From da8b2ab138dacabd196c341707fe53950273de62 Mon Sep 17 00:00:00 2001 From: Lee Eakes Date: Thu, 5 Feb 2015 16:33:25 -0500 Subject: [PATCH 1/2] first commit --- ATUS_Data.ipynb | 1804 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1804 insertions(+) create mode 100644 ATUS_Data.ipynb diff --git a/ATUS_Data.ipynb b/ATUS_Data.ipynb new file mode 100644 index 0000000..2eeecb0 --- /dev/null +++ b/ATUS_Data.ipynb @@ -0,0 +1,1804 @@ +{ + "metadata": { + "name": "", + "signature": "sha256:9a6d28d8b4af1d197051dd717c215446418c79448171268ec9e6bdf4db4c2d5a" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import pandas as pd\n", + "import re" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 36 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import matplotlib.pyplot as plt" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 37 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "%matplotlib inline" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 38 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "summary = pd.read_csv(\"atusdata/atussum_2013/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": 39 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "summary.columns" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 40, + "text": [ + "Index(['tucaseid', 'TUFINLWGT', 'TRYHHCHILD', 'TEAGE', 'TESEX', 'PEEDUCA', 'PTDTRACE', 'PEHSPNON', 'GTMETSTA', 'TELFS', 'TEMJOT', 'TRDPFTPT', 'TESCHENR', 'TESCHLVL', 'TRSPPRES', 'TESPEMPNOT', 'TRERNWA', 'TRCHILDNUM', 'TRSPFTPT', 'TEHRUSLT', 'TUDIARYDAY', 'TRHOLIDAY', 'TRTEC', 'TRTHH', 't010101', 't010102', 't010201', 't010299', 't010301', 't010399', 't010401', 't020101', 't020102', 't020103', 't020104', 't020199', 't020201', 't020202', 't020203', 't020299', 't020301', 't020302', 't020303', 't020399', 't020401', 't020402', 't020499', 't020501', 't020502', 't020599', 't020601', 't020602', 't020699', 't020701', 't020799', 't020801', 't020899', 't020901', 't020902', 't020903', 't020904', 't020905', 't020999', 't029999', 't030101', 't030102', 't030103', 't030104', 't030105', 't030106', 't030108', 't030109', 't030110', 't030111', 't030112', 't030199', 't030201', 't030202', 't030203', 't030299', 't030301', 't030302', 't030303', 't030399', 't030401', 't030402', 't030403', 't030404', 't030405', 't030499', 't030501', 't030502', 't030503', 't030504', 't030599', 't039999', 't040101', 't040102', 't040103', 't040104', ...], dtype='object')" + ] + } + ], + "prompt_number": 40 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pertinent columns:\n", + "\n", + "* TUFINLWGT - statistical weight of respondent\n", + "* TRYHHCHILD - age of youngest child in household Range 0-17\n", + "* TEAGE - age of respondent\n", + "* TESEX - sex of respondent\n", + "* TELFS - working status of respondent 1=Employed@work 2=Employed absent 3=unemp layoff 4=unemp looking 5= not in labor force\n", + "* TRCHILDNUM - number of children in household - Range 0-30\n", + "* TESCHENR - Enrolled in high school or college/univ 1=yes 2=no\n", + "* TESCHLVL - School Level - 1=HS 2=College\n", + "* TUDIARYDAY - day of the week in about which respondent was interviewed... 1=Sunday 7=Saturday" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "summary.head()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
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tucaseidTUFINLWGTTRYHHCHILDTEAGETESEXPEEDUCAPTDTRACEPEHSPNONGTMETSTATELFS...t181501t181599t181601t181801t189999t500101t500103t500105t500106t500107
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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": 41, + "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": 41 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "adults_all = (summary.TEAGE >= 18)\n", + "adults_20 = summary[summary[\"TEAGE\"].isin(range(20,30))]\n", + "adults_30 = adults_20 = summary[summary[\"TEAGE\"].isin(range(20,30))]\n", + "adults_40 = adults_20 = summary[summary[\"TEAGE\"].isin(range(30,40))]\n", + "adults_50 = adults_20 = summary[summary[\"TEAGE\"].isin(range(40,50))]\n", + "adults_65 = (summary.TEAGE >= 65)\n", + "no_children_crit = (summary.TRCHILDNUM == 0)\n", + "male = summary[summary.TESEX == 1]\n", + "female = summary[summary.TESEX == 2]\n" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 104 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def age_range(start, stop):\n", + " return summary[summary['TEAGE'].isin(range(start, stop))]\n", + "#thanks John!" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 43 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def get_max_record(data):\n", + " return data.ix[data.idxmax()]\n", + "\n", + "get_max_record(summary.TEAGE)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 91, + "text": [ + "85" + ] + } + ], + "prompt_number": 91 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#Oldest person surveyed and the Index number they are located at\n", + "print(summary.TEAGE.max())\n", + "print(summary.TEAGE.idxmax())\n", + "print(summary.TEAGE.mean())#Mean age of all surveyed\n", + "\n", + "#Youngest person surveyed and the Index number they are located at\n", + "print(summary.TEAGE.min())\n", + "print(summary.TEAGE.idxmin())" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "85\n", + "106\n", + "48.2798418972\n", + "15\n", + "88\n" + ] + } + ], + "prompt_number": 92 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "#Person who had the most children in their household....\n", + "print(summary.TRCHILDNUM.max())\n", + "print(summary.TRCHILDNUM.idxmax())" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "10\n", + "4612\n" + ] + } + ], + "prompt_number": 88 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "adults_with_no_children = summary[adults_all & no_children_crit]\n", + "adults_with_no_children.info()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "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" + ] + } + ], + "prompt_number": 95 + }, + { + "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()\n", + "#" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
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weightminutes
2 10377056.507734 60
3 7731257.992805 65
4 4725269.227067 90
5 2372791.046351 270
6 5671341.270490 244
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" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 47, + "text": [ + " 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" + ] + } + ], + "prompt_number": 47 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "data['weighted_minutes'] = data.weight * data.minutes\n", + "#this created a new column \"weighted minutes and filled it with the results of the operation" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 48 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "data.head()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
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weightminutesweighted_minutes
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3 7731257.992805 65 5.025318e+08
4 4725269.227067 90 4.252742e+08
5 2372791.046351 270 6.406536e+08
6 5671341.270490 244 1.383807e+09
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" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 49, + "text": [ + " 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" + ] + } + ], + "prompt_number": 49 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "data.weighted_minutes.sum() / data.weight.sum() / 60" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 50, + "text": [ + "3.1709004734759403" + ] + } + ], + "prompt_number": 50 + }, + { + "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": 51 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "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": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 99, + "text": [ + "8.7508537061809992" + ] + } + ], + "prompt_number": 99 + }, + { + "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": 53 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "activity_columns(summary, \"0101\")" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 54, + "text": [ + "['t010101', 't010102']" + ] + } + ], + "prompt_number": 54 + }, + { + "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": 55 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "average_minutes2(adults_with_no_children, \"0101\") / 60" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 73, + "text": [ + "8.750853706181001" + ] + } + ], + "prompt_number": 73 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "average_minutes2(adults_with_no_children, \"01\") / 60" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 57, + "text": [ + "9.5541911543273592" + ] + } + ], + "prompt_number": 57 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "groom_list = []\n", + "grooming_males = groom_list.append(average_minutes2(male, \"010201\"))\n", + "grooming_females = groom_list.append(average_minutes2(female, \"010201\"))\n", + "#groom_list\n", + "\n", + "labels = [\"Male\", \"Female\"]\n", + "groom_list = pd.Series(groom_list, index=labels)\n", + "groom_list.plot(kind=\"bar\", label = \"Average Grooming Time\", figsize=(10, 5))" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 110, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 110 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# grouping\n", + "help(adults_with_no_children.groupby)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "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 result\n", + " >>> data.groupby(func, axis=0).mean()\n", + " \n", + " # DataFrame result\n", + " >>> data.groupby(['col1', 'col2'])['col3'].mean()\n", + " \n", + " # DataFrame with hierarchical index\n", + " >>> data.groupby(['col1', 'col2']).mean()\n", + " \n", + " Returns\n", + " -------\n", + " GroupBy object\n", + "\n" + ] + } + ], + "prompt_number": 58 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "group = summary.groupby([\"TEAGE\", \"TESEX\", \"TRCHILDNUM\"])" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 59 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "group.mean().xs(15).xs(1)" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
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TUDURSTOP TUEC24 TUSTARTTIM \\\n", + "0 -1 -1 -1 ... 2 -1 04:00:00 \n", + "1 -1 -1 -1 ... 1 -1 12:00:00 \n", + "2 -1 -1 -1 ... 1 -1 12:05:00 \n", + "3 -1 -1 -1 ... 2 -1 14:05:00 \n", + "4 -1 -1 -1 ... 1 -1 19:00:00 \n", + "\n", + " TUSTOPTIME TUTIER1CODE TUTIER2CODE TUTIER3CODE TRCODE TRTIER2 TXWHERE \n", + "0 12:00:00 1 1 1 10101 101 0 \n", + "1 12:05:00 11 1 1 110101 1101 0 \n", + "2 14:05:00 12 3 3 120303 1203 0 \n", + "3 19:00:00 6 3 1 60301 603 0 \n", + "4 19:30:00 11 1 1 110101 1101 0 \n", + "\n", + "[5 rows x 205 columns]" + ] + } + ], + "prompt_number": 67 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 67 + } + ], + "metadata": {} + } + ] +} \ No newline at end of file From 83b370fe4d204b8e5374c9b29b32a76aea0408e2 Mon Sep 17 00:00:00 2001 From: Lee Eakes Date: Mon, 9 Feb 2015 07:40:53 -0500 Subject: [PATCH 2/2] Homework commit --- ATUS_Data.ipynb | 2151 +++++++++++++++++++++-------------------------- 1 file changed, 977 insertions(+), 1174 deletions(-) diff --git a/ATUS_Data.ipynb b/ATUS_Data.ipynb index 2eeecb0..1cff936 100644 --- a/ATUS_Data.ipynb +++ b/ATUS_Data.ipynb @@ -1,7 +1,7 @@ { "metadata": { "name": "", - "signature": "sha256:9a6d28d8b4af1d197051dd717c215446418c79448171268ec9e6bdf4db4c2d5a" + "signature": "sha256:85e84ca7cfa86899eedd8e26e6673447137d1035b4dcceabb335d869e305e027" }, "nbformat": 3, "nbformat_minor": 0, @@ -13,12 +13,13 @@ "collapsed": false, "input": [ "import pandas as pd\n", - "import re" + "import re\n", + "import seaborn as sb" ], "language": "python", "metadata": {}, "outputs": [], - "prompt_number": 36 + "prompt_number": 10 }, { "cell_type": "code", @@ -29,7 +30,7 @@ "language": "python", "metadata": {}, "outputs": [], - "prompt_number": 37 + "prompt_number": 11 }, { "cell_type": "code", @@ -40,7 +41,7 @@ "language": "python", "metadata": {}, "outputs": [], - "prompt_number": 38 + "prompt_number": 12 }, { "cell_type": "code", @@ -64,27 +65,7 @@ ] } ], - "prompt_number": 39 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "summary.columns" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 40, - "text": [ - "Index(['tucaseid', 'TUFINLWGT', 'TRYHHCHILD', 'TEAGE', 'TESEX', 'PEEDUCA', 'PTDTRACE', 'PEHSPNON', 'GTMETSTA', 'TELFS', 'TEMJOT', 'TRDPFTPT', 'TESCHENR', 'TESCHLVL', 'TRSPPRES', 'TESPEMPNOT', 'TRERNWA', 'TRCHILDNUM', 'TRSPFTPT', 'TEHRUSLT', 'TUDIARYDAY', 'TRHOLIDAY', 'TRTEC', 'TRTHH', 't010101', 't010102', 't010201', 't010299', 't010301', 't010399', 't010401', 't020101', 't020102', 't020103', 't020104', 't020199', 't020201', 't020202', 't020203', 't020299', 't020301', 't020302', 't020303', 't020399', 't020401', 't020402', 't020499', 't020501', 't020502', 't020599', 't020601', 't020602', 't020699', 't020701', 't020799', 't020801', 't020899', 't020901', 't020902', 't020903', 't020904', 't020905', 't020999', 't029999', 't030101', 't030102', 't030103', 't030104', 't030105', 't030106', 't030108', 't030109', 't030110', 't030111', 't030112', 't030199', 't030201', 't030202', 't030203', 't030299', 't030301', 't030302', 't030303', 't030399', 't030401', 't030402', 't030403', 't030404', 't030405', 't030499', 't030501', 't030502', 't030503', 't030504', 't030599', 't039999', 't040101', 't040102', 't040103', 't040104', ...], dtype='object')" - ] - } - ], - "prompt_number": 40 + "prompt_number": 13 }, { "cell_type": "markdown", @@ -270,7 +251,7 @@ ], "metadata": {}, "output_type": "pyout", - "prompt_number": 41, + "prompt_number": 14, "text": [ " tucaseid TUFINLWGT TRYHHCHILD TEAGE TESEX PEEDUCA \\\n", "0 20130101130004 11899905.662034 12 22 2 40 \n", @@ -297,39 +278,64 @@ ] } ], - "prompt_number": 41 + "prompt_number": 14 + }, + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Filters" + ] }, { "cell_type": "code", "collapsed": false, "input": [ + "male = (summary.TESEX == 1)\n", + "female = (summary.TESEX == 2)\n", + "\n", "adults_all = (summary.TEAGE >= 18)\n", - "adults_20 = summary[summary[\"TEAGE\"].isin(range(20,30))]\n", - "adults_30 = adults_20 = summary[summary[\"TEAGE\"].isin(range(20,30))]\n", - "adults_40 = adults_20 = summary[summary[\"TEAGE\"].isin(range(30,40))]\n", - "adults_50 = adults_20 = summary[summary[\"TEAGE\"].isin(range(40,50))]\n", - "adults_65 = (summary.TEAGE >= 65)\n", - "no_children_crit = (summary.TRCHILDNUM == 0)\n", - "male = summary[summary.TESEX == 1]\n", - "female = summary[summary.TESEX == 2]\n" + "adults_20 = (summary.TEAGE.isin(range(20,30)))\n", + "adults_30 = (summary.TEAGE.isin(range(30,40)))\n", + "adults_40 = (summary.TEAGE.isin(range(40,50)))\n", + "adults_50 = (summary.TEAGE.isin(range(50,60)))\n", + "adults_60 = (summary.TEAGE >= 60)\n", + "adults_20_60 = (summary.TEAGE.isin(range(20,61)))\n", + "\n", + "adults_no_kids = (summary.TRCHILDNUM == 0)\n", + "adults_any_kids = (summary.TRCHILDNUM >= 1)\n", + "adults_one_kid = (summary.TRCHILDNUM == 1)\n", + "adults_two_kids = (summary.TRCHILDNUM == 2)\n", + "adults_onetwo_kids = (summary.TRCHILDNUM.isin(range(1,3)))\n", + "adults_threeplus_kids = (summary.TRCHILDNUM >2)\n", + "adults_any_kids = (summary.TRCHILDNUM >= 1)\n", + "\n", + "young_kid = (summary.TRYHHCHILD >= 10)\n", + "\n", + "weekdays = (summary.TUDIARYDAY.isin(range(2,7)))\n", + "saturday = (summary.TUDIARYDAY == 7)\n", + "sunday = (summary.TUDIARYDAY == 1)\n", + "\n", + "unemployed = (summary.TELFS.isin(range(3,5)))\n", + "employed = (summary.TELFS.isin(range(1,3)))" ], "language": "python", "metadata": {}, "outputs": [], - "prompt_number": 104 + "prompt_number": 64 }, { "cell_type": "code", "collapsed": false, "input": [ "def age_range(start, stop):\n", - " return summary[summary['TEAGE'].isin(range(start, stop))]\n", - "#thanks John!" + " return summary[summary['TEAGE'].isin(range(start, stop))]" ], "language": "python", "metadata": {}, "outputs": [], - "prompt_number": 43 + "prompt_number": 16 }, { "cell_type": "code", @@ -346,26 +352,29 @@ { "metadata": {}, "output_type": "pyout", - "prompt_number": 91, + "prompt_number": 17, "text": [ "85" ] } ], - "prompt_number": 91 + "prompt_number": 17 + }, + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Oldest, Youngest, and Mean Age of all surveyed" + ] }, { "cell_type": "code", "collapsed": false, "input": [ - "#Oldest person surveyed and the Index number they are located at\n", - "print(summary.TEAGE.max())\n", - "print(summary.TEAGE.idxmax())\n", - "print(summary.TEAGE.mean())#Mean age of all surveyed\n", - "\n", - "#Youngest person surveyed and the Index number they are located at\n", - "print(summary.TEAGE.min())\n", - "print(summary.TEAGE.idxmin())" + "print(summary.TEAGE.max())#Oldest person surveyed\n", + "print(summary.TEAGE.min())#Youngest person surveyed\n", + "print(summary.TEAGE.mean())#Mean age of all surveyed\n" ], "language": "python", "metadata": {}, @@ -375,43 +384,73 @@ "stream": "stdout", "text": [ "85\n", - "106\n", - "48.2798418972\n", "15\n", - "88\n" + "48.2798418972\n" ] } ], - "prompt_number": 92 + "prompt_number": 18 + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Oldest Person is 85, Youngest Person is 15, and the Mean Age is 48" + ] }, { "cell_type": "code", "collapsed": false, "input": [ - "#Person who had the most children in their household....\n", - "print(summary.TRCHILDNUM.max())\n", - "print(summary.TRCHILDNUM.idxmax())" + "summary.TEAGE.describe()" ], "language": "python", "metadata": {}, "outputs": [ { - "output_type": "stream", - "stream": "stdout", + "metadata": {}, + "output_type": "pyout", + "prompt_number": 19, "text": [ - "10\n", - "4612\n" + "count 11385.000000\n", + "mean 48.279842\n", + "std 17.909707\n", + "min 15.000000\n", + "25% 34.000000\n", + "50% 48.000000\n", + "75% 62.000000\n", + "max 85.000000\n", + "Name: TEAGE, dtype: float64" ] } ], - "prompt_number": 88 + "prompt_number": 19 + }, + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Which Diary Day was used the most in the Interviews" + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Since its a leisure study, it makes sense that half of the survey would focus on the two weekend days, but I also think that might skew some of the results on topics like Government Services, Work Related Activities, and Education" + ] }, { "cell_type": "code", "collapsed": false, "input": [ - "adults_with_no_children = summary[adults_all & no_children_crit]\n", - "adults_with_no_children.info()" + "days = summary[\"TUDIARYDAY\"].value_counts()\n", + "print(days)\n", + "weekday = {1: \"Sun\", 2: \"Mon\", 3: \"Tues\", 4: \"Weds\", 5: \"Thurs\", 6: \"Fri\", 7: \"Sat\"}\n", + "pd.DataFrame(days).sort_index().T.rename(columns = weekday).T.plot(kind='pie', colors=['r', 'g', 'b', 'c', 'y', 'm', 'orange'],subplots=True, legend=True, autopct='%.2f', fontsize=20, figsize=(10, 10))" ], "language": "python", "metadata": {}, @@ -420,436 +459,190 @@ "output_type": "stream", "stream": "stdout", "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" + "1 2974\n", + "7 2709\n", + "4 1202\n", + "2 1152\n", + "6 1147\n", + "3 1102\n", + "5 1099\n", + "dtype: int64\n" + ] + }, + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 20, + "text": [ + "array([], dtype=object)" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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R4SfFkugQpZQPLnoebjwMvJ38aCmwbn4uT+4pLQViXJ0USyIKHAAF58J9U5Sa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+ "text": [ + "" ] } ], - "prompt_number": 95 + "prompt_number": 20 + }, + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Gender Split across the whole survey" + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "Im a little suprised the split isn't closer to 50/50" + ] }, { "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()\n", - "#" + "gender = summary[\"TESEX\"].value_counts()\n", + "print(gender)\n", + "gender_dict = {1: \"Male\", 2: \"Female\"}\n", + "pd.DataFrame(gender).sort_index().T.rename(columns = gender_dict).T.plot(kind='pie', colors=['b', 'r',], subplots=True, legend=True, autopct='%.2f', fontsize=20, figsize=(7, 7))" ], "language": "python", "metadata": {}, "outputs": [ { - "html": [ - "
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6QtK1rG0zhm/afcOpCTfTfEEvDliUzHhNI5cYla1CiiZpKqS41B1SbqGw3Wd8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\n7//med62BFd5LvI8bxLB8aPvCWZ5AVj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+ "text": [ + "" ] } ], - "prompt_number": 47 + "prompt_number": 21 }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "data['weighted_minutes'] = data.weight * data.minutes\n", - "#this created a new column \"weighted minutes and filled it with the results of the operation" - ], - "language": "python", + "cell_type": "heading", + "level": 1, "metadata": {}, - "outputs": [], - "prompt_number": 48 + "source": [ + "Number of Household Children splits" + ] }, { "cell_type": "code", "collapsed": false, "input": [ - "data.head()" + "#Person who had the most children in their household....\n", + "\n", + "print(summary.TRCHILDNUM.max())\n", + "#print(summary.TRCHILDNUM.idxmax())" ], "language": "python", "metadata": {}, "outputs": [ { - "html": [ - "
\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
weightminutesweighted_minutes
2 10377056.507734 60 6.226234e+08
3 7731257.992805 65 5.025318e+08
4 4725269.227067 90 4.252742e+08
5 2372791.046351 270 6.406536e+08
6 5671341.270490 244 1.383807e+09
\n", - "
" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 49, + "output_type": "stream", + "stream": "stdout", "text": [ - " 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" + "10\n" ] } ], - "prompt_number": 49 + "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ - "data.weighted_minutes.sum() / data.weight.sum() / 60" + "kid_count = summary[\"TRCHILDNUM\"].value_counts()\n", + "print(kid_count)\n", + "kid_count.plot(kind=\"pie\", figsize=(7, 7))" ], "language": "python", "metadata": {}, "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "0 6481\n", + "1 2070\n", + "2 1859\n", + "3 682\n", + "4 225\n", + "5 39\n", + "6 15\n", + "7 11\n", + "9 2\n", + "10 1\n", + "dtype: int64\n" + ] + }, { "metadata": {}, "output_type": "pyout", - "prompt_number": 50, + "prompt_number": 23, "text": [ - "3.1709004734759403" + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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JOkuuw0I5CUKIzFm2/G5bUWMB6ywIITacZctKnGXL7iWEyFhnyWVYKCfhrTn3xyWLL1/D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\nacO4nhd6z4vdnS9uHRp4hnWOXIKFchy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+ "text": [ + "" ] } ], - "prompt_number": 50 - }, - { - "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": 51 + "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ - "sleeping = average_minutes(adults_with_no_children, \"010101\")\n", - "sleepless = average_minutes(adults_with_no_children, \"010102\")\n", - "(sleeping + sleepless) / 60" + "adults_with_no_children = summary[adults_all & adults_no_kids]\n", + "adults_with_no_children.info()\n", + "adults_with_no_children.head()" ], "language": "python", "metadata": {}, "outputs": [ { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 99, + "output_type": "stream", + "stream": "stdout", "text": [ - "8.7508537061809992" - ] - } - ], - "prompt_number": 99 - }, - { - "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": 53 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "activity_columns(summary, \"0101\")" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 54, - "text": [ - "['t010101', 't010102']" - ] - } - ], - "prompt_number": 54 - }, - { - "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": 55 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "average_minutes2(adults_with_no_children, \"0101\") / 60" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 73, - "text": [ - "8.750853706181001" - ] - } - ], - "prompt_number": 73 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "average_minutes2(adults_with_no_children, \"01\") / 60" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 57, - "text": [ - "9.5541911543273592" - ] - } - ], - "prompt_number": 57 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "groom_list = []\n", - "grooming_males = groom_list.append(average_minutes2(male, \"010201\"))\n", - "grooming_females = groom_list.append(average_minutes2(female, \"010201\"))\n", - "#groom_list\n", - "\n", - "labels = [\"Male\", \"Female\"]\n", - "groom_list = pd.Series(groom_list, index=labels)\n", - "groom_list.plot(kind=\"bar\", label = \"Average Grooming Time\", figsize=(10, 5))" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 110, - "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" ] }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 110 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# grouping\n", - "help(adults_with_no_children.groupby)" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "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 result\n", - " >>> data.groupby(func, axis=0).mean()\n", - " \n", - " # DataFrame result\n", - " >>> data.groupby(['col1', 'col2'])['col3'].mean()\n", - " \n", - " # DataFrame with hierarchical index\n", - " >>> data.groupby(['col1', 'col2']).mean()\n", - " \n", - " Returns\n", - " -------\n", - " GroupBy object\n", - "\n" - ] - } - ], - "prompt_number": 58 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "group = summary.groupby([\"TEAGE\", \"TESEX\", \"TRCHILDNUM\"])" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 59 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "group.mean().xs(15).xs(1)" - ], - "language": "python", - "metadata": {}, - "outputs": [ { "html": [ "
\n", @@ -860,13 +653,13 @@ " tucaseid\n", " TUFINLWGT\n", " TRYHHCHILD\n", + " TEAGE\n", + " TESEX\n", " PEEDUCA\n", " PTDTRACE\n", " PEHSPNON\n", " GTMETSTA\n", " TELFS\n", - " TEMJOT\n", - " TRDPFTPT\n", " ...\n", " t181501\n", " t181599\n", @@ -879,301 +672,248 @@ " t500106\n", " t500107\n", " \n", - " \n", - " TRCHILDNUM\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " 1\n", - " 2.013063e+13\n", - " 10279004.678881\n", - " 15.000000\n", - " 35.157895\n", - " 1.842105\n", - " 1.842105\n", - " 1.157895\n", - " 4.789474\n", - " -0.842105\n", - " -0.842105\n", + " 2\n", + " 20130101130123\n", + " 10377056.507734\n", + " -1\n", + " 47\n", + " 2\n", + " 40\n", + " 1\n", + " 2\n", + " 1\n", + " 4\n", " ...\n", + " 25\n", + " 0\n", + " 0\n", + " 0\n", " 0\n", " 0\n", " 0\n", " 0\n", - " 1.315789\n", - " 20.157895\n", - " 11.052632\n", " 0\n", - " 0\n", " 0\n", " \n", " \n", - " 2\n", - " 2.013066e+13\n", - " 8094486.932688\n", - " 11.733333\n", - " 35.133333\n", - " 1.133333\n", - " 1.800000\n", - " 1.066667\n", - " 4.666667\n", - " -0.800000\n", - " -0.800000\n", + " 3\n", + " 20130101130611\n", + " 7731257.992805\n", + " -1\n", + " 50\n", + " 2\n", + " 40\n", + " 1\n", + " 1\n", + " 1\n", + " 1\n", " ...\n", - " 3\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", " 0\n", " 0\n", " 0\n", - " 0.333333\n", - " 1.466667\n", - " 0.000000\n", " 0\n", - " 0\n", " 0\n", " \n", " \n", - " 3\n", - " 2.013056e+13\n", - " 13751422.077139\n", - " 7.000000\n", - " 34.846154\n", - " 1.923077\n", - " 1.692308\n", - " 1.230769\n", - " 4.384615\n", - " -0.538462\n", - " -0.538462\n", + " 4\n", + " 20130101130616\n", + " 4725269.227067\n", + " -1\n", + " 45\n", + " 2\n", + " 40\n", + " 2\n", + " 2\n", + " 1\n", + " 1\n", " ...\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", " 0\n", " 0\n", " 0\n", " 0\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", " 0\n", - " 0\n", " 0\n", " \n", " \n", - " 4\n", - " 2.013050e+13\n", - " 11292383.376507\n", - " 5.333333\n", - " 33.666667\n", - " 1.000000\n", - " 2.000000\n", - " 1.333333\n", - " 3.666667\n", - " -0.333333\n", - " 0.000000\n", + " 5\n", + " 20130101130619\n", + " 2372791.046351\n", + " -1\n", + " 80\n", + " 2\n", + " 38\n", + " 1\n", + " 2\n", + " 1\n", + " 5\n", " ...\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", " 0\n", " 0\n", " 0\n", " 0\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", " 0\n", - " 10\n", " 0\n", " \n", " \n", " 6\n", - " 2.013081e+13\n", - " 6890966.927239\n", - " 6.000000\n", - " 34.000000\n", - " 1.000000\n", - " 2.000000\n", - " 2.000000\n", - " 1.000000\n", - " 2.000000\n", - " 1.000000\n", + " 20130101130658\n", + " 5671341.270490\n", + " -1\n", + " 72\n", + " 1\n", + " 42\n", + " 1\n", + " 1\n", + " 1\n", + " 5\n", " ...\n", + " 0\n", + " 0\n", + " 5\n", + " 0\n", " 0\n", " 0\n", " 0\n", " 0\n", - " 0.000000\n", - " 0.000000\n", - " 0.000000\n", " 0\n", - " 0\n", " 0\n", " \n", " \n", "\n", - "

5 rows \u00d7 410 columns

\n", + "

5 rows \u00d7 413 columns

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" ], "metadata": {}, "output_type": "pyout", - "prompt_number": 60, + "prompt_number": 24, "text": [ - " tucaseid TUFINLWGT TRYHHCHILD PEEDUCA PTDTRACE \\\n", - "TRCHILDNUM \n", - "1 2.013063e+13 10279004.678881 15.000000 35.157895 1.842105 \n", - "2 2.013066e+13 8094486.932688 11.733333 35.133333 1.133333 \n", - "3 2.013056e+13 13751422.077139 7.000000 34.846154 1.923077 \n", - "4 2.013050e+13 11292383.376507 5.333333 33.666667 1.000000 \n", - "6 2.013081e+13 6890966.927239 6.000000 34.000000 1.000000 \n", - "\n", - " PEHSPNON GTMETSTA TELFS TEMJOT TRDPFTPT ... \\\n", - "TRCHILDNUM ... \n", - "1 1.842105 1.157895 4.789474 -0.842105 -0.842105 ... \n", - "2 1.800000 1.066667 4.666667 -0.800000 -0.800000 ... \n", - "3 1.692308 1.230769 4.384615 -0.538462 -0.538462 ... \n", - "4 2.000000 1.333333 3.666667 -0.333333 0.000000 ... \n", - "6 2.000000 2.000000 1.000000 2.000000 1.000000 ... \n", + " tucaseid TUFINLWGT TRYHHCHILD TEAGE TESEX PEEDUCA \\\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", + "5 20130101130619 2372791.046351 -1 80 2 38 \n", + "6 20130101130658 5671341.270490 -1 72 1 42 \n", "\n", - " t181501 t181599 t181601 t181801 t189999 t500101 \\\n", - "TRCHILDNUM \n", - "1 0 0 0 0 1.315789 20.157895 \n", - "2 3 0 0 0 0.333333 1.466667 \n", - "3 0 0 0 0 0.000000 0.000000 \n", - "4 0 0 0 0 0.000000 0.000000 \n", - "6 0 0 0 0 0.000000 0.000000 \n", + " PTDTRACE PEHSPNON GTMETSTA TELFS ... t181501 t181599 t181601 \\\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", + "5 1 2 1 5 ... 0 0 0 \n", + "6 1 1 1 5 ... 0 0 5 \n", "\n", - " t500103 t500105 t500106 t500107 \n", - "TRCHILDNUM \n", - "1 11.052632 0 0 0 \n", - "2 0.000000 0 0 0 \n", - "3 0.000000 0 0 0 \n", - "4 0.000000 0 10 0 \n", - "6 0.000000 0 0 0 \n", + " t181801 t189999 t500101 t500103 t500105 t500106 t500107 \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", + "5 0 0 0 0 0 0 0 \n", + "6 0 0 0 0 0 0 0 \n", "\n", - "[5 rows x 410 columns]" + "[5 rows x 413 columns]" ] } ], - "prompt_number": 60 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Joining files" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "respondents = pd.read_csv(\"atusdata/atusresp_2013/atusresp_2013.dat\")\n", - "activities = pd.read_csv(\"atusdata/atusact_2013/atusact_2013.dat\")" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 61 + "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [ - "respondents.info()" + "data = adults_with_no_children[['TUFINLWGT', 't120303']]\n", + "data = data.rename(columns={\"TUFINLWGT\": \"weight\", \"t120303\": \"minutes\"})\n", + "data.head()" ], "language": "python", "metadata": {}, "outputs": [ { - "output_type": "stream", - "stream": "stdout", + "html": [ + "
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weightminutes
2 10377056.507734 60
3 7731257.992805 65
4 4725269.227067 90
5 2372791.046351 270
6 5671341.270490 244
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" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 25, "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" + " 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" ] } ], - "prompt_number": 62 + "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ - "activities.info()" + "data['weighted_minutes'] = data.weight * data.minutes\n", + "#this created a new column \"weighted minutes\" and filled it with the results of the operation" ], "language": "python", "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "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" - ] - } - ], - "prompt_number": 63 + "outputs": [], + "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [ - "respondents.head()" + "data.head()" ], "language": "python", "metadata": {}, @@ -1185,617 +925,680 @@ " \n", " \n", " \n", - " TUCASEID\n", - " TULINENO\n", - " TUYEAR\n", - " TUMONTH\n", - " TEABSRSN\n", - " TEERN\n", - " TEERNH1O\n", - " TEERNH2\n", - " TEERNHRO\n", - " TEERNHRY\n", - " ...\n", - " TXSPEMPNOT\n", - " TXSPUHRS\n", - " TXTCC\n", - " TXTCCTOT\n", - " TXTCOC\n", - " TXTHH\n", - " TXTNOHH\n", - " TXTO\n", - " TXTOHH\n", - " TXTONHH\n", + " weight\n", + " minutes\n", + " weighted_minutes\n", " \n", " \n", " \n", " \n", - " 0\n", - " 20130101130004\n", - " 1\n", - " 2013\n", - " 1\n", - " -1\n", - " -1\n", - " -1\n", - " -1\n", - " -1\n", - " -1\n", - " ...\n", - " -1\n", - " -1\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " -1\n", - " -1\n", - " -1\n", + " 2\n", + " 10377056.507734\n", + " 60\n", + " 6.226234e+08\n", " \n", " \n", - " 1\n", - " 20130101130112\n", - " 1\n", - " 2013\n", - " 1\n", - " -1\n", - " -1\n", - " -1\n", - " -1\n", - " -1\n", - " 2\n", - " ...\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " -1\n", - " 0\n", - " 0\n", - " -1\n", + " 3\n", + " 7731257.992805\n", + " 65\n", + " 5.025318e+08\n", " \n", " \n", - " 2\n", - " 20130101130123\n", - " 1\n", - " 2013\n", - " 1\n", - " -1\n", - " -1\n", - " -1\n", - " -1\n", - " -1\n", - " -1\n", - " ...\n", - " 0\n", - " 0\n", - " -1\n", - " 0\n", - " 0\n", - " -1\n", - " -1\n", - " -1\n", - " -1\n", - " -1\n", + " 4\n", + " 4725269.227067\n", + " 90\n", + " 4.252742e+08\n", " \n", " \n", - " 3\n", - " 20130101130611\n", - " 1\n", - " 2013\n", - " 1\n", - " -1\n", - " -1\n", - " -1\n", - " -1\n", - " -1\n", - " 2\n", - " ...\n", - " -1\n", - " -1\n", - " -1\n", - " 0\n", - " 0\n", - " -1\n", - " -1\n", - " -1\n", - " -1\n", - " -1\n", + " 5\n", + " 2372791.046351\n", + " 270\n", + " 6.406536e+08\n", " \n", " \n", - " 4\n", - " 20130101130616\n", - " 1\n", - " 2013\n", - " 1\n", - " -1\n", - " -1\n", - " -1\n", - " -1\n", - " -1\n", - " 2\n", - " ...\n", - " -1\n", - " -1\n", - " -1\n", - " 0\n", - " 0\n", - " -1\n", - " -1\n", - " -1\n", - " -1\n", - " -1\n", + " 6\n", + " 5671341.270490\n", + " 244\n", + " 1.383807e+09\n", " \n", " \n", "\n", - "

5 rows \u00d7 175 columns

\n", "" ], "metadata": {}, "output_type": "pyout", - "prompt_number": 64, + "prompt_number": 27, "text": [ - " TUCASEID TULINENO TUYEAR TUMONTH TEABSRSN TEERN TEERNH1O \\\n", - "0 20130101130004 1 2013 1 -1 -1 -1 \n", - "1 20130101130112 1 2013 1 -1 -1 -1 \n", - "2 20130101130123 1 2013 1 -1 -1 -1 \n", - "3 20130101130611 1 2013 1 -1 -1 -1 \n", - "4 20130101130616 1 2013 1 -1 -1 -1 \n", - "\n", - " TEERNH2 TEERNHRO TEERNHRY ... TXSPEMPNOT TXSPUHRS TXTCC \\\n", - "0 -1 -1 -1 ... -1 -1 0 \n", - "1 -1 -1 2 ... 0 0 0 \n", - "2 -1 -1 -1 ... 0 0 -1 \n", - "3 -1 -1 2 ... -1 -1 -1 \n", - "4 -1 -1 2 ... -1 -1 -1 \n", - "\n", - " TXTCCTOT TXTCOC TXTHH TXTNOHH TXTO TXTOHH TXTONHH \n", - "0 0 0 0 0 -1 -1 -1 \n", - "1 0 0 0 -1 0 0 -1 \n", - "2 0 0 -1 -1 -1 -1 -1 \n", - "3 0 0 -1 -1 -1 -1 -1 \n", - "4 0 0 -1 -1 -1 -1 -1 \n", - "\n", - "[5 rows x 175 columns]" + " 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" ] } ], - "prompt_number": 64 + "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ - "activities.head()" + "data.weighted_minutes.sum() / data.weight.sum() / 60" ], "language": "python", "metadata": {}, "outputs": [ { - "html": [ - "
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TUCASEIDTUACTIVITY_NTEWHERETRTCCTOT_LNTRTCC_LNTRTCOC_LNTRTEC_LNTRTHH_LNTRTNOHH_LNTRTOHH_LN...TUDURSTOPTUEC24TUSTARTTIMTUSTOPTIMETUTIER1CODETUTIER2CODETUTIER3CODETRCODETRTIER2TXWHERE
0 20130101130004 1-1 0 0 0-1 0 0-1... 2-1 04:00:00 12:00:00 1 1 1 10101 101 0
1 20130101130004 2 1 5 5 0-1 5 5-1... 1-1 12:00:00 12:05:00 11 1 1 110101 1101 0
2 20130101130004 3 1 120 120 0-1 120 120-1... 1-1 12:05:00 14:05:00 12 3 3 120303 1203 0
3 20130101130004 4 1 0 0 0-1 0 0-1... 2-1 14:05:00 19:00:00 6 3 1 60301 603 0
4 20130101130004 5 1 30 30 0-1 30 30-1... 1-1 19:00:00 19:30:00 11 1 1 110101 1101 0
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5 rows \u00d7 31 columns

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" - ], "metadata": {}, "output_type": "pyout", - "prompt_number": 65, + "prompt_number": 28, "text": [ - " TUCASEID TUACTIVITY_N TEWHERE TRTCCTOT_LN TRTCC_LN TRTCOC_LN \\\n", - "0 20130101130004 1 -1 0 0 0 \n", - "1 20130101130004 2 1 5 5 0 \n", - "2 20130101130004 3 1 120 120 0 \n", - "3 20130101130004 4 1 0 0 0 \n", - "4 20130101130004 5 1 30 30 0 \n", - "\n", - " TRTEC_LN TRTHH_LN TRTNOHH_LN TRTOHH_LN ... TUDURSTOP TUEC24 \\\n", - "0 -1 0 0 -1 ... 2 -1 \n", - "1 -1 5 5 -1 ... 1 -1 \n", - "2 -1 120 120 -1 ... 1 -1 \n", - "3 -1 0 0 -1 ... 2 -1 \n", - "4 -1 30 30 -1 ... 1 -1 \n", - "\n", - " TUSTARTTIM TUSTOPTIME TUTIER1CODE TUTIER2CODE TUTIER3CODE TRCODE \\\n", - "0 04:00:00 12:00:00 1 1 1 10101 \n", - "1 12:00:00 12:05:00 11 1 1 110101 \n", - "2 12:05:00 14:05:00 12 3 3 120303 \n", - "3 14:05:00 19:00:00 6 3 1 60301 \n", - "4 19:00:00 19:30:00 11 1 1 110101 \n", - "\n", - " TRTIER2 TXWHERE \n", - "0 101 0 \n", - "1 1101 0 \n", - "2 1203 0 \n", - "3 603 0 \n", - "4 1101 0 \n", - "\n", - "[5 rows x 31 columns]" + "3.1709004734759403" ] } ], - "prompt_number": 65 + "prompt_number": 28 }, { "cell_type": "code", "collapsed": false, "input": [ - "merged = pd.merge(respondents, activities, left_on=\"TUCASEID\", right_on=\"TUCASEID\")\n", - "merged.info()" + "#This is a fucntions that combines all three cells above this\n", + "\n", + "def average_minutes(data, activity_code):\n", + " activity_col = \"t{}\".format(activity_code)\n", + " data = data[['TUFINLWGT', activity_col]]#this creates a new DataFrame\n", + " data = data.rename(columns={\"TUFINLWGT\": \"weight\", activity_col: \"minutes\"})\n", + " data['weighted_minutes'] = data.weight * data.minutes#this adds a new column to the DataFrame\n", + " return data.weighted_minutes.sum() / data.weight.sum()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 29 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "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": [ { - "output_type": "stream", - "stream": "stdout", + "metadata": {}, + "output_type": "pyout", + "prompt_number": 30, "text": [ - "\n", - "Int64Index: 215576 entries, 0 to 215575\n", - "Columns: 205 entries, TUCASEID to TXWHERE\n", - "dtypes: float64(1), int64(200), object(4)\n", - "memory usage: 338.8+ MB\n" + "8.7508537061809992" ] } ], - "prompt_number": 66 + "prompt_number": 30 + }, + { + "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": 31 }, { "cell_type": "code", "collapsed": false, "input": [ - "merged.head()" + "activity_columns(summary, \"0101\")" ], "language": "python", "metadata": {}, "outputs": [ { - "html": [ - "
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TUCASEIDTULINENOTUYEARTUMONTHTEABSRSNTEERNTEERNH1OTEERNH2TEERNHROTEERNHRY...TUDURSTOPTUEC24TUSTARTTIMTUSTOPTIMETUTIER1CODETUTIER2CODETUTIER3CODETRCODETRTIER2TXWHERE
0 20130101130004 1 2013 1-1-1-1-1-1-1... 2-1 04:00:00 12:00:00 1 1 1 10101 101 0
1 20130101130004 1 2013 1-1-1-1-1-1-1... 1-1 12:00:00 12:05:00 11 1 1 110101 1101 0
2 20130101130004 1 2013 1-1-1-1-1-1-1... 1-1 12:05:00 14:05:00 12 3 3 120303 1203 0
3 20130101130004 1 2013 1-1-1-1-1-1-1... 2-1 14:05:00 19:00:00 6 3 1 60301 603 0
4 20130101130004 1 2013 1-1-1-1-1-1-1... 1-1 19:00:00 19:30:00 11 1 1 110101 1101 0
\n", - "

5 rows \u00d7 205 columns

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" - ], "metadata": {}, "output_type": "pyout", - "prompt_number": 67, + "prompt_number": 32, "text": [ - " TUCASEID TULINENO TUYEAR TUMONTH TEABSRSN TEERN TEERNH1O \\\n", - "0 20130101130004 1 2013 1 -1 -1 -1 \n", - "1 20130101130004 1 2013 1 -1 -1 -1 \n", - "2 20130101130004 1 2013 1 -1 -1 -1 \n", - "3 20130101130004 1 2013 1 -1 -1 -1 \n", - "4 20130101130004 1 2013 1 -1 -1 -1 \n", - "\n", - " TEERNH2 TEERNHRO TEERNHRY ... TUDURSTOP TUEC24 TUSTARTTIM \\\n", - "0 -1 -1 -1 ... 2 -1 04:00:00 \n", - "1 -1 -1 -1 ... 1 -1 12:00:00 \n", - "2 -1 -1 -1 ... 1 -1 12:05:00 \n", - "3 -1 -1 -1 ... 2 -1 14:05:00 \n", - "4 -1 -1 -1 ... 1 -1 19:00:00 \n", - "\n", - " TUSTOPTIME TUTIER1CODE TUTIER2CODE TUTIER3CODE TRCODE TRTIER2 TXWHERE \n", - "0 12:00:00 1 1 1 10101 101 0 \n", - "1 12:05:00 11 1 1 110101 1101 0 \n", - "2 14:05:00 12 3 3 120303 1203 0 \n", - "3 19:00:00 6 3 1 60301 603 0 \n", - "4 19:30:00 11 1 1 110101 1101 0 \n", - "\n", - "[5 rows x 205 columns]" + "['t010101', 't010102']" ] } ], - "prompt_number": 67 + "prompt_number": 32 }, { "cell_type": "code", "collapsed": false, - "input": [], + "input": [ + "def average_minutes2(data, activity_code):\n", + " cols = activity_columns(data, activity_code)#see cell above\n", + " activity_data = data[cols]#creates a new DataFrame\n", + " activity_sums = activity_data.sum(axis=1)#sums the DataFrame on the Columns\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": 67 + "prompt_number": 33 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "average_minutes2(adults_with_no_children, \"0101\") / 60" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stderr", + "text": [ + "-c: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" + ] + }, + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 34, + "text": [ + "8.750853706181001" + ] + } + ], + "prompt_number": 34 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "average_minutes2(adults_with_no_children, \"01\") / 60" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 35, + "text": [ + "9.5541911543273592" + ] + } + ], + "prompt_number": 35 + }, + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Average Time spent on Personal Grooming split by Male&Female" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "groom_list = []\n", + "grooming_males = groom_list.append(average_minutes2(summary[male], \"010201\"))\n", + "grooming_females = groom_list.append(average_minutes2(summary[female], \"010201\"))\n", + "#groom_list\n", + "\n", + "labels = [\"Male\", \"Female\"]\n", + "groom_list = pd.Series(groom_list, index=labels)\n", + "groom_list.plot(kind=\"bar\", label = \"Average Grooming Time\", figsize=(10, 5))\n", + "plt.ylabel(\"Minutes\")\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 49 + }, + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Time Spent Grooming based on # of Kids and Plotted By Age - MALES" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "groom_male_nokids = []\n", + "groom_males_20_no = summary[male & adults_20 & adults_no_kids]\n", + "groom_males_30_no = summary[male & adults_30 & adults_no_kids]\n", + "groom_males_40_no = summary[male & adults_40 & adults_no_kids]\n", + "groom_males_50_no = summary[male & adults_50 & adults_no_kids]\n", + "groom_males_60_no = summary[male & adults_60 & adults_no_kids]\n", + "\n", + "groom_male_onetwo = []\n", + "groom_males_20_onetwo = summary[male & adults_20 & adults_onetwo_kids]\n", + "groom_males_30_onetwo = summary[male & adults_30 & adults_onetwo_kids]\n", + "groom_males_40_onetwo = summary[male & adults_40 & adults_onetwo_kids]\n", + "groom_males_50_onetwo = summary[male & adults_50 & adults_onetwo_kids]\n", + "groom_males_60_onetwo = summary[male & adults_60 & adults_onetwo_kids]\n", + "\n", + "groom_male_three = []\n", + "groom_males_20_three = summary[male & adults_20 & adults_threeplus_kids]\n", + "groom_males_30_three = summary[male & adults_30 & adults_threeplus_kids]\n", + "groom_males_40_three = summary[male & adults_40 & adults_threeplus_kids]\n", + "groom_males_50_three = summary[male & adults_50 & adults_threeplus_kids]\n", + "groom_males_60_three = summary[male & adults_60 & adults_threeplus_kids]\n", + "\n", + "groom_male_nokids.append(average_minutes2(groom_males_20_no, \"010201\"))\n", + "groom_male_nokids.append(average_minutes2(groom_males_30_no, \"010201\"))\n", + "groom_male_nokids.append(average_minutes2(groom_males_40_no, \"010201\"))\n", + "groom_male_nokids.append(average_minutes2(groom_males_50_no, \"010201\"))\n", + "groom_male_nokids.append(average_minutes2(groom_males_60_no, \"010201\"))\n", + "groom_male_onetwo.append(average_minutes2(groom_males_20_onetwo, \"010201\"))\n", + "groom_male_onetwo.append(average_minutes2(groom_males_30_onetwo, \"010201\"))\n", + "groom_male_onetwo.append(average_minutes2(groom_males_40_onetwo, \"010201\"))\n", + "groom_male_onetwo.append(average_minutes2(groom_males_50_onetwo, \"010201\"))\n", + "groom_male_onetwo.append(average_minutes2(groom_males_60_onetwo, \"010201\"))\n", + "groom_male_three.append(average_minutes2(groom_males_20_three, \"010201\"))\n", + "groom_male_three.append(average_minutes2(groom_males_30_three, \"010201\"))\n", + "groom_male_three.append(average_minutes2(groom_males_40_three, \"010201\"))\n", + "groom_male_three.append(average_minutes2(groom_males_50_three, \"010201\"))\n", + "groom_male_three.append(average_minutes2(groom_males_60_three, \"010201\"))\n", + "\n", + "labels = [\"20\",\"30\",\"40\",\"50\",\"60\"]\n", + "groom_list_nokids = pd.Series(groom_male_nokids, index=labels)\n", + "groom_list_onetwo = pd.Series(groom_male_onetwo, index=labels)\n", + "groom_list_three = pd.Series(groom_male_three, index=labels)\n", + "groom_list_nokids.plot(label = \"No Kids\", figsize=(10, 5))\n", + "groom_list_onetwo.plot(label = \"One or Two Kids\", figsize=(10, 5))\n", + "groom_list_three.plot(label = \"Three or More Kids\", figsize=(10, 5))\n", + "plt.ylabel(\"Minutes\")\n", + "plt.xlabel(\"Age\")\n", + "plt.legend()\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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JSXF9W12LjR86dIBt27bwzTdfAVBR0XqmhYCAAJYtW8muXTu4775FPPvsywQEBDS+np6e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\n6r6GhmXvRu7Yiv81TkUSUsNHH7LyN1fTvOJzMg4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+ "text": [ + "" + ] + } + ], + "prompt_number": 55 + }, + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Time Spent Grooming based on # of Kids and Plotted By Age - FEMALES" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "groom_female_no = []\n", + "groom_females_20_no = summary[female & adults_20 & adults_no_kids]\n", + "groom_females_30_no = summary[female & adults_30 & adults_no_kids]\n", + "groom_females_40_no = summary[female & adults_40 & adults_no_kids]\n", + "groom_females_50_no = summary[female & adults_50 & adults_no_kids]\n", + "groom_females_60_no = summary[female & adults_60 & adults_no_kids]\n", + "\n", + "groom_female_onetwo = []\n", + "groom_females_20_onetwo = summary[female & adults_20 & adults_onetwo_kids]\n", + "groom_females_30_onetwo = summary[female & adults_30 & adults_onetwo_kids]\n", + "groom_females_40_onetwo = summary[female & adults_40 & adults_onetwo_kids]\n", + "groom_females_50_onetwo = summary[female & adults_50 & adults_onetwo_kids]\n", + "groom_females_60_onetwo = summary[female & adults_60 & adults_onetwo_kids]\n", + "\n", + "groom_female_three = []\n", + "groom_females_20_three = summary[female & adults_20 & adults_threeplus_kids]\n", + "groom_females_30_three = summary[female & adults_30 & adults_threeplus_kids]\n", + "groom_females_40_three = summary[female & adults_40 & adults_threeplus_kids]\n", + "groom_females_50_three = summary[female & adults_50 & adults_threeplus_kids]\n", + "groom_females_60_three = summary[female & adults_60 & adults_threeplus_kids]\n", + "\n", + "groom_female_no.append(average_minutes2(groom_females_20_no, \"010201\"))\n", + "groom_female_no.append(average_minutes2(groom_females_30_no, \"010201\"))\n", + "groom_female_no.append(average_minutes2(groom_females_40_no, \"010201\"))\n", + "groom_female_no.append(average_minutes2(groom_females_50_no, \"010201\"))\n", + "groom_female_no.append(average_minutes2(groom_females_60_no, \"010201\"))\n", + "groom_female_onetwo.append(average_minutes2(groom_females_20_onetwo, \"010201\"))\n", + "groom_female_onetwo.append(average_minutes2(groom_females_30_onetwo, \"010201\"))\n", + "groom_female_onetwo.append(average_minutes2(groom_females_40_onetwo, \"010201\"))\n", + "groom_female_onetwo.append(average_minutes2(groom_females_50_onetwo, \"010201\"))\n", + "groom_female_onetwo.append(average_minutes2(groom_females_60_onetwo, \"010201\"))\n", + "groom_female_three.append(average_minutes2(groom_females_20_three, \"010201\"))\n", + "groom_female_three.append(average_minutes2(groom_females_30_three, \"010201\"))\n", + "groom_female_three.append(average_minutes2(groom_females_40_three, \"010201\"))\n", + "groom_female_three.append(average_minutes2(groom_females_50_three, \"010201\"))\n", + "groom_female_three.append(average_minutes2(groom_females_60_three, \"010201\"))\n", + "\n", + "labels = [\"20\",\"30\",\"40\",\"50\",\"60\"]\n", + "groom_list_no = pd.Series(groom_female_no, index=labels)\n", + "groom_list_onetwo = pd.Series(groom_female_onetwo, index=labels)\n", + "groom_list_three = pd.Series(groom_female_three, index=labels)\n", + "groom_list_no.plot(label = \"No Kids\", figsize=(10, 5))\n", + "groom_list_onetwo.plot(label = \"One or Two Kids\", figsize=(10, 5))\n", + "groom_list_three.plot(label = \"Three or More Kids\", figsize=(10, 5))\n", + "plt.xlabel(\"Age\")\n", + "plt.ylabel(\"Minutes\")\n", + "plt.legend()\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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0YoyGo59IiElqXMtXjBEpXxGELBsOJDEJbjrGxe1xs7ttH0WNZZQ4y+ke7AEg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\nW0IkJ/sbih/ClhAdHZfW2lrKnl9J5Ztr/I25jzX+xtz98jr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+ "text": [ + "" + ] + } + ], + "prompt_number": 56 + }, + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Time Spent Reading to the Kids & Helping with Homework - Male vs Female" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "study_list = []\n", + "male_study = summary[male & adults_any_kids & young_kid]\n", + "female_study = summary[female & adults_any_kids & young_kid]\n", + "#young_kid is in there to include only the households where the oldest kids is 10 or under\n", + "\n", + "study_list.append(average_minutes2(male_study, \"030102\"))\n", + "study_list.append(average_minutes2(male_study, \"030201\"))\n", + "study_list.append(average_minutes2(female_study, \"030102\"))\n", + "study_list.append(average_minutes2(male_study, \"030201\"))\n", + "\n", + "labels = [\"Male Read\", \"Male Homework\", \"Female Read\", \"Female Homework\"]\n", + "study_list_s = pd.Series(study_list, index=labels)\n", + "study_list_s.plot(kind=\"bar\", title = \"Teaching to the Kids\", figsize=(10, 5))\n", + "plt.ylabel(\"Minutes\")\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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2YKtyY0mSJPWnyZSvlRExs2v5KcDKkvJIkiT1tcmUrxOBHwJPi4gTgV8BJ5SaSpIkqU+t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\n07rAO4ENgHOB5fX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+ "text": [ + "" + ] + } + ], + "prompt_number": 39 + }, + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Job Searching & Interviewing Split Across Unemployeed Male/Female\n", + "Age 20 - 60" + ] + }, + { + "cell_type": "heading", + "level": 3, + "metadata": {}, + "source": [ + "A lot of times seems to be spent searching for jobs vs actually interviewing for one" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "male_nojob = summary[male & adults_20_60 & unemployed & weekdays]\n", + "female_nojob = summary[female & adults_20_60 & unemployed & weekdays]\n", + "\n", + "#050401 Job Search Activites\n", + "#050403 Job Interview\n", + "\n", + "job_list = []\n", + "job_list.append(average_minutes2(male_nojob, \"050401\"))\n", + "job_list.append(average_minutes2(female_nojob, \"050403\"))\n", + "job_list.append(average_minutes2(female_nojob, \"050401\"))\n", + "job_list.append(average_minutes2(female_nojob, \"050403\"))\n", + "\n", + "labels = [\"Male Job Search\", \"Male Interview\", \"Female Job Search\", \"Female Interview\"]\n", + "job_list_s = pd.Series(job_list, index=labels)\n", + "job_list_s.plot(kind=\"bar\", label = \"Teaching to the Kids\", figsize=(10, 5))\n", + "plt.ylabel(\"Minutes\")\n", + "plt.show()\n", + "job_list_s.plot(kind='pie', legend=True, autopct='%.2f', fontsize=20, figsize=(7, 7))\n", + "plt.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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qpVKd512J+fS11DnvacBLmK7Np7TWi53zPosJolmJLWLOtguBP2JavR7EjAP4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\nQwkSIYQQQkgcSpAIIYQQQuL8PxllUK7nM6rzAAAAAElFTkSuQmCC\n", + "text": [ + "" + ] + } + ], + "prompt_number": 54 + }, + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "Eating/Cooking/Cleaning -- Male vs Female during the Week" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "male_weekday = summary[male & adults_all & weekdays]\n", + "female_weekday = summary[female & adults_all & weekdays]\n", + "\n", + "eat_cook_clean = []\n", + "eat_cook_clean.append(average_minutes2(male_weekday, \"1101\"))#eat\n", + "eat_cook_clean.append(average_minutes2(male_weekday, \"020201\"))#cook\n", + "eat_cook_clean.append(average_minutes2(male_weekday, \"020203\"))#kitchen cleanup\n", + "eat_cook_clean.append(average_minutes2(female_weekday, \"1101\"))\n", + "eat_cook_clean.append(average_minutes2(female_weekday, \"020201\"))\n", + "eat_cook_clean.append(average_minutes2(female_weekday, \"020203\"))\n", + "\n", + "labels = [\"Male Eating\", \"Male Cooking\", \"Male Cleaning\", \"Female Eating\", \"Female Cooking\", \"Female Cleaing\"]\n", + "eat_list_s = pd.Series(eat_cook_clean, index=labels)\n", + "eat_list_s.plot(kind=\"bar\", label = \"Time Spent Eating/Cooking/Cleaning during the Week\", figsize=(10, 5))\n", + "plt.ylabel(\"Minutes\")\n", + "plt.show()\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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OiH0z8wbgp8DJEWGPUDkPWFS2nqF4Vwu1FOMHqMfq/5AeBXwDuC0z7fcv4+3A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\n/L6pwj2bvywAAAAASUVORK5CYII=\n", + "text": [ + "" + ] + } + ], + "prompt_number": 76 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# grouping\n", + "help(adults_with_no_children.groupby)" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "group = summary.groupby([\"TEAGE\", \"TESEX\", \"TRCHILDNUM\"])" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "group.head()" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "group.mean().xs(15).xs(1)" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "heading", + "level": 2, + "metadata": {}, + "source": [ + "Joining files" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "respondents = pd.read_csv(\"atusdata/atusresp_2013/atusresp_2013.dat\")\n", + "activities = pd.read_csv(\"atusdata/atusact_2013/atusact_2013.dat\")" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "respondents.info()" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "activities.info()" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "respondents.head()" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "activities.head()" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "merged = pd.merge(respondents, activities, left_on=\"TUCASEID\", right_on=\"TUCASEID\")\n", + "merged.info()" + ], + "language": "python", + "metadata": {}, + "outputs": [] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "merged[1:15]" + ], + "language": "python", + "metadata": {}, + "outputs": [] } ], "metadata": {}