Toronto_Safeplace_code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data and Collection of Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this particular project, we have collected some information on the crime rate from 2014 to 2019 in Toronto"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Based on our problem, some factors should be taken into account in order to take the good decision are listed below:\n",
    "\n",
    "1. Figure out the safest borough based on Toronto Crime Rate.\n",
    "2. Discover the optimal common venues and select the appropriate neighbourhood within the borough.\n",
    "\n",
    "## We will treat the geographical data about Toronto in order to plot the corresponding neighbourhoods which are considered as safe and secure for the opening of a new restaurant.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following dataset which is Toronto Crime rate (https://www.kaggle.com/alincijov/toronto-crime-rate-per-neighbourhood) is analysing.\n",
    "\n",
    "Toronto Neighbourhoods Boundary File includes 2014-2019 Crime Data by Neighbourhood. Counts are available for Assault, Auto Theft, Break and Enter, Robbery, Theft Over and Homicide. Data also includes four year averages and crime rates per 100,000 people by neighbourhood based on 2016 Census Population.\n",
    "\n",
    "In this project, we extract data from the following data sources:\n",
    "1. Toronto Crime rate (https://www.kaggle.com/alincijov/toronto-crime-rate-per-neighbourhood)\n",
    "2. Restaurants data in every neighborhood will be obtained using Foursquare API\n",
    "3. Google Geocoding API will be used to center the hexagon neighborhood.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_repodata.json): done\n",
      "Solving environment: done\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n",
      "Collecting package metadata (current_repodata.json): done\n",
      "Solving environment: done\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n",
      "Libraries imported.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd # library for data analsysis\n",
    "import numpy as np # library to handle data in a vectorized manner\n",
    "import random # library for random number generation\n",
    "\n",
    "!conda install -c conda-forge geopy --yes \n",
    "from geopy.geocoders import Nominatim # conversion an address into latitude and longitude values\n",
    "\n",
    "# libraries for displaying images\n",
    "from IPython.display import Image \n",
    "from IPython.core.display import HTML \n",
    "\n",
    "\n",
    "from IPython.display import display_html\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "import requests # library to handle requests\n",
    "# tranforming json file into a pandas dataframe library\n",
    "from pandas.io.json import json_normalize\n",
    "\n",
    "# Matplotlib and associated plotting modules\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as colors\n",
    "\n",
    "# import k-means from clustering stage\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "\n",
    "!conda install -c conda-forge folium=0.5.0 --yes # uncomment this line if you haven't completed the Foursquare API lab\n",
    "import folium # map rendering library\n",
    "\n",
    "print('Libraries imported.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading from the Dataset\n",
    "We proceed to the Toronto Dataset with the recent crime report from 2008 to 2019.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: lxml in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (4.6.2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>Postal Code</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighbourhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M1A</td>\n",
       "      <td>Not assigned</td>\n",
       "      <td>Not assigned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M2A</td>\n",
       "      <td>Not assigned</td>\n",
       "      <td>Not assigned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M3A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Parkwoods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M4A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Victoria Village</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M5A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Regent Park, Harbourfront</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postal Code           Borough              Neighbourhood\n",
       "0         M1A      Not assigned               Not assigned\n",
       "1         M2A      Not assigned               Not assigned\n",
       "2         M3A        North York                  Parkwoods\n",
       "3         M4A        North York           Victoria Village\n",
       "4         M5A  Downtown Toronto  Regent Park, Harbourfront"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!pip install lxml\n",
    "import lxml\n",
    "url = 'https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M'\n",
    "df = pd.read_html(url, header=0)\n",
    "df = df[0]\n",
    "df.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighbourhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M3A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Parkwoods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M4A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Victoria Village</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M5A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Regent Park, Harbourfront</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M6A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Lawrence Manor, Lawrence Heights</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M7A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Queen's Park, Ontario Provincial Government</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postal Code           Borough                                Neighbourhood\n",
       "0         M3A        North York                                    Parkwoods\n",
       "1         M4A        North York                             Victoria Village\n",
       "2         M5A  Downtown Toronto                    Regent Park, Harbourfront\n",
       "3         M6A        North York             Lawrence Manor, Lawrence Heights\n",
       "4         M7A  Downtown Toronto  Queen's Park, Ontario Provincial Government"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping the rows where Borough is 'Not assigned'\n",
    "df = df[df.Borough != 'Not assigned']\n",
    "\n",
    "# Combining the neighbourhoods with same Postalcode\n",
    "df = df.groupby(['Postal Code','Borough'], sort=False).agg(', '.join)\n",
    "df.reset_index(inplace=True)\n",
    "\n",
    "# Replacing the name of the neighbourhoods which are 'Not assigned' with names of Borough\n",
    "df['Neighbourhood'] = np.where(df['Neighbourhood'] == 'Not assigned',df['Borough'], df['Neighbourhood'])\n",
    "\n",
    "df.shape\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\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",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M1B</td>\n",
       "      <td>43.806686</td>\n",
       "      <td>-79.194353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M1C</td>\n",
       "      <td>43.784535</td>\n",
       "      <td>-79.160497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M1E</td>\n",
       "      <td>43.763573</td>\n",
       "      <td>-79.188711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M1G</td>\n",
       "      <td>43.770992</td>\n",
       "      <td>-79.216917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M1H</td>\n",
       "      <td>43.773136</td>\n",
       "      <td>-79.239476</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postal Code   Latitude  Longitude\n",
       "0         M1B  43.806686 -79.194353\n",
       "1         M1C  43.784535 -79.160497\n",
       "2         M1E  43.763573 -79.188711\n",
       "3         M1G  43.770992 -79.216917\n",
       "4         M1H  43.773136 -79.239476"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Importing the csv file conatining the latitudes and longitudes for various neighbourhoods in Canada\n",
    "latitude_longitude = pd.read_csv('https://cocl.us/Geospatial_data')\n",
    "latitude_longitude.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging the two tables for getting the Latitudes and Longitudes for various neighbourhoods in Canada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Postal Code</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M3A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Parkwoods</td>\n",
       "      <td>43.753259</td>\n",
       "      <td>-79.329656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M4A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Victoria Village</td>\n",
       "      <td>43.725882</td>\n",
       "      <td>-79.315572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M5A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Regent Park, Harbourfront</td>\n",
       "      <td>43.654260</td>\n",
       "      <td>-79.360636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M6A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Lawrence Manor, Lawrence Heights</td>\n",
       "      <td>43.718518</td>\n",
       "      <td>-79.464763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M7A</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Queen's Park, Ontario Provincial Government</td>\n",
       "      <td>43.662301</td>\n",
       "      <td>-79.389494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>M8X</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>The Kingsway, Montgomery Road, Old Mill North</td>\n",
       "      <td>43.653654</td>\n",
       "      <td>-79.506944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>M4Y</td>\n",
       "      <td>Downtown Toronto</td>\n",
       "      <td>Church and Wellesley</td>\n",
       "      <td>43.665860</td>\n",
       "      <td>-79.383160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>M7Y</td>\n",
       "      <td>East Toronto</td>\n",
       "      <td>Business reply mail Processing Centre, South C...</td>\n",
       "      <td>43.662744</td>\n",
       "      <td>-79.321558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>M8Y</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Old Mill South, King's Mill Park, Sunnylea, Hu...</td>\n",
       "      <td>43.636258</td>\n",
       "      <td>-79.498509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>M8Z</td>\n",
       "      <td>Etobicoke</td>\n",
       "      <td>Mimico NW, The Queensway West, South of Bloor,...</td>\n",
       "      <td>43.628841</td>\n",
       "      <td>-79.520999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Postal Code           Borough  \\\n",
       "0           M3A        North York   \n",
       "1           M4A        North York   \n",
       "2           M5A  Downtown Toronto   \n",
       "3           M6A        North York   \n",
       "4           M7A  Downtown Toronto   \n",
       "..          ...               ...   \n",
       "98          M8X         Etobicoke   \n",
       "99          M4Y  Downtown Toronto   \n",
       "100         M7Y      East Toronto   \n",
       "101         M8Y         Etobicoke   \n",
       "102         M8Z         Etobicoke   \n",
       "\n",
       "                                         Neighbourhood   Latitude  Longitude  \n",
       "0                                            Parkwoods  43.753259 -79.329656  \n",
       "1                                     Victoria Village  43.725882 -79.315572  \n",
       "2                            Regent Park, Harbourfront  43.654260 -79.360636  \n",
       "3                     Lawrence Manor, Lawrence Heights  43.718518 -79.464763  \n",
       "4          Queen's Park, Ontario Provincial Government  43.662301 -79.389494  \n",
       "..                                                 ...        ...        ...  \n",
       "98       The Kingsway, Montgomery Road, Old Mill North  43.653654 -79.506944  \n",
       "99                                Church and Wellesley  43.665860 -79.383160  \n",
       "100  Business reply mail Processing Centre, South C...  43.662744 -79.321558  \n",
       "101  Old Mill South, King's Mill Park, Sunnylea, Hu...  43.636258 -79.498509  \n",
       "102  Mimico NW, The Queensway West, South of Bloor,...  43.628841 -79.520999  \n",
       "\n",
       "[103 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latitude_longitude.rename(columns={'Postal Code':'Postal Code'},inplace=True)\n",
    "df = pd.merge(df,latitude_longitude,on='Postal Code')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging the three tables for getting the Neighbourhood with the crime Rate \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>Postal Code</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Hood_ID</th>\n",
       "      <th>Population</th>\n",
       "      <th>Assault_Rate_2019</th>\n",
       "      <th>AutoTheft_Rate_2019</th>\n",
       "      <th>BreakandEnter_Rate_2019</th>\n",
       "      <th>Homicide_Rate_2019</th>\n",
       "      <th>Robbery_Rate_2019</th>\n",
       "      <th>TheftOver_Rate_2019</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M4A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Victoria Village</td>\n",
       "      <td>43.725882</td>\n",
       "      <td>-79.315572</td>\n",
       "      <td>43</td>\n",
       "      <td>17510</td>\n",
       "      <td>753.9</td>\n",
       "      <td>102.8</td>\n",
       "      <td>342.7</td>\n",
       "      <td>5.7</td>\n",
       "      <td>80.0</td>\n",
       "      <td>28.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M6C</td>\n",
       "      <td>York</td>\n",
       "      <td>Humewood-Cedarvale</td>\n",
       "      <td>43.693781</td>\n",
       "      <td>-79.428191</td>\n",
       "      <td>106</td>\n",
       "      <td>14365</td>\n",
       "      <td>320.2</td>\n",
       "      <td>111.4</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69.6</td>\n",
       "      <td>27.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M4E</td>\n",
       "      <td>East Toronto</td>\n",
       "      <td>The Beaches</td>\n",
       "      <td>43.676357</td>\n",
       "      <td>-79.293031</td>\n",
       "      <td>63</td>\n",
       "      <td>21567</td>\n",
       "      <td>375.6</td>\n",
       "      <td>92.7</td>\n",
       "      <td>264.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.2</td>\n",
       "      <td>41.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M1G</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Woburn</td>\n",
       "      <td>43.770992</td>\n",
       "      <td>-79.216917</td>\n",
       "      <td>137</td>\n",
       "      <td>53485</td>\n",
       "      <td>798.4</td>\n",
       "      <td>112.2</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>125.3</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M2H</td>\n",
       "      <td>North York</td>\n",
       "      <td>Hillcrest Village</td>\n",
       "      <td>43.803762</td>\n",
       "      <td>-79.363452</td>\n",
       "      <td>48</td>\n",
       "      <td>16934</td>\n",
       "      <td>407.5</td>\n",
       "      <td>159.4</td>\n",
       "      <td>212.6</td>\n",
       "      <td>5.9</td>\n",
       "      <td>94.5</td>\n",
       "      <td>35.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M4H</td>\n",
       "      <td>East York</td>\n",
       "      <td>Thorncliffe Park</td>\n",
       "      <td>43.705369</td>\n",
       "      <td>-79.349372</td>\n",
       "      <td>55</td>\n",
       "      <td>21108</td>\n",
       "      <td>544.8</td>\n",
       "      <td>61.6</td>\n",
       "      <td>127.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.6</td>\n",
