),(4,4))\n", + "print(R)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 82. Compute a matrix rank (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Stefan van der Walt\n", + "\n", + "Z = np.random.uniform(0,1,(10,10))\n", + "U, S, V = np.linalg.svd(Z) # Singular Value Decomposition\n", + "rank = np.sum(S > 1e-10)\n", + "print(rank)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 83. How to find the most frequent value in an array?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.random.randint(0,10,50)\n", + "print(np.bincount(Z).argmax())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 84. Extract all the contiguous 3x3 blocks from a random 10x10 matrix (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Chris Barker\n", + "\n", + "Z = np.random.randint(0,5,(10,10))\n", + "n = 3\n", + "i = 1 + (Z.shape[0]-3)\n", + "j = 1 + (Z.shape[1]-3)\n", + "C = stride_tricks.as_strided(Z, shape=(i, j, n, n), strides=Z.strides + Z.strides)\n", + "print(C)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 85. Create a 2D array subclass such that Z\\[i,j\\] == Z\\[j,i\\] (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Eric O. Lebigot\n", + "# Note: only works for 2d array and value setting using indices\n", + "\n", + "class Symetric(np.ndarray):\n", + " def __setitem__(self, index, value):\n", + " i,j = index\n", + " super(Symetric, self).__setitem__((i,j), value)\n", + " super(Symetric, self).__setitem__((j,i), value)\n", + "\n", + "def symetric(Z):\n", + " return np.asarray(Z + Z.T - np.diag(Z.diagonal())).view(Symetric)\n", + "\n", + "S = symetric(np.random.randint(0,10,(5,5)))\n", + "S[2,3] = 42\n", + "print(S)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 86. Consider a set of p matrices wich shape (n,n) and a set of p vectors with shape (n,1). How to compute the sum of of the p matrix products at once? (result has shape (n,1)) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Stefan van der Walt\n", + "\n", + "p, n = 10, 20\n", + "M = np.ones((p,n,n))\n", + "V = np.ones((p,n,1))\n", + "S = np.tensordot(M, V, axes=[[0, 2], [0, 1]])\n", + "print(S)\n", + "\n", + "# It works, because:\n", + "# M is (p,n,n)\n", + "# V is (p,n,1)\n", + "# Thus, summing over the paired axes 0 and 0 (of M and V independently),\n", + "# and 2 and 1, to remain with a (n,1) vector." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 87. Consider a 16x16 array, how to get the block-sum (block size is 4x4)? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Robert Kern\n", + "\n", + "Z = np.ones((16,16))\n", + "k = 4\n", + "S = np.add.reduceat(np.add.reduceat(Z, np.arange(0, Z.shape[0], k), axis=0),\n", + " np.arange(0, Z.shape[1], k), axis=1)\n", + "print(S)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 88. How to implement the Game of Life using numpy arrays? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Nicolas Rougier\n", + "\n", + "def iterate(Z):\n", + " # Count neighbours\n", + " N = (Z[0:-2,0:-2] + Z[0:-2,1:-1] + Z[0:-2,2:] +\n", + " Z[1:-1,0:-2] + Z[1:-1,2:] +\n", + " Z[2: ,0:-2] + Z[2: ,1:-1] + Z[2: ,2:])\n", + "\n", + " # Apply rules\n", + " birth = (N==3) & (Z[1:-1,1:-1]==0)\n", + " survive = ((N==2) | (N==3)) & (Z[1:-1,1:-1]==1)\n", + " Z[...] = 0\n", + " Z[1:-1,1:-1][birth | survive] = 1\n", + " return Z\n", + "\n", + "Z = np.random.randint(0,2,(50,50))\n", + "for i in range(100): Z = iterate(Z)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 89. How to get the n largest values of an array (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.arange(10000)\n", + "np.random.shuffle(Z)\n", + "n = 5\n", + "\n", + "# Slow\n", + "print (Z[np.argsort(Z)[-n:]])\n", + "\n", + "# Fast\n", + "print (Z[np.argpartition(-Z,n)[:n]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 90. Given an arbitrary number of vectors, build the cartesian product (every combinations of every item) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true, + "scrolled": true + }, + "outputs": [], + "source": [ + "# Author: Stefan Van der Walt\n", + "\n", + "def cartesian(arrays):\n", + " arrays = [np.asarray(a) for a in arrays]\n", + " shape = (len(x) for x in arrays)\n", + "\n", + " ix = np.indices(shape, dtype=int)\n", + " ix = ix.reshape(len(arrays), -1).T\n", + "\n", + " for n, arr in enumerate(arrays):\n", + " ix[:, n] = arrays[n][ix[:, n]]\n", + "\n", + " return ix\n", + "\n", + "print (cartesian(([1, 2, 3], [4, 5], [6, 7])))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 91. How to create a record array from a regular array? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.array([(\"Hello\", 2.5, 3),\n", + " (\"World\", 3.6, 2)])\n", + "R = np.core.records.fromarrays(Z.T, \n", + " names='col1, col2, col3',\n", + " formats = 'S8, f8, i8')\n", + "print(R)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 92. Consider a large vector Z, compute Z to the power of 3 using 3 different methods (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Ryan G.\n", + "\n", + "x = np.random.rand(5e7)\n", + "\n", + "%timeit np.power(x,3)\n", + "%timeit x*x*x\n", + "%timeit np.einsum('i,i,i->i',x,x,x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 93. Consider two arrays A and B of shape (8,3) and (2,2). How to find rows of A that contain elements of each row of B regardless of the order of the elements in B? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Gabe Schwartz\n", + "\n", + "A = np.random.randint(0,5,(8,3))\n", + "B = np.random.randint(0,5,(2,2))\n", + "\n", + "C = (A[..., np.newaxis, np.newaxis] == B)\n", + "rows = np.where(C.any((3,1)).all(1))[0]\n", + "print(rows)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 94. Considering a 10x3 matrix, extract rows with unequal values (e.g. \\[2,2,3\\]) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Robert Kern\n", + "\n", + "Z = np.random.randint(0,5,(10,3))\n", + "print(Z)\n", + "# solution for arrays of all dtypes (including string arrays and record arrays)\n", + "E = np.all(Z[:,1:] == Z[:,:-1], axis=1)\n", + "U = Z[~E]\n", + "print(U)\n", + "# soluiton for numerical arrays only, will work for any number of columns in Z\n", + "U = Z[Z.max(axis=1) != Z.min(axis=1),:]\n", + "print(U)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 95. Convert a vector of ints into a matrix binary representation (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Warren Weckesser\n", + "\n", + "I = np.array([0, 1, 2, 3, 15, 16, 32, 64, 128])\n", + "B = ((I.reshape(-1,1) & (2**np.arange(8))) != 0).astype(int)\n", + "print(B[:,::-1])\n", + "\n", + "# Author: Daniel T. McDonald\n", + "\n", + "I = np.array([0, 1, 2, 3, 15, 16, 32, 64, 128], dtype=np.uint8)\n", + "print(np.unpackbits(I[:, np.newaxis], axis=1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 96. Given a two dimensional array, how to extract unique rows? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Jaime Fernández del Río\n", + "\n", + "Z = np.random.randint(0,2,(6,3))\n", + "T = np.ascontiguousarray(Z).view(np.dtype((np.void, Z.dtype.itemsize * Z.shape[1])))\n", + "_, idx = np.unique(T, return_index=True)\n", + "uZ = Z[idx]\n", + "print(uZ)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 97. Considering 2 vectors A & B, write the einsum equivalent of inner, outer, sum, and mul function (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "A = np.random.uniform(0,1,10)\n", + "B = np.random.uniform(0,1,10)\n", + "\n", + "np.einsum('i->', A) # np.sum(A)\n", + "np.einsum('i,i->i', A, B) # A * B\n", + "np.einsum('i,i', A, B) # np.inner(A, B)\n", + "np.einsum('i,j->ij', A, B) # np.outer(A, B)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 98. Considering a path described by two vectors (X,Y), how to sample it using equidistant samples (★★★)?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Bas Swinckels\n", + "\n", + "phi = np.arange(0, 10*np.pi, 0.1)\n", + "a = 1\n", + "x = a*phi*np.cos(phi)\n", + "y = a*phi*np.sin(phi)\n", + "\n", + "dr = (np.diff(x)**2 + np.diff(y)**2)**.5 # segment lengths\n", + "r = np.zeros_like(x)\n", + "r[1:] = np.cumsum(dr) # integrate path\n", + "r_int = np.linspace(0, r.max(), 200) # regular spaced path\n", + "x_int = np.interp(r_int, r, x) # integrate path\n", + "y_int = np.interp(r_int, r, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 99. Given an integer n and a 2D array X, select from X the rows which can be interpreted as draws from a multinomial distribution with n degrees, i.e., the rows which only contain integers and which sum to n. (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Evgeni Burovski\n", + "\n", + "X = np.asarray([[1.0, 0.0, 3.0, 8.0],\n", + " [2.0, 0.0, 1.0, 1.0],\n", + " [1.5, 2.5, 1.0, 0.0]])\n", + "n = 4\n", + "M = np.logical_and.reduce(np.mod(X, 1) == 0, axis=-1)\n", + "M &= (X.sum(axis=-1) == n)\n", + "print(X[M])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 100. Compute bootstrapped 95% confidence intervals for the mean of a 1D array X (i.e., resample the elements of an array with replacement N times, compute the mean of each sample, and then compute percentiles over the means). (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Jessica B. Hamrick\n", + "\n", + "X = np.random.randn(100) # random 1D array\n", + "N = 1000 # number of bootstrap samples\n", + "idx = np.random.randint(0, X.size, (N, X.size))\n", + "means = X[idx].mean(axis=1)\n", + "confint = np.percentile(means, [2.5, 97.5])\n", + "print(confint)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/200 solved problems in Python/numpy/100 Numpy exercises.ipynb b/200 solved problems in Python/numpy/100 Numpy exercises.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..c38ccbc16eda6ba170b43bea7d121e99e71e2e01 --- /dev/null +++ b/200 solved problems in Python/numpy/100 Numpy exercises.ipynb @@ -0,0 +1,1710 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "\n", + "# 100 numpy exercises\n", + "\n", + "This is a collection of exercises that have been collected in the numpy mailing list, on stack overflow and in the numpy documentation. The goal of this collection is to offer a quick reference for both old and new users but also to provide a set of exercices for those who teach." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1. Import the numpy package under the name `np` (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2. Print the numpy version and the configuration (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3. Create a null vector of size 10 (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4. How to find the memory size of any array (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5. How to get the documentation of the numpy add function from the command line? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 6. Create a null vector of size 10 but the fifth value which is 1 (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 7. Create a vector with values ranging from 10 to 49 (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 8. Reverse a vector (first element becomes last) (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 9. Create a 3x3 matrix with values ranging from 0 to 8 (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 10. Find indices of non-zero elements from \\[1,2,0,0,4,0\\] (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 11. Create a 3x3 identity matrix (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 12. Create a 3x3x3 array with random values (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 13. Create a 10x10 array with random values and find the minimum and maximum values (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 14. Create a random vector of size 30 and find the mean value (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 15. Create a 2d array with 1 on the border and 0 inside (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 16. How to add a border (filled with 0's) around an existing array? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 17. What is the result of the following expression? (★☆☆)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "0 * np.nan\n", + "np.nan == np.nan\n", + "np.inf > np.nan\n", + "np.nan - np.nan\n", + "0.3 == 3 * 0.1\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 18. Create a 5x5 matrix with values 1,2,3,4 just below the diagonal (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 19. Create a 8x8 matrix and fill it with a checkerboard pattern (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 20. Consider a (6,7,8) shape array, what is the index (x,y,z) of the 100th element?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 21. Create a checkerboard 8x8 matrix using the tile function (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 22. Normalize a 5x5 random matrix (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 23. Create a custom dtype that describes a color as four unsigned bytes (RGBA) (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 24. Multiply a 5x3 matrix by a 3x2 matrix (real matrix product) (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 25. Given a 1D array, negate all elements which are between 3 and 8, in place. (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 26. What is the output of the following script? (★☆☆)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "# Author: Jake VanderPlas\n", + "\n", + "print(sum(range(5),-1))\n", + "from numpy import *\n", + "print(sum(range(5),-1))\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 27. Consider an integer vector Z, which of these expressions are legal? (★☆☆)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "Z**Z\n", + "2 << Z >> 2\n", + "Z <- Z\n", + "1j*Z\n", + "Z/1/1\n", + "ZZ\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 28. What are the result of the following expressions?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "np.array(0) / np.array(0)\n", + "np.array(0) // np.array(0)\n", + "np.array([np.nan]).astype(int).astype(float)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 29. How to round away from zero a float array ? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 30. How to find common values between two arrays? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 31. How to ignore all numpy warnings (not recommended)? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 32. Is the following expressions true? (★☆☆)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```python\n", + "np.sqrt(-1) == np.emath.sqrt(-1)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 33. How to get the dates of yesterday, today and tomorrow? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 34. How to get all the dates corresponding to the month of July 2016? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 35. How to compute ((A+B)\\*(-A/2)) in place (without copy)? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 36. Extract the integer part of a random array using 5 different methods (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 37. Create a 5x5 matrix with row values ranging from 0 to 4 (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 38. Consider a generator function that generates 10 integers and use it to build an array (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 39. Create a vector of size 10 with values ranging from 0 to 1, both excluded (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 40. Create a random vector of size 10 and sort it (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 41. How to sum a small array faster than np.sum? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 42. Consider two random array A and B, check if they are equal (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 43. Make an array immutable (read-only) (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 44. Consider a random 10x2 matrix representing cartesian coordinates, convert them to polar coordinates (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 45. Create random vector of size 10 and replace the maximum value by 0 (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 46. Create a structured array with `x` and `y` coordinates covering the \\[0,1\\]x\\[0,1\\] area (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 47. Given two arrays, X and Y, construct the Cauchy matrix C (Cij =1/(xi - yj))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 48. Print the minimum and maximum representable value for each numpy scalar type (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 49. How to print all the values of an array? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 50. How to find the closest value (to a given scalar) in a vector? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 51. Create a structured array representing a position (x,y) and a color (r,g,b) (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 52. Consider a random vector with shape (100,2) representing coordinates, find point by point distances (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 53. How to convert a float (32 bits) array into an integer (32 bits) in place?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 54. How to read the following file? (★★☆)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```\n", + "1, 2, 3, 4, 5\n", + "6, , , 7, 8\n", + " , , 9,10,11\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 55. What is the equivalent of enumerate for numpy arrays? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 56. Generate a generic 2D Gaussian-like array (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 57. How to randomly place p elements in a 2D array? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 58. Subtract the mean of each row of a matrix (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 59. How to sort an array by the nth column? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 60. How to tell if a given 2D array has null columns? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 61. Find the nearest value from a given value in an array (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 62. Considering two arrays with shape (1,3) and (3,1), how to compute their sum using an iterator? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 63. Create an array class that has a name attribute (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 64. Consider a given vector, how to add 1 to each element indexed by a second vector (be careful with repeated indices)? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 65. How to accumulate elements of a vector (X) to an array (F) based on an index list (I)? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 66. Considering a (w,h,3) image of (dtype=ubyte), compute the number of unique colors (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 67. Considering a four dimensions array, how to get sum over the last two axis at once? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 68. Considering a one-dimensional vector D, how to compute means of subsets of D using a vector S of same size describing subset indices? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 69. How to get the diagonal of a dot product? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 70. Consider the vector \\[1, 2, 3, 4, 5\\], how to build a new vector with 3 consecutive zeros interleaved between each value? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 71. Consider an array of dimension (5,5,3), how to mulitply it by an array with dimensions (5,5)? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 72. How to swap two rows of an array? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 73. Consider a set of 10 triplets describing 10 triangles (with shared vertices), find the set of unique line segments composing all the triangles (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 74. Given an array C that is a bincount, how to produce an array A such that np.bincount(A) == C? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 75. How to compute averages using a sliding window over an array? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 76. Consider a one-dimensional array Z, build a two-dimensional array whose first row is (Z\\[0\\],Z\\[1\\],Z\\[2\\]) and each subsequent row is shifted by 1 (last row should be (Z\\[-3\\],Z\\[-2\\],Z\\[-1\\]) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 77. How to negate a boolean, or to change the sign of a float inplace? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 78. Consider 2 sets of points P0,P1 describing lines (2d) and a point p, how to compute distance from p to each line i (P0\\[i\\],P1\\[i\\])? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 79. Consider 2 sets of points P0,P1 describing lines (2d) and a set of points P, how to compute distance from each point j (P\\[j\\]) to each line i (P0\\[i\\],P1\\[i\\])? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 80. Consider an arbitrary array, write a function that extract a subpart with a fixed shape and centered on a given element (pad with a `fill` value when necessary) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 81. Consider an array Z = \\[1,2,3,4,5,6,7,8,9,10,11,12,13,14\\], how to generate an array R = \\[\\[1,2,3,4\\], \\[2,3,4,5\\], \\[3,4,5,6\\], ..., \\[11,12,13,14\\]\\]? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 82. Compute a matrix rank (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 83. How to find the most frequent value in an array?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 84. Extract all the contiguous 3x3 blocks from a random 10x10 matrix (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 85. Create a 2D array subclass such that Z\\[i,j\\] == Z\\[j,i\\] (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 86. Consider a set of p matrices wich shape (n,n) and a set of p vectors with shape (n,1). How to compute the sum of of the p matrix products at once? (result has shape (n,1)) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 87. Consider a 16x16 array, how to get the block-sum (block size is 4x4)? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 88. How to implement the Game of Life using numpy arrays? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 89. How to get the n largest values of an array (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 90. Given an arbitrary number of vectors, build the cartesian product (every combinations of every item) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true, + "scrolled": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 91. How to create a record array from a regular array? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 92. Consider a large vector Z, compute Z to the power of 3 using 3 different methods (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 93. Consider two arrays A and B of shape (8,3) and (2,2). How to find rows of A that contain elements of each row of B regardless of the order of the elements in B? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 94. Considering a 10x3 matrix, extract rows with unequal values (e.g. \\[2,2,3\\]) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 95. Convert a vector of ints into a matrix binary representation (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 96. Given a two dimensional array, how to extract unique rows? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 97. Considering 2 vectors A & B, write the einsum equivalent of inner, outer, sum, and mul function (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 98. Considering a path described by two vectors (X,Y), how to sample it using equidistant samples (★★★)?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 99. Given an integer n and a 2D array X, select from X the rows which can be interpreted as draws from a multinomial distribution with n degrees, i.e., the rows which only contain integers and which sum to n. (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 100. Compute bootstrapped 95% confidence intervals for the mean of a 1D array X (i.e., resample the elements of an array with replacement N times, compute the mean of each sample, and then compute percentiles over the means). (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/200 solved problems in Python/numpy/100 Numpy exercises_solution.ipynb b/200 solved problems in Python/numpy/100 Numpy exercises_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..7d6eb76d68c2e89cfba5941a30a53461139095b1 --- /dev/null +++ b/200 solved problems in Python/numpy/100 Numpy exercises_solution.ipynb @@ -0,0 +1,2351 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "\n", + "# 100 numpy exercises\n", + "\n", + "This is a collection of exercises that have been collected in the numpy mailing list, on stack overflow and in the numpy documentation. The goal of this collection is to offer a quick reference for both old and new users but also to provide a set of exercices for those who teach." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1. Import the numpy package under the name `np` (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2. Print the numpy version and the configuration (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "print(np.__version__)\n", + "np.show_config()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3. Create a null vector of size 10 (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.zeros(10)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4. How to find the memory size of any array (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.zeros((10,10))\n", + "print(\"%d bytes\" % (Z.size * Z.itemsize))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5. How to get the documentation of the numpy add function from the command line? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%run `python -c \"import numpy; numpy.info(numpy.add)\"`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 6. Create a null vector of size 10 but the fifth value which is 1 (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.zeros(10)\n", + "Z[4] = 1\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 7. Create a vector with values ranging from 10 to 49 (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.arange(10,50)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 8. Reverse a vector (first element becomes last) (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.arange(50)\n", + "Z = Z[::-1]\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 9. Create a 3x3 matrix with values ranging from 0 to 8 (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.arange(9).reshape(3,3)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 10. Find indices of non-zero elements from \\[1,2,0,0,4,0\\] (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "nz = np.nonzero([1,2,0,0,4,0])\n", + "print(nz)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 11. Create a 3x3 identity matrix (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.eye(3)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 12. Create a 3x3x3 array with random values (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.random.random((3,3,3))\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 13. Create a 10x10 array with random values and find the minimum and maximum values (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.random.random((10,10))\n", + "Zmin, Zmax = Z.min(), Z.max()\n", + "print(Zmin, Zmax)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 14. Create a random vector of size 30 and find the mean value (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.random.random(30)\n", + "m = Z.mean()\n", + "print(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 15. Create a 2d array with 1 on the border and 0 inside (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.ones((10,10))\n", + "Z[1:-1,1:-1] = 0\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 16. How to add a border (filled with 0's) around an existing array? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.ones((5,5))\n", + "Z = np.pad(Z, pad_width=1, mode='constant', constant_values=0)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 17. What is the result of the following expression? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "print(0 * np.nan)\n", + "print(np.nan == np.nan)\n", + "print(np.inf > np.nan)\n", + "print(np.nan - np.nan)\n", + "print(0.3 == 3 * 0.1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 18. Create a 5x5 matrix with values 1,2,3,4 just below the diagonal (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.diag(1+np.arange(4),k=-1)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 19. Create a 8x8 matrix and fill it with a checkerboard pattern (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.zeros((8,8),dtype=int)\n", + "Z[1::2,::2] = 1\n", + "Z[::2,1::2] = 1\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 20. Consider a (6,7,8) shape array, what is the index (x,y,z) of the 100th element?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "print(np.unravel_index(100,(6,7,8)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 21. Create a checkerboard 8x8 matrix using the tile function (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.tile( np.array([[0,1],[1,0]]), (4,4))\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 22. Normalize a 5x5 random matrix (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.random.random((5,5))\n", + "Zmax, Zmin = Z.max(), Z.min()\n", + "Z = (Z - Zmin)/(Zmax - Zmin)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 23. Create a custom dtype that describes a color as four unsigned bytes (RGBA) (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "color = np.dtype([(\"r\", np.ubyte, 1),\n", + " (\"g\", np.ubyte, 1),\n", + " (\"b\", np.ubyte, 1),\n", + " (\"a\", np.ubyte, 1)])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 24. Multiply a 5x3 matrix by a 3x2 matrix (real matrix product) (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.dot(np.ones((5,3)), np.ones((3,2)))\n", + "print(Z)\n", + "\n", + "# Alternative solution, in Python 3.5 and above\n", + "Z = np.ones((5,3)) @ np.ones((3,2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 25. Given a 1D array, negate all elements which are between 3 and 8, in place. (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Evgeni Burovski\n", + "\n", + "Z = np.arange(11)\n", + "Z[(3 < Z) & (Z <= 8)] *= -1\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 26. What is the output of the following script? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Jake VanderPlas\n", + "\n", + "print(sum(range(5),-1))\n", + "from numpy import *\n", + "print(sum(range(5),-1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 27. Consider an integer vector Z, which of these expressions are legal? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z**Z\n", + "2 << Z >> 2\n", + "Z <- Z\n", + "1j*Z\n", + "Z/1/1\n", + "ZZ" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 28. What are the result of the following expressions?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "print(np.array(0) / np.array(0))\n", + "print(np.array(0) // np.array(0))\n", + "print(np.array([np.nan]).astype(int).astype(float))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 29. How to round away from zero a float array ? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Charles R Harris\n", + "\n", + "Z = np.random.uniform(-10,+10,10)\n", + "print (np.copysign(np.ceil(np.abs(Z)), Z))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 30. How to find common values between two arrays? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z1 = np.random.randint(0,10,10)\n", + "Z2 = np.random.randint(0,10,10)\n", + "print(np.intersect1d(Z1,Z2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 31. How to ignore all numpy warnings (not recommended)? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Suicide mode on\n", + "defaults = np.seterr(all=\"ignore\")\n", + "Z = np.ones(1) / 0\n", + "\n", + "# Back to sanity\n", + "_ = np.seterr(**defaults)\n", + "\n", + "An equivalent way, with a context manager:\n", + "\n", + "with np.errstate(divide='ignore'):\n", + " Z = np.ones(1) / 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 32. Is the following expressions true? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "np.sqrt(-1) == np.emath.sqrt(-1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 33. How to get the dates of yesterday, today and tomorrow? (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "yesterday = np.datetime64('today', 'D') - np.timedelta64(1, 'D')\n", + "today = np.datetime64('today', 'D')\n", + "tomorrow = np.datetime64('today', 'D') + np.timedelta64(1, 'D')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 34. How to get all the dates corresponding to the month of July 2016? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.arange('2016-07', '2016-08', dtype='datetime64[D]')\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 35. How to compute ((A+B)\\*(-A/2)) in place (without copy)? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "A = np.ones(3)*1\n", + "B = np.ones(3)*2\n", + "C = np.ones(3)*3\n", + "np.add(A,B,out=B)\n", + "np.divide(A,2,out=A)\n", + "np.negative(A,out=A)\n", + "np.multiply(A,B,out=A)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 36. Extract the integer part of a random array using 5 different methods (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.random.uniform(0,10,10)\n", + "\n", + "print (Z - Z%1)\n", + "print (np.floor(Z))\n", + "print (np.ceil(Z)-1)\n", + "print (Z.astype(int))\n", + "print (np.trunc(Z))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 37. Create a 5x5 matrix with row values ranging from 0 to 4 (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.zeros((5,5))\n", + "Z += np.arange(5)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 38. Consider a generator function that generates 10 integers and use it to build an array (★☆☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def generate():\n", + " for x in range(10):\n", + " yield x\n", + "Z = np.fromiter(generate(),dtype=float,count=-1)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 39. Create a vector of size 10 with values ranging from 0 to 1, both excluded (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.linspace(0,1,11,endpoint=False)[1:]\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 40. Create a random vector of size 10 and sort it (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.random.random(10)\n", + "Z.sort()\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 41. How to sum a small array faster than np.sum? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Evgeni Burovski\n", + "\n", + "Z = np.arange(10)\n", + "np.add.reduce(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 42. Consider two random array A and B, check if they are equal (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "A = np.random.randint(0,2,5)\n", + "B = np.random.randint(0,2,5)\n", + "\n", + "# Assuming identical shape of the arrays and a tolerance for the comparison of values\n", + "equal = np.allclose(A,B)\n", + "print(equal)\n", + "\n", + "# Checking both the shape and the element values, no tolerance (values have to be exactly equal)\n", + "equal = np.array_equal(A,B)\n", + "print(equal)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 43. Make an array immutable (read-only) (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.zeros(10)\n", + "Z.flags.writeable = False\n", + "Z[0] = 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 44. Consider a random 10x2 matrix representing cartesian coordinates, convert them to polar coordinates (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.random.random((10,2))\n", + "X,Y = Z[:,0], Z[:,1]\n", + "R = np.sqrt(X**2+Y**2)\n", + "T = np.arctan2(Y,X)\n", + "print(R)\n", + "print(T)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 45. Create random vector of size 10 and replace the maximum value by 0 (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.random.random(10)\n", + "Z[Z.argmax()] = 0\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 46. Create a structured array with `x` and `y` coordinates covering the \\[0,1\\]x\\[0,1\\] area (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.zeros((5,5), [('x',float),('y',float)])\n", + "Z['x'], Z['y'] = np.meshgrid(np.linspace(0,1,5),\n", + " np.linspace(0,1,5))\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 47. Given two arrays, X and Y, construct the Cauchy matrix C (Cij =1/(xi - yj))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Evgeni Burovski\n", + "\n", + "X = np.arange(8)\n", + "Y = X + 0.5\n", + "C = 1.0 / np.subtract.outer(X, Y)\n", + "print(np.linalg.det(C))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 48. Print the minimum and maximum representable value for each numpy scalar type (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "for dtype in [np.int8, np.int32, np.int64]:\n", + " print(np.iinfo(dtype).min)\n", + " print(np.iinfo(dtype).max)\n", + "for dtype in [np.float32, np.float64]:\n", + " print(np.finfo(dtype).min)\n", + " print(np.finfo(dtype).max)\n", + " print(np.finfo(dtype).eps)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 49. How to print all the values of an array? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "np.set_printoptions(threshold=np.nan)\n", + "Z = np.zeros((16,16))\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 50. How to find the closest value (to a given scalar) in a vector? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.arange(100)\n", + "v = np.random.uniform(0,100)\n", + "index = (np.abs(Z-v)).argmin()\n", + "print(Z[index])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 51. Create a structured array representing a position (x,y) and a color (r,g,b) (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.zeros(10, [ ('position', [ ('x', float, 1),\n", + " ('y', float, 1)]),\n", + " ('color', [ ('r', float, 1),\n", + " ('g', float, 1),\n", + " ('b', float, 1)])])\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 52. Consider a random vector with shape (100,2) representing coordinates, find point by point distances (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.random.random((10,2))\n", + "X,Y = np.atleast_2d(Z[:,0], Z[:,1])\n", + "D = np.sqrt( (X-X.T)**2 + (Y-Y.T)**2)\n", + "print(D)\n", + "\n", + "# Much faster with scipy\n", + "import scipy\n", + "# Thanks Gavin Heverly-Coulson (#issue 1)\n", + "import scipy.spatial\n", + "\n", + "Z = np.random.random((10,2))\n", + "D = scipy.spatial.distance.cdist(Z,Z)\n", + "print(D)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 53. How to convert a float (32 bits) array into an integer (32 bits) in place?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.arange(10, dtype=np.int32)\n", + "Z = Z.astype(np.float32, copy=False)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 54. How to read the following file? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from io import StringIO\n", + "\n", + "# Fake file \n", + "s = StringIO(\"\"\"1, 2, 3, 4, 5\\n\n", + " 6, , , 7, 8\\n\n", + " , , 9,10,11\\n\"\"\")\n", + "Z = np.genfromtxt(s, delimiter=\",\", dtype=np.int)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 55. What is the equivalent of enumerate for numpy arrays? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.arange(9).reshape(3,3)\n", + "for index, value in np.ndenumerate(Z):\n", + " print(index, value)\n", + "for index in np.ndindex(Z.shape):\n", + " print(index, Z[index])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 56. Generate a generic 2D Gaussian-like array (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "X, Y = np.meshgrid(np.linspace(-1,1,10), np.linspace(-1,1,10))\n", + "D = np.sqrt(X*X+Y*Y)\n", + "sigma, mu = 1.0, 0.0\n", + "G = np.exp(-( (D-mu)**2 / ( 2.0 * sigma**2 ) ) )\n", + "print(G)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 57. How to randomly place p elements in a 2D array? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Divakar\n", + "\n", + "n = 10\n", + "p = 3\n", + "Z = np.zeros((n,n))\n", + "np.put(Z, np.random.choice(range(n*n), p, replace=False),1)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 58. Subtract the mean of each row of a matrix (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Warren Weckesser\n", + "\n", + "X = np.random.rand(5, 10)\n", + "\n", + "# Recent versions of numpy\n", + "Y = X - X.mean(axis=1, keepdims=True)\n", + "\n", + "# Older versions of numpy\n", + "Y = X - X.mean(axis=1).reshape(-1, 1)\n", + "\n", + "print(Y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 59. How to sort an array by the nth column? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Steve Tjoa\n", + "\n", + "Z = np.random.randint(0,10,(3,3))\n", + "print(Z)\n", + "print(Z[Z[:,1].argsort()])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 60. How to tell if a given 2D array has null columns? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Warren Weckesser\n", + "\n", + "Z = np.random.randint(0,3,(3,10))\n", + "print((~Z.any(axis=0)).any())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 61. Find the nearest value from a given value in an array (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.random.uniform(0,1,10)\n", + "z = 0.5\n", + "m = Z.flat[np.abs(Z - z).argmin()]\n", + "print(m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 62. Considering two arrays with shape (1,3) and (3,1), how to compute their sum using an iterator? (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "A = np.arange(3).reshape(3,1)\n", + "B = np.arange(3).reshape(1,3)\n", + "it = np.nditer([A,B,None])\n", + "for x,y,z in it: z[...] = x + y\n", + "print(it.operands[2])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 63. Create an array class that has a name attribute (★★☆)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "class NamedArray(np.ndarray):\n", + " def __new__(cls, array, name=\"no name\"):\n", + " obj = np.asarray(array).view(cls)\n", + " obj.name = name\n", + " return obj\n", + " def __array_finalize__(self, obj):\n", + " if obj is None: return\n", + " self.info = getattr(obj, 'name', \"no name\")\n", + "\n", + "Z = NamedArray(np.arange(10), \"range_10\")\n", + "print (Z.name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 64. Consider a given vector, how to add 1 to each element indexed by a second vector (be careful with repeated indices)? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Brett Olsen\n", + "\n", + "Z = np.ones(10)\n", + "I = np.random.randint(0,len(Z),20)\n", + "Z += np.bincount(I, minlength=len(Z))\n", + "print(Z)\n", + "\n", + "# Another solution\n", + "# Author: Bartosz Telenczuk\n", + "np.add.at(Z, I, 1)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 65. How to accumulate elements of a vector (X) to an array (F) based on an index list (I)? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Alan G Isaac\n", + "\n", + "X = [1,2,3,4,5,6]\n", + "I = [1,3,9,3,4,1]\n", + "F = np.bincount(I,X)\n", + "print(F)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 66. Considering a (w,h,3) image of (dtype=ubyte), compute the number of unique colors (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Nadav Horesh\n", + "\n", + "w,h = 16,16\n", + "I = np.random.randint(0,2,(h,w,3)).astype(np.ubyte)\n", + "#Note that we should compute 256*256 first. \n", + "#Otherwise numpy will only promote F.dtype to 'uint16' and overfolw will occur\n", + "F = I[...,0]*(256*256) + I[...,1]*256 +I[...,2]\n", + "n = len(np.unique(F))\n", + "print(n)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 67. Considering a four dimensions array, how to get sum over the last two axis at once? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "A = np.random.randint(0,10,(3,4,3,4))\n", + "# solution by passing a tuple of axes (introduced in numpy 1.7.0)\n", + "sum = A.sum(axis=(-2,-1))\n", + "print(sum)\n", + "# solution by flattening the last two dimensions into one\n", + "# (useful for functions that don't accept tuples for axis argument)\n", + "sum = A.reshape(A.shape[:-2] + (-1,)).sum(axis=-1)\n", + "print(sum)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 68. Considering a one-dimensional vector D, how to compute means of subsets of D using a vector S of same size describing subset indices? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Jaime Fernández del Río\n", + "\n", + "D = np.random.uniform(0,1,100)\n", + "S = np.random.randint(0,10,100)\n", + "D_sums = np.bincount(S, weights=D)\n", + "D_counts = np.bincount(S)\n", + "D_means = D_sums / D_counts\n", + "print(D_means)\n", + "\n", + "# Pandas solution as a reference due to more intuitive code\n", + "import pandas as pd\n", + "print(pd.Series(D).groupby(S).mean())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 69. How to get the diagonal of a dot product? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Mathieu Blondel\n", + "\n", + "A = np.random.uniform(0,1,(5,5))\n", + "B = np.random.uniform(0,1,(5,5))\n", + "\n", + "# Slow version \n", + "np.diag(np.dot(A, B))\n", + "\n", + "# Fast version\n", + "np.sum(A * B.T, axis=1)\n", + "\n", + "# Faster version\n", + "np.einsum(\"ij,ji->i\", A, B)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 70. Consider the vector \\[1, 2, 3, 4, 5\\], how to build a new vector with 3 consecutive zeros interleaved between each value? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Warren Weckesser\n", + "\n", + "Z = np.array([1,2,3,4,5])\n", + "nz = 3\n", + "Z0 = np.zeros(len(Z) + (len(Z)-1)*(nz))\n", + "Z0[::nz+1] = Z\n", + "print(Z0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 71. Consider an array of dimension (5,5,3), how to mulitply it by an array with dimensions (5,5)? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "A = np.ones((5,5,3))\n", + "B = 2*np.ones((5,5))\n", + "print(A * B[:,:,None])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 72. How to swap two rows of an array? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Eelco Hoogendoorn\n", + "\n", + "A = np.arange(25).reshape(5,5)\n", + "A[[0,1]] = A[[1,0]]\n", + "print(A)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 73. Consider a set of 10 triplets describing 10 triangles (with shared vertices), find the set of unique line segments composing all the triangles (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Nicolas P. Rougier\n", + "\n", + "faces = np.random.randint(0,100,(10,3))\n", + "F = np.roll(faces.repeat(2,axis=1),-1,axis=1)\n", + "F = F.reshape(len(F)*3,2)\n", + "F = np.sort(F,axis=1)\n", + "G = F.view( dtype=[('p0',F.dtype),('p1',F.dtype)] )\n", + "G = np.unique(G)\n", + "print(G)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 74. Given an array C that is a bincount, how to produce an array A such that np.bincount(A) == C? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Jaime Fernández del Río\n", + "\n", + "C = np.bincount([1,1,2,3,4,4,6])\n", + "A = np.repeat(np.arange(len(C)), C)\n", + "print(A)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 75. How to compute averages using a sliding window over an array? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Jaime Fernández del Río\n", + "\n", + "def moving_average(a, n=3) :\n", + " ret = np.cumsum(a, dtype=float)\n", + " ret[n:] = ret[n:] - ret[:-n]\n", + " return ret[n - 1:] / n\n", + "Z = np.arange(20)\n", + "print(moving_average(Z, n=3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 76. Consider a one-dimensional array Z, build a two-dimensional array whose first row is (Z\\[0\\],Z\\[1\\],Z\\[2\\]) and each subsequent row is shifted by 1 (last row should be (Z\\[-3\\],Z\\[-2\\],Z\\[-1\\]) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Joe Kington / Erik Rigtorp\n", + "from numpy.lib import stride_tricks\n", + "\n", + "def rolling(a, window):\n", + " shape = (a.size - window + 1, window)\n", + " strides = (a.itemsize, a.itemsize)\n", + " return stride_tricks.as_strided(a, shape=shape, strides=strides)\n", + "Z = rolling(np.arange(10), 3)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 77. How to negate a boolean, or to change the sign of a float inplace? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Nathaniel J. Smith\n", + "\n", + "Z = np.random.randint(0,2,100)\n", + "np.logical_not(Z, out=Z)\n", + "\n", + "Z = np.random.uniform(-1.0,1.0,100)\n", + "np.negative(Z, out=Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 78. Consider 2 sets of points P0,P1 describing lines (2d) and a point p, how to compute distance from p to each line i (P0\\[i\\],P1\\[i\\])? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def distance(P0, P1, p):\n", + " T = P1 - P0\n", + " L = (T**2).sum(axis=1)\n", + " U = -((P0[:,0]-p[...,0])*T[:,0] + (P0[:,1]-p[...,1])*T[:,1]) / L\n", + " U = U.reshape(len(U),1)\n", + " D = P0 + U*T - p\n", + " return np.sqrt((D**2).sum(axis=1))\n", + "\n", + "P0 = np.random.uniform(-10,10,(10,2))\n", + "P1 = np.random.uniform(-10,10,(10,2))\n", + "p = np.random.uniform(-10,10,( 1,2))\n", + "print(distance(P0, P1, p))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 79. Consider 2 sets of points P0,P1 describing lines (2d) and a set of points P, how to compute distance from each point j (P\\[j\\]) to each line i (P0\\[i\\],P1\\[i\\])? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Italmassov Kuanysh\n", + "\n", + "# based on distance function from previous question\n", + "P0 = np.random.uniform(-10, 10, (10,2))\n", + "P1 = np.random.uniform(-10,10,(10,2))\n", + "p = np.random.uniform(-10, 10, (10,2))\n", + "print(np.array([distance(P0,P1,p_i) for p_i in p]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 80. Consider an arbitrary array, write a function that extract a subpart with a fixed shape and centered on a given element (pad with a `fill` value when necessary) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Nicolas Rougier\n", + "\n", + "Z = np.random.randint(0,10,(10,10))\n", + "shape = (5,5)\n", + "fill = 0\n", + "position = (1,1)\n", + "\n", + "R = np.ones(shape, dtype=Z.dtype)*fill\n", + "P = np.array(list(position)).astype(int)\n", + "Rs = np.array(list(R.shape)).astype(int)\n", + "Zs = np.array(list(Z.shape)).astype(int)\n", + "\n", + "R_start = np.zeros((len(shape),)).astype(int)\n", + "R_stop = np.array(list(shape)).astype(int)\n", + "Z_start = (P-Rs//2)\n", + "Z_stop = (P+Rs//2)+Rs%2\n", + "\n", + "R_start = (R_start - np.minimum(Z_start,0)).tolist()\n", + "Z_start = (np.maximum(Z_start,0)).tolist()\n", + "R_stop = np.maximum(R_start, (R_stop - np.maximum(Z_stop-Zs,0))).tolist()\n", + "Z_stop = (np.minimum(Z_stop,Zs)).tolist()\n", + "\n", + "r = [slice(start,stop) for start,stop in zip(R_start,R_stop)]\n", + "z = [slice(start,stop) for start,stop in zip(Z_start,Z_stop)]\n", + "R[r] = Z[z]\n", + "print(Z)\n", + "print(R)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 81. Consider an array Z = \\[1,2,3,4,5,6,7,8,9,10,11,12,13,14\\], how to generate an array R = \\[\\[1,2,3,4\\], \\[2,3,4,5\\], \\[3,4,5,6\\], ..., \\[11,12,13,14\\]\\]? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Stefan van der Walt\n", + "\n", + "Z = np.arange(1,15,dtype=np.uint32)\n", + "R = stride_tricks.as_strided(Z,(11,4),(4,4))\n", + "print(R)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 82. Compute a matrix rank (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Stefan van der Walt\n", + "\n", + "Z = np.random.uniform(0,1,(10,10))\n", + "U, S, V = np.linalg.svd(Z) # Singular Value Decomposition\n", + "rank = np.sum(S > 1e-10)\n", + "print(rank)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 83. How to find the most frequent value in an array?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.random.randint(0,10,50)\n", + "print(np.bincount(Z).argmax())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 84. Extract all the contiguous 3x3 blocks from a random 10x10 matrix (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Chris Barker\n", + "\n", + "Z = np.random.randint(0,5,(10,10))\n", + "n = 3\n", + "i = 1 + (Z.shape[0]-3)\n", + "j = 1 + (Z.shape[1]-3)\n", + "C = stride_tricks.as_strided(Z, shape=(i, j, n, n), strides=Z.strides + Z.strides)\n", + "print(C)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 85. Create a 2D array subclass such that Z\\[i,j\\] == Z\\[j,i\\] (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Eric O. Lebigot\n", + "# Note: only works for 2d array and value setting using indices\n", + "\n", + "class Symetric(np.ndarray):\n", + " def __setitem__(self, index, value):\n", + " i,j = index\n", + " super(Symetric, self).__setitem__((i,j), value)\n", + " super(Symetric, self).__setitem__((j,i), value)\n", + "\n", + "def symetric(Z):\n", + " return np.asarray(Z + Z.T - np.diag(Z.diagonal())).view(Symetric)\n", + "\n", + "S = symetric(np.random.randint(0,10,(5,5)))\n", + "S[2,3] = 42\n", + "print(S)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 86. Consider a set of p matrices wich shape (n,n) and a set of p vectors with shape (n,1). How to compute the sum of of the p matrix products at once? (result has shape (n,1)) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Stefan van der Walt\n", + "\n", + "p, n = 10, 20\n", + "M = np.ones((p,n,n))\n", + "V = np.ones((p,n,1))\n", + "S = np.tensordot(M, V, axes=[[0, 2], [0, 1]])\n", + "print(S)\n", + "\n", + "# It works, because:\n", + "# M is (p,n,n)\n", + "# V is (p,n,1)\n", + "# Thus, summing over the paired axes 0 and 0 (of M and V independently),\n", + "# and 2 and 1, to remain with a (n,1) vector." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 87. Consider a 16x16 array, how to get the block-sum (block size is 4x4)? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Robert Kern\n", + "\n", + "Z = np.ones((16,16))\n", + "k = 4\n", + "S = np.add.reduceat(np.add.reduceat(Z, np.arange(0, Z.shape[0], k), axis=0),\n", + " np.arange(0, Z.shape[1], k), axis=1)\n", + "print(S)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 88. How to implement the Game of Life using numpy arrays? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Nicolas Rougier\n", + "\n", + "def iterate(Z):\n", + " # Count neighbours\n", + " N = (Z[0:-2,0:-2] + Z[0:-2,1:-1] + Z[0:-2,2:] +\n", + " Z[1:-1,0:-2] + Z[1:-1,2:] +\n", + " Z[2: ,0:-2] + Z[2: ,1:-1] + Z[2: ,2:])\n", + "\n", + " # Apply rules\n", + " birth = (N==3) & (Z[1:-1,1:-1]==0)\n", + " survive = ((N==2) | (N==3)) & (Z[1:-1,1:-1]==1)\n", + " Z[...] = 0\n", + " Z[1:-1,1:-1][birth | survive] = 1\n", + " return Z\n", + "\n", + "Z = np.random.randint(0,2,(50,50))\n", + "for i in range(100): Z = iterate(Z)\n", + "print(Z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 89. How to get the n largest values of an array (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.arange(10000)\n", + "np.random.shuffle(Z)\n", + "n = 5\n", + "\n", + "# Slow\n", + "print (Z[np.argsort(Z)[-n:]])\n", + "\n", + "# Fast\n", + "print (Z[np.argpartition(-Z,n)[:n]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 90. Given an arbitrary number of vectors, build the cartesian product (every combinations of every item) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true, + "scrolled": true + }, + "outputs": [], + "source": [ + "# Author: Stefan Van der Walt\n", + "\n", + "def cartesian(arrays):\n", + " arrays = [np.asarray(a) for a in arrays]\n", + " shape = (len(x) for x in arrays)\n", + "\n", + " ix = np.indices(shape, dtype=int)\n", + " ix = ix.reshape(len(arrays), -1).T\n", + "\n", + " for n, arr in enumerate(arrays):\n", + " ix[:, n] = arrays[n][ix[:, n]]\n", + "\n", + " return ix\n", + "\n", + "print (cartesian(([1, 2, 3], [4, 5], [6, 7])))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 91. How to create a record array from a regular array? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "Z = np.array([(\"Hello\", 2.5, 3),\n", + " (\"World\", 3.6, 2)])\n", + "R = np.core.records.fromarrays(Z.T, \n", + " names='col1, col2, col3',\n", + " formats = 'S8, f8, i8')\n", + "print(R)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 92. Consider a large vector Z, compute Z to the power of 3 using 3 different methods (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Ryan G.\n", + "\n", + "x = np.random.rand(5e7)\n", + "\n", + "%timeit np.power(x,3)\n", + "%timeit x*x*x\n", + "%timeit np.einsum('i,i,i->i',x,x,x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 93. Consider two arrays A and B of shape (8,3) and (2,2). How to find rows of A that contain elements of each row of B regardless of the order of the elements in B? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Gabe Schwartz\n", + "\n", + "A = np.random.randint(0,5,(8,3))\n", + "B = np.random.randint(0,5,(2,2))\n", + "\n", + "C = (A[..., np.newaxis, np.newaxis] == B)\n", + "rows = np.where(C.any((3,1)).all(1))[0]\n", + "print(rows)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 94. Considering a 10x3 matrix, extract rows with unequal values (e.g. \\[2,2,3\\]) (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Robert Kern\n", + "\n", + "Z = np.random.randint(0,5,(10,3))\n", + "print(Z)\n", + "# solution for arrays of all dtypes (including string arrays and record arrays)\n", + "E = np.all(Z[:,1:] == Z[:,:-1], axis=1)\n", + "U = Z[~E]\n", + "print(U)\n", + "# soluiton for numerical arrays only, will work for any number of columns in Z\n", + "U = Z[Z.max(axis=1) != Z.min(axis=1),:]\n", + "print(U)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 95. Convert a vector of ints into a matrix binary representation (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Warren Weckesser\n", + "\n", + "I = np.array([0, 1, 2, 3, 15, 16, 32, 64, 128])\n", + "B = ((I.reshape(-1,1) & (2**np.arange(8))) != 0).astype(int)\n", + "print(B[:,::-1])\n", + "\n", + "# Author: Daniel T. McDonald\n", + "\n", + "I = np.array([0, 1, 2, 3, 15, 16, 32, 64, 128], dtype=np.uint8)\n", + "print(np.unpackbits(I[:, np.newaxis], axis=1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 96. Given a two dimensional array, how to extract unique rows? (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Jaime Fernández del Río\n", + "\n", + "Z = np.random.randint(0,2,(6,3))\n", + "T = np.ascontiguousarray(Z).view(np.dtype((np.void, Z.dtype.itemsize * Z.shape[1])))\n", + "_, idx = np.unique(T, return_index=True)\n", + "uZ = Z[idx]\n", + "print(uZ)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 97. Considering 2 vectors A & B, write the einsum equivalent of inner, outer, sum, and mul function (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "A = np.random.uniform(0,1,10)\n", + "B = np.random.uniform(0,1,10)\n", + "\n", + "np.einsum('i->', A) # np.sum(A)\n", + "np.einsum('i,i->i', A, B) # A * B\n", + "np.einsum('i,i', A, B) # np.inner(A, B)\n", + "np.einsum('i,j->ij', A, B) # np.outer(A, B)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 98. Considering a path described by two vectors (X,Y), how to sample it using equidistant samples (★★★)?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Bas Swinckels\n", + "\n", + "phi = np.arange(0, 10*np.pi, 0.1)\n", + "a = 1\n", + "x = a*phi*np.cos(phi)\n", + "y = a*phi*np.sin(phi)\n", + "\n", + "dr = (np.diff(x)**2 + np.diff(y)**2)**.5 # segment lengths\n", + "r = np.zeros_like(x)\n", + "r[1:] = np.cumsum(dr) # integrate path\n", + "r_int = np.linspace(0, r.max(), 200) # regular spaced path\n", + "x_int = np.interp(r_int, r, x) # integrate path\n", + "y_int = np.interp(r_int, r, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 99. Given an integer n and a 2D array X, select from X the rows which can be interpreted as draws from a multinomial distribution with n degrees, i.e., the rows which only contain integers and which sum to n. (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Evgeni Burovski\n", + "\n", + "X = np.asarray([[1.0, 0.0, 3.0, 8.0],\n", + " [2.0, 0.0, 1.0, 1.0],\n", + " [1.5, 2.5, 1.0, 0.0]])\n", + "n = 4\n", + "M = np.logical_and.reduce(np.mod(X, 1) == 0, axis=-1)\n", + "M &= (X.sum(axis=-1) == n)\n", + "print(X[M])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 100. Compute bootstrapped 95% confidence intervals for the mean of a 1D array X (i.e., resample the elements of an array with replacement N times, compute the mean of each sample, and then compute percentiles over the means). (★★★)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Author: Jessica B. Hamrick\n", + "\n", + "X = np.random.randn(100) # random 1D array\n", + "N = 1000 # number of bootstrap samples\n", + "idx = np.random.randint(0, X.size, (N, X.size))\n", + "means = X[idx].mean(axis=1)\n", + "confint = np.percentile(means, [2.5, 97.5])\n", + "print(confint)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/200 solved problems in Python/pandas/01_Getting___Knowing_Your_Data/Chipotle/.ipynb_checkpoints/Exercise_with_Solutions-checkpoint.ipynb b/200 solved problems in Python/pandas/01_Getting___Knowing_Your_Data/Chipotle/.ipynb_checkpoints/Exercise_with_Solutions-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..25ff691d8ee2533202d1aa9623ca94a89fc44747 --- /dev/null +++ b/200 solved problems in Python/pandas/01_Getting___Knowing_Your_Data/Chipotle/.ipynb_checkpoints/Exercise_with_Solutions-checkpoint.ipynb @@ -0,0 +1,609 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "\n", + "# Ex2 - Getting and Knowing your Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This time we are going to pull data directly from the internet.\n", + "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called chipo." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "url = 'https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv'\n", + " \n", + "chipo = pd.read_csv(url, sep = '\\t')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. See the first 10 entries" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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order_idquantityitem_namechoice_descriptionitem_price
011Chips and Fresh Tomato SalsaNaN$2.39
111Izze[Clementine]$3.39
211Nantucket Nectar[Apple]$3.39
311Chips and Tomatillo-Green Chili SalsaNaN$2.39
422Chicken Bowl[Tomatillo-Red Chili Salsa (Hot), [Black Beans...$16.98
531Chicken Bowl[Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...$10.98
631Side of ChipsNaN$1.69
741Steak Burrito[Tomatillo Red Chili Salsa, [Fajita Vegetables...$11.75
841Steak Soft Tacos[Tomatillo Green Chili Salsa, [Pinto Beans, Ch...$9.25
951Steak Burrito[Fresh Tomato Salsa, [Rice, Black Beans, Pinto...$9.25
\n", + "
" + ], + "text/plain": [ + " order_id quantity item_name \\\n", + "0 1 1 Chips and Fresh Tomato Salsa \n", + "1 1 1 Izze \n", + "2 1 1 Nantucket Nectar \n", + "3 1 1 Chips and Tomatillo-Green Chili Salsa \n", + "4 2 2 Chicken Bowl \n", + "5 3 1 Chicken Bowl \n", + "6 3 1 Side of Chips \n", + "7 4 1 Steak Burrito \n", + "8 4 1 Steak Soft Tacos \n", + "9 5 1 Steak Burrito \n", + "\n", + " choice_description item_price \n", + "0 NaN $2.39 \n", + "1 [Clementine] $3.39 \n", + "2 [Apple] $3.39 \n", + "3 NaN $2.39 \n", + "4 [Tomatillo-Red Chili Salsa (Hot), [Black Beans... $16.98 \n", + "5 [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou... $10.98 \n", + "6 NaN $1.69 \n", + "7 [Tomatillo Red Chili Salsa, [Fajita Vegetables... $11.75 \n", + "8 [Tomatillo Green Chili Salsa, [Pinto Beans, Ch... $9.25 \n", + "9 [Fresh Tomato Salsa, [Rice, Black Beans, Pinto... $9.25 " + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chipo.head(10)\n", + "# chipo['choice_description'][4]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. What is the number of observations in the dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 4622 entries, 0 to 4621\n", + "Data columns (total 5 columns):\n", + "order_id 4622 non-null int64\n", + "quantity 4622 non-null int64\n", + "item_name 4622 non-null object\n", + "choice_description 3376 non-null object\n", + "item_price 4622 non-null object\n", + "dtypes: int64(2), object(3)\n", + "memory usage: 180.6+ KB\n" + ] + }, + { + "data": { + "text/plain": [ + "4622" + ] + }, + "execution_count": 111, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chipo.info()#\n", + "\n", + "# OR\n", + "\n", + "chipo.shape[0]\n", + "# 4622 observations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. What is the number of columns in the dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5" + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chipo.shape[1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Print the name of all the columns." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index([u'order_id', u'quantity', u'item_name', u'choice_description',\n", + " u'item_price'],\n", + " dtype='object')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chipo.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. How is the dataset indexed?" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "RangeIndex(start=0, stop=4622, step=1)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chipo.index" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. Which was the most ordered item? " + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Chicken Bowl 726\n", + "Name: item_name, dtype: int64" + ] + }, + "execution_count": 139, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chipo.item_name.value_counts().head(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. How many items were ordered?" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "726" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mostOrd = chipo.item_name.value_counts().max() #or mostOrd = chipo[\"item_name\"].max()\n", + "mostOrd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 11. What was the most ordered item in the choice_description column?" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Diet Coke] 134\n", + "[Coke] 123\n", + "[Sprite] 77\n", + "[Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] 42\n", + "[Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] 40\n", + "Name: choice_description, dtype: int64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chipo.choice_description.value_counts().head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 12. How many items were orderd in total?" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4972" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "total_items_orders = chipo.quantity.sum()\n", + "total_items_orders" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 13. Turn the item price into a float" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "dollarizer = lambda x: float(x[1:-1])\n", + "chipo.item_price = chipo.item_price.apply(dollarizer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 14. How much was the revenue for the period in the dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "34500.16000000046" + ] + }, + "execution_count": 130, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chipo.item_price.sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 15. How many orders were made in the period?" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1834" + ] + }, + "execution_count": 130, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chipo.order_id.value_counts().count()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 16. What is the average amount per order?" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "18.811428571428689" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "order_grouped = chipo.groupby(by=['order_id']).sum()\n", + "order_grouped.mean()['item_price']\n", + "\n", + "# Or \n", + "\n", + "#chipo.groupby(by=['order_id']).sum().mean()['item_price']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 17. How many different items are sold?" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "50" + ] + }, + "execution_count": 148, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chipo.item_name.value_counts().count()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/200 solved problems in Python/pandas/01_Getting___Knowing_Your_Data/Chipotle/Exercise_with_Solutions.ipynb b/200 solved problems in Python/pandas/01_Getting___Knowing_Your_Data/Chipotle/Exercise_with_Solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..25ff691d8ee2533202d1aa9623ca94a89fc44747 --- /dev/null +++ b/200 solved problems in Python/pandas/01_Getting___Knowing_Your_Data/Chipotle/Exercise_with_Solutions.ipynb @@ -0,0 +1,609 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "\n", + "# Ex2 - Getting and Knowing your Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This time we are going to pull data directly from the internet.\n", + "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called chipo." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "url = 'https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv'\n", + " \n", + "chipo = pd.read_csv(url, sep = '\\t')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. See the first 10 entries" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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order_idquantityitem_namechoice_descriptionitem_price
011Chips and Fresh Tomato SalsaNaN$2.39
111Izze[Clementine]$3.39
211Nantucket Nectar[Apple]$3.39
311Chips and Tomatillo-Green Chili SalsaNaN$2.39
422Chicken Bowl[Tomatillo-Red Chili Salsa (Hot), [Black Beans...$16.98
531Chicken Bowl[Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...$10.98
631Side of ChipsNaN$1.69
741Steak Burrito[Tomatillo Red Chili Salsa, [Fajita Vegetables...$11.75
841Steak Soft Tacos[Tomatillo Green Chili Salsa, [Pinto Beans, Ch...$9.25
951Steak Burrito[Fresh Tomato Salsa, [Rice, Black Beans, Pinto...$9.25
\n", + "
" + ], + "text/plain": [ + " order_id quantity item_name \\\n", + "0 1 1 Chips and Fresh Tomato Salsa \n", + "1 1 1 Izze \n", + "2 1 1 Nantucket Nectar \n", + "3 1 1 Chips and Tomatillo-Green Chili Salsa \n", + "4 2 2 Chicken Bowl \n", + "5 3 1 Chicken Bowl \n", + "6 3 1 Side of Chips \n", + "7 4 1 Steak Burrito \n", + "8 4 1 Steak Soft Tacos \n", + "9 5 1 Steak Burrito \n", + "\n", + " choice_description item_price \n", + "0 NaN $2.39 \n", + "1 [Clementine] $3.39 \n", + "2 [Apple] $3.39 \n", + "3 NaN $2.39 \n", + "4 [Tomatillo-Red Chili Salsa (Hot), [Black Beans... $16.98 \n", + "5 [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou... $10.98 \n", + "6 NaN $1.69 \n", + "7 [Tomatillo Red Chili Salsa, [Fajita Vegetables... $11.75 \n", + "8 [Tomatillo Green Chili Salsa, [Pinto Beans, Ch... $9.25 \n", + "9 [Fresh Tomato Salsa, [Rice, Black Beans, Pinto... $9.25 " + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chipo.head(10)\n", + "# chipo['choice_description'][4]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. What is the number of observations in the dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 4622 entries, 0 to 4621\n", + "Data columns (total 5 columns):\n", + "order_id 4622 non-null int64\n", + "quantity 4622 non-null int64\n", + "item_name 4622 non-null object\n", + "choice_description 3376 non-null object\n", + "item_price 4622 non-null object\n", + "dtypes: int64(2), object(3)\n", + "memory usage: 180.6+ KB\n" + ] + }, + { + "data": { + "text/plain": [ + "4622" + ] + }, + "execution_count": 111, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chipo.info()#\n", + "\n", + "# OR\n", + "\n", + "chipo.shape[0]\n", + "# 4622 observations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. What is the number of columns in the dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5" + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chipo.shape[1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Print the name of all the columns." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index([u'order_id', u'quantity', u'item_name', u'choice_description',\n", + " u'item_price'],\n", + " dtype='object')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chipo.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. How is the dataset indexed?" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "RangeIndex(start=0, stop=4622, step=1)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chipo.index" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. Which was the most ordered item? " + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Chicken Bowl 726\n", + "Name: item_name, dtype: int64" + ] + }, + "execution_count": 139, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chipo.item_name.value_counts().head(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. How many items were ordered?" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "726" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mostOrd = chipo.item_name.value_counts().max() #or mostOrd = chipo[\"item_name\"].max()\n", + "mostOrd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 11. What was the most ordered item in the choice_description column?" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Diet Coke] 134\n", + "[Coke] 123\n", + "[Sprite] 77\n", + "[Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] 42\n", + "[Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] 40\n", + "Name: choice_description, dtype: int64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chipo.choice_description.value_counts().head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 12. How many items were orderd in total?" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4972" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "total_items_orders = chipo.quantity.sum()\n", + "total_items_orders" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 13. Turn the item price into a float" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "dollarizer = lambda x: float(x[1:-1])\n", + "chipo.item_price = chipo.item_price.apply(dollarizer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 14. How much was the revenue for the period in the dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "34500.16000000046" + ] + }, + "execution_count": 130, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chipo.item_price.sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 15. How many orders were made in the period?" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1834" + ] + }, + "execution_count": 130, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chipo.order_id.value_counts().count()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 16. What is the average amount per order?" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "18.811428571428689" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "order_grouped = chipo.groupby(by=['order_id']).sum()\n", + "order_grouped.mean()['item_price']\n", + "\n", + "# Or \n", + "\n", + "#chipo.groupby(by=['order_id']).sum().mean()['item_price']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 17. How many different items are sold?" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "50" + ] + }, + "execution_count": 148, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chipo.item_name.value_counts().count()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/200 solved problems in Python/pandas/01_Getting___Knowing_Your_Data/Chipotle/Exercises.ipynb b/200 solved problems in Python/pandas/01_Getting___Knowing_Your_Data/Chipotle/Exercises.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..4808ae47e7c0dc7a258b3a0540a38b61cc2233ef --- /dev/null +++ b/200 solved problems in Python/pandas/01_Getting___Knowing_Your_Data/Chipotle/Exercises.ipynb @@ -0,0 +1,298 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Getting and Knowing your Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This time we are going to pull data directly from the internet.\n", + "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called chipo." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. See the first 10 entries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. What is the number of observations in the dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. What is the number of columns in the dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Print the name of all the columns." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. How is the dataset indexed?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. Which was the most ordered item?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. How many items were ordered?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 11. What was the most ordered item in the choice_description column?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 12. How many items were orderd in total?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 13. Turn the item price into a float" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 14. How much was the revenue for the period in the dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 15. How many orders were made in the period?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 16. What is the average amount per order?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 17. How many different items are sold?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/01_Getting___Knowing_Your_Data/Chipotle/Solutions.ipynb b/200 solved problems in Python/pandas/01_Getting___Knowing_Your_Data/Chipotle/Solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..364eeb2cd20b95e565418b431460e5221316d0cf --- /dev/null +++ b/200 solved problems in Python/pandas/01_Getting___Knowing_Your_Data/Chipotle/Solutions.ipynb @@ -0,0 +1,586 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Ex2 - Getting and Knowing your Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This time we are going to pull data directly from the internet.\n", + "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called chipo." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. See the first 10 entries" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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order_idquantityitem_namechoice_descriptionitem_price
011Chips and Fresh Tomato SalsaNaN$2.39
111Izze[Clementine]$3.39
211Nantucket Nectar[Apple]$3.39
311Chips and Tomatillo-Green Chili SalsaNaN$2.39
422Chicken Bowl[Tomatillo-Red Chili Salsa (Hot), [Black Beans...$16.98
531Chicken Bowl[Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...$10.98
631Side of ChipsNaN$1.69
741Steak Burrito[Tomatillo Red Chili Salsa, [Fajita Vegetables...$11.75
841Steak Soft Tacos[Tomatillo Green Chili Salsa, [Pinto Beans, Ch...$9.25
951Steak Burrito[Fresh Tomato Salsa, [Rice, Black Beans, Pinto...$9.25
\n", + "
" + ], + "text/plain": [ + " order_id quantity item_name \\\n", + "0 1 1 Chips and Fresh Tomato Salsa \n", + "1 1 1 Izze \n", + "2 1 1 Nantucket Nectar \n", + "3 1 1 Chips and Tomatillo-Green Chili Salsa \n", + "4 2 2 Chicken Bowl \n", + "5 3 1 Chicken Bowl \n", + "6 3 1 Side of Chips \n", + "7 4 1 Steak Burrito \n", + "8 4 1 Steak Soft Tacos \n", + "9 5 1 Steak Burrito \n", + "\n", + " choice_description item_price \n", + "0 NaN $2.39 \n", + "1 [Clementine] $3.39 \n", + "2 [Apple] $3.39 \n", + "3 NaN $2.39 \n", + "4 [Tomatillo-Red Chili Salsa (Hot), [Black Beans... $16.98 \n", + "5 [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou... $10.98 \n", + "6 NaN $1.69 \n", + "7 [Tomatillo Red Chili Salsa, [Fajita Vegetables... $11.75 \n", + "8 [Tomatillo Green Chili Salsa, [Pinto Beans, Ch... $9.25 \n", + "9 [Fresh Tomato Salsa, [Rice, Black Beans, Pinto... $9.25 " + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. What is the number of observations in the dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 4622 entries, 0 to 4621\n", + "Data columns (total 5 columns):\n", + "order_id 4622 non-null int64\n", + "quantity 4622 non-null int64\n", + "item_name 4622 non-null object\n", + "choice_description 3376 non-null object\n", + "item_price 4622 non-null object\n", + "dtypes: int64(2), object(3)\n", + "memory usage: 180.6+ KB\n" + ] + }, + { + "data": { + "text/plain": [ + "4622" + ] + }, + "execution_count": 111, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. What is the number of columns in the dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "5" + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Print the name of all the columns." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index([u'order_id', u'quantity', u'item_name', u'choice_description',\n", + " u'item_price'],\n", + " dtype='object')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. How is the dataset indexed?" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "RangeIndex(start=0, stop=4622, step=1)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. Which was the most ordered item? " + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Chicken Bowl 726\n", + "Name: item_name, dtype: int64" + ] + }, + "execution_count": 139, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. How many items were ordered?" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "726" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 11. What was the most ordered item in the choice_description column?" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[Diet Coke] 134\n", + "[Coke] 123\n", + "[Sprite] 77\n", + "[Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]] 42\n", + "[Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]] 40\n", + "Name: choice_description, dtype: int64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 12. How many items were orderd in total?" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "4972" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 13. Turn the item price into a float" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 14. How much was the revenue for the period in the dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "34500.16000000046" + ] + }, + "execution_count": 130, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 15. How many orders were made in the period?" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1834" + ] + }, + "execution_count": 130, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 16. What is the average amount per order?" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "18.811428571428689" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 17. How many different items are sold?" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "50" + ] + }, + "execution_count": 148, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/01_Getting___Knowing_Your_Data/Occupation/Exercise_with_Solution.ipynb b/200 solved problems in Python/pandas/01_Getting___Knowing_Your_Data/Occupation/Exercise_with_Solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..ba552f673c607d376bab993c392440b8142293e7 --- /dev/null +++ b/200 solved problems in Python/pandas/01_Getting___Knowing_Your_Data/Occupation/Exercise_with_Solution.ipynb @@ -0,0 +1,1078 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Ex3 - Getting and Knowing your Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This time we are going to pull data directly from the internet.\n", + "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/u.user). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called users and use the 'user_id' as index" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "users = pd.read_table('https://raw.githubusercontent.com/justmarkham/DAT8/master/data/u.user', \n", + " sep='|', index_col='user_id')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. See the first 25 entries" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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agegenderoccupationzip_code
user_id
124Mtechnician85711
253Fother94043
323Mwriter32067
424Mtechnician43537
533Fother15213
642Mexecutive98101
757Madministrator91344
836Madministrator05201
929Mstudent01002
1053Mlawyer90703
1139Fother30329
1228Fother06405
1347Meducator29206
1445Mscientist55106
1549Feducator97301
1621Mentertainment10309
1730Mprogrammer06355
1835Fother37212
1940Mlibrarian02138
2042Fhomemaker95660
2126Mwriter30068
2225Mwriter40206
2330Fartist48197
2421Fartist94533
2539Mengineer55107
\n", + "
" + ], + "text/plain": [ + " age gender occupation zip_code\n", + "user_id \n", + "1 24 M technician 85711\n", + "2 53 F other 94043\n", + "3 23 M writer 32067\n", + "4 24 M technician 43537\n", + "5 33 F other 15213\n", + "6 42 M executive 98101\n", + "7 57 M administrator 91344\n", + "8 36 M administrator 05201\n", + "9 29 M student 01002\n", + "10 53 M lawyer 90703\n", + "11 39 F other 30329\n", + "12 28 F other 06405\n", + "13 47 M educator 29206\n", + "14 45 M scientist 55106\n", + "15 49 F educator 97301\n", + "16 21 M entertainment 10309\n", + "17 30 M programmer 06355\n", + "18 35 F other 37212\n", + "19 40 M librarian 02138\n", + "20 42 F homemaker 95660\n", + "21 26 M writer 30068\n", + "22 25 M writer 40206\n", + "23 30 F artist 48197\n", + "24 21 F artist 94533\n", + "25 39 M engineer 55107" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "users.head(25)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. See the last 10 entries" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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agegenderoccupationzip_code
user_id
93461Mengineer22902
93542Mdoctor66221
93624Mother32789
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93926Fstudent33319
94032Madministrator02215
94120Mstudent97229
94248Flibrarian78209
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" + ], + "text/plain": [ + " age gender occupation zip_code\n", + "user_id \n", + "934 61 M engineer 22902\n", + "935 42 M doctor 66221\n", + "936 24 M other 32789\n", + "937 48 M educator 98072\n", + "938 38 F technician 55038\n", + "939 26 F student 33319\n", + "940 32 M administrator 02215\n", + "941 20 M student 97229\n", + "942 48 F librarian 78209\n", + "943 22 M student 77841" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "users.tail(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. What is the number of observations in the dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "943" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "users.shape[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. What is the number of columns in the dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "users.shape[1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Print the name of all the columns." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index([u'age', u'gender', u'occupation', u'zip_code'], dtype='object')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "users.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. How is the dataset indexed?" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Int64Index([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,\n", + " ...\n", + " 934, 935, 936, 937, 938, 939, 940, 941, 942, 943],\n", + " dtype='int64', name=u'user_id', length=943)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# \"the index\" (aka \"the labels\")\n", + "users.index" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. What is the data type of each column?" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "age int64\n", + "gender object\n", + "occupation object\n", + "zip_code object\n", + "dtype: object" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "users.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 11. Print only the occupation column" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "user_id\n", + "1 technician\n", + "2 other\n", + "3 writer\n", + "4 technician\n", + "5 other\n", + "6 executive\n", + "7 administrator\n", + "8 administrator\n", + "9 student\n", + "10 lawyer\n", + "11 other\n", + "12 other\n", + "13 educator\n", + "14 scientist\n", + "15 educator\n", + "16 entertainment\n", + "17 programmer\n", + "18 other\n", + "19 librarian\n", + "20 homemaker\n", + "21 writer\n", + "22 writer\n", + "23 artist\n", + "24 artist\n", + "25 engineer\n", + "26 engineer\n", + "27 librarian\n", + "28 writer\n", + "29 programmer\n", + "30 student\n", + " ... \n", + "914 other\n", + "915 entertainment\n", + "916 engineer\n", + "917 student\n", + "918 scientist\n", + "919 other\n", + "920 artist\n", + "921 student\n", + "922 administrator\n", + "923 student\n", + "924 other\n", + "925 salesman\n", + "926 entertainment\n", + "927 programmer\n", + "928 student\n", + "929 scientist\n", + "930 scientist\n", + "931 educator\n", + "932 educator\n", + "933 student\n", + "934 engineer\n", + "935 doctor\n", + "936 other\n", + "937 educator\n", + "938 technician\n", + "939 student\n", + "940 administrator\n", + "941 student\n", + "942 librarian\n", + "943 student\n", + "Name: occupation, dtype: object" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "users.occupation \n", + "\n", + "#OR\n", + "\n", + "users['occupation']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 12. How many different occupations there are in this dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "21" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(users.occupation.unique())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 13. What is the most frequent occupation?" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "student 196\n", + "other 105\n", + "educator 95\n", + "administrator 79\n", + "engineer 67\n", + "Name: occupation, dtype: int64" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "users.occupation.value_counts().head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 14. Summarize the DataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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agegenderoccupationzip_code
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" + ], + "text/plain": [ + " age gender occupation zip_code\n", + "count 943.000000 943 943 943\n", + "unique NaN 2 21 795\n", + "top NaN M student 55414\n", + "freq NaN 670 196 9\n", + "mean 34.051962 NaN NaN NaN\n", + "std 12.192740 NaN NaN NaN\n", + "min 7.000000 NaN NaN NaN\n", + "25% 25.000000 NaN NaN NaN\n", + "50% 31.000000 NaN NaN NaN\n", + "75% 43.000000 NaN NaN NaN\n", + "max 73.000000 NaN NaN NaN" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "users.describe(include = \"all\") #Notice is only the numeric column" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 16. Summarize only the occupation column" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "count 943\n", + "unique 21\n", + "top student\n", + "freq 196\n", + "Name: occupation, dtype: object" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "users.occupation.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 17. What is the mean age of users?" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "34.0" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "round(users.age.mean())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 18. What is the age with least occurrence?" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "7 1\n", + "Name: age, dtype: int64" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "users.age.value_counts().tail(1) #7 years, only 1 occurrence" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/01_Getting___Knowing_Your_Data/Occupation/Exercises.ipynb b/200 solved problems in Python/pandas/01_Getting___Knowing_Your_Data/Occupation/Exercises.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..4e832814797783c7dca2f65e67b57af9a4ac7663 --- /dev/null +++ b/200 solved problems in Python/pandas/01_Getting___Knowing_Your_Data/Occupation/Exercises.ipynb @@ -0,0 +1,316 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Ex3 - Getting and Knowing your Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This time we are going to pull data directly from the internet.\n", + "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/u.user). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called users and use the 'user_id' as index" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. See the first 25 entries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. See the last 10 entries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. What is the number of observations in the dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. What is the number of columns in the dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Print the name of all the columns." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. How is the dataset indexed?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. What is the data type of each column?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 11. Print only the occupation column" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 12. How many different occupations there are in this dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 13. What is the most frequent occupation?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 14. Summarize the DataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 15. Summarize all the columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 16. Summarize only the occupation column" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 17. What is the mean age of users?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 18. What is the age with least occurrence?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/01_Getting___Knowing_Your_Data/Occupation/Solutions.ipynb b/200 solved problems in Python/pandas/01_Getting___Knowing_Your_Data/Occupation/Solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..0daad020089d65e91cf8b71d25d565b85f047235 --- /dev/null +++ b/200 solved problems in Python/pandas/01_Getting___Knowing_Your_Data/Occupation/Solutions.ipynb @@ -0,0 +1,1050 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Ex3 - Getting and Knowing your Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This time we are going to pull data directly from the internet.\n", + "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/u.user). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called users and use the 'user_id' as index" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. See the first 25 entries" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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agegenderoccupationzip_code
user_id
124Mtechnician85711
253Fother94043
323Mwriter32067
424Mtechnician43537
533Fother15213
642Mexecutive98101
757Madministrator91344
836Madministrator05201
929Mstudent01002
1053Mlawyer90703
1139Fother30329
1228Fother06405
1347Meducator29206
1445Mscientist55106
1549Feducator97301
1621Mentertainment10309
1730Mprogrammer06355
1835Fother37212
1940Mlibrarian02138
2042Fhomemaker95660
2126Mwriter30068
2225Mwriter40206
2330Fartist48197
2421Fartist94533
2539Mengineer55107
\n", + "
" + ], + "text/plain": [ + " age gender occupation zip_code\n", + "user_id \n", + "1 24 M technician 85711\n", + "2 53 F other 94043\n", + "3 23 M writer 32067\n", + "4 24 M technician 43537\n", + "5 33 F other 15213\n", + "6 42 M executive 98101\n", + "7 57 M administrator 91344\n", + "8 36 M administrator 05201\n", + "9 29 M student 01002\n", + "10 53 M lawyer 90703\n", + "11 39 F other 30329\n", + "12 28 F other 06405\n", + "13 47 M educator 29206\n", + "14 45 M scientist 55106\n", + "15 49 F educator 97301\n", + "16 21 M entertainment 10309\n", + "17 30 M programmer 06355\n", + "18 35 F other 37212\n", + "19 40 M librarian 02138\n", + "20 42 F homemaker 95660\n", + "21 26 M writer 30068\n", + "22 25 M writer 40206\n", + "23 30 F artist 48197\n", + "24 21 F artist 94533\n", + "25 39 M engineer 55107" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. See the last 10 entries" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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agegenderoccupationzip_code
user_id
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93542Mdoctor66221
93624Mother32789
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" + ], + "text/plain": [ + " age gender occupation zip_code\n", + "user_id \n", + "934 61 M engineer 22902\n", + "935 42 M doctor 66221\n", + "936 24 M other 32789\n", + "937 48 M educator 98072\n", + "938 38 F technician 55038\n", + "939 26 F student 33319\n", + "940 32 M administrator 02215\n", + "941 20 M student 97229\n", + "942 48 F librarian 78209\n", + "943 22 M student 77841" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. What is the number of observations in the dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "943" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. What is the number of columns in the dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Print the name of all the columns." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index([u'age', u'gender', u'occupation', u'zip_code'], dtype='object')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. How is the dataset indexed?" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Int64Index([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,\n", + " ...\n", + " 934, 935, 936, 937, 938, 939, 940, 941, 942, 943],\n", + " dtype='int64', name=u'user_id', length=943)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. What is the data type of each column?" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "age int64\n", + "gender object\n", + "occupation object\n", + "zip_code object\n", + "dtype: object" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 11. Print only the occupation column" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "user_id\n", + "1 technician\n", + "2 other\n", + "3 writer\n", + "4 technician\n", + "5 other\n", + "6 executive\n", + "7 administrator\n", + "8 administrator\n", + "9 student\n", + "10 lawyer\n", + "11 other\n", + "12 other\n", + "13 educator\n", + "14 scientist\n", + "15 educator\n", + "16 entertainment\n", + "17 programmer\n", + "18 other\n", + "19 librarian\n", + "20 homemaker\n", + "21 writer\n", + "22 writer\n", + "23 artist\n", + "24 artist\n", + "25 engineer\n", + "26 engineer\n", + "27 librarian\n", + "28 writer\n", + "29 programmer\n", + "30 student\n", + " ... \n", + "914 other\n", + "915 entertainment\n", + "916 engineer\n", + "917 student\n", + "918 scientist\n", + "919 other\n", + "920 artist\n", + "921 student\n", + "922 administrator\n", + "923 student\n", + "924 other\n", + "925 salesman\n", + "926 entertainment\n", + "927 programmer\n", + "928 student\n", + "929 scientist\n", + "930 scientist\n", + "931 educator\n", + "932 educator\n", + "933 student\n", + "934 engineer\n", + "935 doctor\n", + "936 other\n", + "937 educator\n", + "938 technician\n", + "939 student\n", + "940 administrator\n", + "941 student\n", + "942 librarian\n", + "943 student\n", + "Name: occupation, dtype: object" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 12. How many different occupations there are in this dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "21" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 13. What is the most frequent occupation?" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "student 196\n", + "other 105\n", + "educator 95\n", + "administrator 79\n", + "engineer 67\n", + "Name: occupation, dtype: int64" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 14. Summarize the DataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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agegenderoccupationzip_code
count943.000000943943943
uniqueNaN221795
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freqNaN6701969
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std12.192740NaNNaNNaN
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" + ], + "text/plain": [ + " age gender occupation zip_code\n", + "count 943.000000 943 943 943\n", + "unique NaN 2 21 795\n", + "top NaN M student 55414\n", + "freq NaN 670 196 9\n", + "mean 34.051962 NaN NaN NaN\n", + "std 12.192740 NaN NaN NaN\n", + "min 7.000000 NaN NaN NaN\n", + "25% 25.000000 NaN NaN NaN\n", + "50% 31.000000 NaN NaN NaN\n", + "75% 43.000000 NaN NaN NaN\n", + "max 73.000000 NaN NaN NaN" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 16. Summarize only the occupation column" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "count 943\n", + "unique 21\n", + "top student\n", + "freq 196\n", + "Name: occupation, dtype: object" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 17. What is the mean age of users?" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "34.0" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 18. What is the age with least occurrence?" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "7 1\n", + "Name: age, dtype: int64" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/01_Getting___Knowing_Your_Data/World Food Facts/Exercises.ipynb b/200 solved problems in Python/pandas/01_Getting___Knowing_Your_Data/World Food Facts/Exercises.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..0246d3784fb7a1935086f82d72c1b3ebd7bcc2a4 --- /dev/null +++ b/200 solved problems in Python/pandas/01_Getting___Knowing_Your_Data/World Food Facts/Exercises.ipynb @@ -0,0 +1,190 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exercise 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 1. Go to [Kaggle]( https://www.kaggle.com/openfoodfacts/world-food-facts)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Download the dataset to your computer and unzip it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Use the csv file and assign it to a dataframe called food" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. See the first 5 entries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. What is the number of observations in the dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. What is the number of columns in the dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Print the name of all the columns." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. What is the name of 105th column?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. What is the type of the observations of the 105th column?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. How is the dataset indexed?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 11. What is the product name of the 19th observation?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/01_Getting___Knowing_Your_Data/World Food Facts/Exercises_with_solutions.ipynb b/200 solved problems in Python/pandas/01_Getting___Knowing_Your_Data/World Food Facts/Exercises_with_solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..38531e483ae8c80e6d9a15e6e4c30c53678ca2bf --- /dev/null +++ b/200 solved problems in Python/pandas/01_Getting___Knowing_Your_Data/World Food Facts/Exercises_with_solutions.ipynb @@ -0,0 +1,538 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Ex1 - Getting and knowing your Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 1. Go to https://www.kaggle.com/openfoodfacts/world-food-facts" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Download the dataset to your computer and unzip it." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Use the csv file and assign it to a dataframe called food" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "//anaconda/lib/python2.7/site-packages/IPython/core/interactiveshell.py:2723: DtypeWarning: Columns (0,3,5,27,36) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " interactivity=interactivity, compiler=compiler, result=result)\n" + ] + } + ], + "source": [ + "food = pd.read_csv('/Users/guilhermeoliveira/Desktop/world-food-facts/FoodFacts.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. See the first 5 entries" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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codeurlcreatorcreated_tcreated_datetimelast_modified_tlast_modified_datetimeproduct_namegeneric_namequantity...caffeine_100gtaurine_100gph_100gfruits_vegetables_nuts_100gcollagen_meat_protein_ratio_100gcocoa_100gchlorophyl_100gcarbon_footprint_100gnutrition_score_fr_100gnutrition_score_uk_100g
0000000000000012866http://world-en.openfoodfacts.org/product/0000...date-limite-app14470043642015-11-08T17:39:24Z14470043642015-11-08T17:39:24ZPoêlée à la sarladaiseNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
10000000024600http://world-en.openfoodfacts.org/product/0000...date-limite-app14345307042015-06-17T08:45:04Z14345359142015-06-17T10:11:54ZFilet de bœufNaN2.46 kg...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
20000000036252http://world-en.openfoodfacts.org/product/0000...tacinte14222217012015-01-25T21:35:01Z14222218552015-01-25T21:37:35ZLion Peanut x2NaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
30000000039259http://world-en.openfoodfacts.org/product/0000...tacinte14222217732015-01-25T21:36:13Z14222219262015-01-25T21:38:46ZTwix x2NaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
40000000039529http://world-en.openfoodfacts.org/product/0000...teolemon14201470512015-01-01T21:17:31Z14391417402015-08-09T17:35:40ZPack de 2 TwixNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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5 rows × 159 columns

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" + ], + "text/plain": [ + " code url \\\n", + "0 000000000000012866 http://world-en.openfoodfacts.org/product/0000... \n", + "1 0000000024600 http://world-en.openfoodfacts.org/product/0000... \n", + "2 0000000036252 http://world-en.openfoodfacts.org/product/0000... \n", + "3 0000000039259 http://world-en.openfoodfacts.org/product/0000... \n", + "4 0000000039529 http://world-en.openfoodfacts.org/product/0000... \n", + "\n", + " creator created_t created_datetime last_modified_t \\\n", + "0 date-limite-app 1447004364 2015-11-08T17:39:24Z 1447004364 \n", + "1 date-limite-app 1434530704 2015-06-17T08:45:04Z 1434535914 \n", + "2 tacinte 1422221701 2015-01-25T21:35:01Z 1422221855 \n", + "3 tacinte 1422221773 2015-01-25T21:36:13Z 1422221926 \n", + "4 teolemon 1420147051 2015-01-01T21:17:31Z 1439141740 \n", + "\n", + " last_modified_datetime product_name generic_name quantity \\\n", + "0 2015-11-08T17:39:24Z Poêlée à la sarladaise NaN NaN \n", + "1 2015-06-17T10:11:54Z Filet de bœuf NaN 2.46 kg \n", + "2 2015-01-25T21:37:35Z Lion Peanut x2 NaN NaN \n", + "3 2015-01-25T21:38:46Z Twix x2 NaN NaN \n", + "4 2015-08-09T17:35:40Z Pack de 2 Twix NaN NaN \n", + "\n", + " ... caffeine_100g taurine_100g ph_100g \\\n", + "0 ... NaN NaN NaN \n", + "1 ... NaN NaN NaN \n", + "2 ... NaN NaN NaN \n", + "3 ... NaN NaN NaN \n", + "4 ... NaN NaN NaN \n", + "\n", + " fruits_vegetables_nuts_100g collagen_meat_protein_ratio_100g cocoa_100g \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + "\n", + " chlorophyl_100g carbon_footprint_100g nutrition_score_fr_100g \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + "\n", + " nutrition_score_uk_100g \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "\n", + "[5 rows x 159 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "food.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. What is the number of observations in the dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "65503" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "food.shape #will give you both (observations/rows, columns)\n", + "food.shape[0] #will give you only the observations/rows number" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. What is the number of columns in the dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(65503, 159)\n", + "159\n", + "\n", + "RangeIndex: 65503 entries, 0 to 65502\n", + "Columns: 159 entries, code to nutrition_score_uk_100g\n", + "dtypes: float64(103), object(56)\n", + "memory usage: 79.5+ MB\n" + ] + } + ], + "source": [ + "print food.shape #will give you both (observations/rows, columns)\n", + "print food.shape[1] #will give you only the columns number\n", + "\n", + "#OR\n", + "\n", + "food.info() #Columns: 159 entries" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Print the name of all the columns." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index([u'code', u'url', u'creator', u'created_t', u'created_datetime',\n", + " u'last_modified_t', u'last_modified_datetime', u'product_name',\n", + " u'generic_name', u'quantity',\n", + " ...\n", + " u'caffeine_100g', u'taurine_100g', u'ph_100g',\n", + " u'fruits_vegetables_nuts_100g', u'collagen_meat_protein_ratio_100g',\n", + " u'cocoa_100g', u'chlorophyl_100g', u'carbon_footprint_100g',\n", + " u'nutrition_score_fr_100g', u'nutrition_score_uk_100g'],\n", + " dtype='object', length=159)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "food.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. What is the name of 105th column?" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'glucose_100g'" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "food.columns[104]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. What is the type of the observations of the 105th column?" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('float64')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "food.dtypes['glucose_100g']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. How is the dataset indexed?" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "RangeIndex(start=0, stop=65503, step=1)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "food.index" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 11. What is the product name of the 19th observation?" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'Flat Leaf Parsley'" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "food.values[18][7]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/01_Getting___Knowing_Your_Data/World Food Facts/Solutions.ipynb b/200 solved problems in Python/pandas/01_Getting___Knowing_Your_Data/World Food Facts/Solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..6b0c9864469441792dcde6e6a5006f9dd860e5aa --- /dev/null +++ b/200 solved problems in Python/pandas/01_Getting___Knowing_Your_Data/World Food Facts/Solutions.ipynb @@ -0,0 +1,512 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Ex1 - Getting and knowing your Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 1. Go to https://www.kaggle.com/openfoodfacts/world-food-facts" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Download the dataset to your computer and unzip it." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Use the csv file and assign it to a dataframe called food" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "//anaconda/lib/python2.7/site-packages/IPython/core/interactiveshell.py:2723: DtypeWarning: Columns (0,3,5,27,36) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " interactivity=interactivity, compiler=compiler, result=result)\n" + ] + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. See the first 5 entries" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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codeurlcreatorcreated_tcreated_datetimelast_modified_tlast_modified_datetimeproduct_namegeneric_namequantity...caffeine_100gtaurine_100gph_100gfruits_vegetables_nuts_100gcollagen_meat_protein_ratio_100gcocoa_100gchlorophyl_100gcarbon_footprint_100gnutrition_score_fr_100gnutrition_score_uk_100g
0000000000000012866http://world-en.openfoodfacts.org/product/0000...date-limite-app14470043642015-11-08T17:39:24Z14470043642015-11-08T17:39:24ZPoêlée à la sarladaiseNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
10000000024600http://world-en.openfoodfacts.org/product/0000...date-limite-app14345307042015-06-17T08:45:04Z14345359142015-06-17T10:11:54ZFilet de bœufNaN2.46 kg...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
20000000036252http://world-en.openfoodfacts.org/product/0000...tacinte14222217012015-01-25T21:35:01Z14222218552015-01-25T21:37:35ZLion Peanut x2NaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
30000000039259http://world-en.openfoodfacts.org/product/0000...tacinte14222217732015-01-25T21:36:13Z14222219262015-01-25T21:38:46ZTwix x2NaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
40000000039529http://world-en.openfoodfacts.org/product/0000...teolemon14201470512015-01-01T21:17:31Z14391417402015-08-09T17:35:40ZPack de 2 TwixNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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5 rows × 159 columns

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" + ], + "text/plain": [ + " code url \\\n", + "0 000000000000012866 http://world-en.openfoodfacts.org/product/0000... \n", + "1 0000000024600 http://world-en.openfoodfacts.org/product/0000... \n", + "2 0000000036252 http://world-en.openfoodfacts.org/product/0000... \n", + "3 0000000039259 http://world-en.openfoodfacts.org/product/0000... \n", + "4 0000000039529 http://world-en.openfoodfacts.org/product/0000... \n", + "\n", + " creator created_t created_datetime last_modified_t \\\n", + "0 date-limite-app 1447004364 2015-11-08T17:39:24Z 1447004364 \n", + "1 date-limite-app 1434530704 2015-06-17T08:45:04Z 1434535914 \n", + "2 tacinte 1422221701 2015-01-25T21:35:01Z 1422221855 \n", + "3 tacinte 1422221773 2015-01-25T21:36:13Z 1422221926 \n", + "4 teolemon 1420147051 2015-01-01T21:17:31Z 1439141740 \n", + "\n", + " last_modified_datetime product_name generic_name quantity \\\n", + "0 2015-11-08T17:39:24Z Poêlée à la sarladaise NaN NaN \n", + "1 2015-06-17T10:11:54Z Filet de bœuf NaN 2.46 kg \n", + "2 2015-01-25T21:37:35Z Lion Peanut x2 NaN NaN \n", + "3 2015-01-25T21:38:46Z Twix x2 NaN NaN \n", + "4 2015-08-09T17:35:40Z Pack de 2 Twix NaN NaN \n", + "\n", + " ... caffeine_100g taurine_100g ph_100g \\\n", + "0 ... NaN NaN NaN \n", + "1 ... NaN NaN NaN \n", + "2 ... NaN NaN NaN \n", + "3 ... NaN NaN NaN \n", + "4 ... NaN NaN NaN \n", + "\n", + " fruits_vegetables_nuts_100g collagen_meat_protein_ratio_100g cocoa_100g \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + "\n", + " chlorophyl_100g carbon_footprint_100g nutrition_score_fr_100g \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + "\n", + " nutrition_score_uk_100g \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "\n", + "[5 rows x 159 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. What is the number of observations in the dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "65503" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. What is the number of columns in the dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(65503, 159)\n", + "159\n", + "\n", + "RangeIndex: 65503 entries, 0 to 65502\n", + "Columns: 159 entries, code to nutrition_score_uk_100g\n", + "dtypes: float64(103), object(56)\n", + "memory usage: 79.5+ MB\n" + ] + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Print the name of all the columns." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index([u'code', u'url', u'creator', u'created_t', u'created_datetime',\n", + " u'last_modified_t', u'last_modified_datetime', u'product_name',\n", + " u'generic_name', u'quantity',\n", + " ...\n", + " u'caffeine_100g', u'taurine_100g', u'ph_100g',\n", + " u'fruits_vegetables_nuts_100g', u'collagen_meat_protein_ratio_100g',\n", + " u'cocoa_100g', u'chlorophyl_100g', u'carbon_footprint_100g',\n", + " u'nutrition_score_fr_100g', u'nutrition_score_uk_100g'],\n", + " dtype='object', length=159)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. What is the name of 105th column?" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'glucose_100g'" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. What is the type of the observations of the 105th column?" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('float64')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. How is the dataset indexed?" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "RangeIndex(start=0, stop=65503, step=1)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 11. What is the product name of the 19th observation?" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'Flat Leaf Parsley'" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/02_Filtering___Sorting/Chipotle/.ipynb_checkpoints/Exercises-checkpoint.ipynb b/200 solved problems in Python/pandas/02_Filtering___Sorting/Chipotle/.ipynb_checkpoints/Exercises-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..5b5a8c166b8938a658716908e8f3c131d6931793 --- /dev/null +++ b/200 solved problems in Python/pandas/02_Filtering___Sorting/Chipotle/.ipynb_checkpoints/Exercises-checkpoint.ipynb @@ -0,0 +1,159 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "\n", + "# Ex1 - Filtering and Sorting Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This time we are going to pull data directly from the internet.\n", + "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called chipo." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. How many products cost more than $10.00?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. What is the price of each item? \n", + "###### print a data frame with only two columns item_name and item_price" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Sort by the name of the item" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. What was the quantity of the most expensive item ordered?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. How many times were a Veggie Salad Bowl ordered?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. How many times people orderd more than one Canned Soda?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/200 solved problems in Python/pandas/02_Filtering___Sorting/Chipotle/Exercises.ipynb b/200 solved problems in Python/pandas/02_Filtering___Sorting/Chipotle/Exercises.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..5b5a8c166b8938a658716908e8f3c131d6931793 --- /dev/null +++ b/200 solved problems in Python/pandas/02_Filtering___Sorting/Chipotle/Exercises.ipynb @@ -0,0 +1,159 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "\n", + "# Ex1 - Filtering and Sorting Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This time we are going to pull data directly from the internet.\n", + "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called chipo." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. How many products cost more than $10.00?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. What is the price of each item? \n", + "###### print a data frame with only two columns item_name and item_price" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Sort by the name of the item" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. What was the quantity of the most expensive item ordered?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. How many times were a Veggie Salad Bowl ordered?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. How many times people orderd more than one Canned Soda?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/200 solved problems in Python/pandas/02_Filtering___Sorting/Chipotle/Exercises_with_solutions.ipynb b/200 solved problems in Python/pandas/02_Filtering___Sorting/Chipotle/Exercises_with_solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..e487dad97e6af1f4c4789d45c2b5ea6b353faa69 --- /dev/null +++ b/200 solved problems in Python/pandas/02_Filtering___Sorting/Chipotle/Exercises_with_solutions.ipynb @@ -0,0 +1,689 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Ex1 - Filtering and Sorting Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This time we are going to pull data directly from the internet.\n", + "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called chipo." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "url = 'https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv'\n", + " \n", + "chipo = pd.read_csv(url, sep = '\\t')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. How many products cost more than $10.00?" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1130" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# clean the item_price column and transform it in a float\n", + "prices = [float(value[1 : -1]) for value in chipo.item_price]\n", + "\n", + "# reassign the column with the cleaned prices\n", + "chipo.item_price = prices \n", + "\n", + "# make the comparison\n", + "chipo10 = chipo[chipo['item_price'] > 10.00]\n", + "chipo10.head()\n", + "\n", + "len(chipo10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. What is the price of each item? \n", + "###### print a data frame with only two columns item_name and item_price" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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item_nameitem_price
606Steak Salad Bowl11.89
1229Barbacoa Salad Bowl11.89
1132Carnitas Salad Bowl11.89
7Steak Burrito11.75
168Barbacoa Crispy Tacos11.75
39Barbacoa Bowl11.75
738Veggie Soft Tacos11.25
186Veggie Salad Bowl11.25
62Veggie Bowl11.25
57Veggie Burrito11.25
250Chicken Salad10.98
5Chicken Bowl10.98
8Steak Soft Tacos9.25
554Carnitas Crispy Tacos9.25
237Carnitas Soft Tacos9.25
56Barbacoa Soft Tacos9.25
92Steak Crispy Tacos9.25
664Steak Salad8.99
54Steak Bowl8.99
3750Carnitas Salad8.99
21Barbacoa Burrito8.99
27Carnitas Burrito8.99
33Carnitas Bowl8.99
11Chicken Crispy Tacos8.75
12Chicken Soft Tacos8.75
44Chicken Salad Bowl8.75
1653Veggie Crispy Tacos8.49
16Chicken Burrito8.49
1694Veggie Salad8.49
1414Salad7.40
510Burrito7.40
520Crispy Tacos7.40
673Bowl7.40
2986 Pack Soft Drink6.49
10Chips and Guacamole4.45
1Izze3.39
2Nantucket Nectar3.39
674Chips and Mild Fresh Tomato Salsa3.00
111Chips and Tomatillo Red Chili Salsa2.95
233Chips and Roasted Chili Corn Salsa2.95
38Chips and Tomatillo Green Chili Salsa2.95
3Chips and Tomatillo-Green Chili Salsa2.39
300Chips and Tomatillo-Red Chili Salsa2.39
191Chips and Roasted Chili-Corn Salsa2.39
0Chips and Fresh Tomato Salsa2.39
40Chips2.15
6Side of Chips1.69
263Canned Soft Drink1.25
28Canned Soda1.09
34Bottled Water1.09
\n", + "
" + ], + "text/plain": [ + " item_name item_price\n", + "606 Steak Salad Bowl 11.89\n", + "1229 Barbacoa Salad Bowl 11.89\n", + "1132 Carnitas Salad Bowl 11.89\n", + "7 Steak Burrito 11.75\n", + "168 Barbacoa Crispy Tacos 11.75\n", + "39 Barbacoa Bowl 11.75\n", + "738 Veggie Soft Tacos 11.25\n", + "186 Veggie Salad Bowl 11.25\n", + "62 Veggie Bowl 11.25\n", + "57 Veggie Burrito 11.25\n", + "250 Chicken Salad 10.98\n", + "5 Chicken Bowl 10.98\n", + "8 Steak Soft Tacos 9.25\n", + "554 Carnitas Crispy Tacos 9.25\n", + "237 Carnitas Soft Tacos 9.25\n", + "56 Barbacoa Soft Tacos 9.25\n", + "92 Steak Crispy Tacos 9.25\n", + "664 Steak Salad 8.99\n", + "54 Steak Bowl 8.99\n", + "3750 Carnitas Salad 8.99\n", + "21 Barbacoa Burrito 8.99\n", + "27 Carnitas Burrito 8.99\n", + "33 Carnitas Bowl 8.99\n", + "11 Chicken Crispy Tacos 8.75\n", + "12 Chicken Soft Tacos 8.75\n", + "44 Chicken Salad Bowl 8.75\n", + "1653 Veggie Crispy Tacos 8.49\n", + "16 Chicken Burrito 8.49\n", + "1694 Veggie Salad 8.49\n", + "1414 Salad 7.40\n", + "510 Burrito 7.40\n", + "520 Crispy Tacos 7.40\n", + "673 Bowl 7.40\n", + "298 6 Pack Soft Drink 6.49\n", + "10 Chips and Guacamole 4.45\n", + "1 Izze 3.39\n", + "2 Nantucket Nectar 3.39\n", + "674 Chips and Mild Fresh Tomato Salsa 3.00\n", + "111 Chips and Tomatillo Red Chili Salsa 2.95\n", + "233 Chips and Roasted Chili Corn Salsa 2.95\n", + "38 Chips and Tomatillo Green Chili Salsa 2.95\n", + "3 Chips and Tomatillo-Green Chili Salsa 2.39\n", + "300 Chips and Tomatillo-Red Chili Salsa 2.39\n", + "191 Chips and Roasted Chili-Corn Salsa 2.39\n", + "0 Chips and Fresh Tomato Salsa 2.39\n", + "40 Chips 2.15\n", + "6 Side of Chips 1.69\n", + "263 Canned Soft Drink 1.25\n", + "28 Canned Soda 1.09\n", + "34 Bottled Water 1.09" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# delete the duplicates in item_name and quantity\n", + "chipo_filtered = chipo.drop_duplicates(['item_name','quantity'])\n", + "\n", + "# select only the products with quantity equals to 1\n", + "chipo_one_prod = chipo_filtered[chipo_filtered.quantity == 1]\n", + "\n", + "# select only the item_name and item_price columns\n", + "price_per_item = chipo_one_prod[['item_name', 'item_price']]\n", + "\n", + "# sort the values from the most to less expensive\n", + "price_per_item.sort_values(by = \"item_price\", ascending = False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Sort by the name of the item" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "3389 6 Pack Soft Drink\n", + "341 6 Pack Soft Drink\n", + "1849 6 Pack Soft Drink\n", + "1860 6 Pack Soft Drink\n", + "2713 6 Pack Soft Drink\n", + "3422 6 Pack Soft Drink\n", + "553 6 Pack Soft Drink\n", + "1916 6 Pack Soft Drink\n", + "1922 6 Pack Soft Drink\n", + "1937 6 Pack Soft Drink\n", + "3836 6 Pack Soft Drink\n", + "298 6 Pack Soft Drink\n", + "1976 6 Pack Soft Drink\n", + "1167 6 Pack Soft Drink\n", + "3875 6 Pack Soft Drink\n", + "1124 6 Pack Soft Drink\n", + "3886 6 Pack Soft Drink\n", + "2108 6 Pack Soft Drink\n", + "3010 6 Pack Soft Drink\n", + "4535 6 Pack Soft Drink\n", + "4169 6 Pack Soft Drink\n", + "4174 6 Pack Soft Drink\n", + "4527 6 Pack Soft Drink\n", + "4522 6 Pack Soft Drink\n", + "3806 6 Pack Soft Drink\n", + "2389 6 Pack Soft Drink\n", + "3132 6 Pack Soft Drink\n", + "3141 6 Pack Soft Drink\n", + "639 6 Pack Soft Drink\n", + "1026 6 Pack Soft Drink\n", + " ... \n", + "2996 Veggie Salad\n", + "3163 Veggie Salad\n", + "4084 Veggie Salad\n", + "1694 Veggie Salad\n", + "2756 Veggie Salad\n", + "4201 Veggie Salad Bowl\n", + "1884 Veggie Salad Bowl\n", + "455 Veggie Salad Bowl\n", + "3223 Veggie Salad Bowl\n", + "2223 Veggie Salad Bowl\n", + "2269 Veggie Salad Bowl\n", + "4541 Veggie Salad Bowl\n", + "3293 Veggie Salad Bowl\n", + "186 Veggie Salad Bowl\n", + "960 Veggie Salad Bowl\n", + "1316 Veggie Salad Bowl\n", + "2156 Veggie Salad Bowl\n", + "4261 Veggie Salad Bowl\n", + "295 Veggie Salad Bowl\n", + "4573 Veggie Salad Bowl\n", + "2683 Veggie Salad Bowl\n", + "496 Veggie Salad Bowl\n", + "4109 Veggie Salad Bowl\n", + "738 Veggie Soft Tacos\n", + "3889 Veggie Soft Tacos\n", + "2384 Veggie Soft Tacos\n", + "781 Veggie Soft Tacos\n", + "2851 Veggie Soft Tacos\n", + "1699 Veggie Soft Tacos\n", + "1395 Veggie Soft Tacos\n", + "Name: item_name, dtype: object" + ] + }, + "execution_count": 156, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chipo.item_name.sort_values()\n", + "\n", + "# OR\n", + "\n", + "chipo.sort_values(by = \"item_name\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. What was the quantity of the most expensive item ordered?" + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
order_idquantityitem_namechoice_descriptionitem_price
3598144315Chips and Fresh Tomato SalsaNaN44.25
\n", + "
" + ], + "text/plain": [ + " order_id quantity item_name choice_description \\\n", + "3598 1443 15 Chips and Fresh Tomato Salsa NaN \n", + "\n", + " item_price \n", + "3598 44.25 " + ] + }, + "execution_count": 165, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chipo.sort_values(by = \"item_price\", ascending = False).head(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. How many times were a Veggie Salad Bowl ordered?" + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "18" + ] + }, + "execution_count": 174, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chipo_salad = chipo[chipo.item_name == \"Veggie Salad Bowl\"]\n", + "\n", + "len(chipo_salad)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. How many times people orderd more than one Canned Soda?" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "20" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chipo_drink_steak_bowl = chipo[(chipo.item_name == \"Canned Soda\") & (chipo.quantity > 1)]\n", + "len(chipo_drink_steak_bowl)" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/02_Filtering___Sorting/Chipotle/Solutions.ipynb b/200 solved problems in Python/pandas/02_Filtering___Sorting/Chipotle/Solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..74db3b38666e1511d354ebd3816cfd161e9f10de --- /dev/null +++ b/200 solved problems in Python/pandas/02_Filtering___Sorting/Chipotle/Solutions.ipynb @@ -0,0 +1,643 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Filtering and Sorting Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This time we are going to pull data directly from the internet.\n", + "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called chipo." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. How many products cost more than $10.00?" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1130" + ] + }, + "execution_count": 168, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. What is the price of each item? \n", + "###### print a data frame with only two columns item_name and item_price" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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item_nameitem_price
606Steak Salad Bowl11.89
1229Barbacoa Salad Bowl11.89
1132Carnitas Salad Bowl11.89
7Steak Burrito11.75
168Barbacoa Crispy Tacos11.75
39Barbacoa Bowl11.75
738Veggie Soft Tacos11.25
186Veggie Salad Bowl11.25
62Veggie Bowl11.25
57Veggie Burrito11.25
250Chicken Salad10.98
5Chicken Bowl10.98
8Steak Soft Tacos9.25
554Carnitas Crispy Tacos9.25
237Carnitas Soft Tacos9.25
56Barbacoa Soft Tacos9.25
92Steak Crispy Tacos9.25
664Steak Salad8.99
54Steak Bowl8.99
3750Carnitas Salad8.99
21Barbacoa Burrito8.99
27Carnitas Burrito8.99
33Carnitas Bowl8.99
11Chicken Crispy Tacos8.75
12Chicken Soft Tacos8.75
44Chicken Salad Bowl8.75
1653Veggie Crispy Tacos8.49
16Chicken Burrito8.49
1694Veggie Salad8.49
1414Salad7.40
510Burrito7.40
520Crispy Tacos7.40
673Bowl7.40
2986 Pack Soft Drink6.49
10Chips and Guacamole4.45
1Izze3.39
2Nantucket Nectar3.39
674Chips and Mild Fresh Tomato Salsa3.00
111Chips and Tomatillo Red Chili Salsa2.95
233Chips and Roasted Chili Corn Salsa2.95
38Chips and Tomatillo Green Chili Salsa2.95
3Chips and Tomatillo-Green Chili Salsa2.39
300Chips and Tomatillo-Red Chili Salsa2.39
191Chips and Roasted Chili-Corn Salsa2.39
0Chips and Fresh Tomato Salsa2.39
40Chips2.15
6Side of Chips1.69
263Canned Soft Drink1.25
28Canned Soda1.09
34Bottled Water1.09
\n", + "
" + ], + "text/plain": [ + " item_name item_price\n", + "606 Steak Salad Bowl 11.89\n", + "1229 Barbacoa Salad Bowl 11.89\n", + "1132 Carnitas Salad Bowl 11.89\n", + "7 Steak Burrito 11.75\n", + "168 Barbacoa Crispy Tacos 11.75\n", + "39 Barbacoa Bowl 11.75\n", + "738 Veggie Soft Tacos 11.25\n", + "186 Veggie Salad Bowl 11.25\n", + "62 Veggie Bowl 11.25\n", + "57 Veggie Burrito 11.25\n", + "250 Chicken Salad 10.98\n", + "5 Chicken Bowl 10.98\n", + "8 Steak Soft Tacos 9.25\n", + "554 Carnitas Crispy Tacos 9.25\n", + "237 Carnitas Soft Tacos 9.25\n", + "56 Barbacoa Soft Tacos 9.25\n", + "92 Steak Crispy Tacos 9.25\n", + "664 Steak Salad 8.99\n", + "54 Steak Bowl 8.99\n", + "3750 Carnitas Salad 8.99\n", + "21 Barbacoa Burrito 8.99\n", + "27 Carnitas Burrito 8.99\n", + "33 Carnitas Bowl 8.99\n", + "11 Chicken Crispy Tacos 8.75\n", + "12 Chicken Soft Tacos 8.75\n", + "44 Chicken Salad Bowl 8.75\n", + "1653 Veggie Crispy Tacos 8.49\n", + "16 Chicken Burrito 8.49\n", + "1694 Veggie Salad 8.49\n", + "1414 Salad 7.40\n", + "510 Burrito 7.40\n", + "520 Crispy Tacos 7.40\n", + "673 Bowl 7.40\n", + "298 6 Pack Soft Drink 6.49\n", + "10 Chips and Guacamole 4.45\n", + "1 Izze 3.39\n", + "2 Nantucket Nectar 3.39\n", + "674 Chips and Mild Fresh Tomato Salsa 3.00\n", + "111 Chips and Tomatillo Red Chili Salsa 2.95\n", + "233 Chips and Roasted Chili Corn Salsa 2.95\n", + "38 Chips and Tomatillo Green Chili Salsa 2.95\n", + "3 Chips and Tomatillo-Green Chili Salsa 2.39\n", + "300 Chips and Tomatillo-Red Chili Salsa 2.39\n", + "191 Chips and Roasted Chili-Corn Salsa 2.39\n", + "0 Chips and Fresh Tomato Salsa 2.39\n", + "40 Chips 2.15\n", + "6 Side of Chips 1.69\n", + "263 Canned Soft Drink 1.25\n", + "28 Canned Soda 1.09\n", + "34 Bottled Water 1.09" + ] + }, + "execution_count": 176, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Sort by the name of the item" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "3389 6 Pack Soft Drink\n", + "341 6 Pack Soft Drink\n", + "1849 6 Pack Soft Drink\n", + "1860 6 Pack Soft Drink\n", + "2713 6 Pack Soft Drink\n", + "3422 6 Pack Soft Drink\n", + "553 6 Pack Soft Drink\n", + "1916 6 Pack Soft Drink\n", + "1922 6 Pack Soft Drink\n", + "1937 6 Pack Soft Drink\n", + "3836 6 Pack Soft Drink\n", + "298 6 Pack Soft Drink\n", + "1976 6 Pack Soft Drink\n", + "1167 6 Pack Soft Drink\n", + "3875 6 Pack Soft Drink\n", + "1124 6 Pack Soft Drink\n", + "3886 6 Pack Soft Drink\n", + "2108 6 Pack Soft Drink\n", + "3010 6 Pack Soft Drink\n", + "4535 6 Pack Soft Drink\n", + "4169 6 Pack Soft Drink\n", + "4174 6 Pack Soft Drink\n", + "4527 6 Pack Soft Drink\n", + "4522 6 Pack Soft Drink\n", + "3806 6 Pack Soft Drink\n", + "2389 6 Pack Soft Drink\n", + "3132 6 Pack Soft Drink\n", + "3141 6 Pack Soft Drink\n", + "639 6 Pack Soft Drink\n", + "1026 6 Pack Soft Drink\n", + " ... \n", + "2996 Veggie Salad\n", + "3163 Veggie Salad\n", + "4084 Veggie Salad\n", + "1694 Veggie Salad\n", + "2756 Veggie Salad\n", + "4201 Veggie Salad Bowl\n", + "1884 Veggie Salad Bowl\n", + "455 Veggie Salad Bowl\n", + "3223 Veggie Salad Bowl\n", + "2223 Veggie Salad Bowl\n", + "2269 Veggie Salad Bowl\n", + "4541 Veggie Salad Bowl\n", + "3293 Veggie Salad Bowl\n", + "186 Veggie Salad Bowl\n", + "960 Veggie Salad Bowl\n", + "1316 Veggie Salad Bowl\n", + "2156 Veggie Salad Bowl\n", + "4261 Veggie Salad Bowl\n", + "295 Veggie Salad Bowl\n", + "4573 Veggie Salad Bowl\n", + "2683 Veggie Salad Bowl\n", + "496 Veggie Salad Bowl\n", + "4109 Veggie Salad Bowl\n", + "738 Veggie Soft Tacos\n", + "3889 Veggie Soft Tacos\n", + "2384 Veggie Soft Tacos\n", + "781 Veggie Soft Tacos\n", + "2851 Veggie Soft Tacos\n", + "1699 Veggie Soft Tacos\n", + "1395 Veggie Soft Tacos\n", + "Name: item_name, dtype: object" + ] + }, + "execution_count": 156, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. What was the quantity of the most expensive item ordered?" + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
order_idquantityitem_namechoice_descriptionitem_price
3598144315Chips and Fresh Tomato SalsaNaN44.25
\n", + "
" + ], + "text/plain": [ + " order_id quantity item_name choice_description \\\n", + "3598 1443 15 Chips and Fresh Tomato Salsa NaN \n", + "\n", + " item_price \n", + "3598 44.25 " + ] + }, + "execution_count": 165, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. How many times were a Veggie Salad Bowl ordered?" + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "18" + ] + }, + "execution_count": 174, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. How many times people orderd more than one Canned Soda?" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "20" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/02_Filtering___Sorting/Euro12/Exercises.ipynb b/200 solved problems in Python/pandas/02_Filtering___Sorting/Euro12/Exercises.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..64d69237b58c4dddba76a13e3d818433f23c2f17 --- /dev/null +++ b/200 solved problems in Python/pandas/02_Filtering___Sorting/Euro12/Exercises.ipynb @@ -0,0 +1,250 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Ex2 - Filtering and Sorting Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This time we are going to pull data directly from the internet.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/jokecamp/FootballData/master/Euro%202012/Euro%202012%20stats%20TEAM.csv). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called euro12." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Select only the Goal column." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. How many team participated in the Euro2012?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. What is the number of columns in the dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. View only the columns Team, Yellow Cards and Red Cards and assign them to a dataframe called discipline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Sort the teams by Red Cards, then to Yellow Cards" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. Calculate the mean Yellow Cards given per Team" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. Filter teams that scored more than 6 goals" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 11. Select the teams that start with G" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 12. Select the first 7 columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 13. Select all columns except the last 3." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 14. Present only the Shooting Accuracy from England, Italy and Russia" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/02_Filtering___Sorting/Euro12/Exercises_with_Solutions.ipynb b/200 solved problems in Python/pandas/02_Filtering___Sorting/Euro12/Exercises_with_Solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..62766f1c90dec7701180c36650de845ed65fb957 --- /dev/null +++ b/200 solved problems in Python/pandas/02_Filtering___Sorting/Euro12/Exercises_with_Solutions.ipynb @@ -0,0 +1,2198 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Ex2 - Filtering and Sorting Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This time we are going to pull data directly from the internet.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/jokecamp/FootballData/master/Euro%202012/Euro%202012%20stats%20TEAM.csv). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called euro12." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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TeamGoalsShots on targetShots off targetShooting Accuracy% Goals-to-shotsTotal shots (inc. Blocked)Hit WoodworkPenalty goalsPenalties not scored...Saves madeSaves-to-shots ratioFouls WonFouls ConcededOffsidesYellow CardsRed CardsSubs onSubs offPlayers Used
0Croatia4131251.9%16.0%32000...1381.3%41622909916
1Czech Republic4131841.9%12.9%39000...960.1%5373870111119
2Denmark4101050.0%20.0%27100...1066.7%25388407715
3England5111850.0%17.2%40000...2288.1%4345650111116
4France3222437.9%6.5%65100...654.6%3651560111119
5Germany10323247.8%15.6%80210...1062.6%63491240151517
6Greece581830.7%19.2%32111...1365.1%67481291121220
7Italy6344543.0%7.5%110200...2074.1%1018916160181819
8Netherlands2123625.0%4.1%60200...1270.6%35303507715
9Poland2152339.4%5.2%48000...666.7%48563717717
10Portugal6224234.3%9.3%82600...1071.5%739010120141416
11Republic of Ireland171236.8%5.2%28000...1765.4%43511161101017
12Russia593122.5%12.5%59200...1077.0%34434607716
13Spain12423355.9%16.0%100010...1593.8%1028319110171718
14Sweden5171947.2%13.8%39300...861.6%35517709918
15Ukraine272621.2%6.0%38000...1376.5%48314509918
\n", + "

16 rows × 35 columns

\n", + "
" + ], + "text/plain": [ + " Team Goals Shots on target Shots off target \\\n", + "0 Croatia 4 13 12 \n", + "1 Czech Republic 4 13 18 \n", + "2 Denmark 4 10 10 \n", + "3 England 5 11 18 \n", + "4 France 3 22 24 \n", + "5 Germany 10 32 32 \n", + "6 Greece 5 8 18 \n", + "7 Italy 6 34 45 \n", + "8 Netherlands 2 12 36 \n", + "9 Poland 2 15 23 \n", + "10 Portugal 6 22 42 \n", + "11 Republic of Ireland 1 7 12 \n", + "12 Russia 5 9 31 \n", + "13 Spain 12 42 33 \n", + "14 Sweden 5 17 19 \n", + "15 Ukraine 2 7 26 \n", + "\n", + " Shooting Accuracy % Goals-to-shots Total shots (inc. Blocked) \\\n", + "0 51.9% 16.0% 32 \n", + "1 41.9% 12.9% 39 \n", + "2 50.0% 20.0% 27 \n", + "3 50.0% 17.2% 40 \n", + "4 37.9% 6.5% 65 \n", + "5 47.8% 15.6% 80 \n", + "6 30.7% 19.2% 32 \n", + "7 43.0% 7.5% 110 \n", + "8 25.0% 4.1% 60 \n", + "9 39.4% 5.2% 48 \n", + "10 34.3% 9.3% 82 \n", + "11 36.8% 5.2% 28 \n", + "12 22.5% 12.5% 59 \n", + "13 55.9% 16.0% 100 \n", + "14 47.2% 13.8% 39 \n", + "15 21.2% 6.0% 38 \n", + "\n", + " Hit Woodwork Penalty goals Penalties not scored ... \\\n", + "0 0 0 0 ... \n", + "1 0 0 0 ... \n", + "2 1 0 0 ... \n", + "3 0 0 0 ... \n", + "4 1 0 0 ... \n", + "5 2 1 0 ... \n", + "6 1 1 1 ... \n", + "7 2 0 0 ... \n", + "8 2 0 0 ... \n", + "9 0 0 0 ... \n", + "10 6 0 0 ... \n", + "11 0 0 0 ... \n", + "12 2 0 0 ... \n", + "13 0 1 0 ... \n", + "14 3 0 0 ... \n", + "15 0 0 0 ... \n", + "\n", + " Saves made Saves-to-shots ratio Fouls Won Fouls Conceded Offsides \\\n", + "0 13 81.3% 41 62 2 \n", + "1 9 60.1% 53 73 8 \n", + "2 10 66.7% 25 38 8 \n", + "3 22 88.1% 43 45 6 \n", + "4 6 54.6% 36 51 5 \n", + "5 10 62.6% 63 49 12 \n", + "6 13 65.1% 67 48 12 \n", + "7 20 74.1% 101 89 16 \n", + "8 12 70.6% 35 30 3 \n", + "9 6 66.7% 48 56 3 \n", + "10 10 71.5% 73 90 10 \n", + "11 17 65.4% 43 51 11 \n", + "12 10 77.0% 34 43 4 \n", + "13 15 93.8% 102 83 19 \n", + "14 8 61.6% 35 51 7 \n", + "15 13 76.5% 48 31 4 \n", + "\n", + " Yellow Cards Red Cards Subs on Subs off Players Used \n", + "0 9 0 9 9 16 \n", + "1 7 0 11 11 19 \n", + "2 4 0 7 7 15 \n", + "3 5 0 11 11 16 \n", + "4 6 0 11 11 19 \n", + "5 4 0 15 15 17 \n", + "6 9 1 12 12 20 \n", + "7 16 0 18 18 19 \n", + "8 5 0 7 7 15 \n", + "9 7 1 7 7 17 \n", + "10 12 0 14 14 16 \n", + "11 6 1 10 10 17 \n", + "12 6 0 7 7 16 \n", + "13 11 0 17 17 18 \n", + "14 7 0 9 9 18 \n", + "15 5 0 9 9 18 \n", + "\n", + "[16 rows x 35 columns]" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "euro12 = pd.read_csv('https://raw.githubusercontent.com/jokecamp/FootballData/master/Euro%202012/Euro%202012%20stats%20TEAM.csv')\n", + "euro12" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Select only the Goal column." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 4\n", + "1 4\n", + "2 4\n", + "3 5\n", + "4 3\n", + "5 10\n", + "6 5\n", + "7 6\n", + "8 2\n", + "9 2\n", + "10 6\n", + "11 1\n", + "12 5\n", + "13 12\n", + "14 5\n", + "15 2\n", + "Name: Goals, dtype: int64" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "euro12.Goals" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. How many team participated in the Euro2012?" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "16" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "euro12.shape[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. What is the number of columns in the dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 16 entries, 0 to 15\n", + "Data columns (total 35 columns):\n", + "Team 16 non-null object\n", + "Goals 16 non-null int64\n", + "Shots on target 16 non-null int64\n", + "Shots off target 16 non-null int64\n", + "Shooting Accuracy 16 non-null object\n", + "% Goals-to-shots 16 non-null object\n", + "Total shots (inc. Blocked) 16 non-null int64\n", + "Hit Woodwork 16 non-null int64\n", + "Penalty goals 16 non-null int64\n", + "Penalties not scored 16 non-null int64\n", + "Headed goals 16 non-null int64\n", + "Passes 16 non-null int64\n", + "Passes completed 16 non-null int64\n", + "Passing Accuracy 16 non-null object\n", + "Touches 16 non-null int64\n", + "Crosses 16 non-null int64\n", + "Dribbles 16 non-null int64\n", + "Corners Taken 16 non-null int64\n", + "Tackles 16 non-null int64\n", + "Clearances 16 non-null int64\n", + "Interceptions 16 non-null int64\n", + "Clearances off line 15 non-null float64\n", + "Clean Sheets 16 non-null int64\n", + "Blocks 16 non-null int64\n", + "Goals conceded 16 non-null int64\n", + "Saves made 16 non-null int64\n", + "Saves-to-shots ratio 16 non-null object\n", + "Fouls Won 16 non-null int64\n", + "Fouls Conceded 16 non-null int64\n", + "Offsides 16 non-null int64\n", + "Yellow Cards 16 non-null int64\n", + "Red Cards 16 non-null int64\n", + "Subs on 16 non-null int64\n", + "Subs off 16 non-null int64\n", + "Players Used 16 non-null int64\n", + "dtypes: float64(1), int64(29), object(5)\n", + "memory usage: 4.4+ KB\n" + ] + } + ], + "source": [ + "euro12.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. View only the columns Team, Yellow Cards and Red Cards and assign them to a dataframe called discipline" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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TeamYellow CardsRed Cards
0Croatia90
1Czech Republic70
2Denmark40
3England50
4France60
5Germany40
6Greece91
7Italy160
8Netherlands50
9Poland71
10Portugal120
11Republic of Ireland61
12Russia60
13Spain110
14Sweden70
15Ukraine50
\n", + "
" + ], + "text/plain": [ + " Team Yellow Cards Red Cards\n", + "0 Croatia 9 0\n", + "1 Czech Republic 7 0\n", + "2 Denmark 4 0\n", + "3 England 5 0\n", + "4 France 6 0\n", + "5 Germany 4 0\n", + "6 Greece 9 1\n", + "7 Italy 16 0\n", + "8 Netherlands 5 0\n", + "9 Poland 7 1\n", + "10 Portugal 12 0\n", + "11 Republic of Ireland 6 1\n", + "12 Russia 6 0\n", + "13 Spain 11 0\n", + "14 Sweden 7 0\n", + "15 Ukraine 5 0" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# filter only giving the column names\n", + "\n", + "discipline = euro12[['Team', 'Yellow Cards', 'Red Cards']]\n", + "discipline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Sort the teams by Red Cards, then to Yellow Cards" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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TeamYellow CardsRed Cards
6Greece91
9Poland71
11Republic of Ireland61
7Italy160
10Portugal120
13Spain110
0Croatia90
1Czech Republic70
14Sweden70
4France60
12Russia60
3England50
8Netherlands50
15Ukraine50
2Denmark40
5Germany40
\n", + "
" + ], + "text/plain": [ + " Team Yellow Cards Red Cards\n", + "6 Greece 9 1\n", + "9 Poland 7 1\n", + "11 Republic of Ireland 6 1\n", + "7 Italy 16 0\n", + "10 Portugal 12 0\n", + "13 Spain 11 0\n", + "0 Croatia 9 0\n", + "1 Czech Republic 7 0\n", + "14 Sweden 7 0\n", + "4 France 6 0\n", + "12 Russia 6 0\n", + "3 England 5 0\n", + "8 Netherlands 5 0\n", + "15 Ukraine 5 0\n", + "2 Denmark 4 0\n", + "5 Germany 4 0" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "discipline.sort_values(['Red Cards', 'Yellow Cards'], ascending = False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. Calculate the mean Yellow Cards given per Team" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "7.0" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "round(discipline['Yellow Cards'].mean())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. Filter teams that scored more than 6 goals" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
TeamGoalsShots on targetShots off targetShooting Accuracy% Goals-to-shotsTotal shots (inc. Blocked)Hit WoodworkPenalty goalsPenalties not scored...Saves madeSaves-to-shots ratioFouls WonFouls ConcededOffsidesYellow CardsRed CardsSubs onSubs offPlayers Used
5Germany10323247.8%15.6%80210...1062.6%63491240151517
13Spain12423355.9%16.0%100010...1593.8%1028319110171718
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2 rows × 35 columns

\n", + "
" + ], + "text/plain": [ + " Team Goals Shots on target Shots off target Shooting Accuracy \\\n", + "5 Germany 10 32 32 47.8% \n", + "13 Spain 12 42 33 55.9% \n", + "\n", + " % Goals-to-shots Total shots (inc. Blocked) Hit Woodwork Penalty goals \\\n", + "5 15.6% 80 2 1 \n", + "13 16.0% 100 0 1 \n", + "\n", + " Penalties not scored ... Saves made Saves-to-shots ratio \\\n", + "5 0 ... 10 62.6% \n", + "13 0 ... 15 93.8% \n", + "\n", + " Fouls Won Fouls Conceded Offsides Yellow Cards Red Cards Subs on \\\n", + "5 63 49 12 4 0 15 \n", + "13 102 83 19 11 0 17 \n", + "\n", + " Subs off Players Used \n", + "5 15 17 \n", + "13 17 18 \n", + "\n", + "[2 rows x 35 columns]" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "euro12[euro12.Goals > 6]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 11. Select the teams that start with G" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
TeamGoalsShots on targetShots off targetShooting Accuracy% Goals-to-shotsTotal shots (inc. Blocked)Hit WoodworkPenalty goalsPenalties not scored...Saves madeSaves-to-shots ratioFouls WonFouls ConcededOffsidesYellow CardsRed CardsSubs onSubs offPlayers Used
5Germany10323247.8%15.6%80210...1062.6%63491240151517
6Greece581830.7%19.2%32111...1365.1%67481291121220
\n", + "

2 rows × 35 columns

\n", + "
" + ], + "text/plain": [ + " Team Goals Shots on target Shots off target Shooting Accuracy \\\n", + "5 Germany 10 32 32 47.8% \n", + "6 Greece 5 8 18 30.7% \n", + "\n", + " % Goals-to-shots Total shots (inc. Blocked) Hit Woodwork Penalty goals \\\n", + "5 15.6% 80 2 1 \n", + "6 19.2% 32 1 1 \n", + "\n", + " Penalties not scored ... Saves made Saves-to-shots ratio \\\n", + "5 0 ... 10 62.6% \n", + "6 1 ... 13 65.1% \n", + "\n", + " Fouls Won Fouls Conceded Offsides Yellow Cards Red Cards Subs on \\\n", + "5 63 49 12 4 0 15 \n", + "6 67 48 12 9 1 12 \n", + "\n", + " Subs off Players Used \n", + "5 15 17 \n", + "6 12 20 \n", + "\n", + "[2 rows x 35 columns]" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "euro12[euro12.Team.str.startswith('G')]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 12. Select the first 7 columns" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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TeamGoalsShots on targetShots off targetShooting Accuracy% Goals-to-shotsTotal shots (inc. Blocked)
0Croatia4131251.9%16.0%32
1Czech Republic4131841.9%12.9%39
2Denmark4101050.0%20.0%27
3England5111850.0%17.2%40
4France3222437.9%6.5%65
5Germany10323247.8%15.6%80
6Greece581830.7%19.2%32
7Italy6344543.0%7.5%110
8Netherlands2123625.0%4.1%60
9Poland2152339.4%5.2%48
10Portugal6224234.3%9.3%82
11Republic of Ireland171236.8%5.2%28
12Russia593122.5%12.5%59
13Spain12423355.9%16.0%100
14Sweden5171947.2%13.8%39
15Ukraine272621.2%6.0%38
\n", + "
" + ], + "text/plain": [ + " Team Goals Shots on target Shots off target \\\n", + "0 Croatia 4 13 12 \n", + "1 Czech Republic 4 13 18 \n", + "2 Denmark 4 10 10 \n", + "3 England 5 11 18 \n", + "4 France 3 22 24 \n", + "5 Germany 10 32 32 \n", + "6 Greece 5 8 18 \n", + "7 Italy 6 34 45 \n", + "8 Netherlands 2 12 36 \n", + "9 Poland 2 15 23 \n", + "10 Portugal 6 22 42 \n", + "11 Republic of Ireland 1 7 12 \n", + "12 Russia 5 9 31 \n", + "13 Spain 12 42 33 \n", + "14 Sweden 5 17 19 \n", + "15 Ukraine 2 7 26 \n", + "\n", + " Shooting Accuracy % Goals-to-shots Total shots (inc. Blocked) \n", + "0 51.9% 16.0% 32 \n", + "1 41.9% 12.9% 39 \n", + "2 50.0% 20.0% 27 \n", + "3 50.0% 17.2% 40 \n", + "4 37.9% 6.5% 65 \n", + "5 47.8% 15.6% 80 \n", + "6 30.7% 19.2% 32 \n", + "7 43.0% 7.5% 110 \n", + "8 25.0% 4.1% 60 \n", + "9 39.4% 5.2% 48 \n", + "10 34.3% 9.3% 82 \n", + "11 36.8% 5.2% 28 \n", + "12 22.5% 12.5% 59 \n", + "13 55.9% 16.0% 100 \n", + "14 47.2% 13.8% 39 \n", + "15 21.2% 6.0% 38 " + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# use .iloc to slices via the position of the passed integers\n", + "# : means all, 0:7 means from 0 to 7\n", + "\n", + "euro.iloc[: , 0:7]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 13. Select all columns except the last 3." + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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TeamGoalsShots on targetShots off targetShooting Accuracy% Goals-to-shotsTotal shots (inc. Blocked)Hit WoodworkPenalty goalsPenalties not scored...Clean SheetsBlocksGoals concededSaves madeSaves-to-shots ratioFouls WonFouls ConcededOffsidesYellow CardsRed Cards
0Croatia4131251.9%16.0%32000...01031381.3%4162290
1Czech Republic4131841.9%12.9%39000...1106960.1%5373870
2Denmark4101050.0%20.0%27100...11051066.7%2538840
3England5111850.0%17.2%40000...22932288.1%4345650
4France3222437.9%6.5%65100...175654.6%3651560
5Germany10323247.8%15.6%80210...11161062.6%63491240
6Greece581830.7%19.2%32111...12371365.1%67481291
7Italy6344543.0%7.5%110200...21872074.1%1018916160
8Netherlands2123625.0%4.1%60200...0951270.6%3530350
9Poland2152339.4%5.2%48000...083666.7%4856371
10Portugal6224234.3%9.3%82600...21141071.5%739010120
11Republic of Ireland171236.8%5.2%28000...02391765.4%43511161
12Russia593122.5%12.5%59200...0831077.0%3443460
13Spain12423355.9%16.0%100010...5811593.8%1028319110
14Sweden5171947.2%13.8%39300...1125861.6%3551770
15Ukraine272621.2%6.0%38000...0441376.5%4831450
\n", + "

16 rows × 32 columns

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" + ], + "text/plain": [ + " Team Goals Shots on target Shots off target \\\n", + "0 Croatia 4 13 12 \n", + "1 Czech Republic 4 13 18 \n", + "2 Denmark 4 10 10 \n", + "3 England 5 11 18 \n", + "4 France 3 22 24 \n", + "5 Germany 10 32 32 \n", + "6 Greece 5 8 18 \n", + "7 Italy 6 34 45 \n", + "8 Netherlands 2 12 36 \n", + "9 Poland 2 15 23 \n", + "10 Portugal 6 22 42 \n", + "11 Republic of Ireland 1 7 12 \n", + "12 Russia 5 9 31 \n", + "13 Spain 12 42 33 \n", + "14 Sweden 5 17 19 \n", + "15 Ukraine 2 7 26 \n", + "\n", + " Shooting Accuracy % Goals-to-shots Total shots (inc. Blocked) \\\n", + "0 51.9% 16.0% 32 \n", + "1 41.9% 12.9% 39 \n", + "2 50.0% 20.0% 27 \n", + "3 50.0% 17.2% 40 \n", + "4 37.9% 6.5% 65 \n", + "5 47.8% 15.6% 80 \n", + "6 30.7% 19.2% 32 \n", + "7 43.0% 7.5% 110 \n", + "8 25.0% 4.1% 60 \n", + "9 39.4% 5.2% 48 \n", + "10 34.3% 9.3% 82 \n", + "11 36.8% 5.2% 28 \n", + "12 22.5% 12.5% 59 \n", + "13 55.9% 16.0% 100 \n", + "14 47.2% 13.8% 39 \n", + "15 21.2% 6.0% 38 \n", + "\n", + " Hit Woodwork Penalty goals Penalties not scored ... \\\n", + "0 0 0 0 ... \n", + "1 0 0 0 ... \n", + "2 1 0 0 ... \n", + "3 0 0 0 ... \n", + "4 1 0 0 ... \n", + "5 2 1 0 ... \n", + "6 1 1 1 ... \n", + "7 2 0 0 ... \n", + "8 2 0 0 ... \n", + "9 0 0 0 ... \n", + "10 6 0 0 ... \n", + "11 0 0 0 ... \n", + "12 2 0 0 ... \n", + "13 0 1 0 ... \n", + "14 3 0 0 ... \n", + "15 0 0 0 ... \n", + "\n", + " Clean Sheets Blocks Goals conceded Saves made Saves-to-shots ratio \\\n", + "0 0 10 3 13 81.3% \n", + "1 1 10 6 9 60.1% \n", + "2 1 10 5 10 66.7% \n", + "3 2 29 3 22 88.1% \n", + "4 1 7 5 6 54.6% \n", + "5 1 11 6 10 62.6% \n", + "6 1 23 7 13 65.1% \n", + "7 2 18 7 20 74.1% \n", + "8 0 9 5 12 70.6% \n", + "9 0 8 3 6 66.7% \n", + "10 2 11 4 10 71.5% \n", + "11 0 23 9 17 65.4% \n", + "12 0 8 3 10 77.0% \n", + "13 5 8 1 15 93.8% \n", + "14 1 12 5 8 61.6% \n", + "15 0 4 4 13 76.5% \n", + "\n", + " Fouls Won Fouls Conceded Offsides Yellow Cards Red Cards \n", + "0 41 62 2 9 0 \n", + "1 53 73 8 7 0 \n", + "2 25 38 8 4 0 \n", + "3 43 45 6 5 0 \n", + "4 36 51 5 6 0 \n", + "5 63 49 12 4 0 \n", + "6 67 48 12 9 1 \n", + "7 101 89 16 16 0 \n", + "8 35 30 3 5 0 \n", + "9 48 56 3 7 1 \n", + "10 73 90 10 12 0 \n", + "11 43 51 11 6 1 \n", + "12 34 43 4 6 0 \n", + "13 102 83 19 11 0 \n", + "14 35 51 7 7 0 \n", + "15 48 31 4 5 0 \n", + "\n", + "[16 rows x 32 columns]" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# use negative to exclude the last 3 columns\n", + "\n", + "euro.iloc[: , :-3]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 14. Present only the Shooting Accuracy from England, Italy and Russia" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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TeamShooting Accuracy
3England50.0%
7Italy43.0%
12Russia22.5%
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" + ], + "text/plain": [ + " Team Shooting Accuracy\n", + "3 England 50.0%\n", + "7 Italy 43.0%\n", + "12 Russia 22.5%" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# .loc is another way to slice, using the labels of the columns and indexes\n", + "\n", + "euro12.loc[euro12.Team.isin(['England', 'Italy', 'Russia']), ['Team','Shooting Accuracy']]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/02_Filtering___Sorting/Euro12/Solutions.ipynb b/200 solved problems in Python/pandas/02_Filtering___Sorting/Euro12/Solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..b0d5fc0f35f7d2534f26c6a0b447ec3261c8fb66 --- /dev/null +++ b/200 solved problems in Python/pandas/02_Filtering___Sorting/Euro12/Solutions.ipynb @@ -0,0 +1,2161 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Filtering and Sorting Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This time we are going to pull data directly from the internet.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/jokecamp/FootballData/master/Euro%202012/Euro%202012%20stats%20TEAM.csv). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called euro12." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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TeamGoalsShots on targetShots off targetShooting Accuracy% Goals-to-shotsTotal shots (inc. Blocked)Hit WoodworkPenalty goalsPenalties not scored...Saves madeSaves-to-shots ratioFouls WonFouls ConcededOffsidesYellow CardsRed CardsSubs onSubs offPlayers Used
0Croatia4131251.9%16.0%32000...1381.3%41622909916
1Czech Republic4131841.9%12.9%39000...960.1%5373870111119
2Denmark4101050.0%20.0%27100...1066.7%25388407715
3England5111850.0%17.2%40000...2288.1%4345650111116
4France3222437.9%6.5%65100...654.6%3651560111119
5Germany10323247.8%15.6%80210...1062.6%63491240151517
6Greece581830.7%19.2%32111...1365.1%67481291121220
7Italy6344543.0%7.5%110200...2074.1%1018916160181819
8Netherlands2123625.0%4.1%60200...1270.6%35303507715
9Poland2152339.4%5.2%48000...666.7%48563717717
10Portugal6224234.3%9.3%82600...1071.5%739010120141416
11Republic of Ireland171236.8%5.2%28000...1765.4%43511161101017
12Russia593122.5%12.5%59200...1077.0%34434607716
13Spain12423355.9%16.0%100010...1593.8%1028319110171718
14Sweden5171947.2%13.8%39300...861.6%35517709918
15Ukraine272621.2%6.0%38000...1376.5%48314509918
\n", + "

16 rows × 35 columns

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" + ], + "text/plain": [ + " Team Goals Shots on target Shots off target \\\n", + "0 Croatia 4 13 12 \n", + "1 Czech Republic 4 13 18 \n", + "2 Denmark 4 10 10 \n", + "3 England 5 11 18 \n", + "4 France 3 22 24 \n", + "5 Germany 10 32 32 \n", + "6 Greece 5 8 18 \n", + "7 Italy 6 34 45 \n", + "8 Netherlands 2 12 36 \n", + "9 Poland 2 15 23 \n", + "10 Portugal 6 22 42 \n", + "11 Republic of Ireland 1 7 12 \n", + "12 Russia 5 9 31 \n", + "13 Spain 12 42 33 \n", + "14 Sweden 5 17 19 \n", + "15 Ukraine 2 7 26 \n", + "\n", + " Shooting Accuracy % Goals-to-shots Total shots (inc. Blocked) \\\n", + "0 51.9% 16.0% 32 \n", + "1 41.9% 12.9% 39 \n", + "2 50.0% 20.0% 27 \n", + "3 50.0% 17.2% 40 \n", + "4 37.9% 6.5% 65 \n", + "5 47.8% 15.6% 80 \n", + "6 30.7% 19.2% 32 \n", + "7 43.0% 7.5% 110 \n", + "8 25.0% 4.1% 60 \n", + "9 39.4% 5.2% 48 \n", + "10 34.3% 9.3% 82 \n", + "11 36.8% 5.2% 28 \n", + "12 22.5% 12.5% 59 \n", + "13 55.9% 16.0% 100 \n", + "14 47.2% 13.8% 39 \n", + "15 21.2% 6.0% 38 \n", + "\n", + " Hit Woodwork Penalty goals Penalties not scored ... \\\n", + "0 0 0 0 ... \n", + "1 0 0 0 ... \n", + "2 1 0 0 ... \n", + "3 0 0 0 ... \n", + "4 1 0 0 ... \n", + "5 2 1 0 ... \n", + "6 1 1 1 ... \n", + "7 2 0 0 ... \n", + "8 2 0 0 ... \n", + "9 0 0 0 ... \n", + "10 6 0 0 ... \n", + "11 0 0 0 ... \n", + "12 2 0 0 ... \n", + "13 0 1 0 ... \n", + "14 3 0 0 ... \n", + "15 0 0 0 ... \n", + "\n", + " Saves made Saves-to-shots ratio Fouls Won Fouls Conceded Offsides \\\n", + "0 13 81.3% 41 62 2 \n", + "1 9 60.1% 53 73 8 \n", + "2 10 66.7% 25 38 8 \n", + "3 22 88.1% 43 45 6 \n", + "4 6 54.6% 36 51 5 \n", + "5 10 62.6% 63 49 12 \n", + "6 13 65.1% 67 48 12 \n", + "7 20 74.1% 101 89 16 \n", + "8 12 70.6% 35 30 3 \n", + "9 6 66.7% 48 56 3 \n", + "10 10 71.5% 73 90 10 \n", + "11 17 65.4% 43 51 11 \n", + "12 10 77.0% 34 43 4 \n", + "13 15 93.8% 102 83 19 \n", + "14 8 61.6% 35 51 7 \n", + "15 13 76.5% 48 31 4 \n", + "\n", + " Yellow Cards Red Cards Subs on Subs off Players Used \n", + "0 9 0 9 9 16 \n", + "1 7 0 11 11 19 \n", + "2 4 0 7 7 15 \n", + "3 5 0 11 11 16 \n", + "4 6 0 11 11 19 \n", + "5 4 0 15 15 17 \n", + "6 9 1 12 12 20 \n", + "7 16 0 18 18 19 \n", + "8 5 0 7 7 15 \n", + "9 7 1 7 7 17 \n", + "10 12 0 14 14 16 \n", + "11 6 1 10 10 17 \n", + "12 6 0 7 7 16 \n", + "13 11 0 17 17 18 \n", + "14 7 0 9 9 18 \n", + "15 5 0 9 9 18 \n", + "\n", + "[16 rows x 35 columns]" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Select only the Goal column." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 4\n", + "1 4\n", + "2 4\n", + "3 5\n", + "4 3\n", + "5 10\n", + "6 5\n", + "7 6\n", + "8 2\n", + "9 2\n", + "10 6\n", + "11 1\n", + "12 5\n", + "13 12\n", + "14 5\n", + "15 2\n", + "Name: Goals, dtype: int64" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. How many team participated in the Euro2012?" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "16" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. What is the number of columns in the dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 16 entries, 0 to 15\n", + "Data columns (total 35 columns):\n", + "Team 16 non-null object\n", + "Goals 16 non-null int64\n", + "Shots on target 16 non-null int64\n", + "Shots off target 16 non-null int64\n", + "Shooting Accuracy 16 non-null object\n", + "% Goals-to-shots 16 non-null object\n", + "Total shots (inc. Blocked) 16 non-null int64\n", + "Hit Woodwork 16 non-null int64\n", + "Penalty goals 16 non-null int64\n", + "Penalties not scored 16 non-null int64\n", + "Headed goals 16 non-null int64\n", + "Passes 16 non-null int64\n", + "Passes completed 16 non-null int64\n", + "Passing Accuracy 16 non-null object\n", + "Touches 16 non-null int64\n", + "Crosses 16 non-null int64\n", + "Dribbles 16 non-null int64\n", + "Corners Taken 16 non-null int64\n", + "Tackles 16 non-null int64\n", + "Clearances 16 non-null int64\n", + "Interceptions 16 non-null int64\n", + "Clearances off line 15 non-null float64\n", + "Clean Sheets 16 non-null int64\n", + "Blocks 16 non-null int64\n", + "Goals conceded 16 non-null int64\n", + "Saves made 16 non-null int64\n", + "Saves-to-shots ratio 16 non-null object\n", + "Fouls Won 16 non-null int64\n", + "Fouls Conceded 16 non-null int64\n", + "Offsides 16 non-null int64\n", + "Yellow Cards 16 non-null int64\n", + "Red Cards 16 non-null int64\n", + "Subs on 16 non-null int64\n", + "Subs off 16 non-null int64\n", + "Players Used 16 non-null int64\n", + "dtypes: float64(1), int64(29), object(5)\n", + "memory usage: 4.4+ KB\n" + ] + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. View only the columns Team, Yellow Cards and Red Cards and assign them to a dataframe called discipline" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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TeamYellow CardsRed Cards
0Croatia90
1Czech Republic70
2Denmark40
3England50
4France60
5Germany40
6Greece91
7Italy160
8Netherlands50
9Poland71
10Portugal120
11Republic of Ireland61
12Russia60
13Spain110
14Sweden70
15Ukraine50
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" + ], + "text/plain": [ + " Team Yellow Cards Red Cards\n", + "0 Croatia 9 0\n", + "1 Czech Republic 7 0\n", + "2 Denmark 4 0\n", + "3 England 5 0\n", + "4 France 6 0\n", + "5 Germany 4 0\n", + "6 Greece 9 1\n", + "7 Italy 16 0\n", + "8 Netherlands 5 0\n", + "9 Poland 7 1\n", + "10 Portugal 12 0\n", + "11 Republic of Ireland 6 1\n", + "12 Russia 6 0\n", + "13 Spain 11 0\n", + "14 Sweden 7 0\n", + "15 Ukraine 5 0" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Sort the teams by Red Cards, then to Yellow Cards" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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TeamYellow CardsRed Cards
6Greece91
9Poland71
11Republic of Ireland61
7Italy160
10Portugal120
13Spain110
0Croatia90
1Czech Republic70
14Sweden70
4France60
12Russia60
3England50
8Netherlands50
15Ukraine50
2Denmark40
5Germany40
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" + ], + "text/plain": [ + " Team Yellow Cards Red Cards\n", + "6 Greece 9 1\n", + "9 Poland 7 1\n", + "11 Republic of Ireland 6 1\n", + "7 Italy 16 0\n", + "10 Portugal 12 0\n", + "13 Spain 11 0\n", + "0 Croatia 9 0\n", + "1 Czech Republic 7 0\n", + "14 Sweden 7 0\n", + "4 France 6 0\n", + "12 Russia 6 0\n", + "3 England 5 0\n", + "8 Netherlands 5 0\n", + "15 Ukraine 5 0\n", + "2 Denmark 4 0\n", + "5 Germany 4 0" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. Calculate the mean Yellow Cards given per Team" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "7.0" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. Filter teams that scored more than 6 goals" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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TeamGoalsShots on targetShots off targetShooting Accuracy% Goals-to-shotsTotal shots (inc. Blocked)Hit WoodworkPenalty goalsPenalties not scored...Saves madeSaves-to-shots ratioFouls WonFouls ConcededOffsidesYellow CardsRed CardsSubs onSubs offPlayers Used
5Germany10323247.8%15.6%80210...1062.6%63491240151517
13Spain12423355.9%16.0%100010...1593.8%1028319110171718
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2 rows × 35 columns

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" + ], + "text/plain": [ + " Team Goals Shots on target Shots off target Shooting Accuracy \\\n", + "5 Germany 10 32 32 47.8% \n", + "13 Spain 12 42 33 55.9% \n", + "\n", + " % Goals-to-shots Total shots (inc. Blocked) Hit Woodwork Penalty goals \\\n", + "5 15.6% 80 2 1 \n", + "13 16.0% 100 0 1 \n", + "\n", + " Penalties not scored ... Saves made Saves-to-shots ratio \\\n", + "5 0 ... 10 62.6% \n", + "13 0 ... 15 93.8% \n", + "\n", + " Fouls Won Fouls Conceded Offsides Yellow Cards Red Cards Subs on \\\n", + "5 63 49 12 4 0 15 \n", + "13 102 83 19 11 0 17 \n", + "\n", + " Subs off Players Used \n", + "5 15 17 \n", + "13 17 18 \n", + "\n", + "[2 rows x 35 columns]" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 11. Select the teams that start with G" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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TeamGoalsShots on targetShots off targetShooting Accuracy% Goals-to-shotsTotal shots (inc. Blocked)Hit WoodworkPenalty goalsPenalties not scored...Saves madeSaves-to-shots ratioFouls WonFouls ConcededOffsidesYellow CardsRed CardsSubs onSubs offPlayers Used
5Germany10323247.8%15.6%80210...1062.6%63491240151517
6Greece581830.7%19.2%32111...1365.1%67481291121220
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2 rows × 35 columns

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" + ], + "text/plain": [ + " Team Goals Shots on target Shots off target Shooting Accuracy \\\n", + "5 Germany 10 32 32 47.8% \n", + "6 Greece 5 8 18 30.7% \n", + "\n", + " % Goals-to-shots Total shots (inc. Blocked) Hit Woodwork Penalty goals \\\n", + "5 15.6% 80 2 1 \n", + "6 19.2% 32 1 1 \n", + "\n", + " Penalties not scored ... Saves made Saves-to-shots ratio \\\n", + "5 0 ... 10 62.6% \n", + "6 1 ... 13 65.1% \n", + "\n", + " Fouls Won Fouls Conceded Offsides Yellow Cards Red Cards Subs on \\\n", + "5 63 49 12 4 0 15 \n", + "6 67 48 12 9 1 12 \n", + "\n", + " Subs off Players Used \n", + "5 15 17 \n", + "6 12 20 \n", + "\n", + "[2 rows x 35 columns]" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 12. Select the first 7 columns" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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TeamGoalsShots on targetShots off targetShooting Accuracy% Goals-to-shotsTotal shots (inc. Blocked)
0Croatia4131251.9%16.0%32
1Czech Republic4131841.9%12.9%39
2Denmark4101050.0%20.0%27
3England5111850.0%17.2%40
4France3222437.9%6.5%65
5Germany10323247.8%15.6%80
6Greece581830.7%19.2%32
7Italy6344543.0%7.5%110
8Netherlands2123625.0%4.1%60
9Poland2152339.4%5.2%48
10Portugal6224234.3%9.3%82
11Republic of Ireland171236.8%5.2%28
12Russia593122.5%12.5%59
13Spain12423355.9%16.0%100
14Sweden5171947.2%13.8%39
15Ukraine272621.2%6.0%38
\n", + "
" + ], + "text/plain": [ + " Team Goals Shots on target Shots off target \\\n", + "0 Croatia 4 13 12 \n", + "1 Czech Republic 4 13 18 \n", + "2 Denmark 4 10 10 \n", + "3 England 5 11 18 \n", + "4 France 3 22 24 \n", + "5 Germany 10 32 32 \n", + "6 Greece 5 8 18 \n", + "7 Italy 6 34 45 \n", + "8 Netherlands 2 12 36 \n", + "9 Poland 2 15 23 \n", + "10 Portugal 6 22 42 \n", + "11 Republic of Ireland 1 7 12 \n", + "12 Russia 5 9 31 \n", + "13 Spain 12 42 33 \n", + "14 Sweden 5 17 19 \n", + "15 Ukraine 2 7 26 \n", + "\n", + " Shooting Accuracy % Goals-to-shots Total shots (inc. Blocked) \n", + "0 51.9% 16.0% 32 \n", + "1 41.9% 12.9% 39 \n", + "2 50.0% 20.0% 27 \n", + "3 50.0% 17.2% 40 \n", + "4 37.9% 6.5% 65 \n", + "5 47.8% 15.6% 80 \n", + "6 30.7% 19.2% 32 \n", + "7 43.0% 7.5% 110 \n", + "8 25.0% 4.1% 60 \n", + "9 39.4% 5.2% 48 \n", + "10 34.3% 9.3% 82 \n", + "11 36.8% 5.2% 28 \n", + "12 22.5% 12.5% 59 \n", + "13 55.9% 16.0% 100 \n", + "14 47.2% 13.8% 39 \n", + "15 21.2% 6.0% 38 " + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 13. Select all columns except the last 3." + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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TeamGoalsShots on targetShots off targetShooting Accuracy% Goals-to-shotsTotal shots (inc. Blocked)Hit WoodworkPenalty goalsPenalties not scored...Clean SheetsBlocksGoals concededSaves madeSaves-to-shots ratioFouls WonFouls ConcededOffsidesYellow CardsRed Cards
0Croatia4131251.9%16.0%32000...01031381.3%4162290
1Czech Republic4131841.9%12.9%39000...1106960.1%5373870
2Denmark4101050.0%20.0%27100...11051066.7%2538840
3England5111850.0%17.2%40000...22932288.1%4345650
4France3222437.9%6.5%65100...175654.6%3651560
5Germany10323247.8%15.6%80210...11161062.6%63491240
6Greece581830.7%19.2%32111...12371365.1%67481291
7Italy6344543.0%7.5%110200...21872074.1%1018916160
8Netherlands2123625.0%4.1%60200...0951270.6%3530350
9Poland2152339.4%5.2%48000...083666.7%4856371
10Portugal6224234.3%9.3%82600...21141071.5%739010120
11Republic of Ireland171236.8%5.2%28000...02391765.4%43511161
12Russia593122.5%12.5%59200...0831077.0%3443460
13Spain12423355.9%16.0%100010...5811593.8%1028319110
14Sweden5171947.2%13.8%39300...1125861.6%3551770
15Ukraine272621.2%6.0%38000...0441376.5%4831450
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16 rows × 32 columns

