Proj9-Segmenting-and-Clustering-Neighborhoods-in-Toronto.ipynb

{
    "nbformat_minor": 4, 
    "cells": [
        {
            "source": "# Segmenting and Clustering Neighborhoods in Toronto", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 1, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "Requirement already satisfied: beautifulsoup4 in /opt/conda/envs/Python36/lib/python3.6/site-packages (4.7.1)\nRequirement already satisfied: soupsieve>=1.2 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from beautifulsoup4) (1.7.1)\nRequirement already satisfied: geopy in /opt/conda/envs/Python36/lib/python3.6/site-packages (1.20.0)\nRequirement already satisfied: geographiclib<2,>=1.49 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from geopy) (1.49)\nRequirement already satisfied: folium in /opt/conda/envs/Python36/lib/python3.6/site-packages (0.10.0)\nRequirement already satisfied: branca>=0.3.0 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from folium) (0.3.1)\nRequirement already satisfied: requests in /opt/conda/envs/Python36/lib/python3.6/site-packages (from folium) (2.21.0)\nRequirement already satisfied: jinja2>=2.9 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from folium) (2.10)\nRequirement already satisfied: numpy in /opt/conda/envs/Python36/lib/python3.6/site-packages (from folium) (1.15.4)\nRequirement already satisfied: six in /opt/conda/envs/Python36/lib/python3.6/site-packages (from branca>=0.3.0->folium) (1.12.0)\nRequirement already satisfied: idna<2.9,>=2.5 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from requests->folium) (2.8)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from requests->folium) (2019.6.16)\nRequirement already satisfied: urllib3<1.25,>=1.21.1 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from requests->folium) (1.24.1)\nRequirement already satisfied: chardet<3.1.0,>=3.0.2 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from requests->folium) (3.0.4)\nRequirement already satisfied: MarkupSafe>=0.23 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from jinja2>=2.9->folium) (1.1.0)\nRequirement already satisfied: geocoder in /opt/conda/envs/Python36/lib/python3.6/site-packages (1.38.1)\nRequirement already satisfied: six in /opt/conda/envs/Python36/lib/python3.6/site-packages (from geocoder) (1.12.0)\nRequirement already satisfied: requests in /opt/conda/envs/Python36/lib/python3.6/site-packages (from geocoder) (2.21.0)\nRequirement already satisfied: click in /opt/conda/envs/Python36/lib/python3.6/site-packages (from geocoder) (7.0)\nRequirement already satisfied: ratelim in /opt/conda/envs/Python36/lib/python3.6/site-packages (from geocoder) (0.1.6)\nRequirement already satisfied: future in /opt/conda/envs/Python36/lib/python3.6/site-packages (from geocoder) (0.17.1)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from requests->geocoder) (2019.6.16)\nRequirement already satisfied: idna<2.9,>=2.5 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from requests->geocoder) (2.8)\nRequirement already satisfied: urllib3<1.25,>=1.21.1 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from requests->geocoder) (1.24.1)\nRequirement already satisfied: chardet<3.1.0,>=3.0.2 in /opt/conda/envs/Python36/lib/python3.6/site-packages (from requests->geocoder) (3.0.4)\nRequirement already satisfied: decorator in /opt/conda/envs/Python36/lib/python3.6/site-packages (from ratelim->geocoder) (4.3.2)\n"
                }
            ], 
            "source": "!pip install beautifulsoup4\nfrom bs4 import BeautifulSoup # library for scraping from a website\n\nimport numpy as np # library to handle data in a vectorized manner\n\nimport pandas as pd # library for data analsysis\npd.set_option('display.max_columns', None)\npd.set_option('display.max_rows', None)\n\nimport json # library to handle JSON files\n\n!pip install geopy\nfrom geopy.geocoders import Nominatim # convert an address into latitude and longitude values\n\nimport requests # library to handle requests\nfrom pandas.io.json import json_normalize # tranform JSON file into a pandas dataframe\n\n# Matplotlib and associated plotting modules\nimport matplotlib.cm as cm\nimport matplotlib.colors as colors\n\n# import k-means from clustering stage\nfrom sklearn.cluster import KMeans\n\n!pip install folium\nimport folium # map rendering library\n\n!pip install geocoder\nimport geocoder # to get longitude and latitude"
        }, 
        {
            "source": "## 1. Table of neighborhoods in Toronto", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "To create a table of neighborhoods in Toronto, the postcode table on the Wikipedia page was scraped using Beautiful Soup package.", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 2, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 2, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Postcode</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>Not assigned</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>M2A</td>\n      <td>Not assigned</td>\n      <td>Not assigned</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>Harbourfront</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>M5A</td>\n      <td>Downtown Toronto</td>\n      <td>Regent Park</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>M6A</td>\n      <td>North York</td>\n      <td>Lawrence Heights</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>M6A</td>\n      <td>North York</td>\n      <td>Lawrence Manor</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>M7A</td>\n      <td>Queen's Park</td>\n      <td>Not assigned</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>M8A</td>\n      <td>Not assigned</td>\n      <td>Not assigned</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "  Postcode           Borough      Neighborhood\n0      M1A      Not assigned      Not assigned\n1      M2A      Not assigned      Not assigned\n2      M3A        North York         Parkwoods\n3      M4A        North York  Victoria Village\n4      M5A  Downtown Toronto      Harbourfront\n5      M5A  Downtown Toronto       Regent Park\n6      M6A        North York  Lawrence Heights\n7      M6A        North York    Lawrence Manor\n8      M7A      Queen's Park      Not assigned\n9      M8A      Not assigned      Not assigned"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "# Get the webpage\nurl = 'https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M'\npage = requests.get(url).text\n\n# Extract only the table\nsoup = BeautifulSoup(page, 'html.parser')\ntable = soup.find('table', class_='sortable')\n\n# Get the values from the Wikipedia table and store into a dataframe\nrow = [] # initialize row list\n\nfor tr in table.find_all('tr'):                                           # for every row in the original table\n    if tr.find_all('th') == []:                                           # unless it's a header\n        row.append([td.get_text(strip=True) for td in tr.find_all('td')]) # every item w/i 'td' tag appended to row list\n\n# Assign columns names and turn row into a dataframe of neighborhoods\ncolumn_names = ['Postcode', 'Borough', 'Neighborhood']\n\nneighborhoods_raw = pd.DataFrame(row, columns=column_names) # create a raw table of neighborhoods\nneighborhoods_raw.head(10)"
        }, 
        {
            "source": "Next, the table was cleaned.\n1. The cells with a borough that is 'Not assigned' were ignored.\n2. Neighborhoods that are 'Not assigned' were given the same name as their borough.\n3. If two or more neighborhoods share the same postcode, they were put in the same cell separated by commas.", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 3, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "# Rows containing 'Not assigned' boroughs are dropped.\ndrop_index = neighborhoods_raw[neighborhoods_raw['Borough'] == 'Not assigned'].index # get indexes of rows containing 'Not assigned' borough\nneighborhoods = neighborhoods_raw.drop(drop_index, axis=0)                           # rows dropped to create neighborhoods table\nneighborhoods.reset_index(drop=True, inplace=True)                                   # resets the index after dropping the rows"
        }, 
        {
            "execution_count": 4, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "# Assign borough names to neighborhoods when neighborhood is 'Not assigned'\nnh_na = neighborhoods[neighborhoods['Neighborhood'] == 'Not assigned'].index # index of those rows\nneighborhoods.iloc[nh_na, 2] = neighborhoods['Borough'][nh_na]               "
        }, 
        {
            "execution_count": 5, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 5, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Postcode</th>\n      <th>Borough</th>\n      <th>Neighborhood</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>M1B</td>\n      <td>Scarborough</td>\n      <td>Rouge, Malvern</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>M1C</td>\n      <td>Scarborough</td>\n      <td>Highland Creek, Rouge Hill, Port Union</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>M1E</td>\n      <td>Scarborough</td>\n      <td>Guildwood, Morningside, West Hill</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>M1G</td>\n      <td>Scarborough</td>\n      <td>Woburn</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>M1H</td>\n      <td>Scarborough</td>\n      <td>Cedarbrae</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>M1J</td>\n      <td>Scarborough</td>\n      <td>Scarborough Village</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>M1K</td>\n      <td>Scarborough</td>\n      <td>East Birchmount Park, Ionview, Kennedy Park</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>M1L</td>\n      <td>Scarborough</td>\n      <td>Clairlea, Golden Mile, Oakridge</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>M1M</td>\n      <td>Scarborough</td>\n      <td>Cliffcrest, Cliffside, Scarborough Village West</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>M1N</td>\n      <td>Scarborough</td>\n      <td>Birch Cliff, Cliffside West</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>M1P</td>\n      <td>Scarborough</td>\n      <td>Dorset Park, Scarborough Town Centre, Wexford ...</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>M1R</td>\n      <td>Scarborough</td>\n      <td>Maryvale, Wexford</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>M1S</td>\n      <td>Scarborough</td>\n      <td>Agincourt</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>M1T</td>\n      <td>Scarborough</td>\n      <td>Clarks Corners, Sullivan, Tam O'Shanter</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>M1V</td>\n      <td>Scarborough</td>\n      <td>Agincourt North, L'Amoreaux East, Milliken, St...</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>M1W</td>\n      <td>Scarborough</td>\n      <td>L'Amoreaux West</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>M1X</td>\n      <td>Scarborough</td>\n      <td>Upper Rouge</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>M2H</td>\n      <td>North York</td>\n      <td>Hillcrest Village</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>M2J</td>\n      <td>North York</td>\n      <td>Fairview, Henry Farm, Oriole</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>M2K</td>\n      <td>North York</td>\n      <td>Bayview Village</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>M2L</td>\n      <td>North York</td>\n      <td>Silver Hills, York Mills</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>M2M</td>\n      <td>North York</td>\n      <td>Newtonbrook, Willowdale</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>M2N</td>\n      <td>North York</td>\n      <td>Willowdale South</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>M2P</td>\n      <td>North York</td>\n      <td>York Mills West</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>M2R</td>\n      <td>North York</td>\n      <td>Willowdale West</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>M3A</td>\n      <td>North York</td>\n      <td>Parkwoods</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>M3B</td>\n      <td>North York</td>\n      <td>Don Mills North</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>M3C</td>\n      <td>North York</td>\n      <td>Flemingdon Park, Don Mills South</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>M3H</td>\n      <td>North York</td>\n      <td>Bathurst Manor, Downsview North, Wilson Heights</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>M3J</td>\n      <td>North York</td>\n      <td>Northwood Park, York University</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>M3K</td>\n      <td>North York</td>\n      <td>CFB Toronto, Downsview East</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>M3L</td>\n      <td>North York</td>\n      <td>Downsview West</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>M3M</td>\n      <td>North York</td>\n      <td>Downsview Central</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>M3N</td>\n      <td>North York</td>\n      <td>Downsview Northwest</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>M4A</td>\n      <td>North York</td>\n      <td>Victoria Village</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>M4B</td>\n      <td>East York</td>\n      <td>Woodbine Gardens, Parkview Hill</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>M4C</td>\n      <td>East York</td>\n      <td>Woodbine Heights</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>M4E</td>\n      <td>East Toronto</td>\n      <td>The Beaches</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>M4G</td>\n      <td>East York</td>\n      <td>Leaside</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>M4H</td>\n      <td>East York</td>\n      <td>Thorncliffe Park</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>M4J</td>\n      <td>East York</td>\n      <td>East Toronto</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>M4K</td>\n      <td>East Toronto</td>\n      <td>The Danforth West, Riverdale</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>M4L</td>\n      <td>East Toronto</td>\n      <td>The Beaches West, India Bazaar</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>M4M</td>\n      <td>East Toronto</td>\n      <td>Studio District</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>M4N</td>\n      <td>Central Toronto</td>\n      <td>Lawrence Park</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>M4P</td>\n      <td>Central Toronto</td>\n      <td>Davisville North</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>M4R</td>\n      <td>Central Toronto</td>\n      <td>North Toronto West</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>M4S</td>\n      <td>Central Toronto</td>\n      <td>Davisville</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>M4T</td>\n      <td>Central Toronto</td>\n      <td>Moore Park, Summerhill East</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>M4V</td>\n      <td>Central Toronto</td>\n      <td>Deer Park, Forest Hill SE, Rathnelly, South Hi...</td>\n    </tr>\n    <tr>\n      <th>50</th>\n      <td>M4W</td>\n      <td>Downtown Toronto</td>\n      <td>Rosedale</td>\n    </tr>\n    <tr>\n      <th>51</th>\n      <td>M4X</td>\n      <td>Downtown Toronto</td>\n      <td>Cabbagetown, St. James Town</td>\n    </tr>\n    <tr>\n      <th>52</th>\n      <td>M4Y</td>\n      <td>Downtown Toronto</td>\n      <td>Church and Wellesley</td>\n    </tr>\n    <tr>\n      <th>53</th>\n      <td>M5A</td>\n      <td>Downtown Toronto</td>\n      <td>Harbourfront, Regent Park</td>\n    </tr>\n    <tr>\n      <th>54</th>\n      <td>M5B</td>\n      <td>Downtown Toronto</td>\n      <td>Ryerson, Garden District</td>\n    </tr>\n    <tr>\n      <th>55</th>\n      <td>M5C</td>\n      <td>Downtown Toronto</td>\n      <td>St. James Town</td>\n    </tr>\n    <tr>\n      <th>56</th>\n      <td>M5E</td>\n      <td>Downtown Toronto</td>\n      <td>Berczy Park</td>\n    </tr>\n    <tr>\n      <th>57</th>\n      <td>M5G</td>\n      <td>Downtown Toronto</td>\n      <td>Central Bay Street</td>\n    </tr>\n    <tr>\n      <th>58</th>\n      <td>M5H</td>\n      <td>Downtown Toronto</td>\n      <td>Adelaide, King, Richmond</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>M5J</td>\n      <td>Downtown Toronto</td>\n      <td>Harbourfront East, Toronto Islands, Union Station</td>\n    </tr>\n    <tr>\n      <th>60</th>\n      <td>M5K</td>\n      <td>Downtown Toronto</td>\n      <td>Design Exchange, Toronto Dominion Centre</td>\n    </tr>\n    <tr>\n      <th>61</th>\n      <td>M5L</td>\n      <td>Downtown Toronto</td>\n      <td>Commerce Court, Victoria Hotel</td>\n    </tr>\n    <tr>\n      <th>62</th>\n      <td>M5M</td>\n      <td>North York</td>\n      <td>Bedford Park, Lawrence Manor East</td>\n    </tr>\n    <tr>\n      <th>63</th>\n      <td>M5N</td>\n      <td>Central Toronto</td>\n      <td>Roselawn</td>\n    </tr>\n    <tr>\n      <th>64</th>\n      <td>M5P</td>\n      <td>Central Toronto</td>\n      <td>Forest Hill North, Forest Hill West</td>\n    </tr>\n    <tr>\n      <th>65</th>\n      <td>M5R</td>\n      <td>Central Toronto</td>\n      <td>The Annex, North Midtown, Yorkville</td>\n    </tr>\n    <tr>\n      <th>66</th>\n      <td>M5S</td>\n      <td>Downtown Toronto</td>\n      <td>Harbord, University of Toronto</td>\n    </tr>\n    <tr>\n      <th>67</th>\n      <td>M5T</td>\n      <td>Downtown Toronto</td>\n      <td>Chinatown, Grange Park, Kensington Market</td>\n    </tr>\n    <tr>\n      <th>68</th>\n      <td>M5V</td>\n      <td>Downtown Toronto</td>\n      <td>CN Tower, Bathurst Quay, Island airport, Harbo...</td>\n    </tr>\n    <tr>\n      <th>69</th>\n      <td>M5W</td>\n      <td>Downtown Toronto</td>\n      <td>Stn A PO Boxes 25 The Esplanade</td>\n    </tr>\n    <tr>\n      <th>70</th>\n      <td>M5X</td>\n      <td>Downtown Toronto</td>\n      <td>First Canadian Place, Underground city</td>\n    </tr>\n    <tr>\n      <th>71</th>\n      <td>M6A</td>\n      <td>North York</td>\n      <td>Lawrence Heights, Lawrence Manor</td>\n    </tr>\n    <tr>\n      <th>72</th>\n      <td>M6B</td>\n      <td>North York</td>\n      <td>Glencairn</td>\n    </tr>\n    <tr>\n      <th>73</th>\n      <td>M6C</td>\n      <td>York</td>\n      <td>Humewood-Cedarvale</td>\n    </tr>\n    <tr>\n      <th>74</th>\n      <td>M6E</td>\n      <td>York</td>\n      <td>Caledonia-Fairbanks</td>\n    </tr>\n    <tr>\n      <th>75</th>\n      <td>M6G</td>\n      <td>Downtown Toronto</td>\n      <td>Christie</td>\n    </tr>\n    <tr>\n      <th>76</th>\n      <td>M6H</td>\n      <td>West Toronto</td>\n      <td>Dovercourt Village, Dufferin</td>\n    </tr>\n    <tr>\n      <th>77</th>\n      <td>M6J</td>\n      <td>West Toronto</td>\n      <td>Little Portugal, Trinity</td>\n    </tr>\n    <tr>\n      <th>78</th>\n      <td>M6K</td>\n      <td>West Toronto</td>\n      <td>Brockton, Exhibition Place, Parkdale Village</td>\n    </tr>\n    <tr>\n      <th>79</th>\n      <td>M6L</td>\n      <td>North York</td>\n      <td>Downsview, North Park, Upwood Park</td>\n    </tr>\n    <tr>\n      <th>80</th>\n      <td>M6M</td>\n      <td>York</td>\n      <td>Del Ray, Keelesdale, Mount Dennis, Silverthorn</td>\n    </tr>\n    <tr>\n      <th>81</th>\n      <td>M6N</td>\n      <td>York</td>\n      <td>The Junction North, Runnymede</td>\n    </tr>\n    <tr>\n      <th>82</th>\n      <td>M6P</td>\n      <td>West Toronto</td>\n      <td>High Park, The Junction South</td>\n    </tr>\n    <tr>\n      <th>83</th>\n      <td>M6R</td>\n      <td>West Toronto</td>\n      <td>Parkdale, Roncesvalles</td>\n    </tr>\n    <tr>\n      <th>84</th>\n      <td>M6S</td>\n      <td>West Toronto</td>\n      <td>Runnymede, Swansea</td>\n    </tr>\n    <tr>\n      <th>85</th>\n      <td>M7A</td>\n      <td>Queen's Park</td>\n      <td>Queen's Park</td>\n    </tr>\n    <tr>\n      <th>86</th>\n      <td>M7R</td>\n      <td>Mississauga</td>\n      <td>Canada Post Gateway Processing Centre</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>M7Y</td>\n      <td>East Toronto</td>\n      <td>Business Reply Mail Processing Centre 969 Eastern</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>M8V</td>\n      <td>Etobicoke</td>\n      <td>Humber Bay Shores, Mimico South, New Toronto</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>M8W</td>\n      <td>Etobicoke</td>\n      <td>Alderwood, Long Branch</td>\n    </tr>\n    <tr>\n      <th>90</th>\n      <td>M8X</td>\n      <td>Etobicoke</td>\n      <td>The Kingsway, Montgomery Road, Old Mill North</td>\n    </tr>\n    <tr>\n      <th>91</th>\n      <td>M8Y</td>\n      <td>Etobicoke</td>\n      <td>Humber Bay, King's Mill Park, Kingsway Park So...</td>\n    </tr>\n    <tr>\n      <th>92</th>\n      <td>M8Z</td>\n      <td>Etobicoke</td>\n      <td>Kingsway Park South West, Mimico NW, The Queen...</td>\n    </tr>\n    <tr>\n      <th>93</th>\n      <td>M9A</td>\n      <td>Etobicoke</td>\n      <td>Islington Avenue</td>\n    </tr>\n    <tr>\n      <th>94</th>\n      <td>M9B</td>\n      <td>Etobicoke</td>\n      <td>Cloverdale, Islington, Martin Grove, Princess ...</td>\n    </tr>\n    <tr>\n      <th>95</th>\n      <td>M9C</td>\n      <td>Etobicoke</td>\n      <td>Bloordale Gardens, Eringate, Markland Wood, Ol...</td>\n    </tr>\n    <tr>\n      <th>96</th>\n      <td>M9L</td>\n      <td>North York</td>\n      <td>Humber Summit</td>\n    </tr>\n    <tr>\n      <th>97</th>\n      <td>M9M</td>\n      <td>North York</td>\n      <td>Emery, Humberlea</td>\n    </tr>\n    <tr>\n      <th>98</th>\n      <td>M9N</td>\n      <td>York</td>\n      <td>Weston</td>\n    </tr>\n    <tr>\n      <th>99</th>\n      <td>M9P</td>\n      <td>Etobicoke</td>\n      <td>Westmount</td>\n    </tr>\n    <tr>\n      <th>100</th>\n      <td>M9R</td>\n      <td>Etobicoke</td>\n      <td>Kingsview Village, Martin Grove Gardens, Richv...</td>\n    </tr>\n    <tr>\n      <th>101</th>\n      <td>M9V</td>\n      <td>Etobicoke</td>\n      <td>Albion Gardens, Beaumond Heights, Humbergate, ...</td>\n    </tr>\n    <tr>\n      <th>102</th>\n      <td>M9W</td>\n      <td>Etobicoke</td>\n      <td>Northwest</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "    Postcode           Borough  \\\n0        M1B       Scarborough   \n1        M1C       Scarborough   \n2        M1E       Scarborough   \n3        M1G       Scarborough   \n4        M1H       Scarborough   \n5        M1J       Scarborough   \n6        M1K       Scarborough   \n7        M1L       Scarborough   \n8        M1M       Scarborough   \n9        M1N       Scarborough   \n10       M1P       Scarborough   \n11       M1R       Scarborough   \n12       M1S       Scarborough   \n13       M1T       Scarborough   \n14       M1V       Scarborough   \n15       M1W       Scarborough   \n16       M1X       Scarborough   \n17       M2H        North York   \n18       M2J        North York   \n19       M2K        North York   \n20       M2L        North York   \n21       M2M        North York   \n22       M2N        North York   \n23       M2P        North York   \n24       M2R        North York   \n25       M3A        North York   \n26       M3B        North York   \n27       M3C        North York   \n28       M3H        North York   \n29       M3J        North York   \n30       M3K        North York   \n31       M3L        North York   \n32       M3M        North York   \n33       M3N        North York   \n34       M4A        North York   \n35       M4B         East York   \n36       M4C         East York   \n37       M4E      East Toronto   \n38       M4G         East York   \n39       M4H         East York   \n40       M4J         East York   \n41       M4K      East Toronto   \n42       M4L      East Toronto   \n43       M4M      East Toronto   \n44       M4N   Central Toronto   \n45       M4P   Central Toronto   \n46       M4R   Central Toronto   \n47       M4S   Central Toronto   \n48       M4T   Central Toronto   \n49       M4V   Central Toronto   \n50       M4W  Downtown Toronto   \n51       M4X  Downtown Toronto   \n52       M4Y  Downtown Toronto   \n53       M5A  Downtown Toronto   \n54       M5B  Downtown Toronto   \n55       M5C  Downtown Toronto   \n56       M5E  Downtown Toronto   \n57       M5G  Downtown Toronto   \n58       M5H  Downtown Toronto   \n59       M5J  Downtown Toronto   \n60       M5K  Downtown Toronto   \n61       M5L  Downtown Toronto   \n62       M5M        North York   \n63       M5N   Central Toronto   \n64       M5P   Central Toronto   \n65       M5R   Central Toronto   \n66       M5S  Downtown Toronto   \n67       M5T  Downtown Toronto   \n68       M5V  Downtown Toronto   \n69       M5W  Downtown Toronto   \n70       M5X  Downtown Toronto   \n71       M6A        North York   \n72       M6B        North York   \n73       M6C              York   \n74       M6E              York   \n75       M6G  Downtown Toronto   \n76       M6H      West Toronto   \n77       M6J      West Toronto   \n78       M6K      West Toronto   \n79       M6L        North York   \n80       M6M              York   \n81       M6N              York   \n82       M6P      West Toronto   \n83       M6R      West Toronto   \n84       M6S      West Toronto   \n85       M7A      Queen's Park   \n86       M7R       Mississauga   \n87       M7Y      East Toronto   \n88       M8V         Etobicoke   \n89       M8W         Etobicoke   \n90       M8X         Etobicoke   \n91       M8Y         Etobicoke   \n92       M8Z         Etobicoke   \n93       M9A         Etobicoke   \n94       M9B         Etobicoke   \n95       M9C         Etobicoke   \n96       M9L        North York   \n97       M9M        North York   \n98       M9N              York   \n99       M9P         Etobicoke   \n100      M9R         Etobicoke   \n101      M9V         Etobicoke   \n102      M9W         Etobicoke   \n\n                                          Neighborhood  \n0                                       Rouge, Malvern  \n1               Highland Creek, Rouge Hill, Port Union  \n2                    Guildwood, Morningside, West Hill  \n3                                               Woburn  \n4                                            Cedarbrae  \n5                                  Scarborough Village  \n6          East Birchmount Park, Ionview, Kennedy Park  \n7                      Clairlea, Golden Mile, Oakridge  \n8      Cliffcrest, Cliffside, Scarborough Village West  \n9                          Birch Cliff, Cliffside West  \n10   Dorset Park, Scarborough Town Centre, Wexford ...  \n11                                   Maryvale, Wexford  \n12                                           Agincourt  \n13             Clarks Corners, Sullivan, Tam O'Shanter  \n14   Agincourt North, L'Amoreaux East, Milliken, St...  \n15                                     L'Amoreaux West  \n16                                         Upper Rouge  \n17                                   Hillcrest Village  \n18                        Fairview, Henry Farm, Oriole  \n19                                     Bayview Village  \n20                            Silver Hills, York Mills  \n21                             Newtonbrook, Willowdale  \n22                                    Willowdale South  \n23                                     York Mills West  \n24                                     Willowdale West  \n25                                           Parkwoods  \n26                                     Don Mills North  \n27                    Flemingdon Park, Don Mills South  \n28     Bathurst Manor, Downsview North, Wilson Heights  \n29                     Northwood Park, York University  \n30                         CFB Toronto, Downsview East  \n31                                      Downsview West  \n32                                   Downsview Central  \n33                                 Downsview Northwest  \n34                                    Victoria Village  \n35                     Woodbine Gardens, Parkview Hill  \n36                                    Woodbine Heights  \n37                                         The Beaches  \n38                                             Leaside  \n39                                    Thorncliffe Park  \n40                                        East Toronto  \n41                        The Danforth West, Riverdale  \n42                      The Beaches West, India Bazaar  \n43                                     Studio District  \n44                                       Lawrence Park  \n45                                    Davisville North  \n46                                  North Toronto West  \n47                                          Davisville  \n48                         Moore Park, Summerhill East  \n49   Deer Park, Forest Hill SE, Rathnelly, South Hi...  \n50                                            Rosedale  \n51                         Cabbagetown, St. James Town  \n52                                Church and Wellesley  \n53                           Harbourfront, Regent Park  \n54                            Ryerson, Garden District  \n55                                      St. James Town  \n56                                         Berczy Park  \n57                                  Central Bay Street  \n58                            Adelaide, King, Richmond  \n59   Harbourfront East, Toronto Islands, Union Station  \n60            Design Exchange, Toronto Dominion Centre  \n61                      Commerce Court, Victoria Hotel  \n62                   Bedford Park, Lawrence Manor East  \n63                                            Roselawn  \n64                 Forest Hill North, Forest Hill West  \n65                 The Annex, North Midtown, Yorkville  \n66                      Harbord, University of Toronto  \n67           Chinatown, Grange Park, Kensington Market  \n68   CN Tower, Bathurst Quay, Island airport, Harbo...  \n69                     Stn A PO Boxes 25 The Esplanade  \n70              First Canadian Place, Underground city  \n71                    Lawrence Heights, Lawrence Manor  \n72                                           Glencairn  \n73                                  Humewood-Cedarvale  \n74                                 Caledonia-Fairbanks  \n75                                            Christie  \n76                        Dovercourt Village, Dufferin  \n77                            Little Portugal, Trinity  \n78        Brockton, Exhibition Place, Parkdale Village  \n79                  Downsview, North Park, Upwood Park  \n80      Del Ray, Keelesdale, Mount Dennis, Silverthorn  \n81                       The Junction North, Runnymede  \n82                       High Park, The Junction South  \n83                              Parkdale, Roncesvalles  \n84                                  Runnymede, Swansea  \n85                                        Queen's Park  \n86               Canada Post Gateway Processing Centre  \n87   Business Reply Mail Processing Centre 969 Eastern  \n88        Humber Bay Shores, Mimico South, New Toronto  \n89                              Alderwood, Long Branch  \n90       The Kingsway, Montgomery Road, Old Mill North  \n91   Humber Bay, King's Mill Park, Kingsway Park So...  \n92   Kingsway Park South West, Mimico NW, The Queen...  \n93                                    Islington Avenue  \n94   Cloverdale, Islington, Martin Grove, Princess ...  \n95   Bloordale Gardens, Eringate, Markland Wood, Ol...  \n96                                       Humber Summit  \n97                                    Emery, Humberlea  \n98                                              Weston  \n99                                           Westmount  \n100  Kingsview Village, Martin Grove Gardens, Richv...  \n101  Albion Gardens, Beaumond Heights, Humbergate, ...  \n102                                          Northwest  "
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "# Neighborhoods with a same postcode are merged into a single cell, separated by commas\nneighborhoods = neighborhoods.groupby(['Postcode', 'Borough'], as_index=False).agg(lambda x: ', '.join(x))\nneighborhoods"
        }, 
        {
            "execution_count": 6, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 6, 
                    "metadata": {}, 
                    "data": {
                        "text/plain": "(103, 3)"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "neighborhoods.shape"
        }, 
        {
            "source": "# 2. Longitude and latitude of the neighborhoods", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "Next, geocoder package was used to get the latitude and longitude of each postcode of Toronto. I used arcgis instead of google, which the example code in the instructions used, so the latitude and longitude are slightly different.", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 7, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "# Initialize varialbes\nlat = []\nlng = []\nlat_lng_coords = None\n\n# Get postcodes from neighborhoods table\npostal_code = neighborhoods['Postcode']\n\n# Store latitude and longitude values in lat and lng\nfor pc in postal_code:\n    g = geocoder.arcgis('{}, Toronto, Ontario'.format(pc))\n    lat_lng_coords = g.latlng\n    lat.append(lat_lng_coords[0])\n    lng.append(lat_lng_coords[1])"
        }, 
        {
            "source": "The latitude and longitude information, each stored in its own list, were added to the neighborhood dataframe.", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 8, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "# Add the lists containing latitude and longitude values to the neighborhood dataframe\nnh_complete = neighborhoods\nnh_complete['Latitude'] = lat\nnh_complete['Longitude'] = lng"
        }, 
        {
            "execution_count": 9, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 9, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Postcode</th>\n      <th>Borough</th>\n      <th>Neighborhood</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>M1B</td>\n      <td>Scarborough</td>\n      <td>Rouge, Malvern</td>\n      <td>43.811525</td>\n      <td>-79.195517</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>M1C</td>\n      <td>Scarborough</td>\n      <td>Highland Creek, Rouge Hill, Port Union</td>\n      <td>43.785730</td>\n      <td>-79.158750</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>M1E</td>\n      <td>Scarborough</td>\n      <td>Guildwood, Morningside, West Hill</td>\n      <td>43.765690</td>\n      <td>-79.175256</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>M1G</td>\n      <td>Scarborough</td>\n      <td>Woburn</td>\n      <td>43.768359</td>\n      <td>-79.217590</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>M1H</td>\n      <td>Scarborough</td>\n      <td>Cedarbrae</td>\n      <td>43.769688</td>\n      <td>-79.239440</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>M1J</td>\n      <td>Scarborough</td>\n      <td>Scarborough Village</td>\n      <td>43.743125</td>\n      <td>-79.231750</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>M1K</td>\n      <td>Scarborough</td>\n      <td>East Birchmount Park, Ionview, Kennedy Park</td>\n      <td>43.726245</td>\n      <td>-79.263670</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>M1L</td>\n      <td>Scarborough</td>\n      <td>Clairlea, Golden Mile, Oakridge</td>\n      <td>43.713133</td>\n      <td>-79.285055</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>M1M</td>\n      <td>Scarborough</td>\n      <td>Cliffcrest, Cliffside, Scarborough Village West</td>\n      <td>43.723575</td>\n      <td>-79.234976</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>M1N</td>\n      <td>Scarborough</td>\n      <td>Birch Cliff, Cliffside West</td>\n      <td>43.696665</td>\n      <td>-79.260163</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>M1P</td>\n      <td>Scarborough</td>\n      <td>Dorset Park, Scarborough Town Centre, Wexford ...</td>\n      <td>43.759975</td>\n      <td>-79.268974</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>M1R</td>\n      <td>Scarborough</td>\n      <td>Maryvale, Wexford</td>\n      <td>43.750710</td>\n      <td>-79.300560</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>M1S</td>\n      <td>Scarborough</td>\n      <td>Agincourt</td>\n      <td>43.793940</td>\n      <td>-79.267983</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>M1T</td>\n      <td>Scarborough</td>\n      <td>Clarks Corners, Sullivan, Tam O'Shanter</td>\n      <td>43.784725</td>\n      <td>-79.299047</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>M1V</td>\n      <td>Scarborough</td>\n      <td>Agincourt North, L'Amoreaux East, Milliken, St...</td>\n      <td>43.817810</td>\n      <td>-79.280244</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>M1W</td>\n      <td>Scarborough</td>\n      <td>L'Amoreaux West</td>\n      <td>43.800881</td>\n      <td>-79.320740</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>M1X</td>\n      <td>Scarborough</td>\n      <td>Upper Rouge</td>\n      <td>43.834215</td>\n      <td>-79.216701</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>M2H</td>\n      <td>North York</td>\n      <td>Hillcrest Village</td>\n      <td>43.802845</td>\n      <td>-79.356236</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>M2J</td>\n      <td>North York</td>\n      <td>Fairview, Henry Farm, Oriole</td>\n      <td>43.780880</td>\n      <td>-79.347796</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>M2K</td>\n      <td>North York</td>\n      <td>Bayview Village</td>\n      <td>43.781015</td>\n      <td>-79.380542</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>M2L</td>\n      <td>North York</td>\n      <td>Silver Hills, York Mills</td>\n      <td>43.757192</td>\n      <td>-79.379865</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>M2M</td>\n      <td>North York</td>\n      <td>Newtonbrook, Willowdale</td>\n      <td>43.791475</td>\n      <td>-79.413605</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>M2N</td>\n      <td>North York</td>\n      <td>Willowdale South</td>\n      <td>43.768165</td>\n      <td>-79.407420</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>M2P</td>\n      <td>North York</td>\n      <td>York Mills West</td>\n      <td>43.747855</td>\n      <td>-79.400062</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>M2R</td>\n      <td>North York</td>\n      <td>Willowdale West</td>\n      <td>43.777695</td>\n      <td>-79.445797</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>M3A</td>\n      <td>North York</td>\n      <td>Parkwoods</td>\n      <td>43.752440</td>\n      <td>-79.329271</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>M3B</td>\n      <td>North York</td>\n      <td>Don Mills North</td>\n      <td>43.749195</td>\n      <td>-79.361905</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>M3C</td>\n      <td>North York</td>\n      <td>Flemingdon Park, Don Mills South</td>\n      <td>43.721425</td>\n      <td>-79.343453</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>M3H</td>\n      <td>North York</td>\n      <td>Bathurst Manor, Downsview North, Wilson Heights</td>\n      <td>43.757565</td>\n      <td>-79.448191</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>M3J</td>\n      <td>North York</td>\n      <td>Northwood Park, York University</td>\n      <td>43.764665</td>\n      <td>-79.487183</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>M3K</td>\n      <td>North York</td>\n      <td>CFB Toronto, Downsview East</td>\n      <td>43.739026</td>\n      <td>-79.467320</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>M3L</td>\n      <td>North York</td>\n      <td>Downsview West</td>\n      <td>43.740885</td>\n      <td>-79.505027</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>M3M</td>\n      <td>North York</td>\n      <td>Downsview Central</td>\n      <td>43.734585</td>\n      <td>-79.493151</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>M3N</td>\n      <td>North York</td>\n      <td>Downsview Northwest</td>\n      <td>43.755331</td>\n      <td>-79.519590</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>M4A</td>\n      <td>North York</td>\n      <td>Victoria Village</td>\n      <td>43.730421</td>\n      <td>-79.313320</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>M4B</td>\n      <td>East York</td>\n      <td>Woodbine Gardens, Parkview Hill</td>\n      <td>43.707535</td>\n      <td>-79.311773</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>M4C</td>\n      <td>East York</td>\n      <td>Woodbine Heights</td>\n      <td>43.689665</td>\n      <td>-79.307169</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>M4E</td>\n      <td>East Toronto</td>\n      <td>The Beaches</td>\n      <td>43.676845</td>\n      <td>-79.295225</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>M4G</td>\n      <td>East York</td>\n      <td>Leaside</td>\n      <td>43.709765</td>\n      <td>-79.363901</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>M4H</td>\n      <td>East York</td>\n      <td>Thorncliffe Park</td>\n      <td>43.701270</td>\n      <td>-79.349844</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>M4J</td>\n      <td>East York</td>\n      <td>East Toronto</td>\n      <td>43.688765</td>\n      <td>-79.334175</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>M4K</td>\n      <td>East Toronto</td>\n      <td>The Danforth West, Riverdale</td>\n      <td>43.683262</td>\n      <td>-79.355120</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>M4L</td>\n      <td>East Toronto</td>\n      <td>The Beaches West, India Bazaar</td>\n      <td>43.667965</td>\n      <td>-79.314673</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>M4M</td>\n      <td>East Toronto</td>\n      <td>Studio District</td>\n      <td>43.662766</td>\n      <td>-79.334830</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>M4N</td>\n      <td>Central Toronto</td>\n      <td>Lawrence Park</td>\n      <td>43.728160</td>\n      <td>-79.387085</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>M4P</td>\n      <td>Central Toronto</td>\n      <td>Davisville North</td>\n      <td>43.712815</td>\n      <td>-79.388526</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>M4R</td>\n      <td>Central Toronto</td>\n      <td>North Toronto West</td>\n      <td>43.714523</td>\n      <td>-79.406960</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>M4S</td>\n      <td>Central Toronto</td>\n      <td>Davisville</td>\n      <td>43.703395</td>\n      <td>-79.385964</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>M4T</td>\n      <td>Central Toronto</td>\n      <td>Moore Park, Summerhill East</td>\n      <td>43.690655</td>\n      <td>-79.383561</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>M4V</td>\n      <td>Central Toronto</td>\n      <td>Deer Park, Forest Hill SE, Rathnelly, South Hi...</td>\n      <td>43.686083</td>\n      <td>-79.402335</td>\n    </tr>\n    <tr>\n      <th>50</th>\n      <td>M4W</td>\n      <td>Downtown Toronto</td>\n      <td>Rosedale</td>\n      <td>43.681940</td>\n      <td>-79.378474</td>\n    </tr>\n    <tr>\n      <th>51</th>\n      <td>M4X</td>\n      <td>Downtown Toronto</td>\n      <td>Cabbagetown, St. James Town</td>\n      <td>43.668160</td>\n      <td>-79.366602</td>\n    </tr>\n    <tr>\n      <th>52</th>\n      <td>M4Y</td>\n      <td>Downtown Toronto</td>\n      <td>Church and Wellesley</td>\n      <td>43.666585</td>\n      <td>-79.381302</td>\n    </tr>\n    <tr>\n      <th>53</th>\n      <td>M5A</td>\n      <td>Downtown Toronto</td>\n      <td>Harbourfront, Regent Park</td>\n      <td>43.655120</td>\n      <td>-79.362640</td>\n    </tr>\n    <tr>\n      <th>54</th>\n      <td>M5B</td>\n      <td>Downtown Toronto</td>\n      <td>Ryerson, Garden District</td>\n      <td>43.657363</td>\n      <td>-79.378180</td>\n    </tr>\n    <tr>\n      <th>55</th>\n      <td>M5C</td>\n      <td>Downtown Toronto</td>\n      <td>St. James Town</td>\n      <td>43.651210</td>\n      <td>-79.375481</td>\n    </tr>\n    <tr>\n      <th>56</th>\n      <td>M5E</td>\n      <td>Downtown Toronto</td>\n      <td>Berczy Park</td>\n      <td>43.645160</td>\n      <td>-79.373675</td>\n    </tr>\n    <tr>\n      <th>57</th>\n      <td>M5G</td>\n      <td>Downtown Toronto</td>\n      <td>Central Bay Street</td>\n      <td>43.656091</td>\n      <td>-79.384930</td>\n    </tr>\n    <tr>\n      <th>58</th>\n      <td>M5H</td>\n      <td>Downtown Toronto</td>\n      <td>Adelaide, King, Richmond</td>\n      <td>43.649515</td>\n      <td>-79.382503</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>M5J</td>\n      <td>Downtown Toronto</td>\n      <td>Harbourfront East, Toronto Islands, Union Station</td>\n      <td>43.623470</td>\n      <td>-79.391507</td>\n    </tr>\n    <tr>\n      <th>60</th>\n      <td>M5K</td>\n      <td>Downtown Toronto</td>\n      <td>Design Exchange, Toronto Dominion Centre</td>\n      <td>43.647100</td>\n      <td>-79.381531</td>\n    </tr>\n    <tr>\n      <th>61</th>\n      <td>M5L</td>\n      <td>Downtown Toronto</td>\n      <td>Commerce Court, Victoria Hotel</td>\n      <td>43.648205</td>\n      <td>-79.378793</td>\n    </tr>\n    <tr>\n      <th>62</th>\n      <td>M5M</td>\n      <td>North York</td>\n      <td>Bedford Park, Lawrence Manor East</td>\n      <td>43.735460</td>\n      <td>-79.419164</td>\n    </tr>\n    <tr>\n      <th>63</th>\n      <td>M5N</td>\n      <td>Central Toronto</td>\n      <td>Roselawn</td>\n      <td>43.711941</td>\n      <td>-79.419120</td>\n    </tr>\n    <tr>\n      <th>64</th>\n      <td>M5P</td>\n      <td>Central Toronto</td>\n      <td>Forest Hill North, Forest Hill West</td>\n      <td>43.694785</td>\n      <td>-79.414405</td>\n    </tr>\n    <tr>\n      <th>65</th>\n      <td>M5R</td>\n      <td>Central Toronto</td>\n      <td>The Annex, North Midtown, Yorkville</td>\n      <td>43.674840</td>\n      <td>-79.403698</td>\n    </tr>\n    <tr>\n      <th>66</th>\n      <td>M5S</td>\n      <td>Downtown Toronto</td>\n      <td>Harbord, University of Toronto</td>\n      <td>43.663110</td>\n      <td>-79.401801</td>\n    </tr>\n    <tr>\n      <th>67</th>\n      <td>M5T</td>\n      <td>Downtown Toronto</td>\n      <td>Chinatown, Grange Park, Kensington Market</td>\n      <td>43.653570</td>\n      <td>-79.397249</td>\n    </tr>\n    <tr>\n      <th>68</th>\n      <td>M5V</td>\n      <td>Downtown Toronto</td>\n      <td>CN Tower, Bathurst Quay, Island airport, Harbo...