diff --git a/.ipynb_checkpoints/consumer_complaints_charting-checkpoint.ipynb b/.ipynb_checkpoints/consumer_complaints_charting-checkpoint.ipynb new file mode 100644 index 0000000..6bb57f3 --- /dev/null +++ b/.ipynb_checkpoints/consumer_complaints_charting-checkpoint.ipynb @@ -0,0 +1,2931 @@ +{ + "metadata": { + "name": "", + "signature": "sha256:ed0ede28ae9c27b936a6ceb999793942a2e3dcf18d905481b529a7f7c0e59db4" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from datetime import date" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 24 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "%matplotlib inline" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 25 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "complaints = pd.read_csv(\"complaints_dec_2014.csv\")" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 26 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "by_company = complaints[\"Company\"].value_counts()[:10]\n", + "by_company.plot(kind = \"bar\")\n", + "#sns.factorplot(\"Company\", data = by_company, palette =\"muted\")" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 27, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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EfElSV0T0S7qKHLviJO1Eulr7h6R3kmpfF3W18h3WzWrpJXU95V2wDVJdm9oY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\nSS8CTif1g32PVJyrn1RC95t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+ "text": [ + "" + ] + } + ], + "prompt_number": 27 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "by_product = complaints[\"Product\"].value_counts()[:10]\n", + "by_product.plot(kind = \"bar\")\n", + "#sns.factorplot(\"Company\", data = by_company, palette =\"muted\")" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 28, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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C/wZz4/V9wPMj4gzgMWA98BtJ+0TEtcCBwEJS8j9N0njSm8BOpJuyS4BZwI3FtYue/iob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\ng80j7VNZAkwnnS3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+ "text": [ + "" + ] + } + ], + "prompt_number": 29 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "complaints[\"dates\"] = pd.to_datetime(complaints.pop('Date received'), format=\"%m/%d/%Y\")\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 31 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "top_days = complaints.dates.dt.weekday.value_counts()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 32 + }, + { + "cell_type": "code", + "collapsed": true, + "input": [ + "top_days = top_days.sort_index(ascending=True)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 33 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "top_days.index = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 34 + }, + { + "cell_type": "code", + "collapsed": true, + "input": [ + "top_days.plot(kind = \"bar\")" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 35, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 35 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import calendar\n", + "my_list = []\n", + "for n in range(1,32):\n", + " my_list.append(calendar.weekday(2014, 12, n))" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 12 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "freqs = np.unique(my_list, return_counts = True)[1]" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 36 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "the_averages = top_days/freqs" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 37 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "the_averages.plot(kind = \"bar\")" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 38, + "text": [ + "" + ] + }, + { + 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This may be due to the fact that billing mistakes etc happen on the first day of the week and are noticed on the second day. " + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "population = pd.read_csv(\"NST_EST2014_ALLDATA.csv\")" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 39 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "population" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
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0 10 0 0 0 United States 308745538 308758105 309347057 311721632 314112078... 3.168211 3.135081 X X X X 2.941968 3.051932 3.168211 3.135081
1 20 1 0 0 Northeast Region 55317240 55318348 55381690 55635670 55832038... 4.692605 4.674678 -2.891457696 -3.987594326 -3.82818713 -5.111331551 1.578060 0.405624 0.864418 -0.436653
2 20 2 0 0 Midwest Region 66927001 66929898 66972390 67149657 67331458... 1.891826 1.886101 -2.728007872 -2.729468744 -1.942826565 -2.690902253 -1.032761 -0.946140 -0.051001 -0.804801
3 20 3 0 0 South Region 114555744 114562951 114871231 116089908 117346322... 3.064411 3.012706 2.807069634 3.003089966 2.472345639 3.065858744 5.677925 6.055264 5.536757 6.078565
4 20 4 0 0 West Region 71945553 71946908 72121746 72846397 73602260... 3.344905 3.305249 0.266017066 0.754667214 0.724134003 1.383520042 3.304919 3.950244 4.069039 4.688770
5 40 3 6 1 Alabama 4779736 4780127 4785822 4801695 4817484... 1.165832 1.157861 -0.020443249 -0.168413541 0.396415887 0.420101549 1.011941 1.001333 1.562247 1.577963
6 40 4 9 2 Alaska 710231 710249 713856 722572 731081... 3.203618 2.869760 -1.175137215 -1.949571184 -3.789313102 -13.75449375 0.948185 1.835376 -0.585695-10.884734
7 40 4 8 4 Arizona 6392017 6392310 6411999 6472867 6556236... 2.141877 2.129805 1.36951366 5.13128187 3.910475996 6.280635868 3.336628 7.155212 6.052353 8.410441
8 40 3 7 5 Arkansas 2915918 2915958 2922297 2938430 2949300... 1.090035 1.091283 1.3414718 -0.420875278 -0.580562333 -1.313050473 2.317801 0.621971 0.509473 -0.221767
9 40 4 9 6 California 37253956 37254503 37336011 37701901 38062780... 4.207353 4.177389 -1.162079243 -1.173950696 -1.341226344 -0.830982325 2.761377 2.772770 2.866127 3.346406
10 40 4 8 8 Colorado 5029196 5029324 5048575 5119661 5191709... 2.074200 2.010735 5.183396609 5.553675215 6.977583181 7.587162607 6.933159 7.660864 9.051783 9.597898
11 40 1 1 9 Connecticut 3574097 3574096 3579345 3590537 3594362... 4.753602 4.730950 -3.384435058 -5.611491546 -4.731638212 -7.286251924 1.116894 -1.059166 0.021964 -2.555302
12 40 3 5 10 Delaware 897934 897936 899731 907829 916881... 2.608949 2.565489 2.866848127 3.598380017 3.397170979 5.148173903 5.303282 6.221263 6.006120 7.713663
13 40 3 5 11 District of Columbia 601723 601767 605210 620427 635040... 5.871584 5.749218 11.33288241 10.00583847 9.777666334 1.793572497 16.805955 15.595790 15.649250 7.542790
14 40 3 5 12 Florida 18801310 18804623 18852220 19107900 19355257... 5.783717 5.687300 5.540393445 5.125320316 4.918783369 7.01612271 11.359606 10.722573 10.702501 12.703423
15 40 3 5 13 Georgia 9687653 9688681 9714464 9813201 9919000... 2.510526 2.470423 1.105815775 1.852200877 -0.576887568 2.200466631 3.375007 4.426268 1.933638 4.670890
16 40 4 9 15 Hawaii 1360301 1360301 1363950 1378251 1392766... 6.426691 6.074495 -0.728611798 -2.567288472 -0.663156245 -3.635080614 4.254976 4.316105 5.763534 2.439414
17 40 4 8 16 Idaho 1567582 1567652 1570639 1583780 1595590... 1.052850 1.043942 0.058330869 -0.163554415 2.986504627 4.738695787 0.952949 0.848596 4.039355 5.782638
18 40 2 3 17 Illinois 12830632 12831587 12840097 12858725 12873763... 2.505015 2.518554 -5.424762271 -5.690471905 -5.238097733 -7.369175712 -3.182714 -3.382339 -2.733083 -4.850621
19 40 2 3 18 Indiana 6483802 6484192 6490308 6516560 6537632... 1.586775 1.590575 -1.302542626 -1.954161544 -0.231150462 -1.192171554 0.155918 -0.507730 1.355625 0.398403
20 40 2 4 19 Iowa 3046355 3046869 3050295 3064904 3075935... 1.732413 1.731762 0.091575107 -1.398831658 0.150122984 -0.261312787 1.684655 0.189551 1.882536 1.470449
21 40 2 4 20 Kansas 2853118 2853132 2858949 2869965 2885966... 2.106968 2.050753 -3.156968319 -1.789806028 -4.369598429 -4.760146087 -1.406549 0.474641 -2.262630 -2.709393
22 40 3 6 21 Kentucky 4339367 4339349 4349838 4370038 4383465... 1.394732 1.356853 0.597026839 -1.271262488 -0.542180801 -0.858954458 1.778007 0.199920 0.852551 0.497899
23 40 3 7 22 Louisiana 4533372 4533479 4545581 4575972 4604744... 1.651933 1.620656 0.454747125 -0.167960756 -0.496857926 -1.3115694 1.940240 1.525589 1.155076 0.309086
24 40 1 1 23 Maine 1328361 1328361 1327361 1327930 1328592... 1.045424 1.035809 0.061763475 -0.467528596 -1.102625453 0.399429666 0.951308 0.534533 -0.057201 1.435239
25 40 3 5 24 Maryland 5773552 5773785 5788101 5843833 5891819... 4.914731 4.860034 0.062930206 -1.432046554 -1.51387644 -2.56732105 4.604050 3.278216 3.400855 2.292713
26 40 1 1 25 Massachusetts 6547629 6547817 6564073 6612270 6655829... 5.589799 5.542473 -0.523665785 -1.63158264 -0.325484225 -2.431047603 4.789037 3.646943 5.264315 3.111426
27 40 2 3 26 Michigan 9883640 9884133 9876498 9875736 9884781... 2.029017 2.028870 -4.371657403 -3.394243177 -2.98681078 -2.895688474 -2.546041 -1.522632 -0.957793 -0.866818
28 40 2 4 27 Minnesota 5303925 5303925 5310418 5348036 5380615... 2.583619 2.574079 -0.628046056 -1.657431116 -0.414341818 -1.230969132 1.733460 0.748277 2.169278 1.343109
29 40 3 6 28 Mississippi 2967297 2968103 2970811 2978464 2986137... 0.784164 0.751718 -1.960911203 -1.889480956 -1.60546158 -3.134498274 -1.337642 -0.966703 -0.821298 -2.382780
30 40 2 4 29 Missouri 5988927 5988923 5996085 6010544 6025281... 1.454160 1.427261 -2.245426256 -2.240644077 -1.348776549 -1.333607961 -0.987954 -0.800444 0.105384 0.093653
31 40 4 8 30 Montana 989415 989417 990575 997661 1005163... 0.792069 0.751554 3.434199964 3.550986008 5.297948988 4.464191542 4.040768 4.416764 6.090018 5.215746
32 40 2 4 31 Nebraska 1826341 1826341 1829865 1842232 1855487... 2.056139 2.029078 -0.64486314 -0.49165445 -0.478459136 -1.360362109 1.169359 1.514447 1.577680 0.668716
33 40 4 8 32 Nevada 2700551 2700692 2703493 2718586 2755245... 3.032412 3.003591 -2.827328779 5.284415979 4.755226449 8.390945678 -0.080781 8.145301 7.787639 11.394537
34 40 1 1 33 New Hampshire 1316470 1316466 1316517 1318109 1321297... 1.536359 1.529386 -1.636664938 -0.398574528 -1.98644963 0.843200554 -0.302889 1.045690 -0.450090 2.372587
35 40 1 2 34 New Jersey 8791894 8791936 8803580 8842614 8876000... 5.799240 5.784530 -5.110337107 -5.589037608 -5.120561617 -6.21512647 0.454262 -0.199000 0.678679 -0.430596
36 40 4 8 35 New Mexico 2059179 2059192 2064950 2078407 2084594... 1.283954 1.280777 0.030892824 -3.420128893 -5.062940355 -6.784475467 1.098143 -2.078308 -3.778986 -5.503698
37 40 1 2 36 New York 19378102 19378112 19400867 19521745 19607140... 6.051932 6.023999 -4.325454828 -5.94680886 -5.48204938 -7.804947159 1.494350 -0.318997 0.569883 -1.780948
