Week3 - Part 3.ipynb

{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {
                "collapsed": true
            },
            "source": "# Segmenting and Clustering Neighborhoods in Toronto - Part 3"
        },
        {
            "cell_type": "code",
            "execution_count": 19,
            "metadata": {
                "scrolled": true
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Requirement already satisfied: folium in /opt/conda/envs/Python36/lib/python3.6/site-packages (0.11.0)\nRequirement already satisfied: numpy in /opt/conda/envs/Python36/lib/python3.6/site-packages (from folium) (1.15.4)\nRequirement already satisfied: branca>=0.3.0 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from folium) (0.4.1)\nRequirement already satisfied: jinja2>=2.9 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from folium) (2.10)\nRequirement already satisfied: requests in /opt/conda/envs/Python36/lib/python3.6/site-packages (from folium) (2.21.0)\nRequirement already satisfied: MarkupSafe>=0.23 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from jinja2>=2.9->folium) (1.1.0)\nRequirement already satisfied: urllib3<1.25,>=1.21.1 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from requests->folium) (1.24.1)\nRequirement already satisfied: idna<2.9,>=2.5 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from requests->folium) (2.8)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from requests->folium) (2020.4.5.1)\nRequirement already satisfied: chardet<3.1.0,>=3.0.2 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from requests->folium) (3.0.4)\n"
                }
            ],
            "source": "!pip install folium\nimport requests\nimport numpy as np\nfrom geopy.geocoders import Nominatim # convert an address into latitude and longitude values\nfrom pandas.io.json import json_normalize  # tranform JSON file into a pandas dataframe\n\nimport folium # map rendering library\n\nimport pandas as pd\n# import k-means from clustering stage\nfrom sklearn.cluster import KMeans"
        },
        {
            "cell_type": "code",
            "execution_count": 25,
            "metadata": {},
            "outputs": [],
            "source": "TorontoNeighborhoodCoordinates_df = pd.read_csv(\"./TorontoNeighborhoodCoordinates_df.csv\")"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Filtering the Boroughs with Toronto in the names"
        },
        {
            "cell_type": "code",
            "execution_count": 27,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Postal Code</th>\n      <th>Borough</th>\n      <th>Neighborhood</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td>M5A</td>\n      <td>Downtown Toronto</td>\n      <td>Regent Park, Harbourfront</td>\n      <td>43.654260</td>\n      <td>-79.360636</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>M7A</td>\n      <td>Downtown Toronto</td>\n      <td>Queen's Park, Ontario Provincial Government</td>\n      <td>43.662301</td>\n      <td>-79.389494</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>M5B</td>\n      <td>Downtown Toronto</td>\n      <td>Garden District, Ryerson</td>\n      <td>43.657162</td>\n      <td>-79.378937</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>M5C</td>\n      <td>Downtown Toronto</td>\n      <td>St. James Town</td>\n      <td>43.651494</td>\n      <td>-79.375418</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>M4E</td>\n      <td>East Toronto</td>\n      <td>The Beaches</td>\n      <td>43.676357</td>\n      <td>-79.293031</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>M5E</td>\n      <td>Downtown Toronto</td>\n      <td>Berczy Park</td>\n      <td>43.644771</td>\n      <td>-79.373306</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>M5G</td>\n      <td>Downtown Toronto</td>\n      <td>Central Bay Street</td>\n      <td>43.657952</td>\n      <td>-79.387383</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>M6G</td>\n      <td>Downtown Toronto</td>\n      <td>Christie</td>\n      <td>43.669542</td>\n      <td>-79.422564</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>M5H</td>\n      <td>Downtown Toronto</td>\n      <td>Richmond, Adelaide, King</td>\n      <td>43.650571</td>\n      <td>-79.384568</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>M6H</td>\n      <td>West Toronto</td>\n      <td>Dufferin, Dovercourt Village</td>\n      <td>43.669005</td>\n      <td>-79.442259</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>M5J</td>\n      <td>Downtown Toronto</td>\n      <td>Harbourfront East, Union Station, Toronto Islands</td>\n      <td>43.640816</td>\n      <td>-79.381752</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>M6J</td>\n      <td>West Toronto</td>\n      <td>Little Portugal, Trinity</td>\n      <td>43.647927</td>\n      <td>-79.419750</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>M4K</td>\n      <td>East Toronto</td>\n      <td>The Danforth West, Riverdale</td>\n      <td>43.679557</td>\n      <td>-79.352188</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>M5K</td>\n      <td>Downtown Toronto</td>\n      <td>Toronto Dominion Centre, Design Exchange</td>\n      <td>43.647177</td>\n      <td>-79.381576</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>M6K</td>\n      <td>West Toronto</td>\n      <td>Brockton, Parkdale Village, Exhibition Place</td>\n      <td>43.636847</td>\n      <td>-79.428191</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>M4L</td>\n      <td>East Toronto</td>\n      <td>India Bazaar, The Beaches West</td>\n      <td>43.668999</td>\n      <td>-79.315572</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>M5L</td>\n      <td>Downtown Toronto</td>\n      <td>Commerce Court, Victoria Hotel</td>\n      <td>43.648198</td>\n      <td>-79.379817</td>\n    </tr>\n    <tr>\n      <th>54</th>\n      <td>M4M</td>\n      <td>East Toronto</td>\n      <td>Studio District</td>\n      <td>43.659526</td>\n      <td>-79.340923</td>\n    </tr>\n    <tr>\n      <th>61</th>\n      <td>M4N</td>\n      <td>Central Toronto</td>\n      <td>Lawrence Park</td>\n      <td>43.728020</td>\n      <td>-79.388790</td>\n    </tr>\n    <tr>\n      <th>62</th>\n      <td>M5N</td>\n      <td>Central Toronto</td>\n      <td>Roselawn</td>\n      <td>43.711695</td>\n      <td>-79.416936</td>\n    </tr>\n    <tr>\n      <th>67</th>\n      <td>M4P</td>\n      <td>Central Toronto</td>\n      <td>Davisville North</td>\n      <td>43.712751</td>\n      <td>-79.390197</td>\n    </tr>\n    <tr>\n      <th>68</th>\n      <td>M5P</td>\n      <td>Central Toronto</td>\n      <td>Forest Hill North &amp; West, Forest Hill Road Park</td>\n      <td>43.696948</td>\n      <td>-79.411307</td>\n    </tr>\n    <tr>\n      <th>69</th>\n      <td>M6P</td>\n      <td>West Toronto</td>\n      <td>High Park, The Junction South</td>\n      <td>43.661608</td>\n      <td>-79.464763</td>\n    </tr>\n    <tr>\n      <th>73</th>\n      <td>M4R</td>\n      <td>Central Toronto</td>\n      <td>North Toronto West,  Lawrence Park</td>\n      <td>43.715383</td>\n      <td>-79.405678</td>\n    </tr>\n    <tr>\n      <th>74</th>\n      <td>M5R</td>\n      <td>Central Toronto</td>\n      <td>The Annex, North Midtown, Yorkville</td>\n      <td>43.672710</td>\n      <td>-79.405678</td>\n    </tr>\n    <tr>\n      <th>75</th>\n      <td>M6R</td>\n      <td>West Toronto</td>\n      <td>Parkdale, Roncesvalles</td>\n      <td>43.648960</td>\n      <td>-79.456325</td>\n    </tr>\n    <tr>\n      <th>79</th>\n      <td>M4S</td>\n      <td>Central Toronto</td>\n      <td>Davisville</td>\n      <td>43.704324</td>\n      <td>-79.388790</td>\n    </tr>\n    <tr>\n      <th>80</th>\n      <td>M5S</td>\n      <td>Downtown Toronto</td>\n      <td>University of Toronto, Harbord</td>\n      <td>43.662696</td>\n      <td>-79.400049</td>\n    </tr>\n    <tr>\n      <th>81</th>\n      <td>M6S</td>\n      <td>West Toronto</td>\n      <td>Runnymede, Swansea</td>\n      <td>43.651571</td>\n      <td>-79.484450</td>\n    </tr>\n    <tr>\n      <th>83</th>\n      <td>M4T</td>\n      <td>Central Toronto</td>\n      <td>Moore Park, Summerhill East</td>\n      <td>43.689574</td>\n      <td>-79.383160</td>\n    </tr>\n    <tr>\n      <th>84</th>\n      <td>M5T</td>\n      <td>Downtown Toronto</td>\n      <td>Kensington Market, Chinatown, Grange Park</td>\n      <td>43.653206</td>\n      <td>-79.400049</td>\n    </tr>\n    <tr>\n      <th>86</th>\n      <td>M4V</td>\n      <td>Central Toronto</td>\n      <td>Summerhill West, Rathnelly, South Hill, Forest...</td>\n      <td>43.686412</td>\n      <td>-79.400049</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>M5V</td>\n      <td>Downtown Toronto</td>\n      <td>CN Tower, King and Spadina, Railway Lands, Har...</td>\n      <td>43.628947</td>\n      <td>-79.394420</td>\n    </tr>\n    <tr>\n      <th>91</th>\n      <td>M4W</td>\n      <td>Downtown Toronto</td>\n      <td>Rosedale</td>\n      <td>43.679563</td>\n      <td>-79.377529</td>\n    </tr>\n    <tr>\n      <th>92</th>\n      <td>M5W</td>\n      <td>Downtown Toronto</td>\n      <td>Stn A PO Boxes</td>\n      <td>43.646435</td>\n      <td>-79.374846</td>\n    </tr>\n    <tr>\n      <th>96</th>\n      <td>M4X</td>\n      <td>Downtown Toronto</td>\n      <td>St. James Town, Cabbagetown</td>\n      <td>43.667967</td>\n      <td>-79.367675</td>\n    </tr>\n    <tr>\n      <th>97</th>\n      <td>M5X</td>\n      <td>Downtown Toronto</td>\n      <td>First Canadian Place, Underground city</td>\n      <td>43.648429</td>\n      <td>-79.382280</td>\n    </tr>\n    <tr>\n      <th>99</th>\n      <td>M4Y</td>\n      <td>Downtown Toronto</td>\n      <td>Church and Wellesley</td>\n      <td>43.665860</td>\n      <td>-79.383160</td>\n    </tr>\n    <tr>\n      <th>100</th>\n      <td>M7Y</td>\n      <td>East Toronto</td>\n      <td>Business reply mail Processing Centre, South C...</td>\n      <td>43.662744</td>\n      <td>-79.321558</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "    Postal Code           Borough  \\\n2           M5A  Downtown Toronto   \n4           M7A  Downtown Toronto   \n9           M5B  Downtown Toronto   \n15          M5C  Downtown Toronto   \n19          M4E      East Toronto   \n20          M5E  Downtown Toronto   \n24          M5G  Downtown Toronto   \n25          M6G  Downtown Toronto   \n30          M5H  Downtown Toronto   \n31          M6H      West Toronto   \n36          M5J  Downtown Toronto   \n37          M6J      West Toronto   \n41          M4K      East Toronto   \n42          M5K  Downtown Toronto   \n43          M6K      West Toronto   \n47          M4L      East Toronto   \n48          M5L  Downtown Toronto   \n54          M4M      East Toronto   \n61          M4N   Central Toronto   \n62          M5N   Central Toronto   \n67          M4P   Central Toronto   \n68          M5P   Central Toronto   \n69          M6P      West Toronto   \n73          M4R   Central Toronto   \n74          M5R   Central Toronto   \n75          M6R      West Toronto   \n79          M4S   Central Toronto   \n80          M5S  Downtown Toronto   \n81          M6S      West Toronto   \n83          M4T   Central Toronto   \n84          M5T  Downtown Toronto   \n86          M4V   Central Toronto   \n87          M5V  Downtown Toronto   \n91          M4W  Downtown Toronto   \n92          M5W  Downtown Toronto   \n96          M4X  Downtown Toronto   \n97          M5X  Downtown Toronto   \n99          M4Y  Downtown Toronto   \n100         M7Y      East Toronto   \n\n                                          Neighborhood   Latitude  Longitude  \n2                            Regent Park, Harbourfront  43.654260 -79.360636  \n4          Queen's Park, Ontario Provincial Government  43.662301 -79.389494  \n9                             Garden District, Ryerson  43.657162 -79.378937  \n15                                      St. James Town  43.651494 -79.375418  \n19                                         The Beaches  43.676357 -79.293031  \n20                                         Berczy Park  43.644771 -79.373306  \n24                                  Central Bay Street  43.657952 -79.387383  \n25                                            Christie  43.669542 -79.422564  \n30                            Richmond, Adelaide, King  43.650571 -79.384568  \n31                        Dufferin, Dovercourt Village  43.669005 -79.442259  \n36   Harbourfront East, Union Station, Toronto Islands  43.640816 -79.381752  \n37                            Little Portugal, Trinity  43.647927 -79.419750  \n41                        The Danforth West, Riverdale  43.679557 -79.352188  \n42            Toronto Dominion Centre, Design Exchange  43.647177 -79.381576  \n43        Brockton, Parkdale Village, Exhibition Place  43.636847 -79.428191  \n47                      India Bazaar, The Beaches West  43.668999 -79.315572  \n48                      Commerce Court, Victoria Hotel  43.648198 -79.379817  \n54                                     Studio District  43.659526 -79.340923  \n61                                       Lawrence Park  43.728020 -79.388790  \n62                                            Roselawn  43.711695 -79.416936  \n67                                    Davisville North  43.712751 -79.390197  \n68     Forest Hill North & West, Forest Hill Road Park  43.696948 -79.411307  \n69                       High Park, The Junction South  43.661608 -79.464763  \n73                  North Toronto West,  Lawrence Park  43.715383 -79.405678  \n74                 The Annex, North Midtown, Yorkville  43.672710 -79.405678  \n75                              Parkdale, Roncesvalles  43.648960 -79.456325  \n79                                          Davisville  43.704324 -79.388790  \n80                      University of Toronto, Harbord  43.662696 -79.400049  \n81                                  Runnymede, Swansea  43.651571 -79.484450  \n83                         Moore Park, Summerhill East  43.689574 -79.383160  \n84           Kensington Market, Chinatown, Grange Park  43.653206 -79.400049  \n86   Summerhill West, Rathnelly, South Hill, Forest...  43.686412 -79.400049  \n87   CN Tower, King and Spadina, Railway Lands, Har...  43.628947 -79.394420  \n91                                            Rosedale  43.679563 -79.377529  \n92                                      Stn A PO Boxes  43.646435 -79.374846  \n96                         St. James Town, Cabbagetown  43.667967 -79.367675  \n97              First Canadian Place, Underground city  43.648429 -79.382280  \n99                                Church and Wellesley  43.665860 -79.383160  \n100  Business reply mail Processing Centre, South C...  43.662744 -79.321558  "
                    },
                    "execution_count": 27,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "TorontoNeighborhoodCoordinates_df = TorontoNeighborhoodCoordinates_df[TorontoNeighborhoodCoordinates_df[\"Borough\"].str.contains(\"Toronto\")]\nTorontoNeighborhoodCoordinates_df"
        },
        {
            "cell_type": "code",
            "execution_count": 28,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "(39, 5)"
                    },
                    "execution_count": 28,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "TorontoNeighborhoodCoordinates_df.shape"
        },
        {
            "cell_type": "code",
            "execution_count": 29,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "The geograpical coordinate of Toronto City are 43.6534817, -79.3839347.\n"
                }
            ],
            "source": "address = 'Toronto, Ontario, Canada'\n\ngeolocator = Nominatim(user_agent=\"ny_explorer\")\nlocation = geolocator.geocode(address)\nlatitude = location.latitude\nlongitude = location.longitude\nprint('The geograpical coordinate of Toronto City are {}, {}.'.format(latitude, longitude))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Plotting map with pointers of all the Boroughs in Toronto"
        },
        {
            "cell_type": "code",
            "execution_count": 30,
            "metadata": {
                "scrolled": false
            },
            "outputs": [
                {
                    "data": {
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        var popup_2fd49a78310c4b0587c80e6d2ce3437e = L.popup({"maxWidth": "100%"});

