diff --git a/atus_data.py.ipynb b/atus_data.py.ipynb new file mode 100644 index 0000000..568d748 --- /dev/null +++ b/atus_data.py.ipynb @@ -0,0 +1,3871 @@ +{ + "metadata": { + "name": "", + "signature": "sha256:7b6aacc2653575c68059f5455a78f0572feaf5029c092957797b1c3f0d5dad39" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import pandas as pd\n", + "import numpy as np\n", + "import re\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 7 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "%matplotlib inline" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 8 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def activity_columns(data, activity_code):\n", + " \"\"\"For the activity code given, return all columns that fall under that activity.\"\"\"\n", + " col_prefix = \"t{}\".format(activity_code)\n", + " return [column for column in data.columns if re.match(col_prefix, column)]" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 9 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def average_minutes(data, activity_code):\n", + " activity_col = \"t{}\".format(activity_code)\n", + " data = data[['TUFINLWGT', activity_col]]\n", + " data = data.rename(columns={\"TUFINLWGT\": \"weight\", activity_col: \"minutes\"})\n", + " data['weighted_minutes'] = data.weight * data.minutes\n", + " return data.weighted_minutes.sum() / data.weight.sum()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 10 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def average_minutes2(data, activity_code):\n", + " cols = activity_columns(data, activity_code)\n", + " activity_data = data[cols]\n", + " activity_sums = activity_data.sum(axis=1)\n", + " data = data[['TUFINLWGT']]\n", + " data['minutes'] = activity_sums\n", + " data = data.rename(columns={\"TUFINLWGT\": \"weight\"})\n", + " data['weighted_minutes'] = data.weight * data.minutes\n", + " return data.weighted_minutes.sum() / data.weight.sum()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 11 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "summary = pd.read_csv(\"atusdata/atussum_2013.dat\")\n", + "summary.info()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "Int64Index: 11385 entries, 0 to 11384\n", + "Columns: 413 entries, tucaseid to t500107\n", + "dtypes: float64(1), int64(412)\n", + "memory usage: 36.0 MB\n" + ] + } + ], + "prompt_number": 12 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "clean_summary = summary.rename(columns= {\"TEAGE\": \"Age of Respondent\", \"TUFINLWGT\": \"Statistical Weight\",\n", + " \"t120303\": \"Minutes\", \"TRCHILDNUM\": \"Number of Children in Household\", \n", + " \"TRYHHCHILD\": \"Age of Youngest Child in Household\",\n", + " \"TESEX\": \"Sex of Respondent\", \"TELFS\": \"Working Status of Respondent\"})\n", + "clean_summary.head()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
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tucaseidStatistical WeightAge of Youngest Child in HouseholdAge of RespondentSex of RespondentPEEDUCAPTDTRACEPEHSPNONGTMETSTAWorking Status of Respondent...t181501t181599t181601t181801t189999t500101t500103t500105t500106t500107
0 20130101130004 11899905.662034 12 22 2 40 8 2 1 5... 0 0 0 0 0 0 0 0 0 0
1 20130101130112 4447638.009513 1 39 1 43 1 2 1 1... 0 0 0 0 0 0 0 0 0 0
2 20130101130123 10377056.507734 -1 47 2 40 1 2 1 4... 25 0 0 0 0 0 0 0 0 0
3 20130101130611 7731257.992805 -1 50 2 40 1 1 1 1... 0 0 0 0 0 0 0 0 0 0
4 20130101130616 4725269.227067 -1 45 2 40 2 2 1 1... 0 0 0 0 0 0 0 0 0 0
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5 rows \u00d7 413 columns

