Segmenting and Clustering neighborhoods in Toronto.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup\n",
    "import requests\n",
    "import csv\n",
    "import pandas as pd\n",
    "import json\n",
    "from pandas.io.json import json_normalize\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Scraping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# send the GET request\n",
    "data = requests.get('https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M').text\n",
    "\n",
    "# parse data from the html into a beautifulsoup object\n",
    "soup = BeautifulSoup(data, 'lxml')\n",
    "\n",
    "# create three lists to store table data\n",
    "postalCodeList = []\n",
    "boroughList = []\n",
    "neighborhoodList = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# append the data into the respective lists\n",
    "for row in soup.find('table').find_all('tr'):\n",
    "    cells = row.find_all('td')\n",
    "    if(len(cells) > 0):\n",
    "        postalCodeList.append(cells[0].text.rstrip('\\n'))\n",
    "        boroughList.append(cells[1].text.rstrip('\\n'))\n",
    "        neighborhoodList.append(cells[2].text.rstrip('\\n'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "        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>Neighborhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M1A</td>\n",
       "      <td>Not assigned</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M2A</td>\n",
       "      <td>Not assigned</td>\n",
       "      <td></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                Neighborhood\n",
       "0         M1A      Not assigned                            \n",
       "1         M2A      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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a new DataFrame from the three lists\n",
    "toronto_df = pd.DataFrame({\"Postal Code\": postalCodeList,\n",
    "                           \"Borough\": boroughList,\n",
    "                           \"Neighborhood\": neighborhoodList})\n",
    "\n",
    "toronto_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Neighborhood</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                                  Neighborhood\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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop cells with Borough not assigned\n",
    "toronto_dropped = toronto_df[toronto_df.Borough != 'Not assigned'].reset_index(drop=True)\n",
    "toronto_dropped.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "        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>Neighborhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M1B</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Malvern , Rouge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M1C</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Rouge Hill , Port Union , Highland Creek</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M1E</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Guildwood , Morningside , West Hill</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M1G</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Woburn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M1H</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cedarbrae</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postal Code      Borough                              Neighborhood\n",
       "0         M1B  Scarborough                           Malvern , Rouge\n",
       "1         M1C  Scarborough  Rouge Hill , Port Union , Highland Creek\n",
       "2         M1E  Scarborough       Guildwood , Morningside , West Hill\n",
       "3         M1G  Scarborough                                    Woburn\n",
       "4         M1H  Scarborough                                 Cedarbrae"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group multiple neighborhoods having same postal code\n",
    "toronto_group = toronto_dropped.groupby(['Postal Code', 'Borough'], as_index=False).agg(lambda x: \", \".join(x))\n",
    "toronto_group['Neighborhood'] = toronto_group['Neighborhood'].str.replace('/', ',')\n",
    "toronto_group.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(103, 3)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rows of dataframe\n",
    "toronto_group.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adding Lattitude and Longitude to the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import types\n",
    "from botocore.client import Config\n",
    "import ibm_boto3\n",
    "\n",
    "def __iter__(self): return 0\n",
    "\n",
    "# @hidden_cell\n",
    "# The following code accesses a file in your IBM Cloud Object Storage. It includes your credentials.\n",
    "# You might want to remove those credentials before you share the notebook.\n",
    "client_id = ibm_boto3.client(service_name='s3',\n",
    "    ibm_api_key_id='enter ibm_api_key_id',\n",
    "    ibm_auth_endpoint=\"enter ibm_auth_endpoint\",\n",
    "    config=Config(signature_version='oauth'),\n",
    "    endpoint_url='https://s3.eu-geo.objectstorage.service.networklayer.com')\n",
    "\n",
    "body = client_id.get_object(Bucket='bucket',Key='Geospatial_Coordinates.csv')['Body']\n",
    "# add missing __iter__ method, so pandas accepts body as file-like object\n",
    "if not hasattr(body, \"__iter__\"): body.__iter__ = types.MethodType( __iter__, body )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coordinates = pd.read_csv(body)\n",
    "coordinates.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merge the two dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>Neighborhood</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>Scarborough</td>\n",
       "      <td>Malvern , Rouge</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>Scarborough</td>\n",
       "      <td>Rouge Hill , Port Union , Highland Creek</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>Scarborough</td>\n",
       "      <td>Guildwood , Morningside , West Hill</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>Scarborough</td>\n",
       "      <td>Woburn</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>Scarborough</td>\n",
       "      <td>Cedarbrae</td>\n",
       "      <td>43.773136</td>\n",
       "      <td>-79.239476</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postal Code      Borough                              Neighborhood  \\\n",
       "0         M1B  Scarborough                           Malvern , Rouge   \n",
       "1         M1C  Scarborough  Rouge Hill , Port Union , Highland Creek   \n",
       "2         M1E  Scarborough       Guildwood , Morningside , West Hill   \n",
       "3         M1G  Scarborough                                    Woburn   \n",
       "4         M1H  Scarborough                                 Cedarbrae   \n",
       "\n",
       "    Latitude  Longitude  \n",
       "0  43.806686 -79.194353  \n",
       "1  43.784535 -79.160497  \n",
       "2  43.763573 -79.188711  \n",
       "3  43.770992 -79.216917  \n",
       "4  43.773136 -79.239476  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe = pd.merge(toronto_group, coordinates, on='Postal Code')\n",
    "dataframe.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### save these into csv files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "toronto_group.to_csv('toronto_postal_codes.csv', index = False)\n",
    "dataframe.to_csv('final_toronto_geospatial.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataframe has 10 boroughs\n"
     ]
    }
   ],
   "source": [
    "print('The dataframe has {} boroughs'.format(\n",
    "        len(dataframe['Borough'].unique())\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Use geopy library to get the latitude and longitude values of Toronto City"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The geograpical coordinate of Toronto City are 43.6534817, -79.3839347.\n"
     ]
    }
   ],
   "source": [
    "from geopy.geocoders import Nominatim\n",
    "import requests\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as colors\n",
    "from sklearn.cluster import KMeans\n",
    "import folium\n",
    "\n",
    "address = 'Toronto , Ontario'\n",
    "\n",
    "geolocator = Nominatim(user_agent=\"toronto_explorer\")\n",
    "location = geolocator.geocode(address)\n",
    "latitude = location.latitude\n",
    "longitude = location.longitude\n",
    "print('The geograpical coordinate of Toronto City are {}, {}.'.format(latitude, longitude))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create a map of Toronto with neighborhoods superimposed on top"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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            popup_b17add8cad36472aa5f901447c36c9f5.setContent(html_61d3725bfdae4c209efd254a65cecb6b);
        

        circle_marker_4407af4c72d54a279466334bead52d42.bindPopup(popup_b17add8cad36472aa5f901447c36c9f5)
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            var circle_marker_0af3c391c69a47cd859d5d6422294eb8 = L.circleMarker(
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                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
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        var popup_f6b3d8eeb84f4b27aaed5a9fb2e94407 = L.popup({"maxWidth": "100%"});

        
            var html_dbb07f662ec549fa991e707f22447962 = $(`<div id="html_dbb07f662ec549fa991e707f22447962" style="width: 100.0%; height: 100.0%;">Humber Summit, North York</div>`)[0];
            popup_f6b3d8eeb84f4b27aaed5a9fb2e94407.setContent(html_dbb07f662ec549fa991e707f22447962);
        

        circle_marker_0af3c391c69a47cd859d5d6422294eb8.bindPopup(popup_f6b3d8eeb84f4b27aaed5a9fb2e94407)
        ;

        
    
    
            var circle_marker_9d0baee46d6441eb8deb193dc6cf1e49 = L.circleMarker(
                [43.7247659, -79.53224240000002],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_a70d40b94ec24b148fbc94d2182ef330);
        
    
        var popup_9501fdf45bbb4098917d011e967f5311 = L.popup({"maxWidth": "100%"});

        
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            popup_9501fdf45bbb4098917d011e967f5311.setContent(html_166cca2a7f3e42eda5e9c74c40c7da58);
        

        circle_marker_9d0baee46d6441eb8deb193dc6cf1e49.bindPopup(popup_9501fdf45bbb4098917d011e967f5311)
        ;

        
    
    
            var circle_marker_47a280b8a28b472fba9feab8773f7431 = L.circleMarker(
                [43.706876, -79.51818840000001],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_a70d40b94ec24b148fbc94d2182ef330);
        
    
        var popup_7c2ce38691fd496792a08ffceedc1fd6 = L.popup({"maxWidth": "100%"});

        
            var html_a3667ffc7d7240a9b80b1f3a894fc4fa = $(`<div id="html_a3667ffc7d7240a9b80b1f3a894fc4fa" style="width: 100.0%; height: 100.0%;">Weston, York</div>`)[0];
            popup_7c2ce38691fd496792a08ffceedc1fd6.setContent(html_a3667ffc7d7240a9b80b1f3a894fc4fa);
        

        circle_marker_47a280b8a28b472fba9feab8773f7431.bindPopup(popup_7c2ce38691fd496792a08ffceedc1fd6)
        ;

        
    
    
            var circle_marker_a28e15580d0d48429c63bb914d63b2b2 = L.circleMarker(
                [43.696319, -79.53224240000002],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_a70d40b94ec24b148fbc94d2182ef330);
        
    
        var popup_6d749312d370497081bf644acc3810a3 = L.popup({"maxWidth": "100%"});

        
            var html_ee7a910ff2e4469697f6060d1a643f30 = $(`<div id="html_ee7a910ff2e4469697f6060d1a643f30" style="width: 100.0%; height: 100.0%;">Westmount, Etobicoke</div>`)[0];
            popup_6d749312d370497081bf644acc3810a3.setContent(html_ee7a910ff2e4469697f6060d1a643f30);
        

        circle_marker_a28e15580d0d48429c63bb914d63b2b2.bindPopup(popup_6d749312d370497081bf644acc3810a3)
        ;

        
    
    
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                [43.6889054, -79.55472440000001],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_a70d40b94ec24b148fbc94d2182ef330);
        
    
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            var html_f4b4d65a1e264cff8fee33fe01ac3093 = $(`<div id="html_f4b4d65a1e264cff8fee33fe01ac3093" style="width: 100.0%; height: 100.0%;">Kingsview Village , St. Phillips , Martin Grove Gardens , Richview Gardens, Etobicoke</div>`)[0];
            popup_51294d2b9eac464c920f19782a5ec0d4.setContent(html_f4b4d65a1e264cff8fee33fe01ac3093);
        

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        ;

        
    
    
            var circle_marker_c1881050efd344aaa7804b0839a3363f = L.circleMarker(
                [43.739416399999996, -79.5884369],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_a70d40b94ec24b148fbc94d2182ef330);
        
    
        var popup_fc5d9b8099b44cf296d0183a50ed55b6 = L.popup({"maxWidth": "100%"});

        
            var html_86ec68076024485cae88c98016bc5284 = $(`<div id="html_86ec68076024485cae88c98016bc5284" style="width: 100.0%; height: 100.0%;">South Steeles , Silverstone , Humbergate , Jamestown , Mount Olive , Beaumond Heights , Thistletown , Albion Gardens, Etobicoke</div>`)[0];
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        ;

        
    
    
            var circle_marker_379c14d5bdf84c2d82ed5b53638a19a3 = L.circleMarker(
                [43.706748299999994, -79.5940544],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_a70d40b94ec24b148fbc94d2182ef330);
        
    
        var popup_33328c5e26384ae5b76d263efeca61be = L.popup({"maxWidth": "100%"});

        
            var html_107b8b1332824dac9b448a3de8746f1b = $(`<div id="html_107b8b1332824dac9b448a3de8746f1b" style="width: 100.0%; height: 100.0%;">Northwest, Etobicoke</div>`)[0];
            popup_33328c5e26384ae5b76d263efeca61be.setContent(html_107b8b1332824dac9b448a3de8746f1b);
        

        circle_marker_379c14d5bdf84c2d82ed5b53638a19a3.bindPopup(popup_33328c5e26384ae5b76d263efeca61be)
        ;

