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| { | |
| "metadata": { | |
| "language": "Julia", | |
| "name": "Voter data analysis with Julia's DataFrames" | |
| }, | |
| "nbformat": 3, | |
| "nbformat_minor": 0, | |
| "worksheets": [ | |
| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": "# First we load the DataFrames package that we will use for data analysis\nusing DataFrames", | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [], | |
| "prompt_number": 2 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": "# Load data from a CSV file. readtable() returns a DataFrame object containing the data.\nvl = readtable(\"/Users/viral/ELECTIONS/blrsouth-clean.csv\")", | |
| "language": "python", | |
| "metadata": {}, "outputs": [], | |
| "prompt_number": 3 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": "# Data is stored by columns. colnames() gives the names of the columns, picked up from the first line in the CSV file.\n# The fields are: Assembly constituency number, Assembly constituency name, Ward number, Ward name, Polling station number, \n# Voter ID number, Name, Father/Husband name, Age, and Gender.\ncolnames(vl)", | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "metadata": {}, | |
| "output_type": "pyout", | |
| "prompt_number": 4, | |
| "text": "11-element Union(ASCIIString,UTF8String) Array:\n \"ac\" \n \"acname\" \n \"ward\" \n \"wardname\" \n \"ps\" \n \"voterid\" \n \"name\" \n \"fatherhusband\"\n \"house\" \n \"age\" \n \"gender\" " | |
| } | |
| ], | |
| "prompt_number": 4 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": "# Get summary statistics of the data, which may or may not be meaningful.\ndescribe(vl)", | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": "ac\n" | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": "Min 166.0" | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": "\n1st Qu. 167.0\nMedian 171.0\nMean 170.49255111993733\n3rd Qu. 173.0\nMax 175.0\nNAs 0\nNA% 0.0%\n\nacname\nLength 1708042" | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": "\nType UTF8String\nNAs 0\nNA% 0.0%\nUnique 8" | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": "\n\nward\nMin 58.0" | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": "\n1st Qu. 134.0\nMedian 161.0\nMean 155.82925536959863\n3rd Qu. 176.0\nMax 197.0\nNAs 0\nNA% 0.0%\n\nwardname\nLength 1708042\nType UTF8String\nNAs 16245\nNA% 0.95%\nUnique 63" | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": "\n\nps\nMin 1.0" | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": "\n1st Qu. 60.0\nMedian 113.0\nMean 113.00165218419687\n3rd Qu. 167.0\nMax 247.0\nNAs 0\nNA% 0.0%\n\nvoterid\nLength 1708042\nType UTF8String\nNAs 0\nNA% 0.0%\nUnique 1666956" | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": "\n\nname\nLength 1708042\nType UTF8String\nNAs 0\nNA% 0.0%\nUnique 730505" | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": "\n\nfatherhusband\nLength 1708042\nType UTF8String\nNAs 1685\nNA% 0.1%\nUnique 656806" | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": "\n\nhouse\nLength 1708042\nType UTF8String\nNAs 617\nNA% 0.04%\nUnique 127371" | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": "\n\nage\nMin 0.0" | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": "\n1st Qu. 29.0\nMedian 37.0\nMean 39.99865459982834\n3rd Qu. 49.0\nMax 100.0\nNAs 0\nNA% 0.0%\n\ngender\nLength 1708042\nType UTF8String\nNAs 217\nNA% 0.01%\nUnique 3" | |
| } | |
| ], | |
| "prompt_number": 5 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": "# Group data by Assembly constituencies, and count the number of voters in each constituency.\nby(vl, :acname, nrow)", | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "metadata": {}, | |
| "output_type": "pyout", | |