        
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            var circle_marker_ceb3e846f16248e0b36c441d5191ecf9 = 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_2e2fc06615d649739317384f0f226b1f = L.popup({"maxWidth": "100%"});

        
            var html_481fb105e36741699e92f58c2785bf1c = $(`<div id="html_481fb105e36741699e92f58c2785bf1c" style="width: 100.0%; height: 100.0%;">St. James Town, Cabbagetown, Downtown Toronto</div>`)[0];
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            var circle_marker_d25592a3e0f046f08ab1f22fe691d8e8 = L.circleMarker(
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            ).addTo(map_f90923531ada443eb73856640991f61d);
        
    
        var popup_1ed10dd2310641b9aaef93c8107260a0 = L.popup({"maxWidth": "100%"});

        
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        ;

        
    
    
            var circle_marker_b83623df955242c6b0aa529ed6c7a438 = L.circleMarker(
                [43.6627439, -79.321558],
                {"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_f90923531ada443eb73856640991f61d);
        
    
        var popup_cdc9d56e3b7a4c58bd2bf7e801df2bb7 = L.popup({"maxWidth": "100%"});

        
            var html_3e4222fe164f43409fe6592498371950 = $(`<div id="html_3e4222fe164f43409fe6592498371950" style="width: 100.0%; height: 100.0%;">Business reply mail Processing Centre, South Central Letter Processing Plant Toronto, East Toronto</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 0x7f754361f2e8>"
                    },
                    "execution_count": 30,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "map_toronto = folium.Map(location=[latitude, longitude], zoom_start=10)\n\n# add markers to map\nfor lat, lng, borough, neighborhood in zip(TorontoNeighborhoodCoordinates_df['Latitude'], \n                                           TorontoNeighborhoodCoordinates_df['Longitude'], \n                                           TorontoNeighborhoodCoordinates_df['Borough'], \n                                           TorontoNeighborhoodCoordinates_df['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    \nmap_toronto"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Creating function to get venues from FourSquare API"
        },
        {
            "cell_type": "code",
            "execution_count": 31,
            "metadata": {},
            "outputs": [],
            "source": "def getNearbyVenues(names, latitudes, longitudes, radius=500):\n    \n    venues_list=[]\n    CLIENT_ID=\"ASX3DZRL0RJC2H3VLR1YDHFGSBVU2ITFCZ3UUYKRZSIWV0FR\"\n    CLIENT_SECRET=\"SAFPB5VK2BFQHTN3VWKHFHTE0YGCENI5SJWIOCUQWIYKM3DT\"\n    VERSION='20180323'\n    LIMIT=100\n    url = 'https://api.foursquare.com/v2/venues/explore'\n\n    for name, lat, lng in zip(names, latitudes, longitudes):\n        print(name)\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            lat, \n            lng, \n            radius, \n            LIMIT)\n            \n        #print (url)\n        # make the GET request\n        results = requests.get(url).json()[\"response\"]['groups'][0]['items']\n        #print(results)\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": 32,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Regent Park, Harbourfront\nQueen's Park, Ontario Provincial Government\nGarden District, Ryerson\nSt. James Town\nThe Beaches\nBerczy Park\nCentral Bay Street\nChristie\nRichmond, Adelaide, King\nDufferin, Dovercourt Village\nHarbourfront East, Union Station, Toronto Islands\nLittle Portugal, Trinity\nThe Danforth West, Riverdale\nToronto Dominion Centre, Design Exchange\nBrockton, Parkdale Village, Exhibition Place\nIndia Bazaar, The Beaches West\nCommerce Court, Victoria Hotel\nStudio District\nLawrence Park\nRoselawn\nDavisville North\nForest Hill North & West, Forest Hill Road Park\nHigh Park, The Junction South\nNorth Toronto West,  Lawrence Park\nThe Annex, North Midtown, Yorkville\nParkdale, Roncesvalles\nDavisville\nUniversity of Toronto, Harbord\nRunnymede, Swansea\nMoore Park, Summerhill East\nKensington Market, Chinatown, Grange Park\nSummerhill West, Rathnelly, South Hill, Forest Hill SE, Deer Park\nCN Tower, King and Spadina, Railway Lands, Harbourfront West, Bathurst Quay, South Niagara, Island airport\nRosedale\nStn A PO Boxes\nSt. James Town, Cabbagetown\nFirst Canadian Place, Underground city\nChurch and Wellesley\nBusiness reply mail Processing Centre, South Central Letter Processing Plant Toronto\n"
                }
            ],
            "source": "TorontoNeighborhoodCoordinates_df.reset_index(drop=True)\ntoronto_venues = getNearbyVenues(names = TorontoNeighborhoodCoordinates_df['Neighborhood'], \n                                     latitudes = TorontoNeighborhoodCoordinates_df['Latitude'], \n                                     longitudes = TorontoNeighborhoodCoordinates_df['Longitude'] )\n"
        },
        {
            "cell_type": "code",
            "execution_count": 33,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "(1616, 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>Regent Park, Harbourfront</td>\n      <td>43.65426</td>\n      <td>-79.360636</td>\n      <td>Roselle Desserts</td>\n      <td>43.653447</td>\n      <td>-79.362017</td>\n      <td>Bakery</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Regent Park, Harbourfront</td>\n      <td>43.65426</td>\n      <td>-79.360636</td>\n      <td>Tandem Coffee</td>\n      <td>43.653559</td>\n      <td>-79.361809</td>\n      <td>Coffee Shop</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Regent Park, Harbourfront</td>\n      <td>43.65426</td>\n      <td>-79.360636</td>\n      <td>Morning Glory Cafe</td>\n      <td>43.653947</td>\n      <td>-79.361149</td>\n      <td>Breakfast Spot</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Regent Park, Harbourfront</td>\n      <td>43.65426</td>\n      <td>-79.360636</td>\n      <td>Cooper Koo Family YMCA</td>\n      <td>43.653249</td>\n      <td>-79.358008</td>\n      <td>Distribution Center</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Regent Park, Harbourfront</td>\n      <td>43.65426</td>\n      <td>-79.360636</td>\n      <td>Body Blitz Spa East</td>\n      <td>43.654735</td>\n      <td>-79.359874</td>\n      <td>Spa</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "                Neighborhood  Neighborhood Latitude  Neighborhood Longitude  \\\n0  Regent Park, Harbourfront               43.65426              -79.360636   \n1  Regent Park, Harbourfront               43.65426              -79.360636   \n2  Regent Park, Harbourfront               43.65426              -79.360636   \n3  Regent Park, Harbourfront               43.65426              -79.360636   \n4  Regent Park, Harbourfront               43.65426              -79.360636   \n\n                    Venue  Venue Latitude  Venue Longitude  \\\n0        Roselle Desserts       43.653447       -79.362017   \n1           Tandem Coffee       43.653559       -79.361809   \n2      Morning Glory Cafe       43.653947       -79.361149   \n3  Cooper Koo Family YMCA       43.653249       -79.358008   \n4     Body Blitz Spa East       43.654735       -79.359874   \n\n        Venue Category  \n0               Bakery  \n1          Coffee Shop  \n2       Breakfast Spot  \n3  Distribution Center  \n4                  Spa  "
                    },
                    "execution_count": 33,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "print(toronto_venues.shape)\ntoronto_venues.head()"
        },
        {
            "cell_type": "code",
            "execution_count": 34,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "There are 237 uniques categories.\n"
                }
            ],
            "source": "print('There are {} uniques categories.'.format(len(toronto_venues['Venue Category'].unique())))"
        },
        {
            "cell_type": "code",
            "execution_count": 35,
            "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>Yoga Studio</th>\n      <th>Afghan Restaurant</th>\n      <th>Airport</th>\n      <th>Airport Food Court</th>\n      <th>Airport Gate</th>\n      <th>Airport Lounge</th>\n      <th>Airport Service</th>\n      <th>Airport Terminal</th>\n      <th>American Restaurant</th>\n      <th>Antique Shop</th>\n      <th>...</th>\n      <th>Theme Restaurant</th>\n      <th>Toy / Game Store</th>\n      <th>Trail</th>\n      <th>Train Station</th>\n      <th>Vegetarian / Vegan Restaurant</th>\n      <th>Video Game Store</th>\n      <th>Vietnamese Restaurant</th>\n      <th>Wine Bar</th>\n      <th>Wings Joint</th>\n      <th>Women's Store</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</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>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</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>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</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>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</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>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</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 \u00d7 237 columns</p>\n</div>",
                        "text/plain": "   Yoga Studio  Afghan Restaurant  Airport  Airport Food Court  Airport Gate  \\\n0            0                  0        0                   0             0   \n1            0                  0        0                   0             0   \n2            0                  0        0                   0             0   \n3            0                  0        0                   0             0   \n4            0                  0        0                   0             0   \n\n   Airport Lounge  Airport Service  Airport Terminal  American Restaurant  \\\n0               0                0                 0                    0   \n1               0                0                 0                    0   \n2               0                0                 0                    0   \n3               0                0                 0                    0   \n4               0                0                 0                    0   \n\n   Antique Shop  ...  Theme Restaurant  Toy / Game Store  Trail  \\\n0             0  ...                 0                 0      0   \n1             0  ...                 0                 0      0   \n2             0  ...                 0                 0      0   \n3             0  ...                 0                 0      0   \n4             0  ...                 0                 0      0   \n\n   Train Station  Vegetarian / Vegan Restaurant  Video Game Store  \\\n0              0                              0                 0   \n1              0                              0                 0   \n2              0                              0                 0   \n3              0                              0                 0   \n4              0                              0                 0   \n\n   Vietnamese Restaurant  Wine Bar  Wings Joint  Women's Store  \n0                      0         0            0              0  \n1                      0         0            0              0  \n2                      0         0            0              0  \n3                      0         0            0              0  \n4                      0         0            0              0  \n\n[5 rows x 237 columns]"
                    },
                    "execution_count": 35,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# one hot encoding\ntoronto_onehot = pd.get_dummies(toronto_venues[['Venue Category']], prefix=\"\", prefix_sep=\"\")\n\n# add neighborhood column back to dataframe\ntoronto_onehot['Neighborhood'] = toronto_venues['Neighborhood'] \n\n# move neighborhood column to the first column\nfixed_columns = [toronto_onehot.columns[-1]] + list(toronto_onehot.columns[:-1])\ntoronto_onehot = toronto_onehot[fixed_columns]\n\ntoronto_onehot.head()"
        },
        {
            "cell_type": "code",
            "execution_count": 36,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Neighborhood</th>\n      <th>Yoga Studio</th>\n      <th>Afghan Restaurant</th>\n      <th>Airport</th>\n      <th>Airport Food Court</th>\n      <th>Airport Gate</th>\n      <th>Airport Lounge</th>\n      <th>Airport Service</th>\n      <th>Airport Terminal</th>\n      <th>American Restaurant</th>\n      <th>...</th>\n      <th>Theme Restaurant</th>\n      <th>Toy / Game Store</th>\n      <th>Trail</th>\n      <th>Train Station</th>\n      <th>Vegetarian / Vegan Restaurant</th>\n      <th>Video Game Store</th>\n      <th>Vietnamese Restaurant</th>\n      <th>Wine Bar</th>\n      <th>Wings Joint</th>\n      <th>Women's Store</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Berczy Park</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.017857</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Brockton, Parkdale Village, Exhibition Place</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Business reply mail Processing Centre, South C...</td>\n      <td>0.055556</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CN Tower, King and Spadina, Railway Lands, Har...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0625</td>\n      <td>0.0625</td>\n      <td>0.0625</td>\n      <td>0.125</td>\n      <td>0.125</td>\n      <td>0.125</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Central Bay Street</td>\n      <td>0.015625</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.015625</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.015625</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Christie</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Church and Wellesley</td>\n      <td>0.026316</td>\n      <td>0.013158</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.013158</td>\n      <td>...</td>\n      <td>0.013158</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>0.013158</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Commerce Court, Victoria Hotel</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.040000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.020000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.010000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Davisville</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.027778</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>0.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Davisville North</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Dufferin, Dovercourt Village</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>First Canadian Place, Underground city</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.030000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.01</td>\n      <td>0.010000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.010000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Forest Hill North &amp; West, Forest Hill Road Park</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.200000</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>0.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Garden District, Ryerson</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.010000</td>\n      <td>0.010000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Harbourfront East, Union Station, Toronto Islands</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.01</td>\n      <td>0.010000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.010000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>High Park, The Junction South</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>India Bazaar, The Beaches West</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Kensington Market, Chinatown, Grange Park</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.035088</td>\n      <td>0.000000</td>\n      <td>0.052632</td>\n      <td>0.017544</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Lawrence Park</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Little Portugal, Trinity</td>\n      <td>0.022727</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.045455</td>\n      <td>0.000000</td>\n      <td>0.022727</td>\n      <td>0.022727</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Moore Park, Summerhill East</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.333333</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>0.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>North Toronto West,  Lawrence Park</td>\n      <td>0.050000</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Parkdale, Roncesvalles</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Queen's Park, Ontario Provincial Government</td>\n      <td>0.031250</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.031250</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.031250</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>Regent Park, Harbourfront</td>\n      <td>0.022727</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>Richmond, Adelaide, King</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.021739</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.010870</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.01087</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Rosedale</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.250000</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>0.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>Roselawn</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>Runnymede, Swansea</td>\n      <td>0.024390</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.024390</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>St. James Town</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.037975</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.012658</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.012658</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>St. James Town, Cabbagetown</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.021277</td>\n      <td>...</td>\n      <td>0.000000</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>0.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>Stn A PO Boxes</td>\n      <td>0.010870</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.010870</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.010870</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>Studio District</td>\n      <td>0.024390</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.048780</td>\n      <td>...</td>\n      <td>0.000000</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>0.024390</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>Summerhill West, Rathnelly, South Hill, Forest...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.062500</td>\n      <td>...</td>\n      <td>0.000000</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>0.062500</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>The Annex, North Midtown, Yorkville</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.043478</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.043478</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>The Beaches</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.250000</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>0.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>The Danforth West, Riverdale</td>\n      <td>0.023810</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.023810</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.023810</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>0.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>Toronto Dominion Centre, Design Exchange</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.030000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.01</td>\n      <td>0.010000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.010000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>University of Toronto, Harbord</td>\n      <td>0.028571</td>\n      <td>0.000000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.028571</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n    </tr>\n  </tbody>\n</table>\n<p>39 rows \u00d7 237 columns</p>\n</div>",
                        "text/plain": "                                         Neighborhood  Yoga Studio  \\\n0                                         Berczy Park     0.000000   \n1        Brockton, Parkdale Village, Exhibition Place     0.000000   \n2   Business reply mail Processing Centre, South C...     0.055556   \n3   CN Tower, King and Spadina, Railway Lands, Har...     0.000000   \n4                                  Central Bay Street     0.015625   \n5                                            Christie     0.000000   \n6                                Church and Wellesley     0.026316   \n7                      Commerce Court, Victoria Hotel     0.000000   \n8                                          Davisville     0.000000   \n9                                    Davisville North     0.000000   \n10                       Dufferin, Dovercourt Village     0.000000   \n11             First Canadian Place, Underground city     0.000000   \n12    Forest Hill North & West, Forest Hill Road Park     0.000000   \n13                           Garden District, Ryerson     0.000000   \n14  Harbourfront East, Union Station, Toronto Islands     0.000000   \n15                      High Park, The Junction South     0.000000   \n16                     India Bazaar, The Beaches West     0.000000   \n17          Kensington Market, Chinatown, Grange Park     0.000000   \n18                                      Lawrence Park     0.000000   \n19                           Little Portugal, Trinity     0.022727   \n20                        Moore Park, Summerhill East     0.000000   \n21                 North Toronto West,  Lawrence Park     0.050000   \n22                             Parkdale, Roncesvalles     0.000000   \n23        Queen's Park, Ontario Provincial Government     0.031250   \n24                          Regent Park, Harbourfront     0.022727   \n25                           Richmond, Adelaide, King     0.000000   \n26                                           Rosedale     0.000000   \n27                                           Roselawn     0.000000   \n28                                 Runnymede, Swansea     0.024390   \n29                                     St. James Town     0.000000   \n30                        St. James Town, Cabbagetown     0.000000   \n31                                     Stn A PO Boxes     0.010870   \n32                                    Studio District     0.024390   \n33  Summerhill West, Rathnelly, South Hill, Forest...     0.000000   \n34                The Annex, North Midtown, Yorkville     0.000000   \n35                                        The Beaches     0.000000   \n36                       The Danforth West, Riverdale     0.023810   \n37           Toronto Dominion Centre, Design Exchange     0.000000   \n38                     University of Toronto, Harbord     0.028571   \n\n    Afghan Restaurant  Airport  Airport Food Court  Airport Gate  \\\n0            0.000000   0.0000              0.0000        0.0000   \n1            0.000000   0.0000              0.0000        0.0000   \n2            0.000000   0.0000              0.0000        0.0000   \n3            0.000000   0.0625              0.0625        0.0625   \n4            0.000000   0.0000              0.0000        0.0000   \n5            0.000000   0.0000              0.0000        0.0000   \n6            0.013158   0.0000              0.0000        0.0000   \n7            0.000000   0.0000              0.0000        0.0000   \n8            0.000000   0.0000              0.0000        0.0000   \n9            0.000000   0.0000              0.0000        0.0000   \n10           0.000000   0.0000              0.0000        0.0000   \n11           0.000000   0.0000              0.0000        0.0000   \n12           0.000000   0.0000              0.0000        0.0000   \n13           0.000000   0.0000              0.0000        0.0000   \n14           0.000000   0.0000              0.0000        0.0000   \n15           0.000000   0.0000              0.0000        0.0000   \n16           0.000000   0.0000              0.0000        0.0000   \n17           0.000000   0.0000              0.0000        0.0000   \n18           0.000000   0.0000              0.0000        0.0000   \n19           0.000000   0.0000              0.0000        0.0000   \n20           0.000000   0.0000              0.0000        0.0000   \n21           0.000000   0.0000              0.0000        0.0000   \n22           0.000000   0.0000              0.0000        0.0000   \n23           0.000000   0.0000              0.0000        0.0000   \n24           0.000000   0.0000              0.0000        0.0000   \n25           0.000000   0.0000              0.0000        0.0000   \n26           0.000000   0.0000              0.0000        0.0000   \n27           0.000000   0.0000              0.0000        0.0000   \n28           0.000000   0.0000              0.0000        0.0000   \n29           0.000000   0.0000              0.0000        0.0000   \n30           0.000000   0.0000              0.0000        0.0000   \n31           0.000000   0.0000              0.0000        0.0000   \n32           0.000000   0.0000              0.0000        0.0000   \n33           0.000000   0.0000              0.0000        0.0000   \n34           0.000000   0.0000              0.0000        0.0000   \n35           0.000000   0.0000              0.0000        0.0000   \n36           0.000000   0.0000              0.0000        0.0000   \n37           0.000000   0.0000              0.0000        0.0000   \n38           0.000000   0.0000              0.0000        0.0000   \n\n    Airport Lounge  Airport Service  Airport Terminal  American Restaurant  \\\n0            0.000            0.000             0.000             0.000000   \n1            0.000            0.000             0.000             0.000000   \n2            0.000            0.000             0.000             0.000000   \n3            0.125            0.125             0.125             0.000000   \n4            0.000            0.000             0.000             0.000000   \n5            0.000            0.000             0.000             0.000000   \n6            0.000            0.000             0.000             0.013158   \n7            0.000            0.000             0.000             0.040000   \n8            0.000            0.000             0.000             0.000000   \n9            0.000            0.000             0.000             0.000000   \n10           0.000            0.000             0.000             0.000000   \n11           0.000            0.000             0.000             0.030000   \n12           0.000            0.000             0.000             0.000000   \n13           0.000            0.000             0.000             0.000000   \n14           0.000            0.000             0.000             0.000000   \n15           0.000            0.000             0.000             0.000000   \n16           0.000            0.000             0.000             0.000000   \n17           0.000            0.000             0.000             0.000000   \n18           0.000            0.000             0.000             0.000000   \n19           0.000            0.000             0.000             0.000000   \n20           0.000            0.000             0.000             0.000000   \n21           0.000            0.000             0.000             0.000000   \n22           0.000            0.000             0.000             0.000000   \n23           0.000            0.000             0.000             0.000000   \n24           0.000            0.000             0.000             0.000000   \n25           0.000            0.000             0.000             0.021739   \n26           0.000            0.000             0.000             0.000000   \n27           0.000            0.000             0.000             0.000000   \n28           0.000            0.000             0.000             0.000000   \n29           0.000            0.000             0.000             0.037975   \n30           0.000            0.000             0.000             0.021277   \n31           0.000            0.000             0.000             0.010870   \n32           0.000            0.000             0.000             0.048780   \n33           0.000            0.000             0.000             0.062500   \n34           0.000            0.000             0.000             0.043478   \n35           0.000            0.000             0.000             0.000000   \n36           0.000            0.000             0.000             0.023810   \n37           0.000            0.000             0.000             0.030000   \n38           0.000            0.000             0.000             0.000000   \n\n    ...  Theme Restaurant  Toy / Game Store     Trail  Train Station  \\\n0   ...          0.000000          0.000000  0.000000           0.00   \n1   ...          0.000000          0.000000  0.000000           0.00   \n2   ...          0.000000          0.000000  0.000000           0.00   \n3   ...          0.000000          0.000000  0.000000           0.00   \n4   ...          0.000000          0.000000  0.000000           0.00   \n5   ...          0.000000          0.000000  0.000000           0.00   \n6   ...          0.013158          0.000000  0.000000           0.00   \n7   ...          0.000000          0.000000  0.000000           0.00   \n8   ...          0.000000          0.027778  0.000000           0.00   \n9   ...          0.000000          0.000000  0.000000           0.00   \n10  ...          0.000000          0.000000  0.000000           0.00   \n11  ...          0.000000          0.000000  0.000000           0.01   \n12  ...          0.000000          0.000000  0.200000           0.00   \n13  ...          0.000000          0.000000  0.000000           0.00   \n14  ...          0.000000          0.000000  0.000000           0.01   \n15  ...          0.000000          0.000000  0.000000           0.00   \n16  ...          0.000000          0.000000  0.000000           0.00   \n17  ...          0.000000          0.000000  0.000000           0.00   \n18  ...          0.000000          0.000000  0.000000           0.00   \n19  ...          0.000000          0.000000  0.000000           0.00   \n20  ...          0.000000          0.000000  0.333333           0.00   \n21  ...          0.000000          0.000000  0.000000           0.00   \n22  ...          0.000000          0.000000  0.000000           0.00   \n23  ...          0.000000          0.000000  0.000000           0.00   \n24  ...          0.000000          0.000000  0.000000           0.00   \n25  ...          0.000000          0.000000  0.000000           0.00   \n26  ...          0.000000          0.000000  0.250000           0.00   \n27  ...          0.000000          0.000000  0.000000           0.00   \n28  ...          0.000000          0.000000  0.000000           0.00   \n29  ...          0.000000          0.000000  0.000000           0.00   \n30  ...          0.000000          0.000000  0.000000           0.00   \n31  ...          0.000000          0.000000  0.000000           0.00   \n32  ...          0.000000          0.000000  0.000000           0.00   \n33  ...          0.000000          0.000000  0.000000           0.00   \n34  ...          0.000000          0.000000  0.000000           0.00   \n35  ...          0.000000          0.000000  0.250000           0.00   \n36  ...          0.000000          0.000000  0.023810           0.00   \n37  ...          0.000000          0.000000  0.000000           0.01   \n38  ...          0.000000          0.000000  0.000000           0.00   \n\n    Vegetarian / Vegan Restaurant  Video Game Store  Vietnamese Restaurant  \\\n0                        0.017857          0.000000               0.000000   \n1                        0.000000          0.000000               0.000000   \n2                        0.000000          0.000000               0.000000   \n3                        0.000000          0.000000               0.000000   \n4                        0.015625          0.000000               0.000000   \n5                        0.000000          0.000000               0.000000   \n6                        0.000000          0.000000               0.000000   \n7                        0.020000          0.000000               0.000000   \n8                        0.000000          0.000000               0.000000   \n9                        0.000000          0.000000               0.000000   \n10                       0.000000          0.000000               0.000000   \n11                       0.010000          0.000000               0.000000   \n12                       0.000000          0.000000               0.000000   \n13                       0.000000          0.010000               0.010000   \n14                       0.010000          0.000000               0.000000   \n15                       0.000000          0.000000               0.000000   \n16                       0.000000          0.000000               0.000000   \n17                       0.035088          0.000000               0.052632   \n18                       0.000000          0.000000               0.000000   \n19                       0.045455          0.000000               0.022727   \n20                       0.000000          0.000000               0.000000   \n21                       0.000000          0.000000               0.000000   \n22                       0.000000          0.000000               0.000000   \n23                       0.031250          0.000000               0.000000   \n24                       0.000000          0.000000               0.000000   \n25                       0.010870          0.000000               0.000000   \n26                       0.000000          0.000000               0.000000   \n27                       0.000000          0.000000               0.000000   \n28                       0.024390          0.000000               0.000000   \n29                       0.012658          0.000000               0.000000   \n30                       0.000000          0.000000               0.000000   \n31                       0.010870          0.000000               0.000000   \n32                       0.000000          0.000000               0.000000   \n33                       0.000000          0.000000               0.062500   \n34                       0.043478          0.000000               0.000000   \n35                       0.000000          0.000000               0.000000   \n36                       0.000000          0.000000               0.000000   \n37                       0.010000          0.000000               0.000000   \n38                       0.000000          0.028571               0.000000   \n\n    Wine Bar  Wings Joint  Women's Store  \n0   0.000000     0.000000        0.00000  \n1   0.000000     0.000000        0.00000  \n2   0.000000     0.000000        0.00000  \n3   0.000000     0.000000        0.00000  \n4   0.015625     0.000000        0.00000  \n5   0.000000     0.000000        0.00000  \n6   0.000000     0.013158        0.00000  \n7   0.010000     0.000000        0.00000  \n8   0.000000     0.000000        0.00000  \n9   0.000000     0.000000        0.00000  \n10  0.000000     0.000000        0.00000  \n11  0.010000     0.000000        0.00000  \n12  0.000000     0.000000        0.00000  \n13  0.000000     0.000000        0.00000  \n14  0.010000     0.000000        0.00000  \n15  0.000000     0.000000        0.00000  \n16  0.000000     0.000000        0.00000  \n17  0.017544     0.000000        0.00000  \n18  0.000000     0.000000        0.00000  \n19  0.022727     0.000000        0.00000  \n20  0.000000     0.000000        0.00000  \n21  0.000000     0.000000        0.00000  \n22  0.000000     0.000000        0.00000  \n23  0.000000     0.031250        0.00000  \n24  0.000000     0.000000        0.00000  \n25  0.000000     0.000000        0.01087  \n26  0.000000     0.000000        0.00000  \n27  0.000000     0.000000        0.00000  \n28  0.000000     0.000000        0.00000  \n29  0.012658     0.000000        0.00000  \n30  0.000000     0.000000        0.00000  \n31  0.000000     0.000000        0.00000  \n32  0.024390     0.000000        0.00000  \n33  0.000000     0.000000        0.00000  \n34  0.000000     0.000000        0.00000  \n35  0.000000     0.000000        0.00000  \n36  0.000000     0.000000        0.00000  \n37  0.010000     0.000000        0.00000  \n38  0.000000     0.000000        0.00000  \n\n[39 rows x 237 columns]"
                    },
                    "execution_count": 36,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "toronto_grouped = toronto_onehot.groupby('Neighborhood').mean().reset_index()\ntoronto_grouped"
        },
        {
            "cell_type": "code",
            "execution_count": 37,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "          venue  freq\n0   Coffee Shop  0.07\n1  Cocktail Bar  0.05\n2          Caf\u00e9  0.04\n3        Bakery  0.04\n4    Restaurant  0.04\n\n\n               venue  freq\n0               Caf\u00e9  0.14\n1     Breakfast Spot  0.09\n2        Coffee Shop  0.09\n3  Convenience Store  0.05\n4          Nightclub  0.05\n\n\n                venue  freq\n0  Light Rail Station  0.11\n1         Yoga Studio  0.06\n2       Auto Workshop  0.06\n3       Garden Center  0.06\n4              Garden  0.06\n\n\n              venue  freq\n0    Airport Lounge  0.12\n1   Airport Service  0.12\n2  Airport Terminal  0.12\n3   Harbor / Marina  0.06\n4               Bar  0.06\n\n\n                 venue  freq\n0          Coffee Shop  0.17\n1   Italian Restaurant  0.06\n2       Sandwich Place  0.05\n3                 Caf\u00e9  0.05\n4  Japanese Restaurant  0.05\n\n\n                venue  freq\n0       Grocery Store  0.24\n1                Caf\u00e9  0.18\n2                Park  0.12\n3  Italian Restaurant  0.06\n4           Nightclub  0.06\n\n\n                 venue  freq\n0          Coffee Shop  0.08\n1     Sushi Restaurant  0.05\n2  Japanese Restaurant  0.05\n3              Gay Bar  0.04\n4           Restaurant  0.04\n\n\n                 venue  freq\n0          Coffee Shop  0.11\n1           Restaurant  0.07\n2                 Caf\u00e9  0.07\n3                Hotel  0.05\n4  American Restaurant  0.04\n\n\n                venue  freq\n0        Dessert Shop  0.08\n1      Sandwich Place  0.08\n2  Italian Restaurant  0.06\n3    Sushi Restaurant  0.06\n4                Caf\u00e9  0.06\n\n\n                  venue  freq\n0  Gym / Fitness Center  0.11\n1                 Hotel  0.11\n2                  Park  0.11\n3          Dance Studio  0.11\n4      Department Store  0.11\n\n\n         venue  freq\n0     Pharmacy  0.13\n1       Bakery  0.13\n2      Brewery  0.07\n3  Coffee Shop  0.07\n4          Bar  0.07\n\n\n         venue  freq\n0  Coffee Shop  0.10\n1         Caf\u00e9  0.08\n2        Hotel  0.04\n3          Gym  0.04\n4   Restaurant  0.04\n\n\n              venue  freq\n0              Park   0.2\n1     Jewelry Store   0.2\n2             Trail   0.2\n3          Bus Line   0.2\n4  Sushi Restaurant   0.2\n\n\n                venue  freq\n0      Clothing Store  0.09\n1         Coffee Shop  0.08\n2     Bubble Tea Shop  0.03\n3                Caf\u00e9  0.03\n4  Italian Restaurant  0.03\n\n\n            venue  freq\n0     Coffee Shop  0.13\n1        Aquarium  0.05\n2           Hotel  0.04\n3            Caf\u00e9  0.04\n4  Scenic Lookout  0.03\n\n\n                    venue  freq\n0         Thai Restaurant  0.09\n1      Mexican Restaurant  0.09\n2                    Caf\u00e9  0.09\n3     Arts & Crafts Store  0.05\n4  Furniture / Home Store  0.05\n\n\n                  venue  freq\n0                  Park  0.09\n1        Sandwich Place  0.09\n2  Fast Food Restaurant  0.09\n3                   Gym  0.04\n4               Brewery  0.04\n\n\n                   venue  freq\n0                   Caf\u00e9  0.09\n1  Vietnamese Restaurant  0.05\n2                 Bakery  0.05\n3     Mexican Restaurant  0.05\n4            Coffee Shop  0.05\n\n\n                 venue  freq\n0                 Park  0.33\n1             Bus Line  0.33\n2          Swim School  0.33\n3          Yoga Studio  0.00\n4  Monument / Landmark  0.00\n\n\n                           venue  freq\n0                            Bar  0.09\n1                     Restaurant  0.07\n2               Asian Restaurant  0.07\n3                           Caf\u00e9  0.05\n4  Vegetarian / Vegan Restaurant  0.05\n\n\n                 venue  freq\n0           Playground  0.33\n1                Trail  0.33\n2         Intersection  0.33\n3          Yoga Studio  0.00\n4  Monument / Landmark  0.00\n\n\n                venue  freq\n0         Coffee Shop  0.10\n1      Clothing Store  0.10\n2          Shoe Store  0.05\n3  Salon / Barbershop  0.05\n4                Caf\u00e9  0.05\n\n\n                venue  freq\n0      Breakfast Spot  0.14\n1           Gift Shop  0.14\n2           Bookstore  0.07\n3             Dog Run  0.07\n4  Italian Restaurant  0.07\n\n\n                venue  freq\n0         Coffee Shop  0.25\n1         Yoga Studio  0.03\n2  Mexican Restaurant  0.03\n3                Bank  0.03\n4                 Bar  0.03\n\n\n         venue  freq\n0  Coffee Shop  0.16\n1         Park  0.07\n2          Pub  0.07\n3       Bakery  0.07\n4      Theater  0.05\n\n\n            venue  freq\n0     Coffee Shop  0.10\n1            Caf\u00e9  0.05\n2      Restaurant  0.04\n3           Hotel  0.03\n4  Clothing Store  0.03\n\n\n                 venue  freq\n0                 Park  0.50\n1           Playground  0.25\n2                Trail  0.25\n3          Yoga Studio  0.00\n4  Monument / Landmark  0.00\n\n\n                       venue  freq\n0             Ice Cream Shop  0.25\n1                Music Venue  0.25\n2    Health & Beauty Service  0.25\n3                     Garden  0.25\n4  Middle Eastern Restaurant  0.00\n\n\n              venue  freq\n0  Sushi Restaurant  0.07\n1       Pizza Place  0.07\n2       Coffee Shop  0.07\n3              Caf\u00e9  0.07\n4             Diner  0.05\n\n\n                 venue  freq\n0          Coffee Shop  0.06\n1                 Caf\u00e9  0.06\n2         Cocktail Bar  0.05\n3            Gastropub  0.04\n4  American Restaurant  0.04\n\n\n                venue  freq\n0         Coffee Shop  0.06\n1         Pizza Place  0.06\n2              Bakery  0.04\n3                Caf\u00e9  0.04\n4  Italian Restaurant  0.04\n\n\n                 venue  freq\n0          Coffee Shop  0.10\n1                 Caf\u00e9  0.04\n2             Beer Bar  0.03\n3   Italian Restaurant  0.03\n4  Japanese Restaurant  0.03\n\n\n                 venue  freq\n0                 Caf\u00e9  0.10\n1          Coffee Shop  0.07\n2              Brewery  0.05\n3  American Restaurant  0.05\n4               Bakery  0.05\n\n\n                venue  freq\n0         Coffee Shop  0.12\n1                 Pub  0.12\n2          Restaurant  0.06\n3  Light Rail Station  0.06\n4          Sports Bar  0.06\n\n\n               venue  freq\n0               Caf\u00e9  0.13\n1     Sandwich Place  0.13\n2        Coffee Shop  0.09\n3     Cosmetics Shop  0.04\n4  Indian Restaurant  0.04\n\n\n                             venue  freq\n0                            Trail  0.25\n1                Health Food Store  0.25\n2                              Pub  0.25\n3                      Men's Store  0.00\n4  Molecular Gastronomy Restaurant  0.00\n\n\n                    venue  freq\n0        Greek Restaurant  0.21\n1      Italian Restaurant  0.07\n2             Coffee Shop  0.07\n3  Furniture / Home Store  0.05\n4          Ice Cream Shop  0.05\n\n\n                venue  freq\n0         Coffee Shop  0.10\n1               Hotel  0.06\n2                Caf\u00e9  0.06\n3  Seafood Restaurant  0.03\n4  Italian Restaurant  0.03\n\n\n                 venue  freq\n0                 Caf\u00e9  0.14\n1               Bakery  0.06\n2           Restaurant  0.06\n3   Italian Restaurant  0.06\n4  Japanese Restaurant  0.06\n\n\n"
                }
            ],
            "source": "num_top_venues = 5\n\nfor hood in toronto_grouped['Neighborhood']:\n    #print(\"----\"+hood+\"----\")\n    temp = toronto_grouped[toronto_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": "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>Berczy Park</td>\n      <td>Coffee Shop</td>\n      <td>Cocktail Bar</td>\n      <td>Bakery</td>\n      <td>Beer Bar</td>\n      <td>Seafood Restaurant</td>\n      <td>Cheese Shop</td>\n      <td>Caf\u00e9</td>\n      <td>Restaurant</td>\n      <td>Gourmet Shop</td>\n      <td>Pub</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Brockton, Parkdale Village, Exhibition Place</td>\n      <td>Caf\u00e9</td>\n      <td>Coffee Shop</td>\n      <td>Breakfast Spot</td>\n      <td>Pet Store</td>\n      <td>Bakery</td>\n      <td>Intersection</td>\n      <td>Restaurant</td>\n      <td>Italian Restaurant</td>\n      <td>Climbing Gym</td>\n      <td>Bar</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Business reply mail Processing Centre, South C...</td>\n      <td>Light Rail Station</td>\n      <td>Yoga Studio</td>\n      <td>Auto Workshop</td>\n      <td>Garden Center</td>\n      <td>Garden</td>\n      <td>Gym / Fitness Center</td>\n      <td>Fast Food Restaurant</td>\n      <td>Farmers Market</td>\n      <td>Comic Shop</td>\n      <td>Pizza Place</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CN Tower, King and Spadina, Railway Lands, Har...</td>\n      <td>Airport Lounge</td>\n      <td>Airport Service</td>\n      <td>Airport Terminal</td>\n      <td>Coffee Shop</td>\n      <td>Harbor / Marina</td>\n      <td>Plane</td>\n      <td>Rental Car Location</td>\n      <td>Sculpture Garden</td>\n      <td>Bar</td>\n      <td>Boat or Ferry</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Central Bay Street</td>\n      <td>Coffee Shop</td>\n      <td>Italian Restaurant</td>\n      <td>Sandwich Place</td>\n      <td>Caf\u00e9</td>\n      <td>Japanese Restaurant</td>\n      <td>Burger Joint</td>\n      <td>Ice Cream Shop</td>\n      <td>Salad Place</td>\n      <td>Thai Restaurant</td>\n      <td>Bubble Tea Shop</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "                                        Neighborhood 1st Most Common Venue  \\\n0                                        Berczy Park           Coffee Shop   \n1       Brockton, Parkdale Village, Exhibition Place                  Caf\u00e9   \n2  Business reply mail Processing Centre, South C...    Light Rail Station   \n3  CN Tower, King and Spadina, Railway Lands, Har...        Airport Lounge   \n4                                 Central Bay Street           Coffee Shop   \n\n  2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue  \\\n0          Cocktail Bar                Bakery              Beer Bar   \n1           Coffee Shop        Breakfast Spot             Pet Store   \n2           Yoga Studio         Auto Workshop         Garden Center   \n3       Airport Service      Airport Terminal           Coffee Shop   \n4    Italian Restaurant        Sandwich Place                  Caf\u00e9   \n\n  5th Most Common Venue 6th Most Common Venue 7th Most Common Venue  \\\n0    Seafood Restaurant           Cheese Shop                  Caf\u00e9   \n1                Bakery          Intersection            Restaurant   \n2                Garden  Gym / Fitness Center  Fast Food Restaurant   \n3       Harbor / Marina                 Plane   Rental Car Location   \n4   Japanese Restaurant          Burger Joint        Ice Cream Shop   \n\n  8th Most Common Venue 9th Most Common Venue 10th Most Common Venue  \n0            Restaurant          Gourmet Shop                    Pub  \n1    Italian Restaurant          Climbing Gym                    Bar  \n2        Farmers Market            Comic Shop            Pizza Place  \n3      Sculpture Garden                   Bar          Boat or Ferry  \n4           Salad Place       Thai Restaurant        Bubble Tea Shop  "
                    },
                    "execution_count": 38,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "def return_most_common_venues(row, num_top_venues):\n    row_categories = row.iloc[1:]\n    row_categories_sorted = row_categories.sort_values(ascending=False)\n    \n    return row_categories_sorted.index.values[0:num_top_venues]\n\nnum_top_venues = 10\n\nindicators = ['st', 'nd', 'rd']\n\n# create columns according to number of top venues\ncolumns = ['Neighborhood']\nfor 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\nneighborhoods_venues_sorted = pd.DataFrame(columns=columns)\nneighborhoods_venues_sorted['Neighborhood'] = toronto_grouped['Neighborhood']\n\nfor ind in np.arange(toronto_grouped.shape[0]):\n    neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(toronto_grouped.iloc[ind, :], num_top_venues)\n\nneighborhoods_venues_sorted.head()"
        },
        {
            "cell_type": "code",
            "execution_count": 39,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "array([1, 1, 1, 9, 1, 1, 1, 1, 1, 5], dtype=int32)"
                    },
                    "execution_count": 39,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# set number of clusters\nkclusters = 10\n\ntoronto_grouped_clustering = toronto_grouped.drop('Neighborhood', 1)\n\n# run k-means clustering\nkmeans = KMeans(n_clusters=kclusters, random_state=0).fit(toronto_grouped_clustering)\n\n# check cluster labels generated for each row in the dataframe\nkmeans.labels_[0:10] \n"
        },
        {
            "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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td>M5A</td>\n      <td>Downtown Toronto</td>\n      <td>Regent Park, Harbourfront</td>\n      <td>43.654260</td>\n      <td>-79.360636</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>M7A</td>\n      <td>Downtown Toronto</td>\n      <td>Queen's Park, Ontario Provincial Government</td>\n      <td>43.662301</td>\n      <td>-79.389494</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>M5B</td>\n      <td>Downtown Toronto</td>\n      <td>Garden District, Ryerson</td>\n      <td>43.657162</td>\n      <td>-79.378937</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>M5C</td>\n      <td>Downtown Toronto</td>\n      <td>St. James Town</td>\n      <td>43.651494</td>\n      <td>-79.375418</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>M4E</td>\n      <td>East Toronto</td>\n      <td>The Beaches</td>\n      <td>43.676357</td>\n      <td>-79.293031</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "   Postal Code           Borough                                 Neighborhood  \\\n2          M5A  Downtown Toronto                    Regent Park, Harbourfront   \n4          M7A  Downtown Toronto  Queen's Park, Ontario Provincial Government   \n9          M5B  Downtown Toronto                     Garden District, Ryerson   \n15         M5C  Downtown Toronto                               St. James Town   \n19         M4E      East Toronto                                  The Beaches   \n\n     Latitude  Longitude  \n2   43.654260 -79.360636  \n4   43.662301 -79.389494  \n9   43.657162 -79.378937  \n15  43.651494 -79.375418  \n19  43.676357 -79.293031  "
                    },
                    "execution_count": 40,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "TorontoNeighborhoodCoordinates_df.head()"
        },
        {
            "cell_type": "code",
            "execution_count": 41,
            "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>2</th>\n      <td>M5A</td>\n      <td>Downtown Toronto</td>\n      <td>Regent Park, Harbourfront</td>\n      <td>43.654260</td>\n      <td>-79.360636</td>\n      <td>1</td>\n      <td>Coffee Shop</td>\n      <td>Park</td>\n      <td>Pub</td>\n      <td>Bakery</td>\n      <td>Caf\u00e9</td>\n      <td>Breakfast Spot</td>\n      <td>Theater</td>\n      <td>Spa</td>\n      <td>Ice Cream Shop</td>\n      <td>Beer Store</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>M7A</td>\n      <td>Downtown Toronto</td>\n      <td>Queen's Park, Ontario Provincial Government</td>\n      <td>43.662301</td>\n      <td>-79.389494</td>\n      <td>1</td>\n      <td>Coffee Shop</td>\n      <td>Yoga Studio</td>\n      <td>Arts &amp; Crafts Store</td>\n      <td>Restaurant</td>\n      <td>Italian Restaurant</td>\n      <td>Distribution Center</td>\n      <td>Burrito Place</td>\n      <td>Beer Bar</td>\n      <td>Smoothie Shop</td>\n      <td>Diner</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>M5B</td>\n      <td>Downtown Toronto</td>\n      <td>Garden District, Ryerson</td>\n      <td>43.657162</td>\n      <td>-79.378937</td>\n      <td>1</td>\n      <td>Clothing Store</td>\n      <td>Coffee Shop</td>\n      <td>Restaurant</td>\n      <td>Italian Restaurant</td>\n      <td>Bubble Tea Shop</td>\n      <td>Cosmetics Shop</td>\n      <td>Middle Eastern Restaurant</td>\n      <td>Caf\u00e9</td>\n      <td>Japanese Restaurant</td>\n      <td>Fast Food Restaurant</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>M5C</td>\n      <td>Downtown Toronto</td>\n      <td>St. James Town</td>\n      <td>43.651494</td>\n      <td>-79.375418</td>\n      <td>9</td>\n      <td>Coffee Shop</td>\n      <td>Caf\u00e9</td>\n      <td>Cocktail Bar</td>\n      <td>American Restaurant</td>\n      <td>Gastropub</td>\n      <td>Beer Bar</td>\n      <td>Seafood Restaurant</td>\n      <td>Clothing Store</td>\n      <td>Gym</td>\n      <td>Cosmetics Shop</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>M4E</td>\n      <td>East Toronto</td>\n      <td>The Beaches</td>\n      <td>43.676357</td>\n      <td>-79.293031</td>\n      <td>1</td>\n      <td>Health Food Store</td>\n      <td>Pub</td>\n      <td>Trail</td>\n      <td>Women's Store</td>\n      <td>Deli / Bodega</td>\n      <td>Department Store</td>\n      <td>Dessert Shop</td>\n      <td>Diner</td>\n      <td>Discount Store</td>\n      <td>Distribution Center</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "   Postal Code           Borough                                 Neighborhood  \\\n2          M5A  Downtown Toronto                    Regent Park, Harbourfront   \n4          M7A  Downtown Toronto  Queen's Park, Ontario Provincial Government   \n9          M5B  Downtown Toronto                     Garden District, Ryerson   \n15         M5C  Downtown Toronto                               St. James Town   \n19         M4E      East Toronto                                  The Beaches   \n\n     Latitude  Longitude  Cluster Labels 1st Most Common Venue  \\\n2   43.654260 -79.360636               1           Coffee Shop   \n4   43.662301 -79.389494               1           Coffee Shop   \n9   43.657162 -79.378937               1        Clothing Store   \n15  43.651494 -79.375418               9           Coffee Shop   \n19  43.676357 -79.293031               1     Health Food Store   \n\n   2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue  \\\n2                   Park                   Pub                Bakery   \n4            Yoga Studio   Arts & Crafts Store            Restaurant   \n9            Coffee Shop            Restaurant    Italian Restaurant   \n15                  Caf\u00e9          Cocktail Bar   American Restaurant   \n19                   Pub                 Trail         Women's Store   \n\n   5th Most Common Venue 6th Most Common Venue      7th Most Common Venue  \\\n2                   Caf\u00e9        Breakfast Spot                    Theater   \n4     Italian Restaurant   Distribution Center              Burrito Place   \n9        Bubble Tea Shop        Cosmetics Shop  Middle Eastern Restaurant   \n15             Gastropub              Beer Bar         Seafood Restaurant   \n19         Deli / Bodega      Department Store               Dessert Shop   \n\n   8th Most Common Venue 9th Most Common Venue 10th Most Common Venue  \n2                    Spa        Ice Cream Shop             Beer Store  \n4               Beer Bar         Smoothie Shop                  Diner  \n9                   Caf\u00e9   Japanese Restaurant   Fast Food Restaurant  \n15        Clothing Store                   Gym         Cosmetics Shop  \n19                 Diner        Discount Store    Distribution Center  "
                    },
                    "execution_count": 41,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "toronto_merged = TorontoNeighborhoodCoordinates_df.copy(deep=True)\ntoronto_merged.reset_index()\n\n# add clustering labels\ntoronto_merged['Cluster Labels'] = kmeans.labels_\n\n# merge toronto_grouped with toronto_data to add latitude/longitude for each neighborhood\ntoronto_merged = toronto_merged.join(neighborhoods_venues_sorted.set_index('Neighborhood'), on='Neighborhood')\n\ntoronto_merged.head() # check the last columns!"
        },
        {
            "cell_type": "code",
            "execution_count": 42,
            "metadata": {
                "scrolled": false
            },
            "outputs": [
                {
                    "data": {
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            var circle_marker_69f1da91360942a5aeb90735b355f8db = L.circleMarker(
                [43.67635739999999, -79.2930312],
                {"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_43f7661aac59489bbea1e54d4730414e);
        
    
        var popup_923e1949fba24aacb92f5c7434dc8375 = L.popup({"maxWidth": "100%"});

        
            var html_c8acbf13b014437facce530fb3d334bf = $(`<div id="html_c8acbf13b014437facce530fb3d334bf" style="width: 100.0%; height: 100.0%;">The Beaches Cluster 1</div>`)[0];
            popup_923e1949fba24aacb92f5c7434dc8375.setContent(html_c8acbf13b014437facce530fb3d334bf);
        

        circle_marker_69f1da91360942a5aeb90735b355f8db.bindPopup(popup_923e1949fba24aacb92f5c7434dc8375)
        ;

        
    
    
            var circle_marker_4edfdc23115d4447810b7b6f7efea490 = L.circleMarker(
                [43.6447708, -79.3733064],
                {"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_43f7661aac59489bbea1e54d4730414e);
        
    
        var popup_2c6bbec39d5041d795c0b116750d67d3 = L.popup({"maxWidth": "100%"});

        
            var html_ed1fcfe78c9744568ec09f5fe9a95765 = $(`<div id="html_ed1fcfe78c9744568ec09f5fe9a95765" style="width: 100.0%; height: 100.0%;">Berczy Park Cluster 1</div>`)[0];
            popup_2c6bbec39d5041d795c0b116750d67d3.setContent(html_ed1fcfe78c9744568ec09f5fe9a95765);
        

        circle_marker_4edfdc23115d4447810b7b6f7efea490.bindPopup(popup_2c6bbec39d5041d795c0b116750d67d3)
        ;

        
    
    
            var circle_marker_e627bcf8e845478c940c5336e119cbab = L.circleMarker(
                [43.6579524, -79.3873826],
                {"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_43f7661aac59489bbea1e54d4730414e);
        
    
        var popup_fbc0e3fb48574ab3a02480af578f423d = L.popup({"maxWidth": "100%"});

        
            var html_7c546045776b43049aae47118f2869d4 = $(`<div id="html_7c546045776b43049aae47118f2869d4" style="width: 100.0%; height: 100.0%;">Central Bay Street Cluster 1</div>`)[0];
            popup_fbc0e3fb48574ab3a02480af578f423d.setContent(html_7c546045776b43049aae47118f2869d4);
        

        circle_marker_e627bcf8e845478c940c5336e119cbab.bindPopup(popup_fbc0e3fb48574ab3a02480af578f423d)
        ;

        
    
    
            var circle_marker_f7210883ed764f2fb58e99a8db9d7a5a = L.circleMarker(
                [43.669542, -79.4225637],
                {"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_43f7661aac59489bbea1e54d4730414e);
        
    
        var popup_3f7ada44a0c9462694ebab2a41369d11 = L.popup({"maxWidth": "100%"});

        
            var html_42c3b4880fc24058b1fe8a42cce79913 = $(`<div id="html_42c3b4880fc24058b1fe8a42cce79913" style="width: 100.0%; height: 100.0%;">Christie Cluster 1</div>`)[0];
            popup_3f7ada44a0c9462694ebab2a41369d11.setContent(html_42c3b4880fc24058b1fe8a42cce79913);
        

        circle_marker_f7210883ed764f2fb58e99a8db9d7a5a.bindPopup(popup_3f7ada44a0c9462694ebab2a41369d11)
        ;

        
    
    
            var circle_marker_b4dfa5da60a048a190bdc0fccbdffd62 = L.circleMarker(
                [43.65057120000001, -79.3845675],
                {"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_43f7661aac59489bbea1e54d4730414e);
        
    
        var popup_d228f4b68e6e4811b56c0bb6b9328ca6 = L.popup({"maxWidth": "100%"});

        
            var html_dfd3ba19d0c1408dbb6fcf5220fa2893 = $(`<div id="html_dfd3ba19d0c1408dbb6fcf5220fa2893" style="width: 100.0%; height: 100.0%;">Richmond, Adelaide, King Cluster 1</div>`)[0];
            popup_d228f4b68e6e4811b56c0bb6b9328ca6.setContent(html_dfd3ba19d0c1408dbb6fcf5220fa2893);
        

        circle_marker_b4dfa5da60a048a190bdc0fccbdffd62.bindPopup(popup_d228f4b68e6e4811b56c0bb6b9328ca6)
        ;

        
    
    
            var circle_marker_88b047b2b15d445abf7235306e5c9bbe = L.circleMarker(
                [43.66900510000001, -79.4422593],
                {"bubblingMouseEvents": true, "color": "#62fbc4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#62fbc4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_43f7661aac59489bbea1e54d4730414e);
        
    
        var popup_b65285974ee1475796cfcf4973f174c8 = L.popup({"maxWidth": "100%"});

        
            var html_f6d4b3a1a8cd4ef4a277573186be710e = $(`<div id="html_f6d4b3a1a8cd4ef4a277573186be710e" style="width: 100.0%; height: 100.0%;">Dufferin, Dovercourt Village Cluster 5</div>`)[0];
            popup_b65285974ee1475796cfcf4973f174c8.setContent(html_f6d4b3a1a8cd4ef4a277573186be710e);
        

        circle_marker_88b047b2b15d445abf7235306e5c9bbe.bindPopup(popup_b65285974ee1475796cfcf4973f174c8)
        ;

        
    
    
            var circle_marker_263ac6e767da4077b67121d647cec130 = L.circleMarker(
                [43.6408157, -79.38175229999997],
                {"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_43f7661aac59489bbea1e54d4730414e);
        
    
        var popup_a73a5f8edfae4f9a9071b3e24ab6cce0 = L.popup({"maxWidth": "100%"});

        
            var html_1f62f9fb12364bc5a185ac62b6140473 = $(`<div id="html_1f62f9fb12364bc5a185ac62b6140473" style="width: 100.0%; height: 100.0%;">Harbourfront East, Union Station, Toronto Islands Cluster 1</div>`)[0];
            popup_a73a5f8edfae4f9a9071b3e24ab6cce0.setContent(html_1f62f9fb12364bc5a185ac62b6140473);
        

        circle_marker_263ac6e767da4077b67121d647cec130.bindPopup(popup_a73a5f8edfae4f9a9071b3e24ab6cce0)
        ;

        
    
    
            var circle_marker_31283e9af13e4ab3a4e7bec4dfa43fe4 = L.circleMarker(
                [43.647926700000006, -79.4197497],
                {"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_43f7661aac59489bbea1e54d4730414e);
        
    
        var popup_65fd8a045de44554aef02c142a375457 = L.popup({"maxWidth": "100%"});

        
            var html_a4ef70d85d1143e7a09cdc06eca05a14 = $(`<div id="html_a4ef70d85d1143e7a09cdc06eca05a14" style="width: 100.0%; height: 100.0%;">Little Portugal, Trinity Cluster 1</div>`)[0];
            popup_65fd8a045de44554aef02c142a375457.setContent(html_a4ef70d85d1143e7a09cdc06eca05a14);
        

        circle_marker_31283e9af13e4ab3a4e7bec4dfa43fe4.bindPopup(popup_65fd8a045de44554aef02c142a375457)
        ;

        
    
    
            var circle_marker_ce25cd120db44149ba679860ae377dc3 = L.circleMarker(
                [43.6795571, -79.352188],
                {"bubblingMouseEvents": true, "color": "#d4dd80", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#d4dd80", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_43f7661aac59489bbea1e54d4730414e);
        
    
        var popup_7a7f8897e1f741a297715adb1c55ccb2 = L.popup({"maxWidth": "100%"});

        
            var html_69b504d1190f4287b8f534ed9b6ff54b = $(`<div id="html_69b504d1190f4287b8f534ed9b6ff54b" style="width: 100.0%; height: 100.0%;">The Danforth West, Riverdale Cluster 7</div>`)[0];
            popup_7a7f8897e1f741a297715adb1c55ccb2.setContent(html_69b504d1190f4287b8f534ed9b6ff54b);
        

        circle_marker_ce25cd120db44149ba679860ae377dc3.bindPopup(popup_7a7f8897e1f741a297715adb1c55ccb2)
        ;

        
    
    
            var circle_marker_cd69139c248a4e129f58fa77e0daa533 = L.circleMarker(
                [43.6471768, -79.38157640000001],
                {"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_43f7661aac59489bbea1e54d4730414e);
        
    
        var popup_4272e41d3eef430ab1ee51785b772f22 = L.popup({"maxWidth": "100%"});

        
            var html_c092171b077b4c08bcfc015e520d75bb = $(`<div id="html_c092171b077b4c08bcfc015e520d75bb" style="width: 100.0%; height: 100.0%;">Toronto Dominion Centre, Design Exchange Cluster 1</div>`)[0];
            popup_4272e41d3eef430ab1ee51785b772f22.setContent(html_c092171b077b4c08bcfc015e520d75bb);
        

        circle_marker_cd69139c248a4e129f58fa77e0daa533.bindPopup(popup_4272e41d3eef430ab1ee51785b772f22)
        ;

        
    
    
            var circle_marker_78adf8bc69cb4b8dad9208632b963977 = L.circleMarker(
                [43.6368472, -79.42819140000002],
                {"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_43f7661aac59489bbea1e54d4730414e);
        
    
        var popup_260a32914e254b9ea812f45a910e5016 = L.popup({"maxWidth": "100%"});

        
            var html_0a72f603bacf437aa4a63135b131676c = $(`<div id="html_0a72f603bacf437aa4a63135b131676c" style="width: 100.0%; height: 100.0%;">Brockton, Parkdale Village, Exhibition Place Cluster 1</div>`)[0];
            popup_260a32914e254b9ea812f45a910e5016.setContent(html_0a72f603bacf437aa4a63135b131676c);
        

        circle_marker_78adf8bc69cb4b8dad9208632b963977.bindPopup(popup_260a32914e254b9ea812f45a910e5016)
        ;

        
    
    
            var circle_marker_12f5f5cd9b6a4c8aa9e177b6359dc980 = L.circleMarker(
                [43.6689985, -79.31557159999998],
                {"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_43f7661aac59489bbea1e54d4730414e);
        
    
        var popup_bb81290cb79c491389889c694e1adfab = L.popup({"maxWidth": "100%"});

        
            var html_ec0d8935f3574c9b929af128a7de3387 = $(`<div id="html_ec0d8935f3574c9b929af128a7de3387" style="width: 100.0%; height: 100.0%;">India Bazaar, The Beaches West Cluster 1</div>`)[0];
            popup_bb81290cb79c491389889c694e1adfab.setContent(html_ec0d8935f3574c9b929af128a7de3387);
        

        circle_marker_12f5f5cd9b6a4c8aa9e177b6359dc980.bindPopup(popup_bb81290cb79c491389889c694e1adfab)
        ;

        
    
    
            var circle_marker_1dfa11f654e1448fa3a793b502568431 = L.circleMarker(
                [43.6481985, -79.37981690000002],
                {"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_43f7661aac59489bbea1e54d4730414e);
        
    
        var popup_8cdd3e2b2edb4a42b6366c8eace25997 = L.popup({"maxWidth": "100%"});

        
            var html_8befb987018943268242c32180a4b82d = $(`<div id="html_8befb987018943268242c32180a4b82d" style="width: 100.0%; height: 100.0%;">Commerce Court, Victoria Hotel Cluster 1</div>`)[0];
            popup_8cdd3e2b2edb4a42b6366c8eace25997.setContent(html_8befb987018943268242c32180a4b82d);
        

        circle_marker_1dfa11f654e1448fa3a793b502568431.bindPopup(popup_8cdd3e2b2edb4a42b6366c8eace25997)
        ;

        
    
    
            var circle_marker_cfd8df888cdd4c3a8827444a525b6275 = L.circleMarker(
                [43.6595255, -79.340923],
                {"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_43f7661aac59489bbea1e54d4730414e);
        
    
        var popup_dcd792b67b0a47d3b613710f9fc715c8 = L.popup({"maxWidth": "100%"});

        
            var html_4950eb3a661945cab44c480f59f94446 = $(`<div id="html_4950eb3a661945cab44c480f59f94446" style="width: 100.0%; height: 100.0%;">Studio District Cluster 1</div>`)[0];
            popup_dcd792b67b0a47d3b613710f9fc715c8.setContent(html_4950eb3a661945cab44c480f59f94446);
        

        circle_marker_cfd8df888cdd4c3a8827444a525b6275.bindPopup(popup_dcd792b67b0a47d3b613710f9fc715c8)
        ;

        
    
    
            var circle_marker_9b99458008e14e37b45a4f7ee4d69fa1 = L.circleMarker(
                [43.7280205, -79.3887901],
                {"bubblingMouseEvents": true, "color": "#10a2f0", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#10a2f0", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_43f7661aac59489bbea1e54d4730414e);
        
    
        var popup_ae2848a664984cb6bbfe0b6040a611d9 = L.popup({"maxWidth": "100%"});

        
            var html_cf402413e0524cba93cb44bb3c71e5f8 = $(`<div id="html_cf402413e0524cba93cb44bb3c71e5f8" style="width: 100.0%; height: 100.0%;">Lawrence Park Cluster 3</div>`)[0];
            popup_ae2848a664984cb6bbfe0b6040a611d9.setContent(html_cf402413e0524cba93cb44bb3c71e5f8);
        

        circle_marker_9b99458008e14e37b45a4f7ee4d69fa1.bindPopup(popup_ae2848a664984cb6bbfe0b6040a611d9)
        ;

        
    
    
            var circle_marker_2671049db263445c97b465d1cc956886 = L.circleMarker(
                [43.7116948, -79.41693559999999],
                {"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_43f7661aac59489bbea1e54d4730414e);
        
    
        var popup_4866672f14c549bebced41e2cbbebf1c = L.popup({"maxWidth": "100%"});

        
            var html_e82cf7ba83824eabbd9309f5fc3be540 = $(`<div id="html_e82cf7ba83824eabbd9309f5fc3be540" style="width: 100.0%; height: 100.0%;">Roselawn Cluster 1</div>`)[0];
            popup_4866672f14c549bebced41e2cbbebf1c.setContent(html_e82cf7ba83824eabbd9309f5fc3be540);
        

        circle_marker_2671049db263445c97b465d1cc956886.bindPopup(popup_4866672f14c549bebced41e2cbbebf1c)
        ;

        
    
    
            var circle_marker_28f54f128a814f1b83e3c196fcf03de5 = L.circleMarker(
                [43.7127511, -79.3901975],
                {"bubblingMouseEvents": true, "color": "#9cfba4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#9cfba4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_43f7661aac59489bbea1e54d4730414e);
        
    
        var popup_50065158dc954d87989d9bfb65cfd787 = L.popup({"maxWidth": "100%"});

        
            var html_7eb6277f5d4d48f88b5d6b8bdef71ce9 = $(`<div id="html_7eb6277f5d4d48f88b5d6b8bdef71ce9" style="width: 100.0%; height: 100.0%;">Davisville North Cluster 6</div>`)[0];
            popup_50065158dc954d87989d9bfb65cfd787.setContent(html_7eb6277f5d4d48f88b5d6b8bdef71ce9);
        

        circle_marker_28f54f128a814f1b83e3c196fcf03de5.bindPopup(popup_50065158dc954d87989d9bfb65cfd787)
        ;

        
    
    
            var circle_marker_5223ed6eb35e45329ff6009d59325414 = L.circleMarker(
                [43.6969476, -79.41130720000002],
                {"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_43f7661aac59489bbea1e54d4730414e);
        
    
        var popup_d49e44804903492f934e0777bbd4b3d2 = L.popup({"maxWidth": "100%"});

        
            var html_5d8a1a9dc2354375af1637c5a9495951 = $(`<div id="html_5d8a1a9dc2354375af1637c5a9495951" style="width: 100.0%; height: 100.0%;">Forest Hill North &amp; West, Forest Hill Road Park Cluster 1</div>`)[0];
            popup_d49e44804903492f934e0777bbd4b3d2.setContent(html_5d8a1a9dc2354375af1637c5a9495951);
        

        circle_marker_5223ed6eb35e45329ff6009d59325414.bindPopup(popup_d49e44804903492f934e0777bbd4b3d2)
        ;

        
    
    
            var circle_marker_6c8de2013dcb425487895c637dd087d5 = L.circleMarker(
                [43.6616083, -79.46476329999999],
                {"bubblingMouseEvents": true, "color": "#ffa256", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ffa256", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_43f7661aac59489bbea1e54d4730414e);
        
    
        var popup_17f4e0903b9048eab409449badb9c579 = L.popup({"maxWidth": "100%"});

        
            var html_4da91d1bca73455485cebeb04798981d = $(`<div id="html_4da91d1bca73455485cebeb04798981d" style="width: 100.0%; height: 100.0%;">High Park, The Junction South Cluster 8</div>`)[0];
            popup_17f4e0903b9048eab409449badb9c579.setContent(html_4da91d1bca73455485cebeb04798981d);
        

        circle_marker_6c8de2013dcb425487895c637dd087d5.bindPopup(popup_17f4e0903b9048eab409449badb9c579)
        ;

        
    
    
            var circle_marker_6b233af5f3a94c76b01fa8351fc9b466 = L.circleMarker(
                [43.7153834, -79.40567840000001],
                {"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_43f7661aac59489bbea1e54d4730414e);
        
    
        var popup_c94431c03bfb4a98b8eb1939501ae2bf = L.popup({"maxWidth": "100%"});

        
            var html_50a4da9fe3a24504a2e2e87617c27fa3 = $(`<div id="html_50a4da9fe3a24504a2e2e87617c27fa3" style="width: 100.0%; height: 100.0%;">North Toronto West,  Lawrence Park Cluster 1</div>`)[0];
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        circle_marker_6b233af5f3a94c76b01fa8351fc9b466.bindPopup(popup_c94431c03bfb4a98b8eb1939501ae2bf)
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            var circle_marker_3992e97c6b3a44a39d08bc81dbfc5f2d = L.circleMarker(
                [43.6727097, -79.40567840000001],
                {"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}
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        var popup_e1fb1ac8b11142218b9fe8e1c318640c = L.popup({"maxWidth": "100%"});

        
            var html_b920675045534910879a15aef0365961 = $(`<div id="html_b920675045534910879a15aef0365961" style="width: 100.0%; height: 100.0%;">The Annex, North Midtown, Yorkville Cluster 1</div>`)[0];
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        circle_marker_3992e97c6b3a44a39d08bc81dbfc5f2d.bindPopup(popup_e1fb1ac8b11142218b9fe8e1c318640c)
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            var circle_marker_c37619cef8ab4cbd841431621a3973ea = L.circleMarker(
                [43.6489597, -79.456325],
                {"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}
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        var popup_962771e16820452c9c33ffc116e07f9e = L.popup({"maxWidth": "100%"});

        
            var html_e8d625c4715e47548d9b832856d3e88b = $(`<div id="html_e8d625c4715e47548d9b832856d3e88b" style="width: 100.0%; height: 100.0%;">Parkdale, Roncesvalles Cluster 1</div>`)[0];
            popup_962771e16820452c9c33ffc116e07f9e.setContent(html_e8d625c4715e47548d9b832856d3e88b);
        

        circle_marker_c37619cef8ab4cbd841431621a3973ea.bindPopup(popup_962771e16820452c9c33ffc116e07f9e)
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            var circle_marker_dec35797bef6417c89be91d60253c174 = L.circleMarker(
                [43.7043244, -79.3887901],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
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        var popup_6e99fdc37ad6483fb13b50946e0968c2 = L.popup({"maxWidth": "100%"});

        
            var html_9a383bc7eb5c49be9cf5b74eafdc6196 = $(`<div id="html_9a383bc7eb5c49be9cf5b74eafdc6196" style="width: 100.0%; height: 100.0%;">Davisville Cluster 0</div>`)[0];
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        circle_marker_dec35797bef6417c89be91d60253c174.bindPopup(popup_6e99fdc37ad6483fb13b50946e0968c2)
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            var circle_marker_00f1e75e1b0e440b8227c7025c351aaa = L.circleMarker(
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                {"bubblingMouseEvents": true, "color": "#4856fb", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#4856fb", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
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        circle_marker_00f1e75e1b0e440b8227c7025c351aaa.bindPopup(popup_07d62dd6d45241679101dd805bb63d67)
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            var circle_marker_42d63517143440d58bd8d48c6a8652df = L.circleMarker(
                [43.6515706, -79.4844499],
                {"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}
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        var popup_b393da5300474cadb7817fc90fdc3baa = L.popup({"maxWidth": "100%"});

        
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        circle_marker_42d63517143440d58bd8d48c6a8652df.bindPopup(popup_b393da5300474cadb7817fc90fdc3baa)
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            var circle_marker_9552c3d613e14d8594f82794fe7b62a6 = L.circleMarker(
                [43.6895743, -79.38315990000002],
                {"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}
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        var popup_570931e2a95b4abb99c5282666e0d5a6 = L.popup({"maxWidth": "100%"});

        
            var html_682b1686e4af416abd868f921137683d = $(`<div id="html_682b1686e4af416abd868f921137683d" style="width: 100.0%; height: 100.0%;">Moore Park, Summerhill East Cluster 1</div>`)[0];
            popup_570931e2a95b4abb99c5282666e0d5a6.setContent(html_682b1686e4af416abd868f921137683d);
        

        circle_marker_9552c3d613e14d8594f82794fe7b62a6.bindPopup(popup_570931e2a95b4abb99c5282666e0d5a6)
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            var circle_marker_950ba924e67c494f874f8365e677b333 = L.circleMarker(
                [43.6532057, -79.4000493],
                {"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_43f7661aac59489bbea1e54d4730414e);
        
    
        var popup_6c6927029dd84950840f4e3e198ba79f = L.popup({"maxWidth": "100%"});

        
            var html_8532ccecf2124118a12b81161d367207 = $(`<div id="html_8532ccecf2124118a12b81161d367207" style="width: 100.0%; height: 100.0%;">Kensington Market, Chinatown, Grange Park Cluster 1</div>`)[0];
            popup_6c6927029dd84950840f4e3e198ba79f.setContent(html_8532ccecf2124118a12b81161d367207);
        

        circle_marker_950ba924e67c494f874f8365e677b333.bindPopup(popup_6c6927029dd84950840f4e3e198ba79f)
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            var circle_marker_d083d9ff9ef64ed1b6056a5b950dc1e8 = L.circleMarker(
                [43.68641229999999, -79.4000493],
                {"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_43f7661aac59489bbea1e54d4730414e);
        
    
        var popup_8af546eaa0364d7084f261baeb8f631b = L.popup({"maxWidth": "100%"});

        
            var html_afa33612804b4de2aae34bb1d8a195a8 = $(`<div id="html_afa33612804b4de2aae34bb1d8a195a8" style="width: 100.0%; height: 100.0%;">Summerhill West, Rathnelly, South Hill, Forest Hill SE, Deer Park Cluster 1</div>`)[0];
            popup_8af546eaa0364d7084f261baeb8f631b.setContent(html_afa33612804b4de2aae34bb1d8a195a8);
        

        circle_marker_d083d9ff9ef64ed1b6056a5b950dc1e8.bindPopup(popup_8af546eaa0364d7084f261baeb8f631b)
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            var circle_marker_de8cc4f384f84839aba9bd0c54132033 = L.circleMarker(
                [43.6289467, -79.3944199],
                {"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_43f7661aac59489bbea1e54d4730414e);
        
    
        var popup_cd94be21ca21424a88ff166db324a117 = L.popup({"maxWidth": "100%"});

        
            var html_f19c30d461a645189d96fbee29caefbf = $(`<div id="html_f19c30d461a645189d96fbee29caefbf" style="width: 100.0%; height: 100.0%;">CN Tower, King and Spadina, Railway Lands, Harbourfront West, Bathurst Quay, South Niagara, Island airport Cluster 1</div>`)[0];
            popup_cd94be21ca21424a88ff166db324a117.setContent(html_f19c30d461a645189d96fbee29caefbf);
        

        circle_marker_de8cc4f384f84839aba9bd0c54132033.bindPopup(popup_cd94be21ca21424a88ff166db324a117)
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            var circle_marker_979b3db7397a42b085c9f1cd0e4681e1 = L.circleMarker(
                [43.6795626, -79.37752940000001],
                {"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_43f7661aac59489bbea1e54d4730414e);
        
    
        var popup_0fafb021c2174d25b907a1d4958b4fc8 = L.popup({"maxWidth": "100%"});

        
            var html_c21fbb75b5f24feda675712cf04bf982 = $(`<div id="html_c21fbb75b5f24feda675712cf04bf982" style="width: 100.0%; height: 100.0%;">Rosedale Cluster 1</div>`)[0];
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        circle_marker_979b3db7397a42b085c9f1cd0e4681e1.bindPopup(popup_0fafb021c2174d25b907a1d4958b4fc8)
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            var circle_marker_d75b601286a545e398b166f444893a14 = L.circleMarker(
                [43.6464352, -79.37484599999998],
                {"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_43f7661aac59489bbea1e54d4730414e);
        
    
        var popup_44a6c9d5ebdc47f783881d89c3918382 = L.popup({"maxWidth": "100%"});

        
            var html_056bfbd6be174612b446294bf50f9f7f = $(`<div id="html_056bfbd6be174612b446294bf50f9f7f" style="width: 100.0%; height: 100.0%;">Stn A PO Boxes Cluster 1</div>`)[0];
            popup_44a6c9d5ebdc47f783881d89c3918382.setContent(html_056bfbd6be174612b446294bf50f9f7f);
        

        circle_marker_d75b601286a545e398b166f444893a14.bindPopup(popup_44a6c9d5ebdc47f783881d89c3918382)
        ;

        
    
    
            var circle_marker_b41dbcc185e14e97b099bbe16cab7cdf = L.circleMarker(
                [43.667967, -79.3676753],
                {"bubblingMouseEvents": true, "color": "#2adddd", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#2adddd", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_43f7661aac59489bbea1e54d4730414e);
        
    
        var popup_64152e755422472f9dafaa7fca174458 = L.popup({"maxWidth": "100%"});

        
            var html_234f2bdb02d34c86b5659169ebe2f615 = $(`<div id="html_234f2bdb02d34c86b5659169ebe2f615" style="width: 100.0%; height: 100.0%;">St. James Town, Cabbagetown Cluster 4</div>`)[0];
            popup_64152e755422472f9dafaa7fca174458.setContent(html_234f2bdb02d34c86b5659169ebe2f615);
        

        circle_marker_b41dbcc185e14e97b099bbe16cab7cdf.bindPopup(popup_64152e755422472f9dafaa7fca174458)
        ;

        
    
    
            var circle_marker_808098d1248345d0b9c254e489230054 = L.circleMarker(
                [43.6484292, -79.3822802],
                {"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_43f7661aac59489bbea1e54d4730414e);
        
    
        var popup_744f8f1975b74caf9f283e61101724b0 = L.popup({"maxWidth": "100%"});

        
            var html_1c70f8aa66cc4c20b0a5baea894ae2ca = $(`<div id="html_1c70f8aa66cc4c20b0a5baea894ae2ca" style="width: 100.0%; height: 100.0%;">First Canadian Place, Underground city Cluster 1</div>`)[0];
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        circle_marker_808098d1248345d0b9c254e489230054.bindPopup(popup_744f8f1975b74caf9f283e61101724b0)
        ;

        
    
    
            var circle_marker_ad5f33a1cdd04a15a49790320fba6d01 = L.circleMarker(
                [43.6658599, -79.38315990000002],
                {"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_43f7661aac59489bbea1e54d4730414e);
        
    
        var popup_91d6629fa3114cb9b0309931986a7415 = L.popup({"maxWidth": "100%"});

        
            var html_a1eaa9f4277247c39489ea21bdd3090e = $(`<div id="html_a1eaa9f4277247c39489ea21bdd3090e" style="width: 100.0%; height: 100.0%;">Church and Wellesley Cluster 1</div>`)[0];
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        circle_marker_ad5f33a1cdd04a15a49790320fba6d01.bindPopup(popup_91d6629fa3114cb9b0309931986a7415)
        ;

        
    
    
            var circle_marker_1aefdcf5fa53438b9bd0eedd078e83be = L.circleMarker(
                [43.6627439, -79.321558],
                {"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_43f7661aac59489bbea1e54d4730414e);
        
    
        var popup_5b3753797d744d4eb87e6a1f469519b0 = L.popup({"maxWidth": "100%"});

        
            var html_9432fc9f2d6b49f6b417463d68ad6e36 = $(`<div id="html_9432fc9f2d6b49f6b417463d68ad6e36" style="width: 100.0%; height: 100.0%;">Business reply mail Processing Centre, South Central Letter Processing Plant Toronto Cluster 1</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 0x7f75434054a8>"
                    },
                    "execution_count": 42,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# Matplotlib and associated plotting modules\nimport matplotlib.cm as cm\nimport matplotlib.colors as colors\n# create map\nmap_clusters = folium.Map(location=[latitude, longitude], zoom_start=11)\n\n# set color scheme for the clusters\nx = np.arange(kclusters)\nys = [i+x+(i*x)**2 for i in range(kclusters)]\ncolors_array = cm.rainbow(np.linspace(0, 1, len(ys)))\nrainbow = [colors.rgb2hex(i) for i in colors_array]\n\n# add markers to the map\nmarkers_colors = []\nfor lat, lon, poi, cluster in zip(toronto_merged['Latitude'], toronto_merged['Longitude'], toronto_merged['Neighborhood'],kmeans.labels_):\n    label = folium.Popup(str(poi) + ' Cluster ' + str(cluster), parse_html=True)\n    folium.CircleMarker(\n        [lat, lon],\n        radius=5,\n        popup=label,\n        color=rainbow[cluster-1],\n        fill=True,\n        fill_color=rainbow[cluster-1],\n        fill_opacity=0.7).add_to(map_clusters)\n       \nmap_clusters"
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3.6",
            "language": "python",
            "name": "python3"
        },
        "language_info": {
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
            },
            "file_extension": ".py",
            "mimetype": "text/x-python",
            "name": "python",
            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
            "version": "3.6.9"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 1
}