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" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 13, + "text": [ + " tucaseid Statistical Weight Age of Youngest Child in Household \\\n", + "0 20130101130004 11899905.662034 12 \n", + "1 20130101130112 4447638.009513 1 \n", + "2 20130101130123 10377056.507734 -1 \n", + "3 20130101130611 7731257.992805 -1 \n", + "4 20130101130616 4725269.227067 -1 \n", + "\n", + " Age of Respondent Sex of Respondent PEEDUCA PTDTRACE PEHSPNON \\\n", + "0 22 2 40 8 2 \n", + "1 39 1 43 1 2 \n", + "2 47 2 40 1 2 \n", + "3 50 2 40 1 1 \n", + "4 45 2 40 2 2 \n", + "\n", + " GTMETSTA Working Status of Respondent ... t181501 t181599 t181601 \\\n", + "0 1 5 ... 0 0 0 \n", + "1 1 1 ... 0 0 0 \n", + "2 1 4 ... 25 0 0 \n", + "3 1 1 ... 0 0 0 \n", + "4 1 1 ... 0 0 0 \n", + "\n", + " t181801 t189999 t500101 t500103 t500105 t500106 t500107 \n", + "0 0 0 0 0 0 0 0 \n", + "1 0 0 0 0 0 0 0 \n", + "2 0 0 0 0 0 0 0 \n", + "3 0 0 0 0 0 0 0 \n", + "4 0 0 0 0 0 0 0 \n", + "\n", + "[5 rows x 413 columns]" + ] + } + ], + "prompt_number": 13 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "group = clean_summary.groupby([\"Age of Respondent\", \"Sex of Respondent\", \"Working Status of Respondent\"])\n", + "group.head()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
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tucaseidStatistical WeightAge of Youngest Child in HouseholdAge of RespondentSex of RespondentPEEDUCAPTDTRACEPEHSPNONGTMETSTAWorking Status of Respondent...t181501t181599t181601t181801t189999t500101t500103t500105t500106t500107
0 20130101130004 11899905.662034 12 22 2 40 8 2 1 5... 0 0 0 0 0 0 0 0 0 0
1 20130101130112 4447638.009513 1 39 1 43 1 2 1 1... 0 0 0 0 0 0 0 0 0 0
2 20130101130123 10377056.507734 -1 47 2 40 1 2 1 4... 25 0 0 0 0 0 0 0 0 0
3 20130101130611 7731257.992805 -1 50 2 40 1 1 1 1... 0 0 0 0 0 0 0 0 0 0
4 20130101130616 4725269.227067 -1 45 2 40 2 2 1 1... 0 0 0 0 0 0 0 0 0 0
5 20130101130619 2372791.046351 -1 80 2 38 1 2 1 5... 0 0 0 0 0 0 0 0 0 0
6 20130101130658 5671341.270490 -1 72 1 42 1 1 1 5... 0 0 5 0 0 0 0 0 0 0
7 20130101130670 8608413.296903 -1 55 2 38 4 2 1 1... 0 0 0 0 0 0 0 0 120 0
8 20130101130734 1378191.194810 -1 57 2 34 2 2 1 1... 0 0 0 0 0 0 0 0 0 0
9 20130101130735 3905483.253032 4 27 2 38 1 2 1 1... 0 0 0 0 0 0 0 0 0 0
10 20130101130740 4538371.462244 0 59 2 39 2 2 2 1... 0 0 0 0 0 0 0 0 0 0
11 20130101130768 6755514.216327 1 31 1 43 4 2 1 1... 0 0 0 0 0 0 0 0 0 0
12 20130101130799 13506297.294756 -1 52 1 39 1 2 2 1... 0 0 0 0 0 0 0 0 0 0
13 20130101130826 5521732.162587 7 42 2 40 1 2 2 1... 0 0 0 0 0 0 0 0 0 0
14 20130101130839 11791654.393174 -1 66 1 44 1 2 2 5... 0 0 0 0 0 0 0 0 0 0
15 20130101130867 1801834.050978 -1 66 2 39 1 2 1 5... 0 0 0 0 0 0 0 0 140 0
16 20130101130871 6884215.057542 -1 45 1 43 1 2 1 1... 0 0 0 0 0 0 0 0 0 0
17 20130101130891 12569148.198194 -1 59 1 40 1 2 1 2... 0 0 0 0 0 0 0 0 0 0
18 20130101130910 14226152.054254 -1 53 2 44 1 2 1 1... 0 0 0 0 0 0 0 0 0 0
19 20130101130970 12301142.559951 15 43 2 39 1 2 1 4... 0 0 0 0 0 0 0 0 0 0
20 20130101130996 1102916.898147 8 36 2 42 1 2 1 4... 0 0 0 0 0 0 0 0 0 0
21 20130101131007 8128107.650758 -1 53 2 40 1 2 1 1... 0 0 0 0 0 0 0 0 0 0
22 20130101131043 5767745.700187 -1 27 1 44 1 1 1 1... 0 0 0 0 0 0 0 0 0 0
23 20130101131054 9474271.417876 4 59 2 43 2 2 1 5... 0 0 0 0 0 0 0 0 0 0
24 20130101131056 5960041.143926 -1 52 1 44 1 2 2 1... 0 0 0 0 0 0 0 0 0 0
25 20130101131066 3157926.871281 -1 48 1 39 1 2 1 1... 0 0 0 0 0 0 0 0 0 0
26 20130101131096 3359410.785590 15 55 2 39 3 2 2 5... 0 0 0 0 0 0 0 0 0 0
27 20130101131099 17547816.572228 -1 80 2 40 1 2 1 5... 0 0 0 0 0 0 0 0 30 0
28 20130101131112 7823574.493908 0 24 2 37 2 2 1 4... 0 0 0 0 0 0 0 0 55 0
29 20130101131117 9061749.751818 9 49 2 40 1 1 1 1... 0 0 0 0 0 0 0 0 0 0
..................................................................
