{ "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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schoolsexageaddressfamsizePstatusMeduFeduMjobFjob...famrelfreetimegooutDalcWalchealthabsencesG1G2G3
0GPF18UGT3A44at_hometeacher...4341136566
1GPF17UGT3T11at_homeother...5331134556
2GPF15ULE3T11at_homeother...432233107810
3GPF15UGT3T42healthservices...3221152151415
4GPF16UGT3T33otherother...432125461010
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5 rows × 33 columns

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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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schoolsexageaddressfamsizePstatusMeduFeduMjobFjobreasonguardian
0GPF18UGT3A44at_hometeachercoursemother
1GPF17UGT3T11at_homeothercoursefather
2GPF15ULE3T11at_homeotherothermother
3GPF15UGT3T42healthserviceshomemother
4GPF16UGT3T33otherotherhomefather
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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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schoolsexageaddressfamsizePstatusMeduFeduMjobFjobreasonguardian
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": 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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schoolsexageaddressfamsizePstatusMeduFeduMjobFjobreasonguardian
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": [] }, { "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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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": [] }, { "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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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 }