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Created May 22, 2020 03:30
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Capstone_Coursera.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h1>\n",
" Capstone Project</h1>\n",
" <h3 > - provided by Coursera</h3>\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This project shall see how to use location API to find a new location to live with good neighbours"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Hello Capstone Project Course!\n"
]
}
],
"source": [
"print (\" Hello Capstone Project Course!\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Explore and cluster the neighborhoods in Toronto"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### build the code to scrape the following Wikipedia page, https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M, in order to obtain the data that is in the table of postal codes and to transform the data into a pandas dataframe"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"data_url=\"https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M\""
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting BeautifulSoup4\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/66/25/ff030e2437265616a1e9b25ccc864e0371a0bc3adb7c5a404fd661c6f4f6/beautifulsoup4-4.9.1-py3-none-any.whl (115kB)\n",
"\u001b[K |████████████████████████████████| 122kB 25.0MB/s eta 0:00:01\n",
"\u001b[?25hCollecting soupsieve>1.2 (from BeautifulSoup4)\n",
" Downloading https://files.pythonhosted.org/packages/6f/8f/457f4a5390eeae1cc3aeab89deb7724c965be841ffca6cfca9197482e470/soupsieve-2.0.1-py3-none-any.whl\n",
"Installing collected packages: soupsieve, BeautifulSoup4\n",
"Successfully installed BeautifulSoup4-4.9.1 soupsieve-2.0.1\n",
"Requirement already satisfied: requests in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (2.23.0)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from requests) (2020.4.5.1)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from requests) (3.0.4)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from requests) (1.25.9)\n",
"Requirement already satisfied: idna<3,>=2.5 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from requests) (2.9)\n"
]
}
],
"source": [
"#install Beautiful Soup and requests for Web Scaping\n",
"!pip install BeautifulSoup4\n",
"!pip install requests"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting package metadata (current_repodata.json): done\n",
"Solving environment: done\n",
"\n",
"# All requested packages already installed.\n",
"\n",
"Libraries imported.\n"
]
}
],
"source": [
"import numpy as np # library to handle data in a vectorized manner\n",
"\n",
"import pandas as pd # library for data analsysis\n",
"pd.set_option('display.max_columns', None)\n",
"pd.set_option('display.max_rows', None)\n",
"\n",
"import json # library to handle JSON files\n",
"\n",
"!conda install -c conda-forge geopy --yes # uncomment this line if you haven't completed the Foursquare API lab\n",
"from geopy.geocoders import Nominatim # convert an address into latitude and longitude values\n",
"\n",
"import requests # library to handle requests\n",
"from pandas.io.json import json_normalize # tranform JSON file into a pandas dataframe\n",
"\n",
"# Matplotlib and associated plotting modules\n",
"import matplotlib.cm as cm\n",
"import matplotlib.colors as colors\n",
"\n",
"# import k-means from clustering stage\n",
"from sklearn.cluster import KMeans\n",
"\n",
"#!conda install -c conda-forge folium=0.5.0 --yes # uncomment this line if you haven't completed the Foursquare API lab\n",
"import folium # map rendering library\n",
"\n",
"print('Libraries imported.')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"#imports\n",
"from bs4 import BeautifulSoup\n",
"import requests\n",
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"#get html from wiki page and create soup object\n",
"source = requests.get(data_url)\n",
"soup = BeautifulSoup(source.text, 'html')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"#using soup object, iterate the .wikitable to get the data from the HTML page and store it into a list\n",
"data = []\n",
"columns = []\n",
"table = soup.find(class_='wikitable')\n",
"for index, tr in enumerate(table.find_all('tr')):\n",
" section = []\n",
" for td in tr.find_all(['th','td']):\n",
" section.append(td.text.rstrip())\n",
" \n",
" #First row of data is the header\n",
" if (index == 0):\n",
" columns = section\n",
" else:\n",
" data.append(section)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Postal Code</th>\n",
" <th>Borough</th>\n",
" <th>Neighborhood</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>M1A</td>\n",
" <td>Not assigned</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>M2A</td>\n",
" <td>Not assigned</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>M3A</td>\n",
" <td>North York</td>\n",
" <td>Parkwoods</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>M4A</td>\n",
" <td>North York</td>\n",
" <td>Victoria Village</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>M5A</td>\n",
" <td>Downtown Toronto</td>\n",
" <td>Regent Park, Harbourfront</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Postal Code Borough Neighborhood\n",
"0 M1A Not assigned \n",
"1 M2A Not assigned \n",
"2 M3A North York Parkwoods\n",
"3 M4A North York Victoria Village\n",
"4 M5A Downtown Toronto Regent Park, Harbourfront"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#convert list into Pandas DataFrame\n",
"canada_df = pd.DataFrame(data = data,columns = columns)\n",
"canada_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Postal Code</th>\n",
" <th>Borough</th>\n",
" <th>Neighborhood</th>\n",
" </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>"
],
"text/plain": [
" Postal Code Borough Neighborhood\n",
"2 M3A North York Parkwoods\n",
"3 M4A North York Victoria Village\n",
"4 M5A Downtown Toronto Regent Park, Harbourfront\n",
"5 M6A North York Lawrence Manor, Lawrence Heights\n",
"6 M7A Downtown Toronto Queen's Park, Ontario Provincial Government"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Remove Boroughs that are 'Not assigned'\n",
"canada_df = canada_df[canada_df['Borough'] != 'Not assigned']\n",
"canada_df.head()\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Borough</th>\n",
" <th>Neighborhood</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Postal Code</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>M3A</th>\n",
" <td>North York</td>\n",
" <td>Parkwoods</td>\n",
" </tr>\n",
" <tr>\n",
" <th>M4A</th>\n",
" <td>North York</td>\n",
" <td>Victoria Village</td>\n",
" </tr>\n",
" <tr>\n",
" <th>M5A</th>\n",
" <td>Downtown Toronto</td>\n",
" <td>Regent Park, Harbourfront</td>\n",
" </tr>\n",
" <tr>\n",
" <th>M6A</th>\n",
" <td>North York</td>\n",
" <td>Lawrence Manor, Lawrence Heights</td>\n",
" </tr>\n",
" <tr>\n",
" <th>M7A</th>\n",
" <td>Downtown Toronto</td>\n",
" <td>Queen's Park, Ontario Provincial Government</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Borough Neighborhood\n",
"Postal Code \n",
"M3A North York Parkwoods\n",
"M4A North York Victoria Village\n",
"M5A Downtown Toronto Regent Park, Harbourfront\n",
"M6A North York Lawrence Manor, Lawrence Heights\n",
"M7A Downtown Toronto Queen's Park, Ontario Provincial Government"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# More than one neighborhood can exist in one postal code area, combined these into one row with the neighborhoods separated with a comma\n",
"canada_df[\"Neighborhood\"] = canada_df.groupby(\"Postal Code\")[\"Neighborhood\"].transform(lambda neigh: ', '.join(neigh))\n",
"\n",
"#remove duplicates\n",
"canada_df = canada_df.drop_duplicates()\n",
"\n",
"#update index to be postcode if it isn't already\n",
"if(canada_df.index.name != 'Postal Code'):\n",
" canada_df = canada_df.set_index('Postal Code')\n",
" \n",
"canada_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Borough</th>\n",
" <th>Neighborhood</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Postal Code</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>M3A</th>\n",
" <td>North York</td>\n",
" <td>Parkwoods</td>\n",
" </tr>\n",
" <tr>\n",
" <th>M4A</th>\n",
" <td>North York</td>\n",
" <td>Victoria Village</td>\n",
" </tr>\n",
" <tr>\n",
" <th>M5A</th>\n",
" <td>Downtown Toronto</td>\n",
" <td>Regent Park, Harbourfront</td>\n",
" </tr>\n",
" <tr>\n",
" <th>M6A</th>\n",
" <td>North York</td>\n",
" <td>Lawrence Manor, Lawrence Heights</td>\n",
" </tr>\n",
" <tr>\n",
" <th>M7A</th>\n",
" <td>Downtown Toronto</td>\n",
" <td>Queen's Park, Ontario Provincial Government</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Borough Neighborhood\n",
"Postal Code \n",
"M3A North York Parkwoods\n",
"M4A North York Victoria Village\n",
"M5A Downtown Toronto Regent Park, Harbourfront\n",
"M6A North York Lawrence Manor, Lawrence Heights\n",
"M7A Downtown Toronto Queen's Park, Ontario Provincial Government"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# If a cell has a borough but a Not assigned neighborhood, then the neighborhood will be the same as the borough\n",
"canada_df['Neighborhood'].replace(\"Not assigned\", canada_df[\"Borough\"],inplace=True)\n",
"canada_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(103, 2)"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"canada_df.shape\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Latitude and Longitude "
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data downloaded!\n"
]
}
],
"source": [
"!wget -q -O 'Geospatial_Coordinates.csv' http://cocl.us/Geospatial_data\n",
"print('Data downloaded!')"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Postcode</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>M1B</th>\n",
" <td>43.806686</td>\n",
" <td>-79.194353</td>\n",
" </tr>\n",
" <tr>\n",
" <th>M1C</th>\n",
" <td>43.784535</td>\n",
" <td>-79.160497</td>\n",
" </tr>\n",
" <tr>\n",
" <th>M1E</th>\n",
" <td>43.763573</td>\n",
" <td>-79.188711</td>\n",
" </tr>\n",
" <tr>\n",
" <th>M1G</th>\n",
" <td>43.770992</td>\n",
" <td>-79.216917</td>\n",
" </tr>\n",
" <tr>\n",
" <th>M1H</th>\n",
" <td>43.773136</td>\n",
" <td>-79.239476</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Latitude Longitude\n",
"Postcode \n",
"M1B 43.806686 -79.194353\n",
"M1C 43.784535 -79.160497\n",
"M1E 43.763573 -79.188711\n",
"M1G 43.770992 -79.216917\n",
"M1H 43.773136 -79.239476"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Get data lat/long data from csv\n",
"lat_long_coord_df = pd.read_csv(\"Geospatial_Coordinates.csv\")\n",
"\n",
"#rename columns and set the index to be Postcode\n",
"lat_long_coord_df.columns = [\"Postcode\", \"Latitude\", \"Longitude\"]\n",
"if(lat_long_coord_df.index.name != 'Postcode'):\n",
" lat_long_coord_df = lat_long_coord_df.set_index('Postcode')\n",
" \n",
"lat_long_coord_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Borough</th>\n",
" <th>Neighborhood</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Postal Code</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>M3A</th>\n",
" <td>North York</td>\n",
" <td>Parkwoods</td>\n",
" <td>43.753259</td>\n",
" <td>-79.329656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>M4A</th>\n",
" <td>North York</td>\n",
" <td>Victoria Village</td>\n",
" <td>43.725882</td>\n",
" <td>-79.315572</td>\n",
" </tr>\n",
" <tr>\n",
" <th>M5A</th>\n",
" <td>Downtown Toronto</td>\n",
" <td>Regent Park, Harbourfront</td>\n",
" <td>43.654260</td>\n",
" <td>-79.360636</td>\n",
" </tr>\n",
" <tr>\n",
" <th>M6A</th>\n",
" <td>North York</td>\n",
" <td>Lawrence Manor, Lawrence Heights</td>\n",
" <td>43.718518</td>\n",
" <td>-79.464763</td>\n",
" </tr>\n",
" <tr>\n",
" <th>M7A</th>\n",
" <td>Downtown Toronto</td>\n",
" <td>Queen's Park, Ontario Provincial Government</td>\n",
" <td>43.662301</td>\n",
" <td>-79.389494</td>\n",
" </tr>\n",
" <tr>\n",
" <th>M9A</th>\n",
" <td>Etobicoke</td>\n",
" <td>Islington Avenue</td>\n",
" <td>43.667856</td>\n",
" <td>-79.532242</td>\n",
" </tr>\n",
" <tr>\n",
" <th>M1B</th>\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>M3B</th>\n",
" <td>North York</td>\n",
" <td>Don Mills</td>\n",
" <td>43.745906</td>\n",
" <td>-79.352188</td>\n",
" </tr>\n",
" <tr>\n",
" <th>M4B</th>\n",
" <td>East York</td>\n",
" <td>Parkview Hill, Woodbine Gardens</td>\n",
" <td>43.706397</td>\n",
" <td>-79.309937</td>\n",
" </tr>\n",
" <tr>\n",
" <th>M5B</th>\n",
" <td>Downtown Toronto</td>\n",
" <td>Garden District, Ryerson</td>\n",
" <td>43.657162</td>\n",
" <td>-79.378937</td>\n",
" </tr>\n",
" <tr>\n",
" <th>M6B</th>\n",
" <td>North York</td>\n",
" <td>Glencairn</td>\n",
" <td>43.709577</td>\n",
" <td>-79.445073</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Borough Neighborhood \\\n",
"Postal Code \n",
"M3A North York Parkwoods \n",
"M4A North York Victoria Village \n",
"M5A Downtown Toronto Regent Park, Harbourfront \n",
"M6A North York Lawrence Manor, Lawrence Heights \n",
"M7A Downtown Toronto Queen's Park, Ontario Provincial Government \n",
"M9A Etobicoke Islington Avenue \n",
"M1B Scarborough Malvern, Rouge \n",
"M3B North York Don Mills \n",
"M4B East York Parkview Hill, Woodbine Gardens \n",
"M5B Downtown Toronto Garden District, Ryerson \n",
"M6B North York Glencairn \n",
"\n",
" Latitude Longitude \n",
"Postal Code \n",
"M3A 43.753259 -79.329656 \n",
"M4A 43.725882 -79.315572 \n",
"M5A 43.654260 -79.360636 \n",
"M6A 43.718518 -79.464763 \n",
"M7A 43.662301 -79.389494 \n",
"M9A 43.667856 -79.532242 \n",
"M1B 43.806686 -79.194353 \n",
"M3B 43.745906 -79.352188 \n",
"M4B 43.706397 -79.309937 \n",
"M5B 43.657162 -79.378937 \n",
"M6B 43.709577 -79.445073 "
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"canada_df = canada_df.join(lat_long_coord_df)\n",
"canada_df.head(11)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Explore Toronto and creating Maps"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
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" <td>Downtown Toronto</td>\n",
" <td>Queen's Park, Ontario Provincial Government</td>\n",
" <td>43.662301</td>\n",
" <td>-79.389494</td>\n",
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" <th>M5B</th>\n",
" <td>Downtown Toronto</td>\n",
" <td>Garden District, Ryerson</td>\n",
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" <td>-79.378937</td>\n",
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" <th>M5C</th>\n",
" <td>Downtown Toronto</td>\n",
" <td>St. James Town</td>\n",
" <td>43.651494</td>\n",
" <td>-79.375418</td>\n",
" </tr>\n",
" <tr>\n",
" <th>M4E</th>\n",