        
    
</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 0x7fd58fd1c2b0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create map of Toronto using latitude and longitude values\n",
    "map_toronto = folium.Map(location=[latitude, longitude], zoom_start=10)\n",
    "\n",
    "# add markers to map\n",
    "for lat, lng, borough, neighborhood in zip(dataframe['Latitude'], dataframe['Longitude'], dataframe['Borough'], dataframe['Neighborhood']):\n",
    "    label = '{}, {}'.format(neighborhood, borough)\n",
    "    label = folium.Popup(label, parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lng],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color='blue',\n",
    "        fill=True,\n",
    "        fill_color='#3186cc',\n",
    "        fill_opacity=0.7,\n",
    "        parse_html=False).add_to(map_toronto)  \n",
    "    \n",
    "map_toronto"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We segment and cluster only the neighborhoods in Scarborough"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</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>Scarborough</td>\n",
       "      <td>Malvern , Rouge</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>Scarborough</td>\n",
       "      <td>Rouge Hill , Port Union , Highland Creek</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>Scarborough</td>\n",
       "      <td>Guildwood , Morningside , West Hill</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>Scarborough</td>\n",
       "      <td>Woburn</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>Scarborough</td>\n",
       "      <td>Cedarbrae</td>\n",
       "      <td>43.773136</td>\n",
       "      <td>-79.239476</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postal Code      Borough                              Neighborhood  \\\n",
       "0         M1B  Scarborough                           Malvern , Rouge   \n",
       "1         M1C  Scarborough  Rouge Hill , Port Union , Highland Creek   \n",
       "2         M1E  Scarborough       Guildwood , Morningside , West Hill   \n",
       "3         M1G  Scarborough                                    Woburn   \n",
       "4         M1H  Scarborough                                 Cedarbrae   \n",
       "\n",
       "    Latitude  Longitude  \n",
       "0  43.806686 -79.194353  \n",
       "1  43.784535 -79.160497  \n",
       "2  43.763573 -79.188711  \n",
       "3  43.770992 -79.216917  \n",
       "4  43.773136 -79.239476  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scarborough_data = dataframe[dataframe['Borough'] == 'Scarborough'].reset_index(drop=True)\n",
    "scarborough_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The geograpical coordinate of Scarborough are 43.773077, -79.257774.\n"
     ]
    }
   ],
   "source": [
    "address = 'Scarborough, Toronto'\n",
    "\n",
    "geolocator = Nominatim(user_agent=\"toronto_explorer\")\n",
    "location = geolocator.geocode(address)\n",
    "latitude = location.latitude\n",
    "longitude = location.longitude\n",
    "print('The geograpical coordinate of Scarborough are {}, {}.'.format(latitude, longitude))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=<!DOCTYPE html>
<head>    
    <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
    
        <script>
            L_NO_TOUCH = false;
            L_DISABLE_3D = false;
        </script>
    
    <script src="https://cdn.jsdelivr.net/npm/leaflet@1.5.1/dist/leaflet.js"></script>
    <script src="https://code.jquery.com/jquery-1.12.4.min.js"></script>
    <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>
    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet@1.5.1/dist/leaflet.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css"/>
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css"/>
    <link rel="stylesheet" href="https://rawcdn.githack.com/python-visualization/folium/master/folium/templates/leaflet.awesome.rotate.css"/>
    <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>
    <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>
    
            <meta name="viewport" content="width=device-width,
                initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />
            <style>
                #map_4bb86e35ffca4daba633152d0f7aec5d {
                    position: relative;
                    width: 100.0%;
                    height: 100.0%;
                    left: 0.0%;
                    top: 0.0%;
                }
            </style>
        
</head>
<body>    
    
            <div class="folium-map" id="map_4bb86e35ffca4daba633152d0f7aec5d" ></div>
        
</body>
<script>    
    
            var map_4bb86e35ffca4daba633152d0f7aec5d = L.map(
                "map_4bb86e35ffca4daba633152d0f7aec5d",
                {
                    center: [43.773077, -79.257774],
                    crs: L.CRS.EPSG3857,
                    zoom: 10,
                    zoomControl: true,
                    preferCanvas: false,
                }
            );

            

        
    
            var tile_layer_9c35ad62601941448c800b6f647a418c = L.tileLayer(
                "https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",
                {"attribution": "Data by \u0026copy; \u003ca href=\"http://openstreetmap.org\"\u003eOpenStreetMap\u003c/a\u003e, under \u003ca href=\"http://www.openstreetmap.org/copyright\"\u003eODbL\u003c/a\u003e.", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}
            ).addTo(map_4bb86e35ffca4daba633152d0f7aec5d);
        
    
            var circle_marker_40c43b93c0e8446c8bfc7479547def29 = L.circleMarker(
                [43.806686299999996, -79.19435340000001],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_4bb86e35ffca4daba633152d0f7aec5d);
        
    
        var popup_3aee763bdb0244baa20345850f00c48c = L.popup({"maxWidth": "100%"});

        
            var html_d5f13904376a48e7b705124b92455f89 = $(`<div id="html_d5f13904376a48e7b705124b92455f89" style="width: 100.0%; height: 100.0%;">Malvern , Rouge, Scarborough</div>`)[0];
            popup_3aee763bdb0244baa20345850f00c48c.setContent(html_d5f13904376a48e7b705124b92455f89);
        

        circle_marker_40c43b93c0e8446c8bfc7479547def29.bindPopup(popup_3aee763bdb0244baa20345850f00c48c)
        ;

        
    
    
            var circle_marker_01fb8a952e134a718e378e95bc97a1ee = L.circleMarker(
                [43.7845351, -79.16049709999999],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
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        var popup_ee1f3d2290514c4fbf71232b1c2d0d10 = L.popup({"maxWidth": "100%"});

        
            var html_baa256fa2ba4479993d210746797a91c = $(`<div id="html_baa256fa2ba4479993d210746797a91c" style="width: 100.0%; height: 100.0%;">Rouge Hill , Port Union , Highland Creek, Scarborough</div>`)[0];
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        circle_marker_c16d5db5f0404d559483ced06c1b8ce7.bindPopup(popup_0950a3f9737a4316aaa73ae2b7bd5009)
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            var circle_marker_7ea605b4986a453fa2493fc63830a191 = L.circleMarker(
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        var popup_63bf84d90b2c41a5ab7ab1f9362d4c2a = L.popup({"maxWidth": "100%"});

        
            var html_7ee0803f07a34e209d333fac26ee9d7a = $(`<div id="html_7ee0803f07a34e209d333fac26ee9d7a" style="width: 100.0%; height: 100.0%;">Cliffside , Cliffcrest , Scarborough Village West, Scarborough</div>`)[0];
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            popup_5a307bb70dba45f8afac9f6a7a517c7f.setContent(html_1eb5190a2ba2415a8f75dd2be306415d);
        

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                [43.7574096, -79.27330400000001],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_4bb86e35ffca4daba633152d0f7aec5d);
        
    
        var popup_a833d3a6983c40b0922120a7f0e0b926 = L.popup({"maxWidth": "100%"});

        
            var html_d219164c32b14d238df6104ed3d777f2 = $(`<div id="html_d219164c32b14d238df6104ed3d777f2" style="width: 100.0%; height: 100.0%;">Dorset Park , Wexford Heights , Scarborough Town Centre, Scarborough</div>`)[0];
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        circle_marker_04e9a3ba87c045e4bb82f540b661f88b.bindPopup(popup_a833d3a6983c40b0922120a7f0e0b926)
        ;

        
    
    
            var circle_marker_05e5a9f4b33e44069ef67f434dba7c5f = L.circleMarker(
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                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_4bb86e35ffca4daba633152d0f7aec5d);
        
    
        var popup_2f069e3d69b74b208a50472a12c52f5f = L.popup({"maxWidth": "100%"});

        
            var html_5cb5abd958e749e4a5bc07a492d12b97 = $(`<div id="html_5cb5abd958e749e4a5bc07a492d12b97" style="width: 100.0%; height: 100.0%;">Wexford , Maryvale, Scarborough</div>`)[0];
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                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_4bb86e35ffca4daba633152d0f7aec5d);
        
    
        var popup_b38d23b681264152bbadc192e151cdb1 = L.popup({"maxWidth": "100%"});

        
            var html_c197737e567c445bb5fdf14d27caf104 = $(`<div id="html_c197737e567c445bb5fdf14d27caf104" style="width: 100.0%; height: 100.0%;">Agincourt, Scarborough</div>`)[0];
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            ).addTo(map_4bb86e35ffca4daba633152d0f7aec5d);
        
    
        var popup_4d5e14485eb24d669b1e6ba190a2d561 = L.popup({"maxWidth": "100%"});

        
            var html_dc42d88c7fa24a5c9e1c934c33c6fffc = $(`<div id="html_dc42d88c7fa24a5c9e1c934c33c6fffc" style="width: 100.0%; height: 100.0%;">Clarks Corners , Tam O&#39;Shanter , Sullivan, Scarborough</div>`)[0];
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        ;

        
    
    
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            ).addTo(map_4bb86e35ffca4daba633152d0f7aec5d);
        
    
        var popup_26f27b7c0d9f40ac8583ae7b0d21819e = L.popup({"maxWidth": "100%"});

        
            var html_fdc171a19eb240808740ceafe1a186aa = $(`<div id="html_fdc171a19eb240808740ceafe1a186aa" style="width: 100.0%; height: 100.0%;">Milliken , Agincourt North , Steeles East , L&#39;Amoreaux East, Scarborough</div>`)[0];
            popup_26f27b7c0d9f40ac8583ae7b0d21819e.setContent(html_fdc171a19eb240808740ceafe1a186aa);
        

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        ;

        
    
    
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                [43.799525200000005, -79.3183887],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
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            var html_70cf9cf1f6394dc49617daebac613609 = $(`<div id="html_70cf9cf1f6394dc49617daebac613609" style="width: 100.0%; height: 100.0%;">Steeles West , L&#39;Amoreaux West, Scarborough</div>`)[0];
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        circle_marker_bfefc27ee9e3416e9472994c0048a81d.bindPopup(popup_89af8a6682204931b03e0e7091caf07c)
        ;

