Chicago-Crime-Data-Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chicago Crime Data Analysis\n",
    "\n",
    "# Project Group: \n",
    "* Christopher Habib \n",
    "* Siddharth Krishnan\n",
    "* Ali Kashani \n",
    "* Elmaddin Karimov\n",
    "\n",
    "# Curated datasets:\n",
    "\n",
    "https://dev.socrata.com/foundry/data.cityofchicago.org/6zsd-86xi\n",
    "\n",
    "https://data.cityofchicago.org/Health-Human-Services/Census-Data-Selected-socioeconomic-indicators-in-C/kn9c-c2s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Import necessary packages\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "from scipy.stats import linregress\n",
    "from pandas import ExcelWriter\n",
    "from pandas import ExcelFile\n",
    "\n",
    "\n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in the Chicago crime dataset\n",
    "df = pd.read_csv('resources/Crimes_-_2001_to_present.csv')\n",
    "census = pd.read_csv('resources/Census_Data_-_Selected_socioeconomic_indicators_in_Chicago__2008___2012.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### View subset of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Case Number</th>\n",
       "      <th>Date</th>\n",
       "      <th>Block</th>\n",
       "      <th>IUCR</th>\n",
       "      <th>Primary Type</th>\n",
       "      <th>Description</th>\n",
       "      <th>Location Description</th>\n",
       "      <th>Arrest</th>\n",
       "      <th>Domestic</th>\n",
       "      <th>...</th>\n",
       "      <th>Ward</th>\n",
       "      <th>Community Area</th>\n",
       "      <th>FBI Code</th>\n",
       "      <th>X Coordinate</th>\n",
       "      <th>Y Coordinate</th>\n",
       "      <th>Year</th>\n",
       "      <th>Updated On</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4381932</td>\n",
       "      <td>HL677807</td>\n",
       "      <td>10/16/2005 09:30:00 PM</td>\n",
       "      <td>051XX S MENARD AVE</td>\n",
       "      <td>1320</td>\n",
       "      <td>CRIMINAL DAMAGE</td>\n",
       "      <td>TO VEHICLE</td>\n",
       "      <td>STREET</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>14</td>\n",
       "      <td>1138567.0</td>\n",
       "      <td>1869950.0</td>\n",
       "      <td>2005</td>\n",
       "      <td>02/28/2018 03:56:25 PM</td>\n",
       "      <td>41.799263</td>\n",
       "      <td>-87.767377</td>\n",
       "      <td>(41.799263181, -87.767376862)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6833055</td>\n",
       "      <td>HR241329</td>\n",
       "      <td>03/28/2009 11:30:00 PM</td>\n",
       "      <td>034XX W PARKER AVE</td>\n",
       "      <td>0486</td>\n",
       "      <td>BATTERY</td>\n",
       "      <td>DOMESTIC BATTERY SIMPLE</td>\n",
       "      <td>APARTMENT</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>35.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>08B</td>\n",
       "      <td>1152927.0</td>\n",
       "      <td>1918074.0</td>\n",
       "      <td>2009</td>\n",
       "      <td>02/28/2018 03:56:25 PM</td>\n",
       "      <td>41.931049</td>\n",
       "      <td>-87.713440</td>\n",
       "      <td>(41.931049328, -87.713440154)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6833057</td>\n",
       "      <td>HR215483</td>\n",
       "      <td>03/14/2009 01:20:00 PM</td>\n",
       "      <td>072XX S HERMITAGE AVE</td>\n",
       "      <td>0486</td>\n",
       "      <td>BATTERY</td>\n",
       "      <td>DOMESTIC BATTERY SIMPLE</td>\n",
       "      <td>RESIDENCE</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>17.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>08B</td>\n",
       "      <td>1165933.0</td>\n",
       "      <td>1856730.0</td>\n",
       "      <td>2009</td>\n",
       "      <td>02/28/2018 03:56:25 PM</td>\n",
       "      <td>41.762448</td>\n",
       "      <td>-87.667394</td>\n",
       "      <td>(41.762447541, -87.667393762)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6833058</td>\n",
       "      <td>HR241426</td>\n",
       "      <td>03/28/2009 11:00:00 PM</td>\n",
       "      <td>036XX N RAVENSWOOD AVE</td>\n",
       "      <td>0820</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>$500 AND UNDER</td>\n",
       "      <td>STREET</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>47.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>06</td>\n",
       "      <td>1163715.0</td>\n",
       "      <td>1924205.0</td>\n",
       "      <td>2009</td>\n",
       "      <td>02/28/2018 03:56:25 PM</td>\n",
       "      <td>41.947652</td>\n",
       "      <td>-87.673623</td>\n",
       "      <td>(41.947652256, -87.673622996)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6833059</td>\n",
       "      <td>HR241429</td>\n",
       "      <td>03/28/2009 03:00:00 AM</td>\n",
       "      <td>012XX W LOYOLA AVE</td>\n",
       "      <td>0890</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>FROM BUILDING</td>\n",
       "      <td>COLLEGE/UNIVERSITY RESIDENCE HALL</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>06</td>\n",
       "      <td>1166780.0</td>\n",
       "      <td>1943821.0</td>\n",
       "      <td>2009</td>\n",
       "      <td>02/28/2018 03:56:25 PM</td>\n",
       "      <td>42.001414</td>\n",
       "      <td>-87.661792</td>\n",
       "      <td>(42.001413896, -87.661791623)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID Case Number                    Date                   Block  IUCR  \\\n",
       "0  4381932    HL677807  10/16/2005 09:30:00 PM      051XX S MENARD AVE  1320   \n",
       "1  6833055    HR241329  03/28/2009 11:30:00 PM      034XX W PARKER AVE  0486   \n",
       "2  6833057    HR215483  03/14/2009 01:20:00 PM   072XX S HERMITAGE AVE  0486   \n",
       "3  6833058    HR241426  03/28/2009 11:00:00 PM  036XX N RAVENSWOOD AVE  0820   \n",
       "4  6833059    HR241429  03/28/2009 03:00:00 AM      012XX W LOYOLA AVE  0890   \n",
       "\n",
       "      Primary Type              Description  \\\n",
       "0  CRIMINAL DAMAGE               TO VEHICLE   \n",
       "1          BATTERY  DOMESTIC BATTERY SIMPLE   \n",
       "2          BATTERY  DOMESTIC BATTERY SIMPLE   \n",
       "3            THEFT           $500 AND UNDER   \n",
       "4            THEFT            FROM BUILDING   \n",
       "\n",
       "                Location Description  Arrest  Domestic  ...  Ward  \\\n",
       "0                             STREET   False     False  ...  23.0   \n",
       "1                          APARTMENT   False      True  ...  35.0   \n",
       "2                          RESIDENCE   False      True  ...  17.0   \n",
       "3                             STREET   False     False  ...  47.0   \n",
       "4  COLLEGE/UNIVERSITY RESIDENCE HALL   False     False  ...  40.0   \n",
       "\n",
       "   Community Area  FBI Code  X Coordinate Y Coordinate  Year  \\\n",
       "0            56.0        14     1138567.0    1869950.0  2005   \n",
       "1            22.0       08B     1152927.0    1918074.0  2009   \n",
       "2            67.0       08B     1165933.0    1856730.0  2009   \n",
       "3             6.0        06     1163715.0    1924205.0  2009   \n",
       "4             1.0        06     1166780.0    1943821.0  2009   \n",
       "\n",
       "               Updated On   Latitude  Longitude                       Location  \n",
       "0  02/28/2018 03:56:25 PM  41.799263 -87.767377  (41.799263181, -87.767376862)  \n",
       "1  02/28/2018 03:56:25 PM  41.931049 -87.713440  (41.931049328, -87.713440154)  \n",
       "2  02/28/2018 03:56:25 PM  41.762448 -87.667394  (41.762447541, -87.667393762)  \n",
       "3  02/28/2018 03:56:25 PM  41.947652 -87.673623  (41.947652256, -87.673622996)  \n",
       "4  02/28/2018 03:56:25 PM  42.001414 -87.661792  (42.001413896, -87.661791623)  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Community Area Number</th>\n",
       "      <th>COMMUNITY AREA NAME</th>\n",
       "      <th>PERCENT OF HOUSING CROWDED</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Rogers Park</td>\n",
       "      <td>7.7</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>West Ridge</td>\n",
       "      <td>7.8</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Uptown</td>\n",
       "      <td>3.8</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>25.5</td>\n",
       "      <td>37524</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>North Center</td>\n",
       "      <td>0.3</td>\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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Community Area Number COMMUNITY AREA NAME  PERCENT OF HOUSING CROWDED  \\\n",
       "0                    1.0         Rogers Park                         7.7   \n",
       "1                    2.0          West Ridge                         7.8   \n",
       "2                    3.0              Uptown                         3.8   \n",
       "3                    4.0      Lincoln Square                         3.4   \n",
       "4                    5.0        North Center                         0.3   \n",
       "\n",
       "   PERCENT HOUSEHOLDS BELOW POVERTY  PERCENT AGED 16+ UNEMPLOYED  \\\n",
       "0                              23.6                          8.7   \n",
       "1                              17.2                          8.8   \n",
       "2                              24.0                          8.9   \n",
       "3                              10.9                          8.2   \n",
       "4                               7.5                          5.2   \n",
       "\n",
       "   PERCENT AGED 25+ WITHOUT HIGH SCHOOL DIPLOMA  \\\n",
       "0                                          18.2   \n",
       "1                                          20.8   \n",
       "2                                          11.8   \n",
       "3                                          13.4   \n",
       "4                                           4.5   \n",
       "\n",
       "   PERCENT AGED UNDER 18 OR OVER 64  PER CAPITA INCOME   HARDSHIP INDEX  \n",
       "0                              27.5               23939            39.0  \n",
       "1                              38.5               23040            46.0  \n",
       "2                              22.2               35787            20.0  \n",
       "3                              25.5               37524            17.0  \n",
       "4                              26.2               57123             6.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "census.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Case Number', 'Date', 'Block', 'IUCR', 'Primary Type',\n",
       "       'Description', 'Location Description', 'Arrest', 'Domestic', 'Beat',\n",
       "       'District', 'Ward', 'Community Area', 'FBI Code', 'X Coordinate',\n",
       "       'Y Coordinate', 'Year', 'Updated On', 'Latitude', 'Longitude',\n",
       "       'Location'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Community Area Number', 'COMMUNITY AREA NAME',\n",
       "       'PERCENT OF HOUSING CROWDED', 'PERCENT HOUSEHOLDS BELOW POVERTY',\n",
       "       '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'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " census.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What crimes are most prevalent in Chicago?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Check if any rows are missing data and are null\n",
    "df['Primary Type'].isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "THEFT                                1435101\n",
       "BATTERY                              1244650\n",
       "CRIMINAL DAMAGE                       778188\n",
       "NARCOTICS                             714802\n",
       "ASSAULT                               423683\n",
       "OTHER OFFENSE                         423342\n",
       "BURGLARY                              390914\n",
       "MOTOR VEHICLE THEFT                   316892\n",
       "DECEPTIVE PRACTICE                    268020\n",
       "ROBBERY                               258012\n",
       "CRIMINAL TRESPASS                     195178\n",
       "WEAPONS VIOLATION                      72138\n",
       "PROSTITUTION                           68512\n",
       "PUBLIC PEACE VIOLATION                 48148\n",
       "OFFENSE INVOLVING CHILDREN             46018\n",
       "CRIM SEXUAL ASSAULT                    27660\n",
       "SEX OFFENSE                            25445\n",
       "INTERFERENCE WITH PUBLIC OFFICER       15485\n",
       "GAMBLING                               14433\n",
       "LIQUOR LAW VIOLATION                   14109\n",
       "ARSON                                  11250\n",
       "HOMICIDE                                9553\n",
       "KIDNAPPING                              6706\n",
       "INTIMIDATION                            3989\n",
       "STALKING                                3435\n",
       "OBSCENITY                                597\n",
       "CONCEALED CARRY LICENSE VIOLATION        324\n",
       "NON-CRIMINAL                             171\n",
       "PUBLIC INDECENCY                         164\n",
       "OTHER NARCOTIC VIOLATION                 124\n",
       "HUMAN TRAFFICKING                         54\n",
       "NON - CRIMINAL                            38\n",
       "RITUALISM                                 23\n",
       "NON-CRIMINAL (SUBJECT SPECIFIED)           9\n",
       "DOMESTIC VIOLENCE                          1\n",
       "Name: Primary Type, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Count number of observations for each crime\n",
    "df['Primary Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot these for better visualization\n",
    "crime_type_df = df['Primary Type'].value_counts(ascending=True)\n",
    "\n",
    "## Some formatting to make it look nicer\n",
    "fig=plt.figure(figsize=(18, 16))\n",
    "plt.title(\"Frequency of Crimes Per Crime Type\")\n",
    "plt.xlabel(\"Frequency of Crimes\")\n",
    "plt.ylabel(\"Type of Crime\")\n",
    "ax = crime_type_df.plot(kind='barh')\n",
    "ax.get_xaxis().set_major_formatter(plt.FuncFormatter(lambda x, loc: \"{:,}\".format(int(x))))\n",
    "plt.savefig(\"output_data/TypeOFCrime.png\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What year had the most crime?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Check if any rows are missing data and are null\n",
    "df['Year'].isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2002    486754\n",
       "2001    485752\n",
       "2003    475940\n",
       "2004    469383\n",
       "2005    453715\n",
       "2006    448108\n",
       "2007    437011\n",
       "2008    427056\n",
       "2009    392678\n",
       "2010    370309\n",
       "2011    351774\n",
       "2012    335960\n",
       "2013    307089\n",
       "2014    275266\n",
       "2016    268954\n",
       "2017    268015\n",
       "2018    265859\n",
       "2015    264048\n",
       "2019     33497\n",
       "Name: Year, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Count number of reported crimes for each year\n",
    "df['Year'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot these for better visualization\n",
    "crime_year_df = df['Year'].value_counts(ascending=True)\n",
    "\n",
    "## Some formatting to make it look nicer\n",
    "fig=plt.figure(figsize=(10, 8))\n",
    "plt.title(\"Frequency of Crimes Per Year in Chicago\")\n",
    "plt.xlabel(\"Frequency of Crimes\")\n",
    "plt.ylabel(\"Year\")\n",
    "ax = crime_year_df.plot(kind='barh')\n",
    "ax.get_xaxis().set_major_formatter(plt.FuncFormatter(lambda x, loc: \"{:,}\".format(int(x))))\n",
    "plt.savefig(\"output_data/FrequencyOfCrimesPerYear.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What percentage of arrests were successful?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Check if any rows are missing data and are null\n",
    "df['Arrest'].isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    4930863\n",
       "True     1886305\n",
       "Name: Arrest, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Count number of successful arrests for each year\n",
    "df['Arrest'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Convert values into percentages\n",
    "arrest_df = df['Arrest'].value_counts()\n",
    "arrest_percent = (arrest_df / df['Arrest'].sum()) * 100 \n",
    "\n",
    "## Rename Series.name\n",
    "arrest_percent.rename(\"% of Arrests\",inplace=True)\n",
    "\n",
    "## Rename True and False to % Arrested and % Not Arrested\n",
    "arrest_percent.rename({True: '% Arrested', False: '% Not Arrested'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Format pie chart to nicely show percentage and count\n",
    "def make_autopct(values):\n",
    "    def my_autopct(pct):\n",
    "        total = sum(values)\n",
    "        val = int(round(pct*total/100.0))\n",
    "        return '{p:.2f}%  ({v:d})'.format(p=pct,v=val)\n",
    "    return my_autopct\n",
    "\n",
    "## Plot results in a pie chart\n",
    "arrest_percent.plot.pie(fontsize=11,\n",
    "                       autopct=make_autopct(df['Arrest'].value_counts()),\n",
    "                       figsize=(8, 8))\n",
    "plt.savefig(\"output_data/PercentageOfArrests.png\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What are the successful arrest percentages per year?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Arrest</th>\n",
       "      <th>Counts</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>False</td>\n",
       "      <td>343850</td>\n",
       "      <td>70.787151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>True</td>\n",
       "      <td>141902</td>\n",
       "      <td>29.212849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>False</td>\n",
       "      <td>345201</td>\n",
       "      <td>70.918986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>True</td>\n",
       "      <td>141553</td>\n",
       "      <td>29.081014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>False</td>\n",
       "      <td>334368</td>\n",
       "      <td>70.254234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>True</td>\n",
       "      <td>141572</td>\n",
       "      <td>29.745766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>False</td>\n",
       "      <td>324697</td>\n",
       "      <td>69.175279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>True</td>\n",
       "      <td>144686</td>\n",
       "      <td>30.824721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>False</td>\n",
       "      <td>312820</td>\n",
       "      <td>68.946365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>True</td>\n",
       "      <td>140895</td>\n",
       "      <td>31.053635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>False</td>\n",
       "      <td>312724</td>\n",
       "      <td>69.787640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>True</td>\n",
       "      <td>135384</td>\n",
       "      <td>30.212360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>False</td>\n",
       "      <td>305160</td>\n",
       "      <td>69.828906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>True</td>\n",
       "      <td>131851</td>\n",
       "      <td>30.171094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>False</td>\n",
       "      <td>317099</td>\n",
       "      <td>74.252323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>True</td>\n",
       "      <td>109957</td>\n",
       "      <td>25.747677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>False</td>\n",
       "      <td>281913</td>\n",
       "      <td>71.792410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>True</td>\n",
       "      <td>110765</td>\n",
       "      <td>28.207590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>False</td>\n",
       "      <td>269827</td>\n",
       "      <td>72.865364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>True</td>\n",
       "      <td>100482</td>\n",
       "      <td>27.134636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>False</td>\n",
       "      <td>255549</td>\n",
       "      <td>72.645790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>True</td>\n",
       "      <td>96225</td>\n",
       "      <td>27.354210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>False</td>\n",
       "      <td>245388</td>\n",
       "      <td>73.040838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>True</td>\n",
       "      <td>90572</td>\n",
       "      <td>26.959162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>False</td>\n",
       "      <td>220635</td>\n",
       "      <td>71.847249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>True</td>\n",
       "      <td>86454</td>\n",
       "      <td>28.152751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>False</td>\n",
       "      <td>195735</td>\n",
       "      <td>71.107583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>True</td>\n",
       "      <td>79531</td>\n",
       "      <td>28.892417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>False</td>\n",
       "      <td>194199</td>\n",
       "      <td>73.546855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>True</td>\n",
       "      <td>69849</td>\n",
       "      <td>26.453145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>False</td>\n",
       "      <td>216138</td>\n",
       "      <td>80.362441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>True</td>\n",
       "      <td>52816</td>\n",
       "      <td>19.637559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>False</td>\n",
       "      <td>215701</td>\n",
       "      <td>80.480943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>True</td>\n",
       "      <td>52314</td>\n",
       "      <td>19.519057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>False</td>\n",
       "      <td>213494</td>\n",
       "      <td>80.303469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>True</td>\n",
       "      <td>52365</td>\n",
       "      <td>19.696531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>False</td>\n",
       "      <td>26365</td>\n",
       "      <td>78.708541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>True</td>\n",
       "      <td>7132</td>\n",
       "      <td>21.291459</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Arrest  Counts  Percentage\n",
       "Year                            \n",
       "2001   False  343850   70.787151\n",
       "2001    True  141902   29.212849\n",
       "2002   False  345201   70.918986\n",
       "2002    True  141553   29.081014\n",
       "2003   False  334368   70.254234\n",
       "2003    True  141572   29.745766\n",
       "2004   False  324697   69.175279\n",
       "2004    True  144686   30.824721\n",
       "2005   False  312820   68.946365\n",
       "2005    True  140895   31.053635\n",
       "2006   False  312724   69.787640\n",
       "2006    True  135384   30.212360\n",
       "2007   False  305160   69.828906\n",
       "2007    True  131851   30.171094\n",
       "2008   False  317099   74.252323\n",
       "2008    True  109957   25.747677\n",
       "2009   False  281913   71.792410\n",
       "2009    True  110765   28.207590\n",
       "2010   False  269827   72.865364\n",
       "2010    True  100482   27.134636\n",
       "2011   False  255549   72.645790\n",
       "2011    True   96225   27.354210\n",
       "2012   False  245388   73.040838\n",
       "2012    True   90572   26.959162\n",
       "2013   False  220635   71.847249\n",
       "2013    True   86454   28.152751\n",
       "2014   False  195735   71.107583\n",
       "2014    True   79531   28.892417\n",
       "2015   False  194199   73.546855\n",
       "2015    True   69849   26.453145\n",
       "2016   False  216138   80.362441\n",
       "2016    True   52816   19.637559\n",
       "2017   False  215701   80.480943\n",
       "2017    True   52314   19.519057\n",
       "2018   False  213494   80.303469\n",
       "2018    True   52365   19.696531\n",
       "2019   False   26365   78.708541\n",
       "2019    True    7132   21.291459"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Group dataset by year and arrests\n",
    "arrest_per_year = df.groupby('Year')['Arrest'].value_counts().rename('Counts').to_frame()\n",
    "arrest_per_year['Percentage'] = (100 * arrest_per_year / arrest_per_year.groupby(level=0).sum())\n",
    "arrest_per_year.reset_index(level=[1],inplace=True)\n",
    "arrest_per_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Create a line plot for percentages of successful arrests over time (2001 to present)\n",
    "line_plot = arrest_per_year[arrest_per_year['Arrest'] == True]['Percentage']\n",
    "\n",
    "## Configure line plot to make visualizing data cleaner\n",
    "labels = line_plot.index.values\n",
    "fig=plt.figure(figsize=(12, 10))\n",
    "plt.title('Percentages of successful arrests from 2001 to 2019')\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Successful Arrest Percentage\")\n",
    "plt.xticks(line_plot.index, line_plot.index.values)\n",
    "\n",
    "line_plot.plot(grid=True, marker='o', color='mediumvioletred')\n",
    "plt.savefig(\"output_data/SuccessfulArrest.png\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data visualization for crimes based on district from 2001 to present"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculating total number of incidents per district\n",
    "district_crime = df['District'].value_counts(ascending=True)\n",
    "\n",
    "## Data cleaning\n",
    "district_crime.index = district_crime.index.astype(int)\n",
    "district_crime.index = district_crime.index.astype(str)\n",
    "\n",
    "## Plot bar graph for initial visualization\n",
    "fig=plt.figure(figsize=(14, 12))\n",
    "plt.title(\"Frequency of Crimes Per Chicago Police District\")\n",
    "plt.xlabel(\"Frequency of Crimes\")\n",
    "plt.ylabel(\"Chicago Police District No.\")\n",
    "ax = district_crime.plot(kind='barh')\n",
    "ax.get_xaxis().set_major_formatter(plt.FuncFormatter(lambda x, loc: \"{:,}\".format(int(x))))\n",
    "plt.savefig(\"output_data/FrequencyOfCrimesPerPolisDist.png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "THEFT                                2010005\n",
       "BATTERY                              1244650\n",
       "CRIMINAL DAMAGE                       778188\n",
       "NARCOTICS                             714802\n",
       "ASSAULT                               423683\n",
       "OTHER OFFENSE                         423342\n",
       "BURGLARY                              390914\n",
       "DECEPTIVE PRACTICE                    268020\n",
       "CRIMINAL TRESPASS                     195178\n",
       "WEAPONS VIOLATION                      72138\n",
       "PROSTITUTION                           68512\n",
       "PUBLIC PEACE VIOLATION                 48148\n",
       "OFFENSE INVOLVING CHILDREN             46018\n",
       "CRIM SEXUAL ASSAULT                    27660\n",
       "SEX OFFENSE                            25445\n",
       "INTERFERENCE WITH PUBLIC OFFICER       15485\n",
       "GAMBLING                               14433\n",
       "LIQUOR LAW VIOLATION                   14109\n",
       "ARSON                                  11250\n",
       "HOMICIDE                                9553\n",
       "KIDNAPPING                              6706\n",
       "INTIMIDATION                            3989\n",
       "STALKING                                3435\n",
       "OBSCENITY                                597\n",
       "CONCEALED CARRY LICENSE VIOLATION        324\n",
       "NON-CRIMINAL                             171\n",
       "PUBLIC INDECENCY                         164\n",
       "OTHER NARCOTIC VIOLATION                 124\n",
       "HUMAN TRAFFICKING                         54\n",
       "NON - CRIMINAL                            38\n",
       "RITUALISM                                 23\n",
       "NON-CRIMINAL (SUBJECT SPECIFIED)           9\n",
       "DOMESTIC VIOLENCE                          1\n",
       "Name: Primary Type, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Primary Type'] = df['Primary Type'].replace(\n",
    "    {'MOTOR VEHICLE THEFT': 'THEFT', 'ROBBERY': 'THEFT'})\n",
    "df[\"Primary Type\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Community Area</th>\n",
       "      <th>COMMUNITY AREA NAME</th>\n",
       "      <th>PERCENT OF HOUSING CROWDED</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Rogers Park</td>\n",
       "      <td>7.7</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>West Ridge</td>\n",
       "      <td>7.8</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Uptown</td>\n",
       "      <td>3.8</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>25.5</td>\n",
       "      <td>37524</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>North Center</td>\n",
       "      <td>0.3</td>\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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Community Area COMMUNITY AREA NAME  PERCENT OF HOUSING CROWDED  \\\n",
       "0             1.0         Rogers Park                         7.7   \n",
       "1             2.0          West Ridge                         7.8   \n",
       "2             3.0              Uptown                         3.8   \n",
       "3             4.0      Lincoln Square                         3.4   \n",
       "4             5.0        North Center                         0.3   \n",
       "\n",
       "   PERCENT HOUSEHOLDS BELOW POVERTY  PERCENT AGED 16+ UNEMPLOYED  \\\n",
       "0                              23.6                          8.7   \n",
       "1                              17.2                          8.8   \n",
       "2                              24.0                          8.9   \n",
       "3                              10.9                          8.2   \n",
       "4                               7.5                          5.2   \n",
       "\n",
       "   PERCENT AGED 25+ WITHOUT HIGH SCHOOL DIPLOMA  \\\n",
       "0                                          18.2   \n",
       "1                                          20.8   \n",
       "2                                          11.8   \n",
       "3                                          13.4   \n",
       "4                                           4.5   \n",
       "\n",
       "   PERCENT AGED UNDER 18 OR OVER 64  PER CAPITA INCOME   HARDSHIP INDEX  \n",
       "0                              27.5               23939            39.0  \n",
       "1                              38.5               23040            46.0  \n",
       "2                              22.2               35787            20.0  \n",