" <td>East Toronto</td>\n",
" <td>The Beaches</td>\n",
" <td>43.676357</td>\n",
" <td>-79.293031</td>\n",
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"text/plain": [
" Borough Neighborhood \\\n",
"Postal Code \n",
"M5A Downtown Toronto Regent Park, Harbourfront \n",
"M7A Downtown Toronto Queen's Park, Ontario Provincial Government \n",
"M5B Downtown Toronto Garden District, Ryerson \n",
"M5C Downtown Toronto St. James Town \n",
"M4E East Toronto The Beaches \n",
"\n",
" Latitude Longitude \n",
"Postal Code \n",
"M5A 43.654260 -79.360636 \n",
"M7A 43.662301 -79.389494 \n",
"M5B 43.657162 -79.378937 \n",
"M5C 43.651494 -79.375418 \n",
"M4E 43.676357 -79.293031 "
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Filter Canada data to only use boroughs in Toronto\n",
"toronto_df = canada_df[canada_df['Borough'].str.contains('Toronto')]\n",
"toronto_df.head()\n"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
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],
"text/plain": [
"<folium.folium.Map at 0x7f5ce37fa390>"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Show an initial map of the neighborhoods in Toronto\n",
"# create map of Toronto using first entries latitude and longitude values\n",
"map_toronto = folium.Map(location=[toronto_df[\"Latitude\"][0], toronto_df[\"Longitude\"][0]], zoom_start=10)\n",
"\n",
"# add markers to map\n",
"for lat, lng, borough, neighborhood in zip(toronto_df['Latitude'], toronto_df['Longitude'], toronto_df['Borough'], toronto_df['Neighborhood']):\n",
" label = '{}, {}'.format(neighborhood, borough)\n",
" label = folium.Popup(label, parse_html=True)\n",
" folium.CircleMarker(\n",
" [lat, lng],\n",
" radius=5,\n",
" popup=label,\n",
" color='blue',\n",
" fill=True,\n",
" fill_color='#3186cc',\n",
" fill_opacity=0.7,\n",
" parse_html=False).add_to(map_toronto) \n",
" \n",
"map_toronto"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Your credentails:\n",
"CLIENT_ID: Q5SAY0XAYHJ2KXJ3AIRKQJ2IPWSWFCPR5FOA5OFWCBE2DEUF\n",
"CLIENT_SECRET:HK1XAI1VDMPCZWIXOUIUPVJ0ECGRR1OJTE0MEKZIBVRH5PGW\n"
]
}
],
"source": [
"CLIENT_ID = 'Q5SAY0XAYHJ2KXJ3AIRKQJ2IPWSWFCPR5FOA5OFWCBE2DEUF' #'your-client-ID' # your Foursquare ID\n",
"CLIENT_SECRET ='HK1XAI1VDMPCZWIXOUIUPVJ0ECGRR1OJTE0MEKZIBVRH5PGW' # 'your-client-secret' # your Foursquare Secret\n",
"VERSION = '20180605' # Foursquare API version\n",
"\n",
"print('Your credentails:')\n",
"print('CLIENT_ID: ' + CLIENT_ID)\n",
"print('CLIENT_SECRET:' + CLIENT_SECRET)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Latitude and longitude values of Regent Park, Harbourfront are 43.6542599, -79.3606359.\n"
]
}
],
"source": [
"neighborhood_latitude = toronto_df['Latitude'][0] # neighborhood latitude value\n",
"neighborhood_longitude = toronto_df['Longitude'][0] # neighborhood longitude value\n",
"\n",
"neighborhood_name = toronto_df['Neighborhood'][0] # neighborhood name\n",
"\n",
"print('Latitude and longitude values of {} are {}, {}.'.format(neighborhood_name, \n",
" neighborhood_latitude, \n",
" neighborhood_longitude))"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/ipykernel_launcher.py:21: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead\n"
]
},
{
"data": {
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"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>name</th>\n",
" <th>categories</th>\n",
" <th>lat</th>\n",
" <th>lng</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Roselle Desserts</td>\n",
" <td>Bakery</td>\n",
" <td>43.653447</td>\n",
" <td>-79.362017</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Tandem Coffee</td>\n",
" <td>Coffee Shop</td>\n",
" <td>43.653559</td>\n",
" <td>-79.361809</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Morning Glory Cafe</td>\n",
" <td>Breakfast Spot</td>\n",
" <td>43.653947</td>\n",
" <td>-79.361149</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Cooper Koo Family YMCA</td>\n",
" <td>Distribution Center</td>\n",
" <td>43.653249</td>\n",
" <td>-79.358008</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Body Blitz Spa East</td>\n",
" <td>Spa</td>\n",
" <td>43.654735</td>\n",
" <td>-79.359874</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" name categories lat lng\n",
"0 Roselle Desserts Bakery 43.653447 -79.362017\n",
"1 Tandem Coffee Coffee Shop 43.653559 -79.361809\n",
"2 Morning Glory Cafe Breakfast Spot 43.653947 -79.361149\n",
"3 Cooper Koo Family YMCA Distribution Center 43.653249 -79.358008\n",
"4 Body Blitz Spa East Spa 43.654735 -79.359874"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Setup API URL to explore venues near by\n",
"LIMIT = 100\n",
"radius = 500\n",
"url = 'https://api.foursquare.com/v2/venues/explore?client_id={}&client_secret={}&ll={},{}&v={}&radius={}&limit={}'.format(CLIENT_ID, CLIENT_SECRET, neighborhood_latitude, neighborhood_longitude, VERSION, radius, LIMIT)\n",
"neighborhood_json = requests.get(url).json()\n",
"\n",
"# function that extracts the category of the venue\n",
"def get_category_type(row):\n",
" try:\n",
" categories_list = row['categories']\n",
" except:\n",
" categories_list = row['venue.categories']\n",
" \n",
" if len(categories_list) == 0:\n",
" return None\n",
" else:\n",
" return categories_list[0]['name']\n",
" \n",
"venues = neighborhood_json['response']['groups'][0]['items']\n",
" \n",
"nearby_venues = json_normalize(venues) # flatten JSON\n",
"\n",
"# filter columns\n",
"filtered_columns = ['venue.name', 'venue.categories', 'venue.location.lat', 'venue.location.lng']\n",
"nearby_venues =nearby_venues.loc[:, filtered_columns]\n",
"\n",
"# filter the category for each row\n",
"nearby_venues['venue.categories'] = nearby_venues.apply(get_category_type, axis=1)\n",
"\n",
"# clean columns\n",
"nearby_venues.columns = [col.split(\".\")[-1] for col in nearby_venues.columns]\n",
"\n",
"nearby_venues.head()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"def getNearbyVenues(names, latitudes, longitudes, radius=500):\n",
" \n",
" venues_list=[]\n",
" for name, lat, lng in zip(names, latitudes, longitudes):\n",
" print(name)\n",
" \n",
" # create the API request URL\n",
" url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n",
" CLIENT_ID, \n",
" CLIENT_SECRET, \n",
" VERSION, \n",
" lat, \n",
" lng, \n",
" radius, \n",
" LIMIT)\n",
" \n",
" # make the GET request\n",
" results = requests.get(url).json()[\"response\"]['groups'][0]['items']\n",
" \n",
" # return only relevant information for each nearby venue\n",
" venues_list.append([(\n",
" name, \n",
" lat, \n",
" lng, \n",
" v['venue']['name'], \n",
" v['venue']['location']['lat'], \n",
" v['venue']['location']['lng'], \n",
" v['venue']['categories'][0]['name']) for v in results])\n",
"\n",
" nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])\n",
" nearby_venues.columns = ['Neighborhood', \n",
" 'Neighborhood Latitude', \n",
" 'Neighborhood Longitude', \n",
" 'Venue', \n",
" 'Venue Latitude', \n",
" 'Venue Longitude', \n",
" 'Venue Category']\n",
" \n",
" return(nearby_venues)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Regent Park, Harbourfront\n",
"Queen's Park, Ontario Provincial Government\n",
"Garden District, Ryerson\n",
"St. James Town\n",
"The Beaches\n",
"Berczy Park\n",
"Central Bay Street\n",
"Christie\n",
"Richmond, Adelaide, King\n",
"Dufferin, Dovercourt Village\n",
"Harbourfront East, Union Station, Toronto Islands\n",
"Little Portugal, Trinity\n",
"The Danforth West, Riverdale\n",
"Toronto Dominion Centre, Design Exchange\n",
"Brockton, Parkdale Village, Exhibition Place\n",
"India Bazaar, The Beaches West\n",
"Commerce Court, Victoria Hotel\n",
"Studio District\n",
"Lawrence Park\n",
"Roselawn\n",
"Davisville North\n",
"Forest Hill North & West\n",
"High Park, The Junction South\n",
"North Toronto West\n",
"The Annex, North Midtown, Yorkville\n",
"Parkdale, Roncesvalles\n",
"Davisville\n",
"University of Toronto, Harbord\n",
"Runnymede, Swansea\n",
"Moore Park, Summerhill East\n",
"Kensington Market, Chinatown, Grange Park\n",
"Summerhill West, Rathnelly, South Hill, Forest Hill SE, Deer Park\n",
"CN Tower, King and Spadina, Railway Lands, Harbourfront West, Bathurst Quay, South Niagara, Island airport\n",
"Rosedale\n",
"Stn A PO Boxes\n",
"St. James Town, Cabbagetown\n",
"First Canadian Place, Underground city\n",
"Church and Wellesley\n",
"Business reply mail Processing Centre\n"
]
}
],
"source": [
"#Get all Tor\n",
"toronto_venues_df = getNearbyVenues(names=toronto_df['Neighborhood'],\n",
" latitudes=toronto_df['Latitude'],\n",
" longitudes=toronto_df['Longitude']\n",
" )\n"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1623, 7)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Neighborhood</th>\n",
" <th>Neighborhood Latitude</th>\n",
" <th>Neighborhood Longitude</th>\n",
" <th>Venue</th>\n",
" <th>Venue Latitude</th>\n",
" <th>Venue Longitude</th>\n",
" <th>Venue Category</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Regent Park, Harbourfront</td>\n",
" <td>43.65426</td>\n",
" <td>-79.360636</td>\n",
" <td>Roselle Desserts</td>\n",
" <td>43.653447</td>\n",
" <td>-79.362017</td>\n",
" <td>Bakery</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Regent Park, Harbourfront</td>\n",
" <td>43.65426</td>\n",
" <td>-79.360636</td>\n",
" <td>Tandem Coffee</td>\n",
" <td>43.653559</td>\n",
" <td>-79.361809</td>\n",
" <td>Coffee Shop</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Regent Park, Harbourfront</td>\n",
" <td>43.65426</td>\n",
" <td>-79.360636</td>\n",
" <td>Morning Glory Cafe</td>\n",
" <td>43.653947</td>\n",
" <td>-79.361149</td>\n",
" <td>Breakfast Spot</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Regent Park, Harbourfront</td>\n",
" <td>43.65426</td>\n",
" <td>-79.360636</td>\n",
" <td>Cooper Koo Family YMCA</td>\n",
" <td>43.653249</td>\n",
" <td>-79.358008</td>\n",
" <td>Distribution Center</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Regent Park, Harbourfront</td>\n",
" <td>43.65426</td>\n",
" <td>-79.360636</td>\n",
" <td>Body Blitz Spa East</td>\n",
" <td>43.654735</td>\n",
" <td>-79.359874</td>\n",
" <td>Spa</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Neighborhood Neighborhood Latitude Neighborhood Longitude \\\n",
"0 Regent Park, Harbourfront 43.65426 -79.360636 \n",
"1 Regent Park, Harbourfront 43.65426 -79.360636 \n",
"2 Regent Park, Harbourfront 43.65426 -79.360636 \n",
"3 Regent Park, Harbourfront 43.65426 -79.360636 \n",
"4 Regent Park, Harbourfront 43.65426 -79.360636 \n",
"\n",
" Venue Venue Latitude Venue Longitude \\\n",
"0 Roselle Desserts 43.653447 -79.362017 \n",
"1 Tandem Coffee 43.653559 -79.361809 \n",
"2 Morning Glory Cafe 43.653947 -79.361149 \n",
"3 Cooper Koo Family YMCA 43.653249 -79.358008 \n",
"4 Body Blitz Spa East 43.654735 -79.359874 \n",
"\n",
" Venue Category \n",
"0 Bakery \n",
"1 Coffee Shop \n",
"2 Breakfast Spot \n",
"3 Distribution Center \n",
"4 Spa "
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(toronto_venues_df.shape)\n",
"toronto_venues_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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>55</td>\n",
" <td>55</td>\n",
" <td>55</td>\n",
" <td>55</td>\n",
" <td>55</td>\n",
" <td>55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Brockton, Parkdale Village, Exhibition Place</th>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Business reply mail Processing Centre</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>CN Tower, King and Spadina, Railway Lands, Harbourfront West, Bathurst Quay, South Niagara, Island airport</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>Central Bay Street</th>\n",
" <td>66</td>\n",
" <td>66</td>\n",
" <td>66</td>\n",
" <td>66</td>\n",
" <td>66</td>\n",
" <td>66</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Christie</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>Church and Wellesley</th>\n",
" <td>76</td>\n",
" <td>76</td>\n",
" <td>76</td>\n",
" <td>76</td>\n",
" <td>76</td>\n",
" <td>76</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>34</td>\n",
" <td>34</td>\n",
" <td>34</td>\n",
" <td>34</td>\n",
" <td>34</td>\n",
" <td>34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Davisville North</th>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Dufferin, Dovercourt Village</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>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</th>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</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>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>India Bazaar, The Beaches West</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>Kensington Market, Chinatown, Grange Park</th>\n",
" <td>57</td>\n",
" <td>57</td>\n",
" <td>57</td>\n",
" <td>57</td>\n",
" <td>57</td>\n",
" <td>57</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>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>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</th>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Parkdale, Roncesvalles</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>Queen's Park, Ontario Provincial Government</th>\n",
" <td>34</td>\n",
" <td>34</td>\n",
" <td>34</td>\n",
" <td>34</td>\n",
" <td>34</td>\n",
" <td>34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Regent Park, Harbourfront</th>\n",
" <td>47</td>\n",
" <td>47</td>\n",
" <td>47</td>\n",
" <td>47</td>\n",
" <td>47</td>\n",
" <td>47</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Richmond, Adelaide, King</th>\n",
" <td>93</td>\n",
" <td>93</td>\n",
" <td>93</td>\n",
" <td>93</td>\n",
" <td>93</td>\n",
" <td>93</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>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>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>76</td>\n",
" <td>76</td>\n",
" <td>76</td>\n",
" <td>76</td>\n",
" <td>76</td>\n",
" <td>76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>St. James Town, Cabbagetown</th>\n",
" <td>51</td>\n",
" <td>51</td>\n",
" <td>51</td>\n",
" <td>51</td>\n",
" <td>51</td>\n",
" <td>51</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Stn A PO Boxes</th>\n",
" <td>93</td>\n",
" <td>93</td>\n",
" <td>93</td>\n",
" <td>93</td>\n",
" <td>93</td>\n",
" <td>93</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>26</td>\n",
" <td>26</td>\n",
" <td>26</td>\n",
" <td>26</td>\n",
" <td>26</td>\n",
" <td>26</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>42</td>\n",
" <td>42</td>\n",
" <td>42</td>\n",
" <td>42</td>\n",
" <td>42</td>\n",
" <td>42</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>"
],
