Capstone_4.ipynb

{
    "cells": [
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Installing relevant packages"
        },
        {
            "metadata": {
                "scrolled": true
            },
            "cell_type": "code",
            "source": "#install relevant packages\n! pip install geocoder",
            "execution_count": 41,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "Collecting geocoder\n\u001b[?25l  Downloading https://files.pythonhosted.org/packages/4f/6b/13166c909ad2f2d76b929a4227c952630ebaf0d729f6317eb09cbceccbab/geocoder-1.38.1-py2.py3-none-any.whl (98kB)\n\u001b[K     |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 102kB 7.3MB/s ta 0:00:011\n\u001b[?25hRequirement already satisfied: requests in /opt/conda/envs/Python36/lib/python3.6/site-packages (from geocoder) (2.21.0)\nRequirement already satisfied: click in /opt/conda/envs/Python36/lib/python3.6/site-packages (from geocoder) (7.0)\nRequirement already satisfied: six in /opt/conda/envs/Python36/lib/python3.6/site-packages (from geocoder) (1.12.0)\nCollecting ratelim (from geocoder)\n  Downloading https://files.pythonhosted.org/packages/f2/98/7e6d147fd16a10a5f821db6e25f192265d6ecca3d82957a4fdd592cad49c/ratelim-0.1.6-py2.py3-none-any.whl\nRequirement already satisfied: future in /opt/conda/envs/Python36/lib/python3.6/site-packages (from geocoder) (0.17.1)\nRequirement already satisfied: urllib3<1.25,>=1.21.1 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from requests->geocoder) (1.24.1)\nRequirement already satisfied: chardet<3.1.0,>=3.0.2 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from requests->geocoder) (3.0.4)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from requests->geocoder) (2020.6.20)\nRequirement already satisfied: idna<2.9,>=2.5 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from requests->geocoder) (2.8)\nRequirement already satisfied: decorator in /opt/conda/envs/Python36/lib/python3.6/site-packages (from ratelim->geocoder) (4.3.2)\nInstalling collected packages: ratelim, geocoder\nSuccessfully installed geocoder-1.38.1 ratelim-0.1.6\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "!pip install folium",
            "execution_count": 69,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "Collecting folium\n\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a4/f0/44e69d50519880287cc41e7c8a6acc58daa9a9acf5f6afc52bcc70f69a6d/folium-0.11.0-py2.py3-none-any.whl (93kB)\n\u001b[K     |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 102kB 7.4MB/s ta 0:00:011\n\u001b[?25hRequirement already satisfied: requests in /opt/conda/envs/Python36/lib/python3.6/site-packages (from folium) (2.21.0)\nRequirement already satisfied: numpy in /opt/conda/envs/Python36/lib/python3.6/site-packages (from folium) (1.15.4)\nCollecting branca>=0.3.0 (from folium)\n  Downloading https://files.pythonhosted.org/packages/13/fb/9eacc24ba3216510c6b59a4ea1cd53d87f25ba76237d7f4393abeaf4c94e/branca-0.4.1-py3-none-any.whl\nRequirement already satisfied: jinja2>=2.9 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from folium) (2.10)\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)\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: 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: certifi>=2017.4.17 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from requests->folium) (2020.6.20)\nRequirement already satisfied: MarkupSafe>=0.23 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from jinja2>=2.9->folium) (1.1.0)\nInstalling collected packages: branca, folium\nSuccessfully installed branca-0.4.1 folium-0.11.0\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Imorting relevant libraries"
        },
        {
            "metadata": {
                "scrolled": true
            },
            "cell_type": "code",
            "source": "#import relevant libraries\nimport pandas as pd # data analysis\nimport geocoder # convert address to lat and long\nfrom geopy.geocoders import Nominatim \nimport folium # map rendering\nimport requests # handle url requests\nimport numpy as np\nfrom sklearn.cluster import KMeans\nimport matplotlib.cm as cm\nimport matplotlib.colors as colors",
            "execution_count": 119,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Scraping webpage "
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "#scraping wikipedia page using pandas\nlink = 'https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M'\ntables = pd.read_html(link)",
            "execution_count": null,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Converting to dataframe"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "#converting scraped list to dataframe\ntables\ndf = pd.DataFrame(tables[0])",
            "execution_count": 58,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Removing not assigned items"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# Assumption: all not assigned neighborhoods and boroughs occur in pair\ndf = df[df.Borough != 'Not assigned']\ndf.reset_index(inplace=True)",
            "execution_count": 59,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "df.drop(df.columns[[0]],axis=1,inplace=True)\ndf.shape",
            "execution_count": 60,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 60,
                    "data": {
                        "text/plain": "(103, 3)"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Geocoder not working"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "#geocoder not working\nlat_lng_coords = None\nfor index, row in df.iterrows():\n    while (lat_lng_coords is None):\n        g = geocoder.google('{}, Toronto, Ontario'.format(row['Postal Code']))\n        lat_lng_coords = g.latlng\n    row['Latitude'] = lat_lng_coords[0]\n    row['Longitude'] = lat_lng_coords[1]",
            "execution_count": null,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Using alternate approach"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "#using alternate approach\n!wget -q -O 'geospatial_data.csv' https://cocl.us/Geospatial_data\nlat_lng = pd.read_csv('geospatial_data.csv')",
            "execution_count": 52,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "lat_lng.head()",
            "execution_count": 53,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 53,
                    "data": {
                        "text/plain": "  Postal Code   Latitude  Longitude\n0         M1B  43.806686 -79.194353\n1         M1C  43.784535 -79.160497\n2         M1E  43.763573 -79.188711\n3         M1G  43.770992 -79.216917\n4         M1H  43.773136 -79.239476",
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Postal Code</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>M1B</td>\n      <td>43.806686</td>\n      <td>-79.194353</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>M1C</td>\n      <td>43.784535</td>\n      <td>-79.160497</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>M1E</td>\n      <td>43.763573</td>\n      <td>-79.188711</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>M1G</td>\n      <td>43.770992</td>\n      <td>-79.216917</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>M1H</td>\n      <td>43.773136</td>\n      <td>-79.239476</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "#merging dataframes\ndf = df.merge(lat_lng, on ='Postal Code', how ='left')\ndf.head()",
            "execution_count": 61,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 61,
                    "data": {
                        "text/plain": "  Postal Code           Borough                                Neighbourhood  \\\n0         M3A        North York                                    Parkwoods   \n1         M4A        North York                             Victoria Village   \n2         M5A  Downtown Toronto                    Regent Park, Harbourfront   \n3         M6A        North York             Lawrence Manor, Lawrence Heights   \n4         M7A  Downtown Toronto  Queen's Park, Ontario Provincial Government   \n\n    Latitude  Longitude  \n0  43.753259 -79.329656  \n1  43.725882 -79.315572  \n2  43.654260 -79.360636  \n3  43.718518 -79.464763  \n4  43.662301 -79.389494  ",
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Postal Code</th>\n      <th>Borough</th>\n      <th>Neighbourhood</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>M3A</td>\n      <td>North York</td>\n      <td>Parkwoods</td>\n      <td>43.753259</td>\n      <td>-79.329656</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>M4A</td>\n      <td>North York</td>\n      <td>Victoria Village</td>\n      <td>43.725882</td>\n      <td>-79.315572</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>M5A</td>\n      <td>Downtown Toronto</td>\n      <td>Regent Park, Harbourfront</td>\n      <td>43.654260</td>\n      <td>-79.360636</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>M6A</td>\n      <td>North York</td>\n      <td>Lawrence Manor, Lawrence Heights</td>\n      <td>43.718518</td>\n      <td>-79.464763</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>M7A</td>\n      <td>Downtown Toronto</td>\n      <td>Queen's Park, Ontario Provincial Government</td>\n      <td>43.662301</td>\n      <td>-79.389494</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "df.shape",
            "execution_count": 64,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 64,
                    "data": {
                        "text/plain": "(103, 5)"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Finding coordinates for Toronto"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "#Finding coordinates for Toronto\naddress = 'Toronto, Canada'\ngeolocator = Nominatim(user_agent=\"explorer\")\nlocation = geolocator.geocode(address)\nlatitude = location.latitude\nlongitude = location.longitude\nprint('The geograpical coordinate of Toronto are {}, {}.'.format(latitude, longitude))",
            "execution_count": 67,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "The geograpical coordinate of Toronto are 43.6534817, -79.3839347.\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {
                "scrolled": true
            },
            "cell_type": "code",
            "source": "# create map of Toronto latitude and longitude values\nmap_toronto = folium.Map(location=[latitude, longitude], zoom_start=10)\n\n# add markers to map\nfor lat, lng, borough, neighbourhood in zip(df['Latitude'], df['Longitude'], df['Borough'], df['Neighbourhood']):\n    label = '{}, {}'.format(neighbourhood, borough)\n    label = folium.Popup(label, parse_html=True)\n    folium.CircleMarker(\n        [lat, lng],\n        radius=5,\n        popup=label,\n        color='blue',\n        fill=True,\n        fill_color='#3186cc',\n        fill_opacity=0.7,\n        parse_html=False).add_to(map_toronto)\n    \nmap_toronto",
            "execution_count": 74,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 74,
                    "data": {
                        "text/plain": "<folium.folium.Map at 0x7f5ca1af7a90>",
                        "text/html": "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=<!DOCTYPE html>
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            <meta name="viewport" content="width=device-width,
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                #map_6a23895a308e4fc7b5a4a173db5ab345 {
                    position: relative;
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            <div class="folium-map" id="map_6a23895a308e4fc7b5a4a173db5ab345" ></div>
        
