Skip to content

Instantly share code, notes, and snippets.

@namdoan194

namdoan194/Toronto Part 2.ipynb

Last active June 30, 2020 12:32
Show Gist options
  • Star 0 You must be signed in to star a gist
  • Fork 0 You must be signed in to fork a gist
  • Save namdoan194/f11ac92802eac0c48bf18fa8cfc42fa8 to your computer and use it in GitHub Desktop.
Save namdoan194/f11ac92802eac0c48bf18fa8cfc42fa8 to your computer and use it in GitHub Desktop.
Download ZIP
Display the source blob
Display the rendered blob
Raw
Toronto Part 2.ipynb
{
"cells": [
{
"metadata": {},
"cell_type": "code",
"source": "import pandas as pd\nimport numpy as np\nimport requests\nfrom bs4 import BeautifulSoup\nimport os\nfrom sklearn.cluster import KMeans \nfrom geopy.geocoders import Nominatim \nimport matplotlib.cm as cm\nimport matplotlib.colors as colors\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\nprint('Libraries imported.')",
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": "Libraries imported.\n",
"name": "stdout"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "1. Dowload and Explore Dataset"
},
{
"metadata": {},
"cell_type": "code",
"source": "List_url='https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M'\nsource = requests.get(List_url).text",
"execution_count": 2,
"outputs": []
},
{
"metadata": {},
"cell_type": "code",
"source": "soup = BeautifulSoup(source, 'xml')\ntable=soup.find('table')\ncolumn_names=['Postalcode','Borough','Neighborhood']\ndf = pd.DataFrame(columns=column_names)",
"execution_count": 3,
"outputs": []
},
{
"metadata": {},
"cell_type": "code",
"source": "for tr_cell in table.find_all('tr'):\n row_data=[]\n for td_cell in tr_cell.find_all('td'):\n row_data.append(td_cell.text.strip())\n if len(row_data)==3:\n df.loc[len(df)] = row_data\n df.head()",
"execution_count": 4,
"outputs": []
},
{
"metadata": {},
"cell_type": "code",
"source": "df=df[df['Borough']!='Not assigned']",
"execution_count": 5,
"outputs": []
},
{
"metadata": {},
"cell_type": "code",
"source": "df.head()",
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 6,
"data": {
"text/plain": " Postalcode Borough Neighborhood\n2 M3A North York Parkwoods\n3 M4A North York Victoria Village\n4 M5A Downtown Toronto Regent Park, Harbourfront\n5 M6A North York Lawrence Manor, Lawrence Heights\n6 M7A Downtown Toronto Queen's Park, Ontario Provincial Government",
"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>2</th>\n <td>M3A</td>\n <td>North York</td>\n <td>Parkwoods</td>\n </tr>\n <tr>\n <th>3</th>\n <td>M4A</td>\n <td>North York</td>\n <td>Victoria Village</td>\n </tr>\n <tr>\n <th>4</th>\n <td>M5A</td>\n <td>Downtown Toronto</td>\n <td>Regent Park, Harbourfront</td>\n </tr>\n <tr>\n <th>5</th>\n <td>M6A</td>\n <td>North York</td>\n <td>Lawrence Manor, Lawrence Heights</td>\n </tr>\n <tr>\n <th>6</th>\n <td>M7A</td>\n <td>Downtown Toronto</td>\n <td>Queen's Park, Ontario Provincial Government</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "code",
"source": "temp_df=df.groupby('Postalcode')['Neighborhood'].apply(lambda x: \"%s\" % ', '.join(x))\ntemp_df=temp_df.reset_index(drop=False)\ntemp_df.rename(columns={'Neighborhood':'Neighborhood_joined'},inplace=True)",
"execution_count": 7,
"outputs": []
},
{
"metadata": {},
"cell_type": "code",
"source": "df_merge = pd.merge(df, temp_df, on='Postalcode')",
"execution_count": 8,
"outputs": []
},
{
"metadata": {},
"cell_type": "code",
"source": "df_merge.drop(['Neighborhood'],axis=1,inplace=True)",
"execution_count": 9,
"outputs": []
},
{
"metadata": {},
"cell_type": "code",
"source": "df_merge.drop_duplicates(inplace=True)",
"execution_count": 10,
"outputs": []
},
{
"metadata": {},
"cell_type": "code",
"source": "df_merge.rename(columns={'Neighborhood_joined':'Neighborhood'},inplace=True)",
"execution_count": 11,
"outputs": []
},
{
"metadata": {},
"cell_type": "code",
"source": "df_merge.head()",
"execution_count": 12,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 12,
"data": {
"text/plain": " Postalcode Borough Neighborhood\n0 M3A North York Parkwoods\n1 M4A North York Victoria Village\n2 M5A Downtown Toronto Regent Park, Harbourfront\n3 M6A North York Lawrence Manor, Lawrence Heights\n4 M7A Downtown Toronto Queen's Park, Ontario Provincial Government",
"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>M3A</td>\n <td>North York</td>\n <td>Parkwoods</td>\n </tr>\n <tr>\n <th>1</th>\n <td>M4A</td>\n <td>North York</td>\n <td>Victoria Village</td>\n </tr>\n <tr>\n <th>2</th>\n <td>M5A</td>\n <td>Downtown Toronto</td>\n <td>Regent Park, Harbourfront</td>\n </tr>\n <tr>\n <th>3</th>\n <td>M6A</td>\n <td>North York</td>\n <td>Lawrence Manor, Lawrence Heights</td>\n </tr>\n <tr>\n <th>4</th>\n <td>M7A</td>\n <td>Downtown Toronto</td>\n <td>Queen's Park, Ontario Provincial Government</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "code",
"source": "df_merge.shape",
"execution_count": 13,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 13,
"data": {
"text/plain": "(103, 3)"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "code",
"source": "def get_geocode(postal_code):\n # initialize your variable to None\n lat_lng_coords = None\n while(lat_lng_coords is None):\n g = geocoder.google('{}, Toronto, Ontario'.format(postal_code))\n lat_lng_coords = g.latlng\n latitude = lat_lng_coords[0]\n longitude = lat_lng_coords[1]\n return latitude,longitude",
"execution_count": 14,
"outputs": []
},
{
"metadata": {},
"cell_type": "code",
"source": "geo_df=pd.read_csv('http://cocl.us/Geospatial_data')",
"execution_count": 15,
"outputs": []
},
{
"metadata": {},
"cell_type": "code",
"source": "geo_df.head()",
"execution_count": 16,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 16,
"data": {
"text/plain": " Postal Code Latitude Longitude\n0 M1B 43.806686 -79.194353\n1 M1C 43.784535 -79.160497\n2 M1E 43.763573 -79.188711\n3 M1G 43.770992 -79.216917\n4 M1H 43.773136 -79.239476",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Postal Code</th>\n <th>Latitude</th>\n <th>Longitude</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>M1B</td>\n <td>43.806686</td>\n <td>-79.194353</td>\n </tr>\n <tr>\n <th>1</th>\n <td>M1C</td>\n <td>43.784535</td>\n <td>-79.160497</td>\n </tr>\n <tr>\n <th>2</th>\n <td>M1E</td>\n <td>43.763573</td>\n <td>-79.188711</td>\n </tr>\n <tr>\n <th>3</th>\n <td>M1G</td>\n <td>43.770992</td>\n <td>-79.216917</td>\n </tr>\n <tr>\n <th>4</th>\n <td>M1H</td>\n <td>43.773136</td>\n <td>-79.239476</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "code",
"source": "geo_df.rename(columns={'Postal Code':'Postalcode'},inplace=True)\ngeo_merged = pd.merge(geo_df, df_merge, on='Postalcode')",
"execution_count": 17,
"outputs": []
},
{
"metadata": {},
"cell_type": "code",
"source": "geo_merged.head()",
"execution_count": 18,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 18,
"data": {
"text/plain": " Postalcode Latitude Longitude Borough \\\n0 M1B 43.806686 -79.194353 Scarborough \n1 M1C 43.784535 -79.160497 Scarborough \n2 M1E 43.763573 -79.188711 Scarborough \n3 M1G 43.770992 -79.216917 Scarborough \n4 M1H 43.773136 -79.239476 Scarborough \n\n Neighborhood \n0 Malvern, Rouge \n1 Rouge Hill, Port Union, Highland Creek \n2 Guildwood, Morningside, West Hill \n3 Woburn \n4 Cedarbrae ",
"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 <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>43.806686</td>\n <td>-79.194353</td>\n <td>Scarborough</td>\n <td>Malvern, Rouge</td>\n </tr>\n <tr>\n <th>1</th>\n <td>M1C</td>\n <td>43.784535</td>\n <td>-79.160497</td>\n <td>Scarborough</td>\n <td>Rouge Hill, Port Union, Highland Creek</td>\n </tr>\n <tr>\n <th>2</th>\n <td>M1E</td>\n <td>43.763573</td>\n <td>-79.188711</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>43.770992</td>\n <td>-79.216917</td>\n <td>Scarborough</td>\n <td>Woburn</td>\n </tr>\n <tr>\n <th>4</th>\n <td>M1H</td>\n <td>43.773136</td>\n <td>-79.239476</td>\n <td>Scarborough</td>\n <td>Cedarbrae</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "code",
"source": "geo_data=geo_merged[['Postalcode','Borough','Neighborhood','Latitude','Longitude']]\ngeo_data.head()",
"execution_count": 19,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 19,
"data": {
"text/plain": " Postalcode Borough Neighborhood Latitude \\\n0 M1B Scarborough Malvern, Rouge 43.806686 \n1 M1C Scarborough Rouge Hill, Port Union, Highland Creek 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 ",
"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>Malvern, Rouge</td>\n <td>43.806686</td>\n <td>-79.194353</td>\n </tr>\n <tr>\n <th>1</th>\n <td>M1C</td>\n <td>Scarborough</td>\n <td>Rouge Hill, Port Union, Highland Creek</td>\n <td>43.784535</td>\n <td>-79.160497</td>\n </tr>\n <tr>\n <th>2</th>\n <td>M1E</td>\n <td>Scarborough</td>\n <td>Guildwood, Morningside, West Hill</td>\n <td>43.763573</td>\n <td>-79.188711</td>\n </tr>\n <tr>\n <th>3</th>\n <td>M1G</td>\n <td>Scarborough</td>\n <td>Woburn</td>\n <td>43.770992</td>\n <td>-79.216917</td>\n </tr>\n <tr>\n <th>4</th>\n <td>M1H</td>\n <td>Scarborough</td>\n <td>Cedarbrae</td>\n <td>43.773136</td>\n <td>-79.239476</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "code",
"source": "toronto_data=geo_data[geo_data['Borough'].str.contains(\"Toronto\")]\ntoronto_data.head()",
"execution_count": 20,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 20,
"data": {
"text/plain": " Postalcode Borough Neighborhood Latitude \\\n37 M4E East Toronto The Beaches 43.676357 \n41 M4K East Toronto The Danforth West, Riverdale 43.679557 \n42 M4L East Toronto India Bazaar, The Beaches West 43.668999 \n43 M4M East Toronto Studio District 43.659526 \n44 M4N Central Toronto Lawrence Park 43.728020 \n\n Longitude \n37 -79.293031 \n41 -79.352188 \n42 -79.315572 \n43 -79.340923 \n44 -79.388790 ",
