{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 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": [] }, { "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": [] }, { "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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beer_servingsspirit_servingswine_servingstotal_litres_of_pure_alcohol
continent
AF61.47169816.33962316.2641513.007547
AS37.04545560.8409099.0681822.170455
EU193.777778132.555556142.2222228.617778
OC89.68750058.43750035.6250003.381250
SA175.083333114.75000062.4166676.308333
\n", "
" ], "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": [] }, { "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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beer_servingsspirit_servingswine_servingstotal_litres_of_pure_alcohol
continent
AF32.03.02.02.30
AS17.516.01.01.20
EU219.0122.0128.010.00
OC52.537.08.51.75
SA162.5108.512.06.85
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" ], "text/plain": [ " beer_servings spirit_servings wine_servings \\\n", "continent \n", "AF 32.0 3.0 2.0 \n", "AS 17.5 16.0 1.0 \n", "EU 219.0 122.0 128.0 \n", "OC 52.5 37.0 8.5 \n", "SA 162.5 108.5 12.0 \n", "\n", " total_litres_of_pure_alcohol \n", "continent \n", "AF 2.30 \n", "AS 1.20 \n", "EU 10.00 \n", "OC 1.75 \n", "SA 6.85 " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "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": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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meanminmax
continent
AF16.3396230152
AS60.8409090326
EU132.5555560373
OC58.4375000254
SA114.75000025302
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" ], "text/plain": [ " mean min max\n", "continent \n", "AF 16.339623 0 152\n", "AS 60.840909 0 326\n", "EU 132.555556 0 373\n", "OC 58.437500 0 254\n", "SA 114.750000 25 302" ] }, "execution_count": 15, "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 }