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" + ], + "text/plain": [ + " Team Goals Shots on target Shots off target \\\n", + "0 Croatia 4 13 12 \n", + "1 Czech Republic 4 13 18 \n", + "2 Denmark 4 10 10 \n", + "3 England 5 11 18 \n", + "4 France 3 22 24 \n", + "5 Germany 10 32 32 \n", + "6 Greece 5 8 18 \n", + "7 Italy 6 34 45 \n", + "8 Netherlands 2 12 36 \n", + "9 Poland 2 15 23 \n", + "10 Portugal 6 22 42 \n", + "11 Republic of Ireland 1 7 12 \n", + "12 Russia 5 9 31 \n", + "13 Spain 12 42 33 \n", + "14 Sweden 5 17 19 \n", + "15 Ukraine 2 7 26 \n", + "\n", + " Shooting Accuracy % Goals-to-shots Total shots (inc. Blocked) \\\n", + "0 51.9% 16.0% 32 \n", + "1 41.9% 12.9% 39 \n", + "2 50.0% 20.0% 27 \n", + "3 50.0% 17.2% 40 \n", + "4 37.9% 6.5% 65 \n", + "5 47.8% 15.6% 80 \n", + "6 30.7% 19.2% 32 \n", + "7 43.0% 7.5% 110 \n", + "8 25.0% 4.1% 60 \n", + "9 39.4% 5.2% 48 \n", + "10 34.3% 9.3% 82 \n", + "11 36.8% 5.2% 28 \n", + "12 22.5% 12.5% 59 \n", + "13 55.9% 16.0% 100 \n", + "14 47.2% 13.8% 39 \n", + "15 21.2% 6.0% 38 \n", + "\n", + " Hit Woodwork Penalty goals Penalties not scored ... \\\n", + "0 0 0 0 ... \n", + "1 0 0 0 ... \n", + "2 1 0 0 ... \n", + "3 0 0 0 ... \n", + "4 1 0 0 ... \n", + "5 2 1 0 ... \n", + "6 1 1 1 ... \n", + "7 2 0 0 ... \n", + "8 2 0 0 ... \n", + "9 0 0 0 ... \n", + "10 6 0 0 ... \n", + "11 0 0 0 ... \n", + "12 2 0 0 ... \n", + "13 0 1 0 ... \n", + "14 3 0 0 ... \n", + "15 0 0 0 ... \n", + "\n", + " Clean Sheets Blocks Goals conceded Saves made Saves-to-shots ratio \\\n", + "0 0 10 3 13 81.3% \n", + "1 1 10 6 9 60.1% \n", + "2 1 10 5 10 66.7% \n", + "3 2 29 3 22 88.1% \n", + "4 1 7 5 6 54.6% \n", + "5 1 11 6 10 62.6% \n", + "6 1 23 7 13 65.1% \n", + "7 2 18 7 20 74.1% \n", + "8 0 9 5 12 70.6% \n", + "9 0 8 3 6 66.7% \n", + "10 2 11 4 10 71.5% \n", + "11 0 23 9 17 65.4% \n", + "12 0 8 3 10 77.0% \n", + "13 5 8 1 15 93.8% \n", + "14 1 12 5 8 61.6% \n", + "15 0 4 4 13 76.5% \n", + "\n", + " Fouls Won Fouls Conceded Offsides Yellow Cards Red Cards \n", + "0 41 62 2 9 0 \n", + "1 53 73 8 7 0 \n", + "2 25 38 8 4 0 \n", + "3 43 45 6 5 0 \n", + "4 36 51 5 6 0 \n", + "5 63 49 12 4 0 \n", + "6 67 48 12 9 1 \n", + "7 101 89 16 16 0 \n", + "8 35 30 3 5 0 \n", + "9 48 56 3 7 1 \n", + "10 73 90 10 12 0 \n", + "11 43 51 11 6 1 \n", + "12 34 43 4 6 0 \n", + "13 102 83 19 11 0 \n", + "14 35 51 7 7 0 \n", + "15 48 31 4 5 0 \n", + "\n", + "[16 rows x 32 columns]" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 14. Present only the Shooting Accuracy from England, Italy and Russia" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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TeamShooting Accuracy
3England50.0%
7Italy43.0%
12Russia22.5%
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" + ], + "text/plain": [ + " Team Shooting Accuracy\n", + "3 England 50.0%\n", + "7 Italy 43.0%\n", + "12 Russia 22.5%" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/02_Filtering___Sorting/Fictional Army/Exercise.ipynb b/200 solved problems in Python/pandas/02_Filtering___Sorting/Fictional Army/Exercise.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..a15c1fe58d2b70df23b0e3e1ae41957004cc6c9a --- /dev/null +++ b/200 solved problems in Python/pandas/02_Filtering___Sorting/Fictional Army/Exercise.ipynb @@ -0,0 +1,342 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fictional Army - Filtering and Sorting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "This exercise was inspired by this [page](http://chrisalbon.com/python/)\n", + "\n", + "Special thanks to: https://github.com/chrisalbon for sharing the dataset and materials.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. This is the data given as a dictionary" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create an example dataframe about a fictional army\n", + "raw_data = {'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', 'Nighthawks', 'Dragoons', 'Dragoons', 'Dragoons', 'Dragoons', 'Scouts', 'Scouts', 'Scouts', 'Scouts'],\n", + " 'company': ['1st', '1st', '2nd', '2nd', '1st', '1st', '2nd', '2nd','1st', '1st', '2nd', '2nd'],\n", + " 'deaths': [523, 52, 25, 616, 43, 234, 523, 62, 62, 73, 37, 35],\n", + " 'battles': [5, 42, 2, 2, 4, 7, 8, 3, 4, 7, 8, 9],\n", + " 'size': [1045, 957, 1099, 1400, 1592, 1006, 987, 849, 973, 1005, 1099, 1523],\n", + " 'veterans': [1, 5, 62, 26, 73, 37, 949, 48, 48, 435, 63, 345],\n", + " 'readiness': [1, 2, 3, 3, 2, 1, 2, 3, 2, 1, 2, 3],\n", + " 'armored': [1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1],\n", + " 'deserters': [4, 24, 31, 2, 3, 4, 24, 31, 2, 3, 2, 3],\n", + " 'origin': ['Arizona', 'California', 'Texas', 'Florida', 'Maine', 'Iowa', 'Alaska', 'Washington', 'Oregon', 'Wyoming', 'Louisana', 'Georgia']}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Create a dataframe and assign it to a variable called army. \n", + "\n", + "#### Don't forget to include the columns names" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Set the 'origin' colum as the index of the dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Print only the column veterans" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Print the columns 'veterans' and 'deaths'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Print the name of all the columns." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Select the 'deaths', 'size' and 'deserters' columns from Maine and Alaska" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. Select the rows 3 to 7 and the columns 3 to 6" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. Select every row after the fourth row" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 11. Select every row up to the 4th row" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 12. Select the 3rd column up to the 7th column" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 13. Select rows where df.deaths is greater than 50" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 14. Select rows where df.deaths is greater than 500 or less than 50" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 15. Select all the regiments not named \"Dragoons\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 16. Select the rows called Texas and Arizona" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 17. Select the third cell in the row named Arizona" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 18. Select the third cell down in the column named deaths" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/02_Filtering___Sorting/Fictional Army/Exercise_with_solutions.ipynb b/200 solved problems in Python/pandas/02_Filtering___Sorting/Fictional Army/Exercise_with_solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..87308cae0f60e82ed0cbec33f1dacb25db189a8f --- /dev/null +++ b/200 solved problems in Python/pandas/02_Filtering___Sorting/Fictional Army/Exercise_with_solutions.ipynb @@ -0,0 +1,1804 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fictional Army - Filtering and Sorting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "This exercise was inspired by this [page](http://chrisalbon.com/python/)\n", + "\n", + "Special thanks to: https://github.com/chrisalbon for sharing the dataset and materials.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. This is the data given as a dictionary" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create an example dataframe about a fictional army\n", + "raw_data = {'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', 'Nighthawks', 'Dragoons', 'Dragoons', 'Dragoons', 'Dragoons', 'Scouts', 'Scouts', 'Scouts', 'Scouts'],\n", + " 'company': ['1st', '1st', '2nd', '2nd', '1st', '1st', '2nd', '2nd','1st', '1st', '2nd', '2nd'],\n", + " 'deaths': [523, 52, 25, 616, 43, 234, 523, 62, 62, 73, 37, 35],\n", + " 'battles': [5, 42, 2, 2, 4, 7, 8, 3, 4, 7, 8, 9],\n", + " 'size': [1045, 957, 1099, 1400, 1592, 1006, 987, 849, 973, 1005, 1099, 1523],\n", + " 'veterans': [1, 5, 62, 26, 73, 37, 949, 48, 48, 435, 63, 345],\n", + " 'readiness': [1, 2, 3, 3, 2, 1, 2, 3, 2, 1, 2, 3],\n", + " 'armored': [1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1],\n", + " 'deserters': [4, 24, 31, 2, 3, 4, 24, 31, 2, 3, 2, 3],\n", + " 'origin': ['Arizona', 'California', 'Texas', 'Florida', 'Maine', 'Iowa', 'Alaska', 'Washington', 'Oregon', 'Wyoming', 'Louisana', 'Georgia']}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Create a dataframe and assign it to a variable called army. \n", + "\n", + "#### Don't forget to include the columns names" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "army = pd.DataFrame(raw_data, columns = ['regiment', 'company', 'deaths', 'battles', 'size', 'veterans', 'readiness', 'armored', 'deserters', 'origin'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Set the 'origin' colum as the index of the dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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TexasNighthawks2nd2521099623131
FloridaNighthawks2nd6162140026312
MaineDragoons1st434159273203
IowaDragoons1st2347100637114
AlaskaDragoons2nd52389879492024
WashingtonDragoons2nd623849483131
OregonScouts1st62497348202
WyomingScouts1st7371005435103
LouisanaScouts2nd378109963212
GeorgiaScouts2nd3591523345313
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" + ], + "text/plain": [ + " regiment company deaths battles size veterans readiness \\\n", + "origin \n", + "Arizona Nighthawks 1st 523 5 1045 1 1 \n", + "California Nighthawks 1st 52 42 957 5 2 \n", + "Texas Nighthawks 2nd 25 2 1099 62 3 \n", + "Florida Nighthawks 2nd 616 2 1400 26 3 \n", + "Maine Dragoons 1st 43 4 1592 73 2 \n", + "Iowa Dragoons 1st 234 7 1006 37 1 \n", + "Alaska Dragoons 2nd 523 8 987 949 2 \n", + "Washington Dragoons 2nd 62 3 849 48 3 \n", + "Oregon Scouts 1st 62 4 973 48 2 \n", + "Wyoming Scouts 1st 73 7 1005 435 1 \n", + "Louisana Scouts 2nd 37 8 1099 63 2 \n", + "Georgia Scouts 2nd 35 9 1523 345 3 \n", + "\n", + " armored deserters \n", + "origin \n", + "Arizona 1 4 \n", + "California 0 24 \n", + "Texas 1 31 \n", + "Florida 1 2 \n", + "Maine 0 3 \n", + "Iowa 1 4 \n", + "Alaska 0 24 \n", + "Washington 1 31 \n", + "Oregon 0 2 \n", + "Wyoming 0 3 \n", + "Louisana 1 2 \n", + "Georgia 1 3 " + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "army = army.set_index('origin')\n", + "army" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Print only the column veterans" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "origin\n", + "Arizona 1\n", + "California 5\n", + "Texas 62\n", + "Florida 26\n", + "Maine 73\n", + "Iowa 37\n", + "Alaska 949\n", + "Washington 48\n", + "Oregon 48\n", + "Wyoming 435\n", + "Louisana 63\n", + "Georgia 345\n", + "Name: veterans, dtype: int64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "army['veterans']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Print the columns 'veterans' and 'deaths'" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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veteransdeaths
origin
Arizona1523
California552
Texas6225
Florida26616
Maine7343
Iowa37234
Alaska949523
Washington4862
Oregon4862
Wyoming43573
Louisana6337
Georgia34535
\n", + "
" + ], + "text/plain": [ + " veterans deaths\n", + "origin \n", + "Arizona 1 523\n", + "California 5 52\n", + "Texas 62 25\n", + "Florida 26 616\n", + "Maine 73 43\n", + "Iowa 37 234\n", + "Alaska 949 523\n", + "Washington 48 62\n", + "Oregon 48 62\n", + "Wyoming 435 73\n", + "Louisana 63 37\n", + "Georgia 345 35" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "army[['veterans', 'deaths']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Print the name of all the columns." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index([u'regiment', u'company', u'deaths', u'battles', u'size', u'veterans',\n", + " u'readiness', u'armored', u'deserters'],\n", + " dtype='object')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "army.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Select the 'deaths', 'size' and 'deserters' columns from Maine and Alaska" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
deathssizedeserters
origin
Maine4315923
Alaska52398724
\n", + "
" + ], + "text/plain": [ + " deaths size deserters\n", + "origin \n", + "Maine 43 1592 3\n", + "Alaska 523 987 24" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Select all rows with the index label \"Maine\" and \"Alaska\"\n", + "army.loc[['Maine','Alaska'] , [\"deaths\",\"size\",\"deserters\"]]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. Select the rows 3 to 7 and the columns 3 to 6" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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battlessizeveterans
origin
Florida2140026
Maine4159273
Iowa7100637
Alaska8987949
\n", + "
" + ], + "text/plain": [ + " battles size veterans\n", + "origin \n", + "Florida 2 1400 26\n", + "Maine 4 1592 73\n", + "Iowa 7 1006 37\n", + "Alaska 8 987 949" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#\n", + "army.iloc[3:7, 3:6]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. Select every row after the fourth row" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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regimentcompanydeathsbattlessizeveteransreadinessarmoreddeserters
origin
FloridaNighthawks2nd6162140026312
MaineDragoons1st434159273203
IowaDragoons1st2347100637114
AlaskaDragoons2nd52389879492024
WashingtonDragoons2nd623849483131
OregonScouts1st62497348202
WyomingScouts1st7371005435103
LouisanaScouts2nd378109963212
GeorgiaScouts2nd3591523345313
\n", + "
" + ], + "text/plain": [ + " regiment company deaths battles size veterans readiness \\\n", + "origin \n", + "Florida Nighthawks 2nd 616 2 1400 26 3 \n", + "Maine Dragoons 1st 43 4 1592 73 2 \n", + "Iowa Dragoons 1st 234 7 1006 37 1 \n", + "Alaska Dragoons 2nd 523 8 987 949 2 \n", + "Washington Dragoons 2nd 62 3 849 48 3 \n", + "Oregon Scouts 1st 62 4 973 48 2 \n", + "Wyoming Scouts 1st 73 7 1005 435 1 \n", + "Louisana Scouts 2nd 37 8 1099 63 2 \n", + "Georgia Scouts 2nd 35 9 1523 345 3 \n", + "\n", + " armored deserters \n", + "origin \n", + "Florida 1 2 \n", + "Maine 0 3 \n", + "Iowa 1 4 \n", + "Alaska 0 24 \n", + "Washington 1 31 \n", + "Oregon 0 2 \n", + "Wyoming 0 3 \n", + "Louisana 1 2 \n", + "Georgia 1 3 " + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "army.iloc[3:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 11. Select every row up to the 4th row" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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regimentcompanydeathsbattlessizeveteransreadinessarmoreddeserters
origin
ArizonaNighthawks1st523510451114
CaliforniaNighthawks1st524295752024
TexasNighthawks2nd2521099623131
\n", + "
" + ], + "text/plain": [ + " regiment company deaths battles size veterans readiness \\\n", + "origin \n", + "Arizona Nighthawks 1st 523 5 1045 1 1 \n", + "California Nighthawks 1st 52 42 957 5 2 \n", + "Texas Nighthawks 2nd 25 2 1099 62 3 \n", + "\n", + " armored deserters \n", + "origin \n", + "Arizona 1 4 \n", + "California 0 24 \n", + "Texas 1 31 " + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "army.iloc[:3]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 12. Select the 3rd column up to the 7th column" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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sizeveteransreadiness
origin
Arizona104511
California95752
Texas1099623
Florida1400263
Maine1592732
Iowa1006371
Alaska9879492
Washington849483
Oregon973482
Wyoming10054351
Louisana1099632
Georgia15233453
\n", + "
" + ], + "text/plain": [ + " size veterans readiness\n", + "origin \n", + "Arizona 1045 1 1\n", + "California 957 5 2\n", + "Texas 1099 62 3\n", + "Florida 1400 26 3\n", + "Maine 1592 73 2\n", + "Iowa 1006 37 1\n", + "Alaska 987 949 2\n", + "Washington 849 48 3\n", + "Oregon 973 48 2\n", + "Wyoming 1005 435 1\n", + "Louisana 1099 63 2\n", + "Georgia 1523 345 3" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# the first : means all\n", + "# after the comma you select the range\n", + "\n", + "army.iloc[: , 4:7]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 13. Select rows where df.deaths is greater than 50" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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regimentcompanydeathsbattlessizeveteransreadinessarmoreddeserters
origin
ArizonaNighthawks1st523510451114
CaliforniaNighthawks1st524295752024
FloridaNighthawks2nd6162140026312
IowaDragoons1st2347100637114
AlaskaDragoons2nd52389879492024
WashingtonDragoons2nd623849483131
OregonScouts1st62497348202
WyomingScouts1st7371005435103
\n", + "
" + ], + "text/plain": [ + " regiment company deaths battles size veterans readiness \\\n", + "origin \n", + "Arizona Nighthawks 1st 523 5 1045 1 1 \n", + "California Nighthawks 1st 52 42 957 5 2 \n", + "Florida Nighthawks 2nd 616 2 1400 26 3 \n", + "Iowa Dragoons 1st 234 7 1006 37 1 \n", + "Alaska Dragoons 2nd 523 8 987 949 2 \n", + "Washington Dragoons 2nd 62 3 849 48 3 \n", + "Oregon Scouts 1st 62 4 973 48 2 \n", + "Wyoming Scouts 1st 73 7 1005 435 1 \n", + "\n", + " armored deserters \n", + "origin \n", + "Arizona 1 4 \n", + "California 0 24 \n", + "Florida 1 2 \n", + "Iowa 1 4 \n", + "Alaska 0 24 \n", + "Washington 1 31 \n", + "Oregon 0 2 \n", + "Wyoming 0 3 " + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "army[army['deaths'] > 50]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 14. Select rows where df.deaths is greater than 500 or less than 50" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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regimentcompanydeathsbattlessizeveteransreadinessarmoreddeserters
origin
ArizonaNighthawks1st523510451114
TexasNighthawks2nd2521099623131
FloridaNighthawks2nd6162140026312
MaineDragoons1st434159273203
AlaskaDragoons2nd52389879492024
LouisanaScouts2nd378109963212
GeorgiaScouts2nd3591523345313
\n", + "
" + ], + "text/plain": [ + " regiment company deaths battles size veterans readiness \\\n", + "origin \n", + "Arizona Nighthawks 1st 523 5 1045 1 1 \n", + "Texas Nighthawks 2nd 25 2 1099 62 3 \n", + "Florida Nighthawks 2nd 616 2 1400 26 3 \n", + "Maine Dragoons 1st 43 4 1592 73 2 \n", + "Alaska Dragoons 2nd 523 8 987 949 2 \n", + "Louisana Scouts 2nd 37 8 1099 63 2 \n", + "Georgia Scouts 2nd 35 9 1523 345 3 \n", + "\n", + " armored deserters \n", + "origin \n", + "Arizona 1 4 \n", + "Texas 1 31 \n", + "Florida 1 2 \n", + "Maine 0 3 \n", + "Alaska 0 24 \n", + "Louisana 1 2 \n", + "Georgia 1 3 " + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "army[(army['deaths'] > 500) | (army['deaths'] < 50)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 15. Select all the regiments not named \"Dragoons\"" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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regimentcompanydeathsbattlessizeveteransreadinessarmoreddeserters
origin
ArizonaNighthawks1st523510451114
CaliforniaNighthawks1st524295752024
TexasNighthawks2nd2521099623131
FloridaNighthawks2nd6162140026312
OregonScouts1st62497348202
WyomingScouts1st7371005435103
LouisanaScouts2nd378109963212
GeorgiaScouts2nd3591523345313
\n", + "
" + ], + "text/plain": [ + " regiment company deaths battles size veterans readiness \\\n", + "origin \n", + "Arizona Nighthawks 1st 523 5 1045 1 1 \n", + "California Nighthawks 1st 52 42 957 5 2 \n", + "Texas Nighthawks 2nd 25 2 1099 62 3 \n", + "Florida Nighthawks 2nd 616 2 1400 26 3 \n", + "Oregon Scouts 1st 62 4 973 48 2 \n", + "Wyoming Scouts 1st 73 7 1005 435 1 \n", + "Louisana Scouts 2nd 37 8 1099 63 2 \n", + "Georgia Scouts 2nd 35 9 1523 345 3 \n", + "\n", + " armored deserters \n", + "origin \n", + "Arizona 1 4 \n", + "California 0 24 \n", + "Texas 1 31 \n", + "Florida 1 2 \n", + "Oregon 0 2 \n", + "Wyoming 0 3 \n", + "Louisana 1 2 \n", + "Georgia 1 3 " + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "army[(army['regiment'] != 'Dragoons')]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 16. Select the rows called Texas and Arizona" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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regimentcompanydeathsbattlessizeveteransreadinessarmoreddeserters
origin
ArizonaNighthawks1st523510451114
TexasNighthawks2nd2521099623131
\n", + "
" + ], + "text/plain": [ + " regiment company deaths battles size veterans readiness \\\n", + "origin \n", + "Arizona Nighthawks 1st 523 5 1045 1 1 \n", + "Texas Nighthawks 2nd 25 2 1099 62 3 \n", + "\n", + " armored deserters \n", + "origin \n", + "Arizona 1 4 \n", + "Texas 1 31 " + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "army.ix[['Arizona', 'Texas']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 17. Select the third cell in the row named Arizona" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "523" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "army.ix['Arizona', 'deaths']\n", + "\n", + "#OR\n", + "\n", + "army.ix['Arizona', 2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 18. Select the third cell down in the column named deaths" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "25" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "army.ix[2, 'deaths']" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/02_Filtering___Sorting/Fictional Army/Solutions.ipynb b/200 solved problems in Python/pandas/02_Filtering___Sorting/Fictional Army/Solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..ee76cbeaba186ffc05dd08194ac9536e91b0e6e2 --- /dev/null +++ b/200 solved problems in Python/pandas/02_Filtering___Sorting/Fictional Army/Solutions.ipynb @@ -0,0 +1,1760 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fictional Army - Filtering and Sorting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "This exercise was inspired by this [page](http://chrisalbon.com/python/)\n", + "\n", + "Special thanks to: https://github.com/chrisalbon for sharing the dataset and materials.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. This is the data given as a dictionary" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create an example dataframe about a fictional army\n", + "raw_data = {'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', 'Nighthawks', 'Dragoons', 'Dragoons', 'Dragoons', 'Dragoons', 'Scouts', 'Scouts', 'Scouts', 'Scouts'],\n", + " 'company': ['1st', '1st', '2nd', '2nd', '1st', '1st', '2nd', '2nd','1st', '1st', '2nd', '2nd'],\n", + " 'deaths': [523, 52, 25, 616, 43, 234, 523, 62, 62, 73, 37, 35],\n", + " 'battles': [5, 42, 2, 2, 4, 7, 8, 3, 4, 7, 8, 9],\n", + " 'size': [1045, 957, 1099, 1400, 1592, 1006, 987, 849, 973, 1005, 1099, 1523],\n", + " 'veterans': [1, 5, 62, 26, 73, 37, 949, 48, 48, 435, 63, 345],\n", + " 'readiness': [1, 2, 3, 3, 2, 1, 2, 3, 2, 1, 2, 3],\n", + " 'armored': [1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1],\n", + " 'deserters': [4, 24, 31, 2, 3, 4, 24, 31, 2, 3, 2, 3],\n", + " 'origin': ['Arizona', 'California', 'Texas', 'Florida', 'Maine', 'Iowa', 'Alaska', 'Washington', 'Oregon', 'Wyoming', 'Louisana', 'Georgia']}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Create a dataframe and assign it to a variable called army. \n", + "\n", + "#### Don't forget to include the columns names" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Set the 'origin' colum as the index of the dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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regimentcompanydeathsbattlessizeveteransreadinessarmoreddeserters
origin
ArizonaNighthawks1st523510451114
CaliforniaNighthawks1st524295752024
TexasNighthawks2nd2521099623131
FloridaNighthawks2nd6162140026312
MaineDragoons1st434159273203
IowaDragoons1st2347100637114
AlaskaDragoons2nd52389879492024
WashingtonDragoons2nd623849483131
OregonScouts1st62497348202
WyomingScouts1st7371005435103
LouisanaScouts2nd378109963212
GeorgiaScouts2nd3591523345313
\n", + "
" + ], + "text/plain": [ + " regiment company deaths battles size veterans readiness \\\n", + "origin \n", + "Arizona Nighthawks 1st 523 5 1045 1 1 \n", + "California Nighthawks 1st 52 42 957 5 2 \n", + "Texas Nighthawks 2nd 25 2 1099 62 3 \n", + "Florida Nighthawks 2nd 616 2 1400 26 3 \n", + "Maine Dragoons 1st 43 4 1592 73 2 \n", + "Iowa Dragoons 1st 234 7 1006 37 1 \n", + "Alaska Dragoons 2nd 523 8 987 949 2 \n", + "Washington Dragoons 2nd 62 3 849 48 3 \n", + "Oregon Scouts 1st 62 4 973 48 2 \n", + "Wyoming Scouts 1st 73 7 1005 435 1 \n", + "Louisana Scouts 2nd 37 8 1099 63 2 \n", + "Georgia Scouts 2nd 35 9 1523 345 3 \n", + "\n", + " armored deserters \n", + "origin \n", + "Arizona 1 4 \n", + "California 0 24 \n", + "Texas 1 31 \n", + "Florida 1 2 \n", + "Maine 0 3 \n", + "Iowa 1 4 \n", + "Alaska 0 24 \n", + "Washington 1 31 \n", + "Oregon 0 2 \n", + "Wyoming 0 3 \n", + "Louisana 1 2 \n", + "Georgia 1 3 " + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Print only the column veterans" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "origin\n", + "Arizona 1\n", + "California 5\n", + "Texas 62\n", + "Florida 26\n", + "Maine 73\n", + "Iowa 37\n", + "Alaska 949\n", + "Washington 48\n", + "Oregon 48\n", + "Wyoming 435\n", + "Louisana 63\n", + "Georgia 345\n", + "Name: veterans, dtype: int64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Print the columns 'veterans' and 'deaths'" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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veteransdeaths
origin
Arizona1523
California552
Texas6225
Florida26616
Maine7343
Iowa37234
Alaska949523
Washington4862
Oregon4862
Wyoming43573
Louisana6337
Georgia34535
\n", + "
" + ], + "text/plain": [ + " veterans deaths\n", + "origin \n", + "Arizona 1 523\n", + "California 5 52\n", + "Texas 62 25\n", + "Florida 26 616\n", + "Maine 73 43\n", + "Iowa 37 234\n", + "Alaska 949 523\n", + "Washington 48 62\n", + "Oregon 48 62\n", + "Wyoming 435 73\n", + "Louisana 63 37\n", + "Georgia 345 35" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Print the name of all the columns." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index([u'regiment', u'company', u'deaths', u'battles', u'size', u'veterans',\n", + " u'readiness', u'armored', u'deserters'],\n", + " dtype='object')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Select the 'deaths', 'size' and 'deserters' columns from Maine and Alaska" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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deathssizedeserters
origin
Maine4315923
Alaska52398724
\n", + "
" + ], + "text/plain": [ + " deaths size deserters\n", + "origin \n", + "Maine 43 1592 3\n", + "Alaska 523 987 24" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. Select the rows 3 to 7 and the columns 3 to 6" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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battlessizeveterans
origin
Florida2140026
Maine4159273
Iowa7100637
Alaska8987949
\n", + "
" + ], + "text/plain": [ + " battles size veterans\n", + "origin \n", + "Florida 2 1400 26\n", + "Maine 4 1592 73\n", + "Iowa 7 1006 37\n", + "Alaska 8 987 949" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. Select every row after the fourth row" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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regimentcompanydeathsbattlessizeveteransreadinessarmoreddeserters
origin
FloridaNighthawks2nd6162140026312
MaineDragoons1st434159273203
IowaDragoons1st2347100637114
AlaskaDragoons2nd52389879492024
WashingtonDragoons2nd623849483131
OregonScouts1st62497348202
WyomingScouts1st7371005435103
LouisanaScouts2nd378109963212
GeorgiaScouts2nd3591523345313
\n", + "
" + ], + "text/plain": [ + " regiment company deaths battles size veterans readiness \\\n", + "origin \n", + "Florida Nighthawks 2nd 616 2 1400 26 3 \n", + "Maine Dragoons 1st 43 4 1592 73 2 \n", + "Iowa Dragoons 1st 234 7 1006 37 1 \n", + "Alaska Dragoons 2nd 523 8 987 949 2 \n", + "Washington Dragoons 2nd 62 3 849 48 3 \n", + "Oregon Scouts 1st 62 4 973 48 2 \n", + "Wyoming Scouts 1st 73 7 1005 435 1 \n", + "Louisana Scouts 2nd 37 8 1099 63 2 \n", + "Georgia Scouts 2nd 35 9 1523 345 3 \n", + "\n", + " armored deserters \n", + "origin \n", + "Florida 1 2 \n", + "Maine 0 3 \n", + "Iowa 1 4 \n", + "Alaska 0 24 \n", + "Washington 1 31 \n", + "Oregon 0 2 \n", + "Wyoming 0 3 \n", + "Louisana 1 2 \n", + "Georgia 1 3 " + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 11. Select every row up to the 4th row" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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regimentcompanydeathsbattlessizeveteransreadinessarmoreddeserters
origin
ArizonaNighthawks1st523510451114
CaliforniaNighthawks1st524295752024
TexasNighthawks2nd2521099623131
\n", + "
" + ], + "text/plain": [ + " regiment company deaths battles size veterans readiness \\\n", + "origin \n", + "Arizona Nighthawks 1st 523 5 1045 1 1 \n", + "California Nighthawks 1st 52 42 957 5 2 \n", + "Texas Nighthawks 2nd 25 2 1099 62 3 \n", + "\n", + " armored deserters \n", + "origin \n", + "Arizona 1 4 \n", + "California 0 24 \n", + "Texas 1 31 " + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 12. Select the 3rd column up to the 7th column" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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sizeveteransreadiness
origin
Arizona104511
California95752
Texas1099623
Florida1400263
Maine1592732
Iowa1006371
Alaska9879492
Washington849483
Oregon973482
Wyoming10054351
Louisana1099632
Georgia15233453
\n", + "
" + ], + "text/plain": [ + " size veterans readiness\n", + "origin \n", + "Arizona 1045 1 1\n", + "California 957 5 2\n", + "Texas 1099 62 3\n", + "Florida 1400 26 3\n", + "Maine 1592 73 2\n", + "Iowa 1006 37 1\n", + "Alaska 987 949 2\n", + "Washington 849 48 3\n", + "Oregon 973 48 2\n", + "Wyoming 1005 435 1\n", + "Louisana 1099 63 2\n", + "Georgia 1523 345 3" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 13. Select rows where df.deaths is greater than 50" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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regimentcompanydeathsbattlessizeveteransreadinessarmoreddeserters
origin
ArizonaNighthawks1st523510451114
CaliforniaNighthawks1st524295752024
FloridaNighthawks2nd6162140026312
IowaDragoons1st2347100637114
AlaskaDragoons2nd52389879492024
WashingtonDragoons2nd623849483131
OregonScouts1st62497348202
WyomingScouts1st7371005435103
\n", + "
" + ], + "text/plain": [ + " regiment company deaths battles size veterans readiness \\\n", + "origin \n", + "Arizona Nighthawks 1st 523 5 1045 1 1 \n", + "California Nighthawks 1st 52 42 957 5 2 \n", + "Florida Nighthawks 2nd 616 2 1400 26 3 \n", + "Iowa Dragoons 1st 234 7 1006 37 1 \n", + "Alaska Dragoons 2nd 523 8 987 949 2 \n", + "Washington Dragoons 2nd 62 3 849 48 3 \n", + "Oregon Scouts 1st 62 4 973 48 2 \n", + "Wyoming Scouts 1st 73 7 1005 435 1 \n", + "\n", + " armored deserters \n", + "origin \n", + "Arizona 1 4 \n", + "California 0 24 \n", + "Florida 1 2 \n", + "Iowa 1 4 \n", + "Alaska 0 24 \n", + "Washington 1 31 \n", + "Oregon 0 2 \n", + "Wyoming 0 3 " + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 14. Select rows where df.deaths is greater than 500 or less than 50" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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regimentcompanydeathsbattlessizeveteransreadinessarmoreddeserters
origin
ArizonaNighthawks1st523510451114
TexasNighthawks2nd2521099623131
FloridaNighthawks2nd6162140026312
MaineDragoons1st434159273203
AlaskaDragoons2nd52389879492024
LouisanaScouts2nd378109963212
GeorgiaScouts2nd3591523345313
\n", + "
" + ], + "text/plain": [ + " regiment company deaths battles size veterans readiness \\\n", + "origin \n", + "Arizona Nighthawks 1st 523 5 1045 1 1 \n", + "Texas Nighthawks 2nd 25 2 1099 62 3 \n", + "Florida Nighthawks 2nd 616 2 1400 26 3 \n", + "Maine Dragoons 1st 43 4 1592 73 2 \n", + "Alaska Dragoons 2nd 523 8 987 949 2 \n", + "Louisana Scouts 2nd 37 8 1099 63 2 \n", + "Georgia Scouts 2nd 35 9 1523 345 3 \n", + "\n", + " armored deserters \n", + "origin \n", + "Arizona 1 4 \n", + "Texas 1 31 \n", + "Florida 1 2 \n", + "Maine 0 3 \n", + "Alaska 0 24 \n", + "Louisana 1 2 \n", + "Georgia 1 3 " + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 15. Select all the regiments not named \"Dragoons\"" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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regimentcompanydeathsbattlessizeveteransreadinessarmoreddeserters
origin
ArizonaNighthawks1st523510451114
CaliforniaNighthawks1st524295752024
TexasNighthawks2nd2521099623131
FloridaNighthawks2nd6162140026312
OregonScouts1st62497348202
WyomingScouts1st7371005435103
LouisanaScouts2nd378109963212
GeorgiaScouts2nd3591523345313
\n", + "
" + ], + "text/plain": [ + " regiment company deaths battles size veterans readiness \\\n", + "origin \n", + "Arizona Nighthawks 1st 523 5 1045 1 1 \n", + "California Nighthawks 1st 52 42 957 5 2 \n", + "Texas Nighthawks 2nd 25 2 1099 62 3 \n", + "Florida Nighthawks 2nd 616 2 1400 26 3 \n", + "Oregon Scouts 1st 62 4 973 48 2 \n", + "Wyoming Scouts 1st 73 7 1005 435 1 \n", + "Louisana Scouts 2nd 37 8 1099 63 2 \n", + "Georgia Scouts 2nd 35 9 1523 345 3 \n", + "\n", + " armored deserters \n", + "origin \n", + "Arizona 1 4 \n", + "California 0 24 \n", + "Texas 1 31 \n", + "Florida 1 2 \n", + "Oregon 0 2 \n", + "Wyoming 0 3 \n", + "Louisana 1 2 \n", + "Georgia 1 3 " + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 16. Select the rows called Texas and Arizona" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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regimentcompanydeathsbattlessizeveteransreadinessarmoreddeserters
origin
ArizonaNighthawks1st523510451114
TexasNighthawks2nd2521099623131
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" + ], + "text/plain": [ + " regiment company deaths battles size veterans readiness \\\n", + "origin \n", + "Arizona Nighthawks 1st 523 5 1045 1 1 \n", + "Texas Nighthawks 2nd 25 2 1099 62 3 \n", + "\n", + " armored deserters \n", + "origin \n", + "Arizona 1 4 \n", + "Texas 1 31 " + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 17. Select the third cell in the row named Arizona" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "523" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 18. Select the third cell down in the column named deaths" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "25" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/03_Grouping/Alcohol_Consumption/Exercise.ipynb b/200 solved problems in Python/pandas/03_Grouping/Alcohol_Consumption/Exercise.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..7ca680cec742580422e1681a3b459738e1c97e05 --- /dev/null +++ b/200 solved problems in Python/pandas/03_Grouping/Alcohol_Consumption/Exercise.ipynb @@ -0,0 +1,159 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Ex - GroupBy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "GroupBy can be summarizes as Split-Apply-Combine.\n", + "\n", + "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n", + "\n", + "Check out this [Diagram](http://i.imgur.com/yjNkiwL.png) \n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/drinks.csv). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called drinks." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Which continent drinks more beer on average?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. For each continent print the statistics for wine consumption." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Print the mean alcoohol consumption per continent for every column" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Print the median alcoohol consumption per continent for every column" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Print the mean, min and max values for spirit consumption.\n", + "#### This time output a DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/200 solved problems in Python/pandas/03_Grouping/Alcohol_Consumption/Exercise_with_solutions.ipynb b/200 solved problems in Python/pandas/03_Grouping/Alcohol_Consumption/Exercise_with_solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..55258575da3cae36c7d37e1911370fe81e63ea64 --- /dev/null +++ b/200 solved problems in Python/pandas/03_Grouping/Alcohol_Consumption/Exercise_with_solutions.ipynb @@ -0,0 +1,567 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Ex - GroupBy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "GroupBy can be summarizes as Split-Apply-Combine.\n", + "\n", + "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n", + "\n", + "Check out this [Diagram](http://i.imgur.com/yjNkiwL.png) \n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/drinks.csv). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called drinks." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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countrybeer_servingsspirit_servingswine_servingstotal_litres_of_pure_alcoholcontinent
0Afghanistan0000.0AS
1Albania89132544.9EU
2Algeria250140.7AF
3Andorra24513831212.4EU
4Angola21757455.9AF
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" + ], + "text/plain": [ + " country beer_servings spirit_servings wine_servings \\\n", + "0 Afghanistan 0 0 0 \n", + "1 Albania 89 132 54 \n", + "2 Algeria 25 0 14 \n", + "3 Andorra 245 138 312 \n", + "4 Angola 217 57 45 \n", + "\n", + " total_litres_of_pure_alcohol continent \n", + "0 0.0 AS \n", + "1 4.9 EU \n", + "2 0.7 AF \n", + "3 12.4 EU \n", + "4 5.9 AF " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "drinks = pd.read_csv('https://raw.githubusercontent.com/justmarkham/DAT8/master/data/drinks.csv')\n", + "drinks.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Which continent drinks more beer on average?" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "continent\n", + "AF 61.471698\n", + "AS 37.045455\n", + "EU 193.777778\n", + "OC 89.687500\n", + "SA 175.083333\n", + "Name: beer_servings, dtype: float64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "drinks.groupby('continent').beer_servings.mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. For each continent print the statistics for wine consumption." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "continent \n", + "AF count 53.000000\n", + " mean 16.264151\n", + " std 38.846419\n", + " min 0.000000\n", + " 25% 1.000000\n", + " 50% 2.000000\n", + " 75% 13.000000\n", + " max 233.000000\n", + "AS count 44.000000\n", + " mean 9.068182\n", + " std 21.667034\n", + " min 0.000000\n", + " 25% 0.000000\n", + " 50% 1.000000\n", + " 75% 8.000000\n", + " max 123.000000\n", + "EU count 45.000000\n", + " mean 142.222222\n", + " std 97.421738\n", + " min 0.000000\n", + " 25% 59.000000\n", + " 50% 128.000000\n", + " 75% 195.000000\n", + " max 370.000000\n", + "OC count 16.000000\n", + " mean 35.625000\n", + " std 64.555790\n", + " min 0.000000\n", + " 25% 1.000000\n", + " 50% 8.500000\n", + " 75% 23.250000\n", + " max 212.000000\n", + "SA count 12.000000\n", + " mean 62.416667\n", + " std 88.620189\n", + " min 1.000000\n", + " 25% 3.000000\n", + " 50% 12.000000\n", + " 75% 98.500000\n", + " max 221.000000\n", + "dtype: float64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "drinks.groupby('continent').wine_servings.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Print the mean alcoohol consumption per continent for every column" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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beer_servingsspirit_servingswine_servingstotal_litres_of_pure_alcohol
continent
AF61.47169816.33962316.2641513.007547
AS37.04545560.8409099.0681822.170455
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OC89.68750058.43750035.6250003.381250
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" + ], + "text/plain": [ + " beer_servings spirit_servings wine_servings \\\n", + "continent \n", + "AF 61.471698 16.339623 16.264151 \n", + "AS 37.045455 60.840909 9.068182 \n", + "EU 193.777778 132.555556 142.222222 \n", + "OC 89.687500 58.437500 35.625000 \n", + "SA 175.083333 114.750000 62.416667 \n", + "\n", + " total_litres_of_pure_alcohol \n", + "continent \n", + "AF 3.007547 \n", + "AS 2.170455 \n", + "EU 8.617778 \n", + "OC 3.381250 \n", + "SA 6.308333 " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "drinks.groupby('continent').mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Print the median alcoohol consumption per continent for every column" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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countrybeer_servingsspirit_servingswine_servingstotal_litres_of_pure_alcoholcontinent
0Afghanistan0000.0AS
1Albania89132544.9EU
2Algeria250140.7AF
3Andorra24513831212.4EU
4Angola21757455.9AF
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" + ], + "text/plain": [ + " country beer_servings spirit_servings wine_servings \\\n", + "0 Afghanistan 0 0 0 \n", + "1 Albania 89 132 54 \n", + "2 Algeria 25 0 14 \n", + "3 Andorra 245 138 312 \n", + "4 Angola 217 57 45 \n", + "\n", + " total_litres_of_pure_alcohol continent \n", + "0 0.0 AS \n", + "1 4.9 EU \n", + "2 0.7 AF \n", + "3 12.4 EU \n", + "4 5.9 AF " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Which continent drinks more beer on average?" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "continent\n", + "AF 61.471698\n", + "AS 37.045455\n", + "EU 193.777778\n", + "OC 89.687500\n", + "SA 175.083333\n", + "Name: beer_servings, dtype: float64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. For each continent print the statistics for wine consumption." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "continent \n", + "AF count 53.000000\n", + " mean 16.264151\n", + " std 38.846419\n", + " min 0.000000\n", + " 25% 1.000000\n", + " 50% 2.000000\n", + " 75% 13.000000\n", + " max 233.000000\n", + "AS count 44.000000\n", + " mean 9.068182\n", + " std 21.667034\n", + " min 0.000000\n", + " 25% 0.000000\n", + " 50% 1.000000\n", + " 75% 8.000000\n", + " max 123.000000\n", + "EU count 45.000000\n", + " mean 142.222222\n", + " std 97.421738\n", + " min 0.000000\n", + " 25% 59.000000\n", + " 50% 128.000000\n", + " 75% 195.000000\n", + " max 370.000000\n", + "OC count 16.000000\n", + " mean 35.625000\n", + " std 64.555790\n", + " min 0.000000\n", + " 25% 1.000000\n", + " 50% 8.500000\n", + " 75% 23.250000\n", + " max 212.000000\n", + "SA count 12.000000\n", + " mean 62.416667\n", + " std 88.620189\n", + " min 1.000000\n", + " 25% 3.000000\n", + " 50% 12.000000\n", + " 75% 98.500000\n", + " max 221.000000\n", + "dtype: float64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Print the mean alcoohol consumption per continent for every column" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/u.user). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called users." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Discover what is the mean age per occupation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Discover the Male ratio per occupation and sort it from the most to the least" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. For each occupation, calculate the minimum and maximum ages" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. For each combination of occupation and gender, calculate the mean age" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. For each occupation present the percentage of women and men" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/03_Grouping/Occupation/Exercises_with_solutions.ipynb b/200 solved problems in Python/pandas/03_Grouping/Occupation/Exercises_with_solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..9fb68d2377da9d00b9bafadeaeeeb127d653c0a7 --- /dev/null +++ b/200 solved problems in Python/pandas/03_Grouping/Occupation/Exercises_with_solutions.ipynb @@ -0,0 +1,600 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Occupation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/u.user). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called users." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " age gender occupation zip_code\n", + "user_id \n", + "1 24 M technician 85711\n", + "2 53 F other 94043\n", + "3 23 M writer 32067\n", + "4 24 M technician 43537\n", + "5 33 F other 15213" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "users = pd.read_table('https://raw.githubusercontent.com/justmarkham/DAT8/master/data/u.user', \n", + " sep='|', index_col='user_id')\n", + "users.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Discover what is the mean age per occupation" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "occupation\n", + "administrator 38.746835\n", + "artist 31.392857\n", + "doctor 43.571429\n", + "educator 42.010526\n", + "engineer 36.388060\n", + "entertainment 29.222222\n", + "executive 38.718750\n", + "healthcare 41.562500\n", + "homemaker 32.571429\n", + "lawyer 36.750000\n", + "librarian 40.000000\n", + "marketing 37.615385\n", + "none 26.555556\n", + "other 34.523810\n", + "programmer 33.121212\n", + "retired 63.071429\n", + "salesman 35.666667\n", + "scientist 35.548387\n", + "student 22.081633\n", + "technician 33.148148\n", + "writer 36.311111\n", + "Name: age, dtype: float64" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "users.groupby('occupation').age.mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Discover the Male ratio per occupation and sort it from the most to the least" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "doctor 100.000000\n", + "engineer 97.014925\n", + "technician 96.296296\n", + "retired 92.857143\n", + "programmer 90.909091\n", + "executive 90.625000\n", + "scientist 90.322581\n", + "entertainment 88.888889\n", + "lawyer 83.333333\n", + "salesman 75.000000\n", + "educator 72.631579\n", + "student 69.387755\n", + "other 65.714286\n", + "marketing 61.538462\n", + "writer 57.777778\n", + "none 55.555556\n", + "administrator 54.430380\n", + "artist 53.571429\n", + "librarian 43.137255\n", + "healthcare 31.250000\n", + "homemaker 14.285714\n", + "dtype: float64" + ] + }, + "execution_count": 150, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# create a function\n", + "def gender_to_numeric(x):\n", + " if x == 'M':\n", + " return 1\n", + " if x == 'F':\n", + " return 0\n", + "\n", + "# apply the function to the gender column and create a new column\n", + "users['gender_n'] = users['gender'].apply(gender_to_numeric)\n", + "\n", + "\n", + "a = users.groupby('occupation').gender_n.sum() / users.occupation.value_counts() * 100 \n", + "\n", + "# sort to the most male \n", + "a.sort_values(ascending = False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. For each occupation, calculate the minimum and maximum ages" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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minmax
occupation
administrator2170
artist1948
doctor2864
educator2363
engineer2270
entertainment1550
executive2269
healthcare2262
homemaker2050
lawyer2153
librarian2369
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retired5173
salesman1866
scientist2355
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" + ], + "text/plain": [ + " min max\n", + "occupation \n", + "administrator 21 70\n", + "artist 19 48\n", + "doctor 28 64\n", + "educator 23 63\n", + "engineer 22 70\n", + "entertainment 15 50\n", + "executive 22 69\n", + "healthcare 22 62\n", + "homemaker 20 50\n", + "lawyer 21 53\n", + "librarian 23 69\n", + "marketing 24 55\n", + "none 11 55\n", + "other 13 64\n", + "programmer 20 63\n", + "retired 51 73\n", + "salesman 18 66\n", + "scientist 23 55\n", + "student 7 42\n", + "technician 21 55\n", + "writer 18 60" + ] + }, + "execution_count": 151, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "users.groupby('occupation').age.agg(['min', 'max'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. For each combination of occupation and gender, calculate the mean age" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "occupation gender\n", + "administrator F 40.638889\n", + " M 37.162791\n", + "artist F 30.307692\n", + " M 32.333333\n", + "doctor M 43.571429\n", + "educator F 39.115385\n", + " M 43.101449\n", + "engineer F 29.500000\n", + " M 36.600000\n", + "entertainment F 31.000000\n", + " M 29.000000\n", + "executive F 44.000000\n", + " M 38.172414\n", + "healthcare F 39.818182\n", + " M 45.400000\n", + "homemaker F 34.166667\n", + " M 23.000000\n", + "lawyer F 39.500000\n", + " M 36.200000\n", + "librarian F 40.000000\n", + " M 40.000000\n", + "marketing F 37.200000\n", + " M 37.875000\n", + "none F 36.500000\n", + " M 18.600000\n", + "other F 35.472222\n", + " M 34.028986\n", + "programmer F 32.166667\n", + " M 33.216667\n", + "retired F 70.000000\n", + " M 62.538462\n", + "salesman F 27.000000\n", + " M 38.555556\n", + "scientist F 28.333333\n", + " M 36.321429\n", + "student F 20.750000\n", + " M 22.669118\n", + "technician F 38.000000\n", + " M 32.961538\n", + "writer F 37.631579\n", + " M 35.346154\n", + "Name: age, dtype: float64" + ] + }, + "execution_count": 152, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "users.groupby(['occupation', 'gender']).age.mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. For each occupation present the percentage of women and men" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "occupation gender\n", + "administrator F 45.569620\n", + " M 54.430380\n", + "artist F 46.428571\n", + " M 53.571429\n", + "doctor M 100.000000\n", + "educator F 27.368421\n", + " M 72.631579\n", + "engineer F 2.985075\n", + " M 97.014925\n", + "entertainment F 11.111111\n", + " M 88.888889\n", + "executive F 9.375000\n", + " M 90.625000\n", + "healthcare F 68.750000\n", + " M 31.250000\n", + "homemaker F 85.714286\n", + " M 14.285714\n", + "lawyer F 16.666667\n", + " M 83.333333\n", + "librarian F 56.862745\n", + " M 43.137255\n", + "marketing F 38.461538\n", + " M 61.538462\n", + "none F 44.444444\n", + " M 55.555556\n", + "other F 34.285714\n", + " M 65.714286\n", + "programmer F 9.090909\n", + " M 90.909091\n", + "retired F 7.142857\n", + " M 92.857143\n", + "salesman F 25.000000\n", + " M 75.000000\n", + "scientist F 9.677419\n", + " M 90.322581\n", + "student F 30.612245\n", + " M 69.387755\n", + "technician F 3.703704\n", + " M 96.296296\n", + "writer F 42.222222\n", + " M 57.777778\n", + "Name: gender, dtype: float64" + ] + }, + "execution_count": 154, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# create a data frame and apply count to gender\n", + "gender_ocup = users.groupby(['occupation', 'gender']).agg({'gender': 'count'})\n", + "\n", + "# create a DataFrame and apply count for each occupation\n", + "occup_count = users.groupby(['occupation']).agg('count')\n", + "\n", + "# divide the gender_ocup per the occup_count and multiply per 100\n", + "occup_gender = gender_ocup.div(occup_count, level = \"occupation\") * 100\n", + "\n", + "# present all rows from the 'gender column'\n", + "occup_gender.loc[: , 'gender']" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/03_Grouping/Occupation/Solutions.ipynb b/200 solved problems in Python/pandas/03_Grouping/Occupation/Solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..f07ceda010b773b63edbc2f1471461414fad45bb --- /dev/null +++ b/200 solved problems in Python/pandas/03_Grouping/Occupation/Solutions.ipynb @@ -0,0 +1,560 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Occupation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/u.user). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called users." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " age gender occupation zip_code\n", + "user_id \n", + "1 24 M technician 85711\n", + "2 53 F other 94043\n", + "3 23 M writer 32067\n", + "4 24 M technician 43537\n", + "5 33 F other 15213" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Discover what is the mean age per occupation" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "occupation\n", + "administrator 38.746835\n", + "artist 31.392857\n", + "doctor 43.571429\n", + "educator 42.010526\n", + "engineer 36.388060\n", + "entertainment 29.222222\n", + "executive 38.718750\n", + "healthcare 41.562500\n", + "homemaker 32.571429\n", + "lawyer 36.750000\n", + "librarian 40.000000\n", + "marketing 37.615385\n", + "none 26.555556\n", + "other 34.523810\n", + "programmer 33.121212\n", + "retired 63.071429\n", + "salesman 35.666667\n", + "scientist 35.548387\n", + "student 22.081633\n", + "technician 33.148148\n", + "writer 36.311111\n", + "Name: age, dtype: float64" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Discover the Male ratio per occupation and sort it from the most to the least" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "doctor 100.000000\n", + "engineer 97.014925\n", + "technician 96.296296\n", + "retired 92.857143\n", + "programmer 90.909091\n", + "executive 90.625000\n", + "scientist 90.322581\n", + "entertainment 88.888889\n", + "lawyer 83.333333\n", + "salesman 75.000000\n", + "educator 72.631579\n", + "student 69.387755\n", + "other 65.714286\n", + "marketing 61.538462\n", + "writer 57.777778\n", + "none 55.555556\n", + "administrator 54.430380\n", + "artist 53.571429\n", + "librarian 43.137255\n", + "healthcare 31.250000\n", + "homemaker 14.285714\n", + "dtype: float64" + ] + }, + "execution_count": 150, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. For each occupation, calculate the minimum and maximum ages" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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minmax
occupation
administrator2170
artist1948
doctor2864
educator2363
engineer2270
entertainment1550
executive2269
healthcare2262
homemaker2050
lawyer2153
librarian2369
marketing2455
none1155
other1364
programmer2063
retired5173
salesman1866
scientist2355
student742
technician2155
writer1860
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" + ], + "text/plain": [ + " min max\n", + "occupation \n", + "administrator 21 70\n", + "artist 19 48\n", + "doctor 28 64\n", + "educator 23 63\n", + "engineer 22 70\n", + "entertainment 15 50\n", + "executive 22 69\n", + "healthcare 22 62\n", + "homemaker 20 50\n", + "lawyer 21 53\n", + "librarian 23 69\n", + "marketing 24 55\n", + "none 11 55\n", + "other 13 64\n", + "programmer 20 63\n", + "retired 51 73\n", + "salesman 18 66\n", + "scientist 23 55\n", + "student 7 42\n", + "technician 21 55\n", + "writer 18 60" + ] + }, + "execution_count": 151, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. For each combination of occupation and gender, calculate the mean age" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "occupation gender\n", + "administrator F 40.638889\n", + " M 37.162791\n", + "artist F 30.307692\n", + " M 32.333333\n", + "doctor M 43.571429\n", + "educator F 39.115385\n", + " M 43.101449\n", + "engineer F 29.500000\n", + " M 36.600000\n", + "entertainment F 31.000000\n", + " M 29.000000\n", + "executive F 44.000000\n", + " M 38.172414\n", + "healthcare F 39.818182\n", + " M 45.400000\n", + "homemaker F 34.166667\n", + " M 23.000000\n", + "lawyer F 39.500000\n", + " M 36.200000\n", + "librarian F 40.000000\n", + " M 40.000000\n", + "marketing F 37.200000\n", + " M 37.875000\n", + "none F 36.500000\n", + " M 18.600000\n", + "other F 35.472222\n", + " M 34.028986\n", + "programmer F 32.166667\n", + " M 33.216667\n", + "retired F 70.000000\n", + " M 62.538462\n", + "salesman F 27.000000\n", + " M 38.555556\n", + "scientist F 28.333333\n", + " M 36.321429\n", + "student F 20.750000\n", + " M 22.669118\n", + "technician F 38.000000\n", + " M 32.961538\n", + "writer F 37.631579\n", + " M 35.346154\n", + "Name: age, dtype: float64" + ] + }, + "execution_count": 152, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. For each occupation present the percentage of women and men" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "occupation gender\n", + "administrator F 45.569620\n", + " M 54.430380\n", + "artist F 46.428571\n", + " M 53.571429\n", + "doctor M 100.000000\n", + "educator F 27.368421\n", + " M 72.631579\n", + "engineer F 2.985075\n", + " M 97.014925\n", + "entertainment F 11.111111\n", + " M 88.888889\n", + "executive F 9.375000\n", + " M 90.625000\n", + "healthcare F 68.750000\n", + " M 31.250000\n", + "homemaker F 85.714286\n", + " M 14.285714\n", + "lawyer F 16.666667\n", + " M 83.333333\n", + "librarian F 56.862745\n", + " M 43.137255\n", + "marketing F 38.461538\n", + " M 61.538462\n", + "none F 44.444444\n", + " M 55.555556\n", + "other F 34.285714\n", + " M 65.714286\n", + "programmer F 9.090909\n", + " M 90.909091\n", + "retired F 7.142857\n", + " M 92.857143\n", + "salesman F 25.000000\n", + " M 75.000000\n", + "scientist F 9.677419\n", + " M 90.322581\n", + "student F 30.612245\n", + " M 69.387755\n", + "technician F 3.703704\n", + " M 96.296296\n", + "writer F 42.222222\n", + " M 57.777778\n", + "Name: gender, dtype: float64" + ] + }, + "execution_count": 154, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/03_Grouping/Regiment/Exercises.ipynb b/200 solved problems in Python/pandas/03_Grouping/Regiment/Exercises.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..98f544ebac2c16874cddeedb1866f83f3d108265 --- /dev/null +++ b/200 solved problems in Python/pandas/03_Grouping/Regiment/Exercises.ipynb @@ -0,0 +1,219 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Regiment" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "Special thanks to: http://chrisalbon.com/ for sharing the dataset and materials.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Create the DataFrame with the following values:" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "raw_data = {'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', 'Nighthawks', 'Dragoons', 'Dragoons', 'Dragoons', 'Dragoons', 'Scouts', 'Scouts', 'Scouts', 'Scouts'], \n", + " 'company': ['1st', '1st', '2nd', '2nd', '1st', '1st', '2nd', '2nd','1st', '1st', '2nd', '2nd'], \n", + " 'name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze', 'Jacon', 'Ryaner', 'Sone', 'Sloan', 'Piger', 'Riani', 'Ali'], \n", + " 'preTestScore': [4, 24, 31, 2, 3, 4, 24, 31, 2, 3, 2, 3],\n", + " 'postTestScore': [25, 94, 57, 62, 70, 25, 94, 57, 62, 70, 62, 70]}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called regiment.\n", + "#### Don't forget to name each column" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. What is the mean preTestScore from the regiment Nighthawks? " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Present general statistics by company" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. What is the mean each company's preTestScore?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Present the mean preTestScores grouped by regiment and company" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Present the mean preTestScores grouped by regiment and company without heirarchical indexing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. Group the entire dataframe by regiment and company" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. What is the number of observations in each regiment and company" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 11. Iterate over a group and print the name and the whole data from the regiment" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/03_Grouping/Regiment/Exercises_solutions.ipynb b/200 solved problems in Python/pandas/03_Grouping/Regiment/Exercises_solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..fa2b1922044f7c825cfe47c27c2740d3914d7a3e --- /dev/null +++ b/200 solved problems in Python/pandas/03_Grouping/Regiment/Exercises_solutions.ipynb @@ -0,0 +1,762 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Regiment" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "Special thanks to: http://chrisalbon.com/ for sharing the dataset and materials.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Create the DataFrame with the following values:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "raw_data = {'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', 'Nighthawks', 'Dragoons', 'Dragoons', 'Dragoons', 'Dragoons', 'Scouts', 'Scouts', 'Scouts', 'Scouts'], \n", + " 'company': ['1st', '1st', '2nd', '2nd', '1st', '1st', '2nd', '2nd','1st', '1st', '2nd', '2nd'], \n", + " 'name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze', 'Jacon', 'Ryaner', 'Sone', 'Sloan', 'Piger', 'Riani', 'Ali'], \n", + " 'preTestScore': [4, 24, 31, 2, 3, 4, 24, 31, 2, 3, 2, 3],\n", + " 'postTestScore': [25, 94, 57, 62, 70, 25, 94, 57, 62, 70, 62, 70]}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called regiment.\n", + "#### Don't forget to name each column" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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regimentcompanynamepreTestScorepostTestScore
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2Nighthawks2ndAli3157
3Nighthawks2ndMilner262
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5Dragoons1stJacon425
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8Scouts1stSloan262
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10Scouts2ndRiani262
11Scouts2ndAli370
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" + ], + "text/plain": [ + " regiment company name preTestScore postTestScore\n", + "0 Nighthawks 1st Miller 4 25\n", + "1 Nighthawks 1st Jacobson 24 94\n", + "2 Nighthawks 2nd Ali 31 57\n", + "3 Nighthawks 2nd Milner 2 62\n", + "4 Dragoons 1st Cooze 3 70\n", + "5 Dragoons 1st Jacon 4 25\n", + "6 Dragoons 2nd Ryaner 24 94\n", + "7 Dragoons 2nd Sone 31 57\n", + "8 Scouts 1st Sloan 2 62\n", + "9 Scouts 1st Piger 3 70\n", + "10 Scouts 2nd Riani 2 62\n", + "11 Scouts 2nd Ali 3 70" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "regiment = pd.DataFrame(raw_data, columns = raw_data.keys())\n", + "regiment" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. What is the mean preTestScore from the regiment Nighthawks? " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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mean57.6666676.666667
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" + ], + "text/plain": [ + "company 1st 2nd\n", + "regiment \n", + "Dragoons 3.5 27.5\n", + "Nighthawks 14.0 16.5\n", + "Scouts 2.5 2.5" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "regiment.groupby(['regiment', 'company']).preTestScore.mean().unstack()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. Group the entire dataframe by regiment and company" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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2nd2.566.0
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" + ], + "text/plain": [ + " preTestScore postTestScore\n", + "regiment company \n", + "Dragoons 1st 3.5 47.5\n", + " 2nd 27.5 75.5\n", + "Nighthawks 1st 14.0 59.5\n", + " 2nd 16.5 59.5\n", + "Scouts 1st 2.5 66.0\n", + " 2nd 2.5 66.0" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "regiment.groupby(['regiment', 'company']).mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. What is the number of observations in each regiment and company" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "company regiment \n", + "1st Dragoons 2\n", + " Nighthawks 2\n", + " Scouts 2\n", + "2nd Dragoons 2\n", + " Nighthawks 2\n", + " Scouts 2\n", + "dtype: int64" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "regiment.groupby(['company', 'regiment']).size()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 11. Iterate over a group and print the name and the whole data from the regiment" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dragoons\n", + " regiment company name preTestScore postTestScore\n", + "4 Dragoons 1st Cooze 3 70\n", + "5 Dragoons 1st Jacon 4 25\n", + "6 Dragoons 2nd Ryaner 24 94\n", + "7 Dragoons 2nd Sone 31 57\n", + "Nighthawks\n", + " regiment company name preTestScore postTestScore\n", + "0 Nighthawks 1st Miller 4 25\n", + "1 Nighthawks 1st Jacobson 24 94\n", + "2 Nighthawks 2nd Ali 31 57\n", + "3 Nighthawks 2nd Milner 2 62\n", + "Scouts\n", + " regiment company name preTestScore postTestScore\n", + "8 Scouts 1st Sloan 2 62\n", + "9 Scouts 1st Piger 3 70\n", + "10 Scouts 2nd Riani 2 62\n", + "11 Scouts 2nd Ali 3 70\n" + ] + } + ], + "source": [ + "# Group the dataframe by regiment, and for each regiment,\n", + "for name, group in regiment.groupby('regiment'):\n", + " # print the name of the regiment\n", + " print(name)\n", + " # print the data of that regiment\n", + " print(group)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/03_Grouping/Regiment/Solutions.ipynb b/200 solved problems in Python/pandas/03_Grouping/Regiment/Solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..cdb0dc7b06a737fe0d2da59da3166df4b551b7a1 --- /dev/null +++ b/200 solved problems in Python/pandas/03_Grouping/Regiment/Solutions.ipynb @@ -0,0 +1,736 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Regiment" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "Special thanks to: http://chrisalbon.com/ for sharing the dataset and materials.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Create the DataFrame with the following values:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "raw_data = {'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', 'Nighthawks', 'Dragoons', 'Dragoons', 'Dragoons', 'Dragoons', 'Scouts', 'Scouts', 'Scouts', 'Scouts'], \n", + " 'company': ['1st', '1st', '2nd', '2nd', '1st', '1st', '2nd', '2nd','1st', '1st', '2nd', '2nd'], \n", + " 'name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze', 'Jacon', 'Ryaner', 'Sone', 'Sloan', 'Piger', 'Riani', 'Ali'], \n", + " 'preTestScore': [4, 24, 31, 2, 3, 4, 24, 31, 2, 3, 2, 3],\n", + " 'postTestScore': [25, 94, 57, 62, 70, 25, 94, 57, 62, 70, 62, 70]}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called regiment.\n", + "#### Don't forget to name each column" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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regimentcompanynamepreTestScorepostTestScore
0Nighthawks1stMiller425
1Nighthawks1stJacobson2494
2Nighthawks2ndAli3157
3Nighthawks2ndMilner262
4Dragoons1stCooze370
5Dragoons1stJacon425
6Dragoons2ndRyaner2494
7Dragoons2ndSone3157
8Scouts1stSloan262
9Scouts1stPiger370
10Scouts2ndRiani262
11Scouts2ndAli370
\n", + "
" + ], + "text/plain": [ + " regiment company name preTestScore postTestScore\n", + "0 Nighthawks 1st Miller 4 25\n", + "1 Nighthawks 1st Jacobson 24 94\n", + "2 Nighthawks 2nd Ali 31 57\n", + "3 Nighthawks 2nd Milner 2 62\n", + "4 Dragoons 1st Cooze 3 70\n", + "5 Dragoons 1st Jacon 4 25\n", + "6 Dragoons 2nd Ryaner 24 94\n", + "7 Dragoons 2nd Sone 31 57\n", + "8 Scouts 1st Sloan 2 62\n", + "9 Scouts 1st Piger 3 70\n", + "10 Scouts 2nd Riani 2 62\n", + "11 Scouts 2nd Ali 3 70" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. What is the mean preTestScore from the regiment Nighthawks? " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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preTestScorepostTestScore
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Dragoons15.5061.5
Nighthawks15.2559.5
Scouts2.5066.0
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" + ], + "text/plain": [ + " preTestScore postTestScore\n", + "regiment \n", + "Dragoons 15.50 61.5\n", + "Nighthawks 15.25 59.5\n", + "Scouts 2.50 66.0" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Present general statistics by company" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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postTestScorepreTestScore
company
1stcount6.0000006.000000
mean57.6666676.666667
std27.4857548.524475
min25.0000002.000000
25%34.2500003.000000
50%66.0000003.500000
75%70.0000004.000000
max94.00000024.000000
2ndcount6.0000006.000000
mean67.00000015.500000
std14.05702714.652645
min57.0000002.000000
25%58.2500002.250000
50%62.00000013.500000
75%68.00000029.250000
max94.00000031.000000
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" + ], + "text/plain": [ + " postTestScore preTestScore\n", + "company \n", + "1st count 6.000000 6.000000\n", + " mean 57.666667 6.666667\n", + " std 27.485754 8.524475\n", + " min 25.000000 2.000000\n", + " 25% 34.250000 3.000000\n", + " 50% 66.000000 3.500000\n", + " 75% 70.000000 4.000000\n", + " max 94.000000 24.000000\n", + "2nd count 6.000000 6.000000\n", + " mean 67.000000 15.500000\n", + " std 14.057027 14.652645\n", + " min 57.000000 2.000000\n", + " 25% 58.250000 2.250000\n", + " 50% 62.000000 13.500000\n", + " 75% 68.000000 29.250000\n", + " max 94.000000 31.000000" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. What is the mean each company's preTestScore?" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "company\n", + "1st 6.666667\n", + "2nd 15.500000\n", + "Name: preTestScore, dtype: float64" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Present the mean preTestScores grouped by regiment and company" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "regiment company\n", + "Dragoons 1st 3.5\n", + " 2nd 27.5\n", + "Nighthawks 1st 14.0\n", + " 2nd 16.5\n", + "Scouts 1st 2.5\n", + " 2nd 2.5\n", + "Name: preTestScore, dtype: float64" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Present the mean preTestScores grouped by regiment and company without heirarchical indexing" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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company1st2nd
regiment
Dragoons3.527.5
Nighthawks14.016.5
Scouts2.52.5
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" + ], + "text/plain": [ + "company 1st 2nd\n", + "regiment \n", + "Dragoons 3.5 27.5\n", + "Nighthawks 14.0 16.5\n", + "Scouts 2.5 2.5" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. Group the entire dataframe by regiment and company" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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preTestScorepostTestScore
regimentcompany
Dragoons1st3.547.5
2nd27.575.5
Nighthawks1st14.059.5
2nd16.559.5
Scouts1st2.566.0
2nd2.566.0
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" + ], + "text/plain": [ + " preTestScore postTestScore\n", + "regiment company \n", + "Dragoons 1st 3.5 47.5\n", + " 2nd 27.5 75.5\n", + "Nighthawks 1st 14.0 59.5\n", + " 2nd 16.5 59.5\n", + "Scouts 1st 2.5 66.0\n", + " 2nd 2.5 66.0" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. What is the number of observations in each regiment and company" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "company regiment \n", + "1st Dragoons 2\n", + " Nighthawks 2\n", + " Scouts 2\n", + "2nd Dragoons 2\n", + " Nighthawks 2\n", + " Scouts 2\n", + "dtype: int64" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 11. Iterate over a group and print the name and the whole data from the regiment" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dragoons\n", + " regiment company name preTestScore postTestScore\n", + "4 Dragoons 1st Cooze 3 70\n", + "5 Dragoons 1st Jacon 4 25\n", + "6 Dragoons 2nd Ryaner 24 94\n", + "7 Dragoons 2nd Sone 31 57\n", + "Nighthawks\n", + " regiment company name preTestScore postTestScore\n", + "0 Nighthawks 1st Miller 4 25\n", + "1 Nighthawks 1st Jacobson 24 94\n", + "2 Nighthawks 2nd Ali 31 57\n", + "3 Nighthawks 2nd Milner 2 62\n", + "Scouts\n", + " regiment company name preTestScore postTestScore\n", + "8 Scouts 1st Sloan 2 62\n", + "9 Scouts 1st Piger 3 70\n", + "10 Scouts 2nd Riani 2 62\n", + "11 Scouts 2nd Ali 3 70\n" + ] + } + ], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/04_Apply/Students_Alcohol_Consumption/Exercises.ipynb b/200 solved problems in Python/pandas/04_Apply/Students_Alcohol_Consumption/Exercises.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..f8aa25bcff1f0d94ff28bf1d639a096411bcf315 --- /dev/null +++ b/200 solved problems in Python/pandas/04_Apply/Students_Alcohol_Consumption/Exercises.ipynb @@ -0,0 +1,207 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Student Alcohol Consumption" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "This time you will download a dataset from the UCI.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://github.com/guipsamora/pandas_exercises/blob/master/04_Apply/Students_Alcohol_Consumption/student-mat.csv)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called df." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. For the purpose of this exercise slice the dataframe from 'school' until the 'guardian' column" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Create a lambda function that captalize strings." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Capitalize both Mjob and Fjob" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Print the last elements of the data set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Did you notice the original dataframe is still lowercase? Why is that? Fix it and captalize Mjob and Fjob." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. Create a function called majority that return a boolean value to a new column called legal_drinker" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. Multiply every number of the dataset by 10. \n", + "##### I know this makes no sense, don't forget it is just an exercise" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/04_Apply/Students_Alcohol_Consumption/Exercises_with_solutions.ipynb b/200 solved problems in Python/pandas/04_Apply/Students_Alcohol_Consumption/Exercises_with_solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..f64ac64b50e5c279d5396257efbb4493f5148982 --- /dev/null +++ b/200 solved problems in Python/pandas/04_Apply/Students_Alcohol_Consumption/Exercises_with_solutions.ipynb @@ -0,0 +1,1215 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Student Alcohol Consumption" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "This time you will download a dataset from the UCI.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://github.com/guipsamora/pandas_exercises/blob/master/04_Apply/Students_Alcohol_Consumption/student-mat.csv)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called df." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " school sex age address famsize Pstatus Medu Fedu Mjob Fjob ... \\\n", + "0 GP F 18 U GT3 A 4 4 at_home teacher ... \n", + "1 GP F 17 U GT3 T 1 1 at_home other ... \n", + "2 GP F 15 U LE3 T 1 1 at_home other ... \n", + "3 GP F 15 U GT3 T 4 2 health services ... \n", + "4 GP F 16 U GT3 T 3 3 other other ... \n", + "\n", + " famrel freetime goout Dalc Walc health absences G1 G2 G3 \n", + "0 4 3 4 1 1 3 6 5 6 6 \n", + "1 5 3 3 1 1 3 4 5 5 6 \n", + "2 4 3 2 2 3 3 10 7 8 10 \n", + "3 3 2 2 1 1 5 2 15 14 15 \n", + "4 4 3 2 1 2 5 4 6 10 10 \n", + "\n", + "[5 rows x 33 columns]" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('/Users/guilhermeoliveira/Desktop/student/student-mat.csv', sep = ';')\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. For the purpose of this exercise slice the dataframe from 'school' until the 'guardian' column" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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0GPF18UGT3A44at_hometeachercoursemother
1GPF17UGT3T11at_homeothercoursefather
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" + ], + "text/plain": [ + " school sex age address famsize Pstatus Medu Fedu Mjob Fjob \\\n", + "0 GP F 18 U GT3 A 4 4 at_home teacher \n", + "1 GP F 17 U GT3 T 1 1 at_home other \n", + "2 GP F 15 U LE3 T 1 1 at_home other \n", + "3 GP F 15 U GT3 T 4 2 health services \n", + "4 GP F 16 U GT3 T 3 3 other other \n", + "\n", + " reason guardian \n", + "0 course mother \n", + "1 course father \n", + "2 other mother \n", + "3 home mother \n", + "4 home father " + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stud_alcoh = df.loc[: , \"school\":\"guardian\"]\n", + "stud_alcoh.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Create a lambda function that captalize strings." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "captalizer = lambda x: x.upper()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Capitalize both Mjob and Fjob" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 TEACHER\n", + "1 OTHER\n", + "2 OTHER\n", + "3 SERVICES\n", + "4 OTHER\n", + "5 OTHER\n", + "6 OTHER\n", + "7 TEACHER\n", + "8 OTHER\n", + "9 OTHER\n", + "10 HEALTH\n", + "11 OTHER\n", + "12 SERVICES\n", + "13 OTHER\n", + "14 OTHER\n", + "15 OTHER\n", + "16 SERVICES\n", + "17 OTHER\n", + "18 SERVICES\n", + "19 OTHER\n", + "20 OTHER\n", + "21 HEALTH\n", + "22 OTHER\n", + "23 OTHER\n", + "24 HEALTH\n", + "25 SERVICES\n", + "26 OTHER\n", + "27 SERVICES\n", + "28 OTHER\n", + "29 TEACHER\n", + " ... \n", + "365 OTHER\n", + "366 SERVICES\n", + "367 SERVICES\n", + "368 SERVICES\n", + "369 TEACHER\n", + "370 SERVICES\n", + "371 SERVICES\n", + "372 AT_HOME\n", + "373 OTHER\n", + "374 OTHER\n", + "375 OTHER\n", + "376 OTHER\n", + "377 SERVICES\n", + "378 OTHER\n", + "379 OTHER\n", + "380 TEACHER\n", + "381 OTHER\n", + "382 SERVICES\n", + "383 SERVICES\n", + "384 OTHER\n", + "385 OTHER\n", + "386 AT_HOME\n", + "387 OTHER\n", + "388 SERVICES\n", + "389 OTHER\n", + "390 SERVICES\n", + "391 SERVICES\n", + "392 OTHER\n", + "393 OTHER\n", + "394 AT_HOME\n", + "Name: Fjob, dtype: object" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stud_alcoh['Mjob'].apply(captalizer)\n", + "stud_alcoh['Fjob'].apply(captalizer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Print the last elements of the data set." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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391MSM17ULE3T31servicesservicescoursemother
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394MSM19ULE3T11otherat_homecoursefather
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" + ], + "text/plain": [ + " school sex age address famsize Pstatus Medu Fedu Mjob Fjob \\\n", + "390 MS M 20 U LE3 A 2 2 services services \n", + "391 MS M 17 U LE3 T 3 1 services services \n", + "392 MS M 21 R GT3 T 1 1 other other \n", + "393 MS M 18 R LE3 T 3 2 services other \n", + "394 MS M 19 U LE3 T 1 1 other at_home \n", + "\n", + " reason guardian \n", + "390 course other \n", + "391 course mother \n", + "392 course other \n", + "393 course mother \n", + "394 course father " + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stud_alcoh.tail()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Did you notice the original dataframe is still lowercase? Why is that? Fix it and captalize Mjob and Fjob." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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390MSM20ULE3A22SERVICESSERVICEScourseother
391MSM17ULE3T31SERVICESSERVICEScoursemother
392MSM21RGT3T11OTHEROTHERcourseother
393MSM18RLE3T32SERVICESOTHERcoursemother
394MSM19ULE3T11OTHERAT_HOMEcoursefather
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" + ], + "text/plain": [ + " school sex age address famsize Pstatus Medu Fedu Mjob Fjob \\\n", + "390 MS M 20 U LE3 A 2 2 SERVICES SERVICES \n", + "391 MS M 17 U LE3 T 3 1 SERVICES SERVICES \n", + "392 MS M 21 R GT3 T 1 1 OTHER OTHER \n", + "393 MS M 18 R LE3 T 3 2 SERVICES OTHER \n", + "394 MS M 19 U LE3 T 1 1 OTHER AT_HOME \n", + "\n", + " reason guardian \n", + "390 course other \n", + "391 course mother \n", + "392 course other \n", + "393 course mother \n", + "394 course father " + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stud_alcoh['Mjob'] = stud_alcoh['Mjob'].apply(captalizer)\n", + "stud_alcoh['Fjob'] = stud_alcoh['Fjob'].apply(captalizer)\n", + "stud_alcoh.tail()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. Create a function called majority that return a boolean value to a new column called legal_drinker" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def majority(x):\n", + " if x > 17:\n", + " return True\n", + " else:\n", + " return False" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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schoolsexageaddressfamsizePstatusMeduFeduMjobFjobreasonguardianlegal_drinker
0GPF18UGT3A44AT_HOMETEACHERcoursemotherTrue
1GPF17UGT3T11AT_HOMEOTHERcoursefatherFalse
2GPF15ULE3T11AT_HOMEOTHERothermotherFalse
3GPF15UGT3T42HEALTHSERVICEShomemotherFalse
4GPF16UGT3T33OTHEROTHERhomefatherFalse
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" + ], + "text/plain": [ + " school sex age address famsize Pstatus Medu Fedu Mjob Fjob \\\n", + "0 GP F 18 U GT3 A 4 4 AT_HOME TEACHER \n", + "1 GP F 17 U GT3 T 1 1 AT_HOME OTHER \n", + "2 GP F 15 U LE3 T 1 1 AT_HOME OTHER \n", + "3 GP F 15 U GT3 T 4 2 HEALTH SERVICES \n", + "4 GP F 16 U GT3 T 3 3 OTHER OTHER \n", + "\n", + " reason guardian legal_drinker \n", + "0 course mother True \n", + "1 course father False \n", + "2 other mother False \n", + "3 home mother False \n", + "4 home father False " + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stud_alcoh['legal_drinker'] = stud_alcoh['age'].apply(majority)\n", + "stud_alcoh.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. Multiply every number of the dataset by 10. \n", + "##### I know this makes no sense, don't forget it is just an exercise" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def times10(x):\n", + " if type(x) is str:\n", + " return x\n", + " elif type(x) is numpy.int64:\n", + " return 10 * x\n", + " else:\n", + " return" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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schoolsexageaddressfamsizePstatusMeduFeduMjobFjobreasonguardianlegal_drinker
0GPF180UGT3A4040AT_HOMETEACHERcoursemotherNone
1GPF170UGT3T1010AT_HOMEOTHERcoursefatherNone
2GPF150ULE3T1010AT_HOMEOTHERothermotherNone
3GPF150UGT3T4020HEALTHSERVICEShomemotherNone
4GPF160UGT3T3030OTHEROTHERhomefatherNone
5GPM160ULE3T4030SERVICESOTHERreputationmotherNone
6GPM160ULE3T2020OTHEROTHERhomemotherNone
7GPF170UGT3A4040OTHERTEACHERhomemotherNone
8GPM150ULE3A3020SERVICESOTHERhomemotherNone
9GPM150UGT3T3040OTHEROTHERhomemotherNone
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" + ], + "text/plain": [ + " school sex age address famsize Pstatus Medu Fedu Mjob Fjob \\\n", + "0 GP F 180 U GT3 A 40 40 AT_HOME TEACHER \n", + "1 GP F 170 U GT3 T 10 10 AT_HOME OTHER \n", + "2 GP F 150 U LE3 T 10 10 AT_HOME OTHER \n", + "3 GP F 150 U GT3 T 40 20 HEALTH SERVICES \n", + "4 GP F 160 U GT3 T 30 30 OTHER OTHER \n", + "5 GP M 160 U LE3 T 40 30 SERVICES OTHER \n", + "6 GP M 160 U LE3 T 20 20 OTHER OTHER \n", + "7 GP F 170 U GT3 A 40 40 OTHER TEACHER \n", + "8 GP M 150 U LE3 A 30 20 SERVICES OTHER \n", + "9 GP M 150 U GT3 T 30 40 OTHER OTHER \n", + "\n", + " reason guardian legal_drinker \n", + "0 course mother None \n", + "1 course father None \n", + "2 other mother None \n", + "3 home mother None \n", + "4 home father None \n", + "5 reputation mother None \n", + "6 home mother None \n", + "7 home mother None \n", + "8 home mother None \n", + "9 home mother None " + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stud_alcoh.applymap(times10).head(10)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/04_Apply/Students_Alcohol_Consumption/Solutions.ipynb b/200 solved problems in Python/pandas/04_Apply/Students_Alcohol_Consumption/Solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..5faf2f4704697278352ce9121f030cf9288f3f25 --- /dev/null +++ b/200 solved problems in Python/pandas/04_Apply/Students_Alcohol_Consumption/Solutions.ipynb @@ -0,0 +1,1180 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Student Alcohol Consumption" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "This time you will download a dataset from the UCI.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://github.com/guipsamora/pandas_exercises/blob/master/04_Apply/Students_Alcohol_Consumption/student-mat.csv)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called df." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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0GPF18UGT3A44at_hometeacher...4341136566
1GPF17UGT3T11at_homeother...5331134556
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3GPF15UGT3T42healthservices...3221152151415
4GPF16UGT3T33otherother...432125461010
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" + ], + "text/plain": [ + " school sex age address famsize Pstatus Medu Fedu Mjob Fjob ... \\\n", + "0 GP F 18 U GT3 A 4 4 at_home teacher ... \n", + "1 GP F 17 U GT3 T 1 1 at_home other ... \n", + "2 GP F 15 U LE3 T 1 1 at_home other ... \n", + "3 GP F 15 U GT3 T 4 2 health services ... \n", + "4 GP F 16 U GT3 T 3 3 other other ... \n", + "\n", + " famrel freetime goout Dalc Walc health absences G1 G2 G3 \n", + "0 4 3 4 1 1 3 6 5 6 6 \n", + "1 5 3 3 1 1 3 4 5 5 6 \n", + "2 4 3 2 2 3 3 10 7 8 10 \n", + "3 3 2 2 1 1 5 2 15 14 15 \n", + "4 4 3 2 1 2 5 4 6 10 10 \n", + "\n", + "[5 rows x 33 columns]" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. For the purpose of this exercise slice the dataframe from 'school' until the 'guardian' column" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " school sex age address famsize Pstatus Medu Fedu Mjob Fjob \\\n", + "0 GP F 18 U GT3 A 4 4 at_home teacher \n", + "1 GP F 17 U GT3 T 1 1 at_home other \n", + "2 GP F 15 U LE3 T 1 1 at_home other \n", + "3 GP F 15 U GT3 T 4 2 health services \n", + "4 GP F 16 U GT3 T 3 3 other other \n", + "\n", + " reason guardian \n", + "0 course mother \n", + "1 course father \n", + "2 other mother \n", + "3 home mother \n", + "4 home father " + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Create a lambda function that captalize strings." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Capitalize both Mjob and Fjob" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 TEACHER\n", + "1 OTHER\n", + "2 OTHER\n", + "3 SERVICES\n", + "4 OTHER\n", + "5 OTHER\n", + "6 OTHER\n", + "7 TEACHER\n", + "8 OTHER\n", + "9 OTHER\n", + "10 HEALTH\n", + "11 OTHER\n", + "12 SERVICES\n", + "13 OTHER\n", + "14 OTHER\n", + "15 OTHER\n", + "16 SERVICES\n", + "17 OTHER\n", + "18 SERVICES\n", + "19 OTHER\n", + "20 OTHER\n", + "21 HEALTH\n", + "22 OTHER\n", + "23 OTHER\n", + "24 HEALTH\n", + "25 SERVICES\n", + "26 OTHER\n", + "27 SERVICES\n", + "28 OTHER\n", + "29 TEACHER\n", + " ... \n", + "365 OTHER\n", + "366 SERVICES\n", + "367 SERVICES\n", + "368 SERVICES\n", + "369 TEACHER\n", + "370 SERVICES\n", + "371 SERVICES\n", + "372 AT_HOME\n", + "373 OTHER\n", + "374 OTHER\n", + "375 OTHER\n", + "376 OTHER\n", + "377 SERVICES\n", + "378 OTHER\n", + "379 OTHER\n", + "380 TEACHER\n", + "381 OTHER\n", + "382 SERVICES\n", + "383 SERVICES\n", + "384 OTHER\n", + "385 OTHER\n", + "386 AT_HOME\n", + "387 OTHER\n", + "388 SERVICES\n", + "389 OTHER\n", + "390 SERVICES\n", + "391 SERVICES\n", + "392 OTHER\n", + "393 OTHER\n", + "394 AT_HOME\n", + "Name: Fjob, dtype: object" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Print the last elements of the data set." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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391MSM17ULE3T31servicesservicescoursemother
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" + ], + "text/plain": [ + " school sex age address famsize Pstatus Medu Fedu Mjob Fjob \\\n", + "390 MS M 20 U LE3 A 2 2 services services \n", + "391 MS M 17 U LE3 T 3 1 services services \n", + "392 MS M 21 R GT3 T 1 1 other other \n", + "393 MS M 18 R LE3 T 3 2 services other \n", + "394 MS M 19 U LE3 T 1 1 other at_home \n", + "\n", + " reason guardian \n", + "390 course other \n", + "391 course mother \n", + "392 course other \n", + "393 course mother \n", + "394 course father " + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Did you notice the original dataframe is still lowercase? Why is that? Fix it and captalize Mjob and Fjob." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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391MSM17ULE3T31SERVICESSERVICEScoursemother
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393MSM18RLE3T32SERVICESOTHERcoursemother
394MSM19ULE3T11OTHERAT_HOMEcoursefather
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" + ], + "text/plain": [ + " school sex age address famsize Pstatus Medu Fedu Mjob Fjob \\\n", + "390 MS M 20 U LE3 A 2 2 SERVICES SERVICES \n", + "391 MS M 17 U LE3 T 3 1 SERVICES SERVICES \n", + "392 MS M 21 R GT3 T 1 1 OTHER OTHER \n", + "393 MS M 18 R LE3 T 3 2 SERVICES OTHER \n", + "394 MS M 19 U LE3 T 1 1 OTHER AT_HOME \n", + "\n", + " reason guardian \n", + "390 course other \n", + "391 course mother \n", + "392 course other \n", + "393 course mother \n", + "394 course father " + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. Create a function called majority that return a boolean value to a new column called legal_drinker" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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0GPF18UGT3A44AT_HOMETEACHERcoursemotherTrue
1GPF17UGT3T11AT_HOMEOTHERcoursefatherFalse
2GPF15ULE3T11AT_HOMEOTHERothermotherFalse
3GPF15UGT3T42HEALTHSERVICEShomemotherFalse
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" + ], + "text/plain": [ + " school sex age address famsize Pstatus Medu Fedu Mjob Fjob \\\n", + "0 GP F 18 U GT3 A 4 4 AT_HOME TEACHER \n", + "1 GP F 17 U GT3 T 1 1 AT_HOME OTHER \n", + "2 GP F 15 U LE3 T 1 1 AT_HOME OTHER \n", + "3 GP F 15 U GT3 T 4 2 HEALTH SERVICES \n", + "4 GP F 16 U GT3 T 3 3 OTHER OTHER \n", + "\n", + " reason guardian legal_drinker \n", + "0 course mother True \n", + "1 course father False \n", + "2 other mother False \n", + "3 home mother False \n", + "4 home father False " + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. Multiply every number of the dataset by 10. \n", + "##### I know this makes no sense, don't forget it is just an exercise" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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schoolsexageaddressfamsizePstatusMeduFeduMjobFjobreasonguardianlegal_drinker
0GPF180UGT3A4040AT_HOMETEACHERcoursemotherNone
1GPF170UGT3T1010AT_HOMEOTHERcoursefatherNone
2GPF150ULE3T1010AT_HOMEOTHERothermotherNone
3GPF150UGT3T4020HEALTHSERVICEShomemotherNone
4GPF160UGT3T3030OTHEROTHERhomefatherNone
5GPM160ULE3T4030SERVICESOTHERreputationmotherNone
6GPM160ULE3T2020OTHEROTHERhomemotherNone
7GPF170UGT3A4040OTHERTEACHERhomemotherNone
8GPM150ULE3A3020SERVICESOTHERhomemotherNone
9GPM150UGT3T3040OTHEROTHERhomemotherNone
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" + ], + "text/plain": [ + " school sex age address famsize Pstatus Medu Fedu Mjob Fjob \\\n", + "0 GP F 180 U GT3 A 40 40 AT_HOME TEACHER \n", + "1 GP F 170 U GT3 T 10 10 AT_HOME OTHER \n", + "2 GP F 150 U LE3 T 10 10 AT_HOME OTHER \n", + "3 GP F 150 U GT3 T 40 20 HEALTH SERVICES \n", + "4 GP F 160 U GT3 T 30 30 OTHER OTHER \n", + "5 GP M 160 U LE3 T 40 30 SERVICES OTHER \n", + "6 GP M 160 U LE3 T 20 20 OTHER OTHER \n", + "7 GP F 170 U GT3 A 40 40 OTHER TEACHER \n", + "8 GP M 150 U LE3 A 30 20 SERVICES OTHER \n", + "9 GP M 150 U GT3 T 30 40 OTHER OTHER \n", + "\n", + " reason guardian legal_drinker \n", + "0 course mother None \n", + "1 course father None \n", + "2 other mother None \n", + "3 home mother None \n", + "4 home father None \n", + "5 reputation mother None \n", + "6 home mother None \n", + "7 home mother None \n", + "8 home mother None \n", + "9 home mother None " + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/04_Apply/Students_Alcohol_Consumption/student-mat.csv b/200 solved problems in Python/pandas/04_Apply/Students_Alcohol_Consumption/student-mat.csv new file mode 100644 index 0000000000000000000000000000000000000000..6c6518295fec70396b9854b0e57dff383e7c8584 --- /dev/null +++ b/200 solved problems in Python/pandas/04_Apply/Students_Alcohol_Consumption/student-mat.csv @@ -0,0 +1,396 @@ 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+MS,M,18,R,LE3,T,3,2,services,other,course,mother,3,1,0,no,no,no,no,no,yes,yes,no,4,4,1,3,4,5,0,11,12,10 +MS,M,19,U,LE3,T,1,1,other,at_home,course,father,1,1,0,no,no,no,no,yes,yes,yes,no,3,2,3,3,3,5,5,8,9,9 diff --git a/200 solved problems in Python/pandas/04_Apply/US_Crime_Rates/Exercises.ipynb b/200 solved problems in Python/pandas/04_Apply/US_Crime_Rates/Exercises.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..e8fbffa2cae8379d0440673e01175c6db9fbf08c --- /dev/null +++ b/200 solved problems in Python/pandas/04_Apply/US_Crime_Rates/Exercises.ipynb @@ -0,0 +1,179 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# United States - Crime Rates - 1960 - 2014" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "This time you will create a data \n", + "\n", + "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/04_Apply/US_Crime_Rates/US_Crime_Rates_1960_2014.csv). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called crime." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. What is the type of the columns?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Have you noticed that the type of Year is int64. But pandas has a different type to work with Time Series. Let's see it now.\n", + "\n", + "### Step 5. Convert the type of the column Year to datetime64" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Set the Year column as the index of the dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Delete the Total column" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Group the year by decades and sum the values\n", + "\n", + "#### Pay attention to the Population column number, summing this column is a mistake" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. What is the mos dangerous decade to live in the US?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/04_Apply/US_Crime_Rates/Exercises_with_solutions.ipynb b/200 solved problems in Python/pandas/04_Apply/US_Crime_Rates/Exercises_with_solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..81924c1d709e79a7b9af47105830e02bf999da4b --- /dev/null +++ b/200 solved problems in Python/pandas/04_Apply/US_Crime_Rates/Exercises_with_solutions.ipynb @@ -0,0 +1,800 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# United States - Crime Rates - 1960 - 2014" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "This time you will create a data \n", + "\n", + "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/04_Apply/US_Crime_Rates/US_Crime_Rates_1960_2014.csv). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called crime." + ] + }, + { + "cell_type": "code", + "execution_count": 265, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearPopulationTotalViolentPropertyMurderForcible_RapeRobberyAggravated_assaultBurglaryLarceny_TheftVehicle_Theft
01960179323175338420028846030957009110171901078401543209121001855400328200
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" + ], + "text/plain": [ + " Year Population Total Violent Property Murder Forcible_Rape \\\n", + "0 1960 179323175 3384200 288460 3095700 9110 17190 \n", + "1 1961 182992000 3488000 289390 3198600 8740 17220 \n", + "2 1962 185771000 3752200 301510 3450700 8530 17550 \n", + "3 1963 188483000 4109500 316970 3792500 8640 17650 \n", + "4 1964 191141000 4564600 364220 4200400 9360 21420 \n", + "\n", + " Robbery Aggravated_assault Burglary Larceny_Theft Vehicle_Theft \n", + "0 107840 154320 912100 1855400 328200 \n", + "1 106670 156760 949600 1913000 336000 \n", + "2 110860 164570 994300 2089600 366800 \n", + "3 116470 174210 1086400 2297800 408300 \n", + "4 130390 203050 1213200 2514400 472800 " + ] + }, + "execution_count": 265, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "url = \"https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/04_Apply/US_Crime_Rates/US_Crime_Rates_1960_2014.csv\"\n", + "crime = pd.read_csv(url)\n", + "crime.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. What is the type of the columns?" + ] + }, + { + "cell_type": "code", + "execution_count": 266, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 55 entries, 0 to 54\n", + "Data columns (total 12 columns):\n", + "Year 55 non-null int64\n", + "Population 55 non-null int64\n", + "Total 55 non-null int64\n", + "Violent 55 non-null int64\n", + "Property 55 non-null int64\n", + "Murder 55 non-null int64\n", + "Forcible_Rape 55 non-null int64\n", + "Robbery 55 non-null int64\n", + "Aggravated_assault 55 non-null int64\n", + "Burglary 55 non-null int64\n", + "Larceny_Theft 55 non-null int64\n", + "Vehicle_Theft 55 non-null int64\n", + "dtypes: int64(12)\n", + "memory usage: 5.2 KB\n" + ] + } + ], + "source": [ + "crime.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Have you noticed that the type of Year is int64. But pandas has a different type to work with Time Series. Let's see it now.\n", + "\n", + "### Step 5. Convert the type of the column Year to datetime64" + ] + }, + { + "cell_type": "code", + "execution_count": 267, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 55 entries, 0 to 54\n", + "Data columns (total 12 columns):\n", + "Year 55 non-null datetime64[ns]\n", + "Population 55 non-null int64\n", + "Total 55 non-null int64\n", + "Violent 55 non-null int64\n", + "Property 55 non-null int64\n", + "Murder 55 non-null int64\n", + "Forcible_Rape 55 non-null int64\n", + "Robbery 55 non-null int64\n", + "Aggravated_assault 55 non-null int64\n", + "Burglary 55 non-null int64\n", + "Larceny_Theft 55 non-null int64\n", + "Vehicle_Theft 55 non-null int64\n", + "dtypes: datetime64[ns](1), int64(11)\n", + "memory usage: 5.2 KB\n" + ] + } + ], + "source": [ + "# pd.to_datetime(crime)\n", + "crime.Year = pd.to_datetime(crime.Year, format='%Y')\n", + "crime.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Set the Year column as the index of the dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 268, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PopulationTotalViolentPropertyMurderForcible_RapeRobberyAggravated_assaultBurglaryLarceny_TheftVehicle_Theft
Year
1960-01-01179323175338420028846030957009110171901078401543209121001855400328200
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" + ], + "text/plain": [ + " Population Total Violent Property Murder Forcible_Rape \\\n", + "Year \n", + "1960-01-01 179323175 3384200 288460 3095700 9110 17190 \n", + "1961-01-01 182992000 3488000 289390 3198600 8740 17220 \n", + "1962-01-01 185771000 3752200 301510 3450700 8530 17550 \n", + "1963-01-01 188483000 4109500 316970 3792500 8640 17650 \n", + "1964-01-01 191141000 4564600 364220 4200400 9360 21420 \n", + "\n", + " Robbery Aggravated_assault Burglary Larceny_Theft \\\n", + "Year \n", + "1960-01-01 107840 154320 912100 1855400 \n", + "1961-01-01 106670 156760 949600 1913000 \n", + "1962-01-01 110860 164570 994300 2089600 \n", + "1963-01-01 116470 174210 1086400 2297800 \n", + "1964-01-01 130390 203050 1213200 2514400 \n", + "\n", + " Vehicle_Theft \n", + "Year \n", + "1960-01-01 328200 \n", + "1961-01-01 336000 \n", + "1962-01-01 366800 \n", + "1963-01-01 408300 \n", + "1964-01-01 472800 " + ] + }, + "execution_count": 268, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "crime = crime.set_index('Year', drop = True)\n", + "crime.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Delete the Total column" + ] + }, + { + "cell_type": "code", + "execution_count": 269, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PopulationViolentPropertyMurderForcible_RapeRobberyAggravated_assaultBurglaryLarceny_TheftVehicle_Theft
Year
1960-01-0117932317528846030957009110171901078401543209121001855400328200
1961-01-0118299200028939031986008740172201066701567609496001913000336000
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1964-01-01191141000364220420040093602142013039020305012132002514400472800
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" + ], + "text/plain": [ + " Population Violent Property Murder Forcible_Rape Robbery \\\n", + "Year \n", + "1960-01-01 179323175 288460 3095700 9110 17190 107840 \n", + "1961-01-01 182992000 289390 3198600 8740 17220 106670 \n", + "1962-01-01 185771000 301510 3450700 8530 17550 110860 \n", + "1963-01-01 188483000 316970 3792500 8640 17650 116470 \n", + "1964-01-01 191141000 364220 4200400 9360 21420 130390 \n", + "\n", + " Aggravated_assault Burglary Larceny_Theft Vehicle_Theft \n", + "Year \n", + "1960-01-01 154320 912100 1855400 328200 \n", + "1961-01-01 156760 949600 1913000 336000 \n", + "1962-01-01 164570 994300 2089600 366800 \n", + "1963-01-01 174210 1086400 2297800 408300 \n", + "1964-01-01 203050 1213200 2514400 472800 " + ] + }, + "execution_count": 269, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "del crime['Total']\n", + "crime.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Group the year by decades and sum the values\n", + "\n", + "#### Pay attention to the Population column number, summing this column is a mistake" + ] + }, + { + "cell_type": "code", + "execution_count": 270, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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2010318857056607201744095950728674210591749809376414210125170304016983569080
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" + ], + "text/plain": [ + " Population Violent Property Murder Forcible_Rape Robbery \\\n", + "1960 201385000 4134930 45160900 106180 236720 1633510 \n", + "1970 220099000 9607930 91383800 192230 554570 4159020 \n", + "1980 248239000 14074328 117048900 206439 865639 5383109 \n", + "1990 272690813 17527048 119053499 211664 998827 5748930 \n", + "2000 307006550 13968056 100944369 163068 922499 4230366 \n", + "2010 318857056 6072017 44095950 72867 421059 1749809 \n", + "\n", + " Aggravated_assault Burglary Larceny_Theft Vehicle_Theft \n", + "1960 2158520 13321100 26547700 5292100 \n", + "1970 4702120 28486000 53157800 9739900 \n", + "1980 7619130 33073494 72040253 11935411 \n", + "1990 10568963 26750015 77679366 14624418 \n", + "2000 8652124 21565176 67970291 11412834 \n", + "2010 3764142 10125170 30401698 3569080 " + ] + }, + "execution_count": 270, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# To learn more about .resample (https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.resample.html)\n", + "# To learn more about Offset Aliases (http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases)\n", + "\n", + "# Uses resample to sum each decade\n", + "crimes = crime.resample('10AS').sum()\n", + "\n", + "# Uses resample to get the max value only for the \"Population\" column\n", + "population = crime['Population'].resample('10AS').max()\n", + "\n", + "# Updating the \"Population\" column\n", + "crimes['Population'] = population\n", + "\n", + "crimes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. What is the mos dangerous decade to live in the US?" + ] + }, + { + "cell_type": "code", + "execution_count": 276, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Population 2010\n", + "Violent 1990\n", + "Property 1990\n", + "Murder 1990\n", + "Forcible_Rape 1990\n", + "Robbery 1990\n", + "Aggravated_assault 1990\n", + "Burglary 1980\n", + "Larceny_Theft 1990\n", + "Vehicle_Theft 1990\n", + "dtype: int64" + ] + }, + "execution_count": 276, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# apparently the 90s was a pretty dangerous time in the US\n", + "crime.idxmax(0)" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/04_Apply/US_Crime_Rates/Solutions.ipynb b/200 solved problems in Python/pandas/04_Apply/US_Crime_Rates/Solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..8f5234f00995cbb48a5809ddebb82f3d25f7b747 --- /dev/null +++ b/200 solved problems in Python/pandas/04_Apply/US_Crime_Rates/Solutions.ipynb @@ -0,0 +1,763 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# United States - Crime Rates - 1960 - 2014" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "This time you will create a data \n", + "\n", + "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/04_Apply/US_Crime_Rates/US_Crime_Rates_1960_2014.csv). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called crime." + ] + }, + { + "cell_type": "code", + "execution_count": 265, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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YearPopulationTotalViolentPropertyMurderForcible_RapeRobberyAggravated_assaultBurglaryLarceny_TheftVehicle_Theft
01960179323175338420028846030957009110171901078401543209121001855400328200
11961182992000348800028939031986008740172201066701567609496001913000336000
21962185771000375220030151034507008530175501108601645709943002089600366800
319631884830004109500316970379250086401765011647017421010864002297800408300
419641911410004564600364220420040093602142013039020305012132002514400472800
\n", + "
" + ], + "text/plain": [ + " Year Population Total Violent Property Murder Forcible_Rape \\\n", + "0 1960 179323175 3384200 288460 3095700 9110 17190 \n", + "1 1961 182992000 3488000 289390 3198600 8740 17220 \n", + "2 1962 185771000 3752200 301510 3450700 8530 17550 \n", + "3 1963 188483000 4109500 316970 3792500 8640 17650 \n", + "4 1964 191141000 4564600 364220 4200400 9360 21420 \n", + "\n", + " Robbery Aggravated_assault Burglary Larceny_Theft Vehicle_Theft \n", + "0 107840 154320 912100 1855400 328200 \n", + "1 106670 156760 949600 1913000 336000 \n", + "2 110860 164570 994300 2089600 366800 \n", + "3 116470 174210 1086400 2297800 408300 \n", + "4 130390 203050 1213200 2514400 472800 " + ] + }, + "execution_count": 265, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. What is the type of the columns?" + ] + }, + { + "cell_type": "code", + "execution_count": 266, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 55 entries, 0 to 54\n", + "Data columns (total 12 columns):\n", + "Year 55 non-null int64\n", + "Population 55 non-null int64\n", + "Total 55 non-null int64\n", + "Violent 55 non-null int64\n", + "Property 55 non-null int64\n", + "Murder 55 non-null int64\n", + "Forcible_Rape 55 non-null int64\n", + "Robbery 55 non-null int64\n", + "Aggravated_assault 55 non-null int64\n", + "Burglary 55 non-null int64\n", + "Larceny_Theft 55 non-null int64\n", + "Vehicle_Theft 55 non-null int64\n", + "dtypes: int64(12)\n", + "memory usage: 5.2 KB\n" + ] + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Have you noticed that the type of Year is int64. But pandas has a different type to work with Time Series. Let's see it now.\n", + "\n", + "### Step 5. Convert the type of the column Year to datetime64" + ] + }, + { + "cell_type": "code", + "execution_count": 267, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 55 entries, 0 to 54\n", + "Data columns (total 12 columns):\n", + "Year 55 non-null datetime64[ns]\n", + "Population 55 non-null int64\n", + "Total 55 non-null int64\n", + "Violent 55 non-null int64\n", + "Property 55 non-null int64\n", + "Murder 55 non-null int64\n", + "Forcible_Rape 55 non-null int64\n", + "Robbery 55 non-null int64\n", + "Aggravated_assault 55 non-null int64\n", + "Burglary 55 non-null int64\n", + "Larceny_Theft 55 non-null int64\n", + "Vehicle_Theft 55 non-null int64\n", + "dtypes: datetime64[ns](1), int64(11)\n", + "memory usage: 5.2 KB\n" + ] + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Set the Year column as the index of the dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 268, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PopulationTotalViolentPropertyMurderForcible_RapeRobberyAggravated_assaultBurglaryLarceny_TheftVehicle_Theft
Year
1960-01-01179323175338420028846030957009110171901078401543209121001855400328200
1961-01-01182992000348800028939031986008740172201066701567609496001913000336000
1962-01-01185771000375220030151034507008530175501108601645709943002089600366800
1963-01-011884830004109500316970379250086401765011647017421010864002297800408300
1964-01-011911410004564600364220420040093602142013039020305012132002514400472800
\n", + "
" + ], + "text/plain": [ + " Population Total Violent Property Murder Forcible_Rape \\\n", + "Year \n", + "1960-01-01 179323175 3384200 288460 3095700 9110 17190 \n", + "1961-01-01 182992000 3488000 289390 3198600 8740 17220 \n", + "1962-01-01 185771000 3752200 301510 3450700 8530 17550 \n", + "1963-01-01 188483000 4109500 316970 3792500 8640 17650 \n", + "1964-01-01 191141000 4564600 364220 4200400 9360 21420 \n", + "\n", + " Robbery Aggravated_assault Burglary Larceny_Theft \\\n", + "Year \n", + "1960-01-01 107840 154320 912100 1855400 \n", + "1961-01-01 106670 156760 949600 1913000 \n", + "1962-01-01 110860 164570 994300 2089600 \n", + "1963-01-01 116470 174210 1086400 2297800 \n", + "1964-01-01 130390 203050 1213200 2514400 \n", + "\n", + " Vehicle_Theft \n", + "Year \n", + "1960-01-01 328200 \n", + "1961-01-01 336000 \n", + "1962-01-01 366800 \n", + "1963-01-01 408300 \n", + "1964-01-01 472800 " + ] + }, + "execution_count": 268, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Delete the Total column" + ] + }, + { + "cell_type": "code", + "execution_count": 269, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PopulationViolentPropertyMurderForcible_RapeRobberyAggravated_assaultBurglaryLarceny_TheftVehicle_Theft
Year
1960-01-0117932317528846030957009110171901078401543209121001855400328200
1961-01-0118299200028939031986008740172201066701567609496001913000336000
1962-01-0118577100030151034507008530175501108601645709943002089600366800
1963-01-01188483000316970379250086401765011647017421010864002297800408300
1964-01-01191141000364220420040093602142013039020305012132002514400472800
\n", + "
" + ], + "text/plain": [ + " Population Violent Property Murder Forcible_Rape Robbery \\\n", + "Year \n", + "1960-01-01 179323175 288460 3095700 9110 17190 107840 \n", + "1961-01-01 182992000 289390 3198600 8740 17220 106670 \n", + "1962-01-01 185771000 301510 3450700 8530 17550 110860 \n", + "1963-01-01 188483000 316970 3792500 8640 17650 116470 \n", + "1964-01-01 191141000 364220 4200400 9360 21420 130390 \n", + "\n", + " Aggravated_assault Burglary Larceny_Theft Vehicle_Theft \n", + "Year \n", + "1960-01-01 154320 912100 1855400 328200 \n", + "1961-01-01 156760 949600 1913000 336000 \n", + "1962-01-01 164570 994300 2089600 366800 \n", + "1963-01-01 174210 1086400 2297800 408300 \n", + "1964-01-01 203050 1213200 2514400 472800 " + ] + }, + "execution_count": 269, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Group the year by decades and sum the values\n", + "\n", + "#### Pay attention to the Population column number, summing this column is a mistake" + ] + }, + { + "cell_type": "code", + "execution_count": 270, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PopulationViolentPropertyMurderForcible_RapeRobberyAggravated_assaultBurglaryLarceny_TheftVehicle_Theft
19602013850004134930451609001061802367201633510215852013321100265477005292100
19702200990009607930913838001922305545704159020470212028486000531578009739900
19802482390001407432811704890020643986563953831097619130330734947204025311935411
199027269081317527048119053499211664998827574893010568963267500157767936614624418
20003070065501396805610094436916306892249942303668652124215651766797029111412834
2010318857056607201744095950728674210591749809376414210125170304016983569080
\n", + "
" + ], + "text/plain": [ + " Population Violent Property Murder Forcible_Rape Robbery \\\n", + "1960 201385000 4134930 45160900 106180 236720 1633510 \n", + "1970 220099000 9607930 91383800 192230 554570 4159020 \n", + "1980 248239000 14074328 117048900 206439 865639 5383109 \n", + "1990 272690813 17527048 119053499 211664 998827 5748930 \n", + "2000 307006550 13968056 100944369 163068 922499 4230366 \n", + "2010 318857056 6072017 44095950 72867 421059 1749809 \n", + "\n", + " Aggravated_assault Burglary Larceny_Theft Vehicle_Theft \n", + "1960 2158520 13321100 26547700 5292100 \n", + "1970 4702120 28486000 53157800 9739900 \n", + "1980 7619130 33073494 72040253 11935411 \n", + "1990 10568963 26750015 77679366 14624418 \n", + "2000 8652124 21565176 67970291 11412834 \n", + "2010 3764142 10125170 30401698 3569080 " + ] + }, + "execution_count": 270, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. What is the mos dangerous decade to live in the US?" + ] + }, + { + "cell_type": "code", + "execution_count": 276, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Population 2010\n", + "Violent 1990\n", + "Property 1990\n", + "Murder 1990\n", + "Forcible_Rape 1990\n", + "Robbery 1990\n", + "Aggravated_assault 1990\n", + "Burglary 1980\n", + "Larceny_Theft 1990\n", + "Vehicle_Theft 1990\n", + "dtype: int64" + ] + }, + "execution_count": 276, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/04_Apply/US_Crime_Rates/US_Crime_Rates_1960_2014.csv b/200 solved problems in Python/pandas/04_Apply/US_Crime_Rates/US_Crime_Rates_1960_2014.csv new file mode 100644 index 0000000000000000000000000000000000000000..958a86c78bd108a86705e2054abc183011051421 --- /dev/null +++ b/200 solved problems in Python/pandas/04_Apply/US_Crime_Rates/US_Crime_Rates_1960_2014.csv @@ -0,0 +1 @@ +Year,Population,Total,Violent,Property,Murder,Forcible_Rape,Robbery,Aggravated_assault,Burglary,Larceny_Theft,Vehicle_Theft 1960,179323175,3384200,288460,3095700,9110,17190,107840,154320,912100,1855400,328200 1961,182992000,3488000,289390,3198600,8740,17220,106670,156760,949600,1913000,336000 1962,185771000,3752200,301510,3450700,8530,17550,110860,164570,994300,2089600,366800 1963,188483000,4109500,316970,3792500,8640,17650,116470,174210,1086400,2297800,408300 1964,191141000,4564600,364220,4200400,9360,21420,130390,203050,1213200,2514400,472800 1965,193526000,4739400,387390,4352000,9960,23410,138690,215330,1282500,2572600,496900 1966,195576000,5223500,430180,4793300,11040,25820,157990,235330,1410100,2822000,561200 1967,197457000,5903400,499930,5403500,12240,27620,202910,257160,1632100,3111600,659800 1968,199399000,6720200,595010,6125200,13800,31670,262840,286700,1858900,3482700,783600 1969,201385000,7410900,661870,6749000,14760,37170,298850,311090,1981900,3888600,878500 1970,203235298,8098000,738820,7359200,16000,37990,349860,334970,2205000,4225800,928400 1971,206212000,8588200,816500,7771700,17780,42260,387700,368760,2399300,4424200,948200 1972,208230000,8248800,834900,7413900,18670,46850,376290,393090,2375500,4151200,887200 1973,209851000,8718100,875910,7842200,19640,51400,384220,420650,2565500,4347900,928800 1974,211392000,10253400,974720,9278700,20710,55400,442400,456210,3039200,5262500,977100 1975,213124000,11292400,1039710,10252700,20510,56090,470500,492620,3265300,5977700,1009600 1976,214659000,11349700,1004210,10345500,18780,57080,427810,500530,3108700,6270800,966000 1977,216332000,10984500,1029580,9955000,19120,63500,412610,534350,3071500,5905700,977700 1978,218059000,11209000,1085550,10123400,19560,67610,426930,571460,3128300,5991000,1004100 1979,220099000,12249500,1208030,11041500,21460,76390,480700,629480,3327700,6601000,1112800 1980,225349264,13408300,1344520,12063700,23040,82990,565840,672650,3795200,7136900,1131700 1981,229146000,13423800,1361820,12061900,22520,82500,592910,663900,3779700,7194400,1087800 1982,231534000,12974400,1322390,11652000,21010,78770,553130,669480,3447100,7142500,1062400 1983,233981000,12108600,1258090,10850500,19310,78920,506570,653290,3129900,6712800,1007900 1984,236158000,11881800,1273280,10608500,18690,84230,485010,685350,2984400,6591900,1032200 1985,238740000,12431400,1328800,11102600,18980,88670,497870,723250,3073300,6926400,1102900 1986,240132887,13211869,1489169,11722700,20613,91459,542775,834322,3241410,7257153,1224137 1987,242282918,13508700,1483999,12024700,20096,91110,517704,855088,3236184,7499900,1288674 1988,245807000,13923100,1566220,12356900,20680,92490,542970,910090,3218100,7705900,1432900 1989,248239000,14251400,1646040,12605400,21500,94500,578330,951710,3168200,7872400,1564800 1990,248709873,14475600,1820130,12655500,23440,102560,639270,1054860,3073900,7945700,1635900 1991,252177000,14872900,1911770,12961100,24700,106590,687730,1092740,3157200,8142200,1661700 1992,255082000,14438200,1932270,12505900,23760,109060,672480,1126970,2979900,7915200,1610800 1993,257908000,14144800,1926020,12218800,24530,106010,659870,1135610,2834800,7820900,1563100 1994,260341000,13989500,1857670,12131900,23330,102220,618950,1113180,2712800,7879800,1539300 1995,262755000,13862700,1798790,12063900,21610,97470,580510,1099210,2593800,7997700,1472400 1996,265228572,13493863,1688540,11805300,19650,96250,535590,1037050,2506400,7904700,1394200 1997,267637000,13194571,1634770,11558175,18208,96153,498534,1023201,2460526,7743760,1354189 1998,270296000,12475634,1531044,10944590,16914,93103,446625,974402,2329950,7373886,1240754 1999,272690813,11634378,1426044,10208334,15522,89411,409371,911740,2100739,6955520,1152075 2000,281421906,11608072,1425486,10182586,15586,90178,408016,911706,2050992,6971590,1160002 2001,285317559,11876669,1439480,10437480,16037,90863,423557,909023,2116531,7092267,1228391 2002,287973924,11878954,1423677,10455277,16229,95235,420806,891407,2151252,7057370,1246646 2003,290690788,11826538,1383676,10442862,16528,93883,414235,859030,2154834,7026802,1261226 2004,293656842,11679474,1360088,10319386,16148,95089,401470,847381,2144446,6937089,1237851 2005,296507061,11565499,1390745,10174754,16740,94347,417438,862220,2155448,6783447,1235859 2006,299398484,11401511,1418043,9983568,17030,92757,447403,860853,2183746,6607013,1192809 2007,301621157,11251828,1408337,9843481,16929,90427,445125,855856,2176140,6568572,1095769 2008,304374846,11160543,1392628,9767915,16442,90479,443574,842134,2228474,6588046,958629 2009,307006550,10762956,1325896,9337060,15399,89241,408742,812514,2203313,6338095,795652 2010,309330219,10363873,1251248,9112625,14772,85593,369089,781844,2168457,6204601,739565 2011,311587816,10258774,1206031,9052743,14661,84175,354772,752423,2185140,6151095,716508 2012,313873685,10219059,1217067,9001992,14866,85141,355051,762009,2109932,6168874,723186 2013,316497531,9850445,1199684,8650761,14319,82109,345095,726575,1931835,6018632,700294 2014,318857056,9475816,1197987,8277829,14249,84041,325802,741291,1729806,5858496,689527 \ No newline at end of file diff --git a/200 solved problems in Python/pandas/05_Merge/Auto_MPG/Exercises.ipynb b/200 solved problems in Python/pandas/05_Merge/Auto_MPG/Exercises.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..0e4ea8d870f647179d2cf924600e591251496966 --- /dev/null +++ b/200 solved problems in Python/pandas/05_Merge/Auto_MPG/Exercises.ipynb @@ -0,0 +1,155 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# MPG Cars" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "The following exercise utilizes data from [UC Irvine Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Auto+MPG)\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the first dataset [cars1](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/Merge/Auto_MPG/cars1.csv) and [cars2](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/Merge/Auto_MPG/cars2.csv). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " ### Step 3. Assign each to a variable called cars1 and cars2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Ops it seems our first dataset has some unnamed blank columns, fix cars1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. What is the number of observations in each dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Join cars1 and cars2 into a single DataFrame called cars" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Ops there is a column missing, called owners. Create a random number Series from 15,000 to 73,000." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Add the column owners to cars" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/05_Merge/Auto_MPG/Exercises_with_solutions.ipynb b/200 solved problems in Python/pandas/05_Merge/Auto_MPG/Exercises_with_solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..549a8ad34638e8e69086474506d7823ebb5a06af --- /dev/null +++ b/200 solved problems in Python/pandas/05_Merge/Auto_MPG/Exercises_with_solutions.ipynb @@ -0,0 +1,1391 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# MPG Cars" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "The following exercise utilizes data from [UC Irvine Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Auto+MPG)\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the first dataset [cars1](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/Merge/Auto_MPG/cars1.csv) and [cars2](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/Merge/Auto_MPG/cars2.csv). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " ### Step 3. Assign each to a to a variable called cars1 and cars2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " mpg cylinders displacement horsepower weight acceleration model \\\n", + "0 18.0 8 307 130 3504 12.0 70 \n", + "1 15.0 8 350 165 3693 11.5 70 \n", + "2 18.0 8 318 150 3436 11.0 70 \n", + "3 16.0 8 304 150 3433 12.0 70 \n", + "4 17.0 8 302 140 3449 10.5 70 \n", + "\n", + " origin car Unnamed: 9 Unnamed: 10 Unnamed: 11 \\\n", + "0 1 chevrolet chevelle malibu NaN NaN NaN \n", + "1 1 buick skylark 320 NaN NaN NaN \n", + "2 1 plymouth satellite NaN NaN NaN \n", + "3 1 amc rebel sst NaN NaN NaN \n", + "4 1 ford torino NaN NaN NaN \n", + "\n", + " Unnamed: 12 Unnamed: 13 \n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + " mpg cylinders displacement horsepower weight acceleration model \\\n", + "0 33.0 4 91 53 1795 17.4 76 \n", + "1 20.0 6 225 100 3651 17.7 76 \n", + "2 18.0 6 250 78 3574 21.0 76 \n", + "3 18.5 6 250 110 3645 16.2 76 \n", + "4 17.5 6 258 95 3193 17.8 76 \n", + "\n", + " origin car \n", + "0 3 honda civic \n", + "1 1 dodge aspen se \n", + "2 1 ford granada ghia \n", + "3 1 pontiac ventura sj \n", + "4 1 amc pacer d/l \n" + ] + } + ], + "source": [ + "cars1 = pd.read_csv(\"https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/Merge/Auto_MPG/cars1.csv\")\n", + "cars2 = pd.read_csv(\"https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/Merge/Auto_MPG/cars2.csv\")\n", + "\n", + "print cars1.head()\n", + "print cars2.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Ops it seems our first dataset has some unnamed blank columns, fix cars1" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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mpgcylindersdisplacementhorsepowerweightaccelerationmodelorigincar
018.08307130350412.0701chevrolet chevelle malibu
115.08350165369311.5701buick skylark 320
218.08318150343611.0701plymouth satellite
316.08304150343312.0701amc rebel sst
417.08302140344910.5701ford torino
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" + ], + "text/plain": [ + " mpg cylinders displacement horsepower weight acceleration model \\\n", + "0 18.0 8 307 130 3504 12.0 70 \n", + "1 15.0 8 350 165 3693 11.5 70 \n", + "2 18.0 8 318 150 3436 11.0 70 \n", + "3 16.0 8 304 150 3433 12.0 70 \n", + "4 17.0 8 302 140 3449 10.5 70 \n", + "\n", + " origin car \n", + "0 1 chevrolet chevelle malibu \n", + "1 1 buick skylark 320 \n", + "2 1 plymouth satellite \n", + "3 1 amc rebel sst \n", + "4 1 ford torino " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cars1 = cars1.loc[:, \"mpg\":\"car\"]\n", + "cars1.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. What is the number of observations in each dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(198, 9)\n", + "(200, 9)\n" + ] + } + ], + "source": [ + "print cars1.shape\n", + "print cars2.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Join cars1 and cars2 into a single DataFrame called cars" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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mpgcylindersdisplacementhorsepowerweightaccelerationmodelorigincar
018.08307130350412.0701chevrolet chevelle malibu
115.08350165369311.5701buick skylark 320
218.08318150343611.0701plymouth satellite
316.08304150343312.0701amc rebel sst
417.08302140344910.5701ford torino
515.08429198434110.0701ford galaxie 500
614.0845422043549.0701chevrolet impala
714.0844021543128.5701plymouth fury iii
814.08455225442510.0701pontiac catalina
915.0839019038508.5701amc ambassador dpl
1015.08383170356310.0701dodge challenger se
1114.0834016036098.0701plymouth 'cuda 340
1215.0840015037619.5701chevrolet monte carlo
1314.08455225308610.0701buick estate wagon (sw)
1424.0411395237215.0703toyota corona mark ii
1522.0619895283315.5701plymouth duster
1618.0619997277415.5701amc hornet
1721.0620085258716.0701ford maverick
1827.049788213014.5703datsun pl510
1926.049746183520.5702volkswagen 1131 deluxe sedan
2025.0411087267217.5702peugeot 504
2124.0410790243014.5702audi 100 ls
2225.0410495237517.5702saab 99e
2326.04121113223412.5702bmw 2002
2421.0619990264815.0701amc gremlin
2510.08360215461514.0701ford f250
2610.08307200437615.0701chevy c20
2711.08318210438213.5701dodge d200
289.08304193473218.5701hi 1200d
2927.049788213014.5713datsun pl510
..............................
17027.0411288264018.6821chevrolet cavalier wagon
17134.0411288239518.0821chevrolet cavalier 2-door
17231.0411285257516.2821pontiac j2000 se hatchback
17329.0413584252516.0821dodge aries se
17427.0415190273518.0821pontiac phoenix
17524.0414092286516.4821ford fairmont futura
17623.04151?303520.5821amc concord dl
17736.0410574198015.3822volkswagen rabbit l
17837.049168202518.2823mazda glc custom l
17931.049168197017.6823mazda glc custom
18038.0410563212514.7821plymouth horizon miser
18136.049870212517.3821mercury lynx l
18236.0412088216014.5823nissan stanza xe
18336.0410775220514.5823honda accord
18434.0410870224516.9823toyota corolla
18538.049167196515.0823honda civic
18632.049167196515.7823honda civic (auto)
18738.049167199516.2823datsun 310 gx
18825.06181110294516.4821buick century limited
18938.0626285301517.0821oldsmobile cutlass ciera (diesel)
19026.0415692258514.5821chrysler lebaron medallion
19122.06232112283514.7821ford granada l
19232.0414496266513.9823toyota celica gt
19336.0413584237013.0821dodge charger 2.2
19427.0415190295017.3821chevrolet camaro
19527.0414086279015.6821ford mustang gl
19644.049752213024.6822vw pickup
19732.0413584229511.6821dodge rampage
19828.0412079262518.6821ford ranger
19931.0411982272019.4821chevy s-10
\n", + "

398 rows × 9 columns

\n", + "
" + ], + "text/plain": [ + " mpg cylinders displacement horsepower weight acceleration model \\\n", + "0 18.0 8 307 130 3504 12.0 70 \n", + "1 15.0 8 350 165 3693 11.5 70 \n", + "2 18.0 8 318 150 3436 11.0 70 \n", + "3 16.0 8 304 150 3433 12.0 70 \n", + "4 17.0 8 302 140 3449 10.5 70 \n", + "5 15.0 8 429 198 4341 10.0 70 \n", + "6 14.0 8 454 220 4354 9.0 70 \n", + "7 14.0 8 440 215 4312 8.5 70 \n", + "8 14.0 8 455 225 4425 10.0 70 \n", + "9 15.0 8 390 190 3850 8.5 70 \n", + "10 15.0 8 383 170 3563 10.0 70 \n", + "11 14.0 8 340 160 3609 8.0 70 \n", + "12 15.0 8 400 150 3761 9.5 70 \n", + "13 14.0 8 455 225 3086 10.0 70 \n", + "14 24.0 4 113 95 2372 15.0 70 \n", + "15 22.0 6 198 95 2833 15.5 70 \n", + "16 18.0 6 199 97 2774 15.5 70 \n", + "17 21.0 6 200 85 2587 16.0 70 \n", + "18 27.0 4 97 88 2130 14.5 70 \n", + "19 26.0 4 97 46 1835 20.5 70 \n", + "20 25.0 4 110 87 2672 17.5 70 \n", + "21 24.0 4 107 90 2430 14.5 70 \n", + "22 25.0 4 104 95 2375 17.5 70 \n", + "23 26.0 4 121 113 2234 12.5 70 \n", + "24 21.0 6 199 90 2648 15.0 70 \n", + "25 10.0 8 360 215 4615 14.0 70 \n", + "26 10.0 8 307 200 4376 15.0 70 \n", + "27 11.0 8 318 210 4382 13.5 70 \n", + "28 9.0 8 304 193 4732 18.5 70 \n", + "29 27.0 4 97 88 2130 14.5 71 \n", + ".. ... ... ... ... ... ... ... \n", + "170 27.0 4 112 88 2640 18.6 82 \n", + "171 34.0 4 112 88 2395 18.0 82 \n", + "172 31.0 4 112 85 2575 16.2 82 \n", + "173 29.0 4 135 84 2525 16.0 82 \n", + "174 27.0 4 151 90 2735 18.0 82 \n", + "175 24.0 4 140 92 2865 16.4 82 \n", + "176 23.0 4 151 ? 3035 20.5 82 \n", + "177 36.0 4 105 74 1980 15.3 82 \n", + "178 37.0 4 91 68 2025 18.2 82 \n", + "179 31.0 4 91 68 1970 17.6 82 \n", + "180 38.0 4 105 63 2125 14.7 82 \n", + "181 36.0 4 98 70 2125 17.3 82 \n", + "182 36.0 4 120 88 2160 14.5 82 \n", + "183 36.0 4 107 75 2205 14.5 82 \n", + "184 34.0 4 108 70 2245 16.9 82 \n", + "185 38.0 4 91 67 1965 15.0 82 \n", + "186 32.0 4 91 67 1965 15.7 82 \n", + "187 38.0 4 91 67 1995 16.2 82 \n", + "188 25.0 6 181 110 2945 16.4 82 \n", + "189 38.0 6 262 85 3015 17.0 82 \n", + "190 26.0 4 156 92 2585 14.5 82 \n", + "191 22.0 6 232 112 2835 14.7 82 \n", + "192 32.0 4 144 96 2665 13.9 82 \n", + "193 36.0 4 135 84 2370 13.0 82 \n", + "194 27.0 4 151 90 2950 17.3 82 \n", + "195 27.0 4 140 86 2790 15.6 82 \n", + "196 44.0 4 97 52 2130 24.6 82 \n", + "197 32.0 4 135 84 2295 11.6 82 \n", + "198 28.0 4 120 79 2625 18.6 82 \n", + "199 31.0 4 119 82 2720 19.4 82 \n", + "\n", + " origin car \n", + "0 1 chevrolet chevelle malibu \n", + "1 1 buick skylark 320 \n", + "2 1 plymouth satellite \n", + "3 1 amc rebel sst \n", + "4 1 ford torino \n", + "5 1 ford galaxie 500 \n", + "6 1 chevrolet impala \n", + "7 1 plymouth fury iii \n", + "8 1 pontiac catalina \n", + "9 1 amc ambassador dpl \n", + "10 1 dodge challenger se \n", + "11 1 plymouth 'cuda 340 \n", + "12 1 chevrolet monte carlo \n", + "13 1 buick estate wagon (sw) \n", + "14 3 toyota corona mark ii \n", + "15 1 plymouth duster \n", + "16 1 amc hornet \n", + "17 1 ford maverick \n", + "18 3 datsun pl510 \n", + "19 2 volkswagen 1131 deluxe sedan \n", + "20 2 peugeot 504 \n", + "21 2 audi 100 ls \n", + "22 2 saab 99e \n", + "23 2 bmw 2002 \n", + "24 1 amc gremlin \n", + "25 1 ford f250 \n", + "26 1 chevy c20 \n", + "27 1 dodge d200 \n", + "28 1 hi 1200d \n", + "29 3 datsun pl510 \n", + ".. ... ... \n", + "170 1 chevrolet cavalier wagon \n", + "171 1 chevrolet cavalier 2-door \n", + "172 1 pontiac j2000 se hatchback \n", + "173 1 dodge aries se \n", + "174 1 pontiac phoenix \n", + "175 1 ford fairmont futura \n", + "176 1 amc concord dl \n", + "177 2 volkswagen rabbit l \n", + "178 3 mazda glc custom l \n", + "179 3 mazda glc custom \n", + "180 1 plymouth horizon miser \n", + "181 1 mercury lynx l \n", + "182 3 nissan stanza xe \n", + "183 3 honda accord \n", + "184 3 toyota corolla \n", + "185 3 honda civic \n", + "186 3 honda civic (auto) \n", + "187 3 datsun 310 gx \n", + "188 1 buick century limited \n", + "189 1 oldsmobile cutlass ciera (diesel) \n", + "190 1 chrysler lebaron medallion \n", + "191 1 ford granada l \n", + "192 3 toyota celica gt \n", + "193 1 dodge charger 2.2 \n", + "194 1 chevrolet camaro \n", + "195 1 ford mustang gl \n", + "196 2 vw pickup \n", + "197 1 dodge rampage \n", + "198 1 ford ranger \n", + "199 1 chevy s-10 \n", + "\n", + "[398 rows x 9 columns]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cars = cars1.append(cars2)\n", + "cars" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Ops there is a column missing, called owners. Create a random number Series from 15,000 to 73,000." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([29487, 25680, 65268, 31827, 69215, 72602, 52693, 58440, 16183,\n", + " 45014, 32318, 72942, 62163, 35951, 57625, 59355, 36533, 67048,\n", + " 58159, 69743, 25146, 22755, 44966, 46792, 56553, 65013, 55908,\n", + " 69563, 22030, 59561, 15593, 52998, 54795, 16169, 24809, 35580,\n", + " 46590, 38792, 43099, 37166, 21390, 56496, 68606, 21110, 56334,\n", + " 45477, 51961, 27625, 51176, 30796, 61809, 65450, 67375, 23342,\n", + " 27499, 50585, 57302, 56191, 60281, 32865, 58605, 66374, 15315,\n", + " 31791, 28670, 38796, 69214, 41055, 32353, 31574, 65799, 42998,\n", + " 72785, 18415, 31977, 29812, 65439, 21161, 60871, 67151, 22179,\n", + " 32821, 55392, 34586, 67937, 31646, 66397, 35258, 63815, 71291,\n", + " 51130, 27684, 49648, 52691, 50681, 68185, 32635, 51553, 28970,\n", + " 19112, 26035, 67666, 55471, 51477, 62055, 53003, 41265, 18565,\n", + " 48851, 48673, 45832, 67891, 57638, 29240, 41236, 16950, 31449,\n", + " 50528, 22397, 15876, 26414, 16736, 23896, 46104, 17583, 65951,\n", + " 38538, 31443, 19299, 46095, 31239, 19290, 38051, 68575, 61755,\n", + " 22560, 34460, 35395, 34608, 56906, 44895, 48429, 20900, 49770,\n", + " 50513, 59402, 26893, 37233, 19036, 20523, 18765, 46333, 42831,\n", + " 53698, 25218, 63106, 16928, 34901, 43674, 65453, 54428, 68502,\n", + " 19043, 20325, 45039, 29466, 49672, 67972, 30547, 22522, 69354,\n", + " 40489, 72887, 15724, 51442, 65182, 64555, 42138, 72988, 20861,\n", + " 67898, 20768, 36415, 47480, 16820, 48739, 62610, 43473, 23002,\n", + " 43488, 62581, 37724, 63019, 44912, 35595, 59188, 51814, 65283,\n", + " 53479, 27660, 38237, 22957, 47870, 15533, 41944, 51830, 56676,\n", + " 57481, 48529, 72220, 66675, 50099, 30585, 25436, 49195, 26050,\n", + " 24899, 37213, 25870, 67447, 23808, 71275, 67572, 18545, 43553,\n", + " 54858, 23077, 33705, 31282, 26298, 23742, 36110, 51491, 18019,\n", + " 60655, 27453, 35563, 63627, 35315, 56717, 59281, 55634, 18415,\n", + " 59570, 47320, 20110, 18425, 19352, 18032, 31816, 28573, 66030,\n", + " 54723, 21592, 37160, 59518, 35629, 47619, 52359, 34566, 64932,\n", + " 24072, 39445, 31203, 63975, 62041, 70175, 51029, 32058, 19428,\n", + " 65553, 50799, 48190, 68061, 68201, 53389, 15901, 44585, 54723,\n", + " 30446, 63716, 57488, 67134, 22033, 53694, 40002, 24854, 59747,\n", + " 59827, 53378, 53196, 68686, 20784, 28181, 33044, 41694, 39857,\n", + " 57296, 69021, 17359, 29794, 22515, 55877, 22806, 50027, 56787,\n", + " 50844, 17420, 65259, 19141, 40204, 19530, 30116, 34973, 15641,\n", + " 53492, 59574, 59082, 64400, 70163, 43058, 69696, 67996, 26158,\n", + " 32936, 45461, 47390, 32368, 15400, 40895, 16572, 31776, 62121,\n", + " 56704, 39335, 27716, 52565, 50831, 45049, 25173, 25018, 18606,\n", + " 71177, 66288, 46754, 68175, 35829, 24959, 54792, 19059, 29092,\n", + " 58736, 62938, 44733, 17884, 33905, 33965, 24641, 52257, 28178,\n", + " 29515, 37703, 56036, 51556, 23590, 61888, 70224, 53730, 41328,\n", + " 16501, 30360, 54106, 29101, 35631, 56173, 30424, 46887, 23657,\n", + " 17723, 71709, 45270, 30380, 27779, 33774, 36379, 47127, 63625,\n", + " 16750, 65740, 53802, 40995, 37487, 42791, 21825, 69344, 63210,\n", + " 15982, 20259])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nr_owners = np.random.randint(15000, high=73001, size=398, dtype='l')\n", + "nr_owners" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Add the column owners to cars" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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mpgcylindersdisplacementhorsepowerweightaccelerationmodelorigincarowners
19527.0414086279015.6821ford mustang gl21825
19644.049752213024.6822vw pickup69344
19732.0413584229511.6821dodge rampage63210
19828.0412079262518.6821ford ranger15982
19931.0411982272019.4821chevy s-1020259
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" + ], + "text/plain": [ + " mpg cylinders displacement horsepower weight acceleration model \\\n", + "195 27.0 4 140 86 2790 15.6 82 \n", + "196 44.0 4 97 52 2130 24.6 82 \n", + "197 32.0 4 135 84 2295 11.6 82 \n", + "198 28.0 4 120 79 2625 18.6 82 \n", + "199 31.0 4 119 82 2720 19.4 82 \n", + "\n", + " origin car owners \n", + "195 1 ford mustang gl 21825 \n", + "196 2 vw pickup 69344 \n", + "197 1 dodge rampage 63210 \n", + "198 1 ford ranger 15982 \n", + "199 1 chevy s-10 20259 " + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cars['owners'] = nr_owners\n", + "cars.tail()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/05_Merge/Auto_MPG/Solutions.ipynb b/200 solved problems in Python/pandas/05_Merge/Auto_MPG/Solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..27fe4d08030f5b5eddfa3a5055a1936011627e58 --- /dev/null +++ b/200 solved problems in Python/pandas/05_Merge/Auto_MPG/Solutions.ipynb @@ -0,0 +1,1370 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# MPG Cars" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "The following exercise utilizes data from [UC Irvine Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Auto+MPG)\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the first dataset [cars1](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/Merge/Auto_MPG/cars1.csv) and [cars2](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/Merge/Auto_MPG/cars2.csv). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " ### Step 3. Assign each to a to a variable called cars1 and cars2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " mpg cylinders displacement horsepower weight acceleration model \\\n", + "0 18.0 8 307 130 3504 12.0 70 \n", + "1 15.0 8 350 165 3693 11.5 70 \n", + "2 18.0 8 318 150 3436 11.0 70 \n", + "3 16.0 8 304 150 3433 12.0 70 \n", + "4 17.0 8 302 140 3449 10.5 70 \n", + "\n", + " origin car Unnamed: 9 Unnamed: 10 Unnamed: 11 \\\n", + "0 1 chevrolet chevelle malibu NaN NaN NaN \n", + "1 1 buick skylark 320 NaN NaN NaN \n", + "2 1 plymouth satellite NaN NaN NaN \n", + "3 1 amc rebel sst NaN NaN NaN \n", + "4 1 ford torino NaN NaN NaN \n", + "\n", + " Unnamed: 12 Unnamed: 13 \n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + " mpg cylinders displacement horsepower weight acceleration model \\\n", + "0 33.0 4 91 53 1795 17.4 76 \n", + "1 20.0 6 225 100 3651 17.7 76 \n", + "2 18.0 6 250 78 3574 21.0 76 \n", + "3 18.5 6 250 110 3645 16.2 76 \n", + "4 17.5 6 258 95 3193 17.8 76 \n", + "\n", + " origin car \n", + "0 3 honda civic \n", + "1 1 dodge aspen se \n", + "2 1 ford granada ghia \n", + "3 1 pontiac ventura sj \n", + "4 1 amc pacer d/l \n" + ] + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Ops it seems our first dataset has some unnamed blank columns, fix cars1" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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mpgcylindersdisplacementhorsepowerweightaccelerationmodelorigincar
018.08307130350412.0701chevrolet chevelle malibu
115.08350165369311.5701buick skylark 320
218.08318150343611.0701plymouth satellite
316.08304150343312.0701amc rebel sst
417.08302140344910.5701ford torino
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" + ], + "text/plain": [ + " mpg cylinders displacement horsepower weight acceleration model \\\n", + "0 18.0 8 307 130 3504 12.0 70 \n", + "1 15.0 8 350 165 3693 11.5 70 \n", + "2 18.0 8 318 150 3436 11.0 70 \n", + "3 16.0 8 304 150 3433 12.0 70 \n", + "4 17.0 8 302 140 3449 10.5 70 \n", + "\n", + " origin car \n", + "0 1 chevrolet chevelle malibu \n", + "1 1 buick skylark 320 \n", + "2 1 plymouth satellite \n", + "3 1 amc rebel sst \n", + "4 1 ford torino " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. What is the number of observations in each dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(198, 9)\n", + "(200, 9)\n" + ] + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Join cars1 and cars2 into a single DataFrame called cars" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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mpgcylindersdisplacementhorsepowerweightaccelerationmodelorigincar
018.08307130350412.0701chevrolet chevelle malibu
115.08350165369311.5701buick skylark 320
218.08318150343611.0701plymouth satellite
316.08304150343312.0701amc rebel sst
417.08302140344910.5701ford torino
515.08429198434110.0701ford galaxie 500
614.0845422043549.0701chevrolet impala
714.0844021543128.5701plymouth fury iii
814.08455225442510.0701pontiac catalina
915.0839019038508.5701amc ambassador dpl
1015.08383170356310.0701dodge challenger se
1114.0834016036098.0701plymouth 'cuda 340
1215.0840015037619.5701chevrolet monte carlo
1314.08455225308610.0701buick estate wagon (sw)
1424.0411395237215.0703toyota corona mark ii
1522.0619895283315.5701plymouth duster
1618.0619997277415.5701amc hornet
1721.0620085258716.0701ford maverick
1827.049788213014.5703datsun pl510
1926.049746183520.5702volkswagen 1131 deluxe sedan
2025.0411087267217.5702peugeot 504
2124.0410790243014.5702audi 100 ls
2225.0410495237517.5702saab 99e
2326.04121113223412.5702bmw 2002
2421.0619990264815.0701amc gremlin
2510.08360215461514.0701ford f250
2610.08307200437615.0701chevy c20
2711.08318210438213.5701dodge d200
289.08304193473218.5701hi 1200d
2927.049788213014.5713datsun pl510
..............................
17027.0411288264018.6821chevrolet cavalier wagon
17134.0411288239518.0821chevrolet cavalier 2-door
17231.0411285257516.2821pontiac j2000 se hatchback
17329.0413584252516.0821dodge aries se
17427.0415190273518.0821pontiac phoenix
17524.0414092286516.4821ford fairmont futura
17623.04151?303520.5821amc concord dl
17736.0410574198015.3822volkswagen rabbit l
17837.049168202518.2823mazda glc custom l
17931.049168197017.6823mazda glc custom
18038.0410563212514.7821plymouth horizon miser
18136.049870212517.3821mercury lynx l
18236.0412088216014.5823nissan stanza xe
18336.0410775220514.5823honda accord
18434.0410870224516.9823toyota corolla
18538.049167196515.0823honda civic
18632.049167196515.7823honda civic (auto)
18738.049167199516.2823datsun 310 gx
18825.06181110294516.4821buick century limited
18938.0626285301517.0821oldsmobile cutlass ciera (diesel)
19026.0415692258514.5821chrysler lebaron medallion
19122.06232112283514.7821ford granada l
19232.0414496266513.9823toyota celica gt
19336.0413584237013.0821dodge charger 2.2
19427.0415190295017.3821chevrolet camaro
19527.0414086279015.6821ford mustang gl
19644.049752213024.6822vw pickup
19732.0413584229511.6821dodge rampage
19828.0412079262518.6821ford ranger
19931.0411982272019.4821chevy s-10
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398 rows × 9 columns

\n", + "
" + ], + "text/plain": [ + " mpg cylinders displacement horsepower weight acceleration model \\\n", + "0 18.0 8 307 130 3504 12.0 70 \n", + "1 15.0 8 350 165 3693 11.5 70 \n", + "2 18.0 8 318 150 3436 11.0 70 \n", + "3 16.0 8 304 150 3433 12.0 70 \n", + "4 17.0 8 302 140 3449 10.5 70 \n", + "5 15.0 8 429 198 4341 10.0 70 \n", + "6 14.0 8 454 220 4354 9.0 70 \n", + "7 14.0 8 440 215 4312 8.5 70 \n", + "8 14.0 8 455 225 4425 10.0 70 \n", + "9 15.0 8 390 190 3850 8.5 70 \n", + "10 15.0 8 383 170 3563 10.0 70 \n", + "11 14.0 8 340 160 3609 8.0 70 \n", + "12 15.0 8 400 150 3761 9.5 70 \n", + "13 14.0 8 455 225 3086 10.0 70 \n", + "14 24.0 4 113 95 2372 15.0 70 \n", + "15 22.0 6 198 95 2833 15.5 70 \n", + "16 18.0 6 199 97 2774 15.5 70 \n", + "17 21.0 6 200 85 2587 16.0 70 \n", + "18 27.0 4 97 88 2130 14.5 70 \n", + "19 26.0 4 97 46 1835 20.5 70 \n", + "20 25.0 4 110 87 2672 17.5 70 \n", + "21 24.0 4 107 90 2430 14.5 70 \n", + "22 25.0 4 104 95 2375 17.5 70 \n", + "23 26.0 4 121 113 2234 12.5 70 \n", + "24 21.0 6 199 90 2648 15.0 70 \n", + "25 10.0 8 360 215 4615 14.0 70 \n", + "26 10.0 8 307 200 4376 15.0 70 \n", + "27 11.0 8 318 210 4382 13.5 70 \n", + "28 9.0 8 304 193 4732 18.5 70 \n", + "29 27.0 4 97 88 2130 14.5 71 \n", + ".. ... ... ... ... ... ... ... \n", + "170 27.0 4 112 88 2640 18.6 82 \n", + "171 34.0 4 112 88 2395 18.0 82 \n", + "172 31.0 4 112 85 2575 16.2 82 \n", + "173 29.0 4 135 84 2525 16.0 82 \n", + "174 27.0 4 151 90 2735 18.0 82 \n", + "175 24.0 4 140 92 2865 16.4 82 \n", + "176 23.0 4 151 ? 3035 20.5 82 \n", + "177 36.0 4 105 74 1980 15.3 82 \n", + "178 37.0 4 91 68 2025 18.2 82 \n", + "179 31.0 4 91 68 1970 17.6 82 \n", + "180 38.0 4 105 63 2125 14.7 82 \n", + "181 36.0 4 98 70 2125 17.3 82 \n", + "182 36.0 4 120 88 2160 14.5 82 \n", + "183 36.0 4 107 75 2205 14.5 82 \n", + "184 34.0 4 108 70 2245 16.9 82 \n", + "185 38.0 4 91 67 1965 15.0 82 \n", + "186 32.0 4 91 67 1965 15.7 82 \n", + "187 38.0 4 91 67 1995 16.2 82 \n", + "188 25.0 6 181 110 2945 16.4 82 \n", + "189 38.0 6 262 85 3015 17.0 82 \n", + "190 26.0 4 156 92 2585 14.5 82 \n", + "191 22.0 6 232 112 2835 14.7 82 \n", + "192 32.0 4 144 96 2665 13.9 82 \n", + "193 36.0 4 135 84 2370 13.0 82 \n", + "194 27.0 4 151 90 2950 17.3 82 \n", + "195 27.0 4 140 86 2790 15.6 82 \n", + "196 44.0 4 97 52 2130 24.6 82 \n", + "197 32.0 4 135 84 2295 11.6 82 \n", + "198 28.0 4 120 79 2625 18.6 82 \n", + "199 31.0 4 119 82 2720 19.4 82 \n", + "\n", + " origin car \n", + "0 1 chevrolet chevelle malibu \n", + "1 1 buick skylark 320 \n", + "2 1 plymouth satellite \n", + "3 1 amc rebel sst \n", + "4 1 ford torino \n", + "5 1 ford galaxie 500 \n", + "6 1 chevrolet impala \n", + "7 1 plymouth fury iii \n", + "8 1 pontiac catalina \n", + "9 1 amc ambassador dpl \n", + "10 1 dodge challenger se \n", + "11 1 plymouth 'cuda 340 \n", + "12 1 chevrolet monte carlo \n", + "13 1 buick estate wagon (sw) \n", + "14 3 toyota corona mark ii \n", + "15 1 plymouth duster \n", + "16 1 amc hornet \n", + "17 1 ford maverick \n", + "18 3 datsun pl510 \n", + "19 2 volkswagen 1131 deluxe sedan \n", + "20 2 peugeot 504 \n", + "21 2 audi 100 ls \n", + "22 2 saab 99e \n", + "23 2 bmw 2002 \n", + "24 1 amc gremlin \n", + "25 1 ford f250 \n", + "26 1 chevy c20 \n", + "27 1 dodge d200 \n", + "28 1 hi 1200d \n", + "29 3 datsun pl510 \n", + ".. ... ... \n", + "170 1 chevrolet cavalier wagon \n", + "171 1 chevrolet cavalier 2-door \n", + "172 1 pontiac j2000 se hatchback \n", + "173 1 dodge aries se \n", + "174 1 pontiac phoenix \n", + "175 1 ford fairmont futura \n", + "176 1 amc concord dl \n", + "177 2 volkswagen rabbit l \n", + "178 3 mazda glc custom l \n", + "179 3 mazda glc custom \n", + "180 1 plymouth horizon miser \n", + "181 1 mercury lynx l \n", + "182 3 nissan stanza xe \n", + "183 3 honda accord \n", + "184 3 toyota corolla \n", + "185 3 honda civic \n", + "186 3 honda civic (auto) \n", + "187 3 datsun 310 gx \n", + "188 1 buick century limited \n", + "189 1 oldsmobile cutlass ciera (diesel) \n", + "190 1 chrysler lebaron medallion \n", + "191 1 ford granada l \n", + "192 3 toyota celica gt \n", + "193 1 dodge charger 2.2 \n", + "194 1 chevrolet camaro \n", + "195 1 ford mustang gl \n", + "196 2 vw pickup \n", + "197 1 dodge rampage \n", + "198 1 ford ranger \n", + "199 1 chevy s-10 \n", + "\n", + "[398 rows x 9 columns]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Ops there is a column missing, called owners. Create a random number Series from 15,000 to 73,000." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([29487, 25680, 65268, 31827, 69215, 72602, 52693, 58440, 16183,\n", + " 45014, 32318, 72942, 62163, 35951, 57625, 59355, 36533, 67048,\n", + " 58159, 69743, 25146, 22755, 44966, 46792, 56553, 65013, 55908,\n", + " 69563, 22030, 59561, 15593, 52998, 54795, 16169, 24809, 35580,\n", + " 46590, 38792, 43099, 37166, 21390, 56496, 68606, 21110, 56334,\n", + " 45477, 51961, 27625, 51176, 30796, 61809, 65450, 67375, 23342,\n", + " 27499, 50585, 57302, 56191, 60281, 32865, 58605, 66374, 15315,\n", + " 31791, 28670, 38796, 69214, 41055, 32353, 31574, 65799, 42998,\n", + " 72785, 18415, 31977, 29812, 65439, 21161, 60871, 67151, 22179,\n", + " 32821, 55392, 34586, 67937, 31646, 66397, 35258, 63815, 71291,\n", + " 51130, 27684, 49648, 52691, 50681, 68185, 32635, 51553, 28970,\n", + " 19112, 26035, 67666, 55471, 51477, 62055, 53003, 41265, 18565,\n", + " 48851, 48673, 45832, 67891, 57638, 29240, 41236, 16950, 31449,\n", + " 50528, 22397, 15876, 26414, 16736, 23896, 46104, 17583, 65951,\n", + " 38538, 31443, 19299, 46095, 31239, 19290, 38051, 68575, 61755,\n", + " 22560, 34460, 35395, 34608, 56906, 44895, 48429, 20900, 49770,\n", + " 50513, 59402, 26893, 37233, 19036, 20523, 18765, 46333, 42831,\n", + " 53698, 25218, 63106, 16928, 34901, 43674, 65453, 54428, 68502,\n", + " 19043, 20325, 45039, 29466, 49672, 67972, 30547, 22522, 69354,\n", + " 40489, 72887, 15724, 51442, 65182, 64555, 42138, 72988, 20861,\n", + " 67898, 20768, 36415, 47480, 16820, 48739, 62610, 43473, 23002,\n", + " 43488, 62581, 37724, 63019, 44912, 35595, 59188, 51814, 65283,\n", + " 53479, 27660, 38237, 22957, 47870, 15533, 41944, 51830, 56676,\n", + " 57481, 48529, 72220, 66675, 50099, 30585, 25436, 49195, 26050,\n", + " 24899, 37213, 25870, 67447, 23808, 71275, 67572, 18545, 43553,\n", + " 54858, 23077, 33705, 31282, 26298, 23742, 36110, 51491, 18019,\n", + " 60655, 27453, 35563, 63627, 35315, 56717, 59281, 55634, 18415,\n", + " 59570, 47320, 20110, 18425, 19352, 18032, 31816, 28573, 66030,\n", + " 54723, 21592, 37160, 59518, 35629, 47619, 52359, 34566, 64932,\n", + " 24072, 39445, 31203, 63975, 62041, 70175, 51029, 32058, 19428,\n", + " 65553, 50799, 48190, 68061, 68201, 53389, 15901, 44585, 54723,\n", + " 30446, 63716, 57488, 67134, 22033, 53694, 40002, 24854, 59747,\n", + " 59827, 53378, 53196, 68686, 20784, 28181, 33044, 41694, 39857,\n", + " 57296, 69021, 17359, 29794, 22515, 55877, 22806, 50027, 56787,\n", + " 50844, 17420, 65259, 19141, 40204, 19530, 30116, 34973, 15641,\n", + " 53492, 59574, 59082, 64400, 70163, 43058, 69696, 67996, 26158,\n", + " 32936, 45461, 47390, 32368, 15400, 40895, 16572, 31776, 62121,\n", + " 56704, 39335, 27716, 52565, 50831, 45049, 25173, 25018, 18606,\n", + " 71177, 66288, 46754, 68175, 35829, 24959, 54792, 19059, 29092,\n", + " 58736, 62938, 44733, 17884, 33905, 33965, 24641, 52257, 28178,\n", + " 29515, 37703, 56036, 51556, 23590, 61888, 70224, 53730, 41328,\n", + " 16501, 30360, 54106, 29101, 35631, 56173, 30424, 46887, 23657,\n", + " 17723, 71709, 45270, 30380, 27779, 33774, 36379, 47127, 63625,\n", + " 16750, 65740, 53802, 40995, 37487, 42791, 21825, 69344, 63210,\n", + " 15982, 20259])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Add the column owners to cars" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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mpgcylindersdisplacementhorsepowerweightaccelerationmodelorigincarowners
19527.0414086279015.6821ford mustang gl21825
19644.049752213024.6822vw pickup69344
19732.0413584229511.6821dodge rampage63210
19828.0412079262518.6821ford ranger15982
19931.0411982272019.4821chevy s-1020259
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" + ], + "text/plain": [ + " mpg cylinders displacement horsepower weight acceleration model \\\n", + "195 27.0 4 140 86 2790 15.6 82 \n", + "196 44.0 4 97 52 2130 24.6 82 \n", + "197 32.0 4 135 84 2295 11.6 82 \n", + "198 28.0 4 120 79 2625 18.6 82 \n", + "199 31.0 4 119 82 2720 19.4 82 \n", + "\n", + " origin car owners \n", + "195 1 ford mustang gl 21825 \n", + "196 2 vw pickup 69344 \n", + "197 1 dodge rampage 63210 \n", + "198 1 ford ranger 15982 \n", + "199 1 chevy s-10 20259 " + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/05_Merge/Auto_MPG/cars1.csv b/200 solved problems in Python/pandas/05_Merge/Auto_MPG/cars1.csv new file mode 100644 index 0000000000000000000000000000000000000000..44a909a3dc90fdad10eccba9c290dbcce631f75e --- /dev/null +++ b/200 solved problems in Python/pandas/05_Merge/Auto_MPG/cars1.csv @@ -0,0 +1 @@ +mpg,cylinders,displacement,horsepower,weight,acceleration,model,origin,car,,,,, 18.0,8,307,130,3504,12.0,70,1,chevrolet chevelle malibu,,,,, 15.0,8,350,165,3693,11.5,70,1,buick skylark 320,,,,, 18.0,8,318,150,3436,11.0,70,1,plymouth satellite,,,,, 16.0,8,304,150,3433,12.0,70,1,amc rebel sst,,,,, 17.0,8,302,140,3449,10.5,70,1,ford torino,,,,, 15.0,8,429,198,4341,10.0,70,1,ford galaxie 500,,,,, 14.0,8,454,220,4354,9.0,70,1,chevrolet impala,,,,, 14.0,8,440,215,4312,8.5,70,1,plymouth fury iii,,,,, 14.0,8,455,225,4425,10.0,70,1,pontiac catalina,,,,, 15.0,8,390,190,3850,8.5,70,1,amc ambassador dpl,,,,, 15.0,8,383,170,3563,10.0,70,1,dodge challenger se,,,,, 14.0,8,340,160,3609,8.0,70,1,plymouth 'cuda 340,,,,, 15.0,8,400,150,3761,9.5,70,1,chevrolet monte carlo,,,,, 14.0,8,455,225,3086,10.0,70,1,buick estate wagon (sw),,,,, 24.0,4,113,95,2372,15.0,70,3,toyota corona mark ii,,,,, 22.0,6,198,95,2833,15.5,70,1,plymouth duster,,,,, 18.0,6,199,97,2774,15.5,70,1,amc hornet,,,,, 21.0,6,200,85,2587,16.0,70,1,ford maverick,,,,, 27.0,4,97,88,2130,14.5,70,3,datsun pl510,,,,, 26.0,4,97,46,1835,20.5,70,2,volkswagen 1131 deluxe sedan,,,,, 25.0,4,110,87,2672,17.5,70,2,peugeot 504,,,,, 24.0,4,107,90,2430,14.5,70,2,audi 100 ls,,,,, 25.0,4,104,95,2375,17.5,70,2,saab 99e,,,,, 26.0,4,121,113,2234,12.5,70,2,bmw 2002,,,,, 21.0,6,199,90,2648,15.0,70,1,amc gremlin,,,,, 10.0,8,360,215,4615,14.0,70,1,ford f250,,,,, 10.0,8,307,200,4376,15.0,70,1,chevy c20,,,,, 11.0,8,318,210,4382,13.5,70,1,dodge d200,,,,, 9.0,8,304,193,4732,18.5,70,1,hi 1200d,,,,, 27.0,4,97,88,2130,14.5,71,3,datsun pl510,,,,, 28.0,4,140,90,2264,15.5,71,1,chevrolet vega 2300,,,,, 25.0,4,113,95,2228,14.0,71,3,toyota corona,,,,, 25.0,4,98,?,2046,19.0,71,1,ford pinto,,,,, 19.0,6,232,100,2634,13.0,71,1,amc gremlin,,,,, 16.0,6,225,105,3439,15.5,71,1,plymouth satellite custom,,,,, 17.0,6,250,100,3329,15.5,71,1,chevrolet chevelle malibu,,,,, 19.0,6,250,88,3302,15.5,71,1,ford torino 500,,,,, 18.0,6,232,100,3288,15.5,71,1,amc matador,,,,, 14.0,8,350,165,4209,12.0,71,1,chevrolet impala,,,,, 14.0,8,400,175,4464,11.5,71,1,pontiac catalina brougham,,,,, 14.0,8,351,153,4154,13.5,71,1,ford galaxie 500,,,,, 14.0,8,318,150,4096,13.0,71,1,plymouth fury iii,,,,, 12.0,8,383,180,4955,11.5,71,1,dodge monaco (sw),,,,, 13.0,8,400,170,4746,12.0,71,1,ford country squire (sw),,,,, 13.0,8,400,175,5140,12.0,71,1,pontiac safari (sw),,,,, 18.0,6,258,110,2962,13.5,71,1,amc hornet sportabout (sw),,,,, 22.0,4,140,72,2408,19.0,71,1,chevrolet vega (sw),,,,, 19.0,6,250,100,3282,15.0,71,1,pontiac firebird,,,,, 18.0,6,250,88,3139,14.5,71,1,ford mustang,,,,, 23.0,4,122,86,2220,14.0,71,1,mercury capri 2000,,,,, 28.0,4,116,90,2123,14.0,71,2,opel 1900,,,,, 30.0,4,79,70,2074,19.5,71,2,peugeot 304,,,,, 30.0,4,88,76,2065,14.5,71,2,fiat 124b,,,,, 31.0,4,71,65,1773,19.0,71,3,toyota corolla 1200,,,,, 35.0,4,72,69,1613,18.0,71,3,datsun 1200,,,,, 27.0,4,97,60,1834,19.0,71,2,volkswagen model 111,,,,, 26.0,4,91,70,1955,20.5,71,1,plymouth cricket,,,,, 24.0,4,113,95,2278,15.5,72,3,toyota corona hardtop,,,,, 25.0,4,98,80,2126,17.0,72,1,dodge colt hardtop,,,,, 23.0,4,97,54,2254,23.5,72,2,volkswagen type 3,,,,, 20.0,4,140,90,2408,19.5,72,1,chevrolet vega,,,,, 21.0,4,122,86,2226,16.5,72,1,ford pinto runabout,,,,, 13.0,8,350,165,4274,12.0,72,1,chevrolet impala,,,,, 14.0,8,400,175,4385,12.0,72,1,pontiac catalina,,,,, 15.0,8,318,150,4135,13.5,72,1,plymouth fury iii,,,,, 14.0,8,351,153,4129,13.0,72,1,ford galaxie 500,,,,, 17.0,8,304,150,3672,11.5,72,1,amc ambassador sst,,,,, 11.0,8,429,208,4633,11.0,72,1,mercury marquis,,,,, 13.0,8,350,155,4502,13.5,72,1,buick lesabre custom,,,,, 12.0,8,350,160,4456,13.5,72,1,oldsmobile delta 88 royale,,,,, 13.0,8,400,190,4422,12.5,72,1,chrysler newport royal,,,,, 19.0,3,70,97,2330,13.5,72,3,mazda rx2 coupe,,,,, 15.0,8,304,150,3892,12.5,72,1,amc matador (sw),,,,, 13.0,8,307,130,4098,14.0,72,1,chevrolet chevelle concours (sw),,,,, 13.0,8,302,140,4294,16.0,72,1,ford gran torino (sw),,,,, 14.0,8,318,150,4077,14.0,72,1,plymouth satellite custom (sw),,,,, 18.0,4,121,112,2933,14.5,72,2,volvo 145e (sw),,,,, 22.0,4,121,76,2511,18.0,72,2,volkswagen 411 (sw),,,,, 21.0,4,120,87,2979,19.5,72,2,peugeot 504 (sw),,,,, 26.0,4,96,69,2189,18.0,72,2,renault 12 (sw),,,,, 22.0,4,122,86,2395,16.0,72,1,ford pinto (sw),,,,, 28.0,4,97,92,2288,17.0,72,3,datsun 510 (sw),,,,, 23.0,4,120,97,2506,14.5,72,3,toyouta corona mark ii (sw),,,,, 28.0,4,98,80,2164,15.0,72,1,dodge colt (sw),,,,, 27.0,4,97,88,2100,16.5,72,3,toyota corolla 1600 (sw),,,,, 13.0,8,350,175,4100,13.0,73,1,buick century 350,,,,, 14.0,8,304,150,3672,11.5,73,1,amc matador,,,,, 13.0,8,350,145,3988,13.0,73,1,chevrolet malibu,,,,, 14.0,8,302,137,4042,14.5,73,1,ford gran torino,,,,, 15.0,8,318,150,3777,12.5,73,1,dodge coronet custom,,,,, 12.0,8,429,198,4952,11.5,73,1,mercury marquis brougham,,,,, 13.0,8,400,150,4464,12.0,73,1,chevrolet caprice classic,,,,, 13.0,8,351,158,4363,13.0,73,1,ford ltd,,,,, 14.0,8,318,150,4237,14.5,73,1,plymouth fury gran sedan,,,,, 13.0,8,440,215,4735,11.0,73,1,chrysler new yorker brougham,,,,, 12.0,8,455,225,4951,11.0,73,1,buick electra 225 custom,,,,, 13.0,8,360,175,3821,11.0,73,1,amc ambassador brougham,,,,, 18.0,6,225,105,3121,16.5,73,1,plymouth valiant,,,,, 16.0,6,250,100,3278,18.0,73,1,chevrolet nova custom,,,,, 18.0,6,232,100,2945,16.0,73,1,amc hornet,,,,, 18.0,6,250,88,3021,16.5,73,1,ford maverick,,,,, 23.0,6,198,95,2904,16.0,73,1,plymouth duster,,,,, 26.0,4,97,46,1950,21.0,73,2,volkswagen super beetle,,,,, 11.0,8,400,150,4997,14.0,73,1,chevrolet impala,,,,, 12.0,8,400,167,4906,12.5,73,1,ford country,,,,, 13.0,8,360,170,4654,13.0,73,1,plymouth custom suburb,,,,, 12.0,8,350,180,4499,12.5,73,1,oldsmobile vista cruiser,,,,, 18.0,6,232,100,2789,15.0,73,1,amc gremlin,,,,, 20.0,4,97,88,2279,19.0,73,3,toyota carina,,,,, 21.0,4,140,72,2401,19.5,73,1,chevrolet vega,,,,, 22.0,4,108,94,2379,16.5,73,3,datsun 610,,,,, 18.0,3,70,90,2124,13.5,73,3,maxda rx3,,,,, 19.0,4,122,85,2310,18.5,73,1,ford pinto,,,,, 21.0,6,155,107,2472,14.0,73,1,mercury capri v6,,,,, 26.0,4,98,90,2265,15.5,73,2,fiat 124 sport coupe,,,,, 15.0,8,350,145,4082,13.0,73,1,chevrolet monte carlo s,,,,, 16.0,8,400,230,4278,9.5,73,1,pontiac grand prix,,,,, 29.0,4,68,49,1867,19.5,73,2,fiat 128,,,,, 24.0,4,116,75,2158,15.5,73,2,opel manta,,,,, 20.0,4,114,91,2582,14.0,73,2,audi 100ls,,,,, 19.0,4,121,112,2868,15.5,73,2,volvo 144ea,,,,, 15.0,8,318,150,3399,11.0,73,1,dodge dart custom,,,,, 24.0,4,121,110,2660,14.0,73,2,saab 99le,,,,, 20.0,6,156,122,2807,13.5,73,3,toyota mark ii,,,,, 11.0,8,350,180,3664,11.0,73,1,oldsmobile omega,,,,, 20.0,6,198,95,3102,16.5,74,1,plymouth duster,,,,, 21.0,6,200,?,2875,17.0,74,1,ford maverick,,,,, 19.0,6,232,100,2901,16.0,74,1,amc hornet,,,,, 15.0,6,250,100,3336,17.0,74,1,chevrolet nova,,,,, 31.0,4,79,67,1950,19.0,74,3,datsun b210,,,,, 26.0,4,122,80,2451,16.5,74,1,ford pinto,,,,, 32.0,4,71,65,1836,21.0,74,3,toyota corolla 1200,,,,, 25.0,4,140,75,2542,17.0,74,1,chevrolet vega,,,,, 16.0,6,250,100,3781,17.0,74,1,chevrolet chevelle malibu classic,,,,, 16.0,6,258,110,3632,18.0,74,1,amc matador,,,,, 18.0,6,225,105,3613,16.5,74,1,plymouth satellite sebring,,,,, 16.0,8,302,140,4141,14.0,74,1,ford gran torino,,,,, 13.0,8,350,150,4699,14.5,74,1,buick century luxus (sw),,,,, 14.0,8,318,150,4457,13.5,74,1,dodge coronet custom (sw),,,,, 14.0,8,302,140,4638,16.0,74,1,ford gran torino (sw),,,,, 14.0,8,304,150,4257,15.5,74,1,amc matador (sw),,,,, 29.0,4,98,83,2219,16.5,74,2,audi fox,,,,, 26.0,4,79,67,1963,15.5,74,2,volkswagen dasher,,,,, 26.0,4,97,78,2300,14.5,74,2,opel manta,,,,, 31.0,4,76,52,1649,16.5,74,3,toyota corona,,,,, 32.0,4,83,61,2003,19.0,74,3,datsun 710,,,,, 28.0,4,90,75,2125,14.5,74,1,dodge colt,,,,, 24.0,4,90,75,2108,15.5,74,2,fiat 128,,,,, 26.0,4,116,75,2246,14.0,74,2,fiat 124 tc,,,,, 24.0,4,120,97,2489,15.0,74,3,honda civic,,,,, 26.0,4,108,93,2391,15.5,74,3,subaru,,,,, 31.0,4,79,67,2000,16.0,74,2,fiat x1.9,,,,, 19.0,6,225,95,3264,16.0,75,1,plymouth valiant custom,,,,, 18.0,6,250,105,3459,16.0,75,1,chevrolet nova,,,,, 15.0,6,250,72,3432,21.0,75,1,mercury monarch,,,,, 15.0,6,250,72,3158,19.5,75,1,ford maverick,,,,, 16.0,8,400,170,4668,11.5,75,1,pontiac catalina,,,,, 15.0,8,350,145,4440,14.0,75,1,chevrolet bel air,,,,, 16.0,8,318,150,4498,14.5,75,1,plymouth grand fury,,,,, 14.0,8,351,148,4657,13.5,75,1,ford ltd,,,,, 17.0,6,231,110,3907,21.0,75,1,buick century,,,,, 16.0,6,250,105,3897,18.5,75,1,chevroelt chevelle malibu,,,,, 15.0,6,258,110,3730,19.0,75,1,amc matador,,,,, 18.0,6,225,95,3785,19.0,75,1,plymouth fury,,,,, 21.0,6,231,110,3039,15.0,75,1,buick skyhawk,,,,, 20.0,8,262,110,3221,13.5,75,1,chevrolet monza 2+2,,,,, 13.0,8,302,129,3169,12.0,75,1,ford mustang ii,,,,, 29.0,4,97,75,2171,16.0,75,3,toyota corolla,,,,, 23.0,4,140,83,2639,17.0,75,1,ford pinto,,,,, 20.0,6,232,100,2914,16.0,75,1,amc gremlin,,,,, 23.0,4,140,78,2592,18.5,75,1,pontiac astro,,,,, 24.0,4,134,96,2702,13.5,75,3,toyota corona,,,,, 25.0,4,90,71,2223,16.5,75,2,volkswagen dasher,,,,, 24.0,4,119,97,2545,17.0,75,3,datsun 710,,,,, 18.0,6,171,97,2984,14.5,75,1,ford pinto,,,,, 29.0,4,90,70,1937,14.0,75,2,volkswagen rabbit,,,,, 19.0,6,232,90,3211,17.0,75,1,amc pacer,,,,, 23.0,4,115,95,2694,15.0,75,2,audi 100ls,,,,, 23.0,4,120,88,2957,17.0,75,2,peugeot 504,,,,, 22.0,4,121,98,2945,14.5,75,2,volvo 244dl,,,,, 25.0,4,121,115,2671,13.5,75,2,saab 99le,,,,, 33.0,4,91,53,1795,17.5,75,3,honda civic cvcc,,,,, 28.0,4,107,86,2464,15.5,76,2,fiat 131,,,,, 25.0,4,116,81,2220,16.9,76,2,opel 1900,,,,, 25.0,4,140,92,2572,14.9,76,1,capri ii,,,,, 26.0,4,98,79,2255,17.7,76,1,dodge colt,,,,, 27.0,4,101,83,2202,15.3,76,2,renault 12tl,,,,, 17.5,8,305,140,4215,13.0,76,1,chevrolet chevelle malibu classic,,,,, 16.0,8,318,150,4190,13.0,76,1,dodge coronet brougham,,,,, 15.5,8,304,120,3962,13.9,76,1,amc matador,,,,, 14.5,8,351,152,4215,12.8,76,1,ford gran torino,,,,, 22.0,6,225,100,3233,15.4,76,1,plymouth valiant,,,,, 22.0,6,250,105,3353,14.5,76,1,chevrolet nova,,,,, 24.0,6,200,81,3012,17.6,76,1,ford maverick,,,,, 22.5,6,232,90,3085,17.6,76,1,amc hornet,,,,, 29.0,4,85,52,2035,22.2,76,1,chevrolet chevette,,,,, 24.5,4,98,60,2164,22.1,76,1,chevrolet woody,,,,, 29.0,4,90,70,1937,14.2,76,2,vw rabbit,,,,, \ No newline at end of file diff --git a/200 solved problems in Python/pandas/05_Merge/Auto_MPG/cars2.csv b/200 solved problems in Python/pandas/05_Merge/Auto_MPG/cars2.csv new file mode 100644 index 0000000000000000000000000000000000000000..ad1526576577151beaa41e3221cf6813e02a6b75 --- /dev/null +++ b/200 solved problems in Python/pandas/05_Merge/Auto_MPG/cars2.csv @@ -0,0 +1 @@ +mpg,cylinders,displacement,horsepower,weight,acceleration,model,origin,car 33.0,4,91,53,1795,17.4,76,3,honda civic 20.0,6,225,100,3651,17.7,76,1,dodge aspen se 18.0,6,250,78,3574,21.0,76,1,ford granada ghia 18.5,6,250,110,3645,16.2,76,1,pontiac ventura sj 17.5,6,258,95,3193,17.8,76,1,amc pacer d/l 29.5,4,97,71,1825,12.2,76,2,volkswagen rabbit 32.0,4,85,70,1990,17.0,76,3,datsun b-210 28.0,4,97,75,2155,16.4,76,3,toyota corolla 26.5,4,140,72,2565,13.6,76,1,ford pinto 20.0,4,130,102,3150,15.7,76,2,volvo 245 13.0,8,318,150,3940,13.2,76,1,plymouth volare premier v8 19.0,4,120,88,3270,21.9,76,2,peugeot 504 19.0,6,156,108,2930,15.5,76,3,toyota mark ii 16.5,6,168,120,3820,16.7,76,2,mercedes-benz 280s 16.5,8,350,180,4380,12.1,76,1,cadillac seville 13.0,8,350,145,4055,12.0,76,1,chevy c10 13.0,8,302,130,3870,15.0,76,1,ford f108 13.0,8,318,150,3755,14.0,76,1,dodge d100 31.5,4,98,68,2045,18.5,77,3,honda accord cvcc 30.0,4,111,80,2155,14.8,77,1,buick opel isuzu deluxe 36.0,4,79,58,1825,18.6,77,2,renault 5 gtl 25.5,4,122,96,2300,15.5,77,1,plymouth arrow gs 33.5,4,85,70,1945,16.8,77,3,datsun f-10 hatchback 17.5,8,305,145,3880,12.5,77,1,chevrolet caprice classic 17.0,8,260,110,4060,19.0,77,1,oldsmobile cutlass supreme 15.5,8,318,145,4140,13.7,77,1,dodge monaco brougham 15.0,8,302,130,4295,14.9,77,1,mercury cougar brougham 17.5,6,250,110,3520,16.4,77,1,chevrolet concours 20.5,6,231,105,3425,16.9,77,1,buick skylark 19.0,6,225,100,3630,17.7,77,1,plymouth volare custom 18.5,6,250,98,3525,19.0,77,1,ford granada 16.0,8,400,180,4220,11.1,77,1,pontiac grand prix lj 15.5,8,350,170,4165,11.4,77,1,chevrolet monte carlo landau 15.5,8,400,190,4325,12.2,77,1,chrysler cordoba 16.0,8,351,149,4335,14.5,77,1,ford thunderbird 29.0,4,97,78,1940,14.5,77,2,volkswagen rabbit custom 24.5,4,151,88,2740,16.0,77,1,pontiac sunbird coupe 26.0,4,97,75,2265,18.2,77,3,toyota corolla liftback 25.5,4,140,89,2755,15.8,77,1,ford mustang ii 2+2 30.5,4,98,63,2051,17.0,77,1,chevrolet chevette 33.5,4,98,83,2075,15.9,77,1,dodge colt m/m 30.0,4,97,67,1985,16.4,77,3,subaru dl 30.5,4,97,78,2190,14.1,77,2,volkswagen dasher 22.0,6,146,97,2815,14.5,77,3,datsun 810 21.5,4,121,110,2600,12.8,77,2,bmw 320i 21.5,3,80,110,2720,13.5,77,3,mazda rx-4 43.1,4,90,48,1985,21.5,78,2,volkswagen rabbit custom diesel 36.1,4,98,66,1800,14.4,78,1,ford fiesta 32.8,4,78,52,1985,19.4,78,3,mazda glc deluxe 39.4,4,85,70,2070,18.6,78,3,datsun b210 gx 36.1,4,91,60,1800,16.4,78,3,honda civic cvcc 19.9,8,260,110,3365,15.5,78,1,oldsmobile cutlass salon brougham 19.4,8,318,140,3735,13.2,78,1,dodge diplomat 20.2,8,302,139,3570,12.8,78,1,mercury monarch ghia 19.2,6,231,105,3535,19.2,78,1,pontiac phoenix lj 20.5,6,200,95,3155,18.2,78,1,chevrolet malibu 20.2,6,200,85,2965,15.8,78,1,ford fairmont (auto) 25.1,4,140,88,2720,15.4,78,1,ford fairmont (man) 20.5,6,225,100,3430,17.2,78,1,plymouth volare 19.4,6,232,90,3210,17.2,78,1,amc concord 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25.0,6,181,110,2945,16.4,82,1,buick century limited 38.0,6,262,85,3015,17.0,82,1,oldsmobile cutlass ciera (diesel) 26.0,4,156,92,2585,14.5,82,1,chrysler lebaron medallion 22.0,6,232,112,2835,14.7,82,1,ford granada l 32.0,4,144,96,2665,13.9,82,3,toyota celica gt 36.0,4,135,84,2370,13.0,82,1,dodge charger 2.2 27.0,4,151,90,2950,17.3,82,1,chevrolet camaro 27.0,4,140,86,2790,15.6,82,1,ford mustang gl 44.0,4,97,52,2130,24.6,82,2,vw pickup 32.0,4,135,84,2295,11.6,82,1,dodge rampage 28.0,4,120,79,2625,18.6,82,1,ford ranger 31.0,4,119,82,2720,19.4,82,1,chevy s-10 \ No newline at end of file diff --git a/200 solved problems in Python/pandas/05_Merge/Fictitous Names/Exercises.ipynb b/200 solved problems in Python/pandas/05_Merge/Fictitous Names/Exercises.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..61cc55c0fb2cd74cd52e80964a4521a38b8f1ea9 --- /dev/null +++ b/200 solved problems in Python/pandas/05_Merge/Fictitous Names/Exercises.ipynb @@ -0,0 +1,199 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fictitious Names" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "This time you will create a data again \n", + "\n", + "Special thanks to [Chris Albon](http://chrisalbon.com/) for sharing the dataset and materials.\n", + "All the credits to this exercise belongs to him. \n", + "\n", + "In order to understand about it go [here](https://blog.codinghorror.com/a-visual-explanation-of-sql-joins/).\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Create the 3 DataFrames based on the followin raw data" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "raw_data_1 = {\n", + " 'subject_id': ['1', '2', '3', '4', '5'],\n", + " 'first_name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'], \n", + " 'last_name': ['Anderson', 'Ackerman', 'Ali', 'Aoni', 'Atiches']}\n", + "\n", + "raw_data_2 = {\n", + " 'subject_id': ['4', '5', '6', '7', '8'],\n", + " 'first_name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'], \n", + " 'last_name': ['Bonder', 'Black', 'Balwner', 'Brice', 'Btisan']}\n", + "\n", + "raw_data_3 = {\n", + " 'subject_id': ['1', '2', '3', '4', '5', '7', '8', '9', '10', '11'],\n", + " 'test_id': [51, 15, 15, 61, 16, 14, 15, 1, 61, 16]}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign each to a variable called data1, data2, data3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Join the two dataframes along rows and assign all_data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Join the two dataframes along columns and assing to all_data_col" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Print data3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Merge all_data and data3 along the subject_id value" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Merge only the data that has the same 'subject_id' on both data1 and data2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. Merge all values in data1 and data2, with matching records from both sides where available." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/05_Merge/Fictitous Names/Exercises_with_solutions.ipynb b/200 solved problems in Python/pandas/05_Merge/Fictitous Names/Exercises_with_solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..805f867ab5db06763f21e5c0277de5541ddf5aac --- /dev/null +++ b/200 solved problems in Python/pandas/05_Merge/Fictitous Names/Exercises_with_solutions.ipynb @@ -0,0 +1,818 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fictitious Names" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "This time you will create a data again \n", + "\n", + "Special thanks to [Chris Albon](http://chrisalbon.com/) for sharing the dataset and materials.\n", + "All the credits to this exercise belongs to him. \n", + "\n", + "In order to understand about it go to [here](https://blog.codinghorror.com/a-visual-explanation-of-sql-joins/).\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Create the 3 DataFrames based on the followin raw data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "raw_data_1 = {\n", + " 'subject_id': ['1', '2', '3', '4', '5'],\n", + " 'first_name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'], \n", + " 'last_name': ['Anderson', 'Ackerman', 'Ali', 'Aoni', 'Atiches']}\n", + "\n", + "raw_data_2 = {\n", + " 'subject_id': ['4', '5', '6', '7', '8'],\n", + " 'first_name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'], \n", + " 'last_name': ['Bonder', 'Black', 'Balwner', 'Brice', 'Btisan']}\n", + "\n", + "raw_data_3 = {\n", + " 'subject_id': ['1', '2', '3', '4', '5', '7', '8', '9', '10', '11'],\n", + " 'test_id': [51, 15, 15, 61, 16, 14, 15, 1, 61, 16]}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign each to a variable called data1, data2, data3" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " subject_id test_id\n", + "0 1 51\n", + "1 2 15\n", + "2 3 15\n", + "3 4 61\n", + "4 5 16\n", + "5 7 14\n", + "6 8 15\n", + "7 9 1\n", + "8 10 61\n", + "9 11 16" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data1 = pd.DataFrame(raw_data_1, columns = ['subject_id', 'first_name', 'last_name'])\n", + "data2 = pd.DataFrame(raw_data_2, columns = ['subject_id', 'first_name', 'last_name'])\n", + "data3 = pd.DataFrame(raw_data_3, columns = ['subject_id','test_id'])\n", + "\n", + "data3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Join the two dataframes along rows and assign all_data" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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subject_idfirst_namelast_name
01AlexAnderson
12AmyAckerman
23AllenAli
34AliceAoni
45AyoungAtiches
04BillyBonder
15BrianBlack
26BranBalwner
37BryceBrice
48BettyBtisan
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" + ], + "text/plain": [ + " subject_id first_name last_name\n", + "0 1 Alex Anderson\n", + "1 2 Amy Ackerman\n", + "2 3 Allen Ali\n", + "3 4 Alice Aoni\n", + "4 5 Ayoung Atiches\n", + "0 4 Billy Bonder\n", + "1 5 Brian Black\n", + "2 6 Bran Balwner\n", + "3 7 Bryce Brice\n", + "4 8 Betty Btisan" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_data = pd.concat([data1, data2])\n", + "all_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Join the two dataframes along columns and assing to all_data_col" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " subject_id first_name last_name subject_id first_name last_name\n", + "0 1 Alex Anderson 4 Billy Bonder\n", + "1 2 Amy Ackerman 5 Brian Black\n", + "2 3 Allen Ali 6 Bran Balwner\n", + "3 4 Alice Aoni 7 Bryce Brice\n", + "4 5 Ayoung Atiches 8 Betty Btisan" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_data_col = pd.concat([data1, data2], axis = 1)\n", + "all_data_col" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Print data3" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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subject_idfirst_namelast_nametest_id
01AlexAnderson51
12AmyAckerman15
23AllenAli15
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44BillyBonder61
55AyoungAtiches16
65BrianBlack16
77BryceBrice14
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" + ], + "text/plain": [ + " subject_id first_name last_name test_id\n", + "0 1 Alex Anderson 51\n", + "1 2 Amy Ackerman 15\n", + "2 3 Allen Ali 15\n", + "3 4 Alice Aoni 61\n", + "4 4 Billy Bonder 61\n", + "5 5 Ayoung Atiches 16\n", + "6 5 Brian Black 16\n", + "7 7 Bryce Brice 14\n", + "8 8 Betty Btisan 15" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.merge(all_data, data3, on='subject_id')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Merge only the data that has the same 'subject_id' on both data1 and data2" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " subject_id first_name_x last_name_x first_name_y last_name_y\n", + "0 4 Alice Aoni Billy Bonder\n", + "1 5 Ayoung Atiches Brian Black" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.merge(data1, data2, on='subject_id', how='inner')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. Merge all values in data1 and data2, with matching records from both sides where available." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " subject_id first_name_x last_name_x first_name_y last_name_y\n", + "0 1 Alex Anderson NaN NaN\n", + "1 2 Amy Ackerman NaN NaN\n", + "2 3 Allen Ali NaN NaN\n", + "3 4 Alice Aoni Billy Bonder\n", + "4 5 Ayoung Atiches Brian Black\n", + "5 6 NaN NaN Bran Balwner\n", + "6 7 NaN NaN Bryce Brice\n", + "7 8 NaN NaN Betty Btisan" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.merge(data1, data2, on='subject_id', how='outer')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/05_Merge/Fictitous Names/Solutions.ipynb b/200 solved problems in Python/pandas/05_Merge/Fictitous Names/Solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..39b498503e75c72305adec9083db00d12050253d --- /dev/null +++ b/200 solved problems in Python/pandas/05_Merge/Fictitous Names/Solutions.ipynb @@ -0,0 +1,796 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fictitious Names" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "This time you will create a data again \n", + "\n", + "Special thanks to [Chris Albon](http://chrisalbon.com/) for sharing the dataset and materials.\n", + "All the credits to this exercise belongs to him. \n", + "\n", + "In order to understand about it go to [here](https://blog.codinghorror.com/a-visual-explanation-of-sql-joins/).\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Create the 3 DataFrames based on the followin raw data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "raw_data_1 = {\n", + " 'subject_id': ['1', '2', '3', '4', '5'],\n", + " 'first_name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'], \n", + " 'last_name': ['Anderson', 'Ackerman', 'Ali', 'Aoni', 'Atiches']}\n", + "\n", + "raw_data_2 = {\n", + " 'subject_id': ['4', '5', '6', '7', '8'],\n", + " 'first_name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'], \n", + " 'last_name': ['Bonder', 'Black', 'Balwner', 'Brice', 'Btisan']}\n", + "\n", + "raw_data_3 = {\n", + " 'subject_id': ['1', '2', '3', '4', '5', '7', '8', '9', '10', '11'],\n", + " 'test_id': [51, 15, 15, 61, 16, 14, 15, 1, 61, 16]}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign each to a variable called data1, data2, data3" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " subject_id first_name last_name subject_id first_name last_name\n", + "0 1 Alex Anderson 4 Billy Bonder\n", + "1 2 Amy Ackerman 5 Brian Black\n", + "2 3 Allen Ali 6 Bran Balwner\n", + "3 4 Alice Aoni 7 Bryce Brice\n", + "4 5 Ayoung Atiches 8 Betty Btisan" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Print data3" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " subject_id first_name last_name test_id\n", + "0 1 Alex Anderson 51\n", + "1 2 Amy Ackerman 15\n", + "2 3 Allen Ali 15\n", + "3 4 Alice Aoni 61\n", + "4 4 Billy Bonder 61\n", + "5 5 Ayoung Atiches 16\n", + "6 5 Brian Black 16\n", + "7 7 Bryce Brice 14\n", + "8 8 Betty Btisan 15" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Merge only the data that has the same 'subject_id' on both data1 and data2" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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As we are going to create random data don't try to reason of the numbers.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Create 3 differents Series, each of length 100, as follows: \n", + "1. The first a random number from 1 to 4 \n", + "2. The second a random number from 1 to 3\n", + "3. The third a random number from 10,000 to 30,000" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Let's create a DataFrame by joinning the Series by column" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Change the name of the columns to bedrs, bathrs, price_sqr_meter" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Create a one column DataFrame with the values of the 3 Series and assign it to 'bigcolumn'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Ops it seems it is going only until index 99. Is it true?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Reindex the DataFrame so it goes from 0 to 299" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/05_Merge/Housing Market/Exercises_with_solutions.ipynb b/200 solved problems in Python/pandas/05_Merge/Housing Market/Exercises_with_solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..251e5f3054f7c79310d1008923291813912c9a04 --- /dev/null +++ b/200 solved problems in Python/pandas/05_Merge/Housing Market/Exercises_with_solutions.ipynb @@ -0,0 +1,1180 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Housing Market" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "This time we will create our own dataset with fictional numbers to describe a house market. As we are going to create random data don't try to reason of the numbers.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Create 3 differents Series, each of length 100, as follows: \n", + "1. The first a random number from 1 to 4 \n", + "2. The second a random number from 1 to 3\n", + "3. The third a random number from 10,000 to 30,000" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 2\n", + "1 2\n", + "2 4\n", + "3 2\n", + "4 1\n", + "5 1\n", + "6 2\n", + "7 3\n", + "8 3\n", + "9 2\n", + "10 1\n", + "11 2\n", + "12 4\n", + "13 1\n", + "14 2\n", + "15 3\n", + "16 4\n", + "17 4\n", + "18 4\n", + "19 3\n", + "20 2\n", + "21 1\n", + "22 4\n", + "23 1\n", + "24 3\n", + "25 2\n", + "26 3\n", + "27 1\n", + "28 3\n", + "29 4\n", + " ..\n", + "70 4\n", + "71 2\n", + "72 2\n", + "73 4\n", + "74 2\n", + "75 1\n", + "76 2\n", + "77 4\n", + "78 3\n", + "79 2\n", + "80 2\n", + "81 2\n", + "82 4\n", + "83 2\n", + "84 2\n", + "85 2\n", + "86 1\n", + "87 3\n", + "88 1\n", + "89 1\n", + "90 1\n", + "91 3\n", + "92 1\n", + "93 2\n", + "94 3\n", + "95 4\n", + "96 4\n", + "97 2\n", + "98 1\n", + "99 3\n", + "dtype: int64 0 2\n", + "1 3\n", + "2 2\n", + "3 3\n", + "4 3\n", + "5 1\n", + "6 2\n", + "7 1\n", + "8 2\n", + "9 2\n", + "10 2\n", + "11 3\n", + "12 3\n", + "13 1\n", + "14 3\n", + "15 3\n", + "16 3\n", + "17 1\n", + "18 3\n", + "19 3\n", + "20 3\n", + "21 3\n", + "22 1\n", + "23 2\n", + "24 3\n", + "25 2\n", + "26 2\n", + "27 1\n", + "28 3\n", + "29 3\n", + " ..\n", + "70 3\n", + "71 2\n", + "72 2\n", + "73 2\n", + "74 3\n", + "75 2\n", + "76 3\n", + "77 1\n", + "78 1\n", + "79 1\n", + "80 2\n", + "81 1\n", + "82 1\n", + "83 3\n", + "84 1\n", + "85 3\n", + "86 1\n", + "87 2\n", + "88 3\n", + "89 2\n", + "90 2\n", + "91 3\n", + "92 2\n", + "93 2\n", + "94 2\n", + "95 2\n", + "96 2\n", + "97 3\n", + "98 1\n", + "99 1\n", + "dtype: int64 0 16957\n", + "1 24571\n", + "2 28303\n", + "3 14153\n", + "4 23445\n", + "5 21444\n", + "6 16179\n", + "7 22696\n", + "8 18595\n", + "9 27145\n", + "10 14406\n", + "11 15011\n", + "12 17444\n", + "13 26236\n", + "14 23808\n", + "15 21417\n", + "16 15079\n", + "17 13100\n", + "18 21470\n", + "19 17082\n", + "20 21935\n", + "21 26770\n", + "22 10059\n", + "23 11095\n", + "24 25916\n", + "25 17137\n", + "26 22023\n", + "27 21612\n", + "28 11446\n", + "29 29281\n", + " ... \n", + "70 23963\n", + "71 26782\n", + "72 11199\n", + "73 23600\n", + "74 26935\n", + "75 27365\n", + "76 23084\n", + "77 19052\n", + "78 19922\n", + "79 17088\n", + "80 25468\n", + "81 10924\n", + "82 10243\n", + "83 19834\n", + "84 21288\n", + "85 22410\n", + "86 22348\n", + "87 18812\n", + "88 29522\n", + "89 20838\n", + "90 28695\n", + "91 23000\n", + "92 21684\n", + "93 26316\n", + "94 10866\n", + "95 12337\n", + "96 13480\n", + "97 25158\n", + "98 25585\n", + "99 26142\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "s1 = pd.Series(np.random.randint(1, high=5, size=100, dtype='l'))\n", + "s2 = pd.Series(np.random.randint(1, high=4, size=100, dtype='l'))\n", + "s3 = pd.Series(np.random.randint(10000, high=30001, size=100, dtype='l'))\n", + "\n", + "print s1, s2, s3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. 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As we are going to create random data don't try to reason of the numbers.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Create 3 differents Series, each of length 100, as follows: \n", + "1. The first a random number from 1 to 4 \n", + "2. The second a random number from 1 to 3\n", + "3. The third a random number from 10,000 to 30,000" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 2\n", + "1 2\n", + "2 4\n", + "3 2\n", + "4 1\n", + "5 1\n", + "6 2\n", + "7 3\n", + "8 3\n", + "9 2\n", + "10 1\n", + "11 2\n", + "12 4\n", + "13 1\n", + "14 2\n", + "15 3\n", + "16 4\n", + "17 4\n", + "18 4\n", + "19 3\n", + "20 2\n", + "21 1\n", + "22 4\n", + "23 1\n", + "24 3\n", + "25 2\n", + "26 3\n", + "27 1\n", + "28 3\n", + "29 4\n", + " ..\n", + "70 4\n", + "71 2\n", + "72 2\n", + "73 4\n", + "74 2\n", + "75 1\n", + "76 2\n", + "77 4\n", + "78 3\n", + "79 2\n", + "80 2\n", + "81 2\n", + "82 4\n", + "83 2\n", + "84 2\n", + "85 2\n", + "86 1\n", + "87 3\n", + "88 1\n", + "89 1\n", + "90 1\n", + "91 3\n", + "92 1\n", + "93 2\n", + "94 3\n", + "95 4\n", + "96 4\n", + "97 2\n", + "98 1\n", + "99 3\n", + "dtype: int64 0 2\n", + "1 3\n", + "2 2\n", + "3 3\n", + "4 3\n", + "5 1\n", + "6 2\n", + "7 1\n", + "8 2\n", + "9 2\n", + "10 2\n", + "11 3\n", + "12 3\n", + "13 1\n", + "14 3\n", + "15 3\n", + "16 3\n", + "17 1\n", + "18 3\n", + "19 3\n", + "20 3\n", + "21 3\n", + "22 1\n", + "23 2\n", + "24 3\n", + "25 2\n", + "26 2\n", + "27 1\n", + "28 3\n", + "29 3\n", + " ..\n", + "70 3\n", + "71 2\n", + "72 2\n", + "73 2\n", + "74 3\n", + "75 2\n", + "76 3\n", + "77 1\n", + "78 1\n", + "79 1\n", + "80 2\n", + "81 1\n", + "82 1\n", + "83 3\n", + "84 1\n", + "85 3\n", + "86 1\n", + "87 2\n", + "88 3\n", + "89 2\n", + "90 2\n", + "91 3\n", + "92 2\n", + "93 2\n", + "94 2\n", + "95 2\n", + "96 2\n", + "97 3\n", + "98 1\n", + "99 1\n", + "dtype: int64 0 16957\n", + "1 24571\n", + "2 28303\n", + "3 14153\n", + "4 23445\n", + "5 21444\n", + "6 16179\n", + "7 22696\n", + "8 18595\n", + "9 27145\n", + "10 14406\n", + "11 15011\n", + "12 17444\n", + "13 26236\n", + "14 23808\n", + "15 21417\n", + "16 15079\n", + "17 13100\n", + "18 21470\n", + "19 17082\n", + "20 21935\n", + "21 26770\n", + "22 10059\n", + "23 11095\n", + "24 25916\n", + "25 17137\n", + "26 22023\n", + "27 21612\n", + "28 11446\n", + "29 29281\n", + " ... \n", + "70 23963\n", + "71 26782\n", + "72 11199\n", + "73 23600\n", + "74 26935\n", + "75 27365\n", + "76 23084\n", + "77 19052\n", + "78 19922\n", + "79 17088\n", + "80 25468\n", + "81 10924\n", + "82 10243\n", + "83 19834\n", + "84 21288\n", + "85 22410\n", + "86 22348\n", + "87 18812\n", + "88 29522\n", + "89 20838\n", + "90 28695\n", + "91 23000\n", + "92 21684\n", + "93 26316\n", + "94 10866\n", + "95 12337\n", + "96 13480\n", + "97 25158\n", + "98 25585\n", + "99 26142\n", + "dtype: int64\n" + ] + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Let's create a DataFrame by joinning the Series by column" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/Stats/US_Baby_Names/US_Baby_Names_right.csv). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called baby_names." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. See the first 10 entries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Delete the column 'Unnamed: 0' and 'Id'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Is there more male or female names in the dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Group the dataset by name and assign to names" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. How many different names exist in the dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. What is the name with most occurrences?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. How many different names have the least occurrences?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 11. What is the median name occurrence?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 12. What is the standard deviation of names?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 13. Get a summary with the mean, min, max, std and quartiles." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/06_Stats/US_Baby_Names/Exercises_with_solutions.ipynb b/200 solved problems in Python/pandas/06_Stats/US_Baby_Names/Exercises_with_solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..ec6f7ffaa7243fd0b8344685590bd08215f0bbb1 --- /dev/null +++ b/200 solved problems in Python/pandas/06_Stats/US_Baby_Names/Exercises_with_solutions.ipynb @@ -0,0 +1,1026 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# US - Baby Names" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "We are going to use a subset of [US Baby Names](https://www.kaggle.com/kaggle/us-baby-names) from Kaggle. \n", + "In the file it will be names from 2004 until 2014\n", + "\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/Stats/US_Baby_Names/US_Baby_Names_right.csv). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called baby_names." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 1016395 entries, 0 to 1016394\n", + "Data columns (total 7 columns):\n", + "Unnamed: 0 1016395 non-null int64\n", + "Id 1016395 non-null int64\n", + "Name 1016395 non-null object\n", + "Year 1016395 non-null int64\n", + "Gender 1016395 non-null object\n", + "State 1016395 non-null object\n", + "Count 1016395 non-null int64\n", + "dtypes: int64(4), object(3)\n", + "memory usage: 54.3+ MB\n" + ] + } + ], + "source": [ + "baby_names = pd.read_csv('https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/Stats/US_Baby_Names/US_Baby_Names_right.csv')\n", + "baby_names.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. See the first 10 entries" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Count\n", + "Name \n", + "Jacob 242874\n", + "Emma 214852\n", + "Michael 214405\n", + "Ethan 209277\n", + "Isabella 204798" + ] + }, + "execution_count": 116, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# you don't want to sum the Year column, so you delete it\n", + "# del baby_names[\"Year\"]\n", + "\n", + "# group the data\n", + "names = baby_names.groupby(\"Name\").sum()\n", + "\n", + "# print the first 5 observations\n", + "names.head()\n", + "\n", + "# print the size of the dataset\n", + "print names.shape\n", + "\n", + "# sort it from the biggest value to the smallest one\n", + "names.sort_values(\"Count\", ascending = 0).head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. How many different names exist in the dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "17632" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# as we have already grouped by the name, all the names are unique already. \n", + "# get the length of names\n", + "len(names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. What is the name with most occurrences?" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'Jacob'" + ] + }, + "execution_count": 151, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "names.Count.idxmax()\n", + "\n", + "# OR\n", + "\n", + "# names[names.Count == names.Count.max()]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. How many different names have the least occurrences?" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2578" + ] + }, + "execution_count": 138, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(names[names.Count == names.Count.min()])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 11. What is the median name occurrence?" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Count
Name
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Alara49
Alysse49
Ameir49
Anely49
Antonina49
Aveline49
Aziah49
Baily49
Caleah49
Carlota49
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Emmanuela49
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Esli49
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Hareem49
Iven49
Jaice49
Jaiyana49
Jamiracle49
Jelissa49
......
Kyndle49
Kynsley49
Leylanie49
Maisha49
Malillany49
Mariann49
Marquell49
Maurilio49
Mckynzie49
Mehdi49
Nabeel49
Nalleli49
Nassir49
Nazier49
Nishant49
Rebecka49
Reghan49
Ridwan49
Riot49
Rubin49
Ryatt49
Sameera49
Sanjuanita49
Shalyn49
Skylie49
Sriram49
Trinton49
Vita49
Yoni49
Zuleima49
\n", + "

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" + ], + "text/plain": [ + " Count\n", + "count 17632.000000\n", + "mean 2008.932169\n", + "std 11006.069468\n", + "min 5.000000\n", + "25% 11.000000\n", + "50% 49.000000\n", + "75% 337.000000\n", + "max 242874.000000" + ] + }, + "execution_count": 148, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "names.describe()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/06_Stats/US_Baby_Names/Solutions.ipynb b/200 solved problems in Python/pandas/06_Stats/US_Baby_Names/Solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..856c840249d700b90e0744506b89c133911af55e --- /dev/null +++ b/200 solved problems in Python/pandas/06_Stats/US_Baby_Names/Solutions.ipynb @@ -0,0 +1,988 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# US - Baby Names" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "We are going to use a subset of [US Baby Names](https://www.kaggle.com/kaggle/us-baby-names) from Kaggle. \n", + "In the file it will be names from 2004 until 2014\n", + "\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/Stats/US_Baby_Names/US_Baby_Names_right.csv). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called baby_names." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 1016395 entries, 0 to 1016394\n", + "Data columns (total 7 columns):\n", + "Unnamed: 0 1016395 non-null int64\n", + "Id 1016395 non-null int64\n", + "Name 1016395 non-null object\n", + "Year 1016395 non-null int64\n", + "Gender 1016395 non-null object\n", + "State 1016395 non-null object\n", + "Count 1016395 non-null int64\n", + "dtypes: int64(4), object(3)\n", + "memory usage: 54.3+ MB\n" + ] + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. See the first 10 entries" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0IdNameYearGenderStateCount
01134911350Emma2004FAK62
11135011351Madison2004FAK48
21135111352Hannah2004FAK46
31135211353Grace2004FAK44
41135311354Emily2004FAK41
51135411355Abigail2004FAK37
61135511356Olivia2004FAK33
71135611357Isabella2004FAK30
81135711358Alyssa2004FAK29
91135811359Sophia2004FAK28
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" + ], + "text/plain": [ + " Unnamed: 0 Id Name Year Gender State Count\n", + "0 11349 11350 Emma 2004 F AK 62\n", + "1 11350 11351 Madison 2004 F AK 48\n", + "2 11351 11352 Hannah 2004 F AK 46\n", + "3 11352 11353 Grace 2004 F AK 44\n", + "4 11353 11354 Emily 2004 F AK 41\n", + "5 11354 11355 Abigail 2004 F AK 37\n", + "6 11355 11356 Olivia 2004 F AK 33\n", + "7 11356 11357 Isabella 2004 F AK 30\n", + "8 11357 11358 Alyssa 2004 F AK 29\n", + "9 11358 11359 Sophia 2004 F AK 28" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Delete the column 'Unnamed: 0' and 'Id'" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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NameYearGenderStateCount
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" + ], + "text/plain": [ + " Name Year Gender State Count\n", + "0 Emma 2004 F AK 62\n", + "1 Madison 2004 F AK 48\n", + "2 Hannah 2004 F AK 46\n", + "3 Grace 2004 F AK 44\n", + "4 Emily 2004 F AK 41" + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Is there more male or female names in the dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "17632\n" + ] + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Group the dataset by name and assign to names" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(17632, 1)\n" + ] + }, + { + "data": { + "text/html": [ + "
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Count
Name
Jacob242874
Emma214852
Michael214405
Ethan209277
Isabella204798
\n", + "
" + ], + "text/plain": [ + " Count\n", + "Name \n", + "Jacob 242874\n", + "Emma 214852\n", + "Michael 214405\n", + "Ethan 209277\n", + "Isabella 204798" + ] + }, + "execution_count": 116, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. How many different names exist in the dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "17632" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. What is the name with most occurrences?" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'Jacob'" + ] + }, + "execution_count": 151, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. How many different names have the least occurrences?" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2578" + ] + }, + "execution_count": 138, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 11. What is the median name occurrence?" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Name
Aishani49
Alara49
Alysse49
Ameir49
Anely49
Antonina49
Aveline49
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Baily49
Caleah49
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Cristine49
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Iven49
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......
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Kynsley49
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Mckynzie49
Mehdi49
Nabeel49
Nalleli49
Nassir49
Nazier49
Nishant49
Rebecka49
Reghan49
Ridwan49
Riot49
Rubin49
Ryatt49
Sameera49
Sanjuanita49
Shalyn49
Skylie49
Sriram49
Trinton49
Vita49
Yoni49
Zuleima49
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\n", + "
" + ], + "text/plain": [ + " Count\n", + "Name \n", + "Aishani 49\n", + "Alara 49\n", + "Alysse 49\n", + "Ameir 49\n", + "Anely 49\n", + "Antonina 49\n", + "Aveline 49\n", + "Aziah 49\n", + "Baily 49\n", + "Caleah 49\n", + "Carlota 49\n", + "Cristine 49\n", + "Dahlila 49\n", + "Darvin 49\n", + "Deante 49\n", + "Deserae 49\n", + "Devean 49\n", + "Elizah 49\n", + "Emmaly 49\n", + "Emmanuela 49\n", + "Envy 49\n", + "Esli 49\n", + "Fay 49\n", + "Gurshaan 49\n", + "Hareem 49\n", + "Iven 49\n", + "Jaice 49\n", + "Jaiyana 49\n", + "Jamiracle 49\n", + "Jelissa 49\n", + "... ...\n", + "Kyndle 49\n", + "Kynsley 49\n", + "Leylanie 49\n", + "Maisha 49\n", + "Malillany 49\n", + "Mariann 49\n", + "Marquell 49\n", + "Maurilio 49\n", + "Mckynzie 49\n", + "Mehdi 49\n", + "Nabeel 49\n", + "Nalleli 49\n", + "Nassir 49\n", + "Nazier 49\n", + "Nishant 49\n", + "Rebecka 49\n", + "Reghan 49\n", + "Ridwan 49\n", + "Riot 49\n", + "Rubin 49\n", + "Ryatt 49\n", + "Sameera 49\n", + "Sanjuanita 49\n", + "Shalyn 49\n", + "Skylie 49\n", + "Sriram 49\n", + "Trinton 49\n", + "Vita 49\n", + "Yoni 49\n", + "Zuleima 49\n", + "\n", + "[66 rows x 1 columns]" + ] + }, + "execution_count": 144, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 12. What is the standard deviation of names?" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "11006.069467891111" + ] + }, + "execution_count": 147, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 13. Get a summary with the mean, min, max, std and quartiles." + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Count\n", + "count 17632.000000\n", + "mean 2008.932169\n", + "std 11006.069468\n", + "min 5.000000\n", + "25% 11.000000\n", + "50% 49.000000\n", + "75% 337.000000\n", + "max 242874.000000" + ] + }, + "execution_count": 148, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/06_Stats/US_Baby_Names/US_Baby_Names_right.csv b/200 solved problems in Python/pandas/06_Stats/US_Baby_Names/US_Baby_Names_right.csv new file mode 100644 index 0000000000000000000000000000000000000000..46d0a644506819dc1a54eaede398b9e2fe9228f0 --- /dev/null +++ b/200 solved problems in Python/pandas/06_Stats/US_Baby_Names/US_Baby_Names_right.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a77decebf1740ce50b55896ea81a5f9dafc884dd4d6ad671c3a2a8203ca1a4d3 +size 36779091 diff --git a/200 solved problems in Python/pandas/06_Stats/Wind_Stats/Exercises.ipynb b/200 solved problems in Python/pandas/06_Stats/Wind_Stats/Exercises.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..bd7d46f0c5f79795f853daa9eb17bbaf5d450b24 --- /dev/null +++ b/200 solved problems in Python/pandas/06_Stats/Wind_Stats/Exercises.ipynb @@ -0,0 +1,346 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Wind Statistics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "The data have been modified to contain some missing values, identified by NaN. \n", + "Using pandas should make this exercise\n", + "easier, in particular for the bonus question.\n", + "\n", + "You should be able to perform all of these operations without using\n", + "a for loop or other looping construct.\n", + "\n", + "\n", + "1. The data in 'wind.data' has the following format:" + ] + }, + { + "cell_type": "code", + "execution_count": 434, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nYr Mo Dy RPT VAL ROS KIL SHA BIR DUB CLA MUL CLO BEL MAL\\n61 1 1 15.04 14.96 13.17 9.29 NaN 9.87 13.67 10.25 10.83 12.58 18.50 15.04\\n61 1 2 14.71 NaN 10.83 6.50 12.62 7.67 11.50 10.04 9.79 9.67 17.54 13.83\\n61 1 3 18.50 16.88 12.33 10.13 11.17 6.17 11.25 NaN 8.50 7.67 12.75 12.71\\n'" + ] + }, + "execution_count": 434, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "Yr Mo Dy RPT VAL ROS KIL SHA BIR DUB CLA MUL CLO BEL MAL\n", + "61 1 1 15.04 14.96 13.17 9.29 NaN 9.87 13.67 10.25 10.83 12.58 18.50 15.04\n", + "61 1 2 14.71 NaN 10.83 6.50 12.62 7.67 11.50 10.04 9.79 9.67 17.54 13.83\n", + "61 1 3 18.50 16.88 12.33 10.13 11.17 6.17 11.25 NaN 8.50 7.67 12.75 12.71\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " The first three columns are year, month and day. The\n", + " remaining 12 columns are average windspeeds in knots at 12\n", + " locations in Ireland on that day. \n", + "\n", + " More information about the dataset go [here](wind.desc)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://github.com/guipsamora/pandas_exercises/blob/master/Stats/Wind_Stats/wind.data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called data and replace the first 3 columns by a proper datetime index." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Year 2061? Do we really have data from this year? Create a function to fix it and apply it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Set the right dates as the index. Pay attention at the data type, it should be datetime64[ns]." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Compute how many values are missing for each location over the entire record. \n", + "#### They should be ignored in all calculations below. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Compute how many non-missing values there are in total." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Calculate the mean windspeeds of the windspeeds over all the locations and all the times.\n", + "#### A single number for the entire dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. Create a DataFrame called loc_stats and calculate the min, max and mean windspeeds and standard deviations of the windspeeds at each location over all the days \n", + "\n", + "#### A different set of numbers for each location." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. Create a DataFrame called day_stats and calculate the min, max and mean windspeed and standard deviations of the windspeeds across all the locations at each day.\n", + "\n", + "#### A different set of numbers for each day." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 11. Find the average windspeed in January for each location. \n", + "#### Treat January 1961 and January 1962 both as January." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 12. Downsample the record to a yearly frequency for each location." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 13. Downsample the record to a monthly frequency for each location." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 14. Downsample the record to a weekly frequency for each location." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 15. Calculate the mean windspeed for each month in the dataset. \n", + "#### Treat January 1961 and January 1962 as *different* months.\n", + "#### (hint: first find a way to create an identifier unique for each month.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 16. Calculate the min, max and mean windspeeds and standard deviations of the windspeeds across all locations for each week (assume that the first week starts on January 2 1961) for the first 52 weeks." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/06_Stats/Wind_Stats/Exercises_with_solutions.ipynb b/200 solved problems in Python/pandas/06_Stats/Wind_Stats/Exercises_with_solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..4078fca74753edfe4946731d98bd8834366ee97b --- /dev/null +++ b/200 solved problems in Python/pandas/06_Stats/Wind_Stats/Exercises_with_solutions.ipynb @@ -0,0 +1,4631 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Wind Statistics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "The data have been modified to contain some missing values, identified by NaN. \n", + "Using pandas should make this exercise\n", + "easier, in particular for the bonus question.\n", + "\n", + "You should be able to perform all of these operations without using\n", + "a for loop or other looping construct.\n", + "\n", + "\n", + "1. The data in 'wind.data' has the following format:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nYr Mo Dy RPT VAL ROS KIL SHA BIR DUB CLA MUL CLO BEL MAL\\n61 1 1 15.04 14.96 13.17 9.29 NaN 9.87 13.67 10.25 10.83 12.58 18.50 15.04\\n61 1 2 14.71 NaN 10.83 6.50 12.62 7.67 11.50 10.04 9.79 9.67 17.54 13.83\\n61 1 3 18.50 16.88 12.33 10.13 11.17 6.17 11.25 NaN 8.50 7.67 12.75 12.71\\n'" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "Yr Mo Dy RPT VAL ROS KIL SHA BIR DUB CLA MUL CLO BEL MAL\n", + "61 1 1 15.04 14.96 13.17 9.29 NaN 9.87 13.67 10.25 10.83 12.58 18.50 15.04\n", + "61 1 2 14.71 NaN 10.83 6.50 12.62 7.67 11.50 10.04 9.79 9.67 17.54 13.83\n", + "61 1 3 18.50 16.88 12.33 10.13 11.17 6.17 11.25 NaN 8.50 7.67 12.75 12.71\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " The first three columns are year, month and day. The\n", + " remaining 12 columns are average windspeeds in knots at 12\n", + " locations in Ireland on that day. \n", + "\n", + " More information about the dataset go [here](wind.desc)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import datetime" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://github.com/guipsamora/pandas_exercises/blob/master/Stats/Wind_Stats/wind.data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called data and replace the first 3 columns by a proper datetime index." + ] + }, + { + "cell_type": "code", + "execution_count": 414, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Yr_Mo_DyRPTVALROSKILSHABIRDUBCLAMULCLOBELMAL
02061-01-0115.0414.9613.179.29NaN9.8713.6710.2510.8312.5818.5015.04
12061-01-0214.71NaN10.836.5012.627.6711.5010.049.799.6717.5413.83
22061-01-0318.5016.8812.3310.1311.176.1711.25NaN8.507.6712.7512.71
32061-01-0410.586.6311.754.584.542.888.631.795.835.885.4610.88
42061-01-0513.3313.2511.426.1710.718.2111.926.5410.9210.3412.9211.83
\n", + "
" + ], + "text/plain": [ + " Yr_Mo_Dy RPT VAL ROS KIL SHA BIR DUB CLA MUL \\\n", + "0 2061-01-01 15.04 14.96 13.17 9.29 NaN 9.87 13.67 10.25 10.83 \n", + "1 2061-01-02 14.71 NaN 10.83 6.50 12.62 7.67 11.50 10.04 9.79 \n", + "2 2061-01-03 18.50 16.88 12.33 10.13 11.17 6.17 11.25 NaN 8.50 \n", + "3 2061-01-04 10.58 6.63 11.75 4.58 4.54 2.88 8.63 1.79 5.83 \n", + "4 2061-01-05 13.33 13.25 11.42 6.17 10.71 8.21 11.92 6.54 10.92 \n", + "\n", + " CLO BEL MAL \n", + "0 12.58 18.50 15.04 \n", + "1 9.67 17.54 13.83 \n", + "2 7.67 12.75 12.71 \n", + "3 5.88 5.46 10.88 \n", + "4 10.34 12.92 11.83 " + ] + }, + "execution_count": 414, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# parse_dates gets 0, 1, 2 columns and parses them as the index\n", + "data = pd.read_table(\"wind.data\", sep = \"\\s+\", parse_dates = [[0,1,2]]) \n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Year 2061? Do we really have data from this year? Create a function to fix it and apply it." + ] + }, + { + "cell_type": "code", + "execution_count": 415, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Yr_Mo_DyRPTVALROSKILSHABIRDUBCLAMULCLOBELMAL
01961-01-0115.0414.9613.179.29NaN9.8713.6710.2510.8312.5818.5015.04
11961-01-0214.71NaN10.836.5012.627.6711.5010.049.799.6717.5413.83
21961-01-0318.5016.8812.3310.1311.176.1711.25NaN8.507.6712.7512.71
31961-01-0410.586.6311.754.584.542.888.631.795.835.885.4610.88
41961-01-0513.3313.2511.426.1710.718.2111.926.5410.9210.3412.9211.83
\n", + "
" + ], + "text/plain": [ + " Yr_Mo_Dy RPT VAL ROS KIL SHA BIR DUB CLA MUL \\\n", + "0 1961-01-01 15.04 14.96 13.17 9.29 NaN 9.87 13.67 10.25 10.83 \n", + "1 1961-01-02 14.71 NaN 10.83 6.50 12.62 7.67 11.50 10.04 9.79 \n", + "2 1961-01-03 18.50 16.88 12.33 10.13 11.17 6.17 11.25 NaN 8.50 \n", + "3 1961-01-04 10.58 6.63 11.75 4.58 4.54 2.88 8.63 1.79 5.83 \n", + "4 1961-01-05 13.33 13.25 11.42 6.17 10.71 8.21 11.92 6.54 10.92 \n", + "\n", + " CLO BEL MAL \n", + "0 12.58 18.50 15.04 \n", + "1 9.67 17.54 13.83 \n", + "2 7.67 12.75 12.71 \n", + "3 5.88 5.46 10.88 \n", + "4 10.34 12.92 11.83 " + ] + }, + "execution_count": 415, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# The problem is that the dates are 2061 and so on...\n", + "\n", + "# function that uses datetime\n", + "def fix_century(x):\n", + " year = x.year - 100 if x.year > 1989 else x.year\n", + " return datetime.date(year, x.month, x.day)\n", + "\n", + "# apply the function fix_century on the column and replace the values to the right ones\n", + "data['Yr_Mo_Dy'] = data['Yr_Mo_Dy'].apply(fix_century)\n", + "\n", + "# data.info()\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Set the right dates as the index. Pay attention at the data type, it should be datetime64[ns]." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'pd' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# transform Yr_Mo_Dy it to date type datetime64\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Yr_Mo_Dy\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_datetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Yr_Mo_Dy\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# set 'Yr_Mo_Dy' as the index\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Yr_Mo_Dy'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined" + ] + } + ], + "source": [ + "# transform Yr_Mo_Dy it to date type datetime64\n", + "data[\"Yr_Mo_Dy\"] = pd.to_datetime(data[\"Yr_Mo_Dy\"])\n", + "\n", + "# set 'Yr_Mo_Dy' as the index\n", + "data = data.set_index('Yr_Mo_Dy')\n", + "\n", + "data.head()\n", + "# data.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Compute how many values are missing for each location over the entire record. \n", + "#### They should be ignored in all calculations below. " + ] + }, + { + "cell_type": "code", + "execution_count": 423, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "RPT 6\n", + "VAL 3\n", + "ROS 2\n", + "KIL 5\n", + "SHA 2\n", + "BIR 0\n", + "DUB 3\n", + "CLA 2\n", + "MUL 3\n", + "CLO 1\n", + "BEL 0\n", + "MAL 4\n", + "dtype: int64" + ] + }, + "execution_count": 423, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# \"Number of non-missing values for each location: \"\n", + "data.isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Compute how many non-missing values there are in total." + ] + }, + { + "cell_type": "code", + "execution_count": 424, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "RPT 6\n", + "VAL 9\n", + "ROS 10\n", + "KIL 7\n", + "SHA 10\n", + "BIR 12\n", + "DUB 9\n", + "CLA 10\n", + "MUL 9\n", + "CLO 11\n", + "BEL 12\n", + "MAL 8\n", + "dtype: int64" + ] + }, + "execution_count": 424, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# number of columns minus the number of missing values for each location\n", + "data.shape[1] - data.isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Calculate the mean windspeeds of the windspeeds over all the locations and all the times.\n", + "#### A single number for the entire dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 426, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "10.227982360836924" + ] + }, + "execution_count": 426, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# print 'Mean over all values is: '\n", + "data.mean().mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. Create a DataFrame called loc_stats and calculate the min, max and mean windspeeds and standard deviations of the windspeeds at each location over all the days \n", + "\n", + "#### A different set of numbers for each location." + ] + }, + { + "cell_type": "code", + "execution_count": 264, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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minmaxmeanstd
RPT0.6735.8012.3629875.618413
VAL0.2133.3710.6443145.267356
ROS1.5033.8411.6605265.008450
KIL0.0028.466.3064683.605811
SHA0.1337.5410.4558344.936125
BIR0.0026.167.0922543.968683
DUB0.0030.379.7973434.977555
CLA0.0031.088.4950534.499449
MUL0.0025.888.4935904.166872
CLO0.0428.218.7073324.503954
BEL0.1342.3813.1210075.835037
MAL0.6742.5415.5990796.699794
\n", + "
" + ], + "text/plain": [ + " min max mean std\n", + "RPT 0.67 35.80 12.362987 5.618413\n", + "VAL 0.21 33.37 10.644314 5.267356\n", + "ROS 1.50 33.84 11.660526 5.008450\n", + "KIL 0.00 28.46 6.306468 3.605811\n", + "SHA 0.13 37.54 10.455834 4.936125\n", + "BIR 0.00 26.16 7.092254 3.968683\n", + "DUB 0.00 30.37 9.797343 4.977555\n", + "CLA 0.00 31.08 8.495053 4.499449\n", + "MUL 0.00 25.88 8.493590 4.166872\n", + "CLO 0.04 28.21 8.707332 4.503954\n", + "BEL 0.13 42.38 13.121007 5.835037\n", + "MAL 0.67 42.54 15.599079 6.699794" + ] + }, + "execution_count": 264, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "loc_stats = pd.DataFrame()\n", + "\n", + "loc_stats['min'] = data.min() # min\n", + "loc_stats['max'] = data.max() # max \n", + "loc_stats['mean'] = data.mean() # mean\n", + "loc_stats['std'] = data.std() # standard deviations\n", + "\n", + "loc_stats" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. Create a DataFrame called day_stats and calculate the min, max and mean windspeed and standard deviations of the windspeeds across all the locations at each day.\n", + "\n", + "#### A different set of numbers for each day." + ] + }, + { + "cell_type": "code", + "execution_count": 404, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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minmaxmeanstd
01.018.5012.0166674.382798
11.017.5410.4750004.260110
21.018.5010.7550004.664914
31.011.756.1869233.435771
41.013.339.8892313.551768
\n", + "
" + ], + "text/plain": [ + " min max mean std\n", + "0 1.0 18.50 12.016667 4.382798\n", + "1 1.0 17.54 10.475000 4.260110\n", + "2 1.0 18.50 10.755000 4.664914\n", + "3 1.0 11.75 6.186923 3.435771\n", + "4 1.0 13.33 9.889231 3.551768" + ] + }, + "execution_count": 404, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# create the dataframe\n", + "day_stats = pd.DataFrame()\n", + "\n", + "# this time we determine axis equals to one so it gets each row.\n", + "day_stats['min'] = data.min(axis = 1) # min\n", + "day_stats['max'] = data.max(axis = 1) # max \n", + "day_stats['mean'] = data.mean(axis = 1) # mean\n", + "day_stats['std'] = data.std(axis = 1) # standard deviations\n", + "\n", + "day_stats.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 11. Find the average windspeed in January for each location. \n", + "#### Treat January 1961 and January 1962 both as January." + ] + }, + { + "cell_type": "code", + "execution_count": 427, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "RPT 14.847325\n", + "VAL 12.914560\n", + "ROS 13.299624\n", + "KIL 7.199498\n", + "SHA 11.667734\n", + "BIR 8.054839\n", + "DUB 11.819355\n", + "CLA 9.512047\n", + "MUL 9.543208\n", + "CLO 10.053566\n", + "BEL 14.550520\n", + "MAL 18.028763\n", + "dtype: float64" + ] + }, + "execution_count": 427, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# print \"January windspeeds:\"\n", + "\n", + "# creates a new column 'date' and gets the values from the index\n", + "data['date'] = data.index\n", + "\n", + "# creates a column for each value from date\n", + "data['month'] = data['date'].apply(lambda date: date.month)\n", + "data['year'] = data['date'].apply(lambda date: date.year)\n", + "data['day'] = data['date'].apply(lambda date: date.day)\n", + "\n", + "# gets all value from the month 1 and assign to janyary_winds\n", + "january_winds = data.query('month == 1')\n", + "\n", + "# gets the mean from january_winds, using .loc to not print the mean of month, year and day\n", + "january_winds.loc[:,'RPT':\"MAL\"].mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 12. Downsample the record to a yearly frequency for each location." + ] + }, + { + "cell_type": "code", + "execution_count": 428, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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RPTVALROSKILSHABIRDUBCLAMULCLOBELMALdatemonthyearday
Yr_Mo_Dy
1961-01-0115.0414.9613.179.29NaN9.8713.6710.2510.8312.5818.5015.041961-01-01119611
1962-01-019.293.4211.543.502.211.9610.412.793.545.174.387.921962-01-01119621
1963-01-0115.5913.6219.798.3812.2510.0023.4515.7113.5914.3717.5834.131963-01-01119631
1964-01-0125.8022.1318.2113.2521.2914.7914.1219.5813.2516.7528.9621.001964-01-01119641
1965-01-019.5411.929.004.386.085.2110.256.085.718.6312.0417.411965-01-01119651
1966-01-0122.0421.5017.0812.7522.1715.5921.7918.1216.6617.8328.3323.791966-01-01119661
1967-01-016.464.466.503.216.673.7911.383.837.719.0810.6720.911967-01-01119671
1968-01-0130.0417.8816.2516.2521.7912.5418.1616.6218.7517.6222.2527.291968-01-01119681
1969-01-016.131.635.411.082.541.008.502.424.586.349.1716.711969-01-01119691
1970-01-019.592.9611.793.426.134.089.004.467.293.507.3313.001970-01-01119701
1971-01-013.710.794.710.171.421.044.630.751.541.084.219.541971-01-01119711
1972-01-019.293.6314.544.256.754.4213.005.3310.048.548.7119.171972-01-01119721
1973-01-0116.5015.9214.627.418.2911.2113.547.7910.4610.7913.379.711973-01-01119731
1974-01-0123.2116.5416.089.7515.8311.469.5413.5413.8316.6617.2125.291974-01-01119741
1975-01-0114.0413.5411.295.4612.585.588.128.969.295.177.7111.631975-01-01119751
1976-01-0118.3417.6714.838.0016.6210.1313.179.0413.135.7511.3814.961976-01-01119761
1977-01-0120.0411.9220.259.139.298.0410.755.889.009.0014.8825.701977-01-01119771
1978-01-018.337.127.713.548.507.5014.7110.0011.8310.0015.0920.461978-01-01119781
\n", + "
" + ], + "text/plain": [ + " RPT VAL ROS KIL SHA BIR DUB CLA MUL \\\n", + "Yr_Mo_Dy \n", + "1961-01-01 15.04 14.96 13.17 9.29 NaN 9.87 13.67 10.25 10.83 \n", + "1962-01-01 9.29 3.42 11.54 3.50 2.21 1.96 10.41 2.79 3.54 \n", + "1963-01-01 15.59 13.62 19.79 8.38 12.25 10.00 23.45 15.71 13.59 \n", + "1964-01-01 25.80 22.13 18.21 13.25 21.29 14.79 14.12 19.58 13.25 \n", + "1965-01-01 9.54 11.92 9.00 4.38 6.08 5.21 10.25 6.08 5.71 \n", + "1966-01-01 22.04 21.50 17.08 12.75 22.17 15.59 21.79 18.12 16.66 \n", + "1967-01-01 6.46 4.46 6.50 3.21 6.67 3.79 11.38 3.83 7.71 \n", + "1968-01-01 30.04 17.88 16.25 16.25 21.79 12.54 18.16 16.62 18.75 \n", + "1969-01-01 6.13 1.63 5.41 1.08 2.54 1.00 8.50 2.42 4.58 \n", + "1970-01-01 9.59 2.96 11.79 3.42 6.13 4.08 9.00 4.46 7.29 \n", + "1971-01-01 3.71 0.79 4.71 0.17 1.42 1.04 4.63 0.75 1.54 \n", + "1972-01-01 9.29 3.63 14.54 4.25 6.75 4.42 13.00 5.33 10.04 \n", + "1973-01-01 16.50 15.92 14.62 7.41 8.29 11.21 13.54 7.79 10.46 \n", + "1974-01-01 23.21 16.54 16.08 9.75 15.83 11.46 9.54 13.54 13.83 \n", + "1975-01-01 14.04 13.54 11.29 5.46 12.58 5.58 8.12 8.96 9.29 \n", + "1976-01-01 18.34 17.67 14.83 8.00 16.62 10.13 13.17 9.04 13.13 \n", + "1977-01-01 20.04 11.92 20.25 9.13 9.29 8.04 10.75 5.88 9.00 \n", + "1978-01-01 8.33 7.12 7.71 3.54 8.50 7.50 14.71 10.00 11.83 \n", + "\n", + " CLO BEL MAL date month year day \n", + "Yr_Mo_Dy \n", + "1961-01-01 12.58 18.50 15.04 1961-01-01 1 1961 1 \n", + "1962-01-01 5.17 4.38 7.92 1962-01-01 1 1962 1 \n", + "1963-01-01 14.37 17.58 34.13 1963-01-01 1 1963 1 \n", + "1964-01-01 16.75 28.96 21.00 1964-01-01 1 1964 1 \n", + "1965-01-01 8.63 12.04 17.41 1965-01-01 1 1965 1 \n", + "1966-01-01 17.83 28.33 23.79 1966-01-01 1 1966 1 \n", + "1967-01-01 9.08 10.67 20.91 1967-01-01 1 1967 1 \n", + "1968-01-01 17.62 22.25 27.29 1968-01-01 1 1968 1 \n", + "1969-01-01 6.34 9.17 16.71 1969-01-01 1 1969 1 \n", + "1970-01-01 3.50 7.33 13.00 1970-01-01 1 1970 1 \n", + "1971-01-01 1.08 4.21 9.54 1971-01-01 1 1971 1 \n", + "1972-01-01 8.54 8.71 19.17 1972-01-01 1 1972 1 \n", + "1973-01-01 10.79 13.37 9.71 1973-01-01 1 1973 1 \n", + "1974-01-01 16.66 17.21 25.29 1974-01-01 1 1974 1 \n", + "1975-01-01 5.17 7.71 11.63 1975-01-01 1 1975 1 \n", + "1976-01-01 5.75 11.38 14.96 1976-01-01 1 1976 1 \n", + "1977-01-01 9.00 14.88 25.70 1977-01-01 1 1977 1 \n", + "1978-01-01 10.00 15.09 20.46 1978-01-01 1 1978 1 " + ] + }, + "execution_count": 428, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.query('month == 1 and day == 1')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 13. Downsample the record to a monthly frequency for each location." + ] + }, + { + "cell_type": "code", + "execution_count": 429, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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RPTVALROSKILSHABIRDUBCLAMULCLOBELMALdatemonthyearday
Yr_Mo_Dy
1961-01-0115.0414.9613.179.29NaN9.8713.6710.2510.8312.5818.5015.041961-01-01119611
1961-02-0114.2515.129.045.8812.087.1710.173.636.505.509.178.001961-02-01219611
1961-03-0112.6713.1311.796.429.798.5410.2513.29NaN12.2120.62NaN1961-03-01319611
1961-04-018.386.348.336.759.339.5411.678.2111.216.4611.967.171961-04-01419611
1961-05-0115.8713.8815.379.7913.4610.179.9614.049.759.9218.6311.121961-05-01519611
1961-06-0115.929.5912.048.7911.546.049.758.299.3310.3410.6712.121961-06-01619611
1961-07-017.216.837.714.428.464.796.716.005.797.966.968.711961-07-01719611
1961-08-019.595.095.544.638.295.254.215.255.375.418.389.081961-08-01819611
1961-09-015.581.134.963.044.252.254.632.713.676.004.795.411961-09-01919611
1961-10-0114.2512.877.878.0013.007.755.839.007.085.2911.794.041961-10-011019611
1961-11-0113.2113.1314.338.5412.1710.2113.0812.1710.9213.5420.1720.041961-11-011119611
1961-12-019.677.758.003.966.002.757.252.505.585.587.7911.171961-12-011219611
1962-01-019.293.4211.543.502.211.9610.412.793.545.174.387.921962-01-01119621
1962-02-0119.1213.9612.2110.5815.7110.6315.7111.0813.1712.6217.6722.711962-02-01219621
1962-03-018.214.839.004.836.002.217.961.874.083.924.085.411962-03-01319621
1962-04-0114.3312.2511.8710.3714.9211.0019.7911.6714.0915.4616.6223.581962-04-01419621
1962-05-019.629.543.583.338.753.752.252.581.672.377.293.251962-05-01519621
1962-06-015.886.298.675.215.004.255.915.414.799.255.2510.711962-06-01619621
1962-07-018.674.176.926.718.175.6611.179.388.7511.1210.2517.081962-07-01719621
1962-08-014.585.376.042.297.873.714.462.584.004.797.217.461962-08-01819621
1962-09-0110.0012.0810.969.259.297.627.418.757.679.6214.5811.921962-09-01919621
1962-10-0114.587.8319.2110.0811.548.3813.2910.638.2112.9218.0518.121962-10-011019621
1962-11-0116.8813.2516.008.9613.4611.4610.4610.1710.3713.2114.8315.161962-11-011119621
1962-12-0118.3815.4111.756.7912.218.048.4210.835.669.0811.5011.501962-12-011219621
1963-01-0115.5913.6219.798.3812.2510.0023.4515.7113.5914.3717.5834.131963-01-01119631
1963-02-0115.417.6224.6711.429.218.1714.047.547.5410.0810.1717.671963-02-01219631
1963-03-0116.7519.6717.678.8719.0815.3716.2114.2911.299.2119.9219.791963-03-01319631
1963-04-0110.549.5912.467.339.469.5911.7911.879.7910.7113.3718.211963-04-01419631
1963-05-0118.7914.1713.5911.6314.1711.9614.4612.4612.8713.9615.2921.621963-05-01519631
1963-06-0113.376.8712.008.5010.049.4210.9212.9611.7911.0410.9213.671963-06-01619631
...................................................
1976-07-018.501.756.582.132.752.215.372.045.884.504.9610.631976-07-01719761
1976-08-0113.008.388.635.8312.928.2513.009.4210.5811.3414.2120.251976-08-01819761
1976-09-0111.8711.007.386.877.758.3310.346.4610.179.2912.7519.551976-09-01919761
1976-10-0110.966.7110.414.637.585.045.045.546.503.926.795.001976-10-011019761
1976-11-0113.9615.6710.296.4612.799.0810.009.6710.2111.6323.0921.961976-11-011119761
1976-12-0113.4616.429.214.5410.758.6710.884.838.795.918.8313.671976-12-011219761
1977-01-0120.0411.9220.259.139.298.0410.755.889.009.0014.8825.701977-01-01119771
1977-02-0111.839.7111.004.258.588.716.175.668.297.5811.7116.501977-02-01219771
1977-03-018.6314.8310.293.756.638.795.008.127.876.4213.5413.671977-03-01319771
1977-04-0121.6716.0017.3313.5920.8315.9625.6217.6219.4120.6724.3730.091977-04-01419771
1977-05-016.427.128.673.584.584.006.756.133.334.5019.2112.381977-05-01519771
1977-06-017.085.259.712.832.213.505.291.422.000.925.215.631977-06-01619771
1977-07-0115.4116.2917.086.2511.8311.8312.2910.5810.417.2117.377.831977-07-01719771
1977-08-014.332.964.422.330.961.084.961.872.332.0410.509.831977-08-01819771
1977-09-0117.3716.3316.838.5814.4611.8315.0913.9213.2913.8823.2925.171977-09-01919771
1977-10-0116.7515.3412.259.4216.3811.3818.5013.9214.0914.4622.3429.671977-10-011019771
1977-11-0116.7111.5412.174.178.547.1711.126.468.256.2111.0415.631977-11-011119771
1977-12-0113.3710.9212.422.375.796.138.967.386.295.718.5412.421977-12-011219771
1978-01-018.337.127.713.548.507.5014.7110.0011.8310.0015.0920.461978-01-01119781
1978-02-0127.2524.2118.1617.4627.5418.0520.9625.0420.0417.5027.7121.121978-02-01219781
1978-03-0115.046.2116.047.876.426.6712.298.0010.589.335.4117.001978-03-01319781
1978-04-013.427.582.711.383.462.082.674.754.831.677.3313.671978-04-01419781
1978-05-0110.5412.219.085.2911.0010.0811.1713.7511.8711.7912.8727.161978-05-01519781
1978-06-0110.3711.426.466.0411.257.506.465.967.795.465.5010.411978-06-01619781
1978-07-0112.4610.6311.176.7512.929.0412.429.6212.088.0414.0416.171978-07-01719781
1978-08-0119.3315.0920.178.8312.6210.419.3312.339.509.9215.7518.001978-08-01819781
1978-09-018.426.139.875.253.215.717.253.507.336.507.6215.961978-09-01919781
1978-10-019.506.8310.503.886.134.584.216.506.386.5410.6314.091978-10-011019781
1978-11-0113.5916.7511.257.0811.048.338.1711.2910.7511.2523.1325.001978-11-011119781
1978-12-0121.2916.2924.0412.7918.2119.2921.5417.2116.7117.8317.7525.701978-12-011219781
\n", + "

216 rows × 16 columns

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Downsample the record to a weekly frequency for each location." + ] + }, + { + "cell_type": "code", + "execution_count": 430, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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RPTVALROSKILSHABIRDUBCLAMULCLOBELMALdatemonthyearday
Yr_Mo_Dy
1961-01-0115.0414.9613.179.29NaN9.8713.6710.2510.8312.5818.5015.041961-01-01119611
1961-01-0810.969.757.625.919.627.2914.297.629.2510.4616.6216.461961-01-08119618
1961-01-1512.049.6711.752.377.383.132.506.834.755.637.546.751961-01-151196115
1961-01-229.595.889.922.176.875.509.387.046.347.5010.889.921961-01-221196122
1961-01-29NaN23.9122.2917.5424.0819.7022.0020.2521.4619.9527.7123.381961-01-291196129
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\n", + "6544 78 12 2 13.70 12.71 14.29 5.13 9.21 8.04 12.33 6.34 \n", + "6545 78 12 3 21.21 21.34 17.75 11.58 16.75 14.46 17.46 15.29 \n", + "6546 78 12 4 9.92 13.50 7.21 1.71 11.00 7.50 8.38 7.46 \n", + "6547 78 12 5 22.75 20.17 18.58 8.50 15.96 14.29 13.92 12.92 \n", + "6548 78 12 6 29.33 23.87 25.37 16.04 24.46 19.50 24.54 18.58 \n", + "6549 78 12 7 26.63 24.79 24.79 18.16 23.13 19.58 19.92 19.04 \n", + "6550 78 12 8 12.92 12.54 11.25 3.37 6.50 5.96 10.34 6.17 \n", + "6551 78 12 9 18.71 16.92 15.50 6.04 10.37 9.59 10.75 9.13 \n", + "6552 78 12 10 24.92 22.54 16.54 14.62 15.59 13.00 13.21 14.12 \n", + "6553 78 12 11 20.25 19.17 17.83 11.63 17.79 13.37 14.83 13.88 \n", + "6554 78 12 12 23.13 18.63 18.05 8.29 14.33 11.04 10.54 10.13 \n", + "6555 78 12 13 18.84 24.04 14.37 8.33 18.12 12.17 13.00 13.75 \n", + "6556 78 12 14 17.21 19.75 12.71 5.83 13.79 7.33 8.83 5.71 \n", + "6557 78 12 15 13.13 8.92 16.54 6.92 6.00 4.00 12.67 5.88 \n", + "6558 78 12 16 14.88 9.13 17.37 5.21 6.71 2.46 9.13 4.96 \n", + "6559 78 12 17 9.87 3.21 8.04 2.21 3.04 0.54 2.46 1.46 \n", + "6560 78 12 18 9.83 10.88 8.50 1.00 9.08 6.00 2.42 8.25 \n", + "6561 78 12 19 13.88 11.42 10.13 2.33 8.12 6.75 4.75 5.88 \n", + "6562 78 12 20 9.92 3.63 12.38 3.08 3.50 0.42 4.54 2.50 \n", + "6563 78 12 21 12.96 3.83 13.79 4.79 7.12 6.54 11.67 9.25 \n", + "6564 78 12 22 6.21 7.38 13.08 2.54 7.58 5.33 2.46 8.38 \n", + "6565 78 12 23 16.62 13.29 22.21 9.50 14.29 13.08 16.50 17.16 \n", + "6566 78 12 24 8.67 5.63 12.12 4.79 5.09 5.91 12.25 9.25 \n", + "6567 78 12 25 7.21 6.58 7.83 2.67 4.79 4.58 8.71 0.75 \n", + "6568 78 12 26 13.83 11.87 10.34 2.37 6.96 4.29 1.96 3.79 \n", + "6569 78 12 27 17.58 16.96 17.62 8.08 13.21 11.67 14.46 15.59 \n", + "6570 78 12 28 13.21 5.46 13.46 5.00 8.12 9.42 14.33 16.25 \n", + "6571 78 12 29 14.00 10.29 14.42 8.71 9.71 10.54 19.17 12.46 \n", + "6572 78 12 30 18.50 14.04 21.29 9.13 12.75 9.71 18.08 12.87 \n", + "6573 78 12 31 20.33 17.41 27.29 9.59 12.08 10.13 19.25 11.63 \n", + "\n", + " MUL CLO BEL MAL months_num \n", + "0 10.83 12.58 18.50 15.04 1 \n", + "1 9.79 9.67 17.54 13.83 1 \n", + "2 8.50 7.67 12.75 12.71 1 \n", + "3 5.83 5.88 5.46 10.88 1 \n", + "4 10.92 10.34 12.92 11.83 1 \n", + "5 7.17 7.50 8.12 13.17 1 \n", + "6 7.58 7.96 13.96 13.79 1 \n", + "7 9.25 10.46 16.62 16.46 1 \n", + "8 7.79 9.08 13.04 15.37 1 \n", + "9 8.54 9.00 8.58 11.83 1 \n", + "10 5.71 8.67 20.71 16.92 1 \n", + "11 10.37 14.58 15.59 14.09 1 \n", + "12 2.33 3.37 5.25 7.04 1 \n", + "13 0.50 2.67 7.17 5.17 1 \n", + "14 4.75 5.63 7.54 6.75 1 \n", + "15 8.21 7.33 13.04 9.04 1 \n", + "16 8.71 11.38 15.92 16.08 1 \n", + "17 14.67 16.71 8.79 17.96 1 \n", + "18 1.92 2.71 6.87 7.83 1 \n", + "19 3.13 3.37 6.50 6.79 1 \n", + "20 7.04 7.87 6.75 12.42 1 \n", + "21 6.34 7.50 10.88 9.92 1 \n", + "22 10.75 13.17 14.79 20.58 1 \n", + "23 15.37 15.12 23.09 25.25 1 \n", + "24 7.12 12.04 14.04 17.50 1 \n", + "25 16.08 19.08 20.50 25.25 1 \n", + "26 18.66 19.08 26.08 27.63 1 \n", + "27 11.92 11.04 20.30 18.12 1 \n", + "28 21.46 19.95 27.71 23.38 1 \n", + "29 9.92 11.96 18.88 19.25 1 \n", + "... ... ... ... ... ... \n", + "6544 9.21 11.21 9.59 19.95 216 \n", + "6545 15.79 17.50 21.42 25.75 216 \n", + "6546 10.79 10.21 17.88 17.96 216 \n", + "6547 12.96 12.29 17.08 19.83 216 \n", + "6548 21.00 20.58 21.67 34.46 216 \n", + "6549 19.75 21.50 23.04 34.59 216 \n", + "6550 6.63 6.75 9.54 17.33 216 \n", + "6551 9.75 11.08 14.33 15.34 216 \n", + "6552 16.21 16.17 26.08 21.92 216 \n", + "6553 15.54 16.29 18.34 22.83 216 \n", + "6554 11.42 10.50 11.25 13.50 216 \n", + "6555 14.17 15.09 21.50 21.37 216 \n", + "6556 7.96 3.37 5.21 6.92 216 \n", + "6557 7.67 6.08 5.50 17.16 216 \n", + "6558 6.13 5.96 10.92 18.08 216 \n", + "6559 1.29 2.67 5.00 9.08 216 \n", + "6560 4.42 5.88 19.79 19.79 216 \n", + "6561 6.21 8.17 8.33 18.25 216 \n", + "6562 2.13 4.71 3.21 10.29 216 \n", + "6563 8.67 9.00 11.25 20.30 216 \n", + "6564 5.09 5.04 9.92 11.00 216 \n", + "6565 12.71 12.00 18.50 21.50 216 \n", + "6566 10.83 11.71 11.92 31.71 216 \n", + "6567 5.21 5.25 1.21 13.96 216 \n", + "6568 3.04 3.08 4.79 11.96 216 \n", + "6569 14.04 14.00 17.21 40.08 216 \n", + "6570 15.25 18.05 21.79 41.46 216 \n", + "6571 14.50 16.42 18.88 29.58 216 \n", + "6572 12.46 12.12 14.67 28.79 216 \n", + "6573 11.58 11.38 12.08 22.08 216 \n", + "\n", + "[6574 rows x 16 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# call data again but this time don't use parse_dates\n", + "wind_data = pd.read_table(\"wind.data\", sep = \"\\s+\") \n", + "\n", + "# compute the month number for each day in the dataset, there are in total 216 months\n", + "wind_data['months_num'] = (wind_data.iloc[:, 0] - 61) * 12 + wind_data.iloc[:, 1]\n", + "\n", + "wind_data\n", + "\n", + "# group the data according to the months_num and get the mean\n", + "# monthly_data = wind_data.groupby(['months_num']).mean()\n", + "\n", + "# monthly_data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 16. Calculate the min, max and mean windspeeds and standard deviations of the windspeeds across all locations for each week (assume that the first week starts on January 2 1961) for the first 52 weeks." + ] + }, + { + "cell_type": "code", + "execution_count": 433, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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RPTVALROS...CLOBELMAL
minmaxmeanstdminmaxmeanstdminmax...meanstdminmaxmeanstdminmaxmeanstd
Yr_Mo_Dy
1961-01-0810.5818.5013.5414292.6313216.6316.8811.4866673.9495257.6212.33...8.4971431.7049415.4617.5412.4814294.34913910.8816.4613.2385711.773062
1961-01-159.0419.7512.4685713.5553923.5412.088.9671433.1489457.0819.50...7.5714294.0842935.2520.7111.1257145.5522155.1716.9211.0242864.692355
1961-01-224.9219.8313.2042865.3374023.4214.379.8628573.8377857.2920.79...8.1242864.7839526.5015.929.8214293.6265846.7917.9611.4342864.237239
1961-01-2913.6225.0419.8800004.6190619.9623.9116.1414295.17022412.6725.84...15.6400003.71336814.0427.7120.9300005.21072617.5027.6322.5300003.874721
1961-02-0510.5824.2116.8271435.2514089.4624.2115.4600005.1873959.0419.70...9.4600002.8395019.1719.3314.0128574.2108587.1719.2511.9357144.336104
1961-02-1216.0024.5419.6842863.58767711.5421.4216.4171433.60837313.6721.34...14.4400001.74674915.2126.3821.8328574.06375317.0421.8419.1557141.828705
1961-02-196.0422.5015.1300005.06460911.6320.1715.0914293.5750126.1319.41...13.5428572.53136114.0929.6321.1671435.91093810.9622.5816.5842864.685377
1961-02-267.7925.8015.2214297.0207167.0821.5013.6257145.1473486.0822.42...12.7300004.9200649.5923.2116.3042865.0911626.6723.8714.3228576.182283
1961-03-0510.9613.3312.1014290.9977218.8317.0012.9514292.8519558.1713.67...12.3700001.59368511.5823.4517.8428574.3323318.8317.5413.9516673.021387
1961-03-124.8814.799.3766673.7322638.0816.9611.5785713.2301677.5416.38...10.4585713.65511310.2122.7116.7014294.3587595.5422.5414.4200005.769890
\n", + "