"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>37</th>\n <td>M4E</td>\n <td>East Toronto</td>\n <td>The Beaches</td>\n <td>43.676357</td>\n <td>-79.293031</td>\n </tr>\n <tr>\n <th>41</th>\n <td>M4K</td>\n <td>East Toronto</td>\n <td>The Danforth West, Riverdale</td>\n <td>43.679557</td>\n <td>-79.352188</td>\n </tr>\n <tr>\n <th>42</th>\n <td>M4L</td>\n <td>East Toronto</td>\n <td>India Bazaar, The Beaches West</td>\n <td>43.668999</td>\n <td>-79.315572</td>\n </tr>\n <tr>\n <th>43</th>\n <td>M4M</td>\n <td>East Toronto</td>\n <td>Studio District</td>\n <td>43.659526</td>\n <td>-79.340923</td>\n </tr>\n <tr>\n <th>44</th>\n <td>M4N</td>\n <td>Central Toronto</td>\n <td>Lawrence Park</td>\n <td>43.728020</td>\n <td>-79.388790</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "code",
"source": "toronto_data.groupby('Borough')['Neighborhood'].count().plot.bar(figsize=(10,5))\nplt.title('Neighborhoods per Borough: Toronto', fontsize = 20)\nplt.xlabel('Borough', fontsize = 15)\nplt.ylabel('No. Neighborhoods',fontsize = 15)\nplt.xticks(rotation = 'horizontal')\nplt.show()",
"execution_count": 40,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<Figure size 720x360 with 1 Axes>",
"image/png": "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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"metadata": {},
"cell_type": "code",
"source": "CLIENT_ID = 'P3UHP2EBN51LGLSACOIOLSRVSZVCA55NR2XFCVEXUPHGE0BH' # your Foursquare ID\nCLIENT_SECRET = 'PVKHZPMTFH2LFZ1B2P35GSBPBBK1LDSPRRER4EY3LAN4WB45' # your Foursquare Secret\nVERSION = '20180604'",
"execution_count": 21,
"outputs": []
},
{
"metadata": {},
"cell_type": "code",
"source": "def getNearbyVenues(names, latitudes, longitudes):\n radius=500\n LIMIT=100\n venues_list=[]\n for name, lat, lng in zip(names, latitudes, longitudes):\n print(name)\n \n # create the API request URL\n url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n CLIENT_ID, \n CLIENT_SECRET, \n VERSION, \n lat, \n lng, \n radius, \n LIMIT)\n \n # make the GET request\n results = requests.get(url).json()[\"response\"]['groups'][0]['items']\n \n # return only relevant information for each nearby venue\n venues_list.append([(\n name, \n lat, \n lng, \n v['venue']['name'], \n v['venue']['location']['lat'], \n v['venue']['location']['lng'], \n v['venue']['categories'][0]['name']) for v in results])\n\n nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])\n nearby_venues.columns = ['Neighborhood', \n 'Neighborhood Latitude', \n 'Neighborhood Longitude', \n 'Venue', \n 'Venue Latitude', \n 'Venue Longitude', \n 'Venue Category']\n \n return(nearby_venues)",
"execution_count": 22,
"outputs": []
},
{
"metadata": {},
"cell_type": "code",
"source": "toronto_venues = getNearbyVenues(names=toronto_data['Neighborhood'],\n latitudes=toronto_data['Latitude'],\n longitudes=toronto_data['Longitude']\n )",
"execution_count": 23,
"outputs": [
{
"output_type": "stream",
"text": "The Beaches\nThe Danforth West, Riverdale\nIndia Bazaar, The Beaches West\nStudio District\nLawrence Park\nDavisville North\nNorth Toronto West, Lawrence Park\nDavisville\nMoore Park, Summerhill East\nSummerhill West, Rathnelly, South Hill, Forest Hill SE, Deer Park\nRosedale\nSt. James Town, Cabbagetown\nChurch and Wellesley\nRegent Park, Harbourfront\nGarden District, Ryerson\nSt. James Town\nBerczy Park\nCentral Bay Street\nRichmond, Adelaide, King\nHarbourfront East, Union Station, Toronto Islands\nToronto Dominion Centre, Design Exchange\nCommerce Court, Victoria Hotel\nRoselawn\nForest Hill North & West, Forest Hill Road Park\nThe Annex, North Midtown, Yorkville\nUniversity of Toronto, Harbord\nKensington Market, Chinatown, Grange Park\nCN Tower, King and Spadina, Railway Lands, Harbourfront West, Bathurst Quay, South Niagara, Island airport\nStn A PO Boxes\nFirst Canadian Place, Underground city\nChristie\nDufferin, Dovercourt Village\nLittle Portugal, Trinity\nBrockton, Parkdale Village, Exhibition Place\nHigh Park, The Junction South\nParkdale, Roncesvalles\nRunnymede, Swansea\nQueen's Park, Ontario Provincial Government\nBusiness reply mail Processing Centre, South Central Letter Processing Plant Toronto\n",
"name": "stdout"
}
]
},
{
"metadata": {},
"cell_type": "code",
"source": "toronto_venues.head()",
"execution_count": 24,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 24,
"data": {
"text/plain": " Neighborhood Neighborhood Latitude \\\n0 The Beaches 43.676357 \n1 The Beaches 43.676357 \n2 The Beaches 43.676357 \n3 The Beaches 43.676357 \n4 The Danforth West, Riverdale 43.679557 \n\n Neighborhood Longitude Venue Venue Latitude \\\n0 -79.293031 Glen Manor Ravine 43.676821 \n1 -79.293031 The Big Carrot Natural Food Market 43.678879 \n2 -79.293031 Grover Pub and Grub 43.679181 \n3 -79.293031 Upper Beaches 43.680563 \n4 -79.352188 MenEssentials 43.677820 \n\n Venue Longitude Venue Category \n0 -79.293942 Trail \n1 -79.297734 Health Food Store \n2 -79.297215 Pub \n3 -79.292869 Neighborhood \n4 -79.351265 Cosmetics Shop ",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>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>The Beaches</td>\n <td>43.676357</td>\n <td>-79.293031</td>\n <td>Glen Manor Ravine</td>\n <td>43.676821</td>\n <td>-79.293942</td>\n <td>Trail</td>\n </tr>\n <tr>\n <th>1</th>\n <td>The Beaches</td>\n <td>43.676357</td>\n <td>-79.293031</td>\n <td>The Big Carrot Natural Food Market</td>\n <td>43.678879</td>\n <td>-79.297734</td>\n <td>Health Food Store</td>\n </tr>\n <tr>\n <th>2</th>\n <td>The Beaches</td>\n <td>43.676357</td>\n <td>-79.293031</td>\n <td>Grover Pub and Grub</td>\n <td>43.679181</td>\n <td>-79.297215</td>\n <td>Pub</td>\n </tr>\n <tr>\n <th>3</th>\n <td>The Beaches</td>\n <td>43.676357</td>\n <td>-79.293031</td>\n <td>Upper Beaches</td>\n <td>43.680563</td>\n <td>-79.292869</td>\n <td>Neighborhood</td>\n </tr>\n <tr>\n <th>4</th>\n <td>The Danforth West, Riverdale</td>\n <td>43.679557</td>\n <td>-79.352188</td>\n <td>MenEssentials</td>\n <td>43.677820</td>\n <td>-79.351265</td>\n <td>Cosmetics Shop</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "code",
"source": "toronto_venues.groupby('Neighborhood').count()",
"execution_count": 25,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 25,
"data": {
"text/plain": " Neighborhood Latitude \\\nNeighborhood \nBerczy Park 58 \nBrockton, Parkdale Village, Exhibition Place 24 \nBusiness reply mail Processing Centre, South Ce... 17 \nCN Tower, King and Spadina, Railway Lands, Harb... 17 \nCentral Bay Street 65 \nChristie 16 \nChurch and Wellesley 77 \nCommerce Court, Victoria Hotel 100 \nDavisville 35 \nDavisville North 8 \nDufferin, Dovercourt Village 14 \nFirst Canadian Place, Underground city 100 \nForest Hill North & West, Forest Hill Road Park 4 \nGarden District, Ryerson 100 \nHarbourfront East, Union Station, Toronto Islands 100 \nHigh Park, The Junction South 24 \nIndia Bazaar, The Beaches West 24 \nKensington Market, Chinatown, Grange Park 59 \nLawrence Park 3 \nLittle Portugal, Trinity 44 \nMoore Park, Summerhill East 2 \nNorth Toronto West, Lawrence Park 19 \nParkdale, Roncesvalles 15 \nQueen's Park, Ontario Provincial Government 33 \nRegent Park, Harbourfront 45 \nRichmond, Adelaide, King 94 \nRosedale 4 \nRoselawn 2 \nRunnymede, Swansea 37 \nSt. James Town 80 \nSt. James Town, Cabbagetown 44 \nStn A PO Boxes 97 \nStudio District 40 \nSummerhill West, Rathnelly, South Hill, Forest ... 16 \nThe Annex, North Midtown, Yorkville 23 \nThe Beaches 4 \nThe Danforth West, Riverdale 43 \nToronto Dominion Centre, Design Exchange 100 \nUniversity of Toronto, Harbord 35 \n\n Neighborhood Longitude \\\nNeighborhood \nBerczy Park 58 \nBrockton, Parkdale Village, Exhibition Place 24 \nBusiness reply mail Processing Centre, South Ce... 17 \nCN Tower, King and Spadina, Railway Lands, Harb... 17 \nCentral Bay Street 65 \nChristie 16 \nChurch and Wellesley 77 \nCommerce Court, Victoria Hotel 100 \nDavisville 35 \nDavisville North 8 \nDufferin, Dovercourt Village 14 \nFirst Canadian Place, Underground city 100 \nForest Hill North & West, Forest Hill Road Park 4 \nGarden District, Ryerson 100 \nHarbourfront East, Union Station, Toronto Islands 100 \nHigh Park, The Junction South 24 \nIndia Bazaar, The Beaches West 24 \nKensington Market, Chinatown, Grange Park 59 \nLawrence Park 3 \nLittle Portugal, Trinity 44 \nMoore Park, Summerhill East 2 \nNorth Toronto West, Lawrence Park 19 \nParkdale, Roncesvalles 15 \nQueen's Park, Ontario Provincial Government 33 \nRegent Park, Harbourfront 45 \nRichmond, Adelaide, King 94 \nRosedale 4 \nRoselawn 2 \nRunnymede, Swansea 37 \nSt. James Town 80 \nSt. James Town, Cabbagetown 44 \nStn A PO Boxes 97 \nStudio District 40 \nSummerhill West, Rathnelly, South Hill, Forest ... 16 \nThe Annex, North Midtown, Yorkville 23 \nThe Beaches 4 \nThe Danforth West, Riverdale 43 \nToronto Dominion Centre, Design Exchange 100 \nUniversity of Toronto, Harbord 35 \n\n Venue Venue Latitude \\\nNeighborhood \nBerczy Park 58 58 \nBrockton, Parkdale Village, Exhibition Place 24 24 \nBusiness reply mail Processing Centre, South Ce... 17 17 \nCN Tower, King and Spadina, Railway Lands, Harb... 17 17 \nCentral Bay Street 65 65 \nChristie 16 16 \nChurch and Wellesley 77 77 \nCommerce Court, Victoria Hotel 100 100 \nDavisville 35 35 \nDavisville North 8 8 \nDufferin, Dovercourt Village 14 14 \nFirst Canadian Place, Underground city 100 100 \nForest Hill North & West, Forest Hill Road Park 4 4 \nGarden District, Ryerson 100 100 \nHarbourfront East, Union Station, Toronto Islands 100 100 \nHigh Park, The Junction South 24 24 \nIndia Bazaar, The Beaches West 24 24 \nKensington Market, Chinatown, Grange Park 59 59 \nLawrence Park 3 3 \nLittle Portugal, Trinity 44 44 \nMoore Park, Summerhill East 2 2 \nNorth Toronto West, Lawrence Park 19 19 \nParkdale, Roncesvalles 15 15 \nQueen's Park, Ontario Provincial Government 33 33 \nRegent Park, Harbourfront 45 45 \nRichmond, Adelaide, King 94 94 \nRosedale 4 4 \nRoselawn 2 2 \nRunnymede, Swansea 37 37 \nSt. James Town 80 80 \nSt. James Town, Cabbagetown 44 44 \nStn A PO Boxes 97 97 \nStudio District 40 40 \nSummerhill West, Rathnelly, South Hill, Forest ... 