Capstone_Intro-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Capstone Project - The Battle of the Neighborhoods (Week 2)\n",
    "### Applied Data Science Capstone by IBM/Coursera"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of contents\n",
    "* [Introduction: Business Problem](#introduction)\n",
    "* [Data](#data)\n",
    "* [Methodology](#methodology)\n",
    "* [Analysis](#analysis)\n",
    "* [Results and Discussion](#results)\n",
    "* [Conclusion](#conclusion)\n",
    "\n",
    "\n",
    "\n",
    "## Introduction: Business Problem <a name=\"introduction\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The aim of this project is to find a safe and secure location for opening of commercial establishments in Vancouver, Canada. Specifically, this report will be targeted to stakeholders interested in opening any business place like **Grocery Store** in **Vancouver City**, Canada.\n",
    "\n",
    "The first task would be to **choose the safest borough** by analysing crime data for opening a grocery store and **short listing a neighbourhood**, where grocery stores are not amongst the most commom venues, and yet **as close to the city as possible**.\n",
    "\n",
    "We will make use of our data science tools to analyse data and focus on the safest borough and explore its neighborhoods and the 10 most common venues in each neighborhood so that the best neighborhood where grocery store is not amongst the most common venue can be selected."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on definition of our problem, factors that will influence our decission are:\n",
    "* finding the safest borough based on crime statistics\n",
    "* finding the most common venues\n",
    "* choosing the right neighbourhood within the borough\n",
    "\n",
    "We will be using the geographical coordinates of Vancouver to plot neighbourhoods in a borough that is safe and in the city's vicinity, and finally cluster our neighborhoods and present our findings.\n",
    "\n",
    "Following data sources will be needed to extract/generate the required information:\n",
    "\n",
    "- [**Part 1**: Using a real world data set from Kaggle containing the Vancouver Crimes from 2003 to 2019](#part1):  A dataset consisting of the crime statistics of each Neighbourhoof in Vancouver along with type of crime, recorded year, month and hour.\n",
    "\n",
    "- [**Part 2**: Gathering additional information of the list of officially categorized boroughs in Vancouver from Wikipedia.](#part2): Borough information will be used to map the existing data where each neighbourhood can be assigned with the right borough.\n",
    "\n",
    "- [**Part 3**: Creating a new consolidated dataset of the Neighborhoods, along with their boroughs, crime data and the respective Neighbourhood's co-ordinates.](#part3): This data will be fetched using OpenCage Geocoder to find the safest borough and explore the neighbourhood by plotting it on maps using Folium and perform exploratory data analysis.\n",
    "\n",
    "- [**Part 4**: Creating a new consolidated dataset of the Neighborhoods, boroughs, and the most common venues and the respective Neighbourhood along with co-ordinates.](#part4): This data will be fetched using Four Square API to explore the neighbourhood venues and to apply machine learning algorithm to cluster the neighbourhoods and present the findings by plotting it on maps using Folium."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## WEEK-2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###**Part 1:** Using a real world data set from Kaggle containing the Vancouver Crimes from 2003 to 2019<a name=\"part1\"></a>\n",
    "\n",
    "\n",
    "####  Vancouver Crime Report \n",
    "\n",
    "Properties of the Crime Report\n",
    "\n",
    "*   TYPE - Crime type\n",
    "*   YEAR - Recorded year\n",
    "*   MONTH - Recorded month\n",
    "*   DAY - Recorded day\n",
    "*   HOUR - Recorded hour\n",
    "*   MINUTE - Recorded minute\n",
    "*   HUNDRED_BLOCK - Recorded block\n",
    "*   NEIGHBOURHOOD - Recorded neighborhood\n",
    "*   X - GPS longtitude\n",
    "*   Y - GPS latitude\n",
    "\n",
    "Data set URL: https://www.kaggle.com/agilesifaka/vancouver-crime-report/version/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: opencage in c:\\programdata\\anaconda3\\lib\\site-packages (1.2.1)\n",
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      "Requirement already satisfied: Requests>=2.2.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from opencage) (2.22.0)\n",
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      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from Requests>=2.2.0->opencage) (1.24.2)\n",
      "Requirement already satisfied: asn1crypto>=0.21.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from cryptography>=2.3->pyopenssl>=0.15.1->opencage) (0.24.0)\n",
      "Requirement already satisfied: cffi!=1.11.3,>=1.8 in c:\\programdata\\anaconda3\\lib\\site-packages (from cryptography>=2.3->pyopenssl>=0.15.1->opencage) (1.12.3)\n",
      "Requirement already satisfied: pycparser in c:\\programdata\\anaconda3\\lib\\site-packages (from cffi!=1.11.3,>=1.8->cryptography>=2.3->pyopenssl>=0.15.1->opencage) (2.19)\n",
      "Matplotlib version:  3.2.1\n",
      "Libraries imported\n"
     ]
    }
   ],
   "source": [
    "### Importing all the necessary Libraries\n",
    "\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "#Command to install OpenCage Geocoder for fetching Lat and Lng of Neighborhood\n",
    "!pip install opencage\n",
    "\n",
    "#Importing OpenCage Geocoder\n",
    "from opencage.geocoder import OpenCageGeocode\n",
    "\n",
    "# use the inline backend to generate the plots within the browser\n",
    "%matplotlib inline \n",
    "\n",
    "#Importing Matplot lib and associated packages to perform Data Visualisation and Exploratory Data Analysis\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mpl.style.use('ggplot') # optional: for ggplot-like style\n",
    "\n",
    "# check for latest version of Matplotlib\n",
    "print ('Matplotlib version: ', mpl.__version__) # >= 2.0.0\n",
    "\n",
    "# Matplotlib and associated plotting modules\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as colors\n",
    "\n",
    "#Importing folium to visualise Maps and plot based on Lat and Lng\n",
    "import folium\n",
    "\n",
    "#Requests to request web pages by making get requests to FourSquare REST Client\n",
    "import requests\n",
    "\n",
    "#To normalise data returned by FourSquare API\n",
    "from pandas.io.json import json_normalize\n",
    "\n",
    "#Importing KMeans from SciKit library to Classify neighborhoods into clusters\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "print('Libraries imported')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>TYPE</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>DAY</th>\n",
       "      <th>HOUR</th>\n",
       "      <th>NEIGHBOURHOOD</th>\n",
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       "      <th>0</th>\n",
       "      <td>Break and Enter Commercial</td>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
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       "      <td>West End</td>\n",
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       "      <td>2018</td>\n",
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       "      <th>2</th>\n",
       "      <td>Break and Enter Commercial</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
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       "      <td>0</td>\n",
       "      <td>West End</td>\n",
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       "      <th>3</th>\n",
       "      <td>Break and Enter Commercial</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>Central Business District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Break and Enter Commercial</td>\n",
       "      <td>2018</td>\n",
       "      <td>10</td>\n",
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       "      <td>18</td>\n",
       "      <td>Central Business District</td>\n",
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      "text/plain": [
       "                         TYPE  YEAR  MONTH  DAY  HOUR  \\\n",
       "0  Break and Enter Commercial  2018      3    2     6   \n",
       "1  Break and Enter Commercial  2018      6   16    18   \n",
       "2  Break and Enter Commercial  2018     12   12     0   \n",
       "3  Break and Enter Commercial  2018      4    9     6   \n",
       "4  Break and Enter Commercial  2018     10    2    18   \n",
       "\n",
       "               NEIGHBOURHOOD  \n",
       "0                   West End  \n",
       "1                   West End  \n",
       "2                   West End  \n",
       "3  Central Business District  \n",
       "4  Central Business District  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vnc_crime_df = pd.read_csv('https://raw.githubusercontent.com/RamanujaSVL/Coursera_Capstone/master/vancouver_crime_records_2018.csv', index_col=None)\n",
    "\n",
    "#Dropping X,Y which represents Lat, Lng data as Coordinates, the data seems to be corrupt\n",
    "vnc_crime_df.drop(['Unnamed: 0','MINUTE', 'HUNDRED_BLOCK', 'X', 'Y'], axis = 1, inplace = True)\n",
    "\n",
    "#vnc_crime_df.columns\n",
    "\n",
    "vnc_crime_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>38072</th>\n",
       "      <td>Vehicle Collision or Pedestrian Struck (with I...</td>\n",
       "      <td>2018</td>\n",
       "      <td>9</td>\n",
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       "      <td>9</td>\n",
       "      <td>Marpole</td>\n",
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       "      <th>38073</th>\n",
       "      <td>Vehicle Collision or Pedestrian Struck (with I...</td>\n",
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       "      <td>10</td>\n",
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       "      <th>38074</th>\n",
       "      <td>Vehicle Collision or Pedestrian Struck (with I...</td>\n",
       "      <td>2018</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>Mount Pleasant</td>\n",
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       "    <tr>\n",
       "      <th>38075</th>\n",
       "      <td>Vehicle Collision or Pedestrian Struck (with I...</td>\n",
       "      <td>2018</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>Riley Park</td>\n",
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       "    <tr>\n",
       "      <th>38076</th>\n",
       "      <td>Vehicle Collision or Pedestrian Struck (with I...</td>\n",
       "      <td>2018</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>Mount Pleasant</td>\n",
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      "text/plain": [
       "                                                    TYPE  YEAR  MONTH  DAY  \\\n",
       "38072  Vehicle Collision or Pedestrian Struck (with I...  2018      9    7   \n",
       "38073  Vehicle Collision or Pedestrian Struck (with I...  2018     10    1   \n",
       "38074  Vehicle Collision or Pedestrian Struck (with I...  2018      8    5   \n",
       "38075  Vehicle Collision or Pedestrian Struck (with I...  2018      9   10   \n",
       "38076  Vehicle Collision or Pedestrian Struck (with I...  2018      8    4   \n",
       "\n",
       "       HOUR   NEIGHBOURHOOD  \n",
       "38072     9         Marpole  \n",
       "38073    11         Marpole  \n",
       "38074    17  Mount Pleasant  \n",
       "38075    17      Riley Park  \n",
       "38076    16  Mount Pleasant  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vnc_crime_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>DAY</th>\n",
       "      <th>HOUR</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>38077.0</td>\n",
       "      <td>38077.000000</td>\n",
       "      <td>38077.000000</td>\n",
       "      <td>38077.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>6.552932</td>\n",
       "      <td>15.387058</td>\n",
       "      <td>12.673477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.464976</td>\n",
       "      <td>8.713437</td>\n",
       "      <td>7.447335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          YEAR         MONTH           DAY          HOUR\n",
       "count  38077.0  38077.000000  38077.000000  38077.000000\n",
       "mean    2018.0      6.552932     15.387058     12.673477\n",
       "std        0.0      3.464976      8.713437      7.447335\n",
       "min     2018.0      1.000000      1.000000      0.000000\n",
       "25%     2018.0      4.000000      8.000000      7.000000\n",
       "50%     2018.0      7.000000     15.000000     15.000000\n",
       "75%     2018.0     10.000000     23.000000     19.000000\n",
       "max     2018.0     12.000000     31.000000     23.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vnc_crime_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 38077 entries, 0 to 38076\n",
      "Data columns (total 6 columns):\n",
      "TYPE             38077 non-null object\n",
      "YEAR             38077 non-null int64\n",
      "MONTH            38077 non-null int64\n",
      "DAY              38077 non-null int64\n",
      "HOUR             38077 non-null int64\n",
      "NEIGHBOURHOOD    34828 non-null object\n",
      "dtypes: int64(4), object(2)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "vnc_crime_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Break and Enter Commercial</td>\n",
       "      <td>2018</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Break and Enter Commercial</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>West End</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Break and Enter Commercial</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>Central Business District</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Break and Enter Commercial</td>\n",
       "      <td>2018</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>Central Business District</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Type  Year  Month  Day  Hour  \\\n",
       "0  Break and Enter Commercial  2018      3    2     6   \n",
       "1  Break and Enter Commercial  2018      6   16    18   \n",
       "2  Break and Enter Commercial  2018     12   12     0   \n",
       "3  Break and Enter Commercial  2018      4    9     6   \n",
       "4  Break and Enter Commercial  2018     10    2    18   \n",
       "\n",
       "               Neighbourhood  \n",
       "0                   West End  \n",
       "1                   West End  \n",
       "2                   West End  \n",
       "3  Central Business District  \n",
       "4  Central Business District  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### Changing the name of columns to lowercase\n",
    "\n",
    "vnc_crime_df.columns = ['Type', 'Year','Month','Day','Hour','Neighbourhood']\n",
    "vnc_crime_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['West End', 'Central Business District', 'Hastings-Sunrise',\n",
       "       'Grandview-Woodland', 'Mount Pleasant', 'Strathcona',\n",
       "       'Shaughnessy', 'Sunset', 'Fairview', 'Oakridge', 'Marpole',\n",
       "       'Kensington-Cedar Cottage', 'Kitsilano', 'Victoria-Fraserview',\n",
       "       'Kerrisdale', 'West Point Grey', 'Riley Park', 'Arbutus Ridge',\n",
       "       'Renfrew-Collingwood', 'Killarney', 'South Cambie',\n",
       "       'Dunbar-Southlands', 'Stanley Park', 'Musqueam', nan], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###Total Crimes in different Neighborhoods\n",
    "\n",
    "vnc_crime_df['Neighbourhood'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vnc_crime_df['Neighbourhood'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Central Business District    10857\n",
       "West End                      3031\n",
       "Mount Pleasant                2396\n",
       "Strathcona                    1987\n",
       "Kitsilano                     1802\n",
       "Fairview                      1795\n",
       "Renfrew-Collingwood           1762\n",
       "Grandview-Woodland            1761\n",
       "Kensington-Cedar Cottage      1391\n",
       "Hastings-Sunrise              1270\n",
       "Sunset                         967\n",
       "Riley Park                     866\n",
       "Marpole                        828\n",
       "Victoria-Fraserview            600\n",
       "Killarney                      565\n",
       "Oakridge                       499\n",
       "Dunbar-Southlands              474\n",
       "Kerrisdale                     417\n",
       "Shaughnessy                    414\n",
       "West Point Grey                372\n",
       "Arbutus Ridge                  311\n",
       "South Cambie                   292\n",
       "Stanley Park                   154\n",
       "Musqueam                        17\n",
       "Name: Neighbourhood, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vnc_crime_df['Neighbourhood'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Part 2:** Gathering additional information about the Neighborhood from Wikipedia<a name=\"part2\"></a>\n",
    "\n",
    "######As part of data set Borough which the neighborhood was part of was not categorized, so we will create a dictionary of Neighborhood and based on data in the following [Wikipedia page](https://en.wikipedia.org/wiki/List_of_neighbourhoods_in_Vancouver)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the dataframe columns\n",
    "column_names = ['Neighbourhood', 'Borough'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# instantiate the dataframe\n",
    "vnc_neigh_bor = pd.DataFrame(columns=column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Neighbourhood</th>\n",
       "      <th>Borough</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Neighbourhood, Borough]\n",
       "Index: []"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_names\n",
    "vnc_neigh_bor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "vnc_neigh_bor['Neighbourhood'] = vnc_crime_df['Neighbourhood'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Neighbourhood</th>\n",
       "      <th>Borough</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>West End</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Central Business District</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hastings-Sunrise</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Grandview-Woodland</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mount Pleasant</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Strathcona</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Shaughnessy</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Sunset</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Fairview</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Oakridge</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Marpole</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Kensington-Cedar Cottage</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Kitsilano</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Victoria-Fraserview</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kerrisdale</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>West Point Grey</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Riley Park</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Arbutus Ridge</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Renfrew-Collingwood</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Killarney</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>South Cambie</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Dunbar-Southlands</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Stanley Park</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Musqueam</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Neighbourhood Borough\n",
       "0                    West End     NaN\n",
       "1   Central Business District     NaN\n",
       "2            Hastings-Sunrise     NaN\n",
       "3          Grandview-Woodland     NaN\n",
       "4              Mount Pleasant     NaN\n",
       "5                  Strathcona     NaN\n",
       "6                 Shaughnessy     NaN\n",
       "7                      Sunset     NaN\n",
       "8                    Fairview     NaN\n",
       "9                    Oakridge     NaN\n",
       "10                    Marpole     NaN\n",
       "11   Kensington-Cedar Cottage     NaN\n",
       "12                  Kitsilano     NaN\n",
       "13        Victoria-Fraserview     NaN\n",
       "14                 Kerrisdale     NaN\n",
       "15            West Point Grey     NaN\n",
       "16                 Riley Park     NaN\n",
       "17              Arbutus Ridge     NaN\n",
       "18        Renfrew-Collingwood     NaN\n",
       "19                  Killarney     NaN\n",
       "20               South Cambie     NaN\n",
       "21          Dunbar-Southlands     NaN\n",
       "22               Stanley Park     NaN\n",
       "23                   Musqueam     NaN\n",
       "24                        NaN     NaN"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vnc_neigh_bor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Central Business District': 'Central',\n",
       " 'West End': 'Central',\n",
       " 'Stanley Park': 'Central',\n",
       " 'Victoria-Fraserview': 'South Vancouver',\n",
       " 'Killarney': 'South Vancouver',\n",
       " 'Musqueam': 'South Vancouver',\n",
       " 'Mount Pleasant': 'East Side',\n",
       " 'Strathcona': 'East Side',\n",
       " 'Renfrew-Collingwood': 'East Side',\n",
       " 'Grandview-Woodland': 'East Side',\n",
       " 'Kensington-Cedar Cottage': 'East Side',\n",
       " 'Hastings-Sunrise': 'East Side',\n",
       " 'Sunset': 'East Side',\n",
       " 'Riley Park': 'East Side',\n",
       " 'Kitsilano': 'West Side',\n",
       " 'Fairview': 'West Side',\n",
       " 'Marpole': 'West Side',\n",
       " 'Oakridge': 'West Side',\n",
       " 'Dunbar-Southlands': 'West Side',\n",
       " 'Kerrisdale': 'West Side',\n",
       " 'Shaughnessy': 'West Side',\n",
       " 'West Point Grey': 'West Side',\n",
       " 'Arbutus Ridge': 'West Side',\n",
       " 'South Cambie': 'West Side'}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "neigh_bor_dict = {'Central Business District':'Central', 'West End':'Central', 'Stanley Park':'Central', 'Victoria-Fraserview':'South Vancouver',\n",
    "                  'Killarney':'South Vancouver', 'Musqueam':'South Vancouver', 'Mount Pleasant':'East Side', 'Strathcona':'East Side',\n",
    "                  'Renfrew-Collingwood':'East Side', 'Grandview-Woodland':'East Side', 'Kensington-Cedar Cottage':'East Side', 'Hastings-Sunrise':'East Side',\n",
    "                  'Sunset':'East Side', 'Riley Park':'East Side', 'Kitsilano':'West Side', 'Fairview':'West Side',\n",
    "                  'Marpole':'West Side', 'Oakridge':'West Side', 'Dunbar-Southlands':'West Side', 'Kerrisdale':'West Side',\n",
    "                  'Shaughnessy':'West Side', 'West Point Grey':'West Side', 'Arbutus Ridge':'West Side', 'South Cambie':'West Side'}\n",
    "\n",
    "\n",
    "neigh_bor_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Neighbourhood Count 24 Borough Count 4\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>Neighbourhood</th>\n",
       "      <th>Borough</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>West End</td>\n",
       "      <td>Central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Central Business District</td>\n",
       "      <td>Central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hastings-Sunrise</td>\n",
       "      <td>East Side</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Grandview-Woodland</td>\n",
       "      <td>East Side</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mount Pleasant</td>\n",
       "      <td>East Side</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Neighbourhood    Borough\n",
       "0                   West End    Central\n",
       "1  Central Business District    Central\n",
       "2           Hastings-Sunrise  East Side\n",
       "3         Grandview-Woodland  East Side\n",
       "4             Mount Pleasant  East Side"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "for row, neigh in zip(neigh_bor_dict, vnc_neigh_bor['Neighbourhood']):\n",
    "  vnc_neigh_bor.loc[vnc_neigh_bor.Neighbourhood == row, 'Borough'] = neigh_bor_dict.get(row)\n",
    "\n",
    "vnc_neigh_bor.dropna(inplace=True)\n",
    "\n",
    "print(\"Total Neighbourhood Count\",len(vnc_neigh_bor['Neighbourhood']),\"Borough Count\",len(vnc_neigh_bor['Borough'].unique()))\n",
    "\n",
    "vnc_neigh_bor.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging the Crime data Table to include Boroughs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Borough</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Break and Enter Commercial</td>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>West End</td>\n",
       "      <td>Central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Break and Enter Commercial</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>West End</td>\n",
       "      <td>Central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Break and Enter Commercial</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>West End</td>\n",
       "      <td>Central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Break and Enter Commercial</td>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>West End</td>\n",