10 rows × 48 columns

\n", + "
" + ], + "text/plain": [ + " RPT VAL \\\n", + " min max mean std min max mean \n", + "Yr_Mo_Dy \n", + "1961-01-08 10.58 18.50 13.541429 2.631321 6.63 16.88 11.486667 \n", + "1961-01-15 9.04 19.75 12.468571 3.555392 3.54 12.08 8.967143 \n", + "1961-01-22 4.92 19.83 13.204286 5.337402 3.42 14.37 9.862857 \n", + "1961-01-29 13.62 25.04 19.880000 4.619061 9.96 23.91 16.141429 \n", + "1961-02-05 10.58 24.21 16.827143 5.251408 9.46 24.21 15.460000 \n", + "1961-02-12 16.00 24.54 19.684286 3.587677 11.54 21.42 16.417143 \n", + "1961-02-19 6.04 22.50 15.130000 5.064609 11.63 20.17 15.091429 \n", + "1961-02-26 7.79 25.80 15.221429 7.020716 7.08 21.50 13.625714 \n", + "1961-03-05 10.96 13.33 12.101429 0.997721 8.83 17.00 12.951429 \n", + "1961-03-12 4.88 14.79 9.376667 3.732263 8.08 16.96 11.578571 \n", + "\n", + " ROS ... CLO BEL \\\n", + " std min max ... mean std min \n", + "Yr_Mo_Dy ... \n", + "1961-01-08 3.949525 7.62 12.33 ... 8.497143 1.704941 5.46 \n", + "1961-01-15 3.148945 7.08 19.50 ... 7.571429 4.084293 5.25 \n", + "1961-01-22 3.837785 7.29 20.79 ... 8.124286 4.783952 6.50 \n", + "1961-01-29 5.170224 12.67 25.84 ... 15.640000 3.713368 14.04 \n", + "1961-02-05 5.187395 9.04 19.70 ... 9.460000 2.839501 9.17 \n", + "1961-02-12 3.608373 13.67 21.34 ... 14.440000 1.746749 15.21 \n", + "1961-02-19 3.575012 6.13 19.41 ... 13.542857 2.531361 14.09 \n", + "1961-02-26 5.147348 6.08 22.42 ... 12.730000 4.920064 9.59 \n", + "1961-03-05 2.851955 8.17 13.67 ... 12.370000 1.593685 11.58 \n", + "1961-03-12 3.230167 7.54 16.38 ... 10.458571 3.655113 10.21 \n", + "\n", + " MAL \n", + " max mean std min max mean std \n", + "Yr_Mo_Dy \n", + "1961-01-08 17.54 12.481429 4.349139 10.88 16.46 13.238571 1.773062 \n", + "1961-01-15 20.71 11.125714 5.552215 5.17 16.92 11.024286 4.692355 \n", + "1961-01-22 15.92 9.821429 3.626584 6.79 17.96 11.434286 4.237239 \n", + "1961-01-29 27.71 20.930000 5.210726 17.50 27.63 22.530000 3.874721 \n", + "1961-02-05 19.33 14.012857 4.210858 7.17 19.25 11.935714 4.336104 \n", + "1961-02-12 26.38 21.832857 4.063753 17.04 21.84 19.155714 1.828705 \n", + "1961-02-19 29.63 21.167143 5.910938 10.96 22.58 16.584286 4.685377 \n", + "1961-02-26 23.21 16.304286 5.091162 6.67 23.87 14.322857 6.182283 \n", + "1961-03-05 23.45 17.842857 4.332331 8.83 17.54 13.951667 3.021387 \n", + "1961-03-12 22.71 16.701429 4.358759 5.54 22.54 14.420000 5.769890 \n", + "\n", + "[10 rows x 48 columns]" + ] + }, + "execution_count": 433, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# resample data to 'W' week and use the functions\n", + "weekly = data.resample('W').agg(['min','max','mean','std'])\n", + "\n", + "# slice it for the first 52 weeks and locations\n", + "weekly.ix[1:53, \"RPT\":\"MAL\"].head(10)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/06_Stats/Wind_Stats/Solutions.ipynb b/200 solved problems in Python/pandas/06_Stats/Wind_Stats/Solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..a0160b398f8b37bbae5d8eec72edd21ec3b5f7d8 --- /dev/null +++ b/200 solved problems in Python/pandas/06_Stats/Wind_Stats/Solutions.ipynb @@ -0,0 +1,3506 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Wind Statistics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "The data have been modified to contain some missing values, identified by NaN. \n", + "Using pandas should make this exercise\n", + "easier, in particular for the bonus question.\n", + "\n", + "You should be able to perform all of these operations without using\n", + "a for loop or other looping construct.\n", + "\n", + "\n", + "1. The data in 'wind.data' has the following format:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nYr Mo Dy RPT VAL ROS KIL SHA BIR DUB CLA MUL CLO BEL MAL\\n61 1 1 15.04 14.96 13.17 9.29 NaN 9.87 13.67 10.25 10.83 12.58 18.50 15.04\\n61 1 2 14.71 NaN 10.83 6.50 12.62 7.67 11.50 10.04 9.79 9.67 17.54 13.83\\n61 1 3 18.50 16.88 12.33 10.13 11.17 6.17 11.25 NaN 8.50 7.67 12.75 12.71\\n'" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "Yr Mo Dy RPT VAL ROS KIL SHA BIR DUB CLA MUL CLO BEL MAL\n", + "61 1 1 15.04 14.96 13.17 9.29 NaN 9.87 13.67 10.25 10.83 12.58 18.50 15.04\n", + "61 1 2 14.71 NaN 10.83 6.50 12.62 7.67 11.50 10.04 9.79 9.67 17.54 13.83\n", + "61 1 3 18.50 16.88 12.33 10.13 11.17 6.17 11.25 NaN 8.50 7.67 12.75 12.71\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " The first three columns are year, month and day. The\n", + " remaining 12 columns are average windspeeds in knots at 12\n", + " locations in Ireland on that day. \n", + "\n", + " More information about the dataset go [here](wind.desc)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import datetime" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://github.com/guipsamora/pandas_exercises/blob/master/Stats/Wind_Stats/wind.data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called data and replace the first 3 columns by a proper datetime index." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Yr_Mo_DyRPTVALROSKILSHABIRDUBCLAMULCLOBELMAL
02061-01-0115.0414.9613.179.29NaN9.8713.6710.2510.8312.5818.5015.04
12061-01-0214.71NaN10.836.5012.627.6711.5010.049.799.6717.5413.83
22061-01-0318.5016.8812.3310.1311.176.1711.25NaN8.507.6712.7512.71
32061-01-0410.586.6311.754.584.542.888.631.795.835.885.4610.88
42061-01-0513.3313.2511.426.1710.718.2111.926.5410.9210.3412.9211.83
\n", + "
" + ], + "text/plain": [ + " Yr_Mo_Dy RPT VAL ROS KIL SHA BIR DUB CLA MUL \\\n", + "0 2061-01-01 15.04 14.96 13.17 9.29 NaN 9.87 13.67 10.25 10.83 \n", + "1 2061-01-02 14.71 NaN 10.83 6.50 12.62 7.67 11.50 10.04 9.79 \n", + "2 2061-01-03 18.50 16.88 12.33 10.13 11.17 6.17 11.25 NaN 8.50 \n", + "3 2061-01-04 10.58 6.63 11.75 4.58 4.54 2.88 8.63 1.79 5.83 \n", + "4 2061-01-05 13.33 13.25 11.42 6.17 10.71 8.21 11.92 6.54 10.92 \n", + "\n", + " CLO BEL MAL \n", + "0 12.58 18.50 15.04 \n", + "1 9.67 17.54 13.83 \n", + "2 7.67 12.75 12.71 \n", + "3 5.88 5.46 10.88 \n", + "4 10.34 12.92 11.83 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Year 2061? Do we really have data from this year? Create a function to fix it and apply it." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Yr_Mo_DyRPTVALROSKILSHABIRDUBCLAMULCLOBELMAL
01961-01-0115.0414.9613.179.29NaN9.8713.6710.2510.8312.5818.5015.04
11961-01-0214.71NaN10.836.5012.627.6711.5010.049.799.6717.5413.83
21961-01-0318.5016.8812.3310.1311.176.1711.25NaN8.507.6712.7512.71
31961-01-0410.586.6311.754.584.542.888.631.795.835.885.4610.88
41961-01-0513.3313.2511.426.1710.718.2111.926.5410.9210.3412.9211.83
\n", + "
" + ], + "text/plain": [ + " Yr_Mo_Dy RPT VAL ROS KIL SHA BIR DUB CLA MUL \\\n", + "0 1961-01-01 15.04 14.96 13.17 9.29 NaN 9.87 13.67 10.25 10.83 \n", + "1 1961-01-02 14.71 NaN 10.83 6.50 12.62 7.67 11.50 10.04 9.79 \n", + "2 1961-01-03 18.50 16.88 12.33 10.13 11.17 6.17 11.25 NaN 8.50 \n", + "3 1961-01-04 10.58 6.63 11.75 4.58 4.54 2.88 8.63 1.79 5.83 \n", + "4 1961-01-05 13.33 13.25 11.42 6.17 10.71 8.21 11.92 6.54 10.92 \n", + "\n", + " CLO BEL MAL \n", + "0 12.58 18.50 15.04 \n", + "1 9.67 17.54 13.83 \n", + "2 7.67 12.75 12.71 \n", + "3 5.88 5.46 10.88 \n", + "4 10.34 12.92 11.83 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Set the right dates as the index. Pay attention at the data type, it should be datetime64[ns]." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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RPTVALROSKILSHABIRDUBCLAMULCLOBELMAL
Yr_Mo_Dy
1961-01-0115.0414.9613.179.29NaN9.8713.6710.2510.8312.5818.5015.04
1961-01-0214.71NaN10.836.5012.627.6711.5010.049.799.6717.5413.83
1961-01-0318.5016.8812.3310.1311.176.1711.25NaN8.507.6712.7512.71
1961-01-0410.586.6311.754.584.542.888.631.795.835.885.4610.88
1961-01-0513.3313.2511.426.1710.718.2111.926.5410.9210.3412.9211.83
\n", + "
" + ], + "text/plain": [ + " RPT VAL ROS KIL SHA BIR DUB CLA MUL \\\n", + "Yr_Mo_Dy \n", + "1961-01-01 15.04 14.96 13.17 9.29 NaN 9.87 13.67 10.25 10.83 \n", + "1961-01-02 14.71 NaN 10.83 6.50 12.62 7.67 11.50 10.04 9.79 \n", + "1961-01-03 18.50 16.88 12.33 10.13 11.17 6.17 11.25 NaN 8.50 \n", + "1961-01-04 10.58 6.63 11.75 4.58 4.54 2.88 8.63 1.79 5.83 \n", + "1961-01-05 13.33 13.25 11.42 6.17 10.71 8.21 11.92 6.54 10.92 \n", + "\n", + " CLO BEL MAL \n", + "Yr_Mo_Dy \n", + "1961-01-01 12.58 18.50 15.04 \n", + "1961-01-02 9.67 17.54 13.83 \n", + "1961-01-03 7.67 12.75 12.71 \n", + "1961-01-04 5.88 5.46 10.88 \n", + "1961-01-05 10.34 12.92 11.83 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Compute how many values are missing for each location over the entire record. \n", + "#### They should be ignored in all calculations below. " + ] + }, + { + "cell_type": "code", + "execution_count": 423, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "RPT 6\n", + "VAL 3\n", + "ROS 2\n", + "KIL 5\n", + "SHA 2\n", + "BIR 0\n", + "DUB 3\n", + "CLA 2\n", + "MUL 3\n", + "CLO 1\n", + "BEL 0\n", + "MAL 4\n", + "dtype: int64" + ] + }, + "execution_count": 423, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Compute how many non-missing values there are in total." + ] + }, + { + "cell_type": "code", + "execution_count": 424, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "RPT 6\n", + "VAL 9\n", + "ROS 10\n", + "KIL 7\n", + "SHA 10\n", + "BIR 12\n", + "DUB 9\n", + "CLA 10\n", + "MUL 9\n", + "CLO 11\n", + "BEL 12\n", + "MAL 8\n", + "dtype: int64" + ] + }, + "execution_count": 424, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Calculate the mean windspeeds of the windspeeds over all the locations and all the times.\n", + "#### A single number for the entire dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 426, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "10.227982360836924" + ] + }, + "execution_count": 426, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. Create a DataFrame called loc_stats and calculate the min, max and mean windspeeds and standard deviations of the windspeeds at each location over all the days \n", + "\n", + "#### A different set of numbers for each location." + ] + }, + { + "cell_type": "code", + "execution_count": 264, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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minmaxmeanstd
RPT0.6735.8012.3629875.618413
VAL0.2133.3710.6443145.267356
ROS1.5033.8411.6605265.008450
KIL0.0028.466.3064683.605811
SHA0.1337.5410.4558344.936125
BIR0.0026.167.0922543.968683
DUB0.0030.379.7973434.977555
CLA0.0031.088.4950534.499449
MUL0.0025.888.4935904.166872
CLO0.0428.218.7073324.503954
BEL0.1342.3813.1210075.835037
MAL0.6742.5415.5990796.699794
\n", + "
" + ], + "text/plain": [ + " min max mean std\n", + "RPT 0.67 35.80 12.362987 5.618413\n", + "VAL 0.21 33.37 10.644314 5.267356\n", + "ROS 1.50 33.84 11.660526 5.008450\n", + "KIL 0.00 28.46 6.306468 3.605811\n", + "SHA 0.13 37.54 10.455834 4.936125\n", + "BIR 0.00 26.16 7.092254 3.968683\n", + "DUB 0.00 30.37 9.797343 4.977555\n", + "CLA 0.00 31.08 8.495053 4.499449\n", + "MUL 0.00 25.88 8.493590 4.166872\n", + "CLO 0.04 28.21 8.707332 4.503954\n", + "BEL 0.13 42.38 13.121007 5.835037\n", + "MAL 0.67 42.54 15.599079 6.699794" + ] + }, + "execution_count": 264, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. Create a DataFrame called day_stats and calculate the min, max and mean windspeed and standard deviations of the windspeeds across all the locations at each day.\n", + "\n", + "#### A different set of numbers for each day." + ] + }, + { + "cell_type": "code", + "execution_count": 404, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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minmaxmeanstd
01.018.5012.0166674.382798
11.017.5410.4750004.260110
21.018.5010.7550004.664914
31.011.756.1869233.435771
41.013.339.8892313.551768
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" + ], + "text/plain": [ + " min max mean std\n", + "0 1.0 18.50 12.016667 4.382798\n", + "1 1.0 17.54 10.475000 4.260110\n", + "2 1.0 18.50 10.755000 4.664914\n", + "3 1.0 11.75 6.186923 3.435771\n", + "4 1.0 13.33 9.889231 3.551768" + ] + }, + "execution_count": 404, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 11. Find the average windspeed in January for each location. \n", + "#### Treat January 1961 and January 1962 both as January." + ] + }, + { + "cell_type": "code", + "execution_count": 427, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "RPT 14.847325\n", + "VAL 12.914560\n", + "ROS 13.299624\n", + "KIL 7.199498\n", + "SHA 11.667734\n", + "BIR 8.054839\n", + "DUB 11.819355\n", + "CLA 9.512047\n", + "MUL 9.543208\n", + "CLO 10.053566\n", + "BEL 14.550520\n", + "MAL 18.028763\n", + "dtype: float64" + ] + }, + "execution_count": 427, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 12. Downsample the record to a yearly frequency for each location." + ] + }, + { + "cell_type": "code", + "execution_count": 428, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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RPTVALROSKILSHABIRDUBCLAMULCLOBELMALdatemonthyearday
Yr_Mo_Dy
1961-01-0115.0414.9613.179.29NaN9.8713.6710.2510.8312.5818.5015.041961-01-01119611
1962-01-019.293.4211.543.502.211.9610.412.793.545.174.387.921962-01-01119621
1963-01-0115.5913.6219.798.3812.2510.0023.4515.7113.5914.3717.5834.131963-01-01119631
1964-01-0125.8022.1318.2113.2521.2914.7914.1219.5813.2516.7528.9621.001964-01-01119641
1965-01-019.5411.929.004.386.085.2110.256.085.718.6312.0417.411965-01-01119651
1966-01-0122.0421.5017.0812.7522.1715.5921.7918.1216.6617.8328.3323.791966-01-01119661
1967-01-016.464.466.503.216.673.7911.383.837.719.0810.6720.911967-01-01119671
1968-01-0130.0417.8816.2516.2521.7912.5418.1616.6218.7517.6222.2527.291968-01-01119681
1969-01-016.131.635.411.082.541.008.502.424.586.349.1716.711969-01-01119691
1970-01-019.592.9611.793.426.134.089.004.467.293.507.3313.001970-01-01119701
1971-01-013.710.794.710.171.421.044.630.751.541.084.219.541971-01-01119711
1972-01-019.293.6314.544.256.754.4213.005.3310.048.548.7119.171972-01-01119721
1973-01-0116.5015.9214.627.418.2911.2113.547.7910.4610.7913.379.711973-01-01119731
1974-01-0123.2116.5416.089.7515.8311.469.5413.5413.8316.6617.2125.291974-01-01119741
1975-01-0114.0413.5411.295.4612.585.588.128.969.295.177.7111.631975-01-01119751
1976-01-0118.3417.6714.838.0016.6210.1313.179.0413.135.7511.3814.961976-01-01119761
1977-01-0120.0411.9220.259.139.298.0410.755.889.009.0014.8825.701977-01-01119771
1978-01-018.337.127.713.548.507.5014.7110.0011.8310.0015.0920.461978-01-01119781
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" + ], + "text/plain": [ + " RPT VAL ROS KIL SHA BIR DUB CLA MUL \\\n", + "Yr_Mo_Dy \n", + "1961-01-01 15.04 14.96 13.17 9.29 NaN 9.87 13.67 10.25 10.83 \n", + "1962-01-01 9.29 3.42 11.54 3.50 2.21 1.96 10.41 2.79 3.54 \n", + "1963-01-01 15.59 13.62 19.79 8.38 12.25 10.00 23.45 15.71 13.59 \n", + "1964-01-01 25.80 22.13 18.21 13.25 21.29 14.79 14.12 19.58 13.25 \n", + "1965-01-01 9.54 11.92 9.00 4.38 6.08 5.21 10.25 6.08 5.71 \n", + "1966-01-01 22.04 21.50 17.08 12.75 22.17 15.59 21.79 18.12 16.66 \n", + "1967-01-01 6.46 4.46 6.50 3.21 6.67 3.79 11.38 3.83 7.71 \n", + "1968-01-01 30.04 17.88 16.25 16.25 21.79 12.54 18.16 16.62 18.75 \n", + "1969-01-01 6.13 1.63 5.41 1.08 2.54 1.00 8.50 2.42 4.58 \n", + "1970-01-01 9.59 2.96 11.79 3.42 6.13 4.08 9.00 4.46 7.29 \n", + "1971-01-01 3.71 0.79 4.71 0.17 1.42 1.04 4.63 0.75 1.54 \n", + "1972-01-01 9.29 3.63 14.54 4.25 6.75 4.42 13.00 5.33 10.04 \n", + "1973-01-01 16.50 15.92 14.62 7.41 8.29 11.21 13.54 7.79 10.46 \n", + "1974-01-01 23.21 16.54 16.08 9.75 15.83 11.46 9.54 13.54 13.83 \n", + "1975-01-01 14.04 13.54 11.29 5.46 12.58 5.58 8.12 8.96 9.29 \n", + "1976-01-01 18.34 17.67 14.83 8.00 16.62 10.13 13.17 9.04 13.13 \n", + "1977-01-01 20.04 11.92 20.25 9.13 9.29 8.04 10.75 5.88 9.00 \n", + "1978-01-01 8.33 7.12 7.71 3.54 8.50 7.50 14.71 10.00 11.83 \n", + "\n", + " CLO BEL MAL date month year day \n", + "Yr_Mo_Dy \n", + "1961-01-01 12.58 18.50 15.04 1961-01-01 1 1961 1 \n", + "1962-01-01 5.17 4.38 7.92 1962-01-01 1 1962 1 \n", + "1963-01-01 14.37 17.58 34.13 1963-01-01 1 1963 1 \n", + "1964-01-01 16.75 28.96 21.00 1964-01-01 1 1964 1 \n", + "1965-01-01 8.63 12.04 17.41 1965-01-01 1 1965 1 \n", + "1966-01-01 17.83 28.33 23.79 1966-01-01 1 1966 1 \n", + "1967-01-01 9.08 10.67 20.91 1967-01-01 1 1967 1 \n", + "1968-01-01 17.62 22.25 27.29 1968-01-01 1 1968 1 \n", + "1969-01-01 6.34 9.17 16.71 1969-01-01 1 1969 1 \n", + "1970-01-01 3.50 7.33 13.00 1970-01-01 1 1970 1 \n", + "1971-01-01 1.08 4.21 9.54 1971-01-01 1 1971 1 \n", + "1972-01-01 8.54 8.71 19.17 1972-01-01 1 1972 1 \n", + "1973-01-01 10.79 13.37 9.71 1973-01-01 1 1973 1 \n", + "1974-01-01 16.66 17.21 25.29 1974-01-01 1 1974 1 \n", + "1975-01-01 5.17 7.71 11.63 1975-01-01 1 1975 1 \n", + "1976-01-01 5.75 11.38 14.96 1976-01-01 1 1976 1 \n", + "1977-01-01 9.00 14.88 25.70 1977-01-01 1 1977 1 \n", + "1978-01-01 10.00 15.09 20.46 1978-01-01 1 1978 1 " + ] + }, + "execution_count": 428, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 13. Downsample the record to a monthly frequency for each location." + ] + }, + { + "cell_type": "code", + "execution_count": 429, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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RPTVALROSKILSHABIRDUBCLAMULCLOBELMALdatemonthyearday
Yr_Mo_Dy
1961-01-0115.0414.9613.179.29NaN9.8713.6710.2510.8312.5818.5015.041961-01-01119611
1961-02-0114.2515.129.045.8812.087.1710.173.636.505.509.178.001961-02-01219611
1961-03-0112.6713.1311.796.429.798.5410.2513.29NaN12.2120.62NaN1961-03-01319611
1961-04-018.386.348.336.759.339.5411.678.2111.216.4611.967.171961-04-01419611
1961-05-0115.8713.8815.379.7913.4610.179.9614.049.759.9218.6311.121961-05-01519611
1961-06-0115.929.5912.048.7911.546.049.758.299.3310.3410.6712.121961-06-01619611
1961-07-017.216.837.714.428.464.796.716.005.797.966.968.711961-07-01719611
1961-08-019.595.095.544.638.295.254.215.255.375.418.389.081961-08-01819611
1961-09-015.581.134.963.044.252.254.632.713.676.004.795.411961-09-01919611
1961-10-0114.2512.877.878.0013.007.755.839.007.085.2911.794.041961-10-011019611
1961-11-0113.2113.1314.338.5412.1710.2113.0812.1710.9213.5420.1720.041961-11-011119611
1961-12-019.677.758.003.966.002.757.252.505.585.587.7911.171961-12-011219611
1962-01-019.293.4211.543.502.211.9610.412.793.545.174.387.921962-01-01119621
1962-02-0119.1213.9612.2110.5815.7110.6315.7111.0813.1712.6217.6722.711962-02-01219621
1962-03-018.214.839.004.836.002.217.961.874.083.924.085.411962-03-01319621
1962-04-0114.3312.2511.8710.3714.9211.0019.7911.6714.0915.4616.6223.581962-04-01419621
1962-05-019.629.543.583.338.753.752.252.581.672.377.293.251962-05-01519621
1962-06-015.886.298.675.215.004.255.915.414.799.255.2510.711962-06-01619621
1962-07-018.674.176.926.718.175.6611.179.388.7511.1210.2517.081962-07-01719621
1962-08-014.585.376.042.297.873.714.462.584.004.797.217.461962-08-01819621
1962-09-0110.0012.0810.969.259.297.627.418.757.679.6214.5811.921962-09-01919621
1962-10-0114.587.8319.2110.0811.548.3813.2910.638.2112.9218.0518.121962-10-011019621
1962-11-0116.8813.2516.008.9613.4611.4610.4610.1710.3713.2114.8315.161962-11-011119621
1962-12-0118.3815.4111.756.7912.218.048.4210.835.669.0811.5011.501962-12-011219621
1963-01-0115.5913.6219.798.3812.2510.0023.4515.7113.5914.3717.5834.131963-01-01119631
1963-02-0115.417.6224.6711.429.218.1714.047.547.5410.0810.1717.671963-02-01219631
1963-03-0116.7519.6717.678.8719.0815.3716.2114.2911.299.2119.9219.791963-03-01319631
1963-04-0110.549.5912.467.339.469.5911.7911.879.7910.7113.3718.211963-04-01419631
1963-05-0118.7914.1713.5911.6314.1711.9614.4612.4612.8713.9615.2921.621963-05-01519631
1963-06-0113.376.8712.008.5010.049.4210.9212.9611.7911.0410.9213.671963-06-01619631
...................................................
1976-07-018.501.756.582.132.752.215.372.045.884.504.9610.631976-07-01719761
1976-08-0113.008.388.635.8312.928.2513.009.4210.5811.3414.2120.251976-08-01819761
1976-09-0111.8711.007.386.877.758.3310.346.4610.179.2912.7519.551976-09-01919761
1976-10-0110.966.7110.414.637.585.045.045.546.503.926.795.001976-10-011019761
1976-11-0113.9615.6710.296.4612.799.0810.009.6710.2111.6323.0921.961976-11-011119761
1976-12-0113.4616.429.214.5410.758.6710.884.838.795.918.8313.671976-12-011219761
1977-01-0120.0411.9220.259.139.298.0410.755.889.009.0014.8825.701977-01-01119771
1977-02-0111.839.7111.004.258.588.716.175.668.297.5811.7116.501977-02-01219771
1977-03-018.6314.8310.293.756.638.795.008.127.876.4213.5413.671977-03-01319771
1977-04-0121.6716.0017.3313.5920.8315.9625.6217.6219.4120.6724.3730.091977-04-01419771
1977-05-016.427.128.673.584.584.006.756.133.334.5019.2112.381977-05-01519771
1977-06-017.085.259.712.832.213.505.291.422.000.925.215.631977-06-01619771
1977-07-0115.4116.2917.086.2511.8311.8312.2910.5810.417.2117.377.831977-07-01719771
1977-08-014.332.964.422.330.961.084.961.872.332.0410.509.831977-08-01819771
1977-09-0117.3716.3316.838.5814.4611.8315.0913.9213.2913.8823.2925.171977-09-01919771
1977-10-0116.7515.3412.259.4216.3811.3818.5013.9214.0914.4622.3429.671977-10-011019771
1977-11-0116.7111.5412.174.178.547.1711.126.468.256.2111.0415.631977-11-011119771
1977-12-0113.3710.9212.422.375.796.138.967.386.295.718.5412.421977-12-011219771
1978-01-018.337.127.713.548.507.5014.7110.0011.8310.0015.0920.461978-01-01119781
1978-02-0127.2524.2118.1617.4627.5418.0520.9625.0420.0417.5027.7121.121978-02-01219781
1978-03-0115.046.2116.047.876.426.6712.298.0010.589.335.4117.001978-03-01319781
1978-04-013.427.582.711.383.462.082.674.754.831.677.3313.671978-04-01419781
1978-05-0110.5412.219.085.2911.0010.0811.1713.7511.8711.7912.8727.161978-05-01519781
1978-06-0110.3711.426.466.0411.257.506.465.967.795.465.5010.411978-06-01619781
1978-07-0112.4610.6311.176.7512.929.0412.429.6212.088.0414.0416.171978-07-01719781
1978-08-0119.3315.0920.178.8312.6210.419.3312.339.509.9215.7518.001978-08-01819781
1978-09-018.426.139.875.253.215.717.253.507.336.507.6215.961978-09-01919781
1978-10-019.506.8310.503.886.134.584.216.506.386.5410.6314.091978-10-011019781
1978-11-0113.5916.7511.257.0811.048.338.1711.2910.7511.2523.1325.001978-11-011119781
1978-12-0121.2916.2924.0412.7918.2119.2921.5417.2116.7117.8317.7525.701978-12-011219781
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216 rows × 16 columns

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" + ], + "text/plain": [ + " RPT VAL ROS KIL SHA BIR DUB CLA MUL \\\n", + "Yr_Mo_Dy \n", + "1961-01-01 15.04 14.96 13.17 9.29 NaN 9.87 13.67 10.25 10.83 \n", + "1961-02-01 14.25 15.12 9.04 5.88 12.08 7.17 10.17 3.63 6.50 \n", + "1961-03-01 12.67 13.13 11.79 6.42 9.79 8.54 10.25 13.29 NaN \n", + "1961-04-01 8.38 6.34 8.33 6.75 9.33 9.54 11.67 8.21 11.21 \n", + "1961-05-01 15.87 13.88 15.37 9.79 13.46 10.17 9.96 14.04 9.75 \n", + "1961-06-01 15.92 9.59 12.04 8.79 11.54 6.04 9.75 8.29 9.33 \n", + "1961-07-01 7.21 6.83 7.71 4.42 8.46 4.79 6.71 6.00 5.79 \n", + "1961-08-01 9.59 5.09 5.54 4.63 8.29 5.25 4.21 5.25 5.37 \n", + "1961-09-01 5.58 1.13 4.96 3.04 4.25 2.25 4.63 2.71 3.67 \n", + "1961-10-01 14.25 12.87 7.87 8.00 13.00 7.75 5.83 9.00 7.08 \n", + "1961-11-01 13.21 13.13 14.33 8.54 12.17 10.21 13.08 12.17 10.92 \n", + "1961-12-01 9.67 7.75 8.00 3.96 6.00 2.75 7.25 2.50 5.58 \n", + "1962-01-01 9.29 3.42 11.54 3.50 2.21 1.96 10.41 2.79 3.54 \n", + "1962-02-01 19.12 13.96 12.21 10.58 15.71 10.63 15.71 11.08 13.17 \n", + "1962-03-01 8.21 4.83 9.00 4.83 6.00 2.21 7.96 1.87 4.08 \n", + "1962-04-01 14.33 12.25 11.87 10.37 14.92 11.00 19.79 11.67 14.09 \n", + "1962-05-01 9.62 9.54 3.58 3.33 8.75 3.75 2.25 2.58 1.67 \n", + "1962-06-01 5.88 6.29 8.67 5.21 5.00 4.25 5.91 5.41 4.79 \n", + "1962-07-01 8.67 4.17 6.92 6.71 8.17 5.66 11.17 9.38 8.75 \n", + "1962-08-01 4.58 5.37 6.04 2.29 7.87 3.71 4.46 2.58 4.00 \n", + "1962-09-01 10.00 12.08 10.96 9.25 9.29 7.62 7.41 8.75 7.67 \n", + "1962-10-01 14.58 7.83 19.21 10.08 11.54 8.38 13.29 10.63 8.21 \n", + "1962-11-01 16.88 13.25 16.00 8.96 13.46 11.46 10.46 10.17 10.37 \n", + "1962-12-01 18.38 15.41 11.75 6.79 12.21 8.04 8.42 10.83 5.66 \n", + "1963-01-01 15.59 13.62 19.79 8.38 12.25 10.00 23.45 15.71 13.59 \n", + "1963-02-01 15.41 7.62 24.67 11.42 9.21 8.17 14.04 7.54 7.54 \n", + "1963-03-01 16.75 19.67 17.67 8.87 19.08 15.37 16.21 14.29 11.29 \n", + "1963-04-01 10.54 9.59 12.46 7.33 9.46 9.59 11.79 11.87 9.79 \n", + "1963-05-01 18.79 14.17 13.59 11.63 14.17 11.96 14.46 12.46 12.87 \n", + "1963-06-01 13.37 6.87 12.00 8.50 10.04 9.42 10.92 12.96 11.79 \n", + "... ... ... ... ... ... ... ... ... ... \n", + "1976-07-01 8.50 1.75 6.58 2.13 2.75 2.21 5.37 2.04 5.88 \n", + "1976-08-01 13.00 8.38 8.63 5.83 12.92 8.25 13.00 9.42 10.58 \n", + "1976-09-01 11.87 11.00 7.38 6.87 7.75 8.33 10.34 6.46 10.17 \n", + "1976-10-01 10.96 6.71 10.41 4.63 7.58 5.04 5.04 5.54 6.50 \n", + "1976-11-01 13.96 15.67 10.29 6.46 12.79 9.08 10.00 9.67 10.21 \n", + "1976-12-01 13.46 16.42 9.21 4.54 10.75 8.67 10.88 4.83 8.79 \n", + "1977-01-01 20.04 11.92 20.25 9.13 9.29 8.04 10.75 5.88 9.00 \n", + "1977-02-01 11.83 9.71 11.00 4.25 8.58 8.71 6.17 5.66 8.29 \n", + "1977-03-01 8.63 14.83 10.29 3.75 6.63 8.79 5.00 8.12 7.87 \n", + "1977-04-01 21.67 16.00 17.33 13.59 20.83 15.96 25.62 17.62 19.41 \n", + "1977-05-01 6.42 7.12 8.67 3.58 4.58 4.00 6.75 6.13 3.33 \n", + "1977-06-01 7.08 5.25 9.71 2.83 2.21 3.50 5.29 1.42 2.00 \n", + "1977-07-01 15.41 16.29 17.08 6.25 11.83 11.83 12.29 10.58 10.41 \n", + "1977-08-01 4.33 2.96 4.42 2.33 0.96 1.08 4.96 1.87 2.33 \n", + "1977-09-01 17.37 16.33 16.83 8.58 14.46 11.83 15.09 13.92 13.29 \n", + "1977-10-01 16.75 15.34 12.25 9.42 16.38 11.38 18.50 13.92 14.09 \n", + "1977-11-01 16.71 11.54 12.17 4.17 8.54 7.17 11.12 6.46 8.25 \n", + "1977-12-01 13.37 10.92 12.42 2.37 5.79 6.13 8.96 7.38 6.29 \n", + "1978-01-01 8.33 7.12 7.71 3.54 8.50 7.50 14.71 10.00 11.83 \n", + "1978-02-01 27.25 24.21 18.16 17.46 27.54 18.05 20.96 25.04 20.04 \n", + "1978-03-01 15.04 6.21 16.04 7.87 6.42 6.67 12.29 8.00 10.58 \n", + "1978-04-01 3.42 7.58 2.71 1.38 3.46 2.08 2.67 4.75 4.83 \n", + "1978-05-01 10.54 12.21 9.08 5.29 11.00 10.08 11.17 13.75 11.87 \n", + "1978-06-01 10.37 11.42 6.46 6.04 11.25 7.50 6.46 5.96 7.79 \n", + "1978-07-01 12.46 10.63 11.17 6.75 12.92 9.04 12.42 9.62 12.08 \n", + "1978-08-01 19.33 15.09 20.17 8.83 12.62 10.41 9.33 12.33 9.50 \n", + "1978-09-01 8.42 6.13 9.87 5.25 3.21 5.71 7.25 3.50 7.33 \n", + "1978-10-01 9.50 6.83 10.50 3.88 6.13 4.58 4.21 6.50 6.38 \n", + "1978-11-01 13.59 16.75 11.25 7.08 11.04 8.33 8.17 11.29 10.75 \n", + "1978-12-01 21.29 16.29 24.04 12.79 18.21 19.29 21.54 17.21 16.71 \n", + "\n", + " CLO BEL MAL date month year day \n", + "Yr_Mo_Dy \n", + "1961-01-01 12.58 18.50 15.04 1961-01-01 1 1961 1 \n", + "1961-02-01 5.50 9.17 8.00 1961-02-01 2 1961 1 \n", + "1961-03-01 12.21 20.62 NaN 1961-03-01 3 1961 1 \n", + "1961-04-01 6.46 11.96 7.17 1961-04-01 4 1961 1 \n", + "1961-05-01 9.92 18.63 11.12 1961-05-01 5 1961 1 \n", + "1961-06-01 10.34 10.67 12.12 1961-06-01 6 1961 1 \n", + "1961-07-01 7.96 6.96 8.71 1961-07-01 7 1961 1 \n", + "1961-08-01 5.41 8.38 9.08 1961-08-01 8 1961 1 \n", + "1961-09-01 6.00 4.79 5.41 1961-09-01 9 1961 1 \n", + "1961-10-01 5.29 11.79 4.04 1961-10-01 10 1961 1 \n", + "1961-11-01 13.54 20.17 20.04 1961-11-01 11 1961 1 \n", + "1961-12-01 5.58 7.79 11.17 1961-12-01 12 1961 1 \n", + "1962-01-01 5.17 4.38 7.92 1962-01-01 1 1962 1 \n", + "1962-02-01 12.62 17.67 22.71 1962-02-01 2 1962 1 \n", + "1962-03-01 3.92 4.08 5.41 1962-03-01 3 1962 1 \n", + "1962-04-01 15.46 16.62 23.58 1962-04-01 4 1962 1 \n", + "1962-05-01 2.37 7.29 3.25 1962-05-01 5 1962 1 \n", + "1962-06-01 9.25 5.25 10.71 1962-06-01 6 1962 1 \n", + "1962-07-01 11.12 10.25 17.08 1962-07-01 7 1962 1 \n", + "1962-08-01 4.79 7.21 7.46 1962-08-01 8 1962 1 \n", + "1962-09-01 9.62 14.58 11.92 1962-09-01 9 1962 1 \n", + "1962-10-01 12.92 18.05 18.12 1962-10-01 10 1962 1 \n", + "1962-11-01 13.21 14.83 15.16 1962-11-01 11 1962 1 \n", + "1962-12-01 9.08 11.50 11.50 1962-12-01 12 1962 1 \n", + "1963-01-01 14.37 17.58 34.13 1963-01-01 1 1963 1 \n", + "1963-02-01 10.08 10.17 17.67 1963-02-01 2 1963 1 \n", + "1963-03-01 9.21 19.92 19.79 1963-03-01 3 1963 1 \n", + "1963-04-01 10.71 13.37 18.21 1963-04-01 4 1963 1 \n", + "1963-05-01 13.96 15.29 21.62 1963-05-01 5 1963 1 \n", + "1963-06-01 11.04 10.92 13.67 1963-06-01 6 1963 1 \n", + "... ... ... ... ... ... ... ... \n", + "1976-07-01 4.50 4.96 10.63 1976-07-01 7 1976 1 \n", + "1976-08-01 11.34 14.21 20.25 1976-08-01 8 1976 1 \n", + "1976-09-01 9.29 12.75 19.55 1976-09-01 9 1976 1 \n", + "1976-10-01 3.92 6.79 5.00 1976-10-01 10 1976 1 \n", + "1976-11-01 11.63 23.09 21.96 1976-11-01 11 1976 1 \n", + "1976-12-01 5.91 8.83 13.67 1976-12-01 12 1976 1 \n", + "1977-01-01 9.00 14.88 25.70 1977-01-01 1 1977 1 \n", + "1977-02-01 7.58 11.71 16.50 1977-02-01 2 1977 1 \n", + "1977-03-01 6.42 13.54 13.67 1977-03-01 3 1977 1 \n", + "1977-04-01 20.67 24.37 30.09 1977-04-01 4 1977 1 \n", + "1977-05-01 4.50 19.21 12.38 1977-05-01 5 1977 1 \n", + "1977-06-01 0.92 5.21 5.63 1977-06-01 6 1977 1 \n", + "1977-07-01 7.21 17.37 7.83 1977-07-01 7 1977 1 \n", + "1977-08-01 2.04 10.50 9.83 1977-08-01 8 1977 1 \n", + "1977-09-01 13.88 23.29 25.17 1977-09-01 9 1977 1 \n", + "1977-10-01 14.46 22.34 29.67 1977-10-01 10 1977 1 \n", + "1977-11-01 6.21 11.04 15.63 1977-11-01 11 1977 1 \n", + "1977-12-01 5.71 8.54 12.42 1977-12-01 12 1977 1 \n", + "1978-01-01 10.00 15.09 20.46 1978-01-01 1 1978 1 \n", + "1978-02-01 17.50 27.71 21.12 1978-02-01 2 1978 1 \n", + "1978-03-01 9.33 5.41 17.00 1978-03-01 3 1978 1 \n", + "1978-04-01 1.67 7.33 13.67 1978-04-01 4 1978 1 \n", + "1978-05-01 11.79 12.87 27.16 1978-05-01 5 1978 1 \n", + "1978-06-01 5.46 5.50 10.41 1978-06-01 6 1978 1 \n", + "1978-07-01 8.04 14.04 16.17 1978-07-01 7 1978 1 \n", + "1978-08-01 9.92 15.75 18.00 1978-08-01 8 1978 1 \n", + "1978-09-01 6.50 7.62 15.96 1978-09-01 9 1978 1 \n", + "1978-10-01 6.54 10.63 14.09 1978-10-01 10 1978 1 \n", + "1978-11-01 11.25 23.13 25.00 1978-11-01 11 1978 1 \n", + "1978-12-01 17.83 17.75 25.70 1978-12-01 12 1978 1 \n", + "\n", + "[216 rows x 16 columns]" + ] + }, + "execution_count": 429, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 14. Downsample the record to a weekly frequency for each location." + ] + }, + { + "cell_type": "code", + "execution_count": 430, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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RPTVALROSKILSHABIRDUBCLAMULCLOBELMALdatemonthyearday
Yr_Mo_Dy
1961-01-0115.0414.9613.179.29NaN9.8713.6710.2510.8312.5818.5015.041961-01-01119611
1961-01-0810.969.757.625.919.627.2914.297.629.2510.4616.6216.461961-01-08119618
1961-01-1512.049.6711.752.377.383.132.506.834.755.637.546.751961-01-151196115
1961-01-229.595.889.922.176.875.509.387.046.347.5010.889.921961-01-221196122
1961-01-29NaN23.9122.2917.5424.0819.7022.0020.2521.4619.9527.7123.381961-01-291196129
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" + ], + "text/plain": [ + " RPT VAL ROS KIL SHA BIR DUB CLA MUL \\\n", + "Yr_Mo_Dy \n", + "1961-01-01 15.04 14.96 13.17 9.29 NaN 9.87 13.67 10.25 10.83 \n", + "1961-01-08 10.96 9.75 7.62 5.91 9.62 7.29 14.29 7.62 9.25 \n", + "1961-01-15 12.04 9.67 11.75 2.37 7.38 3.13 2.50 6.83 4.75 \n", + "1961-01-22 9.59 5.88 9.92 2.17 6.87 5.50 9.38 7.04 6.34 \n", + "1961-01-29 NaN 23.91 22.29 17.54 24.08 19.70 22.00 20.25 21.46 \n", + "\n", + " CLO BEL MAL date month year day \n", + "Yr_Mo_Dy \n", + "1961-01-01 12.58 18.50 15.04 1961-01-01 1 1961 1 \n", + "1961-01-08 10.46 16.62 16.46 1961-01-08 1 1961 8 \n", + "1961-01-15 5.63 7.54 6.75 1961-01-15 1 1961 15 \n", + "1961-01-22 7.50 10.88 9.92 1961-01-22 1 1961 22 \n", + "1961-01-29 19.95 27.71 23.38 1961-01-29 1 1961 29 " + ] + }, + "execution_count": 430, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 15. Calculate the mean windspeed for each month in the dataset. \n", + "#### Treat January 1961 and January 1962 as *different* months.\n", + "#### (hint: first find a way to create an identifier unique for each month.)" + ] + }, + { + "cell_type": "code", + "execution_count": 432, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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YrMoDyRPTVALROSKILSHABIRDUBCLAMULCLOBELMAL
months_num
161.01.016.014.84133311.98833313.4316137.73677411.0727598.58806511.1848399.2453339.08580610.10741913.88096814.703226
261.02.014.516.26928614.97535714.4414819.23074113.85214310.93750011.89071411.84607111.82142912.71428618.58321415.411786
361.03.016.010.89000011.29645210.7529037.28400010.5093558.8667749.6441949.82967710.29413811.25193516.41096815.720000
461.04.015.510.7226679.4276679.9980005.8306678.4350006.4950006.9253337.0946677.3423337.23700011.14733310.278333
561.05.016.09.8609688.85000010.8180655.9053339.4903236.5748397.6040008.1770978.0393558.49935511.90032312.011613
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" + ], + "text/plain": [ + " Yr Mo Dy RPT VAL ROS KIL \\\n", + "months_num \n", + "1 61.0 1.0 16.0 14.841333 11.988333 13.431613 7.736774 \n", + "2 61.0 2.0 14.5 16.269286 14.975357 14.441481 9.230741 \n", + "3 61.0 3.0 16.0 10.890000 11.296452 10.752903 7.284000 \n", + "4 61.0 4.0 15.5 10.722667 9.427667 9.998000 5.830667 \n", + "5 61.0 5.0 16.0 9.860968 8.850000 10.818065 5.905333 \n", + "\n", + " SHA BIR DUB CLA MUL CLO \\\n", + "months_num \n", + "1 11.072759 8.588065 11.184839 9.245333 9.085806 10.107419 \n", + "2 13.852143 10.937500 11.890714 11.846071 11.821429 12.714286 \n", + "3 10.509355 8.866774 9.644194 9.829677 10.294138 11.251935 \n", + "4 8.435000 6.495000 6.925333 7.094667 7.342333 7.237000 \n", + "5 9.490323 6.574839 7.604000 8.177097 8.039355 8.499355 \n", + "\n", + " BEL MAL \n", + "months_num \n", + "1 13.880968 14.703226 \n", + "2 18.583214 15.411786 \n", + "3 16.410968 15.720000 \n", + "4 11.147333 10.278333 \n", + "5 11.900323 12.011613 " + ] + }, + "execution_count": 432, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 16. Calculate the min, max and mean windspeeds and standard deviations of the windspeeds across all locations for each week (assume that the first week starts on January 2 1961) for the first 52 weeks." + ] + }, + { + "cell_type": "code", + "execution_count": 433, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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RPTVALROS...CLOBELMAL
minmaxmeanstdminmaxmeanstdminmax...meanstdminmaxmeanstdminmaxmeanstd
Yr_Mo_Dy
1961-01-0810.5818.5013.5414292.6313216.6316.8811.4866673.9495257.6212.33...8.4971431.7049415.4617.5412.4814294.34913910.8816.4613.2385711.773062
1961-01-159.0419.7512.4685713.5553923.5412.088.9671433.1489457.0819.50...7.5714294.0842935.2520.7111.1257145.5522155.1716.9211.0242864.692355
1961-01-224.9219.8313.2042865.3374023.4214.379.8628573.8377857.2920.79...8.1242864.7839526.5015.929.8214293.6265846.7917.9611.4342864.237239
1961-01-2913.6225.0419.8800004.6190619.9623.9116.1414295.17022412.6725.84...15.6400003.71336814.0427.7120.9300005.21072617.5027.6322.5300003.874721
1961-02-0510.5824.2116.8271435.2514089.4624.2115.4600005.1873959.0419.70...9.4600002.8395019.1719.3314.0128574.2108587.1719.2511.9357144.336104
1961-02-1216.0024.5419.6842863.58767711.5421.4216.4171433.60837313.6721.34...14.4400001.74674915.2126.3821.8328574.06375317.0421.8419.1557141.828705
1961-02-196.0422.5015.1300005.06460911.6320.1715.0914293.5750126.1319.41...13.5428572.53136114.0929.6321.1671435.91093810.9622.5816.5842864.685377
1961-02-267.7925.8015.2214297.0207167.0821.5013.6257145.1473486.0822.42...12.7300004.9200649.5923.2116.3042865.0911626.6723.8714.3228576.182283
1961-03-0510.9613.3312.1014290.9977218.8317.0012.9514292.8519558.1713.67...12.3700001.59368511.5823.4517.8428574.3323318.8317.5413.9516673.021387
1961-03-124.8814.799.3766673.7322638.0816.9611.5785713.2301677.5416.38...10.4585713.65511310.2122.7116.7014294.3587595.5422.5414.4200005.769890
\n", + "

10 rows × 48 columns

\n", + "
" + ], + "text/plain": [ + " RPT VAL \\\n", + " min max mean std min max mean \n", + "Yr_Mo_Dy \n", + "1961-01-08 10.58 18.50 13.541429 2.631321 6.63 16.88 11.486667 \n", + "1961-01-15 9.04 19.75 12.468571 3.555392 3.54 12.08 8.967143 \n", + "1961-01-22 4.92 19.83 13.204286 5.337402 3.42 14.37 9.862857 \n", + "1961-01-29 13.62 25.04 19.880000 4.619061 9.96 23.91 16.141429 \n", + "1961-02-05 10.58 24.21 16.827143 5.251408 9.46 24.21 15.460000 \n", + "1961-02-12 16.00 24.54 19.684286 3.587677 11.54 21.42 16.417143 \n", + "1961-02-19 6.04 22.50 15.130000 5.064609 11.63 20.17 15.091429 \n", + "1961-02-26 7.79 25.80 15.221429 7.020716 7.08 21.50 13.625714 \n", + "1961-03-05 10.96 13.33 12.101429 0.997721 8.83 17.00 12.951429 \n", + "1961-03-12 4.88 14.79 9.376667 3.732263 8.08 16.96 11.578571 \n", + "\n", + " ROS ... CLO BEL \\\n", + " std min max ... mean std min \n", + "Yr_Mo_Dy ... \n", + "1961-01-08 3.949525 7.62 12.33 ... 8.497143 1.704941 5.46 \n", + "1961-01-15 3.148945 7.08 19.50 ... 7.571429 4.084293 5.25 \n", + "1961-01-22 3.837785 7.29 20.79 ... 8.124286 4.783952 6.50 \n", + "1961-01-29 5.170224 12.67 25.84 ... 15.640000 3.713368 14.04 \n", + "1961-02-05 5.187395 9.04 19.70 ... 9.460000 2.839501 9.17 \n", + "1961-02-12 3.608373 13.67 21.34 ... 14.440000 1.746749 15.21 \n", + "1961-02-19 3.575012 6.13 19.41 ... 13.542857 2.531361 14.09 \n", + "1961-02-26 5.147348 6.08 22.42 ... 12.730000 4.920064 9.59 \n", + "1961-03-05 2.851955 8.17 13.67 ... 12.370000 1.593685 11.58 \n", + "1961-03-12 3.230167 7.54 16.38 ... 10.458571 3.655113 10.21 \n", + "\n", + " MAL \n", + " max mean std min max mean std \n", + "Yr_Mo_Dy \n", + "1961-01-08 17.54 12.481429 4.349139 10.88 16.46 13.238571 1.773062 \n", + "1961-01-15 20.71 11.125714 5.552215 5.17 16.92 11.024286 4.692355 \n", + "1961-01-22 15.92 9.821429 3.626584 6.79 17.96 11.434286 4.237239 \n", + "1961-01-29 27.71 20.930000 5.210726 17.50 27.63 22.530000 3.874721 \n", + "1961-02-05 19.33 14.012857 4.210858 7.17 19.25 11.935714 4.336104 \n", + "1961-02-12 26.38 21.832857 4.063753 17.04 21.84 19.155714 1.828705 \n", + "1961-02-19 29.63 21.167143 5.910938 10.96 22.58 16.584286 4.685377 \n", + "1961-02-26 23.21 16.304286 5.091162 6.67 23.87 14.322857 6.182283 \n", + "1961-03-05 23.45 17.842857 4.332331 8.83 17.54 13.951667 3.021387 \n", + "1961-03-12 22.71 16.701429 4.358759 5.54 22.54 14.420000 5.769890 \n", + "\n", + "[10 rows x 48 columns]" + ] + }, + "execution_count": 433, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/06_Stats/Wind_Stats/wind.data b/200 solved problems in Python/pandas/06_Stats/Wind_Stats/wind.data new file mode 100644 index 0000000000000000000000000000000000000000..a17b74218ad5ee756d76983966ea8d6c99af71a1 --- /dev/null +++ b/200 solved problems in Python/pandas/06_Stats/Wind_Stats/wind.data @@ -0,0 +1,6576 @@ + +Yr Mo Dy RPT VAL ROS KIL SHA BIR DUB CLA MUL CLO BEL MAL +61 1 1 15.04 14.96 13.17 9.29 NaN 9.87 13.67 10.25 10.83 12.58 18.50 15.04 +61 1 2 14.71 NaN 10.83 6.50 12.62 7.67 11.50 10.04 9.79 9.67 17.54 13.83 +61 1 3 18.50 16.88 12.33 10.13 11.17 6.17 11.25 NaN 8.50 7.67 12.75 12.71 +61 1 4 10.58 6.63 11.75 4.58 4.54 2.88 8.63 1.79 5.83 5.88 5.46 10.88 +61 1 5 13.33 13.25 11.42 6.17 10.71 8.21 11.92 6.54 10.92 10.34 12.92 11.83 +61 1 6 13.21 8.12 9.96 6.67 5.37 4.50 10.67 4.42 7.17 7.50 8.12 13.17 +61 1 7 13.50 14.29 9.50 4.96 12.29 8.33 9.17 9.29 7.58 7.96 13.96 13.79 +61 1 8 10.96 9.75 7.62 5.91 9.62 7.29 14.29 7.62 9.25 10.46 16.62 16.46 +61 1 9 12.58 10.83 10.00 4.75 10.37 6.79 8.04 10.13 7.79 9.08 13.04 15.37 +61 1 10 13.37 11.12 19.50 8.33 9.71 6.54 11.42 7.79 8.54 9.00 8.58 11.83 +61 1 11 10.58 9.87 8.42 2.79 8.71 7.25 7.54 8.33 5.71 8.67 20.71 16.92 +61 1 12 19.75 12.08 18.50 10.54 10.29 9.46 15.54 11.50 10.37 14.58 15.59 14.09 +61 1 13 9.92 3.54 8.46 2.96 2.29 0.96 4.63 0.58 2.33 3.37 5.25 7.04 +61 1 14 9.04 5.66 7.08 0.67 2.71 1.38 3.08 2.58 0.50 2.67 7.17 5.17 +61 1 15 12.04 9.67 11.75 2.37 7.38 3.13 2.50 6.83 4.75 5.63 7.54 6.75 +61 1 16 16.42 11.25 15.67 4.71 11.34 6.92 9.25 8.79 8.21 7.33 13.04 9.04 +61 1 17 17.75 14.37 17.33 10.13 13.96 13.37 13.42 11.04 8.71 11.38 15.92 16.08 +61 1 18 19.83 12.04 20.79 18.54 NaN 10.29 17.83 11.38 14.67 16.71 8.79 17.96 +61 1 19 4.92 3.42 7.29 1.04 3.67 3.17 3.71 2.79 1.92 2.71 6.87 7.83 +61 1 20 9.59 11.83 7.96 1.58 7.92 5.00 3.17 4.92 3.13 3.37 6.50 6.79 +61 1 21 14.33 10.25 11.92 6.13 10.04 7.67 8.04 9.17 7.04 7.87 6.75 12.42 +61 1 22 9.59 5.88 9.92 2.17 6.87 5.50 9.38 7.04 6.34 7.50 10.88 9.92 +61 1 23 16.54 9.96 18.54 10.46 13.50 12.67 13.70 13.75 10.75 13.17 14.79 20.58 +61 1 24 25.04 14.83 25.84 15.67 21.46 18.58 20.38 19.38 15.37 15.12 23.09 25.25 +61 1 25 13.62 11.17 12.67 6.04 10.00 9.42 9.25 8.71 7.12 12.04 14.04 17.50 +61 1 26 24.37 18.79 17.50 14.25 18.91 15.67 14.33 15.16 16.08 19.08 20.50 25.25 +61 1 27 22.04 20.79 17.41 16.21 19.04 16.13 16.79 18.29 18.66 19.08 26.08 27.63 +61 1 28 17.67 13.54 13.33 8.87 15.04 11.63 12.25 10.58 11.92 11.04 20.30 18.12 +61 1 29 NaN 23.91 22.29 17.54 24.08 19.70 22.00 20.25 21.46 19.95 27.71 23.38 +61 1 30 12.21 11.42 10.92 7.92 13.08 9.62 14.50 10.21 9.92 11.96 18.88 19.25 +61 1 31 24.21 19.55 16.71 11.96 14.42 10.46 14.88 8.21 10.50 9.96 12.42 13.92 +61 2 1 14.25 15.12 9.04 5.88 12.08 7.17 10.17 3.63 6.50 5.50 9.17 8.00 +61 2 2 20.17 24.21 10.00 6.54 17.37 7.17 10.88 6.08 9.08 7.12 12.08 7.17 +61 2 3 14.37 11.71 11.04 5.09 9.87 5.83 8.50 5.13 6.34 7.38 9.71 8.83 +61 2 4 10.58 9.46 10.92 8.71 12.46 11.46 12.54 11.04 13.04 11.17 16.50 11.71 +61 2 5 22.00 16.75 19.70 11.63 14.25 12.04 13.96 15.54 13.37 13.13 19.33 14.67 +61 2 6 24.50 20.75 20.62 15.37 25.33 17.62 19.17 18.79 18.96 14.46 26.38 21.84 +61 2 7 18.05 14.37 14.88 9.75 10.96 11.17 14.21 9.71 13.04 12.25 15.21 17.58 +61 2 8 24.54 21.42 21.34 11.63 17.04 14.21 16.50 17.33 14.67 17.79 23.79 19.58 +61 2 9 17.41 15.92 13.67 10.71 14.00 12.92 14.37 15.71 14.46 14.67 23.04 19.21 +61 2 10 20.50 11.54 19.67 10.04 8.12 8.08 11.08 12.54 11.12 12.96 17.83 17.75 +61 2 11 16.79 14.00 13.70 9.54 15.83 13.29 17.50 15.50 15.83 14.33 21.17 21.09 +61 2 12 16.00 16.92 17.25 8.38 11.75 10.37 11.71 14.37 10.37 14.62 25.41 17.04 +61 2 13 22.50 19.70 19.41 15.34 16.13 14.62 16.08 14.12 13.96 16.58 28.62 19.67 +61 2 14 17.08 11.63 17.25 12.12 13.75 13.46 15.46 12.29 14.88 15.67 14.09 11.42 +61 2 15 6.04 12.08 6.13 4.21 9.87 6.92 5.17 7.41 8.17 9.21 17.12 10.96 +61 2 16 16.79 20.17 12.67 13.25 15.29 15.12 3.42 14.29 14.79 13.79 21.92 22.58 +61 2 17 15.25 14.09 14.75 9.54 14.62 12.92 6.17 12.58 13.25 14.54 18.21 17.67 +61 2 18 12.08 12.38 12.67 6.04 10.71 9.08 10.88 12.38 10.00 11.34 18.58 13.25 +61 2 19 16.17 15.59 13.70 NaN 13.50 10.96 9.62 11.83 11.58 13.67 29.63 20.54 +61 2 20 11.04 11.08 10.63 8.33 10.92 8.29 7.25 7.75 8.83 11.08 15.09 12.17 +61 2 21 7.79 8.63 6.08 2.21 7.41 4.33 3.75 7.04 4.50 7.41 12.12 6.67 +61 2 22 12.29 13.21 13.54 6.46 11.58 9.21 8.25 10.29 9.21 13.08 15.25 13.21 +61 2 23 22.08 17.46 18.88 12.42 20.50 16.13 16.38 16.92 13.21 18.00 23.21 21.21 +61 2 24 8.71 7.08 11.38 4.83 5.96 4.58 8.17 4.67 6.21 7.00 9.59 9.21 +61 2 25 18.84 16.42 17.41 10.21 16.13 11.38 13.08 12.21 12.33 12.50 16.04 13.92 +61 2 26 25.80 21.50 22.42 15.21 23.09 16.88 21.17 17.25 18.46 20.04 22.83 23.87 +61 2 27 11.00 13.37 11.17 6.87 13.21 8.75 12.75 10.83 10.88 12.67 20.00 17.54 +61 2 28 12.92 12.75 NaN 8.92 16.13 12.29 14.75 14.46 13.96 14.04 18.41 13.17 +61 3 1 12.67 13.13 11.79 6.42 9.79 8.54 10.25 13.29 NaN 12.21 20.62 NaN +61 3 2 12.58 10.04 11.17 7.12 10.29 9.21 10.88 10.50 10.58 12.96 18.38 13.79 +61 3 3 13.33 15.54 13.67 9.59 11.58 8.71 11.29 12.83 8.92 14.09 23.45 16.38 +61 3 4 10.96 8.83 8.17 5.88 8.25 6.00 6.21 5.66 6.25 9.79 12.46 8.83 +61 3 5 11.25 17.00 10.41 10.04 15.46 11.17 5.50 10.34 11.71 10.83 11.58 14.00 +61 3 6 4.88 8.08 7.54 3.13 5.41 4.08 2.83 5.29 5.21 5.09 10.21 5.54 +61 3 7 6.46 10.50 8.00 5.41 10.58 6.58 3.63 6.67 7.08 7.04 16.08 8.75 +61 3 8 7.54 8.33 9.29 8.12 11.00 10.58 1.71 8.50 8.71 9.71 17.58 15.37 +61 3 9 11.96 13.13 10.17 9.46 14.17 12.67 3.75 9.71 12.46 11.54 17.54 18.16 +61 3 10 10.63 10.17 9.04 5.13 10.50 7.92 8.79 7.92 8.87 10.50 12.29 13.50 +61 3 11 14.79 16.96 15.50 8.46 10.00 10.92 10.34 15.67 10.58 13.54 22.71 17.08 +61 3 12 NaN 13.88 16.38 10.25 14.92 13.67 17.12 13.70 16.29 15.79 20.50 22.54 +61 3 13 11.21 11.34 12.33 5.63 11.17 10.58 13.96 10.50 13.88 11.50 19.70 18.88 +61 3 14 12.12 15.29 12.75 5.17 9.38 7.04 8.12 11.12 8.75 9.29 20.04 15.71 +61 3 15 11.12 15.54 13.96 8.42 10.88 8.83 11.54 13.54 8.46 13.92 22.13 14.12 +61 3 16 4.92 12.92 5.25 4.29 8.96 7.04 2.75 5.13 6.00 7.29 20.12 11.42 +61 3 17 15.71 14.71 11.21 8.87 14.37 10.75 13.00 14.29 13.08 15.34 22.79 19.17 +61 3 18 16.88 15.25 12.29 11.04 13.67 10.54 13.17 10.46 13.50 14.67 19.38 22.95 +61 3 19 11.42 9.46 13.46 6.17 6.83 5.79 7.75 6.50 7.04 9.38 11.29 11.34 +61 3 20 13.75 10.41 12.00 10.75 11.46 10.29 15.41 10.04 13.67 13.83 14.29 21.09 +61 3 21 9.54 10.58 15.63 7.29 7.54 7.71 8.17 6.58 8.58 10.50 9.38 14.88 +61 3 22 6.34 4.42 5.71 4.42 5.13 4.29 8.00 4.92 8.00 9.04 8.25 13.13 +61 3 23 6.38 2.58 4.79 5.13 6.46 7.21 10.88 7.38 9.79 8.33 15.21 18.34 +61 3 24 6.29 7.46 6.75 4.54 8.25 6.25 8.46 10.34 10.00 10.29 14.42 15.46 +61 3 25 9.67 11.63 10.41 5.58 10.58 9.92 10.75 13.04 11.92 14.50 21.34 21.54 +61 3 26 15.00 11.63 12.58 8.83 13.33 10.21 13.75 10.54 14.37 13.88 15.37 22.50 +61 3 27 5.88 3.50 5.17 2.75 2.83 3.50 6.75 1.46 6.50 7.12 7.21 15.34 +61 3 28 8.46 10.63 12.04 NaN 7.83 6.79 10.88 10.92 9.54 10.63 18.16 19.58 +61 3 29 18.25 16.29 14.96 12.00 18.34 14.33 16.62 13.37 17.54 14.21 18.63 16.83 +61 3 30 17.75 12.92 11.79 10.13 16.08 14.21 14.79 14.92 NaN 13.46 15.67 13.17 +61 3 31 8.96 8.04 9.13 8.50 10.75 9.54 11.92 9.59 11.25 8.54 11.96 12.21 +61 4 1 8.38 6.34 8.33 6.75 9.33 9.54 11.67 8.21 11.21 6.46 11.96 7.17 +61 4 2 7.62 4.25 5.09 3.67 4.67 6.25 5.25 4.96 6.83 7.00 10.71 13.00 +61 4 3 15.29 8.63 15.67 4.12 10.25 9.04 5.50 8.54 7.58 4.96 11.08 9.38 +61 4 4 13.92 13.67 13.88 6.75 9.79 6.38 6.00 8.54 7.67 8.08 14.50 11.92 +61 4 5 18.12 14.62 18.29 10.63 13.33 10.83 9.25 8.21 10.34 8.50 17.16 13.13 +61 4 6 4.50 8.21 9.29 3.21 5.96 4.33 5.00 7.33 6.13 6.54 10.25 11.50 +61 4 7 8.21 7.04 13.75 3.75 6.00 5.00 5.33 4.00 5.66 3.67 7.62 7.21 +61 4 8 10.34 11.29 11.29 4.42 9.38 7.67 8.71 10.08 9.21 8.42 11.25 11.12 +61 4 9 13.37 11.12 13.08 8.83 11.75 10.21 9.96 9.59 10.54 10.50 10.13 15.34 +61 4 10 5.75 4.83 8.25 3.00 5.88 4.25 5.21 3.58 6.00 4.58 7.62 11.25 +61 4 11 10.50 12.25 12.58 6.96 8.71 8.58 5.66 9.92 9.00 9.67 16.17 12.87 +61 4 12 10.63 10.04 11.38 5.13 5.71 5.13 6.54 4.75 4.50 5.17 8.50 6.38 +61 4 13 15.50 9.29 15.79 9.71 7.29 7.17 8.00 4.75 7.87 7.58 7.17 5.66 +61 4 14 10.50 6.17 4.50 5.00 8.67 5.00 4.29 4.50 5.71 7.79 8.50 7.71 +61 4 15 5.17 6.42 3.92 2.67 3.37 1.54 2.46 2.29 3.58 4.00 5.75 6.79 +61 4 16 4.71 7.00 5.09 2.33 5.21 2.96 2.88 5.17 3.37 4.46 12.67 10.17 +61 4 17 4.00 3.71 3.33 1.58 2.21 0.83 4.46 2.54 3.58 3.83 10.04 7.25 +61 4 18 10.83 12.42 6.04 5.50 11.92 7.08 5.66 9.96 7.46 9.46 12.92 11.58 +61 4 19 16.71 13.96 14.67 11.42 14.37 11.63 12.96 13.25 13.29 14.37 19.21 20.46 +61 4 20 21.09 15.41 17.00 11.63 13.25 11.17 12.25 10.54 11.29 11.58 18.05 20.08 +61 4 21 15.34 14.04 15.87 9.79 11.71 8.79 9.50 12.25 9.00 9.87 17.54 12.29 +61 4 22 8.17 5.66 6.92 4.79 7.87 5.09 7.46 4.83 4.88 7.12 6.75 4.96 +61 4 23 12.21 7.87 7.96 9.67 11.17 10.00 10.71 10.41 11.87 10.63 10.83 10.67 +61 4 24 9.54 8.04 9.13 4.79 7.54 5.75 5.25 7.08 6.00 7.62 9.87 8.46 +61 4 25 14.04 11.96 14.96 7.87 10.75 8.83 13.75 11.96 11.08 11.46 12.17 17.50 +61 4 26 16.29 14.46 6.92 5.88 12.62 7.46 6.00 6.34 7.54 6.00 8.33 12.12 +61 4 27 4.08 7.04 7.92 3.83 3.88 3.13 6.25 3.17 5.41 4.71 7.29 7.75 +61 4 28 5.58 6.50 2.54 1.87 4.21 1.54 2.67 1.38 2.54 2.71 5.13 3.21 +61 4 29 9.62 9.59 6.96 3.83 6.83 3.88 4.04 6.46 4.17 4.12 13.04 2.67 +61 4 30 11.67 11.00 9.54 5.54 9.42 5.79 5.09 8.25 6.96 6.25 12.21 8.75 +61 5 1 15.87 13.88 15.37 9.79 13.46 10.17 9.96 14.04 9.75 9.92 18.63 11.12 +61 5 2 19.04 14.83 14.92 8.67 15.63 11.63 10.46 9.75 11.21 10.71 12.96 12.67 +61 5 3 11.00 10.92 12.87 8.67 10.58 8.42 9.50 10.04 9.62 9.42 15.46 12.25 +61 5 4 9.87 10.29 8.42 7.50 9.42 7.33 5.96 6.25 7.79 8.92 4.79 3.83 +61 5 5 10.63 10.37 9.17 6.46 9.83 8.00 9.13 9.87 10.04 8.63 16.29 12.42 +61 5 6 23.00 19.79 21.21 14.12 22.34 15.04 16.83 17.71 16.29 17.54 28.08 23.13 +61 5 7 18.16 17.71 14.88 14.46 23.16 15.46 18.21 18.05 17.04 15.96 26.63 26.58 +61 5 8 12.79 10.08 12.33 10.75 15.71 14.04 18.58 15.79 17.33 17.92 18.66 26.30 +61 5 9 6.04 3.96 7.00 3.83 4.17 4.00 7.25 3.37 7.08 9.21 6.54 11.38 +61 5 10 4.79 6.21 4.63 1.04 3.42 1.17 3.33 3.79 2.46 1.83 7.58 3.88 +61 5 11 3.54 7.54 5.33 2.08 5.25 1.58 3.67 3.71 2.62 3.04 9.25 5.96 +61 5 12 7.00 15.12 9.17 4.54 11.21 5.91 NaN 4.04 5.25 4.38 10.63 9.50 +61 5 13 11.00 11.54 11.71 8.83 12.83 9.92 5.21 8.71 7.92 8.63 9.17 12.33 +61 5 14 9.25 6.54 7.04 6.00 11.46 8.12 10.04 9.96 10.04 9.75 11.12 3.33 +61 5 15 9.62 3.92 9.75 3.88 5.63 3.46 5.91 5.88 6.38 6.25 6.63 8.42 +61 5 16 15.04 10.17 15.25 5.17 12.92 6.79 8.38 11.12 9.46 9.17 12.00 13.70 +61 5 17 10.00 6.54 15.63 4.88 8.21 6.75 5.83 7.38 8.00 7.96 9.46 8.21 +61 5 18 4.88 3.58 15.96 3.71 6.34 3.83 5.04 3.79 5.00 4.17 7.29 5.91 +61 5 19 5.00 5.29 5.91 3.63 7.25 4.17 6.83 6.79 7.17 9.29 10.83 14.96 +61 5 20 6.21 NaN 10.13 4.83 7.33 5.66 4.71 7.58 7.00 8.67 10.41 12.75 +61 5 21 6.83 4.29 11.67 3.71 5.50 4.50 3.17 6.25 4.92 4.29 10.75 10.34 +61 5 22 6.25 3.67 5.33 2.50 4.17 3.79 2.42 4.21 4.63 5.37 8.21 10.92 +61 5 23 4.96 3.96 3.58 2.42 3.75 2.25 3.13 2.83 4.50 5.54 6.13 8.67 +61 5 24 5.54 5.96 3.71 3.37 3.92 1.63 4.00 3.37 4.92 6.50 8.83 12.29 +61 5 25 7.67 11.17 13.13 5.50 9.13 6.04 6.79 7.96 7.33 10.46 13.88 17.04 +61 5 26 11.79 12.50 20.96 NaN 10.21 6.50 9.33 12.08 9.59 11.46 14.33 16.58 +61 5 27 10.92 6.79 6.46 5.41 10.00 5.25 4.92 7.04 7.29 8.25 10.46 8.00 +61 5 28 9.96 7.67 6.42 3.29 7.41 3.33 5.41 6.42 4.50 5.17 11.79 8.38 +61 5 29 12.04 6.67 15.96 9.87 8.04 8.46 11.71 9.21 11.17 12.04 11.54 17.96 +61 5 30 10.00 4.75 9.21 3.42 7.67 5.25 5.83 7.21 6.34 5.91 8.71 12.92 +61 5 31 7.00 9.79 12.25 4.83 8.25 5.37 6.58 9.29 6.58 7.12 11.87 10.63 +61 6 1 15.92 9.59 12.04 8.79 11.54 6.04 9.75 8.29 9.33 10.34 10.67 12.12 +61 6 2 11.29 6.34 6.92 6.71 12.12 7.79 7.92 8.71 9.83 9.17 12.42 15.71 +61 6 3 7.50 8.29 6.83 5.88 12.87 7.08 10.41 7.87 10.46 10.34 13.88 14.42 +61 6 4 8.50 6.42 7.25 4.67 7.79 5.17 6.21 9.87 7.29 8.33 17.00 11.42 +61 6 5 11.58 11.54 7.50 5.66 9.96 6.04 3.42 9.17 8.08 10.92 16.17 12.29 +61 6 6 8.33 8.54 6.13 3.21 8.58 5.17 4.21 7.96 6.92 6.71 11.42 8.75 +61 6 7 11.92 9.42 8.96 6.34 13.13 8.63 8.17 9.71 9.59 9.87 13.25 16.38 +61 6 8 12.17 8.87 8.17 6.92 11.92 7.46 9.54 9.59 10.58 10.37 11.21 19.83 +61 6 9 9.71 7.79 8.29 4.29 7.75 4.38 8.29 4.38 8.08 8.83 7.79 14.21 +61 6 10 14.42 9.96 11.58 6.79 11.54 4.67 9.71 4.25 7.58 6.87 5.83 8.33 +61 6 11 8.29 6.83 6.04 3.29 7.33 1.87 6.25 3.13 5.41 5.21 5.37 5.96 +61 6 12 9.25 7.08 13.46 5.17 5.66 3.63 9.17 2.88 6.50 6.83 7.29 11.34 +61 6 13 6.13 4.12 6.50 2.92 3.50 1.58 4.67 3.63 4.21 3.08 7.71 6.13 +61 6 14 8.33 12.42 8.71 6.79 11.87 7.12 5.66 8.63 8.08 10.25 20.21 11.83 +61 6 15 9.96 8.17 10.54 5.88 10.25 6.92 8.25 9.29 9.50 9.25 14.58 13.96 +61 6 16 13.88 14.54 13.75 8.29 13.29 10.00 10.54 16.54 11.54 14.71 25.25 20.88 +61 6 17 14.33 11.34 15.50 9.42 12.33 9.04 12.96 9.50 11.00 10.50 14.67 16.33 +61 6 18 12.12 9.29 7.67 7.17 14.92 10.34 13.70 13.25 12.12 12.54 17.75 24.71 +61 6 19 8.92 6.38 7.67 6.42 11.34 7.75 10.37 9.38 10.29 10.17 12.92 16.88 +61 6 20 4.00 3.25 5.13 2.88 8.58 4.46 7.92 6.34 6.50 7.83 11.00 14.25 +61 6 21 8.54 9.50 10.37 6.63 11.50 7.33 10.54 9.83 9.54 10.17 14.71 13.75 +61 6 22 8.83 6.25 6.38 6.42 11.96 8.58 13.29 9.29 11.54 11.00 13.25 18.91 +61 6 23 5.71 4.21 7.79 5.29 11.58 7.33 14.21 7.38 11.46 11.63 14.96 21.50 +61 6 24 6.42 4.92 9.33 4.75 12.17 8.33 14.88 11.87 12.92 15.00 19.08 20.75 +61 6 25 9.00 8.25 9.92 5.96 13.21 8.67 11.87 12.04 10.96 13.00 14.67 15.83 +61 6 26 13.13 11.42 7.62 7.08 12.42 5.96 8.38 7.75 9.67 10.04 10.83 11.63 +61 6 27 9.00 6.34 8.00 5.71 8.04 6.96 10.54 8.96 10.41 10.37 13.29 16.79 +61 6 28 7.75 9.71 9.21 5.83 11.12 6.25 8.79 12.83 7.38 11.58 19.92 16.38 +61 6 29 NaN 10.46 7.96 6.79 12.62 7.08 8.33 9.46 7.08 10.92 20.88 10.79 +61 6 30 12.29 14.37 10.79 10.54 13.83 9.59 4.92 13.70 8.75 12.38 21.87 NaN +61 7 1 7.21 6.83 7.71 4.42 8.46 4.79 6.71 6.00 5.79 7.96 6.96 8.71 +61 7 2 12.04 7.25 9.25 7.04 10.34 4.92 5.83 5.96 6.25 8.63 8.00 8.50 +61 7 3 15.34 10.58 12.17 10.08 18.58 11.38 15.75 14.33 15.46 16.79 20.41 21.29 +61 7 4 17.50 10.75 14.92 12.00 12.62 9.62 14.92 11.08 13.13 13.50 12.46 19.67 +61 7 5 11.50 4.96 7.62 5.83 8.92 6.17 10.08 5.58 8.08 8.75 7.08 12.42 +61 7 6 8.00 3.75 7.62 3.54 8.00 4.58 7.08 6.87 6.25 6.21 10.21 12.08 +61 7 7 11.17 7.21 7.41 8.12 14.29 9.75 NaN 9.96 13.04 11.54 15.34 17.41 +61 7 8 11.21 7.58 7.83 7.87 14.42 8.38 14.04 10.96 11.63 9.33 11.83 16.04 +61 7 9 7.29 5.71 7.67 5.37 10.63 6.00 9.46 6.38 7.54 7.71 8.21 13.00 +61 7 10 8.63 7.87 9.33 5.58 9.46 3.46 2.67 2.96 2.92 4.25 5.04 5.96 +61 7 11 11.08 11.42 10.37 6.87 9.46 5.63 7.41 8.17 6.50 8.38 10.21 13.29 +61 7 12 19.46 16.88 12.29 10.08 15.04 8.83 9.08 11.75 10.50 10.71 17.21 9.92 +61 7 13 16.75 13.79 12.92 12.33 19.95 13.17 16.83 16.17 15.37 14.92 20.25 21.96 +61 7 14 22.50 19.29 14.29 12.42 20.88 10.17 11.83 12.33 10.63 10.88 15.63 14.00 +61 7 15 16.92 14.50 8.00 7.62 12.21 7.92 10.25 10.96 8.92 10.17 14.09 9.92 +61 7 16 21.42 10.88 11.00 10.04 14.67 9.71 13.13 11.21 11.87 11.79 12.21 12.62 +61 7 17 4.25 3.08 5.46 1.46 5.04 1.96 7.08 5.21 5.66 7.62 6.17 9.13 +61 7 18 4.38 6.79 5.29 4.63 7.33 3.88 4.96 7.79 6.50 7.75 9.79 10.83 +61 7 19 5.17 3.42 6.63 2.96 6.75 2.92 3.50 7.29 5.58 7.00 10.13 10.92 +61 7 20 5.88 2.92 4.08 2.42 6.21 2.71 3.04 5.46 5.63 5.37 7.29 8.33 +61 7 21 3.33 4.58 6.96 4.67 5.75 2.79 2.21 5.13 4.25 5.09 7.08 8.25 +61 7 22 3.37 5.29 6.08 3.67 5.54 2.58 4.75 5.25 5.46 5.54 8.00 6.04 +61 7 23 3.04 3.71 12.67 3.29 6.17 2.17 2.21 3.37 4.00 3.92 4.21 5.41 +61 7 24 7.00 4.63 6.42 2.83 4.96 2.92 3.58 4.17 2.21 4.92 9.25 6.13 +61 7 25 15.92 13.79 15.67 8.63 14.33 9.33 12.29 14.09 11.63 15.37 22.17 19.50 +61 7 26 11.46 7.50 10.46 9.92 17.50 13.50 18.79 15.16 15.59 15.75 19.38 25.37 +61 7 27 10.13 8.83 6.79 3.75 9.50 4.54 7.87 6.46 6.83 6.63 7.87 13.00 +61 7 28 16.08 9.13 10.29 7.33 10.54 7.33 9.67 8.67 9.33 8.87 9.38 13.75 +61 7 29 7.21 4.83 6.67 3.42 5.96 3.67 5.79 6.67 5.09 6.54 11.17 9.25 +61 7 30 6.13 10.41 9.17 4.21 7.75 4.58 6.04 9.17 5.58 8.21 10.67 9.33 +61 7 31 7.67 6.71 9.38 4.17 5.25 2.79 3.75 2.25 3.17 5.54 6.17 9.67 +61 8 1 9.59 5.09 5.54 4.63 8.29 5.25 4.21 5.25 5.37 5.41 8.38 9.08 +61 8 2 10.00 5.58 6.71 6.00 8.96 6.67 8.87 7.12 7.79 8.75 8.71 11.87 +61 8 3 16.08 15.79 15.59 8.96 16.92 10.21 11.92 13.04 11.12 12.83 18.54 17.54 +61 8 4 11.00 9.21 10.58 7.54 13.17 9.62 13.54 11.79 11.67 10.21 16.04 20.25 +61 8 5 13.37 12.46 12.79 6.54 15.50 9.00 10.54 11.29 10.50 11.92 15.41 17.12 +61 8 6 8.38 6.71 8.42 5.83 10.04 6.75 7.87 7.12 6.46 8.00 7.92 10.79 +61 8 7 2.88 4.42 6.34 3.04 6.58 3.96 3.88 5.04 3.17 5.71 9.83 5.88 +61 8 8 14.21 9.87 11.63 6.42 12.38 6.96 10.63 7.12 7.38 7.79 10.08 6.83 +61 8 9 12.96 10.00 11.29 6.46 14.58 8.96 11.25 10.37 8.87 9.33 12.50 10.46 +61 8 10 6.63 6.71 6.00 2.50 7.58 3.58 4.67 8.33 2.83 5.41 9.96 10.96 +61 8 11 NaN 6.75 8.29 5.00 8.58 4.83 6.83 6.25 5.79 5.25 5.88 8.38 +61 8 12 10.71 8.71 8.92 4.38 8.29 5.83 6.83 8.25 5.88 NaN 14.29 13.21 +61 8 13 12.96 9.13 8.63 7.75 12.75 8.00 10.92 10.79 10.08 9.92 14.00 15.16 +61 8 14 16.42 10.88 9.87 8.79 16.21 11.46 15.09 13.37 12.87 12.92 15.37 NaN +61 8 15 15.34 8.75 10.54 6.83 12.96 7.54 9.42 8.92 8.79 9.13 9.59 13.04 +61 8 16 13.13 7.25 9.17 8.21 13.29 9.04 12.96 10.54 10.54 12.00 11.00 16.08 +61 8 17 13.62 8.17 9.71 7.00 14.25 8.83 10.21 10.50 10.79 11.25 15.50 14.62 +61 8 18 18.91 12.87 11.46 11.79 20.38 14.25 19.04 17.71 16.13 17.83 21.92 24.30 +61 8 19 18.08 12.42 12.67 10.58 15.87 11.54 13.21 11.04 12.46 11.79 13.79 15.59 +61 8 20 13.75 13.62 14.04 7.00 13.88 10.25 12.58 9.92 10.88 10.79 17.29 16.13 +61 8 21 18.16 14.58 14.62 11.67 21.04 15.34 19.00 17.79 17.50 15.46 20.21 22.29 +61 8 22 13.33 8.58 9.50 7.67 13.83 9.50 12.08 9.83 10.75 9.96 12.87 15.37 +61 8 23 9.62 8.92 10.46 3.96 9.46 6.79 8.17 7.96 7.08 8.00 10.04 12.62 +61 8 24 7.67 6.87 9.67 5.46 7.46 4.25 5.66 4.50 3.96 7.46 5.75 8.29 +61 8 25 14.33 13.59 17.83 8.21 12.17 9.67 10.92 14.00 9.59 15.21 19.55 16.17 +61 8 26 12.38 13.88 14.09 6.71 15.50 10.29 9.79 14.50 10.13 15.50 21.96 20.04 +61 8 27 11.25 13.59 13.21 6.08 13.29 10.13 12.12 16.83 9.71 15.96 23.38 20.62 +61 8 28 13.37 22.00 12.75 12.42 17.62 13.08 9.08 14.58 13.50 12.92 20.75 16.79 +61 8 29 14.46 16.04 12.75 9.17 19.21 12.12 14.12 16.54 12.00 16.13 24.71 22.54 +61 8 30 3.63 4.67 4.88 2.37 5.63 3.17 4.58 7.58 4.58 6.96 14.33 10.34 +61 8 31 4.83 7.04 2.46 3.25 8.50 4.83 2.92 6.42 4.29 6.54 17.16 8.00 +61 9 1 5.58 1.13 4.96 3.04 4.25 2.25 4.63 2.71 3.67 6.00 4.79 5.41 +61 9 2 7.25 3.58 2.42 3.50 6.96 3.13 3.83 6.58 3.92 5.96 7.12 6.96 +61 9 3 11.63 7.29 7.00 5.75 5.58 5.37 5.88 5.88 5.25 7.96 6.79 7.12 +61 9 4 6.79 3.04 4.79 3.17 4.75 2.17 1.25 2.21 2.96 1.75 5.41 3.37 +61 9 5 12.67 10.00 6.38 5.83 10.41 5.83 9.38 8.54 9.04 9.96 13.62 15.04 +61 9 6 17.62 12.54 11.58 10.17 14.54 9.13 14.09 10.79 12.71 11.71 15.83 20.25 +61 9 7 9.21 3.92 7.58 5.83 7.54 5.96 7.87 4.88 7.04 7.41 5.66 10.83 +61 9 8 5.75 9.71 3.63 2.75 7.67 2.67 3.17 4.42 2.50 4.21 8.25 5.79 +61 9 9 16.75 13.59 14.88 10.50 11.42 9.83 6.92 9.50 9.71 10.96 13.92 15.00 +61 9 10 5.00 8.79 8.46 4.17 8.29 4.21 4.83 6.38 4.88 6.96 12.21 6.96 +61 9 11 9.87 8.42 7.29 5.46 12.12 7.92 8.63 8.75 8.75 9.67 13.92 14.67 +61 9 12 20.75 18.66 16.66 10.75 15.75 10.96 11.08 14.50 10.34 12.92 23.91 16.71 +61 9 13 16.71 13.96 15.21 9.67 14.62 10.25 11.75 13.42 9.71 13.00 21.37 15.71 +61 9 14 18.00 16.88 15.54 9.71 15.21 11.12 12.75 13.79 12.21 13.54 23.91 18.25 +61 9 15 20.71 13.96 20.00 12.46 13.70 11.08 13.00 12.92 11.58 14.83 18.63 17.71 +61 9 16 28.75 22.08 26.50 21.09 28.50 20.67 20.79 21.37 21.34 25.21 23.45 33.09 +61 9 17 5.33 7.12 8.12 3.96 8.29 5.58 7.87 7.17 5.88 10.71 13.96 16.75 +61 9 18 9.75 4.21 NaN NaN 5.79 4.46 4.17 NaN 4.33 6.21 7.21 9.21 +61 9 19 8.50 8.25 12.17 7.17 11.54 7.54 8.17 7.96 7.33 10.71 9.08 11.04 +61 9 20 7.08 2.92 5.33 2.62 3.88 1.63 4.79 1.46 2.83 2.83 4.54 5.25 +61 9 21 6.92 7.75 7.79 4.12 7.75 4.25 6.46 7.50 4.38 6.63 13.17 7.12 +61 9 22 9.00 13.62 6.46 5.41 12.92 7.75 4.12 11.63 6.08 7.67 20.96 14.62 +61 9 23 10.25 7.29 8.17 5.83 8.12 6.46 6.08 7.33 6.08 7.50 10.46 12.04 +61 9 24 8.00 6.04 8.04 3.96 7.50 3.21 5.91 5.41 4.21 6.04 11.71 9.42 +61 9 25 10.34 12.92 8.87 5.79 11.58 8.00 8.92 12.50 8.04 13.00 20.17 16.21 +61 9 26 18.46 13.13 15.87 10.17 13.46 10.37 12.46 11.38 10.04 13.50 17.75 15.83 +61 9 27 11.34 6.96 9.38 6.00 11.25 6.34 8.08 6.25 7.00 10.13 14.29 14.29 +61 9 28 15.59 8.67 14.71 7.87 11.29 7.75 9.13 6.21 8.71 9.50 7.79 10.13 +61 9 29 19.12 13.62 17.16 9.59 14.67 9.13 9.21 12.38 9.25 14.00 20.30 15.71 +61 9 30 23.21 NaN 17.58 11.17 20.75 12.00 10.96 15.34 12.08 17.46 21.37 17.16 +61 10 1 14.25 12.87 7.87 8.00 13.00 7.75 5.83 9.00 7.08 5.29 11.79 4.04 +61 10 2 14.09 8.67 13.54 8.54 9.62 9.42 8.71 9.00 10.58 12.12 7.58 19.62 +61 10 3 4.29 4.00 4.96 2.13 3.75 1.79 4.04 2.75 2.42 3.88 4.96 4.83 +61 10 4 3.13 4.42 6.13 1.46 3.79 1.04 3.50 1.87 1.42 2.42 4.92 6.50 +61 10 5 9.75 3.63 8.71 2.83 5.91 3.04 5.63 3.96 4.00 6.17 4.79 8.33 +61 10 6 16.04 16.96 17.83 9.79 9.75 9.71 12.96 9.50 8.67 11.96 9.96 14.42 +61 10 7 16.08 11.75 9.71 5.46 11.38 5.41 6.87 6.96 3.25 4.88 11.63 9.92 +61 10 8 15.37 11.87 10.29 6.63 13.59 8.46 10.17 9.50 9.21 12.21 17.83 16.25 +61 10 9 17.12 12.71 20.96 9.38 10.58 8.25 9.04 9.17 7.46 10.21 8.54 12.83 +61 10 10 14.46 13.25 11.46 9.42 13.17 11.04 11.75 12.38 11.12 15.21 20.46 19.41 +61 10 11 11.96 10.13 10.13 7.79 13.54 9.13 12.71 8.79 10.00 12.00 18.91 21.04 +61 10 12 NaN 8.71 4.67 3.67 9.08 6.54 5.58 6.38 4.00 5.37 12.46 10.75 +61 10 13 10.25 10.34 7.92 6.58 11.29 8.67 9.92 5.96 6.46 7.38 12.50 11.96 +61 10 14 3.71 6.13 4.38 4.63 7.29 4.92 2.96 4.88 3.13 5.71 13.00 12.87 +61 10 15 7.79 6.87 4.21 4.58 9.46 7.21 9.00 7.75 8.42 10.58 15.29 17.96 +61 10 16 12.58 12.67 9.92 9.21 15.63 9.42 14.58 12.42 12.58 12.92 19.58 24.62 +61 10 17 28.62 19.46 19.67 19.38 22.46 16.42 22.17 20.17 18.00 23.13 24.71 32.63 +61 10 18 28.33 18.29 22.63 17.83 18.08 11.96 21.84 17.33 16.17 26.38 24.21 33.45 +61 10 19 17.12 12.67 16.50 9.59 10.25 8.29 13.21 10.63 12.00 13.13 13.96 23.00 +61 10 20 12.67 3.75 10.37 9.42 7.75 7.21 12.08 6.87 9.83 13.00 11.71 21.67 +61 10 21 10.46 7.83 10.71 6.83 9.54 6.83 7.50 6.08 7.38 11.08 10.88 13.46 +61 10 22 25.04 18.88 21.00 16.08 22.25 14.92 17.58 14.42 15.87 15.59 17.29 16.66 +61 10 23 19.25 15.41 15.59 10.79 17.67 12.17 16.21 13.25 14.12 17.75 22.29 25.00 +61 10 24 26.42 21.25 23.09 16.54 24.41 15.46 17.96 18.46 NaN 22.13 27.29 30.88 +61 10 25 21.34 17.00 22.88 13.62 18.21 13.21 18.34 12.12 15.37 17.46 17.12 25.12 +61 10 26 16.29 11.29 17.41 6.58 12.21 8.92 9.79 10.13 8.54 12.12 13.08 12.75 +61 10 27 18.41 12.08 13.00 7.41 12.00 7.79 7.79 8.83 8.54 8.71 12.00 11.96 +61 10 28 6.50 3.29 4.79 2.21 4.58 2.62 9.75 3.71 5.79 6.67 8.50 13.29 +61 10 29 4.33 2.75 5.50 1.71 4.46 2.33 9.42 2.62 4.25 5.83 8.87 9.83 +61 10 30 9.42 8.71 10.50 6.38 11.63 8.71 14.88 10.58 11.08 11.92 17.25 20.62 +61 10 31 13.62 10.75 13.00 8.33 11.50 9.71 15.25 11.96 11.54 11.92 17.75 14.92 +61 11 1 13.21 13.13 14.33 8.54 12.17 10.21 13.08 12.17 10.92 13.54 20.17 20.04 +61 11 2 15.79 13.46 10.13 11.79 17.25 12.21 15.83 14.00 15.09 15.79 19.79 23.58 +61 11 3 9.75 4.83 10.25 6.63 7.29 6.92 11.21 5.75 7.83 8.92 10.96 23.13 +61 11 4 5.88 3.96 6.54 3.79 6.00 4.33 8.50 4.21 5.71 7.67 10.29 13.37 +61 11 5 13.33 12.29 11.04 7.33 10.04 8.58 10.79 11.12 9.04 13.29 18.05 18.71 +61 11 6 7.96 5.83 4.83 3.04 7.92 4.71 8.21 6.63 6.08 8.12 12.58 13.50 +61 11 7 11.83 9.04 12.58 5.13 10.00 6.42 9.29 6.58 7.38 10.88 11.12 13.46 +61 11 8 5.46 5.50 5.29 2.71 7.33 4.33 6.58 2.71 5.33 5.50 7.67 9.71 +61 11 9 7.71 6.21 3.83 1.87 6.21 3.33 8.21 1.42 3.37 3.71 6.34 6.63 +61 11 10 9.13 6.63 6.92 2.50 3.37 2.04 7.71 2.00 1.33 2.62 6.13 5.71 +61 11 11 16.08 10.75 20.41 8.79 8.42 5.17 12.12 5.00 5.71 5.96 9.62 11.46 +61 11 12 14.83 9.87 18.05 8.58 8.12 3.54 13.04 5.46 7.12 8.12 11.00 15.54 +61 11 13 15.00 13.00 32.71 13.33 9.59 10.13 21.87 10.04 10.37 14.04 9.25 13.25 +61 11 14 10.67 10.67 26.63 9.71 8.08 8.42 15.71 4.92 7.50 9.13 5.50 2.75 +61 11 15 8.58 5.50 15.71 5.17 8.17 3.29 6.00 2.83 4.38 4.21 3.83 3.58 +61 11 16 7.50 4.21 9.00 2.42 5.04 1.83 2.29 1.04 0.75 2.00 0.71 7.08 +61 11 17 8.71 5.33 6.46 1.50 5.66 1.25 3.13 0.63 0.92 1.25 2.71 2.00 +61 11 18 13.67 9.33 14.29 5.41 10.25 7.62 9.29 7.75 4.71 5.71 9.59 5.37 +61 11 19 11.46 7.62 14.12 4.25 10.13 5.91 9.33 5.17 2.88 5.88 9.54 6.13 +61 11 20 10.83 3.29 14.46 4.50 7.96 4.71 13.04 7.67 6.50 7.87 7.71 8.71 +61 11 21 14.29 4.96 10.46 4.21 6.00 4.54 7.00 4.96 4.88 8.33 6.50 8.71 +61 11 22 6.46 4.33 8.96 4.83 7.62 5.50 9.17 3.42 4.67 7.38 7.08 10.34 +61 11 23 9.00 9.83 6.34 6.25 10.13 7.41 15.41 9.08 9.33 12.42 20.46 22.58 +61 11 24 11.50 11.79 10.96 7.96 12.75 9.83 16.17 10.00 10.04 14.12 17.37 18.58 +61 11 25 5.75 7.12 5.04 5.00 9.04 6.25 11.83 6.38 6.79 10.04 12.71 15.96 +61 11 26 6.25 10.04 4.42 2.04 8.83 6.29 NaN 6.96 3.71 6.92 10.58 4.25 +61 11 27 7.92 4.67 10.58 4.71 4.17 2.29 5.83 1.92 2.25 3.63 5.50 5.88 +61 11 28 8.92 10.21 8.25 5.21 10.96 8.63 12.79 9.46 8.83 11.17 18.96 19.46 +61 11 29 15.67 15.67 13.00 9.71 15.29 10.79 15.29 11.50 11.25 13.00 19.04 21.29 +61 11 30 23.75 18.71 19.92 14.46 19.92 11.04 16.50 8.63 12.58 10.29 12.54 13.62 +61 12 1 9.67 7.75 8.00 3.96 6.00 2.75 7.25 2.50 5.58 5.58 7.79 11.17 +61 12 2 8.58 5.96 8.83 4.17 8.33 5.79 12.21 3.46 5.66 7.71 8.17 17.29 +61 12 3 13.75 10.13 11.79 3.29 7.83 6.79 7.67 6.25 5.46 4.00 8.25 14.37 +61 12 4 22.83 17.00 19.79 10.37 13.54 12.75 13.79 15.29 9.83 14.50 21.79 19.21 +61 12 5 17.67 14.09 14.92 12.92 13.33 11.83 17.83 11.92 15.12 15.41 19.21 29.33 +61 12 6 11.92 9.21 10.92 6.50 7.79 5.50 12.62 3.13 8.29 7.87 10.71 13.67 +61 12 7 17.96 15.83 12.17 4.50 13.59 8.04 8.87 8.04 5.09 5.75 12.21 7.58 +61 12 8 16.62 12.08 20.54 9.21 12.96 14.54 19.83 10.00 11.92 15.50 16.33 19.41 +61 12 9 10.83 10.96 9.71 3.83 9.21 7.12 10.04 6.96 7.08 9.67 11.08 9.08 +61 12 10 23.71 21.37 20.17 13.04 18.08 13.17 18.84 15.46 12.46 15.46 20.50 15.41 +61 12 11 21.34 16.38 19.00 13.62 20.00 14.83 25.62 14.50 17.25 17.41 22.83 18.34 +61 12 12 15.46 14.88 14.67 6.63 15.46 12.96 16.83 13.46 12.38 13.54 18.34 16.13 +61 12 13 16.25 11.96 16.04 9.83 14.62 11.34 16.66 11.46 11.79 13.59 16.79 18.16 +61 12 14 11.34 17.62 9.71 6.92 14.92 10.63 9.38 9.08 7.75 7.04 14.33 15.09 +61 12 15 18.05 15.71 16.66 12.92 16.75 16.42 10.71 12.50 13.59 13.62 20.62 21.12 +61 12 16 9.29 8.63 9.04 6.50 9.50 7.04 6.92 5.46 7.12 7.54 7.38 6.50 +61 12 17 13.17 9.13 10.92 4.46 8.83 8.67 9.54 6.54 4.71 4.67 5.50 7.17 +61 12 18 12.17 11.63 8.08 1.79 8.71 6.63 4.79 3.88 3.79 3.88 5.83 5.91 +61 12 19 12.38 10.88 8.96 0.58 7.33 2.83 3.54 5.04 1.71 5.75 6.67 5.66 +61 12 20 11.46 9.79 8.63 2.54 7.50 3.37 3.63 3.46 1.25 5.41 5.29 3.92 +61 12 21 13.13 10.00 8.83 3.21 9.96 3.96 4.33 3.71 2.75 2.46 6.50 2.62 +61 12 22 16.21 11.87 15.00 7.62 14.12 9.75 13.59 10.04 8.25 9.17 12.83 12.38 +61 12 23 24.41 16.13 22.13 15.59 20.38 13.59 18.12 14.33 12.54 15.34 17.67 16.62 +61 12 24 20.54 13.50 18.25 9.17 15.50 9.46 8.50 8.63 8.33 11.87 14.92 10.04 +61 12 25 19.58 10.29 18.34 6.50 11.34 8.12 11.92 11.92 10.37 9.54 13.21 13.62 +61 12 26 20.54 16.04 20.91 8.33 11.21 7.08 17.12 12.75 9.04 10.83 13.21 11.34 +61 12 27 9.25 5.21 9.04 4.08 5.91 1.83 2.92 1.87 1.83 1.96 3.75 6.63 +61 12 28 5.04 3.08 2.13 0.42 3.67 2.42 8.96 4.17 3.33 7.41 9.96 15.21 +61 12 29 14.33 13.83 23.21 11.25 9.83 7.25 15.00 8.42 8.25 10.79 10.54 14.62 +61 12 30 16.83 10.25 29.33 11.79 9.17 7.41 17.29 5.75 9.38 9.59 9.17 13.79 +61 12 31 9.87 7.83 7.67 3.75 5.66 3.50 10.04 3.08 5.04 3.79 8.04 14.67 +62 1 1 9.29 3.42 11.54 3.50 2.21 1.96 10.41 2.79 3.54 5.17 4.38 7.92 +62 1 2 6.08 3.13 5.09 0.87 0.42 0.33 8.46 0.00 0.54 4.54 1.96 7.71 +62 1 3 7.75 4.46 6.04 3.17 3.58 0.42 4.58 0.00 3.08 0.92 0.58 6.34 +62 1 4 3.17 4.92 4.38 1.04 3.50 1.79 5.50 3.75 1.63 3.17 12.38 11.71 +62 1 5 11.67 13.04 11.38 4.79 11.42 8.96 15.29 10.75 9.13 11.87 17.04 14.67 +62 1 6 15.04 12.29 12.21 4.71 9.00 6.13 8.46 6.92 5.46 5.91 12.62 10.92 +62 1 7 12.62 11.04 11.34 3.54 11.75 8.04 14.29 8.42 8.63 13.75 18.88 16.71 +62 1 8 21.09 16.62 20.08 12.92 17.41 11.71 17.71 13.50 12.17 16.38 26.12 19.50 +62 1 9 12.29 12.04 10.63 6.50 11.54 8.96 15.29 8.87 10.29 14.50 18.63 21.75 +62 1 10 21.62 20.88 16.46 10.04 15.71 10.96 14.29 12.17 9.00 12.33 16.75 16.79 +62 1 11 29.71 25.46 20.67 17.75 23.09 17.00 25.96 16.21 17.50 17.62 21.92 20.04 +62 1 12 20.91 20.08 16.79 16.62 22.29 14.83 23.54 15.04 19.79 16.83 21.04 18.66 +62 1 13 15.71 14.83 9.92 8.87 13.54 9.67 19.70 10.63 13.21 12.96 19.46 23.33 +62 1 14 10.34 9.42 8.00 5.00 8.58 6.25 12.42 6.79 7.17 8.25 11.25 12.50 +62 1 15 31.13 22.34 26.25 18.25 20.54 17.25 20.38 16.83 16.96 21.67 25.88 23.13 +62 1 16 27.79 26.54 19.83 12.54 20.46 16.25 19.75 16.71 14.29 20.08 29.42 26.50 +62 1 17 20.54 14.79 19.46 12.83 18.41 13.88 22.79 12.75 15.63 19.38 18.66 22.79 +62 1 18 13.33 9.96 10.63 4.12 8.75 8.00 12.46 7.67 7.41 10.00 13.67 13.25 +62 1 19 10.46 9.54 8.46 5.00 10.83 9.42 16.08 10.04 10.54 13.00 19.00 19.92 +62 1 20 11.46 10.17 9.75 3.00 9.46 6.75 10.29 8.04 6.67 12.04 13.92 14.42 +62 1 21 19.29 18.79 17.41 11.63 15.67 12.12 17.29 15.34 14.09 17.92 26.00 21.92 +62 1 22 20.54 21.42 14.33 14.29 22.25 12.92 23.25 16.92 16.88 15.67 26.67 27.00 +62 1 23 14.58 15.75 12.50 5.04 10.83 8.38 12.71 6.87 6.96 8.63 12.54 11.83 +62 1 24 24.13 19.55 18.66 12.62 16.17 12.96 17.25 15.29 12.96 13.75 19.75 12.46 +62 1 25 12.92 13.29 12.96 7.75 13.04 10.92 18.29 9.59 13.04 11.54 16.38 10.58 +62 1 26 5.96 4.08 4.92 2.13 4.54 3.08 7.41 1.92 4.04 4.08 4.29 7.08 +62 1 27 4.46 7.96 8.12 1.83 5.46 2.71 5.17 3.83 5.33 4.25 5.13 3.25 +62 1 28 0.67 4.50 2.96 0.87 1.38 1.21 3.63 1.54 1.71 1.42 8.67 6.79 +62 1 29 7.67 7.58 7.96 5.13 5.88 5.83 2.17 6.17 6.38 5.71 16.96 13.62 +62 1 30 14.12 12.83 12.87 5.25 10.96 9.38 13.33 15.25 9.87 15.54 24.37 20.17 +62 1 31 21.96 17.25 18.75 12.08 16.50 12.25 22.42 17.00 15.87 17.58 23.13 22.25 +62 2 1 19.12 13.96 12.21 10.58 15.71 10.63 15.71 11.08 13.17 12.62 17.67 22.71 +62 2 2 7.67 10.29 8.83 5.09 10.00 7.17 13.17 6.29 8.58 8.46 11.71 11.96 +62 2 3 14.92 11.54 11.67 6.71 13.50 10.04 15.96 11.46 12.42 12.12 18.58 20.71 +62 2 4 20.30 16.96 17.83 11.08 19.46 13.25 18.88 11.29 15.34 13.83 17.08 17.21 +62 2 5 13.83 12.75 12.71 7.62 12.12 10.13 15.75 11.83 12.17 12.38 19.29 19.95 +62 2 6 21.84 18.29 20.25 12.12 18.79 12.29 18.75 18.41 14.21 17.75 28.12 23.16 +62 2 7 30.13 25.17 21.17 16.62 22.58 16.29 25.00 20.00 20.71 20.67 27.04 25.29 +62 2 8 10.92 7.25 11.42 5.79 7.75 5.58 8.21 5.58 6.83 7.54 10.13 11.21 +62 2 9 9.21 7.38 7.96 4.04 10.50 6.46 12.08 7.71 8.17 8.54 14.29 14.46 +62 2 10 14.42 11.21 10.08 7.12 13.08 8.21 14.96 10.17 11.54 12.38 16.62 20.88 +62 2 11 18.38 14.42 15.59 12.79 21.00 14.67 24.41 17.33 19.04 18.38 28.04 29.08 +62 2 12 29.17 22.63 21.34 22.88 30.00 20.58 29.54 21.92 25.29 22.37 25.84 28.91 +62 2 13 26.30 23.38 17.29 12.62 17.21 10.08 15.79 11.87 10.29 12.92 19.50 21.71 +62 2 14 12.96 7.50 12.96 6.96 8.50 5.54 11.71 3.33 7.46 7.62 10.25 13.62 +62 2 15 13.21 10.67 12.08 10.37 18.00 13.67 23.45 17.83 16.50 16.42 21.92 25.75 +62 2 16 26.04 16.83 15.46 18.16 23.42 19.29 28.84 20.75 24.79 25.00 25.33 33.50 +62 2 17 12.87 4.75 9.00 5.13 10.04 6.42 14.12 8.21 8.96 8.67 10.41 14.25 +62 2 18 5.79 5.83 6.46 3.50 6.63 5.63 8.29 5.63 5.79 7.96 13.08 12.29 +62 2 19 10.54 9.50 8.79 3.21 7.96 4.17 7.92 5.58 4.83 6.17 14.17 8.71 +62 2 20 5.66 6.67 4.29 1.46 7.21 3.29 1.46 2.04 1.63 2.08 6.25 3.04 +62 2 21 7.41 5.91 9.96 NaN 3.08 4.00 7.04 3.71 5.29 6.17 7.46 8.00 +62 2 22 15.75 6.58 17.50 6.29 9.62 7.96 15.29 8.25 10.92 9.21 10.13 10.34 +62 2 23 17.50 7.62 19.58 6.96 12.12 8.67 20.21 10.25 13.83 9.54 15.29 10.75 +62 2 24 13.92 8.58 21.37 8.79 11.75 9.29 18.12 10.13 11.00 9.33 11.46 12.50 +62 2 25 16.00 13.37 25.92 12.33 15.34 11.42 22.63 13.13 12.46 12.42 15.75 25.08 +62 2 26 17.88 15.50 27.16 12.38 14.88 10.58 25.62 16.13 13.96 15.37 19.79 27.88 +62 2 27 18.91 13.33 26.92 13.00 15.63 11.00 26.87 17.04 14.33 14.29 21.59 27.25 +62 2 28 13.00 9.29 19.21 6.50 11.12 6.04 13.88 9.04 7.08 9.33 10.96 15.75 +62 3 1 8.21 4.83 9.00 4.83 6.00 2.21 7.96 1.87 4.08 3.92 4.08 5.41 +62 3 2 8.08 7.29 6.54 3.08 8.71 4.63 9.59 6.17 4.29 5.63 11.42 14.96 +62 3 3 11.50 6.00 12.50 4.63 7.67 4.21 6.71 8.12 6.13 5.91 7.58 12.96 +62 3 4 NaN 5.33 13.50 6.13 6.46 2.71 5.33 3.79 4.46 4.04 2.71 7.08 +62 3 5 6.63 3.96 5.88 2.04 3.29 3.08 5.79 1.63 1.67 2.79 2.67 2.46 +62 3 6 15.09 14.37 7.92 5.04 12.62 6.92 8.00 8.42 3.92 5.13 10.75 5.29 +62 3 7 35.80 31.63 30.37 20.79 32.42 24.25 29.58 23.29 19.08 20.75 21.37 18.16 +62 3 8 13.29 11.67 21.29 9.38 14.75 13.08 18.00 12.75 8.75 10.92 13.46 14.71 +62 3 9 8.54 7.17 11.08 3.83 7.96 4.96 9.00 6.58 4.08 4.42 5.83 6.63 +62 3 10 15.50 7.17 5.33 4.38 11.00 2.17 3.83 2.04 3.04 6.13 2.21 10.96 +62 3 11 5.46 2.88 6.54 3.58 6.00 5.71 10.92 9.67 9.83 7.75 7.87 11.21 +62 3 12 6.54 4.42 14.62 4.75 8.00 5.29 7.71 6.58 6.38 6.17 8.67 9.29 +62 3 13 6.71 4.21 7.41 5.04 6.00 2.88 6.83 5.46 4.67 5.29 4.29 9.62 +62 3 14 5.50 9.92 3.88 2.08 8.71 2.17 4.00 3.50 1.71 2.04 6.67 4.67 +62 3 15 15.79 10.83 21.37 10.13 15.75 14.67 16.54 12.87 12.38 15.34 17.92 21.09 +62 3 16 7.00 4.46 18.38 6.38 10.00 7.41 12.58 5.83 8.42 12.46 8.71 NaN +62 3 17 12.71 10.67 11.67 5.75 11.71 8.21 7.71 7.75 6.58 7.67 7.79 11.50 +62 3 18 14.71 11.08 11.83 4.50 11.00 5.33 5.79 7.67 6.04 5.96 7.83 6.75 +62 3 19 10.92 7.87 13.70 5.41 8.29 5.54 9.04 6.54 5.66 6.04 9.21 5.21 +62 3 20 9.21 7.38 14.12 2.29 5.91 1.92 7.67 5.96 5.66 6.34 10.83 12.42 +62 3 21 10.13 5.66 16.08 4.92 5.83 3.71 6.71 3.00 3.83 5.63 3.63 6.67 +62 3 22 4.33 2.25 5.66 2.88 5.29 2.46 5.96 4.04 3.83 5.75 5.50 10.46 +62 3 23 9.42 6.50 16.42 7.04 7.83 5.88 9.42 5.41 6.25 6.00 9.92 9.79 +62 3 24 6.83 3.21 8.25 3.71 5.96 1.00 3.50 3.63 1.46 3.13 6.17 8.96 +62 3 25 17.67 14.62 14.88 10.58 17.92 13.37 19.41 17.83 16.25 18.46 22.46 21.29 +62 3 26 12.92 8.21 8.92 11.12 12.50 9.67 14.88 9.54 13.50 11.79 11.92 13.17 +62 3 27 6.92 3.83 5.91 5.88 6.58 4.96 9.92 5.75 6.29 10.54 9.04 13.13 +62 3 28 14.83 14.12 16.42 9.00 14.33 12.17 13.54 11.46 9.79 11.00 16.21 16.50 +62 3 29 16.66 13.17 14.71 8.71 12.17 8.71 10.83 9.79 8.63 10.17 14.88 18.63 +62 3 30 13.92 6.50 9.83 8.87 12.38 8.46 13.75 9.13 10.37 11.08 11.00 18.50 +62 3 31 18.21 15.46 11.42 11.75 17.88 10.96 16.42 10.83 12.83 11.38 17.79 17.79 +62 4 1 14.33 12.25 11.87 10.37 14.92 11.00 19.79 11.67 14.09 15.46 16.62 23.58 +62 4 2 26.75 20.25 24.00 13.59 18.25 12.79 20.83 14.46 14.12 16.25 18.75 16.08 +62 4 3 17.71 10.46 7.12 8.25 12.62 7.75 10.67 9.50 8.71 10.34 13.17 12.08 +62 4 4 29.04 23.75 15.63 15.71 26.04 16.75 17.83 20.54 16.08 14.62 23.91 13.83 +62 4 5 28.33 16.38 18.08 17.88 21.21 15.16 22.42 18.05 20.83 18.34 21.42 18.16 +62 4 6 14.37 12.87 12.29 10.29 14.79 9.79 17.08 10.79 13.21 11.92 16.96 20.12 +62 4 7 19.29 16.29 17.67 11.96 19.29 12.79 22.50 12.75 16.83 14.33 16.88 17.41 +62 4 8 32.58 23.21 17.88 16.92 24.54 14.17 18.25 15.71 15.41 13.04 22.46 14.67 +62 4 9 17.21 11.63 10.79 10.37 16.17 10.04 16.21 12.50 12.92 12.87 14.46 15.25 +62 4 10 10.13 6.67 7.58 4.96 10.34 5.41 12.04 8.67 9.04 9.00 10.37 14.42 +62 4 11 8.79 5.58 9.67 7.41 11.21 9.42 14.12 8.75 10.34 12.96 10.17 15.71 +62 4 12 4.58 5.58 5.88 1.58 3.96 2.50 3.79 3.96 2.92 4.42 6.21 7.21 +62 4 13 8.92 5.58 13.79 3.75 7.33 4.21 6.58 6.25 5.66 4.71 7.29 13.08 +62 4 14 11.67 10.37 13.33 4.54 7.96 6.04 11.42 9.21 9.21 9.21 9.59 15.79 +62 4 15 13.04 7.83 23.13 7.71 9.67 8.75 13.83 8.25 10.08 9.50 8.63 14.09 +62 4 16 10.17 8.92 27.04 10.88 9.75 8.92 15.34 8.38 8.75 12.42 10.17 13.33 +62 4 17 10.41 5.88 20.12 9.46 7.54 8.29 14.83 8.63 7.21 12.87 11.12 11.63 +62 4 18 10.13 8.17 12.79 6.58 8.92 7.33 11.71 8.63 8.17 11.92 11.17 12.12 +62 4 19 5.58 4.38 6.79 3.63 4.92 3.58 6.21 2.71 2.42 5.21 7.29 7.50 +62 4 20 8.58 8.83 8.79 4.21 8.92 5.71 6.17 8.21 5.58 7.41 14.37 10.25 +62 4 21 5.75 5.09 7.54 2.25 5.13 3.33 3.58 2.62 2.75 2.92 4.63 7.46 +62 4 22 9.96 14.83 9.59 7.38 12.46 7.92 8.58 8.79 7.46 7.71 11.63 7.08 +62 4 23 7.50 7.33 7.62 5.54 9.59 7.38 8.96 7.54 5.91 7.96 10.25 12.58 +62 4 24 5.71 5.46 5.29 2.04 4.08 2.33 5.79 4.92 3.46 4.50 11.75 5.63 +62 4 25 4.04 8.29 4.08 2.04 4.54 3.79 5.83 9.17 4.00 6.25 19.00 11.54 +62 4 26 3.58 2.75 4.63 1.13 2.96 2.42 7.67 3.04 3.58 6.87 7.71 7.79 +62 4 27 6.17 1.42 12.25 3.25 3.46 5.63 6.42 2.79 5.37 3.37 8.50 4.33 +62 4 28 4.75 2.92 10.08 2.71 3.21 3.29 5.17 3.00 4.50 3.17 9.17 2.21 +62 4 29 9.17 7.62 8.54 3.79 6.46 4.79 5.37 6.54 6.75 5.13 6.46 5.91 +62 4 30 6.58 9.71 8.79 4.71 6.08 5.46 5.41 5.63 5.75 4.67 6.92 2.58 +62 5 1 9.62 9.54 3.58 3.33 8.75 3.75 2.25 2.58 1.67 2.37 7.29 3.25 +62 5 2 9.83 11.17 5.46 4.33 9.75 4.42 4.00 5.09 3.54 4.79 6.79 5.29 +62 5 3 9.50 8.33 6.46 4.83 7.38 3.71 3.50 3.13 3.67 4.08 3.63 3.33 +62 5 4 5.54 8.21 8.50 3.46 5.17 1.75 5.83 0.54 2.62 3.42 5.71 3.37 +62 5 5 11.67 11.79 12.58 7.12 10.96 8.08 8.79 11.17 6.04 7.62 14.54 12.42 +62 5 6 14.25 8.38 14.96 6.04 6.63 4.75 4.88 7.41 5.13 7.29 15.09 5.63 +62 5 7 16.88 15.67 16.50 11.38 13.67 10.67 14.09 12.71 10.29 10.41 18.21 11.50 +62 5 8 12.75 11.04 13.08 8.17 9.96 6.71 11.34 9.13 6.29 8.87 14.83 8.17 +62 5 9 8.63 8.25 9.59 1.87 7.87 3.71 7.04 6.29 3.54 4.04 11.38 6.29 +62 5 10 14.92 11.79 9.21 4.08 10.92 5.88 5.96 4.58 3.42 4.04 5.71 8.00 +62 5 11 14.96 14.25 12.67 8.75 11.79 7.67 11.04 11.54 8.42 9.83 12.08 13.75 +62 5 12 16.25 12.62 15.41 10.79 12.50 9.25 10.46 12.08 10.92 13.00 12.29 14.54 +62 5 13 12.42 10.21 6.67 6.83 9.50 6.46 8.75 7.33 7.83 10.00 9.38 9.83 +62 5 14 17.58 9.46 11.04 10.00 13.25 9.21 13.79 11.08 10.17 10.46 10.63 14.79 +62 5 15 13.42 9.75 11.63 7.00 12.87 7.50 9.83 10.25 8.50 9.25 13.92 15.34 +62 5 16 20.17 16.42 16.08 14.62 23.50 16.00 23.25 19.95 20.00 20.04 23.42 32.17 +62 5 17 14.00 14.00 11.50 11.25 17.33 12.17 18.05 13.13 13.59 12.87 17.88 25.25 +62 5 18 14.75 12.17 10.96 7.29 10.79 6.54 11.21 8.08 5.96 8.63 11.54 10.37 +62 5 19 21.04 11.04 15.87 12.29 16.66 10.75 17.12 12.46 14.29 14.09 14.00 17.00 +62 5 20 17.21 13.67 15.50 7.96 13.29 9.33 12.42 8.58 9.96 10.34 12.50 14.09 +62 5 21 18.63 13.17 11.08 7.79 15.41 9.79 11.63 10.29 9.38 8.75 13.25 13.67 +62 5 22 16.96 15.04 12.42 11.67 18.66 13.13 16.00 13.59 13.50 15.83 18.91 16.38 +62 5 23 12.87 10.00 11.25 8.83 14.50 10.17 16.42 12.33 12.00 12.67 14.62 18.79 +62 5 24 10.13 9.33 7.21 7.08 9.29 6.21 11.67 10.21 8.75 12.83 11.04 18.41 +62 5 25 9.59 10.79 17.67 6.83 9.79 6.34 8.00 10.25 7.38 11.08 12.92 18.46 +62 5 26 5.91 8.79 17.04 5.83 8.08 4.42 7.46 8.50 4.67 6.92 14.42 13.75 +62 5 27 6.13 7.62 19.87 7.50 8.04 5.91 9.92 7.92 6.50 8.12 12.50 10.46 +62 5 28 10.58 8.75 10.17 7.96 8.00 4.00 6.83 7.38 6.21 7.46 9.42 11.54 +62 5 29 11.34 9.25 7.62 5.63 10.17 5.79 6.75 9.50 6.34 9.83 10.96 13.88 +62 5 30 6.71 7.87 6.42 4.54 7.46 4.08 7.17 6.42 6.54 9.33 8.00 16.79 +62 5 31 10.88 8.46 20.12 6.58 7.92 5.33 7.83 8.12 6.79 9.83 9.17 14.09 +62 6 1 5.88 6.29 8.67 5.21 5.00 4.25 5.91 5.41 4.79 9.25 5.25 10.71 +62 6 2 4.96 4.63 5.75 2.58 3.92 1.08 3.17 2.79 2.42 1.29 4.12 5.66 +62 6 3 5.58 10.37 4.96 2.29 7.17 3.08 3.00 5.41 3.46 4.17 7.67 9.46 +62 6 4 8.79 11.67 4.04 5.46 10.92 6.00 3.33 8.92 4.50 7.21 7.04 8.71 +62 6 5 10.00 12.12 7.67 6.71 12.75 6.79 8.21 11.67 8.00 10.08 13.17 14.79 +62 6 6 7.67 10.83 4.50 5.37 10.63 6.79 6.08 10.29 5.96 7.54 15.34 9.79 +62 6 7 5.17 7.00 4.00 4.83 6.75 5.71 3.83 5.21 3.58 4.58 12.25 5.50 +62 6 8 2.54 4.29 5.71 2.92 4.67 3.46 5.41 5.83 4.63 5.50 8.79 4.29 +62 6 9 6.87 5.50 6.54 3.79 4.50 4.04 8.29 4.29 5.37 6.00 9.04 13.04 +62 6 10 8.87 5.25 9.08 4.58 4.54 5.29 6.50 5.04 5.33 8.79 6.96 11.34 +62 6 11 8.08 7.87 9.13 4.42 8.83 4.79 7.96 9.59 6.17 8.17 12.96 8.63 +62 6 12 10.54 5.37 12.92 4.21 7.46 5.83 9.08 7.17 6.79 8.08 8.58 11.92 +62 6 13 9.46 13.96 11.71 7.29 10.50 7.96 9.62 14.04 7.92 11.63 23.29 16.75 +62 6 14 14.71 13.21 16.17 8.25 17.33 11.38 14.00 16.62 13.67 17.50 24.41 26.71 +62 6 15 1.00 5.09 6.46 1.21 5.25 4.04 2.79 8.08 6.21 7.12 13.50 11.54 +62 6 16 5.75 5.50 8.08 3.67 7.71 4.42 8.42 7.58 7.38 8.00 13.17 15.37 +62 6 17 13.83 15.59 10.21 8.63 14.54 9.59 10.37 10.67 10.63 9.29 14.04 13.83 +62 6 18 22.13 21.04 22.54 16.50 23.50 14.12 21.50 21.92 18.96 21.79 29.79 26.58 +62 6 19 18.38 15.12 18.91 15.09 23.04 16.38 23.42 18.84 20.25 18.91 25.62 28.38 +62 6 20 11.42 9.71 12.75 6.25 8.83 6.25 12.54 10.21 9.42 10.71 14.33 19.70 +62 6 21 17.58 16.42 19.83 11.67 15.34 10.96 16.04 15.71 12.58 15.87 18.79 19.55 +62 6 22 13.33 10.13 12.96 8.67 15.79 10.13 13.67 11.38 13.29 12.92 18.75 23.71 +62 6 23 11.71 16.21 13.33 10.04 15.75 11.79 16.66 15.04 13.54 14.50 24.46 23.04 +62 6 24 19.67 13.67 14.09 12.21 17.71 14.88 25.29 19.00 19.62 20.41 20.30 27.46 +62 6 25 11.08 7.92 10.50 8.71 15.12 11.21 18.79 13.25 15.12 13.21 16.83 23.54 +62 6 26 15.34 10.63 10.92 8.75 10.96 9.38 14.33 11.08 11.08 13.79 11.38 18.88 +62 6 27 9.00 4.75 7.92 6.08 7.83 6.13 11.92 8.50 8.96 10.75 9.96 16.54 +62 6 28 12.54 6.50 9.25 8.21 11.71 8.71 12.79 10.50 11.87 10.67 11.00 7.33 +62 6 29 10.79 8.54 6.96 6.67 7.75 5.09 6.42 7.83 7.75 9.17 6.42 11.38 +62 6 30 6.50 5.13 4.33 5.13 5.54 3.71 7.12 7.33 6.63 8.71 5.50 12.42 +62 7 1 8.67 4.17 6.92 6.71 8.17 5.66 11.17 9.38 8.75 11.12 10.25 17.08 +62 7 2 14.67 6.67 9.54 10.21 13.67 10.58 17.50 13.00 12.38 15.09 12.42 18.63 +62 7 3 15.83 10.29 10.00 9.59 10.34 7.21 12.87 8.58 10.58 13.25 8.21 16.71 +62 7 4 15.96 10.25 9.21 7.29 11.71 7.62 10.08 10.71 8.71 12.46 13.59 16.25 +62 7 5 11.21 8.58 7.25 6.75 7.41 6.63 7.79 8.96 8.17 12.38 10.50 14.79 +62 7 6 8.04 4.29 6.13 3.17 4.12 2.58 5.46 2.25 4.71 5.17 5.96 5.71 +62 7 7 6.38 8.92 6.96 4.00 5.29 3.54 3.13 4.21 2.17 4.38 10.71 5.09 +62 7 8 8.96 11.38 7.25 5.46 10.58 5.66 4.67 8.96 6.42 6.83 10.21 9.08 +62 7 9 12.58 8.46 6.75 6.00 10.25 5.04 4.63 9.67 9.33 9.79 11.58 15.12 +62 7 10 6.96 5.46 8.92 5.17 8.58 5.37 7.50 10.63 8.58 10.37 9.50 16.96 +62 7 11 8.63 7.75 3.42 2.75 6.13 2.88 5.71 5.83 4.42 7.04 10.58 11.83 +62 7 12 7.58 5.37 7.33 3.92 2.88 2.04 4.75 1.04 1.71 4.50 4.79 6.21 +62 7 13 10.54 11.75 7.92 6.04 11.04 6.50 5.54 8.83 5.54 6.13 8.96 6.71 +62 7 14 5.88 6.42 9.59 4.25 7.25 5.71 8.00 9.71 9.33 8.46 11.12 12.38 +62 7 15 4.17 4.08 16.54 3.75 3.25 3.79 5.79 3.58 4.50 5.04 7.79 4.88 +62 7 16 6.29 4.79 6.00 2.21 6.17 1.75 1.87 0.96 0.37 3.13 5.41 2.29 +62 7 17 12.92 10.54 5.46 6.25 11.29 6.63 4.54 8.83 5.66 9.04 9.33 11.42 +62 7 18 18.88 16.62 15.67 11.00 16.25 12.96 12.33 14.92 11.96 14.92 19.38 20.83 +62 7 19 16.04 15.41 16.21 10.21 18.54 12.08 13.04 15.96 12.87 17.33 23.13 20.08 +62 7 20 7.92 11.00 10.29 8.33 10.00 6.83 9.17 13.67 6.42 12.33 20.38 17.75 +62 7 21 16.33 16.04 13.33 11.08 17.67 11.58 12.96 11.75 11.34 9.59 15.54 5.91 +62 7 22 12.42 9.54 10.75 9.96 15.34 9.17 11.29 8.54 10.08 9.25 9.21 12.21 +62 7 23 3.08 6.17 6.38 1.96 4.42 2.00 5.88 2.96 2.58 4.92 7.54 6.54 +62 7 24 10.04 6.34 8.67 2.62 7.08 2.46 4.38 3.17 2.88 5.58 4.67 8.17 +62 7 25 9.38 7.58 21.46 6.87 7.12 5.13 6.34 8.08 6.13 6.87 11.79 10.92 +62 7 26 10.79 10.75 25.12 10.13 9.00 6.63 10.00 8.92 7.29 9.54 12.04 6.34 +62 7 27 4.75 2.67 5.75 2.50 2.62 0.25 3.67 2.96 0.46 2.04 4.67 4.63 +62 7 28 4.50 4.08 5.25 2.33 3.42 2.29 2.88 6.34 3.13 4.83 7.71 5.50 +62 7 29 8.83 8.12 8.96 4.71 7.83 4.58 6.58 7.46 5.54 5.21 9.79 6.00 +62 7 30 14.17 9.00 11.25 8.21 14.12 9.71 12.17 12.04 10.04 11.50 14.58 18.63 +62 7 31 7.04 7.00 8.08 5.46 10.96 7.33 9.54 9.92 9.67 8.92 12.96 16.46 +62 8 1 4.58 5.37 6.04 2.29 7.87 3.71 4.46 2.58 4.00 4.79 7.21 7.46 +62 8 2 7.46 8.75 6.00 4.71 10.29 6.75 8.38 7.83 6.79 7.38 10.83 9.21 +62 8 3 16.04 11.75 11.54 8.79 10.75 6.71 8.42 9.42 7.46 8.63 11.79 13.62 +62 8 4 9.83 7.58 9.17 5.75 10.29 7.00 14.25 11.08 9.54 11.25 15.09 20.30 +62 8 5 5.88 4.50 7.83 4.25 8.92 6.54 10.75 7.71 7.87 9.59 9.92 19.46 +62 8 6 11.46 8.83 6.75 4.08 6.42 3.63 5.63 7.00 3.63 7.12 11.08 13.75 +62 8 7 14.50 9.29 10.13 7.92 10.67 7.08 10.50 7.25 8.79 9.83 11.12 12.83 +62 8 8 8.87 6.96 9.25 3.42 5.75 4.08 7.12 4.71 5.41 4.88 9.29 10.75 +62 8 9 14.67 11.71 14.96 10.34 14.62 10.00 12.25 12.04 10.75 13.79 19.04 21.79 +62 8 10 13.25 13.50 13.88 9.29 12.54 9.75 14.29 13.88 11.54 13.21 18.79 17.08 +62 8 11 16.92 15.16 16.46 13.00 18.12 12.96 18.50 17.25 17.92 17.37 24.08 29.95 +62 8 12 2.88 4.92 4.96 2.83 5.91 3.50 9.38 5.88 5.75 8.63 7.92 13.67 +62 8 13 10.04 5.25 12.04 4.29 5.75 2.29 5.88 4.58 6.08 3.08 5.50 6.92 +62 8 14 12.00 10.25 11.83 4.63 10.29 4.25 8.67 9.29 9.00 9.92 10.67 12.92 +62 8 15 10.79 10.46 8.08 7.41 12.17 6.83 10.41 8.46 8.58 6.50 11.46 13.21 +62 8 16 11.17 8.25 10.21 6.04 10.96 5.88 11.38 6.79 7.54 7.50 11.50 12.87 +62 8 17 8.21 6.58 8.71 4.71 10.58 6.83 11.25 7.83 6.75 7.08 8.83 10.92 +62 8 18 6.87 10.79 9.21 5.00 7.17 5.63 7.54 5.58 5.17 5.91 12.25 7.29 +62 8 19 14.17 13.62 13.59 9.83 10.79 8.54 10.50 11.67 9.33 11.08 18.63 13.70 +62 8 20 14.29 12.62 12.54 7.38 14.12 7.96 11.25 11.54 8.58 8.75 14.88 10.92 +62 8 21 14.67 12.38 13.37 9.54 15.46 10.34 14.21 14.50 12.42 11.83 17.75 20.75 +62 8 22 11.25 10.96 11.17 8.87 12.92 10.17 15.09 12.33 11.87 12.46 16.13 23.63 +62 8 23 18.84 14.79 17.33 9.25 14.25 10.00 17.04 14.04 11.75 14.25 20.67 21.67 +62 8 24 15.12 14.29 11.50 11.71 17.25 12.96 21.09 15.75 15.96 14.54 19.79 26.87 +62 8 25 13.25 11.54 10.88 8.33 12.17 8.63 15.50 11.04 10.67 11.12 16.50 21.75 +62 8 26 24.67 20.12 19.83 17.75 20.88 15.96 22.13 18.08 19.41 19.75 23.63 19.46 +62 8 27 10.00 8.67 9.83 6.54 9.92 7.25 14.29 7.41 9.83 10.50 10.63 17.25 +62 8 28 6.04 7.04 4.92 2.08 4.71 3.17 5.54 2.29 3.04 4.58 5.63 11.21 +62 8 29 4.83 3.88 5.29 1.87 1.92 1.92 7.75 4.50 3.75 7.29 10.37 15.34 +62 8 30 0.96 2.33 4.17 2.21 4.38 3.83 6.42 5.04 5.00 6.46 9.42 13.96 +62 8 31 6.38 8.38 4.25 3.63 6.58 3.13 5.37 4.67 3.83 5.79 6.29 7.54 +62 9 1 10.00 12.08 10.96 9.25 9.29 7.62 7.41 8.75 7.67 9.62 14.58 11.92 +62 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7.33 11.63 13.79 +62 9 16 13.46 10.63 10.04 7.92 12.67 9.04 11.75 10.54 11.08 9.71 14.79 18.05 +62 9 17 11.34 8.75 10.79 6.54 9.17 7.17 10.58 6.38 7.79 11.34 11.96 17.75 +62 9 18 6.83 5.46 9.38 4.04 4.25 2.46 6.79 2.13 4.63 7.29 5.04 11.25 +62 9 19 6.46 4.71 12.42 1.67 4.42 0.83 2.88 1.04 2.13 1.75 3.17 3.00 +62 9 20 9.08 6.96 16.17 4.21 7.33 3.67 4.63 1.42 2.04 3.37 2.79 0.67 +62 9 21 6.29 3.08 10.54 2.17 4.96 2.50 2.29 1.42 0.87 1.25 4.33 7.54 +62 9 22 4.88 5.25 6.29 2.21 5.88 4.33 6.58 6.08 4.29 7.50 14.29 16.21 +62 9 23 9.25 10.67 9.62 3.75 10.54 6.38 8.33 11.67 6.58 12.54 19.67 16.96 +62 9 24 8.54 9.13 8.50 4.12 9.00 4.79 4.88 4.50 4.29 5.21 6.25 8.50 +62 9 25 12.62 9.33 12.62 6.54 8.67 6.08 10.83 6.87 5.58 7.58 12.12 12.04 +62 9 26 12.00 6.63 12.54 6.75 8.17 4.50 12.00 4.67 8.71 9.00 5.33 17.00 +62 9 27 9.59 10.21 12.17 5.91 10.71 7.38 8.42 6.17 6.13 7.96 6.42 13.75 +62 9 28 11.42 9.54 10.46 7.29 13.75 8.87 11.29 9.00 9.25 10.71 13.83 12.71 +62 9 29 21.37 16.75 18.38 11.50 17.67 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12.25 15.79 +62 10 27 15.16 15.41 12.92 8.25 17.00 11.12 15.63 15.67 14.04 15.67 24.83 25.75 +62 10 28 19.95 14.17 14.09 12.62 14.09 10.75 15.92 13.54 14.37 16.04 19.17 28.12 +62 10 29 12.92 10.79 13.13 5.79 10.96 9.21 11.96 8.71 8.46 10.83 15.79 18.00 +62 10 30 18.25 22.79 11.75 10.67 20.79 11.67 20.75 17.29 16.46 14.58 29.42 29.17 +62 10 31 15.21 12.25 8.38 9.04 13.67 10.58 12.75 11.96 12.25 11.87 18.21 19.04 +62 11 1 16.88 13.25 16.00 8.96 13.46 11.46 10.46 10.17 10.37 13.21 14.83 15.16 +62 11 2 15.96 8.46 13.70 6.13 7.71 7.67 6.04 5.83 4.75 5.83 6.46 9.04 +62 11 3 5.21 3.75 6.17 3.37 8.08 3.88 6.83 1.08 2.79 3.63 5.13 7.00 +62 11 4 13.75 7.29 15.67 7.41 11.54 8.67 10.92 8.29 9.96 12.96 11.58 17.62 +62 11 5 16.08 6.50 18.91 7.96 7.71 9.62 19.75 6.00 11.79 16.54 9.62 23.87 +62 11 6 12.17 8.08 12.46 6.17 7.92 6.79 8.29 8.71 6.75 8.25 9.67 11.71 +62 11 7 2.96 4.33 5.04 2.50 4.71 2.88 3.50 2.79 2.37 3.46 3.13 4.92 +62 11 8 4.12 3.25 11.08 3.42 5.00 3.21 4.42 2.04 2.54 3.04 2.96 3.00 +62 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5.13 5.88 1.87 6.75 6.38 8.38 4.08 2.96 4.92 9.33 8.79 +62 11 23 11.29 12.08 11.63 6.00 11.83 10.34 12.96 12.83 10.13 12.08 15.21 11.83 +62 11 24 20.50 15.21 15.04 11.79 15.50 11.67 11.17 11.96 9.96 11.75 16.58 16.79 +62 11 25 5.88 5.13 5.63 2.50 5.33 3.50 4.00 2.54 2.17 3.04 4.63 6.00 +62 11 26 4.00 2.21 3.21 0.21 2.33 1.00 5.96 0.00 0.83 0.92 2.75 8.42 +62 11 27 4.88 2.67 7.83 3.04 2.37 0.46 5.09 0.00 1.75 1.87 2.46 8.33 +62 11 28 6.17 1.50 6.04 1.63 2.46 0.33 4.88 1.00 1.58 3.04 6.34 12.75 +62 11 29 7.17 4.38 3.58 0.21 3.54 0.21 7.41 0.17 2.96 7.21 9.04 13.13 +62 11 30 12.83 10.00 5.79 1.00 9.38 5.29 4.71 4.38 2.37 3.63 7.79 8.58 +62 12 1 18.38 15.41 11.75 6.79 12.21 8.04 8.42 10.83 5.66 9.08 11.50 11.50 +62 12 2 17.41 18.38 14.71 10.41 16.21 10.17 12.96 9.62 10.00 10.34 14.96 18.21 +62 12 3 17.33 16.13 13.88 8.87 14.04 12.38 10.13 9.75 9.71 10.79 13.96 17.50 +62 12 4 13.62 13.33 10.75 6.46 13.62 10.37 4.79 10.75 6.87 9.04 10.46 13.62 +62 12 5 15.46 16.42 13.00 7.71 10.83 9.96 8.96 7.00 7.83 8.29 18.41 18.58 +62 12 6 13.17 11.67 11.25 7.46 9.92 7.87 4.17 8.38 6.13 8.58 17.21 16.17 +62 12 7 25.46 17.50 22.34 16.21 15.34 12.75 13.13 14.67 9.96 13.96 23.21 24.50 +62 12 8 23.29 18.00 21.59 17.12 21.12 15.67 23.67 13.92 17.46 16.71 21.62 23.42 +62 12 9 23.33 18.88 10.75 16.71 20.12 14.42 20.25 14.79 19.00 17.21 21.04 28.54 +62 12 10 13.13 11.21 8.12 7.58 14.96 9.71 12.67 9.96 10.83 10.75 15.04 20.79 +62 12 11 17.92 15.87 11.63 9.13 17.37 12.21 16.42 12.46 12.29 12.17 19.92 19.46 +62 12 12 30.91 24.79 21.87 16.75 19.92 12.12 19.67 18.88 15.96 23.21 28.33 37.12 +62 12 13 17.16 11.12 15.09 8.42 10.63 8.46 14.00 9.50 10.41 13.70 14.75 23.00 +62 12 14 13.96 13.37 11.08 13.46 20.30 16.33 20.00 18.12 17.33 16.79 21.12 24.04 +62 12 15 29.38 18.46 17.58 24.33 29.46 22.83 28.91 26.67 25.80 28.21 29.25 32.79 +62 12 16 17.83 12.62 13.25 12.42 13.13 11.58 16.54 9.67 12.42 16.54 15.09 25.58 +62 12 17 13.37 11.96 6.46 4.42 12.17 7.83 9.59 8.42 7.08 8.04 14.29 13.92 +62 12 18 16.33 15.37 10.21 10.71 15.34 10.79 15.92 14.42 13.17 12.54 22.08 23.45 +62 12 19 11.25 9.75 8.25 4.79 11.04 8.21 9.87 7.67 8.79 10.58 14.17 16.54 +62 12 20 14.58 14.46 12.21 8.96 16.00 11.96 16.08 9.75 12.12 13.62 15.96 20.12 +62 12 21 5.75 5.17 6.38 2.29 7.54 5.17 10.41 3.50 6.13 6.25 9.87 11.46 +62 12 22 17.83 17.50 6.71 3.58 11.04 9.13 5.71 10.92 7.33 9.46 15.75 13.25 +62 12 23 13.25 11.29 12.21 6.54 11.12 8.96 9.33 9.46 8.12 11.04 15.41 18.12 +62 12 24 9.13 6.08 9.79 2.00 8.42 4.38 4.92 4.29 3.13 4.17 7.75 11.83 +62 12 25 8.00 2.58 8.75 1.79 1.00 0.75 7.00 0.75 2.33 3.67 6.96 12.46 +62 12 26 12.87 5.66 10.08 5.00 8.38 6.50 12.38 6.50 7.25 9.67 11.08 17.29 +62 12 27 13.62 12.96 19.83 8.42 5.66 4.92 11.92 7.21 6.58 5.88 7.67 16.79 +62 12 28 12.67 10.71 17.75 6.83 7.75 4.42 7.79 4.92 5.71 2.96 3.83 7.87 +62 12 29 19.29 13.13 22.79 7.17 11.00 6.54 13.62 8.00 7.21 9.25 9.33 16.88 +62 12 30 22.00 19.70 33.84 19.83 20.08 18.46 28.79 21.59 18.75 18.05 21.87 29.88 +62 12 31 22.67 16.88 28.67 14.12 19.75 17.08 27.79 25.21 19.83 17.79 25.46 37.63 +63 1 1 15.59 13.62 19.79 8.38 12.25 10.00 23.45 15.71 13.59 14.37 17.58 34.13 +63 1 2 13.00 13.92 21.50 6.63 10.67 8.54 20.75 11.46 10.75 14.09 12.83 30.75 +63 1 3 14.09 8.25 20.12 9.00 9.96 9.67 13.83 10.17 9.00 13.70 11.67 27.21 +63 1 4 10.29 1.54 6.67 5.21 6.08 5.13 8.54 8.21 5.91 11.17 11.54 25.54 +63 1 5 7.50 6.04 9.87 3.37 5.96 6.29 13.08 12.46 7.04 11.63 14.96 29.04 +63 1 6 19.70 13.75 19.33 10.46 16.96 12.12 20.91 15.67 13.17 12.71 19.08 24.54 +63 1 7 18.75 18.12 18.29 8.92 16.17 12.46 16.75 11.71 9.87 11.92 14.21 19.58 +63 1 8 20.67 18.79 17.67 7.33 20.41 13.50 16.83 12.83 9.67 12.83 15.34 17.41 +63 1 9 26.30 20.04 25.66 14.96 24.00 16.04 21.67 19.95 16.00 14.58 18.54 25.88 +63 1 10 25.58 19.75 24.00 10.41 20.25 12.00 19.38 16.71 12.25 12.33 17.37 26.58 +63 1 11 18.84 10.58 16.79 4.25 10.75 6.42 9.79 8.79 5.41 7.46 8.17 10.17 +63 1 12 10.04 10.34 8.54 2.08 4.54 1.25 3.96 2.25 2.29 1.58 2.62 4.54 +63 1 13 10.00 9.00 7.25 1.87 2.50 0.75 5.21 2.79 2.37 2.50 3.00 9.46 +63 1 14 11.92 6.83 10.88 4.75 4.33 2.25 10.46 6.54 5.04 6.00 6.63 12.04 +63 1 15 8.50 3.17 5.41 3.83 7.50 4.63 9.17 5.66 5.88 6.83 9.46 12.58 +63 1 16 11.54 9.71 11.38 5.41 8.21 8.21 12.58 10.58 8.83 11.04 12.54 21.25 +63 1 17 20.33 12.67 22.42 10.71 14.21 10.63 15.37 12.75 8.92 10.63 11.46 10.71 +63 1 18 28.21 16.79 27.21 11.38 16.29 13.83 21.50 13.96 13.13 13.67 17.21 25.80 +63 1 19 22.21 15.12 32.25 12.04 19.62 13.42 23.79 17.46 15.50 15.59 14.17 27.75 +63 1 20 19.00 14.25 20.91 11.00 18.50 15.63 19.62 18.88 15.87 14.50 23.04 32.83 +63 1 21 19.25 16.08 17.21 9.17 15.59 9.42 13.83 11.71 11.08 14.25 12.96 23.04 +63 1 22 17.16 12.04 14.33 6.13 13.62 10.50 11.92 8.75 4.58 7.87 10.79 13.79 +63 1 23 14.04 11.42 11.50 5.41 11.29 7.71 2.17 7.08 5.66 5.04 7.21 14.67 +63 1 24 7.04 6.17 8.46 1.79 6.13 3.96 3.04 2.62 2.13 0.46 8.79 12.21 +63 1 25 6.00 2.83 2.75 1.42 4.17 1.71 8.92 2.33 2.04 4.54 4.75 11.08 +63 1 26 7.67 3.75 7.17 1.38 0.92 0.25 8.38 2.08 2.29 4.79 0.13 8.29 +63 1 27 10.54 9.83 6.04 0.67 7.12 2.29 1.63 5.50 1.33 1.92 5.37 5.71 +63 1 28 7.50 6.34 3.37 0.92 5.25 1.08 1.33 1.71 0.29 1.38 1.96 5.37 +63 1 29 5.41 3.25 4.83 1.50 5.17 2.88 7.41 4.12 3.08 4.71 10.17 15.41 +63 1 30 21.42 21.84 25.21 15.46 17.29 13.54 19.62 17.00 14.04 17.83 21.87 27.50 +63 1 31 12.83 8.67 21.96 9.87 7.79 6.79 11.83 7.75 6.08 9.17 8.87 18.63 +63 2 1 15.41 7.62 24.67 11.42 9.21 8.17 14.04 7.54 7.54 10.08 10.17 17.67 +63 2 2 15.04 14.37 26.34 10.71 12.71 8.33 15.16 11.08 9.42 9.46 9.25 15.25 +63 2 3 11.12 4.00 9.87 4.92 5.91 4.00 8.67 4.42 5.00 6.87 5.79 13.79 +63 2 4 12.17 15.46 7.83 4.21 15.67 10.54 9.13 12.83 6.92 8.08 15.92 12.83 +63 2 5 12.62 11.46 28.75 13.13 12.58 12.83 22.00 15.29 15.41 17.41 16.79 29.17 +63 2 6 17.79 9.42 22.67 11.12 14.00 14.79 20.25 15.16 14.79 20.04 15.83 31.00 +63 2 7 11.87 7.75 10.54 7.21 7.92 7.83 11.83 5.54 6.58 7.50 8.71 18.34 +63 2 8 11.87 12.17 13.13 8.54 11.71 9.71 10.37 8.63 6.34 5.66 12.25 11.75 +63 2 9 14.83 9.75 23.33 12.33 13.25 13.67 16.88 14.58 12.67 13.21 13.75 15.37 +63 2 10 19.33 12.71 19.67 10.00 14.33 11.63 15.67 14.29 13.17 12.71 12.33 20.71 +63 2 11 7.71 4.88 11.63 3.63 7.12 4.17 6.79 5.71 4.79 5.29 5.00 11.92 +63 2 12 7.17 2.96 5.79 4.33 4.08 0.13 5.21 1.63 2.42 1.79 7.29 5.09 +63 2 13 18.91 20.12 16.54 10.75 18.00 13.70 12.83 14.79 12.96 16.42 19.58 23.54 +63 2 14 17.41 14.58 19.04 14.50 16.17 12.71 19.17 13.70 14.17 19.33 16.25 29.88 +63 2 15 10.04 10.63 15.92 6.87 12.33 9.21 15.50 13.79 10.63 15.79 14.83 24.25 +63 2 16 12.38 7.71 13.88 7.71 14.21 7.58 13.54 14.29 11.63 12.38 15.50 23.09 +63 2 17 12.46 14.67 12.46 5.33 11.12 6.96 7.83 8.33 6.63 7.33 11.63 13.70 +63 2 18 21.12 17.54 17.08 8.75 17.67 10.67 10.34 11.58 7.92 8.00 13.92 9.50 +63 2 19 16.83 9.79 18.34 9.87 13.42 8.67 14.54 10.04 8.71 9.17 9.75 8.12 +63 2 20 11.75 1.92 14.71 7.54 8.33 4.33 9.92 7.21 7.33 6.04 6.87 8.17 +63 2 21 5.58 2.92 4.67 3.58 4.58 2.17 8.50 1.50 1.42 3.17 5.46 4.54 +63 2 22 4.08 5.58 2.71 1.38 7.33 3.17 5.50 4.42 2.29 4.04 7.33 11.21 +63 2 23 18.58 16.66 14.46 7.04 13.79 10.83 9.33 11.50 6.42 7.67 10.92 13.59 +63 2 24 17.67 20.17 14.37 11.42 17.37 13.67 13.75 13.04 9.42 10.37 19.75 16.79 +63 2 25 19.75 18.88 15.96 13.13 21.17 13.33 11.46 14.79 12.92 12.50 21.59 23.58 +63 2 26 22.83 22.21 20.67 14.17 22.63 16.88 19.25 19.70 15.04 15.12 27.42 30.37 +63 2 27 17.62 16.66 15.50 7.87 15.71 13.00 12.50 12.42 8.58 10.83 16.96 14.54 +63 2 28 19.79 19.95 19.00 9.67 18.54 16.13 16.46 15.25 10.37 13.70 20.62 21.62 +63 3 1 16.75 19.67 17.67 8.87 19.08 15.37 16.21 14.29 11.29 9.21 19.92 19.79 +63 3 2 16.79 15.54 19.83 10.54 15.96 13.70 15.83 14.50 10.54 12.00 18.41 19.25 +63 3 3 12.83 15.83 14.50 7.62 13.70 11.54 14.46 12.25 9.17 9.92 15.34 17.37 +63 3 4 20.08 18.96 18.34 14.58 18.79 15.67 14.42 17.67 14.54 17.41 21.54 21.59 +63 3 5 27.46 22.67 25.08 20.00 21.46 19.95 20.21 23.13 17.21 25.37 32.63 30.09 +63 3 6 16.96 15.21 18.46 12.83 13.92 10.75 9.96 12.33 8.63 12.96 11.54 12.96 +63 3 7 20.58 18.54 19.83 15.29 18.08 13.92 17.88 16.62 15.59 18.84 17.46 17.33 +63 3 8 24.33 20.79 23.50 18.12 22.04 19.12 17.50 18.29 16.21 21.34 19.21 22.17 +63 3 9 18.75 11.54 17.50 11.38 14.00 9.71 13.33 12.38 8.75 13.67 11.75 18.38 +63 3 10 13.13 9.59 13.21 9.42 13.70 11.46 17.33 11.92 14.29 11.67 12.71 11.25 +63 3 11 8.38 3.37 11.79 4.88 5.54 2.92 5.66 2.62 2.88 3.50 3.67 5.37 +63 3 12 5.21 5.29 5.75 0.87 3.29 1.87 6.67 1.67 2.04 3.04 3.17 4.54 +63 3 13 20.79 18.00 16.29 11.12 15.63 12.29 11.46 13.50 10.75 12.29 15.37 17.92 +63 3 14 25.84 17.25 24.71 20.50 16.33 14.54 13.92 16.25 15.87 18.91 19.58 15.46 +63 3 15 15.09 9.92 17.79 11.92 14.58 11.83 14.25 10.83 11.79 14.54 13.92 14.09 +63 3 16 18.25 13.79 18.25 10.67 16.75 12.38 16.62 13.37 14.42 13.83 13.83 15.75 +63 3 17 16.96 10.79 16.54 10.37 12.50 10.37 13.17 11.34 10.21 13.42 12.21 18.79 +63 3 18 14.25 8.46 9.25 9.38 12.33 10.21 12.42 10.41 10.67 12.08 10.50 16.25 +63 3 19 11.00 11.96 9.04 4.75 11.87 6.92 9.54 9.04 7.21 7.21 10.21 7.41 +63 3 20 11.29 8.63 11.21 6.34 9.54 5.46 7.54 7.58 5.79 8.21 6.21 4.83 +63 3 21 4.38 5.66 5.71 1.04 5.83 1.29 4.33 2.13 1.25 3.17 5.17 5.13 +63 3 22 7.54 2.62 13.42 3.50 5.66 3.67 4.17 3.04 2.50 2.96 5.71 3.21 +63 3 23 6.34 8.04 4.88 1.63 6.42 3.88 4.79 7.75 3.04 6.67 14.96 10.25 +63 3 24 26.25 22.37 20.79 14.33 18.50 16.66 19.75 19.95 14.17 18.84 27.16 23.04 +63 3 25 15.54 12.38 14.00 11.96 17.25 14.37 19.55 15.09 16.88 16.17 16.92 15.63 +63 3 26 10.46 7.62 8.25 4.25 8.29 5.88 7.62 10.17 6.58 8.08 12.62 13.92 +63 3 27 12.83 12.21 12.33 8.08 12.54 9.54 13.92 10.79 11.12 12.58 15.50 17.29 +63 3 28 11.50 8.38 11.58 5.41 11.34 8.04 9.83 8.50 7.46 10.17 11.83 14.17 +63 3 29 7.54 8.87 5.33 5.00 10.96 6.13 8.67 8.12 5.41 4.83 9.71 3.08 +63 3 30 15.96 6.63 10.34 7.54 11.75 6.25 9.38 6.42 7.29 5.13 7.21 3.88 +63 3 31 7.41 9.83 8.00 5.13 8.87 7.04 6.83 9.62 6.87 8.96 17.50 17.92 +63 4 1 10.54 9.59 12.46 7.33 9.46 9.59 11.79 11.87 9.79 10.71 13.37 18.21 +63 4 2 3.88 3.13 5.04 3.75 5.46 4.50 8.96 6.50 7.41 6.54 7.08 11.79 +63 4 3 7.54 4.21 5.63 6.58 6.17 4.17 8.71 7.62 7.54 9.08 4.33 10.50 +63 4 4 12.71 9.04 16.83 8.83 10.88 9.00 13.00 10.54 9.79 13.67 13.33 20.71 +63 4 5 13.08 12.46 29.04 11.83 12.12 11.25 20.04 14.75 12.04 17.71 19.29 23.21 +63 4 6 13.50 11.42 25.25 12.83 15.92 16.00 20.50 19.95 17.62 18.25 21.75 32.96 +63 4 7 16.25 13.79 22.75 11.92 16.38 16.38 18.50 18.16 17.04 15.21 16.92 26.25 +63 4 8 9.83 9.46 12.25 5.96 11.08 7.92 10.41 12.50 11.04 12.42 10.50 20.38 +63 4 9 16.46 11.79 15.21 7.29 9.96 12.21 11.67 13.70 12.33 12.50 12.92 20.91 +63 4 10 4.75 4.42 13.59 5.25 7.83 6.79 7.25 8.00 6.34 8.54 11.38 8.83 +63 4 11 16.21 13.37 9.29 8.46 13.75 10.29 9.54 12.83 11.25 9.29 16.83 16.46 +63 4 12 11.12 10.13 11.79 8.38 13.13 11.00 16.00 12.08 12.71 16.00 19.62 24.79 +63 4 13 15.09 11.29 16.38 8.75 13.88 13.54 15.21 12.67 12.38 15.46 15.87 21.79 +63 4 14 19.12 15.54 18.96 11.04 15.16 13.33 16.46 11.29 11.54 13.79 13.70 14.75 +63 4 15 6.79 7.25 9.04 5.46 9.25 5.50 8.87 5.29 6.34 5.63 7.58 10.46 +63 4 16 15.54 15.71 14.62 8.92 14.25 10.63 9.67 13.83 10.29 13.70 20.46 16.38 +63 4 17 11.04 10.83 9.79 6.83 10.96 7.96 8.00 12.71 8.21 12.08 18.91 13.50 +63 4 18 5.66 5.21 9.42 4.67 9.29 7.75 9.92 9.13 8.79 8.29 11.29 11.29 +63 4 19 9.08 10.75 8.79 6.67 12.42 8.87 9.50 8.42 7.17 8.92 9.08 10.21 +63 4 20 25.50 20.67 24.87 17.04 22.00 17.71 21.54 19.29 17.00 19.67 17.92 22.63 +63 4 21 27.08 18.88 23.75 16.54 18.66 15.16 19.58 17.75 16.42 21.84 20.30 24.62 +63 4 22 14.09 12.42 15.12 10.29 14.54 12.21 13.42 13.13 12.42 15.67 16.83 13.70 +63 4 23 9.87 11.67 7.25 5.63 11.87 8.42 6.00 8.71 8.25 10.00 9.67 10.63 +63 4 24 8.54 3.54 5.04 5.79 9.00 7.25 6.67 8.83 7.46 8.87 10.00 9.67 +63 4 25 4.25 6.00 4.92 2.75 4.00 2.00 4.83 2.04 2.83 3.83 8.58 6.92 +63 4 26 5.09 8.87 8.38 5.88 6.79 6.96 5.71 8.29 5.83 6.21 15.09 8.83 +63 4 27 5.37 6.29 10.13 4.00 6.67 7.50 12.12 10.67 9.13 10.46 15.21 15.63 +63 4 28 5.88 6.50 10.21 5.54 8.96 8.04 11.42 7.54 10.46 8.92 12.00 12.71 +63 4 29 11.79 9.08 8.75 7.83 13.08 9.13 11.75 10.92 10.08 11.67 11.96 15.21 +63 4 30 12.83 10.83 12.46 7.67 12.54 10.75 12.33 11.58 10.75 12.08 14.50 16.08 +63 5 1 18.79 14.17 13.59 11.63 14.17 11.96 14.46 12.46 12.87 13.96 15.29 21.62 +63 5 2 18.00 13.75 11.83 12.00 16.50 12.54 15.37 13.96 12.75 12.87 15.83 20.58 +63 5 3 16.13 9.59 10.29 10.00 13.46 9.59 12.96 10.17 11.12 12.54 14.37 13.04 +63 5 4 13.88 12.21 12.58 8.38 14.42 11.63 13.37 13.88 12.46 12.71 17.96 18.21 +63 5 5 12.42 12.25 9.71 10.37 14.75 12.00 14.96 12.50 12.67 12.67 16.38 18.38 +63 5 6 12.38 11.58 12.29 8.25 12.58 9.46 13.67 11.67 10.13 12.62 17.71 15.54 +63 5 7 19.83 16.96 22.34 15.16 17.41 15.34 18.75 16.66 15.37 19.17 22.83 21.00 +63 5 8 14.00 12.87 15.37 7.87 15.04 10.83 11.67 14.29 11.83 14.46 18.54 19.55 +63 5 9 19.79 19.29 16.33 12.58 17.92 13.13 13.88 12.79 12.92 12.62 15.16 14.96 +63 5 10 30.91 18.21 21.12 19.62 26.20 18.38 23.09 17.37 20.08 17.37 20.25 22.21 +63 5 11 11.17 10.34 13.37 8.38 11.08 9.59 12.25 10.83 10.75 11.67 16.66 15.79 +63 5 12 23.75 22.17 23.09 13.42 21.92 16.00 15.59 23.00 15.34 20.88 32.91 24.54 +63 5 13 20.62 18.91 17.04 12.75 24.67 18.34 20.33 21.54 17.62 20.38 26.42 28.01 +63 5 14 13.29 10.75 10.25 7.04 12.62 8.58 13.42 11.87 11.46 12.04 15.75 18.29 +63 5 15 10.21 7.50 8.17 6.34 10.46 8.08 12.83 8.63 10.88 10.88 12.87 15.59 +63 5 16 8.08 9.13 9.59 6.58 11.00 8.83 14.42 10.08 10.67 12.29 13.70 13.62 +63 5 17 9.59 8.21 9.92 7.08 13.13 10.00 13.96 11.08 13.00 12.58 16.04 20.54 +63 5 18 17.79 12.96 13.00 11.42 17.75 14.33 19.33 16.79 17.04 18.12 18.63 24.21 +63 5 19 10.50 9.33 9.71 6.50 11.17 8.29 11.38 8.71 9.42 8.83 11.79 12.54 +63 5 20 16.00 13.50 15.41 9.21 16.88 12.33 14.50 10.96 11.75 10.04 13.33 12.92 +63 5 21 12.54 12.83 11.04 5.29 13.37 7.83 7.21 9.79 8.63 10.46 13.46 15.92 +63 5 22 8.87 6.83 9.87 5.17 7.58 6.00 7.62 5.17 6.87 6.04 8.71 8.17 +63 5 23 5.25 10.46 5.13 4.12 9.59 6.29 7.00 6.54 6.38 5.79 13.04 8.04 +63 5 24 9.59 9.21 6.17 3.88 10.54 5.29 4.96 6.29 5.33 5.04 7.54 8.96 +63 5 25 8.38 8.12 12.33 7.29 8.63 8.04 8.38 8.04 6.71 7.58 14.54 7.29 +63 5 26 11.63 8.58 8.12 6.71 9.71 7.38 9.29 9.62 9.17 11.04 12.38 12.75 +63 5 27 8.21 2.92 10.04 3.71 3.42 1.87 5.13 1.71 3.17 2.67 5.83 4.92 +63 5 28 3.63 5.91 13.83 4.63 7.75 3.71 5.63 5.17 3.67 4.21 5.04 1.75 +63 5 29 7.12 7.62 21.50 7.83 10.08 7.41 10.92 7.12 8.75 7.25 12.21 4.75 +63 5 30 4.88 2.96 17.25 4.67 5.96 6.71 8.25 7.62 7.83 7.54 7.96 11.12 +63 5 31 2.04 2.21 9.04 2.88 2.67 2.17 6.13 3.58 6.46 6.92 4.96 9.42 +63 6 1 13.37 6.87 12.00 8.50 10.04 9.42 10.92 12.96 11.79 11.04 10.92 13.67 +63 6 2 17.62 8.79 20.62 8.63 11.38 10.75 14.46 13.70 13.70 11.79 12.83 16.46 +63 6 3 19.04 9.17 24.13 11.54 12.12 13.04 17.67 14.50 14.00 11.63 14.09 11.75 +63 6 4 8.96 7.17 21.92 7.04 8.79 8.79 9.79 9.08 8.58 7.79 11.21 4.96 +63 6 5 3.37 4.75 16.83 2.21 3.54 3.88 6.00 7.00 6.21 7.08 12.08 7.00 +63 6 6 4.04 3.08 4.00 3.71 4.54 2.54 6.29 6.08 7.00 9.21 6.79 13.46 +63 6 7 6.34 8.04 4.63 4.50 6.96 2.92 3.58 4.25 3.54 4.67 5.71 10.96 +63 6 8 7.17 7.83 3.67 2.58 7.62 4.17 4.25 5.91 4.83 4.83 5.17 3.33 +63 6 9 6.75 3.37 13.25 3.58 4.25 4.38 8.38 7.96 8.42 7.50 6.96 5.96 +63 6 10 7.58 4.58 8.87 3.58 6.54 4.08 5.75 7.58 7.79 7.21 7.87 6.67 +63 6 11 4.12 3.75 7.87 1.21 5.09 2.92 2.92 4.04 4.25 5.09 6.83 4.63 +63 6 12 6.08 4.92 4.42 2.25 5.50 2.21 4.25 4.08 4.38 4.04 8.63 5.63 +63 6 13 10.00 3.13 4.17 5.54 5.91 3.67 4.92 3.96 4.67 8.38 5.71 5.41 +63 6 14 7.62 3.21 3.75 2.62 5.13 2.42 4.83 2.79 4.54 2.88 5.79 2.79 +63 6 15 8.92 8.08 8.04 4.50 9.13 6.58 7.17 11.46 7.67 10.54 14.17 11.87 +63 6 16 5.46 6.63 7.33 4.92 7.50 5.58 8.29 6.71 6.75 6.50 10.25 13.21 +63 6 17 10.25 10.58 11.83 5.83 8.75 4.92 5.88 8.87 4.46 7.71 13.00 8.25 +63 6 18 20.75 14.46 15.34 12.96 20.83 15.29 17.83 17.54 17.50 16.92 21.17 18.54 +63 6 19 7.04 7.67 7.33 5.04 8.54 5.17 8.92 7.50 6.50 8.75 11.12 12.79 +63 6 20 11.38 11.67 13.00 7.62 13.17 9.50 8.67 12.46 10.21 13.25 17.67 13.25 +63 6 21 14.54 10.00 15.34 6.50 11.71 8.79 10.34 10.41 10.96 10.96 13.70 16.08 +63 6 22 10.00 9.62 9.17 8.92 13.79 9.92 11.83 10.67 10.25 9.38 14.17 12.71 +63 6 23 15.59 14.00 14.83 7.96 15.04 10.75 11.87 13.00 9.83 12.71 17.96 16.25 +63 6 24 14.04 11.54 16.38 8.00 16.96 11.67 12.04 10.13 10.79 11.92 13.46 11.54 +63 6 25 9.83 9.25 8.96 6.42 14.50 8.63 8.17 10.79 8.17 8.54 13.37 11.92 +63 6 26 13.17 10.71 10.83 9.17 14.83 9.59 12.71 11.63 10.67 11.54 14.09 12.71 +63 6 27 13.70 12.17 11.79 8.83 13.42 9.75 13.04 13.25 10.92 14.58 14.88 24.25 +63 6 28 16.25 13.13 16.04 9.83 15.29 9.00 12.17 14.25 10.25 13.50 18.88 20.67 +63 6 29 15.09 14.04 15.21 10.54 14.58 9.00 13.50 15.04 11.50 15.54 20.96 18.75 +63 6 30 10.63 10.83 19.00 8.08 8.83 7.25 11.58 10.08 7.38 12.08 13.50 5.29 +63 7 1 3.83 5.50 8.50 4.12 6.79 4.04 8.54 8.25 8.04 9.00 10.75 10.83 +63 7 2 5.21 1.13 5.96 4.33 5.88 4.12 5.09 8.46 8.00 8.71 9.79 16.83 +63 7 3 2.00 2.17 8.25 2.58 4.83 3.13 6.46 5.96 6.34 6.00 6.00 9.00 +63 7 4 2.37 2.42 7.79 2.62 3.04 3.17 8.50 6.29 5.17 8.29 6.54 13.79 +63 7 5 2.67 2.08 7.46 1.92 3.04 2.88 5.17 5.41 4.67 6.04 7.33 7.29 +63 7 6 4.04 5.41 2.83 2.17 4.50 2.79 2.67 6.75 3.33 6.34 10.71 5.17 +63 7 7 7.00 7.83 5.83 4.88 5.17 4.50 6.46 7.75 5.54 7.12 9.46 11.21 +63 7 8 7.96 7.41 7.67 5.29 8.63 7.00 10.92 10.71 9.67 9.25 11.42 15.83 +63 7 9 8.38 8.46 9.50 8.21 12.71 9.87 13.96 11.96 10.17 10.00 12.67 13.21 +63 7 10 9.04 7.83 6.21 5.04 8.96 4.88 8.17 7.08 6.79 7.71 8.00 9.17 +63 7 11 6.67 5.00 7.12 3.13 7.87 2.58 4.46 4.12 2.79 3.21 3.71 5.79 +63 7 12 3.08 5.96 4.25 2.79 5.46 3.37 4.79 5.75 4.75 6.58 7.92 6.46 +63 7 13 10.37 7.96 7.54 5.17 7.21 3.83 5.54 6.42 4.00 4.71 9.04 5.83 +63 7 14 19.46 17.21 19.33 13.50 15.63 12.62 14.71 19.29 12.33 15.83 23.42 18.50 +63 7 15 12.62 10.79 15.87 7.83 11.96 9.08 12.04 10.83 8.75 11.17 14.29 10.58 +63 7 16 8.87 9.17 7.21 6.04 10.29 7.00 10.29 9.50 8.75 8.67 12.50 13.29 +63 7 17 11.75 10.63 10.92 6.25 10.58 7.58 9.75 10.50 7.71 11.92 12.50 15.04 +63 7 18 8.00 6.46 9.04 4.83 7.21 4.88 6.50 8.50 5.21 5.79 12.96 9.08 +63 7 19 7.04 5.37 9.42 5.09 11.12 6.54 10.04 7.92 8.54 10.25 9.96 14.54 +63 7 20 3.96 5.54 5.75 3.88 8.71 4.58 6.50 6.17 4.17 6.13 9.75 9.33 +63 7 21 5.13 8.46 5.17 6.75 8.54 6.13 7.29 11.46 6.54 9.79 16.79 13.33 +63 7 22 3.88 2.08 3.67 1.67 4.08 1.50 6.50 3.46 3.88 4.83 4.75 5.88 +63 7 23 12.29 10.67 12.79 8.83 9.96 7.12 7.87 7.62 7.58 9.46 8.46 7.67 +63 7 24 14.79 11.83 10.63 8.67 13.88 9.33 11.17 12.21 9.62 11.79 12.87 15.83 +63 7 25 15.12 10.75 8.96 7.71 11.71 8.42 11.00 10.75 10.37 11.00 13.88 15.54 +63 7 26 4.75 6.13 4.00 3.00 7.29 2.92 6.50 5.91 4.58 5.63 11.58 7.62 +63 7 27 5.75 10.75 6.58 6.08 10.79 7.96 5.50 10.21 7.21 10.92 19.67 10.08 +63 7 28 5.75 8.38 2.83 4.12 9.96 5.00 5.91 7.83 5.66 7.67 12.38 7.58 +63 7 29 7.75 12.33 5.54 6.75 11.75 8.29 8.21 11.34 7.75 10.79 19.70 12.96 +63 7 30 5.09 12.83 3.08 5.91 9.17 6.83 4.33 9.87 6.54 7.54 16.88 9.79 +63 7 31 5.00 8.79 3.50 3.92 9.75 3.42 4.33 9.75 4.46 4.88 7.87 9.00 +63 8 1 10.21 7.83 13.70 5.37 11.67 5.00 5.41 11.25 5.66 5.37 13.37 3.67 +63 8 2 7.58 7.46 18.25 6.08 9.62 5.00 4.08 8.17 5.41 5.66 11.42 6.96 +63 8 3 5.75 3.29 8.96 3.29 6.38 2.29 2.62 1.79 2.21 3.04 3.79 2.25 +63 8 4 7.00 6.50 4.79 2.37 5.91 1.87 3.13 3.50 3.67 4.29 8.71 3.71 +63 8 5 18.08 12.21 11.08 8.54 11.12 8.58 10.50 9.75 9.92 12.12 10.34 17.75 +63 8 6 11.12 7.50 8.17 5.33 9.62 7.41 7.00 10.71 8.63 8.58 14.12 14.00 +63 8 7 13.13 9.71 11.92 8.00 14.46 11.38 12.58 13.67 12.25 12.25 16.08 19.79 +63 8 8 14.46 7.75 10.83 8.58 13.62 8.96 13.50 10.29 12.25 12.79 12.92 23.29 +63 8 9 9.71 10.41 8.96 5.54 10.67 6.21 7.96 5.96 7.41 7.29 10.29 11.21 +63 8 10 12.92 8.92 10.00 9.38 14.00 11.38 14.09 12.00 12.71 11.50 11.58 10.21 +63 8 11 14.00 10.79 7.29 7.54 13.25 8.87 11.25 10.58 11.34 9.25 12.12 12.50 +63 8 12 12.29 9.29 5.66 6.29 9.29 7.21 8.38 9.25 8.46 10.00 12.17 13.70 +63 8 13 7.96 7.08 6.67 3.67 7.12 4.12 6.58 6.63 5.91 8.96 8.83 10.75 +63 8 14 8.00 7.00 5.04 3.08 8.21 5.33 6.67 7.41 6.58 7.33 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3.63 2.33 4.88 2.46 5.37 4.92 4.83 10.92 +65 7 7 9.46 9.87 5.09 6.00 7.75 4.71 7.75 7.29 6.50 10.58 10.34 15.83 +65 7 8 11.58 7.54 9.17 6.92 9.79 5.83 9.42 6.08 7.83 9.75 9.50 13.21 +65 7 9 7.62 6.00 8.67 5.13 9.38 6.58 7.04 6.58 7.29 7.46 7.87 9.25 +65 7 10 12.38 11.63 10.41 5.66 10.79 7.21 8.04 10.13 7.08 7.08 17.37 12.38 +65 7 11 12.87 8.58 13.37 6.38 10.50 7.25 10.08 9.29 8.71 9.59 14.04 13.83 +65 7 12 7.41 6.63 9.87 2.83 6.46 1.96 5.71 5.83 4.83 5.00 7.54 11.25 +65 7 13 9.42 5.88 10.21 3.88 5.83 3.92 4.50 7.67 5.33 7.25 11.29 16.04 +65 7 14 2.96 8.17 5.79 5.21 7.92 5.09 5.96 7.71 5.46 9.25 13.96 14.96 +65 7 15 5.21 2.62 16.92 3.33 5.88 5.54 8.04 6.79 6.83 6.21 5.04 13.42 +65 7 16 5.29 6.79 6.96 2.33 5.13 1.29 5.41 4.12 4.21 6.21 8.17 12.46 +65 7 17 6.00 10.67 3.50 3.83 7.54 3.04 4.88 4.33 3.71 6.34 13.21 9.29 +65 7 18 13.59 10.13 12.42 8.29 11.17 7.25 10.46 8.87 9.59 11.38 12.54 16.42 +65 7 19 4.54 3.71 12.00 5.83 4.33 7.33 8.83 7.00 7.58 8.71 7.33 15.34 +65 7 20 7.38 5.71 6.83 4.50 7.87 2.62 4.79 4.00 3.37 5.21 5.09 6.87 +65 7 21 10.92 8.08 6.21 4.63 10.04 3.92 7.21 6.79 5.71 6.42 9.17 8.83 +65 7 22 5.33 2.79 4.67 2.00 2.79 0.83 4.79 2.37 2.17 2.75 6.00 8.29 +65 7 23 8.00 4.83 8.87 3.79 5.54 3.33 6.96 7.21 4.42 4.96 10.79 12.21 +65 7 24 7.54 7.21 7.38 5.00 8.58 5.29 10.96 6.63 7.21 7.75 9.71 9.67 +65 7 25 13.79 11.21 9.04 7.21 9.92 7.29 7.79 9.83 8.50 9.13 12.33 16.54 +65 7 26 10.58 7.50 8.67 6.75 9.96 6.63 10.67 7.50 7.29 9.62 10.13 15.92 +65 7 27 15.37 13.17 11.12 7.17 12.12 8.71 12.96 10.04 8.87 9.87 12.50 10.00 +65 7 28 16.21 14.17 14.12 8.50 17.08 9.46 14.09 13.42 11.34 12.79 16.00 16.88 +65 7 29 15.67 10.92 13.46 12.00 19.08 12.75 18.71 14.29 15.04 14.00 16.71 18.58 +65 7 30 8.92 8.92 10.37 4.88 6.46 3.75 5.33 7.29 5.75 7.21 10.29 15.16 +65 7 31 11.08 7.04 9.67 6.54 11.58 9.13 13.29 11.92 9.50 12.00 13.96 18.79 +65 8 1 11.87 10.67 10.67 5.09 10.54 5.66 9.92 7.00 5.79 7.00 9.59 11.96 +65 8 2 11.29 7.92 11.75 4.58 8.38 5.17 7.71 5.75 4.58 6.92 11.46 14.17 +65 8 3 7.46 5.58 5.83 3.13 3.33 1.50 5.63 1.92 2.17 4.08 9.87 7.41 +65 8 4 18.71 16.83 16.25 12.04 14.67 9.54 11.92 12.08 9.46 11.46 17.46 18.63 +65 8 5 13.67 10.79 15.25 8.00 17.96 14.92 17.54 17.00 15.71 17.96 21.34 24.83 +65 8 6 9.46 9.33 9.75 6.83 13.96 8.79 12.33 10.04 9.75 11.75 14.46 18.63 +65 8 7 7.62 4.08 6.71 4.29 9.38 4.79 8.63 6.38 7.96 9.75 8.54 14.83 +65 8 8 5.00 3.37 7.21 3.04 5.46 2.58 3.58 2.08 2.17 2.25 5.96 6.96 +65 8 9 10.75 9.71 8.12 5.58 11.21 7.12 6.54 7.96 4.71 8.63 8.50 12.87 +65 8 10 11.42 10.83 10.88 9.04 13.54 9.96 9.75 11.87 9.42 11.58 14.21 17.79 +65 8 11 10.83 15.00 11.63 10.54 13.29 9.08 11.08 11.08 9.79 12.38 16.92 17.88 +65 8 12 10.58 10.46 10.92 7.62 9.79 10.50 11.17 8.71 10.25 12.42 9.59 13.21 +65 8 13 5.58 3.83 5.83 4.54 2.46 4.88 6.50 3.21 5.54 8.38 3.29 14.46 +65 8 14 10.41 7.33 9.50 5.46 8.46 5.41 7.12 5.63 5.58 7.50 8.63 11.54 +65 8 15 7.41 5.04 6.08 3.75 7.08 3.83 7.21 4.54 4.83 6.38 7.75 8.17 +65 8 16 7.87 4.08 6.92 2.04 3.17 2.00 4.21 2.50 2.33 3.00 8.50 5.21 +65 8 17 7.75 11.17 8.83 4.58 9.21 6.46 7.87 10.34 5.50 10.17 17.41 13.46 +65 8 18 12.79 11.00 12.58 6.79 12.75 9.50 12.67 11.79 10.00 12.38 16.54 17.16 +65 8 19 8.87 8.25 7.54 4.08 11.04 7.67 10.96 9.04 7.62 9.29 16.17 17.12 +65 8 20 17.41 15.21 13.42 8.21 15.12 10.29 14.17 12.75 10.37 13.96 19.29 19.87 +65 8 21 20.58 15.75 12.67 9.25 18.75 10.21 14.88 12.38 11.12 12.79 17.00 19.79 +65 8 22 26.20 23.42 16.71 15.67 20.17 13.59 16.83 16.00 14.04 16.71 22.50 26.67 +65 8 23 10.71 6.58 9.92 5.50 8.96 6.13 11.87 5.66 6.87 9.87 10.37 13.42 +65 8 24 16.96 15.21 12.12 7.71 16.96 10.75 16.42 12.38 12.67 13.50 17.25 20.00 +65 8 25 12.67 11.63 9.25 8.12 16.13 11.63 15.71 12.54 12.00 11.00 16.38 22.50 +65 8 26 8.33 8.21 8.21 3.54 5.58 4.29 7.79 4.58 4.04 5.37 7.62 12.92 +65 8 27 8.54 9.67 12.67 6.29 10.29 7.21 8.58 7.92 6.08 8.42 14.71 14.33 +65 8 28 12.29 10.63 10.75 8.04 16.25 10.83 16.92 12.87 12.46 12.00 18.05 21.29 +65 8 29 11.92 8.00 9.46 8.42 13.70 10.92 15.25 14.33 10.75 11.75 15.29 22.17 +65 8 30 8.67 5.96 8.33 5.17 10.25 6.87 11.87 8.87 8.08 9.33 13.25 20.67 +65 8 31 14.96 10.58 11.46 8.29 10.46 8.79 9.87 7.67 8.04 10.17 10.54 16.54 +65 9 1 9.54 2.92 12.96 5.79 2.62 2.92 7.75 2.21 4.67 5.83 5.75 8.25 +65 9 2 3.37 1.38 7.92 2.04 3.92 0.71 3.29 2.33 0.87 1.17 7.25 4.42 +65 9 3 13.08 11.92 12.00 8.12 10.96 6.29 10.63 8.29 7.12 8.50 11.12 15.96 +65 9 4 21.00 18.54 14.09 11.87 17.29 10.37 13.54 14.25 11.50 14.83 22.17 28.84 +65 9 5 20.62 11.58 13.13 10.63 13.54 8.58 11.29 9.67 10.04 12.42 12.00 19.38 +65 9 6 5.75 5.04 2.79 1.25 5.33 0.92 3.96 2.62 1.00 3.21 6.67 8.63 +65 9 7 6.96 4.58 5.00 2.88 3.54 2.29 5.33 4.46 3.50 4.12 7.67 9.50 +65 9 8 12.25 9.71 9.38 5.83 10.17 5.37 7.29 7.33 5.96 8.58 13.62 20.83 +65 9 9 15.54 9.62 9.87 10.00 13.04 10.04 16.33 11.54 13.29 13.59 16.50 27.04 +65 9 10 18.00 12.38 10.21 10.17 16.13 10.04 14.21 12.92 12.38 13.46 17.25 22.95 +65 9 11 14.42 7.38 12.08 7.41 9.75 6.75 10.92 7.41 8.96 10.29 8.79 16.50 +65 9 12 6.63 7.50 8.75 3.42 7.50 4.29 3.42 4.88 3.50 3.96 8.17 6.29 +65 9 13 2.79 5.63 6.75 2.83 3.71 1.96 2.71 2.00 1.42 1.42 5.25 8.21 +65 9 14 10.04 13.17 12.00 6.83 11.21 7.58 4.25 9.59 7.29 11.42 13.00 14.67 +65 9 15 7.83 5.29 11.96 4.17 6.04 4.38 6.42 7.92 5.09 8.96 12.12 11.50 +65 9 16 9.54 8.12 9.83 4.04 11.58 6.58 9.54 8.71 7.41 11.21 14.96 18.05 +65 9 17 24.87 19.25 19.08 12.21 19.50 12.92 12.71 13.25 12.42 13.29 13.79 15.87 +65 9 18 14.25 9.17 10.75 8.50 12.04 9.13 13.04 8.67 9.92 11.75 11.54 18.21 +65 9 19 13.96 15.83 11.46 7.21 12.71 9.38 9.25 12.00 7.50 11.00 24.71 16.62 +65 9 20 13.37 14.12 12.79 7.75 9.96 8.71 9.54 12.96 6.58 14.29 21.71 19.75 +65 9 21 12.83 17.00 14.54 8.58 12.71 10.92 12.21 14.37 9.59 17.12 19.87 19.70 +65 9 22 10.13 7.92 12.12 4.67 8.79 6.54 8.25 7.50 5.91 8.46 13.50 9.04 +65 9 23 9.54 4.67 10.92 4.58 6.50 3.63 4.21 4.75 3.67 7.38 9.59 11.25 +65 9 24 6.17 4.33 9.38 4.67 6.29 3.63 5.96 4.33 3.46 5.79 7.67 11.38 +65 9 25 9.54 7.29 5.75 4.50 8.67 5.41 4.29 9.38 3.58 5.63 12.62 12.87 +65 9 26 8.83 10.34 3.58 2.58 8.12 4.17 3.42 5.63 2.83 4.50 12.17 8.71 +65 9 27 12.42 11.87 7.96 5.17 11.34 6.79 5.37 7.96 6.04 8.63 11.34 14.00 +65 9 28 10.83 10.88 5.66 2.71 9.71 3.54 2.50 6.87 2.83 6.17 9.92 11.08 +65 9 29 13.29 7.83 10.50 9.13 10.67 6.63 11.63 12.29 9.46 13.75 15.09 25.17 +65 9 30 16.79 12.38 22.04 11.17 10.96 7.75 15.63 9.21 7.75 10.00 13.96 13.46 +65 10 1 10.08 4.29 9.54 6.50 6.71 3.25 8.00 4.38 4.63 5.71 8.38 9.00 +65 10 2 9.59 9.13 8.67 3.92 7.92 5.91 4.67 5.09 5.41 7.92 8.21 11.04 +65 10 3 17.16 13.83 14.58 8.75 14.46 10.00 9.79 11.04 7.25 10.83 13.08 12.25 +65 10 4 7.25 4.63 8.12 5.17 5.88 3.58 6.79 4.58 3.46 7.17 6.79 10.63 +65 10 5 7.17 0.75 4.75 1.79 2.25 0.87 2.13 0.13 1.38 1.71 2.42 2.04 +65 10 6 5.96 4.96 2.54 2.21 3.67 1.83 0.42 2.17 1.42 1.87 2.79 6.25 +65 10 7 8.92 4.67 8.00 3.17 2.13 2.33 4.96 3.75 3.71 5.04 4.71 18.96 +65 10 8 16.50 8.96 14.62 6.04 7.62 8.00 10.71 9.96 11.17 9.33 11.04 14.92 +65 10 9 13.50 7.41 15.12 5.58 8.50 7.38 11.79 8.71 11.71 9.17 12.08 11.54 +65 10 10 13.96 7.83 14.67 4.96 6.75 5.63 9.04 7.17 8.04 7.00 9.92 8.71 +65 10 11 9.62 5.58 7.58 1.50 2.83 1.08 4.00 3.58 4.38 6.87 4.42 10.79 +65 10 12 7.79 5.91 5.58 2.29 4.88 1.83 3.00 2.29 1.71 2.21 4.92 8.42 +65 10 13 7.33 7.96 6.75 3.58 6.13 5.09 3.29 3.92 3.21 7.33 14.37 13.25 +65 10 14 10.58 10.29 6.13 2.88 8.38 5.29 6.75 6.46 5.54 6.54 12.92 14.50 +65 10 15 12.67 8.08 8.21 5.00 9.50 5.50 10.08 7.87 7.62 9.17 12.46 18.96 +65 10 16 4.54 6.92 5.66 1.46 4.25 3.71 5.29 6.96 2.46 7.17 15.12 13.54 +65 10 17 3.67 7.12 3.58 0.75 3.42 3.37 4.96 4.46 4.17 6.42 6.71 9.92 +65 10 18 8.38 5.46 9.29 2.83 3.83 1.46 5.33 2.83 4.42 4.88 5.04 7.04 +65 10 19 14.33 11.71 13.29 5.75 9.87 7.58 9.21 6.83 5.91 8.58 12.17 14.29 +65 10 20 16.88 15.41 15.96 8.12 15.25 12.38 10.46 9.25 5.58 7.67 14.79 16.75 +65 10 21 17.25 14.88 16.04 10.34 16.38 12.33 11.87 12.75 9.59 11.38 14.54 18.54 +65 10 22 10.25 12.25 12.04 7.87 12.33 8.83 8.96 10.00 8.87 9.29 12.50 15.75 +65 10 23 12.04 12.33 10.34 6.00 10.41 7.83 7.87 7.21 4.71 8.29 11.17 13.04 +65 10 24 14.54 15.25 10.08 5.41 13.13 9.87 6.83 9.29 5.25 7.58 11.63 12.08 +65 10 25 11.29 10.21 9.62 6.50 9.46 8.42 7.17 6.75 5.37 6.96 10.29 12.96 +65 10 26 10.21 11.42 11.34 6.38 10.08 8.42 6.08 9.79 7.33 12.58 18.84 17.33 +65 10 27 22.50 19.83 21.25 12.46 16.13 13.33 17.00 18.54 12.46 19.55 25.17 20.67 +65 10 28 18.05 19.21 13.96 12.33 19.21 13.62 20.00 16.92 15.83 18.21 27.37 33.12 +65 10 29 18.25 18.54 14.79 9.50 15.54 11.58 15.12 15.59 11.00 16.17 23.21 24.50 +65 10 30 19.29 16.71 17.08 13.17 19.79 13.59 21.62 14.04 15.09 16.17 23.33 28.25 +65 10 31 25.46 22.00 20.54 12.71 19.95 13.25 19.21 16.92 14.50 17.08 23.16 23.38 +65 11 1 23.42 22.17 18.91 17.00 27.58 20.30 29.17 22.67 24.46 27.29 30.96 37.59 +65 11 2 11.71 11.71 12.67 6.96 9.42 6.54 12.04 5.21 8.33 9.13 12.71 21.79 +65 11 3 15.54 14.67 14.25 8.08 8.87 7.41 11.42 7.33 9.13 10.83 17.79 24.54 +65 11 4 8.17 5.79 11.42 3.17 2.92 1.29 6.04 2.00 2.54 3.50 8.33 9.96 +65 11 5 7.67 6.92 8.83 4.54 6.42 2.67 4.83 0.71 2.37 2.04 4.50 6.92 +65 11 6 21.12 12.29 22.42 10.41 13.08 10.54 16.00 12.38 12.58 12.17 12.71 14.37 +65 11 7 18.63 15.34 13.59 8.67 15.96 10.54 11.58 14.37 11.38 13.00 16.71 25.96 +65 11 8 16.13 8.54 15.12 10.29 12.62 8.12 9.59 8.08 8.75 12.87 12.83 14.92 +65 11 9 13.79 8.08 10.88 8.04 7.92 5.75 9.38 4.63 7.17 9.17 5.79 11.75 +65 11 10 9.83 11.08 11.34 4.29 10.08 5.33 6.96 6.38 5.46 6.54 10.25 8.21 +65 11 11 17.50 15.34 17.41 8.71 14.92 9.38 14.83 12.12 9.46 11.58 11.00 19.67 +65 11 12 8.58 6.17 11.04 2.71 6.04 3.37 6.75 6.29 4.29 7.41 6.96 13.33 +65 11 13 15.29 7.58 13.33 4.29 10.71 6.00 8.50 8.87 6.08 9.04 11.71 16.38 +65 11 14 22.54 13.83 19.70 8.00 17.00 12.00 11.63 14.50 9.29 9.29 15.37 13.59 +65 11 15 23.54 22.00 17.92 7.71 17.41 9.62 11.34 11.67 8.83 7.87 14.42 12.46 +65 11 16 27.21 16.79 33.25 14.92 23.42 18.58 27.58 23.50 19.75 19.70 22.83 33.50 +65 11 17 13.54 12.42 18.84 10.54 12.50 11.17 25.33 17.92 15.92 18.75 20.91 41.25 +65 11 18 4.04 8.75 1.83 1.71 9.33 4.08 15.92 12.96 11.50 14.75 18.54 35.50 +65 11 19 10.00 10.08 9.87 6.25 11.17 8.00 9.75 11.00 8.71 11.63 11.96 25.88 +65 11 20 10.29 9.83 18.96 7.29 9.54 7.71 15.75 10.63 8.67 12.00 10.54 27.92 +65 11 21 13.92 9.62 19.75 9.08 9.79 6.13 10.75 8.54 6.87 10.67 9.13 18.25 +65 11 22 12.04 3.29 14.42 6.17 4.08 3.04 10.67 4.58 6.67 9.08 8.42 15.79 +65 11 23 14.83 13.50 13.04 10.25 14.46 11.46 20.00 11.92 13.25 16.75 18.96 29.38 +65 11 24 8.87 7.50 6.96 1.75 6.25 3.96 7.46 2.50 2.62 4.88 6.38 13.59 +65 11 25 20.00 14.00 17.79 10.17 12.08 8.58 13.75 9.42 8.17 12.00 14.12 18.25 +65 11 26 11.50 11.50 9.54 3.63 10.25 6.83 9.33 6.00 5.25 6.87 11.75 14.29 +65 11 27 19.87 18.25 13.79 11.08 13.54 9.25 16.33 10.41 10.50 12.12 15.09 24.71 +65 11 28 9.38 10.54 7.33 3.33 9.75 4.79 9.50 2.54 5.17 6.29 6.25 12.75 +65 11 29 28.42 24.08 28.46 19.08 18.66 15.00 20.33 14.75 14.42 21.29 23.87 32.05 +65 11 30 14.50 13.33 18.38 12.46 10.50 9.67 19.38 9.50 12.75 15.00 16.33 26.92 +65 12 1 10.29 10.46 10.08 3.83 10.50 8.21 9.96 5.29 6.25 7.12 10.54 12.46 +65 12 2 18.50 23.13 13.79 12.67 21.84 13.37 19.25 15.71 12.58 13.88 24.54 20.04 +65 12 3 24.87 23.33 15.37 13.21 19.25 11.50 15.96 12.38 13.42 11.42 17.12 14.25 +65 12 4 15.63 12.67 10.54 6.58 12.04 7.17 6.46 6.04 5.79 6.46 13.79 12.33 +65 12 5 14.96 17.37 10.63 10.37 17.37 10.00 15.63 11.83 10.58 13.08 20.08 22.25 +65 12 6 21.62 21.29 12.87 14.96 17.58 12.83 18.63 16.62 16.25 15.25 26.08 25.66 +65 12 7 14.71 9.67 11.54 8.63 7.25 6.75 11.00 7.62 7.54 8.96 13.92 17.21 +65 12 8 16.79 16.00 15.00 7.12 15.04 10.54 13.46 11.54 10.67 13.42 16.38 16.46 +65 12 9 26.46 23.21 21.00 17.37 27.29 18.75 24.00 21.75 20.38 20.21 26.38 24.17 +65 12 10 21.87 21.34 11.38 11.21 16.17 8.63 14.29 8.71 10.71 9.00 12.04 17.58 +65 12 11 10.96 10.75 7.92 6.08 10.63 7.08 11.54 6.25 9.42 11.00 11.04 15.29 +65 12 12 18.05 16.13 11.79 6.17 13.04 8.87 8.92 9.96 7.96 7.54 9.96 7.54 +65 12 13 10.79 10.75 10.29 6.75 12.17 8.79 14.29 11.12 9.83 11.46 15.96 15.00 +65 12 14 24.41 21.00 19.00 12.75 18.91 15.16 11.63 14.62 12.58 17.37 18.25 19.29 +65 12 15 14.21 11.50 15.21 7.96 9.62 8.71 8.54 10.17 8.54 13.62 20.08 14.58 +65 12 16 15.67 14.92 11.67 5.83 14.00 9.04 8.21 9.75 7.38 10.71 15.50 13.88 +65 12 17 26.30 20.75 23.09 11.67 16.42 11.34 17.92 14.50 11.29 16.96 18.21 19.58 +65 12 18 5.29 2.83 5.50 1.83 6.04 4.75 7.50 5.63 5.00 8.08 13.04 14.37 +65 12 19 4.96 4.12 6.71 3.63 7.12 4.79 11.71 4.50 6.96 9.92 14.29 21.34 +65 12 20 8.46 2.46 10.63 2.83 2.25 1.75 8.96 1.92 3.33 6.34 7.83 10.29 +65 12 21 8.92 9.13 7.79 1.29 8.63 5.46 9.42 5.88 5.00 7.33 10.67 13.04 +65 12 22 17.12 13.00 16.58 7.12 13.88 9.13 9.38 8.79 7.58 8.67 9.13 10.58 +65 12 23 14.88 15.92 9.59 7.87 12.87 8.08 11.04 7.04 7.29 8.04 11.08 9.21 +65 12 24 8.50 11.04 13.42 4.75 8.08 5.09 7.12 7.04 5.21 7.50 7.92 13.04 +65 12 25 11.00 5.91 11.96 5.79 6.87 3.58 7.38 3.33 5.17 7.25 8.63 15.37 +65 12 26 7.96 6.04 11.17 2.71 5.09 1.13 5.79 1.17 1.29 4.25 4.46 11.21 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10.17 11.25 12.75 17.58 18.63 +66 5 20 8.71 11.79 10.96 6.67 9.13 7.29 9.25 6.46 7.33 8.29 13.83 16.71 +66 5 21 18.08 19.38 18.16 12.04 14.50 12.62 15.96 13.75 10.71 12.67 20.75 12.00 +66 5 22 23.13 19.12 18.08 17.16 22.04 17.50 24.41 19.08 20.25 21.37 25.88 27.04 +66 5 23 14.50 13.33 11.29 9.13 12.75 11.29 15.04 10.79 12.71 13.21 15.87 20.12 +66 5 24 14.92 14.58 12.71 7.08 12.12 9.96 10.75 8.79 9.92 10.29 16.58 10.17 +66 5 25 17.00 15.37 13.37 10.92 18.25 13.00 14.96 13.67 13.33 12.71 18.96 18.00 +66 5 26 13.13 9.17 7.54 8.08 8.67 7.87 11.38 7.41 9.38 11.58 11.54 12.08 +66 5 27 11.67 5.09 10.67 4.58 4.83 4.63 8.17 5.25 7.25 6.71 7.00 12.00 +66 5 28 13.46 13.92 12.08 4.67 8.54 7.54 6.54 6.71 7.87 7.29 9.33 15.09 +66 5 29 15.12 14.46 12.42 5.46 9.38 8.33 5.66 7.67 7.25 8.46 9.08 8.92 +66 5 30 10.92 9.21 7.96 3.37 6.29 4.92 2.62 3.92 4.75 4.79 5.37 4.25 +66 5 31 5.33 3.50 4.12 1.38 2.46 1.58 0.83 2.54 1.79 1.92 5.29 2.54 +66 6 1 7.67 6.25 3.79 4.71 6.00 3.88 4.54 5.37 6.00 8.00 7.25 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10.04 12.71 12.92 15.37 +66 6 16 16.17 13.54 12.38 10.25 14.21 10.67 10.34 10.41 10.92 12.96 12.79 18.21 +66 6 17 12.79 10.71 11.58 7.29 6.83 7.04 8.63 8.54 7.38 10.34 16.00 14.12 +66 6 18 9.17 8.75 11.42 4.83 8.00 6.50 5.58 7.08 6.46 9.00 13.29 8.04 +66 6 19 8.63 8.04 8.87 5.04 6.50 5.66 6.34 3.88 4.75 7.41 8.42 7.17 +66 6 20 7.50 9.42 7.17 2.58 5.66 3.46 4.21 2.37 3.79 5.25 8.21 8.25 +66 6 21 6.50 6.38 9.33 2.46 4.63 2.46 4.71 2.00 4.29 3.25 9.08 4.50 +66 6 22 7.75 5.75 5.79 3.58 3.46 3.42 3.63 1.58 3.46 2.92 5.66 6.50 +66 6 23 6.71 6.83 10.17 5.09 9.00 5.88 8.92 4.38 6.83 6.54 7.54 8.12 +66 6 24 15.12 11.67 11.34 9.38 13.83 11.00 14.42 10.21 12.08 11.87 13.25 15.67 +66 6 25 10.83 9.67 9.87 5.37 11.50 8.50 9.71 7.87 9.42 9.59 13.75 12.38 +66 6 26 12.33 13.67 13.37 7.41 14.79 9.75 10.75 8.12 9.25 11.08 16.88 13.50 +66 6 27 20.50 14.25 15.21 10.63 16.92 11.87 17.00 11.25 13.59 15.67 16.66 21.62 +66 6 28 9.21 5.41 9.33 6.08 8.29 6.46 11.12 4.04 9.42 11.29 9.17 13.50 +66 6 29 7.75 4.00 7.41 2.58 6.21 2.67 4.46 2.21 4.58 3.25 11.29 6.96 +66 6 30 5.83 5.04 6.21 4.04 8.25 5.37 6.42 4.54 6.63 6.54 11.87 14.67 +66 7 1 9.42 5.91 7.41 3.54 7.12 4.17 3.71 5.29 5.33 6.87 13.33 15.87 +66 7 2 9.25 7.25 8.12 4.12 6.83 4.29 4.83 3.29 6.42 7.21 9.00 13.33 +66 7 3 8.21 3.71 6.00 3.54 3.17 0.58 2.88 1.00 2.04 2.83 8.63 6.25 +66 7 4 2.58 4.08 6.25 1.42 4.12 1.08 1.46 0.96 3.37 3.00 7.67 10.63 +66 7 5 14.83 11.21 4.42 5.00 11.75 5.00 1.63 4.25 5.91 5.75 10.96 11.92 +66 7 6 10.08 10.92 9.46 6.54 7.71 5.66 6.96 4.08 7.33 9.17 9.33 12.00 +66 7 7 8.33 4.21 8.21 4.75 8.33 5.13 6.79 4.29 7.21 6.83 8.38 12.92 +66 7 8 7.92 8.50 7.79 3.79 8.00 4.63 5.83 5.63 6.50 7.33 11.50 12.58 +66 7 9 6.71 6.92 6.96 5.09 10.21 6.08 7.67 6.29 7.21 7.12 14.50 15.75 +66 7 10 15.54 13.70 11.04 7.62 13.42 9.87 7.50 11.38 8.67 11.08 16.00 15.37 +66 7 11 11.42 7.50 12.04 6.54 8.79 5.83 7.58 6.58 7.92 6.54 12.04 13.29 +66 7 12 10.25 10.17 10.34 8.87 16.04 10.29 13.79 10.63 12.29 10.83 17.21 20.58 +66 7 13 18.91 15.00 12.21 10.88 17.83 11.63 14.83 12.58 13.75 14.58 18.25 21.34 +66 7 14 14.25 9.46 10.54 8.46 12.62 8.54 10.08 8.08 10.13 9.92 13.79 15.54 +66 7 15 18.75 13.29 11.34 9.54 15.79 10.46 10.25 10.92 10.37 10.96 15.37 12.83 +66 7 16 18.08 12.83 8.96 8.83 13.17 9.04 6.17 8.08 8.33 7.75 12.42 10.83 +66 7 17 12.71 10.08 9.04 6.29 7.50 4.75 4.75 6.38 6.29 7.38 9.62 10.67 +66 7 18 6.29 4.21 7.29 2.21 5.04 1.00 1.38 1.67 2.08 1.83 5.33 2.96 +66 7 19 10.17 7.33 13.88 5.54 9.54 3.83 5.46 5.75 5.50 5.88 6.92 5.63 +66 7 20 13.50 9.46 23.00 9.71 11.83 6.58 9.38 9.13 8.67 8.58 13.59 12.29 +66 7 21 10.96 8.17 17.33 8.08 8.38 6.38 7.17 8.75 7.41 8.96 13.25 9.00 +66 7 22 12.17 6.54 7.21 7.04 10.92 6.42 7.79 7.71 7.92 7.75 11.38 17.16 +66 7 23 9.50 9.79 10.37 7.96 16.42 9.59 13.21 8.54 12.21 10.46 14.71 18.71 +66 7 24 22.34 14.12 14.25 10.46 18.66 10.04 12.46 9.79 11.50 11.92 14.83 16.46 +66 7 25 12.17 10.50 9.83 6.96 13.29 7.62 9.42 8.46 9.46 8.33 14.21 12.00 +66 7 26 16.92 14.50 11.17 8.79 15.41 7.83 10.04 12.00 10.08 11.00 16.25 18.29 +66 7 27 9.29 6.96 8.63 4.88 7.50 4.17 5.46 6.34 6.42 6.00 10.54 14.04 +66 7 28 14.00 10.79 10.13 7.79 16.04 10.25 11.21 12.75 10.92 11.71 14.33 17.58 +66 7 29 17.92 12.29 9.13 9.46 15.67 10.71 10.71 11.46 13.25 10.88 15.67 16.71 +66 7 30 18.66 12.04 9.96 11.38 16.66 10.37 13.17 9.83 11.96 11.79 12.79 21.75 +66 7 31 8.54 7.25 5.88 3.13 6.25 1.75 3.33 1.42 3.63 2.21 4.33 6.87 +66 8 1 6.54 7.29 8.33 3.83 4.79 2.17 5.41 1.67 4.88 5.96 5.09 10.37 +66 8 2 15.46 11.50 13.29 6.04 10.79 6.00 5.41 6.42 5.66 4.96 10.25 10.08 +66 8 3 10.00 8.42 9.46 6.46 8.38 4.29 7.08 7.04 7.25 7.67 7.96 9.46 +66 8 4 14.46 7.50 9.04 7.96 11.79 7.12 10.63 9.21 10.63 9.04 11.54 14.17 +66 8 5 8.50 6.50 9.13 5.96 9.46 6.92 9.50 8.33 8.00 7.29 9.54 12.12 +66 8 6 6.13 6.00 6.96 1.71 5.63 1.79 3.17 1.83 2.17 2.71 5.09 5.91 +66 8 7 10.29 6.50 6.54 5.71 7.96 3.92 5.83 4.17 5.71 2.54 9.33 4.17 +66 8 8 9.96 10.71 9.54 3.88 12.29 6.83 8.25 8.58 8.29 7.54 13.21 10.34 +66 8 9 20.08 17.46 16.38 9.79 18.79 12.92 12.79 14.88 12.29 13.13 18.12 20.00 +66 8 10 14.42 10.25 15.12 7.92 15.29 9.33 17.46 11.42 13.46 13.21 17.67 27.54 +66 8 11 13.21 9.75 12.25 7.41 10.13 6.34 8.58 4.79 5.46 3.54 7.75 9.46 +66 8 12 6.46 3.54 4.42 3.46 6.75 2.42 6.54 3.75 3.83 2.67 6.63 7.08 +66 8 13 12.87 5.33 9.96 6.79 8.58 3.04 9.67 5.29 6.21 5.09 11.42 10.83 +66 8 14 12.25 8.17 6.04 2.96 10.92 2.46 2.88 5.66 3.29 1.92 8.63 8.79 +66 8 15 6.75 6.29 7.50 2.79 4.21 1.87 4.17 3.50 2.96 2.08 11.21 5.09 +66 8 16 9.04 14.96 12.42 5.25 9.92 6.96 8.87 11.83 7.25 9.29 21.46 19.50 +66 8 17 8.38 9.92 10.79 4.71 6.34 4.29 7.00 3.67 5.21 4.00 9.08 7.83 +66 8 18 4.75 4.08 3.58 2.42 3.21 0.37 3.08 1.58 0.96 1.29 9.96 8.29 +66 8 19 4.08 7.41 5.50 4.04 4.54 3.29 4.63 4.33 5.00 6.08 8.00 9.25 +66 8 20 7.29 8.00 8.04 3.21 8.25 3.88 3.46 5.88 4.38 6.46 11.83 10.83 +66 8 21 9.87 10.08 17.37 6.58 9.25 5.91 8.46 8.79 6.46 8.63 14.58 11.21 +66 8 22 9.83 6.46 18.41 4.92 6.96 4.38 6.58 3.79 5.75 6.71 10.67 7.29 +66 8 23 4.96 4.25 8.71 1.87 4.75 0.71 3.21 2.00 1.67 3.67 4.08 5.21 +66 8 24 4.00 10.46 3.58 2.58 7.67 3.13 5.09 1.79 2.08 4.38 8.54 9.38 +66 8 25 10.58 12.96 7.33 4.12 10.21 3.79 6.38 7.29 7.21 8.00 12.92 12.21 +66 8 26 13.29 12.17 11.00 5.96 14.00 6.54 9.17 10.17 8.83 10.17 12.75 17.46 +66 8 27 16.13 15.09 11.12 7.33 15.34 9.00 8.17 11.50 8.75 9.33 13.67 18.00 +66 8 28 15.46 12.12 11.08 6.79 13.96 8.96 8.96 9.62 9.46 10.34 12.00 17.92 +66 8 29 11.42 7.83 11.71 8.29 11.96 9.04 10.25 12.25 9.38 10.13 14.04 18.66 +66 8 30 16.92 12.46 11.25 7.75 11.83 5.50 6.58 8.33 5.79 7.00 12.25 10.04 +66 8 31 14.33 8.25 8.63 7.54 9.67 5.25 6.13 5.29 6.04 6.00 8.79 10.25 +66 9 1 17.41 16.54 15.04 7.83 11.83 7.62 7.62 10.96 6.67 7.00 13.54 11.12 +66 9 2 14.71 11.83 12.96 10.88 16.66 9.96 12.50 10.08 10.88 8.54 11.75 12.29 +66 9 3 19.00 18.84 15.12 10.46 15.96 11.38 14.37 12.21 10.25 14.21 19.41 18.91 +66 9 4 16.46 13.42 15.79 10.29 17.00 11.08 14.67 12.21 11.87 13.50 18.66 24.08 +66 9 5 17.04 16.66 16.38 9.75 16.54 11.63 15.67 15.46 12.83 15.50 24.17 24.83 +66 9 6 10.04 8.96 9.71 7.71 15.75 10.34 16.38 11.63 14.00 13.17 20.25 30.34 +66 9 7 5.63 4.42 2.29 1.96 6.42 3.71 6.75 4.17 4.92 6.75 9.59 16.96 +66 9 8 6.17 6.71 6.75 3.17 4.83 1.29 3.83 0.92 2.54 4.08 7.62 9.42 +66 9 9 10.96 12.62 10.04 6.63 12.25 8.58 7.96 10.71 7.58 10.29 17.83 14.42 +66 9 10 16.88 12.04 14.88 7.04 11.17 8.25 11.96 9.79 7.79 12.21 15.41 11.54 +66 9 11 10.34 5.41 12.58 5.04 6.00 3.54 3.96 6.21 3.54 4.17 15.16 11.63 +66 9 12 15.63 17.04 14.62 8.96 18.29 11.00 13.04 11.34 11.83 12.17 17.79 16.96 +66 9 13 16.50 13.13 9.83 9.00 16.46 10.50 15.41 11.71 11.75 11.25 16.42 19.12 +66 9 14 18.16 16.58 17.50 10.04 20.62 12.33 18.21 16.08 14.04 15.96 22.92 26.92 +66 9 15 17.96 14.96 13.13 11.17 13.88 9.75 16.29 11.67 11.17 15.04 16.25 24.96 +66 9 16 7.75 7.08 6.38 2.62 5.83 4.46 6.13 8.58 5.00 8.38 15.83 16.42 +66 9 17 6.13 7.38 7.08 3.33 8.04 4.92 2.04 5.71 3.71 6.46 10.83 10.34 +66 9 18 8.63 6.21 8.42 1.79 4.67 1.50 1.71 0.63 1.29 1.75 4.92 4.58 +66 9 19 8.00 5.25 5.09 1.63 5.04 1.83 1.21 1.67 0.75 2.25 6.38 8.58 +66 9 20 5.09 3.00 4.00 0.46 3.46 0.58 1.71 0.37 0.79 2.42 6.25 9.04 +66 9 21 5.71 1.83 11.17 0.75 3.37 1.21 1.83 1.17 2.08 3.08 5.25 6.25 +66 9 22 13.00 4.96 13.00 1.50 4.54 1.92 2.71 5.54 6.04 4.38 9.62 4.83 +66 9 23 8.54 9.92 5.91 1.42 7.50 2.33 3.63 3.71 4.50 5.25 7.79 6.34 +66 9 24 6.50 9.54 4.79 2.54 8.12 1.92 2.33 2.92 2.50 1.92 4.50 7.08 +66 9 25 10.96 9.83 5.71 1.83 7.87 2.96 3.88 4.71 4.29 5.13 6.67 7.83 +66 9 26 10.00 9.04 8.04 1.75 8.79 3.17 1.87 5.04 4.00 3.17 6.29 6.46 +66 9 27 12.04 8.38 7.96 2.58 8.75 2.37 4.79 3.75 3.42 4.38 6.17 7.67 +66 9 28 7.83 1.38 8.12 1.17 7.62 2.83 3.88 0.67 1.13 2.92 5.54 7.04 +66 9 29 3.37 3.29 4.38 0.96 5.91 2.25 0.42 1.87 1.50 3.67 7.87 7.50 +66 9 30 5.13 4.92 2.25 2.17 6.08 2.04 2.33 3.00 2.08 5.50 8.83 9.38 +66 10 1 8.00 3.63 9.17 4.04 7.29 2.33 4.25 0.42 2.17 2.00 5.04 6.71 +66 10 2 6.75 6.83 6.13 2.25 7.46 1.75 4.33 2.21 1.54 3.83 6.67 7.62 +66 10 3 16.96 24.17 12.33 12.46 23.38 13.13 21.29 15.79 14.62 17.41 25.25 26.34 +66 10 4 20.17 17.33 29.54 12.87 14.37 11.00 17.79 11.79 11.38 13.67 18.50 22.58 +66 10 5 11.79 9.25 11.12 4.12 9.33 4.83 7.58 7.58 6.04 10.00 12.96 16.25 +66 10 6 13.33 9.13 13.96 6.67 9.04 6.38 9.71 5.25 6.83 8.25 10.00 11.29 +66 10 7 8.92 7.04 7.87 1.79 7.50 3.04 2.00 2.21 2.71 2.75 6.58 7.83 +66 10 8 9.00 5.66 7.62 3.83 6.17 2.13 4.42 2.04 3.29 3.58 6.67 9.04 +66 10 9 11.25 10.46 8.50 3.71 10.71 5.83 3.79 5.71 4.21 6.42 12.08 11.71 +66 10 10 12.87 8.96 10.04 3.96 9.13 4.25 2.83 3.83 3.71 6.04 13.08 11.92 +66 10 11 7.71 10.79 8.83 4.25 10.54 6.21 7.12 7.08 5.50 7.87 14.12 15.12 +66 10 12 22.54 18.91 15.59 11.63 18.96 12.42 13.17 13.17 11.58 13.92 17.46 22.75 +66 10 13 13.67 9.50 9.54 5.04 9.75 5.50 4.75 6.75 5.09 5.00 10.25 12.92 +66 10 14 12.54 8.29 12.17 4.67 8.46 4.50 2.83 3.08 4.00 3.04 5.29 4.08 +66 10 15 11.79 6.38 15.04 7.17 8.04 2.58 5.79 5.17 4.96 6.21 8.79 12.83 +66 10 16 11.67 9.50 7.87 3.67 11.08 5.75 5.63 5.46 4.42 5.50 8.75 10.25 +66 10 17 17.67 12.29 16.42 7.08 14.92 9.33 11.96 10.96 8.29 9.96 15.00 14.37 +66 10 18 11.83 7.92 13.37 6.17 7.96 3.71 10.13 3.88 7.25 10.83 9.04 25.04 +66 10 19 13.79 9.83 8.75 8.00 8.38 6.17 5.88 5.21 5.50 8.17 9.42 15.92 +66 10 20 5.41 6.63 4.21 1.25 6.42 2.75 8.50 3.33 3.88 5.79 7.79 12.96 +66 10 21 8.75 5.88 6.50 3.79 7.75 5.58 9.67 5.33 7.29 7.29 12.96 19.38 +66 10 22 4.75 3.00 3.96 0.71 4.88 2.62 5.91 0.67 1.96 6.04 8.12 13.79 +66 10 23 9.04 6.58 7.58 3.58 5.79 2.79 8.12 2.83 5.58 7.79 10.25 19.79 +66 10 24 12.67 11.50 18.63 7.04 6.04 1.71 7.62 4.75 4.38 6.96 13.37 16.25 +66 10 25 12.29 6.87 14.33 5.83 7.08 1.96 6.00 2.46 3.92 3.75 8.83 13.75 +66 10 26 14.54 13.59 11.21 6.29 9.59 5.54 11.08 7.71 9.46 10.13 18.54 26.12 +66 10 27 19.75 17.16 22.54 12.62 12.87 9.29 14.79 9.67 9.79 14.21 19.62 25.96 +66 10 28 12.38 9.21 18.54 8.17 6.58 2.33 7.75 2.58 4.29 5.09 6.71 13.08 +66 10 29 9.08 2.96 10.46 4.75 5.63 1.08 5.88 1.08 4.00 5.46 3.83 10.34 +66 10 30 6.71 3.79 7.46 1.67 4.50 1.00 5.46 1.50 2.62 3.79 8.42 13.25 +66 10 31 9.71 8.38 8.38 4.42 9.67 6.50 9.92 7.79 7.38 8.63 15.41 23.21 +66 11 1 22.71 21.25 25.75 15.34 18.00 11.92 18.58 15.34 12.75 16.13 27.04 33.12 +66 11 2 16.38 13.75 27.37 10.96 11.38 7.38 13.79 7.79 6.63 8.79 10.37 13.42 +66 11 3 10.67 4.83 7.25 2.42 6.63 3.17 5.17 5.46 3.58 5.58 7.96 16.13 +66 11 4 7.75 11.17 10.75 1.96 9.17 5.04 5.63 6.04 4.75 8.54 8.33 21.50 +66 11 5 13.08 17.67 18.12 9.67 14.88 10.79 17.67 16.29 11.54 14.33 25.08 33.79 +66 11 6 21.09 19.41 26.83 14.00 13.42 13.46 19.87 14.67 10.79 15.75 25.25 30.84 +66 11 7 13.70 10.00 10.37 5.79 7.71 4.96 6.17 5.71 4.46 8.25 11.75 12.12 +66 11 8 7.83 4.21 5.91 2.50 6.50 3.29 7.38 2.79 4.29 5.58 10.58 14.79 +66 11 9 9.00 7.67 6.96 3.17 7.79 4.33 8.71 3.71 7.83 7.38 12.04 21.42 +66 11 10 4.29 5.09 4.42 0.63 5.54 3.54 6.75 1.54 3.33 4.71 6.83 13.59 +66 11 11 13.50 11.46 10.75 3.75 9.79 7.62 7.96 11.17 6.63 10.96 18.91 19.95 +66 11 12 14.58 8.21 12.12 5.17 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6.00 4.25 4.33 5.13 10.79 +68 5 23 11.92 14.46 9.08 5.21 14.42 6.71 5.04 8.87 4.92 7.41 11.58 15.46 +68 5 24 15.16 13.37 15.67 8.71 17.04 12.92 10.08 13.83 10.21 12.46 16.29 19.62 +68 5 25 6.58 4.33 7.41 3.58 5.91 1.08 3.46 5.04 1.92 4.12 6.79 17.37 +68 5 26 8.63 7.50 7.83 4.38 5.83 2.79 3.67 3.00 1.50 2.75 8.25 15.29 +68 5 27 5.33 3.63 7.96 2.54 5.46 1.38 3.29 4.04 1.21 1.67 4.63 6.58 +68 5 28 5.17 5.96 4.50 3.42 5.83 1.63 3.00 4.04 0.96 2.21 11.21 2.67 +68 5 29 4.38 9.83 5.13 3.17 8.54 1.75 4.25 3.83 1.13 3.00 10.58 5.04 +68 5 30 8.50 14.79 5.96 7.71 13.79 8.42 4.63 10.17 6.67 10.13 16.88 15.12 +68 5 31 8.54 10.88 5.33 8.50 7.50 7.79 3.04 9.04 6.92 9.71 18.16 13.13 +68 6 1 8.33 10.17 4.33 5.75 10.04 4.46 4.96 8.46 3.88 5.41 13.67 11.50 +68 6 2 15.54 7.79 7.29 6.46 9.62 3.75 4.54 6.29 3.71 4.29 4.58 4.08 +68 6 3 10.96 9.83 8.08 5.04 8.46 3.58 3.29 6.29 3.83 3.13 14.46 5.00 +68 6 4 9.96 10.25 7.71 5.88 12.67 6.54 7.83 9.33 7.33 7.08 17.04 15.67 +68 6 5 6.58 8.92 6.21 4.38 11.00 6.13 6.13 7.50 6.87 6.63 17.67 17.62 +68 6 6 10.00 10.79 8.00 7.50 13.29 7.92 11.08 10.00 9.50 7.25 16.17 19.00 +68 6 7 6.38 10.29 8.17 5.96 8.71 4.46 6.50 4.63 3.50 2.46 9.50 11.29 +68 6 8 4.71 6.63 4.17 3.67 8.67 4.04 3.54 6.08 3.13 4.38 9.83 6.00 +68 6 9 3.92 3.17 3.33 1.38 5.91 0.75 2.96 3.29 0.67 0.87 7.12 6.50 +68 6 10 3.79 6.38 1.87 3.37 5.50 3.08 1.87 3.46 2.33 2.04 3.08 7.71 +68 6 11 4.54 6.63 5.04 3.71 3.08 1.92 4.67 2.71 4.00 5.00 4.58 5.88 +68 6 12 3.88 3.63 3.17 2.42 5.58 1.96 3.67 3.58 1.96 3.50 7.87 7.33 +68 6 13 4.25 2.17 9.75 2.58 5.96 2.50 3.33 2.88 3.25 2.37 5.88 3.63 +68 6 14 5.79 4.83 16.88 6.50 7.04 2.04 5.33 6.63 2.46 2.13 11.58 8.25 +68 6 15 6.34 2.37 11.12 5.91 7.00 2.00 3.42 3.63 2.54 2.13 12.08 7.00 +68 6 16 4.92 6.96 5.33 3.79 5.33 2.96 3.13 5.00 1.75 3.54 10.71 7.79 +68 6 17 6.75 8.54 8.38 3.29 6.34 2.17 5.13 7.54 1.83 4.50 16.29 4.83 +68 6 18 6.25 6.17 8.12 4.29 10.75 3.79 5.96 5.46 4.46 3.75 12.12 11.46 +68 6 19 9.75 9.13 8.33 5.04 12.38 4.38 5.41 6.87 4.46 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4.71 8.96 7.92 7.79 4.17 11.63 9.46 +68 9 25 14.04 11.96 12.46 5.71 11.87 7.79 8.00 9.08 7.58 8.12 12.92 13.88 +68 9 26 10.00 11.21 9.38 6.17 11.54 7.21 9.67 8.42 7.71 7.08 13.75 15.25 +68 9 27 25.41 23.45 20.54 14.00 21.21 14.54 16.92 17.12 14.58 17.50 27.58 24.50 +68 9 28 18.05 19.38 17.58 11.50 18.84 10.92 17.33 14.58 15.41 13.37 21.29 21.46 +68 9 29 15.46 14.96 11.67 9.67 16.96 9.29 13.54 12.50 11.00 11.08 17.58 18.63 +68 9 30 14.54 14.83 12.42 9.75 19.55 10.46 12.83 11.46 12.38 10.41 15.75 20.41 +68 10 1 14.25 16.79 16.00 8.38 17.79 10.67 17.33 16.21 15.12 14.12 23.83 20.46 +68 10 2 14.83 14.75 15.75 7.25 10.79 7.62 14.00 10.71 10.79 9.00 13.29 15.63 +68 10 3 10.71 11.34 12.83 5.04 7.08 3.50 5.46 9.46 5.71 6.34 10.21 5.21 +68 10 4 13.33 15.34 9.79 7.87 12.62 6.79 4.79 9.87 8.17 8.38 11.08 13.29 +68 10 5 6.25 8.96 8.38 3.83 6.67 3.42 2.04 4.75 4.83 3.75 9.79 7.67 +68 10 6 2.37 5.46 4.54 0.92 2.08 0.33 4.46 0.33 0.92 1.87 3.13 7.87 +68 10 7 4.00 3.46 2.37 0.71 3.96 0.63 1.75 1.54 0.71 1.33 4.08 10.34 +68 10 8 7.92 5.83 12.46 2.67 4.00 1.13 3.21 5.50 2.08 2.54 9.33 7.17 +68 10 9 17.29 15.09 15.00 8.67 12.50 7.87 8.17 11.08 9.38 12.04 17.37 19.12 +68 10 10 18.46 15.96 16.42 9.79 14.75 9.42 12.58 11.83 10.88 11.04 19.92 19.04 +68 10 11 24.83 22.83 21.17 14.00 21.42 13.67 14.42 20.91 15.21 19.17 33.21 27.58 +68 10 12 24.92 22.67 20.17 14.62 27.04 17.50 20.41 20.96 19.55 20.12 34.54 34.46 +68 10 13 15.16 11.79 12.08 7.92 16.29 9.17 16.46 11.54 11.87 10.75 19.87 27.67 +68 10 14 5.50 8.00 5.54 3.96 9.96 5.25 9.71 6.71 6.63 5.25 12.58 17.08 +68 10 15 13.70 15.09 10.54 6.13 15.37 7.21 11.08 11.42 10.34 10.21 21.75 23.16 +68 10 16 11.67 9.46 8.75 6.50 12.92 7.12 12.08 12.46 10.71 9.96 17.62 27.16 +68 10 17 9.62 11.08 7.46 3.67 9.92 5.54 7.62 6.96 6.38 3.13 8.96 12.79 +68 10 18 15.63 17.50 16.08 9.54 17.88 12.83 15.59 15.87 11.96 14.54 21.50 26.96 +68 10 19 19.41 16.54 20.46 14.17 16.46 10.46 13.88 11.75 14.04 15.37 21.09 21.17 +68 10 20 9.87 11.04 9.46 4.75 9.13 6.87 7.50 12.04 7.46 10.96 23.16 18.84 +68 10 21 6.25 5.83 2.17 2.00 6.08 3.00 3.04 5.00 2.83 2.79 6.08 9.13 +68 10 22 5.66 10.04 3.58 3.58 8.67 2.79 1.21 4.75 3.25 2.54 8.92 9.46 +68 10 23 14.37 19.50 9.92 7.67 14.67 9.79 8.96 9.17 7.54 4.88 13.17 11.00 +68 10 24 11.71 7.96 13.46 4.38 7.83 3.71 6.04 7.41 6.25 3.83 10.25 9.67 +68 10 25 5.04 1.58 5.29 1.04 4.50 0.37 1.13 2.21 2.00 1.71 5.83 7.71 +68 10 26 12.21 10.21 8.58 6.17 9.29 5.54 3.54 7.12 5.63 5.29 9.00 12.46 +68 10 27 11.83 8.87 11.87 5.88 10.54 5.37 4.63 6.46 6.67 5.63 8.92 12.17 +68 10 28 18.29 12.87 15.00 7.96 13.37 8.79 9.29 11.87 11.34 14.04 16.38 16.54 +68 10 29 16.33 13.62 12.58 6.96 12.21 6.79 9.04 10.00 9.38 10.13 16.25 16.13 +68 10 30 15.63 9.96 11.96 9.00 11.42 7.96 6.42 7.08 9.75 11.21 8.38 15.34 +68 10 31 11.12 5.00 8.63 4.42 5.63 2.08 3.25 10.00 3.79 7.58 21.54 25.75 +68 11 1 13.70 16.50 11.29 5.50 13.33 5.29 8.92 23.58 11.46 21.75 35.30 37.59 +68 11 2 17.75 21.21 26.34 12.96 19.25 12.79 20.33 16.33 14.58 16.13 24.30 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21.12 17.62 18.66 11.25 14.88 9.21 16.17 18.75 16.29 15.71 18.05 30.71 +69 3 17 21.46 15.37 18.58 12.00 14.29 11.54 15.00 18.46 17.79 13.96 17.58 31.58 +69 3 18 9.38 4.50 15.12 11.12 11.67 10.88 14.67 20.00 16.33 14.62 17.00 37.99 +69 3 19 2.92 7.83 5.79 4.42 6.79 3.00 7.33 8.00 7.00 6.50 10.34 19.95 +69 3 20 6.87 11.04 7.71 4.96 8.96 5.50 4.08 6.38 5.58 7.79 10.54 18.84 +69 3 21 16.00 16.38 12.71 7.62 14.92 9.96 9.50 12.50 8.42 10.50 12.75 18.16 +69 3 22 16.25 14.88 17.79 9.00 14.00 9.25 14.92 12.00 12.71 12.08 14.54 19.70 +69 3 23 7.62 5.54 12.83 4.92 6.13 2.62 9.75 4.96 7.38 4.00 7.41 8.33 +69 3 24 3.58 2.83 8.38 5.04 4.58 1.75 7.04 2.75 3.21 4.79 4.92 12.83 +69 3 25 7.41 4.38 15.29 5.46 5.63 1.75 6.50 3.83 3.33 2.75 7.12 4.96 +69 3 26 6.21 3.00 14.46 4.88 4.88 2.33 5.37 2.67 4.08 2.92 4.29 7.83 +69 3 27 8.17 4.63 13.25 4.25 4.12 1.04 5.33 1.96 3.21 2.04 6.17 6.63 +69 3 28 6.34 4.88 5.50 2.67 7.79 2.42 10.54 6.13 6.79 6.79 10.25 13.59 +69 3 29 11.08 10.58 7.67 7.25 14.75 5.96 13.17 9.79 12.08 9.13 13.59 19.25 +69 3 30 12.87 13.50 10.54 9.92 15.83 8.54 14.54 11.83 13.54 11.63 12.38 14.92 +69 3 31 22.34 16.46 16.08 11.83 22.17 9.75 18.50 9.42 14.54 12.67 11.83 16.62 +69 4 1 12.79 9.79 11.34 7.08 9.08 4.33 9.42 7.46 8.04 10.75 8.67 16.54 +69 4 2 5.75 3.46 6.42 3.42 4.50 0.29 5.46 3.29 2.62 2.21 8.67 10.21 +69 4 3 4.17 3.21 3.04 1.63 4.33 0.29 4.96 3.46 1.17 3.17 12.29 11.00 +69 4 4 5.75 2.88 12.29 3.21 4.63 1.17 3.42 1.50 1.58 1.54 5.63 4.63 +69 4 5 11.21 3.79 16.92 5.91 7.29 4.88 7.87 5.33 7.83 5.25 7.50 7.83 +69 4 6 18.66 5.37 18.38 6.21 9.42 7.41 13.75 10.67 13.62 8.92 11.29 14.29 +69 4 7 12.62 1.63 8.17 3.71 6.79 2.58 6.38 5.33 8.17 6.54 6.71 13.08 +69 4 8 7.46 11.83 6.38 6.96 11.63 7.46 5.04 10.17 9.13 10.83 22.08 18.54 +69 4 9 16.29 9.25 16.13 9.71 8.33 5.96 14.37 7.83 8.58 9.59 11.12 13.75 +69 4 10 6.50 5.83 8.38 4.50 6.75 4.08 9.59 7.75 8.87 7.71 10.34 13.00 +69 4 11 22.88 20.62 18.12 12.12 22.88 12.25 17.62 15.12 17.83 11.71 22.50 21.50 +69 4 12 24.75 20.75 16.50 15.00 22.13 12.08 19.04 14.83 17.37 14.29 20.33 27.67 +69 4 13 11.63 6.46 7.96 6.54 11.08 5.00 9.96 7.92 10.04 8.54 9.62 10.17 +69 4 14 13.62 11.17 9.04 11.25 19.79 10.17 14.50 14.88 15.12 11.46 15.37 17.41 +69 4 15 18.16 12.38 11.54 11.83 17.00 9.75 17.16 11.75 16.50 12.08 14.04 17.96 +69 4 16 7.41 6.83 5.83 4.88 6.71 3.13 9.87 3.79 9.13 8.96 7.25 13.83 +69 4 17 8.04 5.83 9.75 3.08 6.34 2.25 3.71 3.37 4.50 2.88 5.50 9.38 +69 4 18 7.83 10.46 11.21 5.63 9.38 4.67 8.67 5.33 7.12 6.29 6.46 13.88 +69 4 19 16.33 14.21 8.71 5.88 14.33 6.25 6.92 8.75 8.54 10.50 11.63 15.92 +69 4 20 15.87 15.59 11.38 6.58 14.29 7.17 7.41 9.79 8.29 8.38 11.92 19.00 +69 4 21 13.33 12.04 15.34 7.71 13.00 7.71 12.83 13.13 10.08 13.17 19.62 32.05 +69 4 22 21.00 13.79 14.75 12.42 17.54 10.13 12.71 12.21 15.29 13.08 17.04 17.25 +69 4 23 10.04 8.21 11.50 7.54 8.96 5.04 8.67 7.83 9.00 8.75 12.71 10.37 +69 4 24 20.08 16.71 16.50 9.50 14.96 7.96 9.25 8.96 8.50 5.75 10.25 6.83 +69 4 25 14.62 13.29 15.75 7.58 15.96 8.12 14.29 11.54 13.62 13.29 15.21 11.42 +69 4 26 9.79 12.92 7.17 4.75 11.71 3.54 4.17 5.88 6.71 6.13 10.83 10.25 +69 4 27 13.83 13.83 9.42 8.12 15.50 6.38 10.25 10.34 13.17 10.25 15.92 15.16 +69 4 28 16.75 13.62 8.00 7.58 13.70 6.25 6.17 8.25 7.75 7.75 12.50 7.38 +69 4 29 6.00 4.25 4.63 3.96 5.63 0.71 4.71 4.46 4.83 6.71 9.04 13.75 +69 4 30 4.58 3.08 4.29 3.83 5.50 0.33 3.33 3.33 3.04 4.63 5.13 9.67 +69 5 1 5.41 2.96 6.21 2.29 3.46 0.63 4.08 1.33 3.29 4.54 6.13 9.54 +69 5 2 13.08 3.75 16.75 5.88 6.04 4.88 11.38 7.96 9.54 9.25 7.92 20.62 +69 5 3 8.08 4.67 5.58 3.79 6.50 2.00 5.71 3.29 3.46 6.50 5.33 27.88 +69 5 4 5.25 6.42 6.25 2.58 3.71 0.33 4.08 1.33 2.88 2.71 3.29 13.33 +69 5 5 6.46 8.08 2.29 1.67 6.79 0.29 2.83 1.96 2.00 5.04 4.88 4.46 +69 5 6 5.13 4.46 7.46 1.71 5.29 0.96 2.04 1.83 1.83 2.29 5.09 16.04 +69 5 7 5.83 5.79 9.71 2.71 7.29 1.33 4.71 4.50 2.13 4.08 10.00 6.79 +69 5 8 4.08 5.96 8.33 4.08 9.13 2.54 6.87 3.96 5.41 4.42 9.87 4.08 +69 5 9 13.13 10.96 11.34 6.79 12.46 6.63 9.42 6.46 9.87 5.83 10.41 10.00 +69 5 10 14.29 13.88 14.96 7.87 13.33 8.46 8.58 11.83 9.79 9.29 15.92 13.88 +69 5 11 8.04 9.83 12.12 3.67 8.21 3.92 2.29 8.00 3.96 4.50 14.88 8.83 +69 5 12 8.42 10.88 6.83 5.00 9.00 5.25 5.66 6.58 5.29 5.46 7.75 10.34 +69 5 13 6.34 5.71 8.87 4.71 6.58 3.96 5.46 5.41 4.21 7.17 11.12 11.29 +69 5 14 9.04 11.54 11.67 6.92 9.79 5.04 7.54 8.54 5.75 6.42 16.08 7.62 +69 5 15 7.00 7.92 7.71 3.88 7.25 3.75 4.21 6.08 2.58 3.63 9.13 5.71 +69 5 16 12.67 7.67 9.38 7.67 11.38 7.96 10.29 7.50 9.75 7.04 9.75 7.08 +69 5 17 12.67 12.75 9.75 6.75 11.58 6.87 6.71 6.17 6.25 8.71 12.08 15.04 +69 5 18 15.09 11.46 11.12 8.58 11.83 9.17 12.46 8.46 10.08 11.96 13.00 19.58 +69 5 19 5.00 5.13 6.21 4.17 6.38 3.17 3.92 1.79 4.42 5.41 6.04 7.75 +69 5 20 7.87 6.63 7.67 3.50 9.83 5.71 1.96 4.46 2.92 2.33 7.83 8.58 +69 5 21 3.54 5.21 5.50 2.75 4.46 1.38 4.17 2.04 1.63 2.13 5.09 5.41 +69 5 22 12.75 8.54 8.58 2.67 8.58 4.71 3.25 4.12 3.63 4.21 5.96 8.42 +69 5 23 15.79 10.96 15.96 7.75 12.50 12.62 7.96 11.38 11.12 10.34 11.83 17.41 +69 5 24 11.17 7.54 9.17 8.08 11.34 10.25 9.96 7.41 11.58 13.29 12.75 21.79 +69 5 25 11.38 7.62 10.13 6.83 8.96 9.42 7.12 6.38 7.50 8.00 8.46 13.42 +69 5 26 8.92 10.13 8.12 4.29 6.00 4.08 5.17 2.88 2.42 3.79 6.38 12.00 +69 5 27 13.70 12.96 8.79 6.50 13.17 10.46 7.62 8.21 8.38 8.75 10.92 13.54 +69 5 28 11.83 8.75 9.54 5.83 10.67 7.71 6.79 4.88 6.96 3.96 9.04 12.96 +69 5 29 5.83 5.13 11.92 6.21 8.12 8.87 8.29 9.38 9.33 6.75 11.67 10.79 +69 5 30 13.62 7.58 12.71 7.58 9.17 8.92 8.42 10.17 9.00 10.58 13.62 11.92 +69 5 31 10.58 5.33 7.62 6.54 8.38 5.88 6.63 4.04 6.17 7.46 7.08 9.54 +69 6 1 4.46 5.13 6.54 2.50 5.17 2.62 4.71 2.17 4.25 4.33 6.13 8.79 +69 6 2 12.83 12.71 11.92 6.34 12.29 10.00 9.62 9.25 9.42 7.46 14.83 11.63 +69 6 3 14.17 7.46 10.63 7.21 11.63 9.59 8.58 8.38 8.25 7.58 11.92 9.54 +69 6 4 6.58 4.67 12.00 2.62 4.25 3.71 3.42 1.71 4.17 3.13 7.12 10.34 +69 6 5 4.96 4.38 7.58 2.62 4.83 6.25 2.96 3.75 3.37 3.50 9.17 9.79 +69 6 6 2.50 4.21 3.67 0.71 2.25 2.67 0.21 4.08 0.50 1.83 13.54 8.21 +69 6 7 2.17 3.79 2.75 2.42 3.67 3.33 4.42 3.50 1.17 2.17 9.62 4.46 +69 6 8 6.46 3.46 8.75 3.25 2.71 3.71 2.88 2.62 4.38 3.33 5.29 2.21 +69 6 9 11.00 3.83 15.71 4.04 5.09 6.46 3.75 3.79 6.34 4.29 9.21 5.71 +69 6 10 12.58 5.33 16.58 5.58 7.29 9.04 6.50 4.75 6.96 3.04 7.58 6.67 +69 6 11 5.04 4.25 11.21 3.88 5.25 5.41 3.29 1.29 4.63 2.04 3.83 3.96 +69 6 12 4.42 2.88 4.12 2.54 4.42 3.63 2.92 2.62 2.79 2.92 6.46 5.33 +69 6 13 9.59 6.96 7.71 3.83 6.00 4.04 3.79 3.92 2.92 2.17 5.79 5.09 +69 6 14 7.92 8.12 8.42 4.00 7.71 6.50 4.63 3.54 7.41 5.96 5.71 14.04 +69 6 15 15.34 15.71 11.92 8.96 12.46 12.54 10.46 8.04 10.54 11.75 11.96 20.67 +69 6 16 21.96 16.83 10.88 9.33 16.50 13.42 10.37 10.46 9.75 9.92 12.87 14.21 +69 6 17 15.79 16.17 13.37 10.37 15.79 14.09 11.29 11.00 11.58 13.46 11.42 18.05 +69 6 18 11.21 14.79 8.87 7.12 14.29 11.96 8.29 8.46 9.79 7.41 16.25 11.96 +69 6 19 14.37 14.96 12.33 8.29 12.42 12.00 13.42 8.83 11.17 9.46 12.46 11.63 +69 6 20 18.34 14.83 12.00 9.25 15.75 13.92 11.04 10.08 12.08 8.50 16.17 13.21 +69 6 21 11.96 11.54 13.37 7.17 12.25 13.13 9.38 6.50 9.42 8.92 8.29 16.83 +69 6 22 9.17 8.50 10.67 5.00 7.67 7.33 4.17 5.66 5.41 5.79 10.25 13.04 +69 6 23 3.46 4.38 5.37 2.71 4.71 3.42 3.63 3.54 1.71 2.92 9.38 9.59 +69 6 24 6.29 6.29 7.17 3.08 6.71 7.00 7.00 4.67 5.33 4.38 10.34 9.87 +69 6 25 9.29 8.08 10.08 4.38 6.71 6.00 5.71 4.21 5.17 3.46 8.17 12.71 +69 6 26 12.62 13.79 13.04 8.63 13.33 11.29 9.83 11.67 11.75 11.38 19.58 21.54 +69 6 27 9.67 7.54 7.75 6.75 10.63 7.92 9.54 9.33 8.92 9.92 15.09 18.21 +69 6 28 4.96 5.50 7.00 3.67 5.50 3.63 5.37 6.17 4.21 4.00 14.33 12.71 +69 6 29 6.04 7.12 8.29 4.00 5.58 4.46 5.96 10.29 5.83 8.38 19.29 14.67 +69 6 30 5.66 8.50 7.83 2.71 7.46 5.46 6.25 9.75 8.71 7.87 15.46 10.92 +69 7 1 7.67 5.21 12.33 4.54 9.08 6.71 7.92 9.25 9.87 7.79 16.54 14.29 +69 7 2 5.54 6.92 4.63 4.58 7.38 4.46 6.00 3.75 6.96 6.79 9.46 13.04 +69 7 3 6.79 4.42 7.38 3.46 5.00 2.83 4.21 4.96 3.92 5.54 14.83 15.46 +69 7 4 10.13 9.83 10.41 5.37 12.38 9.96 12.96 11.79 12.42 12.87 19.08 26.08 +69 7 5 8.08 7.46 5.88 5.75 10.17 7.54 9.17 8.42 9.96 8.71 16.25 20.08 +69 7 6 13.25 9.75 6.29 5.88 12.17 6.50 7.08 8.08 9.50 8.58 14.46 17.00 +69 7 7 17.88 10.25 12.67 9.00 14.42 9.87 12.21 7.75 12.04 10.71 14.17 18.34 +69 7 8 12.62 6.42 8.33 6.83 10.17 7.50 8.17 6.29 8.96 9.75 10.37 16.79 +69 7 9 7.46 4.83 5.17 4.54 8.67 5.04 7.62 4.83 7.79 6.08 10.17 11.38 +69 7 10 7.92 4.29 7.12 5.63 10.67 8.38 12.38 6.13 10.67 8.38 14.29 20.50 +69 7 11 7.38 5.71 6.96 6.83 11.71 8.54 13.92 8.79 12.62 8.46 16.88 24.54 +69 7 12 6.71 3.46 6.83 4.46 7.75 6.04 8.92 4.83 9.67 7.00 14.67 17.75 +69 7 13 4.04 5.33 3.58 2.92 4.33 2.13 6.67 2.37 4.29 3.67 8.75 11.12 +69 7 14 3.88 5.83 3.96 4.50 6.87 7.12 2.42 4.08 5.33 4.63 10.92 8.21 +69 7 15 8.63 6.92 3.13 4.25 7.67 5.04 3.71 3.63 4.08 3.75 5.88 7.62 +69 7 16 11.42 10.34 8.12 6.38 12.12 7.50 9.00 8.50 9.13 8.33 13.04 15.75 +69 7 17 10.37 9.59 9.00 4.75 7.67 4.83 7.87 5.88 8.17 5.71 12.00 18.91 +69 7 18 18.84 18.54 16.29 8.50 14.75 11.42 12.25 13.88 11.87 13.37 24.41 20.04 +69 7 19 12.33 12.50 14.50 7.83 14.21 10.37 16.25 10.21 15.21 12.21 17.46 21.62 +69 7 20 9.13 9.92 10.79 4.83 8.04 6.92 9.59 10.96 10.41 9.00 17.79 17.16 +69 7 21 10.63 12.62 13.21 5.91 8.42 8.50 8.00 11.67 9.13 10.34 19.62 17.58 +69 7 22 12.83 10.21 13.17 6.87 7.21 8.04 10.71 7.50 8.46 9.46 13.96 14.62 +69 7 23 4.50 5.66 6.46 2.37 6.75 4.33 3.50 5.50 5.37 6.63 10.41 12.58 +69 7 24 5.37 6.25 7.21 2.62 2.71 1.71 2.71 2.50 2.21 1.83 8.42 6.46 +69 7 25 11.46 15.67 10.63 8.17 13.50 10.67 7.96 10.37 9.71 11.71 22.37 19.83 +69 7 26 7.17 5.25 8.71 3.37 4.33 2.08 1.50 2.54 2.62 1.96 9.92 5.58 +69 7 27 6.00 10.46 8.00 4.25 8.21 5.50 4.71 5.88 4.54 7.21 15.25 14.88 +69 7 28 11.87 8.83 6.54 4.04 9.42 4.79 3.42 2.71 6.17 4.63 6.58 5.66 +69 7 29 9.96 10.50 9.21 5.04 10.71 5.46 8.42 5.04 7.58 4.50 13.13 13.17 +69 7 30 14.12 17.79 9.54 7.33 13.79 10.04 7.21 11.29 9.08 10.83 21.67 17.67 +69 7 31 11.08 14.54 7.92 7.08 12.79 9.25 7.04 9.38 9.13 10.83 12.83 13.70 +69 8 1 10.13 6.50 4.29 3.33 10.04 5.54 2.50 4.71 3.50 3.92 6.50 10.96 +69 8 2 10.54 6.63 9.00 3.96 6.34 4.92 6.63 3.25 6.96 5.91 8.83 10.25 +69 8 3 14.37 14.83 11.50 5.29 12.33 8.04 8.00 6.96 8.42 8.17 13.70 10.46 +69 8 4 14.37 16.21 13.54 9.59 14.88 11.04 9.59 10.25 10.88 13.62 22.00 16.83 +69 8 5 12.29 11.25 11.58 6.25 12.50 9.08 7.12 9.50 10.21 11.00 18.25 18.66 +69 8 6 10.04 17.21 6.54 5.66 11.71 7.33 5.63 7.33 6.92 7.54 13.37 11.87 +69 8 7 12.17 17.62 11.38 8.25 16.25 9.79 8.21 8.54 10.92 11.25 16.88 19.50 +69 8 8 8.33 9.92 8.17 5.71 8.42 5.79 5.58 7.54 6.13 4.92 13.33 14.17 +69 8 9 4.54 8.96 5.17 2.37 3.08 1.29 1.54 3.46 2.58 1.42 10.46 3.63 +69 8 10 9.46 13.42 7.67 5.33 10.00 5.58 5.50 5.54 5.50 4.00 9.67 10.00 +69 8 11 10.67 9.08 7.96 5.37 12.25 7.67 6.17 10.92 9.25 10.83 22.13 21.71 +69 8 12 14.92 4.75 15.59 6.71 8.92 5.79 7.21 3.79 7.46 5.96 7.58 11.67 +69 8 13 8.67 2.79 6.58 3.00 2.50 3.33 2.79 1.50 3.88 1.21 1.58 4.88 +69 8 14 3.67 7.17 4.50 1.00 3.29 0.63 2.42 1.00 1.63 1.13 3.71 8.58 +69 8 15 4.54 3.67 2.92 0.83 2.04 1.54 0.71 3.75 1.29 1.21 5.46 2.50 +69 8 16 8.04 3.88 5.00 3.21 3.46 3.13 3.50 1.58 3.92 4.25 5.88 7.79 +69 8 17 9.04 10.13 8.00 3.83 5.17 5.00 3.96 5.88 4.33 5.75 17.71 12.46 +69 8 18 8.38 8.46 9.21 3.71 7.92 5.09 4.92 7.46 6.92 8.21 16.08 12.46 +69 8 19 12.87 11.42 9.75 5.79 14.25 9.25 10.08 7.96 11.00 10.08 19.75 18.46 +69 8 20 16.38 14.92 11.29 10.04 17.50 10.71 13.59 11.96 13.33 13.25 18.54 19.62 +69 8 21 16.42 9.79 9.46 9.04 11.00 8.54 10.21 6.38 10.41 12.17 11.42 19.46 +69 8 22 15.54 10.75 10.75 8.33 11.96 8.12 10.79 6.92 11.08 10.96 14.50 22.37 +69 8 23 16.79 9.96 11.17 8.92 10.41 7.33 9.54 5.29 8.71 10.21 9.17 16.92 +69 8 24 8.96 9.29 8.46 4.42 9.59 6.42 7.87 6.13 7.25 7.71 13.70 14.33 +69 8 25 20.08 10.50 12.21 6.87 12.58 8.58 12.17 8.75 11.71 11.12 14.62 20.62 +69 8 26 19.58 12.83 13.08 9.83 16.08 10.46 15.79 12.04 14.71 14.00 15.00 19.55 +69 8 27 18.63 9.33 11.00 8.46 13.08 8.67 13.00 8.46 12.21 13.88 11.79 20.75 +69 8 28 18.21 8.96 11.00 7.62 12.21 8.25 8.92 6.92 9.42 8.29 11.96 13.00 +69 8 29 7.96 7.17 9.04 5.09 6.96 4.04 5.91 4.54 4.83 6.34 5.29 10.29 +69 8 30 8.83 4.88 5.66 3.17 6.29 2.96 5.21 4.38 4.50 7.33 4.54 8.42 +69 8 31 4.17 7.08 13.50 4.54 6.67 4.71 4.12 4.67 4.75 6.17 7.12 13.00 +69 9 1 5.79 8.08 15.21 4.04 5.91 3.71 6.34 4.21 4.38 5.13 8.92 11.38 +69 9 2 3.37 2.88 11.79 3.83 5.54 2.79 2.08 2.54 2.75 1.92 5.79 6.58 +69 9 3 8.04 4.25 14.09 4.33 6.13 2.50 3.83 2.29 3.46 3.71 2.96 9.25 +69 9 4 4.71 4.21 4.92 3.54 5.17 1.25 3.50 1.50 2.62 3.50 6.42 10.83 +69 9 5 5.96 3.33 6.08 3.00 6.63 1.87 4.92 2.17 3.00 4.42 5.63 11.04 +69 9 6 3.17 1.00 10.58 1.83 4.33 1.08 2.17 0.67 0.83 0.13 7.12 6.00 +69 9 7 1.50 1.25 5.63 1.08 3.21 1.33 2.42 3.33 1.33 1.96 11.79 7.25 +69 9 8 5.96 7.50 5.83 2.13 5.09 2.92 4.00 7.08 3.13 5.83 12.38 12.38 +69 9 9 16.50 15.63 12.67 8.63 14.83 10.04 10.04 9.29 10.41 13.00 18.12 20.71 +69 9 10 12.17 4.96 11.58 5.21 6.75 2.62 5.25 3.33 3.04 3.46 6.54 11.17 +69 9 11 11.87 5.63 6.87 3.00 4.50 3.46 6.83 2.88 4.54 4.04 4.79 11.75 +69 9 12 7.58 7.87 4.54 2.04 6.71 3.25 1.92 6.34 3.17 3.25 13.21 9.62 +69 9 13 5.25 3.04 12.29 2.25 3.58 1.25 3.25 2.37 2.42 1.96 4.96 13.70 +69 9 14 4.25 3.33 14.83 3.50 6.25 5.29 9.21 8.25 6.71 5.25 7.41 13.29 +69 9 15 5.58 3.63 14.12 3.08 6.00 4.71 6.92 7.29 7.33 4.38 7.38 12.00 +69 9 16 10.71 9.21 10.54 4.29 11.83 6.29 7.71 10.29 8.67 6.04 11.25 18.05 +69 9 17 3.83 0.87 10.00 2.75 5.09 3.37 3.75 4.71 4.54 4.33 5.00 7.41 +69 9 18 7.50 5.21 13.25 4.08 6.46 4.17 3.79 5.91 4.96 3.29 6.34 10.17 +69 9 19 9.04 5.13 13.75 4.00 5.96 1.58 3.54 3.29 1.17 0.67 8.21 7.00 +69 9 20 8.83 9.13 10.04 4.25 11.34 7.29 10.50 10.04 9.50 8.04 13.00 15.83 +69 9 21 18.54 16.62 17.54 9.50 17.67 10.67 16.17 15.75 12.83 13.54 22.04 22.88 +69 9 22 7.87 6.29 8.25 3.25 9.42 4.71 7.38 7.29 6.21 6.87 13.88 20.75 +69 9 23 13.08 15.37 13.79 6.42 13.21 10.71 10.37 14.88 9.50 11.00 23.79 21.84 +69 9 24 12.58 11.54 11.46 5.66 11.54 8.58 10.13 10.34 8.92 11.21 15.41 17.88 +69 9 25 10.41 10.58 9.54 4.42 13.88 8.17 9.96 11.83 10.00 10.13 22.13 23.58 +69 9 26 15.83 15.75 14.46 9.00 20.30 11.08 17.12 11.71 14.88 14.04 19.95 23.00 +69 9 27 7.41 6.42 6.83 2.04 7.08 4.00 6.46 5.75 6.17 5.09 14.29 17.33 +69 9 28 16.17 15.34 14.96 8.08 15.63 10.96 16.92 16.38 13.59 15.75 24.75 33.63 +69 9 29 4.00 2.37 8.21 1.29 5.17 1.79 5.75 4.29 4.08 4.83 11.08 18.46 +69 9 30 8.83 7.96 7.29 4.33 11.63 6.58 11.38 9.17 8.54 9.33 15.41 24.41 +69 10 1 6.75 5.13 6.21 1.54 7.54 4.33 7.33 6.87 4.96 7.21 13.46 14.00 +69 10 2 15.09 15.16 13.42 7.00 15.34 10.79 16.42 11.50 13.42 11.25 21.42 23.00 +69 10 3 10.58 9.59 11.08 4.46 10.04 6.87 7.38 11.54 7.46 9.04 18.34 15.71 +69 10 4 12.54 11.25 11.71 7.96 11.58 6.96 8.08 9.75 8.17 7.41 16.00 13.25 +69 10 5 8.79 7.96 11.08 6.50 10.29 5.25 7.25 8.46 7.87 7.62 12.38 14.42 +69 10 6 11.75 14.09 10.92 7.79 13.88 9.13 8.04 11.29 9.67 9.83 21.29 17.71 +69 10 7 10.67 11.04 14.12 5.88 12.67 9.08 10.79 12.54 10.29 12.75 21.75 23.67 +69 10 8 20.08 21.29 18.50 12.12 18.54 11.83 14.00 15.29 10.92 13.25 19.50 18.16 +69 10 9 9.75 11.12 7.41 5.04 9.71 5.88 4.25 7.46 6.75 7.67 6.79 6.29 +69 10 10 4.67 3.17 5.96 1.25 7.08 1.58 1.17 4.21 4.17 1.75 4.71 5.88 +69 10 11 12.87 14.29 9.25 3.04 8.04 5.13 3.58 7.75 5.71 5.21 9.13 8.67 +69 10 12 18.79 21.59 16.17 9.62 16.21 11.12 10.63 13.96 10.92 11.54 16.04 20.00 +69 10 13 14.25 8.50 17.79 9.83 10.50 8.12 11.92 8.12 10.75 12.46 12.08 17.12 +69 10 14 15.63 16.04 13.46 7.46 13.33 9.00 11.50 13.00 10.37 11.71 22.46 21.21 +69 10 15 24.46 23.58 20.62 14.75 19.58 16.42 16.13 19.08 15.63 20.38 31.88 29.38 +69 10 16 19.46 18.79 18.91 13.46 17.00 14.54 9.87 16.29 13.37 16.62 25.58 24.67 +69 10 17 12.38 16.29 15.46 10.67 17.04 9.59 14.37 13.50 14.17 10.79 21.87 25.25 +69 10 18 9.96 4.54 9.38 3.33 11.17 6.34 4.33 7.58 4.71 3.58 4.04 11.29 +69 10 19 5.37 1.21 7.00 0.87 6.08 0.29 1.04 2.58 2.00 1.67 9.59 6.08 +69 10 20 2.42 3.08 2.83 0.29 7.50 2.25 1.38 2.25 2.58 1.71 4.83 6.17 +69 10 21 4.79 1.67 2.17 1.46 6.83 2.83 1.50 1.83 1.58 1.58 2.96 7.62 +69 10 22 9.96 8.29 7.46 3.88 8.25 5.58 3.33 6.25 6.34 5.91 10.75 12.54 +69 10 23 13.29 18.00 13.08 6.63 17.46 10.71 13.88 16.42 12.42 12.21 28.62 27.37 +69 10 24 10.34 12.96 9.33 6.29 15.37 8.21 13.42 12.38 12.42 10.71 20.41 26.34 +69 10 25 7.83 8.54 8.17 6.04 14.37 9.50 12.54 13.13 13.13 8.46 17.16 22.75 +69 10 26 2.62 2.88 6.67 4.08 9.00 5.09 9.29 8.63 9.25 6.04 12.17 15.46 +69 10 27 5.58 6.92 5.50 2.37 6.29 2.79 4.04 5.91 4.46 5.50 10.79 12.92 +69 10 28 9.04 9.17 9.62 5.09 11.00 6.13 8.25 9.67 7.92 9.96 16.54 20.58 +69 10 29 8.83 6.38 11.17 3.13 6.04 3.63 7.46 6.08 5.29 7.25 11.42 22.46 +69 10 30 7.04 3.88 6.75 4.58 10.46 4.58 11.58 10.46 9.59 8.50 16.21 25.17 +69 10 31 4.29 6.75 5.00 3.50 9.71 5.79 8.25 7.33 7.08 10.67 15.63 21.12 +69 11 1 10.63 9.21 11.83 4.75 12.50 9.04 13.37 11.63 12.33 13.62 20.25 20.83 +69 11 2 24.04 24.87 21.37 13.54 22.08 15.21 19.41 17.58 18.41 17.75 23.42 28.25 +69 11 3 22.63 12.38 17.62 5.25 6.42 3.58 4.75 5.71 5.96 5.91 8.63 13.88 +69 11 4 13.13 10.37 10.63 4.54 8.46 5.54 10.00 6.83 8.67 6.96 12.58 20.96 +69 11 5 5.50 6.17 6.38 2.00 7.46 7.00 9.67 6.04 8.33 7.62 14.42 19.12 +69 11 6 14.42 10.13 11.50 2.21 7.12 4.46 2.88 3.67 3.42 3.88 6.54 14.17 +69 11 7 13.59 16.29 12.33 6.67 14.25 8.04 11.29 9.96 9.71 9.08 20.88 23.29 +69 11 8 18.25 21.96 12.25 10.63 19.41 11.21 18.16 16.25 16.33 14.00 23.87 29.46 +69 11 9 21.75 27.33 11.71 9.00 18.00 9.83 10.71 10.88 10.79 8.50 17.46 18.29 +69 11 10 8.38 14.58 8.75 6.04 14.25 9.21 12.87 10.58 9.00 10.37 21.25 22.42 +69 11 11 9.08 6.08 10.67 3.96 5.66 2.83 5.79 2.21 3.42 2.46 5.83 9.75 +69 11 12 9.13 6.92 5.88 2.58 8.25 3.63 7.04 3.54 2.75 3.54 9.00 14.88 +69 11 13 4.79 3.75 2.21 1.17 5.09 2.71 5.41 3.13 2.42 5.46 7.54 17.58 +69 11 14 9.54 3.29 11.38 4.04 4.58 3.25 4.29 0.96 2.37 0.33 6.34 7.33 +69 11 15 12.38 10.71 10.50 6.17 7.79 5.79 9.79 6.17 8.17 5.54 11.00 15.41 +69 11 16 19.87 20.71 20.83 9.13 12.12 9.67 11.96 12.33 10.92 9.92 21.00 22.92 +69 11 17 11.29 9.38 13.67 5.63 6.38 6.46 10.25 6.42 8.92 7.46 11.50 15.34 +69 11 18 9.25 7.83 10.41 7.50 10.29 9.50 16.25 13.54 12.33 10.21 17.04 22.88 +69 11 19 11.79 15.00 12.38 9.04 14.25 12.08 19.25 16.62 16.38 14.96 22.54 30.04 +69 11 20 10.58 14.67 8.50 4.21 11.75 7.17 11.96 8.54 9.17 8.96 13.67 21.42 +69 11 21 3.46 2.75 5.75 2.17 5.75 4.00 2.96 9.42 5.33 7.41 14.17 20.83 +69 11 22 9.13 12.00 9.08 2.33 8.12 4.00 2.58 7.08 4.12 4.75 20.54 34.08 +69 11 23 20.04 19.46 28.21 9.04 16.96 12.04 14.96 13.67 12.62 9.79 17.67 18.75 +69 11 24 12.17 10.58 18.08 6.21 6.63 3.71 8.92 6.58 6.38 6.42 13.04 20.50 +69 11 25 13.33 5.83 17.04 4.67 6.00 3.21 9.50 5.17 6.87 8.12 10.67 18.88 +69 11 26 6.54 3.54 9.04 1.71 4.63 3.29 8.21 4.29 4.75 3.75 8.75 14.46 +69 11 27 12.79 11.63 8.87 6.46 11.25 7.92 13.17 9.42 13.08 9.08 17.41 24.79 +69 11 28 15.21 10.41 13.70 8.08 8.21 7.67 12.38 10.13 8.92 9.13 18.96 29.33 +69 11 29 13.92 8.46 17.00 4.88 5.91 4.75 9.50 4.50 6.58 6.21 14.29 19.46 +69 11 30 7.25 6.63 6.67 1.38 5.29 4.50 7.71 6.29 5.29 4.75 12.50 13.54 +69 12 1 9.62 7.87 7.04 4.42 8.67 5.83 8.96 6.58 8.92 6.71 13.42 20.17 +69 12 2 13.13 15.04 9.96 8.96 16.54 10.71 12.87 14.67 13.67 10.79 17.25 18.16 +69 12 3 19.58 16.96 12.67 9.83 12.67 8.21 12.21 9.42 12.79 8.92 13.92 21.59 +69 12 4 12.25 13.13 14.17 4.75 6.67 4.04 7.21 4.71 6.67 5.50 11.46 16.17 +69 12 5 9.46 12.92 8.33 3.37 9.29 5.75 8.00 8.25 6.67 5.66 11.79 18.79 +69 12 6 17.46 14.62 13.17 8.00 10.25 8.42 11.04 8.92 10.13 7.41 14.09 19.04 +69 12 7 23.04 12.12 13.67 8.58 14.79 9.87 15.54 14.79 16.58 12.33 17.96 21.42 +69 12 8 11.58 7.25 9.83 6.08 6.67 4.71 7.25 7.62 6.58 6.29 10.96 14.83 +69 12 9 5.66 5.75 4.63 1.75 6.63 4.92 5.50 6.96 5.00 5.71 15.12 14.92 +69 12 10 13.13 9.59 10.04 3.46 8.29 7.54 7.50 6.38 8.42 9.54 12.33 15.37 +69 12 11 6.71 3.37 8.33 0.25 4.29 3.33 5.66 1.79 1.54 1.96 7.83 8.75 +69 12 12 3.46 6.21 4.38 0.21 6.71 5.00 2.88 4.75 1.38 0.50 12.87 13.00 +69 12 13 22.67 16.88 16.25 5.21 13.08 10.54 11.34 11.83 9.87 13.21 19.50 22.29 +69 12 14 18.29 22.54 14.96 9.67 17.00 10.79 14.92 12.87 13.13 12.17 18.66 19.17 +69 12 15 17.16 20.12 12.25 7.71 12.79 7.08 12.92 6.79 11.96 5.75 15.87 15.16 +69 12 16 13.00 9.29 10.46 3.17 9.75 8.75 7.12 7.17 6.38 5.25 10.08 15.54 +69 12 17 6.17 4.46 21.37 6.00 3.29 7.04 17.67 7.58 8.87 10.50 12.75 26.83 +69 12 18 11.71 8.42 12.21 4.58 8.46 7.38 10.13 9.54 10.21 10.41 9.54 19.21 +69 12 19 7.62 10.67 13.79 5.21 9.79 9.04 11.79 9.08 10.00 8.63 15.79 22.21 +69 12 20 10.50 13.92 10.00 3.58 9.38 9.38 8.75 9.25 7.38 5.83 13.62 17.21 +69 12 21 24.41 20.41 19.70 11.92 15.50 13.62 14.46 13.96 14.37 14.17 20.50 19.46 +69 12 22 16.38 13.88 12.17 9.29 13.50 8.38 13.33 10.08 13.25 8.79 16.38 21.21 +69 12 23 12.83 13.37 9.79 5.41 11.00 9.25 10.50 11.63 11.00 9.50 16.62 15.00 +69 12 24 7.67 5.75 5.50 2.04 4.71 4.25 4.96 4.96 3.25 2.83 6.79 11.34 +69 12 25 12.83 12.58 8.46 4.17 5.41 4.67 6.21 5.41 5.17 3.92 13.88 10.96 +69 12 26 12.71 6.67 10.75 3.21 2.71 1.25 5.25 2.37 3.25 2.79 5.13 9.50 +69 12 27 6.71 6.21 5.29 0.42 5.96 4.17 3.04 5.25 2.17 3.75 12.46 13.21 +69 12 28 18.34 18.00 15.75 6.08 13.29 11.34 9.71 11.87 9.21 8.75 15.87 18.12 +69 12 29 18.66 15.96 19.38 6.38 13.54 10.08 14.29 11.46 10.58 9.59 13.75 25.04 +69 12 30 16.25 13.25 23.42 8.04 10.04 8.17 16.79 11.42 11.92 11.42 11.50 27.84 +69 12 31 14.42 13.83 27.71 7.08 12.08 10.00 14.58 11.00 12.54 7.12 11.17 17.41 +70 1 1 9.59 2.96 11.79 3.42 6.13 4.08 9.00 4.46 7.29 3.50 7.33 13.00 +70 1 2 9.00 4.29 6.75 4.58 5.79 6.58 8.58 6.21 9.59 6.04 10.17 16.96 +70 1 3 8.08 7.46 11.71 3.58 4.50 3.63 5.13 4.75 3.13 2.50 12.54 18.25 +70 1 4 11.21 7.38 11.58 2.13 3.29 1.13 8.92 4.92 5.54 5.71 7.58 15.54 +70 1 5 10.46 5.29 10.83 3.63 4.75 5.29 12.33 5.83 6.79 6.21 11.83 20.21 +70 1 6 7.92 3.50 8.12 3.79 2.50 4.29 10.34 4.00 6.63 6.08 7.04 16.66 +70 1 7 18.91 17.50 14.75 4.63 12.83 9.00 8.58 9.75 6.92 4.29 10.79 12.17 +70 1 8 22.92 20.75 24.08 10.34 17.96 17.29 19.08 15.54 14.17 11.46 17.12 25.62 +70 1 9 16.17 8.87 18.25 7.41 8.92 9.08 13.54 10.37 10.67 7.50 9.13 19.21 +70 1 10 7.96 5.41 8.25 1.79 3.92 3.50 4.63 2.29 4.50 2.54 1.71 8.50 +70 1 11 5.00 3.71 6.34 3.00 4.00 3.21 5.50 4.83 7.67 6.13 9.21 9.62 +70 1 12 7.96 7.50 6.08 2.79 4.46 4.75 6.13 3.42 4.63 2.62 6.63 8.04 +70 1 13 21.67 18.79 18.41 9.21 15.67 11.54 11.29 14.09 11.38 9.13 13.42 15.96 +70 1 14 8.46 11.58 9.38 4.33 10.00 8.12 9.62 11.34 7.71 7.46 14.33 19.92 +70 1 15 6.71 5.00 6.38 3.00 3.75 3.37 6.00 1.71 3.54 2.37 5.58 5.96 +70 1 16 7.29 10.29 6.17 0.75 6.83 2.54 3.83 4.88 3.75 3.42 7.08 15.75 +70 1 17 20.88 18.88 21.42 10.34 17.33 16.21 16.54 16.29 13.25 14.33 18.88 32.00 +70 1 18 14.42 15.67 15.16 7.87 12.00 10.04 12.87 10.92 12.58 14.37 15.37 21.79 +70 1 19 19.08 22.37 20.91 13.17 18.34 14.96 16.79 15.16 15.37 15.63 23.67 21.87 +70 1 20 22.63 23.67 14.71 11.75 18.16 14.25 9.96 17.71 15.71 17.92 28.12 25.37 +70 1 21 22.08 17.58 17.92 11.54 12.62 12.67 13.33 14.42 13.33 16.75 23.63 22.67 +70 1 22 15.25 15.41 13.13 7.25 13.25 11.38 8.54 10.63 8.25 5.91 12.87 14.75 +70 1 23 17.16 13.08 18.46 7.71 8.21 6.79 11.29 10.08 8.79 8.58 10.83 22.75 +70 1 24 13.88 14.33 13.37 5.54 10.21 9.71 11.08 11.04 9.17 10.63 15.29 21.67 +70 1 25 5.00 4.29 7.58 1.13 4.04 3.42 2.25 4.67 2.62 2.08 10.21 12.33 +70 1 26 4.79 2.17 4.42 0.75 3.21 2.46 7.04 2.21 2.71 2.67 5.91 10.83 +70 1 27 10.54 19.08 7.83 3.17 10.00 7.00 5.17 8.79 5.21 2.88 12.42 9.96 +70 1 28 17.50 17.67 17.83 8.58 17.37 14.62 13.75 14.88 11.42 12.50 18.79 21.25 +70 1 29 12.96 11.08 18.12 6.83 9.83 9.46 10.83 10.63 10.17 8.58 10.58 19.00 +70 1 30 9.79 6.63 14.96 3.17 5.54 3.21 5.66 2.79 2.67 2.17 4.50 8.54 +70 1 31 22.46 21.50 20.91 12.96 19.33 15.67 17.54 15.25 15.46 14.46 21.96 26.83 +70 2 1 22.08 21.79 17.83 13.46 19.75 15.41 18.25 17.41 17.12 17.83 24.08 25.08 +70 2 2 19.33 18.12 9.83 8.75 14.92 9.38 13.96 11.87 13.08 13.62 22.83 28.96 +70 2 3 10.92 11.54 10.46 5.46 10.63 8.71 14.29 10.63 11.63 11.38 19.08 26.83 +70 2 4 9.79 12.46 10.21 4.54 8.54 4.96 5.63 4.79 3.67 1.17 8.00 5.29 +70 2 5 8.38 8.33 14.12 4.63 3.67 2.75 5.33 4.25 4.25 2.17 6.54 13.37 +70 2 6 7.25 7.29 7.54 3.08 5.96 5.29 7.17 5.75 5.75 6.42 12.50 16.13 +70 2 7 18.21 22.25 12.87 11.46 20.08 10.71 17.54 13.46 15.37 12.12 23.25 23.25 +70 2 8 16.92 20.54 10.34 8.33 17.46 8.83 15.50 12.62 13.37 10.41 22.04 20.75 +70 2 9 19.87 26.75 13.13 9.42 20.41 10.34 17.00 13.08 15.75 12.46 23.71 19.70 +70 2 10 10.88 7.79 8.33 4.96 8.42 6.04 10.83 5.83 7.92 4.92 9.92 10.79 +70 2 11 10.79 6.83 7.08 1.96 6.00 2.67 6.50 3.46 3.71 3.04 4.29 9.25 +70 2 12 17.46 10.29 21.71 5.50 8.79 6.17 10.13 6.38 7.54 7.17 6.58 17.83 +70 2 13 17.79 15.83 21.25 7.17 11.83 8.21 8.75 11.04 10.04 7.25 14.71 21.59 +70 2 14 12.54 7.79 11.79 4.04 7.25 4.75 5.91 3.67 5.13 4.38 8.71 14.12 +70 2 15 11.63 11.29 9.33 3.58 4.29 2.71 5.41 2.67 4.67 4.54 10.08 14.71 +70 2 16 7.46 9.71 7.12 1.54 8.63 6.96 7.67 4.63 6.87 4.21 9.33 15.92 +70 2 17 20.91 15.29 14.00 10.54 13.83 9.46 15.79 10.71 15.87 10.58 19.08 25.41 +70 2 18 12.25 10.96 9.62 5.09 9.96 8.38 13.50 10.04 9.62 9.08 15.00 20.96 +70 2 19 19.38 19.83 15.59 10.00 18.21 12.92 16.54 12.58 14.33 12.00 15.04 16.13 +70 2 20 18.29 17.67 13.83 9.67 16.17 11.29 17.04 12.83 14.67 10.04 18.25 22.88 +70 2 21 21.84 20.83 18.96 13.75 24.96 16.33 21.96 16.58 20.41 13.13 22.63 18.12 +70 2 22 16.92 16.71 12.58 9.17 19.75 13.29 16.88 16.00 15.21 16.21 24.83 28.29 +70 2 23 10.13 9.42 9.38 5.54 10.58 8.87 15.29 10.71 13.08 11.75 17.54 27.25 +70 2 24 17.16 15.87 9.17 7.62 14.67 8.29 15.29 10.75 12.33 9.79 19.25 17.54 +70 2 25 23.29 14.96 13.25 10.58 13.25 9.17 10.88 9.92 11.58 8.54 17.88 13.70 +70 2 26 11.34 6.38 10.54 3.42 4.54 2.50 4.33 3.96 4.42 2.17 8.46 8.46 +70 2 27 12.21 10.83 17.88 5.00 5.91 4.92 7.67 8.00 7.12 6.08 9.00 10.41 +70 2 28 9.96 8.38 16.29 4.79 5.41 3.42 6.42 4.63 5.00 3.17 4.58 11.75 +70 3 1 13.13 6.04 8.96 4.63 10.58 7.67 14.62 10.50 11.83 10.17 14.46 22.21 +70 3 2 15.34 15.12 13.79 9.62 10.71 8.33 10.63 11.42 9.59 10.75 19.50 26.16 +70 3 3 15.29 10.34 13.62 5.63 10.13 7.12 10.25 8.21 8.58 6.87 15.54 17.12 +70 3 4 23.04 14.67 16.79 12.21 13.37 10.13 13.62 11.21 12.62 10.41 17.16 20.38 +70 3 5 7.87 5.29 9.13 2.21 6.79 2.92 8.83 3.79 6.34 7.17 8.33 13.79 +70 3 6 11.96 6.83 7.54 3.50 7.87 4.83 8.50 5.21 7.21 5.04 10.54 13.88 +70 3 7 20.38 10.79 9.71 7.54 10.29 8.29 10.29 8.38 11.29 6.46 17.25 20.50 +70 3 8 22.79 13.67 14.29 9.92 12.71 10.34 11.21 12.04 12.71 12.08 20.21 26.46 +70 3 9 12.08 8.63 6.00 4.21 8.29 5.37 7.25 6.00 6.38 7.87 10.71 20.12 +70 3 10 10.54 6.25 8.04 4.58 8.00 5.96 10.08 8.00 8.71 5.54 12.54 14.71 +70 3 11 19.12 17.96 10.71 9.08 17.79 11.17 16.88 14.37 16.33 14.96 21.12 27.67 +70 3 12 24.17 17.21 25.41 13.50 14.79 12.54 14.37 15.09 13.92 16.54 21.42 23.58 +70 3 13 16.50 13.67 31.83 9.96 12.42 9.83 16.75 13.21 11.83 10.96 14.04 15.75 +70 3 14 11.08 7.25 17.29 6.71 7.71 3.08 7.29 5.13 5.46 4.42 4.21 9.96 +70 3 15 12.54 8.42 8.92 5.46 7.21 5.41 8.33 6.34 8.04 7.29 9.87 12.75 +70 3 16 10.41 7.67 8.46 6.58 13.21 8.67 14.92 10.71 13.70 10.54 18.50 23.58 +70 3 17 17.25 16.66 13.08 10.79 19.38 14.62 22.04 18.08 20.71 15.54 20.67 24.37 +70 3 18 22.95 16.96 14.71 13.88 19.04 12.58 19.70 17.12 17.83 17.79 22.34 27.16 +70 3 19 12.17 8.92 11.71 5.91 9.54 8.04 12.25 10.34 11.29 7.71 15.75 17.71 +70 3 20 14.12 13.08 12.58 7.58 12.87 11.21 15.29 12.38 13.92 10.71 16.33 18.41 +70 3 21 7.58 6.63 5.50 2.71 8.29 6.08 7.38 7.38 5.88 5.96 11.67 12.50 +70 3 22 10.21 8.87 8.67 2.62 7.50 5.58 5.25 5.37 4.08 3.25 9.83 7.17 +70 3 23 13.62 17.08 21.17 8.29 11.46 8.71 11.34 10.54 8.92 8.75 12.67 10.08 +70 3 24 11.17 10.92 22.58 6.34 9.46 5.25 8.17 6.50 6.79 5.46 5.41 8.04 +70 3 25 6.38 3.37 6.46 1.87 5.13 3.46 4.25 5.66 4.75 5.00 12.29 16.08 +70 3 26 14.83 11.58 12.00 8.00 11.63 8.33 11.58 9.92 12.79 11.21 15.46 23.67 +70 3 27 13.70 9.83 12.58 7.87 8.96 8.33 11.92 7.71 10.58 10.71 13.33 24.79 +70 3 28 17.71 9.75 13.04 10.08 12.92 8.75 13.46 9.87 12.17 10.79 14.17 19.79 +70 3 29 14.96 13.67 13.29 8.87 15.67 10.17 16.38 14.50 15.37 11.54 19.38 22.42 +70 3 30 18.88 18.79 13.54 9.71 16.92 11.58 19.00 14.29 15.59 12.17 18.75 17.00 +70 3 31 21.42 20.46 17.21 12.46 15.37 11.17 15.16 13.29 13.83 17.37 22.83 34.08 +70 4 1 18.96 12.17 19.25 10.21 10.88 8.87 13.08 8.29 9.83 11.79 15.29 22.29 +70 4 2 12.42 7.00 10.92 6.96 8.96 5.21 10.83 5.91 10.46 8.75 9.67 16.17 +70 4 3 17.79 10.46 9.83 8.33 11.63 8.83 12.04 8.29 12.29 9.71 11.29 17.33 +70 4 4 17.46 9.79 12.00 8.87 10.46 6.67 11.34 6.75 10.21 9.71 9.17 13.92 +70 4 5 18.00 14.04 12.79 10.17 15.63 10.46 14.25 12.21 14.42 11.83 16.38 19.58 +70 4 6 14.88 14.54 23.75 8.75 9.87 9.21 14.42 11.42 9.50 11.21 15.25 23.67 +70 4 7 11.92 9.29 11.17 4.50 7.17 6.17 8.00 8.54 7.92 10.04 12.50 21.42 +70 4 8 12.83 9.33 12.54 6.21 7.46 6.50 7.46 8.12 6.38 10.17 15.09 25.12 +70 4 9 13.21 8.08 8.79 7.17 7.50 8.38 8.92 8.42 8.46 10.50 10.29 23.67 +70 4 10 6.71 3.92 6.75 2.92 4.21 4.17 6.25 5.09 5.71 3.04 6.71 11.63 +70 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11.71 13.29 8.83 4.08 9.79 6.38 6.92 7.58 8.00 6.21 8.17 11.67 +70 4 25 26.71 16.83 21.50 13.21 17.75 13.83 19.41 15.96 16.42 14.71 18.38 24.08 +70 4 26 19.12 13.00 13.88 8.96 12.08 10.13 12.12 9.04 11.42 13.83 11.96 19.79 +70 4 27 12.12 11.58 7.92 5.54 10.34 6.38 7.33 6.58 8.00 7.67 9.38 11.38 +70 4 28 12.75 10.17 8.63 7.41 13.59 9.50 13.83 11.25 14.04 10.92 12.75 18.46 +70 4 29 13.75 9.21 10.88 6.63 8.79 7.38 9.04 5.41 8.21 9.75 6.21 13.08 +70 4 30 8.83 10.34 6.38 3.37 8.58 6.87 8.29 8.38 9.13 6.38 14.71 11.54 +70 5 1 14.17 17.96 15.12 7.08 14.17 11.83 12.75 16.92 11.71 15.41 25.75 19.58 +70 5 2 16.75 18.91 16.00 10.71 17.67 13.62 16.66 16.88 14.00 15.09 27.50 17.58 +70 5 3 13.67 14.83 15.37 9.62 17.21 12.71 9.71 11.21 13.70 15.46 17.67 20.04 +70 5 4 13.50 11.42 9.71 7.58 13.88 9.17 9.25 9.87 9.38 12.42 14.33 17.88 +70 5 5 12.87 11.34 9.59 8.21 9.79 9.59 6.79 7.33 11.17 12.12 11.75 16.00 +70 5 6 11.71 7.08 5.58 5.71 9.79 6.71 3.37 5.46 