"text/plain": [
" Neighborhood Latitude \\\n",
"Neighborhood \n",
"Berczy Park 55 \n",
"Brockton, Parkdale Village, Exhibition Place 22 \n",
"Business reply mail Processing Centre 19 \n",
"CN Tower, King and Spadina, Railway Lands, Harb... 16 \n",
"Central Bay Street 66 \n",
"Christie 17 \n",
"Church and Wellesley 76 \n",
"Commerce Court, Victoria Hotel 100 \n",
"Davisville 34 \n",
"Davisville North 9 \n",
"Dufferin, Dovercourt Village 16 \n",
"First Canadian Place, Underground city 100 \n",
"Forest Hill North & West 5 \n",
"Garden District, Ryerson 100 \n",
"Harbourfront East, Union Station, Toronto Islands 100 \n",
"High Park, The Junction South 23 \n",
"India Bazaar, The Beaches West 23 \n",
"Kensington Market, Chinatown, Grange Park 57 \n",
"Lawrence Park 3 \n",
"Little Portugal, Trinity 43 \n",
"Moore Park, Summerhill East 2 \n",
"North Toronto West 22 \n",
"Parkdale, Roncesvalles 14 \n",
"Queen's Park, Ontario Provincial Government 34 \n",
"Regent Park, Harbourfront 47 \n",
"Richmond, Adelaide, King 93 \n",
"Rosedale 4 \n",
"Roselawn 3 \n",
"Runnymede, Swansea 37 \n",
"St. James Town 76 \n",
"St. James Town, Cabbagetown 51 \n",
"Stn A PO Boxes 93 \n",
"Studio District 40 \n",
"Summerhill West, Rathnelly, South Hill, Forest ... 16 \n",
"The Annex, North Midtown, Yorkville 26 \n",
"The Beaches 4 \n",
"The Danforth West, Riverdale 42 \n",
"Toronto Dominion Centre, Design Exchange 100 \n",
"University of Toronto, Harbord 35 \n",
"\n",
" Neighborhood Longitude \\\n",
"Neighborhood \n",
"Berczy Park 55 \n",
"Brockton, Parkdale Village, Exhibition Place 22 \n",
"Business reply mail Processing Centre 19 \n",
"CN Tower, King and Spadina, Railway Lands, Harb... 16 \n",
"Central Bay Street 66 \n",
"Christie 17 \n",
"Church and Wellesley 76 \n",
"Commerce Court, Victoria Hotel 100 \n",
"Davisville 34 \n",
"Davisville North 9 \n",
"Dufferin, Dovercourt Village 16 \n",
"First Canadian Place, Underground city 100 \n",
"Forest Hill North & West 5 \n",
"Garden District, Ryerson 100 \n",
"Harbourfront East, Union Station, Toronto Islands 100 \n",
"High Park, The Junction South 23 \n",
"India Bazaar, The Beaches West 23 \n",
"Kensington Market, Chinatown, Grange Park 57 \n",
"Lawrence Park 3 \n",
"Little Portugal, Trinity 43 \n",
"Moore Park, Summerhill East 2 \n",
"North Toronto West 22 \n",
"Parkdale, Roncesvalles 14 \n",
"Queen's Park, Ontario Provincial Government 34 \n",
"Regent Park, Harbourfront 47 \n",
"Richmond, Adelaide, King 93 \n",
"Rosedale 4 \n",
"Roselawn 3 \n",
"Runnymede, Swansea 37 \n",
"St. James Town 76 \n",
"St. James Town, Cabbagetown 51 \n",
"Stn A PO Boxes 93 \n",
"Studio District 40 \n",
"Summerhill West, Rathnelly, South Hill, Forest ... 16 \n",
"The Annex, North Midtown, Yorkville 26 \n",
"The Beaches 4 \n",
"The Danforth West, Riverdale 42 \n",
"Toronto Dominion Centre, Design Exchange 100 \n",
"University of Toronto, Harbord 35 \n",
"\n",
" Venue Venue Latitude \\\n",
"Neighborhood \n",
"Berczy Park 55 55 \n",
"Brockton, Parkdale Village, Exhibition Place 22 22 \n",
"Business reply mail Processing Centre 19 19 \n",
"CN Tower, King and Spadina, Railway Lands, Harb... 16 16 \n",
"Central Bay Street 66 66 \n",
"Christie 17 17 \n",
"Church and Wellesley 76 76 \n",
"Commerce Court, Victoria Hotel 100 100 \n",
"Davisville 34 34 \n",
"Davisville North 9 9 \n",
"Dufferin, Dovercourt Village 16 16 \n",
"First Canadian Place, Underground city 100 100 \n",
"Forest Hill North & West 5 5 \n",
"Garden District, Ryerson 100 100 \n",
"Harbourfront East, Union Station, Toronto Islands 100 100 \n",
"High Park, The Junction South 23 23 \n",
"India Bazaar, The Beaches West 23 23 \n",
"Kensington Market, Chinatown, Grange Park 57 57 \n",
"Lawrence Park 3 3 \n",
"Little Portugal, Trinity 43 43 \n",
"Moore Park, Summerhill East 2 2 \n",
"North Toronto West 22 22 \n",
"Parkdale, Roncesvalles 14 14 \n",
"Queen's Park, Ontario Provincial Government 34 34 \n",
"Regent Park, Harbourfront 47 47 \n",
"Richmond, Adelaide, King 93 93 \n",
"Rosedale 4 4 \n",
"Roselawn 3 3 \n",
"Runnymede, Swansea 37 37 \n",
"St. James Town 76 76 \n",
"St. James Town, Cabbagetown 51 51 \n",
"Stn A PO Boxes 93 93 \n",
"Studio District 40 40 \n",
"Summerhill West, Rathnelly, South Hill, Forest ... 16 16 \n",
"The Annex, North Midtown, Yorkville 26 26 \n",
"The Beaches 4 4 \n",
"The Danforth West, Riverdale 42 42 \n",
"Toronto Dominion Centre, Design Exchange 100 100 \n",
"University of Toronto, Harbord 35 35 \n",
"\n",
" Venue Longitude \\\n",
"Neighborhood \n",
"Berczy Park 55 \n",
"Brockton, Parkdale Village, Exhibition Place 22 \n",
"Business reply mail Processing Centre 19 \n",
"CN Tower, King and Spadina, Railway Lands, Harb... 16 \n",
"Central Bay Street 66 \n",
"Christie 17 \n",
"Church and Wellesley 76 \n",
"Commerce Court, Victoria Hotel 100 \n",
"Davisville 34 \n",
"Davisville North 9 \n",
"Dufferin, Dovercourt Village 16 \n",
"First Canadian Place, Underground city 100 \n",
"Forest Hill North & West 5 \n",
"Garden District, Ryerson 100 \n",
"Harbourfront East, Union Station, Toronto Islands 100 \n",
"High Park, The Junction South 23 \n",
"India Bazaar, The Beaches West 23 \n",
"Kensington Market, Chinatown, Grange Park 57 \n",
"Lawrence Park 3 \n",
"Little Portugal, Trinity 43 \n",
"Moore Park, Summerhill East 2 \n",
"North Toronto West 22 \n",
"Parkdale, Roncesvalles 14 \n",
"Queen's Park, Ontario Provincial Government 34 \n",
"Regent Park, Harbourfront 47 \n",
"Richmond, Adelaide, King 93 \n",
"Rosedale 4 \n",
"Roselawn 3 \n",
"Runnymede, Swansea 37 \n",
"St. James Town 76 \n",
"St. James Town, Cabbagetown 51 \n",
"Stn A PO Boxes 93 \n",
"Studio District 40 \n",
"Summerhill West, Rathnelly, South Hill, Forest ... 16 \n",
"The Annex, North Midtown, Yorkville 26 \n",
"The Beaches 4 \n",
"The Danforth West, Riverdale 42 \n",
"Toronto Dominion Centre, Design Exchange 100 \n",
"University of Toronto, Harbord 35 \n",
"\n",
" Venue Category \n",
"Neighborhood \n",
"Berczy Park 55 \n",
"Brockton, Parkdale Village, Exhibition Place 22 \n",
"Business reply mail Processing Centre 19 \n",
"CN Tower, King and Spadina, Railway Lands, Harb... 16 \n",
"Central Bay Street 66 \n",
"Christie 17 \n",
"Church and Wellesley 76 \n",
"Commerce Court, Victoria Hotel 100 \n",
"Davisville 34 \n",
"Davisville North 9 \n",
"Dufferin, Dovercourt Village 16 \n",
"First Canadian Place, Underground city 100 \n",
"Forest Hill North & West 5 \n",
"Garden District, Ryerson 100 \n",
"Harbourfront East, Union Station, Toronto Islands 100 \n",
"High Park, The Junction South 23 \n",
"India Bazaar, The Beaches West 23 \n",
"Kensington Market, Chinatown, Grange Park 57 \n",
"Lawrence Park 3 \n",
"Little Portugal, Trinity 43 \n",
"Moore Park, Summerhill East 2 \n",
"North Toronto West 22 \n",
"Parkdale, Roncesvalles 14 \n",
"Queen's Park, Ontario Provincial Government 34 \n",
"Regent Park, Harbourfront 47 \n",
"Richmond, Adelaide, King 93 \n",
"Rosedale 4 \n",
"Roselawn 3 \n",
"Runnymede, Swansea 37 \n",
"St. James Town 76 \n",
"St. James Town, Cabbagetown 51 \n",
"Stn A PO Boxes 93 \n",
"Studio District 40 \n",
"Summerhill West, Rathnelly, South Hill, Forest ... 16 \n",
"The Annex, North Midtown, Yorkville 26 \n",
"The Beaches 4 \n",
"The Danforth West, Riverdale 42 \n",
"Toronto Dominion Centre, Design Exchange 100 \n",
"University of Toronto, Harbord 35 "
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"toronto_venues_df.groupby('Neighborhood').count()\n"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 242 uniques categories.\n"
]
}
],
"source": [
"print('There are {} uniques categories.'.format(len(toronto_venues_df['Venue Category'].unique())))\n"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Yoga Studio</th>\n",
" <th>Accessories Store</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>Aquarium</th>\n",
" <th>Art Gallery</th>\n",
" <th>Art Museum</th>\n",
" <th>Arts &amp; Crafts Store</th>\n",
" <th>Asian Restaurant</th>\n",
" <th>Athletics &amp; Sports</th>\n",
" <th>Auto Workshop</th>\n",
" <th>BBQ Joint</th>\n",
" <th>Baby Store</th>\n",
" <th>Bagel Shop</th>\n",
" <th>Bakery</th>\n",
" <th>Bank</th>\n",
" <th>Bar</th>\n",
" <th>Baseball Stadium</th>\n",
" <th>Basketball Stadium</th>\n",
" <th>Beach</th>\n",
" <th>Bed &amp; Breakfast</th>\n",
" <th>Beer Bar</th>\n",
" <th>Beer Store</th>\n",
" <th>Belgian Restaurant</th>\n",
" <th>Bistro</th>\n",
" <th>Boat or Ferry</th>\n",
" <th>Bookstore</th>\n",
" <th>Boutique</th>\n",
" <th>Brazilian Restaurant</th>\n",
" <th>Breakfast Spot</th>\n",
" <th>Brewery</th>\n",
" <th>Bubble Tea Shop</th>\n",
" <th>Building</th>\n",
" <th>Burger Joint</th>\n",
" <th>Burrito Place</th>\n",
" <th>Bus Line</th>\n",
" <th>Butcher</th>\n",
" <th>Café</th>\n",
" <th>Cajun / Creole Restaurant</th>\n",
" <th>Camera Store</th>\n",
" <th>Candy Store</th>\n",
" <th>Caribbean Restaurant</th>\n",
" <th>Cheese Shop</th>\n",
" <th>Chinese Restaurant</th>\n",
" <th>Chocolate Shop</th>\n",
" <th>Church</th>\n",
" <th>Climbing Gym</th>\n",
" <th>Clothing Store</th>\n",
" <th>Cocktail Bar</th>\n",
" <th>Coffee Shop</th>\n",
" <th>College Arts Building</th>\n",
" <th>College Auditorium</th>\n",
" <th>College Cafeteria</th>\n",
" <th>College Gym</th>\n",
" <th>College Rec Center</th>\n",
" <th>Colombian Restaurant</th>\n",
" <th>Comfort Food Restaurant</th>\n",
" <th>Comic Shop</th>\n",
" <th>Concert Hall</th>\n",
" <th>Convenience Store</th>\n",
" <th>Convention Center</th>\n",
" <th>Cosmetics Shop</th>\n",
" <th>Coworking Space</th>\n",
" <th>Creperie</th>\n",
" <th>Cuban Restaurant</th>\n",
" <th>Cupcake Shop</th>\n",
" <th>Dance Studio</th>\n",
" <th>Deli / Bodega</th>\n",
" <th>Department Store</th>\n",
" <th>Dessert Shop</th>\n",
" <th>Diner</th>\n",
" <th>Discount Store</th>\n",
" <th>Distribution Center</th>\n",
" <th>Dog Run</th>\n",
" <th>Doner Restaurant</th>\n",
" <th>Donut Shop</th>\n",
" <th>Eastern European Restaurant</th>\n",
" <th>Electronics Store</th>\n",
" <th>Ethiopian Restaurant</th>\n",
" <th>Event Space</th>\n",
" <th>Falafel Restaurant</th>\n",
" <th>Farmers Market</th>\n",
" <th>Fast Food Restaurant</th>\n",
" <th>Filipino Restaurant</th>\n",
" <th>Fish &amp; Chips Shop</th>\n",
" <th>Fish Market</th>\n",
" <th>Flea Market</th>\n",
" <th>Flower Shop</th>\n",
" <th>Food</th>\n",
" <th>Food &amp; Drink Shop</th>\n",
" <th>Food Court</th>\n",
" <th>Food Truck</th>\n",
" <th>Fountain</th>\n",
" <th>French Restaurant</th>\n",
" <th>Fried Chicken Joint</th>\n",
" <th>Frozen Yogurt Shop</th>\n",
" <th>Fruit &amp; Vegetable Store</th>\n",
" <th>Furniture / Home Store</th>\n",
" <th>Gaming Cafe</th>\n",
" <th>Garden</th>\n",
" <th>Garden Center</th>\n",
" <th>Gas Station</th>\n",
" <th>Gastropub</th>\n",
" <th>Gay Bar</th>\n",
" <th>General Entertainment</th>\n",
" <th>General Travel</th>\n",
" <th>German Restaurant</th>\n",
" <th>Gift Shop</th>\n",
" <th>Gluten-free Restaurant</th>\n",
" <th>Gourmet Shop</th>\n",
" <th>Greek Restaurant</th>\n",
" <th>Grocery Store</th>\n",
" <th>Gym</th>\n",
" <th>Gym / Fitness Center</th>\n",
" <th>Harbor / Marina</th>\n",
" <th>Health &amp; Beauty Service</th>\n",
" <th>Health Food Store</th>\n",
" <th>Historic Site</th>\n",
" <th>History Museum</th>\n",
" <th>Hobby Shop</th>\n",
" <th>Home Service</th>\n",
" <th>Hookah Bar</th>\n",
" <th>Hospital</th>\n",
" <th>Hot Dog Joint</th>\n",
" <th>Hotel</th>\n",
" <th>Hotel Bar</th>\n",
" <th>IT Services</th>\n",
" <th>Ice Cream Shop</th>\n",
" <th>Indian Restaurant</th>\n",
" <th>Indie Movie Theater</th>\n",
" <th>Indoor Play Area</th>\n",
" <th>Intersection</th>\n",
" <th>Irish Pub</th>\n",
" <th>Italian Restaurant</th>\n",
" <th>Japanese Restaurant</th>\n",
" <th>Jazz Club</th>\n",
" <th>Jewelry Store</th>\n",
" <th>Juice Bar</th>\n",
" <th>Korean Restaurant</th>\n",
" <th>Lake</th>\n",
" <th>Latin American Restaurant</th>\n",
" <th>Light Rail Station</th>\n",
" <th>Lingerie Store</th>\n",
" <th>Liquor Store</th>\n",
" <th>Lounge</th>\n",
" <th>Mac &amp; Cheese Joint</th>\n",
" <th>Market</th>\n",
" <th>Martial Arts Dojo</th>\n",
" <th>Mediterranean Restaurant</th>\n",
" <th>Men's Store</th>\n",
" <th>Metro Station</th>\n",
" <th>Mexican Restaurant</th>\n",
" <th>Middle Eastern Restaurant</th>\n",
" <th>Miscellaneous Shop</th>\n",
" <th>Modern European Restaurant</th>\n",
" <th>Molecular Gastronomy Restaurant</th>\n",
" <th>Monument / Landmark</th>\n",
" <th>Moroccan Restaurant</th>\n",
" <th>Movie Theater</th>\n",
" <th>Museum</th>\n",
" <th>Music Venue</th>\n",
" <th>Neighborhood</th>\n",
" <th>New American Restaurant</th>\n",
" <th>Nightclub</th>\n",
" <th>Noodle House</th>\n",
" <th>Office</th>\n",
" <th>Opera House</th>\n",
" <th>Optical Shop</th>\n",
" <th>Organic Grocery</th>\n",
" <th>Other Great Outdoors</th>\n",
" <th>Outdoor Sculpture</th>\n",
" <th>Park</th>\n",
" <th>Performing Arts Venue</th>\n",
" <th>Pet Store</th>\n",
" <th>Pharmacy</th>\n",
" <th>Pizza Place</th>\n",
" <th>Plane</th>\n",
" <th>Playground</th>\n",
" <th>Plaza</th>\n",
" <th>Poke Place</th>\n",
" <th>Pool</th>\n",
" <th>Post Office</th>\n",
" <th>Poutine Place</th>\n",
" <th>Pub</th>\n",
" <th>Ramen Restaurant</th>\n",
" <th>Record Shop</th>\n",
" <th>Recording Studio</th>\n",
" <th>Rental Car Location</th>\n",
" <th>Restaurant</th>\n",
" <th>Roof Deck</th>\n",
" <th>Sake Bar</th>\n",
" <th>Salad Place</th>\n",
" <th>Salon / Barbershop</th>\n",
" <th>Sandwich Place</th>\n",
" <th>Scenic Lookout</th>\n",
" <th>Sculpture Garden</th>\n",
" <th>Seafood Restaurant</th>\n",
" <th>Shoe Repair</th>\n",
" <th>Shoe Store</th>\n",
" <th>Shopping Mall</th>\n",
" <th>Skate Park</th>\n",
" <th>Skating Rink</th>\n",
" <th>Smoke Shop</th>\n",
" <th>Smoothie Shop</th>\n",
" <th>Snack Place</th>\n",
" <th>Soup Place</th>\n",
" <th>Spa</th>\n",
" <th>Speakeasy</th>\n",
" <th>Sporting Goods Shop</th>\n",
" <th>Sports Bar</th>\n",
" <th>Stadium</th>\n",
" <th>Stationery Store</th>\n",
" <th>Steakhouse</th>\n",
" <th>Strip Club</th>\n",
" <th>Supermarket</th>\n",
" <th>Sushi Restaurant</th>\n",
" <th>Swim School</th>\n",
" <th>Taco Place</th>\n",
" <th>Tailor Shop</th>\n",
" <th>Taiwanese Restaurant</th>\n",
" <th>Tanning Salon</th>\n",
" <th>Tea Room</th>\n",
" <th>Thai Restaurant</th>\n",
" <th>Theater</th>\n",
" <th>Theme Restaurant</th>\n",
" <th>Toy / Game Store</th>\n",
" <th>Trail</th>\n",
" <th>Train Station</th>\n",
" <th>Vegetarian / Vegan Restaurant</th>\n",
" <th>Video Game Store</th>\n",
" <th>Video Store</th>\n",
" <th>Vietnamese Restaurant</th>\n",
" <th>Wine Bar</th>\n",
" <th>Wings Joint</th>\n",
" <th>Women's Store</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