        
    
    
            var circle_marker_5d64e780844849428ff1cf47bcedd03f = L.circleMarker(
                [43.836124700000006, -79.20563609999999],
                {"bubblingMouseEvents": true, "color": "blue", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3186cc", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_4bb86e35ffca4daba633152d0f7aec5d);
        
    
        var popup_ca0139e5f0bd4972bf05659ea821f3c3 = L.popup({"maxWidth": "100%"});

        
            var html_8b70bf141c5945519c7a82b1dc45cc9d = $(`<div id="html_8b70bf141c5945519c7a82b1dc45cc9d" style="width: 100.0%; height: 100.0%;">Upper Rouge, Scarborough</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 0x7fd58fc3e978>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create map of Scarborough using latitude and longitude values\n",
    "map_scarborough = folium.Map(location=[latitude, longitude], zoom_start=10)\n",
    "\n",
    "# add markers to map\n",
    "for lat, lng, borough, neighborhood in zip(scarborough_data['Latitude'], scarborough_data['Longitude'], scarborough_data['Borough'], scarborough_data['Neighborhood']):\n",
    "    label = '{}, {}'.format(neighborhood, borough)\n",
    "    label = folium.Popup(label, parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lng],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color='blue',\n",
    "        fill=True,\n",
    "        fill_color='#3186cc',\n",
    "        fill_opacity=0.7,\n",
    "        parse_html=False).add_to(map_scarborough)  \n",
    "    \n",
    "map_scarborough"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define Foursquare Credentials and Version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# @hidden_cell\n",
    "\n",
    "CLIENT_ID = 'enter foursquare client id' # your Foursquare ID\n",
    "CLIENT_SECRET = 'enter foursquare client secret' # your Foursquare Secret\n",
    "VERSION = 'enter foursquare api version' # Foursquare API version\n",
    "\n",
    "print('Your credentails:')\n",
    "print('CLIENT_ID: ' + CLIENT_ID)\n",
    "print('CLIENT_SECRET:' + CLIENT_SECRET)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's explore the first neighborhood in our dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Malvern , Rouge'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scarborough_data.loc[0, 'Neighborhood']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get the neighborhood's latitude and longitude values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Latitude and longitude values of Malvern , Rouge are 43.806686299999996, -79.19435340000001.\n"
     ]
    }
   ],
   "source": [
    "neighborhood_latitude = scarborough_data.loc[0, 'Latitude'] # neighborhood latitude value\n",
    "neighborhood_longitude = scarborough_data.loc[0, 'Longitude'] # neighborhood longitude value\n",
    "\n",
    "neighborhood_name = scarborough_data.loc[0, 'Neighborhood'] # neighborhood name\n",
    "\n",
    "print('Latitude and longitude values of {} are {}, {}.'.format(neighborhood_name, \n",
    "                                                               neighborhood_latitude, \n",
    "                                                               neighborhood_longitude))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now, let's get the top 100 venues that are in Malvern , Rouge within a radius of 500 meters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'meta': {'code': 200, 'requestId': '5e9c70dac546f3001be9edd5'},\n",
       " 'response': {'warning': {'text': \"There aren't a lot of results near you. Try something more general, reset your filters, or expand the search area.\"},\n",
       "  'headerLocation': 'Malvern',\n",
       "  'headerFullLocation': 'Malvern, Toronto',\n",
       "  'headerLocationGranularity': 'neighborhood',\n",
       "  'totalResults': 1,\n",
       "  'suggestedBounds': {'ne': {'lat': 43.8111863045, 'lng': -79.18812958073042},\n",
       "   'sw': {'lat': 43.80218629549999, 'lng': -79.2005772192696}},\n",
       "  'groups': [{'type': 'Recommended Places',\n",
       "    'name': 'recommended',\n",
       "    'items': [{'reasons': {'count': 0,\n",
       "       'items': [{'summary': 'This spot is popular',\n",
       "         'type': 'general',\n",
       "         'reasonName': 'globalInteractionReason'}]},\n",
       "      'venue': {'id': '4bb6b9446edc76b0d771311c',\n",
       "       'name': 'Wendy’s',\n",
       "       'location': {'crossStreet': 'Morningside & Sheppard',\n",
       "        'lat': 43.80744841934756,\n",
       "        'lng': -79.19905558052072,\n",
       "        'labeledLatLngs': [{'label': 'display',\n",
       "          'lat': 43.80744841934756,\n",
       "          'lng': -79.19905558052072}],\n",
       "        'distance': 387,\n",
       "        'cc': 'CA',\n",
       "        'city': 'Toronto',\n",
       "        'state': 'ON',\n",
       "        'country': 'Canada',\n",
       "        'formattedAddress': ['Toronto ON', 'Canada']},\n",
       "       'categories': [{'id': '4bf58dd8d48988d16e941735',\n",
       "         'name': 'Fast Food Restaurant',\n",
       "         'pluralName': 'Fast Food Restaurants',\n",
       "         'shortName': 'Fast Food',\n",
       "         'icon': {'prefix': 'https://ss3.4sqi.net/img/categories_v2/food/fastfood_',\n",
       "          'suffix': '.png'},\n",
       "         'primary': True}],\n",
       "       'photos': {'count': 0, 'groups': []}},\n",
       "      'referralId': 'e-0-4bb6b9446edc76b0d771311c-0'}]}]}}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LIMIT = 100 \n",
    "radius = 500 \n",
    "\n",
    "url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n",
    "    CLIENT_ID, \n",
    "    CLIENT_SECRET, \n",
    "    VERSION, \n",
    "    neighborhood_latitude, \n",
    "    neighborhood_longitude, \n",
    "    radius, \n",
    "    LIMIT)\n",
    "\n",
    "results = requests.get(url).json()\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function that extracts the category of the venue\n",
    "def get_category_type(row):\n",
    "    try:\n",
    "        categories_list = row['categories']\n",
    "    except:\n",
    "        categories_list = row['venue.categories']\n",
    "        \n",
    "    if len(categories_list) == 0:\n",
    "        return None\n",
    "    else:\n",
    "        return categories_list[0]['name']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Clean the json and structure it into a pandas dataframe"
   ]
  },
  {
   "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>name</th>\n",
       "      <th>categories</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wendy’s</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>43.807448</td>\n",
       "      <td>-79.199056</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name            categories        lat        lng\n",
       "0  Wendy’s  Fast Food Restaurant  43.807448 -79.199056"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "venues = results['response']['groups'][0]['items']\n",
    "    \n",
    "nearby_venues = json_normalize(venues) # flatten JSON\n",
    "\n",
    "# filter columns\n",
    "filtered_columns = ['venue.name', 'venue.categories', 'venue.location.lat', 'venue.location.lng']\n",
    "nearby_venues =nearby_venues.loc[:, filtered_columns]\n",
    "\n",
    "# filter the category for each row\n",
    "nearby_venues['venue.categories'] = nearby_venues.apply(get_category_type, axis=1)\n",
    "\n",
    "# clean columns\n",
    "nearby_venues.columns = [col.split(\".\")[-1] for col in nearby_venues.columns]\n",
    "\n",
    "nearby_venues.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 venues were returned by Foursquare.\n"
     ]
    }
   ],
   "source": [
    "print('{} venues were returned by Foursquare.'.format(nearby_venues.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Function to repeat the same process to all the neighborhoods in Scarborough"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getNearbyVenues(names, latitudes, longitudes, radius=500):\n",
    "    \n",
    "    venues_list=[]\n",
    "    for name, lat, lng in zip(names, latitudes, longitudes):\n",
    "        print(name)\n",
    "            \n",
    "        # create the API request URL\n",
    "        url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n",
    "            CLIENT_ID, \n",
    "            CLIENT_SECRET, \n",
    "            VERSION, \n",
    "            lat, \n",
    "            lng, \n",
    "            radius, \n",
    "            LIMIT)\n",
    "            \n",
    "        # make the GET request\n",
    "        results = requests.get(url).json()[\"response\"]['groups'][0]['items']\n",
    "        \n",
    "        # return only relevant information for each nearby venue\n",
    "        venues_list.append([(\n",
    "            name, \n",
    "            lat, \n",
    "            lng, \n",
    "            v['venue']['name'], \n",
    "            v['venue']['location']['lat'], \n",
    "            v['venue']['location']['lng'],  \n",
    "            v['venue']['categories'][0]['name']) for v in results])\n",
    "\n",
    "    nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])\n",
    "    nearby_venues.columns = ['Neighborhood', \n",
    "                  'Neighborhood Latitude', \n",
    "                  'Neighborhood Longitude', \n",
    "                  'Venue', \n",
    "                  'Venue Latitude', \n",
    "                  'Venue Longitude', \n",
    "                  'Venue Category']\n",
    "    \n",
    "    return(nearby_venues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Malvern , Rouge\n",
      "Rouge Hill , Port Union , Highland Creek\n",
      "Guildwood , Morningside , West Hill\n",
      "Woburn\n",
      "Cedarbrae\n",
      "Scarborough Village\n",
      "Kennedy Park , Ionview , East Birchmount Park\n",
      "Golden Mile , Clairlea , Oakridge\n",
      "Cliffside , Cliffcrest , Scarborough Village West\n",
      "Birch Cliff , Cliffside West\n",
      "Dorset Park , Wexford Heights , Scarborough Town Centre\n",
      "Wexford , Maryvale\n",
      "Agincourt\n",
      "Clarks Corners , Tam O'Shanter , Sullivan\n",
      "Milliken , Agincourt North , Steeles East , L'Amoreaux East\n",
      "Steeles West , L'Amoreaux West\n",
      "Upper Rouge\n"
     ]
    }
   ],
   "source": [
    "scarborough_venues = getNearbyVenues(names=scarborough_data['Neighborhood'],\n",
    "                                   latitudes=scarborough_data['Latitude'],\n",
    "                                   longitudes=scarborough_data['Longitude']\n",
    "                                  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(96, 7)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Neighborhood Latitude</th>\n",
       "      <th>Neighborhood Longitude</th>\n",
       "      <th>Venue</th>\n",
       "      <th>Venue Latitude</th>\n",
       "      <th>Venue Longitude</th>\n",
       "      <th>Venue Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Malvern , Rouge</td>\n",
       "      <td>43.806686</td>\n",
       "      <td>-79.194353</td>\n",
       "      <td>Wendy’s</td>\n",
       "      <td>43.807448</td>\n",
       "      <td>-79.199056</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rouge Hill , Port Union , Highland Creek</td>\n",
       "      <td>43.784535</td>\n",
       "      <td>-79.160497</td>\n",
       "      <td>Royal Canadian Legion</td>\n",
       "      <td>43.782533</td>\n",
       "      <td>-79.163085</td>\n",
       "      <td>Bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rouge Hill , Port Union , Highland Creek</td>\n",
       "      <td>43.784535</td>\n",
       "      <td>-79.160497</td>\n",
       "      <td>SEBS Engineering Inc. (Sustainable Energy and ...</td>\n",
       "      <td>43.782371</td>\n",
       "      <td>-79.156820</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Guildwood , Morningside , West Hill</td>\n",
       "      <td>43.763573</td>\n",
       "      <td>-79.188711</td>\n",
       "      <td>G &amp; G Electronics</td>\n",
       "      <td>43.765309</td>\n",
       "      <td>-79.191537</td>\n",
       "      <td>Electronics Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Guildwood , Morningside , West Hill</td>\n",
       "      <td>43.763573</td>\n",
       "      <td>-79.188711</td>\n",
       "      <td>Big Bite Burrito</td>\n",
       "      <td>43.766299</td>\n",
       "      <td>-79.190720</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Neighborhood  Neighborhood Latitude  \\\n",
       "0                           Malvern , Rouge              43.806686   \n",
       "1  Rouge Hill , Port Union , Highland Creek              43.784535   \n",
       "2  Rouge Hill , Port Union , Highland Creek              43.784535   \n",
       "3       Guildwood , Morningside , West Hill              43.763573   \n",
       "4       Guildwood , Morningside , West Hill              43.763573   \n",
       "\n",
       "   Neighborhood Longitude                                              Venue  \\\n",
       "0              -79.194353                                            Wendy’s   \n",
       "1              -79.160497                              Royal Canadian Legion   \n",
       "2              -79.160497  SEBS Engineering Inc. (Sustainable Energy and ...   \n",
       "3              -79.188711                                  G & G Electronics   \n",
       "4              -79.188711                                   Big Bite Burrito   \n",
       "\n",
       "   Venue Latitude  Venue Longitude              Venue Category  \n",
       "0       43.807448       -79.199056        Fast Food Restaurant  \n",
       "1       43.782533       -79.163085                         Bar  \n",
       "2       43.782371       -79.156820  Construction & Landscaping  \n",
       "3       43.765309       -79.191537           Electronics Store  \n",
       "4       43.766299       -79.190720          Mexican Restaurant  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(scarborough_venues.shape)\n",
    "scarborough_venues.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " #### No. of venues were returned for each neighborhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood Latitude</th>\n",
       "      <th>Neighborhood Longitude</th>\n",
       "      <th>Venue</th>\n",
       "      <th>Venue Latitude</th>\n",
       "      <th>Venue Longitude</th>\n",
       "      <th>Venue Category</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Agincourt</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Birch Cliff , Cliffside West</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cedarbrae</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clarks Corners , Tam O'Shanter , Sullivan</th>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cliffside , Cliffcrest , Scarborough Village West</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dorset Park , Wexford Heights , Scarborough Town Centre</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Golden Mile , Clairlea , Oakridge</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guildwood , Morningside , West Hill</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kennedy Park , Ionview , East Birchmount Park</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malvern , Rouge</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Milliken , Agincourt North , Steeles East , L'Amoreaux East</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rouge Hill , Port Union , Highland Creek</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scarborough Village</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steeles West , L'Amoreaux West</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wexford , Maryvale</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Woburn</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Neighborhood Latitude  \\\n",
       "Neighborhood                                                                \n",
       "Agincourt                                                               4   \n",
       "Birch Cliff , Cliffside West                                            4   \n",
       "Cedarbrae                                                               8   \n",
       "Clarks Corners , Tam O'Shanter , Sullivan                              14   \n",
       "Cliffside , Cliffcrest , Scarborough Village West                       2   \n",
       "Dorset Park , Wexford Heights , Scarborough Tow...                      6   \n",
       "Golden Mile , Clairlea , Oakridge                                       9   \n",
       "Guildwood , Morningside , West Hill                                     7   \n",
       "Kennedy Park , Ionview , East Birchmount Park                           8   \n",
       "Malvern , Rouge                                                         1   \n",
       "Milliken , Agincourt North , Steeles East , L'A...                      3   \n",
       "Rouge Hill , Port Union , Highland Creek                                2   \n",
       "Scarborough Village                                                     3   \n",
       "Steeles West , L'Amoreaux West                                         15   \n",
       "Wexford , Maryvale                                                      7   \n",
       "Woburn                                                                  3   \n",
       "\n",
       "                                                    Neighborhood Longitude  \\\n",
       "Neighborhood                                                                 \n",
       "Agincourt                                                                4   \n",
       "Birch Cliff , Cliffside West                                             4   \n",
       "Cedarbrae                                                                8   \n",
       "Clarks Corners , Tam O'Shanter , Sullivan                               14   \n",
       "Cliffside , Cliffcrest , Scarborough Village West                        2   \n",
       "Dorset Park , Wexford Heights , Scarborough Tow...                       6   \n",
       "Golden Mile , Clairlea , Oakridge                                        9   \n",
       "Guildwood , Morningside , West Hill                                      7   \n",
       "Kennedy Park , Ionview , East Birchmount Park                            8   \n",
       "Malvern , Rouge                                                          1   \n",
       "Milliken , Agincourt North , Steeles East , L'A...                       3   \n",