7.67 5.54 9.46 11.79 +70 5 7 15.34 10.41 8.67 5.75 9.54 5.09 5.91 7.67 6.21 5.75 8.54 12.38 +70 5 8 7.87 8.58 7.96 3.79 8.46 6.46 7.92 6.42 8.83 9.29 5.58 17.08 +70 5 9 7.96 7.12 17.12 5.88 9.42 9.71 12.50 10.08 11.12 10.83 9.33 15.75 +70 5 10 11.71 7.96 17.00 6.79 8.79 10.58 10.21 10.67 10.75 9.54 12.79 17.41 +70 5 11 6.08 6.96 3.54 2.42 4.38 5.25 5.75 4.58 4.79 6.87 8.58 16.71 +70 5 12 6.25 4.08 15.63 4.12 4.25 7.04 7.25 5.75 7.33 5.79 10.25 9.17 +70 5 13 5.37 3.67 6.58 2.92 3.21 3.21 2.54 6.21 2.58 1.75 12.12 6.21 +70 5 14 3.33 5.63 5.17 1.21 4.25 1.71 3.25 6.42 4.54 4.25 9.71 4.12 +70 5 15 8.79 9.59 8.63 4.08 11.12 5.09 3.83 7.92 5.41 3.71 7.38 3.58 +70 5 16 10.92 6.67 6.96 4.88 6.67 6.08 7.17 4.75 5.91 6.08 8.50 12.29 +70 5 17 3.83 2.42 6.21 0.92 5.50 3.67 2.79 4.46 4.08 3.13 12.38 9.25 +70 5 18 5.71 4.33 8.54 2.83 6.42 5.33 8.54 8.96 8.04 7.04 16.88 15.75 +70 5 19 13.33 8.92 8.33 7.12 13.42 9.54 12.04 10.92 12.67 11.83 14.75 20.96 +70 5 20 11.63 12.42 11.00 8.12 17.25 12.21 15.83 12.04 16.42 12.25 19.50 26.25 +70 5 21 21.84 13.42 13.46 10.92 18.96 13.25 17.37 12.75 16.92 16.29 16.25 25.50 +70 5 22 9.46 5.71 8.75 4.83 8.79 6.79 8.87 6.29 9.04 7.08 10.46 14.75 +70 5 23 7.92 13.92 8.04 4.88 11.54 6.87 8.75 7.12 6.08 8.21 20.33 13.70 +70 5 24 13.46 13.59 12.87 6.42 14.25 8.42 10.08 10.79 10.63 11.04 20.96 19.25 +70 5 25 9.92 9.21 8.58 7.54 13.62 10.29 14.17 11.29 13.04 10.96 17.00 20.41 +70 5 26 6.92 6.92 6.63 5.25 10.54 7.71 13.37 8.12 11.17 6.96 14.17 19.58 +70 5 27 6.34 8.25 7.58 3.46 6.17 4.75 6.46 10.88 5.58 5.09 17.67 9.42 +70 5 28 10.67 7.92 6.83 4.50 10.00 5.79 5.33 6.08 7.17 6.17 10.79 11.96 +70 5 29 10.00 9.25 8.25 4.63 11.04 7.12 6.04 8.92 7.62 8.54 14.83 16.21 +70 5 30 10.50 7.62 7.96 5.66 11.79 9.21 10.58 10.83 11.04 8.04 13.92 17.08 +70 5 31 7.92 7.67 9.67 7.00 12.54 9.46 13.37 9.50 12.21 9.79 14.54 17.75 +70 6 1 6.34 5.88 8.54 4.38 8.79 4.75 4.12 6.08 5.96 3.71 11.38 9.83 +70 6 2 5.37 7.12 5.50 2.75 6.71 4.58 6.50 7.54 4.92 6.08 10.96 12.96 +70 6 3 11.92 18.58 8.00 6.96 13.83 8.17 6.75 9.71 8.71 9.13 14.12 15.29 +70 6 4 17.75 13.54 12.92 6.67 12.33 8.79 11.08 10.29 10.04 9.38 9.54 16.50 +70 6 5 14.29 9.38 14.17 6.34 9.71 8.00 8.33 7.75 9.50 7.29 8.17 13.92 +70 6 6 10.21 6.38 19.17 4.46 5.25 6.83 5.91 5.71 7.67 5.58 8.46 9.29 +70 6 7 4.96 3.83 15.21 3.71 4.71 4.54 5.04 3.67 6.21 4.50 7.29 6.63 +70 6 8 5.46 2.42 4.71 3.37 3.88 2.17 3.42 4.00 6.58 5.63 4.83 11.08 +70 6 9 2.21 2.54 5.33 1.96 3.17 0.71 1.46 3.29 2.00 2.13 5.33 8.54 +70 6 10 6.54 2.46 5.96 1.58 6.13 1.33 1.63 2.33 1.50 1.79 4.04 3.79 +70 6 11 6.92 2.58 7.54 1.71 4.54 1.63 2.42 1.63 2.37 0.71 3.21 5.17 +70 6 12 4.12 1.71 5.91 2.58 3.04 2.37 2.25 4.04 3.88 2.13 7.58 3.79 +70 6 13 2.71 3.08 6.79 1.50 2.46 2.75 3.13 2.92 3.37 1.96 7.62 8.75 +70 6 14 6.54 2.96 16.58 5.63 5.66 6.50 5.21 4.12 6.42 2.50 12.33 3.13 +70 6 15 6.58 5.83 6.71 2.46 4.79 3.46 1.75 5.88 3.00 3.63 9.25 7.75 +70 6 16 7.46 5.41 18.46 6.75 9.29 6.42 9.25 6.42 8.08 4.50 11.67 10.17 +70 6 17 11.50 5.58 11.75 4.38 10.71 5.66 11.96 8.54 10.79 8.21 11.12 16.38 +70 6 18 6.87 3.71 4.75 1.83 6.87 4.29 2.00 4.96 1.96 2.46 8.21 8.58 +70 6 19 11.08 6.71 7.29 1.92 3.37 1.50 2.42 3.96 1.92 0.79 7.58 3.08 +70 6 20 15.71 13.21 13.25 6.25 13.29 8.12 7.21 10.08 8.08 8.25 18.66 10.58 +70 6 21 22.71 13.04 17.41 14.83 17.71 14.17 15.41 12.75 16.75 17.83 13.29 27.33 +70 6 22 14.71 11.46 12.12 5.96 11.67 7.08 8.92 8.67 6.42 7.38 13.33 8.29 +70 6 23 13.04 11.87 12.83 5.41 12.21 8.29 8.33 9.54 8.79 9.21 14.09 14.04 +70 6 24 15.12 14.71 15.00 9.00 18.75 11.38 14.50 11.63 13.67 11.21 19.83 19.70 +70 6 25 10.50 9.17 10.96 6.00 10.46 7.54 9.29 9.04 10.58 9.92 16.00 20.54 +70 6 26 9.79 9.50 10.88 5.88 11.08 7.38 7.83 7.33 8.46 7.67 10.21 11.54 +70 6 27 6.38 7.08 8.67 3.75 11.04 6.79 4.79 7.29 6.63 7.83 10.21 9.79 +70 6 28 16.29 12.75 11.54 8.29 13.83 9.79 13.67 9.87 12.87 11.04 16.92 19.62 +70 6 29 16.04 12.04 12.71 10.25 14.92 11.79 15.92 10.00 13.79 14.50 15.46 22.21 +70 6 30 12.17 10.92 10.13 6.71 12.96 8.33 7.87 10.46 10.75 9.08 16.71 16.25 +70 7 1 20.79 17.12 12.50 11.67 20.30 13.54 19.95 14.92 17.41 15.37 23.00 29.63 +70 7 2 20.96 13.17 13.25 10.63 18.54 13.13 18.29 10.63 16.42 15.21 16.17 24.33 +70 7 3 8.92 4.12 9.17 4.75 10.00 6.46 9.42 5.88 9.71 7.62 12.08 14.04 +70 7 4 10.92 9.13 8.12 6.87 12.21 9.25 12.12 7.96 10.29 7.33 11.54 16.79 +70 7 5 10.71 8.83 10.92 5.13 8.83 8.12 9.00 9.13 8.87 9.04 19.12 19.08 +70 7 6 7.79 7.29 9.62 4.21 7.29 6.46 7.08 9.71 7.83 8.00 13.59 11.00 +70 7 7 4.50 5.50 4.67 2.67 2.25 3.25 3.21 6.46 3.46 5.00 14.50 8.17 +70 7 8 12.62 11.17 10.17 7.29 11.17 7.79 6.34 8.25 8.75 7.87 16.79 13.70 +70 7 9 13.92 10.17 11.42 7.92 13.42 10.41 12.62 10.96 12.46 11.46 14.83 19.87 +70 7 10 11.83 9.17 10.79 6.34 12.75 9.62 10.79 9.13 12.25 8.29 16.17 20.79 +70 7 11 13.50 11.38 12.25 7.38 16.17 11.12 15.87 10.63 13.25 13.50 20.17 25.29 +70 7 12 13.42 10.08 13.70 7.96 16.46 12.42 17.50 13.96 16.42 13.29 21.34 23.09 +70 7 13 12.38 9.62 10.88 6.21 14.96 11.21 14.58 9.83 13.37 9.92 16.25 21.84 +70 7 14 21.54 15.29 14.29 9.38 14.79 9.87 12.87 10.50 14.09 11.34 15.96 23.83 +70 7 15 17.12 9.96 13.88 9.42 11.54 8.96 12.46 7.62 12.25 12.00 11.75 20.21 +70 7 16 10.63 3.71 6.34 3.63 8.38 5.17 7.41 4.88 8.33 5.83 10.34 13.59 +70 7 17 7.62 4.42 7.71 3.92 8.25 5.96 8.71 4.63 9.08 4.75 13.59 16.08 +70 7 18 8.96 8.79 8.38 5.66 12.46 7.75 11.04 6.04 10.21 6.92 12.67 18.91 +70 7 19 14.67 8.25 9.46 6.67 9.75 6.63 8.21 6.29 10.17 8.25 11.17 14.79 +70 7 20 14.33 8.33 11.54 7.21 9.13 5.88 9.33 6.96 10.54 8.71 12.62 20.38 +70 7 21 6.54 4.12 8.83 4.83 8.42 6.46 9.17 7.04 9.54 7.71 10.37 14.71 +70 7 22 7.38 3.04 8.08 4.04 7.17 5.63 5.79 4.38 6.25 5.54 9.75 11.71 +70 7 23 13.37 6.50 15.87 6.50 6.17 4.38 4.58 2.62 4.54 2.08 4.92 5.75 +70 7 24 18.79 11.92 14.71 8.87 16.88 11.42 10.79 8.67 10.58 8.42 13.42 19.12 +70 7 25 10.46 9.13 10.37 6.54 10.54 7.50 10.34 5.75 9.46 8.12 9.13 12.92 +70 7 26 11.08 9.21 9.50 4.63 11.75 8.00 8.08 6.67 10.34 4.92 12.87 8.96 +70 7 27 10.46 10.67 12.46 5.37 11.29 7.17 7.79 4.79 8.21 2.58 7.46 5.37 +70 7 28 10.21 9.96 8.58 4.67 10.96 7.87 7.41 6.75 9.00 7.38 10.00 10.63 +70 7 29 11.12 10.04 8.21 4.29 11.58 6.87 6.96 7.92 8.96 7.96 15.54 16.92 +70 7 30 13.67 15.96 13.96 6.67 13.88 10.67 12.17 13.67 12.21 13.04 20.58 20.12 +70 7 31 7.67 6.96 11.34 3.83 6.63 5.33 4.67 5.21 7.25 5.54 10.04 5.79 +70 8 1 3.92 8.92 7.50 3.88 4.42 3.88 2.58 5.09 3.37 3.75 13.96 12.38 +70 8 2 4.96 9.83 4.50 2.46 5.50 4.92 2.62 3.54 4.00 5.25 11.67 6.54 +70 8 3 7.17 13.59 6.79 3.37 8.17 3.88 4.50 3.33 4.92 5.09 5.04 8.87 +70 8 4 2.67 7.92 9.42 1.75 3.96 1.54 2.25 2.04 3.79 3.50 6.00 9.25 +70 8 5 10.17 4.29 12.17 2.83 5.17 5.66 5.37 4.54 8.42 3.50 11.29 6.75 +70 8 6 4.63 3.17 7.79 1.79 3.33 3.46 3.04 2.54 5.54 4.83 10.92 4.96 +70 8 7 5.63 2.79 4.33 1.67 6.04 2.37 1.29 2.29 0.58 2.17 9.92 4.42 +70 8 8 13.50 6.75 4.21 3.96 6.67 5.79 4.46 4.33 7.38 5.46 7.50 11.04 +70 8 9 11.17 8.38 9.54 7.17 14.62 9.59 13.13 8.58 13.29 9.46 15.71 18.29 +70 8 10 10.08 9.08 8.92 6.34 11.25 7.46 8.29 7.04 9.17 7.62 6.58 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2.04 5.41 4.67 5.88 7.33 5.75 6.04 13.75 19.67 +70 10 18 9.79 11.29 8.29 7.96 16.29 11.54 19.62 14.29 18.91 14.92 23.71 32.71 +70 10 19 22.67 17.71 17.41 11.63 18.66 12.96 18.58 15.29 17.12 15.41 23.42 32.58 +70 10 20 24.92 17.92 22.58 13.13 16.88 10.67 16.33 15.29 15.83 13.75 24.75 35.92 +70 10 21 15.16 10.13 17.25 7.58 9.08 6.21 10.96 8.04 9.13 8.54 13.21 21.59 +70 10 22 9.00 4.67 7.58 3.25 3.58 4.50 6.87 4.12 5.25 4.00 9.29 12.25 +70 10 23 10.50 11.08 9.92 3.83 10.96 9.00 12.25 12.54 11.71 11.21 17.50 19.79 +70 10 24 14.67 13.33 12.54 6.75 13.17 11.17 15.87 13.25 13.21 11.71 13.79 14.42 +70 10 25 17.62 12.92 14.96 8.58 14.33 9.96 12.21 9.25 9.29 7.96 11.17 15.96 +70 10 26 5.54 6.13 6.17 4.46 7.58 5.79 8.54 6.71 7.17 8.67 12.50 20.58 +70 10 27 14.46 14.96 12.67 5.63 13.04 9.25 10.17 9.17 9.67 7.62 12.75 14.42 +70 10 28 13.70 13.13 12.58 7.04 12.54 8.00 9.33 8.83 9.04 3.96 15.09 12.21 +70 10 29 17.33 16.75 16.50 6.96 13.62 10.92 13.17 13.25 12.54 12.21 20.75 21.21 +70 10 30 20.30 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6.13 9.54 5.29 10.92 4.71 11.50 8.00 7.67 20.46 +71 3 27 3.96 5.83 7.46 2.04 6.75 3.75 5.79 5.54 5.46 5.37 13.70 13.96 +71 3 28 14.17 12.25 13.17 7.46 12.62 10.25 9.96 10.54 11.50 13.88 17.25 20.30 +71 3 29 3.67 1.54 6.58 2.96 5.33 1.13 3.21 2.62 4.38 3.83 6.63 6.13 +71 3 30 3.92 4.08 5.75 1.08 5.29 1.67 2.04 3.71 4.92 3.50 6.87 5.21 +71 3 31 3.83 1.96 5.29 2.00 3.25 0.96 3.42 1.21 3.96 3.13 6.04 4.33 +71 4 1 15.96 9.42 11.79 8.54 14.37 11.54 9.29 10.63 10.92 10.63 16.79 19.00 +71 4 2 17.29 16.13 20.79 9.67 15.34 13.37 14.79 15.63 14.79 10.21 17.33 20.79 +71 4 3 12.92 13.17 24.04 7.62 11.79 8.08 12.96 8.04 10.75 7.50 11.96 15.12 +71 4 4 9.38 7.08 19.17 6.38 8.96 4.75 8.25 3.54 7.12 4.33 5.17 12.25 +71 4 5 7.29 2.67 5.46 3.13 4.33 0.50 1.46 1.29 3.92 2.46 2.88 11.92 +71 4 6 4.17 4.54 6.04 1.79 3.21 0.79 1.42 1.83 1.63 3.17 5.63 8.12 +71 4 7 7.46 5.83 13.17 4.38 7.12 2.25 2.92 4.50 3.75 2.92 5.96 7.12 +71 4 8 8.42 5.37 18.25 7.25 7.00 3.92 6.63 2.96 6.04 2.54 4.38 3.71 +71 4 9 9.75 6.42 19.70 6.50 7.92 4.58 5.17 4.25 6.83 3.63 6.58 4.58 +71 4 10 8.17 3.79 11.42 3.29 5.37 3.50 4.33 2.25 6.08 2.42 6.13 3.88 +71 4 11 6.25 1.87 9.17 1.92 2.58 0.96 1.42 1.25 2.75 1.50 5.13 3.75 +71 4 12 14.04 9.79 10.79 4.38 7.00 5.33 5.79 6.96 8.21 5.58 9.46 7.62 +71 4 13 10.79 4.12 9.62 4.25 8.25 4.71 5.63 5.96 6.46 4.54 4.46 13.79 +71 4 14 7.41 1.79 7.96 1.42 2.46 2.04 2.13 2.08 4.71 1.38 6.67 4.50 +71 4 15 6.38 1.96 8.42 2.33 6.71 3.29 2.29 3.83 3.83 3.17 7.96 8.46 +71 4 16 14.79 9.21 12.12 9.17 14.33 9.71 12.00 10.08 12.75 11.87 13.88 21.84 +71 4 17 11.42 7.33 11.46 5.91 13.75 9.29 13.75 10.29 12.21 11.63 17.67 22.13 +71 4 18 11.04 9.00 12.71 6.63 13.54 10.25 15.83 10.08 15.00 11.96 16.04 23.83 +71 4 19 5.00 4.71 8.67 3.63 7.58 5.46 6.38 5.33 9.17 8.17 8.83 13.67 +71 4 20 9.25 11.42 5.17 3.25 7.29 4.29 3.88 4.38 6.00 4.38 7.46 9.96 +71 4 21 10.13 4.71 6.42 4.21 6.46 7.58 7.50 4.42 8.17 8.79 12.21 15.67 +71 4 22 13.92 9.75 6.71 4.50 7.46 3.92 1.50 6.21 6.75 5.33 6.67 10.88 +71 4 23 18.34 9.00 15.87 9.46 9.46 7.83 11.25 6.34 14.21 12.92 5.66 15.12 +71 4 24 12.25 8.33 10.54 8.17 9.92 8.08 12.62 6.75 9.17 11.21 7.67 18.66 +71 4 25 12.08 13.33 11.79 5.75 15.29 7.67 8.46 12.12 10.25 8.54 14.75 18.41 +71 4 26 9.75 6.79 16.46 5.96 9.17 7.38 11.21 10.37 9.21 7.79 13.88 14.04 +71 4 27 5.46 6.87 8.17 2.92 5.41 2.50 3.83 6.58 5.83 6.54 10.88 15.92 +71 4 28 5.79 1.46 5.83 0.87 3.63 1.21 2.37 2.29 4.08 2.13 3.83 8.71 +71 4 29 6.42 3.79 3.71 1.63 4.08 2.33 2.75 2.33 1.75 0.67 7.79 7.46 +71 4 30 5.21 5.79 4.21 1.83 5.17 2.46 2.83 3.13 3.04 3.63 6.71 8.12 +71 5 1 3.46 1.92 6.83 0.83 1.83 0.37 1.42 1.96 1.54 0.50 4.17 4.00 +71 5 2 6.00 7.54 6.00 2.25 3.46 3.58 3.50 6.25 3.71 4.21 15.67 7.54 +71 5 3 12.92 15.21 7.92 5.50 12.21 8.17 4.33 9.33 8.63 8.71 16.54 13.08 +71 5 4 12.33 13.42 7.08 5.21 11.58 7.46 5.41 6.71 8.96 10.00 11.67 10.50 +71 5 5 16.21 19.46 12.38 7.83 16.50 13.13 7.21 12.38 11.96 10.46 14.33 17.83 +71 5 6 14.12 16.50 10.88 5.25 11.75 6.92 6.54 8.96 9.17 7.58 11.83 17.04 +71 5 7 17.54 15.96 13.59 10.29 14.71 10.71 8.63 10.67 12.29 11.17 10.75 17.25 +71 5 8 15.41 12.67 13.08 9.13 11.34 9.71 7.87 10.00 11.29 12.46 16.58 12.67 +71 5 9 16.42 15.46 14.54 9.17 12.00 10.83 11.46 12.38 12.75 13.92 20.71 18.79 +71 5 10 12.92 13.04 14.21 7.75 11.58 8.83 10.04 15.25 10.88 12.75 24.37 18.34 +71 5 11 5.41 6.04 4.12 1.92 4.38 2.83 1.63 7.41 4.33 6.67 14.71 13.67 +71 5 12 10.08 1.83 14.50 3.33 3.67 3.33 5.17 2.37 4.96 2.29 6.71 6.75 +71 5 13 17.16 6.25 13.75 4.63 5.41 6.08 7.17 8.21 10.96 7.25 8.00 14.21 +71 5 14 7.87 3.29 5.33 3.58 3.37 2.62 4.42 3.37 6.00 5.09 7.21 10.75 +71 5 15 20.91 12.00 13.75 9.04 11.96 8.63 11.12 9.25 12.33 11.87 16.54 21.34 +71 5 16 15.92 11.00 7.21 5.71 13.33 5.75 9.46 10.34 9.67 6.38 17.33 21.84 +71 5 17 12.04 8.96 8.67 5.66 9.92 5.79 7.38 7.46 9.92 9.13 13.21 13.04 +71 5 18 8.87 4.58 6.71 3.92 6.21 3.04 4.25 5.17 7.71 6.04 8.00 13.50 +71 5 19 8.50 3.08 5.71 2.42 3.25 1.38 1.54 2.88 3.00 2.33 6.67 11.96 +71 5 20 7.96 3.46 8.33 2.54 3.21 1.00 1.21 2.71 3.04 4.33 5.54 9.46 +71 5 21 6.92 6.13 8.04 2.46 4.96 3.37 3.46 8.00 4.33 4.58 13.54 9.08 +71 5 22 11.83 7.33 7.62 4.71 8.25 4.21 3.00 6.08 6.25 6.38 7.75 11.00 +71 5 23 16.71 11.54 10.63 6.75 11.12 5.17 4.67 10.13 8.04 9.96 15.54 15.79 +71 5 24 8.08 7.54 7.33 2.33 4.29 2.58 2.54 5.00 5.46 4.88 12.67 16.71 +71 5 25 6.04 3.25 15.54 3.46 3.13 3.96 7.04 2.54 5.71 2.75 5.63 9.83 +71 5 26 8.29 7.29 8.75 4.79 9.71 5.58 7.46 6.96 8.04 7.83 12.96 16.21 +71 5 27 17.92 11.38 9.75 6.29 13.88 7.17 10.63 10.34 10.58 9.62 17.37 18.08 +71 5 28 8.25 9.67 9.25 3.92 10.67 5.33 8.50 6.58 6.83 6.83 9.29 9.29 +71 5 29 7.17 6.67 9.17 4.83 6.54 4.96 6.17 6.00 6.25 7.41 11.04 13.79 +71 5 30 8.87 8.46 10.13 4.17 10.04 4.63 4.88 7.58 5.50 5.75 10.71 7.50 +71 5 31 5.09 4.46 5.71 1.87 3.75 1.00 4.04 1.87 2.25 1.67 2.67 5.50 +71 6 1 7.17 12.25 6.04 3.54 8.21 4.25 6.42 5.46 4.75 5.63 6.71 8.92 +71 6 2 13.46 10.58 12.29 4.75 7.04 5.29 6.13 7.58 8.33 6.25 9.21 11.67 +71 6 3 13.54 9.04 25.50 6.04 9.75 10.00 8.63 9.67 12.04 9.00 10.92 10.46 +71 6 4 12.29 2.92 23.71 5.25 7.87 8.96 9.96 7.58 10.54 7.21 9.75 10.41 +71 6 5 7.17 4.38 20.83 5.58 8.29 5.58 7.75 7.67 6.46 5.09 11.75 10.41 +71 6 6 3.96 3.25 15.12 3.83 4.67 2.67 4.71 5.33 3.29 2.08 12.04 9.25 +71 6 7 4.96 3.92 12.08 3.29 5.04 1.83 4.00 4.75 3.25 2.62 9.04 8.54 +71 6 8 9.08 8.42 7.08 3.42 10.04 4.88 4.38 10.13 6.17 5.41 16.83 14.67 +71 6 9 13.21 11.38 24.08 8.96 10.50 7.38 10.13 7.92 7.67 6.25 15.34 12.04 +71 6 10 9.38 13.37 22.75 7.08 10.83 5.88 8.29 8.96 7.12 6.29 14.04 14.33 +71 6 11 16.33 10.92 12.29 7.33 11.00 5.13 9.54 8.46 9.25 7.46 12.83 16.58 +71 6 12 11.04 8.17 8.29 4.79 9.71 3.67 4.50 4.88 6.21 3.50 7.12 21.12 +71 6 13 10.88 8.63 9.83 3.33 7.71 3.17 3.88 5.75 4.00 4.63 11.34 15.96 +71 6 14 15.25 12.71 25.62 8.58 11.12 5.79 9.67 11.87 8.08 8.00 17.21 19.75 +71 6 15 15.00 9.59 11.34 6.46 12.50 6.58 8.04 7.29 9.96 6.75 11.08 14.88 +71 6 16 12.42 7.96 9.42 6.38 12.54 6.92 9.75 6.96 10.46 8.25 10.54 14.88 +71 6 17 7.33 6.38 8.04 4.12 7.25 4.00 6.21 3.42 5.50 6.42 6.71 11.29 +71 6 18 19.12 17.21 12.50 7.00 16.17 8.63 7.96 9.29 10.08 4.04 13.92 12.42 +71 6 19 14.58 10.04 11.58 6.96 12.33 5.96 7.41 7.12 7.71 3.37 7.46 10.34 +71 6 20 16.42 9.75 11.63 9.04 14.04 8.71 11.25 8.71 11.63 8.87 12.33 13.70 +71 6 21 13.54 11.29 11.75 7.21 13.21 8.58 9.83 11.29 12.12 11.71 16.08 21.62 +71 6 22 7.38 4.50 6.75 2.79 5.96 2.54 6.04 1.92 5.29 5.29 4.42 10.17 +71 6 23 4.79 6.71 4.63 3.08 2.79 2.08 3.83 3.88 2.88 5.13 7.33 3.88 +71 6 24 8.71 10.00 12.00 3.83 9.96 5.04 5.79 9.21 7.67 7.62 14.92 10.13 +71 6 25 14.29 12.87 13.21 7.33 11.38 6.21 6.71 8.58 8.92 8.33 12.50 10.71 +71 6 26 14.46 12.21 11.83 8.79 16.21 10.25 12.92 11.92 13.33 11.42 16.13 14.50 +71 6 27 10.75 9.08 11.29 6.21 13.00 7.00 10.21 8.67 10.88 8.50 15.09 16.66 +71 6 28 8.54 7.75 9.54 4.96 8.38 4.54 6.75 4.71 8.00 5.63 7.17 13.33 +71 6 29 3.96 7.29 8.38 3.67 5.25 3.25 3.79 5.63 6.46 1.46 6.21 4.54 +71 6 30 6.87 7.04 7.33 4.42 9.50 5.04 3.71 6.04 7.21 4.54 14.04 9.13 +71 7 1 5.46 8.12 5.33 2.67 6.42 2.96 7.29 8.67 6.21 5.96 16.75 9.33 +71 7 2 5.75 10.34 6.42 4.42 10.79 4.58 3.37 6.17 5.37 7.04 15.12 10.67 +71 7 3 4.79 6.34 6.29 4.42 6.42 3.33 5.17 4.12 5.13 3.04 4.33 7.50 +71 7 4 4.54 4.25 6.25 2.37 5.17 2.67 2.37 4.75 4.63 3.79 7.75 6.92 +71 7 5 8.04 4.29 6.29 2.58 4.25 1.00 3.88 2.54 3.54 1.87 7.25 9.08 +71 7 6 4.88 2.25 6.38 1.63 2.96 0.71 3.63 1.79 2.42 0.96 6.87 4.88 +71 7 7 6.96 5.91 4.71 4.58 9.00 4.63 5.09 4.29 5.71 5.91 4.71 6.79 +71 7 8 7.96 9.13 7.62 4.46 7.92 4.17 6.54 6.00 5.79 4.21 10.25 12.08 +71 7 9 9.75 7.33 18.50 5.13 7.87 3.00 6.21 2.33 4.38 3.46 4.04 6.50 +71 7 10 5.71 2.75 6.96 1.83 5.41 1.75 3.83 4.50 4.00 4.04 11.29 13.46 +71 7 11 9.17 6.17 5.13 3.96 8.38 3.79 8.29 5.63 6.79 6.67 8.87 15.16 +71 7 12 6.21 11.42 22.04 7.29 9.92 6.04 9.46 7.21 7.87 7.50 10.13 12.62 +71 7 13 7.00 7.67 15.16 6.54 8.17 3.67 4.83 3.83 5.91 4.96 6.54 11.29 +71 7 14 9.29 6.75 6.13 5.71 6.87 3.46 8.04 4.50 8.17 7.33 6.25 16.17 +71 7 15 13.54 8.04 10.34 6.92 10.63 6.50 12.67 9.21 11.83 10.75 10.29 21.67 +71 7 16 7.75 7.71 13.04 6.42 8.83 4.83 8.50 6.17 7.46 7.58 7.33 15.34 +71 7 17 6.34 5.83 9.79 3.67 5.71 0.83 6.50 2.62 3.71 2.00 3.25 7.71 +71 7 18 6.83 2.75 6.83 1.96 6.46 2.33 4.79 2.79 2.58 1.87 4.83 7.38 +71 7 19 9.79 3.04 7.41 2.67 7.21 2.71 7.38 4.04 5.66 4.50 5.79 8.08 +71 7 20 6.25 5.04 6.34 1.17 3.92 1.21 4.58 1.87 2.17 0.92 5.29 7.67 +71 7 21 10.37 10.92 7.67 3.63 9.83 3.08 4.50 2.08 2.71 1.33 5.83 5.29 +71 7 22 9.21 7.50 9.17 3.96 9.21 5.71 7.25 6.58 7.25 4.29 8.54 20.54 +71 7 23 11.12 7.41 9.50 5.13 11.58 7.04 7.17 7.87 7.00 6.58 7.62 24.37 +71 7 24 11.58 5.66 11.00 4.04 9.29 3.96 7.50 3.46 7.17 4.38 9.38 14.92 +71 7 25 8.63 5.88 9.59 6.00 7.29 4.50 8.71 1.83 5.88 3.92 9.42 5.63 +71 7 26 5.63 4.33 9.00 1.38 5.37 1.46 3.00 3.67 2.58 0.92 9.83 4.12 +71 7 27 8.42 5.25 5.66 2.79 5.04 1.79 2.92 1.75 4.42 1.21 6.13 2.88 +71 7 28 7.50 12.33 6.75 4.50 9.62 4.54 4.67 6.58 5.63 5.88 16.96 10.00 +71 7 29 11.29 13.50 7.79 7.08 12.92 7.67 3.63 8.71 8.08 8.25 18.25 10.92 +71 7 30 7.38 8.87 6.71 5.00 9.92 3.17 5.58 5.88 6.04 5.00 8.08 9.71 +71 7 31 12.38 7.58 10.54 4.67 9.67 5.50 7.96 6.00 7.12 3.17 7.67 9.17 +71 8 1 7.71 6.00 9.54 2.75 8.75 5.09 6.25 2.21 5.71 3.88 5.25 9.33 +71 8 2 7.25 6.71 8.46 5.58 8.87 5.37 7.67 3.25 5.75 3.63 6.71 6.29 +71 8 3 11.87 5.54 11.34 5.66 9.46 7.67 9.59 7.62 7.58 6.54 8.21 13.79 +71 8 4 9.92 7.75 11.92 4.71 7.08 3.63 6.92 4.08 5.63 2.75 4.83 13.46 +71 8 5 5.21 5.29 8.79 2.42 5.91 4.25 5.37 4.58 5.75 3.83 10.17 10.25 +71 8 6 11.92 6.71 7.04 5.33 9.62 5.63 7.67 7.58 8.96 7.58 12.25 20.21 +71 8 7 8.33 6.21 9.46 3.67 8.25 4.12 7.67 4.79 6.13 4.21 9.59 9.50 +71 8 8 9.25 7.21 9.92 4.71 13.50 7.12 12.25 7.17 10.25 6.46 12.58 14.50 +71 8 9 7.41 5.41 6.50 3.71 7.54 3.46 8.17 4.88 7.00 5.17 10.21 14.50 +71 8 10 15.83 8.33 12.58 7.83 10.88 4.00 7.58 4.12 7.58 4.92 8.21 8.08 +71 8 11 8.46 6.92 7.92 3.37 7.71 2.46 6.17 3.33 5.66 1.67 6.87 8.54 +71 8 12 11.08 7.00 9.04 3.37 9.59 3.92 6.29 7.21 7.29 5.21 7.33 8.83 +71 8 13 6.92 3.71 6.58 3.50 7.08 3.54 5.79 5.41 6.25 6.00 10.83 18.46 +71 8 14 12.04 9.08 15.59 6.79 10.92 8.08 13.08 11.38 11.08 12.92 19.41 23.38 +71 8 15 6.38 6.08 8.92 4.12 7.38 1.63 5.63 4.88 6.50 8.21 9.29 13.70 +71 8 16 5.29 2.92 6.13 1.46 4.38 0.46 4.50 1.33 2.00 1.42 3.96 7.50 +71 8 17 11.29 3.17 17.37 3.21 6.21 3.42 4.21 3.88 3.79 1.67 6.75 5.41 +71 8 18 9.92 7.29 19.92 5.71 7.41 6.21 9.42 4.33 7.46 3.79 9.13 5.13 +71 8 19 7.33 5.04 16.96 4.75 7.79 3.21 5.96 2.92 4.83 1.50 5.29 2.17 +71 8 20 13.50 2.46 6.67 5.50 7.17 3.83 7.08 2.67 4.58 3.88 5.88 5.33 +71 8 21 5.17 3.71 5.00 2.92 2.50 1.17 4.71 2.04 1.38 0.79 10.79 4.71 +71 8 22 9.67 12.79 9.46 3.83 7.75 3.25 5.41 7.71 4.46 3.58 13.88 6.75 +71 8 23 9.87 7.25 8.67 3.54 9.21 3.75 3.50 7.38 6.79 5.33 11.58 9.92 +71 8 24 4.21 0.83 7.17 1.79 3.46 0.21 1.46 0.08 1.92 0.13 1.63 3.92 +71 8 25 3.79 3.92 6.29 3.00 5.04 3.04 2.79 5.25 4.54 5.88 12.79 11.42 +71 8 26 9.42 9.62 8.00 4.12 11.46 6.54 6.46 8.83 8.12 8.46 13.00 15.67 +71 8 27 14.21 10.46 11.96 6.17 13.62 8.42 11.29 8.21 11.04 9.83 16.33 17.41 +71 8 28 18.29 13.92 15.75 6.96 12.79 7.08 10.79 5.71 9.54 5.83 11.71 16.08 +71 8 29 14.62 10.96 11.63 7.67 14.46 8.79 11.04 6.21 10.67 6.00 13.17 13.75 +71 8 30 12.00 10.50 9.13 5.41 15.16 8.04 11.58 8.12 11.08 8.25 13.75 17.33 +71 8 31 15.12 13.96 13.33 7.21 17.37 11.34 14.67 12.12 13.54 13.25 21.34 21.21 +71 9 1 11.08 8.54 10.08 7.17 13.70 11.50 13.25 10.29 12.75 12.83 17.33 24.87 +71 9 2 12.75 9.54 13.88 5.58 11.75 8.92 12.04 9.33 9.92 8.50 14.79 17.50 +71 9 3 6.38 1.46 8.67 3.04 1.83 2.25 2.83 1.38 1.25 2.83 9.62 14.62 +71 9 4 2.50 3.79 3.63 1.71 2.29 0.92 4.63 1.33 3.13 3.54 6.46 13.46 +71 9 5 10.08 6.71 5.09 3.37 10.17 6.21 5.04 4.12 3.92 3.21 6.63 9.42 +71 9 6 11.67 11.21 6.34 5.25 10.29 8.92 6.83 4.92 6.29 5.33 7.17 12.46 +71 9 7 10.08 8.29 7.00 4.79 9.42 6.87 6.71 5.25 6.83 6.00 6.83 14.71 +71 9 8 11.04 8.04 8.67 4.50 10.63 6.58 5.46 6.42 7.58 7.71 6.67 15.12 +71 9 9 12.67 7.62 10.46 5.41 11.00 11.12 6.08 9.42 10.67 11.34 11.25 19.08 +71 9 10 9.79 10.25 8.54 4.88 12.17 9.71 8.96 9.67 10.13 11.83 13.37 19.70 +71 9 11 10.46 6.50 10.17 5.17 10.54 8.21 8.21 6.71 8.92 8.17 9.62 15.92 +71 9 12 5.33 1.83 4.54 0.87 3.71 0.92 1.67 0.83 0.33 2.04 1.63 4.21 +71 9 13 1.79 7.12 4.92 1.13 5.29 2.79 2.54 1.50 2.50 1.25 10.13 5.13 +71 9 14 3.21 5.63 3.29 0.87 3.17 2.00 4.25 1.67 1.58 0.79 10.50 4.42 +71 9 15 3.46 3.79 3.88 0.96 1.46 1.46 3.92 0.67 0.92 0.29 6.17 3.00 +71 9 16 5.54 7.75 6.00 2.33 8.46 5.46 3.67 2.71 3.00 2.50 12.12 7.87 +71 9 17 12.25 9.00 8.17 5.21 11.08 7.29 7.33 4.54 5.75 5.63 13.21 11.04 +71 9 18 3.46 2.75 2.96 1.54 4.54 1.92 1.58 1.83 0.83 1.13 4.21 10.46 +71 9 19 8.50 2.37 5.09 2.25 6.00 4.04 6.96 3.33 4.38 3.13 7.41 13.67 +71 9 20 6.42 2.42 3.42 2.08 6.13 3.50 5.13 2.92 6.21 3.92 8.92 14.71 +71 9 21 2.46 1.87 2.37 1.04 3.42 2.33 2.83 2.96 3.46 2.83 7.67 13.21 +71 9 22 4.79 1.50 12.38 1.33 2.67 1.08 2.00 1.08 0.67 0.37 4.29 2.62 +71 9 23 4.92 5.04 4.46 1.04 3.92 2.04 6.00 4.21 4.63 4.58 10.75 14.67 +71 9 24 10.75 2.17 18.41 2.79 7.29 6.92 6.17 4.83 6.38 2.71 8.46 10.00 +71 9 25 9.83 6.83 8.00 4.21 9.33 4.88 6.54 5.00 5.71 4.67 10.13 13.67 +71 9 26 16.29 17.16 11.54 7.79 16.66 10.13 8.71 8.38 8.42 7.21 12.54 14.88 +71 9 27 10.54 3.58 9.00 4.92 8.21 5.04 8.38 3.04 6.08 3.75 4.42 9.96 +71 9 28 7.12 6.50 6.50 2.13 5.46 4.92 4.17 4.58 3.71 3.58 12.67 12.54 +71 9 29 15.50 13.08 14.46 6.38 9.71 8.12 10.79 4.54 8.50 6.29 8.25 10.71 +71 9 30 10.29 13.04 9.21 4.38 10.00 7.83 7.41 5.66 7.12 4.50 13.75 15.71 +71 10 1 11.08 11.54 11.38 8.17 9.38 7.29 5.71 7.46 7.96 8.54 14.58 15.63 +71 10 2 10.04 6.96 5.50 3.58 8.46 5.75 4.79 3.79 4.25 3.08 7.17 8.79 +71 10 3 10.00 3.13 6.83 2.67 6.00 2.67 2.92 1.04 3.08 2.71 2.21 5.71 +71 10 4 6.08 10.67 4.63 2.67 7.62 5.54 5.17 3.71 5.50 5.33 8.63 11.87 +71 10 5 13.88 13.42 12.46 7.75 13.42 12.08 7.04 8.33 10.50 10.25 14.17 20.41 +71 10 6 12.75 10.83 10.92 7.00 11.63 10.17 10.79 10.58 10.71 12.87 22.17 20.67 +71 10 7 7.46 6.92 8.75 3.58 8.00 6.50 8.67 10.08 8.58 11.25 15.00 10.25 +71 10 8 4.29 6.29 6.00 3.21 8.87 6.54 9.75 7.38 7.87 8.75 7.41 10.96 +71 10 9 5.13 6.00 6.08 2.75 9.59 5.91 9.92 5.96 8.17 7.38 14.42 18.79 +71 10 10 17.46 16.62 15.79 8.83 16.75 13.21 16.92 13.75 14.46 15.34 20.25 24.75 +71 10 11 9.46 6.25 9.67 4.17 10.08 6.87 8.33 4.83 8.25 5.96 14.96 19.70 +71 10 12 8.25 7.67 14.71 3.79 6.96 4.50 7.33 5.09 6.58 5.88 10.96 19.95 +71 10 13 13.59 11.87 20.67 4.58 9.21 6.87 12.46 6.00 8.29 7.00 5.66 12.08 +71 10 14 13.29 10.08 12.04 4.79 9.92 6.58 6.38 7.71 7.71 9.25 15.87 16.71 +71 10 15 19.41 12.21 18.46 8.42 14.33 10.88 11.92 9.71 11.75 13.79 20.58 23.16 +71 10 16 10.08 9.46 10.54 3.25 8.79 6.42 6.42 5.50 7.17 7.58 12.54 14.50 +71 10 17 22.42 19.92 18.16 11.21 19.41 14.79 17.00 12.96 15.63 14.25 21.34 22.75 +71 10 18 22.46 19.21 19.00 8.54 18.21 12.29 16.62 12.21 12.87 12.58 23.29 24.87 +71 10 19 17.04 17.83 14.88 9.87 17.96 11.79 17.08 10.67 13.33 13.37 21.87 26.08 +71 10 20 14.50 11.38 10.75 4.83 12.33 8.54 14.21 4.88 9.54 10.17 15.67 25.17 +71 10 21 26.20 20.54 21.67 12.00 17.54 14.92 16.79 18.16 14.79 19.12 32.63 28.67 +71 10 22 13.25 14.42 17.71 9.08 10.96 9.59 14.46 12.25 13.88 14.92 26.25 21.59 +71 10 23 12.38 12.96 11.29 6.42 8.58 7.92 6.71 6.21 7.12 8.29 15.21 17.67 +71 10 24 13.00 6.21 12.38 4.88 8.29 7.00 8.04 4.04 7.25 6.34 6.17 11.96 +71 10 25 10.21 10.00 10.63 2.08 8.33 5.79 7.12 4.63 6.63 3.88 8.75 10.13 +71 10 26 12.21 11.25 12.92 5.04 11.96 9.04 10.46 8.25 7.62 7.50 12.96 16.92 +71 10 27 14.37 13.00 13.70 4.58 13.25 8.75 10.67 8.00 7.92 8.58 16.08 9.92 +71 10 28 16.29 13.33 18.21 8.29 13.92 13.00 13.75 10.79 11.12 11.63 16.08 19.70 +71 10 29 9.75 8.17 7.38 3.75 10.92 7.71 7.92 8.38 8.08 10.50 20.71 20.83 +71 10 30 15.75 8.29 14.09 6.42 10.96 9.54 10.71 10.29 9.96 11.54 21.37 24.87 +71 10 31 8.63 7.87 9.29 2.92 8.83 7.00 6.21 8.21 6.21 9.08 19.17 19.55 +71 11 1 14.21 10.92 13.50 6.08 9.75 10.67 9.79 10.08 9.13 12.33 20.62 21.59 +71 11 2 17.00 12.21 18.08 10.46 10.37 10.25 14.96 10.83 10.96 12.46 18.00 25.12 +71 11 3 14.04 12.38 14.71 6.34 9.92 8.08 9.00 11.54 7.83 10.37 22.42 21.29 +71 11 4 18.71 15.16 17.08 9.17 14.37 12.42 15.12 14.25 12.29 16.13 10.71 12.38 +71 11 5 16.04 9.92 12.42 6.67 10.21 7.75 11.96 5.33 12.38 7.46 13.29 20.96 +71 11 6 15.83 12.17 12.46 6.75 11.29 7.46 9.54 7.25 9.83 10.25 15.54 22.29 +71 11 7 14.33 12.83 12.17 5.96 14.79 10.79 13.92 10.17 13.59 11.50 19.41 27.79 +71 11 8 23.96 18.25 16.71 10.83 15.16 10.08 15.12 11.25 15.50 15.12 24.30 38.04 +71 11 9 17.62 11.87 20.00 6.71 9.17 6.58 10.21 4.71 7.62 7.00 14.09 24.13 +71 11 10 7.75 2.92 10.50 1.42 4.92 1.87 6.21 1.63 4.67 3.25 8.33 14.42 +71 11 11 4.25 4.42 7.17 0.92 4.79 0.96 2.88 0.87 1.79 1.29 5.71 10.88 +71 11 12 5.37 4.88 7.00 3.33 8.92 5.33 9.08 5.13 8.12 6.50 12.25 16.08 +71 11 13 13.88 12.25 11.50 7.12 10.37 8.54 10.00 6.71 9.87 9.96 15.96 25.96 +71 11 14 9.29 5.46 9.33 3.17 6.83 3.54 7.58 4.21 6.21 6.42 9.38 18.29 +71 11 15 7.54 8.17 6.46 4.58 11.29 7.75 14.37 7.00 12.46 10.67 18.54 24.08 +71 11 16 11.21 9.96 10.41 8.04 12.54 8.75 14.37 7.08 13.37 9.87 12.29 27.16 +71 11 17 11.34 12.17 9.33 7.17 14.17 8.67 12.17 7.92 11.17 8.71 12.71 16.83 +71 11 18 16.92 11.17 15.96 9.87 14.00 9.62 11.63 7.21 9.59 8.83 13.42 24.83 +71 11 19 12.75 6.13 15.59 6.13 7.04 5.09 11.34 3.42 7.96 7.87 10.63 22.17 +71 11 20 18.75 18.91 15.34 9.87 19.67 12.96 13.13 12.21 13.33 9.04 20.67 22.71 +71 11 21 23.58 20.62 21.50 15.63 18.84 14.50 20.33 12.96 16.54 16.92 24.58 35.92 +71 11 22 18.21 16.17 16.83 9.71 12.67 9.59 12.38 8.50 11.54 8.00 18.96 24.46 +71 11 23 20.54 19.04 16.62 9.96 15.09 9.59 12.50 10.41 12.29 10.54 17.37 20.96 +71 11 24 6.00 1.21 8.63 2.21 3.04 1.29 7.08 1.33 4.04 2.33 5.63 12.58 +71 11 25 5.91 4.17 6.87 2.71 8.42 5.79 12.42 3.54 8.83 6.25 10.46 14.37 +71 11 26 7.92 6.58 5.50 3.71 9.54 5.96 9.21 6.96 9.38 7.71 16.33 17.12 +71 11 27 11.63 10.13 8.83 4.50 9.38 7.46 9.62 5.71 8.92 8.33 13.46 17.33 +71 11 28 7.50 8.67 5.83 1.29 7.17 4.67 9.04 2.17 6.46 5.13 10.75 14.21 +71 11 29 11.67 10.79 12.04 3.21 11.79 8.00 7.83 7.62 7.67 10.00 19.75 21.37 +71 11 30 16.29 16.08 8.33 4.00 11.34 5.79 9.08 5.83 8.08 4.21 18.79 15.29 +71 12 1 13.04 10.37 9.00 3.42 8.25 5.50 8.54 3.00 7.04 4.54 10.71 14.12 +71 12 2 12.50 10.29 12.96 3.46 6.17 1.96 5.13 1.46 3.58 2.17 6.04 9.87 +71 12 3 3.33 7.92 6.34 0.37 5.58 4.67 7.41 5.41 5.00 4.92 14.33 18.12 +71 12 4 4.08 6.67 7.12 2.08 5.09 5.25 9.67 4.54 6.58 7.00 13.04 16.38 +71 12 5 11.50 6.79 6.08 2.46 8.58 4.08 4.96 7.04 4.33 7.62 16.75 16.71 +71 12 6 5.29 4.04 4.46 1.04 4.67 2.79 5.96 3.75 3.75 5.75 10.63 18.00 +71 12 7 5.46 0.42 5.09 0.50 1.63 0.46 8.33 0.04 2.17 4.08 7.54 17.79 +71 12 8 4.67 0.67 5.75 0.33 2.13 1.71 8.42 1.21 4.17 4.17 10.83 16.38 +71 12 9 6.96 3.75 6.17 4.67 7.58 5.41 11.12 7.08 9.71 9.71 13.88 22.95 +71 12 10 7.96 6.21 7.04 5.25 8.87 6.75 11.58 7.12 11.54 9.08 15.87 20.75 +71 12 11 9.13 7.71 6.67 3.37 8.17 7.17 12.29 5.63 8.79 10.75 15.29 21.00 +71 12 12 16.33 12.75 13.67 6.13 10.25 9.42 10.88 10.34 9.29 12.25 21.75 20.79 +71 12 13 15.79 10.83 13.92 6.29 10.83 8.38 12.08 6.58 9.75 10.67 15.12 19.38 +71 12 14 20.38 18.58 19.12 11.25 17.58 14.00 17.04 15.00 14.62 18.54 26.34 28.08 +71 12 15 19.00 15.83 16.29 8.17 13.50 12.29 15.37 15.63 13.33 15.96 26.67 29.04 +71 12 16 17.00 16.96 14.46 7.12 11.83 11.17 12.29 10.75 10.79 12.75 20.96 21.59 +71 12 17 18.12 16.21 14.54 8.75 12.62 9.96 9.17 11.75 11.54 14.96 26.50 24.83 +71 12 18 26.50 20.25 20.00 15.09 17.58 14.21 14.62 14.21 16.29 17.29 28.04 26.42 +71 12 19 25.00 23.54 19.70 14.58 23.91 16.71 19.33 16.96 17.08 17.46 29.20 31.42 +71 12 20 21.84 21.17 20.08 10.63 20.38 16.46 21.92 17.54 18.71 18.50 28.16 29.95 +71 12 21 10.46 8.08 10.34 6.50 11.42 8.42 15.41 11.08 13.92 13.25 23.13 30.13 +71 12 22 7.58 5.09 10.83 3.04 4.21 1.38 3.96 0.54 1.87 1.58 7.46 14.88 +71 12 23 8.12 8.08 10.13 3.00 9.33 7.41 13.13 8.46 8.87 9.38 22.88 24.58 +71 12 24 18.46 15.25 16.38 7.83 12.29 11.96 16.42 14.92 13.62 15.67 27.84 26.04 +71 12 25 22.08 15.96 15.04 10.08 14.96 12.46 11.79 11.67 12.00 14.75 15.79 19.46 +71 12 26 15.12 12.75 17.04 7.79 11.17 8.21 11.04 9.25 8.83 7.08 12.04 13.88 +71 12 27 8.75 6.13 9.04 3.13 7.00 7.41 13.13 12.96 11.12 10.88 19.25 27.00 +71 12 28 13.25 9.67 19.12 7.50 11.34 7.33 13.25 9.21 9.46 7.75 9.04 15.46 +71 12 29 12.42 10.00 24.25 7.46 10.25 6.00 14.58 6.67 9.21 8.63 5.66 17.46 +71 12 30 13.04 6.34 27.67 8.42 11.54 4.12 16.21 6.71 10.46 7.29 6.67 16.46 +71 12 31 14.88 10.50 26.08 8.46 13.50 10.04 21.04 10.25 13.54 11.34 12.12 27.33 +72 1 1 9.29 3.63 14.54 4.25 6.75 4.42 13.00 5.33 10.04 8.54 8.71 19.17 +72 1 2 8.96 1.46 12.00 4.50 3.21 0.83 6.04 0.67 3.79 0.79 1.75 7.83 +72 1 3 6.83 0.67 9.25 2.29 2.46 0.17 3.88 0.21 2.88 1.25 2.21 5.88 +72 1 4 8.46 0.87 5.41 2.04 4.79 0.29 2.62 0.92 1.63 1.96 4.38 6.58 +72 1 5 10.88 9.17 5.88 0.87 2.04 0.13 2.75 1.38 1.08 1.13 4.83 7.29 +72 1 6 17.00 13.92 13.54 4.46 12.29 10.25 7.62 7.75 7.87 6.17 9.79 13.54 +72 1 7 13.92 12.00 19.12 8.00 12.50 12.08 16.46 11.63 11.12 13.42 14.67 25.04 +72 1 8 14.46 10.29 16.17 8.79 12.58 11.29 17.00 9.21 13.29 15.59 11.34 28.33 +72 1 9 11.00 7.41 8.50 3.04 7.54 4.29 5.25 3.37 5.79 4.21 5.00 11.08 +72 1 10 20.88 18.34 18.12 10.17 14.04 10.08 14.04 9.29 12.17 13.62 17.83 21.29 +72 1 11 13.29 12.17 13.29 4.96 8.83 6.67 11.34 6.08 8.17 9.38 16.04 16.13 +72 1 12 19.83 14.42 18.16 7.58 13.42 10.25 11.87 9.38 11.38 13.59 18.08 23.09 +72 1 13 8.63 4.58 9.29 2.17 4.08 1.87 4.12 0.75 3.46 2.71 4.50 8.92 +72 1 14 23.96 20.62 19.50 9.17 18.05 12.50 16.08 10.54 13.08 13.21 15.04 19.12 +72 1 15 9.17 10.71 15.16 6.34 10.37 7.54 15.34 7.58 10.21 10.46 11.00 21.96 +72 1 16 23.67 20.62 20.17 8.79 16.83 12.75 16.58 12.50 12.33 12.71 15.16 21.04 +72 1 17 14.50 18.71 10.25 5.91 18.41 12.75 12.38 12.87 13.25 11.25 13.96 21.62 +72 1 18 20.25 15.96 15.75 7.79 14.17 10.37 12.33 9.25 12.17 12.50 19.50 22.83 +72 1 19 10.34 10.88 6.96 4.46 10.04 5.46 11.25 3.96 9.00 7.33 12.21 21.25 +72 1 20 7.54 10.37 6.38 3.67 8.87 5.83 10.41 4.29 7.92 5.96 12.67 19.70 +72 1 21 7.21 7.29 7.12 1.46 7.71 4.75 10.41 3.54 6.75 6.13 10.83 16.46 +72 1 22 12.79 13.42 11.42 4.92 10.00 8.75 13.70 8.08 10.71 10.54 12.58 14.75 +72 1 23 20.41 17.00 18.46 8.92 13.92 11.29 13.00 12.08 12.46 14.50 22.29 23.00 +72 1 24 13.00 16.13 10.63 5.50 12.04 6.34 14.00 5.13 9.42 9.33 15.09 20.04 +72 1 25 13.17 14.46 10.79 4.46 11.67 7.71 9.13 6.87 9.04 8.04 14.67 17.37 +72 1 26 22.71 19.12 15.71 10.21 17.62 9.87 12.87 8.75 12.71 8.71 17.54 19.50 +72 1 27 26.42 14.83 22.13 15.21 15.00 12.29 18.05 10.71 16.62 19.55 19.25 33.25 +72 1 28 11.29 7.25 23.29 7.00 8.42 3.13 14.04 3.37 8.38 6.79 7.25 10.83 +72 1 29 12.29 8.58 19.29 6.38 8.83 5.00 14.92 4.63 9.17 6.58 8.63 14.21 +72 1 30 11.25 10.13 10.34 2.37 8.08 2.58 6.92 4.29 5.00 4.38 10.08 12.29 +72 1 31 22.50 22.29 19.08 8.00 20.96 14.50 12.50 14.33 11.50 10.25 20.41 22.17 +72 2 1 12.87 11.00 15.83 6.79 9.04 9.04 17.29 6.83 10.41 13.50 11.63 28.33 +72 2 2 33.84 26.38 28.16 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7.92 6.38 12.67 12.58 +72 6 29 6.92 9.21 7.04 3.46 5.25 3.33 5.09 5.09 4.67 4.75 14.83 9.87 +72 6 30 14.83 13.29 13.21 7.12 12.83 9.38 9.79 11.96 10.96 12.00 21.84 17.79 +72 7 1 11.83 11.87 8.08 6.04 13.04 9.29 12.71 9.71 10.63 8.04 17.29 17.12 +72 7 2 9.00 10.08 8.63 5.21 9.42 7.04 9.04 5.88 8.54 7.21 9.17 12.21 +72 7 3 14.50 13.92 13.25 6.04 13.92 6.42 8.75 4.29 7.83 3.79 6.87 8.75 +72 7 4 14.58 10.04 16.58 5.17 10.34 6.00 7.62 4.92 7.92 4.33 11.71 7.50 +72 7 5 7.58 9.04 8.46 5.04 9.71 6.13 8.42 6.46 8.96 5.75 13.37 16.66 +72 7 6 9.17 11.50 8.29 2.83 10.13 5.33 5.00 8.08 5.91 5.17 12.96 10.67 +72 7 7 12.46 8.71 12.25 5.91 11.63 7.54 10.92 6.04 10.83 7.83 11.67 15.46 +72 7 8 6.79 7.17 5.71 2.33 7.50 3.88 4.17 5.04 6.38 4.00 12.17 12.42 +72 7 9 11.79 12.96 9.08 5.58 12.38 8.25 9.92 8.92 10.21 7.25 15.71 15.79 +72 7 10 13.92 14.46 12.75 7.87 18.05 10.92 15.25 11.17 13.46 11.21 18.58 22.37 +72 7 11 9.50 8.42 9.08 3.58 7.75 4.63 8.04 5.71 7.21 4.63 11.58 13.67 +72 7 12 10.04 10.08 11.87 6.08 9.87 6.50 7.29 9.08 8.50 8.17 14.46 16.33 +72 7 13 3.25 7.12 6.46 3.08 4.38 3.00 3.79 4.67 4.12 3.96 7.71 8.71 +72 7 14 2.75 3.96 4.50 1.21 3.17 1.00 2.50 2.92 2.88 0.67 9.08 4.08 +72 7 15 4.75 1.96 9.42 1.54 2.33 2.67 3.54 1.33 4.75 1.38 4.50 4.88 +72 7 16 6.25 3.92 15.00 4.04 3.79 4.21 4.33 2.42 6.34 2.62 7.54 4.21 +72 7 17 11.54 5.37 19.04 5.46 6.63 6.29 6.58 5.58 7.92 5.13 9.33 2.92 +72 7 18 12.67 4.71 17.04 4.50 6.38 8.00 9.96 5.66 8.58 3.54 10.29 4.75 +72 7 19 3.37 0.96 9.92 2.79 3.63 2.42 4.08 2.13 4.79 1.83 9.71 2.50 +72 7 20 2.13 3.08 10.25 2.33 5.46 1.63 3.67 5.21 3.79 1.71 12.00 6.17 +72 7 21 3.00 1.42 10.46 2.50 4.83 2.88 3.08 2.67 4.29 2.58 12.87 7.50 +72 7 22 4.38 2.50 7.87 3.79 4.08 1.17 3.21 0.67 2.62 0.79 5.21 8.67 +72 7 23 4.42 3.92 5.00 2.37 5.75 1.75 4.29 2.13 4.00 1.46 3.33 4.08 +72 7 24 6.92 3.96 5.09 2.46 6.38 1.38 5.46 5.00 4.42 4.08 11.83 8.42 +72 7 25 4.17 3.63 3.88 3.58 4.63 0.54 4.21 2.67 3.63 1.50 12.87 9.04 +72 7 26 4.54 2.58 9.87 2.25 3.46 1.21 4.04 1.33 4.38 1.29 6.50 4.96 +72 7 27 4.71 3.54 5.79 1.29 4.17 0.79 2.33 2.21 1.92 0.96 5.25 6.04 +72 7 28 7.96 5.50 5.50 3.46 5.75 2.62 3.75 3.54 6.00 1.87 8.71 4.21 +72 7 29 7.62 6.29 7.50 4.17 4.33 3.21 6.17 1.71 5.75 3.25 7.79 7.04 +72 7 30 10.92 13.54 8.33 4.54 10.71 5.25 5.09 7.41 7.08 4.71 14.83 10.34 +72 7 31 22.42 19.41 9.00 8.00 15.83 7.29 8.33 10.67 10.13 6.29 17.25 12.79 +72 8 1 21.87 15.16 17.37 11.46 13.79 10.75 12.42 10.37 13.00 10.92 15.41 19.29 +72 8 2 14.88 10.46 8.25 7.25 10.08 5.13 11.50 8.33 10.71 10.17 13.08 18.12 +72 8 3 12.29 8.63 11.29 4.04 5.33 2.46 8.63 5.37 6.34 4.71 12.42 10.46 +72 8 4 13.62 11.08 10.75 8.12 13.62 10.21 17.54 11.54 13.17 10.79 13.08 18.05 +72 8 5 11.46 12.46 11.42 6.34 9.21 6.63 11.21 8.08 9.08 7.00 14.62 13.83 +72 8 6 14.04 13.75 13.25 7.33 10.13 6.25 9.83 8.92 6.21 7.38 17.08 13.62 +72 8 7 13.88 10.71 14.12 7.08 10.13 8.21 12.62 8.92 10.17 8.04 11.50 12.50 +72 8 8 14.79 12.58 11.00 8.25 13.21 8.04 14.12 9.71 11.96 10.54 12.67 15.16 +72 8 9 6.92 7.75 6.92 4.67 8.12 6.08 9.87 6.83 8.71 6.38 11.83 16.50 +72 8 10 5.75 7.12 6.67 2.58 7.41 4.79 7.17 5.54 6.46 4.42 7.96 10.50 +72 8 11 11.46 14.17 7.87 5.66 8.67 6.38 6.92 9.21 8.08 6.75 21.37 15.75 +72 8 12 15.96 9.17 10.41 6.92 8.08 9.59 9.92 7.41 10.58 12.12 10.71 18.75 +72 8 13 6.04 4.79 4.50 1.96 1.87 0.87 3.92 1.17 2.50 1.71 5.00 5.91 +72 8 14 6.17 5.91 5.63 3.04 3.54 2.17 7.04 4.42 4.88 4.08 9.33 10.50 +72 8 15 7.83 5.63 7.92 3.00 4.58 1.67 6.67 2.25 4.25 2.54 7.21 10.54 +72 8 16 8.29 7.46 10.29 5.04 8.75 7.29 11.54 10.88 10.41 8.63 18.91 20.71 +72 8 17 17.37 10.25 12.71 9.21 11.83 9.13 15.12 11.25 13.08 10.63 13.25 19.75 +72 8 18 9.96 5.04 8.08 3.42 5.37 4.25 8.87 3.50 7.75 6.38 8.38 14.75 +72 8 19 4.92 2.13 6.29 1.75 5.54 3.92 8.58 3.79 6.79 4.00 12.79 15.37 +72 8 20 10.34 6.87 7.17 4.08 8.00 5.41 8.25 5.21 8.12 5.41 6.29 11.17 +72 8 21 5.83 3.75 7.79 1.63 2.04 0.75 4.29 1.13 2.62 2.67 3.17 8.71 +72 8 22 3.75 2.21 4.67 1.42 1.54 0.92 2.62 1.42 2.79 1.42 3.50 6.75 +72 8 23 7.21 2.96 5.50 2.88 2.92 3.00 7.92 4.04 5.33 4.71 7.29 14.58 +72 8 24 7.25 1.96 9.62 3.42 1.63 1.79 5.00 2.54 4.88 4.12 4.21 9.67 +72 8 25 7.62 2.33 17.83 3.29 3.63 5.09 5.88 3.17 6.34 3.29 8.17 7.17 +72 8 26 8.04 1.42 9.54 1.25 2.17 1.38 6.71 2.54 6.00 3.54 5.79 10.58 +72 8 27 10.34 2.54 10.41 3.33 5.50 2.71 6.13 4.25 6.08 3.13 4.38 9.87 +72 8 28 8.17 5.09 11.42 2.25 4.00 2.04 6.83 2.92 6.13 3.92 4.42 10.54 +72 8 29 6.08 1.63 8.12 2.13 3.92 1.50 6.92 2.62 6.21 5.37 3.21 11.08 +72 8 30 5.37 4.08 9.62 2.67 3.13 0.08 4.88 2.13 2.83 1.25 3.79 7.75 +72 8 31 7.00 2.46 16.04 2.29 3.46 3.04 5.33 2.17 4.92 2.13 6.54 5.66 +72 9 1 8.83 1.92 12.67 2.92 2.50 1.29 3.67 1.79 4.46 1.67 3.75 3.83 +72 9 2 6.08 4.67 11.17 3.13 2.58 2.08 4.75 2.58 4.00 2.71 6.63 6.83 +72 9 3 8.71 6.08 19.50 5.66 7.38 4.46 6.38 6.58 6.71 5.00 11.46 11.38 +72 9 4 4.58 5.83 18.46 5.71 6.29 4.29 5.91 6.83 5.21 3.17 13.17 12.08 +72 9 5 3.96 2.08 5.09 1.13 2.08 0.67 3.96 0.54 1.96 0.29 5.09 4.04 +72 9 6 8.54 4.88 8.67 3.00 4.79 2.67 5.96 4.33 4.67 4.63 11.29 11.67 +72 9 7 10.46 7.87 7.83 4.46 7.67 5.09 10.00 4.63 8.96 5.25 9.50 14.42 +72 9 8 10.54 10.71 19.62 3.42 5.13 2.58 6.17 2.54 3.58 2.58 6.54 6.92 +72 9 9 14.21 8.79 14.58 6.54 8.54 4.67 10.67 4.71 9.46 7.38 10.83 14.92 +72 9 10 10.46 7.38 8.04 4.21 7.62 6.21 11.58 5.37 9.21 7.54 12.17 18.38 +72 9 11 9.83 9.29 8.25 4.83 8.25 6.58 12.38 6.13 9.92 7.04 12.58 14.25 +72 9 12 9.96 6.79 6.92 5.00 6.17 4.08 10.67 5.37 9.17 6.17 10.63 16.08 +72 9 13 13.33 10.17 9.59 6.00 9.08 5.54 10.13 6.75 8.83 7.04 12.67 16.04 +72 9 14 6.54 4.67 13.62 3.58 3.42 0.87 5.00 0.96 3.37 1.08 5.66 2.83 +72 9 15 5.33 2.67 5.37 1.54 1.92 1.42 4.54 1.96 2.33 0.79 3.79 4.38 +72 9 16 6.96 3.33 7.00 2.75 3.25 2.00 7.58 2.88 6.00 1.63 6.58 5.63 +72 9 17 7.21 5.63 9.13 2.75 4.42 1.00 3.88 5.17 2.37 0.71 5.41 5.29 +72 9 18 7.75 4.46 8.00 2.71 2.17 0.96 4.50 2.46 2.21 1.33 3.58 8.00 +72 9 19 6.79 4.42 12.42 3.08 1.87 1.17 5.83 2.83 2.79 1.38 2.83 5.25 +72 9 20 5.25 6.58 7.41 2.37 4.17 3.88 6.17 9.59 4.71 6.87 14.42 15.12 +72 9 21 6.63 7.92 8.87 3.21 3.46 2.33 7.08 5.75 4.75 5.33 9.29 13.54 +72 9 22 6.63 7.17 6.17 1.04 3.54 2.21 4.38 5.17 3.25 0.75 8.50 7.67 +72 9 23 16.00 12.96 11.87 3.46 5.41 5.50 7.87 8.42 8.00 3.96 9.33 11.25 +72 9 24 12.75 12.62 13.33 4.46 8.08 4.88 10.29 8.79 8.04 4.42 7.38 12.75 +72 9 25 11.42 11.08 10.75 3.37 5.88 3.88 6.63 6.21 5.21 4.71 8.54 12.50 +72 9 26 8.79 9.79 6.25 1.50 3.08 1.71 3.54 3.71 2.00 2.29 6.17 6.00 +72 9 27 4.63 5.63 3.42 1.00 2.25 1.17 3.50 2.17 1.71 0.46 4.71 5.83 +72 9 28 6.29 9.13 5.58 1.79 5.71 3.63 2.54 6.00 3.54 4.38 13.08 10.88 +72 9 29 15.92 14.88 11.71 7.50 10.54 9.25 8.33 11.92 9.13 10.13 24.54 19.00 +72 9 30 23.04 16.62 15.63 11.63 12.42 11.34 11.50 15.09 13.29 14.46 21.25 20.04 +72 10 1 14.17 12.62 11.17 6.38 11.29 8.25 11.42 10.37 9.21 9.25 15.04 20.38 +72 10 2 9.59 6.50 10.25 3.96 2.46 2.29 9.13 7.00 7.71 4.63 7.67 13.54 +72 10 3 10.41 3.54 5.75 1.25 1.42 0.33 3.50 2.75 4.08 1.08 3.17 7.46 +72 10 4 10.54 5.17 8.71 1.29 1.79 0.46 4.04 3.33 4.17 1.67 3.46 3.75 +72 10 5 18.88 16.66 11.92 5.21 9.42 5.41 7.04 10.00 9.50 7.04 13.00 11.08 +72 10 6 15.12 12.79 7.62 3.46 8.12 4.54 5.75 7.46 6.63 5.83 9.50 12.00 +72 10 7 10.88 12.79 10.58 5.66 9.38 6.21 5.50 7.46 6.92 5.33 11.08 12.96 +72 10 8 16.71 14.17 15.92 10.13 11.38 10.25 10.08 11.79 12.54 14.09 14.96 17.75 +72 10 9 10.41 10.88 11.54 5.46 6.17 4.29 7.71 6.00 7.00 6.79 10.17 11.67 +72 10 10 13.75 13.33 10.83 5.46 7.92 4.38 9.38 10.17 7.75 7.33 18.71 17.88 +72 10 11 8.71 9.08 12.62 4.96 6.63 5.09 11.25 7.67 7.79 5.58 13.50 9.33 +72 10 12 8.67 6.38 11.54 2.37 3.88 2.17 5.75 4.67 3.29 2.08 8.38 12.83 +72 10 13 7.17 5.83 13.17 3.71 2.79 3.33 5.91 3.13 4.46 1.79 3.88 3.33 +72 10 14 9.00 6.83 16.17 5.50 5.58 4.83 8.87 6.54 6.75 4.17 8.92 6.75 +72 10 15 12.71 7.87 13.46 5.04 6.38 4.83 10.29 8.54 7.50 5.13 8.67 6.13 +72 10 16 11.58 6.79 12.38 4.00 5.13 3.04 6.75 5.54 4.63 2.88 6.13 9.83 +72 10 17 8.79 5.91 14.83 4.92 7.29 3.58 10.50 7.38 7.04 3.04 6.25 8.33 +72 10 18 11.87 6.75 15.46 5.83 6.83 3.71 11.34 7.41 7.41 1.87 5.09 7.38 +72 10 19 8.63 7.96 13.62 4.92 5.63 1.54 5.83 6.29 5.13 3.63 9.13 14.09 +72 10 20 9.17 7.50 16.25 5.50 5.21 2.96 9.08 6.58 6.75 6.96 11.67 17.79 +72 10 21 8.46 5.13 10.96 4.67 5.75 4.71 10.88 8.04 8.75 5.79 12.17 20.41 +72 10 22 11.46 12.58 10.67 8.04 11.25 9.92 20.62 15.63 15.12 14.25 20.25 28.21 +72 10 23 10.00 12.83 8.79 6.63 9.83 7.08 14.00 10.54 10.67 9.79 13.83 23.16 +72 10 24 4.83 7.96 6.46 2.75 5.79 4.17 9.33 5.58 6.71 5.50 11.71 15.41 +72 10 25 11.83 11.34 8.87 4.21 9.21 5.83 9.17 10.25 8.71 9.25 19.38 20.04 +72 10 26 17.50 15.34 17.00 10.13 13.46 10.96 12.38 13.37 13.33 14.33 16.46 23.04 +72 10 27 11.92 14.09 13.00 5.09 7.38 4.08 8.33 8.33 5.33 5.04 15.83 12.25 +72 10 28 17.04 16.83 14.12 8.04 11.58 9.71 11.96 12.46 11.67 12.54 22.37 20.54 +72 10 29 22.00 17.41 21.42 12.29 15.00 12.62 12.87 14.58 14.12 16.46 25.17 25.75 +72 10 30 3.67 5.21 4.42 1.42 4.42 3.75 6.42 4.75 5.09 4.71 11.42 18.58 +72 10 31 10.71 6.25 11.04 3.88 4.38 1.13 4.58 3.37 3.75 1.71 12.54 13.13 +72 11 1 4.79 8.38 8.25 2.33 5.63 7.17 9.25 11.21 8.63 9.59 17.75 18.91 +72 11 2 7.54 7.33 11.46 2.13 4.08 5.63 8.12 10.41 7.75 9.13 18.41 19.55 +72 11 3 9.42 8.38 9.87 3.33 5.04 4.12 8.50 6.38 6.46 4.63 7.33 9.71 +72 11 4 12.25 11.67 12.96 4.17 4.92 3.25 3.79 5.63 5.41 3.17 7.67 6.71 +72 11 5 4.92 6.42 6.38 2.37 3.17 2.67 3.88 5.96 3.92 3.50 9.38 10.71 +72 11 6 7.12 10.13 12.92 3.00 4.88 6.08 8.46 10.75 9.25 8.42 14.79 17.62 +72 11 7 9.79 8.46 10.79 3.13 3.96 2.25 6.50 3.46 5.79 3.37 10.83 15.75 +72 11 8 7.38 7.79 5.71 1.42 5.83 4.38 7.67 7.41 6.21 6.50 15.54 20.96 +72 11 9 26.83 23.00 21.84 13.29 15.92 13.50 18.29 16.75 15.71 16.33 26.75 33.21 +72 11 10 15.67 19.08 10.63 8.71 12.46 7.12 15.54 13.13 12.12 11.04 23.25 28.96 +72 11 11 17.83 18.25 11.38 9.42 11.79 8.33 15.83 13.59 14.54 11.12 22.17 27.25 +72 11 12 21.50 20.12 15.96 9.04 12.71 7.41 12.33 10.63 10.88 8.79 13.62 14.46 +72 11 13 8.46 8.63 8.08 3.92 5.46 4.25 10.17 5.50 8.33 5.46 8.12 16.04 +72 11 14 8.50 6.54 8.08 2.58 3.71 2.04 7.67 2.37 5.41 3.58 7.29 12.92 +72 11 15 7.29 7.29 5.71 1.04 3.71 3.08 8.63 4.25 5.88 4.38 10.04 18.96 +72 11 16 7.92 11.04 14.29 3.63 4.12 1.58 6.71 4.29 3.46 1.29 6.87 13.75 +72 11 17 12.25 10.41 13.62 3.37 4.38 2.08 9.25 4.92 5.00 4.04 11.08 14.50 +72 11 18 17.83 14.21 15.75 6.21 10.88 7.83 10.88 8.33 7.75 8.75 11.38 18.00 +72 11 19 18.63 14.79 15.04 6.21 6.96 5.33 8.29 6.54 5.96 3.83 7.33 13.17 +72 11 20 19.62 26.46 14.37 11.87 17.58 9.42 14.50 10.37 13.00 6.34 12.25 11.54 +72 11 21 14.96 16.88 13.50 9.33 11.50 8.54 18.91 12.33 14.46 9.87 16.71 13.92 +72 11 22 8.71 8.83 7.87 3.88 6.00 3.79 9.96 5.79 7.83 5.66 12.42 22.63 +72 11 23 10.21 6.13 11.50 3.75 3.75 2.33 10.83 4.54 8.58 8.29 10.00 22.88 +72 11 24 7.29 3.46 9.50 3.08 2.25 0.29 6.71 1.75 2.83 1.67 4.29 8.46 +72 11 25 5.37 2.62 8.12 2.62 6.29 3.42 11.75 6.67 8.33 5.41 11.00 18.71 +72 11 26 7.50 10.17 5.96 1.67 5.29 3.83 7.87 7.00 3.58 4.92 14.83 16.46 +72 11 27 23.38 22.46 19.75 12.96 13.92 13.96 15.96 18.58 14.17 17.67 27.58 25.84 +72 11 28 13.08 17.00 9.62 6.21 11.50 8.79 15.00 10.21 11.17 11.83 18.79 25.46 +72 11 29 18.21 17.58 17.83 9.96 11.71 11.34 16.96 12.04 12.33 11.67 18.88 21.62 +72 11 30 13.17 17.04 10.34 6.29 11.21 9.04 14.62 10.46 9.75 9.29 16.92 18.00 +72 12 1 20.58 17.92 18.46 9.62 16.75 13.21 18.29 12.75 13.83 13.83 22.42 26.20 +72 12 2 6.00 10.21 4.75 0.87 7.17 4.92 5.37 5.58 3.71 1.71 8.08 9.59 +72 12 3 16.71 16.88 12.29 7.46 10.13 9.46 13.25 10.67 8.67 8.00 14.00 17.08 +72 12 4 20.21 22.79 16.83 11.00 12.00 10.79 19.38 10.71 13.04 11.54 14.25 20.54 +72 12 5 25.58 20.46 22.08 10.37 18.46 15.25 21.71 17.92 16.79 16.29 27.50 29.83 +72 12 6 16.88 17.67 11.58 5.54 12.08 9.59 12.87 8.83 7.75 8.50 12.87 18.63 +72 12 7 11.38 14.46 11.46 6.83 11.75 9.83 15.29 8.87 10.17 8.21 12.38 12.54 +72 12 8 11.08 10.08 7.21 2.08 4.67 3.67 6.87 3.67 3.21 2.13 4.75 6.92 +72 12 9 11.75 12.67 10.04 3.71 8.67 8.08 13.37 10.00 8.50 8.46 17.25 20.08 +72 12 10 15.46 18.50 12.67 6.42 13.88 11.00 17.50 9.33 11.79 11.50 19.00 22.71 +72 12 11 24.13 22.04 20.62 13.08 17.37 14.88 15.92 16.13 13.29 16.38 29.08 26.54 +72 12 12 20.25 18.63 17.33 10.17 16.42 14.62 16.92 16.46 14.04 16.17 27.21 26.25 +72 12 13 14.88 14.17 16.29 7.62 8.75 9.50 11.46 11.29 9.38 11.54 22.92 25.62 +72 12 14 25.96 23.45 20.04 18.46 19.04 15.71 19.08 19.12 15.12 14.92 24.25 20.79 +72 12 15 17.50 23.42 16.66 11.38 15.71 13.46 14.54 14.37 12.75 11.12 19.46 19.87 +72 12 16 16.25 16.13 15.21 9.96 12.50 10.88 13.88 12.08 11.38 10.92 15.29 20.17 +72 12 17 20.71 25.17 17.12 9.79 18.41 13.70 17.71 16.62 12.83 11.42 20.38 25.66 +72 12 18 9.87 16.04 14.33 8.25 13.75 11.58 15.71 10.63 9.38 6.17 13.79 25.50 +72 12 19 9.59 13.37 8.96 6.46 7.33 7.29 5.29 9.87 8.87 10.46 25.46 19.83 +72 12 20 8.46 12.08 8.42 5.17 7.46 7.17 2.13 9.59 6.54 5.37 20.54 18.16 +72 12 21 1.79 2.21 3.96 0.37 1.71 0.92 2.08 2.25 0.71 0.92 6.17 9.59 +72 12 22 5.91 9.00 4.75 2.17 6.08 3.79 2.62 5.46 4.54 4.17 14.88 15.29 +72 12 23 13.00 10.71 10.29 3.04 6.87 4.88 8.96 5.58 8.17 6.87 13.88 18.08 +72 12 24 15.00 16.62 13.96 5.17 11.38 9.29 9.71 9.96 9.00 7.41 14.42 14.88 +72 12 25 26.71 23.54 23.33 16.50 23.63 18.25 21.46 21.37 19.75 21.29 27.46 31.25 +72 12 26 14.29 12.33 11.96 5.41 8.92 6.54 11.08 9.13 9.59 9.17 12.62 15.79 +72 12 27 19.04 11.58 21.42 10.92 12.29 12.87 18.41 13.00 12.42 9.79 12.42 14.50 +72 12 28 30.42 20.30 17.08 14.12 14.17 11.96 14.33 12.42 13.70 11.54 15.87 16.04 +72 12 29 8.04 7.83 9.25 3.37 4.79 3.58 5.88 5.33 5.50 4.58 17.21 14.67 +72 12 30 13.62 14.67 11.83 7.54 11.04 8.54 7.83 12.00 11.29 12.12 21.04 25.00 +72 12 31 13.83 14.46 15.87 9.75 8.71 11.00 10.67 11.54 11.50 10.75 18.00 17.50 +73 1 1 16.50 15.92 14.62 7.41 8.29 11.21 13.54 7.79 10.46 10.79 13.37 9.71 +73 1 2 15.75 12.12 15.04 8.00 8.79 10.41 13.25 9.67 9.04 10.17 12.29 14.04 +73 1 3 7.38 9.33 9.17 2.21 3.13 2.54 5.33 1.63 4.46 0.71 2.83 5.63 +73 1 4 2.92 4.58 4.63 1.00 2.75 0.96 4.50 0.46 3.08 0.79 4.12 4.17 +73 1 5 6.42 6.79 6.25 1.33 3.29 1.00 2.71 1.79 3.13 0.58 3.67 4.83 +73 1 6 10.58 9.04 7.54 2.25 5.96 3.00 5.79 4.50 5.54 3.08 5.37 6.04 +73 1 7 12.08 10.88 10.13 4.25 6.13 4.96 5.63 6.34 5.25 4.08 6.46 7.87 +73 1 8 7.75 8.12 3.54 0.63 5.21 2.88 2.67 1.25 2.58 0.33 5.00 6.79 +73 1 9 12.17 12.08 6.21 1.50 8.38 5.41 3.13 6.42 3.08 1.21 8.63 7.79 +73 1 10 14.46 14.37 11.58 3.58 9.08 6.42 8.29 7.54 6.21 5.37 10.71 10.29 +73 1 11 15.79 14.75 15.71 7.38 8.58 10.29 11.58 10.34 8.42 8.42 12.79 13.04 +73 1 12 22.37 16.92 22.04 13.54 15.16 14.37 17.41 13.79 15.37 16.66 16.66 23.63 +73 1 13 12.71 2.67 13.42 5.46 7.33 6.63 10.00 4.25 8.50 7.62 4.83 14.79 +73 1 14 22.00 21.71 18.05 11.08 17.16 13.17 12.96 10.79 11.87 11.46 16.08 17.12 +73 1 15 14.33 19.62 11.71 10.88 17.33 14.00 22.04 16.54 15.83 18.96 25.37 30.37 +73 1 16 7.38 4.17 4.42 1.75 4.63 3.33 7.92 2.21 4.42 3.75 7.00 14.09 +73 1 17 6.42 3.08 8.38 1.54 2.83 0.13 4.50 0.79 0.67 0.04 7.12 6.50 +73 1 18 23.67 22.37 15.87 7.67 18.58 13.50 12.17 15.09 11.87 11.58 22.67 23.45 +73 1 19 17.21 8.42 25.08 13.75 10.08 13.17 25.12 7.75 19.12 20.83 8.63 32.66 +73 1 20 11.17 12.00 7.50 4.96 9.59 5.21 11.12 7.46 8.33 6.83 14.25 17.54 +73 1 21 11.58 8.17 7.62 4.63 7.83 5.71 14.71 5.50 10.13 7.71 11.38 24.25 +73 1 22 10.96 12.58 11.71 2.75 9.00 7.25 11.29 7.71 7.54 5.75 13.42 16.33 +73 1 23 10.34 12.25 8.92 3.33 9.42 7.96 12.87 11.25 9.33 8.08 17.29 14.50 +73 1 24 13.37 16.96 10.67 4.29 10.04 7.54 9.54 12.50 9.17 10.21 21.00 19.55 +73 1 25 15.54 14.17 12.75 7.67 12.00 11.38 12.83 10.83 12.50 10.63 16.25 16.75 +73 1 26 15.37 16.75 13.13 7.33 14.79 11.21 16.71 12.12 13.33 11.08 17.88 21.29 +73 1 27 15.50 16.42 6.04 5.04 13.29 5.58 6.83 8.38 8.00 3.00 9.46 10.75 +73 1 28 16.08 7.67 7.96 6.75 9.71 7.75 13.08 10.41 11.58 7.62 13.04 15.00 +73 1 29 10.21 11.42 8.04 4.21 7.25 6.92 9.59 10.17 9.38 10.54 15.34 16.79 +73 1 30 15.25 18.50 10.75 7.79 12.08 8.71 14.71 10.63 11.54 10.50 18.50 18.66 +73 1 31 15.25 14.46 8.21 5.13 11.46 7.41 11.08 8.38 8.46 7.12 12.96 11.00 +73 2 1 14.79 12.33 6.67 6.67 10.37 6.58 8.00 6.34 8.75 6.08 9.92 9.38 +73 2 2 4.00 3.54 4.29 1.63 2.62 0.37 4.25 2.58 3.92 2.96 6.21 6.79 +73 2 3 12.71 12.17 7.96 4.79 8.17 7.08 6.34 7.87 6.79 8.21 17.96 13.37 +73 2 4 17.88 13.08 12.75 6.21 10.37 7.33 9.67 9.33 8.50 10.54 14.88 17.92 +73 2 5 10.54 10.54 8.46 5.00 10.08 7.38 13.42 8.38 11.12 9.50 17.04 20.62 +73 2 6 12.83 12.92 11.67 8.33 16.29 12.21 20.08 11.63 17.50 14.96 19.58 27.08 +73 2 7 17.21 13.00 15.92 7.92 12.79 9.13 12.71 7.71 11.46 11.17 16.42 22.46 +73 2 8 13.70 10.37 9.54 5.21 9.75 5.79 8.79 6.50 8.92 7.41 11.08 15.29 +73 2 9 17.92 16.38 12.29 10.13 11.17 9.04 14.09 9.96 13.13 9.25 18.25 20.83 +73 2 10 18.46 17.67 13.67 10.58 15.75 10.29 14.71 12.83 16.00 14.09 18.46 27.71 +73 2 11 16.96 18.75 12.29 11.04 19.12 13.00 18.50 14.92 16.79 14.46 23.09 27.04 +73 2 12 28.01 28.16 17.58 16.08 28.62 18.58 24.21 22.67 23.63 19.67 30.63 35.75 +73 2 13 21.25 22.50 13.17 10.50 20.21 11.71 16.88 15.79 16.54 15.21 26.16 28.42 +73 2 14 18.46 19.00 10.13 8.25 12.04 7.96 10.88 9.62 10.41 5.04 20.21 13.59 +73 2 15 19.67 21.29 12.92 8.42 15.00 8.67 7.92 9.71 9.96 5.41 18.54 11.38 +73 2 16 11.12 9.50 9.46 2.88 6.96 2.50 3.88 5.25 5.04 1.63 10.71 13.00 +73 2 17 13.67 5.71 9.21 2.88 11.17 3.50 2.79 4.75 4.54 2.33 4.88 9.59 +73 2 18 8.21 6.00 5.04 2.88 7.75 4.38 9.08 6.29 7.08 5.09 12.33 17.04 +73 2 19 10.58 8.33 7.79 5.66 9.67 7.21 12.00 8.87 10.88 5.96 11.17 17.58 +73 2 20 8.79 8.38 6.79 5.13 9.83 7.29 11.58 7.83 12.17 9.17 14.12 19.75 +73 2 21 14.54 13.50 10.00 7.41 13.17 8.46 13.13 9.21 12.79 12.12 13.37 19.41 +73 2 22 19.50 16.54 11.04 11.75 17.41 10.46 17.83 14.42 18.29 13.88 21.59 31.46 +73 2 23 19.58 15.92 11.29 13.21 17.62 13.04 16.38 15.29 18.12 16.96 20.08 33.09 +73 2 24 11.08 9.00 10.25 4.29 7.67 4.54 9.13 4.50 7.33 9.13 7.96 20.83 +73 2 25 8.38 14.92 8.17 1.87 9.59 6.04 5.46 5.58 6.29 4.58 11.21 10.21 +73 2 26 11.42 8.21 6.79 0.63 8.46 0.33 3.17 0.96 1.75 1.79 3.33 8.67 +73 2 27 13.04 10.25 11.83 5.50 9.83 7.58 6.96 6.71 8.79 7.92 10.50 17.96 +73 2 28 9.21 9.67 6.58 3.00 6.71 4.42 4.58 5.13 7.04 5.33 8.67 11.12 +73 3 1 17.83 15.16 14.46 8.17 12.75 10.79 13.54 11.12 12.04 12.50 16.21 20.04 +73 3 2 18.25 19.08 15.16 9.13 15.25 11.38 14.83 17.79 13.00 15.59 22.50 24.37 +73 3 3 17.12 8.87 15.59 4.00 5.46 4.54 7.46 6.46 7.21 6.38 11.34 14.12 +73 3 4 13.08 8.75 13.25 6.50 8.08 6.54 8.71 6.67 8.79 8.42 12.04 17.96 +73 3 5 8.42 10.54 6.00 4.17 9.96 5.17 7.71 6.63 8.63 8.12 14.46 18.12 +73 3 6 17.16 11.87 13.62 8.71 12.87 8.92 13.13 9.54 12.87 11.46 13.00 19.87 +73 3 7 5.58 9.13 6.71 1.21 4.33 3.46 6.58 6.25 5.29 5.29 10.46 13.54 +73 3 8 14.29 18.16 8.79 4.67 10.96 6.63 5.21 8.83 7.41 7.87 12.75 14.83 +73 3 9 12.62 15.67 7.87 5.04 10.58 8.67 6.46 7.25 5.75 4.33 8.42 14.04 +73 3 10 12.83 15.71 6.87 3.17 10.21 4.08 3.79 4.96 4.17 2.42 4.42 7.58 +73 3 11 11.17 11.58 7.75 1.92 7.17 2.67 3.04 6.38 5.04 3.04 3.79 6.92 +73 3 12 6.42 8.42 11.83 2.21 3.96 2.04 6.58 3.75 6.42 4.33 3.50 9.17 +73 3 13 8.50 7.04 9.79 1.79 4.71 1.87 3.50 3.54 3.92 3.00 2.25 5.00 +73 3 14 6.83 7.17 10.46 2.37 3.21 1.96 4.54 3.25 5.00 2.54 3.21 6.29 +73 3 15 6.54 5.54 9.54 1.33 4.58 0.37 2.92 1.42 1.83 0.92 3.00 6.54 +73 3 16 5.21 5.63 8.58 3.58 4.25 1.33 3.17 0.75 3.88 3.46 3.00 12.54 +73 3 17 7.04 4.00 7.29 3.08 3.54 0.75 4.92 2.67 4.79 4.17 3.71 10.46 +73 3 18 4.42 4.79 8.12 3.46 4.83 1.54 6.96 5.29 6.96 7.25 6.96 12.67 +73 3 19 5.88 3.13 8.33 3.17 4.75 2.29 3.67 2.50 4.67 0.37 4.12 4.75 +73 3 20 4.17 7.67 4.00 2.67 5.21 2.42 3.83 2.79 3.33 2.71 6.67 5.17 +73 3 21 7.50 11.96 6.96 5.21 9.08 6.71 7.12 7.50 7.17 6.42 18.75 15.59 +73 3 22 11.25 12.04 11.12 3.96 9.21 7.12 9.00 13.67 7.21 10.83 21.25 20.08 +73 3 23 16.75 18.25 13.04 11.04 16.29 12.54 8.38 14.04 12.62 14.17 22.63 21.37 +73 3 24 22.58 20.91 18.58 12.50 17.50 14.12 16.38 15.59 16.04 16.83 24.75 25.37 +73 3 25 18.58 14.37 12.38 9.50 15.34 9.50 13.75 12.50 13.42 11.54 16.79 20.67 +73 3 26 11.00 14.92 8.75 5.29 11.17 6.29 8.21 7.75 6.38 6.21 13.37 13.96 +73 3 27 16.29 17.08 15.41 11.34 16.46 10.63 11.25 12.46 12.79 14.83 17.79 24.50 +73 3 28 12.04 9.46 11.87 5.83 9.04 5.83 9.79 7.83 7.87 7.04 12.08 18.00 +73 3 29 14.29 16.00 13.88 7.58 11.67 10.67 12.17 16.08 11.83 13.59 23.00 26.75 +73 3 30 18.50 13.96 14.54 10.21 15.25 10.79 14.12 11.21 13.59 12.12 18.41 22.37 +73 3 31 14.92 16.04 13.37 10.00 16.66 12.87 18.88 14.04 17.75 15.79 23.87 27.75 +73 4 1 17.41 17.08 13.59 8.04 12.00 10.04 12.42 10.88 11.63 10.83 14.58 18.34 +73 4 2 28.71 20.46 22.34 15.63 20.50 13.29 17.41 13.25 15.37 14.12 18.29 21.71 +73 4 3 14.62 13.83 12.58 7.46 10.46 9.46 12.21 10.50 10.08 10.54 18.58 19.95 +73 4 4 19.62 18.84 20.41 11.67 18.34 14.79 21.54 15.67 18.91 16.83 22.42 28.16 +73 4 5 12.17 14.21 10.79 8.54 13.67 11.71 17.00 17.62 16.66 15.29 22.25 28.21 +73 4 6 20.21 15.92 14.50 11.12 17.67 13.88 18.08 14.54 17.96 16.92 21.17 28.16 +73 4 7 16.71 10.92 14.83 7.79 8.87 8.92 11.46 7.83 10.08 10.54 14.21 22.00 +73 4 8 19.00 14.58 18.54 9.46 9.29 9.67 12.04 9.33 10.21 11.12 14.88 21.25 +73 4 9 11.46 7.08 10.34 6.21 7.41 3.46 8.00 4.38 5.66 3.63 8.63 7.96 +73 4 10 13.75 13.83 8.12 5.54 9.59 6.87 9.00 8.63 9.21 8.63 13.17 15.96 +73 4 11 19.21 15.37 11.75 9.87 10.58 9.83 10.04 11.42 10.63 11.75 13.96 15.75 +73 4 12 19.75 14.09 13.37 10.83 13.83 10.83 10.75 11.21 13.79 12.62 13.54 14.96 +73 4 13 8.63 5.75 7.04 4.79 5.46 5.04 8.08 4.25 7.21 7.38 6.58 12.75 +73 4 14 10.88 7.38 6.87 5.46 6.58 4.58 8.54 4.42 8.42 7.00 7.25 12.04 +73 4 15 7.79 6.63 7.29 4.63 6.63 4.58 6.63 5.96 8.08 6.38 8.12 10.92 +73 4 16 10.71 8.50 8.87 6.34 7.04 6.00 8.46 6.96 8.42 8.63 7.96 13.08 +73 4 17 9.08 8.92 8.42 5.00 5.29 4.08 8.71 5.04 6.38 7.62 7.92 14.09 +73 4 18 13.21 10.46 12.92 8.71 9.67 9.79 12.96 8.79 9.54 12.08 11.00 17.75 +73 4 19 11.17 12.58 15.21 7.41 11.04 8.71 9.08 9.83 11.54 13.83 12.42 17.29 +73 4 20 15.16 12.38 12.54 8.38 10.63 9.46 12.96 9.46 11.29 14.25 13.67 21.21 +73 4 21 16.33 13.46 18.41 8.83 13.21 10.41 11.71 11.17 10.50 12.79 14.17 20.41 +73 4 22 13.79 10.96 25.88 10.21 9.87 8.25 14.37 9.25 10.46 12.21 15.59 17.92 +73 4 23 14.67 15.41 29.79 11.25 13.46 12.29 20.17 14.33 14.21 17.75 21.21 22.54 +73 4 24 16.00 14.00 20.62 8.83 13.50 12.17 15.54 12.42 13.04 12.29 14.88 16.42 +73 4 25 10.54 9.25 10.58 3.08 6.87 6.83 5.04 6.34 7.41 4.38 10.41 6.42 +73 4 26 4.50 3.58 5.63 2.13 4.33 1.96 3.79 3.29 3.25 2.58 4.58 10.13 +73 4 27 4.25 4.96 5.91 2.42 4.50 1.83 1.75 3.75 2.83 3.29 6.54 8.42 +73 4 28 8.25 10.79 10.41 4.17 6.08 3.58 5.58 5.83 5.46 4.96 9.83 13.92 +73 4 29 14.75 12.96 13.13 5.71 10.63 6.00 6.63 8.33 6.92 4.42 9.59 8.38 +73 4 30 12.50 13.50 8.63 6.25 10.08 8.08 8.12 8.38 8.12 6.87 16.62 17.75 +73 5 1 11.92 6.38 8.46 4.04 8.00 5.71 10.58 4.88 8.79 6.42 8.17 15.37 +73 5 2 12.58 11.04 10.17 4.38 7.29 5.66 7.62 5.54 6.58 3.71 8.83 9.67 +73 5 3 16.29 14.09 14.79 9.59 13.21 11.34 11.75 15.09 13.25 12.00 17.33 24.08 +73 5 4 14.71 7.17 18.25 8.12 10.83 10.17 12.17 7.29 9.71 9.38 9.38 16.66 +73 5 5 12.00 13.59 11.63 6.25 11.08 8.54 10.92 8.29 10.21 8.50 12.08 12.87 +73 5 6 13.04 10.67 12.08 6.38 11.71 9.29 9.00 9.54 12.25 10.37 15.46 12.75 +73 5 7 22.46 17.08 13.50 11.50 14.83 11.92 12.38 12.17 13.92 13.79 16.17 21.87 +73 5 8 15.75 12.54 8.75 8.38 14.37 10.37 12.87 12.08 12.21 9.79 14.71 17.58 +73 5 9 16.00 15.00 14.37 8.42 16.38 10.04 14.50 11.92 14.17 11.92 17.79 20.17 +73 5 10 21.50 16.38 13.59 12.21 19.29 12.96 17.16 13.54 16.92 15.83 19.04 26.83 +73 5 11 11.00 11.00 8.29 7.25 11.63 8.63 11.12 10.29 11.87 9.59 14.09 17.88 +73 5 12 16.58 15.75 14.67 8.46 15.12 11.71 14.00 14.42 12.96 13.92 20.54 21.79 +73 5 13 13.17 11.12 12.33 5.91 9.38 6.96 7.96 7.00 8.21 9.04 8.96 14.96 +73 5 14 5.17 9.54 9.38 3.92 5.58 4.50 3.29 4.17 5.88 6.29 5.66 11.12 +73 5 15 9.67 6.58 11.12 3.00 5.33 3.46 4.08 4.04 6.29 2.92 4.67 9.92 +73 5 16 19.04 20.54 12.38 7.92 13.96 11.12 9.13 12.21 11.42 11.25 12.71 18.12 +73 5 17 21.79 17.88 17.83 11.58 17.37 14.50 14.29 18.50 15.63 16.92 16.79 22.42 +73 5 18 15.50 14.46 10.46 6.83 13.33 10.41 9.29 13.08 10.67 10.13 12.96 20.88 +73 5 19 9.75 7.08 17.92 6.79 9.54 10.79 13.04 11.17 13.37 11.12 12.04 20.54 +73 5 20 10.46 11.34 14.58 7.33 10.75 12.87 12.92 14.37 13.04 13.37 17.67 26.08 +73 5 21 13.33 8.79 9.54 6.17 10.25 9.87 6.54 10.29 9.08 10.17 11.83 19.75 +73 5 22 9.38 7.83 8.58 6.17 9.46 6.96 8.54 6.04 8.25 7.38 6.17 14.00 +73 5 23 8.00 4.92 5.54 2.75 7.41 3.58 4.79 2.13 5.75 5.79 3.21 8.21 +73 5 24 6.58 9.96 8.83 3.33 6.13 4.04 5.46 4.21 5.21 3.58 7.50 5.50 +73 5 25 11.58 13.50 9.62 6.58 12.33 9.00 4.75 7.83 7.87 6.87 10.71 12.04 +73 5 26 6.04 7.79 8.08 4.79 7.33 5.88 5.09 6.58 6.67 5.54 9.25 12.92 +73 5 27 9.04 3.54 5.88 1.96 5.04 4.04 2.25 4.12 5.71 4.75 7.87 8.21 +73 5 28 3.04 3.83 5.63 2.50 5.83 2.33 4.83 2.33 6.13 4.17 4.21 11.42 +73 5 29 11.71 9.96 9.96 3.96 8.50 5.00 2.96 5.75 6.17 3.58 9.08 7.08 +73 5 30 11.04 12.08 9.38 6.25 11.12 7.41 9.21 9.17 9.71 8.00 14.37 13.08 +73 5 31 18.25 14.29 11.25 7.92 12.46 7.17 8.00 7.71 9.29 9.42 11.92 16.71 +73 6 1 14.92 11.34 10.54 5.25 11.21 7.79 8.12 9.54 8.71 9.67 16.46 17.21 +73 6 2 14.92 13.79 14.50 6.50 13.92 8.46 8.58 8.17 10.25 7.25 11.87 8.75 +73 6 3 9.17 7.41 10.21 5.91 10.34 7.17 6.87 7.79 8.38 7.38 10.13 12.83 +73 6 4 3.37 4.08 5.66 2.13 4.08 1.08 3.17 2.04 1.54 1.13 5.29 7.62 +73 6 5 3.79 5.21 5.21 1.46 5.63 4.17 3.71 4.63 4.38 2.50 13.37 13.25 +73 6 6 5.09 3.13 7.62 3.00 4.21 1.63 3.21 1.79 2.79 0.96 8.92 10.75 +73 6 7 4.50 4.25 5.58 2.37 5.66 1.96 1.04 3.21 2.04 1.75 5.25 5.04 +73 6 8 11.42 6.79 4.50 3.96 9.42 6.13 6.63 5.79 7.17 4.00 11.12 13.62 +73 6 9 12.00 10.54 7.75 8.08 14.88 10.46 14.79 11.75 12.04 10.75 15.25 20.33 +73 6 10 12.71 8.42 7.87 6.87 11.79 8.79 10.00 8.46 8.96 8.25 10.88 12.38 +73 6 11 9.96 7.96 9.92 4.46 10.21 7.25 9.67 9.33 9.92 7.83 17.37 18.63 +73 6 12 16.17 16.75 16.29 9.87 14.83 12.33 13.75 16.33 13.83 15.59 23.13 27.16 +73 6 13 9.29 8.17 8.79 6.38 10.34 7.96 11.04 8.92 10.17 11.54 13.17 21.50 +73 6 14 7.12 13.25 9.00 5.54 7.96 6.29 5.37 7.41 5.46 5.75 18.05 12.29 +73 6 15 10.37 12.58 11.54 7.46 11.17 8.75 8.17 8.58 10.00 10.41 12.83 15.46 +73 6 16 12.54 14.96 13.17 7.17 11.92 9.71 9.04 11.71 9.29 11.71 20.30 17.92 +73 6 17 11.96 10.25 11.21 5.75 11.25 9.17 11.21 9.83 10.37 8.71 15.21 13.46 +73 6 18 10.96 12.71 11.92 5.88 9.92 8.38 7.79 13.08 8.38 9.29 20.25 17.29 +73 6 19 16.38 12.62 11.04 7.92 15.75 9.08 10.00 10.13 11.04 11.08 16.62 17.12 +73 6 20 14.50 6.54 13.67 7.62 10.17 7.08 10.88 4.17 9.67 8.67 8.83 12.04 +73 6 21 6.38 6.96 6.54 1.67 4.75 2.54 3.04 5.71 3.21 2.54 14.67 4.63 +73 6 22 6.92 4.46 5.71 3.00 7.00 4.42 2.75 4.54 5.75 4.92 12.87 7.92 +73 6 23 10.29 5.04 7.92 4.58 5.71 3.04 4.50 3.04 4.29 3.37 6.50 8.83 +73 6 24 6.54 3.21 4.46 1.87 4.79 2.08 2.71 1.46 1.96 2.67 7.21 5.09 +73 6 25 4.92 6.58 5.13 1.92 5.66 2.46 2.54 3.79 4.12 4.71 9.25 9.21 +73 6 26 3.75 4.67 13.13 3.13 5.54 2.46 3.63 1.42 3.71 2.13 4.21 5.41 +73 6 27 4.50 6.29 12.00 2.83 5.58 3.37 2.67 3.00 4.46 3.63 7.17 9.21 +73 6 28 14.21 9.87 10.25 3.75 8.29 5.41 5.96 9.75 7.38 7.71 17.41 13.17 +73 6 29 18.05 16.50 16.21 8.46 12.92 11.58 13.21 13.88 12.00 13.21 19.21 17.79 +73 6 30 19.46 12.42 16.66 13.04 11.25 12.08 12.79 9.83 13.17 15.21 18.05 17.33 +73 7 1 14.71 8.17 13.62 9.21 8.50 7.67 10.79 3.63 9.54 8.92 8.67 8.04 +73 7 2 8.00 12.17 7.75 5.37 9.83 7.33 6.00 7.79 6.92 6.08 15.50 10.00 +73 7 3 11.21 8.75 11.50 8.08 6.00 6.13 9.38 7.50 6.58 7.54 13.75 16.83 +73 7 4 4.50 9.83 6.08 3.75 3.58 2.75 3.58 5.21 3.33 4.79 11.67 9.21 +73 7 5 6.96 10.04 5.29 4.67 8.96 8.00 5.91 9.29 7.21 9.08 18.84 16.29 +73 7 6 16.21 11.21 7.25 6.42 10.04 6.54 6.42 4.04 7.83 8.58 9.33 12.54 +73 7 7 6.54 4.71 8.25 4.25 5.75 3.83 4.33 2.92 5.58 5.09 6.83 9.75 +73 7 8 4.88 9.13 5.13 3.58 4.21 3.71 4.08 3.96 3.29 3.46 14.50 7.38 +73 7 9 8.71 8.46 9.83 3.37 7.87 5.41 5.50 5.83 5.91 6.71 13.13 12.38 +73 7 10 5.91 4.96 6.79 4.50 9.50 6.71 10.00 4.63 9.25 5.83 13.00 13.88 +73 7 11 13.29 8.04 9.50 5.83 10.58 7.62 10.92 7.50 11.04 9.17 11.00 13.17 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4.88 3.37 2.25 14.09 9.50 +73 9 6 8.96 10.88 10.58 4.42 8.29 6.71 5.63 10.79 7.21 8.04 19.58 18.12 +73 9 7 3.88 9.92 7.25 1.79 5.17 3.13 4.92 6.25 4.29 6.00 16.38 13.70 +73 9 8 8.50 3.83 6.87 1.04 5.37 1.83 1.83 1.25 2.83 1.79 9.50 5.96 +73 9 9 5.25 0.83 11.83 1.92 3.17 1.42 2.04 2.46 2.88 3.00 9.42 6.83 +73 9 10 9.46 1.75 15.50 3.25 6.08 5.66 6.79 4.29 6.63 3.50 8.08 9.92 +73 9 11 10.92 11.08 6.50 2.79 8.79 2.54 4.88 4.50 3.96 3.21 6.08 8.92 +73 9 12 11.71 11.50 10.41 4.12 9.33 4.96 4.04 7.17 5.88 4.42 8.50 6.92 +73 9 13 14.92 10.34 15.96 6.21 8.67 6.71 10.17 9.67 7.75 4.71 9.67 11.42 +73 9 14 17.12 13.04 14.58 5.75 9.87 7.83 5.00 10.17 8.92 7.04 10.79 16.79 +73 9 15 16.04 14.12 11.21 6.83 14.71 9.79 6.79 12.75 10.04 7.54 14.83 17.12 +73 9 16 11.50 7.46 13.59 5.79 6.25 6.25 6.04 7.04 7.67 6.21 10.83 12.50 +73 9 17 9.83 10.00 9.46 2.54 9.33 6.00 8.42 7.41 7.17 6.00 14.96 12.50 +73 9 18 17.04 11.54 17.79 4.42 9.25 5.00 6.25 4.08 4.46 2.96 8.29 10.34 +73 9 19 6.04 3.58 7.75 3.71 3.00 2.75 6.29 3.67 4.79 4.04 7.21 10.58 +73 9 20 5.13 6.79 9.42 3.75 5.71 2.54 2.96 3.92 3.63 2.96 6.17 8.54 +73 9 21 11.12 10.75 10.88 5.54 8.42 6.67 9.17 7.04 8.25 6.00 12.50 11.87 +73 9 22 16.83 15.67 14.75 8.50 11.58 8.21 11.71 8.29 10.13 8.29 15.54 19.38 +73 9 23 8.79 7.17 10.92 4.75 6.38 2.54 6.25 2.54 4.67 4.58 7.79 10.88 +73 9 24 12.33 13.25 10.00 3.79 9.25 5.17 4.12 7.29 5.13 4.33 12.67 16.96 +73 9 25 8.87 10.37 9.00 2.79 7.00 2.67 5.66 5.79 4.04 4.42 13.62 12.67 +73 9 26 10.83 9.13 11.08 5.79 8.92 5.71 8.12 5.66 6.87 6.79 13.21 16.42 +73 9 27 16.62 18.91 14.54 9.21 18.34 11.04 14.75 13.96 13.00 13.67 19.08 24.92 +73 9 28 16.21 23.21 13.21 9.79 17.71 9.08 16.42 11.00 11.21 11.63 17.79 22.08 +73 9 29 22.88 20.04 16.71 12.79 16.33 9.79 13.50 13.46 12.62 12.17 19.55 29.29 +73 9 30 18.16 12.62 17.16 8.54 10.04 6.17 10.17 7.33 8.71 9.42 11.12 20.21 +73 10 1 10.08 8.38 8.21 3.42 6.08 2.46 6.21 4.46 4.33 5.46 8.08 13.33 +73 10 2 8.33 5.96 12.83 5.04 5.46 2.17 3.58 3.50 3.75 4.29 3.88 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16.21 13.29 22.50 16.21 20.75 17.46 20.04 20.79 24.00 33.71 +73 11 13 13.21 13.62 9.79 9.87 17.04 12.42 12.87 12.17 13.46 13.75 18.88 27.29 +73 11 14 12.67 16.08 9.33 7.41 13.17 6.71 7.38 7.75 8.58 7.79 11.50 19.25 +73 11 15 7.71 11.25 6.63 4.29 8.17 4.42 7.87 8.17 8.83 9.83 11.83 21.17 +73 11 16 9.46 8.12 9.04 3.96 6.63 4.04 6.21 4.92 5.58 7.29 9.59 19.70 +73 11 17 13.96 10.21 8.17 3.17 11.12 6.87 2.75 7.54 6.79 6.83 14.17 16.62 +73 11 18 13.59 9.83 15.04 7.12 10.75 7.50 10.04 11.25 12.33 13.75 16.71 24.37 +73 11 19 8.50 5.91 10.13 3.17 6.58 1.71 4.33 4.29 4.58 7.62 5.83 18.38 +73 11 20 6.46 6.08 6.92 0.46 7.33 2.46 0.63 2.42 1.92 2.46 3.08 8.87 +73 11 21 6.67 8.33 7.54 2.42 8.08 3.25 0.00 3.42 4.92 2.79 8.25 13.50 +73 11 22 6.50 5.71 8.21 2.00 5.63 2.79 3.29 2.75 5.17 4.50 5.88 16.04 +73 11 23 5.41 4.92 6.25 3.25 7.75 4.46 8.38 6.63 8.58 8.58 11.63 18.41 +73 11 24 10.75 12.87 10.04 8.33 15.21 8.38 13.04 12.00 12.96 14.79 15.16 26.83 +73 11 25 9.71 8.21 10.92 3.92 7.08 2.21 4.67 4.33 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9.83 3.92 7.12 2.54 6.04 9.87 5.29 6.71 13.46 11.29 +74 6 1 16.54 15.92 15.46 9.87 13.67 9.13 9.33 13.75 12.08 12.17 22.34 19.70 +74 6 2 13.00 9.54 11.83 6.54 13.88 7.38 12.04 12.46 11.38 10.71 18.54 22.00 +74 6 3 8.54 6.50 6.42 4.50 9.83 4.75 9.21 10.29 7.21 8.83 13.88 20.17 +74 6 4 6.46 8.46 6.34 4.12 8.04 2.92 5.63 8.54 6.08 5.33 14.79 12.83 +74 6 5 17.04 14.62 11.46 7.58 15.12 9.04 11.75 13.75 10.79 8.96 18.46 18.88 +74 6 6 17.50 17.46 13.42 10.17 19.58 12.25 17.50 17.29 13.62 13.29 18.96 23.54 +74 6 7 8.12 5.29 10.41 4.38 8.04 4.83 10.21 9.79 8.17 7.71 11.54 17.00 +74 6 8 14.96 10.00 8.17 5.88 13.70 5.66 7.21 9.38 7.41 9.71 12.00 12.33 +74 6 9 18.96 11.63 10.63 8.00 16.00 8.63 10.34 11.46 10.21 12.46 12.87 19.50 +74 6 10 14.92 9.79 10.63 8.38 13.96 8.12 11.38 11.58 9.92 10.37 13.00 18.05 +74 6 11 15.00 5.37 9.29 7.00 11.87 7.96 14.12 12.50 9.62 11.63 12.71 19.67 +74 6 12 4.79 2.88 4.46 1.67 4.25 0.87 3.42 2.88 2.83 1.29 7.41 10.17 +74 6 13 6.50 7.04 7.25 4.88 6.87 4.96 5.33 7.41 7.17 6.25 9.38 13.83 +74 6 14 4.46 4.50 4.79 3.25 5.91 2.58 4.25 7.62 2.58 3.92 9.83 8.54 +74 6 15 8.08 8.04 13.04 4.33 6.58 2.67 3.29 5.88 4.21 4.00 5.83 9.38 +74 6 16 8.50 5.54 6.25 2.13 4.96 2.37 2.37 5.54 3.46 3.04 10.54 4.54 +74 6 17 14.67 10.21 8.21 5.21 12.12 5.37 6.17 9.21 6.13 6.87 10.25 8.87 +74 6 18 12.00 9.92 11.04 4.42 9.92 5.29 7.46 7.92 7.75 7.21 12.92 10.04 +74 6 19 10.92 12.62 11.75 4.17 7.71 5.04 5.75 11.17 6.79 5.33 15.63 9.62 +74 6 20 12.29 12.96 7.87 5.66 11.46 7.87 7.38 10.67 8.54 7.46 13.00 11.83 +74 6 21 17.12 20.17 7.08 5.63 11.08 7.17 6.08 12.00 9.21 6.92 11.21 13.83 +74 6 22 20.12 19.41 14.04 7.08 13.75 9.79 11.54 16.25 11.54 9.42 13.59 17.29 +74 6 23 10.17 2.29 10.08 4.50 7.50 8.00 8.50 11.58 9.67 7.67 10.37 13.08 +74 6 24 7.62 4.25 13.67 3.21 6.00 4.46 6.87 6.75 6.83 5.17 7.92 8.63 +74 6 25 12.46 6.50 18.00 3.63 6.38 8.42 8.42 8.38 9.00 5.66 11.38 8.54 +74 6 26 6.58 1.79 18.50 4.58 8.38 7.25 11.38 10.25 10.21 7.46 9.79 12.00 +74 6 27 8.38 3.88 19.00 4.96 8.08 5.63 10.29 9.25 8.04 7.12 9.71 7.87 +74 6 28 4.17 3.17 6.38 2.79 3.37 1.38 4.17 3.63 4.29 5.63 5.37 6.79 +74 6 29 6.00 4.42 7.00 2.62 6.00 3.50 3.17 5.75 3.75 4.21 7.50 6.71 +74 6 30 12.79 8.71 10.63 4.46 7.46 4.88 5.25 9.59 4.63 5.91 11.34 11.46 +74 7 1 13.29 6.92 10.37 7.17 10.13 8.29 12.96 11.08 9.79 12.12 11.79 20.50 +74 7 2 17.58 12.83 13.17 5.09 9.46 6.13 7.58 8.79 7.41 6.17 8.58 7.87 +74 7 3 15.09 13.21 13.62 6.42 13.62 8.21 11.12 9.75 9.25 8.04 11.92 14.58 +74 7 4 14.50 11.08 12.83 6.13 11.34 6.46 8.21 8.67 8.00 2.79 11.04 8.54 +74 7 5 12.87 9.04 10.96 7.92 13.59 8.79 12.42 12.08 10.37 9.83 13.92 18.21 +74 7 6 6.17 6.08 8.46 2.04 6.25 2.46 7.41 4.58 5.17 5.83 7.50 12.50 +74 7 7 7.62 11.54 9.50 4.29 8.29 5.88 7.00 10.63 6.96 7.12 18.16 9.38 +74 7 8 13.88 13.42 12.87 7.58 13.59 8.67 7.96 12.92 10.34 10.58 18.08 15.75 +74 7 9 11.87 10.04 10.37 5.41 12.58 6.71 10.41 9.54 8.87 8.38 16.46 16.46 +74 7 10 16.71 13.75 12.92 7.08 14.88 9.04 11.54 14.37 12.00 12.04 18.25 19.25 +74 7 11 11.25 9.67 10.37 7.00 13.29 7.87 10.67 12.00 9.96 10.46 16.08 18.29 +74 7 12 9.13 7.00 8.33 4.88 9.83 6.08 8.50 8.54 5.96 8.67 10.83 15.63 +74 7 13 13.79 8.63 6.79 4.42 8.92 5.63 5.91 8.29 4.71 7.71 11.58 14.62 +74 7 14 15.25 12.08 10.83 5.29 10.08 6.13 7.62 9.54 7.71 6.75 14.29 10.88 +74 7 15 18.58 15.34 15.63 8.54 16.25 9.75 12.50 12.75 10.37 9.50 17.08 14.00 +74 7 16 21.00 14.71 14.62 10.37 14.37 10.58 11.17 14.58 10.54 13.13 20.50 25.37 +74 7 17 14.50 6.25 9.59 6.25 8.75 4.88 7.12 6.34 6.25 7.08 9.62 13.50 +74 7 18 5.83 4.54 7.29 1.42 5.04 3.04 7.50 4.38 3.83 3.92 10.37 14.58 +74 7 19 6.96 4.29 7.41 5.17 11.75 7.04 13.17 9.21 9.17 8.63 14.96 19.75 +74 7 20 11.79 6.75 7.25 6.21 10.46 6.83 11.71 10.88 8.33 9.21 12.79 19.62 +74 7 21 7.25 9.04 9.29 5.29 12.87 8.25 16.29 12.12 11.87 11.25 17.92 23.21 +74 7 22 15.54 10.41 14.33 9.13 14.88 10.41 17.29 12.12 12.17 11.38 18.46 23.58 +74 7 23 9.29 9.08 9.17 5.29 8.83 5.58 10.08 7.87 8.00 5.63 12.17 17.58 +74 7 24 16.46 10.58 8.92 6.42 11.79 5.91 8.58 8.21 7.17 7.67 10.54 14.46 +74 7 25 9.92 8.92 9.67 4.33 9.46 6.17 7.87 9.33 6.75 5.79 11.92 13.83 +74 7 26 9.46 9.33 9.04 3.88 8.46 5.37 6.79 10.79 7.12 6.46 14.58 12.92 +74 7 27 11.00 10.46 12.92 5.71 13.25 7.46 10.63 8.83 9.71 8.00 12.17 15.00 +74 7 28 12.83 9.46 10.29 6.42 12.08 7.62 11.29 9.04 10.34 7.87 12.08 15.59 +74 7 29 12.58 12.62 14.00 6.34 13.21 7.08 8.50 4.04 7.21 4.00 7.29 15.00 +74 7 30 12.87 13.46 13.42 5.21 12.17 6.50 6.00 5.13 8.00 2.04 7.38 3.75 +74 7 31 13.54 7.38 13.04 4.58 8.42 2.88 3.13 3.71 4.12 2.33 5.83 8.33 +74 8 1 6.29 6.04 7.25 3.00 7.33 4.63 5.96 8.46 7.21 5.21 11.46 16.96 +74 8 2 5.17 5.83 7.17 3.04 7.08 4.50 6.83 8.46 6.29 6.46 10.37 14.54 +74 8 3 3.75 4.58 11.00 1.92 3.13 0.63 2.50 3.42 1.63 3.33 9.08 13.59 +74 8 4 5.54 4.96 14.83 3.37 4.17 1.79 3.79 1.79 3.67 1.75 4.29 6.92 +74 8 5 10.41 15.83 9.67 6.21 11.46 8.04 4.17 10.41 9.00 7.75 13.83 12.67 +74 8 6 13.25 16.38 11.50 8.38 13.42 8.92 5.29 12.00 11.42 11.12 14.37 17.92 +74 8 7 10.67 11.96 11.87 6.67 10.63 8.29 7.12 10.58 11.38 7.12 12.12 18.21 +74 8 8 9.50 8.58 9.13 3.54 6.92 3.88 5.71 6.46 5.66 5.66 10.29 14.04 +74 8 9 14.09 11.63 10.92 4.42 10.04 4.96 7.62 7.96 7.87 5.91 10.58 10.37 +74 8 10 16.92 12.46 11.46 6.79 12.12 7.25 11.38 10.54 9.38 9.17 13.04 16.46 +74 8 11 7.38 9.46 9.75 3.88 7.75 5.66 7.00 8.29 7.92 7.79 12.42 16.83 +74 8 12 9.96 9.96 11.25 5.29 11.54 6.34 10.63 9.62 10.92 9.42 15.75 19.29 +74 8 13 7.41 4.71 8.79 1.75 4.46 1.96 4.79 3.83 4.21 3.37 7.21 8.50 +74 8 14 18.71 17.21 17.00 7.29 13.37 10.13 10.25 14.37 12.50 11.21 15.71 18.41 +74 8 15 19.55 16.46 16.79 9.87 13.59 11.29 14.25 19.41 13.42 12.54 23.25 22.34 +74 8 16 7.71 7.87 11.58 4.46 9.59 7.04 10.96 10.04 7.75 9.13 10.75 17.08 +74 8 17 5.09 5.33 7.67 2.25 6.13 2.13 5.88 3.25 4.46 3.67 5.29 10.96 +74 8 18 7.41 4.08 8.96 3.96 5.13 2.04 5.46 6.00 4.38 5.29 6.42 9.59 +74 8 19 5.71 7.41 6.08 2.50 4.83 3.54 4.54 8.25 4.12 4.25 15.75 11.17 +74 8 20 10.34 13.79 10.79 5.37 10.04 7.83 8.87 12.50 10.67 10.37 19.41 19.33 +74 8 21 8.29 7.00 10.21 3.71 8.38 5.66 7.83 6.13 7.33 5.96 9.25 13.50 +74 8 22 7.96 11.58 7.04 3.17 6.87 3.79 4.92 6.13 5.96 4.83 10.88 8.87 +74 8 23 13.79 12.46 14.09 6.71 10.37 8.21 9.59 11.75 11.21 11.67 16.88 16.42 +74 8 24 15.16 16.21 13.88 6.13 9.79 8.46 10.04 14.54 10.79 11.29 20.25 19.92 +74 8 25 14.54 12.92 12.58 5.29 13.70 8.08 10.54 11.08 11.96 9.71 16.96 19.83 +74 8 26 15.67 11.71 8.42 4.83 9.92 5.50 6.67 6.34 5.79 6.00 11.17 12.33 +74 8 27 12.00 12.17 8.63 3.92 9.75 6.17 8.50 9.62 7.46 7.21 16.96 12.96 +74 8 28 17.67 14.04 15.04 7.75 12.12 8.87 7.71 13.59 11.12 11.92 19.41 20.33 +74 8 29 7.46 6.71 9.67 2.04 4.58 2.54 7.46 4.38 5.63 4.38 7.83 9.62 +74 8 30 8.67 7.21 13.37 4.04 5.96 4.71 7.46 6.08 7.21 6.04 7.96 10.67 +74 8 31 5.37 7.54 5.79 2.25 4.75 3.54 3.33 5.63 5.83 4.21 4.04 12.08 +74 9 1 13.50 11.75 7.38 3.00 8.17 3.88 4.42 6.42 4.92 3.63 11.79 10.67 +74 9 2 22.75 17.88 19.95 11.04 14.58 10.54 12.92 9.83 11.12 9.00 6.83 15.37 +74 9 3 16.04 15.34 12.50 6.83 12.96 6.58 10.54 6.71 8.42 8.33 6.13 14.67 +74 9 4 16.21 15.37 14.58 7.17 16.17 9.59 12.79 9.38 12.25 6.63 12.46 15.50 +74 9 5 16.50 11.92 13.37 4.96 11.87 6.42 8.04 8.38 8.63 6.83 8.58 11.12 +74 9 6 20.17 16.33 14.71 7.54 13.04 7.92 8.29 8.96 8.96 5.88 10.29 10.50 +74 9 7 29.54 20.54 23.21 13.70 21.71 13.83 16.21 12.62 14.54 11.29 15.04 26.42 +74 9 8 16.58 11.38 15.71 5.63 13.54 8.33 10.88 8.79 10.67 7.87 10.71 16.83 +74 9 9 10.21 9.08 8.92 3.42 7.83 5.04 6.92 5.91 7.29 4.38 9.29 11.87 +74 9 10 11.08 13.67 12.67 5.00 9.59 7.96 8.00 9.71 9.59 8.42 16.83 19.25 +74 9 11 16.71 13.00 12.67 5.54 12.46 9.33 9.13 10.34 9.17 6.71 9.17 11.71 +74 9 12 13.13 9.42 14.83 7.08 9.13 7.54 9.42 9.92 10.54 9.21 9.50 19.46 +74 9 13 10.00 9.75 9.13 5.75 10.83 7.54 10.37 9.75 9.25 7.92 13.50 18.05 +74 9 14 14.50 14.29 12.38 6.34 10.92 7.33 5.46 9.00 8.04 7.04 10.79 14.83 +74 9 15 10.92 7.00 6.67 3.58 6.13 3.00 2.79 5.37 3.04 4.38 7.08 10.25 +74 9 16 8.83 9.29 8.71 3.54 5.58 4.63 6.13 9.08 5.75 5.79 11.00 13.96 +74 9 17 12.87 10.63 11.34 5.46 7.87 4.50 7.67 6.38 6.13 6.96 10.46 19.33 +74 9 18 5.21 3.96 6.21 1.50 3.08 1.67 4.58 5.09 2.62 4.75 9.54 14.67 +74 9 19 10.00 8.25 7.75 4.79 10.58 6.21 9.50 10.96 9.67 8.58 14.71 23.16 +74 9 20 12.79 10.29 12.96 5.41 10.75 7.04 9.67 12.00 9.87 9.38 18.54 19.83 +74 9 21 14.71 18.88 11.12 7.71 14.75 7.67 13.37 13.04 11.42 10.88 23.04 27.12 +74 9 22 17.92 17.71 13.08 7.83 15.29 8.87 12.21 12.42 11.50 9.92 17.46 20.62 +74 9 23 27.00 25.08 19.70 10.54 16.62 9.59 11.17 12.92 11.00 10.04 18.46 26.63 +74 9 24 15.96 16.04 11.58 7.54 11.29 7.58 10.25 9.17 8.83 8.33 14.50 18.38 +74 9 25 24.04 20.21 17.29 12.00 18.58 11.12 13.00 13.75 12.92 9.87 19.92 19.25 +74 9 26 8.50 7.21 7.33 2.37 4.29 2.88 8.04 2.88 5.17 3.88 6.42 13.21 +74 9 27 14.12 9.13 12.42 4.46 6.79 3.17 6.46 6.46 4.42 2.96 10.37 8.83 +74 9 28 15.92 16.71 10.92 5.83 11.21 6.46 8.38 9.79 7.17 6.46 17.08 10.92 +74 9 29 14.12 10.96 8.50 3.96 8.50 4.00 7.83 7.71 5.79 7.33 12.62 11.12 +74 9 30 5.09 3.33 7.08 1.58 1.79 1.50 7.46 4.54 3.63 4.83 7.50 13.59 +74 10 1 8.54 4.08 8.33 3.96 5.50 2.00 6.79 4.88 4.63 4.08 7.33 16.54 +74 10 2 19.95 14.21 15.96 9.08 12.83 8.08 13.92 11.38 10.75 11.21 17.16 25.75 +74 10 3 13.88 10.41 19.41 7.21 5.91 4.67 9.92 8.92 7.87 9.59 13.67 25.21 +74 10 4 16.46 13.17 15.87 7.75 10.00 7.33 10.17 9.75 8.08 11.00 15.71 23.67 +74 10 5 10.54 8.42 11.42 4.88 6.29 4.79 9.62 6.38 4.88 6.96 11.87 18.00 +74 10 6 19.41 18.79 11.67 6.96 17.83 8.50 13.29 13.54 10.71 8.58 22.63 19.25 +74 10 7 25.08 18.12 17.96 10.25 13.88 8.87 12.67 12.92 9.33 9.87 20.00 25.46 +74 10 8 13.33 9.21 13.50 5.54 8.38 5.21 9.33 7.62 5.63 6.04 13.37 17.12 +74 10 9 10.88 4.75 9.83 3.00 4.42 1.96 8.38 6.38 5.13 4.12 9.79 15.37 +74 10 10 13.08 9.92 13.17 5.50 8.83 4.04 10.00 7.62 6.46 7.41 12.50 20.83 +74 10 11 15.16 9.17 13.96 5.04 7.54 6.08 9.75 6.50 6.87 9.25 11.29 18.38 +74 10 12 9.67 3.33 10.54 2.67 5.75 2.17 8.33 5.46 4.21 3.42 9.08 10.50 +74 10 13 8.12 5.09 4.79 2.33 6.63 2.04 8.17 3.96 5.54 3.42 8.46 11.71 +74 10 14 4.17 7.38 2.71 0.46 6.04 1.96 4.00 5.29 3.75 3.42 10.25 9.71 +74 10 15 8.12 2.71 3.79 2.00 2.67 0.79 3.37 1.29 1.08 1.04 3.37 5.37 +74 10 16 9.38 8.33 3.88 2.08 6.79 2.29 5.41 5.83 3.58 2.54 10.50 10.83 +74 10 17 14.79 9.67 10.71 4.21 8.96 5.04 7.87 9.21 6.75 6.79 13.67 16.00 +74 10 18 19.41 16.33 14.58 8.08 16.33 7.96 14.37 9.46 11.21 8.71 14.92 19.21 +74 10 19 13.21 13.08 7.08 5.83 11.00 5.29 11.71 8.21 7.92 5.29 15.96 19.46 +74 10 20 23.79 17.75 13.92 11.75 17.62 10.25 15.59 14.09 11.67 8.50 20.50 26.34 +74 10 21 16.29 11.75 14.62 9.38 8.96 6.46 11.87 8.21 7.87 6.87 14.92 21.59 +74 10 22 16.92 12.79 14.88 6.96 9.79 6.34 10.96 8.12 7.83 8.50 15.75 23.58 +74 10 23 10.08 8.08 13.33 4.88 5.71 3.50 8.87 5.41 6.04 7.29 8.75 16.29 +74 10 24 8.04 8.33 6.17 3.21 8.50 4.25 10.79 6.42 8.08 6.08 12.50 20.58 +74 10 25 13.96 8.71 7.87 7.17 11.08 6.71 13.67 11.71 10.71 7.12 13.79 18.08 +74 10 26 16.04 11.34 9.33 8.29 14.83 8.58 15.59 12.08 12.21 10.29 15.50 23.21 +74 10 27 26.50 17.33 15.34 15.75 19.92 14.12 22.29 18.66 15.87 20.88 22.46 36.63 +74 10 28 19.50 18.75 17.21 12.96 14.00 9.46 15.92 13.59 12.87 12.79 18.96 31.91 +74 10 29 16.38 12.71 19.12 8.92 10.67 7.33 13.13 8.67 9.54 9.25 12.33 24.25 +74 10 30 12.83 5.46 12.29 5.58 7.92 5.88 11.42 7.58 7.67 6.96 9.04 15.34 +74 10 31 9.25 6.79 7.67 3.88 6.54 4.21 8.54 6.29 6.29 4.67 8.38 8.96 +74 11 1 10.63 11.29 7.54 2.92 6.83 3.42 6.92 8.58 6.42 5.54 11.21 12.12 +74 11 2 14.17 12.87 13.21 5.29 11.67 7.12 9.21 10.63 10.25 11.00 15.46 17.37 +74 11 3 19.95 15.04 14.09 11.12 16.17 9.38 12.71 10.41 11.21 8.38 12.54 12.75 +74 11 4 6.63 3.92 10.96 2.46 2.67 1.04 5.96 3.67 2.58 2.17 7.21 8.29 +74 11 5 9.59 13.37 8.96 3.71 9.92 5.41 6.79 9.54 7.83 6.25 15.96 18.54 +74 11 6 8.46 4.63 12.17 2.83 5.58 1.13 5.41 2.33 5.17 1.63 1.96 6.34 +74 11 7 10.41 11.46 7.83 3.42 8.83 5.33 5.33 10.46 7.50 6.00 14.37 17.75 +74 11 8 13.59 12.58 12.21 6.42 10.41 6.67 11.21 9.83 10.58 8.83 12.96 17.25 +74 11 9 18.29 17.29 16.62 10.00 18.34 11.92 20.04 17.67 16.79 15.75 25.29 34.96 +74 11 10 22.71 21.87 20.17 12.33 20.38 11.75 19.25 18.16 15.46 15.63 24.96 27.54 +74 11 11 14.58 19.21 10.58 8.00 14.50 9.62 20.21 13.00 14.00 13.29 20.08 31.54 +74 11 12 13.54 13.54 9.67 4.21 11.08 5.41 11.75 7.83 8.79 6.83 10.79 19.21 +74 11 13 27.58 22.25 21.92 13.92 17.37 12.12 12.79 12.33 13.88 10.17 11.08 16.50 +74 11 14 22.63 18.66 24.37 13.92 20.04 13.25 19.08 9.87 18.25 14.29 8.79 17.29 +74 11 15 7.71 4.21 8.38 3.21 7.08 4.38 10.63 7.33 9.04 8.58 11.67 21.75 +74 11 16 4.33 4.25 5.91 1.58 6.21 4.00 9.62 7.33 8.54 7.58 13.88 19.87 +74 11 17 4.25 2.33 5.63 0.33 3.88 1.46 5.88 1.63 3.88 2.00 2.96 10.04 +74 11 18 7.04 2.17 8.33 0.75 1.87 0.13 8.25 0.37 2.75 3.04 1.25 10.79 +74 11 19 11.92 14.37 7.29 2.17 9.17 2.75 4.71 5.75 3.42 3.42 5.54 9.54 +74 11 20 15.41 13.37 20.41 6.29 10.88 4.88 14.29 11.63 10.00 6.58 8.50 12.75 +74 11 21 8.79 7.92 19.55 5.21 6.87 3.63 9.25 8.12 7.29 3.00 4.38 14.17 +74 11 22 6.67 3.79 8.08 2.67 5.91 1.50 4.83 3.13 4.29 2.21 3.25 12.92 +74 11 23 14.67 9.62 9.79 6.29 8.54 4.17 9.71 3.79 6.17 3.33 2.75 6.17 +74 11 24 18.79 19.83 14.33 10.58 20.88 11.96 18.00 18.84 16.25 16.66 23.63 28.84 +74 11 25 12.67 13.17 11.08 7.29 14.29 7.96 17.79 10.54 13.25 13.25 16.66 26.63 +74 11 26 4.00 8.58 4.38 1.63 8.46 2.83 6.96 4.54 4.54 4.04 7.62 11.25 +74 11 27 22.71 23.00 13.42 13.54 24.04 13.42 22.50 20.04 17.83 18.63 25.12 31.88 +74 11 28 20.04 16.33 13.50 13.21 19.41 10.96 20.67 13.33 14.67 15.25 18.63 30.71 +74 11 29 9.08 10.29 8.96 2.29 7.41 4.29 9.96 7.08 7.46 4.96 9.38 14.04 +74 11 30 12.92 12.96 10.37 5.00 14.37 7.08 13.29 12.12 11.12 8.21 14.04 18.79 +74 12 1 17.16 19.12 16.17 7.87 16.66 11.12 14.62 15.75 14.92 13.96 23.13 23.79 +74 12 2 11.38 10.37 12.83 5.04 9.46 5.04 9.87 6.87 9.25 8.83 11.25 19.46 +74 12 3 17.50 18.25 15.16 7.92 15.16 11.83 12.04 15.96 12.92 13.54 21.87 21.17 +74 12 4 12.96 16.83 10.04 7.50 17.37 8.58 17.41 14.29 13.75 13.08 21.62 26.83 +74 12 5 12.04 11.29 8.54 5.54 12.12 6.25 15.75 11.58 12.33 9.29 14.00 23.83 +74 12 6 13.92 13.54 10.29 6.04 16.38 7.87 17.04 10.79 14.12 10.21 16.08 23.33 +74 12 7 14.88 13.79 10.96 10.13 22.00 10.37 21.67 16.08 17.62 12.25 19.58 24.79 +74 12 8 16.83 17.75 14.88 9.29 15.59 10.46 14.92 13.83 14.71 13.96 20.04 25.08 +74 12 9 14.29 18.58 9.21 8.75 16.29 8.08 17.46 14.92 13.59 12.71 21.25 26.00 +74 12 10 18.25 22.54 14.25 11.29 21.04 10.41 20.21 15.50 15.67 12.21 21.21 29.38 +74 12 11 20.12 16.38 15.00 9.87 14.58 7.62 13.75 10.63 10.50 10.00 14.29 21.92 +74 12 12 29.50 19.87 15.50 16.08 22.83 13.79 17.58 19.70 16.88 15.75 17.92 22.92 +74 12 13 14.42 14.50 11.63 8.75 15.79 8.96 13.13 14.12 13.08 10.50 18.29 19.67 +74 12 14 12.00 13.88 10.92 7.87 13.13 8.04 14.88 13.96 11.71 9.62 16.29 21.84 +74 12 15 9.71 11.12 8.12 4.25 9.38 5.17 10.58 8.83 9.50 8.71 12.79 21.00 +74 12 16 21.21 22.25 15.09 12.75 23.13 13.33 20.79 21.09 17.92 16.75 25.12 29.95 +74 12 17 25.12 24.96 13.96 15.12 24.58 15.96 25.46 22.37 18.58 19.08 26.75 38.25 +74 12 18 12.04 12.46 8.46 6.54 15.00 6.13 16.66 9.29 12.71 9.46 11.79 20.17 +74 12 19 23.25 21.37 19.70 9.87 15.25 11.00 18.63 20.83 16.66 15.46 27.33 27.88 +74 12 20 26.54 23.00 22.75 13.88 16.04 12.17 17.46 17.50 16.88 15.25 21.50 23.50 +74 12 21 25.00 20.67 22.21 11.67 15.37 11.38 16.25 17.37 16.00 15.16 24.41 26.00 +74 12 22 22.58 19.55 17.88 10.13 15.50 9.67 15.21 16.58 14.00 14.17 19.55 22.42 +74 12 23 27.54 23.54 24.00 18.88 24.87 17.54 17.75 22.42 25.00 23.58 25.04 33.04 +74 12 24 10.41 12.75 8.79 3.79 11.87 5.29 11.12 7.21 10.34 8.04 13.33 20.79 +74 12 25 27.29 25.29 22.67 13.21 20.17 12.58 17.83 16.92 16.46 13.67 18.16 20.67 +74 12 26 25.41 22.21 19.55 12.87 20.50 12.71 19.50 14.50 16.75 12.87 15.16 17.79 +74 12 27 22.50 22.00 17.46 10.58 22.17 12.71 20.50 17.41 17.29 13.88 19.79 27.33 +74 12 28 25.84 22.83 20.88 14.96 25.92 16.62 25.33 20.04 22.63 16.88 15.75 25.96 +74 12 29 14.83 11.58 12.71 9.29 13.70 8.00 14.96 11.42 13.75 9.04 12.92 22.50 +74 12 30 13.29 13.42 13.17 3.63 9.75 6.34 11.08 12.17 10.83 9.00 18.16 22.13 +74 12 31 16.04 16.29 15.21 8.42 13.67 9.75 15.25 16.13 15.04 13.46 18.54 18.46 +75 1 1 14.04 13.54 11.29 5.46 12.58 5.58 8.12 8.96 9.29 5.17 7.71 11.63 +75 1 2 9.17 11.46 9.13 2.54 8.71 4.58 8.58 13.75 10.67 10.54 17.79 20.96 +75 1 3 9.54 5.63 7.29 3.50 7.71 3.33 9.96 7.12 8.46 7.38 11.25 21.84 +75 1 4 5.37 6.00 9.38 3.50 8.96 5.83 16.38 10.17 12.75 11.46 16.38 24.87 +75 1 5 11.92 12.25 12.17 5.75 14.92 10.50 21.59 13.13 16.38 14.58 22.13 30.88 +75 1 6 19.83 17.08 17.08 10.21 19.29 10.08 19.12 12.87 15.59 12.87 18.58 23.33 +75 1 7 11.58 10.79 8.75 6.00 12.62 6.38 10.88 10.29 9.46 5.09 11.46 13.29 +75 1 8 10.71 11.00 8.75 5.83 11.46 7.17 10.63 11.17 10.71 6.96 16.21 16.33 +75 1 9 14.71 14.79 12.25 6.29 12.29 8.50 14.25 15.71 13.50 12.29 19.87 20.62 +75 1 10 21.96 17.46 17.75 10.21 13.25 8.54 11.83 12.46 13.37 12.12 22.92 17.46 +75 1 11 22.13 18.12 19.00 11.12 15.67 10.04 17.41 14.42 16.42 13.37 24.92 21.84 +75 1 12 18.79 21.09 17.46 10.96 17.29 10.71 15.79 14.96 16.17 15.09 27.33 27.42 +75 1 13 33.12 25.58 25.88 19.00 21.79 17.50 19.62 20.91 21.00 21.54 28.71 28.71 +75 1 14 19.12 12.54 19.92 10.13 11.92 7.96 13.96 12.29 12.50 13.83 20.33 26.54 +75 1 15 18.05 11.58 16.50 10.58 12.96 7.83 10.83 9.08 12.67 10.17 12.96 17.79 +75 1 16 8.29 8.00 8.67 1.75 9.42 2.92 5.88 6.92 7.62 4.29 10.13 14.33 +75 1 17 8.00 10.71 6.54 3.75 10.08 4.04 9.62 7.17 9.71 6.08 11.46 9.17 +75 1 18 14.37 10.37 11.29 6.13 8.17 3.29 7.25 6.54 7.08 4.58 12.58 9.54 +75 1 19 22.37 22.08 17.67 9.21 16.33 11.00 12.12 16.62 13.88 12.92 23.63 22.46 +75 1 20 20.50 22.29 12.62 9.33 19.29 11.50 16.79 16.33 16.00 15.79 29.88 29.71 +75 1 21 19.55 18.29 13.25 8.58 14.21 8.33 14.42 12.12 13.88 10.50 17.50 20.79 +75 1 22 26.50 29.50 21.29 14.67 26.16 15.50 20.91 21.34 20.58 19.55 32.30 32.33 +75 1 23 20.62 23.63 14.62 12.87 22.29 14.75 19.50 20.12 18.96 16.13 31.49 34.54 +75 1 24 16.25 14.37 12.50 6.75 11.38 7.29 10.92 10.00 11.46 9.54 16.58 22.08 +75 1 25 19.41 16.42 15.29 8.83 13.59 8.21 13.17 10.71 12.38 10.71 17.12 23.13 +75 1 26 22.21 14.88 17.88 7.96 11.83 8.12 10.25 11.54 11.29 10.13 18.50 19.95 +75 1 27 18.66 19.46 11.21 6.46 12.50 5.29 6.67 7.62 7.41 5.66 13.59 17.37 +75 1 28 16.29 13.67 14.04 8.92 14.12 8.67 14.75 12.21 13.37 11.46 17.25 26.16 +75 1 29 17.62 18.38 10.54 4.50 10.46 7.00 7.87 10.92 9.67 6.34 16.04 17.29 +75 1 30 21.09 20.58 17.96 10.71 16.88 11.96 14.92 19.12 17.04 16.04 26.92 28.58 +75 1 31 18.34 18.66 14.25 9.04 13.83 9.17 14.58 14.09 13.75 10.83 23.75 22.17 +75 2 1 12.50 17.46 12.12 5.83 11.12 6.29 9.62 13.92 10.67 9.75 22.95 18.16 +75 2 2 11.96 21.46 8.87 6.34 12.46 6.08 4.42 9.75 10.29 7.79 18.91 11.08 +75 2 3 10.75 15.37 8.12 5.96 10.88 5.41 6.08 8.42 9.79 6.21 14.46 12.92 +75 2 4 12.04 9.33 9.54 4.00 7.83 3.67 4.12 7.33 4.21 4.67 7.41 10.92 +75 2 5 12.38 7.12 11.38 3.25 7.87 3.50 8.12 6.34 8.29 5.29 7.67 9.42 +75 2 6 19.00 17.79 15.92 5.54 11.71 6.46 8.46 11.63 9.42 8.08 15.96 17.33 +75 2 7 15.87 14.00 11.42 6.42 10.58 6.63 8.58 13.00 11.75 7.29 16.46 17.00 +75 2 8 15.41 14.17 10.54 4.08 11.29 5.83 7.54 9.42 9.62 5.71 12.38 14.92 +75 2 9 12.79 12.29 8.67 2.79 9.46 2.37 5.21 7.71 8.00 3.33 8.87 9.04 +75 2 10 14.25 17.54 10.04 5.33 11.63 7.00 7.00 8.92 8.46 3.50 10.67 11.29 +75 2 11 14.00 11.63 13.46 4.88 10.41 7.79 5.66 9.29 7.54 3.88 10.96 7.87 +75 2 12 7.46 6.04 9.00 2.62 4.50 2.67 4.67 3.04 3.79 0.92 6.08 6.54 +75 2 13 11.63 15.59 7.33 5.04 12.25 7.46 9.79 8.33 10.54 5.17 14.58 8.87 +75 2 14 14.21 8.58 9.92 1.96 7.67 2.21 4.21 3.92 5.91 1.38 3.75 7.29 +75 2 15 9.17 7.17 9.21 3.33 9.42 5.54 6.96 7.04 8.38 6.50 9.92 14.88 +75 2 16 14.42 14.00 16.96 10.50 11.96 11.42 12.38 11.83 13.92 13.59 19.25 23.16 +75 2 17 13.42 13.54 10.92 7.33 13.25 9.54 14.00 12.67 14.00 10.37 20.08 23.25 +75 2 18 3.71 5.29 4.33 0.67 3.13 0.63 4.00 3.04 2.46 1.50 8.00 11.34 +75 2 19 17.46 17.62 16.17 7.92 16.33 11.71 10.63 12.42 12.79 11.63 17.88 23.42 +75 2 20 9.67 7.50 16.08 4.58 5.17 1.67 6.00 1.63 4.33 1.13 6.25 6.58 +75 2 21 8.67 3.08 10.92 4.46 5.00 5.50 5.21 3.67 7.62 5.96 16.25 16.50 +75 2 22 16.46 14.42 14.37 8.17 12.92 9.96 7.92 12.75 12.79 12.92 21.00 22.25 +75 2 23 10.67 9.42 11.34 5.63 8.04 5.13 6.96 5.37 7.21 3.96 5.79 11.50 +75 2 24 11.38 15.29 9.13 5.91 10.71 8.21 6.34 10.71 10.54 7.29 12.54 14.96 +75 2 25 16.88 21.29 14.17 6.42 12.71 10.67 9.21 11.67 11.00 7.38 14.96 19.87 +75 2 26 18.08 19.17 15.83 6.42 16.38 10.50 9.25 10.46 10.46 7.96 15.50 18.00 +75 2 27 15.46 17.04 11.54 3.96 15.16 7.29 7.87 8.79 8.79 4.71 11.42 10.41 +75 2 28 11.21 16.88 12.71 5.75 13.83 7.83 7.92 9.96 9.33 5.37 12.29 9.54 +75 3 1 12.79 11.46 14.62 5.09 10.00 6.83 10.13 7.54 8.25 3.75 8.04 11.42 +75 3 2 11.67 11.42 13.46 5.71 10.17 7.21 10.00 7.96 9.71 5.79 10.37 12.92 +75 3 3 4.33 5.04 7.87 2.29 3.96 2.92 7.38 4.88 6.67 3.88 9.75 12.12 +75 3 4 4.63 5.46 5.66 0.50 3.42 0.00 1.63 1.54 0.54 0.25 5.50 5.41 +75 3 5 7.87 7.21 5.83 1.83 3.50 2.17 4.88 3.92 3.96 5.00 8.92 9.33 +75 3 6 14.62 14.71 14.00 7.46 14.50 9.83 14.46 11.71 13.54 13.17 19.70 22.37 +75 3 7 13.79 14.54 10.63 7.33 14.62 8.33 14.00 9.79 12.71 12.04 18.96 20.83 +75 3 8 9.50 6.54 7.21 3.17 6.79 2.58 5.33 5.37 5.91 5.83 11.87 14.54 +75 3 9 17.04 13.08 12.21 6.13 10.54 6.25 10.08 8.08 9.38 9.04 15.12 16.33 +75 3 10 15.29 13.83 19.00 7.04 10.83 8.33 12.96 11.08 10.46 11.92 16.92 21.75 +75 3 11 18.84 13.92 28.33 11.25 14.29 12.92 19.29 11.83 16.00 15.34 15.59 19.00 +75 3 12 10.63 9.04 15.21 4.79 7.50 5.91 10.75 7.12 9.75 8.25 6.63 17.16 +75 3 13 9.00 5.00 16.00 5.04 6.29 2.79 7.62 4.33 6.83 6.25 5.17 8.63 +75 3 14 12.38 6.42 15.54 5.54 6.63 2.88 6.46 4.38 6.87 7.00 4.54 9.17 +75 3 15 10.37 6.00 15.09 4.79 4.21 1.87 7.83 5.71 5.50 7.04 9.13 18.63 +75 3 16 11.38 9.46 12.17 3.04 4.21 3.21 5.58 5.13 5.83 4.92 4.12 10.67 +75 3 17 12.42 8.63 13.37 3.37 5.37 4.17 7.79 6.63 6.38 5.54 7.46 11.67 +75 3 18 12.83 11.12 13.21 4.54 7.96 4.21 10.50 7.00 6.29 7.04 7.92 12.08 +75 3 19 11.58 8.92 18.34 5.41 8.58 4.46 9.62 5.91 6.58 6.50 8.25 14.33 +75 3 20 10.71 6.63 20.54 4.50 6.13 2.46 8.71 6.96 8.87 6.04 7.71 15.67 +75 3 21 16.33 14.46 14.50 7.83 11.54 10.13 11.12 9.83 12.29 11.96 14.88 22.34 +75 3 22 16.54 13.00 9.59 7.62 11.46 6.63 10.92 9.04 10.13 8.87 13.04 14.21 +75 3 23 15.63 9.54 10.58 6.25 11.87 7.21 10.63 9.13 9.83 10.96 12.21 19.75 +75 3 24 17.16 7.67 12.08 8.00 9.71 6.75 11.67 8.12 10.21 12.42 9.04 22.95 +75 3 25 8.50 6.29 8.75 3.75 7.08 5.66 10.88 7.71 10.50 8.79 11.04 10.58 +75 3 26 9.21 8.63 13.29 3.08 6.29 5.63 7.33 7.54 8.96 7.00 14.42 16.71 +75 3 27 15.63 11.87 17.79 8.42 9.25 5.21 10.17 8.38 10.96 10.58 13.67 19.70 +75 3 28 15.87 12.67 12.17 6.00 10.21 7.62 9.42 9.29 10.75 10.00 16.17 20.62 +75 3 29 15.37 12.54 10.79 7.08 9.46 6.46 9.62 8.25 9.33 9.83 18.05 20.12 +75 3 30 16.08 16.96 15.12 7.17 9.75 6.54 8.58 8.42 9.50 9.71 15.87 14.62 +75 3 31 12.79 10.83 8.29 6.58 8.87 6.29 9.46 7.62 8.96 9.59 12.92 15.59 +75 4 1 16.50 11.54 10.50 7.71 9.25 8.08 11.71 8.92 11.04 12.17 13.79 18.16 +75 4 2 17.50 15.12 16.75 8.87 12.87 8.25 10.75 12.17 11.34 10.88 17.41 19.29 +75 4 3 15.04 11.54 17.58 8.04 9.29 7.08 11.79 9.08 12.04 14.83 15.16 25.62 +75 4 4 12.62 10.63 18.34 6.58 6.46 3.46 9.79 8.04 9.17 8.42 11.54 16.75 +75 4 5 9.92 7.33 13.54 4.79 5.29 2.00 7.00 4.92 7.12 4.88 10.41 11.92 +75 4 6 12.50 7.75 13.83 6.92 6.34 3.71 8.33 6.29 8.29 5.88 8.04 13.21 +75 4 7 21.84 15.83 15.00 11.58 16.00 10.92 17.71 14.04 15.71 20.54 20.91 33.04 +75 4 8 20.21 17.12 17.00 11.79 12.12 11.58 15.92 12.75 13.50 15.46 20.33 31.25 +75 4 9 15.63 9.08 13.13 7.54 8.71 8.04 10.54 6.38 9.46 10.41 13.50 18.63 +75 4 10 16.54 11.00 9.87 7.46 12.62 9.62 11.79 12.29 12.54 10.83 15.00 19.25 +75 4 11 14.88 7.87 8.08 6.21 9.46 7.41 12.83 8.96 11.63 10.34 12.50 18.41 +75 4 12 6.96 3.33 6.79 4.92 7.58 5.91 10.29 6.79 9.42 7.67 9.59 12.42 +75 4 13 12.33 14.29 10.37 5.66 8.33 6.63 9.08 11.50 10.13 9.75 16.42 14.00 +75 4 14 18.46 15.59 15.96 7.87 12.83 9.38 10.00 7.87 11.87 7.62 11.71 10.58 +75 4 15 13.08 9.13 7.75 3.54 6.96 2.62 4.46 3.96 5.88 4.08 8.38 6.71 +75 4 16 8.25 11.87 8.17 3.58 7.58 5.54 5.54 8.12 9.54 6.50 14.92 11.17 +75 4 17 10.17 11.50 10.92 4.71 8.42 6.63 8.38 9.00 8.92 8.38 15.92 12.00 +75 4 18 7.41 11.08 5.46 3.92 10.54 6.92 6.79 8.75 9.50 6.67 19.87 13.17 +75 4 19 13.62 13.21 8.92 5.41 13.04 8.63 9.79 10.08 10.63 8.71 17.54 18.50 +75 4 20 19.62 16.04 17.71 7.87 10.08 9.00 11.38 9.54 12.42 9.33 11.34 9.62 +75 4 21 10.46 7.62 13.50 4.17 5.54 4.00 4.67 7.71 8.79 7.50 9.62 7.25 +75 4 22 4.58 5.63 3.29 2.42 7.41 2.29 4.04 4.71 5.88 6.83 6.54 9.17 +75 4 23 8.08 5.71 10.79 4.21 5.75 3.00 2.54 3.50 6.71 5.54 4.17 9.96 +75 4 24 4.58 1.96 12.96 2.21 4.17 1.87 1.21 1.46 4.17 1.92 6.13 5.25 +75 4 25 4.25 1.42 6.38 1.92 3.63 0.17 1.87 3.54 2.17 2.13 4.79 2.29 +75 4 26 8.75 4.12 6.29 3.00 5.96 3.17 3.08 4.54 6.04 4.75 7.92 12.12 +75 4 27 7.62 9.62 12.71 4.75 6.92 3.67 4.83 3.75 5.75 5.00 8.75 13.42 +75 4 28 10.67 11.00 9.21 4.88 12.50 6.79 7.21 9.04 11.42 8.79 17.88 18.29 +75 4 29 14.17 16.50 10.34 8.17 16.42 9.83 12.96 11.96 12.83 13.25 19.33 23.25 +75 4 30 18.50 15.75 14.92 8.38 16.50 9.87 10.41 11.34 13.33 12.67 17.88 19.33 +75 5 1 22.54 16.04 17.16 7.75 13.92 8.75 13.88 11.04 13.42 11.46 15.25 21.67 +75 5 2 14.46 10.96 11.38 6.92 14.88 2.50 4.38 2.42 4.54 5.66 6.67 15.41 +75 5 3 6.25 6.29 4.92 1.71 3.54 0.29 2.54 2.42 1.04 2.04 6.63 6.38 +75 5 4 1.63 3.96 5.37 0.46 4.08 0.63 0.92 1.42 3.42 1.67 3.83 3.79 +75 5 5 6.13 4.21 15.71 3.88 5.09 3.96 5.63 3.58 5.46 3.37 7.83 7.33 +75 5 6 8.79 8.63 21.59 6.54 8.12 5.54 6.96 5.04 7.54 5.09 13.75 6.29 +75 5 7 11.83 9.33 27.67 8.21 7.58 6.54 12.04 6.04 9.29 6.75 9.83 10.54 +75 5 8 17.92 13.13 24.00 8.67 15.50 7.04 11.08 13.54 12.17 10.79 15.41 10.88 +75 5 9 16.66 15.41 13.67 7.00 14.09 8.08 7.41 12.96 11.25 13.17 13.92 19.62 +75 5 10 15.96 11.17 9.46 6.13 10.92 8.42 9.54 9.83 10.67 12.25 12.67 20.46 +75 5 11 11.42 9.04 9.17 3.58 7.00 3.33 5.33 5.17 5.83 5.88 10.37 11.58 +75 5 12 13.54 10.04 11.87 5.21 11.63 7.75 7.33 8.25 11.00 7.00 11.58 13.54 +75 5 13 6.79 6.13 5.91 1.67 5.09 1.63 2.13 2.92 3.92 3.83 8.00 8.96 +75 5 14 6.87 4.67 9.54 2.71 8.08 4.50 6.21 7.62 6.17 7.46 14.50 14.79 +75 5 15 10.04 9.25 22.17 5.33 8.63 5.63 7.87 5.41 6.21 3.71 14.17 10.71 +75 5 16 6.71 7.08 14.92 3.54 5.00 0.79 3.13 4.50 2.29 2.29 6.46 5.58 +75 5 17 6.29 2.83 11.58 1.87 3.75 0.83 2.21 1.92 2.04 2.04 4.33 6.08 +75 5 18 6.21 4.71 3.46 1.87 3.46 1.08 3.92 3.75 1.92 2.62 8.83 5.83 +75 5 19 4.38 6.46 3.04 1.50 3.13 1.33 4.04 5.41 3.50 4.21 15.54 13.54 +75 5 20 9.67 2.50 10.00 2.50 4.29 4.04 2.54 2.96 3.58 4.04 9.08 14.25 +75 5 21 6.79 2.75 13.42 3.92 5.00 4.17 3.71 5.41 6.42 5.37 15.75 18.71 +75 5 22 6.38 4.96 16.66 5.54 7.71 4.42 6.21 7.79 8.50 9.96 14.67 18.88 +75 5 23 8.21 9.59 10.79 4.50 6.42 4.71 5.88 7.17 8.12 9.29 10.88 15.92 +75 5 24 9.00 4.04 13.33 4.08 6.38 2.88 5.71 4.83 6.38 4.58 11.58 10.00 +75 5 25 11.75 5.75 16.50 4.79 6.54 5.88 7.96 6.63 8.21 7.12 10.50 12.21 +75 5 26 10.29 5.96 19.67 6.38 7.96 7.71 9.62 9.29 10.08 7.54 12.87 7.29 +75 5 27 12.83 8.21 23.58 7.38 9.50 8.75 9.92 9.59 9.25 7.67 11.42 8.25 +75 5 28 12.25 6.67 24.54 4.83 9.92 7.83 6.87 9.59 9.67 5.79 11.58 7.12 +75 5 29 6.92 5.17 16.21 3.33 4.29 3.21 3.92 5.83 4.88 2.21 12.67 4.33 +75 5 30 9.42 4.42 12.75 2.96 6.46 4.08 5.33 7.79 7.17 3.33 19.08 13.88 +75 5 31 9.33 7.17 7.92 3.21 5.71 3.42 3.00 7.21 5.09 5.79 9.38 13.54 +75 6 1 10.96 8.79 9.50 4.75 9.62 5.58 8.38 9.75 9.29 8.75 11.75 18.34 +75 6 2 23.21 16.58 18.79 13.13 15.92 12.08 17.21 15.46 14.58 20.00 20.25 32.79 +75 6 3 9.62 4.92 10.37 3.88 5.58 3.33 5.91 4.79 6.46 6.58 6.29 11.21 +75 6 4 11.83 12.71 10.54 4.96 8.87 4.83 3.75 6.96 8.04 5.79 14.12 12.83 +75 6 5 12.33 16.17 11.46 7.50 12.87 9.92 9.13 12.71 13.25 10.50 17.58 14.17 +75 6 6 11.12 18.38 12.12 9.25 16.54 10.67 7.46 11.67 14.54 13.04 18.75 22.79 +75 6 7 6.63 9.50 5.04 3.04 4.96 3.83 2.88 8.17 6.04 5.79 14.54 10.75 +75 6 8 7.67 8.96 2.92 3.54 8.54 4.92 3.92 7.00 6.96 6.50 6.79 9.04 +75 6 9 3.96 3.67 7.00 1.79 6.08 2.46 4.58 4.00 5.75 4.67 6.29 10.04 +75 6 10 7.00 7.54 3.67 1.25 6.42 1.00 1.58 4.33 2.75 0.54 5.75 5.91 +75 6 11 7.96 1.96 2.88 1.63 4.00 1.25 1.58 3.54 2.00 2.00 10.67 6.54 +75 6 12 10.17 6.58 5.88 2.88 5.46 1.75 3.75 4.96 4.58 4.54 6.42 10.13 +75 6 13 10.08 3.96 6.87 4.17 9.29 5.46 7.58 7.21 7.12 6.92 11.08 14.62 +75 6 14 13.33 9.29 8.92 6.71 11.92 7.92 9.00 10.83 9.46 9.92 13.08 17.88 +75 6 15 10.54 10.58 8.67 4.71 9.08 6.04 5.63 8.00 7.00 5.71 11.46 13.79 +75 6 16 9.79 7.92 7.38 1.92 6.54 2.13 3.33 4.25 4.96 3.13 7.50 9.21 +75 6 17 10.75 8.79 11.12 3.75 7.33 3.96 4.42 8.21 7.38 5.58 11.71 10.92 +75 6 18 15.00 15.21 14.75 8.33 12.75 9.79 10.46 14.79 12.58 12.58 21.50 20.46 +75 6 19 15.37 12.96 16.13 9.21 12.71 8.96 11.71 14.09 12.21 11.00 20.33 19.38 +75 6 20 5.41 1.79 6.38 2.00 4.88 1.25 4.42 4.33 5.37 3.63 8.25 11.08 +75 6 21 5.79 3.96 9.33 1.92 4.17 2.29 3.54 3.67 6.50 4.12 5.00 15.63 +75 6 22 5.75 3.21 6.13 2.25 4.71 1.63 2.50 6.38 2.67 3.63 7.00 8.63 +75 6 23 11.38 6.75 5.71 4.83 6.87 4.04 6.04 6.13 6.21 5.04 7.96 11.54 +75 6 24 8.25 9.46 12.58 4.96 6.58 3.79 4.08 5.75 7.41 6.13 6.17 8.79 +75 6 25 6.17 6.25 9.62 2.37 5.71 1.04 1.79 4.50 3.42 3.37 5.63 9.96 +75 6 26 9.50 9.71 17.00 5.88 9.42 5.91 5.46 9.46 8.87 8.12 10.41 14.96 +75 6 27 10.54 9.54 14.79 4.29 7.21 3.67 6.87 9.00 8.83 3.92 13.33 10.96 +75 6 28 10.54 5.54 13.62 3.71 5.75 5.58 6.04 7.00 9.00 4.33 8.58 8.04 +75 6 29 8.17 4.71 11.54 2.50 4.25 0.25 1.83 4.63 3.37 1.17 5.21 7.25 +75 6 30 9.13 3.21 5.21 2.21 5.54 1.50 1.63 3.83 3.25 4.46 4.67 10.67 +75 7 1 6.25 3.25 5.50 1.92 3.46 1.33 2.92 2.75 2.37 2.21 6.13 11.46 +75 7 2 4.96 4.54 3.50 2.29 4.46 3.54 4.79 4.25 4.50 2.08 5.91 7.00 +75 7 3 10.83 5.17 6.21 3.58 5.41 2.83 3.96 4.67 6.42 3.37 7.67 5.63 +75 7 4 10.08 8.00 6.83 3.42 7.21 2.46 4.12 7.29 4.38 2.00 12.54 6.17 +75 7 5 8.08 4.50 4.33 3.08 4.54 2.62 4.71 6.21 8.50 4.42 13.04 7.75 +75 7 6 7.33 4.63 9.96 2.00 5.79 1.58 4.63 3.83 5.58 3.00 6.87 7.87 +75 7 7 14.33 6.83 16.00 4.63 7.92 6.54 8.50 9.29 10.08 6.13 8.79 9.54 +75 7 8 11.71 6.96 12.83 6.17 8.63 8.92 8.67 12.00 11.25 6.71 10.71 13.21 +75 7 9 10.34 6.92 9.67 5.04 10.41 6.87 8.29 9.62 8.83 6.13 9.29 16.58 +75 7 10 10.08 7.25 12.08 5.46 7.12 3.42 6.67 2.96 7.87 3.33 7.17 7.96 +75 7 11 7.83 7.58 8.79 4.33 7.75 5.00 9.13 6.67 7.96 4.58 6.67 8.83 +75 7 12 12.79 12.71 11.25 5.50 10.46 5.75 5.96 7.79 9.13 5.04 7.67 10.96 +75 7 13 13.37 8.92 12.87 7.12 9.92 7.00 6.58 5.17 10.08 6.34 9.13 9.71 +75 7 14 15.41 10.75 15.87 7.62 9.62 6.54 10.71 5.41 11.17 8.00 9.92 9.54 +75 7 15 15.04 11.58 13.54 6.96 13.75 9.29 14.37 9.62 12.21 10.17 12.38 16.08 +75 7 16 1.71 3.63 7.29 3.17 4.46 2.71 7.58 5.37 5.63 4.50 8.25 4.67 +75 7 17 8.67 6.50 2.92 2.13 6.67 2.92 2.79 4.17 2.71 2.04 5.33 7.00 +75 7 18 7.00 9.54 7.96 3.96 7.71 4.63 5.21 5.71 6.67 4.29 13.37 5.37 +75 7 19 11.79 12.58 12.00 5.04 8.42 6.67 8.54 11.21 10.63 8.79 16.21 12.46 +75 7 20 10.13 10.71 9.71 5.13 10.75 5.58 9.79 7.75 9.71 6.08 15.25 15.75 +75 7 21 12.58 13.17 13.08 5.71 15.59 9.08 13.79 12.75 13.25 11.71 20.83 21.34 +75 7 22 18.88 16.29 20.04 9.71 20.12 11.71 17.29 11.08 15.63 12.25 16.88 15.16 +75 7 23 15.96 14.88 13.42 8.54 15.67 9.13 14.62 13.42 13.13 10.83 16.75 15.87 +75 7 24 16.75 12.87 12.04 7.96 15.75 8.67 13.75 11.79 11.04 10.21 15.09 22.46 +75 7 25 7.87 7.58 8.96 2.54 9.59 4.75 9.59 6.67 9.46 7.33 13.88 13.59 +75 7 26 4.17 5.41 9.13 3.00 6.87 4.79 9.46 7.08 8.63 5.71 15.12 15.63 +75 7 27 5.33 2.83 5.21 1.83 3.17 0.92 4.08 3.00 2.92 1.46 11.54 11.08 +75 7 28 6.34 6.04 6.04 1.96 4.88 3.33 5.83 10.17 7.38 7.92 17.62 15.46 +75 7 29 9.00 6.25 12.04 3.50 5.75 4.83 6.04 8.75 8.71 6.92 12.62 13.96 +75 7 30 7.25 9.25 11.96 2.62 6.58 3.54 3.75 5.83 6.96 5.17 7.38 10.21 +75 7 31 9.21 10.41 21.42 5.21 6.87 3.46 6.04 4.25 6.54 2.50 3.88 6.87 +75 8 1 8.12 5.29 16.38 2.96 4.29 0.83 2.71 2.58 3.37 1.63 3.71 13.92 +75 8 2 7.41 1.08 11.12 2.29 2.54 0.46 3.33 1.58 4.75 2.37 4.29 7.29 +75 8 3 5.83 2.46 5.54 2.62 2.00 1.25 4.38 0.46 4.08 1.96 3.71 5.21 +75 8 4 9.50 5.13 5.25 5.17 8.17 4.71 3.83 6.63 7.92 6.75 7.29 11.71 +75 8 5 12.46 15.12 10.96 9.33 14.96 8.63 7.75 9.50 11.63 8.92 8.29 15.87 +75 8 6 14.71 15.75 11.46 10.63 14.62 9.25 9.46 12.04 13.70 12.29 20.12 17.79 +75 8 7 8.92 10.58 6.46 5.09 10.34 6.42 4.83 9.46 9.71 8.54 20.08 14.21 +75 8 8 8.42 3.29 8.08 3.79 4.67 1.54 4.83 2.88 3.50 3.29 7.87 6.00 +75 8 9 17.83 8.46 11.79 7.92 7.54 4.75 5.29 8.92 9.54 6.67 9.21 14.50 +75 8 10 8.08 3.54 5.63 2.75 2.67 1.04 0.92 2.54 4.75 0.46 6.54 6.50 +75 8 11 6.13 5.41 5.79 3.92 5.58 2.29 2.75 1.38 5.41 2.96 4.79 6.54 +75 8 12 11.92 7.58 9.59 5.41 9.04 4.63 5.88 4.96 7.83 1.92 6.75 4.00 +75 8 13 6.04 4.38 5.13 3.00 4.96 2.92 2.62 4.75 7.46 4.54 6.42 9.38 +75 8 14 6.67 7.38 7.33 3.96 6.71 3.83 3.25 6.38 6.54 2.42 12.08 6.79 +75 8 15 6.87 7.87 6.42 3.46 8.96 4.96 5.75 6.00 8.79 6.25 10.75 8.46 +75 8 16 8.46 4.92 10.13 4.92 6.17 5.17 9.54 7.58 8.00 8.12 8.50 12.38 +75 8 17 3.67 5.29 4.67 1.79 3.37 0.92 2.79 2.13 0.87 2.25 8.38 8.21 +75 8 18 4.92 6.96 5.46 2.71 3.58 2.79 5.33 4.92 6.54 5.13 9.00 11.08 +75 8 19 14.25 13.50 13.92 6.17 11.92 6.92 8.38 11.96 11.21 8.92 15.25 14.25 +75 8 20 14.67 10.54 12.92 6.29 14.17 9.08 11.50 10.58 12.08 10.96 16.04 19.75 +75 8 21 13.42 9.87 8.25 4.33 10.00 5.71 8.42 7.50 8.50 7.21 12.29 18.75 +75 8 22 12.12 8.25 10.00 7.00 9.13 5.66 9.42 7.08 8.46 7.87 9.13 16.38 +75 8 23 9.50 6.92 8.63 4.88 8.58 5.09 6.29 7.96 8.08 7.21 11.63 13.37 +75 8 24 5.46 4.17 6.46 2.17 5.58 3.33 6.34 6.00 7.29 3.54 10.00 12.38 +75 8 25 5.37 5.83 6.34 1.96 3.83 2.42 5.00 6.92 6.38 4.88 12.71 13.70 +75 8 26 3.13 5.66 7.21 1.46 1.46 1.13 2.46 6.96 5.37 4.33 12.58 11.12 +75 8 27 4.21 3.75 3.46 1.50 2.08 1.58 3.00 3.04 2.96 1.29 7.00 4.75 +75 8 28 4.58 2.75 3.33 1.50 2.75 0.75 3.17 2.75 2.75 2.25 7.62 7.83 +75 8 29 12.87 9.42 14.50 5.75 7.04 4.46 8.29 8.46 7.83 7.58 11.50 17.41 +75 8 30 17.50 9.71 16.62 9.25 8.12 6.79 14.00 6.58 10.17 10.63 8.04 16.00 +75 8 31 8.08 3.46 9.13 3.96 2.46 1.75 6.42 2.88 4.79 1.46 8.67 9.79 +75 9 1 5.96 2.71 5.29 1.67 2.96 0.58 5.50 3.25 2.25 1.46 12.42 11.04 +75 9 2 7.33 7.58 8.87 3.58 6.17 3.83 7.46 8.63 7.21 4.67 14.25 14.42 +75 9 3 9.08 5.54 7.71 3.88 4.96 2.33 6.54 2.62 6.71 5.50 8.54 12.38 +75 9 4 9.17 7.83 9.17 5.04 8.29 5.29 9.71 6.79 8.46 6.63 15.37 17.12 +75 9 5 7.08 6.34 7.50 3.88 6.50 3.13 7.04 4.92 6.83 3.83 10.50 13.54 +75 9 6 7.21 7.00 6.46 1.63 6.25 3.17 5.09 5.17 6.21 3.88 7.54 7.79 +75 9 7 6.08 4.00 5.66 1.96 2.08 1.38 4.96 4.96 3.92 1.92 9.83 8.21 +75 9 8 11.50 9.75 11.38 5.75 10.25 6.75 10.00 9.71 10.13 9.42 17.62 18.79 +75 9 9 16.62 13.17 13.00 5.54 13.62 7.87 11.50 9.92 12.08 9.29 15.34 18.75 +75 9 10 10.37 9.59 9.42 3.37 9.79 5.79 9.33 6.96 8.75 6.42 12.21 14.83 +75 9 11 15.83 16.92 12.46 5.63 14.62 8.12 8.79 6.75 9.29 3.33 12.29 12.12 +75 9 12 16.88 10.67 12.54 8.33 12.92 8.50 12.87 8.63 10.58 10.21 13.21 18.75 +75 9 13 10.25 8.83 13.75 3.54 5.17 3.92 9.25 4.96 5.71 5.00 8.92 16.46 +75 9 14 20.50 12.83 16.46 8.21 10.41 8.17 12.92 9.38 10.13 11.08 18.12 25.46 +75 9 15 10.37 7.21 10.25 3.25 5.33 2.92 7.75 5.91 6.50 6.46 12.62 16.33 +75 9 16 5.63 3.88 7.83 1.38 3.96 4.21 4.92 5.00 7.46 3.92 9.83 13.42 +75 9 17 9.04 5.71 14.50 4.21 4.63 3.79 6.29 6.04 7.25 5.04 10.71 14.17 +75 9 18 9.38 5.83 8.96 4.42 4.75 4.12 6.63 5.04 5.96 5.00 8.58 13.08 +75 9 19 14.37 17.08 11.83 5.71 13.00 9.29 8.25 13.25 11.54 11.17 18.91 18.75 +75 9 20 12.46 11.96 11.04 5.50 13.13 8.33 11.42 9.62 11.29 10.96 19.58 25.58 +75 9 21 8.75 10.21 7.17 3.79 7.00 5.50 9.29 8.08 8.50 7.79 18.00 18.63 +75 9 22 22.21 20.91 19.67 12.04 16.21 14.96 16.25 19.29 17.54 18.12 28.79 30.54 +75 9 23 12.33 10.54 11.87 6.83 12.42 7.92 12.25 10.00 10.67 10.46 19.33 26.96 +75 9 24 23.54 20.08 19.04 9.38 15.37 12.96 15.67 18.25 15.37 14.62 25.50 25.41 +75 9 25 13.50 12.12 11.50 6.58 12.67 8.04 12.42 10.67 11.87 10.21 13.59 17.75 +75 9 26 11.50 6.71 8.75 3.88 5.25 3.58 8.12 4.17 7.04 4.50 7.25 14.83 +75 9 27 19.75 15.04 18.41 9.25 13.92 10.58 14.12 11.21 12.21 13.13 12.62 19.12 +75 9 28 20.33 16.92 17.33 8.67 13.59 8.50 9.96 10.13 10.71 9.46 13.21 15.79 +75 9 29 19.79 12.75 17.83 11.71 12.46 10.29 14.42 10.63 14.46 14.46 14.88 22.37 +75 9 30 13.17 11.25 14.88 4.79 8.92 6.75 8.67 6.34 8.29 6.83 7.71 13.46 +75 10 1 13.79 11.04 14.67 6.29 10.29 7.04 11.25 6.46 10.50 6.17 9.17 16.71 +75 10 2 19.00 17.00 16.04 8.38 12.75 9.42 9.75 9.71 11.04 8.42 11.50 16.58 +75 10 3 13.29 13.25 11.17 5.79 14.71 9.04 13.88 11.38 12.38 11.63 22.79 24.67 +75 10 4 16.25 15.46 15.46 8.12 20.67 12.46 18.46 14.67 16.04 14.00 26.71 29.08 +75 10 5 14.25 14.83 15.21 5.91 12.25 8.50 13.33 10.34 11.58 10.29 18.05 25.00 +75 10 6 3.25 3.21 4.38 1.71 1.83 1.17 5.09 2.13 3.88 3.54 8.71 12.46 +75 10 7 7.21 7.12 4.08 1.25 3.63 1.46 2.08 3.21 3.63 1.25 5.21 5.54 +75 10 8 4.21 5.75 6.58 0.33 1.75 0.17 1.08 1.13 0.29 0.21 5.17 6.96 +75 10 9 4.92 3.21 3.75 1.00 3.21 0.79 4.58 2.67 3.25 2.46 8.58 8.25 +75 10 10 12.08 12.12 14.17 5.50 9.17 5.29 8.38 6.38 7.96 4.79 11.21 12.54 +75 10 11 8.42 3.79 13.42 3.92 4.42 1.87 6.00 2.88 5.09 1.67 6.08 8.38 +75 10 12 5.37 7.12 4.92 0.96 4.96 2.13 1.46 3.50 4.17 2.42 10.58 8.63 +75 10 13 17.62 15.79 13.13 3.37 13.04 9.25 7.96 11.21 7.04 7.83 16.13 13.17 +75 10 14 9.83 9.29 11.54 3.00 10.25 6.08 7.08 5.54 8.58 9.08 11.00 16.00 +75 10 15 16.21 12.17 12.58 5.00 10.37 6.13 6.96 6.46 7.75 5.13 7.17 11.96 +75 10 16 12.12 10.08 7.62 3.50 8.50 4.63 5.09 5.66 6.67 5.71 10.58 9.21 +75 10 17 4.21 5.13 5.91 0.42 3.54 0.29 6.04 1.54 2.25 1.25 4.46 5.58 +75 10 18 11.21 16.75 6.42 2.21 10.08 4.67 4.29 7.00 6.08 4.71 11.21 12.04 +75 10 19 17.79 22.46 16.50 9.71 19.38 12.83 14.00 14.33 12.83 12.50 22.79 24.41 +75 10 20 22.00 24.41 18.21 9.67 23.13 13.70 16.04 15.87 15.34 15.37 24.04 27.84 +75 10 21 23.83 25.84 18.88 9.59 22.79 14.37 16.66 16.71 14.75 12.50 20.67 24.04 +75 10 22 27.42 22.54 24.41 14.42 24.41 19.55 21.42 19.29 20.30 19.79 23.50 32.46 +75 10 23 13.75 16.42 15.92 8.29 12.75 8.25 11.75 10.54 13.62 12.17 20.88 19.75 +75 10 24 17.50 17.25 15.29 9.00 13.46 8.79 8.04 12.46 13.00 12.46 24.58 22.08 +75 10 25 17.75 21.04 14.42 12.62 18.84 12.71 9.96 16.04 17.08 15.34 26.71 27.25 +75 10 26 14.67 18.16 12.42 9.50 15.00 12.17 3.46 14.33 16.21 12.17 19.17 23.63 +75 10 27 12.21 18.79 10.25 7.25 14.29 8.42 2.29 9.87 12.83 8.04 13.92 17.71 +75 10 28 10.75 15.54 11.54 8.29 12.25 9.46 5.50 7.41 11.54 7.00 13.88 13.79 +75 10 29 15.63 18.08 13.83 7.92 14.00 10.63 9.46 9.92 12.79 8.29 11.46 15.09 +75 10 30 19.92 17.96 17.21 9.50 14.29 9.79 11.42 12.75 13.37 12.92 19.04 20.58 +75 10 31 15.25 10.34 15.16 7.41 10.79 7.92 12.46 9.83 11.92 11.34 12.00 19.12 +75 11 1 14.12 11.92 9.71 4.08 8.54 3.67 9.92 4.79 6.58 6.17 11.21 12.71 +75 11 2 15.54 15.29 13.75 6.46 14.62 9.13 9.71 12.21 11.08 10.29 22.88 21.62 +75 11 3 9.75 12.46 8.25 4.79 12.33 5.88 11.67 8.58 9.54 8.75 20.08 22.46 +75 11 4 17.92 13.79 13.92 5.33 12.83 8.46 12.50 11.29 12.42 12.29 19.04 21.21 +75 11 5 10.00 10.13 8.46 2.08 9.08 2.54 6.38 3.63 7.08 5.25 10.71 14.21 +75 11 6 8.75 4.67 6.87 1.25 5.29 1.96 8.58 2.54 4.88 4.38 6.83 16.29 +75 11 7 3.33 2.46 8.38 0.37 3.08 0.08 5.50 0.63 0.37 0.75 5.88 8.25 +75 11 8 9.08 7.08 14.71 1.67 7.00 3.25 8.79 4.71 4.67 3.58 7.29 8.67 +75 11 9 12.42 8.92 16.13 4.25 6.21 3.71 11.67 7.04 5.54 5.79 7.96 11.92 +75 11 10 6.46 7.21 8.00 3.29 4.50 1.96 6.13 4.42 6.87 6.50 4.29 16.42 +75 11 11 7.33 4.96 8.00 0.67 2.58 0.29 5.66 2.75 6.29 3.50 4.21 10.25 +75 11 12 17.04 14.12 16.04 5.79 11.96 8.58 9.71 8.67 7.58 5.79 7.92 12.04 +75 11 13 11.38 9.00 11.71 3.67 11.67 5.63 6.96 6.83 5.54 4.83 6.46 12.67 +75 11 14 9.75 9.67 9.25 2.54 7.71 3.88 4.96 4.17 5.58 3.25 5.58 9.75 +75 11 15 13.67 14.00 11.75 5.17 11.34 6.54 9.87 8.54 9.87 7.75 11.54 14.00 +75 11 16 27.63 20.25 23.38 15.12 18.21 11.04 18.21 15.00 13.96 15.67 22.25 36.08 +75 11 17 22.75 19.12 23.16 13.00 13.00 7.75 14.00 10.41 11.08 11.46 18.96 29.63 +75 11 18 9.21 7.33 9.96 2.17 8.00 3.08 11.54 5.91 6.75 5.66 10.67 16.25 +75 11 19 12.87 13.00 9.83 8.50 16.96 10.46 18.54 14.50 14.42 14.21 16.92 25.08 +75 11 20 11.04 5.13 7.79 3.37 5.71 2.46 7.08 2.67 4.21 3.88 3.67 9.79 +75 11 21 2.67 2.17 1.50 0.58 2.71 0.37 0.63 2.04 0.42 1.21 5.79 8.12 +75 11 22 18.21 17.29 10.37 8.71 13.42 9.25 9.38 11.96 11.63 11.12 18.75 19.00 +75 11 23 14.29 7.50 12.21 5.83 8.33 5.66 9.21 5.75 10.29 9.38 12.25 14.25 +75 11 24 9.17 10.88 8.71 3.33 10.13 5.13 10.92 8.17 8.63 8.54 14.92 17.83 +75 11 25 13.13 15.37 9.83 5.96 12.42 6.17 11.87 8.50 10.41 8.21 15.29 17.16 +75 11 26 14.83 11.38 7.92 4.42 10.79 6.00 12.00 7.21 9.54 8.21 15.79 17.08 +75 11 27 19.00 18.41 16.46 9.71 19.46 10.50 18.66 12.29 14.92 14.58 20.17 28.42 +75 11 28 7.50 6.25 6.34 2.50 6.96 3.25 10.83 5.41 8.54 6.92 8.67 21.37 +75 11 29 6.38 4.63 4.83 0.17 3.63 0.92 8.38 1.87 5.00 5.21 7.67 20.04 +75 11 30 10.96 10.34 6.71 0.71 8.00 4.25 10.63 4.04 6.34 5.21 8.79 16.25 +75 12 1 20.30 20.04 18.16 8.54 18.91 9.71 13.96 11.00 14.17 10.17 17.71 21.04 +75 12 2 26.96 22.08 20.67 14.33 19.55 12.92 16.62 16.88 15.83 17.83 26.38 36.08 +75 12 3 14.09 10.04 13.54 5.83 7.62 4.04 10.46 6.92 8.00 6.92 12.38 23.00 +75 12 4 7.38 5.37 7.50 2.79 7.92 3.42 11.29 6.21 10.41 6.50 9.75 18.00 +75 12 5 8.75 4.42 5.96 2.54 5.33 1.67 10.46 5.09 7.17 6.75 7.21 17.21 +75 12 6 7.79 3.67 7.92 2.58 2.92 0.96 6.29 2.29 5.58 4.67 5.58 12.33 +75 12 7 8.33 3.58 6.79 3.25 4.83 2.50 8.87 4.38 6.83 5.96 9.79 20.17 +75 12 8 6.29 2.29 10.04 2.04 2.21 0.13 6.04 0.67 2.08 3.25 4.58 12.58 +75 12 9 5.63 3.96 7.25 1.17 1.25 0.13 7.29 0.21 2.29 1.75 5.13 13.67 +75 12 10 4.33 5.50 6.92 0.58 1.13 0.00 5.71 0.08 1.83 0.92 4.58 8.79 +75 12 11 2.21 3.00 3.08 0.58 4.79 2.37 5.33 5.25 5.63 4.42 13.21 17.46 +75 12 12 20.00 16.75 18.08 9.67 10.00 6.38 11.63 7.62 9.83 8.54 19.00 24.83 +75 12 13 13.62 7.00 18.50 4.08 3.71 1.50 9.50 1.96 6.63 4.67 6.79 12.87 +75 12 14 8.29 1.54 9.08 1.04 2.37 1.13 8.42 2.08 6.71 4.21 6.50 15.67 +75 12 15 8.58 2.62 9.92 2.04 1.21 0.21 7.92 1.71 3.37 2.17 4.08 13.00 +75 12 16 9.08 3.00 10.50 3.54 2.62 2.00 9.42 3.83 7.12 5.88 10.17 18.54 +75 12 17 16.17 13.33 24.41 8.50 8.12 5.96 12.04 5.79 9.00 6.71 11.04 16.62 +75 12 18 10.67 2.50 9.87 1.79 3.04 0.67 10.00 2.13 4.75 3.42 6.92 14.83 +75 12 19 6.71 0.54 6.92 1.21 0.54 0.25 6.67 1.08 2.58 2.08 4.46 12.21 +75 12 20 3.37 2.50 3.92 0.33 1.67 3.04 9.17 2.25 6.79 4.79 8.54 14.75 +75 12 21 9.87 9.96 8.92 3.08 7.87 5.71 12.33 6.79 10.58 9.21 16.88 20.04 +75 12 22 10.58 11.87 7.96 4.79 8.04 6.87 10.88 10.00 10.83 11.46 20.38 21.25 +75 12 23 16.17 14.04 14.29 7.17 8.71 6.87 12.50 9.33 12.08 10.50 13.70 20.58 +75 12 24 12.67 12.62 7.92 3.29 8.42 4.33 12.21 8.58 10.58 8.12 15.46 21.75 +75 12 25 18.71 12.33 12.58 9.33 12.71 8.75 13.96 11.00 12.87 13.59 15.79 23.00 +75 12 26 8.38 5.29 6.96 4.29 7.04 4.00 11.38 5.91 10.17 7.25 14.21 20.00 +75 12 27 12.92 12.92 11.92 5.79 7.38 6.83 13.17 13.04 11.96 12.42 20.08 21.96 +75 12 28 16.29 15.04 14.37 8.00 9.87 8.33 13.04 11.67 13.21 12.71 14.71 19.17 +75 12 29 12.42 13.62 12.04 5.25 8.46 6.42 9.46 8.75 9.42 8.71 14.58 17.50 +75 12 30 19.17 15.54 18.34 9.04 13.17 11.25 17.79 14.71 16.25 15.71 21.92 30.91 +75 12 31 15.59 12.33 13.42 2.37 4.08 1.17 7.08 4.25 5.91 6.34 11.38 19.55 +76 1 1 18.34 17.67 14.83 8.00 16.62 10.13 13.17 9.04 13.13 5.75 11.38 14.96 +76 1 2 29.20 25.29 20.25 15.46 23.58 14.88 18.96 17.25 17.62 18.29 24.71 27.54 +76 1 3 11.25 9.59 7.62 5.46 9.46 4.00 13.29 6.38 10.13 9.00 12.12 23.29 +76 1 4 12.67 12.79 12.92 6.46 12.92 8.71 13.62 10.41 13.13 6.96 14.04 17.12 +76 1 5 12.79 11.67 12.58 4.96 9.71 6.08 15.63 10.13 13.04 10.63 15.16 19.04 +76 1 6 11.17 12.08 13.37 3.33 9.92 7.50 14.83 13.33 13.13 10.96 18.71 19.33 +76 1 7 17.79 15.75 15.71 5.88 10.29 9.67 14.58 14.83 13.67 15.59 23.63 25.70 +76 1 8 15.59 16.96 15.41 7.87 11.08 9.50 14.37 5.50 11.34 5.25 4.21 11.42 +76 1 9 15.21 14.62 11.75 3.92 7.08 4.50 7.17 2.88 7.83 1.46 5.33 8.00 +76 1 10 18.00 20.75 14.29 12.96 23.29 15.04 21.29 18.63 19.12 16.38 23.83 28.42 +76 1 11 14.12 11.67 11.25 9.54 16.62 10.92 19.12 18.46 16.38 14.62 19.00 26.71 +76 1 12 12.58 12.12 11.67 6.92 14.25 8.29 16.42 10.88 14.33 10.88 17.41 23.50 +76 1 13 13.79 14.46 10.63 4.83 8.46 5.63 11.46 9.83 11.50 9.08 16.46 18.29 +76 1 14 9.13 8.87 8.67 4.79 9.13 5.21 11.92 8.96 10.96 10.46 16.75 22.04 +76 1 15 2.92 7.41 4.67 2.04 4.92 2.88 8.38 5.75 8.29 7.46 11.21 16.54 +76 1 16 1.63 4.33 4.29 0.21 1.17 0.96 8.25 1.17 5.17 4.46 6.38 14.50 +76 1 17 5.13 7.38 3.67 0.63 5.71 2.25 3.42 4.63 4.38 4.33 10.54 14.04 +76 1 18 16.50 15.09 11.46 7.54 12.83 8.12 13.83 12.29 12.58 12.71 18.88 25.00 +76 1 19 19.79 21.00 16.88 11.96 20.83 12.96 20.75 18.66 19.41 16.25 25.75 30.63 +76 1 20 24.25 23.50 17.58 15.34 26.25 16.75 25.96 21.29 22.50 21.79 29.33 40.12 +76 1 21 18.54 19.17 12.50 11.17 21.46 14.71 20.79 19.33 18.63 18.58 23.54 32.55 +76 1 22 20.58 19.70 15.67 10.71 20.12 14.29 21.67 18.54 20.17 18.71 22.83 29.29 +76 1 23 22.54 18.41 13.88 10.83 16.46 9.00 14.12 12.58 12.92 10.00 20.21 23.54 +76 1 24 19.92 17.25 19.08 8.96 10.00 8.08 13.29 8.50 12.38 12.46 22.13 32.91 +76 1 25 16.96 10.34 17.46 5.83 7.71 5.13 10.21 3.00 9.50 7.75 14.00 23.58 +76 1 26 11.96 7.62 11.00 6.54 7.25 5.09 11.46 7.21 10.00 9.04 12.87 20.88 +76 1 27 13.13 12.04 9.13 4.04 9.96 6.04 9.25 7.08 8.08 6.87 11.21 13.79 +76 1 28 20.30 11.34 20.79 12.21 12.29 11.08 16.71 7.41 16.50 18.08 6.54 21.34 +76 1 29 26.00 18.71 27.12 15.87 21.75 20.21 23.13 21.87 22.21 20.12 21.87 29.33 +76 1 30 21.17 14.37 26.08 12.33 17.46 16.83 23.50 19.12 18.63 16.29 17.88 34.25 +76 1 31 11.46 9.50 17.67 4.88 6.96 4.67 18.21 8.92 11.63 7.96 8.54 22.13 +76 2 1 9.04 6.13 12.04 3.04 3.83 1.58 11.58 5.54 5.66 6.17 8.00 13.79 +76 2 2 10.96 10.96 14.33 3.88 6.46 4.08 11.21 8.58 7.33 7.46 10.54 17.33 +76 2 3 13.37 10.96 15.37 4.88 9.83 5.09 10.58 8.63 8.79 5.91 7.04 15.34 +76 2 4 11.42 10.54 17.41 6.04 8.75 6.58 13.62 9.67 11.21 9.46 10.46 22.50 +76 2 5 11.46 11.87 14.00 4.33 7.21 3.08 12.79 7.87 11.12 10.75 9.04 18.79 +76 2 6 8.42 12.46 4.33 2.04 6.46 1.96 4.58 5.46 7.12 4.79 9.29 11.96 +76 2 7 16.75 16.08 12.04 9.17 14.04 9.50 9.50 10.88 13.62 9.87 17.21 18.08 +76 2 8 10.50 12.71 9.75 4.42 7.79 4.88 11.17 7.58 9.21 7.33 15.12 13.88 +76 2 9 16.46 19.08 16.83 8.71 15.79 11.29 16.17 16.83 16.58 15.71 27.58 28.71 +76 2 10 14.46 19.75 11.25 7.54 13.92 7.41 15.16 11.96 13.04 11.21 23.16 22.58 +76 2 11 16.88 18.46 10.67 8.50 16.79 7.17 17.12 12.67 13.29 10.83 21.87 23.87 +76 2 12 18.63 19.95 15.21 11.87 19.00 11.63 17.41 15.34 15.12 12.21 17.79 22.21 +76 2 13 12.12 7.25 14.04 5.63 5.09 2.37 8.79 3.21 4.38 3.54 5.83 8.38 +76 2 14 6.96 7.08 6.96 2.75 3.54 3.92 4.25 7.41 7.96 6.04 13.50 16.96 +76 2 15 8.00 12.46 13.33 5.00 6.38 4.08 9.42 4.96 6.67 6.63 6.00 11.12 +76 2 16 11.67 9.83 10.88 3.33 6.75 2.75 8.50 5.54 6.42 5.41 8.54 11.42 +76 2 17 9.04 11.34 7.79 2.75 9.13 5.75 4.38 8.25 8.21 6.42 14.71 14.88 +76 2 18 5.29 11.25 5.13 1.17 6.17 2.21 2.92 5.58 4.88 2.37 11.34 12.54 +76 2 19 16.04 19.33 12.00 4.92 12.71 7.38 10.08 10.13 10.34 5.58 13.92 16.25 +76 2 20 17.25 21.84 14.42 9.59 15.25 10.41 12.25 13.54 13.29 10.00 17.54 22.13 +76 2 21 12.62 13.79 12.83 7.25 9.59 5.09 10.17 7.00 10.63 8.87 11.04 13.96 +76 2 22 18.16 18.84 15.46 10.83 13.88 11.71 12.08 14.67 15.92 16.92 22.71 25.25 +76 2 23 14.46 7.79 14.37 7.12 6.54 3.79 11.29 6.29 8.17 6.50 10.34 14.62 +76 2 24 5.66 8.38 8.75 4.88 7.58 5.21 13.29 14.62 11.38 10.63 21.04 25.58 +76 2 25 10.08 11.46 14.33 5.04 8.58 6.21 14.17 13.33 13.70 13.79 19.79 22.67 +76 2 26 4.42 7.29 5.88 2.00 3.88 1.83 8.46 7.00 7.12 6.08 14.25 15.37 +76 2 27 7.25 10.71 6.42 4.58 7.04 3.17 5.75 6.58 7.71 6.79 11.50 11.29 +76 2 28 8.54 10.54 9.46 4.92 7.50 5.29 6.71 8.04 10.58 9.96 14.75 16.83 +76 2 29 10.41 10.79 11.08 5.96 8.58 6.21 11.71 10.58 10.34 10.17 17.16 22.29 +76 3 1 6.13 4.79 3.42 3.21 7.50 2.50 6.50 3.67 5.54 4.04 7.75 11.42 +76 3 2 7.96 7.75 7.50 4.00 8.92 5.37 6.96 5.25 7.79 5.79 8.87 11.46 +76 3 3 7.62 12.46 9.62 5.09 8.83 4.54 10.04 8.33 10.21 8.42 15.54 13.29 +76 3 4 11.92 14.92 10.34 6.75 12.08 6.96 7.87 12.12 11.67 11.75 17.62 19.12 +76 3 5 15.50 19.38 9.83 9.38 18.00 13.83 11.54 15.12 16.25 15.83 17.75 23.96 +76 3 6 21.00 18.54 21.17 10.25 20.21 15.00 20.79 12.75 16.79 14.33 16.29 23.91 +76 3 7 20.25 21.17 17.37 8.33 19.08 10.83 14.33 13.04 12.17 12.71 15.09 20.96 +76 3 8 10.88 13.92 5.91 3.29 11.46 3.25 7.54 7.00 8.08 6.75 11.67 9.29 +76 3 9 20.00 16.38 10.75 6.83 14.92 9.08 7.58 9.42 12.17 8.38 16.50 20.08 +76 3 10 23.96 18.71 18.46 12.04 18.75 11.75 12.71 13.67 16.46 15.92 17.83 22.92 +76 3 11 14.83 9.13 13.00 8.17 9.96 6.79 11.34 9.46 11.00 11.87 11.58 16.04 +76 3 12 25.33 24.04 19.67 11.12 20.21 13.96 15.92 17.92 15.37 13.13 16.71 21.71 +76 3 13 19.04 15.09 14.29 8.25 11.25 6.29 12.00 7.83 8.38 7.33 11.42 11.17 +76 3 14 12.87 14.83 12.21 6.50 13.13 9.00 15.37 11.92 13.21 14.04 16.50 24.25 +76 3 15 3.71 8.50 3.17 0.67 4.00 1.33 9.59 6.04 7.87 7.29 10.21 19.00 +76 3 16 8.21 9.54 9.33 3.83 9.21 3.88 10.79 7.41 10.54 8.25 11.63 17.16 +76 3 17 7.50 5.29 9.25 2.62 3.00 0.83 8.04 2.21 4.54 6.08 5.63 8.17 +76 3 18 16.88 16.62 13.46 5.58 13.62 6.38 11.12 11.42 10.67 7.54 12.08 11.79 +76 3 19 13.46 10.96 18.29 8.50 11.79 10.88 14.58 11.42 13.92 13.21 13.04 26.12 +76 3 20 19.29 18.00 22.67 14.92 16.92 14.09 16.88 14.12 19.83 19.67 20.04 29.17 +76 3 21 9.54 10.21 7.58 3.67 8.33 3.88 6.83 6.21 7.75 8.54 9.38 11.21 +76 3 22 12.33 5.37 11.46 5.13 3.83 1.79 5.71 2.50 6.92 4.71 9.83 10.00 +76 3 23 8.83 11.83 7.83 4.83 10.17 6.34 5.96 7.12 11.25 8.54 12.54 20.17 +76 3 24 11.83 12.96 13.13 6.46 11.92 6.46 12.96 10.79 12.87 12.00 15.41 18.79 +76 3 25 18.46 17.75 18.88 8.96 19.83 10.34 15.29 11.54 15.96 10.96 17.58 16.96 +76 3 26 13.92 17.21 13.04 9.00 17.29 10.00 16.58 14.04 15.59 14.21 23.00 24.41 +76 3 27 14.25 14.62 14.54 9.13 20.46 12.17 20.88 17.75 20.83 18.41 23.21 28.58 +76 3 28 17.21 16.13 18.16 9.17 17.04 13.04 18.71 17.50 19.67 19.75 24.41 31.20 +76 3 29 16.17 14.54 13.83 9.92 17.16 11.12 17.00 14.00 16.04 15.67 20.30 26.25 +76 3 30 14.29 12.83 12.21 9.17 16.71 8.50 15.63 13.54 15.12 13.79 17.41 20.46 +76 3 31 14.96 14.21 13.13 9.92 16.71 10.41 17.00 12.67 16.21 16.96 20.54 25.00 +76 4 1 7.58 9.21 7.33 3.96 7.96 3.37 6.87 4.00 7.75 7.58 10.37 13.67 +76 4 2 17.88 12.17 13.08 7.79 10.67 5.41 10.79 7.87 9.17 9.67 16.17 18.63 +76 4 3 14.96 12.17 12.83 6.67 13.13 8.08 12.87 10.96 13.50 14.33 17.79 25.50 +76 4 4 10.67 9.50 8.50 5.46 10.63 5.66 12.21 8.92 12.08 10.58 14.79 18.08 +76 4 5 13.79 8.21 9.42 8.00 13.04 8.46 16.38 14.29 14.62 13.79 16.33 23.91 +76 4 6 20.96 10.37 8.54 7.75 14.37 7.79 14.71 13.42 13.21 14.75 14.33 19.92 +76 4 7 13.21 12.25 15.09 6.79 12.04 5.58 10.63 8.04 9.29 10.58 11.08 13.79 +76 4 8 7.54 3.54 7.87 2.62 4.50 2.04 7.21 4.25 5.79 5.58 8.54 13.96 +76 4 9 5.96 2.17 5.13 2.08 3.79 1.33 6.00 3.33 5.79 5.17 9.79 13.67 +76 4 10 11.50 11.67 11.17 5.33 8.54 7.46 10.34 15.87 13.08 12.87 27.00 23.33 +76 4 11 16.88 14.50 16.71 8.21 10.13 5.88 11.34 7.50 9.79 7.96 13.75 14.88 +76 4 12 12.75 9.46 15.46 6.04 5.88 2.54 8.29 4.17 8.04 6.75 12.79 10.41 +76 4 13 20.91 16.46 14.09 10.04 15.87 12.17 15.00 15.83 15.50 15.25 22.54 25.04 +76 4 14 27.84 21.37 20.38 13.79 17.79 12.92 17.67 15.50 15.79 17.58 20.46 26.87 +76 4 15 14.71 10.13 15.96 6.29 8.38 4.83 8.79 4.96 8.63 8.08 9.50 11.96 +76 4 16 6.50 2.67 4.79 1.50 5.50 0.58 6.46 2.54 5.91 4.17 11.17 15.87 +76 4 17 4.92 3.96 5.13 1.54 5.46 3.33 9.42 4.96 8.50 6.83 12.79 16.17 +76 4 18 4.33 0.54 5.75 0.63 2.50 0.21 5.63 1.46 2.13 1.42 7.96 7.17 +76 4 19 11.29 8.42 11.54 1.46 4.46 1.38 5.88 5.29 7.58 5.71 6.34 9.25 +76 4 20 13.88 10.67 10.63 3.29 6.13 4.21 8.00 6.75 9.21 7.92 9.13 15.50 +76 4 21 17.71 11.00 13.37 5.83 9.25 9.59 8.83 11.21 11.04 8.42 11.96 12.42 +76 4 22 16.04 12.25 16.42 6.83 10.41 10.17 14.79 10.21 11.42 8.38 11.50 15.29 +76 4 23 10.29 7.25 14.37 4.12 6.25 3.67 9.96 6.21 6.38 4.63 9.83 7.50 +76 4 24 5.88 3.33 14.96 4.38 4.42 3.33 9.87 3.63 6.25 3.67 8.75 5.71 +76 4 25 8.96 3.67 17.71 4.71 4.79 5.00 11.25 5.75 8.38 7.54 11.54 5.96 +76 4 26 11.38 5.21 17.25 5.13 5.91 6.00 8.25 5.54 8.33 6.25 11.75 8.21 +76 4 27 12.21 6.34 14.79 5.88 7.04 5.21 9.59 5.04 7.33 4.75 7.29 9.21 +76 4 28 9.92 7.29 11.34 2.71 5.75 3.08 7.29 4.21 5.96 3.79 6.87 6.63 +76 4 29 6.04 3.79 5.75 1.87 3.46 0.29 3.54 1.42 1.25 3.17 7.58 11.25 +76 4 30 9.29 5.17 5.04 1.00 3.08 0.58 5.54 3.25 4.46 3.17 13.33 8.83 +76 5 1 16.66 13.46 13.04 7.17 13.13 8.46 11.96 11.17 10.71 10.54 18.29 19.29 +76 5 2 14.79 12.08 13.54 6.87 12.71 8.33 12.38 9.42 11.75 7.96 17.12 18.71 +76 5 3 17.83 13.75 11.75 10.08 16.46 9.96 14.96 10.96 13.88 13.25 17.92 15.87 +76 5 4 7.41 2.25 8.54 2.79 3.29 1.83 7.08 3.63 4.46 4.38 9.33 8.12 +76 5 5 8.96 7.41 6.54 2.46 4.12 2.75 5.54 4.00 6.08 3.88 13.29 9.54 +76 5 6 5.66 3.50 12.46 1.54 2.25 1.92 5.66 3.50 7.92 6.75 10.17 11.42 +76 5 7 8.29 9.54 6.17 2.67 5.71 4.29 5.33 6.54 6.79 6.00 13.83 11.21 +76 5 8 9.46 7.92 7.67 2.75 5.54 3.37 7.25 9.04 6.58 7.21 19.75 16.54 +76 5 9 9.96 8.25 6.79 3.96 5.46 3.88 4.33 6.29 6.50 5.63 11.17 13.17 +76 5 10 12.33 11.34 10.21 6.38 10.71 6.29 11.25 8.00 9.46 8.58 16.17 17.79 +76 5 11 16.04 13.75 12.83 8.17 14.92 9.62 14.17 11.58 14.09 11.42 18.91 11.96 +76 5 12 25.17 15.63 15.67 12.67 19.41 13.04 16.29 13.37 14.96 14.04 19.41 23.71 +76 5 13 11.58 9.29 11.04 5.33 6.34 6.17 8.96 9.21 10.46 9.21 15.63 14.17 +76 5 14 21.00 16.54 17.83 11.00 13.92 11.12 13.37 11.29 15.63 16.29 23.33 21.12 +76 5 15 18.41 14.37 18.84 8.96 14.04 8.75 13.25 8.08 13.75 12.38 13.92 15.41 +76 5 16 23.63 21.34 20.79 11.83 15.96 11.96 16.50 15.79 16.75 18.46 23.21 24.13 +76 5 17 19.17 11.50 20.08 10.79 9.25 4.83 13.88 8.50 10.79 8.71 12.21 14.88 +76 5 18 7.62 6.21 8.46 3.25 4.29 2.29 7.04 4.00 6.00 4.25 7.04 7.38 +76 5 19 12.21 11.83 7.67 3.63 7.58 3.08 7.08 2.62 5.09 3.54 6.13 4.50 +76 5 20 14.17 8.63 8.25 6.79 10.71 6.25 8.38 6.17 8.42 7.04 12.54 9.08 +76 5 21 15.37 15.16 12.17 5.88 10.37 7.62 9.83 9.13 11.21 11.21 18.41 17.46 +76 5 22 13.62 14.21 13.00 7.58 10.17 8.96 10.54 9.08 13.21 9.25 18.71 16.58 +76 5 23 14.17 16.38 11.79 8.50 14.04 10.50 9.50 11.83 15.54 11.00 19.00 20.88 +76 5 24 15.50 15.21 12.83 7.71 15.46 9.42 11.08 11.63 15.34 12.12 21.54 25.04 +76 5 25 13.46 10.71 7.87 6.34 11.54 7.41 10.00 9.21 10.46 9.83 17.12 16.54 +76 5 26 7.38 7.08 5.88 1.92 2.17 0.83 7.12 2.37 5.00 3.88 9.67 10.00 +76 5 27 6.79 11.54 8.00 2.50 5.83 2.42 3.29 2.58 4.75 2.67 13.33 5.29 +76 5 28 9.13 8.67 5.13 2.62 9.33 3.50 4.63 5.00 8.04 6.04 10.21 11.04 +76 5 29 11.83 10.00 10.25 2.79 7.00 3.83 3.33 6.42 6.29 4.58 12.29 6.00 +76 5 30 15.92 13.42 14.09 6.63 13.00 6.54 8.29 10.13 11.79 7.54 17.54 12.71 +76 5 31 9.96 11.42 10.41 3.29 9.75 5.09 6.29 6.75 9.25 5.88 11.50 9.42 +76 6 1 13.04 8.83 10.79 7.21 10.83 6.96 12.92 8.25 9.96 10.92 11.34 14.75 +76 6 2 5.37 3.25 6.54 2.04 2.13 0.04 4.29 1.50 3.83 2.50 6.34 6.79 +76 6 3 5.58 5.04 6.13 1.13 1.54 0.71 3.04 1.42 2.88 1.71 7.00 5.09 +76 6 4 8.04 10.21 10.29 3.42 4.12 4.00 5.75 8.00 6.79 5.63 14.88 11.83 +76 6 5 8.71 10.37 13.08 3.21 4.17 4.88 6.50 10.50 9.33 8.50 16.58 16.04 +76 6 6 6.96 10.88 11.46 4.04 4.96 5.21 7.87 9.79 8.83 8.21 17.79 14.37 +76 6 7 11.29 13.37 6.83 3.83 10.21 6.67 5.54 5.13 8.38 5.75 11.34 9.71 +76 6 8 12.83 13.54 12.83 7.08 9.29 9.33 7.75 9.13 10.54 9.13 13.54 12.54 +76 6 9 14.17 13.92 14.21 7.71 13.62 9.59 10.83 10.25 12.42 10.17 17.88 16.79 +76 6 10 12.33 14.21 12.87 7.33 12.33 9.75 10.83 10.46 11.42 9.62 19.08 18.12 +76 6 11 19.04 16.79 16.38 9.38 11.79 11.50 10.63 13.25 14.04 12.50 20.67 19.67 +76 6 12 11.25 9.75 13.96 5.00 10.21 7.33 13.13 11.75 11.17 10.13 19.62 21.34 +76 6 13 12.21 11.54 13.08 6.46 9.13 8.12 10.79 12.46 10.37 11.04 20.33 20.83 +76 6 14 7.75 7.17 10.71 4.00 6.04 6.00 11.92 5.91 10.13 6.58 16.21 18.46 +76 6 15 7.79 7.46 10.21 1.25 3.04 2.83 9.79 7.58 7.71 5.29 13.75 15.00 +76 6 16 8.75 7.12 8.87 3.83 4.04 4.42 3.37 4.38 5.91 3.21 9.13 6.54 +76 6 17 12.79 7.75 8.25 4.04 5.50 4.46 7.00 5.71 7.75 5.37 13.37 8.75 +76 6 18 14.09 9.62 11.79 6.21 10.75 8.63 11.67 9.21 10.37 10.50 15.34 19.79 +76 6 19 2.92 2.50 5.29 3.67 5.21 5.09 11.08 6.17 9.33 6.63 14.46 20.33 +76 6 20 7.83 6.92 8.42 4.08 4.75 4.17 9.96 4.58 7.75 6.50 9.04 14.54 +76 6 21 10.79 11.96 11.04 5.33 8.21 8.17 7.12 7.00 10.37 8.00 12.75 12.92 +76 6 22 13.88 14.21 11.00 8.67 12.83 9.83 8.96 7.33 12.83 10.83 10.04 12.96 +76 6 23 10.41 12.33 11.04 6.46 9.67 8.29 10.04 9.38 11.04 10.17 18.75 13.67 +76 6 24 10.54 13.25 11.46 7.12 10.71 9.46 10.37 10.96 12.58 8.79 20.46 15.34 +76 6 25 6.00 11.04 5.46 3.46 8.46 6.17 7.00 5.33 7.12 5.21 7.79 5.04 +76 6 26 7.29 6.04 9.17 2.62 6.17 5.09 7.41 7.46 8.00 6.67 15.16 13.67 +76 6 27 4.46 3.83 4.92 1.96 3.21 2.67 3.42 7.08 5.37 4.88 16.50 20.25 +76 6 28 4.33 1.92 5.46 2.08 2.21 1.54 5.58 2.50 3.21 3.04 8.63 6.04 +76 6 29 5.29 1.00 6.38 1.75 1.63 0.54 5.58 2.25 4.21 2.92 9.50 6.54 +76 6 30 8.96 1.38 11.04 3.21 4.25 3.21 6.04 3.67 6.87 4.12 8.25 9.29 +76 7 1 8.50 1.75 6.58 2.13 2.75 2.21 5.37 2.04 5.88 4.50 4.96 10.63 +76 7 2 9.50 4.21 5.71 2.67 3.00 1.92 4.67 3.50 5.29 3.37 5.96 4.96 +76 7 3 5.66 6.00 5.71 2.37 2.21 1.54 4.88 3.00 5.41 2.96 7.50 7.17 +76 7 4 4.96 5.96 4.75 3.79 5.04 3.21 4.08 2.46 6.79 4.08 7.21 8.42 +76 7 5 8.12 7.21 5.21 3.17 6.17 2.67 1.54 4.17 4.83 1.67 7.87 5.09 +76 7 6 5.91 3.79 8.71 3.00 1.58 1.29 4.12 2.96 5.21 2.37 8.08 6.63 +76 7 7 3.04 2.00 5.58 1.67 1.29 0.29 5.50 0.92 5.58 2.88 4.12 4.17 +76 7 8 9.04 8.75 6.54 4.67 7.50 5.09 4.29 3.42 6.21 4.67 5.37 7.83 +76 7 9 14.58 14.12 13.08 6.29 14.21 7.96 10.34 10.46 11.42 8.58 17.33 11.79 +76 7 10 18.08 16.17 12.46 9.67 15.67 11.67 9.13 9.59 13.67 11.75 21.21 16.08 +76 7 11 11.92 9.29 9.21 6.83 11.00 8.46 9.33 5.54 10.41 9.08 10.37 16.66 +76 7 12 8.17 9.25 7.46 6.34 8.29 5.83 5.75 6.75 8.71 6.50 10.83 11.63 +76 7 13 15.75 15.12 12.54 7.08 15.71 9.46 9.04 7.00 10.83 8.58 9.29 11.08 +76 7 14 11.46 11.17 10.88 6.42 10.41 7.87 10.34 7.58 9.96 8.87 16.58 13.70 +76 7 15 12.62 9.67 13.50 6.67 11.21 8.17 10.08 6.79 11.58 8.67 11.71 14.67 +76 7 16 7.17 7.79 7.46 2.37 9.87 4.58 6.54 6.83 7.50 5.13 14.58 15.00 +76 7 17 8.33 9.29 8.25 2.92 7.46 4.21 5.79 5.58 7.87 4.63 12.33 10.75 +76 7 18 14.92 13.13 13.88 8.25 13.13 9.42 10.25 10.54 12.21 13.50 16.38 14.96 +76 7 19 9.87 7.41 8.92 5.04 9.67 5.83 9.96 5.91 8.50 8.46 10.50 15.79 +76 7 20 11.71 8.33 8.54 5.75 9.04 6.08 8.83 5.21 8.83 8.63 10.17 16.75 +76 7 21 9.13 6.29 7.96 3.92 6.67 4.12 7.92 3.67 7.29 6.42 8.42 11.83 +76 7 22 6.42 4.21 5.75 2.21 3.37 2.54 5.63 2.75 5.29 3.83 8.17 11.67 +76 7 23 8.46 6.46 7.25 4.42 5.96 3.79 8.96 3.79 7.04 6.13 8.08 12.75 +76 7 24 10.08 8.67 7.96 4.17 6.42 4.25 6.58 3.42 7.33 6.17 8.92 11.71 +76 7 25 9.25 9.54 7.00 3.92 4.75 4.50 5.58 3.67 7.17 6.46 10.25 10.71 +76 7 26 6.21 5.41 7.41 4.00 3.71 2.08 5.09 3.08 6.50 6.13 6.67 9.54 +76 7 27 8.87 6.17 7.38 4.04 4.33 2.75 4.92 3.33 7.12 6.58 8.50 12.54 +76 7 28 7.58 6.38 8.25 4.38 7.62 4.42 5.50 6.08 7.08 6.71 10.79 13.13 +76 7 29 10.34 6.04 5.96 4.88 7.50 4.38 9.25 6.63 8.08 7.71 11.17 17.00 +76 7 30 13.04 9.54 8.12 6.25 10.96 6.83 9.87 6.63 9.42 8.25 13.70 19.55 +76 7 31 11.63 8.29 8.29 4.29 8.38 5.33 7.71 5.91 7.17 7.96 11.17 15.09 +76 8 1 13.00 8.38 8.63 5.83 12.92 8.25 13.00 9.42 10.58 11.34 14.21 20.25 +76 8 2 8.83 7.21 7.83 4.92 9.75 6.96 10.58 7.29 9.29 9.59 11.71 19.95 +76 8 3 5.54 5.00 5.79 4.79 5.13 3.58 5.91 2.71 6.79 6.29 7.38 10.88 +76 8 4 7.33 3.04 5.58 3.29 3.54 3.63 6.75 3.88 5.00 5.25 8.79 13.59 +76 8 5 4.71 2.13 5.83 2.54 4.38 2.75 4.92 3.37 4.92 3.04 9.96 13.50 +76 8 6 6.04 3.88 5.41 2.21 3.92 2.92 3.46 2.00 4.38 2.29 8.42 10.58 +76 8 7 6.75 3.79 6.17 2.33 3.50 2.54 3.08 1.00 4.54 4.25 4.63 7.54 +76 8 8 4.33 1.17 8.83 2.25 3.25 1.50 1.67 1.17 1.54 1.17 4.54 4.12 +76 8 9 5.04 2.71 7.04 2.04 4.08 1.38 1.67 0.63 2.42 0.96 3.04 3.67 +76 8 10 5.50 2.71 4.46 1.42 2.58 1.75 2.67 2.62 3.42 3.58 8.96 4.67 +76 8 11 6.25 6.96 6.21 3.04 5.83 3.96 4.71 5.37 4.38 4.83 14.00 12.00 +76 8 12 6.63 6.25 5.83 3.04 4.29 3.42 1.46 2.62 4.21 3.17 5.91 7.25 +76 8 13 8.25 3.29 9.08 1.92 3.50 1.50 2.42 0.87 3.96 1.71 4.54 5.33 +76 8 14 7.29 2.67 14.50 1.96 3.13 2.54 3.79 2.42 7.33 4.58 6.38 8.71 +76 8 15 4.21 1.46 8.54 1.13 0.92 1.75 3.42 1.46 3.46 4.21 1.87 7.12 +76 8 16 5.46 3.75 5.96 1.38 2.75 2.42 1.87 0.67 2.79 1.25 3.08 6.67 +76 8 17 7.17 6.21 6.21 2.00 4.92 3.17 2.00 3.00 4.96 1.96 4.63 6.58 +76 8 18 4.67 4.96 9.62 1.33 4.12 4.04 4.38 3.08 7.62 4.25 4.50 11.21 +76 8 19 7.17 4.50 9.71 2.00 3.21 2.58 3.00 4.12 8.38 5.96 7.12 7.67 +76 8 20 9.83 10.41 11.08 2.00 2.83 2.79 4.04 3.54 6.83 4.58 5.88 8.46 +76 8 21 11.42 12.12 9.92 2.96 4.54 5.33 6.87 4.58 7.12 5.63 7.29 9.00 +76 8 22 15.34 10.92 9.59 4.75 6.83 8.04 7.08 6.92 10.21 7.92 8.33 13.08 +76 8 23 9.75 6.58 6.79 3.37 2.50 5.09 5.79 4.92 9.59 5.75 5.25 15.25 +76 8 24 5.96 4.42 7.04 4.29 4.88 5.54 6.04 3.33 7.21 5.46 4.42 8.71 +76 8 25 4.38 2.54 4.12 0.92 2.13 1.96 4.12 0.79 1.83 0.87 3.50 3.92 +76 8 26 4.17 2.08 14.46 3.58 3.63 2.83 3.88 2.08 3.46 1.00 7.83 4.63 +76 8 27 7.33 4.96 17.41 5.33 5.37 4.88 8.87 2.83 6.58 3.42 9.25 4.50 +76 8 28 9.46 6.83 20.75 5.09 6.17 5.13 13.59 4.83 9.08 7.12 9.17 12.25 +76 8 29 10.17 8.21 15.04 5.41 6.75 5.17 9.67 5.13 7.92 6.13 7.96 8.87 +76 8 30 15.46 10.92 13.88 6.58 8.25 6.92 9.21 4.46 8.83 7.58 9.21 11.38 +76 8 31 9.42 6.29 7.71 3.71 5.96 4.96 8.63 3.13 5.71 4.25 7.71 11.25 +76 9 1 11.87 11.00 7.38 6.87 7.75 8.33 10.34 6.46 10.17 9.29 12.75 19.55 +76 9 2 14.75 13.46 16.17 7.79 7.79 7.67 11.63 6.50 10.13 10.54 12.92 21.84 +76 9 3 9.83 4.79 8.12 4.75 4.29 5.79 10.04 3.71 8.71 5.63 8.83 15.16 +76 9 4 8.92 4.04 5.88 3.79 3.00 5.37 8.08 3.21 7.25 6.04 7.54 14.00 +76 9 5 4.54 4.29 5.17 1.75 1.00 2.33 3.79 1.83 4.12 4.08 5.46 10.17 +76 9 6 3.42 3.00 2.96 1.63 0.87 0.96 4.46 0.87 2.25 2.25 5.96 12.75 +76 9 7 4.75 4.75 3.96 1.29 1.58 2.13 4.38 0.54 0.58 1.13 4.67 8.96 +76 9 8 10.13 9.33 9.46 7.04 9.62 8.75 13.17 10.34 11.04 9.83 18.25 23.00 +76 9 9 24.54 20.00 20.50 15.09 16.08 13.17 21.25 12.96 16.21 17.37 21.54 35.13 +76 9 10 15.12 13.67 10.29 5.17 11.83 8.04 10.54 7.25 9.96 7.38 16.54 22.34 +76 9 11 23.33 22.37 22.67 13.29 17.83 15.04 20.54 14.83 15.37 18.88 25.12 32.08 +76 9 12 17.33 13.17 22.21 8.96 11.25 8.54 12.33 6.04 10.46 10.58 11.46 18.08 +76 9 13 8.71 8.25 5.71 2.13 9.71 5.21 6.71 6.25 8.00 5.13 12.75 18.12 +76 9 14 20.83 17.29 20.62 9.67 14.37 11.21 12.17 11.04 12.33 12.29 19.25 29.63 +76 9 15 12.08 7.50 14.12 6.04 8.08 7.00 6.58 3.63 7.92 7.96 10.34 18.12 +76 9 16 5.66 5.54 5.33 1.63 4.29 1.29 3.08 0.42 2.50 1.79 4.75 8.17 +76 9 17 12.21 10.46 8.71 4.17 11.71 9.04 4.42 5.54 8.04 6.46 9.42 12.42 +76 9 18 7.21 6.92 8.33 4.38 7.21 7.38 5.66 3.92 8.92 5.54 7.67 15.83 +76 9 19 12.83 14.21 13.37 7.00 11.25 10.00 9.75 7.58 12.71 8.25 12.79 18.88 +76 9 20 11.79 14.58 10.17 6.58 14.21 8.75 7.25 8.04 12.25 10.71 15.92 15.87 +76 9 21 6.25 6.38 6.67 1.83 4.17 3.79 3.21 2.17 4.63 3.46 8.71 6.92 +76 9 22 5.21 6.92 2.88 1.13 4.12 2.37 1.63 1.13 3.00 1.13 4.33 5.54 +76 9 23 17.04 16.00 13.88 6.17 15.04 9.62 6.34 10.96 10.71 6.46 11.67 18.08 +76 9 24 13.59 14.04 14.71 7.71 12.12 11.46 15.92 14.79 14.54 11.25 17.50 28.96 +76 9 25 10.92 12.58 5.88 3.67 8.42 6.42 10.13 7.17 10.34 9.92 15.34 33.17 +76 9 26 11.63 11.00 9.42 3.83 7.41 6.38 8.17 5.66 9.62 5.88 7.33 19.08 +76 9 27 11.25 9.50 11.17 4.71 9.38 6.83 7.04 9.08 9.59 4.67 10.88 16.71 +76 9 28 13.08 12.38 11.04 4.83 10.96 8.29 7.71 7.71 10.50 5.00 9.38 14.75 +76 9 29 13.50 9.54 7.83 3.37 10.75 5.88 6.96 9.00 8.92 5.63 11.83 8.79 +76 9 30 8.79 8.50 9.38 3.13 6.75 4.83 5.25 3.83 7.83 3.37 6.71 6.92 +76 10 1 10.96 6.71 10.41 4.63 7.58 5.04 5.04 5.54 6.50 3.92 6.79 5.00 +76 10 2 12.38 7.58 7.50 3.92 6.34 4.04 2.67 3.50 4.79 3.17 7.38 10.13 +76 10 3 12.38 10.63 12.08 6.34 11.21 8.67 8.96 8.75 10.75 9.00 14.37 15.83 +76 10 4 10.21 9.33 5.17 2.96 9.59 6.13 5.41 5.41 6.38 2.50 11.87 11.17 +76 10 5 22.04 20.71 17.46 10.21 18.25 13.62 11.54 16.29 13.62 12.50 21.54 21.46 +76 10 6 21.62 20.67 19.95 12.96 23.09 17.12 19.21 21.50 19.87 21.92 34.83 36.51 +76 10 7 16.33 13.08 12.12 5.75 11.96 8.58 7.71 6.42 7.46 5.88 12.00 16.58 +76 10 8 8.29 6.34 6.29 2.79 5.91 5.29 3.04 4.46 6.04 5.33 8.00 11.54 +76 10 9 5.66 6.21 7.54 1.38 6.58 5.75 1.63 7.92 5.37 6.08 14.67 15.12 +76 10 10 11.12 13.04 10.29 6.04 12.21 10.29 7.41 11.54 10.79 11.42 19.58 19.25 +76 10 11 17.16 14.71 10.67 5.50 11.96 7.92 5.29 9.79 9.38 8.38 13.17 13.50 +76 10 12 8.63 7.41 9.00 6.96 8.58 7.25 8.33 7.00 9.08 9.25 11.96 21.12 +76 10 13 7.92 7.29 5.88 2.50 7.79 4.63 2.25 4.50 5.21 3.13 8.79 11.46 +76 10 14 28.08 16.00 26.83 13.88 15.59 12.08 13.83 9.71 12.00 12.42 12.12 16.54 +76 10 15 15.59 10.41 12.83 10.25 10.96 10.67 14.83 8.42 11.58 13.62 12.79 27.08 +76 10 16 12.92 12.54 10.37 3.63 10.54 7.58 4.88 5.29 5.58 4.88 7.21 11.42 +76 10 17 13.96 9.67 14.79 6.79 9.54 10.08 13.50 9.29 11.04 12.50 9.83 22.50 +76 10 18 8.63 4.00 7.71 2.04 5.46 4.08 1.54 2.46 4.38 3.63 4.58 18.46 +76 10 19 9.00 12.08 4.58 0.42 9.71 5.29 2.42 5.71 5.50 4.83 9.87 12.75 +76 10 20 16.38 13.62 9.50 3.00 11.75 6.50 6.96 6.50 5.13 6.42 14.50 8.87 +76 10 21 14.46 13.67 13.59 5.37 14.17 8.29 9.17 9.79 10.71 11.17 19.50 17.88 +76 10 22 9.62 5.09 8.79 2.83 7.92 5.50 6.04 3.75 6.79 6.67 8.79 16.08 +76 10 23 11.87 7.33 10.58 5.17 7.67 5.91 6.21 5.66 6.71 6.83 9.42 13.42 +76 10 24 6.08 3.04 5.50 1.21 3.92 4.50 1.92 0.63 3.29 4.12 8.25 14.62 +76 10 25 4.58 1.79 7.50 1.42 0.87 0.83 0.92 0.17 1.46 3.13 7.12 6.17 +76 10 26 12.79 12.58 7.25 2.17 10.04 5.21 2.58 6.04 5.37 3.88 9.59 7.50 +76 10 27 7.87 6.34 7.08 2.46 7.38 5.96 6.54 9.13 6.83 6.38 13.08 17.58 +76 10 28 9.71 3.83 10.08 2.75 2.88 1.83 2.58 2.42 2.67 4.46 5.37 8.71 +76 10 29 12.79 5.83 14.67 5.66 4.79 4.96 6.71 4.83 6.13 7.54 9.17 11.87 +76 10 30 10.71 3.25 13.79 4.50 4.79 2.33 4.46 2.92 4.29 5.83 3.50 9.25 +76 10 31 15.50 11.96 10.04 5.96 12.17 8.17 4.21 9.59 9.42 10.13 17.33 18.05 +76 11 1 13.96 15.67 10.29 6.46 12.79 9.08 10.00 9.67 10.21 11.63 23.09 21.96 +76 11 2 12.71 15.04 8.83 4.79 11.34 8.83 11.79 7.75 9.71 9.59 18.88 18.71 +76 11 3 9.21 8.12 6.17 2.96 7.46 7.50 7.54 6.00 8.17 7.92 15.16 20.00 +76 11 4 10.46 12.29 9.33 4.75 9.92 9.08 5.96 7.87 9.04 8.63 15.41 18.63 +76 11 5 17.37 15.50 11.38 5.96 13.25 9.17 9.62 8.71 11.54 12.92 18.58 19.25 +76 11 6 15.37 12.29 10.29 4.46 12.21 10.46 5.17 9.33 9.75 10.50 12.46 16.21 +76 11 7 10.83 10.41 7.79 2.54 9.83 8.29 8.08 6.67 6.71 7.33 11.29 15.67 +76 11 8 9.79 9.13 6.17 1.71 8.58 8.04 5.33 7.33 5.71 7.87 10.34 17.88 +76 11 9 7.00 4.29 4.33 0.75 4.88 4.08 5.75 2.17 1.87 3.75 11.25 13.21 +76 11 10 6.13 5.79 4.88 1.33 4.79 6.04 8.38 1.63 3.58 4.21 7.71 13.50 +76 11 11 3.50 2.83 3.04 0.13 2.88 3.08 4.96 1.08 0.58 0.92 7.25 8.71 +76 11 12 6.29 1.79 9.38 1.75 0.25 0.83 8.83 0.00 0.92 1.42 1.92 6.00 +76 11 13 7.75 9.50 4.88 0.21 5.50 5.66 6.58 3.29 2.67 4.29 7.54 9.75 +76 11 14 15.21 9.17 13.33 6.21 8.83 10.17 11.38 5.91 10.00 8.83 8.42 18.63 +76 11 15 10.88 14.09 9.87 4.17 8.12 10.13 8.29 8.75 8.04 9.13 20.41 19.50 +76 11 16 9.67 6.42 10.71 3.79 1.58 3.96 8.63 1.50 3.17 3.88 5.54 9.96 +76 11 17 10.96 13.92 11.38 4.25 9.54 8.67 7.41 9.79 7.79 9.92 16.00 17.54 +76 11 18 9.79 6.87 10.50 3.79 2.71 5.66 7.50 4.50 6.17 4.04 7.38 8.25 +76 11 19 6.21 3.92 5.50 0.54 1.92 0.96 2.17 0.87 0.17 1.21 2.96 4.38 +76 11 20 5.66 2.71 6.50 1.00 0.17 0.67 3.71 0.08 0.79 1.92 3.71 9.29 +76 11 21 7.50 1.42 9.59 1.79 1.29 2.75 8.46 1.54 2.25 5.54 6.38 14.33 +76 11 22 7.62 3.92 9.38 2.50 3.46 3.46 7.83 2.37 2.88 5.75 6.46 12.42 +76 11 23 9.87 3.96 6.87 1.42 3.08 2.92 8.54 1.75 3.21 4.29 6.08 12.00 +76 11 24 7.75 2.96 6.25 1.79 4.29 6.83 11.21 5.13 6.00 7.21 13.96 17.75 +76 11 25 17.46 15.25 12.17 5.83 9.17 11.00 13.04 12.42 11.71 13.83 22.54 22.17 +76 11 26 18.00 16.38 15.92 8.71 13.00 12.38 16.75 12.04 13.00 14.21 16.71 17.04 +76 11 27 20.46 19.38 16.00 10.58 16.08 14.09 15.50 13.67 13.92 15.71 25.75 24.87 +76 11 28 16.38 19.29 15.16 8.71 18.16 13.33 21.84 12.83 13.92 15.00 28.79 29.75 +76 11 29 13.46 10.83 11.21 4.25 10.37 8.38 13.21 5.37 9.29 9.54 16.21 20.41 +76 11 30 11.63 10.17 8.96 3.75 8.33 5.29 8.29 3.71 4.54 4.38 8.87 12.79 +76 12 1 13.46 16.42 9.21 4.54 10.75 8.67 10.88 4.83 8.79 5.91 8.83 13.67 +76 12 2 13.08 13.46 9.54 3.33 7.41 6.13 10.00 3.75 4.88 5.04 10.58 11.29 +76 12 3 12.12 12.62 7.50 1.96 6.54 4.17 5.75 1.08 3.08 2.75 6.58 8.17 +76 12 4 10.29 9.62 9.21 1.50 3.50 2.67 11.00 0.63 3.63 4.67 4.04 8.87 +76 12 5 16.88 13.37 14.42 5.33 11.50 9.04 11.83 6.83 8.83 7.67 12.75 15.29 +76 12 6 21.12 22.71 15.96 8.54 19.08 12.71 16.75 11.17 13.17 13.79 18.08 18.88 +76 12 7 16.00 18.05 13.37 6.96 14.09 11.79 15.46 5.04 11.38 9.50 9.54 14.92 +76 12 8 12.21 8.75 8.12 3.83 7.08 5.88 