HW1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "sns.set(style=\"darkgrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 1 - Analyzing 311 Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Used the following datasets from Chicago Open data portal\n",
    "files = [\"data/311_Service_Requests_-_Graffiti_Removal.csv\",\n",
    "         \"data/311_Service_Requests_-_Pot_Holes_Reported.csv\",\n",
    "         \"data/311_Service_Requests_-_Sanitation_Code_Complaints.csv\",\n",
    "         \"data/311_Service_Requests_-_Vacant_and_Abandoned_Buildings_Reported.csv\"]\n",
    "hhs_file = \"data/Census_Data_-_Selected_socioeconomic_indicators_in_Chicago__2008___2012.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2821: DtypeWarning: Columns (4,10) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  if self.run_code(code, result):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['community area', 'completion date', 'creation date',\n",
       "       'current activity', 'latitude', 'location', 'longitude',\n",
       "       'most recent action', 'number of potholes filled on block',\n",
       "       'police district', 'service request number', 'service request type',\n",
       "       'ssa', 'status', 'street address', 'type of service request', 'ward',\n",
       "       'what is the nature of this code violation?',\n",
       "       'what type of surface is the graffiti on?',\n",
       "       'where is the graffiti located?', 'x coordinate', 'y coordinate',\n",
       "       'zip code', 'response time', 'community area number',\n",
       "       'community area name', 'percent of housing crowded',\n",
       "       'percent households below poverty', 'percent aged 16+ unemployed',\n",
       "       'percent aged 25+ without high school diploma',\n",
       "       'percent aged under 18 or over 64', 'per capita income ',\n",
       "       'hardship index', 'year'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read data and massage the data\n",
    "frames = [ pd.read_csv(file) for file in files ]\n",
    "for frame in frames:\n",
    "    frame.columns = map(str.lower, frame.columns)\n",
    "df = pd.concat(frames)\n",
    "\n",
    "# combine zip and zip code\n",
    "df['zip code'].fillna(df['zip'], inplace=True)\n",
    "del df['zip']\n",
    "\n",
    "# drop other columns\n",
    "df.drop(df.columns[[0,1,2,3,4,9,10,11,12,13,14,17]], axis=1, inplace=True)\n",
    "\n",
    "# drop rows with NANs in essential columns\n",
    "df.dropna(subset=[\"community area\",\"zip code\",\"ward\",\"police district\", \"creation date\", \"completion date\", \"latitude\"], inplace=True)\n",
    "\n",
    "# combine status\n",
    "df.replace(\"Pot Hole in Street\",\"Pothole in Street\", inplace=True)\n",
    "\n",
    "# convert to categories\n",
    "cat = df[[\"type of service request\", \"status\", \"what type of surface is the graffiti on?\"]]\n",
    "df[[\"type of service request\", \"status\", \"what type of surface is the graffiti on?\"]] = cat.apply(lambda x: x.astype('category'))\n",
    "\n",
    "# convert to integer\n",
    "ints = df[[\"community area\", \"police district\", \"ward\", \"zip code\"]]\n",
    "df[[\"community area\",\"police district\", \"ward\", \"zip code\"]] = ints.apply(lambda x: x.astype('int'))\n",
    "\n",
    "# convert to datetime\n",
    "df['creation date'] = pd.to_datetime(df['creation date'],infer_datetime_format=True)\n",
    "df['completion date'] = pd.to_datetime(df['completion date'],infer_datetime_format=True)\n",
    "df['response time'] = ((df['completion date'] - df['creation date']) / np.timedelta64(1, 'D')).astype(int)\n",
    "\n",
    "# combine with Chicago HHS data\n",
    "hhs = pd.read_csv(hhs_file)\n",
    "hhs.columns = map(str.lower, hhs.columns)\n",
    "hhs.dropna(subset=[\"community area number\"], inplace=True)\n",
    "hhs[[\"community area number\"]] = hhs[[\"community area number\"]].apply(lambda x: x.astype('int'))\n",
    "\n",
    "df = pd.merge(df, hhs, how='inner', left_on=\"community area\", right_on=\"community area number\")\n",
    "\n",
    "#extract year\n",
    "df['year'] = df['creation date'].dt.year\n",
    "\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Taking a sample for speed\n",
    "df = df.sample(10000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 311 Calls by police district"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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5uerZs6dycpiiAQCoC5enx5977jkNHjxYAQEBkqRFixapcePGLg9csca0YjnLrFmzKj/S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\nAADYBE0bAACboGkDAGAT/x9S5CbKpzJlGgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f701080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(y=\"police district\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 311 Requests by Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cOY0aNUrr1q3TtWvX9O233+bIn5SUpCpVquTYVqZMGVksFsXGxqpFixZavXq1\n3nzzTYWFhSkjI0OzZ8/We++9pyVLlqhkyZKSpN27dyshIUFxcXFasWKF3n77bd28eTPHefv06aMt\nW7ZIunc5sVWrVvbjpXuXLLdv3661a9dq7dq1Onv2rH2WknTx4kU1aNBAS5Ys0YYNG7R27VpZrVYN\nHTpUnTt3VlBQkH3f3LLfL7SdOnXSqlWrVL58ee3Zs+dX/hcHAKB4KpAVqbNnz8pms2nWrFmSpG+/\n/VZDhgzRE088oQoVKmjGjBny8vJSYmKiGjVqJEmqWrWqbDabJMnf319paWm5ntvPz0/Lly/Xzp07\nZbPZlJmZmWeO06dPq0mTJpKkOnXq6NKlS5LurTBVqlRJklSpUqWf/a4HH3xQFy9eVGBgoH3bgQMH\n5Ofnp9OnT6tLly6S7hVGm82mpKQklSlTRmXLlpUkNWzYUJJ08uRJHT16VMHBwZKkzMxMnT9/XqVL\nl7aft27durp165YSExO1ceNGhYaG5shy5swZPfbYYypRooQkqXHjxvruu+/sz/v6+urbb7/V3r17\nZbPZfvHesNyyX716VZL0hz/8Ic95AACA3BXIitSJEyc0ffp0+1/q1atXV+nSpWW1WhUeHq6ZM2dq\n9uzZKl++vAzDkCRZLJZfPOf9/ZYuXaoGDRpo7ty56tChQ47js7OzcxwTEBCgr7/+WpJ0/Phx+fn5\n5et39ejRQ0uWLNGdO3ckSVevXtWkSZN09+7dHOdMTEzUzZs3VaFCBd28eVPXrl2TJPsKV40aNfTE\nE09o5cqVWr58uTp27PizlS5J6tmzp1auXKnU1FTVqlUrx3M1atTQ4cOHlZmZKcMwtH//flWvXt3+\n/MaNG+Xj46OYmBgNHjxYqampMgxDbm5uvziP+9l9fX3zNRMAAPBzBbIi1b59e50+fVq9evWSl5eX\nDMPQ+PHj5ePjo65du2rAgAEqVaqU/Pz8lJSUlK9zBgQEaNy4cerVq5eioqK0bds2+fj4yGq1Kj09\nXY8++qhef/11BQQE2I8ZP368wsPDtXTpUmVmZmrGjBn5+l0NGzZUnz59NHjwYLm7uys1NVVjxoxR\nYGCgKlasqEmTJmnHjh1KTU3V9OnT5e7urilTpuill15SmTJl5O5+b6xt27bVV199pf79++vOnTtq\n166dfdXtx7p06aLWrVvnelP4I488oo4dO6pfv37Kzs7W448/rnbt2ik+Pl6S1Lx5c40dO1aHDh2S\nh4eHqlWYcZ01AAAJD0lEQVSrpqSkJNWuXVuxsbGqW7eu/VzDhg3LNTsAADDHYtxf0gHyadvAFws7\ngtM0iXnrV+3v7++jy5dvFVCaoou55I3Z5I655I655K2gZ+Pvn/ubsPhATgAAAJMoUgAAACZRpAAA\nAEyiSAEAAJhEkQIAADCJIgUAAGASRQoAAMAkihQAAIBJFCkAAACTKFIAAAAmUaQAAABMokgBAACY\nRJECAAAwiSIFAABgEkUKAADAJIoUAACASRQpAAAAkyhSAAAAJlGkAAAATKJIAQAAmESRAgAAMIki\nBQAAYBJFCgAAwCSKFAAAgEkUKQAAAJMoUgAAACZRpAAAAEyiSAEAAJhEkQIAADCJIgUAAGASRQoA\nAMAkihQAAIBJFCkAAACTKFIAAAAmUaQAAABMci/sACh6nlnxni5fvlXYMQAAKHSsSAEAAJhEkQIA\nADCJIgUAAGASRQoAAMAkihQAAIBJFCkAAACTKFIAAAAmUaQAAABMokgBAACYZDEMwyjsEAAAAEUR\nK1IAAAAmUaQAAABMokgBAACYRJECAAAwiSIFAABgEkUKAADAJPfCDoCiIzs7WxERETpx4oQ8PDwU\nFRWlatWqFXYsp/nXv/6luXPnauXKlTp79qwmTJggi8WiWrVqaerUqXJzc9P777+vtWvXyt3dXSNG\njFCbNm2UmpqqkJAQXb16Vd7e3oqOjtYDDzxQ2C/nv5aRkaFJkybp/PnzSk9P14gRI1SzZs1iPxdJ\nysrK0uTJk/X999/LYrFo2rRp8vT0ZDb/dvXqVfXo0UNLly6Vu7s7c/m37t27y2azSZIqV66s4cOH\nMxtJixYt0ieffKKMjAz169dPTZs2da25GEA+7dixwwgNDTUMwzC++eYbY/jw4YWcyHkWL15sdO7c\n2ejdu7dhGIYxbNgwY+/evYZhGEZ4eLixc+dOIykpyejcubORlpZm3Lx50/7z0qVLjbfeesswDMPY\nunWrERkZWWiv47e0YcMGIyoqyjAMw7h+/brRqlUr5vJvH330kTFhwgTDMAxj7969xvDhw5nNv6Wn\npxt//vOfjfbt2xunTp1iLv+WmppqPPvsszm2MZt7f36GDRtmZGVlGSkpKcZbb73lcnPh0h7y7cCB\nA/rTn/4kSWrQoIGOHDlSyImcp2rVqpo/f7798dGjR9W0aVNJUsuWLfXFF1/o8OHDatiwoTw8POTj\n46OqVasqPj4+x9xatmypL7/8slBew2+tQ4cOGjVqlCTJMAxZrVbm8m/t2rVTZGSkJOnChQsqXbo0\ns/m36OhoPffccypfvrwk/izdFx8fr7t372rw4MEaOHCgDh06xGwk/eMf/1Dt2rU1cuRIDR8+XK1b\nt3a5uVCkkG8pKSn2ZWdJslqtyszMLMREzvP000/L3f0/V8INw5DFYpEkeXt769atW0pJSZGPj499\nH29vb6WkpOTYfn/f3wNvb2/ZbDalpKTo1Vdf1WuvvcZcfsTd3V2hoaGKjIxUly5dmI2kjRs36oEH\nHrD/xSbxZ+m+kiVL6qWXXtKSJUs0bdo0jRs3jtlIun79uo4cOaI333zTZedCkUK+2Ww23b592/44\nOzs7R7koTtzc/vNH5/bt2ypduvTP5nP79m35+Pjk2H5/39+LixcvauDAgXr22WfVpUsX5vIT0dHR\n2rFjh8LDw5WWlmbfXlxn88EHH+iLL75QcHCwjh8/rtDQUF27ds3+fHGdiyRVr15dXbt2lcViUfXq\n1eXr66urV6/any+us/H19dWTTz4pDw8P1ahRQ56enjnKkCvMhSKFfGvUqJH27NkjSTp06JBq165d\nyIkKzx/+8Aft27dPkrRnzx41btxY9evX14EDB5SWlqZbt27p9OnTql27tho1aqTdu3fb93388ccL\nM/pv5sqVKxo8eLBCQkLUq1cvSczlvs2bN2vRokWSpFKlSslisejRRx8t9rNZvXq1Vq1apZUrV6pO\nnTqKjo5Wy5Yti/1cJGnDhg2aPXu2JCkxMVEpKSn64x//WOxn8/jjj+vzzz+XYRhKTEzU3bt31bx5\nc5eaC19ajHy7/669kydPyjAMzZw5UwEBAYUdy2kSEhI0ZswYvf/++/r+++8VHh6ujIwM1ahRQ1FR\nUbJarXr//fe1bt06GYahYcOG6emnn9bdu3cVGhqqy5cvq0SJEoqJiZG/v39hv5z/WlRUlLZv364a\nNWrYt4WFhSkqKqpYz0WS7ty5o4kTJ+rKlSvKzMzUkCFDFBAQUOz/n/mx4OBgRUREyM3NjblISk9P\n18SJE3XhwgVZLBaNGzdOZcuWZTaSXn/9de3bt0+GYWj06NGqXLmyS82FIgUAAGASl/YAAABMokgB\nAACYRJECAAAwiSIFAABgEkUKAADAJIoUAPzOnDt3TpMmTSrsGECxQJECgN+ZCxcu6Ny5c4UdAygW\n+BwpACgEhmFo7ty52rVrl6xWq/r27auWLVtqypQpSk5OlpeXl8LCwlS/fn1NmDBBTZs2VY8ePSRJ\njzzyiE6cOKH58+crMTFRZ8+e1fnz59W7d2+NGDFCXbp0UUJCgrp166apU6cW8isFft+K5xelAUAh\n+/vf/66DBw/qr3/9qzIyMtS/f3+tWbNGY8eOVfv27XXo0CGNGjVKO3bs+MXznDhxQqtXr9atW7fU\nrl07DRgwQJMnT9aCBQsoUYATcGkPAArB/v371bFjR3l4eMjb21tr1qzR9evX1b59e0lSgwYNVKZM\nGZ05c+YXz/PEE0/Iw8ND5cqVk6+vb4F8uz2AvFGkAKAQuLvnvCBw7tw5/fROC8MwlJWVJYvFYn8u\nIyMjxz6enp72n3+8HwDnoEgBQCFo0qSJPvroI2VkZOju3bt67bXXZLFYtHPnTknSoUOHdOXKFdWq\nVUu+vr46deqUJGnXrl0Oz221WpWZmVmg+QHcQ5ECgELw1FNPqVGjRurRo4d69eqlgQMHKi4uTitX\nrlSXLl00ffp0zZ8/Xx4eHurfv7+++uordenSRQcPHnT47fUBAQG6deuWQkJCnPRqgOKLd+0BAACY\nxIoUAACASRQpAAAAkyhSAAAAJlGkAAAATKJIAQAAmESRAgAAMIkiBQAAYBJFCgAAwKT/B+h3BSs4\nAs6wAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115176da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(y=\"type of service request\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top Ten Community Areas by Request Count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "West Town          547\n",
       "South Lawndale     508\n",
       "Logan Square       468\n",
       "Belmont Cragin     383\n",
       "Lower West Side    374\n",
       "Brighton Park      345\n",
       "Irving Park        310\n",
       "Gage Park          293\n",
       "Avondale           275\n",
       "Lake View          249\n",
       "Name: community area name, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['community area name'].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# building a community area variable dataframe\n",
    "indicators = df[['community area name','percent households below poverty', 'percent aged 16+ unemployed',\n",
    "       'percent aged 25+ without high school diploma',\n",
    "       'percent aged under 18 or over 64', 'per capita income ',\n",
    "       'hardship index']].groupby('community area name').max()\n",
    "\n",
    "indicators.join(df['community area name'].value_counts())\n",
    "\n",
    "# Adding response time\n",
    "response_time = df.groupby('community area name')['response time'].mean()\n",
    "indicators = indicators.join(response_time)\n",
    "\n",
    "# Adding request counts\n",
    "indicators['total_requests'] = 0\n",
    "\n",
    "request_types = df['type of service request'].unique()\n",
    "\n",
    "for request_type in request_types:\n",
    "    t = df[df['type of service request'] == request_type]\n",
    "    t = t['community area name'].value_counts()\n",
    "    t = t.rename('num' + request_type)\n",
    "    indicators = indicators.join(t)\n",
    "    indicators[t.name].fillna(0, inplace=True)\n",
    "    indicators['total_requests'] = indicators['total_requests'] + indicators[t.name]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>percent households below poverty</th>\n",
       "      <th>percent aged 16+ unemployed</th>\n",
       "      <th>percent aged 25+ without high school diploma</th>\n",
       "      <th>percent aged under 18 or over 64</th>\n",
       "      <th>per capita income</th>\n",
       "      <th>hardship index</th>\n",
       "      <th>response time</th>\n",
       "      <th>total_requests</th>\n",
       "      <th>numGraffiti Removal</th>\n",
       "      <th>numPothole in Street</th>\n",
       "      <th>numSanitation Code Violation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>community area name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Albany Park</th>\n",
       "      <td>19.2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>32.9</td>\n",
       "      <td>32.0</td>\n",
       "      <td>21323</td>\n",
       "      <td>53.0</td>\n",
       "      <td>11.038835</td>\n",
       "      <td>206.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>48</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Archer Heights</th>\n",
       "      <td>14.1</td>\n",
       "      <td>16.5</td>\n",
       "      <td>35.9</td>\n",
       "      <td>39.2</td>\n",
       "      <td>16134</td>\n",
       "      <td>67.0</td>\n",
       "      <td>6.951087</td>\n",
       "      <td>184.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>26</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armour Square</th>\n",
       "      <td>40.1</td>\n",
       "      <td>16.7</td>\n",
       "      <td>34.5</td>\n",
       "      <td>38.3</td>\n",
       "      <td>16148</td>\n",
       "      <td>82.0</td>\n",
       "      <td>6.981481</td>\n",
       "      <td>54.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>20</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ashburn</th>\n",
       "      <td>10.4</td>\n",
       "      <td>11.7</td>\n",
       "      <td>17.7</td>\n",
       "      <td>36.9</td>\n",
       "      <td>23482</td>\n",
       "      <td>37.0</td>\n",
       "      <td>17.123894</td>\n",
       "      <td>113.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>55</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Auburn Gresham</th>\n",
       "      <td>27.6</td>\n",
       "      <td>28.3</td>\n",
       "      <td>18.5</td>\n",
       "      <td>41.9</td>\n",
       "      <td>15528</td>\n",
       "      <td>74.0</td>\n",
       "      <td>23.675000</td>\n",
       "      <td>120.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>75</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austin</th>\n",
       "      <td>28.6</td>\n",
       "      <td>22.6</td>\n",
       "      <td>24.4</td>\n",
       "      <td>37.9</td>\n",
       "      <td>15957</td>\n",
       "      <td>73.0</td>\n",
       "      <td>19.011561</td>\n",
       "      <td>173.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>97</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avalon Park</th>\n",
       "      <td>17.2</td>\n",
       "      <td>21.1</td>\n",
       "      <td>10.6</td>\n",
       "      <td>39.3</td>\n",
       "      <td>24454</td>\n",
       "      <td>41.0</td>\n",
       "      <td>16.260870</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avondale</th>\n",
       "      <td>15.3</td>\n",
       "      <td>9.2</td>\n",
       "      <td>24.7</td>\n",
       "      <td>31.0</td>\n",
       "      <td>20039</td>\n",
       "      <td>42.0</td>\n",
       "      <td>10.378182</td>\n",
       "      <td>275.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>37</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belmont Cragin</th>\n",
       "      <td>18.7</td>\n",
       "      <td>14.6</td>\n",
       "      <td>37.3</td>\n",
       "      <td>37.3</td>\n",
       "      <td>15461</td>\n",
       "      <td>70.0</td>\n",
       "      <td>6.310705</td>\n",
       "      <td>383.0</td>\n",
       "      <td>291.0</td>\n",
       "      <td>71</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beverly</th>\n",
       "      <td>5.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>40.5</td>\n",
       "      <td>39523</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.711864</td>\n",
       "      <td>59.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>46</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bridgeport</th>\n",
       "      <td>18.9</td>\n",
       "      <td>13.7</td>\n",
       "      <td>22.2</td>\n",
       "      <td>31.3</td>\n",
       "      <td>22694</td>\n",
       "      <td>43.0</td>\n",
       "      <td>5.948052</td>\n",
       "      <td>154.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>22</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brighton Park</th>\n",
       "      <td>23.6</td>\n",
       "      <td>13.9</td>\n",
       "      <td>45.1</td>\n",
       "      <td>39.3</td>\n",
       "      <td>13089</td>\n",
       "      <td>84.0</td>\n",
       "      <td>6.686957</td>\n",
       "      <td>345.0</td>\n",
       "      <td>299.0</td>\n",
       "      <td>38</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burnside</th>\n",
       "      <td>33.0</td>\n",
       "      <td>18.6</td>\n",
       "      <td>19.3</td>\n",
       "      <td>42.7</td>\n",
       "      <td>12515</td>\n",
       "      <td>79.0</td>\n",
       "      <td>20.714286</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calumet Heights</th>\n",
       "      <td>11.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>28887</td>\n",
       "      <td>38.0</td>\n",
       "      <td>33.054054</td>\n",
       "      <td>37.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>28</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chatham</th>\n",
       "      <td>27.8</td>\n",
       "      <td>24.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>40.3</td>\n",
       "      <td>18881</td>\n",
       "      <td>60.0</td>\n",
       "      <td>20.880000</td>\n",
       "      <td>50.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>32</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chicago Lawn</th>\n",
       "      <td>27.9</td>\n",
       "      <td>17.1</td>\n",
       "      <td>31.2</td>\n",
       "      <td>40.6</td>\n",
       "      <td>13231</td>\n",
       "      <td>80.0</td>\n",
       "      <td>8.771429</td>\n",
       "      <td>175.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>50</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clearing</th>\n",
       "      <td>8.9</td>\n",
       "      <td>9.5</td>\n",
       "      <td>18.8</td>\n",
       "      <td>37.6</td>\n",
       "      <td>25113</td>\n",
       "      <td>29.0</td>\n",
       "      <td>5.641791</td>\n",
       "      <td>67.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>36</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Douglas</th>\n",
       "      <td>29.6</td>\n",
       "      <td>18.2</td>\n",
       "      <td>14.3</td>\n",
       "      <td>30.7</td>\n",
       "      <td>23791</td>\n",
       "      <td>47.0</td>\n",
       "      <td>13.272727</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dunning</th>\n",
       "      <td>10.6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>16.2</td>\n",
       "      <td>33.6</td>\n",
       "      <td>26282</td>\n",
       "      <td>28.0</td>\n",
       "      <td>21.126316</td>\n",
       "      <td>95.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>49</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East Garfield Park</th>\n",
       "      <td>42.4</td>\n",
       "      <td>19.6</td>\n",
       "      <td>21.3</td>\n",
       "      <td>43.2</td>\n",
       "      <td>12961</td>\n",
       "      <td>83.0</td>\n",
       "      <td>24.474576</td>\n",
       "      <td>59.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>27</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East Side</th>\n",
       "      <td>19.2</td>\n",
       "      <td>12.1</td>\n",
       "      <td>31.9</td>\n",
       "      <td>42.8</td>\n",
       "      <td>17104</td>\n",
       "      <td>64.0</td>\n",
       "      <td>10.071429</td>\n",
       "      <td>98.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>20</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Edgewater</th>\n",
       "      <td>18.2</td>\n",
       "      <td>9.2</td>\n",
       "      <td>9.7</td>\n",
       "      <td>23.8</td>\n",
       "      <td>33385</td>\n",
       "      <td>19.0</td>\n",
       "      <td>10.054054</td>\n",
       "      <td>111.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>35</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Edison Park</th>\n",
       "      <td>3.3</td>\n",
       "      <td>6.5</td>\n",
       "      <td>7.4</td>\n",
       "      <td>35.3</td>\n",
       "      <td>40959</td>\n",
       "      <td>8.0</td>\n",
       "      <td>23.600000</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Englewood</th>\n",
       "      <td>46.6</td>\n",
       "      <td>28.0</td>\n",
       "      <td>28.5</td>\n",
       "      <td>42.5</td>\n",
       "      <td>11888</td>\n",
       "      <td>94.0</td>\n",
       "      <td>18.180000</td>\n",
       "      <td>50.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>22</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Forest Glen</th>\n",
       "      <td>7.5</td>\n",
       "      <td>6.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>40.5</td>\n",
       "      <td>44164</td>\n",
       "      <td>11.0</td>\n",
       "      <td>27.803030</td>\n",
       "      <td>66.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>57</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fuller Park</th>\n",
       "      <td>51.2</td>\n",
       "      <td>33.9</td>\n",
       "      <td>26.6</td>\n",
       "      <td>44.9</td>\n",
       "      <td>10432</td>\n",
       "      <td>97.0</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gage Park</th>\n",
       "      <td>23.4</td>\n",
       "      <td>18.2</td>\n",
       "      <td>51.5</td>\n",
       "      <td>38.8</td>\n",
       "      <td>12171</td>\n",
       "      <td>93.0</td>\n",
       "      <td>9.529010</td>\n",
       "      <td>293.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>56</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Garfield Ridge</th>\n",
       "      <td>8.8</td>\n",
       "      <td>11.3</td>\n",
       "      <td>19.3</td>\n",
       "      <td>38.1</td>\n",
       "      <td>26353</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6.782313</td>\n",
       "      <td>147.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>67</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grand Boulevard</th>\n",
       "      <td>29.3</td>\n",
       "      <td>24.3</td>\n",
       "      <td>15.9</td>\n",
       "      <td>39.5</td>\n",
       "      <td>23472</td>\n",
       "      <td>57.0</td>\n",
       "      <td>6.948718</td>\n",
       "      <td>39.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>21</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greater Grand Crossing</th>\n",
       "      <td>29.6</td>\n",
       "      <td>23.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>41.0</td>\n",
       "      <td>17285</td>\n",
       "      <td>66.0</td>\n",
       "      <td>17.623188</td>\n",
       "      <td>69.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>40</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Near North Side</th>\n",
       "      <td>12.9</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>22.6</td>\n",
       "      <td>88669</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.602410</td>\n",
       "      <td>166.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>84</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Near South Side</th>\n",
       "      <td>13.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>7.4</td>\n",
       "      <td>21.8</td>\n",
       "      <td>59077</td>\n",
       "      <td>7.0</td>\n",
       "      <td>24.857143</td>\n",
       "      <td>35.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>16</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Near West Side</th>\n",
       "      <td>20.6</td>\n",
       "      <td>10.7</td>\n",
       "      <td>9.6</td>\n",
       "      <td>22.2</td>\n",
       "      <td>44689</td>\n",
       "      <td>15.0</td>\n",
       "      <td>14.562771</td>\n",
       "      <td>231.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>82</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New City</th>\n",
       "      <td>29.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>41.5</td>\n",
       "      <td>38.9</td>\n",
       "      <td>12765</td>\n",
       "      <td>91.0</td>\n",
       "      <td>8.435897</td>\n",
       "      <td>195.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>49</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Center</th>\n",
       "      <td>7.5</td>\n",
       "      <td>5.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>26.2</td>\n",
       "      <td>57123</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.491228</td>\n",
       "      <td>171.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>38</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Lawndale</th>\n",
       "      <td>43.1</td>\n",
       "      <td>21.2</td>\n",
       "      <td>27.6</td>\n",
       "      <td>42.7</td>\n",
       "      <td>12034</td>\n",
       "      <td>87.0</td>\n",
       "      <td>11.506494</td>\n",
       "      <td>77.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>30</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Park</th>\n",
       "      <td>13.2</td>\n",
       "      <td>9.9</td>\n",
       "      <td>14.4</td>\n",
       "      <td>39.0</td>\n",
       "      <td>26576</td>\n",
       "      <td>33.0</td>\n",
       "      <td>8.218750</td>\n",
       "      <td>64.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>22</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norwood Park</th>\n",
       "      <td>5.4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>39.5</td>\n",
       "      <td>32875</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.765306</td>\n",
       "      <td>98.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>74</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O'Hare</th>\n",
       "      <td>15.4</td>\n",
       "      <td>7.1</td>\n",
       "      <td>10.9</td>\n",
       "      <td>30.3</td>\n",
       "      <td>25828</td>\n",
       "      <td>24.0</td>\n",
       "      <td>37.333333</td>\n",
       "      <td>21.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oakland</th>\n",
       "      <td>39.7</td>\n",
       "      <td>28.7</td>\n",
       "      <td>18.4</td>\n",
       "      <td>40.4</td>\n",
       "      <td>19252</td>\n",
       "      <td>78.0</td>\n",
       "      <td>13.200000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portage Park</th>\n",
       "      <td>11.6</td>\n",
       "      <td>12.6</td>\n",
       "      <td>19.3</td>\n",
       "      <td>34.0</td>\n",
       "      <td>24336</td>\n",
       "      <td>35.0</td>\n",
       "      <td>9.869198</td>\n",
       "      <td>237.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>62</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pullman</th>\n",
       "      <td>21.6</td>\n",
       "      <td>22.8</td>\n",
       "      <td>13.1</td>\n",
       "      <td>38.6</td>\n",
       "      <td>20588</td>\n",
       "      <td>51.0</td>\n",
       "      <td>7.222222</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Riverdale</th>\n",
       "      <td>56.5</td>\n",
       "      <td>34.6</td>\n",
       "      <td>27.5</td>\n",
       "      <td>51.5</td>\n",
       "      <td>8201</td>\n",
       "      <td>98.0</td>\n",
       "      <td>74.142857</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rogers Park</th>\n",
       "      <td>23.6</td>\n",
       "      <td>8.7</td>\n",
       "      <td>18.2</td>\n",
       "      <td>27.5</td>\n",
       "      <td>23939</td>\n",
       "      <td>39.0</td>\n",
       "      <td>13.232877</td>\n",
       "      <td>146.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>53</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Roseland</th>\n",
       "      <td>19.8</td>\n",
       "      <td>20.3</td>\n",
       "      <td>16.9</td>\n",
       "      <td>41.2</td>\n",
       "      <td>17949</td>\n",
       "      <td>52.0</td>\n",
       "      <td>12.175926</td>\n",
       "      <td>108.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>78</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Chicago</th>\n",
       "      <td>29.8</td>\n",
       "      <td>19.7</td>\n",
       "      <td>26.6</td>\n",
       "      <td>41.1</td>\n",
       "      <td>16579</td>\n",
       "      <td>75.0</td>\n",
       "      <td>8.047059</td>\n",
       "      <td>85.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>29</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Deering</th>\n",
       "      <td>29.2</td>\n",
       "      <td>16.3</td>\n",
       "      <td>21.0</td>\n",
       "      <td>39.5</td>\n",
       "      <td>14685</td>\n",
       "      <td>65.0</td>\n",
       "      <td>10.049180</td>\n",
       "      <td>61.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>32</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Lawndale</th>\n",
       "      <td>30.7</td>\n",
       "      <td>15.8</td>\n",
       "      <td>54.8</td>\n",
       "      <td>33.8</td>\n",
       "      <td>10402</td>\n",
       "      <td>96.0</td>\n",
       "      <td>5.832677</td>\n",
       "      <td>508.0</td>\n",
       "      <td>451.0</td>\n",
       "      <td>44</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Shore</th>\n",
       "      <td>31.1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>35.7</td>\n",
       "      <td>19398</td>\n",
       "      <td>55.0</td>\n",
       "      <td>23.320988</td>\n",
       "      <td>81.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>56</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uptown</th>\n",
       "      <td>24.0</td>\n",
       "      <td>8.9</td>\n",
       "      <td>11.8</td>\n",
       "      <td>22.2</td>\n",
       "      <td>35787</td>\n",
       "      <td>20.0</td>\n",
       "      <td>13.355140</td>\n",
       "      <td>107.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>28</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington Height</th>\n",
       "      <td>16.9</td>\n",
       "      <td>20.8</td>\n",
       "      <td>13.7</td>\n",
       "      <td>42.6</td>\n",
       "      <td>19713</td>\n",
       "      <td>48.0</td>\n",
       "      <td>17.565217</td>\n",
       "      <td>69.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>49</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington Park</th>\n",
       "      <td>42.1</td>\n",
       "      <td>28.6</td>\n",
       "      <td>25.4</td>\n",
       "      <td>42.8</td>\n",
       "      <td>13785</td>\n",
       "      <td>88.0</td>\n",
       "      <td>10.307692</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Elsdon</th>\n",
       "      <td>15.6</td>\n",
       "      <td>16.7</td>\n",
       "      <td>37.0</td>\n",
       "      <td>37.7</td>\n",
       "      <td>15754</td>\n",
       "      <td>69.0</td>\n",
       "      <td>6.364286</td>\n",
       "      <td>140.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>24</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Englewood</th>\n",
       "      <td>34.4</td>\n",
       "      <td>35.9</td>\n",
       "      <td>26.3</td>\n",
       "      <td>40.7</td>\n",
       "      <td>11317</td>\n",
       "      <td>89.0</td>\n",
       "      <td>16.184211</td>\n",
       "      <td>76.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>42</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Garfield Park</th>\n",
       "      <td>41.7</td>\n",
       "      <td>25.8</td>\n",
       "      <td>24.5</td>\n",
       "      <td>43.6</td>\n",
       "      <td>10934</td>\n",
       "      <td>92.0</td>\n",
       "      <td>23.542857</td>\n",
       "      <td>35.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>19</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Lawn</th>\n",
       "      <td>14.9</td>\n",
       "      <td>9.6</td>\n",
       "      <td>33.6</td>\n",
       "      <td>39.6</td>\n",
       "      <td>16907</td>\n",
       "      <td>56.0</td>\n",
       "      <td>5.466667</td>\n",
       "      <td>165.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>64</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Pullman</th>\n",
       "      <td>25.9</td>\n",
       "      <td>19.4</td>\n",
       "      <td>20.5</td>\n",
       "      <td>42.1</td>\n",
       "      <td>16563</td>\n",
       "      <td>62.0</td>\n",
       "      <td>21.797101</td>\n",
       "      <td>69.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>41</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Ridge</th>\n",
       "      <td>17.2</td>\n",
       "      <td>8.8</td>\n",
       "      <td>20.8</td>\n",
       "      <td>38.5</td>\n",
       "      <td>23040</td>\n",
       "      <td>46.0</td>\n",
       "      <td>20.853211</td>\n",
       "      <td>218.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>85</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Town</th>\n",
       "      <td>14.7</td>\n",
       "      <td>6.6</td>\n",
       "      <td>12.9</td>\n",
       "      <td>21.7</td>\n",
       "      <td>43198</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.628885</td>\n",
       "      <td>547.0</td>\n",
       "      <td>417.0</td>\n",
       "      <td>96</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Woodlawn</th>\n",
       "      <td>30.7</td>\n",
       "      <td>23.4</td>\n",
       "      <td>16.5</td>\n",
       "      <td>36.1</td>\n",
       "      <td>18672</td>\n",
       "      <td>58.0</td>\n",
       "      <td>14.657895</td>\n",
       "      <td>38.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>27</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        percent households below poverty  \\\n",
       "community area name                                        \n",
       "Albany Park                                         19.2   \n",
       "Archer Heights                                      14.1   \n",
       "Armour Square                                       40.1   \n",
       "Ashburn                                             10.4   \n",
       "Auburn Gresham                                      27.6   \n",
       "Austin                                              28.6   \n",
       "Avalon Park                                         17.2   \n",
       "Avondale                                            15.3   \n",
       "Belmont Cragin                                      18.7   \n",
       "Beverly                                              5.1   \n",
       "Bridgeport                                          18.9   \n",
       "Brighton Park                                       23.6   \n",
       "Burnside                                            33.0   \n",
       "Calumet Heights                                     11.5   \n",
       "Chatham                                             27.8   \n",
       "Chicago Lawn                                        27.9   \n",
       "Clearing                                             8.9   \n",
       "Douglas                                             29.6   \n",
       "Dunning                                             10.6   \n",
       "East Garfield Park                                  42.4   \n",
       "East Side                                           19.2   \n",
       "Edgewater                                           18.2   \n",
       "Edison Park                                          3.3   \n",
       "Englewood                                           46.6   \n",
       "Forest Glen                                          7.5   \n",
       "Fuller Park                                         51.2   \n",
       "Gage Park                                           23.4   \n",
       "Garfield Ridge                                       8.8   \n",
       "Grand Boulevard                                     29.3   \n",
       "Greater Grand Crossing                              29.6   \n",
       "...                                                  ...   \n",
       "Near North Side                                     12.9   \n",
       "Near South Side                                     13.8   \n",
       "Near West Side                                      20.6   \n",
       "New City                                            29.0   \n",
       "North Center                                         7.5   \n",
       "North Lawndale                                      43.1   \n",
       "North Park                                          13.2   \n",
       "Norwood Park                                         5.4   \n",
       "O'Hare                                              15.4   \n",
       "Oakland                                             39.7   \n",
       "Portage Park                                        11.6   \n",
       "Pullman                                             21.6   \n",
       "Riverdale                                           56.5   \n",
       "Rogers Park                                         23.6   \n",
       "Roseland                                            19.8   \n",
       "South Chicago                                       29.8   \n",
       "South Deering                                       29.2   \n",
       "South Lawndale                                      30.7   \n",
       "South Shore                                         31.1   \n",
       "Uptown                                              24.0   \n",
       "Washington Height                                   16.9   \n",
       "Washington Park                                     42.1   \n",
       "West Elsdon                                         15.6   \n",
       "West Englewood                                      34.4   \n",
       "West Garfield Park                                  41.7   \n",
       "West Lawn                                           14.9   \n",
       "West Pullman                                        25.9   \n",
       "West Ridge                                          17.2   \n",
       "West Town                                           14.7   \n",
       "Woodlawn                                            30.7   \n",
       "\n",
       "                        percent aged 16+ unemployed  \\\n",
       "community area name                                   \n",
       "Albany Park                                    10.0   \n",
       "Archer Heights                                 16.5   \n",
       "Armour Square                                  16.7   \n",
       "Ashburn                                        11.7   \n",
       "Auburn Gresham                                 28.3   \n",
       "Austin                                         22.6   \n",
       "Avalon Park                                    21.1   \n",
       "Avondale                                        9.2   \n",
       "Belmont Cragin                                 14.6   \n",
       "Beverly                                         8.0   \n",
       "Bridgeport                                     13.7   \n",
       "Brighton Park                                  13.9   \n",
       "Burnside                                       18.6   \n",
       "Calumet Heights                                20.0   \n",
       "Chatham                                        24.0   \n",
       "Chicago Lawn                                   17.1   \n",
       "Clearing                                        9.5   \n",
       "Douglas                                        18.2   \n",
       "Dunning                                        10.0   \n",
       "East Garfield Park                             19.6   \n",
       "East Side                                      12.1   \n",
       "Edgewater                                       9.2   \n",
       "Edison Park                                     6.5   \n",
       "Englewood                                      28.0   \n",
       "Forest Glen                                     6.8   \n",
       "Fuller Park                                    33.9   \n",
       "Gage Park                                      18.2   \n",
       "Garfield Ridge                                 11.3   \n",
       "Grand Boulevard                                24.3   \n",
       "Greater Grand Crossing                         23.0   \n",
       "...                                             ...   \n",
       "Near North Side                                 7.0   \n",
       "Near South Side                                 4.9   \n",
       "Near West Side                                 10.7   \n",
       "New City                                       23.0   \n",
       "North Center                                    5.2   \n",
       "North Lawndale                                 21.2   \n",
       "North Park                                      9.9   \n",
       "Norwood Park                                    9.0   \n",
       "O'Hare                                          7.1   \n",
       "Oakland                                        28.7   \n",
       "Portage Park                                   12.6   \n",
       "Pullman                                        22.8   \n",
       "Riverdale                                      34.6   \n",
       "Rogers Park                                     8.7   \n",
       "Roseland                                       20.3   \n",
       "South Chicago                                  19.7   \n",
       "South Deering                                  16.3   \n",
       "South Lawndale                                 15.8   \n",
       "South Shore                                    20.0   \n",
       "Uptown                                          8.9   \n",
       "Washington Height                              20.8   \n",
       "Washington Park                                28.6   \n",
       "West Elsdon                                    16.7   \n",
       "West Englewood                                 35.9   \n",
       "West Garfield Park                             25.8   \n",
       "West Lawn                                       9.6   \n",
       "West Pullman                                   19.4   \n",
       "West Ridge                                      8.8   \n",
       "West Town                                       6.6   \n",
       "Woodlawn                                       23.4   \n",
       "\n",
       "                        percent aged 25+ without high school diploma  \\\n",
       "community area name                                                    \n",
       "Albany Park                                                     32.9   \n",
       "Archer Heights                                                  35.9   \n",
       "Armour Square                                                   34.5   \n",
       "Ashburn                                                         17.7   \n",
       "Auburn Gresham                                                  18.5   \n",
       "Austin                                                          24.4   \n",
       "Avalon Park                                                     10.6   \n",
       "Avondale                                                        24.7   \n",
       "Belmont Cragin                                                  37.3   \n",
       "Beverly                                                          3.7   \n",
       "Bridgeport                                                      22.2   \n",
       "Brighton Park                                                   45.1   \n",
       "Burnside                                                        19.3   \n",
       "Calumet Heights                                                 11.0   \n",
       "Chatham                                                         14.5   \n",
       "Chicago Lawn                                                    31.2   \n",
       "Clearing                                                        18.8   \n",
       "Douglas                                                         14.3   \n",
       "Dunning                                                         16.2   \n",
       "East Garfield Park                                              21.3   \n",
       "East Side                                                       31.9   \n",
       "Edgewater                                                        9.7   \n",
       "Edison Park                                                      7.4   \n",
       "Englewood                                                       28.5   \n",
       "Forest Glen                                                      4.9   \n",
       "Fuller Park                                                     26.6   \n",
       "Gage Park                                                       51.5   \n",
       "Garfield Ridge                                                  19.3   \n",
       "Grand Boulevard                                                 15.9   \n",
       "Greater Grand Crossing                                          16.5   \n",
       "...                                                              ...   \n",
       "Near North Side                                                  2.5   \n",
       "Near South Side                                                  7.4   \n",
       "Near West Side                                                   9.6   \n",
       "New City                                                        41.5   \n",
       "North Center                                                     4.5   \n",
       "North Lawndale                                                  27.6   \n",
       "North Park                                                      14.4   \n",
       "Norwood Park                                                    11.5   \n",
       "O'Hare                                                          10.9   \n",
       "Oakland                                                         18.4   \n",
       "Portage Park                                                    19.3   \n",
       "Pullman                                                         13.1   \n",
       "Riverdale                                                       27.5   \n",
       "Rogers Park                                                     18.2   \n",
       "Roseland                                                        16.9   \n",
       "South Chicago                                                   26.6   \n",
       "South Deering                                                   21.0   \n",
       "South Lawndale                                                  54.8   \n",
       "South Shore                                                     14.0   \n",
       "Uptown                                                          11.8   \n",
       "Washington Height                                               13.7   \n",
       "Washington Park                                                 25.4   \n",
       "West Elsdon                                                     37.0   \n",
       "West Englewood                                                  26.3   \n",
       "West Garfield Park                                              24.5   \n",
       "West Lawn                                                       33.6   \n",
       "West Pullman                                                    20.5   \n",
       "West Ridge                                                      20.8   \n",
       "West Town                                                       12.9   \n",
       "Woodlawn                                                        16.5   \n",
       "\n",
       "                        percent aged under 18 or over 64  per capita income   \\\n",
       "community area name                                                            \n",
       "Albany Park                                         32.0               21323   \n",
       "Archer Heights                                      39.2               16134   \n",
       "Armour Square                                       38.3               16148   \n",
       "Ashburn                                             36.9               23482   \n",
       "Auburn Gresham                                      41.9               15528   \n",
       "Austin                                              37.9               15957   \n",
       "Avalon Park                                         39.3               24454   \n",
       "Avondale                                            31.0               20039   \n",
       "Belmont Cragin                                      37.3               15461   \n",
       "Beverly                                             40.5               39523   \n",
       "Bridgeport                                          31.3               22694   \n",
       "Brighton Park                                       39.3               13089   \n",
       "Burnside                                            42.7               12515   \n",
       "Calumet Heights                                     44.0               28887   \n",
       "Chatham                                             40.3               18881   \n",
       "Chicago Lawn                                        40.6               13231   \n",
       "Clearing                                            37.6               25113   \n",
       "Douglas                                             30.7               23791   \n",
       "Dunning                                             33.6               26282   \n",
       "East Garfield Park                                  43.2               12961   \n",
       "East Side                                           42.8               17104   \n",
       "Edgewater                                           23.8               33385   \n",
       "Edison Park                                         35.3               40959   \n",
       "Englewood                                           42.5               11888   \n",
       "Forest Glen                                         40.5               44164   \n",
       "Fuller Park                                         44.9               10432   \n",
       "Gage Park                                           38.8               12171   \n",
       "Garfield Ridge                                      38.1               26353   \n",
       "Grand Boulevard                                     39.5               23472   \n",
       "Greater Grand Crossing                              41.0               17285   \n",
       "...                                                  ...                 ...   \n",
       "Near North Side                                     22.6               88669   \n",
       "Near South Side                                     21.8               59077   \n",
       "Near West Side                                      22.2               44689   \n",
       "New City                                            38.9               12765   \n",
       "North Center                                        26.2               57123   \n",
       "North Lawndale                                      42.7               12034   \n",
       "North Park                                          39.0               26576   \n",
       "Norwood Park                                        39.5               32875   \n",
       "O'Hare                                              30.3               25828   \n",
       "Oakland                                             40.4               19252   \n",
       "Portage Park                                        34.0               24336   \n",
       "Pullman                                             38.6               20588   \n",
       "Riverdale                                           51.5                8201   \n",
       "Rogers Park                                         27.5               23939   \n",
       "Roseland                                            41.2               17949   \n",
       "South Chicago                                       41.1               16579   \n",
       "South Deering                                       39.5               14685   \n",
       "South Lawndale                                      33.8               10402   \n",
       "South Shore                                         35.7               19398   \n",
       "Uptown                                              22.2               35787   \n",
       "Washington Height                                   42.6               19713   \n",
       "Washington Park                                     42.8               13785   \n",
       "West Elsdon                                         37.7               15754   \n",
       "West Englewood                                      40.7               11317   \n",
       "West Garfield Park                                  43.6               10934   \n",
       "West Lawn                                           39.6               16907   \n",
       "West Pullman                                        42.1               16563   \n",
       "West Ridge                                          38.5               23040   \n",
       "West Town                                           21.7               43198   \n",
       "Woodlawn                                            36.1               18672   \n",
       "\n",
       "                        hardship index  response time  total_requests  \\\n",
       "community area name                                                     \n",
       "Albany Park                       53.0      11.038835           206.0   \n",
       "Archer Heights                    67.0       6.951087           184.0   \n",
       "Armour Square                     82.0       6.981481            54.0   \n",
       "Ashburn                           37.0      17.123894           113.0   \n",
       "Auburn Gresham                    74.0      23.675000           120.0   \n",
       "Austin                            73.0      19.011561           173.0   \n",
       "Avalon Park                       41.0      16.260870            23.0   \n",
       "Avondale                          42.0      10.378182           275.0   \n",
       "Belmont Cragin                    70.0       6.310705           383.0   \n",
       "Beverly                           12.0      11.711864            59.0   \n",
       "Bridgeport                        43.0       5.948052           154.0   \n",
       "Brighton Park                     84.0       6.686957           345.0   \n",
       "Burnside                          79.0      20.714286             7.0   \n",
       "Calumet Heights                   38.0      33.054054            37.0   \n",
       "Chatham                           60.0      20.880000            50.0   \n",
       "Chicago Lawn                      80.0       8.771429           175.0   \n",
       "Clearing                          29.0       5.641791            67.0   \n",
       "Douglas                           47.0      13.272727            22.0   \n",
       "Dunning                           28.0      21.126316            95.0   \n",
       "East Garfield Park                83.0      24.474576            59.0   \n",
       "East Side                         64.0      10.071429            98.0   \n",
       "Edgewater                         19.0      10.054054           111.0   \n",
       "Edison Park                        8.0      23.600000            25.0   \n",
       "Englewood                         94.0      18.180000            50.0   \n",
       "Forest Glen                       11.0      27.803030            66.0   \n",
       "Fuller Park                       97.0      53.000000             9.0   \n",
       "Gage Park                         93.0       9.529010           293.0   \n",
       "Garfield Ridge                    32.0       6.782313           147.0   \n",
       "Grand Boulevard                   57.0       6.948718            39.0   \n",
       "Greater Grand Crossing            66.0      17.623188            69.0   \n",
       "...                                ...            ...             ...   \n",
       "Near North Side                    1.0      12.602410           166.0   \n",
       "Near South Side                    7.0      24.857143            35.0   \n",
       "Near West Side                    15.0      14.562771           231.0   \n",
       "New City                          91.0       8.435897           195.0   \n",
       "North Center                       6.0       7.491228           171.0   \n",
       "North Lawndale                    87.0      11.506494            77.0   \n",
       "North Park                        33.0       8.218750            64.0   \n",
       "Norwood Park                      21.0      21.765306            98.0   \n",
       "O'Hare                            24.0      37.333333            21.0   \n",
       "Oakland                           78.0      13.200000             5.0   \n",
       "Portage Park                      35.0       9.869198           237.0   \n",
       "Pullman                           51.0       7.222222            18.0   \n",
       "Riverdale                         98.0      74.142857             7.0   \n",
       "Rogers Park                       39.0      13.232877           146.0   \n",
       "Roseland                          52.0      12.175926           108.0   \n",
       "South Chicago                     75.0       8.047059            85.0   \n",
       "South Deering                     65.0      10.049180            61.0   \n",
       "South Lawndale                    96.0       5.832677           508.0   \n",
       "South Shore                       55.0      23.320988            81.0   \n",
       "Uptown                            20.0      13.355140           107.0   \n",
       "Washington Height                 48.0      17.565217            69.0   \n",
       "Washington Park                   88.0      10.307692            26.0   \n",
       "West Elsdon                       69.0       6.364286           140.0   \n",
       "West Englewood                    89.0      16.184211            76.0   \n",
       "West Garfield Park                92.0      23.542857            35.0   \n",
       "West Lawn                         56.0       5.466667           165.0   \n",
       "West Pullman                      62.0      21.797101            69.0   \n",
       "West Ridge                        46.0      20.853211           218.0   \n",
       "West Town                         10.0      11.628885           547.0   \n",
       "Woodlawn                          58.0      14.657895            38.0   \n",
       "\n",
       "                        numGraffiti Removal  numPothole in Street  \\\n",
       "community area name                                                 \n",
       "Albany Park                           150.0                    48   \n",
       "Archer Heights                        149.0                    26   \n",
       "Armour Square                          33.0                    20   \n",
       "Ashburn                                45.0                    55   \n",
       "Auburn Gresham                          8.0                    75   \n",
       "Austin                                 39.0                    97   \n",
       "Avalon Park                             0.0                    21   \n",
       "Avondale                              228.0                    37   \n",
       "Belmont Cragin                        291.0                    71   \n",
       "Beverly                                 7.0                    46   \n",
       "Bridgeport                            125.0                    22   \n",
       "Brighton Park                         299.0                    38   \n",
       "Burnside                                1.0                     4   \n",
       "Calumet Heights                         4.0                    28   \n",
       "Chatham                                 5.0                    32   \n",
       "Chicago Lawn                          102.0                    50   \n",
       "Clearing                               29.0                    36   \n",
       "Douglas                                 4.0                    15   \n",
       "Dunning                                35.0                    49   \n",
       "East Garfield Park                     18.0                    27   \n",
       "East Side                              73.0                    20   \n",
       "Edgewater                              68.0                    35   \n",
       "Edison Park                             5.0                    19   \n",
       "Englewood                              12.0                    22   \n",
       "Forest Glen                             6.0                    57   \n",
       "Fuller Park                             1.0                     6   \n",
       "Gage Park                             229.0                    56   \n",
       "Garfield Ridge                         70.0                    67   \n",
       "Grand Boulevard                        10.0                    21   \n",
       "Greater Grand Crossing                  2.0                    40   \n",
       "...                                     ...                   ...   \n",
       "Near North Side                        73.0                    84   \n",
       "Near South Side                        17.0                    16   \n",
       "Near West Side                        138.0                    82   \n",
       "New City                              128.0                    49   \n",
       "North Center                          122.0                    38   \n",
       "North Lawndale                         37.0                    30   \n",
       "North Park                             38.0                    22   \n",
       "Norwood Park                            7.0                    74   \n",
       "O'Hare                                  4.0                    15   \n",
       "Oakland                                 1.0                     4   \n",
       "Portage Park                          158.0                    62   \n",
       "Pullman                                 2.0                    14   \n",
       "Riverdale                               0.0                     7   \n",
       "Rogers Park                            82.0                    53   \n",
       "Roseland                               12.0                    78   \n",
       "South Chicago                          44.0                    29   \n",
       "South Deering                          26.0                    32   \n",
       "South Lawndale                        451.0                    44   \n",
       "South Shore                             8.0                    56   \n",
       "Uptown                                 67.0                    28   \n",
       "Washington Height                       2.0                    49   \n",
       "Washington Park                         7.0                    15   \n",
       "West Elsdon                           113.0                    24   \n",
       "West Englewood                         20.0                    42   \n",
       "West Garfield Park                      4.0                    19   \n",
       "West Lawn                              97.0                    64   \n",
       "West Pullman                           12.0                    41   \n",
       "West Ridge                            114.0                    85   \n",
       "West Town                             417.0                    96   \n",
       "Woodlawn                                4.0                    27   \n",
       "\n",
       "                        numSanitation Code Violation  \n",
       "community area name                                   \n",
       "Albany Park                                      8.0  \n",
       "Archer Heights                                   9.0  \n",
       "Armour Square                                    1.0  \n",
       "Ashburn                                         13.0  \n",
       "Auburn Gresham                                  37.0  \n",
       "Austin                                          37.0  \n",
       "Avalon Park                                      2.0  \n",
       "Avondale                                        10.0  \n",
       "Belmont Cragin                                  21.0  \n",
       "Beverly                                          6.0  \n",
       "Bridgeport                                       7.0  \n",
       "Brighton Park                                    8.0  \n",
       "Burnside                                         2.0  \n",
       "Calumet Heights                                  5.0  \n",
       "Chatham                                         13.0  \n",
       "Chicago Lawn                                    23.0  \n",
       "Clearing                                         2.0  \n",
       "Douglas                                          3.0  \n",
       "Dunning                                         11.0  \n",
       "East Garfield Park                              14.0  \n",
       "East Side                                        5.0  \n",
       "Edgewater                                        8.0  \n",
       "Edison Park                                      1.0  \n",
       "Englewood                                       16.0  \n",
       "Forest Glen                                      3.0  \n",
       "Fuller Park                                      2.0  \n",
       "Gage Park                                        8.0  \n",
       "Garfield Ridge                                  10.0  \n",
       "Grand Boulevard                                  8.0  \n",
       "Greater Grand Crossing                          27.0  \n",
       "...                                              ...  \n",
       "Near North Side                                  9.0  \n",
       "Near South Side                                  2.0  \n",
       "Near West Side                                  11.0  \n",
       "New City                                        18.0  \n",
       "North Center                                    11.0  \n",
       "North Lawndale                                  10.0  \n",
       "North Park                                       4.0  \n",
       "Norwood Park                                    17.0  \n",
       "O'Hare                                           2.0  \n",
       "Oakland                                          0.0  \n",
       "Portage Park                                    17.0  \n",
       "Pullman                                          2.0  \n",
       "Riverdale                                        0.0  \n",
       "Rogers Park                                     11.0  \n",
       "Roseland                                        18.0  \n",
       "South Chicago                                   12.0  \n",
       "South Deering                                    3.0  \n",
       "South Lawndale                                  13.0  \n",
       "South Shore                                     17.0  \n",
       "Uptown                                          12.0  \n",
       "Washington Height                               18.0  \n",
       "Washington Park                                  4.0  \n",
       "West Elsdon                                      3.0  \n",
       "West Englewood                                  14.0  \n",
       "West Garfield Park                              12.0  \n",
       "West Lawn                                        4.0  \n",
       "West Pullman                                    16.0  \n",
       "West Ridge                                      19.0  \n",
       "West Town                                       34.0  \n",
       "Woodlawn                                         7.0  \n",
       "\n",
       "[77 rows x 11 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indicators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graffiti locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tNC655BIuuOAC1q5da+nK2aXeQaFpGi7N4DUDJ3Jq7/E8dvA37I+PN5VxQrCH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\n5RZifzrRACq+XwHxbJqxdHxew1mKU2WLujil8ln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uW7duJSYmJnDzgzGey9DQUH7961/z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\n4iSEEMJw/h+yD+e/iz6+QgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1157fc160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import kendalltau\n",
    "x = indicators['per capita income ']\n",
    "y = indicators['numGraffiti Removal']\n",
    "ax = sns.jointplot(x, y, kind=\"hex\", stat_func=kendalltau, color=\"#4CB391\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### Poverty and Graffiti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4AXa7HbfeeqsZsRER0SSVs3JyuVz43ve+Z0YsREREAPJITo888gh+/etfIxwO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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116457a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = indicators['percent households below poverty']\n",
    "y = indicators['numGraffiti Removal']\n",
    "ax = sns.jointplot(x, y, kind=\"hex\", color=\"#333333\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hardship and Potholes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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L1G35RdmzjfsDap+r34PkejZnTitXrrR4eVxcnOzBiIQltltEK6Gx5KMPfA/q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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11749b208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = indicators['hardship index']\n",
    "y = indicators['numPothole in Street']\n",
    "ax = sns.jointplot(x, y, stat_func=kendalltau, color=\"#325925\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Avg Response Time by Community Area (top 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "community area name\n",
       "Riverdale             74.142857\n",
       "Fuller Park           53.000000\n",
       "O'Hare                37.333333\n",
       "Calumet Heights       33.054054\n",
       "Forest Glen           27.803030\n",
       "Lake View             24.915663\n",
       "Near South Side       24.857143\n",
       "East Garfield Park    24.474576\n",
       "Auburn Gresham        23.675000\n",
       "Edison Park           23.600000\n",
       "Name: response time, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indicators['response time'].sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DB/ahsDAP48e/apX9PflkGPbuTTP7/LRpEzFnTgz8/Pxw7NhRPPLIo1Y5viWuXbuGBQv+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\nyTCEiIhIMgwhIiKSzP8DjEQMYq86+oMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11645bb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = indicators['percent aged 25+ without high school diploma']\n",
    "y = indicators['response time']\n",
    "ax = sns.jointplot(x, y, stat_func=kendalltau, color=\"blue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YBiEiIhKGQYiIiIRhECIiImEYhIiISBgGISIiEoZBiIiIhGEQIiLyM+vXB2P9+mDRxfAJ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\nEBERCfP/AD3KyByw6YEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1160a2c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = indicators['hardship index']\n",
    "y = indicators['response time']\n",
    "ax = sns.jointplot(x, y, stat_func=kendalltau, color=\"blue\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Types over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0ufOQA0+s2YPaVifW75Te8Bo6BlBZH2283viiJqGhBIDmzkFU1PXguY+qAADr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\npuIrIiISZ/8CREjOMOjXwBIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c6e3dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rZVuf3/SS/fv3x4IFC/DII48AAIqKijB+/HgAwMUXX4yNGzfi8ssv97mO7OwUxMVpZCiu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\nFmlBX4jcJL62671AgA0Ykdf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C2SxA225AnFqNlKQ4qFTA0L5ZOFTWiLqmDvxqWB4qatuQ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\n2QwCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d6accc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AvbqkBfKzzz6L5uZm3HfffRgYGMCiRYt0Annjxo1YtmwZ5syZU5GCiiiYrCuw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\nhfe368Ah9PSU7u8fGJF6loP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iiTZqs7mGHzxoxSr+qRfI/mjIxo9NzdcUqatMpy4eu0AQ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\nshBCCOEE/gPaaKMWHD0BSAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e0cee10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.patches as mpatches\n",
    "import matplotlib.pyplot as plt\n",
    "for t in df['type of service request'].unique():\n",
    "    ax = df[df[\"type of service request\"] == t][['creation date', \"type of service request\"]].groupby('creation date').count().plot()\n",
    "    p = mpatches.Patch(color='blue', label=t)\n",
    "    plt.legend(handles=[p])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary of findings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "- Graffiti is the most frequent service request. Graffiti removal has been consistently reported in the data we've analyzed.\n",
    "\n",
    "- Graffiti request tend to be in lower income areas, either by per capita income or households in poverty\n",
    "\n",
    "- There doesn't appear to be any strong linear relationship between hardship index and potholes\n",
    "\n",
    "- Potholes were an mostly an issue in 2014. They are reported less currently. Sanitation and Abandon Buildings were not reported often\n",
    "\n",
    "- There doesn't appear to be any strong linear relationship between hardship or adults without a HS diploma and response time "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Problem 2 - Data Augmentation with ACS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "from requests.auth import HTTPBasicAuth\n",
    "\n",
    "API_KEY = '9032d3c94c7f4afe905da54f889af02a6b51f63f'\n",
    "request_url = \"http://citysdk.commerce.gov\"\n",
    "\n",
    "variables = ['income','population','poverty','median_contract_rent','education_bachelors','poverty_family']\n",
    "\n",
    "def acs_data (zip_code):\n",
    "    request_obj = {\n",
    "        'level': 'tract',\n",
    "        'zip': int(zip_code),\n",
    "        'sublevel': False,\n",
    "        'api': 'acs5',\n",
    "        'year': 2014,\n",
    "        'variables': variables\n",
    "        }\n",
    "\n",
    "    data = None\n",
    "    \n",
    "    response = requests.post(request_url, auth=HTTPBasicAuth(API_KEY, None), json=request_obj)\n",
    "    if response:\n",
    "        data = response.json()\n",
    "        data = data['features'][0]['properties']\n",
    "\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# pull data by from zip code\n",
    "codes = df[['zip code']]\n",
    "codes = codes.drop_duplicates()\n",
    "\n",
    "acs = codes.apply(acs_data, axis=1)\n",
    "acs = acs.apply(pd.Series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "acs_ = acs\n",
    "# humanize\n",
    "columns = ['B01003_001E', 'B15003_022E', 'B17001_002E','B17012_002E', 'B19013_001E', 'B25058_001E']\n",
    "ref = {\"B15003_022E\": \"education_bachelors\",\n",
    "        \"B19013_001E\":   \"income\",\n",
    "        \"B25058_001E\":   \"median_contract_rent\",\n",
    "        \"B01003_001E\":   \"population\",\n",
    "        \"B17001_002E\":   \"poverty\",\n",
    "        \"B17012_002E\":   \"poverty_family\"}\n",
    "new_name = [ref[key] for key in columns]\n",
    "acs = acs[columns]\n",
    "acs.columns = new_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# combine zip with acs\n",
    "acs = pd.concat([acs,codes], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# combine df with acs on zip\n",
    "df = df.merge(acs, on='zip code')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# combine all indicators\n",
    "new_indicators = df.groupby('community area name').max()\n",
    "new_indicators = new_indicators.merge(indicators)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Type by Community Area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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t2yIlJQVJSUno27evvm18fHwddYOIiIiIDKnKJ654enoiPT1d/1pEYGJiAgCw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\nIlL4f6VmVJFD9tWnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1158d6588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(df, col=\"type of service request\")\n",
    "ax = g.map(plt.hist, \"community area\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What types of blocks get “Vacant and Abandoned Buildings Reported”?\n",
    "There are realtively very few Vacant and Abandoned Building reports.\n",
    "\n",
    "#### What types of blocks get “Sanitation Code Complaints”?\n",
    "community area 47, near the Chatam neighborhood, which has lower incomes and 18% unemployment\n",
    "\n",
    "#### Does that change over time in the data you collected?\n",
    "There is a consitent bimodal distribution over the years, but it has changed slightly.\n",
    "\n",
    "#### What is the difference in blocks that get “Vacant and Abandoned Buildings Reported” vs “Sanitation Code Complaints”?\n",
    "I don't have any data in my sample on these"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Graffiti Removal', 'Sanitation Code Violation', 'Pothole in Street'], dtype=object)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['type of service request'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>community area</th>\n",
       "      <th>completion date</th>\n",
       "      <th>creation date</th>\n",
       "      <th>latitude</th>\n",
       "      <th>location</th>\n",
       "      <th>longitude</th>\n",
       "      <th>number of potholes filled on block</th>\n",
       "      <th>police district</th>\n",
       "      <th>service request number</th>\n",
       "      <th>service request type</th>\n",
       "      <th>...</th>\n",
       "      <th>percent aged 16+ unemployed</th>\n",
       "      <th>percent aged 25+ without high school diploma</th>\n",
       "      <th>percent aged under 18 or over 64</th>\n",
       "      <th>per capita income</th>\n",
       "      <th>hardship index</th>\n",
       "      <th>year</th>\n",
       "      <th>total_requests</th>\n",
       "      <th>numGraffiti Removal</th>\n",
       "      <th>numPothole in Street</th>\n",
       "      <th>numSanitation Code Violation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [community area, completion date, creation date, latitude, location, longitude, number of potholes filled on block, police district, service request number, service request type, ssa, status, street address, type of service request, ward, x coordinate, y coordinate, zip code, response time, community area number, percent of housing crowded, percent households below poverty, percent aged 16+ unemployed, percent aged 25+ without high school diploma, percent aged under 18 or over 64, per capita income , hardship index, year, total_requests, numGraffiti Removal, numPothole in Street, numSanitation Code Violation]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 32 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_indicators[new_indicators['community area'] == 47]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sanitation Code Violations over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d5a4ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = df[df['type of service request'] == 'Sanitation Code Violation']\n",
    "g = sns.FacetGrid(data, col=\"year\")\n",
    "g = g.map(plt.hist, \"community area\", color=\"r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assume you are running the 311 call center for Chicago. You get a call from 7500 S Wolcott Ave.\n",
    "\n",
    "Of the four types of requests you have data for, which request type is the most likely given the call came from 7500 S Wolcott Ave? What are the probabilities for each type of request?\n",
    "Let’s now assume that a call comes in about Graffiti Removal. Which is more likely – that the call came from Lawndale or Uptown? How much more or less likely is it to be from Lawndale versus Uptown?\n",
    "Now assume that you don’t have access to all the raw data and you know the following things:\n",
    "\n",
    "There are a total of 1000 calls, 600 from Englewood and 400 from Uptown. \n",
    "Of the 600 calls from Englewood, 100 of them are about Graffiti Removal. \n",
    "Of the 400 calls from Uptown, 160 are about Graffiti Removal. \n",
    "\n",
    "If a call comes about Graffiti Removal, how much more/less likely is it \n",
    "that the call came from Englewood versus Uptown? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "engleside prob =  0.0019877422560874605\n",
      "lawndale prob =  0.00612887195626967\n"
     ]
    }
   ],
   "source": [
    "total_graffiti = indicators['numGraffiti Removal'].sum()\n",
    "total_requests = indicators['total_requests'].sum()\n",
    "graffiti_prob = (indicators['numGraffiti Removal'].sum() / total_requests)\n",
    "\n",
    "neighborhood = \"Englewood\"\n",
    "# Englewood\n",
    "graffiti_area = indicators[indicators.index == neighborhood]['numGraffiti Removal'].sum()\n",
    "graffiti_given_area_prob = (graffiti_area / indicators[indicators.index == neighborhood]['total_requests'].sum())\n",
    "area_prob = (indicators[indicators.index == neighborhood]['total_requests'].sum() / total_requests)\n",
    "\n",
    "eng_given_graffiti = graffiti_given_area_prob * area_prob / graffiti_prob\n",
    "\n",
    "# Lawndale\n",
    "neighborhood = \"North Lawndale\"\n",
    "graffiti_area = indicators[indicators.index == neighborhood]['numGraffiti Removal'].sum()\n",
    "graffiti_given_area_prob = (graffiti_area / indicators[indicators.index == neighborhood]['total_requests'].sum())\n",
    "area_prob = (indicators[indicators.index == neighborhood]['total_requests'].sum() / total_requests)\n",
    "\n",
    "lwn_given_graffiti = graffiti_given_area_prob * area_prob / graffiti_prob\n",
    "\n",
    "print(\"engleside prob = \", eng_given_graffiti)\n",
    "print(\"lawndale prob = \", lwn_given_graffiti)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answer:\n",
    "Using Bayes Rule...\n",
    "Prob(Area | Graffiti) = [Prob(Graffiti | Area) * Prob(Area)]/ Prob(Graffiti)\n",
    "\n",
    "For the real data, given a graffitti request it is 3x more likely that the neighboorhood is \"North Lawndale\".\n",
    "\n",
    "With the synthetic data, the prob a call is 60% and 40% for Englewood and Uptown.\n",
    "- Prob(Englewood | Graffiti) =  [100/600 x 600/1000]/(100/600) = 60%\n",
    "- Prob(Uptown | Graffiti) = [160/400 x 400/1000]/(160/400) = 40%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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