ACS.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Code for August 14, 2016 post on Medium\n",
    "\n",
    "# not using the Census API because they don't have the most recent 5-year ACS available (2014)\n",
    "# using help from https://stharrold.github.io/20160110-etl-census-with-python.html for replicate weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "% pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "path_acs = # redacted for anonymity\n",
    "path_acs_hh = os.path.join(path_acs, 'OpenEastBay_household.csv')\n",
    "path_acs_person = os.path.join(path_acs, 'OpenEastBay_person.csv')\n",
    "path_acs_emp = os.path.join(path_acs, 'OpenEastBay_employment.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>datanum</th>\n",
       "      <th>serial</th>\n",
       "      <th>hhwt</th>\n",
       "      <th>city</th>\n",
       "      <th>gq</th>\n",
       "      <th>pernum</th>\n",
       "      <th>perwt</th>\n",
       "      <th>repwtp</th>\n",
       "      <th>bpl</th>\n",
       "      <th>...</th>\n",
       "      <th>repwtp71</th>\n",
       "      <th>repwtp72</th>\n",
       "      <th>repwtp73</th>\n",
       "      <th>repwtp74</th>\n",
       "      <th>repwtp75</th>\n",
       "      <th>repwtp76</th>\n",
       "      <th>repwtp77</th>\n",
       "      <th>repwtp78</th>\n",
       "      <th>repwtp79</th>\n",
       "      <th>repwtp80</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 2014</td>\n",
       "      <td> 5</td>\n",
       "      <td> 327896</td>\n",
       "      <td> 11</td>\n",
       "      <td> 4930</td>\n",
       "      <td> 1</td>\n",
       "      <td> 1</td>\n",
       "      <td> 12</td>\n",
       "      <td> 1</td>\n",
       "      <td> 500</td>\n",
       "      <td>...</td>\n",
       "      <td>  5</td>\n",
       "      <td> 4</td>\n",
       "      <td> 15</td>\n",
       "      <td> 10</td>\n",
       "      <td> 16</td>\n",
       "      <td> 21</td>\n",
       "      <td>  3</td>\n",
       "      <td>  9</td>\n",
       "      <td> 11</td>\n",
       "      <td>  4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 2014</td>\n",
       "      <td> 5</td>\n",
       "      <td> 327903</td>\n",
       "      <td> 12</td>\n",
       "      <td> 4930</td>\n",
       "      <td> 1</td>\n",
       "      <td> 1</td>\n",
       "      <td> 12</td>\n",
       "      <td> 1</td>\n",
       "      <td>  26</td>\n",
       "      <td>...</td>\n",
       "      <td> 12</td>\n",
       "      <td> 3</td>\n",
       "      <td> 10</td>\n",
       "      <td>  3</td>\n",
       "      <td> 20</td>\n",
       "      <td> 20</td>\n",
       "      <td> 12</td>\n",
       "      <td> 11</td>\n",
       "      <td> 12</td>\n",
       "      <td> 21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 2014</td>\n",
       "      <td> 5</td>\n",
       "      <td> 327903</td>\n",
       "      <td> 12</td>\n",
       "      <td> 4930</td>\n",
       "      <td> 1</td>\n",
       "      <td> 2</td>\n",
       "      <td>  9</td>\n",
       "      <td> 1</td>\n",
       "      <td> 200</td>\n",
       "      <td>...</td>\n",
       "      <td>  9</td>\n",
       "      <td> 3</td>\n",
       "      <td> 10</td>\n",
       "      <td>  2</td>\n",
       "      <td> 13</td>\n",
       "      <td> 14</td>\n",
       "      <td>  9</td>\n",
       "      <td>  8</td>\n",
       "      <td>  7</td>\n",
       "      <td>  9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 2014</td>\n",
       "      <td> 5</td>\n",
       "      <td> 327903</td>\n",
       "      <td> 12</td>\n",
       "      <td> 4930</td>\n",
       "      <td> 1</td>\n",
       "      <td> 3</td>\n",
       "      <td> 17</td>\n",
       "      <td> 1</td>\n",
       "      <td> 200</td>\n",
       "      <td>...</td>\n",
       "      <td> 21</td>\n",
       "      <td> 4</td>\n",
       "      <td> 17</td>\n",
       "      <td>  6</td>\n",
       "      <td> 27</td>\n",
       "      <td> 31</td>\n",
       "      <td> 17</td>\n",
       "      <td> 16</td>\n",
       "      <td> 14</td>\n",
       "      <td> 24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 2014</td>\n",
       "      <td> 5</td>\n",
       "      <td> 327919</td>\n",
       "      <td> 14</td>\n",
       "      <td> 4930</td>\n",
       "      <td> 1</td>\n",
       "      <td> 1</td>\n",
       "      <td> 14</td>\n",
       "      <td> 1</td>\n",
       "      <td> 210</td>\n",
       "      <td>...</td>\n",
       "      <td> 16</td>\n",
       "      <td> 3</td>\n",
       "      <td>  4</td>\n",
       "      <td> 28</td>\n",
       "      <td> 21</td>\n",
       "      <td>  4</td>\n",
       "      <td> 16</td>\n",
       "      <td> 27</td>\n",
       "      <td>  4</td>\n",
       "      <td> 13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 97 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  datanum  serial  hhwt  city  gq  pernum  perwt  repwtp  bpl ...   \\\n",
       "0  2014        5  327896    11  4930   1       1     12       1  500 ...    \n",
       "1  2014        5  327903    12  4930   1       1     12       1   26 ...    \n",
       "2  2014        5  327903    12  4930   1       2      9       1  200 ...    \n",
       "3  2014        5  327903    12  4930   1       3     17       1  200 ...    \n",
       "4  2014        5  327919    14  4930   1       1     14       1  210 ...    \n",
       "\n",
       "   repwtp71  repwtp72  repwtp73  repwtp74  repwtp75  repwtp76  repwtp77  \\\n",
       "0         5         4        15        10        16        21         3   \n",
       "1        12         3        10         3        20        20        12   \n",
       "2         9         3        10         2        13        14         9   \n",
       "3        21         4        17         6        27        31        17   \n",
       "4        16         3         4        28        21         4        16   \n",
       "\n",
       "   repwtp78  repwtp79  repwtp80  \n",
       "0         9        11         4  \n",
       "1        11        12        21  \n",
       "2         8         7         9  \n",
       "3        16        14        24  \n",
       "4        27         4        13  \n",
       "\n",
       "[5 rows x 97 columns]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Person-level ACS data\n",
    "person = pd.read_csv(path_acs_person)\n",
    "person.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Defining a function to compute the weighted proportions of a categorical variable\n",
    "def wtd_prop(df, var):\n",
    "    wtd = df.groupby(by=['city', var])['perwt'].sum().unstack()\n",
    "    wtdT = wtd.transpose()\n",
    "    wtdT.pct = wtdT.div(wtdT.sum(axis=0), axis=1)\n",
    "    return wtdT.pct\n",
    "\n",
    "# renaming city var as Oakland and NYC\n",
    "person[\"city\"].replace({4610: \"New York\", 4930: \"Oakland\"}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "city     New York   Oakland\n",
      "empstat                    \n",
      "0        0.190047  0.185126\n",
      "1        0.461463  0.482360\n",
      "2        0.051796  0.060239\n",
      "3        0.296694  0.272274\n"
     ]
    }
   ],
   "source": [
    "# distribution of employment variable\n",
    "print wtd_prop(person, 'empstat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "city     New York  Oakland\n",
      "empstat                   \n",
      "1        0.899084  0.88898\n",
      "2        0.100916  0.11102\n"
     ]
    }
   ],
   "source": [
    "# proportion employed out of those who are eligible (taking out the people not in labor force and NA)\n",
    "print wtd_prop(person.loc[(person[\"empstat\"] == 1) | (person[\"empstat\"] == 2)], 'empstat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average travel time to work among the employed: city\n",
      "New York    39.603626\n",
      "Oakland     29.195720\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# (weighted) average travel time to work among the employed\n",
    "person_emp = person.loc[(person[\"empstat\"] == 1)]\n",
    "\n",
    "# removing the 0's because they indicate \"Not Applicable\"\n",
    "# (see https://usa.ipums.org/usa-action/variables/TRANTIME#codes_section)\n",
    "grouped = person_emp[(person_emp[\"trantime\"] != 0)].groupby('city')\n",
    "minifun = lambda x: np.average(x.trantime, weights=x.perwt)\n",
    "print \"Average travel time to work among the employed: \" + str(grouped.apply(minifun))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAEZCAYAAAC5AHPcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nE4iZmRXiBGJmZoU4gZiZWSH/AyF3jOohD+eHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c601310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAEZCAYAAABb3GilAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nuOhQ1XoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b18b610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram of travel time to work - NOTE THAT THIS IS UNWEIGHTED\n",
    "fig = plt.figure(figsize=(6,4))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "numBins = 50\n",
    "ax.hist(person_emp[(person_emp.trantime != 0) & (person_emp.city == \"Oakland\")][['trantime']].as_matrix(), \\\n",
    "        numBins, alpha=0.8, color = 'cornflowerblue')\n",
    "ax.tick_params(right = \"off\")\n",
    "ax.tick_params(top = \"off\")\n",
    "ax.set_xlabel('Length of commute (minutes)')\n",
    "ax.set_ylabel('Number of people')\n",
    "ax.set_title('Oakland')\n",
    "plt.show()\n",
    "\n",
    "fig = plt.figure(figsize=(6,4))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "numBins = 50\n",
    "ax.hist(person_emp[(person_emp.trantime != 0) & (person_emp.city == \"New York\")][['trantime']].as_matrix(), \\\n",
    "        numBins, alpha=0.8, color = 'red')\n",
    "ax.tick_params(right = \"off\")\n",
    "ax.tick_params(top = \"off\")\n",
    "ax.set_xlabel('Length of commute (minutes)')\n",
    "ax.set_ylabel('Number of people')\n",
    "ax.set_title('New York')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "city       New York    Oakland\n",
      "tranwork                      \n",
      "0          2.323771   3.283316\n",
      "10        26.210846  63.234784\n",
      "20         0.066586   0.311494\n",
      "31        11.391420   7.543208\n",
      "32         0.263647   0.244144\n",
      "33        41.621221  10.136681\n",
      "34         1.578094   0.369435\n",
      "35         0.966757   0.080226\n",
      "36         0.236852   0.150052\n",
      "40         0.943412   2.693508\n",
      "50         9.817891   4.295548\n",
      "60         0.605812   1.435646\n",
      "70         3.973691   6.221958\n"
     ]
    }
   ],
   "source": [
    "# Type of transportation used to get to work (using person-weights)\n",
    "weightedT = wtd_prop(person_emp, 'tranwork')\n",
    "# Percent not proportion\n",
    "weightedT['New York'] = weightedT['New York'] * 100\n",
    "weightedT['Oakland'] = weightedT['Oakland'] * 100\n",
    "\n",
    "print weightedT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "city                       New York    Oakland\n",
      "tranwork                                      \n",
      "NA                         2.323771   3.283316\n",
      "Auto, truck, or van       26.210846  63.234784\n",
      "Motorcycle                 0.066586   0.311494\n",
      "Bus or trolley bus        11.391420   7.543208\n",
      "Streetcar or trolley car   0.263647   0.244144\n",
      "Subway or elevated        41.621221  10.136681\n",
      "Railroad                   1.578094   0.369435\n",
      "Taxicab                    0.966757   0.080226\n",
      "Ferryboat                  0.236852   0.150052\n",
      "Bicycle                    0.943412   2.693508\n",
      "Walked only                9.817891   4.295548\n",
      "Other                      0.605812   1.435646\n",
      "Worked at home             3.973691   6.221958\n"
     ]
    }
   ],
   "source": [
    "# renaming rows and columns for nice graphing\n",
    "weightedT = weightedT.rename(index={0: 'NA', 10: 'Auto, truck, or van', 20: 'Motorcycle', \\\n",
    "                      31: 'Bus or trolley bus', 32: 'Streetcar or trolley car', \\\n",
    "                      33: 'Subway or elevated', 34: 'Railroad', 35: 'Taxicab', \\\n",
    "                      36: 'Ferryboat', 40: 'Bicycle', 50: 'Walked only', \\\n",
    "                      60: 'Other', 70: 'Worked at home'})\n",
    "print weightedT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.3237710743741631, 26.210845942421006, 0.066586151118138939, 11.391419698709987, 0.26364691856827582, 41.621221034894567, 1.5780943754286469, 0.96675724665876028, 0.2368516345382651, 0.94341188787172292, 9.8178906378885831, 0.6058120605236208, 3.9736913370042615]\n"
     ]
    }
   ],
   "source": [
    "dfList = weightedT['New York'].tolist()\n",
    "print dfList"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAARcAAAEQCAYAAACa1jBsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJtMW/j/ylcDEC8u/TAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e005d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# graphing the modes of commute\n",
    "N = 13\n",
    "ind = np.arange(N)  # the x locations for the groups\n",
    "width = 0.4      # the width of the bars\n",
    "\n",
    "fig = plt.figure(figsize=(4,2))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_xticklabels(ax.xaxis.get_majorticklabels(), rotation=90)\n",
    "\n",
    "rects1 = ax.bar(ind, weightedT['New York'].tolist(), width, color = 'red')\n",
    "rects2 = ax.bar(ind+width, weightedT['Oakland'].tolist(), width, color = 'cornflowerblue')\n",
    "ax.tick_params(right=\"off\")\n",
    "ax.tick_params(top=\"off\")\n",
    "ax.set_ylabel('Percent')\n",
    "ax.set_xticks(ind+width)\n",
    "ax.set_xticklabels( ('NA', 'Auto, truck, or van', 'Motorcycle', 'Bus or trolley bus', 'Streetcar or trolley car', \\\n",
    "                    'Subway or elevated', 'Railroad', 'Taxicab', 'Ferryboat', 'Bicycle', 'Walked only', 'Other', \\\n",
    "                    'Worked at home') )\n",
    "\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "             ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(10)\n",
    "\n",
    "nyc_leg = mpatches.Patch(color='red', label='New York')\n",
    "oak_leg = mpatches.Patch(color='cornflowerblue', label='Oakland')\n",
    "ax.legend(handles=[nyc_leg, oak_leg])\n",
    "\n",
    "ax.set_title('Primary means of transportation to work \\n in New York and Oakland')\n",
    "\n",
    "plt.savefig('/Users/laura/Dropbox/datamatters/fig1.png', bbox_inches='tight', dpi = 150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>datanum</th>\n",
       "      <th>serial</th>\n",
       "      <th>hhwt</th>\n",
       "      <th>city</th>\n",
       "      <th>gq</th>\n",
       "      <th>pernum</th>\n",
       "      <th>perwt</th>\n",
       "      <th>repwtp</th>\n",
       "      <th>nchild</th>\n",
       "      <th>...</th>\n",
       "      <th>repwtp71</th>\n",
       "      <th>repwtp72</th>\n",
       "      <th>repwtp73</th>\n",
       "      <th>repwtp74</th>\n",
       "      <th>repwtp75</th>\n",
       "      <th>repwtp76</th>\n",
       "      <th>repwtp77</th>\n",
       "      <th>repwtp78</th>\n",
       "      <th>repwtp79</th>\n",
       "      <th>repwtp80</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 2014</td>\n",
       "      <td> 5</td>\n",
       "      <td> 327896</td>\n",
       "      <td> 1100</td>\n",
       "      <td> 4930</td>\n",
       "      <td> 1</td>\n",
       "      <td> 1</td>\n",
       "      <td> 1200</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td>  5</td>\n",
       "      <td> 4</td>\n",
       "      <td> 15</td>\n",
       "      <td> 10</td>\n",
       "      <td> 16</td>\n",
       "      <td> 21</td>\n",
       "      <td>  3</td>\n",
       "      <td>  9</td>\n",
       "      <td> 11</td>\n",
       "      <td>  4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 2014</td>\n",
       "      <td> 5</td>\n",
       "      <td> 327903</td>\n",
       "      <td> 1200</td>\n",
       "      <td> 4930</td>\n",
       "      <td> 1</td>\n",
       "      <td> 1</td>\n",
       "      <td> 1200</td>\n",
       "      <td> 1</td>\n",
       "      <td> 1</td>\n",
       "      <td>...</td>\n",
       "      <td> 12</td>\n",
       "      <td> 3</td>\n",
       "      <td> 10</td>\n",
       "      <td>  3</td>\n",
       "      <td> 20</td>\n",
       "      <td> 20</td>\n",
       "      <td> 12</td>\n",
       "      <td> 11</td>\n",
       "      <td> 12</td>\n",
       "      <td> 21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 2014</td>\n",
       "      <td> 5</td>\n",
       "      <td> 327903</td>\n",
       "      <td> 1200</td>\n",
       "      <td> 4930</td>\n",
       "      <td> 1</td>\n",
       "      <td> 2</td>\n",
       "      <td>  900</td>\n",
       "      <td> 1</td>\n",
       "      <td> 1</td>\n",
       "      <td>...</td>\n",
       "      <td>  9</td>\n",
       "      <td> 3</td>\n",
       "      <td> 10</td>\n",
       "      <td>  2</td>\n",
       "      <td> 13</td>\n",
       "      <td> 14</td>\n",
       "      <td>  9</td>\n",
       "      <td>  8</td>\n",
       "      <td>  7</td>\n",
       "      <td>  9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 2014</td>\n",
       "      <td> 5</td>\n",
       "      <td> 327903</td>\n",
       "      <td> 1200</td>\n",
       "      <td> 4930</td>\n",
       "      <td> 1</td>\n",
       "      <td> 3</td>\n",
       "      <td> 1700</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 21</td>\n",
       "      <td> 4</td>\n",
       "      <td> 17</td>\n",
       "      <td>  6</td>\n",
       "      <td> 27</td>\n",
       "      <td> 31</td>\n",
       "      <td> 17</td>\n",
       "      <td> 16</td>\n",
       "      <td> 14</td>\n",
       "      <td> 24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 2014</td>\n",
       "      <td> 5</td>\n",
       "      <td> 327919</td>\n",
       "      <td> 1400</td>\n",
       "      <td> 4930</td>\n",
       "      <td> 1</td>\n",
       "      <td> 1</td>\n",
       "      <td> 1400</td>\n",
       "      <td> 1</td>\n",
       "      <td> 2</td>\n",
       "      <td>...</td>\n",
       "      <td> 16</td>\n",
       "      <td> 3</td>\n",
       "      <td>  4</td>\n",
       "      <td> 28</td>\n",
       "      <td> 21</td>\n",
       "      <td>  4</td>\n",
       "      <td> 16</td>\n",
       "      <td> 27</td>\n",
       "      <td>  4</td>\n",
       "      <td> 13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  datanum  serial  hhwt  city  gq  pernum  perwt  repwtp  nchild  \\\n",
       "0  2014        5  327896  1100  4930   1       1   1200       1       0   \n",
       "1  2014        5  327903  1200  4930   1       1   1200       1       1   \n",
       "2  2014        5  327903  1200  4930   1       2    900       1       1   \n",
       "3  2014        5  327903  1200  4930   1       3   1700       1       0   \n",
       "4  2014        5  327919  1400  4930   1       1   1400       1       2   \n",
       "\n",
       "    ...    repwtp71  repwtp72  repwtp73  repwtp74  repwtp75  repwtp76  \\\n",
       "0   ...           5         4        15        10        16        21   \n",
       "1   ...          12         3        10         3        20        20   \n",
       "2   ...           9         3        10         2        13        14   \n",
       "3   ...          21         4        17         6        27        31   \n",
       "4   ...          16         3         4        28        21         4   \n",
       "\n",
       "   repwtp77  repwtp78  repwtp79  repwtp80  \n",
       "0         3         9        11         4  \n",
       "1        12        11        12        21  \n",
       "2         9         8         7         9  \n",
       "3        17        16        14        24  \n",
       "4        16        27         4        13  \n",
       "\n",
       "[5 rows x 100 columns]"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what about the unemployed?\n",
    "employment = pd.read_csv(path_acs_emp)\n",
    "employment.head() # quick look at the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# just keeping the people who are non-student unemployed \n",
    "unemp = employment.loc[(person[\"empstat\"] == 2)]\n",
    "unemp = unemp.loc[(unemp[\"gradeatt\"] == 0)]\n",
    "unemp.head()\n",
    "\n",
    "# again renaming city var as Oakland and NYC\n",
    "unemp[\"city\"].replace({4610: \"New York\", 4930: \"Oakland\"}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "city     New York   Oakland\n",
      "hcovany                    \n",
      "0        0.322653  0.405933\n",
      "1        0.677347  0.594067\n"
     ]
    }
   ],
   "source": [
    "# About the unemployed - proportion without health insurance\n",
    "unemp[\"hcovany\"].replace(1, 0, inplace = True)\n",
    "unemp[\"hcovany\"].replace(2, 1, inplace = True)\n",
    "print wtd_prop(unemp, 'hcovany') # 1-no, no health insurance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.67734674075\n",
      "0.59406709715\n"
     ]
    }
   ],
   "source": [
    "# Is the difference in health insurance coverage between Oakland and NYC statistically significant?\n",
    "# Using replicate weights to ge the correct standard errors\n",
    "# Creating iterable list of replicate weights\n",
    "hwt = 'repwtp'\n",
    "hwts = [hwt+str(inum) for inum in range(1, 81)] # ['repwt1', ..., 'repwt80']\n",
    "\n",
    "# saving Oakland and NYC means\n",
    "props = wtd_prop(unemp, 'hcovany')\n",
    "\n",
    "nyc_mean = props.iloc[1]['New York']\n",
    "oak_mean = props.iloc[1]['Oakland']\n",
    "\n",
    "print nyc_mean\n",
    "print oak_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard error for proportion without health insurance in New York is 0.00504878969964\n",
      "Standard error for proportion without health insurance in Oakland is 0.0188740114305\n"
     ]
    }
   ],
   "source": [
    "# variance of the point estimate x_0 where x_i are the replicate weight estimates \n",
    "# variance(x_0) = (4/80) * sum(x_i - x_0)^2 (square root to get standard error)\n",
    "nyc_se = []\n",
    "oak_se = []\n",
    "\n",
    "nyc = unemp[unemp[\"city\"] == \"New York\"]\n",
    "oak = unemp[unemp[\"city\"] == \"Oakland\"]\n",
    "\n",
    "for weight in hwts:\n",
    "    nyc_se.append(float((np.average(nyc[\"hcovany\"], weights = nyc[weight]) - nyc_mean) ** 2))\n",
    "nyc_se = (np.sum(nyc_se) * .05) ** .5    \n",
    "for weight in hwts:\n",
    "    oak_se.append(float((np.average(oak[\"hcovany\"], weights = oak[weight]) - oak_mean) ** 2))\n",
    "oak_se = (np.sum(oak_se) * .05) ** .5\n",
    "    \n",
    "print \"Standard error for proportion without health insurance in New York is \" + str(nyc_se)\n",
    "print \"Standard error for proportion without health insurance in Oakland is \" + str(oak_se)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percent of unemployed without health insurance, New York = 67.7% (CI = 66.7% , 68.7%)\n",
      "Percent of unemployed without health insurance, Oakland = 59.4% (CI = 55.7% , 63.1%)\n"
     ]
    }
   ],
   "source": [
    "# Point estimate and confidence interval for proportion with health insurance\n",
    "print \"Percent of unemployed without health insurance, New York = \" + str(round(nyc_mean * 100, 1)) + \\\n",
    "    \"% (CI = \" + str(round(((nyc_mean - (1.96*nyc_se)) * 100), 1)) + \"% , \" + \\\n",
    "    str(round(((nyc_mean + (1.96*nyc_se)) * 100), 1)) + \"%)\"\n",
    "print \"Percent of unemployed without health insurance, Oakland = \" + str(round(oak_mean * 100, 1)) + \\\n",
    "    \"% (CI = \" + str(round(((oak_mean - (1.96*oak_se)) * 100), 1)) + \"% , \" + \\\n",
    "    str(round(((oak_mean + (1.96*oak_se)) * 100), 1)) + \"%)\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       New York    Oakland\n",
      "mean  67.734674  59.406710\n",
      "se     0.989563   3.699306\n"
     ]
    }
   ],
   "source": [
    "# Graphing\n",
    "forgraph = pd.DataFrame({'New York' : pd.Series([nyc_mean*100, (1.96*nyc_se) * 100], index=['mean', 'se']), \n",
    "                           'Oakland' : pd.Series([oak_mean*100, (1.96*oak_se) * 100], index=['mean', 'se'])})\n",
    "print forgraph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOEAAACvCAYAAAAL4blmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e4ea3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(3,2))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "ind = np.arange(2)\n",
    "width = 0.5\n",
    "\n",
    "rects1 = ax.bar(1, forgraph.iloc[0,0], width,\n",
    "                color='red', yerr = forgraph.iloc[1,0],\n",
    "                error_kw=dict(elinewidth=2,ecolor='black'))\n",
    "\n",
    "rects1 = ax.bar(2, forgraph.iloc[0,1], width,\n",
    "                color='cornflowerblue', yerr = forgraph.iloc[1,1],\n",
    "                error_kw=dict(elinewidth=2,ecolor='black'))\n",
    "ax.tick_params(right=\"off\")\n",
    "ax.tick_params(top=\"off\")\n",
    "ax.set_xlim(.5,3)\n",
    "ax.set_xticks([1.25, 2.25])\n",
    "ax.axes.set_ylim([0,100])\n",
    "xTickMarks = [\"New York\", \"Oakland\"]\n",
    "xtickNames = ax.set_xticklabels(xTickMarks)\n",
    "ax.set_ylabel('Percent')\n",
    "ax.set_xlabel('City')\n",
    "ax.set_title('Percent of unemployed that do not have health\\n insurance in New York and Oakland')\n",
    "\n",
    "for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +\n",
    "             ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    item.set_fontsize(8)\n",
    "\n",
    "plt.savefig('/Users/laura/Dropbox/datamatters/fig2.png', bbox_inches='tight', dpi = 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "city\n",
      "New York    0.610400\n",
      "Oakland     0.673011\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# average number of children in the household\n",
    "grouped = unemp.groupby('city')\n",
    "minifun = lambda x: np.average(x.nchild, weights = x.perwt)\n",
    "print grouped.apply(minifun)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "city                       New York   Oakland\n",
      "educ                                         \n",
      "NA or no schooling         0.021641  0.026120\n",
      "Nursery school to grade 4  0.008347  0.015148\n",
      "Grade 5, 6, 7, or 8        0.048234  0.057921\n",
      "Grade 9                    0.024032  0.022916\n",
      "Grade 10                   0.032762  0.026751\n",
      "Grade 11                   0.049573  0.047094\n",
      "Grade 12                   0.383328  0.340001\n",
      "1 year of college          0.134797  0.171239\n",
      "2 years of college         0.060965  0.061368\n",
      "4 years of college         0.168593  0.163762\n",
      "5+ years of college        0.067730  0.067680\n",
      "0.518125\n",
      "0.51124\n"
     ]
    }
   ],
   "source": [
    "# Level of education\n",
    "educstat = wtd_prop(unemp, 'educ')\n",
    "# renaming for easy reading\n",
    "educstat = educstat.rename(index={0: 'NA or no schooling', 1: 'Nursery school to grade 4', 2: 'Grade 5, 6, 7, or 8', \\\n",
    "                      3: 'Grade 9', 4: 'Grade 10', \\\n",
    "                      5: 'Grade 11', 6: 'Grade 12', 7: '1 year of college', \\\n",
    "                      8: '2 years of college', 10: '4 years of college', 11: '5+ years of college'})\n",
    "print educstat\n",
    "# half have completed either high school or one year of college\n",
    "print 0.383328 + 0.134797 \n",
    "print 0.340001 + 0.171239"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}