       "3                              25.5               37524            17.0  \n",
       "4                              26.2               57123             6.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "census = census.rename(columns={\"Community Area Number\":\"Community Area\"})\n",
    "census.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "crime08 = df.loc[df[\"Year\"]==2008]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "crime09 = df.loc[df[\"Year\"]==2009]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "crime10 = df.loc[df[\"Year\"]==2010]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "crime11 = df.loc[df[\"Year\"]==2011]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "crime12 = df.loc[df[\"Year\"]==2012]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "crime_data=pd.merge(crime08,census,on=\"Community Area\")\n",
    "crime_data1=pd.merge(crime09,census,on=\"Community Area\")\n",
    "crime_data2=pd.merge(crime10,census,on=\"Community Area\")\n",
    "crime_data3=pd.merge(crime11,census,on=\"Community Area\")\n",
    "crime_data4=pd.merge(crime12,census,on=\"Community Area\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Case Number', 'Date', 'Block', 'IUCR', 'Primary Type',\n",
       "       'Description', 'Location Description', 'Arrest', 'Domestic', 'Beat',\n",
       "       'District', 'Ward', 'Community Area', 'FBI Code', 'X Coordinate',\n",
       "       'Y Coordinate', 'Year', 'Updated On', 'Latitude', 'Longitude',\n",
       "       'Location', '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'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime2=pd.concat([crime_data, crime_data1,crime_data2,crime_data3,crime_data4])\n",
    "crime2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Case Number</th>\n",
       "      <th>Date</th>\n",
       "      <th>Block</th>\n",
       "      <th>IUCR</th>\n",
       "      <th>Primary Type</th>\n",
       "      <th>Description</th>\n",
       "      <th>Location Description</th>\n",
       "      <th>Arrest</th>\n",
       "      <th>Domestic</th>\n",
       "      <th>...</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Location</th>\n",
       "      <th>COMMUNITY AREA NAME</th>\n",
       "      <th>PERCENT OF HOUSING CROWDED</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6833176</td>\n",
       "      <td>HP731989</td>\n",
       "      <td>12/13/2008 01:30:00 PM</td>\n",
       "      <td>022XX W LAWRENCE AVE</td>\n",
       "      <td>0810</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>OVER $500</td>\n",
       "      <td>STREET</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.684872</td>\n",
       "      <td>(41.968671122, -87.684871598)</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>25.5</td>\n",
       "      <td>37524</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6851983</td>\n",
       "      <td>HR258044</td>\n",
       "      <td>11/01/2008 08:00:00 AM</td>\n",
       "      <td>054XX N CAMPBELL AVE</td>\n",
       "      <td>0890</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>FROM BUILDING</td>\n",
       "      <td>RESIDENCE</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.691758</td>\n",
       "      <td>(41.980090021, -87.691758346)</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>25.5</td>\n",
       "      <td>37524</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6865551</td>\n",
       "      <td>HR270948</td>\n",
       "      <td>05/15/2008 08:00:00 AM</td>\n",
       "      <td>025XX W GUNNISON ST</td>\n",
       "      <td>0840</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>FINANCIAL ID THEFT: OVER $300</td>\n",
       "      <td>RESIDENCE</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.692709</td>\n",
       "      <td>(41.969678337, -87.692708598)</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>25.5</td>\n",
       "      <td>37524</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6899379</td>\n",
       "      <td>HR305897</td>\n",
       "      <td>09/01/2008 10:00:00 AM</td>\n",
       "      <td>051XX N CALIFORNIA AVE</td>\n",
       "      <td>0840</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>FINANCIAL ID THEFT: OVER $300</td>\n",
       "      <td>HOSPITAL BUILDING/GROUNDS</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.698898</td>\n",
       "      <td>(41.975151721, -87.698898226)</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>25.5</td>\n",
       "      <td>37524</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6071217</td>\n",
       "      <td>HP170745</td>\n",
       "      <td>02/08/2008 06:00:00 PM</td>\n",
       "      <td>053XX N BELL AVE</td>\n",
       "      <td>0810</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>OVER $500</td>\n",
       "      <td>STREET</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.685585</td>\n",
       "      <td>(41.978150877, -87.685584529)</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>25.5</td>\n",
       "      <td>37524</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6411847</td>\n",
       "      <td>HP496117</td>\n",
       "      <td>08/05/2008 07:30:00 AM</td>\n",
       "      <td>018XX W LAWRENCE AVE</td>\n",
       "      <td>0820</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>$500 AND UNDER</td>\n",
       "      <td>SIDEWALK</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.674703</td>\n",
       "      <td>(41.968816304, -87.674702715)</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>25.5</td>\n",
       "      <td>37524</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>6584745</td>\n",
       "      <td>HP655750</td>\n",
       "      <td>10/23/2008 11:20:00 AM</td>\n",
       "      <td>051XX N DAMEN AVE</td>\n",
       "      <td>0820</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>$500 AND UNDER</td>\n",
       "      <td>SCHOOL, PUBLIC, BUILDING</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.679375</td>\n",
       "      <td>(41.975099794, -87.679374655)</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>25.5</td>\n",
       "      <td>37524</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>6628041</td>\n",
       "      <td>HP699814</td>\n",
       "      <td>11/23/2008 07:00:00 PM</td>\n",
       "      <td>055XX N WESTERN AVE</td>\n",
       "      <td>0810</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>OVER $500</td>\n",
       "      <td>STREET</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.689386</td>\n",
       "      <td>(41.981915352, -87.689386142)</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>25.5</td>\n",
       "      <td>37524</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>6652596</td>\n",
       "      <td>HP724814</td>\n",
       "      <td>12/09/2008 11:30:00 AM</td>\n",
       "      <td>055XX N CAMPBELL AVE</td>\n",
       "      <td>0820</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>$500 AND UNDER</td>\n",
       "      <td>VEHICLE NON-COMMERCIAL</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.691825</td>\n",
       "      <td>(41.981904308, -87.691824725)</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>25.5</td>\n",
       "      <td>37524</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>6705301</td>\n",
       "      <td>HR119992</td>\n",
       "      <td>12/18/2008 01:18:00 PM</td>\n",
       "      <td>048XX N DAMEN AVE</td>\n",
       "      <td>0890</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>FROM BUILDING</td>\n",
       "      <td>RESIDENCE PORCH/HALLWAY</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.679202</td>\n",
       "      <td>(41.969341473, -87.679201891)</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>25.5</td>\n",
       "      <td>37524</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>6099525</td>\n",
       "      <td>HP195525</td>\n",
       "      <td>02/26/2008 09:30:00 AM</td>\n",
       "      <td>051XX N FRANCISCO AVE</td>\n",
       "      <td>0810</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>OVER $500</td>\n",
       "      <td>STREET</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>25.5</td>\n",
       "      <td>37524</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>6183263</td>\n",
       "      <td>HP272795</td>\n",
       "      <td>04/10/2008 07:40:00 PM</td>\n",
       "      <td>026XX W BERWYN AVE</td>\n",
       "      <td>0320</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>STRONGARM - NO WEAPON</td>\n",
       "      <td>STREET</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>25.5</td>\n",
       "      <td>37524</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>6183266</td>\n",
       "      <td>HP272394</td>\n",
       "      <td>04/10/2008 03:55:00 PM</td>\n",
       "      <td>049XX N CLAREMONT AVE</td>\n",
       "      <td>0880</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>PURSE-SNATCHING</td>\n",
       "      <td>SIDEWALK</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>25.5</td>\n",
       "      <td>37524</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>6308921</td>\n",
       "      <td>HP398913</td>\n",
       "      <td>06/16/2008 07:45:00 AM</td>\n",
       "      <td>046XX N RAVENSWOOD AVE</td>\n",
       "      <td>0810</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>OVER $500</td>\n",
       "      <td>SIDEWALK</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>25.5</td>\n",
       "      <td>37524</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>6313675</td>\n",
       "      <td>HP403488</td>\n",
       "      <td>06/19/2008 12:00:00 AM</td>\n",
       "      <td>021XX W LAWRENCE AVE</td>\n",
       "      <td>0820</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>$500 AND UNDER</td>\n",
       "      <td>STREET</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>25.5</td>\n",
       "      <td>37524</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>6492885</td>\n",
       "      <td>HP568531</td>\n",
       "      <td>09/11/2008 09:00:00 AM</td>\n",
       "      <td>049XX N WOLCOTT AVE</td>\n",
       "      <td>0810</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>OVER $500</td>\n",
       "      <td>STREET</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>25.5</td>\n",
       "      <td>37524</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>6538877</td>\n",
       "      <td>HP606299</td>\n",
       "      <td>10/02/2008 08:15:00 PM</td>\n",
       "      <td>050XX N OAKLEY AVE</td>\n",
       "      <td>0810</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>OVER $500</td>\n",
       "      <td>STREET</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>25.5</td>\n",
       "      <td>37524</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>6547318</td>\n",
       "      <td>HP621395</td>\n",
       "      <td>10/08/2008 04:00:00 PM</td>\n",
       "      <td>053XX N LINCOLN AVE</td>\n",
       "      <td>0810</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>OVER $500</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>25.5</td>\n",
       "      <td>37524</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>6675411</td>\n",
       "      <td>HP748142</td>\n",
       "      <td>12/23/2008 08:00:00 AM</td>\n",
       "      <td>027XX W ARGYLE ST</td>\n",
       "      <td>0890</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>FROM BUILDING</td>\n",
       "      <td>RESIDENCE PORCH/HALLWAY</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>25.5</td>\n",
       "      <td>37524</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>6927823</td>\n",
       "      <td>HR332074</td>\n",
       "      <td>12/01/2008 07:00:00 PM</td>\n",
       "      <td>052XX N ROCKWELL ST</td>\n",
       "      <td>0810</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>OVER $500</td>\n",
       "      <td>RESIDENCE</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>25.5</td>\n",
       "      <td>37524</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>6950818</td>\n",
       "      <td>HR355857</td>\n",
       "      <td>01/01/2008 12:01:00 AM</td>\n",
       "      <td>044XX N WINCHESTER AVE</td>\n",
       "      <td>0820</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>$500 AND UNDER</td>\n",
       "      <td>STREET</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>25.5</td>\n",
       "      <td>37524</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>7177425</td>\n",
       "      <td>HR587405</td>\n",
       "      <td>11/02/2008 12:08:00 PM</td>\n",
       "      <td>027XX W RASCHER AVE</td>\n",
       "      <td>0820</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>$500 AND UNDER</td>\n",
       "      <td>RESIDENCE</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>25.5</td>\n",
       "      <td>37524</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>7313762</td>\n",
       "      <td>HS118041</td>\n",
       "      <td>01/13/2008 09:00:00 AM</td>\n",
       "      <td>026XX W FOSTER AVE</td>\n",
       "      <td>0840</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>FINANCIAL ID THEFT: OVER $300</td>\n",
       "      <td>RESIDENCE</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>25.5</td>\n",
       "      <td>37524</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>7363193</td>\n",
       "      <td>HS165754</td>\n",
       "      <td>07/10/2008 10:00:00 AM</td>\n",
       "      <td>024XX W BERWYN AVE</td>\n",
       "      <td>0841</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>FINANCIAL ID THEFT:$300 &amp;UNDER</td>\n",
       "      <td>RESIDENCE</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>25.5</td>\n",
       "      <td>37524</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>7683704</td>\n",
       "      <td>HS489610</td>\n",
       "      <td>07/08/2008 06:00:00 AM</td>\n",
       "      <td>046XX N LEAVITT ST</td>\n",
       "      <td>0841</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>FINANCIAL ID THEFT:$300 &amp;UNDER</td>\n",
       "      <td>RESIDENCE</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>25.5</td>\n",
       "      <td>37524</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>7925518</td>\n",
       "      <td>HT157195</td>\n",
       "      <td>11/01/2008 12:01:00 AM</td>\n",
       "      <td>054XX N CALIFORNIA AVE</td>\n",
       "      <td>0843</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>ATTEMPT FINANCIAL IDENTITY THEFT</td>\n",
       "      <td>APARTMENT</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>25.5</td>\n",
       "      <td>37524</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>7952101</td>\n",
       "      <td>HT184278</td>\n",
       "      <td>10/01/2008 09:00:00 AM</td>\n",
       "      <td>051XX N CALIFORNIA AVE</td>\n",
       "      <td>0840</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>FINANCIAL ID THEFT: OVER $300</td>\n",
       "      <td>HOSPITAL BUILDING/GROUNDS</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>25.5</td>\n",
       "      <td>37524</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>7995307</td>\n",
       "      <td>HT226873</td>\n",
       "      <td>08/08/2008 12:00:00 PM</td>\n",
       "      <td>026XX W CARMEN AVE</td>\n",
       "      <td>0840</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>FINANCIAL ID THEFT: OVER $300</td>\n",
       "      <td>RESIDENCE</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>25.5</td>\n",
       "      <td>37524</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>6144561</td>\n",
       "      <td>HP235631</td>\n",
       "      <td>03/20/2008 06:20:00 PM</td>\n",
       "      <td>048XX N LINCOLN AVE</td>\n",
       "      <td>0870</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>POCKET-PICKING</td>\n",
       "      <td>DRUG STORE</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.688662</td>\n",
       "      <td>(41.968763818, -87.688661831)</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>25.5</td>\n",
       "      <td>37524</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>6144610</td>\n",
       "      <td>HP235279</td>\n",
       "      <td>01/23/2008 12:00:00 PM</td>\n",
       "      <td>027XX W CATALPA AVE</td>\n",
       "      <td>0840</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>FINANCIAL ID THEFT: OVER $300</td>\n",
       "      <td>APARTMENT</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.697723</td>\n",
       "      <td>(41.981305949, -87.697722731)</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>25.5</td>\n",
       "      <td>37524</td>\n",
       "      <td>17.0</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",
       "      <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>335896</th>\n",
       "      <td>8942486</td>\n",
       "      <td>HV614544</td>\n",
       "      <td>12/22/2012 06:00:00 PM</td>\n",
       "      <td>016XX E 85TH PL</td>\n",
       "      <td>0820</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>$500 AND UNDER</td>\n",
       "      <td>STREET</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.584429</td>\n",
       "      <td>(41.739663953, -87.584429076)</td>\n",
       "      <td>Avalon Park</td>\n",
       "      <td>1.4</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335903</th>\n",
       "      <td>8945566</td>\n",
       "      <td>HV617974</td>\n",
       "      <td>12/27/2012 10:00:00 AM</td>\n",
       "      <td>083XX S BENNETT AVE</td>\n",
       "      <td>0920</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>ATT: AUTOMOBILE</td>\n",
       "      <td>STREET</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.578257</td>\n",
       "      <td>(41.743284459, -87.578256672)</td>\n",
       "      <td>Avalon Park</td>\n",
       "      <td>1.4</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335904</th>\n",
       "      <td>8945703</td>\n",
       "      <td>HV617956</td>\n",
       "      <td>12/17/2012 10:00:00 AM</td>\n",
       "      <td>081XX S DANTE AVE</td>\n",
       "      <td>0840</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>FINANCIAL ID THEFT: OVER $300</td>\n",
       "      <td>RESIDENCE</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.589372</td>\n",
       "      <td>(41.746919077, -87.589372434)</td>\n",
       "      <td>Avalon Park</td>\n",
       "      <td>1.4</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335905</th>\n",
       "      <td>8946047</td>\n",
       "      <td>HV618072</td>\n",
       "      <td>12/27/2012 11:15:00 AM</td>\n",
       "      <td>082XX S SOUTH CHICAGO AVE</td>\n",
       "      <td>0330</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>AGGRAVATED</td>\n",
       "      <td>SIDEWALK</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.577855</td>\n",
       "      <td>(41.745941051, -87.577855437)</td>\n",
       "      <td>Avalon Park</td>\n",
       "      <td>1.4</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335906</th>\n",
       "      <td>8946171</td>\n",
       "      <td>HV618445</td>\n",
       "      <td>12/27/2012 01:45:00 PM</td>\n",
       "      <td>016XX E 87TH ST</td>\n",
       "      <td>0810</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>OVER $500</td>\n",
       "      <td>STREET</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.584215</td>\n",
       "      <td>(41.736934718, -87.584215302)</td>\n",
       "      <td>Avalon Park</td>\n",
       "      <td>1.4</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335913</th>\n",
       "      <td>8948343</td>\n",
       "      <td>HV620500</td>\n",
       "      <td>12/28/2012 10:30:00 PM</td>\n",
       "      <td>079XX S ANTHONY AVE</td>\n",
       "      <td>0910</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>AUTOMOBILE</td>\n",
       "      <td>STREET</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.587271</td>\n",
       "      <td>(41.750503234, -87.587271496)</td>\n",
       "      <td>Avalon Park</td>\n",
       "      <td>1.4</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335916</th>\n",
       "      <td>8948524</td>\n",
       "      <td>HV620340</td>\n",
       "      <td>12/29/2012 04:00:00 AM</td>\n",
       "      <td>081XX S STONY ISLAND AVE</td>\n",
       "      <td>0810</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>OVER $500</td>\n",
       "      <td>HOTEL/MOTEL</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.585439</td>\n",
       "      <td>(41.747393001, -87.585438607)</td>\n",
       "      <td>Avalon Park</td>\n",
       "      <td>1.4</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335919</th>\n",
       "      <td>8949545</td>\n",
       "      <td>HV621908</td>\n",
       "      <td>12/30/2012 01:55:00 PM</td>\n",
       "      <td>086XX S STONY ISLAND AVE</td>\n",
       "      <td>031A</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>ARMED: HANDGUN</td>\n",
       "      <td>ALLEY</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.585234</td>\n",
       "      <td>(41.738184527, -87.585234022)</td>\n",
       "      <td>Avalon Park</td>\n",
       "      <td>1.4</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335920</th>\n",
       "      <td>8949667</td>\n",
       "      <td>HV622093</td>\n",
       "      <td>12/30/2012 05:15:00 PM</td>\n",
       "      <td>017XX E 83RD ST</td>\n",
       "      <td>0930</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>THEFT/RECOVERY: AUTOMOBILE</td>\n",
       "      <td>STREET</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.581452</td>\n",
       "      <td>(41.744260428, -87.581451527)</td>\n",
       "      <td>Avalon Park</td>\n",
       "      <td>1.4</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335922</th>\n",
       "      <td>8949721</td>\n",
       "      <td>HV622121</td>\n",
       "      <td>12/30/2012 03:35:00 PM</td>\n",
       "      <td>013XX E 79TH ST</td>\n",
       "      <td>0860</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>RETAIL THEFT</td>\n",
       "      <td>SMALL RETAIL STORE</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.591409</td>\n",
       "      <td>(41.751430158, -87.591409248)</td>\n",
       "      <td>Avalon Park</td>\n",
       "      <td>1.4</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335925</th>\n",
       "      <td>8950257</td>\n",
       "      <td>HV622625</td>\n",
       "      <td>12/30/2012 08:30:00 PM</td>\n",
       "      <td>080XX S KENWOOD AVE</td>\n",
       "      <td>0810</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>OVER $500</td>\n",
       "      <td>STREET</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.591774</td>\n",
       "      <td>(41.748679981, -87.591774393)</td>\n",
       "      <td>Avalon Park</td>\n",
       "      <td>1.4</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335926</th>\n",
       "      <td>8950378</td>\n",
       "      <td>HV622745</td>\n",
       "      <td>12/22/2012 03:00:00 PM</td>\n",
       "      <td>081XX S BLACKSTONE AVE</td>\n",
       "      <td>0841</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>FINANCIAL ID THEFT:$300 &amp;UNDER</td>\n",
       "      <td>RESIDENCE</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.588236</td>\n",
       "      <td>(41.747265728, -87.5882361)</td>\n",
       "      <td>Avalon Park</td>\n",
       "      <td>1.4</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335927</th>\n",
       "      <td>8950520</td>\n",
       "      <td>HV623027</td>\n",
       "      <td>12/31/2012 02:02:00 PM</td>\n",
       "      <td>013XX E 79TH ST</td>\n",
       "      <td>0860</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>RETAIL THEFT</td>\n",
       "      <td>SMALL RETAIL STORE</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.591497</td>\n",
       "      <td>(41.751427982, -87.591497227)</td>\n",
       "      <td>Avalon Park</td>\n",
       "      <td>1.4</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335928</th>\n",
       "      <td>8950549</td>\n",
       "      <td>HV622919</td>\n",
       "      <td>12/31/2012 01:15:00 PM</td>\n",
       "      <td>079XX S AVALON AVE</td>\n",
       "      <td>0910</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>AUTOMOBILE</td>\n",
       "      <td>PARKING LOT/GARAGE(NON.RESID.)</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.594262</td>\n",
       "      <td>(41.750943595, -87.594262333)</td>\n",
       "      <td>Avalon Park</td>\n",
       "      <td>1.4</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335931</th>\n",
       "      <td>8955922</td>\n",
       "      <td>HW104830</td>\n",
       "      <td>12/23/2012 06:00:00 PM</td>\n",
       "      <td>084XX S BENNETT AVE</td>\n",
       "      <td>0920</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>ATT: AUTOMOBILE</td>\n",
       "      <td>STREET</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.578232</td>\n",
       "      <td>(41.7419341, -87.578232186)</td>\n",
       "      <td>Avalon Park</td>\n",
       "      <td>1.4</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335932</th>\n",
       "      <td>8956685</td>\n",
       "      <td>HW105934</td>\n",
       "      <td>12/26/2012 08:00:00 PM</td>\n",
       "      <td>083XX S KIMBARK AVE</td>\n",
       "      <td>0820</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>$500 AND UNDER</td>\n",
       "      <td>STREET</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.592332</td>\n",
       "      <td>(41.743197716, -87.592332154)</td>\n",
       "      <td>Avalon Park</td>\n",
       "      <td>1.4</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335933</th>\n",
       "      <td>8958410</td>\n",
       "      <td>HW107730</td>\n",
       "      <td>12/12/2012 09:00:00 AM</td>\n",
       "      <td>078XX S WOODLAWN AVE</td>\n",
       "      <td>0820</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>$500 AND UNDER</td>\n",
       "      <td>STREET</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.595525</td>\n",
       "      <td>(41.752721782, -87.595524619)</td>\n",
       "      <td>Avalon Park</td>\n",
       "      <td>1.4</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335938</th>\n",
       "      <td>8982518</td>\n",
       "      <td>HW129304</td>\n",
       "      <td>10/17/2012 11:35:00 AM</td>\n",
       "      <td>021XX E 87TH ST</td>\n",
       "      <td>0820</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>$500 AND UNDER</td>\n",
       "      <td>SCHOOL, PUBLIC, BUILDING</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.572998</td>\n",
       "      <td>(41.737094305, -87.572998178)</td>\n",
       "      <td>Avalon Park</td>\n",
       "      <td>1.4</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335939</th>\n",
       "      <td>8997878</td>\n",
       "      <td>HW145099</td>\n",
       "      <td>12/10/2012 09:00:00 PM</td>\n",
       "      <td>079XX S AVALON AVE</td>\n",
       "      <td>0890</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>FROM BUILDING</td>\n",
       "      <td>RESIDENCE</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.594257</td>\n",
       "      <td>(41.750776158, -87.594256924)</td>\n",
       "      <td>Avalon Park</td>\n",
       "      <td>1.4</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335940</th>\n",
       "      <td>9000681</td>\n",
       "      <td>HW147531</td>\n",
       "      <td>12/17/2012 09:15:00 AM</td>\n",
       "      <td>011XX E 81ST ST</td>\n",
       "      <td>0810</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>OVER $500</td>\n",
       "      <td>APARTMENT</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.596241</td>\n",
       "      <td>(41.747731753, -87.596241374)</td>\n",
       "      <td>Avalon Park</td>\n",
       "      <td>1.4</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335942</th>\n",
       "      <td>9066987</td>\n",
       "      <td>HW211527</td>\n",
       "      <td>05/10/2012 09:00:00 AM</td>\n",
       "      <td>079XX S DANTE AVE</td>\n",
       "      <td>0840</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>FINANCIAL ID THEFT: OVER $300</td>\n",
       "      <td>RESIDENCE</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.589459</td>\n",
       "      <td>(41.750564068, -87.589458612)</td>\n",
       "      <td>Avalon Park</td>\n",
       "      <td>1.4</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335943</th>\n",
       "      <td>9074729</td>\n",
       "      <td>HW219596</td>\n",
       "      <td>04/01/2012 09:00:00 AM</td>\n",
       "      <td>078XX S AVALON AVE</td>\n",
       "      <td>0840</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>FINANCIAL ID THEFT: OVER $300</td>\n",
       "      <td>RESIDENCE</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.594302</td>\n",
       "      <td>(41.752637081, -87.594301543)</td>\n",
       "      <td>Avalon Park</td>\n",
       "      <td>1.4</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335944</th>\n",
       "      <td>9077923</td>\n",
       "      <td>HW222911</td>\n",
       "      <td>10/01/2012 12:01:00 AM</td>\n",
       "      <td>016XX E 84TH ST</td>\n",
       "      <td>0840</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>FINANCIAL ID THEFT: OVER $300</td>\n",
       "      <td>ANIMAL HOSPITAL</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.584258</td>\n",
       "      <td>(41.742398911, -87.584258056)</td>\n",
       "      <td>Avalon Park</td>\n",
       "      <td>1.4</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335945</th>\n",
       "      <td>9145024</td>\n",
       "      <td>HW290253</td>\n",
       "      <td>05/25/2012 07:00:00 AM</td>\n",
       "      <td>079XX S KIMBARK AVE</td>\n",
       "      <td>0841</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>FINANCIAL ID THEFT:$300 &amp;UNDER</td>\n",
       "      <td>APARTMENT</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.593042</td>\n",
       "      <td>(41.750957692, -87.593041821)</td>\n",
       "      <td>Avalon Park</td>\n",
       "      <td>1.4</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335946</th>\n",
       "      <td>9174086</td>\n",
       "      <td>HW318284</td>\n",
       "      <td>10/11/2012 09:00:00 AM</td>\n",
       "      <td>084XX S CREGIER AVE</td>\n",
       "      <td>0840</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>FINANCIAL ID THEFT: OVER $300</td>\n",
       "      <td>RESIDENCE</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.580676</td>\n",
       "      <td>(41.741917205, -87.580676404)</td>\n",
       "      <td>Avalon Park</td>\n",
       "      <td>1.4</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335947</th>\n",
       "      <td>9195876</td>\n",
       "      <td>HW339433</td>\n",
       "      <td>12/12/2012 12:00:00 AM</td>\n",
       "      <td>080XX S WOODLAWN AVE</td>\n",
       "      <td>0843</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>ATTEMPT FINANCIAL IDENTITY THEFT</td>\n",
       "      <td>RESIDENCE</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.595425</td>\n",
       "      <td>(41.748582738, -87.595425432)</td>\n",
       "      <td>Avalon Park</td>\n",
       "      <td>1.4</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335948</th>\n",
       "      <td>9209990</td>\n",
       "      <td>HW355989</td>\n",
       "      <td>01/04/2012 09:00:00 AM</td>\n",
       "      <td>084XX S BENNETT AVE</td>\n",
       "      <td>0840</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>FINANCIAL ID THEFT: OVER $300</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.578228</td>\n",
       "      <td>(41.741654154, -87.578228138)</td>\n",
       "      <td>Avalon Park</td>\n",
       "      <td>1.4</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335949</th>\n",
       "      <td>9287125</td>\n",
       "      <td>HW430221</td>\n",
       "      <td>01/01/2012 10:00:00 AM</td>\n",
       "      <td>012XX E 79TH ST</td>\n",
       "      <td>0840</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>FINANCIAL ID THEFT: OVER $300</td>\n",
       "      <td>APARTMENT</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.593806</td>\n",
       "      <td>(41.751407191, -87.593806252)</td>\n",
       "      <td>Avalon Park</td>\n",
       "      <td>1.4</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335950</th>\n",
       "      <td>9385510</td>\n",
       "      <td>HW529093</td>\n",
       "      <td>05/27/2012 12:01:00 AM</td>\n",
       "      <td>019XX E 84TH ST</td>\n",
       "      <td>0840</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>FINANCIAL ID THEFT: OVER $300</td>\n",
       "      <td>RESIDENCE</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.577936</td>\n",
       "      <td>(41.742486504, -87.57793624)</td>\n",
       "      <td>Avalon Park</td>\n",
       "      <td>1.4</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335951</th>\n",
       "      <td>9404253</td>\n",
       "      <td>HW547528</td>\n",
       "      <td>11/28/2012 09:00:00 AM</td>\n",
       "      <td>085XX S BENNETT AVE</td>\n",
       "      <td>0841</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>FINANCIAL ID THEFT:$300 &amp;UNDER</td>\n",
       "      <td>RESIDENCE</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.578198</td>\n",
       "      <td>(41.739815274, -87.578198384)</td>\n",
       "      <td>Avalon Park</td>\n",
       "      <td>1.4</td>\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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>560459 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             ID Case Number                    Date  \\\n",
       "0       6833176    HP731989  12/13/2008 01:30:00 PM   \n",
       "2       6851983    HR258044  11/01/2008 08:00:00 AM   \n",
       "3       6865551    HR270948  05/15/2008 08:00:00 AM   \n",
       "5       6899379    HR305897  09/01/2008 10:00:00 AM   \n",
       "6       6071217    HP170745  02/08/2008 06:00:00 PM   \n",
       "15      6411847    HP496117  08/05/2008 07:30:00 AM   \n",
       "25      6584745    HP655750  10/23/2008 11:20:00 AM   \n",
       "26      6628041    HP699814  11/23/2008 07:00:00 PM   \n",
       "27      6652596    HP724814  12/09/2008 11:30:00 AM   \n",
       "28      6705301    HR119992  12/18/2008 01:18:00 PM   \n",
       "31      6099525    HP195525  02/26/2008 09:30:00 AM   \n",
       "32      6183263    HP272795  04/10/2008 07:40:00 PM   \n",
       "33      6183266    HP272394  04/10/2008 03:55:00 PM   \n",
       "38      6308921    HP398913  06/16/2008 07:45:00 AM   \n",
       "39      6313675    HP403488  06/19/2008 12:00:00 AM   \n",
       "46      6492885    HP568531  09/11/2008 09:00:00 AM   \n",
       "47      6538877    HP606299  10/02/2008 08:15:00 PM   \n",
       "48      6547318    HP621395  10/08/2008 04:00:00 PM   \n",
       "52      6675411    HP748142  12/23/2008 08:00:00 AM   \n",
       "55      6927823    HR332074  12/01/2008 07:00:00 PM   \n",
       "56      6950818    HR355857  01/01/2008 12:01:00 AM   \n",
       "58      7177425    HR587405  11/02/2008 12:08:00 PM   \n",
       "60      7313762    HS118041  01/13/2008 09:00:00 AM   \n",
       "61      7363193    HS165754  07/10/2008 10:00:00 AM   \n",
       "63      7683704    HS489610  07/08/2008 06:00:00 AM   \n",
       "64      7925518    HT157195  11/01/2008 12:01:00 AM   \n",
       "65      7952101    HT184278  10/01/2008 09:00:00 AM   \n",
       "66      7995307    HT226873  08/08/2008 12:00:00 PM   \n",
       "76      6144561    HP235631  03/20/2008 06:20:00 PM   \n",
       "77      6144610    HP235279  01/23/2008 12:00:00 PM   \n",
       "...         ...         ...                     ...   \n",
       "335896  8942486    HV614544  12/22/2012 06:00:00 PM   \n",
       "335903  8945566    HV617974  12/27/2012 10:00:00 AM   \n",
       "335904  8945703    HV617956  12/17/2012 10:00:00 AM   \n",
       "335905  8946047    HV618072  12/27/2012 11:15:00 AM   \n",
       "335906  8946171    HV618445  12/27/2012 01:45:00 PM   \n",
       "335913  8948343    HV620500  12/28/2012 10:30:00 PM   \n",
       "335916  8948524    HV620340  12/29/2012 04:00:00 AM   \n",
       "335919  8949545    HV621908  12/30/2012 01:55:00 PM   \n",
       "335920  8949667    HV622093  12/30/2012 05:15:00 PM   \n",
       "335922  8949721    HV622121  12/30/2012 03:35:00 PM   \n",
       "335925  8950257    HV622625  12/30/2012 08:30:00 PM   \n",
       "335926  8950378    HV622745  12/22/2012 03:00:00 PM   \n",
       "335927  8950520    HV623027  12/31/2012 02:02:00 PM   \n",
       "335928  8950549    HV622919  12/31/2012 01:15:00 PM   \n",
       "335931  8955922    HW104830  12/23/2012 06:00:00 PM   \n",
       "335932  8956685    HW105934  12/26/2012 08:00:00 PM   \n",
       "335933  8958410    HW107730  12/12/2012 09:00:00 AM   \n",
       "335938  8982518    HW129304  10/17/2012 11:35:00 AM   \n",
       "335939  8997878    HW145099  12/10/2012 09:00:00 PM   \n",
       "335940  9000681    HW147531  12/17/2012 09:15:00 AM   \n",
       "335942  9066987    HW211527  05/10/2012 09:00:00 AM   \n",
       "335943  9074729    HW219596  04/01/2012 09:00:00 AM   \n",
       "335944  9077923    HW222911  10/01/2012 12:01:00 AM   \n",
       "335945  9145024    HW290253  05/25/2012 07:00:00 AM   \n",
       "335946  9174086    HW318284  10/11/2012 09:00:00 AM   \n",
       "335947  9195876    HW339433  12/12/2012 12:00:00 AM   \n",
       "335948  9209990    HW355989  01/04/2012 09:00:00 AM   \n",
       "335949  9287125    HW430221  01/01/2012 10:00:00 AM   \n",
       "335950  9385510    HW529093  05/27/2012 12:01:00 AM   \n",
       "335951  9404253    HW547528  11/28/2012 09:00:00 AM   \n",
       "\n",
       "                            Block  IUCR Primary Type  \\\n",
       "0            022XX W LAWRENCE AVE  0810        THEFT   \n",
       "2            054XX N CAMPBELL AVE  0890        THEFT   \n",
       "3             025XX W GUNNISON ST  0840        THEFT   \n",
       "5          051XX N CALIFORNIA AVE  0840        THEFT   \n",
       "6                053XX N BELL AVE  0810        THEFT   \n",
       "15           018XX W LAWRENCE AVE  0820        THEFT   \n",
       "25              051XX N DAMEN AVE  0820        THEFT   \n",
       "26            055XX N WESTERN AVE  0810        THEFT   \n",
       "27           055XX N CAMPBELL AVE  0820        THEFT   \n",
       "28              048XX N DAMEN AVE  0890        THEFT   \n",
       "31          051XX N FRANCISCO AVE  0810        THEFT   \n",
       "32             026XX W BERWYN AVE  0320        THEFT   \n",
       "33          049XX N CLAREMONT AVE  0880        THEFT   \n",
       "38         046XX N RAVENSWOOD AVE  0810        THEFT   \n",
       "39           021XX W LAWRENCE AVE  0820        THEFT   \n",
       "46            049XX N WOLCOTT AVE  0810        THEFT   \n",
       "47             050XX N OAKLEY AVE  0810        THEFT   \n",
       "48            053XX N LINCOLN AVE  0810        THEFT   \n",
       "52              027XX W ARGYLE ST  0890        THEFT   \n",
       "55            052XX N ROCKWELL ST  0810        THEFT   \n",
       "56         044XX N WINCHESTER AVE  0820        THEFT   \n",
       "58            027XX W RASCHER AVE  0820        THEFT   \n",
       "60             026XX W FOSTER AVE  0840        THEFT   \n",
       "61             024XX W BERWYN AVE  0841        THEFT   \n",
       "63             046XX N LEAVITT ST  0841        THEFT   \n",
       "64         054XX N CALIFORNIA AVE  0843        THEFT   \n",
       "65         051XX N CALIFORNIA AVE  0840        THEFT   \n",
       "66             026XX W CARMEN AVE  0840        THEFT   \n",
       "76            048XX N LINCOLN AVE  0870        THEFT   \n",
       "77            027XX W CATALPA AVE  0840        THEFT   \n",
       "...                           ...   ...          ...   \n",
       "335896            016XX E 85TH PL  0820        THEFT   \n",
       "335903        083XX S BENNETT AVE  0920        THEFT   \n",
       "335904          081XX S DANTE AVE  0840        THEFT   \n",
       "335905  082XX S SOUTH CHICAGO AVE  0330        THEFT   \n",
       "335906            016XX E 87TH ST  0810        THEFT   \n",
       "335913        079XX S ANTHONY AVE  0910        THEFT   \n",
       "335916   081XX S STONY ISLAND AVE  0810        THEFT   \n",
       "335919   086XX S STONY ISLAND AVE  031A        THEFT   \n",
       "335920            017XX E 83RD ST  0930        THEFT   \n",
       "335922            013XX E 79TH ST  0860        THEFT   \n",
       "335925        080XX S KENWOOD AVE  0810        THEFT   \n",
       "335926     081XX S BLACKSTONE AVE  0841        THEFT   \n",
       "335927            013XX E 79TH ST  0860        THEFT   \n",
       "335928         079XX S AVALON AVE  0910        THEFT   \n",
       "335931        084XX S BENNETT AVE  0920        THEFT   \n",
       "335932        083XX S KIMBARK AVE  0820        THEFT   \n",
       "335933       078XX S WOODLAWN AVE  0820        THEFT   \n",
       "335938            021XX E 87TH ST  0820        THEFT   \n",
       "335939         079XX S AVALON AVE  0890        THEFT   \n",
       "335940            011XX E 81ST ST  0810        THEFT   \n",
       "335942          079XX S DANTE AVE  0840        THEFT   \n",
       "335943         078XX S AVALON AVE  0840        THEFT   \n",
       "335944            016XX E 84TH ST  0840        THEFT   \n",
       "335945        079XX S KIMBARK AVE  0841        THEFT   \n",
       "335946        084XX S CREGIER AVE  0840        THEFT   \n",
       "335947       080XX S WOODLAWN AVE  0843        THEFT   \n",
       "335948        084XX S BENNETT AVE  0840        THEFT   \n",
       "335949            012XX E 79TH ST  0840        THEFT   \n",
       "335950            019XX E 84TH ST  0840        THEFT   \n",
       "335951        085XX S BENNETT AVE  0841        THEFT   \n",
       "\n",
       "                             Description            Location Description  \\\n",
       "0                              OVER $500                          STREET   \n",
       "2                          FROM BUILDING                       RESIDENCE   \n",
       "3          FINANCIAL ID THEFT: OVER $300                       RESIDENCE   \n",
       "5          FINANCIAL ID THEFT: OVER $300       HOSPITAL BUILDING/GROUNDS   \n",
       "6                              OVER $500                          STREET   \n",
       "15                        $500 AND UNDER                        SIDEWALK   \n",
       "25                        $500 AND UNDER        SCHOOL, PUBLIC, BUILDING   \n",
       "26                             OVER $500                          STREET   \n",
       "27                        $500 AND UNDER          VEHICLE NON-COMMERCIAL   \n",
       "28                         FROM BUILDING         RESIDENCE PORCH/HALLWAY   \n",
       "31                             OVER $500                          STREET   \n",
       "32                 STRONGARM - NO WEAPON                          STREET   \n",
       "33                       PURSE-SNATCHING                        SIDEWALK   \n",
       "38                             OVER $500                        SIDEWALK   \n",
       "39                        $500 AND UNDER                          STREET   \n",
       "46                             OVER $500                          STREET   \n",
       "47                             OVER $500                          STREET   \n",
       "48                             OVER $500                           OTHER   \n",
       "52                         FROM BUILDING         RESIDENCE PORCH/HALLWAY   \n",
       "55                             OVER $500                       RESIDENCE   \n",
       "56                        $500 AND UNDER                          STREET   \n",
       "58                        $500 AND UNDER                       RESIDENCE   \n",
       "60         FINANCIAL ID THEFT: OVER $300                       RESIDENCE   \n",
       "61        FINANCIAL ID THEFT:$300 &UNDER                       RESIDENCE   \n",
       "63        FINANCIAL ID THEFT:$300 &UNDER                       RESIDENCE   \n",
       "64      ATTEMPT FINANCIAL IDENTITY THEFT                       APARTMENT   \n",
       "65         FINANCIAL ID THEFT: OVER $300       HOSPITAL BUILDING/GROUNDS   \n",
       "66         FINANCIAL ID THEFT: OVER $300                       RESIDENCE   \n",
       "76                        POCKET-PICKING                      DRUG STORE   \n",
       "77         FINANCIAL ID THEFT: OVER $300                       APARTMENT   \n",
       "...                                  ...                             ...   \n",
       "335896                    $500 AND UNDER                          STREET   \n",
       "335903                   ATT: AUTOMOBILE                          STREET   \n",
       "335904     FINANCIAL ID THEFT: OVER $300                       RESIDENCE   \n",
       "335905                        AGGRAVATED                        SIDEWALK   \n",
       "335906                         OVER $500                          STREET   \n",
       "335913                        AUTOMOBILE                          STREET   \n",
       "335916                         OVER $500                     HOTEL/MOTEL   \n",
       "335919                    ARMED: HANDGUN                           ALLEY   \n",
       "335920        THEFT/RECOVERY: AUTOMOBILE                          STREET   \n",
       "335922                      RETAIL THEFT              SMALL RETAIL STORE   \n",
       "335925                         OVER $500                          STREET   \n",
       "335926    FINANCIAL ID THEFT:$300 &UNDER                       RESIDENCE   \n",
       "335927                      RETAIL THEFT              SMALL RETAIL STORE   \n",
       "335928                        AUTOMOBILE  PARKING LOT/GARAGE(NON.RESID.)   \n",
       "335931                   ATT: AUTOMOBILE                          STREET   \n",
       "335932                    $500 AND UNDER                          STREET   \n",
       "335933                    $500 AND UNDER                          STREET   \n",
       "335938                    $500 AND UNDER        SCHOOL, PUBLIC, BUILDING   \n",
       "335939                     FROM BUILDING                       RESIDENCE   \n",
       "335940                         OVER $500                       APARTMENT   \n",
       "335942     FINANCIAL ID THEFT: OVER $300                       RESIDENCE   \n",
       "335943     FINANCIAL ID THEFT: OVER $300                       RESIDENCE   \n",
       "335944     FINANCIAL ID THEFT: OVER $300                 ANIMAL HOSPITAL   \n",
       "335945    FINANCIAL ID THEFT:$300 &UNDER                       APARTMENT   \n",
       "335946     FINANCIAL ID THEFT: OVER $300                       RESIDENCE   \n",
       "335947  ATTEMPT FINANCIAL IDENTITY THEFT                       RESIDENCE   \n",
       "335948     FINANCIAL ID THEFT: OVER $300                           OTHER   \n",
       "335949     FINANCIAL ID THEFT: OVER $300                       APARTMENT   \n",
       "335950     FINANCIAL ID THEFT: OVER $300                       RESIDENCE   \n",
       "335951    FINANCIAL ID THEFT:$300 &UNDER                       RESIDENCE   \n",
       "\n",
       "        Arrest  Domestic  ...  Longitude                       Location  \\\n",
       "0        False     False  ... -87.684872  (41.968671122, -87.684871598)   \n",
       "2        False     False  ... -87.691758  (41.980090021, -87.691758346)   \n",
       "3        False     False  ... -87.692709  (41.969678337, -87.692708598)   \n",
       "5        False     False  ... -87.698898  (41.975151721, -87.698898226)   \n",
       "6        False     False  ... -87.685585  (41.978150877, -87.685584529)   \n",
       "15       False     False  ... -87.674703  (41.968816304, -87.674702715)   \n",
       "25       False     False  ... -87.679375  (41.975099794, -87.679374655)   \n",
       "26       False     False  ... -87.689386  (41.981915352, -87.689386142)   \n",
       "27       False     False  ... -87.691825  (41.981904308, -87.691824725)   \n",
       "28       False     False  ... -87.679202  (41.969341473, -87.679201891)   \n",
       "31       False     False  ...        NaN                            NaN   \n",
       "32       False     False  ...        NaN                            NaN   \n",
       "33       False     False  ...        NaN                            NaN   \n",
       "38       False     False  ...        NaN                            NaN   \n",
       "39       False     False  ...        NaN                            NaN   \n",
       "46       False     False  ...        NaN                            NaN   \n",
       "47       False     False  ...        NaN                            NaN   \n",
       "48       False     False  ...        NaN                            NaN   \n",
       "52       False     False  ...        NaN                            NaN   \n",
       "55       False     False  ...        NaN                            NaN   \n",
       "56       False     False  ...        NaN                            NaN   \n",
       "58       False     False  ...        NaN                            NaN   \n",
       "60       False     False  ...        NaN                            NaN   \n",
       "61       False     False  ...        NaN                            NaN   \n",
       "63       False     False  ...        NaN                            NaN   \n",
       "64       False     False  ...        NaN                            NaN   \n",
       "65       False     False  ...        NaN                            NaN   \n",
       "66       False     False  ...        NaN                            NaN   \n",
       "76       False     False  ... -87.688662  (41.968763818, -87.688661831)   \n",
       "77       False     False  ... -87.697723  (41.981305949, -87.697722731)   \n",
       "...        ...       ...  ...        ...                            ...   \n",
       "335896   False     False  ... -87.584429  (41.739663953, -87.584429076)   \n",
       "335903   False     False  ... -87.578257  (41.743284459, -87.578256672)   \n",
       "335904   False     False  ... -87.589372  (41.746919077, -87.589372434)   \n",
       "335905   False     False  ... -87.577855  (41.745941051, -87.577855437)   \n",
       "335906   False     False  ... -87.584215  (41.736934718, -87.584215302)   \n",
       "335913   False     False  ... -87.587271  (41.750503234, -87.587271496)   \n",
       "335916   False     False  ... -87.585439  (41.747393001, -87.585438607)   \n",
       "335919   False     False  ... -87.585234  (41.738184527, -87.585234022)   \n",
       "335920   False     False  ... -87.581452  (41.744260428, -87.581451527)   \n",
       "335922    True     False  ... -87.591409  (41.751430158, -87.591409248)   \n",
       "335925   False     False  ... -87.591774  (41.748679981, -87.591774393)   \n",
       "335926   False     False  ... -87.588236    (41.747265728, -87.5882361)   \n",
       "335927    True     False  ... -87.591497  (41.751427982, -87.591497227)   \n",
       "335928   False     False  ... -87.594262  (41.750943595, -87.594262333)   \n",
       "335931   False     False  ... -87.578232    (41.7419341, -87.578232186)   \n",
       "335932   False     False  ... -87.592332  (41.743197716, -87.592332154)   \n",
       "335933   False     False  ... -87.595525  (41.752721782, -87.595524619)   \n",
       "335938   False     False  ... -87.572998  (41.737094305, -87.572998178)   \n",
       "335939   False     False  ... -87.594257  (41.750776158, -87.594256924)   \n",
       "335940   False     False  ... -87.596241  (41.747731753, -87.596241374)   \n",
       "335942   False     False  ... -87.589459  (41.750564068, -87.589458612)   \n",
       "335943   False     False  ... -87.594302  (41.752637081, -87.594301543)   \n",
       "335944   False     False  ... -87.584258  (41.742398911, -87.584258056)   \n",
       "335945   False     False  ... -87.593042  (41.750957692, -87.593041821)   \n",
       "335946   False     False  ... -87.580676  (41.741917205, -87.580676404)   \n",
       "335947   False     False  ... -87.595425  (41.748582738, -87.595425432)   \n",
       "335948   False     False  ... -87.578228  (41.741654154, -87.578228138)   \n",
       "335949   False     False  ... -87.593806  (41.751407191, -87.593806252)   \n",
       "335950   False     False  ... -87.577936   (41.742486504, -87.57793624)   \n",
       "335951   False     False  ... -87.578198  (41.739815274, -87.578198384)   \n",
       "\n",
       "        COMMUNITY AREA NAME  PERCENT OF HOUSING CROWDED  \\\n",
       "0            Lincoln Square                         3.4   \n",
       "2            Lincoln Square                         3.4   \n",
       "3            Lincoln Square                         3.4   \n",
       "5            Lincoln Square                         3.4   \n",
       "6            Lincoln Square                         3.4   \n",
       "15           Lincoln Square                         3.4   \n",
       "25           Lincoln Square                         3.4   \n",
       "26           Lincoln Square                         3.4   \n",
       "27           Lincoln Square                         3.4   \n",
       "28           Lincoln Square                         3.4   \n",
       "31           Lincoln Square                         3.4   \n",
       "32           Lincoln Square                         3.4   \n",
       "33           Lincoln Square                         3.4   \n",
       "38           Lincoln Square                         3.4   \n",
       "39           Lincoln Square                         3.4   \n",
       "46           Lincoln Square                         3.4   \n",
       "47           Lincoln Square                         3.4   \n",
       "48           Lincoln Square                         3.4   \n",
       "52           Lincoln Square                         3.4   \n",
       "55           Lincoln Square                         3.4   \n",
       "56           Lincoln Square                         3.4   \n",
       "58           Lincoln Square                         3.4   \n",
       "60           Lincoln Square                         3.4   \n",
       "61           Lincoln Square                         3.4   \n",
       "63           Lincoln Square                         3.4   \n",
       "64           Lincoln Square                         3.4   \n",
       "65           Lincoln Square                         3.4   \n",
       "66           Lincoln Square                         3.4   \n",
       "76           Lincoln Square                         3.4   \n",
       "77           Lincoln Square                         3.4   \n",
       "...                     ...                         ...   \n",
       "335896          Avalon Park                         1.4   \n",
       "335903          Avalon Park                         1.4   \n",
       "335904          Avalon Park                         1.4   \n",
       "335905          Avalon Park                         1.4   \n",
       "335906          Avalon Park                         1.4   \n",
       "335913          Avalon Park                         1.4   \n",
       "335916          Avalon Park                         1.4   \n",
       "335919          Avalon Park                         1.4   \n",
       "335920          Avalon Park                         1.4   \n",
       "335922          Avalon Park                         1.4   \n",
       "335925          Avalon Park                         1.4   \n",
       "335926          Avalon Park                         1.4   \n",
       "335927          Avalon Park                         1.4   \n",
       "335928          Avalon Park                         1.4   \n",
       "335931          Avalon Park                         1.4   \n",
       "335932          Avalon Park                         1.4   \n",
       "335933          Avalon Park                         1.4   \n",
       "335938          Avalon Park                         1.4   \n",
       "335939          Avalon Park                         1.4   \n",
       "335940          Avalon Park                         1.4   \n",
       "335942          Avalon Park                         1.4   \n",
       "335943          Avalon Park                         1.4   \n",
       "335944          Avalon Park                         1.4   \n",
       "335945          Avalon Park                         1.4   \n",
       "335946          Avalon Park                         1.4   \n",
       "335947          Avalon Park                         1.4   \n",
       "335948          Avalon Park                         1.4   \n",
       "335949          Avalon Park                         1.4   \n",
       "335950          Avalon Park                         1.4   \n",
       "335951          Avalon Park                         1.4   \n",
       "\n",
       "       PERCENT HOUSEHOLDS BELOW POVERTY  PERCENT AGED 16+ UNEMPLOYED  \\\n",
       "0                                  10.9                          8.2   \n",
       "2                                  10.9                          8.2   \n",
       "3                                  10.9                          8.2   \n",
       "5                                  10.9                          8.2   \n",
       "6                                  10.9                          8.2   \n",
       "15                                 10.9                          8.2   \n",
       "25                                 10.9                          8.2   \n",
       "26                                 10.9                          8.2   \n",
       "27                                 10.9                          8.2   \n",
       "28                                 10.9                          8.2   \n",
       "31                                 10.9                          8.2   \n",
       "32                                 10.9                          8.2   \n",
       "33                                 10.9                          8.2   \n",
       "38                                 10.9                          8.2   \n",
       "39                                 10.9                          8.2   \n",
       "46                                 10.9                          8.2   \n",
       "47                                 10.9                          8.2   \n",
       "48                                 10.9                          8.2   \n",
       "52                                 10.9                          8.2   \n",
       "55                                 10.9                          8.2   \n",
       "56                                 10.9                          8.2   \n",
       "58                                 10.9                          8.2   \n",
       "60                                 10.9                          8.2   \n",
       "61                                 10.9                          8.2   \n",
       "63                                 10.9                          8.2   \n",
       "64                                 10.9                          8.2   \n",
       "65                                 10.9                          8.2   \n",
       "66                                 10.9                          8.2   \n",
       "76                                 10.9                          8.2   \n",
       "77                                 10.9                          8.2   \n",
       "...                                 ...                          ...   \n",
       "335896                             17.2                         21.1   \n",
       "335903                             17.2                         21.1   \n",
       "335904                             17.2                         21.1   \n",
       "335905                             17.2                         21.1   \n",
       "335906                             17.2                         21.1   \n",
       "335913                             17.2                         21.1   \n",
       "335916                             17.2                         21.1   \n",
       "335919                             17.2                         21.1   \n",
       "335920                             17.2                         21.1   \n",
       "335922                             17.2                         21.1   \n",
       "335925                             17.2                         21.1   \n",
       "335926                             17.2                         21.1   \n",
       "335927                             17.2                         21.1   \n",
       "335928                             17.2                         21.1   \n",
       "335931                             17.2                         21.1   \n",
       "335932                             17.2                         21.1   \n",
       "335933                             17.2                         21.1   \n",
       "335938                             17.2                         21.1   \n",
       "335939                             17.2                         21.1   \n",
       "335940                             17.2                         21.1   \n",
       "335942                             17.2                         21.1   \n",
       "335943                             17.2                         21.1   \n",
       "335944                             17.2                         21.1   \n",
       "335945                             17.2                         21.1   \n",
       "335946                             17.2                         21.1   \n",
       "335947                             17.2                         21.1   \n",
       "335948                             17.2                         21.1   \n",
       "335949                             17.2                         21.1   \n",
       "335950                             17.2                         21.1   \n",
       "335951                             17.2                         21.1   \n",
       "\n",
       "        PERCENT AGED 25+ WITHOUT HIGH SCHOOL DIPLOMA  \\\n",
       "0                                               13.4   \n",
       "2                                               13.4   \n",
       "3                                               13.4   \n",
       "5                                               13.4   \n",
       "6                                               13.4   \n",
       "15                                              13.4   \n",
       "25                                              13.4   \n",
       "26                                              13.4   \n",
       "27                                              13.4   \n",
       "28                                              13.4   \n",
       "31                                              13.4   \n",
       "32                                              13.4   \n",
       "33                                              13.4   \n",
       "38                                              13.4   \n",
       "39                                              13.4   \n",
       "46                                              13.4   \n",
       "47                                              13.4   \n",
       "48                                              13.4   \n",
       "52                                              13.4   \n",
       "55                                              13.4   \n",
       "56                                              13.4   \n",
       "58                                              13.4   \n",
       "60                                              13.4   \n",
       "61                                              13.4   \n",
       "63                                              13.4   \n",
       "64                                              13.4   \n",
       "65                                              13.4   \n",
       "66                                              13.4   \n",
       "76                                              13.4   \n",
       "77                                              13.4   \n",
       "...                                              ...   \n",
       "335896                                          10.6   \n",
       "335903                                          10.6   \n",
       "335904                                          10.6   \n",
       "335905                                          10.6   \n",
       "335906                                          10.6   \n",
       "335913                                          10.6   \n",
       "335916                                          10.6   \n",
       "335919                                          10.6   \n",
       "335920                                          10.6   \n",
       "335922                                          10.6   \n",
       "335925                                          10.6   \n",
       "335926                                          10.6   \n",
       "335927                                          10.6   \n",
       "335928                                          10.6   \n",
       "335931                                          10.6   \n",
       "335932                                          10.6   \n",
       "335933                                          10.6   \n",
       "335938                                          10.6   \n",
       "335939                                          10.6   \n",
       "335940                                          10.6   \n",
       "335942                                          10.6   \n",
       "335943                                          10.6   \n",
       "335944                                          10.6   \n",
       "335945                                          10.6   \n",
       "335946                                          10.6   \n",
       "335947                                          10.6   \n",
       "335948                                          10.6   \n",
       "335949                                          10.6   \n",
       "335950                                          10.6   \n",
       "335951                                          10.6   \n",
       "\n",
       "        PERCENT AGED UNDER 18 OR OVER 64 PER CAPITA INCOME   HARDSHIP INDEX  \n",
       "0                                   25.5              37524            17.0  \n",
       "2                                   25.5              37524            17.0  \n",
       "3                                   25.5              37524            17.0  \n",
       "5                                   25.5              37524            17.0  \n",
       "6                                   25.5              37524            17.0  \n",
       "15                                  25.5              37524            17.0  \n",
       "25                                  25.5              37524            17.0  \n",
       "26                                  25.5              37524            17.0  \n",
       "27                                  25.5              37524            17.0  \n",
       "28                                  25.5              37524            17.0  \n",
       "31                                  25.5              37524            17.0  \n",
       "32                                  25.5              37524            17.0  \n",
       "33                                  25.5              37524            17.0  \n",
       "38                                  25.5              37524            17.0  \n",
       "39                                  25.5              37524            17.0  \n",
       "46                                  25.5              37524            17.0  \n",
       "47                                  25.5              37524            17.0  \n",
       "48                                  25.5              37524            17.0  \n",
       "52                                  25.5              37524            17.0  \n",
       "55                                  25.5              37524            17.0  \n",
       "56                                  25.5              37524            17.0  \n",
       "58                                  25.5              37524            17.0  \n",
       "60                                  25.5              37524            17.0  \n",
       "61                                  25.5              37524            17.0  \n",
       "63                                  25.5              37524            17.0  \n",
       "64                                  25.5              37524            17.0  \n",
       "65                                  25.5              37524            17.0  \n",
       "66                                  25.5              37524            17.0  \n",
       "76                                  25.5              37524            17.0  \n",
       "77                                  25.5              37524            17.0  \n",
       "...                                  ...                ...             ...  \n",
       "335896                              39.3              24454            41.0  \n",
       "335903                              39.3              24454            41.0  \n",
       "335904                              39.3              24454            41.0  \n",
       "335905                              39.3              24454            41.0  \n",
       "335906                              39.3              24454            41.0  \n",
       "335913                              39.3              24454            41.0  \n",
       "335916                              39.3              24454            41.0  \n",
       "335919                              39.3              24454            41.0  \n",
       "335920                              39.3              24454            41.0  \n",
       "335922                              39.3              24454            41.0  \n",
       "335925                              39.3              24454            41.0  \n",
       "335926                              39.3              24454            41.0  \n",
       "335927                              39.3              24454            41.0  \n",
       "335928                              39.3              24454            41.0  \n",
       "335931                              39.3              24454            41.0  \n",
       "335932                              39.3              24454            41.0  \n",
       "335933                              39.3              24454            41.0  \n",
       "335938                              39.3              24454            41.0  \n",
       "335939                              39.3              24454            41.0  \n",
       "335940                              39.3              24454            41.0  \n",
       "335942                              39.3              24454            41.0  \n",
       "335943                              39.3              24454            41.0  \n",
       "335944                              39.3              24454            41.0  \n",
       "335945                              39.3              24454            41.0  \n",
       "335946                              39.3              24454            41.0  \n",
       "335947                              39.3              24454            41.0  \n",
       "335948                              39.3              24454            41.0  \n",
       "335949                              39.3              24454            41.0  \n",
       "335950                              39.3              24454            41.0  \n",
       "335951                              39.3              24454            41.0  \n",
       "\n",
       "[560459 rows x 30 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theft=crime2.loc[crime2[\"Primary Type\"]==\"THEFT\"]\n",
    "theft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Case Number</th>\n",
       "      <th>Date</th>\n",
       "      <th>Block</th>\n",
       "      <th>IUCR</th>\n",
       "      <th>Primary Type</th>\n",
       "      <th>Description</th>\n",
       "      <th>Location Description</th>\n",
       "      <th>Arrest</th>\n",
       "      <th>Domestic</th>\n",
       "      <th>...</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Location</th>\n",
       "      <th>COMMUNITY AREA NAME</th>\n",
       "      <th>PERCENT OF HOUSING CROWDED</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6834557</td>\n",
       "      <td>HR242656</td>\n",
       "      <td>12/01/2008 12:00:00 AM</td>\n",
       "      <td>028XX W LAWRENCE AVE</td>\n",
       "      <td>1310</td>\n",
       "      <td>CRIMINAL DAMAGE</td>\n",
       "      <td>TO PROPERTY</td>\n",
       "      <td>RESIDENCE</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.700196</td>\n",
       "      <td>(41.9685234, -87.700195851)</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>25.5</td>\n",
       "      <td>37524</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6887280</td>\n",
       "      <td>HP590816</td>\n",
       "      <td>09/24/2008 03:40:36 PM</td>\n",
       "      <td>019XX W WINONA ST</td>\n",
       "      <td>2022</td>\n",
       "      <td>NARCOTICS</td>\n",
       "      <td>POSS: COCAINE</td>\n",
       "      <td>RESIDENCE</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.677172</td>\n",
       "      <td>(41.974735967, -87.677171863)</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>25.5</td>\n",
       "      <td>37524</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6089363</td>\n",
       "      <td>HP187633</td>\n",
       "      <td>02/20/2008 05:30:00 PM</td>\n",
       "      <td>047XX N MAPLEWOOD AVE</td>\n",
       "      <td>0460</td>\n",
       "      <td>BATTERY</td>\n",
       "      <td>SIMPLE</td>\n",
       "      <td>RESIDENCE</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.692548</td>\n",
       "      <td>(41.967751008, -87.69254773)</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>25.5</td>\n",
       "      <td>37524</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6094492</td>\n",
       "      <td>HP189514</td>\n",
       "      <td>02/14/2008 09:00:00 PM</td>\n",
       "      <td>051XX N LINCOLN AVE</td>\n",
       "      <td>2820</td>\n",
       "      <td>OTHER OFFENSE</td>\n",
       "      <td>TELEPHONE THREAT</td>\n",
       "      <td>BARBERSHOP</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.692196</td>\n",
       "      <td>(41.974549183, -87.69219642)</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>25.5</td>\n",
       "      <td>37524</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6174296</td>\n",
       "      <td>HP262992</td>\n",
       "      <td>04/05/2008 12:25:00 PM</td>\n",
       "      <td>025XX W FARRAGUT AVE</td>\n",
       "      <td>0560</td>\n",
       "      <td>ASSAULT</td>\n",
       "      <td>SIMPLE</td>\n",
       "      <td>RESIDENCE PORCH/HALLWAY</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.692946</td>\n",
       "      <td>(41.976773428, -87.692946416)</td>\n",
       "      <td>Lincoln Square</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.2</td>\n",
       "      <td>13.4</td>\n",
       "      <td>25.5</td>\n",
       "      <td>37524</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID Case Number                    Date                  Block  IUCR  \\\n",
       "1  6834557    HR242656  12/01/2008 12:00:00 AM   028XX W LAWRENCE AVE  1310   \n",
       "4  6887280    HP590816  09/24/2008 03:40:36 PM      019XX W WINONA ST  2022   \n",
       "7  6089363    HP187633  02/20/2008 05:30:00 PM  047XX N MAPLEWOOD AVE  0460   \n",
       "8  6094492    HP189514  02/14/2008 09:00:00 PM    051XX N LINCOLN AVE  2820   \n",
       "9  6174296    HP262992  04/05/2008 12:25:00 PM   025XX W FARRAGUT AVE  0560   \n",
       "\n",
       "      Primary Type       Description     Location Description  Arrest  \\\n",
       "1  CRIMINAL DAMAGE       TO PROPERTY                RESIDENCE   False   \n",
       "4        NARCOTICS     POSS: COCAINE                RESIDENCE    True   \n",
       "7          BATTERY            SIMPLE                RESIDENCE   False   \n",
       "8    OTHER OFFENSE  TELEPHONE THREAT               BARBERSHOP   False   \n",
       "9          ASSAULT            SIMPLE  RESIDENCE PORCH/HALLWAY   False   \n",
       "\n",
       "   Domestic  ...  Longitude                       Location  \\\n",
       "1     False  ... -87.700196    (41.9685234, -87.700195851)   \n",
       "4     False  ... -87.677172  (41.974735967, -87.677171863)   \n",
       "7     False  ... -87.692548   (41.967751008, -87.69254773)   \n",
       "8     False  ... -87.692196   (41.974549183, -87.69219642)   \n",
       "9     False  ... -87.692946  (41.976773428, -87.692946416)   \n",
       "\n",
       "   COMMUNITY AREA NAME  PERCENT OF HOUSING CROWDED  \\\n",
       "1       Lincoln Square                         3.4   \n",
       "4       Lincoln Square                         3.4   \n",
       "7       Lincoln Square                         3.4   \n",
       "8       Lincoln Square                         3.4   \n",
       "9       Lincoln Square                         3.4   \n",
       "\n",
       "  PERCENT HOUSEHOLDS BELOW POVERTY  PERCENT AGED 16+ UNEMPLOYED  \\\n",
       "1                             10.9                          8.2   \n",
       "4                             10.9                          8.2   \n",
       "7                             10.9                          8.2   \n",
       "8                             10.9                          8.2   \n",
       "9                             10.9                          8.2   \n",
       "\n",
       "   PERCENT AGED 25+ WITHOUT HIGH SCHOOL DIPLOMA  \\\n",
       "1                                          13.4   \n",
       "4                                          13.4   \n",
       "7                                          13.4   \n",
       "8                                          13.4   \n",
       "9                                          13.4   \n",
       "\n",
       "   PERCENT AGED UNDER 18 OR OVER 64 PER CAPITA INCOME   HARDSHIP INDEX  \n",
       "1                              25.5              37524            17.0  \n",
       "4                              25.5              37524            17.0  \n",
       "7                              25.5              37524            17.0  \n",
       "8                              25.5              37524            17.0  \n",
       "9                              25.5              37524            17.0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_theft = crime2.loc[crime2[\"Primary Type\"]!=\"THEFT\"]\n",
    "no_theft.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54.8"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theft[\"PERCENT AGED 25+ WITHOUT HIGH SCHOOL DIPLOMA\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.5"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theft[\"PERCENT AGED 25+ WITHOUT HIGH SCHOOL DIPLOMA\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nasir\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High School Graduate Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0-5%</th>\n",
       "      <td>99.358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5-10%</th>\n",
       "      <td>33.626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10-15%</th>\n",
       "      <td>118.199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15-20%</th>\n",
       "      <td>83.236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-25%</th>\n",
       "      <td>78.196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25-30%</th>\n",
       "      <td>51.724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-35%</th>\n",
       "      <td>30.704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35-40%</th>\n",
       "      <td>29.352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-45%</th>\n",
       "      <td>17.390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45-50%</th>\n",
       "      <td>5.033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50-55%</th>\n",
       "      <td>13.641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        High School Graduate Percentage\n",
       "0-5%                             99.358\n",
       "5-10%                            33.626\n",
       "10-15%                          118.199\n",
       "15-20%                           83.236\n",
       "20-25%                           78.196\n",
       "25-30%                           51.724\n",
       "30-35%                           30.704\n",
       "35-40%                           29.352\n",
       "40-45%                           17.390\n",
       "45-50%                            5.033\n",
       "50-55%                           13.641"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the bins in which Data will be held\n",
    "# Bins are 0, 60, 70, 80, 90, 100\n",
    "bins = [0, 5, 10, 15, 20, 25,30,35,40,45,50,55]\n",
    "\n",
    "# Create the names for the four bins\n",
    "group_names = [\"0-5%\", \"5-10%\", \"10-15%\", \"15-20%\", \"20-25%\",\"25-30%\",\"30-35%\",\"35-40%\",\"40-45%\",\"45-50%\",\"50-55%\"]\n",
    "\n",
    "theft[\"High School Graduate Percentage\"] = pd.cut(theft[\"PERCENT AGED 25+ WITHOUT HIGH SCHOOL DIPLOMA\"], bins, labels=group_names)\n",
    "a=theft[\"High School Graduate Percentage\"].value_counts()/1000\n",
    "\n",
    "a1=pd.DataFrame(a.sort_index())\n",
    "a1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a1.plot(kind=\"bar\", figsize=(20,3),color='r')\n",
    "\n",
    "# Set a title for the chart\n",
    "figure = plt.gcf() \n",
    "figure.set_size_inches(8, 6)\n",
    "plt.title(\"Theft (thousands) vs % Without High School Diploma\")\n",
    "plt.savefig(\"output_data/Theft_Without_High_School_Diploma.png\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35.9"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theft[\"PERCENT AGED 16+ UNEMPLOYED\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.7"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theft[\"PERCENT AGED 16+ UNEMPLOYED\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nasir\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unemployment %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0-5%</th>\n",
       "      <td>22.524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5-10%</th>\n",
       "      <td>194.910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10-15%</th>\n",
       "      <td>74.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15-20%</th>\n",
       "      <td>106.816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-25%</th>\n",
       "      <td>108.321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25-30%</th>\n",
       "      <td>37.502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-35%</th>\n",
       "      <td>3.173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35-40%</th>\n",
       "      <td>12.963</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unemployment %\n",
       "0-5%            22.524\n",
       "5-10%          194.910\n",
       "10-15%          74.250\n",
       "15-20%         106.816\n",
       "20-25%         108.321\n",
       "25-30%          37.502\n",
       "30-35%           3.173\n",
       "35-40%          12.963"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the bins in which Data will be held\n",
    "# Bins are 0, 60, 70, 80, 90, 100\n",
    "bins = [0, 5, 10, 15, 20, 25,30,35,40]\n",
    "\n",
    "# Create the names for the four bins\n",
    "group_names = [\"0-5%\", \"5-10%\", \"10-15%\", \"15-20%\", \"20-25%\",\"25-30%\",\"30-35%\",\"35-40%\"]\n",
    "\n",
    "theft[\"Unemployment %\"] = pd.cut(theft[\"PERCENT AGED 16+ UNEMPLOYED\"], bins, labels=group_names)\n",
    "b=theft[\"Unemployment %\"].value_counts()/1000\n",
    "\n",
    "b1=pd.DataFrame(b.sort_index())\n",
    "b1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nD4MGDQJgyy23ZMaMGcycOZOnn36anXfeGYB//etf7LTTTmXX9Ve/+hXLstdee7HXXnsBMHr0aPbff3+eeeYZzj//fNZee20uvvhiVl111WW+T3s5qM3MmkzPnj158803F2mbO3fuIhfkWGWVVQDo2rUr8+fPB9LFOy655BL22WefReYdP378wtdD6hpufd6lS5eF88PiXxLaPk+dsiz2mg022IA+ffowbtw4/vjHPzJmzJjFai1dbumyu3btyl577cUNN9xQ9vdRbl3bY968eYwePZp7772XvffemzvuuIPrr7+eMWPG8OUvf7nd77MsvtZ3zqo9ErTCkaJm1hhWX311+vbty4MPPgikkL7nnnv41Kc+tdT59tlnHy677DI++OADAJ599ln+8Y9/LNeyb7nlFhYsWMD06dN54YUX2GyzzRaZ/ulPf3phCI8fP55evXqxxhprAHDsscdyxBFHMGLECLp27druZe6444488sgjC7vZ582bx7PPPrvUeXr06ME777yz1Nf84Ac/4MQTT6Rbt27885//RBJdunRh3rx57a6tPRzUZma1FlHdn3a49tprOeeccxgyZAh77LEHo0aNYpNNNlnqPMceeyxbbLEF2267LVtttRXHH3/8cu2BAmy22Wbsuuuu7Lffflx++eV07959kelnnXUWLS0tDB48mNNOO43Ro0cvnHbggQfy7rvvLtLt3R69e/fmmmuu4fDDD184SK1tl3tbw4YN47bbbis7mAzg1VdfpaWlheHDhwPwjW98gx133JHRo0fz+c9/frnqWxaV62bobEOHDo2WlpZal5Gfzt7LzeCzYNYMpk6dyuabb17rMjrdyJEjOeCAA/jc5z63QvO3tLRw8sknlw3O3JX7m0uaFBFDlzWvj1GbmVn2zj33XC677LJFjk03C+9R58x71GYNqVn3qJtZJXvUPkZtZlYDOewkWeeo9G/toDYz62Tdu3dnzpw5Dusm0Ho/6raD5paHj1GbmXWyfv36MXPmTHyL3+bQvXt3+vXrt8LzO6jNzDpZt27dFrm4iNnSuOvbzMwsYw5qMzOzjDmozczMMuagNjMzy5iD2szMLGMOajMzs4w5qM3MzDLmoDYzM8uYg9rMzCxjDmozM7OMOajNzMwy5qA2MzPLmIPazMwsYw5qMzOzjDmozczMMuagNjMzy5iD2szMLGMOajMzs4w5qM3MzDK2zKCWdLWk1yU9VdJ2k6TJxc8MSZOL9v6S/lky7fKOLN7MzKzRrdSO11wD/AS4trUhIg5tfSzpAuCtktdPj4gh1SrQzMysmS0zqCPi95L6l5smScAIYI/qlmVmZmZQ+THqXYBZEfFcSdvGkv4kaYKkXSp8fzMzs6bWnq7vpTkcuKHk+WvAhhExR9J2wO2StoyIt9vOKOk44DiADTfcsMIyzMzMGtMK71FLWgk4GLiptS0i3o+IOcXjScB0YNNy80fEFRExNCKG9u7de0XLMDMza2iVdH1/BpgWETNbGyT1ltS1eDwAGAi8UFmJZmZmzas9p2fdAPwB2EzSTElfKiYdxqLd3gCfBqZIehK4FTghIuZWs2AzM7Nm0p5R34cvoX1kmbaxwNjKyzIzMzPwlcnMzMyy5qA2MzPLmIPazMwsYw5qMzOzjDmozczMMuagNjMzy5iD2szMLGMOajMzs4w5qM3MzDLmoDYzM8uYg9rMzCxjDmozM7OMOajNzMwy5qA2MzPLmIPazMwsYw5qMzOzjDmozczMMuagNjMzy5iD2szMLGMOajMzs4w5qM3MzDLmoDYzM8uYg9rMzCxjDmozM7OMOajNzMwy5qA2MzPLmIPazMwsYw5qMzOzjDmozczMMuagNjMzy5iD2szMLGPLDGpJV0t6XdJTJW1nSXpF0uTiZ/+SaadLel7SM5L26ajCzczMmkF79qivAfYt035RRAwpfu4GkLQFcBiwZTHPpZK6VqtYMzOzZrPMoI6I3wNz2/l+w4EbI+L9iHgReB7YoYL6zMzMmlolx6i/LmlK0TW+dtG2PvByyWtmFm2LkXScpBZJLbNnz66gDDMzs8a1okF9GbAJMAR4DbigaFeZ10a5N4iIKyJiaEQM7d279wqWYWZm1thWKKgjYlZEfBgRC4Cf81H39kxgg5KX9gNeraxEMzOz5rVCQS2pb8nTzwKtI8LvBA6TtIqkjYGBwGOVlWhmZta8VlrWCyTdAOwG9JI0ExgF7CZpCKlbewZwPEBE/EXSzcDTwHzgaxHxYceUbmZm1vgUUfYQcqcaOnRotLS01LqM/KjcIf8OlMFnwcysWUiaFBFDl/U6X5nMzMwsYw5qMzOzjDmozczMMuagNjMzy5iD2szMLGMOajMzs4w5qM3MzDLmoDYzM8uYg9rMzCxjDmozM7OMOajNzMwy5qA2MzPLmIPazMwsYw5qMzOzjDmozczMMuagNjMzy5iD2szMLGMOajMzs4w5qM3MzDLmoDYzM8uYg9rMzCxjDmozM7OMOajNzMwy5qA2MzPLmIPazMwsYw5qMzOzjDmozczMMuagNjMzy5iD2szMLGMOajMzs4w5qM3MzDK2zKCWdLWk1yU9VdL2Q0nTJE2RdJuktYr2/pL+KWly8XN5RxZvZmbW6NqzR30NsG+btvuBrSJiMPAscHrJtOkRMaT4OaE6ZZqZmTWnZQZ1RPwemNum7b6ImF88nQj064DazMzMml41jlEfA/y25PnGkv4kaYKkXZY0k6TjJLVIapk9e3YVyjAzM2s8FQW1pP8HzAfGFE2vARtGxDbAKcD1ktYoN29EXBERQyNiaO/evSspw8zMrGGtcFBLOgo4APhCRARARLwfEXOKx5OA6cCm1SjUzMysGa1QUEvaFzgVODAi5pW095bUtXg8ABgIvFCNQs3MzJrRSst6gaQbgN2AXpJmAqNIo7xXAe6XBDCxGOH9aeBsSfOBD4ETImJu2Tc2MzOzZVpmUEfE4WWar1rCa8cCYystyszMzBJfmczMzCxjDmozM7OMOajNzMwy5qA2MzPLmIPazMwsYw5qMzOzjDmozczMMuagNjMzy5iD2szMLGMOajMzs4w5qM3MzDLmoDYzM8uYg9rMzCxjDmozM7OMOajNzMwy5qA2MzPLmIPazMwsYw5qMzOzjDmozczMMuagNjMzy5iD2szMLGMr1boAM6tDUucuL6Jzl2eWEe9Rm5mZZcx71GYdxXudZlYF3qM2MzPLmIPazMwsYw5qMzOzjDmozczMMuagNjMzy5iD2szMLGMOajMzs4y1K6glXS3pdUlPlbStI+l+Sc8V/65dtEvSjyU9L2mKpG07qngzM7NG19496muAfdu0nQY8GBEDgQeL5wD7AQOLn+OAyyov08zMrDm1K6gj4vfA3DbNw4HRxePRwEEl7ddGMhFYS1LfahRrZmbWbCo5Rt0nIl4DKP5dt2hfH3i55HUzi7ZFSDpOUoukltmzZ1dQhpmZWePqiMFk5S5wvNhFiCPiiogYGhFDe/fu3QFlmJmZ1b9KgnpWa5d28e/rRftMYIOS1/UDXq1gOWZmZk2rkqC+EziqeHwUcEdJ+5HF6O8dgbdau8jNzMxs+bTrNpeSbgB2A3pJmgmMAs4Fbpb0JeCvwCHFy+8G9geeB+YBR1e5ZjMzs6bRrqCOiMOXMGnPMq8N4GuVFGVmZmaJr0xmZmaWMQe1mZlZxhzUZmZmGXNQm5mZZcxBbWZmljEHtZmZWcYc1GZmZhlzUJuZmWXMQW1mZpYxB7WZmVnGHNRmZmYZc1CbmZllzEFtZmaWMQe1mZlZxhzUZmZmGXNQm5mZZcxBbWZmljEHtZmZWcYc1GZmZhlzUJuZmWXMQW1mZpYxB7WZmVnGHNRmZmYZc1CbmZllzEFtZmaWMQe1mZlZxhzUZmZmGXNQm5mZZcxBbWZmljEHtZmZWcYc1GZmZhlbaUVnlLQZcFNJ0wDgTGAt4MvA7KL9fyPi7hWu0MzMrImtcFBHxDPAEABJXYFXgNuAo4GLIuL8qlRoZmbWxKrV9b0nMD0iXqrS+5mZmRnVC+rDgBtKnn9d0hRJV0tau9wMko6T1CKpZfbs2eVeYmZm1vQqDmpJKwMHArcUTZcBm5C6xV8DLig3X0RcERFDI2Jo7969Ky3D6pHUuT9mZnWoGnvU+wFPRMQsgIiYFREfRsQC4OfADlVYhpmZWVOqRlAfTkm3t6S+JdM+CzxVhWWYmZk1pRUe9Q0gaVVgL+D4kuYfSBoCBDCjzTQzMzNbDhUFdUTMA3q2aftiRRWZmZnZQr4ymZmZWcYc1GZmZhlzUJuZmWXMQW1mZpYxB7WZmVnGHNRmZmYZc1CbmZllzEFtZmaWMQe1mZlZxhzUZmZmGXNQm5mZZcxBbWZmljEHtZmZWcYc1GZmZhlzUJuZmWXMQW1mZpYxB7WZmVnGHNRmZmYZc1CbmZllzEFtZmaWMQe1mZlZxhzUZmZmGXNQm5mZZcxBbWZmljEHtZmZWcYc1GZmZhlzUJuZmWXMQW1mZpYxB7WZmVnGHNRmZmYZc1CbmZllbKVK30DSDOAd4ENgfkQMlbQOcBPQH5gBjIiINytdlpmZWbOp1h717hExJCKGFs9PAx6MiIHAg8VzMzMzW04d1fU9HBhdPB4NHNRByzEzM2to1QjqAO6TNEnScUVbn4h4DaD4d922M0k6TlKLpJbZs2dXoQwzM7PGU/ExamDniHhV0rrA/ZKmtWemiLgCuAJg6NChUYU6zMzMGk7Fe9QR8Wrx7+vAbcAOwCxJfQGKf1+vdDlmZmbNqKKglrSapB6tj4G9gaeAO4GjipcdBdxRyXLMzMyaVaVd332A2yS1vtf1EXGPpMeBmyV9CfgrcEiFyzEzM2tKFQV1RLwAbF2mfQ6wZyXvbWZmZr4ymZmZWdYc1GZmZhlzUJuZmWXMQW1mZpYxB7WZmVnGHNRmZmYZc1CbmZllzEFtZmaWMQe1mZlZxhzUZmZmGXNQm5mZZcxBbWZmljEHtZmZWcYc1GZmZhmr9H7UZmaNR+rc5UV07vKsrniP2szMLGMOajMzs4w5qM3MzDLmoDYzM8uYg9rMzCxjDmozM7OMOajNzMwy5qA2MzPLmIPazMwsYw5qMzOzjDmozczMMuagNjMzy5iD2szMLGMOajMzs4w5qM3MzDLmoDYzM8vYCge1pA0k/U7SVEl/kXRi0X6WpFckTS5+9q9euWZmZs1lpQrmnQ98IyKekNQDmCTp/mLaRRFxfuXlmZmZNbcVDuqIeA14rXj8jqSpwPrVKszMzMyqdIxaUn9gG+CPRdPXJU2RdLWktZcwz3GSWiS1zJ49uxplmJmZNZyKg1rS6sBY4KSIeBu4DNgEGELa476g3HwRcUVEDI2Iob179660DDMzs4ZUUVBL6kYK6TER8SuAiJgVER9GxALg58AOlZdpZmbWnCoZ9S3gKmBqRFxY0t635GWfBZ5a8fLMzMyaWyWjvncGvgj8WdLkou1/gcMlDQECmAEcX1GFSyN12FuXFdG5yzMzs6ZXyajvh4FySXn3ipdjZmZmpXxlMjMzs4w5qM3MzDLmoDYzM8uYg9rMzCxjDmozM7OMOajNzMwy5qA2MzPLmIPazMwsYw5qMzOzjDmozczMMuagNjMzy5iD2szMLGMOajMzs4xVcptLMzOz/DTYLZC9R21mZpYxB7WZmVnGHNRmZmYZc1CbmZllzEFtZmaWMQe1mZlZxhzUZmZmGXNQm5mZZcxBbWZmljEHtZmZWcYc1GZmZhlzUJuZmWXMQW1mZpYxB7WZmVnGfJtLM7Nm02C3gWx03qM2MzPLWIcFtaR9JT0j6XlJp3XUcszMzBpZhwS1pK7AT4H9gC2AwyVt0RHLMjMza2QdtUe9A/B8RLwQEf8CbgSGd9CyzMzMGlZHDSZbH3i55PlM4JOlL5B0HHBc8fRdSc90UC3l9ALeWO65OnsAxorz+pXj9ctBI68beP3K8/otyUbteVFHBXW5qhcZ9hcRVwBXdNDyl0pSS0QMrcWyO4PXr7418vo18rqB16/e5bp+HdX1PRPYoOR5P+DVDlqWmZlZw+qooH4cGChpY0krA4cBd3bQsszMzBpWh3R9R8R8SV8H7gW6AldHxF86YlkrqCZd7p2oWm1wAAAOl0lEQVTI61ffGnn9GnndwOtX77JcP4WvGGNmZpYtX5nMzMwsYw5qMzOzjDmozczMMuagNjMzy1hT3uZSUndg5Yh4u9a1VFuxbl8AVgWuj4g5NS7JzBqApN7AicDHgMsi4vkal1RVOa9f0+1RSzqWdNrYbyR9r9b1dICLSV/A3gNur3EtZgtJ2lHSOEmPSDqo1vVUm6RPSLpO0lhJO9W6ng5wAfB74B7ghhrX0hGyXb+GD2pJw9o0fSYido2IXYD/qEVN1STpekmblDStA4whfdDWrk1VHacJNvYNs36SPt6m6RTgQGBf4DudX1F1Fb1Xpb4DnA2cBlzW+RVVl6R7JO1S0rQyMKP4WaUWNVVTPa1fwwc1sLWkOyRtXTyfImmMpOuAnC7CsqLOAL4j6XxJawLnk64Cdx9wVi0Lq4Ym2Ng38vpdLun/SgLt78DngUOBRjjs9GtJXyx5/gHQv/j5sBYFVdmhwPCSnYH/A84EzgW+WtPKqqNu1q8pLnhSbAzPLp6eCawOrBoRU2pXVXVJ+hQptH8DXBoRjbChQNLtwCTghxHxnqQrgBZgAXB0ROxc0wIr1ATrN4x03G80MJYU1KsCN0TE7FrWVilJXYGvAAcA3wWeAf6btH5XRMS0GpZXNZIGkNbvFeA7EfFWjUuqqnpYv2YJ6h6kb7gDSXspj1NsGGtaWBVIWpu08fuAdN/vg4AjgR9FxF21rK1aGnljD02xfl1Jeyj/AXw3Ih6qcUlVVfRknQn0Bf4vIqbXuKSqKALsK6Rty0+ATUg7A3fRADsD9bR+Dd/1Lekc0l7mg8DuEXEg8CRpMNkXlzpzfbgdeB/oDvwyIq4FhgHbSWqIG6FExK+BfYC1gF8Bz0TEjxshxKBx10/SgZIeBsYBT5FuzvNZSTe0GVdRlyR9UtKtpOPRvyB1nX635DBUvbuBNLBqImnb8lBE7EM6bHFfTSurjvpZv4ho6B9gcvGvgCdK2lcCTqx1fVVYv6dIe189gZY20/rWur4qrN+BwMPABGB3UphdSPpPtkmt6/P6LXXdpgCrAb2Ax0raBwI31rq+Kqzfn0h7YVsDj5S07wrcW+v6qrB+TwIDgEHAH9pM+1it62um9Wv4ru9i0FiQzo17OSJOrnFJVSXpYOAbpK79syPigRqXVFWSpgA7kf5+d0fEDkX7QNLxpMNqWV+lGnn9JD0EXENat30j4oDaVlRdklqAU0lflE+JiN1rXFJVSfp30rblX8C5EfFkjUuqqnpav4YPagBJg4APokEGdzSTJtjYN+z6SeoFHE46Bnh9NNgFhiRtChxP2tBfGhEv17gkW0GS1gEiIt6sdS3lNEVQl5K0MbAN8HQjBHdxLOw00iCydYvm14E7SN8S/16r2qqhCTb2Db1+bUlaJyLm1rqOapLUB1if1HP3akTMqnFJVSFp34i4p3i8JumQzPakw20n1/t6StoQ+AGwB/AW6fDoGqQxFadFxIzaVbeoZhhMdnvJ4+GkP8Iw4A5JI2tVVxXdTDo/dfeI6BkRPUnHOt8EbqlpZVUQEW9ExCURcXlriBXffhtCRLxBGnH6J+Azkj5bDFJSjUurmKQzSh5vIelZYJKkGZI+WcPSqkLSEEkTgfGkDf4PgQmSJkratqbFVUfplRsvAF4jbTsfB35Wk4qq6ybgNtJYnoER8QnSyP3bSWfQZKPh96gl/SkitikePwp8ISJeLPZkHoyIrZf+DnmT9ExEbLa80+qFpDMi4pzi8Rak/0TdSN9+D42IP9ayvkpJ2hu4FHiOdB4nQD/gE8BXIyKv0afLQdITEbFt8fg3wE8i4reSdiCdPvjvta2wMpImA8e3/QxK2hH4WQNsW0r/fpMjYkjJtEWe1yNJz0XEwOWdVgvNcFOO0m8iK0XEi5D2ZCQtqFFN1fSSpP8BRrd2RRVdcSOBRjhmdjBwTvH4h6SR+gs39kBdb+xJ12b/TNtutuIQzd3A5rUoqgOsFxG/BYiIxyR9rNYFVcFq5b4oRsRESavVoqAqW1fSKRRdwpIUH+3ZNUJv7CRJl5KuX9C6rdwAOIrUw5WNZgjqrSW9TfqwrSLp4xHxN0krA11rXFs1HEo6Rj1BUusx6lmky4iOqFlVHaMRN/YrATPLtL9C6jmoZwOKc/kF9JO0akTMK6bV+7oB/LboKbiWRTf0R5LOz613Pwd6FI9Hk06zm11c6XFyzaqqniOBLwHfJo0xEOn/4p3AVTWsazEN3/W9JJLWAjaPiD/UuhZbMkl/J93RRsCOwEatG3tJT0XEVrWsr1KSTid9obqRRTf2hwE3R8T3a1VbpSTt2qZpUkS8W/T4fC4iflqLuqpJ0v6kc+EX2dBHxN01LcwaSlMGtaQDokEur7k0ko6OiF/Uuo5KNMnGfgvKb+yfrmlh1tSKAY2HkA4f3koaHT0cmAZcHhGNcOhwEZKejYhNa11HW80a1AsHSTQySX+NiA1rXYc1p+Ia38eSBsfdExGPlExbOEiwXhVdwKNIN1A5E/gv0piKaaSxFK/VsLyKFcdv1yXd/vFt0q0ffw3sD8yKiBNrWF7FJL1D+hJSeobFqsA80jnVa9SksDKaNagXjgSvd8WVrcpOAjaNiKzuq1pNkq6IiONqXUclJK0BnE4Ks7sj4oaSaZdGRFa321sekq4kbfgeA74ITIiIU4ppdf9lWdI9pPsIrEa6kUrrfeCHkwYIDq9heRWT9OeIGCSpG/A30mlM/5K0EvCniBhU4xIrIukSYE3gWyUDcV+MiI1rW9nimiKoJf0b6T9P60UJ3gBujYipNS2sCiTNIt3Qoe0VdQQ8GhHrdX5V1bOUc6YFPBkR/TqznmqTNJZ0atZE4BjShU8+HxHv13uYSZoSEYOLxyuRTkNrvcDLxHr/stzm1M9Feq8a5PSl0vW7JyL2LZlW9+sHIGk70tkkt5OuZ/B8RAyobVWLa/hR35JOJW0YbiR9s4e093KDpBsj4tyaFVcddwGrR8RiozAlje/8cqpuNvASi3ZPtXZXrVt2jvqySUT8Z/H4dkn/Dxgn6cBaFlUlK7c+iIj5wHGSziRddGj1mlVVPaWnKF27lGn16m+SVo+Id9uE9MdJl02texExSdJngK+TbozTvcYlldXwe9TF1ZC2jIgP2rSvDPwlp5PabXGSngP2jIi/lpn2ckRsUIOyqkbSVNLnc0FJ21HA/5C+gG1Us+IqpHRDnOtaL0NZ0n4scFlE1PUpWpLOBn4QEe+2af8E6fK9n6tNZR2rOEd8tYh4vda1VJOkvsA2OY7Yb4RvfcuyACjX/du3mNZwJNX1cds2fgSsvYRpP+jMQjrIr0mjaReKiNF8dFefuhURR7QN6aL9ynoPaYCIOLNtSBftzzdwSJ8VEf9otJAGKAb/HVTrOspphj3qfUnHHp7jo/NUNyRdovHr5TYk9a7ej21a42qEAYBLI+muaKA7oLXV6NuWXNev4Y9RR8Q9Srej24FFz1N9PCI+rGlxHafub+iwNN7Y17WhtS6gg61f6wI6WENvW0h3HsxOwwc1QHH8b2Kt6+gMkj4FjJW0dz3f0GEZvLGvX1luCKsoq2tEd4Dtal1ARyodNJeTZjhG3dAkPVby+Mukbv6uwChJp9WssI7ljX2dynVDWC0RcUyta6gWSStJOl7SPZKmSHoS+I2kE4pzq+uapMElj7tJOkPSnZK+J2nVWtbWVsMfo250bc51fBzYPyJmFyMzJ9b7RQmsfklak3Qxl4OA3kXz68AdpFHRf69VbdUgad/WMS7Ful4IbA88BZzcehGNeiXpBtK97kfz0Y1j+pHuLrVORBxaq9qqQYvexvMCoCfwC9LntWdEHFnL+kp5j7r+dZG0tqSepC9eswEi4h/A/NqWVjlJa0o6V9I0SXOKn6lF21q1rq9SxWDH1sdrSrqq2Hu5vrieeT27mXQhnt0iomdE9AR2L9puqWll1fG9kscXAK8Bw4DHgZ/VpKLq2jYivhIREyNiZvEzMSK+AtT1xWoKpcfb9wS+HBETgFOArC7m4qCuf2sCk4AWYJ3iYgRIWp3GGPjhjX396h8R50XE31obIuJvEXEe6cyLRjI0Is6IiJci4iKgf60LqoI3JR0iaWFOSOoi6VAWvxJiPVpT0sGS/hNYpfVaG5G6mbPqam6KwWSNLCL6L2HSAuCznVhKR+lfbNgXKjb850lqmOOBhaEll2W8qLjwST17SdL/AKNLrqXcBxjJR6dK1rN1JZ1C+kK8hiTFR8cSG2En6DDgPOCnSrebBVgL+F0xrd5NIH0pBpgoqU9EzCp2dt6oYV2LcVA3qEj3bH6x1nVUgTf29etQ4DRgQvE3C2AWcCfpHtz17udAj+LxaNJ1zGcXG/rFLulbbyJihqQLST0904HNSfeEfzoi6n7bEhFHS/oksCAiHpe0haQvANMiYs9a11fKg8ksa5LWJm3sh/PRtb1bN/bnRkRdd8FJGtWm6dJiMODHSZenzGZAy4oobojTjzSw8d2S9oUDsepZsX7rA39stPUrPpv7kXbo7iddi2IC8Bng3oj4bg3Lq1g9rZ+D2uqWpKMj4he1rqOj1Pv6Sfpv4GvAVNLgnBMj4o5iWpZXgFoekv6LdDOHRl2/P5PWaxXSbS77RcTbkj5G+mIyeKlvkLl6Wj93fVs9+zbpdIpGVe/r92Vgu4h4V1J/4FZJ/SPiYhpjoONxNPb6zS+u3jhP0vSIeBsgIv4pqRHuk1A36+egtqxJmrKkSUC9n77U6OvXtbU7uDjeuRspzDaiMYKs0dfvX5JWLca7LLwiWXHOeFZBtoLqZv0c1Ja7PsA+LH46iIBHO7+cqmvk9fubpCFR3Cu92PM8ALgaaIQL8TT6+n06It6HhZdhbtWNdNGTelc36+egttzdRbov82KjaCWN7/xyqq6R1+9I2lx0JyLmA0dKqvdzxKHB1681xMq0v0Fmpy+tiHpaPw8mMzMzy1i9n6dpZmbW0BzUZmZmGXNQm5mZZcxBbWZmlrH/D8MpBHLbdWFiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "b1.plot(kind=\"bar\", figsize=(20,3),color='r')\n",
    "\n",
    "# Set a title for the chart\n",
    "figure = plt.gcf() \n",
    "figure.set_size_inches(8, 6)\n",
    "plt.title(\"Theft (thousands) vs % Unemployed\")\n",
    "plt.savefig(\"output_data/Theft_vs_Unemployed.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56.5"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theft[\"PERCENT HOUSEHOLDS BELOW POVERTY\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.3"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theft[\"PERCENT HOUSEHOLDS BELOW POVERTY\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nasir\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>relative</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0-5%</th>\n",
       "      <td>1.667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5-10%</th>\n",
       "      <td>18.324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10-15%</th>\n",
       "      <td>159.886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15-20%</th>\n",
       "      <td>96.147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-25%</th>\n",
       "      <td>57.111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25-30%</th>\n",
       "      <td>120.416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-35%</th>\n",
       "      <td>57.452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35-40%</th>\n",
       "      <td>1.075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-45%</th>\n",
       "      <td>33.246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45-50%</th>\n",
       "      <td>11.962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50-55%</th>\n",
       "      <td>1.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55-60%</th>\n",
       "      <td>1.273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        relative\n",
       "0-5%       1.667\n",
       "5-10%     18.324\n",
       "10-15%   159.886\n",
       "15-20%    96.147\n",
       "20-25%    57.111\n",
       "25-30%   120.416\n",
       "30-35%    57.452\n",
       "35-40%     1.075\n",
       "40-45%    33.246\n",
       "45-50%    11.962\n",
       "50-55%     1.900\n",
       "55-60%     1.273"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the bins in which Data will be held\n",
    "# Bins are 0, 60, 70, 80, 90, 100\n",
    "bins = [0, 5, 10, 15, 20, 25,30,35,40,45,50,55,60]\n",
    "\n",
    "# Create the names for the four bins\n",
    "group_names = [\"0-5%\", \"5-10%\", \"10-15%\", \"15-20%\", \"20-25%\",\"25-30%\",\"30-35%\",\"35-40%\",\"40-45%\",\"45-50%\",\"50-55%\",\"55-60%\"]\n",
    "\n",
    "theft[\"% Houses Under Poverty\"] = pd.cut(theft[\"PERCENT HOUSEHOLDS BELOW POVERTY\"], bins, labels=group_names)\n",
    "c=theft[\"% Houses Under Poverty\"].value_counts()\n",
    "c_df = pd.DataFrame(c)\n",
    "\n",
    "c_df[\"relative\"]=c_df[\"% Houses Under Poverty\"]/1000\n",
    "c1=pd.DataFrame(c_df[\"relative\"].sort_index())\n",
    "c1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "c1.plot(kind=\"bar\", figsize=(20,3),color='r')\n",
    "\n",
    "# Set a title for the chart\n",
    "figure = plt.gcf() \n",
    "figure.set_size_inches(8, 6)\n",
    "plt.title(\"Theft (thousands) vs Houses Under Poverty\")\n",
    "plt.savefig(\"output_data/Theft_vs_Houses_Under_Poverty.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.8"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theft[\"PERCENT OF HOUSING CROWDED\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theft[\"PERCENT OF HOUSING CROWDED\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nasir\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>% Crowded Housing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0-4%</th>\n",
       "      <td>332.288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4-8%</th>\n",
       "      <td>144.309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8-12%</th>\n",
       "      <td>51.899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12-16%</th>\n",
       "      <td>31.963</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        % Crowded Housing\n",
       "0-4%              332.288\n",
       "4-8%              144.309\n",
       "8-12%              51.899\n",
       "12-16%             31.963"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the bins in which Data will be held\n",
    "# Bins are 0, 60, 70, 80, 90, 100\n",
    "bins = [0, 4,8,12,16]\n",
    "\n",
    "# Create the names for the four bins\n",
    "group_names = [\"0-4%\", \"4-8%\", \"8-12%\", \"12-16%\", ]\n",
    "\n",
    "theft[\"% Crowded Housing\"] = pd.cut(theft[\"PERCENT OF HOUSING CROWDED\"], bins, labels=group_names)\n",
    "d=theft[\"% Crowded Housing\"].value_counts()/1000\n",
    "\n",
    "d1 = pd.DataFrame(d)\n",
    "d1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "d1.plot(kind=\"bar\", figsize=(20,3),color='r')\n",
    "\n",
    "# Set a title for the chart\n",
    "figure = plt.gcf() \n",
    "figure.set_size_inches(8, 6)\n",
    "plt.title(\"Theft (thousands) vs % Crowded Housing\")\n",
    "plt.savefig(\"output_data/Theft_vs_%Crowded_Housing.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "88669"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theft[\"PER CAPITA INCOME \"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8201"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theft[\"PER CAPITA INCOME \"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Nasir\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Per Capita Income Bracket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0-10,000</th>\n",
       "      <td>1.273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10,000-20,000</th>\n",
       "      <td>273.766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20,000-30,000</th>\n",
       "      <td>94.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30,000-40,000</th>\n",
       "      <td>49.528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40,000-50,000</th>\n",
       "      <td>47.298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50,000-60,000</th>\n",
       "      <td>8.993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60,000-70,000</th>\n",
       "      <td>40.758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70,000-80,000</th>\n",
       "      <td>15.522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80,000-90,000</th>\n",
       "      <td>29.196</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Per Capita Income Bracket\n",
       "0-10,000                           1.273\n",
       "10,000-20,000                    273.766\n",
       "20,000-30,000                     94.125\n",
       "30,000-40,000                     49.528\n",
       "40,000-50,000                     47.298\n",
       "50,000-60,000                      8.993\n",
       "60,000-70,000                     40.758\n",
       "70,000-80,000                     15.522\n",
       "80,000-90,000                     29.196"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the bins in which Data will be held\n",
    "# Bins are 0, 60, 70, 80, 90, 100\n",
    "bins = [0, 10000,20000,30000,40000,50000,60000,70000,80000,90000]\n",
    "\n",
    "# Create the names for the four bins\n",
    "group_names = [\"0-10,000\", \"10,000-20,000\", \"20,000-30,000\", \"30,000-40,000\",\"40,000-50,000\",\"50,000-60,000\",\"60,000-70,000\",\"70,000-80,000\",\"80,000-90,000\" ]\n",
    "\n",
    "theft[\"Per Capita Income Bracket\"] = pd.cut(theft[\"PER CAPITA INCOME \"], bins, labels=group_names)\n",
    "e=theft[\"Per Capita Income Bracket\"].value_counts()/1000\n",
    "e1=pd.DataFrame(e.sort_index())\n",
    "e1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "e1.plot(kind=\"bar\", figsize=(20,3),color='r')\n",
    "\n",
    "# Set a title for the chart\n",
    "figure = plt.gcf() \n",
    "figure.set_size_inches(8, 6)\n",
    "plt.title(\"Theft (thousands) vs Per Capita Income Bracket\")\n",
    "plt.savefig(\"output_data/Theft_vs_Per_Capita_Income_Bracket.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "98.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theft['HARDSHIP INDEX'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theft['HARDSHIP INDEX'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hardship Bracket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0-10</th>\n",
       "      <td>247.906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10-20</th>\n",
       "      <td>132.678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-30</th>\n",
       "      <td>111.649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-40</th>\n",
       "      <td>123.577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-50</th>\n",
       "      <td>111.349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50-60</th>\n",
       "      <td>250.097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60-70</th>\n",
       "      <td>160.882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70-80</th>\n",
       "      <td>299.700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80-90</th>\n",
       "      <td>250.560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90-100</th>\n",
       "      <td>188.494</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Hardship Bracket\n",
       "0-10             247.906\n",
       "10-20            132.678\n",
       "20-30            111.649\n",
       "30-40            123.577\n",
       "40-50            111.349\n",
       "50-60            250.097\n",
       "60-70            160.882\n",
       "70-80            299.700\n",
       "80-90            250.560\n",
       "90-100           188.494"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the bins in which Data will be held\n",
    "# Bins are 0, 60, 70, 80, 90, 100\n",
    "bins = [0, 10,20,30,40,50,60,70,80,90,100]\n",
    "\n",
    "# Create the names for the four bins\n",
    "group_names = [\"0-10\", \"10-20\", \"20-30\", \"30-40\",\"40-50\",\"50-60\",\"60-70\",\"70-80\",\"80-90\",\"90-100\" ]\n",
    "\n",
    "crime2[\"Hardship Bracket\"] = pd.cut(crime2['HARDSHIP INDEX'], bins, labels=group_names)\n",
    "f=crime2[\"Hardship Bracket\"].value_counts()/1000\n",
    "f1=pd.DataFrame(f.sort_index())\n",
    "f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f1.plot(kind=\"bar\", figsize=(20,3),color='r')\n",
    "\n",
    "# Set a title for the chart\n",
    "figure = plt.gcf() \n",
    "figure.set_size_inches(8, 6)\n",
    "plt.title(\"Theft (thousands) vs Hardship Bracket\")\n",
    "plt.savefig(\"output_data/Theft_vs_Hardship_Bracket.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Case Number</th>\n",
       "      <th>Date</th>\n",
       "      <th>Block</th>\n",
       "      <th>IUCR</th>\n",
       "      <th>Primary Type</th>\n",
       "      <th>Description</th>\n",
       "      <th>Location Description</th>\n",
       "      <th>Arrest</th>\n",
       "      <th>Domestic</th>\n",
       "      <th>...</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Location</th>\n",
       "      <th>COMMUNITY AREA NAME</th>\n",
       "      <th>PERCENT OF HOUSING CROWDED</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4381932</td>\n",
       "      <td>HL677807</td>\n",
       "      <td>10/16/2005 09:30:00 PM</td>\n",
       "      <td>051XX S MENARD AVE</td>\n",
       "      <td>1320</td>\n",
       "      <td>CRIMINAL DAMAGE</td>\n",
       "      <td>TO VEHICLE</td>\n",
       "      <td>STREET</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.767377</td>\n",
       "      <td>(41.799263181, -87.767376862)</td>\n",
       "      <td>Garfield Ridge</td>\n",
       "      <td>2.6</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6833177</td>\n",
       "      <td>HR241485</td>\n",
       "      <td>03/27/2009 02:30:00 PM</td>\n",
       "      <td>052XX S KEATING AVE</td>\n",
       "      <td>5002</td>\n",
       "      <td>OTHER OFFENSE</td>\n",
       "      <td>OTHER VEHICLE OFFENSE</td>\n",
       "      <td>RESIDENCE-GARAGE</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.741665</td>\n",
       "      <td>(41.79760506, -87.741664754)</td>\n",
       "      <td>Garfield Ridge</td>\n",
       "      <td>2.6</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6833415</td>\n",
       "      <td>HR242017</td>\n",
       "      <td>03/29/2009 06:00:00 PM</td>\n",
       "      <td>057XX S MAYFIELD AVE</td>\n",
       "      <td>1320</td>\n",
       "      <td>CRIMINAL DAMAGE</td>\n",
       "      <td>TO VEHICLE</td>\n",
       "      <td>STREET</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.769448</td>\n",
       "      <td>(41.788032748, -87.769448408)</td>\n",
       "      <td>Garfield Ridge</td>\n",
       "      <td>2.6</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6833782</td>\n",
       "      <td>HR242256</td>\n",
       "      <td>03/30/2009 04:42:02 AM</td>\n",
       "      <td>054XX S LUNA AVE</td>\n",
       "      <td>0610</td>\n",
       "      <td>BURGLARY</td>\n",
       "      <td>FORCIBLE ENTRY</td>\n",
       "      <td>RESIDENCE-GARAGE</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.761118</td>\n",
       "      <td>(41.79374085, -87.761117923)</td>\n",
       "      <td>Garfield Ridge</td>\n",
       "      <td>2.6</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6834334</td>\n",
       "      <td>HR225410</td>\n",
       "      <td>03/19/2009 04:00:00 PM</td>\n",
       "      <td>051XX S CICERO AVE</td>\n",
       "      <td>1150</td>\n",
       "      <td>DECEPTIVE PRACTICE</td>\n",
       "      <td>CREDIT CARD FRAUD</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-87.742953</td>\n",
       "      <td>(41.799911401, -87.742952841)</td>\n",
       "      <td>Garfield Ridge</td>\n",
       "      <td>2.6</td>\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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID Case Number                    Date                 Block  IUCR  \\\n",
       "0  4381932    HL677807  10/16/2005 09:30:00 PM    051XX S MENARD AVE  1320   \n",
       "1  6833177    HR241485  03/27/2009 02:30:00 PM   052XX S KEATING AVE  5002   \n",
       "2  6833415    HR242017  03/29/2009 06:00:00 PM  057XX S MAYFIELD AVE  1320   \n",
       "3  6833782    HR242256  03/30/2009 04:42:02 AM      054XX S LUNA AVE  0610   \n",
       "4  6834334    HR225410  03/19/2009 04:00:00 PM    051XX S CICERO AVE  1150   \n",
       "\n",
       "         Primary Type            Description Location Description  Arrest  \\\n",
       "0     CRIMINAL DAMAGE             TO VEHICLE               STREET   False   \n",
       "1       OTHER OFFENSE  OTHER VEHICLE OFFENSE     RESIDENCE-GARAGE   False   \n",
       "2     CRIMINAL DAMAGE             TO VEHICLE               STREET   False   \n",
       "3            BURGLARY         FORCIBLE ENTRY     RESIDENCE-GARAGE   False   \n",
       "4  DECEPTIVE PRACTICE      CREDIT CARD FRAUD                OTHER   False   \n",
       "\n",
       "   Domestic  ...  Longitude                       Location  \\\n",
       "0     False  ... -87.767377  (41.799263181, -87.767376862)   \n",
       "1     False  ... -87.741665   (41.79760506, -87.741664754)   \n",
       "2     False  ... -87.769448  (41.788032748, -87.769448408)   \n",
       "3     False  ... -87.761118   (41.79374085, -87.761117923)   \n",
       "4     False  ... -87.742953  (41.799911401, -87.742952841)   \n",
       "\n",
       "   COMMUNITY AREA NAME  PERCENT OF HOUSING CROWDED  \\\n",
       "0       Garfield Ridge                         2.6   \n",
       "1       Garfield Ridge                         2.6   \n",
       "2       Garfield Ridge                         2.6   \n",
       "3       Garfield Ridge                         2.6   \n",
       "4       Garfield Ridge                         2.6   \n",
       "\n",
       "  PERCENT HOUSEHOLDS BELOW POVERTY  PERCENT AGED 16+ UNEMPLOYED  \\\n",
       "0                              8.8                         11.3   \n",
       "1                              8.8                         11.3   \n",
       "2                              8.8                         11.3   \n",
       "3                              8.8                         11.3   \n",
       "4                              8.8                         11.3   \n",
       "\n",
       "   PERCENT AGED 25+ WITHOUT HIGH SCHOOL DIPLOMA  \\\n",
       "0                                          19.3   \n",
       "1                                          19.3   \n",
       "2                                          19.3   \n",
       "3                                          19.3   \n",
       "4                                          19.3   \n",
       "\n",
       "   PERCENT AGED UNDER 18 OR OVER 64 PER CAPITA INCOME   HARDSHIP INDEX  \n",
       "0                              38.1              26353            32.0  \n",
       "1                              38.1              26353            32.0  \n",
       "2                              38.1              26353            32.0  \n",
       "3                              38.1              26353            32.0  \n",
       "4                              38.1              26353            32.0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df = pd.merge(df,census,on=\"Community Area\")\n",
    "merged_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Community Area\n",
       "1.0      28084\n",
       "2.0      24042\n",
       "3.0      26018\n",
       "4.0      12638\n",
       "5.0      10956\n",
       "6.0      38661\n",
       "7.0      29028\n",
       "8.0      59903\n",
       "9.0       1856\n",
       "10.0      8074\n",
       "11.0      7395\n",
       "12.0      3451\n",
       "13.0      6239\n",
       "14.0     16997\n",
       "15.0     24217\n",
       "16.0     22348\n",
       "17.0     12081\n",
       "18.0      4647\n",
       "19.0     37205\n",
       "20.0     11491\n",
       "21.0     18219\n",
       "22.0     39921\n",
       "23.0     60240\n",
       "24.0     55715\n",
       "25.0    121674\n",
       "26.0     33161\n",
       "27.0     34661\n",
       "28.0     50856\n",
       "29.0     53101\n",
       "30.0     31764\n",
       "         ...  \n",
       "48.0     10647\n",
       "49.0     52939\n",
       "50.0      7021\n",
       "51.0     12700\n",
       "52.0      9737\n",
       "53.0     31535\n",
       "54.0      7312\n",
       "55.0      4120\n",
       "56.0     14550\n",
       "57.0      7072\n",
       "58.0     18506\n",
       "59.0      7807\n",
       "60.0     12147\n",
       "61.0     39466\n",
       "62.0      7599\n",
       "63.0     18849\n",
       "64.0      7586\n",
       "65.0     14273\n",
       "66.0     50802\n",
       "67.0     57107\n",
       "68.0     51748\n",
       "69.0     47227\n",
       "70.0     17492\n",
       "71.0     55010\n",
       "72.0      6793\n",
       "73.0     22419\n",
       "74.0      4190\n",
       "75.0     15618\n",
       "76.0     10156\n",
       "77.0     18068\n",
       "Name: ID, Length: 77, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime_by_area=crime2.groupby(\"Community Area\")\n",
    "crime_count = crime_by_area.count()\n",
    "crime_count[\"ID\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Community Area\n",
       "1.0      7862\n",
       "2.0      7658\n",
       "3.0      8533\n",
       "4.0      4646\n",
       "5.0      4563\n",
       "6.0     18094\n",
       "7.0     15522\n",
       "8.0     29196\n",
       "9.0       481\n",
       "10.0     2489\n",
       "11.0     2136\n",
       "12.0     1114\n",
       "13.0     2212\n",
       "14.0     4997\n",
       "15.0     7715\n",
       "16.0     7442\n",
       "17.0     3691\n",
       "18.0     1520\n",
       "19.0    11315\n",
       "20.0     2848\n",
       "21.0     6287\n",
       "22.0    15579\n",
       "23.0    13289\n",
       "24.0    23469\n",
       "25.0    26471\n",
       "26.0     6988\n",
       "27.0     7789\n",
       "28.0    22234\n",
       "29.0    11224\n",
       "30.0     8328\n",
       "        ...  \n",
       "48.0     3620\n",
       "49.0    13007\n",
       "50.0     1693\n",
       "51.0     3314\n",
       "52.0     2000\n",
       "53.0     7300\n",
       "54.0     1273\n",
       "55.0     1071\n",
       "56.0     3819\n",
       "57.0     2372\n",
       "58.0     5033\n",
       "59.0     2698\n",
       "60.0     3427\n",
       "61.0     9248\n",
       "62.0     2376\n",
       "63.0     5313\n",
       "64.0     1844\n",
       "65.0     5565\n",
       "66.0    12789\n",
       "67.0    12963\n",
       "68.0    11962\n",
       "69.0    11623\n",
       "70.0     4904\n",
       "71.0    12887\n",
       "72.0     2359\n",
       "73.0     6166\n",
       "74.0     1186\n",
       "75.0     4344\n",
       "76.0     3339\n",
       "77.0     6481\n",
       "Name: ID, Length: 77, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theft_by_area = theft.groupby(\"Community Area\")\n",
    "theft_count = theft_by_area.count()\n",
    "theft_count[\"ID\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Case Number</th>\n",
       "      <th>Date</th>\n",
       "      <th>Block</th>\n",
       "      <th>IUCR</th>\n",
       "      <th>Primary Type</th>\n",
       "      <th>Description</th>\n",
       "      <th>Location Description</th>\n",
       "      <th>Arrest</th>\n",
       "      <th>Domestic</th>\n",
       "      <th>...</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Location</th>\n",
       "      <th>COMMUNITY AREA NAME</th>\n",
       "      <th>PERCENT OF HOUSING CROWDED</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Community Area</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",
       "      <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>1.0</th>\n",
       "      <td>20222</td>\n",
       "      <td>20222</td>\n",
       "      <td>20222</td>\n",
       "      <td>20222</td>\n",
       "      <td>20222</td>\n",
       "      <td>20222</td>\n",
       "      <td>20222</td>\n",
       "      <td>20220</td>\n",
       "      <td>20222</td>\n",
       "      <td>20222</td>\n",
       "      <td>...</td>\n",
       "      <td>20073</td>\n",
       "      <td>20073</td>\n",
       "      <td>20222</td>\n",
       "      <td>20222</td>\n",
       "      <td>20222</td>\n",
       "      <td>20222</td>\n",
       "      <td>20222</td>\n",
       "      <td>20222</td>\n",
       "      <td>20222</td>\n",
       "      <td>20222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>16384</td>\n",
       "      <td>16384</td>\n",
       "      <td>16384</td>\n",
       "      <td>16384</td>\n",
       "      <td>16384</td>\n",
       "      <td>16384</td>\n",
       "      <td>16384</td>\n",
       "      <td>16382</td>\n",
       "      <td>16384</td>\n",
       "      <td>16384</td>\n",
       "      <td>...</td>\n",
       "      <td>16268</td>\n",
       "      <td>16268</td>\n",
       "      <td>16384</td>\n",
       "      <td>16384</td>\n",
       "      <td>16384</td>\n",
       "      <td>16384</td>\n",
       "      <td>16384</td>\n",
       "      <td>16384</td>\n",
       "      <td>16384</td>\n",
       "      <td>16384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>17485</td>\n",
       "      <td>17485</td>\n",
       "      <td>17485</td>\n",
       "      <td>17485</td>\n",
       "      <td>17485</td>\n",
       "      <td>17485</td>\n",
       "      <td>17485</td>\n",
       "      <td>17482</td>\n",
       "      <td>17485</td>\n",
       "      <td>17485</td>\n",
       "      <td>...</td>\n",
       "      <td>17383</td>\n",
       "      <td>17383</td>\n",
       "      <td>17485</td>\n",
       "      <td>17485</td>\n",
       "      <td>17485</td>\n",
       "      <td>17485</td>\n",
       "      <td>17485</td>\n",
       "      <td>17485</td>\n",
       "      <td>17485</td>\n",
       "      <td>17485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>7992</td>\n",
       "      <td>7992</td>\n",
       "      <td>7992</td>\n",
       "      <td>7992</td>\n",
       "      <td>7992</td>\n",
       "      <td>7992</td>\n",
       "      <td>7992</td>\n",
       "      <td>7991</td>\n",
       "      <td>7992</td>\n",
       "      <td>7992</td>\n",
       "      <td>...</td>\n",
       "      <td>7945</td>\n",
       "      <td>7945</td>\n",
       "      <td>7992</td>\n",
       "      <td>7992</td>\n",
       "      <td>7992</td>\n",
       "      <td>7992</td>\n",
       "      <td>7992</td>\n",
       "      <td>7992</td>\n",
       "      <td>7992</td>\n",
       "      <td>7992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>6393</td>\n",
       "      <td>6393</td>\n",
       "      <td>6393</td>\n",
       "      <td>6393</td>\n",
       "      <td>6393</td>\n",
       "      <td>6393</td>\n",
       "      <td>6393</td>\n",
       "      <td>6391</td>\n",
       "      <td>6393</td>\n",
       "      <td>6393</td>\n",
       "      <td>...</td>\n",
       "      <td>6350</td>\n",
       "      <td>6350</td>\n",
       "      <td>6393</td>\n",
       "      <td>6393</td>\n",
       "      <td>6393</td>\n",
       "      <td>6393</td>\n",
       "      <td>6393</td>\n",
       "      <td>6393</td>\n",
       "      <td>6393</td>\n",
       "      <td>6393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.0</th>\n",
       "      <td>20567</td>\n",
       "      <td>20567</td>\n",
       "      <td>20567</td>\n",
       "      <td>20567</td>\n",
       "      <td>20567</td>\n",
       "      <td>20567</td>\n",
       "      <td>20567</td>\n",
       "      <td>20566</td>\n",
       "      <td>20567</td>\n",
       "      <td>20567</td>\n",
       "      <td>...</td>\n",
       "      <td>20432</td>\n",
       "      <td>20432</td>\n",
       "      <td>20567</td>\n",
       "      <td>20567</td>\n",
       "      <td>20567</td>\n",
       "      <td>20567</td>\n",
       "      <td>20567</td>\n",
       "      <td>20567</td>\n",
       "      <td>20567</td>\n",
       "      <td>20567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7.0</th>\n",
       "      <td>13506</td>\n",
       "      <td>13506</td>\n",
       "      <td>13506</td>\n",
       "      <td>13506</td>\n",
       "      <td>13506</td>\n",
       "      <td>13506</td>\n",
       "      <td>13506</td>\n",
       "      <td>13506</td>\n",
       "      <td>13506</td>\n",
       "      <td>13506</td>\n",
       "      <td>...</td>\n",
       "      <td>13398</td>\n",
       "      <td>13398</td>\n",
       "      <td>13506</td>\n",
       "      <td>13506</td>\n",
       "      <td>13506</td>\n",
       "      <td>13506</td>\n",
       "      <td>13506</td>\n",
       "      <td>13506</td>\n",
       "      <td>13506</td>\n",
       "      <td>13506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.0</th>\n",
       "      <td>30707</td>\n",
       "      <td>30707</td>\n",
       "      <td>30707</td>\n",
       "      <td>30707</td>\n",
       "      <td>30707</td>\n",
       "      <td>30707</td>\n",
       "      <td>30707</td>\n",
       "      <td>30701</td>\n",
       "      <td>30707</td>\n",
       "      <td>30707</td>\n",
       "      <td>...</td>\n",
       "      <td>30468</td>\n",
       "      <td>30468</td>\n",
       "      <td>30707</td>\n",
       "      <td>30707</td>\n",
       "      <td>30707</td>\n",
       "      <td>30707</td>\n",
       "      <td>30707</td>\n",
       "      <td>30707</td>\n",
       "      <td>30707</td>\n",
       "      <td>30707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.0</th>\n",
       "      <td>1375</td>\n",
       "      <td>1375</td>\n",
       "      <td>1375</td>\n",
       "      <td>1375</td>\n",
       "      <td>1375</td>\n",
       "      <td>1375</td>\n",
       "      <td>1375</td>\n",
       "      <td>1375</td>\n",
       "      <td>1375</td>\n",
       "      <td>1375</td>\n",
       "      <td>...</td>\n",
       "      <td>1356</td>\n",
       "      <td>1356</td>\n",
       "      <td>1375</td>\n",
       "      <td>1375</td>\n",
       "      <td>1375</td>\n",
       "      <td>1375</td>\n",
       "      <td>1375</td>\n",
       "      <td>1375</td>\n",
       "      <td>1375</td>\n",
       "      <td>1375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>5585</td>\n",
       "      <td>5585</td>\n",
       "      <td>5585</td>\n",
       "      <td>5585</td>\n",
       "      <td>5585</td>\n",
       "      <td>5585</td>\n",
       "      <td>5585</td>\n",
       "      <td>5585</td>\n",
       "      <td>5585</td>\n",
       "      <td>5585</td>\n",
       "      <td>...</td>\n",
       "      <td>5534</td>\n",
       "      <td>5534</td>\n",
       "      <td>5585</td>\n",
       "      <td>5585</td>\n",
       "      <td>5585</td>\n",
       "      <td>5585</td>\n",
       "      <td>5585</td>\n",
       "      <td>5585</td>\n",
       "      <td>5585</td>\n",
       "      <td>5585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>5259</td>\n",
       "      <td>5259</td>\n",
       "      <td>5259</td>\n",
       "      <td>5259</td>\n",
       "      <td>5259</td>\n",
       "      <td>5259</td>\n",
       "      <td>5259</td>\n",
       "      <td>5258</td>\n",
       "      <td>5259</td>\n",
       "      <td>5259</td>\n",
       "      <td>...</td>\n",
       "      <td>5228</td>\n",
       "      <td>5228</td>\n",
       "      <td>5259</td>\n",
       "      <td>5259</td>\n",
       "      <td>5259</td>\n",
       "      <td>5259</td>\n",
       "      <td>5259</td>\n",
       "      <td>5259</td>\n",
       "      <td>5259</td>\n",
       "      <td>5259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>2337</td>\n",
       "      <td>2337</td>\n",
       "      <td>2337</td>\n",
       "      <td>2337</td>\n",
       "      <td>2337</td>\n",
       "      <td>2337</td>\n",
       "      <td>2337</td>\n",
       "      <td>2337</td>\n",
       "      <td>2337</td>\n",
       "      <td>2337</td>\n",
       "      <td>...</td>\n",
       "      <td>2309</td>\n",
       "      <td>2309</td>\n",
       "      <td>2337</td>\n",
       "      <td>2337</td>\n",
       "      <td>2337</td>\n",
       "      <td>2337</td>\n",
       "      <td>2337</td>\n",
       "      <td>2337</td>\n",
       "      <td>2337</td>\n",
       "      <td>2337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.0</th>\n",
       "      <td>4027</td>\n",
       "      <td>4027</td>\n",
       "      <td>4027</td>\n",
       "      <td>4027</td>\n",
       "      <td>4027</td>\n",
       "      <td>4027</td>\n",
       "      <td>4027</td>\n",
       "      <td>4027</td>\n",
       "      <td>4027</td>\n",
       "      <td>4027</td>\n",
       "      <td>...</td>\n",
       "      <td>4004</td>\n",
       "      <td>4004</td>\n",
       "      <td>4027</td>\n",
       "      <td>4027</td>\n",
       "      <td>4027</td>\n",
       "      <td>4027</td>\n",
       "      <td>4027</td>\n",
       "      <td>4027</td>\n",
       "      <td>4027</td>\n",
       "      <td>4027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.0</th>\n",
       "      <td>12000</td>\n",
       "      <td>12000</td>\n",
       "      <td>12000</td>\n",
       "      <td>12000</td>\n",
       "      <td>12000</td>\n",
       "      <td>12000</td>\n",
       "      <td>12000</td>\n",
       "      <td>12000</td>\n",
       "      <td>12000</td>\n",
       "      <td>12000</td>\n",
       "      <td>...</td>\n",
       "      <td>11922</td>\n",
       "      <td>11922</td>\n",
       "      <td>12000</td>\n",
       "      <td>12000</td>\n",
       "      <td>12000</td>\n",
       "      <td>12000</td>\n",
       "      <td>12000</td>\n",
       "      <td>12000</td>\n",
       "      <td>12000</td>\n",
       "      <td>12000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.0</th>\n",
       "      <td>16502</td>\n",
       "      <td>16502</td>\n",
       "      <td>16502</td>\n",
       "      <td>16502</td>\n",
       "      <td>16502</td>\n",
       "      <td>16502</td>\n",
       "      <td>16502</td>\n",
       "      <td>16501</td>\n",
       "      <td>16502</td>\n",
       "      <td>16502</td>\n",
       "      <td>...</td>\n",
       "      <td>16394</td>\n",
       "      <td>16394</td>\n",
       "      <td>16502</td>\n",
       "      <td>16502</td>\n",
       "      <td>16502</td>\n",
       "      <td>16502</td>\n",
       "      <td>16502</td>\n",
       "      <td>16502</td>\n",
       "      <td>16502</td>\n",
       "      <td>16502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.0</th>\n",
       "      <td>14906</td>\n",
       "      <td>14905</td>\n",
       "      <td>14906</td>\n",
       "      <td>14906</td>\n",
       "      <td>14906</td>\n",
       "      <td>14906</td>\n",
       "      <td>14906</td>\n",
       "      <td>14904</td>\n",
       "      <td>14906</td>\n",
       "      <td>14906</td>\n",
       "      <td>...</td>\n",
       "      <td>14807</td>\n",
       "      <td>14807</td>\n",
       "      <td>14906</td>\n",
       "      <td>14906</td>\n",
       "      <td>14906</td>\n",
       "      <td>14906</td>\n",
       "      <td>14906</td>\n",
       "      <td>14906</td>\n",
       "      <td>14906</td>\n",
       "      <td>14906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17.0</th>\n",
       "      <td>8390</td>\n",
       "      <td>8390</td>\n",
       "      <td>8390</td>\n",
       "      <td>8390</td>\n",
       "      <td>8390</td>\n",
       "      <td>8390</td>\n",
       "      <td>8390</td>\n",
       "      <td>8390</td>\n",
       "      <td>8390</td>\n",
       "      <td>8390</td>\n",
       "      <td>...</td>\n",
       "      <td>8328</td>\n",
       "      <td>8328</td>\n",
       "      <td>8390</td>\n",
       "      <td>8390</td>\n",
       "      <td>8390</td>\n",
       "      <td>8390</td>\n",
       "      <td>8390</td>\n",
       "      <td>8390</td>\n",
       "      <td>8390</td>\n",
       "      <td>8390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18.0</th>\n",
       "      <td>3127</td>\n",
       "      <td>3127</td>\n",
       "      <td>3127</td>\n",
       "      <td>3127</td>\n",
       "      <td>3127</td>\n",
       "      <td>3127</td>\n",
       "      <td>3127</td>\n",
       "      <td>3126</td>\n",
       "      <td>3127</td>\n",
       "      <td>3127</td>\n",
       "      <td>...</td>\n",
       "      <td>3100</td>\n",
       "      <td>3100</td>\n",
       "      <td>3127</td>\n",
       "      <td>3127</td>\n",
       "      <td>3127</td>\n",
       "      <td>3127</td>\n",
       "      <td>3127</td>\n",
       "      <td>3127</td>\n",
       "      <td>3127</td>\n",
       "      <td>3127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19.0</th>\n",
       "      <td>25890</td>\n",
       "      <td>25890</td>\n",
       "      <td>25890</td>\n",
       "      <td>25890</td>\n",
       "      <td>25890</td>\n",
       "      <td>25890</td>\n",
       "      <td>25890</td>\n",
       "      <td>25882</td>\n",
       "      <td>25890</td>\n",
       "      <td>25890</td>\n",
       "      <td>...</td>\n",
       "      <td>25659</td>\n",
       "      <td>25659</td>\n",
       "      <td>25890</td>\n",
       "      <td>25890</td>\n",
       "      <td>25890</td>\n",
       "      <td>25890</td>\n",
       "      <td>25890</td>\n",
       "      <td>25890</td>\n",
       "      <td>25890</td>\n",
       "      <td>25890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.0</th>\n",
       "      <td>8643</td>\n",
       "      <td>8643</td>\n",
       "      <td>8643</td>\n",
       "      <td>8643</td>\n",
       "      <td>8643</td>\n",
       "      <td>8643</td>\n",
       "      <td>8643</td>\n",
       "      <td>8642</td>\n",
       "      <td>8643</td>\n",
       "      <td>8643</td>\n",
       "      <td>...</td>\n",
       "      <td>8566</td>\n",
       "      <td>8566</td>\n",
       "      <td>8643</td>\n",
       "      <td>8643</td>\n",
       "      <td>8643</td>\n",
       "      <td>8643</td>\n",
       "      <td>8643</td>\n",
       "      <td>8643</td>\n",
       "      <td>8643</td>\n",
       "      <td>8643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21.0</th>\n",
       "      <td>11932</td>\n",
       "      <td>11932</td>\n",
       "      <td>11932</td>\n",
       "      <td>11932</td>\n",
       "      <td>11932</td>\n",
       "      <td>11932</td>\n",
       "      <td>11932</td>\n",
       "      <td>11930</td>\n",
       "      <td>11932</td>\n",
       "      <td>11932</td>\n",
       "      <td>...</td>\n",
       "      <td>11824</td>\n",
       "      <td>11824</td>\n",
       "      <td>11932</td>\n",
       "      <td>11932</td>\n",
       "      <td>11932</td>\n",
       "      <td>11932</td>\n",
       "      <td>11932</td>\n",
       "      <td>11932</td>\n",
       "      <td>11932</td>\n",
       "      <td>11932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22.0</th>\n",
       "      <td>24342</td>\n",
       "      <td>24342</td>\n",
       "      <td>24342</td>\n",
       "      <td>24342</td>\n",
       "      <td>24342</td>\n",
       "      <td>24342</td>\n",
       "      <td>24342</td>\n",
       "      <td>24339</td>\n",
       "      <td>24342</td>\n",
       "      <td>24342</td>\n",
       "      <td>...</td>\n",
       "      <td>24084</td>\n",
       "      <td>24084</td>\n",
       "      <td>24342</td>\n",
       "      <td>24342</td>\n",
       "      <td>24342</td>\n",
       "      <td>24342</td>\n",
       "      <td>24342</td>\n",
       "      <td>24342</td>\n",
       "      <td>24342</td>\n",
       "      <td>24342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23.0</th>\n",
       "      <td>46951</td>\n",
       "      <td>46951</td>\n",
       "      <td>46951</td>\n",
       "      <td>46951</td>\n",
       "      <td>46951</td>\n",
       "      <td>46951</td>\n",
       "      <td>46951</td>\n",
       "      <td>46951</td>\n",
       "      <td>46951</td>\n",
       "      <td>46951</td>\n",
       "      <td>...</td>\n",
       "      <td>46508</td>\n",
       "      <td>46508</td>\n",
       "      <td>46951</td>\n",
       "      <td>46951</td>\n",
       "      <td>46951</td>\n",
       "      <td>46951</td>\n",
       "      <td>46951</td>\n",
       "      <td>46951</td>\n",
       "      <td>46951</td>\n",
       "      <td>46951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24.0</th>\n",
       "      <td>32246</td>\n",
       "      <td>32246</td>\n",
       "      <td>32246</td>\n",
       "      <td>32246</td>\n",
       "      <td>32246</td>\n",
       "      <td>32246</td>\n",
       "      <td>32246</td>\n",
       "      <td>32241</td>\n",
       "      <td>32246</td>\n",
       "      <td>32246</td>\n",
       "      <td>...</td>\n",
       "      <td>31986</td>\n",
       "      <td>31986</td>\n",
       "      <td>32246</td>\n",
       "      <td>32246</td>\n",
       "      <td>32246</td>\n",
       "      <td>32246</td>\n",
       "      <td>32246</td>\n",
       "      <td>32246</td>\n",
       "      <td>32246</td>\n",
       "      <td>32246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25.0</th>\n",
       "      <td>95203</td>\n",
       "      <td>95203</td>\n",
       "      <td>95203</td>\n",
       "      <td>95203</td>\n",
       "      <td>95203</td>\n",
       "      <td>95203</td>\n",
       "      <td>95203</td>\n",
       "      <td>95193</td>\n",
       "      <td>95203</td>\n",
       "      <td>95203</td>\n",
       "      <td>...</td>\n",
       "      <td>94465</td>\n",
       "      <td>94465</td>\n",
       "      <td>95203</td>\n",
       "      <td>95203</td>\n",
       "      <td>95203</td>\n",
       "      <td>95203</td>\n",
       "      <td>95203</td>\n",
       "      <td>95203</td>\n",
       "      <td>95203</td>\n",
       "      <td>95203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26.0</th>\n",
       "      <td>26173</td>\n",
       "      <td>26173</td>\n",
       "      <td>26173</td>\n",
       "      <td>26173</td>\n",
       "      <td>26173</td>\n",
       "      <td>26173</td>\n",
       "      <td>26173</td>\n",
       "      <td>26171</td>\n",
       "      <td>26173</td>\n",
       "      <td>26173</td>\n",
       "      <td>...</td>\n",
       "      <td>25959</td>\n",
       "      <td>25959</td>\n",
       "      <td>26173</td>\n",
       "      <td>26173</td>\n",
       "      <td>26173</td>\n",
       "      <td>26173</td>\n",
       "      <td>26173</td>\n",
       "      <td>26173</td>\n",
       "      <td>26173</td>\n",
       "      <td>26173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27.0</th>\n",
       "      <td>26872</td>\n",
       "      <td>26872</td>\n",
       "      <td>26872</td>\n",
       "      <td>26872</td>\n",
       "      <td>26872</td>\n",
       "      <td>26872</td>\n",
       "      <td>26872</td>\n",
       "      <td>26865</td>\n",
       "      <td>26872</td>\n",
       "      <td>26872</td>\n",
       "      <td>...</td>\n",
       "      <td>26636</td>\n",
       "      <td>26636</td>\n",
       "      <td>26872</td>\n",
       "      <td>26872</td>\n",
       "      <td>26872</td>\n",
       "      <td>26872</td>\n",
       "      <td>26872</td>\n",
       "      <td>26872</td>\n",
       "      <td>26872</td>\n",
       "      <td>26872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28.0</th>\n",
       "      <td>28622</td>\n",
       "      <td>28621</td>\n",
       "      <td>28622</td>\n",
       "      <td>28622</td>\n",
       "      <td>28622</td>\n",
       "      <td>28622</td>\n",
       "      <td>28622</td>\n",
       "      <td>28615</td>\n",
       "      <td>28622</td>\n",
       "      <td>28622</td>\n",
       "      <td>...</td>\n",
       "      <td>28401</td>\n",
       "      <td>28401</td>\n",
       "      <td>28622</td>\n",
       "      <td>28622</td>\n",
       "      <td>28622</td>\n",
       "      <td>28622</td>\n",
       "      <td>28622</td>\n",
       "      <td>28622</td>\n",
       "      <td>28622</td>\n",
       "      <td>28622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29.0</th>\n",
       "      <td>41877</td>\n",
       "      <td>41877</td>\n",
       "      <td>41877</td>\n",
       "      <td>41877</td>\n",
       "      <td>41877</td>\n",
       "      <td>41877</td>\n",
       "      <td>41877</td>\n",
       "      <td>41872</td>\n",
       "      <td>41877</td>\n",
       "      <td>41877</td>\n",
       "      <td>...</td>\n",
       "      <td>41232</td>\n",
       "      <td>41232</td>\n",
       "      <td>41877</td>\n",
       "      <td>41877</td>\n",
       "      <td>41877</td>\n",
       "      <td>41877</td>\n",
       "      <td>41877</td>\n",
       "      <td>41877</td>\n",
       "      <td>41877</td>\n",
       "      <td>41877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30.0</th>\n",
       "      <td>23436</td>\n",
       "      <td>23436</td>\n",
       "      <td>23436</td>\n",
       "      <td>23436</td>\n",
       "      <td>23436</td>\n",
       "      <td>23436</td>\n",
       "      <td>23436</td>\n",
       "      <td>23435</td>\n",
       "      <td>23436</td>\n",
       "      <td>23436</td>\n",
       "      <td>...</td>\n",
       "      <td>23232</td>\n",
       "      <td>23232</td>\n",
       "      <td>23436</td>\n",
       "      <td>23436</td>\n",
       "      <td>23436</td>\n",
       "      <td>23436</td>\n",
       "      <td>23436</td>\n",
       "      <td>23436</td>\n",
       "      <td>23436</td>\n",
       "      <td>23436</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",
       "      <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>48.0</th>\n",
       "      <td>7027</td>\n",
       "      <td>7027</td>\n",
       "      <td>7027</td>\n",
       "      <td>7027</td>\n",
       "      <td>7027</td>\n",
       "      <td>7027</td>\n",
       "      <td>7027</td>\n",
       "      <td>7026</td>\n",
       "      <td>7027</td>\n",
       "      <td>7027</td>\n",
       "      <td>...</td>\n",
       "      <td>6975</td>\n",
       "      <td>6975</td>\n",
       "      <td>7027</td>\n",
       "      <td>7027</td>\n",
       "      <td>7027</td>\n",
       "      <td>7027</td>\n",
       "      <td>7027</td>\n",
       "      <td>7027</td>\n",
       "      <td>7027</td>\n",
       "      <td>7027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49.0</th>\n",
       "      <td>39932</td>\n",
       "      <td>39932</td>\n",
       "      <td>39932</td>\n",
       "      <td>39932</td>\n",
       "      <td>39932</td>\n",
       "      <td>39932</td>\n",
       "      <td>39932</td>\n",
       "      <td>39922</td>\n",
       "      <td>39932</td>\n",
       "      <td>39932</td>\n",
       "      <td>...</td>\n",
       "      <td>39500</td>\n",
       "      <td>39500</td>\n",
       "      <td>39932</td>\n",
       "      <td>39932</td>\n",
       "      <td>39932</td>\n",
       "      <td>39932</td>\n",
       "      <td>39932</td>\n",
       "      <td>39932</td>\n",
       "      <td>39932</td>\n",
       "      <td>39932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50.0</th>\n",
       "      <td>5328</td>\n",
       "      <td>5328</td>\n",
       "      <td>5328</td>\n",
       "      <td>5328</td>\n",
       "      <td>5328</td>\n",
       "      <td>5328</td>\n",
       "      <td>5328</td>\n",
       "      <td>5325</td>\n",
       "      <td>5328</td>\n",
       "      <td>5328</td>\n",
       "      <td>...</td>\n",
       "      <td>5266</td>\n",
       "      <td>5266</td>\n",
       "      <td>5328</td>\n",
       "      <td>5328</td>\n",
       "      <td>5328</td>\n",
       "      <td>5328</td>\n",
       "      <td>5328</td>\n",
       "      <td>5328</td>\n",
       "      <td>5328</td>\n",
       "      <td>5328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51.0</th>\n",
       "      <td>9386</td>\n",
       "      <td>9386</td>\n",
       "      <td>9386</td>\n",
       "      <td>9386</td>\n",
       "      <td>9386</td>\n",
       "      <td>9386</td>\n",
       "      <td>9386</td>\n",
       "      <td>9386</td>\n",
       "      <td>9386</td>\n",
       "      <td>9386</td>\n",
       "      <td>...</td>\n",
       "      <td>9331</td>\n",
       "      <td>9331</td>\n",
       "      <td>9386</td>\n",
       "      <td>9386</td>\n",
       "      <td>9386</td>\n",
       "      <td>9386</td>\n",
       "      <td>9386</td>\n",
       "      <td>9386</td>\n",
       "      <td>9386</td>\n",
       "      <td>9386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52.0</th>\n",
       "      <td>7737</td>\n",
       "      <td>7737</td>\n",
       "      <td>7737</td>\n",
       "      <td>7737</td>\n",
       "      <td>7737</td>\n",
       "      <td>7737</td>\n",
       "      <td>7737</td>\n",
       "      <td>7737</td>\n",
       "      <td>7737</td>\n",
       "      <td>7737</td>\n",
       "      <td>...</td>\n",
       "      <td>7683</td>\n",
       "      <td>7683</td>\n",
       "      <td>7737</td>\n",
       "      <td>7737</td>\n",
       "      <td>7737</td>\n",
       "      <td>7737</td>\n",
       "      <td>7737</td>\n",
       "      <td>7737</td>\n",
       "      <td>7737</td>\n",
       "      <td>7737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53.0</th>\n",
       "      <td>24235</td>\n",
       "      <td>24235</td>\n",
       "      <td>24235</td>\n",
       "      <td>24235</td>\n",
       "      <td>24235</td>\n",
       "      <td>24235</td>\n",
       "      <td>24235</td>\n",
       "      <td>24230</td>\n",
       "      <td>24235</td>\n",
       "      <td>24235</td>\n",
       "      <td>...</td>\n",
       "      <td>24004</td>\n",
       "      <td>24004</td>\n",
       "      <td>24235</td>\n",
       "      <td>24235</td>\n",
       "      <td>24235</td>\n",
       "      <td>24235</td>\n",
       "      <td>24235</td>\n",
       "      <td>24235</td>\n",
       "      <td>24235</td>\n",
       "      <td>24235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54.0</th>\n",
       "      <td>6039</td>\n",
       "      <td>6039</td>\n",
       "      <td>6039</td>\n",
       "      <td>6039</td>\n",
       "      <td>6039</td>\n",
       "      <td>6039</td>\n",
       "      <td>6039</td>\n",
       "      <td>6039</td>\n",
       "      <td>6039</td>\n",
       "      <td>6039</td>\n",
       "      <td>...</td>\n",
       "      <td>5984</td>\n",
       "      <td>5984</td>\n",
       "      <td>6039</td>\n",
       "      <td>6039</td>\n",
       "      <td>6039</td>\n",
       "      <td>6039</td>\n",
       "      <td>6039</td>\n",
       "      <td>6039</td>\n",
       "      <td>6039</td>\n",
       "      <td>6039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55.0</th>\n",
       "      <td>3049</td>\n",
       "      <td>3049</td>\n",
       "      <td>3049</td>\n",
       "      <td>3049</td>\n",
       "      <td>3049</td>\n",
       "      <td>3049</td>\n",
       "      <td>3049</td>\n",
       "      <td>3049</td>\n",
       "      <td>3049</td>\n",
       "      <td>3049</td>\n",
       "      <td>...</td>\n",
       "      <td>3027</td>\n",
       "      <td>3027</td>\n",
       "      <td>3049</td>\n",
       "      <td>3049</td>\n",
       "      <td>3049</td>\n",
       "      <td>3049</td>\n",
       "      <td>3049</td>\n",
       "      <td>3049</td>\n",
       "      <td>3049</td>\n",
       "      <td>3049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56.0</th>\n",
       "      <td>10731</td>\n",
       "      <td>10731</td>\n",
       "      <td>10731</td>\n",
       "      <td>10731</td>\n",
       "      <td>10731</td>\n",
       "      <td>10731</td>\n",
       "      <td>10731</td>\n",
       "      <td>10730</td>\n",
       "      <td>10731</td>\n",
       "      <td>10731</td>\n",
       "      <td>...</td>\n",
       "      <td>10648</td>\n",
       "      <td>10648</td>\n",
       "      <td>10731</td>\n",
       "      <td>10731</td>\n",
       "      <td>10731</td>\n",
       "      <td>10731</td>\n",
       "      <td>10731</td>\n",
       "      <td>10731</td>\n",
       "      <td>10731</td>\n",
       "      <td>10731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57.0</th>\n",
       "      <td>4700</td>\n",
       "      <td>4700</td>\n",
       "      <td>4700</td>\n",
       "      <td>4700</td>\n",
       "      <td>4700</td>\n",
       "      <td>4700</td>\n",
       "      <td>4700</td>\n",
       "      <td>4700</td>\n",
       "      <td>4700</td>\n",
       "      <td>4700</td>\n",
       "      <td>...</td>\n",
       "      <td>4670</td>\n",
       "      <td>4670</td>\n",
       "      <td>4700</td>\n",
       "      <td>4700</td>\n",
       "      <td>4700</td>\n",
       "      <td>4700</td>\n",
       "      <td>4700</td>\n",
       "      <td>4700</td>\n",
       "      <td>4700</td>\n",
       "      <td>4700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58.0</th>\n",
       "      <td>13473</td>\n",
       "      <td>13473</td>\n",
       "      <td>13473</td>\n",
       "      <td>13473</td>\n",
       "      <td>13473</td>\n",
       "      <td>13473</td>\n",
       "      <td>13473</td>\n",
       "      <td>13473</td>\n",
       "      <td>13473</td>\n",
       "      <td>13473</td>\n",
       "      <td>...</td>\n",
       "      <td>13372</td>\n",
       "      <td>13372</td>\n",
       "      <td>13473</td>\n",
       "      <td>13473</td>\n",
       "      <td>13473</td>\n",
       "      <td>13473</td>\n",
       "      <td>13473</td>\n",
       "      <td>13473</td>\n",
       "      <td>13473</td>\n",
       "      <td>13473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59.0</th>\n",
       "      <td>5109</td>\n",
       "      <td>5109</td>\n",
       "      <td>5109</td>\n",
       "      <td>5109</td>\n",
       "      <td>5109</td>\n",
       "      <td>5109</td>\n",
       "      <td>5109</td>\n",
       "      <td>5109</td>\n",
       "      <td>5109</td>\n",
       "      <td>5109</td>\n",
       "      <td>...</td>\n",
       "      <td>5070</td>\n",
       "      <td>5070</td>\n",
       "      <td>5109</td>\n",
       "      <td>5109</td>\n",
       "      <td>5109</td>\n",
       "      <td>5109</td>\n",
       "      <td>5109</td>\n",
       "      <td>5109</td>\n",
       "      <td>5109</td>\n",
       "      <td>5109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60.0</th>\n",
       "      <td>8720</td>\n",
       "      <td>8720</td>\n",
       "      <td>8720</td>\n",
       "      <td>8720</td>\n",
       "      <td>8720</td>\n",
       "      <td>8720</td>\n",
       "      <td>8720</td>\n",
       "      <td>8720</td>\n",
       "      <td>8720</td>\n",
       "      <td>8720</td>\n",
       "      <td>...</td>\n",
       "      <td>8668</td>\n",
       "      <td>8668</td>\n",
       "      <td>8720</td>\n",
       "      <td>8720</td>\n",
       "      <td>8720</td>\n",
       "      <td>8720</td>\n",
       "      <td>8720</td>\n",
       "      <td>8720</td>\n",
       "      <td>8720</td>\n",
       "      <td>8720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61.0</th>\n",
       "      <td>30218</td>\n",
       "      <td>30218</td>\n",
       "      <td>30218</td>\n",
       "      <td>30218</td>\n",
       "      <td>30218</td>\n",
       "      <td>30218</td>\n",
       "      <td>30218</td>\n",
       "      <td>30217</td>\n",
       "      <td>30218</td>\n",
       "      <td>30218</td>\n",
       "      <td>...</td>\n",
       "      <td>30011</td>\n",
       "      <td>30011</td>\n",
       "      <td>30218</td>\n",
       "      <td>30218</td>\n",
       "      <td>30218</td>\n",
       "      <td>30218</td>\n",
       "      <td>30218</td>\n",
       "      <td>30218</td>\n",
       "      <td>30218</td>\n",
       "      <td>30218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62.0</th>\n",
       "      <td>5223</td>\n",
       "      <td>5223</td>\n",
       "      <td>5223</td>\n",
       "      <td>5223</td>\n",
       "      <td>5223</td>\n",
       "      <td>5223</td>\n",
       "      <td>5223</td>\n",
       "      <td>5223</td>\n",
       "      <td>5223</td>\n",
       "      <td>5223</td>\n",
       "      <td>...</td>\n",
       "      <td>5194</td>\n",
       "      <td>5194</td>\n",
       "      <td>5223</td>\n",
       "      <td>5223</td>\n",
       "      <td>5223</td>\n",
       "      <td>5223</td>\n",
       "      <td>5223</td>\n",
       "      <td>5223</td>\n",
       "      <td>5223</td>\n",
       "      <td>5223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63.0</th>\n",
       "      <td>13536</td>\n",
       "      <td>13536</td>\n",
       "      <td>13536</td>\n",
       "      <td>13536</td>\n",
       "      <td>13536</td>\n",
       "      <td>13536</td>\n",
       "      <td>13536</td>\n",
       "      <td>13536</td>\n",
       "      <td>13536</td>\n",
       "      <td>13536</td>\n",
       "      <td>...</td>\n",
       "      <td>13430</td>\n",
       "      <td>13430</td>\n",
       "      <td>13536</td>\n",
       "      <td>13536</td>\n",
       "      <td>13536</td>\n",
       "      <td>13536</td>\n",
       "      <td>13536</td>\n",
       "      <td>13536</td>\n",
       "      <td>13536</td>\n",
       "      <td>13536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64.0</th>\n",
       "      <td>5742</td>\n",
       "      <td>5742</td>\n",
       "      <td>5742</td>\n",
       "      <td>5742</td>\n",
       "      <td>5742</td>\n",
       "      <td>5742</td>\n",
       "      <td>5742</td>\n",
       "      <td>5741</td>\n",
       "      <td>5742</td>\n",
       "      <td>5742</td>\n",
       "      <td>...</td>\n",
       "      <td>5695</td>\n",
       "      <td>5695</td>\n",
       "      <td>5742</td>\n",
       "      <td>5742</td>\n",
       "      <td>5742</td>\n",
       "      <td>5742</td>\n",
       "      <td>5742</td>\n",
       "      <td>5742</td>\n",
       "      <td>5742</td>\n",
       "      <td>5742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65.0</th>\n",
       "      <td>8708</td>\n",
       "      <td>8708</td>\n",
       "      <td>8708</td>\n",
       "      <td>8708</td>\n",
       "      <td>8708</td>\n",
       "      <td>8708</td>\n",
       "      <td>8708</td>\n",
       "      <td>8707</td>\n",
       "      <td>8708</td>\n",
       "      <td>8708</td>\n",
       "      <td>...</td>\n",
       "      <td>8653</td>\n",
       "      <td>8653</td>\n",
       "      <td>8708</td>\n",
       "      <td>8708</td>\n",
       "      <td>8708</td>\n",
       "      <td>8708</td>\n",
       "      <td>8708</td>\n",
       "      <td>8708</td>\n",
       "      <td>8708</td>\n",
       "      <td>8708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66.0</th>\n",
       "      <td>38013</td>\n",
       "      <td>38013</td>\n",
       "      <td>38013</td>\n",
       "      <td>38013</td>\n",
       "      <td>38013</td>\n",
       "      <td>38013</td>\n",
       "      <td>38013</td>\n",
       "      <td>38008</td>\n",
       "      <td>38013</td>\n",
       "      <td>38013</td>\n",
       "      <td>...</td>\n",
       "      <td>37769</td>\n",
       "      <td>37769</td>\n",
       "      <td>38013</td>\n",
       "      <td>38013</td>\n",
       "      <td>38013</td>\n",
       "      <td>38013</td>\n",
       "      <td>38013</td>\n",
       "      <td>38013</td>\n",
       "      <td>38013</td>\n",
       "      <td>38013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67.0</th>\n",
       "      <td>44144</td>\n",
       "      <td>44144</td>\n",
       "      <td>44144</td>\n",
       "      <td>44144</td>\n",
       "      <td>44144</td>\n",
       "      <td>44144</td>\n",
       "      <td>44144</td>\n",
       "      <td>44139</td>\n",
       "      <td>44144</td>\n",
       "      <td>44144</td>\n",
       "      <td>...</td>\n",
       "      <td>43864</td>\n",
       "      <td>43864</td>\n",
       "      <td>44144</td>\n",
       "      <td>44144</td>\n",
       "      <td>44144</td>\n",
       "      <td>44144</td>\n",
       "      <td>44144</td>\n",
       "      <td>44144</td>\n",
       "      <td>44144</td>\n",
       "      <td>44144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68.0</th>\n",
       "      <td>39786</td>\n",
       "      <td>39786</td>\n",
       "      <td>39786</td>\n",
       "      <td>39786</td>\n",
       "      <td>39786</td>\n",
       "      <td>39786</td>\n",
       "      <td>39786</td>\n",
       "      <td>39784</td>\n",
       "      <td>39786</td>\n",
       "      <td>39786</td>\n",
       "      <td>...</td>\n",
       "      <td>39548</td>\n",
       "      <td>39548</td>\n",
       "      <td>39786</td>\n",
       "      <td>39786</td>\n",
       "      <td>39786</td>\n",
       "      <td>39786</td>\n",
       "      <td>39786</td>\n",
       "      <td>39786</td>\n",
       "      <td>39786</td>\n",
       "      <td>39786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69.0</th>\n",
       "      <td>35604</td>\n",
       "      <td>35604</td>\n",
       "      <td>35604</td>\n",
       "      <td>35604</td>\n",
       "      <td>35604</td>\n",
       "      <td>35604</td>\n",
       "      <td>35604</td>\n",
       "      <td>35597</td>\n",
       "      <td>35604</td>\n",
       "      <td>35604</td>\n",
       "      <td>...</td>\n",
       "      <td>35364</td>\n",
       "      <td>35364</td>\n",
       "      <td>35604</td>\n",
       "      <td>35604</td>\n",
       "      <td>35604</td>\n",
       "      <td>35604</td>\n",
       "      <td>35604</td>\n",
       "      <td>35604</td>\n",
       "      <td>35604</td>\n",
       "      <td>35604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70.0</th>\n",
       "      <td>12588</td>\n",
       "      <td>12588</td>\n",
       "      <td>12588</td>\n",
       "      <td>12588</td>\n",
       "      <td>12588</td>\n",
       "      <td>12588</td>\n",
       "      <td>12588</td>\n",
       "      <td>12586</td>\n",
       "      <td>12588</td>\n",
       "      <td>12588</td>\n",
       "      <td>...</td>\n",
       "      <td>12500</td>\n",
       "      <td>12500</td>\n",
       "      <td>12588</td>\n",
       "      <td>12588</td>\n",
       "      <td>12588</td>\n",
       "      <td>12588</td>\n",
       "      <td>12588</td>\n",
       "      <td>12588</td>\n",
       "      <td>12588</td>\n",
       "      <td>12588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71.0</th>\n",
       "      <td>42123</td>\n",
       "      <td>42123</td>\n",
       "      <td>42123</td>\n",
       "      <td>42123</td>\n",
       "      <td>42123</td>\n",
       "      <td>42123</td>\n",
       "      <td>42123</td>\n",
       "      <td>42117</td>\n",
       "      <td>42123</td>\n",
       "      <td>42123</td>\n",
       "      <td>...</td>\n",
       "      <td>41729</td>\n",
       "      <td>41729</td>\n",
       "      <td>42123</td>\n",
       "      <td>42123</td>\n",
       "      <td>42123</td>\n",
       "      <td>42123</td>\n",
       "      <td>42123</td>\n",
       "      <td>42123</td>\n",
       "      <td>42123</td>\n",
       "      <td>42123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72.0</th>\n",
       "      <td>4434</td>\n",
       "      <td>4434</td>\n",
       "      <td>4434</td>\n",
       "      <td>4434</td>\n",
       "      <td>4434</td>\n",
       "      <td>4434</td>\n",
       "      <td>4434</td>\n",
       "      <td>4432</td>\n",
       "      <td>4434</td>\n",
       "      <td>4434</td>\n",
       "      <td>...</td>\n",
       "      <td>4400</td>\n",
       "      <td>4400</td>\n",
       "      <td>4434</td>\n",
       "      <td>4434</td>\n",
       "      <td>4434</td>\n",
       "      <td>4434</td>\n",
       "      <td>4434</td>\n",
       "      <td>4434</td>\n",
       "      <td>4434</td>\n",
       "      <td>4434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73.0</th>\n",
       "      <td>16253</td>\n",
       "      <td>16253</td>\n",
       "      <td>16253</td>\n",
       "      <td>16253</td>\n",
       "      <td>16253</td>\n",
       "      <td>16253</td>\n",
       "      <td>16253</td>\n",
       "      <td>16247</td>\n",
       "      <td>16253</td>\n",
       "      <td>16253</td>\n",
       "      <td>...</td>\n",
       "      <td>16131</td>\n",
       "      <td>16131</td>\n",
       "      <td>16253</td>\n",
       "      <td>16253</td>\n",
       "      <td>16253</td>\n",
       "      <td>16253</td>\n",
       "      <td>16253</td>\n",
       "      <td>16253</td>\n",
       "      <td>16253</td>\n",
       "      <td>16253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74.0</th>\n",
       "      <td>3004</td>\n",
       "      <td>3004</td>\n",
       "      <td>3004</td>\n",
       "      <td>3004</td>\n",
       "      <td>3004</td>\n",
       "      <td>3004</td>\n",
       "      <td>3004</td>\n",
       "      <td>3004</td>\n",
       "      <td>3004</td>\n",
       "      <td>3004</td>\n",
       "      <td>...</td>\n",
       "      <td>2986</td>\n",
       "      <td>2986</td>\n",
       "      <td>3004</td>\n",
       "      <td>3004</td>\n",
       "      <td>3004</td>\n",
       "      <td>3004</td>\n",
       "      <td>3004</td>\n",
       "      <td>3004</td>\n",
       "      <td>3004</td>\n",
       "      <td>3004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75.0</th>\n",
       "      <td>11274</td>\n",
       "      <td>11274</td>\n",
       "      <td>11274</td>\n",
       "      <td>11274</td>\n",
       "      <td>11274</td>\n",
       "      <td>11274</td>\n",
       "      <td>11274</td>\n",
       "      <td>11271</td>\n",
       "      <td>11274</td>\n",
       "      <td>11274</td>\n",
       "      <td>...</td>\n",
       "      <td>11195</td>\n",
       "      <td>11195</td>\n",
       "      <td>11274</td>\n",
       "      <td>11274</td>\n",
       "      <td>11274</td>\n",
       "      <td>11274</td>\n",
       "      <td>11274</td>\n",
       "      <td>11274</td>\n",
       "      <td>11274</td>\n",
       "      <td>11274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76.0</th>\n",
       "      <td>6817</td>\n",
       "      <td>6817</td>\n",
       "      <td>6817</td>\n",
       "      <td>6817</td>\n",
       "      <td>6817</td>\n",
       "      <td>6817</td>\n",
       "      <td>6817</td>\n",
       "      <td>6816</td>\n",
       "      <td>6817</td>\n",
       "      <td>6817</td>\n",
       "      <td>...</td>\n",
       "      <td>6693</td>\n",
       "      <td>6693</td>\n",
       "      <td>6817</td>\n",
       "      <td>6817</td>\n",
       "      <td>6817</td>\n",
       "      <td>6817</td>\n",
       "      <td>6817</td>\n",
       "      <td>6817</td>\n",
       "      <td>6817</td>\n",
       "      <td>6817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77.0</th>\n",
       "      <td>11587</td>\n",
       "      <td>11586</td>\n",
       "      <td>11587</td>\n",
       "      <td>11587</td>\n",
       "      <td>11587</td>\n",
       "      <td>11587</td>\n",
       "      <td>11587</td>\n",
       "      <td>11587</td>\n",
       "      <td>11587</td>\n",
       "      <td>11587</td>\n",
       "      <td>...</td>\n",
       "      <td>11529</td>\n",
       "      <td>11529</td>\n",
       "      <td>11587</td>\n",
       "      <td>11587</td>\n",
       "      <td>11587</td>\n",
       "      <td>11587</td>\n",
       "      <td>11587</td>\n",
       "      <td>11587</td>\n",
       "      <td>11587</td>\n",
       "      <td>11587</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   ID  Case Number   Date  Block   IUCR  Primary Type  \\\n",
       "Community Area                                                          \n",
       "1.0             20222        20222  20222  20222  20222         20222   \n",
       "2.0             16384        16384  16384  16384  16384         16384   \n",
       "3.0             17485        17485  17485  17485  17485         17485   \n",
       "4.0              7992         7992   7992   7992   7992          7992   \n",
       "5.0              6393         6393   6393   6393   6393          6393   \n",
       "6.0             20567        20567  20567  20567  20567         20567   \n",
       "7.0             13506        13506  13506  13506  13506         13506   \n",
       "8.0             30707        30707  30707  30707  30707         30707   \n",
       "9.0              1375         1375   1375   1375   1375          1375   \n",
       "10.0             5585         5585   5585   5585   5585          5585   \n",
       "11.0             5259         5259   5259   5259   5259          5259   \n",
       "12.0             2337         2337   2337   2337   2337          2337   \n",
       "13.0             4027         4027   4027   4027   4027          4027   \n",
       "14.0            12000        12000  12000  12000  12000         12000   \n",
       "15.0            16502        16502  16502  16502  16502         16502   \n",
       "16.0            14906        14905  14906  14906  14906         14906   \n",
       "17.0             8390         8390   8390   8390   8390          8390   \n",
       "18.0             3127         3127   3127   3127   3127          3127   \n",
       "19.0            25890        25890  25890  25890  25890         25890   \n",
       "20.0             8643         8643   8643   8643   8643          8643   \n",
       "21.0            11932        11932  11932  11932  11932         11932   \n",
       "22.0            24342        24342  24342  24342  24342         24342   \n",
       "23.0            46951        46951  46951  46951  46951         46951   \n",
       "24.0            32246        32246  32246  32246  32246         32246   \n",
       "25.0            95203        95203  95203  95203  95203         95203   \n",
       "26.0            26173        26173  26173  26173  26173         26173   \n",
       "27.0            26872        26872  26872  26872  26872         26872   \n",
       "28.0            28622        28621  28622  28622  28622         28622   \n",
       "29.0            41877        41877  41877  41877  41877         41877   \n",
       "30.0            23436        23436  23436  23436  23436         23436   \n",
       "...               ...          ...    ...    ...    ...           ...   \n",
       "48.0             7027         7027   7027   7027   7027          7027   \n",
       "49.0            39932        39932  39932  39932  39932         39932   \n",
       "50.0             5328         5328   5328   5328   5328          5328   \n",
       "51.0             9386         9386   9386   9386   9386          9386   \n",
       "52.0             7737         7737   7737   7737   7737          7737   \n",
       "53.0            24235        24235  24235  24235  24235         24235   \n",
       "54.0             6039         6039   6039   6039   6039          6039   \n",
       "55.0             3049         3049   3049   3049   3049          3049   \n",
       "56.0            10731        10731  10731  10731  10731         10731   \n",
       "57.0             4700         4700   4700   4700   4700          4700   \n",
       "58.0            13473        13473  13473  13473  13473         13473   \n",
       "59.0             5109         5109   5109   5109   5109          5109   \n",
       "60.0             8720         8720   8720   8720   8720          8720   \n",
       "61.0            30218        30218  30218  30218  30218         30218   \n",
       "62.0             5223         5223   5223   5223   5223          5223   \n",
       "63.0            13536        13536  13536  13536  13536         13536   \n",
       "64.0             5742         5742   5742   5742   5742          5742   \n",
       "65.0             8708         8708   8708   8708   8708          8708   \n",
       "66.0            38013        38013  38013  38013  38013         38013   \n",
       "67.0            44144        44144  44144  44144  44144         44144   \n",
       "68.0            39786        39786  39786  39786  39786         39786   \n",
       "69.0            35604        35604  35604  35604  35604         35604   \n",
       "70.0            12588        12588  12588  12588  12588         12588   \n",
       "71.0            42123        42123  42123  42123  42123         42123   \n",
       "72.0             4434         4434   4434   4434   4434          4434   \n",
       "73.0            16253        16253  16253  16253  16253         16253   \n",
       "74.0             3004         3004   3004   3004   3004          3004   \n",
       "75.0            11274        11274  11274  11274  11274         11274   \n",
       "76.0             6817         6817   6817   6817   6817          6817   \n",
       "77.0            11587        11586  11587  11587  11587         11587   \n",
       "\n",
       "                Description  Location Description  Arrest  Domestic  ...  \\\n",
       "Community Area                                                       ...   \n",
       "1.0                   20222                 20220   20222     20222  ...   \n",
       "2.0                   16384                 16382   16384     16384  ...   \n",
       "3.0                   17485                 17482   17485     17485  ...   \n",
       "4.0                    7992                  7991    7992      7992  ...   \n",
       "5.0                    6393                  6391    6393      6393  ...   \n",
       "6.0                   20567                 20566   20567     20567  ...   \n",
       "7.0                   13506                 13506   13506     13506  ...   \n",
       "8.0                   30707                 30701   30707     30707  ...   \n",
       "9.0                    1375                  1375    1375      1375  ...   \n",
       "10.0                   5585                  5585    5585      5585  ...   \n",
       "11.0                   5259                  5258    5259      5259  ...   \n",
       "12.0                   2337                  2337    2337      2337  ...   \n",
       "13.0                   4027                  4027    4027      4027  ...   \n",
       "14.0                  12000                 12000   12000     12000  ...   \n",
       "15.0                  16502                 16501   16502     16502  ...   \n",
       "16.0                  14906                 14904   14906     14906  ...   \n",
       "17.0                   8390                  8390    8390      8390  ...   \n",
       "18.0                   3127                  3126    3127      3127  ...   \n",
       "19.0                  25890                 25882   25890     25890  ...   \n",
       "20.0                   8643                  8642    8643      8643  ...   \n",
       "21.0                  11932                 11930   11932     11932  ...   \n",
       "22.0                  24342                 24339   24342     24342  ...   \n",
       "23.0                  46951                 46951   46951     46951  ...   \n",
       "24.0                  32246                 32241   32246     32246  ...   \n",
       "25.0                  95203                 95193   95203     95203  ...   \n",
       "26.0                  26173                 26171   26173     26173  ...   \n",
       "27.0                  26872                 26865   26872     26872  ...   \n",
       "28.0                  28622                 28615   28622     28622  ...   \n",
       "29.0                  41877                 41872   41877     41877  ...   \n",
       "30.0                  23436                 23435   23436     23436  ...   \n",
       "...                     ...                   ...     ...       ...  ...   \n",
       "48.0                   7027                  7026    7027      7027  ...   \n",
       "49.0                  39932                 39922   39932     39932  ...   \n",
       "50.0                   5328                  5325    5328      5328  ...   \n",
       "51.0                   9386                  9386    9386      9386  ...   \n",
       "52.0                   7737                  7737    7737      7737  ...   \n",
       "53.0                  24235                 24230   24235     24235  ...   \n",
       "54.0                   6039                  6039    6039      6039  ...   \n",
       "55.0                   3049                  3049    3049      3049  ...   \n",
       "56.0                  10731                 10730   10731     10731  ...   \n",
       "57.0                   4700                  4700    4700      4700  ...   \n",
       "58.0                  13473                 13473   13473     13473  ...   \n",
       "59.0                   5109                  5109    5109      5109  ...   \n",
       "60.0                   8720                  8720    8720      8720  ...   \n",
       "61.0                  30218                 30217   30218     30218  ...   \n",
       "62.0                   5223                  5223    5223      5223  ...   \n",
       "63.0                  13536                 13536   13536     13536  ...   \n",
       "64.0                   5742                  5741    5742      5742  ...   \n",
       "65.0                   8708                  8707    8708      8708  ...   \n",
       "66.0                  38013                 38008   38013     38013  ...   \n",
       "67.0                  44144                 44139   44144     44144  ...   \n",
       "68.0                  39786                 39784   39786     39786  ...   \n",
       "69.0                  35604                 35597   35604     35604  ...   \n",
       "70.0                  12588                 12586   12588     12588  ...   \n",
       "71.0                  42123                 42117   42123     42123  ...   \n",
       "72.0                   4434                  4432    4434      4434  ...   \n",
       "73.0                  16253                 16247   16253     16253  ...   \n",
       "74.0                   3004                  3004    3004      3004  ...   \n",
       "75.0                  11274                 11271   11274     11274  ...   \n",
       "76.0                   6817                  6816    6817      6817  ...   \n",
       "77.0                  11587                 11587   11587     11587  ...   \n",
       "\n",
       "                Longitude  Location  COMMUNITY AREA NAME  \\\n",
       "Community Area                                             \n",
       "1.0                 20073     20073                20222   \n",
       "2.0                 16268     16268                16384   \n",
       "3.0                 17383     17383                17485   \n",
       "4.0                  7945      7945                 7992   \n",
       "5.0                  6350      6350                 6393   \n",
       "6.0                 20432     20432                20567   \n",
       "7.0                 13398     13398                13506   \n",
       "8.0                 30468     30468                30707   \n",
       "9.0                  1356      1356                 1375   \n",
       "10.0                 5534      5534                 5585   \n",
       "11.0                 5228      5228                 5259   \n",
       "12.0                 2309      2309                 2337   \n",
       "13.0                 4004      4004                 4027   \n",
       "14.0                11922     11922                12000   \n",
       "15.0                16394     16394                16502   \n",
       "16.0                14807     14807                14906   \n",
       "17.0                 8328      8328                 8390   \n",
       "18.0                 3100      3100                 3127   \n",
       "19.0                25659     25659                25890   \n",
       "20.0                 8566      8566                 8643   \n",
       "21.0                11824     11824                11932   \n",
       "22.0                24084     24084                24342   \n",
       "23.0                46508     46508                46951   \n",
       "24.0                31986     31986                32246   \n",
       "25.0                94465     94465                95203   \n",
       "26.0                25959     25959                26173   \n",
       "27.0                26636     26636                26872   \n",
       "28.0                28401     28401                28622   \n",
       "29.0                41232     41232                41877   \n",
       "30.0                23232     23232                23436   \n",
       "...                   ...       ...                  ...   \n",
       "48.0                 6975      6975                 7027   \n",
       "49.0                39500     39500                39932   \n",
       "50.0                 5266      5266                 5328   \n",
       "51.0                 9331      9331                 9386   \n",
       "52.0                 7683      7683                 7737   \n",
       "53.0                24004     24004                24235   \n",
       "54.0                 5984      5984                 6039   \n",
       "55.0                 3027      3027                 3049   \n",
       "56.0                10648     10648                10731   \n",
       "57.0                 4670      4670                 4700   \n",
       "58.0                13372     13372                13473   \n",
       "59.0                 5070      5070                 5109   \n",
       "60.0                 8668      8668                 8720   \n",
       "61.0                30011     30011                30218   \n",
       "62.0                 5194      5194                 5223   \n",
       "63.0                13430     13430                13536   \n",
       "64.0                 5695      5695                 5742   \n",
       "65.0                 8653      8653                 8708   \n",
       "66.0                37769     37769                38013   \n",
       "67.0                43864     43864                44144   \n",
       "68.0                39548     39548                39786   \n",
       "69.0                35364     35364                35604   \n",
       "70.0                12500     12500                12588   \n",
       "71.0                41729     41729                42123   \n",
       "72.0                 4400      4400                 4434   \n",
       "73.0                16131     16131                16253   \n",
       "74.0                 2986      2986                 3004   \n",
       "75.0                11195     11195                11274   \n",
       "76.0                 6693      6693                 6817   \n",
       "77.0                11529     11529                11587   \n",
       "\n",
       "                PERCENT OF HOUSING CROWDED  PERCENT HOUSEHOLDS BELOW POVERTY  \\\n",
       "Community Area                                                                 \n",
       "1.0                                  20222                             20222   \n",
       "2.0                                  16384                             16384   \n",
       "3.0                                  17485                             17485   \n",
       "4.0                                   7992                              7992   \n",
       "5.0                                   6393                              6393   \n",
       "6.0                                  20567                             20567   \n",
       "7.0                                  13506                             13506   \n",
       "8.0                                  30707                             30707   \n",
       "9.0                                   1375                              1375   \n",
       "10.0                                  5585                              5585   \n",
       "11.0                                  5259                              5259   \n",
       "12.0                                  2337                              2337   \n",
       "13.0                                  4027                              4027   \n",
       "14.0                                 12000                             12000   \n",
       "15.0                                 16502                             16502   \n",
       "16.0                                 14906                             14906   \n",
       "17.0                                  8390                              8390   \n",
       "18.0                                  3127                              3127   \n",
       "19.0                                 25890                             25890   \n",
       "20.0                                  8643                              8643   \n",
       "21.0                                 11932                             11932   \n",
       "22.0                                 24342                             24342   \n",
       "23.0                                 46951                             46951   \n",
       "24.0                                 32246                             32246   \n",
       "25.0                                 95203                             95203   \n",
       "26.0                                 26173                             26173   \n",
       "27.0                                 26872                             26872   \n",
       "28.0                                 28622                             28622   \n",
       "29.0                                 41877                             41877   \n",
       "30.0                                 23436                             23436   \n",
       "...                                    ...                               ...   \n",
       "48.0                                  7027                              7027   \n",
       "49.0                                 39932                             39932   \n",
       "50.0                                  5328                              5328   \n",
       "51.0                                  9386                              9386   \n",
       "52.0                                  7737                              7737   \n",
       "53.0                                 24235                             24235   \n",
       "54.0                                  6039                              6039   \n",
       "55.0                                  3049                              3049   \n",
       "56.0                                 10731                             10731   \n",
       "57.0                                  4700                              4700   \n",
       "58.0                                 13473                             13473   \n",
       "59.0                                  5109                              5109   \n",
       "60.0                                  8720                              8720   \n",
       "61.0                                 30218                             30218   \n",
       "62.0                                  5223                              5223   \n",
       "63.0                                 13536                             13536   \n",
       "64.0                                  5742                              5742   \n",
       "65.0                                  8708                              8708   \n",
       "66.0                                 38013                             38013   \n",
       "67.0                                 44144                             44144   \n",
       "68.0                                 39786                             39786   \n",
       "69.0                                 35604                             35604   \n",
       "70.0                                 12588                             12588   \n",
       "71.0                                 42123                             42123   \n",
       "72.0                                  4434                              4434   \n",
       "73.0                                 16253                             16253   \n",
       "74.0                                  3004                              3004   \n",
       "75.0                                 11274                             11274   \n",
       "76.0                                  6817                              6817   \n",
       "77.0                                 11587                             11587   \n",
       "\n",
       "                PERCENT AGED 16+ UNEMPLOYED  \\\n",
       "Community Area                                \n",
       "1.0                                   20222   \n",
       "2.0                                   16384   \n",
       "3.0                                   17485   \n",
       "4.0                                    7992   \n",
       "5.0                                    6393   \n",
       "6.0                                   20567   \n",
       "7.0                                   13506   \n",
       "8.0                                   30707   \n",
       "9.0                                    1375   \n",
       "10.0                                   5585   \n",
       "11.0                                   5259   \n",
       "12.0                                   2337   \n",
       "13.0                                   4027   \n",
       "14.0                                  12000   \n",
       "15.0                                  16502   \n",
       "16.0                                  14906   \n",
       "17.0                                   8390   \n",
       "18.0                                   3127   \n",
       "19.0                                  25890   \n",
       "20.0                                   8643   \n",
       "21.0                                  11932   \n",
       "22.0                                  24342   \n",
       "23.0                                  46951   \n",
       "24.0                                  32246   \n",
       "25.0                                  95203   \n",
       "26.0                                  26173   \n",
       "27.0                                  26872   \n",
       "28.0                                  28622   \n",
       "29.0                                  41877   \n",
       "30.0                                  23436   \n",
       "...                                     ...   \n",
       "48.0                                   7027   \n",
       "49.0                                  39932   \n",
       "50.0                                   5328   \n",
       "51.0                                   9386   \n",
       "52.0                                   7737   \n",
       "53.0                                  24235   \n",
       "54.0                                   6039   \n",
       "55.0                                   3049   \n",
       "56.0                                  10731   \n",
       "57.0                                   4700   \n",
       "58.0                                  13473   \n",
       "59.0                                   5109   \n",
       "60.0                                   8720   \n",
       "61.0                                  30218   \n",
       "62.0                                   5223   \n",
       "63.0                                  13536   \n",
       "64.0                                   5742   \n",
       "65.0                                   8708   \n",
       "66.0                                  38013   \n",
       "67.0                                  44144   \n",
       "68.0                                  39786   \n",
       "69.0                                  35604   \n",
       "70.0                                  12588   \n",
       "71.0                                  42123   \n",
       "72.0                                   4434   \n",
       "73.0                                  16253   \n",
       "74.0                                   3004   \n",
       "75.0                                  11274   \n",
       "76.0                                   6817   \n",
       "77.0                                  11587   \n",
       "\n",
       "                PERCENT AGED 25+ WITHOUT HIGH SCHOOL DIPLOMA  \\\n",
       "Community Area                                                 \n",
       "1.0                                                    20222   \n",
       "2.0                                                    16384   \n",
       "3.0                                                    17485   \n",
       "4.0                                                     7992   \n",
       "5.0                                                     6393   \n",
       "6.0                                                    20567   \n",
       "7.0                                                    13506   \n",
       "8.0                                                    30707   \n",
       "9.0                                                     1375   \n",
       "10.0                                                    5585   \n",
       "11.0                                                    5259   \n",
       "12.0                                                    2337   \n",
       "13.0                                                    4027   \n",
       "14.0                                                   12000   \n",
       "15.0                                                   16502   \n",
       "16.0                                                   14906   \n",
       "17.0                                                    8390   \n",
       "18.0                                                    3127   \n",
       "19.0                                                   25890   \n",
       "20.0                                                    8643   \n",
       "21.0                                                   11932   \n",
       "22.0                                                   24342   \n",
       "23.0                                                   46951   \n",
       "24.0                                                   32246   \n",
       "25.0                                                   95203   \n",
       "26.0                                                   26173   \n",
       "27.0                                                   26872   \n",
       "28.0                                                   28622   \n",
       "29.0                                                   41877   \n",
       "30.0                                                   23436   \n",
       "...                                                      ...   \n",
       "48.0                                                    7027   \n",
       "49.0                                                   39932   \n",
       "50.0                                                    5328   \n",
       "51.0                                                    9386   \n",
       "52.0                                                    7737   \n",
       "53.0                                                   24235   \n",
       "54.0                                                    6039   \n",
       "55.0                                                    3049   \n",
       "56.0                                                   10731   \n",
       "57.0                                                    4700   \n",
       "58.0                                                   13473   \n",
       "59.0                                                    5109   \n",
       "60.0                                                    8720   \n",
       "61.0                                                   30218   \n",
       "62.0                                                    5223   \n",
       "63.0                                                   13536   \n",
       "64.0                                                    5742   \n",
       "65.0                                                    8708   \n",
       "66.0                                                   38013   \n",
       "67.0                                                   44144   \n",
       "68.0                                                   39786   \n",
       "69.0                                                   35604   \n",
       "70.0                                                   12588   \n",
       "71.0                                                   42123   \n",
       "72.0                                                    4434   \n",
       "73.0                                                   16253   \n",
       "74.0                                                    3004   \n",
       "75.0                                                   11274   \n",
       "76.0                                                    6817   \n",
       "77.0                                                   11587   \n",
       "\n",
       "                PERCENT AGED UNDER 18 OR OVER 64  PER CAPITA INCOME   \\\n",
       "Community Area                                                         \n",
       "1.0                                        20222               20222   \n",
       "2.0                                        16384               16384   \n",
       "3.0                                        17485               17485   \n",
       "4.0                                         7992                7992   \n",
       "5.0                                         6393                6393   \n",
       "6.0                                        20567               20567   \n",
       "7.0                                        13506               13506   \n",
       "8.0                                        30707               30707   \n",
       "9.0                                         1375                1375   \n",
       "10.0                                        5585                5585   \n",
       "11.0                                        5259                5259   \n",
       "12.0                                        2337                2337   \n",
       "13.0                                        4027                4027   \n",
       "14.0                                       12000               12000   \n",
       "15.0                                       16502               16502   \n",
       "16.0                                       14906               14906   \n",
       "17.0                                        8390                8390   \n",
       "18.0                                        3127                3127   \n",
       "19.0                                       25890               25890   \n",
       "20.0                                        8643                8643   \n",
       "21.0                                       11932               11932   \n",
       "22.0                                       24342               24342   \n",
       "23.0                                       46951               46951   \n",
       "24.0                                       32246               32246   \n",
       "25.0                                       95203               95203   \n",
       "26.0                                       26173               26173   \n",
       "27.0                                       26872               26872   \n",
       "28.0                                       28622               28622   \n",
       "29.0                                       41877               41877   \n",
       "30.0                                       23436               23436   \n",
       "...                                          ...                 ...   \n",
       "48.0                                        7027                7027   \n",
       "49.0                                       39932               39932   \n",
       "50.0                                        5328                5328   \n",
       "51.0                                        9386                9386   \n",
       "52.0                                        7737                7737   \n",
       "53.0                                       24235               24235   \n",
       "54.0                                        6039                6039   \n",
       "55.0                                        3049                3049   \n",
       "56.0                                       10731               10731   \n",
       "57.0                                        4700                4700   \n",
       "58.0                                       13473               13473   \n",
       "59.0                                        5109                5109   \n",
       "60.0                                        8720                8720   \n",
       "61.0                                       30218               30218   \n",
       "62.0                                        5223                5223   \n",
       "63.0                                       13536               13536   \n",
       "64.0                                        5742                5742   \n",
       "65.0                                        8708                8708   \n",
       "66.0                                       38013               38013   \n",
       "67.0                                       44144               44144   \n",
       "68.0                                       39786               39786   \n",
       "69.0                                       35604               35604   \n",
       "70.0                                       12588               12588   \n",
       "71.0                                       42123               42123   \n",
       "72.0                                        4434                4434   \n",
       "73.0                                       16253               16253   \n",
       "74.0                                        3004                3004   \n",
       "75.0                                       11274               11274   \n",
       "76.0                                        6817                6817   \n",
       "77.0                                       11587               11587   \n",
       "\n",
       "                HARDSHIP INDEX  \n",
       "Community Area                  \n",
       "1.0                      20222  \n",
       "2.0                      16384  \n",
       "3.0                      17485  \n",
       "4.0                       7992  \n",
       "5.0                       6393  \n",
       "6.0                      20567  \n",
       "7.0                      13506  \n",
       "8.0                      30707  \n",
       "9.0                       1375  \n",
       "10.0                      5585  \n",
       "11.0                      5259  \n",
       "12.0                      2337  \n",
       "13.0                      4027  \n",
       "14.0                     12000  \n",
       "15.0                     16502  \n",
       "16.0                     14906  \n",
       "17.0                      8390  \n",
       "18.0                      3127  \n",
       "19.0                     25890  \n",
       "20.0                      8643  \n",
       "21.0                     11932  \n",
       "22.0                     24342  \n",
       "23.0                     46951  \n",
       "24.0                     32246  \n",
       "25.0                     95203  \n",
       "26.0                     26173  \n",
       "27.0                     26872  \n",
       "28.0                     28622  \n",
       "29.0                     41877  \n",
       "30.0                     23436  \n",
       "...                        ...  \n",
       "48.0                      7027  \n",
       "49.0                     39932  \n",
       "50.0                      5328  \n",
       "51.0                      9386  \n",
       "52.0                      7737  \n",
       "53.0                     24235  \n",
       "54.0                      6039  \n",
       "55.0                      3049  \n",
       "56.0                     10731  \n",
       "57.0                      4700  \n",
       "58.0                     13473  \n",
       "59.0                      5109  \n",
       "60.0                      8720  \n",
       "61.0                     30218  \n",
       "62.0                      5223  \n",
       "63.0                     13536  \n",
       "64.0                      5742  \n",
       "65.0                      8708  \n",
       "66.0                     38013  \n",
       "67.0                     44144  \n",
       "68.0                     39786  \n",
       "69.0                     35604  \n",
       "70.0                     12588  \n",
       "71.0                     42123  \n",
       "72.0                      4434  \n",
       "73.0                     16253  \n",
       "74.0                      3004  \n",
       "75.0                     11274  \n",
       "76.0                      6817  \n",
       "77.0                     11587  \n",
       "\n",
       "[77 rows x 29 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_theft_by_area = no_theft.groupby(\"Community Area\")\n",
    "no_theft_count = no_theft_by_area.count()\n",
    "no_theft_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_1sampResult(statistic=Community Area\n",
       "1.0     -18.075757\n",
       "2.0     -12.717247\n",
       "3.0     -14.254433\n",
       "4.0      -1.000566\n",
       "5.0       1.231914\n",
       "6.0     -18.557437\n",
       "7.0      -8.699062\n",
       "8.0     -32.714627\n",
       "9.0       8.237908\n",
       "10.0      2.360021\n",
       "11.0      2.815174\n",
       "12.0      6.894790\n",
       "13.0      4.535258\n",
       "14.0     -6.596426\n",
       "15.0    -12.881995\n",
       "16.0    -10.653704\n",
       "17.0     -1.556243\n",
       "18.0      5.791814\n",
       "19.0    -25.989263\n",
       "20.0     -1.909475\n",
       "21.0     -6.501486\n",
       "22.0    -23.827988\n",
       "23.0    -55.394055\n",
       "24.0    -34.863337\n",
       "25.0   -122.762175\n",
       "26.0    -26.384380\n",
       "27.0    -27.360305\n",
       "28.0    -29.803607\n",
       "29.0    -48.309875\n",
       "30.0    -22.563056\n",
       "           ...    \n",
       "48.0      0.346740\n",
       "49.0    -45.594319\n",
       "50.0      2.718838\n",
       "51.0     -2.946831\n",
       "52.0     -0.644542\n",
       "53.0    -23.678598\n",
       "54.0      1.726159\n",
       "55.0      5.900715\n",
       "56.0     -4.824683\n",
       "57.0      3.595634\n",
       "58.0     -8.652988\n",
       "59.0      3.024599\n",
       "60.0     -2.016980\n",
       "61.0    -32.031899\n",
       "62.0      2.865436\n",
       "63.0     -8.740947\n",
       "64.0      2.140822\n",
       "65.0     -2.000226\n",
       "66.0    -42.915064\n",
       "67.0    -51.474998\n",
       "68.0    -45.390478\n",
       "69.0    -39.551684\n",
       "70.0     -7.417375\n",
       "71.0    -48.653333\n",
       "72.0      3.967016\n",
       "73.0    -12.534348\n",
       "74.0      5.963543\n",
       "75.0     -5.582805\n",
       "76.0      0.639937\n",
       "77.0     -6.019807\n",
       "Name: ID, Length: 77, dtype: float64, pvalue=array([3.05135896e-29, 1.66345588e-20, 3.57960078e-23, 3.20211703e-01,\n",
       "       2.21779250e-01, 5.95341045e-30, 5.03487342e-13, 1.55938872e-46,\n",
       "       3.85537357e-12, 2.08411408e-02, 6.20557824e-03, 1.38505071e-09,\n",
       "       2.11088192e-05, 5.00520308e-09, 8.49366978e-21, 9.75890683e-17,\n",
       "       1.23805053e-01, 1.48471258e-07, 1.47749813e-39, 5.99751310e-02,\n",
       "       7.51277605e-09, 5.31756130e-37, 3.38242789e-63, 1.67057776e-48,\n",
       "       3.80331354e-89, 5.25485738e-40, 4.31005357e-41, 1.11964701e-43,\n",
       "       8.36024843e-59, 2.02257221e-35, 8.73079645e-11, 7.19959254e-23,\n",
       "       9.32740875e-01, 8.01635924e-05, 1.39625480e-06, 1.89067981e-07,\n",
       "       8.21838247e-05, 1.82279638e-24, 9.41926090e-01, 4.78604385e-18,\n",
       "       7.98977309e-02, 5.15861743e-38, 7.47815925e-64, 3.24099287e-41,\n",
       "       3.74123010e-01, 1.24033297e-42, 6.19126376e-10, 7.29744614e-01,\n",
       "       5.87257895e-57, 8.11272066e-03, 4.26101341e-03, 5.21164654e-01,\n",
       "       8.10906546e-37, 8.83821989e-02, 9.45683276e-08, 7.06343946e-06,\n",
       "       5.72799345e-04, 6.17034568e-13, 3.39492508e-03, 4.72296934e-02,\n",
       "       6.95967294e-46, 5.38295207e-03, 4.18509876e-13, 3.54949271e-02,\n",
       "       4.90485532e-02, 4.94178481e-55, 7.74435041e-61, 8.15739737e-57,\n",
       "       1.87762247e-52, 1.42410306e-10, 4.96159218e-59, 1.63456439e-04,\n",
       "       3.52159794e-20, 7.28103079e-08, 3.50024112e-07, 5.24138327e-01,\n",
       "       5.75683426e-08]))"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#t-test\n",
    "a=stats.ttest_1samp(theft_count[\"ID\"]/1000, no_theft_count[\"ID\"]/1000)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p is Ttest_1sampResult(statistic=Community Area\n",
      "1.0     -18.075757\n",
      "2.0     -12.717247\n",
      "3.0     -14.254433\n",
      "4.0      -1.000566\n",
      "5.0       1.231914\n",
      "6.0     -18.557437\n",
      "7.0      -8.699062\n",
      "8.0     -32.714627\n",
      "9.0       8.237908\n",
      "10.0      2.360021\n",
      "11.0      2.815174\n",
      "12.0      6.894790\n",
      "13.0      4.535258\n",
      "14.0     -6.596426\n",
      "15.0    -12.881995\n",
      "16.0    -10.653704\n",
      "17.0     -1.556243\n",
      "18.0      5.791814\n",
      "19.0    -25.989263\n",
      "20.0     -1.909475\n",
      "21.0     -6.501486\n",
      "22.0    -23.827988\n",
      "23.0    -55.394055\n",
      "24.0    -34.863337\n",
      "25.0   -122.762175\n",
      "26.0    -26.384380\n",
      "27.0    -27.360305\n",
      "28.0    -29.803607\n",
      "29.0    -48.309875\n",
      "30.0    -22.563056\n",
      "           ...    \n",
      "48.0      0.346740\n",
      "49.0    -45.594319\n",
      "50.0      2.718838\n",
      "51.0     -2.946831\n",
      "52.0     -0.644542\n",
      "53.0    -23.678598\n",
      "54.0      1.726159\n",
      "55.0      5.900715\n",
      "56.0     -4.824683\n",
      "57.0      3.595634\n",
      "58.0     -8.652988\n",
      "59.0      3.024599\n",
      "60.0     -2.016980\n",
      "61.0    -32.031899\n",
      "62.0      2.865436\n",
      "63.0     -8.740947\n",
      "64.0      2.140822\n",
      "65.0     -2.000226\n",
      "66.0    -42.915064\n",
      "67.0    -51.474998\n",
      "68.0    -45.390478\n",
      "69.0    -39.551684\n",
      "70.0     -7.417375\n",
      "71.0    -48.653333\n",
      "72.0      3.967016\n",
      "73.0    -12.534348\n",
      "74.0      5.963543\n",
      "75.0     -5.582805\n",
      "76.0      0.639937\n",
      "77.0     -6.019807\n",
      "Name: ID, Length: 77, dtype: float64, pvalue=array([3.05135896e-29, 1.66345588e-20, 3.57960078e-23, 3.20211703e-01,\n",
      "       2.21779250e-01, 5.95341045e-30, 5.03487342e-13, 1.55938872e-46,\n",
      "       3.85537357e-12, 2.08411408e-02, 6.20557824e-03, 1.38505071e-09,\n",
      "       2.11088192e-05, 5.00520308e-09, 8.49366978e-21, 9.75890683e-17,\n",
      "       1.23805053e-01, 1.48471258e-07, 1.47749813e-39, 5.99751310e-02,\n",
      "       7.51277605e-09, 5.31756130e-37, 3.38242789e-63, 1.67057776e-48,\n",
      "       3.80331354e-89, 5.25485738e-40, 4.31005357e-41, 1.11964701e-43,\n",
      "       8.36024843e-59, 2.02257221e-35, 8.73079645e-11, 7.19959254e-23,\n",
      "       9.32740875e-01, 8.01635924e-05, 1.39625480e-06, 1.89067981e-07,\n",
      "       8.21838247e-05, 1.82279638e-24, 9.41926090e-01, 4.78604385e-18,\n",
      "       7.98977309e-02, 5.15861743e-38, 7.47815925e-64, 3.24099287e-41,\n",
      "       3.74123010e-01, 1.24033297e-42, 6.19126376e-10, 7.29744614e-01,\n",
      "       5.87257895e-57, 8.11272066e-03, 4.26101341e-03, 5.21164654e-01,\n",
      "       8.10906546e-37, 8.83821989e-02, 9.45683276e-08, 7.06343946e-06,\n",
      "       5.72799345e-04, 6.17034568e-13, 3.39492508e-03, 4.72296934e-02,\n",
      "       6.95967294e-46, 5.38295207e-03, 4.18509876e-13, 3.54949271e-02,\n",
      "       4.90485532e-02, 4.94178481e-55, 7.74435041e-61, 8.15739737e-57,\n",
      "       1.87762247e-52, 1.42410306e-10, 4.96159218e-59, 1.63456439e-04,\n",
      "       3.52159794e-20, 7.28103079e-08, 3.50024112e-07, 5.24138327e-01,\n",
      "       5.75683426e-08])).\n",
      "The difference is significant.\n",
      "p is Ttest_1sampResult(statistic=Community Area\n",
      "1.0     -18.075757\n",
      "2.0     -12.717247\n",
      "3.0     -14.254433\n",
      "4.0      -1.000566\n",
      "5.0       1.231914\n",
      "6.0     -18.557437\n",
      "7.0      -8.699062\n",
      "8.0     -32.714627\n",
      "9.0       8.237908\n",
      "10.0      2.360021\n",
      "11.0      2.815174\n",
      "12.0      6.894790\n",
      "13.0      4.535258\n",
      "14.0     -6.596426\n",
      "15.0    -12.881995\n",
      "16.0    -10.653704\n",
      "17.0     -1.556243\n",
      "18.0      5.791814\n",
      "19.0    -25.989263\n",
      "20.0     -1.909475\n",
      "21.0     -6.501486\n",
      "22.0    -23.827988\n",
      "23.0    -55.394055\n",
      "24.0    -34.863337\n",
      "25.0   -122.762175\n",
      "26.0    -26.384380\n",
      "27.0    -27.360305\n",
      "28.0    -29.803607\n",
      "29.0    -48.309875\n",
      "30.0    -22.563056\n",
      "           ...    \n",
      "48.0      0.346740\n",
      "49.0    -45.594319\n",
      "50.0      2.718838\n",
      "51.0     -2.946831\n",
      "52.0     -0.644542\n",
      "53.0    -23.678598\n",
      "54.0      1.726159\n",
      "55.0      5.900715\n",
      "56.0     -4.824683\n",
      "57.0      3.595634\n",
      "58.0     -8.652988\n",
      "59.0      3.024599\n",
      "60.0     -2.016980\n",
      "61.0    -32.031899\n",
      "62.0      2.865436\n",
      "63.0     -8.740947\n",
      "64.0      2.140822\n",
      "65.0     -2.000226\n",
      "66.0    -42.915064\n",
      "67.0    -51.474998\n",
      "68.0    -45.390478\n",
      "69.0    -39.551684\n",
      "70.0     -7.417375\n",
      "71.0    -48.653333\n",
      "72.0      3.967016\n",
      "73.0    -12.534348\n",
      "74.0      5.963543\n",
      "75.0     -5.582805\n",
      "76.0      0.639937\n",
      "77.0     -6.019807\n",
      "Name: ID, Length: 77, dtype: float64, pvalue=array([3.05135896e-29, 1.66345588e-20, 3.57960078e-23, 3.20211703e-01,\n",
      "       2.21779250e-01, 5.95341045e-30, 5.03487342e-13, 1.55938872e-46,\n",
      "       3.85537357e-12, 2.08411408e-02, 6.20557824e-03, 1.38505071e-09,\n",
      "       2.11088192e-05, 5.00520308e-09, 8.49366978e-21, 9.75890683e-17,\n",
      "       1.23805053e-01, 1.48471258e-07, 1.47749813e-39, 5.99751310e-02,\n",
      "       7.51277605e-09, 5.31756130e-37, 3.38242789e-63, 1.67057776e-48,\n",
      "       3.80331354e-89, 5.25485738e-40, 4.31005357e-41, 1.11964701e-43,\n",
      "       8.36024843e-59, 2.02257221e-35, 8.73079645e-11, 7.19959254e-23,\n",
      "       9.32740875e-01, 8.01635924e-05, 1.39625480e-06, 1.89067981e-07,\n",
      "       8.21838247e-05, 1.82279638e-24, 9.41926090e-01, 4.78604385e-18,\n",
      "       7.98977309e-02, 5.15861743e-38, 7.47815925e-64, 3.24099287e-41,\n",
      "       3.74123010e-01, 1.24033297e-42, 6.19126376e-10, 7.29744614e-01,\n",
      "       5.87257895e-57, 8.11272066e-03, 4.26101341e-03, 5.21164654e-01,\n",
      "       8.10906546e-37, 8.83821989e-02, 9.45683276e-08, 7.06343946e-06,\n",
      "       5.72799345e-04, 6.17034568e-13, 3.39492508e-03, 4.72296934e-02,\n",
      "       6.95967294e-46, 5.38295207e-03, 4.18509876e-13, 3.54949271e-02,\n",
      "       4.90485532e-02, 4.94178481e-55, 7.74435041e-61, 8.15739737e-57,\n",
      "       1.87762247e-52, 1.42410306e-10, 4.96159218e-59, 1.63456439e-04,\n",
      "       3.52159794e-20, 7.28103079e-08, 3.50024112e-07, 5.24138327e-01,\n",
      "       5.75683426e-08])).\n",
      "The difference is significant.\n"
     ]
    }
   ],
   "source": [
    "for a1 in a:\n",
    "    print(\"p is {}.\".format(p))\n",
    "    if a1[1] < 0.05:\n",
    "        print(\"The difference is significant.\")\n",
    "    else:\n",
    "        print(\"The difference is not significant.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06621804206886148"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[1].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_axis = theft_count[\"ID\"]/1000\n",
    "fake = no_theft_count[\"ID\"]/1000\n",
    "(slope, intercept, _, _, _) = linregress(x_axis, fake)\n",
    "fit = slope * x_axis + intercept\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "fig.suptitle(\"Crime vs Theft Data\", fontsize=16, fontweight=\"bold\")\n",
    "\n",
    "\n",
    "\n",
    "ax.set_xlabel(\"Number of Thefts per Area (thousands)\")\n",
    "ax.set_ylabel(\"Number of Crimes per Area (thousands)\")\n",
    "\n",
    "ax.plot(x_axis, fake, linewidth=0, marker='o')\n",
    "ax.plot(x_axis, fit, 'b--')\n",
    "plt.savefig(\"output_data/Crime_VS_Theft.png\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Exporting data to excel for more analysis\n",
    "writer = ExcelWriter('output_data/Chicago Crime.xlsx')\n",
    "no_theft_count.to_excel(writer,'Sheet1',index=True)\n",
    "theft_count.to_excel(writer,'Sheet2',index=True)\n",
    "writer.save()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Community Area\n",
       "1.0      2741\n",
       "2.0      2355\n",
       "3.0      2636\n",
       "4.0      1289\n",
       "5.0       907\n",
       "6.0      3118\n",
       "7.0      1872\n",
       "8.0      4583\n",
       "9.0       184\n",
       "10.0      895\n",
       "11.0      828\n",
       "12.0      339\n",
       "13.0      541\n",
       "14.0     1687\n",
       "15.0     2352\n",
       "16.0     1862\n",
       "17.0     1187\n",
       "18.0      351\n",
       "19.0     3655\n",
       "20.0     1319\n",
       "21.0     1530\n",
       "22.0     3061\n",
       "23.0     7477\n",
       "24.0     4102\n",
       "25.0    14884\n",
       "26.0     5234\n",
       "27.0     4728\n",
       "28.0     4460\n",
       "29.0     7021\n",
       "30.0     3535\n",
       "        ...  \n",
       "48.0     1029\n",
       "49.0     5619\n",
       "50.0      850\n",
       "51.0     1612\n",
       "52.0     1098\n",
       "53.0     3400\n",
       "54.0     1151\n",
       "55.0      453\n",
       "56.0     1409\n",
       "57.0      652\n",
       "58.0     2256\n",
       "59.0      702\n",
       "60.0     1255\n",
       "61.0     3811\n",
       "62.0      691\n",
       "63.0     1911\n",
       "64.0      712\n",
       "65.0     1211\n",
       "66.0     4931\n",
       "67.0     5991\n",
       "68.0     5458\n",
       "69.0     5140\n",
       "70.0     1812\n",
       "71.0     5970\n",
       "72.0      601\n",
       "73.0     2267\n",
       "74.0      474\n",
       "75.0     1488\n",
       "76.0     1450\n",
       "77.0     1477\n",
       "Name: ID, Length: 77, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding 2014 data to test prediction\n",
    "crime14=df.loc[df[\"Year\"]==2014]\n",
    "test_data=pd.merge(crime14,census,on=\"Community Area\")\n",
    "theft_test =test_data.loc[test_data[\"Primary Type\"]==\"THEFT\"]\n",
    "not_theft_test=test_data.loc[test_data[\"Primary Type\"]!=\"THEFT\"]\n",
    "ttg=theft_test.groupby(\"Community Area\")\n",
    "nttg=not_theft_test.groupby(\"Community Area\")\n",
    "ttg_count=ttg.count()\n",
    "nttg_count=nttg.count()\n",
    "ttg_list=ttg_count[\"ID\"]\n",
    "nttg_list=nttg_count[\"ID\"]\n",
    "nttg_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Community Area\n",
       "1.0     1993.278752\n",
       "2.0     1985.784175\n",
       "3.0     2212.495142\n",
       "4.0     1144.517861\n",
       "5.0     1191.358970\n",
       "6.0     4837.470879\n",
       "7.0     4161.085268\n",
       "8.0     8262.492755\n",
       "9.0      115.887112\n",
       "10.0     674.233129\n",
       "11.0     756.673480\n",
       "12.0     232.053061\n",
       "13.0     625.518375\n",
       "14.0    1344.997807\n",
       "15.0    1948.311288\n",
       "16.0    1860.250003\n",
       "17.0     719.200593\n",
       "18.0     329.482568\n",
       "19.0    2472.931707\n",
       "20.0     889.702229\n",
       "21.0    1577.329706\n",
       "22.0    3825.702929\n",
       "23.0    3282.346067\n",
       "24.0    6077.823440\n",
       "25.0    7323.796934\n",
       "26.0    1849.008137\n",
       "27.0    2036.372572\n",
       "28.0    6450.678666\n",
       "29.0    3173.674694\n",
       "30.0    2319.292870\n",
       "           ...     \n",
       "48.0    1054.582932\n",
       "49.0    3312.324376\n",
       "50.0     702.337794\n",
       "51.0    1208.221769\n",
       "52.0     548.698957\n",
       "53.0    1826.524405\n",
       "54.0     441.901229\n",
       "55.0     327.608923\n",
       "56.0     983.384447\n",
       "57.0     762.294413\n",
       "58.0    1232.579145\n",
       "59.0     670.485840\n",
       "60.0    1028.351911\n",
       "61.0    2725.873694\n",
       "62.0     612.402865\n",
       "63.0    1341.250518\n",
       "64.0     543.078024\n",
       "65.0    1680.380146\n",
       "66.0    2916.985418\n",
       "67.0    3031.277723\n",
       "68.0    3104.349853\n",
       "69.0    3027.530435\n",
       "70.0    1427.438158\n",
       "71.0    3791.977330\n",
       "72.0     569.309045\n",
       "73.0    1749.704987\n",
       "74.0     370.702743\n",
       "75.0    1241.947367\n",
       "76.0    1060.203865\n",
       "77.0    1603.560727\n",
       "Name: ID, Length: 77, dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction = ttg_list*slope+intercept\n",
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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