16 16 \nThe Annex, North Midtown, Yorkville 23 23 \nThe Beaches 4 4 \nThe Danforth West, Riverdale 43 43 \nToronto Dominion Centre, Design Exchange 100 100 \nUniversity of Toronto, Harbord 35 35 \n\n Venue Longitude \\\nNeighborhood \nBerczy Park 58 \nBrockton, Parkdale Village, Exhibition Place 24 \nBusiness reply mail Processing Centre, South Ce... 17 \nCN Tower, King and Spadina, Railway Lands, Harb... 17 \nCentral Bay Street 65 \nChristie 16 \nChurch and Wellesley 77 \nCommerce Court, Victoria Hotel 100 \nDavisville 35 \nDavisville North 8 \nDufferin, Dovercourt Village 14 \nFirst Canadian Place, Underground city 100 \nForest Hill North & West, Forest Hill Road Park 4 \nGarden District, Ryerson 100 \nHarbourfront East, Union Station, Toronto Islands 100 \nHigh Park, The Junction South 24 \nIndia Bazaar, The Beaches West 24 \nKensington Market, Chinatown, Grange Park 59 \nLawrence Park 3 \nLittle Portugal, Trinity 44 \nMoore Park, Summerhill East 2 \nNorth Toronto West, Lawrence Park 19 \nParkdale, Roncesvalles 15 \nQueen's Park, Ontario Provincial Government 33 \nRegent Park, Harbourfront 45 \nRichmond, Adelaide, King 94 \nRosedale 4 \nRoselawn 2 \nRunnymede, Swansea 37 \nSt. James Town 80 \nSt. James Town, Cabbagetown 44 \nStn A PO Boxes 97 \nStudio District 40 \nSummerhill West, Rathnelly, South Hill, Forest ... 16 \nThe Annex, North Midtown, Yorkville 23 \nThe Beaches 4 \nThe Danforth West, Riverdale 43 \nToronto Dominion Centre, Design Exchange 100 \nUniversity of Toronto, Harbord 35 \n\n Venue Category \nNeighborhood \nBerczy Park 58 \nBrockton, Parkdale Village, Exhibition Place 24 \nBusiness reply mail Processing Centre, South Ce... 17 \nCN Tower, King and Spadina, Railway Lands, Harb... 17 \nCentral Bay Street 65 \nChristie 16 \nChurch and Wellesley 77 \nCommerce Court, Victoria Hotel 100 \nDavisville 35 \nDavisville North 8 \nDufferin, Dovercourt Village 14 \nFirst Canadian Place, Underground city 100 \nForest Hill North & West, Forest Hill Road Park 4 \nGarden District, Ryerson 100 \nHarbourfront East, Union Station, Toronto Islands 100 \nHigh Park, The Junction South 24 \nIndia Bazaar, The Beaches West 24 \nKensington Market, Chinatown, Grange Park 59 \nLawrence Park 3 \nLittle Portugal, Trinity 44 \nMoore Park, Summerhill East 2 \nNorth Toronto West, Lawrence Park 19 \nParkdale, Roncesvalles 15 \nQueen's Park, Ontario Provincial Government 33 \nRegent Park, Harbourfront 45 \nRichmond, Adelaide, King 94 \nRosedale 4 \nRoselawn 2 \nRunnymede, Swansea 37 \nSt. James Town 80 \nSt. James Town, Cabbagetown 44 \nStn A PO Boxes 97 \nStudio District 40 \nSummerhill West, Rathnelly, South Hill, Forest ... 16 \nThe Annex, North Midtown, Yorkville 23 \nThe Beaches 4 \nThe Danforth West, Riverdale 43 \nToronto Dominion Centre, Design Exchange 100 \nUniversity of Toronto, Harbord 35 ",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Neighborhood Latitude</th>\n <th>Neighborhood Longitude</th>\n <th>Venue</th>\n <th>Venue Latitude</th>\n <th>Venue Longitude</th>\n <th>Venue Category</th>\n </tr>\n <tr>\n <th>Neighborhood</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>Berczy Park</th>\n <td>58</td>\n <td>58</td>\n <td>58</td>\n <td>58</td>\n <td>58</td>\n <td>58</td>\n </tr>\n <tr>\n <th>Brockton, Parkdale Village, Exhibition Place</th>\n <td>24</td>\n <td>24</td>\n <td>24</td>\n <td>24</td>\n <td>24</td>\n <td>24</td>\n </tr>\n <tr>\n <th>Business reply mail Processing Centre, South Central Letter Processing Plant Toronto</th>\n <td>17</td>\n <td>17</td>\n <td>17</td>\n <td>17</td>\n <td>17</td>\n <td>17</td>\n </tr>\n <tr>\n <th>CN Tower, King and Spadina, Railway Lands, Harbourfront West, Bathurst Quay, South Niagara, Island airport</th>\n <td>17</td>\n <td>17</td>\n <td>17</td>\n <td>17</td>\n <td>17</td>\n <td>17</td>\n </tr>\n <tr>\n <th>Central Bay Street</th>\n <td>65</td>\n <td>65</td>\n <td>65</td>\n <td>65</td>\n <td>65</td>\n <td>65</td>\n </tr>\n <tr>\n <th>Christie</th>\n <td>16</td>\n <td>16</td>\n <td>16</td>\n <td>16</td>\n <td>16</td>\n <td>16</td>\n </tr>\n <tr>\n <th>Church and Wellesley</th>\n <td>77</td>\n <td>77</td>\n <td>77</td>\n <td>77</td>\n <td>77</td>\n <td>77</td>\n </tr>\n <tr>\n <th>Commerce Court, Victoria Hotel</th>\n <td>100</td>\n <td>100</td>\n <td>100</td>\n <td>100</td>\n <td>100</td>\n <td>100</td>\n </tr>\n <tr>\n <th>Davisville</th>\n <td>35</td>\n <td>35</td>\n <td>35</td>\n <td>35</td>\n <td>35</td>\n <td>35</td>\n </tr>\n <tr>\n <th>Davisville North</th>\n <td>8</td>\n <td>8</td>\n <td>8</td>\n <td>8</td>\n <td>8</td>\n <td>8</td>\n </tr>\n <tr>\n <th>Dufferin, Dovercourt Village</th>\n <td>14</td>\n <td>14</td>\n <td>14</td>\n <td>14</td>\n <td>14</td>\n <td>14</td>\n </tr>\n <tr>\n <th>First Canadian Place, Underground city</th>\n <td>100</td>\n <td>100</td>\n <td>100</td>\n <td>100</td>\n <td>100</td>\n <td>100</td>\n </tr>\n <tr>\n <th>Forest Hill North &amp; West, Forest Hill Road Park</th>\n <td>4</td>\n <td>4</td>\n <td>4</td>\n <td>4</td>\n <td>4</td>\n <td>4</td>\n </tr>\n <tr>\n <th>Garden District, Ryerson</th>\n <td>100</td>\n <td>100</td>\n <td>100</td>\n <td>100</td>\n <td>100</td>\n <td>100</td>\n </tr>\n <tr>\n <th>Harbourfront East, Union Station, Toronto Islands</th>\n <td>100</td>\n <td>100</td>\n <td>100</td>\n <td>100</td>\n <td>100</td>\n <td>100</td>\n </tr>\n <tr>\n <th>High Park, The Junction South</th>\n <td>24</td>\n <td>24</td>\n <td>24</td>\n <td>24</td>\n <td>24</td>\n <td>24</td>\n </tr>\n <tr>\n <th>India Bazaar, The Beaches West</th>\n <td>24</td>\n <td>24</td>\n <td>24</td>\n <td>24</td>\n <td>24</td>\n <td>24</td>\n </tr>\n <tr>\n <th>Kensington Market, Chinatown, Grange Park</th>\n <td>59</td>\n <td>59</td>\n <td>59</td>\n <td>59</td>\n <td>59</td>\n <td>59</td>\n </tr>\n <tr>\n <th>Lawrence Park</th>\n <td>3</td>\n <td>3</td>\n <td>3</td>\n <td>3</td>\n <td>3</td>\n <td>3</td>\n </tr>\n <tr>\n <th>Little Portugal, Trinity</th>\n <td>44</td>\n <td>44</td>\n <td>44</td>\n <td>44</td>\n <td>44</td>\n <td>44</td>\n </tr>\n <tr>\n <th>Moore Park, Summerhill East</th>\n <td>2</td>\n <td>2</td>\n <td>2</td>\n <td>2</td>\n <td>2</td>\n <td>2</td>\n </tr>\n <tr>\n <th>North Toronto West, Lawrence Park</th>\n <td>19</td>\n <td>19</td>\n <td>19</td>\n <td>19</td>\n <td>19</td>\n <td>19</td>\n </tr>\n <tr>\n <th>Parkdale, Roncesvalles</th>\n <td>15</td>\n <td>15</td>\n <td>15</td>\n <td>15</td>\n <td>15</td>\n <td>15</td>\n </tr>\n <tr>\n <th>Queen's Park, Ontario Provincial Government</th>\n <td>33</td>\n <td>33</td>\n <td>33</td>\n <td>33</td>\n <td>33</td>\n <td>33</td>\n </tr>\n <tr>\n <th>Regent Park, Harbourfront</th>\n <td>45</td>\n <td>45</td>\n <td>45</td>\n <td>45</td>\n <td>45</td>\n <td>45</td>\n </tr>\n <tr>\n <th>Richmond, Adelaide, King</th>\n <td>94</td>\n <td>94</td>\n <td>94</td>\n <td>94</td>\n <td>94</td>\n <td>94</td>\n </tr>\n <tr>\n <th>Rosedale</th>\n <td>4</td>\n <td>4</td>\n <td>4</td>\n <td>4</td>\n <td>4</td>\n <td>4</td>\n </tr>\n <tr>\n <th>Roselawn</th>\n <td>2</td>\n <td>2</td>\n <td>2</td>\n <td>2</td>\n <td>2</td>\n <td>2</td>\n </tr>\n <tr>\n <th>Runnymede, Swansea</th>\n <td>37</td>\n <td>37</td>\n <td>37</td>\n <td>37</td>\n <td>37</td>\n <td>37</td>\n </tr>\n <tr>\n <th>St. James Town</th>\n <td>80</td>\n <td>80</td>\n <td>80</td>\n <td>80</td>\n <td>80</td>\n <td>80</td>\n </tr>\n <tr>\n <th>St. James Town, Cabbagetown</th>\n <td>44</td>\n <td>44</td>\n <td>44</td>\n <td>44</td>\n <td>44</td>\n <td>44</td>\n </tr>\n <tr>\n <th>Stn A PO Boxes</th>\n <td>97</td>\n <td>97</td>\n <td>97</td>\n <td>97</td>\n <td>97</td>\n <td>97</td>\n </tr>\n <tr>\n <th>Studio District</th>\n <td>40</td>\n <td>40</td>\n <td>40</td>\n <td>40</td>\n <td>40</td>\n <td>40</td>\n </tr>\n <tr>\n <th>Summerhill West, Rathnelly, South Hill, Forest Hill SE, Deer Park</th>\n <td>16</td>\n <td>16</td>\n <td>16</td>\n <td>16</td>\n <td>16</td>\n <td>16</td>\n </tr>\n <tr>\n <th>The Annex, North Midtown, Yorkville</th>\n <td>23</td>\n <td>23</td>\n <td>23</td>\n <td>23</td>\n <td>23</td>\n <td>23</td>\n </tr>\n <tr>\n <th>The Beaches</th>\n <td>4</td>\n <td>4</td>\n <td>4</td>\n <td>4</td>\n <td>4</td>\n <td>4</td>\n </tr>\n <tr>\n <th>The Danforth West, Riverdale</th>\n <td>43</td>\n <td>43</td>\n <td>43</td>\n <td>43</td>\n <td>43</td>\n <td>43</td>\n </tr>\n <tr>\n <th>Toronto Dominion Centre, Design Exchange</th>\n <td>100</td>\n <td>100</td>\n <td>100</td>\n <td>100</td>\n <td>100</td>\n <td>100</td>\n </tr>\n <tr>\n <th>University of Toronto, Harbord</th>\n <td>35</td>\n <td>35</td>\n <td>35</td>\n <td>35</td>\n <td>35</td>\n <td>35</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "code",
"source": "toronto_onehot = pd.get_dummies(toronto_venues[['Venue Category']], prefix=\"\", prefix_sep=\"\")\ntoronto_onehot.drop(['Neighborhood'],axis=1,inplace=True) \ntoronto_onehot.insert(loc=0, column='Neighborhood', value=toronto_venues['Neighborhood'] )\ntoronto_onehot.shape",
"execution_count": 26,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 26,
"data": {
"text/plain": "(1622, 233)"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "code",
"source": "toronto_grouped = toronto_onehot.groupby('Neighborhood').mean().reset_index()\ntoronto_grouped.head()",