| "prompt_number": 6, | |
| "text": "8x2 DataFrame:\n acname x1\n[1,] \"B.T.M Layout (Gen)\" 194162\n[2,] \"Basavanagudi (Gen)\" 192398\n[3,] \"Bommanahalli (Gen)\" 306560\n[4,] \"Chickpet (Gen)\" 182521\n[5,] \"Govindarajanagar (Gen)\" 232770\n[6,] \"Jayanagar (Gen)\" 164522\n[7,] \"Padmanabanagar (Gen)\" 211384\n[8,] \"Vijayanagar (Gen)\" 223725\n" | |
| } | |
| ], | |
| "prompt_number": 6 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": "# Any operation that can be performed on a dataframe can be performed on a grouped dataframe.\n# For example, compute the mean of the ages of people by constituency.\n@time by(vl, :acname, x->mean(x[:age]))", | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": "\n\nelapsed time: " | |
| }, | |
| { | |
| "metadata": {}, | |
| "output_type": "pyout", | |
| "prompt_number": 7, | |
| "text": "8x2 DataFrame:\n acname x1\n[1,] \"B.T.M Layout (Gen)\" 39.9261\n[2,] \"Basavanagudi (Gen)\" 42.3307\n[3,] \"Bommanahalli (Gen)\" 37.3427\n[4,] \"Chickpet (Gen)\" 40.2869\n[5,] \"Govindarajanagar (Gen)\" 39.7533\n[6,] \"Jayanagar (Gen)\" 41.9118\n[7,] \"Padmanabanagar (Gen)\" 40.8328\n[8,] \"Vijayanagar (Gen)\" 39.5204\n" | |
| } | |
| ], | |
| "prompt_number": 7 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": "# Since Polling Station numbers are not globally unique in our data, we combine them with Assembly constituency numbers, and\n# create a new column in our dataframe.\nvl[\"psno\"] = 1000*vl[:ac]+vl[:ps]", | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "metadata": {}, | |
| "output_type": "pyout", | |
| "prompt_number": 8, | |
| "text": "1708042-element Int64 DataArray:\n 166001\n 166001\n 166001\n 166001\n 166001\n 166001\n 166001\n 166001\n 166001\n 166001\n \u22ee\n 175239\n 175239\n 175239\n 175239\n 175239\n 175239\n 175239\n 175239\n 175239\n 175239" | |
| } | |
| ], | |
| "prompt_number": 8 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": "# Now, count number of people registered at every polling station\npspop = by(vl, :psno, nrow)", | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "metadata": {}, | |
| "output_type": "pyout", | |
| "prompt_number": 9, | |
| "text": "1748x2 DataFrame:\n psno x1\n[1,] 166001 1448\n[2,] 166002 968\n[3,] 166003 1216\n[4,] 166004 693\n[5,] 166005 966\n[6,] 166006 919\n[7,] 166007 1281\n[8,] 166008 1056\n[9,] 166009 1020\n[10,] 166010 592\n[11,] 166011 731\n[12,] 166012 546\n[13,] 166013 2047\n[14,] 166014 1445\n[15,] 166015 1787\n[16,] 166016 1770\n[17,] 166017 1331\n[18,] 166018 752\n[19,] 166019 1029\n[20,] 166020 837\n :\n[1729,] 175220 1470\n[1730,] 175221 966\n[1731,] 175222 2530\n[1732,] 175223 2154\n[1733,] 175224 1797\n[1734,] 175225 1324\n[1735,] 175226 1010\n[1736,] 175227 1058\n[1737,] 175228 890\n[1738,] 175229 1374\n[1739,] 175230 1389\n[1740,] 175231 1277\n[1741,] 175232 1381\n[1742,] 175233 1159\n[1743,] 175234 1191\n[1744,] 175235 1223\n[1745,] 175236 1437\n[1746,] 175237 1387\n[1747,] 175238 1236\n[1748,] 175239 874\n" | |
| } | |
| ], | |
| "prompt_number": 9 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": "# Look at the distribution of the people registered at Polling Stations.\n# We will use the PyPlot package to plot a graph, which calls Python's matplotlib from Julia.\nusing PyPlot\nplot(1:1748, sort(vector(pspop[:x1])), color=\"red\", linewidth=2.0)\ntitle(\"Number of registered voters per polling station\")", | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "metadata": {}, | |
| "output_type": "pyout", | |
| "prompt_number": 10, | |
| "text": "PyObject <matplotlib.text.Text object at 0x11086be90>" | |
| }, | |
| { | |
| "metadata": {}, | |
| "output_type": "display_data", | |
| "png": 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