10.75 4.12 7.08 6.75 11.50 18.91 +76 12 9 4.54 3.00 3.71 0.17 4.71 3.29 7.25 0.75 1.33 4.08 3.92 16.66 +76 12 10 4.46 2.17 4.54 0.58 1.87 2.50 9.00 0.37 2.04 4.17 4.79 11.92 +76 12 11 4.58 8.54 3.17 0.00 4.83 5.33 2.71 2.62 1.50 2.42 8.79 11.08 +76 12 12 12.92 11.96 4.58 0.33 6.04 4.17 1.21 2.88 0.92 1.75 6.21 8.92 +76 12 13 20.21 16.83 10.50 4.00 11.87 8.75 1.21 4.79 5.79 5.04 8.00 11.17 +76 12 14 15.92 14.29 12.71 4.08 11.29 8.54 5.33 6.58 7.67 7.83 7.50 12.33 +76 12 15 14.17 10.71 14.58 4.04 8.25 7.17 5.66 3.75 6.42 5.63 3.46 14.12 +76 12 16 7.62 8.08 10.92 2.33 8.96 6.87 6.63 6.42 6.29 5.41 7.75 14.62 +76 12 17 8.29 4.67 9.38 0.67 4.12 2.92 5.63 2.83 3.67 3.75 8.38 15.96 +76 12 18 14.00 8.58 14.67 4.21 6.71 4.58 9.92 4.88 6.83 7.58 7.00 14.29 +76 12 19 9.79 3.54 8.54 3.37 6.13 2.58 3.17 2.92 3.42 6.42 2.88 17.21 +76 12 20 7.54 1.58 6.50 1.96 2.46 0.96 6.21 0.33 2.67 2.37 1.08 4.58 +76 12 21 8.87 9.59 8.75 2.37 10.58 4.58 6.50 7.04 4.25 2.92 9.46 9.00 +76 12 22 12.25 9.04 12.00 4.88 9.92 8.63 9.71 8.79 8.21 10.08 10.67 21.42 +76 12 23 12.38 11.38 12.92 3.58 9.75 8.38 6.63 8.63 7.50 6.67 8.29 17.96 +76 12 24 13.83 12.42 11.96 3.29 7.33 8.04 8.25 8.04 8.00 7.29 7.96 15.09 +76 12 25 9.59 6.13 14.83 4.38 5.75 4.96 5.96 3.92 5.00 4.88 4.67 12.00 +76 12 26 8.21 6.34 9.08 1.38 0.75 0.79 6.50 1.21 2.08 3.21 0.67 11.92 +76 12 27 9.04 6.21 7.87 2.83 4.79 6.96 11.67 4.42 7.38 8.58 14.50 23.42 +76 12 28 9.62 5.96 13.59 3.71 3.83 4.29 7.58 4.71 5.71 5.54 8.46 16.58 +76 12 29 23.83 16.38 17.16 7.25 13.08 12.38 12.33 11.71 13.83 16.04 13.37 23.33 +76 12 30 15.34 11.46 16.04 5.83 8.67 8.29 9.67 2.67 8.71 8.46 6.17 15.50 +76 12 31 8.67 8.83 9.38 3.67 5.37 4.58 7.92 1.79 4.46 4.38 6.38 15.67 +77 1 1 20.04 11.92 20.25 9.13 9.29 8.04 10.75 5.88 9.00 9.00 14.88 25.70 +77 1 2 9.75 1.54 12.54 1.83 3.79 2.75 9.46 1.21 2.33 4.58 8.25 12.38 +77 1 3 13.21 16.66 9.25 1.71 9.46 8.17 7.87 8.96 7.75 7.21 22.25 21.59 +77 1 4 20.12 15.92 18.91 6.75 10.25 11.83 15.41 14.09 12.83 14.71 20.50 27.16 +77 1 5 12.50 9.50 12.04 4.67 9.25 6.87 11.50 8.00 8.33 9.75 13.33 18.08 +77 1 6 5.09 0.96 6.87 0.37 1.79 2.00 8.04 1.54 4.12 5.58 10.08 17.88 +77 1 7 5.29 3.00 6.67 1.67 4.25 3.54 8.04 1.54 6.58 6.34 8.29 16.46 +77 1 8 8.54 6.46 8.25 4.42 8.04 6.08 11.38 6.50 9.67 8.08 13.00 17.16 +77 1 9 19.92 16.00 9.96 9.79 17.12 10.96 12.71 11.42 12.08 11.08 20.12 20.96 +77 1 10 18.16 13.59 16.46 9.38 12.17 8.67 10.75 10.21 8.54 10.96 22.08 31.75 +77 1 11 12.12 10.37 13.17 4.25 5.13 3.17 7.62 4.75 3.54 3.58 12.75 15.09 +77 1 12 7.79 1.75 7.54 1.21 5.54 1.42 4.96 2.88 1.87 1.67 7.17 11.04 +77 1 13 26.92 19.12 24.96 14.09 18.46 13.59 17.04 12.62 12.58 14.17 13.79 22.95 +77 1 14 25.84 15.79 24.54 15.00 16.66 13.50 22.08 9.67 14.58 21.04 18.38 34.29 +77 1 15 17.75 11.79 14.42 10.41 11.04 9.79 11.75 6.34 8.63 9.92 12.04 17.96 +77 1 16 8.04 5.50 6.46 1.29 3.63 1.67 7.17 1.75 2.79 4.00 3.54 14.00 +77 1 17 19.46 18.05 15.12 6.00 12.87 10.96 9.21 10.04 8.21 8.58 12.12 16.08 +77 1 18 11.42 8.21 15.37 7.08 9.92 11.58 14.83 9.00 10.83 14.09 11.25 24.25 +77 1 19 14.58 17.62 10.25 5.58 10.63 9.83 7.17 10.54 8.67 10.50 16.17 17.79 +77 1 20 22.37 15.09 22.50 13.88 15.83 16.62 17.12 14.37 16.54 18.50 15.46 26.12 +77 1 21 15.29 10.88 15.34 9.42 12.54 13.04 14.79 10.71 14.67 18.75 11.83 27.04 +77 1 22 3.92 6.83 5.96 0.58 4.00 4.88 4.63 5.50 4.79 7.38 10.13 17.88 +77 1 23 4.67 8.75 4.08 0.92 4.58 5.54 2.04 4.29 3.50 4.42 10.75 12.71 +77 1 24 15.50 14.75 14.46 7.71 12.58 11.87 10.34 11.00 11.63 13.04 15.34 19.00 +77 1 25 16.71 10.96 13.59 6.96 11.00 9.29 10.88 6.67 10.96 12.33 6.83 15.79 +77 1 26 8.50 11.83 7.71 2.50 6.38 5.66 4.96 2.96 4.75 4.75 6.13 7.50 +77 1 27 8.75 6.50 11.83 7.41 11.25 11.38 12.17 12.71 12.17 10.71 13.92 22.95 +77 1 28 9.29 11.17 15.96 6.00 9.00 8.29 8.58 8.08 8.87 9.17 8.63 14.09 +77 1 29 6.21 6.08 8.29 2.83 2.92 3.25 2.54 2.29 2.00 3.37 6.04 11.58 +77 1 30 17.62 12.04 13.04 5.25 10.08 11.04 12.79 6.75 12.00 9.00 7.50 15.71 +77 1 31 10.17 3.08 9.92 4.33 4.50 2.88 4.75 1.29 1.87 2.92 3.54 10.79 +77 2 1 11.83 9.71 11.00 4.25 8.58 8.71 6.17 5.66 8.29 7.58 11.71 16.50 +77 2 2 16.38 13.33 15.54 8.79 11.12 10.75 9.21 9.25 11.21 11.34 12.46 17.75 +77 2 3 14.21 14.04 13.88 7.46 11.67 11.58 18.29 9.50 12.67 12.92 16.08 20.04 +77 2 4 13.88 15.09 12.17 4.00 11.00 10.88 10.50 7.46 9.33 9.17 11.50 15.75 +77 2 5 14.58 14.75 12.67 5.96 10.21 8.71 10.08 7.00 7.83 9.08 13.96 17.83 +77 2 6 19.38 17.79 15.96 6.04 10.75 9.71 7.58 5.79 7.25 5.71 8.25 13.13 +77 2 7 18.75 18.96 15.04 10.00 15.63 12.50 16.66 9.96 12.87 10.58 13.79 14.42 +77 2 8 5.46 8.96 7.62 3.17 6.63 7.83 7.96 3.04 5.88 3.71 4.17 8.79 +77 2 9 9.67 10.50 10.25 4.00 7.00 9.25 9.96 4.75 9.13 6.34 6.21 15.04 +77 2 10 11.75 8.46 10.08 6.34 11.96 9.92 12.25 8.87 10.21 3.58 12.12 27.84 +77 2 11 3.13 2.62 7.08 2.13 3.50 6.34 9.13 3.08 7.25 4.63 7.33 26.25 +77 2 12 6.04 6.83 6.29 1.63 4.17 4.83 5.25 2.92 5.54 6.54 7.04 22.21 +77 2 13 8.12 5.58 9.83 2.17 7.71 6.38 5.46 4.96 6.96 7.75 8.46 12.04 +77 2 14 11.34 9.59 7.46 2.08 7.25 2.92 8.21 1.96 4.29 4.08 7.54 6.83 +77 2 15 13.70 12.21 11.63 4.75 10.13 10.96 11.50 8.08 9.54 8.54 11.83 12.29 +77 2 16 5.58 8.54 6.25 1.13 6.67 6.87 5.91 4.50 6.00 5.79 11.08 12.83 +77 2 17 17.71 15.12 15.59 7.62 14.29 11.75 11.04 10.04 9.50 8.83 11.71 15.87 +77 2 18 11.46 9.67 10.17 5.37 8.75 8.04 15.09 8.21 11.29 12.12 14.21 17.33 +77 2 19 14.42 11.92 10.79 7.12 10.63 10.00 13.04 9.71 11.25 9.83 11.79 12.92 +77 2 20 11.63 10.21 11.25 4.83 8.25 8.83 9.08 7.00 8.63 7.92 9.33 21.59 +77 2 21 9.83 9.87 8.46 2.08 8.33 5.00 3.83 8.96 6.08 10.21 13.50 23.09 +77 2 22 10.17 15.09 10.96 4.58 11.46 9.87 10.96 11.83 9.75 11.34 16.83 19.92 +77 2 23 12.54 15.21 20.62 10.25 14.37 14.37 17.92 13.50 14.75 15.37 17.12 24.37 +77 2 24 8.67 8.87 10.88 5.63 11.46 10.29 17.71 10.92 12.04 13.29 14.37 27.00 +77 2 25 5.50 3.08 13.00 2.13 3.17 6.58 10.46 6.50 7.41 8.67 6.21 14.71 +77 2 26 14.62 14.33 10.04 3.42 11.34 7.58 8.71 8.75 6.46 6.04 9.42 11.63 +77 2 27 25.41 24.79 22.58 11.50 20.41 17.79 17.92 16.62 14.67 12.00 15.79 23.21 +77 2 28 19.67 18.05 19.38 10.46 17.33 15.63 16.71 14.21 14.88 10.96 16.38 20.54 +77 3 1 8.63 14.83 10.29 3.75 6.63 8.79 5.00 8.12 7.87 6.42 13.54 13.67 +77 3 2 12.62 15.34 15.59 7.33 13.04 12.87 13.25 12.21 13.29 13.04 22.46 22.17 +77 3 3 11.12 12.50 12.92 7.29 11.21 10.25 16.71 11.96 14.79 14.29 18.25 23.29 +77 3 4 12.67 11.08 11.54 6.92 11.75 10.79 18.66 13.79 13.92 13.50 17.92 24.92 +77 3 5 8.29 8.08 10.67 2.92 5.91 7.04 11.04 6.83 7.08 8.58 10.67 17.33 +77 3 6 11.87 15.09 11.29 8.25 13.83 12.54 9.42 6.42 12.54 8.96 9.67 12.50 +77 3 7 16.58 15.96 14.92 7.21 13.13 12.50 14.00 11.92 12.12 9.79 15.96 18.79 +77 3 8 26.20 24.00 18.66 13.33 20.50 18.38 17.67 18.54 18.05 19.79 29.29 27.37 +77 3 9 21.17 15.96 15.16 9.83 17.46 13.62 16.88 13.75 14.42 15.50 16.38 21.71 +77 3 10 33.84 26.92 24.41 20.08 23.75 20.58 20.21 18.71 21.92 22.75 23.09 27.58 +77 3 11 14.33 12.42 13.13 7.41 14.92 12.12 14.29 11.12 13.42 13.50 14.62 22.46 +77 3 12 9.79 9.29 10.54 4.12 8.92 7.75 9.67 6.00 8.21 5.63 8.08 13.25 +77 3 13 12.92 11.92 15.29 5.88 9.79 8.63 13.33 8.79 10.46 9.92 11.12 16.13 +77 3 14 17.46 17.79 14.71 7.75 16.38 11.38 16.33 12.29 13.29 12.87 21.04 20.62 +77 3 15 28.33 19.55 22.00 16.79 18.88 15.67 19.79 16.42 18.46 18.21 19.87 26.16 +77 3 16 24.75 18.84 17.25 11.63 15.21 13.21 15.34 14.79 14.88 16.29 22.92 19.75 +77 3 17 27.50 18.75 20.21 14.46 17.00 14.88 18.34 17.50 15.96 17.54 20.88 23.54 +77 3 18 16.79 14.62 14.04 7.33 17.79 13.08 15.41 12.54 12.79 12.17 13.88 15.83 +77 3 19 21.29 15.75 12.12 8.83 14.67 11.38 11.08 12.58 9.54 9.50 14.46 13.62 +77 3 20 16.79 11.83 25.04 8.50 11.83 10.41 17.41 9.21 9.79 10.83 15.75 16.88 +77 3 21 14.54 11.08 28.25 9.75 10.46 8.58 18.21 7.38 10.79 7.71 9.79 14.58 +77 3 22 10.54 8.29 17.71 5.71 6.92 8.08 13.50 7.00 7.29 7.00 8.54 14.46 +77 3 23 9.33 3.96 8.67 2.88 6.54 5.46 12.08 5.13 8.17 7.33 7.92 18.29 +77 3 24 11.58 11.83 4.96 2.79 9.42 6.00 9.71 5.25 6.00 5.66 5.41 10.29 +77 3 25 19.12 19.50 14.62 7.58 17.67 14.25 12.08 12.92 10.58 8.75 12.79 14.04 +77 3 26 14.75 13.67 13.50 7.25 15.21 12.87 12.54 12.25 10.29 6.96 12.04 17.58 +77 3 27 14.50 15.09 30.84 10.41 12.87 12.08 19.33 13.42 12.17 12.87 19.79 24.13 +77 3 28 12.17 8.38 21.34 5.91 6.38 5.37 10.13 5.04 6.34 9.33 6.58 14.67 +77 3 29 8.58 5.50 5.54 2.04 3.58 4.71 7.79 4.54 4.58 5.54 13.67 12.62 +77 3 30 25.04 19.50 20.38 13.13 18.79 16.75 14.12 15.92 16.71 19.17 28.42 29.79 +77 3 31 26.16 22.17 24.08 13.79 21.67 18.41 19.46 19.87 19.46 19.70 26.96 25.12 +77 4 1 21.67 16.00 17.33 13.59 20.83 15.96 25.62 17.62 19.41 20.67 24.37 30.09 +77 4 2 12.17 6.79 10.13 6.17 9.21 8.67 12.33 9.08 9.62 11.29 13.54 22.79 +77 4 3 19.70 12.46 20.04 10.08 13.29 12.25 14.92 11.83 11.67 15.34 20.00 30.04 +77 4 4 12.96 9.21 9.21 5.00 6.17 5.96 9.38 5.41 7.04 7.46 12.25 14.92 +77 4 5 8.63 7.25 7.25 4.33 7.21 7.17 11.17 5.96 7.83 9.46 10.79 16.46 +77 4 6 14.37 13.21 15.04 8.46 11.17 11.38 15.67 10.79 11.34 17.83 18.25 27.54 +77 4 7 18.12 14.71 17.75 7.58 12.25 11.00 13.17 10.00 9.96 15.12 16.33 22.46 +77 4 8 15.75 9.79 10.21 5.91 10.13 8.58 8.63 8.42 8.08 8.33 15.54 13.33 +77 4 9 11.50 9.96 9.79 4.21 7.58 7.62 6.13 4.33 5.63 6.92 6.75 9.62 +77 4 10 17.33 12.83 9.96 9.13 11.67 10.21 13.83 8.33 9.83 10.00 12.46 15.75 +77 4 11 11.96 10.96 11.04 6.04 11.34 10.08 13.04 10.17 10.67 9.17 14.71 15.00 +77 4 12 17.21 15.71 13.62 10.50 18.34 15.54 21.62 16.38 16.62 15.41 18.25 22.21 +77 4 13 19.29 13.42 12.54 10.25 15.29 12.87 18.54 10.83 12.21 16.50 16.46 22.50 +77 4 14 14.79 8.54 10.46 6.83 10.88 8.96 14.12 6.25 9.29 11.34 10.58 17.83 +77 4 15 6.87 6.21 5.88 1.96 2.42 3.50 8.00 2.79 2.67 4.54 8.00 7.41 +77 4 16 11.08 9.75 9.75 3.54 7.21 7.04 8.42 4.92 4.83 5.50 10.37 11.17 +77 4 17 7.08 6.04 14.12 4.04 5.25 5.83 8.58 3.67 5.13 6.96 12.96 12.87 +77 4 18 9.79 9.62 9.17 3.67 8.92 9.13 8.21 6.87 8.00 7.17 7.33 9.79 +77 4 19 8.12 6.67 10.34 2.75 3.92 5.71 7.75 5.21 5.29 6.79 9.17 15.87 +77 4 20 13.13 13.67 14.54 7.21 9.67 10.25 14.29 12.04 12.12 11.04 18.34 15.37 +77 4 21 21.59 19.75 19.70 12.33 15.37 13.17 18.46 16.62 15.34 15.59 22.79 18.63 +77 4 22 20.30 17.58 20.04 12.46 21.25 15.59 26.42 16.33 18.12 18.71 23.75 22.63 +77 4 23 16.96 18.12 14.50 10.63 16.88 12.38 20.25 11.46 12.83 12.00 17.50 20.12 +77 4 24 14.29 13.67 12.54 7.21 10.54 10.04 13.21 9.17 10.29 8.79 14.33 13.08 +77 4 25 17.96 14.96 16.79 9.04 14.21 12.92 14.50 15.87 13.21 16.04 25.21 26.63 +77 4 26 13.70 15.34 9.42 7.83 15.92 10.41 14.37 11.21 11.79 10.75 19.50 21.96 +77 4 27 22.63 17.37 19.87 11.17 15.50 12.38 17.54 7.79 12.25 11.75 16.66 18.54 +77 4 28 16.88 14.54 11.58 7.54 14.37 10.67 14.09 11.58 10.08 10.54 17.62 14.88 +77 4 29 17.33 13.70 11.04 7.58 14.37 10.17 13.79 8.33 10.83 12.79 16.88 21.46 +77 4 30 15.50 10.96 7.04 5.63 11.04 8.67 10.58 7.96 8.29 8.79 17.12 17.29 +77 5 1 6.42 7.12 8.67 3.58 4.58 4.00 6.75 6.13 3.33 4.50 19.21 12.38 +77 5 2 6.63 5.29 10.75 3.42 4.63 3.46 7.41 5.46 4.71 4.12 13.59 10.71 +77 5 3 12.79 7.38 11.58 6.21 7.29 7.21 11.04 6.75 8.29 9.96 10.37 15.83 +77 5 4 8.83 4.25 6.21 2.46 4.79 6.92 8.96 5.41 6.25 9.25 11.38 15.12 +77 5 5 8.75 4.75 7.50 1.79 3.29 5.71 7.96 3.08 3.17 3.50 8.25 12.08 +77 5 6 7.58 6.71 8.54 3.67 6.87 7.50 9.87 5.79 6.42 6.63 11.63 15.59 +77 5 7 11.71 10.13 11.38 8.87 12.67 11.63 17.08 13.00 13.00 15.16 16.38 25.75 +77 5 8 8.79 8.38 8.54 5.79 7.12 7.17 8.87 5.37 6.42 5.75 7.71 9.33 +77 5 9 13.54 9.75 9.21 4.83 9.13 6.83 6.58 4.96 5.09 3.71 8.21 6.87 +77 5 10 5.41 8.17 7.62 3.67 4.42 6.21 7.67 4.04 4.58 5.04 12.33 12.87 +77 5 11 17.41 14.71 16.66 7.71 14.09 11.58 15.71 12.62 12.71 13.37 18.08 15.87 +77 5 12 17.92 14.33 9.17 6.38 12.04 9.46 10.75 7.67 8.42 9.62 14.96 18.21 +77 5 13 7.96 5.00 10.58 5.09 5.25 5.33 7.96 4.46 4.63 7.50 8.21 11.92 +77 5 14 8.42 9.83 6.29 3.83 6.13 4.83 6.67 3.75 4.46 3.13 7.75 7.62 +77 5 15 14.21 12.67 17.04 5.75 8.75 9.17 8.71 7.79 6.34 6.29 11.04 10.25 +77 5 16 12.17 11.79 23.16 7.62 7.79 5.46 9.62 8.96 5.91 7.29 12.29 14.79 +77 5 17 6.87 4.63 16.33 3.37 2.96 4.79 5.09 3.37 4.71 3.58 4.63 9.71 +77 5 18 3.88 4.42 12.21 1.25 2.33 1.50 4.71 2.42 4.12 5.09 9.46 9.92 +77 5 19 4.54 5.41 5.96 1.83 2.21 2.96 4.50 5.25 3.67 2.04 13.25 8.83 +77 5 20 6.38 6.04 15.63 4.17 2.79 6.25 10.83 3.13 7.46 4.38 5.71 12.92 +77 5 21 12.71 7.41 22.46 7.58 7.04 8.71 11.96 4.92 7.87 5.91 7.67 9.00 +77 5 22 11.38 9.25 18.63 6.17 5.17 9.21 8.08 5.04 7.12 4.04 8.08 4.08 +77 5 23 12.29 6.63 16.46 4.29 6.08 8.08 9.04 2.92 6.58 3.67 10.41 4.54 +77 5 24 11.92 7.00 20.17 4.50 7.33 8.83 11.58 7.17 9.62 7.33 11.17 12.33 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16.13 18.79 +77 6 8 9.00 6.87 7.08 3.17 6.87 4.71 6.71 5.58 5.63 5.63 11.08 13.25 +77 6 9 7.41 8.50 12.83 3.00 6.67 3.75 7.58 4.29 5.88 3.79 12.33 9.79 +77 6 10 14.12 12.75 27.25 8.29 10.88 8.17 16.00 10.08 9.54 10.04 19.62 20.79 +77 6 11 13.75 10.58 21.00 8.08 9.46 10.17 14.79 8.63 9.83 10.96 18.34 19.12 +77 6 12 7.00 6.79 8.58 4.63 6.34 7.38 7.67 4.33 6.63 6.17 8.17 9.33 +77 6 13 12.87 8.08 9.25 4.58 7.00 6.63 6.21 5.91 5.58 5.58 9.46 7.00 +77 6 14 11.92 11.17 18.66 6.87 9.87 8.79 11.08 8.75 9.33 9.29 13.62 16.62 +77 6 15 7.21 9.21 20.50 4.79 6.08 6.87 11.00 5.54 7.46 7.54 14.00 9.83 +77 6 16 8.50 2.58 18.00 2.21 6.34 9.46 10.00 5.83 9.87 7.46 12.29 9.62 +77 6 17 11.46 4.29 15.59 2.88 8.38 9.25 9.42 7.58 9.21 5.25 13.04 5.71 +77 6 18 6.21 7.00 13.79 3.25 7.38 6.00 5.54 6.00 4.92 4.29 13.50 12.25 +77 6 19 7.08 7.29 8.75 3.00 7.21 5.58 4.00 6.87 6.50 5.83 10.50 12.00 +77 6 20 4.83 4.54 12.54 1.96 4.88 2.83 4.00 1.00 3.37 1.46 9.29 4.88 +77 6 21 4.83 4.38 10.75 2.04 5.63 2.25 2.96 2.21 1.67 2.42 5.79 4.79 +77 6 22 6.00 3.25 4.21 1.71 3.79 3.58 2.79 1.17 2.92 2.96 2.96 3.58 +77 6 23 7.33 7.71 6.50 3.50 6.08 6.29 5.00 3.71 4.42 5.37 8.33 8.33 +77 6 24 13.50 11.71 10.79 4.12 10.41 8.17 6.04 4.50 5.54 4.12 9.38 6.25 +77 6 25 16.25 7.04 9.38 6.50 10.17 9.04 8.79 6.00 7.62 8.63 9.13 12.42 +77 6 26 9.79 6.92 9.38 4.38 9.38 7.79 9.75 5.04 9.25 7.12 12.12 11.58 +77 6 27 11.08 7.54 12.12 4.21 7.92 6.71 12.25 4.71 8.79 5.83 6.00 7.08 +77 6 28 14.58 8.71 8.42 5.75 9.33 8.12 7.08 5.66 6.63 7.00 9.71 10.79 +77 6 29 13.08 11.25 7.25 5.25 13.25 8.92 7.50 7.00 7.46 6.25 12.46 12.54 +77 6 30 19.21 17.46 16.00 9.21 17.83 12.25 14.96 8.04 12.71 11.50 15.67 15.04 +77 7 1 15.41 16.29 17.08 6.25 11.83 11.83 12.29 10.58 10.41 7.21 17.37 7.83 +77 7 2 15.59 15.87 15.34 8.46 13.62 11.79 13.08 11.96 11.63 11.79 24.50 17.33 +77 7 3 6.25 12.62 9.00 4.83 10.54 8.96 7.33 8.29 8.54 10.21 19.79 12.50 +77 7 4 6.71 12.17 4.08 4.71 10.04 7.83 4.17 6.46 8.58 9.42 15.54 10.79 +77 7 5 5.79 1.13 7.33 2.37 1.58 2.62 4.21 2.08 6.17 4.21 10.00 5.09 +77 7 6 4.46 3.08 9.71 2.08 3.13 4.42 6.21 0.21 5.79 3.33 6.87 7.17 +77 7 7 8.87 9.21 6.50 1.92 8.12 4.21 4.29 5.25 2.83 3.37 8.21 7.87 +77 7 8 18.08 12.25 13.88 7.04 10.37 7.58 8.42 7.92 8.12 9.00 12.71 12.46 +77 7 9 8.96 8.79 20.33 6.42 8.25 6.96 8.87 6.87 6.42 4.17 14.62 6.25 +77 7 10 8.58 5.00 16.33 4.04 7.29 7.21 8.42 4.33 9.29 6.00 10.46 4.50 +77 7 11 7.25 4.17 15.71 3.46 2.83 5.66 6.79 2.79 5.71 4.92 8.42 5.58 +77 7 12 6.58 2.75 12.50 3.13 6.08 7.67 9.04 5.09 7.17 3.75 14.58 6.17 +77 7 13 4.63 3.08 14.33 4.58 5.00 5.79 6.79 3.92 4.67 3.08 12.00 5.04 +77 7 14 4.79 2.46 8.46 2.75 4.42 2.67 6.17 0.37 3.79 2.50 4.50 6.34 +77 7 15 9.38 5.21 6.38 3.50 5.58 6.08 7.17 5.13 5.71 5.37 11.00 10.37 +77 7 16 11.00 6.87 7.54 3.46 6.58 4.29 9.08 5.66 5.50 5.09 12.46 13.50 +77 7 17 18.63 13.54 14.17 8.79 15.54 11.29 14.17 12.08 12.38 11.34 18.25 19.04 +77 7 18 15.46 10.75 11.12 9.00 15.67 12.04 18.16 12.42 11.83 14.00 18.12 18.75 +77 7 19 14.04 12.71 11.00 7.92 15.59 12.04 17.50 11.63 12.46 10.67 17.92 18.00 +77 7 20 16.25 9.33 10.63 7.96 11.71 9.25 15.29 7.62 10.21 11.83 11.63 18.21 +77 7 21 5.50 8.42 7.92 2.96 8.46 6.46 7.83 2.71 7.38 5.88 10.37 10.00 +77 7 22 8.04 10.37 9.67 4.25 9.96 8.29 13.21 9.08 10.04 9.59 16.58 13.08 +77 7 23 18.79 14.96 15.92 8.87 13.21 12.04 15.50 12.83 12.75 15.25 18.08 19.04 +77 7 24 13.33 13.62 11.50 9.38 15.12 11.58 17.21 11.83 11.63 12.71 16.92 23.21 +77 7 25 20.33 16.25 13.67 11.79 15.59 12.29 15.41 9.83 11.04 13.33 15.34 23.29 +77 7 26 12.79 7.92 8.21 5.71 11.42 7.33 11.58 6.46 7.79 8.08 10.63 15.63 +77 7 27 13.17 7.87 6.46 5.25 9.38 6.71 9.42 4.83 7.12 6.08 9.83 11.00 +77 7 28 10.63 7.00 11.42 4.63 6.96 6.25 9.21 5.75 6.96 7.58 9.00 12.96 +77 7 29 8.75 3.96 6.87 3.92 4.42 4.79 7.38 2.04 4.54 4.08 9.96 11.71 +77 7 30 10.88 7.04 5.17 4.25 7.54 6.17 7.41 4.29 6.87 7.71 8.12 12.79 +77 7 31 8.42 3.88 7.08 4.33 3.63 3.50 7.21 2.00 4.08 4.67 6.87 12.29 +77 8 1 4.33 2.96 4.42 2.33 0.96 1.08 4.96 1.87 2.33 2.04 10.50 9.83 +77 8 2 5.46 5.00 7.96 2.88 6.17 4.50 8.87 3.00 4.71 4.17 9.62 10.29 +77 8 3 11.29 6.58 9.54 4.67 7.00 5.83 9.62 4.33 7.12 4.50 11.42 10.41 +77 8 4 16.79 14.12 15.16 7.17 13.59 10.79 14.88 9.59 13.00 11.71 15.41 15.79 +77 8 5 8.75 8.87 11.42 4.92 5.04 5.46 9.00 4.42 5.79 6.83 9.46 13.00 +77 8 6 6.71 2.71 13.37 2.88 1.71 2.79 6.34 1.75 3.54 2.75 4.58 7.29 +77 8 7 4.54 3.67 6.75 2.29 1.54 2.37 7.04 1.04 3.96 3.50 4.25 7.38 +77 8 8 3.92 3.08 4.54 1.42 1.92 2.17 4.46 1.38 3.63 2.79 3.75 9.87 +77 8 9 5.79 2.50 5.46 1.92 1.42 1.96 3.71 1.13 2.33 1.79 5.91 5.04 +77 8 10 5.04 10.25 5.41 3.79 6.63 5.37 5.83 3.75 5.75 5.88 11.96 9.83 +77 8 11 6.87 11.87 5.79 5.25 8.67 7.54 6.50 4.58 7.21 6.25 11.79 11.08 +77 8 12 6.29 13.25 4.75 3.54 9.29 7.17 7.21 3.67 5.54 7.00 6.04 12.25 +77 8 13 12.00 16.33 4.88 3.88 10.63 7.54 6.54 6.29 7.08 6.75 12.96 16.96 +77 8 14 7.58 6.67 6.96 5.46 7.50 8.63 7.87 5.33 7.17 5.83 9.75 11.75 +77 8 15 3.17 2.92 7.08 2.75 1.13 3.71 7.67 1.00 5.63 5.33 5.63 12.54 +77 8 16 11.71 7.79 18.00 6.21 7.71 9.25 15.37 7.54 10.75 8.38 10.21 16.00 +77 8 17 13.67 10.58 27.88 10.00 10.75 10.63 19.17 8.12 11.34 10.46 13.13 11.92 +77 8 18 10.08 4.83 18.00 4.04 5.41 4.25 9.17 3.75 4.96 3.46 7.62 6.29 +77 8 19 6.50 3.42 11.75 3.42 4.46 4.17 7.12 1.33 4.29 3.29 5.04 3.96 +77 8 20 10.88 5.58 12.75 5.41 6.58 4.88 7.46 2.83 5.83 3.96 6.58 3.08 +77 8 21 11.83 5.37 8.63 4.58 4.12 2.79 7.96 2.83 4.38 1.50 6.50 8.50 +77 8 22 6.79 6.21 15.21 3.63 3.50 4.29 9.21 1.54 4.67 3.92 4.71 11.21 +77 8 23 11.83 12.42 10.04 6.63 12.67 8.67 8.54 6.83 7.96 7.21 10.63 10.50 +77 8 24 13.62 9.25 17.00 6.54 8.83 11.00 16.29 6.04 10.83 12.58 8.83 23.42 +77 8 25 11.04 9.59 9.96 3.96 11.42 7.62 9.67 3.33 7.79 4.58 8.21 6.79 +77 8 26 9.92 12.33 9.21 5.29 13.00 7.25 10.67 9.21 7.25 9.21 18.84 24.08 +77 8 27 19.38 11.08 17.83 8.00 9.13 8.38 10.54 5.66 7.17 7.08 10.00 14.29 +77 8 28 10.88 11.25 10.29 2.71 6.79 7.79 9.08 12.00 9.13 8.75 22.29 20.67 +77 8 29 12.17 8.21 11.34 5.17 9.67 8.00 11.58 10.00 9.25 9.50 17.67 20.88 +77 8 30 9.79 3.58 8.92 3.83 3.21 4.17 7.46 1.87 2.96 1.58 9.54 5.96 +77 8 31 7.62 7.29 10.75 3.50 4.79 4.75 7.46 4.83 4.58 3.96 11.75 10.58 +77 9 1 17.37 16.33 16.83 8.58 14.46 11.83 15.09 13.92 13.29 13.88 23.29 25.17 +77 9 2 12.75 11.87 11.17 6.54 12.92 10.25 14.37 11.04 10.41 12.50 18.96 24.25 +77 9 3 11.12 11.75 9.38 4.63 10.00 8.46 12.12 9.17 7.67 8.04 19.38 19.12 +77 9 4 19.25 15.83 17.00 7.50 15.04 12.54 16.04 12.71 13.04 15.04 21.71 26.12 +77 9 5 9.00 10.50 8.17 4.71 9.71 7.46 11.63 7.29 9.25 8.58 16.25 20.58 +77 9 6 16.66 12.42 14.12 7.92 12.12 9.21 14.46 8.00 10.29 9.75 14.25 17.00 +77 9 7 6.46 3.17 4.75 1.08 3.37 4.21 8.17 2.75 4.88 4.33 11.50 17.33 +77 9 8 10.92 7.46 8.38 4.83 9.21 7.21 10.58 5.13 8.33 6.87 12.00 14.54 +77 9 9 11.38 11.34 10.71 5.33 8.42 8.50 11.75 8.83 8.21 7.33 15.87 17.29 +77 9 10 15.75 15.54 18.50 8.83 13.88 11.04 20.46 11.08 12.46 9.79 17.21 15.41 +77 9 11 10.29 8.75 12.25 6.21 10.29 10.83 14.04 10.67 12.42 12.08 17.12 22.92 +77 9 12 8.79 6.96 8.92 4.17 3.04 4.38 7.92 2.75 4.25 5.50 7.00 14.09 +77 9 13 3.88 4.42 4.29 1.54 2.79 2.83 4.58 2.88 3.92 3.92 9.21 11.87 +77 9 14 3.21 3.54 4.92 1.63 2.29 3.04 8.58 1.63 4.75 5.21 9.29 17.04 +77 9 15 4.58 2.25 8.71 1.38 2.46 1.42 2.42 0.42 1.79 0.83 4.83 8.58 +77 9 16 12.04 5.21 11.25 3.63 7.71 8.12 13.46 7.41 9.33 6.96 10.71 16.17 +77 9 17 12.71 12.71 14.00 4.46 9.38 7.50 10.58 6.04 6.96 5.71 11.00 14.04 +77 9 18 10.34 6.54 13.96 3.21 5.46 5.83 8.38 3.00 5.00 4.04 8.58 6.54 +77 9 19 9.92 4.63 11.83 3.33 3.67 4.50 8.50 2.42 4.58 3.42 6.92 7.29 +77 9 20 9.38 7.33 10.37 2.96 5.37 4.79 7.50 2.83 4.96 4.21 7.29 8.87 +77 9 21 6.46 2.71 8.21 2.42 4.29 2.83 5.13 0.29 1.67 0.29 4.33 2.08 +77 9 22 8.92 11.42 7.00 1.87 7.54 3.29 5.00 3.67 4.83 4.08 8.63 9.71 +77 9 23 24.79 23.09 15.71 10.00 20.58 14.46 15.21 12.46 11.71 11.75 16.79 18.25 +77 9 24 17.00 13.13 18.38 10.41 14.37 16.08 20.12 12.58 14.75 17.79 17.54 27.04 +77 9 25 13.83 12.46 12.46 4.46 11.12 8.42 11.08 10.29 8.92 9.00 15.16 16.88 +77 9 26 12.50 12.04 12.08 6.04 9.71 8.87 8.67 10.21 9.54 10.34 14.92 15.59 +77 9 27 14.50 15.54 11.67 7.58 10.37 7.54 9.83 9.46 9.00 7.08 12.08 13.21 +77 9 28 19.83 18.05 15.46 12.54 21.34 15.34 21.09 18.05 16.42 16.46 23.75 29.63 +77 9 29 20.33 17.04 17.25 10.96 21.71 13.37 20.30 14.67 16.46 15.04 23.79 28.84 +77 9 30 20.21 18.25 17.75 10.41 19.70 13.13 21.12 16.04 15.75 15.37 22.17 25.46 +77 10 1 16.75 15.34 12.25 9.42 16.38 11.38 18.50 13.92 14.09 14.46 22.34 29.67 +77 10 2 7.41 5.46 7.62 2.92 6.75 5.33 10.79 6.17 7.83 7.00 9.75 16.29 +77 10 3 14.62 13.54 14.29 5.09 10.13 8.21 10.17 9.75 10.08 6.87 14.62 15.16 +77 10 4 13.33 14.33 9.62 5.63 12.79 8.63 11.21 9.59 9.92 7.71 14.79 16.29 +77 10 5 7.33 6.34 6.54 1.42 6.63 4.58 7.75 5.83 7.00 6.87 11.54 15.71 +77 10 6 17.25 18.08 21.00 8.75 11.75 8.17 14.96 12.00 10.63 10.08 19.79 21.67 +77 10 7 9.75 8.96 9.08 4.58 9.75 7.33 10.88 9.87 9.13 9.04 12.17 17.37 +77 10 8 8.00 4.25 8.08 2.83 5.37 6.54 10.41 10.08 9.96 10.04 16.38 18.25 +77 10 9 16.33 15.41 11.79 8.21 16.58 10.71 15.25 13.33 11.79 9.75 17.58 13.46 +77 10 10 12.21 11.54 7.62 3.71 8.00 6.21 9.75 6.71 6.75 6.34 9.04 13.21 +77 10 11 25.92 20.46 20.96 13.46 18.05 13.67 16.29 17.37 16.58 17.46 24.25 24.04 +77 10 12 16.83 15.83 14.67 8.25 16.50 12.87 16.71 17.92 14.83 17.12 26.58 28.46 +77 10 13 13.88 14.83 11.79 5.66 12.67 8.67 10.58 13.00 10.50 7.41 16.42 15.83 +77 10 14 13.21 14.04 13.08 7.46 13.62 10.37 12.54 12.12 12.79 7.92 15.21 20.25 +77 10 15 17.00 18.08 15.67 8.38 15.46 14.58 18.21 14.71 14.50 8.38 16.29 22.79 +77 10 16 13.08 18.41 11.29 4.75 14.71 10.50 11.29 13.17 11.67 8.96 16.21 20.38 +77 10 17 12.50 14.79 12.54 5.79 12.46 13.29 13.33 9.08 10.21 6.63 12.54 16.17 +77 10 18 13.50 13.70 13.70 6.96 12.17 11.58 12.17 11.17 13.46 9.75 12.29 20.79 +77 10 19 15.34 11.21 13.79 7.87 9.21 9.87 12.04 11.96 12.67 11.46 15.29 17.12 +77 10 20 8.33 10.13 10.04 3.21 6.25 6.58 8.92 8.00 7.67 7.54 10.96 13.17 +77 10 21 14.62 13.37 13.75 8.29 10.88 9.21 11.83 9.92 12.83 9.79 10.63 12.58 +77 10 22 18.25 11.50 15.16 6.13 9.25 7.33 10.58 9.71 9.75 10.37 14.54 18.63 +77 10 23 22.29 20.08 16.00 7.87 14.00 12.50 16.50 14.46 13.70 12.71 21.25 21.59 +77 10 24 15.79 11.21 12.67 7.25 9.67 9.46 14.88 11.92 11.63 10.63 19.41 22.58 +77 10 25 10.67 12.71 8.83 4.08 8.12 8.79 9.79 12.83 8.46 8.92 21.87 18.38 +77 10 26 20.04 18.05 15.25 8.83 13.79 12.42 13.00 14.67 13.96 14.09 28.75 25.84 +77 10 27 11.79 13.96 8.54 6.04 12.17 8.50 13.25 12.29 11.25 11.17 24.67 24.96 +77 10 28 14.25 12.83 11.12 4.79 12.83 9.33 14.92 14.54 13.37 10.37 22.67 23.42 +77 10 29 23.91 18.41 16.42 10.96 14.46 12.08 14.37 16.21 14.50 16.50 25.66 25.41 +77 10 30 27.71 20.08 25.12 15.00 13.54 12.92 18.05 17.41 14.54 16.79 26.67 27.12 +77 10 31 13.25 15.83 9.13 5.79 11.21 8.21 11.75 8.42 9.87 8.42 16.71 18.54 +77 11 1 16.71 11.54 12.17 4.17 8.54 7.17 11.12 6.46 8.25 6.21 11.04 15.63 +77 11 2 11.54 10.92 7.38 3.92 9.00 6.67 8.46 7.87 7.50 6.42 15.16 15.50 +77 11 3 15.59 18.91 12.33 8.75 15.46 9.96 18.29 12.79 12.42 10.92 20.96 27.50 +77 11 4 14.62 14.17 10.54 4.75 10.13 8.38 12.62 9.25 9.13 8.75 14.83 19.79 +77 11 5 20.33 17.75 16.46 9.00 13.50 10.63 15.29 13.17 12.42 12.96 16.96 20.38 +77 11 6 20.38 19.67 15.63 9.17 14.50 12.12 17.46 17.04 13.88 15.25 24.37 28.12 +77 11 7 21.17 17.16 19.33 10.21 14.88 12.21 19.33 9.04 12.83 11.21 16.71 19.38 +77 11 8 16.17 16.83 15.29 6.13 14.50 11.58 16.88 13.79 13.13 12.67 24.71 26.87 +77 11 9 16.66 18.21 15.34 7.62 11.58 10.79 12.12 12.00 10.58 8.75 15.79 15.25 +77 11 10 23.91 23.79 20.04 13.46 15.34 13.96 17.79 22.50 15.71 19.12 33.34 26.38 +77 11 11 30.84 30.34 26.42 18.05 25.80 17.71 24.13 21.29 19.21 20.71 25.84 29.58 +77 11 12 21.12 22.71 14.37 10.92 21.59 12.58 19.29 16.38 14.33 12.83 23.42 30.13 +77 11 13 15.25 13.96 11.54 8.75 14.29 10.96 16.21 13.25 13.54 13.21 18.75 27.75 +77 11 14 27.92 25.25 19.33 18.71 28.46 19.50 28.16 24.00 20.79 22.58 30.91 38.66 +77 11 15 23.21 17.83 13.54 12.50 16.13 11.83 18.79 14.54 13.54 14.21 22.21 32.42 +77 11 16 18.79 18.63 12.62 8.25 15.16 10.54 13.54 12.25 10.67 6.63 21.50 17.41 +77 11 17 22.29 15.63 16.42 8.25 10.92 8.83 13.59 10.04 10.17 10.29 17.37 27.25 +77 11 18 11.34 4.38 11.75 4.17 6.42 4.83 11.38 6.83 7.38 6.96 12.62 20.83 +77 11 19 17.21 18.88 14.54 10.46 17.00 12.00 19.92 14.09 14.67 13.17 22.25 29.29 +77 11 20 20.46 18.66 16.71 8.08 10.41 7.71 13.33 9.83 10.41 8.71 17.16 26.08 +77 11 21 20.38 16.25 18.46 10.00 10.88 9.92 14.17 11.58 12.08 11.63 20.75 31.42 +77 11 22 16.29 10.63 12.21 6.08 8.42 8.00 13.46 9.17 9.83 10.63 14.92 24.21 +77 11 23 18.00 19.87 15.00 11.42 19.38 13.75 23.83 19.70 17.37 16.58 26.38 34.83 +77 11 24 18.46 13.00 14.00 9.87 9.50 9.54 17.08 10.63 10.34 14.79 14.25 31.88 +77 11 25 5.88 4.46 8.46 1.00 1.79 1.96 7.54 2.08 2.13 1.79 2.13 8.12 +77 11 26 9.83 11.63 5.33 0.25 3.92 2.46 4.04 5.46 1.87 3.54 6.92 10.37 +77 11 27 9.75 10.58 8.71 0.83 7.12 4.83 4.00 6.79 3.04 3.92 10.00 10.46 +77 11 28 8.67 10.83 9.25 1.58 2.17 1.71 5.71 3.17 2.13 4.04 4.04 7.71 +77 11 29 9.00 6.75 5.21 0.71 2.08 0.96 1.87 1.38 0.00 0.04 6.13 8.46 +77 11 30 6.63 4.12 8.46 0.46 3.79 1.75 2.13 2.21 0.21 1.29 5.04 8.33 +77 12 1 13.37 10.92 12.42 2.37 5.79 6.13 8.96 7.38 6.29 5.71 8.54 12.42 +77 12 2 21.79 15.12 20.96 10.25 15.96 15.00 20.33 14.54 14.83 14.17 15.34 24.30 +77 12 3 26.20 24.87 24.17 11.25 22.71 20.21 24.08 18.96 17.88 18.38 19.55 29.95 +77 12 4 25.17 23.91 22.04 11.54 22.00 21.34 22.46 21.84 16.21 18.16 22.54 28.12 +77 12 5 16.33 17.04 19.25 9.21 14.29 14.00 20.21 15.34 13.83 15.04 16.38 25.84 +77 12 6 10.63 4.38 15.09 5.75 7.96 8.75 18.34 13.46 12.29 11.04 12.62 31.96 +77 12 7 14.71 11.54 16.58 6.83 11.92 12.58 15.75 17.12 13.70 14.00 16.04 33.45 +77 12 8 6.63 4.54 10.83 3.75 4.58 4.75 9.00 6.42 5.33 4.79 8.12 15.83 +77 12 9 15.63 15.63 15.67 6.79 12.42 11.04 14.17 10.54 11.38 10.71 12.96 19.58 +77 12 10 15.29 14.62 11.71 5.21 10.13 8.21 9.75 11.50 10.21 10.83 18.00 20.00 +77 12 11 20.79 13.96 17.92 7.50 10.25 8.75 12.33 7.92 8.92 7.46 9.50 11.21 +77 12 12 11.71 14.09 11.25 5.71 12.62 8.38 12.33 7.71 9.38 7.29 11.67 12.42 +77 12 13 11.38 13.04 12.38 3.58 8.63 8.75 13.42 13.17 10.37 10.13 16.66 15.04 +77 12 14 10.96 11.42 11.79 2.96 7.00 7.92 13.79 13.96 9.38 11.38 19.67 21.75 +77 12 15 7.75 12.08 6.75 2.21 6.54 4.50 7.58 7.75 6.25 5.54 9.04 12.58 +77 12 16 8.25 9.17 6.79 3.33 7.08 5.46 6.54 3.75 7.21 5.09 7.46 12.67 +77 12 17 5.41 4.83 7.38 1.21 4.00 1.71 2.67 1.25 1.33 0.67 2.29 9.87 +77 12 18 4.92 2.21 5.09 1.13 2.37 2.33 2.71 1.71 3.46 2.50 2.37 7.54 +77 12 19 7.50 7.71 6.25 3.37 9.17 6.71 4.79 4.12 6.71 4.04 5.83 11.29 +77 12 20 13.00 16.33 14.29 7.25 13.79 10.21 11.67 9.83 11.38 6.42 11.67 11.46 +77 12 21 17.62 17.88 18.71 8.67 14.17 14.54 14.17 13.88 11.87 10.50 13.83 24.33 +77 12 22 19.70 14.37 17.00 9.83 11.42 10.25 10.71 9.42 10.83 11.29 13.83 17.62 +77 12 23 19.58 15.16 17.79 9.87 11.58 10.41 12.96 8.17 8.92 7.29 11.42 12.79 +77 12 24 17.79 14.25 14.75 11.67 17.67 12.62 21.75 13.54 15.25 12.96 16.88 23.16 +77 12 25 10.29 8.54 9.33 3.17 8.21 6.46 10.96 8.67 9.62 7.50 13.08 15.34 +77 12 26 11.96 14.58 9.75 4.08 10.54 7.75 11.96 9.87 10.92 7.79 15.12 14.42 +77 12 27 22.08 18.16 15.16 11.04 13.75 10.67 16.08 12.54 11.12 10.54 19.46 26.96 +77 12 28 19.70 12.75 15.63 9.75 10.00 5.83 12.58 9.59 10.00 8.83 14.54 26.83 +77 12 29 14.33 10.41 9.92 7.12 14.21 9.42 15.71 11.75 12.33 10.75 15.50 23.71 +77 12 30 21.75 13.96 12.12 10.88 17.29 11.21 16.83 14.58 13.00 12.00 16.33 26.30 +77 12 31 15.09 7.62 8.79 7.08 10.63 7.58 15.59 11.54 12.25 9.08 14.12 19.55 +78 1 1 8.33 7.12 7.71 3.54 8.50 7.50 14.71 10.00 11.83 10.00 15.09 20.46 +78 1 2 14.62 11.83 10.50 7.41 14.21 9.62 17.08 13.46 13.50 11.67 22.63 27.92 +78 1 3 20.67 17.29 14.54 12.12 18.91 13.54 21.96 16.62 15.09 15.50 20.33 28.04 +78 1 4 12.96 10.67 7.62 6.42 13.88 8.50 8.38 11.83 5.91 3.75 14.29 15.29 +78 1 5 12.12 10.46 11.29 3.37 8.79 7.87 13.88 15.04 12.54 13.17 20.41 23.50 +78 1 6 16.50 13.83 14.21 6.42 9.87 9.54 11.29 12.87 11.12 13.21 20.41 21.62 +78 1 7 6.00 3.04 7.08 2.04 4.71 3.75 8.08 6.87 5.46 6.17 12.08 15.63 +78 1 8 9.21 11.58 6.63 2.04 8.38 7.83 8.12 12.04 7.25 10.46 19.75 20.75 +78 1 9 14.88 17.46 12.12 8.17 13.75 9.08 17.33 11.00 11.71 9.83 18.75 22.63 +78 1 10 16.38 21.54 14.42 9.42 16.83 9.50 16.62 10.29 12.17 10.29 19.46 19.79 +78 1 11 25.96 23.00 23.50 13.37 17.16 13.75 19.41 13.42 14.33 15.12 23.63 34.70 +78 1 12 13.00 8.67 18.66 6.29 6.38 5.29 11.58 4.54 5.91 6.13 9.87 19.79 +78 1 13 7.38 3.04 8.83 1.46 3.88 1.83 9.87 4.63 3.96 3.25 6.04 15.50 +78 1 14 4.67 1.08 8.04 2.79 2.33 0.71 4.17 1.21 1.67 2.37 3.96 10.63 +78 1 15 2.00 3.88 6.67 0.87 4.12 2.29 1.58 6.13 3.54 4.50 9.96 14.42 +78 1 16 14.79 11.92 12.83 5.37 8.50 5.04 7.87 6.38 7.04 5.46 8.75 13.25 +78 1 17 12.83 5.83 7.87 2.75 6.54 3.46 8.33 5.09 4.96 4.88 9.33 12.71 +78 1 18 12.92 10.63 12.42 4.79 11.87 7.67 7.92 8.50 8.04 8.12 13.62 17.00 +78 1 19 14.46 15.09 9.92 5.96 13.42 5.29 10.29 8.54 8.12 5.04 15.79 15.46 +78 1 20 15.00 11.75 10.21 6.46 11.83 7.17 13.79 11.79 10.92 10.08 14.88 19.58 +78 1 21 21.54 19.17 15.54 9.42 16.96 11.50 11.08 15.09 12.62 14.42 20.54 20.88 +78 1 22 15.12 14.62 10.79 7.71 13.88 8.38 15.71 10.08 11.92 10.25 13.29 20.38 +78 1 23 15.75 12.12 14.37 9.29 14.42 10.21 13.50 11.92 13.96 11.50 12.42 18.41 +78 1 24 11.21 5.37 8.17 6.83 7.41 6.58 13.92 8.54 9.75 8.83 12.29 25.75 +78 1 25 8.25 10.25 7.58 2.75 10.29 4.67 8.33 6.46 6.25 5.04 10.79 12.58 +78 1 26 13.08 11.29 10.04 6.67 13.75 7.00 16.50 10.88 12.04 10.96 17.88 26.42 +78 1 27 18.54 9.59 15.67 6.42 10.34 7.04 11.63 9.38 9.46 7.58 11.42 24.87 +78 1 28 35.38 29.88 18.00 15.96 26.92 15.67 15.87 26.34 15.04 17.75 34.42 35.83 +78 1 29 29.38 18.54 28.08 17.12 17.50 13.75 25.54 15.67 18.08 20.50 19.12 38.20 +78 1 30 9.62 8.71 9.59 2.71 7.58 3.54 6.08 6.08 5.33 4.46 10.41 12.83 +78 1 31 10.50 8.79 9.54 4.42 10.58 5.46 8.00 5.71 6.50 6.38 6.54 17.37 +78 2 1 27.25 24.21 18.16 17.46 27.54 18.05 20.96 25.04 20.04 17.50 27.71 21.12 +78 2 2 13.29 9.67 12.54 9.25 11.54 8.12 14.54 10.25 10.75 12.38 10.13 22.34 +78 2 3 7.50 11.25 8.54 2.96 10.92 6.87 8.12 7.41 8.46 4.04 11.75 17.58 +78 2 4 9.21 11.08 7.67 4.67 12.42 7.25 8.75 11.63 9.87 6.92 16.54 16.42 +78 2 5 14.04 14.50 9.25 5.75 14.29 7.00 13.46 9.04 10.08 6.04 15.87 15.63 +78 2 6 8.58 8.46 8.21 2.62 10.00 4.88 9.59 8.33 7.83 4.33 9.96 12.04 +78 2 7 3.42 0.79 6.08 1.29 3.46 0.58 5.50 1.83 2.67 1.46 2.17 7.21 +78 2 8 10.29 6.83 13.17 3.04 9.17 2.79 9.38 8.00 7.38 4.46 4.71 11.29 +78 2 9 13.33 10.25 20.30 7.00 10.41 5.96 15.71 9.13 9.33 6.54 5.37 11.96 +78 2 10 9.21 6.83 12.96 3.92 6.29 2.37 6.79 7.00 3.92 2.83 9.71 10.75 +78 2 11 19.70 11.67 12.38 5.88 11.38 8.63 5.09 12.71 9.08 7.17 10.63 17.88 +78 2 12 13.04 5.96 13.62 5.96 5.29 1.46 9.42 4.38 6.83 7.00 2.62 11.17 +78 2 13 13.79 4.04 9.67 6.83 6.00 5.17 9.08 7.08 6.79 7.87 8.71 22.34 +78 2 14 6.75 5.25 9.17 2.58 3.79 0.50 5.75 3.25 2.79 3.33 3.29 10.37 +78 2 15 21.79 23.09 16.25 6.87 16.58 10.79 11.87 14.46 10.79 8.58 11.96 19.41 +78 2 16 19.55 23.29 15.46 5.54 12.29 9.42 14.46 15.50 11.63 9.25 14.37 20.58 +78 2 17 14.67 17.75 13.50 4.67 10.21 8.29 13.33 13.59 10.04 7.79 12.12 17.79 +78 2 18 27.96 25.46 26.46 14.37 23.83 18.05 23.42 23.21 17.92 15.59 16.83 29.46 +78 2 19 26.34 13.50 28.96 14.71 19.38 18.54 27.71 23.67 22.08 16.38 15.75 31.96 +78 2 20 12.67 5.91 19.67 9.92 9.17 10.54 16.75 13.42 14.17 11.96 9.13 26.75 +78 2 21 13.92 13.67 11.92 4.88 10.54 7.83 10.08 9.33 8.46 5.91 6.63 14.62 +78 2 22 21.17 16.58 19.25 12.67 15.00 12.62 15.37 13.17 14.04 12.29 8.92 21.37 +78 2 23 15.34 15.00 15.83 10.08 12.25 10.88 13.00 12.17 14.42 10.17 9.62 19.79 +78 2 24 18.71 17.08 16.71 12.17 13.88 12.71 14.92 15.50 13.92 13.42 19.04 20.41 +78 2 25 5.54 9.21 10.63 4.54 6.79 5.21 9.87 6.83 8.83 6.08 7.83 16.75 +78 2 26 2.67 6.29 3.50 1.04 3.17 0.42 3.54 2.79 1.17 0.75 2.92 6.83 +78 2 27 16.29 13.88 13.21 5.50 10.25 6.79 6.29 10.25 7.92 6.38 6.83 12.83 +78 2 28 10.00 8.79 13.42 5.04 7.67 8.33 10.54 10.96 10.25 10.21 8.79 17.71 +78 3 1 15.04 6.21 16.04 7.87 6.42 6.67 12.29 8.00 10.58 9.33 5.41 17.00 +78 3 2 5.66 6.96 10.92 4.04 3.88 3.13 6.38 4.79 6.58 4.92 4.17 13.70 +78 3 3 3.96 7.25 5.17 1.71 4.08 1.58 2.29 3.25 1.63 2.17 2.79 7.00 +78 3 4 5.00 5.75 8.33 1.87 1.58 0.29 4.67 2.08 2.88 2.21 2.75 9.71 +78 3 5 12.62 17.37 9.83 5.96 10.92 8.29 6.08 10.71 10.08 7.62 10.71 13.21 +78 3 6 15.00 16.21 13.04 9.71 13.29 10.67 8.58 12.83 12.79 10.34 18.00 21.87 +78 3 7 18.00 17.50 17.12 8.96 12.58 12.75 14.17 13.75 13.42 13.17 21.84 22.00 +78 3 8 8.87 7.29 9.42 5.13 7.79 6.29 10.67 7.71 8.75 8.17 6.50 14.46 +78 3 9 8.54 14.17 11.29 4.17 7.87 7.38 2.79 10.04 8.46 5.13 16.13 15.67 +78 3 10 9.21 14.00 11.75 6.71 9.59 8.83 4.63 9.67 8.42 5.66 12.17 11.38 +78 3 11 11.67 12.21 12.50 5.75 10.13 7.41 3.63 9.04 9.08 6.29 13.04 15.29 +78 3 12 16.08 12.87 13.04 7.33 12.25 10.00 13.13 11.54 11.63 10.17 11.96 16.29 +78 3 13 21.04 19.00 17.75 9.71 12.67 10.71 11.79 13.00 12.92 11.17 14.37 17.83 +78 3 14 23.63 24.30 18.54 15.79 23.71 18.50 21.87 21.34 19.79 16.13 21.29 15.83 +78 3 15 15.63 16.17 12.00 9.62 16.33 10.04 16.08 10.50 12.83 8.83 16.33 19.25 +78 3 16 13.13 14.29 8.08 5.09 8.92 6.29 9.13 8.04 7.29 7.33 11.79 18.96 +78 3 17 9.54 7.00 10.41 3.29 5.21 3.83 6.50 6.54 5.17 4.63 5.13 11.08 +78 3 18 8.04 9.29 10.46 4.54 6.71 6.63 7.83 8.25 8.12 6.00 11.75 15.34 +78 3 19 18.41 18.08 14.54 11.54 17.67 14.37 17.00 18.29 15.63 14.00 23.75 27.16 +78 3 20 19.25 17.41 13.29 11.04 18.54 11.29 13.13 13.59 12.38 7.83 18.21 19.04 +78 3 21 17.54 15.92 12.54 9.17 14.75 10.00 14.29 12.83 11.92 9.50 17.71 22.17 +78 3 22 20.67 20.46 17.92 11.54 20.30 11.71 18.00 12.87 14.42 12.21 16.46 19.29 +78 3 23 19.70 21.46 14.71 12.58 21.54 13.04 20.12 18.58 17.33 13.70 23.29 28.29 +78 3 24 16.88 14.83 11.75 8.42 16.46 9.92 15.46 13.62 14.46 12.21 15.63 21.25 +78 3 25 18.21 19.95 14.37 11.12 18.88 12.29 17.25 13.83 14.83 11.71 19.58 24.71 +78 3 26 15.54 17.62 14.46 8.50 14.21 10.41 15.92 14.29 13.59 10.83 17.37 22.92 +78 3 27 22.88 20.30 19.67 13.67 20.79 15.75 20.17 19.21 18.46 15.16 22.42 27.84 +78 3 28 25.54 22.75 21.00 14.96 19.17 15.29 18.08 17.67 17.83 17.83 19.83 24.83 +78 3 29 17.16 16.66 17.46 10.96 18.46 13.04 20.08 14.21 17.25 15.00 19.00 24.83 +78 3 30 14.75 13.75 12.96 8.08 14.83 9.75 11.46 11.34 11.58 9.00 12.17 16.46 +78 3 31 9.04 5.63 13.00 6.34 6.75 8.00 12.54 10.21 11.25 11.12 9.71 22.29 +78 4 1 3.42 7.58 2.71 1.38 3.46 2.08 2.67 4.75 4.83 1.67 7.33 13.67 +78 4 2 8.21 6.04 3.58 2.29 4.25 2.17 4.71 5.75 5.96 4.71 8.71 14.88 +78 4 3 5.41 2.58 15.25 2.96 5.33 7.54 6.71 8.04 9.59 5.17 9.38 15.87 +78 4 4 8.42 6.67 24.04 5.71 8.33 9.29 9.83 7.17 9.54 5.71 10.96 8.38 +78 4 5 10.21 6.25 26.54 5.96 8.79 8.12 9.50 6.83 9.33 5.37 8.58 10.13 +78 4 6 8.58 6.04 18.38 4.38 6.25 6.42 7.58 5.33 7.04 2.96 6.17 9.46 +78 4 7 6.04 5.46 13.88 3.67 5.46 4.29 6.50 4.21 4.42 2.33 6.42 5.58 +78 4 8 4.75 2.92 10.41 2.00 2.58 1.46 2.04 1.67 1.42 1.75 5.13 6.54 +78 4 9 11.54 11.04 7.79 6.21 9.62 6.08 8.33 8.25 9.33 9.17 15.71 20.91 +78 4 10 17.12 14.17 16.04 8.96 10.96 7.96 11.79 10.46 9.83 10.71 16.79 22.71 +78 4 11 14.21 13.04 10.46 7.04 11.46 7.79 9.50 8.58 9.38 7.00 14.33 14.25 +78 4 12 14.12 13.04 9.25 8.33 10.79 8.25 8.58 7.75 8.29 8.25 11.21 12.87 +78 4 13 23.13 17.75 17.00 13.29 16.88 12.54 14.25 15.46 13.21 12.92 17.25 24.58 +78 4 14 10.04 4.42 9.71 5.46 5.71 4.96 9.46 5.54 6.83 8.71 5.58 12.75 +78 4 15 6.54 10.21 5.46 3.29 6.96 3.71 4.08 3.13 2.83 1.63 9.46 4.96 +78 4 16 15.71 16.83 9.50 6.46 13.29 7.92 5.83 10.96 8.04 5.96 10.29 8.42 +78 4 17 20.88 15.46 18.91 11.92 17.92 16.29 15.16 14.58 16.92 11.96 17.29 15.92 +78 4 18 8.12 8.12 8.12 2.29 9.92 4.04 8.12 4.08 5.37 6.25 6.08 14.92 +78 4 19 15.75 18.88 12.00 7.41 14.29 10.67 11.12 11.87 12.62 11.29 14.17 17.79 +78 4 20 24.41 27.71 19.87 13.92 26.38 14.79 11.25 13.79 13.25 11.00 12.79 18.84 +78 4 21 13.79 13.04 9.87 7.71 15.00 7.75 7.38 5.63 7.17 5.83 8.21 4.33 +78 4 22 13.67 19.38 8.38 6.38 14.83 9.92 9.50 10.21 10.67 9.54 13.79 14.33 +78 4 23 17.33 20.12 10.08 7.38 14.42 8.75 8.42 11.54 11.08 10.08 15.21 14.42 +78 4 24 15.12 11.92 11.63 7.83 12.54 11.42 9.50 15.04 12.67 11.21 15.46 18.21 +78 4 25 13.25 11.04 14.83 4.58 10.46 9.21 13.00 11.96 11.25 9.00 18.05 17.67 +78 4 26 8.38 12.58 14.17 4.46 8.12 4.75 8.54 9.04 6.87 3.71 13.42 10.83 +78 4 27 5.66 4.63 6.34 3.00 4.50 2.92 6.50 4.00 4.79 4.79 7.58 16.42 +78 4 28 6.29 7.62 3.58 2.21 6.71 2.25 3.33 4.50 3.17 2.25 6.17 12.17 +78 4 29 9.96 9.38 16.17 3.67 8.29 3.88 7.25 6.25 6.71 5.25 9.62 15.83 +78 4 30 14.09 13.75 21.54 7.46 13.04 11.83 17.16 12.83 13.70 13.79 14.58 26.92 +78 5 1 10.54 12.21 9.08 5.29 11.00 10.08 11.17 13.75 11.87 11.79 12.87 27.16 +78 5 2 13.83 11.87 7.92 4.08 10.41 7.25 9.62 11.83 9.33 11.04 13.13 23.21 +78 5 3 10.37 9.46 9.08 4.04 9.46 6.04 3.96 6.04 7.50 5.75 10.08 13.96 +78 5 4 4.67 9.71 5.41 2.46 7.67 4.83 5.96 7.58 5.33 7.67 11.38 10.67 +78 5 5 14.67 10.00 9.96 7.17 9.42 5.37 8.79 7.17 8.17 8.87 10.63 10.41 +78 5 6 10.46 5.75 8.33 3.83 4.46 3.29 5.41 2.92 3.96 3.79 3.29 5.54 +78 5 7 7.33 8.54 5.58 3.42 6.50 4.21 3.67 5.13 4.96 4.63 6.58 14.92 +78 5 8 9.00 9.50 7.54 3.96 8.67 7.08 6.34 8.12 7.83 6.83 8.54 16.50 +78 5 9 4.96 3.29 13.62 3.29 5.63 4.21 4.79 4.38 6.25 4.42 5.17 5.58 +78 5 10 5.09 4.29 4.79 1.87 3.71 3.17 5.04 5.75 4.58 3.37 12.50 5.96 +78 5 11 11.50 7.33 8.67 6.67 10.88 6.92 10.88 9.54 9.87 9.17 14.46 15.12 +78 5 12 24.46 15.21 14.29 11.67 17.54 10.54 13.79 13.83 12.38 12.17 17.58 21.37 +78 5 13 16.21 11.38 9.96 8.25 13.13 9.17 10.08 10.08 9.38 11.08 12.87 15.04 +78 5 14 14.21 10.25 6.34 6.58 13.08 6.29 4.58 8.96 6.50 6.17 10.75 4.83 +78 5 15 6.34 4.63 4.83 1.04 4.96 1.17 2.37 2.92 2.42 2.08 7.58 3.88 +78 5 16 4.29 2.33 4.54 1.92 2.71 1.21 3.33 2.33 1.38 2.92 5.21 4.25 +78 5 17 4.75 4.12 6.79 2.29 6.08 2.58 3.88 5.71 4.75 5.54 5.75 7.33 +78 5 18 4.88 2.83 8.67 1.63 3.25 2.79 3.17 2.71 2.96 1.42 5.41 5.25 +78 5 19 4.25 1.63 5.88 1.75 3.58 2.50 3.04 5.25 5.54 4.21 5.91 6.21 +78 5 20 2.83 2.08 4.29 1.54 2.83 1.13 2.29 3.17 2.79 3.46 4.12 12.29 +78 5 21 2.42 3.54 7.46 1.46 2.75 0.54 3.04 1.67 3.42 4.50 4.33 11.58 +78 5 22 7.54 6.54 6.17 2.79 2.42 2.46 2.92 2.96 3.04 2.88 8.00 5.46 +78 5 23 11.87 15.50 7.92 5.17 8.96 4.58 8.50 7.00 7.67 8.04 14.62 14.67 +78 5 24 16.13 9.96 9.00 8.08 9.17 7.12 8.29 7.54 8.21 10.17 10.34 12.71 +78 5 25 3.92 3.37 5.04 1.08 5.88 3.04 3.92 6.71 3.46 4.46 11.83 11.50 +78 5 26 4.50 3.25 3.50 1.21 5.13 1.58 0.83 3.17 3.17 2.29 9.29 8.71 +78 5 27 3.46 2.37 4.04 1.46 1.75 0.04 1.67 1.29 1.00 0.96 6.29 5.63 +78 5 28 2.62 6.67 3.96 1.96 3.25 3.29 2.13 4.04 2.75 3.63 8.83 4.04 +78 5 29 3.25 7.33 3.50 3.08 5.04 3.17 4.88 4.46 4.96 6.08 11.87 6.42 +78 5 30 6.87 6.71 3.54 2.54 7.25 4.12 4.88 5.00 4.63 4.83 6.58 8.04 +78 5 31 9.17 12.38 4.25 4.38 9.62 8.17 4.83 9.04 7.96 9.50 11.38 15.21 +78 6 1 10.37 11.42 6.46 6.04 11.25 7.50 6.46 5.96 7.79 5.46 5.50 10.41 +78 6 2 7.75 10.83 7.33 4.12 10.34 6.08 2.79 4.88 6.50 4.96 9.17 6.42 +78 6 3 10.71 8.83 6.63 3.79 9.59 5.83 4.54 6.25 6.79 6.29 6.79 11.46 +78 6 4 16.83 13.54 9.96 7.62 12.96 10.00 5.63 9.54 9.79 10.00 9.71 16.33 +78 6 5 21.54 12.12 13.83 10.71 16.13 11.54 11.75 12.29 13.54 13.96 12.38 17.12 +78 6 6 10.04 9.92 9.21 5.17 13.25 9.96 9.29 9.75 10.58 8.58 14.25 16.08 +78 6 7 10.37 9.87 8.96 5.58 11.79 8.12 7.12 9.38 7.46 7.00 11.54 16.25 +78 6 8 7.50 7.83 5.75 4.00 7.04 4.46 6.34 6.13 6.04 8.54 11.21 19.46 +78 6 9 13.46 9.59 9.67 6.29 10.08 8.17 9.50 8.38 9.42 10.83 13.13 21.17 +78 6 10 12.54 9.17 10.04 6.17 9.96 8.46 8.71 7.41 9.67 10.13 10.29 17.54 +78 6 11 8.71 5.58 8.96 3.88 6.21 5.50 8.04 4.25 7.50 8.46 8.63 16.83 +78 6 12 7.00 6.63 13.54 6.00 7.41 7.25 6.58 7.67 7.79 8.12 10.58 15.25 +78 6 13 7.00 5.04 8.00 3.04 6.71 4.46 5.04 3.79 4.46 2.50 8.46 11.12 +78 6 14 5.17 8.21 7.21 4.42 9.04 6.96 4.75 6.92 7.75 5.33 12.21 14.79 +78 6 15 11.54 10.13 7.46 5.33 10.21 6.79 4.08 7.83 6.71 6.67 9.54 13.83 +78 6 16 15.16 15.41 25.70 11.25 11.04 10.75 16.50 11.79 11.63 11.29 16.00 19.17 +78 6 17 12.83 11.34 28.58 10.37 8.00 7.87 12.42 8.92 7.12 7.17 15.25 8.29 +78 6 18 7.17 4.63 8.46 4.79 7.54 4.21 1.71 5.33 3.92 2.50 7.75 8.25 +78 6 19 13.50 7.58 6.63 4.21 8.29 4.25 7.41 4.75 6.54 5.17 10.88 15.16 +78 6 20 9.96 7.96 7.75 3.46 7.50 5.09 4.54 8.46 5.88 5.88 13.00 15.75 +78 6 21 10.04 8.21 10.71 5.33 10.37 5.46 3.04 4.96 5.09 3.63 7.71 10.25 +78 6 22 8.46 9.25 6.92 2.37 6.96 2.96 6.21 5.00 5.50 3.54 9.79 11.92 +78 6 23 17.58 15.25 11.17 9.29 13.29 11.50 10.34 11.21 10.13 12.21 19.29 26.96 +78 6 24 17.58 12.38 10.13 10.41 16.33 11.00 12.67 11.83 12.25 10.83 17.00 18.71 +78 6 25 11.83 11.08 9.08 6.08 9.87 7.71 9.33 8.92 9.67 8.33 13.54 18.00 +78 6 26 13.92 8.17 11.21 7.38 11.83 8.46 9.42 7.00 9.04 8.63 10.67 14.54 +78 6 27 10.25 7.71 10.04 5.54 11.12 8.46 10.79 9.83 8.96 7.62 14.09 17.00 +78 6 28 9.08 8.50 9.54 6.54 12.50 9.08 14.00 7.79 11.34 7.33 10.79 15.92 +78 6 29 13.88 10.34 9.25 6.50 15.25 8.96 9.67 9.13 8.71 7.96 13.42 13.96 +78 6 30 9.83 7.71 9.42 5.91 10.00 7.87 9.17 8.71 9.04 7.58 12.46 14.42 +78 7 1 12.46 10.63 11.17 6.75 12.92 9.04 12.42 9.62 12.08 8.04 14.04 16.17 +78 7 2 16.04 14.42 12.75 10.46 15.79 11.17 11.92 10.34 10.79 5.33 10.41 15.00 +78 7 3 25.37 17.04 14.09 11.79 19.12 13.92 13.70 14.75 13.67 11.58 20.12 21.59 +78 7 4 25.84 17.41 17.71 14.83 16.25 14.83 13.70 15.50 14.71 17.58 19.62 28.25 +78 7 5 25.04 18.08 13.88 12.08 13.70 12.67 12.38 12.83 12.79 17.83 17.79 24.08 +78 7 6 18.21 10.71 13.08 9.83 10.54 10.17 11.79 11.25 11.54 13.92 12.54 19.00 +78 7 7 11.87 6.92 9.59 5.46 11.83 8.08 11.92 9.33 10.41 8.42 11.75 16.75 +78 7 8 11.50 10.71 9.29 6.63 13.50 9.38 11.67 8.58 9.71 6.92 11.38 11.42 +78 7 9 8.54 7.54 6.25 3.92 8.96 6.29 4.42 6.29 6.08 1.33 10.08 6.42 +78 7 10 4.12 4.29 4.58 1.96 3.88 3.29 3.88 2.83 3.17 2.79 4.71 5.83 +78 7 11 11.21 4.00 3.92 2.46 7.08 2.83 2.83 5.79 6.58 4.25 5.21 6.71 +78 7 12 6.67 2.17 3.63 0.75 3.58 1.13 1.63 1.33 1.50 1.50 4.17 2.13 +78 7 13 3.13 3.96 5.17 1.33 2.67 0.96 0.83 2.75 0.54 1.50 10.13 7.75 +78 7 14 6.25 2.62 8.00 3.13 4.96 3.46 3.17 4.58 4.12 4.17 11.63 9.75 +78 7 15 5.09 3.46 6.21 3.08 6.04 4.21 2.79 4.92 4.54 5.50 8.29 12.25 +78 7 16 4.71 2.21 6.00 3.04 4.12 2.54 2.33 2.58 3.33 2.79 6.25 8.63 +78 7 17 6.38 6.25 7.17 3.29 4.21 3.58 1.83 4.71 3.04 3.88 5.63 9.67 +78 7 18 15.00 8.00 7.62 5.29 11.38 6.34 7.17 8.00 8.83 8.33 11.21 12.21 +78 7 19 17.12 9.25 9.33 8.42 13.88 10.13 11.87 11.42 10.88 10.37 13.59 18.16 +78 7 20 13.83 6.63 6.83 7.71 10.13 7.75 7.58 5.66 7.54 7.04 8.58 13.08 +78 7 21 9.71 10.54 8.67 3.96 7.50 6.17 4.67 7.83 6.87 5.33 15.59 8.50 +78 7 22 19.58 17.88 17.08 10.29 15.37 13.88 13.83 17.12 14.09 14.04 24.17 21.96 +78 7 23 13.96 11.50 12.71 7.08 12.83 10.13 10.08 10.37 11.42 9.83 18.16 19.92 +78 7 24 12.67 10.08 10.54 5.71 12.92 9.08 9.33 8.58 10.41 8.12 15.46 19.29 +78 7 25 12.25 12.33 15.29 7.58 10.17 8.50 8.42 8.79 9.87 9.08 16.92 17.04 +78 7 26 13.13 13.70 12.21 5.46 9.46 7.79 7.04 9.33 8.42 5.46 14.29 9.96 +78 7 27 14.96 14.62 15.63 9.25 13.29 12.46 10.50 12.87 13.67 13.17 20.12 20.38 +78 7 28 10.83 13.59 9.79 6.54 11.08 9.92 9.92 11.96 10.67 9.67 19.55 17.08 +78 7 29 8.00 7.87 6.58 5.75 7.38 7.12 4.08 5.79 6.13 6.17 11.63 10.63 +78 7 30 13.37 10.79 7.62 6.13 5.13 6.08 2.92 6.50 5.96 5.37 8.38 7.71 +78 7 31 20.58 13.08 14.83 11.04 12.04 9.54 12.17 10.04 10.88 10.41 10.71 19.08 +78 8 1 19.33 15.09 20.17 8.83 12.62 10.41 9.33 12.33 9.50 9.92 15.75 18.00 +78 8 2 11.83 8.71 10.71 5.41 7.62 4.54 4.50 5.13 4.79 1.50 10.25 5.17 +78 8 3 10.46 5.58 6.34 4.75 8.87 6.29 9.62 6.17 7.54 5.58 8.38 10.67 +78 8 4 5.25 5.58 6.46 2.13 3.29 2.25 5.54 0.83 4.42 2.37 5.46 12.71 +78 8 5 10.50 9.92 7.50 1.87 7.92 2.79 1.38 1.29 2.71 0.79 5.66 3.92 +78 8 6 9.38 8.46 8.08 4.17 8.71 5.17 6.92 2.08 6.58 4.17 7.38 10.29 +78 8 7 13.04 9.87 8.29 4.46 7.17 5.54 5.96 5.41 6.71 5.54 9.83 14.54 +78 8 8 13.54 7.46 6.21 6.58 9.79 8.63 7.75 6.79 7.71 6.63 10.25 15.59 +78 8 9 7.38 3.79 5.58 3.37 4.50 4.50 3.17 1.87 5.17 3.42 5.46 7.12 +78 8 10 5.63 13.13 4.54 3.21 7.96 5.63 3.08 5.09 5.79 3.25 10.63 5.91 +78 8 11 13.50 13.96 11.00 8.12 11.63 9.21 6.42 8.63 12.75 10.29 14.17 15.79 +78 8 12 5.88 6.87 7.25 3.79 7.62 6.29 5.54 4.04 5.29 3.83 7.96 10.75 +78 8 13 11.83 12.12 10.58 4.00 8.04 7.12 6.25 9.92 9.21 6.67 16.54 13.79 +78 8 14 14.09 11.54 12.79 5.17 7.79 6.25 7.50 7.21 8.21 5.83 11.67 9.50 +78 8 15 14.37 12.08 11.96 5.83 11.54 8.79 8.54 6.63 9.29 5.91 9.29 10.17 +78 8 16 10.88 6.25 8.12 6.13 10.58 8.87 10.83 8.25 9.92 9.21 12.75 16.17 +78 8 17 8.00 12.92 7.71 3.79 9.17 6.96 4.79 7.58 8.46 5.46 17.58 13.46 +78 8 18 17.16 18.25 13.92 12.00 16.75 12.58 9.92 13.33 15.87 15.09 21.79 27.25 +78 8 19 5.41 6.04 10.41 3.00 5.04 5.29 3.88 5.58 7.50 6.71 14.96 9.25 +78 8 20 7.79 11.58 8.63 3.96 8.50 7.50 5.41 11.04 9.46 7.38 16.96 15.04 +78 8 21 14.92 14.42 15.00 7.96 13.00 10.79 12.38 11.42 13.46 9.96 9.29 7.41 +78 8 22 10.04 9.08 10.00 4.67 8.92 5.54 4.33 3.54 7.21 4.71 6.92 12.96 +78 8 23 3.04 2.13 6.42 1.42 2.58 1.21 1.25 1.38 5.00 3.50 6.67 11.42 +78 8 24 3.88 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12.96 3.83 13.79 4.79 7.12 6.54 11.67 9.25 8.67 9.00 11.25 20.30 +78 12 22 6.21 7.38 13.08 2.54 7.58 5.33 2.46 8.38 5.09 5.04 9.92 11.00 +78 12 23 16.62 13.29 22.21 9.50 14.29 13.08 16.50 17.16 12.71 12.00 18.50 21.50 +78 12 24 8.67 5.63 12.12 4.79 5.09 5.91 12.25 9.25 10.83 11.71 11.92 31.71 +78 12 25 7.21 6.58 7.83 2.67 4.79 4.58 8.71 0.75 5.21 5.25 1.21 13.96 +78 12 26 13.83 11.87 10.34 2.37 6.96 4.29 1.96 3.79 3.04 3.08 4.79 11.96 +78 12 27 17.58 16.96 17.62 8.08 13.21 11.67 14.46 15.59 14.04 14.00 17.21 40.08 +78 12 28 13.21 5.46 13.46 5.00 8.12 9.42 14.33 16.25 15.25 18.05 21.79 41.46 +78 12 29 14.00 10.29 14.42 8.71 9.71 10.54 19.17 12.46 14.50 16.42 18.88 29.58 +78 12 30 18.50 14.04 21.29 9.13 12.75 9.71 18.08 12.87 12.46 12.12 14.67 28.79 +78 12 31 20.33 17.41 27.29 9.59 12.08 10.13 19.25 11.63 11.58 11.38 12.08 22.08 diff --git a/200 solved problems in Python/pandas/06_Stats/Wind_Stats/wind.desc b/200 solved problems in Python/pandas/06_Stats/Wind_Stats/wind.desc new file mode 100644 index 0000000000000000000000000000000000000000..c26f90c12e43653f575c6ca960e14c31e98614ae --- /dev/null +++ b/200 solved problems in Python/pandas/06_Stats/Wind_Stats/wind.desc @@ -0,0 +1,23 @@ +wind daily average wind speeds for 1961-1978 at 12 synoptic meteorological + stations in the Republic of Ireland (Haslett and raftery 1989). + +These data were analyzed in detail in the following article: + Haslett, J. and Raftery, A. E. (1989). Space-time Modelling with + Long-memory Dependence: Assessing Ireland's Wind Power Resource + (with Discussion). Applied Statistics 38, 1-50. + +Each line corresponds to one day of data in the following format: +year, month, day, average wind speed at each of the stations in the order given +in Fig.4 of Haslett and Raftery : + RPT, VAL, ROS, KIL, SHA, BIR, DUB, CLA, MUL, CLO, BEL, MAL + +Fortan format : ( i2, 2i3, 12f6.2) + +The data are in knots, not in m/s. + +Permission granted for unlimited distribution. + +Please report all anomalies to fraley@stat.washington.edu + +Be aware that the dataset is 532494 bytes long (thats over half a +Megabyte). Please be sure you want the data before you request it. diff --git a/200 solved problems in Python/pandas/07_Visualization/Chipotle/Exercise_with_Solutions.ipynb b/200 solved problems in Python/pandas/07_Visualization/Chipotle/Exercise_with_Solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..39a64b0cbaf27fad1d435b57e8fedf6a10e394e5 --- /dev/null +++ b/200 solved problems in Python/pandas/07_Visualization/Chipotle/Exercise_with_Solutions.ipynb @@ -0,0 +1,355 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Visualizing Chipotle's Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This time we are going to pull data directly from the internet.\n", + "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import collections\n", + "import matplotlib.pyplot as plt \n", + "\n", + "# set this so the \n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called chipo." + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "url = 'https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv'\n", + " \n", + "chipo = pd.read_csv(url, sep = '\\t')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. See the first 10 entries" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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order_idquantityitem_namechoice_descriptionitem_price
011Chips and Fresh Tomato SalsaNaN$2.39
111Izze[Clementine]$3.39
211Nantucket Nectar[Apple]$3.39
311Chips and Tomatillo-Green Chili SalsaNaN$2.39
422Chicken Bowl[Tomatillo-Red Chili Salsa (Hot), [Black Beans...$16.98
531Chicken Bowl[Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...$10.98
631Side of ChipsNaN$1.69
741Steak Burrito[Tomatillo Red Chili Salsa, [Fajita Vegetables...$11.75
841Steak Soft Tacos[Tomatillo Green Chili Salsa, [Pinto Beans, Ch...$9.25
951Steak Burrito[Fresh Tomato Salsa, [Rice, Black Beans, Pinto...$9.25
\n", + "
" + ], + "text/plain": [ + " order_id quantity item_name \\\n", + "0 1 1 Chips and Fresh Tomato Salsa \n", + "1 1 1 Izze \n", + "2 1 1 Nantucket Nectar \n", + "3 1 1 Chips and Tomatillo-Green Chili Salsa \n", + "4 2 2 Chicken Bowl \n", + "5 3 1 Chicken Bowl \n", + "6 3 1 Side of Chips \n", + "7 4 1 Steak Burrito \n", + "8 4 1 Steak Soft Tacos \n", + "9 5 1 Steak Burrito \n", + "\n", + " choice_description item_price \n", + "0 NaN $2.39 \n", + "1 [Clementine] $3.39 \n", + "2 [Apple] $3.39 \n", + "3 NaN $2.39 \n", + "4 [Tomatillo-Red Chili Salsa (Hot), [Black Beans... $16.98 \n", + "5 [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou... $10.98 \n", + "6 NaN $1.69 \n", + "7 [Tomatillo Red Chili Salsa, [Fajita Vegetables... $11.75 \n", + "8 [Tomatillo Green Chili Salsa, [Pinto Beans, Ch... $9.25 \n", + "9 [Fresh Tomato Salsa, [Rice, Black Beans, Pinto... $9.25 " + ] + }, + "execution_count": 134, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chipo.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Create a histogram of the top 5 items bought" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# get the Series of the names\n", + "x = chipo.item_name\n", + "\n", + "# use the Counter class from collections to create a dictionary with keys(text) and frequency\n", + "letter_counts = Counter(x)\n", + "\n", + "# convert the dictionary to a DataFrame\n", + "df = pd.DataFrame.from_dict(letter_counts, orient='index')\n", + "\n", + "# sort the values from the top to the least value and slice the first 5 items\n", + "df = df[0].sort_values(ascending = True)[45:50]\n", + "\n", + "# create the plot\n", + "df.plot(kind='bar')\n", + "\n", + "# Set the title and labels\n", + "plt.xlabel('Items')\n", + "plt.ylabel('Price')\n", + "plt.title('Most ordered Chipotle\\'s Items')\n", + "\n", + "# show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Create a scatterplot with the number of items orderered per order price\n", + "#### Hint: Price should be in the X-axis and Items ordered in the Y-axis" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0, 40.0)" + ] + }, + "execution_count": 136, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# create a list of prices\n", + "chipo.item_price = [float(value[1:-1]) for value in chipo.item_price] # strip the dollar sign and trailing space\n", + "\n", + "# then groupby the orders and sum\n", + "orders = chipo.groupby('order_id').sum()\n", + "\n", + "# creates the scatterplot\n", + "# plt.scatter(orders.quantity, orders.item_price, s = 50, c = 'green')\n", + "plt.scatter(x = orders.item_price, y = orders.quantity, s = 50, c = 'green')\n", + "\n", + "# Set the title and labels\n", + "plt.xlabel('Order Price')\n", + "plt.ylabel('Items ordered')\n", + "plt.title('Number of items ordered per order price')\n", + "plt.ylim(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BONUS: Create a question and a graph to answer your own question." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/07_Visualization/Chipotle/Exercises.ipynb b/200 solved problems in Python/pandas/07_Visualization/Chipotle/Exercises.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..43f4d72885e2a9870800d6695b99fee2fe31a143 --- /dev/null +++ b/200 solved problems in Python/pandas/07_Visualization/Chipotle/Exercises.ipynb @@ -0,0 +1,147 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Visualizing Chipotle's Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This time we are going to pull data directly from the internet.\n", + "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import collections\n", + "import matplotlib.pyplot as plt \n", + "\n", + "# set this so the graphs open internally\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called chipo." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. See the first 10 entries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Create a histogram of the top 5 items bought" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Create a scatterplot with the number of items orderered per order price\n", + "#### Hint: Price should be in the X-axis and Items ordered in the Y-axis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. BONUS: Create a question and a graph to answer your own question." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/07_Visualization/Chipotle/Solutions.ipynb b/200 solved problems in Python/pandas/07_Visualization/Chipotle/Solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..ba39d5c9063742500d4f80f82debc7077325d82b --- /dev/null +++ b/200 solved problems in Python/pandas/07_Visualization/Chipotle/Solutions.ipynb @@ -0,0 +1,310 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Visualizing Chipotle's Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This time we are going to pull data directly from the internet.\n", + "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import collections\n", + "import matplotlib.pyplot as plt \n", + "\n", + "# set this so the \n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called chipo." + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. See the first 10 entries" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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order_idquantityitem_namechoice_descriptionitem_price
011Chips and Fresh Tomato SalsaNaN$2.39
111Izze[Clementine]$3.39
211Nantucket Nectar[Apple]$3.39
311Chips and Tomatillo-Green Chili SalsaNaN$2.39
422Chicken Bowl[Tomatillo-Red Chili Salsa (Hot), [Black Beans...$16.98
531Chicken Bowl[Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...$10.98
631Side of ChipsNaN$1.69
741Steak Burrito[Tomatillo Red Chili Salsa, [Fajita Vegetables...$11.75
841Steak Soft Tacos[Tomatillo Green Chili Salsa, [Pinto Beans, Ch...$9.25
951Steak Burrito[Fresh Tomato Salsa, [Rice, Black Beans, Pinto...$9.25
\n", + "
" + ], + "text/plain": [ + " order_id quantity item_name \\\n", + "0 1 1 Chips and Fresh Tomato Salsa \n", + "1 1 1 Izze \n", + "2 1 1 Nantucket Nectar \n", + "3 1 1 Chips and Tomatillo-Green Chili Salsa \n", + "4 2 2 Chicken Bowl \n", + "5 3 1 Chicken Bowl \n", + "6 3 1 Side of Chips \n", + "7 4 1 Steak Burrito \n", + "8 4 1 Steak Soft Tacos \n", + "9 5 1 Steak Burrito \n", + "\n", + " choice_description item_price \n", + "0 NaN $2.39 \n", + "1 [Clementine] $3.39 \n", + "2 [Apple] $3.39 \n", + "3 NaN $2.39 \n", + "4 [Tomatillo-Red Chili Salsa (Hot), [Black Beans... $16.98 \n", + "5 [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou... $10.98 \n", + "6 NaN $1.69 \n", + "7 [Tomatillo Red Chili Salsa, [Fajita Vegetables... $11.75 \n", + "8 [Tomatillo Green Chili Salsa, [Pinto Beans, Ch... $9.25 \n", + "9 [Fresh Tomato Salsa, [Rice, Black Beans, Pinto... $9.25 " + ] + }, + "execution_count": 134, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Create a histogram of the top 5 items bought" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Create a scatterplot with the number of items orderered per order price\n", + "#### Hint: Price should be in the X-axis and Items ordered in the Y-axis" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0, 40.0)" + ] + }, + "execution_count": 136, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. BONUS: Create a question and a graph to answer your own question." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/07_Visualization/Online_Retail/Exercises.ipynb b/200 solved problems in Python/pandas/07_Visualization/Online_Retail/Exercises.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..cba1cdf7d35dfeca8648d7d4908d488381c95951 --- /dev/null +++ b/200 solved problems in Python/pandas/07_Visualization/Online_Retail/Exercises.ipynb @@ -0,0 +1,139 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Online Retails Purchase" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/Visualization/Online_Retail/Online_Retail.csv). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called online_rt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Create a histogram with the 10 countries that have the most 'Quantity' ordered except UK" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Exclude negative Quatity entries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Create a scatterplot with the Quantity per UnitPrice by CustomerID for the top 3 Countries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BONUS: Create your own question and answer it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/07_Visualization/Online_Retail/Exercises_with_solutions_code.ipynb b/200 solved problems in Python/pandas/07_Visualization/Online_Retail/Exercises_with_solutions_code.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..b2f0d538ca9c625f89ff8f1f547909b32a9532e6 --- /dev/null +++ b/200 solved problems in Python/pandas/07_Visualization/Online_Retail/Exercises_with_solutions_code.ipynb @@ -0,0 +1,423 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Online Retails Purchase" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# set the graphs to show in the jupyter notebook\n", + "%matplotlib inline\n", + "\n", + "# set seabor graphs to a better style\n", + "sns.set(style=\"ticks\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/Visualization/Online_Retail/Online_Retail.csv). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called online_rt" + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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InvoiceNoStockCodeDescriptionQuantityInvoiceDateUnitPriceCustomerIDCountry
053636585123AWHITE HANGING HEART T-LIGHT HOLDER612/1/10 8:262.5517850.0United Kingdom
153636571053WHITE METAL LANTERN612/1/10 8:263.3917850.0United Kingdom
253636584406BCREAM CUPID HEARTS COAT HANGER812/1/10 8:262.7517850.0United Kingdom
353636584029GKNITTED UNION FLAG HOT WATER BOTTLE612/1/10 8:263.3917850.0United Kingdom
453636584029ERED WOOLLY HOTTIE WHITE HEART.612/1/10 8:263.3917850.0United Kingdom
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" + ], + "text/plain": [ + " InvoiceNo StockCode Description Quantity \\\n", + "0 536365 85123A WHITE HANGING HEART T-LIGHT HOLDER 6 \n", + "1 536365 71053 WHITE METAL LANTERN 6 \n", + "2 536365 84406B CREAM CUPID HEARTS COAT HANGER 8 \n", + "3 536365 84029G KNITTED UNION FLAG HOT WATER BOTTLE 6 \n", + "4 536365 84029E RED WOOLLY HOTTIE WHITE HEART. 6 \n", + "\n", + " InvoiceDate UnitPrice CustomerID Country \n", + "0 12/1/10 8:26 2.55 17850.0 United Kingdom \n", + "1 12/1/10 8:26 3.39 17850.0 United Kingdom \n", + "2 12/1/10 8:26 2.75 17850.0 United Kingdom \n", + "3 12/1/10 8:26 3.39 17850.0 United Kingdom \n", + "4 12/1/10 8:26 3.39 17850.0 United Kingdom " + ] + }, + "execution_count": 198, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "url = 'https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/Visualization/Online_Retail/Online_Retail.csv'\n", + "online_rt = pd.read_csv(url)\n", + "\n", + "online_rt.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Create a histogram with the 10 countries that have the most 'Quantity' ordered except UK" + ] + }, + { + "cell_type": "code", + "execution_count": 199, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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TUy8BiYwRF4kiouriuUVh0qRJmDhxIrp37y4cgvjjjz/w5ptvIiEhQS8BiYiI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\naYOeYzF9+nRcvnwZAQEBmDp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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# group by the Country\n", + "countries = online_rt.groupby('Country').sum()\n", + "\n", + "# sort the value and get the first 10 after UK\n", + "countries = countries.sort_values(by = 'Quantity',ascending = False)[1:11]\n", + "\n", + "# create the plot\n", + "countries['Quantity'].plot(kind='bar')\n", + "\n", + "# Set the title and labels\n", + "plt.xlabel('Countries')\n", + "plt.ylabel('Quantity')\n", + "plt.title('10 Countries with more orders')\n", + "\n", + "# show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Exclude negative Quatity entries" + ] + }, + { + "cell_type": "code", + "execution_count": 200, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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InvoiceNoStockCodeDescriptionQuantityInvoiceDateUnitPriceCustomerIDCountry
053636585123AWHITE HANGING HEART T-LIGHT HOLDER612/1/10 8:262.5517850.0United Kingdom
153636571053WHITE METAL LANTERN612/1/10 8:263.3917850.0United Kingdom
253636584406BCREAM CUPID HEARTS COAT HANGER812/1/10 8:262.7517850.0United Kingdom
353636584029GKNITTED UNION FLAG HOT WATER BOTTLE612/1/10 8:263.3917850.0United Kingdom
453636584029ERED WOOLLY HOTTIE WHITE HEART.612/1/10 8:263.3917850.0United Kingdom
\n", + "
" + ], + "text/plain": [ + " InvoiceNo StockCode Description Quantity \\\n", + "0 536365 85123A WHITE HANGING HEART T-LIGHT HOLDER 6 \n", + "1 536365 71053 WHITE METAL LANTERN 6 \n", + "2 536365 84406B CREAM CUPID HEARTS COAT HANGER 8 \n", + "3 536365 84029G KNITTED UNION FLAG HOT WATER BOTTLE 6 \n", + "4 536365 84029E RED WOOLLY HOTTIE WHITE HEART. 6 \n", + "\n", + " InvoiceDate UnitPrice CustomerID Country \n", + "0 12/1/10 8:26 2.55 17850.0 United Kingdom \n", + "1 12/1/10 8:26 3.39 17850.0 United Kingdom \n", + "2 12/1/10 8:26 2.75 17850.0 United Kingdom \n", + "3 12/1/10 8:26 3.39 17850.0 United Kingdom \n", + "4 12/1/10 8:26 3.39 17850.0 United Kingdom " + ] + }, + "execution_count": 200, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "online_rt = online_rt[online_rt.Quantity > 0]\n", + "online_rt.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Create a scatterplot with the Quantity per UnitPrice by CustomerID for the top 3 Countries" + ] + }, + { + "cell_type": "code", + "execution_count": 201, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# groupby CustomerID\n", + "customers = online_rt.groupby(['CustomerID','Country']).sum()\n", + "\n", + "# there is an outlier with negative price\n", + "customers = customers[customers.UnitPrice > 0]\n", + "\n", + "# get the value of the index and put in the column Country\n", + "customers['Country'] = customers.index.get_level_values(1)\n", + "\n", + "# top three countries\n", + "top_countries = ['Netherlands', 'EIRE', 'Germany']\n", + "\n", + "# filter the dataframe to just select ones in the top_countries\n", + "customers = customers[customers['Country'].isin(top_countries)]\n", + "\n", + "################\n", + "# Grap Section #\n", + "################\n", + "\n", + "# creates the FaceGrid\n", + "g = sns.FacetGrid(customers, col=\"Country\")\n", + "\n", + "# map over a make a scatterplot\n", + "g.map(plt.scatter, \"Quantity\", \"UnitPrice\", alpha=1)\n", + "\n", + "# adds legend\n", + "g.add_legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BONUS: Create your own question and answer it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/07_Visualization/Online_Retail/Online_Retail.csv b/200 solved problems in Python/pandas/07_Visualization/Online_Retail/Online_Retail.csv new file mode 100644 index 0000000000000000000000000000000000000000..c5efb8c98069d0acb858e91636973b361f0c1651 --- /dev/null +++ b/200 solved problems in Python/pandas/07_Visualization/Online_Retail/Online_Retail.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5c1b5517919301b1da060b3dc486614f487da43515a9b2a52709e2b04d5da575 +size 43954909 diff --git a/200 solved problems in Python/pandas/07_Visualization/Online_Retail/Solutions.ipynb b/200 solved problems in Python/pandas/07_Visualization/Online_Retail/Solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..2464f943aa3284b4eb5feb7a7feae3452d4790a7 --- /dev/null +++ b/200 solved problems in Python/pandas/07_Visualization/Online_Retail/Solutions.ipynb @@ -0,0 +1,370 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Online Retails Purchase" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# set the graphs to show in the jupyter notebook\n", + "%matplotlib inline\n", + "\n", + "# set seabor graphs to a better style\n", + "sns.set(style=\"ticks\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/Visualization/Online_Retail/Online_Retail.csv). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called online_rt" + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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053636585123AWHITE HANGING HEART T-LIGHT HOLDER612/1/10 8:262.5517850.0United Kingdom
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453636584029ERED WOOLLY HOTTIE WHITE HEART.612/1/10 8:263.3917850.0United Kingdom
\n", + "
" + ], + "text/plain": [ + " InvoiceNo StockCode Description Quantity \\\n", + "0 536365 85123A WHITE HANGING HEART T-LIGHT HOLDER 6 \n", + "1 536365 71053 WHITE METAL LANTERN 6 \n", + "2 536365 84406B CREAM CUPID HEARTS COAT HANGER 8 \n", + "3 536365 84029G KNITTED UNION FLAG HOT WATER BOTTLE 6 \n", + "4 536365 84029E RED WOOLLY HOTTIE WHITE HEART. 6 \n", + "\n", + " InvoiceDate UnitPrice CustomerID Country \n", + "0 12/1/10 8:26 2.55 17850.0 United Kingdom \n", + "1 12/1/10 8:26 3.39 17850.0 United Kingdom \n", + "2 12/1/10 8:26 2.75 17850.0 United Kingdom \n", + "3 12/1/10 8:26 3.39 17850.0 United Kingdom \n", + "4 12/1/10 8:26 3.39 17850.0 United Kingdom " + ] + }, + "execution_count": 198, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Create a histogram with the 10 countries that have the most 'Quantity' ordered except UK" + ] + }, + { + "cell_type": "code", + "execution_count": 199, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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TUy8BiYwRF4kiouriuUVh0qRJmDhxIrp37y4cgvjjjz/w5ptvIiEhQS8BiYiI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\naYOeYzF9+nRcvnwZAQEBmDp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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Exclude negative Quatity entries" + ] + }, + { + "cell_type": "code", + "execution_count": 200, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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InvoiceNoStockCodeDescriptionQuantityInvoiceDateUnitPriceCustomerIDCountry
053636585123AWHITE HANGING HEART T-LIGHT HOLDER612/1/10 8:262.5517850.0United Kingdom
153636571053WHITE METAL LANTERN612/1/10 8:263.3917850.0United Kingdom
253636584406BCREAM CUPID HEARTS COAT HANGER812/1/10 8:262.7517850.0United Kingdom
353636584029GKNITTED UNION FLAG HOT WATER BOTTLE612/1/10 8:263.3917850.0United Kingdom
453636584029ERED WOOLLY HOTTIE WHITE HEART.612/1/10 8:263.3917850.0United Kingdom
\n", + "
" + ], + "text/plain": [ + " InvoiceNo StockCode Description Quantity \\\n", + "0 536365 85123A WHITE HANGING HEART T-LIGHT HOLDER 6 \n", + "1 536365 71053 WHITE METAL LANTERN 6 \n", + "2 536365 84406B CREAM CUPID HEARTS COAT HANGER 8 \n", + "3 536365 84029G KNITTED UNION FLAG HOT WATER BOTTLE 6 \n", + "4 536365 84029E RED WOOLLY HOTTIE WHITE HEART. 6 \n", + "\n", + " InvoiceDate UnitPrice CustomerID Country \n", + "0 12/1/10 8:26 2.55 17850.0 United Kingdom \n", + "1 12/1/10 8:26 3.39 17850.0 United Kingdom \n", + "2 12/1/10 8:26 2.75 17850.0 United Kingdom \n", + "3 12/1/10 8:26 3.39 17850.0 United Kingdom \n", + "4 12/1/10 8:26 3.39 17850.0 United Kingdom " + ] + }, + "execution_count": 200, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Create a scatterplot with the Quantity per UnitPrice by CustomerID for the top 3 Countries" + ] + }, + { + "cell_type": "code", + "execution_count": 201, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BONUS: Create your own question and answer it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/07_Visualization/Scores/Exercises.ipynb b/200 solved problems in Python/pandas/07_Visualization/Scores/Exercises.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..d887c2ef25bfd43de690ed40e6d95dd663f12f37 --- /dev/null +++ b/200 solved problems in Python/pandas/07_Visualization/Scores/Exercises.ipynb @@ -0,0 +1,200 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Scores" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "This time you will create the data.\n", + "\n", + "***Exercise based on [Chris Albon](http://chrisalbon.com/) work, the credits belong to him.***\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Create the DataFrame that should look like the one below." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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first_namelast_nameagefemalepreTestScorepostTestScore
0JasonMiller420425
1MollyJacobson5212494
2TinaAli3613157
3JakeMilner240262
4AmyCooze731370
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" + ], + "text/plain": [ + " first_name last_name age female preTestScore postTestScore\n", + "0 Jason Miller 42 0 4 25\n", + "1 Molly Jacobson 52 1 24 94\n", + "2 Tina Ali 36 1 31 57\n", + "3 Jake Milner 24 0 2 62\n", + "4 Amy Cooze 73 1 3 70" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Create a Scatterplot of preTestScore and postTestScore, with the size of each point determined by age\n", + "#### Hint: Don't forget to place the labels" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Create a Scatterplot of preTestScore and postTestScore.\n", + "### This time the size should be 4.5 times the postTestScore and the color determined by sex" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BONUS: Create your own question and answer it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/07_Visualization/Scores/Exercises_with_solutions_code.ipynb b/200 solved problems in Python/pandas/07_Visualization/Scores/Exercises_with_solutions_code.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..664e94d21753d875d3d322eb6b0792ca6b0fc378 --- /dev/null +++ b/200 solved problems in Python/pandas/07_Visualization/Scores/Exercises_with_solutions_code.ipynb @@ -0,0 +1,273 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Scores" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "This time you will create the data.\n", + "\n", + "***Exercise based on [Chris Albon](http://chrisalbon.com/) work, the credits belong to him.***\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Create the DataFrame it should look like below." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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first_namelast_nameagefemalepreTestScorepostTestScore
0JasonMiller420425
1MollyJacobson5212494
2TinaAli3613157
3JakeMilner240262
4AmyCooze731370
\n", + "
" + ], + "text/plain": [ + " first_name last_name age female preTestScore postTestScore\n", + "0 Jason Miller 42 0 4 25\n", + "1 Molly Jacobson 52 1 24 94\n", + "2 Tina Ali 36 1 31 57\n", + "3 Jake Milner 24 0 2 62\n", + "4 Amy Cooze 73 1 3 70" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data = {'first_name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], \n", + " 'last_name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze'], \n", + " 'female': [0, 1, 1, 0, 1],\n", + " 'age': [42, 52, 36, 24, 73], \n", + " 'preTestScore': [4, 24, 31, 2, 3],\n", + " 'postTestScore': [25, 94, 57, 62, 70]}\n", + "\n", + "df = pd.DataFrame(raw_data, columns = ['first_name', 'last_name', 'age', 'female', 'preTestScore', 'postTestScore'])\n", + "\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Create a Scatterplot of preTestScore and postTestScore, with the size of each point determined by age\n", + "#### Hint: Don't forget to place the labels" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(df.preTestScore, df.postTestScore, s=df.age)\n", + "\n", + "#set labels and titles\n", + "plt.title(\"preTestScore x postTestScore\")\n", + "plt.xlabel('preTestScore')\n", + "plt.ylabel('preTestScore')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Create a Scatterplot of preTestScore and postTestScore.\n", + "### This time the size should be 4.5 times the postTestScore and the color determined by sex" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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5YnKSMLOeKCJYs2YNlZWV9O3bt8f1Kl3wN9OZmVnnSWLgwJJcYCmajtokbtsq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\nyUnCzMwyOUmYmVmm/w+eYh5l4kpEgwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(df.preTestScore, df.postTestScore, s= df.postTestScore * 4.5, c = df.female)\n", + "\n", + "#set labels and titles\n", + "plt.title(\"preTestScore x postTestScore\")\n", + "plt.xlabel('preTestScore')\n", + "plt.ylabel('preTestScore')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BONUS: Create your own question and answer it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/07_Visualization/Scores/Solutions.ipynb b/200 solved problems in Python/pandas/07_Visualization/Scores/Solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..cdc25cf69f1c7e924f36dd020bd15b8646def529 --- /dev/null +++ b/200 solved problems in Python/pandas/07_Visualization/Scores/Solutions.ipynb @@ -0,0 +1,248 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Scores" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "This time you will create the data.\n", + "\n", + "***Exercise based on [Chris Albon](http://chrisalbon.com/) work, the credits belong to him.***\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Create the DataFrame it should look like below." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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first_namelast_nameagefemalepreTestScorepostTestScore
0JasonMiller420425
1MollyJacobson5212494
2TinaAli3613157
3JakeMilner240262
4AmyCooze731370
\n", + "
" + ], + "text/plain": [ + " first_name last_name age female preTestScore postTestScore\n", + "0 Jason Miller 42 0 4 25\n", + "1 Molly Jacobson 52 1 24 94\n", + "2 Tina Ali 36 1 31 57\n", + "3 Jake Milner 24 0 2 62\n", + "4 Amy Cooze 73 1 3 70" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Create a Scatterplot of preTestScore and postTestScore, with the size of each point determined by age\n", + "#### Hint: Don't forget to place the labels" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Create a Scatterplot of preTestScore and postTestScore.\n", + "### This time the size should be 4.5 times the postTestScore and the color determined by sex" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BONUS: Create your own question and answer it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/07_Visualization/Tips/Exercises.ipynb b/200 solved problems in Python/pandas/07_Visualization/Tips/Exercises.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..baf0804a415d26c71a9b5924b5f8354507403c57 --- /dev/null +++ b/200 solved problems in Python/pandas/07_Visualization/Tips/Exercises.ipynb @@ -0,0 +1,238 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tips" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "This exercise was created based on the tutorial and documentation from [Seaborn](https://stanford.edu/~mwaskom/software/seaborn/index.html) \n", + "The dataset being used is tips from Seaborn.\n", + "\n", + "### Step 1. Import the necessary libraries:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/Visualization/Tips/tips.csv). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called tips" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Delete the Unnamed 0 column" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Plot the total_bill column histogram" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Create a scatter plot presenting the relationship between total_bill and tip" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Create one image with the relationship of total_bill, tip and size.\n", + "#### Hint: It is just one function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Present the realationship between days and total_bill value" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. Create a scatter plot with the day as the y-axis and tip as the x-axis, differ the dots by sex" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. Create a box plot presenting the total_bill per day differetiation the time (Dinner or Lunch)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 11. Create two histograms of the tip value based for Dinner and Lunch. They must be side by side." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 12. Create two scatterplots graphs, one for Male and another for Female, presenting the total_bill value and tip relationship, differing by smoker or no smoker\n", + "### They must be side by side." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BONUS: Create your own question and answer it using a graph." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/07_Visualization/Tips/Exercises_with_code_and_solutions.ipynb b/200 solved problems in Python/pandas/07_Visualization/Tips/Exercises_with_code_and_solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..28f9cff5aac37c177a6fb9a278af441706633563 --- /dev/null +++ b/200 solved problems in Python/pandas/07_Visualization/Tips/Exercises_with_code_and_solutions.ipynb @@ -0,0 +1,577 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tips" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "This exercise was created based on the tutorial and documentation from [Seaborn](https://stanford.edu/~mwaskom/software/seaborn/index.html) \n", + "The dataset being used is tips from Seaborn.\n", + "\n", + "### Step 1. Import the necessary libraries:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "# visualization libraries\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "\n", + "# print the graphs in the notebook\n", + "% matplotlib inline\n", + "\n", + "# set seaborn style to white\n", + "sns.set_style(\"white\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/Visualization/Tips/tips.csv). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called tips" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Plot the total_bill column histogram" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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3cWvGeLl+vXB7DgXGc889R0ZGBmPGyBRBb2Mfv0gIc3El7isgQIdOH9xv24yJ\ngRRXtvHN8VYmpw7H5yxnGUK4O4cCIyEhgZycHNLS0vpcW3vevHlOK0y4B/sZhgx4X5AAPy1pY6LY\ne7iOb0obmZIqO9kKz3XWwKirqyMmJoawsJ5Pl4WFhX3aJTCGNkVRKKlsITYikGCddEFeqMkpUXxT\n2sDXxaeYkByBn4+cZQjPdNbAWLZsGRs3biQ3N5dXXnmFJUuWDFZdwg3UNbXT1m4mbUykq0vxaH4+\nGqaMjWZXUQ37ik8xc+IwV5ckxAU567RaRVHst99//32nFyPci31LcxnwvmiXjI5EF+DD/qP1GNvN\nri5HiAty1sD4/jS/74eH8A4y4D1wfLRqLpsQi9WmsOdQnavLEeKCOLy9ucwR9z4llS2oVJAcL9M9\nB8LYxDDCg/05XN5Eo6HD1eUIcd7OOoZRUlLCrFmzgJ4B8N7bcmnWoc9mUyitaiEuSi9bdA8QtUrF\nFZcMY3Pecb48UMPNVyXJBzHhUc4aGB999NFg1SHcTE2jifZOCzMmyPjFQBoRG0RCtJ4TdW1U1LYx\nclj/6zeEcEdnDQy5/Kr3KjnRDMiGgwNNpVJxZVoc67YW80XhSRJi9K4uSQiHOTyGIbxLSZUMeDtL\nRIg/E5MjMRjNHChtcHU5QjhMAkP0q+REC2oVJMVJl4kzzBgfg5+vhvxDdbR3ykWWhGeQwBCnsVpt\nHKs2MCI2GH9fh3aPEefJ31fLzAmxdFts7DnS5OpyhHCIBIY4TdUpI11mqyzYc7IJoyKICQ+kvNZE\nYUmjq8sR4pycGhiKorBixQqys7NZvHgxlZWVfdq3b99OZmYm2dnZrF+/HgCLxcKvfvUr7rrrLhYu\nXMj27dudWaLoR0llz4C3bGnuXCqVimunxqNSwT8/OEpnl3RNCffm1MDYunUrZrOZtWvX8sgjj5Cb\nm2tvs1gsrFq1ildffZU1a9awbt06mpqaeO+99wgLC+PNN9/k5Zdf5ne/+50zSxT9KKmULUEGS0RI\nABNGhtBg6OKtj4tdXY4QZ+XUwCgoKCA9PR2AtLQ0ioqK7G1lZWUkJiai1+vx8fFh6tSp5OfnM3fu\nXB566CEAbDYbWq30oQ+2oyea0WrUskZgkEweHUpUqD+bPivjeHX/V+8Twh04NTCMRiNBQUH277Va\nLTabrd82nU5HW1sbAQEBBAYGYjQaeeihh3j44YedWaL4gU6zhePVrSTHh8jFfgaJVqPmJz9KwWZT\n+Ov6/VhERfR9AAAbl0lEQVRtsm+bcE9ODQy9Xo/JZLJ/b7PZUKvV9jaj0WhvM5lMBAf3fKKtqanh\nnnvuYf78+fzoRz9yZoniB8qqDFhtCqmJ4a4uxatMGh3O1ZfGcfRECx9+edzV5QjRL6cGxpQpU9i5\ncycA+/fvJyUlxd6WnJxMRUUFra2tmM1m8vPzmTx5Mg0NDSxdupRf/vKXzJ8/35nliX4UV/RM8Ryb\nKAv2BttPb5uILsCH1z84TF1Tu6vLEeI0Tg2MOXPm4OvrS3Z2NqtWrSInJ4fNmzezfv16tFotOTk5\nLFmyhEWLFpGVlUV0dDQvvvgira2trF69mrvvvpvFixdjNsv1AwbLkYqeGVISGIMvLMif+26bSEeX\nheff+lq6poTbceqIskqlYuXKlX3uS0pKst/OyMggIyOjT/sTTzzBE0884cyyxBkoikJxRRPhwX5E\nhQa4uhyvdN20BHYfrGXXNzVs+rSU268b4+qShLCThXvCrr6lg6bWLsYmhsu22y6iUql4IDONsCA/\n3thymGMnZdaUcB8SGMKu+NvuqFTpjnKpEL0f/3XHpVisCn/6VwHmbqurSxICkMAQ31NsH7+QGVKu\nNm1cDD+6YiQnatt47YNDri5HCMDJYxjCsxypaEKjVsklWQeZoigYDKd3Pd1+TQL7iut477NjjB8R\nxMRRZz7zCw4Olm5E4XQSGAKAbouVsioDScNlh9rB1t5u5KNdTYSHR5zWNjUljP981cH/rS/ilivi\nCPA7fTFle7uJWzPGExIiQS+cS/4yCABKKw1YrDbpjnKRgAAdOv3pW7Ho9DBzgsKuohq+KGri1vRR\nqNVyJiFcQ8YwBACHjvdsrz0h6fRPucK1Lh0bRdLwYE7WG9l9sNbV5QgvJmcYXkxRFFpbWwEoPFoH\nQHykT7/96f0xGAwoyOIyZ1OpVMyaNoJ/bzvK18WniI0IJGm4dD+JwSeB4cVaW1t579NDBAQEcrC8\nGX2Alq+POP4JtqG+Dp0+BL3eiUUKAPx8Ncy9fCTv7Chha/4JFs5KIUTv5+qyhJeRwPBygYE6Oq0+\nmLttJA0P6bcf/UxMpjYnViZ+KDI0gGumxLMtv5IPd5WTed0YtBrpVRaDR37bBDUNPTsKD4/UubgS\ncS6pieFMGBVBo6GTTwuqUBTpEhSDRwJDUP1tYAyLkMDwBFelDSc6LJDiE818XXzK1eUILyKBIahp\nMBHgpyU0SPrEPYFWo+ZHV45EH+DDV0W1lNeazn2QEANAAsPLGTu6MXZ0MyxCJyuFPYjO34ebrkzC\nR6vm8wP1lJ1sdXVJwgtIYHi5uuYuAIbJ+IXHiQwN4PrLErHZFP5nXRGnmuWiS8K5JDC8XF1TJyCB\n4alGDgtmemo4BqOZ3/1jN+2d3a4uSQxhEhherqaxA18fNVFhcsEkTzUuMZhZ04ZTXtPKs2v2YrHa\nXF2SGKIkMLxYfUsHbR0W4qL0qGX8wmOpVCoW3ziaaeNi+PrIKf73rX3Y5PKuwgkkMLzYweMtAMRH\ny1JtT6dRq3l08TTGjQxn574q/v5ekazREANOAsOLHTzec8Gk+OggF1ciBoK/r5ZfL72MEbFBvP/5\nMdZvK3F1SWKIkcDwUoqicOh4MwF+GsJk/cWQERToy2/vv5yosADWfHiYj74qd3VJYgiRwPBSJ2rb\nMJi6GRbuL+svhpiIkAB+e//lBOt8Wf12IV8UnnR1SWKIkMDwUoUl9QAMi5DZUUNRfHQQT903Ez9f\nDX98o4CvimpcXZIYAiQwvFRhSQMggTGUjUkIY8VPL0erVfPs6/nsPVzn6pKEh5PA8EIWq42iYw3E\nhAegD5Ad7oeyCaMiWLF0Jmq1mmde3cP+o7JZobhwEhhe6NDxRto7LUxKlut3e4NLRkfy5E9mAPC7\nV/bwTVmDiysSnkoCwwvlH+rpmpg8RgLDW1w6NprH752BzWbjt3//ioPHGl1dkvBAEhheKP9QHX6+\nGsaNDHV1KWIQTRsXw6OLp2Ox2ljx8i77xAchHCWB4WWqG4ycrDcyeUwUvlqNq8sRg2zmxGHk3DsD\nq1Xht3//iq+PyJiGcJwEhpfZ+2131PTxMS6uRLjKjPGx/HrJZQD87pXd7DlU6+KKhKdwamAoisKK\nFSvIzs5m8eLFVFZW9mnfvn07mZmZZGdns379+j5thYWF3H333c4szyvlfzu1cto4CYyhQlEUDAbD\neX0lD/PjkUWXoFHDM//cQ94BWdwnzs2pcyq3bt2K2Wxm7dq1FBYWkpuby+rVqwGwWCysWrWKDRs2\n4Ofnx6JFi5g1axbh4eH8/e9/591330Wnk2s0DKT2zm6KyhoZFRdCREgABoPZ1SWJAdDebuSjXU2E\nh0ec97HXXRrDJwW1/OH1vTyUbeW6aSOcUKEYKpx6hlFQUEB6ejoAaWlpFBUV2dvKyspITExEr9fj\n4+PD1KlTyc/PByAxMZEXXnjBmaV5pcKSeixWG9Pl7GLICQjQodMHn/fXqBHRXD89lgA/Lc+/tY+N\nn5a6+q0IN+bUwDAajQQFfbcTqlarxWaz9dum0+loa2sDYM6cOWg0MiA70PIKe7aHmDEh1sWVCHcS\nHerPr++9lIgQf155/yCvbj4oW6OLfjk1MPR6PSaTyf69zWZDrVbb24xGo73NZDIRHBzszHK8Wle3\nlT2HaoiNCGRMgkynFX3FR+v4w4PpxEXpeGdHKf+3bj9WuXKf+AGnBsaUKVPYuXMnAPv37yclJcXe\nlpycTEVFBa2trZjNZvLz85k8eXKf4+VTzsDZe7iOji4rV6XFye60ol/R4YE8+2A6oxNC2Zp/gtzX\n8unssri6LOFGnDroPWfOHPLy8sjOzgYgNzeXzZs309HRQVZWFjk5OSxZsgRFUcjKyiI6OrrP8fKH\nbeB8vr9nFkz65DgXVyLcWYjej6eXXUHuq/nsPljLoy98wZM/uUyu+S4AJweGSqVi5cqVfe5LSkqy\n387IyCAjI6PfY+Pi4li7dq0zy/ManV0W8g/VMTxSR9Jw6fYTZxfo78NvfjqTFzce4KOvKvjvP+/k\nyZ/MYGyibCXj7WThnhfIP1SHudtK+mTpjhKO8dGqeSAzjfvmTaTV2EXO6jw+Lag894FiSJO9rb3A\n54XSHSXOrHfhX3+umRRJWOAk/vrOQf70r685WtFA1rVJqNV9P3gEBwfLhxEvIIExxBmMXeQfqmVE\nbBCJw6Q7SpzOkYV/10+PZdvXdbyfd4L8w6e4elIUgf7ab483cWvGeEJCQgarZOEiEhhD3Lb8SixW\nhRsuS3R1KcKN9S78OxOdHu6YHcb2vZUcqzbw/q4aZk1PIDFWPoR4ExnDGMIUReGjr8rx0aq5dlqC\nq8sRHs7PV8ONlydyVdpwusxWNn9xnE+/rqLbIus1vIWcYQxhRWWNVDeYyJgaT1Cgr6vLEUOASqUi\nbUwUcVF6tuaf4OCxRipqDITqtVw+6eKeW8ZB3J8ExhD20VcVANw4c6RrCxFDTmRoAFnXjWHPoTr2\nFZ/ihU1lfPBVLdNSw9H5n/+fFRkH8QwSGENUq8lM3oFq4qP1jE+S+fNi4Gk0ai6/ZBgRgWYKSk0c\nrzVRWd/BpWOjuDQlCh+5QNe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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# create histogram\n", + "ttbill = sns.distplot(tips.total_bill);\n", + "\n", + "# set lables and titles\n", + "ttbill.set(xlabel = 'Value', ylabel = 'Frequency', title = \"Total Bill\")\n", + "\n", + "# take out the right and upper borders\n", + "sns.despine()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Create a scatter plot presenting the relationship between total_bill and tip" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.jointplot(x =\"total_bill\", y =\"tip\", data = tips)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Create one image with the relationship of total_bill, tip and size.\n", + "#### Hint: It is just one function." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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WBr0Oc616XLYPwu4cQl3D8FblJoMOG67Px9ry+YjXabG2PAd25xAS9Tr09A3i\nluXzMT8zCZ+e78HAoAeCICBrrhF9Ax7/BmQcqaBISk8xYt+YHJ5tMvfyuGwfQNqcRP9mY112+dMT\nhVlJokTPBTb51TdjyZQBhSAIcDgc/pUXDoeDyxmJwiCYjbHGjljoE3ToHxjCzddlI9VqgE/w4fAn\nLdi4egGq94/uL7Bx9QK8/u5n/lUeQ0NeFGRbsffQGX8p7cpSmz/YuGvdYvzmndO4a91i0XDuFysL\nsffwGew9fGbGF9wh9dNAEI0oyK1qbTHrcbbF4Q+QC8Lw8Hf7IEr03L6Ne3mMNWVA8Y1vfAObNm3C\nmjVrIAgC3n333bAmaRLNVpOtQff6BHxY34aGs92wmuPR7/Jg0fxknGuzo7Wr33+DTNAN16WTDg3b\nnUO4Zfl85M1LQlfPILrsLgwOeXFTqc2/46ghIQ43X5eNrFQT7H3Dv3+lT3ye3v7Roj2c+qBIax+z\nGZhG0g7FkNsracvfGXSiYm4rl7JC7IgpA4p3330XL7zwAmprayEIAn7605/iueeew6ZNm6LRP6KY\nNdka9Nr6dux6pc5/XGWpDb87+Bm+unahaAThq2sXAgBSreLktex0MzauXog975wSbQJ217rRZXiu\nIa9/dOPQ1XPmZYnX6Y/UngFmfsEdUr+0OYnYP2akbeuGJbLO5/EKE35e5Ii1QlThNmlA8c1vfhOn\nTp1CZ2cnGhoa/DeXl19+GVlZ/KZCJNdka9AnS8Ts6xcX+unrd2PzLYtxqacf92woQmuXE9npZnxh\nVQEAoKNLPGfc2dOP8uIMFOWlQAMBN1xbDq1GA0NCHHKzrCgvykCKJfFqgGOBVqPBvLmmmCi4Q+rX\nEeZS2RN9XuSKtUJU4TZpQPH888+jp6cHzz77LHbu3Dn6CzodUlNTo9I5otlosm3K0+Ykil5PtehR\n/dZp3H/nNdBAA13ccHAwkjw5L02SkZ5qhM8n4PWDf/VX3wTg3y3R6xsdkdBAg/LiTFk7KRJNR0aq\neJlzRoppkiODc+3CNOz74Nxoe8FcWecDYq8QVbhNGlCYzWaYzWa8+OKL0ewP0axXUZKJHdvKcbbF\ngSt9Lswx6/Hwps+hvasPG1cv8Getj5QmHnB58Ma7n2FZUQY+OtWJwSEvvnTTApgM8aLj7b2D/qDh\nQodj3F4hsbbzIc0s8Toftm5YgtbLTtjmmqCPl5fzsOKaLI4mRNmUORREFF1arQYCNPjNO6PZ5JWl\nNhRmW3FZw/rXAAAgAElEQVTmot2flFl4dW+CbocL61fm4XxHL+aYEnCuzYEX3ziB9OREHPr4Ai7b\nhysOjlTCXFaUIVoVMhI4BLPqhChSfEIcmtuHr2+Px4fCbHn5CRxNiD4GFEQKGbuaw2KKR16mBWXF\nmRAA1J/tEh07MOjBgMvjTzIzGXTIzjDji5WFmJOUgF/+TyMAjCurPbKEFBieOsnNTBq3Q+JI4MCE\nM1KSS7I7rnS3XFI/BhRECploNYdXAAQI6OkVL+EcKUAFDAcT61fm4dPzPTDqdbhsH01ek96Ee/uH\n8MXKQlhM8cjNtKC8OBO19e2ibcpHAgcmnJGSfL7wr8qg6GJAoUKCz4empqagji0sLGShsRlqotUc\nzW12DLq9iNdpcefNCzAw6IbVlAANAAGj24yPXQ66cfUC/7+l+3Hkz7PijhsK/G2vT4AAYVyQAXCI\nmJTVG4FVGRRdDChUaKD3Ep5+6TKM1jMBj+u3d6J612YsWrQoSj2jcJpoNUdulhUXO3vxu4OjAcPI\nNMY37ihCZakNWkkJwZbO4WTN8x29iNdp8fXbi9DR3Y/cTAvWr8gTHTuceDk6KrJ9WwVLapMqpEjq\nqSRb9JMcSWqlWEBx4sQJ/OhHP0J1dTXOnz+PJ554AlqtFgsXLkRVVZVS3VINozUd5mSb0t2gCBqe\nYihH/dUcipHRgoamy/4SxEa9Dol6HUwGHa70DiJtTuK4ksQ5GUmI0wIWYwKsZj1yM8zYtGbiIJOJ\nl6RW/QNDolVJAy6OUMw0igQUL7/8Mvbu3QuTaXid8a5du/DYY4+hrKwMVVVVqKmpwdq1a5XoGlHU\nDE8xzMPKpfP8O4/+tuY0TInx+MOhs/7jNq5egGVFGUi26uHodaPL4cLmWxejr38IPX1D2PdBE5wu\nDypLbXj93c/g8RZiWVHWhCMPTLwktTIb9Xh1X6O/fc9t8jYHG/L4cODYOTS3O5CXZcGty/Ogu1qu\nniJDkYAiNzcXL7zwAv73//7fAIZ3NC0rKwMAVFZW4oMPPmBAQbNKXX073j/RgoFBDwqyLDAZdP6N\nvFo6+2AyxmNo0CfKnbj71sW4pjANyUkGXOl14aPG4c2+rvS68GF9+4QjD0y8pHAZCYKl9UxC1dPr\nEo3MSROTp+vAsXPY/fuT/rYgQJRPROGnSECxbt06tLSMZvOO3TPAZDKht7dXiW4RKeZ8h8Of4V7X\n0CFa/pmQEAe3x4d2SSniju4BfO2WJVhekonfHfzUv0vjR40dyEk3TxhQBEq8DPcDgmJbuAuhJRkT\n8OZ7o8nod9+6OMDRU2tudwRsU/ipIilTqx0dhnI6nf6t0olmC2ltCKNBh1uWz4fVpMeVXheOnmzD\nbavyRcfkZJgBDAcJ8zMsomJVoUxlsFImTUe483Gu9A6K247BSY4MjnSzu9xMPlciTRUBRXFxMerq\n6lBeXo7Dhw9jxYoVSneJKKqK81NEtSHS5iTiQkcf4uO1qKm7AJNBB0ffIL66diHsfUOYazUg92pA\nAYRnKoMJmzQd+VlW0RRF/jx5+ThzksSrOubIXOVx6/I8CMLwyMREK54o/FQRUDz++ON46qmn4Ha7\nUVhYiPXr1yvdpWnzer04cybwMs9ga0vQ7BOn1aCy1IahIS8KbFacbbVDp9XiwLFzuOe2IrjdXvzm\nnU/9x1eWDq8AWlY8/MAPRw0JJmzSdPggLkS16tp5ss6XlKgTrfJISpT3eNLptMyZiDLFAgqbzYY9\ne/YAAPLy8lBdXa1UV8LizJkz2Prkr2G0pk96TNfFRqRmy8tcptgxNmehb2C4rPbqZdn49YHRPTxW\nL8tG55V+pFrEa/R1cVrZ3wilQhnlYN7F7HWuTZKj0ObAyqWhBxV9A55xScc0s6hihCJWTFU7ot/e\nEcXekNqNzVm46eqIg0ZSZEKj0SA304K5VvHW5R6vD74xyczhEMooB/MuZq8kU4KobTYmTHJkcLol\nORPSNqkfAwoihYzNWTje2IGtG5ZgyC3esjltjgHrV+RBq9Vg64YlONV8RbKSQ94ws1zMu5i9XC63\nP4ciUa+Da1BeISqzMV7cToyf5EhSKwYURAoZm7PgdHmQk2FBnAaim/TCnGR/MZ5wrOQIN+ZdzF7Z\n6Rb8ct/o9XjDtRWyzpdsThBd+9YkeSMeFH0MKIgUsqwoAw/cuXS4kl+mBeVFGdBqNfAK8OcxjGzc\nBaizKJUa+0TREe6//dqKPLjcZ9FyqQ+2NDNurcgLT0cpahhQECnkeGOHqJJfijXRn8Mw3aJUSlFj\nnyg6wv23//NfL4lKb2enJ/G6mmFY2JxIIRPlHxDNVvw8zHwMKIgUwvwDolH8PMx8nPIgUgjzD4hG\n8fMw8zGgmMEEny+o6puFhYWIi4uLQo9oOsZWkWApKJppwl3UjPk4Mx8DihlsoPcSnn7pMozWyUt+\n99s7Ub1rMxYtWhTFnlEwWBSKZjJevyTFgGKGm6o6J6kXi0LRTMbrl6SYlEmkECah0UzG65ekOEJB\npBAmodFMxuuXpFQTUAiCgO985zs4ffo0EhIS8OyzzyInJ0fpbhFFDJPQaCbj9UtSqpnyqKmpwdDQ\nEPbs2YN/+Id/wK5du5TuEhEREQVJNQHFRx99hBtvvBEAcO211+Ivf/mLwj0iIiKiYKkmoOjr60NS\nUpK/rdPp4PP5AvwGERERqYVqAgqz2Qyn0+lv+3w+aLWq6R4REREFoJon9nXXXYdDhw4BAP785z+z\nEBMREdEMoppVHuvWrcORI0fwta99DQCYlBkmwZbnBliim4iIQqeagEKj0eCZZ55RuhsxJ5jy3ABL\ndBMRkTyqCSgocliem4iIIk01ORREREQ0czGgICIiItkYUBAREZFsDCiIiIhINiZlEgAuLyUiInkY\nUBAALi8lIiJ5GFCQH5eXEhFRqJhDQURERLIxoCAiIiLZOOUxhfqGU/j3X78NXXxCwOMEVxeAlOh0\nSkHBJm8ycZOIaHZhQDGFiy1taOxORUJiUsDjEnsuYDYEFMEkbzJxk4ho9mFAQdMWruRNr9eLM2cC\nryoZwREPIiJ1UyygeOedd/DWW2/hn/7pnwAAJ06cwLPPPgudTofrr78ejzzyiFJdI5mCnRZpamrC\n0y8dhdGaHvA4jngQEamfIgHFs88+iyNHjqCoqMj/WlVVFf7t3/4N2dnZuP/++3Hq1CksWbJEie6R\nTMHWtOi62IjU7CIuVSUiigGKBBTXXXcd1q1bh//6r/8CAPT19cHtdiM7OxsAcMMNN+CDDz5gQDGD\nBTMt0m/viFJviIgo0iIaUPzud7/DL3/5S9Fru3btwoYNG1BbW+t/zel0wmw2+9smkwkXL16c9Lxe\nrxcA0N7eHuYej9fV1YX+nhYMDRgDHud2dKEfgVeCDPR2A9BM+Z7hPC4W3rPf3on29nYYjYH/BtOR\nmZkJnS78l380r02KTbw2Sa2mujYjGlBs2rQJmzZtmvI4k8mEvr4+f9vpdMJisUx6/KVLlwAAd999\nt/xOhlnPFD8fDOKYcB8XC+95332/CeKo4P3pT3/yj4iFk5qvTZoZeG2SWk11bapilYfZbEZCQgIu\nXLiA7OxsvP/++wGTMq+55hr86le/QlpaGjP/KSSZmZkROS+vTZKL1yap1VTXpioCCgB45pln8O1v\nfxs+nw+rVq3C5z73uUmPNRgMKCsri2LviILDa5PUitcmRZpGEARB6U4QERHRzMa9PIiIiEg2BhRE\nREQkGwMKIiIiko0BBREREcnGgIKIiIhkY0BBREREsjGgICIiItkYUBAREZFsDCiIiIhINgYURERE\nJBsDCiIiIpKNAQURERHJplhAceLECWzduhUA0NjYiLvvvhv33HMP/v7v/x7d3d1KdYuIiIhCoEhA\n8fLLL2Pnzp1wu90AgOeeew5PP/00Xn31Vaxbtw4vvfSSEt0iIiKiECkSUOTm5uKFF17wt3/84x9j\n8eLFAACPxwO9Xq9Et4iIiChEigQU69atQ1xcnL89d+5cAMDHH3+MX//619i2bVvA3/d4PLh48SI8\nHk8ku0k0bbw2Sa14bVKkqSYpc9++fXjmmWfw0ksvITk5OeCx7e3t+PznP4/29vYo9Y4oOLw2Sa14\nbVKk6ZTuAADs3bsXv/3tb1FdXQ2LxaJ0d4iIiGiaFA8ofD4fnnvuOcybNw/f/OY3odFoUFFRgUce\neUTprhEREVGQFAsobDYb9uzZAwD48MMPleoGERERhYFqciiIiIho5mJAQURERLIxoCAiIiLZGFAQ\nERGRbAwoiIiISDYGFERERCQbAwoiIiKSjQEFERERycaAgoiIiGRjQEFERESyMaAgIiIi2RhQEBER\nkWwMKIiIiEg2BhREREQkGwMKIiIiko0BBREREcnGgIKIiIhkUyygOHHiBLZu3QoAOH/+PDZv3owt\nW7bgmWeeUapLREREFCJFAoqXX34ZO3fuhNvtBgDs2rULjz32GF577TX4fD7U1NQo0S1SIa9PwNGT\nbdhz4BSOnWyDzyco3SUimgF474g+RQKK3NxcvPDCC/52fX09ysrKAACVlZU4evSoEt0iFaqtb8dz\nr9TiV2+fxrOv1OLD+nalu0REMwDvHdGnSECxbt06xMXF+duCMBo5mkwm9Pb2KtEtUqHmNnvANhHR\nRHjviD5VJGVqtaPdcDqdsFgsCvaG1CQvyypq50raREQT4b0j+nRKdwAAiouLUVdXh/Lychw+fBgr\nVqxQukukEhUlmdi+rQLNbXbkZlmxvCRT6S4R0QzAe0f0qSKgePzxx/HUU0/B7XajsLAQ69evV7pL\npBJarQYrl2Zh5dIspbtCRDMI7x3Rp1hAYbPZsGfPHgBAXl4eqqurleoKERERyaSKHAoiIiKa2RhQ\nEBERkWwMKIiIiEg2BhREREQkGwMKIiIiko0BBREREcmmijoURMDwZj619e1obrMjL8uKipJMaLUa\npbtFRASA96ipMKAg1RjZzGfE9m0VLEpDRKrBe1RgnPIg1eBmPkSkZrxHBcaAglSDm/kQkZrxHhUY\npzxINbiZDxGpGe9RgTGgINXgZj5EpGa8RwXGKQ8iIiKSjQEFERERycaAgoiIiGRjQEFERESyqSYp\n0+Px4PHHH0dLSwt0Oh2++93vIj8/X+luERERURBUM0Jx6NAh+Hw+7NmzBw8//DB+/OMfK90lIiIi\nCpJqAoq8vDx4vV4IgoDe3l7Ex8cr3SUiIiIKkmqmPEwmEy5evIj169ejp6cHu3fvVrpLREREFCTV\njFC88soruPHGG/H222/jzTffxOOPP46hoSGlu0VERERBUM0IhdVqhU433J2kpCR4PB74fD6Fe0VE\nRETBUE1A8fWvfx3bt2/H3XffDY/Hg3/4h3+AwWBQultEREQUBNUEFEajET/5yU+U7gaFwOsTUFvf\njuY2O/KyrKgoyYRWq1G6W0REYcV7XWCqCSho5qqtb8dzr9T629u3VXDzHCKKObzXBaaapEyauZrb\n7AHbRESxgPe6wBhQkGx5WVZRO1fSJiKKBbzXBcYpD5KtoiQT27dVoLnNjtwsK5aXZCrdJSKisOO9\nLjAGFCSbVqvByqVZnEskopjGe11gnPIgIiIi2RhQEBERkWwMKIiIiEg2BhREREQkGwMKIiIiko2r\nPGhCSpSYZVlbIlIztd+jlO4fAwqakBIlZlnWlojUTO33KKX7xykPmpASJWZZ1paI1Ezt9yil+8cR\nihku2CGu6Q6FKVFilmVtiShcIjH8n59lRWWpDQODHhj1OuTPU9c9Sul7KAOKGS7YIa7pDoUpUWKW\nZW2JKFwiMfzvg4DDn7T426uunSfrfOGm9D2UAcUMN9EQ10QfmmCPG6FEiVmWtSWicJnuPS+4czrG\ntVcuVU9QofQ9lDkUM1ywQ1xKD4UREUVTJO55vI8GpqoRipdeegkHDx6E2+3G5s2bsXHjRqW7pHrB\nDnEpPRRGRBRNkbjn8T4amKyAoqurCx999BHi4uJQVlYGqzX0aK22thaffPIJ9uzZg/7+fvznf/6n\nnK7NGsEOcSk9FEZEFE2RuOfxPhpYyFMee/fuxd/+7d/ij3/8I9544w3ccccdOHToUMgdef/997Fo\n0SI8/PDDeOihh7B69eqQz0XT5/UJOHqyDXsOnMKxk23w+QSlu0REpCq8TwYW8gjFiy++iDfeeAMZ\nGRkAgJaWFjz44IO46aabQjrflStX0Nrait27d+PChQt46KGH8NZbb4XaPZrEZEuplC6IQkQUTpFY\nNsr7ZGAhBxRmsxlpaWn+ts1mQ3x8fMgdmTNnDgoLC6HT6ZCfnw+9Xo/u7m6kpKSEfM7ZJpgP0GQf\niEhkRFN4eb1enDlzJujjCwsLERcXF8EeEYVPuAOASDz8eZ8MLOSAYtGiRbjvvvuwceNGxMXFYf/+\n/UhPT8cf/vAHAMCXvvSlaZ1v2bJlqK6uxrZt29DR0QGXy4Xk5ORQuzcrBfMBmuwDwexl9Ttz5gy2\nPvlrGK3pUx7bb+9E9a7NWLRoURR6RiRfuAOASDz8eZ8MLOSAQhAEpKen47333gMAJCYmIjExER9+\n+CGA6QcUN998M44fP45NmzZBEARUVVVBo1HPpitqNTaq12o1MBl0cLo8ACb+AE32gWD28sxgtKbD\nnGxTuhtEYXex0yGqQtnS6QAQegAQiYc/75OBhRxQ7Nq1K5z9AAB8+9vfDvs5Y500qq8stfkruU30\nARr5QJxrsyPJmICWTgeOnRx+ndnLRKQUoz5eVIVySd5SWeeLxMN/bAomv+6ON+2A4oEHHsDu3bux\nZs0a0QiCIAjQarWoqakJawcpMOmwXnKSAXffunjSD9DIsicATC4iItXo7R8Stfsk7emKxBJPJmUG\nNu2A4nvf+x4AoLi4GNu3b4cgCNBoNBAEAU8++WTYO0iBSYf1SgpSg7rAmVxERGoyE/ITeN8MbNoB\nxXe+8x2cOnUKnZ2daGxs9L/u9XqRlcX/Y6Mt1GG9mfDhJaLZYybkJ/C+Gdi0A4rnn38ePT09ePbZ\nZ7Fz587RE+l0SE1NDWvnaGqhDuvNhA8vEc0eM6EKJe+bgU07oDCbzTCbzXjxxRcj0Z8ZJRKFU6LR\nr2D7PeTx4cCxc2hudyAvy4Jbl+dBp+N+ckQUfmq9n4416PGh9XIfOq4MQB8fB4/Hh4SE0Gu9zIT/\n5ulQ1eZgM41aE3Sm6teH9W3Y9UrdmJ+XT7gF74Fj57D79yf9bUEA7rihIEK9JqLZTK3307H2f9CE\nV/7YMPqCRoM7b14Q8vlmwn/zdDCgkCHaCTpjo9ncLAu0Gg2aWsWRrdcnoP5sl+j36s92iSLghrPd\nop//+a+X0NzmQH6WFT4IaG5zIC/LivPtDtFxTa12+HxCwAhaGnEvK8rA8caOoCLwWIvWiSh4Z1vF\n99OmVnn300jcT9ou94narZL2dF0Ic+0NpUeVGVDIEO0EnUA1J0Yi29r6dvT0ukS/d6XXhb2HR4+z\nmMQl0uPj4vCrt0+LzgcAf3dHsei4JGMCPqxvD/ghl/bxgTuXikY5AkXgsRatE1Hw9JIHX7zMB2Ek\n7idzrYmidqrFIOt8cVqt6J6bbysOcPTUlB5VZkAhQ7QTdKQjIgODHtHPRvbkON7Y4Y968+dZ8D/v\nN4mOK5hn9f88Ua+Dc2Bo3PkAwOvzYeuGJbjQ0YdUqwGHPr4AQ0JcwA/luFEbyShHoFEcLskKH8Hn\nQ1NT09QHgnt+kDpc6hkQ3Zcu9wzIOl8k7icO55Coj739blnn6+hyBmxP17j7raQdaQwoZIh2VrJ0\nRCRRP/rnGxkdycuywuny+KPe65ak+0txjxxXVpwJrzD8AUsyJqB6//DyX6NefDnY0i3QAKjef2rc\n+0iNDC9KhxRzsyyS9uSjOFySFT4DvZfw9EuXYbQG3kyMe36QWmSmGPGfY/ITtt0h79t6JO4ntjQT\n3nzvrL/94JflVfOU3h/nZ1omOTI4edL7rczzTRcDihlEPCIynEORk24WjY4sK8rAA3cuHZ5Dy7Rg\nXXkuUi2JolGUsYGQzycgxTr88/x5Vqy6dh6a2xyic+7YVoHzHQ44nG4AwoR5FCPDiyaDDpWlNiQn\nGVBSkIryooxx7x/cfx+XZMnFfT9oJrl9VQF8AC529iE73YwvrJI3VD/2XpibZUF5UYbsPqZYDKIR\nCrlTHrcuz4MgDI8k5GZasH5FnqrON10MKGaQiUZEll8jHh053tghmkNLsSYGHEWZ6JzSFR8CRkcp\n9h4+E3AX05HRkbtvXew/JthRnJmwDp2IIiMhIQ4bVy8M2/mk98JUS6Lse8tfznaLch6SkwxYMcEK\nuWDpdNqw5jiE+3zTxaICMWaiecOpeH0Cjp5sw54Dp3DsZBt8PkH082DOyekKIlKTUO6FU0m2JKCy\n1Iby4gzcVGpDiiVB9jljCUcoYkwoD/apsqGDOSenK4hITSLxJccQrxONUBTlpcg+ZyxhQBFjKkoy\ng8p5GGuqbOhgggVOVxCRmkTiS45DsgOqtD3bMaBQiekUYQl0rADgsn0Ap5qvwKjX4V/2fIJHv6YJ\n+KCfKpJnsEBEkTYw5MW+I2dxsbMP89PNuH1Vgayy1pG4byWZxFMcSUZOeYzFgEIlAk07SAMIjQaT\nHltb3y5KRKostY0bcZCer6wog9MVRKSot4814WyLHQODHpxxe7H/WBO+WBl6WetIcLncolUerkF5\ndSjCTelqw6oLKLq6urBx40b84he/QH5+vtLdiZpA0w7SYGPrhiWTHjtR8SvpiMNkwQtHIIhIKY6+\nIVF+QnqKUcHeTCw73YJf7huty3PDtRUK9mY8pasNqyqg8Hg8qKqqgsEgb23vTCSddjAbE7DnwCnk\nZVnHBQmXrgxgXXkOPjjZBqdLHDBIz3Pd4vRxIw6sSElEauMccAdsq4Hak8+VvrerKqB4/vnncddd\nd2H37t1KdyVkwQ45SY8rXZyOB+9cinPtDljNelzocAAC0DfggdvtxbryHHh8AvoG3DAlxsPeN4jb\nb8jHXGsiBEHwBx9lRRmipMxkiwFvHzuHs612/2Yx4ch+VnpojYhiS3aGWbRRVk66Wdb5nC4P9h05\ni5ZLfchJG87JMBjkPfI8PgFd9gF0OVxIMiVMmfAebUov31dNQPHGG28gNTUVq1atws9//nOluxOy\nYIecptpEq7LUhrQ5iXj93c/87ZHhwLqGDnyxshBNrQ60d/WLhgm3bxseghtbiGrs7woCcNv1+bKj\nbKWH1ogotrgGvaJ7mbQs9XTtO3IWr+5r9Ld9AL7yeXkl5pXefGsqSo+gqCqg0Gg0OHLkCE6dOoXH\nH38cL774IlJTU5Xu2rQEO+Q01SZaA4Me9PQNitpj9fYPIVGvG/f6RMVbRJuItTvCkv2s9NAaEcWW\nlkt9AdtKnw9QfvOtqSi9Ik81AcVrr73m//fWrVvxj//4jzMumACCH3Iaf5w4Gk/U6zDHrPe3pRt3\npVoM2PdBE8ok9enNxgS0d/XjplIbjjd2wOnyiDcRm8ZmMYGmNZQeWiOi2GJLE09x2ObKm/LIlp4v\nTd75AGCe5BzzZPYx1qgmoBhLo1HPnNR0BTvkJD1OpwVWL8uGRqNBkjEBJkMcDnzY7J9TzE43Yfu2\n8qsbdw1vDJYQr/Vv6HWuzYGE+Di8tr/Rv7vo1g1LkJ2eBHvfIIwG3bQ3iwk0raH00BoRxRajXout\n65egtcuJeakmGA3ydoYonJeErRuWoPWyE/PmmrDAliS/kz6faNkoBJ/8c8YQVQYUr776qtJdCFmw\nQ07S4/YcOIV3P7ro//k9ty3BZfvg6Dbki9Oxcuk80cZd4o3BNHintlm0VbnD6cb1nwt945pzAaY1\nlB5aI6LYMtdqQsO5FgwMeuDx+HDj38jbKfevLQ5/Lhkw/AWrdIm8+9WFTqcoz0MfH3rhrVikyoAi\nlk02jZCXZYXJoMNNpTZYzQZc7nHhng1FaO92IivVBAheHDvZii67C+faHUhOMsCcqEN7dz9saWb4\nfF7MzxiOwI16HY43dsBiipfVV2lVOHOYqsJxhQgRSbm94m/7Xq+8b/9arW90xGOuCTqt/NGEeWkm\ncXuuaZIjZycGFBEWbJXLipJM3HN7Mey9g/j1gdOoLLVh3wfn/MdVltqAZrsoOq4sHY7g//u9k7jn\ntiL/ihAA2HzLYuRlWnD0ZFvID+5IVYXjChEikrrQKU5wPN/hwPUIfZRCq9GhqcPuH/EotMnP8zIb\n4kX3RHOivC9tsYYBRYQFW+VSq9Wgr38IrZedAMav6pC2pa9JM5idLg+8goDnXqnzvzbdB3ekqsJx\nhQgRSXm8gugL01c/v1DW+fr63eLKm8nyK2/29A2Kzim3VkasYUARYdKH5/AOoKNGKmLmZ1nh8fiQ\nEK/FTaU2JOjECUlWUwKsZj3qGjr8r41dvSEdeispSJX94I5U4iVXiBCRVK/k3tjbL29E1OkKf+VN\n3rsCY0ARYdILsKQgxf+wNxsT8Nr+4cIr61fmiaYs/tfahdi4egFaOvuQkBCHnAwzPr3Qg6+sWYju\nXhdSLMM5FJ9e6EFlqQ37P2jCxtULcL6jF0tyk7G8JBOCIIjee/4kS0bHTsvkZ1nhg4DmNgfyrgYR\n4R494AoRIpKS5ifY0uTlJ6QnJ4raaSmJkxwZvGVFGXjgzqVobncgL9OCcsmy/dmOAUWETfTwHHnM\nf3y6A2VFGdBqNTjf0Sv6vS77IGrqzvvbA4Me/+hEZakN59ocmDfXjCMn2vzHnO/oRV1DB9ZV5EKr\n1cDeNyia77OPKZQ1NogwmxLw2r7h5aZjq2oCkclv4AoRIpLySZZken3ykih7Atz/QvVRYwcaz3Vj\nYNCDAZcHc+ckSlbbzW4MKCJsoofnsZNteP/E8PKoVIsBKRY9uh2D2LRmAXRaDWrqzmNOkl60f8fC\nnDlYYLOis2cAqRYD4uO1sJr0ovcqyk3Guopc/zf+s62jSZwmgw6ZqUb85sApJJkSMDDgxqtjllSN\nBGqmmRcAACAASURBVBITVd6c6MHv9Qmoq2/37xlSUpCCsqJMHG/s4OqNaTpytBZ9fc4pj2ttvTjl\nMUQzlcM5iOw0Mzq6+5GRaoTDKS8AmGPRI06rRZfdhVSrAVaz/ATK9m5xrlp7l7zqm7G24o0BhQLO\ndzj8D/rKUhveOtDs/1llqQ0bVubjjf/7GZYVZYj27xg7erC2PAeuIS9uWT4fRkM8hoY8MBri0dxm\nhwbDIyN5Y6pvLivKwG9r/upvrynLEfVpJJCQVuScbI6wtr4d759o8fdn7+Ez4/Yj4eqN4Pzw5f0Y\n0BdOeVzP+eMwpi+OQo+Ios9k0OPV/aN7b9yzoUjW+XweiKaR5Z4PGJ/omZkqL9Ez1la8MaBQwNjE\nzIlWc1zs7IPT5Qm40iPJmIDXD45+WDauXoCfSx7mty7PgyCM7t8xlkVSY+K6xelYlDPHX3lzuCLn\n5PkNzW328aMZ0jr3XL0RlIQEA7yJU1fx0yXInwMmUquRFW6Ttaer5XJfwHYo7H1DonaPpD1dsbbi\njQFFFI1MExj1ceNGCEYk6nVIsRoAjB8tGLuqQ5rB3GV3idojF+bITnjHTrZh35Fz/p/39Q+hstSG\n5CQDSgpSsVwy1Da2IudE8rKsuNgp/oDmSZI+mQFNRMHKnCv+tp8hM4kyN0McpM/PkF96W5romZ5s\nkHW+WFs1woAiiqTTBABw798W4+u3L0FH9wCSjAlIMsaj/bIT5cUZiNdp8fXbi3DpygCSjPHosrtQ\nXpwxHHRYxBdy3jwL8PFoW3phjk0ONRsT4Bp0w5ZuGRdIBKuiJBMaDTA/M8mfQ1FelIkUayJXbxDR\ntBn1WmxcvcCf82AyyCtrbUszic6XLXPVCADkZSaJzin9EjVdsbbijQFFhEyUbNPcZsfQkFd0nGvQ\ni6/dMlrs6qe//QQHPhxd3XHL8vn4/75ait/WnEZN3QX/69+4o8h/Ic7PsiBeq8HWDUv8D3fphTl2\nAelcayIqSvJlJf9otRosvyZrXIYzV28QUShaLvWjt9+NgUEPfIIA19D4Yn7T8f/OdGPv4TP+tqey\nEBXXhL63EQAsK86CR9D4A4CyYnkBQKyteGNAESEjyTYmgw7LijJwtqUb8bp45GZZcKy+3X9cXJwW\ntX9pQ8mCNLx1tAk+YTgf4tDHF3DZPoi0OYk4drINBVkW0RIoW1oSll8zfCEePdmGf/zP0cSekoLU\nccGCGpJ/Yi2jmYjCJyPFiP850uBv/90X5CVRSld1WGXubQSIv5jxzjUeA4oIGUm2GVmpsXH1Avzm\nnUasLc8RBQb9LjcO/bkFzR29eHXfaIbz3bcuhmvICw0EPPtKLbZvK8eN19r8yzR9ggCfT4BWqwkq\nsUcNyT9qCGqISJ0EQRDdGyV1+aYtKTFBvO9GGDY3rLs6bT2SPK/RSHd9nt0YUETISLKN92qxlp7e\n4TXVdueQqHz2mrIcDAx6xu3F0d7dD7fHB6Neh/LiDFzo6EVupsW/He/ew2f8D+RgEnvUkPyjhqCG\niNTpfEefKL8sXrL9wHRdkey7kR2GfTfGLvkHhnPIGFCMYkARISPJNhc6HKjefwobVy+AyaDD/Iwk\nUUAhCMLVKQzxxT4v1YTqt06hstSGuoYO1DV0YNsdRf6I26jXoaXTASArqMQeNST/qCGoISJ1ypbc\nA6X3xOmSLo23hGGEosvhCtie7VQTUHg8Hmzfvh0tLS1wu9148MEHsWbNGqW7FbKRZJvmNjtMhuGy\nrzf+jQ1XegextjwHducQFs+fAw008Ak+2HsHRdnDPX0ubFqzEPs/aPKfc3DQJ4qOi/KWirYn/+ra\nxZPmJKgh+UcNQQ0RqVOiXovNtywerpSZYoRRL2+EwuP1iu6pXp936l+awlyreHXdXIu8ZaOxRjUB\nxZtvvonk5GT84Ac/gN1ux5e+9KUZHVCMyMuyYllRhmiFxsiow7y5Juw9fBYAcFOpDYfGBAuVpTZo\nnG44XaOZzv2D4toTbV3OccWs1DyFoIaghojUqcsxhP/zp9Fqvl+RuX25s98rqpR51zr5VWZzM8TJ\n8ZNtuDhbqSag2LBhA9avXw9geJMYnU41XQtoZOXCuTY7kkwJcLncyEm3wO3z4f/99TLMxnjMMYuH\n2kYqTCYn6fHlmwtgSoxHt2MQ99y2BJeuDCBrrgmGhDg0tTrwjTuKodEISNDpYJfUtk+26FFZasPQ\nkBfZGWb85cwlNLXaYUjQIjstCWXFwxuRjaysyM2yQKvRoKl1eFdRryCgoakbFlM88jItKCvmqgsi\nUoZOK4yOUKQaIcgeURCPUADyRyiKCuaiub0XLZf7YEszY2nBXHk9jLGVb6p5aicmDlcg6+vrw6OP\nPopvfetbCvcoONLlobo4LfoGPKLIeOPqBaLfmTfXjMpSHVyDHmg0WlTvP+3/WWWpDZ9dtIumNh64\ncyl+/vuTMBl0qCy1QRenxcKcOdAAo8fVD//um+81obLUhjMtDnivZkmPXVkxsh+IdFfRylIbvAI4\nejDLCD4fmpqapj7wqsLCQsTFySs4RLEh3A/DeF18WPfyiNfF4zfvhO98APDW0SZRHwHgK59fFPL5\nYm3lm2oCCgBoa2vDI488gi1btuC2225TujtBkS4PBYDy4gzRMS2dffjKmoXo7nXB7fGhprYZTpcH\nN1+XPe580v0xgNE9MpwuDw5/0oLy4gz09Y+vIT/yuyP/K11VMdExY1/nqovZZ6D3Ep5+6TKM1jNT\nHttv70T1rs1YtCj0GyjFjg/r27DrlTp/e/u28ilL9gcS7r03IrGXh3Q1nrQ9XbG28k01AcXly5dx\n77334umnn8aKFSuU7k7QRlYujH1AS/fgSEiIgyEhDvPmmvzLPgFcHYYTS9TroNdpRas5CudZxx2T\nm2UdV1hlZK+Pkf8NdMxE+4Rw1cXsZLSmw5xsU7obNMM0nO0WtevPdssKKOZnJonue7kZ8lZ55IR5\n1UgkzhlrK99UE1Ds3r0bDocDP/vZz/DCCy9Ao9Hg5ZdfRkKC/KU+kTR2eejIctDjjR24+9bF6HK4\nkJSYgCSjDs6BISQZ47F1wxK0Xe5HRkoiEvVadPcOYuuGJehyuJBqMaDL7kJ6ciJe+Z/RYbXrPzcP\n27eVo/7scL5DbqYF5VdLvo7dn6PL3o+71i2GQa+FLS1p3DEjORQ56Wbkz7Pi+s/N8+dQjD0nEdFU\nLJLKk9L2dBkTNaJp2GsWzpF1vnXL8+AThkcmbHPN2LA8T9b5AOD2VQXwYXhkwpZmxhdWFcg6X6yt\nfFNNQLFjxw7s2LFD6W5M28jKheUlmcjJsKC5zY6E+Dj893tnUFwwF112FxbNT4ZWG4cTZ66gcJ4F\nXp8Pn17ogVGvwzUFqXD0D8Hj8cGYGI8v37wQvzv4qeg9zrc78LVblkwY/QezakJ6zNhCLNd/Tl5t\neyKanfIyxSsecmWueGi66BzfXh76+Q7/+aIo3yHRoPPvvhwqg0EnK2dCKtZWvqkmoJjpxlaJ7Xa4\nUFwwF4c/aYHJoEOiXgddnBZGvQ4dVwbw7kcX/cdmphrxx/ebsKwoAx+f6sTQkBe5WeIPplarwbGT\nbdNKeoq17GEiUpey4kx4Bfi/Xcsd4Zw7R1LjYYIp4ekYyT2brE3hx4BCpiGPDweOnUNTqx1JxgQc\n+vgCSgrmYmDQA5NBh/Ur83C+oxdGvQ7HGzvGlWnt6RsSJXTWNXRg5zcqsH1bBerPduFKrwtvvPsZ\nnC7PtDKAYy17mIjUJdzfrp0DQ6Jlnv2u8Ynn05En+WImdwSFpsaAQqYDx85h95jiUhtXL8BbR8/h\nSzcvQKJeJ1o+WllqG1cO1pZmxskzl0WvNbXa8bVbluBipwNXel0oLkgVldoORqxlD5PyuMSUIslo\nSBAlrW/dsETW+daW58I15MXFzj7kpJtxS0Wu3C7SFBhQyCQdRuuyD9d212oExOu0uKnUhuONHXC6\nPBgY9MAQr8XqZdmwmPQoKUjFssXpEARBtL/HSKavUR8vSlJakrc06H7FWvYwKY9LTCmSuu0u0SqP\nbru8fTI+Pt2JV/44uh161lwzv1RFGAMKmaTDalmpJqxfmTeuWNXhT1pQlJuC/kH31STOLH9Ow5du\nWoCsueZxmb69kloTE9WemEwo2cPMu6CpcIkpRUp6ihG9LaMjqxkpRlnn4yht9DGgkOnW5XkQBOBM\nix1WUwLeqT2HfJt4uZNWo8HWDUvwpZsX4HhjB5rb7NBA439gTzYXKWeUIZT5TeZdEJFSnC63aEQ2\nI1VeQMFR2uhjQCGTTqfFHTcU4NjJNjx79WFcUiD+v9UnCMjJsOB4Y0fAB7Z0hKCsKCOqa5QZ0ROR\nUnqd7oDt6Yq1Gg8zAQOKACaaAhi72VZ+lhX/f3v3Ht9Eme8P/JNL2zRNk5ZeSFqwpSCghfWASwER\nDqByWd0VBTwrF2VfvBBYUZdVF1rwgnLXdY/ugWPRVVzA5bgCshdE5PJbFJWiqyxbBVZoC5SmLW1J\nmrRNmmR+f5RkkzRt0k7STMvn/U87M888802eb9JvZ55kXBBQVmFGToYOBfNGoKzCDJ0mDtkZWlRf\nbUSKVoUsvRbDB/fGnv/3L4y4ubfnEx/+f7DbOkPQVX/UWdETUajcn3ArM5qRbdBi8shsKJWdv+V4\nit/HRlN0caLi62nf8dAdsKBoR6A/8MC/b7blf4Otgnl5+OmkwDOTPz9V4TODedywzFZ/sKN9hoAV\nPRGFyv8TboIAUV8cFauQ+XxRVoyI4oSigwVFOwL9gffmf4Ot9goA/32TE1Wt/mBH+wwBK3oiClW4\nvziqvNrq8w9afCw/ctzdsKBoR6A/8N6fefC/wVZ7BYB/X7k5Ka0+QcEzBETUXYT7i6OyM33fI7Mz\neMm1u2FB0Y62/sC71/XL0GHMLRkoqzAHLQBCKRZ4hoCIugv3J9zKjGZk6bWYMipbVH9TRmYDYeyP\nuh4Lina09Qfef10ot+xlsUBEPYn7E25S7Y+6Hme9EBERkWgsKIiIiEg0XvIgorDryI3EeBMxop6B\nBQURhV2oNxKzXjXixYVj0K9fv5D6ZfFBJF2SKSgEQcDzzz+PM2fOIDY2FmvWrEHfvn2jHRYRdVIo\nNxJrMFXi2S2f8w6mRD2AZAqKgwcPwm63Y+fOnTh58iTWrVuHzZs3RzssIoow3sGUqGeQTEHx1Vdf\nYezYsQCAW265Bf/85z+jHBERSUlH5mU4nU4ACHp5JNR2brzkQtQ2yRQUFosFiYmJnmWlUgmXywW5\nvPUHUdxvAkajscvio55Fr9dDqQx/+ncmN03VZXCp7EHbNdaWwiFTBW0HAI31tQBkYWsXqbYd6bP2\n8hk8/fK3UGl6BW1rqjyPuISkoG1DbQcATZZa/M8zP0VOTmS/K0FKuUnkLVhuSqag0Gg0sFqtnuW2\nigkAqK6uBgDMnj27S2KjnufQoUPo06dP2PuNeG5WfI2rITa1ASG1DbVdpNp2pE93+5Da1YTWNtR2\nALBgwaEQW3Zet81N6vGC5aZkCorhw4fjyJEjmDJlCr755pt2J14NGTIEO3bsQFpaGk8/Uqfo9ZG5\nTwpzk8RibpJUBctNmSAIQhfF0i7vT3kAwLp160L+KBkRERFFl2QKCiIiIuq++NXbREREJBoLCiIi\nIhKNBQURERGJxoKCiIiIRGNBQURERKKxoCAiIiLRWFAQERGRaCwoiIiISDQWFERERCQaCwoiIiIS\njQUFERERicaCgoiIiESTzO3L9+zZg927d0Mmk8Fms+H06dM4duwYNBpNtEMjIiKiICR5t9EXXngB\nN910E2bOnBntUIiIiCgEkrvkcerUKXz//fcsJoiIiLoRyRUUW7ZswZIlS9pt43A4cOnSJTgcji6K\niig0zE2SKuYmRZqkCor6+nqUlpYiLy+v3XZGoxF33HEHjEZjF0VGFBrmJkkVc5MiTVIFxYkTJzBq\n1Khoh0FEREQdJJlPeQBASUkJ+vbtG+0wiIgoCpxOJ86dOxdS2/79+0OhUEQ4IuoISRUU8+fPj3YI\nREQUJefOncPc/Heh1qW3267BVIVt62Zh4MCBXRQZhUJSBQUREV3f1Lp0aJIzox0GdYKk5lAQERFR\n98SCgoiIiERjQUFERESisaAgIiIi0VhQEBERkWgsKIiIiEg0FhREREQkGgsKIiIiEo0FBREREYnG\ngoKIiIhEY0FBREREorGgICIiItEkdXOwLVu24PDhw2hubsasWbMwffr0aIdEREREIZBMQVFUVISv\nv/4aO3fuRENDA956661oh0REREQhkkxB8emnn2LgwIH4+c9/DqvVil/96lfRDqlHcLoEFBUbUVZh\nQrZBh7xcPQSg1Tq5XBZ0P/82XRFrpI9JrYU6DnaHCwe+KEWZ0YxsgxaTR2ZDqez8VVSOP1H3JpmC\noq6uDpcvX0ZhYSEuXryIxYsXY//+/dEOq9srKjZi7dYiz3LBvDwAaLVu9FBD0P3823RFrJE+JrUW\n6jgc+KIUhXtOeZYFAbjn9pyIH5eIpEkykzKTkpIwduxYKJVK9OvXD3FxcaitrY12WN1eWYWp1XKg\ndaHsF2nROCa1Fuo4lBnN7S5H6rhEJE2SKShuvfVWfPLJJwCAyspKNDU1ITk5OcpRdX/ZBp3PcpZB\nF3BdKPtFWjSOSa2FOg7ZBq1vO702YLtwH5eIpEkylzzGjx+PL7/8EjNmzIAgCHjuuecgk/H6qVh5\nuXoUzMtDWYUJWQYdRubqASDgulD2i0as1LVCHYfJI7MhCC1nJrL0WkwZld0lxyUiaZJMQQEATz31\nVLRD6HHkchlGDzW0uhYdaF0o+0VSNI5JrYU6DkqlXNScic4el4ikSTKXPIiIiKj7YkFBREREorGg\nICIiItFYUBAREZFoLCiIiIhINBYUREREJBoLCiIiIhKNBQURERGJxoKCiIiIRGNBQURERKKxoCAi\nIiLRWFAQERGRaCwoiIiISDRJ3W30/vvvh0ajAQD06dMHa9eujXJEREREFArJFBR2ux0A8Pvf/z7K\nkRAREVFHSaagOH36NBoaGjB//nw4nU4sXboUt9xyS7TDkhSnS0BRsRFlFSZkG3TIy9VDLpd1eN8s\ngxZymQwXKs1Qx8WgvsHe4f4iESO1r6PPbaTHwu5w4eMvSlFqNCM5UYX+mVqMuNnA8Sa6TkmmoFCp\nVJg/fz5mzpyJ0tJSLFiwAB999BHkck7zcCsqNmLt1iLPcsG8PIweaujUvuOGZQIAjn5d3qn+IhEj\nta+jz22kx+LAF6Uo3HPKszxuWCZcgozjTXSdksxf6+zsbPzkJz/x/J6UlITq6uooRyUtZRWmdpc7\nsm+jzYFGm6PT/YV6nHD0SS06+txGeizKjGaf5Uabg+NNdB2TTEGxa9curF+/HgBQWVkJq9WKtLS0\nKEclLdkGnc9ylt9yR/aNj1NCHed7gqoj/YV6nHD0SS06+txGeiyyDVqf5fg4Jceb6DommUseM2bM\nQH5+PmbNmgW5XI61a9fycoefvFw9CublXZsHocPIXH0n922ZQ3Gx0ozB2UNhabB3uL9IxEjt6+hz\nG+mxmDwyGxDQModCo0L/PlqMuJnjTXS9kkxBERMTg5dffjnaYUiaXN5yfboz16gD7TtySPivdYuJ\nkdrX0ec20mOhVMpx9+05EembiLofngIgIiIi0VhQEBERkWgsKIiIiEg0FhREREQkGgsKIiIiEo0F\nBREREYnGgoKIiIhEY0FBREREorGgICIiItFYUBAREZFoLCiIiIhINBYUREREJBoLCiIiIhJNcgVF\nTU0Nxo8fj5KSkmiHQkRERCGSVEHhcDjw3HPPQaVSRTsUIiIi6gBltAPwtmHDBjz44IMoLCyMdihd\nzukSUFRsRFmFCVkGLRQyoKSiHsbaBvQzJCI9OR4XKi0w1jTAkKpGc7MT/TKT8MOb9Pjqu0pcqDSj\n1tyEVF08rlps0CXEoqquARlpiYDgQo3ZjtycXhiZa4AAoKi4AiXlZtRZmtDPoEUvrQr/PF8LbUIM\nsvVa/PBmPWwOF/YdO4+LlfXITEtAf4MW/zFYD7lcBqdLwIliIy5UmmG22pGRmoAmuwO1Xsdxt3M/\nrmyDDnm5LfsHYne4cOCLUpQZzcg2aDF5ZDaUSknVvFFlbXJg37HzKK+2oG+aBlNG98Opc1dwsdKM\nGnMTemlVnrETAJworsCFSjOq6pqQpIlDepIKE0dkQSaX4XhxBf51oQ5qVQyuXG1EojoW6b3i0dzs\nQum15//OEVn4+5kqXKoyQx0XA5PVDlWsAldMjchM0+A/h/XFR8dLUVXbgFRdPIy1VvRJ0+DuMTlQ\nKOWtxr0l79pel2XQQi6ToeRy+7nSkZxqSzj6ICJfkikodu/ejZSUFIwZMwavv/56tMPpckXFRqzd\nWuRZnjVpEN49cAYAMG5YJmrNNuw68r1n+/QJA7B26wksvG8oviutxdGvy322/f7D057lccMycfTr\ncuw9eg4F8/IAAJ+evOyzj7uN+3enAFy+YsHWv3zrE5PNCYweakBRsRGfniwP2If7OO523o/LvT6Q\nA1+UonDPKc+yIAD33J4T4jPY8+07dh6/3/edZ9klAKVGc6sxcAotv58pq/PJmXHDMmF3CkjRxWPd\n1hPXxss3p7zbN9md2PqXb31yw93Pnz85hYZGB37/4XcYNywT+z4r/XdcADJSNa3GHUDQdd7HaitX\nOpJTbQlHH0TkSzL//u3evRvHjh3D3Llzcfr0aSxbtgw1NTXRDqvLlFWYfJYraxs8vzfaHKgxNfls\ndy+XGc1otDkCbvPe3/s4ZRWmVvt4LzfaHCirMOFSlaVVTO44g/Xh3a69x+mzzWhud/l6V17tOx7l\nVywBx8A9xoHyoMxo9oxBsLxxj39b41x+JfD2S1WWgOMeyrpAOeSvIznVlnD0QUS+JHOGYvv27Z7f\n586dixdeeAEpKSlRjKhrZRt0Psu9e6k9v6vjlEjR+c4rcS9nGbRobHIE3OYWH/fvYc4y6CADWhUL\n3m3i45TIMugQF6NoFVPWtTizDbp2+/Bu5y3Lb9lbtkHr21avbaPl9alvmsZnOTNVA4fDt+hyj50M\nLWcYWm3Ta5GqiwfQklfe/POmb7omYDv3OGemBd7eJ12DzFTfWN0xBVsXKIf8dSSn2hKOPojIl2QK\nCm8y2fV3LTMvV4+CeXmea8mxCmDOlMGeORT6XvFQTx3cMociRY1mhxMF80ZgxE16pOnicYM+EbXm\nJqTo4mGy2DB36mBUe82hSE5UXZvboAcAyGQCMlI1qLM0IdugRYpWheREFbQJMcjSazHiZj0cDhdc\nAC5W1iMjNQH9M7QYNljviVcmA27QJ8JstcOQmgCb3dHqOL6PS+dZH8jkkdkQhJYzE1l6LaaMyo70\n096t3D0mBy60nKnITNPg7tH9cOr8FWTpE3HF3IQUrcozdgAglwlQTx10bQ5FLNKS4nHHiCzI5TIU\nzBuBsxfq8NCPbsKVq43QqGOgT1Zj0X1DUXrt+Z+UlwVDqgblVWYMzh4Ks9WOuFgFakyNWHjfUEwc\n3hdyuQxVtQ14aOpNMNZakZGmwY/H5ECplAcc9/bXtcyh6JuuaTdXOpJTbQlHH0TkSyYIghDtIDrq\n0qVLuOOOO3Do0CH06dMn2uEQeTA3Saq6Q26ePXsWC9cfhCY5s912lrpyFC6/EwMHDuyiyCgUYZ9D\nUV5ejp/97GeYNGkSqqqq8NBDD+HSpUvhPgwRERFJSNgLimeffRbz589HQkIC0tLScM8992DZsmXh\nPgwRERFJSNgLirq6Otx+++0QBAEymQwPPPAALBZL8B2JiIio2wp7QaFSqWA0Gj0TK7/88kvExsaG\n+zBEREQkIWH/lMfy5cuxcOFCXLhwAffeey9MJhNeffXVcB+GiIiIJCTsBcUPfvADvP/++ygtLYXT\n6UROTg4qKyvDfRgiIiKSkLBf8hg+fDgOHz6MG2+8EYMHD0ZsbCwef/zxcB+GiIiIJCTsBUVycjLe\neustvPLKK5513fCrLoiIiKgDwl5QaLVabNu2DUajEQsWLEB9fT3kcsncMoSIiIgiIOx/6QVBQGxs\nLDZu3IhRo0bhgQceQH19fbgPQ0RERBIS9oJi7Nixnt/nz5+P/Px8nqEgIiLq4cL2KY/q6mqkpaXh\nwQcfxOXLlz3rBwwYgLfffjtchyEiIiIJCltBsXLlShQWFmLOnDmQyWSeb8p0O3ToULgORURERBIT\ntmsRhYWFAIDf/OY3mD17Nvbv34+srCxYLBY8/fTT4ToMERERSVDYJzesWbMGQ4cOxYEDB6BSqfDB\nBx/gjTfeCLqfy+VCQUEBHnzwQcyePRvff/99uEMjIiKiCAn7N2W6XC6MGDECTz75JCZNmgSDwQCn\n0xl0v8OHD0Mmk+EPf/gDioqK8Morr2Dz5s3hDq/L2B0uHPiiFGVGM7INWkwemQ2lMnD91mh34sNj\n53GxyoLevdRQx8lhttiQqFGh3mqHyWKHITUBCgVgbxZgrLGiT5oGU0b3w8l/VeNCpRlmqx0pWhWq\nTY3ITNMgVafChcp6qONiUN9gRz+DDk5BwL8u1iE+TgmztRn6VDUam5oRr4pBU1Mz+qRrkZerh1wu\n84nP6RJQVGxEWYUJ2QYd8nL1EIBW6/z38+fu51KV2RNXsH0DHTvYcboz78d7g14Lk8WG85dNyDZo\n8Z/D+uLA8VJcrLIgI1WNhqZmxMYooVYpoJQrYG6wQ5cYB6vVDpO1GUNyeuHyFSsuVNYjM00DpQKo\nNdug08ShurYRCeoYJKqVUMcpkJyoxk05qdj/eQnKqy3om6bB3WNyoFIpA8aWbdDh1pt648vvKkMa\nm7bGsbuMb3eJkyiawl5QxMfH46233sLx48fx7LPP4p133kFCQkLQ/e68805MnDgRAFBeXg6dThfu\n0LrUgS9KUbjnlGdZEIB7bs8J2HbfsfPY+pdvPcvTJwxAXIwSZy9cxdGvyz3rZ00ahHcPnPEsEQqu\nSgAAG9VJREFUO1wCSivMPm3GDcvEnz85hXHDMgHAs23csEwc/brc89O7vXv9O/tOo2BeHkYPNfjE\nV1RsxNqtRZ7lgnl5ANBqnf9+/tz9+MfQ3r6Bjh3sON2Z/+P1fq4aGh34/YffebZNnzAA7350BtMn\nDMCuI9+3aq9UyDzr3e1rzTZ88LfzPv1n9U7EP86Vo8xY79O/C8DMOwa2GdvC+4b65HhnxrG7jG93\niZMomsJ+yePll19GQ0MDXnvtNeh0OlRVVeHXv/51aMHI5Vi+fDnWrFmDH//4x+EOrUuVGc3tLnu7\nVOV7e/caUxMqaxvQaHP4rK+sbfBZLq+2tGrjXm60OXy2ea9vqz0AlFWYWj8Wv3VlFaaA64Jxt/GP\nob19O3Oc7sz/8Xk/V+VXWueJ90//9t7r3cuBxv9yjRWNNker/surfZdbjYV/jndiHLvL+HaXOImi\nKexnKHr37o0lS5Z4ljs6IXP9+vWoqanBzJkzsW/fPqhUqnCH2CWyDVqf5Sy9to2WwA3pGp/lFJ0K\ncTEKOJwun/W9e6l9ljPTNHA4fN/U4+OUnp/eJ2TV19a7fwZqDwBZhtZnhrL91mUZdPA/2Rtov7b6\n8Y+hvX0DHbsn83+88V7PVWZa6zzx/gn4Prfe693L/l+DHx+nREZKApodrlb9+y/7x5btl9OdGcfu\nMr7dJU6iaJIJErnRxt69e1FZWYlHHnkEFosF06ZNw759+xAbG9uq7aVLl3DHHXfg0KFD6NOnTxSi\nDc7hcGH/tTkUWXotpoxqew6F3e7EX9xzKJLjoVYpYLbYoNWoYL42h0KfokaMUgbbtTkUGWka3D26\nH05+X40yY8scil5aFa6YGpGZqkFqkgoXK+uhiouBpcGOfhk6OF1+cyhS1GiyNUMVF4MmWzMy07UY\nGeDasMsl4Pi168dZBh1G5uoBoNW6YNeU3f2UV5k9cQXbN9CxpXztWmxuej9e7zkUWXotJg7vi/3H\nS3GpygJ9ihqNtpY5FAkqBRTX5lAkaeJgaWiZQ/GD/r1wqfraHIpUDZRKvzkU8THQqJVIiFMgSavG\n0JxU/PXaHIrMNA1+7DeHwn8sRtzUGyeuzaHo7Dh2l/HtLnG2pzu8b549exYL1x+EJjmz3XaWunIU\nLr8TAwcObLcddS3JFBSNjY3Iz8/HlStX4HA4sHDhQkyYMCFg2+7wwqD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C6P5Onz6NhoYG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\niIhE+//yIo6VcM5ynAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.pairplot(tips)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Present the realationship between days and total_bill value" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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uVZxFQ7rtdM1dVVV0lJTiE3fuawfk//0ZOku7h3W2FxRQ8MLLjPvDo/1Zpugn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\niYiIPAiDm4iIyIMwuImIiDzI/wPvE39ImLp8oAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.stripplot(x = \"day\", y = \"total_bill\", data = tips, jitter = True);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. Create a scatter plot with the day as the y-axis and tip as the x-axis, differ the dots by sex" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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zsmz04ouuV21LPZWtNdhsNhRFcdpWWN7Ao6/toKHJCMDgMc3kqLYCUNdSz192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\noGXLlql169Z1Ht+iE3Lw4MHasGGDJCk3N1cJCQk+rsh/nT9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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.stripplot(x = \"tip\", y = \"day\", hue = \"sex\", data = tips, jitter = True);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. Create a box plot presenting the total_bill per day differetiation the time (Dinner or Lunch)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.boxplot(x = \"day\", y = \"total_bill\", hue = \"time\", data = tips);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 11. Create two histograms of the tip value based for Dinner and Lunch. They must be side by side." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# better seaborn style\n", + "sns.set(style = \"ticks\")\n", + "\n", + "# creates FacetGrid\n", + "g = sns.FacetGrid(tips, col = \"time\")\n", + "g.map(plt.hist, \"tip\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 12. Create two scatterplots graphs, one for Male and another for Female, presenting the total_bill value and tip relationship, differing by smoker or no smoker\n", + "### They must be side by side." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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U8Otf/7pJxxFCiOYS7fNtW7J77rmHl156iX/961/0798fpRS9e/dG0zRSUlKCscrn85GT\nk0Pfvn0xm80A9O/fn8OHDwOwc+dOlFJYLBYKCwvJy8tj+vTpKKUoLy8nJycHgB49eoSs7mcNuPHx\n8bRt2zb4uFOnTlitTT+xioqKOHbsGEuXLuXo0aNMnz6dNWvWNPl4QggRKg0NpLKARPN5//33ufXW\nW0lPT2f69OkcPHgwmCxXlSFe9e9OnTqRnZ1NIBAAYPfu3dxwww0APPbYY3z88ce88sor3HHHHXTp\n0oWXX34Zq9XKihUr6NWrFwAmU+gm85w14Pbp04e7776bW265BbPZzIcffkhaWhpvv/02AOPGjWtU\ngcnJyaSnp2OxWOjRowd2u53CwkJSUlLqfH9mZiaLFy9uVBlCtFTSHhqnsT3RhgbSaJ9v25JdeOGF\nPPHEE8THx9OuXTvS03/MBK/6b1L175SUFCZOnMjEiRMxDINRo0bRp0+f4Hvuv/9+JkyYwMiRI5k5\ncyZ33HEHfr+fPn368Itf/CLkdddU9UuCOsyaNeuMB3jmmWcaVeB//vMfXnvtNV5++WXy8vK4/fbb\nWbNmTY0/1Nnk5OQwYsQIsrKy6Ny5c6PKF6KlkfZQvzfW7g8GUKUUvbs4z9gTzVy5m6KyH4OnM8HB\njAmXnPa+5Wv3c6ARxxUCGtDDbWxAPZthw4axc+dOxo8fj1KKp59+ulHBVggh6lN7tam84rRG9UQb\nuhJTtM+3FdGp3oB77733snTpUoYPH14jICqlMJlMrFvX9FVcHn300SZ/Vggh6lO12pSmaRS6S3DZ\nT2Ip69vgpQxrB9LRGV15Y+3+04akZb6taIp6A+78+fOByvHy2bNno5QKnrRnG2YWQojmUHu1qfbt\nTThjnA3uidYOpNWHpCU5SpyregPu3Llz2b9/PydOnODrr78OPh8IBOjQoUNEKieEEI1Re7WptvEp\nTMhoeoCU5CgRSvUG3Oeee47i4mIWLFjAnDlzfvyAxUJqampEKieEEI0R6tWmZHcdEUr1Btz4+Hji\n4+N56aWXIlkfcR6SRQBEtAj1alOSHCVCSTYvEEFNDZxvZn3D5i+P4dcNrBYTuh7gjusuikCNRThE\nw76y4dCU7yXJUa3H9u3bue+++/jggw+Cq1X94Q9/ID09vdHrTdRH9sMVQVWT/ovKPBw4WsTqDQcb\n9LnPs/OpcPvx6wYVbj+fZ+eHuaYinKJhX9lwaOz3cnn8vLF2P5krd7N87X7cHn+EairOJvdEOW99\n8i2r1n/LDwUVITuuzWYLa1Kw9HBbqLqu5lXAfMYebNMTRKoy2MHrC5Bf5GL52v0hG1puqT2uaBUN\n+8rWp65RGAUNGplp7PeqverUyqxvsVpMwXKuvaIj677fGNbzMtS3a1rC7Z+CEjfL13yNx1+5XOPB\n3BLuufFikhLO/W+fkZGBUorly5czefLk4PP/+Mc/+OCDD7BYLAwePJhHHnmkSceXHm4LVdfV/Nl6\nsClJjuBapI1JELm0bxqxdgt6wEApRZzD0qgeclO+iwgfpyO5xnkQTXsl13UON3RkprHfq/YF6K7s\nvBrlvJC1KuznZVNHnSJ1vObw9eHCYLCFyouIfYeLQnJsTdOYO3cu//znPzly5AgA5eXlrFmzhpUr\nV7JixQoOHz7Mhg0bmnR8CbgtVF1X82frwd48NJ3eXZw4Exz07uJscILIhBF9GDaoC84EO22SY+nQ\nNj6kUyiiucfVEo3tN5L0lG44HYmkp3SLqn1l6zqHGzoy09jvVfsCVKHVPA+9JWE/L0M9LaklTHNK\nirNjGNU3KYCUxNCNLCQlJTFr1iwef/xxlFJ4vV4GDBgQ3MRg0KBBfPvtt006tgwpt1C15yM6Y5Lx\nn2WKQ1MTRKo+p6DG+rKhmkJR13cR4RPN+8rWNU1HwVmn7lQOpR6moKQzKUm9GDs0nZiz7HpWO0PZ\npwf4/nhpsJxkeyJKuUJ+Xla/hXLC5kdTPbFo9pC0qZYwzeni9FS+O1bMl9+eRAGDL2hH327OkJZx\n9dVX8/HHH7Nq1Sruu+8+vvzySwzDQNM0du7c2eQkKgm4LVSd8xF7mcM6xeHmoemszPqGz7Pz0VD4\n9QBuj/+c7xGFem6lOH/VN03nbOd1U7bTq30B6vb4a5Rz7RXj+fj7DSE/L6svT+lICuDlEE7fJSFp\nsy1hmpOmadw4pBejf9IdNHDYwhPGZs+ezdatW4mPj2fMmDHcdtttKKUYNGgQI0c27b/1WXcLikay\nO0r0auzuLOLcSXs4u4buAhQNlu54nSJPafCx05HIvYOnNGONRKhID1eEVEu4RyRanlANpUYiy1du\nobRckjQlQqqpmc5ChNOYn3TF49U5ll+Ox6szJqNrk44TiSzfaE5aE+dGergR1tLnlLaEe0Ti/NDQ\n3qbL7+ZP/1lJQWIh1oRYrL4LWLP1SJNudURiBCeak9bEuWm2gFtQUMAtt9zCP/7xD3r06NFc1Yi4\n2vt1vpe9rs7G1RyBORTDZbIUnoiUhiZCvZ+dRZ73GAGTQsdFge1rCkqSmlRmS8jyFc2nWYaUdV3n\n6aefxuFofSdrQ+eUNsdiDy1hUrxoPRra2yxyF2OzWFCAhoZfczU5UDZ1rroQ0Ew93Oeee46JEyey\ndOnS5ii+UZra06zvcw1NiKgemA1DsePb78nbt7tRPc/G9lgl4UlEmsvv5u19a/nih30AXNCmD/qx\ndHYfOIkrPhtniuLS9K7cfNFoYqwOClyF/HnrK5R4SvCYLKSqn2LX4lFKkZhgYuVX79fZ5tqnFPFD\nIfh0nXb2FG4amk5BWSl/ylpFkbeEZHsiD48YjzMh4Yz1DeUITktYZlE0TsR7uKtWrSI1NZUrr7yS\n82FGUlN7mvV9rqEJEdWXoTt+sgJXmanRPc/G9lgl4UlE2vvZWWw9+jkF7mJOuor4z4GdbMjZyEnL\nV5Rr+RwvKWTLgf3B9vPnra+QU3Kccp8bn1bCUdtGjuWX4/bq0PZgvW2ud9seXNy1IyMu6s+s624j\nxmHlT1mrOFqRg0svJ6cilz9mvRXR7y4jStFl5syZLFu2LPi4oqKCMWPGkJ2dHbIyIt7DXbVqFZqm\nsXnzZvbv38/jjz/OSy+9VO+m9pmZmSxevDjCtfxRU5cVrO9zDU2IqL7Yw8mAjTh/n+CxGtrzbGyP\nVRKeol9zt4dQK3IXoysdqDxHA+joJjdYFBqVG2LoASPYfko8Py6n6AsYYPbSsW1lD/ero8dIbdPw\nNlfkLcFE5ftNVC7VGEkyotR0x8ry2JnzBQA/6TyQdgltz/mY8+bN45ZbbmHEiBGkp6ezaNEibrvt\nNvr27XvOx64S8YD7+uuvB/89depUfvvb39YbbAFmzJjBjBkzajxXNdE/Epo6J+5c59JV/5FYXrif\nA2VFoDWs51k1VHUgpwi3V6d9ahxmk3bWz0nCU/Rr7vYQak5HMhbNgg8dpRRmLBgBG15bAZqtAoUF\nTUsJtp8keyJl3srlFA1lYCUGqAxYymdHKW+D21yyPZFyvQwTGgYKp71piVRNJQlYTVPoKmblnvfw\nBHwAHCrO4c6BvyDJcebbAWfjdDp56qmnePLJJ3n44YfJyclh3rx5ZGdns2DBAgCSk5NZuHAhPp+P\nhx56CKUUPp+PuXPn0q/f2X87m3UebtXVXTRr6py4UM6la2yiRtVQVXK8AxSUlPskwUNEHZfHjzun\nK5aKTuB3kBKTzNW9LsNht2AyNDRlBlMApQWC7edXP72LzkkdiLfFkGhuQ2f3z4DKC9H/k3h5o9rc\nwyPG0yWuM7GWeLrEdeahEbeE5nv53az86n2W7nidlXvex+2vu+cqCVhNs7/gYDDYQuXfO/vkdyE5\n9rBhw+jZsyezZ8/m2WefBeCpp57i6aef5tVXX2XIkCH89a9/Zc+ePTidTv72t7/xm9/8Brfb3aDj\nN+s83FdffbU5i2+Qps6JC+Vcusb0PF0eP9v3Hqekwo/NYqJDm3jaJMdIz1VEnbc3HORwbgXJ2gCS\nVH96JzqZNKgfOw/8CfQYONV7tRrxwURFZ2wy84Y/DJy+tvFNQ9OJcfQHKtvB6k/OnJDkTEjgt+Pu\nDPn3aujUPxlRapokWwKGUpiqOmxK4XQkhuz448aNw+v10rZt5TD1wYMHmTdvHlA5w6Zbt24MHTqU\nw4cPM336dKxWK9OnT2/QsWXhi2bi8vh5Y+3XbPg8F7/ykdAth//TL472iW1Oy4RuTKb02xsO4vLq\n+Pw6fl3j+MnykO+kIUQo1HcPs/ZQb6I1kTfW7g8GzzEZXVm79Qh5BRXkFblok+Rgz4Ey8goqSEuN\n4+ah6U3arCBU8itO8kPZCfyGH6vJSqI9PiLlQstfWAfgwrTeHC4+yp4T2WgKBna8mN5twreWQ8+e\nPVm0aBHt27fn888/5+TJk2zdupW2bdvy8ssvs3v3bp5//nn++c9/nvVYEnBDqLGBMWvHUdxeHVPH\nAxQHivn8Oxt9u1WcdkVc+4p59b41WMyWOsspKPHQPjWOvAIXPj1AjMMqQ1UiKtV3D/PhEeP5Y9Zb\nFHlLcNqT6GoeUCN4/u+BfBx2C8fyKyh3+zjyQxmaBoWlXkpdvmCvN2Ao8goq8OkBCku9p3rA1rAH\npRMVBZT7Ku8ze3U/JyoKQnbss2lo7/p8pmka1/UdwYj0n6GhYbfYwlre008/zWOPPUYgEMBkMrFg\nwQKSkpJ4+OGH+X//7/9hGAYPPPBAg44lATeEGnOyF5R48AcUmqahWT0oTPh1o85M6NoZz1/8sI/k\nmKQ6y6n6EeuUFh/crUfm9olwa8qc0vq2c6w91Ju5cne1Oenw/Q9l2G1mXG4/FosZvx7AbjPj0wPB\nnnJKkoM9B/OpcPvRNC04/DxpdL+wB6W02FSK3CX4DR2ryUJabErIjn02TZ1VcT5yWOxhOe7ll1/O\n5ZdfHnx80UUX8dprr532vr///e+NPrYE3BBqzMmekuTAatbQdYXyOcDqIRDQyMkro2uvTjXeWzvj\nGSoXw/ihoAK/HqCw4HvG9qrcd1am9ojm0NT9Zq0WMymJdjRN4/DxUlZmfYvVYqoRuKv3hI+fLEcp\nhV830A2F7vVjt1lQSmGzmCsXwIizoesByl1+AoYiKc5GhzZxwSHrcAeltnFtKPVVBNtr2/g2IT3+\nmchOQ9FNAm4INeRkrxrOKk4qpNslPo5+lYYnvwdm82HikxXKFYv/eA+otlVn7Q3Y/bqfLQe+weXV\nAYXLawr+wEkihmgODZlTWr0XnJBgwtbhEFuKD2PY7LTxX4hZs7ErOw9noqNG4K7sCX/Lzm9yKI7f\niyXVCwEHsSfT0QJW+vduQ0GJhzRnLO1S4/DrAQ4fLyUh1ka524emgclEcMj6XIPS2Yaka7fXSO72\n05xli7OTgBtCDTnZq4azDENRTgUdLgB/bjrJMR0w65U/WKVlRo3P1M54dvs97MouwG+UYlGxtPFf\neNZJ8w29b9Uaki5E6DVkTmn1XvB+zy60ilI0i4ZLL+Mk+0jzDUDXdE7YvkDXXFhULInFl54amlYU\nO74GcxF+NCwODyndc7iy3dWnXWBWDUG3S42FAtA0aky7GdtvJKv3rmXXoRyUz06Hiq64T40QNUT1\nIekT5YXs3fQiHRPSarSX5rpvKjsNRTcJuCFU+2R3efz886O97PjmGK74bJKcBmXGSczKgccbwKfr\nGKYilOMEBXos3RhIfoGPk8VuZi3ZRJozNph1Wf3HIMbqYGDyzzhw6serIZPmG3rfqjUkXYjQO9Ot\njJPFLv6w/DO+yy3BZDLRtVMMJY4jBAIVWM0mNIsNLLH0bufEXbaP70t/IKArDFVEQbGLv75n5dNj\nm/HGHkWhMDx2/CYPRSYXOwvN+D70MfaKXqzdeoSCEg+5+WVYLSasFjMd28bRu4uzRlCOsTogry/x\nBWlomsb3ZRWsXP81MV2OkF9xkhMVBaTFptI27vQZA1BzSDrfdRJ/wE+M1dGo9lJ1YZtffpITFYWk\nxafSNjY1WJ5c+LZMEnDD6O0NB9n85TFK47/C0IopLwLsLqwWD35PDMpejqYBVi9+zctxtQeL6ovH\nq1NQ4q6RdVn7Kr72D9zon3StMXWidpBu6H2r1pR0IULnTLcy/rD8M47klREwwOv3c8ifjWaqAAL4\njQBmk0EtU/XaAAAZo0lEQVSbVJg0vB+fvbWOQEARUAqUhm7ysDFnIz5rIQoFZh9anBcNDUOZKDZO\nsCN/M9mvl+CwW9A0DdupRKo0Z1y9eQy1h8D3lG4ntdDLD2UnKPe5KHKXUOo7fcYA1ByS9gV07GZb\n8DgNbS9VF7bHy/Ko8Lkp9pRQmlAeLE8ufFumZl1pqqUrKPHg1w2weILrwmr+OMzKCrodFbCALy74\nIxGXGKBzu4Rg9rLHp3Msv4KNu3JYvnY/bo8fqLw6fu/AWspSdtLughxuGt6dtduOnHEh9OqbIZzp\nvlVD3ydEQxWVVS63aLeZsZjNYHFjN9uxmi2YNDNmk5m0uMrlXTXdgcmkVWbvawrld6Cb3FgtZjRf\nLATsoIEWsFe2HTR0zR0sA8BiMdGxbQIzJlwSzGuorfZGHZqt8vN+w3/q//V6A2j1VeTaxacG696Y\n9lJ1YVtVTlW5VeXJhW/LJD3cEKo9NSIhzorVYsKtO1BWT2XyhqaREOiCvagnJQlfYYorxaRpJMbZ\nSLYmocoUNosJn1/H6zMod/mwWMxs+Owoum5wx3UX1nn1W1DS+YxJKw1NppCkCxFqyQl2yly+U0HX\nRLwjCXusB5deeTskzhaD057MG2v34znWFcNcijK5UT472snuWLocJTEOKjwabp8JkxEDylx5OwWF\nRcWQkGCvDJwNvMVSe4RItevMkdIcrCYrXt2P1WSpN4BWv3Xk9nua1F6qeslWkwWfXrlARvXyJNu4\nZZKAG0K1p0Z075DElf07suMbcKnKe7ixpgTa+C8i5ZJ4/EZb9rs+Q7N5GdijM9emD+fDT3NJirOT\nV1jBoWOVDc5iNuHy6uzKzuOO6y6s8+o3JanXGZNWGppMIUkXIlSqLkDbJDk4UejCbNZokxTDjBsm\n8p/cjcE9cPu3vwDXkW5s33MUv64weftiNWuYNY1Ep51LOvcgtssRijwl5P1gkODtRbH1WzyqHE2P\n4f+kXM7YG3ux5tQ93IZMh6s9BO72d+e97HUk2uNP3cNNoW18m7MG0Ka2l6oL20RbHCdchaTFpdI2\nLjVYnlz4tkwScBuoIRP78woqOJZfubKNzWImMdbGQ5MGccd1FwGj6jnywBqPqv8IPPj8fzhZ7EI7\ntUuQOrWVWF1Xv2Nl/q2IMtUvQLt1SKyRvDSpzTgmDRgXfO+vstZT4dHx+gIYShFntbL0iRHV2tjA\nWkf/2Wnlnct0uBirg+v7juD97CwsJnNlolLf8CUqnS1Qy4VvyyQBt4Gq/3jkl5bwzJqtdOpkwelI\nZmT3oaz5NJed3+TiTfoWs8OHW3dwvGjAaccpcBXy562vUOIpIcmeyK9+ehfO2LqHiwb2bcunXx7D\nrxtYLSYu7Vu5mHZdV78x1tYz/1YyOKNbjaxks4keHRI5Wewh+/situ39gYF923LriD41LlgVGh6v\njh4wMBSUlHn5deYmMvp3bNCqVbU1ZeUrSVQS4SZJUw1UPauxwPY1ed5jFHlKOVj0PS9kvcW3R4sw\n2nyHFleCsngwxZWgpxw47Th/2vx3DuQfJb+slAMnc/jj5r/VW+atI/pwZf+OJMbZibVXXhu5Pf7g\n1e+9g6cw4eLrW12wqfphrPr7v5e9rrmrJKqpykrWDYXb4yf7+yIKSlx4/QFOFrv49MtjpyX1Dezb\nloAyOJXHhAJOFLnqTABsiKoL5PqSCOsiiUoi3CTgNlD1rEa/5sJmqQyAmqZR5K0c3jXbfJhMJswm\nEw67FbPDf9pxjhYXYhgE/3e0uLDeMqsvfZeSFMPh46VN+vFpaeSHMfpU7n61n8yVu/n+hzJAw241\nYTabCBgGZrMJu9VUmZGrG6cl9d06og8JMTbM5h/3yHZ7AxzKLWXb3h+CGfoN1ZCVr2qTDH0RbjKk\n3EDVsxr9NieOpHKgsmEmWOPIs+4CRwkYHsxGIrF2CwN7dA5+vmqIy12uYVgDlcvfoDDpZ16Auyk/\nHC2dZHBGn+q3XJRSeH06MQ4rDpuG3WoLJv4ppbBaTKQmOU4b9h16aSeydhzF5dExVGUvt9ztw2b9\ncenShg4VN2Tlq9okUUmEW8QDrq7rzJ49m9zcXPx+P9OmTWP48OGRrkajVc9qdPv71WiYnngv2787\nQGwgBh8B2iZaGNzlIkZ2GxpcjOJYfhkWiwkjdyCBLp+jWX2YAnaSPRlnLLcpPxwtnfwwRp+CEg+G\n5uekdR9xvSug1ESsqx+p8QnM+MUAPt6Rw67sPBQal/Zty02nLmBrZvUnkpYSR3GZh9IKL0qB2Wyi\nQ5v44IVmfZsk1A7E12Z0Zc2pejU0iVASlUS4RTzgvvvuuzidThYtWkRJSQnjxo07LwJudbUb5tId\nr9O5XcKpRyk4HYlMuPh63li7P/jjcLzAhd1qJsYcT8XBK9A0cCY66NL9zFt3ye4/p5MfxuiTkuRg\nv2cLbnMBoJHawcKQfgEmXHwVAHdcdyF3XHdhMOHt1T27+LbYQ5zWBzM2NE2jtMLP5Re158DRouBe\nt/ExthobD9Q34lM7EK/h3LKWhQiHiAfca6+9ljFjxgBgGAYWy/kxql1QVsrz697kmO8QZk0j1dSF\n9oFLcMbHccjjIUc/hB8XGopkRxLHCk+y5ctjnChyoQANCOg6uqFhGAZ2m4W0lBjSUmLPWG4od/+R\n7F7RULV7jGMyugbXKk5JcnDtFR1Z9/3G4NrDKfFOvCXH8bnNaEqjxB/grU1fsOLjfZiSCrBYIMEe\nQ2KiGa9e2Xs9qVcQsOzFKHGCHku3iku4efilZLu3Yo0rJt5tpXfMIDqmJDP6J1355wf72LH3OB5/\ngKR4Ox1S44KBOK+4tMamB7HFF/PPz9/ivwe/QDcMOli78dio23AmJDT4O197RUfWfPfJj/OF213A\nzRddK21GNFnEo11MTAwA5eXlPPjggzz00EORrkKT/ClrFYfd+zE0nYBhUBr4hnIM/N+lU5FUit/p\nAs1AaVDqKeN3n/yFopKB+HQDpRRmkwkFxNjNaJoFm8WErhsR7bHKtAfRULWnwW3MzUJZvVhVLKml\nF5CdtZWYlIof1x62laArH8oaQHni8fh1lNmDyelBM+sEzDolgXIqSi0YBDAMhdJAsypMCcUor59c\n75cs/W8OMSkVdNZsKKXomnKSCRdn8Mba/Wz+IhfdMAgYipJyL22qjfgU2vfh0k9iQsOHi+/M/2Hv\ngXK8AS8Kxfeeb/hj1ls1NrY/03cuLHXzQtZWii3f4dI9KKXYlrsLq8UqbUY0WbN0L48fP84DDzzA\nlClT+PnPf37G92ZmZrJ48eII1ax+he4SdCOA0k5lc2gBdFz4dQPD7AOlUZX0rZk1KgLlxNgs6LrC\nUAqLWSM5wUGntPjgMZ0JjkbPLzwXkt17/otUe6g9Da4skE+MyYqOiwLb15i8XmI1a421h23EYyg3\nfr8Do9yMZnWBw03l+E7lsQKByovSH2lgMkDTUBYPRd4SYrXKNlH9HC0o8eAPKEwmEzF2E9ZT6yVX\ntZ/27U0U59rw64HKdZftFZRV6KdKqFwCsshb0uDvXDX7IGDWg4/9hi5tRpyTiAfckydPctddd/HU\nU0+RkXHmhCGAGTNmMGPGjBrP5eTkMGLEiHBVsU5+twVl0cCsUCg0Q8OixaIsJiq8NojXQAtU7nBi\nKBLM8RgWEw67uXK92BgrKYmNW+811CS79/wXqfZQPVmvchqc+dStEQ2vqsBc4eDQsWJ8JoVmDuA1\nAuj40Yglvqg/rgIfpvbfglEMZgVVnzbsaGY/hgqAYap8/tT/awEHCdYEcvLy0QMGFrOJrr06Betj\nNWv4dWpkOldpG5dCaduy4Lnt8VsodXkJEEChMKHhtCc1+DsrpUi2J1KiFeDjVHa1ySJtRpyTiM/D\nXbp0KaWlpbz44otMnTqV22+/HZ/PF+lqNFoP6yVQ3AHD60Dzx2B3d6G7ZQDJ8Xa0/J5Q0AkCNjAs\naK4kBlhH8ZOL2tMmyUGb5Fiu7N+Rx6YMoncXJ84ER40NsSOl+i4n6SndJLtX1OvmoenBc7VdgpOe\nHZOIc1ixWDTMAQdJ7gsozouhLD+W8nKFGTNJcTHEWe0kdDtKr46JxJT2xihqj/LHYNYTSDa3pb2j\nC0n+njiMFDTDhgpYUK5ErL4UhncZSk/rpShXIspnR7kS8R/vEazPlQM61WhP1dtP7XP7Vz/9H4b3\nGky8JYEYUzzdHX15aMQtDf7Ovbs4eXjEeDK6XEpqTDJtYp38pPNAaTPinGiqaqb3eaTqij4rK4vO\nnTuf/QMhMGvJJg6fKES1+Q5l8ZBsT+SKDldxOLcimFFpGGAyQXyMrc6Nr4UIh3C3h9o74hzZ04Yt\nu/Mrt57UwNr9KxKSDPp1r9ymzulI5N7BU+pM0iNgZv6/X+P70hw0IDHOxpW9L2DSJTcCkLlyN0Vl\nP841dyY4mDHhkpB/JyGaw/mRIhxhdU2uT3PGclT7HJ+lCAUUGR6yDv+HmOILSXNWJoIVl3lIjHPQ\nPjUuqhapaMq6skJUUQEzem4vfCUe/EkOTpw8gV83UFA5Iuy14/KUcLToGGW+coyAif3flRNrsxKb\n7MJsMddI0vNpbixW0LVyivwG67/9jJsuGk2M1XHasG5inC04l13OXXG+k4Bbh7om16elxkG5B81k\nQhkKZSg8qoKAu3I4vGPbOJyJdmLslqhbpKK+xQKEqI/L4+fN9V+zp3Q7Re5irMTSLnAx+UUujp0o\nq/Fe/YfuWJN2UeguQ6HAUJxQBzG5bCQbSXRKi6+RAKV8dvxaGQHNj4aGx/AGg3Hteed+PSDnrmgx\nJODWoa7J9b+84SKyXotF19yV+ZYmDbMRQ3yMDU2D3l2cjMno2qg9OWsLV09UlocUjfX2hoNsz9+M\n21yAK6Bj0soxW/fhz0unwhvAbNbQA5V3o2wmO4nWFDx4UQQwgKrNJH16ZZZv9SS9ixMvJ6/wEGgK\nTZlIsjiDwbj2vPPMlbvl3BUthgTcOiTGWdlzIB9/QGE1a3Rrn0iMw8qQzkPZkb+ZkkAZuttKfEXf\n0+7VnsvVd7h6orI8pGisghLPqYvLyo3gDaXQNRfF5d7KEZ5qU3viYyx4KyyYHRYCGCgMTGjEBTrQ\nNi4ep8NSYwnOW6++gINrenHCexybxUL7lJh6s3/l3BUtiQTc+mgAKrjJAFT+UNg22MgrqOCE30W7\nbnGkpcSGLNs4XD1RWR5SNFZKkgNLXgx+XNhsZgxlkGBLxGU1EwhULj5R1TLcvgCJpb1p0y6WQOwJ\nisu9xBsducR5BROuvuC0UZoYh5XZ101s0HrY0XzuysptorEk4NahtMJP57SEGo/hzMsshqLxhetq\nPpTLQ4rW4eah6ejrfewp3Y5m8zKwR2duumg0K+MP8sHmQ1TORjewmc3ExVjp2jYJZ6AjM65tWEZx\nQ9fDjuZzV1ZuE43VIgPu2YJfXa+rgDl4/zQ3vwybxYzFYmpw4Gts46vrfm00X82L1iXGYeWOa/sD\n/Ws8P2FEH/YfLuR4gQufPwBQuShGtXbSWnp+snKbaKwWGXDPFvzqel3P7RW8f2q1mPDrAdo6Yxsc\n+Brb+Oq7XxutV/NCQGUgfuquDFZvOMiJQhd5hRWkOWNplxoXbCetpecnK7eJxmqRAfdswa+u133V\n7p9aLWbSnHGNmnDf2MYnmcPifHW2Yd7W0vOTfZlFY7XIgHu24FfX6/5zvH/a2MYn2ZeipWotPT/Z\nl1k0VosMuGcLfnW+3st8TvdPG9v45H6taKmk5ydE3VpkwD1b8Kvzdeu5zaFtrGjOvhTiXEjPT4i6\nRXy3ICGEEKI1koArhBBCRIAEXCGEECICJOAKIYQQERDxpCmlFHPnziU7OxubzcaCBQvo0qVLpKsh\nhBBCRFTEe7jr1q3D5/OxYsUKHnnkEZ555plIV0EIIYSIuIgH3M8++4yrrroKgAEDBvDVV19FugpC\nCCFExEV8SLm8vJyEhB934rFYLBiGgcnU8NgfCFQumv7DDz+EvH5ChFv79u2xWELX9KQ9iPNZqNtD\nNIv4t4yPj6eioiL4+GzBNjMzk8WLF9f52uTJk0NePyHCLSsri86dOzfps9IeREtzLu3hfKMppVQk\nC/zoo49Yv349zzzzDLt37+bFF19k2bJljTqGx+NhwIABfPTRR5jN5jDV9HQjRowgKysrYuVJmS2z\nzL1794b0il7ag5R5PpcZ6vYQzSL+LUeNGsXmzZu57bbbAJqUNOVwVC70361bt5DWrSGa40pMymxZ\nZYb6x0Xag5R5PpfZWoItNEPA1TSNefPmRbpYIYQQolnJwhdCCCFEBEjAFUIIISLAPHfu3LnNXYmm\n+slPfiJlSplSZpiPK2VKmS2tzOYS8SxlIYQQojWSIWUhhBAiAiTgCiGEEBEgAVcIIYSIAAm4Qggh\nRARIwBVCCCEi4LxbUyvSG9h/8cUX/O///i+vvfYaR44c4YknnsBkMtG7d2+efvrpkJal6zqzZ88m\nNzcXv9/PtGnT6NWrV1jLNAyDOXPmcOjQIUwmE/PmzcNms4W1zCoFBQXccsst/OMf/8BsNoe9zJtv\nvpn4+Higcgm7adOmhb3MZcuW8cknn+D3+5k0aRKDBw8OaZnSHqQ9NFVLbA9RT51nPvroI/XEE08o\npZTavXu3mj59etjK+utf/6quv/56deuttyqllJo2bZrasWOHUkqpp556Sn388cchLe+tt95SCxcu\nVEopVVJSooYNGxb2Mj/++GM1e/ZspZRS27ZtU9OnTw97mUop5ff71f33369Gjx6tvvvuu7CX6fV6\n1U033VTjuXCXuW3bNjVt2jSllFIVFRUqMzMz5GVKe5D20BQttT1Eu/NuSDmSG9h369aNJUuWBB/v\n3buXyy67DIAhQ4awZcuWkJZ37bXX8uCDDwKVe5yazWb27dsX1jJHjhzJ7373OwCOHTtGUlJS2MsE\neO6555g4cSJpaWkopcJe5v79+3G5XNx1113ceeedfPHFF2Ev87///S99+vThvvvuY/r06QwbNizk\nZUp7kPbQFC21PUS78y7g1reBfTiMGjWqxnZnqtoaIXFxcZSVlYW0vJiYGGJjYykvL+fBBx/koYce\nCnuZACaTiSeeeIL58+dz/fXXh73MVatWkZqaypVXXhksq/p/w3CU6XA4uOuuu3j55ZeZO3cujz76\naNi/Z1FREV999RV//vOfg2WG+ntKe5D20BQttT1Eu/PuHm5jN7APperlVFRUkJiYGPIyjh8/zgMP\nPMCUKVO47rrr+P3vfx/2MgGeffZZCgoKGD9+PF6vN6xlrlq1Ck3T2Lx5M9nZ2Tz++OMUFRWFtczu\n3bsHt6/r3r07ycnJ7Nu3L6xlJicnk56ejsVioUePHtjtdvLy8kJaprQHaQ9N0VLbQ7Q773q4l156\nKRs2bABg9+7d9OnTJ2JlX3jhhezYsQOAjRs3MmjQoJAe/+TJk9x111089thj3HTTTQBccMEFYS3z\nnXfeYdmyZQDY7XZMJhMXX3wx27dvD1uZr7/+Oq+99hqvvfYa/fr1Y9GiRVx11VVh/Z5vvfUWzz77\nLAB5eXmUl5dz5ZVXhvV7Dho0iE2bNgXLdLvdZGRkhLRMaQ/SHpqipbaHaHfe9XBDsYF9Uz3++OP8\n5je/we/3k56ezpgxY0J6/KVLl1JaWsqLL77IkiVL0DSNJ598kvnz54etzGuuuYZZs2YxZcoUdF1n\nzpw59OzZkzlz5oStzLqE+287fvx4Zs2axaRJkzCZTDz77LMkJyeH9XsOGzaMnTt3Mn78+GA2cadO\nnUJaprQHaQ9N0VLbQ7STzQuEEEKICDjvhpSFEEKI85EEXCGEECICJOAKIYQQESABVwghhIgACbhC\nCCFEBEjAFUIIISJAAm4LUF5ezv3333/G98yaNYvjx4+f8T1Tp04NTravS25uLsOHD6/ztXvvvZf8\n/HxWr17NrFmzABg+fDjHjh07S+2FCC1pDyJanXcLX4jTFRcXs3///jO+Z9u2bYRiyrWmaXU+v3Tp\n0nM+thChIO1BRCvp4bYACxYs4MSJE8yYMYNVq1YxduxYbrjhBmbNmoXL5WLZsmWcOHGCe+65h5KS\nEj788ENuvfVWxo0bx5gxY9i5c2eDy/J6vfzqV7/ixhtvZObMmcHFxuXqXUQLaQ8iWknAbQHmzJlD\nWloaM2fO5C9/+QvLly/n3XffJSYmhiVLlnDPPfeQlpbGX//6VxITE1m5ciVLly7l7bff5u677+bl\nl19ucFkFBQXccccdvPPOO3Tp0iW4XVt9V/pCRJq0BxGtJOC2EEoptm/fzvDhw4M7bkyYMKHG/pJK\nKTRNIzMzk02bNvHnP/+Z1atX43K5GlxOz549GThwIAA33HBDcOFxWSFURBNpDyIaScBtQZRSpzX0\nQCBQ47HL5WL8+PHk5uYyePBgpk6d2qgfh9r7oVoskgYgopO0BxFtJOC2AFWbjg8ePJj169dTWloK\nwMqVK8nIyAi+JxAIcPjwYcxmM9OmTSM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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "g = sns.FacetGrid(tips, col = \"sex\", hue = \"smoker\")\n", + "g.map(plt.scatter, \"total_bill\", \"tip\", alpha =.7)\n", + "\n", + "g.add_legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BONUS: Create your own question and answer it using a graph." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/07_Visualization/Tips/Solutions.ipynb b/200 solved problems in Python/pandas/07_Visualization/Tips/Solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..538fd060fb0d0b8cfd0f54be2d3b6408c425c81a --- /dev/null +++ b/200 solved problems in Python/pandas/07_Visualization/Tips/Solutions.ipynb @@ -0,0 +1,537 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tips" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "This exercise was created based on the tutorial and documentation from [Seaborn](https://stanford.edu/~mwaskom/software/seaborn/index.html) \n", + "The dataset being used is tips from Seaborn.\n", + "\n", + "### Step 1. Import the necessary libraries:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "# visualization libraries\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "\n", + "# print the graphs in the notebook\n", + "% matplotlib inline\n", + "\n", + "# set seaborn style to white\n", + "sns.set_style(\"white\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/Visualization/Tips/tips.csv). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called tips" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0total_billtipsexsmokerdaytimesize
0016.991.01FemaleNoSunDinner2
1110.341.66MaleNoSunDinner3
2221.013.50MaleNoSunDinner3
3323.683.31MaleNoSunDinner2
4424.593.61FemaleNoSunDinner4
\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 total_bill tip sex smoker day time size\n", + "0 0 16.99 1.01 Female No Sun Dinner 2\n", + "1 1 10.34 1.66 Male No Sun Dinner 3\n", + "2 2 21.01 3.50 Male No Sun Dinner 3\n", + "3 3 23.68 3.31 Male No Sun Dinner 2\n", + "4 4 24.59 3.61 Female No Sun Dinner 4" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Delete the Unnamed 0 column" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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total_billtipsexsmokerdaytimesize
016.991.01FemaleNoSunDinner2
110.341.66MaleNoSunDinner3
221.013.50MaleNoSunDinner3
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\n", + "