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                {
                    center: [43.6534817, -79.3839347],
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                    zoom: 10,
                    zoomControl: true,
                    preferCanvas: false,
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                {"attribution": "Data by \u0026copy; \u003ca href=\"http://openstreetmap.org\"\u003eOpenStreetMap\u003c/a\u003e, under \u003ca href=\"http://www.openstreetmap.org/copyright\"\u003eODbL\u003c/a\u003e.", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}
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            var circle_marker_a526da1eee7a4c6f872bf538f28865cd = 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_b5d4839bca8d4dd981ebb46a1d6490ec = L.popup({"maxWidth": "100%"});

        
            var html_65b4b396872d4d73b59ede7413e597b3 = $(`<div id="html_65b4b396872d4d73b59ede7413e597b3" style="width: 100.0%; height: 100.0%;">Parkwoods, North York</div>`)[0];
            popup_b5d4839bca8d4dd981ebb46a1d6490ec.setContent(html_65b4b396872d4d73b59ede7413e597b3);
        

        circle_marker_a526da1eee7a4c6f872bf538f28865cd.bindPopup(popup_b5d4839bca8d4dd981ebb46a1d6490ec)
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            ).addTo(map_6a23895a308e4fc7b5a4a173db5ab345);
        
    
        var popup_f93dc6ed97594d5db7f7fb3683407bbc = L.popup({"maxWidth": "100%"});

        
            var html_50ec8952853e4b588a90f5c8eeca679e = $(`<div id="html_50ec8952853e4b588a90f5c8eeca679e" style="width: 100.0%; height: 100.0%;">Victoria Village, North York</div>`)[0];
            popup_f93dc6ed97594d5db7f7fb3683407bbc.setContent(html_50ec8952853e4b588a90f5c8eeca679e);
        

        circle_marker_3611a232799f41d4a5a3fc3c6b23a9d9.bindPopup(popup_f93dc6ed97594d5db7f7fb3683407bbc)
        ;

        
    
    
            var circle_marker_cb7f353a01554dd68b2091068a995c36 = L.circleMarker(
                [43.6542599, -79.3606359],
                {"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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            popup_fb606d2714f5445696cbfdda9b2c2f91.setContent(html_d2fcaa9700e84b02aa58581291f21a19);
        