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" <td>Regent Park, Harbourfront</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>Regent Park, Harbourfront</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>Regent Park, Harbourfront</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" Strip Club Supermarket Sushi Restaurant Swim School Taco Place \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" Tailor Shop Taiwanese Restaurant Tanning Salon Tea Room \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" Thai Restaurant Theater Theme Restaurant Toy / Game Store Trail \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" Train Station Vegetarian / Vegan Restaurant Video Game Store \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" Video Store Vietnamese Restaurant Wine Bar Wings Joint Women's Store \n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 "
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# one hot encoding\n",
"toronto_onehot = pd.get_dummies(toronto_venues_df[['Venue Category']], prefix=\"\", prefix_sep=\"\")\n",
"\n",
"# add neighborhood column back to dataframe\n",
"toronto_onehot['Neighborhood'] = toronto_venues_df['Neighborhood'] \n",
"\n",
"# move neighborhood column to the first column\n",
"fixed_columns = [toronto_onehot.columns[-1]] + list(toronto_onehot.columns[:-1])\n",
"toronto_onehot = toronto_onehot[fixed_columns]\n",
"\n",
"toronto_onehot.head()"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
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" vertical-align: middle;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Neighborhood</th>\n",
" <th>Yoga Studio</th>\n",
" <th>Accessories Store</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>Aquarium</th>\n",
" <th>Art Gallery</th>\n",
" <th>Art Museum</th>\n",
" <th>Arts &amp; Crafts Store</th>\n",
" <th>Asian Restaurant</th>\n",
" <th>Athletics &amp; Sports</th>\n",
" <th>Auto Workshop</th>\n",
" <th>BBQ Joint</th>\n",
" <th>Baby Store</th>\n",
" <th>Bagel Shop</th>\n",
" <th>Bakery</th>\n",
" <th>Bank</th>\n",
" <th>Bar</th>\n",
" <th>Baseball Stadium</th>\n",
" <th>Basketball Stadium</th>\n",
" <th>Beach</th>\n",
" <th>Bed &amp; Breakfast</th>\n",
" <th>Beer Bar</th>\n",
" <th>Beer Store</th>\n",
" <th>Belgian Restaurant</th>\n",
" <th>Bistro</th>\n",
" <th>Boat or Ferry</th>\n",
" <th>Bookstore</th>\n",
" <th>Boutique</th>\n",
" <th>Brazilian Restaurant</th>\n",
" <th>Breakfast Spot</th>\n",
" <th>Brewery</th>\n",
" <th>Bubble Tea Shop</th>\n",
" <th>Building</th>\n",
" <th>Burger Joint</th>\n",
" <th>Burrito Place</th>\n",
" <th>Bus Line</th>\n",
" <th>Butcher</th>\n",
" <th>Café</th>\n",
" <th>Cajun / Creole Restaurant</th>\n",
" <th>Camera Store</th>\n",
" <th>Candy Store</th>\n",
" <th>Caribbean Restaurant</th>\n",
" <th>Cheese Shop</th>\n",
" <th>Chinese Restaurant</th>\n",
" <th>Chocolate Shop</th>\n",
" <th>Church</th>\n",
" <th>Climbing Gym</th>\n",
" <th>Clothing Store</th>\n",
" <th>Cocktail Bar</th>\n",
" <th>Coffee Shop</th>\n",
" <th>College Arts Building</th>\n",
" <th>College Auditorium</th>\n",
" <th>College Cafeteria</th>\n",
" <th>College Gym</th>\n",
" <th>College Rec Center</th>\n",
" <th>Colombian Restaurant</th>\n",
" <th>Comfort Food Restaurant</th>\n",
" <th>Comic Shop</th>\n",
" <th>Concert Hall</th>\n",
" <th>Convenience Store</th>\n",
" <th>Convention Center</th>\n",
" <th>Cosmetics Shop</th>\n",
" <th>Coworking Space</th>\n",
" <th>Creperie</th>\n",
" <th>Cuban Restaurant</th>\n",
" <th>Cupcake Shop</th>\n",
" <th>Dance Studio</th>\n",
" <th>Deli / Bodega</th>\n",
" <th>Department Store</th>\n",
" <th>Dessert Shop</th>\n",
" <th>Diner</th>\n",
" <th>Discount Store</th>\n",
" <th>Distribution Center</th>\n",
" <th>Dog Run</th>\n",
" <th>Doner Restaurant</th>\n",
" <th>Donut Shop</th>\n",
" <th>Eastern European Restaurant</th>\n",
" <th>Electronics Store</th>\n",
" <th>Ethiopian Restaurant</th>\n",
" <th>Event Space</th>\n",
" <th>Falafel Restaurant</th>\n",
" <th>Farmers Market</th>\n",
" <th>Fast Food Restaurant</th>\n",
" <th>Filipino Restaurant</th>\n",
" <th>Fish &amp; Chips Shop</th>\n",
" <th>Fish Market</th>\n",
" <th>Flea Market</th>\n",
" <th>Flower Shop</th>\n",
" <th>Food</th>\n",
" <th>Food &amp; Drink Shop</th>\n",
" <th>Food Court</th>\n",
" <th>Food Truck</th>\n",
" <th>Fountain</th>\n",
" <th>French Restaurant</th>\n",
" <th>Fried Chicken Joint</th>\n",
" <th>Frozen Yogurt Shop</th>\n",
" <th>Fruit &amp; Vegetable Store</th>\n",
" <th>Furniture / Home Store</th>\n",
" <th>Gaming Cafe</th>\n",
" <th>Garden</th>\n",
" <th>Garden Center</th>\n",
" <th>Gas Station</th>\n",
" <th>Gastropub</th>\n",
" <th>Gay Bar</th>\n",
" <th>General Entertainment</th>\n",
" <th>General Travel</th>\n",
" <th>German Restaurant</th>\n",
" <th>Gift Shop</th>\n",
" <th>Gluten-free Restaurant</th>\n",
" <th>Gourmet Shop</th>\n",
" <th>Greek Restaurant</th>\n",
" <th>Grocery Store</th>\n",
" <th>Gym</th>\n",
" <th>Gym / Fitness Center</th>\n",
" <th>Harbor / Marina</th>\n",
" <th>Health &amp; Beauty Service</th>\n",
" <th>Health Food Store</th>\n",
" <th>Historic Site</th>\n",
" <th>History Museum</th>\n",
" <th>Hobby Shop</th>\n",
" <th>Home Service</th>\n",
" <th>Hookah Bar</th>\n",
" <th>Hospital</th>\n",
" <th>Hot Dog Joint</th>\n",
" <th>Hotel</th>\n",
" <th>Hotel Bar</th>\n",
" <th>IT Services</th>\n",
" <th>Ice Cream Shop</th>\n",
" <th>Indian Restaurant</th>\n",
" <th>Indie Movie Theater</th>\n",
" <th>Indoor Play Area</th>\n",
" <th>Intersection</th>\n",
" <th>Irish Pub</th>\n",
" <th>Italian Restaurant</th>\n",
" <th>Japanese Restaurant</th>\n",
" <th>Jazz Club</th>\n",
" <th>Jewelry Store</th>\n",
" <th>Juice Bar</th>\n",
" <th>Korean Restaurant</th>\n",
" <th>Lake</th>\n",
" <th>Latin American Restaurant</th>\n",
" <th>Light Rail Station</th>\n",
" <th>Lingerie Store</th>\n",
" <th>Liquor Store</th>\n",
" <th>Lounge</th>\n",
" <th>Mac &amp; Cheese Joint</th>\n",
" <th>Market</th>\n",
" <th>Martial Arts Dojo</th>\n",
" <th>Mediterranean Restaurant</th>\n",
" <th>Men's Store</th>\n",
" <th>Metro Station</th>\n",
" <th>Mexican Restaurant</th>\n",
" <th>Middle Eastern Restaurant</th>\n",
" <th>Miscellaneous Shop</th>\n",
" <th>Modern European Restaurant</th>\n",
" <th>Molecular Gastronomy Restaurant</th>\n",
" <th>Monument / Landmark</th>\n",
" <th>Moroccan Restaurant</th>\n",
" <th>Movie Theater</th>\n",
" <th>Museum</th>\n",
" <th>Music Venue</th>\n",
" <th>New American Restaurant</th>\n",
" <th>Nightclub</th>\n",
" <th>Noodle House</th>\n",
" <th>Office</th>\n",
" <th>Opera House</th>\n",
" <th>Optical Shop</th>\n",
" <th>Organic Grocery</th>\n",
" <th>Other Great Outdoors</th>\n",
" <th>Outdoor Sculpture</th>\n",
" <th>Park</th>\n",
" <th>Performing Arts Venue</th>\n",
" <th>Pet Store</th>\n",
" <th>Pharmacy</th>\n",
" <th>Pizza Place</th>\n",
" <th>Plane</th>\n",
" <th>Playground</th>\n",
" <th>Plaza</th>\n",
" <th>Poke Place</th>\n",
" <th>Pool</th>\n",
" <th>Post Office</th>\n",
" <th>Poutine Place</th>\n",
" <th>Pub</th>\n",
" <th>Ramen Restaurant</th>\n",
" <th>Record Shop</th>\n",
" <th>Recording Studio</th>\n",
" <th>Rental Car Location</th>\n",
" <th>Restaurant</th>\n",
" <th>Roof Deck</th>\n",
" <th>Sake Bar</th>\n",
" <th>Salad Place</th>\n",
" <th>Salon / Barbershop</th>\n",
" <th>Sandwich Place</th>\n",
" <th>Scenic Lookout</th>\n",
" <th>Sculpture Garden</th>\n",
" <th>Seafood Restaurant</th>\n",
" <th>Shoe Repair</th>\n",
" <th>Shoe Store</th>\n",
" <th>Shopping Mall</th>\n",
" <th>Skate Park</th>\n",
" <th>Skating Rink</th>\n",
" <th>Smoke Shop</th>\n",
" <th>Smoothie Shop</th>\n",
" <th>Snack Place</th>\n",
" <th>Soup Place</th>\n",
" <th>Spa</th>\n",
" <th>Speakeasy</th>\n",
" <th>Sporting Goods Shop</th>\n",
" <th>Sports Bar</th>\n",
" <th>Stadium</th>\n",
" <th>Stationery Store</th>\n",
" <th>Steakhouse</th>\n",
" <th>Strip Club</th>\n",
" <th>Supermarket</th>\n",
" <th>Sushi Restaurant</th>\n",
" <th>Swim School</th>\n",
" <th>Taco Place</th>\n",
" <th>Tailor Shop</th>\n",
" <th>Taiwanese Restaurant</th>\n",
" <th>Tanning Salon</th>\n",
" <th>Tea Room</th>\n",
" <th>Thai Restaurant</th>\n",
" <th>Theater</th>\n",
" <th>Theme Restaurant</th>\n",
" <th>Toy / Game Store</th>\n",
" <th>Trail</th>\n",
" <th>Train Station</th>\n",
" <th>Vegetarian / Vegan Restaurant</th>\n",
" <th>Video Game Store</th>\n",
" <th>Video Store</th>\n",
" <th>Vietnamese Restaurant</th>\n",
" <th>Wine Bar</th>\n",
" <th>Wings Joint</th>\n",
" <th>Women's Store</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
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" <tr>\n",
" <th>1</th>\n",
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" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Business reply mail Processing Centre</td>\n",
" <td>0.052632</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>CN Tower, King and Spadina, Railway Lands, Har...</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.0625</td>\n",
" <td>0.0625</td>\n",
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" <td>0.125</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Central Bay Street</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Christie</td>\n",
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" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
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" <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>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Church and Wellesley</td>\n",
" <td>0.026316</td>\n",
" <td>0.000000</td>\n",
" <td>0.013158</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>0.013158</td>\n",
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" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.013158</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>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.013158</td>\n",
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" <td>0.0000</td>\n",
" <td>0.013158</td>\n",
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" <td>0.013158</td>\n",
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" <td>0.026316</td>\n",
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" <td>0.013158</td>\n",
" <td>0.013158</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.026316</td>\n",
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" <td>0.000000</td>\n",
" <td>0.013158</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.013158</td>\n",
" <td>0.000000</td>\n",
" <td>0.065789</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000</td>\n",
" <td>0.013158</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.013158</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.013158</td>\n",
" <td>0.000000</td>\n",
" <td>0.013158</td>\n",
" <td>0.013158</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.013158</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.013158</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>0.013158</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.00000</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>0.026316</td>\n",
" <td>0.039474</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",
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" <td>0.000000</td>\n",
" <td>0.013158</td>\n",
" <td>0.013158</td>\n",
" <td>0.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.013158</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.013158</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.026316</td>\n",
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" <td>0.000000</td>\n",
" <td>0.013158</td>\n",
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" <td>0.000000</td>\n",
" <td>0.013158</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.013158</td>\n",
" <td>0.026316</td>\n",
" <td>0.026316</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.013158</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.013158</td>\n",
" <td>0.0000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.026316</td>\n",
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" <td>0.013158</td>\n",
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" <td>0.013158</td>\n",
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" <td>0.013158</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Commerce Court, Victoria Hotel</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>0.040000</td>\n",
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" <td>0.00</td>\n",
" <td>0.010000</td>\n",
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" <td>0.010000</td>\n",
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" <td>0.020000</td>\n",
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" <td>0.010000</td>\n",
" <td>0.00</td>\n",
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" <td>0.020000</td>\n",
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" <td>0.0000</td>\n",
" <td>0.010000</td>\n",
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" <td>0.020000</td>\n",
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" <td>0.010000</td>\n",
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" <td>0.070000</td>\n",
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" <td>0.020000</td>\n",
" <td>0.110000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000</td>\n",
" <td>0.010000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.010000</td>\n",
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" <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>0.010000</td>\n",
" <td>0.010000</td>\n",
" <td>0.010000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.00000</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>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.040000</td>\n",
" <td>0.010000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.010000</td>\n",
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" <td>0.00</td>\n",
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" <td>0.010000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.010000</td>\n",
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" <td>0.070000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.010000</td>\n",
" <td>0.010000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.030000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
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" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.010000</td>\n",
" <td>0.0000</td>\n",
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" <td>0.000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.020000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.00</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
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" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Davisville</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.00</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.029412</td>\n",
" <td>0.00</td>\n",
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" <td>0.000000</td>\n",
" <td>0.029412</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Davisville North</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Dufferin, Dovercourt Village</td>\n",
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" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</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>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</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>0.000000</td>\n",
" <td>0.062500</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>0.000000</td>\n",
" <td>0.062500</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>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.062500</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.125000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0625</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>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>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>0.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0625</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.00</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>First Canadian Place, Underground city</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>0.030000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.030000</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>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.010000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.080000</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>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.100000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.030000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <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>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
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" <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>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
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" <td>0.010000</td>\n",
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" <td>0.010000</td>\n",
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" <td>0.040000</td>\n",
" <td>0.010000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.010000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.010000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.030000</td>\n",
" <td>0.010000</td>\n",
" <td>0.010000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.030000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
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" <td>0.020000</td>\n",
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" <td>0.00000</td>\n",
" <td>0.01</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Forest Hill North &amp; West</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
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" <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",
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" <td>0.0000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.20000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Garden District, Ryerson</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
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" <td>0.00</td>\n",
" <td>0.010000</td>\n",
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" <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>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.010000</td>\n",
" <td>0.010000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.010000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.020000</td>\n",
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" <td>0.010000</td>\n",
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" <td>0.030000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.090000</td>\n",
" <td>0.000000</td>\n",
" <td>0.080000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.030000</td>\n",
" <td>0.000</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>0.010000</td>\n",
" <td>0.010000</td>\n",
" <td>0.020000</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>0.010000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</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>0.000000</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>0.00000</td>\n",
" <td>0.00000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.010000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.030000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.010000</td>\n",
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" <td>0.010000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.010000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.030000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.010000</td>\n",
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" <td>0.000</td>\n",
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" <td>0.0000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.020000</td>\n",
" <td>0.010000</td>\n",
" <td>0.020000</td>\n",
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" <td>0.00000</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Harbourfront East, Union Station, Toronto Islands</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.05</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.010000</td>\n",
" <td>0.020000</td>\n",
" <td>0.02</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.0000</td>\n",
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" <td>0.030000</td>\n",
" <td>0.010000</td>\n",
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" <td>0.040000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.130000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.00</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.010000</td>\n",
" <td>0.010000</td>\n",
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" <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",
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" <td>0.000000</td>\n",
" <td>0.030000</td>\n",
" <td>0.00000</td>\n",
" <td>0.00000</td>\n",
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" <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>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.010000</td>\n",
" <td>0.01</td>\n",
" <td>0.010000</td>\n",
" <td>0.010000</td>\n",
" <td>0.010000</td>\n",
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" <td>0.01</td>\n",
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" <td>0.010000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
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" <td>0.020000</td>\n",
" <td>0.010000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.030000</td>\n",
" <td>0.01</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.03</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.01</td>\n",
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" <td>0.030000</td>\n",
" <td>0.0100</td>\n",
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" <td>0.000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0100</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.010000</td>\n",
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" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.01</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>High Park, The Junction South</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>0.000000</td>\n",
" <td>0.043478</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
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" <td>0.043478</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.043478</td>\n",
" <td>0.000000</td>\n",
" <td>0.043478</td>\n",
" <td>0.00</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.043478</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.086957</td>\n",
" <td>0.043478</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.00</td>\n",
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" <td>0.043478</td>\n",
" <td>0.000000</td>\n",
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" <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>0.043478</td>\n",
" <td>0.00000</td>\n",
" <td>0.00000</td>\n",
" <td>0.043478</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.043478</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>India Bazaar, The Beaches West</td>\n",
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" <td>0.000</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Kensington Market, Chinatown, Grange Park</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Lawrence Park</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Little Portugal, Trinity</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Moore Park, Summerhill East</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>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>0.000000</td>\n",
" <td>0.0000</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>0.000000</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>0.000000</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>0.000000</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>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000</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>0.000000</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>0.000000</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>0.000000</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>0.000000</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>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.00000</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>0.000000</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>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0000</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",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <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",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <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>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>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>0.0000</td>\n",
" <td>0.500000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0000</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>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.0000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.50000</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>North Toronto West</td>\n",
" <td>0.045455</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.0000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.181818</td>\n",
" <td>0.000000</td>\n",
" <td>0.090909</td>\n",
" <td>0.000000</td>\n",
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" <td>0.00</td>\n",
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" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.00000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.0000</td>\n",
" <td>0.045455</td>\n",
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" <td>0.045455</td>\n",
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" <td>0.00</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>Parkdale, Roncesvalles</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.00</td>\n",
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" <td>0.142857</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>Queen's Park, Ontario Provincial Government</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>Regent Park, Harbourfront</td>\n",
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" <td>0.00</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>Richmond, Adelaide, King</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <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",
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" <td>0.00000</td>\n",
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" <td>0.000000</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>Rosedale</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>Roselawn</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>Runnymede, Swansea</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>St. James Town</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>St. James Town, Cabbagetown</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>Stn A PO Boxes</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>Studio District</td>\n",
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" <td>0.100000</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>Summerhill West, Rathnelly, South Hill, Forest...</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>The Annex, North Midtown, Yorkville</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.038462</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.115385</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>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.076923</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.038462</td>\n",
" <td>0.000</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>0.000000</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>0.038462</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>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.038462</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>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.00000</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>0.000000</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>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.038462</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.038462</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.038462</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>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.038462</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>0.038462</td>\n",
" <td>0.000000</td>\n",
" <td>0.038462</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>0.000000</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>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.038462</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.038462</td>\n",
" <td>0.038462</td>\n",
" <td>0.0000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.038462</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.115385</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.038462</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.00</td>\n",
" <td>0.038462</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",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>The Beaches</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>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>0.000000</td>\n",
" <td>0.0000</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>0.000000</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>0.000000</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>0.000000</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>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000</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",
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" <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>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <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>0.000000</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>0.00000</td>\n",
" <td>0.00000</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>0.000000</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>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000000</td>\n",
" <td>0.250000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>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>0.0000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.250000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.25000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>The Danforth West, Riverdale</td>\n",
" <td>0.023810</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" <td>0.023810</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.023810</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0000</td>\n",
" <td>0.023810</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.023810</td>\n",
" <td>0.023810</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>0.023810</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.023810</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>0.000000</td>\n",
" <td>0.071429</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.00</td>\n",
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" <td>0.000</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",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.02381</td>\n",
" <td>0.02381</td>\n",
" <td>0.047619</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.214286</td>\n",
" <td>0.023810</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>Toronto Dominion Centre, Design Exchange</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>University of Toronto, Harbord</td>\n",
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" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Neighborhood Yoga Studio \\\n",
"0 Berczy Park 0.000000 \n",
"1 Brockton, Parkdale Village, Exhibition Place 0.000000 \n",
"2 Business reply mail Processing Centre 0.052632 \n",
"3 CN Tower, King and Spadina, Railway Lands, Har... 0.000000 \n",
"4 Central Bay Street 0.015152 \n",
"5 Christie 0.000000 \n",
"6 Church and Wellesley 0.026316 \n",
"7 Commerce Court, Victoria Hotel 0.000000 \n",
"8 Davisville 0.000000 \n",
"9 Davisville North 0.000000 \n",
"10 Dufferin, Dovercourt Village 0.000000 \n",
"11 First Canadian Place, Underground city 0.000000 \n",
"12 Forest Hill North & West 0.000000 \n",
"13 Garden District, Ryerson 0.000000 \n",
"14 Harbourfront East, Union Station, Toronto Islands 0.000000 \n",
"15 High Park, The Junction South 0.000000 \n",
"16 India Bazaar, The Beaches West 0.000000 \n",
"17 Kensington Market, Chinatown, Grange Park 0.000000 \n",
"18 Lawrence Park 0.000000 \n",
"19 Little Portugal, Trinity 0.023256 \n",
"20 Moore Park, Summerhill East 0.000000 \n",
"21 North Toronto West 0.045455 \n",
"22 Parkdale, Roncesvalles 0.000000 \n",
"23 Queen's Park, Ontario Provincial Government 0.029412 \n",
"24 Regent Park, Harbourfront 0.021277 \n",
"25 Richmond, Adelaide, King 0.000000 \n",
"26 Rosedale 0.000000 \n",
"27 Roselawn 0.000000 \n",
"28 Runnymede, Swansea 0.027027 \n",
"29 St. James Town 0.000000 \n",
"30 St. James Town, Cabbagetown 0.000000 \n",
"31 Stn A PO Boxes 0.010753 \n",
"32 Studio District 0.025000 \n",
"33 Summerhill West, Rathnelly, South Hill, Forest... 0.000000 \n",
"34 The Annex, North Midtown, Yorkville 0.000000 \n",
"35 The Beaches 0.000000 \n",
"36 The Danforth West, Riverdale 0.023810 \n",
"37 Toronto Dominion Centre, Design Exchange 0.000000 \n",
"38 University of Toronto, Harbord 0.028571 \n",
"\n",
" Accessories Store Afghan Restaurant Airport Airport Food Court \\\n",
"0 0.000000 0.000000 0.0000 0.0000 \n",
"1 0.000000 0.000000 0.0000 0.0000 \n",
"2 0.000000 0.000000 0.0000 0.0000 \n",
"3 0.000000 0.000000 0.0625 0.0625 \n",
"4 0.000000 0.000000 0.0000 0.0000 \n",
"5 0.000000 0.000000 0.0000 0.0000 \n",
"6 0.000000 0.013158 0.0000 0.0000 \n",
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"8 0.000000 0.000000 0.0000 0.0000 \n",
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"12 0.000000 0.000000 0.0000 0.0000 \n",
"13 0.000000 0.000000 0.0000 0.0000 \n",
"14 0.000000 0.000000 0.0000 0.0000 \n",
"15 0.000000 0.000000 0.0000 0.0000 \n",
"16 0.000000 0.000000 0.0000 0.0000 \n",
"17 0.000000 0.000000 0.0000 0.0000 \n",
"18 0.000000 0.000000 0.0000 0.0000 \n",
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"20 0.000000 0.000000 0.0000 0.0000 \n",
"21 0.000000 0.000000 0.0000 0.0000 \n",
"22 0.000000 0.000000 0.0000 0.0000 \n",
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"25 0.010753 0.000000 0.0000 0.0000 \n",
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"29 0.000000 0.000000 0.0000 0.0000 \n",
"30 0.000000 0.000000 0.0000 0.0000 \n",
"31 0.000000 0.000000 0.0000 0.0000 \n",
"32 0.000000 0.000000 0.0000 0.0000 \n",
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"34 0.000000 0.000000 0.0000 0.0000 \n",
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"36 0.000000 0.000000 0.0000 0.0000 \n",
"37 0.000000 0.000000 0.0000 0.0000 \n",
"38 0.000000 0.000000 0.0000 0.0000 \n",
"\n",
" Airport Gate Airport Lounge Airport Service Airport Terminal \\\n",
"0 0.0000 0.000 0.000 0.000 \n",
"1 0.0000 0.000 0.000 0.000 \n",
"2 0.0000 0.000 0.000 0.000 \n",
"3 0.0625 0.125 0.125 0.125 \n",
"4 0.0000 0.000 0.000 0.000 \n",
"5 0.0000 0.000 0.000 0.000 \n",
"6 0.0000 0.000 0.000 0.000 \n",
"7 0.0000 0.000 0.000 0.000 \n",
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"9 0.0000 0.000 0.000 0.000 \n",
"10 0.0000 0.000 0.000 0.000 \n",
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"12 0.0000 0.000 0.000 0.000 \n",
"13 0.0000 0.000 0.000 0.000 \n",
"14 0.0000 0.000 0.000 0.000 \n",
"15 0.0000 0.000 0.000 0.000 \n",
"16 0.0000 0.000 0.000 0.000 \n",
"17 0.0000 0.000 0.000 0.000 \n",
"18 0.0000 0.000 0.000 0.000 \n",
"19 0.0000 0.000 0.000 0.000 \n",
"20 0.0000 0.000 0.000 0.000 \n",
"21 0.0000 0.000 0.000 0.000 \n",
"22 0.0000 0.000 0.000 0.000 \n",
"23 0.0000 0.000 0.000 0.000 \n",
"24 0.0000 0.000 0.000 0.000 \n",
"25 0.0000 0.000 0.000 0.000 \n",
"26 0.0000 0.000 0.000 0.000 \n",
"27 0.0000 0.000 0.000 0.000 \n",
"28 0.0000 0.000 0.000 0.000 \n",
"29 0.0000 0.000 0.000 0.000 \n",
"30 0.0000 0.000 0.000 0.000 \n",
"31 0.0000 0.000 0.000 0.000 \n",
"32 0.0000 0.000 0.000 0.000 \n",
"33 0.0000 0.000 0.000 0.000 \n",
"34 0.0000 0.000 0.000 0.000 \n",
"35 0.0000 0.000 0.000 0.000 \n",
"36 0.0000 0.000 0.000 0.000 \n",
"37 0.0000 0.000 0.000 0.000 \n",
"38 0.0000 0.000 0.000 0.000 \n",
"\n",
" American Restaurant Antique Shop Aquarium Art Gallery Art Museum \\\n",
"0 0.000000 0.000000 0.00 0.018182 0.000000 \n",
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"15 0.000000 0.043478 0.00 0.000000 0.000000 \n",
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"24 0.000000 0.021277 0.00 0.021277 0.000000 \n",
"25 0.021505 0.000000 0.00 0.010753 0.010753 \n",
"26 0.000000 0.000000 0.00 0.000000 0.000000 \n",
"27 0.000000 0.000000 0.00 0.000000 0.000000 \n",
"28 0.000000 0.000000 0.00 0.000000 0.000000 \n",
"29 0.039474 0.000000 0.00 0.013158 0.000000 \n",
"30 0.019608 0.000000 0.00 0.000000 0.000000 \n",
"31 0.010753 0.010753 0.00 0.021505 0.000000 \n",
"32 0.050000 0.000000 0.00 0.000000 0.000000 \n",
"33 0.062500 0.000000 0.00 0.000000 0.000000 \n",
"34 0.038462 0.000000 0.00 0.000000 0.000000 \n",
"35 0.000000 0.000000 0.00 0.000000 0.000000 \n",
"36 0.023810 0.000000 0.00 0.000000 0.000000 \n",
"37 0.030000 0.000000 0.00 0.010000 0.000000 \n",
"38 0.000000 0.000000 0.00 0.000000 0.000000 \n",
"\n",
" Arts & Crafts Store Asian Restaurant Athletics & Sports Auto Workshop \\\n",
"0 0.000000 0.000000 0.000000 0.000000 \n",
"1 0.000000 0.000000 0.000000 0.000000 \n",
"2 0.000000 0.000000 0.000000 0.052632 \n",
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"\n",
" BBQ Joint Baby Store Bagel Shop Bakery Bank Bar \\\n",
"0 0.018182 0.000000 0.018182 0.036364 0.000000 0.000000 \n",
"1 0.000000 0.000000 0.000000 0.045455 0.000000 0.045455 \n",
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"10 0.000000 0.000000 0.000000 0.125000 0.062500 0.062500 \n",
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"\n",
" Baseball Stadium Basketball Stadium Beach Bed & Breakfast Beer Bar \\\n",
"0 0.00 0.018182 0.018182 0.000000 0.036364 \n",
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"\n",
" Beer Store Belgian Restaurant Bistro Boat or Ferry Bookstore \\\n",
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"\n",
" Boutique Brazilian Restaurant Breakfast Spot Brewery Bubble Tea Shop \\\n",
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"\n",
" Building Burger Joint Burrito Place Bus Line Butcher Café \\\n",
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"38 0.000000 0.000000 0.00 0.000000 0.000000 \n",
"\n",
" Snack Place Soup Place Spa Speakeasy Sporting Goods Shop \\\n",
"0 0.000000 0.000000 0.000000 0.000000 0.018182 \n",
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"\n",
" Sports Bar Stadium Stationery Store Steakhouse Strip Club \\\n",
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"\n",
" Supermarket Sushi Restaurant Swim School Taco Place Tailor Shop \\\n",
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"\n",
" Taiwanese Restaurant Tanning Salon Tea Room Thai Restaurant Theater \\\n",
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"37 0.000000 0.00 0.020000 0.000000 0.010000 \n",
"38 0.000000 0.00 0.000000 0.000000 0.028571 \n",
"\n",
" Theme Restaurant Toy / Game Store Trail Train Station \\\n",
"0 0.000000 0.000000 0.00000 0.00 \n",
"1 0.000000 0.000000 0.00000 0.00 \n",
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"37 0.000000 0.000000 0.00000 0.01 \n",
"38 0.000000 0.000000 0.00000 0.00 \n",
"\n",
" Vegetarian / Vegan Restaurant Video Game Store Video Store \\\n",
"0 0.018182 0.000000 0.000000 \n",
"1 0.000000 0.000000 0.000000 \n",
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"\n",
" Vietnamese Restaurant Wine Bar Wings Joint Women's Store \n",
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]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"toronto_grouped = toronto_onehot.groupby('Neighborhood').mean().reset_index()\n",
"toronto_grouped"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"----Berczy Park----\n",
" venue freq\n",
"0 Coffee Shop 0.07\n",
"1 Cocktail Bar 0.05\n",
"2 Beer Bar 0.04\n",
"3 Seafood Restaurant 0.04\n",
"4 Cheese Shop 0.04\n",
"\n",
"\n",
"----Brockton, Parkdale Village, Exhibition Place----\n",
" venue freq\n",
"0 Café 0.14\n",
"1 Breakfast Spot 0.09\n",
"2 Coffee Shop 0.09\n",
"3 Climbing Gym 0.05\n",
"4 Bar 0.05\n",
"\n",
"\n",
"----Business reply mail Processing Centre----\n",
" venue freq\n",
"0 Light Rail Station 0.11\n",
"1 Yoga Studio 0.05\n",
"2 Auto Workshop 0.05\n",
"3 Pizza Place 0.05\n",
"4 Comic Shop 0.05\n",
"\n",
"\n",
"----CN Tower, King and Spadina, Railway Lands, Harbourfront West, Bathurst Quay, South Niagara, Island airport----\n",
" venue freq\n",
"0 Airport Lounge 0.12\n",
"1 Airport Service 0.12\n",
"2 Airport Terminal 0.12\n",
"3 Boutique 0.06\n",
"4 Harbor / Marina 0.06\n",
"\n",
"\n",
"----Central Bay Street----\n",
" venue freq\n",
"0 Coffee Shop 0.17\n",
"1 Italian Restaurant 0.06\n",
"2 Café 0.05\n",
"3 Sandwich Place 0.05\n",
"4 Japanese Restaurant 0.03\n",
"\n",
"\n",
"----Christie----\n",
" venue freq\n",
"0 Grocery Store 0.24\n",
"1 Café 0.18\n",
"2 Park 0.12\n",
"3 Nightclub 0.06\n",
"4 Baby Store 0.06\n",
"\n",
"\n",
"----Church and Wellesley----\n",
" venue freq\n",
"0 Sushi Restaurant 0.07\n",
"1 Coffee Shop 0.07\n",
"2 Japanese Restaurant 0.07\n",
"3 Gay Bar 0.04\n",
"4 Restaurant 0.04\n",
"\n",
"\n",
"----Commerce Court, Victoria Hotel----\n",
" venue freq\n",
"0 Coffee Shop 0.11\n",
"1 Café 0.07\n",
"2 Restaurant 0.07\n",
"3 Hotel 0.05\n",
"4 Gym 0.04\n",
"\n",
"\n",
"----Davisville----\n",
" venue freq\n",
"0 Sandwich Place 0.09\n",
"1 Dessert Shop 0.09\n",
"2 Sushi Restaurant 0.06\n",
"3 Café 0.06\n",
"4 Pizza Place 0.06\n",
"\n",
"\n",
"----Davisville North----\n",
" venue freq\n",
"0 Food & Drink Shop 0.11\n",
"1 Park 0.11\n",
"2 Convenience Store 0.11\n",
"3 Gym 0.11\n",
"4 Sandwich Place 0.11\n",
"\n",
"\n",
"----Dufferin, Dovercourt Village----\n",
" venue freq\n",
"0 Pharmacy 0.12\n",
"1 Bakery 0.12\n",
"2 Bar 0.06\n",
"3 Brewery 0.06\n",
"4 Music Venue 0.06\n",
"\n",
"\n",
"----First Canadian Place, Underground city----\n",
" venue freq\n",
"0 Coffee Shop 0.10\n",
"1 Café 0.08\n",
"2 Japanese Restaurant 0.04\n",
"3 Gym 0.04\n",
"4 Restaurant 0.04\n",
"\n",
"\n",
"----Forest Hill North & West----\n",
" venue freq\n",
"0 Trail 0.2\n",
"1 Park 0.2\n",
"2 Sushi Restaurant 0.2\n",
"3 Bus Line 0.2\n",
"4 Jewelry Store 0.2\n",
"\n",
"\n",
"----Garden District, Ryerson----\n",
" venue freq\n",
"0 Clothing Store 0.09\n",
"1 Coffee Shop 0.08\n",
"2 Bubble Tea Shop 0.03\n",
"3 Café 0.03\n",
"4 Italian Restaurant 0.03\n",
"\n",
"\n",
"----Harbourfront East, Union Station, Toronto Islands----\n",
" venue freq\n",
"0 Coffee Shop 0.13\n",
"1 Aquarium 0.05\n",
"2 Hotel 0.04\n",
"3 Café 0.04\n",
"4 Brewery 0.03\n",
"\n",
"\n",
"----High Park, The Junction South----\n",
" venue freq\n",
"0 Mexican Restaurant 0.09\n",
"1 Café 0.09\n",
"2 Thai Restaurant 0.09\n",
"3 Flea Market 0.04\n",
"4 Fried Chicken Joint 0.04\n",
"\n",
"\n",
"----India Bazaar, The Beaches West----\n",
" venue freq\n",
"0 Fast Food Restaurant 0.09\n",
"1 Park 0.09\n",
"2 Sandwich Place 0.09\n",
"3 Brewery 0.04\n",
"4 Coffee Shop 0.04\n",
"\n",
"\n",
"----Kensington Market, Chinatown, Grange Park----\n",
" venue freq\n",
"0 Café 0.09\n",
"1 Mexican Restaurant 0.05\n",
"2 Bakery 0.05\n",
"3 Vietnamese Restaurant 0.05\n",
"4 Coffee Shop 0.05\n",
"\n",
"\n",
"----Lawrence Park----\n",
" venue freq\n",
"0 Park 0.33\n",
"1 Bus Line 0.33\n",
"2 Swim School 0.33\n",
"3 Yoga Studio 0.00\n",
"4 Moroccan Restaurant 0.00\n",
"\n",
"\n",
"----Little Portugal, Trinity----\n",
" venue freq\n",
"0 Bar 0.09\n",
"1 Restaurant 0.07\n",
"2 Asian Restaurant 0.07\n",
"3 Café 0.05\n",
"4 Vegetarian / Vegan Restaurant 0.05\n",
"\n",
"\n",
"----Moore Park, Summerhill East----\n",
" venue freq\n",
"0 Playground 0.5\n",
"1 Trail 0.5\n",
"2 Yoga Studio 0.0\n",
"3 Movie Theater 0.0\n",
"4 Market 0.0\n",
"\n",
"\n",
"----North Toronto West----\n",
" venue freq\n",
"0 Clothing Store 0.18\n",
"1 Coffee Shop 0.09\n",
"2 Yoga Studio 0.05\n",
"3 Mexican Restaurant 0.05\n",
"4 Dessert Shop 0.05\n",
"\n",
"\n",
"----Parkdale, Roncesvalles----\n",
" venue freq\n",
"0 Gift Shop 0.14\n",
"1 Breakfast Spot 0.14\n",
"2 Restaurant 0.07\n",
"3 Movie Theater 0.07\n",
"4 Bar 0.07\n",
"\n",
"\n",
"----Queen's Park, Ontario Provincial Government----\n",
" venue freq\n",
"0 Coffee Shop 0.21\n",
"1 Sushi Restaurant 0.06\n",
"2 Yoga Studio 0.03\n",
"3 Café 0.03\n",
"4 Beer Bar 0.03\n",
"\n",
"\n",
"----Regent Park, Harbourfront----\n",
" venue freq\n",
"0 Coffee Shop 0.17\n",
"1 Pub 0.06\n",
"2 Bakery 0.06\n",
"3 Park 0.06\n",
"4 Breakfast Spot 0.04\n",
"\n",
"\n",
"----Richmond, Adelaide, King----\n",
" venue freq\n",
"0 Coffee Shop 0.10\n",
"1 Café 0.05\n",
"2 Restaurant 0.04\n",
"3 Hotel 0.03\n",
"4 Deli / Bodega 0.03\n",
"\n",
"\n",
"----Rosedale----\n",
" venue freq\n",
"0 Park 0.50\n",
"1 Playground 0.25\n",
"2 Trail 0.25\n",
"3 Yoga Studio 0.00\n",
"4 Mexican Restaurant 0.00\n",
"\n",
"\n",
"----Roselawn----\n",
" venue freq\n",
"0 Health & Beauty Service 0.33\n",
"1 Home Service 0.33\n",
"2 Garden 0.33\n",
"3 Park 0.00\n",
"4 Outdoor Sculpture 0.00\n",
"\n",
"\n",
"----Runnymede, Swansea----\n",
" venue freq\n",
"0 Café 0.08\n",
"1 Pizza Place 0.08\n",
"2 Sushi Restaurant 0.05\n",
"3 Italian Restaurant 0.05\n",
"4 Pub 0.05\n",
"\n",
"\n",
"----St. James Town----\n",
" venue freq\n",
"0 Coffee Shop 0.07\n",
"1 Café 0.07\n",
"2 Cocktail Bar 0.05\n",
"3 Gastropub 0.04\n",
"4 American Restaurant 0.04\n",
"\n",
"\n",
"----St. James Town, Cabbagetown----\n",
" venue freq\n",
"0 Pizza Place 0.06\n",
"1 Park 0.06\n",
"2 Restaurant 0.06\n",
"3 Coffee Shop 0.06\n",
"4 Bakery 0.04\n",
"\n",
"\n",
"----Stn A PO Boxes----\n",
" venue freq\n",
"0 Coffee Shop 0.10\n",
"1 Café 0.04\n",
"2 Italian Restaurant 0.03\n",
"3 Japanese Restaurant 0.03\n",
"4 Seafood Restaurant 0.03\n",
"\n",
"\n",
"----Studio District----\n",
" venue freq\n",
"0 Café 0.10\n",
"1 Coffee Shop 0.08\n",
"2 Brewery 0.05\n",
"3 Bakery 0.05\n",
"4 American Restaurant 0.05\n",
"\n",
"\n",
"----Summerhill West, Rathnelly, South Hill, Forest Hill SE, Deer Park----\n",
" venue freq\n",
"0 Coffee Shop 0.12\n",
"1 Pub 0.12\n",
"2 Light Rail Station 0.06\n",
"3 Liquor Store 0.06\n",
"4 Sports Bar 0.06\n",
"\n",
"\n",
"----The Annex, North Midtown, Yorkville----\n",
" venue freq\n",
"0 Sandwich Place 0.12\n",
"1 Café 0.12\n",
"2 Coffee Shop 0.08\n",
"3 Flower Shop 0.04\n",
"4 BBQ Joint 0.04\n",
"\n",
"\n",
"----The Beaches----\n",
" venue freq\n",
"0 Trail 0.25\n",
"1 Health Food Store 0.25\n",
"2 Pub 0.25\n",
"3 Yoga Studio 0.00\n",
"4 Moroccan Restaurant 0.00\n",
"\n",
"\n",
"----The Danforth West, Riverdale----\n",
" venue freq\n",
"0 Greek Restaurant 0.21\n",
"1 Italian Restaurant 0.07\n",
"2 Coffee Shop 0.07\n",
"3 Restaurant 0.05\n",
"4 Ice Cream Shop 0.05\n",
"\n",
"\n",
"----Toronto Dominion Centre, Design Exchange----\n",
" venue freq\n",
"0 Coffee Shop 0.10\n",
"1 Café 0.06\n",
"2 Hotel 0.06\n",
"3 Restaurant 0.05\n",
"4 American Restaurant 0.03\n",
"\n",
"\n",
"----University of Toronto, Harbord----\n",
" venue freq\n",
"0 Café 0.14\n",
"1 Restaurant 0.06\n",
"2 Italian Restaurant 0.06\n",
"3 Japanese Restaurant 0.06\n",
"4 Bar 0.06\n",
"\n",
"\n"
]
}
],
"source": [
"num_top_venues = 5\n",
"\n",
"for hood in toronto_grouped['Neighborhood']:\n",
" print(\"----\"+hood+\"----\")\n",
" temp = toronto_grouped[toronto_grouped['Neighborhood'] == hood].T.reset_index()\n",
" temp.columns = ['venue','freq']\n",
" temp = temp.iloc[1:]\n",
" temp['freq'] = temp['freq'].astype(float)\n",
" temp = temp.round({'freq': 2})\n",
" print(temp.sort_values('freq', ascending=False).reset_index(drop=True).head(num_top_venues))\n",
" print('\\n')"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"#method to sort venues\n",
"def return_most_common_venues(row, num_top_venues):\n",
" row_categories = row.iloc[1:]\n",
" row_categories_sorted = row_categories.sort_values(ascending=False)\n",
" \n",
" return row_categories_sorted.index.values[0:num_top_venues]"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Neighborhood</th>\n",
" <th>1st Most Common Venue</th>\n",
" <th>2nd Most Common Venue</th>\n",
" <th>3rd Most Common Venue</th>\n",
" <th>4th Most Common Venue</th>\n",
" <th>5th Most Common Venue</th>\n",
" <th>6th Most Common Venue</th>\n",
" <th>7th Most Common Venue</th>\n",
" <th>8th Most Common Venue</th>\n",
" <th>9th Most Common Venue</th>\n",
" <th>10th Most Common Venue</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Berczy Park</td>\n",
" <td>Coffee Shop</td>\n",
" <td>Cocktail Bar</td>\n",
" <td>Bakery</td>\n",
" <td>Beer Bar</td>\n",
" <td>Seafood Restaurant</td>\n",
" <td>Cheese Shop</td>\n",
" <td>Café</td>\n",
" <td>Restaurant</td>\n",
" <td>Clothing Store</td>\n",
" <td>Eastern European Restaurant</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Brockton, Parkdale Village, Exhibition Place</td>\n",
" <td>Café</td>\n",
" <td>Breakfast Spot</td>\n",
" <td>Coffee Shop</td>\n",
" <td>Performing Arts Venue</td>\n",
" <td>Stadium</td>\n",
" <td>Bar</td>\n",
" <td>Intersection</td>\n",
" <td>Italian Restaurant</td>\n",
" <td>Restaurant</td>\n",
" <td>Climbing Gym</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Business reply mail Processing Centre</td>\n",
" <td>Light Rail Station</td>\n",
" <td>Yoga Studio</td>\n",
" <td>Pizza Place</td>\n",
" <td>Butcher</td>\n",
" <td>Restaurant</td>\n",
" <td>Burrito Place</td>\n",
" <td>Auto Workshop</td>\n",
" <td>Fast Food Restaurant</td>\n",
" <td>Spa</td>\n",
" <td>Farmers Market</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 Terminal</td>\n",
" <td>Airport Lounge</td>\n",
" <td>Boat or Ferry</td>\n",
" <td>Boutique</td>\n",
" <td>Bar</td>\n",
" <td>Plane</td>\n",
" <td>Coffee Shop</td>\n",
" <td>Sculpture Garden</td>\n",
" <td>Airport Gate</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>Café</td>\n",
" <td>Middle Eastern Restaurant</td>\n",
" <td>Salad Place</td>\n",
" <td>Restaurant</td>\n",
" <td>Bar</td>\n",
" <td>Bubble Tea Shop</td>\n",
" <td>Burger Joint</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Neighborhood 1st Most Common Venue \\\n",
"0 Berczy Park Coffee Shop \n",
"1 Brockton, Parkdale Village, Exhibition Place Café \n",
"2 Business reply mail Processing Centre Light Rail Station \n",
"3 CN Tower, King and Spadina, Railway Lands, Har... Airport Service \n",
"4 Central Bay Street Coffee Shop \n",
"\n",
" 2nd Most Common Venue 3rd Most Common Venue 4th Most Common Venue \\\n",
"0 Cocktail Bar Bakery Beer Bar \n",
"1 Breakfast Spot Coffee Shop Performing Arts Venue \n",
"2 Yoga Studio Pizza Place Butcher \n",
"3 Airport Terminal Airport Lounge Boat or Ferry \n",
"4 Italian Restaurant Sandwich Place Café \n",
"\n",
" 5th Most Common Venue 6th Most Common Venue 7th Most Common Venue \\\n",
"0 Seafood Restaurant Cheese Shop Café \n",
"1 Stadium Bar Intersection \n",
"2 Restaurant Burrito Place Auto Workshop \n",
"3 Boutique Bar Plane \n",
"4 Middle Eastern Restaurant Salad Place Restaurant \n",
"\n",
" 8th Most Common Venue 9th Most Common Venue 10th Most Common Venue \n",
"0 Restaurant Clothing Store Eastern European Restaurant \n",
"1 Italian Restaurant Restaurant Climbing Gym \n",
"2 Fast Food Restaurant Spa Farmers Market \n",
"3 Coffee Shop Sculpture Garden Airport Gate \n",
"4 Bar Bubble Tea Shop Burger Joint "
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"num_top_venues = 10\n",
"\n",
"indicators = ['st', 'nd', 'rd']\n",
"\n",
"# create columns according to number of top venues\n",
"columns = ['Neighborhood']\n",
"for ind in np.arange(num_top_venues):\n",
" try:\n",
" columns.append('{}{} Most Common Venue'.format(ind+1, indicators[ind]))\n",
" except:\n",
" columns.append('{}th Most Common Venue'.format(ind+1))\n",
"\n",
"# create a new dataframe\n",
"neighborhoods_venues_sorted = pd.DataFrame(columns=columns)\n",
"neighborhoods_venues_sorted['Neighborhood'] = toronto_grouped['Neighborhood']\n",
"\n",
"for 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",
"\n",
"neighborhoods_venues_sorted.head()"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" vertical-align: middle;\n",
" }\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></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",
" <tr>\n",
" <th>Postal Code</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></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>M5A</th>\n",
" <td>Downtown Toronto</td>\n",
" <td>Regent Park, Harbourfront</td>\n",
" <td>43.654260</td>\n",
" <td>-79.360636</td>\n",
" <td>5</td>\n",
" <td>Coffee Shop</td>\n",
" <td>Pub</td>\n",
" <td>Park</td>\n",
" <td>Bakery</td>\n",
" <td>Breakfast Spot</td>\n",
" <td>Theater</td>\n",
" <td>Café</td>\n",
" <td>French Restaurant</td>\n",
" <td>Health Food Store</td>\n",
" <td>Historic Site</td>\n",
" </tr>\n",
" <tr>\n",
" <th>M7A</th>\n",
" <td>Downtown Toronto</td>\n",
" <td>Queen's Park, Ontario Provincial Government</td>\n",
" <td>43.662301</td>\n",
" <td>-79.389494</td>\n",
" <td>5</td>\n",
" <td>Coffee Shop</td>\n",
" <td>Sushi Restaurant</td>\n",
" <td>Yoga Studio</td>\n",
" <td>College Cafeteria</td>\n",
" <td>Smoothie Shop</td>\n",
" <td>Beer Bar</td>\n",
" <td>Sandwich Place</td>\n",
" <td>Burger Joint</td>\n",
" <td>Burrito Place</td>\n",
" <td>Café</td>\n",
" </tr>\n",
" <tr>\n",
" <th>M5B</th>\n",
" <td>Downtown Toronto</td>\n",
" <td>Garden District, Ryerson</td>\n",
" <td>43.657162</td>\n",
" <td>-79.378937</td>\n",
" <td>0</td>\n",
" <td>Clothing Store</td>\n",
" <td>Coffee Shop</td>\n",
" <td>Restaurant</td>\n",
" <td>Middle Eastern Restaurant</td>\n",
" <td>Japanese Restaurant</td>\n",
" <td>Italian Restaurant</td>\n",
" <td>Cosmetics Shop</td>\n",
" <td>Café</td>\n",
" <td>Bubble Tea Shop</td>\n",
" <td>Fast Food Restaurant</td>\n",
" </tr>\n",
" <tr>\n",
" <th>M5C</th>\n",
" <td>Downtown Toronto</td>\n",
" <td>St. James Town</td>\n",
" <td>43.651494</td>\n",
" <td>-79.375418</td>\n",
" <td>0</td>\n",
" <td>Coffee Shop</td>\n",
" <td>Café</td>\n",
" <td>Cocktail Bar</td>\n",
" <td>American Restaurant</td>\n",
" <td>Gastropub</td>\n",
" <td>Cosmetics Shop</td>\n",
" <td>Moroccan Restaurant</td>\n",
" <td>Department Store</td>\n",
" <td>Lingerie Store</td>\n",
" <td>Gym</td>\n",
" </tr>\n",
" <tr>\n",
" <th>M4E</th>\n",
" <td>East Toronto</td>\n",
" <td>The Beaches</td>\n",
" <td>43.676357</td>\n",
" <td>-79.293031</td>\n",
" <td>6</td>\n",
" <td>Trail</td>\n",
" <td>Pub</td>\n",
" <td>Health Food Store</td>\n",
" <td>Doner Restaurant</td>\n",
" <td>Dessert Shop</td>\n",
" <td>Diner</td>\n",
" <td>Discount Store</td>\n",
" <td>Distribution Center</td>\n",
" <td>Dog Run</td>\n",
" <td>Women's Store</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Borough Neighborhood \\\n",
"Postal Code \n",
"M5A Downtown Toronto Regent Park, Harbourfront \n",
"M7A Downtown Toronto Queen's Park, Ontario Provincial Government \n",
"M5B Downtown Toronto Garden District, Ryerson \n",
"M5C Downtown Toronto St. James Town \n",
"M4E East Toronto The Beaches \n",
"\n",
" Latitude Longitude Cluster Labels 1st Most Common Venue \\\n",
"Postal Code \n",
"M5A 43.654260 -79.360636 5 Coffee Shop \n",
"M7A 43.662301 -79.389494 5 Coffee Shop \n",
"M5B 43.657162 -79.378937 0 Clothing Store \n",
"M5C 43.651494 -79.375418 0 Coffee Shop \n",
"M4E 43.676357 -79.293031 6 Trail \n",
"\n",
" 2nd Most Common Venue 3rd Most Common Venue \\\n",
"Postal Code \n",
"M5A Pub Park \n",
"M7A Sushi Restaurant Yoga Studio \n",
"M5B Coffee Shop Restaurant \n",
"M5C Café Cocktail Bar \n",
"M4E Pub Health Food Store \n",
"\n",
" 4th Most Common Venue 5th Most Common Venue \\\n",
"Postal Code \n",
"M5A Bakery Breakfast Spot \n",
"M7A College Cafeteria Smoothie Shop \n",
"M5B Middle Eastern Restaurant Japanese Restaurant \n",
"M5C American Restaurant Gastropub \n",
"M4E Doner Restaurant Dessert Shop \n",
"\n",
" 6th Most Common Venue 7th Most Common Venue 8th Most Common Venue \\\n",
"Postal Code \n",
"M5A Theater Café French Restaurant \n",
"M7A Beer Bar Sandwich Place Burger Joint \n",
"M5B Italian Restaurant Cosmetics Shop Café \n",
"M5C Cosmetics Shop Moroccan Restaurant Department Store \n",
"M4E Diner Discount Store Distribution Center \n",
"\n",
" 9th Most Common Venue 10th Most Common Venue \n",
"Postal Code \n",
"M5A Health Food Store Historic Site \n",
"M7A Burrito Place Café \n",
"M5B Bubble Tea Shop Fast Food Restaurant \n",
"M5C Lingerie Store Gym \n",
"M4E Dog Run Women's Store "
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# set number of clusters\n",
"kclusters = int(len(toronto_df[\"Neighborhood\"].unique()) / 4)\n",
"toronto_grouped_clustering = toronto_grouped.drop('Neighborhood', 1)\n",
"\n",
"# run k-means clustering\n",
"kmeans = KMeans(n_clusters=kclusters, random_state=1).fit(toronto_grouped_clustering)\n",
"\n",
"# add clustering labels\n",
"neighborhoods_venues_sorted.insert(0, 'Cluster Labels', kmeans.labels_)\n",
"\n",
"toronto_merged = toronto_df.copy()\n",
"toronto_merged.rename(columns={'Neighborhood':'Neighborhood'}, inplace=True)\n",
"# merge toronto_grouped with toronto_data to add latitude/longitude for each neighborhood\n",
"toronto_merged = toronto_merged.join(neighborhoods_venues_sorted.set_index('Neighborhood'), on='Neighborhood')\n",
"\n",
"toronto_merged.head()"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
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                popup_1612e9efd68348d9a5e3a92c718355a4.setContent(html_4b62b4552b354f42ae988ca8fefee01e);
            