"execution_count": 27,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 27,
"data": {
"text/plain": " Neighborhood Afghan Restaurant \\\n0 Berczy Park 0.0 \n1 Brockton, Parkdale Village, Exhibition Place 0.0 \n2 Business reply mail Processing Centre, South C... 0.0 \n3 CN Tower, King and Spadina, Railway Lands, Har... 0.0 \n4 Central Bay Street 0.0 \n\n Airport Airport Food Court Airport Gate Airport Lounge \\\n0 0.000000 0.000000 0.000000 0.000000 \n1 0.000000 0.000000 0.000000 0.000000 \n2 0.000000 0.000000 0.000000 0.000000 \n3 0.058824 0.058824 0.058824 0.117647 \n4 0.000000 0.000000 0.000000 0.000000 \n\n Airport Service Airport Terminal American Restaurant Antique Shop ... \\\n0 0.000000 0.000000 0.0 0.0 ... \n1 0.000000 0.000000 0.0 0.0 ... \n2 0.000000 0.000000 0.0 0.0 ... \n3 0.176471 0.117647 0.0 0.0 ... \n4 0.000000 0.000000 0.0 0.0 ... \n\n Toy / Game Store Trail Train Station Vegetarian / Vegan Restaurant \\\n0 0.0 0.0 0.0 0.017241 \n1 0.0 0.0 0.0 0.000000 \n2 0.0 0.0 0.0 0.000000 \n3 0.0 0.0 0.0 0.000000 \n4 0.0 0.0 0.0 0.015385 \n\n Video Game Store Vietnamese Restaurant Wine Bar Wine Shop \\\n0 0.0 0.0 0.000000 0.0 \n1 0.0 0.0 0.000000 0.0 \n2 0.0 0.0 0.000000 0.0 \n3 0.0 0.0 0.000000 0.0 \n4 0.0 0.0 0.015385 0.0 \n\n Women's Store Yoga Studio \n0 0.0 0.000000 \n1 0.0 0.000000 \n2 0.0 0.058824 \n3 0.0 0.000000 \n4 0.0 0.015385 \n\n[5 rows x 233 columns]",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Neighborhood</th>\n <th>Afghan Restaurant</th>\n <th>Airport</th>\n <th>Airport Food Court</th>\n <th>Airport Gate</th>\n <th>Airport Lounge</th>\n <th>Airport Service</th>\n <th>Airport Terminal</th>\n <th>American Restaurant</th>\n <th>Antique Shop</th>\n <th>...</th>\n <th>Toy / Game Store</th>\n <th>Trail</th>\n <th>Train Station</th>\n <th>Vegetarian / Vegan Restaurant</th>\n <th>Video Game Store</th>\n <th>Vietnamese Restaurant</th>\n <th>Wine Bar</th>\n <th>Wine Shop</th>\n <th>Women's Store</th>\n <th>Yoga Studio</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Berczy Park</td>\n <td>0.0</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>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.017241</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.000000</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.000000</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Brockton, Parkdale Village, Exhibition Place</td>\n <td>0.0</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>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.000000</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.000000</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.000000</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Business reply mail Processing Centre, South C...</td>\n <td>0.0</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>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.000000</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.000000</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.058824</td>\n </tr>\n <tr>\n <th>3</th>\n <td>CN Tower, King and Spadina, Railway Lands, Har...</td>\n <td>0.0</td>\n <td>0.058824</td>\n <td>0.058824</td>\n <td>0.058824</td>\n <td>0.117647</td>\n <td>0.176471</td>\n <td>0.117647</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.000000</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.000000</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.000000</td>\n </tr>\n <tr>\n <th>4</th>\n <td>Central Bay Street</td>\n <td>0.0</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>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.015385</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.015385</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.015385</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows \u00d7 233 columns</p>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "code",
"source": "def return_most_common_venues(row, num_top_venues):\n row_categories = row.iloc[1:]\n row_categories_sorted = row_categories.sort_values(ascending=False)\n \n return row_categories_sorted.index.values[0:num_top_venues]",
"execution_count": 31,
"outputs": []
},
{
"metadata": {},
"cell_type": "code",
"source": "num_top_venues = 10\n\nindicators = ['st', 'nd', 'rd']\n\n# create columns according to number of top venues\ncolumns = ['Neighborhood']\nfor ind in np.arange(num_top_venues):\n try:\n columns.append('{}{} Most Common Venue'.format(ind+1, indicators[ind]))\n except:\n columns.append('{}th Most Common Venue'.format(ind+1))\n\n# create a new dataframe\nneighborhoods_venues_sorted = pd.DataFrame(columns=columns)\nneighborhoods_venues_sorted['Neighborhood'] = toronto_grouped['Neighborhood']\n\nfor ind in np.arange(toronto_grouped.shape[0]):\n neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(toronto_grouped.iloc[ind, :], num_top_venues)\n\nneighborhoods_venues_sorted.head()",
"execution_count": 32,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 32,
"data": {
"text/plain": " Neighborhood 1st Most Common Venue \\\n0 Berczy Park Coffee Shop \n1 Brockton, Parkdale Village, Exhibition Place Caf\u00e9 \n2 Business reply mail Processing Centre, South C... Yoga Studio \n3 CN Tower, King and Spadina, Railway Lands, Har... Airport Service \n4 Central Bay Street Coffee Shop \n\n 2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue \\\n0 Cocktail Bar Seafood Restaurant Bakery \n1 Bakery Breakfast Spot Coffee Shop \n2 Auto Workshop Burrito Place Light Rail Station \n3 Airport Lounge Airport Terminal Boutique \n4 Italian Restaurant Sandwich Place Japanese Restaurant \n\n 5th Most Common Venue 6th Most Common Venue 7th Most Common Venue \\\n0 Restaurant Caf\u00e9 Beer Bar \n1 Gym Stadium Burrito Place \n2 Farmers Market Fast Food Restaurant Butcher \n3 Coffee Shop Airport Airport Food Court \n4 Caf\u00e9 Thai Restaurant Salad Place \n\n 8th Most Common Venue 9th Most Common Venue 10th Most Common Venue \n0 Cheese Shop Department Store Japanese Restaurant \n1 Restaurant Climbing Gym Performing Arts Venue \n2 Restaurant Recording Studio Brewery \n3 Airport Gate Sculpture Garden Rental Car Location \n4 Bar Burger Joint Bubble Tea Shop ",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Neighborhood</th>\n <th>1st Most Common Venue</th>\n <th>2nd Most Common Venue</th>\n <th>3rd Most Common Venue</th>\n <th>4th Most Common Venue</th>\n <th>5th Most Common Venue</th>\n <th>6th Most Common Venue</th>\n <th>7th Most Common Venue</th>\n <th>8th Most Common Venue</th>\n <th>9th Most Common Venue</th>\n <th>10th Most Common Venue</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Berczy Park</td>\n <td>Coffee Shop</td>\n <td>Cocktail Bar</td>\n <td>Seafood Restaurant</td>\n <td>Bakery</td>\n <td>Restaurant</td>\n <td>Caf\u00e9</td>\n <td>Beer Bar</td>\n <td>Cheese Shop</td>\n <td>Department Store</td>\n <td>Japanese Restaurant</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Brockton, Parkdale Village, Exhibition Place</td>\n <td>Caf\u00e9</td>\n <td>Bakery</td>\n <td>Breakfast Spot</td>\n <td>Coffee Shop</td>\n <td>Gym</td>\n <td>Stadium</td>\n <td>Burrito Place</td>\n <td>Restaurant</td>\n <td>Climbing Gym</td>\n <td>Performing Arts Venue</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Business reply mail Processing Centre, South C...</td>\n <td>Yoga Studio</td>\n <td>Auto Workshop</td>\n <td>Burrito Place</td>\n <td>Light Rail Station</td>\n <td>Farmers Market</td>\n <td>Fast Food Restaurant</td>\n <td>Butcher</td>\n <td>Restaurant</td>\n <td>Recording Studio</td>\n <td>Brewery</td>\n </tr>\n <tr>\n <th>3</th>\n <td>CN Tower, King and Spadina, Railway Lands, Har...</td>\n <td>Airport Service</td>\n <td>Airport Lounge</td>\n <td>Airport Terminal</td>\n <td>Boutique</td>\n <td>Coffee Shop</td>\n <td>Airport</td>\n <td>Airport Food Court</td>\n <td>Airport Gate</td>\n <td>Sculpture Garden</td>\n <td>Rental Car Location</td>\n </tr>\n <tr>\n <th>4</th>\n <td>Central Bay Street</td>\n <td>Coffee Shop</td>\n <td>Italian Restaurant</td>\n <td>Sandwich Place</td>\n <td>Japanese Restaurant</td>\n <td>Caf\u00e9</td>\n <td>Thai Restaurant</td>\n <td>Salad Place</td>\n <td>Bar</td>\n <td>Burger Joint</td>\n <td>Bubble Tea Shop</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "code",
"source": "# set number of clusters\nkclusters = 5\n\ntoronto_grouped_clustering = toronto_grouped.drop('Neighborhood', 1)\n\n# run k-means clustering\nkmeans = KMeans(n_clusters=kclusters, random_state=0).fit(toronto_grouped_clustering)\n\n# check cluster labels generated for each row in the dataframe\nkmeans.labels_[0:10]",
"execution_count": 33,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 33,
"data": {
"text/plain": "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32)"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "code",
"source": "# add clustering labels\nneighborhoods_venues_sorted.insert(0, 'Cluster Labels', kmeans.labels_)\n\ntoronto_merged = toronto_data\n\n# merge toronto_grouped with toronto_data to add latitude/longitude for each neighborhood\ntoronto_merged = toronto_merged.join(neighborhoods_venues_sorted.set_index('Neighborhood'), on='Neighborhood')\n\ntoronto_merged.head()\n",
"execution_count": 34,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 34,
"data": {