       "      <td>Central</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Break and Enter Commercial</td>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>West End</td>\n",
       "      <td>Central</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Type  Year  Month  Day  Hour Neighbourhood  Borough\n",
       "0  Break and Enter Commercial  2018      3    2     6      West End  Central\n",
       "1  Break and Enter Commercial  2018      6   16    18      West End  Central\n",
       "2  Break and Enter Commercial  2018     12   12     0      West End  Central\n",
       "3  Break and Enter Commercial  2018      3    2     3      West End  Central\n",
       "4  Break and Enter Commercial  2018      3   17    11      West End  Central"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vnc_boroughs_crime = pd.merge(vnc_crime_df,vnc_neigh_bor, on='Neighbourhood')\n",
    "\n",
    "vnc_boroughs_crime.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>34828.0</td>\n",
       "      <td>34828.000000</td>\n",
       "      <td>34828.000000</td>\n",
       "      <td>34828.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>6.555559</td>\n",
       "      <td>15.374785</td>\n",
       "      <td>13.827007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.465889</td>\n",
       "      <td>8.699461</td>\n",
       "      <td>6.678152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Year         Month           Day          Hour\n",
       "count  34828.0  34828.000000  34828.000000  34828.000000\n",
       "mean    2018.0      6.555559     15.374785     13.827007\n",
       "std        0.0      3.465889      8.699461      6.678152\n",
       "min     2018.0      1.000000      1.000000      0.000000\n",
       "25%     2018.0      4.000000      8.000000      9.000000\n",
       "50%     2018.0      7.000000     15.000000     15.000000\n",
       "75%     2018.0     10.000000     23.000000     19.000000\n",
       "max     2018.0     12.000000     31.000000     23.000000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vnc_boroughs_crime.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Type             0\n",
       "Year             0\n",
       "Month            0\n",
       "Day              0\n",
       "Hour             0\n",
       "Neighbourhood    0\n",
       "Borough          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vnc_boroughs_crime.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34828"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(vnc_boroughs_crime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "vnc_boroughs_crime.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34828"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(vnc_boroughs_crime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Central            14042\n",
       "East Side          12400\n",
       "West Side           7204\n",
       "South Vancouver     1182\n",
       "Name: Borough, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vnc_boroughs_crime['Borough'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Methodology<a name=\"methodology\"></a>\n",
    "\n",
    "Categorized the methodologysection into two parts:\n",
    "\n",
    "- [**Exploratory Data Analysis**:](#eda) Visualise the crime repots in different Vancouver boroughs to idenity the safest borough and normalise the neighborhoods of that borough. We will Use the resulting data and find 10 most common venues in each neighborhood.\n",
    "\n",
    "\n",
    "- [**Modelling**:](#mdl) To help stakeholders choose the right neighborhood within a borough we will be clustering similar neighborhoods using K - means clustering which is a form of unsupervised machine learning algorithm that clusters data based on predefined cluster size. We will use K-Means clustering to address this problem so as to group data based on existing venues which will help in the decision making process."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exploratory Data Analysis<a name=\"eda\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"10\" halign=\"left\">Year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type</th>\n",
       "      <th>Break and Enter Commercial</th>\n",
       "      <th>Break and Enter Residential/Other</th>\n",
       "      <th>Mischief</th>\n",
       "      <th>Other Theft</th>\n",
       "      <th>Theft from Vehicle</th>\n",
       "      <th>Theft of Bicycle</th>\n",
       "      <th>Theft of Vehicle</th>\n",
       "      <th>Vehicle Collision or Pedestrian Struck (with Fatality)</th>\n",
       "      <th>Vehicle Collision or Pedestrian Struck (with Injury)</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Borough</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>Central</th>\n",
       "      <td>787</td>\n",
       "      <td>198</td>\n",
       "      <td>2280</td>\n",
       "      <td>2489</td>\n",
       "      <td>6871</td>\n",
       "      <td>857</td>\n",
       "      <td>245</td>\n",
       "      <td>1</td>\n",
       "      <td>314</td>\n",
       "      <td>14042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East Side</th>\n",
       "      <td>786</td>\n",
       "      <td>1043</td>\n",
       "      <td>2192</td>\n",
       "      <td>1674</td>\n",
       "      <td>4754</td>\n",
       "      <td>678</td>\n",
       "      <td>605</td>\n",
       "      <td>8</td>\n",
       "      <td>660</td>\n",
       "      <td>12400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Vancouver</th>\n",
       "      <td>49</td>\n",
       "      <td>156</td>\n",
       "      <td>187</td>\n",
       "      <td>88</td>\n",
       "      <td>483</td>\n",
       "      <td>36</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "      <td>1182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Side</th>\n",
       "      <td>403</td>\n",
       "      <td>1000</td>\n",
       "      <td>1062</td>\n",
       "      <td>696</td>\n",
       "      <td>2838</td>\n",
       "      <td>588</td>\n",
       "      <td>225</td>\n",
       "      <td>3</td>\n",
       "      <td>389</td>\n",
       "      <td>7204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>2025</td>\n",
       "      <td>2397</td>\n",
       "      <td>5721</td>\n",
       "      <td>4947</td>\n",
       "      <td>14946</td>\n",
       "      <td>2159</td>\n",
       "      <td>1146</td>\n",
       "      <td>13</td>\n",
       "      <td>1474</td>\n",
       "      <td>34828</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      Year                                    \\\n",
       "Type            Break and Enter Commercial Break and Enter Residential/Other   \n",
       "Borough                                                                        \n",
       "Central                                787                               198   \n",
       "East Side                              786                              1043   \n",
       "South Vancouver                         49                               156   \n",
       "West Side                              403                              1000   \n",
       "All                                   2025                              2397   \n",
       "\n",
       "                                                                          \\\n",
       "Type            Mischief Other Theft Theft from Vehicle Theft of Bicycle   \n",
       "Borough                                                                    \n",
       "Central             2280        2489               6871              857   \n",
       "East Side           2192        1674               4754              678   \n",
       "South Vancouver      187          88                483               36   \n",
       "West Side           1062         696               2838              588   \n",
       "All                 5721        4947              14946             2159   \n",
       "\n",
       "                                  \\\n",
       "Type            Theft of Vehicle   \n",
       "Borough                            \n",
       "Central                      245   \n",
       "East Side                    605   \n",
       "South Vancouver               71   \n",
       "West Side                    225   \n",
       "All                         1146   \n",
       "\n",
       "                                                                        \\\n",
       "Type            Vehicle Collision or Pedestrian Struck (with Fatality)   \n",
       "Borough                                                                  \n",
       "Central                                                          1       \n",
       "East Side                                                        8       \n",
       "South Vancouver                                                  1       \n",
       "West Side                                                        3       \n",
       "All                                                             13       \n",
       "\n",
       "                                                                             \n",
       "Type            Vehicle Collision or Pedestrian Struck (with Injury)    All  \n",
       "Borough                                                                      \n",
       "Central                                                        314    14042  \n",
       "East Side                                                      660    12400  \n",
       "South Vancouver                                                111     1182  \n",
       "West Side                                                      389     7204  \n",
       "All                                                           1474    34828  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "####Pivoting the table to better understand the data by crimes per borough\n",
    "\n",
    "vnc_crime_cat = pd.pivot_table(vnc_boroughs_crime,\n",
    "                               values=['Year'],\n",
    "                               index=['Borough'],\n",
    "                               columns=['Type'],\n",
    "                               aggfunc=len,\n",
    "                               fill_value=0,\n",
    "                               margins=True)\n",
    "vnc_crime_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#####Merging the Pivoted Column with other columns\n",
    "\n",
    "vnc_crime_cat.reset_index(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Borough</th>\n",
       "      <th colspan=\"10\" halign=\"left\">Year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type</th>\n",
       "      <th></th>\n",
       "      <th>Break and Enter Commercial</th>\n",
       "      <th>Break and Enter Residential/Other</th>\n",
       "      <th>Mischief</th>\n",
       "      <th>Other Theft</th>\n",
       "      <th>Theft from Vehicle</th>\n",
       "      <th>Theft of Bicycle</th>\n",
       "      <th>Theft of Vehicle</th>\n",
       "      <th>Vehicle Collision or Pedestrian Struck (with Fatality)</th>\n",
       "      <th>Vehicle Collision or Pedestrian Struck (with Injury)</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Central</td>\n",
       "      <td>787</td>\n",
       "      <td>198</td>\n",
       "      <td>2280</td>\n",
       "      <td>2489</td>\n",
       "      <td>6871</td>\n",
       "      <td>857</td>\n",
       "      <td>245</td>\n",
       "      <td>1</td>\n",
       "      <td>314</td>\n",
       "      <td>14042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>East Side</td>\n",
       "      <td>786</td>\n",
       "      <td>1043</td>\n",
       "      <td>2192</td>\n",
       "      <td>1674</td>\n",
       "      <td>4754</td>\n",
       "      <td>678</td>\n",
       "      <td>605</td>\n",
       "      <td>8</td>\n",
       "      <td>660</td>\n",
       "      <td>12400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>South Vancouver</td>\n",
       "      <td>49</td>\n",
       "      <td>156</td>\n",
       "      <td>187</td>\n",
       "      <td>88</td>\n",
       "      <td>483</td>\n",
       "      <td>36</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "      <td>1182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>West Side</td>\n",
       "      <td>403</td>\n",
       "      <td>1000</td>\n",
       "      <td>1062</td>\n",
       "      <td>696</td>\n",
       "      <td>2838</td>\n",
       "      <td>588</td>\n",
       "      <td>225</td>\n",
       "      <td>3</td>\n",
       "      <td>389</td>\n",
       "      <td>7204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>All</td>\n",
       "      <td>2025</td>\n",
       "      <td>2397</td>\n",
       "      <td>5721</td>\n",
       "      <td>4947</td>\n",
       "      <td>14946</td>\n",
       "      <td>2159</td>\n",
       "      <td>1146</td>\n",
       "      <td>13</td>\n",
       "      <td>1474</td>\n",
       "      <td>34828</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Borough                       Year  \\\n",
       "Type                  Break and Enter Commercial   \n",
       "0             Central                        787   \n",
       "1           East Side                        786   \n",
       "2     South Vancouver                         49   \n",
       "3           West Side                        403   \n",
       "4                 All                       2025   \n",
       "\n",
       "                                                             \\\n",
       "Type Break and Enter Residential/Other Mischief Other Theft   \n",
       "0                                  198     2280        2489   \n",
       "1                                 1043     2192        1674   \n",
       "2                                  156      187          88   \n",
       "3                                 1000     1062         696   \n",
       "4                                 2397     5721        4947   \n",
       "\n",
       "                                                           \\\n",
       "Type Theft from Vehicle Theft of Bicycle Theft of Vehicle   \n",
       "0                  6871              857              245   \n",
       "1                  4754              678              605   \n",
       "2                   483               36               71   \n",
       "3                  2838              588              225   \n",
       "4                 14946             2159             1146   \n",
       "\n",
       "                                                             \\\n",
       "Type Vehicle Collision or Pedestrian Struck (with Fatality)   \n",
       "0                                                     1       \n",
       "1                                                     8       \n",
       "2                                                     1       \n",
       "3                                                     3       \n",
       "4                                                    13       \n",
       "\n",
       "                                                                  \n",
       "Type Vehicle Collision or Pedestrian Struck (with Injury)    All  \n",
       "0                                                   314    14042  \n",
       "1                                                   660    12400  \n",
       "2                                                   111     1182  \n",
       "3                                                   389     7204  \n",
       "4                                                  1474    34828  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vnc_crime_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "vnc_crime_cat.columns = vnc_crime_cat.columns.map(''.join)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Borough</th>\n",
       "      <th>YearBreak and Enter Commercial</th>\n",
       "      <th>YearBreak and Enter Residential/Other</th>\n",
       "      <th>YearMischief</th>\n",
       "      <th>YearOther Theft</th>\n",
       "      <th>YearTheft from Vehicle</th>\n",
       "      <th>YearTheft of Bicycle</th>\n",
       "      <th>YearTheft of Vehicle</th>\n",
       "      <th>YearVehicle Collision or Pedestrian Struck (with Fatality)</th>\n",
       "      <th>YearVehicle Collision or Pedestrian Struck (with Injury)</th>\n",
       "      <th>YearAll</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Central</td>\n",
       "      <td>787</td>\n",
       "      <td>198</td>\n",
       "      <td>2280</td>\n",
       "      <td>2489</td>\n",
       "      <td>6871</td>\n",
       "      <td>857</td>\n",
       "      <td>245</td>\n",
       "      <td>1</td>\n",
       "      <td>314</td>\n",
       "      <td>14042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>East Side</td>\n",
       "      <td>786</td>\n",
       "      <td>1043</td>\n",
       "      <td>2192</td>\n",
       "      <td>1674</td>\n",
       "      <td>4754</td>\n",
       "      <td>678</td>\n",
       "      <td>605</td>\n",
       "      <td>8</td>\n",
       "      <td>660</td>\n",
       "      <td>12400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>South Vancouver</td>\n",
       "      <td>49</td>\n",
       "      <td>156</td>\n",
       "      <td>187</td>\n",
       "      <td>88</td>\n",
       "      <td>483</td>\n",
       "      <td>36</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "      <td>1182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>West Side</td>\n",
       "      <td>403</td>\n",
       "      <td>1000</td>\n",
       "      <td>1062</td>\n",
       "      <td>696</td>\n",
       "      <td>2838</td>\n",
       "      <td>588</td>\n",
       "      <td>225</td>\n",
       "      <td>3</td>\n",
       "      <td>389</td>\n",
       "      <td>7204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>All</td>\n",
       "      <td>2025</td>\n",
       "      <td>2397</td>\n",
       "      <td>5721</td>\n",
       "      <td>4947</td>\n",
       "      <td>14946</td>\n",
       "      <td>2159</td>\n",
       "      <td>1146</td>\n",
       "      <td>13</td>\n",
       "      <td>1474</td>\n",
       "      <td>34828</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Borough  YearBreak and Enter Commercial  \\\n",
       "0          Central                             787   \n",
       "1        East Side                             786   \n",
       "2  South Vancouver                              49   \n",
       "3        West Side                             403   \n",
       "4              All                            2025   \n",
       "\n",
       "   YearBreak and Enter Residential/Other  YearMischief  YearOther Theft  \\\n",
       "0                                    198          2280             2489   \n",
       "1                                   1043          2192             1674   \n",
       "2                                    156           187               88   \n",
       "3                                   1000          1062              696   \n",
       "4                                   2397          5721             4947   \n",
       "\n",
       "   YearTheft from Vehicle  YearTheft of Bicycle  YearTheft of Vehicle  \\\n",
       "0                    6871                   857                   245   \n",
       "1                    4754                   678                   605   \n",
       "2                     483                    36                    71   \n",
       "3                    2838                   588                   225   \n",
       "4                   14946                  2159                  1146   \n",
       "\n",
       "   YearVehicle Collision or Pedestrian Struck (with Fatality)  \\\n",
       "0                                                  1            \n",
       "1                                                  8            \n",
       "2                                                  1            \n",
       "3                                                  3            \n",
       "4                                                 13            \n",
       "\n",
       "   YearVehicle Collision or Pedestrian Struck (with Injury)  YearAll  \n",
       "0                                                314           14042  \n",
       "1                                                660           12400  \n",
       "2                                                111            1182  \n",
       "3                                                389            7204  \n",
       "4                                               1474           34828  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vnc_crime_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "vnc_crime_cat.rename(columns={'YearAll':'Total'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Borough</th>\n",
       "      <th>YearBreak and Enter Commercial</th>\n",
       "      <th>YearBreak and Enter Residential/Other</th>\n",
       "      <th>YearMischief</th>\n",
       "      <th>YearOther Theft</th>\n",
       "      <th>YearTheft from Vehicle</th>\n",
       "      <th>YearTheft of Bicycle</th>\n",
       "      <th>YearTheft of Vehicle</th>\n",
       "      <th>YearVehicle Collision or Pedestrian Struck (with Fatality)</th>\n",
       "      <th>YearVehicle Collision or Pedestrian Struck (with Injury)</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Central</td>\n",
       "      <td>787</td>\n",
       "      <td>198</td>\n",
       "      <td>2280</td>\n",
       "      <td>2489</td>\n",
       "      <td>6871</td>\n",
       "      <td>857</td>\n",
       "      <td>245</td>\n",
       "      <td>1</td>\n",
       "      <td>314</td>\n",
       "      <td>14042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>East Side</td>\n",
       "      <td>786</td>\n",
       "      <td>1043</td>\n",
       "      <td>2192</td>\n",
       "      <td>1674</td>\n",
       "      <td>4754</td>\n",
       "      <td>678</td>\n",
       "      <td>605</td>\n",
       "      <td>8</td>\n",
       "      <td>660</td>\n",
       "      <td>12400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>South Vancouver</td>\n",
       "      <td>49</td>\n",
       "      <td>156</td>\n",
       "      <td>187</td>\n",
       "      <td>88</td>\n",
       "      <td>483</td>\n",
       "      <td>36</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "      <td>1182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>West Side</td>\n",
       "      <td>403</td>\n",
       "      <td>1000</td>\n",
       "      <td>1062</td>\n",
       "      <td>696</td>\n",
       "      <td>2838</td>\n",
       "      <td>588</td>\n",
       "      <td>225</td>\n",
       "      <td>3</td>\n",
       "      <td>389</td>\n",
       "      <td>7204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>All</td>\n",
       "      <td>2025</td>\n",
       "      <td>2397</td>\n",
       "      <td>5721</td>\n",
       "      <td>4947</td>\n",
       "      <td>14946</td>\n",
       "      <td>2159</td>\n",
       "      <td>1146</td>\n",
       "      <td>13</td>\n",
       "      <td>1474</td>\n",
       "      <td>34828</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Borough  YearBreak and Enter Commercial  \\\n",
       "0          Central                             787   \n",
       "1        East Side                             786   \n",
       "2  South Vancouver                              49   \n",
       "3        West Side                             403   \n",
       "4              All                            2025   \n",
       "\n",
       "   YearBreak and Enter Residential/Other  YearMischief  YearOther Theft  \\\n",
       "0                                    198          2280             2489   \n",
       "1                                   1043          2192             1674   \n",
       "2                                    156           187               88   \n",
       "3                                   1000          1062              696   \n",
       "4                                   2397          5721             4947   \n",
       "\n",
       "   YearTheft from Vehicle  YearTheft of Bicycle  YearTheft of Vehicle  \\\n",
       "0                    6871                   857                   245   \n",
       "1                    4754                   678                   605   \n",
       "2                     483                    36                    71   \n",
       "3                    2838                   588                   225   \n",
       "4                   14946                  2159                  1146   \n",
       "\n",
       "   YearVehicle Collision or Pedestrian Struck (with Fatality)  \\\n",
       "0                                                  1            \n",
       "1                                                  8            \n",
       "2                                                  1            \n",
       "3                                                  3            \n",
       "4                                                 13            \n",
       "\n",
       "   YearVehicle Collision or Pedestrian Struck (with Injury)  Total  \n",
       "0                                                314         14042  \n",
       "1                                                660         12400  \n",
       "2                                                111          1182  \n",
       "3                                                389          7204  \n",
       "4                                               1474         34828  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vnc_crime_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Borough</th>\n",