       "Rouge Hill , Port Union , Highland Creek                                 2   \n",
       "Scarborough Village                                                      3   \n",
       "Steeles West , L'Amoreaux West                                          15   \n",
       "Wexford , Maryvale                                                       7   \n",
       "Woburn                                                                   3   \n",
       "\n",
       "                                                    Venue  Venue Latitude  \\\n",
       "Neighborhood                                                                \n",
       "Agincourt                                               4               4   \n",
       "Birch Cliff , Cliffside West                            4               4   \n",
       "Cedarbrae                                               8               8   \n",
       "Clarks Corners , Tam O'Shanter , Sullivan              14              14   \n",
       "Cliffside , Cliffcrest , Scarborough Village West       2               2   \n",
       "Dorset Park , Wexford Heights , Scarborough Tow...      6               6   \n",
       "Golden Mile , Clairlea , Oakridge                       9               9   \n",
       "Guildwood , Morningside , West Hill                     7               7   \n",
       "Kennedy Park , Ionview , East Birchmount Park           8               8   \n",
       "Malvern , Rouge                                         1               1   \n",
       "Milliken , Agincourt North , Steeles East , L'A...      3               3   \n",
       "Rouge Hill , Port Union , Highland Creek                2               2   \n",
       "Scarborough Village                                     3               3   \n",
       "Steeles West , L'Amoreaux West                         15              15   \n",
       "Wexford , Maryvale                                      7               7   \n",
       "Woburn                                                  3               3   \n",
       "\n",
       "                                                    Venue Longitude  \\\n",
       "Neighborhood                                                          \n",
       "Agincourt                                                         4   \n",
       "Birch Cliff , Cliffside West                                      4   \n",
       "Cedarbrae                                                         8   \n",
       "Clarks Corners , Tam O'Shanter , Sullivan                        14   \n",
       "Cliffside , Cliffcrest , Scarborough Village West                 2   \n",
       "Dorset Park , Wexford Heights , Scarborough Tow...                6   \n",
       "Golden Mile , Clairlea , Oakridge                                 9   \n",
       "Guildwood , Morningside , West Hill                               7   \n",
       "Kennedy Park , Ionview , East Birchmount Park                     8   \n",
       "Malvern , Rouge                                                   1   \n",
       "Milliken , Agincourt North , Steeles East , L'A...                3   \n",
       "Rouge Hill , Port Union , Highland Creek                          2   \n",
       "Scarborough Village                                               3   \n",
       "Steeles West , L'Amoreaux West                                   15   \n",
       "Wexford , Maryvale                                                7   \n",
       "Woburn                                                            3   \n",
       "\n",
       "                                                    Venue Category  \n",
       "Neighborhood                                                        \n",
       "Agincourt                                                        4  \n",
       "Birch Cliff , Cliffside West                                     4  \n",
       "Cedarbrae                                                        8  \n",
       "Clarks Corners , Tam O'Shanter , Sullivan                       14  \n",
       "Cliffside , Cliffcrest , Scarborough Village West                2  \n",
       "Dorset Park , Wexford Heights , Scarborough Tow...               6  \n",
       "Golden Mile , Clairlea , Oakridge                                9  \n",
       "Guildwood , Morningside , West Hill                              7  \n",
       "Kennedy Park , Ionview , East Birchmount Park                    8  \n",
       "Malvern , Rouge                                                  1  \n",
       "Milliken , Agincourt North , Steeles East , L'A...               3  \n",
       "Rouge Hill , Port Union , Highland Creek                         2  \n",
       "Scarborough Village                                              3  \n",
       "Steeles West , L'Amoreaux West                                  15  \n",
       "Wexford , Maryvale                                               7  \n",
       "Woburn                                                           3  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scarborough_venues.groupby('Neighborhood').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Unique categories curated from all the returned venues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 58 uniques categories.\n"
     ]
    }
   ],
   "source": [
    "print('There are {} uniques categories.'.format(len(scarborough_venues['Venue Category'].unique())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze Each Neighborhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Neighborhood</th>\n",
       "      <th>American Restaurant</th>\n",
       "      <th>Athletics &amp; Sports</th>\n",
       "      <th>Auto Garage</th>\n",
       "      <th>Bakery</th>\n",
       "      <th>Bank</th>\n",
       "      <th>Bar</th>\n",
       "      <th>Breakfast Spot</th>\n",
       "      <th>Bubble Tea Shop</th>\n",
       "      <th>Bus Line</th>\n",
       "      <th>...</th>\n",
       "      <th>Sandwich Place</th>\n",
       "      <th>Shopping Mall</th>\n",
       "      <th>Skating Rink</th>\n",
       "      <th>Soccer Field</th>\n",
       "      <th>Supermarket</th>\n",
       "      <th>Thai Restaurant</th>\n",
       "      <th>Thrift / Vintage Store</th>\n",
       "      <th>Train Station</th>\n",
       "      <th>Vietnamese Restaurant</th>\n",
       "      <th>Women's Store</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Malvern , Rouge</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rouge Hill , Port Union , Highland Creek</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rouge Hill , Port Union , Highland Creek</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Guildwood , Morningside , West Hill</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Guildwood , Morningside , West Hill</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Neighborhood  American Restaurant  \\\n",
       "0                           Malvern , Rouge                    0   \n",
       "1  Rouge Hill , Port Union , Highland Creek                    0   \n",
       "2  Rouge Hill , Port Union , Highland Creek                    0   \n",
       "3       Guildwood , Morningside , West Hill                    0   \n",
       "4       Guildwood , Morningside , West Hill                    0   \n",
       "\n",
       "   Athletics & Sports  Auto Garage  Bakery  Bank  Bar  Breakfast Spot  \\\n",
       "0                   0            0       0     0    0               0   \n",
       "1                   0            0       0     0    1               0   \n",
       "2                   0            0       0     0    0               0   \n",
       "3                   0            0       0     0    0               0   \n",
       "4                   0            0       0     0    0               0   \n",
       "\n",
       "   Bubble Tea Shop  Bus Line  ...  Sandwich Place  Shopping Mall  \\\n",
       "0                0         0  ...               0              0   \n",
       "1                0         0  ...               0              0   \n",
       "2                0         0  ...               0              0   \n",
       "3                0         0  ...               0              0   \n",
       "4                0         0  ...               0              0   \n",
       "\n",
       "   Skating Rink  Soccer Field  Supermarket  Thai Restaurant  \\\n",
       "0             0             0            0                0   \n",
       "1             0             0            0                0   \n",
       "2             0             0            0                0   \n",
       "3             0             0            0                0   \n",
       "4             0             0            0                0   \n",
       "\n",
       "   Thrift / Vintage Store  Train Station  Vietnamese Restaurant  Women's Store  \n",
       "0                       0              0                      0              0  \n",
       "1                       0              0                      0              0  \n",
       "2                       0              0                      0              0  \n",
       "3                       0              0                      0              0  \n",
       "4                       0              0                      0              0  \n",
       "\n",
       "[5 rows x 59 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# one hot encoding\n",
    "scarborough_onehot = pd.get_dummies(scarborough_venues[['Venue Category']], prefix=\"\", prefix_sep=\"\")\n",
    "\n",
    "# add neighborhood column back to dataframe\n",
    "scarborough_onehot['Neighborhood'] = scarborough_venues['Neighborhood'] \n",
    "\n",
    "# move neighborhood column to the first column\n",
    "fixed_columns = [scarborough_onehot.columns[-1]] + list(scarborough_onehot.columns[:-1])\n",
    "scarborough_onehot = scarborough_onehot[fixed_columns]\n",
    "\n",
    "scarborough_onehot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(96, 59)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scarborough_onehot.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Group rows by neighborhood and by taking the mean of the frequency of occurrence of each category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>Neighborhood</th>\n",
       "      <th>American Restaurant</th>\n",
       "      <th>Athletics &amp; Sports</th>\n",
       "      <th>Auto Garage</th>\n",
       "      <th>Bakery</th>\n",
       "      <th>Bank</th>\n",
       "      <th>Bar</th>\n",
       "      <th>Breakfast Spot</th>\n",
       "      <th>Bubble Tea Shop</th>\n",
       "      <th>Bus Line</th>\n",
       "      <th>...</th>\n",
       "      <th>Sandwich Place</th>\n",
       "      <th>Shopping Mall</th>\n",
       "      <th>Skating Rink</th>\n",
       "      <th>Soccer Field</th>\n",
       "      <th>Supermarket</th>\n",
       "      <th>Thai Restaurant</th>\n",
       "      <th>Thrift / Vintage Store</th>\n",
       "      <th>Train Station</th>\n",
       "      <th>Vietnamese Restaurant</th>\n",
       "      <th>Women's Store</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Agincourt</td>\n",
       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Birch Cliff , Cliffside West</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cedarbrae</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Clarks Corners , Tam O'Shanter , Sullivan</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.071429</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cliffside , Cliffcrest , Scarborough Village West</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Dorset Park , Wexford Heights , Scarborough To...</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Golden Mile , Clairlea , Oakridge</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Guildwood , Morningside , West Hill</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Kennedy Park , Ionview , East Birchmount Park</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Malvern , Rouge</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Milliken , Agincourt North , Steeles East , L'...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Rouge Hill , Port Union , Highland Creek</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Scarborough Village</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Steeles West , L'Amoreaux West</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Wexford , Maryvale</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Woburn</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Neighborhood  American Restaurant  \\\n",
       "0                                           Agincourt                  0.0   \n",
       "1                        Birch Cliff , Cliffside West                  0.0   \n",
       "2                                           Cedarbrae                  0.0   \n",
       "3           Clarks Corners , Tam O'Shanter , Sullivan                  0.0   \n",
       "4   Cliffside , Cliffcrest , Scarborough Village West                  0.5   \n",
       "5   Dorset Park , Wexford Heights , Scarborough To...                  0.0   \n",
       "6                   Golden Mile , Clairlea , Oakridge                  0.0   \n",
       "7                 Guildwood , Morningside , West Hill                  0.0   \n",
       "8       Kennedy Park , Ionview , East Birchmount Park                  0.0   \n",
       "9                                     Malvern , Rouge                  0.0   \n",
       "10  Milliken , Agincourt North , Steeles East , L'...                  0.0   \n",
       "11           Rouge Hill , Port Union , Highland Creek                  0.0   \n",
       "12                                Scarborough Village                  0.0   \n",
       "13                     Steeles West , L'Amoreaux West                  0.0   \n",
       "14                                 Wexford , Maryvale                  0.0   \n",
       "15                                             Woburn                  0.0   \n",
       "\n",
       "    Athletics & Sports  Auto Garage    Bakery      Bank  Bar  Breakfast Spot  \\\n",
       "0                0.000     0.000000  0.000000  0.000000  0.0        0.250000   \n",
       "1                0.000     0.000000  0.000000  0.000000  0.0        0.000000   \n",
       "2                0.125     0.000000  0.125000  0.125000  0.0        0.000000   \n",
       "3                0.000     0.000000  0.000000  0.071429  0.0        0.000000   \n",
       "4                0.000     0.000000  0.000000  0.000000  0.0        0.000000   \n",
       "5                0.000     0.000000  0.000000  0.000000  0.0        0.000000   \n",
       "6                0.000     0.000000  0.222222  0.000000  0.0        0.000000   \n",
       "7                0.000     0.000000  0.000000  0.142857  0.0        0.142857   \n",
       "8                0.000     0.000000  0.000000  0.000000  0.0        0.000000   \n",
       "9                0.000     0.000000  0.000000  0.000000  0.0        0.000000   \n",
       "10               0.000     0.000000  0.000000  0.000000  0.0        0.000000   \n",
       "11               0.000     0.000000  0.000000  0.000000  0.5        0.000000   \n",
       "12               0.000     0.000000  0.000000  0.000000  0.0        0.000000   \n",
       "13               0.000     0.000000  0.000000  0.066667  0.0        0.066667   \n",
       "14               0.000     0.142857  0.142857  0.000000  0.0        0.142857   \n",
       "15               0.000     0.000000  0.000000  0.000000  0.0        0.000000   \n",
       "\n",
       "    Bubble Tea Shop  Bus Line  ...  Sandwich Place  Shopping Mall  \\\n",
       "0          0.000000  0.000000  ...        0.000000       0.000000   \n",
       "1          0.000000  0.000000  ...        0.000000       0.000000   \n",
       "2          0.000000  0.000000  ...        0.000000       0.000000   \n",
       "3          0.000000  0.000000  ...        0.000000       0.071429   \n",
       "4          0.000000  0.000000  ...        0.000000       0.000000   \n",
       "5          0.000000  0.000000  ...        0.000000       0.000000   \n",
       "6          0.000000  0.222222  ...        0.000000       0.000000   \n",
       "7          0.000000  0.000000  ...        0.000000       0.000000   \n",
       "8          0.000000  0.000000  ...        0.000000       0.000000   \n",
       "9          0.000000  0.000000  ...        0.000000       0.000000   \n",
       "10         0.000000  0.000000  ...        0.000000       0.000000   \n",
       "11         0.000000  0.000000  ...        0.000000       0.000000   \n",
       "12         0.000000  0.000000  ...        0.000000       0.000000   \n",
       "13         0.066667  0.000000  ...        0.066667       0.000000   \n",
       "14         0.000000  0.000000  ...        0.142857       0.142857   \n",
       "15         0.000000  0.000000  ...        0.000000       0.000000   \n",
       "\n",
       "    Skating Rink  Soccer Field  Supermarket  Thai Restaurant  \\\n",
       "0           0.25      0.000000     0.000000         0.000000   \n",
       "1           0.25      0.000000     0.000000         0.000000   \n",
       "2           0.00      0.000000     0.000000         0.125000   \n",
       "3           0.00      0.000000     0.000000         0.071429   \n",
       "4           0.00      0.000000     0.000000         0.000000   \n",
       "5           0.00      0.000000     0.000000         0.000000   \n",
       "6           0.00      0.111111     0.000000         0.000000   \n",
       "7           0.00      0.000000     0.000000         0.000000   \n",
       "8           0.00      0.000000     0.000000         0.000000   \n",
       "9           0.00      0.000000     0.000000         0.000000   \n",
       "10          0.00      0.000000     0.000000         0.000000   \n",
       "11          0.00      0.000000     0.000000         0.000000   \n",
       "12          0.00      0.000000     0.000000         0.000000   \n",
       "13          0.00      0.000000     0.066667         0.000000   \n",
       "14          0.00      0.000000     0.000000         0.000000   \n",
       "15          0.00      0.000000     0.000000         0.000000   \n",
       "\n",
       "    Thrift / Vintage Store  Train Station  Vietnamese Restaurant  \\\n",
       "0                 0.000000          0.000               0.000000   \n",
       "1                 0.000000          0.000               0.000000   \n",
       "2                 0.000000          0.000               0.000000   \n",
       "3                 0.000000          0.000               0.000000   \n",
       "4                 0.000000          0.000               0.000000   \n",
       "5                 0.166667          0.000               0.166667   \n",
       "6                 0.000000          0.000               0.000000   \n",
       "7                 0.000000          0.000               0.000000   \n",
       "8                 0.000000          0.125               0.000000   \n",
       "9                 0.000000          0.000               0.000000   \n",
       "10                0.000000          0.000               0.000000   \n",
       "11                0.000000          0.000               0.000000   \n",
       "12                0.000000          0.000               0.000000   \n",
       "13                0.066667          0.000               0.000000   \n",
       "14                0.000000          0.000               0.142857   \n",
       "15                0.000000          0.000               0.000000   \n",
       "\n",
       "    Women's Store  \n",
       "0        0.000000  \n",
       "1        0.000000  \n",
       "2        0.000000  \n",
       "3        0.000000  \n",
       "4        0.000000  \n",
       "5        0.000000  \n",
       "6        0.000000  \n",
       "7        0.000000  \n",
       "8        0.000000  \n",
       "9        0.000000  \n",
       "10       0.000000  \n",
       "11       0.000000  \n",
       "12       0.333333  \n",
       "13       0.000000  \n",
       "14       0.000000  \n",
       "15       0.000000  \n",
       "\n",
       "[16 rows x 59 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scarborough_grouped = scarborough_onehot.groupby('Neighborhood').mean().reset_index()\n",
    "scarborough_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16, 59)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scarborough_grouped.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Print each neighborhood along with the top 5 most common venues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----Agincourt----\n",
      "                       venue  freq\n",
      "0  Latin American Restaurant  0.25\n",
      "1                     Lounge  0.25\n",
      "2             Breakfast Spot  0.25\n",
      "3               Skating Rink  0.25\n",
      "4        American Restaurant  0.00\n",
      "\n",
      "\n",
      "----Birch Cliff , Cliffside West----\n",
      "                   venue  freq\n",
      "0  General Entertainment  0.25\n",
      "1           Skating Rink  0.25\n",
      "2                   Café  0.25\n",
      "3        College Stadium  0.25\n",
      "4    American Restaurant  0.00\n",
      "\n",
      "\n",
      "----Cedarbrae----\n",
      "                  venue  freq\n",
      "0      Hakka Restaurant  0.12\n",
      "1  Caribbean Restaurant  0.12\n",
      "2                Bakery  0.12\n",
      "3                  Bank  0.12\n",
      "4    Athletics & Sports  0.12\n",
      "\n",
      "\n",
      "----Clarks Corners , Tam O'Shanter , Sullivan----\n",
      "                 venue  freq\n",
      "0          Pizza Place  0.14\n",
      "1    Convenience Store  0.07\n",
      "2   Chinese Restaurant  0.07\n",
      "3             Pharmacy  0.07\n",
      "4  Fried Chicken Joint  0.07\n",
      "\n",
      "\n",
      "----Cliffside , Cliffcrest , Scarborough Village West----\n",
      "                 venue  freq\n",
      "0  American Restaurant   0.5\n",
      "1                Motel   0.5\n",
      "2            Pet Store   0.0\n",
      "3   Italian Restaurant   0.0\n",
      "4        Jewelry Store   0.0\n",
      "\n",
      "\n",
      "----Dorset Park , Wexford Heights , Scarborough Town Centre----\n",
      "                    venue  freq\n",
      "0       Indian Restaurant  0.33\n",
      "1               Pet Store  0.17\n",
      "2   Vietnamese Restaurant  0.17\n",
      "3  Thrift / Vintage Store  0.17\n",
      "4      Chinese Restaurant  0.17\n",
      "\n",
      "\n",
      "----Golden Mile , Clairlea , Oakridge----\n",
      "           venue  freq\n",
      "0       Bus Line  0.22\n",
      "1         Bakery  0.22\n",
      "2   Soccer Field  0.11\n",
      "3  Metro Station  0.11\n",
      "4   Intersection  0.11\n",
      "\n",
      "\n",
      "----Guildwood , Morningside , West Hill----\n",
      "                 venue  freq\n",
      "0         Intersection  0.14\n",
      "1       Breakfast Spot  0.14\n",
      "2    Electronics Store  0.14\n",
      "3  Rental Car Location  0.14\n",
      "4   Mexican Restaurant  0.14\n",
      "\n",
      "\n",
      "----Kennedy Park , Ionview , East Birchmount Park----\n",
      "                venue  freq\n",
      "0      Discount Store  0.25\n",
      "1          Hobby Shop  0.12\n",
      "2         Coffee Shop  0.12\n",
      "3  Chinese Restaurant  0.12\n",
      "4    Department Store  0.12\n",
      "\n",
      "\n",
      "----Malvern , Rouge----\n",
      "                  venue  freq\n",
      "0  Fast Food Restaurant   1.0\n",
      "1   American Restaurant   0.0\n",
      "2             Pet Store   0.0\n",
      "3         Jewelry Store   0.0\n",
      "4     Korean Restaurant   0.0\n",
      "\n",
      "\n",
      "----Milliken , Agincourt North , Steeles East , L'Amoreaux East----\n",
      "                 venue  freq\n",
      "0           Playground  0.33\n",
      "1          Coffee Shop  0.33\n",
      "2                 Park  0.33\n",
      "3  American Restaurant  0.00\n",
      "4            Pet Store  0.00\n",
      "\n",
      "\n",
      "----Rouge Hill , Port Union , Highland Creek----\n",
      "                        venue  freq\n",
      "0  Construction & Landscaping   0.5\n",
      "1                         Bar   0.5\n",
      "2                    Pharmacy   0.0\n",
      "3               Jewelry Store   0.0\n",
      "4           Korean Restaurant   0.0\n",
      "\n",
      "\n",
      "----Scarborough Village----\n",
      "                venue  freq\n",
      "0       Women's Store  0.33\n",
      "1       Jewelry Store  0.33\n",
      "2          Playground  0.33\n",
      "3           Pet Store  0.00\n",
      "4  Italian Restaurant  0.00\n",
      "\n",
      "\n",
      "----Steeles West , L'Amoreaux West----\n",
      "                  venue  freq\n",
      "0  Fast Food Restaurant  0.13\n",
      "1    Chinese Restaurant  0.13\n",
      "2        Sandwich Place  0.07\n",
      "3              Pharmacy  0.07\n",
      "4           Pizza Place  0.07\n",
      "\n",
      "\n",
      "----Wexford , Maryvale----\n",
      "                       venue  freq\n",
      "0                Auto Garage  0.14\n",
      "1                     Bakery  0.14\n",
      "2      Vietnamese Restaurant  0.14\n",
      "3             Breakfast Spot  0.14\n",
      "4  Middle Eastern Restaurant  0.14\n",
      "\n",
      "\n",
      "----Woburn----\n",
      "                 venue  freq\n",
      "0          Coffee Shop  0.67\n",
      "1    Korean Restaurant  0.33\n",
      "2  American Restaurant  0.00\n",
      "3             Pharmacy  0.00\n",
      "4        Jewelry Store  0.00\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "num_top_venues = 5\n",
    "\n",
    "for hood in scarborough_grouped['Neighborhood']:\n",
    "    print(\"----\"+hood+\"----\")\n",
    "    temp = scarborough_grouped[scarborough_grouped['Neighborhood'] == 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": "markdown",
   "metadata": {},
   "source": [
    "#### Put this into a *pandas* dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to sort the venues in descending order\n",
    "\n",
    "def return_most_common_venues(row, num_top_venues):\n",
    "    row_categories = row.iloc[1:]\n",
    "    row_categories_sorted = row_categories.sort_values(ascending=False)\n",
    "    \n",
    "    return row_categories_sorted.index.values[0:num_top_venues]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>Neighborhood</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>Agincourt</td>\n",
       "      <td>Skating Rink</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Latin American Restaurant</td>\n",
       "      <td>Lounge</td>\n",
       "      <td>Women's Store</td>\n",
       "      <td>College Stadium</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Birch Cliff , Cliffside West</td>\n",
       "      <td>College Stadium</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Skating Rink</td>\n",
       "      <td>Café</td>\n",
       "      <td>Women's Store</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cedarbrae</td>\n",
       "      <td>Hakka Restaurant</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Caribbean Restaurant</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>Athletics &amp; Sports</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Clarks Corners , Tam O'Shanter , Sullivan</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Noodle House</td>\n",
       "      <td>Shopping Mall</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cliffside , Cliffcrest , Scarborough Village West</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Motel</td>\n",
       "      <td>College Stadium</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Discount Store</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Neighborhood 1st Most Common Venue  \\\n",
       "0                                          Agincourt          Skating Rink   \n",
       "1                       Birch Cliff , Cliffside West       College Stadium   \n",
       "2                                          Cedarbrae      Hakka Restaurant   \n",
       "3          Clarks Corners , Tam O'Shanter , Sullivan           Pizza Place   \n",
       "4  Cliffside , Cliffcrest , Scarborough Village West   American Restaurant   \n",
       "\n",
       "   2nd Most Common Venue      3rd Most Common Venue 4th Most Common Venue  \\\n",
       "0         Breakfast Spot  Latin American Restaurant                Lounge   \n",
       "1  General Entertainment               Skating Rink                  Café   \n",
       "2                 Bakery       Caribbean Restaurant   Fried Chicken Joint   \n",
       "3           Noodle House              Shopping Mall     Convenience Store   \n",
       "4                  Motel            College Stadium         Grocery Store   \n",
       "\n",
       "   5th Most Common Venue 6th Most Common Venue  7th Most Common Venue  \\\n",
       "0          Women's Store       College Stadium  General Entertainment   \n",
       "1          Women's Store         Grocery Store            Gas Station   \n",
       "2                   Bank           Gas Station        Thai Restaurant   \n",
       "3               Pharmacy           Coffee Shop     Italian Restaurant   \n",
       "4  General Entertainment           Gas Station    Fried Chicken Joint   \n",
       "\n",
       "  8th Most Common Venue  9th Most Common Venue      10th Most Common Venue  \n",
       "0           Gas Station    Fried Chicken Joint        Fast Food Restaurant  \n",
       "1   Fried Chicken Joint   Fast Food Restaurant           Electronics Store  \n",
       "2    Athletics & Sports  General Entertainment  Construction & Landscaping  \n",
       "3                  Bank   Fast Food Restaurant             Thai Restaurant  \n",
       "4  Fast Food Restaurant      Electronics Store              Discount Store  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create the new dataframe and display the top 10 venues for each neighborhood\n",
    "\n",
    "num_top_venues = 10\n",
    "\n",
    "indicators = ['st', 'nd', 'rd']\n",
    "\n",
    "# create columns according to number of top venues\n",
    "columns = ['Neighborhood']\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['Neighborhood'] = scarborough_grouped['Neighborhood']\n",
    "\n",
    "for ind in np.arange(scarborough_grouped.shape[0]):\n",
    "    neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(scarborough_grouped.iloc[ind, :], num_top_venues)\n",
    "\n",
    "neighborhoods_venues_sorted.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cluster Neighborhoods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 4, 1, 1, 1, 1, 2], dtype=int32)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Run k-means to cluster the neighborhood into 5 clusters\n",
    "\n",
    "# set number of clusters\n",
    "kclusters = 5\n",
    "\n",
    "scarborough_grouped_clustering = scarborough_grouped.drop('Neighborhood', 1)\n",
    "\n",
    "# run k-means clustering\n",
    "kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(scarborough_grouped_clustering)\n",
    "\n",
    "# check cluster labels generated for each row in the dataframe\n",
    "kmeans.labels_[0:10] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>Neighborhood</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Cluster Labels</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M1B</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Malvern , Rouge</td>\n",
       "      <td>43.806686</td>\n",
       "      <td>-79.194353</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Women's Store</td>\n",
       "      <td>College Stadium</td>\n",
       "      <td>Hakka Restaurant</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Discount Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M1C</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Rouge Hill , Port Union , Highland Creek</td>\n",
       "      <td>43.784535</td>\n",
       "      <td>-79.160497</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Women's Store</td>\n",
       "      <td>College Stadium</td>\n",
       "      <td>Hakka Restaurant</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M1E</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Guildwood , Morningside , West Hill</td>\n",
       "      <td>43.763573</td>\n",
       "      <td>-79.188711</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Medical Center</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Rental Car Location</td>\n",
       "      <td>Women's Store</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Cosmetics Shop</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>3.0</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Korean Restaurant</td>\n",
       "      <td>College Stadium</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Discount Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M1H</td>\n",
       "      <td>Scarborough</td>\n",
       "      <td>Cedarbrae</td>\n",
       "      <td>43.773136</td>\n",
       "      <td>-79.239476</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Hakka Restaurant</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Caribbean Restaurant</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>Athletics &amp; Sports</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Postal Code      Borough                              Neighborhood  \\\n",
       "0         M1B  Scarborough                           Malvern , Rouge   \n",
       "1         M1C  Scarborough  Rouge Hill , Port Union , Highland Creek   \n",
       "2         M1E  Scarborough       Guildwood , Morningside , West Hill   \n",
       "3         M1G  Scarborough                                    Woburn   \n",
       "4         M1H  Scarborough                                 Cedarbrae   \n",
       "\n",
       "    Latitude  Longitude  Cluster Labels 1st Most Common Venue  \\\n",
       "0  43.806686 -79.194353             2.0  Fast Food Restaurant   \n",
       "1  43.784535 -79.160497             0.0                   Bar   \n",
       "2  43.763573 -79.188711             1.0    Mexican Restaurant   \n",
       "3  43.770992 -79.216917             3.0           Coffee Shop   \n",
       "4  43.773136 -79.239476             1.0      Hakka Restaurant   \n",
       "\n",
       "        2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue  \\\n",
       "0               Women's Store       College Stadium      Hakka Restaurant   \n",
       "1  Construction & Landscaping         Women's Store       College Stadium   \n",
       "2                Intersection                  Bank        Medical Center   \n",
       "3           Korean Restaurant       College Stadium         Grocery Store   \n",
       "4                      Bakery  Caribbean Restaurant   Fried Chicken Joint   \n",
       "\n",
       "   5th Most Common Venue  6th Most Common Venue  7th Most Common Venue  \\\n",
       "0          Grocery Store  General Entertainment            Gas Station   \n",
       "1       Hakka Restaurant          Grocery Store  General Entertainment   \n",
       "2         Breakfast Spot      Electronics Store    Rental Car Location   \n",
       "3  General Entertainment            Gas Station    Fried Chicken Joint   \n",
       "4                   Bank            Gas Station        Thai Restaurant   \n",
       "\n",
       "  8th Most Common Venue  9th Most Common Venue      10th Most Common Venue  \n",
       "0   Fried Chicken Joint      Electronics Store              Discount Store  \n",
       "1           Gas Station    Fried Chicken Joint        Fast Food Restaurant  \n",
       "2         Women's Store         Discount Store              Cosmetics Shop  \n",
       "3  Fast Food Restaurant      Electronics Store              Discount Store  \n",
       "4    Athletics & Sports  General Entertainment  Construction & Landscaping  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create a new dataframe that includes the cluster as well as the top 10 venues for each neighborhood\n",
    "\n",
    "# add clustering labels\n",
    "neighborhoods_venues_sorted.insert(0, 'Cluster Labels', kmeans.labels_)\n",
    "\n",
    "scarborough_merged = scarborough_data\n",
    "\n",
    "# merge toronto_grouped with toronto_data to add latitude/longitude for each neighborhood\n",
    "scarborough_merged = scarborough_merged.join(neighborhoods_venues_sorted.set_index('Neighborhood'), on='Neighborhood')\n",
    "\n",
    "scarborough_merged.head() # check the last columns!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
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                {"attribution": "Data by \u0026copy; \u003ca href=\"http://openstreetmap.org\"\u003eOpenStreetMap\u003c/a\u003e, under \u003ca href=\"http://www.openstreetmap.org/copyright\"\u003eODbL\u003c/a\u003e.", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}
            ).addTo(map_0c46b01c9eeb4bf0bdb15ada6a6b16d6);
        