" + ], + "text/plain": [ + " total_bill tip sex smoker day time size\n", + "0 16.99 1.01 Female No Sun Dinner 2\n", + "1 10.34 1.66 Male No Sun Dinner 3\n", + "2 21.01 3.50 Male No Sun Dinner 3\n", + "3 23.68 3.31 Male No Sun Dinner 2\n", + "4 24.59 3.61 Female No Sun Dinner 4" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Plot the total_bill column histogram" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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3cWvGeLl+vXB7DgXGc889R0ZGBmPGyBRBb2Mfv0gIc3El7isgQIdOH9xv24yJ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\nrzVRWd/BpWOjuDQlCh+5QNe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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Create a scatter plot presenting the relationship between total_bill and tip" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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RaVBMg6lPnz5YvXq1e/vQoUO46qqrAADXXXcddu/eHcvDE1GCMlmbsXLjPjy+agdWbtwH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\nEf6UlpZi3Lhx2L5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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Create one image with the relationship of total_bill, tip and size.\n", + "#### Hint: It is just one function." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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WBr0Oc616XLYPwu4cQl3D8FblJoMOG67Px9ry+YjXabG2PAd25xAS9Tr09A3i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\nLr8TAwcObLcddS3JFBSNjY3Iz8/HlStX4HA4sHDhQkyYMCFg2+7wwqD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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Present the realationship between days and total_bill value" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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uVZxFQ7rtdM1dVVV0lJTiE3fuawfk//0ZOku7h3W2FxRQ8MLLjPvDo/1Zpugn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\niYiIPAiDm4iIyIMwuImIiDzI/wPvE39ImLp8oAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. Create a scatter plot with the day as the y-axis and tip as the x-axis, differ the dots by sex" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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zsmz04ouuV21LPZWtNdhsNhRFcdpWWN7Ao6/toKHJCMDgMc3kqLYCUNdSz192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\noGXLlql169Z1Ht+iE3Lw4MHasGGDJCk3N1cJCQk+rsh/nT9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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. Create a box plot presenting the total_bill per day differetiation the time (Dinner or Lunch)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 11. Create two histograms of the tip value based for Dinner and Lunch. They must be side by side." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 12. Create two scatterplots graphs, one for Male and another for Female, presenting the total_bill value and tip relationship, differing by smoker or no smoker\n", + "### They must be side by side." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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U8Otf/7pJxxFCiOYS7fNtW7J77rmHl156iX/961/0798fpRS9e/dG0zRSUlKCscrn85GT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\nwMqVK8nIyAi+JxAIcPjwYcxmM9OmTSM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+ { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "This exercise is based on the titanic Disaster dataset avaiable at [Kaggle](https://www.kaggle.com/c/titanic). \n", + "To know more about the variables check [here](https://www.kaggle.com/c/titanic/data)\n", + "\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/Visualization/Titanic_Desaster/train.csv). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable titanic " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Set PassengerId as the index " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Create a pie chart presenting the male/female proportion" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Create a scatterplot with the Fare payed and the Age, differ the plot color by gender" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. How many people survived?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Create a histogram with the Fare payed" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BONUS: Create your own question and answer it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/07_Visualization/Titanic_Desaster/Exercises_code_with_solutions.ipynb b/200 solved problems in Python/pandas/07_Visualization/Titanic_Desaster/Exercises_code_with_solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..94aa215c187818ed66415b285b07c5210ff69b58 --- /dev/null +++ b/200 solved problems in Python/pandas/07_Visualization/Titanic_Desaster/Exercises_code_with_solutions.ipynb @@ -0,0 +1,586 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Visualizing the Titanic Disaster" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "This exercise is based on the titanic Disaster dataset avaiable at [Kaggle](https://www.kaggle.com/c/titanic). \n", + "To know more about the variables check [here](https://www.kaggle.com/c/titanic/data)\n", + "\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import numpy as np\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/Visualization/Titanic_Desaster/train.csv). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable titanic " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
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" + ], + "text/plain": [ + " PassengerId Survived Pclass \\\n", + "0 1 0 3 \n", + "1 2 1 1 \n", + "2 3 1 3 \n", + "3 4 1 1 \n", + "4 5 0 3 \n", + "\n", + " Name Sex Age SibSp \\\n", + "0 Braund, Mr. Owen Harris male 22.0 1 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", + "2 Heikkinen, Miss. Laina female 26.0 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", + "4 Allen, Mr. William Henry male 35.0 0 \n", + "\n", + " Parch Ticket Fare Cabin Embarked \n", + "0 0 A/5 21171 7.2500 NaN S \n", + "1 0 PC 17599 71.2833 C85 C \n", + "2 0 STON/O2. 3101282 7.9250 NaN S \n", + "3 0 113803 53.1000 C123 S \n", + "4 0 373450 8.0500 NaN S " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "url = 'https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/Visualization/Titanic_Desaster/train.csv'\n", + "\n", + "titanic = pd.read_csv(url)\n", + "\n", + "titanic.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Set PassengerId as the index " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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SurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
PassengerId
103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
503Allen, Mr. William Henrymale35.0003734508.0500NaNS
\n", + "
" + ], + "text/plain": [ + " Survived Pclass \\\n", + "PassengerId \n", + "1 0 3 \n", + "2 1 1 \n", + "3 1 3 \n", + "4 1 1 \n", + "5 0 3 \n", + "\n", + " Name Sex Age \\\n", + "PassengerId \n", + "1 Braund, Mr. Owen Harris male 22.0 \n", + "2 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 \n", + "3 Heikkinen, Miss. Laina female 26.0 \n", + "4 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 \n", + "5 Allen, Mr. William Henry male 35.0 \n", + "\n", + " SibSp Parch Ticket Fare Cabin Embarked \n", + "PassengerId \n", + "1 1 0 A/5 21171 7.2500 NaN S \n", + "2 1 0 PC 17599 71.2833 C85 C \n", + "3 0 0 STON/O2. 3101282 7.9250 NaN S \n", + "4 1 0 113803 53.1000 C123 S \n", + "5 0 0 373450 8.0500 NaN S " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic.set_index('PassengerId').head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Create a pie chart presenting the male/female proportion" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# sum the instances of males and females\n", + "males = (titanic['Sex'] == 'male').sum()\n", + "females = (titanic['Sex'] == 'female').sum()\n", + "\n", + "# put them into a list called proportions\n", + "proportions = [males, females]\n", + "\n", + "# Create a pie chart\n", + "plt.pie(\n", + " # using proportions\n", + " proportions,\n", + " \n", + " # with the labels being officer names\n", + " labels = ['Males', 'Females'],\n", + " \n", + " # with no shadows\n", + " shadow = False,\n", + " \n", + " # with colors\n", + " colors = ['blue','red'],\n", + " \n", + " # with one slide exploded out\n", + " explode = (0.15 , 0),\n", + " \n", + " # with the start angle at 90%\n", + " startangle = 90,\n", + " \n", + " # with the percent listed as a fraction\n", + " autopct = '%1.1f%%'\n", + " )\n", + "\n", + "# View the plot drop above\n", + "plt.axis('equal')\n", + "\n", + "# Set labels\n", + "plt.title(\"Sex Proportion\")\n", + "\n", + "# View the plot\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Create a scatterplot with the Fare payed and the Age, differ the plot color by gender" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-5, 85)" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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eSICq7WIHmU8Bf6qUigHHgIe11p5S6svAk/jNaZ/WWucucrrERbQa5qysZnJ/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\nTiMRM0bMauXVCfiU/t90NG7n197x7mK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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# creates the plot using\n", + "lm = sns.lmplot(x = 'Age', y = 'Fare', data = titanic, hue = 'Sex', fit_reg=False)\n", + "\n", + "# set title\n", + "lm.set(title = 'Fare x Age')\n", + "\n", + "# get the axes object and tweak it\n", + "axes = lm.axes\n", + "axes[0,0].set_ylim(-5,)\n", + "axes[0,0].set_xlim(-5,85)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. How many people survived?" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "342" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic.Survived.sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Create a histogram with the Fare payed" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# sort the values from the top to the least value and slice the first 5 items\n", + "df = titanic.Fare.sort_values(ascending = False)\n", + "df\n", + "\n", + "# create bins interval using numpy\n", + "binsVal = np.arange(0,600,10)\n", + "binsVal\n", + "\n", + "# create the plot\n", + "plt.hist(df, bins = binsVal)\n", + "\n", + "# Set the title and labels\n", + "plt.xlabel('Fare')\n", + "plt.ylabel('Frequency')\n", + "plt.title('Fare Payed Histrogram')\n", + "\n", + "# show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BONUS: Create your own question and answer it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/07_Visualization/Titanic_Desaster/Solutions.ipynb b/200 solved problems in Python/pandas/07_Visualization/Titanic_Desaster/Solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..7824ff8a7b5650a63f53af74f801304d50c571d4 --- /dev/null +++ b/200 solved problems in Python/pandas/07_Visualization/Titanic_Desaster/Solutions.ipynb @@ -0,0 +1,505 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Visualizing the Titanic Disaster" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "This exercise is based on the titanic Disaster dataset avaiable at [Kaggle](https://www.kaggle.com/c/titanic). \n", + "To know more about the variables check [here](https://www.kaggle.com/c/titanic/data)\n", + "\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import numpy as np\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/Visualization/Titanic_Desaster/train.csv). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable titanic " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
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" + ], + "text/plain": [ + " PassengerId Survived Pclass \\\n", + "0 1 0 3 \n", + "1 2 1 1 \n", + "2 3 1 3 \n", + "3 4 1 1 \n", + "4 5 0 3 \n", + "\n", + " Name Sex Age SibSp \\\n", + "0 Braund, Mr. Owen Harris male 22.0 1 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", + "2 Heikkinen, Miss. Laina female 26.0 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", + "4 Allen, Mr. William Henry male 35.0 0 \n", + "\n", + " Parch Ticket Fare Cabin Embarked \n", + "0 0 A/5 21171 7.2500 NaN S \n", + "1 0 PC 17599 71.2833 C85 C \n", + "2 0 STON/O2. 3101282 7.9250 NaN S \n", + "3 0 113803 53.1000 C123 S \n", + "4 0 373450 8.0500 NaN S " + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Set PassengerId as the index " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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SurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
PassengerId
103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
503Allen, Mr. William Henrymale35.0003734508.0500NaNS
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" + ], + "text/plain": [ + " Survived Pclass \\\n", + "PassengerId \n", + "1 0 3 \n", + "2 1 1 \n", + "3 1 3 \n", + "4 1 1 \n", + "5 0 3 \n", + "\n", + " Name Sex Age \\\n", + "PassengerId \n", + "1 Braund, Mr. Owen Harris male 22.0 \n", + "2 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 \n", + "3 Heikkinen, Miss. Laina female 26.0 \n", + "4 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 \n", + "5 Allen, Mr. William Henry male 35.0 \n", + "\n", + " SibSp Parch Ticket Fare Cabin Embarked \n", + "PassengerId \n", + "1 1 0 A/5 21171 7.2500 NaN S \n", + "2 1 0 PC 17599 71.2833 C85 C \n", + "3 0 0 STON/O2. 3101282 7.9250 NaN S \n", + "4 1 0 113803 53.1000 C123 S \n", + "5 0 0 373450 8.0500 NaN S " + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Create a pie chart presenting the male/female proportion" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Create a scatterplot with the Fare payed and the Age, differ the plot color by gender" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-5, 85)" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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eSICq7WIHmU8Bf6qUigHHgIe11p5S6svAk/jNaZ/WWucucrrERbQa5qysZnJ/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\nTiMRM0bMauXVCfiU/t90NG7n197x7mK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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. How many people survived?" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "342" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Create a histogram with the Fare payed" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BONUS: Create your own question and answer it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/07_Visualization/Titanic_Desaster/train.csv b/200 solved problems in Python/pandas/07_Visualization/Titanic_Desaster/train.csv new file mode 100644 index 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Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Create a data dictionary" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "raw_data = {\"name\": ['Bulbasaur', 'Charmander','Squirtle','Caterpie'],\n", + " \"evolution\": ['Ivysaur','Charmeleon','Wartortle','Metapod'],\n", + " \"type\": ['grass', 'fire', 'water', 'bug'],\n", + " \"hp\": [45, 39, 44, 45],\n", + " \"pokedex\": ['yes', 'no','yes','no'] \n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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3Metapod45Caterpienobug
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" + ], + "text/plain": [ + " evolution hp name pokedex type\n", + "0 Ivysaur 45 Bulbasaur yes grass\n", + "1 Charmeleon 39 Charmander no fire\n", + "2 Wartortle 44 Squirtle yes water\n", + "3 Metapod 45 Caterpie no bug" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pokemon = pd.DataFrame(raw_data)\n", + "pokemon.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Ops...it seems the DataFrame columns are in alphabetical order. Place the order of the columns as name, type, hp, evolution, pokedex" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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nametypehpevolutionpokedex
0Bulbasaurgrass45Ivysauryes
1Charmanderfire39Charmeleonno
2Squirtlewater44Wartortleyes
3Caterpiebug45Metapodno
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" + ], + "text/plain": [ + " name type hp evolution pokedex\n", + "0 Bulbasaur grass 45 Ivysaur yes\n", + "1 Charmander fire 39 Charmeleon no\n", + "2 Squirtle water 44 Wartortle yes\n", + "3 Caterpie bug 45 Metapod no" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pokemon = pokemon[['name', 'type', 'hp', 'evolution','pokedex']]\n", + "pokemon" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Add another column called place, and insert what you have in mind." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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nametypehpevolutionpokedexplace
0Bulbasaurgrass45Ivysauryespark
1Charmanderfire39Charmeleonnostreet
2Squirtlewater44Wartortleyeslake
3Caterpiebug45Metapodnoforest
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" + ], + "text/plain": [ + " name type hp evolution pokedex place\n", + "0 Bulbasaur grass 45 Ivysaur yes park\n", + "1 Charmander fire 39 Charmeleon no street\n", + "2 Squirtle water 44 Wartortle yes lake\n", + "3 Caterpie bug 45 Metapod no forest" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pokemon['place'] = ['park','street','lake','forest']\n", + "pokemon" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Present the type of each column" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "name object\n", + "type object\n", + "hp int64\n", + "evolution object\n", + "pokedex object\n", + "dtype: object" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pokemon.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BONUS: Create your own question and answer it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/08_Creating_Series_and_DataFrames/Pokemon/Exercises.ipynb b/200 solved problems in Python/pandas/08_Creating_Series_and_DataFrames/Pokemon/Exercises.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..8a3751399c7257b052b267237dfc4b14c8f25b56 --- /dev/null +++ b/200 solved problems in Python/pandas/08_Creating_Series_and_DataFrames/Pokemon/Exercises.ipynb @@ -0,0 +1,215 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pokemon" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "This time you will create the data.\n", + "\n", + "\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Create a data dictionary that looks like the DataFrame below" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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evolutionhpnamepokedextype
0Ivysaur45Bulbasauryesgrass
1Charmeleon39Charmandernofire
2Wartortle44Squirtleyeswater
3Metapod45Caterpienobug
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" + ], + "text/plain": [ + " evolution hp name pokedex type\n", + "0 Ivysaur 45 Bulbasaur yes grass\n", + "1 Charmeleon 39 Charmander no fire\n", + "2 Wartortle 44 Squirtle yes water\n", + "3 Metapod 45 Caterpie no bug" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Ops...it seems the DataFrame columns are in alphabetical order. Place the order of the columns as name, type, hp, evolution, pokedex" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Add another column called place, and insert what you have in mind." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Present the type of each column" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BONUS: Create your own question and answer it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/08_Creating_Series_and_DataFrames/Pokemon/Solutions.ipynb b/200 solved problems in Python/pandas/08_Creating_Series_and_DataFrames/Pokemon/Solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..3a0cebbf450fcae382b0b62e47ca5867e71b8b6e --- /dev/null +++ b/200 solved problems in Python/pandas/08_Creating_Series_and_DataFrames/Pokemon/Solutions.ipynb @@ -0,0 +1,150 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pokemon" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "This time you will create the data.\n", + "\n", + "\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Create a data dictionary" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Ops...it seems the DataFrame columns are in alphabetical order. Place the order of the columns as name, type, hp, evolution, pokedex" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Add another column called place, and insert what you have in mind." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Present the type of each column" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BONUS: Create your own question and answer it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/09_Time_Series/Apple_Stock/Exercises-with-solutions-code.ipynb b/200 solved problems in Python/pandas/09_Time_Series/Apple_Stock/Exercises-with-solutions-code.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..6191320fd94615dcf4ad3ddd79e483c10e20ac4f --- /dev/null +++ b/200 solved problems in Python/pandas/09_Time_Series/Apple_Stock/Exercises-with-solutions-code.ipynb @@ -0,0 +1,716 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Apple Stock" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "We are going to use Apple's stock price.\n", + "\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# visualization\n", + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/09_Time_Series/Apple_Stock/appl_1980_2014.csv)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable apple" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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DateOpenHighLowCloseVolumeAdj Close
02014-07-0896.2796.8093.9295.356513000095.35
12014-07-0794.1495.9994.1095.975630540095.97
22014-07-0393.6794.1093.2094.032289180094.03
32014-07-0293.8794.0693.0993.482842090093.48
42014-07-0193.5294.0793.1393.523817020093.52
\n", + "
" + ], + "text/plain": [ + " Date Open High Low Close Volume Adj Close\n", + "0 2014-07-08 96.27 96.80 93.92 95.35 65130000 95.35\n", + "1 2014-07-07 94.14 95.99 94.10 95.97 56305400 95.97\n", + "2 2014-07-03 93.67 94.10 93.20 94.03 22891800 94.03\n", + "3 2014-07-02 93.87 94.06 93.09 93.48 28420900 93.48\n", + "4 2014-07-01 93.52 94.07 93.13 93.52 38170200 93.52" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "url = 'https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/09_Time_Series/Apple_Stock/appl_1980_2014.csv'\n", + "apple = pd.read_csv(url)\n", + "\n", + "apple.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Check out the type of the columns" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Date object\n", + "Open float64\n", + "High float64\n", + "Low float64\n", + "Close float64\n", + "Volume int64\n", + "Adj Close float64\n", + "dtype: object" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "apple.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Transform the Date column as a datetime type" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 2014-07-08\n", + "1 2014-07-07\n", + "2 2014-07-03\n", + "3 2014-07-02\n", + "4 2014-07-01\n", + "Name: Date, dtype: datetime64[ns]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "apple.Date = pd.to_datetime(apple.Date)\n", + "\n", + "apple['Date'].head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Set the date as the index" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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OpenHighLowCloseVolumeAdj Close
Date
2014-07-0896.2796.8093.9295.356513000095.35
2014-07-0794.1495.9994.1095.975630540095.97
2014-07-0393.6794.1093.2094.032289180094.03
2014-07-0293.8794.0693.0993.482842090093.48
2014-07-0193.5294.0793.1393.523817020093.52
\n", + "
" + ], + "text/plain": [ + " Open High Low Close Volume Adj Close\n", + "Date \n", + "2014-07-08 96.27 96.80 93.92 95.35 65130000 95.35\n", + "2014-07-07 94.14 95.99 94.10 95.97 56305400 95.97\n", + "2014-07-03 93.67 94.10 93.20 94.03 22891800 94.03\n", + "2014-07-02 93.87 94.06 93.09 93.48 28420900 93.48\n", + "2014-07-01 93.52 94.07 93.13 93.52 38170200 93.52" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "apple = apple.set_index('Date')\n", + "\n", + "apple.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Is there any duplicate dates?" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# NO! All are unique\n", + "apple.index.is_unique" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Ops...it seems the index is from the most recent date. Make the first entry the oldest date." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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OpenHighLowCloseVolumeAdj Close
Date
1980-12-1228.7528.8728.7528.751172584000.45
1980-12-1527.3827.3827.2527.25439712000.42
1980-12-1625.3725.3725.2525.25264320000.39
1980-12-1725.8726.0025.8725.87216104000.40
1980-12-1826.6326.7526.6326.63183624000.41
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" + ], + "text/plain": [ + " Open High Low Close Volume Adj Close\n", + "Date \n", + "1980-12-12 28.75 28.87 28.75 28.75 117258400 0.45\n", + "1980-12-15 27.38 27.38 27.25 27.25 43971200 0.42\n", + "1980-12-16 25.37 25.37 25.25 25.25 26432000 0.39\n", + "1980-12-17 25.87 26.00 25.87 25.87 21610400 0.40\n", + "1980-12-18 26.63 26.75 26.63 26.63 18362400 0.41" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "apple.sort_index(ascending = True).head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. Get the last business day of each month" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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OpenHighLowCloseVolumeAdj Close
Date
1980-12-3130.48153830.56769230.44307730.443077258625230.473077
1981-01-3031.75476231.82666731.65476231.65476272498660.493810
1981-02-2726.48000026.57210526.40789526.40789542318310.411053
1981-03-3124.93772725.01681824.83636424.83636479626900.387727
1981-04-3027.28666727.36809527.22714327.22714363920000.423333
\n", + "
" + ], + "text/plain": [ + " Open High Low Close Volume Adj Close\n", + "Date \n", + "1980-12-31 30.481538 30.567692 30.443077 30.443077 25862523 0.473077\n", + "1981-01-30 31.754762 31.826667 31.654762 31.654762 7249866 0.493810\n", + "1981-02-27 26.480000 26.572105 26.407895 26.407895 4231831 0.411053\n", + "1981-03-31 24.937727 25.016818 24.836364 24.836364 7962690 0.387727\n", + "1981-04-30 27.286667 27.368095 27.227143 27.227143 6392000 0.423333" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "apple_month = apple.resample('BM').mean()\n", + "\n", + "apple_month.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. What is the difference in days between the first day and the oldest" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "12261" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(apple.index.max() - apple.index.min()).days" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 11. How many months in the data we have?" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "404" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "apple_months = apple.resample('BM').mean()\n", + "\n", + "len(apple_months.index)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 12. Plot the 'Adj Close' value. Set the size of the figure to 13.5 x 9 inches" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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8MQ7RXZ7a2lzgsLZwq1/Hd4UiWAAAAAAlxBtbEd3lqa3N74pUKPEtFtXVsduC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\nt0fmfdky6N49Mq5durTuM6++Gr70pXj+5z9HYSx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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# makes the plot and assign it to a variable\n", + "appl_open = apple['Adj Close'].plot(title = \"Apple Stock\")\n", + "\n", + "# changes the size of the graph\n", + "fig = appl_open.get_figure()\n", + "fig.set_size_inches(13.5, 9)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BONUS: Create your own question and answer it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/09_Time_Series/Apple_Stock/Exercises.ipynb b/200 solved problems in Python/pandas/09_Time_Series/Apple_Stock/Exercises.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..ac13ee9230e341b2a95a77cb44f3d07ef1079f4e --- /dev/null +++ b/200 solved problems in Python/pandas/09_Time_Series/Apple_Stock/Exercises.ipynb @@ -0,0 +1,246 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Apple Stock" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "We are going to use Apple's stock price.\n", + "\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/09_Time_Series/Apple_Stock/appl_1980_2014.csv)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable apple" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Check out the type of the columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Transform the Date column as a datetime type" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Set the date as the index" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Is there any duplicate dates?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Ops...it seems the index is from the most recent date. Make the first entry the oldest date." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. Get the last business day of each month" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. What is the difference in days between the first day and the oldest" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 11. How many months in the data we have?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 12. Plot the 'Adj Close' value. Set the size of the figure to 13.5 x 9 inches" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BONUS: Create your own question and answer it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python [default]", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/09_Time_Series/Apple_Stock/Solutions.ipynb b/200 solved problems in Python/pandas/09_Time_Series/Apple_Stock/Solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..b3c39c08f28c03e7146ba1af1f8fc3b2ada4b447 --- /dev/null +++ b/200 solved problems in Python/pandas/09_Time_Series/Apple_Stock/Solutions.ipynb @@ -0,0 +1,681 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Apple Stock" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "We are going to use Apple's stock price.\n", + "\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# visualization\n", + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/09_Time_Series/Apple_Stock/appl_1980_2014.csv)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable apple" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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42014-07-0193.5294.0793.1393.523817020093.52
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" + ], + "text/plain": [ + " Date Open High Low Close Volume Adj Close\n", + "0 2014-07-08 96.27 96.80 93.92 95.35 65130000 95.35\n", + "1 2014-07-07 94.14 95.99 94.10 95.97 56305400 95.97\n", + "2 2014-07-03 93.67 94.10 93.20 94.03 22891800 94.03\n", + "3 2014-07-02 93.87 94.06 93.09 93.48 28420900 93.48\n", + "4 2014-07-01 93.52 94.07 93.13 93.52 38170200 93.52" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Check out the type of the columns" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Date object\n", + "Open float64\n", + "High float64\n", + "Low float64\n", + "Close float64\n", + "Volume int64\n", + "Adj Close float64\n", + "dtype: object" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Transform the Date column as a datetime type" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0 2014-07-08\n", + "1 2014-07-07\n", + "2 2014-07-03\n", + "3 2014-07-02\n", + "4 2014-07-01\n", + "Name: Date, dtype: datetime64[ns]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Set the date as the index" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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OpenHighLowCloseVolumeAdj Close
Date
2014-07-0896.2796.8093.9295.356513000095.35
2014-07-0794.1495.9994.1095.975630540095.97
2014-07-0393.6794.1093.2094.032289180094.03
2014-07-0293.8794.0693.0993.482842090093.48
2014-07-0193.5294.0793.1393.523817020093.52
\n", + "
" + ], + "text/plain": [ + " Open High Low Close Volume Adj Close\n", + "Date \n", + "2014-07-08 96.27 96.80 93.92 95.35 65130000 95.35\n", + "2014-07-07 94.14 95.99 94.10 95.97 56305400 95.97\n", + "2014-07-03 93.67 94.10 93.20 94.03 22891800 94.03\n", + "2014-07-02 93.87 94.06 93.09 93.48 28420900 93.48\n", + "2014-07-01 93.52 94.07 93.13 93.52 38170200 93.52" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Is there any duplicate dates?" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# NO! All are unique" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Ops...it seems the index is from the most recent date. Make the first entry the oldest date." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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OpenHighLowCloseVolumeAdj Close
Date
1980-12-1228.7528.8728.7528.751172584000.45
1980-12-1527.3827.3827.2527.25439712000.42
1980-12-1625.3725.3725.2525.25264320000.39
1980-12-1725.8726.0025.8725.87216104000.40
1980-12-1826.6326.7526.6326.63183624000.41
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" + ], + "text/plain": [ + " Open High Low Close Volume Adj Close\n", + "Date \n", + "1980-12-12 28.75 28.87 28.75 28.75 117258400 0.45\n", + "1980-12-15 27.38 27.38 27.25 27.25 43971200 0.42\n", + "1980-12-16 25.37 25.37 25.25 25.25 26432000 0.39\n", + "1980-12-17 25.87 26.00 25.87 25.87 21610400 0.40\n", + "1980-12-18 26.63 26.75 26.63 26.63 18362400 0.41" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. Get the last business day of each month" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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OpenHighLowCloseVolumeAdj Close
Date
1980-12-3130.48153830.56769230.44307730.443077258625230.473077
1981-01-3031.75476231.82666731.65476231.65476272498660.493810
1981-02-2726.48000026.57210526.40789526.40789542318310.411053
1981-03-3124.93772725.01681824.83636424.83636479626900.387727
1981-04-3027.28666727.36809527.22714327.22714363920000.423333
\n", + "
" + ], + "text/plain": [ + " Open High Low Close Volume Adj Close\n", + "Date \n", + "1980-12-31 30.481538 30.567692 30.443077 30.443077 25862523 0.473077\n", + "1981-01-30 31.754762 31.826667 31.654762 31.654762 7249866 0.493810\n", + "1981-02-27 26.480000 26.572105 26.407895 26.407895 4231831 0.411053\n", + "1981-03-31 24.937727 25.016818 24.836364 24.836364 7962690 0.387727\n", + "1981-04-30 27.286667 27.368095 27.227143 27.227143 6392000 0.423333" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. What is the difference in days between the first day and the oldest" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "12261" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 11. How many months in the data we have?" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "404" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 12. Plot the 'Adj Close' value. Set the size of the figure to 13.5 x 9 inches" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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8MQ7RXZ7a2lzgsLZwq1/Hd4UiWAAAAAAlxBtbEd3lqa3N74pUKPEtFtXVsduC\n1lL4WwIAAAClq6Eh8bSy7e3hBYv4FotC1yFJIU5IBQAAAJSeQYPcjFBSbItFS0vhWwriAwUtFgAA\nAEAJeestt41usdiyRRo8uLB1eC0WXrAIs8WCYAEAAABkyBi3jW6x2LJFqq0tbB1esOjXz21psQAA\nAABKUHyLxZAhhb0/LRYAAABAGfj61/3Vtk8/XfrTnwp7f2/aWy9IeC0VBAsAAACgBHjTzb70knTj\njd2PF4q3KJ/XNSt+EHchESwAAACAFFau9B/cE/Gmng1Da2vsa6+lom/fwtdCsAAAAABSWLUq9fub\nN/v7he4KFR9qKiqk3XbzWy4KiXUsAAAAgBRStVZI/qrXEyZIU6fmv55oiVpL3n+/sDV4aLEAAAAA\ncvDFL7ptGLNChdkNKx7BAgAAAEjBG8fwwguJ36+sdLMzbdtW+AXyotfRCBvBAgAAAEihsdFt//a3\nxO9HIm52pj59/G5RhXL22VJ9fWHvmQxjLAAAAIAUtm512z59Er/f0RHO9K6SG6R9xBHh3DsewQIA\nAABIobnZbeODxYAB0qmnurEVIFgAAAAAKXljLIYNk775Tf/4eee5lorVq8Opq9gQLAAAAIAUvJmX\nhg2T7rjDP15ZKb34YvGMcQgbg7cBAACAFLwWi02bYo/36SO9/nrh6ylWBAsAAAAgBa/FYv362OOV\nleEN2i5GBAsAAAAgBS9YXH117HFj3ABuOAQLAAAAIAWvK1RHR+zxSESqrS18PcWKYAEAAACk4LVY\nxItEpKFDC1tLMSNYAAAAACl4LRbxGhqkE08sbC3FjGABAAAApJCsxeLmm6Wvf93tf+1rLJRHsAAA\nAABSaGuTKqKemi+5xN+vqnLbhx+WBg8ubF3FhmABAAAApNDaKnV2+q8nTvT3a2rctqWlsDUVI4IF\nAAAAkEJ8V6hzz/X3vRYLawtXT7EiWAAAAAApRA/erq31u0Uddlg49RSryrALAAAAAIpZdIuFMW57\n6qnStGnh1FOsCBYAAABAColmhfrjH2Nf779/YWopZnSFAgAAAFKI7grltVjEq+CpmmABAAAApJJs\nHYtoyQJHb0JXKAAAAPRaxkgbN0pDhyY/p7VVGjjQrbSdyC9+IU2dmp/6SomxIc2NZYyxYd0bAAAA\nkFyw+OADaZddkp+z++5SZaW0eLE0bJi0YUPh6itGxhhZa7u10dAVCgAAAL1Se7vb7rqra7VI5OWX\npfffl/r1kpp8AAAgAElEQVT1c6/p8pQcwQIAAAC90ve/7+9v2pT4nLfectv+/fNfT6kjWAAAAKBX\n+u//9ve3bUt8jhcovBaLkSPzW1MpI1gAAACgV6qMmsbok08Sn+MFi5oat/3HP/JbUykjWAAAAKBX\nuvRSf//449125kzpvfe6n3vggW47fHj+6ypVBAsAAAD0SpFI7OvLL5fuuSe2VaKlRfrKV6Qrr4xd\nKA/dESwAAADQKzU1xb7+1a/c9tvf9tes2LpVGjzYzQZVVVXY+koNwQIAAAC9jjHd16OIHnNxzTXS\nu++6YDFkSGFrK1UECwAAAPQqXheoNWtij3d0+Pu//KW0117Sli0Ei3QRLAAAANCrNDa67TPP9Hwu\nwSJ9BAsAAAD0Kt74iUSGDZNOOMHtV1T4YyzQM4IFAAAAepVHHkn+XmVl7Crbra3+GhZIjWABAACA\nXmXnnZO/V1kpPfSQ/7qjQ+rTJ/81lQOCBQAAAHqVbdv8/UGDYsdQxK9tEYkQLNJFsAAAAECvEj3G\nYt066YEH/Ndr1/r7nZ0uWERPQ4vkCBYAAADoVaJbLKqr3U8ybW0Ei3QRLAAAANCreMGioutJOL77\nkxc0hg1jgbxMECwAAADQq3jBwmuJaG2NfX/xYrfduFF64QVp6NDC1VbKCBYAAADoVbxg4Q3KbmmJ\nfX/nnaWPP/Zf19YWpq5SR7AAAABAr+IN3vZaLI49Vnr0Uenzn/fPGT7c36fFIj0ECwAAAPQq8S0W\nAwdKJ54oTZ7sr7Ldt69/fr9+ha2vVBlrbTg3NsaGdW8AAAD0TsuXSzvuKF11lfSlL0n77OO/Z62b\nYtYLHMb4x+Ezxshaa+KP02IBAACAXuOoo9x28uTYUCG5IBG9GN6BBxaurnJAsAAAAECvMXas244c\n2fO5rLidGYIFAAAAeoU//EF6/nnXEnH44T2fb7p19kEqBAsAAACULWul5mbp1lulr33NHXv88fQ+\nW1WVv7rKEcECAAAAZeuyy6T+/aULLvCPRU8lm8qAAfmpqVwRLAAAAFC2/vrX2NfpdIHyECwyQ7AA\nAABA2dq0Kfb1pEnpf/baa6UHHwy2nnJWGXYBAAAAQL7Er0FRU5P+Z3fayf0gPbRYAAAAoGw1NcW+\n3nXXcOroDVh5GwAAAGUrfsrYzk6mkc0VK28DAACg19l//9jXhIr8IVgAAACgbE2ZEnYFvQfBAgAA\nAGWrIuppd+zY8OroDQgWAAAAKFudnf5+e3t4dfQGBAsAAACUrbY2f7+jI7w6eoOc17EwxlRIekXS\nSmvtScaYoZL+IGkHSR9JOs1auyXX+wAAAACZamyUfvQjqU8f1qTItyAWyPu+pMWSBne9vlzSfGvt\ndcaYyyRd0XUMAAAAKKjGRumYY6Rp08KupPzl1BXKGLO9pOmSbo86fLKku7v275Z0Si73AAAAALLV\n1CT17x92Fb1DrmMsfiPpEknRK92NstaulSRr7RpJI3O8BwAAAJCV9eul2tqwq+gdsg4WxpjPS1pr\nrX1DUqqlRlheGwAAAAXX0CC9/760xx5hV9I75DLG4hBJJxljpkvqJ2mQMWaOpDXGmFHW2rXGmNGS\nPkl2gVmzZn26X1dXp7q6uhzKAQAAAHyDBrltVVW4dZS6+vp61dfX93iesTb3BgVjzBGSftQ1K9R1\nkjZYa3/VNXh7qLW22+BtY4wN4t4AAABAIqarTw2PnMEyxsha263HUj7WsbhW0rHGmCWSju56DQAA\nAKCMBdJikdWNabEAAABAHtFikR+FbLEAAAAAQtXeHnYFvQ/BAgAAAGWnqSnsCnofggUAAADKDsGi\n8AgWAAAAKDuNjWFX0PsQLAAAAFB2aLEoPIIFAAAAys7q1VL//tIHH4RdSe/BdLMAAAAoO/vtJ73+\nOlPN5gPTzQIAAKAsGSPdeWfssfPPl/bfP5x6eiuCBQAAAEreN74R+7qyUpo0KZxaeiuCBQAAAErK\n009LxxyT+pz2dhcuUDgECwAAAJSUhQtduIgeP7HPPrHnNDZKAwYUtq7ejmABAACAkuIFhuXL/WNv\nvhl7zoYN0vDhhasJBAsAAACUmD593PaWW5Kfs3GjNGxYYeqBQ7AAAABASfHGTixdKv3Xf7n9z3xG\n+stfpDlz3GtaLAqPIS0AAAAoKV6LxQsvSA8/7PatlWbOlLZulb74Renjj6XRo8OrsTeixQIAAAAl\npaLrCXbdOv9YJOJChSRdeqnbr60tfG29GcECAAAAJaWzs/ux6MHb220nvfGG1K9f4WoCwQIAAAAl\nJhJJ/X5Dg9sOHJj/WuAjWAAAAKCk9BQs2tpcN6jtty9MPXAIFgAAACgpqYLFwQdLra3ShAmFqwcO\nwQIAAAAlJVWw2LxZ+r//86ekReEQLAAAAFBSUgWL995z2759C1MLfAQLAAAAlJT33+/5HFosCo9g\nAQAAgJLyv/+b/L1TT3Xb5ubC1AIfwQIAAABl47rr3HbLlnDr6I0IFgAAACh51krjx0vjxrnXTU3h\n1tMbGWttODc2xoZ1bwAAAJQuY7ofi36s9N7nUTM/jDGy1nb7XaDFAgAAACXj3/+WamqkUaPc64MO\nkjZsCLcmOAQLAAAAlIzrr5daWqTqavd6yBBp2LDu59XUFLYuECwAAABQQnbYQTrhBKmz071ONK3s\nsmXStm2FrQsECwAAAJSQt9+WjjrKHz8xe3b3c3baiXUswsDgbQAAAJQMY6Rzz5U++1lp5Upp1qyw\nK+p9kg3eJlgAAACgZNTUSH//uxu0jXAwKxQAAABK3ujR0ogRYVeBRGixAAAAQElobXUtFh0dUp8+\nYVfTe9FiAQAAgJL27LNuS6goTgQLAAAAlIQ77gi7AqRCVygAAACUBNPV+YZHyHDRFQoAAAAlizBR\n/AgWAAAAKHqtrW47cGC4dSA5ggUAAACKXkODmxFq1aqwK0EyBAsAAAAUvcWLpdpaafDgsCtBMgQL\nAAAAFL1PPpH23TfsKpAKwQIAAABFa+lSt41EGF9R7CrDLgAAAABIJHp62Y4OqZIn16JGiwUAAACK\nTvwg7UiEFbeLHcECAAAARWf77WNfEyyKH8ECAAAARa2jw003yxiL4kawAAAAQNFYssRtDz3UP/br\nX0sffyyNHh1OTUgPwQIAAACh27ZN2rJF2mMP6a9/lYYM8desmDNHWrlSGjcu3BqRGsECAAAAodtz\nT2nXXd3+Kae47k933OFeV1e7wdxjx4ZXH3pGsAAAAEDo1qyR1q93+5GI+6mtda/feEN65hnXioHi\nRbAAAABA6MaMiX3d0iJVVcUe69+/cPUgcwQLAACAMnf99dLuu4ddRWoVcU+lmzf7LRae+KCB4kKw\nAAAAKHOXXCK9/37YVaTmrVExc6bbvvOONGJE4nNQnAgWAAAAZa6uLuwKeua1WEQvjBcfLJhutrgR\nLAAAAMrYwoXSRx+5/Ugk1FJSShQsqqqkYcPc/gsvSIMGFb4upK8y7AIAAACQP3V1UlOT2+/oKN7u\nRF6w8Nau8Gzc6LZ7713YepA5ggUAAEAZOucct+6DFyokqbMzvHpSaW/3V9yODxZnnCHNnUtrRSkw\n1tpwbmyMDeveAAAA5c4Y15Worc0/1tAgDRiQ/Py//c0tUrfLLoWpMfrenmeflY48UrrwQum//quw\ndSA9xhhZa038ccZYAAAAlKnoUCFJyf5N1zs+bZq/+nUhHXywvz9ggHT44dLJJxe+DuSGYAEAAFBm\n1qzx9ydO9PeTdYV69NH81tOTxkbpi190LSoHHCD9/e/SMceEWxMyR7AAAAAoMzNmuO2pp0pbt/rH\nkwWLMFsHxoyR3n5bmj49eTctlAaCBQAAQJl5/nm3bWyMbb0oxsHbXn3etLIoXQQLAACAMnPuuW7b\n2Oi2558vDRmSfIzF1Kmxr7dty19t8UaOLNy9kF8ECwAAgDLjjauoqnLbGTNcl6iGhsTn77df7OvB\ng11LgrXS+vX5q1PyQ80+++T3Psg/ggUAAECZ8bo8tbS47aBBLiTsuGPi8xMFjj33lO67T9puu7yU\n+KmWFqm+vvBT3CJ4BAsAAIAy09np1oZoapI+8xlpr71Sn9/Q0L1L0ubN0gcfuP3ly/NTp+SCRXV1\n/q6PwiFYAAAAlJlIxHWDamrqvpJ1Io2N0pw5fpDweAvXXXFF8DV6CBblg2ABAABQZubMkVpbpebm\n9B7aGxrcrEzxrRa1tW57333B1yi5lpVly9ILPyh+BAsAAIAy8/bbbrt+vTRwYM/nb9vmzvMGe3vW\nrQu+tmhXXulCzYQJ+b0PCoNgAQAAUKYaG6XJk91+slYBa6VVq9xCddXV0mOP+e+tXi0ddJB0yin5\nqW/+fLft2zc/10dhESwAAADKSGenVFnpT+P64Ydu++ij0qGHdj9/61YXQIYMca+9xfUkN4B75Mjk\n09Tm4pvflF59NXFNKE0ECwAAgDLyu99JHR3SuHHutbc+RJ8+iVfenjUr9vW3v+3vb9vmpoGdPz/5\n4nrZaGyU7rjD7Xd0BHddhItgAQAAUEa87kV77um2/fq5bUWFmy0q3tKlsa933NEfWzFvnrTbbm6/\ntTW4Gg86yN/v0ye46yJcBAsAAIAy4i2K53Vt8gJBnz6Jg8Xmzd2PjRjh7w8f7hbYCzJYLFrk7996\na3DXRbgIFgAAAGXECw/eNLPnn++2ybpCbdmS+npVVVJNjR9YgjB9uttOmeJ+UB4qwy4AAAAAwTn4\nYGntWn+mJW8K2fXrpdde635+ohaLaFVVLqQE2WLhreTNwnjlhRYLAACAMrJsmVRX52aGiuZ1b2pv\njz1+/PGpr1dV5bpVbdoUTH3r17uuUMZIhx0WzDVRHAgWAAAAZeT226Vf/ar72hCf+YwLF/EBYcwY\n6eqru1/nC19w2+pqN6Dba2XIlbcgXmendMMNwVwTxYFgAQAAUGZ22inxonPDhkkbNvivN26Ufv7z\n7ituS9KkSW4bibjB242NwdTW3OzPVIXyQrAAAAAoE95aE088kTgs9O8vNTX5r+vr3TbRoO7ttnPb\nhoZgB28TLMpX1sHCGLO9MeYZY8wiY8zbxpiLuo4PNcbMM8YsMcY8aYwZEly5AAAASKa52W3HjZNO\nOEH68Y9j34+fGcpbUTvRInUXXeS248e7tTEefDCYGh94QHrjjWCuheKSy6xQHZJ+aK19wxgzUNKr\nxph5ks6RNN9ae50x5jJJV0i6PIBaAQAAkEJDg2tpGDjQ/fz857Hvx69l4bVeJGrdqKz0W0BWrnQ/\nQfjzn4O5DopP1sHCWrtG0pqu/QZjzLuStpd0sqQjuk67W1K9CBYAAAB519DgAkUy8cHCmyGqooc+\nLNdd56awDcJJJ0mjRgVzLRSXQMZYGGN2lDRV0kuSRllr10qfho+RQdwDAAAAqW3bllmw8LpO9e+f\n+rrV1VJbW+71SW6sRk1NMNdCcck5WHR1g3pQ0vettQ2SbNwp8a8BAAAQkNtu8wPCxo1u5qdkknWF\nOvXU1PeoqgouWDQ3EyzKVU4rbxtjKuVCxRxr7cNdh9caY0ZZa9caY0ZL+iTZ52fNmvXpfl1dnerq\n6nIpBwAAoFexVjrvPLcuxGc+I738sjR8ePLz44PF7Nnu8z11Tco1WDQ3S+efL915Jy0Wpai+vl71\n3hRiKeQULCTdKWmxtfamqGOPSDpb0q8kzZT0cILPSYoNFgAAAMjMc8+57dat0oknumAxc2by8+OD\nhZReYMg1WKxYIf3ud9Kee0r33uvGWaB0xDcAzJ49O+F5WQcLY8whks6U9LYx5nW5Lk9XygWKB4wx\n50paLum0bO8BAACA5LzF7laulFatcvvxK2tHiw4W3oxPBx7Y832qqvwQk0udl13mttXV2V8LxSuX\nWaEWSOqT5O1jsr0uAAAA0uOtht3S4q9J8cgjyc+PDhatrW47ZUrP9+nTR/r3v90aGD3NIJXI+vWx\nrydOzPwaKH6svA0AAFCizj7b3/dCxlVXJT8/Olhs2+bGY3zucz3fxwsTXhjJVHywGDo0u+uguBEs\nAAAAStShh7rt3Ln+6tnnnpv8/PhgMWhQevcJOljU1mZ3HRS3XAdvAwAAICTeA/+iRf6x0aOTn//0\n027la2szCxZ9ujq/ZxssvDEWHmaFKk+0WAAAAJSgzk7pH//ofjzVwOitW/395cvdoO90eMGipSX9\n+jyLFrkQc/PN7rW32jfKD8ECAACgBL3wQvepYzNxxhmpZ5CK5rWMzJ2b2T3a2qS993bTzQ4a5FpK\nKukvU7YIFgAAACWoXz+3jZ4G9tprU39mzBi3/Z//8Qd7p8MLFr/9bfqfkfyxFR98IA0enNlnUXoI\nFgAAACWorU2aNEk65BD/2C67pP7MU0+57Xe/67b9+6d3r2wHb3vBYsmS9MdzoHQRLAAAAEpQa6ub\nLjZ6XYkBA1J/ZtKk2NdNTenda+pUt800HETPBkWLRfmjlxsAAECJWbzYdUuKH6g9bVpm1/nOd9I7\nb/hwafp06fDDM7v+0Uf7+7RYlD+CBQAAQInxWh6mT489bkxm1zn//PTP3WuvzK7vravhGTgw/c+i\nNNEVCgAAoIREP7A//ri/X5HFU503jWw6Kiu7h4VU3nsv9jVdocofwQIAAKCEXHxx4uO77Zbe5ydP\n9vfTHWMhxa7anY4PPoh9TbAof3SFAgAAKCHRi+I9/bS/n+76EG+95XdpmjAh/ftmGiy++EW3veKK\n7FpTUHoIFgAAACXkrLOk116T6uqko47yj3vrWqSjvd0FhUzGTFRWuiluM/XLX2b+GZQmggUAAEAJ\n6d9fOvts6bbb/GNLlmTW1Sib1a/79MlsjIUkffWrmd8HpYuGKQAAgBJy333SkCFS377+sYkTpdGj\n83vf9eulZ55J71xr3faKK/JXD4qPsd7vfKFvbIwN694AAAClyhg3TewttxT+vpIfGlJpb5dqajIb\nk4HSYYyRtbZbRzpaLAAAAEpES4vbjhsXXg0HHNDz2IyGhsLUguJCsAAAACgR//mfbvujH4VXwyuv\n9HzOCSdInZ35rwXFha5QAAAAJSKT7kj5urcnVQ1h1on8S9YVilmhAAAASsSxx0oHHRR2FUBidIUC\nAAAoEevWSaecEs69Z80K574oHQQLAACAErFqVXgDt6dPT/3+2rXSDTe4/YMOkh5/PP81obgQLAAA\nAEpAa6u0ebM0cmQ490+0qF70AO177pEuvtjt//Of0vbbF6YuFA+CBQAAQAn43/9160NUhPT0Fr0g\nnyQ1NbnVuD3eVLjWugX8CBa9D8ECAACgiHzwgdTY2P14e3vP3ZEKaf16t331Vbddt85tN25061gM\nHBhOXQgPwQIAAKCI7LabdOaZ/uuVK6UFC1wLwb77hlfXqFGxr884w22fecZta2rcdu5ct+J2fAsH\nyh/TzQIAABQJb90HrzVAkk46SXr9dWm77aRLLgmnLsndP9qCBW576aUuVNTXu9fPP1/QslBECBYA\nAABFwhsMfcgh/rHXX3fbdeuk8eMLX1M6LrrI349E/NYM9C50hQIAAAiZMdIuu7hxFJK0ZYv/3rHH\nSmPGuP0JEwpfW6YeesiNsUDvQ7AAAAAoAsuWuXEUkptWduVK6corpaeekq66yh3fddfw6vNcdlnP\n5zzySP7rQPEhWAAAAISoo8PfP/ZYt928WbrzTumaa9zr885z4y/CWsMi2lFHJV7TYq+9pEmTCl8P\nigfBAgAAIEPWJp4SNhuffOI/qL/2mts++aT06KNuf++9XVepYmCtdNxxbjE8KXagdkuLdNppbn/E\niMLXhvARLAAAADK0YIFbpyF65elsnXNObKuF55VX3Pbaa3O/R9CqqtzWG/shSW1tfpet5ubC14Tw\nESwAAAAytHWr2wYxSHnePLf95z/9Y5df7u+fcELu9wiaN8h8552lVavcfmur2/bvzxoWvRXTzQIA\nAGTI+xf55mZp8ODsrzNsmNsuXOgvQPfkk26sxVlnFe+YhbY2f3/sWLf1vpPFiwtfD4oDwQIAACBD\n0cEiW9ZKmza5/e23l0aPlo44QtpxRzemolhDhSTttFP3Y17rzQ47FLYWFA+6QgEAAGTImxZ248bs\nP79hg//aa62or5cmTsyptII47DB/lXBJGjcuvFpQPAgWAAAAGXr7bbc9++zsPv/Vr7qBz4MHS//4\nR/HM+pStBQvoAgXJ2Oi4WcgbG2PDujcAAEA2Ghqkv/5VOv10/1g2jzPRQYLHIZQaY4ystd3iMGMs\nAAAA0nTWWdLDD8ce6+yUKjLoAzJ/frA1AcWCrlAAAABpig4V553ntr//vbR8udtfuVL6059Sd5Hy\nVte+5x7pvffyUiYQCrpCAQAApMnrwvSNb0g33yz16+de77ij6yK1997+ufGPOevWuRWpKyqkL3xB\neuSRgpQMBI6uUAAAADlYuNBtP/zQLQwX7aOPuneHam+PXShu5Eg3nawk3Xhj3soEQkNXKAAAgDSs\nWOG2Q4cmfv/WW2NfR4+l8Fal/vvf3TY+mADlgGABAACQhnnzXBeoZMHi5pv9/ZNPltav9197oUSS\npk3LT31A2AgWAAAAabjtNmnQoNhj3/iGNHZs7LFf/MIN8p4xwz+2Zo2//8QT+asRCBPBAgAAoAeL\nFrntuefGHr/9dmnVKunoo/1jV14pXXCB23/1Vbddt86trv2LX+S/ViAszAoFAADQA282qM2bpSFD\nur/f0uLPEOU93nifsdaFDWMIFigPyWaFosUCAACgBzvsII0fnzhUSFJNjdt+5zvd3zPGjbHYddf8\n1QcUA4IFAABACp2dbgG8++5Lfd6zz0pXXeW/fvFFf//ee/3wAZQrggUAAEAKBx7otiNGpD6vrk4a\nM8Z/fdBB0okn+q8JFih3BAsAAIAkpk/3B2APH57ZZ42RHn1UmjXLva6uDrQ0oOgQLAAAABK4/vrY\nqWF7arFIZuZMt41uzQDKEbNCAQAAxLFWqoj659c//1k65ZTsrrVpkzRsmLR1a/d1MIBSxKxQAAAA\naVq3zm2PPdZt9947+2vV1kr330+oQPmjxQIAACDKAw9IjzwiLVggLV0qvfCCdNhh/roUQG+XrMWC\nYAEAABAlOkDwqAJ0R1coAADQax1/vAsMr7/uVs/eti3xebfeKo0d6/b/+c/C1QeUA1osAABA2UvU\njSn+MWT1amncOLe/dCkrZQPJ0GIBAAB6rd12S3x840bp44/d/rPPuu3++0s771yYuoByQrAAAABl\n7brrXAvE+ee715ddJvXp4/aPPdZ1fdqwwbVYfPOb0sKFsVPNAkgP/9kAAICydtddbvuTn7jZni67\nTKqsdFPKtra690aMkC69VBo/Prw6gVLHGAsAADLwxhuuH/5224VdCdJ15JEuNJxwgnvd2irV1CQ+\nd8MGt5gdgOSYbhYAgAB4g4D5K6x4bdvmfsaMkfr2lSIRNxPUkCHu/fhVtX/7W9c16sQTpQkTwqkZ\nKCXJgkVlGMUAAFCK3n/f31+yRNp99/BqQXKnny499ph08cUuVJxxhh8qJBcOrZWef1566CHpe98L\nr1agnNBiAQBAmtKZstRzyy1SW5v0gx/ktyZ017+/1Nzsv372WamuLrRygLJDiwUAAGnq7JTeekua\nOtU/Nnduz597/XVpl12kwYOlCy90x555Rnr00fzUicQmTJCGD5e+8AXppJOkvfYKuyKgd6DFAgCA\nODfc4LrRLF0q/e1vbjahLVv89w86yF+VuaVFqq52oWK//dyxzs7YPvz8dVc477wjTZ4srV/vwgWA\n4LFAHgAAafrkE7fdbTfX/94LFTvv7N6bPt0/d4cd3LSlXqiQpFWrpFGj/NeRSP5rhrRmjQsVEjM7\nAWEgWAAAEMdb2yDaSSdJH37oppm96iq/FWLtWqmhIfbc8ePd8T/9Kfn1ELynnnJhsKMj8XgYAPlF\nsAAAoIu17oH0pptij8+aJf36193Pv/NOt915Z7eNDhA//KH0+c/npUwk0NYmzZghffnL/qraAAqL\nMRYAAHRZvdotfuftDx/uxk+sWRPbtcnT2Rn7EGutG3Px4x+7MDJokDRggGu9GDiwIL+EXssbbN/S\nIlVVhV0NUN4YYwEAQAJtbW49A0m6914XJjo73eJqVVXSSy8lDhWSG6BdUSFNmuR3jaqpcYO/Bw3y\nz+Hf0fLve99zv0+ECiA8TDcLAOi1rHUtEpL0l79Il17qujVF988/6KDU1+hpYHZFhQsqyI9Fi6S9\n93b7O+0Ubi1Ab0eLBYBPLVzoHqh+8Yvgrvncc/4MO0CxWb/e3z/lFLd9991g70GwCM6KFa6b2dtv\nu65q777rh4pFi9zgegDhYYwFAHV0uCkzV6/2j3V2Zj+rysMP+w9pngMPlObPl9rbpYcekr7xjdh5\n/ntirQsoo0e7VXQPPzyzzwPx1q2TRo6MPVZX5/58BWn4cOn991lTIRttbe5n4ED3/45E3ZwGD45d\nYwRA/jHGAr3KU0+5h1j07J57pL59/VDxxz+67Xe/m/j8bdtiZ75ZvdoNlty40b1etswPFSNGSMcc\n41Ygfvll9wAwfLj07W+7Aa9PP526tkhEOuQQF3AqKlyokKQjj5TOOku64grpZz+TGhvd8TvvdOf+\n5jeZfw/oPf7yF/fnxAsV8+ZJDzzgFsQLOlRImbdYRCLS4493n8I2CNu2lcZ4j2OOcS0R1dVurMpx\nx/mh4sUXpYkT3e/XmDFu0DaAImGtDeXH3RoIzvr11k6bZq37a9P9LFsWdlW+9nZrV6+2duPGxO93\ndlq7ZIm1bW2Fq+nBB9331KePtbNnW9va6o7fcYc7ftpp1j73nH/+G2/Efr977hn7epdd3Pbkk62N\nRGLv9dZb/nlr11p7yiluv77e2nfesbax0X0H119v7W23ue+qf3//M1/5irXz51u7ZYu1778fe9/4\nn5qa7vcPSyRi7aJF7teWyrvvWnvAAdYecYS1f/mL+/OydWtBSiwLmzdb++GHid+75BJrzzrL2j/+\n0YO5V50AABjiSURBVP352G47/8/K22/nvzbJ2nnz0jt30SJrjz7ar+/dd91/H8OGudep/hytWmXt\nCy+4ayxaFPvfREVF9/9OXnwxmF9fOlpbrX3tNWvXrLH2s5+19rvfjf3ut2619uqr3f9vrrgits4B\nA9z2s5/lvwmgWHQ9x3d/vk90sBA/BAsEIRKxtrnZ/WXUr5//F9GMGdaOHm3tnDn+uW1t1v7859b+\n+c/uL+c333QP1Pvua21HhzvW3u4CypFHWnvQQe5h9uKLrf3b39w15s2z9umnuz+0dna6h/RkDzbx\nD+Rr1rjP3H+/e6A//PDuf+nPmOEeDuK1tVn7xBPWPvmke+CYP989hDc0WPu5z7mH6tmzkz9Yd3Za\nO3eu+4tdsvbyy7uf8/bbsbWcdpq/P2SI+w691zvv7P7i33VXa487zgWGpqaef++stXbgwNj71NZ2\n/x6SBTFPa6v/oPjoo+6YZO0FF6RXQ5B+9jP3Xey/v7XLl7vv+tJL/V/b+PHWPvCA+z1sb3d/dj/3\nOWtHjEgekk46yb/+ihXWzpzprtHRUfhfX1juvtvaQYPcn+l//ct9d21t/kP2kiX+93XHHbEPn+3t\nib/X994rXP3R9z3nnNjfu0succfnzEkdlr2ft97qHi4iEff/s1SfmznT2gMP7P5nbf78/Pya29ut\nvesua6urU9c1ZIjb7rZb7PHzzus5jAMID8ECJct7QN640dp//MM98EvuX3bj/5KK/oto4kR3bNAg\nFzz23rv7+d6/Asb/jB9v7YknWvutb/n/Ch//M3Wqq2X//V1A8Y6PHm3tr3/tQsHcudaOHWttVZV7\nQHzzTWt32qn7tc4/3z1g3n+/tRdeaO3pp1u7xx7WTpli7VVXWbtwobUbNriHaO9f+pP9nHyyvz9q\nlHsQ8zz3XGyrzk03Jf/evbB0yy3u+/jWt6y99dZgWwI+/NAFpDvvtPa++6z9zW9cCFu71n0Pa9Zk\nd92LL3a/vupqa5cuTXxOJOIe1Nvb3etly9z3P3GitX37WltZae3vfmftypWxD7GexkZr//Qna3/6\nU2snT3a/f8l+Tw44wMb8y2v0T2Wla6WZM8fV0tLi7vnaa+7+ya75rW9Z+4UvWPvKK9l9R6Xg5pvT\ne9iWrB0+3D04J3rPGBc+7rzThY6XXy7sr2PFiu41tbRYe8ghyX89S5a4P3M77OBev/qqtRMmxJ7z\nwx9ae8YZscemT3fh6pJL3P8zN21yLRmJ1NT4n3vrLRd44v/7fvxx9w8Y6TzkP/aYaxmJbhHyfsaO\ndf/NzJ5t7fPPu/Pvvdfa/fZz7199tfv/W0NDTl81gAJJFiwYvB2wDRukm2+WDj1UOvrosKspbdZK\nt9/u+uNHmzZNWrxYOu006eCD3UwgZ54pDR0qDRvmnzd9uvTEE7GfXbFCeucdaeVK9/64cW7/lVf+\nf3v3HmdVXe5x/PNwH1AwjSFFuQWZmiBoCIYHDgnoycRSC6VUOFoeo4um5/TqYkOWmpaiotDFW3K0\n1LycrNRMUdI081KJ4iVBbiKI4DgODjDznD+e33Y20wAzs2fvzd5836/Xfs3ee6219289s/Zav/uC\nffaJftDPPw+f//yWA5dXr4Zf/jLGDtTWRj/se+6J50cdBZ/+dIwnuPFG+NnPos//iy/Gtt/8Jpx4\nYuPMJe7xPXffDWPHxriD5gYhr14dc7IfcEDsY0bHjjGv/tChsT+9e8cYh+XLYfjw+Kw1a+CQQ2I5\nwH77RX/yhx6KdWbPhtGj2z44uxS8+WaM55g6FVasgHPPjX7anTrBtGlw/fWN644cGWNAMqqq4oZo\n114bA0czDjwQpk+HD34Qjjkm3svc5Xft2hhgeued0Ud+3jxYvz7Gl2TfQG3pUjjrrEjfVVfF/2Zb\n/4eHHoK//S1m7JowIe4svGBBDF7PGD48Pue882Kq1OrqOM7mzoUTToB99912nCDikrnvQluOi9//\nPo7r1txp2j1+M2+8EbOHPfBAjAG44or4f8ybF+tNnx7/i113jfPqnXfC+PFxrGfGUr3xRvy/ly6N\nMVannRbvX3op7LUXfPazrd+n9uQe+7ZkCQwb1vh+//7x3sKFsPfe0KvX1j+jtjY+53e/g9/8JmIH\ncNhhcM018OEPty5Ny5fHea+pfv1g//3jHJdt/PgYQL377vG/XrUqxj3V1MSYh4cfjv//qlUxCP7U\nU3UHbJFytbXB23krWJjZkcAsYoD4Ne7+wybLS7JgMX/+fMaOHcctt0QGdcyYyBi+/HJc8Ju6+ebI\njHbrVvi0lqKFCyMDPGPGfG65ZRwAkyfHPPKVlXDSSVBR0bLPqq2NO942NERGY+BAGDIkf2lvKnN4\nt0fmfdky6N49Mq5durTuM6++Gr70pXj+5z9HYSx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Python/pandas/09_Time_Series/Apple_Stock/appl_1980_2014.csv new file mode 100644 index 0000000000000000000000000000000000000000..9cdff615a6615464cd0d66002ce610f271d6dd38 --- /dev/null +++ b/200 solved problems in Python/pandas/09_Time_Series/Apple_Stock/appl_1980_2014.csv @@ -0,0 +1,8466 @@ +Date,Open,High,Low,Close,Volume,Adj Close +2014-07-08,96.27,96.80,93.92,95.35,65130000,95.35 +2014-07-07,94.14,95.99,94.10,95.97,56305400,95.97 +2014-07-03,93.67,94.10,93.20,94.03,22891800,94.03 +2014-07-02,93.87,94.06,93.09,93.48,28420900,93.48 +2014-07-01,93.52,94.07,93.13,93.52,38170200,93.52 +2014-06-30,92.10,93.73,92.09,92.93,49482300,92.93 +2014-06-27,90.82,92.00,90.77,91.98,64006800,91.98 +2014-06-26,90.37,91.05,89.80,90.90,32595800,90.90 +2014-06-25,90.21,90.70,89.65,90.36,36852200,90.36 +2014-06-24,90.75,91.74,90.19,90.28,38988300,90.28 +2014-06-23,91.32,91.62,90.60,90.83,43618200,90.83 +2014-06-20,91.85,92.55,90.90,90.91,100813200,90.91 +2014-06-19,92.29,92.30,91.34,91.86,35486400,91.86 +2014-06-18,92.27,92.29,91.35,92.18,33493800,92.18 +2014-06-17,92.31,92.70,91.80,92.08,29689800,92.08 +2014-06-16,91.51,92.75,91.45,92.20,35561000,92.20 +2014-06-13,92.20,92.44,90.88,91.28,54525000,91.28 +2014-06-12,94.04,94.12,91.90,92.29,54749000,92.29 +2014-06-11,94.13,94.76,93.47,93.86,45681000,93.86 +2014-06-10,94.73,95.05,93.57,94.25,62777000,94.25 +2014-06-09,92.70,93.88,91.75,93.70,75415000,93.70 +2014-06-06,649.90,651.26,644.47,645.57,87484600,92.22 +2014-06-05,646.20,649.37,642.61,647.35,75951400,92.48 +2014-06-04,637.44,647.89,636.11,644.82,83870500,92.12 +2014-06-03,628.46,638.74,628.25,637.54,73177300,91.08 +2014-06-02,633.96,634.83,622.50,628.65,92337700,89.81 +2014-05-30,637.98,644.17,628.90,633.00,141005200,90.43 +2014-05-29,627.85,636.87,627.77,635.38,94118500,90.77 +2014-05-28,626.02,629.83,623.78,624.01,78870400,89.14 +2014-05-27,615.88,625.86,615.63,625.63,87216500,89.38 +2014-05-23,607.25,614.73,606.47,614.13,58052400,87.73 +2014-05-22,606.60,609.85,604.10,607.27,50190000,86.75 +2014-05-21,603.83,606.70,602.06,606.31,49214900,86.62 +2014-05-20,604.51,606.40,600.73,604.71,58709000,86.39 +2014-05-19,597.85,607.33,597.33,604.59,79438800,86.37 +2014-05-16,588.63,597.53,585.40,597.51,69064100,85.36 +2014-05-15,594.70,596.60,588.04,588.82,57711500,84.12 +2014-05-14,592.43,597.40,591.74,593.87,41601000,84.84 +2014-05-13,592.00,594.54,590.70,593.76,39934300,84.82 +2014-05-12,587.49,593.66,587.40,592.83,53302200,84.69 +2014-05-09,584.54,586.25,580.33,585.54,72899400,83.65 +2014-05-08,588.25,594.41,586.40,587.99,57574300,84.00 +2014-05-07,595.25,597.29,587.73,592.33,70716100,84.15 +2014-05-06,601.80,604.41,594.41,594.41,93641100,84.44 +2014-05-05,590.14,601.00,590.00,600.96,71766800,85.37 +2014-05-02,592.34,594.20,589.71,592.58,47878600,84.18 +2014-05-01,592.00,594.80,586.36,591.48,61012000,84.03 +2014-04-30,592.64,599.43,589.80,590.09,114160200,83.83 +2014-04-29,593.74,595.98,589.51,592.33,84344400,84.15 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+1980-12-16,25.37,25.37,25.25,25.25,26432000,0.39 +1980-12-15,27.38,27.38,27.25,27.25,43971200,0.42 +1980-12-12,28.75,28.87,28.75,28.75,117258400,0.45 diff --git a/200 solved problems in Python/pandas/09_Time_Series/Getting_Financial_Data/Exercises.ipynb b/200 solved problems in Python/pandas/09_Time_Series/Getting_Financial_Data/Exercises.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..aafa0b4b442e6d954c9d957319763c387491ca11 --- /dev/null +++ b/200 solved problems in Python/pandas/09_Time_Series/Getting_Financial_Data/Exercises.ipynb @@ -0,0 +1,200 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Getting Financial Data - Google Finance" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "This time you will get data from a website.\n", + "\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Create your time range (start and end variables). The start date should be 01/01/2015 and the end should today (whatever your today is)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Select the Apple, Tesla, Twitter, IBM, LinkedIn stocks symbols and assign them to a variable called stocks" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Read the data from google, assign to df and print it" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. What is the type of structure of df ?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Print all the Items axis values\n", + "#### To learn more about the Panel structure go to [documentation](http://pandas.pydata.org/pandas-docs/stable/dsintro.html#panel) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Good, now we know the data avaiable. Create a dataFrame called vol, with the Volume values." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Aggregate the data of Volume to weekly\n", + "#### Hint: Be careful to not sum data from the same week of 2015 and other years." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. Find all the volume traded in the year of 2015" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BONUS: Create your own question and answer it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/09_Time_Series/Getting_Financial_Data/Exercises_with_solutions_and_code.ipynb b/200 solved problems in Python/pandas/09_Time_Series/Getting_Financial_Data/Exercises_with_solutions_and_code.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..2cdbb206a3e4839d2157a8ce67269aaf030f1deb --- /dev/null +++ b/200 solved problems in Python/pandas/09_Time_Series/Getting_Financial_Data/Exercises_with_solutions_and_code.ipynb @@ -0,0 +1,485 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Getting Financial Data - Google Finance" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "This time you will get data from a website.\n", + "\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "# package to extract data from various Internet sources into a DataFrame\n", + "# make sure you have it installed\n", + "from pandas_datareader import data, wb\n", + "\n", + "# package for dates\n", + "import datetime as dt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Create your time range (start and end variables). The start date should be 01/01/2015 and the end should today (whatever your today is)" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "datetime.datetime(2015, 1, 1, 0, 0)" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "start = dt.datetime(2015, 1, 1)\n", + "\n", + "end = dt.datetime.today()\n", + "\n", + "start" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Select the Apple, Tesla, Twitter, IBM, LinkedIn stocks symbols and assign them to a variable called stocks" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "stocks = ['AAPL', 'TSLA', 'IBM', 'LNKD']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Read the data from google, assign to df and print it" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Dimensions: 5 (items) x 399 (major_axis) x 4 (minor_axis)\n", + "Items axis: Open to Volume\n", + "Major_axis axis: 2015-01-02 00:00:00 to 2016-08-02 00:00:00\n", + "Minor_axis axis: AAPL to TSLA" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = web.DataReader(stocks, 'google', start, end)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. What is the type of structure of df ?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# 'pandas.core.panel.Panel'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Print all the Items axis values\n", + "#### To learn more about the Panel structure go to [documentation](http://pandas.pydata.org/pandas-docs/stable/dsintro.html#panel) " + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index([u'Open', u'High', u'Low', u'Close', u'Volume'], dtype='object')" + ] + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.items" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Good, now we know the data avaiable. Create a dataFrame called vol, with the Volume values." + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " AAPL IBM LNKD TSLA\n", + "Date \n", + "2015-01-02 53204626.0 5525466.0 1203743.0 4764443.0\n", + "2015-01-05 64285491.0 4880389.0 1400562.0 5368477.0\n", + "2015-01-06 65797116.0 6146712.0 2006546.0 6261936.0\n", + "2015-01-07 40105934.0 4701839.0 985016.0 2968390.0\n", + "2015-01-08 59364547.0 4241113.0 1293955.0 3442509.0" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vol = df['Volume']\n", + "vol.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Aggregate the data of Volume to weekly\n", + "#### Hint: Be careful to not sum data from the same week of 2015 and other years." + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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AAPLIBMLNKDTSLA
weekyear
1201553204626.05525466.01203743.04764443.0
2016343422014.025233098.06630485.020967926.0
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" + ], + "text/plain": [ + " AAPL IBM LNKD TSLA\n", + "week year \n", + "1 2015 53204626.0 5525466.0 1203743.0 4764443.0\n", + " 2016 343422014.0 25233098.0 6630485.0 20967926.0\n", + "2 2015 283252615.0 24458400.0 7203125.0 22709607.0\n", + " 2016 302072797.0 29379214.0 9160521.0 22997290.0\n", + "3 2015 304226647.0 23263206.0 7084168.0 30799137.0" + ] + }, + "execution_count": 132, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vol['week'] = vol.index.week\n", + "vol['year'] = vol.index.year\n", + "\n", + "week = vol.groupby(['week','year']).sum()\n", + "week.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. Find all the volume traded in the year of 2015" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " AAPL IBM LNKD TSLA\n", + "year \n", + "2015 1.301994e+10 1.100959e+09 440376163.0 1.085839e+09\n", + "2016 6.081474e+09 6.585250e+08 453233878.0 7.540962e+08" + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "del vol['week']\n", + "vol['year'] = vol.index.year\n", + "\n", + "year = vol.groupby(['year']).sum()\n", + "year" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BONUS: Create your own question and answer it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/09_Time_Series/Getting_Financial_Data/Solutions.ipynb b/200 solved problems in Python/pandas/09_Time_Series/Getting_Financial_Data/Solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..cad558f8d4f4217d7f6ae8e33f995c3d015dfda8 --- /dev/null +++ b/200 solved problems in Python/pandas/09_Time_Series/Getting_Financial_Data/Solutions.ipynb @@ -0,0 +1,468 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Getting Financial Data - Google Finance" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "This time you will get data from a website.\n", + "\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "# package to extract data from various Internet sources into a DataFrame\n", + "# make sure you have it installed\n", + "from pandas_datareader import data, wb\n", + "\n", + "# package for dates\n", + "import datetime as dt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Create your time range (start and end variables). The start date should be 01/01/2015 and the end should today (whatever your today is)" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "datetime.datetime(2015, 1, 1, 0, 0)" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Select the Apple, Tesla, Twitter, IBM, LinkedIn stocks symbols and assign them to a variable called stocks" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['AAPL', 'TSLA', 'IBM', 'LNKD']" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Read the data from google, assign to df and print it" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Dimensions: 5 (items) x 399 (major_axis) x 4 (minor_axis)\n", + "Items axis: Open to Volume\n", + "Major_axis axis: 2015-01-02 00:00:00 to 2016-08-02 00:00:00\n", + "Minor_axis axis: AAPL to TSLA" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. What is the type of structure of df ?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# 'pandas.core.panel.Panel'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Print all the Items axis values\n", + "#### To learn more about the Panel structure go to [documentation](http://pandas.pydata.org/pandas-docs/stable/dsintro.html#panel) " + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index([u'Open', u'High', u'Low', u'Close', u'Volume'], dtype='object')" + ] + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Good, now we know the data avaiable. Create a dataFrame called vol, with the Volume values." + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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AAPLIBMLNKDTSLA
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2015-01-0253204626.05525466.01203743.04764443.0
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" + ], + "text/plain": [ + " AAPL IBM LNKD TSLA\n", + "Date \n", + "2015-01-02 53204626.0 5525466.0 1203743.0 4764443.0\n", + "2015-01-05 64285491.0 4880389.0 1400562.0 5368477.0\n", + "2015-01-06 65797116.0 6146712.0 2006546.0 6261936.0\n", + "2015-01-07 40105934.0 4701839.0 985016.0 2968390.0\n", + "2015-01-08 59364547.0 4241113.0 1293955.0 3442509.0" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Aggregate the data of Volume to weekly\n", + "#### Hint: Be careful to not sum data from the same week of 2015 and other years." + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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AAPLIBMLNKDTSLA
weekyear
1201553204626.05525466.01203743.04764443.0
2016343422014.025233098.06630485.020967926.0
22015283252615.024458400.07203125.022709607.0
2016302072797.029379214.09160521.022997290.0
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" + ], + "text/plain": [ + " AAPL IBM LNKD TSLA\n", + "week year \n", + "1 2015 53204626.0 5525466.0 1203743.0 4764443.0\n", + " 2016 343422014.0 25233098.0 6630485.0 20967926.0\n", + "2 2015 283252615.0 24458400.0 7203125.0 22709607.0\n", + " 2016 302072797.0 29379214.0 9160521.0 22997290.0\n", + "3 2015 304226647.0 23263206.0 7084168.0 30799137.0" + ] + }, + "execution_count": 132, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. Find all the volume traded in the year of 2015" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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year
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" + ], + "text/plain": [ + " AAPL IBM LNKD TSLA\n", + "year \n", + "2015 1.301994e+10 1.100959e+09 440376163.0 1.085839e+09\n", + "2016 6.081474e+09 6.585250e+08 453233878.0 7.540962e+08" + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BONUS: Create your own question and answer it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/09_Time_Series/Investor_Flow_of_Funds_US/Exercises.ipynb b/200 solved problems in Python/pandas/09_Time_Series/Investor_Flow_of_Funds_US/Exercises.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..8e0420625d12870df7618838dbf6084d1ce65f2a --- /dev/null +++ b/200 solved problems in Python/pandas/09_Time_Series/Investor_Flow_of_Funds_US/Exercises.ipynb @@ -0,0 +1,203 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Investor - Flow of Funds - US" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "Special thanks to: https://github.com/rgrp for sharing the dataset.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/datasets/investor-flow-of-funds-us/master/data/weekly.csv). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. What is the frequency of the dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Set the column Date as the index." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. What is the type of the index?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Set the index to a DatetimeIndex type" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Change the frequency to monthly, sum the values and assign it to monthly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. You will notice that it filled the dataFrame with months that don't have any data with NaN. Let's drop these rows." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. Good, now we have the monthly data. Now change the frequency to year." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BONUS: Create your own question and answer it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/09_Time_Series/Investor_Flow_of_Funds_US/Exercises_with_code_and_solutions.ipynb b/200 solved problems in Python/pandas/09_Time_Series/Investor_Flow_of_Funds_US/Exercises_with_code_and_solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..a60c955f6a0b8b9a1906ba030752a7e301c18091 --- /dev/null +++ b/200 solved problems in Python/pandas/09_Time_Series/Investor_Flow_of_Funds_US/Exercises_with_code_and_solutions.ipynb @@ -0,0 +1,1218 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Investor - Flow of Funds - US" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "Special thanks to: https://github.com/rgrp for sharing the dataset.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/datasets/investor-flow-of-funds-us/master/data/weekly.csv). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called " + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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DateTotal EquityDomestic EquityWorld EquityHybridTotal BondTaxable BondMunicipal BondTotal
02012-12-05-7426-6060-1367-74531742101107-2183
12012-12-12-8783-7520-126312318181598219-6842
22012-12-19-5496-5470-26-731033472-3369-5466
32012-12-26-4451-4076-37555026103333-722-1291
42013-01-02-11156-9622-1533-15823832103280-8931
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" + ], + "text/plain": [ + " Date Total Equity Domestic Equity World Equity Hybrid \\\n", + "0 2012-12-05 -7426 -6060 -1367 -74 \n", + "1 2012-12-12 -8783 -7520 -1263 123 \n", + "2 2012-12-19 -5496 -5470 -26 -73 \n", + "3 2012-12-26 -4451 -4076 -375 550 \n", + "4 2013-01-02 -11156 -9622 -1533 -158 \n", + "\n", + " Total Bond Taxable Bond Municipal Bond Total \n", + "0 5317 4210 1107 -2183 \n", + "1 1818 1598 219 -6842 \n", + "2 103 3472 -3369 -5466 \n", + "3 2610 3333 -722 -1291 \n", + "4 2383 2103 280 -8931 " + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "url = 'https://raw.githubusercontent.com/datasets/investor-flow-of-funds-us/master/data/weekly.csv'\n", + "df = pd.read_csv(url)\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. What is the frequency of the dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# weekly data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Set the column Date as the index." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Total EquityDomestic EquityWorld EquityHybridTotal BondTaxable BondMunicipal BondTotal
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2012-12-05-7426-6060-1367-74531742101107-2183
2012-12-12-8783-7520-126312318181598219-6842
2012-12-19-5496-5470-26-731033472-3369-5466
2012-12-26-4451-4076-37555026103333-722-1291
2013-01-02-11156-9622-1533-15823832103280-8931
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" + ], + "text/plain": [ + " Total Equity Domestic Equity World Equity Hybrid Total Bond \\\n", + "Date \n", + "2012-12-05 -7426 -6060 -1367 -74 5317 \n", + "2012-12-12 -8783 -7520 -1263 123 1818 \n", + "2012-12-19 -5496 -5470 -26 -73 103 \n", + "2012-12-26 -4451 -4076 -375 550 2610 \n", + "2013-01-02 -11156 -9622 -1533 -158 2383 \n", + "\n", + " Taxable Bond Municipal Bond Total \n", + "Date \n", + "2012-12-05 4210 1107 -2183 \n", + "2012-12-12 1598 219 -6842 \n", + "2012-12-19 3472 -3369 -5466 \n", + "2012-12-26 3333 -722 -1291 \n", + "2013-01-02 2103 280 -8931 " + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = df.set_index('Date')\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. What is the type of the index?" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index([u'2012-12-05', u'2012-12-12', u'2012-12-19', u'2012-12-26',\n", + " u'2013-01-02', u'2013-01-09', u'2014-04-02', u'2014-04-09',\n", + " u'2014-04-16', u'2014-04-23', u'2014-04-30', u'2014-05-07',\n", + " u'2014-05-14', u'2014-05-21', u'2014-05-28', u'2014-06-04',\n", + " u'2014-06-11', u'2014-06-18', u'2014-06-25', u'2014-07-02',\n", + " u'2014-07-09', u'2014-07-30', u'2014-08-06', u'2014-08-13',\n", + " u'2014-08-20', u'2014-08-27', u'2014-09-03', u'2014-09-10',\n", + " u'2014-11-05', u'2014-11-12', u'2014-11-19', u'2014-11-25',\n", + " u'2015-01-07', u'2015-01-14', u'2015-01-21', u'2015-01-28',\n", + " u'2015-02-04', u'2015-02-11', u'2015-03-04', u'2015-03-11',\n", + " u'2015-03-18', u'2015-03-25', u'2015-04-01', u'2015-04-08'],\n", + " dtype='object', name=u'Date')" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.index\n", + "# it is a 'object' type" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Set the index to a DatetimeIndex type" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.tseries.index.DatetimeIndex" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.index = pd.to_datetime(df.index)\n", + "type(df.index)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Change the frequency to monthly, sum the values and assign it to monthly." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Total EquityDomestic EquityWorld EquityHybridTotal BondTaxable BondMunicipal BondTotal
Date
2012-12-31-26156.0-23126.0-3031.0526.09848.012613.0-2765.0-15782.0
2013-01-313661.0-1627.05288.02730.012149.09414.02735.018540.0
2013-02-28NaNNaNNaNNaNNaNNaNNaNNaN
2013-03-31NaNNaNNaNNaNNaNNaNNaNNaN
2013-04-30NaNNaNNaNNaNNaNNaNNaNNaN
2013-05-31NaNNaNNaNNaNNaNNaNNaNNaN
2013-06-30NaNNaNNaNNaNNaNNaNNaNNaN
2013-07-31NaNNaNNaNNaNNaNNaNNaNNaN
2013-08-31NaNNaNNaNNaNNaNNaNNaNNaN
2013-09-30NaNNaNNaNNaNNaNNaNNaNNaN
2013-10-31NaNNaNNaNNaNNaNNaNNaNNaN
2013-11-30NaNNaNNaNNaNNaNNaNNaNNaN
2013-12-31NaNNaNNaNNaNNaNNaNNaNNaN
2014-01-31NaNNaNNaNNaNNaNNaNNaNNaN
2014-02-28NaNNaNNaNNaNNaNNaNNaNNaN
2014-03-31NaNNaNNaNNaNNaNNaNNaNNaN
2014-04-3010842.01048.09794.04931.08493.07193.01300.024267.0
2014-05-31-2203.0-8720.06518.03172.013767.010192.03576.014736.0
2014-06-302319.0-6546.08865.04588.09715.07551.02163.016621.0
2014-07-31-7051.0-11128.04078.02666.07506.07026.0481.03122.0
2014-08-311943.0-5508.07452.01885.01897.0-1013.02910.05723.0
2014-09-30-2767.0-6596.03829.01599.03984.02479.01504.02816.0
2014-10-31NaNNaNNaNNaNNaNNaNNaNNaN
2014-11-30-2753.0-7239.04485.0729.014528.011566.02962.012502.0
2014-12-31NaNNaNNaNNaNNaNNaNNaNNaN
2015-01-313471.0-1164.04635.01729.07368.02762.04606.012569.0
2015-02-285508.03509.01999.01752.09099.07443.01656.016359.0
2015-03-315691.0-8176.013867.02829.09138.07267.01870.017657.0
2015-04-30379.0-4628.05007.0970.0423.0514.0-91.01772.0
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" + ], + "text/plain": [ + " Total Equity Domestic Equity World Equity Hybrid Total Bond \\\n", + "Date \n", + "2012-12-31 -26156.0 -23126.0 -3031.0 526.0 9848.0 \n", + "2013-01-31 3661.0 -1627.0 5288.0 2730.0 12149.0 \n", + "2013-02-28 NaN NaN NaN NaN NaN \n", + "2013-03-31 NaN NaN NaN NaN NaN \n", + "2013-04-30 NaN NaN NaN NaN NaN \n", + "2013-05-31 NaN NaN NaN NaN NaN \n", + "2013-06-30 NaN NaN NaN NaN NaN \n", + "2013-07-31 NaN NaN NaN NaN NaN \n", + "2013-08-31 NaN NaN NaN NaN NaN \n", + "2013-09-30 NaN NaN NaN NaN NaN \n", + "2013-10-31 NaN NaN NaN NaN NaN \n", + "2013-11-30 NaN NaN NaN NaN NaN \n", + "2013-12-31 NaN NaN NaN NaN NaN \n", + "2014-01-31 NaN NaN NaN NaN NaN \n", + "2014-02-28 NaN NaN NaN NaN NaN \n", + "2014-03-31 NaN NaN NaN NaN NaN \n", + "2014-04-30 10842.0 1048.0 9794.0 4931.0 8493.0 \n", + "2014-05-31 -2203.0 -8720.0 6518.0 3172.0 13767.0 \n", + "2014-06-30 2319.0 -6546.0 8865.0 4588.0 9715.0 \n", + "2014-07-31 -7051.0 -11128.0 4078.0 2666.0 7506.0 \n", + "2014-08-31 1943.0 -5508.0 7452.0 1885.0 1897.0 \n", + "2014-09-30 -2767.0 -6596.0 3829.0 1599.0 3984.0 \n", + "2014-10-31 NaN NaN NaN NaN NaN \n", + "2014-11-30 -2753.0 -7239.0 4485.0 729.0 14528.0 \n", + "2014-12-31 NaN NaN NaN NaN NaN \n", + "2015-01-31 3471.0 -1164.0 4635.0 1729.0 7368.0 \n", + "2015-02-28 5508.0 3509.0 1999.0 1752.0 9099.0 \n", + "2015-03-31 5691.0 -8176.0 13867.0 2829.0 9138.0 \n", + "2015-04-30 379.0 -4628.0 5007.0 970.0 423.0 \n", + "\n", + " Taxable Bond Municipal Bond Total \n", + "Date \n", + "2012-12-31 12613.0 -2765.0 -15782.0 \n", + "2013-01-31 9414.0 2735.0 18540.0 \n", + "2013-02-28 NaN NaN NaN \n", + "2013-03-31 NaN NaN NaN \n", + "2013-04-30 NaN NaN NaN \n", + "2013-05-31 NaN NaN NaN \n", + "2013-06-30 NaN NaN NaN \n", + "2013-07-31 NaN NaN NaN \n", + "2013-08-31 NaN NaN NaN \n", + "2013-09-30 NaN NaN NaN \n", + "2013-10-31 NaN NaN NaN \n", + "2013-11-30 NaN NaN NaN \n", + "2013-12-31 NaN NaN NaN \n", + "2014-01-31 NaN NaN NaN \n", + "2014-02-28 NaN NaN NaN \n", + "2014-03-31 NaN NaN NaN \n", + "2014-04-30 7193.0 1300.0 24267.0 \n", + "2014-05-31 10192.0 3576.0 14736.0 \n", + "2014-06-30 7551.0 2163.0 16621.0 \n", + "2014-07-31 7026.0 481.0 3122.0 \n", + "2014-08-31 -1013.0 2910.0 5723.0 \n", + "2014-09-30 2479.0 1504.0 2816.0 \n", + "2014-10-31 NaN NaN NaN \n", + "2014-11-30 11566.0 2962.0 12502.0 \n", + "2014-12-31 NaN NaN NaN \n", + "2015-01-31 2762.0 4606.0 12569.0 \n", + "2015-02-28 7443.0 1656.0 16359.0 \n", + "2015-03-31 7267.0 1870.0 17657.0 \n", + "2015-04-30 514.0 -91.0 1772.0 " + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "monthly = df.resample('M').sum()\n", + "monthly" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. You will notice that it filled the dataFrame with months that don't have any data with NaN. Let's drop these rows." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Total EquityDomestic EquityWorld EquityHybridTotal BondTaxable BondMunicipal BondTotal
Date
2012-12-31-26156.0-23126.0-3031.0526.09848.012613.0-2765.0-15782.0
2013-01-313661.0-1627.05288.02730.012149.09414.02735.018540.0
2014-04-3010842.01048.09794.04931.08493.07193.01300.024267.0
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2015-02-285508.03509.01999.01752.09099.07443.01656.016359.0
2015-03-315691.0-8176.013867.02829.09138.07267.01870.017657.0
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" + ], + "text/plain": [ + " Total Equity Domestic Equity World Equity Hybrid Total Bond \\\n", + "Date \n", + "2012-12-31 -26156.0 -23126.0 -3031.0 526.0 9848.0 \n", + "2013-01-31 3661.0 -1627.0 5288.0 2730.0 12149.0 \n", + "2014-04-30 10842.0 1048.0 9794.0 4931.0 8493.0 \n", + "2014-05-31 -2203.0 -8720.0 6518.0 3172.0 13767.0 \n", + "2014-06-30 2319.0 -6546.0 8865.0 4588.0 9715.0 \n", + "2014-07-31 -7051.0 -11128.0 4078.0 2666.0 7506.0 \n", + "2014-08-31 1943.0 -5508.0 7452.0 1885.0 1897.0 \n", + "2014-09-30 -2767.0 -6596.0 3829.0 1599.0 3984.0 \n", + "2014-11-30 -2753.0 -7239.0 4485.0 729.0 14528.0 \n", + "2015-01-31 3471.0 -1164.0 4635.0 1729.0 7368.0 \n", + "2015-02-28 5508.0 3509.0 1999.0 1752.0 9099.0 \n", + "2015-03-31 5691.0 -8176.0 13867.0 2829.0 9138.0 \n", + "2015-04-30 379.0 -4628.0 5007.0 970.0 423.0 \n", + "\n", + " Taxable Bond Municipal Bond Total \n", + "Date \n", + "2012-12-31 12613.0 -2765.0 -15782.0 \n", + "2013-01-31 9414.0 2735.0 18540.0 \n", + "2014-04-30 7193.0 1300.0 24267.0 \n", + "2014-05-31 10192.0 3576.0 14736.0 \n", + "2014-06-30 7551.0 2163.0 16621.0 \n", + "2014-07-31 7026.0 481.0 3122.0 \n", + "2014-08-31 -1013.0 2910.0 5723.0 \n", + "2014-09-30 2479.0 1504.0 2816.0 \n", + "2014-11-30 11566.0 2962.0 12502.0 \n", + "2015-01-31 2762.0 4606.0 12569.0 \n", + "2015-02-28 7443.0 1656.0 16359.0 \n", + "2015-03-31 7267.0 1870.0 17657.0 \n", + "2015-04-30 514.0 -91.0 1772.0 " + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "monthly = monthly.dropna()\n", + "monthly" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. Good, now we have the monthly data. Now change the frequency to year." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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2012-01-01-26156.0-23126.0-3031.0526.09848.012613.0-2765.0-15782.0
2013-01-013661.0-1627.05288.02730.012149.09414.02735.018540.0
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2015-01-0115049.0-10459.025508.07280.026028.017986.08041.048357.0
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" + ], + "text/plain": [ + " Total Equity Domestic Equity World Equity Hybrid Total Bond \\\n", + "Date \n", + "2012-01-01 -26156.0 -23126.0 -3031.0 526.0 9848.0 \n", + "2013-01-01 3661.0 -1627.0 5288.0 2730.0 12149.0 \n", + "2014-01-01 330.0 -44689.0 45021.0 19570.0 59890.0 \n", + "2015-01-01 15049.0 -10459.0 25508.0 7280.0 26028.0 \n", + "\n", + " Taxable Bond Municipal Bond Total \n", + "Date \n", + "2012-01-01 12613.0 -2765.0 -15782.0 \n", + "2013-01-01 9414.0 2735.0 18540.0 \n", + "2014-01-01 44994.0 14896.0 79787.0 \n", + "2015-01-01 17986.0 8041.0 48357.0 " + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "year = monthly.resample('AS-JAN').sum()\n", + "year" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BONUS: Create your own question and answer it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/09_Time_Series/Investor_Flow_of_Funds_US/Solutions.ipynb b/200 solved problems in Python/pandas/09_Time_Series/Investor_Flow_of_Funds_US/Solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..318f9d4b48c5adc61639a07a21083e170fbc1e69 --- /dev/null +++ b/200 solved problems in Python/pandas/09_Time_Series/Investor_Flow_of_Funds_US/Solutions.ipynb @@ -0,0 +1,1196 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Investor - Flow of Funds - US" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "Special thanks to: https://github.com/rgrp for sharing the dataset.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/datasets/investor-flow-of-funds-us/master/data/weekly.csv). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called " + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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DateTotal EquityDomestic EquityWorld EquityHybridTotal BondTaxable BondMunicipal BondTotal
02012-12-05-7426-6060-1367-74531742101107-2183
12012-12-12-8783-7520-126312318181598219-6842
22012-12-19-5496-5470-26-731033472-3369-5466
32012-12-26-4451-4076-37555026103333-722-1291
42013-01-02-11156-9622-1533-15823832103280-8931
\n", + "
" + ], + "text/plain": [ + " Date Total Equity Domestic Equity World Equity Hybrid \\\n", + "0 2012-12-05 -7426 -6060 -1367 -74 \n", + "1 2012-12-12 -8783 -7520 -1263 123 \n", + "2 2012-12-19 -5496 -5470 -26 -73 \n", + "3 2012-12-26 -4451 -4076 -375 550 \n", + "4 2013-01-02 -11156 -9622 -1533 -158 \n", + "\n", + " Total Bond Taxable Bond Municipal Bond Total \n", + "0 5317 4210 1107 -2183 \n", + "1 1818 1598 219 -6842 \n", + "2 103 3472 -3369 -5466 \n", + "3 2610 3333 -722 -1291 \n", + "4 2383 2103 280 -8931 " + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. What is the frequency of the dataset?" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# weekly data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Set the column Date as the index." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Total EquityDomestic EquityWorld EquityHybridTotal BondTaxable BondMunicipal BondTotal
Date
2012-12-05-7426-6060-1367-74531742101107-2183
2012-12-12-8783-7520-126312318181598219-6842
2012-12-19-5496-5470-26-731033472-3369-5466
2012-12-26-4451-4076-37555026103333-722-1291
2013-01-02-11156-9622-1533-15823832103280-8931
\n", + "
" + ], + "text/plain": [ + " Total Equity Domestic Equity World Equity Hybrid Total Bond \\\n", + "Date \n", + "2012-12-05 -7426 -6060 -1367 -74 5317 \n", + "2012-12-12 -8783 -7520 -1263 123 1818 \n", + "2012-12-19 -5496 -5470 -26 -73 103 \n", + "2012-12-26 -4451 -4076 -375 550 2610 \n", + "2013-01-02 -11156 -9622 -1533 -158 2383 \n", + "\n", + " Taxable Bond Municipal Bond Total \n", + "Date \n", + "2012-12-05 4210 1107 -2183 \n", + "2012-12-12 1598 219 -6842 \n", + "2012-12-19 3472 -3369 -5466 \n", + "2012-12-26 3333 -722 -1291 \n", + "2013-01-02 2103 280 -8931 " + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. What is the type of the index?" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index([u'2012-12-05', u'2012-12-12', u'2012-12-19', u'2012-12-26',\n", + " u'2013-01-02', u'2013-01-09', u'2014-04-02', u'2014-04-09',\n", + " u'2014-04-16', u'2014-04-23', u'2014-04-30', u'2014-05-07',\n", + " u'2014-05-14', u'2014-05-21', u'2014-05-28', u'2014-06-04',\n", + " u'2014-06-11', u'2014-06-18', u'2014-06-25', u'2014-07-02',\n", + " u'2014-07-09', u'2014-07-30', u'2014-08-06', u'2014-08-13',\n", + " u'2014-08-20', u'2014-08-27', u'2014-09-03', u'2014-09-10',\n", + " u'2014-11-05', u'2014-11-12', u'2014-11-19', u'2014-11-25',\n", + " u'2015-01-07', u'2015-01-14', u'2015-01-21', u'2015-01-28',\n", + " u'2015-02-04', u'2015-02-11', u'2015-03-04', u'2015-03-11',\n", + " u'2015-03-18', u'2015-03-25', u'2015-04-01', u'2015-04-08'],\n", + " dtype='object', name=u'Date')" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# it is a 'object' type" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Set the index to a DatetimeIndex type" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.tseries.index.DatetimeIndex" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Change the frequency to monthly, sum the values and assign it to monthly." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Total EquityDomestic EquityWorld EquityHybridTotal BondTaxable BondMunicipal BondTotal
Date
2012-12-31-26156.0-23126.0-3031.0526.09848.012613.0-2765.0-15782.0
2013-01-313661.0-1627.05288.02730.012149.09414.02735.018540.0
2013-02-28NaNNaNNaNNaNNaNNaNNaNNaN
2013-03-31NaNNaNNaNNaNNaNNaNNaNNaN
2013-04-30NaNNaNNaNNaNNaNNaNNaNNaN
2013-05-31NaNNaNNaNNaNNaNNaNNaNNaN
2013-06-30NaNNaNNaNNaNNaNNaNNaNNaN
2013-07-31NaNNaNNaNNaNNaNNaNNaNNaN
2013-08-31NaNNaNNaNNaNNaNNaNNaNNaN
2013-09-30NaNNaNNaNNaNNaNNaNNaNNaN
2013-10-31NaNNaNNaNNaNNaNNaNNaNNaN
2013-11-30NaNNaNNaNNaNNaNNaNNaNNaN
2013-12-31NaNNaNNaNNaNNaNNaNNaNNaN
2014-01-31NaNNaNNaNNaNNaNNaNNaNNaN
2014-02-28NaNNaNNaNNaNNaNNaNNaNNaN
2014-03-31NaNNaNNaNNaNNaNNaNNaNNaN
2014-04-3010842.01048.09794.04931.08493.07193.01300.024267.0
2014-05-31-2203.0-8720.06518.03172.013767.010192.03576.014736.0
2014-06-302319.0-6546.08865.04588.09715.07551.02163.016621.0
2014-07-31-7051.0-11128.04078.02666.07506.07026.0481.03122.0
2014-08-311943.0-5508.07452.01885.01897.0-1013.02910.05723.0
2014-09-30-2767.0-6596.03829.01599.03984.02479.01504.02816.0
2014-10-31NaNNaNNaNNaNNaNNaNNaNNaN
2014-11-30-2753.0-7239.04485.0729.014528.011566.02962.012502.0
2014-12-31NaNNaNNaNNaNNaNNaNNaNNaN
2015-01-313471.0-1164.04635.01729.07368.02762.04606.012569.0
2015-02-285508.03509.01999.01752.09099.07443.01656.016359.0
2015-03-315691.0-8176.013867.02829.09138.07267.01870.017657.0
2015-04-30379.0-4628.05007.0970.0423.0514.0-91.01772.0
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" + ], + "text/plain": [ + " Total Equity Domestic Equity World Equity Hybrid Total Bond \\\n", + "Date \n", + "2012-12-31 -26156.0 -23126.0 -3031.0 526.0 9848.0 \n", + "2013-01-31 3661.0 -1627.0 5288.0 2730.0 12149.0 \n", + "2013-02-28 NaN NaN NaN NaN NaN \n", + "2013-03-31 NaN NaN NaN NaN NaN \n", + "2013-04-30 NaN NaN NaN NaN NaN \n", + "2013-05-31 NaN NaN NaN NaN NaN \n", + "2013-06-30 NaN NaN NaN NaN NaN \n", + "2013-07-31 NaN NaN NaN NaN NaN \n", + "2013-08-31 NaN NaN NaN NaN NaN \n", + "2013-09-30 NaN NaN NaN NaN NaN \n", + "2013-10-31 NaN NaN NaN NaN NaN \n", + "2013-11-30 NaN NaN NaN NaN NaN \n", + "2013-12-31 NaN NaN NaN NaN NaN \n", + "2014-01-31 NaN NaN NaN NaN NaN \n", + "2014-02-28 NaN NaN NaN NaN NaN \n", + "2014-03-31 NaN NaN NaN NaN NaN \n", + "2014-04-30 10842.0 1048.0 9794.0 4931.0 8493.0 \n", + "2014-05-31 -2203.0 -8720.0 6518.0 3172.0 13767.0 \n", + "2014-06-30 2319.0 -6546.0 8865.0 4588.0 9715.0 \n", + "2014-07-31 -7051.0 -11128.0 4078.0 2666.0 7506.0 \n", + "2014-08-31 1943.0 -5508.0 7452.0 1885.0 1897.0 \n", + "2014-09-30 -2767.0 -6596.0 3829.0 1599.0 3984.0 \n", + "2014-10-31 NaN NaN NaN NaN NaN \n", + "2014-11-30 -2753.0 -7239.0 4485.0 729.0 14528.0 \n", + "2014-12-31 NaN NaN NaN NaN NaN \n", + "2015-01-31 3471.0 -1164.0 4635.0 1729.0 7368.0 \n", + "2015-02-28 5508.0 3509.0 1999.0 1752.0 9099.0 \n", + "2015-03-31 5691.0 -8176.0 13867.0 2829.0 9138.0 \n", + "2015-04-30 379.0 -4628.0 5007.0 970.0 423.0 \n", + "\n", + " Taxable Bond Municipal Bond Total \n", + "Date \n", + "2012-12-31 12613.0 -2765.0 -15782.0 \n", + "2013-01-31 9414.0 2735.0 18540.0 \n", + "2013-02-28 NaN NaN NaN \n", + "2013-03-31 NaN NaN NaN \n", + "2013-04-30 NaN NaN NaN \n", + "2013-05-31 NaN NaN NaN \n", + "2013-06-30 NaN NaN NaN \n", + "2013-07-31 NaN NaN NaN \n", + "2013-08-31 NaN NaN NaN \n", + "2013-09-30 NaN NaN NaN \n", + "2013-10-31 NaN NaN NaN \n", + "2013-11-30 NaN NaN NaN \n", + "2013-12-31 NaN NaN NaN \n", + "2014-01-31 NaN NaN NaN \n", + "2014-02-28 NaN NaN NaN \n", + "2014-03-31 NaN NaN NaN \n", + "2014-04-30 7193.0 1300.0 24267.0 \n", + "2014-05-31 10192.0 3576.0 14736.0 \n", + "2014-06-30 7551.0 2163.0 16621.0 \n", + "2014-07-31 7026.0 481.0 3122.0 \n", + "2014-08-31 -1013.0 2910.0 5723.0 \n", + "2014-09-30 2479.0 1504.0 2816.0 \n", + "2014-10-31 NaN NaN NaN \n", + "2014-11-30 11566.0 2962.0 12502.0 \n", + "2014-12-31 NaN NaN NaN \n", + "2015-01-31 2762.0 4606.0 12569.0 \n", + "2015-02-28 7443.0 1656.0 16359.0 \n", + "2015-03-31 7267.0 1870.0 17657.0 \n", + "2015-04-30 514.0 -91.0 1772.0 " + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. You will notice that it filled the dataFrame with months that don't have any data with NaN. Let's drop these rows." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Total EquityDomestic EquityWorld EquityHybridTotal BondTaxable BondMunicipal BondTotal
Date
2012-12-31-26156.0-23126.0-3031.0526.09848.012613.0-2765.0-15782.0
2013-01-313661.0-1627.05288.02730.012149.09414.02735.018540.0
2014-04-3010842.01048.09794.04931.08493.07193.01300.024267.0
2014-05-31-2203.0-8720.06518.03172.013767.010192.03576.014736.0
2014-06-302319.0-6546.08865.04588.09715.07551.02163.016621.0
2014-07-31-7051.0-11128.04078.02666.07506.07026.0481.03122.0
2014-08-311943.0-5508.07452.01885.01897.0-1013.02910.05723.0
2014-09-30-2767.0-6596.03829.01599.03984.02479.01504.02816.0
2014-11-30-2753.0-7239.04485.0729.014528.011566.02962.012502.0
2015-01-313471.0-1164.04635.01729.07368.02762.04606.012569.0
2015-02-285508.03509.01999.01752.09099.07443.01656.016359.0
2015-03-315691.0-8176.013867.02829.09138.07267.01870.017657.0
2015-04-30379.0-4628.05007.0970.0423.0514.0-91.01772.0
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" + ], + "text/plain": [ + " Total Equity Domestic Equity World Equity Hybrid Total Bond \\\n", + "Date \n", + "2012-01-01 -26156.0 -23126.0 -3031.0 526.0 9848.0 \n", + "2013-01-01 3661.0 -1627.0 5288.0 2730.0 12149.0 \n", + "2014-01-01 330.0 -44689.0 45021.0 19570.0 59890.0 \n", + "2015-01-01 15049.0 -10459.0 25508.0 7280.0 26028.0 \n", + "\n", + " Taxable Bond Municipal Bond Total \n", + "Date \n", + "2012-01-01 12613.0 -2765.0 -15782.0 \n", + "2013-01-01 9414.0 2735.0 18540.0 \n", + "2014-01-01 44994.0 14896.0 79787.0 \n", + "2015-01-01 17986.0 8041.0 48357.0 " + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BONUS: Create your own question and answer it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/10_Deleting/Iris/Exercises.ipynb b/200 solved problems in Python/pandas/10_Deleting/Iris/Exercises.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..e2e1ad9025a0f6fbe32df67abb13722961b121de --- /dev/null +++ b/200 solved problems in Python/pandas/10_Deleting/Iris/Exercises.ipynb @@ -0,0 +1,225 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Iris" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "This exercise may seem a little bit strange, but keep doing it.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called iris" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Create columns for the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# 1. sepal_length (in cm)\n", + "# 2. sepal_width (in cm)\n", + "# 3. petal_length (in cm)\n", + "# 4. petal_width (in cm)\n", + "# 5. class" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Is there any missing value in the dataframe?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Lets set the values of the rows 10 to 29 of the column 'petal_length' to NaN" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Good, now lets substitute the NaN values to 1.0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Now let's delete the column class" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. Set the first 3 rows as NaN" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. Delete the rows that have NaN" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 11. Reset the index so it begins with 0 again" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BONUS: Create your own question and answer it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/10_Deleting/Iris/Exercises_with_solutions_and_code.ipynb b/200 solved problems in Python/pandas/10_Deleting/Iris/Exercises_with_solutions_and_code.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..b6a1dca98c2544bb6d02d6e24298600c6a096c24 --- /dev/null +++ b/200 solved problems in Python/pandas/10_Deleting/Iris/Exercises_with_solutions_and_code.ipynb @@ -0,0 +1,1483 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Iris" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "This exercise may seem a little bit strange, but keep doing it.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called iris" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width class\n", + "0 4.9 3.0 1.4 0.2 Iris-setosa\n", + "1 4.7 3.2 1.3 0.2 Iris-setosa\n", + "2 4.6 3.1 1.5 0.2 Iris-setosa\n", + "3 5.0 3.6 1.4 0.2 Iris-setosa\n", + "4 5.4 3.9 1.7 0.4 Iris-setosa" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 1. sepal_length (in cm)\n", + "# 2. sepal_width (in cm)\n", + "# 3. petal_length (in cm)\n", + "# 4. petal_width (in cm)\n", + "# 5. class\n", + "\n", + "iris.columns = ['sepal_length','sepal_width', 'petal_length', 'petal_width', 'class']\n", + "iris.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Is there any missing value in the dataframe?" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "sepal_length 0\n", + "sepal_width 0\n", + "petal_length 0\n", + "petal_width 0\n", + "class 0\n", + "dtype: int64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.isnull(iris).sum()\n", + "# nice no missing value" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Lets set the values of the rows 10 to 29 of the column 'petal_length' to NaN" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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04.93.01.40.2Iris-setosa
14.73.21.30.2Iris-setosa
24.63.11.50.2Iris-setosa
35.03.61.40.2Iris-setosa
45.43.91.70.4Iris-setosa
54.63.41.40.3Iris-setosa
65.03.41.50.2Iris-setosa
74.42.91.40.2Iris-setosa
84.93.11.50.1Iris-setosa
95.43.71.50.2Iris-setosa
104.83.4NaN0.2Iris-setosa
114.83.0NaN0.1Iris-setosa
124.33.0NaN0.1Iris-setosa
135.84.0NaN0.2Iris-setosa
145.74.4NaN0.4Iris-setosa
155.43.9NaN0.4Iris-setosa
165.13.5NaN0.3Iris-setosa
175.73.8NaN0.3Iris-setosa
185.13.8NaN0.3Iris-setosa
195.43.4NaN0.2Iris-setosa
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width class\n", + "0 4.9 3.0 1.4 0.2 Iris-setosa\n", + "1 4.7 3.2 1.3 0.2 Iris-setosa\n", + "2 4.6 3.1 1.5 0.2 Iris-setosa\n", + "3 5.0 3.6 1.4 0.2 Iris-setosa\n", + "4 5.4 3.9 1.7 0.4 Iris-setosa\n", + "5 4.6 3.4 1.4 0.3 Iris-setosa\n", + "6 5.0 3.4 1.5 0.2 Iris-setosa\n", + "7 4.4 2.9 1.4 0.2 Iris-setosa\n", + "8 4.9 3.1 1.5 0.1 Iris-setosa\n", + "9 5.4 3.7 1.5 0.2 Iris-setosa\n", + "10 4.8 3.4 NaN 0.2 Iris-setosa\n", + "11 4.8 3.0 NaN 0.1 Iris-setosa\n", + "12 4.3 3.0 NaN 0.1 Iris-setosa\n", + "13 5.8 4.0 NaN 0.2 Iris-setosa\n", + "14 5.7 4.4 NaN 0.4 Iris-setosa\n", + "15 5.4 3.9 NaN 0.4 Iris-setosa\n", + "16 5.1 3.5 NaN 0.3 Iris-setosa\n", + "17 5.7 3.8 NaN 0.3 Iris-setosa\n", + "18 5.1 3.8 NaN 0.3 Iris-setosa\n", + "19 5.4 3.4 NaN 0.2 Iris-setosa" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iris.iloc[10:30,2:3] = np.nan\n", + "iris.head(20)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Good, now lets substitute the NaN values to 1.0" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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04.93.01.40.2Iris-setosa
14.73.21.30.2Iris-setosa
24.63.11.50.2Iris-setosa
35.03.61.40.2Iris-setosa
45.43.91.70.4Iris-setosa
54.63.41.40.3Iris-setosa
65.03.41.50.2Iris-setosa
74.42.91.40.2Iris-setosa
84.93.11.50.1Iris-setosa
95.43.71.50.2Iris-setosa
104.83.41.00.2Iris-setosa
114.83.01.00.1Iris-setosa
124.33.01.00.1Iris-setosa
135.84.01.00.2Iris-setosa
145.74.41.00.4Iris-setosa
155.43.91.00.4Iris-setosa
165.13.51.00.3Iris-setosa
175.73.81.00.3Iris-setosa
185.13.81.00.3Iris-setosa
195.43.41.00.2Iris-setosa
205.13.71.00.4Iris-setosa
214.63.61.00.2Iris-setosa
225.13.31.00.5Iris-setosa
234.83.41.00.2Iris-setosa
245.03.01.00.2Iris-setosa
255.03.41.00.4Iris-setosa
265.23.51.00.2Iris-setosa
275.23.41.00.2Iris-setosa
284.73.21.00.2Iris-setosa
294.83.11.00.2Iris-setosa
..................
1196.93.25.72.3Iris-virginica
1205.62.84.92.0Iris-virginica
1217.72.86.72.0Iris-virginica
1226.32.74.91.8Iris-virginica
1236.73.35.72.1Iris-virginica
1247.23.26.01.8Iris-virginica
1256.22.84.81.8Iris-virginica
1266.13.04.91.8Iris-virginica
1276.42.85.62.1Iris-virginica
1287.23.05.81.6Iris-virginica
1297.42.86.11.9Iris-virginica
1307.93.86.42.0Iris-virginica
1316.42.85.62.2Iris-virginica
1326.32.85.11.5Iris-virginica
1336.12.65.61.4Iris-virginica
1347.73.06.12.3Iris-virginica
1356.33.45.62.4Iris-virginica
1366.43.15.51.8Iris-virginica
1376.03.04.81.8Iris-virginica
1386.93.15.42.1Iris-virginica
1396.73.15.62.4Iris-virginica
1406.93.15.12.3Iris-virginica
1415.82.75.11.9Iris-virginica
1426.83.25.92.3Iris-virginica
1436.73.35.72.5Iris-virginica
1446.73.05.22.3Iris-virginica
1456.32.55.01.9Iris-virginica
1466.53.05.22.0Iris-virginica
1476.23.45.42.3Iris-virginica
1485.93.05.11.8Iris-virginica
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width\n", + "0 4.9 3.0 1.4 0.2\n", + "1 4.7 3.2 1.3 0.2\n", + "2 4.6 3.1 1.5 0.2\n", + "3 5.0 3.6 1.4 0.2\n", + "4 5.4 3.9 1.7 0.4" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "del iris['class']\n", + "iris.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. Set the first 3 rows as NaN" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width\n", + "0 NaN NaN NaN NaN\n", + "1 NaN NaN NaN NaN\n", + "2 NaN NaN NaN NaN\n", + "3 5.0 3.4 1.5 0.2\n", + "4 4.4 2.9 1.4 0.2" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iris.iloc[0:3 ,:] = np.nan\n", + "iris.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. Delete the rows that have NaN" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width\n", + "3 5.0 3.4 1.5 0.2\n", + "4 4.4 2.9 1.4 0.2\n", + "5 4.9 3.1 1.5 0.1\n", + "6 5.4 3.7 1.5 0.2\n", + "7 4.8 3.4 1.0 0.2" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iris = iris.dropna(how='any')\n", + "iris.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 11. Reset the index so it begins with 0 again" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width\n", + "0 5.0 3.4 1.5 0.2\n", + "1 4.4 2.9 1.4 0.2\n", + "2 4.9 3.1 1.5 0.1\n", + "3 5.4 3.7 1.5 0.2\n", + "4 4.8 3.4 1.0 0.2" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "iris = iris.reset_index(drop = True)\n", + "iris.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BONUS: Create your own question and answer it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/10_Deleting/Iris/Solutions.ipynb b/200 solved problems in Python/pandas/10_Deleting/Iris/Solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..bb67ac43a8449dfbe113d29b3a58433bf08a0286 --- /dev/null +++ b/200 solved problems in Python/pandas/10_Deleting/Iris/Solutions.ipynb @@ -0,0 +1,1454 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Iris" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "This exercise may seem a little bit strange, but keep doing it.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called iris" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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5.13.51.40.2Iris-setosa
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" + ], + "text/plain": [ + " 5.1 3.5 1.4 0.2 Iris-setosa\n", + "0 4.9 3.0 1.4 0.2 Iris-setosa\n", + "1 4.7 3.2 1.3 0.2 Iris-setosa\n", + "2 4.6 3.1 1.5 0.2 Iris-setosa\n", + "3 5.0 3.6 1.4 0.2 Iris-setosa\n", + "4 5.4 3.9 1.7 0.4 Iris-setosa" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Create columns for the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width class\n", + "0 4.9 3.0 1.4 0.2 Iris-setosa\n", + "1 4.7 3.2 1.3 0.2 Iris-setosa\n", + "2 4.6 3.1 1.5 0.2 Iris-setosa\n", + "3 5.0 3.6 1.4 0.2 Iris-setosa\n", + "4 5.4 3.9 1.7 0.4 Iris-setosa" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 1. sepal_length (in cm)\n", + "# 2. sepal_width (in cm)\n", + "# 3. petal_length (in cm)\n", + "# 4. petal_width (in cm)\n", + "# 5. class" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Is there any missing value in the dataframe?" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "sepal_length 0\n", + "sepal_width 0\n", + "petal_length 0\n", + "petal_width 0\n", + "class 0\n", + "dtype: int64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Lets set the values of the rows 10 to 29 of the column 'petal_length' to NaN" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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04.93.01.40.2Iris-setosa
14.73.21.30.2Iris-setosa
24.63.11.50.2Iris-setosa
35.03.61.40.2Iris-setosa
45.43.91.70.4Iris-setosa
54.63.41.40.3Iris-setosa
65.03.41.50.2Iris-setosa
74.42.91.40.2Iris-setosa
84.93.11.50.1Iris-setosa
95.43.71.50.2Iris-setosa
104.83.4NaN0.2Iris-setosa
114.83.0NaN0.1Iris-setosa
124.33.0NaN0.1Iris-setosa
135.84.0NaN0.2Iris-setosa
145.74.4NaN0.4Iris-setosa
155.43.9NaN0.4Iris-setosa
165.13.5NaN0.3Iris-setosa
175.73.8NaN0.3Iris-setosa
185.13.8NaN0.3Iris-setosa
195.43.4NaN0.2Iris-setosa
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sepal_lengthsepal_widthpetal_lengthpetal_widthclass
04.93.01.40.2Iris-setosa
14.73.21.30.2Iris-setosa
24.63.11.50.2Iris-setosa
35.03.61.40.2Iris-setosa
45.43.91.70.4Iris-setosa
54.63.41.40.3Iris-setosa
65.03.41.50.2Iris-setosa
74.42.91.40.2Iris-setosa
84.93.11.50.1Iris-setosa
95.43.71.50.2Iris-setosa
104.83.41.00.2Iris-setosa
114.83.01.00.1Iris-setosa
124.33.01.00.1Iris-setosa
135.84.01.00.2Iris-setosa
145.74.41.00.4Iris-setosa
155.43.91.00.4Iris-setosa
165.13.51.00.3Iris-setosa
175.73.81.00.3Iris-setosa
185.13.81.00.3Iris-setosa
195.43.41.00.2Iris-setosa
205.13.71.00.4Iris-setosa
214.63.61.00.2Iris-setosa
225.13.31.00.5Iris-setosa
234.83.41.00.2Iris-setosa
245.03.01.00.2Iris-setosa
255.03.41.00.4Iris-setosa
265.23.51.00.2Iris-setosa
275.23.41.00.2Iris-setosa
284.73.21.00.2Iris-setosa
294.83.11.00.2Iris-setosa
..................
1196.93.25.72.3Iris-virginica
1205.62.84.92.0Iris-virginica
1217.72.86.72.0Iris-virginica
1226.32.74.91.8Iris-virginica
1236.73.35.72.1Iris-virginica
1247.23.26.01.8Iris-virginica
1256.22.84.81.8Iris-virginica
1266.13.04.91.8Iris-virginica
1276.42.85.62.1Iris-virginica
1287.23.05.81.6Iris-virginica
1297.42.86.11.9Iris-virginica
1307.93.86.42.0Iris-virginica
1316.42.85.62.2Iris-virginica
1326.32.85.11.5Iris-virginica
1336.12.65.61.4Iris-virginica
1347.73.06.12.3Iris-virginica
1356.33.45.62.4Iris-virginica
1366.43.15.51.8Iris-virginica
1376.03.04.81.8Iris-virginica
1386.93.15.42.1Iris-virginica
1396.73.15.62.4Iris-virginica
1406.93.15.12.3Iris-virginica
1415.82.75.11.9Iris-virginica
1426.83.25.92.3Iris-virginica
1436.73.35.72.5Iris-virginica
1446.73.05.22.3Iris-virginica
1456.32.55.01.9Iris-virginica
1466.53.05.22.0Iris-virginica
1476.23.45.42.3Iris-virginica
1485.93.05.11.8Iris-virginica
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width\n", + "0 4.9 3.0 1.4 0.2\n", + "1 4.7 3.2 1.3 0.2\n", + "2 4.6 3.1 1.5 0.2\n", + "3 5.0 3.6 1.4 0.2\n", + "4 5.4 3.9 1.7 0.4" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. Set the first 3 rows as NaN" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width\n", + "0 NaN NaN NaN NaN\n", + "1 NaN NaN NaN NaN\n", + "2 NaN NaN NaN NaN\n", + "3 5.0 3.4 1.5 0.2\n", + "4 4.4 2.9 1.4 0.2" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. Delete the rows that have NaN" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width\n", + "3 5.0 3.4 1.5 0.2\n", + "4 4.4 2.9 1.4 0.2\n", + "5 4.9 3.1 1.5 0.1\n", + "6 5.4 3.7 1.5 0.2\n", + "7 4.8 3.4 1.0 0.2" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 11. Reset the index so it begins with 0 again" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width\n", + "0 5.0 3.4 1.5 0.2\n", + "1 4.4 2.9 1.4 0.2\n", + "2 4.9 3.1 1.5 0.1\n", + "3 5.4 3.7 1.5 0.2\n", + "4 4.8 3.4 1.0 0.2" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BONUS: Create your own question and answer it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/10_Deleting/Wine/Exercises.ipynb b/200 solved problems in Python/pandas/10_Deleting/Wine/Exercises.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..09cc35c57eef73656fa6cdf59a508336e873c88b --- /dev/null +++ b/200 solved problems in Python/pandas/10_Deleting/Wine/Exercises.ipynb @@ -0,0 +1,293 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Wine" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "This exercise is a adaptation from the UCI Wine dataset.\n", + "The only pupose is to practice deleting data with pandas.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called wine" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4. Delete the first, fourth, seventh, nineth, eleventh, thirteenth and fourteenth columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Assign the columns as below:\n", + "\n", + "The attributes are (dontated by Riccardo Leardi, riclea '@' anchem.unige.it): \n", + "1) alcohol \n", + "2) malic_acid \n", + "3) alcalinity_of_ash \n", + "4) magnesium \n", + "5) flavanoids \n", + "6) proanthocyanins \n", + "7) hue " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Set the values of the first 3 rows from alcohol as NaN" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Now set the value of the rows 3 and 4 of magnesium as NaN" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Fill the value of NaN with the number 10 in alcohol and 100 in magnesium" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. Count the number of missing values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. Create an array of 10 random numbers up until 10" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 11. Set the rows of the random numbers in the column" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 12. How many missing values do we have?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 14. Print only the non-null values in alcohol" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 13. Delete the rows that contain missing values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 15. Reset the index, so it starts with 0 again" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BONUS: Create your own question and answer it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/10_Deleting/Wine/Exercises_code_and_solutions.ipynb b/200 solved problems in Python/pandas/10_Deleting/Wine/Exercises_code_and_solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..188d7c8aaa5ba116497a957fd62118f44439ce62 --- /dev/null +++ b/200 solved problems in Python/pandas/10_Deleting/Wine/Exercises_code_and_solutions.ipynb @@ -0,0 +1,1392 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Wine" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "This exercise is a adaptation from the UCI Wine dataset.\n", + "The only pupose is to practice deleting data with pandas.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called wine" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " alcohol malic_acid alcalinity_of_ash magnesium flavanoids \\\n", + "0 10.00 1.78 11.2 100.0 2.76 \n", + "1 10.00 2.36 18.6 101.0 3.24 \n", + "2 10.00 1.95 16.8 100.0 3.49 \n", + "3 13.24 2.59 21.0 100.0 2.69 \n", + "4 14.20 1.76 15.2 112.0 3.39 \n", + "\n", + " proanthocyanins hue \n", + "0 1.28 4.38 \n", + "1 2.81 5.68 \n", + "2 2.18 7.80 \n", + "3 1.82 4.32 \n", + "4 1.97 6.75 " + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wine.alcohol.fillna(10, inplace = True)\n", + "\n", + "wine.magnesium.fillna(100, inplace = True)\n", + "\n", + "wine.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. Count the number of missing values" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "alcohol 0\n", + "malic_acid 0\n", + "alcalinity_of_ash 0\n", + "magnesium 0\n", + "flavanoids 0\n", + "proanthocyanins 0\n", + "hue 0\n", + "dtype: int64" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wine.isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. Create an array of 10 random numbers up until 10" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([6, 6, 7, 4, 9, 4, 0, 1, 0, 8])" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "random = np.random.randint(10, size = 10)\n", + "random" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 11. Set the rows of the random numbers in the column" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " alcohol malic_acid alcalinity_of_ash magnesium flavanoids \\\n", + "0 NaN 1.78 11.2 100.0 2.76 \n", + "1 NaN 2.36 18.6 101.0 3.24 \n", + "2 10.00 1.95 16.8 100.0 3.49 \n", + "3 13.24 2.59 21.0 100.0 2.69 \n", + "4 NaN 1.76 15.2 112.0 3.39 \n", + "5 14.39 1.87 14.6 96.0 2.52 \n", + "6 NaN 2.15 17.6 121.0 2.51 \n", + "7 NaN 1.64 14.0 97.0 2.98 \n", + "8 NaN 1.35 16.0 98.0 3.15 \n", + "9 NaN 2.16 18.0 105.0 3.32 \n", + "\n", + " proanthocyanins hue \n", + "0 1.28 4.38 \n", + "1 2.81 5.68 \n", + "2 2.18 7.80 \n", + "3 1.82 4.32 \n", + "4 1.97 6.75 \n", + "5 1.98 5.25 \n", + "6 1.25 5.05 \n", + "7 1.98 5.20 \n", + "8 1.85 7.22 \n", + "9 2.38 5.75 " + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wine.alcohol[random] = np.nan\n", + "wine.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 12. How many missing values do we have?" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "alcohol 7\n", + "malic_acid 0\n", + "alcalinity_of_ash 0\n", + "magnesium 0\n", + "flavanoids 0\n", + "proanthocyanins 0\n", + "hue 0\n", + "dtype: int64" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wine.isnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 14. Print only the non-null values in alcohol" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2 10.00\n", + "3 13.24\n", + "5 14.39\n", + "10 14.12\n", + "11 13.75\n", + "12 14.75\n", + "13 14.38\n", + "14 13.63\n", + "15 14.30\n", + "16 13.83\n", + "17 14.19\n", + "18 13.64\n", + "19 14.06\n", + "20 12.93\n", + "21 13.71\n", + "22 12.85\n", + "23 13.50\n", + "24 13.05\n", + "25 13.39\n", + "26 13.30\n", + "27 13.87\n", + "28 14.02\n", + "29 13.73\n", + "30 13.58\n", + "31 13.68\n", + "32 13.76\n", + "33 13.51\n", + "34 13.48\n", + "35 13.28\n", + "36 13.05\n", + " ... \n", + "147 13.32\n", + "148 13.08\n", + "149 13.50\n", + "150 12.79\n", + "151 13.11\n", + "152 13.23\n", + "153 12.58\n", + "154 13.17\n", + "155 13.84\n", + "156 12.45\n", + "157 14.34\n", + "158 13.48\n", + "159 12.36\n", + "160 13.69\n", + "161 12.85\n", + "162 12.96\n", + "163 13.78\n", + "164 13.73\n", + "165 13.45\n", + "166 12.82\n", + "167 13.58\n", + "168 13.40\n", + "169 12.20\n", + "170 12.77\n", + "171 14.16\n", + "172 13.71\n", + "173 13.40\n", + "174 13.27\n", + "175 13.17\n", + "176 14.13\n", + "Name: alcohol, dtype: float64" + ] + }, + "execution_count": 108, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mask = wine.alcohol.notnull()\n", + "mask\n", + "\n", + "wine.alcohol[mask]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 13. Delete the rows that contain missing values" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " alcohol malic_acid alcalinity_of_ash magnesium flavanoids \\\n", + "2 10.00 1.95 16.8 100.0 3.49 \n", + "3 13.24 2.59 21.0 100.0 2.69 \n", + "5 14.39 1.87 14.6 96.0 2.52 \n", + "10 14.12 1.48 16.8 95.0 2.43 \n", + "11 13.75 1.73 16.0 89.0 2.76 \n", + "\n", + " proanthocyanins hue \n", + "2 2.18 7.80 \n", + "3 1.82 4.32 \n", + "5 1.98 5.25 \n", + "10 1.57 5.00 \n", + "11 1.81 5.60 " + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wine = wine.dropna(axis = 0, how = \"any\")\n", + "wine.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 15. Reset the index, so it starts with 0 again" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " alcohol malic_acid alcalinity_of_ash magnesium flavanoids \\\n", + "0 10.00 1.95 16.8 100.0 3.49 \n", + "1 13.24 2.59 21.0 100.0 2.69 \n", + "2 14.39 1.87 14.6 96.0 2.52 \n", + "3 14.12 1.48 16.8 95.0 2.43 \n", + "4 13.75 1.73 16.0 89.0 2.76 \n", + "\n", + " proanthocyanins hue \n", + "0 2.18 7.80 \n", + "1 1.82 4.32 \n", + "2 1.98 5.25 \n", + "3 1.57 5.00 \n", + "4 1.81 5.60 " + ] + }, + "execution_count": 110, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wine = wine.reset_index(drop = True)\n", + "wine.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BONUS: Create your own question and answer it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/pandas/10_Deleting/Wine/Solutions.ipynb b/200 solved problems in Python/pandas/10_Deleting/Wine/Solutions.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..0e76021c571de60a0c6db06c6effde9a77b12210 --- /dev/null +++ b/200 solved problems in Python/pandas/10_Deleting/Wine/Solutions.ipynb @@ -0,0 +1,1357 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Wine" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Introduction:\n", + "\n", + "This exercise is a adaptation from the UCI Wine dataset.\n", + "The only pupose is to practice deleting data with pandas.\n", + "\n", + "### Step 1. Import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 2. Import the dataset from this [address](https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 3. Assign it to a variable called wine" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 14.23 1.71 15.6 127 3.06 2.29 5.64\n", + "0 13.20 1.78 11.2 100 2.76 1.28 4.38\n", + "1 13.16 2.36 18.6 101 3.24 2.81 5.68\n", + "2 14.37 1.95 16.8 113 3.49 2.18 7.80\n", + "3 13.24 2.59 21.0 118 2.69 1.82 4.32\n", + "4 14.20 1.76 15.2 112 3.39 1.97 6.75" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 5. Assign the columns as below:\n", + "\n", + "The attributes are (dontated by Riccardo Leardi, riclea '@' anchem.unige.it): \n", + "1) alcohol \n", + "2) malic_acid \n", + "3) alcalinity_of_ash \n", + "4) magnesium \n", + "5) flavanoids \n", + "6) proanthocyanins \n", + "7) hue " + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " alcohol malic_acid alcalinity_of_ash magnesium flavanoids \\\n", + "0 13.20 1.78 11.2 100 2.76 \n", + "1 13.16 2.36 18.6 101 3.24 \n", + "2 14.37 1.95 16.8 113 3.49 \n", + "3 13.24 2.59 21.0 118 2.69 \n", + "4 14.20 1.76 15.2 112 3.39 \n", + "\n", + " proanthocyanins hue \n", + "0 1.28 4.38 \n", + "1 2.81 5.68 \n", + "2 2.18 7.80 \n", + "3 1.82 4.32 \n", + "4 1.97 6.75 " + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 6. Set the values of the first 3 rows from alcohol as NaN" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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alcoholmalic_acidalcalinity_of_ashmagnesiumflavanoidsproanthocyaninshue
0NaN1.7811.21002.761.284.38
1NaN2.3618.61013.242.815.68
2NaN1.9516.81133.492.187.80
313.242.5921.01182.691.824.32
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" + ], + "text/plain": [ + " alcohol malic_acid alcalinity_of_ash magnesium flavanoids \\\n", + "0 NaN 1.78 11.2 100 2.76 \n", + "1 NaN 2.36 18.6 101 3.24 \n", + "2 NaN 1.95 16.8 113 3.49 \n", + "3 13.24 2.59 21.0 118 2.69 \n", + "4 14.20 1.76 15.2 112 3.39 \n", + "\n", + " proanthocyanins hue \n", + "0 1.28 4.38 \n", + "1 2.81 5.68 \n", + "2 2.18 7.80 \n", + "3 1.82 4.32 \n", + "4 1.97 6.75 " + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 7. Now set the value of the rows 3 and 4 of magnesium as NaN" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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0NaN1.7811.2100.02.761.284.38
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" + ], + "text/plain": [ + " alcohol malic_acid alcalinity_of_ash magnesium flavanoids \\\n", + "0 NaN 1.78 11.2 100.0 2.76 \n", + "1 NaN 2.36 18.6 101.0 3.24 \n", + "2 NaN 1.95 16.8 NaN 3.49 \n", + "3 13.24 2.59 21.0 NaN 2.69 \n", + "4 14.20 1.76 15.2 112.0 3.39 \n", + "\n", + " proanthocyanins hue \n", + "0 1.28 4.38 \n", + "1 2.81 5.68 \n", + "2 2.18 7.80 \n", + "3 1.82 4.32 \n", + "4 1.97 6.75 " + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 8. Fill the value of NaN with the number 10 in alcohol and 100 in magnesium" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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alcoholmalic_acidalcalinity_of_ashmagnesiumflavanoidsproanthocyaninshue
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\n", + "
" + ], + "text/plain": [ + " alcohol malic_acid alcalinity_of_ash magnesium flavanoids \\\n", + "0 10.00 1.78 11.2 100.0 2.76 \n", + "1 10.00 2.36 18.6 101.0 3.24 \n", + "2 10.00 1.95 16.8 100.0 3.49 \n", + "3 13.24 2.59 21.0 100.0 2.69 \n", + "4 14.20 1.76 15.2 112.0 3.39 \n", + "\n", + " proanthocyanins hue \n", + "0 1.28 4.38 \n", + "1 2.81 5.68 \n", + "2 2.18 7.80 \n", + "3 1.82 4.32 \n", + "4 1.97 6.75 " + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 9. Count the number of missing values" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "alcohol 0\n", + "malic_acid 0\n", + "alcalinity_of_ash 0\n", + "magnesium 0\n", + "flavanoids 0\n", + "proanthocyanins 0\n", + "hue 0\n", + "dtype: int64" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 10. Create an array of 10 random numbers up until 10" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([6, 6, 7, 4, 9, 4, 0, 1, 0, 8])" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# the number will be randoms, so yours will be different" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 11. Set the rows of the random numbers in the column" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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alcoholmalic_acidalcalinity_of_ashmagnesiumflavanoidsproanthocyaninshue
0NaN1.7811.2100.02.761.284.38
1NaN2.3618.6101.03.242.815.68
210.001.9516.8100.03.492.187.80
313.242.5921.0100.02.691.824.32
4NaN1.7615.2112.03.391.976.75
514.391.8714.696.02.521.985.25
6NaN2.1517.6121.02.511.255.05
7NaN1.6414.097.02.981.985.20
8NaN1.3516.098.03.151.857.22
9NaN2.1618.0105.03.322.385.75
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" + ], + "text/plain": [ + " alcohol malic_acid alcalinity_of_ash magnesium flavanoids \\\n", + "0 NaN 1.78 11.2 100.0 2.76 \n", + "1 NaN 2.36 18.6 101.0 3.24 \n", + "2 10.00 1.95 16.8 100.0 3.49 \n", + "3 13.24 2.59 21.0 100.0 2.69 \n", + "4 NaN 1.76 15.2 112.0 3.39 \n", + "5 14.39 1.87 14.6 96.0 2.52 \n", + "6 NaN 2.15 17.6 121.0 2.51 \n", + "7 NaN 1.64 14.0 97.0 2.98 \n", + "8 NaN 1.35 16.0 98.0 3.15 \n", + "9 NaN 2.16 18.0 105.0 3.32 \n", + "\n", + " proanthocyanins hue \n", + "0 1.28 4.38 \n", + "1 2.81 5.68 \n", + "2 2.18 7.80 \n", + "3 1.82 4.32 \n", + "4 1.97 6.75 \n", + "5 1.98 5.25 \n", + "6 1.25 5.05 \n", + "7 1.98 5.20 \n", + "8 1.85 7.22 \n", + "9 2.38 5.75 " + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# the number will be randoms, so yours will be different" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 12. How many missing values do we have?" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "alcohol 7\n", + "malic_acid 0\n", + "alcalinity_of_ash 0\n", + "magnesium 0\n", + "flavanoids 0\n", + "proanthocyanins 0\n", + "hue 0\n", + "dtype: int64" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# the number will be randoms, so yours will be different" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 14. Print only the non-null values in alcohol" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2 10.00\n", + "3 13.24\n", + "5 14.39\n", + "10 14.12\n", + "11 13.75\n", + "12 14.75\n", + "13 14.38\n", + "14 13.63\n", + "15 14.30\n", + "16 13.83\n", + "17 14.19\n", + "18 13.64\n", + "19 14.06\n", + "20 12.93\n", + "21 13.71\n", + "22 12.85\n", + "23 13.50\n", + "24 13.05\n", + "25 13.39\n", + "26 13.30\n", + "27 13.87\n", + "28 14.02\n", + "29 13.73\n", + "30 13.58\n", + "31 13.68\n", + "32 13.76\n", + "33 13.51\n", + "34 13.48\n", + "35 13.28\n", + "36 13.05\n", + " ... \n", + "147 13.32\n", + "148 13.08\n", + "149 13.50\n", + "150 12.79\n", + "151 13.11\n", + "152 13.23\n", + "153 12.58\n", + "154 13.17\n", + "155 13.84\n", + "156 12.45\n", + "157 14.34\n", + "158 13.48\n", + "159 12.36\n", + "160 13.69\n", + "161 12.85\n", + "162 12.96\n", + "163 13.78\n", + "164 13.73\n", + "165 13.45\n", + "166 12.82\n", + "167 13.58\n", + "168 13.40\n", + "169 12.20\n", + "170 12.77\n", + "171 14.16\n", + "172 13.71\n", + "173 13.40\n", + "174 13.27\n", + "175 13.17\n", + "176 14.13\n", + "Name: alcohol, dtype: float64" + ] + }, + "execution_count": 108, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# the number will be randoms, so yours will be different" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 13. Delete the rows that contain missing values" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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alcoholmalic_acidalcalinity_of_ashmagnesiumflavanoidsproanthocyaninshue
210.001.9516.8100.03.492.187.80
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" + ], + "text/plain": [ + " alcohol malic_acid alcalinity_of_ash magnesium flavanoids \\\n", + "2 10.00 1.95 16.8 100.0 3.49 \n", + "3 13.24 2.59 21.0 100.0 2.69 \n", + "5 14.39 1.87 14.6 96.0 2.52 \n", + "10 14.12 1.48 16.8 95.0 2.43 \n", + "11 13.75 1.73 16.0 89.0 2.76 \n", + "\n", + " proanthocyanins hue \n", + "2 2.18 7.80 \n", + "3 1.82 4.32 \n", + "5 1.98 5.25 \n", + "10 1.57 5.00 \n", + "11 1.81 5.60 " + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# the number will be randoms, so yours will be different" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 15. Reset the index, so it starts with 0 again" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " alcohol malic_acid alcalinity_of_ash magnesium flavanoids \\\n", + "0 10.00 1.95 16.8 100.0 3.49 \n", + "1 13.24 2.59 21.0 100.0 2.69 \n", + "2 14.39 1.87 14.6 96.0 2.52 \n", + "3 14.12 1.48 16.8 95.0 2.43 \n", + "4 13.75 1.73 16.0 89.0 2.76 \n", + "\n", + " proanthocyanins hue \n", + "0 2.18 7.80 \n", + "1 1.82 4.32 \n", + "2 1.98 5.25 \n", + "3 1.57 5.00 \n", + "4 1.81 5.60 " + ] + }, + "execution_count": 110, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BONUS: Create your own question and answer it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/200 solved problems in Python/recursion/a_power_by_b.ipynb b/200 solved problems in Python/recursion/a_power_by_b.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..11e640c31b8b459b864dd0c44ea8d3595e03781d --- /dev/null +++ b/200 solved problems in Python/recursion/a_power_by_b.ipynb @@ -0,0 +1,36 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to calculate the value of 'a' to the power 'b'." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/recursion/a_power_by_b_solution.ipynb b/200 solved problems in Python/recursion/a_power_by_b_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..7d75c633b24621f06f1a0e3dd3f9907ad35898be --- /dev/null +++ b/200 solved problems in Python/recursion/a_power_by_b_solution.ipynb @@ -0,0 +1,54 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "81\n" + ] + } + ], + "source": [ + "# Write a Python program to calculate the value of 'a' to the power 'b'.\n", + "\n", + "def power(a,b):\n", + "\tif b==0:\n", + "\t\treturn 1\n", + "\telif a==0:\n", + "\t\treturn 0\n", + "\telif b==1:\n", + "\t\treturn a\n", + "\telse:\n", + "\t\treturn a*power(a,b-1)\n", + "\n", + "print(power(3,4))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/recursion/fibonaci.ipynb b/200 solved problems in Python/recursion/fibonaci.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..3203b2dc8cc2999881ca082871ba0df9a3aaac63 --- /dev/null +++ b/200 solved problems in Python/recursion/fibonaci.ipynb @@ -0,0 +1,36 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to solve the Fibonacci sequence using recursion." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/recursion/fibonaci_solution.ipynb b/200 solved problems in Python/recursion/fibonaci_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..35178e6950257f9bdcb811a72a68828d6da920b5 --- /dev/null +++ b/200 solved problems in Python/recursion/fibonaci_solution.ipynb @@ -0,0 +1,50 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13\n" + ] + } + ], + "source": [ + "# Write a Python program to solve the Fibonacci sequence using recursion.\n", + "\n", + "def fibonacci(n):\n", + " if n == 1 or n == 2:\n", + " return 1\n", + " else:\n", + " return (fibonacci(n - 1) + (fibonacci(n - 2)))\n", + "\n", + "print(fibonacci(7))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/recursion/fractorial.ipynb b/200 solved problems in Python/recursion/fractorial.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..2793cb75cef9f74fc0f431f3640be81b8faaeb99 --- /dev/null +++ b/200 solved problems in Python/recursion/fractorial.ipynb @@ -0,0 +1,36 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to get the factorial of a non-negative integer. " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/recursion/fractorial_solution.ipynb b/200 solved problems in Python/recursion/fractorial_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..b4d0f2d284742479de1bcd5bd79bb5af435d40ed --- /dev/null +++ b/200 solved problems in Python/recursion/fractorial_solution.ipynb @@ -0,0 +1,50 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "120\n" + ] + } + ], + "source": [ + "# Write a Python program to get the factorial of a non-negative integer.\n", + "\n", + "def factorial(n):\n", + " if n <= 1:\n", + " return 1\n", + " else:\n", + " return n * (factorial(n - 1))\n", + " \n", + "print(factorial(5))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/recursion/greatest_gcd.ipynb b/200 solved problems in Python/recursion/greatest_gcd.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..21d4beab16aaa22449ae8f083a6d688df1b7b52f --- /dev/null +++ b/200 solved problems in Python/recursion/greatest_gcd.ipynb @@ -0,0 +1,36 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to find the greatest common divisor (gcd) of two integers." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/recursion/greatest_gcd_solution.ipynb b/200 solved problems in Python/recursion/greatest_gcd_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..aea13f2126db12ffc95cd9beb5afbdb60dd5065e --- /dev/null +++ b/200 solved problems in Python/recursion/greatest_gcd_solution.ipynb @@ -0,0 +1,54 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2\n" + ] + } + ], + "source": [ + "# Write a Python program to find the greatest common divisor (gcd) of two integers.\n", + "\n", + "def Recurgcd(a, b):\n", + "\tlow = min(a, b)\n", + "\thigh = max(a, b)\n", + "\n", + "\tif low == 0:\n", + "\t\treturn high\n", + "\telif low == 1:\n", + "\t\treturn 1\n", + "\telse:\n", + "\t\treturn Recurgcd(low, high%low)\n", + "print(Recurgcd(12,14))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/recursion/sum_of_list.ipynb b/200 solved problems in Python/recursion/sum_of_list.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..bb9fb692164dce831010accdcc31615ba23940ac --- /dev/null +++ b/200 solved problems in Python/recursion/sum_of_list.ipynb @@ -0,0 +1,36 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to calculate the sum of a list of numbers. (in recursion fashion)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/recursion/sum_of_list_solution.ipynb b/200 solved problems in Python/recursion/sum_of_list_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..83650bce2064014adbc186dd431f0713f7c15c96 --- /dev/null +++ b/200 solved problems in Python/recursion/sum_of_list_solution.ipynb @@ -0,0 +1,50 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "24\n" + ] + } + ], + "source": [ + "# Write a Python program to calculate the sum of a list of numbers. (in recursion fashion)\n", + "\n", + "def list_sum(num_List):\n", + " if len(num_List) == 1:\n", + " return num_List[0]\n", + " else:\n", + " return num_List[0] + list_sum(num_List[1:])\n", + " \n", + "print(list_sum([2, 4, 5, 6, 7]))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/regex/all_word_contain_5_chracters.ipynb b/200 solved problems in Python/regex/all_word_contain_5_chracters.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..234b1c9ec5edfbda276bda5f81b448bb9026bc1d --- /dev/null +++ b/200 solved problems in Python/regex/all_word_contain_5_chracters.ipynb @@ -0,0 +1,40 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to find all five characters long word in a string.\n", + "# Input\n", + "# 'The quick brown fox jumps over the lazy dog.'\n", + "# Output\n", + "# ['quick', 'brown', 'jumps']" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/regex/all_word_contain_5_chracters_solution.ipynb b/200 solved problems in Python/regex/all_word_contain_5_chracters_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..c52426847784e53fbe0909dd5428ce5ac481077d --- /dev/null +++ b/200 solved problems in Python/regex/all_word_contain_5_chracters_solution.ipynb @@ -0,0 +1,50 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['quick', 'brown', 'jumps']\n" + ] + } + ], + "source": [ + "# Write a Python program to find all five characters long word in a string.\n", + "# Input\n", + "# 'The quick brown fox jumps over the lazy dog.'\n", + "# Output\n", + "# ['quick', 'brown', 'jumps']\n", + "\n", + "import re\n", + "text = 'The quick brown fox jumps over the lazy dog.'\n", + "print(re.findall(r\"\\b\\w{5}\\b\", text))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/regex/check_string.ipynb b/200 solved problems in Python/regex/check_string.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..49a9404ba53874dfcd3af097b7a8cc5eccc8a44e --- /dev/null +++ b/200 solved problems in Python/regex/check_string.ipynb @@ -0,0 +1,42 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to check that a string contains only a certain set of characters (in this case a-z, A-Z and 0-9).\n", + "# Input\n", + "# \"ABCDEFabcdef123450\"\n", + "# \"*&%@#!}{\"\n", + "# Output\n", + "# True \n", + "# False" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/regex/check_string_solution.ipynb b/200 solved problems in Python/regex/check_string_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..2ded2609f98d1c63606ae699d8307ce2595c07bf --- /dev/null +++ b/200 solved problems in Python/regex/check_string_solution.ipynb @@ -0,0 +1,58 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "False\n" + ] + } + ], + "source": [ + "# Write a Python program to check that a string contains only a certain set of characters (in this case a-z, A-Z and 0-9).\n", + "# Input\n", + "# \"ABCDEFabcdef123450\"\n", + "# \"*&%@#!}{\"\n", + "# Output\n", + "# True \n", + "# False\n", + "\n", + "import re\n", + "def is_allowed_specific_char(string):\n", + " charRe = re.compile(r'[^a-zA-Z0-9.]')\n", + " string = charRe.search(string)\n", + " return not bool(string)\n", + "\n", + "print(is_allowed_specific_char(\"ABCDEFabcdef123450\")) \n", + "print(is_allowed_specific_char(\"*&%@#!}{\"))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/regex/find_substring.ipynb b/200 solved problems in Python/regex/find_substring.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..544262d4e98e7e7b1c7074bef7e929e20fb6a30d --- /dev/null +++ b/200 solved problems in Python/regex/find_substring.ipynb @@ -0,0 +1,45 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to find the occurrence and position of the substrings within a string.\n", + "# \n", + "# Input\n", + "# text = 'Python exercises, PHP exercises, C# exercises'\n", + "# pattern = 'exercises'\n", + "# \n", + "# Output\n", + "# Found \"exercises\" at 7:16 \n", + "# Found \"exercises\" at 22:31 \n", + "# Found \"exercises\" at 36:45" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/regex/find_substring_solution.ipynb b/200 solved problems in Python/regex/find_substring_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..7d00e9bc078e23a6209e7d5bb12d96063ddb092a --- /dev/null +++ b/200 solved problems in Python/regex/find_substring_solution.ipynb @@ -0,0 +1,62 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found \"exercises\" at 7:16\n", + "Found \"exercises\" at 22:31\n", + "Found \"exercises\" at 36:45\n" + ] + } + ], + "source": [ + "# Write a Python program to find the occurrence and position of the substrings within a string.\n", + "# \n", + "# Input\n", + "# text = 'Python exercises, PHP exercises, C# exercises'\n", + "# pattern = 'exercises'\n", + "# \n", + "# Output\n", + "# Found \"exercises\" at 7:16 \n", + "# Found \"exercises\" at 22:31 \n", + "# Found \"exercises\" at 36:45\n", + "\n", + "import re\n", + "text = 'Python exercises, PHP exercises, C# exercises'\n", + "pattern = 'exercises'\n", + "for match in re.finditer(pattern, text):\n", + " s = match.start()\n", + " e = match.end()\n", + " print('Found \"%s\" at %d:%d' % (text[s:e], s, e))\n", + "\t" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/regex/find_url.ipynb b/200 solved problems in Python/regex/find_url.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..5d858bfd6136e383a4d7862232860a288e611065 --- /dev/null +++ b/200 solved problems in Python/regex/find_url.ipynb @@ -0,0 +1,42 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to find urls in a string.\n", + "# \n", + "# Input\n", + "# '

Contents :

Python ExamplesEven More Examples'\n", + "# \n", + "# Output\n", + "# Urls: ['https://w3resource.com', 'http://github.com']" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/regex/find_url_solution.ipynb b/200 solved problems in Python/regex/find_url_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..0fffde753a5c405bfa656bcb2314f7be6ba4a5bd --- /dev/null +++ b/200 solved problems in Python/regex/find_url_solution.ipynb @@ -0,0 +1,55 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original string:

Contents :

Python ExamplesEven More Examples\n", + "Urls: ['https://w3resource.com', 'http://github.com']\n" + ] + } + ], + "source": [ + "# Write a Python program to find urls in a string.\n", + "# \n", + "# Input\n", + "# '

Contents :

Python ExamplesEven More Examples'\n", + "# \n", + "# Output\n", + "# Urls: ['https://w3resource.com', 'http://github.com']\n", + "\n", + "import re\n", + "text = '

Contents :

Python ExamplesEven More Examples'\n", + "urls = re.findall('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', text)\n", + "print(\"Original string: \",text)\n", + "print(\"Urls: \",urls)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/regex/keep_alphanumeric_only.ipynb b/200 solved problems in Python/regex/keep_alphanumeric_only.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..765b7d2048de23f6ce175dbb85e2b22555729f22 --- /dev/null +++ b/200 solved problems in Python/regex/keep_alphanumeric_only.ipynb @@ -0,0 +1,40 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to remove everything except alphanumeric characters from a string.\n", + "# Input\n", + "# '**//Python Exercises// - 12. '\n", + "# Output\n", + "# PythonExercises12" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/regex/keep_alphanumeric_only_solution.ipynb b/200 solved problems in Python/regex/keep_alphanumeric_only_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..9a74d7606e8c5a89d7d4aa4c01e56a783e48537d --- /dev/null +++ b/200 solved problems in Python/regex/keep_alphanumeric_only_solution.ipynb @@ -0,0 +1,51 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "PythonExercises12\n" + ] + } + ], + "source": [ + "# Write a Python program to remove everything except alphanumeric characters from a string.\n", + "# Input\n", + "# '**//Python Exercises// - 12. '\n", + "# Output\n", + "# PythonExercises12\n", + "\n", + "import re\n", + "text1 = '**//Python Exercises// - 12. '\n", + "pattern = re.compile('[\\W_]+')\n", + "print(pattern.sub('', text1))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/regex/remove_parenthesis.ipynb b/200 solved problems in Python/regex/remove_parenthesis.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..4917f0c349e780de6f7aa5b471cac9f133b9a969 --- /dev/null +++ b/200 solved problems in Python/regex/remove_parenthesis.ipynb @@ -0,0 +1,45 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to remove the parenthesis area in a string.\n", + "# \n", + "# Input\n", + "# [\"example (.com)\", \"w3resource\", \"github (.com)\", \"stackoverflow (.com)\"]\n", + "# \n", + "# Output\n", + "# example \n", + "# w3resource \n", + "# github \n", + "# stackoverflow " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/regex/remove_parenthesis_solution.ipynb b/200 solved problems in Python/regex/remove_parenthesis_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..3d7ee0b91d6d8fb74e7789353ba617a4f9af6f4e --- /dev/null +++ b/200 solved problems in Python/regex/remove_parenthesis_solution.ipynb @@ -0,0 +1,59 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "example\n", + "w3resource\n", + "github\n", + "stackoverflow\n" + ] + } + ], + "source": [ + "# Write a Python program to remove the parenthesis area in a string.\n", + "# \n", + "# Input\n", + "# [\"example (.com)\", \"w3resource\", \"github (.com)\", \"stackoverflow (.com)\"]\n", + "# \n", + "# Output\n", + "# example \n", + "# w3resource \n", + "# github \n", + "# stackoverflow\n", + "\n", + "import re\n", + "items = [\"example (.com)\", \"w3resource\", \"github (.com)\", \"stackoverflow (.com)\"]\n", + "for item in items:\n", + " print(re.sub(r\" ?\\([^)]+\\)\", \"\", item))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/regex/remove_white_space.ipynb b/200 solved problems in Python/regex/remove_white_space.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..9479010f74edafccc88039151bdde6205a9c2840 --- /dev/null +++ b/200 solved problems in Python/regex/remove_white_space.ipynb @@ -0,0 +1,41 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Remove all whitespaces from a string\n", + "# \n", + "# Input\n", + "# ' Python Exercises '\n", + "# Output\n", + "# PythonExercises" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/regex/remove_white_space_solution.ipynb b/200 solved problems in Python/regex/remove_white_space_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..57d65978310af9949082359613860593498040e6 --- /dev/null +++ b/200 solved problems in Python/regex/remove_white_space_solution.ipynb @@ -0,0 +1,53 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original string: Python Exercises \n", + "Without extra spaces: PythonExercises\n" + ] + } + ], + "source": [ + "# Remove all whitespaces from a string\n", + "# \n", + "# Input\n", + "# ' Python Exercises '\n", + "# Output\n", + "# PythonExercises\n", + "\n", + "import re\n", + "text1 = ' Python Exercises '\n", + "print(\"Original string:\",text1)\n", + "print(\"Without extra spaces:\",re.sub(r'\\s+', '',text1))\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/regex/remove_zero.ipynb b/200 solved problems in Python/regex/remove_zero.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..fe234b0e4da0be72d8aa26aa2a49796e00fa85d4 --- /dev/null +++ b/200 solved problems in Python/regex/remove_zero.ipynb @@ -0,0 +1,40 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to remove leading zeros from an IP address.\n", + "# Input\n", + "# \"216.08.094.196\"\n", + "# Output\n", + "# 216.8.94.196" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/regex/remove_zero_solution.ipynb b/200 solved problems in Python/regex/remove_zero_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..3771e8621ccb74393f85dc0bcc05a236e5e96b5f --- /dev/null +++ b/200 solved problems in Python/regex/remove_zero_solution.ipynb @@ -0,0 +1,51 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "216.8.94.196\n" + ] + } + ], + "source": [ + "# Write a Python program to remove leading zeros from an IP address.\n", + "# Input\n", + "# \"216.08.094.196\"\n", + "# Output\n", + "# 216.8.94.196\n", + "\n", + "import re\n", + "ip = \"216.08.094.196\"\n", + "string = re.sub('\\.[0]*', '.', ip)\n", + "print(string)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/search/.ipynb_checkpoints/binary_search_implement_solution-checkpoint.ipynb b/200 solved problems in Python/search/.ipynb_checkpoints/binary_search_implement_solution-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..7ebf5f90afd60d6d43ce1348616f31638c5e9fc8 --- /dev/null +++ b/200 solved problems in Python/search/.ipynb_checkpoints/binary_search_implement_solution-checkpoint.ipynb @@ -0,0 +1,64 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "False\n", + "True\n" + ] + } + ], + "source": [ + "# Write a Python program for binary search. \n", + "# Binary Search : In computer science, a binary search or half-interval search algorithm finds the position of a target value\n", + "# within a sorted array. The binary search algorithm can be classified as a dichotomies divide-and-conquer search algorithm and \n", + "# executes in logarithmic time.\n", + "\n", + "def binary_search(item_listac,item):\n", + "\tfirst = 0\n", + "\tlast = len(item_list)-1\n", + "\tfound = False\n", + "\twhile( first<=last and not found):\n", + "\t\tmid = (first + last)//2\n", + "\t\tif item_list[mid] == item :\n", + "\t\t\tfound = True\n", + "\t\telse:\n", + "\t\t\tif item < item_list[mid]:\n", + "\t\t\t\tlast = mid - 1\n", + "\t\t\telse:\n", + "\t\t\t\tfirst = mid + 1\t\n", + "\treturn found\n", + "\t\n", + "print(binary_search([1,2,3,5,8], 6))\n", + "print(binary_search([1,2,3,5,8], 5))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/search/binary_search_implement.ipynb b/200 solved problems in Python/search/binary_search_implement.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..cb14999c011c6e44a04271000d25cfb0e9d1f5b4 --- /dev/null +++ b/200 solved problems in Python/search/binary_search_implement.ipynb @@ -0,0 +1,39 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program for binary search. \n", + "# Binary Search : In computer science, a binary search or half-interval search algorithm finds the position of a target value\n", + "# within a sorted array. The binary search algorithm can be classified as a dichotomies divide-and-conquer search algorithm and \n", + "# executes in logarithmic time." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/search/binary_search_implement_solution.ipynb b/200 solved problems in Python/search/binary_search_implement_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..7ebf5f90afd60d6d43ce1348616f31638c5e9fc8 --- /dev/null +++ b/200 solved problems in Python/search/binary_search_implement_solution.ipynb @@ -0,0 +1,64 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "False\n", + "True\n" + ] + } + ], + "source": [ + "# Write a Python program for binary search. \n", + "# Binary Search : In computer science, a binary search or half-interval search algorithm finds the position of a target value\n", + "# within a sorted array. The binary search algorithm can be classified as a dichotomies divide-and-conquer search algorithm and \n", + "# executes in logarithmic time.\n", + "\n", + "def binary_search(item_listac,item):\n", + "\tfirst = 0\n", + "\tlast = len(item_list)-1\n", + "\tfound = False\n", + "\twhile( first<=last and not found):\n", + "\t\tmid = (first + last)//2\n", + "\t\tif item_list[mid] == item :\n", + "\t\t\tfound = True\n", + "\t\telse:\n", + "\t\t\tif item < item_list[mid]:\n", + "\t\t\t\tlast = mid - 1\n", + "\t\t\telse:\n", + "\t\t\t\tfirst = mid + 1\t\n", + "\treturn found\n", + "\t\n", + "print(binary_search([1,2,3,5,8], 6))\n", + "print(binary_search([1,2,3,5,8], 5))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/search/sequential search.ipynb b/200 solved problems in Python/search/sequential search.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..d81a1ad559bce49b7a15348190b2ce715fc04718 --- /dev/null +++ b/200 solved problems in Python/search/sequential search.ipynb @@ -0,0 +1,38 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program for sequential search.\n", + "# Sequential Search: In computer science, linear search or sequential search is a method for finding a particular value in a list\n", + "# that checks each element in sequence until the desired element is found or the list is exhausted. The list need not be ordered.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/search/sequential search_solution.ipynb b/200 solved problems in Python/search/sequential search_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..a2a2906139b8baf505ffe922febabfb46732be59 --- /dev/null +++ b/200 solved problems in Python/search/sequential search_solution.ipynb @@ -0,0 +1,60 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(True, 3)\n" + ] + } + ], + "source": [ + "# Write a Python program for sequential search.\n", + "# Sequential Search: In computer science, linear search or sequential search is a method for finding a particular value in a list\n", + "# that checks each element in sequence until the desired element is found or the list is exhausted. The list need not be ordered.\n", + "\n", + "\n", + "def Sequential_Search(dlist, item):\n", + "\n", + " pos = 0\n", + " found = False\n", + " \n", + " while pos < len(dlist) and not found:\n", + " if dlist[pos] == item:\n", + " found = True\n", + " else:\n", + " pos = pos + 1\n", + " \n", + " return found, pos\n", + "\n", + "print(Sequential_Search([11,23,58,31,56,77,43,12,65,19],31))\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/sort/buble_sort_implement.ipynb b/200 solved problems in Python/sort/buble_sort_implement.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..0e9795a6fc692b5ed4835124b958c5f540cc3a30 --- /dev/null +++ b/200 solved problems in Python/sort/buble_sort_implement.ipynb @@ -0,0 +1,56 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to sort a list of elements using the bubble sort algorithm.\n", + "# Note : According to Wikipedia \"Bubble sort, sometimes referred to as sinking sort, is a simple sorting algorithm that\n", + "# repeatedly steps through the list to be sorted, compares each pair of adjacent items and swaps them if they are in the \n", + "# wrong order. The pass through the list is repeated until no swaps are needed, which indicates that the list is sorted. \n", + "# The algorithm, which is a comparison sort, is named for the way smaller elements \"bubble\" to the top of the list. \n", + "# Although the algorithm is simple, it is too slow and impractical for most problems even when compared to insertion sort. \n", + "# It can be practical if the input is usually in sort order but may occasionally have some out-of-order elements nearly \n", + "# in position.\n", + "\n" + ] + }, + { + "attachments": { + "bubble-short.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![bubble-short.png](attachment:bubble-short.png)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/sort/buble_sort_implement_solution.ipynb b/200 solved problems in Python/sort/buble_sort_implement_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..d3ab12dd9172e61a26e1a04d34c600d1789f3618 --- /dev/null +++ b/200 solved problems in Python/sort/buble_sort_implement_solution.ipynb @@ -0,0 +1,55 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to sort a list of elements using the bubble sort algorithm.\n", + "# Note : According to Wikipedia \"Bubble sort, sometimes referred to as sinking sort, is a simple sorting algorithm that\n", + "# repeatedly steps through the list to be sorted, compares each pair of adjacent items and swaps them if they are in the \n", + "# wrong order. The pass through the list is repeated until no swaps are needed, which indicates that the list is sorted. \n", + "# The algorithm, which is a comparison sort, is named for the way smaller elements \"bubble\" to the top of the list. \n", + "# Although the algorithm is simple, it is too slow and impractical for most problems even when compared to insertion sort. \n", + "# It can be practical if the input is usually in sort order but may occasionally have some out-of-order elements nearly \n", + "# in position.\n", + "\n", + "def bubbleSort(nlist):\n", + " for passnum in range(len(nlist)-1,0,-1):\n", + " for i in range(passnum):\n", + " if nlist[i]>nlist[i+1]:\n", + " temp = nlist[i]\n", + " nlist[i] = nlist[i+1]\n", + " nlist[i+1] = temp\n", + "\n", + "nlist = [14,46,43,27,57,41,45,21,70]\n", + "bubbleSort(nlist)\n", + "print(nlist)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/sort/insertion_sort_implement.ipynb b/200 solved problems in Python/sort/insertion_sort_implement.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..59b145cf4f59fbbc8a34e250f69ec90cff43c795 --- /dev/null +++ b/200 solved problems in Python/sort/insertion_sort_implement.ipynb @@ -0,0 +1,51 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to sort a list of elements using the insertion sort algorithm.\n", + "# Note: According to Wikipedia \"Insertion sort is a simple sorting algorithm that builds \n", + "# the final sorted array (or list) one item at a time. It is much less efficient on large\n", + "# lists than more advanced algorithms such as quicksort, heapsort, or merge sort.\"" + ] + }, + { + "attachments": { + "insertion-sort.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![insertion-sort.png](attachment:insertion-sort.png)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/sort/insertion_sort_implement_solution.ipynb b/200 solved problems in Python/sort/insertion_sort_implement_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..c7d4cdb971bc72f2e901736eec6b4c017a8e5561 --- /dev/null +++ b/200 solved problems in Python/sort/insertion_sort_implement_solution.ipynb @@ -0,0 +1,82 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to sort a list of elements using the insertion sort algorithm.\n", + "# Note: According to Wikipedia \"Insertion sort is a simple sorting algorithm that builds \n", + "# the final sorted array (or list) one item at a time. It is much less efficient on large\n", + "# lists than more advanced algorithms such as quicksort, heapsort, or merge sort.\"" + ] + }, + { + "attachments": { + "insertion-sort.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![insertion-sort.png](attachment:insertion-sort.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[14, 21, 27, 41, 43, 45, 46, 57, 70]\n" + ] + } + ], + "source": [ + "def insertionSort(nlist):\n", + " for index in range(1,len(nlist)):\n", + "\n", + " currentvalue = nlist[index]\n", + " position = index\n", + "\n", + " while position>0 and nlist[position-1]>currentvalue:\n", + " nlist[position]=nlist[position-1]\n", + " position = position-1\n", + "\n", + " nlist[position]=currentvalue\n", + "\n", + "nlist = [14,46,43,27,57,41,45,21,70]\n", + "insertionSort(nlist)\n", + "print(nlist)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/sort/merge_sort_implement.ipynb b/200 solved problems in Python/sort/merge_sort_implement.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..49df0e2b2580fa6d1f798bda9ef129c41c5021e8 --- /dev/null +++ b/200 solved problems in Python/sort/merge_sort_implement.ipynb @@ -0,0 +1,57 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to sort a list of elements using the merge sort algorithm.\n", + "# Note: According to Wikipedia \"Merge sort (also commonly spelled mergesort) is an O (n log n)\n", + "# comparison-based sorting algorithm. Most implementations produce a stable sort, which means that \n", + "# the implementation preserves the input order of equal elements in the sorted output.\"\n", + "\n", + "# Algorithm:\n", + "# Conceptually, a merge sort works as follows :\n", + "\n", + "# Divide the unsorted list into n sublists, each containing 1 element (a list of 1 element is considered sorted).\n", + "# Repeatedly merge sublists to produce new sorted sublists until there is only 1 sublist remaining. This will be the sorted list." + ] + }, + { + "attachments": { + "merge_sort.png": { + "image/png": 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AwzR39gb67CrP/BMS7ezKEguozgB8Z5KvNaiDhmPVua6fgYYeErb+bxf1rnK76kTOJ1cHuNLBZfQHoBmkDFZFohXh9UG6dnvRXYEGtC2AxtfrQEOdSnbVpMfYiurkNnUe7fiY0PUeku3xftAZUFFFoInCLAcf7Qi3mmjOo2VQ3dB2d7yZ0+Fx8ov/X48UqXrtz8ruvgTaaJ284q769mzUH1WhrNUuSqCN8X+bi3xWV+zhrBha1FWLXCEnPwPAc2gd7AREJQ8JtA76bi/S/jwjwyPRU1Rn/WIcSQyRYXY07sjnjDpX1QnI6cYrl2awewfdoXAxNgz0xrcdUPulTIa6n8ajCbrUeNMxMOsmiXabJNrTONoi0bCqwjn3kA3eorxYbsM/Rq4st3UcCD7wSwNLzpEE+CgPyKcNJIDz5RfvjifWoae7qhdSzgyGWriIpLZy9ULK9cvNt4uo0tGaiRadrIKwxzj+HgqE5GEcx2NXpCTCR+e514PJxe51+/KyebtCVLZ1JVCAap315m1viuGkTV7eDnI0+9vroY+q/eOkxy6T7nG9cvCD18pTfjnoeUvsdC+ZCS31SaDN/wcaU88+vVpHo6ESQKuJ+dkRzZKWrNN+doRyHiEsQJCOic6DEtxe2O1IcruJHw3jAnB1qPMMVieG1HNkQIPNYdTVpvcSaCY6rzOggYP/l9VpkOMTfR4ZaP3tPJaSNDlHmexV3taDjPYmyfNpkpII4ylzD7Z6f3lZS25z0UxTl+DW9Z0lm7s2sSzOIy3WdX9j1f4Vvd1w/pV2M+r5NUR3NbtGp1LGNnJvaCPRzyMDSASVJ6P6MVfpWRZEZnlHWroBWaN5RTzWWqdcAC8XpCrX4GGD0x4jBRCiQJBanRcwWjN7g+wGwXqhe0MhRohiwtFVmSzmNaqUTGOAsxFyWkUCqB2BmQdSBWmKS0qMuXHGzqK/xuok0LZwp329NEADIEbkEidTCWhswFxIsvrYecg60Mnfo9IF0PQGj79/AlrHKxLuHAFexNEdjI5JYuAd4S6Fr5LZThLQRmvI/clmyDdbwqwbjXU9tUDLZNjxTzplMz3Y2cpCOkxmxkBymQ+x58xoDAYlo1EmfkGKCrPVQFOY6aB6zWiX1NEIVBvh0q60X0AudDRdEGCNItO6NzT+OQTMKAwjlK5o6DSiNPO9rjiAZN91K0BID/3W1GdXtCTEIXLv47g1aD00/lgpdt5KmdW1UNfpIjtqVQA7+juppBvfWTI+cOyMPUjWGI3tX9Edf0FZOK5RZxKgBUUmoyEROZeCU8toGkNPBT4tBqADkkX2yFATcIGAVs9ys/3AIoVn8luOKQrQAATIvqvZCEcxE+Dl//pFDe0MFlM/LX20chgV4lsU+3bZGQ2Tk8BoBilgCqGRksnfLG+cNixtTb1WtWh1NC9OGjuFp7zr8OLxyk/nl4rRsjuyTNe9UaApGu9kjs3U4ehEjJdAs9Y5Siwo2HiWTV469P4pLw394aXe9LR4q69rZ7SXMRgN7S+UpBvJ8v2z8GSdTmxzckaTAzHskQ/Jb+oJaLVY5YoMDyVc0/XH+CfRmcOi2NUcdsWKMYKrzka7UuDt42MVyRijKVEJso4+qH6jMSDGKEhM6tLM3FtLfnGFb7CcK6+wfd2NzCgSf554OW+A9z33KTSBUOLINAgNr0UyJjToG0uKA7WtKLZc3t3IaNoMYTKglUni32QMxek3AAcmUdB24bsCotZjZ3UuePeRPaO/s6Je+wNhEGi8ljWBptAsHRp2nXZXJEZ3oGoUotPTdy3u7zzZSYFWW8g/+nuJpERTFkwd6wGwk+aRy25aP1rlBXRdEGLTgZY2Zw5IYR7PFUDz2RwgKBWDdxgD0NR8mpCsdxWcanIqAI+tH40uDQUdZzVCF4RwrER9EhSjFkPB97yrAFqrd8V9UzE5pTKCTbKeIiA0mEyYCLQNXjcxRaB1Jjuc0SZobM4ynVTf7glD9I7+EVDnXXs/ax3q9PrKziEYz3pD6E8uleW4ytDRIHSncQ0Dg3grOOW16/tehb8Wlo6Nb5O3IQDV8VwEbWXiPSP1b66WOpculhaSlm9G0iIFUkt6ZEg7wM28HBk47IR7ADSJKvcG5KWgTFaOlbvVCVetAI2cpgBUdOV8tIsNcMJhu+d0RxPJEJoxmXHu6meUV523b7xez82JJXUMB1PLaIjsyScMXdl/HVugoUUArZSiQPwIZbu7fFy0I2W0rglTB6n4kJh1gtFuFaOJpGZ/CrS1lsACVkbzK9oDoQY01efvH/fOr8o7TaCxDYCmKoEvUgU5w4xkZl6fDvt2zI5x4qwZj/eIqfkY8HzOSeplVL45BZLjnlebJuRWZ8toKitDdCqRCcwwHw2MRqDxs5Z6Wi3cG3QMA2iKtHpkIERn4q2dvZHUHtZT6EWp/Ze+WOg11GXMDbIi2UvcFWO0DVZsn8NypRGsm6P0obnH/f7AwR5M0g/LxRJd9aiLcFz2FsonFKZvZkg+Q19Q/FkUTKvbGf2dAf45ozn0pwpo4OlQAUzp15g0OUWbBNqEBUhjStLPNpJHmeQgHOogno2uMaLi5uMYHlAhEJo6Xy2wYjBqhzcOs1CAB3xcodKrJBWWUxT9EmjBCFAsXY5Ii0WguRSdHA4I0cl/n7m2jKYwr5W1n4GmiVAIkvON0erb5IZQJExGMUFPUMwme9Spo5n4/AASAB6GUWpFbo0sFCZzuh57O7rWuoZn3T5Qul1JmkD73RcDGlpk9x2MgTWNgRZoG2OtDpGd8o6LrxlNG09tmxNumnxGRnNaPxEmyWhJY1zTgkJ4ZrTGnGAyqmQLY4d6RM/Gcchu/BCPVicsxD2nXChw9oAYHbG/oJdh+FwoDIsbA/vKGAC6xEUbxsCf/C5TIa37uehkROZK6qaC34b258ix8Q6LMxqghGLsjdF6MppyjG7gJMjN1SqMgbVCh6p4AmPbuRJuTtdbqfxo0NfdSwW+Es/WRfMgQTl6I4yyPxIxywn6NThP8pC+I3xzVMes2bkRnSl3ow3KwVdh9JRM8zcWhg68GK5rMJDFpqzBNDNhGfCloGAmdOafN/+RdAxCadil6Nz9t+iUNN0b55sijdyW7g1aD7ACfG6Q4I645f+KyZZAayc+5tjGiU8UISkKjOSZQI8jaxhYIClJS9AizUhAiu29cTLa5xHv3YH2M6O9S8eUg4AiJGld10Un+r6+bcAwxxJ6OwGVDuB3f7nU3bTGKqB43soFmT4IvP0r0FDYttFfki5D/OIG5LXILhB7uhIL4RWPsaAsf6ouREmCjVxMoJG3ZsO+6cXLqZQcJQhGU0CQ0aBaQQDSj0bRebbJtgq7TywAWohOYi1H0sVzEqLzDxAXH0y38/M1pD/DZWY4ELnjG0rFK+HF5zsM2IGzADSsyMvKZMAQQBuEDR5M9gOEKcKjL8g9vhU0OLfLMXXM34oSzdrqJovwzTy24EQAhUCjMRD9uQVs7o1GdJ6hOgJoecofgQb7ARybbQqtIlUdoJJ2vf28OiDNWS6AxifbaBQEXrBckprFwJ2maqHD0FqyBFp8fJ0eNf7rFYYlqZcdDzA+6Uc7YobGj0C7hMOWKNO1BdqOQOOHJwRa6v+I00n2rADg/rimMcBdeRKdHX+PrXVQICyXxYBWim4E2vVl2zPJEq1KfgwgvqzCf2UK162Ag+KcKciv35p+h+Ore2a0G9y/r09Aqxht2oTwNlXNyMv8g3OrM9vQvcHFddXinh1mqe7nXLY0xpFtAMV8hEZ0SvrZr5ZqWQxP81P3HIIS8RZA+0qg7Sk6Zb3rvCEH2u12Sx1NPRqNw1bau8O21dEgMqn710Azt4b9HFxtJaxqYZk/N4iEvnAhUfUIA4C0lCDJ5WJY6g55C4sFNuzAWPfF4vW8XCLJiNV0x8KdksVSOhaP/Kid3RV/bL3UPPjSMP89vriHdf2ogAZ3LMF08MHS0N0oOl1HY70EmnbMeq3DtquAdivWpgyDCUs+p0wXMDkKkeP2o+gE9wXrVWHgBXDXJ9kF9yUwlfCIxVaHq76H3qkE1bGBBmiKJwPaTfwYZnXKQkNTQAUP3FXHGHaKr5hWCyvTPRiyaSDGY5SpZrQwBhiM0VpLwB/QVMur0PsBOcSm/VNXW+oq+BFoSFI3AElil6KMgFe0IKMZpfW9mpD6uj5RXFAXmO8TaFL3vRhVrWgMePZmCj8GVld4bSTHZNceUOx3a9MSexoTfUF7V53QqRkDt6mQ0fY1o6W57XyWjIakZGbuRWzERzAak/aAEbGcnTkXhNBx0RmiqNHRADMfLpBd7d4QgmpEpxgER3VvHMXqBNDuhBqAdgDQZCdQ2x1k0x4EbJh8xPFNw7Kf2rHlCpnzbQ20+i7wRGqgYUnGS41Nf8kFW6GFGccGwqgWnWiIN8zI60I4rgV9ioaN5EdEfa/AOxS8YLStL+VhY53oD5iAfocmxlrlZv31BrQU07JH/o1A4ykVxTylqQTslIXXQnByAHUOtAKglZ+BNhedOZzSDKoDPc+iM2CVL6oWnUyk6KQYw6Ls4zoaxjppf9rYEh3+cG9czhcTneo+s5EmzGGTlgpRTgfyP5TbEV+cdw6UxRhb68/pmJj4SMIn1hE67p5AxjStgextyEg7iyKCBsLSgDb8F9AaHW1l4WHHh2HkfA0F0fv3p5Zuy7yuibWDHnzcoJvLtbMJsltDHygJczuGQTC8xhmcPc0m2QE0cqH1KU0x6Pu6UJ6DXTrbFNRQ8WkPKp4ZRGdhUjNGYzXQCoGVCpv5iKdALvnLqK5V/dPVWQMNSws0xZju4KYVmAnQOE2In94F0ITP7s+iE4QmQUXnwUWndni9VqIzdDSeuvnQaS4603kYv7HOQx80YsGsToYGaARVaHOS7JFdLA1dKHZMSRRhHExxW8LdvxuW2zj2LZ0r32Xd3rwHH1FnJfl+k01Kb5Ao6O+ajLbsk9HWkh/60RitVKfU26YEg6fOCwvnhAC+6UdjKJ4L/WImOtOnPQda1xgMc6vTEJjj7jkyUA1iAXoZCDoVagCBz7QQqOz34DchrKN/TPCVQPsC0PYaw344aAJAy38At//aN0QD1IGvVMsQ178Ov4twb1DTdDnJLU2EQQKBhpRAAz9r2Utm6LERCXIE6rumCqByBzSprjF6BhopbVgeARuk+zkoSTbS73vWkcAma8m6zseRKRa4jla6RnQO/Y2M1pc8JR0Q2F+y0FtK8ef/AprrHDmCBzgF0EyUpC6WOl4tUifnPM9qCKAh0c7tluCmgANtRwsRSpYDDdwGz8Wv+xdm08JTpgjz6dpiOxBoOww15b+jk7/UEHC1UE+ZXz7MZrGY38zZLIFGEpO4cpENVMgAraUga6GpxWIBPC0WgyyLodf8ohfzE4ekXJKyEUia/euvtDpzhu0Cr1Brk9IWw27UxPYbZ2uB1ofd+6odbFbMLb9+a3b7GAY/nyTY39IM2NJ3ATTme2WyEUJOT7mWUxYOaZB/j7yOh+STJ/stHbaAg2tlfQu02t/tKbMt0+pE2mHaRxtGDkSowWyHGrgynsu50qgj5FQCDbxzJdAUJnsBGtR7+PiT0XTiRhgDR3wyhRGocG9IZQxwwqTwv9OIaUJ//8xoGVwziFkZ/xB2JrqN40AQDQwYWEEkfziTyBnEO/vZ211VfRDrYBmLalKHnfilT8kWaKnJRkaGCdqaAs3YskbQ1um0cbENjtzRQDu9KUMBAcdzQFZvw6eg8/hXfdovP11CZwvcoLlX9O1TfEcN/ZCeUkhp9CqFRCntDsbTPGJfa4bcPO050pQqJAJX1FeV2P4ZtGwVKLWwvW/RMT29wXk2KTgxFhoti+pZ91GtMz+XGDWjy36e/DIdr3V+M9FhbFnUiS+6cNJY6VQe7eqgMY9moNnjPYIBLnj+PRj43QuaN4A1AquhFJAelYGFyLHSZwPoHAv4gCeAttZhVB0Lcy6fh9F2xxB7olEAaBjk0Bq3aupYPNUkoWsRtI6adzmg1KHaYmWOd9Ao6ahJz947f0qeX1OTG5d3Y/mYu8/2Z/HRKN3zX9MZQNJ3lacbGTcXMCuvt/iUqDOUd9M0GpVar/X0D3GGY2UGMEG7vKgu0L7cdH57c6YMKf+5kDpDOsPLmaa1qL106Rtgbnc+VKzSLoBN6hTStHIvkVMbfGBVoMF0GkDn/X4/DnTW7tFpDAkt52NXO/buSPmu09qixzWpLSnJONoY81BlpdHiDz/tVYA/alkRi17i5EboUAzRlkQeFMfghcy2D1/bNpbSkuu66zCOJOyg8UpkMQ+lrheg1yK5BK56ZvwmKJM7XeCmt1EarSdL6+qNd9dorHX+Emie37flycsXodE+S6MhFDWNdkmjOXie6cgbBZBGk+n05G1W+Pc82gYa84FJFvTGmCN/OR+2vzmU10oVZexInuc6sCV12916oHDYHpjU31EoSA0eXaMdlNSkBNs4YIHOXRsNqQNGdBqHNNswRQriACAFS4NElRKsjay92fJXajR/OZCZ9Vlr6XkdXhMmpv1AhelVOk4XbtZL6aYTmgsNTrUorjDWhSzCV2VAkaBB0IKBDwQDD/lotvJ2vf39lZbzy6/r2EF7ADQkbAnaM0Fzjn2djL//VuqlTGfqsXhPoDz05mECU5Oh5KSiKQtI6TiPbjpT0vajsFwdtFm2tEArL28On8Lh00YnDsKmwZ3TW2TUK9pIUvfRdhP7M2ivnaTWePbps9PhCNO5bgCK1CuRODGeOqHBOCXh/Gkp87nmT69lRHpToBGnLSWC7FSCltffdtDeX4B2PTPqVDDAb+DRl3tCQNQJHF2P8SqN/JKWuvWEQWfd0+CF2fZaq8JUbZRzKvdTs1nrrHc3jMnZQIPmOkAfpIM+GuckUteF58WAlTbrNNEHZTiowFZdAmIHDPpFkRQNqwI5Uy0+qUO8kQPqaI3AgU6i/yfiMTj0B3cRZFClL0DDnMeB/G+kozswUQHCKI0YUWfOpTkUgsUjD8lH4ZX52ygYYCW+1BQORFr1qcuEkEd7PkCR+2i8Z/16+/zuoH19J2iPBE15tALtvYMWwcCeOqv6hliLpcDTGpCxqM42RdUUccYZcYLXdpy22Bpz8s/uJtmIogu+xXrhStcKq3kGaCNAQ3irDstwYvKlvAKNkOok2KBdh3VkT3vC1i7MrAn7BlasAbTVg4FJtrNgUqBVwSTgEnkSC6owyS7soLGVoEG/hC3LoyKt39G4J2y1co2zg/YLuCAtdj0aaNf19k9+MQrr6AwGcMxDeizrmh9Ci3TZCKoNKlSgmdxBA2YAjUs5tRWCKtWvJKyvZPK4TvsHccHvP8Pt956ij46z4gQJMsLddPLMu0aT7oS4DFhBunhaGwfuLmemxUSdE3PUqTaHaW/UUiYL9jxUGqdculT0r0ELXmgua9wLdq80GkctOSb6vVEvy8ZmxgfY1fWRCZmNdtBEGsxbgqabpZRRe3zwM4xROH/7RIv7Twy7XoK6HkiMZAnKz8Zze8ULz7ODlpztPhqiouJsVKFJoCmPhgao9C7SCh4NNEF1Ry/cNCexCxpoeKeoTXEoEVUTKbKIu49WppOZ5aV0gbVQSFR++0U8wkkc2QDDGaCNGbspvQGaRgetcxaj8tEGBvV/20EbHTTafOuXHIWTll7x+B76V1I3TZE/6iIQF+qjjxw6JvR7CQrBgHw0/0gXfv+6krRVVP+TRfVnK6qHxUS4mRkVscUeylet/cP1JCiclDWp1udSMOAjWk7A9Qq0CAbmyb3Uwkfjdvj/VCZ7Ho0hKBu2kDJoytXWPIJxSTfjlGdk3lQXS9M5X5nOAUHvbv2e/FXHjbM3W7yN5YeiG6cPPRRAixgYIv5oi6BNghaY7JWBWwONW2zFcTlmWXav0qcK7hlt7qYTgkbIN4Azmj6DBN6aF5XMCcP9UG46UensoPkKoNnaKANoWAK0/ZsLyZhAQx9WXqzdekfgAFppMlvP/C8jaICLazhi9MDoo2EiNZo6jTDNyZPqD4fiofVxJ6BLTp6N8yQyvv4gvtpwAiHAicIqaVtnOnEiS6DdACBBs67ihwgCHCu6+4wgscjrg2rzEdZUgP32CYBGl1FBQgetos6Enqk7ecI6lNo1s4UiVDqM6Q1VvbzLJW+mYuP7LuYCikhIWIPpDNB206mqJ01nXvjouKFVUd0RBsmRmnXib4w6t4s0bamwv5kVf5zyyaGCKo9PZBypsGoFmnWSNMeOI1KkfVYRdjhVOlxbTwYLcaqF6VqDJDCfGk3jomb+UODHqCdsZG0J2qor6VxnkYPbGqXRRmg0Q3twxrkg6evUnkjC3GgMsZvsH4RFcMP3CyeFk5UFKRPJPsPOyqup6p6Vgf2e+H5nSGi0p2s0r0gZOLzxDu3/QVNR/XXUuYOmazxbSx22FeGmAixX/lGYlHkr0DaQrKkPaWlOHQVtiokz90O3NNBCCwvNN504mlhbY8/ZSYOIQGGRjZ9BGx20sYP2Re/OhtJfInghM0KiAdokaEugze4v8lRQppTB8xqVsL3NpQxKBRIvM3dVeJDpFGcU0nTy07J6UR1CabEP6zKPZqC5Sx8JW34UpIH2/QelKH4LtXJnvy4/6EnP7J3lp/z8ej1T++BPmHEyVZWSyizvm6yLxAGdKW//UnZtuY3rSPSqG4ou4bayllnBAF6KAH8mQAwkP45H8Y0zcse96Kk6px5k7Lk9Q0siVaJodfuk3qSKMxwKKxZrlWzbpW7ov5YC+qxpqbvL9aXNNE1B6PLaZZq2XUONzhSheMDLlGURZBYvZ37fCt1o9QqCF6P29PmNpgWcJyW/C1GhDMHeDDzCQBl7sFlFtcyW2kK4eoCYRRrUUcvKfTY0pZFgoKdyjgs6Cs/G2EtEQLhlCMx5MU0WVBDM/C1ZO6NzzudrGuGQ77LlO9zJ0RRNqoghe4PJG+RowNxioJvNYdv40fKN33JIzfBX6GitwRlC1Phz2vHQz9y3QHeGsZCixWo79L8vl3uWl6BQ37og38sNTxCp5C1CntDC8CBL5wfpDHTwWfpNtUTfWTiaK2z+fU8WM6COFtTIokMjyLSvscXASGyDXFYhR96IhxPiFdA8+i6dM0fUQlCRhOKik3rLemU8VZGFhumOxBe1PMvcvUoaQHGdLbgKOZ6V5G4H9Xq9mugER7NltgG0I/IebwFN+pvVGS9xV23PVx7ffwHar5tAi1heAs1+GQtKQpcaqSzdxFQ6LCLITvjxAgG05IKLQsk+EzECClmn36dXJjcgcLDO93EzWNxD4uGkXI5S9z2/D8jg38vikCR1pTSpP6tM3f8KtNXIS1qNeDiKTuYWJ0czi9jd0frJuD8o5GtgSgkc2vptYY/MovTIQIrONgTVGgO1AQqFyvxoNAaUl82yY7kzZG8gx1HOCC6psCyVIFHqG1YnsEugme/2FtD+vAKaQS3MHrjgLci0Dvdr4KwVaS35WnQabDbIvd7X1+5Zrm4hB5wUTqnt3bdAAyY32s3KnN7hZ3zfFkmPAB+Az6d4AhV/S2TW2vkJ5EeGqlbCknyRyUk+x+Bo1NHwcP90oMnmyR4wPmEMUNqthLmlEgL4uS5K2DHshjRRa4yuXzpv+5YeQJOYDC60HE2gSE0oIGcI2GtkyleOh9g8cLqnztBcMFnzFbFO5p8x7CQtQaLbpoTXT5/xRMUvcZzuM0Cq9Z/9qpxnYfzgD1IKQj2uq6+KS0vbg405wryZQCygwTPBcqDQBHw2tYdWftud0OyWZIKHRwItLQ7ZrTPAh82ARhcaIwO0Gs4KHROgm5UWHf+gVLkK6hh+uDPI7HyBSqXIwMBNhsBqbZEGezgFhwX9M4GA8QW0Q0crAF+ITgpKrdPqjBRAvWoXkqkFooAnHPGikNrqtLWMUmLGa8w45TL9aAf60WYTnQI4Ag1+NFiaqCMfjW5a8MIozVr6FdC+peES3sD00PJITcayuDL/EN4yc1k4vlC3QNO9u+JLXlSSHbShGf2gONC2/caBhuFXuGBScopvwm6dC0U5QA+gudekDw/wBRqb/MzPABwzNIs+xVk7KFUhCUgAYK965/P2nbi6Bhp8LtLSxjgh/3xKX/PaPdORzJQJTs7E1hXQGle/0COQT9fKDfdGtdpVTpsSSu3ewEKndcLQbaCRownQ5oUcbVbRqRztSKfGbEATWwFAw4xh8kJfN5TwsviXTwlAxlmq/FdBAE+usxA3zTTmXmA3mVlYZem6QZAlB7T/Toh2/UneBwAcAj3SuwMsh37uO/1FQYnuBObjg3blkCTPHTpXHQcgL+9z5qPUs4JRZ7wrmlDOO6EqwMSmeFJRBYRCSBLfxiaBYgANbc8JIJ14lREmRXYZYU56Ric9QPyTQZNi2zVdKoUWYg3ROdJuyOllqCzrNBMgzTDIMKxbndxNauVCm8lucl3wvdufTPPefzAlA+6Nme4NgRdEKOJVYH6WPfkz3BuyYbB4YU647wD7SJxrLQE+YTgKPYfwm/vSaQSMLqyIpv+/nGBuLqqHtxcMKm0R2fS8bYwBkANoCT4l7MJx4p0BND2bIH9DKC9Pi9RYeKoQPD2Z4nLZbN4LxCAtBMHqgw2MmTYVo8fDueikT3ZNN+1IRGhL2xSIek68ZO7w2IhOz/xjwzkaapb0TKH40vS29hX3fJ2jQSw5WkYGPAyutqPmyb7xTRcKtON8hNysgWZT3Pd7AI2D5Jq0EftKjibHjKhxjwy05HQONNd6aS+V9Djwp+2cgXVSdVK8KUe2BrCngT0HilSpu3mD9RfRrwVaC10g4Sx0c5k52XAljVXw1zQOXzoI2hDV7/rkpuiZBManYFIwKAE0lt0CdkZeXg0MFY88aSz5cOtCoGFj9BZoovMYPgrPIabGCyqVQIpXWA6mzYEOSDfGQE7fiNW6uFjD74BGjKXoBBfDyw73zN4QgO0ZxoToVKxhNsrsOtriOlqIzmqZIC0Z5UpjgIjL0Bxiv0wqZCqCK2eFocTRHLWtvASwBtlk53FA0wnR6tjzbkCRMz0eqXfdoWOUFJ1RoJ0XAq0tDrRM9oipxQ/KKj0GcVDYHAiKCdJwjCjFopJvcQNQrmzo8QB5LCPFoQ6cHpIxbBw+nF6d5GZ8xozHgZSROQc4WmFBgzTiDP0Z+e3RwN18NmIvE9jp4bXk+uATKToTaJlte0NHa9KEZqQJSWRgPtmsJ053WoKjQXTqfToIhts7xuLwM2PpCrTwnrVL7qVjQz9ux9NiYup/GWUrq0I2kohwCAat0tbyQtJP+NX2QCQOCTQ/IQ98Vn50ehGsnN5wt+/s3AzPN07QcTJ3lmoJd4rcDvhMhGXwZR39Eray3FL5fJ9XYQxgYDo97hczI5WOhzsLefevxTnaj+BoWiyzxJ2RII+V6EQfral5eWK6buYQDsNTP1bo3Q0dLSaC4orle8eK6OlII9MxAxQOW8uHRFbZG5LMFGjHE0QnTIBZCiwAuN1wg9xp+WZkloByoK3laPDsJdA8VuuzN2ugIaJuf1PKJmQLSUMRpoWwaDDWgRAfSEy0EmjboeZofRgD3LSn++mJEo7JvQGaf/ll27vjJEF+eMwhwp+M8gR2aUAL0fl47l93+jofc7oBaltprzEwgeYmao48lhGi7m+ARjI9uIDUV6CRyn5asxth2c761M7aaPMgcyauAg0bike6gTl7R+MefIkeNQEajYGPWTnasQKaNNTg9DdW03uWKl/rQrsGGlM3sFVIC2HqaflU/dcj+XxEhRoR2uXvP9Q6WkpVFm30PLsb9ptnsDRpW8cWaI5X4zDGjoBK2XETO/f8etId9IoftFnmXcIBgousroBRlSugvRe62bbpITa/aq8DJ9A28WgJNAJJToqnxTEGbzpakSZyNjPWabKQmHNzP/ITUNNYxRY5BHoAJDFRhqmTfAWSrT4J2WWhqHw7aVidWsfr2BVB9MXW7o1rHQ1WJ1eR9KA6IPc7oNVZ281MXCigEUgfwcbC5MtCeGUr+RjKoK0BCNR27p8KrRfwnaFW5gYCLdlcCzTjm25VGEdrpSfqcLv5816epqfLDkDLn3B55NIMkX/S0xey0uIKHa5KuQB0SrUM8tIDaC1H85mCPinCo2OKLGZrwr0RkQG07c5I9gxjgNSYc7pOQYqaKWs458JpUq7cGxmSakQn4fEKjuaiE7k/8x+ay4E5AkKWay4vOZeqXQsIySKxJlBYI1zYwUrManKWzVQY0zFWcEmGdz5YF0GhHz2whJ4/3EmpDgN2uwra8F2Omrohp+Q7QwANtTEpUFH2JxToaHPXFnYmrPULBMDP9+Cg+sPbCGSkGH4SZ1x9v/K5rQltF9dEupmofUQ+Ls9qDBcg2IiyHU46YQg62nkRSp2+3rMhJbTT34TofhvNs/HwO1FKR7J38Ilci8MlKQUX+Yzs4G4Z9vwr+RLfPaF+NOpo85sZA3KE6Az3RqS2mehsXw0Q/ItCPBOE3S09onKHbQbllAVQZBJr8X/QpXgk/r4aoDjN48Ba3Q24LvWE3mB7uBYcTU5ZwmU2+ZdrdWUMgHYxAkfOC/N20cauNV238GxwQK8KhygBNApWpQrNgeZ096qQuDKvh2exUByQo7XNEkF1uuPWOKNqhhocLCc+VJnopqFZlavA+HJEdHPEAsp/Wm3CbV9nb8hBcYPYJTjaB42Bo66hfGQymkQN4Eaz7EYm0kZ8PiFGfkaO5mnqma+eUy2YooeKigUlTMYbG33MC5FQAw0YY2EzhSZOUc9q6Qn/OUF08v47YOqG6OxdSibQpG6MgYTKSTV4qWcdORG149nSUBlwmPNuNk4wQ1FH59KfSS0YIjkaIZdAQ4nLtThomhDGhiT3WIx25lHRvEytzI7BtmIRItf+G6D52opSodECjW5bpTEB2yMDyrGgo7l7Yz56UF2EKKceg/9hqHzhKL7JgUYtTAHnsU6KT9J18+n5NtGjr4HWmHbUwwJSd1ZBcqLFJmhNsat3agZsjp+aT3aSQT6nTQCrApqQyecaoF2kMwE/VJEBoTKIoCPz1T6nrhQh69XnpJ4LqX2/19PdBgWKHUfmECcbAtShHfi8AlWONim1bCwEJc9VSgs0jwNny3hhzDvEMXX+EoodGyToxhTKkYyPkdDgaAk0oZgJgLRDcJorjuZrmTlH+1A/2mH+ABP745RAWwi0N8uwfaMfTW6vQ1B7Is5WDA8djZwsV2n3+Re5oAaLuTULIBepsV3NzIidBNr37wk0lO93N8u/Q7+fpOtR6s9roJ2UbNQEGqhXohMMB2BJ3dzJK/CuoJZinVtF3plWKWl9TMHgSj1wMWorOiPUINBrgQZGJ0wrRCcxlonqJdLVwxgwo5V0Gglar9PqhCwC0CJNKDnarwCa1gSay7oE2t6B9loBTUXn25HT6wCvN0cXTM1GdHrKhkczDfCZVcfGjzGisKSYAgA7m9aTFVqc12qsGQAAyf/O0WTb2m+507NPZRbXQAOZMncYTEfrSG1Fp8ABVNBePIDUmcq2EWJSw8n2BWgkP4ClZedusM41tevtKQA0xL8YGRCONvZaAXMr6mUJNDKl1urEXhBgZ1if+IKXxEVnitTRl3VoFhfWUk37pqAKRueXzYeGPY2BG+6Nw9sfx4/lNtB+ftXRGH9IoDngc1pDLE2TAAygpTGQUzDVU1s7ZztahrdF53dr/kNoctCdx7tA4TucaC+8S1Sod8GSbtDR7iiVSWZJY0CpNUcj+kGle+MRI1PkeufOvk9I3rkFGsgPNsZFOz9u5T7vXKRg4Ienvi9G7QxohUAb3RhY3TYGei+UD9TUQnfTkYIKSo9YqgOT2CQ+o1CdowaXP5+niCfQBAOe93plDIBNYZnRn/SjfUB0MjVN4TZLMYetRwbc6sxJyYBxis5YfzKtTpOk+e4dxs9+0EXrWYL+TzdHqhdC6I5AcnSl6LQW8cXrqEE4Hk/uGxFmEaYrNjaEHP64DB1oZwdaH0Wo0T6fTzW50Jp0aimklvjRC+pSXiR5yeSeeCxdsX9CZ5QzqYUDZ5oxd7KxlqO1pmY1fYeBUEclFL1QzZgYR3AWnLmOBr7G4lO6aIdG+KlZauCv8KMBaPHyYyCl1dE8BCWImml1IjCg3rRlsenpB76+LoCmR4ag0hggutyl107Hh4LmFrTUljzwH/KuLsdtJAeP2iOrBVs+Rh76ZZG75AQDzAH22W4km4URGOhDb5EfyY9VJVvtxczT0HK5VLLsDvyF/2TNtVdnry6IQcWkwoN+sx3mu4KhTDvqaACavXnn+lys//lHYW20LjxQX5aHEJ2A2ohV+ok9TaSs9p6ovJwqtrDqKtUcyvmff/xbJtX78Wa2DzSS1SZKEjoavr21zuupDPhItiGRiXM0fHIYAw5LGb2oX38oNOUwnfvI1gjYcaTOR+OWF1V8vctHKyGoHwCa9uq7IE1olaN5BlLl3oj2gw60EJgnBtiQjIJMhnG2ss3W6iTrqbWw/T2C/KTVCUZHN65dAr4INBCApjEnLlXE9zFIIWNAqwUaTgIXeLLuqneV0mLMGNRjANBobs7tbV77etIbKDrB0cCtTD1DxR9Uf5mckJC0gKOZcw28QKP3Hr465k6cjY7mNQTsnpYdtmj9U+toEus0h+1V89Gumla7ITrpsI2A00fT+EDEu8UHQLAE5nLwf5r7NPUVaKN83KJpciGLAXfJEuHHAEENNLLPYYsgUgcfiYtcegpAgVaBlhLS+Q5e5wfO6xzN46Z05I902MolmSSr0wtlA+KCOdPR8BdEv5xDNPjC4EADyqKpDFmKzT5eRIz2xoDDpY51agjqEiGoa3lGCOpWhaAUuWXiBcF59wk3K+GolYkG0eBHi0RuDb/NhwQ0SiQPl++fIEUUIUWOpiodhkKVjuZEqRquldZxDPAzXGE0Yhw5ktcBjEPW9iCFeaanrILmGiK6M++EGk+gAcQ+t1MAipjnbLAzmUCmBpaho8mRdTQwA4116hz5XUdyL5OX1NFEi2Ka0C2MgfeLSE6PDOiOwdfi3ihWp2dvXLCBnaUJnTuOBtimTWvNxQIi/ODaQLHhrKMy7CwCGPYB9cJymz7B0b4pQ8pAGwm0CbHMSDLitLEVUlB2oqzHcsPR5g2ONnccbbzD0agvktZ1tC526cYA0mM84u+ik91Y14G2oFkde+6ae+Gj3gIIQPuZgWbwYDNlcjTxo1maENwb3OP1/Uz3hoxAr+8iz7pNZP3SGDii5Zm3y8RRBqaZtTwjKURPAQ0Maz90HI1Ag3B8xNGm4SFHk7U1GhrROZLiDDiiGyLbk+tAI78SCsMkpghetFobRr+dVidmdIoAcQDaIYBWKLVE0jYxEjb0xEfnH/qjv2SgfXRAKxArZzAGbsKprh6C+q4c7fpL3WdXNUxddHp8QXCl+FIMR0ZjOf77r38U/fMpsjeCo4FuLCb2sKfQ+y0ZAxoZuLrD9p0cDftJsVg4SGDszOwDYjw2bGDvMzSHkANxNDQJwkGCmNIBtuJzpM4M+tF6q3MHVmdW50iGFqqbL5GjtXojZVdHvdW5ztGGWJBjeMjRZH1YMwZ4WUbqaDbt3hDXZ+NzZHMeDz1gHcZAbrQqL+iqdgzjs94KH55T52gYCkcj0FRfy8YA/Wimo10vwtsKKXOTRG4IXa97gfMkCpil0Yx+r+WXMx5gsU7944E0FPBmfdUUKEYE9k/SujHAVTxBQ5kG0LiqQ9b2bd5m2Pb0hI42flJHO9idD40Bemk5TVI8F5XlWPqhDqrLMUKUWrATjg7tJRbdBFBcVFW1c2JlIj8xpPIBmWDDxZt408QY+CXgUj4G0YnYqNWg0OpE2Im7VXtjN2uQWabmm0VLoMVUAeuuSI9R+ilRqpSjmZlVfZZoX9IYwIIPA441HS3WqaPhcApmx5VqmtGJt9tz6q1Omj9dIoFe5ToZbK2jRc0D78O7eqBlzAu+TtTRKqC5CRrJ3gua5KP/PFgZtyU3TvZTpnDYvqaN/c8PHbb/MYctYp3ff9QczYyBs459mlAWnWUShfdAIFrWRX8NSiTMJgINtHsSaLxt2n5TCqAyRB+ilrDrHWtAyNMUHO3QW52ISUF+UfBy5s950+rM0yGL8fa/R2MAB5O0xQN7AI9agsAmvb7lPSgH1V8EdKscTZCSgPYeQPtBoDE3CFF41m8iqK5mBzeSQcaZRQPYSsTy0Q+pIKIvmoNooma1DbTVdWFYhKsjqQcWNDNQcthOI9bBMXqO1f7YvEqi6rn5Rk6Bw4Z6oJGjtTrawCk5GkB8V410PjaqS9P/BgNWWKOqomkbSUuKPKFIJTlsJR4lHM3kXdRfUkeD1Qmgwb1xNY6GLT3VEhBOCNEJpa+MFJ0o/HuxdpZLDTT9b0Cgnfx/yzxmAvtwePVAy1DbBCCMAZsDdg3QGCNYF50AlD3xoDWA6TbNc+0V9JN9/RWTfuC0H9A7coUPEmgrxgBFp9cMzMHRpnsAH3jv3PBO+Jw8QdID7cj1jsjAK/b59J4vrzipjYFbJTpvSBMi0ODeUIn5Q6xOsRGkUOBCjlbQamlowdE83mU9Byk6vZG+MLV5WdiBHUgjWwtFGyjogbZ7HmiELHU0PD4FNCBhnaNh+GspGQO0RDug9cYA+Z1PHxJuXP/28NsRaKqmebfKBmjySECDH023MoQ3PwPNNiKT5yWA9h3BTbkCL601V+Nm/Ph0cDSTmICUvHoZ1GLFKChORfU0egUIIawCGgi0UJQm4klpW3Rm8tsBK6FhUgzu5PPjvvtAm8a6MrnjaOs4Az6dE4LclBaKFdYvG2WgjRtAO6yFoFhKym+a+miYEC0PDL1Ib7LDrQ+Dl+YdvX0kcsJsBEcD1gCXCmi3ABoSH+GwvWgW2tU4GjbkQfPt0NEY9IwW4eh65HUzaPcGlGm404BGYyB1H9gH0KZtoG0TdPvevZFPM9D2g60P2Q1S1a+wb5bMZen/o0f3NUH18bAtOnuOhrICfE9vdWL1vjEAjsb9OnBoe3BvRyS/p5V9Rh6kTNguXhV41dS8aF0oO2x/XVJQ/XK1AKfi0eLxuB8bs/jWO7brgXXQQJqGpZ6xe4g3IQHRXIcUw49bCcweaM/bnBNlpWVP8nSqOBrdHgHFfWt1yrI7evHehjF0MXVcr9nIGtp41Z0ZyU7iW+YkseW0w2/mVI6gJ4A2J6AhCmpAK6RAo8OWxoBzNPju2aLPWdIZFiTyhS66k1j40SA6L5cIcJ7dGBBrAK1DycfMHEDjEeGqONftY0CwYeQvD7zlUpQqrK2UARf0EGCrlCMDOsihM4hSNzANU9OUUafP7LsitGR1xcUWQVpWjZLsg9jWoUws6ciXW92J57nybjgcLEuNruE6IEozpoIstZQGnd2pN+wdvV8DupciC2cxjhap3ASauzei688ZBcTK0QC094sC7XsGmnV3T0BT9SyAxrqTRe3Oxfit9ayR3pZRv7+AlzXaavwE7HdBubYNtN0Us21Wx/dUU2FcSaoOMQ6yxi4L9C/bQ0DS/kp4b/fjgmwFr/5CoNXA2tLqO8c/MWcDaO7hm3PhMh/LojPStrQ8DdbBiF4g2m54OeKRGvjBEDXI4QGOFtvfFYQl0UkdTXK83eosGpqwQIhOGYTUQ4t8NHOhueiUM9QKW7zptFj8o/YxORf4LNDK8Rw1xS31qV+mMQBNEaM8dUrR1XO0v4eGeUvFY/CSQGO0iyKR0MP1rOu7l60HGvVoz8jVDC8gTH7f09IXpyChQ1FWDjMGzqwlFlfszYFGjmYhg5tyNN3m56PiaIAXu1QWtnr0ntrICDqMB2zWdlKy8sI6Z5nyZdKRQCPKfg/atbSJO2hl3yadfBPlDMce4578q7E6CTQQfTA+kJCsU6/S2sQr4RoKlFzyg85jmiB32RgLiCvRGYsznXhwP4PTgg/v9StqEDZ5vAAaikTx6p0hvS1kjgzogRAUIwNhdZ6RYQu63Lxz9+W3X7Q6pWWa5EM6R8MNoeKBp6V+jqajWcr5if0tratGS9SoATD6a2uj4AGwNoHmtVE2yTqaEC4qDXJ0QKMxQD9azpp9YEdui7+BonPYiHVikfoipzHPV4D8sfkgCPyaLScBw49yjses3Rm1oQvyVkVR8w0ba9G5CrR3A9o5cTQHWsGZtoLMotNuKBCTAd4NSGX4akVsAl/qVfPq1OXEerBOn2nC2hM52gbQnsvvhh4WJzJ1pHlF6DDKAf5DoE0+pR8t+8uYgtug6zmCw8setDr5qL7sDrAHV8lqq/OOMTCvGwP6oF9FB3A34WiIdS4LY53SL22Do0F0uj8NxsD7lRyNQOs5Wgbaa6WjKb6WqBBedEDqJvOxMKbGiikq8BzQdk/xNnl+m74VvJVZ4HCHL9NvpdmZy1X0EpYyUefn9O+hYcMYaPxoGLIcvY/wNCcKhy6NTu1Of+qB9kPYqwClKyxkxy5R9KMJZtzNgT7dPzqgUXSeE9CAtNfM0V5ePdRJ94aeLi5F1RjI//ipMwa89FdmQVtK2TbQds3lSY6GqKONa6KzT0aTKV0WmXFs0/Nsr4+JE2jrxoC8xElvIFMi5wfZZdpa9xRW5zJr2wW1C47YLOjFjYHISezdG55ie1HLE+6NCmhXjQwQaAVfsWWifaqZHWbmIi8TQBP3RhlOBrQTLCShmUz+IUfb7b/eAdq024IXmFbjR6um8qavzrhY8ilDA7TpjQmS/GVYKiLzrAr9nUjjzWtAE1oHmhP/9i7Xjk/c4ZZbbDSOEJQKqrA6+fNTdNZWp+r/jeg8R2QAiY8aGRBOpyK3wNSZ2Idvf2yeu+KbRY9e9MdHnyDzZ7CKrlHL6KuaEtB2BrQA0aqh+eXT3jMgtwfaAMQG/8QrHbZ41YV9aj7pGlRb3jJy7OVTYDjdM9LqeMNfAQpj1LoG4hFiPcrp9SKQ8RmOlvH0SaAxE3IMY2A5IMP2JKEBWADcmgx+NDA1hZpJTe8VqnhDx0dEBiQHrVDlsNWH9ah9xYieGsfIbvQmguJuiSZIh9hsZlUHvsvR9qQGZ0/QLgcH6DxrjAFWthNoePLHhacjMtZGWSARYjxxy5RhgCcUsKeyKXGo22M8PABatXLI4VMmmNO94W2Yo+mQ+NDwrP1o6w7bV4t13nS4hY4m7rJyXBiCOiPSzhCUQC2AFhztGF3CsX2Y7E8haHOgga3RsZgJNvgG0NZ8Z9Mj1exLWf36tt99mYLxCUqSsxZG6Bfld2/ZDG3cG7gyvDm+QASUzAirGJmuMbwVspWc3VbR9AhttVuf89rRmrY0ONwF2qAtmWNlG2jCNmbEOmdkdivQCh0daMuLWZ1RVIxYJ/uO/myBdr7BjyaKmbfelmICKGZIAYekVVVPbYFWR1PDVwAlgQqgC/9uS8fLESc7yeUhI41NYIRA66D2lTp9T/0y0EVM2Z1fuWoCFqKSilvUt2M0Hkh/zBudrAzI4yWkZHnPA8v0wOmMNwYxxL7uR+Oz8WhQfkOSBAyJJxxbQDvNiaP5rjcEmj4UaEfvpcyKAnbSVsx4PApAe//tuzpqJZgOjibLEJ3AG0Un+i0A0voFZnUeDuo/RmUDop+qro2N+ITV+QRHI/2+IUB595e06NToaARaiknF4k6GCmhDBho7v1RAo/kKRSvk8YjrW0r+sGEtkFMxW7HqyjFQN+79aDQuh+mRjgYpJFgj0I4N0BaVnhgs+o0dCKwFZM/RCkF0XkR0ii2wElSXe5KOFhxNA/kAu+ALCeexqxAY+5qOdgdo8vSg+RrOyuMB1L5meN0DWjkgOotFWQMNmNhTdA64BJ28KqEiUeHky9s4meCkgkBWtaqjvREYn1Hk5i57o3dW5pSlOQGtyq1sQT4fvIgdijZr15cNoBVEoN2LPlCOeWuNgQZo17s6moxgjtZ0WxtSIv/S6u2X0ATmrtmBzf9HuBmzMAhDQZggUdrRQYRgh9pYKAiCJVunLO79//+l7+oRHgb1MAXlq/eGIyaapPE2ZSrdJfnYQJ20THEshoJErgXEuxwqIfTfO6r5tzgikj7K2SKNh7SkKaDClegy4VoMEicQDQBtQKABYazUsatpxD3MAfKdwquS2NInE3ymgCnooU+wRgeNSt86uYlUTwayR+cmaOsMUALG1xxYmKjXoyFnadPcadC4UWAdqnFw9v9dgyY2SDN7NFSDkaPq0UTMGw4l/aGP4hpXBM27Ouk2f5bn7G48vdb1PBZFIlohHIheCEqIoQTRpnvkRJm5gGjTFScZ8D389upwwZqTOoK1vt+tg8RLEFXIfYO4YKx99Nd9H1RyUT4knPbxP8au/SWOZAvPGNzRcXbo6inSnQQMIeQaXWfIzUOAwQW4Zli8OCuBBeKCKEJACATYgECQXfw5f/Q971PV3RNv6byczzqnu78+p+qcetj8KCt5G40enOkct4nGg2wbFm1DiDZiU/Y9D2/ctYl2p0TD6ekURzOLhnE09J7qOrUzYYvmehuNuMaTUGgzeSUaaC3Hk5V80LzF0fbAUf0+K4dctnc+vO1hefn+v4/oL/VwWM4e7M0mOSL2Xj5jxGRY1/Xsp70ZvS8ZUQGEETVhSkCsz6gyqSMaokQAPGYDQPxYj/7PgChzRK7H+ng2fOp6xOB6UAEEQKTGulGJQfrjGeNXyxllx9uDA65IV5fjKcw8hYVeKB2L60HaZBV5W9IbCoXUeHszoh4g1xqdAZxXjG4zCdh+o4CtxdEgUMu9Tkxu2vhtdrpYdHEs2ct2jOySbTl4NJBHk51o6x1E63dbNPp98WTIZedZ6Fl592qXL3A9mwKC2FDuPMOvAvxEQuD3giBwXe68BwCDGMH/SQh+j3UoxusoAfHmHj0OuA7Vw6Ws1sPr4DKbDkAM06EB2Z6wnNnBOtVSrpBjmky65QjCwmlS+mmvdeDBTSWdJzvZmfJW8fCQzMAodZ0WsMWgfu46lWkJ0cR1phaNNlsUi6aMw4Jcc4u25r3OTd18DZhmSyGvtmitpdD+jZ7zOZ3i7Q98QquelLc7QyqPfj94TiceEFwi/CIYESUhXuwiojZEu44X23SVJoAIJqOKjCgBMQU9ELJCD0VAMSkVwEwPQXTr4VL6AOFK4BBC5OMBjURMDRDwv/XkHjlP6oacyuXUiOjY4dTn9vXTuQ+6nRm+ISumy41t6aaXMsJ2zHn1MVmznGi6mWs30f6+vYVQGpo0mpgikzmTrgASDJ6QZunARxqtwalO0Ew6LLI1SntQO7/h0iYaPOTMP4t8gf0efrlP5gOIJoheVipGTIhGOSIwG3uIQIAjfiUOVb1ockiKkWRYr9ZjalLyYnoQYiII19L1mLyZQhONsM8C6fBJbh2G1KTI+v1yckQIKWKCbD3o6sT6On98ibzXqa0e49eYd5NSoknDXAxMJ9GYNd1EA4t2y5kBtGgZ0eDXicaBtDwFNUI18C4weuVGOuu8e87aikco6OJs8zlzAsTI57UWi1YqoheMCMKB8pBpJIgQvZ5KEEy0CSOEZs5Wo1H5Az308rb0CKpHAxFUgOtRg0UzSMWIKqY0KqGSAULKe+RMcjn58QLRtM3vLEs7A+nf05W7bWcV9FP44D0Kx6lFo4w32p5k8x5d04wZhwVpBCwz18nT7dx1ItmIalDImNF/UXjOXCf/kEXTlWnGyXLjWlb3OoVxew8on04tkg96zkJI7tGX0CpWon3oRQR4EYTYqwnU4fzxcx9fPgKGEQVUSmcdHxXRrQfcC0JF00OQilACuB4KMz3cdX5oGLzHUolZtMl9cob58eZynGibXUTzPzrTNjOi6bbGut8/z0ACAyPLrXjA9o66AuQAOdfJ/U5qfWVEI4tmvc4m0WwjAeTyeEsmPMEPZ9I7VzgZcE6PJrnlK6AlBs2ylwO6f3eq1E75yf9l+ynQ6O3zergTgvHneiGWgBBKxZ1etObb56LQW10QJCXqVat61wV+GSqpQ2i0Wg+hEUgxPaJBHJHpUZ3Oi/npueqxi1ZxjyF5IUTFlRxOfzp4XsIZ6a2SUzfkMDI/3qkkr2Qm4KrOAPxKZ8DYyP1QX0qewh66MZl3BqBw1GtDW/Kp67zLXCdOT/+muU5e4xHQPnqDLZoN3wA7ikYTrSlK3ZL19TTJa/r2hW8esJW8NT/oyTIC3Ebb/iXo5T2+KoovF3bqHpLrRAR95qf5TfT7/LeaOgOAsDPeO58D0aw8ZKIBwklUzYuYI6aZHmFZUDnqRdFDEcgIQlXHV4tieWl6EGJXpFQVaYFlfq5SSrZoAHHHeg5kvDqm9wEVYcgEIEoe0OQGNTE5SLQnLMf8O91aqZzNzaZFa2UXPGDLW5jhLxbKJuraY5xjtE39W73OlGj46GqjdWcGIAHlRNtIiJYGbNfMdaI75w0U6IOHN+5xnRLeYKLt2619Uizw2gjTKjADTLT9oJCjsxM4p0Hb+4yQOiqhGrJE4BkiryNHTDNEvBKisR6CcD2CUHFRnLEejjACLxenvXA0L07lX8DUMNH23a1ezKmSUzKkUknf5XyCA3JNcjmpRcxuLZDjrrPvyy90lL56Tr1uchm9jcau816idVs0zwz8s5JotNms5OWRY0Cyn7G/uYYWDbiG3tNI1WXRGhMjuyyaEe29NHfjcfHlPJyfLJbmL2ZEtMl7vb+PCiy9pDxii/ber94l2gAHRKyDpVTMs8IAXsdU9eBSNBzozBAsJ54Vc+DYcVGcWx0fGSGkOS+uWNic5FZcR58gWqoTOODe+QnSqKJKxKIZJFauaK5r9AMMcmu5rgdyPdqz3/1TPn7XdyJI42jiOvGCU6pgjXfwoYUJZIGhuw0l2veGRfMU1K1uBAvzUW5t/DYZMyIaVIdE0zba2HKd3FTUu8AnpjeJ5h/oczbi1on2/J02i6/m8BTPi8LaY6+QaIgwph0dpUQL4RURDRDGknlxtiiCY17V2C8FBDe4WnWQlKnqQZpcXEUOPUQynY4wucviDL85La5djynqIXrGo+KY3hQL+GBSBlZJAEGXxQkd0+K0UkUOp3sKiZE00V5DS46XSzSLfjQJ0bx0E80CtnItORwqcQ5ypjhOjaamoCulqeJrtuW6rmQlSzU2ep06Hu1vWgme5q7LeLRv5jPxCbgqRCO3yR1bkDTCTkhKNBvx2LJo/Q7XuW77jCnRDu1EFkt+Kawl/IyIdlh5Ly3A11XSjH42AaIhQsuX4nOvuLGPgNgmBIc9BJcSzerwC3q05NdED5ESe1ZBhcBi6XpMVQ9iyDH7XTRsjpDD5TZ8vC7OWNxFcMhAISzANTHIgR+v3Vp2QIyQvcc6wmnZJVKi8TRJmo5C6Sh60mWr8IKrRYNXSz+NmGg29DEPb9w50bTXKePRaLx3K2ALb5pxNOQ0z+tcZ/Lrhlc898kpl6Y3fSROqzPwsBHe/IyNG2vYANEEIX+Mck6tq4YWzeu4hEaRcpHxXoebucwqch0ihVW4PpsXJ8fRIICYpohAFQR0w6YHNtJFStTgSLgujkUwIN5M+1iJMi0sFxfYGbiGCqwSghC+Uk0WJ1hHS056a1VyQCpH8375Mst5Up3/MrY5wzplGN0VzwvHbwCxhVEOjnPg1yNmHhBC15nVbDhHw5BByegNHfjoQ7nNoqHb9Taa5zrpZYzUprRT4jrZ3jrRVncGuMPtC7c70Sp6OjpeIlO8ne5Ec5JEi2QiIifJfImQhcMdEeGXBMXcogVAZDTqHZ8UWE6MnIz4zRFXxSXWdV0UpofX4ZHYU6wjqB6HyiJK1wZQE+QsWI6J2cNaYkuTyuUcuBy/tRbJ8b5x19kdsJWHZwaYaLavMX9G/nFnAC87pQh+HqNJs6S6z+uEJ3gP/FGuGdEwvMFDuZlot0Q0jaPlFo2rNYsGL8B3s2hky1YTzedEmUUTjjnRvBzDiT8RkxA7iBbQokVLADaJFk9P3GA1EF6qHBATknD4gsh+OQdrVLEebSoW80sM2GlFjgjS+IcCZvGaKaOIfmaM4L8/sxxGVQhZdzmhQk0CIfR420SbL0HYYpHJ6faZ/recaGMhmmSesCE2hofOGRit7nXeeUacArZYmq4TWEa5TiWabojtRPPwBolgQSBfduW8j2hech/a76e7JSqNPNt9aiaNHePbJ10WTcANt3c0P29615ZjdIBL+eiIKkA890sFoMviihnf5bDQ0pBFMj2EaJ8kt9A7XxbLc1STHWEFCDZX3rdlHqIcLO46rfEPmvQqRpgcJxppR7cWuk4VBAh1nbLuQxaw9Rcs1hmQ7Lp2BsjMYR8Tk9lw6bGNphZtpO0085pi0dh1Uq+AOwRQcM4AEi3PdfLEvJxo341obtFGPLXTd3zZlKR63qXJ1kMYDHKLZm20GdHBy3kx15MYuDMwow95+0pMR+SG/IwB4bjQcoO1EooQuzP7l5bJC4CYmh7ReQ8o0wMQpofGluenF8XS9GCElnAxn3M4NyRS+gaJ2NBbHBEA5bCqQ4ZYcNo7QHq8ADmwSgLdWnZAlXZ+pvlS+96/bAdsx1uasEZ+YRqArJtmoEZIN1vVM7NoSLPMosEDS7uNdvtP3uvEb3187UaaVB/56A2Oo8n2iKhjPusmDZ7JCz/UdUpx1/nkXaWNjcumufmPhjdi2pB3YnIABOtQj0YhX3yma/lYEduMiOLW2uEN1kPK0XlkSZXqYeEN0eOC+5Rni+tGHUajE4s7R0cMFBIQsiyOzEY7ROUE1oS+WBRNXZVXdmsBJFgoxlc9+b/aaOmeizxDBVhmbTRexRZe0ul2bdeJDypKNG+j/YNLIFCUFg2Zlu/J2nu8sMtIRiIhs8mLe2fAdc/DG82Fw9x1tohWvzdTdopn6gj8hJYdJJojOqwRIiwKGrxPKA4sR8SOOgJJmbIewiJQJFa9s0JiCzlC/PsRO9BL1cNCunrXnAVu8EmJO2UesAUPvcBGAsrhnhCJ2VM5nzJNgss5SOW4DS9U1xqGCXErpjlMSEtGtHXtDGAUnrMBFJlttdE2aAKxDKtdY5dp2U6aP9AOb0hnACJpPsJWlhxqu858PNoo7wxQ7rXV6+xaIA1/1gcp0ch17nLaJoCrWi4+V9DyXRwnyaFDSkEpR3KDFzGH7CkZB93cpA0wSUGJK22RleswPaCWcAIBg3g2TxNMhyZF0gsnENP/UpyYHrXrEUJILA17wagpqNIrOS/wUFGOhlmEiwQh2aAJI4If7wEhAnUW4uPgJi/y0UyanYF8//aW60SiqbscSWdgFdF4I2xZvEANEhJtI4uj4Qvwied13rZd5zcsq4jGOc5Nsmg8mNZcp8b9MJtu1FoVR8unA0uW2VJ/HlUIWVK926IBYiIpc2ml0T+Kp6k8qf6DOqIk1R1RiSKn+J718LS7sPGUkt3qHhUxsTqWN0wzVMURAxMT2JZRWdKnqEl116QCTRYL0iST4wgPIAeygoSAeF1XUp0eKzsDtiOZ9/CUgMA5ZKBs87/GpgZ51ux1IoG0jaZLItgI27yNRo0zivjyEDQsIGAMNky336H8BKpkt4sXXVi47+uoePH1HfFbz3XWlom++FLwYIaQDQKq6/1KIF4I9UIQw8TYhBQSdZhQjQbLShshepgiNxCwBaLmA4lADyaAdgagV6l61ES0PNsdQ2o2nzLRVBHqcC4XeMAhETMgXSvXpABNkuNli+ZyEBmxOannDKh4Txxtvd1GE/YpZmtMX3jAFv3qyAO2SDRhB/lRIpom1dtttHxyihZzuWQcsSpZHplznZT0JLqjQmLR8hTUpmfVG+vAOvMeGNHKunwtJ8znBEQc/LojcwbK4RAQrRIIUQNidwiIICESDc7zLQ4IplFZdtWBUmpEsB6mghXTY4oI0sO/DSDF9EAE6iEAL6yHD3xMFbEErInZe7Hrxxv8OZWzOxSEoyqtxOYMDLTn2Y6jubmTyUXmOn/+kevknVltuh1bsvboDRr4eGdEw006s84AAshnyq5PZtGAYGzRkNmci0h2zWomAfTFPSdzrTH6UVdpwft3uP0rJWXstLGD2vfJKSUivER6YkT9ERE11MHWJcUErYMo4HVULEEROt4fENFjHEEZs//UhnIDosVF0dQR2T3jmk7QXAlE6g8dlew1dG1D/kCiuZw2Ig9v+Przxrwk1GlzBiTRuUVpKJrzxvsMjH08mnQGtI22Ya5TAhVu0dpEw2/YokkQhC2aEE0tGlozHiFCzhqeQSdPQYmbb4U3nGk2VihfDcEmpzy3IfJ2fV4+HCYkKQXhly844u2TWusIqSlCxGRYTnhWQQmIKudIBESpJIE6atfDbdHDOp2cwggvrilZmpV6yJyBA6mkEswnqwTFyNjIuiUnRq0FHSNrststx4nmi2rhw952jeRqrjiEbbRNdJpk6SicJSmosYznHiHV0l4nFqFQmhlg1wk/SLpb7wygUdMtYDvjaGRJke2WGcC8//o9vU7ZjCQJpPHUFCLa17/g/nzdMBTv9rPpdoRwi2eIulRE7YiIJosRpZO1lDoMxIhaSGJSomMcMVFEuf3aAaEXXA9GlBOW4lff9fgILNqth6Wo6igW4/NX8D3J8dKSU3fL6bZoyLnmYsvMPV/khUybDN9GzyXT7SRCD/aFG1Bq0ciB8u8d/pIrbaegbnu4pR0vvw1u05asAvCGbAHrnhNk8mM0kjQrqcRLPORDnLxL01z/ud9BNJyccvj1T5qktvNLL/gct9d0W8NjNj04JAogwghCCPp+AogXgCjLLgRBFAEfHBEMIXW8cSmBMFEQDAEETQWvCSEl1+OA6nA9epkeOoF4omIemzmKCNEJxAyZNOQElkOQP0TO6uP9I+8MyNuOzAAvKzq2tZFtLievisBLVOVtNHjyNtqGuE7ZIlZ7nTzwET6lnQHkGw58bFk0oBi8ukVrJtW3kmFCYtHWf2DRdKUnWTvIytc/S16WYHv//dtAZ+zXV4/klA2f7j94sEOcE0QvMGJGk7kZ8S9A4M+kq44aEP/+iRBQCFH1tA4RXe8/AAReXNcjah2kxl/7g4Ovde2a9notPfp9kMIl12Oix9IfkKoIzythMnMlA6rFIZFrUTn1fn+9JSeKHCiIID6Ju7T17LtmqotxMx5a2ZL4KFm20VrW6+StvzhHYPutwxP1PdOh3Eq0bxqwRSYywNtodxsy7tEzA6OUaFtCNCBcF9H6OcngRS3a//i6wpWGYSBM6WaxgECHg2KZKRyTBhiEgSCwXzJAEAaiCPgA/hX85SP41n65yyV1Wfex5XLp1+sVQpNLIBdyBZdhLe3zpx0WimF3t1y2N6vYsNrY2dodM3aRUYPhytKCIeqiSYxGbWCEnrbR8FNsBRu1GMn9uL3e2Or943tkYwvGFow6+nFZFYd2kIbMD2YgEuTHTDlSe0eK4nD+fa/mczBEO/2+b19y8uiJI5cz+D7Fkx8vw4ED0HXo9F823YLS7Ob4tgnS0JligdTRfBo7DQbQ6341JVlYRMuGTt5Cl6gzJBjWxCk8cdRgYLypfoyY9zluCUA4CN/PLhx13b4DpDTdCP1rCbhedWVqnRmztTIizEj2jo/9pn+Ne1YMSmEAjrzKPxTyZ3SPD72Fz/dPzy9G7+OLXBFBtgIchdYkjJohy6G3pXgxA1mesVtYEU5iaZ1kVYLMJOiPuXNpbRv7AjiyRvdayPbuv+m+AgQDeBPHECBehUA3A9N4ICndFAbmGwyp4Y93AX/oOc977kNKWnuY9tS6V5HUPJxfzvM+mv12RyoqsZ0ToKkCM9BWKwZtSaVOBI43MFkuZEt/OFBMoylv6IYVPppFnQfUaCiq0dh0ErI2OQWOqKhOsANXCL9OWLc13wvRVJrPN+x/uEMIZNE9FAKLP6brfR03/e96kVvbMwppw3vxCrf0qYEhRpuvIeDwip/RB5IVH9fQ6oXrjRd5ur/yPf4ASBW2VQVtZUuPWv6GzweeKUFnVoDT+RPO40Ue0SKfDg5ommZgS4BdmQKT09G8Uotv9n6z4+Vvk6daeUnHogMfGwSMU/9Il+bRFispBGh6g4qRNq3zdLSoU3ZXDzPVyU6SRuOBj1/ZRwOBOwbaSUFj3Yb/ELQVm04GDelawqHfr/3tmNguXg2bTiZNONOYAKE5V4xTd7YIHVSyqPTPoaaLN8QZw/Vls3d974NU2Urv1NmOjNH9hjuUJlzUPtkFLV661p+xUwGS9vHxC7/t3sIzu9/a3FvUEDrwkcdsp6DJ1urE2FzGoGHSFiQJBqBPTefhWfNoiBcSaKZTHmdNOJ8F24lfkRb9Y+A7PEBWrW+yiTVViKc9NNlUTjOdTvQLcjY4J6T8cgFqyilBOy31pDhvz4S2x7ObDba6aPNu8yfe6o1Pokqo44YuW4La7mZ4ym09sTZGNNnVgPpAKhX65LJhRFoMSHuankBM102jwaFzdGW9DV4HYRmiTgsGsAKlGwInwQD0BBzHj2gZZVN/BI3SG6bRXiY02lw1mppO9tGWPFhOTWf7iulk6PhtqtV01g93AsW/A5rJmTqNDhM0khWEALI1gZjYm+1uTZqO0AzUuKCjmkSj6ZGDJvrLThQw7TVUrM7bT6ol0ro3QKuqPNjs5ED2pFJAzHUzHmHbLXjrASHM5kEhLZpGg5Mj84Sg/f/5mdIbU6DNDTTZ6xgP8Qop55JUBkSjyZaR9obmon5UX/9x97sqM5Z3vxTy34FGNDE+yhDy9HCP1wbngx91/fiULRFP52b5JkELWk6v+gg0sd6xv6if3RmZ0inbgfMMX5SrxyeL/jPPzLw8ni6J0kbzObFfJFEnnAED0M+SqJNAA1bUR7OELfIEx2gwEJegTpzeMI2Gn9yGckc+mqU3VqLRirmDiQXVbQP+uA/mymi7SKNdjJqCVns8x0Dz4Y68M2qkd1cf7q4UCq/C1nICNGkNtFJSjWbPs1r7NqnSk49PT60VnEZAC/PtBLSVgcaMGWi2PtpsPpbeMNAwpCTQKDNLPtozmU720QgzGesxPwYfjaILiToj0BYpaI2WoNpi5VQNBmLQfsfWA2du/dOBBhK17s87P4yshPR0fz0GWjJY3ToFzXRYKQ2vvZSARh1xc+4mZk+PH181ndjZ5sP4QidtOhiYTQUDIs8kEgwcNBg4vATT+WwaLTedOgsqAY32GEg1GoludG/vqE9FqALE9vcxJP+CvPteo+tUJqj13v3193hA4XbbfQmadeSjZ1Ent04u2h1zKXLTyR3LmOn0SThhQNujDaRjGuiZZCaWzyyayFeFjLZa513WyU3LggEydQjEaDBwtGAA4EPTaT6agUYa7cgJ24Ut8gJIU8dLJbN2LeYMMGvq/HNQpOK8JDgcZgf29xIe9qbRWC7RaGcqteTZWsPiwe8+SFrDRPF62O5S0GruVBu6ajzqdHzaMB7NSDDgqDdgXCXyzRqtjXJtQFo5w9PbR1xUb+i3yQM0So0m7rnu13lijXacTG9kGs1AOxx4KYRnUmcGGsgpB21BoHUxaN3YD19uIRL5aPWwf3SRpErtPwRtWnoAbvckHBWgDYPfb34z0Jy8HLv00IQwgbc2d1EwYGsqvR514kHnLM23Suq53Jr1bLoRH42DudJ0Lgi0RQkaHAtyqQw08dHmHAwUPpqBBoRxNR1BE0RlDpQslmwaDTWnaLVlVyUlKMCuRI7fIo6QFDR384idyb8M2uWoeTg+fUZa1r4QLtZCUOc8PTCgOnMVgsb3BDTs/EjUqaBV3wza27sej6HI7/zuca96LNdtptFaFN2rh2zmktFbWa1TF0uWBFoJ2slAs6jzkEedcEc0Grx4MQUQ/gTqo824aA9fU/NoZMY1y2ED0s2C8kkaDNTEmZ9UaT8ctDU2n269n9Boa0+kgVlygxPrOmSmk8/dZDDgmyLqLIIBe8b22q9eAc2eiwX8yXJVtNQrs/mcsoMEr5CMEg8T4mY+T0CjEpQMMHth04mgqen8+pXHo0nyDAnD+/w/edErsseCmUQfNK1zhrR3rWUzpHvtT03+pHkHXuSsSjj7yUDr4fjtNmxbnIl5Zl82NyHp4SdAM6nsVHmyoFQTJNQJmObj6Wq0CUQSzI9ptIK0OOrs0qizGzWdzBjnzuiFsGnUWWo0FVRnfMIaDXmDysDXuAQFgUICGuuxudagGLTlgjdOl3FoVdNZnTbSYEXsbleclA5L0EwuBO1y2Hz95QtXCVKM5LvtCca1W++2D3TFDb0TqTW48FEWuHYlaNOm00ALIcGkKitH/cmrEj4H6IC0YoStbRAqplNY7KLxaCjI3YozauKiU2VgseLsVxIMxAMfE432NQwTgkPiUf6f6NmhsWROBTnqFlzItzwavBqpW5SFNCwVmOnEsVb+euN+etAccAbSv7m3NtSjxkkdkcx0VrGoxs9BszLnuaUogulhs2+G5jUfLa0MoLliQZWGcYF4ajK5N96H+DRj44mnLyRIEAcC4qPRsu8y2y710UKtk1GNQJvRF6Woc6npjUa68s/FxGspGDjDX8O6BC2SHw3a7W9r5zn2LKGx3Yx7767vn9Kb2OhPCIe26yih+3Yw0Aho0rKogz8t05PS9tsddtMaDaWLa50tazSeCLVYYk851LA8GppR2cLfap0hGDg8RxrtBTSapDfgyjEpQYGIj4Z8McjYLUHAQ+PvZ0J7s+6WhlOC2HrmzLtJjWZyCWiXo3b7KVJboz6anV3dfbga99Eql6eqZTLrlOtnz1XR3rVqC23lqTMSHUyamMfR9AbdUM1GVzs6X4rXBgeFnmEJWxv4aKCxl2ZjbWm7RDWdIKjRStA410uZOdnmmmOPJaU3yH0MCVuSMrRGEfCGSjjjyH8cNJMfBFqP38znwhxOgwbt58fr6Ka0wlJ2Y8x0Omp1HJxtoE1Hmqw9z4IaaV1SgtKOQBPAWjKdyxb6hjyzxYLAAsA4KoimdZ601pmnNww0YouDgQMHAzQ/PUSdZDcRLzOdCzGdVMDPfTTpDLc8UTvQYFMPnA3lbujjZajx2tQZoI0SNw1i3z/t3FvC5lAR3G1vvN2qKg0GStCEogg0C5MYwQw0vlaKuv3KYDAdetW+BJkSHnRriEX5jii9QQqMtQjPdVuSJcMGLaaskidL2KpGE9N5NI2G2omw4ho7+mghj0b+Gd04wYupPJ0kpkh8NCxC6Zw7qXXqYDSQsRqBxuHIGbSTGi2VCzXaNGpvabgPuzWO/85lSqOh/LXZsRqr4MXX61GN5sUuRhSEk1HQVJozRGiUvqFBtzowMhebN9Dpuor84k2hZuqjWbFgwRVOIoRE8mgadR7UR3tGjRbyaJzhENCOCloaDJzEdNKiCDSxc3QMelXG29RWw/tNBVKCBgD8V6C9LX//NRJtine/VkUWzqTCud9+YpTyPNo6/AtKMOQ3bOuiRoKBSdNpmYszTGcrHQ66JWWQ5NGUL91TYKmrccOrk02HlwQa6TXxz7QKdWLWyMECyUzns5pOUGUoHG1qHg0NrtUDpJ+tZhIN0C4Hkm0Jq+62qyyNGEedgyyN9n7TQDuWKABISit5BmgXo7Z2d3/qN/VdgvUom+NCX4ehog6bOvm0Qd1l6xLmoHE7LrnpVARLFvW3RKTFE/FakAS0lkAT0yl0Ue1JTOeCy086VPFoGg0AMo0mppO0mPhoKFzntMpAAhprNiYYejGdPN2ua6L1ECZqnYqc/7hF5FwwnYacgPIWae5C0FwKmh+rb949nDPgwwNpn++v4Gofa8N61HRakJmkN3ym0aoEtCQQqKIwS7WcKD39qBzOgYQRaaTGStAwuJMgkysDvCq3BpgE2hzP5gYa4mIazfJoL5ZHgyCAok7WbnA1Np2yRowGrDzHc84SQOtsxcf2ddA8vSEft1hXf8NHu3Ao92U6ra/d/R+BwPobpkwlbtjt/b4wnbHBLcxuNi7yddDglYEEd4SobLVaoy5f7b3FQbcFaDL8jL0zSWZg5MklqBy0eQQalaAINGZNo04aSHtAwXJ6AO1goMFxNB+NYJUT9NEENMrfdeqj5VO6KqcDH5OVqq62XM5ZI1bZr1FAmZIzQDuLtN497r+n8u4zgXpUDpoJX0gDCfbyWO8lf3/6eC2ZklzUbH6bi5bmzZ4e3w9eLA32Kgpca8EAymoRg7Zk0BYMWhQMnFipsSP2QpUm5kyDAbxxCEOEWJPR86bRJGerCk32CF2aRmtlVmuZdm5CcPXr1oMM6qINLhu80ff1u5Exs0IfTis26WM72nOjoPU98/ZOnjAAXfhPPULierFxtZUkNzdr910i+kkwwXoUUtFXPGzSu+p10+n94PD74JuYAnIWdcpkqgEvsEaDR0TVxaFWCdbEVAItZtLw7kKjtaLRVlF6YyU+2oJ+7dQtaHnHpU4bENaIMjxXjQYvFqAL0xuq0Q7wcQwadvOjjd4gpUlClCljXVOpDtau/BkTzipoEtNZYzWaSZHB2NTV3LPgh71i0teluF/K+ccpm/QJTMarmJtr584gzYeNE/z+8dbJT4g9HwND5KLyVa+gSc9AClfm/YealL6T1Jhl9BTL5296m7JXFR8gaW3px9EBhAlxnWzRs+DpKczcChqJOuPRGyhRmpYKAiCE19fnAjR4QqynabSjarSljq8ll1G2Tix+Cn0nImm896vt4Aen4x5TH81zesMpV0gMtIExvAzQ9BE4DqmcYsl41LSJ6raeH+FLJLV+eRCPnCGB3yum0TzWo35F6no2fcAJ/6DiLBjH9YQxdpXCOwym/SyYbwZJjWXDhHJoqpifKgPq9v7KjE5+u1MbKlWoFa/CwdtawClC1skyaTO2okSIevS8bugYaHiJfDRmEYvqhFfQaKdYo8kWA+qjpVvyZKM3tErQ/Y/85ngCY5gYvjYXjZDKJpoofbH0zBHSp1cYKLadvT0n4iIi9TQy3Z6A2753l4nv0RJS8KnirauRYGSnV7Bj4FCbxTkP+afVKfOAVZ3lA2tbsynjok/hqw2Dbs10rqSys0QFtmw7LDTKnIEiGIDmNLMJnUDMtEazYOBAoB0CaKTRLOqEjkCTTTPUR7MJxMp/01QGWiz/kG4FqQ3DQLCF9mIXAylZBGIDyKg9CQIihQKFgi76RG/5QV/RX3eHXUdRahogE+wgadiNhomije2431YGiJirvRNXEpSE73gsZJB+nM5QDvAeGEzrKAmmAmMNrAygp3Af5D2p62+A1gHHT0xRXmyHgSpzQZL7WJhUBTsDJoswxGCx6CB30HZJKqp21b8E3r+Z7jpOqnuH41czmq0RarSx3WErnpvgNFwKWP5He7IrkX0x0FWdi9FOxcDPHe7ZkE40sX2zqrMZDSHbimZGs4cFptGMZlvM6XHFYiNOcd5uFuzyR33N805bgxxepI8zWu6c8Yy2Q5cLSXzmNy2GzxZjQRYfCWNoDMTwYFjgcHjQIA4khzQXQb59koXvBfu9f6vTq9UrsnTTDZYFmXW6Xie7+jlclp/eTpBggpyQI0TF+GBZwoUgAU4DRdHSQFYN7H9Zu5qfNpIs7qwhwTagru5SugkjIhQlBgJWPr0HLyON5Fgjz8Iw0ewhHCIQEhKrSDlspDkQLpH2gMQfvfW+q7rtmU3ICxgwv9QXv36vXr2qemQ82deM7C/8PMt0LoadFHjpBsU6+eYqVG4gunvjWvbZ8roZRQboLoSUaAQ1jQYlGNE000APRImWMqwT6evVwX/46saN/lu+4HDzl7UuSzW4uziAb/IAYkTZ2nrzy1qhiIXng24FRSjCOygDin0IkEF7B8uQWhwUAggqo6qqQfvJzgCKaDSk0mpoLvXk+e9/pbf+bBV3kWrJqZay5bW7lXUXpCCEd1E7qhzb8WvSGa9DVkhLw6BScdYXHLKKbjgtAmJxZQAd7xZzxz1Kom5zsx7fAS+51EGp4P4gjq+Hb0EwGKXXVoHoxkeJAKBC0znafxPTSVl6bKuH3P6OvOXzylAzNqIDG+MSx/lOFMs1ebyOg5x3+5uu5SWn4Otn23a77Ev8w+RVf9PjJdUuoBwgSAavh+uE7fbfyA3xXEbOCKgFBGuJEBv8Hwe/tqEahrRiyHqOd5JWAzBrtxZtiNXisKkVcKCC7hKiYIRHjA8I6gsgrKXa2xK7q4h1piUOmae0G4AgNncHw0WAwK3MNCLxuOdEtCHxLA2m45w/fEhEfRWVCuclxognTZ+AaHx6mLJc6+6Na14o4wgUrKmBM6BEC+/ZHO06DarTft2lJAS1bJEBviRkMU7LYcI3TBdvWyiaZ+JVH3oMCXgYsfG25YQkriSEXO4OiArKEJEyYNAqRACXiw1AeE1mjGUwYiEUUnWxIVIL6BuB5AFyiwVfcWyehFwUwABoSMR4p52Be+ipHdRUL5CAKJA8dvn32zJJZyFlKAJqUXGKyDkTUDrumlaWCoGUU0EsjqDSMJ0dyTywuqKmky591DCBmU4SjC8hmYBoFOuU7E8aGRDLaZe8yFEBcQZCxYnXadxqbuVe5HHdFJapbO3CSFRENESoGCJIjkQrAniT/rexxGMZekF8PqMMRbSHmEYgQBy1xEUQyorxPTSadKaU7nqtJS8KbKq1IxGv7RhSGT9Gv5MyUqJteh+X4ARRvBvu6LinUkp31/ZUo0l+O9JoSaxzeZm8zmX1OtUZULOJxKGFfTOdEBnAu5FtjgZvSAgK5nIkvPERi1sS07ksczSQxSbR8Ce74hFfKQvI5oN4QLwMPeorRvDv+MURQjWaIVTvlb7lBDFURGnVGNOCImkbRP483jsfVfP9iGZNheaU8geuirS75cwHa7DHCOdFnzkyj/48ICrNFVSQ6XWca/3cupuvDevj7jUXkKNxJ6JZ6mG0nzZH40MhK0w0OCuAREu9TtNgcv27Ec28TjGdnyPTKUhyBuZ4nZC1ghZs5+5J0eAvdfht8tx5HuKtARGNEGWU21KotDWoiGgFIISDCrEywC4iwiU1GYKIhhAT5xzpEpgjfzeNVkgtpm5mdJckBVE79hhhRDShAVGEb+grR4ihjrsxnpWsNGTYsQjnLKKxcos1mu1/vWkSrbmOZkQLGu0zEe86fHxhyNw5mqaFouOmtvGRPmcLJK3p64CWrfiLZJUDhDHkItMRdIDYR43WNx5iynymWygD0igOCaEa4CKj/J+Wu65N1bCULuZ9iyEst7mwg2oxJtsfGmoptLsiRyPMkAei2e0YIb21ua0hwqy/L3b5XIClDtk7IJqNuwDial4OOyI4H+O9Gz27e6MXGFY3nUt1ognT5izYikYjolEIyoj2187Aak+cAXZaFuGTV29TkcySloc8+3giCuc+aqMU4UaZDo13jKgAofJ+lGVxNs5mRk8oA0URC1SIo7IPM5RxAkmiEd+4dzLprofeHp5KxzhxqLajxI5kk2wkTMPugk8Zd+b90QgSLbI0Uot66M2EemOIewgxAroSnk553BHCCi12Bjoa3WHypc4AnxZYXiFnQDZa1J0B8TojZwB0mTgDTLTriGh0DvlGiHajROulROM7xOGHhlcQZ+11rQNMlTo6UXVEJNnVVILj44Ns4s1GPt2uUKPtWqYxogkNMiK4DM/MgzIsF7og2liNyJkRzRIdf48ToVF3lc7HpGqoIXuAMMARJN+HV+yYx0THwV1AhOXK/SNDCOnntAyHvZkAZGzdHcq4p0+nmRLM6CmqgYICEqPqwYceJsYPyTjGB+3gdUUyXqfLG0GS5Q31Oq+YaJfmdcYLtsiwxhwtSLCfQjSwoOIZdOo3CVEKcZFp9jGkhz7IDkXPD3IkyRuZeo05677p+wGazodvWk4tyGkWxLzGARHtB/kf4wwETSi9Uw6QaNtRQ8A6k3LjycuguCXRzHT+wKtW/jgbHUOfM1BYVAs52ZZC/owaPPKKCERLhqw84DEbE8KFzijCEyRLbSMgdNwT1uuwDsBfYBHTaeto6nWizDWdQZpztC9N04mxTjCaV+J1XjLREmcgfADZqAaOdTLRJKFFRzWahdNpI8dO6PDGa2XN2YgTiUvvnyFJtl8L4Hw8HtN4xIhXgIjt4rFgrIxHhoAy/qAJnSHuDq0Qd3KmVHZeIbc/enwvEM0ackiZ0o+yCyHNMyCJtWOcTfEJm6j+dYiwMlzrNDuAZ2I8OeIxUoQK9IbEyrjD1TjhIz2dOiKJM0AZhyOirfLhlFWODCyv6Mk7iQyA/ylZFOPDKXKNLQSgYHlDiPa5sWBrao03HIUyanO0lfocLTGdndocbb+lMjnE8Z7oo7WJE/l9z753Wct2zohXVkZAfsw+GEbLsFpml9EGiAz0+BCrInVmkFsz7Xkg2staI/w4O4wb8tjacTId49IFKDZD7AWEMqK8ILa2xidNhPVGxHtG3JFxbz6djiGLqbAlpQ1p8AddBNoF6fHVi3hNcrq8gSoNPjW7HbPIljdmE+2yTjS8WqjhDASWLfc6s4nWdAbu27MmPiM8nY4mC0ASRfiUJIJ4JQiHj+ZRyxvGyogFHt4yQiDRftY3Plwcj7KDqW+lkBny9US7nxDNl0FvTeJaHnM7rMcX2TRqB/iU92OLdwLOwEWEAOMqCOtNWkY87uet+Ol0CkmE3c8OxZw6nfC5uoISGMbJOmebzhuek5npvKybTljegHfi/WgyURPTSc5AbDqRaLLNdtGIBoJESzKmcIed0Gg8hRmw00l4ShIweDWibe8j0YSafnRomLSMVKO5FLEAhQj5pgfopx3UIEFuybR2TIGz7BQqvECT5aUWRJQKOvdHtXakNAqtzEAOXA1hMmWErNw8fQQazcbdns6kGrtEMl5gJ1OaeJ0rvPGRvU6IsEtOCxCKHhHrAn9qRLsiorHXacftUJNZZADKscgA0AzttuRy7NSJZtvvJEYgHZZOT21IjCQ+0nmuTjRIZH2f05yXraMDADHGECnRsAwrFBDtiIsOvb3TUTYld0A12m112u+JRptmo9PSfciaRItW/IIyusBvtR170lJ8K5tMPkBbJ1PtLuk8Hym9I0e9wWp8QAxt3FFGhzawczUavlA0nedoICuwP4hMJyfpxNeV+hwNGDNXo13N9Dpl44fsbovPDBjR0E+JV19myp0ZpvNIVFrT7M01nTq7Gr2H2NMM01mmZTiXItpxNRcfaZ59hq1SyBymfQ3lEl3DmiY1nQni/WF2CD5pajq5M456coEThsmZSxCMl960sDeCYKKpHB3owBqEmYVfbMHWvM4OeZ1/4yxgQIA/MZ1f/sJ0Xl2K16mTOkq3IrHOJfqEyvTMwKouvTTv52VdTJeJotc5SH0i8DpHyUReEK5JNEN40RIskyxBbCe1uJnOQFqNd0DHBHL7OxXa3F1bnh4dndScgW1rx8lodGprqjbVH8Q9QYehpB4ZohmfAgSXAUSzQlx4Oh0BzPd5lxBt7vIGWMr68sYNES18bRJNzw0Dz+ZotJubxHSKRkMOp2cGgicQXmtzNNK+Jmo619XNPg2TFo/moAxfHa8qEMIU0IzlDUT4lgeiqTQQImWzDCAaQDwvtb2H+h4gyiCgum5JtnvYGZaTMbbmmJY3dGnC2lEe0NK1iROEGtPDbIxYUIsuLgN/4t7E41YCYpiM+xQfTBTnpCEx0XghlLc+kqz2kuWNFTo6sAp0ALcgMDAxnV+i5Q0klFwedNUgmsCu4WtqOsPL8pLexBfqDmoViSZWHYjWuCFNQlBvnC5QHtHy0Zk4XH1YsIXlWH/ecrC+4WHA8It3hKgCjQAB4iIy6sSmD0SDddJynvntc2TgDZdwAu3wrWNTNb4PkYGvljkLtiT+aDImA3oqje3zYqs+eMdpYNwFxMswAwME+08XExgzaCtpPSsDpYTeCMK6O4zH3UdPp0IappO+5aA6ZUzhpGPod+I6Ggus3uM9af8n0S6/XEVLZ6DDQNgrMNO5hIKxB3MGghtic7QevNRMp3mdlUVLDmlim03J6+YQ1HouCA+gbKKUKgMifwc0ihExkagMRaREKw2RhqAOgrMP0/BjaRlBvk0aISjZHjTODsKa/sfsoIybiggSI4C2wwJM+nBmU2prKWW8RISGg603FF8ICAlBpSILti4KQdUm10w9TRuximcG8AoMyjNgzgASDUOcdNcLfgPkQdGsUNdXX1pXNWeAyGjLG8RWZG1tHW2lB4xLNRoHBuYH1cuWOxmxI04hJ58ExEt+14YdIsS4f9aizEYkhrh6YN41NNr9AokGEN7Tyu04Yt5CIdW3EW2hTjSoRYh1hLWMThzpLQ6qr2s7DtGe/RFek6C6lUGaCuVQA/NANNplUEbR0AnpNR6yvWTcUzPgsBANQaVnI9EhAMeTIwNBejhPW01DUPCNLthep+tol01nIFmwhX9KNAuqG9GAxpyhp6nRGkZTNVqXHi0Hq/AnHzPYieBbJT97FRANECUbQnoxdVUQjapdl2zfaiLoGU8R56wlmGiqBrAdB1M1xQz5BhGvNN4mBE0FjVaSM/CeGuJkV5S2w8Tzp20TMm3kTw+zydnU+ZarI7gUR72xvuRAtG0bd+diNQ/jrkQzmRFU73FkIHxJlzfgcgTICZWYzohosO8Mkqjz4RQ4C4XxdTv9ZF7nknmdZjrBYvfM6+zF1LIT1Pam7LB94ZQApb2c93E3/xAR+KZ49SWP0Fa/izRihGksKcud9+1UwQskVioulFHk4FJSNWGYca+q96g/HyBoiwq5/YVsoNGsqUZ6riWHWqJ2sEinuR17SRkpxhCVInzZrGVvx8YdayqpLc66awu2ekDZguwduh8N/U70A5YbGx/h68zIAAj8DN8ju5oarWE6U40G9UgIirxOOUQDxNMrn1PBMdn4Eef5JK501Ofdrm45BQS9m+yO3C2ERoZIxRA5IKCAFOV3q0o1WoUQIbL8jR5AQ2qm81vNqHQmFeehqbLf39phnbV2ICLPGeGcPhAOyyiUioDw6ZOHCCQazI113B0B4dUaMqxxiw8Q9zjCKQu2UQgqCapTDhXcISSZ+BvOwLV6nZ+voo2PdLRzZqzzS0I0OALFXqek4a6bTzs6QOcoHtH+dn7xqK3CvEiIVhEiHbWAAEBONMq7242DFmUoIzonhWWkFikgqodwWgOJZg2JqnGhEGDrdyFaW7ob1eDwGYNaCqhFEKpPHZNNOrNniFTKCFExwif99TBkpK5qhThiLVdTKdGidTQ92Elcw0+MdVKiAd69wcsbnFDRTKcF1a9xEpYQLUzUom1C4A7o8gaIbROCYmkxje7LBY2GJ9WDoFWP7XyaZkxOhtVNwWs60ZOzLSkIYUrPB0QV0SiHMlyjjLwixOxarAwgWgH1BIjj3fNaiEBuK7/ffTLE45mVNMQn3c2RaNIOMWeGMKIV2hlf74xR0QbkAXDZYxl4RjWwSCHJYQ2AYPV5vE0IRM8M0MH0Fc4sQFu5l5bxalmiwQ2ZTkoO0HQGmGGovmzB9jryOgGpISgjGl/Ax8kayRnAHKKoyewuwXmm8yUd4c37T8vo2XwBKogP/ezzt/2nkUrberEN7+Zw+He4j389KMP+fg7KACEEfwsIFUZUcG63/VgguxGk1EIAcnumtR+vVXDYqYKG4BSfG7IBVUh3K22Hj9thCABgGUYzHxAVI4Z4xDin7tohCC0jH/xLx73L4+6jcYffBMis6/qAaBQcwA2HK3ZmQIm2JERbUqKBJESD6T/kFqDLksFawi/sKoQbdQZAbB0NKgJKgyKlrLAwT4tNZ/hQxqXSr3I+vb/7ho/m//hMrieoHu622/1AoQjhCMHvdR/uLjxY+4QDXwXED69aJSHWqq4iFvpEygrLcL7lqQxDIAQAVA2Mu0AKgHz6DW8fegKvt5H+Q+qY1ZI2pH2X2jEfce8eIEByRDhG5PReQNxZDIgiGjIv3c25DBz3bjV33ItPv2m6n8YyRwd0CZqs+rnOsE47i2hEM/r5RnL0pM7AZewM0G8BhmLLG3iJLVQBdfF+tDrReky0Xm2S9o+f1raLvADpFtv7/fv399e3C5GNQVgceB4Q8Gv4fESIDUM8/Glh4d9/lzdyKWMD4fDOxuB5e2GLyii6eZELQv5HERCBaFv723AxBQpAfg6QXCGf/rkQTB9fcfXtCx3QXe0MNuQlN6SL3d25+3xnPyAa7WD59Clckrazpp0rNhoDMujdWfwVaqFCuoboSi2wvkmQHBBUjY07dteIdqfhdfJJENRveDiFE3jCP5BVjH3zaWJkSUOjBdsJ/OKrRb9ijkb7K+mkOs3RQNTOz8vZeed/tF27b+JcFl90wQ5igvQVaawpJtKsC0sUNCFIFHaxipCQPmXFTCJBRBMpq6RJO9qhSReJP3rP8z7wNa9ZfmD72iZhhvw4z3vP+fXS+f7127dvsAF08FVwPTSA4fXXAP/y9te/XoAko+prI64NrVy6jv24viJrI653/JLBfwa5+XP8ehle05vX8Q3eJUEU8IFE7wOw+jww7dvi3/BB4TX3mcnoO7Ii7Xx3V7bxXcqh0Evi+P7j6kctqU7CjJKbKtFQX4YSTVWndQa0VvKndQYo24ScU9UZEC200aTjgNhoSjTEF8pH9Dqw9QMbrRtt3CG9UlztTCkZnHq1D0mKtOPFkk37nyM8VjOgUla72c5pL3cyvuiQ0ZZl9h4hN3qc+xdmVa7D0yXa7ci9wXZH4yFtdeTeVWn9lyYzrIZreJwmXLuWujTAASA7V7VRhhFsNSsr5DirukFPYp1cyCuHyeuEMc3XobmulzKJ40IVGz6UHyyWAJ9OdZJPgBItpjq31wzUVCcAlTbPuVTVCYxrJNrzvfRmYBR8CBtBzeHJXAjKPvL4+T77C66Mb921bUpm22eZ0IpOdGd/CgdEsXnAu9cZ7ODu6Wjn5vne+501WoUXUsdAi+d7KetdzWAwpHFB9c0LbsmeFFrxkTe4Wijt7FU9uFG3Vl+0mtWI1pU1A8Q13JRoHElTbsnMMdpvSI4JYKR2l9SmQnaR6lw32mgo3CJE0xkcqDq7oUTzTMvURjfS8Y9Wqt9LC8OHueumbpQ9erSSanyDw/x+ZeRaQESTxfgmVDPhPX0xvVtOdXODmt3jWyPnp2N8M088sRgrqwb8y92FnEcKqG9qcrx2c3UDDJSGoX7ZbhBpggI3afLZKQo4Jy4e1NKz2wHdKYooUJ09X3X20AGU8AYrtLjXiXSJ2GjErt9AtDXFZwnyOmbnBYlB63WKGOthgwFp4KJJdP9rI82NSMhbyf4+g4tUtTzRytRU3Sk3jDnKn9wIGQIw+16XzJjBeBAW61YYFlYhPFJmsVvRN1y9NfQ3aB8G+tdaLutORWcug7wmB8XTzWHwujQmSxCtWZWktrenfqypaFOiVxOKopCXF7gnftI5cLHA2tVMxllFyhZfscMZwPRPT1aq92DTtorOGVBDXojHDe7WRLQPSp6DRAOGIecaJFrM6xSQ1+lSUFJi1GNeV2T13QtYBfI5eXBybd8f89dPJo1Z3Sv3/u/IyNbLXqYZq9TTcffTRHBgn9CgQVBazbR2ORFFgeNa/yN7ogcHaRlFAycH8O9UzbB+vKvvbRt7aSqqy10H3Ep1OKqlDk/cOZJtJLzxaSXaf1Wi/RaioRsaJ1pEdWpdSY2jhSmo2sTH2xEcpUGDj4Sxn2i3IyXD7XjQPheEvSOwA//QFchRbJ1CtMSwKzBMxLTLxzcFkiwxfotPGnfk0VIe6aVU6u8f1kxl/IMHYa4T46SY3yTVab3OS3hc8BQ0VqE0cBMfN7gBQhuNXU0mGq8kAJcz8DpjRKPALYOdgTCOFiVa+eaap5xANNN+frPjVSUK7wwSjVHey1iJcyjmUpG+fEM9mJxAtJT25Rs1pGKsqtSQRAuJlgQdyOiiRaoHuU17PtWX80bqZ1nJK1yRZNr3EKimOGKrK9WBa3YGhxCN/E2dk0ZugawOJkKtyev8LapzHRBtw0RTivrhDc3df8ENPWDuWKvz0SLLBQDjGzQN/kCijQeWCY8j8SazM4m0bDKi4RxPjm02Rd1fxgP40fnpS/bGA6VdPkxeZ85GI45ZieZkmiMan/gSrbVPor3Pgks92+SOcUnhDW5jApttpUgc48TkxjoDsYDtturckmjwEKL5i1NAbqIYRYLjRguaSdp6aTLvi8POQet9SRTTLkgO5kCivS89ItxV7TPxLDOGYnKjidWjR/mfOR/el6cun2Lv8n0Z0u42sfFG17bM4Mg169QTFV/eYR/ROuNbX3WqVdYTr7NPDccQHECTZQLNRNvsIRpWFKKV6myjfYbOANbdkKbqMvGRPE+UrKgzbZpMm60FXcLvfoKfUyQq0RKHw4iW3f0ECaFnL9P22cARlrLEg+KYTrUJbuwKnES2VFwBH6snvWclGmpOEWn7EZFtQkTh1/LNj/3aulUMdQY4vY0PdQYwqKbOAGHje50XSB4O2MrctPU/1njkWWpwSVWnWnZU/t1JtC+UVN+y0foS4wsbKKvqnIzY3TnJ61RXgG7xYfRIIdhzIJP3y2r0OpxskxHba/MTmJbDNhkFPYOS1yl/Sum21ykqMu51hlX14pCE4fuUwp9OooVxNNdCkRLrcYmmVAmdgQ/bTADDtIGN5lJQUdWpRENdzaqTl6rbcg0AUZ2OaOUbfqvqRGsdaKOZ8sn4gqW6Q77NzfmoNn1kSXYS2conVaQ70CzR8vKNfE5ArroTz2JEc/3tWnH4kdtW4AzoqehOR0uVFXbGo1WdX/aoTtrERpPwRoRotJKgKam+sUl17nUdOAMk0ZwdKQ3+7H89SceDQgjGh0SNW2ejNVpnlJMa/x3IL4zdEyXOhqdV+ygEVtp4MKcGP8kp+rNNPx8ahqQ78ffVnAGFH7utjb0X0YGGfComzgqDAgUT10Gqu1yC/6n1hC53SrTPJmcA2SU2Gg7hgkuqs1WnRPuIEI0rcDjVqUJ3W1q3qmUnVJ3JMV6nYVcguHG/ap8LGqw7iWh4zKsl8AyHJxpq1XLop+mc7oQttUTTr21DN07CIV4n7d+nauwQt/iOtErhtgOswvpiluEO1RynoChc6yQacsfN3iBmCdHERqNYrV0FtVEbzfZgrKnObRsNqRax0Yq7n5T1bCaagWecaCy47h5lrBiMb8/MtOnr0UzTiSB3j3lyakUFQ64Aa8zQ70xZkQYyrNWxIQ49MLP8a3uIprrTJ5pbcOdFb5FekH3iOBps7IRqs8MLCdjSdoHCTWZv+KpTiIawzgCRTDcimq5KEaJpCkqJ5oLKaqwpJgv+WhVFjWiJIk60jJ5msmDCeffKe7hzTlSr7Kh3cBJtsuCM5alEmyyIUbl/tXziewaJFko0a3Sp9b83jualoOwlCqhvv5gkB2603o5tNJFovDhFJRrCep0YmUDBRooUDkg0znWubRxtbVNQgCAF9bGJ5zp7/cDrrEk0PJZP2iN3J9GSuEQzWbt8qk0KwjzUGWGywZFvYI0qcVxMfppIyxMgFd7Jfe05WYi0C4jmcgA6iMMpVDfc6ln8sKiFN7gMQt9KNCoopO2uRaFpZoBJpnIJBg0pKLT+uTMPyTy2/oVa+Cu4HaMUW8BlAqw60SFBTc5qPeZ1FrBPx38Lv4o04eSJOgOuBa/L9ERyj+Oi1o0Y7OP2eTGr2kfBpgXGhy+iimcF/o52eVeipQBPoiWxGRzhJTLsAGndGXAsHd0ygZFo+sdkacYVlMkdQIrxAkvYkHaU7r4gym1cUh02WTMAO1kahclzTUHBEJi25QwwRYmvmoLSKi/IcJ4Yp2pd5FkIcAVSFuTNEq2ZaECuahmZNgRZovOimp3mDlSr5GSiqStQR3lvnQHiSEeJhluD6tzrDGhFTnyWz76NxmZQn109VZ2EL1Z1sugJnYFP6wxgkJadgY/1Wp0Bno/Gcs6z0TT6RvTSUo+wk/lo+Oy7+Wgta1tqvo2zT+n0kT+OQgnmJZ6MxY5Q/fQhdvmuglvngxlcHamcRftPH405NEFmBH5aYPqoOtQHxoDnqJ0l5ylqoQbWjqFPkO4kmuweRjru9VCUScNhABFNeUZC7II2ZgTVaeT4BulLtdF0FRQ9fRsNOAeQfmQk9ILldup1SqkitAkpA9VxKaiuqNAwvNF9WKhpimkTX4zRrEftaK+oK87JIouFPB6nlmgmcjujIz0iyPLgLMqA1ZFMNog2uQJ15HGTrdbEOJ8s8iSG0SQxYnKkdaLRpFpBJ5zE4YQeHq0zEHoDraIzekiFojbX2e9vEQ1dQY2jsUDTOJoSDWQUbW4B8TqWVCfvc+07Ax+BM8DcpRCKhDi6nBmQaRuhjcZb+SQiPnUSLTFHpDrLBmts9Oh4dSz2/2CGGdW/8Jgd+PutK7ALJJcadafJh+AKRFGWQyv1WnWixZ2BNJwiSR5qLTMgZCvLFgsIcjUxRsvRs1CihZkBJZpKNAqgOaJ9BkQL42iiOgXELw5vUApKVKiQDMAFAaPOgOKqC183sdESZ6MlNPvADJujG4qrYRZlQnVHhyzOF70Xh9nHNQP8Gt9mBodHeQNXQ7MDuOhht5V2VSQRaP4zJ5K58IY+UF8wh1Jx8MVqiy4ODiH0At2J8Gdv9JxEkyKP7HVyHI2SQxJH09DGBcfRyOvcSNS2FkejzbfReO+rTo6XoOqEdyaudTvM/n63QXU+LVEue84AS7ZiyGog30m0TAJacehcnhiM7jLa6sj0VrPEWj2ZI0NpualWsVlF7XlO0yDne90BcCWiyFF3ImySM8wtFQUpxJTusLCSJ50ULfqLaCgggF4fPWwV4kPS2RKLHN641NkbTDRwES4CZwD2zhmQ/BNN1ghynQChIGfViZRBCkrqv8sCYsCl9A5lryCIowmmj/hlC71OXDo7RInWskavURzmCmSch+I4WxPR9i0tyIQekTtqBR4ds50+7JuuNt9JtOlDEo2uoe5MTJ5QVMJlBhQFnDvShKPW7mkcXb1eliIjANp/rA9wPEPzHI9EMAnghqpzE1WduImNFjoDQrSojaY1OFxSvd/1A7babMALBIrJ6YhmbARNo7TNhvlkGhNaJshDZSeZYeozNL9idHtshGMyPSRS225k2mQat2CH5HfCPm3yOveZa/IH0MyUQi22lP1OvM15RO4fQSVexEijtIANb3CTQyBaKNEkxKFEQ1p5RAtVJ7wiJJoKRaKus9H6OsM2bqO1aLR6alnox5AQeJaoJFviYQ7KClRZE3tMWWaNNDPNDFLNuTfUX5bHEq2s/qyc96ri9eoxLCYG7oQ2Go0k+tpiBLMbda/XiohYUxegtUDd6QK2/jQJXgWFGQFWnT2I23NVDFBuLmBLDxVtvo32QUrUT0HFJdpn6HUGy+0um1QnHjvP/6PsDFoThqE4zljrsMcWHu1CtDpxrrBRHTC9jIGX3qSll8F22zdoT4Nd/OTLe89nLDaCfzUS+eWvL4lPbEg7y3VHgA8o8BlWGe6gneTg1GpZem6tN+CQBqiWmLAmuTY1h3ntIbF1evy+EXHFRCtNMNoZTHZbe/PZpXB/6G9lv74/3rcaOh0pKrSusLsz7G0odJ+gGuKMI0JbWQdov4b211TWA3BEzV0WBuhGm1N48dMuQfHlqfen5xi1E40O2P5JRjPaM0Go5EKW3WLXOY5GWdaegM9mNP5y3aUqDkWj6UvxqFQUBlwfB4k/HyBhGMKekHhVXIuIWFwY17Rp2CmQpsacxU3vjTkZYzESAvmYCDMXUxUepZCYEkGe4027uC6lSbhYPpy932dWEyFSB4JeCYjYDfqUYbEulYAmXPmo8aFplOx8X8w7BInNaTxi13i0TfJ888/Z1f3EcSTxJZs1ucWHpmda7tnlLj4UZMwlQYSF5HQr8nKbVQRitY9nHhAICQnJkh+wZOmIX5DuAYk/+rq+uxkGvNdebLNTXTXd/euqruqv5GhRQBdtPRLTSWchqOlszgzc26Hvd/kYDeJmNwC021aNhumLNBoGblmj6eF7JR7OVW3R8V2dzv5vR9/1+Syy8tWLSBET0EQKOCmOKGr8so4U7Rrl59HHa+JEzENQ5iUy/1pP5SMKYf6avquBYoQMjCJ44lEBwfX1q6F5C8+nWJgK+dXADQ6d9VoYfKNupGi8kcmLFLy2tpmW86xwNJzDrMp8ucnc7//taCuyJYq/9v4xwv8326OOH6DgpdxoLCm6AQvBAGe2WZjjaJTkeMbcGVCNdqeXct40TGc+RkvO7QCOqTMAPysMtBV2Bshk6l8bVPSyevW9ntDaCfuvvqUD6OrtF0RR10BhR9ciBT7Ybh9mMfN+9TtkZd5ZVqEA5po8UpTxgxQRzTVTeCJA8TVw+Hi9TUHbL0uZPBeUm8nrAkXZz+U5T/KoQtgZaKaNg35VWWEwu0uKO1huFFfF13T8Y2yPqn68Paq6BvF0Rc8KLnvsreBOzhxofzagIQqaXicmA5o4A+ANJHsGGIq85uMbABevrDXTufoyW1jLToAt6qQN9phi0csqdrBdKJQByX8/wOp+PepurFEX5KvSlS5SAMnrQftwfn1EWXezE487AbIC83eRoiQKex6ARMQPuspDhZt4pFjIdCK3KnKLjLzhNpdXmzygyuWNum1puIFZ662/yRHu8K+zrAA0qcsk+YRiiSia7aHi6ZgNgBuMzOTKft5A/FU6RkPTaXOdqI0eBdotA+1GgIZfPli9oXMDWRwN8Cb7Ovno5pWeXEGsyTTamnYwORHa7W3WDDSl8Plh3HubBLR2pxAarYKsmMf4Q9aI0pgVG75W8V7Ye6Ao+0DB4v0wIfH4gtXCQFNuUhK5Uc1FeXWUN3oTNSjLU5wFpqioQpZ/anEHoKZqZO6IO7EIsbhlfIJAq0i8T6sDKSJB9XrQ1h6OKGJxaXkQAa3X48PRQJ2AZuGALTsDqtEeHaPlzgB6ALnpjF/qGO0mH6PdPxijYZhDxmg99Tp7dhofFexgr9NMb7exXl8wBSFA654pqpaWpkjUOmUdZhBGBm8H/RphdNCvTbxriD8gaAyMBwg3iiry6C4GtLoGeUHLEpxwq6VfVfDOrRXSmtDqamG8lchT1negr6r68doeYKdtbQ8H4gGsiek0oK1YeINmBhBosj+JgKBTUKraUqDdCtDM68QfRhbkQyPa9DpB7uoqznVmXmd26Ejv71Cwb9ebF+r42IfiyEFqfp36aE7nI8WTKmWdmVu+oeTf24JaGxJF0NsxLKF41Ggl8NDHZr/d3lbE+WihKXuSZ6ozl9evqbgmj4SZvDLqvFagUVbvct4BZEXmJRhGqxAjgtI7qBAGWt4eRky1vaJAg0l1cgZWM2cA/pExGjqd6gzc6xgN4ZRPquOCNNNo8AAoWKGJRuOZ9aZGa8bReg2NthPk/pg87YpGq3es6q6KQDerBKYYtM+WrwPz5B6I93IJpWQlCubmgDk9NOZd4mHjKU8PjcdCOxOImzDzeiVLsOJm8ubjKdwQFdIKaUuclVPIYbJLY7SDSDEEyVmXdshcgFYhj2Z7hF00nUscdscpqFVICDncm57H0TiMRg4ogiP6ixhCw3SPGs1Wb+RAuzOg3fJCXLuOWMdoeqEFTkFBoKU5RlOgfSfXo+LNW9MPZx0ntoJqvlrbt4YZF9pjfeenbcDCE81aQ1ZJp+OiSAxRTUDbUgrvx/rcoXikqOEF6atZUXwqimKi9gbFL5BUnocfKO7sHH4heWg6QZ69MKTxqcp7DmhQXOcx73xcXBzmhVna0ArxWJhiCoVxwXNtvwOvk8QTFXZOqzIeo8kKW9rNmTkDcgoC20s7UkhXcN+LSoPNAbTq7PMfTwHtXkynJuCIihMkgb0G8xk1LZr1bD1ab+lPBrQd7V7HWPLxmfSndQaadtPJSSRJlfr6c6bTdKHDqwjlF8qKFPi7ME945xSdy/hCFwWmiVLUi4/RpDDQ0phOlBuP0XaMYD7sTMbF3AnF6yeBtmZ1eTaeAu95VpglYM6K1AqTVkhPxTv88do5fSAK3ake7SabTrhUPTed3zRN531uOmFYRqbzDoD2RwNoQIJAo4kngiglYEk3WojXucJznWI6Vx8znXap1mHx4TScHhczAcP+dwA0objsTORmVIt4rT0JNMiqZuQ8zwxZiSIQzjLmXihA/KXYEnlsPBYznSoPjPlJMT7puMOiODV5oy7Ic3LR60UI8GJjp/IQaG9agGbF9cexLjuxLidpTS0RBZuEQm2sMef28NI9/QzqxNojBRo2pwZsV58FGi9Du38GaHBuMu2NAoOZOgOSaDFa1J0NZ6AHyjUfo6lGW9vW2+wuxlS9Zr9+QKABBTX+ZDKRDsbjjB+i7eq2Nitk1cDouDjBzMZ8ABTSiMrcah8pSLwHaWcX/BCbQijcQkAzeTPSZfPiqjNkeQi0bX2hKZm+T4XKgwrptk1CUVbnsFMdE4e504vqAGhr2+KOSmGsvCEyXzLx1jldWmXgyOnSG73Inw+DB8dTr7Nr12gYt0iA1jSd+L1oNPybgiGpMxAZPxbeQG+Fj95IZgc2Rq9Ug7AqK3QU5nYBaEBBNPQ0c4h2Y0sPW5sVsrpAOT8U70MCJAdZkYLjTYGYJ0konPh9k5k980KxiOVM5fmi8ASGmckbfb0B8iA5d3Y4oSJfmLwnTafV1FVxAjnc5CwtzFKkCBhP0cKYN40U0h5AErhzfrIr73YhvGE3WvTUdPKaboii4Zb1qGNo8Q7ddI3gQ2hE0KkFTDXaXeJ1itlEX1OuwMA/nNh0Ro0W2QnQwBmQqSczmcuyY2BjdJSHwN376Rw0nNYrUHDV+0exMHwCaEdiSvz5dJ5k9pwVKSwZcx+IQnmg13p1Mp4eH+rtip5x/uUplTctIpjQZKs81GhHpDK9iL0qDhN5IzGczQRZeRwwK87m408XVxoTcgy0I2QqhSmOD5FCCwPtYQO0zofp+6xnp0AjX7M51wmYykwn+YiohQQu5AywjqIja2Fy09ajWRwNctw/4gx8JaaTL9KIN6cQxgxoaYpF3001+ORwVsztxtZf+wC03QwLU8UCUQzaA1nro10voBjPUiBxVqJwypGeG8W3EN7Y1XHM4THf8x6IHHksMqeeybsozoHzVVE4lTdCedTUTDgvjoPNFfVHrauE3sSa8sFzj4FXnVLWIDX1AimQvRUGwK613ctq+7zgzqnt8SC8AUjDIbjtH4Y/zTgaJMSKeJ0W3gCg8RSUAQ0NKwMNM0FYRMdoBDTQaKugMAHm6AP8Sd6seZ06F8wJbg6xHSE5Kvq7N0QRmCBIsxhanmxW56iTz499C9CSYNMwe+6NwtFQbwb+3/m4OHQceEOKBZLKo7KOzz2EDwqTN+o+6FdR61zZFCxQyJR6M2FWjNf6yPR9J9CrYnLwqtCt4TE6IzNAkVJwbccx8y73c2ed01l7ANAo2UaVXvzYsXyg23iuk/d2yt0WqtEUaGw677IxWsTcA9N5T6ZTkuxQocugANZot/FlWkwnAG3nwUhhXszhN2+mc0dxKFhxRM7W7QlnQNz9CcaiMjMQdnGCieOXxtxw7Ml07ujjqw8g97y4wPw2RPxi66nyUAqrlE+FyoO5TpMX/OmsmEG4VuS1mE7GHmTlVAA85VVJIJnOHceB2BALwxTGHIC2EyTMNz/uOGCFz72YTtNoUYHIXCf8B+NooNMQaGkc7Z5GaA3TeSNAw4CtaTSeC7h5zHTy7SlsOqNQW70B9jy5liffGG0DWMezzKfFWCYlO+IMGIlhwcZo7aZTsx5OC0ifCrK8wVFWpQgKNPUVjIKFq14pCqx5geLCzoCocIhPj+dn0xlylTEaUgQOho3PCZQmb9S+xBayKtAmeYEIaEjhbSrP++jSBpkgYAqxrdY5rT00YNujhY8vG1NQqMgeNZ33bXE0MJYy1/nfmyRgi4fbQhKv8/aB1/kS5u8pYIuKlabS0yR7vdCdDp5HBOdaOaHT9PcVC/rYB3Dah08HExyZKU2U1YIXhPAcaDmFwmxyKqN4z1l+WNTrNHmXZxN0b06KK9ex4gKFpOPxmY3XhCLirAVpELwgDPnZFIHm8uIux7pETlQY7G85RQ/bQ4Yxmqw93sESL25M8gsolpDsGQBtBmG1VZlUB5Qp0G4ZaC0aLRrRZGYgAxqlOwMaY5mlwBohtp2KtUyjbf3qHbo4p8UcY2WFRqverlnAVsas6RgNKNB0Dp8Ij3qXopQttGRdj+LxixxoTpkTD9e5BAUTX9BDoHXmRHyMYC4yqU7yOM1R6/hj7l9U3C5UCMs/t0kDkzeCiwWeDNh69FTnsaTwqvCFFGZJmHNhlCIIhbRHJzQ6Z2AKSOwMoOU0r9M0Gqg00WiECgNaQ6PdZED7464NaPf5pDryZ6DhVBQdooVAIxuaJpnyYJ/8PQ1PWV/RFBRQuKCtz6Umb9KtPz3XicwNpa6YPjbBZDHdKT8PStFFCh62HBfvcXh+wuyAYiGvE+WpfiyOY/D+Q3HsH5+CctbSlyoPNNrjQFuONVVDVo56H3YCvCpLsykoZmWF4SzBKJIUQH8n7TGgIRCN1OCzgmM01mg404nXTMhOdfIEaIzGmCOwiNeJ3iWvR0uBdtMEWtN0Em9CODgjmHDLekRdPjPAU+Y8JOHogaRNBhpQkAUhLEiI6RIonvI6MWsQjDqzuy4yL2NWouCnuSuvFCVThBAnEKfZBOJmHaG4wMwAyVO3B0eN47NUXheLa3OhKLDwJm/0U+sYDRcoBPZWCaEziaNhTS0hBUrH2o68p3NWeUpRAQ+nA0nu2UphXqdc2glAM9MpQJPNKXR8nmm0e174CBCS5Y74eU6jQe4WoOkyIUg0BQUpA5osbfGItA8FrjjwyaqY5ThVXCadjGnzdTrdpxblWLLsroNrXozC4KWwROZGIS+IAVun4g/a4sXPvxE5A6cmjzRaxeumgo4eTV4NGq1Np8Ws9Y6o7/MZ1OVQMiNzAFrdLIy3ulzChURSw2ZCmIKBZjvVIYYGy9J6skxolbzOr8wZuDfTSbNQZDgRaDFBbCOqssR0IuZoOfedAC16ncABExhmMZi51xlfx7xOuVyNdkvbUjyns4hSyXsDrFei8PbM0t4BLhkbPrfM0CVrrwIvxB4ojCoU78XEiltGFMrDAEisjGKBZPLIkZT/DIXbSOR5FcevphRdTs1jrwBGsqwxLTTVVJ0tfBTJSr13UPaVIsuftoeYTnUGyPlsjNFAnb1UrzPTaDJGM2fgM33YGeCArcXROBsAFn+R06owjKJe5yrCrOl1wkc0WjXYM3WjWHq72Weglf3+aE+AaGjzQNG6lNt1cT0aLMNOtZkyt60n5WDvEjkHe+6EYggU/Xd7Pql6LzzK/wdo+EYmz4pblbyguI4UDXmB32gE6qzVdEJN6UYAL10nQFbenFJxhTg1qi5h3kMeaXso0KC41esBRdBoCqrdGfgGQcCHCXHAVoZpt7KWEUMddOGAQK4JtLsEaLcJ0HQtN6hRBhomA5qdO05Ai2BYk9pxtkZns45PWKNVkcKFvA+Gfd4u0RIwRaD1S2TuH3RhyKpAAwrFl1fmNe0IEAqXKApnPBbzOmUzzJ5P5RG3so8aTYqbyzMKmBloQm2ZgFYic1uXrsxljxNXiNfnRlGjvhIeLFz6vSceJQINhtrPAs1MJ8TRbhFh0REwoN0R0O5yoH2muSe+K4VoeQoq8zrlunYQQ4fY8n4sOlrULlDv4VFoDDTQ1i7DgrfdZRu0x3drXSr+kmi+fwc4g5Zui9gOQaOVlNUMAWft6xjNmFsi8S0UxmNxjab73Zry6oq9zqr9jcrXo3jjZ9u+TmReY1aLvuWb6SqgaC8uarRGexgFAQ03QH0R0MC86SUqvB9KAKP+5w1iDe0parTPiUbj87lFo0Feuw9Wt9zBj9y1p84AeZ0W3qhlS6tOpfuwv7NV4tf15gsAWl0jRaLQIwUR1JvtJ06tXyMWa8oq7Pf/FbPC99ebcRcU1B5RaGLmJYh/s35QAi2IN+kBKWrIG8V3F5iCOtDidlwmj4vbxTeqH5dXYYW0pg3Lqk7jpRPmVb25tDHoq3jbUAYUJYkHZ8DaQ1JgHlUsbm8ZBme8wnb1aaDpIWYv0R0QjUaTAxxRA0CR10nhjc+6gdiAdstAY4jymhBEMCGO9wxAopUlzTO5//1RN+m/+o3c+rfrR2s1fffx+ujFj1g5NWim3/9iFJKtPmo/tfGfvwBegAiZX3YcZa2J+S9Hw/1thitRGHP++uhroqiSF3S5+IU02v7ra8rWfyivpOJ2qbj9pryS5UUz2ZJ+vCbWUFOU1SfFva6Pln4efPxYZsyHkeK7suQqPloW8Xl7IMyA6uN/0hW2zwJNfngfFFpPBVgCtBsCmnqdCrQbAhoqNcGZLjySaYGX6nVC+h9lV9DaMAiFAUfS5ah0DCJJWJLtkgCjpetglgUIobDLxloGW8ilMMCjXsp+wf71NGosa4T2SxTVz6c+IE+FPP8t0cwdpW1HogBphOTuqiBhJJIQoQBBtgfAz1lsClBqGX0ehkXr9o39vSexZsID4ao/yJmgzBm33d8ohq6XwiWDGIIdoCmBIT/TQxplERrvD8np3gIwJyx29AfldBe+azuwfCARgqbpzGoqQAHkj4Xv+zn/hFIbVng6jAemPWM2aMiqTOM65S/adF6evkYTQXkSEtHvZDjeUE6QZebgHM2Yzp9R02m/aBMJfeOY8mEr/xk43g1YPNNFlkhgnOBkAFZvR++BB5oKO7G6aD6cpovuygw70bVAtPuiY3W1Fi4AhIxaPKO0VVed54x591pjJzoKBMq1mK+DlYFm643j3ds2JTY6VAkdyVCtewd0y+mbqh+UbJH1Dkfzp8SJbDPd/JF27ayNM10YZWxpw8aGLdxMHxWCt0jzygYXcmUEhhdDNjbEYZtAlrhxu2S/Jl0gP/o7t7lpRiJOTmxJ9khJZD86l+cczRGgIU07/ngwwKUbhBiTgSIYxRrNEbZm8lGX6xSgeYTthUtBgSCjRgn/2Ec7rLKCu/NRH6ibMbzECfjoURxWMvmcEpFZPX1PDL/q9L3qmpr463iAj1/fcUunxKC26FGy1rJJoi11p+Hws+Rw0jQdsjSDytXtLW3hGcIgzSwK/7bSOGZkBJu8h4LT7S2xXf2S6d/hpyxAYMdsLB1QVr8qauWwWRXScovKtbxpt9nSRBPwhd3HNisCmnmDJ/Z3d6pfSVIdlpxUj4MBLhMSjWbmf/cIW6E3cKeAR2MmDuWCRAjbC4K53JzS5dG87fUdB6EyIyE9GWgKlnz5brdDXS22W62GG130d/vXejRfjL4gar44i97Qpg2fSsl6T0DbbkvVJ+V2m/fJIw/RJaoKXEirWHgWj9uKOjmsNhWsbLtxBpqbPSAWeVP2Xm2yLGpRLEHodOKS6o6wBWw50/kaaDQhNbqZgb+o4uj9N9rDJdWl8Zin0fAviUZzKSijz3yg1XM5Cwu0AkWxUqvnGMk/r4eA9rSOv2lEkP6BUOqXeW2n7v68KDxc6Y/vX8+ZjFEJgUFCIJ9vWuDD6C18ZP1fELpyH2jwyMB00NUMxs/1eEq3I3ONsGULRQBJtpPjTsl1YqDnCh8pDyVxoAQCVx7Q3gaB1hsMvEkwwMJxhZhmooaJR7uUblDpeexnl0VWWaDhQtpgVVgFP6sKWN21Q0Dbt72z3tUDABk9bRRtNF9qTqbPO1zPbgZ6YM8qhUij800LD+ZpafcWaF0ytyjaPU+gDLZTrmMLK16hyDsR1DzZrDIBGoeeDDRpQXzlA23iKmzTQEN99RYCzfJoMb0hQIMNS29wAoKmrZraYODSTzzZmZ/nNeMqy9yFKB/PTX5zXSvant32AU3Bz6zHdOn9etgtF424+XRjTzn8jFJuOKN0t/US3oBBVRLgStUvM6V6CI76+qYHg6qo6msGWrXaiL4TMGV+Y6gs8tLMiFETqw2uxGDiYooiPVQwJxRqNAGa8GgEF3vLwCvPS8VRp9Vo4SQvr7HplGCAk6jCo/nVG06ZWcgV2a61ipt0vPPREHi71rQIVCJpa9TjJa2awS6IT7z+Z/bF1mPnAG1n+1MlOgzsWmdCkyKDOn1/yn8L2wtKoGRfFdlkYXumL36KOY07p9C7MdBIzI6Ln/xFItDEdF4S0PD2p7RG+2aBBg/Ho0nUaWcUisuEXlM+mh8M8HwyE74LSoIBUc78Y+S04VNmi8lgc07H84YrYtZ3vUBT1OY6EhX1iIrGEZ+EkHav1Reg1u7P1H+9tvN5Iy0G8HzTMiplMI9lea980+m31CqgybO9mn+tsoq3Qu8sbkoW9yEAjP1axS2I8SF3qk8k6pTMAAcD751gQKJO4stM1PmXgMYRgNy6QvtK1BkEAyg0AwfN+XcRAo1R1gHaQyNnyECjp7teHxoO5ZuHXtPJeNIpKOk/T0OaaOHC1s9i7PzDHxokXFSEtBwXcLojVd4O9iWjwVESaE9/OkCjTTvI6Kqq4nkjH7VgKmg7TG+mRTiN0wbRhT/wIJNJM3PHPJqLOhEcjrB1wcBrBLTIdMIOgUZ7N0CTyWylQUsUdcK8CEHU2WyNKxrSGxykN0cTtw8Bbbvt11n9sl9H0cT5ggi/a/XHCY7mOOpXaTBYEubkfNPCZE+3zPYGL8uE6cyYksQrGiXH7cVcNJrDl7M0SaAFby3m4qNJ1DnhqHMyZdPpCNsU0FijvXaA9hID7S0BNOnbCb/VUChSMQ5/2AUDQdfuit00YGSt2i5MT2ZlL8XDKSe5W8tVPwCZWKqFGtQtltGffa37+u9/9IcbsQNfmwYavjychKTF8+Uz7kqOgzLSwVneHEWjmd6wnn7LmiOiiNjw7HCU3ioRjljFjfu6p7D9/HXE7dB04uRklCWAh/XRcC1gIyXkAe2tH2hBUp1x9u7ojW+GsOW/wX/vimZpG0/4nnnX+db1Xa6Ar61sX56CAeeCgfXeNEC65Q9Xp3TKsu1ruzSkiYB+17yJYcEX5GOHa1r6/mSar6W3+XzTAqfb019x85zfMLQioKlscyoEaFX23MIiK/ymqZmfgiZA9QPt1Ab3DJCQi2Zb9HiZgQkCTXqEMb//bjoHyJrARBW2nILy6Y13GApynaLRrACoCWiwijQar/hBHeEN0IzpzG0Kqp6LfZjl/A2kv8FZOuo7HYa+93YZKLfz5fzD9Uj71lwFckvBZs4Ex0yUVlJmPUBrd0KbRaYTVu0uQ2FaYzEXGiOLNJqxqYQ2gzmXsEK0oe0M7hkAonRMzlrIo4HI1KJc6C9lQkSLIb3B8ScBCoFGPXp8H+0lNJ3S6z8MBi4maKTFR5MqIfHRwszA7Pu4orMoXDCgbDAwy+hzc/F+b4+58710j9T4Ypv/Zf1xH22Wu8aNUTu7WVGWEb8RwXHE+YMYaYt/7Xsm6nQ7CaeBcgON3gpuvptsSNyNCC67TToPxzHHATInGgYDEnVekUeOPP30ApSaTFsldYqc7kSk0ALFNOzsDQbIiHK8ykeaSV4EXty2EUEuU76nc53zWi6XKBjAx3XNAaiL92MlodgepfPXQ+iwrTapk/BXmDQ4XH/EcupurqLjpF3Xt0qAJhSGisQOxkj7d1ESxFwwkBHc6PFzYchKtJrPLX/UgY9miLKYse0C7XQvUSdnBqQXlJcZuLI8GqsbMZ3cQ4XpfdFocs9AADRpC/Wa0mgSdZLYqJNlYjXaZaTRsn0Li4q35frCDQkGDF+bU7yfBpokkVID7XJA06wOprmwGmEX/k+KuHv6Q4gkvtajdwOYeXxtDmfUC7OA/HCRp3xaoskCH42pb7mGqUimWKDHQlATl6zDn4UIy7pthxdzLOaWejRGF6gv06LnykadgK+IRyN241VaWUvdGb6Uu6D+JxW2sB4A2kUINCnl5hlnkqbztGE22gHNq94Avrbk5rrqsVFDTFajkopm3H+IBaeSUqHPijrncKBk/ZcxX1tKjmDbwDItOGjRmTuBwy3Qig7Q8KPMXLPYrGgeaK+0iEnFLVx62DMQbI6w8JPqkynzaBgDTHCLaFRSZzYYYHXGsYDno4lW45DgxZnOv9Z0CtDeOxot5tEAaVF/azGgx8bmccVJk/PPK9h4aPADrPJS4v3bHo22Xw9VZ6RlfefU3ddKhbSeL/QZcYPZV3ejgYeG1yWdL4ymQ08YjLvGwtaxEVwJWvxWxDBY+F2Jl/ewWWUiTN76zyHClrpG86YEA/RExwxdNLCYXq5TNNrre6jREFaW3mAl1vXROA71gPbaD7RvEwe08fdE1InsDrsNcVKdiCHZAH+7x3T+GP1Qalmn67gTlbfa0PJSQSRVjvWS1ufLGZVG2ulRlWTSmiMsWJPJ+cZyW9JgGUEN0yd5LG4wAFpez6liLQSaW2cpxtZTFfVc7h4WfmMyFXrju5kSYXplTee3kLBloAUpqJdOMMApKljS893e1wnieLRvdFPnlEJdEDKa3DvR59Go2hP42hOfKAMtVN95yXytwueMP9u+WO4M1kFrXMxrH1jg9sjQuaINfjQHJsNWVmN8opO2k/laK78HTCd9GDHS/jxJtF44scHAn6eiyirX/rp59ENLKRay60iyzGk01BnNI8+4ITEA+WjsoNkUFKoa1DoEDtRCgxrtr2g0VyYkHaIYaUmNhr/S99FQrSLczES2lIQSae8ux34/9cCA5uv9jWmo3i5VCT+jhEoDaVMapQEPXw0mt8X0wk7Pmy85aU+bzyXgwykN2j1v4KJdknIbJXQaDfois7/fLw23ISIOGQHtfolBgAParoZ1zG4UsvapztB0cu5pV4+F3pj0AY2spyTWUQ2Zu6DQMXM+GgMKVgFhS6aTI09GZBpoFwQ0ijhB0G7CQ8AVZgaIr3U4E+JWybT5wNdyLMAhPa5UqlnibZrfWN8NMPTbR/tKKS4VgtXnBA/X6kO2t74Odorqay3Q1ncDRFp3UJiy/5xGcz4IM5E3s2kRAC2rdwi0yvj5oUbLQl0m+/ioq3c2GJhQMCD0BnhqNhiYcjAgxIat3nBRJ+HsJfTRXnwfjXNRhkdDER5Npv62pnM65X4WpMkktuRch4TZsyIW59/OCFt5WZpMck8CMUXNKvxS+0SXmyf7y25F1ehPYU0po6m0GjycEl6/EZB9SPtN6Y9c8fkOqNDuoEK5rlVErylDbwAjSfbCAa1pcCO4A9J7jp0kiVs4PGhpAQ8mbxF33JUYc1FSkihTPjoezSXVAVAhYfvmNBqZTkYa5zrxCMp3OnoD8/iIN6KIp5wMS/zX89pdLU5vS4ogn9cWciVHZCnMqJHWsTumR/8sPNwMJxMUZxHOA1p8uBrSaT+Yr9WwSiOtNEjJlZxvWvJcRYM5yL4tQ9PpeSEwWPCdTxZou5qQhyAb0miXCaCh7Gqer2qa9tEuLI+GGoiDAXbQ4OEHAzHQpB5N5qzihgMuMxBrNAQZ2W/RaEYyuyqQr6045AyiTrns4NORtkcU0qNFSXrrKl2/sWt1nz4jFBrRpgLkK7JffyRm1bs2MU+ICSd3rTGc9nzjiEAGE/A7HXLfdIZx1eqAW77pLOolVXKI7YyizqSPRsINWOudqDFqxXSFqysGGglsesEA44yjTsDPB4H2wkDzJhQCYXfPAA00GmHb+GjfAx8ts4XqpwOec+ikFSxVcXM6lEajqTmW0+cpTdFHma0OaRdNqQhXtlRIf0Kb6TMqjZ4PAjSV0GrPB2UFzyiNspI/jBho28c+oGUFDhKoKgs0sp0u2dkFmrwlo7GPNm4aITjQdNJ3TNqNowPwm6b0uBDCFiSIOnEhprPjo71FPhqQaXFm4N0PBoSwnQRRZyaZAXgyX1uJU2Bqi41QfS2LLVkwYIn0QlS3r0ePR8OSyb72pdYJ6mP9pNwuKcyprsdValwLeuXwYQGbxwYWDw1o23xUdgzizCk0XcJooN9+40E5kWoiOaTR8hsO0guEEy6qnKXMYTDPfR6twLgzTwOtqvySQXo63SACthO/UUQZf8EEMwIX56KwxlomeSGtJh33g6iTowFXJkRAe7EazQES7abMj5YMBkCjkYMmPBpKFtwF1Rwp+8ZXTpWDyyBQq1DfNw/KAE1CevuNu4S4omW7jFgscPc16xttnHbrIElyUwesG/0G708ou9LmPdpBB0hU8AYfHsNz+CYG84tKYWnD+m04IwFXzutwEKPPgP7ge63Z+ZfUSsaIKouqXN8V7joWaN1j3Fk5V6ZyPpofDcSmk+V+R7Mks+kkH41NJ+owyaY708m2jxdSaxZqtL+dqBM2JTOAwEuUCSHgODMgdRwXCPiJz6Ndulzn4fh/wq7npWEYCoO0lwoCKYToyBKrTAcoqwJxMMHdWuplQ0Tvntwxuaj+A/uvl2QvaxKW7euvpP1a+MKjr328vnbUAjd6wdDeouMuvxaSAnmHU+ja9/iucyHmaTpvZ5ocFPCbtZeTkEW99sOb4e4YFMf4FXV2BLbOxwENP9+jkZ+icUBvO/ccKSCllxq94mn7IWftPaR9vZrBprCCwYfuMjec64nuRIcw9Jarl9M+wzZwnfavwGd6tqHUoJrQCZRthNeD8K3zPw5v/EEcbe2lcodxNGtiFuA6E4YmVIEApvpNU1VDhNyuofjwXOdCsZD7bLiI6ElDqthzYenYpL80gquXSt+9ojOmjBAEGFf3zc3joHR9VpzfZhkfWALpGVfFjjFl9fHArq93HOsVC2c4ab0AKXFsZ1gShEikF1CUyhkYbHSDM4mSesnnXZ7v0euNCFttGLsal7aSLZ6uZuuLVe4kQ+YaW62srzWtiNW6yxIqD4whGAxSFx61IIogFAoFLBRetwDCAwL+0Tvn656ZufnobDb243en9zf3d8/MmTMzZ5FinWxJYESGXSfKK4x1PnDX+UDb7TQyoM6A15ZYNJ7eKHedCxqCSrpOmd6goPpiEJbl8vxPPhytvjrY2KLzgN9vr9W4/Ln7RoX2n485Hf1VG4e9+/g0HkU9X6eKa7XfAG4rLqp67Tg1N79/vJNzwbaP6ZDXg+PBb3JBvdn6fb9Bv8wJ0apUAMHHln28a7am9Jwv4O6m8/3z63PVTcLXCFborj2diwrwhQsm8X1OQXQ1aP+m0yGVr1W+cMrVsyoihO9r4vt80Ca+dY9Y5J3qOmG7jBk7Ma86dKdq0ajgKRxokrAXpL4zdQaiEBR7DrRTXSwaLXxM5tH89EbiddIvcDzQvENStbXdg+Bo4MOXop73oUX7g2WA2EqC/frHex6xcRlgwzcYLlmcGF6vrb9PRbH59SO2fHLSYXONz0Lc4VNB9bRERTQa8Ox3Zhm0ucEd1JVP4Hu3/p5VQ3wbY/kyXY+N1nYAX6h8TfkSvEHnrx7BaphwwnaT2RBfo2xy1Fa+UwVEHvE1zBcQjXynNI+WWDR1BhYeJCI+xhnQpdxp16nboCaEoOQg+IVwjKZJO4tY53mOXHfeWk1vYiAFgGcL5ehxIbQX/yODs/KWNSPE99agOb7eHfG4n8szbCJfNWL1wG+AY9VgZAAvZWMdjyFtH1sXHv7t6OzW3KcT2KCGb+/pKfAGoICAO3g3dYAGfnI/h6pCvvgLvSke+Ed8bcCXCeRf7+pHc1EglJoS+UqJq+YuMxRa3sDDdIVvhfnmcprtKvO1MV9B7NP6IC8tEdoT1BdJy6sM9UXzXmiJwChNnLAli6Ze5witGW8ZDldvLJS6Tq9t3ECMS2vF4XxMWyJokuYcjf32luaTY0IbZMKPxKJJSzK2eGctYr0Z99jIGYBnWhe4FqtVl08xhpd/9TXj9MdbOXwbhVZffe1Eg3CzjMgb6z+Rxb9fD/nqMfbAIUehaTmvpXy5gTbatUae147iWbZztJXbWzbiy82To9AowC5KQ4tWj/lWDLKpk9AEATz1jhHBQpMVtlFkAE6S4nk0jHnS/kso0VJumcMdyalBFPXUCVvd2FmeR3tInQEQOTkDOEZLVj/+C8RT17a0mtbkcC0QGr+yHqxYLYJNnin0FxGcKzcKT4WW1969HVP/1g4cEvwBhQYIG93vLSBWSIozZnzn+hFfM4ZDYqI81gkuxTJO4cpXi/JlgalFC/maMXzRojHCESjhy50UeHrw4SMwljD/MK2w1W1QKjRQSrR6g4X2Ayc6xBkAGzdZaAtUfN0YgqLsKRRzHTeP9gpaZ+XAVm6N2DTrDNHaCxqTHtK67xgOJCEUFwe87d4Y5ZzXES6lpY1p9vANn5BkJa0fytt2kELFOSOm1zBGEC0YIk4VmvJN5IN880A8f1HbyM1YzsHghG/jqGQsc+Yrdcd8ZaZWIgeSjyYpyrdKiLCymO8+TB9EzgBP2C7IGA1HT5Kwk5cvkhFjZ0AnbNMxGlo0PtqKhUZXlsdomnQstGj4pWM0YBIVKw3VTCxagv3UMzJOM6Y5zqLlCHf4MZ+zXjHSMM28lpeExjmp0voNXL4nuS+SxG+hTuogtBmdZ1852CibBfBtqJVSvqyxsw7mjKoY5ZtaNOSrxQIw5ItztxrneyR8kzGd8q0SAt58C9WZUJR79ToITfZ1RkKDRw8qgw8nGwidgQUcdk2KDEjXSbO6AOSgOu7RiyMDNFWrx1YtTRPatlI8ucl6X66knV63I6HNM1Z02NE0dIawpWe6bSCNEoDsZSfM+KpwLS1MO+eS+rUzCRLhWcjhOMywdEky1ncmM8doLbypSphxLBteV5yzBm8qHaPl2/wuwd1nvaxzyfQVqyVqyksvzJuTmK9GkjkHV5jWT+ko3yoiIoP2OcsCvh+WlquTnQGMDLApU2dgpEH10YSgOmUgpr+APpUsGpnByRZtrNCKw0TO87VDGVe7U0jql3WuqHUtmLSjF2rRACuEuxenQJlwUJr1ktAIbjQfYfC2Q9VjE2m6Uv1Ux0YuqT2l3W/4yRiyHJy4c35G16l8+dn2ehf8jyLfVig0IIBozLLf7Xw7i/gqNG4ey9koe2cR38e6bQX1xnk5EzrKlzN3qs4sCL4nfA/X1/fRXEhQfTGMDIgzIBNpCzSAB5sUOwP3o9QZkOkNFJqO0TwSro4OeVmQzVa0aSAeo2FQXVbYntd39P3OvvgsqafZsPCuGrWjN6HQVtje2C404ze5ULCp0HZkdOMq19CI2i0rPE1WnNYvQ7YDSUVsLWVFc1mUkosQU3tO+Lt+wPci61w4e5JllwWHxpFOjSnfyiVmZHbdrBPyTYUGfLmcclN2Q74kssCiIV/Or2WATsK3igjufeV17QV895cX6RSCJ8l6NLVoYHbKFm30ffIyIREaHv7Op7zQPFq565QsUCK05Vho8gOElg+K7Ik3HWB4KZbHeCrJGG3gCovW9VqwjDNA+7dUOc/ygeqgk12EGV+x6nHp1wGR1H9LoyJIne4RxdDs6kZuU748Ymas87ymfIeU+fcs+6Q3FYqnX/DtQkZmh5ncW0b4pkJjvvC5zk4dXnZmnVbNElOvU/kynYRvNcllz6+r8v3gxVV0ncsqtF9mCk12N4HSJizlHok7imC6nKOkDxoZ+KWIDCzPENpeQSMboizCVPKx0NYVG+KI+XbJogGc2/lL9tkq3kDV9TFCa7IwtX4te5hPsYkiwadId2ukRgOIWV1nqx/yzdBt7WZDMY3boXheKN+rEzRNLrsJ+aYWTfl+4uTV3SsT8AVjFo/Rmlwb0YnLHgqtaZmhvq4B333IuuT/xwC6/8HL0GhmCxTHmrsXod2z0CZuTlGhSVR9VFg0/4Uanbh648k0Z4Bss9UG/BykkW6y0KglCWtLQnCMTYUGcOo5r7MzJ1n7LVc9zhkY2ElCtr5Z381vrAyK9/nzp4sOZu/VSYTpFq0V3JSVf8C4CphO5cBRcuVrJCJgvR98EvEVoMILW3kFzsCnwKtths7AI7ZowAYK0+kpHQsyqipCXtdK9k1uHxDLPDpbwnQS4G8uh0LjgdR4i/Z9JF1nOdYJe+xAgeJ1PrDXqUu5uWaQF21O4cgAr0d7rPNouKvz0flakHTON/rJsHemzAZeaH6QJm94G7AmFoJiU+UAnMEdGPap0bcCTy0aWBA3vn53WFsBhKsYi0//5DTJ/m4AMWvxRr+tfG+yazI/WcgBBmnKV4oxxvexpxHfVGjK198WOKk9wCtf3uOJciNnYE+dKaQDVzjlW91EvpIK2b+uUHXA98MylKUwMoA7n6hTi7tO6PdAMKMgBCXnc8uELUcG4Dd09AuJzF+H/+nqDahdNhAvL8Jk7RLnHKNgAO6EwOXonE/gVdCYMMo+8c1zilPPRoQGRVuy1LUx1JSV00c4XHB2CqEqHcZWpgktrl/jTCw0/C07grZy3emJEUDEjJ5ThEblpNe5xgmDDLsiFk+M1eINzidkP5uv8VV+9sLoZCch32C/CnedwtdVnKdDV1jl+ysiBGI7Q4etYpWvrINMd0HBTD3tgkKvEz4UXH+IhMaboMp7BuCjYzSAwYFq6nWmXSfnIl4Mu85q0nW+5kcp3ccZMCauNu46AesmjqHKXSdW7Sqm27k0hNeLXXOc0HZv7Vih0YjkHSKK+dwvMHy51mGTR3ihtabutmv1A74VtolZhqws8p0v5jfmkS/fwuUwG17Kazah62S+vmQ98C/o5pSvLJDXMVo4Pf3FSwmuMAXf/QLRMvBgTuGPdcACCOireMIWP7xVACOclOe6SN1K02mgEjgseepSbgpBoZdAzgDlu2ahlefRxPdMnIFH4Xq0eIBv0JXvcGehzkAw2jVjhYbY1Ov0cOcMTJtAwb6Er3YKT52BstDgGkcy8ggTIKKGR8T8rBI4AwYnbDtnV9+GBQcMK4lywJvhu73qgPGL+aZCK/gaJ/OuWWYqTviWhUZsTES6F/AFRBEs8K+rtZWY7z4+yfLmlCV48DTBRf2nOImU8Zrk4uVTOAM/UGh/i9C85kJnAIMEhTOAQuMKSWLemuEYTTcMsNB49QZ5nbtiNa6z62KMjJbLboG7HwitDtjWWIvmAJs8U6qahPaNlGYET/DUGdiZ5tV6GRHCkpt2maAQMT8r1ql8K1ddB9Hdi+wT/YHlm1I3EmZLHdzzaecKIAnfdB5tV+gNe+Sl9rKQrx7GwUITvobpGJxAUb4esUJkXfi6ZsqXt0Fhyl8QGho2EdoTEpovkr3/IRLaSIX2/UcktL9VaCMWmozX2A5irjGyaPDPoOuxxGrHrjMtflKyiJpQn9nNbiw/io1GbNHyRAgzJ2yLZ+Ok65w2YSvTk+X6DTwpENpK+5DHU1fktFxkQwHdAmJWeUZ8eZDQhZpOs+vkpuZSvtc4XdFyFTttwpb5gnuayc05rToQGo2SA772qrhC+YIz0D60XMOJ6IzaxXqEdp1LeiBf4XUucMIBiKjjD1pZC10nWqfJK2yL1Rojj3zg+bPIGRgfguKAazWa3uAShpWGyQjWNGvshWlMaaLQAJvOozUORSYuFU5/QghqwvQJhWTmCOF48v0zjNBpxooQM4SWcuhmp376/gv7koY5SN67eYwpWcs2uZdhMSFfgJX5Gpz0PpGbU76cRsVfJKs3GmTzTURH+Xop5rvpUKEX8N0nAwZlEURGE7ZL1KnBRzwB7EEXCnHQTAULBwQnB/Fh8n4vtu9FrBPTp9zHFk3HaPdyPpoKzWt+vNBqTSFx1ZEJA6ORYO46oe0BawWsFkqDzC0/3taWp6o1dsAz/Vw1GISS0HIIMkvpRV7qSw2qY20O7/ZbdhYhZgtN+YJJg+J7RWOiSD+QjfgO1ZgkQXXEKrxwUXs9gA/RyZCq37wqzjWhrnMV2PCrpNFRZeOlCAgUnrZ6iKCT+KBUoduUMZpXnCaFArWR0NAUje7HrbD9Wzan/N9bNBQYRQYQgrBRIbT71BlACwpir5b3dWpkgBY84WDk6ksvXHLQ5EVUsrrZYxsbaMmt48GLs6zK/sRlQlKcI/lasgc5GY90PVr6AusioJwWPtZ3pQ5/t36G08lv9/KaR8wUmvKtsDNwGRipnDnEfLU4E/KNz7jFqmV0fz3MoCkJzHz9VtlXmMGTR2m6LMqTgMaH+efbikVXoE6rN/JG2CKWBnG2aBGyaMt4ks8SLnnUPQPSdS6kXSf8TNej/ZA8A9J1ct8KEIZpCIrSKPoPVgx69h89vDY53ZnO6d3OZbUe6cCqGI6LFbbakn4loxEMfCzP9BKW9uNKIbgG0vWHZXg8dH+xsYLL/BQrc0zGvV3B1WZeaCJemzhsHvFTFk35auyKyvG6viwhX0IpWvnieXFq0bjqhK7hpmwc0ZZiPgUGIwPENxzJGniBhU2VEK6IqLiE72rRdcK5ongkt24g9h+JDLAjQEITi6YJLSAAEC3lRnuWWLT7WGhcrVo0iErg7SzG82hcZLlyUgxZ+1o9FBpjhbRCHWDRGkRCm7jyG+A5Ca0VWTTcChLN69HX7UtaqL3JCCmK2HqZgxRn7h9uRjdViMga4cvQiK9DjBTlq9s6Q7hNV5Fa5ktpfPS8IWFjy0u5gS8KrZEDQiEx333IyVMao4kZW9DIgP+97pWDn7zwkZQWL+WW6Q0QGkWpqNwLHr6L3XsP0RhNJ2wXl0tjNG55w+8rUXKHsjllTpT2Ajey5PSUIrxg49nSPu4J45YXqCvg44Lq8DxWRUeqpoOXebA5JSeECcV2INtXphUYP/aVrwjBMYcGcaAS8TW6jSXkm3qdwb4XU3wFfKPD56jrrNeUr14DfHVzSs6IW6lQ+e7Dk/1poYlKaAkafmGhrOkaVC87A+ALkKea7Bl4SJyBZdheJ8MzCUHJLigoL4Utq8cc4/sNjem9Tmr5v0iUdcbqQzpeA9JjvM5ag6q2BpAEtwDHMlZosA90w3H13DPjdrsGCw0K957S7i1C/FzXWVe+WgNzqLPXOYGvg45N+eYp3xzwCHeBcJQvL+OWvpP3debtjbgrUTaM8C2iUeKYb5U2EFP4yX/7/rMsNNLDAwsNJRZmt6PRmZ69Ma7rJH2R0Dgy8BB3nfC9HIWgZMaWymBdNgVvaT9Seb1Nf5rn7399MydlFze2IlaNCWHrAH4fd527OT5Uhd9iN7IN8iN4OmF7V5MNxEG7H+y2+WZezm98gIsb8QZiA4icEHB45NQCHOSmdFos4DvHFQR8V3e3LGsy5htbtF3aQJxD1bYSwpmvngpP7sDmO90eLGyFbwPYQNcJVU7m68dEdErVMueBSi0az6Oh1/kARZYL8Z4BME8jTWgB/7NF80UsGguNu06sAnJnQ+UYGdBdUBqCkvKIP8/XatxuO7t8RMDB8fYaScS7UZsvdIz2dI2fBmMdYRtyjsFmfOLiZpsfKsGdVN3wUsrrAtcy998/ajlWn7ebxzSq3toYrMqm+Xwwf7BT4xsjhGFEAx6wr3cw8+yNp+2xfEUM7c3i9gu+ii3xjYSm8BUPtwLH20W+GE7XnLGvfl9hfJkvNsTgUYGotfvH4JYY5QtjtAG4eeh2LlY59TDmGZBIOnyJ4BKLdk8W7fuoZNHKQtMx2qgYo3mhccWUTY/PsP2HsOtpaRiG4pRaR6unFsa2hhowYagpCBO6S4YiBT2a8/YJduwu4s2zH9qXf12apdsPUl4eP8J7pLw2ry+prd6I1KXfPgziO78rDKqSf0yXvAI3jIaLa4nYQMxnoMwd7kNRHLlXHsRcUQf0HisR+CsBr/T40Eq+nDJeWnp+aBhQXnhnzfUYQCnZro4vlAktBMydZ5TsK102MAr8Xc1AOeIAF7EHwXNgB+kZ+KuCmRvRItpVRRb0FzQdSxJgHIqAv5li3LP9Ok2cPFp6zKPdmjwahDebsNVp/X5R8KcjmpR/fr1SbhCHn6CgYwqMdEQzj2M3osn3xNSrsNUt0l2CXSAEzYqI6oVSf0ATbVEYLWrpyeFki3P0J6B62L/WpD1hGuy+1eD15xsaw0YdDX5p1wANGyRBaOxEWekvGgeNVR7bxca1HiNsBNXoxL/RJsm6IdgDUBXQcwNz9Bhtv/AoyM32n7Vre02kyeKjlXQbNMICNUDBwCzsNNAgfFlgVBDQJ1dGGATHBEzwJRCJL/O4s05ehA8Q9o/ec6lrV3dH/fZoX0ypSXX/cu51zqTbjo0Bdm9ExkA5R9M62m8faH/WA42tTsPRWrRU2bo3NNBwi6gBpA9Rbe4Md35fLluxvecXWBRKH+M7K/iEhWodDqhPLP6OMoSo3djW7xP+QMCwYPCMSt6Cy6KasrwqKrUsQuq9unOVZaKKYASuG5M9SWOgpasZXXpqkEoCJknhhFIGrx+49etk0yirys2P9uSRcoPgGfrRmhZoutQjsbN6oDnRScFPbXXuD4HoxL0DWuxHc0F159i4CYHGBUbTr9NdIwBaltkM20wAXoS5J3O5iMq1m1qMEdzMXXXjIn7f9MEHq/IrkToMLRYKvgdHBA8V24OKd9ooFuH00vNrP6pMBRVHs6X1lSUw46g3VBYEBxL24GYOZBkjTSSNEqBB/WBTsdZSrrMiYQxVnEG/CDKvODyMUm1R1o2AuSHqaLldmegk5kYbuV517So8Hgt+NORkgdXJQDt4xgDpaNYY6LIx4HO0mBqGo1F+8auU98VuA050ev01F3I4cPU5CXEKOV7ljd7KnrmdorLVBGOYxwXzQT4V0wfFHVKmtv5oLKtfpzVAE/Rw439jbMoNjpXWyUVYJRZoi+1wwFdBZMx5w0J8BEICJyFNyq+JyGo52m5sayFz4VqCHJcW3Y0ZW/17XhsZt/BHvbv/gMYAp+eQ5enVsGWAOWOAeJq2Ou0yE4QP+tGCDNtaPxovtzMBe7+hBbEzZGXxKqic8MUp7FpyjmX/a5recdthBFmaCE0JX316vkj5UOYSjRmc0Kxiyh9w5ZaL1BsWO0EJDWOhEIVcY/lhZkZizoiDotYWiFE+GspJ+WeUqWC7pJjmq5Qr4ciOFUhh/WS8cFO5SjzKIqAl6fiJQcYb87ZcL+sYP10TbXY+smBnRSfysccdgIzvsW5xp3uqNxFsHU58NGswjXujFRoDCCDnsH1DYwBoD/Tb19FAyNb70ToFP9pNpY5GHbGStZw7fiYCFc2/PdsnAk0dhW+Yy9e6hhXcsVVUSV3XG0PSl4pTW5/FXxb+QE3kcOSEdDjI/IoAB4CEGTsWlmlRGdOVIKDNt6/YptMFnWKONn9NkYHBRsIzT2xaZN6YvzICJ/NC+Xcgh7zJ3IrOdpvBhiUfsaEiirMK0clAgX3LiM6jjXXuSXS+vR+CMn40k/iIjpXydZ3a02FJl+MewBVlnUEUgJY45jOTG2WwxOLQU/nLaSGDzijqlK4EwkOh0uKvP7CfLnJAHqwHWkwbOeuVNlpByOCWwQNnDMfMgSyzLCwiAtqzfA44WgnQZvdpDnfDsTUq453TfZndazVseB2R4Wft9mBI8hKeberVad1nLSZndVIxbkSJEYC837PoLBR5AeIiL15kAPHFel0sOrtadLa1Hy2moDMPA82qadpkcupu5rgT3pszaSI3pYapEa7iGY2LCimmNAqX1mQoeycOnt/jBzFk/xQVAlPDabmEGX9dZsurRNSTAxrMF/Z+HD0CWv8LwIpBpg3PlKUnoK//RbOC9b3DV3ACQMMK8Ka2KNV/J4whA2uRtm5CUAfr3jgy0EIdLSwt6kTn25FFp5aYzhhAoFHRR9s5pdOxQGvb1lbObPG5sQaaVdPyxJADGuvPbAio81o1iQc5rQfB42sNKwIUugbZMUWD9RSraUYfLJLgLmQ44yWq+6fREq7aSk5FglQJtO9D2BebWeiffB82NI13Xodrvk8e3mCU2/abstyWozUtR2uGfrRjMTKwL/jRjkWg8ZKBgo52sEH1Ww6q89LOyBho2F5XPqVErKbZf0dhiHR9NgTOb6g52NZ3o1br0ZVQdShUSnBzMVEloQUMnk3qitQ0+s7wD2DD0hgCzNxO5mgDOcwSD2p4HgJt0E8YVyHYyNU06Jv79HMeyyL2omGhx59zFp1wm5uh6CR4MdCaDmj/DYB21EALOxDv394H2gGAxiBudVxQ/bZYEsEy4TKOlpOahu2HEWmBMYAPNgTE+bdTyvrOJjKv+fAakVDfm0zQ4CVAUxVqmtDK/z1cD5SfwNNPRlqWSJnE5DuNVlOdAslPJp3nvZoC7sg++yrhmHs2AN0sk776Q5K2ppuZa6sTHoy6JnM4dm9QUB0Bp61OBE4YVNeJj4xBLiUK+0qgtQzQmKN1IqA1yoBmOZpV0/AiBDoaSUBWadT5HE2t5YOoGf9S2+h/mLv+KtWIEXrwMjVNRH5knHCW4KgJi5zB02ZydZekBZwxMb4mj9QzKuJoSJNHK3nWq5ijuTMcpXUD5nmrrU7cqFy2XkisZal22OIOqMTq3H84ouwE3FENDg00AqXR0XDX9KxOBHWHGoQGQANLuJajpTmraZGOxnYfGgIZtV86k6bypc69sfpVCSBEofvktoY3CXkJzgSpaWxsiDiSwDNOTrMDnOtjun1lkRADjc8Wz/p1UXTCi8WzUW2geXqZ6ETCuzp+RpOz3bzlBvpc2IWdZ1Z0toqikytrAHH17aBFz58f9tzUDgMDbHUGxoB2jwTujS7/cgaaCUG1S0Qn7YNgAKhpUaiTmn+hWnw2xoT1b5RGDpTX6F9Uo1CFDWVjUgoHLyFS0yJ7QFnTR5xFV0tqqkj+DZFUUbaZ6NG0YXW3RsrBqs0kN639f05joLnmiT+n3Y4pJ4RuNL1uGGPdqKebVercMIWbWaCtadxjJBELJREYcbGOdjiaRcdE1PwndNjeekC7tkBzS9fp4YvOBNW0XZGjhREBdS7QnuQ4hpkLNY2f8KSm+aFzYVS4bBUPXsLSlLBqmgphpmjG4gzK9BHme5fcpUkl/Xrho58QyVsj//Wi07iA5HUJacnUbkvKr9VhKECc1tEAb4iEEGiHA6+4Y6DZdk8YQ9cLiOuBdqgHWlsD7doZA4ZclpoFmlPTRg5omQ4MkH/8Qqr2byjc/VqJSpwBCu1H4RzeVvPGc8gBqreWm6CDI40oPWMDn3NoJVnZT6poPSoBmn61Hjk3+nTFDRZdTB3IFkOYrm66cLilNA4gzuFgbNGuIDoPiByLs/0hFp2/jz7QDgHQDnVAu/U4mnNzOOJpFuJOV6Cm9SjIp6MxGDLvzbabq0tpIPs1nFBgSF2J0iFEoQPaolzGkmDHwcuQZtU0LytE6RiIoC8vpfr5lrXKToDoIIZwfe8Em6Ih0PJ02MtTQ89jz2TA2xW09n8e33DokzgbGgM3ZAx0qPMYt4fiVG7uStc6NLmYHgPN6Whve8fRdGTg4EUGTB44J1ByATYuMd+19dHaXS0s4e/06700PHuZKfWI1DSTJJSJzEQELiYpkUdU3Ou7IR7rG/3X+Dfc4EWi06hp4c+8GbMhdB5JyVpuSNYY+Dq07euLVuf116EXHFzN8Dx3wc5AVVutXe0q3FvRiZtdS9xE9sMAYbggawuBRowMgQYn+xBoXNqFeRl+ns45MtAyGbZU48UWFHIgC/WzEGg5bBz0NEDjtVBsCFxKqi9H1bd5tBbV7C73UTjqV+LFDl7E1jZbBpXLCxE4Yx61QDvZ+Mxgvk5wxkAbrVNrdaaB1Zk3Rmu/B7H8UWEMkM92++9bynq0QXTbprNZIjqdjrZHiq3OPQLtCK91rJN9aa70XstQs8OlscAAQY5GuhlsvPop4bKPdlrwgl45nOGlyVmGsppGAgLTFMgQADFyMU3lQ11T/RoIj9aFFA1RDhWBg5eScKkCAh7FGIgo+0gdcaKQqATayy8CmsWa7954+cWRG+YC05+lQCPdH5S0n9An8zYGGnnOIqABHWKg/S4F2htyNLPKDh0aTnS6sLou8dGhJQOcveE1hg2YWiA6kzSz2tp4C960dAkos4kyf4XGclGZ+HW1eK5S4BSi0H89GyEQKmg2ugRjhNIoNw1nLIrpReJkgvkmJWSANtmkiYVawWELYx5H243LgcYNrnfjLmU93hIZ9wZRaHUiuggmtu77MbI6GWjA4rgAh6+jsQeNv8GJTrICGGjU/My5N2JjAHHmgGaEZ2a8aYORED0hTJLD5bJzIcfV2vhmUuPNZRQ6/0as69nkbhi8gFgFM0HPnoCNDQFnh54PNJhvUkoMtOdFfgdXOuJoGG56Xvgc7X6N4CoD2jW44e/XnIjGPfwxJgDKOQcfEQct2OkMW5OvyDllnAgEx3Cl+vHD2wExtmcnm1llcGix2GWO1gqMARad3Rs2BjiCzrp/MQet4QGNp87KBappr8NF1psNyD9+Kc6YwN6v8GIhJ7oXlUALF50oipyrcoccDp5JysKW1bTeHIAMMyZDoOJPOtW/IcpRRkB7nGDRl6SUoz1O4E45pjb84bs90wJ7G/4AwMGaAbYGnDHQ0RX5CGimSSw8tTHg6hvs/RY9ADTYEfb2VnTCCGKu3mHbbaP4NBytwiCgSTqOBicUPXmVWynHYrUdPrJaLEpUFpeLX08jOatyXyg1rDRnBaLQR9bTWJU7fqMAw8lJkIq+EfEl533Uyx7kcEMz1l5kFU73JML5VgYG0nS6SkWeWyWNdknKHG26auDBZHTNJ+U+W9pu5hOqh8AMjduYsI6ms2p1ySpdrIp1NNgqdbQ/j2R3sh/NBtXxwwy0A5OXyq0jUDe6ArzmanbndLNUH1k1E0ZHE8mgD0ibiomUcvvg3T6lQpZ0GquTlWHK+pA6oFBELgwlIuAo3/lxHtKEU8u2oEqaGasqKxlzzk7wb4gypGW4a6T9QZp41ZNzl4+WX/dHjdyTOrvHQKUOgXa9e2y3scsd+0tZOW/Sg5HQAcZje/YDPjhTaB8A7bdvDDBHK/rR0A7gHZHLsOWaDGx1ai7WSD2rk6ZnErpznpjXUhKvx+tQAk3FAg8blCPir8jOvhxUAFI4fLgldpQsAZBWFFJ3nxv0K9giHgZ9ONSTiHFmWHJvvpVSLsA2kVuasROV54tPnK8QWTnQ0nT4PWf/7B2+zJ3DFric/J4yMQzv+4FngLDm6L5/jUEBtjpBaBJTI2KgcT6aKd2ui4mGQPMWpxzBYes42t5EBo5FjkYhe7Q9GdEsOrtOdEaRgQZvpCIw0HJrdk7WEknMYbeVM//uZP9f/8ZkEyxpyeyvWoqYS0ktvcrkuNqe5p4VEWvjFCikqQL9DGlG2CXkX6KnTSWE1WKhmQGc7tIvfRMQyJmjeTral36Yj3o9HPgiE7c2PIngMBy0CWhNBhocOwZmnDPmgMYqPYWg/FjnUQfV39AYQJhZ0XmMRadePaAXJQQ6GhoEEdAsAat2OVF4JTi1ZYn/e6uNlDKbbbeyvxj44LLnJ7M5cbWRT1WRgfEuWqoUoTByYSjlOSfwNQ/iz7N31H/n11AKl3iab5og1ADXcMAZ24UqqhSpoo6WAuYrSoUnIux+5jr2pL6qljQa9zMGnGVdj56SluswJwKNk/QfJxhUR09a17o3+IFmQBhUP7ToiGBzVieDyQPa8Q2Bpg1PJM3ReKfT0lhBa2lgd3mhOgpzr28/QkoTndLSVa2jCZw77JnEYDFcSblZWb1MVfhK3yW1kAtRocXPX2pWKxEKxbtLUJTSg/WkeOO9UsVB/NcSVzDjBw+SKtbuxPt8Dea7xDfEohOzFuYaZQgtRFresAboy9wtqyNrAPIgQ2Kg0dbtTh514Q1mZWxn8uO/oTFwdMbA/qBFJ3OuUEfDxz7S0XQICp5IZHLYmsydLhoDhotFKxyYGv/449OnTx9h0/uPH+Gp6Z932eCp9/c/Pkbk3nF1Gr3IKk39c/8/Vd/+x2c1fwlv9WL87WMVfRsvNLep462V0/kE0xksrnpPn2snrE7y375sp3SMVTQMlc/9r6Rrbk6/PY9JjDoa7D5/+8QE4//6FNC3lx2GoBzQWsahYeKb9UB7C4B2/MCmABD4NlyxZG0H4I4PFEO95QXLHbYG4AkbR6HKFhB//h9p1/fTRpKEAa9CMgHkGc96xnZixwkJkPUiJ8Bmc17QSRiWhQvi4F6SnDhQTtxGFykPIEUnwkuke0Dij76uqq9/zYzxrK4Ws44pj+eb+VxdXV1d1UojkTRoryzvLKy0ahGkE7x9XqmQRhAprSCK2o+X6wuPW4GjsVgyUej334qDUvXOeRTxwdNaa2VhZ/1xOzJSq7/62XeqLt7T+dIjp55G5//B0DaSZgJHS4vgGMCpgtN4UfnO1QBg8xGdtLHoeAu3BdJ+A9E8eUFEq5+fRxDG8PhxO4i0nF94PHv+fLLeMX8F5CfOFXp4f9YjmmvRConGzEFFDc6k5R9uP1bsoxUNnWQpsRlGWmuTUWOy3Sv20Z5y7VguFtfbWkXBub0nUg+uFkT1ytOVlArkKbEaD5QGJKqXzt8oJMCjDh2cpLW3tcgFPFcPe0uBlj/7XuCji1eBoz4Rs3o7lQJ7F6+2xp3GTyt81hpO7AAOagSn+dNKxPChEYassVQLagDsjrlj4huFTtqjzvtzjYGL/IWMAbjenz+aduS5Uqe6e0EEddIndbIMdOXaf+LsDbIqM1ISAeFaG7CFdZOArcT6ZTIAi0ahWgyd2kczewawUV0XhuGm6hg6p6Tyguy2Q4gjV0gIQgUshVNSwBKyVgfsdLlCGmktgEaY1YiWS64YvP69MCwr9U0Vz/trTinXjQW672kQdA59eu5dnAe1WiDqkJDUSWoX54fj/EWUhtWAE/phOGma0s1cbs6vpFoj8S9JSv+ly+VmoK+//5Hz0fJEq3deXRC2J8AQEpljhYGZ/OpiE90ucYvqfIU0ZJySQKYT7mxKm35kPGYtmrDLCW+4RIOPdmXXOq+w1lkQR/vGv6SiHxFYkpFmeB4itYRsLm0+SWh+PWIQuu8boMRSkjftrFBrnBQaCWCysEZERYqb/098Y0fu6vIqjm2a8ObKKqNxGFkWUgcnpXG5Uq+RZaxtZstnFZb/Jjgx3g+ymhrErBEpjS1TejxGHeNUNCrNsVT77hnHN4pt2lHn9YVAdiUB5NcX/eeS6wCqHekrlGhFFqhHQX92FuGNuxjK/PCGrBUYi8aDIPENnfy5Byxq2JYOb/ABueaf1EejKS8qaGWsmWadfMFbMGcQNub8BeusPOs6Grg5LLHSIJOzXjK78B9IYs0SLUCJfhSeNtJFnXVvIaBJvCR16BqJuyjhPo71XfIEDGDLbgUYRMtcklCrJUYjZzULwhuVv6tskCKiVaaP0nNgSMBhFERnDOfBpk6GFDmykMF7fD+64mD0lT2RJsRTNrwBsjHbiGw3Mu7x4rob3hCzpp5j6DRE+/pHiDbnEs3OOul/EGmssK7tg2u0Fpe5mn0FGgzRSmw0GmXzN37/a2FvQ/AMt9ORDfaQN/2ReSeNoA5x1MHL26SpiBYJYAEUE5AYgNNILFoqGomHNjSAx6enIb5RbNG4+0UEDOCNwRARBlk6nxzVdwSSKPWUiMajV2YycIOhs8yskymmiHb130vMOkmQvWFnnUjRZZFW13O00klJI7Sij/AGghpYQLMyv15L213puQOyhXi6usREI4tW63o8g4SrbRCt3Ka2vxS8GlKFfjS38aU58ZIGuRzRghrU0c0y1OXlXzLReOFgjEVrd+0XKsEDgKUXUNDOAk6gMRpwdjeUwjtq6Ayk/U+S/eYShlpAPRc9ixYBspVQvia4Qqj2OGoykB86bzDrJNoJ0a54WSozdF4WWjQmnmfRuIn/LGadbnceUM8MnX0L+eM2nphWXhXRsNLk+0pAw/hlaaJVeCNU/mXpBKZuO/yl8KNtoBjXiTneErqou4NsaIkC9VulIr3LYNAOBtJ4DCJwPI3YM2nQGCFeii0lCmFzSm7oZAwA6WABhlQTDTaB+59NFEudfDQK3GIPVBHRZvxZJ7GFPXzfokkZIRDta8Fa5zdLNO4Na4jGljRHtGwkTZr1GRlUCbsZS8hH0xr8crL7Ybv6+YTsHjRKW7QfVf4GGJPpsdLDwVkG22jT5PQ2hEmDj9YjKxaiwyHJUN4K9THSJTjSyOVYWkcOwLSmghOZfo0gcVM1wdvePz3DvxlwpQzTKj+q+MadrE3jp0dpSpBZQo3BdOGqRWrWmRk6e9KBS75Qn6RzKNRl6JwtiqPdhX825S+qY8sJZp2XmHVKdRebYUt/tDvV8U4QzffRePKZJ1ouvGEbsg7fvcl0FsbQqQ0aWt5vD04cjUb5jVD5+EbT7dYau2fAhH+YJZqoJ1BRHKiqn2GI99ejUkTrg9b71CFzOEDbTwPHAwwevNNn1O3cCtit/ajw+jzTxu7IQg4VBk20OET/yk09bEoFWKhDzo4Huj8s1Puz3H54Ruqwm8kAMhPZn3LWOq+x1snJ2td2MnCtTBqGziudYatoBVdN5+bqJShmMQK29IF0Cs5kwCUZJs9Oy+Wh7dmNi7DW6jSezbeUBron7lY/q769b0yLadYomwD2K2W1Qbym2GYiO9ymdq+291GTe0o33SQLUtchmBAne6ZPmNTHjeBdAswu2vH2B4E90HGMRMF563XdflcdvOMmxMc0lJcBbHy00fGNh4wBUg35k+MY15gwTEJg0QKlHnvU97qM95EmhEqis1MkuZUBrFhiMnDtTQaurjNDp8TRrm0c7S4TjUdfZ88AM3uW5T6XR7vvx9Gwxc42kY/RQ3449C1a3ONZp2XChwH9PrZaSa8c0SpcM/5fhUTbMxaqqc8gTsDjJdXdi+pFueqmBdjJB8wELO3LWLRGLC3rhtVdpw13GAOO0rA82GdbFh6oHv8QpYE43TibpvAW8mzyiDEI6hMmu9v+6kmw6QUISD2xgZbTqkM0Uk/7NHIyx/yhk0mh+GGJdjN61gmiZSYD9CBhW5afDKjPUBRztnTekzouOHnxNCHzK3V2TyAaA1zzl3zd6wZmdT/0tESDUm3GSBOFXgomA52Xbj/VxGuiPdELNr0cC6jji7GP3qGGmr0yRKvjs052hwyyKvcacNabSuPMvR4hWb19pmJIGqUi1II3t/UYQycgMwZAMLNghUFGTWPRBHKoveh33rDTM5MBSbOlklXc545yIFH+E8E0XjjArBMBfyYRtxSQ4ttehi3NAaTDgN2pLhSTmSyn2E5xTVPx0eZu89Fae7hxHoUgL9mi7TlRWvFGDzyilZMKCtlmvv877Q3+eI/qhmv1bK9iVocP9+njuwF1iLbTw3oZoglgM638+EWOkAAONPTZcLQHll4TbaxFI3+M8BZGN6aPZPAm6hoMlkr1TBztnlKPNRPDz9VP/k2q82TAXRLwLJr8Nvs6tY92YwO2aj1dmHV1RdkbOaKRGKLREZQgZDKDoXOGG7jQfqz8EhReUEMnLEToWzSsIQaKaNCADHf3HZ4pDbruzbKFXvKboB4uuasSCZ2BdUjivQxzmjusLrPG3TdVkjeWp3sliLYOOGfy5TpQ74/j2MKpdB3AH6qndOyP1Sq4GDLg0WLMFm+EyhHthRBNQxYM24wBiAnDJERu0RFDDvnjT6sHmZu0B6IVD53fuD2PEA1DZ3atE3NMHUfz1joviWrENjqYpqowDUSbmSKi0QAKomU78+j9AUwjjAq4zfbrbokWx+bK0K31iFa+0MvrgvuimAOPLGSyb+MMRjDHXHWZNSYTpwPlasWlidYkODZSqszJxwSNtUPQCEQD2sFpknxy3KKNzliiQRTezJwTQhgAgme+ggHR50Rh4P1RkEkhGmSwP5Elmp0MYAmKSaYtWjHRLp3sjSt05bn0M2zpFaYacjd0mhAfD+sDTGspYDTHnQZuDW/0BaJ8v4EBEvLQCQ1r9w6yQ2f5QrZ52an9YAKXdA48WJGEGEeQJeSqQz5+Zu94+wPeW3Lo7Juh+Xi/un/M2AGP4EADAqNZrcailNwOOJcoVDx0moXjhDGEp/ATAdmLo91zDODBG7pSYl7t0DlnZ50zdtbJ/pQQLVcS4doj2lfE0ZBhmxs6PR8N6ZTO0Emu4Ugf7Z7GIZMBSLGPBg1rBo63B673PH67HRRUIVs8bfrevUhScAbw7iueul2exPTBzh1KrHUqOKGMQyeDwannKXizH4gK2A4OThDQgcbIxEq/IN97Fd/IVnp5oV6Bd5+/7iFD5oDtJIIDMhnAOtlQYsvVL+aECXJf1SMgntEQxhYts2eAovgYRjmOduP5aFfGR7tEhi1WO12i2SIvsGXCZQpvzMp2UrXT794Yi9ZwfXGKBTr3EeENzbHT6impunPTXlmLxoE0xDf8xct+wSVHCGMR8QpP3czRhscTMRNNmySo3yZu8OLN4ARAjSC8YVZHToYIp30ktNCg0x9HNHqqCveaBG4Ib08/IsiIFQ7JosZArTFIm7tpxNJInSXcrUK+aMiJUu/b7U8SSbPbh2HRwDF6TTwtu9YpZeA5ydZPE5KyHLQEBbblVwZuprAERUSb8YZOfkBg0VpLG9nbnNBDQDPRlEYopkzGzGH1A90FaDTK7g23jXr8tc7lDNFCa95yEVhX/aR6kDAJKMoB9TJEw7z17LT6LvbtWcxwugpwmJyFCXsJwySkAfSUFoCgMX7clH4+Cu+dIjlKG+L0JoRhQjAkBkPkrnUqUeo4w93tqhYbsLVDpyxxztIvppoIsY28NbANQ6cz67yGRVOuma298Y21KOHDs2hTvNsFh2aLJpMBqYhA+7JygmggLUEVmvBYL0FpjYRd108xXFe+UH9oCeoZFbJtSmeK/JqSHToT1wFRzClUj/n2f5pIlDv/zkYGSi9BYVpT5VvnLEFxHM0CHm6/OY6PP5vpz62AK74QXmp6mvPSCAMuM2EIJwSDhTzpVek76pC6lXi7KqeXsHrap+12EMre5wgal0p21wiUcBKHNHe9IZ6hTayOo9FT9tHQacCJoxFVEd4QO2lSuRXFhWhc+4OyuXM0K1xUhysOWbCL6ohjDxBQsBppAwZrfHUoxDeamcXLWh1GVFs0E7T7YSkKNrFX2KgHdfwdZ/PlAFP/sPyiOqL+29vCNf5wAG40ocGSHDAXByeINSakUa5eGsc3dFOtSsWWR5t+mAaIgScKA5/DgQBGYgByBXXAVtQhYWwn5qKO8IYdOkEu+FP0f0wG8uEN/i2zTmIXfLRLbdHwPl1H6AYpu8ZHm6GURxIUE7ovuznBMFembRIQoIahibS/1ImPUZ9vJN3bz9UvH3ahSxppaYuWL2SLoTNtd+PEDaogJS5EmlDTJ1rU7kKRzgbBzrOyaULNbiuAz5NY0FjqJMBvJU0o9CYDx/qfL6My+WgQhZeLTbtFk6cnyUdLkfdjMMDlAoZM9kYnqnXlZJMY30XGKwYt6EuijjZfCG+gQKMX3iCRkkBOKjcsGhMNPtrXr/DRLtlH8ycDIBosGtuyGRCNi7xMTo/K3uB8MFdwF1YbIBppeH8SgUajUia2gfwN89zLzV5eO/OODdniTMbQV0eepGUJ+EnqtVIWDXCSLOhQwYk6DWgw25l+OH5oAJejGQrZqr9ku/Q8ZMjm/GPAAOQ02lR01PKc89EiD7J/hVyiIWBLYoj2zSXaNxtH0/loV2atU1myLNEKJwN33cnAXfpwFOSGj2Y7JmNZwKRyN1bdRM9QzNfiQhAg8dHTMGOr0kjLJz7ScCOFbH1i7kRBGvQWzxzaQGjgzPlolNecIq/ZqocJjbNBKYuG3HX9dllhbzIcuzklVYAROdMkc3PXm+OIdkd1Lv7ujsILgrlDJzGHILMl9tnzQzsITPaGzUdLA1YP5VwSR52JNuUwbUZWBoqJhiXOQqJdgWgow4G+Pd8Q5bhxdqpTzqNYNLMyMMerAqYSgt8Anh9CtKC1mjNoawtRlLJFWyeNJ6vmmmMJXmlgF1SlDNEKE4XAnKDWW8zSLNxwNqd4vAxquEsu9xOol9icQp8HwLhnBg4b6G7mkoTQjA3gvBS2tvA7XttK7wQ5NRhCC1xjmKSmnUbM5hScifFhN7A5xRQQwo4klKyCJ+XNOnXi442KZyAfDSsDvBYla51X8NHcgC1K+PGx7NCJrcNU8XFuzmTWFlbmFqKlS114CZjNJNhuZ4iWtrrencXusygom4/2txGFbLGZbIG2i4QO1bbavM0x3fRmqc0dqw7XmN8XbvE+jYhcurFEo/Ne8jYFuJvpWIN2jjLg0NdIgxrF0cZug2pSM3bVVeuf08VEiyLCgIM7kHk3qyKawzMxgBayWV/HFcJ2u5kp9Bm7r+49/QtEm8pPBm5kNHSXoIyPlrdorHzjbCBG+sZd1C1FeGNuVnc7GJlhSzBq2B6ML9daf4k2rwdpuiBEw45b6xkpDbnl6UKZOBrpFBey7adyuVp9Y6WI5z1iDV31w6x6RNpRW6snzIEedrtHh7CeI6XbwDb0+g+h/jSGQy8ynO56kJod0zE54LgkeCMB9mVUg1iF163Mrdt19onpVBtgU0MWDEHKmNNDjqEZoukt1gpyrH1SVhdJNznAIMtPlGFrK28w1258H02iZ/D+9RLUpVi0a+ujXRsfjUQXvpVjwUdD/1negEVs424HI4n2E7YBB8FS/dEa37bF7l4rgKR7P68tyzPS2FojjJ5GZ69ceOOZKnfGjXoyug+IsEye5X53ETUZeu0gkjoMS/P+wR+02SClWXXSZfVxtF9bjgKRNsGhO0dwUgO4stbBCaV0SchgJgw4SqHRvJVomO+ooZPw3qk8c7um8ENhQP0FwRADA6Q9b8pW0WNaIMsVml9kY0bqKV5d+kVK1pLwTfd9NKYGLBpq13pEo5KPStQz9tFo4LRxNLsyIBX8LNGIzMRpLvrINfikPJrno7mt+6efrrdrWp6sL9R76y3zQtTq/fr9L45GtASNwGrYG1umkC1GQMfOHTZSOho92q31nfqCfFxEj2j9EO3UtZB6jSXQ6kuRAdBg9dszYFdXLODW+sJDBUegBIDz6/cX7VpgNerQIG5EAWn4MqL5mLJdCi9x64XXgFj18dxcSWs+5CVAUrJ+mM3o2mykEf4IdTofvOPtIRkS3mrH6468CwopQ37AFvnYWOvUETVkclzb8AYJU82fdfpEIxE/UCwaL0HdL7ZoYN3TB48eqR/1eMA/9FTLPNeT+3H+wUjZ+IONegoqKD7dogPRr5xsPYVOVn2r8A3zT5tjCrbRHwjOVjGarY0mlQz8tw9461bAI1qnUME93YgYROOA/3Mi28YDvuRWHthnPzs2AS71/KPRMv8/3q7dtY2tiaOMHizyCm5xmu0/FQYVt7m2wEVcBYObgB0bnODG4MVp0stqBLcQ7B/9zfO8ssvqSuuMtY+sZcKxfp6Z38ycGUoLLMoFTxwuZXMKQ4tFycDOpts1Yge9j8bpJjKfe/bU2Ecj2cess/EuGgKVyQCzTuUe1P+vNI02T7ClZ+nCJx0fz/GQFqrx5E5YwjVX71EwH7I5g3BkI9tE7vJoW/W6whOkDSWhbTBA2n0SQOovodeQV3bOm5OCnq7rL+0/Vtn7oBdoY3HNcL3Z1JSZTbzG3/e5tuN++2emPEFDZ4TJSdpFvfD9rulmztMFtF5/Qf64Dhco6Wy90ZLMgAGNISRZdDGfWwaaaTT6l/fRdpt208kEVpkth1ak+/t83ppUl5vRzJbGvwYR/2couxN9EghaP3Q4MI6mjV5aKFsUA/nqbtNn7cCBSoYHf14FiMjlqD+AKr5d1U7/BmzZ8ZvyeaHQLlxMS/ENumFJBwiGCQOzF3c7k6hm3DI91WlskVhdPHy+L2SGBWowvPLk/jMpCsIbC9g2LHQTB2w1uamsE4+g0cx07hho/I8+09kEoJVUJmRkoJBTAJkty+AVYtim02AMT+4V7sYnijW+JkPc48atdUtHBYcMcKq/ylsHEftvcckHr6tTyBebPtc/wtZhLxnQHtbuRV1mHZiSj62w4dF4c3/jXoq5AG3BczoZaLp5OAVaIAN7Y50J0GxLJxECZp3mo2l4A9+luU727izXySE0v7OPp7ZIwFa7o8Xx2nguzwi/WAxgQXTWDGm0AQS4UKjPsL06RLZojqqn6TGPN2GLOazgkhW8cCzQFFYv7kWL0UA0GtkNDzH+3U//Hr3V7hcno4NGy8KdoYfirXu619l2ZSloW0Sss/RNXgRpTazRUta5YwYgAVsBmmYGUDYaR1M7az5ayAxIoyL20XigOzmKPMtx0iFaXWdQs2vkpF25C5ieCDRzq7iRbZ981dGZcMg7L+Wdgwotma/HKzTbiYLr1VS6sc4MaDz+CY3n+oEemEbDIxOLtuM7L4sJA40bx7JfhHeMtk8SRxPlhuJN585PIDagqUYz07nZGRn4l1nnRk0nX6XzhtAIbzptRk9jppN33GWmM7GbCjdOfHoKbiKdCy9hqI8SG7v2Y1L0FIyhJ6X16upHscl/jYcVXDIbUYCj9ZmYB1yvGsoUaCOWKcPtHIkYLvn+PBoxMslFR67+cu7bnF008b6lD7xUUJxpB/iFzE1B8YWPjQ9vENRku90uCW9wMspYJ97tRDRWu5ctA5aCSkwnostynYh5BpVJZjrtgusHBZmJbri+PhloBoXKuTH0kUL0vG7HvfEwG/E+vMAYl4wwq05w0JY8qmEKrqZ7cUsYZCI2xN58l/snd0v3Ue1Dm+lEb+4nIsuAVs7FR7Nhw1xWraaT85MikUaTFo9mOslgoqAOo7t3MZ0bDaORmEbbGOjY27NtKikZ4P0pZbtGC8uywbC5AEifr7uBPG3UFas+q1QBEYLe4Sw62fEjTKe2cjsBZyrLKa6XjWhGBsxD4RNhjQiBaDq1LtlHVCgR+D6iW50/zDqElJlV1vKu8TJ0EhLhQZvi0udxNPbRuLsLlwltEWgkAWjbVtbJ7MCAVkqZUIkUGC8Wr02BJi6BmM12oC2Bt2PCQDiDL46HefUAlwgBKsFuncJE4GllfHNgAVzyKTiLMIXrpTlu57npZO+Mbqai1sgovjEbkMxTm0YzIkBfMqWTZEFAw5cEaw1oiQg2ZLhrANqefTQB2vve16OxKM7IqPIgWTGdzDolCJywztJYZ+ueAVmTz0QJ0ACPyHwCuSnLG5pqNJA8y5jfbqkqIwT0n/cSgcos84ACFQY4vimeT8PZFNfLEEuA5o2mwo2BxYRAHJsWKfDLiEAhICNDhcJ1G6U2diw10ckkMTedMnp440tqmWt2AG2jpGEr3a1siJRuHlbWyQnPBbNOgtvcIhzdplPhNoMZS8YGPlOAY7DIwdeDTF00ifbuN+OoI97HHye45GPV2TixkS+0EepcgKZnIH0WA012cDIhiBla5q8xEdAcAWev0UwGoJVaXWtAI0mAxqkkA9o+Ado+Axq7bAkZ2EVkQEHmk+rlQskAHqxjW4CWt06eTVuANoZXdwMwTKyKvR92s/s9sNuqKzNQKREY3m4a131zN0fbzQRouN5Eo/HNaBaUGR3slwkhMBsTI06EiIAVFHo/DUXCG/MF207E3acEaI3XaD1A6zadOyEDTUwGmlYyQDY0BVous26gjQG4deEQbrc1egG+7c8QeGtapfG4ygaeQzU40HRSqKspEnwqzii+8SUFGsooY/wKKSYEWeZJtnvQC2H4zB+g+WfkmZFQrNYPg5rj44XWbgRhuBnorFly08hmztx0blGEAqD4eqGmUd6qnCLJdVoczec609QAvoLkQMsDHENpj1W8P6WfENxp7gpaDOuHCOiVljyGk3E2/cfVKdDEbrJk0xJHZBx/ZXP6zeUpiAhw+aoPnImP1m06Ux+tafiksf6QVLdcpxU+bveaF9i3mk68yoZ3rd5goJ1JUl3q0VpF+I0ocToFoHmRjbDPAANZKudWB3nuFROCkLfPiUB3BckgSQxcMm9FPRpnJs79M9X5lPaIJ6Oqj4ZizB9NKBMCq9KIqzaYCBDqlAyQcD9Pi6OV0mzDFz4ysAxoeync4JMBbd8CNKnesLrHbqCFMqEQsOURPfHMsTSJpkq7G2hwdwfTK7eGoegA8n1YHqRbRG+14FuIQMU4GF6AiS8uufsdBwNN6zcwvOGBxuhSMqAqLQgTAuvtYhKIAGNO/Z8i8dHQmpYyk6lk06nt3lU6NVrEOt/NdJJGazGdu1bTiS+erU2mnGBvcTTvYfabThP4G5Zw4a4Gwhk1sj0EHwBCCPi+PSMAw+uzEGCpcMnLMZys0Ka39SvbSzOdvneLHEnhsxCCNKzB8rCufyK2pHxVh8JqHI0c8DPt6CO5TskMIBQ6gZYn1SOgsUbb5SkoS6orBbAUlI2BkkCtaTSffpoZpwnikdYCNJLvUt0DMEh841DFooQgxSVwRgDiErHhRQqaTlZoVr8RfDRhnMY6p3QOEXOUcyYExSSxnHPKCEyk4cC8YHTFPlrJAPvEIj6adhIy1dMYauhmo6xziycfR9sStkIpNz6w5vAkUQoKJUlBoTDbJcznQDMuEMvUTKciLCMDgEUIy4F8tGv3fLiz9Fq718pXOVYh5f7xgktuQzEcCDN/j+uNQ+Acz1DvzDNQPouukwwBSlwKjUv+3zkhzaCneo0N6YJMJ2k2lDMhob6brUrTArQ4YLsljSabUxRoloLa9gDN8lzcjY/wX/SYzk6N5oeADPXh8kaog6H2tUZCwDCTA4QIVB8PtJWrV3C8OjOZYXzD8poGr+CjmRjaLEPgPyIUJQKTALRC+ACPgjWTmbNO640WKjY6gJb7aHyVMiF69QKt8UDLfLR8OGxMBrpM5x21lBvIAbqs14fXr2kgIzyR0qA/Irzk6micmWDa1EwnSQo0q6b1qSc8mBDkRCDJSyklmIvw7uHIR+NqRMsQic+ulRvx5pRG69E869wS60yBto2B1nRqNBatRyPdKj5kAjLPb3zKsw1oWmY7kBoB5/5L+FQyBBUHMq4AmAjAHwEaLvk0HqAk07kEaMFQRtVndC8PlBDYTvWHgjMCYZ9KwaJkYCEXIQNnKJwZMKD5MC0DrTuOxjW1ZDq3sUYju9mu0fbJBmIznWUCNCk8z8QwRuv8HWhLSg5wmS0MQ/OeDktSVnKuhBBcXVXjy/WrlAZB9SdsJy35BJyBAQ3z87PzFGgiCjF+FjV4tAzBw8Nkcrn+RUQgds1QpK6WgSbjExVgiLCF3wVlZKDRutrYR9snSXWEmJrOdqDt8UIarIN1Ls4YX4iwCZ6KuOgx4wKzFt8MQm/f5VLLbAeBWcUmqV+AD8sQXNPPPDr3pIb043FWcZnt8TjjB4wuXK9ptFHmmtnfeYI0PL3V9du1+zWafHPuwgxppNHonMTRUBBb6qPpTs4EaDsPtL3f34TWUoAWfLTtv5wZwG9LhtMm21kKqgl7Bnzz0lITA4uFkYFMAvB6fDQWZfvV8PGN/h+onaOf+eFQrirF4ccLLvlUu4kvim+0Ay3yXXLP+cWhvBSTn3R9mIgWo9OETSUdJfFLvJQ6xVCB9smAxu1mpSW3ZAX4CO3fo1JuqknbbRmB7xywJV2m1pUeGtCyJi/qBJbkGJYCNDzmwWiO2gvTOny0MYytzHY8iGBj18M0isF6tXbOPRNCUW6uetA+bJktJI+m/wloXN83neF6Z7w5JaWXIwNZDji6uV+72n1XwN08SPyMdVgr0BBeqtE0/+RZp8DL4mj4LE9BZWTgXQO2lg8gwFmzZLoN9Wgsvkfagoeqy8xO24bih45lpjOu5qaziXXJdDBYfKMvUJtXBKF8IRpIciNvgd9+7K+htRwuuRvSfTADSTnhieIbeIlYpxac5r3q4oqgmpY8KWTJTxgwU6Dh5YyBhicbJRGAlptO2+VrRMBnCRg7iKLGNqdYwHarGo3DaKbRNAUVA8333miMDHwqjQzINMVRV6ntLNNoue1En3ag+Ia76EAYtO5H/3zhSMZjvK6fH6s4vW+3IMRhYJV24x6PNpww1Y2yS1yv3I4y1hlnm1PRJU8KWvL3b5xMl3y6ia8TOmPTGQOtyYGGsBCgGesUl03CG9YSYSe+mZrOjZnOvZpOpRSaQDXTGbNOpAQCtAXZd23gkNQMHQg0qjmFQRKJrm5FBOQqwzYEwNuto4Cxu7le2fda3LTqI8psj/fPQuV2XVsBtwBtGtsT6cBxrsE0ywfMaMn1/cQ9XUchNc2lq7NWzL1GYwbYrtGajjgaPeHIre3r5Ez6ZiuscxtYZ1oy1MhwWOOfOqZHWWeRsc5RK+uEADDAVw60Je5QHOLDBE5Xp8+6e3ko1uDyx8Xj6ir6rlGCXBXCkLzz0d0cDTTw0+yW1sjW9txZN4QgEzni0b3F5c+Lb/cPhC3+YnBJ1Ez3p1Njx16gEbwyNSask/65zZLq241otPfMdGaFj6LRfKCDd/oFMmABv/B3o+cQ3iDvrUOjaYCjGuLjq760B9L+T9vV/bRxZXHAxsaOQR57jAd7Q5qG4oAIIkCS3SWsVImkaRDZtItWTVJViVJt0ihSHvvA+sVSHiz5j97zec+9zBi8eDiM7YYctT3j35xzz/ftnWWgm/IKafd+saAcRiEHb8TOizr4sgDHpDAzcnknLBRiZOkZzazn7Z1nN5dvwhXQM7p+Ju2wtgO/+Be84B1+iPRPP9cIaHMKtFoKaKNLgKbThMwZ8DXaWVqjASHIpPFFNRp6vbyy085oaTLTiTQWaFRzOj3ZINuQCn/pJg2kKrw2vt1+1tvrNhtCt5KVu8XiN8SBrypyLPe+3WCOKnPkHkgzkQtXBhr02eEgW8WYyzwRfXMHpElQYpV51WSurvxzfv7vILMQy2x3JQGOe9RBjKtSKGBbC8qEWMcB5saYzgEGbPuhRvsKQEPAKdMQKCgT4sV2I685hbwRNJ1AYfVG2Ns5OyHQClBzmo9l+j0rnfVwr8qzkasbW0/2ebn4k+M71YTGujZvLRcf6uxZ4gAG4tio0rRI+LKW84+ugciXtwpfqtFA3mygfQcyEyUkEVDMEvGtaCwDRwM+ncxAdFd0DGrjJgVsK0u0iomzAwgC0Wg6TIiS6hawRfyQRkNEWcCWxr9/HZPrDDSal+uED/M68f8BoSYZArwqmRpt/iKgnebTR9wZE99Y45ue8MzmWIYYP1he5XGyyXZHOKqrwGGzsomjShz5+gMF3mQ7NdA4vrFpFtOAphKxzCIyycy0jRxN4WCRTeakgTJTcwoATGYlz2Ffp7/ZjiIRnukckem8DGjDtOkc+c6A5jqBzHQCyuCqiOnMSHROqtFoG0VneqDZINtwKDsBamNXUKZbd3AKvQ5lp/nuwIHUpnUTwpEwR75kCzgmauEMyW4gD7LVvuyCNKgQyUB0kiiyh8dkZigSh5JxNIGja6FbXkAMVlR0zEJgOjPOaMMsoFkKSpmsC0rtrzoDmhlAm43OiaagQmdAXZ2yOgM27DEkG/4LZ6tOJw9voNUaN5S9SwPmY2+nerTfc0CjvRr68Edu9vt+T4DWyfeMpsdJlP7/VWhBUUKrVVaglQxoZQBak2UmUrEiJ/MrBBpxoJj00hujHEu80E5LHcV0hhqNIGKmE0g0mivllsLH/kWmc5RpOsOALVzaQByu6MkA2mzaUVf60Hozvem0QbYh2eIcwxHT3ZVqE2CkHG4rmbECR4Ic+efVP3z8sXh6Og5ppcmoAPKGQJOZwWgYQSLd8902ue5uJwS0x3ZXIv/GxMCBMu+hQkONhuZTTaeuuUbIZZYJmXLDP6nXqUAbGtCyK2zRGZClLNwzQLlO1mgVm7vrxdHKk2q0Ur5ltuDvj1kFZovbY/zkb2B/QxfnNIEj0r06bWVSjmLuFKHIdgMmBlpoPDW+4YZr0vIA0mgJyhzFIpJRe39VTKfelXbwbJHMCDQIkmoySkrCtHd4JJv7zRkw0znw4mgQ0hier94YeF7nQLxOB7SBH97gMiEK7fHomWAYdxk/ypYKoLf0nGQmC4eeYoFP/mURBrSdyPYi/fGULSdCabcpQEt23GP9ok50hOxt2ObYRNOZv0oDka82tqrgioXWsfKlrMRIIyoj0Hb4KGoqy9Y1MtCAI8WA2Ixo5SPF0ebokoiD5tMHeGnAFin7jIYd6sOUM2Cl3P0hkuBUTacBbZRpOvGtIkVN4+JoMpsbrvQpDbz9PCZXWaFQal1r5G7mQZ0+xJDATRcOpfcKNDU1eWu0Dl3UOn0FdVY2r5MaoThOa/kAbydv22D0WfY/q8wV5hCK6eF6ajJnAK02h2osDbSR2cuhAm2y8MbQTKcoN9RoWr0RBmx52zU2zIQIs/KBi4BmKxqg5jQHZ4AG2RayF1CLPjt6fYK3vB2sl1aONl7RU/16jCPPulvzkB9d0W5KS1l5vVz61Pph8zzQZmmdPUnkVsj/dsBAi1UihKIpcXu4bA85fKkUkUfzSS9aBIWkcTTMD/DoWaazwaVA0xQUIkzT6xjq5bI0Xi/ln9GAOAUlmxSDgrQyCysBRJ7lOxZoloV6nIcz90vrTVqjHbq94fGR7DmPXWipCzDCpfsdXWr99n1oT4DjMH/LySIXTq8GtEJZ/gnSxKrRuChNHLG1PZI50teL+p/1yJNopUIckcopm9R1nSxwoDPAh3FqTpFF6tacAiS9UIORO62Z18lwwuxm33KdjD+GFzJarhMsJoc3HNDwkvCGX2FbAQq8TgMaN7POh0BToml80kc8ReLaCi9arQzTuc3Awevo6AiBZrRFGm3bYmxHL4g54MgJXeEhDBYFXMHh5BgGp6CAWq1s08kyR7K58h0+XzNtk2iPOZT04Yr1bcsBba52Q9rGUZ2p18mmjcrHBGgYux14VnRASDOvk3Kd0kDsTOfQTCe++2c0v0yIzmg8H43epfk0NX5DZkUbwFKWk8psp6/IKRR1kG0ItOUZnxhoCqZdAtqyBTY///H6ADaRM9KUI2fSymKcfT8xzgpZqPtb66Fztbwy2zWUyEIWB/XXJDULFe3e2qsIhz1cgsOIPnbljAYXaRWgpZo2jahGg/q0sMeTDOnQ1vhbz8DYM5rkOhf8XCeSNqfUXM8AgsvPcQa5Tg2oCdGkuECj+QEOANmUZ6GIBr2kgXbc5oBGbEDzYYQc9rg/P6nj839yjUBzrdOT6zOK/J93BjY3yxDfUI0mkSW68WsgkeXbvtQ/B1Iz0I4FefZwSfwnIg78eslscsEQarTQdLJGI6/ApcY9jTbgZuJ+1hnNgKapUj8FRTSXSkGxLoM3bXZOaTQdCC2DbEOgKb2hCQVTQq2Dg2zTQNu1pzsFtH9UV5gjdtGNl3H07qD+3OPIOaluIp+ul9YntZuz550BpF9b32sUyQYHo+nctePCu/pLljo2iSrIIRTjw4V04t2VVwAu1CneEATAkud1LqS8ziFZTHi5OFrf+jqpZ0BLubO9TqqyNdOJFtqFN2oCNHhlms60RsOXQxlcXD5NfcSdvAbZZgONN1ePBZoLs33B93f199cGNEUV1F7gitcJgMawygIaxDfcXS0beUBrzxyAYYzq9UhgRRLNG9DiGB+umTY+XOymIsfejUXqGkbfT9zABT+pPsdoAFxYZoDisJYZSPcMXBjeGFwItBpVcrNGq0xgOkONVi5oDub031jvH01zSCvIINs00MCPjzsxoSzbdB4CRycMbNb/vC7T6UAFIheKExpO8gBC07k5C+4AyKuT3pk0BbWDiIowLP3yBN4yTOcO+AkgtHu4Yny4jOMGpZ8WPaCNMqo3EGiMEafRzOukXKdpNAVahukkfKaBhmRAkz0vfq4z1Winnwa0sJpb1vVM/RViodDVnQEKABz9Rn/5tH4tQDNgsciBRrvY27QzGhGd0XTQiy2wZvKO+kcHKFCUBppyWJkB8BiHJtXpVYOX53VawHY4JNN55gK2Z34cre8yA304o30lp0A724fwYn0Ib5zT4qOZbAeVXCfG8dBeLsqZDD8010lCG878qdGh6cSX3Ol1mmY7vfG89/FjGmgrQbIvK7yx4rD3FqwI0Ov6C+O4JqChyOsTBzbK9w1o9+GnTOPoYJAtOwN45y2SxsELqnd6XnckJ/2Iwxt2V/ThshAIcNTmOKbhUlC894mPZgq0wVDOaGz+rJS7z93DoddpZUIaR+PZ8aFGY18grdHIDaBaWzSbN847A9wuANJnBWxFoxUlwJFHbgdy1dJmYkDboICtPbd+MQMGbJlDfnlS/ww4g4AAgpI5DjvXATQSeSJ9ZslijtYyzW6WwL9qYXyDFtkZAdBAIhQwBBoIxBJRmdCqytymhyvSh4s5uhTJqCnQyJYBMdAIBGEcbWQnrcGIbChSGEcbepkBsa4DH2gDM52UhGCccbsM48yGJgd7Yc8tH5vNApreedxdk8f3KYt6wnq0HYOZmM50Ckrp7QHl/V7ORHmnoAxoJvJkCm2WHQC+CtJrxyUbVL9hGBNTsuZLFJlZjFSiCnKI+gofrog50OvUBuLzzgDHbMWGwuVpNK3nVjj91zujfc0+o7EFJahK4SPii2PENQprsKuZVb2hoyCESv4e4qJXvUFgU3/gUeuXzrT9dmiOPoQpBj9lHuNVr+On1Av1Ei+pHrHx/FLHgC19C8qRJ8z88rmPtp67MJbwgeRXkW8gE/oCZRh59mm2ROgy0ylJdRbYgjqRSgQwkqR6Wx+upxg8fNk2jj2qsK0s1ugFTgFc/PXPsaYRc8o2VDSaYEyrN+AioPWpTEg0WrCuk4AGb/Jv4MAcI21BtriTRqPRG1q0EYZszSvwsuqlDGdAcSYtTJ3p4xudVFK9yY93ZIUz8r7rKmzt8baKNDQ1uZYJFYxU5EkiG+CdexrNRjyivaRBLxqqdTmoNZHIkkoGtN2GAM2UnjxcIjvdlW6tZme0MI4maUk9ow0GgUbrK9CAhqjArJRb007E7NWjof3V+S6jkdbzYksMabRFbJKxRdeB10mktY9yyMjIdVowjXYw/YgNlNGU8Q0ac5ZV+NjWe260v4JAE45IlJ59O8aRK86MQORJUk/8l3pGC+n71q+cSjb/ywofjdqRyewXPkYqaCxc/xGOV4Az/4ymzgAQL9JJOQNaJhQCbThM9QzYpmsFGptOAZoczQRomlS/4bzOirUOy4UiB+GNUhbQlGghxfTfpcU3glLulf2MEsC7PesZSJCjTZdRBBzXB7R12lB0epk+ox91BsTlpINvCc9oEN9gjbYJbxZHO4QWk5X9ju8ExVqNljDQ9K6Yjo/cXUkwvLEEhEn1RVtzXWPlxvADGkmu0z+jnbnMALwB0NTrDONoZ30XsB3SD5Il1ek/UfMCtkscqaUT25jMwPxFplPVmQY48t+HKa0nXXdP2wqmB71qIkC7VW0AB93suO20XgQcwJKT6UwdvWRD0SXxM4IZw0s1mi5uwD+gQ1GylcOciyqvicxt67RxMgNBHI04ku5+TH9nPG3lIGcAUlDaMyDOwELgDAxcZkDj+wg2zXXyqT9MQUkVmphOm7U8MmdAVGltUcuE/Dl88GFWVD/NDboIaOt8RlsvTtFHbHN/QEmkgJZQ9/ATupd6Qo6k9SxRjQYcamtSzWn56zNrnb488wQ/9wONpmusMXS02WrpzlfVaHhGowZCkihOSdTAZrrHyuEr+VhaDNOmE19WYYsRVjmjDT3TqRqN7SeZTtVoQXjjzHcGVKONtGdAoxsctMUypUUEW4Wm19qG2JRGE7LwBn4o+TWmWHOa0yDb8Ix22NRW2ciKmx/scN92M+kVbh9au61+McTRbOD3cqt3PTg71QG+0jyXDTTVaOgS8C+0EQUvGmQ7685o5gxgEzBJpPJEMcncoKerRxqtyTK7m9Lmu9JgDlBp6GpyEzFhTgdKjTyNZoWPIzmu4RtmOJEAWQC2dCm3Am2s6cTAXS1IqkOkFgO2SOnqDRU+1Gih26nETbU5zVb33dcH2/iE0uO7/ASaB8A03r193CXwoUY7Lt7bTvDmNpkD7ztxVJv0bSXJMQElX5xJSoTnARUMZplAE0yJKZ0Fsvb0H1qf2PHyvc6Hr6pCqyjzTCQSCSXHle9WAFHM8Qw5ZgKORnJM6sSfj+Y0mk0TAhJ0jcIzGqAoBTTVaMQfTHynqK9YTUIZjTGC4xiZ7CWcXcUB20CjpZtTLjadRtTDNB11oAzk40/nJjvuH642lbqHveWtw677RaO79fin4v5eiqPpOO5s5TMRoZPCW5HKbAk+l5hOPqoViJM0WolNZ3kT6zdkyrt8EH3HEjU8ifhPiKJu7/dH838FjobIeEfuSpUlxrvyP+aurTeK7Ahruum5MBcJR42c3rVCll0nWDFaZze2J0LIfkBelEgRLB6QbMQLDxb7wvvAi6V9QPKPTtVXVV3n+HRj2AaNi+mLx+3B7fnmq+upQk6dHQGu4MD7PhHgeeEj7c0ZIOCp73iuqhPCyPpQN3lBjZDx2NJtNPxzG+0CPIbR/SzudUp5EJ0kkQ4jNB0iz0CLMRYzmiwt6SK5N7INA6Pb33/bJo8ePuHhnX5FKg+rL1D72yB4km4ZanNzZ7MBZDXQcFAXVKwQARrt6H4Rq9yKe6L98O2dO/RolCfzslyMjuns37LxPpIH8DGHQXgjTKqHqtNTUDr0GoylilFdUHwVV28gN9XkdfJeVad7nRma6mopt0duTUbJrJ6BVthiSxmt2zriCpvGN261DgGOQJM/eFyWr/nZqsodnd63Dxv2Xxpn4gLt8tDNNsWpQOtrdlPR53k9tKyl+EbPI0s4muhp/W2Zkrg4Kcuj5ziVFnw4HXPx2QwEMuEDa61hGkfTdKTG0QLfESQFFwBAIg5jAaO56lx62yo6ieJopjotBZUNCVq6CgrdNzTLCTqLbrN3qUwIWzujAWkn3dYRY4LYPld5YSKKi+Uqc9k50p6gGXfVRFibX7C5aHsvl82T8o2qxFQGsep0oLkzsDWg+/WEcjpvx2al2wCBfeosuveWXDb0EZ1J5zGdmTibGtDoAXwBaEMD2rlwGnaXnAEKpYXOgKXMmdHO3wN9Ft6wQjXNEsSMNrxIGA0hPBm3Ny4+uoAY4piLGW0QIE1m13SWssQ7m6AsbWoLOsvtKia1Krnu68HsGf5Rma1E/q8CWi6+QNCbSdRlUZYho10SfVMMaMevy3L++lgGJo6LmQzDNMyNZiR8xhQi88wnumWuOtEmjYGmPqLOGTDV2QA0KaoNqjfeYep1u+qsGU0G0o6n8iuGJIZ93VY0ES3ZwHZD0eXSMcBRgavgu94ScDiEAtSYMmQ6O3kQjrfLnewqfTlHXPUV6Izy43TLbYvDcuX8/g3/Tt39QCJqLHvlcyI2F+Y7NVQKGdUDncM68pCMs9N9NnLAZbzHsEQAjQ7SxXYyRjwBfRcnzGjxmgGz0aBCsbnXqWaZDrc2Z6BebrfU8AZqh2Kg4eFAS200dLEVHtbPDe2czdyeSFVnGt+4MT/b78ghthAqb3H7wFq50Bnz57MuXNUNZ30Ildn28zCOlouGjBmtL2G0sCwGe8Q3Fvgje/FGMKJHDiMQ3uKIjLOFzn4iUfsMwiAT/cknM6jRLPO2VVZhi01Luc+V0bR6w3q3RM4A4Q2MBqDBCRAw8hVahKZJdVWaDLZmoE2bgdauOjXKkafegDdpybs1sm0ejp87O8E620ONzmqA5vkAuuU8VZox0HhHAqCpAGgAFDWyRZ6vSGUUqM79U7rjQzphAgOaWFXKqdpoU6gqfiCziTcc4HJnAJtGI9Qg8wrbwBlgDvPwhibV6RRMxowWZwYuZOnAcGheZxujjQxoXvR4NaOlaXWdXdNNnpwd3GoixdweoLOz8kA83GpFQOur0C2nvoCkT6zCURmtD6C5M6ChW7LyNLRhAVvQm5ltYLTe7pM5G2f0hTEaHtggOssC6BuB1NQ2m9KGZcSSTfcdbDQr9gH62mw0oAqMZqozTqprBE7iaJYZGGqGdYqUOiYhJ3VoIZulNlq77pTWrhViE3n1VQb1AGdGZ6JKV8ZnPt51k4YGXC1hTh0HITGKb8AybmU0ksM9Ns4KCNAlRas6AZMFxAavMwsZbRgxGkTKt9X6D3OdKBQiweKUOryx9OoNnILR3OuE6nSgBeENRIonWso9g8tiY/g6As1Hbt7qNg3zMSz+ZpCZdbZtgY5qdTgbwDCjZGX/04G2UwPNRvE8L0+K3keA5saZzrAweOlDBSzGDAbVSWfDBGgEiEh1tsXRzmvV+d5UpwNNbTRsmD12YYx2YUCDF5IJ0LIIaLhJHFx8QRTkStWJWDlm13RFWllWLc4A9qAz//5qcJa74U+3nLCWOwN9V530ZKQ67e9dlgNntERgnM0PCV9m+4u7qQcwmcXRhEC0jRAAppuVbYsfoIWPIdCQVWJWc2cAos4AvE5jNPrSwxuMt/rnLH+K//VjNtrInAEXsRriOBqkUXXK7JrujWzhC6RSgc7OmM48+7hSxSmtp/LLKKudgV4droVEH1zzKul+i2LQArTj13MKFu6GJMcCyOFtmwWqcwaRbmhTNcwy2YY6u9+9TmmHEAENLKbTE9/hvE6qv4czgNai4nXWEBXVSYJXBWcCXhMGvqvOghlt0MZoJEUTozUB7RlTWlWW+10tp7TRi+eUmM4W7nx+ZRMt77fjDNpyi0/olkFujrOY0QYpo7FIp2CPb9TrzpzdRjDO/rsPVBm0jMckQAsmc0bLJrpsOAtsNJwAX4KxRHUCaGqhgcTUqcTKYpTTcq5ziYv0cmE+05lq/OE/tfINBGx5vgDpdLEkg6Raz3VmQ8A2ZbTUSEPNKWDRuZFtVQFK/rYbnVU58LU64XuN1hHX3bkvN1ricC1i3FCX1oAaqrP+s5+Wv+G8rmrgZZ647jkZZycLem6LsOc+qBvU4DZzO+kh8Y6RjBgAr9hACxeAzvrw6Xz184jR4AxoKt1ttDCOBkwCbOa2AnSx6swmfGDIYenAOFGdvJmYx52qzpTS+rnMrulaKcGNXnLEZV07unX2tdXl5w+te1meqr12aSQ4bc5vPV3H6JrTVCcXChnqcKL1p8dinEXTYdUZmJnXKdjC7BsdOkw7qCxgK7TRTHWaoVZnBqQfmkfS6FSKhLyU+50HbE11mjMA7Suq04B2YUCLCh+R69QoM0lkozmZfwrQNjnM9QMFODrF61FtFEFVut2pswnsrRRnDjQPcGxensQZAY1PAkaDCNAg1MhWTuP5m2ScnVHkLJWAC8T/xJNio6HvHYGOMeeMlmmCwGaaYKa65zqHpgA/1KrUgRakoJjg6jjaMnEG0C0ZHoE7A2jEx7ajqk5bR2zi0HKdKV94NKjJG6hQc9otkFrBodBOsbw3Ojvbe8FP0FZVKwRaGuGhW76qwSNjzAwzE5uXvihPPI6pZ6O3MM4IUYCTcgGJhzf4KgaXBNIYXGOYRGNNehrQJgnQLugEqc+4cgi+gQENXmXKaK1Ak7gtY1a9AVszMCXwW3u0mMecwWM2K65itHxTLKwniIJ1jW9UIaUZnVWWZF8hzlI5LV9vJks2I0bzKK2Lf7DLs57aXqZOxTiDKVPPtTEDZzzTbp3SdlubDmCxLjsDOgFKU5xsmLvqlEFN7nW66tTUuAGNQxgJo71vdwYAL7yq2mjD2EbLLoU3xnJPtEGU0/zg+GoePkZSseLrJvm83K4cY54K8KeuFc526JZx8hGgOaPpFvbV50a2WiYE2RXjbEtAJ26DaxuEZnkbO9A0KSCjeIRJbDES4IUmL6bXgAttK8qbq05ZQ8DAMyUqKSgrE/LMQKo6bc1dCjSQahpHs0Sur1Y3Z8C9zna3M99EmW31JRu9GJ2x5FV13XDWR5mtoywBGqCVMJqbKhzfwLkKR84Oji1ollaqORfweIix6FTo0LoeDfjTPCdvTHUTcJt7nQq0DED7oK5A5HUijra0xSlp9QbA6KozARomG7DqRM9kT0El6jNd2znoXQU0LGPqxjuVN7I93A7pDBhbNc5SrD3rH529kW7ucfinZ2tRHGhRKZCZadTItmA55MMbMs6O9ouwgQDApPCajVV14hAz2nSiFa3mcM7g97nqNAJjjMX1aKY4tWaWYGY22tLmDFgcjTEV9N7QA1gyttE47ToVZ2AqzkBaosLFUY4ziGY+La1CJ2mRLZDQYVxPjt2DV+XR0WNG6/b8IHE2Vx/XSLFGt7yzBefSWm3AVxrUQAtUJz9sZogg7fkp3+/b4ng+P2bjbG9RiJgpAy5QZ0BC/7MQaFMALbsMNJx7GgpAG7bYaAhRCNREW4qNFqvO94I7EmMzpOXNRpNKNANaps4A76WbroU3ClOdkQPkXP8pjFZpmS0iHZ8rVtA4L1n2tIb2lVtnqwda88SAAcpstfAsbCiNY6+nQIsYDay2RYfiud5vrzgoS/pQzV9q4hN7683vQAM9YLua0TCik7baRvOKIPEAauM/ZrRzqE5RjNCXhLG2wkdAFKAdnrc4A/B7UxsNm1kDxvQ+i7mfpgYsTK7ooprTP6bncrbA8sOzkuQlfbmN9+Bg21NNK1ecKdCszNabyWEzoNlKRVedAYrw/CGA9ltvH8eD3QE/7WmCGGiFshnEGK0daMpmDjTaHGiegiLKioC2bIijtTkDy6udgSybJSkom9lpyAqkh7+gTgGMk+l2YimkTjNwXpUkYpyRnIZq9To5AuHSALrlqN+qfBcnWpRs/kGDnJYkx8URHx4HsbRCwxuh6kzitvqcRtfgDNBDYxxjbBnKujUX0MxowJXnOoOk+tKdAVOddLmrTuwUaB/ageZepzd5qf1s15thtxuviU//5oIXqjnthgvKaLKZJ4SGPpKklKvV46xd6JabVSd/XhMbzaRQOaGPU7EoIQvXlnzGsHGg0QlUJlht6ozGX2FyOoll1I3RlNQusgRoqH6kTeBhFbYGPG+EZjYaoc0ZzS6gHaAmL6A2WtZio4lLg4dLrzHXSY8WGw17WVhenlWd5nrtz8sXRmjl3qv8elhog/apTjsU4Og70Fx1DpqA5r0PNFVO97soTo8gpz0jNCMqU52FBWzH2MaqQdN6tAy9a7UptzmdVryt9WjuDEAAJXBaklRfhkn1BGgfWDxuq2VCABl9EHS6nqWgRq46be9jLBRUrj8TrzNSopAu64gBtsVexdmAFw+wiO5awCy8zfgyHsK/Q+uIYxTKzyj28HCgaSWQp9UXe0VSqBUDTb8fAg3kEKvOma5WoZOJqU4sG5hkXtw9sTga+MfKhUT3kYSMxsj6sPxdVefvqY2GAxgtAhrI1Pqjya+CD0RAZ0nrKihNHIuE0VKkCVIOzg5o32VS4bY/U1XXzj7LZeeSa5ktoyp3RgORpV6nf6jrsNIxvnbzzJZycjLTVSejCx6n8BcOMvxVU1Dw7kRVifIKGS1o8Hhhawa017ElAoTWCHfidaYBWwDNc50skk03rzO7YEHyazJF8smANg5VpwIsKRMyhsPuCqB9Wplt9dcfb7fKP3cYWw8/dsUz/q/q19jQo3+J1/g0yT8baXmaPl+cPVZTH7sEaN//uLGxcXuD5JcNl9vYP8WfHVekgh94OqM3xa/49Vc/kePG04kCDQZa5HX63KfUGVDtt0ycgXeELZLUGXjnLW5t2kCUVPcyoeHEnAEQKj4btq6T95L3iJGGw5VAc6F1xABd+3t755v1tbWbayTr6/e+u//L3366+6c1lb+s39+hK+7SFSrJFTf//PcbeI34im9wxbq8Bl2RfyYb/gE/oCfgkgBHBLSBE9nWnXvra+v2q35z/zb/qjf9ZraLwm9m3W7mZnC7/+uNotf4Dq9hP8FX7PIqKATVwHjgE2Y3L+AA9qLldr6AGPSGIwEGNluS64zr0bApGdrilLCUWxiNxBnN8htpAsqBZlBD+NEX9SdA85pTN2SaNOTuT9ow7ua9fzz6GXNn/vXoP3dvqqzdpivW0NFwHVegoeGlK37m18AlfAW6cdsV67jiM6Nv/6ft6n7aupK4HTAGY0c+9m2ugW5SdqlJ3DYikHSrAKutRGhFNqgPrlZkt4gqEi27lfIYaYmlCikPSPzRO99zjq8vNCSMv66dyeXO8c9z5uvMuY6/WWfqVx+ByAQuA5q0caHb5xvSNU+EMXF7JEHnTu0bGZCuCaPidkncPwEHMPs5lIM/7ACHOQNSxsEeKMJKICaZp1tjmQEx0ZIUFCQGJI52mk6dqtHOgEEWTZlGI9tMgUYFG/gCVyU7DczZ1DmRkrQAjRzOFWKLTEDaDO9d058qkn+nKxswgF1uBkrESLpzF3o7dqC98cNp4MCvoQccwdtOAwcNq3AwYXNNoTwHDsJZDzhowfvHygjEVdpjWBvAM4hMAVp+MNBs6oyEySpGcqkoDOzW791RQ8yB/404tg56vS5z+Ijlfo4FsdEQXFpda6YaULw4RfJQ2ixZ9BIBJg1vsAuaBGxHsTOQTJ0IsbTwETHuUyfH0eDuppphzj3xKIrrPnzZ3Ik1p8NSoIGyW2aQPJANXQkoOTUDBhB1paU70t11B2LwBsjK0Wt0HnyWj7dIZqAtsC/xkXA22UYTBSbriMuB5uIGFFQoA2E6JEzVOYJ24Q3AEKwjtHP45qXEpQNyQL4BTZtIvL01uwWEN4Jh4nVq4aNGWaVMCEiiY3z3/TqLawZk1YD9T44KF4AGL8U4Gjk4JXOnpQskvVJipVGZ7WVb1rA28pbuTo9XeeQXlydyhKzyBbZ0TzlyHnI/h0CRkHbzEycDC5sCQcu9gdUFxUDbRlXkwgR5sUvFNtw9EVfkyQlMzrEF4naRw3ViwnEAuoy/VyLfo1OKHYuZAQKJNtU71QXEHFYTrzNNQVlSnVNP7E3YOqgz/TuzutyOp875FkKfG/GVkK0Aozcp0HB0y1Wa710zuckwAW1bhpXHTIb4/kPRV8KR+7gKnJyjhxy8d8hiDDXgAK24jbrzBvRZCUGvlvojGBMFmmu0Wl3FdV2Gj5wutYf6qoa2BHAYg7ASR5eBJucINiAuLgHNp07bV6Dp+5tfeHfkCy5uTLxOvONNNiAWN7Q013lOvTcmAO0CSbfsNKDd5nxsXK+RrOVKNVoJ0OoTgDbzyyf/8o9SmkaVxhvM6ERi/csD/kAf4LiSFQccukWlDT9xAIw2ZGMeV2SZoJXP0QOgMYL+IM7ew+W0j2LPG7broWGZEqCR1ynFVSxuJiL4JZu4A+YIYGSm8hpHVYZsnDLlOMDvk/JRnF3n7u/mDMgUKQU9eswGlgHNUulvgUZjuU4FGtAIzTSx0c7cRjMoJ8vt2DlBr7MwdRJVJ7et8vbc5XPno/4QsuJw0OdHSv0+arSOb5mVhcpJvL9ur8dAY45c4bMYccC4ItA6a/DPuWHUab3zXttYXFOb1ZkEXCCyDoumAbT8bMX2TfN9EfPgl7pRjcR1aTPhQBjVtojDWERg5ejcO5DVm62ojLtJuoxtfwOXp54uihotAtppKdBGDLTTItBmDWjFUm60Hz2OVtMFN4mRZgf16tVAIy/gu0++HSrIitQHk2TpSTTqmwg0nwkYaMDh9FsbWY6ROQcO2km9Qxw5za77baKd6Bw3Ftjwji1yI2yByDos1aoBjY5ZXKHA0ri4aOqTuAQskWUXZAlqTeDUmQ7I4Q+b7Vd7fg7VaLh+khcNUIhDNJom2S3XaZ3Szjw/oF7nhVcJwZF4nYAxaYlAuFSWokYrVG+IC0yGWgw07Ft5JdCql2q0vu5d41TUaI01nBaJdl6+iDd7zZfFAluzbybLDjffAEsWcUwt0zly+Ym/UqAF1IHEceP2WV2JjnAd8SM8rlaLQEv3eT3cjAReZhg5h8ui4opGA/0dbPdbpB/8HKDRSHdAtAq+0CYcaJqdlgyIx4nUlFLbCy7okGiaV28ASVMhrt4oeJ341rAIXNqITxdBsZGmD7TUEqDNK9DmgNj3vAbQFGlbv/5VLLLh1ASijZpzwVkbqeL0BLavZg4g/5Hv6qySEUc/5WjnikM/x3Xo+hrtn1BZjOUa40AbANCWEmECqqzkUmsqTE6y8EvMsSQcx+h545bDrw8rhy9Q6YWcOXzq1H2m8cGAgqfERjujlU4SQGOvszB1avXGSJq8/M4pKNVohjKbjkmPRc4AEeU6JY7WbF4KNByzeJ8BTz+llCi04eUBDtBGD0NQ3Ozs7MRAy/IvCUYPK04/R1jMeUP+KeDIgjIcvQpiRDvHDYKsqNEkwDFIgCZx7ToKk7uAIg1bl3SpxFEJKgu9uD4Hjo0acWQ6HC/oJ0oqLaNzKNAkkS5KRbSXep0e3kiAho9T2a+TgaZJdSoRSsqEOFUAfHBwhTPADi+1z/0DQHM/QIevHGiONKo5LffkACR3eMxluHFDfqd1gtEdh1EFtgsXoDnHMp7DwLqvh4t8yvWbBJqUGdcTGvz7k5/I9C9OnSiuU0bSeFQWLnUgHCQxypKJxZ+ruDXgyI9lyE7aL2nYdo58QDY4JNs0jQbP6H66jUZ6DUnbIMCDSXvRJuENekNbpFhX7hJnoMRG0207Z68GWoGuQprTL79+VwIzZFzZfu6eEwEthHGgAYfSa9icfpe/moBQCgWO305ebspmzyHIOW4KZUBylBL00KgWgVYbVAd6qS4NAC1El1oXjhxuQWXxwO462WjPo6n3CJ2BE/dMgQO/SqJbLVFtDD3CHKNMW4taZ7NzokJ4423kdb599477oxHQzq8GGnd+FKClGm2uCLRUm3my8wqgub0PZbas0Yo2GjkD60l0u/0mRCrtbw3UV+s+ceIUoTrgWDgArMAhobiw96KN9CI+xw3gTHfctCO9Iay+htYGJRrNhHFpEnGrypGBMCpLCAnHEqSn7HdZEJe9TqKmtX5HjWY0mzbik6KeojOgQBs50Ky1KNx0AzwgC9hyI6HYRhN4UZYfrotL5jBie6lGq9qrg85WWpSpNCizVZAVKQWajbuP6zD+bjb3KyFiCcBBGs1HvrK/C9/ez5u77PHnzHE9mr4MZnE7xykxzzRuNoAy2wlAg3GLhck29+Hy3rRF+CyguLWYY79NsrT3NMULHKjR1m3A2rugFHNk8SEjoLFGs8I0nzoRA1b4SE/p1Mntp8S4L9Fo7nWOzhKvU3DKJSG2gFh0KbasIg8YwabL7ajPFgEtqUqTDTwKifWZy4AGXc37qNJKvM41mzozBloeT50H0ytLyJGjxfUD/XALNhqew0IkJ6/RJv5591XmHDehzyQnYod1GQQaFhBZ2uzFUyeloNYymhdFmgAqPBGmxgNispDie1XJJOFEUycNSBCNdqIsPmScfZrluzZJ5pjZuNcJB+4MsBvpU2eJRlOgidfJIAOKNBoCmgidzVuSELNl9UBJvyokw5WT1tmmQCuLcMzo5rzTM1c6AwUUBXEGwjHlBXY2DycAbQM5pHjIEMumT3ZtoEEO3hYQFomFVFHTim5UaBjgEOSp18kt3mNngKXJ4EIzkT92Bjwdh2qLjhxodyzf297dkWFjuRcDDIgkBTxglgJNEgL8rLuLEWrcRhulQAMq2GgjsdEmhzekxbzXWcJF8VbXnPCsaf9dWi9g6x1El8XLdowuB9oQDDGoOS3NDaCvHln/eYQiD17IsO+122/a/BBu57DJZeeQPgcWTjUE4PjY8yZoqBKNpgEzELmqb/m1JkB7mMlPYe8NSuKhw4yE4eBFIOHCzmFGSm83+IAczG0teYhkv21Ay4wDgcbgMsWCBw60eKU6gU0LOEijYcVjIbxxOhbeGAkueTNstdNioJ0J0Frq+pKpSCsaWk2aOqlxsroFJfsOwD0G2mUV3dNQZvsTK7cigQX2II5gokvpdB8imDPLyhH2wCIRijgOpvgcIeDwH3GQ/GV7n6FI57gBb1PrzGTPanwfr0cEkWMbjQaMgObi7rWNyPKv5HipNeJgIB3toiw5yiKZgOMlApoP2UkkbiYD4kCDzXHUPor7iV6kbat0+rSmLR7eIKDpSnUEGrx7601etKUokTbkHps6pQqNNk1pgTkWT526BEw8Tu7KVWfCf+GbkwKtBGlYZsvKrUBpCiqMz4ucggKOLFcVlrCE3DjM7XzR/i2vQFTgZSUsapLqGouwIriVtHcpTp3mEAwGILL//qbkYxg0FDf3kgyaF7M8TUF11rTgEWSpsCzGQUDrEQc+DtvtvZArS6hkyEH90lotqZYgv8+253HAwYGloBR8iBT1OgmLgDCNqJ1JKe1opBrtTDWaWHdSBB7H0RB0mOCiP8eIF402z8sBVaPNF7xOdz2vBpqX2ZapjThlHiSO5kBbjZLqxwWgGYdkqgPdPfmHFjRzfNSYhpIJrcrNurYM6lUQOdJoVZsFWBinDKVxYZKkusviHF2w0YhDR+llm2hfRidb7dzboLXDiCoBlms0LhOabKOd/TEbbfTON+8XG40czyLQLBmlUyfebzdTG22+pkXdhbUqYqM5uY1WllmHvWvKwxv3NIkc5SitKsbKhJQ4vOEcXUhSEcex/vvR610Kcnoh0cL1cFafCDOJldWrqY2GpM1v8ABENhut6tatCmNpMjcmM7rUWiLu0ev2Lsii+gs5Dpgjs2Dc/q5Xb2TrjR4WPs43E6DpBv14KGVCXGdrNhqqMMt10rwYB2yLQBvFuU5BKjWJ9PCGr4LirgjNeXQF3OtMcaUfqDegXidPpokzcEl2ABbVlpQdStEiwWycHi8g0IwjKJL4kROHQrEHtYKOUjlbII4eAq3/gQptKg1q4LqcZOpMGmDindYRc8EjPmlJnxY+FiiIMDXmEGlDcUA2II7WQQ4h58qIg6ZOXS7gcbRmVI/G/oAtjVPzynOdlO0cxRqNW1axjSab9/valLNC9YZqNM514otcBtYs4bWxMzDnW1nNa7eay6ZOGeNyoEHN6eRvtM+l3AuPdaiYAh7dX+3huCYcHsRAOgaODgLNy8ETxOZa7I16deYjhc747l4nv8QVB/T6HyizhU9Ek9mPFH8TIEyWUxmnL1BBcXlFgHCYjgcu59DCxy5x5DYedC7k6FDh463bqsQ0n+3hDUaA1HHD3REmKqpYvUFAw8oNAxo8QUKdt4JisLG/WrTRJEcgKc8m7aZouU6uf8Q74m1S+SPcPGBrLfTL851YZjucKXEGuhMXpzyBYcUK2z5OrsiRqryMODqq0YhDq7hzGf38yWpXgTac+UCckYzmEiU2GpCOhmMNRBbbbIBws6nTF6fk5tKwuAw0EyaLf3rK0ZOpMx0QRC0NSIc4QHlYoJYesnZAAIckGXW4+7JOSXuKXoqS6ucW6EiAxjYafHKWFj66RjtTjaZo5x4cqNUEaJISkD7kaQ9bI98GUKbOUqTNwKLaiZMXroKi9Yoby1rJx2VWOa4d67pGawCHYYzzNV8cyDJG43jm30vGy+2Uo49b+XygNlNspc4ATptslUU2Gh6CyPgyqCbkwqRLbUDcLtxgYmQoIofbCr66kLxO1HmNzsZyUEFxOIKKy0AznLG9BAcYeRAEuCdgXR7ZGTg1Z0D2Uic9Bm8gegYm2rvRu7ej30dkowGLN3zUOJqn0uWPEcp5NRRXpEUB2xo5A3ykGi1twjHebVSGvRxo/WKZrRZDwvozRFqPV8NmqqzW7jZ4vezqNHDQUmHmYAbl6CIHJNU7OMTCQeE05eg0kOPDvc0puhVJNVqCwkEd4PVf6HRmAUcbNgGaC6OXCpLQeukaiMvyAofAKKi4JExty8+Rxed4wJ/e+x8GrUSLtW6xWaZT50SgnReAhsgSoJnXSUAjTVYZJUDT3jCqGTUz4DvDSlId+wmRjeYajRCGz0WgecpzEtAmpgdmKMAxSaVBbQdYUR3Cw4M7z8jxz+8vP/8LfoD46z2f+mqBxg85vmeOCnAsNbRzwPMhcnSJHc6hHNo0odeAcwzh9mFAK+sfZJEN12h1xBkFOKhLUBVJDbWvWetE4ga/1AZcau3zBX2TCINykIjP54wjFbdn59DwRouTnggy26PTS7a9+syAJvU+YnfhP3nhI+BNbLRznDqJAfHm1dwC2GLho/gETe75mE6dGEpDmpsEtDKNVrpIpWQd8RCXWz7evtvpCi1tr975cnvpbgPfwFNn6cvFKeTg9/jBBnMwP3EAgB9v30MGPcf3ytFhDm6t278mxrypduoMCMYiH8AOqJU5lNnafvReBv/5xr1uQdyGCTOHHHjtDef4MwrTUI7WXOsb5ECeTrdBHBvA4eegRlZkC4FqY6Dxk9po3AuBAmqu4jQ4S2aaIWdEBMcS6MDwxinbaEgj2ljR4mg8iUY2mhV1S8gW8G9AAyL3OHUGov62lhq4GmhO2M225Jv+auXTMXpmLysIAed4hnd8OK0sRucwtpiWVRVdSfUyrirc47Yt8lytaTg2BRrBS/dEtN7uMnQ/fvpZCT39bIXilj/C4dOn9MlTuOMx3+GzLTBvWq0f6d1k2pqdNWcATST5lhONhk8S3mDFJrWPpxazLcbR3sHcSbGO33nq1NQo3MumzlnZGFZ2z2jxpsisxmxB5wRnoD4W1pgxZ8D72Za0f4QAx3dDeHkvmvbX4bTPwn//R5luGuvkN/mECa6uJjT2BYB1uk3ZWhz3QZH4KNJxVGbr5A1E9Q29F/jxRogcIOdeoTSvAIG8VC0IT7yKbg4OWryLNZfQUi9Fdi1lPzFJcSLQ6AvnV8ZaOnXiW1JjXJKmy+3O1etUxFFbISCdOl2jwT3ODBAv/i1PQambC5Fbnzptm+7b1CDNU1BpckABV9RodVcLidqidcTXABquCR1qQn741bfQf59n4f6VCP0/cVfz2kaWxJGaVo9oSTfl5ttGgCAQwWLLECDCwEzAlxDLXrCNL4EY5+LjgiIYAgEE80dPVf3q473ulu1MEk9Z/V53u3czr/1Tfb+qwQ+Ti8oOqzPwlRvhXhdzfTs9LCskVHV3QOSJSWyvWl2X0uIJZZCH4/XqkotE1/g92oyVKmrQh2QyAgylxa8kPKp0QmQA0XUhL5YMmwCiM/WrqYrlE+PO1DYRomwOZKJzG6JzlwGNqAk0iE7ypJnVSR8vzs1HiMzgaPaO6QC0Hs3opnY9d3hmXw+SNpGs9duzMwLWp1N668c3/xtwhvgeA/enYcw5tPtvFnz4zzwFGp5xBMIgoCVLHtp8gJb8GDApyU1LY+bBi9ByuPle1nv1SYCneOJytPwhJmYxJsGTOcs0K8hPFFeInLNKpom0xtF4QDggrE4ClVudTFIBnlBGpDoa8zGiTYhOeOAUtVF7A9hGqFOsThGeJjpLW3oqOjs5Gh8iNx/do/J+etMNi8F+oBGgWGyCeb254hrwpytnmLNfBrSixdVQ7jg4HJF/z3wOoKF1xcfpVQ+dqtvyAF9jvMdo7yQ0R9eAC11vXatkkZLvwdHQRl2L19am/2BnAPYK4I/rDrWMoyHWGRyNgJekCUFWGkfbbjgKAO8G+zm6ONrOIgO7zGELsEXVZFTVFdE5MdEJVu6u23ZogKfSWVsiOrtIetfkVDzh7224WV4f0Vu/vF4+/X/940Bznb8K0YlDvmEt0TmocnWMllzO8Zqy9hMyGLomgiIDU61NnpafZb2fl2MiaYxiOhqy7aHA1bX3goKWTdOIiZ8UZagGq0Oaa1idfKDaS9+B9lsKtNDR4EcjXClH2zZF5/YR0fnVRGe/n+lokwlWMa6tvviw5d6w2LpNVVU+BrSCS7t+N9BAhytuJXL09g3fWuhNFqv7rcefpJ6RiMxEJw64xao20LLXQgctuepqXR1A6+HbjA9Oy+Fa13vBEhIqmHwgLidgZXTZpwl5ZjALRDOzTH3oZiE6vZOdNhjWBmM8KKyML2VW5zaAJkgL98YO5kG2Wb0NNJi33wO0fdXSov/A4GGtSfYRfx/Q5PPp5nZK/VPu/B+Y/QwoZU0C9gINC1ukHC2A1QRafPHsHZGDowUzgRgPtctLTMbj7qVPz9W9wnFo5thEONpIgSaG5liBhiYp0af/caCB18joxkDO0WIDMSwB96iJ1bnbCtAaOho9/xTROQ7RGdGBLtEZfWJ1GoSWgkoce3rXfD9He/OWRcjvq0NADPxMz38lyTLwyTkaDb0HgNbLgEZL7vXSFxcVwQxW9JpVZkqa1oWud40EGmwbEnZHTzILGLOrEx0GWIwK9LRfBX9MUhIKJY2aJhB0tL8Ud4wr26ViTVK+mgNt22UMfNmI5PyTz110bnOgIZ6V56MReVI342s0kSVxkRf9btHsfL4NsSAgLGuq2Ekz7l2znxNVhYzZreX1OxYhUMyeh6rQzhKORosrUo5mDRGtmaRcdhAtGfZm5RptFixOKh0yLa8viXcffV7WQiwdhSA6RzWJSkIOg4ppPAqdvz82DcxgNRI1rO+bRDAnnXjoFODANpQUaJL+Q58uoEFH+yZA2zIxLlXoRmw952iQmxLoZJcMPDfKy3Gk6n8kPDrAHGiPyTK4OynndC8zmuHuIuWA8GW8GTw7VUQLAC3T0TLmFZ3FMIejI/Et0pLhosVP5rBttq3Geq8uADvlaEKa80wD/GjEugxo3vdVUIZ0fCJGp3rTgEQPQYHBuDFAEHAmZHtUHC4hOgWDJjq3Ijq3XaLT2kH91uZoURQBoTG1ZWgCM8+MgWFXrTR9uY/xNMk5XRiSOqFWuSt+cA7b/m72b8AMqdotHU1uxx66EJ3xJnRwCNGSm9YmvslEWhGgllnWe3t6P6xhdTIbc46G/nUkDiWCSbzNgTbOgAY4hf9MxoSj8akzNOdoDJgQnTh4Am0UXFvUfoR7o62jCcmcik44UYLL9vuuowFomvwIK0jI35VRxuCib/NDwvN8+u4puOEgk9j2q1eEvhng94wEPBnQMEB0YqE0V03RGaye9YrSo0205ARoPREW6OZU1w600ta7rtnZIWxLxKU4ydACxUJQcNjCGBVjs+GwDY+tjf3UYRuiE0DbAGgt0clTVsP2Cydv4xRAyznaxg2IABrNTR1NtquPs4qP0NPyfZxVr93dAmGCSDl1ruQUsQHsI27v7szjSQgyHb1dzqxwMU+gZ+FndDhHK8zhX5h0LKOr02Bga++iSvwd1KEIeKQffE91hBuWLijIhPUK7kBD2ApKfBuikwZnADSx0IkOxPCpCfTQNaUvOp11G0MlUZA2EjCnRgY0D2nSJ3S0b182GdAYeXiQJrTxcS7IQIs0IVgd3lwdaBuF6BzyKty9ET6gJtIcaPh4q8oWzRecZjt4hCzIVIVZWj2v3ARBN0v7VABdVZ6kEYHMbtFZ0pLt1fGPi86aqCTM3EExO69Ban8KWRdO5WhD3ABHE9FK5xPnaDyalWlmQV9Q5oEgdORR9wZUMxOd4G050IhyY8ActsjeCI4Gp4jqZ80QVFQuwn/q2IFWO9BCR6tdRwshmupoAxWdQV2OjOqaHBxdSprdjCATqFjM8LtnxFnUngqgLTzoFDGmOIkwCc5wqVGnz9NTh1kADUo+B5lueb0MIEAKr3xMFDoas6da2vYyh0J4iW6i/aap+dGMB4RsDU3mCPeGVxDCXgEJPAlM5CzkH1hUJD7Cj8aYgx9tY/wrNqs7WWItT3ySmSO0nMjeeAxowFq8VkiPTqAFFdq7JqO0aLIHmSS+WdgxmP2DCOY/ShPpDoDxDMhhzHbVabDXYyU2BC1vjxtAG2pWjAeZiLM5wXs5SYGGgNKI20CMkDcryUKao8oQRejAwRqF0WA8aAgKdkGQFUBLRafcD4qq3C5V+Zytzg12ROUhKB4jMrDTCBRCUDBNEo7G0OoGWnS5BZmh7u+fhrMHS3AWlHOKqzMZoihPBJnyrOmfTcXe24adAFIAi7BD15igb5nQzLsHB9CCaMkKtB6AxtpZBJlKODGMYHwJZCz/VE7RH1q7I6q1YI42yeFgJI7SrjwjaEQ0GmfR8rUgr/ACb0YUfQRewhhwha1lDGxzqzMOFZ1q7Jo/Ra1OdqaBo6EtvEw0WrZQhILz0DpefKgyGVUdvWsSd0ZhZ58k6HJz1wWLXwmwdl1tmvRskXRDL3tW9AADxlKUNljf0ZMNQxAtWYHGI6xOCzLRy4TtxRSKfxmg0xn3h0CcROFFcZsIidOslg/2CmhACkBriU4imghmDrTI3jAyJmfZG0gd2oQfLdoOm//sayY7oaP5Tiy3OkULgNUppveElxWFRTEk1KyJEA0pH/hLz6R3zWB2rmrX+1dyL4JMxS8DmpWol9GuYvBtAT1MkTKMi7Jnd828dr8sxiY5vy97d7eXZXlelvPy43rOaZARZCpToA0zoGlb9CE4mgFtLFKVgcb3caEhKDqVM7kikiBnWrE2qnGbMUDnrsQDLhvDWMbRdtgERWf7gPbVgBZN8fDPpkDTyMDE89FKXW5aVLTWrSgNa7NZznY/0GYFXLLT298t4rm6XQ1mHmSKB38l0BYp0BY2BNCCozlztnZOwdFSI6AbaGEUEEZlySQ4V9NVr5Qg05SCTP4lHirxiQAqgpzYl6Y6Gqtg2kuMbVATkwIlswdCQOq+StkwoCkUuRNNa28H+0JpIO1lJwMkqiAsB1pTR/M21wCa+TKco6mWKFmPbOpAI8PXBaEB2J+iHGQ6Gr7OGbyiWhioS7u+uzmaEl3DwjyevsuDTJpauKDPjwKtKAIlsb28FZT1MzxqFL8sMp2Ap+z/KLsBv22D7q9kyZ/L4cXx9DKCTDkNMQFukbMhQXQmYBBqM4QO3eQL5G5rlQ09GyGRY9IX2RVA++sJQGNSoG1VYhKemu6Nb5u9xgDYo8U6458G+ODgow//DEV0atEqWSCWvZ/0i62iRKkLJm+OpkyMq+LwmE9vKchE4FrsgVXx4w4xKFuYw28R55jzstp2U2PlttcJl6CwM5s6Gv8EXWDJF+X6eCp0el8KiJhKD0HJAK0fISjYkogBwI9W06gdrIc15okHMiMKhd5i/b5wNBm05BkimSnMHGhMBjQBjw10uQ9oO7op7A/GQPg6wNlgDDATBdzhyeMvgSxEcEakotO+ZDQ2oQWK7bGR2hzf77b0PGS5cSxWJkvM6VGumP08oBUs/wbVGUxhPqEx9cTa+cMczVu6oozNwBw5ESPPIwM40yKigN9allyudb3rEr3cOADDR6l2lxRCDKBZGtAwCUHlQBO9mmGGAzXe1UMLoGHrZnC0HGiakwZsROJjl45GQAPehAhbulMdjyUlq7DhjsiMAWCdP8DZBEDzzSlYrpndFvydhOhswS7laPjkQJthYqTd3vDJzZTpqGGZVsg4+3H3BvAlP0WFK4VJsz81hsLu5feJvEaVXPdwM20hgCPlZWFkGtKuyiuslyGGaIvACuKPMYbGALhV85AH1etJX31lkiYUHA0kqn9wNOsh4WlCMAZsJydKVYXVCWwgYagTaPRB8SBmbJb4SFd5rHPHBzy34Gg0YmOCxzq5QDdEJ2dvuEkDD6ADzQyEOgcZgJZYYYOc4NsvZny6KAhpK/qbvbp7f3rJUrTK/P4L38R7Jpcd9S5m+XURYQWcIVhEU1B+9cTUb8vaaOpoLE/nxtnmCjQviN9UveZ0ENJW67uPst4LBtqkpoOJRnHWcjaj9T0M0Sm4whmsy7qGSkYnuMMpthrVQcCAzx1odIIhgurgbzzghz5hdRK56PzSKTrdjxY62hfNR9s9SUezekIShh3W+4DWdtgKzekopa4JvXEaQhvPMYSUs8N3y0JZXHG+OqezKvfFZ3ubAquYdFgEiu2y8II/s4Gr/z3X0ULzckfHfiyGPgYwqWowl1nBNAf/0oTGTu3e2jmvL5ew3Gm9ytGkg0NoJxAb7uOAFYCnGE+I1agDXYUOq3BD5A1p3FPNglHfuuQDXRCdzNUaOpq2UDcdDfUPTEcDOdB4u0Cio20z0bnZdQNNAguAvVVNDtHp7o1a3lFUFo3g5suTFwcHL/g4aNKLk0P+o9ETbTrAdPIfAsYZnuimk/8yZuKJg/YTh1U8gmfkoT8w4ZGcS5ke5l7YMwyEvDOe7QrXyNa2Yf7y5ACrjcHnk0N+ip7ALfwue+rk5ZpDUfLEHwdddPKSMYYnEvrw4QMfPL1+KUJn+Vru4JcNev1/bBWGRzaJDPQl8th02DZFJ9C4C/ZmVbndr8GTxDp3CUcTuG33czSijKORGjnBxmbhaGPw8wnPoaPBVvqbtOt9aWvZ2mkPqm+qZc/eg3trXlLukdw0VuWeoycIwX6yUgRzpVwBBUQpCIUD/XgFKxRBQOgffdfvNZMYey53zN5q8mRm1uTJmjUza9bsdd6Umro7v37s76x3y7Ip6Wo6wxcLvQyx/7H/KyA0Nb9uAUV2pvLIEb8wohHEzhN5zGMxbf6/7Hb2LZOGbp0tttG07/NEbtlpUhI6ApMTbe8ZedooMZzN9Iw8bzrDOWg1qywhcnk6veXlZ9qVES9fG6QpTWJ+GSuyfrokxwmYt0a2OQUvt9GeHAzc64IlrQb4YIDS7XNLUJyJ7xrAGhjDSKF5hDS4ULHR2Ty8BYq989yxBW+vtyUUVFn1P+22YqCISRJGqWq3tzc2CFGVTbsLiNhShKbtf/0GCGybNiFCKwqiVMRvnEdbEC1InkcDj+3f5je2OYhTu6FMAFHnxWzMO9UyjZYOAnxlIO1SjWi00sR1mS3PBiFKqFhTKSKXB1qt4j/LCXkUsbQk7SoIzmMlQcCHs12ByGU50SiNQfLBgBNt8b8gmg8GJokmHra0qK5EgxcRLJpQMvNdUIvJKgFXhlZlX3JoUTPSdC2XH2Kj9XawMUDefhqs9y3HgEMKDAHBCREBHkERDbfH1tARQaLtE6LbbjsCi0FEREDUUhQxnAdI1VRcjGYSa4WUAAHripM4Z9v9+am31HMRDbTeDtW7goJEmFwekRh5Zm2SyFMCotfrEBQQH0LkVknkadrbp3vcJg20K0kbvBRBLEExVbtpSGJDRG+U1Zsf2nUSt1ivLKrdj1dqoblvrY0x3UZD1fVNHB9v8U/VaA+3d6rRkgnbO+s64f5010m0l8BVSzoYMEdiSGyCauTHud46665PyrHAsVh3Nzm44upWr8NfZUQ4KCACUgOIYafyPDwBosJ3EkJKWUsDJHMpjJgfekWitTsXUxLjse+kybS/vPMJeGhxQH0w0Ftvktoa76EgJtrZ0NrE2sPkqai2p4YIkxLjs4jYQ0TTPNGuFclzdvpybyCZsKxRrlob5exGQ23c87pmQjS1wp4l2m1ONHRttA2d4sp9K13ng9lovD1dnCjZnTJZgjLHEdk3jItQQC5IFmREN1T7WACTEs3UWU1Sw+3tR6bAEIhWOSJaIFZBNEyjpkFE5O92MESjVORS4IVrLUgQCCiBaAxpKNCrI6JCqtUt1E6m0Z5PdrRJtrjB/SprI5enlchTqcSQ3pAq4isERTgVm7KRPJyMgRElEK2DWUAeDvF2FaJhw1aNN33NtTEIaTQm2JRGwySd5TNE0+kNDewCyVXZA4ew5Ri2bqM50WTC1s+CglqYjcbuvzLqhIv3C7+i6TSyznx1XYnWSIx7ay0JQr5pRGsDwmnmabNyfVVpHtFZQnkgRzCPpvrbWtDXg7IaEWXDRCuhGHqffMMFU28yn23yH1SV/8jIMn+OTTTlGd908aDXKRN5srTZMI0yeRQYWrXIc4YaDfMI+lotMJMHEQ3kkZUQrF1L0HnLewMpJiQanPGbTDT35YZkNhpvFtAxgVtoPySIbUI0uAO/nGhAIdVo8Is02m2u0eBlneEQ1vm+TjUINfw7z7zIqJMnbdBG4/kbts74Dok12if9TL8U3iw1hvQvkWiKCHVsASQgkuBvV5VoZftTcJUYa9Np2/ASE638JLzJPuAICNVojXfPbj2FCJlUWAytCvDSkQ8o/bATT8wxT3MLyWBgjkyBvJd3eZhoA5WHpZZP3+U5tVZzjuXynBKL8lK8XVmjCcS/eE5IgDSk0SS4e9Z1MsPk6anBAF52isW9DgZ0y4DHQsi7Tjyf52caTYqVJSia3SONxk5NZPlrPARWZkQwuc+hxdK3b+2oSGV+12VtBIh47RDW8pjiu67QqOnnjRWSPIRo/VrNO/qVIaTr7DsF/ROMCpExJ94m0/yERlvwpSSAi0aTRPIk9axzeYBouTyshUPQ2nLX+UblSU6oMIRQsU8vXAMuTCCUaN70iIneMFCRyok23XWaDssSMsRduTEArbsJwZM6GBD1Bt0nEk13qsPP3TfRaOYiaaNPXxlYtNgbpNFsMOAabVkcPVWvkXt3b73qyhF+B+fH+UH7caXirpMQoVUzJG2zFWQAIziFo8/F4ddL7ypWiIrriPD0p+YSMA8hWuOQcUHpQDomhhitprtO12jCLtsOnMaj4ltvAHVRK+BkVIxOLnJ5hmlta61u0DYpK6ARIuipILU9xNq6PKd7nIdw7ALP4z/Spo2IQKKljQLtVoyvglWEBgNGNNEo8ptnG+jXDKLdThCNkhONQgh9+w42mqwMwH846kQGcqcLuU0R7VHVp2/EEqLBBeyyUSe7rbNvAd7xgibbFJ4VmDIV/m7QEI02YysYJFd6gzYj9HM5xiYvRpfTiORtF6O0oHddIdpm0I/2sxCNNU7gTBbE83eGRnOaIS6nmRINu1og2mbNJMF6YG0vktqyqb/pjeDVDfiAtyPRRJ46Ym3/zUQzeZBonoefaeftijYaVqQV06/W4eE51MwqckNLAfSR0qjA59GIbfBLAnDoDz6SeTQLJSqOad51km/Q3UNGNPhBq02Jxj2xElldue2ggUUedXLX+fo1XLTWZoub1mnqIBSa7IMc1XFwcMAN6h1cnzQaIgKZaAbxw5vAvkKEnY369aJ1cVyMfSzmCLN6xofp2YqxT0TTTNAkkjKiIlp912juIivuwHLyeZoIksbUtri8Gy+gLv+fHqV/MIK7Va5fng1dnmAkIFlEHhwMNIgQkNTW5WGifYi1CHEMrVJDqxxYu3LXWX1QtXlejM7reFQUxHmB3CQ2mdloqQ6TwMjELWUYscoHAxIOmRO+4BrtVon2/Smicc5455KwZKkLr+/TMXfokcm7UGkUYBqNHVnwriOC3vpgNwJh2EYwokm7/oMGA4NdH3wRpIb2gquFCNJX3V35TD6PEHMh+RjC8oh8MD0kLSQwYmFIkEi68/JzbukJhJPYXlPEmk002+REqdeRc6rCBZ6hj3p6lMhDGs3kaUl1D1F2q8optpowT2vr8jiiJhnCVXHM/cGJtSsQbWlvAMUY5c/x10nxxSQGovnJ1q7RFp8k2r2taE4T7dsU0R4eniYaJR5GSFJasxK1uGgSw9YGA0tINEgy1aGhXmj3jdloOy1Ppq6knd9XSDRAhBTiKSJiCxA6+Q2qLBJoTXkiiLSUUXGuR9MbYj6FHIx9Nt0h4G/BnPlviKZ7BNyh0Qs6KI7suFAv6Gzo8kSvLqVICKDRQPIIVFsiT5YHaLQdk+/L4TlbHpfervvQdQKk9lYNAWs05v/XAmRy4zGDIOVLUJCyo+2AU/8r0XwwwKoM09ODAbbRuG58pIVotFfTGo0SEe2jSpuzKLClgET7CH/PIBoitgCRzPXC/c/kiF1CDAFh6WvxZ57Luy4QbRcgUYr988v5CI+CtRQwk/T4ib9INOel+zKCxMKAyyM5J/9zWhfUaKk8Xw+5ui4PEk3k0doeUm09D5jesHaN4+ISBwNf0nbFrpMyiVFbNdRoBEd4m0BufEWT7hLTJ9sHB0ygz97P6fwxYaPZgcK2r3OCaN9wwpaJBokpatMb4mqra52QmGhmOfJgYGlJjH9SZrLCifwyQ63XWclZlKVdItpKnA3ZRRohAn8gQWMdjQ9PJhBUiuRyhSwsDmWWshbE/BAhIdL4/ui4wHScZ2KUyUg19/OuU5GQiGgkscv0pThKC0KimTxRqltkiFPMIwgRrbYhked0D/OotckEkeXxEiD2xOfiiqtSaI8CmQjRfhjR7plnSjU5vMLTtEZzx8dHuFIbDd250fuRXNNad0hBeqdQVPi8SMyywYAc07kkofjwWpKIXOLBznwTdqUhEajJnmZRaF0TSQAxk2g1Ew0R1/z1BIv2sDim/wIhjGhSzmgMzWjzBTUhaK1zJbTcSIdPeHR4lHwy20NbTMpGmBZZK3HE9lHoNHyD6+La5eQwIXTNRCN5+InROIrQkeURoumscghQ2xCuRkBXl4cRPFiPRYFH9iPC2pU12sq11uLocHRF0yiFrh/srtuiOjxMoxHt3ILSUHm6BOWDgTvxfcR/7fJRJ8YZ5ZPt4A4aDV9xNyHMkW52/M9Ls9FsIyCvDKCNxvvsZTCgbra8ym6Tab3BypNdZ8Qv7Bp3e88QTbvOfC3mpDjxDzLNI8BrxwDSXIJ3nWkxX77iR3TFXZpChmDO8w9x62ddZ0o0fo4uGHVaXSLcodf7ks/onMFXy+WB6mZCB+k6KY9aaovPQ22BdUnXuWLLeQWa+BHlsXbtMtFqW9VjnQf3mjsGrMiNBBGiB/6RLUGZjebTt8w14plrNNRkd3jNsNG+ff/+nYhGJ91xV2tdpxHN/NF4T5YRjQNvWJCXZdRo2nWSbrOuc33/KRaB2nfT2BDTSi++r7a3EBHTifSLYuTLSO+7NBgQA+xgdOG5yLDsPREtL8aNdB8MpDuyfka0+Smi6R0krm1Cb1yML/x7xrUdAkKVkVXXhAvvgSSUB2DyhjF5iGguDk6xrcGLgOB2RcTNokACXjRhOzq5/PdYy3nflq7zXld+NDLB1IStL7Dj5UtQt7wC5eH4OODjLKLdEQzflGk09aa1ebRFnUcjoulGVKKUaC/SaLrhUMLA9dbX/5hkkeuaTzTYHygihwgCabT+Ry3211U0UB0zBPdFRwWmQ7yE0oCgRXXKhN9ycGFFeTFDt9FsI9ysrnOGRpOu0yS+HI2ugsviEqs8rSOtrfX14VODGs1bLVptXR4kWgcRAak5lplnQGi70vQGZhKlJge4CBzOUffV9PiUEY31ChMsT+qgqHFF2RMNeGUaDfpJvXKNBobaI0+etb7DK7Kwjp2rLqoTa3XC1hbV2eWRdRlus7czetTLka884eTkTBbRPKkjHJIjhoa44D7zoPgcVV9liBiOCksACDZh+8suQMQ+ueTBxHkxNtoDZOj6CjfP/OXBgJ3lJ/fewOQ59hUMlwe7TkMk1bWqAEkUEZEjhdY2tEweQLz54KONk5jLg4jlpJjWCXPxGMcEPqcrWmPZT+33rlMGfsI8iSbkcRJkRZ3nYIlBs6Y3HlCjPeArzxANCnOimXIlFw49EYHjc7Eis3Av3nXSOkiYZtH1mi3IdKrNSaLNXIIaZ2Zva00QQ0LU071iCIL4ewo5xlxghuM8KybTaM93nTYB4nHf7NAOX2C6kgJCLs8py2MtkQhdc5vwEtRUbU2efAlq7aIojgTh7XoDNppnclAcw4rKVxmYhgiQBm00SHZyorrup+kHG2Wm0R5TjXY3g2jAs0mifcdX7n5GtMWcaHy+4yt25V5i499tNGh5vLuNVnXfpdSJ8Ykl83fmA6QpTCFsVQ9HnZEwtSKGiEjdWYui5swU8QtBuIwacsHu6gQhtUKGLzbYzyeJpP3cPJrzzCI9yPQGFET5HlEpeFN54kqbfMlcYp9Ptaow0aTVXOYTFM0QeyninLvfcTSJK+o6HRJPikLXiD0T6qHEFnKNRuaZz6NRcquKeKdnWqTeG77djrkHf7Jb94NqNHZQe5ZoULQSTZJteeZNquIIibGs+EQ12awPNlrpTjMT3mDmNMOI4AyrryecgMq+GhtfC/BTiIRzhPgAue5QC63O3YQQUXMuNGEb4QHPEGS4QESbl1GnGmh0e4powDAP86A0Y6KJxONCUyrPPthoKo8TbcoJqNRW89pe1yoP2mhQSq2+P1djbBVv1zZrtKrdN4+Oo8+pH0mv2wBE6MU3mVvgAWfuj2Zbe3/o3s5pNyG4iIGk3/D2OINo8A7JAdIE0UipiveGUE3ChRjRliRGb0o07FXJhe938xEL6Q6VPnqTDhNEHXyoFRjBNCJEbCWvyu06QwggKEhLwcGAQKZTIMgTg4G551cGRIulGk2I1nhBk/I0UJBKHN3nOBLOJAYaISLKaudUqzVngkDbwfIJjmhQXTEk+JujfLGo2cxNSNnlSwPsqIE/ttjpdpnPo90+3XU+mI323brOXKM50bKwVe7xiDtOnV/ALZy01dCWFkjIPTfYe0Mdm62zc+2NDTIUZ+9ph1REEEl21dnbCVYLEBBKtCwPL+cjIxZ6WSbREVxMlRBt/mc2mp9kktpo8g8WVM6UpySJtSqRSCYpqDzmyi1PB2WSycNEa8DZW+UIqTjEoqXegDJRrZdbJA1mwiERJBSHrapTomUnTBbNUVQZqi8h2kwbbXowQLvu+Gy7e51Hu8/m0VCNcRVkUd1cuc1G42i95ITGm59k+4AQDRt33XdIaNO+XWmLfzzuEGOEp0AI2XpCHGFE7a1FiBJeMMQAELkHbaRSGuwXLZM0xdAKXBGACKNSJ47Md1ufomGpMk1Ip+PPDRwMTBYUXB5IJLEjPEWWp2lWU0SkH0ouj2i0Zp12F7o56u0K6oog1TrvPGCcQ6p2idMbP3x6Q60ymysTkywnWrY5HV+2OTQKjMzhuL/xyoB0naDgWrx1gHYSPOZEE7It8nHHTjSLSP8aw1dJtGTSbDphizd8ks/A5U0U7e7vIGXac/6xWcmOHyYaISg5QvZkkrM3IbL9eDUiqkaJxoiYfbdrRJBWRKIRKau89wxcjBMNfp4edS5MhQHFRzbgZO1GRCsbrW0uT9OQvhJ5qPNnGjqi3VaiucRRKqvyANG6IE5ZKmKqXVeJaCyxGr+eiUKIV/gRTxMNDHVhQ67RmFE6GCAVxjaaxOIDLk3aaEA0Xut8tAlbLEhLNaLRjQ9St66TT0SgiPavOIYq9pwWroSncWUDMaay6b9LLKi3v3ds869vILZRFCNKerZBBP1ZKiLPo7QtxmWSR6yjIRrYtLsw3KloTyRVRPmokAbyzlcG4NLksYGeT4x48RN5oC6JPFHmN0ImT69HlW0oj2h2QiJP72YHKdeQOCJM3q43izdYTF6RSJBKikGN5kRjH8RUo3FileZEY+LdC2ce5bC6W7roVXnDPdPvFvd4skbj53ONBhcv32tMNppvsQOhKOwjjzxBlfGwk7UaEI8tNj1krd+I6N3N33dbAZvt/T9WK2nTZnNurt80jqDWCICQdwHixQIhSke0BFEKYh4QlGNDiJogeR4IAX1imQhkGzNmiI8ErIOcbaMZ1ud4Td31Kc9ypjxz/2fy5FXBVwnx6jXkkSICy9NYHksvAcF4b5OkXd9svlwEyBv6f0aj9BcXfQkqOfb1MSeanynmGo0sLYtdhU/x9QjMUo022XU+Pkk0cef2U2iJVbi5kwcpSjQfdYLBxmHt8cZ0o91Ry2eDsgIlX1Vlt7vf/7iyv9OtNA3O/jk398/VAf3ThosQOzuVI1ZhXgsRkkpEfNzvYB5tegwgaoYhsBgrpY3/VogAFiAE38DP5BWhTCxUnqf8n+eSd6ooj+c7LfEqfP+2zlSeUqryK8lD1+AMHBSG+1BZSR2XhwQCBDY9lZK26363bcUCYnF5iBJLvgNu2G67VMjqfyi7et42kiR6JEBJhIbMNrtgA0fMNvHKBwd2ZBhwsoGsBWzjEgMn2InDA0RCxAKEYcA/+qreq6rX3aSIvZqP7hkOaYl6ru+uugIx/UsO2xFoPzqg/SWOZpcFtJ2AJh3tQUBjCArFH6nF4fPLj/YjOFqUNb2aw7nBWuHr0epkPN3J7sjqtP3Zr0n/tK2jZ6Y8LzabZ/aS7Th6sifQqvyZvcJ3//OPt59fY2a38BmAiH2GKP8ZvsmeQAW8fEQPiJ79JiRRA9NR7ahF/cVwb6nfWKTfGLrFuSf4jb789dd/2dbRv3jnGZvzvLQru+OP2d7RS2jP60m3R/rzjoFzwCtjAJyeBppNJDqjsqNP4TUrq9NMTlqduThlv90qBMUmxcjmFkcDoZ0FRGd68+bS0cLqvF4TcJH4yFPUhF+um/5rC3O968/S0kasA5X5qnyynTcbWnu+8MMH6zt+b5WqXz/fbFJcyYk/1ofV5Ual8o7U+/h3/ZV0VpAwOSczu+d6uBVKVQ+/7SI8s/F6wdWw7NHA9CrQIr7T5cp6pnz7+GYVZXQs4LfiS5P3pp5f22HixKbVsZqhyxA6Wd5MdRwj7wvK0BHQjGw4x9EENLQcfgig1fHghRAUGZAxEEBzzld+NBfKctiKo/2kwzbdatUzmS2t8AtLR8t8NMbcF+jioS/6BEU2l+2zi3aJW4sQDJvnX1gi/e2r9/bwJpMqfLJBksUGH8P35psrsT/C5VFxEhdK31arXw7nOZro5EuJL9lI9n0AXIzU+ZyygCUbqXeQ2NNptfzzq3fmub9x+WEQmwxIqFSrVPqraUV/E7UZjDaw1LaAxrEqQoF1CGiV4tgnBjkpf8NnPPwiQ1AOucbqdHYWq6AObZrQ3h4SR+v9aKWjKRqAA7+eO2zF0VhBdZ7qGSDHmmlM6g5WN5s1MJtpC6BBAs6q7t2FEWAEuBA8gbbNzad3jrUX717fRMOlDQ70Lkycxilwuwk9X0BLrpasNNgQQYYhTwSatrgIvtZBVD2pOV/0MljlSTaoUBItJwE0lJu1gUCbbqPnwJuvd3PGXlCFAhCMytoYVwDYlRAmF3sWTZlgxKmLhW2in3F2UMU0cMkrmgNKE6LxWWlCiEHhYPUDgAtAo+iMoDpe2/UcjW65lqPZmBUZomVouG2prAFw+JqWBBoTb6MTWdVcHZv35F+EU7lCDV+DAk48kOUBkr5/efUBlatffUFPHD5nIz9Fxdf9FsE0iE6cMo2xWJkwco6jaTxL6vntj+J7YKOmtUvL6AYGjkaGRo5mV8HKfv/47b/suQmITXNvWOcje0CDswFoGRePhWoFNEAGk2QUcWL5gzbxMazOYm/B0TI0INGp0mgSnQ/S0YyT7QJo+72AlsaAGaMwBiiTU05b9m4BjT/QcayzgLaK5mqIE5CbAWzJ0XqMtUTsJPuxsW1vefEbRjXABNowvXn9Dn0f3n29y77lfBbzZFnRC0WKYDSfQ3V2/fOzwJmMAXUZGjmapj2NatpSQDNCNih6A7NhAHOrIEpX6fY2Aiu7ByvzfujX6I4+sbg2EMmeddhsNOxNtjtMfZzwMPgdzkYTzsAXJkzREBiltwUzo0DNWePpwFHFHp8CGjnaXkCTMQCoGrwkOn2T6CyGrDUDLOtM0ekHNNlClazOJLTqUWf1kQwVPdCqYjoJ0xzbm+8/k7F9/PIcQOPdeDc4JGY8SXQKh0CZC1LCTNLuMpjbwNFmA1W57ZSY2gN2iMCjKSI3SEwHoRqaVqLyHViZWTvfbn2BLNgem4exqxiyTQFJ25asr41K2wk0QspBIs8FzmBafjPww1HGAAFEGo0BO53gaFvWfx+ABgMgdTRxtN0OHA3IhHuDHC3KjGaBBLJUsllK7vmkVSpQXB1ukAFAXmTdRttrfqMu30aOFpORoyXNRGNQiIV8sOj3+euPYGxv/niZ7nmpZhykss388ClaN0lJq88XyPWviqMlenI8Q/T2qGb7ImrgQGD6wbZziNStFs6vvqGR3f2Hz3960wACDbqIyutPQBpK1OUKbrcLVmy16VMcxNcZoKlZSSs6VaYso0SAX+pS6lEtYwCGgG9b5AtFJwuOPPb7Rxedu4MdcNzuTgAN6CpwBWOL+mhsWpslEWpxCvWQVHrb7upNzoOK8YSwUm9FtZeQioURQ7tktw0QvfxETvDv18+FrCABrVL8S2VLJuTTtgd1vHTM0VK2xnaGZPfMEnfJ0XyCmfrU3Xx9dw9W9voWVugaQDNuZq9Rai4ddGi5T45mZ9ia1NFoCQB6aiJse6g5+ttVRiHmEJ2l9if4Ul9LrGW2re3iaOVHMwCRo3EI0XlQ9sbjk340As1IxoADzmhiHaHonGKE9DO2hUEjeQFNolPOs5JOSZA5wo2YEeCD27E1pwtiLUVrALT1ewwYHUUn5iknhXgpWYX/y3qEA5+LmNPouB2BNuhoIt5Sxw9nZUYfPt+pYwX8bBSdWeIQmhl1NARkICjB2AAu29fQlhNpjiH50cAzetE5+tG6FSmatrFOomvwo+0gLh1j0tFkDJCjKTIwcDQQfgqJTvnR2DmlRKcRuJqdqLQZpehUQ8UnvWj8w/biStLsvO+0gLa43LR+DzpLZjkQcKrFovbmcavX9f041tHGGMATlkCAS6JTuXrYljAG0PlwsTZW9kuwMvgf4bidrt1du3RjgHnLgBZsTSdanY4sKs2ccU9tGqIz/Wjqh5I1anEAX5hJL0uDkEBLFuabQlB22rZAg17mdEj3xtbGvQPtAKA9Gsj20N2OdTRSevPA0XwISh3tymUoAAe7gEujwNjXtDxVl09BAglUTsg+iK6ngCZkca/ZQmzQWvDY8OXV2/B7+CtWRU+iE08KOXJ9LZ7iaGJ6wqEgdpKk4i+Cs4vTL3A2GbikYDRWdk9WZt8dLUsmKSynyTDmGtjaAMaW/ACazxxWBrOQeURWTbNkYgu0tCPlR+NjPs+/adYpLk9HeNQSkg8nQ1AZ0WQEAPJyOwLtkUA7oEPUALT0pIGN9TpayH92FrVJtIRfrqY0BoySf8W3zFmRGvl3as/TQSLRE9iDR7asTvk9Pt3Jj8aXL1Q/T3Sao82OORrP52nmAFOGN/Zl8rkEnl06K7u/p1YWlSWAvyV1NPtGoZTZC2hQjZ6IsPUJOwPdKaD9PAk0GXN+ksqPZA28gUwvDQS+loGC0+6NvyLxcUerE+ByBW0nPxqsTgDNRaeDLB22ZJYEGQmyPfLSQNQhJToRgVqlHw1AgwhIjC1bjrZ8ureijMmngAZf7cDR+AaO8eqCwLozv8c9/R7OzM6AdtHd1FVH6SMZMaW5zjkRBcSgd6ExurSyld93hCFUvljBn7tCxyOXmQQYYwCUkkbBlkag/XwCaLiJEVBUZeKjNKFItpWKNlqdPumsTjYetoGtn35AkMrqPAB//3hE4cdtic5aNKDaa+lxIeDkYaZJY6dQPdUSHjGCtRECekAXT9SHSilTqoT8+jgInNHq9GlSyFiGLPEw7YZNAjHMg9cRxfnjrqKecqR1DfVPuDfORAbOE39ZGQMyklA8rtXKYD8CWPD/T9fQyRAoNw3Ng+oeOEB7N7g5MK5RBBEbSEO0fUW/VwIzHbUYnVqOZtNeRytsBZjI0oCxiHpjTkmKkeocNtAPJjsqBAX/BoAGHc1fAzYRguKnko5jnXPiC+FOY+Y0Bui+xe6XMMGjHl+ldzcczeZHovQSh5iVUSHsCdkphNakgHNBL5p5QMNtgIWbAtpFgetiYJ+JZUzBbP8u0E5ztGUODrv3wcpevUdQ3checa+Fw2SywaUCoktr26CmrQEYHsHQjjkaSMEbcbRkWalrcyQxWQPFWwivuMkh8yBHjlYxTyU+uicN5PzKrVBwtP0eQHPRud0mR4NfV/ltADbOFeukjBdHM9L/mzlRBnXVJlVudDAGCDRMe1LsuYCmmDfwB+pFJ7z+A9D0XEmz2y/1x7U7G9xNY2DUF3vvvsLiZ2ElRtZPqDpgghlUR9rEDEHBemKVHLIlgxjcsnMCDe7YFbyxOFBsk1yKqUECGtuiV2yJxibtTVylZCL0BKdEj6xOTJk/m+4NrUYBwBB+ikUAAZxqZneAYeD7Q3C0g/vRDIIHz+rO5+kiQfJkUVNo1I8K1PL3dHDxYiLowNFWTq7RrlUS/hhY4i8i+ut9N/Ir2IzjaiTRCDR5dR0el4mGNt8DWCKkya7ksOXbCmsS1BK9ohNAc/0rNf411dOI9l6/f/Uh0I4Ipyv7dor45gSgJUfzdLMA2jWAxtjLNPFBQo5+pSDHI27jQI9hgnTuxHN42jEjPEMzS0mqNCG1OCxlzC4ItB8FNFZ6RNc636q+KIDGOmmAlK0ZAGOzwwB3OCTQdgCa2vhzgd/Pc0DL3ophFkTsjf0HlssTxsDID1LXVxZH9QSWjkZRNhoDmAhockyUyZiOuvev3t7T7+GA2mw2wbE2rd1JupSj7TzJrElWrd+QwRIPpDesjKF0IHDFfCAADegCkvzmSY5mtzDF926Dg4mEm7gtVcwfwaUSH8n+qpWdkYDGGacyBhJo6kAsoHEqoG17oNkEibahoyXQtiPQbEvKH8F2Aa38HMn0V+mtxgnJLjiWQtcZL7qYG6ElY2AEGoZBR7vogRaIcaUvtwyrpt/jzae7y3SeXW6ORKemim2coGU/L92TvyvK37Ss7D/rdUJsRW92Zm+gvLSTzfweEjMMiMjNYFtqH2ycw3VrKETi0BwEABZJ9QckmQHJPtaJrI6oJfVWJ7lb80jpaISWQQXwipi6C0t2tvbF6Tt60uBH2z7aTdPRvjve0Had8JJq1oA9DOJguJnciRDUVMbAfO3EXsSQDBVUB0HfxbAkDX8l6WjETCuu7KaApvNoDJRmVcEjAaZTx+6U73HZ6Wi5DTRbno1jziRqFUqgKthpZe0C/uuUsUrlZkyAlpITY51sSpkczU7Jr3xmW9C8NwZ+yhjAH3EuY8B2kRTxrI/NaRabBVUnOypluyeAtjsEPZhehp52Atp3Zm/gWbcWymHbc7Ry7YVCSa9yic6M2Bop/YnODsiOhURnY/OPCjjcYcRM2/jXtl5H05kYLOVdk7wbO+b4u0u5T7/H2z/u7GmhWtwrBbBwiJlI6bjDzTB37CStTJXKaQIwlRY6movLJfWNqVjWxJYh0MjsnRGCmqCyOUW+o/jYVOda4+3nUXSWi02ICyF5CmjEAMofEGo7goMx9XwZQrQXnZnVjbjnLkQnDNCDrM6HAhomikJdBdBaqxM56xwdXGh5x+/BiYJBQFN8RsZAueEVfmqARgD4pvWT8UTpUPqIQpzmnej0z5ciePfpTXizbkpD+39IIA5WFSMLxDWszFAHuegnNpx3kYibmb5tNygLryEmoeQm0FYOvnXAay2gAVj5R1CCYwLMx/BzgKM16UC4UgN+KWZDrDMuY4E58eATDDWSt21HHe0BfrQtrIA9ROeju27hYEurk7pfIpwQq/q5jqgUnVOIzslm7kxMl+KqCaqPCnPnQhBVN0K4LPJwUB3pZJKDIj3wd8nyxX97Dr8H8z34mefFpmjZX6lFKTjaXWaZf8tkx5CMAcS0FfyIinK2M0PNdqwhACjX1OHsRNWXugkctjC+Or1f5mSK0lqtqdoDLHY3JD4Kd20IipAcpKxy0sgFW2PgAHLPrJGdLfa03dud79+po9kTndUpXxyIQjMjG5V2PqlqMmaTb+T5WATGzCG5N9aDw5YMAZOghFA6yy46l0IBS/yLj4+ic7Q6O2OAM5w3XP6Xbvp3n26ewtPyHMiUaMZf5jayMN99vWEqLba1HeHsyOKYC3f6R/o2c2intDptyhVPMD65KiOtTsY503sul1lS6GjFwWpCoEli4jToaEQWpqMfrTgax11IUrsojrYvoB0OCbTvAFrqaLtOR6N9Ef+O8rmjTjgpMBVrBiLjExPDlju0nWh1BrXZG5gWXFSvsyxL8R27a6ciPdGBhpjSrUZHw6aQl8CJHGs+814cCLgYDU1+hgwY8dMIaSxDW7v7/DY/iKTfWQGp+DLK6vT7K5u60uGzBdS3FVK113B5+7Ci1RlJ24TcqqmEMvE8ZQhK+Y70ecCbKyLeBLQAUuUFJag4J+06YyD4EgYaAwm0A4BmktKA9lhAQwh0iwRbAi1CWxgLaKC5gMZfSotTBDTnY1MArXQ0fKUrnENfCV62VC92k5lleV5cqG/5UVi9gJYhSTls9QYtOcGQ0feqsYdFLr+llrigp4srQp5QyEZWJjFOvGVCubFGgfUE0Oi/HYC2tqmDibPrdG9EJ4c5ibamGjgxrTHzMwCfSOu2rdwbQJyshJKqOPOQkCWDfCJ7g8uLeemYJENKoBm6JDqdo+0aoH0/GOw829bu7z2+DoiyDi4pkGckq5MOWyWmqf7pVAXUZBsgGIUcDxrtBNqJlC4lzo7Z3KIeaIUvXQ5Aq9wjARMHp0MISn6PW2InjzYQoFNGAHznW6mVST+FZxauHihbXLtkE8LHRGNYmutiTuBVEdWzKb/HQpfDC9FOT53xmf4ImfY4hdyRsRkTdUgBZWCR8CpjYIgMxBqoXG3H8JMO3hwctj/CGEBNhK1inTY1MO64wpjYGtwbtDftzB9SOppYGy/IuP2Mb4s+xpUT9I9Y7FlAkxYtl4LsUJx11doQ8pHJ2Fyc1dGMRh1N9QpE9HsoqbqeaIyPUTZvZq6V3ZdWpshA1ieHpu+ztTs07EXqaBkZKKuToJvcAZJRY3CmHmgUjaf9aPOQkPKjATZ9rLMLmzNoPmTYYhZWZ+NHS4CFVx9WgJMNFeuEHw2p3Lu97Xs3NcHR9rQ6DWzsRMYPIVgx7/xoV1VCV6l2BJmKJ8fEyNE2L6DZBuMJCkpKTHgtRQkFoSbRp5w1AU1R7zFj8ci94aRzQGQAWt27+1p+D4GtCWa1GXXkgvdgZdVpYb2GDsZs/3Bo+AHzCBc2gXykzUR1n3zKZwhGJdDkpcUtfLN4cg4aOZqjjHCKUJM4mic4thwNr52PdcoYICBizmOHgRzNhy3JJ14A3qH13VwbFJ3ft7ALUPPFsYZE8EgY8rNTis7ar/yY40fm75giX12FEmsAGtdLSHTSBX4tp1pH/foV4W1MrEjgEXd6taoYH3G0PJ+zIUm33yrfI8H1rXS3u/8sQa2BGVYnampQduKqFZ3U6LM1OOUldp/NI5mMM5sIaFcCms1s6o/g5iSOJqU/rM50S9koqzPxRV1L6+psU/YGZuRopIajcQp0wTYoY6BwdjCghS522LrVaeEBAO0AoNHq9EiUU0JLfrRW3+QPbZeEV7mfBbFjoNmxygxcqiluFtRyH9/Sx6RSnZtkb8xrTUaUpYGUNcTouEoTnaKF6p2lnsdD8a5WLDLT2mB0/yIk4scXt4GzFx9dl5dClz8vISlp25NrbAE0WktL+GaZmzyfJ0dDtZwGaGRXfpnqV6r6pibXek3+tyfK6P+ispNavR0Y2wzbMj95TeL417ExUHiQMQAW1Lhwd6j0nu4Nc24QaAcHms0s1hmRgV5HA6BBoTmyDnyXj8Z+xABUpAmVpkbDc0U7gCiDXuKH0RKOzd5lIMm3KcTwpvTwyyE9DJNRRxNj1CerjEfnR8NTNiPeCuYJnPeq7/H7L29vHfS3L375/fZL1V+xpxSuBXdW8oZWcvqE8tMJjN0I7J1r6Qx/9MYuMaPVOa3wVUOcGq0jC8hpAjhLfNK94QDMei5AWwO0Yz+auEkf66RADO6FufxoQIYioDVDidoxMnB4yAzbR+doB9v38HnALjACL6x/Tq0882f7qWIJjUljJ85pZheRowF70HhphMLzHeUnrsPaD8ZQANowNbtAIpKRKSqgXS76PhM94s4vXyLyO2Z04/IRZEgzoQpHWdimYXViYKkW6v0+w5XzbmwrcLMJHA0xJihmtlN3nYcB6kjC6Fxs7adGAWsdmeBhNpBwxQUAsdPqbJXqSMUXzwL6GILqg+oPI9ByDafBx6c4oItFFSG84HbA3mm3JUd7dI52CNFpF8AcyvShsFBxtCY00PLfarhX6Z1TMrfRGKDEZOgE6HKeBqWF+NK6R2z0fZ5eLDnTzPcNrc7OGOjMhSGi3pCsTk6VNXJKUWQtK+R7EGmOM3rbxk9XZMlHujeK6PEA6nBps0DiChv2WIgC+xMyFIpbJKFVTtoEmmOXfyywSJQRcpXBnQ6DkaMFYZLLTU4AbZdAGzmatDYsvBs4GhcNbPeAHEWnW512Z88stTIGHpKULj5wtLAGsLfGgE+SEJKCTUWOlm61Jb9rBQ8yA/c8yxG8GgReSnRSEMrq3NQ79ZbNiFDZCSPQNLwB0j4QcG96iGHn4iaaBIgzwcAGUS/NQgZOqJoPTHFJMCz0jDH5lJq/bRnIpALMSmjHDtv5PE2AtCsDX7LjVNhAC4+GhKEc+xCUKISbfGqyQ6mjdaJTi4n3exOddNjaQcwdsiokOKaRDayYllG0U360VcU6xcviq3W/UdR9wTSLxdu8xdX/2DRjFIBBGIrSQzTbHwpZBceuJYtbwVB6lt6+GkNLwA+i4T+iLoIhqWKIwgQCQTgQwQcVoAgvTWclzAVqRMthRIQ8LpBt7FI8dksd6vWO59hv+yGsRzyEjqGZ+0OWBL8UQZnt0qkGL+CZrbvjEsXMV8vS6yCXQF0Yqy/Wl7Hrf20byeK+QCBxnKCRRKXUXHLX7NKUy5lQ3x4Hzu5PrjlSYkKOJTQLISFQCBQK7MICTc52iHEL/aPvfX8zVrrcxJFk66OZJ+vj9968NyMdHiFpUcV9k2iuyxrDhIxJURyNmBT1OiXD6b3ORlJ9GmcGwISiFQWiTZ8kGiwlMiwSiKyqlxMfTW4Z0hFFxr9UikkS5ajEpnOdVACS7u/bdU6lXec7ey8ODvrdnVxLvQdXBxFSAPHjwcGPhqjzuruM6EIdL7wOQ9ReByPocAQIYjdvG6JPrdDu33759bcCECjpb7/+8lskRx8RLsf6PxNJm3KA4/8zQqihIj0ZEQSMJ0DqPzjfo87mz9vtP2rniJRew42m0Jr7QenoDTesSW/BzaUqLzWd9zw0zU0nuXFmOnEWFKox7gxMwHJaCgoAbDctmyo206gukmiQJspvCNGo+KhidDcsLbolU1eQf1s8MIY2Duo2l+Ll4U+V3DS/19YCzzs/wAcJ1IIoq+Se+bkgckfAbkbU+FGhdSDaEYEQOX5aJ4ja6nhPCDksN0TBdfCjYExSl6PGRpfkkPLj91tbBzWKFYtaAkRaLopteEjTwW4ka0jPt00Pr3spcteIKFslI/QE8nf/8vBGc7656i90zTwsa2W5M4DvWPEkSXVcYmcAaSO5zinlmnC1mFJnYMKdAbSZcyWa+WhAVU/bk2im0URcjwbqpC5hF2wzvTpLcTROSeECX5ojkEFE+g323siTuPgxIHwBi7z/HSDyAi9w7y8tKYEQcgH7361v1wWQscA6pAKvo2bELm0Cwh/3hoii1jpiOSoUJJUDEUWuCC2pHFtRHU05CtRXnXdCTIAQplKIVtJZeVdbLVWrKknY4O10vzOEtxPi8+0eJb1OJFs0G8VmURr/YtP64Ckop2BzmNAdEox6AvSOCzBvThqN/f4pD+VezNB0zifuo6Ht5PCG+mjUbmo6iWiaYl9hlea36pVphx7e4BgbT5CS6WLU3Wd9hmt5sMzb/VZS9g/0a13vGyLoc/KeG6IGxJ+wjhwR8fO1AiN2sY5VQNSAwVaCPy6IETUhnpYjJHLUjCi5EqGJy7HeZY3CdYRQkTAhOhc63bxdeDOB/x2ysrIXSWLn89zbOVqJ2ylNVEecK62+MR4Ndjq/lF3moJmZ1DQAu1LpMCEygEwX0WgYryUaEaEmi/mEwxszItp0gmTDWBoh7psaTaiOwqa9zmjgMBUjmmo0ncG+IlMR+T3dxE8CuvjixxOdVen1fd8Kh+3cKVDUZ6XpIi2HeKST5CylaqmIHBBE1iJGlKXVURdaR31G5Cj9UdRLctT1mVOZt5pyBCdRJAd6YOvExZxFrRgj6ENha2dFz6YUUeg/aDtMtMLboZ3eTgGI8we7QZXeZo/j7lLcosragxrm7Xt4g+6BRpYS+eZTUu5pi+g1nYJpRNOJa8g4Leb4mkwmSrQJ7aY4mvho0ZA3TAzEXReZZuOxWXPG4qK59I6bTlhw3AhfHZ2mDR8ijL5WYJGVhEeiJ5xFQS8fIyIKMKJcqqMGjioFEo7YBVaSuBzhaUTfWon3p3LUBSGCEsnlYAO97s1ULKyfDP8mNjtQS23tVIFB1o5qNGnHirfDGk2MkaouXkmv0+9/Ros0QsaO+rJGY6J5wJZMpKy0M/DJMgN3n6Zz7QzMZsA3eIFGo0gHxdeAn+LlUT0gUdTZQBHp34eZY4mdAeWfdUJ9yGTHYrpoPnVKKGg0tBKV6o/bzDQOWwLoqgGieMsEc0RghJPkbaLNQqAqCJEbgppJOaB1rLMcejgt+e/pVkJlLqVLSgj6MBgTTQ6kyOYmsLV+G1zdsSgVQYp2Dixa83aqltNM2mGi5YW148XP9wivhptOC9jyMu0jiA/OHKQX0y6546OFZ8lHM6KhS0YajQqNP3ONNkWNNptheENTUAARL44DtlaaPhrZex/cSUXYpRxjuyndAvgnhklyHVYSaJME3wZenO19+10Pskhh/PAKLx8jmByCMH3AiHVGUAmnA3yAuJAAEDsxQjCxIglex7vYP/uQZakcq1RH4KN8v8qxq4hWXFyOV0j4rY1uARA5l2qcZaNRlg0BYifTWfFaUh4p5KgTy1o12inIdKYPqqY/7dAZjVKNRlSSWcJ/nIJyosHrkxJtTndF1jgazevk0RsyC4rCHtxfpejIvU1G+BJNll+DVcN0ypBPLD5dhZIpFEdjR8z3I+cs0KbTDLp1+7V8p8OLkyyDtemDQ6EAIKgEQkR0YcQqIlgFXQ4yLIPLSjGOMDV2fDPKPl7pO22laL8u2Vjh8nIwsobSVhiDDbkpN0RtiDHKMcqGJgd1F/hkSqHrTUZlKA8SPtyt0XQixCzrJfxwbo79fFmj1d4OnE2Wja9b1k4OCElHp0aHP5J/74QqGb1Y9jExqk+loO4SjYZbsE25zgW+puyjTaZgPaUz4KYTFCMzGIqwmuumV2o6NRJoGdt4IgS9cVXXcV+uw6kXZCP6aL19/jkO6dLAd2feyw97GBJABH3CiEyJyAgiiamAcXZatYaD7FRJwohVriPQ9TthLl7plfqh2+4LwhXdeDQa4drlWDdEhftJkFSODZSDNYuTKJEDf1e9fbOHo8SUgyDEEYTgIcjHK/7hnJbWDhpGkMTaGXM7F95Owb3OJ300nwFFhTVaUpInjZl9A2Xl26Tc2De7p9cc+wDIHyIa9jrnZD3JdM40M3DXTEFRykGHQJp5dqKJMtZhA7hwoq050WghSVA2p0o1m6kHPtphS8pwCExKvvtnOVxgRgR4JQi2N8/YTT/kK3GZ3SBPhtlAMY4wfZZ9vGxdnmTjyhA5Eg0QXq4zKIkchvD9iRwFI8ysj5Zd9X+jXex0a4AIba5uhNkmSI1Eo1oCH3wiwg79fAESSXKRDS7wnLJLfs8IikClPlr07PTYR2toNOFgOt2umRngVHs6wpZvnzy3FNRkgqbz8RH4toCC7zAptUBKTkmpkaGFl9Ba/tWWIn0SH834s2YWssPdT/bR7N6EotwQoqCV7s5PkRNCV8/LG9QCEaLVQPylhr4cIDimNczQyoQYVEIdhNCf/M0AFkDJrIxbWcU6jFlhMLrIMsSVhtgAxBP7VQ6UtE+IqiqJRF4ckZzMcGzKLFTaTEe/kUB8PsETG45Og9aCRENEYMgYdVkoT7NbPxv00YwvptG+SbR0v/YMdEuJhsukb6rh2y9k/5RoUWcAubWYUXjjD4imN4H0bD++TCDp0lDhFJT3CTwFxeaSUbSDV040YGK3y+FNJtoopdHfXiHRuu/pgpQp0Tga+j0jxL++Oh4y6CatY7Xf3Q/K1DH6VeF3qqdSxLrKQa18zD6ELKtCCQBD9PcwZNzY73JssqROotA4FxJVy4fbi0F2Qj+NgOAKIdDr3DMLfSsmcXjl5wtEg0rM+o4YkI2lFmzn3MdhJEpKPlHWMFl8HKMn1RkfTU6RpDr+c5jtLkmqz2nKHWzBin20+WRO1JpRrlOS6jg0DbbMdJJthkWi0dgzlNiLBmmo8MqjHNHAR9Jv/MJcJ/NrU0fO0KvbbzX0lffWu0iBfmVOd4poMWIL62AOaAjkuIlIA3EfwI3TIAYiVh1Rta5xJzSE7Zocq/1+FUJo7NdWIFkmdVROoiqR42hzBSAW2Thmb/FEBEMIdQb6dtT49yvsDNxinV7LispayddRhiH8QoMjLEsoPnQ6wPGLXE0nGtHJA7Z3Nm1YnynAmgdfQoh7XsKLNRojJDDGQyDBcs7/D9MpR8adAd5UgZ1oPihAXTYdryaOmqq65dkruNHt6ffsNPIP+ki0nmqaEBpEKwGxCgjtrFawPIVr5yVwHbF2GR6Pyb8OZdWSVragDiuDsZlfl2PDEL4/lWPTz8VJ5KfTpyBZzzMTIESruh5kxyobQta+OqSCCrIRhEC8mvJHgESy3mTXJf20Mv5+Kqzk/CEJbOJm+ox+3sYr6KYzddKauc4HjaQqPWFpcTQcAkRDhOZEL/L/Z7MFdAaaRCMgD+qmqIcRTe+1oK02NZpF1rxvYDe5TwZGfqVVUvhLC4lGc6q98MsXQtN0Okm8XAxGt6YAnCSVJ56qY6RAaYDyRVJHOD0J0pDL4Qjfn8pBkqpeJRK1kEQxFY/wdD2EcvsRP76O7DxAvgLRWO8SmbMPJVUTrJ2UaMfZ4DqAfmaiUfKu76YzHdDY1GiRy5XOc9It72lqr1NynRyW4BSUjJ5lok0nSKY5EG0B9JrNdM4AmU6aITWnCBxpNDwA64rH7PqgJaOV3mlEXHx9RLE8JibtdUKR5zHTlv6Boeja1Wroq/AebEmn+6JhXN0IImILEJr1uRxn48uGcd0ARFpOMQ4CpXSEkn0IUTjwCLOsDLEc61RHc7/LsUlycK+TSNQSErkJR0gQX5ALVuSmkzRaP/jJ3gauxtpBiJ8NB2t+z2AZtXMunLKQu773ML9nz31EtX1kd0iOD5G3dAASz3w0fb41Uw/XZBux1znXXCfwDMhG4Q1Qf4i71yNpUGXqozVTUKKQWVMzrXiB27QWamF4Q4vEeXlyhjvQDdMpDjRQcb8ylVdlT3YX9kPFvYHBQGKXjtghN53CaNQULsOlRkDKpMsRAioJLCNcxHJsQB04aiTd762wHFRh83z0XPBk3IJfKkbULUAgvAEQek9kHtLGKLNa9kAvWiUlhZ8hFXKVjRXxvXYGYGHPC1tmjdx1SomGrHElRmkA2uIwGayJEPiC9zR/c84P4+EsVGQ6bTgQkozDG7PpZDZB1wxNJwU99EjuqyLpuDUWyWRxH82L90Mf1KD63d/EX9Oi5pNvJe2d+ZRG3N+H6BQhrGBnT/GCOMKYgRxwMriKctql1rFpdVTX2XVIKVABgoIX6qOjiuCicYcYUR6PMi2xHBzeEBVnJBqpHAEQ4KNZFCVUV9kpYi88ooeQr1+xncCacUzpKfYHHYIIJfTVkLYustuSP3n+hlJQnjWX7NKSevom0chDx06AE+0+JdqdEg0ohhqNb1xLiuqO1vMFmk+gFflonz8vFo9gRB9nE1JuyL85+2iUUiBd6ESLtCu7kuqbOX90/CO97A0UTU5pkNbvlgDpKQs+ugZwrr2FwJLHOJuIQIgNrKNiU3XhoSleEGLLQ6mXfHmHZtQQ0QcqRnK4onE5YkTQ/akcm31DXI1OOZw6juQgoiEkqNX7AJCBRvVZVP9GAvr4Vo23c4QIreN0NOSqroVoiDg3l17UWhpH88dN+zTP6JZlUrgGcsTuIqLdGdFoUjDRBTsAoKnIRWPNNZ0grWaLx8cWEAzeoEpDjYY4SUHRkVMewQYUw/rp3/imkrCEQDb802SAOPxGtCjvFN/2ynZhOoUVSYBFmRrG/X6NKeQcEZWbzjKOKhBJih6P2TqmJDUupP+AdXAamp1wwIyzD8+pr+etIFlVDltAQ46gFFTeU8L7fpOD6FxLn6NyEiVyrOHpUn+hJDs/Agts6bIKmhEIh9UChpWPy4qq8XaOvgJCIyvD7ARSBx+zk/hszn0OU5QxN7dMFves0UhjNTXanWx/Sk0ncAKPkkPuYx9tLj7a3OJoC8x1fn4kjbaYIfVYz6ECVI02xyNVo31JTaf8Gqx/EGVsba6KdK3TXqcFdSN3zVPVFZiptJxxCtkRqkB0TQggmiLGmRakLbncZzSaUBGhCleDNPCwVEcZCIY2Gt5UgrA6lGq+3+XYYgRZPWrldyCRyIGIPmg0hBhZrz6CHET4yk537StC9Fwv2Bcc+/kCFxlRcUun5C9K5rbkSs5NU9E6LUI0X1lhV19smHVAJY7GG6jOcJOfl4hLes6wajR6cOLENBow7NFN5+NihmSjONo9x9HmUDSOJt0SUap23xlcKJE4s2Rc8lvHmRaLzCgTTfZ1kGicQ06LJJn1AgMCmfMkAlNQtSAiPoa4jg1qxS/v6OaYKVUhIieiOaL6lhy1tFI25VCiJa0giVyOGlrZJAg2zHWwIjaImE6vpXU9zjIQ1iGc69Tz1c7AZVRJjT4aG8hmUv1Be282/8N8bcamEzvtfrW2wQRFrSf6TjsDU4+j4VjHRyDZTIn2WYnG9APIgt00PIo1Gg/d1Tga/Ztka3YLe9NoHrC1qLRkRpGTsnLPjkxn232StBy0VaPliCiXORAAodoo9p8CrqwO1Wi1OkcpUSqoA7TEltbhbC0RV7EcUkcuyfsqxPuDSkp1LNE0luOowxCXL+CGC9Leg84AQ9yOM7e9nXNHKIraDNJO8epcLCbPztQglSy0j6D9OtdnPpQ7GVDBH6myoxzAvcyu414naLSIaEAj4NNixkT7zESbIdHgE/bcJnCEPEpFA7Y2ksMNvORc/WehRPOMrT31VjoNUtbsX7OkTDQ1fCFl0mHbLl8tCAQ4pDzMnQLt1wGuvPXxGAt1FEijFW6FZ7X48bigKQGGiGkYIjmUrIKgV0jlcERYnlVQ2bnwcP/XITnREESQAh2wB66FztMh1g4OAnJJgotRiaw1IIhcDaKlPpr1OmElDhduRb1OiW14rxPWaCznSCvYEE8LrSZm0ekmaBP2xSYLpNXj4vPn1gLU2zLRJlMedjuBMkfWyQ1KuWmKssGCRbaziB5ZLP1RIaDN9fI7RFvxQAd+aXX7jWqhlGdikLC8wf02fjrYAPkCXX1cAyIulJX0OnKqw+nBVQVopWbEnsmRjJ5O5ahRjqAIgYocStb8DUf9qrAkR06xC2rvTcRSHbBY5KzRHuR8kUOmq7Qd0Wg1IZ6aZiGIhzW86Qau/N5CX5KArE5002fwWNczNZ383jaUrk90Bu44dwm8oc7AQjsDC+8MLGRMB0KIpZFGI46z5tRWbDgH+QFiPI0/EdG4+NpXTjT55tuHMmJBbcH+s7bP+cFSEyJU7y15/IyuL5tO2mKEqwCpI2eSUCvvlalaR45Vt/ewjsJacX1TvY/lqAFgcpCkIofoPGsl/dVQHQCiLmWBWyBqqtOsGSDaHlHO2ynj8yVXX9phhOz28627bjolNZlqNCGRbNtGOuCMd4g6jAmmKXTYEh9tmvhooNnAbrJGm4FGm/0XTeeMNNpMNNoi0mhoPzVQbAHblGh+Vwdml/U6mWne/fRHy2rx6XnbSAX4f/lXmVBL5TV8LN88Idq1IAAQGPEOuCUUoEnaXkcrqoNuNPCvzR6ADSFU8laQrD2woI6I5Nhti97sMVblKFkXUR0qx1IrZSwHE21bptQzxEWVWfB9UHoIr70dZmIJtej5AsIk8QII+Soj08lsEZ40NZqaUBtHq1EsxqizZB4ckoGJRgWN6fxJolGvk9gFRJvNPsMW+GqPbD5BuwHVpjyUYyGdATXPLAH9G/HtRoBe1BVLiUac0rQnr7Ss/UfmkOfFy7N/sC766exlm0vd3l7rAIK1HiKIRozIGbK90vnzruB3CFEKgo6BI7f/vgUI5hG1UnoddUF1dBDxbTmgjs4myEEQl6PkOgTxdc3kSCXV0ltbw9OVAhAyrM9NVKi8B9/ef3ZzOeHG2WBj2w+ASNsp03befUrvS8UvT6rTlvUMXKN5fv1p0xkNKmIz14yjMevcdM4WLWCZajT4R/rBDiEazsPjGaFIM0preTLCkwQgCes3E19Dz1xk0pfZzGavE8O7L8FoSdnp9/79rNd/BZvtIi/y/FX/Z0ARos2ILiF2cj0EEEDXl6/aAFDEs2e97k5hiC4iqI48bmUH3yKxuI4O1pHKgXXwUTvSSlMO/YAkBUQN24YwOfA4QKCqh0rauUK0GQEBBH3cnrfzKjnfHI/Br50QjNl5Ie14w/8j7Mpy48aBaJCvBkbtM8w5BrlGJjnKADZai1uWDOjQU/Xq1ULK3SmbZJGUWl6eaqUoEUi914mvhZrzzxItU1DoquiCtAlnYEVYIpwBBdc2rkJqg6nXKRCbLbyx3QVvDNi6jTausNGq14mrnVUnCgVXoCsDtgfTmzZ7ULMmVaAdv/55SD9+Qtv+eHqEBuz+dMT350eoxL3++DtGyEX3x09sHfL0M7Dtw/Mj8MvEZc706yce7jn0U36D2CT98x8E1K9/hX5rAYGXBiNvNK1cfXZJc84k0Ljmx4QIGuaZPOetLer0OCfQigzApBCrqpPLhCxq9u0uqvMG1SlF7TQDGlAacbSw0bjsFjU3ASlAK0kpEvgQbdXrtF7sQoRBjfnmBqXK86E838YP5SpkK3WNVw5jyr7gAClxHmeuA58MvOTTDWh4rRzhAGeth4qnGU74aTZDs9PXPV3txQsvQ14FrI76SRjkafrl9yjKYTyXxB6f5m35jY2KmtBFk7DGVGuqmjKhN+lYoq6p0EJVgxrjIVmPna1TAG1VoK0AGr1JGRw3y3DCGdjhDBSg7WanibDbkACdNso4zzBgT290TJjWZ6JQLaSH4Q2gSAnzHsvtgIZ+rsk9IjufmVNCIxJagzRA0RXYCKigxgzP4jn8aoEGHpVSAg3AB6E3kI2fhUeBDhfQyJTEC1rRoLPgcD7SoxPCsQGLEvcf3kQnNZdkWymv68p1rj3QvARcqJaoG9PwgZzCUJD0Kb8QpQ+JthrQVgfahGw45NLUAG2sQNsL0O53B9ouQJvvI4G2FaBNCTRTqKa3m9BftTQ7iUYK1Qm2eJ3f86nPdjeiwZZ5GMv3zYIXFiPXAAi2GsYEzxu8Frr6Lv7O+uH2xWEhm+MHxFVQcbzm0hKA1ewEN1SgQUQBaOaNyyBwdDAolDatsqzQgjEgHWXfAsweLpf8nXSxCIOl5DoJy56gMd29jNySe52ZGaD1lLlOA90auU6gDrnO3kZTOSbOgKCO4Q0F2a7DKsoq0FZLk64TgUawWSwFWQlUXObE1EasOu8p/6JFdYYQCKEHPmF4UIsp9RItNZ2gzrSlkNbYq1kLICXMXwJTV604BATWhl+0Y4xxeRh3PuI53zEcm91XVDUSzW4pxQyBFvHFCxEVAEsiej6PEF4JHpBpwFaXBFLKoyahdNC6HsocDyUa6YHq5HdJUDGOpiH8CG+sa9po+nC6PzOANFRRndJT0CnqtpRo6/iF6mThgrhmLflzoNXcVCYJgK2lqE6Mty9j4QBwRRrIJtAeqk6taEUVACeaGxnFs4dU2DzbJRq4xLyXTOqCvSTQVPnFL5pAA8wyBJmGbQM0KUu10Vx1hoxCBWpVJ+ikOpPAh+p0kmHpS3tOQVnQTBmHlFRrOgNCY6rOu6nOmapzi9Ub8DxHHIzYBpbbjqqO9fpU2VTmBmnpB9C8Yhi5KFFDyXEpiU1QGEbeUD+lkiSLfao5ilc+U2Fe/RWVJsUgVIZ4LcgDoNE48wbs2UbTklOJbrildYfBgT8LBznOBoZAEbiJ5SOA1pEBiR19N0oiN++/fC3KxRFaXx1QP5YssV0MmpAFRCmEBkVHiip6nZihjYaeOwMCEYEVDLTJgDZLEQKgEmi327f7x/0OG00Dt/uM4Tugt40q2gBRUZymOkdVopBtvGgBmvsHWqWLIMQ/RybVw+KnIKB0SaGSljjdvEEa1Bf8b3U6dzw/hEVHgcbWgSYwBU55GjqAZ9pcbGie4bAY4+VRlL6QruDRBrGjePJ/P9cacAREhgAI7Rfs4c6AYUoIn1KWypw3oGKixqUfhzBWXEuIrZNEU0RpTaBhtmQGqisBAKJAubmNhkfsahxtK3G0XYAm34q2BmgjgDYH0CYHmnTGyVyDCjQKuhqkacLO5SV5Qkt4nQG95jVF2ko5wNI7ONloQ+N1DoDmSy9FZEIniR/a+nq51GX09IyPN1zFs13xs3DwBLQDk18B7RJAQyM1Oe/kuukGaGA458xhulQYAuhso4HnYJsD55gbYl53Q/lIHboBRvcBXG8iviHaUso2Krxgi42b0mxqkWs25hk+5v12E6DtXwJtq0DDLjFqrwFoAJkDTYo/MPPWAC3kd953aZK5/UX85AZYjUTjHCQaZrUIUV0ZaFgGa160pVuKnYogxF4o0KQvA35soXBV0TwEGqgHGoiN/ZJUbgzinIBGTAjz6T5j5oS0cLHF0kk0tvAuUqKRME+WKviRRGtUJ0FXwAnZkPl3mn5VoknhEyop0RR4QhMhs80BtNvNgPZ+l47QbnGP2XKd3AIS4BoNxVCYFhpGwQWJKqp1hnLTXakSrQ221exowaSHQKqOuPhTVVq5V4oASHqdLtGqXTQMRaJhDJ3QlpcvVSc0qwfFMrZFYp9V9YDjcPTTGUBBw4gZvQEPbxSJJqVKtNcq0ZQAAHqDYFKrOUTQ90wSW0bYGZVIoKEk0AJbBFd0yJnOess9RqvqnKSFsbaR7megqTBjIC29zm3uwxuaIp1G8z89vEHVKUqfQAN1cvmzo3pnNfcgmKQjY0qNMZKWcIafLgutH9hF6fyhRglhiqGHqjNB4Q29Y14YvN8M8ZYIZcspqDFWVSdqlOJ1mtBJoL0G0KDwIsYfy3WUV8KOK2JuMSmJ+WpEQcNgEhnvWBRbpRLh2HqdZrPFPxef1ToDWiuyVHXCnpoEJXQGVKJpy3wAgbbfPnz1hlTwEkYcN/kjUbTGcHXIMf5EWvcLHwGdbnetXqKBqxItXxLfv7aDfA80oKu6VywHgdapK8BOC8bQ4YaH6fn16391gAznGG9tgbb0WYqehmoAuGeb+hb5zOpJuPRcvrTR8t6ktlheTxIN8MrFgwQaYUUMCXSsZsL8TQhTTmtwTAJpvSq+polaUso2AWTqY27ype0stCXQdgDtJtLsdv/4EIm2i5m2i9epQ5BomzmbJtHgbUYcbfVgCgrNQ95KqEwCt/mz3rFucnBdCBzHOdDyyITh0QMNlECrQg76NlQg9VUCymnpJRqGMBYSDaUDmmtBDIZ0rJkotJmCWjhTJBoDZn2I4xyw5USNo6EmJdDq/uxoYkPtDmhauGUephpiH59Bjus1pFEWSQB3BmBpjTC7HthoCG/MoTrvc6rOLVXntCqJ3pwiBRWX5itlgUD+YhG9/USnue+68HPGdJLx2xmgI52M3s/HQGNXxIW/Ia1TnUEYSSvKLaviDMQcEUSeMGwPwPCXqpNoFYYQ9vR2hPnDxtCGnEfjyeQc7S4+AEnVeQIa/x+uOqX/UKJVoPGs8nAKPt+zn/k2Hr42HaGvcR37XOfmuc45kurC2SLI6gxM4nquKhPtkWJgCqCy69HyJ54+P5/lOrUJ2421n9GuqlvSPgGDgsEDHQKt7gJYgaYC41pi9WAfBWw5gkk2JIT+nbN5DLlDXPMFbYI/aFkic9Em1SOcvIQEjEzUcmlf18q8lf+5EmjAAdCkvPQCGQm0t3LjMwJ1BprBEVLCVVHIgrhe+AgUjJ0zQB07lSfVVS4h8I9cJ5+CojOwa8QWOhXCbd6Dt5yBnjdrzYAJPhPRYYCPEi3WLcXeRnZ3BM7IZSKNVDdWjVfiElEX8lFZA8MLfmhno11of/GfW5yBLs/aOx0cruPknXmuOnFMNRQOtFXDcSbWW7zSyC9bBKPUdfoJNAosAg08Stzir89TUJnrrASo1jUd8ADOe28ATZPGzyYEZFVhSqM0GjhmIXib0hUFqUpTsLV/fEh5/3YTG02+d+mrmTbvABpAtxWgUfnO46pKmTkCqk6Lo9U7yOFUpHqNd4BjlarzbIyAjjPQDH+hjlASaAouHAB2YSHOlB4ATQhAAzQ6x4R0VuP9YLzcnmKYme4GaEfJAbsgL6pzIQevczl7nWwINEcJppNsNIaaSefD/0yByIYEgPG/29toEF7YLFTRAdkjkimAtgfQ7gq093d3BnZzBtT5VFVKNQpwSVGta/YeruT+J9V4scGSlOdEvaVZl73HYxMuMnEyI7y+VisqZpg8EtHut8AYWFn41gKNVM38zuvkDxdUwfy1vZiyr3aiyTsoHROdBb/EVtl55v+lXcuW6jYQ3GanDzYDzDFm4CYfna6qflk2k0X6SnJbxlwMRfVLePD4X6NOStwNRW5/VJM02d72/S0Rdt7NGIPTZFcFszYl/uTYlnL3FbaQMGfGbkyfkd0SaN9itB4M/A60h3Gh/6JYBpPFdi2rvASjXTqj5b3oW+y8lExm/y2ZgVb2tBjtvHrctPBzpvhgF/FSqYlUpElmA1gghPaR0Wag8bUU0KR53zGavFA1iFgrLCbwBOWL2IrcWZnO5XLqo0GmnzzNqzfG0FCkqIXc4+LPXIHnpibP/4JWpvN+I6NNppO202whS1AC2nNdr+vVZF1f61OgM8g9iEaMfqaHCaJJdBjRjf+1Sl9VZ8fYgAatEKWWP5wKYzJLAOgUaHT8T4H2LhYpjpJWlOaDztT8/MA6dwJa4KHu9iRpGls+pjCjGWUdKW9pdROCvNe1doApIKv/SZ24R7VOsv15hW3z7KjtfTTKm8MgfQ3oA7v2WY3twmEAgNYNb4NQxAgIXsaXtTEuY2ybDcipAQNjGByGjT83G+9j5OoN1Tq/BbTn+gTQADgHWrDbS0BTCs7ZjTrTbDDTKLGTSDfGLicLH6mcS8Q4/b0xPbLhqfdCcXLTfhINnVDohZtkken0AkTHfFmpPFdbdom2mj6bROv+gl8eWy/NdW0J/ibGD+lFbTp5wkpaW8Q5ZTrdg68UWOT3td3i8cALXa8BfjAWA4QupmnY8G8bmMJjjVGGtZuNhiRr4DED11DKbAAZw3A1bPN6YWSa1trj+VoNXvTR1qsHAyhFsRNfLEcBk0DXre6gVsHAXQ6b376bXWpcYpCvB9ctVR1/caO7pzPQ3qHDr040eBoNxRtp1kx7i3vSdJLROtBmbBTQiqBivk3XtjGtIzBfi4OhChrQBPqCNYQzorTptVC45lCm7x2MVr+cBKq4FWwuXwegXTrQdGSR8Mm76fway6A+aC5tF536Bc1gZ7KNgQ85gXYbd7S7gGaSQHsAaK/xSKDBFXuCuAA0A5dFnWv5aN/y0R6vnY/2PftoN/lo2sh0bmU6CT9FKkegqWHQdR/joDQnIv7OaBg++mgFtMp4BFizegztI9D04O60SS05CwaErV66aNT03gUDxa4LBEe66XxHsrtcXUAqgUYWS6DVT8UJsCPQNk+a5dnluQlo3AG6uDOAORz4gmLb+50gGwW0nz3QkOT6LqAZbkwZNtox/7UTiOtqeDPTeXUf7SXAPa5wzK6eR7MH0ruT4ElZbiDk9IeL0Tel6mxg/CueK6CpZqvdeNugYDZLCKlLCmjKTVdgK3fpFGixNLCAVg746bpyHfHp7pyfAi2nz5ehCLZL99HCTpausxxRTjOCE9+WM6Bx/n3pQEsTIqBlGJaWshiNlRxfz9OTcumjCfPDZsci1RoB58X18UXVOo3p3ZppQNsNEScwMH6Iuxloj6uApvTG+ueP+WgmBJxNGfxsj/YV+KMv5yvaGA+oCXIAmP5cmYBGTgPWzoHGcLnWcr6hpl2ArqahJTI70BwMZz4aROZy9pocJsvko82mM1rPJjdCFd2mEsiBTiBpFprP+bckw2xp9atdk8q9fgZaZzQPNHeM1lb9t+KkCtMVnG0t1QnNff2Fx00XnAaDAp5uOvZDpe8GHw0OObz/u+0a2YwfWFEze7b7aKbzhQyaTOfqPprtGG2V6TQObHk0Ygqy2aYWPqLqmX4ANv4+lKSrW5AoDBQrHN7RLM9LN61ugT/9CqMm1Zjj7NkBnnh0wN9lruKOh3V/mr6cw9WoyWu6VQZ264imYBntIDkr+McwB6s9MEHvNpi71AGYBJrtFKMF0LYGNM5xh1xA3GAewmMSbLWkWp+z61wN9MMOk8mA8MKokz47GIf0g87DSpHlClsw2vpnpTxhQtfrEzoLBZAH7KgE3hvo7DvjT60SQWVLVS6k8fQq3XzyNXduMx0NAmgmo9kQivUEWuYbIwSc1pCqNQ/oL+7tlywRHJ/SqTpPxz9LLm2WdNDPXmbde0CMHRcWmhMadGi6fq8cyq+HimlopqOTxcp0hl1MhYc70Lbta5/eWHp6oyTYVZL8ip4fEZ8QmkOVG0CPBQKl8gmxMHwUwIUrtomsNYD2tK2A9iygWdbjyfChgPa9AxqtJcupNgBeTcTQ0qj7VZS9TMo6T2/gcIsgdFpYoZ66lIb2XgCJOUv5KaWw48k6ayblnu9Uc/zkYZ2lq6J2DjRdSysBm5xytneMe2wApxj1VGxS4nzoHDd3YGZGw9iqSdTAV0r2U+5qjACBJOo3DJ7XAmXRg+IG9o/IeEHgbmEGax1hLI24VsifnrBVDs2aCYIGnlgJWz13JmxhShVt8iqiQJGidG16azqGgVsGgIdap/SeBaPhXfbe1O95NO3WM0hzvSxV9/O0mco+asIMDx+lTUtTgFt5NIcq9dRq+bRJnDY/65mPxlv9t+9iNnSJ79YSSDbAX9aDIisrsCcX8KBYUnOaV8/KADoBCe8cJFNEZs0rA9SUQbteQVk2FqM54NCfK+kM4GQM+mK1FIOCAW2I7DKddN3KdLrZv0OkppPAQ+emU/pEeEl0FffNv/0v0/lPOThpc+eok8MMNPQ6KZz7SrlMUjenm/3F2XSWaHcynWiHYEDmKkxnfEupOhzcoiYu/KQ5j8Z2egFlLiHSUjyBknnepEF2jly9g00m8L+FCzpmoDQH2nVvOldsV2BPQoIzLJIL7TStHrL2g2ABXQxHvN0j78Hgk2Zb8Cqg6fX5oO5vIgTX2N9RDyvqaCRBKu7LVrUWmc4EADe/BwOzbWwFfrdCySZtQae2Gps3kHwRno2xSCTB2t2HszInDZMOiJiGOgEti8nIjm/uwKMpzs8Ac0PjA9C1s+mZ5LwV/MLUTFpWNikCl5c4N3n9MKqqcGITREYu82L6gw7XKqA9Ba4ONGOyOIAHMS6FrU2gWZOnVkBDv9Ni3/QPnIoNuG1rkJNcOA0iS6sSTXpwTEtN6bMKd6c81s6D+eOc8Iv4j1KuOKQtofnMaGU667CkSLJRQtCDuAAT4R44YuhLR04izVVq/aMXHtRt3Pr3j8hCMLF3MivvgmHv2U0+WooQKo0SSoSc1vR3c0gdk48GC5qMdmuMZhoLlrCeD0/Y0nRaZeBJgB19NFMwj70C2usz0G4FNMFLlhzq7iqg5s8e4mL1/X0fS1B9tWcsaNE7WiAtrReNpjxacFpOkk3Y0LlXfpGW27gPUwTWqNPHo48mjaAuaur/Va01kNYZjb0DLeYW6Aglvd4XjIaOB+abNH3/2N7qGdJXQFNfD1e0U7jv+DR0BQbllBFX5DBEAiAxAEvYeclowg2DqYTp/Hv20Vhmdx8NTyF0mWAR5EcfTcEL4dXTGxAPBiq9od4C0GIoGtHJGdHJUXhZZGgXqJiXxh3r/BLDQGG6WCQ+U2qaX9hMoaZe6cwAGNX2sngG58LJpMw+mrTc7XMC+VwZYNNGmDjWOmczfiT6SJZ305kbbcPg6pMq70Ya+cEGNBLGJuoAi2FQRAiF5tJzGECa4k0QlQqcRmIFtMl0Em7P//LRblmCErhFUnddTYki+VikBqmxy7RmuWXQj0uc30SNe3GZd0Iv3y0iPZ7bfLS+ujeGvl61TkObKwN1fgCtYFfkgZ0ssYnRAiXarwVFWXZt/qSNjTJ1MZo7Ev256Tx6FEVxTGh6NhZDAk15gclflEArB7sYTaaK1lOgY3c2YjAAhas3VFSvqJPiPtoTKvNoFXW+Hj1hq7wJnzzTKRHn3oLownRK0+vT0L9wlK2T9CmjLcKuDs8pcGj/h9GKnAqdAcsioRN5T2pWa+eos5Ty0fLlFWd30yk1lMyCKRi4nAYDetgpo4kLZqDNjKZPx0kNHUgSu8k1SummE2ymtbUECaDywqxKUKsnbP8FpE9icPM/TG4AAAAASUVORK5CYII=" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![merge_sort.png](attachment:merge_sort.png)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/sort/merge_sort_implement_solution.ipynb b/200 solved problems in Python/sort/merge_sort_implement_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..7ed5ab5a8010df9371cad85889970feafe5cb8a7 --- /dev/null +++ b/200 solved problems in Python/sort/merge_sort_implement_solution.ipynb @@ -0,0 +1,140 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to sort a list of elements using the merge sort algorithm.\n", + "# Note: According to Wikipedia \"Merge sort (also commonly spelled mergesort) is an O (n log n)\n", + "# comparison-based sorting algorithm. Most implementations produce a stable sort, which means that \n", + "# the implementation preserves the input order of equal elements in the sorted output.\"\n", + "\n", + "# Algorithm:\n", + "# Conceptually, a merge sort works as follows :\n", + "\n", + "# Divide the unsorted list into n sublists, each containing 1 element (a list of 1 element is considered sorted).\n", + "# Repeatedly merge sublists to produce new sorted sublists until there is only 1 sublist remaining. This will be the sorted list." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Splitting [14, 46, 43, 27, 57, 41, 45, 21, 70]\n", + "Splitting [14, 46, 43, 27]\n", + "Splitting [14, 46]\n", + "Splitting [14]\n", + "Merging [14]\n", + "Splitting [46]\n", + "Merging [46]\n", + "Merging [14, 46]\n", + "Splitting [43, 27]\n", + "Splitting [43]\n", + "Merging [43]\n", + "Splitting [27]\n", + "Merging [27]\n", + "Merging [27, 43]\n", + "Merging [14, 27, 43, 46]\n", + "Splitting [57, 41, 45, 21, 70]\n", + "Splitting [57, 41]\n", + "Splitting [57]\n", + "Merging [57]\n", + "Splitting [41]\n", + "Merging [41]\n", + "Merging [41, 57]\n", + "Splitting [45, 21, 70]\n", + "Splitting [45]\n", + "Merging [45]\n", + "Splitting [21, 70]\n", + "Splitting [21]\n", + "Merging [21]\n", + "Splitting [70]\n", + "Merging [70]\n", + "Merging [21, 70]\n", + "Merging [21, 45, 70]\n", + "Merging [21, 41, 45, 57, 70]\n", + "Merging [14, 21, 27, 41, 43, 45, 46, 57, 70]\n", + "[14, 21, 27, 41, 43, 45, 46, 57, 70]\n" + ] + } + ], + "source": [ + "def mergeSort(nlist):\n", + " print(\"Splitting \",nlist)\n", + " if len(nlist)>1:\n", + " mid = len(nlist)//2\n", + " lefthalf = nlist[:mid]\n", + " righthalf = nlist[mid:]\n", + "\n", + " mergeSort(lefthalf)\n", + " mergeSort(righthalf)\n", + " i=j=k=0 \n", + " while i < len(lefthalf) and j < len(righthalf):\n", + " if lefthalf[i] < righthalf[j]:\n", + " nlist[k]=lefthalf[i]\n", + " i=i+1\n", + " else:\n", + " nlist[k]=righthalf[j]\n", + " j=j+1\n", + " k=k+1\n", + "\n", + " while i < len(lefthalf):\n", + " nlist[k]=lefthalf[i]\n", + " i=i+1\n", + " k=k+1\n", + "\n", + " while j < len(righthalf):\n", + " nlist[k]=righthalf[j]\n", + " j=j+1\n", + " k=k+1\n", + " print(\"Merging \",nlist)\n", + "\n", + "nlist = [14,46,43,27,57,41,45,21,70]\n", + "mergeSort(nlist)\n", + "print(nlist)" + ] + }, + { + "attachments": { + "merge_sort.png": { + 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![merge_sort.png](attachment:merge_sort.png)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/sort/quick_sort_implement.ipynb b/200 solved problems in Python/sort/quick_sort_implement.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..5f79b6a19ea42a95bd2875732aeb117b9f9690ed --- /dev/null +++ b/200 solved problems in Python/sort/quick_sort_implement.ipynb @@ -0,0 +1,41 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to sort a list of elements using the quick sort algorithm.\n", + "# Note : According to Wikipedia \"Quicksort is a comparison sort, meaning that it can \n", + "# sort items of any type for which a \"less-than\" relation (formally, a total order) is defined. \n", + "# In efficient implementations it is not a stable sort, meaning that the relative order of equal \n", + "# sort items is not preserved. Quicksort can operate in-place on an array, requiring small additional\n", + "# amounts of memory to perform the sorting.\"" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/sort/quick_sort_implement_solution.ipynb b/200 solved problems in Python/sort/quick_sort_implement_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..d6b1737dce9d1813724f85dddfdc3ea776c8a3a5 --- /dev/null +++ b/200 solved problems in Python/sort/quick_sort_implement_solution.ipynb @@ -0,0 +1,92 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[17, 20, 26, 31, 44, 54, 55, 77, 93]\n" + ] + } + ], + "source": [ + "# Write a Python program to sort a list of elements using the quick sort algorithm.\n", + "# Note : According to Wikipedia \"Quicksort is a comparison sort, meaning that it can \n", + "# sort items of any type for which a \"less-than\" relation (formally, a total order) is defined. \n", + "# In efficient implementations it is not a stable sort, meaning that the relative order of equal \n", + "# sort items is not preserved. Quicksort can operate in-place on an array, requiring small additional\n", + "# amounts of memory to perform the sorting.\"\n", + "\n", + "def quickSort(data_list):\n", + " quickSortHlp(data_list,0,len(data_list)-1)\n", + "\n", + "def quickSortHlp(data_list,first,last):\n", + " if first < last:\n", + "\n", + " splitpoint = partition(data_list,first,last)\n", + "\n", + " quickSortHlp(data_list,first,splitpoint-1)\n", + " quickSortHlp(data_list,splitpoint+1,last)\n", + "\n", + "\n", + "def partition(data_list,first,last):\n", + " pivotvalue = data_list[first]\n", + "\n", + " leftmark = first+1\n", + " rightmark = last\n", + "\n", + " done = False\n", + " while not done:\n", + "\n", + " while leftmark <= rightmark and data_list[leftmark] <= pivotvalue:\n", + " leftmark = leftmark + 1\n", + "\n", + " while data_list[rightmark] >= pivotvalue and rightmark >= leftmark:\n", + " rightmark = rightmark -1\n", + "\n", + " if rightmark < leftmark:\n", + " done = True\n", + " else:\n", + " temp = data_list[leftmark]\n", + " data_list[leftmark] = data_list[rightmark]\n", + " data_list[rightmark] = temp\n", + "\n", + " temp = data_list[first]\n", + " data_list[first] = data_list[rightmark]\n", + " data_list[rightmark] = temp\n", + "\n", + "\n", + " return rightmark\n", + "\n", + "data_list = [54,26,93,17,77,31,44,55,20]\n", + "quickSort(data_list)\n", + "print(data_list)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/sort/selection_sort_implement.ipynb b/200 solved problems in Python/sort/selection_sort_implement.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..f36edaab4fd5d07e806ebdaca7ea9ded9232cfaf --- /dev/null +++ b/200 solved problems in Python/sort/selection_sort_implement.ipynb @@ -0,0 +1,49 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to sort a list of elements using the selection sort algorithm.\n", + "# Note : The selection sort improves on the bubble sort by making only one exchange for every pass through the list." + ] + }, + { + "attachments": { + "selection-short.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![selection-short.png](attachment:selection-short.png)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/sort/selection_sort_implement_solution.ipynb b/200 solved problems in Python/sort/selection_sort_implement_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..57dcf4957374a7a58e09da3ae34ace0a6832e124 --- /dev/null +++ b/200 solved problems in Python/sort/selection_sort_implement_solution.ipynb @@ -0,0 +1,79 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to sort a list of elements using the selection sort algorithm.\n", + "# Note : The selection sort improves on the bubble sort by making only one exchange for every pass through the list." + ] + }, + { + "attachments": { + "selection-short.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![selection-short.png](attachment:selection-short.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[14, 21, 27, 41, 43, 45, 46, 57, 70]\n" + ] + } + ], + "source": [ + "def selectionSort(nlist):\n", + " for fillslot in range(len(nlist)-1,0,-1):\n", + " maxpos=0\n", + " for location in range(1,fillslot+1):\n", + " if nlist[location]>nlist[maxpos]:\n", + " maxpos = location\n", + "\n", + " temp = nlist[fillslot]\n", + " nlist[fillslot] = nlist[maxpos]\n", + " nlist[maxpos] = temp\n", + "\n", + "nlist = [14,46,43,27,57,41,45,21,70]\n", + "selectionSort(nlist)\n", + "print(nlist)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/sort/shell_sort_implement.ipynb b/200 solved problems in Python/sort/shell_sort_implement.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..64e86b05ea71e5e83eb33b2f366f1a0f9caf1042 --- /dev/null +++ b/200 solved problems in Python/sort/shell_sort_implement.ipynb @@ -0,0 +1,41 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to sort a list of elements using shell sort algorithm.\n", + "# Note : According to Wikipedia \"Shell sort or Shell's method, is an in-place comparison sort. \n", + "# It can be seen as either a generalization of sorting by exchange (bubble sort) or sorting by insertion (insertion sort). \n", + "# The method starts by sorting pairs of elements far apart from each other, then progressively reducing the gap between\n", + "# elements to be compared. Starting with far apart elements can move some out-of-place elements into position faster than\n", + "# a simple nearest neighbor exchange.\"" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/sort/shell_sort_implement_solution.ipynb b/200 solved problems in Python/sort/shell_sort_implement_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..cde89532efe45efd229128634cf3738afc9416ba --- /dev/null +++ b/200 solved problems in Python/sort/shell_sort_implement_solution.ipynb @@ -0,0 +1,77 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to sort a list of elements using shell sort algorithm.\n", + "# Note : According to Wikipedia \"Shell sort or Shell's method, is an in-place comparison sort. \n", + "# It can be seen as either a generalization of sorting by exchange (bubble sort) or sorting by insertion (insertion sort). \n", + "# The method starts by sorting pairs of elements far apart from each other, then progressively reducing the gap between\n", + "# elements to be compared. Starting with far apart elements can move some out-of-place elements into position faster than\n", + "# a simple nearest neighbor exchange.\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def shellSort(alist):\n", + " sublistcount = len(alist)//2\n", + " while sublistcount > 0:\n", + " for start_position in range(sublistcount):\n", + " gap_InsertionSort(alist, start_position, sublistcount)\n", + "\n", + " print(\"After increments of size\",sublistcount, \"The list is\",nlist)\n", + "\n", + " sublistcount = sublistcount // 2\n", + "\n", + "def gap_InsertionSort(nlist,start,gap):\n", + " for i in range(start+gap,len(nlist),gap):\n", + "\n", + " current_value = nlist[i]\n", + " position = i\n", + "\n", + " while position>=gap and nlist[position-gap]>current_value:\n", + " nlist[position]=nlist[position-gap]\n", + " position = position-gap\n", + "\n", + " nlist[position]=current_value\n", + "\n", + "\n", + "nlist = [14,46,43,27,57,41,45,21,70]\n", + "shellSort(nlist)\n", + "print(nlist)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/string/.ipynb_checkpoints/caesar_encryption-checkpoint.ipynb b/200 solved problems in Python/string/.ipynb_checkpoints/caesar_encryption-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..68d2751ec563613a80b4c5eebd215edb3fad6be8 --- /dev/null +++ b/200 solved problems in Python/string/.ipynb_checkpoints/caesar_encryption-checkpoint.ipynb @@ -0,0 +1,47 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program to create a Caesar encryption\n", + "\n", + "# Note : In cryptography, a Caesar cipher, also known as Caesar's cipher, the shift cipher, Caesar's code or Caesar shift, is one of the simplest and most widely known encryption techniques.\n", + "# It is a type of substitution cipher in which each letter in the plaintext is replaced by a letter some fixed number of positions down the alphabet.\n", + "# For example, with a left shift of 3, D would be replaced by A, E would become B, and so on.\n", + "# The method is named after Julius Caesar, who used it in his private correspondence.\n", + "\n", + "# plaintext: defend the east wall of the castle\n", + "# ciphertext: efgfoe uif fbtu xbmm pg uif dbtumf" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/string/.ipynb_checkpoints/caesar_encryption_solution-checkpoint.ipynb b/200 solved problems in Python/string/.ipynb_checkpoints/caesar_encryption_solution-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..4cb2c594e723179fc5792759a1b393ed2de0a7f5 --- /dev/null +++ b/200 solved problems in Python/string/.ipynb_checkpoints/caesar_encryption_solution-checkpoint.ipynb @@ -0,0 +1,82 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "['e', 'f', 'g', 'f', 'o', 'e', 'u', 'i', 'f', 'f', 'b', 't', 'u', 'x', 'b', 'm', 'm', 'p', 'g', 'u', 'i', 'f', 'd', 'b', 't', 'u', 'm', 'f']\n", + "\n" + ] + } + ], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program to create a Caesar encryption\n", + "\n", + "# Note : In cryptography, a Caesar cipher, also known as Caesar's cipher, the shift cipher, Caesar's code or Caesar shift, is one of the simplest and most widely known encryption techniques.\n", + "# It is a type of substitution cipher in which each letter in the plaintext is replaced by a letter some fixed number of positions down the alphabet.\n", + "# For example, with a left shift of 3, D would be replaced by A, E would become B, and so on.\n", + "# The method is named after Julius Caesar, who used it in his private correspondence.\n", + "\n", + "# plaintext: defend the east wall of the castle\n", + "# ciphertext: efgfoe uif fbtu xbmm pg uif dbtumf\n", + "\n", + "def caesar_encrypt(realText, step):\n", + "\toutText = []\n", + "\tcryptText = []\n", + "\t\n", + "\tuppercase = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']\n", + "\tlowercase = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']\n", + "\n", + "\tfor eachLetter in realText:\n", + "\t\tif eachLetter in uppercase:\n", + "\t\t\tindex = uppercase.index(eachLetter)\n", + "\t\t\tcrypting = (index + step) % 26\n", + "\t\t\tcryptText.append(crypting)\n", + "\t\t\tnewLetter = uppercase[crypting]\n", + "\t\t\toutText.append(newLetter)\n", + "\t\telif eachLetter in lowercase:\n", + "\t\t\tindex = lowercase.index(eachLetter)\n", + "\t\t\tcrypting = (index + step) % 26\n", + "\t\t\tcryptText.append(crypting)\n", + "\t\t\tnewLetter = lowercase[crypting]\n", + "\t\t\toutText.append(newLetter)\n", + "\treturn outText\n", + "\n", + "code = caesar_encrypt('defend the east wall of the castle', 1)\n", + "print()\n", + "print(code)\n", + "print()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/string/.ipynb_checkpoints/count_character-checkpoint.ipynb b/200 solved problems in Python/string/.ipynb_checkpoints/count_character-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..f3022ae8a8f097ef57bcf47f1f528ea59651516b --- /dev/null +++ b/200 solved problems in Python/string/.ipynb_checkpoints/count_character-checkpoint.ipynb @@ -0,0 +1,37 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to count the number of characters (character frequency) in a string.\n", + "# Expected Result : {'o': 3, 'g': 2, '.': 1, 'e': 1, 'l': 1, 'm': 1, 'c': 1}" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/string/.ipynb_checkpoints/count_character_solution-checkpoint.ipynb b/200 solved problems in Python/string/.ipynb_checkpoints/count_character_solution-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..848f1f343be23b9b4caec1d6b241cb25611001af --- /dev/null +++ b/200 solved problems in Python/string/.ipynb_checkpoints/count_character_solution-checkpoint.ipynb @@ -0,0 +1,56 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'g': 2, 'o': 2, 'l': 1, 'e': 1}\n" + ] + } + ], + "source": [ + "# Write a Python program to count the number of characters (character frequency) in a string.\n", + "# Sample String : google'\n", + "# Expected Result : {'g': 2, 'o': 2, 'l': 1, 'e': 1}\n", + "\n", + "def char_frequency(str1):\n", + " dict = {}\n", + " for n in str1:\n", + " keys = dict.keys()\n", + " if n in keys:\n", + " dict[n] += 1\n", + " else:\n", + " dict[n] = 1\n", + " return dict\n", + "\n", + "print(char_frequency('google'))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/string/caesar_encryption.ipynb b/200 solved problems in Python/string/caesar_encryption.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..68d2751ec563613a80b4c5eebd215edb3fad6be8 --- /dev/null +++ b/200 solved problems in Python/string/caesar_encryption.ipynb @@ -0,0 +1,47 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program to create a Caesar encryption\n", + "\n", + "# Note : In cryptography, a Caesar cipher, also known as Caesar's cipher, the shift cipher, Caesar's code or Caesar shift, is one of the simplest and most widely known encryption techniques.\n", + "# It is a type of substitution cipher in which each letter in the plaintext is replaced by a letter some fixed number of positions down the alphabet.\n", + "# For example, with a left shift of 3, D would be replaced by A, E would become B, and so on.\n", + "# The method is named after Julius Caesar, who used it in his private correspondence.\n", + "\n", + "# plaintext: defend the east wall of the castle\n", + "# ciphertext: efgfoe uif fbtu xbmm pg uif dbtumf" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/string/caesar_encryption_solution.ipynb b/200 solved problems in Python/string/caesar_encryption_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..4cb2c594e723179fc5792759a1b393ed2de0a7f5 --- /dev/null +++ b/200 solved problems in Python/string/caesar_encryption_solution.ipynb @@ -0,0 +1,82 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "['e', 'f', 'g', 'f', 'o', 'e', 'u', 'i', 'f', 'f', 'b', 't', 'u', 'x', 'b', 'm', 'm', 'p', 'g', 'u', 'i', 'f', 'd', 'b', 't', 'u', 'm', 'f']\n", + "\n" + ] + } + ], + "source": [ + "# ---------------------------------------------------------------\n", + "# python best courses https://courses.tanpham.org/\n", + "# ---------------------------------------------------------------\n", + "# Write a Python program to create a Caesar encryption\n", + "\n", + "# Note : In cryptography, a Caesar cipher, also known as Caesar's cipher, the shift cipher, Caesar's code or Caesar shift, is one of the simplest and most widely known encryption techniques.\n", + "# It is a type of substitution cipher in which each letter in the plaintext is replaced by a letter some fixed number of positions down the alphabet.\n", + "# For example, with a left shift of 3, D would be replaced by A, E would become B, and so on.\n", + "# The method is named after Julius Caesar, who used it in his private correspondence.\n", + "\n", + "# plaintext: defend the east wall of the castle\n", + "# ciphertext: efgfoe uif fbtu xbmm pg uif dbtumf\n", + "\n", + "def caesar_encrypt(realText, step):\n", + "\toutText = []\n", + "\tcryptText = []\n", + "\t\n", + "\tuppercase = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']\n", + "\tlowercase = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']\n", + "\n", + "\tfor eachLetter in realText:\n", + "\t\tif eachLetter in uppercase:\n", + "\t\t\tindex = uppercase.index(eachLetter)\n", + "\t\t\tcrypting = (index + step) % 26\n", + "\t\t\tcryptText.append(crypting)\n", + "\t\t\tnewLetter = uppercase[crypting]\n", + "\t\t\toutText.append(newLetter)\n", + "\t\telif eachLetter in lowercase:\n", + "\t\t\tindex = lowercase.index(eachLetter)\n", + "\t\t\tcrypting = (index + step) % 26\n", + "\t\t\tcryptText.append(crypting)\n", + "\t\t\tnewLetter = lowercase[crypting]\n", + "\t\t\toutText.append(newLetter)\n", + "\treturn outText\n", + "\n", + "code = caesar_encrypt('defend the east wall of the castle', 1)\n", + "print()\n", + "print(code)\n", + "print()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/string/character_exchange.ipynb b/200 solved problems in Python/string/character_exchange.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..6e33c0be98dc4ac6d27b4f169f73df81e5899407 --- /dev/null +++ b/200 solved problems in Python/string/character_exchange.ipynb @@ -0,0 +1,36 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to change a given string to a new string where the first and last chars have been exchanged" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/string/character_exchange_solution.ipynb b/200 solved problems in Python/string/character_exchange_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..75a7b8065811f4a395c5e1cc025a39900e3e5751 --- /dev/null +++ b/200 solved problems in Python/string/character_exchange_solution.ipynb @@ -0,0 +1,49 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dbca\n", + "52341\n" + ] + } + ], + "source": [ + "# Write a Python program to change a given string to a new string where the first and last chars have been exchanged\n", + "\n", + "def change_sring(str1):\n", + " return str1[-1:] + str1[1:-1] + str1[:1]\n", + " \n", + "print(change_sring('abcd'))\n", + "print(change_sring('12345'))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/string/count_character.ipynb b/200 solved problems in Python/string/count_character.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..f3022ae8a8f097ef57bcf47f1f528ea59651516b --- /dev/null +++ b/200 solved problems in Python/string/count_character.ipynb @@ -0,0 +1,37 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to count the number of characters (character frequency) in a string.\n", + "# Expected Result : {'o': 3, 'g': 2, '.': 1, 'e': 1, 'l': 1, 'm': 1, 'c': 1}" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/string/count_character_solution.ipynb b/200 solved problems in Python/string/count_character_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..848f1f343be23b9b4caec1d6b241cb25611001af --- /dev/null +++ b/200 solved problems in Python/string/count_character_solution.ipynb @@ -0,0 +1,56 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'g': 2, 'o': 2, 'l': 1, 'e': 1}\n" + ] + } + ], + "source": [ + "# Write a Python program to count the number of characters (character frequency) in a string.\n", + "# Sample String : google'\n", + "# Expected Result : {'g': 2, 'o': 2, 'l': 1, 'e': 1}\n", + "\n", + "def char_frequency(str1):\n", + " dict = {}\n", + " for n in str1:\n", + " keys = dict.keys()\n", + " if n in keys:\n", + " dict[n] += 1\n", + " else:\n", + " dict[n] = 1\n", + " return dict\n", + "\n", + "print(char_frequency('google'))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/string/html tag.ipynb b/200 solved problems in Python/string/html tag.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..85728c63e7dbf2c6e3ee47e5f2fecb2a494039ac --- /dev/null +++ b/200 solved problems in Python/string/html tag.ipynb @@ -0,0 +1,39 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python function to create the HTML string with tags around the word(s).\n", + "# Sample function and result : \n", + "# add_tags('i', 'Python') -> 'Python'\n", + "# add_tags('b', 'Python Tutorial') -> 'Python Tutorial '\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/string/html tag_solution.ipynb b/200 solved problems in Python/string/html tag_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..c876af657e8ff52883a3c2d5ffff718ab2976e51 --- /dev/null +++ b/200 solved problems in Python/string/html tag_solution.ipynb @@ -0,0 +1,52 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Python\n", + "Python Tutorial\n" + ] + } + ], + "source": [ + "# Write a Python function to create the HTML string with tags around the word(s).\n", + "# Sample function and result : \n", + "# add_tags('i', 'Python') -> 'Python'\n", + "# add_tags('b', 'Python Tutorial') -> 'Python Tutorial '\n", + "\n", + "def add_tags(tag, word):\n", + "\treturn \"<%s>%s\" % (tag, word, tag)\n", + "\n", + "print(add_tags('i', 'Python'))\n", + "print(add_tags('b', 'Python Tutorial'))\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/string/longest_word.ipynb b/200 solved problems in Python/string/longest_word.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..d4842e0971da7eaa28e6daf6cac594777b08ff1f --- /dev/null +++ b/200 solved problems in Python/string/longest_word.ipynb @@ -0,0 +1,36 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python function that takes a list of words and returns the length of the longest one" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/string/longest_word_solution.ipynb b/200 solved problems in Python/string/longest_word_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..f528f0d2962a9ffe57dc10026cab1bbea357d26f --- /dev/null +++ b/200 solved problems in Python/string/longest_word_solution.ipynb @@ -0,0 +1,51 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Exercises\n" + ] + } + ], + "source": [ + "# Write a Python function that takes a list of words and returns the length of the longest one\n", + "\n", + "def find_longest_word(words_list):\n", + " word_len = []\n", + " for n in words_list:\n", + " word_len.append((len(n), n))\n", + " word_len.sort()\n", + " return word_len[-1][1]\n", + "\n", + "print(find_longest_word([\"PHP\", \"Exercises\", \"Backend\"]))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/string/remove nth character.ipynb b/200 solved problems in Python/string/remove nth character.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..45e47eb7ed49e9ac1df31e6f0a7a7d402a4c7dc0 --- /dev/null +++ b/200 solved problems in Python/string/remove nth character.ipynb @@ -0,0 +1,36 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to remove the nth index character from a nonempty string" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/string/remove nth character_solution.ipynb b/200 solved problems in Python/string/remove nth character_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..81410cc13cd1923f6c191c65e59ce71d14df937a --- /dev/null +++ b/200 solved problems in Python/string/remove nth character_solution.ipynb @@ -0,0 +1,53 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ython\n", + "Pyton\n", + "Pytho\n" + ] + } + ], + "source": [ + "# Write a Python program to remove the nth index character from a nonempty string\n", + "\n", + "def remove_char(str, n):\n", + " first_part = str[:n] \n", + " last_pasrt = str[n+1:]\n", + " return first_part + last_pasrt\n", + " \n", + "print(remove_char('Python', 0))\n", + "print(remove_char('Python', 3))\n", + "print(remove_char('Python', 5))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/string/revert_word_in_string.ipynb b/200 solved problems in Python/string/revert_word_in_string.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..978aea1cfe46d862cc6a7a86c99ae067d9d9333f --- /dev/null +++ b/200 solved problems in Python/string/revert_word_in_string.ipynb @@ -0,0 +1,38 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# 'The quick brown fox jumps over the lazy dog.'\n", + "# input : \"The quick brown fox jumps over the lazy dog.\"\n", + "# output : \"dog. lazy the over jumps fox brown quick The \"" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/string/revert_word_in_string_solution.ipynb b/200 solved problems in Python/string/revert_word_in_string_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..c9fe5f31cdfb6408aa2faa2358bd0d8545025696 --- /dev/null +++ b/200 solved problems in Python/string/revert_word_in_string_solution.ipynb @@ -0,0 +1,52 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dog. lazy the over jumps fox brown quick The\n", + "Exercises. Python\n" + ] + } + ], + "source": [ + "# 'The quick brown fox jumps over the lazy dog.'\n", + "# input : \"The quick brown fox jumps over the lazy dog.\"\n", + "# output : \"dog. lazy the over jumps fox brown quick The \"\n", + "\n", + "def reverse_string_words(text):\n", + " for line in text.split('\\n'):\n", + " return(' '.join(line.split()[::-1]))\n", + " \n", + "print(reverse_string_words(\"The quick brown fox jumps over the lazy dog.\"))\n", + "print(reverse_string_words(\"Python Exercises.\"))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/string/string_length.ipynb b/200 solved problems in Python/string/string_length.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..d31fd6e6d2cf1d44bbfad5e5cc953ce10b9a31ad --- /dev/null +++ b/200 solved problems in Python/string/string_length.ipynb @@ -0,0 +1,36 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to calculate the length of a string." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/string/string_length_solution.ipynb b/200 solved problems in Python/string/string_length_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..3793d2e3f140cd2ffe1f8e8f8f52bc2b893f163a --- /dev/null +++ b/200 solved problems in Python/string/string_length_solution.ipynb @@ -0,0 +1,49 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "11\n" + ] + } + ], + "source": [ + "# Write a Python program to calculate the length of a string.\n", + "\n", + "def string_length(str1):\n", + " count = 0\n", + " for char in str1:\n", + " count += 1\n", + " return count\n", + "print(string_length('hello world'))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/string/unique_word.ipynb b/200 solved problems in Python/string/unique_word.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..ba6f4996c0f941885c7615de0257facf49bd10ba --- /dev/null +++ b/200 solved problems in Python/string/unique_word.ipynb @@ -0,0 +1,38 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program that accepts a comma separated sequence of words as input and prints the unique words in sorted form (alphanumerically)\n", + "# Sample Words : red, white, black, red, green, black\n", + "# Expected Result : black, green, red, white,red" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/string/unique_word_solution.ipynb b/200 solved problems in Python/string/unique_word_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..63f81c69ad3ecb446ce87cce2e77db87abdb6c48 --- /dev/null +++ b/200 solved problems in Python/string/unique_word_solution.ipynb @@ -0,0 +1,40 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program that accepts a comma separated sequence of words as input and prints the unique words in sorted form (alphanumerically)\n", + "\n", + "items = input(\"Input comma separated sequence of words\")\n", + "words = [word for word in items.split(\",\")]\n", + "print(\",\".join(sorted(list(set(words)))))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/string/word_count.ipynb b/200 solved problems in Python/string/word_count.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..1dce4bceda9884e87b792770a12937e237437289 --- /dev/null +++ b/200 solved problems in Python/string/word_count.ipynb @@ -0,0 +1,36 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to count the occurrences of each word in a given sentence" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/200 solved problems in Python/string/word_count_solution.ipynb b/200 solved problems in Python/string/word_count_solution.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..6dd48810062a8a53c26e78dd73fd9ca23aeb63b8 --- /dev/null +++ b/200 solved problems in Python/string/word_count_solution.ipynb @@ -0,0 +1,50 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Write a Python program to count the occurrences of each word in a given sentence\n", + "\n", + "def word_count(str):\n", + " counts = dict()\n", + " words = str.split()\n", + "\n", + " for word in words:\n", + " if word in counts:\n", + " counts[word] += 1\n", + " else:\n", + " counts[word] = 1\n", + "\n", + " return counts\n", + "\n", + "print( word_count('the quick brown fox jumps over the lazy dog.'))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}