            circle_marker_e03e20b9d4b04f50ad1dd968afab374a.bindPopup(popup_1612e9efd68348d9a5e3a92c718355a4);

            
        
    
            var circle_marker_69ccdcf610e14cd2a1563aa6bd45f511 = L.circleMarker(
                [43.667967,-79.3676753],
                {
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  "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_5b353eb23e5d4cb293c1acfc48875342);
            
    
            var popup_d91e9e687a474c4493ee1dff0205055c = L.popup({maxWidth: '300'});

            
                var html_f050a1c2fa594360aee91ecce9145171 = $('<div id="html_f050a1c2fa594360aee91ecce9145171" style="width: 100.0%; height: 100.0%;">St. James Town, Cabbagetown Cluster 5</div>')[0];
                popup_d91e9e687a474c4493ee1dff0205055c.setContent(html_f050a1c2fa594360aee91ecce9145171);
            

            circle_marker_69ccdcf610e14cd2a1563aa6bd45f511.bindPopup(popup_d91e9e687a474c4493ee1dff0205055c);

            
        
    
            var circle_marker_badf2cb641364be596f10492c47e0b0d = L.circleMarker(
                [43.6484292,-79.3822802],
                {
  "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_5b353eb23e5d4cb293c1acfc48875342);
            
    
            var popup_684f5d2890f24d888abfdfc71806f3e5 = L.popup({maxWidth: '300'});

            
                var html_af9c164249024a3eab057eafc18364ee = $('<div id="html_af9c164249024a3eab057eafc18364ee" style="width: 100.0%; height: 100.0%;">First Canadian Place, Underground city Cluster 5</div>')[0];
                popup_684f5d2890f24d888abfdfc71806f3e5.setContent(html_af9c164249024a3eab057eafc18364ee);
            

            circle_marker_badf2cb641364be596f10492c47e0b0d.bindPopup(popup_684f5d2890f24d888abfdfc71806f3e5);

            
        
    
            var circle_marker_48131208efbf4814bfc952a56f33687f = L.circleMarker(
                [43.6658599,-79.38315990000001],
                {
  "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_5b353eb23e5d4cb293c1acfc48875342);
            
    
            var popup_a6b46c9bbdce4cf1827d9cfbe7c0f422 = L.popup({maxWidth: '300'});

            
                var html_8e9921cb416f4373b1979425fc8ca693 = $('<div id="html_8e9921cb416f4373b1979425fc8ca693" style="width: 100.0%; height: 100.0%;">Church and Wellesley Cluster 5</div>')[0];
                popup_a6b46c9bbdce4cf1827d9cfbe7c0f422.setContent(html_8e9921cb416f4373b1979425fc8ca693);
            

            circle_marker_48131208efbf4814bfc952a56f33687f.bindPopup(popup_a6b46c9bbdce4cf1827d9cfbe7c0f422);

            
        
    
            var circle_marker_cd87e07b350d4b76a90020d867e52039 = 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_5b353eb23e5d4cb293c1acfc48875342);
            
    
            var popup_0cfdbf335e6b4fe88e5e7d020333d9c7 = L.popup({maxWidth: '300'});

            
                var html_542d3ec3fc2e4d8294731891bc9d3ac4 = $('<div id="html_542d3ec3fc2e4d8294731891bc9d3ac4" style="width: 100.0%; height: 100.0%;">Business reply mail Processing Centre Cluster 0</div>')[0];
                popup_0cfdbf335e6b4fe88e5e7d020333d9c7.setContent(html_542d3ec3fc2e4d8294731891bc9d3ac4);
            

            circle_marker_cd87e07b350d4b76a90020d867e52039.bindPopup(popup_0cfdbf335e6b4fe88e5e7d020333d9c7);

            
        
</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
],
"text/plain": [
"<folium.folium.Map at 0x7f5d2c6f73c8>"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"map_clusters = folium.Map(location=[toronto_df[\"Latitude\"][0], toronto_df[\"Longitude\"][0]], zoom_start=11)\n",
"\n",
"# set color scheme for the clusters\n",
"x = np.arange(kclusters)\n",
"ys = [i + x + (i*x)**2 for i in range(kclusters)]\n",
"colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))\n",
"rainbow = [colors.rgb2hex(i) for i in colors_array]\n",
"\n",
"# add markers to the map\n",
"markers_colors = []\n",
"for lat, lon, poi, cluster in zip(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",
" \n",
"map_clusters"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python",
"language": "python",
"name": "conda-env-python-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.10"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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