From 6cc9ae46eefc19bd28a6d8cb1800ddee91596b49 Mon Sep 17 00:00:00 2001 From: taddeimania Date: Wed, 17 Jun 2015 15:04:27 -0400 Subject: [PATCH 1/3] live demonstration starting point --- Starting Point.ipynb | 936 +++++++++++++++++++++++++++++++++++++++---- 1 file changed, 864 insertions(+), 72 deletions(-) diff --git a/Starting Point.ipynb b/Starting Point.ipynb index 2e6fffc..3fc71e8 100644 --- a/Starting Point.ipynb +++ b/Starting Point.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 27, + "execution_count": 76, "metadata": { "collapsed": false }, @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 77, "metadata": { "collapsed": false }, @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 78, "metadata": { "collapsed": false }, @@ -36,31 +36,30 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 79, "metadata": { "collapsed": false }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "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" - ] + "data": { + "text/plain": [ + "(11385, 413)" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "summary = pd.read_csv(\"atusdata/atussum_2013.dat\")\n", - "summary.info()" + "summary.shape" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 80, "metadata": { "collapsed": false }, @@ -76,7 +75,7 @@ " dtype='object', length=413)" ] }, - "execution_count": 31, + "execution_count": 80, "metadata": {}, "output_type": "execute_result" } @@ -85,6 +84,598 @@ "summary.columns" ] }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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tucaseidTUFINLWGTTRYHHCHILDTEAGETESEX
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1201301011301124447638.0095131391
22013010113012310377056.507734-1472
3201301011306117731257.992805-1502
4201301011306164725269.227067-1452
5201301011306192372791.046351-1802
6201301011306585671341.270490-1721
7201301011306708608413.296903-1552
8201301011307341378191.194810-1572
9201301011307353905483.2530324272
10201301011307404538371.4622440592
11201301011307686755514.2163271311
122013010113079913506297.294756-1521
13201301011308265521732.1625877422
142013010113083911791654.393174-1661
15201301011308671801834.050978-1662
16201301011308716884215.057542-1451
172013010113089112569148.198194-1591
182013010113091014226152.054254-1532
192013010113097012301142.55995115432
20201301011309961102916.8981478362
21201301011310078128107.650758-1532
22201301011310435767745.700187-1271
23201301011310549474271.4178764592
24201301011310565960041.143926-1521
25201301011310663157926.871281-1481
26201301011310963359410.78559015552
272013010113109917547816.572228-1802
28201301011311127823574.4939080242
29201301011311179061749.7518189492
..................
11355201312121320938140631.838710-1541
11356201312121321007853305.097899-1561
113572013121213210117993815.9695668402
113582013121213211810952198.7733724411
113592013121213217810969610.362669-1611
11360201312121321799075032.4488593292
113612013121213219326070055.668273-1311
11362201312121322124479526.683095-1432
11363201312121322571661291.183740-1361
11364201312121322849980183.671597-1592
113652013121213231029229820.168242-1492
11366201312121323112709393.926463-1771
11367201312121323174417006.113982-1802
11368201312121323296753944.4944613262
11369201312121323673799315.033354-1851
11370201312121323704681607.563005-1622
11371201312121323778820012.0638391312
11372201312121323854571613.77940217502
11373201312121323926454041.312322-1681
11374201312121323997697603.8165603311
11375201312121324047045546.57438015462
11376201312121324183404156.99594914431
113772013121213242610775395.416617-1542
11378201312121324377548528.958862-1591
11379201312121324484844414.70353010412
11380201312121324584469643.600730-1852
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113822013121213246923557969.1101589431
113832013121213247520450051.67550116481
11384201312121324883397480.2881140401
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\n", " \n", " \n", @@ -525,7 +1119,7 @@ "6 5671341.270490 244 1.383807e+09" ] }, - "execution_count": 39, + "execution_count": 89, "metadata": {}, "output_type": "execute_result" } @@ -536,7 +1130,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 90, "metadata": { "collapsed": false }, @@ -547,19 +1141,19 @@ "211.67427866070051" ] }, - "execution_count": 40, + "execution_count": 90, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Minutes on average spent watching TV (unweighted) - DO NOT USE\n", - "data.minutes.sum() / len(data)" + "data.minutes.sum() / len(data)\n" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 91, "metadata": { "collapsed": false }, @@ -570,7 +1164,7 @@ "190.25402840855642" ] }, - "execution_count": 41, + "execution_count": 91, "metadata": {}, "output_type": "execute_result" } @@ -582,7 +1176,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 92, "metadata": { "collapsed": false }, @@ -598,7 +1192,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 93, "metadata": { "collapsed": false }, @@ -609,7 +1203,7 @@ "8.7508537061809992" ] }, - "execution_count": 43, + "execution_count": 93, "metadata": {}, "output_type": "execute_result" } @@ -622,7 +1216,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 94, "metadata": { "collapsed": false }, @@ -636,7 +1230,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 95, "metadata": { "collapsed": false }, @@ -647,7 +1241,7 @@ "['t010101', 't010102']" ] }, - "execution_count": 45, + "execution_count": 95, "metadata": {}, "output_type": "execute_result" } @@ -658,7 +1252,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 96, "metadata": { "collapsed": false }, @@ -677,7 +1271,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 97, "metadata": { "collapsed": false }, @@ -686,7 +1280,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/clinton/src/github.com/tiyd-python-2015-05/atus-analysis/.direnv/python-3.4.3/lib/python3.4/site-packages/IPython/kernel/__main__.py:6: SettingWithCopyWarning: \n", + "/Users/taddeimania/Developer/class-notes/.direnv/python-3.4.3/lib/python3.4/site-packages/IPython/kernel/__main__.py:6: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", @@ -699,7 +1293,7 @@ "8.750853706181001" ] }, - "execution_count": 47, + "execution_count": 97, "metadata": {}, "output_type": "execute_result" } @@ -710,7 +1304,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 98, "metadata": { "collapsed": false }, @@ -719,7 +1313,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/clinton/src/github.com/tiyd-python-2015-05/atus-analysis/.direnv/python-3.4.3/lib/python3.4/site-packages/IPython/kernel/__main__.py:6: SettingWithCopyWarning: \n", + "/Users/taddeimania/Developer/class-notes/.direnv/python-3.4.3/lib/python3.4/site-packages/IPython/kernel/__main__.py:6: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", @@ -732,7 +1326,7 @@ "9.5541911543273592" ] }, - "execution_count": 48, + "execution_count": 98, "metadata": {}, "output_type": "execute_result" } @@ -743,7 +1337,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 99, "metadata": { "collapsed": false }, @@ -813,19 +1407,19 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 100, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "respondents = pd.read_csv(\"atusdata/atusresp_2013/atusresp_2013.dat\")\n", - "activities = pd.read_csv(\"atusdata/atusact_2013/atusact_2013.dat\")" + "respondents = pd.read_csv(\"atusdata/atusresp_2013.dat\")\n", + "activities = pd.read_csv(\"atusdata/atusact_2013.dat\")" ] }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 101, "metadata": { "collapsed": false }, @@ -848,7 +1442,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 102, "metadata": { "collapsed": false }, @@ -902,7 +1496,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 103, "metadata": { "collapsed": false }, @@ -910,7 +1504,7 @@ { "data": { "text/html": [ - "
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" - ], - "text/plain": [ - " tucaseid TUFINLWGT TRYHHCHILD TEAGE TESEX\n", - "0 20130101130004 11899905.662034 12 22 2\n", - "1 20130101130112 4447638.009513 1 39 1\n", - "2 20130101130123 10377056.507734 -1 47 2\n", - "3 20130101130611 7731257.992805 -1 50 2\n", - "4 20130101130616 4725269.227067 -1 45 2\n", - "5 20130101130619 2372791.046351 -1 80 2\n", - "6 20130101130658 5671341.270490 -1 72 1\n", - "7 20130101130670 8608413.296903 -1 55 2\n", - "8 20130101130734 1378191.194810 -1 57 2\n", - "9 20130101130735 3905483.253032 4 27 2\n", - "10 20130101130740 4538371.462244 0 59 2\n", - "11 20130101130768 6755514.216327 1 31 1\n", - "12 20130101130799 13506297.294756 -1 52 1\n", - "13 20130101130826 5521732.162587 7 42 2\n", - "14 20130101130839 11791654.393174 -1 66 1\n", - "15 20130101130867 1801834.050978 -1 66 2\n", - "16 20130101130871 6884215.057542 -1 45 1\n", - "17 20130101130891 12569148.198194 -1 59 1\n", - "18 20130101130910 14226152.054254 -1 53 2\n", - "19 20130101130970 12301142.559951 15 43 2\n", - "20 20130101130996 1102916.898147 8 36 2\n", - "21 20130101131007 8128107.650758 -1 53 2\n", - "22 20130101131043 5767745.700187 -1 27 1\n", - "23 20130101131054 9474271.417876 4 59 2\n", - "24 20130101131056 5960041.143926 -1 52 1\n", - "25 20130101131066 3157926.871281 -1 48 1\n", - "26 20130101131096 3359410.785590 15 55 2\n", - "27 20130101131099 17547816.572228 -1 80 2\n", - "28 20130101131112 7823574.493908 0 24 2\n", - "29 20130101131117 9061749.751818 9 49 2\n", - "... ... ... ... ... ...\n", - "11355 20131212132093 8140631.838710 -1 54 1\n", - "11356 20131212132100 7853305.097899 -1 56 1\n", - "11357 20131212132101 17993815.969566 8 40 2\n", - "11358 20131212132118 10952198.773372 4 41 1\n", - "11359 20131212132178 10969610.362669 -1 61 1\n", - "11360 20131212132179 9075032.448859 3 29 2\n", - "11361 20131212132193 26070055.668273 -1 31 1\n", - "11362 20131212132212 4479526.683095 -1 43 2\n", - "11363 20131212132257 1661291.183740 -1 36 1\n", - "11364 20131212132284 9980183.671597 -1 59 2\n", - "11365 20131212132310 29229820.168242 -1 49 2\n", - "11366 20131212132311 2709393.926463 -1 77 1\n", - "11367 20131212132317 4417006.113982 -1 80 2\n", - "11368 20131212132329 6753944.494461 3 26 2\n", - "11369 20131212132367 3799315.033354 -1 85 1\n", - "11370 20131212132370 4681607.563005 -1 62 2\n", - "11371 20131212132377 8820012.063839 1 31 2\n", - "11372 20131212132385 4571613.779402 17 50 2\n", - "11373 20131212132392 6454041.312322 -1 68 1\n", - "11374 20131212132399 7697603.816560 3 31 1\n", - "11375 20131212132404 7045546.574380 15 46 2\n", - "11376 20131212132418 3404156.995949 14 43 1\n", - "11377 20131212132426 10775395.416617 -1 54 2\n", - "11378 20131212132437 7548528.958862 -1 59 1\n", - "11379 20131212132448 4844414.703530 10 41 2\n", - "11380 20131212132458 4469643.600730 -1 85 2\n", - "11381 20131212132462 4103676.895062 -1 60 1\n", - "11382 20131212132469 23557969.110158 9 43 1\n", - "11383 20131212132475 20450051.675501 16 48 1\n", - "11384 20131212132488 3397480.288114 0 40 1\n", - "\n", - "[11385 rows x 5 columns]" - ] - }, - "execution_count": 81, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "summary[[\"tucaseid\", \"TUFINLWGT\", \"TRYHHCHILD\", \"TEAGE\", \"TESEX\"]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pertinent columns:\n", - "\n", - "* TUFINLWGT - statistical weight of respondent\n", - "* TRYHHCHILD - age of youngest child in household\n", - "* TEAGE - age of respondent\n", - "* TESEX - sex of respondent\n", - "* TELFS - working status of respondent\n", - "* TRCHILDNUM - number of children in household" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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tucaseidTUFINLWGTTRYHHCHILDTEAGETESEXPEEDUCAPTDTRACEPEHSPNONGTMETSTATELFS...t181501t181599t181601t181801t189999t500101t500103t500105t500106t500107
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" - ], - "text/plain": [ - " 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]" - ] - }, - "execution_count": 82, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "summary.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 83, + "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "adults_crit = summary.TEAGE >= 18\n", - "kids_crit = summary.TEAGE < 18\n", - "no_children_crit = summary.TRCHILDNUM == 0\n", - "yes_children_crit = summary.TRCHILDNUM > 0" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 10953 entries, 0 to 11384\n", - "Columns: 413 entries, tucaseid to t500107\n", - "dtypes: float64(1), int64(412)\n", - "memory usage: 34.6 MB\n" - ] - } - ], - "source": [ - "adults = summary[adults_crit]\n", - "adults.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 6481 entries, 2 to 11381\n", - "Columns: 413 entries, tucaseid to t500107\n", - "dtypes: float64(1), int64(412)\n", - "memory usage: 20.5 MB\n" - ] - } - ], - "source": [ - "people_with_no_children = summary[no_children_crit]\n", - "people_with_no_children.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 6481 entries, 2 to 11381\n", - "Columns: 413 entries, tucaseid to t500107\n", - "dtypes: float64(1), int64(412)\n", - "memory usage: 20.5 MB\n" - ] - } - ], - "source": [ - "adults_with_no_children = summary[adults_crit & no_children_crit]\n", - "adults_with_no_children.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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weightminutes
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" - ], - "text/plain": [ - " weight minutes\n", - "2 10377056.507734 60\n", - "3 7731257.992805 65\n", - "4 4725269.227067 90\n", - "5 2372791.046351 270\n", - "6 5671341.270490 244" - ] - }, - "execution_count": 87, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data = adults_with_no_children[['TUFINLWGT', 't120303']]\n", - "\n", - "data = data.rename(columns={\"TUFINLWGT\": \"weight\", \"t120303\": \"minutes\"})\n", - "data.head()" + "summary = pd.read_csv(\"atusdata/atussum_2013.dat\")\n", + "# = pd.read_csv(\"atusdata/atusact_2013.dat\")\n", + "# = pd.read_csv(\"atusdata/atuscps_2013.dat\")\n", + "#respondant = pd.read_csv(\"atusdata/atusresp_2013.dat\")\n", + "# = pd.read_csv(\"atusdata/atusrost_2013.dat\")\n", + "# = pd.read_csv(\"atusdata/atusrostec_2013.dat\")\n", + "# = pd.read_csv(\"atusdata/atussum_2013.dat\")\n", + "# = pd.read_csv(\"atusdata/atuswho_2013.dat\")" ] }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "data['weighted_minutes'] = data.weight * data.minutes" + "basic_selection = [\"tucaseid\", \"TUFINLWGT\", \"TRCHILDNUM\", \"TEAGE\", \"TESEX\"]" ] }, { "cell_type": "code", - "execution_count": 89, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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weightminutesweighted_minutes
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" - ], - "text/plain": [ - " weight minutes weighted_minutes\n", - "2 10377056.507734 60 6.226234e+08\n", - "3 7731257.992805 65 5.025318e+08\n", - "4 4725269.227067 90 4.252742e+08\n", - "5 2372791.046351 270 6.406536e+08\n", - "6 5671341.270490 244 1.383807e+09" - ] - }, - "execution_count": 89, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "211.67427866070051" - ] - }, - "execution_count": 90, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Minutes on average spent watching TV (unweighted) - DO NOT USE\n", - "data.minutes.sum() / len(data)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "190.25402840855642" - ] - }, - "execution_count": 91, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Minutes on average spent watching TV (weighted)\n", - "data.weighted_minutes.sum() / data.weight.sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 92, + "execution_count": 64, "metadata": { "collapsed": false }, @@ -1184,39 +74,33 @@ "source": [ "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 = data[['weight', \"minutes\"]]\n", " data['weighted_minutes'] = data.weight * data.minutes\n", " return data.weighted_minutes.sum() / data.weight.sum()" ] }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 33, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "8.7508537061809992" - ] - }, - "execution_count": 93, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "sleeping = average_minutes(adults_with_no_children, \"010101\")\n", - "sleepless = average_minutes(adults_with_no_children, \"010102\")\n", - "(sleeping + sleepless) / 60 # hours" + "def stdev_minutes(data, activity_code):\n", + " data_mean = average_minutes(data, activity_code)\n", + " num_non0_obs = summary[summary.TUFINLWGT != 0].TUFINLWGT.count()\n", + " activity_col = \"t{}\".format(activity_code)\n", + " data = data.rename(columns={\"TUFINLWGT\": \"weight\", activity_col: \"minutes\"})\n", + " data = data[[\"weight\", \"minutes\"]]\n", + " data['weighted_ss'] = data.weight * (data.minutes - data_mean)**2\n", + " return math.sqrt(data.weighted_ss.sum()/(((num_non0_obs-1)/num_non0_obs)*data.weight.sum()))" ] }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 34, "metadata": { "collapsed": false }, @@ -1230,191 +114,34 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 112, "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['t010101', 't010102']" - ] - }, - "execution_count": 95, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "activity_columns(summary, \"0101\")" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "metadata": { - "collapsed": false + "collapsed": true }, "outputs": [], "source": [ - "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()" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/taddeimania/Developer/class-notes/.direnv/python-3.4.3/lib/python3.4/site-packages/IPython/kernel/__main__.py:6: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" - ] - }, - { - "data": { - "text/plain": [ - "8.750853706181001" - ] - }, - "execution_count": 97, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "average_minutes2(adults_with_no_children, \"0101\") / 60" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/taddeimania/Developer/class-notes/.direnv/python-3.4.3/lib/python3.4/site-packages/IPython/kernel/__main__.py:6: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" - ] - }, - { - "data": { - "text/plain": [ - "9.5541911543273592" - ] - }, - "execution_count": 98, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "average_minutes2(adults_with_no_children, \"01\") / 60" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on method groupby in module pandas.core.generic:\n", - "\n", - "groupby(by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=False) method of pandas.core.frame.DataFrame instance\n", - " Group series using mapper (dict or key function, apply given function\n", - " to group, return result as series) or by a series of columns\n", - " \n", - " Parameters\n", - " ----------\n", - " by : mapping function / list of functions, dict, Series, or tuple /\n", - " list of column names.\n", - " Called on each element of the object index to determine the groups.\n", - " If a dict or Series is passed, the Series or dict VALUES will be\n", - " used to determine the groups\n", - " axis : int, default 0\n", - " level : int, level name, or sequence of such, default None\n", - " If the axis is a MultiIndex (hierarchical), group by a particular\n", - " level or levels\n", - " as_index : boolean, default True\n", - " For aggregated output, return object with group labels as the\n", - " index. Only relevant for DataFrame input. as_index=False is\n", - " effectively \"SQL-style\" grouped output\n", - " sort : boolean, default True\n", - " Sort group keys. Get better performance by turning this off\n", - " group_keys : boolean, default True\n", - " When calling apply, add group keys to index to identify pieces\n", - " squeeze : boolean, default False\n", - " reduce the dimensionaility of the return type if possible,\n", - " otherwise return a consistent type\n", - " \n", - " Examples\n", - " --------\n", - " DataFrame results\n", - " \n", - " >>> data.groupby(func, axis=0).mean()\n", - " >>> data.groupby(['col1', 'col2'])['col3'].mean()\n", - " \n", - " DataFrame with hierarchical index\n", - " \n", - " >>> data.groupby(['col1', 'col2']).mean()\n", - " \n", - " Returns\n", - " -------\n", - " GroupBy object\n", - "\n" - ] - } - ], - "source": [ - "# grouping\n", - "help(adults_with_no_children.groupby)" + "def hypothesis_test_plot(data, group_var, test_var):\n", + " data = data[[group_var, test_var, \"TUFINLWGT\"]]\n", + " data_grouped = data.groupby(group_var)\n", + " frame = pd.DataFrame()\n", + " for group in data_grouped:\n", + " count = group[1].TUFINLWGT.count()\n", + " mean = average_minutes(group[1], test_var[1:])\n", + " stdev = stdev_minutes(group[1], test_var[1:])\n", + " frame = frame.append({group_var: group[0], \"mean\": mean, \"error\": (stdev*1.96/math.sqrt(count))}, ignore_index=True)\n", + " frame.index = frame.pop(group_var)\n", + " plot = frame.plot(kind=\"bar\", yerr=\"error\", figsize=(12, 8))\n", + " return (frame, plot)\n", + " " ] }, { "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Joining files" - ] - }, - { - "cell_type": "code", - "execution_count": 100, "metadata": { - "collapsed": false + "collapsed": true }, - "outputs": [], "source": [ - "respondents = pd.read_csv(\"atusdata/atusresp_2013.dat\")\n", - "activities = pd.read_csv(\"atusdata/atusact_2013.dat\")" + "###Household & personal organization and planning (020302) vs Television and movies (not religious) (120303)" ] }, { @@ -1423,21 +150,9 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 11385 entries, 0 to 11384\n", - "Columns: 175 entries, TUCASEID to TXTONHH\n", - "dtypes: float64(1), int64(172), object(2)\n", - "memory usage: 15.3+ MB\n" - ] - } - ], + "outputs": [], "source": [ - "respondents.info()" + "current = summary[basic_selection + [\"t020302\", \"t120303\"]]" ] }, { @@ -1446,875 +161,70 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Int64Index: 215576 entries, 0 to 215575\n", - "Data columns (total 31 columns):\n", - "TUCASEID 215576 non-null int64\n", - "TUACTIVITY_N 215576 non-null int64\n", - "TEWHERE 215576 non-null int64\n", - "TRTCCTOT_LN 215576 non-null int64\n", - "TRTCC_LN 215576 non-null int64\n", - "TRTCOC_LN 215576 non-null int64\n", - "TRTEC_LN 215576 non-null int64\n", - "TRTHH_LN 215576 non-null int64\n", - "TRTNOHH_LN 215576 non-null int64\n", - "TRTOHH_LN 215576 non-null int64\n", - "TRTONHH_LN 215576 non-null int64\n", - "TRTO_LN 215576 non-null int64\n", - "TRWBELIG 215576 non-null int64\n", - "TUACTDUR 215576 non-null int64\n", - "TUACTDUR24 215576 non-null int64\n", - "TUCC5 215576 non-null int64\n", - "TUCC5B 215576 non-null int64\n", - "TUCC7 215576 non-null int64\n", - "TUCC8 215576 non-null int64\n", - "TUCUMDUR 215576 non-null int64\n", - "TUCUMDUR24 215576 non-null int64\n", - "TUDURSTOP 215576 non-null int64\n", - "TUEC24 215576 non-null int64\n", - "TUSTARTTIM 215576 non-null object\n", - "TUSTOPTIME 215576 non-null object\n", - "TUTIER1CODE 215576 non-null int64\n", - "TUTIER2CODE 215576 non-null int64\n", - "TUTIER3CODE 215576 non-null int64\n", - "TRCODE 215576 non-null int64\n", - "TRTIER2 215576 non-null int64\n", - "TXWHERE 215576 non-null int64\n", - "dtypes: int64(29), object(2)\n", - "memory usage: 52.6+ MB\n" - ] - } - ], + "outputs": [], "source": [ - "activities.info()" + "current_grouped = current.groupby(\"TESEX\")" ] }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 107, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " TUCASEID TUACTIVITY_N TXTCOC\n", - "0 20130101130004 1 0\n", - "1 20130101130004 2 0\n", - "2 20130101130004 3 0\n", - "3 20130101130004 4 0\n", - "4 20130101130004 5 0" - ] - }, - "execution_count": 106, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "merged[[\"TUCASEID\", \"TUACTIVITY_N\", \"TXTCOC\"]].head()" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "telfs = pd.Series({1: \"Employed - at work\",\n", - " 2: \"Employed - absent\",\n", - " 3: \"Unemployed - laid off\",\n", - " 4: \"Unemployed - looking\",\n", - " 5: \"Not in labor force\"})" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { + "image/png": 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"text/plain": [ - "1 Employed - at work\n", - "2 Employed - absent\n", - "3 Unemployed - laid off\n", - "4 Unemployed - looking\n", - "5 Not in labor force\n", - "dtype: object" + "" ] }, - "execution_count": 70, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "telfs" + "newframe.plot(kind=\"bar\", yerr=\"stdev\", figsize=(12, 8))" ] }, { "cell_type": "code", - "execution_count": 108, + "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "summary.TELFS = summary.TELFS.map(telfs)" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0 Not in labor force\n", - "1 Employed - at work\n", - "2 Unemployed - looking\n", - "3 Employed - at work\n", - "4 Employed - at work\n", - "5 Not in labor force\n", - "6 Not in labor force\n", - "7 Employed - at work\n", - "8 Employed - at work\n", - "9 Employed - at work\n", - "10 Employed - at work\n", - "11 Employed - at work\n", - "12 Employed - at work\n", - "13 Employed - at work\n", - "14 Not in labor force\n", - "15 Not in labor force\n", - "16 Employed - at work\n", - "17 Employed - absent\n", - "18 Employed - at work\n", - "19 Unemployed - looking\n", - "20 Unemployed - looking\n", - "21 Employed - at work\n", - "22 Employed - at work\n", - "23 Not in labor force\n", - "24 Employed - at work\n", - "25 Employed - at work\n", - "26 Not in labor force\n", - "27 Not in labor force\n", - "28 Unemployed - looking\n", - "29 Employed - at work\n", - " ... \n", - "11355 Employed - absent\n", - "11356 Employed - at work\n", - "11357 Not in labor force\n", - "11358 Employed - at work\n", - "11359 Employed - at work\n", - "11360 Employed - at work\n", - "11361 Employed - at work\n", - "11362 Employed - at work\n", - "11363 Not in labor force\n", - "11364 Not in labor force\n", - "11365 Employed - at work\n", - "11366 Not in labor force\n", - "11367 Employed - at work\n", - "11368 Employed - at work\n", - "11369 Not in labor force\n", - "11370 Employed - at work\n", - "11371 Employed - at work\n", - "11372 Employed - at work\n", - "11373 Not in labor force\n", - "11374 Employed - at work\n", - "11375 Employed - at work\n", - "11376 Employed - at work\n", - "11377 Employed - at work\n", - "11378 Not in labor force\n", - "11379 Employed - at work\n", - "11380 Not in labor force\n", - "11381 Employed - at work\n", - "11382 Employed - at work\n", - "11383 Employed - at work\n", - "11384 Not in labor force\n", - "Name: TELFS, dtype: object" - ] - }, - "execution_count": 109, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "summary.TELFS" + "hypothesis_test_plot(summary, \"TEAGE\", \"t020501\")[1]" ] }, { From 741607f7abdaeb66c9f4f72d83bed0b998f7e224 Mon Sep 17 00:00:00 2001 From: PJ Passalacqua Date: Tue, 21 Jul 2015 14:14:50 -0400 Subject: [PATCH 3/3] initial progress --- .gitignore | 1 + Init_Notebook.ipynb | 373 +++++++++++++++++++++++++++++++++++++++++++ Starting Point.ipynb | 261 ------------------------------ 3 files changed, 374 insertions(+), 261 deletions(-) create mode 100644 Init_Notebook.ipynb delete mode 100644 Starting Point.ipynb diff --git a/.gitignore b/.gitignore index ad47670..5a34a36 100644 --- a/.gitignore +++ b/.gitignore @@ -112,3 +112,4 @@ crashlytics-build.properties atusdata/ .direnv/ +categories_to_consider.txt diff --git a/Init_Notebook.ipynb b/Init_Notebook.ipynb new file mode 100644 index 0000000..e456bb8 --- /dev/null +++ b/Init_Notebook.ipynb @@ -0,0 +1,373 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import re\n", + "import math\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "summary = pd.read_csv(\"atusdata/atussum_2013.dat\")\n", + "# = pd.read_csv(\"atusdata/atusact_2013.dat\")\n", + "# = pd.read_csv(\"atusdata/atuscps_2013.dat\")\n", + "#respondant = pd.read_csv(\"atusdata/atusresp_2013.dat\")\n", + "# = pd.read_csv(\"atusdata/atusrost_2013.dat\")\n", + "# = pd.read_csv(\"atusdata/atusrostec_2013.dat\")\n", + "# = pd.read_csv(\"atusdata/atussum_2013.dat\")\n", + "# = pd.read_csv(\"atusdata/atuswho_2013.dat\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "basic_selection = [\"tucaseid\", \"TUFINLWGT\", \"TRCHILDNUM\", \"TEAGE\", \"TESEX\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def average_minutes(data, activity_code):\n", + " activity_col = \"t{}\".format(activity_code)\n", + " data = data.rename(columns={\"TUFINLWGT\": \"weight\", activity_col: \"minutes\"})\n", + " data = data[['weight', \"minutes\"]]\n", + " data['weighted_minutes'] = data.weight * data.minutes\n", + " return data.weighted_minutes.sum() / data.weight.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def stdev_minutes(data, activity_code):\n", + " data_mean = average_minutes(data, activity_code)\n", + " num_non0_obs = summary[summary.TUFINLWGT != 0].TUFINLWGT.count()\n", + " activity_col = \"t{}\".format(activity_code)\n", + " data = data.rename(columns={\"TUFINLWGT\": \"weight\", activity_col: \"minutes\"})\n", + " data = data[[\"weight\", \"minutes\"]]\n", + " data['weighted_ss'] = data.weight * (data.minutes - data_mean)**2\n", + " return math.sqrt(data.weighted_ss.sum()/(((num_non0_obs-1)/num_non0_obs)*data.weight.sum()))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "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)]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def hypothesis_test_plot(data, group_var, test_var):\n", + " data = data[[group_var, test_var, \"TUFINLWGT\"]]\n", + " data_grouped = data.groupby(group_var)\n", + " frame = pd.DataFrame()\n", + " for group in data_grouped:\n", + " count = group[1].TUFINLWGT.count()\n", + " mean = average_minutes(group[1], test_var[1:])\n", + " stdev = stdev_minutes(group[1], test_var[1:])\n", + " frame = frame.append({group_var: group[0], \"mean\": mean, \"error\": (stdev*1.96/math.sqrt(count))}, ignore_index=True)\n", + " frame.index = frame.pop(group_var)\n", + " plot = frame.plot(kind=\"bar\", yerr=\"error\", figsize=(12, 8))\n", + " return (frame, plot)\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "###Household & personal organization and planning (020302) vs Television and movies (not religious) (120303)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "current = summary[basic_selection + [\"t020302\", \"t120303\"]]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.core.groupby.DataFrameGroupBy" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "current_grouped = current.groupby(\"TESEX\")\n", + "type(current_grouped)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " mean stdev\n", + "TESEX \n", + "1 178.932708 5.079676\n", + "2 153.844224 3.905072" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "newframe = pd.DataFrame()\n", + "\n", + "for group in current_grouped:\n", + " count = group[1].TUFINLWGT.count()\n", + " mean = average_minutes(group[1], \"120303\")\n", + " stdev = stdev_minutes(group[1], \"120303\")\n", + " newframe = newframe.append({\"TESEX\": group[0], \"mean\": mean, \"stdev\": (stdev*1.96/math.sqrt(count))}, ignore_index=True)\n", + "newframe.index = newframe.pop(\"TESEX\")\n", + "newframe" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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3fwOwIxgDbGh3G+RnVc+umplbZuYbZ+YXZ+aVM/Ose5adma+cmV/eHWX+O7vn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\nAQCgEowBAKASjAEAoKr/HwoW/tGIEOCLAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "temp_data = summary\n", + "temp_data[\"Decade_Age\"] = temp_data.TEAGE //10 * 10\n", + "hypothesis_test_plot(summary, \"Decade_Age\", \"t120302\")[1]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.4.3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Starting Point.ipynb b/Starting Point.ipynb deleted file mode 100644 index 15ef476..0000000 --- a/Starting Point.ipynb +++ /dev/null @@ -1,261 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import re\n", - "import math\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "summary = pd.read_csv(\"atusdata/atussum_2013.dat\")\n", - "# = pd.read_csv(\"atusdata/atusact_2013.dat\")\n", - "# = pd.read_csv(\"atusdata/atuscps_2013.dat\")\n", - "#respondant = pd.read_csv(\"atusdata/atusresp_2013.dat\")\n", - "# = pd.read_csv(\"atusdata/atusrost_2013.dat\")\n", - "# = pd.read_csv(\"atusdata/atusrostec_2013.dat\")\n", - "# = pd.read_csv(\"atusdata/atussum_2013.dat\")\n", - "# = pd.read_csv(\"atusdata/atuswho_2013.dat\")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "basic_selection = [\"tucaseid\", \"TUFINLWGT\", \"TRCHILDNUM\", \"TEAGE\", \"TESEX\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def average_minutes(data, activity_code):\n", - " activity_col = \"t{}\".format(activity_code)\n", - " data = data.rename(columns={\"TUFINLWGT\": \"weight\", activity_col: \"minutes\"})\n", - " data = data[['weight', \"minutes\"]]\n", - " data['weighted_minutes'] = data.weight * data.minutes\n", - " return data.weighted_minutes.sum() / data.weight.sum()" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def stdev_minutes(data, activity_code):\n", - " data_mean = average_minutes(data, activity_code)\n", - " num_non0_obs = summary[summary.TUFINLWGT != 0].TUFINLWGT.count()\n", - " activity_col = \"t{}\".format(activity_code)\n", - " data = data.rename(columns={\"TUFINLWGT\": \"weight\", activity_col: \"minutes\"})\n", - " data = data[[\"weight\", \"minutes\"]]\n", - " data['weighted_ss'] = data.weight * (data.minutes - data_mean)**2\n", - " return math.sqrt(data.weighted_ss.sum()/(((num_non0_obs-1)/num_non0_obs)*data.weight.sum()))" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "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)]" - ] - }, - { - "cell_type": "code", - "execution_count": 112, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def hypothesis_test_plot(data, group_var, test_var):\n", - " data = data[[group_var, test_var, \"TUFINLWGT\"]]\n", - " data_grouped = data.groupby(group_var)\n", - " frame = pd.DataFrame()\n", - " for group in data_grouped:\n", - " count = group[1].TUFINLWGT.count()\n", - " mean = average_minutes(group[1], test_var[1:])\n", - " stdev = stdev_minutes(group[1], test_var[1:])\n", - " frame = frame.append({group_var: group[0], \"mean\": mean, \"error\": (stdev*1.96/math.sqrt(count))}, ignore_index=True)\n", - " frame.index = frame.pop(group_var)\n", - " plot = frame.plot(kind=\"bar\", yerr=\"error\", figsize=(12, 8))\n", - " return (frame, plot)\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "###Household & personal organization and planning (020302) vs Television and movies (not religious) (120303)" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "current = summary[basic_selection + [\"t020302\", \"t120303\"]]" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "current_grouped = current.groupby(\"TESEX\")" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "newframe = pd.DataFrame()\n", - "\n", - "for group in current_grouped:\n", - " count = group[1].TUFINLWGT.count()\n", - " mean = average_minutes(group[1], \"120303\")\n", - " stdev = stdev_minutes(group[1], \"120303\")\n", - " newframe = newframe.append({\"TESEX\": group[0], \"mean\": mean, \"stdev\": (stdev*1.96/math.sqrt(count))}, ignore_index=True)\n", - "newframe.index = newframe.pop(\"TESEX\")" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 108, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "newframe.plot(kind=\"bar\", yerr=\"stdev\", figsize=(12, 8))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "hypothesis_test_plot(summary, \"TEAGE\", \"t020501\")[1]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.4.3" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -}