Toronto Geospatial.ipynb

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    "cells": [
        {
            "source": "# Scraping Data Table of Toronto Neighborhoods with 'M' Postal Code", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "Importing the necessary libraries (and ones I might use later on).", 
            "cell_type": "markdown", 
            "metadata": {
                "collapsed": true
            }
        }, 
        {
            "execution_count": 1, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "import pandas as pd\nimport numpy as np\nimport csv\nfrom bs4 import BeautifulSoup\nimport requests"
        }, 
        {
            "source": "Scraping and Cleaning Up Table (the full explanations can be found in the 'Clustering and Segmenting' notebook.", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 20, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 20, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PostalCode</th>\n      <th>Borough</th>\n      <th>Neighborhood</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>M1B</td>\n      <td>Scarborough</td>\n      <td>Rouge,Malvern</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>M1C</td>\n      <td>Scarborough</td>\n      <td>Highland Creek,Rouge Hill,Port Union</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>M1E</td>\n      <td>Scarborough</td>\n      <td>Guildwood,Morningside,West Hill</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>M1G</td>\n      <td>Scarborough</td>\n      <td>Woburn</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>M1H</td>\n      <td>Scarborough</td>\n      <td>Cedarbrae</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "  PostalCode      Borough                          Neighborhood\n0        M1B  Scarborough                         Rouge,Malvern\n1        M1C  Scarborough  Highland Creek,Rouge Hill,Port Union\n2        M1E  Scarborough       Guildwood,Morningside,West Hill\n3        M1G  Scarborough                                Woburn\n4        M1H  Scarborough                             Cedarbrae"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "# using BeautifulSoup to scrape the data table from wikipedia.\nurl=requests.get('https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M').text\nsource=BeautifulSoup(url, 'lxml')\n\ntable=source.find('table', class_='wikitable sortable')\n\nrows=table.findAll('tr')\n\ndata=[]\nfor row in rows:\n    data.append([t.text.strip() for t in row.findAll('td')])\n\n# appending the data table into a dataframe\n\ndf=pd.DataFrame(data, columns=['PostalCode', 'Borough', 'Neighborhood']) \ndf = df[~df['PostalCode'].isnull()] #to filter out bad rows\ndf.drop(df[df['Borough']=='Not assigned'].index, axis=0, inplace=True) #deleting 'Not assigned' cells.\ndf1=df.reset_index()\ndf2=df1.groupby('PostalCode').agg(lambda x: ','.join(x)) # grouping nrighborhoods with the same postal codes.\ndf2.loc[df2['Neighborhood']==\"Not assigned\",'Neighborhood']=df2.loc[df2['Neighborhood']==\"Not assigned\",'Borough'] # assigning the unnamed neighborhoods with their borough names.\ndf3=df2.reset_index()\ndf3['Borough']= df3['Borough'].str.replace('nan|[{}\\s]','').str.split(',').apply(set).str.join(',').str.strip(',').str.replace(\",{2,}\",\",\") #deleting replicates of borough names from each postal code.\n\ndf3.head() # displaying the first 10 rows of the appended and cleansed data of Toronto neighborhoods with 'M' postal codes."
        }, 
        {
            "source": "# Reading the Geospatial File for the Latitudes and Longtitues of the Toronto Postal Codes", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "Obtaining the latitudes and longtitudes of each Toronto 'M' postal codes from the csv file since the geocoder was unresponsive.", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 3, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 3, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Postal Code</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>M1B</td>\n      <td>43.806686</td>\n      <td>-79.194353</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>M1C</td>\n      <td>43.784535</td>\n      <td>-79.160497</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>M1E</td>\n      <td>43.763573</td>\n      <td>-79.188711</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>M1G</td>\n      <td>43.770992</td>\n      <td>-79.216917</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>M1H</td>\n      <td>43.773136</td>\n      <td>-79.239476</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "  Postal Code   Latitude  Longitude\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"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "df_coor=pd.read_csv('http://cocl.us/Geospatial_data')\ndf_coor.head()"
        }, 
        {
            "source": "Renaming the 'Postal Code' column to 'PostalCode' to prepare the table for merging.", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 4, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "df_coor.rename(columns={'Postal Code':'PostalCode'}, inplace=True)"
        }, 
        {
            "execution_count": 5, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 5, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PostalCode</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>M1B</td>\n      <td>43.806686</td>\n      <td>-79.194353</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>M1C</td>\n      <td>43.784535</td>\n      <td>-79.160497</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>M1E</td>\n      <td>43.763573</td>\n      <td>-79.188711</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>M1G</td>\n      <td>43.770992</td>\n      <td>-79.216917</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>M1H</td>\n      <td>43.773136</td>\n      <td>-79.239476</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "  PostalCode   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"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "df_coor.head()"
        }, 
        {
            "source": "Merging the data tables ", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 21, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 21, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PostalCode</th>\n      <th>Borough</th>\n      <th>Neighborhood</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>M1B</td>\n      <td>Scarborough</td>\n      <td>Rouge,Malvern</td>\n      <td>43.806686</td>\n      <td>-79.194353</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>M1C</td>\n      <td>Scarborough</td>\n      <td>Highland Creek,Rouge Hill,Port Union</td>\n      <td>43.784535</td>\n      <td>-79.160497</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>M1E</td>\n      <td>Scarborough</td>\n      <td>Guildwood,Morningside,West Hill</td>\n      <td>43.763573</td>\n      <td>-79.188711</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>M1G</td>\n      <td>Scarborough</td>\n      <td>Woburn</td>\n      <td>43.770992</td>\n      <td>-79.216917</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>M1H</td>\n      <td>Scarborough</td>\n      <td>Cedarbrae</td>\n      <td>43.773136</td>\n      <td>-79.239476</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "  PostalCode      Borough                          Neighborhood   Latitude  \\\n0        M1B  Scarborough                         Rouge,Malvern  43.806686   \n1        M1C  Scarborough  Highland Creek,Rouge Hill,Port Union  43.784535   \n2        M1E  Scarborough       Guildwood,Morningside,West Hill  43.763573   \n3        M1G  Scarborough                                Woburn  43.770992   \n4        M1H  Scarborough                             Cedarbrae  43.773136   \n\n   Longitude  \n0 -79.194353  \n1 -79.160497  \n2 -79.188711  \n3 -79.216917  \n4 -79.239476  "
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "df_Toronto=pd.merge(df3, df_coor, on='PostalCode', how='inner')\ndf_Toronto.head()"
        }, 
        {
            "execution_count": 7, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 7, 
                    "metadata": {}, 
                    "data": {
                        "text/plain": "(103, 5)"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "df_Toronto.shape"
        }, 
        {
            "source": "There are 103 'M' postal codes in Toronto.", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "# Visualizing the Toronto Neighborhoods", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "Importing the necessary libraries for maps/ visualization.", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 8, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "Solving environment: done\n\n# All requested packages already installed.\n\n"
                }
            ], 
            "source": "!conda install -c conda-forge folium=0.5.0 --yes\nimport folium"
        }, 
        {
            "execution_count": 43, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "from folium import plugins\nimport json\nfrom pandas.io.json import json_normalize"
        }, 
        {
            "execution_count": 11, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "Solving environment: done\n\n## Package Plan ##\n\n  environment location: /opt/conda/envs/Python36\n\n  added / updated specs: \n    - geopy\n\n\nThe following packages will be downloaded:\n\n    package                    |            build\n    ---------------------------|-----------------\n    geographiclib-1.49         |             py_0          32 KB  conda-forge\n    geopy-1.20.0               |             py_0          57 KB  conda-forge\n    ------------------------------------------------------------\n                                           Total:          90 KB\n\nThe following NEW packages will be INSTALLED:\n\n    geographiclib: 1.49-py_0   conda-forge\n    geopy:         1.20.0-py_0 conda-forge\n\n\nDownloading and Extracting Packages\ngeographiclib-1.49   | 32 KB     | ##################################### | 100% \ngeopy-1.20.0         | 57 KB     | ##################################### | 100% \nPreparing transaction: done\nVerifying transaction: done\nExecuting transaction: done\n"
                }
            ], 
            "source": "!conda install -c conda-forge geopy --yes \nfrom geopy.geocoders import Nominatim # convert an address into latitude and longitude values"
        }, 
        {
            "execution_count": 12, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "The geograpical coordinate of Toronto are 43.653963, -79.387207.\n"
                }
            ], 
            "source": "address = 'Toronto, Ontario'\n\ngeolocator = Nominatim(user_agent=\"ca_explorer\")\nlocation = geolocator.geocode(address)\nlatitude = location.latitude\nlongitude = location.longitude\nprint('The geograpical coordinate of Toronto are {}, {}.'.format(latitude, longitude))"
        }, 
        {
            "execution_count": 30, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 30, 
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}
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                var html_a6808ff0b8304ce8bee2277ce5125347 = $('<div id="html_a6808ff0b8304ce8bee2277ce5125347" style="width: 100.0%; height: 100.0%;">Bloordale Gardens,Eringate,Markland Wood,Old Burnhamthorpe, Etobicoke</div>')[0];
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  "fill": true,
  "fillColor": "#3186cc",
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  "fillRule": "evenodd",
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  "lineJoin": "round",
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}
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                var html_f0549bb6ee6d4fd5b1255a0135dc7e4d = $('<div id="html_f0549bb6ee6d4fd5b1255a0135dc7e4d" style="width: 100.0%; height: 100.0%;">Humber Summit, NorthYork</div>')[0];
                popup_9bfbccf5a94d45ccbf270646b7689f67.setContent(html_f0549bb6ee6d4fd5b1255a0135dc7e4d);
            

            circle_marker_a47c5e8602cb49609c9c7d5d2d43e262.bindPopup(popup_9bfbccf5a94d45ccbf270646b7689f67);

            
        
    
            var circle_marker_aa17998184b940ad9cb574df5b733cbc = L.circleMarker(
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  "bubblingMouseEvents": true,
  "color": "orange",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_61edee2414a64bf0a9fe1042ac33c335);
            
    
            var popup_1a6a69d896754cada97982710d60294d = L.popup({maxWidth: '300'});

            
                var html_5e2367b11efc4a87b560f33f9401a670 = $('<div id="html_5e2367b11efc4a87b560f33f9401a670" style="width: 100.0%; height: 100.0%;">Emery,Humberlea, NorthYork</div>')[0];
                popup_1a6a69d896754cada97982710d60294d.setContent(html_5e2367b11efc4a87b560f33f9401a670);
            

            circle_marker_aa17998184b940ad9cb574df5b733cbc.bindPopup(popup_1a6a69d896754cada97982710d60294d);

            
        
    
            var circle_marker_7babcf08daa24b5cb4b5651a2011de6d = L.circleMarker(
                [43.706876,-79.51818840000001],
                {
  "bubblingMouseEvents": true,
  "color": "orange",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_61edee2414a64bf0a9fe1042ac33c335);
            
    
            var popup_64d537b24b874361bf64d6cf503e66cf = L.popup({maxWidth: '300'});

            
                var html_5df72d604e9b42edaccb0b5ed1062ff5 = $('<div id="html_5df72d604e9b42edaccb0b5ed1062ff5" style="width: 100.0%; height: 100.0%;">Weston, York</div>')[0];
                popup_64d537b24b874361bf64d6cf503e66cf.setContent(html_5df72d604e9b42edaccb0b5ed1062ff5);
            

            circle_marker_7babcf08daa24b5cb4b5651a2011de6d.bindPopup(popup_64d537b24b874361bf64d6cf503e66cf);

            
        
    
            var circle_marker_2effedc269394bb49a142f8b951f8f21 = L.circleMarker(
                [43.696319,-79.53224240000002],
                {
  "bubblingMouseEvents": true,
  "color": "orange",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_61edee2414a64bf0a9fe1042ac33c335);
            
    
            var popup_4c5ab8092ccb4b3e95c4b0ad34430f3a = L.popup({maxWidth: '300'});

            
                var html_d2cc6b84543e45688bf6b11cff34b884 = $('<div id="html_d2cc6b84543e45688bf6b11cff34b884" style="width: 100.0%; height: 100.0%;">Westmount, Etobicoke</div>')[0];
                popup_4c5ab8092ccb4b3e95c4b0ad34430f3a.setContent(html_d2cc6b84543e45688bf6b11cff34b884);
            

            circle_marker_2effedc269394bb49a142f8b951f8f21.bindPopup(popup_4c5ab8092ccb4b3e95c4b0ad34430f3a);

            
        
    
            var circle_marker_c40eafb0bcf549bdb2657ab925977386 = L.circleMarker(
                [43.6889054,-79.55472440000001],
                {
  "bubblingMouseEvents": true,
  "color": "orange",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_61edee2414a64bf0a9fe1042ac33c335);
            
    
            var popup_0b34f49b021c4d4986be2f8d3aff2aac = L.popup({maxWidth: '300'});

            
                var html_4a9dc9ed3b594950bcc44bf896a33e7c = $('<div id="html_4a9dc9ed3b594950bcc44bf896a33e7c" style="width: 100.0%; height: 100.0%;">Kingsview Village,Martin Grove Gardens,Richview Gardens,St. Phillips, Etobicoke</div>')[0];
                popup_0b34f49b021c4d4986be2f8d3aff2aac.setContent(html_4a9dc9ed3b594950bcc44bf896a33e7c);
            

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                [43.739416399999996,-79.5884369],
                {
  "bubblingMouseEvents": true,
  "color": "orange",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_61edee2414a64bf0a9fe1042ac33c335);
            
    
            var popup_0e19905909234f66bae487b8778e0fc0 = L.popup({maxWidth: '300'});

            
                var html_3427f1f9489f49f399f0f7bac4f6401f = $('<div id="html_3427f1f9489f49f399f0f7bac4f6401f" style="width: 100.0%; height: 100.0%;">Albion Gardens,Beaumond Heights,Humbergate,Jamestown,Mount Olive,Silverstone,South Steeles,Thistletown, Etobicoke</div>')[0];
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            var circle_marker_0d44d8eb25b14a30acf3d42caadfbe0e = L.circleMarker(
                [43.706748299999994,-79.5940544],
                {
  "bubblingMouseEvents": true,
  "color": "orange",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_61edee2414a64bf0a9fe1042ac33c335);
            
    
            var popup_f382633b96404767a5c6ed1b5e12b87c = L.popup({maxWidth: '300'});

            
                var html_45c90801a3fe4079a3c75598cd7426fd = $('<div id="html_45c90801a3fe4079a3c75598cd7426fd" style="width: 100.0%; height: 100.0%;">Northwest, Etobicoke</div>')[0];
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            circle_marker_0d44d8eb25b14a30acf3d42caadfbe0e.bindPopup(popup_f382633b96404767a5c6ed1b5e12b87c);

            
        
</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>", 
                        "text/plain": "<folium.folium.Map at 0x7fd800968cf8>"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "# create map of New York using latitude and longitude values\nmap_toronto = folium.Map(location=[latitude, longitude], zoom_start=10)\n\n# add markers to map\nfor lat, lng, borough, neighborhood in zip(df_Toronto['Latitude'], df_Toronto['Longitude'], df_Toronto['Borough'], df_Toronto['Neighborhood']):\n    label = '{}, {}'.format(neighborhood, borough)\n    label = folium.Popup(label, parse_html=True)\n    folium.CircleMarker(\n        [lat, lng],\n        radius=5,\n        popup=label,\n        color='orange',\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": 16, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "Your credentails:\nCLIENT_ID: 03KM0T015RKGIIC5OH30NX5WL1HPALMFUZC4BWRZHEG3QXDK\nCLIENT_SECRET:QZTKR2AJBYLPXDEAIE5FIAXE31QEOYG0TRQ34CXZ350HPLVY\n"
                }
            ], 
            "source": "CLIENT_ID = '03KM0T015RKGIIC5OH30NX5WL1HPALMFUZC4BWRZHEG3QXDK' # your Foursquare ID\nCLIENT_SECRET = 'QZTKR2AJBYLPXDEAIE5FIAXE31QEOYG0TRQ34CXZ350HPLVY' # your Foursquare Secret\nVERSION = '20180605' # Foursquare API version\n\nprint('Your credentails:')\nprint('CLIENT_ID: ' + CLIENT_ID)\nprint('CLIENT_SECRET:' + CLIENT_SECRET)"
        }, 
        {
            "source": "Exploring Downtown Toronto", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 31, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 31, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PostalCode</th>\n      <th>Borough</th>\n      <th>Neighborhood</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>M4W</td>\n      <td>DowntownToronto</td>\n      <td>Rosedale</td>\n      <td>43.679563</td>\n      <td>-79.377529</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>M4X</td>\n      <td>DowntownToronto</td>\n      <td>Cabbagetown,St. James Town</td>\n      <td>43.667967</td>\n      <td>-79.367675</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>M4Y</td>\n      <td>DowntownToronto</td>\n      <td>Church and Wellesley</td>\n      <td>43.665860</td>\n      <td>-79.383160</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>M5A</td>\n      <td>DowntownToronto</td>\n      <td>Harbourfront,Regent Park</td>\n      <td>43.654260</td>\n      <td>-79.360636</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>M5B</td>\n      <td>DowntownToronto</td>\n      <td>Ryerson,Garden District</td>\n      <td>43.657162</td>\n      <td>-79.378937</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "  PostalCode          Borough                Neighborhood   Latitude  \\\n0        M4W  DowntownToronto                    Rosedale  43.679563   \n1        M4X  DowntownToronto  Cabbagetown,St. James Town  43.667967   \n2        M4Y  DowntownToronto        Church and Wellesley  43.665860   \n3        M5A  DowntownToronto    Harbourfront,Regent Park  43.654260   \n4        M5B  DowntownToronto     Ryerson,Garden District  43.657162   \n\n   Longitude  \n0 -79.377529  \n1 -79.367675  \n2 -79.383160  \n3 -79.360636  \n4 -79.378937  "
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "downtown_data=df_Toronto[df_Toronto['Borough']=='DowntownToronto'].reset_index(drop=True)\ndowntown_data.head()"
        }, 
        {
            "execution_count": 29, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "The geograpical coordinate of Downtown Toronto are 43.655115, -79.380219.\n"
                }
            ], 
            "source": "address = 'Downtown Toronto, Ontario'\n\ngeolocator = Nominatim(user_agent=\"ca_explorer\")\nlocation = geolocator.geocode(address)\nlatitude = location.latitude\nlongitude = location.longitude\nprint('The geograpical coordinate of Downtown Toronto are {}, {}.'.format(latitude, longitude))"
        }, 
        {
            "execution_count": 33, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 33, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe src=\"data:text/html;charset=utf-8;base64,<!DOCTYPE html>
<head>    
    <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
    <script>L_PREFER_CANVAS = false; L_NO_TOUCH = false; L_DISABLE_3D = false;</script>
    <script src="https://cdn.jsdelivr.net/npm/leaflet@1.2.0/dist/leaflet.js"></script>
    <script src="https://ajax.googleapis.com/ajax/libs/jquery/1.11.1/jquery.min.js"></script>
    <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>
    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet@1.2.0/dist/leaflet.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css"/>
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css"/>
    <link rel="stylesheet" href="https://rawgit.com/python-visualization/folium/master/folium/templates/leaflet.awesome.rotate.css"/>
    <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>
    <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>
    
            <style> #map_aae4379099f149959c812aabb314bd24 {
                position : relative;
                width : 100.0%;
                height: 100.0%;
                left: 0.0%;
                top: 0.0%;
                }
            </style>
        
