Get_Complaints.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Data Management: 311 complaints Dataset\n",
    "\n",
    "## This notebook:\n",
    "**is currently under development**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__  import print_function, division\n",
    "import pylab as pl\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "%pylab inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/cusp/yc3300/PUIdata\n"
     ]
    }
   ],
   "source": [
    "PUIdata = os.getenv('PUIDATA')\n",
    "print(PUIdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2017-12-06 17:00:30--  https://data.cityofnewyork.us/api/views/erm2-nwe9/rows.csv?accessType=DOWNLOAD\n",
      "Resolving data.cityofnewyork.us... 52.206.68.26\n",
      "Connecting to data.cityofnewyork.us|52.206.68.26|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: unspecified [text/csv]\n",
      "Saving to: “rows.csv?accessType=DOWNLOAD.2”\n",
      "\n",
      "    [                     <=>             ] 1,131,396,625 3.86M/s              "
     ]
    }
   ],
   "source": [
    "!wget https://nycopendata.socrata.com/api/views/erm2-nwe9/rows.csv?accessType=DOWNLOAD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.system('mv rows.csv?accessType=DOWNLOAD ' + PUIdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "!mv $PUIDATA/rows.csv?accessType=DOWNLOAD $PUIDATA/311data.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/rh/anaconda/root/envs/PUI2016_Python2/lib/python2.7/site-packages/IPython/core/interactiveshell.py:2717: DtypeWarning: Columns (8,17,39,40,41,42,43,44,45,46,47,48,49) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unique Key</th>\n",
       "      <th>Created Date</th>\n",
       "      <th>Closed Date</th>\n",
       "      <th>Agency</th>\n",
       "      <th>Agency Name</th>\n",
       "      <th>Complaint Type</th>\n",
       "      <th>Descriptor</th>\n",
       "      <th>Location Type</th>\n",
       "      <th>Incident Zip</th>\n",
       "      <th>Incident Address</th>\n",
       "      <th>...</th>\n",
       "      <th>Bridge Highway Name</th>\n",
       "      <th>Bridge Highway Direction</th>\n",
       "      <th>Road Ramp</th>\n",
       "      <th>Bridge Highway Segment</th>\n",
       "      <th>Garage Lot Name</th>\n",
       "      <th>Ferry Direction</th>\n",
       "      <th>Ferry Terminal Name</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>32159845</td>\n",
       "      <td>12/08/2015 10:50:06 PM</td>\n",
       "      <td>12/08/2015 11:01:27 PM</td>\n",
       "      <td>NYPD</td>\n",
       "      <td>New York City Police Department</td>\n",
       "      <td>Noise - Residential</td>\n",
       "      <td>Loud Music/Party</td>\n",
       "      <td>Residential Building/House</td>\n",
       "      <td>10024</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.789615</td>\n",
       "      <td>-73.973609</td>\n",
       "      <td>(40.789614901346546, -73.9736085726657)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32159846</td>\n",
       "      <td>12/08/2015 01:46:00 PM</td>\n",
       "      <td>12/10/2015 01:30:00 PM</td>\n",
       "      <td>DOT</td>\n",
       "      <td>Department of Transportation</td>\n",
       "      <td>Traffic Signal Condition</td>\n",
       "      <td>Vehicle Signal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11691</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.593321</td>\n",
       "      <td>-73.772713</td>\n",
       "      <td>(40.593320648977645, -73.77271336808198)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32159847</td>\n",
       "      <td>12/08/2015 04:23:24 PM</td>\n",
       "      <td>12/08/2015 08:28:35 PM</td>\n",
       "      <td>NYPD</td>\n",
       "      <td>New York City Police Department</td>\n",
       "      <td>Noise - Residential</td>\n",
       "      <td>Loud Music/Party</td>\n",
       "      <td>Residential Building/House</td>\n",
       "      <td>10453</td>\n",
       "      <td>1660 ANDREWS AVENUE</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.848560</td>\n",
       "      <td>-73.919118</td>\n",
       "      <td>(40.84855984187605, -73.91911817593288)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32159848</td>\n",
       "      <td>12/08/2015 07:44:26 PM</td>\n",
       "      <td>12/08/2015 11:20:53 PM</td>\n",
       "      <td>NYPD</td>\n",
       "      <td>New York City Police Department</td>\n",
       "      <td>Noise - Residential</td>\n",
       "      <td>Loud Music/Party</td>\n",
       "      <td>Residential Building/House</td>\n",
       "      <td>10468</td>\n",
       "      <td>2461 DAVIDSON AVENUE</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.863187</td>\n",
       "      <td>-73.901949</td>\n",
       "      <td>(40.86318677236925, -73.90194938707823)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32159849</td>\n",
       "      <td>12/08/2015 10:43:28 AM</td>\n",
       "      <td>12/08/2015 11:21:39 AM</td>\n",
       "      <td>NYPD</td>\n",
       "      <td>New York City Police Department</td>\n",
       "      <td>Noise - Street/Sidewalk</td>\n",
       "      <td>Loud Music/Party</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "      <td>10036</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.755807</td>\n",
       "      <td>-73.986403</td>\n",
       "      <td>(40.75580743192962, -73.98640311324542)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unique Key            Created Date             Closed Date Agency  \\\n",
       "0    32159845  12/08/2015 10:50:06 PM  12/08/2015 11:01:27 PM   NYPD   \n",
       "1    32159846  12/08/2015 01:46:00 PM  12/10/2015 01:30:00 PM    DOT   \n",
       "2    32159847  12/08/2015 04:23:24 PM  12/08/2015 08:28:35 PM   NYPD   \n",
       "3    32159848  12/08/2015 07:44:26 PM  12/08/2015 11:20:53 PM   NYPD   \n",
       "4    32159849  12/08/2015 10:43:28 AM  12/08/2015 11:21:39 AM   NYPD   \n",
       "\n",
       "                       Agency Name            Complaint Type  \\\n",
       "0  New York City Police Department       Noise - Residential   \n",
       "1     Department of Transportation  Traffic Signal Condition   \n",
       "2  New York City Police Department       Noise - Residential   \n",
       "3  New York City Police Department       Noise - Residential   \n",
       "4  New York City Police Department   Noise - Street/Sidewalk   \n",
       "\n",
       "         Descriptor               Location Type Incident Zip  \\\n",
       "0  Loud Music/Party  Residential Building/House        10024   \n",
       "1    Vehicle Signal                         NaN        11691   \n",
       "2  Loud Music/Party  Residential Building/House        10453   \n",
       "3  Loud Music/Party  Residential Building/House        10468   \n",
       "4  Loud Music/Party             Street/Sidewalk        10036   \n",
       "\n",
       "       Incident Address                    ...                     \\\n",
       "0                   NaN                    ...                      \n",
       "1                   NaN                    ...                      \n",
       "2   1660 ANDREWS AVENUE                    ...                      \n",
       "3  2461 DAVIDSON AVENUE                    ...                      \n",
       "4                   NaN                    ...                      \n",
       "\n",
       "  Bridge Highway Name Bridge Highway Direction Road Ramp  \\\n",
       "0                 NaN                      NaN       NaN   \n",
       "1                 NaN                      NaN       NaN   \n",
       "2                 NaN                      NaN       NaN   \n",
       "3                 NaN                      NaN       NaN   \n",
       "4                 NaN                      NaN       NaN   \n",
       "\n",