38 40 3 5 37 North Carolina 9535483 9535691 9559488 9651502 9748181... 2.260641 2.140972 3.172142612 3.299538451 3.88690203 3.663640478 5.047944 5.772981 6.147543 5.804612
39 40 2 4 38 North Dakota 672591 672591 674345 685242 701705... 1.915034 1.763091 9.145424309 15.58963681 23.1894509 12.26510057 10.679714 17.804574 25.104485 14.028192
40 40 2 3 39 Ohio 11536504 11536725 11540070 11544757 11550901... 1.518581 1.513932 -3.188587898 -3.204065457 -1.912302891 -1.574969153 -1.819983 -1.762236 -0.393722 -0.061037
41 40 3 7 40 Oklahoma 3751351 3751616 3759481 3786527 3817059... 1.508701 1.472222 1.674262736 2.350732931 3.599656175 1.132299656 3.007153 3.955502 5.108357 2.604522
42 40 4 9 41 Oregon 3831074 3831073 3837083 3867644 3898684... 1.728175 1.737588 2.957664821 3.376112881 2.489666212 5.740470711 4.528908 4.932576 4.217842 7.478058
43 40 1 2 42 Pennsylvania 12702379 12702884 12711077 12743995 12770043... 2.272758 2.273109 -0.546767261 -1.358703001 -2.483940274 -2.459901351 1.578782 0.797051 -0.211183 -0.186792
44 40 1 1 44 Rhode Island 1052567 1052931 1053078 1052020 1052637... 4.106380 4.069191 -5.839158082 -5.02599711 -4.874664707 -3.212669318 -2.058812 -1.031997 -0.768284 0.856522
45 40 3 5 45 South Carolina 4625364 4625401 4636290 4673054 4722621... 1.379318 1.310648 3.191202302 5.613008113 5.954363293 8.040888713 4.292891 7.229497 7.333681 9.351536
46 40 2 4 46 South Dakota 814180 814191 816192 824171 834504... 1.751176 1.706026 2.449457833 5.243944715 5.197575735 0.661688306 3.990580 7.059852 6.948752 2.367714
47 40 3 6 47 Tennessee 6346105 6346275 6356628 6398389 6455177... 1.417493 1.404195 2.452525152 4.286281332 2.089798328 3.757448001 3.696271 5.729927 3.507291 5.161643
48 40 3 7 48 Texas 25145561 25146104 25245717 25657477 26094422... 3.201213 3.166214 4.577944559 5.630981773 4.428284006 5.778507385 7.675275 8.724163 7.629497 8.944721
49 40 4 8 49 Utah 2763885 2763885 2774346 2815324 2855194... 1.888857 1.869754 -0.328820843 -0.030685027 1.920812174 -0.422533597 1.419762 1.759980 3.809669 1.447220
50 40 1 1 50 Vermont 625741 625745 625792 626450 626138... 1.150844 1.147264 -0.752250763 -2.604208247 -1.09817054 -2.471643515 0.255542 -1.516859 0.052674 -1.324380
51 40 3 5 51 Virginia 8001024 8001023 8025376 8110188 8193422... 4.195152 4.098421 1.331716697 0.603915329 0.304183119 -2.458329804 4.975221 5.003800 4.499335 1.640092
52 40 4 9 53 Washington 6724540 6724543 6741911 6822112 6896325... 3.455210 3.396158 3.446470122 1.962176887 2.250746157 3.998924994 6.416385 5.370291 5.705957 7.395083
53 40 3 5 54 West Virginia 1852994 1853033 1854176 1854982 1856313... 0.631283 0.628523 0.586116849 0.562067957 -1.252861257 -1.484372912 1.143656 1.171559 -0.621579 -0.855850
54 40 2 3 55 Wisconsin 5686986 5687289 5689268 5708785 5724888... 1.129419 1.139079 -1.094923844 -1.653886726 -1.369743442 -1.727052792 -0.058431 -0.619224 -0.240324 -0.587974
55 40 4 8 56 Wyoming 563626 563767 564358 567631 576893... 0.858535 0.830923 -0.362194332 9.651173763 4.54782108 -4.577788133 0.323325 10.610525 5.406356 -3.746865
56 40 X X 72 Puerto Rico 3725789 3726157 3721527 3686771 3642281...-15.222737-15.422783 X X X X-12.354795-15.033868-15.222737-15.422783
\n", + "

57 rows \u00d7 76 columns

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RINTERNATIONALMIG2013 RINTERNATIONALMIG2014 \\\n", + "0 ... 3.168211 3.135081 \n", + "1 ... 4.692605 4.674678 \n", + "2 ... 1.891826 1.886101 \n", + "3 ... 3.064411 3.012706 \n", + "4 ... 3.344905 3.305249 \n", + "5 ... 1.165832 1.157861 \n", + "6 ... 3.203618 2.869760 \n", + "7 ... 2.141877 2.129805 \n", + "8 ... 1.090035 1.091283 \n", + "9 ... 4.207353 4.177389 \n", + "10 ... 2.074200 2.010735 \n", + "11 ... 4.753602 4.730950 \n", + "12 ... 2.608949 2.565489 \n", + "13 ... 5.871584 5.749218 \n", + "14 ... 5.783717 5.687300 \n", + "15 ... 2.510526 2.470423 \n", + "16 ... 6.426691 6.074495 \n", + "17 ... 1.052850 1.043942 \n", + "18 ... 2.505015 2.518554 \n", + "19 ... 1.586775 1.590575 \n", + "20 ... 1.732413 1.731762 \n", + "21 ... 2.106968 2.050753 \n", + "22 ... 1.394732 1.356853 \n", + "23 ... 1.651933 1.620656 \n", + "24 ... 1.045424 1.035809 \n", + "25 ... 4.914731 4.860034 \n", + "26 ... 5.589799 5.542473 \n", + "27 ... 2.029017 2.028870 \n", + "28 ... 2.583619 2.574079 \n", + "29 ... 0.784164 0.751718 \n", + "30 ... 1.454160 1.427261 \n", + "31 ... 0.792069 0.751554 \n", + "32 ... 2.056139 2.029078 \n", + "33 ... 3.032412 3.003591 \n", + "34 ... 1.536359 1.529386 \n", + "35 ... 5.799240 5.784530 \n", + "36 ... 1.283954 1.280777 \n", + "37 ... 6.051932 6.023999 \n", + "38 ... 2.260641 2.140972 \n", + "39 ... 1.915034 1.763091 \n", + "40 ... 1.518581 1.513932 \n", + "41 ... 1.508701 1.472222 \n", + "42 ... 1.728175 1.737588 \n", + "43 ... 2.272758 2.273109 \n", + "44 ... 4.106380 4.069191 \n", + "45 ... 1.379318 1.310648 \n", + "46 ... 1.751176 1.706026 \n", + "47 ... 1.417493 1.404195 \n", + "48 ... 3.201213 3.166214 \n", + "49 ... 1.888857 1.869754 \n", + "50 ... 1.150844 1.147264 \n", + "51 ... 4.195152 4.098421 \n", + "52 ... 3.455210 3.396158 \n", + "53 ... 0.631283 0.628523 \n", + "54 ... 1.129419 1.139079 \n", + "55 ... 0.858535 0.830923 \n", + "56 ... -15.222737 -15.422783 \n", + "\n", + " RDOMESTICMIG2011 RDOMESTICMIG2012 RDOMESTICMIG2013 RDOMESTICMIG2014 \\\n", + "0 X X X X \n", + "1 -2.891457696 -3.987594326 -3.82818713 -5.111331551 \n", + "2 -2.728007872 -2.729468744 -1.942826565 -2.690902253 \n", + "3 2.807069634 3.003089966 2.472345639 3.065858744 \n", + "4 0.266017066 0.754667214 0.724134003 1.383520042 \n", + "5 -0.020443249 -0.168413541 0.396415887 0.420101549 \n", + "6 -1.175137215 -1.949571184 -3.789313102 -13.75449375 \n", + "7 1.36951366 5.13128187 3.910475996 6.280635868 \n", + "8 1.3414718 -0.420875278 -0.580562333 -1.313050473 \n", + "9 -1.162079243 -1.173950696 -1.341226344 -0.830982325 \n", + "10 5.183396609 5.553675215 6.977583181 7.587162607 \n", + "11 -3.384435058 -5.611491546 -4.731638212 -7.286251924 \n", + "12 2.866848127 3.598380017 3.397170979 5.148173903 \n", + "13 11.33288241 10.00583847 9.777666334 1.793572497 \n", + "14 5.540393445 5.125320316 4.918783369 7.01612271 \n", + "15 1.105815775 1.852200877 -0.576887568 2.200466631 \n", + "16 -0.728611798 -2.567288472 -0.663156245 -3.635080614 \n", 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NAMECENSUS2010POP
5 Alabama 4779736
6 Alaska 710231
7 Arizona 6392017
8 Arkansas 2915918
9 California 37253956
10 Colorado 5029196
11 Connecticut 3574097
12 Delaware 897934
13 District of Columbia 601723
14 Florida 18801310
15 Georgia 9687653
16 Hawaii 1360301
17 Idaho 1567582
18 Illinois 12830632
19 Indiana 6483802
20 Iowa 3046355
21 Kansas 2853118
22 Kentucky 4339367
23 Louisiana 4533372
24 Maine 1328361
25 Maryland 5773552
26 Massachusetts 6547629
27 Michigan 9883640
28 Minnesota 5303925
29 Mississippi 2967297
30 Missouri 5988927
31 Montana 989415
32 Nebraska 1826341
33 Nevada 2700551
34 New Hampshire 1316470
35 New Jersey 8791894
36 New Mexico 2059179
37 New York 19378102
38 North Carolina 9535483
39 North Dakota 672591
40 Ohio 11536504
41 Oklahoma 3751351
42 Oregon 3831074
43 Pennsylvania 12702379
44 Rhode Island 1052567
45 South Carolina 4625364
46 South Dakota 814180
47 Tennessee 6346105
48 Texas 25145561
49 Utah 2763885
50 Vermont 625741
51 Virginia 8001024
52 Washington 6724540
53 West Virginia 1852994
54 Wisconsin 5686986
55 Wyoming 563626
56 Puerto Rico 3725789
\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 43, + "text": [ + " NAME CENSUS2010POP\n", + "5 Alabama 4779736\n", + "6 Alaska 710231\n", + "7 Arizona 6392017\n", + "8 Arkansas 2915918\n", + "9 California 37253956\n", + "10 Colorado 5029196\n", + "11 Connecticut 3574097\n", + "12 Delaware 897934\n", + "13 District of Columbia 601723\n", + "14 Florida 18801310\n", + "15 Georgia 9687653\n", + "16 Hawaii 1360301\n", + "17 Idaho 1567582\n", + "18 Illinois 12830632\n", + "19 Indiana 6483802\n", + "20 Iowa 3046355\n", + "21 Kansas 2853118\n", + "22 Kentucky 4339367\n", + "23 Louisiana 4533372\n", + "24 Maine 1328361\n", + "25 Maryland 5773552\n", + "26 Massachusetts 6547629\n", + "27 Michigan 9883640\n", + "28 Minnesota 5303925\n", + "29 Mississippi 2967297\n", + "30 Missouri 5988927\n", + "31 Montana 989415\n", + "32 Nebraska 1826341\n", + "33 Nevada 2700551\n", + "34 New Hampshire 1316470\n", + "35 New Jersey 8791894\n", + "36 New Mexico 2059179\n", + "37 New York 19378102\n", + "38 North Carolina 9535483\n", + "39 North Dakota 672591\n", + "40 Ohio 11536504\n", + "41 Oklahoma 3751351\n", + "42 Oregon 3831074\n", + "43 Pennsylvania 12702379\n", + "44 Rhode Island 1052567\n", + "45 South Carolina 4625364\n", + "46 South Dakota 814180\n", + "47 Tennessee 6346105\n", + "48 Texas 25145561\n", + "49 Utah 2763885\n", + "50 Vermont 625741\n", + "51 Virginia 8001024\n", + "52 Washington 6724540\n", + "53 West Virginia 1852994\n", + "54 Wisconsin 5686986\n", + "55 Wyoming 563626\n", + "56 Puerto Rico 3725789" + ] + } + ], + "prompt_number": 43 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "states = {\n", + " 'AK': 'Alaska',\n", + " 'AL': 'Alabama',\n", + " 'AR': 'Arkansas',\n", + " 'AS': 'American Samoa',\n", + " 'AZ': 'Arizona',\n", + " 'CA': 'California',\n", + " 'CO': 'Colorado',\n", + " 'CT': 'Connecticut',\n", + " 'DC': 'District of Columbia',\n", + " 'DE': 'Delaware',\n", + " 'FL': 'Florida',\n", + " 'GA': 'Georgia',\n", + " 'GU': 'Guam',\n", + " 'HI': 'Hawaii',\n", + " 'IA': 'Iowa',\n", + " 'ID': 'Idaho',\n", + " 'IL': 'Illinois',\n", + " 'IN': 'Indiana',\n", + " 'KS': 'Kansas',\n", + " 'KY': 'Kentucky',\n", + " 'LA': 'Louisiana',\n", + " 'MA': 'Massachusetts',\n", + " 'MD': 'Maryland',\n", + " 'ME': 'Maine',\n", + " 'MI': 'Michigan',\n", + " 'MN': 'Minnesota',\n", + " 'MO': 'Missouri',\n", + " 'MP': 'Northern Mariana Islands',\n", + " 'MS': 'Mississippi',\n", + " 'MT': 'Montana',\n", + " 'NA': 'National',\n", + " 'NC': 'North Carolina',\n", + " 'ND': 'North Dakota',\n", + " 'NE': 'Nebraska',\n", + " 'NH': 'New Hampshire',\n", + " 'NJ': 'New Jersey',\n", + " 'NM': 'New Mexico',\n", + " 'NV': 'Nevada',\n", + " 'NY': 'New York',\n", + " 'OH': 'Ohio',\n", + " 'OK': 'Oklahoma',\n", + " 'OR': 'Oregon',\n", + " 'PA': 'Pennsylvania',\n", + " 'PR': 'Puerto Rico',\n", + " 'RI': 'Rhode Island',\n", + " 'SC': 'South Carolina',\n", + " 'SD': 'South Dakota',\n", + " 'TN': 'Tennessee',\n", + " 'TX': 'Texas',\n", + " 'UT': 'Utah',\n", + " 'VA': 'Virginia',\n", + " 'VI': 'Virgin Islands',\n", + " 'VT': 'Vermont',\n", + " 'WA': 'Washington',\n", + " 'WI': 'Wisconsin',\n", + " 'WV': 'West Virginia',\n", + " 'WY': 'Wyoming'\n", + "}\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 45 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "new_list = []\n", + "for item in complaints[\"State\"]:\n", + " new_list.append(states.get(item))" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 73 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "state_freq = pd.DataFrame(new_list)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 74 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "state_freq = state_freq[0].value_counts()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 75 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "state_freq = state_freq.sort_index()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 76 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "state_freq.columns = [\"State\" ,\"Frequency\"]" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 77 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "state_freq" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 78, + "text": [ + "Alabama 147\n", + "Alaska 15\n", + "American Samoa 1\n", + "Arizona 213\n", + "Arkansas 59\n", + "California 1591\n", + "Colorado 180\n", + "Connecticut 109\n", + "Delaware 44\n", + "District of Columbia 82\n", + "Florida 1093\n", + "Georgia 512\n", + "Hawaii 48\n", + "Idaho 39\n", + "Illinois 427\n", + "Indiana 132\n", + "Iowa 51\n", + "Kansas 56\n", + "Kentucky 59\n", + "Louisiana 127\n", + "Maine 39\n", + "Maryland 342\n", + "Massachusetts 200\n", + "Michigan 287\n", + "Minnesota 135\n", + "Mississippi 57\n", + "Missouri 119\n", + "Montana 14\n", + "Nebraska 37\n", + "Nevada 159\n", + "New Hampshire 46\n", + "New Jersey 465\n", + "New Mexico 55\n", + "New York 733\n", + "North Carolina 287\n", + "North Dakota 8\n", + "Ohio 348\n", + "Oklahoma 93\n", + "Oregon 120\n", + "Pennsylvania 418\n", + "Puerto Rico 27\n", + "Rhode Island 40\n", + "South Carolina 130\n", + "South Dakota 22\n", + "Tennessee 192\n", + "Texas 1099\n", + "Utah 70\n", + "Vermont 18\n", + "Virgin Islands 5\n", + "Virginia 373\n", + "Washington 231\n", + "West Virginia 26\n", + "Wisconsin 143\n", + "Wyoming 8\n", + "Length: 54, dtype: int64" + ] + } + ], + "prompt_number": 78 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "state_pop = state_pop.sort(\"NAME\")" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 52 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "state_freq" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 79, + "text": [ + "Alabama 147\n", + "Alaska 15\n", + "American Samoa 1\n", + "Arizona 213\n", + "Arkansas 59\n", + "California 1591\n", + "Colorado 180\n", + "Connecticut 109\n", + "Delaware 44\n", + "District of Columbia 82\n", + "Florida 1093\n", + "Georgia 512\n", + "Hawaii 48\n", + "Idaho 39\n", + "Illinois 427\n", + "Indiana 132\n", + "Iowa 51\n", + "Kansas 56\n", + "Kentucky 59\n", + "Louisiana 127\n", + "Maine 39\n", + "Maryland 342\n", + "Massachusetts 200\n", + "Michigan 287\n", + "Minnesota 135\n", + "Mississippi 57\n", + "Missouri 119\n", + "Montana 14\n", + "Nebraska 37\n", + "Nevada 159\n", + "New Hampshire 46\n", + "New Jersey 465\n", + "New Mexico 55\n", + "New York 733\n", + "North Carolina 287\n", + "North Dakota 8\n", + "Ohio 348\n", + "Oklahoma 93\n", + "Oregon 120\n", + "Pennsylvania 418\n", + "Puerto Rico 27\n", + "Rhode Island 40\n", + "South Carolina 130\n", + "South Dakota 22\n", + "Tennessee 192\n", + "Texas 1099\n", + "Utah 70\n", + "Vermont 18\n", + "Virgin Islands 5\n", + "Virginia 373\n", + "Washington 231\n", + "West Virginia 26\n", + "Wisconsin 143\n", + "Wyoming 8\n", + "Length: 54, dtype: int64" + ] + } + ], + "prompt_number": 79 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "len(state_freq.values)\n", + "per_cap_compliants = state_freq.values/state_pop[\"CENSUS2010POP\"]\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 111 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "len(state_pop[\"CENSUS2010POP\"])" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 112, + "text": [ + "52" + ] + } + ], + "prompt_number": 112 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "type(state_freq)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 113, + "text": [ + "pandas.core.series.Series" + ] + } + ], + "prompt_number": 113 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "state_freq.pop(\"American Samoa\")" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'American Samoa'", + "output_type": "pyerr", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mstate_freq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"American Samoa\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/Users/Daniel/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mpop\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m 479\u001b[0m \u001b[0mReturn\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mdrop\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mframe\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mRaise\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mfound\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 480\u001b[0m \"\"\"\n\u001b[0;32m--> 481\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 482\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 483\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/Daniel/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 507\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 508\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 509\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 510\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 511\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misscalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/Daniel/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/core/index.py\u001b[0m in \u001b[0;36mget_value\u001b[0;34m(self, series, key)\u001b[0m\n\u001b[1;32m 1429\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1430\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1431\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1432\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# pragma: no cover\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1433\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/Daniel/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/core/index.py\u001b[0m in \u001b[0;36mget_value\u001b[0;34m(self, series, key)\u001b[0m\n\u001b[1;32m 1415\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1416\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1417\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1418\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1419\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minferred_type\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'integer'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'boolean'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/Daniel/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_value (pandas/index.c:3096)\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m/Users/Daniel/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_value (pandas/index.c:2827)\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m/Users/Daniel/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3687)\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m/Users/Daniel/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/hashtable.so\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12310)\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m/Users/Daniel/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/hashtable.so\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12261)\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'American Samoa'" + ] + } + ], + "prompt_number": 114 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "state_freq" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 115, + "text": [ + "Alabama 0.000031\n", + "Alaska 0.000021\n", + "Arizona 0.000033\n", + "Arkansas 0.000020\n", + "California 0.000043\n", + "Colorado 0.000036\n", + "Connecticut 0.000030\n", + "Delaware 0.000049\n", + "District of Columbia 0.000136\n", + "Florida 0.000058\n", + "Georgia 0.000053\n", + "Hawaii 0.000035\n", + "Idaho 0.000025\n", + "Illinois 0.000033\n", + "Indiana 0.000020\n", + "Iowa 0.000017\n", + "Kansas 0.000020\n", + "Kentucky 0.000014\n", + "Louisiana 0.000028\n", + "Maine 0.000029\n", + "Maryland 0.000059\n", + "Massachusetts 0.000031\n", + "Michigan 0.000029\n", + "Minnesota 0.000025\n", + "Mississippi 0.000019\n", + "Missouri 0.000020\n", + "Montana 0.000014\n", + "Nebraska 0.000020\n", + "Nevada 0.000059\n", + "New Hampshire 0.000035\n", + "New Jersey 0.000053\n", + "New Mexico 0.000027\n", + "New York 0.000038\n", + "North Carolina 0.000030\n", + "North Dakota 0.000012\n", + "Ohio 0.000030\n", + "Oklahoma 0.000025\n", + "Oregon 0.000031\n", + "Pennsylvania 0.000033\n", + "Puerto Rico 0.000007\n", + "Rhode Island 0.000038\n", + "South Carolina 0.000028\n", + "South Dakota 0.000027\n", + "Tennessee 0.000030\n", + "Texas 0.000044\n", + "Utah 0.000025\n", + "Vermont 0.000029\n", + "Virginia 0.000047\n", + "Washington 0.000034\n", + "West Virginia 0.000014\n", + "Wisconsin 0.000025\n", + "Wyoming 0.000014\n", + "Name: CENSUS2010POP, Length: 52, dtype: float64" + ] + } + ], + "prompt_number": 115 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "state_freq.pop(\"Virgin Islands\")" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'Virgin Islands'", + "output_type": "pyerr", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mstate_freq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Virgin Islands\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/Users/Daniel/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mpop\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m 479\u001b[0m \u001b[0mReturn\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mdrop\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mframe\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mRaise\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mfound\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 480\u001b[0m \"\"\"\n\u001b[0;32m--> 481\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 482\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 483\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/Daniel/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 507\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 508\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 509\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 510\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 511\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misscalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/Daniel/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/core/index.py\u001b[0m