10944 20131211131561 19157276.189876 -1 53 1 43 1 1 1 2... 0 0 0 0 0 0 0 0 0 0
10974 20131211131684 8129899.722607 9 37 2 43 1 1 1 2... 0 0 0 0 0 0 0 0 0 0
10983 20131211131711 7173690.902000 -1 49 2 44 1 2 1 2... 0 0 0 0 0 0 0 0 0 0
10989 20131211131731 8273287.652065 7 48 1 40 1 2 1 2... 0 0 0 0 0 45 0 0 0 0
11025 20131211131846 6363893.610860 -1 76 2 46 1 2 1 1... 0 0 0 0 0 0 0 0 0 0
11031 20131211131872 3011403.286058 11 45 2 43 1 2 1 3... 30 0 0 0 0 0 0 0 0 0
11039 20131211131887 5697485.501321 4 32 1 43 1 2 1 5... 0 0 0 0 0 0 0 0 0 0
11040 20131211131892 14029146.125332 17 17 1 37 4 2 1 2... 0 0 0 0 9 0 15 0 0 0
11054 20131211131929 2006720.834387 -1 46 2 39 2 2 1 4... 0 0 0 0 0 0 0 0 0 0
11082 20131211132016 4498295.055811 -1 22 1 39 2 2 1 5... 0 0 0 0 0 0 0 0 0 0
11084 20131211132021 4882686.859344 10 59 1 43 1 2 1 2... 0 0 0 0 0 0 0 0 0 0
11157 20131211132275 4014032.858350 -1 54 2 46 1 2 2 2... 0 0 0 0 0 15 0 0 0 0
11180 20131211132351 52632328.828751 -1 23 2 43 1 2 1 2... 0 0 0 0 0 0 0 0 0 0
11201 20131211132417 1779890.726839 -1 66 2 44 1 2 3 2... 0 0 0 0 250 0 0 0 0 0
11214 20131211132455 2192707.928641 -1 22 1 40 1 1 1 4... 0 0 0 0 0 0 0 0 0 0
11237 20131212130092 4436344.008165 1 26 2 39 1 2 2 2... 0 0 0 0 0 0 0 0 0 0
11248 20131212130593 6033256.735098 -1 38 1 39 2 2 1 4... 0 0 0 0 0 0 0 0 0 0
11261 20131212130751 1871037.464092 -1 43 1 41 1 2 1 4... 0 0 0 0 0 0 0 0 0 0
11262 20131212130754 1985563.638093 0 31 2 44 1 2 1 2... 0 0 0 0 0 0 0 0 0 0
11269 20131212130863 1311156.259538 7 48 2 44 1 2 1 2... 0 0 0 0 0 0 0 0 0 0
11271 20131212130890 13053986.051667 -1 50 1 39 1 2 1 3... 0 0 0 0 0 0 0 0 0 0
11273 20131212130899 5643283.112760 8 61 1 43 2 2 1 4... 0 0 0 0 0 0 0 0 150 0
11290 20131212131079 5979163.598864 -1 41 1 35 1 1 1 4... 0 0 0 0 0 10 0 0 0 0
11295 20131212131157 3264654.964583 3 36 1 44 1 2 2 2... 0 0 0 0 0 0 0 0 0 0
11304 20131212131322 1784286.956725 -1 60 1 41 1 2 1 2... 0 0 0 0 0 0 0 0 0 0
11316 20131212131530 4434448.973399 -1 33 2 44 1 2 1 2... 0 0 0 0 45 1200 0 0 0 0
11322 20131212131598 4032736.345657 -1 46 1 42 1 1 2 2... 0 0 0 0 0 0 0 0 0 0
11334 20131212131776 5985504.301408 9 42 1 37 1 1 1 3... 0 0 0 0 0 0 0 0 0 0
11337 20131212131799 5424512.023010 12 16 1 37 1 2 3 3... 0 0 0 0 0 0 0 0 0 0
11355 20131212132093 8140631.838710 -1 54 1 40 1 2 1 2... 0 0 0 0 0 0 0 0 0 0
\n", + "

2059 rows \u00d7 413 columns

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" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 14, + "text": [ + " tucaseid Statistical Weight Age of Youngest Child in Household \\\n", + "0 20130101130004 11899905.662034 12 \n", + "1 20130101130112 4447638.009513 1 \n", + "2 20130101130123 10377056.507734 -1 \n", + "3 20130101130611 7731257.992805 -1 \n", + "4 20130101130616 4725269.227067 -1 \n", + "5 20130101130619 2372791.046351 -1 \n", + "6 20130101130658 5671341.270490 -1 \n", + "7 20130101130670 8608413.296903 -1 \n", + "8 20130101130734 1378191.194810 -1 \n", + "9 20130101130735 3905483.253032 4 \n", + "10 20130101130740 4538371.462244 