       "      <th>YearBreak and Enter Commercial</th>\n",
       "      <th>YearBreak and Enter Residential/Other</th>\n",
       "      <th>YearMischief</th>\n",
       "      <th>YearOther Theft</th>\n",
       "      <th>YearTheft from Vehicle</th>\n",
       "      <th>YearTheft of Bicycle</th>\n",
       "      <th>YearTheft of Vehicle</th>\n",
       "      <th>YearVehicle Collision or Pedestrian Struck (with Fatality)</th>\n",
       "      <th>YearVehicle Collision or Pedestrian Struck (with Injury)</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Central</td>\n",
       "      <td>787</td>\n",
       "      <td>198</td>\n",
       "      <td>2280</td>\n",
       "      <td>2489</td>\n",
       "      <td>6871</td>\n",
       "      <td>857</td>\n",
       "      <td>245</td>\n",
       "      <td>1</td>\n",
       "      <td>314</td>\n",
       "      <td>14042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>East Side</td>\n",
       "      <td>786</td>\n",
       "      <td>1043</td>\n",
       "      <td>2192</td>\n",
       "      <td>1674</td>\n",
       "      <td>4754</td>\n",
       "      <td>678</td>\n",
       "      <td>605</td>\n",
       "      <td>8</td>\n",
       "      <td>660</td>\n",
       "      <td>12400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>South Vancouver</td>\n",
       "      <td>49</td>\n",
       "      <td>156</td>\n",
       "      <td>187</td>\n",
       "      <td>88</td>\n",
       "      <td>483</td>\n",
       "      <td>36</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "      <td>1182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>West Side</td>\n",
       "      <td>403</td>\n",
       "      <td>1000</td>\n",
       "      <td>1062</td>\n",
       "      <td>696</td>\n",
       "      <td>2838</td>\n",
       "      <td>588</td>\n",
       "      <td>225</td>\n",
       "      <td>3</td>\n",
       "      <td>389</td>\n",
       "      <td>7204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Borough  YearBreak and Enter Commercial  \\\n",
       "0          Central                             787   \n",
       "1        East Side                             786   \n",
       "2  South Vancouver                              49   \n",
       "3        West Side                             403   \n",
       "\n",
       "   YearBreak and Enter Residential/Other  YearMischief  YearOther Theft  \\\n",
       "0                                    198          2280             2489   \n",
       "1                                   1043          2192             1674   \n",
       "2                                    156           187               88   \n",
       "3                                   1000          1062              696   \n",
       "\n",
       "   YearTheft from Vehicle  YearTheft of Bicycle  YearTheft of Vehicle  \\\n",
       "0                    6871                   857                   245   \n",
       "1                    4754                   678                   605   \n",
       "2                     483                    36                    71   \n",
       "3                    2838                   588                   225   \n",
       "\n",
       "   YearVehicle Collision or Pedestrian Struck (with Fatality)  \\\n",
       "0                                                  1            \n",
       "1                                                  8            \n",
       "2                                                  1            \n",
       "3                                                  3            \n",
       "\n",
       "   YearVehicle Collision or Pedestrian Struck (with Injury)  Total  \n",
       "0                                                314         14042  \n",
       "1                                                660         12400  \n",
       "2                                                111          1182  \n",
       "3                                                389          7204  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To ignore bottom 'All' in Borough\n",
    "vnc_crime_cat = vnc_crime_cat.head(4)\n",
    "vnc_crime_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"10\" halign=\"left\">Year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type</th>\n",
       "      <th>Break and Enter Commercial</th>\n",
       "      <th>Break and Enter Residential/Other</th>\n",
       "      <th>Mischief</th>\n",
       "      <th>Other Theft</th>\n",
       "      <th>Theft from Vehicle</th>\n",
       "      <th>Theft of Bicycle</th>\n",
       "      <th>Theft of Vehicle</th>\n",
       "      <th>Vehicle Collision or Pedestrian Struck (with Fatality)</th>\n",
       "      <th>Vehicle Collision or Pedestrian Struck (with Injury)</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighbourhood</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>Arbutus Ridge</th>\n",
       "      <td>12</td>\n",
       "      <td>78</td>\n",
       "      <td>49</td>\n",
       "      <td>18</td>\n",
       "      <td>111</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Central Business District</th>\n",
       "      <td>551</td>\n",
       "      <td>124</td>\n",
       "      <td>1812</td>\n",
       "      <td>2034</td>\n",
       "      <td>5301</td>\n",
       "      <td>640</td>\n",
       "      <td>165</td>\n",
       "      <td>0</td>\n",
       "      <td>230</td>\n",
       "      <td>10857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dunbar-Southlands</th>\n",
       "      <td>8</td>\n",
       "      <td>106</td>\n",
       "      <td>81</td>\n",
       "      <td>31</td>\n",
       "      <td>199</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fairview</th>\n",
       "      <td>138</td>\n",
       "      <td>73</td>\n",
       "      <td>233</td>\n",
       "      <td>297</td>\n",
       "      <td>692</td>\n",
       "      <td>245</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>1795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grandview-Woodland</th>\n",
       "      <td>148</td>\n",
       "      <td>162</td>\n",
       "      <td>304</td>\n",
       "      <td>215</td>\n",
       "      <td>634</td>\n",
       "      <td>110</td>\n",
       "      <td>123</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>1761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hastings-Sunrise</th>\n",
       "      <td>48</td>\n",
       "      <td>117</td>\n",
       "      <td>195</td>\n",
       "      <td>107</td>\n",
       "      <td>607</td>\n",
       "      <td>52</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>1270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kensington-Cedar Cottage</th>\n",
       "      <td>62</td>\n",
       "      <td>145</td>\n",
       "      <td>255</td>\n",
       "      <td>148</td>\n",
       "      <td>541</td>\n",
       "      <td>69</td>\n",
       "      <td>71</td>\n",
       "      <td>3</td>\n",
       "      <td>97</td>\n",
       "      <td>1391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kerrisdale</th>\n",
       "      <td>24</td>\n",
       "      <td>97</td>\n",
       "      <td>49</td>\n",
       "      <td>9</td>\n",
       "      <td>172</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Killarney</th>\n",
       "      <td>34</td>\n",
       "      <td>72</td>\n",
       "      <td>90</td>\n",
       "      <td>31</td>\n",
       "      <td>240</td>\n",
       "      <td>19</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kitsilano</th>\n",
       "      <td>106</td>\n",
       "      <td>165</td>\n",
       "      <td>320</td>\n",
       "      <td>154</td>\n",
       "      <td>755</td>\n",
       "      <td>189</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>61</td>\n",
       "      <td>1802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marpole</th>\n",
       "      <td>44</td>\n",
       "      <td>125</td>\n",
       "      <td>134</td>\n",
       "      <td>75</td>\n",
       "      <td>290</td>\n",
       "      <td>34</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>87</td>\n",
       "      <td>828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mount Pleasant</th>\n",
       "      <td>205</td>\n",
       "      <td>124</td>\n",
       "      <td>353</td>\n",
       "      <td>493</td>\n",
       "      <td>822</td>\n",
       "      <td>232</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>2396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Musqueam</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oakridge</th>\n",
       "      <td>19</td>\n",
       "      <td>123</td>\n",
       "      <td>64</td>\n",
       "      <td>63</td>\n",
       "      <td>164</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Renfrew-Collingwood</th>\n",
       "      <td>91</td>\n",
       "      <td>156</td>\n",
       "      <td>243</td>\n",
       "      <td>472</td>\n",
       "      <td>569</td>\n",
       "      <td>37</td>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "      <td>102</td>\n",
       "      <td>1762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Riley Park</th>\n",
       "      <td>35</td>\n",
       "      <td>122</td>\n",
       "      <td>140</td>\n",
       "      <td>53</td>\n",
       "      <td>378</td>\n",
       "      <td>52</td>\n",
       "      <td>39</td>\n",
       "      <td>2</td>\n",
       "      <td>45</td>\n",
       "      <td>866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shaughnessy</th>\n",
       "      <td>12</td>\n",
       "      <td>120</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>187</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Cambie</th>\n",
       "      <td>22</td>\n",
       "      <td>42</td>\n",
       "      <td>41</td>\n",
       "      <td>38</td>\n",
       "      <td>111</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stanley Park</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>109</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Strathcona</th>\n",
       "      <td>160</td>\n",
       "      <td>124</td>\n",
       "      <td>527</td>\n",
       "      <td>81</td>\n",
       "      <td>821</td>\n",
       "      <td>108</td>\n",
       "      <td>76</td>\n",
       "      <td>2</td>\n",
       "      <td>88</td>\n",
       "      <td>1987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunset</th>\n",
       "      <td>37</td>\n",
       "      <td>93</td>\n",
       "      <td>175</td>\n",
       "      <td>105</td>\n",
       "      <td>382</td>\n",
       "      <td>18</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Victoria-Fraserview</th>\n",
       "      <td>15</td>\n",
       "      <td>80</td>\n",
       "      <td>94</td>\n",
       "      <td>57</td>\n",
       "      <td>239</td>\n",
       "      <td>15</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West End</th>\n",
       "      <td>230</td>\n",
       "      <td>72</td>\n",
       "      <td>460</td>\n",
       "      <td>455</td>\n",
       "      <td>1461</td>\n",
       "      <td>203</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>72</td>\n",
       "      <td>3031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Point Grey</th>\n",
       "      <td>18</td>\n",
       "      <td>71</td>\n",
       "      <td>50</td>\n",
       "      <td>11</td>\n",
       "      <td>157</td>\n",
       "      <td>32</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>2025</td>\n",
       "      <td>2397</td>\n",
       "      <td>5721</td>\n",
       "      <td>4947</td>\n",
       "      <td>14946</td>\n",
       "      <td>2159</td>\n",
       "      <td>1146</td>\n",
       "      <td>13</td>\n",
       "      <td>1474</td>\n",
       "      <td>34828</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Year  \\\n",
       "Type                      Break and Enter Commercial   \n",
       "Neighbourhood                                          \n",
       "Arbutus Ridge                                     12   \n",
       "Central Business District                        551   \n",
       "Dunbar-Southlands                                  8   \n",
       "Fairview                                         138   \n",
       "Grandview-Woodland                               148   \n",
       "Hastings-Sunrise                                  48   \n",
       "Kensington-Cedar Cottage                          62   \n",
       "Kerrisdale                                        24   \n",
       "Killarney                                         34   \n",
       "Kitsilano                                        106   \n",
       "Marpole                                           44   \n",
       "Mount Pleasant                                   205   \n",
       "Musqueam                                           0   \n",
       "Oakridge                                          19   \n",
       "Renfrew-Collingwood                               91   \n",
       "Riley Park                                        35   \n",
       "Shaughnessy                                       12   \n",
       "South Cambie                                      22   \n",
       "Stanley Park                                       6   \n",
       "Strathcona                                       160   \n",
       "Sunset                                            37   \n",
       "Victoria-Fraserview                               15   \n",
       "West End                                         230   \n",
       "West Point Grey                                   18   \n",
       "All                                             2025   \n",
       "\n",
       "                                                                      \\\n",
       "Type                      Break and Enter Residential/Other Mischief   \n",
       "Neighbourhood                                                          \n",
       "Arbutus Ridge                                            78       49   \n",
       "Central Business District                               124     1812   \n",
       "Dunbar-Southlands                                       106       81   \n",
       "Fairview                                                 73      233   \n",
       "Grandview-Woodland                                      162      304   \n",
       "Hastings-Sunrise                                        117      195   \n",
       "Kensington-Cedar Cottage                                145      255   \n",
       "Kerrisdale                                               97       49   \n",
       "Killarney                                                72       90   \n",
       "Kitsilano                                               165      320   \n",
       "Marpole                                                 125      134   \n",
       "Mount Pleasant                                          124      353   \n",
       "Musqueam                                                  4        3   \n",
       "Oakridge                                                123       64   \n",
       "Renfrew-Collingwood                                     156      243   \n",
       "Riley Park                                              122      140   \n",
       "Shaughnessy                                             120       41   \n",
       "South Cambie                                             42       41   \n",
       "Stanley Park                                              2        8   \n",
       "Strathcona                                              124      527   \n",
       "Sunset                                                   93      175   \n",
       "Victoria-Fraserview                                      80       94   \n",
       "West End                                                 72      460   \n",
       "West Point Grey                                          71       50   \n",
       "All                                                    2397     5721   \n",
       "\n",
       "                                                                           \\\n",
       "Type                      Other Theft Theft from Vehicle Theft of Bicycle   \n",
       "Neighbourhood                                                               \n",
       "Arbutus Ridge                      18                111               12   \n",
       "Central Business District        2034               5301              640   \n",
       "Dunbar-Southlands                  31                199               16   \n",
       "Fairview                          297                692              245   \n",
       "Grandview-Woodland                215                634              110   \n",
       "Hastings-Sunrise                  107                607               52   \n",
       "Kensington-Cedar Cottage          148                541               69   \n",
       "Kerrisdale                          9                172               13   \n",
       "Killarney                          31                240               19   \n",
       "Kitsilano                         154                755              189   \n",
       "Marpole                            75                290               34   \n",
       "Mount Pleasant                    493                822              232   \n",
       "Musqueam                            0                  4                2   \n",
       "Oakridge                           63                164               18   \n",
       "Renfrew-Collingwood               472                569               37   \n",
       "Riley Park                         53                378               52   \n",
       "Shaughnessy                         0                187               10   \n",
       "South Cambie                       38                111               19   \n",
       "Stanley Park                        0                109               14   \n",
       "Strathcona                         81                821              108   \n",
       "Sunset                            105                382               18   \n",
       "Victoria-Fraserview                57                239               15   \n",
       "West End                          455               1461              203   \n",
       "West Point Grey                    11                157               32   \n",
       "All                              4947              14946             2159   \n",
       "\n",
       "                                            \\\n",
       "Type                      Theft of Vehicle   \n",
       "Neighbourhood                                \n",
       "Arbutus Ridge                           12   \n",
       "Central Business District              165   \n",
       "Dunbar-Southlands                        9   \n",
       "Fairview                                55   \n",
       "Grandview-Woodland                     123   \n",
       "Hastings-Sunrise                        74   \n",
       "Kensington-Cedar Cottage                71   \n",
       "Kerrisdale                              11   \n",
       "Killarney                               33   \n",
       "Kitsilano                               51   \n",
       "Marpole                                 39   \n",
       "Mount Pleasant                          67   \n",
       "Musqueam                                 2   \n",
       "Oakridge                                18   \n",
       "Renfrew-Collingwood                     92   \n",
       "Riley Park                              39   \n",
       "Shaughnessy                             11   \n",
       "South Cambie                             8   \n",
       "Stanley Park                             3   \n",
       "Strathcona                              76   \n",
       "Sunset                                  63   \n",
       "Victoria-Fraserview                     36   \n",
       "West End                                77   \n",
       "West Point Grey                         11   \n",
       "All                                   1146   \n",
       "\n",
       "                                                                                  \\\n",
       "Type                      Vehicle Collision or Pedestrian Struck (with Fatality)   \n",
       "Neighbourhood                                                                      \n",
       "Arbutus Ridge                                                              1       \n",
       "Central Business District                                                  0       \n",
       "Dunbar-Southlands                                                          1       \n",
       "Fairview                                                                   0       \n",
       "Grandview-Woodland                                                         0       \n",
       "Hastings-Sunrise                                                           0       \n",
       "Kensington-Cedar Cottage                                                   3       \n",
       "Kerrisdale                                                                 0       \n",
       "Killarney                                                                  0       \n",
       "Kitsilano                                                                  1       \n",
       "Marpole                                                                    0       \n",
       "Mount Pleasant                                                             0       \n",
       "Musqueam                                                                   0       \n",
       "Oakridge                                                                   0       \n",
       "Renfrew-Collingwood                                                        0       \n",
       "Riley Park                                                                 2       \n",
       "Shaughnessy                                                                0       \n",
       "South Cambie                                                               0       \n",
       "Stanley Park                                                               0       \n",
       "Strathcona                                                                 2       \n",
       "Sunset                                                                     1       \n",
       "Victoria-Fraserview                                                        1       \n",
       "West End                                                                   1       \n",
       "West Point Grey                                                            0       \n",
       "All                                                                       13       \n",
       "\n",
       "                                                                                \\\n",
       "Type                      Vehicle Collision or Pedestrian Struck (with Injury)   \n",
       "Neighbourhood                                                                    \n",
       "Arbutus Ridge                                                             18     \n",
       "Central Business District                                                230     \n",
       "Dunbar-Southlands                                                         23     \n",
       "Fairview                                                                  62     \n",
       "Grandview-Woodland                                                        65     \n",
       "Hastings-Sunrise                                                          70     \n",
       "Kensington-Cedar Cottage                                                  97     \n",
       "Kerrisdale                                                                42     \n",
       "Killarney                                                                 46     \n",
       "Kitsilano                                                                 61     \n",
       "Marpole                                                                   87     \n",
       "Mount Pleasant                                                           100     \n",
       "Musqueam                                                                   2     \n",
       "Oakridge                                                                  30     \n",
       "Renfrew-Collingwood                                                      102     \n",
       "Riley Park                                                                45     \n",
       "Shaughnessy                                                               33     \n",
       "South Cambie                                                              11     \n",
       "Stanley Park                                                              12     \n",
       "Strathcona                                                                88     \n",
       "Sunset                                                                    93     \n",
       "Victoria-Fraserview                                                       63     \n",
       "West End                                                                  72     \n",
       "West Point Grey                                                           22     \n",
       "All                                                                     1474     \n",
       "\n",
       "                                  \n",
       "Type                         All  \n",
       "Neighbourhood                     \n",
       "Arbutus Ridge                311  \n",
       "Central Business District  10857  \n",
       "Dunbar-Southlands            474  \n",
       "Fairview                    1795  \n",
       "Grandview-Woodland          1761  \n",
       "Hastings-Sunrise            1270  \n",
       "Kensington-Cedar Cottage    1391  \n",
       "Kerrisdale                   417  \n",
       "Killarney                    565  \n",
       "Kitsilano                   1802  \n",
       "Marpole                      828  \n",
       "Mount Pleasant              2396  \n",
       "Musqueam                      17  \n",
       "Oakridge                     499  \n",
       "Renfrew-Collingwood         1762  \n",
       "Riley Park                   866  \n",
       "Shaughnessy                  414  \n",
       "South Cambie                 292  \n",
       "Stanley Park                 154  \n",
       "Strathcona                  1987  \n",
       "Sunset                       967  \n",
       "Victoria-Fraserview          600  \n",
       "West End                    3031  \n",
       "West Point Grey              372  \n",
       "All                        34828  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "####Pivoting the table to better understand the data by crimes per neighborhood\n",
    "\n",
    "vnc_crime_neigh = pd.pivot_table(vnc_boroughs_crime,\n",
    "                               values=['Year'],\n",
    "                               index=['Neighbourhood'],\n",
    "                               columns=['Type'],\n",
    "                               aggfunc=len,\n",
    "                               fill_value=0,\n",
    "                               margins=True)\n",
    "vnc_crime_neigh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#####Merging the Pivoted Column with other columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Neighbourhood</th>\n",
       "      <th>YearBreak and Enter Commercial</th>\n",
       "      <th>YearBreak and Enter Residential/Other</th>\n",
       "      <th>YearMischief</th>\n",
       "      <th>YearOther Theft</th>\n",
       "      <th>YearTheft from Vehicle</th>\n",
       "      <th>YearTheft of Bicycle</th>\n",
       "      <th>YearTheft of Vehicle</th>\n",
       "      <th>YearVehicle Collision or Pedestrian Struck (with Fatality)</th>\n",
       "      <th>YearVehicle Collision or Pedestrian Struck (with Injury)</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arbutus Ridge</td>\n",
       "      <td>12</td>\n",
       "      <td>78</td>\n",
       "      <td>49</td>\n",
       "      <td>18</td>\n",
       "      <td>111</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Central Business District</td>\n",
       "      <td>551</td>\n",
       "      <td>124</td>\n",
       "      <td>1812</td>\n",
       "      <td>2034</td>\n",
       "      <td>5301</td>\n",
       "      <td>640</td>\n",
       "      <td>165</td>\n",
       "      <td>0</td>\n",
       "      <td>230</td>\n",
       "      <td>10857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dunbar-Southlands</td>\n",
       "      <td>8</td>\n",
       "      <td>106</td>\n",
       "      <td>81</td>\n",
       "      <td>31</td>\n",
       "      <td>199</td>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fairview</td>\n",
       "      <td>138</td>\n",
       "      <td>73</td>\n",
       "      <td>233</td>\n",
       "      <td>297</td>\n",
       "      <td>692</td>\n",
       "      <td>245</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>1795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Grandview-Woodland</td>\n",
       "      <td>148</td>\n",
       "      <td>162</td>\n",
       "      <td>304</td>\n",
       "      <td>215</td>\n",
       "      <td>634</td>\n",
       "      <td>110</td>\n",
       "      <td>123</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>1761</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Neighbourhood  YearBreak and Enter Commercial  \\\n",
       "0              Arbutus Ridge                              12   \n",
       "1  Central Business District                             551   \n",
       "2          Dunbar-Southlands                               8   \n",
       "3                   Fairview                             138   \n",
       "4         Grandview-Woodland                             148   \n",
       "\n",
       "   YearBreak and Enter Residential/Other  YearMischief  YearOther Theft  \\\n",
       "0                                     78            49               18   \n",
       "1                                    124          1812             2034   \n",
       "2                                    106            81               31   \n",
       "3                                     73           233              297   \n",
       "4                                    162           304              215   \n",
       "\n",
       "   YearTheft from Vehicle  YearTheft of Bicycle  YearTheft of Vehicle  \\\n",
       "0                     111                    12                    12   \n",
       "1                    5301                   640                   165   \n",
       "2                     199                    16                     9   \n",
       "3                     692                   245                    55   \n",
       "4                     634                   110                   123   \n",
       "\n",
       "   YearVehicle Collision or Pedestrian Struck (with Fatality)  \\\n",
       "0                                                  1            \n",
       "1                                                  0            \n",
       "2                                                  1            \n",
       "3                                                  0            \n",
       "4                                                  0            \n",
       "\n",
       "   YearVehicle Collision or Pedestrian Struck (with Injury)  Total  \n",
       "0                                                 18           311  \n",
       "1                                                230         10857  \n",
       "2                                                 23           474  \n",
       "3                                                 62          1795  \n",
       "4                                                 65          1761  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vnc_crime_neigh.reset_index(inplace = True)\n",
    "vnc_crime_neigh.columns = vnc_crime_neigh.columns.map(''.join)\n",
    "vnc_crime_neigh.rename(columns={'YearAll':'Total'}, inplace=True)\n",
    "\n",
    "vnc_crime_neigh.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YearBreak and Enter Commercial</th>\n",
       "      <th>YearBreak and Enter Residential/Other</th>\n",
       "      <th>YearMischief</th>\n",
       "      <th>YearOther Theft</th>\n",
       "      <th>YearTheft from Vehicle</th>\n",
       "      <th>YearTheft of Bicycle</th>\n",
       "      <th>YearTheft of Vehicle</th>\n",
       "      <th>YearVehicle Collision or Pedestrian Struck (with Fatality)</th>\n",
       "      <th>YearVehicle Collision or Pedestrian Struck (with Injury)</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>506.250000</td>\n",
       "      <td>599.250000</td>\n",
       "      <td>1430.25000</td>\n",
       "      <td>1236.750000</td>\n",
       "      <td>3736.500000</td>\n",
       "      <td>539.750000</td>\n",
       "      <td>286.500000</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>368.500000</td>\n",
       "      <td>8707.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>354.409721</td>\n",
       "      <td>488.189427</td>\n",
       "      <td>997.26572</td>\n",
       "      <td>1060.087221</td>\n",
       "      <td>2723.536977</td>\n",
       "      <td>353.955153</td>\n",
       "      <td>226.117226</td>\n",
       "      <td>3.304038</td>\n",
       "      <td>227.060198</td>\n",
       "      <td>5801.870618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>49.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>187.00000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>483.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>1182.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>314.500000</td>\n",
       "      <td>187.500000</td>\n",
       "      <td>843.25000</td>\n",
       "      <td>544.000000</td>\n",
       "      <td>2249.250000</td>\n",
       "      <td>450.000000</td>\n",
       "      <td>186.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>263.250000</td>\n",
       "      <td>5698.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>594.500000</td>\n",
       "      <td>599.000000</td>\n",
       "      <td>1627.00000</td>\n",
       "      <td>1185.000000</td>\n",
       "      <td>3796.000000</td>\n",
       "      <td>633.000000</td>\n",
       "      <td>235.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>351.500000</td>\n",
       "      <td>9802.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>786.250000</td>\n",
       "      <td>1010.750000</td>\n",
       "      <td>2214.00000</td>\n",
       "      <td>1877.750000</td>\n",
       "      <td>5283.250000</td>\n",
       "      <td>722.750000</td>\n",
       "      <td>335.000000</td>\n",
       "      <td>4.250000</td>\n",
       "      <td>456.750000</td>\n",
       "      <td>12810.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>787.000000</td>\n",
       "      <td>1043.000000</td>\n",
       "      <td>2280.00000</td>\n",
       "      <td>2489.000000</td>\n",
       "      <td>6871.000000</td>\n",
       "      <td>857.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>660.000000</td>\n",
       "      <td>14042.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       YearBreak and Enter Commercial  YearBreak and Enter Residential/Other  \\\n",
       "count                        4.000000                               4.000000   \n",
       "mean                       506.250000                             599.250000   \n",
       "std                        354.409721                             488.189427   \n",
       "min                         49.000000                             156.000000   \n",
       "25%                        314.500000                             187.500000   \n",
       "50%                        594.500000                             599.000000   \n",
       "75%                        786.250000                            1010.750000   \n",
       "max                        787.000000                            1043.000000   \n",
       "\n",
       "       YearMischief  YearOther Theft  YearTheft from Vehicle  \\\n",
       "count       4.00000         4.000000                4.000000   \n",
       "mean     1430.25000      1236.750000             3736.500000   \n",
       "std       997.26572      1060.087221             2723.536977   \n",
       "min       187.00000        88.000000              483.000000   \n",
       "25%       843.25000       544.000000             2249.250000   \n",
       "50%      1627.00000      1185.000000             3796.000000   \n",
       "75%      2214.00000      1877.750000             5283.250000   \n",
       "max      2280.00000      2489.000000             6871.000000   \n",
       "\n",
       "       YearTheft of Bicycle  YearTheft of Vehicle  \\\n",
       "count              4.000000              4.000000   \n",
       "mean             539.750000            286.500000   \n",
       "std              353.955153            226.117226   \n",
       "min               36.000000             71.000000   \n",
       "25%              450.000000            186.500000   \n",
       "50%              633.000000            235.000000   \n",
       "75%              722.750000            335.000000   \n",
       "max              857.000000            605.000000   \n",
       "\n",
       "       YearVehicle Collision or Pedestrian Struck (with Fatality)  \\\n",
       "count                                           4.000000            \n",
       "mean                                            3.250000            \n",
       "std                                             3.304038            \n",
       "min                                             1.000000            \n",
       "25%                                             1.000000            \n",
       "50%                                             2.000000            \n",
       "75%                                             4.250000            \n",
       "max                                             8.000000            \n",
       "\n",
       "       YearVehicle Collision or Pedestrian Struck (with Injury)         Total  \n",
       "count                                           4.000000             4.000000  \n",
       "mean                                          368.500000          8707.000000  \n",
       "std                                           227.060198          5801.870618  \n",
       "min                                           111.000000          1182.000000  \n",
       "25%                                           263.250000          5698.500000  \n",
       "50%                                           351.500000          9802.000000  \n",
       "75%                                           456.750000         12810.500000  \n",
       "max                                           660.000000         14042.000000  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vnc_crime_cat.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EXploring by Visualizing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#####Sorting the data by crimes per neighborhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>Neighbourhood</th>\n",
       "      <th>YearBreak and Enter Commercial</th>\n",
       "      <th>YearBreak and Enter Residential/Other</th>\n",
       "      <th>YearMischief</th>\n",
       "      <th>YearOther Theft</th>\n",
       "      <th>YearTheft from Vehicle</th>\n",
       "      <th>YearTheft of Bicycle</th>\n",
       "      <th>YearTheft of Vehicle</th>\n",
       "      <th>YearVehicle Collision or Pedestrian Struck (with Fatality)</th>\n",
       "      <th>YearVehicle Collision or Pedestrian Struck (with Injury)</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Central Business District</td>\n",
       "      <td>551</td>\n",
       "      <td>124</td>\n",
       "      <td>1812</td>\n",
       "      <td>2034</td>\n",
       "      <td>5301</td>\n",
       "      <td>640</td>\n",
       "      <td>165</td>\n",
       "      <td>0</td>\n",
       "      <td>230</td>\n",
       "      <td>10857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>West End</td>\n",
       "      <td>230</td>\n",
       "      <td>72</td>\n",
       "      <td>460</td>\n",
       "      <td>455</td>\n",
       "      <td>1461</td>\n",
       "      <td>203</td>\n",
       "      <td>77</td>\n",
       "      <td>1</td>\n",
       "      <td>72</td>\n",
       "      <td>3031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Mount Pleasant</td>\n",
       "      <td>205</td>\n",
       "      <td>124</td>\n",
       "      <td>353</td>\n",
       "      <td>493</td>\n",
       "      <td>822</td>\n",
       "      <td>232</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>2396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Strathcona</td>\n",
       "      <td>160</td>\n",
       "      <td>124</td>\n",
       "      <td>527</td>\n",
       "      <td>81</td>\n",
       "      <td>821</td>\n",
       "      <td>108</td>\n",
       "      <td>76</td>\n",
       "      <td>2</td>\n",
       "      <td>88</td>\n",
       "      <td>1987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Kitsilano</td>\n",
       "      <td>106</td>\n",
       "      <td>165</td>\n",
       "      <td>320</td>\n",
       "      <td>154</td>\n",
       "      <td>755</td>\n",
       "      <td>189</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>61</td>\n",
       "      <td>1802</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Neighbourhood  YearBreak and Enter Commercial  \\\n",
       "1   Central Business District                             551   \n",
       "22                   West End                             230   \n",
       "11             Mount Pleasant                             205   \n",
       "19                 Strathcona                             160   \n",
       "9                   Kitsilano                             106   \n",
       "\n",
       "    YearBreak and Enter Residential/Other  YearMischief  YearOther Theft  \\\n",
       "1                                     124          1812             2034   \n",
       "22                                     72           460              455   \n",
       "11                                    124           353              493   \n",
       "19                                    124           527               81   \n",
       "9                                     165           320              154   \n",
       "\n",
       "    YearTheft from Vehicle  YearTheft of Bicycle  YearTheft of Vehicle  \\\n",
       "1                     5301                   640                   165   \n",
       "22                    1461                   203                    77   \n",
       "11                     822                   232                    67   \n",
       "19                     821                   108                    76   \n",
       "9                      755                   189                    51   \n",
       "\n",
       "    YearVehicle Collision or Pedestrian Struck (with Fatality)  \\\n",
       "1                                                   0            \n",
       "22                                                  1            \n",
       "11                                                  0            \n",
       "19                                                  2            \n",
       "9                                                   1            \n",
       "\n",
       "    YearVehicle Collision or Pedestrian Struck (with Injury)  Total  \n",
       "1                                                 230         10857  \n",
       "22                                                 72          3031  \n",
       "11                                                100          2396  \n",
       "19                                                 88          1987  \n",
       "9                                                  61          1802  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vnc_crime_neigh.sort_values(['Total'], ascending = False, axis = 0, inplace = True )\n",
    "\n",
    "crime_neigh_top5 = vnc_crime_neigh.iloc[1:6]\n",
    "crime_neigh_top5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#####Five Neighborhoods with highest crime\n",
    "\n",
    "per_neigh = crime_neigh_top5[['Neighbourhood','Total']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>Neighbourhood</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Central Business District</td>\n",
       "      <td>10857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>West End</td>\n",
       "      <td>3031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Mount Pleasant</td>\n",
       "      <td>2396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Strathcona</td>\n",
       "      <td>1987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Kitsilano</td>\n",
       "      <td>1802</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Neighbourhood  Total\n",
       "1   Central Business District  10857\n",
       "22                   West End   3031\n",
       "11             Mount Pleasant   2396\n",
       "19                 Strathcona   1987\n",
       "9                   Kitsilano   1802"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "per_neigh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "per_neigh.set_index('Neighbourhood',inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Central Business District</th>\n",
       "      <td>10857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West End</th>\n",
       "      <td>3031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mount Pleasant</th>\n",
       "      <td>2396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Strathcona</th>\n",
       "      <td>1987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kitsilano</th>\n",
       "      <td>1802</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Total\n",
       "Neighbourhood                   \n",
       "Central Business District  10857\n",
       "West End                    3031\n",
       "Mount Pleasant              2396\n",
       "Strathcona                  1987\n",
       "Kitsilano                   1802"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "per_neigh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "ax = per_neigh.plot(kind='bar', figsize=(10, 6), rot=0)\n",
    "\n",
    "ax.set_ylabel('Number of Crimes')\n",
    "ax.set_xlabel('Neighbourhood')\n",
    "ax.set_title('Neighbourhoods in Vancouver with the Highest crimes')\n",
    "\n",
    "for p in ax.patches:\n",
    "    ax.annotate(np.round(p.get_height(),decimals=2), \n",
    "                (p.get_x()+p.get_width()/2., p.get_height()), \n",
    "                ha='center', \n",
    "                va='center', \n",
    "                xytext=(0, 10), \n",
    "                textcoords='offset points',\n",
    "                fontsize = 14,\n",
    "               )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>Neighbourhood</th>\n",
       "      <th>YearBreak and Enter Commercial</th>\n",
       "      <th>YearBreak and Enter Residential/Other</th>\n",
       "      <th>YearMischief</th>\n",
       "      <th>YearOther Theft</th>\n",
       "      <th>YearTheft from Vehicle</th>\n",
       "      <th>YearTheft of Bicycle</th>\n",
       "      <th>YearTheft of Vehicle</th>\n",
       "      <th>YearVehicle Collision or Pedestrian Struck (with Fatality)</th>\n",
       "      <th>YearVehicle Collision or Pedestrian Struck (with Injury)</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>West Point Grey</td>\n",
       "      <td>18</td>\n",
       "      <td>71</td>\n",
       "      <td>50</td>\n",
       "      <td>11</td>\n",
       "      <td>157</td>\n",
       "      <td>32</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arbutus Ridge</td>\n",
       "      <td>12</td>\n",
       "      <td>78</td>\n",
       "      <td>49</td>\n",
       "      <td>18</td>\n",
       "      <td>111</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>South Cambie</td>\n",
       "      <td>22</td>\n",
       "      <td>42</td>\n",
       "      <td>41</td>\n",
       "      <td>38</td>\n",
       "      <td>111</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Stanley Park</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>109</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Musqueam</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Neighbourhood  YearBreak and Enter Commercial  \\\n",
       "23  West Point Grey                              18   \n",
       "0     Arbutus Ridge                              12   \n",
       "17     South Cambie                              22   \n",
       "18     Stanley Park                               6   \n",
       "12         Musqueam                               0   \n",
       "\n",
       "    YearBreak and Enter Residential/Other  YearMischief  YearOther Theft  \\\n",
       "23                                     71            50               11   \n",
       "0                                      78            49               18   \n",
       "17                                     42            41               38   \n",
       "18                                      2             8                0   \n",
       "12                                      4             3                0   \n",
       "\n",
       "    YearTheft from Vehicle  YearTheft of Bicycle  YearTheft of Vehicle  \\\n",
       "23                     157                    32                    11   \n",
       "0                      111                    12                    12   \n",
       "17                     111                    19                     8   \n",
       "18                     109                    14                     3   \n",
       "12                       4                     2                     2   \n",
       "\n",
       "    YearVehicle Collision or Pedestrian Struck (with Fatality)  \\\n",
       "23                                                  0            \n",
       "0                                                   1            \n",
       "17                                                  0            \n",
       "18                                                  0            \n",
       "12                                                  0            \n",
       "\n",
       "    YearVehicle Collision or Pedestrian Struck (with Injury)  Total  \n",
       "23                                                 22           372  \n",
       "0                                                  18           311  \n",
       "17                                                 11           292  \n",
       "18                                                 12           154  \n",
       "12                                                  2            17  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#####Five Neighborhoods with lowest crime\n",
    "\n",
    "crime_neigh_low = vnc_crime_neigh.tail(5)\n",
    "crime_neigh_low"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "per_neigh = crime_neigh_low[['Neighbourhood','Total']]\n",
    "\n",
    "per_neigh.set_index('Neighbourhood',inplace = True)\n",
    "\n",
    "ax = per_neigh.plot(kind='bar', figsize=(10, 6), rot=0)\n",
    "\n",
    "ax.set_ylabel('Number of Crimes')\n",
    "ax.set_xlabel('Neighbourhood')\n",
    "ax.set_title('Neighbourhoods in Vancouver with the lowest crimes')\n",
    "\n",
    "for p in ax.patches:\n",
    "    ax.annotate(np.round(p.get_height(),decimals=2), \n",
    "                (p.get_x()+p.get_width()/2., p.get_height()), \n",
    "                ha='center', \n",
    "                va='center', \n",
    "                xytext=(0, 10), \n",
    "                textcoords='offset points',\n",
    "                fontsize = 14,\n",
    "               )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "####Borough is Vancouver with Highest Crime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"10\" halign=\"left\">Year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type</th>\n",
       "      <th>Break and Enter Commercial</th>\n",
       "      <th>Break and Enter Residential/Other</th>\n",
       "      <th>Mischief</th>\n",
       "      <th>Other Theft</th>\n",
       "      <th>Theft from Vehicle</th>\n",
       "      <th>Theft of Bicycle</th>\n",
       "      <th>Theft of Vehicle</th>\n",
       "      <th>Vehicle Collision or Pedestrian Struck (with Fatality)</th>\n",
       "      <th>Vehicle Collision or Pedestrian Struck (with Injury)</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Borough</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>Central</th>\n",
       "      <td>787</td>\n",
       "      <td>198</td>\n",
       "      <td>2280</td>\n",
       "      <td>2489</td>\n",
       "      <td>6871</td>\n",
       "      <td>857</td>\n",
       "      <td>245</td>\n",
       "      <td>1</td>\n",
       "      <td>314</td>\n",
       "      <td>14042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East Side</th>\n",
       "      <td>786</td>\n",
       "      <td>1043</td>\n",
       "      <td>2192</td>\n",
       "      <td>1674</td>\n",
       "      <td>4754</td>\n",
       "      <td>678</td>\n",
       "      <td>605</td>\n",
       "      <td>8</td>\n",
       "      <td>660</td>\n",
       "      <td>12400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Vancouver</th>\n",
       "      <td>49</td>\n",
       "      <td>156</td>\n",
       "      <td>187</td>\n",
       "      <td>88</td>\n",
       "      <td>483</td>\n",
       "      <td>36</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "      <td>1182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Side</th>\n",
       "      <td>403</td>\n",
       "      <td>1000</td>\n",
       "      <td>1062</td>\n",
       "      <td>696</td>\n",
       "      <td>2838</td>\n",
       "      <td>588</td>\n",
       "      <td>225</td>\n",
       "      <td>3</td>\n",
       "      <td>389</td>\n",
       "      <td>7204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>2025</td>\n",
       "      <td>2397</td>\n",
       "      <td>5721</td>\n",
       "      <td>4947</td>\n",
       "      <td>14946</td>\n",
       "      <td>2159</td>\n",
       "      <td>1146</td>\n",
       "      <td>13</td>\n",
       "      <td>1474</td>\n",
       "      <td>34828</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      Year                                    \\\n",
       "Type            Break and Enter Commercial Break and Enter Residential/Other   \n",
       "Borough                                                                        \n",
       "Central                                787                               198   \n",
       "East Side                              786                              1043   \n",
       "South Vancouver                         49                               156   \n",
       "West Side                              403                              1000   \n",
       "All                                   2025                              2397   \n",
       "\n",
       "                                                                          \\\n",
       "Type            Mischief Other Theft Theft from Vehicle Theft of Bicycle   \n",
       "Borough                                                                    \n",
       "Central             2280        2489               6871              857   \n",
       "East Side           2192        1674               4754              678   \n",
       "South Vancouver      187          88                483               36   \n",
       "West Side           1062         696               2838              588   \n",
       "All                 5721        4947              14946             2159   \n",
       "\n",
       "                                  \\\n",
       "Type            Theft of Vehicle   \n",
       "Borough                            \n",
       "Central                      245   \n",
       "East Side                    605   \n",
       "South Vancouver               71   \n",
       "West Side                    225   \n",
       "All                         1146   \n",
       "\n",
       "                                                                        \\\n",
       "Type            Vehicle Collision or Pedestrian Struck (with Fatality)   \n",
       "Borough                                                                  \n",
       "Central                                                          1       \n",
       "East Side                                                        8       \n",
       "South Vancouver                                                  1       \n",
       "West Side                                                        3       \n",
       "All                                                             13       \n",
       "\n",
       "                                                                             \n",
       "Type            Vehicle Collision or Pedestrian Struck (with Injury)    All  \n",
       "Borough                                                                      \n",
       "Central                                                        314    14042  \n",
       "East Side                                                      660    12400  \n",
       "South Vancouver                                                111     1182  \n",
       "West Side                                                      389     7204  \n",
       "All                                                           1474    34828  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vnc_crime_cat = pd.pivot_table(vnc_boroughs_crime,\n",
    "                               values=['Year'],\n",
    "                               index=['Borough'],\n",
    "                               columns=['Type'],\n",
    "                               aggfunc=len,\n",
    "                               fill_value=0,\n",
    "                               margins=True)\n",
    "vnc_crime_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "vnc_crime_cat.reset_index(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Borough</th>\n",
       "      <th colspan=\"10\" halign=\"left\">Year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type</th>\n",
       "      <th></th>\n",
       "      <th>Break and Enter Commercial</th>\n",
       "      <th>Break and Enter Residential/Other</th>\n",
       "      <th>Mischief</th>\n",
       "      <th>Other Theft</th>\n",
       "      <th>Theft from Vehicle</th>\n",
       "      <th>Theft of Bicycle</th>\n",
       "      <th>Theft of Vehicle</th>\n",
       "      <th>Vehicle Collision or Pedestrian Struck (with Fatality)</th>\n",
       "      <th>Vehicle Collision or Pedestrian Struck (with Injury)</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Central</td>\n",
       "      <td>787</td>\n",
       "      <td>198</td>\n",
       "      <td>2280</td>\n",
       "      <td>2489</td>\n",
       "      <td>6871</td>\n",
       "      <td>857</td>\n",
       "      <td>245</td>\n",
       "      <td>1</td>\n",
       "      <td>314</td>\n",
       "      <td>14042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>East Side</td>\n",
       "      <td>786</td>\n",
       "      <td>1043</td>\n",
       "      <td>2192</td>\n",
       "      <td>1674</td>\n",
       "      <td>4754</td>\n",
       "      <td>678</td>\n",
       "      <td>605</td>\n",
       "      <td>8</td>\n",
       "      <td>660</td>\n",
       "      <td>12400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>South Vancouver</td>\n",
       "      <td>49</td>\n",
       "      <td>156</td>\n",
       "      <td>187</td>\n",
       "      <td>88</td>\n",
       "      <td>483</td>\n",
       "      <td>36</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "      <td>1182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>West Side</td>\n",
       "      <td>403</td>\n",
       "      <td>1000</td>\n",
       "      <td>1062</td>\n",
       "      <td>696</td>\n",
       "      <td>2838</td>\n",
       "      <td>588</td>\n",
       "      <td>225</td>\n",
       "      <td>3</td>\n",
       "      <td>389</td>\n",
       "      <td>7204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>All</td>\n",
       "      <td>2025</td>\n",
       "      <td>2397</td>\n",
       "      <td>5721</td>\n",
       "      <td>4947</td>\n",
       "      <td>14946</td>\n",
       "      <td>2159</td>\n",
       "      <td>1146</td>\n",
       "      <td>13</td>\n",
       "      <td>1474</td>\n",
       "      <td>34828</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Borough                       Year  \\\n",
       "Type                  Break and Enter Commercial   \n",
       "0             Central                        787   \n",
       "1           East Side                        786   \n",
       "2     South Vancouver                         49   \n",
       "3           West Side                        403   \n",
       "4                 All                       2025   \n",
       "\n",
       "                                                             \\\n",
       "Type Break and Enter Residential/Other Mischief Other Theft   \n",
       "0                                  198     2280        2489   \n",
       "1                                 1043     2192        1674   \n",
       "2                                  156      187          88   \n",
       "3                                 1000     1062         696   \n",
       "4                                 2397     5721        4947   \n",
       "\n",
       "                                                           \\\n",
       "Type Theft from Vehicle Theft of Bicycle Theft of Vehicle   \n",
       "0                  6871              857              245   \n",
       "1                  4754              678              605   \n",
       "2                   483               36               71   \n",
       "3                  2838              588              225   \n",
       "4                 14946             2159             1146   \n",
       "\n",
       "                                                             \\\n",
       "Type Vehicle Collision or Pedestrian Struck (with Fatality)   \n",
       "0                                                     1       \n",
       "1                                                     8       \n",
       "2                                                     1       \n",
       "3                                                     3       \n",
       "4                                                    13       \n",
       "\n",
       "                                                                  \n",
       "Type Vehicle Collision or Pedestrian Struck (with Injury)    All  \n",
       "0                                                   314    14042  \n",
       "1                                                   660    12400  \n",
       "2                                                   111     1182  \n",
       "3                                                   389     7204  \n",
       "4                                                  1474    34828  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vnc_crime_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Borough</th>\n",
       "      <th>Break and Enter Commercial</th>\n",
       "      <th>Break and Enter Residential</th>\n",
       "      <th>Mischief</th>\n",
       "      <th>Other</th>\n",
       "      <th>Theft from Vehicle</th>\n",
       "      <th>Theft of Bicycle</th>\n",
       "      <th>Theft of Vehicle</th>\n",
       "      <th>Vehicle Collision or Pedestrian Struck (with Fatality)</th>\n",
       "      <th>Vehicle Collision or Pedestrian Struck (with Injury)</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Central</td>\n",
       "      <td>787</td>\n",
       "      <td>198</td>\n",
       "      <td>2280</td>\n",
       "      <td>2489</td>\n",
       "      <td>6871</td>\n",
       "      <td>857</td>\n",
       "      <td>245</td>\n",
       "      <td>1</td>\n",
       "      <td>314</td>\n",
       "      <td>14042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>East Side</td>\n",
       "      <td>786</td>\n",
       "      <td>1043</td>\n",
       "      <td>2192</td>\n",
       "      <td>1674</td>\n",
       "      <td>4754</td>\n",
       "      <td>678</td>\n",
       "      <td>605</td>\n",
       "      <td>8</td>\n",
       "      <td>660</td>\n",
       "      <td>12400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>South Vancouver</td>\n",
       "      <td>49</td>\n",
       "      <td>156</td>\n",
       "      <td>187</td>\n",
       "      <td>88</td>\n",
       "      <td>483</td>\n",
       "      <td>36</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "      <td>1182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>West Side</td>\n",
       "      <td>403</td>\n",
       "      <td>1000</td>\n",
       "      <td>1062</td>\n",
       "      <td>696</td>\n",
       "      <td>2838</td>\n",
       "      <td>588</td>\n",
       "      <td>225</td>\n",
       "      <td>3</td>\n",
       "      <td>389</td>\n",
       "      <td>7204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Borough  Break and Enter Commercial  Break and Enter Residential  \\\n",
       "0          Central                         787                          198   \n",
       "1        East Side                         786                         1043   \n",
       "2  South Vancouver                          49                          156   \n",
       "3        West Side                         403                         1000   \n",
       "\n",
       "   Mischief  Other  Theft from Vehicle  Theft of Bicycle  Theft of Vehicle  \\\n",
       "0      2280   2489                6871               857               245   \n",
       "1      2192   1674                4754               678               605   \n",
       "2       187     88                 483                36                71   \n",
       "3      1062    696                2838               588               225   \n",
       "\n",
       "   Vehicle Collision or Pedestrian Struck (with Fatality)  \\\n",
       "0                                                  1        \n",
       "1                                                  8        \n",
       "2                                                  1        \n",
       "3                                                  3        \n",
       "\n",
       "   Vehicle Collision or Pedestrian Struck (with Injury)  Total  \n",
       "0                                                314     14042  \n",
       "1                                                660     12400  \n",
       "2                                                111      1182  \n",
       "3                                                389      7204  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vnc_crime_cat.columns = vnc_crime_cat.columns.map(''.join)\n",
    "vnc_crime_cat.rename(columns={'YearAll':'Total',\n",
    "                              'YearBreak and Enter Commercial' : 'Break and Enter Commercial',\n",
    "                              'YearBreak and Enter Residential/Other' : 'Break and Enter Residential',\n",
    "                              'YearMischief' : 'Mischief',\n",
    "                              'YearOther Theft' : 'Other',\n",
    "                              'YearTheft from Vehicle' : 'Theft from Vehicle',\n",
    "                              'YearTheft of Bicycle' : 'Theft of Bicycle',\n",
    "                              'YearTheft of Vehicle' : 'Theft of Vehicle',\n",
    "                              'YearVehicle Collision or Pedestrian Struck (with Fatality)' : 'Vehicle Collision or Pedestrian Struck (with Fatality)',\n",
    "                              'YearVehicle Collision or Pedestrian Struck (with Injury)' : 'Vehicle Collision or Pedestrian Struck (with Injury)'}, inplace=True)\n",
    "# To ignore bottom All in Borough\n",
    "vnc_crime_cat = vnc_crime_cat.head(4)\n",
    "vnc_crime_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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49tlny9z3c889x6xZs5g3bx579uzhvffe45133uH555+31omKimLp0qV8++23/PHHH4wePdrm1qKXlxcjRozgxRdf5KuvvmL37t288MIL7Nq1q9w9UE8//TTffPMNf/75Jzt37mT58uU0bNiQWrVqlWs/l+qxxx4jKyuL2NhYevXqZfO/+Ouvv56PPvqI7du3k5qayoMPPljuhMHb25uxY8cyadIk5syZw+7du0lLS+P1118HHLuWHh4e3HLLLbz55pukpaWxZcsWHnnkEZtbcOcMHjyYTz75hI8++ohHHnnEZt0rr7zCl19+yejRo0lNTeXPP/9kzZo1DBs27JKe+ouMjCQ7O5tVq1ZZE7fIyEhWr15NRkZGmclco0aNcHFx4ZtvviEjI4Pjx4+X+/jnCw4O5scff+Tvv/8mMzPT4TkCBw4cSHBwML179+bbb7/lwIED/Pzzz7z++usVkghcye/Oxa5nbm4uTz31FImJiezfv59t27axZs0a661YOPu5rV+/nvT0dDIzM+0e52JtuKJFRkYSFRXFvffey4oVK9i3bx9btmyx/h0DWLBgAfPmzSMtLY2//vqLjz76iJycHJtzFeemZE6czoYNG6hfvz7169fnxhtvZM6cOcTFxfHcc89Z68yfP5+ePXsyaNAgbrrpJn788Ue++uorWrRoUea+n3zySV555RWmTJlCq1ateOONN4iLi7PpkRo3bhy9e/emf//+dOnSBR8fH+6//36b/bzxxhvceeedPPTQQ3To0IHs7GyGDBli98nHshiGwahRo7jhhhvo2rUrJ0+eZPXq1eX+h+1SNWzYkF69epGdnc3w4cNt1i1cuJDi4mI6dOhA3759uf3222nfvn25j/Hqq68yefJkZs6cyQ033MBtt91mM3bJkWv5/vvv4+3tTefOnRkwYADDhw+nfv36JY710EMPcezYMTIyMnjooYds1nXv3p3ExES2b99Oly5dCA0NZfTo0dSqVeuS5u2rV68erVq1olatWrRp0waA0NBQ6tSpQ9OmTW16TS5Ut25dXn/9deLi4qhfvz533313uY9/vpdffpnjx49z/fXXExAQwN9//+3QdjVr1uT7778nPDycoUOH0rx5c+69915++eUXGjVqdFkxleZKfXcudj3d3NzIzs5m2LBhtGzZkp49e1K3bl0+/vhj6z6mTp3Kli1bCA4OtvYA23OxNlyRTCYTq1at4t5772XMmDG0aNGC3r178/XXX9O0aVMAfH19WbhwIREREbRs2ZJp06bx73//29pjLM7PZFzOYAsRcVhkZCS+vr588cUXVR2KiFPRd0ekbHoAQqQCbN++na1bt3LzzTdTUFDA4sWLWb9+vV47JnIR+u6IlJ+SOZEKYDKZeOedd4iNjaW4uJgWLVqwYsUKTdQpchH67oiUn26zioiIiDgxPQAhIiIi4sSUzImIiIg4MSVzTi45OZm77rqLoKAgTCYTH3zwQal1hw8fjslk4u2337YpP336NDExMfj7++Pl5cVdd93FwYMH7e4jMzPTeqzz511KSkri7rvvpn79+nh6ehIaGsr7779/Rc5RRERESvePfwDiwpn8nc1ff/1Fo0aN6NOnDyNHjiQ7O9vuOX311Vds3LiRevXqceLECZs648eP59tvv2X27NkEBwczevRoevbsyZo1a6yvbjpn8ODBtGjRgvT0dA4fPmx9ddOaNWto3Lgxjz76KIGBgSQlJTF8+HDy8vKsk/nK1cff37/UyVRFzqe2IuWh9mJfaW9eUc+ck+vRowfPPfccffr0sb6j8kIHDx5k4sSJzJkzp8R7EU+cOMGnn37Kiy++SNeuXWnTpg0zZsxg165dbNiwwabu/PnzycvLY8SIESWOERsby7hx42jfvj2NGjVi8ODB9OrVS9MJiIiIVDAlc1e5wsJCoqOjiY2NpVmzZiXW//rrr5w5c4Zu3bpZy4KCgmjWrBmbN2+2lu3YsYO5c+cyY8aMUpPGC+Xm5uLj43P5JyEiIiKlUjJ3lXv77bfx9fVl8ODBdtcfPXoUV1dX/Pz8bMr9/f3JyMgA4NSpU0RHR/Pqq6/afUWSPd999x0//PADAwcOvLwTEBERkTL948fMXc1++uknPv/8c7799ttyb2sYhvX9nxMmTKB9+/b07t3boW1TUlJ4+umneeWVV6zvpBQREblchmGQn59PcXFxpb2jurIZhoGLiws1a9Z0+ByVzF3FNm7cyJEjR2wSqqKiIiZPnsy8efPYsmULAQEBFBUVYbFYMJvN1npZWVl06tQJgB9++IH09HQ+//xz4GxDAwgLCyM6Oprx48dbt/vll194+OGHeeaZZ0rtDRQREbkU+fn5XHPNNSXGf19tCgsLyc/Px8PDw6H6V/en8Q83ePDgEr1pAwcO5O677+ahhx4CIDQ0lGuuuYbk5GTrU6fp6ens2bOH8PBwAD7++GPOnDlj3UdaWhpjxoxh2bJlBAcHW8s3bdrEI488wpgxY3j88ccr+vREROQfpri4+KpP5ADc3Nw4ffq04/UrMBapBCdPnmT//v3A2Uaenp7Ojh078PX1JSgoCH9/f5v6bm5uBAYGEhISAkDt2rUZMGAAr732GmazmSZNmjBq1ChatmxJly5dAGjatKnNPiwWCwAhISHWsXYbN27kkUceYfDgwdx7773W8Xaurq42PX4iIiKX6mq9tWpPec5VD0A4ubS0NHr27EnPnj3Jz8/n7bffpmfPnrz11lsO72PSpEn06tWLJ598koiICLy8vPjggw9KzDFXls8++4y8vDzeffdd2rRpY/254447LuW0REREqhWLxcKtt97KrbfeSlhYGO3atbMun5tz9Zx58+aRl5d30X3269ePtLS0y47NZJwbAPUP5eyTBl9pmqhRykPtRRyltiLlUVp7OXXqFJ6entblosfvuqLHdZ23yqF6U6dOxcvLiyeeeMLu+o4dO7J69eoSM0VcqF+/fkyYMIGbbrqpxLoLzxU0abCIiIjIFbVhwwZuu+02evTowZgxYzh9+jQLFizgyJEj3H///fTr1w84+6alXr160b179xKv1LwSlMyJiIiIlFN+fj6jR4/mnXfeYd26dRQWFrJo0SKGDRtG3bp1+fzzz1m2bBkA48aNY/Xq1SQkJLBp0yZ+++23KxpLpSRzc+fO5bHHHmPs2LEl1q1atYoHHniAEydOAGenvXj//feJiYnhmWeeYd++fda6SUlJxMbGEhsbS1JSkrV83759jB07lpiYGN5//33+4XeORUREpIIVFxdz3XXXWR8SvP/++/n555/t1v3Pf/5jHd/+xx9/sGfPnisaS6UkcxERETz//PMlyjMzM9m+fbvNE5fbtm3j8OHDzJw5k+HDhzN//nzg7Kuhli1bxpQpU5gyZQrLli0jNzcXODvQcMSIEcycOZPDhw+TmppaGaclIiIi/1AXjmcrzd9//817773H0qVLSUhIoEePHuTn51/RWCplapJWrVpZp6o434cffsjAgQNtnrzcvHkzXbt2xWQy0bx5c06ePEl2djY7d+4kNDQUb29v4Oz8aKmpqbRu3Zq8vDyaN28OQNeuXUlJSXG6Nw9c6UGcl+pIVQdwHkcHooqIiFS2/Px8/vvf/7J//36Cg4P54osvrJPte3t7k5ubi5+fHzk5OXh4eFC7dm2OHj3K+vXrufnmm69oLFU2z9zmzZvx8/OjcePGNuUWi8Wmp85sNmOxWEq8ocDPz89u+bn6pUlISCAhIQGAuLi4EvOwVZXqlERVF9Xl2kjp3NzcdJ3EIWorUh6ltZcjR47YTBpcVAHHdYSLiwuenp7MmDGDJ554gsLCQsLCwhg6dChubm48/PDDPPzwwwQGBrJixQpCQ0OJjIykUaNGdOjQAVdXV9zc3DCZTNbfL+Tu7u7wd6ZKkrnTp0+zfPlyXnzxxRLr7I13K23iPJPJVO7xcVFRUURFRVmX9ah89aVrU/1puglxlNqKlEdp7eX06dM2c6Be6Ts4hYWFDtUbPXq09fe1a9eW2MeQIUMYMmSIdXnatGl2j3XuNZn2jnv69OkSn0G1mprkyJEjZGRk8Oyzz/LUU0+RlZXFuHHjOHbsGGaz2Sb4rKwsfH198fPzIysry1pusVjw9fXFbDbblGdlZV10XhcRERGRq0WVJHPXXXcd8+fPZ86cOcyZMwez2cwbb7xBnTp1CA8PJzk5GcMw2L17N56envj6+hIWFkZaWhq5ubnk5uaSlpZGWFgYvr6+eHh4sHv3bgzDIDk52fpOUREREZGrXaXcZo2Pj+e3334jJyeHJ554ggceeIDIyEi7ddu0acPWrVuJjY2lRo0aREdHA2cHE953330899xzwNlZk889DPHYY48xd+5cCgoKCAsLc7qHH0REREQulV7nVU1e51VdnmatTvQ0a/WncVDiKLUVKY/S2svJkyfx8vKqgogqn71zrVZj5kRERETKy8XFxeGHFJxZYWEhLi6Op2hVNjWJiIiISHnUrFmT/Px8Tp8+XepMF87OMAxcXFyoWbOmw9somRMRERGnYDKZ8PDwqOowqh3dZhURERFxYkrmRERERJyYkjkRERERJ6ZkTkRERMSJKZkTERERcWJK5kREREScmJI5ERERESemZE5ERETEiSmZExEREXFiSuZEREREnJiSOREREREnpmRO5B9i06ZNDBkyhHbt2hEUFMTSpUut686cOcPkyZOJiooiJCSENm3a8NRTT/G///3P7r4Mw2DgwIG4u7vz1Vdf2aw7duwYMTExtGjRghYtWhATE8Px48dt6uzatYv77ruPpk2b0q5dO6ZPn45hGFf+pEVE/gGUzIn8Q5w8eZLrr7+el19+mZo1a9qsy8vLY/v27cTExLBmzRref/990tPTGThwIIWFhSX29d577+HiYv/Px9NPP82OHTtYvHgxS5YsYceOHcTGxlrX5+Tk8OCDD+Lv78/XX3/NK6+8wjvvvMN77713ZU9YROQfwq2qAxCRytGjRw969OgBwOjRo23W1a5dm08//dSm7I033qB79+7s2bOHli1bWsvT0tKYP38+a9as4aabbrLZZs+ePaxfv56VK1fSvn17637uuece9u7dS0hICMuXLycvL4/4+Hg8PDxo0aIFe/bsYd68eYwYMQKTyVQRpy8ictVSz5yI2JWTkwOAj4+PtSw3N5fo6GjeeOMN/P39S2yzZcsWvLy8CA8Pt5a1b98eT09PtmzZYq3ToUMHPDw8rHUiIiI4fPgw//3vfyvqdERErlpK5kSkhIKCAl555RVuvfVWGjRoYC0fP3483bt3t/bwXSgjIwOz2WzTu2YymfD39ycjIwOAo0ePEhAQYLPdueVzdURExHG6zSoiNgoLC4mJieHEiRMsXLjQWr5s2TJ+++03vvnmmzK3t3eb1DCMEgnehetL21ZERMqmnjkRsSosLCQ6Oppdu3axdOlS/Pz8rOt++OEHdu/eTfPmzbnuuuu47rrrAHjyySfp27cvAIGBgWRmZto8mWoYBllZWdbet4CAgBI9cJmZmdZ1IiJSPuqZExHg7PQk0dHR/P777yxbtozAwECb9ePGjeOJJ56wKevRowcTJkygZ8+eALRr146TJ0+yefNm6wMQmzdv5tSpU7Rr185aZ8qUKeTn51ufqk1OTqZevXo0bNiwok9TROSqo2RO5B/i5MmT7N+/H4Di4mLS09PZsWMHvr6+1K1blxEjRpCWlsYHH3yAyWSy9p7VqlULDw8P6tevT/369Uvst0GDBjRq1AiAZs2a0b17d8aPH8+bb76JYRiMHz/eOn8dwD333MP06dMZPXo0I0eOZN++fcyZM4fRo0frNquIyCUwGf/wmTrT09OrOgQAih6/q6pDqHZc562q6hCuKhs3buT+++8vUX7//fczduxYOnXqZHe7adOm0b9/f7vrgoKCeO+99+jTp4+1LDs7m5deeolvv/0WgNtuu43XXnvN5qnYXbt28cILL5CamoqPjw8PP/ywkrmrnL+/v/V2usjFqL3Yd/4DaedTMqdkrtpSMlf96Q+uOEptRcpD7cW+0pI5PQAhIiIi4sSUzImIiIg4MSVzIiIiIk5MyZyIiIiIE9PUJCJOpjo9LHOkqgM4jx6YEZF/KoqUbXEAACAASURBVPXMiYiIiDgxJXMiIiIiTkzJnIiIiIgTUzInIiIi4sSUzImIiIg4sUp5mnXu3Lls3boVHx8fpk6dCsDixYvZsmULbm5u1K1bl+joaLy8vABYsWIFiYmJuLi4MHToUMLCwgBITU1l4cKFFBcX06NHD/r27QtARkYG8fHx5ObmEhwcTExMDG5uelBXRERErn6V0jMXERHB888/b1MWGhrK1KlTefvtt6lfvz4rVqwA4ODBg2zcuJFp06bxwgsvsGDBAoqLiykuLmbBggU8//zzTJ8+nR9//JGDBw8CsGTJEnr37s3MmTPx8vIiMTGxMk5LREREpMpVSjLXqlUrvL29bcpuuukmXF1dAWjevDkWiwWAlJQUOnfuzDXXXENgYCD16tVj79697N27l3r16lG3bl3c3Nzo3LkzKSkpGIbBzp076dSpE3A2cUxJSamM0xIRERGpctXiXmRiYiKdO3cGwGKx0KxZM+s6Pz8/a6JnNput5WazmT179pCTk4Onp6c1MTy/vj0JCQkkJCQAEBcXh7+//xU/n0tRnSZfrS6qy7WpbtRW7FN7qd7c3Nx0jcRhai/lU+XJ3PLly3F1daVLly4AGIZht569cpPJVO7jRUVFERUVZV3OzMws9z6kcujaSHmovVRv/v7+ukbiMLUX+xo0aGC3vEqfZk1KSmLLli3ExsZaEzOz2UxWVpa1jsViwc/Pr0R5VlYWvr6+1KpVi1OnTlFUVGRTX0REROSfoMqSudTUVL788kvGjRuHu7u7tTw8PJyNGzdy5swZMjIyOHToECEhITRt2pRDhw6RkZFBYWEhGzduJDw8HJPJROvWrdm0aRNwNkEMDw+vqtMSERERqVSVcps1Pj6e3377jZycHJ544gkeeOABVqxYQWFhIa+++ioAzZo1Y/jw4TRs2JCbb76ZMWPG4OLiwrBhw3BxOZtzPvroo0yePJni4mK6d+9Ow4YNARg4cCDx8fF8+umnBAcHExkZWRmnJSIiIlLlTEZpg9T+IdLT06s6BACKHr+rqkOodlznrarqEKoltRX71F6qN42BkvJQe7GvWo6ZExEREZHLo2RORERExIkpmRMRERFxYkrmRERERJyYkjkRERERJ6ZkTkRERMSJKZkTERERcWJK5kREREScmJI5ERERESemZE5ERETEiSmZExEREXFiSuZEREREnJiSOREREREnpmRORERExIkpmRMRERFxYkrmRERERJyYkjkRERERJ6ZkTkRERMSJKZkTERERcWJK5kREREScmJI5ERERESemZE5ERETEiSmZExEREXFiSuZEREREnJiSOREREREnpmRORERExIkpmRMRERFxYkrmRERERJyYkjkRERERJ6ZkTkRERMSJKZkTERERcWJK5kREREScmJI5ERERESemZE5ERETEiSmZExEREXFibpVxkLlz57J161Z8fHyYOnUqALm5uUyfPp2jR48SEBDA6NGj8fb2xjAMFi5cyLZt23B3dyc6OpomTZoAkJSUxPLlywG49957iYiIAGDfvn3MmTOHgoIC2rRpw9ChQzGZTJVxaiIiIiJVqlJ65iIiInj++edtylauXMmNN97IzJkzufHGG1m5ciUA27Zt4/Dhw8ycOZPhw4czf/584Gzyt2zZMqZMmcKUKVNYtmwZubm5AMybN48RI0Ywc+ZMDh8+TGpqamWcloiIiEiVq5RkrlWrVnh7e9uUpaSk0K1bNwC6detGSkoKAJs3b6Zr166YTCaaN2/OyZMnyc7OJjU1ldDQULy9vfH29iY0NJTU1FSys7PJy8ujefPmmEwmunbtat2XiIiIyNWuysbMHT9+HF9fXwB8fX05ceIEABaLBX9/f2s9s9mMxWLBYrFgNput5X5+fnbLz9UXERER+SeolDFz5WEYRomy0sa/mUwmu/XLkpCQQEJCAgBxcXE2iWNVOlLVAVRD1eXaVDdqK/apvVRvbm5uukbiMLWX8qmyZM7Hx4fs7Gx8fX3Jzs6mdu3awNmetczMTGu9rKwsfH198fPz47fffrOWWywWWrVqhdlsJisry6a+n59fqceNiooiKirKunz+saR60bWR8lB7qd78/f11jcRhai/2NWjQwG55ld1mDQ8P5/vvvwfg+++/p3379tby5ORkDMNg9+7deHp64uvrS1hYGGlpaeTm5pKbm0taWhphYWH4+vri4eHB7t27MQyD5ORkwsPDq+q0RERERCpVpfTMxcfH89tvv5GTk8MTTzzBAw88QN++fZk+fTqJiYn4+/szZswYANq0acPWrVuJjY2lRo0aREdHA+Dt7c19993Hc889B0C/fv2sD1U89thjzJ07l4KCAsLCwmjTpk1lnJaIiIhIlTMZ5R10BhQUFODi4oKbW7Ubcldu6enpVR0CAEWP31XVIVQ7rvNWVXUI1ZLain1qL9WbbptJeai92HdZt1kXLVrE3r17Adi6dStDhw5lyJAhbN68+cpFKCIiIiLl5lAy98MPP9CwYUMAli1bRkxMDP/617/45JNPKjQ4ERERESmbQ/dJT58+jbu7Ozk5ORw5coROnToBenpMREREpKo5lMw1aNCADRs2cPjwYUJDQwE4ceIENWrUqNDgRERERKRsDt1mHTZsGGvXrmXHjh30798fgLS0NGtiJyIiIiJVw6GeuZCQEF577TWbsi5dutClS5cKCUpEREREHOPw3CK//vorP/74I8ePH2f8+PH8+eef5OXlccMNN1RkfCIiIiJSBodus65evZp58+ZRv359du3aBUCNGjX49NNPKzQ4ERERESmbQ8ncN998w4QJE+jbty8uLmc3CQoKqjYT7oqIiIj8UzmUzOXl5eHv729TVlhYeFW8AUJERETEmTmUzLVs2ZKVK1falK1evZrWrVtXSFAiIiIi4hiHkrlHH32UX375haeeeor8/HxGjhzJpk2bGDx4cEXHJyIiIiJlcOg+qa+vL6+//jp79+4lMzMTs9lMSEiIdfyciIiIiFQNhwe9mUwmmjVrRrNmzSoyHhEREREpB4eSuQMHDvDhhx9y4MAB8vPzbdZ98sknFRKYiIiIiFycQ8ncjBkz6NixI0OHDtX7WEVERESqEYeSuWPHjtG/f39MJlNFxyMiIiIi5eDQEwzdunXjhx9+qOhYRERERKScHOqZ69u3Ly+++CIrVqzAx8fHZt3EiRMrJDARERERuTiHkrlp06YRGBhIhw4dNGZOREREpBpx+GnW999/X6/vEhEREalmHH6d18GDBys6FhEREREpJ4e62gICAnjttdfo0KFDiTFz/fv3r5DAREREROTiHErmCgoKaNu2LYWFhWRlZVV0TCIiIiLiIIeSuejo6IqOQ0REREQuQanJXEZGBoGBgQAcOXKk1B3UrVv3ykclIiIiIg4pNZl75plnWLRoEQCxsbGl7mDp0qVXPioRERERcUipydy5RA6UsImIiIhUVxedmqS4uJiYmBjOnDlTGfGIiIiISDlcNJlzcXHBxcWFgoKCyohHRERERMrBoadZ77jjDuLj47nnnnvw8/PDZDJZ1+kBCBEREZGq41Ay9/777wPw66+/llin8XQiIiIiVcehZE4Jm4iIiEj1VOaYuYKCAv7++2+76/7++2+NoxMRERGpYmUmc19++SWJiYl21yUlJbFq1aoKCUpEREREHFPmbdaNGzcyYcIEu+v69OnDq6++Sr9+/S4rgK+++orExERMJhMNGzYkOjqaY8eOER8fT25uLsHBwcTExODm5saZM2eYPXs2+/bto1atWowaNcr6looVK1aQmJiIi4sLQ4cOJSws7LLiEhEREXEGZfbMWSwW/Pz87K7z8/PDYrFc1sEtFgurV68mLi6OqVOnUlxczMaNG1myZAm9e/dm5syZeHl5WXsHExMT8fLyYtasWfTu3ZuPPvoIgIMHD7Jx40amTZvGCy+8wIIFCyguLr6s2EREREScQZnJXM2aNcnMzLS7LjMzE3d398sOoLi4mIKCAoqKiigoKKBOnTrs3LmTTp06ARAREUFKSgoAmzdvJiIiAoBOnTqxY8cODMMgJSWFzp07c8011xAYGEi9evXYu3fvZccmIiIiUt2Vmcy1adOGTz75xO66Tz/9lLZt217Wwf38/Ljzzjt58sknGT58OJ6enjRp0gRPT09cXV2tdc71AFosFsxmMwCurq54enqSk5NjU37hNiIiIiJXszLHzA0YMIAXXniBZ599lg4dOuDr60t2dja//PILeXl5vPbaa5d18NzcXFJSUpgzZw6enp5MmzaN1NTUUusbhlGizGQy2S0vTUJCAgkJCQDExcXh7+9f/sArwJGqDqAaqi7XprpRW7FP7aV6c3Nz0zUSh6m9lE+ZyVydOnV44403+Oqrr0hNTSU3Nxdvb2/atWtHnz598Pb2vqyDb9++ncDAQGrXrg1Ax44d+eOPPzh16hRFRUW4urrajNszm81kZWVhNpspKiri1KlTeHt7W8vPKWusX1RUFFFRUdbl0m4jS9XTtZHyUHup3vz9/XWNxGFqL/Y1aNDAbvlF383q7e3NgAEDmDx5MjNmzGDy5MkMGDDgshM5OHux9uzZw+nTpzEMg+3bt3PttdfSunVrNm3aBJydAiU8PByAdu3akZSUBMCmTZto3bo1JpOJ8PBwNm7cyJkzZ8jIyODQoUOEhIRcdnwiIiJSPh07diQoKKjEz8MPPwzArFmzuOOOO7j++uu58cYbGTx4ML///rvNPgzDYOrUqbRt25amTZvSr18//vjjD7vHy8/PJyoqiqCgINLS0ir8/Kojh94AUVGaNWtGp06dGDduHK6urjRu3JioqCjatm1LfHw8n376KcHBwURGRgIQGRnJ7NmziYmJwdvbm1GjRgHQsGFDbr75ZsaMGYOLiwvDhg3DxeWieaqIiIhcYd988w1FRUXW5YyMDG6//XbuvPNOAH766ScGDx7MTTfdhGEYvP322wwYMID169fj6+sLwNSpU3nvvfeYPn06TZs2Zfr06Tz44IMkJyeX6Ex69dVXqV+/Prt27aq8k6xmTEZ5BpxdhdLT06s6BACKHr+rqkOodlznaVJqe9RW7FN7qd502+yfa8aMGbz77rts3boVDw+PEutPnjxJixYtWLBgAbfddhuGYRAeHs4jjzzCyJEjAcjLy+Omm25iwoQJ1h4+gLVr1xIXF8e///1vIiIi+Oabb7jpppsq7dwq2yXfZhURERG5FIZh8Omnn3LvvffaTeTg7MOQxcXF1KlTBzj7utDDhw/TrVs3ax0PDw86duzI5s2brWXp6ek899xzzJo1i5o1a1bsiVRzpSZzL7zwgvX3zz//vFKCERERkatHcnIyf//9Nw8++GCpdV566SVat25Nu3btgLO3ZQECAgJs6gUEBHD06FEAioqKiImJYfjw4dxwww0VFL3zKDWZS09Pp6CgADj7yi0RERGR8vjoo48ICwsrNeGaNGkSKSkpzJs3zzq/bGkMw8BkMgEwc+ZM3NzcGDFixBWP2RmV+gBE+/btGTlyJIGBgRQUFDBx4kS79V5++eUKC05EREScU2ZmJt9++y2TJ0+2u37ixImsWrWKzz//nEaNGlnLz71z/ejRowQFBdns79zccz/++CM///yzzXYAd955J3fddRezZ8++0qdTrZWazEVHR/P777+TkZHB3r176d69e2XGJSIiIk5s6dKl1KhRg7vvvrvEupdeeokvv/ySZcuWlZhK7LrrrqNevXokJycTFhYGnJ1+5JdffuHFF18EYNq0aZw6dcq6zZEjR3jooYeYNWsW7du3r8Czqp7KnJqkRYsWtGjRgsLCQus7UUVERETKYhgGH3/8MXfffXeJqUSef/55vvjiCxYsWICPj491jJyXlxdeXl6YTCZiYmJ4/fXXCQkJoUmTJsyYMQMvLy/uuece4GzCdz4vLy8AGjduXOoTn1czh+aZi4yMZMeOHSQnJ5OdnY2vry9du3bVoEMREREpYePGjRw4cMDu7c4PP/wQgP79+9uUjxkzhrFjxwIwduxYsrKyeOGFFzh+/Dht2rTh448/viIvLLgaOTTP3Lp16/jkk0+IjIwkICCAzMxMEhMT6d+/v82rsZyR5pmrvjRvmH1qK/apvVRvmmdOykPtxb7Seh0d6plbtWoVL774Io0bN7aWde7cmalTpzp9MiciIiLizByaNDgnJ4drr73WpqxBgwbk5uZWSFAiIiIi4hiHkrkWLVqwaNEiTp8+DZx9qmTx4sU0b968QoMTERERkbI5dJv18ccfJz4+niFDhuDt7U1ubi7Nmze3vjNNRERERKqGQ8mcr68vL7/8MllZWdanWc1mc0XHJiIiIpWkOj1cdaSqAziPMzxc5VAyd47ZbFYSJyIiIlKNODRmTkRERESqJyVzIiIiIk7soslccXExO3bsoLCwsDLiEREREZFyuGgy5+LiwptvvombW7mG14mIiIhIJXDoNmvLli3ZvXt3RcciIiIiIuXkUHdbQEAAr7/+OuHh4ZjNZkwmk3XdhS/KFREREZHK41AyV1BQQPv27QGwWCwVGpCIiIiIOM6hZC46Orqi4xARERGRS+DwUw0HDx5k06ZNHD9+nGHDhpGens6ZM2do1KhRRcYnIiIiImVw6AGIn376iYkTJ2KxWEhOTgYgLy+PRYsWVWhwIiIiIlI2h3rmPvvsMyZMmEDjxo356aefAGjUqBEHDhyoyNhERERE5CIc6pk7fvx4idupJpPJ5qlWEREREal8DiVzTZo0sd5ePefHH38kJCSkQoISEREREcc4dJt16NChvPbaayQmJnL69GkmT55Meno6L774YkXHJyIiIiJlcCiZCwoKIj4+ni1bttCuXTvMZjPt2rWjZs2aFR2fiIiIiJTB4alJ3N3dadGiBRaLBT8/PyVyIiIiItWAQ8lcZmYmM2fOZM+ePXh5eXHy5ElCQkKIjY0lICCgomMUERERkVI49ADEnDlzaNKkCQsXLmT+/PksXLiQpk2bMmfOnIqOT0RERETK4FAyt2/fPgYNGmS9tVqzZk0GDRrEvn37KjQ4ERERESmbQ8lcs2bN2Lt3r03Zn3/+SfPmzSskKBERERFxTKlj5pYuXWr9vW7durz++uu0bdsWs9lMVlYW27Zt4//+7/8qJUgRERERsa/UZC4rK8tmuWPHjgCcOHGCa665hg4dOlBQUFCx0YmIiIhImUpN5qKjoyslgJMnT/Luu+/y3//+F5PJxJNPPkmDBg2YPn06R48eJSAggNGjR+Pt7Y1hGCxcuJBt27bh7u5OdHQ0TZo0ASApKYnly5cDcO+99xIREVEp8YuIiIhUJYfnmTt9+jSHDx8mPz/fpvz666+/rAAWLlxIWFgYY8eOpbCwkNOnT7NixQpuvPFG+vbty8qVK1m5ciWDBg1i27ZtHD582DpNyvz585kyZQq5ubksW7aMuLg4AMaPH094eDje3t6XFZuIiIhIdedQMvf999/z/vvv4+bmRo0aNWzWvfPOO5d88FOnTrFr1y6eeuqps8G4ueHm5kZKSgqTJk0CoFu3bkyaNIlBgwaxefNmunbtislkonnz5pw8eZLs7Gx27txJaGioNXkLDQ0lNTVVY/pERETkqudQMrdkyRLGjh1LaGjoFT14RkYGtWvXZu7cufz11180adKEIUOGcPz4cXx9fQHw9fXlxIkTAFgsFvz9/a3bm81mLBYLFosFs9lsLffz88Nisdg9ZkJCAgkJCQDExcXZ7K8qHanqAKqh6nJtqhu1FfvUXqo3Nzc3XaNqTn9b7HOGdutQMufm5karVq2u+MGLiorYv38/jz76KM2aNWPhwoWsXLmy1PqGYZQoM5lMduuWVh4VFUVUVJR1OTMzs5xRS2XRtZHyUHup3vz9/XWNxClVp3bboEEDu+UOzTPXv39/Fi1aZO0hu1LMZjNms5lmzZoB0KlTJ/bv34+Pjw/Z2dkAZGdnU7t2bWv98z/UrKwsfH198fPzs3n61mKxWHv2RERERK5mDvXMNWjQgM8++4y1a9eWWHf+fHTlVadOHcxmM+np6TRo0IDt27dz7bXXcu211/L999/Tt29fvv/+e9q3bw9AeHg4a9as4ZZbbmHPnj14enri6+tLWFgYn3zyCbm5uQCkpaXx0EMPXXJcIiIiIs7CoWRu1qxZdO3alc6dO5d4AOJyPfroo8ycOZPCwkICAwOJjo7GMAymT59OYmIi/v7+jBkzBoA2bdqwdetWYmNjqVGjhnX6FG9vb+677z6ee+45APr166cnWUVEROQfwaFkLjc3l/79+5c6Du1yNG7c2DqlyPleeumlEmUmk4nHHnvM7n4iIyOJjIy84vGJiIiIVGcOjZmLiIggOTm5omMRERERkXJyqGdu7969rFmzhuXLl1OnTh2bdS+//HKFBCYiIiIiF+dQMtejRw969OhR0bGIiIiISDk5lMzpPaciIiIi1ZNDyVxiYmKp6/TQgYiIiEjVcSiZ27Bhg83ysWPHOHz4MC1atFAyJyIiIlKFHErmJk6cWKIsMTGR//3vf1c8IBERERFxnENTk9gTERFR5u1XEREREal4DvXMFRcX2ywXFBSQnJyMl5dXhQQlIiIiIo5xKJl78MEHS5T5+fkxYsSIKx6QiIiIiDjOoWRu9uzZNsvu7u7Url27QgISEREREcc5lMwFBARUdBwiIiIicgnKTOYu9qouk8nESy+9dEUDEhERERHHlZnMdenSxW65xWJh9erVnD59ukKCEhERERHHlJnMXTghcE5ODitWrGDdunV07tyZfv36VWhwIiIiIlI2h8bMnTp1ilWrVrF27Vratm3LG2+8Qb169So6NhERERG5iDKTuYKCAr7++mu++uorWrVqxSuvvELDhg0rKzYRERERuYgyk7mnnnqK4uJi7rrrLpo2bcrx48c5fvy4TZ0bbrihQgMUERERkdKVmczVqFEDgG+//dbuepPJVGIOOhERERGpPGUmc3PmzKmsOERERETkErhUdQAiIiIicumUzImIiIg4MSVzIiIiIk5MyZyIiIiIE1MyJyIiIuLElMyJiIiIODElcyIiIiJOTMmciIiIiBNTMiciIiLixJTMiYiIiDgxJXMiIiIiTkzJnIiIiIgTUzInIiIi4sSUzImIiIg4MSVzIiIiIk7MraoDACguLmb8+PH4+fkxfvx4MjIyiI+PJzc3l+DgYGJiYnBzc+PMmTPMnj2bffv2UatWLUaNGkVgYCAAK1asIDExERcXF4YOHUpYWFgVn5WIiIhIxasWPXPffPMNQUFB1uUlS5bQu3dvZs6ciZeXF4mJiQAkJibi5eXFrFmz6N27Nx999BEABw8eZOPGjUybNo0XXniBBQsWUFxcXCXnIiIiIlKZqjyZy8rKYuvWrfTo0QMAwzDYuXMnnTp1AiAiIoKUlBQANm/eTEREBACdOnVix44dGIZBSkoKnTt35pprriEwMJB69eqxd+/eKjkfERERkcpU5bdZP/jgAwYNGkReXh4AOTk5eHp64urqCoCfnx8WiwUAi8WC2WwGwNXVFU9PT3JycrBYLDRr1sy6z/O3uVBCQgIJCQkAxMXF4e/vX2HnVh5HqjqAaqi6XJvqRm3FPrWX6s3NzU3XqJrT3xb7nKHdVmkyt2XLFnx8fGjSpAk7d+68aH3DMEqUmUwmu+WliYqKIioqyrqcmZnp8LZSuXRtpDzUXqo3f39/XSNxStWp3TZo0MBueZUmc3/88QebN29m27ZtFBQUkJeXxwcffMCpU6coKirC1dUVi8WCn58fAGazmaysLMxmM0VFRZw6dQpvb29r+TnnbyMiIiJyNavSMXMPPfQQ7777LnPmzGHUqFHccMMNxMbG0rp1azZt2gRAUlIS4eHhALRr146kpCQANm3aROvWrTGZTISHh7Nx40bOnDlDRkYGhw4dIiQkpKpOS0RERKTSVPmYOXsGDhxIfHw8n376KcHBwURGRgIQGRnJ7NmziYmJwdvbm1GjRgHQsGFDbr75ZsaMGYOLiwvDhg3DxaXKn+0QERERqXAmozwDzq5C6enpVR0CAEWP31XVIVQ7rvNWVXUI1ZLain1qL9WbxsxVf/rbYl91+ttS2pg5dV+JiIiIODElcyIiIiJOTMmciIiIiBNTMiciIiLixJTMiYiIiDgxJXMiIiIiTkzJnIiIiIgTUzInIiIi4sSUzImIiIg4MSVzIiIiIk5MyZyIiIiIE1MyJyIiIuLElMyJiIiIODElcyIiIiJOTMmciIiIiBNTMiciIiLixJTMiYiIiDgxJXMiIiIiTkzJnIiIiIgTUzInIiIi4sSUzImIiIg4MSVzIiIiIk5MyZyIiIiIE1MyJyIiIuLElMyJiIiIODElcyIiIiJOTMmciIiIiBNTMiciIiLixJTMiYiIiDgxJXMiIiIiTkzJnIiIiIgTUzInIiIi4sSUzImIiIg4MSVzIiIiIk5MyZyIiIiIE3OryoNnZmYyZ84cjh07hslkIioqijvuuIPc3FymT5/O0aNHCQgIYPTo0Xh7e2MYBgsXLmTbtm24u7sTHR1NkyZNAEhKSmL58uUA3HvvvURERFThmYmIiIhUjipN5lxdXXn44Ydp0qQJeXl5jB8/ntDQUJKSkrjxxhvp27cvK1euZOXKlQwaNIht27Zx+PBhZs6cyZ49e5g/fz5TpkwhNzeXZcuWERcXB8D48eMJDw/H29u7Kk9PREREpMJV6W1WX19fa8+ah4cHQUFBWCwWUlJS6NatGwDdunUjJSUFgM2bN9O1a1dMJhPNmzfn5MmTZGdnk5qaSmhoKN7e3nh7exMaGkpqamqVnZeIiIhIZak2Y+YyMjLYv38/ISEhHD9+HF9fX+BswnfixAkALBYL/v7+1m3MZjMWiwWLxYLZbLaW+/n5YbFYKvcERERERKpAld5mPSc/P5+pU6cyZMgQPD09S61nGEaJlsNFUwAAFlJJREFUMpPJZLduaeUJCQkkJCQAEBcXZ5McVqUjVR1ANVRdrk11o7Zin9pL9ebm5qZrVM3pb4t9ztBuqzyZKywsZOrUqXTp0oWOHTsC4OPjQ3Z2Nr6+vmRnZ1O79v+3d+9hVVX5H8ffHK4qUlxTEMQbOApIKYhomWla6eNYoU5qk+OTjoOpjXiZnIcnf00O4gXzOk4qaj4zmplddBo1dbyipZhMigOKlxHBEI+EgIBw+P3h434kwGIS4eDn9Rd777X3Wevsxdrfs9bea7sAt3vicnNzjX2vXbuGq6srbm5upKamGuvNZjOdOnWq9vP69etHv379jOW7jycNi86N1IbqS8Pm4eGhcyRWqSHVW29v72rX1+swa0VFBStWrMDHx4dBgwYZ67t168a+ffsA2LdvH2FhYcb6/fv3U1FRQXp6Ok2bNsXV1ZXQ0FBSUlIoKCigoKCAlJQUQkND66VMIiKN0ZEjRxg9ejRdu3bFx8eHDz/8sNL2L774ghEjRhAcHIyPjw9JSUlVjpGTk8PEiRMJDQ2lffv29OvXz5iFAODSpUvExMTQo0cP2rVrR48ePYiLi+PmzZt1Xj4Ra1avPXNpaWns378fPz8/pk2bBsArr7zCkCFDWLhwIXv27MHDw4MpU6YA8Pjjj3P8+HEmTZqEg4MD0dHRADg7O/Pyyy/z1ltvARAVFaUnWUVE7qPCwkICAwOJiopi8uTJVbYXFRXRrVs3XnrppWq3A0yePJm8vDwSExNxd3fnn//8J5MmTcLb25uIiAjOnj1LeXk5cXFxtGnThjNnzjBjxgyuX7/O3Llz67qIIlbLpqK6G9EeIllZWfWdBQDKxw6u7yw0OLYrP6/vLDRIqivVU315cDp06MC7777L8OHDq2wzm80EBwfz0UcfERkZaaz38PDA1dW1yn7h4eGMGTOG8ePHV/tZa9euZd68eZw6der+F0QqUdtSvYbUtjTIYVYREXl4hIeHs3XrVsxmMxaLhR07dnDt2jV69epV4z4FBQU8+uijDzCXItan3h+AEBGRh8OKFSuIjo4mODgYOzs7HBwcWL58OUFBQdWmv3z5MitWrGDixIkPOKci1kXBnIiIPBBz587FbDazceNG3Nzc2LFjB5MnT+bjjz+mc+fOldJevXqVESNG8NRTTzFu3Lh6yrGIdVAwJyIidS4jI4PExER27txpBG6dO3fmq6++Ys2aNcyfP99Im5OTw7BhwwgMDGTx4sU1zhsqIrfpnjkREalzd6YXsbW1rbTe1tYWi8ViLH/33XdERUXRoUMHli9fjp2d+hxEfoz+S0RE5EcVFhZy/vx5ACwWC1lZWZw8eRJXV1d8fHy4fv06ly9fNl6/eOHCBVxcXPDy8sLLy4vAwED8/f2ZOXMmsbGxuLq6sn37dvbv309iYiIAV65cISoqihYtWjBr1qxKr2V0d3evEgiKyG0K5kRE5EelpKQwdOhQY3n+/PnMnz+foUOH8t5777Fz505jTlDAmDt0ypQpxMTEYG9vz/r164mLi2P06NEUFhbi7+9PQkIC/fv3B25PEn/+/HnOnz9PeHh4pc8/cuQIvr6+D6CkItZH88xpnrkGqyHN7dOQqK5UT/WlYdPrvBo+tS3Va0hti+aZExEREWmEFMyJiIiIWDEFcyIiIiJWTMGciIiIiBVTMCciIiJixTQ1iYhII9ZQnlD8rr4zcJeG9HSiyP2gnjkRERERK6ZgTkRERMSKKZgTERERsWIK5kRERESsmII5ERERESumYE5ERETEiimYExEREbFiCuZERERErJiCORERERErpmBORERExIopmBMRERGxYgrmRERERKyYgjkRERERK6ZgTkRERMSKKZgTERERsWIK5kRERESsmII5ERERESumYE5ERETEiimYExEREbFiCuZERERErJiCORERERErZlffGbifTpw4wZo1a7BYLPTt25chQ4bUd5ZERERE6lSj6ZmzWCysXr2amTNnsnDhQg4dOkRmZmZ9Z0tERESkTjWaYO7s2bO0aNGCxx57DDs7OyIjIzl69Gh9Z0tERESkTjWaYM5sNuPu7m4su7u7Yzab6zFHIiIiInWv0dwzV1FRUWWdjY1NlXW7du1i165dAMyZMwdvb+86z9tP8o9j9Z0DsRaqK1Ibqi/yU6muWK1G0zPn7u7OtWvXjOVr167h6upaJV2/fv2YM2cOc+bMeZDZsxp/+MMf6jsLYkVUX+SnUl2R2lB9qZ1GE8y1a9eO7OxscnJyKCsrIykpiW7dutV3tkRERETqVKMZZrW1tWXMmDHMnj0bi8VCnz598PX1re9siYiIiNSpRhPMATzxxBM88cQT9Z0Nq9avX7/6zoJYEdUX+alUV6Q2VF9qx6aiuicHRERERMQqNJp75kREREQeRo1qmFUgLy+PtWvXkpGRgZ2dHV5eXrz22mu1noJl7969hISE4ObmVqv9Nm3ahJOTE4MHD67VflK3hg8fjp+fn7Hcs2fPWr/u7tSpU9jZ2REYGFhlW15eHitWrODatWuUlZXh5eXFW2+9hdlsZs2aNcTExFTZZ9asWbz66qu0a9eu9gWSSrZs2cLBgwcxmUzY2Ngwbtw4OnToUOvj/PAcL1u2jK5duxIREVHjPrNmzWLIkCGEhoYa6/7xj3+QnZ3N66+/XvvCSIO3du1aPD09GThwIACzZ8/G3d2d8ePHA/DBBx/g5ubGoEGDfvIx1b78PArmGpGKigrmzZtH7969efPNNwG4cOEC33///f8UzPn6+lYbzFksFkwmdepaEwcHB+bNm/ezjnHq1CmcnJyqbWw3bdpESEgIL7zwAgAXL14EwM3NrdqGVu6f9PR0kpOTiY+Px97envz8fMrKyv6nY93rHNekZ8+eJCUlVQrmkpKSGDVq1P+Uh/pWXl6Ora1tfWejQQsMDOTw4cMMHDgQi8VCfn4+RUVFxva0tDRGjx5dq2Oqffl5FMw1Ind+2fTv399Y5+/vD8Dnn3/O4cOHuXXrFuHh4QwbNoycnBzi4uIIDAwkPT0dNzc3pk+fzvHjx8nIyGDx4sU4ODgwe/Zsfv/739OnTx9SUlJ47rnnuHnzJrt376asrIzHHnuMiRMn4ujoWE8ll//V5s2bSU5OprS0lICAAMaNG4eNjQ1ffPEFX375Jba2trRq1YoRI0bw5ZdfYjKZOHDgAGPGjOEXv/iFcZzr168TEhJiLLdu3RqAnJwc4uPjWbBgAaWlpSxfvpzMzEx8fHwoLS010qekpLBp0yajPkVHR+Pk5PTgvggrdv36dZo3b469vT0ALi4uxrZvv/2W9evXU15eTrt27Rg7diz29vZMmDCBuLg4XFxcyMjIYP369URHR1c5xwCpqals27aNvLw8Ro0aVaWXLiIigo0bN3Lr1i3s7e3JycnBbDbTsWNHVq5cSUZGBqWlpURERDBs2DAAJkyYQO/evUlOTqasrIwpU6bg4+NDcXExiYmJZGRkYGNjQ1RUFBERERw8eJBPPvkEgMcff9wIFF999VXWr18PwJEjR0hOTuY3v/kN06ZNY8mSJZhMJkpKSnjzzTdZsmQJubm5rF69mvz8fBwdHfntb3+Lj48Py5Ytw9nZmQsXLtCmTRt+/etf1+1Js3KBgYGsW7cOgMzMTHx9fcnLy6OgoABHR0cuX75MmzZtOHfuHOvWraO4uBgXFxeio6NxdXVV+1IHFMw1Iv/9739p06ZNlfUpKSlkZ2fz5z//mYqKCubOnUtqaioeHh5kZ2czefJkxo8fT0JCAkeOHOGpp55i+/btVbqo7e3t+dOf/gTAjRs3jKeNNm7cyJ49e3j++ecfTEGl1kpLS5k2bZqx/OKLLxIZGclzzz1HVFQUAEuWLCE5OZlu3brx2WefsXTpUuzt7SksLKRZs2Y8++yzNQ6hDxgwgPfee48dO3YQHBzM008/XaVXd+fOnTg4ODB//nwuXrzIjBkzAMjPz2fLli3Exsbi5OTEp59+yrZt24x8yb116dKFzZs3M3nyZIKDg4mMjKRTp07GxS02NhZvb2+WLl3Kzp07jaGxH/Ly8qpyjvfs2UNeXh7vvPMOWVlZxMfHVwnmmjdvTvv27Tlx4gRhYWEkJSURGRmJjY0Nr7zyCs7OzlgsFt555x0uXrxoXIibN29OfHw8O3bsYOvWrYwfP57NmzfTtGlTFixYAEBBQQFms5m//e1vxMfH06xZM959912+/vprwsPDqy1H06ZNad26NampqQQFBZGcnEyXLl2ws7Pj/fffZ+zYsbRs2ZIzZ86watUq3n77bQCys7OJjY3VqMNP4Obmhq2tLbm5uaSlpREQEIDZbCY9Pd34/gESExOZPn06Li4uJCUlsWHDBqKjo9W+1AEFcw+BlJQU/v3vfzN9+nQAiouLuXLlCh4eHnh5eRm9d23btuXq1as1HicyMtL4+9KlS2zcuJHCwkKKi4vp0qVLnZZBfp6ahllPnjzJ559/TklJCQUFBfj6+tKtWzf8/PxYvHgxYWFhNV407xYaGsrSpUs5ceIE33zzDTNmzDAuyHekpqYawyStW7c2GvwzZ86QmZlJbGwsAGVlZQQEBPzcIj80nJyciI+P5/Tp05w6dYqFCxcycuRI/P398fLyMm6x6N27Nzt27KgxmKtJWFgYJpOJVq1a8f3331ebpmfPnhw6dIiwsDAOHTrE7373O+D2cOvu3bspLy/n+vXrZGZmGue9e/fuwO125+uvvwZu9yTeuUUEwNnZmaNHj9K5c2ejx/HJJ5/k9OnT96yXkZGRJCUlERQUxKFDhxgwYADFxcWkpaWRkJBgpLt7ODoiIkKBXC0EBgaSlpZGWloagwYNqhTMBQQEkJWVxaVLl4wOAIvFYryVSe3L/adgrhHx9fXlq6++qnbbkCFDePbZZyuty8nJMYZmAEwmU6Wu6R+6exh12bJlTJs2DX9/f/bu3cupU6d+Zu7lQSstLWX16tXExcXh4eHBpk2bjPP/1ltvkZqayrFjx/j4448rXQBr4uzsTK9evejVqxdz5swhNTWVtm3b/uh+FRUVBAcHV7qIS+2YTCY6d+5M586d8fPzY+/evcaPtJrS35mV6tatW/c89t1tRE0zWYWHh/PBBx9w7tw5SktLadu2LTk5OWzdupW4uDicnZ1ZtmxZpc+ys7Mz8lJeXm6s/+E7te81e9bdae9uu7p168bf//53CgoKOHfuHEFBQRQXF9OsWbMa7x1tbMNudS0gIIC0tDQuXbqEn58fHh4ebNu2jSZNmtCnTx8AWrVqxezZs6vsq/bl/tPPkEYkKCiIW7dusWvXLmPd2bNnadKkCf/6178oLi4GwGw21/gL+w4nJydu3rxZ4/bi4mJcXV0pKyvjwIED96cA8kDdubC6uLhQXFxs/BCwWCzk5uYSFBTEqFGjKCoqori4mCZNmhh16IdOnjxJSUkJADdv3uS7777Dw8OjUppOnTpx8OBB4PYtAXduYr5zUbhy5QoAJSUlZGVl3f8CN1JZWVlkZ2cbyxcuXMDT0xNvb29ycnKM73X//v106tQJuD2keu7cOeD2vWZ33Osc34uTkxOdOnXiL3/5Cz179gSgqKgIJycnmjZtSl5eHidOnPjR44SEhLB9+3ZjuaCggA4dOpCamkp+fj4Wi4VDhw4Z5XjkkUfIzMzEYrEYvXt38tO+fXvWrFlD165dMZlMNG3aFC8vLw4fPgzcvshfuHCh1mWV2zp27Mjx48dxdnbGZDLh7OxMYWEh6enpBAQE4O3tTX5+Punp6cDtHrFLly6pfakj6plrRGxsbJg6dSpr167ls88+w97eHk9PT0aPHk2zZs344x//CNxu6CZOnHjPIYWnn36alStXGg9A/NDw4cOZOXMmnp6e+Pn53TPwk/r3w3vmQkNDGTlyJH379iUmJgYvLy/j/kiLxcKSJUuMp9MGDhxIs2bN6Nq1KwkJCRw9erTKDcrnzp1j9erV2NraUlFRwTPPPEP79u3Jyckx0vTv35/ly5czdepU/P39ad++PXA7mJwwYQKLFi0yAsxf/epXtX4C+2F156GBwsJCbG1tadGiBePGjcPBwYHo6GgSEhKMByDu9M5HRUWxYsUKPvnkE+M8AFXOcW306tWL+fPnGz0g/v7++Pv7G/Xrpzwh+/LLL7Nq1SpiYmIwmUxERUXRvXt3RowYwf/93/8Btx+ACAsLA2DkyJHEx8fj7u6Or69vpWAgMjKShIQEZs2aZaybNGkSK1euZMuWLZSVldGzZ8979mBKzfz8/Lhx4wa9evWqtO7Oww4AMTExrFmzhqKiIsrLy3nhhRdo2bKl2pc6oDdAiIiIiFgxDbOKiIiIWDEFcyIiIiJWTMGciIiIiBVTMCciIiJixRTMiYiIiFgxBXMiIg1QTk4Ow4YNqzShrohIdTTPnIg8dCZMmEBeXh4mkwk7OzsCAgIYO3ZslYlIRUSsgXrmROShNGPGDNavX89f//pXHnnkERITE2t9DPWaiUhDoJ45EXmoOTg4EBERwbp164Dbr6FKTEzkm2++wdHRkb59+/Liiy9iMpnYu3cvu3fvpl27duzbt48BAwYwePDgGtNv2rSJK1euMGnSJOD20Okbb7zBhg0bsLW1JScnh2XLlnH+/Hk6dOhAy5YtKSoqMtIDHDhwgA8//JDS0lIGDhzISy+9VC/fk4g0XArmROShVlJSQlJSEh06dAAgMTGRoqIili5dyo0bN5g9ezaurq4888wzAJw5c4bIyEhWrVpFeXk577///j3T38uiRYsIDAwkNjaWs2fPEhcXR9euXSul+c9//sOiRYvIyspi5syZhIeH06pVq/v/RYiI1dIwq4g8lObNm8fo0aN57bXX+Pbbbxk8eDAWi4WkpCRGjBhBkyZN8PLyYtCgQezfv9/Yz9XVleeffx5bW1vs7Ox+NH1NcnNzycjIYPjw4djZ2dGxY8cqgRzA0KFDcXBwwN/fn9atWxsvEBcRuUM9cyLyUJo2bRohISFYLBaOHj3K22+/zdy5cykrK6v0IISnpydms9lYvntbfn7+j6avidlsxtnZGUdHx0rHzs3NrZTu0UcfNf52dHSs9DJ5ERFQz5yIPORMJhPdu3fHZDJx5swZbG1tKwVUubm5uLm5Vbuvi4vLPdM7OTlRWlpqbMvLyzP+dnV1paCggJKSkkr7iojUloI5EXmoVVRUcPToUQoLC/H19aVHjx5s2LCBmzdvcvXqVbZt28aTTz5Z7b4mk+me6f39/Tl9+jS5ubkUFRXx6aefGvt6enrSrl07PvroI8rKykhPTyc5OfmBlFlEGhcNs4rIQyk+Ph6TyYSNjQ2enp5MmDABX19fxowZQ2JiIm+88QYODg707duXPn361Hice6UPCQmhR48eTJ06lebNm/PLX/6SY8eOGftOnDiR5cuXM2bMGNq3b09kZCQWi6XOyy4ijYtNRUVFRX1nQkREYOHChfj4+DBs2LD6zoqIWBENs4qI1JOzZ89y5coVLBYLJ06c4NixY4SFhdV3tkTEymiYVUSknuTl5bFgwQJu3LiBu7s7r7/+Om3atKnvbImIldEwq4iIiIgV0zCriIiIiBVTMCciIiJixRTMiYiIiFgxBXMiIiIiVkzBnIiIiIgVUzAnIiIiYsX+H+rrcKJ+4qxrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "per_borough = vnc_crime_cat[['Borough','Total']]\n",
    "\n",
    "per_borough.set_index('Borough',inplace = True)\n",
    "\n",
    "ax = per_borough.plot(kind='bar', figsize=(10, 6), rot=0)\n",
    "\n",
    "ax.set_ylabel('Number of Crimes')\n",
    "ax.set_xlabel('Borough')\n",
    "ax.set_title('Boroughs in Vancouver with the Highest crimes')\n",
    "\n",
    "for p in ax.patches:\n",
    "    ax.annotate(np.round(p.get_height(),decimals=2), \n",
    "                (p.get_x()+p.get_width()/2., p.get_height()), \n",
    "                ha='center', \n",
    "                va='center', \n",
    "                xytext=(0, 10), \n",
    "                textcoords='offset points',\n",
    "                fontsize = 14,\n",
    "               )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Based on exploratory data analysis it is clear that South Vancouver has the lowest crimes\n",
    "\n",
    "##### Since South Vancouver has very little number of neighborhoods and opening a commercial establishment would not be viable, we can choose the next borough with lowest crime which is **West Side**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "####Different types of crimes recorded in the West Side Borough\n",
    "\n",
    "####West side was chosen because crime type Break and enter Commercial is also low amongst other crimes types which makes West Side ideal destination for opening of commercial establishments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " vnc_ws_df = vnc_crime_cat[vnc_crime_cat['Borough'] == 'West Side']\n",
    "\n",
    " vnc_ws_df = vnc_ws_df.sort_values(['Total'], ascending = True, axis = 0)\n",
    "\n",
    "vnc_ws = vnc_ws_df[['Borough','Theft of Vehicle', 'Break and Enter Commercial','Break and Enter Residential','Mischief','Other',\n",
    "                 'Theft from Vehicle','Vehicle Collision or Pedestrian Struck (with Fatality)','Theft of Bicycle',\n",
    "                 'Vehicle Collision or Pedestrian Struck (with Injury)']]\n",
    "\n",
    "\n",
    "vnc_ws.set_index('Borough',inplace = True)\n",
    "\n",
    "ax = vnc_ws.plot(kind='bar', figsize=(10, 6), rot=0)\n",
    "\n",
    "ax.set_ylabel('Number of Crimes')\n",
    "ax.set_xlabel('Borough')\n",
    "ax.set_title('Different Kind of Crimes in West Side Borough')\n",
    "\n",
    "for p in ax.patches:\n",
    "    ax.annotate(np.round(p.get_height(),decimals=3), \n",
    "                (p.get_x()+p.get_width()/3., p.get_height()), \n",
    "                ha='center', \n",
    "                va='center', \n",
    "                xytext=(5, 10), \n",
    "                textcoords='offset points',\n",
    "                fontsize = 14\n",
    "               )\n",
    "    ax.legend(loc='upper left', bbox_to_anchor=(1.00, 0.5))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Part 3**: Creating a new consolidated dataset of the Neighborhoods, along with their boroughs, crime data and the respective Neighbourhood's co-ordinates.<a name=\"part3\"></a>: \n",
    "\n",
    "####This data will be fetched using OpenCage Geocoder to find the safest borough and explore the neighbourhood by plotting it on maps using Folium and perform exploratory data analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Neighbourhoods in West Side Borough 10\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['Shaughnessy', 'Fairview', 'Oakridge', 'Marpole', 'Kitsilano',\n",
       "       'Kerrisdale', 'West Point Grey', 'Arbutus Ridge', 'South Cambie',\n",
       "       'Dunbar-Southlands'], dtype=object)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#####Restricting the rows in the data frame to only those with West side as Borough\n",
    "\n",
    "vnc_ws_neigh = vnc_boroughs_crime\n",
    "\n",
    "#vnc_ws_neigh.drop(['Type','Year', 'Month', 'Day', 'Hour'], axis = 1, inplace = True)\n",
    "vnc_ws_neigh = vnc_ws_neigh[vnc_ws_neigh['Borough'] == 'West Side']\n",
    "vnc_ws_neigh.reset_index(inplace=True, drop=True)\n",
    "\n",
    "print('Number of Neighbourhoods in West Side Borough', len(vnc_ws_neigh['Neighbourhood'].unique()))\n",
    "\n",
    "vnc_ws_neigh['Neighbourhood'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[49.2518626, 49.2641128, 49.2308288, 49.2092233, 49.2694099, 49.2346728, 49.2644843, 49.2409677, 49.2466847, 49.2534601] [-123.1380226, -123.1268352, -123.1311342, -123.1361495, -123.155267, -123.1553893, -123.1854326, -123.1670008, -123.120915, -123.1850439]\n"
     ]
    }
   ],
   "source": [
    "#####Creating a new Data frame with Lat, Lng being fetched from OpenCage geocoder\n",
    "\n",
    "Latitude = []\n",
    "Longitude = []\n",
    "Borough = []\n",
    "Neighbourhood = vnc_ws_neigh['Neighbourhood'].unique()\n",
    "\n",
    "\n",
    "\n",
    "key = '830323b5ca694362904814ff0a11b803'\n",
    "geocoder = OpenCageGeocode(key)\n",
    "\n",
    "for i in range(len(Neighbourhood)):\n",
    "    address = '{}, Vancouver, BC, Canada'.format(Neighbourhood[i])\n",
    "    location = geocoder.geocode(address)\n",
    "    Latitude.append(location[0]['geometry']['lat'])\n",
    "    Longitude.append(location[0]['geometry']['lng'])\n",
    "    Borough.append('West Side')\n",
    "print(Latitude, Longitude)\n",
    "\n",
    "#print('The geograpical coordinate of Vancouver City are {}, {}.'.format(latitude, longitude))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Neighbourhood</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shaughnessy</td>\n",
       "      <td>West Side</td>\n",
       "      <td>49.251863</td>\n",
       "      <td>-123.138023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fairview</td>\n",
       "      <td>West Side</td>\n",
       "      <td>49.264113</td>\n",
       "      <td>-123.126835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Oakridge</td>\n",
       "      <td>West Side</td>\n",
       "      <td>49.230829</td>\n",
       "      <td>-123.131134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Marpole</td>\n",
       "      <td>West Side</td>\n",
       "      <td>49.209223</td>\n",
       "      <td>-123.136150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kitsilano</td>\n",
       "      <td>West Side</td>\n",
       "      <td>49.269410</td>\n",
       "      <td>-123.155267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Kerrisdale</td>\n",
       "      <td>West Side</td>\n",
       "      <td>49.234673</td>\n",
       "      <td>-123.155389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>West Point Grey</td>\n",
       "      <td>West Side</td>\n",
       "      <td>49.264484</td>\n",
       "      <td>-123.185433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Arbutus Ridge</td>\n",
       "      <td>West Side</td>\n",
       "      <td>49.240968</td>\n",
       "      <td>-123.167001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>South Cambie</td>\n",
       "      <td>West Side</td>\n",
       "      <td>49.246685</td>\n",
       "      <td>-123.120915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Dunbar-Southlands</td>\n",
       "      <td>West Side</td>\n",
       "      <td>49.253460</td>\n",
       "      <td>-123.185044</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Neighbourhood    Borough   Latitude   Longitude\n",
       "0        Shaughnessy  West Side  49.251863 -123.138023\n",
       "1           Fairview  West Side  49.264113 -123.126835\n",
       "2           Oakridge  West Side  49.230829 -123.131134\n",
       "3            Marpole  West Side  49.209223 -123.136150\n",
       "4          Kitsilano  West Side  49.269410 -123.155267\n",
       "5         Kerrisdale  West Side  49.234673 -123.155389\n",
       "6    West Point Grey  West Side  49.264484 -123.185433\n",
       "7      Arbutus Ridge  West Side  49.240968 -123.167001\n",
       "8       South Cambie  West Side  49.246685 -123.120915\n",
       "9  Dunbar-Southlands  West Side  49.253460 -123.185044"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "####Glimpse of the new Data Frame with Neighborhoods in West Side Borough of Vancoouver along with centroid of their co-ordinates\n",
    "\n",
    "ws_neig_dict = {'Neighbourhood': Neighbourhood,'Borough':Borough,'Latitude': Latitude,'Longitude':Longitude}\n",
    "ws_neig_geo = pd.DataFrame(data=ws_neig_dict, columns=['Neighbourhood', 'Borough', 'Latitude', 'Longitude'], index=None)\n",
    "\n",
    "ws_neig_geo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The geograpical coordinate of Vancouver, Canada are 49.2608724, -123.1139529.\n"
     ]
    }
   ],
   "source": [
    "####Fetching the Geographical co-ordiantes of Vancouver to plot on Map\n",
    "\n",
    "address = 'Vancouver, BC, Canada'\n",
    "\n",
    "location = geocoder.geocode(address)\n",
    "latitude = location[0]['geometry']['lat']\n",
    "longitude = location[0]['geometry']['lng']\n",
    "\n",
    "print('The geograpical coordinate of Vancouver, Canada are {}, {}.'.format(latitude, longitude))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
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            var popup_6754a1d3cced41d18ef12db0c6907180 = L.popup({maxWidth: '300'});

            
                var html_c6bb4c5e482346f8b052e5492afb17ec = $('<div id="html_c6bb4c5e482346f8b052e5492afb17ec" style="width: 100.0%; height: 100.0%;">South Cambie, West Side</div>')[0];
                popup_6754a1d3cced41d18ef12db0c6907180.setContent(html_c6bb4c5e482346f8b052e5492afb17ec);
            

            circle_marker_2e8d2d98ef504b2bbec10c694e1c101c.bindPopup(popup_6754a1d3cced41d18ef12db0c6907180);

            
        
    
            var circle_marker_11df88acbf464566a5718f253a618149 = L.circleMarker(
                [49.2534601,-123.1850439],
                {
  "bubblingMouseEvents": true,
  "color": "red",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_1cecfe301742471490361f6dd60465ec);
            
    
            var popup_a36cb3d4d7cb470084a0ae5afc87509b = L.popup({maxWidth: '300'});

            
                var html_9779d46738f0485bbd7c803b2ce8bfa6 = $('<div id="html_9779d46738f0485bbd7c803b2ce8bfa6" style="width: 100.0%; height: 100.0%;">Dunbar-Southlands, West Side</div>')[0];
                popup_a36cb3d4d7cb470084a0ae5afc87509b.setContent(html_9779d46738f0485bbd7c803b2ce8bfa6);
            

            circle_marker_11df88acbf464566a5718f253a618149.bindPopup(popup_a36cb3d4d7cb470084a0ae5afc87509b);

            
        
</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x1be6195e4e0>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "####Using Folium to plot Vancouver City's West Side Borough and it's Neighborhoods\n",
    "\n",
    "van_map = folium.Map(location=[latitude, longitude], zoom_start=12)\n",
    "\n",
    "# add markers to map\n",
    "for lat, lng, borough, neighborhood in zip(ws_neig_geo['Latitude'], ws_neig_geo['Longitude'], ws_neig_geo['Borough'], ws_neig_geo['Neighbourhood']):\n",
    "    label = '{}, {}'.format(neighborhood, borough)\n",
    "    label = folium.Popup(label, parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lng],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color='red',\n",
    "        fill=True,\n",
    "        fill_color='#3186cc',\n",
    "        fill_opacity=0.7,\n",
    "        parse_html=False).add_to(van_map)  \n",
    "    \n",
    "van_map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Part 4**: Creating a new consolidated dataset of the Neighborhoods, boroughs, and the most common venues and the respective Neighbourhood along with co-ordinates.<a name=\"part4\"></a>: \n",
    "#####This data will be fetched using Four Square API to explore the neighbourhood venues and to apply machine learning algorithm to cluster the neighbourhoods and present the findings by plotting it on maps using Folium."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your credentails:\n",
      "CLIENT_ID: DYRHCJNF5ZKWHDDCBSPNFCJOTYXC0FTI22VWRHMSJJ0KY1WB\n",
      "CLIENT_SECRET:LCPCZU0PT4YMX31PU1MCN0CL2CZYDOJQ4S0ODKFQNRD1DPFA\n"
     ]
    }
   ],
   "source": [
    "#Four Square Credentials\n",
    "\n",
    "CLIENT_ID = 'DYRHCJNF5ZKWHDDCBSPNFCJOTYXC0FTI22VWRHMSJJ0KY1WB'\n",
    "CLIENT_SECRET = 'LCPCZU0PT4YMX31PU1MCN0CL2CZYDOJQ4S0ODKFQNRD1DPFA'\n",
    "VERSION = '20191101'\n",
    "LIMIT = 100\n",
    "\n",
    "print('Your credentails:')\n",
    "print('CLIENT_ID: ' + CLIENT_ID)\n",
    "print('CLIENT_SECRET:' + CLIENT_SECRET)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "####Defining a function to fetch top 10 venues around a given neighborhood\n",
    "\n",
    "def getNearbyVenues(names, latitudes, longitudes, radius=500):\n",
    "    \n",
    "    venues_list=[]\n",
    "    for name, lat, lng in zip(names, latitudes, longitudes):\n",
    "        print(name)\n",
    "            \n",
    "        # create the API request URL\n",
    "        url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n",
    "            CLIENT_ID, \n",
    "            CLIENT_SECRET, \n",
    "            VERSION, \n",
    "            lat, \n",
    "            lng, \n",
    "            radius, \n",
    "            LIMIT)\n",
    "            \n",
    "        # make the GET request\n",
    "        results = requests.get(url).json()[\"response\"]['groups'][0]['items']\n",
    "        \n",
    "        # return only relevant information for each nearby venue\n",
    "        venues_list.append([(\n",
    "            name, \n",
    "            lat, \n",
    "            lng, \n",
    "            v['venue']['name'],  \n",
    "            v['venue']['categories'][0]['name']) for v in results])\n",
    "\n",
    "    nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])\n",
    "    nearby_venues.columns = ['Neighbourhood', \n",
    "                  'Neighborhood Latitude', \n",
    "                  'Neighborhood Longitude', \n",
    "                  'Venue', \n",
    "                  'Venue Category']\n",
    "    \n",
    "    return(nearby_venues)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shaughnessy\n",
      "Fairview\n",
      "Oakridge\n",
      "Marpole\n",
      "Kitsilano\n",
      "Kerrisdale\n",
      "West Point Grey\n",
      "Arbutus Ridge\n",
      "South Cambie\n",
      "Dunbar-Southlands\n"
     ]
    }
   ],
   "source": [
    "####Generating Venues\n",
    "\n",
    "vnc_ws_venues = getNearbyVenues(names=ws_neig_geo['Neighbourhood'],\n",
    "                                   latitudes=ws_neig_geo['Latitude'],\n",
    "                                   longitudes=ws_neig_geo['Longitude']\n",
    "                                  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(224, 5)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>Neighbourhood</th>\n",
       "      <th>Neighborhood Latitude</th>\n",
       "      <th>Neighborhood Longitude</th>\n",
       "      <th>Venue</th>\n",
       "      <th>Venue Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shaughnessy</td>\n",
       "      <td>49.251863</td>\n",
       "      <td>-123.138023</td>\n",
       "      <td>Angus Park</td>\n",
       "      <td>Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Shaughnessy</td>\n",
       "      <td>49.251863</td>\n",
       "      <td>-123.138023</td>\n",
       "      <td>Crepe &amp; Cafe</td>\n",
       "      <td>French Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Fairview</td>\n",
       "      <td>49.264113</td>\n",
       "      <td>-123.126835</td>\n",
       "      <td>Gyu-Kaku Japanese BBQ</td>\n",
       "      <td>BBQ Joint</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fairview</td>\n",
       "      <td>49.264113</td>\n",
       "      <td>-123.126835</td>\n",
       "      <td>CRESCENT nail and spa</td>\n",
       "      <td>Nail Salon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fairview</td>\n",
       "      <td>49.264113</td>\n",
       "      <td>-123.126835</td>\n",
       "      <td>Charleson Park</td>\n",
       "      <td>Park</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Neighbourhood  Neighborhood Latitude  Neighborhood Longitude  \\\n",
       "0   Shaughnessy              49.251863             -123.138023   \n",
       "1   Shaughnessy              49.251863             -123.138023   \n",
       "2      Fairview              49.264113             -123.126835   \n",
       "3      Fairview              49.264113             -123.126835   \n",
       "4      Fairview              49.264113             -123.126835   \n",
       "\n",
       "                   Venue     Venue Category  \n",
       "0             Angus Park               Park  \n",
       "1           Crepe & Cafe  French Restaurant  \n",
       "2  Gyu-Kaku Japanese BBQ          BBQ Joint  \n",
       "3  CRESCENT nail and spa         Nail Salon  \n",
       "4         Charleson Park               Park  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "####Data frame containing venues for each neighborhood in West Side\n",
    "\n",
    "print(vnc_ws_venues.shape)\n",
    "vnc_ws_venues.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>Venue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Arbutus Ridge</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dunbar-Southlands</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fairview</th>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kerrisdale</th>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kitsilano</th>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marpole</th>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oakridge</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shaughnessy</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Cambie</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Point Grey</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Venue\n",
       "Neighbourhood           \n",
       "Arbutus Ridge          4\n",
       "Dunbar-Southlands      7\n",
       "Fairview              26\n",
       "Kerrisdale            37\n",
       "Kitsilano             46\n",
       "Marpole               28\n",
       "Oakridge               7\n",
       "Shaughnessy            2\n",
       "South Cambie          18\n",
       "West Point Grey       49"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "####Venue Count per neighborhood\n",
    "\n",
    "vnc_ws_venues.groupby('Neighbourhood').count().drop(['Neighborhood Latitude','Neighborhood Longitude','Venue Category'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 86 uniques categories.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print('There are {} uniques categories.'.format(len(vnc_ws_venues['Venue Category'].unique())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>Neighbourhood</th>\n",
       "      <th>American Restaurant</th>\n",
       "      <th>Arts &amp; Crafts Store</th>\n",
       "      <th>Asian Restaurant</th>\n",
       "      <th>BBQ Joint</th>\n",
       "      <th>Bakery</th>\n",
       "      <th>Bank</th>\n",
       "      <th>Bar</th>\n",
       "      <th>Beach</th>\n",
       "      <th>Bookstore</th>\n",
       "      <th>...</th>\n",
       "      <th>Taiwanese Restaurant</th>\n",
       "      <th>Tea Room</th>\n",
       "      <th>Tennis Court</th>\n",
       "      <th>Thai Restaurant</th>\n",
       "      <th>Thrift / Vintage Store</th>\n",
       "      <th>Vegetarian / Vegan Restaurant</th>\n",
       "      <th>Vietnamese Restaurant</th>\n",
       "      <th>Wine Shop</th>\n",
       "      <th>Women's Store</th>\n",
       "      <th>Yoga Studio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shaughnessy</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Shaughnessy</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Fairview</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fairview</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fairview</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 87 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Neighbourhood  American Restaurant  Arts & Crafts Store  Asian Restaurant  \\\n",
       "0   Shaughnessy                    0                    0                 0   \n",
       "1   Shaughnessy                    0                    0                 0   \n",
       "2      Fairview                    0                    0                 0   \n",
       "3      Fairview                    0                    0                 0   \n",
       "4      Fairview                    0                    0                 0   \n",
       "\n",
       "   BBQ Joint  Bakery  Bank  Bar  Beach  Bookstore  ...  Taiwanese Restaurant  \\\n",
       "0          0       0     0    0      0          0  ...                     0   \n",
       "1          0       0     0    0      0          0  ...                     0   \n",
       "2          1       0     0    0      0          0  ...                     0   \n",
       "3          0       0     0    0      0          0  ...                     0   \n",
       "4          0       0     0    0      0          0  ...                     0   \n",
       "\n",
       "   Tea Room  Tennis Court  Thai Restaurant  Thrift / Vintage Store  \\\n",
       "0         0             0                0                       0   \n",
       "1         0             0                0                       0   \n",
       "2         0             0                0                       0   \n",
       "3         0             0                0                       0   \n",
       "4         0             0                0                       0   \n",
       "\n",
       "   Vegetarian / Vegan Restaurant  Vietnamese Restaurant  Wine Shop  \\\n",
       "0                              0                      0          0   \n",
       "1                              0                      0          0   \n",
       "2                              0                      0          0   \n",
       "3                              0                      0          0   \n",
       "4                              0                      0          0   \n",
       "\n",
       "   Women's Store  Yoga Studio  \n",
       "0              0            0  \n",
       "1              0            0  \n",
       "2              0            0  \n",
       "3              0            0  \n",
       "4              0            0  \n",
       "\n",
       "[5 rows x 87 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#####One Hot Encoding to Analyze Each Neighborhood\n",
    "\n",
    "\n",
    "# one hot encoding\n",
    "vnc_onehot = pd.get_dummies(vnc_ws_venues[['Venue Category']], prefix=\"\", prefix_sep=\"\")\n",
    "\n",
    "# add neighborhood column back to dataframe\n",
    "vnc_onehot['Neighbourhood'] = vnc_ws_venues['Neighbourhood'] \n",
    "\n",
    "# move neighborhood column to the first column\n",
    "fixed_columns = [vnc_onehot.columns[-1]] + list(vnc_onehot.columns[:-1])\n",
    "vnc_onehot = vnc_onehot[fixed_columns]\n",
    "\n",
    "vnc_onehot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(224, 87)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vnc_onehot.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>Neighbourhood</th>\n",
       "      <th>American Restaurant</th>\n",
       "      <th>Arts &amp; Crafts Store</th>\n",
       "      <th>Asian Restaurant</th>\n",
       "      <th>BBQ Joint</th>\n",
       "      <th>Bakery</th>\n",
       "      <th>Bank</th>\n",
       "      <th>Bar</th>\n",
       "      <th>Beach</th>\n",
       "      <th>Bookstore</th>\n",
       "      <th>...</th>\n",
       "      <th>Taiwanese Restaurant</th>\n",
       "      <th>Tea Room</th>\n",
       "      <th>Tennis Court</th>\n",
       "      <th>Thai Restaurant</th>\n",
       "      <th>Thrift / Vintage Store</th>\n",
       "      <th>Vegetarian / Vegan Restaurant</th>\n",
       "      <th>Vietnamese Restaurant</th>\n",
       "      <th>Wine Shop</th>\n",
       "      <th>Women's Store</th>\n",
       "      <th>Yoga Studio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arbutus Ridge</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dunbar-Southlands</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Fairview</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kerrisdale</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.027027</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.027027</td>\n",
       "      <td>0.027027</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.027027</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.027027</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kitsilano</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.021739</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.021739</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.021739</td>\n",
       "      <td>0.043478</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.021739</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.021739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Marpole</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Oakridge</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Shaughnessy</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>South Cambie</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>West Point Grey</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020408</td>\n",
       "      <td>0.040816</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020408</td>\n",
       "      <td>0.020408</td>\n",
       "      <td>0.020408</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.040816</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020408</td>\n",
       "      <td>0.040816</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020408</td>\n",
       "      <td>0.020408</td>\n",
       "      <td>0.020408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 87 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Neighbourhood  American Restaurant  Arts & Crafts Store  \\\n",
       "0      Arbutus Ridge             0.000000             0.000000   \n",
       "1  Dunbar-Southlands             0.000000             0.000000   \n",
       "2           Fairview             0.000000             0.000000   \n",
       "3         Kerrisdale             0.000000             0.000000   \n",
       "4          Kitsilano             0.043478             0.000000   \n",
       "5            Marpole             0.000000             0.000000   \n",
       "6           Oakridge             0.000000             0.000000   \n",
       "7        Shaughnessy             0.000000             0.000000   \n",
       "8       South Cambie             0.000000             0.000000   \n",
       "9    West Point Grey             0.000000             0.020408   \n",
       "\n",
       "   Asian Restaurant  BBQ Joint    Bakery      Bank       Bar     Beach  \\\n",
       "0          0.000000   0.000000  0.250000  0.000000  0.000000  0.000000   \n",
       "1          0.000000   0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2          0.076923   0.038462  0.000000  0.038462  0.000000  0.000000   \n",
       "3          0.027027   0.000000  0.027027  0.027027  0.000000  0.000000   \n",
       "4          0.021739   0.000000  0.086957  0.000000  0.000000  0.021739   \n",
       "5          0.000000   0.000000  0.000000  0.035714  0.035714  0.000000   \n",
       "6          0.000000   0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "7          0.000000   0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "8          0.000000   0.000000  0.000000  0.055556  0.000000  0.000000   \n",
       "9          0.040816   0.000000  0.020408  0.020408  0.020408  0.000000   \n",
       "\n",
       "   Bookstore  ...  Taiwanese Restaurant  Tea Room  Tennis Court  \\\n",
       "0   0.000000  ...              0.000000  0.000000      0.000000   \n",
       "1   0.000000  ...              0.000000  0.000000      0.000000   \n",
       "2   0.000000  ...              0.000000  0.000000      0.000000   \n",
       "3   0.000000  ...              0.000000  0.054054      0.000000   \n",
       "4   0.000000  ...              0.000000  0.043478      0.021739   \n",
       "5   0.000000  ...              0.035714  0.000000      0.000000   \n",
       "6   0.000000  ...              0.000000  0.000000      0.000000   \n",
       "7   0.000000  ...              0.000000  0.000000      0.000000   \n",
       "8   0.000000  ...              0.000000  0.000000      0.000000   \n",
       "9   0.040816  ...              0.000000  0.000000      0.000000   \n",
       "\n",
       "   Thai Restaurant  Thrift / Vintage Store  Vegetarian / Vegan Restaurant  \\\n",
       "0         0.000000                0.000000                       0.000000   \n",
       "1         0.000000                0.000000                       0.000000   \n",
       "2         0.000000                0.000000                       0.000000   \n",
       "3         0.027027                0.000000                       0.000000   \n",
       "4         0.043478                0.000000                       0.021739   \n",
       "5         0.035714                0.000000                       0.000000   \n",
       "6         0.000000                0.000000                       0.000000   \n",
       "7         0.000000                0.000000                       0.000000   \n",
       "8         0.000000                0.000000                       0.000000   \n",
       "9         0.000000                0.020408                       0.040816   \n",
       "\n",
       "   Vietnamese Restaurant  Wine Shop  Women's Store  Yoga Studio  \n",
       "0               0.000000   0.000000       0.000000     0.000000  \n",
       "1               0.000000   0.000000       0.000000     0.000000  \n",
       "2               0.038462   0.000000       0.000000     0.000000  \n",
       "3               0.027027   0.000000       0.000000     0.000000  \n",
       "4               0.000000   0.000000       0.000000     0.021739  \n",
       "5               0.035714   0.000000       0.000000     0.000000  \n",
       "6               0.142857   0.000000       0.000000     0.000000  \n",
       "7               0.000000   0.000000       0.000000     0.000000  \n",
       "8               0.055556   0.000000       0.000000     0.000000  \n",
       "9               0.000000   0.020408       0.020408     0.020408  \n",
       "\n",
       "[10 rows x 87 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vnc_ws_grouped = vnc_onehot.groupby('Neighbourhood').mean().reset_index()\n",
    "vnc_ws_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 87)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vnc_ws_grouped.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----Arbutus Ridge----\n",
      "            venue  freq\n",
      "0   Grocery Store  0.25\n",
      "1          Bakery  0.25\n",
      "2             Spa  0.25\n",
      "3  Nightlife Spot  0.25\n",
      "4            Park  0.00\n",
      "\n",
      "\n",
      "----Dunbar-Southlands----\n",
      "                 venue  freq\n",
      "0     Sushi Restaurant  0.14\n",
      "1         Liquor Store  0.14\n",
      "2  Japanese Restaurant  0.14\n",
      "3   Italian Restaurant  0.14\n",
      "4       Ice Cream Shop  0.14\n",
      "\n",
      "\n",
      "----Fairview----\n",
      "                 venue  freq\n",
      "0          Coffee Shop  0.15\n",
      "1     Asian Restaurant  0.08\n",
      "2                 Park  0.08\n",
      "3    Korean Restaurant  0.04\n",
      "4  Szechuan Restaurant  0.04\n",
      "\n",
      "\n",
      "----Kerrisdale----\n",
      "                venue  freq\n",
      "0         Coffee Shop  0.11\n",
      "1  Chinese Restaurant  0.08\n",
      "2            Tea Room  0.05\n",
      "3      Sandwich Place  0.05\n",
      "4            Pharmacy  0.05\n",
      "\n",
      "\n",
      "----Kitsilano----\n",
      "                 venue  freq\n",
      "0               Bakery  0.09\n",
      "1  American Restaurant  0.04\n",
      "2     Sushi Restaurant  0.04\n",
      "3    French Restaurant  0.04\n",
      "4       Ice Cream Shop  0.04\n",
      "\n",
      "\n",
      "----Marpole----\n",
      "                venue  freq\n",
      "0    Sushi Restaurant  0.11\n",
      "1         Pizza Place  0.07\n",
      "2     Bubble Tea Shop  0.07\n",
      "3  Chinese Restaurant  0.07\n",
      "4  Dim Sum Restaurant  0.04\n",
      "\n",
      "\n",
      "----Oakridge----\n",
      "                   venue  freq\n",
      "0                   Park  0.14\n",
      "1       Sushi Restaurant  0.14\n",
      "2   Fast Food Restaurant  0.14\n",
      "3         Sandwich Place  0.14\n",
      "4  Vietnamese Restaurant  0.14\n",
      "\n",
      "\n",
      "----Shaughnessy----\n",
      "                   venue  freq\n",
      "0                   Park   0.5\n",
      "1      French Restaurant   0.5\n",
      "2    American Restaurant   0.0\n",
      "3                    Pub   0.0\n",
      "4  Portuguese Restaurant   0.0\n",
      "\n",
      "\n",
      "----South Cambie----\n",
      "              venue  freq\n",
      "0       Coffee Shop  0.28\n",
      "1          Bus Stop  0.17\n",
      "2  Malay Restaurant  0.06\n",
      "3         Juice Bar  0.06\n",
      "4     Grocery Store  0.06\n",
      "\n",
      "\n",
      "----West Point Grey----\n",
      "                 venue  freq\n",
      "0  Japanese Restaurant  0.06\n",
      "1                 Café  0.06\n",
      "2          Coffee Shop  0.06\n",
      "3     Asian Restaurant  0.04\n",
      "4     Sushi Restaurant  0.04\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "####Top 5 most common venues across neighborhoods\n",
    "num_top_venues = 5\n",
    "\n",
    "for hood in vnc_ws_grouped['Neighbourhood']:\n",
    "    print(\"----\"+hood+\"----\")\n",
    "    temp = vnc_ws_grouped[vnc_ws_grouped['Neighbourhood'] == hood].T.reset_index()\n",
    "    temp.columns = ['venue','freq']\n",
    "    temp = temp.iloc[1:]\n",
    "    temp['freq'] = temp['freq'].astype(float)\n",
    "    temp = temp.round({'freq': 2})\n",
    "    print(temp.sort_values('freq', ascending=False).reset_index(drop=True).head(num_top_venues))\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "####Now let's create the new dataframe and display the top 10 venues for each neighborhood.\n",
    "def return_most_common_venues(row, num_top_venues):\n",
    "    row_categories = row.iloc[1:]\n",
    "    row_categories_sorted = row_categories.sort_values(ascending=False)\n",
    "    \n",
    "    return row_categories_sorted.index.values[0:num_top_venues]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_top_venues = 10\n",
    "\n",
    "indicators = ['st', 'nd', 'rd']\n",
    "\n",
    "\n",
    "# create columns according to number of top venues\n",
    "columns = ['Neighbourhood']\n",
    "for ind in np.arange(num_top_venues):\n",
    "    try:\n",
    "        columns.append('{}{} Most Common Venue'.format(ind+1, indicators[ind]))\n",
    "    except:\n",
    "        columns.append('{}th Most Common Venue'.format(ind+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    }\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>Neighbourhood</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arbutus Ridge</td>\n",
       "      <td>Spa</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Nightlife Spot</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Dim Sum Restaurant</td>\n",
       "      <td>Diner</td>\n",
       "      <td>Falafel Restaurant</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dunbar-Southlands</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Liquor Store</td>\n",
       "      <td>Salon / Barbershop</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Sporting Goods Shop</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Ice Cream Shop</td>\n",
       "      <td>Gym Pool</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Dessert Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Fairview</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Park</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "      <td>Korean Restaurant</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Nail Salon</td>\n",
       "      <td>Diner</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Falafel Restaurant</td>\n",
       "      <td>Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kerrisdale</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Liquor Store</td>\n",
       "      <td>Portuguese Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kitsilano</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Ice Cream Shop</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>French Restaurant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Neighbourhood 1st Most Common Venue 2nd Most Common Venue  \\\n",
       "0      Arbutus Ridge                   Spa                Bakery   \n",
       "1  Dunbar-Southlands    Italian Restaurant          Liquor Store   \n",
       "2           Fairview           Coffee Shop                  Park   \n",
       "3         Kerrisdale           Coffee Shop    Chinese Restaurant   \n",
       "4          Kitsilano                Bakery   American Restaurant   \n",
       "\n",
       "  3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue  \\\n",
       "0         Grocery Store        Nightlife Spot           Yoga Studio   \n",
       "1    Salon / Barbershop      Sushi Restaurant   Sporting Goods Shop   \n",
       "2      Asian Restaurant     Korean Restaurant              Pharmacy   \n",
       "3              Pharmacy              Tea Room      Sushi Restaurant   \n",
       "4   Japanese Restaurant       Thai Restaurant              Tea Room   \n",
       "\n",
       "  6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "0          Dessert Shop    Dim Sum Restaurant                 Diner   \n",
       "1   Japanese Restaurant        Ice Cream Shop              Gym Pool   \n",
       "2            Nail Salon                 Diner          Camera Store   \n",
       "3        Sandwich Place   Japanese Restaurant           Pizza Place   \n",
       "4           Coffee Shop      Sushi Restaurant        Ice Cream Shop   \n",
       "\n",
       "  9th Most Common Venue 10th Most Common Venue  \n",
       "0    Falafel Restaurant   Fast Food Restaurant  \n",
       "1            Food Truck           Dessert Shop  \n",
       "2    Falafel Restaurant             Restaurant  \n",
       "3          Liquor Store  Portuguese Restaurant  \n",
       "4            Food Truck      French Restaurant  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# create a new dataframe\n",
    "neighborhoods_venues_sorted = pd.DataFrame(columns=columns)\n",
    "neighborhoods_venues_sorted['Neighbourhood'] = vnc_ws_grouped['Neighbourhood']\n",
    "\n",
    "for ind in np.arange(vnc_ws_grouped.shape[0]):\n",
    "    neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(vnc_ws_grouped.iloc[ind, :], num_top_venues)\n",
    "\n",
    "neighborhoods_venues_sorted.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cluster Neighbourhoods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# set number of clusters\n",
    "kclusters = 5\n",
    "\n",
    "vnc_grouped_clustering = vnc_ws_grouped.drop('Neighbourhood', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run k-means clustering\n",
    "kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(vnc_grouped_clustering)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 4, 0, 0, 0, 0, 3, 2, 0, 0])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check cluster labels generated for each row in the dataframe\n",
    "kmeans.labels_[0:10] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add clustering labels\n",
    "neighborhoods_venues_sorted.insert(0, 'Cluster Labels', kmeans.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Cluster Labels</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shaughnessy</td>\n",
       "      <td>West Side</td>\n",
       "      <td>49.251863</td>\n",
       "      <td>-123.138023</td>\n",
       "      <td>2</td>\n",
       "      <td>French Restaurant</td>\n",
       "      <td>Park</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Dim Sum Restaurant</td>\n",
       "      <td>Diner</td>\n",
       "      <td>Falafel Restaurant</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Food &amp; Drink Shop</td>\n",
       "      <td>Food Truck</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fairview</td>\n",
       "      <td>West Side</td>\n",
       "      <td>49.264113</td>\n",
       "      <td>-123.126835</td>\n",
       "      <td>0</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Park</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "      <td>Korean Restaurant</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Nail Salon</td>\n",
       "      <td>Diner</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Falafel Restaurant</td>\n",
       "      <td>Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Oakridge</td>\n",
       "      <td>West Side</td>\n",
       "      <td>49.230829</td>\n",
       "      <td>-123.131134</td>\n",
       "      <td>3</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Bubble Tea Shop</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Park</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Dim Sum Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Marpole</td>\n",
       "      <td>West Side</td>\n",
       "      <td>49.209223</td>\n",
       "      <td>-123.136150</td>\n",
       "      <td>0</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Bubble Tea Shop</td>\n",
       "      <td>Shanghai Restaurant</td>\n",
       "      <td>Liquor Store</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Dim Sum Restaurant</td>\n",
       "      <td>Plaza</td>\n",
       "      <td>Falafel Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kitsilano</td>\n",
       "      <td>West Side</td>\n",
       "      <td>49.269410</td>\n",
       "      <td>-123.155267</td>\n",
       "      <td>0</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Ice Cream Shop</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>French Restaurant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Neighbourhood    Borough   Latitude   Longitude  Cluster Labels  \\\n",
       "0   Shaughnessy  West Side  49.251863 -123.138023               2   \n",
       "1      Fairview  West Side  49.264113 -123.126835               0   \n",
       "2      Oakridge  West Side  49.230829 -123.131134               3   \n",
       "3       Marpole  West Side  49.209223 -123.136150               0   \n",
       "4     Kitsilano  West Side  49.269410 -123.155267               0   \n",
       "\n",
       "  1st Most Common Venue 2nd Most Common Venue  3rd Most Common Venue  \\\n",
       "0     French Restaurant                  Park            Yoga Studio   \n",
       "1           Coffee Shop                  Park       Asian Restaurant   \n",
       "2     Convenience Store       Bubble Tea Shop  Vietnamese Restaurant   \n",
       "3      Sushi Restaurant           Pizza Place     Chinese Restaurant   \n",
       "4                Bakery   American Restaurant    Japanese Restaurant   \n",
       "\n",
       "  4th Most Common Venue 5th Most Common Venue 6th Most Common Venue  \\\n",
       "0          Dessert Shop    Dim Sum Restaurant                 Diner   \n",
       "1     Korean Restaurant              Pharmacy            Nail Salon   \n",
       "2                  Park        Sandwich Place  Fast Food Restaurant   \n",
       "3       Bubble Tea Shop   Shanghai Restaurant          Liquor Store   \n",
       "4       Thai Restaurant              Tea Room           Coffee Shop   \n",
       "\n",
       "  7th Most Common Venue 8th Most Common Venue 9th Most Common Venue  \\\n",
       "0    Falafel Restaurant  Fast Food Restaurant     Food & Drink Shop   \n",
       "1                 Diner          Camera Store    Falafel Restaurant   \n",
       "2      Sushi Restaurant         Deli / Bodega          Dessert Shop   \n",
       "3          Dessert Shop    Dim Sum Restaurant                 Plaza   \n",
       "4      Sushi Restaurant        Ice Cream Shop            Food Truck   \n",
       "\n",
       "  10th Most Common Venue  \n",
       "0             Food Truck  \n",
       "1             Restaurant  \n",
       "2     Dim Sum Restaurant  \n",
       "3     Falafel Restaurant  \n",
       "4      French Restaurant  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vancouver_merged = ws_neig_geo\n",
    "\n",
    "# merge toronto_grouped with Vancouver data to add latitude/longitude for each neighborhood\n",
    "vancouver_merged = vancouver_merged.join(neighborhoods_venues_sorted.set_index('Neighbourhood'), on='Neighbourhood')\n",
    "\n",
    "vancouver_merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
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                var html_293962ff3ba54e56bcbb5a376875b573 = $('<div id="html_293962ff3ba54e56bcbb5a376875b573" style="width: 100.0%; height: 100.0%;">Oakridge Cluster 3</div>')[0];
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            circle_marker_a81f4eaaf55b4539a6c29d7db62ab417.bindPopup(popup_5b8c339d6d6b417db476ae78d6f21e05);

            
        
    
            var circle_marker_651292ec61314f048a639d24679a1452 = L.circleMarker(
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            var popup_9dd5e45d217249e3a4f07939a88d1759 = L.popup({maxWidth: '300'});

            
                var html_9eb844c325be4a9e8cd52d55c222d379 = $('<div id="html_9eb844c325be4a9e8cd52d55c222d379" style="width: 100.0%; height: 100.0%;">Marpole Cluster 0</div>')[0];
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            circle_marker_651292ec61314f048a639d24679a1452.bindPopup(popup_9dd5e45d217249e3a4f07939a88d1759);

            
        
    
            var circle_marker_03d8d5dc1d58484b9df52fdb6a083fd4 = L.circleMarker(
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  "bubblingMouseEvents": true,
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            var popup_7efe9c767eed46b59f6b2becf14256e2 = L.popup({maxWidth: '300'});

            
                var html_2b2a1228420d42869c59c4025bc05d03 = $('<div id="html_2b2a1228420d42869c59c4025bc05d03" style="width: 100.0%; height: 100.0%;">Kitsilano Cluster 0</div>')[0];
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            circle_marker_03d8d5dc1d58484b9df52fdb6a083fd4.bindPopup(popup_7efe9c767eed46b59f6b2becf14256e2);

            
        
    
            var circle_marker_203e2aedb6974025a3dc2a17c391cab5 = L.circleMarker(
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}
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            var popup_3b1b87a509a8491fa5decf8962e736e0 = L.popup({maxWidth: '300'});

            
                var html_109c964342be4d0999736009a5af9ab5 = $('<div id="html_109c964342be4d0999736009a5af9ab5" style="width: 100.0%; height: 100.0%;">Kerrisdale Cluster 0</div>')[0];
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            circle_marker_203e2aedb6974025a3dc2a17c391cab5.bindPopup(popup_3b1b87a509a8491fa5decf8962e736e0);

            
        
    
            var circle_marker_6af76d939d0a4337baf78d597b1a7198 = L.circleMarker(
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}
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            var popup_f45d20202b1f4cc68e19df7c5d2a784a = L.popup({maxWidth: '300'});

            
                var html_4beecf080a7943dd9f858d590a9b71bb = $('<div id="html_4beecf080a7943dd9f858d590a9b71bb" style="width: 100.0%; height: 100.0%;">West Point Grey Cluster 0</div>')[0];
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            var popup_e9ce84fc4ed34e149e8cff8e57920787 = L.popup({maxWidth: '300'});

            
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            var circle_marker_ff6b64803f994142b745239bd1ce2df1 = L.circleMarker(
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            var popup_af5673de00824a8f8a11a04ec367783c = L.popup({maxWidth: '300'});

            
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            circle_marker_ff6b64803f994142b745239bd1ce2df1.bindPopup(popup_af5673de00824a8f8a11a04ec367783c);

            
        
    
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                {
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  "fillColor": "#ffb360",
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                ).addTo(map_d22eef810afd46ba87904e1b22fe2355);
            
    
            var popup_8e8352308f9c44a49b873d4a6abd9832 = L.popup({maxWidth: '300'});

            
                var html_7333b9e93d3d4e77890cf7e13d5f9cd1 = $('<div id="html_7333b9e93d3d4e77890cf7e13d5f9cd1" style="width: 100.0%; height: 100.0%;">Dunbar-Southlands Cluster 4</div>')[0];
                popup_8e8352308f9c44a49b873d4a6abd9832.setContent(html_7333b9e93d3d4e77890cf7e13d5f9cd1);
            

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</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x1be619a1780>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# create map\n",
    "map_clusters = folium.Map(location=[latitude, longitude], zoom_start=12)\n",
    "\n",
    "# set color scheme for the clusters\n",
    "x = np.arange(kclusters)\n",
    "ys = [i + x + (i*x)**2 for i in range(kclusters)]\n",
    "colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))\n",
    "rainbow = [colors.rgb2hex(i) for i in colors_array]\n",
    "\n",
    "# add markers to the map\n",
    "markers_colors = []\n",
    "for lat, lon, poi, cluster in zip(vancouver_merged['Latitude'], vancouver_merged['Longitude'], vancouver_merged['Neighbourhood'], vancouver_merged['Cluster Labels']):\n",
    "    label = folium.Popup(str(poi) + ' Cluster ' + str(cluster), parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lon],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color=rainbow[cluster-1],\n",
    "        fill=True,\n",
    "        fill_color=rainbow[cluster-1],\n",
    "        fill_opacity=0.7).add_to(map_clusters)\n",
    "       \n",
    "map_clusters\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis\n",
    "\n",
    "#### Examining the resulting Clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>Borough</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>West Side</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Park</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "      <td>Korean Restaurant</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Nail Salon</td>\n",
       "      <td>Diner</td>\n",
       "      <td>Camera Store</td>\n",
       "      <td>Falafel Restaurant</td>\n",
       "      <td>Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>West Side</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Bubble Tea Shop</td>\n",
       "      <td>Shanghai Restaurant</td>\n",
       "      <td>Liquor Store</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Dim Sum Restaurant</td>\n",
       "      <td>Plaza</td>\n",
       "      <td>Falafel Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>West Side</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Ice Cream Shop</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>French Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>West Side</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Tea Room</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Liquor Store</td>\n",
       "      <td>Portuguese Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>West Side</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Café</td>\n",
       "      <td>Sporting Goods Shop</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Vegetarian / Vegan Restaurant</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Bookstore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>West Side</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Bus Stop</td>\n",
       "      <td>Juice Bar</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Light Rail Station</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Liquor Store</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Park</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Borough 1st Most Common Venue 2nd Most Common Venue  \\\n",
       "1  West Side           Coffee Shop                  Park   \n",
       "3  West Side      Sushi Restaurant           Pizza Place   \n",
       "4  West Side                Bakery   American Restaurant   \n",
       "5  West Side           Coffee Shop    Chinese Restaurant   \n",
       "6  West Side   Japanese Restaurant           Coffee Shop   \n",
       "8  West Side           Coffee Shop              Bus Stop   \n",
       "\n",
       "  3rd Most Common Venue  4th Most Common Venue 5th Most Common Venue  \\\n",
       "1      Asian Restaurant      Korean Restaurant              Pharmacy   \n",
       "3    Chinese Restaurant        Bubble Tea Shop   Shanghai Restaurant   \n",
       "4   Japanese Restaurant        Thai Restaurant              Tea Room   \n",
       "5              Pharmacy               Tea Room      Sushi Restaurant   \n",
       "6                  Café    Sporting Goods Shop                   Pub   \n",
       "8             Juice Bar  Vietnamese Restaurant    Light Rail Station   \n",
       "\n",
       "  6th Most Common Venue          7th Most Common Venue 8th Most Common Venue  \\\n",
       "1            Nail Salon                          Diner          Camera Store   \n",
       "3          Liquor Store                   Dessert Shop    Dim Sum Restaurant   \n",
       "4           Coffee Shop               Sushi Restaurant        Ice Cream Shop   \n",
       "5        Sandwich Place            Japanese Restaurant           Pizza Place   \n",
       "6           Pizza Place  Vegetarian / Vegan Restaurant      Asian Restaurant   \n",
       "8                  Bank                   Liquor Store         Grocery Store   \n",
       "\n",
       "  9th Most Common Venue 10th Most Common Venue  \n",
       "1    Falafel Restaurant             Restaurant  \n",
       "3                 Plaza     Falafel Restaurant  \n",
       "4            Food Truck      French Restaurant  \n",
       "5          Liquor Store  Portuguese Restaurant  \n",
       "6      Sushi Restaurant              Bookstore  \n",
       "8                  Park       Sushi Restaurant  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "####Cluster 1\n",
    "vancouver_merged.loc[vancouver_merged['Cluster Labels'] == 0, vancouver_merged.columns[[1] + list(range(5, vancouver_merged.shape[1]))]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>Borough</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>West Side</td>\n",
       "      <td>Spa</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Nightlife Spot</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Dim Sum Restaurant</td>\n",
       "      <td>Diner</td>\n",
       "      <td>Falafel Restaurant</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Borough 1st Most Common Venue 2nd Most Common Venue  \\\n",
       "7  West Side                   Spa                Bakery   \n",
       "\n",
       "  3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue  \\\n",
       "7         Grocery Store        Nightlife Spot           Yoga Studio   \n",
       "\n",
       "  6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "7          Dessert Shop    Dim Sum Restaurant                 Diner   \n",
       "\n",
       "  9th Most Common Venue 10th Most Common Venue  \n",
       "7    Falafel Restaurant   Fast Food Restaurant  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "####Cluster 2\n",
    "vancouver_merged.loc[vancouver_merged['Cluster Labels'] == 1, vancouver_merged.columns[[1] + list(range(5, vancouver_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>Borough</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>West Side</td>\n",
       "      <td>French Restaurant</td>\n",
       "      <td>Park</td>\n",
       "      <td>Yoga Studio</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Dim Sum Restaurant</td>\n",
       "      <td>Diner</td>\n",
       "      <td>Falafel Restaurant</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Food &amp; Drink Shop</td>\n",
       "      <td>Food Truck</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Borough 1st Most Common Venue 2nd Most Common Venue  \\\n",
       "0  West Side     French Restaurant                  Park   \n",
       "\n",
       "  3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue  \\\n",
       "0           Yoga Studio          Dessert Shop    Dim Sum Restaurant   \n",
       "\n",
       "  6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "0                 Diner    Falafel Restaurant  Fast Food Restaurant   \n",
       "\n",
       "  9th Most Common Venue 10th Most Common Venue  \n",
       "0     Food & Drink Shop             Food Truck  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "####Cluster 3\n",
    "vancouver_merged.loc[vancouver_merged['Cluster Labels'] == 2, vancouver_merged.columns[[1] + list(range(5, vancouver_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>Borough</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>West Side</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Bubble Tea Shop</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Park</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Deli / Bodega</td>\n",
       "      <td>Dessert Shop</td>\n",
       "      <td>Dim Sum Restaurant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Borough 1st Most Common Venue 2nd Most Common Venue  \\\n",
       "2  West Side     Convenience Store       Bubble Tea Shop   \n",
       "\n",
       "   3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue  \\\n",
       "2  Vietnamese Restaurant                  Park        Sandwich Place   \n",
       "\n",
       "  6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "2  Fast Food Restaurant      Sushi Restaurant         Deli / Bodega   \n",
       "\n",
       "  9th Most Common Venue 10th Most Common Venue  \n",
       "2          Dessert Shop     Dim Sum Restaurant  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "####Cluster 4\n",
    "vancouver_merged.loc[vancouver_merged['Cluster Labels'] == 3, vancouver_merged.columns[[1] + list(range(5, vancouver_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>Borough</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>West Side</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Liquor Store</td>\n",
       "      <td>Salon / Barbershop</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Sporting Goods Shop</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Ice Cream Shop</td>\n",
       "      <td>Gym Pool</td>\n",
       "      <td>Food Truck</td>\n",
       "      <td>Dessert Shop</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Borough 1st Most Common Venue 2nd Most Common Venue  \\\n",
       "9  West Side    Italian Restaurant          Liquor Store   \n",
       "\n",
       "  3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue  \\\n",
       "9    Salon / Barbershop      Sushi Restaurant   Sporting Goods Shop   \n",
       "\n",
       "  6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "9   Japanese Restaurant        Ice Cream Shop              Gym Pool   \n",
       "\n",
       "  9th Most Common Venue 10th Most Common Venue  \n",
       "9            Food Truck           Dessert Shop  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "####Cluster 5\n",
    "vancouver_merged.loc[vancouver_merged['Cluster Labels'] == 4, vancouver_merged.columns[[1] + list(range(5, vancouver_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results and Discussion\n",
    "\n",
    "The objective of the business problem was to help stakeholders identify one of the safest borough in Vancouver, and an appropriate neighborhood within the borough to set up a commercial establishment especially a Grocery store. This has been achieved by first making use of Vancouver crime data to identify a safe borugh with considerable number of neighborhood for any business to be viable. After selecting the borough it was imperative to choose the right neighborhood where grocery shops were not among venues in a close proximity to each other. We achieved this by grouping the neighborhoods into clusters to assist the stakeholders by providing them with relavent data about venues and safety of a given neighborhood.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "We have explored the crime data to understand different types of crimes in all neighborhoods of Vancouver and later categorized them into different boroughs, this helped us group the neighborhoods into boroughs and choose the safest borough first. Once we confirmed the borough the number of neighborhoods for consideration also comes down, we further shortlist the neighborhoods based on the common venues, to choose a neighborhood which best suits the business problem.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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