</head>
<body>    
    
            <div class="folium-map" id="map_aae4379099f149959c812aabb314bd24" ></div>
        
</body>
<script>    
    

            
                var bounds = null;
            

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  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_aae4379099f149959c812aabb314bd24);
            
    
            var popup_e0940198291548138456a1417c45ea13 = L.popup({maxWidth: '300'});

            
                var html_668b79f24622462d93417405df0e555e = $('<div id="html_668b79f24622462d93417405df0e555e" style="width: 100.0%; height: 100.0%;">Christie</div>')[0];
                popup_e0940198291548138456a1417c45ea13.setContent(html_668b79f24622462d93417405df0e555e);
            

            circle_marker_963ffef30ed74a87b24faa7fb1114c34.bindPopup(popup_e0940198291548138456a1417c45ea13);

            
        
</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>", 
                        "text/plain": "<folium.folium.Map at 0x7fd8030b0d68>"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "# create map of Manhattan using latitude and longitude values\nmap_downtown = folium.Map(location=[latitude, longitude], zoom_start=11)\n\n# add markers to map\nfor lat, lng, label in zip(downtown_data['Latitude'], downtown_data['Longitude'], downtown_data['Neighborhood']):\n    label = folium.Popup(label, parse_html=True)\n    folium.CircleMarker(\n        [lat, lng],\n        radius=5,\n        popup=label,\n        color='pink',\n        fill=True,\n        fill_color='#3186cc',\n        fill_opacity=0.7,\n        parse_html=False).add_to(map_downtown)  \n    \nmap_downtown"
        }, 
        {
            "execution_count": 34, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 34, 
                    "metadata": {}, 
                    "data": {
                        "text/plain": "'Rosedale'"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "downtown_data.loc[0, 'Neighborhood']"
        }, 
        {
            "execution_count": 36, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "Latitude and longitude values of Rosedale are 43.6795626, -79.37752940000001.\n"
                }
            ], 
            "source": "neighborhood_latitude = downtown_data.loc[0, 'Latitude'] # neighborhood latitude value\nneighborhood_longitude = downtown_data.loc[0, 'Longitude'] # neighborhood longitude value\n\nneighborhood_name = downtown_data.loc[0, 'Neighborhood'] # neighborhood name\n\nprint('Latitude and longitude values of {} are {}, {}.'.format(neighborhood_name, \n                                                               neighborhood_latitude, \n                                                               neighborhood_longitude))"
        }, 
        {
            "source": "### Exploring the top 100 venues in Rosedale in the radius of 800 meters.", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 38, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 38, 
                    "metadata": {}, 
                    "data": {
                        "text/plain": "'https://api.foursquare.com/v2/venues/explore?client_id=03KM0T015RKGIIC5OH30NX5WL1HPALMFUZC4BWRZHEG3QXDK&client_secret=QZTKR2AJBYLPXDEAIE5FIAXE31QEOYG0TRQ34CXZ350HPLVY&ll=43.6795626,-79.37752940000001&v=20180605&radius=800&limit=100'"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "radius=800\nLIMIT=100\n\nurl1='https://api.foursquare.com/v2/venues/explore?client_id={}&client_secret={}&ll={},{}&v={}&radius={}&limit={}'.format(CLIENT_ID, CLIENT_SECRET, neighborhood_latitude, neighborhood_longitude, VERSION, radius, LIMIT)\nurl1"
        }, 
        {
            "execution_count": 40, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "results = requests.get(url1).json()\n# results"
        }, 
        {
            "execution_count": 41, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "# function that extracts the category of the venue\ndef get_category_type(row):\n    try:\n        categories_list = row['categories']\n    except:\n        categories_list = row['venue.categories']\n        \n    if len(categories_list) == 0:\n        return None\n    else:\n        return categories_list[0]['name']"
        }, 
        {
            "execution_count": 44, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 44, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>categories</th>\n      <th>lat</th>\n      <th>lng</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Summerhill Market</td>\n      <td>Grocery Store</td>\n      <td>43.686265</td>\n      <td>-79.375458</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Craigleigh Gardens</td>\n      <td>Park</td>\n      <td>43.678099</td>\n      <td>-79.371586</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>TD Canada Trust</td>\n      <td>Bank</td>\n      <td>43.686094</td>\n      <td>-79.376549</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>TD Canada Trust</td>\n      <td>Bank</td>\n      <td>43.672484</td>\n      <td>-79.377162</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Mooredale House</td>\n      <td>Building</td>\n      <td>43.678631</td>\n      <td>-79.380091</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "                 name     categories        lat        lng\n0   Summerhill Market  Grocery Store  43.686265 -79.375458\n1  Craigleigh Gardens           Park  43.678099 -79.371586\n2     TD Canada Trust           Bank  43.686094 -79.376549\n3     TD Canada Trust           Bank  43.672484 -79.377162\n4     Mooredale House       Building  43.678631 -79.380091"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "venues = results['response']['groups'][0]['items']\n    \nnearby_venues = json_normalize(venues) # flatten JSON\n\n# filter columns\nfiltered_columns = ['venue.name', 'venue.categories', 'venue.location.lat', 'venue.location.lng']\nnearby_venues =nearby_venues.loc[:, filtered_columns]\n\n# filter the category for each row\nnearby_venues['venue.categories'] = nearby_venues.apply(get_category_type, axis=1)\n\n# clean columns\nnearby_venues.columns = [col.split(\".\")[-1] for col in nearby_venues.columns]\n\nnearby_venues.head()"
        }, 
        {
            "execution_count": 45, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "11 venues were returned by Foursquare.\n"
                }
            ], 
            "source": "print('{} venues were returned by Foursquare.'.format(nearby_venues.shape[0]))"
        }, 
        {
            "source": "### Exploring Neighborhoods in Downtown Toronto", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 46, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "def getNearbyVenues(names, latitudes, longitudes, radius=800):\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        url2 = '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        results1 = requests.get(url2).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 results1])\n\n    nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])\n    nearby_venues.columns = ['Neighborhood', \n                  'Neighborhood Latitude', \n                  'Neighborhood Longitude', \n                  'Venue', \n                  'Venue Latitude', \n                  'Venue Longitude', \n                  'Venue Category']\n    \n    return(nearby_venues)"
        }, 
        {
            "execution_count": 47, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "Rosedale\nCabbagetown,St. James Town\nChurch and Wellesley\nHarbourfront,Regent Park\nRyerson,Garden District\nSt. James Town\nBerczy Park\nCentral Bay Street\nAdelaide,King,Richmond\nHarbourfront East,Toronto Islands,Union Station\nDesign Exchange,Toronto Dominion Centre\nCommerce Court,Victoria Hotel\nHarbord,University of Toronto\nChinatown,Grange Park,Kensington Market\nCN Tower,Bathurst Quay,Island airport,Harbourfront West,King and Spadina,Railway Lands,South Niagara\nStn A PO Boxes 25 The Esplanade\nFirst Canadian Place,Underground city\nChristie\n"
                }
            ], 
            "source": "downtown_venues = getNearbyVenues(names=downtown_data['Neighborhood'],\n                                   latitudes=downtown_data['Latitude'],\n                                   longitudes=downtown_data['Longitude']\n                                  )\n"
        }, 
        {
            "execution_count": 50, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "(1559, 7)\n"
                }, 
                {
                    "execution_count": 50, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Neighborhood</th>\n      <th>Neighborhood Latitude</th>\n      <th>Neighborhood Longitude</th>\n      <th>Venue</th>\n      <th>Venue Latitude</th>\n      <th>Venue Longitude</th>\n      <th>Venue Category</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Rosedale</td>\n      <td>43.679563</td>\n      <td>-79.377529</td>\n      <td>Summerhill Market</td>\n      <td>43.686265</td>\n      <td>-79.375458</td>\n      <td>Grocery Store</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Rosedale</td>\n      <td>43.679563</td>\n      <td>-79.377529</td>\n      <td>Craigleigh Gardens</td>\n      <td>43.678099</td>\n      <td>-79.371586</td>\n      <td>Park</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Rosedale</td>\n      <td>43.679563</td>\n      <td>-79.377529</td>\n      <td>TD Canada Trust</td>\n      <td>43.686094</td>\n      <td>-79.376549</td>\n      <td>Bank</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Rosedale</td>\n      <td>43.679563</td>\n      <td>-79.377529</td>\n      <td>TD Canada Trust</td>\n      <td>43.672484</td>\n      <td>-79.377162</td>\n      <td>Bank</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Rosedale</td>\n      <td>43.679563</td>\n      <td>-79.377529</td>\n      <td>Mooredale House</td>\n      <td>43.678631</td>\n      <td>-79.380091</td>\n      <td>Building</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "  Neighborhood  Neighborhood Latitude  Neighborhood Longitude  \\\n0     Rosedale              43.679563              -79.377529   \n1     Rosedale              43.679563              -79.377529   \n2     Rosedale              43.679563              -79.377529   \n3     Rosedale              43.679563              -79.377529   \n4     Rosedale              43.679563              -79.377529   \n\n                Venue  Venue Latitude  Venue Longitude Venue Category  \n0   Summerhill Market       43.686265       -79.375458  Grocery Store  \n1  Craigleigh Gardens       43.678099       -79.371586           Park  \n2     TD Canada Trust       43.686094       -79.376549           Bank  \n3     TD Canada Trust       43.672484       -79.377162           Bank  \n4     Mooredale House       43.678631       -79.380091       Building  "
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "print(downtown_venues.shape)\ndowntown_venues.head()"
        }, 
        {