    
            var circle_marker_b9f2f2d7128847ab9b2a527fe6580566 = L.circleMarker(
                [43.806686299999996, -79.19435340000001],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_0c46b01c9eeb4bf0bdb15ada6a6b16d6);
        
    
        var popup_90cd87eb867d4a7fa51ed9656daba8c0 = L.popup({"maxWidth": "100%"});

        
            var html_6408e9d2ee134fcc94391a6cbef33a67 = $(`<div id="html_6408e9d2ee134fcc94391a6cbef33a67" style="width: 100.0%; height: 100.0%;">Malvern , Rouge Cluster 2.0</div>`)[0];
            popup_90cd87eb867d4a7fa51ed9656daba8c0.setContent(html_6408e9d2ee134fcc94391a6cbef33a67);
        

        circle_marker_b9f2f2d7128847ab9b2a527fe6580566.bindPopup(popup_90cd87eb867d4a7fa51ed9656daba8c0)
        ;

        
    
    
            var circle_marker_9306531a87414e24a887c9728a236766 = L.circleMarker(
                [43.7845351, -79.16049709999999],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_0c46b01c9eeb4bf0bdb15ada6a6b16d6);
        
    
        var popup_f61486321e8a47b18d28960f9bf39be1 = L.popup({"maxWidth": "100%"});

        
            var html_e1bacaa2fdeb469eaabdbaf49242538f = $(`<div id="html_e1bacaa2fdeb469eaabdbaf49242538f" style="width: 100.0%; height: 100.0%;">Rouge Hill , Port Union , Highland Creek Cluster 0.0</div>`)[0];
            popup_f61486321e8a47b18d28960f9bf39be1.setContent(html_e1bacaa2fdeb469eaabdbaf49242538f);
        

        circle_marker_9306531a87414e24a887c9728a236766.bindPopup(popup_f61486321e8a47b18d28960f9bf39be1)
        ;

        
    
    
            var circle_marker_9743a372ab6344f3bee6f013701b323e = L.circleMarker(
                [43.7635726, -79.1887115],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_0c46b01c9eeb4bf0bdb15ada6a6b16d6);
        
    
        var popup_29e40f5cf34b4affa581cfd74d42d02b = L.popup({"maxWidth": "100%"});

        
            var html_e7506e8f0e2246db890707a4200970cf = $(`<div id="html_e7506e8f0e2246db890707a4200970cf" style="width: 100.0%; height: 100.0%;">Guildwood , Morningside , West Hill Cluster 1.0</div>`)[0];
            popup_29e40f5cf34b4affa581cfd74d42d02b.setContent(html_e7506e8f0e2246db890707a4200970cf);
        

        circle_marker_9743a372ab6344f3bee6f013701b323e.bindPopup(popup_29e40f5cf34b4affa581cfd74d42d02b)
        ;

        
    
    
            var circle_marker_7b52f3a518224adb9aaff27b0014ae6f = L.circleMarker(
                [43.7709921, -79.21691740000001],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_0c46b01c9eeb4bf0bdb15ada6a6b16d6);
        
    
        var popup_5408bf81b18a4c3a88200c1098297210 = L.popup({"maxWidth": "100%"});

        
            var html_74a6203c2fda4ac9ad20dfc4973ed119 = $(`<div id="html_74a6203c2fda4ac9ad20dfc4973ed119" style="width: 100.0%; height: 100.0%;">Woburn Cluster 3.0</div>`)[0];
            popup_5408bf81b18a4c3a88200c1098297210.setContent(html_74a6203c2fda4ac9ad20dfc4973ed119);
        

        circle_marker_7b52f3a518224adb9aaff27b0014ae6f.bindPopup(popup_5408bf81b18a4c3a88200c1098297210)
        ;

        
    
    
            var circle_marker_69c845af439c44929ee4fee28d358918 = L.circleMarker(
                [43.773136, -79.23947609999999],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_0c46b01c9eeb4bf0bdb15ada6a6b16d6);
        
    
        var popup_cacea91b84b24dffb8ef9568ada9b8e3 = L.popup({"maxWidth": "100%"});

        
            var html_16beb4f9b0cb4aae8abf3d04a5d64db7 = $(`<div id="html_16beb4f9b0cb4aae8abf3d04a5d64db7" style="width: 100.0%; height: 100.0%;">Cedarbrae Cluster 1.0</div>`)[0];
            popup_cacea91b84b24dffb8ef9568ada9b8e3.setContent(html_16beb4f9b0cb4aae8abf3d04a5d64db7);
        

        circle_marker_69c845af439c44929ee4fee28d358918.bindPopup(popup_cacea91b84b24dffb8ef9568ada9b8e3)
        ;

        
    
    
            var circle_marker_401131f22c5545548b31cea76e3687fc = L.circleMarker(
                [43.7447342, -79.23947609999999],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_0c46b01c9eeb4bf0bdb15ada6a6b16d6);
        
    
        var popup_2787bff9ba784c5d8d5ba50dea04b9ee = L.popup({"maxWidth": "100%"});

        
            var html_783d359639ff42a28d78c2de6d6bbc44 = $(`<div id="html_783d359639ff42a28d78c2de6d6bbc44" style="width: 100.0%; height: 100.0%;">Scarborough Village Cluster 1.0</div>`)[0];
            popup_2787bff9ba784c5d8d5ba50dea04b9ee.setContent(html_783d359639ff42a28d78c2de6d6bbc44);
        

        circle_marker_401131f22c5545548b31cea76e3687fc.bindPopup(popup_2787bff9ba784c5d8d5ba50dea04b9ee)
        ;

        
    
    
            var circle_marker_d91dbca12dd745ab80b5caa9c2b73a47 = L.circleMarker(
                [43.7279292, -79.26202940000002],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_0c46b01c9eeb4bf0bdb15ada6a6b16d6);
        
    
        var popup_eb8d0fc4bac440c59e840ee4e6ad1ef0 = L.popup({"maxWidth": "100%"});

        
            var html_5956a484d8014e559e117e558bd53b56 = $(`<div id="html_5956a484d8014e559e117e558bd53b56" style="width: 100.0%; height: 100.0%;">Kennedy Park , Ionview , East Birchmount Park Cluster 1.0</div>`)[0];
            popup_eb8d0fc4bac440c59e840ee4e6ad1ef0.setContent(html_5956a484d8014e559e117e558bd53b56);
        

        circle_marker_d91dbca12dd745ab80b5caa9c2b73a47.bindPopup(popup_eb8d0fc4bac440c59e840ee4e6ad1ef0)
        ;

        
    
    
            var circle_marker_71019bc89d614bcc8f107a35918525f7 = L.circleMarker(
                [43.711111700000004, -79.2845772],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_0c46b01c9eeb4bf0bdb15ada6a6b16d6);
        
    
        var popup_cb440af67a964bda968c208b1ed056de = L.popup({"maxWidth": "100%"});

        
            var html_93100ec5704b41138c20743d2801b1ac = $(`<div id="html_93100ec5704b41138c20743d2801b1ac" style="width: 100.0%; height: 100.0%;">Golden Mile , Clairlea , Oakridge Cluster 1.0</div>`)[0];
            popup_cb440af67a964bda968c208b1ed056de.setContent(html_93100ec5704b41138c20743d2801b1ac);
        

        circle_marker_71019bc89d614bcc8f107a35918525f7.bindPopup(popup_cb440af67a964bda968c208b1ed056de)
        ;

        
    
    
            var circle_marker_30b9b449970f4821b7aadb2916c05143 = L.circleMarker(
                [43.716316, -79.23947609999999],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_0c46b01c9eeb4bf0bdb15ada6a6b16d6);
        
    
        var popup_f35b8ad60d87476dad0e864dcce60256 = L.popup({"maxWidth": "100%"});

        
            var html_18adf72fd1c34234b78eefab76fc7588 = $(`<div id="html_18adf72fd1c34234b78eefab76fc7588" style="width: 100.0%; height: 100.0%;">Cliffside , Cliffcrest , Scarborough Village West Cluster 4.0</div>`)[0];
            popup_f35b8ad60d87476dad0e864dcce60256.setContent(html_18adf72fd1c34234b78eefab76fc7588);
        

        circle_marker_30b9b449970f4821b7aadb2916c05143.bindPopup(popup_f35b8ad60d87476dad0e864dcce60256)
        ;

        
    
    
            var circle_marker_854a691a6b18438384208244ee8afabb = L.circleMarker(
                [43.692657000000004, -79.2648481],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_0c46b01c9eeb4bf0bdb15ada6a6b16d6);
        
    
        var popup_6064584f5ef04a6fb7e29023cd7b45f2 = L.popup({"maxWidth": "100%"});

        
            var html_78699e8796e24acea3749dcab21b2f60 = $(`<div id="html_78699e8796e24acea3749dcab21b2f60" style="width: 100.0%; height: 100.0%;">Birch Cliff , Cliffside West Cluster 1.0</div>`)[0];
            popup_6064584f5ef04a6fb7e29023cd7b45f2.setContent(html_78699e8796e24acea3749dcab21b2f60);
        

        circle_marker_854a691a6b18438384208244ee8afabb.bindPopup(popup_6064584f5ef04a6fb7e29023cd7b45f2)
        ;

        
    
    
            var circle_marker_f42e4f76afda45e4870607e74a0edd7a = L.circleMarker(
                [43.7574096, -79.27330400000001],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_0c46b01c9eeb4bf0bdb15ada6a6b16d6);
        
    
        var popup_1f58efd8802b465983c99f40deae65df = L.popup({"maxWidth": "100%"});

        
            var html_c25a0b7222f54a2e87411c4dea8f6bad = $(`<div id="html_c25a0b7222f54a2e87411c4dea8f6bad" style="width: 100.0%; height: 100.0%;">Dorset Park , Wexford Heights , Scarborough Town Centre Cluster 1.0</div>`)[0];
            popup_1f58efd8802b465983c99f40deae65df.setContent(html_c25a0b7222f54a2e87411c4dea8f6bad);
        

        circle_marker_f42e4f76afda45e4870607e74a0edd7a.bindPopup(popup_1f58efd8802b465983c99f40deae65df)
        ;

        
    
    
            var circle_marker_070231c185354113b88e8176672c6935 = L.circleMarker(
                [43.750071500000004, -79.2958491],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_0c46b01c9eeb4bf0bdb15ada6a6b16d6);
        
    
        var popup_709a83202cbc4e15947a77eff3ea7c98 = L.popup({"maxWidth": "100%"});

        
            var html_2ae415f64ea442c0bc0b5132e03aec3f = $(`<div id="html_2ae415f64ea442c0bc0b5132e03aec3f" style="width: 100.0%; height: 100.0%;">Wexford , Maryvale Cluster 1.0</div>`)[0];
            popup_709a83202cbc4e15947a77eff3ea7c98.setContent(html_2ae415f64ea442c0bc0b5132e03aec3f);
        

        circle_marker_070231c185354113b88e8176672c6935.bindPopup(popup_709a83202cbc4e15947a77eff3ea7c98)
        ;

        
    
    
            var circle_marker_1fdeb072515e456093c5ae43f45429af = L.circleMarker(
                [43.7942003, -79.26202940000002],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_0c46b01c9eeb4bf0bdb15ada6a6b16d6);
        
    
        var popup_0419f7b8ebe044079f7711bb0940a2d3 = L.popup({"maxWidth": "100%"});

        
            var html_0cff5c7d70764a77ad5b0e2ceceb3143 = $(`<div id="html_0cff5c7d70764a77ad5b0e2ceceb3143" style="width: 100.0%; height: 100.0%;">Agincourt Cluster 1.0</div>`)[0];
            popup_0419f7b8ebe044079f7711bb0940a2d3.setContent(html_0cff5c7d70764a77ad5b0e2ceceb3143);
        

        circle_marker_1fdeb072515e456093c5ae43f45429af.bindPopup(popup_0419f7b8ebe044079f7711bb0940a2d3)
        ;

        
    
    
            var circle_marker_63f687a401d847cd9162d57bbfc4807b = L.circleMarker(
                [43.7816375, -79.3043021],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_0c46b01c9eeb4bf0bdb15ada6a6b16d6);
        
    
        var popup_50eb5d683b00434b8a9be7fee38c6905 = L.popup({"maxWidth": "100%"});

        
            var html_0a6cab65e6e74dc39c62517d00550313 = $(`<div id="html_0a6cab65e6e74dc39c62517d00550313" style="width: 100.0%; height: 100.0%;">Clarks Corners , Tam O&#39;Shanter , Sullivan Cluster 1.0</div>`)[0];
            popup_50eb5d683b00434b8a9be7fee38c6905.setContent(html_0a6cab65e6e74dc39c62517d00550313);
        

        circle_marker_63f687a401d847cd9162d57bbfc4807b.bindPopup(popup_50eb5d683b00434b8a9be7fee38c6905)
        ;

        
    
    
            var circle_marker_8f81eac0f8094962ae08e3afc4aba2b3 = L.circleMarker(
                [43.8152522, -79.2845772],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_0c46b01c9eeb4bf0bdb15ada6a6b16d6);
        
    
        var popup_b69ff1d6ba0349ada659e141273e59a3 = L.popup({"maxWidth": "100%"});

        
            var html_e3b9823691d5481c927c952ca653681e = $(`<div id="html_e3b9823691d5481c927c952ca653681e" style="width: 100.0%; height: 100.0%;">Milliken , Agincourt North , Steeles East , L&#39;Amoreaux East Cluster 3.0</div>`)[0];
            popup_b69ff1d6ba0349ada659e141273e59a3.setContent(html_e3b9823691d5481c927c952ca653681e);
        

        circle_marker_8f81eac0f8094962ae08e3afc4aba2b3.bindPopup(popup_b69ff1d6ba0349ada659e141273e59a3)
        ;

        
    
    
            var circle_marker_4d96867cb8714125b196fe1a64d61743 = L.circleMarker(
                [43.799525200000005, -79.3183887],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_0c46b01c9eeb4bf0bdb15ada6a6b16d6);
        
    
        var popup_4cf77ae6168146a69d48ddd87b147996 = L.popup({"maxWidth": "100%"});

        
            var html_dd24390194ea4fd9b2bd6e36ef2f2d13 = $(`<div id="html_dd24390194ea4fd9b2bd6e36ef2f2d13" style="width: 100.0%; height: 100.0%;">Steeles West , L&#39;Amoreaux West Cluster 1.0</div>`)[0];
            popup_4cf77ae6168146a69d48ddd87b147996.setContent(html_dd24390194ea4fd9b2bd6e36ef2f2d13);
        

        circle_marker_4d96867cb8714125b196fe1a64d61743.bindPopup(popup_4cf77ae6168146a69d48ddd87b147996)
        ;

        
    
    
            var circle_marker_7ddd6240a286401faab5d66d644b3bd1 = L.circleMarker(
                [43.836124700000006, -79.20563609999999],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_0c46b01c9eeb4bf0bdb15ada6a6b16d6);
        
    
        var popup_e39a69d38f3f479196e655beefd99c52 = L.popup({"maxWidth": "100%"});

        
            var html_64007b4dbfbe428295730094c94ed690 = $(`<div id="html_64007b4dbfbe428295730094c94ed690" style="width: 100.0%; height: 100.0%;">Upper Rouge Cluster nan</div>`)[0];
            popup_e39a69d38f3f479196e655beefd99c52.setContent(html_64007b4dbfbe428295730094c94ed690);
        

        circle_marker_7ddd6240a286401faab5d66d644b3bd1.bindPopup(popup_e39a69d38f3f479196e655beefd99c52)
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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 0x7fd58e3ab5c0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#visualize the resulting clusters\n",
    "\n",
    "# create map\n",
    "map_clusters = folium.Map(location=[latitude, longitude], zoom_start=11)\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(scarborough_merged['Latitude'], scarborough_merged['Longitude'], scarborough_merged['Neighborhood'], scarborough_merged['Cluster Labels']):\n",
    "    label = folium.Popup(str(poi) + ' Cluster ' + str(cluster), parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lon],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color=rainbow[int(cluster)],\n",
    "        fill=True,\n",
    "        fill_color=rainbow[int(cluster)],\n",
    "        fill_opacity=0.7).add_to(map_clusters)\n",
    "       \n",
    "map_clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examine Clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Examine each cluster and determine the discriminating venue categories that distinguish each cluster. Based on the defining categories, you can then assign a name to each cluster. I will leave this exercise to you."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Borough</th>\n",
       "      <th>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>1</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Women's Store</td>\n",
       "      <td>College Stadium</td>\n",
       "      <td>Hakka Restaurant</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Borough  Cluster Labels 1st Most Common Venue  \\\n",
       "1  Scarborough             0.0                   Bar   \n",
       "\n",
       "        2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue  \\\n",
       "1  Construction & Landscaping         Women's Store       College Stadium   \n",
       "\n",
       "  5th Most Common Venue 6th Most Common Venue  7th Most Common Venue  \\\n",
       "1      Hakka Restaurant         Grocery Store  General Entertainment   \n",
       "\n",
       "  8th Most Common Venue 9th Most Common Venue 10th Most Common Venue  \n",
       "1           Gas Station   Fried Chicken Joint   Fast Food Restaurant  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scarborough_merged.loc[scarborough_merged['Cluster Labels'] == 0, scarborough_merged.columns[[1] + list(range(5, scarborough_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Borough</th>\n",
       "      <th>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>2</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Mexican Restaurant</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Medical Center</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Rental Car Location</td>\n",
       "      <td>Women's Store</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Hakka Restaurant</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Caribbean Restaurant</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "      <td>Athletics &amp; Sports</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Women's Store</td>\n",
       "      <td>Jewelry Store</td>\n",
       "      <td>Playground</td>\n",
       "      <td>College Stadium</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Train Station</td>\n",
       "      <td>Department Store</td>\n",
       "      <td>Bus Station</td>\n",
       "      <td>Hobby Shop</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Construction &amp; Landscaping</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Bus Line</td>\n",
       "      <td>Ice Cream Shop</td>\n",
       "      <td>Soccer Field</td>\n",
       "      <td>Intersection</td>\n",
       "      <td>Metro Station</td>\n",
       "      <td>Park</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>General Entertainment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>1.0</td>\n",
       "      <td>College Stadium</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Skating Rink</td>\n",
       "      <td>Café</td>\n",
       "      <td>Women's Store</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Indian Restaurant</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Thrift / Vintage Store</td>\n",
       "      <td>Pet Store</td>\n",
       "      <td>College Stadium</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Vietnamese Restaurant</td>\n",
       "      <td>Auto Garage</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Shopping Mall</td>\n",
       "      <td>Middle Eastern Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Skating Rink</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Latin American Restaurant</td>\n",
       "      <td>Lounge</td>\n",
       "      <td>Women's Store</td>\n",
       "      <td>College Stadium</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Noodle House</td>\n",
       "      <td>Shopping Mall</td>\n",
       "      <td>Convenience Store</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Italian Restaurant</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Thai Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Chinese Restaurant</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Breakfast Spot</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Thrift / Vintage Store</td>\n",
       "      <td>Supermarket</td>\n",
       "      <td>Bank</td>\n",
       "      <td>Cosmetics Shop</td>\n",
       "      <td>Sandwich Place</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Borough  Cluster Labels 1st Most Common Venue  2nd Most Common Venue  \\\n",
       "2   Scarborough             1.0    Mexican Restaurant           Intersection   \n",
       "4   Scarborough             1.0      Hakka Restaurant                 Bakery   \n",
       "5   Scarborough             1.0         Women's Store          Jewelry Store   \n",
       "6   Scarborough             1.0        Discount Store     Chinese Restaurant   \n",
       "7   Scarborough             1.0                Bakery               Bus Line   \n",
       "9   Scarborough             1.0       College Stadium  General Entertainment   \n",
       "10  Scarborough             1.0     Indian Restaurant     Chinese Restaurant   \n",
       "11  Scarborough             1.0        Sandwich Place  Vietnamese Restaurant   \n",
       "12  Scarborough             1.0          Skating Rink         Breakfast Spot   \n",
       "13  Scarborough             1.0           Pizza Place           Noodle House   \n",
       "15  Scarborough             1.0    Chinese Restaurant   Fast Food Restaurant   \n",
       "\n",
       "        3rd Most Common Venue   4th Most Common Venue 5th Most Common Venue  \\\n",
       "2                        Bank          Medical Center        Breakfast Spot   \n",
       "4        Caribbean Restaurant     Fried Chicken Joint                  Bank   \n",
       "5                  Playground         College Stadium         Grocery Store   \n",
       "6               Train Station        Department Store           Bus Station   \n",
       "7              Ice Cream Shop            Soccer Field          Intersection   \n",
       "9                Skating Rink                    Café         Women's Store   \n",
       "10      Vietnamese Restaurant  Thrift / Vintage Store             Pet Store   \n",
       "11                Auto Garage                  Bakery        Breakfast Spot   \n",
       "12  Latin American Restaurant                  Lounge         Women's Store   \n",
       "13              Shopping Mall       Convenience Store              Pharmacy   \n",
       "15             Breakfast Spot           Grocery Store           Coffee Shop   \n",
       "\n",
       "     6th Most Common Venue      7th Most Common Venue  \\\n",
       "2        Electronics Store        Rental Car Location   \n",
       "4              Gas Station            Thai Restaurant   \n",
       "5    General Entertainment                Gas Station   \n",
       "6               Hobby Shop                Coffee Shop   \n",
       "7            Metro Station                       Park   \n",
       "9            Grocery Store                Gas Station   \n",
       "10         College Stadium      General Entertainment   \n",
       "11           Shopping Mall  Middle Eastern Restaurant   \n",
       "12         College Stadium      General Entertainment   \n",
       "13             Coffee Shop         Italian Restaurant   \n",
       "15  Thrift / Vintage Store                Supermarket   \n",
       "\n",
       "         8th Most Common Venue  9th Most Common Venue  \\\n",
       "2                Women's Store         Discount Store   \n",
       "4           Athletics & Sports  General Entertainment   \n",
       "5          Fried Chicken Joint   Fast Food Restaurant   \n",
       "6   Construction & Landscaping          Grocery Store   \n",
       "7                          Bar         Breakfast Spot   \n",
       "9          Fried Chicken Joint   Fast Food Restaurant   \n",
       "10                 Gas Station    Fried Chicken Joint   \n",
       "11           Electronics Store      Convenience Store   \n",
       "12                 Gas Station    Fried Chicken Joint   \n",
       "13                        Bank   Fast Food Restaurant   \n",
       "15                        Bank         Cosmetics Shop   \n",
       "\n",
       "        10th Most Common Venue  \n",
       "2               Cosmetics Shop  \n",
       "4   Construction & Landscaping  \n",
       "5            Electronics Store  \n",
       "6        General Entertainment  \n",
       "7        General Entertainment  \n",
       "9            Electronics Store  \n",
       "10        Fast Food Restaurant  \n",
       "11              Cosmetics Shop  \n",
       "12        Fast Food Restaurant  \n",
       "13             Thai Restaurant  \n",
       "15              Sandwich Place  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scarborough_merged.loc[scarborough_merged['Cluster Labels'] == 1, scarborough_merged.columns[[1] + list(range(5, scarborough_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Borough</th>\n",
       "      <th>Cluster Labels</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Women's Store</td>\n",
       "      <td>College Stadium</td>\n",
       "      <td>Hakka Restaurant</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Discount Store</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Borough  Cluster Labels 1st Most Common Venue 2nd Most Common Venue  \\\n",
       "0  Scarborough             2.0  Fast Food Restaurant         Women's Store   \n",
       "\n",
       "  3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue  \\\n",
       "0       College Stadium      Hakka Restaurant         Grocery Store   \n",
       "\n",
       "   6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "0  General Entertainment           Gas Station   Fried Chicken Joint   \n",
       "\n",
       "  9th Most Common Venue 10th Most Common Venue  \n",
       "0     Electronics Store         Discount Store  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scarborough_merged.loc[scarborough_merged['Cluster Labels'] == 2, scarborough_merged.columns[[1] + list(range(5, scarborough_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <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>3</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Korean Restaurant</td>\n",
       "      <td>College Stadium</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Discount Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Scarborough</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Park</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Playground</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Discount Store</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Borough  Cluster Labels 1st Most Common Venue 2nd Most Common Venue  \\\n",
       "3   Scarborough             3.0           Coffee Shop     Korean Restaurant   \n",
       "14  Scarborough             3.0                  Park           Coffee Shop   \n",
       "\n",
       "   3rd Most Common Venue 4th Most Common Venue  5th Most Common Venue  \\\n",
       "3        College Stadium         Grocery Store  General Entertainment   \n",
       "14            Playground         Grocery Store  General Entertainment   \n",
       "\n",
       "   6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "3            Gas Station   Fried Chicken Joint  Fast Food Restaurant   \n",
       "14           Gas Station   Fried Chicken Joint  Fast Food Restaurant   \n",
       "\n",
       "   9th Most Common Venue 10th Most Common Venue  \n",
       "3      Electronics Store         Discount Store  \n",
       "14     Electronics Store         Discount Store  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scarborough_merged.loc[scarborough_merged['Cluster Labels'] == 3, scarborough_merged.columns[[1] + list(range(5, scarborough_merged.shape[1]))]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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",
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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",
       "      <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>Scarborough</td>\n",
       "      <td>4.0</td>\n",
       "      <td>American Restaurant</td>\n",
       "      <td>Motel</td>\n",
       "      <td>College Stadium</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>General Entertainment</td>\n",
       "      <td>Gas Station</td>\n",
       "      <td>Fried Chicken Joint</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Electronics Store</td>\n",
       "      <td>Discount Store</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       Borough  Cluster Labels 1st Most Common Venue 2nd Most Common Venue  \\\n",
       "8  Scarborough             4.0   American Restaurant                 Motel   \n",
       "\n",
       "  3rd Most Common Venue 4th Most Common Venue  5th Most Common Venue  \\\n",
       "8       College Stadium         Grocery Store  General Entertainment   \n",
       "\n",
       "  6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "8           Gas Station   Fried Chicken Joint  Fast Food Restaurant   \n",
       "\n",
       "  9th Most Common Venue 10th Most Common Venue  \n",
       "8     Electronics Store         Discount Store  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scarborough_merged.loc[scarborough_merged['Cluster Labels'] == 4, scarborough_merged.columns[[1] + list(range(5, scarborough_merged.shape[1]))]]"
   ]
  }
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