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                [43.718517999999996, -79.46476329999999],
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            var html_4e22353c60ad42dcb57c50969497ed91 = $(`<div id="html_4e22353c60ad42dcb57c50969497ed91" style="width: 100.0%; height: 100.0%;">The Kingsway, Montgomery Road, Old Mill North, Etobicoke</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>"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Defining Foursquare credentials"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "CLIENT_ID = '315JI21RCJPMNU5QHYDBU1SL3VRE5KFVYGKHE2YMTZQFPVLI' # your Foursquare ID\nCLIENT_SECRET = 'YXJFLJVB5DER5KC2TRX1BIOWB1GMKBTOXMJ1LRSUISVNSHXZ' # your Foursquare Secret\nVERSION = '20180605' # Foursquare API version",
            "execution_count": 75,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Filtering dataframe for boroughs containing Toronto only"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "df.Borough.unique()",
            "execution_count": 84,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 84,
                    "data": {
                        "text/plain": "array(['North York', 'Downtown Toronto', 'Etobicoke', 'Scarborough',\n       'East York', 'York', 'East Toronto', 'West Toronto',\n       'Central Toronto', 'Mississauga'], dtype=object)"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "df_toronto = df[(df.Borough == 'Downtown Toronto')|(df.Borough == 'East Toronto')|(df.Borough == 'West Toronto')|(df.Borough == 'Central Toronto') ]",
            "execution_count": 85,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "df_toronto.head()",
            "execution_count": 89,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 89,
                    "data": {
                        "text/plain": "   Postal Code           Borough                                Neighbourhood  \\\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  ",
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Postal Code</th>\n      <th>Borough</th>\n      <th>Neighbourhood</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </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>"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Function to explore neighbourhoods"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "def getNearbyVenues(names, latitudes, longitudes, radius=500):\n    \n    venues_list=[]\n    for name, lat, lng in zip(names, latitudes, longitudes):\n        print(name)\n            \n        # create the API request URL\n        url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n            CLIENT_ID, \n            CLIENT_SECRET, \n            VERSION, \n            lat, \n            lng, \n            radius, \n            LIMIT)\n            \n        # make the GET request\n        results = requests.get(url).json()[\"response\"]['groups'][0]['items']\n        \n        # return only relevant information for each nearby venue\n        venues_list.append([(\n            name, \n            lat, \n            lng, \n            v['venue']['name'], \n            v['venue']['location']['lat'], \n            v['venue']['location']['lng'],  \n            v['venue']['categories'][0]['name']) for v in results])\n\n    nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])\n    nearby_venues.columns = ['Neighbourhood', \n                  'Neighbourhood Latitude', \n                  'Neighbourhood Longitude', \n                  'Venue', \n                  'Venue Latitude', \n                  'Venue Longitude', \n                  'Venue Category']\n    \n    return(nearby_venues)",
            "execution_count": 100,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "LIMIT = 100\ntoronto_venues = getNearbyVenues(names=df_toronto['Neighbourhood'],\n                                   latitudes=df_toronto['Latitude'],\n                                   longitudes=df_toronto['Longitude']\n                                  )",
            "execution_count": 101,
            "outputs": [
                {
                    "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",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "toronto_venues.head()",
            "execution_count": 102,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 102,
                    "data": {
                        "text/plain": "               Neighbourhood  Neighbourhood Latitude  Neighbourhood 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  Cooper Koo Family YMCA       43.653249       -79.358008   \n3     Body Blitz Spa East       43.654735       -79.359874   \n4          Impact Kitchen       43.656369       -79.356980   \n\n        Venue Category  \n0               Bakery  \n1          Coffee Shop  \n2  Distribution Center  \n3                  Spa  \n4           Restaurant  ",
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Neighbourhood</th>\n      <th>Neighbourhood Latitude</th>\n      <th>Neighbourhood 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>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>3</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    <tr>\n      <th>4</th>\n      <td>Regent Park, Harbourfront</td>\n      <td>43.65426</td>\n      <td>-79.360636</td>\n      <td>Impact Kitchen</td>\n      <td>43.656369</td>\n      <td>-79.356980</td>\n      <td>Restaurant</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "len(toronto_venues['Venue Category'].unique())",
            "execution_count": 99,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 99,
                    "data": {
                        "text/plain": "233"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# one hot encoding\ntoronto_onehot = pd.get_dummies(toronto_venues[['Venue Category']], prefix=\"\", prefix_sep=\"\")\n\n# add neighbourhood column back to dataframe\ntoronto_onehot['Neighbourhood'] = toronto_venues['Neighbourhood'] \n\n# move neighbourhood 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()",
            "execution_count": 103,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 103,
                    "data": {
                        "text/plain": "               Neighbourhood  Afghan Restaurant  Airport  Airport Food Court  \\\n0  Regent Park, Harbourfront                  0        0                   0   \n1  Regent Park, Harbourfront                  0        0                   0   \n2  Regent Park, Harbourfront                  0        0                   0   \n3  Regent Park, Harbourfront                  0        0                   0   \n4  Regent Park, Harbourfront                  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  Aquarium  ...  Toy / Game Store  Trail  Train Station  \\\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   Vegetarian / Vegan Restaurant  Video Game Store  Vietnamese Restaurant  \\\n0                              0                 0                      0   \n1                              0                 0                      0   \n2                              0                 0                      0   \n3                              0                 0                      0   \n4                              0                 0                      0   \n\n   Wine Bar  Wine Shop  Women's Store  Yoga Studio  \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 234 columns]",
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Neighbourhood</th>\n      <th>Afghan Restaurant</th>\n      <th>Airport</th>\n      <th>Airport Food Court</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>Aquarium</th>\n      <th>...</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>Wine Shop</th>\n      <th>Women's Store</th>\n      <th>Yoga Studio</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Regent Park, Harbourfront</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>Regent Park, Harbourfront</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>Regent Park, Harbourfront</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>Regent Park, Harbourfront</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>Regent Park, Harbourfront</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 234 columns</p>\n</div>"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "toronto_grouped = toronto_onehot.groupby('Neighbourhood').mean().reset_index()\ntoronto_grouped",
            "execution_count": 104,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 104,
                    "data": {
                        "text/plain": "                                        Neighbourhood  Afghan Restaurant  \\\n0                                         Berczy Park           0.000000   \n1        Brockton, Parkdale Village, Exhibition Place           0.000000   \n2   Business reply mail Processing Centre, South C...           0.000000   \n3   CN Tower, King and Spadina, Railway Lands, Har...           0.000000   \n4                                  Central Bay Street           0.000000   \n5                                            Christie           0.000000   \n6                                Church and Wellesley           0.013333   \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           0.000000   \n20                        Moore Park, Summerhill East           0.000000   \n21                  North Toronto West, Lawrence Park           0.000000   \n22                             Parkdale, Roncesvalles           0.000000   \n23        Queen's Park, Ontario Provincial Government           0.000000   \n24                          Regent Park, Harbourfront           0.000000   \n25                           Richmond, Adelaide, King           0.000000   \n26                                           Rosedale           0.000000   \n27                                           Roselawn           0.000000   \n28                                 Runnymede, Swansea           0.000000   \n29                                     St. James Town           0.000000   \n30                        St. James Town, Cabbagetown           0.000000   \n31                                     Stn A PO Boxes           0.000000   \n32                                    Studio District           0.000000   \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           0.000000   \n37           Toronto Dominion Centre, Design Exchange           0.000000   \n38                     University of Toronto, Harbord           0.000000   \n\n     Airport  Airport Food Court  Airport Lounge  Airport Service  \\\n0   0.000000            0.000000        0.000000              0.0   \n1   0.000000            0.000000        0.000000              0.0   \n2   0.000000            0.000000        0.000000              0.0   \n3   0.066667            0.066667        0.133333              0.2   \n4   0.000000            0.000000        0.000000              0.0   \n5   0.000000            0.000000        0.000000              0.0   \n6   0.000000            0.000000        0.000000              0.0   \n7   0.000000            0.000000        0.000000              0.0   \n8   0.000000            0.000000        0.000000              0.0   \n9   0.000000            0.000000        0.000000              0.0   \n10  0.000000            0.000000        0.000000              0.0   \n11  0.000000            0.000000        0.000000              0.0   \n12  0.000000            0.000000        0.000000              0.0   \n13  0.000000            0.000000        0.000000              0.0   \n14  0.000000            0.000000        0.000000              0.0   \n15  0.000000            0.000000        0.000000              0.0   \n16  0.000000            0.000000        0.000000              0.0   \n17  0.000000            0.000000        0.000000              0.0   \n18  0.000000            0.000000        0.000000              0.0   \n19  0.000000            0.000000        0.000000              0.0   \n20  0.000000            0.000000        0.000000              0.0   \n21  0.000000            0.000000        0.000000              0.0   \n22  0.000000            0.000000        0.000000              0.0   \n23  0.000000            0.000000        0.000000              0.0   \n24  0.000000            0.000000        0.000000              0.0   \n25  0.000000            0.000000        0.000000              0.0   \n26  0.000000            0.000000        0.000000              0.0   \n27  0.000000            0.000000        0.000000              0.0   \n28  0.000000            0.000000        0.000000              0.0   \n29  0.000000            0.000000        0.000000              0.0   \n30  0.000000            0.000000        0.000000              0.0   \n31  0.000000            0.000000        0.000000              0.0   \n32  0.000000            0.000000        0.000000              0.0   \n33  0.000000            0.000000        0.000000              0.0   \n34  0.000000            0.000000        0.000000              0.0   \n35  0.000000            0.000000        0.000000              0.0   \n36  0.000000            0.000000        0.000000              0.0   \n37  0.000000            0.000000        0.000000              0.0   \n38  0.000000            0.000000        0.000000              0.0   \n\n    Airport Terminal  American Restaurant  Antique Shop  Aquarium  ...  \\\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           0.133333             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           0.000000             0.013333      0.000000      0.00  ...   \n7           0.000000             0.040000      0.000000      0.00  ...   \n8           0.000000             0.000000      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             0.030000      0.000000      0.00  ...   \n12          0.000000             0.000000      0.000000      0.00  ...   \n13          0.000000             0.000000      0.000000      0.00  ...   \n14          0.000000             0.000000      0.000000      0.05  ...   \n15          0.000000             0.000000      0.041667      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      0.000000      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      0.022222      0.00  ...   \n25          0.000000             0.020000      0.000000      0.00  ...   \n26          0.000000             0.000000      0.000000      0.00  ...   \n27          0.000000             0.000000      0.000000      0.00  ...   \n28          0.000000             0.000000      0.000000      0.00  ...   \n29          0.000000             0.034091      0.000000      0.00  ...   \n30          0.000000             0.022727      0.000000      0.00  ...   \n31          0.000000             0.010309      0.010309      0.00  ...   \n32          0.000000             0.048780      0.000000      0.00  ...   \n33          0.000000             0.062500      0.000000      0.00  ...   \n34          0.000000             0.000000      0.000000      0.00  ...   \n35          0.000000             0.000000      0.000000      0.00  ...   \n36          0.000000             0.023810      0.000000      0.00  ...   \n37          0.000000             0.030000      0.000000      0.00  ...   \n38          0.000000             0.000000      0.000000      0.00  ...   \n\n    Toy / Game Store    Trail  Train Station  Vegetarian / Vegan Restaurant  \\\n0           0.000000  0.00000           0.00                       0.017544   \n1           0.000000  0.00000           0.00                       0.000000   \n2           0.000000  0.00000           0.00                       0.000000   \n3           0.000000  0.00000           0.00                       0.000000   \n4           0.000000  0.00000           0.00                       0.015873   \n5           0.000000  0.00000           0.00                       0.000000   \n6           0.000000  0.00000           0.00                       0.000000   \n7           0.000000  0.00000           0.00                       0.020000   \n8           0.030303  0.00000           0.00                       0.000000   \n9           0.000000  0.00000           0.00                       0.000000   \n10          0.000000  0.00000           0.00                       0.000000   \n11          0.000000  0.00000           0.01                       0.010000   \n12          0.000000  0.25000           0.00                       0.000000   \n13          0.010000  0.00000           0.00                       0.000000   \n14          0.000000  0.00000           0.01                       0.010000   \n15          0.000000  0.00000           0.00                       0.000000   \n16          0.000000  0.00000           0.00                       0.000000   \n17          0.000000  0.00000           0.00                       0.061538   \n18          0.000000  0.00000           0.00                       0.000000   \n19          0.000000  0.00000           0.00                       0.021277   \n20          0.000000  0.00000           0.00                       0.000000   \n21          0.000000  0.00000           0.00                       0.000000   \n22          0.000000  0.00000           0.00                       0.000000   \n23          0.000000  0.00000           0.00                       0.000000   \n24          0.000000  0.00000           0.00                       0.000000   \n25          0.000000  0.00000           0.00                       0.010000   \n26          0.000000  0.25000           0.00                       0.000000   \n27          0.000000  0.00000           0.00                       0.000000   \n28          0.000000  0.00000           0.00                       0.030303   \n29          0.000000  0.00000           0.00                       0.011364   \n30          0.000000  0.00000           0.00                       0.000000   \n31          0.000000  0.00000           0.00                       0.010309   \n32          0.000000  0.00000           0.00                       0.000000   \n33          0.000000  0.00000           0.00                       0.000000   \n34          0.000000  0.00000           0.00                       0.045455   \n35          0.000000  0.25000           0.00                       0.000000   \n36          0.000000  0.02381           0.00                       0.000000   \n37          0.000000  0.00000           0.01                       0.010000   \n38          0.000000  0.00000           0.00                       0.000000   \n\n    Video Game Store  Vietnamese Restaurant  Wine Bar  Wine Shop  \\\n0           0.000000               0.000000  0.000000   0.000000   \n1           0.000000               0.000000  0.000000   0.000000   \n2           0.000000               0.000000  0.000000   0.000000   \n3           0.000000               0.000000  0.000000   0.000000   \n4           0.000000               0.000000  0.015873   0.000000   \n5           0.000000               0.000000  0.000000   0.000000   \n6           0.000000               0.000000  0.000000   0.000000   \n7           0.000000               0.000000  0.010000   0.000000   \n8           0.000000               0.000000  0.000000   0.000000   \n9           0.000000               0.000000  0.000000   0.000000   \n10          0.000000               0.000000  0.000000   0.000000   \n11          0.000000               0.000000  0.010000   0.000000   \n12          0.000000               0.000000  0.000000   0.000000   \n13          0.000000               0.010000  0.010000   0.000000   \n14          0.000000               0.000000  0.010000   0.000000   \n15          0.000000               0.000000  0.000000   0.000000   \n16          0.000000               0.000000  0.000000   0.000000   \n17          0.000000               0.046154  0.015385   0.000000   \n18          0.000000               0.000000  0.000000   0.000000   \n19          0.000000               0.042553  0.021277   0.000000   \n20          0.000000               0.000000  0.000000   0.000000   \n21          0.000000               0.000000  0.000000   0.000000   \n22          0.000000               0.000000  0.000000   0.000000   \n23          0.000000               0.000000  0.000000   0.000000   \n24          0.000000               0.000000  0.000000   0.022222   \n25          0.000000               0.000000  0.000000   0.000000   \n26          0.000000               0.000000  0.000000   0.000000   \n27          0.000000               0.000000  0.000000   0.000000   \n28          0.000000               0.000000  0.000000   0.000000   \n29          0.000000               0.000000  0.011364   0.000000   \n30          0.000000               0.000000  0.000000   0.000000   \n31          0.000000               0.000000  0.000000   0.000000   \n32          0.000000               0.000000  0.024390   0.000000   \n33          0.000000               0.062500  0.000000   0.000000   \n34          0.000000               0.000000  0.000000   0.000000   \n35          0.000000               0.000000  0.000000   0.000000   \n36          0.000000               0.000000  0.000000   0.000000   \n37          0.000000               0.000000  0.010000   0.000000   \n38          0.028571               0.000000  0.000000   0.000000   \n\n    Women's Store  Yoga Studio  \n0            0.00     0.000000  \n1            0.00     0.000000  \n2            0.00     0.052632  \n3            0.00     0.000000  \n4            0.00     0.015873  \n5            0.00     0.000000  \n6            0.00     0.026667  \n7            0.00     0.000000  \n8            0.00     0.000000  \n9            0.00     0.000000  \n10           0.00     0.000000  \n11           0.00     0.000000  \n12           0.00     0.000000  \n13           0.00     0.000000  \n14           0.00     0.000000  \n15           0.00     0.000000  \n16           0.00     0.000000  \n17           0.00     0.000000  \n18           0.00     0.000000  \n19           0.00     0.021277  \n20           0.00     0.000000  \n21           0.00     0.047619  \n22           0.00     0.000000  \n23           0.00     0.029412  \n24           0.00     0.022222  \n25           0.01     0.000000  \n26           0.00     0.000000  \n27           0.00     0.000000  \n28           0.00     0.030303  \n29           0.00     0.000000  \n30           0.00     0.000000  \n31           0.00     0.010309  \n32           0.00     0.024390  \n33           0.00     0.000000  \n34           0.00     0.000000  \n35           0.00     0.000000  \n36           0.00     0.023810  \n37           0.00     0.000000  \n38           0.00     0.028571  \n\n[39 rows x 234 columns]",
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Neighbourhood</th>\n      <th>Afghan Restaurant</th>\n      <th>Airport</th>\n      <th>Airport Food Court</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>Aquarium</th>\n      <th>...</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>Wine Shop</th>\n      <th>Women's Store</th>\n      <th>Yoga Studio</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Berczy Park</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n      <td>0.00</td>\n      <td>0.017544</td>\n      <td>0.000000</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    </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>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00000</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>0.00</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Business reply mail Processing Centre, South C...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00000</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>0.00</td>\n      <td>0.052632</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>0.066667</td>\n      <td>0.066667</td>\n      <td>0.133333</td>\n      <td>0.2</td>\n      <td>0.133333</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00000</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>0.00</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Central Bay Street</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n      <td>0.00</td>\n      <td>0.015873</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.015873</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.015873</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>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00000</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>0.00</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Church and Wellesley</td>\n      <td>0.013333</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.013333</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00000</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>0.00</td>\n      <td>0.026667</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>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.040000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00000</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>0.00</td>\n      <td>0.000000</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>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.030303</td>\n      <td>0.00000</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>0.00</td>\n      <td>0.000000</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>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00000</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>0.00</td>\n      <td>0.000000</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>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00000</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>0.00</td>\n      <td>0.000000</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>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.030000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00000</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>0.00</td>\n      <td>0.000000</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>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.25000</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>0.00</td>\n      <td>0.000000</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>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.010000</td>\n      <td>0.00000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.010000</td>\n      <td>0.010000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</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>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.05</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00000</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>0.00</td>\n      <td>0.000000</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>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.041667</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00000</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>0.00</td>\n      <td>0.000000</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>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00000</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>0.00</td>\n      <td>0.000000</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>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n      <td>0.00</td>\n      <td>0.061538</td>\n      <td>0.000000</td>\n      <td>0.046154</td>\n      <td>0.015385</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</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>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00000</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>0.00</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Little Portugal, Trinity</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n      <td>0.00</td>\n      <td>0.021277</td>\n      <td>0.000000</td>\n      <td>0.042553</td>\n      <td>0.021277</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.021277</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>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00000</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>0.00</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>North Toronto West, Lawrence Park</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00000</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>0.00</td>\n      <td>0.047619</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>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00000</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>0.00</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Queen's Park, Ontario Provincial Government</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00000</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>0.00</td>\n      <td>0.029412</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>Regent Park, Harbourfront</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.022222</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00000</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>0.022222</td>\n      <td>0.00</td>\n      <td>0.022222</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>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.020000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n      <td>0.00</td>\n      <td>0.010000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.01</td>\n      <td>0.000000</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>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.25000</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>0.00</td>\n      <td>0.000000</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>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00000</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>0.00</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>Runnymede, Swansea</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n      <td>0.00</td>\n      <td>0.030303</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.030303</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>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.034091</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n      <td>0.00</td>\n      <td>0.011364</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.011364</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</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>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.022727</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00000</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>0.00</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>Stn A PO Boxes</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.010309</td>\n      <td>0.010309</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n      <td>0.00</td>\n      <td>0.010309</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.010309</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>Studio District</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.048780</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00000</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>0.00</td>\n      <td>0.024390</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>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.062500</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00000</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>0.00</td>\n      <td>0.000000</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>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n      <td>0.00</td>\n      <td>0.045455</td>\n      <td>0.000000</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    </tr>\n    <tr>\n      <th>35</th>\n      <td>The Beaches</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.25000</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>0.00</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>The Danforth West, Riverdale</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.023810</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.02381</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>0.00</td>\n      <td>0.023810</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>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.030000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00000</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>0.00</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>University of Toronto, Harbord</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00000</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>0.00</td>\n      <td>0.028571</td>\n    </tr>\n  </tbody>\n</table>\n<p>39 rows \u00d7 234 columns</p>\n</div>"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "num_top_venues = 5\n\nfor hood in toronto_grouped['Neighbourhood']:\n    print(\"----\"+hood+\"----\")\n    temp = toronto_grouped[toronto_grouped['Neighbourhood'] == hood].T.reset_index()\n    temp.columns = ['venue','freq']\n    temp = temp.iloc[1:]\n    temp['freq'] = temp['freq'].astype(float)\n    temp = temp.round({'freq': 2})\n    print(temp.sort_values('freq', ascending=False).reset_index(drop=True).head(num_top_venues))\n    print('\\n')",
            "execution_count": 105,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "----Berczy Park----\n                venue  freq\n0         Coffee Shop  0.11\n1                Caf\u00e9  0.04\n2        Cocktail Bar  0.04\n3  Seafood Restaurant  0.04\n4         Cheese Shop  0.04\n\n\n----Brockton, Parkdale Village, Exhibition Place----\n            venue  freq\n0            Caf\u00e9  0.12\n1       Nightclub  0.08\n2     Coffee Shop  0.08\n3          Bakery  0.08\n4  Breakfast Spot  0.08\n\n\n----Business reply mail Processing Centre, South Central Letter Processing Plant Toronto----\n                  venue  freq\n0    Light Rail Station  0.11\n1           Yoga Studio  0.05\n2                   Spa  0.05\n3  Gym / Fitness Center  0.05\n4         Garden Center  0.05\n\n\n----CN Tower, King and Spadina, Railway Lands, Harbourfront West, Bathurst Quay, South Niagara, Island airport----\n              venue  freq\n0   Airport Service  0.20\n1    Airport Lounge  0.13\n2  Airport Terminal  0.13\n3   Harbor / Marina  0.07\n4               Bar  0.07\n\n\n----Central Bay Street----\n                 venue  freq\n0          Coffee Shop  0.17\n1       Sandwich Place  0.06\n2                 Caf\u00e9  0.05\n3   Italian Restaurant  0.05\n4  Japanese Restaurant  0.05\n\n\n----Christie----\n                venue  freq\n0       Grocery Store  0.24\n1                Caf\u00e9  0.18\n2                Park  0.12\n3          Baby Store  0.06\n4  Athletics & Sports  0.06\n\n\n----Church and Wellesley----\n                 venue  freq\n0          Coffee Shop  0.11\n1  Japanese Restaurant  0.07\n2     Sushi Restaurant  0.05\n3           Restaurant  0.04\n4              Gay Bar  0.04\n\n\n----Commerce Court, Victoria Hotel----\n                 venue  freq\n0          Coffee Shop  0.14\n1                 Caf\u00e9  0.07\n2           Restaurant  0.07\n3                Hotel  0.06\n4  American Restaurant  0.04\n\n\n----Davisville----\n            venue  freq\n0     Pizza Place  0.09\n1  Sandwich Place  0.09\n2    Dessert Shop  0.09\n3            Caf\u00e9  0.06\n4             Gym  0.06\n\n\n----Davisville North----\n              venue  freq\n0       Pizza Place  0.12\n1              Park  0.12\n2    Breakfast Spot  0.12\n3  Department Store  0.12\n4             Hotel  0.12\n\n\n----Dufferin, Dovercourt Village----\n       venue  freq\n0   Pharmacy  0.12\n1     Bakery  0.12\n2    Brewery  0.06\n3        Bar  0.06\n4  Pet Store  0.06\n\n\n----First Canadian Place, Underground city----\n                 venue  freq\n0          Coffee Shop  0.12\n1                 Caf\u00e9  0.08\n2                Hotel  0.05\n3           Restaurant  0.04\n4  Japanese Restaurant  0.04\n\n\n----Forest Hill North & West, Forest Hill Road Park----\n               venue  freq\n0           Bus Line  0.25\n1              Trail  0.25\n2   Sushi Restaurant  0.25\n3      Jewelry Store  0.25\n4  Afghan Restaurant  0.00\n\n\n----Garden District, Ryerson----\n                 venue  freq\n0       Clothing Store  0.10\n1          Coffee Shop  0.09\n2                 Caf\u00e9  0.04\n3  Japanese Restaurant  0.03\n4      Bubble Tea Shop  0.03\n\n\n----Harbourfront East, Union Station, Toronto Islands----\n         venue  freq\n0  Coffee Shop  0.13\n1     Aquarium  0.05\n2         Caf\u00e9  0.04\n3        Hotel  0.04\n4      Brewery  0.03\n\n\n----High Park, The Junction South----\n                       venue  freq\n0         Mexican Restaurant  0.08\n1                       Caf\u00e9  0.08\n2            Thai Restaurant  0.08\n3  Cajun / Creole Restaurant  0.04\n4                Flea Market  0.04\n\n\n----India Bazaar, The Beaches West----\n                venue  freq\n0         Pizza Place  0.06\n1  Italian Restaurant  0.06\n2   Fish & Chips Shop  0.06\n3             Brewery  0.06\n4       Movie Theater  0.06\n\n\n----Kensington Market, Chinatown, Grange Park----\n                           venue  freq\n0                           Caf\u00e9  0.08\n1                    Coffee Shop  0.06\n2  Vegetarian / Vegan Restaurant  0.06\n3             Mexican Restaurant  0.05\n4          Vietnamese Restaurant  0.05\n\n\n----Lawrence Park----\n               venue  freq\n0               Park  0.33\n1           Bus Line  0.33\n2        Swim School  0.33\n3  Afghan Restaurant  0.00\n4             Museum  0.00\n\n\n----Little Portugal, Trinity----\n                   venue  freq\n0                    Bar  0.11\n1            Coffee Shop  0.06\n2  Vietnamese Restaurant  0.04\n3            Men's Store  0.04\n4                   Caf\u00e9  0.04\n\n\n----Moore Park, Summerhill East----\n                   venue  freq\n0             Playground  0.33\n1                   Park  0.33\n2           Tennis Court  0.33\n3              Pet Store  0.00\n4  Performing Arts Venue  0.00\n\n\n----North Toronto West, Lawrence Park----\n            venue  freq\n0     Coffee Shop  0.10\n1  Clothing Store  0.10\n2     Yoga Studio  0.05\n3      Bagel Shop  0.05\n4       Pet Store  0.05\n\n\n----Parkdale, Roncesvalles----\n                         venue  freq\n0               Breakfast Spot  0.14\n1                    Gift Shop  0.14\n2                      Dog Run  0.07\n3                   Restaurant  0.07\n4  Eastern European Restaurant  0.07\n\n\n----Queen's Park, Ontario Provincial Government----\n                   venue  freq\n0            Coffee Shop  0.26\n1                  Diner  0.06\n2            Yoga Studio  0.03\n3                   Caf\u00e9  0.03\n4  General Entertainment  0.03\n\n\n----Regent Park, Harbourfront----\n            venue  freq\n0     Coffee Shop  0.18\n1          Bakery  0.07\n2            Park  0.07\n3             Pub  0.07\n4  Breakfast Spot  0.04\n\n\n----Richmond, Adelaide, King----\n            venue  freq\n0     Coffee Shop  0.10\n1            Caf\u00e9  0.05\n2  Clothing Store  0.04\n3           Hotel  0.04\n4      Restaurant  0.04\n\n\n----Rosedale----\n         venue  freq\n0         Park  0.50\n1   Playground  0.25\n2        Trail  0.25\n3  Music Venue  0.00\n4  Men's Store  0.00\n\n\n----Roselawn----\n                venue  freq\n0              Garden   0.5\n1        Home Service   0.5\n2   Afghan Restaurant   0.0\n3         Men's Store   0.0\n4  Mexican Restaurant   0.0\n\n\n----Runnymede, Swansea----\n                venue  freq\n0                Caf\u00e9  0.09\n1         Coffee Shop  0.09\n2                 Pub  0.06\n3         Pizza Place  0.06\n4  Italian Restaurant  0.06\n\n\n----St. James Town----\n            venue  freq\n0     Coffee Shop  0.06\n1            Caf\u00e9  0.06\n2      Restaurant  0.05\n3  Clothing Store  0.05\n4    Cocktail Bar  0.03\n\n\n----St. James Town, Cabbagetown----\n                venue  freq\n0         Coffee Shop  0.07\n1          Restaurant  0.07\n2                Caf\u00e9  0.07\n3  Italian Restaurant  0.05\n4                 Pub  0.05\n\n\n----Stn A PO Boxes----\n                venue  freq\n0         Coffee Shop  0.12\n1  Italian Restaurant  0.04\n2                Caf\u00e9  0.04\n3          Restaurant  0.03\n4            Beer Bar  0.03\n\n\n----Studio District----\n                 venue  freq\n0                 Caf\u00e9  0.10\n1          Coffee Shop  0.07\n2              Brewery  0.05\n3               Bakery  0.05\n4  American Restaurant  0.05\n\n\n----Summerhill West, Rathnelly, South Hill, Forest Hill SE, Deer Park----\n          venue  freq\n0           Pub  0.12\n1   Coffee Shop  0.12\n2  Liquor Store  0.06\n3    Bagel Shop  0.06\n4    Restaurant  0.06\n\n\n----The Annex, North Midtown, Yorkville----\n               venue  freq\n0               Caf\u00e9  0.14\n1     Sandwich Place  0.14\n2        Coffee Shop  0.09\n3           Pharmacy  0.05\n4  Indian Restaurant  0.05\n\n\n----The Beaches----\n               venue  freq\n0  Health Food Store  0.25\n1       Neighborhood  0.25\n2                Pub  0.25\n3              Trail  0.25\n4        Men's Store  0.00\n\n\n----The Danforth West, Riverdale----\n                    venue  freq\n0        Greek Restaurant  0.19\n1             Coffee Shop  0.07\n2      Italian Restaurant  0.07\n3  Furniture / Home Store  0.05\n4          Ice Cream Shop  0.05\n\n\n----Toronto Dominion Centre, Design Exchange----\n         venue  freq\n0  Coffee Shop  0.13\n1        Hotel  0.07\n2         Caf\u00e9  0.06\n3   Restaurant  0.04\n4  Salad Place  0.03\n\n\n----University of Toronto, Harbord----\n                 venue  freq\n0                 Caf\u00e9  0.14\n1  Japanese Restaurant  0.06\n2       Sandwich Place  0.06\n3            Bookstore  0.06\n4               Bakery  0.06\n\n\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "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]",
            "execution_count": 106,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "num_top_venues = 10\n\nindicators = ['st', 'nd', 'rd']\n\n# create columns according to number of top venues\ncolumns = ['Neighbourhood']\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\nneighbourhoods_venues_sorted = pd.DataFrame(columns=columns)\nneighbourhoods_venues_sorted['Neighbourhood'] = toronto_grouped['Neighbourhood']\n\nfor ind in np.arange(toronto_grouped.shape[0]):\n    neighbourhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(toronto_grouped.iloc[ind, :], num_top_venues)\n\nneighbourhoods_venues_sorted.head()",
            "execution_count": 128,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 128,
                    "data": {
                        "text/plain": "                                       Neighbourhood 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 Service   \n4                                 Central Bay Street           Coffee Shop   \n\n  2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue  \\\n0    Seafood Restaurant                Bakery        Farmers Market   \n1                Bakery        Breakfast Spot             Nightclub   \n2           Yoga Studio         Auto Workshop            Skate Park   \n3        Airport Lounge      Airport Terminal       Harbor / Marina   \n4        Sandwich Place                  Caf\u00e9    Italian Restaurant   \n\n  5th Most Common Venue   6th Most Common Venue 7th Most Common Venue  \\\n0            Restaurant             Cheese Shop          Cocktail Bar   \n1           Coffee Shop  Furniture / Home Store            Restaurant   \n2         Burrito Place                 Butcher                   Spa   \n3      Sculpture Garden     Rental Car Location                 Plane   \n4   Japanese Restaurant        Department Store       Bubble Tea Shop   \n\n  8th Most Common Venue 9th Most Common Venue 10th Most Common Venue  \n0                  Caf\u00e9              Beer Bar       Department Store  \n1               Stadium    Italian Restaurant           Intersection  \n2        Farmers Market  Fast Food Restaurant             Restaurant  \n3                   Bar         Boat or Ferry     Airport Food Court  \n4           Salad Place          Burger Joint     Miscellaneous Shop  ",
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Neighbourhood</th>\n      <th>1st Most Common Venue</th>\n      <th>2nd Most Common Venue</th>\n      <th>3rd Most Common Venue</th>\n      <th>4th Most Common Venue</th>\n      <th>5th Most Common Venue</th>\n      <th>6th Most Common Venue</th>\n      <th>7th Most Common Venue</th>\n      <th>8th Most Common Venue</th>\n      <th>9th Most Common Venue</th>\n      <th>10th Most Common Venue</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Berczy Park</td>\n      <td>Coffee Shop</td>\n      <td>Seafood Restaurant</td>\n      <td>Bakery</td>\n      <td>Farmers Market</td>\n      <td>Restaurant</td>\n      <td>Cheese Shop</td>\n      <td>Cocktail Bar</td>\n      <td>Caf\u00e9</td>\n      <td>Beer Bar</td>\n      <td>Department Store</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Brockton, Parkdale Village, Exhibition Place</td>\n      <td>Caf\u00e9</td>\n      <td>Bakery</td>\n      <td>Breakfast Spot</td>\n      <td>Nightclub</td>\n      <td>Coffee Shop</td>\n      <td>Furniture / Home Store</td>\n      <td>Restaurant</td>\n      <td>Stadium</td>\n      <td>Italian Restaurant</td>\n      <td>Intersection</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>Skate Park</td>\n      <td>Burrito Place</td>\n      <td>Butcher</td>\n      <td>Spa</td>\n      <td>Farmers Market</td>\n      <td>Fast Food Restaurant</td>\n      <td>Restaurant</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CN Tower, King and Spadina, Railway Lands, Har...</td>\n      <td>Airport Service</td>\n      <td>Airport Lounge</td>\n      <td>Airport Terminal</td>\n      <td>Harbor / Marina</td>\n      <td>Sculpture Garden</td>\n      <td>Rental Car Location</td>\n      <td>Plane</td>\n      <td>Bar</td>\n      <td>Boat or Ferry</td>\n      <td>Airport Food Court</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Central Bay Street</td>\n      <td>Coffee Shop</td>\n      <td>Sandwich Place</td>\n      <td>Caf\u00e9</td>\n      <td>Italian Restaurant</td>\n      <td>Japanese Restaurant</td>\n      <td>Department Store</td>\n      <td>Bubble Tea Shop</td>\n      <td>Salad Place</td>\n      <td>Burger Joint</td>\n      <td>Miscellaneous Shop</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# set number of clusters\nkclusters = 3\n\ntoronto_grouped_clustering = toronto_grouped.drop('Neighbourhood', 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] ",
            "execution_count": 129,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 129,
                    "data": {
                        "text/plain": "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32)"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# add clustering labels\nneighbourhoods_venues_sorted.insert(0, 'Cluster Labels', kmeans.labels_)\n\ntoronto_merged = df_toronto\n\n# merge toronto_grouped with toronto_data to add latitude/longitude for each neighborhood\ntoronto_merged = toronto_merged.join(neighbourhoods_venues_sorted.set_index('Neighbourhood'), on='Neighbourhood')\n\ntoronto_merged.head() # check the last columns!",
            "execution_count": 130,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 130,
                    "data": {
                        "text/plain": "   Postal Code           Borough                                Neighbourhood  \\\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               0           Coffee Shop   \n4   43.662301 -79.389494               0           Coffee Shop   \n9   43.657162 -79.378937               0        Clothing Store   \n15  43.651494 -79.375418               0                  Caf\u00e9   \n19  43.676357 -79.293031               0          Neighborhood   \n\n   2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue  \\\n2                 Bakery                  Park                   Pub   \n4                  Diner              Creperie              Beer Bar   \n9            Coffee Shop                  Caf\u00e9        Cosmetics Shop   \n15           Coffee Shop            Restaurant        Clothing Store   \n19     Health Food Store                   Pub                 Trail   \n\n   5th Most Common Venue        6th Most Common Venue 7th Most Common Venue  \\\n2                   Caf\u00e9               Breakfast Spot               Theater   \n4          Smoothie Shop               Sandwich Place         Burrito Place   \n9        Bubble Tea Shop           Italian Restaurant   Japanese Restaurant   \n15          Cocktail Bar               Cosmetics Shop   American Restaurant   \n19     Electronics Store  Eastern European Restaurant   Dumpling Restaurant   \n\n   8th Most Common Venue 9th Most Common Venue 10th Most Common Venue  \n2             Beer Store        Ice Cream Shop         Chocolate Shop  \n4                   Caf\u00e9    College Auditorium                   Park  \n9                  Diner             Bookstore                  Hotel  \n15        Breakfast Spot                  Park    Moroccan Restaurant  \n19            Donut Shop         Deli / Bodega       Doner Restaurant  ",
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Postal Code</th>\n      <th>Borough</th>\n      <th>Neighbourhood</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n      <th>Cluster Labels</th>\n      <th>1st Most Common Venue</th>\n      <th>2nd Most Common Venue</th>\n      <th>3rd Most Common Venue</th>\n      <th>4th Most Common Venue</th>\n      <th>5th Most Common Venue</th>\n      <th>6th Most Common Venue</th>\n      <th>7th Most Common Venue</th>\n      <th>8th Most Common Venue</th>\n      <th>9th Most Common Venue</th>\n      <th>10th Most Common Venue</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>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>0</td>\n      <td>Coffee Shop</td>\n      <td>Bakery</td>\n      <td>Park</td>\n      <td>Pub</td>\n      <td>Caf\u00e9</td>\n      <td>Breakfast Spot</td>\n      <td>Theater</td>\n      <td>Beer Store</td>\n      <td>Ice Cream Shop</td>\n      <td>Chocolate Shop</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>0</td>\n      <td>Coffee Shop</td>\n      <td>Diner</td>\n      <td>Creperie</td>\n      <td>Beer Bar</td>\n      <td>Smoothie Shop</td>\n      <td>Sandwich Place</td>\n      <td>Burrito Place</td>\n      <td>Caf\u00e9</td>\n      <td>College Auditorium</td>\n      <td>Park</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>0</td>\n      <td>Clothing Store</td>\n      <td>Coffee Shop</td>\n      <td>Caf\u00e9</td>\n      <td>Cosmetics Shop</td>\n      <td>Bubble Tea Shop</td>\n      <td>Italian Restaurant</td>\n      <td>Japanese Restaurant</td>\n      <td>Diner</td>\n      <td>Bookstore</td>\n      <td>Hotel</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>0</td>\n      <td>Caf\u00e9</td>\n      <td>Coffee Shop</td>\n      <td>Restaurant</td>\n      <td>Clothing Store</td>\n      <td>Cocktail Bar</td>\n      <td>Cosmetics Shop</td>\n      <td>American Restaurant</td>\n      <td>Breakfast Spot</td>\n      <td>Park</td>\n      <td>Moroccan Restaurant</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>0</td>\n      <td>Neighborhood</td>\n      <td>Health Food Store</td>\n      <td>Pub</td>\n      <td>Trail</td>\n      <td>Electronics Store</td>\n      <td>Eastern European Restaurant</td>\n      <td>Dumpling Restaurant</td>\n      <td>Donut Shop</td>\n      <td>Deli / Bodega</td>\n      <td>Doner Restaurant</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# 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['Neighbourhood'], toronto_merged['Cluster Labels']):\n    label = folium.Popup(str(poi) + ' Cluster ' + str(cluster), parse_html=True)\n    folium.CircleMarker(\n        [lat, lon],\n        radius=5,\n        popup=label,\n        color=rainbow[cluster-1],\n        fill=True,\n        fill_color=rainbow[cluster-1],\n        fill_opacity=0.7).add_to(map_clusters)\n       \nmap_clusters",
            "execution_count": 131,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 131,
                    "data": {
                        "text/plain": "<folium.folium.Map at 0x7f5c9851fb00>",
                        "text/html": "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=<!DOCTYPE html>
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        <script>
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            var circle_marker_80f2a8c064e347f49f327fc400eaa94a = L.circleMarker(
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                {"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 circle_marker_c8013f838e2346dd8d11d1305c4c5e1a = L.circleMarker(
                [43.6408157, -79.38175229999999],
                {"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}
            ).addTo(map_923f228c3972479ba18b94ffe8be8173);
        
    
        var popup_8c09d929db27492fae4d38cedcdb9d69 = L.popup({"maxWidth": "100%"});

        
            var html_358ddbce55ca4b468d0d99c96212ec3b = $(`<div id="html_358ddbce55ca4b468d0d99c96212ec3b" style="width: 100.0%; height: 100.0%;">Harbourfront East, Union Station, Toronto Islands Cluster 0</div>`)[0];
            popup_8c09d929db27492fae4d38cedcdb9d69.setContent(html_358ddbce55ca4b468d0d99c96212ec3b);
        

        circle_marker_c8013f838e2346dd8d11d1305c4c5e1a.bindPopup(popup_8c09d929db27492fae4d38cedcdb9d69)
        ;

        
    
    
            var circle_marker_81c9f4becf194a8cbde865569a20c0f0 = L.circleMarker(
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                {"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}
            ).addTo(map_923f228c3972479ba18b94ffe8be8173);
        
    
        var popup_d9f78095c49f4730bcad5662d207b27b = L.popup({"maxWidth": "100%"});

        
            var html_8fd22b89c49b4795bc086d41f1d4e421 = $(`<div id="html_8fd22b89c49b4795bc086d41f1d4e421" style="width: 100.0%; height: 100.0%;">Little Portugal, Trinity Cluster 0</div>`)[0];
            popup_d9f78095c49f4730bcad5662d207b27b.setContent(html_8fd22b89c49b4795bc086d41f1d4e421);
        

        circle_marker_81c9f4becf194a8cbde865569a20c0f0.bindPopup(popup_d9f78095c49f4730bcad5662d207b27b)
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            var circle_marker_4244c1bd8d084df6bf558bdc1b437a3d = L.circleMarker(
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                {"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_270488f6e9984b448266ec8b98a2996e = L.popup({"maxWidth": "100%"});

        
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            popup_270488f6e9984b448266ec8b98a2996e.setContent(html_826fac4d103a49e5abbed209c5b05e38);
        

        circle_marker_4244c1bd8d084df6bf558bdc1b437a3d.bindPopup(popup_270488f6e9984b448266ec8b98a2996e)
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            var circle_marker_03dd375403d84331ba89a9fff5b5f121 = L.circleMarker(
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                {"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_89f9244db9f24bfd946bb1bee7c4c33e = L.popup({"maxWidth": "100%"});

        
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            popup_89f9244db9f24bfd946bb1bee7c4c33e.setContent(html_e36bd5d97e8a4c3a9636de0eefcec486);
        

        circle_marker_03dd375403d84331ba89a9fff5b5f121.bindPopup(popup_89f9244db9f24bfd946bb1bee7c4c33e)
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            var circle_marker_854b2b0179b049c8b6e4a3f4a7d258e6 = L.circleMarker(
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                {"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_a8d4acffc48d459688e2b1ad8a82a19e = L.popup({"maxWidth": "100%"});

        
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            popup_a8d4acffc48d459688e2b1ad8a82a19e.setContent(html_3b4bf7ec11bb41549bd1efba459179e6);
        

        circle_marker_854b2b0179b049c8b6e4a3f4a7d258e6.bindPopup(popup_a8d4acffc48d459688e2b1ad8a82a19e)
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            var circle_marker_5ab630a4b6334934b6a808e4194388a9 = L.circleMarker(
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        var popup_4706b688ee294d3eb5262617d53c892b = L.popup({"maxWidth": "100%"});

        
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            popup_4706b688ee294d3eb5262617d53c892b.setContent(html_756f370f1eee4d8b8ceb866198818117);
        

        circle_marker_5ab630a4b6334934b6a808e4194388a9.bindPopup(popup_4706b688ee294d3eb5262617d53c892b)
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            var circle_marker_ac95d7e4d652428d8edcb32989fb46ae = L.circleMarker(
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        var popup_ac085af1d57d49c38a1b20f25c1d95dc = L.popup({"maxWidth": "100%"});

        
            var html_4ba00721f55448609c87fb0e6b04e1f6 = $(`<div id="html_4ba00721f55448609c87fb0e6b04e1f6" style="width: 100.0%; height: 100.0%;">Commerce Court, Victoria Hotel Cluster 0</div>`)[0];
            popup_ac085af1d57d49c38a1b20f25c1d95dc.setContent(html_4ba00721f55448609c87fb0e6b04e1f6);
        

        circle_marker_ac95d7e4d652428d8edcb32989fb46ae.bindPopup(popup_ac085af1d57d49c38a1b20f25c1d95dc)
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            var circle_marker_b2522fe091074ffeabf1941403aa8151 = L.circleMarker(
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                {"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}
            ).addTo(map_923f228c3972479ba18b94ffe8be8173);
        
    
        var popup_5a860da62c1f485ba2b0314440b77dfd = L.popup({"maxWidth": "100%"});

        
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            popup_5a860da62c1f485ba2b0314440b77dfd.setContent(html_bef0f555da704127ad6cf3c82b50b346);
        

        circle_marker_b2522fe091074ffeabf1941403aa8151.bindPopup(popup_5a860da62c1f485ba2b0314440b77dfd)
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            var circle_marker_cb1a331d6ab6460aaf72e65ab374c291 = L.circleMarker(
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            ).addTo(map_923f228c3972479ba18b94ffe8be8173);
        
    
        var popup_33cde130061f4d2daf1536e6dc9e8612 = L.popup({"maxWidth": "100%"});

        
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        circle_marker_cb1a331d6ab6460aaf72e65ab374c291.bindPopup(popup_33cde130061f4d2daf1536e6dc9e8612)
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            var circle_marker_c6bc9f24a9714b39a6eb59947f766eeb = L.circleMarker(
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                {"bubblingMouseEvents": true, "color": "#80ffb4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#80ffb4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_923f228c3972479ba18b94ffe8be8173);
        
    
        var popup_7ea02107b4e949bc97e38bde336fb067 = L.popup({"maxWidth": "100%"});

        
            var html_058ee5d39d804f11bf5776d27a028402 = $(`<div id="html_058ee5d39d804f11bf5776d27a028402" style="width: 100.0%; height: 100.0%;">Roselawn Cluster 2</div>`)[0];
            popup_7ea02107b4e949bc97e38bde336fb067.setContent(html_058ee5d39d804f11bf5776d27a028402);
        

        circle_marker_c6bc9f24a9714b39a6eb59947f766eeb.bindPopup(popup_7ea02107b4e949bc97e38bde336fb067)
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            var circle_marker_bdbf6afd6a614d8eb066b33f170e8a06 = L.circleMarker(
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                {"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}
            ).addTo(map_923f228c3972479ba18b94ffe8be8173);
        
    
        var popup_9a5cb6d678db4d74b118177cfeaa97d7 = L.popup({"maxWidth": "100%"});

        
            var html_077c5336cae04c3fa386592f73456f30 = $(`<div id="html_077c5336cae04c3fa386592f73456f30" style="width: 100.0%; height: 100.0%;">Davisville North Cluster 0</div>`)[0];
            popup_9a5cb6d678db4d74b118177cfeaa97d7.setContent(html_077c5336cae04c3fa386592f73456f30);
        

        circle_marker_bdbf6afd6a614d8eb066b33f170e8a06.bindPopup(popup_9a5cb6d678db4d74b118177cfeaa97d7)
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            var circle_marker_506a2606776843f6929d551c85a9b2ba = L.circleMarker(
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                {"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}
            ).addTo(map_923f228c3972479ba18b94ffe8be8173);
        
    
        var popup_15e2b193aeb44dce8d3a9de498687077 = L.popup({"maxWidth": "100%"});

        
            var html_aafa6d561fcc4a9c976cbc960833c115 = $(`<div id="html_aafa6d561fcc4a9c976cbc960833c115" style="width: 100.0%; height: 100.0%;">Forest Hill North &amp; West, Forest Hill Road Park Cluster 0</div>`)[0];
            popup_15e2b193aeb44dce8d3a9de498687077.setContent(html_aafa6d561fcc4a9c976cbc960833c115);
        

        circle_marker_506a2606776843f6929d551c85a9b2ba.bindPopup(popup_15e2b193aeb44dce8d3a9de498687077)
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            var circle_marker_b181ab74420c4aa7877c2aad14f88087 = L.circleMarker(
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                {"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}
            ).addTo(map_923f228c3972479ba18b94ffe8be8173);
        
    
        var popup_0294454bb47146a6aae03127d92bf0ac = L.popup({"maxWidth": "100%"});

        
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            popup_0294454bb47146a6aae03127d92bf0ac.setContent(html_f1953015c883405e83bbf116fc13624c);
        

        circle_marker_b181ab74420c4aa7877c2aad14f88087.bindPopup(popup_0294454bb47146a6aae03127d92bf0ac)
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            var circle_marker_cc74e8ccea1540f096943e2213835f09 = L.circleMarker(
                [43.7153834, -79.40567840000001],
                {"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}
            ).addTo(map_923f228c3972479ba18b94ffe8be8173);
        
    
        var popup_a7cf503ad79245f3be01c2815af55840 = L.popup({"maxWidth": "100%"});

        
            var html_c829e573cb244e37819a46f4569c8235 = $(`<div id="html_c829e573cb244e37819a46f4569c8235" style="width: 100.0%; height: 100.0%;">North Toronto West, Lawrence Park Cluster 0</div>`)[0];
            popup_a7cf503ad79245f3be01c2815af55840.setContent(html_c829e573cb244e37819a46f4569c8235);
        

        circle_marker_cc74e8ccea1540f096943e2213835f09.bindPopup(popup_a7cf503ad79245f3be01c2815af55840)
        ;

        
    
    
            var circle_marker_e3f5b2af8e5f426d964ef8e03c9bac95 = L.circleMarker(
                [43.6727097, -79.40567840000001],
                {"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}
            ).addTo(map_923f228c3972479ba18b94ffe8be8173);
        
    
        var popup_5a26b06dc12e452cb6489b9d23a4da4e = L.popup({"maxWidth": "100%"});

        
            var html_3878a301c29d4ddb93ceebcbddc34f1a = $(`<div id="html_3878a301c29d4ddb93ceebcbddc34f1a" style="width: 100.0%; height: 100.0%;">The Annex, North Midtown, Yorkville Cluster 0</div>`)[0];
            popup_5a26b06dc12e452cb6489b9d23a4da4e.setContent(html_3878a301c29d4ddb93ceebcbddc34f1a);
        

        circle_marker_e3f5b2af8e5f426d964ef8e03c9bac95.bindPopup(popup_5a26b06dc12e452cb6489b9d23a4da4e)
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            var circle_marker_47b25f0dff7844f7a9f04345aae61712 = L.circleMarker(
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                {"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}
            ).addTo(map_923f228c3972479ba18b94ffe8be8173);
        
    
        var popup_185c28fb701c423ea2b3ad7b2d6842ba = L.popup({"maxWidth": "100%"});

        
            var html_e1a509d54db549a6954fee36ee2b84ed = $(`<div id="html_e1a509d54db549a6954fee36ee2b84ed" style="width: 100.0%; height: 100.0%;">Parkdale, Roncesvalles Cluster 0</div>`)[0];
            popup_185c28fb701c423ea2b3ad7b2d6842ba.setContent(html_e1a509d54db549a6954fee36ee2b84ed);
        

        circle_marker_47b25f0dff7844f7a9f04345aae61712.bindPopup(popup_185c28fb701c423ea2b3ad7b2d6842ba)
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            var circle_marker_58fbff55523a4920ba884ef249352e8a = L.circleMarker(
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                {"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}
            ).addTo(map_923f228c3972479ba18b94ffe8be8173);
        
    
        var popup_aeebda1f965543a58f4200bcfb93c8aa = L.popup({"maxWidth": "100%"});

        
            var html_4eba5b0c7bd548d5ad03c081ccd08163 = $(`<div id="html_4eba5b0c7bd548d5ad03c081ccd08163" style="width: 100.0%; height: 100.0%;">Davisville Cluster 0</div>`)[0];
            popup_aeebda1f965543a58f4200bcfb93c8aa.setContent(html_4eba5b0c7bd548d5ad03c081ccd08163);
        

        circle_marker_58fbff55523a4920ba884ef249352e8a.bindPopup(popup_aeebda1f965543a58f4200bcfb93c8aa)
        ;

        
    
    
            var circle_marker_0576adb59a194395a8f9d2ec505cb140 = L.circleMarker(
                [43.6626956, -79.4000493],
                {"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}
            ).addTo(map_923f228c3972479ba18b94ffe8be8173);
        
    
        var popup_0d4d2479e7d94a5bbed0dfbf5c2969fd = L.popup({"maxWidth": "100%"});

        
            var html_59606fe7e03a46ff9bd7ef9b06cf8e35 = $(`<div id="html_59606fe7e03a46ff9bd7ef9b06cf8e35" style="width: 100.0%; height: 100.0%;">University of Toronto, Harbord Cluster 0</div>`)[0];
            popup_0d4d2479e7d94a5bbed0dfbf5c2969fd.setContent(html_59606fe7e03a46ff9bd7ef9b06cf8e35);
        

        circle_marker_0576adb59a194395a8f9d2ec505cb140.bindPopup(popup_0d4d2479e7d94a5bbed0dfbf5c2969fd)
        ;

        
    
    
            var circle_marker_7e6fd01e1363464e999d716cf19baf86 = L.circleMarker(
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                {"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}
            ).addTo(map_923f228c3972479ba18b94ffe8be8173);
        
    
        var popup_96298bfe449746a69d38f8af50c08ee2 = L.popup({"maxWidth": "100%"});

        
            var html_d84be2cc56c64c4486fadc0f1b845136 = $(`<div id="html_d84be2cc56c64c4486fadc0f1b845136" style="width: 100.0%; height: 100.0%;">Runnymede, Swansea Cluster 0</div>`)[0];
            popup_96298bfe449746a69d38f8af50c08ee2.setContent(html_d84be2cc56c64c4486fadc0f1b845136);
        

        circle_marker_7e6fd01e1363464e999d716cf19baf86.bindPopup(popup_96298bfe449746a69d38f8af50c08ee2)
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            var circle_marker_8c2257e460cc42bb9b5a70ba85f0c98c = L.circleMarker(
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        var popup_507395d76a6b447ba0f5ad55fa34940c = L.popup({"maxWidth": "100%"});

        
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        circle_marker_8c2257e460cc42bb9b5a70ba85f0c98c.bindPopup(popup_507395d76a6b447ba0f5ad55fa34940c)
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            var circle_marker_e9a60da9e9b345c0b449fcb858bbc952 = L.circleMarker(
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            ).addTo(map_923f228c3972479ba18b94ffe8be8173);
        
    
        var popup_a70f64fb001b4baeada4b61f9b5df7d6 = L.popup({"maxWidth": "100%"});

        
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        circle_marker_e9a60da9e9b345c0b449fcb858bbc952.bindPopup(popup_a70f64fb001b4baeada4b61f9b5df7d6)
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            var circle_marker_8717397fb790478d96303a255fe288ed = L.circleMarker(
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                {"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}
            ).addTo(map_923f228c3972479ba18b94ffe8be8173);
        
    
        var popup_671357c7344949f7ae37da94e0a1d812 = L.popup({"maxWidth": "100%"});

        
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            popup_671357c7344949f7ae37da94e0a1d812.setContent(html_3b141d2b1a0f4f4b89dc15ed6dc5fe4a);
        

        circle_marker_8717397fb790478d96303a255fe288ed.bindPopup(popup_671357c7344949f7ae37da94e0a1d812)
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            var circle_marker_45fac1ddbd3f4b7fa49f9fd3976ac798 = L.circleMarker(
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        var popup_8b1c78bb13f542c9b8db44cb8855fb1d = L.popup({"maxWidth": "100%"});

        
            var html_21c6763e5020499f8eba61bc62af2748 = $(`<div id="html_21c6763e5020499f8eba61bc62af2748" style="width: 100.0%; height: 100.0%;">CN Tower, King and Spadina, Railway Lands, Harbourfront West, Bathurst Quay, South Niagara, Island airport Cluster 0</div>`)[0];
            popup_8b1c78bb13f542c9b8db44cb8855fb1d.setContent(html_21c6763e5020499f8eba61bc62af2748);
        

        circle_marker_45fac1ddbd3f4b7fa49f9fd3976ac798.bindPopup(popup_8b1c78bb13f542c9b8db44cb8855fb1d)
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            var circle_marker_b127f797d9084ac4aa6dab4df5268a5a = L.circleMarker(
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        var popup_ad6c3beda9f441bea7c99e40fba23477 = L.popup({"maxWidth": "100%"});

        
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        circle_marker_b127f797d9084ac4aa6dab4df5268a5a.bindPopup(popup_ad6c3beda9f441bea7c99e40fba23477)
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            var circle_marker_373073341b8d4c49a7cd43886d4159af = L.circleMarker(
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        var popup_4c69b08174864d33b3963b649d0c7330 = L.popup({"maxWidth": "100%"});

        
            var html_cb53e4392b7e4f9bb299ba0f4f3b4609 = $(`<div id="html_cb53e4392b7e4f9bb299ba0f4f3b4609" style="width: 100.0%; height: 100.0%;">Stn A PO Boxes Cluster 0</div>`)[0];
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        circle_marker_373073341b8d4c49a7cd43886d4159af.bindPopup(popup_4c69b08174864d33b3963b649d0c7330)
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            var circle_marker_872a4d8fa9854668b52a9a6f6b7fb136 = L.circleMarker(
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                {"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}
            ).addTo(map_923f228c3972479ba18b94ffe8be8173);
        
    
        var popup_979207abeb6a46a6a89f62e4706e8baf = L.popup({"maxWidth": "100%"});

        
            var html_b8d4631ecbc34ec5999ac366d804681f = $(`<div id="html_b8d4631ecbc34ec5999ac366d804681f" style="width: 100.0%; height: 100.0%;">St. James Town, Cabbagetown Cluster 0</div>`)[0];
            popup_979207abeb6a46a6a89f62e4706e8baf.setContent(html_b8d4631ecbc34ec5999ac366d804681f);
        

        circle_marker_872a4d8fa9854668b52a9a6f6b7fb136.bindPopup(popup_979207abeb6a46a6a89f62e4706e8baf)
        ;

        
    
    
            var circle_marker_ff5471064c884b95b5087ec23193c2bf = L.circleMarker(
                [43.6484292, -79.3822802],
                {"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}
            ).addTo(map_923f228c3972479ba18b94ffe8be8173);
        
    
        var popup_5e5816e965e64c559e04927c690425b5 = L.popup({"maxWidth": "100%"});

        
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            popup_5e5816e965e64c559e04927c690425b5.setContent(html_b998f5b5dc794d009e3485324d031a78);
        

        circle_marker_ff5471064c884b95b5087ec23193c2bf.bindPopup(popup_5e5816e965e64c559e04927c690425b5)
        ;

        
    
    
            var circle_marker_2b31ef95e34f4dbcae783a7e34ad04c7 = L.circleMarker(
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                {"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}
            ).addTo(map_923f228c3972479ba18b94ffe8be8173);
        
    
        var popup_bafb3707d8144239892f383ee90afa07 = L.popup({"maxWidth": "100%"});

        
            var html_e01a41d1eeca4bab9e454f131805fc63 = $(`<div id="html_e01a41d1eeca4bab9e454f131805fc63" style="width: 100.0%; height: 100.0%;">Church and Wellesley Cluster 0</div>`)[0];
            popup_bafb3707d8144239892f383ee90afa07.setContent(html_e01a41d1eeca4bab9e454f131805fc63);
        

        circle_marker_2b31ef95e34f4dbcae783a7e34ad04c7.bindPopup(popup_bafb3707d8144239892f383ee90afa07)
        ;

        
    
    
            var circle_marker_d9e2332662f24e7cb7618eb2208b6063 = L.circleMarker(
                [43.6627439, -79.321558],
                {"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}
            ).addTo(map_923f228c3972479ba18b94ffe8be8173);
        
    
        var popup_43738d33e55049cba5668861692fb03f = L.popup({"maxWidth": "100%"});

        
            var html_42ef69f077f14b3d9babe9611774cfdc = $(`<div id="html_42ef69f077f14b3d9babe9611774cfdc" style="width: 100.0%; height: 100.0%;">Business reply mail Processing Centre, South Central Letter Processing Plant Toronto Cluster 0</div>`)[0];
            popup_43738d33e55049cba5668861692fb03f.setContent(html_42ef69f077f14b3d9babe9611774cfdc);
        

        circle_marker_d9e2332662f24e7cb7618eb2208b6063.bindPopup(popup_43738d33e55049cba5668861692fb03f)
        ;

        
    
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