            "execution_count": 51, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "There are 217 uniques categories.\n"
                }
            ], 
            "source": "downtown_venues.groupby('Neighborhood').count()\nprint('There are {} uniques categories.'.format(len(downtown_venues['Venue Category'].unique())))"
        }, 
        {
            "source": "## Analyzing Each Neighborhood", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 52, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 52, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Yoga Studio</th>\n      <th>Afghan Restaurant</th>\n      <th>Airport</th>\n      <th>Airport Food Court</th>\n      <th>Airport Gate</th>\n      <th>Airport Lounge</th>\n      <th>Airport Service</th>\n      <th>Airport Terminal</th>\n      <th>American Restaurant</th>\n      <th>Animal Shelter</th>\n      <th>...</th>\n      <th>Trail</th>\n      <th>Train Station</th>\n      <th>Tunnel</th>\n      <th>University</th>\n      <th>Vegetarian / Vegan Restaurant</th>\n      <th>Video Game Store</th>\n      <th>Video Store</th>\n      <th>Vietnamese Restaurant</th>\n      <th>Wine Bar</th>\n      <th>Wings Joint</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \u00d7 217 columns</p>\n</div>", 
                        "text/plain": "   Yoga Studio  Afghan Restaurant  Airport  Airport Food Court  Airport Gate  \\\n0            0                  0        0                   0             0   \n1            0                  0        0                   0             0   \n2            0                  0        0                   0             0   \n3            0                  0        0                   0             0   \n4            0                  0        0                   0             0   \n\n   Airport Lounge  Airport Service  Airport Terminal  American Restaurant  \\\n0               0                0                 0                    0   \n1               0                0                 0                    0   \n2               0                0                 0                    0   \n3               0                0                 0                    0   \n4               0                0                 0                    0   \n\n   Animal Shelter  ...  Trail  Train Station  Tunnel  University  \\\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  Video Store  \\\n0                              0                 0            0   \n1                              0                 0            0   \n2                              0                 0            0   \n3                              0                 0            0   \n4                              0                 0            0   \n\n   Vietnamese Restaurant  Wine Bar  Wings Joint  \n0                      0         0            0  \n1                      0         0            0  \n2                      0         0            0  \n3                      0         0            0  \n4                      0         0            0  \n\n[5 rows x 217 columns]"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "# one hot encoding\ndowntown_onehot = pd.get_dummies(downtown_venues[['Venue Category']], prefix=\"\", prefix_sep=\"\")\n\n# add neighborhood column back to dataframe\ndowntown_onehot['Neighborhood'] = downtown_venues['Neighborhood'] \n\n# move neighborhood column to the first column\nfixed_columns = [downtown_onehot.columns[-1]] + list(downtown_onehot.columns[:-1])\ndowntown_onehot = downtown_onehot[fixed_columns]\n\ndowntown_onehot.head()"
        }, 
        {
            "execution_count": 53, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 53, 
                    "metadata": {}, 
                    "data": {
                        "text/plain": "(1559, 217)"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "downtown_onehot.shape"
        }, 
        {
            "source": "### Grouping rows by neighborhood and by taking the mean of the frequency of occurrence of each category", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 54, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 54, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Neighborhood</th>\n      <th>Yoga Studio</th>\n      <th>Afghan Restaurant</th>\n      <th>Airport</th>\n      <th>Airport Food Court</th>\n      <th>Airport Gate</th>\n      <th>Airport Lounge</th>\n      <th>Airport Service</th>\n      <th>Airport Terminal</th>\n      <th>American Restaurant</th>\n      <th>...</th>\n      <th>Trail</th>\n      <th>Train Station</th>\n      <th>Tunnel</th>\n      <th>University</th>\n      <th>Vegetarian / Vegan Restaurant</th>\n      <th>Video Game Store</th>\n      <th>Video Store</th>\n      <th>Vietnamese Restaurant</th>\n      <th>Wine Bar</th>\n      <th>Wings Joint</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Adelaide,King,Richmond</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.03</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.020000</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Berczy Park</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.01</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.010000</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CN Tower,Bathurst Quay,Island airport,Harbourf...</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.037037</td>\n      <td>0.037037</td>\n      <td>0.037037</td>\n      <td>0.074074</td>\n      <td>0.111111</td>\n      <td>0.037037</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.037037</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Cabbagetown,St. James Town</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Central Bay Street</td>\n      <td>0.010000</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>0.000000</td>\n      <td>0.01</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.01</td>\n      <td>0.020000</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.01</td>\n      <td>0.00</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Chinatown,Grange Park,Kensington Market</td>\n      <td>0.010000</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>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.070000</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.04</td>\n      <td>0.01</td>\n      <td>0.00</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Christie</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.016393</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Church and Wellesley</td>\n      <td>0.010000</td>\n      <td>0.01</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.01</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.01</td>\n      <td>0.01</td>\n      <td>0.01</td>\n      <td>0.00</td>\n      <td>0.01</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Commerce Court,Victoria Hotel</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.03</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.01</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.020000</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.01</td>\n      <td>0.00</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Design Exchange,Toronto Dominion Centre</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.03</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.01</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.010000</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.01</td>\n      <td>0.00</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>First Canadian Place,Underground city</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.03</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.01</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.010000</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.01</td>\n      <td>0.00</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Harbord,University of Toronto</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.040000</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.01</td>\n      <td>0.00</td>\n      <td>0.00</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Harbourfront East,Toronto Islands,Union Station</td>\n      <td>0.010000</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>0.000000</td>\n      <td>0.01</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.01</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.010000</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.01</td>\n      <td>0.00</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Harbourfront,Regent Park</td>\n      <td>0.010989</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>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Rosedale</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>...</td>\n      <td>0.181818</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Ryerson,Garden District</td>\n      <td>0.010000</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>0.000000</td>\n      <td>0.02</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.010000</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.01</td>\n      <td>0.00</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>St. James Town</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.03</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.010000</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Stn A PO Boxes 25 The Esplanade</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.02</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.00</td>\n      <td>0.010000</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n      <td>0.00</td>\n    </tr>\n  </tbody>\n</table>\n<p>18 rows \u00d7 217 columns</p>\n</div>", 
                        "text/plain": "                                         Neighborhood  Yoga Studio  \\\n0                              Adelaide,King,Richmond     0.000000   \n1                                         Berczy Park     0.000000   \n2   CN Tower,Bathurst Quay,Island airport,Harbourf...     0.000000   \n3                          Cabbagetown,St. James Town     0.000000   \n4                                  Central Bay Street     0.010000   \n5             Chinatown,Grange Park,Kensington Market     0.010000   \n6                                            Christie     0.000000   \n7                                Church and Wellesley     0.010000   \n8                       Commerce Court,Victoria Hotel     0.000000   \n9             Design Exchange,Toronto Dominion Centre     0.000000   \n10              First Canadian Place,Underground city     0.000000   \n11                      Harbord,University of Toronto     0.000000   \n12    Harbourfront East,Toronto Islands,Union Station     0.010000   \n13                           Harbourfront,Regent Park     0.010989   \n14                                           Rosedale     0.000000   \n15                            Ryerson,Garden District     0.010000   \n16                                     St. James Town     0.000000   \n17                    Stn A PO Boxes 25 The Esplanade     0.000000   \n\n    Afghan Restaurant   Airport  Airport Food Court  Airport Gate  \\\n0                0.00  0.000000            0.000000      0.000000   \n1                0.00  0.000000            0.000000      0.000000   \n2                0.00  0.037037            0.037037      0.037037   \n3                0.00  0.000000            0.000000      0.000000   \n4                0.00  0.000000            0.000000      0.000000   \n5                0.00  0.000000            0.000000      0.000000   \n6                0.00  0.000000            0.000000      0.000000   \n7                0.01  0.000000            0.000000      0.000000   \n8                0.00  0.000000            0.000000      0.000000   \n9                0.00  0.000000            0.000000      0.000000   \n10               0.00  0.000000            0.000000      0.000000   \n11               0.00  0.000000            0.000000      0.000000   \n12               0.00  0.000000            0.000000      0.000000   \n13               0.00  0.000000            0.000000      0.000000   \n14               0.00  0.000000            0.000000      0.000000   \n15               0.00  0.000000            0.000000      0.000000   \n16               0.00  0.000000            0.000000      0.000000   \n17               0.00  0.000000            0.000000      0.000000   \n\n    Airport Lounge  Airport Service  Airport Terminal  American Restaurant  \\\n0         0.000000         0.000000          0.000000                 0.03   \n1         0.000000         0.000000          0.000000                 0.00   \n2         0.074074         0.111111          0.037037                 0.00   \n3         0.000000         0.000000          0.000000                 0.00   \n4         0.000000         0.000000          0.000000                 0.01   \n5         0.000000         0.000000          0.000000                 0.00   \n6         0.000000         0.000000          0.000000                 0.00   \n7         0.000000         0.000000          0.000000                 0.01   \n8         0.000000         0.000000          0.000000                 0.03   \n9         0.000000         0.000000          0.000000                 0.03   \n10        0.000000         0.000000          0.000000                 0.03   \n11        0.000000         0.000000          0.000000                 0.00   \n12        0.000000         0.000000          0.000000                 0.01   \n13        0.000000         0.000000          0.000000                 0.00   \n14        0.000000         0.000000          0.000000                 0.00   \n15        0.000000         0.000000          0.000000                 0.02   \n16        0.000000         0.000000          0.000000                 0.03   \n17        0.000000         0.000000          0.000000                 0.02   \n\n    ...     Trail  Train Station    Tunnel  University  \\\n0   ...  0.000000           0.00  0.000000        0.00   \n1   ...  0.000000           0.01  0.000000        0.00   \n2   ...  0.000000           0.00  0.037037        0.00   \n3   ...  0.000000           0.00  0.000000        0.00   \n4   ...  0.000000           0.00  0.000000        0.01   \n5   ...  0.000000           0.00  0.000000        0.00   \n6   ...  0.000000           0.00  0.000000        0.00   \n7   ...  0.000000           0.00  0.000000        0.00   \n8   ...  0.000000           0.01  0.000000        0.00   \n9   ...  0.000000           0.01  0.000000        0.00   \n10  ...  0.000000           0.01  0.000000        0.00   \n11  ...  0.000000           0.00  0.000000        0.00   \n12  ...  0.000000           0.01  0.000000        0.00   \n13  ...  0.000000           0.00  0.000000        0.00   \n14  ...  0.181818           0.00  0.000000        0.00   \n15  ...  0.000000           0.00  0.000000        0.00   \n16  ...  0.000000           0.00  0.000000        0.00   \n17  ...  0.000000           0.00  0.000000        0.00   \n\n    Vegetarian / Vegan Restaurant  Video Game Store  Video Store  \\\n0                        0.020000              0.00         0.00   \n1                        0.010000              0.00         0.00   \n2                        0.000000              0.00         0.00   \n3                        0.000000              0.00         0.00   \n4                        0.020000              0.00         0.00   \n5                        0.070000              0.00         0.00   \n6                        0.016393              0.00         0.00   \n7                        0.000000              0.01         0.01   \n8                        0.020000              0.00         0.00   \n9                        0.010000              0.00         0.00   \n10                       0.010000              0.00         0.00   \n11                       0.040000              0.00         0.00   \n12                       0.010000              0.00         0.00   \n13                       0.000000              0.00         0.00   \n14                       0.000000              0.00         0.00   \n15                       0.010000              0.00         0.00   \n16                       0.010000              0.00         0.00   \n17                       0.010000              0.00         0.00   \n\n    Vietnamese Restaurant  Wine Bar  Wings Joint  \n0                    0.00      0.00         0.00  \n1                    0.00      0.00         0.00  \n2                    0.00      0.00         0.00  \n3                    0.00      0.00         0.00  \n4                    0.00      0.01         0.00  \n5                    0.04      0.01         0.00  \n6                    0.00      0.00         0.00  \n7                    0.01      0.00         0.01  \n8                    0.00      0.01         0.00  \n9                    0.00      0.01         0.00  \n10                   0.00      0.01         0.00  \n11                   0.01      0.00         0.00  \n12                   0.00      0.01         0.00  \n13                   0.00      0.00         0.00  \n14                   0.00      0.00         0.00  \n15                   0.00      0.01         0.00  \n16                   0.00      0.00         0.00  \n17                   0.00      0.00         0.00  \n\n[18 rows x 217 columns]"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "downtown_grouped = downtown_onehot.groupby('Neighborhood').mean().reset_index()\ndowntown_grouped"
        }, 
        {
            "execution_count": 55, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 55, 
                    "metadata": {}, 
                    "data": {
                        "text/plain": "(18, 217)"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "downtown_grouped.shape"
        }, 
        {
            "source": "#### Top 5 Venues in Each Neighborhood", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 59, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "----Adelaide,King,Richmond----\n              venue  freq\n0       Coffee Shop  0.08\n1              Caf\u00e9  0.08\n2        Steakhouse  0.04\n3             Hotel  0.04\n4  Sushi Restaurant  0.03\n\n\n----Berczy Park----\n                 venue  freq\n0          Coffee Shop  0.09\n1                 Caf\u00e9  0.06\n2                Hotel  0.05\n3           Restaurant  0.05\n4  Japanese Restaurant  0.04\n\n\n----CN Tower,Bathurst Quay,Island airport,Harbourfront West,King and Spadina,Railway Lands,South Niagara----\n                 venue  freq\n0      Airport Service  0.11\n1      Harbor / Marina  0.11\n2  Rental Car Location  0.07\n3          Coffee Shop  0.07\n4     Sculpture Garden  0.07\n\n\n----Cabbagetown,St. James Town----\n         venue  freq\n0  Coffee Shop  0.09\n1   Restaurant  0.06\n2  Pizza Place  0.04\n3     Pharmacy  0.04\n4         Caf\u00e9  0.04\n\n\n----Central Bay Street----\n                       venue  freq\n0                Coffee Shop  0.08\n1  Middle Eastern Restaurant  0.03\n2             Ice Cream Shop  0.03\n3                       Caf\u00e9  0.03\n4                       Park  0.03\n\n\n----Chinatown,Grange Park,Kensington Market----\n                           venue  freq\n0                           Caf\u00e9  0.09\n1  Vegetarian / Vegan Restaurant  0.07\n2                            Bar  0.06\n3             Mexican Restaurant  0.04\n4          Vietnamese Restaurant  0.04\n\n\n----Christie----\n               venue  freq\n0  Korean Restaurant  0.11\n1      Grocery Store  0.08\n2        Coffee Shop  0.08\n3               Caf\u00e9  0.07\n4  Indian Restaurant  0.05\n\n\n----Church and Wellesley----\n                 venue  freq\n0          Coffee Shop  0.10\n1  Japanese Restaurant  0.05\n2     Sushi Restaurant  0.03\n3                 Caf\u00e9  0.03\n4         Dance Studio  0.02\n\n\n----Commerce Court,Victoria Hotel----\n            venue  freq\n0     Coffee Shop  0.11\n1           Hotel  0.07\n2      Restaurant  0.06\n3            Caf\u00e9  0.06\n4  Breakfast Spot  0.03\n\n\n----Design Exchange,Toronto Dominion Centre----\n         venue  freq\n0  Coffee Shop  0.09\n1        Hotel  0.08\n2         Caf\u00e9  0.08\n3   Restaurant  0.04\n4       Bakery  0.04\n\n\n----First Canadian Place,Underground city----\n         venue  freq\n0  Coffee Shop  0.11\n1         Caf\u00e9  0.08\n2        Hotel  0.08\n3   Restaurant  0.04\n4      Theater  0.03\n\n\n----Harbord,University of Toronto----\n                           venue  freq\n0                           Caf\u00e9  0.08\n1                     Restaurant  0.06\n2                    Coffee Shop  0.04\n3                         Bakery  0.04\n4  Vegetarian / Vegan Restaurant  0.04\n\n\n----Harbourfront East,Toronto Islands,Union Station----\n                venue  freq\n0         Coffee Shop  0.10\n1               Hotel  0.07\n2  Italian Restaurant  0.04\n3            Aquarium  0.04\n4                Caf\u00e9  0.04\n\n\n----Harbourfront,Regent Park----\n                venue  freq\n0         Coffee Shop  0.15\n1  Italian Restaurant  0.05\n2                Park  0.04\n3                Caf\u00e9  0.04\n4          Restaurant  0.04\n\n\n----Rosedale----\n         venue  freq\n0         Park  0.27\n1        Trail  0.18\n2         Bank  0.18\n3  Candy Store  0.09\n4   Playground  0.09\n\n\n----Ryerson,Garden District----\n                       venue  freq\n0                Coffee Shop  0.08\n1             Clothing Store  0.05\n2  Middle Eastern Restaurant  0.04\n3                       Caf\u00e9  0.03\n4                 Restaurant  0.03\n\n\n----St. James Town----\n         venue  freq\n0  Coffee Shop  0.08\n1         Caf\u00e9  0.07\n2   Restaurant  0.05\n3        Hotel  0.05\n4       Bakery  0.04\n\n\n----Stn A PO Boxes 25 The Esplanade----\n                 venue  freq\n0          Coffee Shop  0.11\n1                 Caf\u00e9  0.07\n2  Japanese Restaurant  0.04\n3             Beer Bar  0.04\n4           Restaurant  0.04\n\n\n"
                }
            ], 
            "source": "num_top_venues = 5\n\nfor hood in downtown_grouped['Neighborhood']:\n    print(\"----\"+hood+\"----\")\n    temp = downtown_grouped[downtown_grouped['Neighborhood'] == hood].T.reset_index()\n    temp.columns = ['venue','freq']\n    temp = temp.iloc[1:]\n    temp['freq'] = temp['freq'].astype(float)\n    temp = temp.round({'freq': 2})\n    print(temp.sort_values('freq', ascending=False).reset_index(drop=True).head(num_top_venues))\n    print('\\n')"
        }, 
        {
            "source": "Converting the data above into a pandas dataframe in a descending order", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 60, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "def return_most_common_venues(row, num_top_venues):\n    row_categories = row.iloc[1:]\n    row_categories_sorted = row_categories.sort_values(ascending=False)\n    \n    return row_categories_sorted.index.values[0:num_top_venues]"
        }, 
        {
            "source": "### Top 10 Venues in Each Neighborhood", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 63, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 63, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Neighborhood</th>\n      <th>1st Most Common Venue</th>\n      <th>2nd Most Common Venue</th>\n      <th>3rd Most Common Venue</th>\n      <th>4th Most Common Venue</th>\n      <th>5th Most Common Venue</th>\n      <th>6th Most Common Venue</th>\n      <th>7th Most Common Venue</th>\n      <th>8th Most Common Venue</th>\n      <th>9th Most Common Venue</th>\n      <th>10th Most Common Venue</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Adelaide,King,Richmond</td>\n      <td>Coffee Shop</td>\n      <td>Caf\u00e9</td>\n      <td>Hotel</td>\n      <td>Steakhouse</td>\n      <td>American Restaurant</td>\n      <td>Bar</td>\n      <td>Pizza Place</td>\n      <td>Theater</td>\n      <td>Sushi Restaurant</td>\n      <td>Gym</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Berczy Park</td>\n      <td>Coffee Shop</td>\n      <td>Caf\u00e9</td>\n      <td>Restaurant</td>\n      <td>Hotel</td>\n      <td>Beer Bar</td>\n      <td>Japanese Restaurant</td>\n      <td>Park</td>\n      <td>Breakfast Spot</td>\n      <td>Cocktail Bar</td>\n      <td>Steakhouse</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CN Tower,Bathurst Quay,Island airport,Harbourf...</td>\n      <td>Harbor / Marina</td>\n      <td>Airport Service</td>\n      <td>Coffee Shop</td>\n      <td>Rental Car Location</td>\n      <td>Airport Lounge</td>\n      <td>Sculpture Garden</td>\n      <td>Boat or Ferry</td>\n      <td>Bar</td>\n      <td>Plane</td>\n      <td>Music Venue</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Cabbagetown,St. James Town</td>\n      <td>Coffee Shop</td>\n      <td>Restaurant</td>\n      <td>Caf\u00e9</td>\n      <td>Pizza Place</td>\n      <td>Pharmacy</td>\n      <td>Gastropub</td>\n      <td>Thai Restaurant</td>\n      <td>Pub</td>\n      <td>Beer Store</td>\n      <td>Park</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Central Bay Street</td>\n      <td>Coffee Shop</td>\n      <td>Japanese Restaurant</td>\n      <td>Middle Eastern Restaurant</td>\n      <td>Caf\u00e9</td>\n      <td>Italian Restaurant</td>\n      <td>Ice Cream Shop</td>\n      <td>Park</td>\n      <td>Burger Joint</td>\n      <td>Sushi Restaurant</td>\n      <td>Burrito Place</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "                                        Neighborhood 1st Most Common Venue  \\\n0                             Adelaide,King,Richmond           Coffee Shop   \n1                                        Berczy Park           Coffee Shop   \n2  CN Tower,Bathurst Quay,Island airport,Harbourf...       Harbor / Marina   \n3                         Cabbagetown,St. James Town           Coffee Shop   \n4                                 Central Bay Street           Coffee Shop   \n\n  2nd Most Common Venue      3rd Most Common Venue 4th Most Common Venue  \\\n0                  Caf\u00e9                      Hotel            Steakhouse   \n1                  Caf\u00e9                 Restaurant                 Hotel   \n2       Airport Service                Coffee Shop   Rental Car Location   \n3            Restaurant                       Caf\u00e9           Pizza Place   \n4   Japanese Restaurant  Middle Eastern Restaurant                  Caf\u00e9   \n\n  5th Most Common Venue 6th Most Common Venue 7th Most Common Venue  \\\n0   American Restaurant                   Bar           Pizza Place   \n1              Beer Bar   Japanese Restaurant                  Park   \n2        Airport Lounge      Sculpture Garden         Boat or Ferry   \n3              Pharmacy             Gastropub       Thai Restaurant   \n4    Italian Restaurant        Ice Cream Shop                  Park   \n\n  8th Most Common Venue 9th Most Common Venue 10th Most Common Venue  \n0               Theater      Sushi Restaurant                    Gym  \n1        Breakfast Spot          Cocktail Bar             Steakhouse  \n2                   Bar                 Plane            Music Venue  \n3                   Pub            Beer Store                   Park  \n4          Burger Joint      Sushi Restaurant          Burrito Place  "
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "num_top_venues = 10\n\nindicators = ['st', 'nd', 'rd']\n\n# create columns according to number of top venues\ncolumns = ['Neighborhood']\nfor ind in np.arange(num_top_venues):\n    try:\n        columns.append('{}{} Most Common Venue'.format(ind+1, indicators[ind]))\n    except:\n        columns.append('{}th Most Common Venue'.format(ind+1))\n\n# create a new dataframe\nneighborhoods_venues_sorted = pd.DataFrame(columns=columns)\nneighborhoods_venues_sorted['Neighborhood'] = downtown_grouped['Neighborhood']\n\nfor ind in np.arange(downtown_grouped.shape[0]):\n    neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(downtown_grouped.iloc[ind, :], num_top_venues)\n\nneighborhoods_venues_sorted.head()"
        }, 
        {
            "source": "## Clustering Neighborhood", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 68, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "from sklearn.cluster import KMeans\nimport matplotlib.cm as cm\nimport matplotlib.colors as colors"
        }, 
        {
            "execution_count": 72, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 72, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PostalCode</th>\n      <th>Borough</th>\n      <th>Neighborhood</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n      <th>Cluster Labels</th>\n      <th>1st Most Common Venue</th>\n      <th>2nd Most Common Venue</th>\n      <th>3rd Most Common Venue</th>\n      <th>4th Most Common Venue</th>\n      <th>5th Most Common Venue</th>\n      <th>6th Most Common Venue</th>\n      <th>7th Most Common Venue</th>\n      <th>8th Most Common Venue</th>\n      <th>9th Most Common Venue</th>\n      <th>10th Most Common Venue</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>M4W</td>\n      <td>DowntownToronto</td>\n      <td>Rosedale</td>\n      <td>43.679563</td>\n      <td>-79.377529</td>\n      <td>2</td>\n      <td>Park</td>\n      <td>Trail</td>\n      <td>Bank</td>\n      <td>Playground</td>\n      <td>Grocery Store</td>\n      <td>Building</td>\n      <td>Candy Store</td>\n      <td>Dessert Shop</td>\n      <td>Diner</td>\n      <td>Discount Store</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>M4X</td>\n      <td>DowntownToronto</td>\n      <td>Cabbagetown,St. James Town</td>\n      <td>43.667967</td>\n      <td>-79.367675</td>\n      <td>1</td>\n      <td>Coffee Shop</td>\n      <td>Restaurant</td>\n      <td>Caf\u00e9</td>\n      <td>Pizza Place</td>\n      <td>Pharmacy</td>\n      <td>Gastropub</td>\n      <td>Thai Restaurant</td>\n      <td>Pub</td>\n      <td>Beer Store</td>\n      <td>Park</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>M4Y</td>\n      <td>DowntownToronto</td>\n      <td>Church and Wellesley</td>\n      <td>43.665860</td>\n      <td>-79.383160</td>\n      <td>1</td>\n      <td>Coffee Shop</td>\n      <td>Japanese Restaurant</td>\n      <td>Sushi Restaurant</td>\n      <td>Caf\u00e9</td>\n      <td>Gastropub</td>\n      <td>Gay Bar</td>\n      <td>Italian Restaurant</td>\n      <td>Mediterranean Restaurant</td>\n      <td>Diner</td>\n      <td>Dance Studio</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>M5A</td>\n      <td>DowntownToronto</td>\n      <td>Harbourfront,Regent Park</td>\n      <td>43.654260</td>\n      <td>-79.360636</td>\n      <td>1</td>\n      <td>Coffee Shop</td>\n      <td>Italian Restaurant</td>\n      <td>Restaurant</td>\n      <td>Caf\u00e9</td>\n      <td>Park</td>\n      <td>Bakery</td>\n      <td>Pub</td>\n      <td>Gym / Fitness Center</td>\n      <td>Theater</td>\n      <td>Cosmetics Shop</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>M5B</td>\n      <td>DowntownToronto</td>\n      <td>Ryerson,Garden District</td>\n      <td>43.657162</td>\n      <td>-79.378937</td>\n      <td>1</td>\n      <td>Coffee Shop</td>\n      <td>Clothing Store</td>\n      <td>Middle Eastern Restaurant</td>\n      <td>Caf\u00e9</td>\n      <td>Tea Room</td>\n      <td>Restaurant</td>\n      <td>Ramen Restaurant</td>\n      <td>Gastropub</td>\n      <td>Cosmetics Shop</td>\n      <td>Gym</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "  PostalCode          Borough                Neighborhood   Latitude  \\\n0        M4W  DowntownToronto                    Rosedale  43.679563   \n1        M4X  DowntownToronto  Cabbagetown,St. James Town  43.667967   \n2        M4Y  DowntownToronto        Church and Wellesley  43.665860   \n3        M5A  DowntownToronto    Harbourfront,Regent Park  43.654260   \n4        M5B  DowntownToronto     Ryerson,Garden District  43.657162   \n\n   Longitude  Cluster Labels 1st Most Common Venue 2nd Most Common Venue  \\\n0 -79.377529               2                  Park                 Trail   \n1 -79.367675               1           Coffee Shop            Restaurant   \n2 -79.383160               1           Coffee Shop   Japanese Restaurant   \n3 -79.360636               1           Coffee Shop    Italian Restaurant   \n4 -79.378937               1           Coffee Shop        Clothing Store   \n\n       3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue  \\\n0                       Bank            Playground         Grocery Store   \n1                       Caf\u00e9           Pizza Place              Pharmacy   \n2           Sushi Restaurant                  Caf\u00e9             Gastropub   \n3                 Restaurant                  Caf\u00e9                  Park   \n4  Middle Eastern Restaurant                  Caf\u00e9              Tea Room   \n\n  6th Most Common Venue 7th Most Common Venue     8th Most Common Venue  \\\n0              Building           Candy Store              Dessert Shop   \n1             Gastropub       Thai Restaurant                       Pub   \n2               Gay Bar    Italian Restaurant  Mediterranean Restaurant   \n3                Bakery                   Pub      Gym / Fitness Center   \n4            Restaurant      Ramen Restaurant                 Gastropub   \n\n  9th Most Common Venue 10th Most Common Venue  \n0                 Diner         Discount Store  \n1            Beer Store                   Park  \n2                 Diner           Dance Studio  \n3               Theater         Cosmetics Shop  \n4        Cosmetics Shop                    Gym  "
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "# set number of clusters\nkclusters = 5\n\ndowntown_grouped_clustering = downtown_grouped.drop('Neighborhood', 1)\n\n# run k-means clustering\nkmeans = KMeans(n_clusters=kclusters, random_state=0).fit(downtown_grouped_clustering)\n\n# check cluster labels generated for each row in the dataframe\nkmeans.labels_[0:10] \n\n# add clustering labels\ndowntown_merged['Cluster Labels'] = kmeans.labels_\n\ndowntown_merged = downtown_data\n\n# merge toronto_grouped with toronto_data to add latitude/longitude for each neighborhood\ndowntown_merged = downtown_merged.join(neighborhoods_venues_sorted.set_index('Neighborhood'), on='Neighborhood')\n\ndowntown_merged.head() # check the last columns!"
        }, 
        {
            "source": "## Visualizing Using a Map", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 83, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 83, 
                    "metadata": {}, 
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</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>", 
                        "text/plain": "<folium.folium.Map at 0x7fd7f906f048>"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "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(downtown_merged['Latitude'], downtown_merged['Longitude'], downtown_merged['Neighborhood'], downtown_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"
        }, 
        {
            "source": "## Examining Clusters", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "### Examining each cluster and determine the discriminating venue categories that distinguish it.", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "### Cluster 1", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 77, 
            "cell_type": "code", 
            "metadata": {
                "scrolled": true
            }, 
            "outputs": [
                {
                    "execution_count": 77, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Borough</th>\n      <th>Cluster Labels</th>\n      <th>1st Most Common Venue</th>\n      <th>2nd Most Common Venue</th>\n      <th>3rd Most Common Venue</th>\n      <th>4th Most Common Venue</th>\n      <th>5th Most Common Venue</th>\n      <th>6th Most Common Venue</th>\n      <th>7th Most Common Venue</th>\n      <th>8th Most Common Venue</th>\n      <th>9th Most Common Venue</th>\n      <th>10th Most Common Venue</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>5</th>\n      <td>DowntownToronto</td>\n      <td>0</td>\n      <td>Coffee Shop</td>\n      <td>Caf\u00e9</td>\n      <td>Restaurant</td>\n      <td>Hotel</td>\n      <td>Bakery</td>\n      <td>Breakfast Spot</td>\n      <td>Gastropub</td>\n      <td>Seafood Restaurant</td>\n      <td>American Restaurant</td>\n      <td>Cosmetics Shop</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>DowntownToronto</td>\n      <td>0</td>\n      <td>Coffee Shop</td>\n      <td>Caf\u00e9</td>\n      <td>Restaurant</td>\n      <td>Hotel</td>\n      <td>Beer Bar</td>\n      <td>Japanese Restaurant</td>\n      <td>Park</td>\n      <td>Breakfast Spot</td>\n      <td>Cocktail Bar</td>\n      <td>Steakhouse</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>DowntownToronto</td>\n      <td>0</td>\n      <td>Coffee Shop</td>\n      <td>Caf\u00e9</td>\n      <td>Hotel</td>\n      <td>Steakhouse</td>\n      <td>American Restaurant</td>\n      <td>Bar</td>\n      <td>Pizza Place</td>\n      <td>Theater</td>\n      <td>Sushi Restaurant</td>\n      <td>Gym</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>DowntownToronto</td>\n      <td>0</td>\n      <td>Coffee Shop</td>\n      <td>Hotel</td>\n      <td>Caf\u00e9</td>\n      <td>Aquarium</td>\n      <td>Italian Restaurant</td>\n      <td>Restaurant</td>\n      <td>Brewery</td>\n      <td>Scenic Lookout</td>\n      <td>Park</td>\n      <td>Deli / Bodega</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>DowntownToronto</td>\n      <td>0</td>\n      <td>Coffee Shop</td>\n      <td>Hotel</td>\n      <td>Caf\u00e9</td>\n      <td>Bakery</td>\n      <td>Restaurant</td>\n      <td>Steakhouse</td>\n      <td>Gastropub</td>\n      <td>Bar</td>\n      <td>Concert Hall</td>\n      <td>American Restaurant</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>DowntownToronto</td>\n      <td>0</td>\n      <td>Coffee Shop</td>\n      <td>Hotel</td>\n      <td>Caf\u00e9</td>\n      <td>Restaurant</td>\n      <td>Seafood Restaurant</td>\n      <td>Gastropub</td>\n      <td>Concert Hall</td>\n      <td>Breakfast Spot</td>\n      <td>Japanese Restaurant</td>\n      <td>American Restaurant</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>DowntownToronto</td>\n      <td>0</td>\n      <td>Coffee Shop</td>\n      <td>Caf\u00e9</td>\n      <td>Japanese Restaurant</td>\n      <td>Restaurant</td>\n      <td>Beer Bar</td>\n      <td>Bakery</td>\n      <td>Gastropub</td>\n      <td>Seafood Restaurant</td>\n      <td>Hotel</td>\n      <td>Cocktail Bar</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>DowntownToronto</td>\n      <td>0</td>\n      <td>Coffee Shop</td>\n      <td>Hotel</td>\n      <td>Caf\u00e9</td>\n      <td>Restaurant</td>\n      <td>Steakhouse</td>\n      <td>Theater</td>\n      <td>Gastropub</td>\n      <td>American Restaurant</td>\n      <td>Japanese Restaurant</td>\n      <td>Bakery</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "            Borough  Cluster Labels 1st Most Common Venue  \\\n5   DowntownToronto               0           Coffee Shop   \n6   DowntownToronto               0           Coffee Shop   \n8   DowntownToronto               0           Coffee Shop   \n9   DowntownToronto               0           Coffee Shop   \n10  DowntownToronto               0           Coffee Shop   \n11  DowntownToronto               0           Coffee Shop   \n15  DowntownToronto               0           Coffee Shop   \n16  DowntownToronto               0           Coffee Shop   \n\n   2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue  \\\n5                   Caf\u00e9            Restaurant                 Hotel   \n6                   Caf\u00e9            Restaurant                 Hotel   \n8                   Caf\u00e9                 Hotel            Steakhouse   \n9                  Hotel                  Caf\u00e9              Aquarium   \n10                 Hotel                  Caf\u00e9                Bakery   \n11                 Hotel                  Caf\u00e9            Restaurant   \n15                  Caf\u00e9   Japanese Restaurant            Restaurant   \n16                 Hotel                  Caf\u00e9            Restaurant   \n\n   5th Most Common Venue 6th Most Common Venue 7th Most Common Venue  \\\n5                 Bakery        Breakfast Spot             Gastropub   \n6               Beer Bar   Japanese Restaurant                  Park   \n8    American Restaurant                   Bar           Pizza Place   \n9     Italian Restaurant            Restaurant               Brewery   \n10            Restaurant            Steakhouse             Gastropub   \n11    Seafood Restaurant             Gastropub          Concert Hall   \n15              Beer Bar                Bakery             Gastropub   \n16            Steakhouse               Theater             Gastropub   \n\n   8th Most Common Venue 9th Most Common Venue 10th Most Common Venue  \n5     Seafood Restaurant   American Restaurant         Cosmetics Shop  \n6         Breakfast Spot          Cocktail Bar             Steakhouse  \n8                Theater      Sushi Restaurant                    Gym  \n9         Scenic Lookout                  Park          Deli / Bodega  \n10                   Bar          Concert Hall    American Restaurant  \n11        Breakfast Spot   Japanese Restaurant    American Restaurant  \n15    Seafood Restaurant                 Hotel           Cocktail Bar  \n16   American Restaurant   Japanese Restaurant                 Bakery  "
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "downtown_merged.loc[downtown_merged['Cluster Labels'] == 0, downtown_merged.columns[[1] + list(range(5, downtown_merged.shape[1]))]]\n"
        }, 
        {
            "source": "Distinguished by coffee shops, cafes, and hotels.", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "### Cluster 2", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 79, 
            "cell_type": "code", 
            "metadata": {
                "scrolled": true
            }, 
            "outputs": [
                {
                    "execution_count": 79, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Borough</th>\n      <th>Cluster Labels</th>\n      <th>1st Most Common Venue</th>\n      <th>2nd Most Common Venue</th>\n      <th>3rd Most Common Venue</th>\n      <th>4th Most Common Venue</th>\n      <th>5th Most Common Venue</th>\n      <th>6th Most Common Venue</th>\n      <th>7th Most Common Venue</th>\n      <th>8th Most Common Venue</th>\n      <th>9th Most Common Venue</th>\n      <th>10th Most Common Venue</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>DowntownToronto</td>\n      <td>1</td>\n      <td>Coffee Shop</td>\n      <td>Restaurant</td>\n      <td>Caf\u00e9</td>\n      <td>Pizza Place</td>\n      <td>Pharmacy</td>\n      <td>Gastropub</td>\n      <td>Thai Restaurant</td>\n      <td>Pub</td>\n      <td>Beer Store</td>\n      <td>Park</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>DowntownToronto</td>\n      <td>1</td>\n      <td>Coffee Shop</td>\n      <td>Japanese Restaurant</td>\n      <td>Sushi Restaurant</td>\n      <td>Caf\u00e9</td>\n      <td>Gastropub</td>\n      <td>Gay Bar</td>\n      <td>Italian Restaurant</td>\n      <td>Mediterranean Restaurant</td>\n      <td>Diner</td>\n      <td>Dance Studio</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>DowntownToronto</td>\n      <td>1</td>\n      <td>Coffee Shop</td>\n      <td>Italian Restaurant</td>\n      <td>Restaurant</td>\n      <td>Caf\u00e9</td>\n      <td>Park</td>\n      <td>Bakery</td>\n      <td>Pub</td>\n      <td>Gym / Fitness Center</td>\n      <td>Theater</td>\n      <td>Cosmetics Shop</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>DowntownToronto</td>\n      <td>1</td>\n      <td>Coffee Shop</td>\n      <td>Clothing Store</td>\n      <td>Middle Eastern Restaurant</td>\n      <td>Caf\u00e9</td>\n      <td>Tea Room</td>\n      <td>Restaurant</td>\n      <td>Ramen Restaurant</td>\n      <td>Gastropub</td>\n      <td>Cosmetics Shop</td>\n      <td>Gym</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>DowntownToronto</td>\n      <td>1</td>\n      <td>Coffee Shop</td>\n      <td>Japanese Restaurant</td>\n      <td>Middle Eastern Restaurant</td>\n      <td>Caf\u00e9</td>\n      <td>Italian Restaurant</td>\n      <td>Ice Cream Shop</td>\n      <td>Park</td>\n      <td>Burger Joint</td>\n      <td>Sushi Restaurant</td>\n      <td>Burrito Place</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>DowntownToronto</td>\n      <td>1</td>\n      <td>Korean Restaurant</td>\n      <td>Coffee Shop</td>\n      <td>Grocery Store</td>\n      <td>Caf\u00e9</td>\n      <td>Indian Restaurant</td>\n      <td>Diner</td>\n      <td>Karaoke Bar</td>\n      <td>Japanese Restaurant</td>\n      <td>Park</td>\n      <td>Cocktail Bar</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "            Borough  Cluster Labels 1st Most Common Venue  \\\n1   DowntownToronto               1           Coffee Shop   \n2   DowntownToronto               1           Coffee Shop   \n3   DowntownToronto               1           Coffee Shop   \n4   DowntownToronto               1           Coffee Shop   \n7   DowntownToronto               1           Coffee Shop   \n17  DowntownToronto               1     Korean Restaurant   \n\n   2nd Most Common Venue      3rd Most Common Venue 4th Most Common Venue  \\\n1             Restaurant                       Caf\u00e9           Pizza Place   \n2    Japanese Restaurant           Sushi Restaurant                  Caf\u00e9   \n3     Italian Restaurant                 Restaurant                  Caf\u00e9   \n4         Clothing Store  Middle Eastern Restaurant                  Caf\u00e9   \n7    Japanese Restaurant  Middle Eastern Restaurant                  Caf\u00e9   \n17           Coffee Shop              Grocery Store                  Caf\u00e9   \n\n   5th Most Common Venue 6th Most Common Venue 7th Most Common Venue  \\\n1               Pharmacy             Gastropub       Thai Restaurant   \n2              Gastropub               Gay Bar    Italian Restaurant   \n3                   Park                Bakery                   Pub   \n4               Tea Room            Restaurant      Ramen Restaurant   \n7     Italian Restaurant        Ice Cream Shop                  Park   \n17     Indian Restaurant                 Diner           Karaoke Bar   \n\n       8th Most Common Venue 9th Most Common Venue 10th Most Common Venue  \n1                        Pub            Beer Store                   Park  \n2   Mediterranean Restaurant                 Diner           Dance Studio  \n3       Gym / Fitness Center               Theater         Cosmetics Shop  \n4                  Gastropub        Cosmetics Shop                    Gym  \n7               Burger Joint      Sushi Restaurant          Burrito Place  \n17       Japanese Restaurant                  Park           Cocktail Bar  "
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "downtown_merged.loc[downtown_merged['Cluster Labels'] == 1, downtown_merged.columns[[1] + list(range(5, downtown_merged.shape[1]))]]\n"
        }, 
        {
            "source": "Distinguished by ethnic restaurants: Japanese, Korean, Middle Eastern.", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "### Cluster 3", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 80, 
            "cell_type": "code", 
            "metadata": {
                "scrolled": true
            }, 
            "outputs": [
                {
                    "execution_count": 80, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Borough</th>\n      <th>Cluster Labels</th>\n      <th>1st Most Common Venue</th>\n      <th>2nd Most Common Venue</th>\n      <th>3rd Most Common Venue</th>\n      <th>4th Most Common Venue</th>\n      <th>5th Most Common Venue</th>\n      <th>6th Most Common Venue</th>\n      <th>7th Most Common Venue</th>\n      <th>8th Most Common Venue</th>\n      <th>9th Most Common Venue</th>\n      <th>10th Most Common Venue</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>DowntownToronto</td>\n      <td>2</td>\n      <td>Park</td>\n      <td>Trail</td>\n      <td>Bank</td>\n      <td>Playground</td>\n      <td>Grocery Store</td>\n      <td>Building</td>\n      <td>Candy Store</td>\n      <td>Dessert Shop</td>\n      <td>Diner</td>\n      <td>Discount Store</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "           Borough  Cluster Labels 1st Most Common Venue  \\\n0  DowntownToronto               2                  Park   \n\n  2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue  \\\n0                 Trail                  Bank            Playground   \n\n  5th Most Common Venue 6th Most Common Venue 7th Most Common Venue  \\\n0         Grocery Store              Building           Candy Store   \n\n  8th Most Common Venue 9th Most Common Venue 10th Most Common Venue  \n0          Dessert Shop                 Diner         Discount Store  "
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "downtown_merged.loc[downtown_merged['Cluster Labels'] == 2, downtown_merged.columns[[1] + list(range(5, downtown_merged.shape[1]))]]\n"
        }, 
        {
            "source": "Distinguished by parks, trails, and banks.", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "### Cluster 4", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 81, 
            "cell_type": "code", 
            "metadata": {
                "scrolled": true
            }, 
            "outputs": [
                {
                    "execution_count": 81, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Borough</th>\n      <th>Cluster Labels</th>\n      <th>1st Most Common Venue</th>\n      <th>2nd Most Common Venue</th>\n      <th>3rd Most Common Venue</th>\n      <th>4th Most Common Venue</th>\n      <th>5th Most Common Venue</th>\n      <th>6th Most Common Venue</th>\n      <th>7th Most Common Venue</th>\n      <th>8th Most Common Venue</th>\n      <th>9th Most Common Venue</th>\n      <th>10th Most Common Venue</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>14</th>\n      <td>DowntownToronto</td>\n      <td>3</td>\n      <td>Harbor / Marina</td>\n      <td>Airport Service</td>\n      <td>Coffee Shop</td>\n      <td>Rental Car Location</td>\n      <td>Airport Lounge</td>\n      <td>Sculpture Garden</td>\n      <td>Boat or Ferry</td>\n      <td>Bar</td>\n      <td>Plane</td>\n      <td>Music Venue</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "            Borough  Cluster Labels 1st Most Common Venue  \\\n14  DowntownToronto               3       Harbor / Marina   \n\n   2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue  \\\n14       Airport Service           Coffee Shop   Rental Car Location   \n\n   5th Most Common Venue 6th Most Common Venue 7th Most Common Venue  \\\n14        Airport Lounge      Sculpture Garden         Boat or Ferry   \n\n   8th Most Common Venue 9th Most Common Venue 10th Most Common Venue  \n14                   Bar                 Plane            Music Venue  "
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "downtown_merged.loc[downtown_merged['Cluster Labels'] == 3, downtown_merged.columns[[1] + list(range(5, downtown_merged.shape[1]))]]\n"
        }, 
        {
            "source": "Distiguished by a marina (including boats and ferries) and airport services (including car rentals and lounges).", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "### Cluster 5", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 82, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 82, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Borough</th>\n      <th>Cluster Labels</th>\n      <th>1st Most Common Venue</th>\n      <th>2nd Most Common Venue</th>\n      <th>3rd Most Common Venue</th>\n      <th>4th Most Common Venue</th>\n      <th>5th Most Common Venue</th>\n      <th>6th Most Common Venue</th>\n      <th>7th Most Common Venue</th>\n      <th>8th Most Common Venue</th>\n      <th>9th Most Common Venue</th>\n      <th>10th Most Common Venue</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>12</th>\n      <td>DowntownToronto</td>\n      <td>4</td>\n      <td>Caf\u00e9</td>\n      <td>Restaurant</td>\n      <td>Vegetarian / Vegan Restaurant</td>\n      <td>Coffee Shop</td>\n      <td>Bakery</td>\n      <td>Bubble Tea Shop</td>\n      <td>Bookstore</td>\n      <td>Pizza Place</td>\n      <td>Pub</td>\n      <td>Italian Restaurant</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>DowntownToronto</td>\n      <td>4</td>\n      <td>Caf\u00e9</td>\n      <td>Vegetarian / Vegan Restaurant</td>\n      <td>Bar</td>\n      <td>Mexican Restaurant</td>\n      <td>Vietnamese Restaurant</td>\n      <td>Bakery</td>\n      <td>Coffee Shop</td>\n      <td>Dessert Shop</td>\n      <td>Chinese Restaurant</td>\n      <td>Caribbean Restaurant</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "            Borough  Cluster Labels 1st Most Common Venue  \\\n12  DowntownToronto               4                  Caf\u00e9   \n13  DowntownToronto               4                  Caf\u00e9   \n\n            2nd Most Common Venue          3rd Most Common Venue  \\\n12                     Restaurant  Vegetarian / Vegan Restaurant   \n13  Vegetarian / Vegan Restaurant                            Bar   \n\n   4th Most Common Venue  5th Most Common Venue 6th Most Common Venue  \\\n12           Coffee Shop                 Bakery       Bubble Tea Shop   \n13    Mexican Restaurant  Vietnamese Restaurant                Bakery   \n\n   7th Most Common Venue 8th Most Common Venue 9th Most Common Venue  \\\n12             Bookstore           Pizza Place                   Pub   \n13           Coffee Shop          Dessert Shop    Chinese Restaurant   \n\n   10th Most Common Venue  \n12     Italian Restaurant  \n13   Caribbean Restaurant  "
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "downtown_merged.loc[downtown_merged['Cluster Labels'] == 4, downtown_merged.columns[[1] + list(range(5, downtown_merged.shape[1]))]]\n"
        }, 
        {
            "source": "Distinguished by vegetarian/ vegan restaurants, bakeries, and cafes.", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": null, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": ""
        }
    ], 
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3.6", 
            "name": "python3", 
            "language": "python"
        }, 
        "language_info": {
            "mimetype": "text/x-python", 
            "nbconvert_exporter": "python", 
            "version": "3.6.8", 
            "name": "python", 
            "file_extension": ".py", 
            "pygments_lexer": "ipython3", 
            "codemirror_mode": {
                "version": 3, 
                "name": "ipython"
            }
        }
    }, 
    "nbformat": 4
}