in \u001b[0;36mget_value\u001b[0;34m(self, series, key)\u001b[0m\n\u001b[1;32m 1429\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1430\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1431\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1432\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# pragma: no cover\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1433\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/Daniel/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/core/index.py\u001b[0m in \u001b[0;36mget_value\u001b[0;34m(self, series, key)\u001b[0m\n\u001b[1;32m 1415\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1416\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1417\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1418\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1419\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minferred_type\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'integer'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'boolean'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/Daniel/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_value (pandas/index.c:3096)\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m/Users/Daniel/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_value (pandas/index.c:2827)\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m/Users/Daniel/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3687)\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m/Users/Daniel/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/hashtable.so\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12310)\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m/Users/Daniel/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/hashtable.so\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12261)\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'Virgin Islands'" + ] + } + ], + "prompt_number": 127 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "state_freq = state_freq.values/state_pop[\"CENSUS2010POP\"]" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 117 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "per_capita = pd.DataFrame({\"State\": state_pop[\"NAME\"],\"Rate\": state_freq.values})" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 120 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "per_capita = per_capita.sort(\"Rate\")" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 122 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "per_capita[\"Rate\"][-10:].plot(kind = \"bar\")" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 126, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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Columbia,601723,601767,605210,620427,635040,649111,658893,3443,15217,14613,14071,9782,2311,9196,9234,9595,9647,1180,4675,4541,5046,5228,1131,4521,4693,4549,4419,733,3354,3509,3770,3760,1426,6945,6281,6278,1173,2159,10299,9790,10048,4933,153,397,130,-526,430,15.00607439,14.71006406,14.94372547,14.75071942,7.628686144,7.233961546,7.858888869,7.993859346,7.377388248,7.476102518,7.084836596,6.756860071,5.47307237,5.589951787,5.871583638,5.749217892,11.33288241,10.00583847,9.777666334,1.793572497,16.80595478,15.59579025,15.64924997,7.542790389 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+40,1,1,23,Maine,1328361,1328361,1327361,1327930,1328592,1328702,1330089,-1000,569,662,110,1387,3135,12631,12796,12748,12662,3105,13028,12751,12988,13137,30,-397,45,-240,-475,239,1181,1331,1389,1377,-1276,82,-621,-1465,531,-1037,1263,710,-76,1908,7,-297,-93,426,-46,9.51383483,9.633648809,9.594723053,9.524629804,9.812860436,9.599769925,9.775357939,9.88193506,-0.299025606,0.033878884,-0.180634886,-0.357305256,0.889544686,1.002062095,1.045424405,1.035809133,0.061763475,-0.467528596,-1.102625453,0.399429666,0.951308162,0.534533499,-0.057201047,1.435238798 +40,3,5,24,Maryland,5773552,5773785,5788101,5843833,5891819,5938737,5976407,14316,55732,47986,46918,37670,18139,73548,72718,72854,73047,10622,43665,43458,46481,47373,7517,29883,29260,26373,25674,6111,26411,27639,29072,28954,840,366,-8403,-8955,-15295,6951,26777,19236,20117,13659,-152,-928,-510,428,-1663,12.64587643,12.39266468,12.31624279,12.26120305,7.507779876,7.40615008,7.857787918,7.951729329,5.138096554,4.986514597,4.458454869,4.309473725,4.541119301,4.71026237,4.914730973,4.860033584,0.062930206,-1.432046554,-1.51387644,-2.56732105,4.604049507,3.278215816,3.400854533,2.292712535 +40,1,1,25,Massachusetts,6547629,6547817,6564073,6612270,6655829,6708874,6745408,16256,48197,43559,53045,36534,17727,73181,72222,72841,73248,12295,54011,52420,54262,55020,5432,19170,19802,18579,18228,7074,35001,35018,37353,37285,3714,-3450,-10824,-2175,-16354,10788,31551,24194,35178,20931,36,-2524,-437,-712,-2625,11.10793792,10.88656333,10.90050411,10.88842942,8.198177598,7.901659462,8.120195413,8.178808799,2.909760318,2.984903866,2.780308698,2.709620625,5.312703229,5.278525582,5.589798741,5.542473393,-0.523665785,-1.63158264,-0.325484225,-2.431047603,4.789037444,3.646942942,5.264314516,3.11142579 +40,2,3,26,Michigan,9883640,9884133,9876498,9875736,9884781,9898193,9909877,-7635,-762,9045,13412,11684,28387,113987,113070,112871,112748,22154,89879,89074,89586,90366,6233,24108,23996,23285,22382,3799,18030,18492,20070,20094,-18225,-43175,-33536,-29544,-28679,-14426,-25145,-15044,-9474,-8585,558,275,93,-399,-2113,11.54168182,11.44403256,11.41092335,11.38404701,9.100641477,9.015351167,9.05687891,9.124160002,2.44104034,2.428681395,2.354044443,2.259887006,1.825616282,1.87161095,2.029017477,2.028870051,-4.371657403,-3.394243177,-2.98681078,-2.895688474,-2.546041121,-1.522632227,-0.957793302,-0.866818423 +40,2,4,27,Minnesota,5303925,5303925,5310418,5348036,5380615,5422060,5457173,6493,37618,32579,41445,35113,17087,68374,68050,69028,68982,9335,39979,39306,39702,39728,7752,28395,28744,29326,29254,2792,12585,12905,13955,14002,-3979,-3347,-8891,-2238,-6696,-1187,9238,4014,11717,7306,-72,-15,-179,402,-1447,12.8300033,12.68565824,12.7797976,12.6814087,7.501838447,7.327295855,7.350401637,7.303456043,5.328164854,5.358362389,5.42939596,5.377952655,2.361505712,2.405707857,2.583619335,2.574078522,-0.628046056,-1.657431116,-0.414341818,-1.230969132,1.733459656,0.748276741,2.169277517,1.34310939 +40,3,6,28,Mississippi,2967297,2968103,2970811,2978464,2986137,2992206,2994079,2708,7653,7673,6069,1873,9995,39738,39440,38396,38206,6634,29131,29022,29355,29573,3361,10607,10418,9041,8633,579,1854,2752,2344,2250,-1129,-5833,-5635,-4799,-9382,-550,-3979,-2883,-2455,-7132,-103,1025,138,-517,372,13.35893869,13.22469013,12.84503081,12.76451088,9.793126053,9.731413719,9.820446903,9.880251274,3.565812641,3.493276415,3.024583902,2.884259603,0.623269222,0.922777567,0.784163773,0.751718303,-1.960911203,-1.889480956,-1.60546158,-3.134498274,-1.337641982,-0.966703389,-0.821297808,-2.382779971 +40,2,4,29,Missouri,5988927,5988923,5996085,6010544,6025281,6044917,6063589,7162,14459,14737,19636,18672,19159,76243,75429,75293,74988,13185,55985,55630,55646,56033,5974,20258,19799,19647,18955,1858,7549,8667,8776,8641,-361,-13480,-13484,-8140,-8074,1497,-5931,-4817,636,567,-309,132,-245,-647,-850,12.70015089,12.53408055,12.47585168,12.38600369,9.325681671,9.244069268,9.220395556,9.255146754,3.37446922,3.290011279,3.255456124,3.130856936,1.257472018,1.440200402,1.454160073,1.427261134,-2.245426256,-2.240644077,-1.348776549,-1.333607961,-0.987954238,-0.800443675,0.105383524,0.093653172 +40,4,8,30,Montana,989415,989417,990575,997661,1005163,1014864,1023579,1158,7086,7502,9701,8715,3017,11998,12024,12183,12243,2389,9126,8960,8819,9089,628,2872,3064,3364,3154,204,603,867,800,766,391,3414,3556,5351,4550,595,4017,4423,6151,5316,-65,197,15,186,245,12.0689898,12.00704605,12.06221501,12.01210924,9.179996741,8.947366319,8.731566459,8.917590534,2.888993057,3.059679732,3.330648551,3.094518709,0.606567832,0.865777522,0.792068621,0.751554005,3.434199964,3.550986008,5.297948988,4.464191542,4.040767796,4.41676353,6.090017609,5.215745547 +40,2,4,31,Nebraska,1826341,1826341,1829865,1842232,1855487,1868969,1881503,3524,12367,13255,13482,12534,6385,25738,25761,25940,25961,3657,15621,15217,14805,14658,2728,10117,10544,11135,11303,799,3331,3709,3829,3805,92,-1184,-909,-891,-2551,891,2147,2800,2938,1254,-95,103,-89,-591,-23,14.01814821,13.93345465,13.92955105,13.84412415,8.507945188,8.230479385,7.950154331,7.816616149,5.510203026,5.702975267,5.979396723,6.027508004,1.814222228,2.006101599,2.056139205,2.02907794,-0.64486314,-0.49165445,-0.478459136,-1.360362109,1.169359088,1.51444715,1.577680069,0.668715831 +40,4,8,32,Nevada,2700551,2700692,2703493,2718586,2755245,2791494,2839099,2801,15093,36659,36249,47605,8791,35843,34706,35075,35153,4948,20206,20410,21255,21702,3843,15637,14296,13820,13451,1543,7446,7830,8410,8456,-2557,-7665,14463,13188,23623,-1014,-219,22293,21598,32079,-28,-325,70,831,2075,13.22112791,12.6806984,12.64707065,12.48642905,7.453229656,7.457300015,7.663962555,7.708601918,5.767898255,5.223398384,4.983108093,4.777827131,2.746547957,2.860884817,3.032412378,3.003591274,-2.827328779,5.284415979,4.755226449,8.390945678,-0.080780822,8.145300796,7.787638827,11.39453695 +40,1,1,33,New Hampshire,1316470,1316466,1316517,1318109,1321297,1322616,1326813,51,1592,3188,1319,4197,3105,12992,12368,12422,12338,2531,10734,10502,10756,10834,574,2258,1866,1666,1504,417,1757,1906,2031,2026,-933,-2156,-526,-2626,1117,-516,-399,1380,-595,3143,-7,-267,-58,248,-450,9.862500408,9.371805626,9.396678333,9.313704953,8.148405125,7.957851123,8.136425064,8.17836598,1.714095283,1.413954503,1.260253269,1.135338973,1.333775648,1.444264353,1.536359177,1.529386143,-1.636664938,-0.398574528,-1.98644963,0.843200554,-0.302889291,1.045689826,-0.450090453,2.372586697 +40,1,2,34,New Jersey,8791894,8791936,8803580,8842614,8876000,8911502,8938175,11644,39034,33386,35502,26673,26301,106684,104515,103993,103440,17094,71062,68985,71354,71699,9207,35622,35530,32639,31741,11113,49097,47752,51577,51626,-8583,-45089,-49515,-45541,-55469,2530,4008,-1763,6036,-3843,-93,-596,-381,-3173,-1225,12.0914459,11.79719813,11.69281668,11.59012569,8.054088037,7.786726434,8.022936554,8.033646771,4.037357858,4.010471699,3.669880121,3.556478921,5.564599369,5.390037844,5.799240388,5.784530443,-5.110337107,-5.589037608,-5.120561617,-6.21512647,0.454262262,-0.198999764,0.678678771,-0.430596027 +40,4,8,35,New Mexico,2059179,2059192,2064950,2078407,2084594,2086895,2085572,5758,13457,6187,2301,-1323,6937,27798,27032,26899,26805,3807,16401,16468,16677,16863,3130,11397,10564,10222,9942,515,2211,2793,2678,2672,2014,64,-7119,-10560,-14154,2529,2275,-4326,-7882,-11482,99,-215,-51,-39,217,13.41810517,12.98678525,12.896594,12.84851384,7.916768939,7.91160031,7.99570609,8.082987834,5.501336235,5.075184945,4.900887908,4.765526007,1.067250541,1.34182048,1.283954003,1.280777056,0.030892824,-3.420128893,-5.062940355,-6.784475467,1.098143365,-2.078308413,-3.778986352,-5.503698412 +40,1,2,36,New York,19378102,19378112,19400867,19521745,19607140,19695680,19746227,22755,120878,85395,88540,50547,59639,243107,239921,240500,240710,35129,149748,146889,152069,154357,24510,93359,93032,88431,86353,24130,113261,110105,118929,118799,-24674,-84179,-116346,-107730,-153921,-544,29082,-6241,11199,-35122,-1211,-1563,-1396,-11090,-684,12.49181324,12.26311458,12.23830758,12.20579928,7.694653175,7.507957357,7.738325138,7.827055624,4.797160067,4.75515722,4.499982444,4.378743655,5.819804694,5.627811782,6.051932151,6.023998789,-4.325454828,-5.94680886,-5.48204938,-7.804947159,1.494349865,-0.318997079,0.569882772,-1.78094837 +40,3,5,37,North Carolina,9535483,9535691,9559488,9651502,9748181,9848917,9943964,23797,92014,96679,100736,95047,30753,121042,119564,120100,120099,18680,79948,79505,83582,83750,12073,41094,40059,36518,36349,5103,18018,23992,22151,21188,6610,30470,32005,38086,36257,11713,48488,55997,60237,57445,11,2432,623,3981,1253,12.60132872,12.32638698,12.25691682,12.13557541,8.323152529,8.196525686,8.530038478,8.462638663,4.27817619,4.129861297,3.726878337,3.672936749,1.875801299,2.473442478,2.260640836,2.140971797,3.172142612,3.299538451,3.88690203,3.663640478,5.047943911,5.77298093,6.147542866,5.804612275 +40,2,4,38,North Dakota,672591,672591,674345,685242,701705,723857,739482,1754,10897,16463,22152,15625,2178,9201,9888,10329,10780,1499,6054,5936,5772,5879,679,3147,3952,4557,4901,287,1043,1536,1365,1290,815,6217,10811,16529,8974,1102,7260,12347,17894,10264,-27,490,164,-299,460,13.53499261,14.25865588,14.49112701,14.73342814,8.905645612,8.559807981,8.09785895,8.035048611,4.629347,5.698847901,6.393268059,6.698379528,1.534289457,2.214936836,1.915034211,1.763091122,9.145424309,15.58963681,23.1894509,12.26510057,10.67971377,17.80457364,25.10448511,14.02819169 +40,2,3,39,Ohio,11536504,11536725,11540070,11544757,11550901,11572005,11594163,3345,4687,6144,21104,22158,34321,137935,137702,138338,137999,26465,110730,111480,111234,111233,7856,27205,26222,27104,26766,3532,15797,16650,17557,17536,-7875,-36804,-37000,-22109,-18243,-4343,-21007,-20350,-4552,-707,-168,-1511,272,-1448,-3901,11.95027366,11.92449247,11.96545106,11.91383918,9.593314258,9.653762625,9.621109042,9.603055628,2.3569594,2.270729849,2.344342013,2.310783553,1.368604582,1.441829456,1.518580753,1.513931868,-3.188587898,-3.204065457,-1.912302891,-1.574969153,-1.819983316,-1.762236001,-0.393722139,-0.061037285 +40,3,7,40,Oklahoma,3751351,3751616,3759481,3786527,3817059,3853118,3878051,7865,27046,30532,36059,24933,13102,52565,52352,53025,53091,8174,37636,36941,37123,37703,4928,14929,15411,15902,15388,1245,5029,6101,5786,5691,1582,6317,8937,13805,4377,2827,11346,15038,19591,10068,110,771,83,566,-523,13.93186967,13.77034468,13.82627806,13.73427486,9.975075563,9.716731027,9.679828771,9.753505582,3.956794109,4.05361365,4.146449293,3.980769273,1.332890185,1.604769118,1.508700516,1.472222377,1.674262736,2.350732931,3.599656175,1.132299656,3.007152921,3.955502049,5.108356691,2.604522033 +40,4,9,41,Oregon,3831074,3831073,3837083,3867644,3898684,3928068,3970239,6010,30561,31040,29384,42171,11071,45386,44910,45010,45209,7777,32546,32928,32700,33295,3294,12840,11982,12310,11914,1389,6053,6044,6763,6862,1436,11394,13110,9743,22670,2825,17447,19154,16506,29532,-109,274,-96,568,725,11.78133891,11.56531117,11.50157818,11.44776976,8.448320103,8.479683063,8.355956596,8.430920702,3.333018808,3.085628112,3.145621581,3.016849054,1.571243212,1.55646272,1.728175366,1.737587562,2.957664821,3.376112881,2.489666212,5.740470711,4.528908033,4.932575601,4.217841577,7.478058273 +40,1,2,42,Pennsylvania,12702379,12702884,12711077,12743995,12770043,12781296,12787209,8193,32918,26048,11253,5913,35307,142967,142112,142485,142032,30129,127759,125570,127820,128600,5178,15208,16542,14665,13432,5509,27053,27501,29036,29060,-1681,-6959,-17333,-31734,-31448,3828,20094,10168,-2698,-2388,-813,-2384,-662,-714,-5131,11.23288907,11.13990659,11.15284017,11.10991824,10.0379995,9.843208668,10.00495512,10.05925063,1.194889569,1.29669792,1.147885048,1.050667609,2.125548889,2.155754413,2.272757604,2.273109046,-0.546767261,-1.358703001,-2.483940274,-2.459901351,1.578781628,0.797051412,-0.21118267,-0.186792306 +40,1,1,44,Rhode Island,1052567,1052931,1053078,1052020,1052637,1053354,1055173,147,-1058,617,717,1819,2749,11002,11006,10845,10918,2336,9761,9314,9399,9543,413,1241,1692,1446,1375,860,3979,4203,4324,4290,-1142,-6146,-5289,-5133,-3387,-282,-2167,-1086,-809,903,16,-132,11,80,-459,10.45272002,10.45871132,10.29918931,10.35604477,9.27367752,8.850848381,8.925964071,9.051816742,1.179042496,1.607862944,1.373225242,1.304228023,3.780346568,3.993999972,4.106380322,4.069191431,-5.839158082,-5.02599711,-4.874664707,-3.212669318,-2.058811514,-1.031997138,-0.768284385,0.856522112 +40,3,5,45,South Carolina,4625364,4625401,4636290,4673054,4722621,4771929,4832482,10889,36764,49567,49308,60553,14620,57473,57524,57040,57401,9683,42034,42084,43978,44230,4937,15439,15440,13062,13171,1597,5128,7594,6548,6294,4280,14854,26369,28267,38614,5877,19982,33963,34815,44908,75,1343,164,1431,2474,12.34737915,12.24478284,12.01531405,11.95304949,9.030496671,8.958164262,9.263840835,9.210351369,3.316882479,3.286618577,2.751473214,2.742698121,1.101688798,1.616488438,1.379317609,1.310647785,3.191202302,5.613008113,5.954363293,8.040888713,4.2928911,7.22949655,7.333680901,9.351536497 +40,2,4,46,South Dakota,814180,814191,816192,824171,834504,845510,853175,2001,7979,10333,11006,7665,2948,11789,11902,12158,12217,1937,7298,7431,6839,6893,1011,4491,4471,5319,5324,261,1264,1506,1471,1449,801,2009,4349,4366,562,1062,3273,5855,5837,2011,-72,215,7,-150,330,14.37364778,14.35121407,14.47368891,14.38406768,8.898030497,8.960163986,8.14159882,8.115689489,5.475617287,5.391050085,6.33209009,6.268378187,1.5411223,1.815907275,1.751175883,1.706025543,2.449457833,5.243944715,5.197575735,0.661688306,3.990580134,7.05985199,6.948751618,2.367713849 +40,3,6,47,Tennessee,6346105,6346275,6356628,6398389,6455177,6497269,6549352,10353,41761,56788,42092,52083,19800,78609,80307,80200,80172,14311,60205,60625,61827,61664,5489,18404,19682,18373,18508,1852,7932,9278,9180,9160,3110,15641,27547,13534,24511,4962,23573,36825,22714,33671,-98,-216,281,1005,-96,12.32597338,12.4956763,12.38376134,12.29007879,9.44020694,9.433179866,9.546768232,9.452869061,2.88576644,3.062496431,2.836993105,2.837209727,1.243745892,1.443646067,1.417492881,1.404195002,2.452525152,4.286281332,2.089798328,3.757448001,3.696271044,5.729927399,3.50729121,5.161643003 +40,3,7,48,Texas,25145561,25146104,25245717,25657477,26094422,26505637,26956958,99613,411760,436945,411215,451321,94517,381746,378107,385197,386786,40853,168055,169031,176885,178395,53664,213691,209076,208312,208391,15393,78832,80039,84192,84637,29507,116516,145707,116464,154467,44900,195348,225746,200656,239104,1049,2721,2123,2247,3826,14.99890164,14.61229471,14.64625734,14.46940613,6.602925545,6.532359325,6.725657855,6.673637896,8.395976095,8.079935386,7.920599481,7.795768238,3.097330199,3.09318118,3.201213139,3.166213686,4.577944559,5.630981773,4.428284006,5.778507385,7.675274758,8.724162953,7.629497146,8.944721071 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Rico,3725789,3726157,3721527,3686771,3642281,3595839,3548397,-4630,-34756,-44490,-46442,-47442,9603,41247,40285,37874,36479,7136,30239,29683,29224,28829,2467,11008,10602,8650,7650,-7097,-45764,-55092,-55092,-55092,X,X,X,X,X,-7097,-45764,-55092,-55092,-55092,0,0,0,0,0,11.1353512,10.99323623,10.46514841,10.21214865,8.163548497,8.100092618,8.075025007,8.070562059,2.9718027,2.893143615,2.390123402,2.141586588,-12.35479458,-15.03386795,-15.2227374,-15.42278279,X,X,X,X,-12.35479458,-15.03386795,-15.2227374,-15.42278279 diff --git a/consumer_complaints_charting.ipynb b/consumer_complaints_charting.ipynb new file mode 100644 index 0000000..6bb57f3 --- /dev/null +++ b/consumer_complaints_charting.ipynb @@ -0,0 +1,2931 @@ +{ + "metadata": { + "name": "", + "signature": "sha256:ed0ede28ae9c27b936a6ceb999793942a2e3dcf18d905481b529a7f7c0e59db4" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from datetime import date" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 24 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "%matplotlib inline" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 25 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "complaints = pd.read_csv(\"complaints_dec_2014.csv\")" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 26 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "by_company = complaints[\"Company\"].value_counts()[:10]\n", + "by_company.plot(kind = \"bar\")\n", + "#sns.factorplot(\"Company\", data = by_company, palette =\"muted\")" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 27, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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EfElSV0T0S7qKHLviJO1Eulr7h6R3kmpfF3W18h3WzWrpJXU95V2wDVJdm9oY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\nSS8CTif1g32PVJyrn1RC95t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+ "text": [ + "" + ] + } + ], + "prompt_number": 27 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "by_product = complaints[\"Product\"].value_counts()[:10]\n", + "by_product.plot(kind = \"bar\")\n", + "#sns.factorplot(\"Company\", data = by_company, palette =\"muted\")" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 28, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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C/wZz4/V9wPMj4gzgMWA98BtJ+0TEtcCBwEJS8j9N0njSm8BOpJuyS4BZwI3FtYue/iob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\ng80j7VNZAkwnnS3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+ "text": [ + "" + ] + } + ], + "prompt_number": 29 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "complaints[\"dates\"] = pd.to_datetime(complaints.pop('Date received'), format=\"%m/%d/%Y\")\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 31 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "top_days = complaints.dates.dt.weekday.value_counts()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 32 + }, + { + "cell_type": "code", + "collapsed": true, + "input": [ + "top_days = top_days.sort_index(ascending=True)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 33 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "top_days.index = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 34 + }, + { + "cell_type": "code", + "collapsed": true, + "input": [ + "top_days.plot(kind = \"bar\")" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 35, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 35 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import calendar\n", + "my_list = []\n", + "for n in range(1,32):\n", + " my_list.append(calendar.weekday(2014, 12, n))" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 12 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "freqs = np.unique(my_list, return_counts = True)[1]" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 36 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "the_averages = top_days/freqs" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 37 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "the_averages.plot(kind = \"bar\")" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 38, + "text": [ + "" + ] + }, + { + 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This may be due to the fact that billing mistakes etc happen on the first day of the week and are noticed on the second day. " + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "population = pd.read_csv(\"NST_EST2014_ALLDATA.csv\")" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 39 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "population" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
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0 10 0 0 0 United States 308745538 308758105 309347057 311721632 314112078... 3.168211 3.135081 X X X X 2.941968 3.051932 3.168211 3.135081
1 20 1 0 0 Northeast Region 55317240 55318348 55381690 55635670 55832038... 4.692605 4.674678 -2.891457696 -3.987594326 -3.82818713 -5.111331551 1.578060 0.405624 0.864418 -0.436653
2 20 2 0 0 Midwest Region 66927001 66929898 66972390 67149657 67331458... 1.891826 1.886101 -2.728007872 -2.729468744 -1.942826565 -2.690902253 -1.032761 -0.946140 -0.051001 -0.804801
3 20 3 0 0 South Region 114555744 114562951 114871231 116089908 117346322... 3.064411 3.012706 2.807069634 3.003089966 2.472345639 3.065858744 5.677925 6.055264 5.536757 6.078565
4 20 4 0 0 West Region 71945553 71946908 72121746 72846397 73602260... 3.344905 3.305249 0.266017066 0.754667214 0.724134003 1.383520042 3.304919 3.950244 4.069039 4.688770
5 40 3 6 1 Alabama 4779736 4780127 4785822 4801695 4817484... 1.165832 1.157861 -0.020443249 -0.168413541 0.396415887 0.420101549 1.011941 1.001333 1.562247 1.577963
6 40 4 9 2 Alaska 710231 710249 713856 722572 731081... 3.203618 2.869760 -1.175137215 -1.949571184 -3.789313102 -13.75449375 0.948185 1.835376 -0.585695-10.884734
7 40 4 8 4 Arizona 6392017 6392310 6411999 6472867 6556236... 2.141877 2.129805 1.36951366 5.13128187 3.910475996 6.280635868 3.336628 7.155212 6.052353 8.410441
8 40 3 7 5 Arkansas 2915918 2915958 2922297 2938430 2949300... 1.090035 1.091283 1.3414718 -0.420875278 -0.580562333 -1.313050473 2.317801 0.621971 0.509473 -0.221767
9 40 4 9 6 California 37253956 37254503 37336011 37701901 38062780... 4.207353 4.177389 -1.162079243 -1.173950696 -1.341226344 -0.830982325 2.761377 2.772770 2.866127 3.346406
10 40 4 8 8 Colorado 5029196 5029324 5048575 5119661 5191709... 2.074200 2.010735 5.183396609 5.553675215 6.977583181 7.587162607 6.933159 7.660864 9.051783 9.597898
11 40 1 1 9 Connecticut 3574097 3574096 3579345 3590537 3594362... 4.753602 4.730950 -3.384435058 -5.611491546 -4.731638212 -7.286251924 1.116894 -1.059166 0.021964 -2.555302
12 40 3 5 10 Delaware 897934 897936 899731 907829 916881... 2.608949 2.565489 2.866848127 3.598380017 3.397170979 5.148173903 5.303282 6.221263 6.006120 7.713663
13 40 3 5 11 District of Columbia 601723 601767 605210 620427 635040... 5.871584 5.749218 11.33288241 10.00583847 9.777666334 1.793572497 16.805955 15.595790 15.649250 7.542790
14 40 3 5 12 Florida 18801310 18804623 18852220 19107900 19355257... 5.783717 5.687300 5.540393445 5.125320316 4.918783369 7.01612271 11.359606 10.722573 10.702501 12.703423
15 40 3 5 13 Georgia 9687653 9688681 9714464 9813201 9919000... 2.510526 2.470423 1.105815775 1.852200877 -0.576887568 2.200466631 3.375007 4.426268 1.933638 4.670890
16 40 4 9 15 Hawaii 1360301 1360301 1363950 1378251 1392766... 6.426691 6.074495 -0.728611798 -2.567288472 -0.663156245 -3.635080614 4.254976 4.316105 5.763534 2.439414
17 40 4 8 16 Idaho 1567582 1567652 1570639 1583780 1595590... 1.052850 1.043942 0.058330869 -0.163554415 2.986504627 4.738695787 0.952949 0.848596 4.039355 5.782638
18 40 2 3 17 Illinois 12830632 12831587 12840097 12858725 12873763... 2.505015 2.518554 -5.424762271 -5.690471905 -5.238097733 -7.369175712 -3.182714 -3.382339 -2.733083 -4.850621
19 40 2 3 18 Indiana 6483802 6484192 6490308 6516560 6537632... 1.586775 1.590575 -1.302542626 -1.954161544 -0.231150462 -1.192171554 0.155918 -0.507730 1.355625 0.398403
20 40 2 4 19 Iowa 3046355 3046869 3050295 3064904 3075935... 1.732413 1.731762 0.091575107 -1.398831658 0.150122984 -0.261312787 1.684655 0.189551 1.882536 1.470449
21 40 2 4 20 Kansas 2853118 2853132 2858949 2869965 2885966... 2.106968 2.050753 -3.156968319 -1.789806028 -4.369598429 -4.760146087 -1.406549 0.474641 -2.262630 -2.709393
22 40 3 6 21 Kentucky 4339367 4339349 4349838 4370038 4383465... 1.394732 1.356853 0.597026839 -1.271262488 -0.542180801 -0.858954458 1.778007 0.199920 0.852551 0.497899
23 40 3 7 22 Louisiana 4533372 4533479 4545581 4575972 4604744... 1.651933 1.620656 0.454747125 -0.167960756 -0.496857926 -1.3115694 1.940240 1.525589 1.155076 0.309086
24 40 1 1 23 Maine 1328361 1328361 1327361 1327930 1328592... 1.045424 1.035809 0.061763475 -0.467528596 -1.102625453 0.399429666 0.951308 0.534533 -0.057201 1.435239
25 40 3 5 24 Maryland 5773552 5773785 5788101 5843833 5891819... 4.914731 4.860034 0.062930206 -1.432046554 -1.51387644 -2.56732105 4.604050 3.278216 3.400855 2.292713
26 40 1 1 25 Massachusetts 6547629 6547817 6564073 6612270 6655829... 5.589799 5.542473 -0.523665785 -1.63158264 -0.325484225 -2.431047603 4.789037 3.646943 5.264315 3.111426
27 40 2 3 26 Michigan 9883640 9884133 9876498 9875736 9884781... 2.029017 2.028870 -4.371657403 -3.394243177 -2.98681078 -2.895688474 -2.546041 -1.522632 -0.957793 -0.866818
28 40 2 4 27 Minnesota 5303925 5303925 5310418 5348036 5380615... 2.583619 2.574079 -0.628046056 -1.657431116 -0.414341818 -1.230969132 1.733460 0.748277 2.169278 1.343109
29 40 3 6 28 Mississippi 2967297 2968103 2970811 2978464 2986137... 0.784164 0.751718 -1.960911203 -1.889480956 -1.60546158 -3.134498274 -1.337642 -0.966703 -0.821298 -2.382780
30 40 2 4 29 Missouri 5988927 5988923 5996085 6010544 6025281... 1.454160 1.427261 -2.245426256 -2.240644077 -1.348776549 -1.333607961 -0.987954 -0.800444 0.105384 0.093653
31 40 4 8 30 Montana 989415 989417 990575 997661 1005163... 0.792069 0.751554 3.434199964 3.550986008 5.297948988 4.464191542 4.040768 4.416764 6.090018 5.215746
32 40 2 4 31 Nebraska 1826341 1826341 1829865 1842232 1855487... 2.056139 2.029078 -0.64486314 -0.49165445 -0.478459136 -1.360362109 1.169359 1.514447 1.577680 0.668716
33 40 4 8 32 Nevada 2700551 2700692 2703493 2718586 2755245... 3.032412 3.003591 -2.827328779 5.284415979 4.755226449 8.390945678 -0.080781 8.145301 7.787639 11.394537
34 40 1 1 33 New Hampshire 1316470 1316466 1316517 1318109 1321297... 1.536359 1.529386 -1.636664938 -0.398574528 -1.98644963 0.843200554 -0.302889 1.045690 -0.450090 2.372587
35 40 1 2 34 New Jersey 8791894 8791936 8803580 8842614 8876000... 5.799240 5.784530 -5.110337107 -5.589037608 -5.120561617 -6.21512647 0.454262 -0.199000 0.678679 -0.430596
36 40 4 8 35 New Mexico 2059179 2059192 2064950 2078407 2084594... 1.283954 1.280777 0.030892824 -3.420128893 -5.062940355 -6.784475467 1.098143 -2.078308 -3.778986 -5.503698
37 40 1 2 36 New York 19378102 19378112 19400867 19521745 19607140... 6.051932 6.023999 -4.325454828 -5.94680886 -5.48204938 -7.804947159 1.494350 -0.318997 0.569883 -1.780948
38 40 3 5 37 North Carolina 9535483 9535691 9559488 9651502 9748181... 2.260641 2.140972 3.172142612 3.299538451 3.88690203 3.663640478 5.047944 5.772981 6.147543 5.804612
39 40 2 4 38 North Dakota 672591 672591 674345 685242 701705... 1.915034 1.763091 9.145424309 15.58963681 23.1894509 12.26510057 10.679714 17.804574 25.104485 14.028192
40 40 2 3 39 Ohio 11536504 11536725 11540070 11544757 11550901... 1.518581 1.513932 -3.188587898 -3.204065457 -1.912302891 -1.574969153 -1.819983 -1.762236 -0.393722 -0.061037
41 40 3 7 40 Oklahoma 3751351 3751616 3759481 3786527 3817059... 1.508701 1.472222 1.674262736 2.350732931 3.599656175 1.132299656 3.007153 3.955502 5.108357 2.604522
42 40 4 9 41 Oregon 3831074 3831073 3837083 3867644 3898684... 1.728175 1.737588 2.957664821 3.376112881 2.489666212 5.740470711 4.528908 4.932576 4.217842 7.478058
43 40 1 2 42 Pennsylvania 12702379 12702884 12711077 12743995 12770043... 2.272758 2.273109 -0.546767261 -1.358703001 -2.483940274 -2.459901351 1.578782 0.797051 -0.211183 -0.186792
44 40 1 1 44 Rhode Island 1052567 1052931 1053078 1052020 1052637... 4.106380 4.069191 -5.839158082 -5.02599711 -4.874664707 -3.212669318 -2.058812 -1.031997 -0.768284 0.856522
45 40 3 5 45 South Carolina 4625364 4625401 4636290 4673054 4722621... 1.379318 1.310648 3.191202302 5.613008113 5.954363293 8.040888713 4.292891 7.229497 7.333681 9.351536
46 40 2 4 46 South Dakota 814180 814191 816192 824171 834504... 1.751176 1.706026 2.449457833 5.243944715 5.197575735 0.661688306 3.990580 7.059852 6.948752 2.367714
47 40 3 6 47 Tennessee 6346105 6346275 6356628 6398389 6455177... 1.417493 1.404195 2.452525152 4.286281332 2.089798328 3.757448001 3.696271 5.729927 3.507291 5.161643
48 40 3 7 48 Texas 25145561 25146104 25245717 25657477 26094422... 3.201213 3.166214 4.577944559 5.630981773 4.428284006 5.778507385 7.675275 8.724163 7.629497 8.944721
49 40 4 8 49 Utah 2763885 2763885 2774346 2815324 2855194... 1.888857 1.869754 -0.328820843 -0.030685027 1.920812174 -0.422533597 1.419762 1.759980 3.809669 1.447220
50 40 1 1 50 Vermont 625741 625745 625792 626450 626138... 1.150844 1.147264 -0.752250763 -2.604208247 -1.09817054 -2.471643515 0.255542 -1.516859 0.052674 -1.324380
51 40 3 5 51 Virginia 8001024 8001023 8025376 8110188 8193422... 4.195152 4.098421 1.331716697 0.603915329 0.304183119 -2.458329804 4.975221 5.003800 4.499335 1.640092
52 40 4 9 53 Washington 6724540 6724543 6741911 6822112 6896325... 3.455210 3.396158 3.446470122 1.962176887 2.250746157 3.998924994 6.416385 5.370291 5.705957 7.395083
53 40 3 5 54 West Virginia 1852994 1853033 1854176 1854982 1856313... 0.631283 0.628523 0.586116849 0.562067957 -1.252861257 -1.484372912 1.143656 1.171559 -0.621579 -0.855850
54 40 2 3 55 Wisconsin 5686986 5687289 5689268 5708785 5724888... 1.129419 1.139079 -1.094923844 -1.653886726 -1.369743442 -1.727052792 -0.058431 -0.619224 -0.240324 -0.587974
55 40 4 8 56 Wyoming 563626 563767 564358 567631 576893... 0.858535 0.830923 -0.362194332 9.651173763 4.54782108 -4.577788133 0.323325 10.610525 5.406356 -3.746865
56 40 X X 72 Puerto Rico 3725789 3726157 3721527 3686771 3642281...-15.222737-15.422783 X X X X-12.354795-15.033868-15.222737-15.422783
\n", + "

57 rows \u00d7 76 columns

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RINTERNATIONALMIG2013 RINTERNATIONALMIG2014 \\\n", + "0 ... 3.168211 3.135081 \n", + "1 ... 4.692605 4.674678 \n", + "2 ... 1.891826 1.886101 \n", + "3 ... 3.064411 3.012706 \n", + "4 ... 3.344905 3.305249 \n", + "5 ... 1.165832 1.157861 \n", + "6 ... 3.203618 2.869760 \n", + "7 ... 2.141877 2.129805 \n", + "8 ... 1.090035 1.091283 \n", + "9 ... 4.207353 4.177389 \n", + "10 ... 2.074200 2.010735 \n", + "11 ... 4.753602 4.730950 \n", + "12 ... 2.608949 2.565489 \n", + "13 ... 5.871584 5.749218 \n", + "14 ... 5.783717 5.687300 \n", + "15 ... 2.510526 2.470423 \n", + "16 ... 6.426691 6.074495 \n", + "17 ... 1.052850 1.043942 \n", + "18 ... 2.505015 2.518554 \n", + "19 ... 1.586775 1.590575 \n", + "20 ... 1.732413 1.731762 \n", + "21 ... 2.106968 2.050753 \n", + "22 ... 1.394732 1.356853 \n", + "23 ... 1.651933 1.620656 \n", + "24 ... 1.045424 1.035809 \n", + "25 ... 4.914731 4.860034 \n", + "26 ... 5.589799 5.542473 \n", + "27 ... 2.029017 2.028870 \n", + "28 ... 2.583619 2.574079 \n", + "29 ... 0.784164 0.751718 \n", + "30 ... 1.454160 1.427261 \n", + "31 ... 0.792069 0.751554 \n", + "32 ... 2.056139 2.029078 \n", + "33 ... 3.032412 3.003591 \n", + "34 ... 1.536359 1.529386 \n", + "35 ... 5.799240 5.784530 \n", + "36 ... 1.283954 1.280777 \n", + "37 ... 6.051932 6.023999 \n", + "38 ... 2.260641 2.140972 \n", + "39 ... 1.915034 1.763091 \n", + "40 ... 1.518581 1.513932 \n", + "41 ... 1.508701 1.472222 \n", + "42 ... 1.728175 1.737588 \n", + "43 ... 2.272758 2.273109 \n", + "44 ... 4.106380 4.069191 \n", + "45 ... 1.379318 1.310648 \n", + "46 ... 1.751176 1.706026 \n", + "47 ... 1.417493 1.404195 \n", + "48 ... 3.201213 3.166214 \n", + "49 ... 1.888857 1.869754 \n", + "50 ... 1.150844 1.147264 \n", + "51 ... 4.195152 4.098421 \n", + "52 ... 3.455210 3.396158 \n", + "53 ... 0.631283 0.628523 \n", + "54 ... 1.129419 1.139079 \n", + "55 ... 0.858535 0.830923 \n", + "56 ... -15.222737 -15.422783 \n", + "\n", + " RDOMESTICMIG2011 RDOMESTICMIG2012 RDOMESTICMIG2013 RDOMESTICMIG2014 \\\n", + "0 X X X X \n", + "1 -2.891457696 -3.987594326 -3.82818713 -5.111331551 \n", + "2 -2.728007872 -2.729468744 -1.942826565 -2.690902253 \n", + "3 2.807069634 3.003089966 2.472345639 3.065858744 \n", + "4 0.266017066 0.754667214 0.724134003 1.383520042 \n", + "5 -0.020443249 -0.168413541 0.396415887 0.420101549 \n", + "6 -1.175137215 -1.949571184 -3.789313102 -13.75449375 \n", + "7 1.36951366 5.13128187 3.910475996 6.280635868 \n", + "8 1.3414718 -0.420875278 -0.580562333 -1.313050473 \n", + "9 -1.162079243 -1.173950696 -1.341226344 -0.830982325 \n", + "10 5.183396609 5.553675215 6.977583181 7.587162607 \n", + "11 -3.384435058 -5.611491546 -4.731638212 -7.286251924 \n", + "12 2.866848127 3.598380017 3.397170979 5.148173903 \n", + "13 11.33288241 10.00583847 9.777666334 1.793572497 \n", + "14 5.540393445 5.125320316 4.918783369 7.01612271 \n", + "15 1.105815775 1.852200877 -0.576887568 2.200466631 \n", + "16 -0.728611798 -2.567288472 -0.663156245 -3.635080614 \n", 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NAMECENSUS2010POP
5 Alabama 4779736
6 Alaska 710231
7 Arizona 6392017
8 Arkansas 2915918
9 California 37253956
10 Colorado 5029196
11 Connecticut 3574097
12 Delaware 897934
13 District of Columbia 601723
14 Florida 18801310
15 Georgia 9687653
16 Hawaii 1360301
17 Idaho 1567582
18 Illinois 12830632
19 Indiana 6483802
20 Iowa 3046355
21 Kansas 2853118
22 Kentucky 4339367
23 Louisiana 4533372
24 Maine 1328361
25 Maryland 5773552
26 Massachusetts 6547629
27 Michigan 9883640
28 Minnesota 5303925
29 Mississippi 2967297
30 Missouri 5988927
31 Montana 989415
32 Nebraska 1826341
33 Nevada 2700551
34 New Hampshire 1316470
35 New Jersey 8791894
36 New Mexico 2059179
37 New York 19378102
38 North Carolina 9535483
39 North Dakota 672591
40 Ohio 11536504
41 Oklahoma 3751351
42 Oregon 3831074
43 Pennsylvania 12702379
44 Rhode Island 1052567
45 South Carolina 4625364
46 South Dakota 814180
47 Tennessee 6346105
48 Texas 25145561
49 Utah 2763885
50 Vermont 625741
51 Virginia 8001024
52 Washington 6724540
53 West Virginia 1852994
54 Wisconsin 5686986
55 Wyoming 563626
56 Puerto Rico 3725789
\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 43, + "text": [ + " NAME CENSUS2010POP\n", + "5 Alabama 4779736\n", + "6 Alaska 710231\n", + "7 Arizona 6392017\n", + "8 Arkansas 2915918\n", + "9 California 37253956\n", + "10 Colorado 5029196\n", + "11 Connecticut 3574097\n", + "12 Delaware 897934\n", + "13 District of Columbia 601723\n", + "14 Florida 18801310\n", + "15 Georgia 9687653\n", + "16 Hawaii 1360301\n", + "17 Idaho 1567582\n", + "18 Illinois 12830632\n", + "19 Indiana 6483802\n", + "20 Iowa 3046355\n", + "21 Kansas 2853118\n", + "22 Kentucky 4339367\n", + "23 Louisiana 4533372\n", + "24 Maine 1328361\n", + "25 Maryland 5773552\n", + "26 Massachusetts 6547629\n", + "27 Michigan 9883640\n", + "28 Minnesota 5303925\n", + "29 Mississippi 2967297\n", + "30 Missouri 5988927\n", + "31 Montana 989415\n", + "32 Nebraska 1826341\n", + "33 Nevada 2700551\n", + "34 New Hampshire 1316470\n", + "35 New Jersey 8791894\n", + "36 New Mexico 2059179\n", + "37 New York 19378102\n", + "38 North Carolina 9535483\n", + "39 North Dakota 672591\n", + "40 Ohio 11536504\n", + "41 Oklahoma 3751351\n", + "42 Oregon 3831074\n", + "43 Pennsylvania 12702379\n", + "44 Rhode Island 1052567\n", + "45 South Carolina 4625364\n", + "46 South Dakota 814180\n", + "47 Tennessee 6346105\n", + "48 Texas 25145561\n", + "49 Utah 2763885\n", + "50 Vermont 625741\n", + "51 Virginia 8001024\n", + "52 Washington 6724540\n", + "53 West Virginia 1852994\n", + "54 Wisconsin 5686986\n", + "55 Wyoming 563626\n", + "56 Puerto Rico 3725789" + ] + } + ], + "prompt_number": 43 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "states = {\n", + " 'AK': 'Alaska',\n", + " 'AL': 'Alabama',\n", + " 'AR': 'Arkansas',\n", + " 'AS': 'American Samoa',\n", + " 'AZ': 'Arizona',\n", + " 'CA': 'California',\n", + " 'CO': 'Colorado',\n", + " 'CT': 'Connecticut',\n", + " 'DC': 'District of Columbia',\n", + " 'DE': 'Delaware',\n", + " 'FL': 'Florida',\n", + " 'GA': 'Georgia',\n", + " 'GU': 'Guam',\n", + " 'HI': 'Hawaii',\n", + " 'IA': 'Iowa',\n", + " 'ID': 'Idaho',\n", + " 'IL': 'Illinois',\n", + " 'IN': 'Indiana',\n", + " 'KS': 'Kansas',\n", + " 'KY': 'Kentucky',\n", + " 'LA': 'Louisiana',\n", + " 'MA': 'Massachusetts',\n", + " 'MD': 'Maryland',\n", + " 'ME': 'Maine',\n", + " 'MI': 'Michigan',\n", + " 'MN': 'Minnesota',\n", + " 'MO': 'Missouri',\n", + " 'MP': 'Northern Mariana Islands',\n", + " 'MS': 'Mississippi',\n", + " 'MT': 'Montana',\n", + " 'NA': 'National',\n", + " 'NC': 'North Carolina',\n", + " 'ND': 'North Dakota',\n", + " 'NE': 'Nebraska',\n", + " 'NH': 'New Hampshire',\n", + " 'NJ': 'New Jersey',\n", + " 'NM': 'New Mexico',\n", + " 'NV': 'Nevada',\n", + " 'NY': 'New York',\n", + " 'OH': 'Ohio',\n", + " 'OK': 'Oklahoma',\n", + " 'OR': 'Oregon',\n", + " 'PA': 'Pennsylvania',\n", + " 'PR': 'Puerto Rico',\n", + " 'RI': 'Rhode Island',\n", + " 'SC': 'South Carolina',\n", + " 'SD': 'South Dakota',\n", + " 'TN': 'Tennessee',\n", + " 'TX': 'Texas',\n", + " 'UT': 'Utah',\n", + " 'VA': 'Virginia',\n", + " 'VI': 'Virgin Islands',\n", + " 'VT': 'Vermont',\n", + " 'WA': 'Washington',\n", + " 'WI': 'Wisconsin',\n", + " 'WV': 'West Virginia',\n", + " 'WY': 'Wyoming'\n", + "}\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 45 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "new_list = []\n", + "for item in complaints[\"State\"]:\n", + " new_list.append(states.get(item))" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 73 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "state_freq = pd.DataFrame(new_list)" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 74 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "state_freq = state_freq[0].value_counts()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 75 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "state_freq = state_freq.sort_index()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 76 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "state_freq.columns = [\"State\" ,\"Frequency\"]" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 77 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "state_freq" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 78, + "text": [ + "Alabama 147\n", + "Alaska 15\n", + "American Samoa 1\n", + "Arizona 213\n", + "Arkansas 59\n", + "California 1591\n", + "Colorado 180\n", + "Connecticut 109\n", + "Delaware 44\n", + "District of Columbia 82\n", + "Florida 1093\n", + "Georgia 512\n", + "Hawaii 48\n", + "Idaho 39\n", + "Illinois 427\n", + "Indiana 132\n", + "Iowa 51\n", + "Kansas 56\n", + "Kentucky 59\n", + "Louisiana 127\n", + "Maine 39\n", + "Maryland 342\n", + "Massachusetts 200\n", + "Michigan 287\n", + "Minnesota 135\n", + "Mississippi 57\n", + "Missouri 119\n", + "Montana 14\n", + "Nebraska 37\n", + "Nevada 159\n", + "New Hampshire 46\n", + "New Jersey 465\n", + "New Mexico 55\n", + "New York 733\n", + "North Carolina 287\n", + "North Dakota 8\n", + "Ohio 348\n", + "Oklahoma 93\n", + "Oregon 120\n", + "Pennsylvania 418\n", + "Puerto Rico 27\n", + "Rhode Island 40\n", + "South Carolina 130\n", + "South Dakota 22\n", + "Tennessee 192\n", + "Texas 1099\n", + "Utah 70\n", + "Vermont 18\n", + "Virgin Islands 5\n", + "Virginia 373\n", + "Washington 231\n", + "West Virginia 26\n", + "Wisconsin 143\n", + "Wyoming 8\n", + "Length: 54, dtype: int64" + ] + } + ], + "prompt_number": 78 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "state_pop = state_pop.sort(\"NAME\")" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 52 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "state_freq" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 79, + "text": [ + "Alabama 147\n", + "Alaska 15\n", + "American Samoa 1\n", + "Arizona 213\n", + "Arkansas 59\n", + "California 1591\n", + "Colorado 180\n", + "Connecticut 109\n", + "Delaware 44\n", + "District of Columbia 82\n", + "Florida 1093\n", + "Georgia 512\n", + "Hawaii 48\n", + "Idaho 39\n", + "Illinois 427\n", + "Indiana 132\n", + "Iowa 51\n", + "Kansas 56\n", + "Kentucky 59\n", + "Louisiana 127\n", + "Maine 39\n", + "Maryland 342\n", + "Massachusetts 200\n", + "Michigan 287\n", + "Minnesota 135\n", + "Mississippi 57\n", + "Missouri 119\n", + "Montana 14\n", + "Nebraska 37\n", + "Nevada 159\n", + "New Hampshire 46\n", + "New Jersey 465\n", + "New Mexico 55\n", + "New York 733\n", + "North Carolina 287\n", + "North Dakota 8\n", + "Ohio 348\n", + "Oklahoma 93\n", + "Oregon 120\n", + "Pennsylvania 418\n", + "Puerto Rico 27\n", + "Rhode Island 40\n", + "South Carolina 130\n", + "South Dakota 22\n", + "Tennessee 192\n", + "Texas 1099\n", + "Utah 70\n", + "Vermont 18\n", + "Virgin Islands 5\n", + "Virginia 373\n", + "Washington 231\n", + "West Virginia 26\n", + "Wisconsin 143\n", + "Wyoming 8\n", + "Length: 54, dtype: int64" + ] + } + ], + "prompt_number": 79 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "len(state_freq.values)\n", + "per_cap_compliants = state_freq.values/state_pop[\"CENSUS2010POP\"]\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 111 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "len(state_pop[\"CENSUS2010POP\"])" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 112, + "text": [ + "52" + ] + } + ], + "prompt_number": 112 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "type(state_freq)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 113, + "text": [ + "pandas.core.series.Series" + ] + } + ], + "prompt_number": 113 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "state_freq.pop(\"American Samoa\")" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'American Samoa'", + "output_type": "pyerr", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mstate_freq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"American Samoa\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/Users/Daniel/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mpop\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m 479\u001b[0m \u001b[0mReturn\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mdrop\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mframe\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mRaise\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mfound\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 480\u001b[0m \"\"\"\n\u001b[0;32m--> 481\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 482\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 483\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/Daniel/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 507\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 508\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 509\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 510\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 511\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misscalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/Daniel/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/core/index.py\u001b[0m in \u001b[0;36mget_value\u001b[0;34m(self, series, key)\u001b[0m\n\u001b[1;32m 1429\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1430\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1431\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1432\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# pragma: no cover\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1433\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/Daniel/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/core/index.py\u001b[0m in \u001b[0;36mget_value\u001b[0;34m(self, series, key)\u001b[0m\n\u001b[1;32m 1415\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1416\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1417\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1418\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1419\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minferred_type\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'integer'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'boolean'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/Daniel/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_value (pandas/index.c:3096)\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m/Users/Daniel/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_value (pandas/index.c:2827)\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m/Users/Daniel/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3687)\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m/Users/Daniel/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/hashtable.so\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12310)\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m/Users/Daniel/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/hashtable.so\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12261)\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'American Samoa'" + ] + } + ], + "prompt_number": 114 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "state_freq" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 115, + "text": [ + "Alabama 0.000031\n", + "Alaska 0.000021\n", + "Arizona 0.000033\n", + "Arkansas 0.000020\n", + "California 0.000043\n", + "Colorado 0.000036\n", + "Connecticut 0.000030\n", + "Delaware 0.000049\n", + "District of Columbia 0.000136\n", + "Florida 0.000058\n", + "Georgia 0.000053\n", + "Hawaii 0.000035\n", + "Idaho 0.000025\n", + "Illinois 0.000033\n", + "Indiana 0.000020\n", + "Iowa 0.000017\n", + "Kansas 0.000020\n", + "Kentucky 0.000014\n", + "Louisiana 0.000028\n", + "Maine 0.000029\n", + "Maryland 0.000059\n", + "Massachusetts 0.000031\n", + "Michigan 0.000029\n", + "Minnesota 0.000025\n", + "Mississippi 0.000019\n", + "Missouri 0.000020\n", + "Montana 0.000014\n", + "Nebraska 0.000020\n", + "Nevada 0.000059\n", + "New Hampshire 0.000035\n", + "New Jersey 0.000053\n", + "New Mexico 0.000027\n", + "New York 0.000038\n", + "North Carolina 0.000030\n", + "North Dakota 0.000012\n", + "Ohio 0.000030\n", + "Oklahoma 0.000025\n", + "Oregon 0.000031\n", + "Pennsylvania 0.000033\n", + "Puerto Rico 0.000007\n", + "Rhode Island 0.000038\n", + "South Carolina 0.000028\n", + "South Dakota 0.000027\n", + "Tennessee 0.000030\n", + "Texas 0.000044\n", + "Utah 0.000025\n", + "Vermont 0.000029\n", + "Virginia 0.000047\n", + "Washington 0.000034\n", + "West Virginia 0.000014\n", + "Wisconsin 0.000025\n", + "Wyoming 0.000014\n", + "Name: CENSUS2010POP, Length: 52, dtype: float64" + ] + } + ], + "prompt_number": 115 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "state_freq.pop(\"Virgin Islands\")" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'Virgin Islands'", + "output_type": "pyerr", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mstate_freq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Virgin Islands\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/Users/Daniel/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mpop\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m 479\u001b[0m \u001b[0mReturn\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mdrop\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mframe\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mRaise\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mfound\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 480\u001b[0m \"\"\"\n\u001b[0;32m--> 481\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 482\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 483\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/Daniel/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 507\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 508\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 509\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 510\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 511\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misscalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/Daniel/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/core/index.py\u001b[0m in \u001b[0;36mget_value\u001b[0;34m(self, series, key)\u001b[0m\n\u001b[1;32m 1429\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1430\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1431\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1432\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# pragma: no cover\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1433\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/Daniel/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/core/index.py\u001b[0m in \u001b[0;36mget_value\u001b[0;34m(self, series, key)\u001b[0m\n\u001b[1;32m 1415\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1416\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1417\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1418\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1419\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minferred_type\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'integer'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'boolean'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/Users/Daniel/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_value (pandas/index.c:3096)\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m/Users/Daniel/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_value (pandas/index.c:2827)\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m/Users/Daniel/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3687)\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m/Users/Daniel/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/hashtable.so\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12310)\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m/Users/Daniel/.pyenv/versions/sandbox/lib/python3.4/site-packages/pandas/hashtable.so\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12261)\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'Virgin Islands'" + ] + } + ], + "prompt_number": 127 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "state_freq = state_freq.values/state_pop[\"CENSUS2010POP\"]" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 117 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "per_capita = pd.DataFrame({\"State\": state_pop[\"NAME\"],\"Rate\": state_freq.values})" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 120 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "per_capita = per_capita.sort(\"Rate\")" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 122 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "per_capita[\"Rate\"][-10:].plot(kind = \"bar\")" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 126, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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