0 \n", + "11 20130101130768 6755514.216327 1 \n", + "12 20130101130799 13506297.294756 -1 \n", + "13 20130101130826 5521732.162587 7 \n", + "14 20130101130839 11791654.393174 -1 \n", + "15 20130101130867 1801834.050978 -1 \n", + "16 20130101130871 6884215.057542 -1 \n", + "17 20130101130891 12569148.198194 -1 \n", + "18 20130101130910 14226152.054254 -1 \n", + "19 20130101130970 12301142.559951 15 \n", + "20 20130101130996 1102916.898147 8 \n", + "21 20130101131007 8128107.650758 -1 \n", + "22 20130101131043 5767745.700187 -1 \n", + "23 20130101131054 9474271.417876 4 \n", + "24 20130101131056 5960041.143926 -1 \n", + "25 20130101131066 3157926.871281 -1 \n", + "26 20130101131096 3359410.785590 15 \n", + "27 20130101131099 17547816.572228 -1 \n", + "28 20130101131112 7823574.493908 0 \n", + "29 20130101131117 9061749.751818 9 \n", + "... ... ... ... \n", + "10944 20131211131561 19157276.189876 -1 \n", + "10974 20131211131684 8129899.722607 9 \n", + "10983 20131211131711 7173690.902000 -1 \n", + "10989 20131211131731 8273287.652065 7 \n", + "11025 20131211131846 6363893.610860 -1 \n", + "11031 20131211131872 3011403.286058 11 \n", + "11039 20131211131887 5697485.501321 4 \n", + "11040 20131211131892 14029146.125332 17 \n", + "11054 20131211131929 2006720.834387 -1 \n", + "11082 20131211132016 4498295.055811 -1 \n", + "11084 20131211132021 4882686.859344 10 \n", + "11157 20131211132275 4014032.858350 -1 \n", + "11180 20131211132351 52632328.828751 -1 \n", + "11201 20131211132417 1779890.726839 -1 \n", + "11214 20131211132455 2192707.928641 -1 \n", + "11237 20131212130092 4436344.008165 1 \n", + "11248 20131212130593 6033256.735098 -1 \n", + "11261 20131212130751 1871037.464092 -1 \n", + "11262 20131212130754 1985563.638093 0 \n", + "11269 20131212130863 1311156.259538 7 \n", + "11271 20131212130890 13053986.051667 -1 \n", + "11273 20131212130899 5643283.112760 8 \n", + "11290 20131212131079 5979163.598864 -1 \n", + "11295 20131212131157 3264654.964583 3 \n", + "11304 20131212131322 1784286.956725 -1 \n", + "11316 20131212131530 4434448.973399 -1 \n", + "11322 20131212131598 4032736.345657 -1 \n", + "11334 20131212131776 5985504.301408 9 \n", + "11337 20131212131799 5424512.023010 12 \n", + "11355 20131212132093 8140631.838710 -1 \n", + "\n", + " Age of Respondent Sex of Respondent PEEDUCA PTDTRACE PEHSPNON \\\n", + "0 22 2 40 8 2 \n", + "1 39 1 43 1 2 \n", + "2 47 2 40 1 2 \n", + "3 50 2 40 1 1 \n", + "4 45 2 40 2 2 \n", + "5 80 2 38 1 2 \n", + "6 72 1 42 1 1 \n", + "7 55 2 38 4 2 \n", + "8 57 2 34 2 2 \n", + "9 27 2 38 1 2 \n", + "10 59 2 39 2 2 \n", + "11 31 1 43 4 2 \n", + "12 52 1 39 1 2 \n", + "13 42 2 40 1 2 \n", + "14 66 1 44 1 2 \n", + "15 66 2 39 1 2 \n", + "16 45 1 43 1 2 \n", + "17 59 1 40 1 2 \n", + "18 53 2 44 1 2 \n", + "19 43 2 39 1 2 \n", + "20 36 2 42 1 2 \n", + "21 53 2 40 1 2 \n", + "22 27 1 44 1 1 \n", + "23 59 2 43 2 2 \n", + "24 52 1 44 1 2 \n", + "25 48 1 39 1 2 \n", + "26 55 2 39 3 2 \n", + "27 80 2 40 1 2 \n", + "28 24 2 37 2 2 \n", + "29 49 2 40 1 1 \n", + "... ... ... ... ... ... \n", + "10944 53 1 43 1 1 \n", + "10974 37 2 43 1 1 \n", + "10983 49 2 44 1 2 \n", + "10989 48 1 40 1 2 \n", + "11025 76 2 46 1 2 \n", + "11031 45 2 43 1 2 \n", + "11039 32 1 43 1 2 \n", + "11040 17 1 37 4 2 \n", + "11054 46 2 39 2 2 \n", + "11082 22 1 39 2 2 \n", + "11084 59 1 43 1 2 \n", + "11157 54 2 46 1 2 \n", + "11180 23 2 43 1 2 \n", + "11201 66 2 44 1 2 \n", + "11214 22 1 40 1 1 \n", + "11237 26 2 39 1 2 \n", + "11248 38 1 39 2 2 \n", + "11261 43 1 41 1 2 \n", + "11262 31 2 44 1 2 \n", + "11269 48 2 44 1 2 \n", + "11271 50 1 39 1 2 \n", + "11273 61 1 43 2 2 \n", + "11290 41 1 35 1 1 \n", + "11295 36 1 44 1 2 \n", + "11304 60 1 41 1 2 \n", + "11316 33 2 44 1 2 \n", + "11322 46 1 42 1 1 \n", + "11334 42 1 37 1 1 \n", + "11337 16 1 37 1 2 \n", + "11355 54 1 40 1 2 \n", + "\n", + " GTMETSTA Working Status of Respondent ... t181501 t181599 \\\n", + "0 1 5 ... 0 0 \n", + "1 1 1 ... 0 0 \n", + "2 1 4 ... 25 0 \n", + "3 1 1 ... 0 0 \n", + "4 1 1 ... 0 0 \n", + "5 1 5 ... 0 0 \n", + "6 1 5 ... 0 0 \n", + "7 1 1 ... 0 0 \n", + "8 1 1 ... 0 0 \n", + "9 1 1 ... 0 0 \n", + "10 2 1 ... 0 0 \n", + "11 1 1 ... 0 0 \n", + "12 2 1 ... 0 0 \n", + "13 2 1 ... 0 0 \n", + "14 2 5 ... 0 0 \n", + "15 1 5 ... 0 0 \n", + "16 1 1 ... 0 0 \n", + "17 1 2 ... 0 0 \n", + "18 1 1 ... 0 0 \n", + "19 1 4 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tucaseidTUFINLWGTTRYHHCHILDTEAGETESEXPEEDUCAPTDTRACEPEHSPNONGTMETSTATELFS...t181501t181599t181601t181801t189999t500101t500103t500105t500106t500107
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4 20130101130616 4725269.227067 -1 45 2 40 2 2 1 1... 0 0 0 0 0 0 0 0 0 0
\n", + "

5 rows \u00d7 413 columns

\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 15, + "text": [ + " tucaseid TUFINLWGT TRYHHCHILD TEAGE TESEX PEEDUCA \\\n", + "0 20130101130004 11899905.662034 12 22 2 40 \n", + "1 20130101130112 4447638.009513 1 39 1 43 \n", + "2 20130101130123 10377056.507734 -1 47 2 40 \n", + "3 20130101130611 7731257.992805 -1 50 2 40 \n", + "4 20130101130616 4725269.227067 -1 45 2 40 \n", + "\n", + " PTDTRACE PEHSPNON GTMETSTA TELFS ... t181501 t181599 t181601 \\\n", + "0 8 2 1 5 ... 0 0 0 \n", + "1 1 2 1 1 ... 0 0 0 \n", + "2 1 2 1 4 ... 25 0 0 \n", + "3 1 1 1 1 ... 0 0 0 \n", + "4 2 2 1 1 ... 0 0 0 \n", + "\n", + " t181801 t189999 t500101 t500103 t500105 t500106 t500107 \n", + "0 0 0 0 0 0 0 0 \n", + "1 0 0 0 0 0 0 0 \n", + "2 0 0 0 0 0 0 0 \n", + "3 0 0 0 0 0 0 0 \n", + "4 0 0 0 0 0 0 0 \n", + "\n", + "[5 rows x 413 columns]" + ] + } + ], + "prompt_number": 15 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "summary.tail()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
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tucaseidTUFINLWGTTRYHHCHILDTEAGETESEXPEEDUCAPTDTRACEPEHSPNONGTMETSTATELFS...t181501t181599t181601t181801t189999t500101t500103t500105t500106t500107
11380 20131212132458 4469643.600730 -1 85 2 44 1 2 1 5... 0 0 0 0 0 0 0 0 0 0
11381 20131212132462 4103676.895062 -1 60 1 39 1 1 1 1... 0 0 0 0 0 0 0 0 0 0
11382 20131212132469 23557969.110158 9 43 1 39 1 1 1 1... 0 0 0 0 0 0 0 0 0 0
11383 20131212132475 20450051.675501 16 48 1 39 1 2 1 1... 0 0 0 0 0 150 0 0 0 0
11384 20131212132488 3397480.288114 0 40 1 40 1 1 1 5... 0 0 0 0 25 30 0 0 0 0
\n", + "

5 rows \u00d7 413 columns

\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 16, + "text": [ + " tucaseid TUFINLWGT TRYHHCHILD TEAGE TESEX PEEDUCA \\\n", + "11380 20131212132458 4469643.600730 -1 85 2 44 \n", + "11381 20131212132462 4103676.895062 -1 60 1 39 \n", + "11382 20131212132469 23557969.110158 9 43 1 39 \n", + "11383 20131212132475 20450051.675501 16 48 1 39 \n", + "11384 20131212132488 3397480.288114 0 40 1 40 \n", + "\n", + " PTDTRACE PEHSPNON GTMETSTA TELFS ... t181501 t181599 \\\n", + "11380 1 2 1 5 ... 0 0 \n", + "11381 1 1 1 1 ... 0 0 \n", + "11382 1 1 1 1 ... 0 0 \n", + "11383 1 2 1 1 ... 0 0 \n", + "11384 1 1 1 5 ... 0 0 \n", + "\n", + " t181601 t181801 t189999 t500101 t500103 t500105 t500106 t500107 \n", + "11380 0 0 0 0 0 0 0 0 \n", + "11381 0 0 0 0 0 0 0 0 \n", + "11382 0 0 0 0 0 0 0 0 \n", + "11383 0 0 0 150 0 0 0 0 \n", + "11384 0 0 25 30 0 0 0 0 \n", + "\n", + "[5 rows x 413 columns]" + ] + } + ], + "prompt_number": 16 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#def activity_columns(data, activity_code):\n", + "# \"\"\"For the activity code given, return all columns that fall under that activity.\"\"\"\n", + "# col_prefix = \"t{}\".format(activity_code)\n", + "# return [column for column in data.columns if re.match(col_prefix, column)]" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 17 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "clean_summary.groupby(\"Age of Respondent\").size().nlargest(50)\n", + "\"\"\" 352 80 year olds took this survey?\"\"\"" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 18, + "text": [ + "' 352 80 year olds took this survey?'" + ] + } + ], + "prompt_number": 18 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "data = adults_with_no_children[['TUFINLWGT', 't120303']]\n", + "data = data.rename(columns={\"TUFINLWGT\": \"weight\", \"t120303\": \"minutes\"})\n", + "data.head()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'adults_with_no_children' is not defined", + "output_type": "pyerr", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\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[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madults_with_no_children\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'TUFINLWGT'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m't120303'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"TUFINLWGT\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"weight\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"t120303\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"minutes\"\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'adults_with_no_children' is not defined" + ] + } + ], + "prompt_number": 19 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "age_of_participants = pd.DataFrame(summary.groupby(\"TEAGE\").size())\n", + "age_of_participants" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "clean_summary.groupby(\"Age of Respondent\").size().plot(kind=\"bar\", figsize=(18,8))\n", + "plt.ylabel(\"Number of Surveys\")" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 21, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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AdCeAAAAAALoTQAAAAADdCSAAAACA7lwFAw6xfa/bnLh2MwAAgAACDrHZrtuc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\nt46rA3SolleH6+er/5kVOq6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tucaseidWeightAge of Youngest Child in HouseholdAge of RespondentSex of RespondentPEEDUCAPTDTRACEPEHSPNONGTMETSTAWorking Status of Respondent...t181501t181599t181601t181801t189999t500101t500103t500105t500106t500107
0 20130101130004 11899905.662034 12 22 2 40 8 2 1 5... 0 0 0 0 0 0 0 0 0 0
1 20130101130112 4447638.009513 1 39 1 43 1 2 1 1... 0 0 0 0 0 0 0 0 0 0
2 20130101130123 10377056.507734 -1 47 2 40 1 2 1 4... 25 0 0 0 0 0 0 0 0 0
3 20130101130611 7731257.992805 -1 50 2 40 1 1 1 1... 0 0 0 0 0 0 0 0 0 0
4 20130101130616 4725269.227067 -1 45 2 40 2 2 1 1... 0 0 0 0 0 0 0 0 0 0
5 20130101130619 2372791.046351 -1 80 2 38 1 2 1 5... 0 0 0 0 0 0 0 0 0 0
6 20130101130658 5671341.270490 -1 72 1 42 1 1 1 5... 0 0 5 0 0 0 0 0 0 0
7 20130101130670 8608413.296903 -1 55 2 38 4 2 1 1... 0 0 0 0 0 0 0 0 120 0
8 20130101130734 1378191.194810 -1 57 2 34 2 2 1 1... 0 0 0 0 0 0 0 0 0 0
9 20130101130735 3905483.253032 4 27 2 38 1 2 1 1... 0 0 0 0 0 0 0 0 0 0
10 20130101130740 4538371.462244 0 59 2 39 2 2 2 1... 0 0 0 0 0 0 0 0 0 0
11 20130101130768 6755514.216327 1 31 1 43 4 2 1 1... 0 0 0 0 0 0 0 0 0 0
12 20130101130799 13506297.294756 -1 52 1 39 1 2 2 1... 0 0 0 0 0 0 0 0 0 0
13 20130101130826 5521732.162587 7 42 2 40 1 2 2 1... 0 0 0 0 0 0 0 0 0 0
14 20130101130839 11791654.393174 -1 66 1 44 1 2 2 5... 0 0 0 0 0 0 0 0 0 0
15 20130101130867 1801834.050978 -1 66 2 39 1 2 1 5... 0 0 0 0 0 0 0 0 140 0
16 20130101130871 6884215.057542 -1 45 1 43 1 2 1 1... 0 0 0 0 0 0 0 0 0 0
17 20130101130891 12569148.198194 -1 59 1 40 1 2 1 2... 0 0 0 0 0 0 0 0 0 0
18 20130101130910 14226152.054254 -1 53 2 44 1 2 1 1... 0 0 0 0 0 0 0 0 0 0
19 20130101130970 12301142.559951 15 43 2 39 1 2 1 4... 0 0 0 0 0 0 0 0 0 0
20 20130101130996 1102916.898147 8 36 2 42 1 2 1 4... 0 0 0 0 0 0 0 0 0 0
21 20130101131007 8128107.650758 -1 53 2 40 1 2 1 1... 0 0 0 0 0 0 0 0 0 0
22 20130101131043 5767745.700187 -1 27 1 44 1 1 1 1... 0 0 0 0 0 0 0 0 0 0
23 20130101131054 9474271.417876 4 59 2 43 2 2 1 5... 0 0 0 0 0 0 0 0 0 0
24 20130101131056 5960041.143926 -1 52 1 44 1 2 2 1... 0 0 0 0 0 0 0 0 0 0
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25 rows \u00d7 413 columns

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Number of surveys taken
Age of Respondent
15 105
16 164
17 163
18 109
19 89
20 87
21 84
22 75
23 104
24 119
25 121
26 128
27 151
28 173
29 185
30 209
31 222
32 221
33 198
34 217
35 211
36 235
37 217
38 188
39 217
40 214
41 208
42 222
43 235
44 202
......
52 212
53 193
54 208
55 177
56 217
57 217
58 204
59 192
60 185
61 205
62 173
63 162
64 167
65 175
66 205
67 164
68 143
69 140
70 135
71 112
72 94
73 95
74 95
75 84
76 76
77 103
78 83
79 77
80 352
85 260
\n", + "

67 rows \u00d7 1 columns

\n", + "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 26, + "text": [ + " Number of surveys taken\n", + "Age of Respondent \n", + "15 105\n", + "16 164\n", + "17 163\n", + "18 109\n", + "19 89\n", + "20 87\n", + "21 84\n", + "22 75\n", + "23 104\n", + "24 119\n", + "25 121\n", + "26 128\n", + "27 151\n", + "28 173\n", + "29 185\n", + "30 209\n", + "31 222\n", + "32 221\n", + "33 198\n", + "34 217\n", + "35 211\n", + "36 235\n", + "37 217\n", + "38 188\n", + "39 217\n", + "40 214\n", + "41 208\n", + "42 222\n", + "43 235\n", + "44 202\n", + "... ...\n", + "52 212\n", + "53 193\n", + "54 208\n", + "55 177\n", + "56 217\n", + "57 217\n", + "58 204\n", + "59 192\n", + "60 185\n", + "61 205\n", + "62 173\n", + "63 162\n", + "64 167\n", + "65 175\n", + "66 205\n", + "67 164\n", + "68 143\n", + "69 140\n", + "70 135\n", + "71 112\n", + "72 94\n", + "73 95\n", + "74 95\n", + "75 84\n", + "76 76\n", + "77 103\n", + "78 83\n", + "79 77\n", + "80 352\n", + "85 260\n", + "\n", + "[67 rows x 1 columns]" + ] + } + ], + "prompt_number": 26 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def activity_columns(data, activity_code):\n", + " \"\"\"For the activity code given, return all columns that fall under that activity.\"\"\"\n", + " col_prefix = \"t{}\".format(activity_code)\n", + " return [column for column in data.columns if re.match(col_prefix, column)]" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def average_minutes(data, activity_code):\n", + " activity_col = \"t{}\".format(activity_code)\n", + " data = data[['TUFINLWGT', activity_col]]\n", + " data = data.rename(columns={\"TUFINLWGT\": \"weight\", activity_col: \"minutes\"})\n", + " data['weighted_minutes'] = data.weight * data.minutes\n", + " return data.weighted_minutes.sum() / data.weight.sum()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 27 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "gambling = average_minutes(\"Age of Respondent\", \"120404\")\n", + "gambling\n", + "#sleeping = average_minutes(adults_with_no_children, \"010101\")\n", + "#sleepless = average_minutes(adults_with_no_children, \"010102\")\n", + "#(sleeping + sleepless) / 60" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "string indices must be integers", + "output_type": "pyerr", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\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[0mgambling\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maverage_minutes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Age of Respondent\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"120404\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mgambling\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m#sleeping = average_minutes(adults_with_no_children, \"010101\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m#sleepless = average_minutes(adults_with_no_children, \"010102\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m#(sleeping + sleepless) / 60\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36maverage_minutes\u001b[0;34m(data, activity_code)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0maverage_minutes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactivity_code\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mactivity_col\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"t{}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mactivity_code\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'TUFINLWGT'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactivity_col\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"TUFINLWGT\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"weight\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactivity_col\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"minutes\"\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'weighted_minutes'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mminutes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: string indices must be integers" + ] + } + ], + "prompt_number": 31 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#gamble_v_religion = pd.DataFrame(clean_summary.groupby(\"Age of Respondent\").size(), columns= [\"Gambling\"])\n", + "clean_summary.groupby(\"Working Status of Respondent\").size().plot(kind=\"bar\", figsize=(18,8))\n", + "plt.ylabel(\"Number of Surveys\")" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 39, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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