"text/plain": " Postalcode Borough Neighborhood Latitude \\\n37 M4E East Toronto The Beaches 43.676357 \n41 M4K East Toronto The Danforth West, Riverdale 43.679557 \n42 M4L East Toronto India Bazaar, The Beaches West 43.668999 \n43 M4M East Toronto Studio District 43.659526 \n44 M4N Central Toronto Lawrence Park 43.728020 \n\n Longitude Cluster Labels 1st Most Common Venue 2nd Most Common Venue \\\n37 -79.293031 0 Health Food Store Trail \n41 -79.352188 0 Greek Restaurant Italian Restaurant \n42 -79.315572 0 Park Sandwich Place \n43 -79.340923 0 Caf\u00e9 Coffee Shop \n44 -79.388790 4 Park Swim School \n\n 3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue \\\n37 Pub Yoga Studio Deli / Bodega \n41 Coffee Shop Bookstore Restaurant \n42 Fast Food Restaurant Pizza Place Gym \n43 Bakery Gastropub American Restaurant \n44 Bus Line Yoga Studio Dessert Shop \n\n 6th Most Common Venue 7th Most Common Venue 8th Most Common Venue \\\n37 Ethiopian Restaurant Electronics Store Eastern European Restaurant \n41 Ice Cream Shop Furniture / Home Store Yoga Studio \n42 Brewery Burrito Place Restaurant \n43 Brewery Yoga Studio Fish Market \n44 Event Space Ethiopian Restaurant Electronics Store \n\n 9th Most Common Venue 10th Most Common Venue \n37 Donut Shop Doner Restaurant \n41 Liquor Store Spa \n42 Pub Pet Store \n43 Italian Restaurant Bookstore \n44 Eastern European Restaurant Donut Shop ",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>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>37</th>\n <td>M4E</td>\n <td>East Toronto</td>\n <td>The Beaches</td>\n <td>43.676357</td>\n <td>-79.293031</td>\n <td>0</td>\n <td>Health Food Store</td>\n <td>Trail</td>\n <td>Pub</td>\n <td>Yoga Studio</td>\n <td>Deli / Bodega</td>\n <td>Ethiopian Restaurant</td>\n <td>Electronics Store</td>\n <td>Eastern European Restaurant</td>\n <td>Donut Shop</td>\n <td>Doner Restaurant</td>\n </tr>\n <tr>\n <th>41</th>\n <td>M4K</td>\n <td>East Toronto</td>\n <td>The Danforth West, Riverdale</td>\n <td>43.679557</td>\n <td>-79.352188</td>\n <td>0</td>\n <td>Greek Restaurant</td>\n <td>Italian Restaurant</td>\n <td>Coffee Shop</td>\n <td>Bookstore</td>\n <td>Restaurant</td>\n <td>Ice Cream Shop</td>\n <td>Furniture / Home Store</td>\n <td>Yoga Studio</td>\n <td>Liquor Store</td>\n <td>Spa</td>\n </tr>\n <tr>\n <th>42</th>\n <td>M4L</td>\n <td>East Toronto</td>\n <td>India Bazaar, The Beaches West</td>\n <td>43.668999</td>\n <td>-79.315572</td>\n <td>0</td>\n <td>Park</td>\n <td>Sandwich Place</td>\n <td>Fast Food Restaurant</td>\n <td>Pizza Place</td>\n <td>Gym</td>\n <td>Brewery</td>\n <td>Burrito Place</td>\n <td>Restaurant</td>\n <td>Pub</td>\n <td>Pet Store</td>\n </tr>\n <tr>\n <th>43</th>\n <td>M4M</td>\n <td>East Toronto</td>\n <td>Studio District</td>\n <td>43.659526</td>\n <td>-79.340923</td>\n <td>0</td>\n <td>Caf\u00e9</td>\n <td>Coffee Shop</td>\n <td>Bakery</td>\n <td>Gastropub</td>\n <td>American Restaurant</td>\n <td>Brewery</td>\n <td>Yoga Studio</td>\n <td>Fish Market</td>\n <td>Italian Restaurant</td>\n <td>Bookstore</td>\n </tr>\n <tr>\n <th>44</th>\n <td>M4N</td>\n <td>Central Toronto</td>\n <td>Lawrence Park</td>\n <td>43.728020</td>\n <td>-79.388790</td>\n <td>4</td>\n <td>Park</td>\n <td>Swim School</td>\n <td>Bus Line</td>\n <td>Yoga Studio</td>\n <td>Dessert Shop</td>\n <td>Event Space</td>\n <td>Ethiopian Restaurant</td>\n <td>Electronics Store</td>\n <td>Eastern European Restaurant</td>\n <td>Donut Shop</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "code",
"source": "neighborhoods_venues_sorted.head()",
"execution_count": 35,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 35,
"data": {
"text/plain": " Cluster Labels Neighborhood \\\n0 0 Berczy Park \n1 0 Brockton, Parkdale Village, Exhibition Place \n2 0 Business reply mail Processing Centre, South C... \n3 0 CN Tower, King and Spadina, Railway Lands, Har... \n4 0 Central Bay Street \n\n 1st Most Common Venue 2nd Most Common Venue 3rd Most Common Venue \\\n0 Coffee Shop Cocktail Bar Seafood Restaurant \n1 Caf\u00e9 Bakery Breakfast Spot \n2 Yoga Studio Auto Workshop Burrito Place \n3 Airport Service Airport Lounge Airport Terminal \n4 Coffee Shop Italian Restaurant Sandwich Place \n\n 4th Most Common Venue 5th Most Common Venue 6th Most Common Venue \\\n0 Bakery Restaurant Caf\u00e9 \n1 Coffee Shop Gym Stadium \n2 Light Rail Station Farmers Market Fast Food Restaurant \n3 Boutique Coffee Shop Airport \n4 Japanese Restaurant Caf\u00e9 Thai Restaurant \n\n 7th Most Common Venue 8th Most Common Venue 9th Most Common Venue \\\n0 Beer Bar Cheese Shop Department Store \n1 Burrito Place Restaurant Climbing Gym \n2 Butcher Restaurant Recording Studio \n3 Airport Food Court Airport Gate Sculpture Garden \n4 Salad Place Bar Burger Joint \n\n 10th Most Common Venue \n0 Japanese Restaurant \n1 Performing Arts Venue \n2 Brewery \n3 Rental Car Location \n4 Bubble Tea Shop ",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Cluster Labels</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>0</td>\n <td>Berczy Park</td>\n <td>Coffee Shop</td>\n <td>Cocktail Bar</td>\n <td>Seafood Restaurant</td>\n <td>Bakery</td>\n <td>Restaurant</td>\n <td>Caf\u00e9</td>\n <td>Beer Bar</td>\n <td>Cheese Shop</td>\n <td>Department Store</td>\n <td>Japanese Restaurant</td>\n </tr>\n <tr>\n <th>1</th>\n <td>0</td>\n <td>Brockton, Parkdale Village, Exhibition Place</td>\n <td>Caf\u00e9</td>\n <td>Bakery</td>\n <td>Breakfast Spot</td>\n <td>Coffee Shop</td>\n <td>Gym</td>\n <td>Stadium</td>\n <td>Burrito Place</td>\n <td>Restaurant</td>\n <td>Climbing Gym</td>\n <td>Performing Arts Venue</td>\n </tr>\n <tr>\n <th>2</th>\n <td>0</td>\n <td>Business reply mail Processing Centre, South C...</td>\n <td>Yoga Studio</td>\n <td>Auto Workshop</td>\n <td>Burrito Place</td>\n <td>Light Rail Station</td>\n <td>Farmers Market</td>\n <td>Fast Food Restaurant</td>\n <td>Butcher</td>\n <td>Restaurant</td>\n <td>Recording Studio</td>\n <td>Brewery</td>\n </tr>\n <tr>\n <th>3</th>\n <td>0</td>\n <td>CN Tower, King and Spadina, Railway Lands, Har...</td>\n <td>Airport Service</td>\n <td>Airport Lounge</td>\n <td>Airport Terminal</td>\n <td>Boutique</td>\n <td>Coffee Shop</td>\n <td>Airport</td>\n <td>Airport Food Court</td>\n <td>Airport Gate</td>\n <td>Sculpture Garden</td>\n <td>Rental Car Location</td>\n </tr>\n <tr>\n <th>4</th>\n <td>0</td>\n <td>Central Bay Street</td>\n <td>Coffee Shop</td>\n <td>Italian Restaurant</td>\n <td>Sandwich Place</td>\n <td>Japanese Restaurant</td>\n <td>Caf\u00e9</td>\n <td>Thai Restaurant</td>\n <td>Salad Place</td>\n <td>Bar</td>\n <td>Burger Joint</td>\n <td>Bubble Tea Shop</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "code",
"source": "address = 'Toronto, CA'\n\ngeolocator = Nominatim(user_agent=\"ny_explorer\")\nlocation = geolocator.geocode(address)\nlatitude = location.latitude\nlongitude = location.longitude\nprint('The geograpical coordinate of Manhattan are {}, {}.'.format(latitude, longitude))",
"execution_count": 36,
"outputs": [
{
"output_type": "stream",
"text": "The geograpical coordinate of Manhattan are 43.6534817, -79.3839347.\n",
"name": "stdout"
}
]
},
{
"metadata": {},
"cell_type": "code",
"source": "! pip install folium",
"execution_count": 37,
"outputs": [
{
"output_type": "stream",
"text": "Collecting folium\n\u001b[?25l Downloading https://files.pythonhosted.org/packages/a4/f0/44e69d50519880287cc41e7c8a6acc58daa9a9acf5f6afc52bcc70f69a6d/folium-0.11.0-py2.py3-none-any.whl (93kB)\n\u001b[K |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 102kB 8.3MB/s ta 0:00:011\n\u001b[?25hCollecting branca>=0.3.0 (from folium)\n Downloading https://files.pythonhosted.org/packages/13/fb/9eacc24ba3216510c6b59a4ea1cd53d87f25ba76237d7f4393abeaf4c94e/branca-0.4.1-py3-none-any.whl\nRequirement already satisfied: requests in /opt/conda/envs/Python36/lib/python3.6/site-packages (from folium) (2.21.0)\nRequirement already satisfied: numpy in /opt/conda/envs/Python36/lib/python3.6/site-packages (from folium) (1.15.4)\nRequirement already satisfied: jinja2>=2.9 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from folium) (2.10)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from requests->folium) (2020.6.20)\nRequirement already satisfied: chardet<3.1.0,>=3.0.2 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from requests->folium) (3.0.4)\nRequirement already satisfied: idna<2.9,>=2.5 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from requests->folium) (2.8)\nRequirement already satisfied: urllib3<1.25,>=1.21.1 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from requests->folium) (1.24.1)\nRequirement already satisfied: MarkupSafe>=0.23 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from jinja2>=2.9->folium) (1.1.0)\nInstalling collected packages: branca, folium\nSuccessfully installed branca-0.4.1 folium-0.11.0\n",
"name": "stdout"
}
]
},
{
"metadata": {},
"cell_type": "code",
"source": "import folium",
"execution_count": 38,
"outputs": []
},
{
"metadata": {},
"cell_type": "code",
"source": "map_clusters = folium.Map(location=[latitude, longitude], zoom_start=11)\n\n# set color scheme for the clusters\nx = np.arange(kclusters)\nys = [i + x + (i*x)**2 for i in range(kclusters)]\ncolors_array = cm.rainbow(np.linspace(0, 1, len(ys)))\nrainbow = [colors.rgb2hex(i) for i in colors_array]\n\n# add markers to the map\nmarkers_colors = []\nfor lat, lon, poi, cluster in zip(toronto_merged['Latitude'], toronto_merged['Longitude'], toronto_merged['Neighborhood'], toronto_merged['Cluster Labels']):\n label = folium.Popup(str(poi) + ' Cluster ' + str(cluster), parse_html=True)\n folium.CircleMarker(\n [lat, lon],\n radius=5,\n popup=label,\n color=rainbow[cluster-1],\n fill=True,\n fill_color=rainbow[cluster-1],\n fill_opacity=0.7).add_to(map_clusters)\n \nmap_clusters",
"execution_count": 39,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 39,
"data": {
"text/plain": "<folium.folium.Map at 0x7fc44a118c88>",
"text/html": "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=<!DOCTYPE html>
<head>    
    <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
    