       "      <td>23.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>M1J</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Scarborough Village</td>\n",
       "      <td>43.744734</td>\n",
       "      <td>-79.239476</td>\n",
       "      <td>139</td>\n",
       "      <td>16724</td>\n",
       "      <td>1046.4</td>\n",
       "      <td>107.6</td>\n",
       "      <td>239.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>167.4</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>M2K</td>\n",
       "      <td>North York</td>\n",
       "      <td>Bayview Village</td>\n",
       "      <td>43.786947</td>\n",
       "      <td>-79.385975</td>\n",
       "      <td>52</td>\n",
       "      <td>21396</td>\n",
       "      <td>308.5</td>\n",
       "      <td>172.9</td>\n",
       "      <td>172.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65.4</td>\n",
       "      <td>60.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>M9L</td>\n",
       "      <td>North York</td>\n",
       "      <td>Humber Summit</td>\n",
       "      <td>43.756303</td>\n",
       "      <td>-79.565963</td>\n",
       "      <td>21</td>\n",
       "      <td>12416</td>\n",
       "      <td>950.4</td>\n",
       "      <td>1087.3</td>\n",
       "      <td>459.1</td>\n",
       "      <td>24.2</td>\n",
       "      <td>225.5</td>\n",
       "      <td>177.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>M9N</td>\n",
       "      <td>York</td>\n",
       "      <td>Weston</td>\n",
       "      <td>43.706876</td>\n",
       "      <td>-79.518188</td>\n",
       "      <td>113</td>\n",
       "      <td>17992</td>\n",
       "      <td>1089.4</td>\n",
       "      <td>272.3</td>\n",
       "      <td>322.4</td>\n",
       "      <td>11.1</td>\n",
       "      <td>144.5</td>\n",
       "      <td>22.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postal Code       Borough        Neighbourhood   Latitude  Longitude  \\\n",
       "0         M4A    North York     Victoria Village  43.725882 -79.315572   \n",
       "1         M6C          York   Humewood-Cedarvale  43.693781 -79.428191   \n",
       "2         M4E  East Toronto          The Beaches  43.676357 -79.293031   \n",
       "3         M1G   Scarborough               Woburn  43.770992 -79.216917   \n",
       "4         M2H    North York    Hillcrest Village  43.803762 -79.363452   \n",
       "5         M4H     East York     Thorncliffe Park  43.705369 -79.349372   \n",
       "6         M1J   Scarborough  Scarborough Village  43.744734 -79.239476   \n",
       "7         M2K    North York      Bayview Village  43.786947 -79.385975   \n",
       "8         M9L    North York        Humber Summit  43.756303 -79.565963   \n",
       "9         M9N          York               Weston  43.706876 -79.518188   \n",
       "\n",
       "   Hood_ID  Population  Assault_Rate_2019  AutoTheft_Rate_2019  \\\n",
       "0       43       17510              753.9                102.8   \n",
       "1      106       14365              320.2                111.4   \n",
       "2       63       21567              375.6                 92.7   \n",
       "3      137       53485              798.4                112.2   \n",
       "4       48       16934              407.5                159.4   \n",
       "5       55       21108              544.8                 61.6   \n",
       "6      139       16724             1046.4                107.6   \n",
       "7       52       21396              308.5                172.9   \n",
       "8       21       12416              950.4               1087.3   \n",
       "9      113       17992             1089.4                272.3   \n",
       "\n",
       "   BreakandEnter_Rate_2019  Homicide_Rate_2019  Robbery_Rate_2019  \\\n",
       "0                    342.7                 5.7               80.0   \n",
       "1                    181.0                 0.0               69.6   \n",
       "2                    264.3                 0.0               74.2   \n",
       "3                    187.0                 0.0              125.3   \n",
       "4                    212.6                 5.9               94.5   \n",
       "5                    127.9                 0.0               61.6   \n",
       "6                    239.2                 0.0              167.4   \n",
       "7                    172.9                 0.0               65.4   \n",
       "8                    459.1                24.2              225.5   \n",
       "9                    322.4                11.1              144.5   \n",
       "\n",
       "   TheftOver_Rate_2019  \n",
       "0                 28.6  \n",
       "1                 27.8  \n",
       "2                 41.7  \n",
       "3                 15.0  \n",
       "4                 35.4  \n",
       "5                 23.7  \n",
       "6                 12.0  \n",
       "7                 60.8  \n",
       "8                177.2  \n",
       "9                 22.2  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toronto_Crime_df = pd.read_csv('https://raw.githubusercontent.com/Ellis0312/The-Battle-of-Neighborhoods/main/Neighbourhood_Crime_Rates.csv',index_col='OBJECTID')\n",
    "toronto_Crime_df.drop(['Assault_2014','Assault_2015','Assault_2016','Assault_2017','Assault_2018','Assault_2019','Assault_AVG','Assault_CHG','AutoTheft_2014','AutoTheft_2015','AutoTheft_2016','AutoTheft_2017','AutoTheft_2018','AutoTheft_2019','AutoTheft_AVG','AutoTheft_CHG','BreakandEnter_2014','BreakandEnter_2015','BreakandEnter_2016','BreakandEnter_2017','BreakandEnter_2018','BreakandEnter_2019','BreakandEnter_AVG','BreakandEnter_CHG','Homicide_2014','Homicide_2015','Homicide_2016','Homicide_2017','Homicide_2018','Homicide_2019','Homicide_CHG','Homicide_AVG','Robbery_2014','Robbery_2015', 'Robbery_2016','Robbery_2017','Robbery_2018','Robbery_2019','Robbery_AVG','Robbery_CHG','TheftOver_2014','TheftOver_2015','TheftOver_2016','TheftOver_2017','TheftOver_2018','TheftOver_2019','TheftOver_AVG','TheftOver_CHG','Shape__Area','Shape__Length'], axis = 1, inplace = True)\n",
    "toronto_Crime_df\n",
    "map_toronto_df = pd.merge(df,toronto_Crime_df,on='Neighbourhood')\n",
    "map_toronto_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After Data Cleaning of these datasets, we perform the total crime rate in the different types of crime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Postal Code</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Hood_ID</th>\n",
       "      <th>Population</th>\n",
       "      <th>Assault_Rate_2019</th>\n",
       "      <th>AutoTheft_Rate_2019</th>\n",
       "      <th>BreakandEnter_Rate_2019</th>\n",
       "      <th>Homicide_Rate_2019</th>\n",
       "      <th>Robbery_Rate_2019</th>\n",
       "      <th>TheftOver_Rate_2019</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M4A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Victoria Village</td>\n",
       "      <td>43.725882</td>\n",
       "      <td>-79.315572</td>\n",
       "      <td>43</td>\n",
       "      <td>17510</td>\n",
       "      <td>753.9</td>\n",
       "      <td>102.8</td>\n",
       "      <td>342.7</td>\n",
       "      <td>5.7</td>\n",
       "      <td>80.0</td>\n",
       "      <td>28.6</td>\n",
       "      <td>1313.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M6C</td>\n",
       "      <td>York</td>\n",
       "      <td>Humewood-Cedarvale</td>\n",
       "      <td>43.693781</td>\n",
       "      <td>-79.428191</td>\n",
       "      <td>106</td>\n",
       "      <td>14365</td>\n",
       "      <td>320.2</td>\n",
       "      <td>111.4</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69.6</td>\n",
       "      <td>27.8</td>\n",
       "      <td>710.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M4E</td>\n",
       "      <td>East Toronto</td>\n",
       "      <td>The Beaches</td>\n",
       "      <td>43.676357</td>\n",
       "      <td>-79.293031</td>\n",
       "      <td>63</td>\n",
       "      <td>21567</td>\n",
       "      <td>375.6</td>\n",
       "      <td>92.7</td>\n",
       "      <td>264.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.2</td>\n",
       "      <td>41.7</td>\n",
       "      <td>848.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M1G</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Woburn</td>\n",
       "      <td>43.770992</td>\n",
       "      <td>-79.216917</td>\n",
       "      <td>137</td>\n",
       "      <td>53485</td>\n",
       "      <td>798.4</td>\n",
       "      <td>112.2</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>125.3</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1237.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M2H</td>\n",
       "      <td>North York</td>\n",
       "      <td>Hillcrest Village</td>\n",
       "      <td>43.803762</td>\n",
       "      <td>-79.363452</td>\n",
       "      <td>48</td>\n",
       "      <td>16934</td>\n",
       "      <td>407.5</td>\n",
       "      <td>159.4</td>\n",
       "      <td>212.6</td>\n",
       "      <td>5.9</td>\n",
       "      <td>94.5</td>\n",
       "      <td>35.4</td>\n",
       "      <td>915.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M4H</td>\n",
       "      <td>East York</td>\n",
       "      <td>Thorncliffe Park</td>\n",
       "      <td>43.705369</td>\n",
       "      <td>-79.349372</td>\n",
       "      <td>55</td>\n",
       "      <td>21108</td>\n",
       "      <td>544.8</td>\n",
       "      <td>61.6</td>\n",
       "      <td>127.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.6</td>\n",
       "      <td>23.7</td>\n",
       "      <td>819.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>M1J</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Scarborough Village</td>\n",
       "      <td>43.744734</td>\n",
       "      <td>-79.239476</td>\n",
       "      <td>139</td>\n",
       "      <td>16724</td>\n",
       "      <td>1046.4</td>\n",
       "      <td>107.6</td>\n",
       "      <td>239.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>167.4</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1572.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>M2K</td>\n",
       "      <td>North York</td>\n",
       "      <td>Bayview Village</td>\n",
       "      <td>43.786947</td>\n",
       "      <td>-79.385975</td>\n",
       "      <td>52</td>\n",
       "      <td>21396</td>\n",
       "      <td>308.5</td>\n",
       "      <td>172.9</td>\n",
       "      <td>172.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65.4</td>\n",
       "      <td>60.8</td>\n",
       "      <td>780.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>M9L</td>\n",
       "      <td>North York</td>\n",
       "      <td>Humber Summit</td>\n",
       "      <td>43.756303</td>\n",
       "      <td>-79.565963</td>\n",
       "      <td>21</td>\n",
       "      <td>12416</td>\n",
       "      <td>950.4</td>\n",
       "      <td>1087.3</td>\n",
       "      <td>459.1</td>\n",
       "      <td>24.2</td>\n",
       "      <td>225.5</td>\n",
       "      <td>177.2</td>\n",
       "      <td>2923.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>M9N</td>\n",
       "      <td>York</td>\n",
       "      <td>Weston</td>\n",
       "      <td>43.706876</td>\n",
       "      <td>-79.518188</td>\n",
       "      <td>113</td>\n",
       "      <td>17992</td>\n",
       "      <td>1089.4</td>\n",
       "      <td>272.3</td>\n",
       "      <td>322.4</td>\n",
       "      <td>11.1</td>\n",
       "      <td>144.5</td>\n",
       "      <td>22.2</td>\n",
       "      <td>1861.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postal Code       Borough        Neighbourhood   Latitude  Longitude  \\\n",
       "0         M4A    North York     Victoria Village  43.725882 -79.315572   \n",
       "1         M6C          York   Humewood-Cedarvale  43.693781 -79.428191   \n",
       "2         M4E  East Toronto          The Beaches  43.676357 -79.293031   \n",
       "3         M1G   Scarborough               Woburn  43.770992 -79.216917   \n",
       "4         M2H    North York    Hillcrest Village  43.803762 -79.363452   \n",
       "5         M4H     East York     Thorncliffe Park  43.705369 -79.349372   \n",
       "6         M1J   Scarborough  Scarborough Village  43.744734 -79.239476   \n",
       "7         M2K    North York      Bayview Village  43.786947 -79.385975   \n",
       "8         M9L    North York        Humber Summit  43.756303 -79.565963   \n",
       "9         M9N          York               Weston  43.706876 -79.518188   \n",
       "\n",
       "   Hood_ID  Population  Assault_Rate_2019  AutoTheft_Rate_2019  \\\n",
       "0       43       17510              753.9                102.8   \n",
       "1      106       14365              320.2                111.4   \n",
       "2       63       21567              375.6                 92.7   \n",
       "3      137       53485              798.4                112.2   \n",
       "4       48       16934              407.5                159.4   \n",
       "5       55       21108              544.8                 61.6   \n",
       "6      139       16724             1046.4                107.6   \n",
       "7       52       21396              308.5                172.9   \n",
       "8       21       12416              950.4               1087.3   \n",
       "9      113       17992             1089.4                272.3   \n",
       "\n",
       "   BreakandEnter_Rate_2019  Homicide_Rate_2019  Robbery_Rate_2019  \\\n",
       "0                    342.7                 5.7               80.0   \n",
       "1                    181.0                 0.0               69.6   \n",
       "2                    264.3                 0.0               74.2   \n",
       "3                    187.0                 0.0              125.3   \n",
       "4                    212.6                 5.9               94.5   \n",
       "5                    127.9                 0.0               61.6   \n",
       "6                    239.2                 0.0              167.4   \n",
       "7                    172.9                 0.0               65.4   \n",
       "8                    459.1                24.2              225.5   \n",
       "9                    322.4                11.1              144.5   \n",
       "\n",
       "   TheftOver_Rate_2019   Total  \n",
       "0                 28.6  1313.7  \n",
       "1                 27.8   710.0  \n",
       "2                 41.7   848.5  \n",
       "3                 15.0  1237.9  \n",
       "4                 35.4   915.3  \n",
       "5                 23.7   819.6  \n",
       "6                 12.0  1572.6  \n",
       "7                 60.8   780.5  \n",
       "8                177.2  2923.7  \n",
       "9                 22.2  1861.9  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Calulating the sum of the all crimes committed and Sorting the data\n",
    "\n",
    "crime_toronto_total = pd.concat([map_toronto_df,pd.DataFrame(map_toronto_df[[\"Assault_Rate_2019\", \"AutoTheft_Rate_2019\", \"BreakandEnter_Rate_2019\",\"Homicide_Rate_2019\",\"Robbery_Rate_2019\",\"TheftOver_Rate_2019\"]].sum(axis=1),columns=['Total'])],axis=1)\n",
    "crime_toronto_total\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sorting the data in order to get the neighbourhood with the top 5 highest crime rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Postal Code</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Hood_ID</th>\n",
       "      <th>Population</th>\n",
       "      <th>Assault_Rate_2019</th>\n",
       "      <th>AutoTheft_Rate_2019</th>\n",
       "      <th>BreakandEnter_Rate_2019</th>\n",
       "      <th>Homicide_Rate_2019</th>\n",
       "      <th>Robbery_Rate_2019</th>\n",
       "      <th>TheftOver_Rate_2019</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>M9N</td>\n",
       "      <td>York</td>\n",
       "      <td>Weston</td>\n",
       "      <td>43.706876</td>\n",
       "      <td>-79.518188</td>\n",
       "      <td>113</td>\n",
       "      <td>17992</td>\n",
       "      <td>1089.4</td>\n",
       "      <td>272.3</td>\n",
       "      <td>322.4</td>\n",
       "      <td>11.1</td>\n",
       "      <td>144.5</td>\n",
       "      <td>22.2</td>\n",
       "      <td>1861.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>M1J</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Scarborough Village</td>\n",
       "      <td>43.744734</td>\n",
       "      <td>-79.239476</td>\n",
       "      <td>139</td>\n",
       "      <td>16724</td>\n",
       "      <td>1046.4</td>\n",
       "      <td>107.6</td>\n",
       "      <td>239.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>167.4</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1572.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M4A</td>\n",
       "      <td>North York</td>\n",
       "      <td>Victoria Village</td>\n",
       "      <td>43.725882</td>\n",
       "      <td>-79.315572</td>\n",
       "      <td>43</td>\n",
       "      <td>17510</td>\n",
       "      <td>753.9</td>\n",
       "      <td>102.8</td>\n",
       "      <td>342.7</td>\n",
       "      <td>5.7</td>\n",
       "      <td>80.0</td>\n",
       "      <td>28.6</td>\n",
       "      <td>1313.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M1G</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Woburn</td>\n",
       "      <td>43.770992</td>\n",
       "      <td>-79.216917</td>\n",
       "      <td>137</td>\n",
       "      <td>53485</td>\n",
       "      <td>798.4</td>\n",
       "      <td>112.2</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>125.3</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1237.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M2H</td>\n",
       "      <td>North York</td>\n",
       "      <td>Hillcrest Village</td>\n",
       "      <td>43.803762</td>\n",
       "      <td>-79.363452</td>\n",
       "      <td>48</td>\n",
       "      <td>16934</td>\n",
       "      <td>407.5</td>\n",
       "      <td>159.4</td>\n",
       "      <td>212.6</td>\n",
       "      <td>5.9</td>\n",
       "      <td>94.5</td>\n",
       "      <td>35.4</td>\n",
       "      <td>915.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postal Code      Borough        Neighbourhood   Latitude  Longitude  \\\n",
       "9         M9N         York               Weston  43.706876 -79.518188   \n",
       "6         M1J  Scarborough  Scarborough Village  43.744734 -79.239476   \n",
       "0         M4A   North York     Victoria Village  43.725882 -79.315572   \n",
       "3         M1G  Scarborough               Woburn  43.770992 -79.216917   \n",
       "4         M2H   North York    Hillcrest Village  43.803762 -79.363452   \n",
       "\n",
       "   Hood_ID  Population  Assault_Rate_2019  AutoTheft_Rate_2019  \\\n",
       "9      113       17992             1089.4                272.3   \n",
       "6      139       16724             1046.4                107.6   \n",
       "0       43       17510              753.9                102.8   \n",
       "3      137       53485              798.4                112.2   \n",
       "4       48       16934              407.5                159.4   \n",
       "\n",
       "   BreakandEnter_Rate_2019  Homicide_Rate_2019  Robbery_Rate_2019  \\\n",
       "9                    322.4                11.1              144.5   \n",
       "6                    239.2                 0.0              167.4   \n",
       "0                    342.7                 5.7               80.0   \n",
       "3                    187.0                 0.0              125.3   \n",
       "4                    212.6                 5.9               94.5   \n",
       "\n",
       "   TheftOver_Rate_2019   Total  \n",
       "9                 22.2  1861.9  \n",
       "6                 12.0  1572.6  \n",
       "0                 28.6  1313.7  \n",
       "3                 15.0  1237.9  \n",
       "4                 35.4   915.3  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sorting the data with the highest rate top 5\n",
    "\n",
    "crime_toronto_total.sort_values(['Total'], ascending = False, axis = 0, inplace = True )\n",
    "\n",
    "crime_toronto_top5 = crime_toronto_total.iloc[1:6]\n",
    "crime_toronto_top5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bar charts Visualization of the top 5 highest crime rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "crime_neighbourhood = crime_toronto_top5[['Neighbourhood','Total']]\n",
    "\n",
    "crime_neighbourhood.set_index('Neighbourhood',inplace = True)\n",
    "\n",
    "ax_x_y = crime_neighbourhood.plot(kind='bar', figsize=(10, 10), rot = 0)\n",
    "\n",
    "ax_x_y .set_title('Neighbourhoods with the highest rate of crime')\n",
    "ax_x_y .set_ylabel('Number of Crimes')\n",
    "ax_x_y .set_xlabel('Neighbourhood')\n",
    "\n",
    "for t in ax_x_y.patches:\n",
    "    ax_x_y.annotate(np.round(t.get_height(),decimals=2), \n",
    "                (t.get_x()+t.get_width()/2., t.get_height()), \n",
    "                ha='center', \n",
    "                va='center', \n",
    "                xytext=(0, 10), \n",
    "                textcoords='offset points',\n",
    "                fontsize = 12,\n",
    "               )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sorting the data in order to get the neighbourhood with the top 5 lowest crime rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Postal Code</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Hood_ID</th>\n",
       "      <th>Population</th>\n",
       "      <th>Assault_Rate_2019</th>\n",
       "      <th>AutoTheft_Rate_2019</th>\n",
       "      <th>BreakandEnter_Rate_2019</th>\n",
       "      <th>Homicide_Rate_2019</th>\n",
       "      <th>Robbery_Rate_2019</th>\n",
       "      <th>TheftOver_Rate_2019</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>M2K</td>\n",
       "      <td>North York</td>\n",
       "      <td>Bayview Village</td>\n",
       "      <td>43.786947</td>\n",
       "      <td>-79.385975</td>\n",
       "      <td>52</td>\n",
       "      <td>21396</td>\n",
       "      <td>308.5</td>\n",
       "      <td>172.9</td>\n",
       "      <td>172.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65.4</td>\n",
       "      <td>60.8</td>\n",
       "      <td>780.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M4H</td>\n",
       "      <td>East York</td>\n",
       "      <td>Thorncliffe Park</td>\n",
       "      <td>43.705369</td>\n",
       "      <td>-79.349372</td>\n",
       "      <td>55</td>\n",
       "      <td>21108</td>\n",
       "      <td>544.8</td>\n",
       "      <td>61.6</td>\n",
       "      <td>127.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.6</td>\n",
       "      <td>23.7</td>\n",
       "      <td>819.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M4E</td>\n",
       "      <td>East Toronto</td>\n",
       "      <td>The Beaches</td>\n",
       "      <td>43.676357</td>\n",
       "      <td>-79.293031</td>\n",
       "      <td>63</td>\n",
       "      <td>21567</td>\n",
       "      <td>375.6</td>\n",
       "      <td>92.7</td>\n",
       "      <td>264.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.2</td>\n",
       "      <td>41.7</td>\n",
       "      <td>848.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M2H</td>\n",
       "      <td>North York</td>\n",
       "      <td>Hillcrest Village</td>\n",
       "      <td>43.803762</td>\n",
       "      <td>-79.363452</td>\n",
       "      <td>48</td>\n",
       "      <td>16934</td>\n",
       "      <td>407.5</td>\n",
       "      <td>159.4</td>\n",
       "      <td>212.6</td>\n",
       "      <td>5.9</td>\n",
       "      <td>94.5</td>\n",
       "      <td>35.4</td>\n",
       "      <td>915.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M1G</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Woburn</td>\n",
       "      <td>43.770992</td>\n",
       "      <td>-79.216917</td>\n",
       "      <td>137</td>\n",
       "      <td>53485</td>\n",
       "      <td>798.4</td>\n",
       "      <td>112.2</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>125.3</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1237.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postal Code       Borough      Neighbourhood   Latitude  Longitude  Hood_ID  \\\n",
       "7         M2K    North York    Bayview Village  43.786947 -79.385975       52   \n",
       "5         M4H     East York   Thorncliffe Park  43.705369 -79.349372       55   \n",
       "2         M4E  East Toronto        The Beaches  43.676357 -79.293031       63   \n",
       "4         M2H    North York  Hillcrest Village  43.803762 -79.363452       48   \n",
       "3         M1G   Scarborough             Woburn  43.770992 -79.216917      137   \n",
       "\n",
       "   Population  Assault_Rate_2019  AutoTheft_Rate_2019  \\\n",
       "7       21396              308.5                172.9   \n",
       "5       21108              544.8                 61.6   \n",
       "2       21567              375.6                 92.7   \n",
       "4       16934              407.5                159.4   \n",
       "3       53485              798.4                112.2   \n",
       "\n",
       "   BreakandEnter_Rate_2019  Homicide_Rate_2019  Robbery_Rate_2019  \\\n",
       "7                    172.9                 0.0               65.4   \n",
       "5                    127.9                 0.0               61.6   \n",
       "2                    264.3                 0.0               74.2   \n",
       "4                    212.6                 5.9               94.5   \n",
       "3                    187.0                 0.0              125.3   \n",
       "\n",
       "   TheftOver_Rate_2019   Total  \n",
       "7                 60.8   780.5  \n",
       "5                 23.7   819.6  \n",
       "2                 41.7   848.5  \n",
       "4                 35.4   915.3  \n",
       "3                 15.0  1237.9  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crime_toronto_total.sort_values(['Total'], ascending = True, axis = 0, inplace = True )\n",
    "\n",
    "crime_toronto_top5_low = crime_toronto_total.iloc[1:6]\n",
    "crime_toronto_top5_low"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nnXUWP/zhD5dpb9oL1x4WLFjAiy++yJIlS9o07dSpU5kyZQoAV1xxBbvvvjsAffv2Ze7cuQC8+uqrrL322vTr14/nn3+eW265pd3rtkdNkiR1uClTprD33nvz05/+lA9/+MNMnjx5mfGNX6cxZ84c5syZwxZbbLHs+KlTycxWb8W0aNEinnnmGebOncuUKVMYMGAAc+bMWW5tvXr14tJLL+WQQw5h0aJF7LjjjpxwwgkAHH/88ey9994MGTKEO+64gx122IERI0aw2Wabscsuu6zoblgug5okSepQTz/9NHvuuSff/va3+cxnPtPqtJnJwoULl2lbsmQJL730Eptvvnmr83bv3p3NNtts6eNp06Yt97DmmWeeuXT4/vvvf9v4k08+mZNPPnnp48suu6zZ5YwdO7bV9bSVhz4lSVK7W7RoEQsWLGDx4sUsXryYBQsWsGjRIqZPn85HP/pRTjrppKW9VLVmzZrFm2++CRTnk82cOZN11llnmWleeuklunXrRt++fVutoXGdmckrr7zCiy++yJAhQ9pvIzuAPWqSJKnd/dd//Rff/e53lz6+8sorOeOMM4gInnzySb773e8uM/7ee+8FYN68eUyfPp0lS5bQvXt3+vfvz0YbbbTMsmfPnt3sDdznzp3L448/zvve9z4A5s+fz7PPPsvixYvp2bMnw4cPp3fv3vXa5LqIzOzsGuqioaEhJ0yY0NllSJKkNpg8eTJbbbXVKn9j9szk0Ucffdt3xUXEfZn5tvtNeehTkiR1ul69ejF79mxW1Q4kKELa7Nmz6dWrV5vn8dCnJEnqdEOHDmXatGnMmjWrs0upq169ejF06NA2T29QkyRJnW7NNdds9as2Vlce+pQkSaooe9QkSdIKG3baTZ1dQl01fuFuZ7NHTZIkqaIMapIkSRVlUJMkSaoog5okSVJFGdQkSZIqyqAmSZJUUQY1SZKkijKoSZIkVZRBTZIkqaIMapIkSRVlUJMkSaoog5okSVJF1S2oRcQlEfFCRDxc0/bDiHg0Ih6MiD9ExLo1406PiCkR8VhE7FXT/v6IeKgcd0FERL1qliRJqpJ69qhdBoxs0nYbsG1mbg/8CzgdICK2AQ4DRpTzXBQR3cp5fgYcD2xZ/jRdpiRJ0iqpbkEtM/8KzGnSdmtmLiof3g0MLYcPAK7JzIWZ+RQwBdgpIoYA62TmXZmZwOXAgfWqWZIkqUo68xy1zwK3lMMbAc/WjJtWtm1UDjdtb1ZEHB8REyJiwqxZs9q5XEmSpI7VKUEtIv4TWARc1djUzGTZSnuzMvPizGzIzIbBgwe/80IlSZI6UfeOXmFEHA18AvhYeTgTip6yjWsmGwrMKNuHNtMuSZK0yuvQHrWIGAl8A9g/M+fXjLoROCwiekbEcIqLBsZn5kxgbkR8sLza8yjgho6sWZIkqbPUrUctIq4G9gAGRcQ04AyKqzx7AreV37Jxd2aekJmTIuJa4BGKQ6InZebiclEnUlxB2pvinLZbkCRJWg3ULahl5uHNNP9vK9OfA5zTTPsEYNt2LE2SJKlL8M4EkiRJFWVQkyRJqiiDmiRJUkUZ1CRJkirKoCZJklRRBjVJkqSKMqhJkiRVlEFNkiSpogxqkiRJFWVQkyRJqiiDmiRJUkUZ1CRJkirKoCZJklRRBjVJkqSKMqhJkiRVlEFNkiSpogxqkiRJFWVQkyRJqiiDmiRJUkUZ1CRJkirKoCZJklRRBjVJkqSKMqhJkiRVlEFNkiSpogxqkiRJFWVQkyRJqiiDmiRJUkUZ1CRJkirKoCZJklRRBjVJkqSKMqhJkiRVlEFNkiSpogxqkiRJFWVQkyRJqiiDmiRJUkUZ1CRJkirKoCZJklRRBjVJkqSKMqhJkiRVlEFNkiSpogxqkiRJFWVQkyRJqiiDmiRJUkUZ1CRJkirKoCZJklRRBjVJkqSKMqhJkiRVlEFNkiSpogxqkiRJFWVQkyRJqiiDmiRJUkUZ1CRJkirKoCZJklRRBjVJkqSKMqhJkiRVlEFNkiSpogxqkiRJFWVQkyRJqiiDmiRJUkUZ1CRJkirKoCZJklRRBjVJkqSKMqhJkiRVlEFNkiSpogxqkiRJFWVQkyRJqiiDmiRJUkUZ1CRJkirKoCZJklRRBjVJkqSKMqhJkiRVlEFNkiSpogxqkiRJFWVQkyRJqiiDmiRJUkXVLahFxCUR8UJEPFzTNiAibouIx8vf/WvGnR4RUyLisYjYq6b9/RHxUDnugoiIetUsSZJUJfXsUbsMGNmk7TRgTGZuCYwpHxMR2wCHASPKeS6KiG7lPD8Djge2LH+aLlOSJGmVVLeglpl/BeY0aT4AGF0OjwYOrGm/JjMXZuZTwBRgp4gYAqyTmXdlZgKX18wjSZK0Suvoc9TWz8yZAOXv9cr2jYBna6abVrZtVA43bW9WRBwfERMiYsKsWbPatXBJkqSOVpWLCZo77yxbaW9WZl6cmQ2Z2TB48OB2K06SJKkzdHRQe748nEn5+4WyfRqwcc10Q4EZZfvQZtolSZJWeR0d1G4Eji6HjwZuqGk/LCJ6RsRwiosGxpeHR+dGxAfLqz2PqplHkiRplda9XguOiKuBPYBBETENOAM4F7g2Io4DngEOAcjMSRFxLfAIsAg4KTMXl4s6keIK0t7ALeWPJEnSKq9uQS0zD29h1MdamP4c4Jxm2icA27ZjaZIkSV1CVS4mkCRJUhMGNUmSpIoyqEmSJFWUQU2SJKmiDGqSJEkVZVCTJEmqKIOaJElSRRnUJEmSKsqgJkmSVFEGNUmSpIoyqEmSJFWUQU2SJKmiDGqSJEkVZVCTJEmqKIOaJElSRRnUJEldxuTJk/noRz9Kv3792GKLLfjDH/4AwBtvvMHBBx/MsGHDiAjGjh27zHxnnnkma665Jn369Fn68+STTza7jkceeYSGhgb69+9P//792XPPPXnkkUfqvWlSswxqkqQuYdGiRRxwwAF84hOfYM6cOVx88cUceeSR/Otf/wJg11135corr2SDDTZodv5PfepTzJs3b+nPZptt1ux0G264Iddddx1z5szhxRdfZP/99+ewww6r23ZJrTGoSZK6hEcffZQZM2bw5S9/mW7duvHRj36UXXbZhSuuuIIePXrwpS99iV133ZVu3bq9o/Wsu+66S3vmMpNu3boxZcqUdtoKacV07+wCJElqi8xstu3hhx9u0/x//OMfGTBgAEOGDGHUqFGceOKJrU6/7rrrMm/ePJYsWcJZZ521UjVL75Q9apKkLmGrrbZivfXW44c//CFvvvkmt956K+PGjWP+/PnLnffQQw9l8uTJzJo1i1/+8pecddZZXH311a3O8/LLL/PKK69w4YUXssMOO7TXZkgrxKAmSeoS1lxzTa6//npuuukmNthgA370ox9x6KGHMnTo0OXOu80227DhhhvSrVs3dt55Z0499VSuu+665c639tprc8IJJ3DUUUfxwgsvtMdmSCvEQ5+SpC5j++23Z9y4cUsf77zzzhx99NErvJzG88/aYsmSJcyfP5/p06ez3nrrrfC6pHfCHjVJUpfx4IMPsmDBAubPn895553HzJkzOeaYYwBYuHAhCxYsAIqv61iwYMHSMHbDDTfw0ksvkZmMHz+eCy64gAMOOKDZddx2223cf//9LF68mFdffZWvfOUr9O/fn6233rpDtlGqZVCTJHUZV1xxBUOGDGG99dZjzJgx3HbbbfTs2ROAd7/73fTu3Zvp06ez11570bt3b55++mkArrnmGrbYYgv69u3LUUcdxTe+8Y1leuJGjBjBVVddBRTnph1++OH069ePzTffnClTpvDnP/+ZXr16dfwGa7UXbe367WoaGhpywoQJnV2GJEmrpGGn3dTZJdTV1HP37dD1RcR9mdnQtN0eNUmSpIoyqEmSJFWUQU2SJKmiDGqSJEkV5feoSZI6hSejS8tnj5okSVJFGdQkSZIqyqAmSZJUUQY1SZKkijKoSZIkVZRBTZIkqaIMapIkSRVlUJMkSaoog5okSVJFGdQkSZIqyqAmSZJUUQY1SZKkijKoSVqtTJ06lX322Yf+/fuzwQYbMGrUKBYtWrTMNN/97neJCG6//falbQsXLuSEE05g/fXXZ8CAAey3335Mnz69xXVEBH369Fn6c/bZZ9d1uyStmgxqklYrX/ziF1lvvfWYOXMmEydOZNy4cVx00UVLxz/xxBNcd911DBkyZJn5fvrTn3LXXXfx4IMPMmPGDNZdd11OPvnkVtf18ssvM2/ePObNm8e3v/3tumyPpFWbQU3SauWpp57i0EMPpVevXmywwQaMHDmSSZMmLR0/atQovv/979OjR4+3zbfXXnux/vrr06tXLw477LBl5pOkejCoSVqtnHrqqVxzzTXMnz+f6dOnc8sttzBy5EgAfvvb39KjRw/22Weft8133HHHceeddzJjxgzmz5/PVVddxd57793qujbddFOGDh3Ksccey4svvliX7ZG0ajOoSVqt7L777kyaNIl11lmHoUOH0tDQwIEHHsi8efP45je/yfnnn9/sfO9617vYZJNN2GijjVhnnXWYPHky3/nOd5qddtCgQdx77708/fTT3HfffcydO5cjjjiijlslaVVlUJO02liyZAl77bUXBx10EK+99hovvvgiL730Et/4xjc444wz+MxnPsPw4cObnffEE09kwYIFzJ49m9dee42DDjqoxR61Pn360NDQQPfu3Vl//fW58MILufXWW3n11VfruXmSVkEGNUmrjTlz5vDss88yatQoevbsycCBAzn22GO5+eabGTNmDBdccAEbbLABG2ywAc8++yyHHnoo3//+9wF44IEHOOaYYxgwYAA9e/bk5JNPZvz48W06pBkRAGRmXbdP0qrHoCZptTFo0CCGDx/Oz372MxYtWsTLL7/M6NGjec973sOYMWN4+OGHmThxIhMnTmTDDTfkF7/4BSeddBIAO+64I5dffjmvvPIKb775JhdddBEbbrghgwYNett67rnnHh577DGWLFnC7NmzOeWUU9hjjz3o169fR2+ypC7OoCZptfL73/+eP//5zwwePJgtttiC7t2785Of/ISBAwcu7U3bYIMN6NatG/3796dPnz4AnHfeefTq1Ystt9ySwYMHc/PNN/OHP/xh6XJHjBjBVVddBcCTTz7JyJEj6du3L9tuuy09e/bk6quv7pTtldS1xaraFd/Q0JATJkzo7DIkSS0YdtpNnV1CXU09d9/OLqGufP7aV0Tcl5kNTdvtUZMkSaoog5okSVJFGdQkSZIqyqAmrYSWbuz9xhtvcPDBBzNs2DAigrFjxy4z38svv8zRRx/Neuutx3rrrceZZ57Z6nrmz5/PF7/4RQYNGkS/fv3Ybbfd6rdRkqTKMahJK6G1G3vvuuuuXHnllWywwQZvm+/LX/4y8+fPZ+rUqYwfP54rrriCSy+9tMX1HH/88cyZM4fJkyczZ84cfvKTn9RtmyRJ1dO9swuQuqKnnnqKUaNGve3G3j169OBLX/oSAN26dXvbfH/84x+55ZZbWGuttRg2bBjHHXccl1xyCccee+zbpn3ssce48cYbmTZtGuussw4A73//++u6XV2RV55JWpXZoyathNZu7L08tV+Jk5k8/PDDzU53zz33sOmmm3LGGWcwaNAgtttuO373u9+1S/2SpK7BoCathJZu7L08I0eO5Nxzz2Xu3LlMmTKFSy65hPnz5zc77bRp03j44Yfp168fM2bM4MILL+Too49m8uTJ7bw1kqSqMqhJK6i1G3svzwUXXEDv3r3ZcsstOeCAAzj88MMZOnRos9P27t2bNddck29961v06NGD3XffnY985CPceuut7b1JkqSKMqhJK6i1G3svz4ABA7jqqqt47rnnmDRpEkuWLGGnnXZqdtrtt9++vUuXJHUxBjVpBbV2Y2+AhQsXsmDBAgDeeOMNFixYsPS8tCeeeILZs2ezePFibrnlFi6++GK+9a1vNbue3XbbjU022YTvfe97LFq0iDvvvJOxY8ey1157dcyGSpI6nUFNWgkt3dgb4N3vfje9e/dm+vTp7LXXXvTu3Zunn34agPvuu4/tttuOvn37cvrpp3PVVVcxYsSIpcutvbH3mmuuyQ033MDNN99Mv379+PznP8/ll1/OVltt1fEbLEnqFH49h7QS3vve977ty2wbTZ06tcX5Dj30UA499NAWx0+aNGmZxyNGjOCuu+5amRIlSasAe9QkSZIqyqAmSZJUUQY1SZKkijKoSZIkVZQXE2i1570iJUlVZY+aJElSRRnUOkGfPn2W+enWrRsnn3zy0vHXXnstW2+9NX379mWbbbbh+uuvXzouM/nGN77BwIEDGThwIF//+teXucl3ralTpxIRy6zr7LPPrvfmSZKkduKhz04wb968pcOvvfYa66+/PocccggA06dP58gjj+SGG25g5MiR3HzzzRxyyCFMnTqV9dZbj4svvpjrr7+eBx54gIjg4x//OJttthknnHBCi+t7+eWX6d7dp1qSpK7GHrVOdt1117Heeuvx4Q9/GIBp06ax7rrrsvfeexMR7Lvvvqy99to88cQTAIwePZqvfvWrDB06lI022oivfvWrXHbZZZ24BZIkqV46JahFxJcjYlJEPBwRV0dEr4gYEBG3RcTj5e/+NdOfHhFTIuKxiFilbnQ4evRojjrqKCICgIaGBrbeemtuvPFGFi9ezPXXX0/Pnj2X3qB70qRJS+8pCfCe97znbd9m39Smm27K0KFDOfbYY3nxxRfrtzGSJKlddXhQi4iNgFOAhszcFugGHAacBozJzC2BMeVjImKbcvwIYCRwUUR06+i66+GZZ55h3LhxHH300UvbunXrxlFHHcWnP/1pevbsyac//Wl+8YtfsPbaawPFYdN+/fotnb5fv37Mmzev2fPUBg0axL333svTTz/Nfffdx9y5czniiCPqv2GSJKlddNahz+5A74joDqwFzAAOAEaX40cDB5bDBwDXZObCzHwKmALs1LHl1sfll1/OrrvuyvDhw5e23X777Xz9619n7NixvPHGG4wbN47Pfe5zTJw4ESguRHj11VeXTv/qq6/Sp0+fpT1ytfr06UNDQwPdu3dn/fXX58ILL+TWW29dZn5JklRdHR7UMnM6cB7wDDATeCUzbwXWz8yZ5TQzgfXKWTYCnq1ZxLSy7W0i4viImBARE2bNmlWvTWg3l19++TK9aQATJ05kt912o6GhgTXWWIMdd9yRD3zgA9x+++1AcZPuBx54YOn0DzzwACNGjGjT+hrDXEtXiUqSpGrpjEOf/Sl6yYYDGwJrR8SRrc3STFuzSSMzL87MhsxsGDx48Dsvto7+8Y9/MH369KVXezbacccd+dvf/ra0B+3+++/nb3/729Jz1I466ih+/OMfM336dGbMmMGPfvQjjjnmmGbXcc899/DYY4+xZMkSZs+ezSmnnMIee+yxzKFTSZJUXZ1x6HNP4KnMnJWZbwK/B3YGno+IIQDl7xfK6acBG9fMP5TiUGmXNnr0aA466CD69u27TPvuu+/OmWeeycEHH0zfvn355Cc/yTe/+U3+7d/+DYAvfOEL7Lfffmy33XZsu+227LvvvnzhC19YOv+IESO46qqrAHjyyScZOXIkffv2Zdttt6Vnz55cffXVHbeRkiTpHemML9d6BvhgRKwFvA58DJgAvAYcDZxb/r6hnP5G4NcR8WOKHrgtgfEdXXR7+8UvftHiuFGjRjFq1Khmx0UEP/jBD/jBD37Q7PjaK0APP/xwDj/88HdWqCRJ6jQdHtQy856IuA74J7AIuB+4GOgDXBsRx1GEuUPK6SdFxLXAI+X0J2Xm4o6uW5IkqaN1ytfVZ+YZwBlNmhdS9K41N/05wDn1rkuSJKlKvDOBJElSRRnUJEmSKso7dbeTYafd1Nkl1M3Uc/ft7BIkSVot2aMmSZJUUQY1SZKkijKoSZIkVZRBTZIkqaIMapIkSRVlUJMkSaoog5okSVJFGdQkSZIqyqAmSZJUUQY1SZKkilqhoBYR/SNi+3oVI0mSpLcsN6hFxNiIWCciBgAPAJdGxI/rX5okSdLqrS09av0y81XgIODSzHw/sGd9y5IkSVJbglr3iBgCHAr8qc71SJIkqdSWoHYW8Bfgicy8NyI2Ax6vb1mSJEnqvrwJMvO3wG9rHj8JfLKeRUmSJKltFxO8KyLGRMTD5ePtI+Jb9S9NkiRp9daWQ5+/BE4H3gTIzAeBw+pZlCRJktoW1NbKzPFN2hbVoxhJkiS9pS1B7cWI2BxIgIg4GJhZ16okSZK0/IsJgJOAi4GtImI68BRwZF2rkiRJUpuu+nwS2DMi1gbWyMy59S9LkiRJyw1qEbEucBQwjOLLbwHIzFPqWZgkSdLqri2HPm8G7gYeApbUtxxJkiQ1aktQ65WZX6l7JZIkSVpGW676vCIiPh8RQyJiQONP3SuTJElazbWlR+0N4IfAf1J+RUf5e7N6FSVJkqS2BbWvAFtk5ov1LkaSJElvacuhz0nA/HoXIkmSpGW1pUdtMTAxIu4AFjY2+vUckiRJ9dWWoHZ9+SNJkqQO1JY7E4zuiEIkSZK0rBaDWkRcm5mHRsRDvHW151KZuX1dK5MkSVrNtdajdmr5+xMdUYgkSZKW1WJQy8yZEdEN+N/M3LMDa5IkSRLL+XqOzFwMzI+Ifh1UjyRJkkptuepzAfBQRNwGvNbY6NdzSJIk1VdbgtpN5Y8kSZI6UGtXfQ4GBjf9eo6I2BZ4vt6FSZIkre5aO0ftf4DBzbRvBPy0PuVIkiSpUWtBbbvMHNe0MTP/AvgdapIkSXXWWlBbcyXHSZIkqR20FtQej4h9mjZGxN7Ak/UrSZIkSdD6VZ9fBv4UEYcC95VtDcCH8G4FkiRJdddij1pm/gvYDhgHDCt/xgHbl+MkSZJUR61+j1pmLgQu7aBaJEmSVKPVW0hJkiSp8xjUJEmSKqrFoBYRY8rf3++4ciRJktSotXPUhkTE7sD+EXENELUjM/Ofda1MkiRpNddaUPsOcBowFPhxk3EJfLReRUmSJKmVoJaZ1wHXRcS3M/PsDqxJkiRJLOfrOQAy8+yI2B/YrWwam5l/qm9ZkiRJWu5VnxHxPeBU4JHy59SyTZIkSXW03B41YF/gvZm5BCAiRgP3A6fXszBJkqTVXVu/R23dmuF+dahDkiRJTbSlR+17wP0RcQfFV3Tshr1pkiRJddeWiwmujoixwI4UQe0bmflcvQuTJEla3bWlR43MnAncWOdaJEmSVMN7fUqSJFWUQU2SJKmiWg1qEbFGRDzcUcVIkiTpLa0GtfK70x6IiE06qB5JkiSV2nIxwRBgUkSMB15rbMzM/etWlSRJktoU1L5b9yokSZL0Nm35HrVxEbEpsGVm3h4RawHd6l+aJEnS6q0tN2X/PHAd8IuyaSPg+jrWJEmSJNr29RwnAbsArwJk5uPAevUsSpIkSW0Lagsz843GBxHRHcj6lSRJkiRoW1AbFxHfBHpHxMeB3wJ/rG9ZkiRJaktQOw2YBTwEfAG4GfhWPYuSJElS2676XBIRo4F7KA55PpaZHvqUJEmqs7Zc9bkv8ARwAXAhMCUi9n4nK42IdSPiuoh4NCImR8SHImJARNwWEY+Xv/vXTH96REyJiMciYq93sm5JkqSuoi2HPn8EfCQz98jM3YGPAD95h+v9KfDnzNwKeA8wmeIQ65jM3BIYUz4mIrYBDgNGACOBiyLC73GTJEmrvLYEtRcyc0rN4yeBF1Z2hRGxDrAb8L8AmflGZr4MHACMLicbDRxYDh8AXJOZCzPzKWAKsNPKrl+SJKmraPEctYg4qBycFBE3A9dSnKN2CHDvO1jnZhQXJ1waEe8B7gNOBdbPzJkAmTkzIhq/q20j4O6a+aeVbc3VfDxwPMAmm3gfeUmS1LW11qO2X/nTC3ge2B3YgyJk9W95tuXqDrwP+Flm7kBxo/fTWpk+mmlr9mKGzLw4Mxsys2Hw4MHvoERJkqTO12KPWmYeW6d1TgOmZeY95ePrKILa8xExpOxNG8Jbh1enARvXzD8UmFGn2iRJkiqjLVd9Do+IH0fE7yPixsaflV1hZj4HPBsR7y6bPgY8AtwIHF22HQ3cUA7fCBwWET0jYjiwJTB+ZdcvSZLUVSz3e9QobsD+vxR3I1jSTus9GbgqInpQXJxwLEVovDYijgOeoTgXjsycFBHXUoS5RcBJmbm4neqQJEmqrLYEtQWZeUF7rjQzJwINzYz6WAvTnwOc0541SJIkVV1bgtpPI+IM4FZgYWNjZv6zblVJkiSpTUFtO+AzwEd569Bnlo8lSZJUJ20Jav8ObJaZb9S7GEmSJL2lLXcmeABYt851SJIkqYm29KitDzwaEfey7Dlq+9etKkmSJLUpqJ1R9yokSZL0NssNapk5riMKkSRJ0rKWG9QiYi5v3VuzB7Am8FpmrlPPwiRJklZ3belR61v7OCIOBHaqV0GSJEkqtOWqz2Vk5vX4HWqSJEl115ZDnwfVPFyD4tZP2cLkkiRJaidtuepzv5rhRcBU4IC6VCNJkqSl2nKO2rEdUYgkSZKW1WJQi4jvtDJfZubZdahHkiRJpdZ61F5rpm1t4DhgIGBQkyRJqqMWg1pm/qhxOCL6AqcCxwLXAD9qaT5JkiS1j1bPUYuIAcBXgCOA0cD7MvOljihMkiRpddfaOWo/BA4CLga2y8x5HVaVJEmSWv3C268CGwLfAmZExKvlz9yIeLVjypMkSVp9tXaO2grftUCSJEntxzAmSZJUUQY1SZKkijKoSZIkVZRBTZIkqaIMapIkSRVlUJMkSaoog5okSVJFGdQkSZIqyqAmSZJUUQY1SZKkijKoSZIkVZRBTZIkqaIMapIkSRVlUJMkSaoog5okSVJFGdQkSZIqyqAmSZJUUQY1SZKkijKoSZIkVZRBTZIkqaIMapIkSRVlUJMkSaoog5okSVJFGdQkSZIqyqAmSZJUUQY1SZKkijKoSZIkVZRBTZIkqaIMapIkSRVlUJMkSaoog5okSVJFGdQkSZIqyqAmSZJUUQY1SZKkijKoSZIkVZRBTZIkqaIMapIkSRVlUJMkSaoog5okSVJFGdQkSZIqyqAmSZJUUQY1SZKkijKoSZIkVZRBTZIkqaIMapIkSRVlUJMkSaoog5okSVJFGdQkSZIqyqAmSZJUUQY1SZKkijKoSZIkVZRBTZIkqaIMapIkSRVlUJMkSaoog5okSVJFdVpQi4huEXF/RPypfDwgIm6LiMfL3/1rpj09IqZExGMRsVdn1SxJktSROrNH7VRgcs3j04AxmbklMKZ8TERsAxwGjABGAhdFRLcOrlWSJKnDdUpQi4ihwL7Ar2qaDwBGl8OjgQNr2q/JzIWZ+RQwBdipg0qVJEnqNJ3Vo3Y+8HVgSU3b+pk5E6D8vV7ZvhHwbM1008q2t4mI4yNiQkRMmDVrVrsXLUmS1JE6PKhFxCeAFzLzvrbO0kxbNjdhZl6cmQ2Z2TB48OCVrlGSJKkKunfCOncB9o+IfYBewDoRcSXwfEQMycyZETEEeKGcfhqwcc38Q4EZHVqxJElSJ+jwHrXMPD0zh2bmMIqLBP4vM48EbgSOLic7GrihHL4ROCwiekbEcGBLYHwHly1JktThOqNHrSXnAtdGxHHAM8AhAJk5KSKuBR4BFgEnZebizitTkiSpY3RqUMvMscDYcng28LEWpjsHOKfDCpMkSaoA70wgSZJUUQY1SZKkijKoSZIkVZRBTZIkqaIMapIkSRVlUJMkSaoog5okSVJFGdQkSZIqyqAmSZJUUQY1SZKkijKoSZIkVZRBTZIkqaIMapIkSRVlUJMkSaoog5okSVJFGdQkSZIqyqAmSZJUUQY1SZKkijKoSZIkVZRBTZIkqaIMapIkSRVlUJMkSaoog5okSVJFGdQkSZIqyqAmSZJUUQY1SZKkijKoSZIkVZRBTZIkqaIMapIkSRVlUJMkSaoog5okSVJFGdQkSZIqyqAmSZJUUQY1SZKkijKoSZIkVZRBTZIkqaIMapIkSRVlUJMkSaoog5okSVJFGdQkSZIqyqAmSZJUUQY1SZKkijKoSZIkVZRBTZIkqaIMapIkSRVlUJMkSaoog5okSVJFGdQkSZIqyqAmSZJUUQY1SZKkijKoSZIkVZRBTZIkqaIMapIkSRVlUJMkSaoog5okSVJFGdQkSZIqyqAmSZJUUQY1SZKkijKoSZIkVZRBTZIkqaIMapIkSRVlUJMkSaoog5okSVJFGdQkSZIqyqAmSZJUUQY1SZKkijKoSZIkVZRBTZIkqaIMapIkSRVlUJMkSaoog5okSVJFGdQkSZIqyqAmSZJUUQY1SZKkiurwoBYRG0fEHRExOSImRcSpZfuAiLgtIh4vf/evmef0iJgSEY9FxF4dXbMkSVJn6IwetUXAVzNza+CDwEkRsQ1wGjAmM7cExpSPKccdBowARgIXRUS3TqhbkiSpQ3V4UMvMmZn5z3J4LjAZ2Ag4ABhdTjYaOLAcPgC4JjMXZuZTwBRgpw4tWpIkqRN06jlqETEM2AG4B1g/M2dCEeaA9crJNgKerZltWtnW3PKOj4gJETFh1qxZdatbkiSpI3RaUIuIPsDvgC9l5qutTdpMWzY3YWZenJkNmdkwePDg9ihTkiSp03RKUIuINSlC2lWZ+fuy+fmIGFKOHwK8ULZPAzaumX0oMKOjapUkSeosnXHVZwD/C0zOzB/XjLoROLocPhq4oab9sIjoGRHDgS2B8R1VryRJUmfp3gnr3AX4DPBQREws274JnAtcGxHHAc8AhwBk5qSIuBZ4hOKK0ZMyc3GHVy1JktTBOjyoZebfaf68M4CPtTDPOcA5dStKkiSpgrwzgSRJUkUZ1CRJkirKoCZJklRRBjVJkqSKMqhJkiRVlEFNkiSpogxqkiRJFWVQkyRJqiiDmiRJUkUZ1CRJkirKoCZJklRRBjVJkqSKMqhJkiRVlEFNkiSpogxqkiRJFWVQkyRJqiiDmiRJUkUZ1CRJkirKoCZJklRRBjVJkqSKMqhJkiRVlEFNkiSpogxqkiRJFWVQkyRJqiiDmiRJUkUZ1CRJkirKoCZJklRRBjVJkqSKMqhJkiRVlEFNkiSpogxqkiRJFWVQkyRJqiiDmiRJUkUZ1CRJkirKoCZJklRRBjVJkqSKMqhJkiRVlEFNkiSpogxqkiRJFWVQkyRJqiiDmiRJUkUZ1CRJkirKoCZJklRRBjVJkqSKMqhJkiRVlEFNkiSpogxqkiRJFWVQkyRJqiiDmiRJUkUZ1CRJkirKoCZJklRRBjVJkqSKMqhJkiRVlEFNkiSpogxqkiRJFWVQkyRJqiiDmiRJUkUZ1CRJkirKoCZJklRRBjVJkqSKMqhJkiRVlEFNkiSpogxqkiRJFWVQkyRJqiiDmiRJUkUZ1CRJkirKoCZJklRRBjVJkqSKMqhJkiRVlEFNkiSpogxqkiRJFWVQkyRJqiiDmiRJUkUZ1CRJkirKoCZJklRRXSaoRcTIiHgsIqZExGmdXY8kSVK9dYmgFhHdgP8H7A1sAxweEdt0blWSJEn11SWCGrATMCUzn8zMN4BrgAM6uSZJkqS6iszs7BqWKyIOBkZm5ufKx58BPpCZo5pMdzxwfPnw3cBjHVpoxxoEvNjZRWil+Nx1bT5/XZfPXde2qj9/m2bm4KaN3TujkpUQzbS9LWFm5sXAxfUvp/NFxITMbOjsOrTifO66Np+/rsvnrmtbXZ+/rnLocxqwcc3jocCMTqpFkiSpQ3SVoHYvsGVEDI+IHsBhwI2dXJMkSVJddYlDn5m5KCJGAX8BugGXZOakTi6rs60Wh3hXUT53XZvPX9flc9e1rZbPX5e4mECSJGl11FUOfUqSJK12DGqSJEkVZVBrRUQsjoiJEfFARPwzInZu5+U3RMQF7bi8sRGxV5O2L0XERRGxf+OttyLizIj4Wjl8Wfk9dauUiBhYPncTI+K5iJheDr8cEY90cC1jI6KhHL45ItYth0+JiMkRcVVE9IyI28saP9XG5Z5Zs10PR8T+K1jX1IgYtMIb1Mnq9dxGxDERMatc1qSIuC4i1mrn2rvkPl9RETGvyeNjIuLCcviEiDiqHF76+VP7PqlDPcMi4tMtjHsqIt7dpO38iPh6Z9S6qomIn0TEl2oe/yUiflXz+EcR8ZUW5l0t3i/LY1Br3euZ+d7MfA9wOvC99lx4Zk7IzFPacZFXU1wRW+sw4OrMvDEzz23HdVVaZs4un7v3Aj8HflIOvxdYsrLLjYh3dAFOZu6TmS+XD78I7JOZRwA7AGuWNf9mBRbZuF2HAJdERJve0+Vt2bqkej23pd+Uyx4BvAG0KTSr7TLz55l5+TtZxkq8D4cBzQY1ijvdLP3cLN9DB1O8Ft5xreIfwM6wdN8OAkbUjN8ZuLO9V9qVP+OaMqi13TrASwAR0ScixpS9bA9FxAFl+9kRcWrjDBFxTtlr8puI2Kem/bKI+GRE7BERfyrb1o6ISyLi3oi4v2aZN0fE9uXw/RHxnZp1fa5JjdcBn4iInuU0w4ANgb/X/kfbkoj4Trn+hyPi4oiIsn3HiHgwIu6KiB9GxMNle7fy8b3l+C+s7M7tYN0i4pdlr8mtEdEbICLeGxF3l9vyh4joX7aPjYj/johxwKnl4+9HxPiI+FdEfLicrltEnFe+Jh6MiJObrrjxP8SI+DmwGXBjRHwDuBJ4b9mbs3lEvD8ixkXEfeV/oENa26DMnAwsAgZFxPXlfJOiuFtH47rnRcRZEXEP8KGa9t4R8eeI+Pw73bEV0NJzu3m5jfdFxN8iYqvWFlIGgbV56z0/OCJ+V77W742IXcr2nSLiH+V78x9R9sws57Vwcs1nx1bl9C29/0eUr7OJ5XK2bPc91sGipke/lWlGlvvogYgYUzPfxRFxK3B5K8/J7vFWj+v9EdEXOBf4cNn25Sara/oP7m7A1Mx8uo21/iwiJpSvue/WtO8TEY9GxN8j4oJYzmf9KuxOyqBGEdAeBuZGRP8o/lZtDax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      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "crime_neighbourhood = crime_toronto_top5_low[['Neighbourhood','Total']]\n",
    "\n",
    "crime_neighbourhood.set_index('Neighbourhood',inplace = True)\n",
    "\n",
    "ax_x_y = crime_neighbourhood.plot(kind='bar', figsize=(10, 10), rot = 0)\n",
    "\n",
    "ax_x_y .set_title('Neighbourhoods with the lowest rate of crime')\n",
    "ax_x_y .set_ylabel('Number of Crimes')\n",
    "ax_x_y .set_xlabel('Neighbourhood')\n",
    "\n",
    "for t in ax_x_y.patches:\n",
    "    ax_x_y.annotate(np.round(t.get_height(),decimals=2), \n",
    "                (t.get_x()+t.get_width()/2., t.get_height()), \n",
    "                ha='center', \n",
    "                va='center', \n",
    "                xytext=(0, 10), \n",
    "                textcoords='offset points',\n",
    "                fontsize = 12,\n",
    "               )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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 0x7fd5eb13fcf8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Visualizing all the Neighbourhoods of the above data frame using Folium\n",
    "\n",
    "map_toronto = folium.Map(location=[43.651070,-79.347015],zoom_start=10)\n",
    "\n",
    "for lat,lng,borough,neighbourhood in zip(map_toronto_df['Latitude'],map_toronto_df['Longitude'],map_toronto_df['Borough'],map_toronto_df['Neighbourhood']):\n",
    "    label = '{}, {}'.format(neighbourhood, borough)\n",
    "    label = folium.Popup(label, parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "    [lat,lng],\n",
    "    radius=5,\n",
    "    popup=label,\n",
    "    color='blue',\n",
    "    fill=True,\n",
    "    fill_color='#3186cc',\n",
    "    fill_opacity=0.7,\n",
    "    parse_html=False).add_to(map_toronto)\n",
    "map_toronto\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After analysis of the top 5 lowest crime rate, East York Borough is considered to be the safest place for the establishment of a new restaurant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "East_York_df = crime_toronto_total[crime_toronto_total['Borough'] == 'East York']\n",
    "\n",
    "East_York_df = East_York_df.sort_values(['Total'], ascending = True, axis = 0)\n",
    "\n",
    "East_York = East_York_df[['Borough','Assault_Rate_2019', 'AutoTheft_Rate_2019','BreakandEnter_Rate_2019','Homicide_Rate_2019','Robbery_Rate_2019',\n",
    "                 'TheftOver_Rate_2019']]\n",
    "\n",
    "\n",
    "East_York.set_index('Borough',inplace = True)\n",
    "\n",
    "ax_x_y = East_York.plot(kind='bar', figsize=(10, 6), rot=0)\n",
    "\n",
    "ax_x_y.set_title('Different Kind of Crimes in East York Borough')\n",
    "ax_x_y.set_ylabel('Number of Crimes')\n",
    "ax_x_y.set_xlabel('Borough')\n",
    "\n",
    "for t in ax_x_y.patches:\n",
    "    ax_x_y.annotate(np.round(t.get_height(),decimals=2), \n",
    "                (t.get_x()+t.get_width()/2.,t.get_height()), \n",
    "                ha='center', \n",
    "                va='center', \n",
    "                xytext=(3, 10), \n",
    "                textcoords='offset points',\n",
    "                fontsize = 12\n",
    "               )\n",
    "    ax_x_y.legend(loc='upper right')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to have an interactive map of the East York Borough"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=<!DOCTYPE html>
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  "lineJoin": "round",
  "opacity": 1.0,
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  "stroke": true,
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                {
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  "fillColor": "#3186cc",
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  "lineJoin": "round",
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                var html_1cb9ef8607db459c8cae8dd8dfc7d5b4 = $('<div id="html_1cb9ef8607db459c8cae8dd8dfc7d5b4" style="width: 100.0%; height: 100.0%;">East Toronto, Broadview North (Old East York), East York</div>')[0];
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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 0x7fd6541a93c8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df['Borough'].str.contains('East York',regex=False)]\n",
    "#Visualizing all the Neighbourhoods of the above data frame using Folium\n",
    "\n",
    "map_toronto = folium.Map(location=[43.651070,-79.347015],zoom_start=10)\n",
    "\n",
    "for lat,lng,borough,neighbourhood in zip(df['Latitude'],df['Longitude'],df['Borough'],df['Neighbourhood']):\n",
    "    label = '{}, {}'.format(neighbourhood, borough)\n",
    "    label = folium.Popup(label, parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "    [lat,lng],\n",
    "    radius=5,\n",
    "    popup=label,\n",
    "    color='blue',\n",
    "    fill=True,\n",
    "    fill_color='#3186cc',\n",
    "    fill_opacity=0.7,\n",
    "    parse_html=False).add_to(map_toronto)\n",
    "map_toronto"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using Foursquare API to get the venue names near the different neighbourhood of East York"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "CLIENT_ID = 'XVY0YGK3DX5QGHMN2TGSK2EWA55P3JNPIVC5QVW5SGIGUI2L'\n",
    "CLIENT_SECRET = 'T53Z3HT4W5DVALRIPBK2DPD4NFOCISMUTMNBLNW13KEJTAIJ'\n",
    "VERSION = '20191101'\n",
    "LIMIT = 100\n",
    "\n",
    "\n",
    "def nearbyVenuesNeigh(names, latitudes, longitudes, radius=1000):\n",
    "    \n",
    "    venues_list=[]\n",
    "    for venue_name, venue_latitude, venue_longtitude in zip(names, latitudes, longitudes):\n",
    "        print(venue_name)\n",
    "            \n",
    "        # use Foursquare API\n",
    "        url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(CLIENT_ID, CLIENT_SECRET, VERSION,venue_latitude, venue_longtitude, radius, LIMIT)\n",
    "            \n",
    "        # GET request\n",
