{ "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
\n", "
" ], "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": [ "
\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", "
NameYearGenderStateCount
0Emma2004FAK62
1Madison2004FAK48
2Hannah2004FAK46
3Grace2004FAK44
4Emily2004FAK41
\n", "
" ], "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": [ "
\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", "
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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Count
Name
Aishani49
Alara49
Alysse49
Ameir49
Anely49
Antonina49
Aveline49
Aziah49
Baily49
Caleah49
Carlota49
Cristine49
Dahlila49
Darvin49
Deante49
Deserae49
Devean49
Elizah49
Emmaly49
Emmanuela49
Envy49
Esli49
Fay49
Gurshaan49
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", "

66 rows × 1 columns

\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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Count
count17632.000000
mean2008.932169
std11006.069468
min5.000000
25%11.000000
50%49.000000
75%337.000000
max242874.000000
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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 }