        <script>
            L_NO_TOUCH = false;
            L_DISABLE_3D = false;
        </script>
    
    <script src="https://cdn.jsdelivr.net/npm/leaflet@1.6.0/dist/leaflet.js"></script>
    <script src="https://code.jquery.com/jquery-1.12.4.min.js"></script>
    <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>
    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet@1.6.0/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://rawcdn.githack.com/python-visualization/folium/master/folium/templates/leaflet.awesome.rotate.css"/>
    <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>
    <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>
    
            <meta name="viewport" content="width=device-width,
                initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />
            <style>
                #map_523e05a2f6e14516a7245c6c913efb09 {
                    position: relative;
                    width: 100.0%;
                    height: 100.0%;
                    left: 0.0%;
                    top: 0.0%;
                }
            </style>
        
</head>
<body>    
    
            <div class="folium-map" id="map_523e05a2f6e14516a7245c6c913efb09" ></div>
        
</body>
<script>    
    
            var map_523e05a2f6e14516a7245c6c913efb09 = L.map(
                "map_523e05a2f6e14516a7245c6c913efb09",
                {
                    center: [43.6534817, -79.3839347],
                    crs: L.CRS.EPSG3857,
                    zoom: 11,
                    zoomControl: true,
                    preferCanvas: false,
                }
            );

            

        
    
            var tile_layer_edf71583e93c4885a4eace0d46995205 = L.tileLayer(
                "https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",
                {"attribution": "Data by \u0026copy; \u003ca href=\"http://openstreetmap.org\"\u003eOpenStreetMap\u003c/a\u003e, under \u003ca href=\"http://www.openstreetmap.org/copyright\"\u003eODbL\u003c/a\u003e.", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
            var circle_marker_60b45a0e0d7a4959ae8f9ab292d3678b = L.circleMarker(
                [43.67635739999999, -79.2930312],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_62f5d8f5be8b49519ee93ec46fe93c6d = L.popup({"maxWidth": "100%"});

        
            var html_ba6f8d4ddf3746348e938f1f3cde089f = $(`<div id="html_ba6f8d4ddf3746348e938f1f3cde089f" style="width: 100.0%; height: 100.0%;">The Beaches Cluster 0</div>`)[0];
            popup_62f5d8f5be8b49519ee93ec46fe93c6d.setContent(html_ba6f8d4ddf3746348e938f1f3cde089f);
        

        circle_marker_60b45a0e0d7a4959ae8f9ab292d3678b.bindPopup(popup_62f5d8f5be8b49519ee93ec46fe93c6d)
        ;

        
    
    
            var circle_marker_a04cc8ffb71f483a84a73d1143625fe1 = L.circleMarker(
                [43.6795571, -79.352188],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_22dbdf45b37c4c09aa7a8f2cc279e857 = L.popup({"maxWidth": "100%"});

        
            var html_eb6d9e80f69f472ab82ebb6de53c50ac = $(`<div id="html_eb6d9e80f69f472ab82ebb6de53c50ac" style="width: 100.0%; height: 100.0%;">The Danforth West, Riverdale Cluster 0</div>`)[0];
            popup_22dbdf45b37c4c09aa7a8f2cc279e857.setContent(html_eb6d9e80f69f472ab82ebb6de53c50ac);
        

        circle_marker_a04cc8ffb71f483a84a73d1143625fe1.bindPopup(popup_22dbdf45b37c4c09aa7a8f2cc279e857)
        ;

        
    
    
            var circle_marker_d7597d71950346949071e91646aca560 = L.circleMarker(
                [43.6689985, -79.31557159999998],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_d02b99888fb04f8cbfe2766c2e6ad1b8 = L.popup({"maxWidth": "100%"});

        
            var html_76fddca868934a6aa4740560a294070e = $(`<div id="html_76fddca868934a6aa4740560a294070e" style="width: 100.0%; height: 100.0%;">India Bazaar, The Beaches West Cluster 0</div>`)[0];
            popup_d02b99888fb04f8cbfe2766c2e6ad1b8.setContent(html_76fddca868934a6aa4740560a294070e);
        

        circle_marker_d7597d71950346949071e91646aca560.bindPopup(popup_d02b99888fb04f8cbfe2766c2e6ad1b8)
        ;

        
    
    
            var circle_marker_03849ee4d5a44fc5a21ff7006b0c5d5f = L.circleMarker(
                [43.6595255, -79.340923],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_ff1c1f7d42ca4b79a1aa08b6e869c4b4 = L.popup({"maxWidth": "100%"});

        
            var html_01123ac478b84998bfd4fbf37e9a911a = $(`<div id="html_01123ac478b84998bfd4fbf37e9a911a" style="width: 100.0%; height: 100.0%;">Studio District Cluster 0</div>`)[0];
            popup_ff1c1f7d42ca4b79a1aa08b6e869c4b4.setContent(html_01123ac478b84998bfd4fbf37e9a911a);
        

        circle_marker_03849ee4d5a44fc5a21ff7006b0c5d5f.bindPopup(popup_ff1c1f7d42ca4b79a1aa08b6e869c4b4)
        ;

        
    
    
            var circle_marker_fe51e6b67a54494bbb29df4241b06ff2 = L.circleMarker(
                [43.7280205, -79.3887901],
                {"bubblingMouseEvents": true, "color": "#ffb360", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ffb360", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_e27eb5023bd145c4a1066e1cda84569f = L.popup({"maxWidth": "100%"});

        
            var html_4082ca3575414490a66d85283886e4c7 = $(`<div id="html_4082ca3575414490a66d85283886e4c7" style="width: 100.0%; height: 100.0%;">Lawrence Park Cluster 4</div>`)[0];
            popup_e27eb5023bd145c4a1066e1cda84569f.setContent(html_4082ca3575414490a66d85283886e4c7);
        

        circle_marker_fe51e6b67a54494bbb29df4241b06ff2.bindPopup(popup_e27eb5023bd145c4a1066e1cda84569f)
        ;

        
    
    
            var circle_marker_a430ae36bd134e11a0919f248abe11c7 = L.circleMarker(
                [43.7127511, -79.3901975],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_4cc33fb9cd4f442fba3409d1d357237a = L.popup({"maxWidth": "100%"});

        
            var html_66474023c9f24de6a28f9539cd8287a1 = $(`<div id="html_66474023c9f24de6a28f9539cd8287a1" style="width: 100.0%; height: 100.0%;">Davisville North Cluster 0</div>`)[0];
            popup_4cc33fb9cd4f442fba3409d1d357237a.setContent(html_66474023c9f24de6a28f9539cd8287a1);
        

        circle_marker_a430ae36bd134e11a0919f248abe11c7.bindPopup(popup_4cc33fb9cd4f442fba3409d1d357237a)
        ;

        
    
    
            var circle_marker_5b2bf28eee8b4e92b288005f91862b85 = L.circleMarker(
                [43.7153834, -79.40567840000001],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_7827d6b5c9f047feb13abe62a2afe04c = L.popup({"maxWidth": "100%"});

        
            var html_c0a34ddd703e4bbba81ba326b4afc228 = $(`<div id="html_c0a34ddd703e4bbba81ba326b4afc228" style="width: 100.0%; height: 100.0%;">North Toronto West,  Lawrence Park Cluster 0</div>`)[0];
            popup_7827d6b5c9f047feb13abe62a2afe04c.setContent(html_c0a34ddd703e4bbba81ba326b4afc228);
        

        circle_marker_5b2bf28eee8b4e92b288005f91862b85.bindPopup(popup_7827d6b5c9f047feb13abe62a2afe04c)
        ;

        
    
    
            var circle_marker_4332554521cd461b9d651dcaf611590f = L.circleMarker(
                [43.7043244, -79.3887901],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_7dc74cda855f4437bcfefb8e15ee0acd = L.popup({"maxWidth": "100%"});

        
            var html_19b5744908d044aaade8661c7043ae5b = $(`<div id="html_19b5744908d044aaade8661c7043ae5b" style="width: 100.0%; height: 100.0%;">Davisville Cluster 0</div>`)[0];
            popup_7dc74cda855f4437bcfefb8e15ee0acd.setContent(html_19b5744908d044aaade8661c7043ae5b);
        

        circle_marker_4332554521cd461b9d651dcaf611590f.bindPopup(popup_7dc74cda855f4437bcfefb8e15ee0acd)
        ;

        
    
    
            var circle_marker_9e83c560614f4e32b965ffdb11a95b42 = L.circleMarker(
                [43.6895743, -79.38315990000001],
                {"bubblingMouseEvents": true, "color": "#8000ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#8000ff", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_14b7c9ddb00d41a6b38cc7eaa3abd608 = L.popup({"maxWidth": "100%"});

        
            var html_9db6fe7ac9504e44a40504ce213566e7 = $(`<div id="html_9db6fe7ac9504e44a40504ce213566e7" style="width: 100.0%; height: 100.0%;">Moore Park, Summerhill East Cluster 1</div>`)[0];
            popup_14b7c9ddb00d41a6b38cc7eaa3abd608.setContent(html_9db6fe7ac9504e44a40504ce213566e7);
        

        circle_marker_9e83c560614f4e32b965ffdb11a95b42.bindPopup(popup_14b7c9ddb00d41a6b38cc7eaa3abd608)
        ;

        
    
    
            var circle_marker_96978224062541a68f6c44c43e1eaa85 = L.circleMarker(
                [43.68641229999999, -79.4000493],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_69be5ce6f3004ef49e9ee46a7555a680 = L.popup({"maxWidth": "100%"});

        
            var html_4a30dc802f1b40acbe637b9149d40b67 = $(`<div id="html_4a30dc802f1b40acbe637b9149d40b67" style="width: 100.0%; height: 100.0%;">Summerhill West, Rathnelly, South Hill, Forest Hill SE, Deer Park Cluster 0</div>`)[0];
            popup_69be5ce6f3004ef49e9ee46a7555a680.setContent(html_4a30dc802f1b40acbe637b9149d40b67);
        

        circle_marker_96978224062541a68f6c44c43e1eaa85.bindPopup(popup_69be5ce6f3004ef49e9ee46a7555a680)
        ;

        
    
    
            var circle_marker_b858c7675df54337a2d0ba5c8f77311c = L.circleMarker(
                [43.6795626, -79.37752940000001],
                {"bubblingMouseEvents": true, "color": "#00b5eb", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#00b5eb", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_a8ec7e064195472b910741692df26736 = L.popup({"maxWidth": "100%"});

        
            var html_58b341c2e3f2414d8a148750e8932b1a = $(`<div id="html_58b341c2e3f2414d8a148750e8932b1a" style="width: 100.0%; height: 100.0%;">Rosedale Cluster 2</div>`)[0];
            popup_a8ec7e064195472b910741692df26736.setContent(html_58b341c2e3f2414d8a148750e8932b1a);
        

        circle_marker_b858c7675df54337a2d0ba5c8f77311c.bindPopup(popup_a8ec7e064195472b910741692df26736)
        ;

        
    
    
            var circle_marker_dc68f89e1947443c8798cbf9ff24bbe7 = L.circleMarker(
                [43.667967, -79.3676753],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_45c3468843a349cd9ebb48e49528cf53 = L.popup({"maxWidth": "100%"});

        
            var html_2c234e2bd3ea446daa03da7c4d2a10d6 = $(`<div id="html_2c234e2bd3ea446daa03da7c4d2a10d6" style="width: 100.0%; height: 100.0%;">St. James Town, Cabbagetown Cluster 0</div>`)[0];
            popup_45c3468843a349cd9ebb48e49528cf53.setContent(html_2c234e2bd3ea446daa03da7c4d2a10d6);
        

        circle_marker_dc68f89e1947443c8798cbf9ff24bbe7.bindPopup(popup_45c3468843a349cd9ebb48e49528cf53)
        ;

        
    
    
            var circle_marker_578332cd82bb436cb5099d8cb983f3b7 = L.circleMarker(
                [43.6658599, -79.38315990000001],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_6f27cee3b4df4d63a5cd07d0ec0ccc73 = L.popup({"maxWidth": "100%"});

        
            var html_dfdd28d939194517aa835aa2e7d4ca7b = $(`<div id="html_dfdd28d939194517aa835aa2e7d4ca7b" style="width: 100.0%; height: 100.0%;">Church and Wellesley Cluster 0</div>`)[0];
            popup_6f27cee3b4df4d63a5cd07d0ec0ccc73.setContent(html_dfdd28d939194517aa835aa2e7d4ca7b);
        

        circle_marker_578332cd82bb436cb5099d8cb983f3b7.bindPopup(popup_6f27cee3b4df4d63a5cd07d0ec0ccc73)
        ;

        
    
    
            var circle_marker_8f7b5da9575f43729f396a4f6751b9ea = L.circleMarker(
                [43.6542599, -79.3606359],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_08c27de0de4d4dadb0a6b28c2db6fde8 = L.popup({"maxWidth": "100%"});

        
            var html_583c08a0bf744684bc54e93677fc708a = $(`<div id="html_583c08a0bf744684bc54e93677fc708a" style="width: 100.0%; height: 100.0%;">Regent Park, Harbourfront Cluster 0</div>`)[0];
            popup_08c27de0de4d4dadb0a6b28c2db6fde8.setContent(html_583c08a0bf744684bc54e93677fc708a);
        

        circle_marker_8f7b5da9575f43729f396a4f6751b9ea.bindPopup(popup_08c27de0de4d4dadb0a6b28c2db6fde8)
        ;

        
    
    
            var circle_marker_9a41c29cf25a4b3997ee1762bed0273b = L.circleMarker(
                [43.6571618, -79.37893709999999],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_0148e6748b5e4a92abe805608899182d = L.popup({"maxWidth": "100%"});

        
            var html_67fb91ba96004a2da602fa81a202606a = $(`<div id="html_67fb91ba96004a2da602fa81a202606a" style="width: 100.0%; height: 100.0%;">Garden District, Ryerson Cluster 0</div>`)[0];
            popup_0148e6748b5e4a92abe805608899182d.setContent(html_67fb91ba96004a2da602fa81a202606a);
        

        circle_marker_9a41c29cf25a4b3997ee1762bed0273b.bindPopup(popup_0148e6748b5e4a92abe805608899182d)
        ;

        
    
    
            var circle_marker_979cd659539a4c20894ba567224b4d42 = L.circleMarker(
                [43.6514939, -79.3754179],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_3026b005c74043c786dcec8fa464dcf4 = L.popup({"maxWidth": "100%"});

        
            var html_cc72461a29a743539989919dbdd7d21b = $(`<div id="html_cc72461a29a743539989919dbdd7d21b" style="width: 100.0%; height: 100.0%;">St. James Town Cluster 0</div>`)[0];
            popup_3026b005c74043c786dcec8fa464dcf4.setContent(html_cc72461a29a743539989919dbdd7d21b);
        

        circle_marker_979cd659539a4c20894ba567224b4d42.bindPopup(popup_3026b005c74043c786dcec8fa464dcf4)
        ;

        
    
    
            var circle_marker_14b1967468334f5faeb4b6ad30a9d623 = L.circleMarker(
                [43.644770799999996, -79.3733064],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_84790a147d104c0cae6ad655fef4c6c6 = L.popup({"maxWidth": "100%"});

        
            var html_aabcbe69375748e6b31aa3604dfe72ef = $(`<div id="html_aabcbe69375748e6b31aa3604dfe72ef" style="width: 100.0%; height: 100.0%;">Berczy Park Cluster 0</div>`)[0];
            popup_84790a147d104c0cae6ad655fef4c6c6.setContent(html_aabcbe69375748e6b31aa3604dfe72ef);
        

        circle_marker_14b1967468334f5faeb4b6ad30a9d623.bindPopup(popup_84790a147d104c0cae6ad655fef4c6c6)
        ;

        
    
    
            var circle_marker_aa2bded432fd4ba4b6fd665e0e94ac21 = L.circleMarker(
                [43.6579524, -79.3873826],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_5f3091a47ae141dc816166c12f4b1fc7 = L.popup({"maxWidth": "100%"});

        
            var html_7381aed885e6453592a70533d4e2ef8b = $(`<div id="html_7381aed885e6453592a70533d4e2ef8b" style="width: 100.0%; height: 100.0%;">Central Bay Street Cluster 0</div>`)[0];
            popup_5f3091a47ae141dc816166c12f4b1fc7.setContent(html_7381aed885e6453592a70533d4e2ef8b);
        

        circle_marker_aa2bded432fd4ba4b6fd665e0e94ac21.bindPopup(popup_5f3091a47ae141dc816166c12f4b1fc7)
        ;

        
    
    
            var circle_marker_6f8feae8463d49268f806310b1965d70 = L.circleMarker(
                [43.65057120000001, -79.3845675],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_1fa0cf4256b04c3299fec427025dc8c7 = L.popup({"maxWidth": "100%"});

        
            var html_3b690234f1734a408fefdbdc0607cb12 = $(`<div id="html_3b690234f1734a408fefdbdc0607cb12" style="width: 100.0%; height: 100.0%;">Richmond, Adelaide, King Cluster 0</div>`)[0];
            popup_1fa0cf4256b04c3299fec427025dc8c7.setContent(html_3b690234f1734a408fefdbdc0607cb12);
        

        circle_marker_6f8feae8463d49268f806310b1965d70.bindPopup(popup_1fa0cf4256b04c3299fec427025dc8c7)
        ;

        
    
    
            var circle_marker_33dbb6533fe846ff9157a44432f54c8a = L.circleMarker(
                [43.6408157, -79.38175229999999],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_594b0c5332034c6b9f67258b18b1ece5 = L.popup({"maxWidth": "100%"});

        
            var html_f085b60e647d4c3593feadcfde425b46 = $(`<div id="html_f085b60e647d4c3593feadcfde425b46" style="width: 100.0%; height: 100.0%;">Harbourfront East, Union Station, Toronto Islands Cluster 0</div>`)[0];
            popup_594b0c5332034c6b9f67258b18b1ece5.setContent(html_f085b60e647d4c3593feadcfde425b46);
        

        circle_marker_33dbb6533fe846ff9157a44432f54c8a.bindPopup(popup_594b0c5332034c6b9f67258b18b1ece5)
        ;

        
    
    
            var circle_marker_7289093cc7124b7a90e42039b0665f82 = L.circleMarker(
                [43.6471768, -79.38157640000001],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_16a163f5ae784893bb01041ca7d6f4ec = L.popup({"maxWidth": "100%"});

        
            var html_190f6d2b2962446ca4e3af3d0ca2a67b = $(`<div id="html_190f6d2b2962446ca4e3af3d0ca2a67b" style="width: 100.0%; height: 100.0%;">Toronto Dominion Centre, Design Exchange Cluster 0</div>`)[0];
            popup_16a163f5ae784893bb01041ca7d6f4ec.setContent(html_190f6d2b2962446ca4e3af3d0ca2a67b);
        

        circle_marker_7289093cc7124b7a90e42039b0665f82.bindPopup(popup_16a163f5ae784893bb01041ca7d6f4ec)
        ;

        
    
    
            var circle_marker_8014a737f24d48309eb9da0bb50eb844 = L.circleMarker(
                [43.6481985, -79.37981690000001],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_908b77ba27884f8a8deacf2d84ee7200 = L.popup({"maxWidth": "100%"});

        
            var html_bad1852cfd9a469ea311bd9a3cd64ea4 = $(`<div id="html_bad1852cfd9a469ea311bd9a3cd64ea4" style="width: 100.0%; height: 100.0%;">Commerce Court, Victoria Hotel Cluster 0</div>`)[0];
            popup_908b77ba27884f8a8deacf2d84ee7200.setContent(html_bad1852cfd9a469ea311bd9a3cd64ea4);
        

        circle_marker_8014a737f24d48309eb9da0bb50eb844.bindPopup(popup_908b77ba27884f8a8deacf2d84ee7200)
        ;

        
    
    
            var circle_marker_4f2a4c177fa34559bbd446e2aa8cd059 = L.circleMarker(
                [43.7116948, -79.41693559999999],
                {"bubblingMouseEvents": true, "color": "#80ffb4", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#80ffb4", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_dac397c3e9f54eac805fdfbc560f5f7f = L.popup({"maxWidth": "100%"});

        
            var html_8eb4c4d1d4f440488b2b3658d2417f4a = $(`<div id="html_8eb4c4d1d4f440488b2b3658d2417f4a" style="width: 100.0%; height: 100.0%;">Roselawn Cluster 3</div>`)[0];
            popup_dac397c3e9f54eac805fdfbc560f5f7f.setContent(html_8eb4c4d1d4f440488b2b3658d2417f4a);
        

        circle_marker_4f2a4c177fa34559bbd446e2aa8cd059.bindPopup(popup_dac397c3e9f54eac805fdfbc560f5f7f)
        ;

        
    
    
            var circle_marker_f2c975ed264a445c89cbae32e0ef762c = L.circleMarker(
                [43.6969476, -79.41130720000001],
                {"bubblingMouseEvents": true, "color": "#00b5eb", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#00b5eb", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_29657e7d35c9470d9f4f5b2163fe944e = L.popup({"maxWidth": "100%"});

        
            var html_57646902e90c4078bece0c0bbc6cdfef = $(`<div id="html_57646902e90c4078bece0c0bbc6cdfef" style="width: 100.0%; height: 100.0%;">Forest Hill North &amp; West, Forest Hill Road Park Cluster 2</div>`)[0];
            popup_29657e7d35c9470d9f4f5b2163fe944e.setContent(html_57646902e90c4078bece0c0bbc6cdfef);
        

        circle_marker_f2c975ed264a445c89cbae32e0ef762c.bindPopup(popup_29657e7d35c9470d9f4f5b2163fe944e)
        ;

        
    
    
            var circle_marker_71be307a663d456083fd2dbc5528e228 = L.circleMarker(
                [43.6727097, -79.40567840000001],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_136413891f6d42ec90e222e21080b7d3 = L.popup({"maxWidth": "100%"});

        
            var html_d77021bdb4974534b19bff744cfa5264 = $(`<div id="html_d77021bdb4974534b19bff744cfa5264" style="width: 100.0%; height: 100.0%;">The Annex, North Midtown, Yorkville Cluster 0</div>`)[0];
            popup_136413891f6d42ec90e222e21080b7d3.setContent(html_d77021bdb4974534b19bff744cfa5264);
        

        circle_marker_71be307a663d456083fd2dbc5528e228.bindPopup(popup_136413891f6d42ec90e222e21080b7d3)
        ;

        
    
    
            var circle_marker_5607e30f7d564838ba3d2dac3b1bc9d2 = L.circleMarker(
                [43.6626956, -79.4000493],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_4e393bd98f1d481db06bcf5d9b57c1ad = L.popup({"maxWidth": "100%"});

        
            var html_2bde054e07ae4c258402ed55ba9a6d49 = $(`<div id="html_2bde054e07ae4c258402ed55ba9a6d49" style="width: 100.0%; height: 100.0%;">University of Toronto, Harbord Cluster 0</div>`)[0];
            popup_4e393bd98f1d481db06bcf5d9b57c1ad.setContent(html_2bde054e07ae4c258402ed55ba9a6d49);
        

        circle_marker_5607e30f7d564838ba3d2dac3b1bc9d2.bindPopup(popup_4e393bd98f1d481db06bcf5d9b57c1ad)
        ;

        
    
    
            var circle_marker_a28c48ddeeeb4bdc8831fffcca0b005f = L.circleMarker(
                [43.6532057, -79.4000493],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_b41a115d51574145b009136bbbab28d0 = L.popup({"maxWidth": "100%"});

        
            var html_010764f481e74601b5b3b12f9ed660d9 = $(`<div id="html_010764f481e74601b5b3b12f9ed660d9" style="width: 100.0%; height: 100.0%;">Kensington Market, Chinatown, Grange Park Cluster 0</div>`)[0];
            popup_b41a115d51574145b009136bbbab28d0.setContent(html_010764f481e74601b5b3b12f9ed660d9);
        

        circle_marker_a28c48ddeeeb4bdc8831fffcca0b005f.bindPopup(popup_b41a115d51574145b009136bbbab28d0)
        ;

        
    
    
            var circle_marker_3c81464120b545ee9c0e1296fff79448 = L.circleMarker(
                [43.6289467, -79.3944199],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_58660e8c34f84377993647482759255c = L.popup({"maxWidth": "100%"});

        
            var html_4dcbf5117fd744a2acb963454af7e494 = $(`<div id="html_4dcbf5117fd744a2acb963454af7e494" style="width: 100.0%; height: 100.0%;">CN Tower, King and Spadina, Railway Lands, Harbourfront West, Bathurst Quay, South Niagara, Island airport Cluster 0</div>`)[0];
            popup_58660e8c34f84377993647482759255c.setContent(html_4dcbf5117fd744a2acb963454af7e494);
        

        circle_marker_3c81464120b545ee9c0e1296fff79448.bindPopup(popup_58660e8c34f84377993647482759255c)
        ;

        
    
    
            var circle_marker_88a5d1c8a24b47e28066912583c7c196 = L.circleMarker(
                [43.6464352, -79.37484599999999],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_36ff55078b544ecfa6d30370ede44696 = L.popup({"maxWidth": "100%"});

        
            var html_96926c33c2814035bb535e9ad48ae7f2 = $(`<div id="html_96926c33c2814035bb535e9ad48ae7f2" style="width: 100.0%; height: 100.0%;">Stn A PO Boxes Cluster 0</div>`)[0];
            popup_36ff55078b544ecfa6d30370ede44696.setContent(html_96926c33c2814035bb535e9ad48ae7f2);
        

        circle_marker_88a5d1c8a24b47e28066912583c7c196.bindPopup(popup_36ff55078b544ecfa6d30370ede44696)
        ;

        
    
    
            var circle_marker_260d188bc91e4b66b9f45fed45b37947 = L.circleMarker(
                [43.6484292, -79.3822802],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_7707d841eafd43ecabca21d4794328a2 = L.popup({"maxWidth": "100%"});

        
            var html_7b6fc7d2ab2e4a86a83ab26bf772ea5c = $(`<div id="html_7b6fc7d2ab2e4a86a83ab26bf772ea5c" style="width: 100.0%; height: 100.0%;">First Canadian Place, Underground city Cluster 0</div>`)[0];
            popup_7707d841eafd43ecabca21d4794328a2.setContent(html_7b6fc7d2ab2e4a86a83ab26bf772ea5c);
        

        circle_marker_260d188bc91e4b66b9f45fed45b37947.bindPopup(popup_7707d841eafd43ecabca21d4794328a2)
        ;

        
    
    
            var circle_marker_0287a0eb1e6e411f9fc206f46d0573ca = L.circleMarker(
                [43.669542, -79.4225637],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_3ed1dd611c4248719b7952e7e967327b = L.popup({"maxWidth": "100%"});

        
            var html_146a4ca1bb70419eaae36bf20474a993 = $(`<div id="html_146a4ca1bb70419eaae36bf20474a993" style="width: 100.0%; height: 100.0%;">Christie Cluster 0</div>`)[0];
            popup_3ed1dd611c4248719b7952e7e967327b.setContent(html_146a4ca1bb70419eaae36bf20474a993);
        

        circle_marker_0287a0eb1e6e411f9fc206f46d0573ca.bindPopup(popup_3ed1dd611c4248719b7952e7e967327b)
        ;

        
    
    
            var circle_marker_7a7c376066164525ac6ed43a7c21fa80 = L.circleMarker(
                [43.66900510000001, -79.4422593],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_391df07f94fd46b7a27d3116ed529706 = L.popup({"maxWidth": "100%"});

        
            var html_94a1afb25a35412bbc085e83774ac042 = $(`<div id="html_94a1afb25a35412bbc085e83774ac042" style="width: 100.0%; height: 100.0%;">Dufferin, Dovercourt Village Cluster 0</div>`)[0];
            popup_391df07f94fd46b7a27d3116ed529706.setContent(html_94a1afb25a35412bbc085e83774ac042);
        

        circle_marker_7a7c376066164525ac6ed43a7c21fa80.bindPopup(popup_391df07f94fd46b7a27d3116ed529706)
        ;

        
    
    
            var circle_marker_226c2d7dd1cc434b97946a150a53ab27 = L.circleMarker(
                [43.647926700000006, -79.4197497],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_c28220a9570a4c17aeda973ef98c1337 = L.popup({"maxWidth": "100%"});

        
            var html_b4d19f7a86d346edbdab14f72ae0e9c2 = $(`<div id="html_b4d19f7a86d346edbdab14f72ae0e9c2" style="width: 100.0%; height: 100.0%;">Little Portugal, Trinity Cluster 0</div>`)[0];
            popup_c28220a9570a4c17aeda973ef98c1337.setContent(html_b4d19f7a86d346edbdab14f72ae0e9c2);
        

        circle_marker_226c2d7dd1cc434b97946a150a53ab27.bindPopup(popup_c28220a9570a4c17aeda973ef98c1337)
        ;

        
    
    
            var circle_marker_f5ecb7074ad148dfbf1093ad665e0efd = L.circleMarker(
                [43.6368472, -79.42819140000002],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_b0bf5062c80746e6891d53a62d4540a6 = L.popup({"maxWidth": "100%"});

        
            var html_3aa18ca98f9d4f519831b57ece5d0d96 = $(`<div id="html_3aa18ca98f9d4f519831b57ece5d0d96" style="width: 100.0%; height: 100.0%;">Brockton, Parkdale Village, Exhibition Place Cluster 0</div>`)[0];
            popup_b0bf5062c80746e6891d53a62d4540a6.setContent(html_3aa18ca98f9d4f519831b57ece5d0d96);
        

        circle_marker_f5ecb7074ad148dfbf1093ad665e0efd.bindPopup(popup_b0bf5062c80746e6891d53a62d4540a6)
        ;

        
    
    
            var circle_marker_ce77817d0da34d16a11d64a7195c9d59 = L.circleMarker(
                [43.6616083, -79.46476329999999],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_1b60b7e49b0b4cfc8862720c8a99c769 = L.popup({"maxWidth": "100%"});

        
            var html_d532dbf70bb14613adeb2063da95ce3d = $(`<div id="html_d532dbf70bb14613adeb2063da95ce3d" style="width: 100.0%; height: 100.0%;">High Park, The Junction South Cluster 0</div>`)[0];
            popup_1b60b7e49b0b4cfc8862720c8a99c769.setContent(html_d532dbf70bb14613adeb2063da95ce3d);
        

        circle_marker_ce77817d0da34d16a11d64a7195c9d59.bindPopup(popup_1b60b7e49b0b4cfc8862720c8a99c769)
        ;

        
    
    
            var circle_marker_ab5d14e74f6e46e2a226116e38a796f2 = L.circleMarker(
                [43.6489597, -79.456325],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_41499e5324e841cea631a5e8a938ff1f = L.popup({"maxWidth": "100%"});

        
            var html_a99a19080ef747eb83114b21753e6a04 = $(`<div id="html_a99a19080ef747eb83114b21753e6a04" style="width: 100.0%; height: 100.0%;">Parkdale, Roncesvalles Cluster 0</div>`)[0];
            popup_41499e5324e841cea631a5e8a938ff1f.setContent(html_a99a19080ef747eb83114b21753e6a04);
        

        circle_marker_ab5d14e74f6e46e2a226116e38a796f2.bindPopup(popup_41499e5324e841cea631a5e8a938ff1f)
        ;

        
    
    
            var circle_marker_5f28124796d5423c93871745ac417bca = L.circleMarker(
                [43.6515706, -79.4844499],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_b6c870b3ec6d46efb5b2cce3f19d2d4b = L.popup({"maxWidth": "100%"});

        
            var html_7316f3f1257643dfad052a95c37942fb = $(`<div id="html_7316f3f1257643dfad052a95c37942fb" style="width: 100.0%; height: 100.0%;">Runnymede, Swansea Cluster 0</div>`)[0];
            popup_b6c870b3ec6d46efb5b2cce3f19d2d4b.setContent(html_7316f3f1257643dfad052a95c37942fb);
        

        circle_marker_5f28124796d5423c93871745ac417bca.bindPopup(popup_b6c870b3ec6d46efb5b2cce3f19d2d4b)
        ;

        
    
    
            var circle_marker_9326ca2735c841a0a98532521b671361 = L.circleMarker(
                [43.6623015, -79.3894938],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_46c479e1466b4fa8aaab5187a4836217 = L.popup({"maxWidth": "100%"});

        
            var html_88b4845ce7a046f7a913c8fcb6be4b05 = $(`<div id="html_88b4845ce7a046f7a913c8fcb6be4b05" style="width: 100.0%; height: 100.0%;">Queen&#39;s Park, Ontario Provincial Government Cluster 0</div>`)[0];
            popup_46c479e1466b4fa8aaab5187a4836217.setContent(html_88b4845ce7a046f7a913c8fcb6be4b05);
        

        circle_marker_9326ca2735c841a0a98532521b671361.bindPopup(popup_46c479e1466b4fa8aaab5187a4836217)
        ;

        
    
    
            var circle_marker_c5affd0011b94b48932a76b2d219a650 = L.circleMarker(
                [43.6627439, -79.321558],
                {"bubblingMouseEvents": true, "color": "#ff0000", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#ff0000", "fillOpacity": 0.7, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(map_523e05a2f6e14516a7245c6c913efb09);
        
    
        var popup_8f323ec98a024b6195a0e52b35842c33 = L.popup({"maxWidth": "100%"});

        
            var html_66ddc341d6654789a7004506158d5911 = $(`<div id="html_66ddc341d6654789a7004506158d5911" style="width: 100.0%; height: 100.0%;">Business reply mail Processing Centre, South Central Letter Processing Plant Toronto Cluster 0</div>`)[0];
            popup_8f323ec98a024b6195a0e52b35842c33.setContent(html_66ddc341d6654789a7004506158d5911);
        

        circle_marker_c5affd0011b94b48932a76b2d219a650.bindPopup(popup_8f323ec98a024b6195a0e52b35842c33)
        ;

        
    
</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
}
],
"metadata": {
"kernelspec": {
"name": "python3",
"display_name": "Python 3.6",
"language": "python"
},
"language_info": {
"name": "python",
"version": "3.6.9",
"mimetype": "text/x-python",
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"pygments_lexer": "ipython3",
"nbconvert_exporter": "python",
"file_extension": ".py"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment