From 506eb943dd545b0cc2591ad457bc9463c56d1ce9 Mon Sep 17 00:00:00 2001 From: Hannah Date: Sun, 31 May 2015 17:32:57 -0400 Subject: [PATCH 1/2] Initial Commit --- atus_analysis.ipynb | 307 +++++++++++++++++++++++++++++++++ atus_analysis/data_analysis.py | 104 +++++++++++ 2 files changed, 411 insertions(+) create mode 100644 atus_analysis.ipynb create mode 100644 atus_analysis/data_analysis.py diff --git a/atus_analysis.ipynb b/atus_analysis.ipynb new file mode 100644 index 0000000..00a87c2 --- /dev/null +++ b/atus_analysis.ipynb @@ -0,0 +1,307 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import re\n", + "import sys\n", + "sys.path.append('/Users/Hannah/Documents/PythonProjects/atus-analysis/atus_analysis')\n", + "from data_analysis import *\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(7.3016452733976838, 3.9399426598089073, 4.0832551717906513)\n" + ] + } + ], + "source": [ + "print(amt_leisure_time_by_age())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Checking methods against ATUS conclusions:
\n", + "People ages 25-24 spend 4.1 hours on leisure activities
\n", + "People ages 35-44 spend 4 hours on leisure activities
\n", + "People age 75 and above spend 7.3 hours on leisure activities
" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "result = corr_sports_school()\n", + "new_result = result.ix[1:]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "new_result.plot(kind='bar', title='In School Vs. Time Spent Exercising')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "People who are not in school spend more time exercising." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Correlations:
\n", + "Taking classes and age --> -0.216473
\n", + "Relaxing & thinking and age --> 0.111234
" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "monthly_data = avg_hours_tv_per_month()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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kiyTNlnSjpHU6F24Yha8BO+ayRL/bDlgKOLfJdpfm7d7Z8Yj6W9TTK2jEmrqk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\nyxdho496eFmM/VJB0eb+YHM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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "monthly_data.plot(title='Mean Hours of TV vs. Month')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected, TV watching decreases during the summer months" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(8.6265733580559285, 8.6735150444399256, 8.6836391549060625)\n" + ] + } + ], + "source": [ + "print(avg_sleep_by_kid_age())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This ended up not being very exciting. People with a kid younger than 2 got 8.6 hours of sleep, people with a kid got 8.67 hours and people without a kid got 8.69 hours." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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aNSv2289VQ400c+aTdHUtbci+e3vXYurUjRuyb0salX/Ou8Zr5Hevr6+P7u7u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\njXIuCMzMRrn/D0p/1ttNESVpAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "avgs = avg_leisure_by_overtime_earnings()\n", + "y = [avgs[0], avgs[1], avgs[2]]\n", + "x = range(3)\n", + "plt.bar(x,y)\n", + "plt.xticks(range(3), [\"Low\", \"Med\", \"High\"], ha='left')\n", + "plt.xlabel(\"Earnings\")\n", + "plt.ylabel(\"Hours of Leisure Activities\")\n", + "plt.title(\"Leisure Time vs. Overtime Earnings\")\n", + "plt.style.use('bmh')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This one was also not as exciting as I had hoped...it seems these people have found a way to earn money while being leisurely. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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A15ZuCVVYxlgNzxgPiuiE11JnDknnV0c7Se8vXxbyr6+LwxyRMcaYthLRCS+YGl6gHwxO\n5/+O7QnAX78q4p9LLOmZ1mc1PGO8J6IT3sGaPKgbvz6uFwI8Na+IlxZtDndIxhhjQiyiE96hXKZ+\n0sCu/OZ4J+k9u2ATLyzc1HqBmQ7PanjGeE90uAMIp4kDuuIT4aGP1/PCws3UKlw8KhMRCXdoxhhj\nWllEt/AOpoYXaEL/Ltx4Yh98An9ftJln529CVVshOtORWQ3PGO+J6ITXWk7K68wtJzlJ7+UlxTw1\nr8iSnjHGtDMRnfAOpYYX6Pi+nbn15FyiBF75egtPflloSc8cNKvhGeM9EZ3wWttxuZ24fUIu0T7h\ntW+28sQXlvSMMaa9iOiE1xo1vEBH9+7EHRNyifEJry/bymOfF1jSMy1mNTxjvCeiE16oHNkrjTtP\nySUmSvjv8m388dMCai3pGWNMRIvohNeaNbxA43qmcfcpfYmJEt76bhuPzt1oSc8EzWp4xnhPRCe8\nUBuTk8o9E/sSGyW8s6KE332ygZpaS3rGGBOJIjrhhaKGF2hUj1TuOTWPuGgfM1du52FLeiYIVsMz\nxnsiOuG1lZHZKdx3al/io328v2o7D3y03pKeMcZEmIhOeKGs4QUanpXCfZPySIjx8eGaHUybs86S\nnmmU1fCM8Z6ITnhtbVhmMlMn5ZEY4+OjtTuZ+uE6qi3pGWNMRIjohNcWNbxAQ7onc/9p/UiM8fFJ\n/k7um51PVU1tm8dhvM1qeMZ4T0QnvHA5LCOJ6ZP7kRwbxafrd3Hv7HXss6RnjDGeFtEJry1reIEG\npicxbXI/UuKi+HzDLu55P5991Zb0jMNqeMZ4T0QnvHAb0C2RByb3IzUuii83lnLX+2st6RljjEdF\ndMILRw0vUF7XRB6Y3J+0+GjmF5Rx56y17LWk1+FZDc8Y74nohOcVfbsm8MDkfqTFR7OgsIw73ltD\npSU9Y4zxlIhOeOGs4QXK7ZLAQ1P60TkhmkVFu7l95hr2VNWEOywTJlbDM8Z7IjrheU3vzgk8OKU/\nXRKjWbJpN7da0jPGGM+I6ITnhRpeoF6d4nl4Sn+6JcbwzeZybnl3DRX7LOl1NFbDM8Z7IjrheVWP\ntHgeOr0/6UkxLCt2kl65JT1jjAmriE54XqrhBcpOjeOhKf3JSI5h+ZZybnpnNbv3Voc7LNNGrIZn\njPcEnfBEZJCInC8il7m3y0XkslAGF+my3KTXPTmWFVsruOmdNZRZ0jPGmLAIKuGJyC3AYuDXwEXu\n7UL3Z9h4sYYXKDMljodP709WSiwrt1Vw44zVlFZa0mvvrIZnjPcE28K7Fhinqkeo6kn+t2APJCJP\ni0ixiCz1W3eXiBSIyCL3dprftptFZJWIfCciE4P/lbwnIzmWh07vT3ZqHKtL9nDDjNXssqRnjDFt\nKtiEVwGsOMRjPQNMClinwCOqOtK9vQMgIoOB84HB7mMeF5EDYvVyDS9QelIsD0/pT05aHGu37+GG\nt1exY09VuMMyIWI1PGO8J9iEdzvwBxHJFhGf/y3YA6nqJ8COBjZJA+vOBF5W1SpVXQesBsYFeyyv\n6poUw4NT+tMzLY78HZXc8PZqdlRY0jPGmLYQbMJ6FrgCKACq/W6t8Wn9KxFZIiJPiUgnd122e6w6\nBUCPwAdGQg0vUNfEGB6a0p/eneJZv7OS695eRYklvXbHanjGeE90kPv1DdHx/wz81r1/D/AwcHkj\n+x5wafGPPvqI+fPn06tXLwDS0tIYNmxYfXdS3YeOF5cfmNKPy373CsvW7ON64IHJ/fhu0Veeic+W\nbdmW28/y3LlzeemllwDo1asXGRkZjB8/no5GVA/II43v7HRhdgeKVbXFZ0cWkT7Am6o6rKltInIT\ngKpOc7e9C9ypql/6P2b27Nk6atSolobhGbsqq7lxxmrWbt9DdmocD07pR3pSbLjDMsa0cwsXLmT8\n+PENlZPatWCnJaSKyPNAJVAIVIrI8yKSdigHF5Esv8WzgboRnP8FfiwisSKSC/QHvjqUY3lRWnw0\nD0zuR7+uCRSV7uW6t1axZfe+cIdljDHtUrA1vD8CScBQINHv5x+DPZCIvAx8BgwUkY3upPXpIvK1\niCwBTsCZ/oCqLgdeAZYD7wC/0AaaopFYwwuUGh/N9Mn9GNAtkU1l+/jNW6vYXLY33GGZQ2Q1PGO8\nJ9ga3iSgr6qWu8srReRSYG2wB1LVnzSw+ukm9p8KTA32+SNZSlw0007L4+Z317BiawXXvb2KByf3\nJys1LtyhGWNMuxFsC28PkB6wrhtOF2fYRNI8vOYkx0Uz7bR+HJaRyJbdVVz39ioKd1lLL1LZPDxj\nvCfYhPc3YJaIXCUip4nIz4H3gL+GLrSOJyk2iqmT+jGkexJby6u4/u1VFOwK63cKY4xpN4JNePcB\n9wPn4kwd+CEwHbg3RHEFpT3U8AI5SS+PYZnJbKtwWnobdlrSizRWwzPGe4JKeOp4WlXHq+pgVZ2g\nqk81NJDEHLqEmCjuPbUvI7KS2V5RzfVvr2L9jj3hDssYYyJao/PwROQiVX3BvX85DUz8BlDVRgee\nhFqkz8NrTmV1LXe+t4ZFRbvp5I7mzO2SEO6wjDERzubhHch/VOVFTdxMiMRH+/jtxDxG90hhZ2U1\nN8xYzdoSa+kZY8zBaDThqepkv/snBl4WqKWXBwqF9ljDCxQX7ePuU/oyJieFXZXVXD9jFWtKKsId\nlmmG1fCM8Z5gz7SyqJH181s3HNOQ2Ggfd53SlyN6plK2t4YbZqxm1TZLesYY0xLBjtLsF7hCRITQ\nnVQ6KO1pHl5zYqN83D4hl6N6pVG2t4YbZ6xmxdby5h9owsLm4RnjPU0mPBF5QUReAOLcc2e+4Lfu\nY2BZm0RpACfp3Ta+D8f0TmP3PifpfbvFkp4xxgSjuRbeGvemfvfX4FyQ9UWcC7WGTUeo4QWKifJx\n6/hcjsvtREVVLTe/s5plxbvDHZYJYDU8Y7ynyXNpqupdACLyhaq+2yYRmWZF+4RbTurDdFnHnLU7\nueXdNdx3ah5DM5PDHZoxxnhWsBPP33Uv1TNMRE4SkZPrbqEOsCkdqYYXKMon3HhiH07O68yeqlpu\neXcNX28qC3dYxmU1PGO8J9hRmscC64GPgPeBV3HOpfm30IVmmhPlE64/oTcT+nehsrqWW99dw6Ii\nS3rGGNOQYEdp/h54UFW7AKXuz98Cfw5ZZEHoiDW8QFE+4TfH9eLUAV3YW6PcPnMNCwtLwx1Wh2c1\nPGO8J9iE1x8n6QHUnY5mGu4FW014RfmEa4/rxWkDu7KvRrnjvbXML7CkZ4wx/oJNeLuANPd+kYgM\nATrjXAU9bDpyDS+QT4Rrju3J6YO6sa9GuXPWWr7auCvcYXVYVsMzxnuCTXivA3WnGnsa+ABYiFPL\nMx7hE+FXx+RwxuBuVNUod8/K5/P1lvSMMQaCH6V5jar+3b3/EPAj4H+AK0IYW7OshncgEeGXR+Vw\n9pB0qmqVe2bn89n6neEOq8OxGp4x3hPsKM0eItKlbllVPwG+BDJDFZg5eCLCVUf24IdD06muVe55\nP5+5+Zb0jDEdW7Bdmv8BegSsy8Hp6gwbq+E1TkS48ogenDc8gxqFez/I5+O1O8IdVodhNTxjvCfY\nhDdAVZcGrFsKHNbK8ZhWJCJcPjabn4zoTq3C1A/X8eGa7eEOyxhjwiLYhLdFRPoHrMsDtrVyPC1i\nNbzmiQiXjsnipyMzqVWYPmc976+ypBdqVsMzxnuCTXhPA6+JyA9EZLCInAG8BjwVutBMaxERLhmd\nxcWjnKT34EfreW9lSbjDMsaYNtXkyaP9TAeqgIdwancbcU4r9kiI4gqK1fBa5sJRWfhEeHbBJh7+\neAO1CpMGdg13WO2S1fCM8Z6gEp6q1gAPujcTwS4YmUmUT3hqXhGPfLKBGlWmDOoW7rCMMSbkgkp4\nIjIe55p4B1DVD1o1ohZYvHgxo0aNCtfhI9b5I7rjE/jrV0U8OncjtbXKDwanhzusdmXu3LnWyjPG\nY4Lt0nyK/RNeOhCH07XZt7WDMqF37vDuRPmEJ74o5I+fFVCrcOYQS3rGmPYr2C7NPv7LIhIF3AaE\n9VLbVsM7NOcMzSBKhMc+L+CxzwuoUeWcoRnhDqtdsNadMd4T7CjN/bg1vanADa0bjmlrZw5J51dH\n5wDwxBeFvPp1cZgjMsaY0DiohOc6BahprUAOhs3Dax0/GJzONcf2BODJr4r45xJLeofK5uEZ4z3B\nDlrZGLAqEYgHftHqEZmwmDKoG1Ei/O6TDTw1r4haVX5yuJ0q1RjTfgQ7aOWigOVyYKWqhvXaM1bD\na12TBnbFJ/Dwxxt4Zv4mamqVC0dlhTusiGQ1PGO8J9hBK3NCHIfxiIkDuuIT4aGP1/P8ws3UKFw8\nKhMRaf7BxhjjYY0mPBF5IWBV3bQE8buPql4cgriCYvPwQmNC/y5E+Zzzbv590WZqa5VLx2RZ0msB\nm4dnjPc0NWhlDbDave0EzgKicObeRQFnuutNO3RSXhduPqkPPoGXlxTz9LwiVBs894AxxkSERlt4\nqnpX3X0ReQ+Y4l74tW7dscAdIY2uGVbDC60T+nbGJ8LUD/L559dbqFG4Yly2tfSCYK07Y7wn2GkJ\nRwJfBKz7EjiqdcMxXnNcbiduG59LtE94dekWnvii0Fp6xpiIFGzCWwTcLyIJACKSiDPxfFGoAguG\nzcNrG8f06cTt43OJ8QmvL9vKY58XWNJrhs3DM8Z7gk14lwLHAKUisgXYBRwLXBKiuIzHHNU7jTtP\nySUmSvjv8m388dMCai3pGWMiSFAJT1XzVfUonKucnwH0U9WjVDU/pNE1w2p4bWtczzTuPqUvMVHC\nW99tc660YEmvQVbDM8Z7mkx4InJEwKqtqvqFqq53t58dssiMJ43JSeWeiX2JjRLeWVHC7z7ZQE2t\nJT1jjPc118J7P2C5MGD5+VaMpcWshhceo3qkcs+pecRFCTNXbudhS3oHsBqeMd7T0pNH23h0A8DI\n7BTum5RHfLSP91dt58GP1lvSM8Z42qFcLSHsrIYXXsOznKSXEOPjgzU7mD5nnSU9l9XwjPGeiE54\nJvyGZSYzdVIeiTE+5qzdyf0frqPakp4xxoOaS3hJIrKx7gakBiwntkGMjbIanjcM6Z7M/af1IzHG\nx8f5O7lvdj5VNbXhDiusrIZnjPc0d7WEk5vZHvRXeRF5GpgCbFHVYe66LsA/gd7AOuA8Vd3pbrsZ\nuAznIrNXq+p7wR7LtL3DMpKYPrkfN7+zhk/X7+LeD9Zx28l9iImyTgRjjDdIW50xQ0SOA3YDz/sl\nvAeAbar6gIjcCHRW1ZtEZDDwEjAW6IEzWnSAqu7XbJg9e7ba1RK8ZeW2Cm5+ZzVle2s4omcqt0/I\nJdaSnjGesnDhQsaPH9/hBiG22SeRe+LpHQGrzwCec+8/h3NFBnCuxPCyqlap6jqcKzaMa4s4zaEZ\n0C2RByb3IzUuii83lnL3rHz2VXfs7k1jjDeE+6t3d1Utdu8XA93d+9lAgd9+BTgtvf1YDc+b8rom\n8sDk/qTFRzOvoJQ7Z61lbwdLelbDM8Z7grrieVtQVRWRpvpXD9j20UcfMX/+fHr16gVAWloaw4YN\nqx8SXvehY8ttv9y3awLnddnCX74sZAHDuOO9NZyatInYKJ8n4rNlW+5Iy3PnzuWll14CoFevXmRk\nZDB+/Hg6mkZreCJSpKrZ7v2nVfWyQz6YSB/gTb8a3nfAiaq6WUSygA9VdZCI3ASgqtPc/d4F7lTV\nL/2fz2p43rd+xx5umLGaHXuqGZGVzG8n9iUhJircYRnToVkN70AxItLVvX9uiI7/X76/4sIlwBt+\n638sIrEikgv0B74KUQwmhHp3TuDBKf3pkhjNkk27uW3mWvZU1YQ7LGNMB9RUwvsLUD/fzn/+nd9t\nQ7AHEpGXgc+Age5jfwZMA04RkZU4UyCmAajqcuAVYDnwDvALbaApajW8yNCrUzwPTelP18QYlm7e\nzS3vrqFiX/tOelbDM8Z7Gq3hqeptIvIk0At4D7iQQziXpqr+pJFNExrZfyrORWZNO5CT5iS962es\nYllxObe8u4b7JuWRFGvdm8aYthHUPDwRmaCqgVdOCDur4UWeTaV7uX7GKrbsrmJQeiJTJ+WRHOeZ\nsVPGdAiYbwb2AAAdJklEQVRWw2vabBG5TEQ+FJGVIvKBu9zhXjBzaLJS43hoSn+6J8fy3dYKbnpn\nDWV7q8MdljGmAwg24d0C3Ai8DFwN/AO4Hrg1RHEFxWp4kSkzJY6HT+9PVkosK7dVcOOM1ZRWtq+k\nZzU8Y7wn2IR3BTBRVZ9U1XdV9UlgEnBl6EIz7VlGciwPTulPdmocq0ucqQu72lnSM8Z4S7AJLxHY\nFrCuBIhv3XBaxq6HF9kykmN5aEo/ctLiWLt9Dze8vYqde6rCHVarsOvhGeM9wSa8d4EXRWSQiCSI\nyGHA88DM0IVmOoJuSU5Lr2daHPk7Krl+xmp2VLSPpGeM8ZZgE96vgDJgCVAOLHZ//ipEcQXFanjt\nQ9fEGB6a0p/eneJZv6OS695eRUmEJz2r4RnjPUElPFXdpaoX43RtZgGJqnpR3bXrjDlUnRNjeGBK\nP/p0jmfjrr1c//YqSsojO+kZY7ylRVdLUNUaVS1WVU+cJsNqeO1L54QYHpzSn75dEijYtZfr3l7F\n1vJ94Q7roFgNzxjvCfflgYzZT1p8NA9M7ke/rgkUlu7lurdWsWV3ZCY9Y4y3RHTCsxpe+5QaH830\nyf0Y0C2RTWX7+M1bq9hctjfcYbWI1fCM8Z6ITnim/UqJi2baaXkMTE+kePc+rnt7FZsiLOkZY7wl\nqIQnIieLSF/3fpaIPC8iz4hIZmjDa5rV8Nq35Lhopp3Wj8MyEtmyu4rr3lpFUWlkJD2r4RnjPcG2\n8B4H6k6D8QjOVRYUeDIUQRlTJyk2iqmT+jGkexJby52kV7irMtxhGWMiULAJL1tVN4hIDHAq8P+A\nq4BjQhZZEKyG1zE4SS+PYZnJbKuo4jdvr2LjTm8nPavhGeM9wSa8Urf78nhgmaqW4VwbLyZkkRnj\nJyEmintP7cuIrGS2V1Rz3dur2LDD20nPGOMtwSa8PwJfAS/hdG+C07r7NhRBBctqeB1LQkwU95ya\nx8jsZHbscZLeuh17wh1Wg6yGZ4z3BHumlenAKcDRqvqyu7oA+J9QBWZMQ+Kjffx2Yh6je6Sws7Ka\n699ezdoSbyY9Y4y3tGRawlqgh4ic7y4XAfmtH1LwrIbXMcVF+7j7lL6MyUlhV2U1N8xYxZqSinCH\ntR+r4RnjPcFOSxgGrMQZlfmUu/oEv/vGtKnYaB93ndKXI3qmUrq3hhtmrGbVNm8lPWOMtwTbwnsC\nuFNVBwF1Z/SdAxwXiqCCZTW8ji02ysftE3I5qlcaZXtruHHGalZsLQ93WIDV8IzxomAT3mDghYB1\nFUBC64ZjTMvERvm4bXwfjumdxu59Ndz0zhq+3eKNpGeM8ZZgE956YEzAurHAqtYNp2WshmcAYqJ8\n3Do+l+NyO1G+r4ab31nNsuLdYY3JanjGeE+wCe824C0R+S0QKyK3AK8Ct4csMmNaINon3HJSH07o\n24mKqlpueXcN32wOb9IzxnhLsNMS3gImAenAR0Av4GxVnRnC2JplNTzjL8on3HRiH07K68weN+l9\nvSk8Sc9qeMZ4T7CjNM9V1UWq+nNVnayqV6nqAhH5UagDNKYlonzCDSf0ZkK/zlRW13LrzDUsLioL\nd1jGGA8Itkvz6UbW/7W1AjkYVsMzDYnyCb85vjenDujC3upabp+5hoWFpW0ag9XwjPGeJhOeiPQV\nkTznrvQNuJ0C2CkujCdF+YRrj+vFaQO7srdGueO9tcwvaNukZ4zxluhmtq9u5D5AMXBXq0bTQlbD\nM03xiXDNsT3xCbz9XQl3zlrLnRNyGdczLeTHthqeMd7TZAtPVX2q6gPm1t33u2Wp6l/aKE5jDopP\nhKuP6ckZg7tRVaPcPSufLzbsCndYxpgwCHaU5vGhDuRgWA3PBENE+OVROZw9JJ2qWuW37+fz2fqd\nIT2m1fCM8Z7mujQBEJFPGtmkXk2GxvgTEa46sgc+gde+2co97+dz68m5HJvbKdyhGWPaSFAJjwNP\nEp0JXA682LrhtIzV8ExLiAhXHtEDnwj/WrqFez/I55aT+nB8386tfiyr4RnjPUElPFV9NnCdiLwK\nPAPc3coxGRMyIsL/jMsmyif8Y0kxUz9cR43CSXmtn/SMMd7SkuvhBSoERrRWIAfDanjmYIgIPxuT\nxU9HZlKrMH3OOmav3t6qx7AanjHeE2wN73JA/VYlAecAn4ciKGNCTUS4ZHQWUQLPL9zMA3PWU1Or\nTBzQNdyhGWNCJNga3kXsn/DKgU+B37V6RC1gNTxzqC4clYVPhGcXbOLhjzdQqzBp4KEnPavhGeM9\nwdbwTgxxHMaEzQUjM/H54Ol5m3jkkw3UqjJ5ULdwh2WMaWVB1/BEJE1ExonIyf63UAbXHKvhmdby\n4xGZXDEuG4Dfz93IW99uO6TnsxqeMd4TbA3vUuAxYDfOlc795bZyTMaExbnDuxPlE574opA/fLqR\nmlrlzCHp4Q7LGNNKgq3hTQV+pKrvhDKYlrIanmlt5wzNwCfC458X8NjnBdSqcvbQjBY/j9XwjPGe\nYLs0o4D3QhmIMV5x1pB0/vfoHAD+/EUhry7dEuaIjDGtIdiENx24XUQOZd5eq7MangmVMwanc82x\nPQF48stCXllS3KLHWw3PGO8Jtkvz10B34AYRKfFbr6raq/XDMib8pgzqRpQIv/tkA3+bV0SNKj85\nPDPcYRljDlKwCe/CkEZxkKyGZ0Jt0sCu+AQe/ngDz8zfRE2tcuGorGYfZzU8Y7wn2Hl4c0IchzGe\nNXFAV3wiPPTxep5fuJlahYtGZSIi4Q7NGNMCjSY8EblNVe9179+Dc6aVuv/wuvuqqneEPMpGLF68\nmFGjRoXr8KYDmdC/C1E+mD5nPS8u2kyNKpeOzmo06c2dO9daecZ4TFMtvB5+93uy/6nFwE14rRGE\niKwDSoEaoEpVx4lIF+CfQG9gHXCeqob2qp3GNOGkvC74RLj/w3W8vLiY2lrlsrHZ1tIzJkI0mvBU\n9ed+9y8NcRwKnKiq/qesvwmYpaoPiMiN7vJN/g+yGp5payf07YxPhKkf5PPPr7dQo3DFuAOTnrXu\njPGeJqcZiEiv5m6tGEvg1+QzgOfc+88BZ7XisYw5aMflduK28blE+4RXl27hiS8LUW2Vzg5jTAg1\nN69uHZDv/mzolt9KcSjwvojMF5Er3HXdVbVu8lMxzrSI/dg8PBMux/TpxO3jc4nxCa9/s5XHPy/Y\nL+nZPDxjvKe5UZpLgATgeeBFnIu+hqJgcYyqbhKRdGCWiHznv1FVVUQO+Ar90UcfMX/+fHr1chqa\naWlpDBs2rL47qe5Dx5ZtORTLNRuXclanct7Y1Z3/LN/G6q/ncfaQdI4/7jhPxGfLtly3PHfuXF56\n6SUAevXqRUZGBuPHj6ejkea6YkRkGHAJcD6wHCf5/VtV94QkIJE7cU5SfQVOXW+ziGQBH6rqIP99\nZ8+erTZK04Tb/IJS7py1lqoa5bSBXbnm2J74bCCL8bCFCxcyfvz4DvcmbfZUYaq6VFWvA/rgXPD1\ndGCTiLRKphGRRBFJce8nAROBpcB/cRIt7s83WuN4xrS2MTmp/PaUvsRGCe+sKOF37jX1jDHe0pJz\nY/YHjgeOBhYBrTVFoDvwiYg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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "avgs = education_time_by_age()\n", + "y = [avgs[0], avgs[1], avgs[2]]\n", + "x = range(3)\n", + "plt.plot(x,y)\n", + "plt.xticks(range(3), [\"Teens\", \"Twenties\", \"Over Thirty\"], ha='left')\n", + "plt.xlabel(\"Age\")\n", + "plt.ylabel(\"Minutes of Education\")\n", + "plt.title(\"Average Education Time Vs. Age\")\n", + "plt.style.use('bmh')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Obviously this one makes sense and turned out as expected." + ] + }, + { + "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 +} diff --git a/atus_analysis/data_analysis.py b/atus_analysis/data_analysis.py new file mode 100644 index 0000000..eeda48b --- /dev/null +++ b/atus_analysis/data_analysis.py @@ -0,0 +1,104 @@ +import pandas as pd +import re + +summary = pd.read_csv("atusdata/atussum_2013/atussum_2013.dat") +activity = pd.read_csv("atusdata/atusact_2013/atusact_2013.dat") +respondent = pd.read_csv("atusdata/atusresp_2013/atusresp_2013.dat") +summary = summary.rename(columns={'tucaseid':'TUCASEID'}) + + +def activity_columns(data, activity_code): + col_prefix = "t{}".format(activity_code) + return [column for column in data.columns if re.match(col_prefix, column)] + +def average_minutes(data, cols): + activity_data = data[cols] + activity_sums = activity_data.sum(axis=1) + data = data[['TUFINLWGT']] + data['minutes'] = activity_sums + data = data.rename(columns={"TUFINLWGT": "weight"}) + data['weighted_minutes'] = data.weight * data.minutes + return data.weighted_minutes.sum() / data.weight.sum() + +def amt_leisure_time_by_age(): + all_cols = activity_columns(summary, '12') + all_cols.extend(activity_columns(summary, '13')) + older_ages_crit = summary.TEAGE >= 75 + old = summary[older_ages_crit] + mid = summary[summary.TEAGE.isin([35,36,37,38,39,40,41,42,43,44])] + young = summary[summary.TEAGE.isin([25,26,27,28,29,30,31,32,33,34])] + old_avg = average_minutes(old, all_cols) / 60 + mid_avg = average_minutes(mid, all_cols) / 60 + young_avg = average_minutes(young, all_cols) / 60 + return old_avg, mid_avg, young_avg + +def corr_sports_school(): + relevant_respondent = respondent[["TUCASEID", "TESCHENR"]] + sports_list = ['TUCASEID'] + sports_list.extend(activity_columns(summary, '1301')) + relevant_respondent.index = relevant_respondent.pop("TUCASEID") + in_school = relevant_respondent.rename(columns={'TESCHENR':'In_school'}) + sports_data = summary[sports_list] + sports_data.index = sports_data.pop('TUCASEID') + summed_exercise = sports_data.sum(axis=1) + exercise_frame = summed_exercise.to_frame(name='Exercise') + result = pd.merge(exercise_frame, in_school, left_index=True, right_index=True) + result['ID'] = result.index + result.index = result.pop('In_school') + ids = result.pop("ID") + return result['Exercise'].groupby(result.index).sum() + +def avg_hours_tv_per_month(): + month_data = respondent[['TUCASEID', 'TUFINLWGT', 'TUMONTH']] + month_data = month_data.rename(columns={'TUFINLWGT':'Weight', 'TUMONTH':'Month'}) + tv_data = summary[['TUCASEID','t120303']] + tv_data = tv_data.rename(columns={'t120303':'TV_mins'}) + data = pd.merge(month_data, tv_data, left_on='TUCASEID', right_on='TUCASEID') + return data['TV_mins'].groupby(data['Month']).mean() / 60 + +def avg_sleep_by_kid_age(): + kid_data = respondent[['TUCASEID', 'TRHHCHILD','TRYHHCHILD']] + sleep_data = summary[['TUCASEID', 'TUFINLWGT', 't010101']] + data = pd.merge(kid_data, sleep_data, right_on='TUCASEID', left_on='TUCASEID') + yes_kids = data[data['TRHHCHILD'] == 1] + no_kids = data[data['TRHHCHILD'] == 2] + yes_kids['weighted_mins'] = yes_kids['t010101'] * yes_kids['TUFINLWGT'] + avg_yes = (yes_kids.weighted_mins.sum() / yes_kids.TUFINLWGT.sum()) / 60 + no_kids['weighted_mins'] = no_kids['t010101'] * no_kids['TUFINLWGT'] + avg_no = (no_kids.weighted_mins.sum() / no_kids.TUFINLWGT.sum()) / 60 + + young_kids = yes_kids[yes_kids['TRYHHCHILD'] < 3] + young_kids['weighted_mins'] = young_kids['t010101'] * young_kids['TUFINLWGT'] + avg_young = (young_kids.weighted_mins.sum() / young_kids.TUFINLWGT.sum()) / 60 + return (avg_young, avg_yes, avg_no) + +def avg_leisure_by_overtime_earnings(): + all_cols = activity_columns(summary, '12') + all_cols.extend(activity_columns(summary, '13')) + all_cols.append('TUCASEID') + leisure_data = summary[all_cols] + overtime_earnings = respondent[['TUCASEID', 'TUFINLWGT', 'TEERN']] + overtime_earnings = overtime_earnings[overtime_earnings['TEERN'] > 0] + merged = pd.merge(leisure_data, overtime_earnings, left_on="TUCASEID", right_on="TUCASEID") + low = merged[merged['TEERN'] < 5000] + med = merged[merged['TEERN'].isin(range(5000,10001))] + high = merged[merged['TEERN'] > 10000] + cols = activity_columns(summary, '12') + cols.extend(activity_columns(summary, '13')) + low_avg = average_minutes(low, cols) / 60 + med_avg = average_minutes(med, cols) / 60 + high_avg = average_minutes(high, cols) / 60 + return low_avg, med_avg, high_avg + +def education_time_by_age(): + cols = activity_columns(summary, '06') + cols.extend(['TUCASEID', 'TEAGE', 'TUFINLWGT']) + activity_data = summary[cols] + teens = activity_data[activity_data['TEAGE'] < 20] + twenties = activity_data[activity_data['TEAGE'].isin([20,21,22,23,24,25,26,27,28,29])] + over_thirty = activity_data[activity_data['TEAGE'] >= 30] + the_cols = activity_columns(summary, '06') + teens_avg = average_minutes(teens, the_cols) + twenties_avg = average_minutes(twenties, the_cols) + over_thirty_avg = average_minutes(over_thirty, the_cols) + return teens_avg, twenties_avg, over_thirty_avg \ No newline at end of file From b58d856dbca781767244c202b3da003eb3a8b598 Mon Sep 17 00:00:00 2001 From: Hannah Date: Sun, 31 May 2015 17:38:45 -0400 Subject: [PATCH 2/2] ReadMe added --- README.md | 63 +------------------------------------------------------ 1 file changed, 1 insertion(+), 62 deletions(-) diff --git a/README.md b/README.md index 88b27af..0fe5dcb 100644 --- a/README.md +++ b/README.md @@ -1,64 +1,3 @@ # American Time Use Analysis -## Description - -Use the U.S. Department of Labor's data on Americans' time use for research and analysis. - -## Objectives - -### Learning Objectives - -After completing this assignment, you should understand: - -* How to use public data for analysis -* How to translate data from CSVs to relational databases -* How to publish your own data analysis as a notebook - -### Performance Objectives - -After completing this assignment, you should be able to: - -* Use pandas to parse and analyze data -* Use matplotlib to chart data -* Clean data - -## Details - -### Deliverables - -* A Git repo called atus-analysis containing at least: - * `README.md` file explaining how to run your project - * a `requirements.txt` file - * an IPython notebook with your analysis - * a suite of tests for your project - -### Requirements - -* Passing unit tests -* No PEP8 or Pyflakes warnings or errors - -## Normal Mode - -The U.S. Bureau of Labor Statistics publishes yearly data about Americans' use -of their time: [American Time Use Survey](http://www.bls.gov/tus/home.htm#data). -This data is used to find out information like how many hours the average person -spends per day doing household activities. You can see -[the 2013 survey results](http://www.bls.gov/news.release/atus.nr0.htm) -for more examples. You should use these results to double-check your logic as well. - -You will download the 2013 files and use these to do analysis. The questions you are trying to answer are up to you, but they -should at least: - -* Compare different populations (people with children and people without, people of differing age groups, men and women, or other groupings) -* Answer macro-level questions and micro-level questions (for example, the amount of leisure for the macro-level, the types of things people do for leisure for the micro-level) - -Your final analysis should be in the form of an IPython Notebook with both -narrative analysis and supporting charts. Your supporting code should be in -normal Python files. - - - -## Additional Resources - -* [How to use ATUS microdata files](http://www.bls.gov/tus/howto.htm) -* [ATUS Coding Lexicon - you will need this](http://www.bls.gov/tus/lexicons.htm) +The ipython notebook (atus_analysis.ipynb) has my findings in it, with the functions all contained in the data_analysis.py file.