    "        results = requests.get(url).json()[\"response\"]['groups'][0]['items']\n",
    "        \n",
    "        # get information for each nearby venue\n",
    "        venues_list.append([(\n",
    "            venue_name, \n",
    "            venue_latitude, \n",
    "            venue_longtitude, \n",
    "            i['venue']['name'], \n",
    "            i['venue']['categories'][0]['name']) for i 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', 'Neighborhood Latitude', 'Neighborhood Longitude', 'Venue', 'Venue Category']\n",
    "    \n",
    "    return(nearby_venues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parkview Hill, Woodbine Gardens\n",
      "Woodbine Heights\n",
      "Leaside\n",
      "Thorncliffe Park\n",
      "East Toronto, Broadview North (Old East York)\n"
     ]
    }
   ],
   "source": [
    "east_York_venues = nearbyVenuesNeigh(names=df['Neighbourhood'],\n",
    "                                   latitudes=df['Latitude'],\n",
    "                                   longitudes=df['Longitude']\n",
    "                                  )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the near by venue of  East York"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\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>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>Parkview Hill, Woodbine Gardens</td>\n",
       "      <td>43.706397</td>\n",
       "      <td>-79.309937</td>\n",
       "      <td>Jawny Bakers</td>\n",
       "      <td>Gastropub</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Parkview Hill, Woodbine Gardens</td>\n",
       "      <td>43.706397</td>\n",
       "      <td>-79.309937</td>\n",
       "      <td>Toronto Climbing Academy</td>\n",
       "      <td>Rock Climbing Spot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Parkview Hill, Woodbine Gardens</td>\n",
       "      <td>43.706397</td>\n",
       "      <td>-79.309937</td>\n",
       "      <td>East York Gymnastics</td>\n",
       "      <td>Gym / Fitness Center</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Parkview Hill, Woodbine Gardens</td>\n",
       "      <td>43.706397</td>\n",
       "      <td>-79.309937</td>\n",
       "      <td>Muddy York Brewing Co.</td>\n",
       "      <td>Brewery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Parkview Hill, Woodbine Gardens</td>\n",
       "      <td>43.706397</td>\n",
       "      <td>-79.309937</td>\n",
       "      <td>Peek Freans Cookie Outlet</td>\n",
       "      <td>Bakery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>East Toronto, Broadview North (Old East York)</td>\n",
       "      <td>43.685347</td>\n",
       "      <td>-79.338106</td>\n",
       "      <td>Raj Kapuri Paan &amp; Snacks</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>East Toronto, Broadview North (Old East York)</td>\n",
       "      <td>43.685347</td>\n",
       "      <td>-79.338106</td>\n",
       "      <td>Pape Flower Market</td>\n",
       "      <td>Flower Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>East Toronto, Broadview North (Old East York)</td>\n",
       "      <td>43.685347</td>\n",
       "      <td>-79.338106</td>\n",
       "      <td>Phyllo Cafe</td>\n",
       "      <td>Pastry Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>East Toronto, Broadview North (Old East York)</td>\n",
       "      <td>43.685347</td>\n",
       "      <td>-79.338106</td>\n",
       "      <td>Pie in the Sky Studios</td>\n",
       "      <td>Performing Arts Venue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>East Toronto, Broadview North (Old East York)</td>\n",
       "      <td>43.685347</td>\n",
       "      <td>-79.338106</td>\n",
       "      <td>Wine Rack</td>\n",
       "      <td>Wine Shop</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>249 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     Neighbourhood  Neighborhood Latitude  \\\n",
       "0                  Parkview Hill, Woodbine Gardens              43.706397   \n",
       "1                  Parkview Hill, Woodbine Gardens              43.706397   \n",
       "2                  Parkview Hill, Woodbine Gardens              43.706397   \n",
       "3                  Parkview Hill, Woodbine Gardens              43.706397   \n",
       "4                  Parkview Hill, Woodbine Gardens              43.706397   \n",
       "..                                             ...                    ...   \n",
       "244  East Toronto, Broadview North (Old East York)              43.685347   \n",
       "245  East Toronto, Broadview North (Old East York)              43.685347   \n",
       "246  East Toronto, Broadview North (Old East York)              43.685347   \n",
       "247  East Toronto, Broadview North (Old East York)              43.685347   \n",
       "248  East Toronto, Broadview North (Old East York)              43.685347   \n",
       "\n",
       "     Neighborhood Longitude                      Venue         Venue Category  \n",
       "0                -79.309937               Jawny Bakers              Gastropub  \n",
       "1                -79.309937   Toronto Climbing Academy     Rock Climbing Spot  \n",
       "2                -79.309937       East York Gymnastics   Gym / Fitness Center  \n",
       "3                -79.309937     Muddy York Brewing Co.                Brewery  \n",
       "4                -79.309937  Peek Freans Cookie Outlet                 Bakery  \n",
       "..                      ...                        ...                    ...  \n",
       "244              -79.338106   Raj Kapuri Paan & Snacks      Indian Restaurant  \n",
       "245              -79.338106         Pape Flower Market            Flower Shop  \n",
       "246              -79.338106                Phyllo Cafe            Pastry Shop  \n",
       "247              -79.338106     Pie in the Sky Studios  Performing Arts Venue  \n",
       "248              -79.338106                  Wine Rack              Wine Shop  \n",
       "\n",
       "[249 rows x 5 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "east_York_venues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regrouping the neighbourhood together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>Venue</th>\n",
       "      <th>Venue Category</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>East Toronto, Broadview North (Old East York)</th>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leaside</th>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parkview Hill, Woodbine Gardens</th>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thorncliffe Park</th>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Woodbine Heights</th>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Venue  Venue Category\n",
       "Neighbourhood                                                       \n",
       "East Toronto, Broadview North (Old East York)     95              95\n",
       "Leaside                                           59              59\n",
       "Parkview Hill, Woodbine Gardens                   19              19\n",
       "Thorncliffe Park                                  49              49\n",
       "Woodbine Heights                                  27              27"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "east_York_venues.groupby('Neighbourhood').count().drop(['Neighborhood Latitude','Neighborhood Longitude'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Coffee Shop</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Café</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greek Restaurant</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pizza Place</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sandwich Place</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Count\n",
       "Coffee Shop          20\n",
       "Café                  9\n",
       "Greek Restaurant      9\n",
       "Pizza Place           8\n",
       "Sandwich Place        7"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nearby_venues_unique = east_York_venues['Venue Category'].value_counts().to_frame(name='Count')\n",
    "nearby_venues_unique.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Getting the venue of all restaurants in East York"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>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>7</th>\n",
       "      <td>Parkview Hill, Woodbine Gardens</td>\n",
       "      <td>43.706397</td>\n",
       "      <td>-79.309937</td>\n",
       "      <td>Harvey's</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Woodbine Heights</td>\n",
       "      <td>43.695344</td>\n",
       "      <td>-79.318389</td>\n",
       "      <td>Little Coxwell Restaurant</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Woodbine Heights</td>\n",
       "      <td>43.695344</td>\n",
       "      <td>-79.318389</td>\n",
       "      <td>Thai Fusion</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Woodbine Heights</td>\n",
       "      <td>43.695344</td>\n",
       "      <td>-79.318389</td>\n",
       "      <td>Kouzina</td>\n",
       "      <td>Greek Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Leaside</td>\n",
       "      <td>43.709060</td>\n",
       "      <td>-79.363452</td>\n",
       "      <td>The Leaside Pub</td>\n",
       "      <td>Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Leaside</td>\n",
       "      <td>43.709060</td>\n",
       "      <td>-79.363452</td>\n",
       "      <td>Kintako Japanese Restaurant</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Leaside</td>\n",
       "      <td>43.709060</td>\n",
       "      <td>-79.363452</td>\n",
       "      <td>Mt Everest Restaurant</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Leaside</td>\n",
       "      <td>43.709060</td>\n",
       "      <td>-79.363452</td>\n",
       "      <td>Mucho Burrito Fresh Mexican Grill</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Leaside</td>\n",
       "      <td>43.709060</td>\n",
       "      <td>-79.363452</td>\n",
       "      <td>Select Sandwich</td>\n",
       "      <td>Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Leaside</td>\n",
       "      <td>43.709060</td>\n",
       "      <td>-79.363452</td>\n",
       "      <td>San Francesco</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Leaside</td>\n",
       "      <td>43.709060</td>\n",
       "      <td>-79.363452</td>\n",
       "      <td>Nigiri-Ya</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>Thorncliffe Park</td>\n",
       "      <td>43.705369</td>\n",
       "      <td>-79.349372</td>\n",
       "      <td>Iqbal Kebab &amp; Sweet Centre</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>Thorncliffe Park</td>\n",
       "      <td>43.705369</td>\n",
       "      <td>-79.349372</td>\n",
       "      <td>Bamiyan Kabob</td>\n",
       "      <td>Afghan Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>Thorncliffe Park</td>\n",
       "      <td>43.705369</td>\n",
       "      <td>-79.349372</td>\n",
       "      <td>Pasha Authentic Turkish Cuisine</td>\n",
       "      <td>Turkish Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>Thorncliffe Park</td>\n",
       "      <td>43.705369</td>\n",
       "      <td>-79.349372</td>\n",
       "      <td>Faley Restaurant</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>Thorncliffe Park</td>\n",
       "      <td>43.705369</td>\n",
       "      <td>-79.349372</td>\n",
       "      <td>A&amp;W</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>Thorncliffe Park</td>\n",
       "      <td>43.705369</td>\n",
       "      <td>-79.349372</td>\n",
       "      <td>Hakka Garden</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>Thorncliffe Park</td>\n",
       "      <td>43.705369</td>\n",
       "      <td>-79.349372</td>\n",
       "      <td>Afghan Cuisine</td>\n",
       "      <td>Afghan Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>Thorncliffe Park</td>\n",
       "      <td>43.705369</td>\n",
       "      <td>-79.349372</td>\n",
       "      <td>Swiss Chalet</td>\n",
       "      <td>Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>Thorncliffe Park</td>\n",
       "      <td>43.705369</td>\n",
       "      <td>-79.349372</td>\n",
       "      <td>Anatolian's Turkish Restaurant</td>\n",
       "      <td>Turkish Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>Thorncliffe Park</td>\n",
       "      <td>43.705369</td>\n",
       "      <td>-79.349372</td>\n",
       "      <td>Kandahar Kabab</td>\n",
       "      <td>Middle Eastern Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>Thorncliffe Park</td>\n",
       "      <td>43.705369</td>\n",
       "      <td>-79.349372</td>\n",
       "      <td>Trendee Chinese Cuisine</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Thorncliffe Park</td>\n",
       "      <td>43.705369</td>\n",
       "      <td>-79.349372</td>\n",
       "      <td>San Francesco</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Thorncliffe Park</td>\n",
       "      <td>43.705369</td>\n",
       "      <td>-79.349372</td>\n",
       "      <td>Naan &amp; Kabob</td>\n",
       "      <td>Middle Eastern Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>East Toronto, Broadview North (Old East York)</td>\n",
       "      <td>43.685347</td>\n",
       "      <td>-79.338106</td>\n",
       "      <td>Mr. Pide</td>\n",
       "      <td>Turkish Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>East Toronto, Broadview North (Old East York)</td>\n",
       "      <td>43.685347</td>\n",
       "      <td>-79.338106</td>\n",
       "      <td>The Wren</td>\n",
       "      <td>American Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>East Toronto, Broadview North (Old East York)</td>\n",
       "      <td>43.685347</td>\n",
       "      <td>-79.338106</td>\n",
       "      <td>Rendez-Vous Restaurant Bar &amp; Cafe</td>\n",
       "      <td>Ethiopian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>East Toronto, Broadview North (Old East York)</td>\n",
       "      <td>43.685347</td>\n",
       "      <td>-79.338106</td>\n",
       "      <td>Athens Restaurant &amp; Bar</td>\n",
       "      <td>Greek Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>East Toronto, Broadview North (Old East York)</td>\n",
       "      <td>43.685347</td>\n",
       "      <td>-79.338106</td>\n",
       "      <td>Danforth Dragon Restaurant</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>East Toronto, Broadview North (Old East York)</td>\n",
       "      <td>43.685347</td>\n",
       "      <td>-79.338106</td>\n",
       "      <td>City Betty</td>\n",
       "      <td>American Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>East Toronto, Broadview North (Old East York)</td>\n",
       "      <td>43.685347</td>\n",
       "      <td>-79.338106</td>\n",
       "      <td>Sala Modern Thai Kitchen &amp; Bar</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>East Toronto, Broadview North (Old East York)</td>\n",
       "      <td>43.685347</td>\n",
       "      <td>-79.338106</td>\n",
       "      <td>Al Mandi Restaurant مطعم المندي</td>\n",
       "      <td>Middle Eastern Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>East Toronto, Broadview North (Old East York)</td>\n",
       "      <td>43.685347</td>\n",
       "      <td>-79.338106</td>\n",
       "      <td>Ha gow Dim Sum House</td>\n",
       "      <td>Dim Sum Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>East Toronto, Broadview North (Old East York)</td>\n",
       "      <td>43.685347</td>\n",
       "      <td>-79.338106</td>\n",
       "      <td>Nakayoshi: Ramen Fine Japanese Cuisine</td>\n",
       "      <td>Ramen Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>East Toronto, Broadview North (Old East York)</td>\n",
       "      <td>43.685347</td>\n",
       "      <td>-79.338106</td>\n",
       "      <td>Lalibela</td>\n",
       "      <td>Ethiopian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>East Toronto, Broadview North (Old East York)</td>\n",
       "      <td>43.685347</td>\n",
       "      <td>-79.338106</td>\n",
       "      <td>Folia Grill</td>\n",
       "      <td>Greek Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>East Toronto, Broadview North (Old East York)</td>\n",
       "      <td>43.685347</td>\n",
       "      <td>-79.338106</td>\n",
       "      <td>Zorba Mezedes</td>\n",
       "      <td>Greek Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>East Toronto, Broadview North (Old East York)</td>\n",
       "      <td>43.685347</td>\n",
       "      <td>-79.338106</td>\n",
       "      <td>Tzatziki</td>\n",
       "      <td>Greek Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>East Toronto, Broadview North (Old East York)</td>\n",
       "      <td>43.685347</td>\n",
       "      <td>-79.338106</td>\n",
       "      <td>A&amp;W</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>East Toronto, Broadview North (Old East York)</td>\n",
       "      <td>43.685347</td>\n",
       "      <td>-79.338106</td>\n",
       "      <td>Akropolis Pastries</td>\n",
       "      <td>Greek Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>East Toronto, Broadview North (Old East York)</td>\n",
       "      <td>43.685347</td>\n",
       "      <td>-79.338106</td>\n",
       "      <td>Wazema Ethiopian Restaurant</td>\n",
       "      <td>Ethiopian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>East Toronto, Broadview North (Old East York)</td>\n",
       "      <td>43.685347</td>\n",
       "      <td>-79.338106</td>\n",
       "      <td>A&amp;W</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>East Toronto, Broadview North (Old East York)</td>\n",
       "      <td>43.685347</td>\n",
       "      <td>-79.338106</td>\n",
       "      <td>Number One Chinese Restaurant</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>East Toronto, Broadview North (Old East York)</td>\n",
       "      <td>43.685347</td>\n",
       "      <td>-79.338106</td>\n",
       "      <td>Makkah Restaurant</td>\n",
       "      <td>Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>East Toronto, Broadview North (Old East York)</td>\n",
       "      <td>43.685347</td>\n",
       "      <td>-79.338106</td>\n",
       "      <td>KFC</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>East Toronto, Broadview North (Old East York)</td>\n",
       "      <td>43.685347</td>\n",
       "      <td>-79.338106</td>\n",
       "      <td>El Sol Mexican Gallery Restaurant</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>East Toronto, Broadview North (Old East York)</td>\n",
       "      <td>43.685347</td>\n",
       "      <td>-79.338106</td>\n",
       "      <td>Kings Bar and Grill</td>\n",
       "      <td>Greek Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>East Toronto, Broadview North (Old East York)</td>\n",
       "      <td>43.685347</td>\n",
       "      <td>-79.338106</td>\n",
       "      <td>Pape Village</td>\n",
       "      <td>Greek Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>East Toronto, Broadview North (Old East York)</td>\n",
       "      <td>43.685347</td>\n",
       "      <td>-79.338106</td>\n",
       "      <td>Pita King &amp; Grill</td>\n",
       "      <td>Middle Eastern Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>East Toronto, Broadview North (Old East York)</td>\n",
       "      <td>43.685347</td>\n",
       "      <td>-79.338106</td>\n",
       "      <td>Pho Challenge</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>East Toronto, Broadview North (Old East York)</td>\n",
       "      <td>43.685347</td>\n",
       "      <td>-79.338106</td>\n",
       "      <td>Paul's Spaghetti</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>East Toronto, Broadview North (Old East York)</td>\n",
       "      <td>43.685347</td>\n",
       "      <td>-79.338106</td>\n",
       "      <td>East York Restaurant</td>\n",
       "      <td>Greek Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>East Toronto, Broadview North (Old East York)</td>\n",
       "      <td>43.685347</td>\n",
       "      <td>-79.338106</td>\n",
       "      <td>Raj Kapuri Paan &amp; Snacks</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     Neighbourhood  Neighborhood Latitude  \\\n",
       "7                  Parkview Hill, Woodbine Gardens              43.706397   \n",
       "27                                Woodbine Heights              43.695344   \n",
       "41                                Woodbine Heights              43.695344   \n",
       "44                                Woodbine Heights              43.695344   \n",
       "56                                         Leaside              43.709060   \n",
       "60                                         Leaside              43.709060   \n",
       "75                                         Leaside              43.709060   \n",
       "91                                         Leaside              43.709060   \n",
       "96                                         Leaside              43.709060   \n",
       "99                                         Leaside              43.709060   \n",
       "100                                        Leaside              43.709060   \n",
       "108                               Thorncliffe Park              43.705369   \n",
       "109                               Thorncliffe Park              43.705369   \n",
       "112                               Thorncliffe Park              43.705369   \n",
       "123                               Thorncliffe Park              43.705369   \n",
       "126                               Thorncliffe Park              43.705369   \n",
       "127                               Thorncliffe Park              43.705369   \n",
       "130                               Thorncliffe Park              43.705369   \n",
       "140                               Thorncliffe Park              43.705369   \n",
       "141                               Thorncliffe Park              43.705369   \n",
       "142                               Thorncliffe Park              43.705369   \n",
       "148                               Thorncliffe Park              43.705369   \n",
       "152                               Thorncliffe Park              43.705369   \n",
       "153                               Thorncliffe Park              43.705369   \n",
       "157  East Toronto, Broadview North (Old East York)              43.685347   \n",
       "161  East Toronto, Broadview North (Old East York)              43.685347   \n",
       "162  East Toronto, Broadview North (Old East York)              43.685347   \n",
       "165  East Toronto, Broadview North (Old East York)              43.685347   \n",
       "169  East Toronto, Broadview North (Old East York)              43.685347   \n",
       "171  East Toronto, Broadview North (Old East York)              43.685347   \n",
       "174  East Toronto, Broadview North (Old East York)              43.685347   \n",
       "176  East Toronto, Broadview North (Old East York)              43.685347   \n",
       "180  East Toronto, Broadview North (Old East York)              43.685347   \n",
       "186  East Toronto, Broadview North (Old East York)              43.685347   \n",
       "187  East Toronto, Broadview North (Old East York)              43.685347   \n",
       "191  East Toronto, Broadview North (Old East York)              43.685347   \n",
       "192  East Toronto, Broadview North (Old East York)              43.685347   \n",
       "194  East Toronto, Broadview North (Old East York)              43.685347   \n",
       "195  East Toronto, Broadview North (Old East York)              43.685347   \n",
       "200  East Toronto, Broadview North (Old East York)              43.685347   \n",
       "202  East Toronto, Broadview North (Old East York)              43.685347   \n",
       "207  East Toronto, Broadview North (Old East York)              43.685347   \n",
       "215  East Toronto, Broadview North (Old East York)              43.685347   \n",
       "216  East Toronto, Broadview North (Old East York)              43.685347   \n",
       "217  East Toronto, Broadview North (Old East York)              43.685347   \n",
       "223  East Toronto, Broadview North (Old East York)              43.685347   \n",
       "232  East Toronto, Broadview North (Old East York)              43.685347   \n",
       "236  East Toronto, Broadview North (Old East York)              43.685347   \n",
       "237  East Toronto, Broadview North (Old East York)              43.685347   \n",
       "238  East Toronto, Broadview North (Old East York)              43.685347   \n",
       "241  East Toronto, Broadview North (Old East York)              43.685347   \n",
       "242  East Toronto, Broadview North (Old East York)              43.685347   \n",
       "244  East Toronto, Broadview North (Old East York)              43.685347   \n",
       "\n",
       "     Neighborhood Longitude                                   Venue  \\\n",
       "7                -79.309937                                Harvey's   \n",
       "27               -79.318389               Little Coxwell Restaurant   \n",
       "41               -79.318389                             Thai Fusion   \n",
       "44               -79.318389                                 Kouzina   \n",
       "56               -79.363452                         The Leaside Pub   \n",
       "60               -79.363452             Kintako Japanese Restaurant   \n",
       "75               -79.363452                   Mt Everest Restaurant   \n",
       "91               -79.363452       Mucho Burrito Fresh Mexican Grill   \n",
       "96               -79.363452                         Select Sandwich   \n",
       "99               -79.363452                           San Francesco   \n",
       "100              -79.363452                               Nigiri-Ya   \n",
       "108              -79.349372              Iqbal Kebab & Sweet Centre   \n",
       "109              -79.349372                           Bamiyan Kabob   \n",
       "112              -79.349372         Pasha Authentic Turkish Cuisine   \n",
       "123              -79.349372                        Faley Restaurant   \n",
       "126              -79.349372                                     A&W   \n",
       "127              -79.349372                            Hakka Garden   \n",
       "130              -79.349372                          Afghan Cuisine   \n",
       "140              -79.349372                            Swiss Chalet   \n",
       "141              -79.349372          Anatolian's Turkish Restaurant   \n",
       "142              -79.349372                          Kandahar Kabab   \n",
       "148              -79.349372                 Trendee Chinese Cuisine   \n",
       "152              -79.349372                           San Francesco   \n",
       "153              -79.349372                            Naan & Kabob   \n",
       "157              -79.338106                                Mr. Pide   \n",
       "161              -79.338106                                The Wren   \n",
       "162              -79.338106       Rendez-Vous Restaurant Bar & Cafe   \n",
       "165              -79.338106                 Athens Restaurant & Bar   \n",
       "169              -79.338106              Danforth Dragon Restaurant   \n",
       "171              -79.338106                              City Betty   \n",
       "174              -79.338106          Sala Modern Thai Kitchen & Bar   \n",
       "176              -79.338106         Al Mandi Restaurant مطعم المندي   \n",
       "180              -79.338106                    Ha gow Dim Sum House   \n",
       "186              -79.338106  Nakayoshi: Ramen Fine Japanese Cuisine   \n",
       "187              -79.338106                                Lalibela   \n",
       "191              -79.338106                             Folia Grill   \n",
       "192              -79.338106                           Zorba Mezedes   \n",
       "194              -79.338106                                Tzatziki   \n",
       "195              -79.338106                                     A&W   \n",
       "200              -79.338106                      Akropolis Pastries   \n",
       "202              -79.338106             Wazema Ethiopian Restaurant   \n",
       "207              -79.338106                                     A&W   \n",
       "215              -79.338106           Number One Chinese Restaurant   \n",
       "216              -79.338106                       Makkah Restaurant   \n",
       "217              -79.338106                                     KFC   \n",
       "223              -79.338106       El Sol Mexican Gallery Restaurant   \n",
       "232              -79.338106                     Kings Bar and Grill   \n",
       "236              -79.338106                            Pape Village   \n",
       "237              -79.338106                       Pita King & Grill   \n",
       "238              -79.338106                           Pho Challenge   \n",
       "241              -79.338106                        Paul's Spaghetti   \n",
       "242              -79.338106                    East York Restaurant   \n",
       "244              -79.338106                Raj Kapuri Paan & Snacks   \n",
       "\n",
       "                Venue Category  \n",
       "7         Fast Food Restaurant  \n",
       "27             Thai Restaurant  \n",
       "41             Thai Restaurant  \n",
       "44            Greek Restaurant  \n",
       "56                  Restaurant  \n",
       "60            Sushi Restaurant  \n",
       "75           Indian Restaurant  \n",
       "91          Mexican Restaurant  \n",
       "96                  Restaurant  \n",
       "99          Italian Restaurant  \n",
       "100        Japanese Restaurant  \n",
       "108          Indian Restaurant  \n",
       "109          Afghan Restaurant  \n",
       "112         Turkish Restaurant  \n",
       "123          Indian Restaurant  \n",
       "126       Fast Food Restaurant  \n",
       "127          Indian Restaurant  \n",
       "130          Afghan Restaurant  \n",
       "140                 Restaurant  \n",
       "141         Turkish Restaurant  \n",
       "142  Middle Eastern Restaurant  \n",
       "148           Asian Restaurant  \n",
       "152         Italian Restaurant  \n",
       "153  Middle Eastern Restaurant  \n",
       "157         Turkish Restaurant  \n",
       "161        American Restaurant  \n",
       "162       Ethiopian Restaurant  \n",
       "165           Greek Restaurant  \n",
       "169           Asian Restaurant  \n",
       "171        American Restaurant  \n",
       "174            Thai Restaurant  \n",
       "176  Middle Eastern Restaurant  \n",
       "180         Dim Sum Restaurant  \n",
       "186           Ramen Restaurant  \n",
       "187       Ethiopian Restaurant  \n",
       "191           Greek Restaurant  \n",
       "192           Greek Restaurant  \n",
       "194           Greek Restaurant  \n",
       "195       Fast Food Restaurant  \n",
       "200           Greek Restaurant  \n",
       "202       Ethiopian Restaurant  \n",
       "207       Fast Food Restaurant  \n",
       "215         Chinese Restaurant  \n",
       "216                 Restaurant  \n",
       "217       Fast Food Restaurant  \n",
       "223         Mexican Restaurant  \n",
       "232           Greek Restaurant  \n",
       "236           Greek Restaurant  \n",
       "237  Middle Eastern Restaurant  \n",
       "238      Vietnamese Restaurant  \n",
       "241         Italian Restaurant  \n",
       "242           Greek Restaurant  \n",
       "244          Indian Restaurant  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "east_york_Restaurant= east_York_venues[east_York_venues['Venue Category'].str.contains('Restaurant',regex=False)]\n",
    "east_york_Restaurant"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analyze Each Neighbourhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighbourhood</th>\n",
       "      <th>Afghan Restaurant</th>\n",
       "      <th>American Restaurant</th>\n",
       "      <th>Asian Restaurant</th>\n",
       "      <th>Chinese Restaurant</th>\n",
       "      <th>Dim Sum Restaurant</th>\n",
       "      <th>Ethiopian Restaurant</th>\n",
       "      <th>Fast Food Restaurant</th>\n",
       "      <th>Greek Restaurant</th>\n",
       "      <th>Indian Restaurant</th>\n",
       "      <th>Italian Restaurant</th>\n",
       "      <th>Japanese Restaurant</th>\n",
       "      <th>Mexican Restaurant</th>\n",
       "      <th>Middle Eastern Restaurant</th>\n",
       "      <th>Ramen Restaurant</th>\n",
       "      <th>Restaurant</th>\n",
       "      <th>Sushi Restaurant</th>\n",
       "      <th>Thai Restaurant</th>\n",
       "      <th>Turkish Restaurant</th>\n",
       "      <th>Vietnamese Restaurant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Parkview Hill, Woodbine Gardens</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>1</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Woodbine Heights</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",
       "      <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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Woodbine Heights</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",
       "      <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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Woodbine Heights</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>1</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Leaside</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",
       "      <td>0</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Neighbourhood  Afghan Restaurant  American Restaurant  \\\n",
       "7   Parkview Hill, Woodbine Gardens                  0                    0   \n",
       "27                 Woodbine Heights                  0                    0   \n",
       "41                 Woodbine Heights                  0                    0   \n",
       "44                 Woodbine Heights                  0                    0   \n",
       "56                          Leaside                  0                    0   \n",
       "\n",
       "    Asian Restaurant  Chinese Restaurant  Dim Sum Restaurant  \\\n",
       "7                  0                   0                   0   \n",
       "27                 0                   0                   0   \n",
       "41                 0                   0                   0   \n",
       "44                 0                   0                   0   \n",
       "56                 0                   0                   0   \n",
       "\n",
       "    Ethiopian Restaurant  Fast Food Restaurant  Greek Restaurant  \\\n",
       "7                      0                     1                 0   \n",
       "27                     0                     0                 0   \n",
       "41                     0                     0                 0   \n",
       "44                     0                     0                 1   \n",
       "56                     0                     0                 0   \n",
       "\n",
       "    Indian Restaurant  Italian Restaurant  Japanese Restaurant  \\\n",
       "7                   0                   0                    0   \n",
       "27                  0                   0                    0   \n",
       "41                  0                   0                    0   \n",
       "44                  0                   0                    0   \n",
       "56                  0                   0                    0   \n",
       "\n",
       "    Mexican Restaurant  Middle Eastern Restaurant  Ramen Restaurant  \\\n",
       "7                    0                          0                 0   \n",
       "27                   0                          0                 0   \n",
       "41                   0                          0                 0   \n",
       "44                   0                          0                 0   \n",
       "56                   0                          0                 0   \n",
       "\n",
       "    Restaurant  Sushi Restaurant  Thai Restaurant  Turkish Restaurant  \\\n",
       "7            0                 0                0                   0   \n",
       "27           0                 0                1                   0   \n",
       "41           0                 0                1                   0   \n",
       "44           0                 0                0                   0   \n",
       "56           1                 0                0                   0   \n",
       "\n",
       "    Vietnamese Restaurant  \n",
       "7                       0  \n",
       "27                      0  \n",
       "41                      0  \n",
       "44                      0  \n",
       "56                      0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# one hot encoding\n",
    "east_york_onehot = pd.get_dummies(east_york_Restaurant[['Venue Category']], prefix=\"\", prefix_sep=\"\")\n",
    "\n",
    "# add neighborhood column back to dataframe\n",
    "east_york_onehot['Neighbourhood'] = east_york_Restaurant['Neighbourhood'] \n",
    "\n",
    "# move neighborhood column to the first column\n",
    "fixed_columns = [east_york_onehot.columns[-1]] + list(east_york_onehot.columns[:-1])\n",
    "east_york_onehot =east_york_onehot[fixed_columns]\n",
    "\n",
    "east_york_onehot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>Afghan Restaurant</th>\n",
       "      <th>American Restaurant</th>\n",
       "      <th>Asian Restaurant</th>\n",
       "      <th>Chinese Restaurant</th>\n",
       "      <th>Dim Sum Restaurant</th>\n",
       "      <th>Ethiopian Restaurant</th>\n",
       "      <th>Fast Food Restaurant</th>\n",
       "      <th>Greek Restaurant</th>\n",
       "      <th>Indian Restaurant</th>\n",
       "      <th>Italian Restaurant</th>\n",
       "      <th>Japanese Restaurant</th>\n",
       "      <th>Mexican Restaurant</th>\n",
       "      <th>Middle Eastern Restaurant</th>\n",
       "      <th>Ramen Restaurant</th>\n",
       "      <th>Restaurant</th>\n",
       "      <th>Sushi Restaurant</th>\n",
       "      <th>Thai Restaurant</th>\n",
       "      <th>Turkish Restaurant</th>\n",
       "      <th>Vietnamese Restaurant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>East Toronto, Broadview North (Old East York)</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.068966</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.103448</td>\n",
       "      <td>0.103448</td>\n",
       "      <td>0.275862</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.068966</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.034483</td>\n",
       "      <td>0.034483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Leaside</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>0.142857</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.285714</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>2</th>\n",
       "      <td>Parkview Hill, Woodbine Gardens</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>1.000000</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",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Thorncliffe Park</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Woodbine Heights</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>0.333333</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>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Neighbourhood  Afghan Restaurant  \\\n",
       "0  East Toronto, Broadview North (Old East York)           0.000000   \n",
       "1                                        Leaside           0.000000   \n",
       "2                Parkview Hill, Woodbine Gardens           0.000000   \n",
       "3                               Thorncliffe Park           0.153846   \n",
       "4                               Woodbine Heights           0.000000   \n",
       "\n",
       "   American Restaurant  Asian Restaurant  Chinese Restaurant  \\\n",
       "0             0.068966          0.034483            0.034483   \n",
       "1             0.000000          0.000000            0.000000   \n",
       "2             0.000000          0.000000            0.000000   \n",
       "3             0.000000          0.076923            0.000000   \n",
       "4             0.000000          0.000000            0.000000   \n",
       "\n",
       "   Dim Sum Restaurant  Ethiopian Restaurant  Fast Food Restaurant  \\\n",
       "0            0.034483              0.103448              0.103448   \n",
       "1            0.000000              0.000000              0.000000   \n",
       "2            0.000000              0.000000              1.000000   \n",
       "3            0.000000              0.000000              0.076923   \n",
       "4            0.000000              0.000000              0.000000   \n",
       "\n",
       "   Greek Restaurant  Indian Restaurant  Italian Restaurant  \\\n",
       "0          0.275862           0.034483            0.034483   \n",
       "1          0.000000           0.142857            0.142857   \n",
       "2          0.000000           0.000000            0.000000   \n",
       "3          0.000000           0.230769            0.076923   \n",
       "4          0.333333           0.000000            0.000000   \n",
       "\n",
       "   Japanese Restaurant  Mexican Restaurant  Middle Eastern Restaurant  \\\n",
       "0             0.000000            0.034483                   0.068966   \n",
       "1             0.142857            0.142857                   0.000000   \n",
       "2             0.000000            0.000000                   0.000000   \n",
       "3             0.000000            0.000000                   0.153846   \n",
       "4             0.000000            0.000000                   0.000000   \n",
       "\n",
       "   Ramen Restaurant  Restaurant  Sushi Restaurant  Thai Restaurant  \\\n",
       "0          0.034483    0.034483          0.000000         0.034483   \n",
       "1          0.000000    0.285714          0.142857         0.000000   \n",
       "2          0.000000    0.000000          0.000000         0.000000   \n",
       "3          0.000000    0.076923          0.000000         0.000000   \n",
       "4          0.000000    0.000000          0.000000         0.666667   \n",
       "\n",
       "   Turkish Restaurant  Vietnamese Restaurant  \n",
       "0            0.034483               0.034483  \n",
       "1            0.000000               0.000000  \n",
       "2            0.000000               0.000000  \n",
       "3            0.153846               0.000000  \n",
       "4            0.000000               0.000000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "east_york_grouped = east_york_onehot.groupby('Neighbourhood').mean().reset_index()\n",
    "east_york_grouped"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get each neighborhood along with the top 5 most common venues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----East Toronto, Broadview North (Old East York)----\n",
      "                       venue  freq\n",
      "0           Greek Restaurant  0.28\n",
      "1       Ethiopian Restaurant  0.10\n",
      "2       Fast Food Restaurant  0.10\n",
      "3        American Restaurant  0.07\n",
      "4  Middle Eastern Restaurant  0.07\n",
      "\n",
      "\n",
      "----Leaside----\n",
      "                 venue  freq\n",
      "0           Restaurant  0.29\n",
      "1   Italian Restaurant  0.14\n",
      "2  Japanese Restaurant  0.14\n",
      "3     Sushi Restaurant  0.14\n",
      "4    Indian Restaurant  0.14\n",
      "\n",
      "\n",
      "----Parkview Hill, Woodbine Gardens----\n",
      "                  venue  freq\n",
      "0  Fast Food Restaurant   1.0\n",
      "1     Afghan Restaurant   0.0\n",
      "2   Japanese Restaurant   0.0\n",
      "3    Turkish Restaurant   0.0\n",
      "4       Thai Restaurant   0.0\n",
      "\n",
      "\n",
      "----Thorncliffe Park----\n",
      "                       venue  freq\n",
      "0          Indian Restaurant  0.23\n",
      "1          Afghan Restaurant  0.15\n",
      "2         Turkish Restaurant  0.15\n",
      "3  Middle Eastern Restaurant  0.15\n",
      "4       Fast Food Restaurant  0.08\n",
      "\n",
      "\n",
      "----Woodbine Heights----\n",
      "                 venue  freq\n",
      "0      Thai Restaurant  0.67\n",
      "1     Greek Restaurant  0.33\n",
      "2    Afghan Restaurant  0.00\n",
      "3  Japanese Restaurant  0.00\n",
      "4   Turkish Restaurant  0.00\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "num_top_venues = 5\n",
    "\n",
    "for hood in east_york_grouped['Neighbourhood']:\n",
    "    print(\"----\"+hood+\"----\")\n",
    "    temp = east_york_grouped[east_york_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": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>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>East Toronto, Broadview North (Old East York)</td>\n",
       "      <td>Greek Restaurant</td>\n",
       "      <td>Ethiopian Restaurant</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Middle Eastern Restaurant</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Dim Sum Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Leaside</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Parkview Hill, Woodbine Gardens</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Dim Sum Restaurant</td>\n",
       "      <td>Ethiopian Restaurant</td>\n",
       "      <td>Greek Restaurant</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Thorncliffe Park</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "      <td>Afghan Restaurant</td>\n",
       "      <td>Middle Eastern Restaurant</td>\n",
       "      <td>Turkish Restaurant</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Woodbine Heights</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>Greek Restaurant</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Dim Sum Restaurant</td>\n",
       "      <td>Ethiopian Restaurant</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Neighbourhood 1st Most Common Venue  \\\n",
       "0  East Toronto, Broadview North (Old East York)      Greek Restaurant   \n",
       "1                                        Leaside            Restaurant   \n",
       "2                Parkview Hill, Woodbine Gardens  Fast Food Restaurant   \n",
       "3                               Thorncliffe Park     Indian Restaurant   \n",
       "4                               Woodbine Heights       Thai Restaurant   \n",
       "\n",
       "   2nd Most Common Venue      3rd Most Common Venue 4th Most Common Venue  \\\n",
       "0   Ethiopian Restaurant       Fast Food Restaurant   American Restaurant   \n",
       "1     Italian Restaurant           Sushi Restaurant    Mexican Restaurant   \n",
       "2  Vietnamese Restaurant          Indian Restaurant   American Restaurant   \n",
       "3      Afghan Restaurant  Middle Eastern Restaurant    Turkish Restaurant   \n",
       "4       Greek Restaurant      Vietnamese Restaurant     Indian Restaurant   \n",
       "\n",
       "       5th Most Common Venue  6th Most Common Venue 7th Most Common Venue  \\\n",
       "0  Middle Eastern Restaurant  Vietnamese Restaurant     Indian Restaurant   \n",
       "1        Japanese Restaurant      Indian Restaurant  Fast Food Restaurant   \n",
       "2           Asian Restaurant     Chinese Restaurant    Dim Sum Restaurant   \n",
       "3           Asian Restaurant   Fast Food Restaurant    Italian Restaurant   \n",
       "4        American Restaurant       Asian Restaurant    Chinese Restaurant   \n",
       "\n",
       "  8th Most Common Venue 9th Most Common Venue 10th Most Common Venue  \n",
       "0      Asian Restaurant    Chinese Restaurant     Dim Sum Restaurant  \n",
       "1   American Restaurant      Asian Restaurant     Chinese Restaurant  \n",
       "2  Ethiopian Restaurant      Greek Restaurant     Italian Restaurant  \n",
       "3            Restaurant    Mexican Restaurant    Japanese Restaurant  \n",
       "4    Dim Sum Restaurant  Ethiopian Restaurant   Fast Food Restaurant  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_top_venues = 10\n",
    "\n",
    "indicators = ['st', 'nd', 'rd']\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))\n",
    "\n",
    "# create a new dataframe\n",
    "neighborhoods_venues_sorted = pd.DataFrame(columns=columns)\n",
    "neighborhoods_venues_sorted['Neighbourhood'] = east_york_grouped['Neighbourhood']\n",
    "\n",
    "for ind in np.arange(east_york_grouped.shape[0]):\n",
    "    neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(east_york_grouped.iloc[ind, :], num_top_venues)\n",
    "\n",
    "neighborhoods_venues_sorted.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "K Means Clustering of the each neighbourhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 4, 2, 1, 0], dtype=int32)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#k=5\n",
    "#East_York_clustering = east_york_Restaurant.drop(['Venue','Venue Category','Neighbourhood'],1)\n",
    "#kmeans = KMeans(n_clusters = k,random_state=0).fit(East_York_clustering)\n",
    "#kmeans.labels_\n",
    "#east_york_Restaurant.insert(0, 'Cluster Labels', kmeans.labels_)\n",
    "#east_york_Restaurant\n",
    "\n",
    "# set number of clusters\n",
    "kclusters = 5\n",
    "\n",
    "east_york_grouped_clustering = east_york_grouped.drop('Neighbourhood', 1)\n",
    "\n",
    "# run k-means clustering\n",
    "kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(east_york_grouped_clustering)\n",
    "\n",
    "# check cluster labels generated for each row in the dataframe\n",
    "kmeans.labels_[0:10] \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Postal Code</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighbourhood</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>8</th>\n",
       "      <td>M4B</td>\n",
       "      <td>East York</td>\n",
       "      <td>Parkview Hill, Woodbine Gardens</td>\n",
       "      <td>43.706397</td>\n",
       "      <td>-79.309937</td>\n",
       "      <td>2</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Dim Sum Restaurant</td>\n",
       "      <td>Ethiopian Restaurant</td>\n",
       "      <td>Greek Restaurant</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>M4C</td>\n",
       "      <td>East York</td>\n",
       "      <td>Woodbine Heights</td>\n",
       "      <td>43.695344</td>\n",
       "      <td>-79.318389</td>\n",
       "      <td>0</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>Greek Restaurant</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Dim Sum Restaurant</td>\n",
       "      <td>Ethiopian Restaurant</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>M4G</td>\n",
       "      <td>East York</td>\n",
       "      <td>Leaside</td>\n",
       "      <td>43.709060</td>\n",
       "      <td>-79.363452</td>\n",
       "      <td>4</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>M4H</td>\n",
       "      <td>East York</td>\n",
       "      <td>Thorncliffe Park</td>\n",
       "      <td>43.705369</td>\n",
       "      <td>-79.349372</td>\n",
       "      <td>1</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "      <td>Afghan Restaurant</td>\n",
       "      <td>Middle Eastern Restaurant</td>\n",
       "      <td>Turkish Restaurant</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Japanese Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>M4J</td>\n",
       "      <td>East York</td>\n",
       "      <td>East Toronto, Broadview North (Old East York)</td>\n",
       "      <td>43.685347</td>\n",
       "      <td>-79.338106</td>\n",
       "      <td>3</td>\n",
       "      <td>Greek Restaurant</td>\n",
       "      <td>Ethiopian Restaurant</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Middle Eastern Restaurant</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Dim Sum Restaurant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Postal Code    Borough                                  Neighbourhood  \\\n",
       "8          M4B  East York                Parkview Hill, Woodbine Gardens   \n",
       "14         M4C  East York                               Woodbine Heights   \n",
       "23         M4G  East York                                        Leaside   \n",
       "29         M4H  East York                               Thorncliffe Park   \n",
       "35         M4J  East York  East Toronto, Broadview North (Old East York)   \n",
       "\n",
       "     Latitude  Longitude  Cluster Labels 1st Most Common Venue  \\\n",
       "8   43.706397 -79.309937               2  Fast Food Restaurant   \n",
       "14  43.695344 -79.318389               0       Thai Restaurant   \n",
       "23  43.709060 -79.363452               4            Restaurant   \n",
       "29  43.705369 -79.349372               1     Indian Restaurant   \n",
       "35  43.685347 -79.338106               3      Greek Restaurant   \n",
       "\n",
       "    2nd Most Common Venue      3rd Most Common Venue 4th Most Common Venue  \\\n",
       "8   Vietnamese Restaurant          Indian Restaurant   American Restaurant   \n",
       "14       Greek Restaurant      Vietnamese Restaurant     Indian Restaurant   \n",
       "23     Italian Restaurant           Sushi Restaurant    Mexican Restaurant   \n",
       "29      Afghan Restaurant  Middle Eastern Restaurant    Turkish Restaurant   \n",
       "35   Ethiopian Restaurant       Fast Food Restaurant   American Restaurant   \n",
       "\n",
       "        5th Most Common Venue  6th Most Common Venue 7th Most Common Venue  \\\n",
       "8            Asian Restaurant     Chinese Restaurant    Dim Sum Restaurant   \n",
       "14        American Restaurant       Asian Restaurant    Chinese Restaurant   \n",
       "23        Japanese Restaurant      Indian Restaurant  Fast Food Restaurant   \n",
       "29           Asian Restaurant   Fast Food Restaurant    Italian Restaurant   \n",
       "35  Middle Eastern Restaurant  Vietnamese Restaurant     Indian Restaurant   \n",
       "\n",
       "   8th Most Common Venue 9th Most Common Venue 10th Most Common Venue  \n",
       "8   Ethiopian Restaurant      Greek Restaurant     Italian Restaurant  \n",
       "14    Dim Sum Restaurant  Ethiopian Restaurant   Fast Food Restaurant  \n",
       "23   American Restaurant      Asian Restaurant     Chinese Restaurant  \n",
       "29            Restaurant    Mexican Restaurant    Japanese Restaurant  \n",
       "35      Asian Restaurant    Chinese Restaurant     Dim Sum Restaurant  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add clustering labels\n",
    "neighborhoods_venues_sorted.insert(0, 'Cluster Labels', kmeans.labels_)\n",
    "\n",
    "east_york_merged = df\n",
    "\n",
    "# match/merge SE London data with latitude/longitude for each neighborhood\n",
    "east_york_merged_latlong = east_york_merged.join(neighborhoods_venues_sorted.set_index('Neighbourhood'), on = 'Neighbourhood')\n",
    "east_york_merged_latlong.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "<folium.folium.Map at 0x7fd5ebdc1518>"
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     },
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     "metadata": {},
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   ],
   "source": [
    "# create map\n",
    "\n",
    "map_clusters = folium.Map(location=[43.651070,-79.347015],zoom_start=10)\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(east_york_merged_latlong['Latitude'], east_york_merged_latlong['Longitude'],east_york_merged_latlong['Neighbourhood'], east_york_merged_latlong['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",
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    "       \n",
    "map_clusters"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Analysis of the 5 clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>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",
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       "      <th>14</th>\n",
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       "      <td>0</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>Greek Restaurant</td>\n",
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       "      Borough  Cluster Labels 1st Most Common Venue 2nd Most Common Venue  \\\n",
       "14  East York               0       Thai Restaurant      Greek Restaurant   \n",
       "\n",
       "    3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue  \\\n",
       "14  Vietnamese Restaurant     Indian Restaurant   American Restaurant   \n",
       "\n",
       "   6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "14      Asian Restaurant    Chinese Restaurant    Dim Sum Restaurant   \n",
       "\n",
       "   9th Most Common Venue 10th Most Common Venue  \n",
       "14  Ethiopian Restaurant   Fast Food Restaurant  "
      ]
     },
     "execution_count": 29,
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   "metadata": {},
   "source": [
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   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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       "      <td>East York</td>\n",
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      "text/plain": [
       "      Borough  Cluster Labels 1st Most Common Venue 2nd Most Common Venue  \\\n",
       "29  East York               1     Indian Restaurant     Afghan Restaurant   \n",
       "\n",
       "        3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue  \\\n",
       "29  Middle Eastern Restaurant    Turkish Restaurant      Asian Restaurant   \n",
       "\n",
       "   6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "29  Fast Food Restaurant    Italian Restaurant            Restaurant   \n",
       "\n",
       "   9th Most Common Venue 10th Most Common Venue  \n",
       "29    Mexican Restaurant    Japanese Restaurant  "
      ]
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   ],
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    "east_york_merged_latlong.loc[east_york_merged_latlong['Cluster Labels'] == 1, east_york_merged_latlong.columns[[1] + list(range(5, east_york_merged_latlong.shape[1]))]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cluster 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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       "      <td>East York</td>\n",
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       "      <td>Fast Food Restaurant</td>\n",
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      "text/plain": [
       "     Borough  Cluster Labels 1st Most Common Venue  2nd Most Common Venue  \\\n",
       "8  East York               2  Fast Food Restaurant  Vietnamese Restaurant   \n",
       "\n",
       "  3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue  \\\n",
       "8     Indian Restaurant   American Restaurant      Asian Restaurant   \n",
       "\n",
       "  6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "8    Chinese Restaurant    Dim Sum Restaurant  Ethiopian Restaurant   \n",
       "\n",
       "  9th Most Common Venue 10th Most Common Venue  \n",
       "8      Greek Restaurant     Italian Restaurant  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "east_york_merged_latlong.loc[east_york_merged_latlong['Cluster Labels'] == 2, east_york_merged_latlong.columns[[1] + list(range(5, east_york_merged_latlong.shape[1]))]]"
   ]
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   "metadata": {},
   "source": [
    "Cluster 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
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       "      <th>Borough</th>\n",
       "      <th>Cluster Labels</th>\n",
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       "      <th>3rd Most Common Venue</th>\n",
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       "      <td>East York</td>\n",
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       "      Borough  Cluster Labels 1st Most Common Venue 2nd Most Common Venue  \\\n",
       "35  East York               3      Greek Restaurant  Ethiopian Restaurant   \n",
       "\n",
       "   3rd Most Common Venue 4th Most Common Venue      5th Most Common Venue  \\\n",
       "35  Fast Food Restaurant   American Restaurant  Middle Eastern Restaurant   \n",
       "\n",
       "    6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "35  Vietnamese Restaurant     Indian Restaurant      Asian Restaurant   \n",
       "\n",
       "   9th Most Common Venue 10th Most Common Venue  \n",
       "35    Chinese Restaurant     Dim Sum Restaurant  "
      ]
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     "execution_count": 32,
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    "east_york_merged_latlong.loc[east_york_merged_latlong['Cluster Labels'] == 3, east_york_merged_latlong.columns[[1] + list(range(5, east_york_merged_latlong.shape[1]))]]"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cluster 5"
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  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Borough</th>\n",
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       "      <th>3rd Most Common Venue</th>\n",
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       "      <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",
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       "      <th>23</th>\n",
       "      <td>East York</td>\n",
       "      <td>4</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Sushi Restaurant</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Japanese Restaurant</td>\n",
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       "      <td>Chinese Restaurant</td>\n",
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       "      Borough  Cluster Labels 1st Most Common Venue 2nd Most Common Venue  \\\n",
       "23  East York               4            Restaurant    Italian Restaurant   \n",
       "\n",
       "   3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue  \\\n",
       "23      Sushi Restaurant    Mexican Restaurant   Japanese Restaurant   \n",
       "\n",
       "   6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "23     Indian Restaurant  Fast Food Restaurant   American Restaurant   \n",
       "\n",
       "   9th Most Common Venue 10th Most Common Venue  \n",
       "23      Asian Restaurant     Chinese Restaurant  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "east_york_merged_latlong.loc[east_york_merged_latlong['Cluster Labels'] == 4, east_york_merged_latlong.columns[[1] + list(range(5, east_york_merged_latlong.shape[1]))]]"
   ]
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