       "  Bridge Highway Segment Garage Lot Name Ferry Direction Ferry Terminal Name  \\\n",
       "0                    NaN             NaN             NaN                 NaN   \n",
       "1                    NaN             NaN             NaN                 NaN   \n",
       "2                    NaN             NaN             NaN                 NaN   \n",
       "3                    NaN             NaN             NaN                 NaN   \n",
       "4                    NaN             NaN             NaN                 NaN   \n",
       "\n",
       "    Latitude  Longitude                                  Location  \n",
       "0  40.789615 -73.973609   (40.789614901346546, -73.9736085726657)  \n",
       "1  40.593321 -73.772713  (40.593320648977645, -73.77271336808198)  \n",
       "2  40.848560 -73.919118   (40.84855984187605, -73.91911817593288)  \n",
       "3  40.863187 -73.901949   (40.86318677236925, -73.90194938707823)  \n",
       "4  40.755807 -73.986403   (40.75580743192962, -73.98640311324542)  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(PUIdata + \"/311data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'Unique Key', u'Created Date', u'Closed Date', u'Agency',\n",
       "       u'Agency Name', u'Complaint Type', u'Descriptor', u'Location Type',\n",
       "       u'Incident Zip', u'Incident Address', u'Street Name', u'Cross Street 1',\n",
       "       u'Cross Street 2', u'Intersection Street 1', u'Intersection Street 2',\n",
       "       u'Address Type', u'City', u'Landmark', u'Facility Type', u'Status',\n",
       "       u'Due Date', u'Resolution Description',\n",
       "       u'Resolution Action Updated Date', u'Community Board', u'Borough',\n",
       "       u'X Coordinate (State Plane)', u'Y Coordinate (State Plane)',\n",
       "       u'Park Facility Name', u'Park Borough', u'School Name',\n",
       "       u'School Number', u'School Region', u'School Code',\n",
       "       u'School Phone Number', u'School Address', u'School City',\n",
       "       u'School State', u'School Zip', u'School Not Found',\n",
       "       u'School or Citywide Complaint', u'Vehicle Type',\n",
       "       u'Taxi Company Borough', u'Taxi Pick Up Location',\n",
       "       u'Bridge Highway Name', u'Bridge Highway Direction', u'Road Ramp',\n",
       "       u'Bridge Highway Segment', u'Garage Lot Name', u'Ferry Direction',\n",
       "       u'Ferry Terminal Name', u'Latitude', u'Longitude', u'Location'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Complaint Type</th>\n",
       "      <th>Incident Zip</th>\n",
       "      <th>Location Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Noise - Residential</td>\n",
       "      <td>10024</td>\n",
       "      <td>Residential Building/House</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Traffic Signal Condition</td>\n",
       "      <td>11691</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Noise - Residential</td>\n",
       "      <td>10453</td>\n",
       "      <td>Residential Building/House</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Noise - Residential</td>\n",
       "      <td>10468</td>\n",
       "      <td>Residential Building/House</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Noise - Street/Sidewalk</td>\n",
       "      <td>10036</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Complaint Type Incident Zip               Location Type\n",
       "0       Noise - Residential        10024  Residential Building/House\n",
       "1  Traffic Signal Condition        11691                         NaN\n",
       "2       Noise - Residential        10453  Residential Building/House\n",
       "3       Noise - Residential        10468  Residential Building/House\n",
       "4   Noise - Street/Sidewalk        10036             Street/Sidewalk"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(df, columns = [['Complaint Type','Incident Zip','Location Type']])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#df['closetime'] = pd.to_datetime(df['Closed Date'])-pd.to_datetime(df['Created Date'])\n",
    "# df['closetime'] = (pd.to_datetime(df['Closed Date'])-pd.to_datetime(df['Created Date'])).dt.seconds / 3600. #ignore this, this is not calculated for the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.dropna(subset = ['Incident Zip'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['Incident Zip'] = pd.to_numeric(df['Incident Zip'],errors = 'coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Complaint Type\n",
       "AGENCY                                    8\n",
       "APPLIANCE                             85202\n",
       "ATF                                     429\n",
       "Adopt-A-Basket                          503\n",
       "Advocate - Lien                           1\n",
       "Advocate - Other                         79\n",
       "Advocate - RPIE                           4\n",
       "Advocate-Business Tax                     3\n",
       "Advocate-Co-opCondo Abatement            41\n",
       "Advocate-Commercial Exemptions           10\n",
       "Advocate-Personal Exemptions            378\n",
       "Advocate-Prop Class Incorrect             6\n",
       "Advocate-Prop Refunds/Credits            31\n",
       "Advocate-Property Value                  23\n",
       "Advocate-SCRIE/DRIE                      13\n",
       "Advocate-UBT                              5\n",
       "Agency Issues                             1\n",
       "Air Quality                           57407\n",
       "Alzheimer's Care                       2097\n",
       "Animal Abuse                          38295\n",
       "Animal Facility - No Permit             541\n",
       "Animal in a Park                      13056\n",
       "Asbestos                              15732\n",
       "Asbestos/Garbage Nuisance                 1\n",
       "BEST/Site Safety                       6000\n",
       "Beach/Pool/Sauna Complaint             1356\n",
       "Benefit Card Replacement                  1\n",
       "Bereavement Support Group               200\n",
       "Bike Rack Condition                     876\n",
       "Bike/Roller/Skate Chronic              3332\n",
       "                                      ...  \n",
       "Taxi Compliment                           2\n",
       "Taxi Report                            8211\n",
       "Taxpayer Advocate Inquiry               148\n",
       "Teaching/Learning/Instruction           364\n",
       "Traffic                               30389\n",
       "Traffic Signal Condition             197772\n",
       "Traffic/Illegal Parking               14047\n",
       "Trans Fat                                28\n",
       "Transportation Provider Complaint        97\n",
       "Trapping Pigeon                           2\n",
       "UNSANITARY CONDITION                 293792\n",
       "Unleashed Dog                          6012\n",
       "Unlicensed Dog                            1\n",
       "Unsanitary Animal Facility              681\n",
       "Unsanitary Animal Pvt Property        17904\n",
       "Unsanitary Pigeon Condition            4972\n",
       "Urinating in Public                    3492\n",
       "Utility Program                        1287\n",
       "VACANT APARTMENT                          6\n",
       "Vacant Lot                            14331\n",
       "Vending                               32156\n",
       "Violation of Park Rules               14051\n",
       "WATER LEAK                           124323\n",
       "Water Conservation                    29361\n",
       "Water Maintenance                        13\n",
       "Water Quality                          9147\n",
       "Water System                         485255\n",
       "Weatherization                         1155\n",
       "Window Guard                           2658\n",
       "X-Ray Machine/Equipment                  92\n",
       "Name: Incident Zip, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Complaint Type')['Incident Zip'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Because there are too many comlaint types, I will only be looking into \"Traffic complaint\" on different zip code distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Complaint Type</th>\n",
       "      <th>Incident Zip</th>\n",
       "      <th>Location Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>532</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>10022.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>533</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>10016.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>10463.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>11101.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>11414.0</td>\n",
       "      <td>Highway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>10454.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2032</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>10065.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2054</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>11432.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2229</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>10022.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2230</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>10305.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2841</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>11432.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2858</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>11215.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2928</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>11412.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2929</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>10128.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2962</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>11378.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3124</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>10314.0</td>\n",
       "      <td>Highway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3246</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>10016.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3256</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>10017.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3258</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>11101.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3259</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>11205.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3278</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>11205.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3281</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>10022.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3292</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>11375.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4493</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>11432.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4543</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>10022.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4615</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>10022.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4616</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>10029.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4636</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>10022.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4637</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>10031.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4729</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>10016.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16417492</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>10075.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16417508</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>11432.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16417509</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>11205.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16418212</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>11236.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16418314</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>10021.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16418315</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>11230.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16418337</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>11432.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16418382</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>10459.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16418383</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>10021.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16418941</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>11432.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16418988</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>10016.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16418989</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>10075.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16419050</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>11434.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16419752</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>11432.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16419861</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>11236.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16421087</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>11432.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16421350</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>11419.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16422082</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>10017.0</td>\n",
       "      <td>Roadway Tunnel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16422084</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>10009.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16422156</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>11213.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16422980</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>11215.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16422992</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>10022.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16423717</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>10022.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16423718</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>11101.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16423824</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>10465.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16424501</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>10016.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16424502</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>10021.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16425152</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>10016.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16425153</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>10001.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16425156</th>\n",
       "      <td>Traffic</td>\n",
       "      <td>10312.0</td>\n",
       "      <td>Street/Sidewalk</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30389 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Complaint Type  Incident Zip    Location Type\n",
       "532             Traffic       10022.0  Street/Sidewalk\n",
       "533             Traffic       10016.0  Street/Sidewalk\n",
       "677             Traffic       10463.0  Street/Sidewalk\n",
       "1307            Traffic       11101.0  Street/Sidewalk\n",
       "1456            Traffic       11414.0          Highway\n",
       "2020            Traffic       10454.0  Street/Sidewalk\n",
       "2032            Traffic       10065.0  Street/Sidewalk\n",
       "2054            Traffic       11432.0  Street/Sidewalk\n",
       "2229            Traffic       10022.0  Street/Sidewalk\n",
       "2230            Traffic       10305.0  Street/Sidewalk\n",
       "2841            Traffic       11432.0  Street/Sidewalk\n",
       "2858            Traffic       11215.0  Street/Sidewalk\n",
       "2928            Traffic       11412.0  Street/Sidewalk\n",
       "2929            Traffic       10128.0  Street/Sidewalk\n",
       "2962            Traffic       11378.0  Street/Sidewalk\n",
       "3124            Traffic       10314.0          Highway\n",
       "3246            Traffic       10016.0  Street/Sidewalk\n",
       "3256            Traffic       10017.0  Street/Sidewalk\n",
       "3258            Traffic       11101.0  Street/Sidewalk\n",
       "3259            Traffic       11205.0  Street/Sidewalk\n",
       "3278            Traffic       11205.0  Street/Sidewalk\n",
       "3281            Traffic       10022.0  Street/Sidewalk\n",
       "3292            Traffic       11375.0  Street/Sidewalk\n",
       "4493            Traffic       11432.0  Street/Sidewalk\n",
       "4543            Traffic       10022.0  Street/Sidewalk\n",
       "4615            Traffic       10022.0  Street/Sidewalk\n",
       "4616            Traffic       10029.0  Street/Sidewalk\n",
       "4636            Traffic       10022.0  Street/Sidewalk\n",
       "4637            Traffic       10031.0  Street/Sidewalk\n",
       "4729            Traffic       10016.0  Street/Sidewalk\n",
       "...                 ...           ...              ...\n",
       "16417492        Traffic       10075.0  Street/Sidewalk\n",
       "16417508        Traffic       11432.0  Street/Sidewalk\n",
       "16417509        Traffic       11205.0  Street/Sidewalk\n",
       "16418212        Traffic       11236.0  Street/Sidewalk\n",
       "16418314        Traffic       10021.0  Street/Sidewalk\n",
       "16418315        Traffic       11230.0  Street/Sidewalk\n",
       "16418337        Traffic       11432.0  Street/Sidewalk\n",
       "16418382        Traffic       10459.0  Street/Sidewalk\n",
       "16418383        Traffic       10021.0  Street/Sidewalk\n",
       "16418941        Traffic       11432.0  Street/Sidewalk\n",
       "16418988        Traffic       10016.0  Street/Sidewalk\n",
       "16418989        Traffic       10075.0  Street/Sidewalk\n",
       "16419050        Traffic       11434.0  Street/Sidewalk\n",
       "16419752        Traffic       11432.0  Street/Sidewalk\n",
       "16419861        Traffic       11236.0  Street/Sidewalk\n",
       "16421087        Traffic       11432.0  Street/Sidewalk\n",
       "16421350        Traffic       11419.0  Street/Sidewalk\n",
       "16422082        Traffic       10017.0   Roadway Tunnel\n",
       "16422084        Traffic       10009.0  Street/Sidewalk\n",
       "16422156        Traffic       11213.0  Street/Sidewalk\n",
       "16422980        Traffic       11215.0  Street/Sidewalk\n",
       "16422992        Traffic       10022.0  Street/Sidewalk\n",
       "16423717        Traffic       10022.0  Street/Sidewalk\n",
       "16423718        Traffic       11101.0  Street/Sidewalk\n",
       "16423824        Traffic       10465.0  Street/Sidewalk\n",
       "16424501        Traffic       10016.0  Street/Sidewalk\n",
       "16424502        Traffic       10021.0  Street/Sidewalk\n",
       "16425152        Traffic       10016.0  Street/Sidewalk\n",
       "16425153        Traffic       10001.0  Street/Sidewalk\n",
       "16425156        Traffic       10312.0  Street/Sidewalk\n",
       "\n",
       "[30389 rows x 3 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df['Complaint Type'] == 'Traffic']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Incident Zip</th>\n",
       "      <th>Complaint Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10001.0</td>\n",
       "      <td>363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10002.0</td>\n",
       "      <td>353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10003.0</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10004.0</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10005.0</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10006.0</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10007.0</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10009.0</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10010.0</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10011.0</td>\n",
       "      <td>264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10012.0</td>\n",
       "      <td>391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10013.0</td>\n",
       "      <td>1774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10014.0</td>\n",
       "      <td>380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>10016.0</td>\n",
       "      <td>729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10017.0</td>\n",
       "      <td>260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10018.0</td>\n",
       "      <td>470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>10019.0</td>\n",
       "      <td>427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>10020.0</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>10021.0</td>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10022.0</td>\n",
       "      <td>656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>10023.0</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>10024.0</td>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>10025.0</td>\n",
       "      <td>251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>10026.0</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>10027.0</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>10028.0</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>10029.0</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>10030.0</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>10031.0</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>11385.0</td>\n",
       "      <td>251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>11411.0</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>11412.0</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>11413.0</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>11414.0</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>11415.0</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>11416.0</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>11417.0</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>11418.0</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>11419.0</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>11420.0</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>11421.0</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>11422.0</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>11423.0</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>11426.0</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>11427.0</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>11428.0</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>11429.0</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>11430.0</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>11432.0</td>\n",
       "      <td>879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>11433.0</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>11434.0</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>11435.0</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>11436.0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>11691.0</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>11692.0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>11693.0</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>11694.0</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>11695.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>11697.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>195 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Incident Zip  Complaint Type\n",
       "0         10000.0              20\n",
       "1         10001.0             363\n",
       "2         10002.0             353\n",
       "3         10003.0             183\n",
       "4         10004.0             176\n",
       "5         10005.0              31\n",
       "6         10006.0              80\n",
       "7         10007.0              87\n",
       "8         10009.0              88\n",
       "9         10010.0             182\n",
       "10        10011.0             264\n",
       "11        10012.0             391\n",
       "12        10013.0            1774\n",
       "13        10014.0             380\n",
       "14        10016.0             729\n",
       "15        10017.0             260\n",
       "16        10018.0             470\n",
       "17        10019.0             427\n",
       "18        10020.0              17\n",
       "19        10021.0             320\n",
       "20        10022.0             656\n",
       "21        10023.0             285\n",
       "22        10024.0             198\n",
       "23        10025.0             251\n",
       "24        10026.0              61\n",
       "25        10027.0             156\n",
       "26        10028.0             210\n",
       "27        10029.0             181\n",
       "28        10030.0              25\n",
       "29        10031.0              43\n",
       "..            ...             ...\n",
       "165       11385.0             251\n",
       "166       11411.0              42\n",
       "167       11412.0              79\n",
       "168       11413.0             113\n",
       "169       11414.0              58\n",
       "170       11415.0              56\n",
       "171       11416.0              48\n",
       "172       11417.0              96\n",
       "173       11418.0              70\n",
       "174       11419.0              47\n",
       "175       11420.0             158\n",
       "176       11421.0              59\n",
       "177       11422.0              99\n",
       "178       11423.0              69\n",
       "179       11426.0              59\n",
       "180       11427.0              53\n",
       "181       11428.0              29\n",
       "182       11429.0              46\n",
       "183       11430.0              50\n",
       "184       11432.0             879\n",
       "185       11433.0              86\n",
       "186       11434.0             139\n",
       "187       11435.0             122\n",
       "188       11436.0              40\n",
       "189       11691.0              52\n",
       "190       11692.0              18\n",
       "191       11693.0              23\n",
       "192       11694.0              42\n",
       "193       11695.0               5\n",
       "194       11697.0               8\n",
       "\n",
       "[195 rows x 2 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.groupby('Incident Zip')['Complaint Type'].count().reset_index() #The Complaint Type will be the count of complaint on traffic within that zipcode area\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "100   194    0   194    0     0    194      0 --:--:-- --:--:-- --:--:--   832\n"
     ]
    }
   ],
   "source": [
    "import shapely\n",
    "from shapely import geometry\n",
    "import collections\n",
    "import geopandas as gp\n",
    "from fiona.crs import from_epsg\n",
    "    \n",
    "!curl -O https://data.cityofnewyork.us/download/i8iw-xf4u/application%2Fzip\n",
    "os.system(PUIdata + 'unzip -d application\\%2Fzip')\n",
    "zipsNYC = gp.GeoDataFrame.from_file(PUIdata + \"/ZIP_CODE_040114.shp\")\n",
    "zipsNYC.crs = from_epsg(2263) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "zipsNYC['ZIP'] = pd.to_numeric(zipsNYC.ZIPCODE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ZIPCODE</th>\n",
       "      <th>BLDGZIP</th>\n",
       "      <th>PO_NAME</th>\n",
       "      <th>POPULATION</th>\n",
       "      <th>AREA</th>\n",
       "      <th>STATE</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>ST_FIPS</th>\n",
       "      <th>CTY_FIPS</th>\n",
       "      <th>URL</th>\n",
       "      <th>SHAPE_AREA</th>\n",
       "      <th>SHAPE_LEN</th>\n",
       "      <th>geometry</th>\n",
       "      <th>ZIP</th>\n",
       "      <th>Incident Zip</th>\n",
       "      <th>Complaint Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11436</td>\n",
       "      <td>0</td>\n",
       "      <td>Jamaica</td>\n",
       "      <td>18681.0</td>\n",
       "      <td>2.269930e+07</td>\n",
       "      <td>NY</td>\n",
       "      <td>Queens</td>\n",
       "      <td>36</td>\n",
       "      <td>081</td>\n",
       "      <td>http://www.usps.com/</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>POLYGON ((1038098.251871482 188138.3800067157,...</td>\n",
       "      <td>11436</td>\n",
       "      <td>11436.0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11213</td>\n",
       "      <td>0</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>62426.0</td>\n",
       "      <td>2.963100e+07</td>\n",
       "      <td>NY</td>\n",
       "      <td>Kings</td>\n",
       "      <td>36</td>\n",
       "      <td>047</td>\n",
       "      <td>http://www.usps.com/</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>POLYGON ((1001613.712964058 186926.4395172149,...</td>\n",
       "      <td>11213</td>\n",
       "      <td>11213.0</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11212</td>\n",
       "      <td>0</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>83866.0</td>\n",
       "      <td>4.197210e+07</td>\n",
       "      <td>NY</td>\n",
       "      <td>Kings</td>\n",
       "      <td>36</td>\n",
       "      <td>047</td>\n",
       "      <td>http://www.usps.com/</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>POLYGON ((1011174.275535807 183696.33770971, 1...</td>\n",
       "      <td>11212</td>\n",
       "      <td>11212.0</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11225</td>\n",
       "      <td>0</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>56527.0</td>\n",
       "      <td>2.369863e+07</td>\n",
       "      <td>NY</td>\n",
       "      <td>Kings</td>\n",
       "      <td>36</td>\n",
       "      <td>047</td>\n",
       "      <td>http://www.usps.com/</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>POLYGON ((995908.3654508889 183617.6128015518,...</td>\n",
       "      <td>11225</td>\n",
       "      <td>11225.0</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11218</td>\n",
       "      <td>0</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>72280.0</td>\n",
       "      <td>3.686880e+07</td>\n",
       "      <td>NY</td>\n",
       "      <td>Kings</td>\n",
       "      <td>36</td>\n",
       "      <td>047</td>\n",
       "      <td>http://www.usps.com/</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>POLYGON ((991997.1134308875 176307.4958601296,...</td>\n",
       "      <td>11218</td>\n",
       "      <td>11218.0</td>\n",
       "      <td>248</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ZIPCODE BLDGZIP   PO_NAME  POPULATION          AREA STATE  COUNTY ST_FIPS  \\\n",
       "0   11436       0   Jamaica     18681.0  2.269930e+07    NY  Queens      36   \n",
       "1   11213       0  Brooklyn     62426.0  2.963100e+07    NY   Kings      36   \n",
       "2   11212       0  Brooklyn     83866.0  4.197210e+07    NY   Kings      36   \n",
       "3   11225       0  Brooklyn     56527.0  2.369863e+07    NY   Kings      36   \n",
       "4   11218       0  Brooklyn     72280.0  3.686880e+07    NY   Kings      36   \n",
       "\n",
       "  CTY_FIPS                   URL  SHAPE_AREA  SHAPE_LEN  \\\n",
       "0      081  http://www.usps.com/         0.0        0.0   \n",
       "1      047  http://www.usps.com/         0.0        0.0   \n",
       "2      047  http://www.usps.com/         0.0        0.0   \n",
       "3      047  http://www.usps.com/         0.0        0.0   \n",
       "4      047  http://www.usps.com/         0.0        0.0   \n",
       "\n",
       "                                            geometry    ZIP  Incident Zip  \\\n",
       "0  POLYGON ((1038098.251871482 188138.3800067157,...  11436       11436.0   \n",
       "1  POLYGON ((1001613.712964058 186926.4395172149,...  11213       11213.0   \n",
       "2  POLYGON ((1011174.275535807 183696.33770971, 1...  11212       11212.0   \n",
       "3  POLYGON ((995908.3654508889 183617.6128015518,...  11225       11225.0   \n",
       "4  POLYGON ((991997.1134308875 176307.4958601296,...  11218       11218.0   \n",
       "\n",
       "   Complaint Type  \n",
       "0              40  \n",
       "1              96  \n",
       "2              85  \n",
       "3             105  \n",
       "4             248  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = zipsNYC.merge(df1, left_on='ZIP', right_on='Incident Zip')\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f818b095150>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nM2bMYMGCBbXkjY8/eRhVfeRqLhiFzmAwGAyGZkBuSSUdW4b5DNOhZSh5JZWn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\ns6dfZanKc+bMmTVkcWXw4MHk5eVx7Ng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9dq5Q8YZdFJ95FuFyF5ajEXviwClGY07uQvIH\nS0ZMrNVvYaRGBjgCSf3ZE7PsKiY5oDM68t8XlRzoxDdK6bFCOcLNNadOY9fpV2Vtt3PxVVyzqBKp\n+5H24DKrYyJwyBbCSyn56Ec/yvz58/niF7+Y15je85738OCDD5JMJtm9ezfbt2/nzDNHnG/k2muv\nZdmyZQC89NJLzJ07d+j+++67Dyklq1atIhwODy1n7nfbbbfx5ptv8uabbxYcIB26R26/rVu30tPT\nwznnnDN07f7776exsZGGhgZ++MMfcsstt4wI5vaP+d577wXg3nvv5Zprrsn7s0w2FdApiqIcJ/re\nXIPoGf8s136h084g1Z+edehviSBSB/KpBS48Y7TbRoi2DKCvfBVPdPSx6cb4q0YITRuWYHcyeGI9\nJPtz70eTjoUQAkuO/FymzL44Jg8JvBx0NE8RH734BHYuvpr2qZkPkbRPnceu06/m1vOmI+w4mh1H\nw0LDRmrerEuAK1as4Pe//z3Lli0bSuuxdGk6KcVjjz3GtGnTWLlyJVdddRXvete7ADjxxBO54YYb\nWLBgAZdffjl33nnn0BLmlVdeyd696RoBt99+O4888ggnnXQSX/va1/jVr3411Kauro45c+bw8Y9/\nnLvuuivrzyWT2tpavvjFL/K73/2OadOmsWnTpqH3HnrooYwB3QMPPMCNN96Y95Loxz72saF0Lbff\nfjvPPfcc9fX1PPfcc9x+++0T9lnGSxxveXoXL14sD82ToyiK8o+g7Zm/0v3VzPmzCiVDYeqfXE68\n/Q2Cyd00/PYFkn9/CYqCFH/yZioXVZPP9+FAYw/BTbn/Tt7S5+Xhb/92XGM2fB5u/3bupcLxii48\nk+C07Icc4gRx170L6VjI3UsROEjSAWcytIDAwFvD2m+xFzK/Pj3r6eACIdAGExI7uh/d7UdKyQsb\ndvK5P75N3donmb3mbwT7O4iEKti5+Cp2nX41P7tpIRecUIVweRFCx7GSgEQzfId9T5cy0ubNm5k/\nf3iibiHEWinl4vH2rfbQKYqiHCe8eh++JWcSf2Vk6ohCabPqMUJhks09pGyN5PMv4333ZVS/fwlu\nPb9lTTNh49v6Vu6GQF1pOiA76bJzOfOCeaxbtYPVDz9X0JgN3+jpKyaSjMZJV6IcnY8I5s4ncKrP\nR8y4FCsZwe5vxle9CMOMwcDoPxeBjdT8OLgBidAHT6qacS6uL+avn1nMb1dU8fgZ76bf0QlpNtcs\nquQn501nZlXl0MlWAM3wpvtQwdxxTwV0iqIoxwvDQ3LnngnpqviGf8ZOJfA5vXS3RAl+5EZqLjmB\ndNnu/CS3NhKw80uX4bYTXP/Vf6IuZKI7vVx6diW7102nY2f+ud9cbiPvtuPhbt2DrK9C5CiPYQgT\np/UFrOnvwl9eRyTZjxnrwVs8lYhnFsHk7oz3CSTCjuIIN7o3fdJUSgdhxwCYWerlG++u5xvvHl4R\nw9F8w4I52H/SUgVz/wjUHjpFUZTjgJSS2Ntv4vT0jb+zOfOofNfVxDq2IQTENu6i8qLs9VwPFe9O\nEGjdUdA99cEYupNeZtSkw7WfuoqqefkfvjA87tyNJoA70U+sLb/Tq5qQpDq3I4QgWLMIb/FUNJcb\nLdUzrJ1gZOoLIU3seA/SsXFSsaxhmUTkVRFCOXIme4ubCugURVGOA9FNq5D9vdS/fzGBdy4ZV1/h\nD96K0HVEbzrFiCscxNDyn5mTUsLmreMaA0BRcZCPf+zsvOu9Hq4ZOgDRsCv/tmZ/+n+FQHO5iXU1\njshL5yFOV+/AsC99gUwfaJAS4SSzPiPXoQflyJJS0tXVlbWqxXipJVdFUZRjnJSS+IqlFActREpS\nUw3Rj15NyhT0LF+Ptbsx/85mz6X6uhsYaFxDkHQgUnrmbApZao009RIcyJ4ANx9SCATpeq+vBf0k\nI7Gs7XXP4QvofH1txLsT+Epzf0EL58Cys2MlsTo3YkkdlziQ7LdGa6ClEzo7fSPP6UqyrppK9MFD\nECqgO5p5vV6mTZs2af2rgE5RFOUYF317BVZfHx7tQM6zQKqHAKBdeApteQR0MhTGOPciqj78SZKR\nDvwDG4eCCI8r/2DOTtl4tm8s9CNk5B3owBEahp3kpMvOYc2jz2dtb3hH1pudTPbuZiidk0fLA4FW\npOVN3FNOhaZnh7UwhE2tvnNM44i4phA64Yox3ascP9SSq6IoyjHMsUyclX8BM/Phg5DdjWtObV59\nzbz9m4TmnUiqexeaGNt+n9iOfRhmfEz3HsplJ4mF0slk556Sey+dcRiXXAH8HQ2k4mbOdo5+YG9b\naOZZeEJV2MIgRvbSWvnS7In5eSvHNhXQKYqiHMMiyx/BiPcSLsocgAmgdMlJOfvR5p+Mu7iE/m3P\nYkQyn77MJdmfItC0ZUz3jmZ/5YeAP/eCkn6YAzqBJLkjd+UIj9lBtGP47JvpKsUz651EjBpsOb6v\nYq/Tj2Pld5pYOX6pgE5RFOUYlexoxvfmUkx/MZ7U6CWmwnY3rtm1o74vTjuL2T+4k4GWDZDswSOy\nb8AfjbmjcUJLbyW8Jfj70wFTyszdr+sw7qHbz9+6g1TMytrGECZ2f/PQayEEgVnnY/jDhOZehqh/\nH0k5fLnYlEbegZ4mJJHdy0lGJq5KiHLsUQGdoijKMSr5yiNojoWeY4kz2yydLK9izs9/C4bA6d5K\nQGQ/eDAaM2Hj75iYHHj7adIaKuU1MJA7yHQZh39buObYWOs341iZA04pIeKeTnDm2URaN9Hx+nPs\nued/6Fl9IPmzyxPALls07L5kaB5xX34pWyypg+5Bdx2etC3K0UkdilAURTkGpbpa8TWswRE6nkTu\n3HNhp4eu2hk43T2Uvu9d9L2wGv9JcxEnX4zu99O7bRlFYgAhICVdpKQHkAgJfi2Ws8xXoqmL4ATO\nzkXCNQT7WoZe72vsyHmP7joyX2m+vn1EN4cJnjRj2PWoVo7uxBG+CqxkFF/XKlru/hvOmjXEgOin\nv8jMT34OgGD1AvojewmmmnCkgFg7eMvyen6yZBGhmpMn5LNIKUn07kXoLryhqgnpUzk8VECnKIpy\njJFSklz6v3gdh3i4mkDf3pz3CCRlF56ClbIJXv0hQqIfxxPAe92NxNq3o6W60DSIOn68IkFQO5A4\nN+F4sB0NnxYnU3EEaTt4msd2QnM0+iEVJho35O7fZegTOoZCePftQi6YhtAPLHy57V6S/lo0TSfR\ntpmuv7yBc1Ct8egffoX5wQ9jhNJlxIKzLya69W8YNWcT8AZJ9DRDjvzFKekmODX3Hsl8SWmjtzyP\nS9j0B+YRqj1nwvpWJpcK6BRFUY4xsa1r8LdvJxKeRrCvOfcNg8JOD9LQiK99Bv+H/h92Ikqipxmn\nYz0GKZKOm4A2csnVq6WXOy2pE3O8CCkxtBRukd47Ft0XIZCMTMyHGyQPSbwW6+3PeY/mOnIBnW6b\nxLsT+CsOnGg1hIUR34HVbbLnjyuwlr047B4x0E/3yuVUvetqADRNR4TrcPvDaC4PnvBU6Mz+3FSg\nDu9B06dWYoDYvk2Eas8a0+fQNBeW8OIiSjC6hUhrmGD1gjH1pRxeKqBTFEU5hkgpkSsfwTR8+Af2\nFXSvQCKkjau7CS0QRnh9yG2P4SGJKVy4RfYUHC5hExTRwXGkZ/OEY6IXUDUhH6bLiy8yfIn1/Bvf\nyePf/z0AJ1+5hPhAjO3L1w1rI4QgbkpMW5C0IWlB0pQMxG36+lP09cUZ6Ikw0NlLrKsHadsIXcfl\n8RCsLCVcXkxVdZhplV6mhiVuvbBEvXZXHxwU0Jkxk7bl24j89XlET3fGe6y+4RUjglMXDv1ec3mQ\nkqzL3cIdGPq9Y5kkdj9PwO7BcRajaWMLcB3hYn92Y6d/D6iA7pigAjpFUZRjyMCrjxPoTicKLnSG\nDsDRdMTln0ZoGtFtzxIYPNHqJvtJzUMJAQERo2tTK51/e5PQwloqi020MR61i/tL8cR60JCY3hBG\nZHiliZMqLULf+ThuQ6fSiOFoLpzrT0PDRnNsNGmzqdniFz97iWhr7lQih+rb3UALsGn/59N1wrNm\nUjGrhuppZVRXeJheIvAZmaOrlC3pbuwg1pcisa+b5PZGnI2bEKlk1hqsVvfoU3BCd5HEg4fMB0JM\n6UJGWpDyJIQQRLcvJeD0pGMxx4KxBnTeKoin92UGrX30N7w25hk/5fBRAZ2iKMoxwjFTuN94CoCk\nN0TgoEMD+YpXzyc0cz6RvRsIOJlnjQrR9dcVsKeB/j0N9JWU4lkwF0/Yy5TSVEGlqBzdQ9JfisuM\nITIUMRdArXdwWdcBnJGziQtrNDovP5Plv318jJ/mAGnb9O7YRe+OXWw/6HqwZirlc2ZQUlFMMp6i\nu7WTgX0dRPe1UTK7jsv8B/b+5fPp7f7Rl5KFEJieKXhSI08PR/zzKKo9G8/gzzgV7cFj94FIB9vx\n7kYClfX5ftzh5PDgPhjdRP+uFKG688fWn3JYqIBOURTlGBFZ8wyBZDrfnK27SQRK8Ua78gochlTX\nY8b70HveHvd4Oje0It8+0I/o6Sa1YhUpoH/qNMrOqKc8lF/CW3eiD8OMYWsGltuX8zDAaE6f42eF\nYeCYuSs4jEWkZS+Rlr00ZHgvFYtBHgmQD+b09476XqxzN0ZqZE3cmFZCUe3ZwwJmd6CEfnc1QTM9\nY+skcu85HI1whyAx/Jor3kqidy/e4qlj7leZXCqgUxRFOQZIKdHfXjb02h9NL9WZho9koAxvpBOX\nlRjtdgASRZUEl1xHZNeLBA/9xi6QY1p03vvkqMGk2NtM9+PN9J26CE9ZEVOrnFGXY1PuAO5UOoLT\nHRMjMUDSG84rHcuhijww7axTaXxlde7GE8yKJ4Bg1jbS5ULMnA26DkKgF5eO3l+siyDDcwzGKSI4\n/9rMN7iDMBjHymQPib5WvOHqQj4CUkrEwIFKISnphhnvxFdUWdCMq3L4qYBOURTlGBB5Yxn+/rYR\n1w0zjtHbjINGJDwNX6R9RMqP/cRFtxDvbiCYahr3eJqefAvRmnvJ137jTWJA+5UXMaVs5KxZPFCO\n7fIOBXQA3nh6H1gsWIkn2o0uC9vfd/6SOu5f8Xr65MZh5A4GcrYxLnsP9d/9cV79+Srm4vS/Payu\nrtRG/9r2VS4gtWsXbpHCk9yHEbggr+ccyu1EQKT36FnlpxNU+eiOCapShKIoylEu2daIe/n9Wdto\nOAT7mpEIIuEaHKHhiAOb4qPV8/HOOQXRviZLL/mJ7Bsg/vjTBd3T98Jqdm6O0x01kAcFWkLKjAc7\nBOCPtJMMlBQ8vrpywdzLxhbMjEdpde7Axzt/Yc42+xm+EDH/8H1wfqeH/q2Zf/ZWcgANO32vMIlu\nfwbHsfN+HoC0LZKu9Kxh0lVKcMq8gu5XjhwV0CmKohylHDNF3/P3Ix78z5zLqfu57CTBvhZMw0/S\nX0o0VI1E4L74n4g2v4mX7GXCcpFSsvd3zyLMworBi3gMe806Oh99kW3P7KSxCfr0Eryx0euPJn3F\n+CO5K0RkcsUF1bjD4THdO1YV/tylt9yVhc12BWvPJoFv2DUhMwdpnnA1zvTL0sukQMDpJLblCQaa\n3iCyb0tez5OOhctO79N0W73Dgm/l6KYCOkVRlKNQvHEL8Xu/RvDNv6FbueuYHsqTiuCLduAy48Rm\nnopnSi2ugW3jHlfbq7uRGzaMqw/R0UbixRWYyezBgunOvh8tm5BHcN77Lxnz/WNRFc+956/3iT8z\nsG1z3n1qmo5Rd2W6HNh+oZmjt/WF0Q9KQeOXvfj73kRzefN6nu724ZSfDoBbpIh3NeQ9VuXIUgGd\noijKUSj10gPYCCKhqZiGL/cNGURCNRiJfjwX3kisYweecR6ESEVT9N7/xLj6OFjIV9gsX6HOm+uh\nbP4Jk/qM/TS3m0As98lSZ8ULJNtH7oXMxvCFiLvKgXS6kmD1iaO2FS43DsPzzwkBjpn/zKy/sp4E\n6QTJRmD0QxvK0UUFdIqiKEch4fET6NtLsH8vhhknFqwkWcCMVayoikB/C7HZZ+Mur4GO9eMeU/PD\nqxB9o6fZKITntJNxmyPLjE0koQmuvO5UxpztuBAFLE22/+CbWNHC8rJII4QjBcGZZ2Y9bZrobsIS\nnhHX9e436d/5EsmBHLXEYLB/QRIvhu/wLlsrY6cCOkVRlKOQLB6+18pI5RcASCDuL0M3Ezi6QeDi\nmxjYsxofA+MaT+eGVlJ/f2FcfQxxG1Sdmkc+M+mM+1G1pYL5V1w47n5ycUyTgUBxXm2FP4ArkPtE\n7MH8004n5pmRtZxXvKcFb9sL+BhZV9dDAiOxFyvek/NZjmNjyDimp7CUJ8qRpQI6RVGUo1Fy+BJZ\n0leMJzXyizoT2/CiWwniCy7G1iS+SH4b4kcz0NJH5//8MWMFh7Eoue6deFPZA0yJwD2GPHSZXHHe\nFDwlhZ+WLVSjuyivdiU3faTgvl2eAKH67HsC7WT2n6mHBHb39qxtAFKRTnThQJYUKcrRRwV0iqIo\nRyPjkGWzPIMpAQT7WrAMH8Hz30+qaWX6y/ngrmxn2OlF23SI7hsg1jlyn1Xvri5a/uu3iMj4Zvj2\nMxbMpdydu694UeWw3HTjEXDDkhsunpC+stm9dWdep0L1cJhkVydSShyrsBx7Wfv1BLP+Z2JLjUDd\nRTn78YaqiPjn4Y3twszjoIdydFABnaIoylHINevkYa8FkpQ79zKdM/jXur3wHaTaG9FefpZY14FA\nzTFt4q9tIL5yA5E3dpJYvhax7AX8b65C7hmecLhzQyv7vnPPhO2bk7rOlHPmIMgd9BycQ28inDPH\nTcVJCya0z0PF9rXRHa7I2a7zsx9h979+mp61ryEmcH+fr2QaMVflqO/HjUp0tz+vvoIzz8QSXpK7\nny84l51yZKiATlEU5SjknT78dGagvxWXGScSnoalZ853lvAVY/pC9FfOJXDOu7FeeRhvpBP3utUM\nNHQTW7mBxJrN+Prb8fW3EWjbhSfajRjcq+bv2EN09WbivUkGWvrp/NHvEMnxnYw9WMl1l+JL5Z7x\ncYSOLzL65n0HDbvA5UAhBFdeczJCn9hA8VADeaYHkW+sZmDNKhIFnnjNxTtzCZbM/BmNypMzXs9E\n03Tcs69AVCyaqKEpk0wFdIqiKEchzRfEEcP/itZkuhqEkJJIeBq2OBDUxIqq0G0TT7wXrWoWyeZt\n+AeTyep2iuCWtfj69uHra836XH93M95VLxNZ8RYiVXj+u9EYJ8yh3J/fEmq8qBLdyZzSJOkNYXqC\nxIumFDyGGcWC+ncuKfi+QlijVrcFWVYB809CW5LeCxdf+xq+KRN78MDwhUl4Z4wcl9QxfKGC+nJ5\niwhU1GU9iKEcPdSOR0VRlKNQfPdGPKOc8tQdk2BfM6bhI+6vQreS+AcOzPS4TziD1NbVjExekb/Y\nxp3juHs4qWlMWTIXLY/ZOQDNGh7M2ZpByhvC0Q280S50x8ST7CcSriHQ15IlhBquP8Gk13fNdi5X\ndHUge7qY+cwq9j10P6XveNfkDEL3IGU6/9x+Gg6aK3clC+XYpWboFEVRjkL2qw/nbGOYcYJ9LXij\nB5YnTXcQ74x56C1jP9lqurxYO3aP+f5Dlb7vXXkttUI6eLNdHmzNANLLrylfGNvlxRdpR3fMobbB\nvhai4Wl59dsWgd/9ZhXbnn258A9QgFm9e7O+LxyHPd+8ndrbvkho3ugJgscjNOscosbwGUxNSCJ7\nXpuU5ylHBxXQKYqiHGXie7bgb8udXmK/g2eokicswexpx9vdOObnR2V+G+fz4Tv/TCo8uSso7Kc7\nJsH+dFAUCU8jESjDF+3EMKNoGWYsg33NRIpzB3VVQag7bfKrRsR9uZM/27u2T3qNVKPypOHlwoBA\nfCfR9vz/u1KOLSqgUxRFOcqk1j8/pvscTSdw9tXEV/8t72XITGLt+eW7y4d/eu5Tn5nsX1b2R9oB\n8MSznLTNMzY6+6QSSupnj2k8+YobuQ9FiL1NxBobJnUcnnA1KTG8ZJwQ4HSNLyehcvRSAZ2iKMpR\nxE4l8DasG9O98emnoBeVYDRuGNcYEg2Zlw1FODR8Y1Yeuv74N/a26xOehuRgmp1fTdjyAHzk1jOp\nPv2USRuLmefn7Fnx0qSNAdKnVC1fDUk5fCel7kxMbj/l6KMCOkVRlKNIfNMqXAUUUj+YccbVxHeu\nxzPQPubnx+pOZOqXbgZAn1Ez7D3/dZeP6VBB5JmXaVi+h5g7v9JYhTKS+c8o+t3w4RvmU3fxuZMy\nFiePgFf6Ahglk1/0PlS3BGPOew75I9MmfblXOTJUQKcoinIUcXaOcXaudAa+mfOwVj855mdLoaHX\nzcCMmRjnnsns736ciju+AoBxxqmUnr8Q37WXFzxLB2Dt3kPSmpyvHMOMFTQD6NIFN10xg5OvvWzC\nx2Ln8bOZ9puHqbriPRP+7Exc3iAWxtBrKzATMYY/P+XopwI6RVGUo4SUEmPf2Daty1MuJdneNJR7\nbiyipy3B67IIlerU3XZles+VZaPX1zHjs9fh9zrMeP/Z1P72mxR94p/A50GbNQPPpRdmH5uu0RAf\npQAAIABJREFUU/rBqyhxukdtY+lukvbYvpKiRdVosrBqBkITXLOkjPNvvWZMAeporByTX9LrJTR/\nck63jiZVuggpIaqV4aucd1ifrRw+Kg+doijKUcIe6MGumQc7C0svYXrDBE65kMgj/01glNx1ucRn\nzCVYPjJzXUltiPJv3MLBGdY8hsPUC+dgLvk6iaiDy6PR7nFjTJuC2dBC4ullGPPr8c+fhe41CPgk\nvlT28mEdZjn9D/8Fbe5cvFNKcPtdGC6BYUjcuoOhWWha5sBLjqN81kUn+in+7PtY+r9PYCfHn0h5\nwMz+8xeJBAPbNlM0d/64n5WvoqkLMUumI3pbMPzhw/Zc5fBSAZ2iKMpRItnRDKdfWlBA57i8JC77\nBIZt4d27aUzPNQMhjHl1CDFylksXo085GbqDEQJwmPnB8wCQS2bTUuSj0t2PaSfx61HIcWbBdiCy\nqQFhmsiNG4lvhEN3EUpdh6opGNOnEpoSpDSYHArwBGAaPowx7j1cNN3F9kvOYctTLwLgDocprp1O\naXU5A90DdDc0MW1uHdtXvJZzD2FnWyfkyPrSu3L5YQ3oIF1BwvCpYO54pgI6RVGUo4RlmYj+/A80\nOLpB6v23p1NUtO7CbZu5bzqEBKwzz8WvTUwB9nhKpzKcomFlN5rXw+wFvqzt2/sNelZvh1g0a6oV\nYduwtwVrbwvdQFe4mPB5i6gsSRHoayESnobR1zzmcZ91WjXz532AqaUaJV45tM9s25ZSZKIdMbCX\n/pMW0vbWBmpOW8SJfki43LSkBLvXvIGTSket1VMroTf7OIzS8jGPU1FGowI6RVGUo4CUDtJOYWxb\nlVd7RzdIXv9VgqVlmJqb+L4GxlLYKXr6BRR5JiaYsxwNXnqJxg1dyI0bsYJFOPNOZrQVUcsR9Dy/\nFopLIVJY7jvR10v/0hfpKynFs/AEAjNieKXApY/tBOeMEqBk/0APhJZur0FqMLg7sdjN/AvPoKq3\nDQbS71cDZectZvULr+IpLWVOfPR9gvvZMZU6RJl4KqBTFEU5CkTfWo7uceNpzr1sKjWd1A1fJ1ha\nBoCuu5B9hacqSVTPyrhvbqxcmsPO15phS/pghogM0B11U16Uec11b4tERAYQi89FNu8Z0zNFTzep\n5StJAd0eL66FC5hxgh9jjIHdwSxHkGpoGXpd0deRsV1tVxORC85lbqwDTzKWs9/4hjfgA/887vEp\nysHUKVdFUZSjgHxrGe6qGUQvuBmpG1nbxpZ8kMBgMHfgYv7ltYYsmDeRBzyRUiL3DA/MupatY0+j\nHJGyJJJyEd+yG+8nPo+zbmJqjIpkAnvtOnY9upadm+J0R40x51yTUtKwcQCacpdQE0JwUl8zHjO/\nQxWp117BMQtfHleUbNQMnaIoyhEW3bIa3aXh8XnxnHQ2qfpTSb7xAv51fxvRVmo6njnDKx3YtgV5\nlJw6WKxuIQHDGte4D7b1Ww8gG5sQ8eEHE0R/H8mXXqXBMDBOOYmKuhBFHpP2XRG0OfNI/PIn4ypT\nlolIxLHXrqNzLXRUVOE7cQ4VU3R8Rv5Ly3v2SJw310/wyAbH19FG18qXqbjgHZPSv/KPSc3QKYqi\nHEFSOvDKQ1jzlwxdc3s9FJ1zOakPfY/43HOGtY+dcQ0e3/DgzbJMRNEhM3Y5iJnTxz7oDORABBEf\nfblRmCbWmnXsffgltq/twdy4Ba28ckLHkPG5HW0kXlxB45+Ws311F61dLiwnewjZHTVILV85qePq\n+vWdSGdsKWYUJRMV0CmKohxB0fUv4+lvw1s7Mo2FLxgkeOlNJG78T5JTT0BqOsw+bUQ7xzLRSqoA\nMG1Bw25n1KoMeztdtCbCeF351T/NRyqagva2vNoKKZGbNiFiUWQigTxMVQuElMitWxhY+hI7H13H\nrq1JeuOZl7b72xKTPh65fi1Nv7t70p+j/ONQAZ2iKMoRIqWDeP0JYufdgNs7+uGEQFk5vmtvw7z5\nW2ieke0c20QLpHOMGbpEMzQant1M3yEBi+UIBl5ey8DDS9n21V/hmKMvQXZtaad3V1den6NrXSPC\nKnz51tnXAuGSgu8bLxGPYa1eQ/ufX2THhgiOM3yfnWNO3FJ0NpEH7yXRvu+wPEs5/qmATlEU5TBz\nbBukpP+1pWg+P4GFZ+e8RwiBJ1iE28iw9Vk6aMFSGnba7Fg/QHzNBkRXJ/sef5WmFkFqcLaurUND\nRNPpQYRhYMYzb8y3UjZ9a7bjKwvk9Xkia8ZWbsyYvxDRmzvNx2TRyssw3BqR8DSioalEQlOJFk3B\nmDPnsDxftLfS+MPvsPfRB2l97E+H5ZnK8SvnoQghxHTgPmAK6dovv5RS/vSg9/8V+AFQIaXsFOls\njD8FrgRiwIellOsG234I+LfBW78jpbx38PrpwO8AH7AU+JyUUgohSoE/AbVAA3CDlLJnnJ9ZURTl\niHFsm2hfBy4dgiefhbNgMbqe37+to/EERYGRiXqFAG9lDaapId96a+iQgTBTxJe9wm7DQD9xAXbT\n3gMHEBr3sPsz/41+xumUvfM0Sk6oQAhBvDdO408eo+Tq8/GEcx+0sBIm9lsbCj7YIENh3JVTOCxn\nPf0+jLqZeKZX4SkN4vEIvDKOyxpcWh1oGdZcnnUdsaf/jujunNxxzZ7L9C9+Hd+U6sl9jvIPIZ9T\nrhbwJSnlOiFEEbBWCPGclHLTYLB3KXDwue4rgPrBX2cBvwDOGgzO/hNYTDo5+VohxBODAdovgE8A\nq0gHdJcDTwG3A89LKe8QQtw++Pqr4/7UiqIoR0i0v4si/4GlUN2dXzBnWjb+LMuyAHrFFDItFgrT\nxHlz/YigS1gmzspVdKxcRfv0GfiXnE7s2eWIrk6E68K8xtX11l5Eagw1UANBYquWF35fHrSaKYTO\nWYQn6MarJfGY+5MWW0AvuaJIfdda3Be8E/MvD07K+PYLf/DWYcGcdBwQYqhKhaIUImdAJ6VsBVoH\nfz8ghNgM1ACbgB8DXwEeP+iWa4D7ZDr5zyohRLEQohq4CHhOStkNIIR4DrhcCPEiEJJSrhy8fh9w\nLemA7prB+wDuBV5EBXSKohyjbMvCN5ZyDkAyZRL0Z54x21+z3jO7PmNAlw/R1Ej8gcahoC+6cx8V\ni2py3jewZuvYnhcMITsLT4Z8KK26Eld1Fe6KElzhAB6/C6+WwmMOgE36V4G8PU2EL7iZzkkO6OzB\n5W8pJanuLnZ/5TacHVsIfuhTzPjIpyf12crxp6A8dEKIWuBU4DUhxHuAFinl+kP+NVEDNB30unnw\nWrbrzRmuA1QNBpRIKVuFEBnPuAshPkF6ho8ZM2YU8pEURVEOm9hAF0W+wtN/JlMmAd/os3P7/wq2\nOvI7aZqPxMp1tM+spPyUqWiGnrGNY9pYb4yc+cuHVlaBk+f+OfcpC/BMq8JdHEBoAAKv28FjRdCd\n/SGsDfSPOYg7mJASt9UBdfWwa/v4OssiuX0LHS89T3zXdiL/8wOEbSGA6NK/gArolALl/TeLECII\nPAJ8nvS89deByzI1zXBNjuF63qSUvwR+CbB48eLx13tRFEWZYLZl4c9zefVgkVh6n5fHPXr1CG0w\notPLKsY2uAxEaws9P/413TXTOeGOWxEZCrJ2bWpDjLEuqQiFEZaZ8S97rbwUV1UF7hlT8JYXUSz6\n0GQKOCjVysRlXcnIs20F4Zs/St+3b5+0Z6ReX0nnk48Aw78IZTKBlFItvSoFySugE0IYpIO5+6WU\njwohTgJmAftn56YB64QQZ5KeYTs4Y+U0YO/g9YsOuf7i4PVpGdoDtAkhqgdn56qB8c/PK4qiHAG6\ny0U0btDxq1/gxKLU3HILnrLsyYD3L7OapkUkliDg82T8ktcG11ztzsy1RsfDfcr8jMEcQP/rY1tu\nBbDWr0U/aRGueXPwzKrBU1qExwNeJ4bL3r8nzwJ6Cvwn/sQwkhFC8+roDZcg+ibnLJ5o25v5jeY9\n9G98i/DCUzK/rygZ5Pzn4uCp1V8Dm6WUPwKQUm6QUlZKKWullLWkg7LTpJT7gCeAW0Ta2UDf4LLp\nM8BlQogSIUQJ6dm9ZwbfGxBCnD34rFs4sCfvCeBDg7//EMP36imKohxTDFsSe+BeEstfRPeNPK16\nqNRgnjjDcBH0e0mmLKKxkQcQ9MGALnjmOcii8ISOOXTyrIzXpeNgriu8NFboy/9GzWNPMeehR5ny\n+X9l1hlVTC23KNN6CJo9BwVzR569cw3u8y857M8VUtLx6AOH/bnKsS2f+f/zgH8GLhFCvDn468os\n7ZcCu4AdwD3A/wEYPAzxbeD1wV/f2n9AAvg08KvBe3aSPhABcAdwqRBiO+nTtHcU8NkURVGOKru/\n/gUIF1P7s7tw+f0523vcwxdRvB6DgN9DLJEinjiw5qhpGtJxkKYJycKrHHiuugZpjFzSlbqL8JzM\ns4jd2zoRfb0FPaf4G9+j5vr3Eqquwu1x4wr4iZXXFTzew0XrbUMLFB2RZ9udHdjJBI55WBK7KMeB\nfE65vkLmfW4Ht6k96PcSuG2Udr8BfpPh+hpgYYbrXYCqXqwoyjGvbenjOC2NzHv4MTR37qOusURy\n1DQlfm/6/oGBCO5kBE/5FBzHIdXRVnAKEePs86j79//Euv3r9G3bTv+TjxN/4hGEbSNmz8YY5Vhu\n3+v5HxaQLoPKn/+S8lOHLyH6vQYDp10Oz95V0JgPFydUgdMwyrLoJJN9veie3HkAFWU/VSlCURRl\nkvVtfIuu//gSMpWic9WqvO45tBxVJvLNF9Ee+h79z9yPGelFjqH8VtENNwPgMlyUnTifWV+9ndqn\nXiD4fz6P95SR9WVhMM3Ghi14rr4ur2dMuee+EcEcpKtfUF1HMpB9L+GRoteehKu0/Ig822lqwIrF\njsizlWOTCugURVEmkZNKsfffvoCwTMTeJrq+8nm2fPLj9G3cOOo9Ukq8ntFPtQKYA714N76AJh0C\nO1bhtDciXIWnRAmfcvKIa/5QkOm33ELNZ79GtPRsYp7pSHlgoSYmy6j9ze+p+7d/p+Lu3yEz1Jfd\nL/Tlf6N0/gmjvh/wGcj3fpVEaErBY58syWA5yeu+hn/BucSXPZX7hkkgujvZ85PvYcViRBsbSPWq\nIklKdiqgUxRFmUQNd/90RAJduX4tLR//ENu/8R/E29pIdHSy+dYPseuHP8DsHyAWT+HSM+d+2y/+\nyhPo9oF9dNKx8VTnTgR8MPeFl+ANjL6Xz20YFJVUEpx2Ksy6kljFEmLlSyiqX4I/HAKg/JSTmfnY\nU2j1hwRtwSICH7+N6uuuzToGXddw+91oH/g68ZLpWdseLubU+fjrTqL5D7+e1Dx0uaQeuo/t585n\nz3vfwY7Lz6HxN784YmNRjn4qoFMURZkknS8vI/GbO5ElI5fthONgPf0ku69/D7tu+yRs3kDyzw+w\n/YbrSDY1ZujtgMS+Jvy71oy47iQKOxARet+NebfVdZ1gqJRguHTEe4HSYup+83u8190wdG3K3b9j\nxkc/ijZKypODGS4dQ7PR3vsVYlX1eY9pMkih4T3zKgDMfUdo/5wY/jMTloVIxIn+7A42X38pLQ/9\n4YiMSzm6FT4/ryiKouSU6u2h/T++hJASOkev4CAsExp3H7iQiBOann2mzVz+KMahydmEINW+L/8B\nCkH45JHLrWNlGC5qv/JV2s85DycWo2R25nQno3G5dBxpIa79EvHdbyHfWoa3bRuadCZsjPmIV86h\nqCpdcaj8Pe9j70P3HdbnA2iLz8Y1vRbz0T+OfHPnNvq/+3WM0nIq33n5YR+bcvRSM3SKoiiToPFn\n30cMlraSUwpYCtVdtD3zTLpQewbRrevxt+8YeVsgnE5bkifvu9+L2zPGwrKjEEJQdcH5hC64cEz3\nuw0XPpeJPn0O2rs/g/jkncQu/DDRmoU4WvY9hRNFP/M9Q78XGVK5HA7OmlUITcP/qS+MeE+WV1F1\n9wMULz7rCIxMOZqpgE5RFGWCJdr3kRos6SR1HXq68r5XRAfo/3/fZstNH6D95ZeHvefYFmJV5vzq\nwnDjra1DenMnLMbvp+TmW0Z92zHHXlcrEksQ8I8v3YbPaxBwO0iZhNmnEHjfV9A+dSeRWWeOqT/T\nHQBACkG8rBZHZP7qS/lK8NWfOvQ6n/Qyk0FIh9Sf/0D47PMRJ58+dF0abmp+dDelZ52Lu7jkiIxN\nOXqpgE5RFGWCNd/146F8cM7sE9AKCOiGNOw8cCL27fSJ2Mi6l/FEMpf3kpZJorEBkYhn7daursEJ\nl9D5h3tJdo0cV7ytjS1XvYud3/0vrHjhaTNcuo5l2QXfl4nh0inyuSDZQ9K2CV33OeJltYWPyYyT\nKKrC9BZjl88gcf4/kwyO3NeYmjJnWGm1rmeeHM/wh5FlFWjnXojvY5/B/+kvol90GSxclA74R5Hq\n7qTup79CCg0ZKGLqPQ8SPvnUUdsr/9jUHjpFUZQJFNm9g9Rf/4wAnLIKtB1bxtVf+kTsLbRffhXT\nq0dPGqwHwsgswZzUdJw589B2bEY4DuZfH2Xn35/G9/4PMvWDN+MpKQag9Y9/RPT3kXriEdrmL6Dm\nuvxyzQFYlo0jJS5X9hO6hdJ1Da+TwrZMXO/4MPKhbyIKKPAqpIN3IL2P0b31Zdj6MpHZ52AtuAj/\n6keG+vKcefWw+6IP/yF7Vv0spBCIeQvxX3I5/jknUH7RO0fU4ZVSkuxsJ7J5I6n2ffQ/81fk668O\nvT+w4iWMcAmirp6y275E8aLTD32MogxRAZ2iKMoEavnx9xB2OsGv9PnRujLPqBVCSElRyMYwM8+Y\nSQR6UQlWR3vG9+0pNQjHRt82PPediMdI3Pdrdj74B4yLLqX8ve8lsfTxoSCm/4nHqHjHO3CHQjnH\nmEyZuHQdr3tyvlY0TSMW6SY4fS59s84guHv1uPoL7lxJvO7TxKvq8bdtIxGeSmDagRO2jmlCdKDg\nfqUvgHHRpUz95OcI1GYvayaEwFtRhbeiCoDq997Ijq99FuuZvwKQfPSPdMZjzH/k2YLHofzjUUuu\niqIoE6Rvw5vYy58HwD5xEWRI9Ot739VQFCyoX628lDJPdNT3TW8ITXdhtbWOeM+un4/WthctywlY\nkUpiPfsk+z71EcRA/4E3Nr+NnUcqFMtOL7HqupZXmpKx8hgaSIl3yfU42vgDR1fldMRJFwOgXzW8\nYqVwudDqF+TsQ06vpeS/fkrd39dQ88DfqHt8GfXf+2nOYC4ToWnM/vaPoHZ2+rWUWEsfo2fd66Me\nklGU/VRApyiKMkH2/eJHCCmRmgZmCmGP3EvmnVtL3Z1foegTN+fdr9PZTdPmPmLucOb3PenkwLJ9\neEDnhEvQGnamU6eMxcw6fJWVOZulUjYe9+SfCDVcOrFoP57K6Tg3fYf4Oz5BZNFVROacS7x0BrZ+\n4BCDrWcfT8obxl0xncDJ52Pe9F94q4enWRFCUPkvX0aKLIuu9fOZ++DfmHLVtXjKKwjNX4i3cnwV\nLzS3m5k//y3UHZgtbP3qbWy56d3EmvaMq2/l+KaWXBVFUSZA/+a3sVe+nN47N2ce+rZNGdv1/uL3\nVPz8C/jnzaSQBb3k6+tpfn097ksvprzUIeg+qG7rYLxmnHw65u50ShOnfj50dyLGcWJVJpM03ncf\nJeeeR3B23dAesH1PP41RXEzZ2WcTjSUJ+Ecv/TXRHDMGhPFUzcAzmC9uaLzSIb57E1bzFmSsj+DG\n50ftJ1V7Kr7BAwmHBnP7lZ51Lu2Lz0W+vmLEe1JoTP+vH+MKFDbbmg//9Jmc8Ken2P2Db5F66D4Q\nGnL7ZhquvQSqa3DNnc+M27857uBROb6ogE5RFGUCtP78+wgp6Q+EaeqKMlfTMZyRM3TBG69BIui4\n84GCnyENN8nX1tESjWCccRo1s7x4XA7uSAd2Mo6nbg4pTcOpm4u+ffO4P5PY10L0rp8QvesnyLJy\nXKecjquiksRD9xP67L8izzoLl+vwLvQEvAbJRBxPhvQsQmj46xYSw8Hu7YDRy+WClV+gW/GRT9Oe\nIaATs2ZTNHd+vsMumGYYzP6/36ZzycWUnX8x/Zs2sPc//hV2bsVu3sOuV1/Cf9NHmPbxz+Dy5ZGq\nRjnuqSVXRVGUcep46XmcV19CSsmq9hhvL1vOa1rxiHbScFF65hwAwjdcUfBeOmdWPaK/D2HbWKte\np+GxNTQ2QTQuiK77OzUf/DBi0Rno4zxZm4no6sRe9gzJP/0+nSetuYlYPHVYllqHjUMIUrG+rG3c\nlTMQWvaTtv5dq+lf/mjO55Wdcz7MHbmXThSPLIE2GcovuAQhBOETT+aEB55MpzsBRCJO/Dd3su09\nF9L6l4eQUmJFo1ixwlPNKMcHFdApiqKMg5NK0fGDbwLQXFZDz46dANQHDyyARL3pwC38iX/C63bQ\nhKRyYWV6L90n/ylrLrKh54SK0XYPLxQvEnESL65g78Mvsfd/f43Z38f0f/8uMlA0UR9vVGbrXhDp\n1BsHSyRNEsn8K1aMhc+dPhwxGqG5cLaszNqH5lj4V/8Zx8o9VmPewhHXnK0bibce3lqvmtvNnB/+\nAv+nvoAMpfdTio42er/xZbbceBV7fvxd2p964rCOSTl6qIBOURRlHJp++wtEc3qz+tbW7qHrq7c0\nsrtsGgnDyzObm3nFXUH5WcP3ahm6w9QL5lD7q//E//53Z39Q5ZRR98MJKZHr32D31z5LoHY23ve8\nb3wfKg9OawsBn4dkyiIaTxKJm0RNHdxF4C4imtKI224GkukgL2VauTvNU9J0IMthBd0fRKRyz1QJ\nYOC5e3O2q/nkZ2FW/bBrIhohunNbznsnmuZyMfNTn2fe39cw9f6/woLBerxbN5L68x/ovfd/D/uY\nlKODCugURVHGKNnVSfS+XwLQGyyha8uBL/hYWxurl73K0q2tmP39lM6vxdAzzyp53Q7Trz2dab/8\nJu4Lz838sP4+nBw1YeXrr9L067uY8dmvIid5w7xsa8VxHIQQOFJDdwcIFBXj9QXw+gIEQiX4AkUU\nhcvwhqdga34syx4xozcWwsi9Zyxw038QPfXdROZfkrEqRDJYjukOQh4zdL6a6cx/7O9Mf/TvuK64\nFmbORhaFsA9O8XKYaW434RNPpv6eB5DlB51ETo6efFo5vqlDEYqiKGPU9NM7ENEIANuczCc9zYH0\nWdb6c+fl7C8QkMz6xGX0vfcSOn/5Z6yNB/bCic52ZPHIfXmHivzq51jXvI/Sz3yV7m9+BZFHwDIW\n+qIzkO4SPH4X+Zxx9QWKkE6AaF87Qd/Y991F4ibBkrKc7TSXQeiSD2LHIiR2n0gsGQMh0Nw+nGQM\n9/QTcJdPxTtKXddMgnX11H/vp2Me+2RwBYKEPvYvmC3NWO2tVHzw1iM9JOUIERPxr6WjyeLFi+Wa\nNWuO9DAURTnORbZvpfEDlyMch35/mKdf34y0Mi8ram43X37lZ3j8+Rd7lxK6GyN0/ew+5L527LkL\nRk2Fcijjmg8w55vfx4rH2fbOMxBjqHgw6riEIHjbl5l2y8fHVLw+lUxgJ3rxeQoP6mzHwdaLcHu8\nBd+rKEcrIcRaKeXi8fajZugURVHGoOXH30UMZu9fn9RHDeYAKk5ZWFAwB+ktYuFqHx0uL87cE9Ea\nd+V9b2pl+sQtUkJZOTJcjAgWgW0jG3ZkTHicL8/7/5kZH7std8NRuD1eUhSTSvXjNvR0PVPTxrQk\naDpIhyJf5q+mWNKhqEQFc4qSiQroFEVRCtT9+kqcV18EoK24ir0vvZ61fXjGFKSUI4qz57L32bcR\nzY0UWupetO+jZ80qSs84hwVPvEj7c0vpefJRNH8AuXAR1rKnhpf4ytfsucz8wv8t/L5DuD1eTCGI\nJmP4/CG8Pp2hME1KYn1tGC4Nw3XgkydTFoHQyL1wiqKkqUMRiqIoBZBS0vbf3xn6/fqW7hx3wLZH\nlnL3h37Gjjdb8n5Ooj9B7PGxF2XveeZJABzLovOnd+C89BzWU3/BfvxPaKefU3B/MlhE3S8fmLAk\ntobbQ6CoBO3QlC1C4C+eghGsxNKLGIibDMRtjED5yLbHiJ51r5Pq7TnSw1COcyqgUxRFKUDb3/4C\nW97GRrDSXTGUdy6X9nXr+es37mVbYhYDTiBn+5bHVuNMnY4TLhnTOM09uwHofPn5obQq+zlrViJz\nnJg9VMmX/h1P2eGdIXMZbopKplBUUnnMBnMNP/s++z7yPnZ97qM4WZblFWW8VECnKIqSJ+k4dN/9\nEwDeDk2lac26gu5f+IUvEKWIHak6dpmziI9yMnagpQ/z+ZfQd2xBRAew5y5A+vyFDdZOBw89f3lo\nxFsi0o+omoKcXptXV56bPkL1dR8o7PkK0YZdxO69GwC5fi3bP/cxulYun5DULYpyKBXQKYqi5Gnv\nn/+IaGqgL1DM1ldXF3Rv5XnnUX7WWYOvBH12EZuT9TSa00jJ4duZWx94cejggrCs9OlWIdKBXZ4z\nVcKdDhbt9WszN1i/Fro64ewLkNWjz9bJaTOpvuUTeT0zl0RvKwN738bJUOP2eNP0+1+x5wNXIOwD\ns3LOihdo//Q/sfUTNxHZla76kWjfhzWY+kZRxkMdilAURcmDk0rS98ufIYC9niJkASdFha5z4uc/\nP/K60OiyS+mxi6l0dVKpt9PzdgvyjTdGto1F0bdtwikuRZZVou3MUa/VMzj7Z4yeHkTEIrDqZQgE\n4YJLka+vQMQPqrBQP5859zyAu3hsy74AjmMT2fE8utmHR0ZxC0m0bzeBE65A046vr6DdP7kD78xZ\nBOadyMAvf4ZIJjK2k6+/SuMNV1D0ma8Qf2sdelkFU266lUBt3WEesXI8Ob7+36QoijJJmn57N6Kz\nDQDLLmzJbOZ730tw1qxR33fQ2GdV0pEqxn3/r8l2Flbr7YbebpwpU5FuD3rj7szt/Ol9ep4LLyX1\nyP1ZxyeiEXj5OYq//WOSO7dhd3UgbYfa27+BMVgzdKwie1YRNAcPgwx+sIDTSXTb0wQMuo9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u4DB9kdnoOpeegJTyen5D+UnGnTyV315gIrjkZ/8Hco0Qj+kyfY7M1y/VuupHZWCd0kBokcP17y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ccBpJbv3W7dSoYRRpcPCa1bz18Mjir/XnncvVP/0edbOG1t4pioLbm18NoaqqpK/7DNqfv48+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\nvBvNld8WYX1SnV388P7XCjL2YAw7zSVXnpL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fnAzrJhaKOxDgvf/8J55AoKj3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\nxN5ddL78ChXLluZ13CknngBA9/EnIh2Hfe+5kqov3krXT+7FfDZ/BY37hD/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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8307274e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = pl.figure(figsize=(10,10))\n",
    "ax = fig.add_subplot(111)\n",
    "zipsNYC.plot(ax=ax, color = 'black',alpha=0.2)\n",
    "df2.plot(ax=ax, column = 'Complaint Type', cmap = 'OrRd', scheme = 'quantiles', legend = True)\n",
    "pl.title('Number of 311 Traffic Complaints Across different Zipcodes in New York City',size = 15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fig.1 Number of 311 Traffic Complaints Across different zipcodes in New York City\n",
    "This map shows the number of 311 traffic complaints people have filed in different areas/zipcodes. Apparently, Manhattan has the highest number of 311 traffic complanits due to high demand in transportation and high population. But surprisingly we have found out that Staten Island also has a high number of compalint on traffic. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}