</td>\n      <td>43.640815</td>\n      <td>-79.399538</td>\n    </tr>\n    <tr>\n      <th>69</th>\n      <td>M5W</td>\n      <td>Downtown Toronto</td>\n      <td>Stn A PO Boxes 25 The Esplanade</td>\n      <td>43.648690</td>\n      <td>-79.385440</td>\n    </tr>\n    <tr>\n      <th>70</th>\n      <td>M5X</td>\n      <td>Downtown Toronto</td>\n      <td>First Canadian Place, Underground city</td>\n      <td>43.648280</td>\n      <td>-79.381461</td>\n    </tr>\n    <tr>\n      <th>71</th>\n      <td>M6A</td>\n      <td>North York</td>\n      <td>Lawrence Heights, Lawrence Manor</td>\n      <td>43.723125</td>\n      <td>-79.451589</td>\n    </tr>\n    <tr>\n      <th>72</th>\n      <td>M6B</td>\n      <td>North York</td>\n      <td>Glencairn</td>\n      <td>43.707990</td>\n      <td>-79.448367</td>\n    </tr>\n    <tr>\n      <th>73</th>\n      <td>M6C</td>\n      <td>York</td>\n      <td>Humewood-Cedarvale</td>\n      <td>43.692105</td>\n      <td>-79.430355</td>\n    </tr>\n    <tr>\n      <th>74</th>\n      <td>M6E</td>\n      <td>York</td>\n      <td>Caledonia-Fairbanks</td>\n      <td>43.688640</td>\n      <td>-79.451015</td>\n    </tr>\n    <tr>\n      <th>75</th>\n      <td>M6G</td>\n      <td>Downtown Toronto</td>\n      <td>Christie</td>\n      <td>43.668781</td>\n      <td>-79.420710</td>\n    </tr>\n    <tr>\n      <th>76</th>\n      <td>M6H</td>\n      <td>West Toronto</td>\n      <td>Dovercourt Village, Dufferin</td>\n      <td>43.665087</td>\n      <td>-79.438705</td>\n    </tr>\n    <tr>\n      <th>77</th>\n      <td>M6J</td>\n      <td>West Toronto</td>\n      <td>Little Portugal, Trinity</td>\n      <td>43.648485</td>\n      <td>-79.417742</td>\n    </tr>\n    <tr>\n      <th>78</th>\n      <td>M6K</td>\n      <td>West Toronto</td>\n      <td>Brockton, Exhibition Place, Parkdale Village</td>\n      <td>43.639410</td>\n      <td>-79.424362</td>\n    </tr>\n    <tr>\n      <th>79</th>\n      <td>M6L</td>\n      <td>North York</td>\n      <td>Downsview, North Park, Upwood Park</td>\n      <td>43.713810</td>\n      <td>-79.488305</td>\n    </tr>\n    <tr>\n      <th>80</th>\n      <td>M6M</td>\n      <td>York</td>\n      <td>Del Ray, Keelesdale, Mount Dennis, Silverthorn</td>\n      <td>43.694545</td>\n      <td>-79.484643</td>\n    </tr>\n    <tr>\n      <th>81</th>\n      <td>M6N</td>\n      <td>York</td>\n      <td>The Junction North, Runnymede</td>\n      <td>43.675825</td>\n      <td>-79.482052</td>\n    </tr>\n    <tr>\n      <th>82</th>\n      <td>M6P</td>\n      <td>West Toronto</td>\n      <td>High Park, The Junction South</td>\n      <td>43.659975</td>\n      <td>-79.462874</td>\n    </tr>\n    <tr>\n      <th>83</th>\n      <td>M6R</td>\n      <td>West Toronto</td>\n      <td>Parkdale, Roncesvalles</td>\n      <td>43.647870</td>\n      <td>-79.449762</td>\n    </tr>\n    <tr>\n      <th>84</th>\n      <td>M6S</td>\n      <td>West Toronto</td>\n      <td>Runnymede, Swansea</td>\n      <td>43.649885</td>\n      <td>-79.474929</td>\n    </tr>\n    <tr>\n      <th>85</th>\n      <td>M7A</td>\n      <td>Queen's Park</td>\n      <td>Queen's Park</td>\n      <td>43.661102</td>\n      <td>-79.391035</td>\n    </tr>\n    <tr>\n      <th>86</th>\n      <td>M7R</td>\n      <td>Mississauga</td>\n      <td>Canada Post Gateway Processing Centre</td>\n      <td>43.648690</td>\n      <td>-79.385440</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>M7Y</td>\n      <td>East Toronto</td>\n      <td>Business Reply Mail Processing Centre 969 Eastern</td>\n      <td>43.648690</td>\n      <td>-79.385440</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>M8V</td>\n      <td>Etobicoke</td>\n      <td>Humber Bay Shores, Mimico South, New Toronto</td>\n      <td>43.609870</td>\n      <td>-79.498178</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>M8W</td>\n      <td>Etobicoke</td>\n      <td>Alderwood, Long Branch</td>\n      <td>43.601131</td>\n      <td>-79.538785</td>\n    </tr>\n    <tr>\n      <th>90</th>\n      <td>M8X</td>\n      <td>Etobicoke</td>\n      <td>The Kingsway, Montgomery Road, Old Mill North</td>\n      <td>43.653690</td>\n      <td>-79.511117</td>\n    </tr>\n    <tr>\n      <th>91</th>\n      <td>M8Y</td>\n      <td>Etobicoke</td>\n      <td>Humber Bay, King's Mill Park, Kingsway Park So...</td>\n      <td>43.632765</td>\n      <td>-79.489601</td>\n    </tr>\n    <tr>\n      <th>92</th>\n      <td>M8Z</td>\n      <td>Etobicoke</td>\n      <td>Kingsway Park South West, Mimico NW, The Queen...</td>\n      <td>43.624630</td>\n      <td>-79.526950</td>\n    </tr>\n    <tr>\n      <th>93</th>\n      <td>M9A</td>\n      <td>Etobicoke</td>\n      <td>Islington Avenue</td>\n      <td>43.662242</td>\n      <td>-79.528379</td>\n    </tr>\n    <tr>\n      <th>94</th>\n      <td>M9B</td>\n      <td>Etobicoke</td>\n      <td>Cloverdale, Islington, Martin Grove, Princess ...</td>\n      <td>43.649692</td>\n      <td>-79.553945</td>\n    </tr>\n    <tr>\n      <th>95</th>\n      <td>M9C</td>\n      <td>Etobicoke</td>\n      <td>Bloordale Gardens, Eringate, Markland Wood, Ol...</td>\n      <td>43.648573</td>\n      <td>-79.578250</td>\n    </tr>\n    <tr>\n      <th>96</th>\n      <td>M9L</td>\n      <td>North York</td>\n      <td>Humber Summit</td>\n      <td>43.759500</td>\n      <td>-79.556852</td>\n    </tr>\n    <tr>\n      <th>97</th>\n      <td>M9M</td>\n      <td>North York</td>\n      <td>Emery, Humberlea</td>\n      <td>43.733760</td>\n      <td>-79.537522</td>\n    </tr>\n    <tr>\n      <th>98</th>\n      <td>M9N</td>\n      <td>York</td>\n      <td>Weston</td>\n      <td>43.704855</td>\n      <td>-79.517552</td>\n    </tr>\n    <tr>\n      <th>99</th>\n      <td>M9P</td>\n      <td>Etobicoke</td>\n      <td>Westmount</td>\n      <td>43.696300</td>\n      <td>-79.530399</td>\n    </tr>\n    <tr>\n      <th>100</th>\n      <td>M9R</td>\n      <td>Etobicoke</td>\n      <td>Kingsview Village, Martin Grove Gardens, Richv...</td>\n      <td>43.686915</td>\n      <td>-79.557276</td>\n    </tr>\n    <tr>\n      <th>101</th>\n      <td>M9V</td>\n      <td>Etobicoke</td>\n      <td>Albion Gardens, Beaumond Heights, Humbergate, ...</td>\n      <td>43.743205</td>\n      <td>-79.584701</td>\n    </tr>\n    <tr>\n      <th>102</th>\n      <td>M9W</td>\n      <td>Etobicoke</td>\n      <td>Northwest</td>\n      <td>43.711740</td>\n      <td>-79.579181</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "    Postcode           Borough  \\\n0        M1B       Scarborough   \n1        M1C       Scarborough   \n2        M1E       Scarborough   \n3        M1G       Scarborough   \n4        M1H       Scarborough   \n5        M1J       Scarborough   \n6        M1K       Scarborough   \n7        M1L       Scarborough   \n8        M1M       Scarborough   \n9        M1N       Scarborough   \n10       M1P       Scarborough   \n11       M1R       Scarborough   \n12       M1S       Scarborough   \n13       M1T       Scarborough   \n14       M1V       Scarborough   \n15       M1W       Scarborough   \n16       M1X       Scarborough   \n17       M2H        North York   \n18       M2J        North York   \n19       M2K        North York   \n20       M2L        North York   \n21       M2M        North York   \n22       M2N        North York   \n23       M2P        North York   \n24       M2R        North York   \n25       M3A        North York   \n26       M3B        North York   \n27       M3C        North York   \n28       M3H        North York   \n29       M3J        North York   \n30       M3K        North York   \n31       M3L        North York   \n32       M3M        North York   \n33       M3N        North York   \n34       M4A        North York   \n35       M4B         East York   \n36       M4C         East York   \n37       M4E      East Toronto   \n38       M4G         East York   \n39       M4H         East York   \n40       M4J         East York   \n41       M4K      East Toronto   \n42       M4L      East Toronto   \n43       M4M      East Toronto   \n44       M4N   Central Toronto   \n45       M4P   Central Toronto   \n46       M4R   Central Toronto   \n47       M4S   Central Toronto   \n48       M4T   Central Toronto   \n49       M4V   Central Toronto   \n50       M4W  Downtown Toronto   \n51       M4X  Downtown Toronto   \n52       M4Y  Downtown Toronto   \n53       M5A  Downtown Toronto   \n54       M5B  Downtown Toronto   \n55       M5C  Downtown Toronto   \n56       M5E  Downtown Toronto   \n57       M5G  Downtown Toronto   \n58       M5H  Downtown Toronto   \n59       M5J  Downtown Toronto   \n60       M5K  Downtown Toronto   \n61       M5L  Downtown Toronto   \n62       M5M        North York   \n63       M5N   Central Toronto   \n64       M5P   Central Toronto   \n65       M5R   Central Toronto   \n66       M5S  Downtown Toronto   \n67       M5T  Downtown Toronto   \n68       M5V  Downtown Toronto   \n69       M5W  Downtown Toronto   \n70       M5X  Downtown Toronto   \n71       M6A        North York   \n72       M6B        North York   \n73       M6C              York   \n74       M6E              York   \n75       M6G  Downtown Toronto   \n76       M6H      West Toronto   \n77       M6J      West Toronto   \n78       M6K      West Toronto   \n79       M6L        North York   \n80       M6M              York   \n81       M6N              York   \n82       M6P      West Toronto   \n83       M6R      West Toronto   \n84       M6S      West Toronto   \n85       M7A      Queen's Park   \n86       M7R       Mississauga   \n87       M7Y      East Toronto   \n88       M8V         Etobicoke   \n89       M8W         Etobicoke   \n90       M8X         Etobicoke   \n91       M8Y         Etobicoke   \n92       M8Z         Etobicoke   \n93       M9A         Etobicoke   \n94       M9B         Etobicoke   \n95       M9C         Etobicoke   \n96       M9L        North York   \n97       M9M        North York   \n98       M9N              York   \n99       M9P         Etobicoke   \n100      M9R         Etobicoke   \n101      M9V         Etobicoke   \n102      M9W         Etobicoke   \n\n                                          Neighborhood   Latitude  Longitude  \n0                                       Rouge, Malvern  43.811525 -79.195517  \n1               Highland Creek, Rouge Hill, Port Union  43.785730 -79.158750  \n2                    Guildwood, Morningside, West Hill  43.765690 -79.175256  \n3                                               Woburn  43.768359 -79.217590  \n4                                            Cedarbrae  43.769688 -79.239440  \n5                                  Scarborough Village  43.743125 -79.231750  \n6          East Birchmount Park, Ionview, Kennedy Park  43.726245 -79.263670  \n7                      Clairlea, Golden Mile, Oakridge  43.713133 -79.285055  \n8      Cliffcrest, Cliffside, Scarborough Village West  43.723575 -79.234976  \n9                          Birch Cliff, Cliffside West  43.696665 -79.260163  \n10   Dorset Park, Scarborough Town Centre, Wexford ...  43.759975 -79.268974  \n11                                   Maryvale, Wexford  43.750710 -79.300560  \n12                                           Agincourt  43.793940 -79.267983  \n13             Clarks Corners, Sullivan, Tam O'Shanter  43.784725 -79.299047  \n14   Agincourt North, L'Amoreaux East, Milliken, St...  43.817810 -79.280244  \n15                                     L'Amoreaux West  43.800881 -79.320740  \n16                                         Upper Rouge  43.834215 -79.216701  \n17                                   Hillcrest Village  43.802845 -79.356236  \n18                        Fairview, Henry Farm, Oriole  43.780880 -79.347796  \n19                                     Bayview Village  43.781015 -79.380542  \n20                            Silver Hills, York Mills  43.757192 -79.379865  \n21                             Newtonbrook, Willowdale  43.791475 -79.413605  \n22                                    Willowdale South  43.768165 -79.407420  \n23                                     York Mills West  43.747855 -79.400062  \n24                                     Willowdale West  43.777695 -79.445797  \n25                                           Parkwoods  43.752440 -79.329271  \n26                                     Don Mills North  43.749195 -79.361905  \n27                    Flemingdon Park, Don Mills South  43.721425 -79.343453  \n28     Bathurst Manor, Downsview North, Wilson Heights  43.757565 -79.448191  \n29                     Northwood Park, York University  43.764665 -79.487183  \n30                         CFB Toronto, Downsview East  43.739026 -79.467320  \n31                                      Downsview West  43.740885 -79.505027  \n32                                   Downsview Central  43.734585 -79.493151  \n33                                 Downsview Northwest  43.755331 -79.519590  \n34                                    Victoria Village  43.730421 -79.313320  \n35                     Woodbine Gardens, Parkview Hill  43.707535 -79.311773  \n36                                    Woodbine Heights  43.689665 -79.307169  \n37                                         The Beaches  43.676845 -79.295225  \n38                                             Leaside  43.709765 -79.363901  \n39                                    Thorncliffe Park  43.701270 -79.349844  \n40                                        East Toronto  43.688765 -79.334175  \n41                        The Danforth West, Riverdale  43.683262 -79.355120  \n42                      The Beaches West, India Bazaar  43.667965 -79.314673  \n43                                     Studio District  43.662766 -79.334830  \n44                                       Lawrence Park  43.728160 -79.387085  \n45                                    Davisville North  43.712815 -79.388526  \n46                                  North Toronto West  43.714523 -79.406960  \n47                                          Davisville  43.703395 -79.385964  \n48                         Moore Park, Summerhill East  43.690655 -79.383561  \n49   Deer Park, Forest Hill SE, Rathnelly, South Hi...  43.686083 -79.402335  \n50                                            Rosedale  43.681940 -79.378474  \n51                         Cabbagetown, St. James Town  43.668160 -79.366602  \n52                                Church and Wellesley  43.666585 -79.381302  \n53                           Harbourfront, Regent Park  43.655120 -79.362640  \n54                            Ryerson, Garden District  43.657363 -79.378180  \n55                                      St. James Town  43.651210 -79.375481  \n56                                         Berczy Park  43.645160 -79.373675  \n57                                  Central Bay Street  43.656091 -79.384930  \n58                            Adelaide, King, Richmond  43.649515 -79.382503  \n59   Harbourfront East, Toronto Islands, Union Station  43.623470 -79.391507  \n60            Design Exchange, Toronto Dominion Centre  43.647100 -79.381531  \n61                      Commerce Court, Victoria Hotel  43.648205 -79.378793  \n62                   Bedford Park, Lawrence Manor East  43.735460 -79.419164  \n63                                            Roselawn  43.711941 -79.419120  \n64                 Forest Hill North, Forest Hill West  43.694785 -79.414405  \n65                 The Annex, North Midtown, Yorkville  43.674840 -79.403698  \n66                      Harbord, University of Toronto  43.663110 -79.401801  \n67           Chinatown, Grange Park, Kensington Market  43.653570 -79.397249  \n68   CN Tower, Bathurst Quay, Island airport, Harbo...  43.640815 -79.399538  \n69                     Stn A PO Boxes 25 The Esplanade  43.648690 -79.385440  \n70              First Canadian Place, Underground city  43.648280 -79.381461  \n71                    Lawrence Heights, Lawrence Manor  43.723125 -79.451589  \n72                                           Glencairn  43.707990 -79.448367  \n73                                  Humewood-Cedarvale  43.692105 -79.430355  \n74                                 Caledonia-Fairbanks  43.688640 -79.451015  \n75                                            Christie  43.668781 -79.420710  \n76                        Dovercourt Village, Dufferin  43.665087 -79.438705  \n77                            Little Portugal, Trinity  43.648485 -79.417742  \n78        Brockton, Exhibition Place, Parkdale Village  43.639410 -79.424362  \n79                  Downsview, North Park, Upwood Park  43.713810 -79.488305  \n80      Del Ray, Keelesdale, Mount Dennis, Silverthorn  43.694545 -79.484643  \n81                       The Junction North, Runnymede  43.675825 -79.482052  \n82                       High Park, The Junction South  43.659975 -79.462874  \n83                              Parkdale, Roncesvalles  43.647870 -79.449762  \n84                                  Runnymede, Swansea  43.649885 -79.474929  \n85                                        Queen's Park  43.661102 -79.391035  \n86               Canada Post Gateway Processing Centre  43.648690 -79.385440  \n87   Business Reply Mail Processing Centre 969 Eastern  43.648690 -79.385440  \n88        Humber Bay Shores, Mimico South, New Toronto  43.609870 -79.498178  \n89                              Alderwood, Long Branch  43.601131 -79.538785  \n90       The Kingsway, Montgomery Road, Old Mill North  43.653690 -79.511117  \n91   Humber Bay, King's Mill Park, Kingsway Park So...  43.632765 -79.489601  \n92   Kingsway Park South West, Mimico NW, The Queen...  43.624630 -79.526950  \n93                                    Islington Avenue  43.662242 -79.528379  \n94   Cloverdale, Islington, Martin Grove, Princess ...  43.649692 -79.553945  \n95   Bloordale Gardens, Eringate, Markland Wood, Ol...  43.648573 -79.578250  \n96                                       Humber Summit  43.759500 -79.556852  \n97                                    Emery, Humberlea  43.733760 -79.537522  \n98                                              Weston  43.704855 -79.517552  \n99                                           Westmount  43.696300 -79.530399  \n100  Kingsview Village, Martin Grove Gardens, Richv...  43.686915 -79.557276  \n101  Albion Gardens, Beaumond Heights, Humbergate, ...  43.743205 -79.584701  \n102                                          Northwest  43.711740 -79.579181  "
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "nh_complete"
        }, 
        {
            "source": "# 3. Explore and cluster the neighborhoods", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "I have decided to only look at the neighborhoods that were a part of the original city of Toronto before the amalgamation of 1998. Thus, only the boroughs with 'Toronto' in the name were included in the new dataframe. It turns out there are 38 different postcodes in the original city of Toronto.", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 10, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 10, 
                    "metadata": {}, 
                    "data": {
                        "text/plain": "(38, 5)"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "# New dataframe with only the original city included\ntoronto = nh_complete[nh_complete['Borough'].str.find('Toronto') != -1].reset_index(drop=True)\ntoronto.shape"
        }, 
        {
            "source": "A map of Toronto was created to visualize the city and all the neighborhoods (locations corresponding to the postcodes).", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 11, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 11, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe src=\"data:text/html;charset=utf-8;base64,<!DOCTYPE html>
<head>    
    <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
    <script>L_PREFER_CANVAS = false; L_NO_TOUCH = false; L_DISABLE_3D = false;</script>
    <script src="https://cdn.jsdelivr.net/npm/leaflet@1.2.0/dist/leaflet.js"></script>
    <script src="https://ajax.googleapis.com/ajax/libs/jquery/1.11.1/jquery.min.js"></script>
    <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>
    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet@1.2.0/dist/leaflet.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css"/>
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css"/>
    <link rel="stylesheet" href="https://rawgit.com/python-visualization/folium/master/folium/templates/leaflet.awesome.rotate.css"/>
    <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>
    <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>
    
            <style> #map_73dfb77953b044a6aaf2235fda14a46d {
                position : relative;
                width : 100.0%;
                height: 100.0%;
                left: 0.0%;
                top: 0.0%;
                }
            </style>
        
</head>
<body>    
    
            <div class="folium-map" id="map_73dfb77953b044a6aaf2235fda14a46d" ></div>
        
</body>
<script>    
    

            
                var bounds = null;
            

            var map_73dfb77953b044a6aaf2235fda14a46d = L.map(
                                  'map_73dfb77953b044a6aaf2235fda14a46d',
                                  {center: [43.648690000000045,-79.38543999999996],
                                  zoom: 11,
                                  maxBounds: bounds,
                                  layers: [],
                                  worldCopyJump: false,
                                  crs: L.CRS.EPSG3857
                                 });
            
        
    
            var tile_layer_0960f9c6cbbc4810a2a936fc422830c3 = L.tileLayer(
                'https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png',
                {
  "attribution": null,
  "detectRetina": false,
  "maxZoom": 18,
  "minZoom": 1,
  "noWrap": false,
  "subdomains": "abc"
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
        
    
            var circle_marker_7322116746d24b2e8e07501af0186846 = L.circleMarker(
                [43.67684518300007,-79.29522499999996],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_6771162b3ebc4b84a1f5458cd4230ada = L.popup({maxWidth: '300'});

            
                var html_8624fd1c48ae4f6c86a2283b2c53c9d0 = $('<div id="html_8624fd1c48ae4f6c86a2283b2c53c9d0" style="width: 100.0%; height: 100.0%;">M4E, East Toronto</div>')[0];
                popup_6771162b3ebc4b84a1f5458cd4230ada.setContent(html_8624fd1c48ae4f6c86a2283b2c53c9d0);
            

            circle_marker_7322116746d24b2e8e07501af0186846.bindPopup(popup_6771162b3ebc4b84a1f5458cd4230ada);

            
        
    
            var circle_marker_643960e0370f4118a4a406da3a1cfe3d = L.circleMarker(
                [43.68326150000007,-79.35511999999994],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_7ee7ded539bb4d738d597f30c1db0adc = L.popup({maxWidth: '300'});

            
                var html_127b3e2fc97c4b2585e2819bca8c663e = $('<div id="html_127b3e2fc97c4b2585e2819bca8c663e" style="width: 100.0%; height: 100.0%;">M4K, East Toronto</div>')[0];
                popup_7ee7ded539bb4d738d597f30c1db0adc.setContent(html_127b3e2fc97c4b2585e2819bca8c663e);
            

            circle_marker_643960e0370f4118a4a406da3a1cfe3d.bindPopup(popup_7ee7ded539bb4d738d597f30c1db0adc);

            
        
    
            var circle_marker_71ef8b5c6dd943438da70c3b85249f30 = L.circleMarker(
                [43.66796500000004,-79.31467251099997],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_37c8272d210747c7a0f981207c4f069b = L.popup({maxWidth: '300'});

            
                var html_ee270485ef36419892b8f73cf6830103 = $('<div id="html_ee270485ef36419892b8f73cf6830103" style="width: 100.0%; height: 100.0%;">M4L, East Toronto</div>')[0];
                popup_37c8272d210747c7a0f981207c4f069b.setContent(html_ee270485ef36419892b8f73cf6830103);
            

            circle_marker_71ef8b5c6dd943438da70c3b85249f30.bindPopup(popup_37c8272d210747c7a0f981207c4f069b);

            
        
    
            var circle_marker_20ee7fc851ed40a9bc60425a5932dd4d = L.circleMarker(
                [43.662765652000076,-79.33482999999995],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_75cb895216084842b036f561d8367e30 = L.popup({maxWidth: '300'});

            
                var html_77128114b57447199685a2d05f20ccb0 = $('<div id="html_77128114b57447199685a2d05f20ccb0" style="width: 100.0%; height: 100.0%;">M4M, East Toronto</div>')[0];
                popup_75cb895216084842b036f561d8367e30.setContent(html_77128114b57447199685a2d05f20ccb0);
            

            circle_marker_20ee7fc851ed40a9bc60425a5932dd4d.bindPopup(popup_75cb895216084842b036f561d8367e30);

            
        
    
            var circle_marker_5134907fd51048f48c5496bf1266f573 = L.circleMarker(
                [43.72816000000006,-79.38708518799996],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_b8713bc4fed5412a9aef4a995733673c = L.popup({maxWidth: '300'});

            
                var html_acf7a1c8a3c747e7afb1d118e3d2e150 = $('<div id="html_acf7a1c8a3c747e7afb1d118e3d2e150" style="width: 100.0%; height: 100.0%;">M4N, Central Toronto</div>')[0];
                popup_b8713bc4fed5412a9aef4a995733673c.setContent(html_acf7a1c8a3c747e7afb1d118e3d2e150);
            

            circle_marker_5134907fd51048f48c5496bf1266f573.bindPopup(popup_b8713bc4fed5412a9aef4a995733673c);

            
        
    
            var circle_marker_054c3555ef92408cb3dfc948b4b74648 = L.circleMarker(
                [43.712815000000035,-79.38852582199996],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_26ac5dd83f7a4c0bb2a4cbb6b28a39b5 = L.popup({maxWidth: '300'});

            
                var html_539b783dda75401197b7e35c1e7d3294 = $('<div id="html_539b783dda75401197b7e35c1e7d3294" style="width: 100.0%; height: 100.0%;">M4P, Central Toronto</div>')[0];
                popup_26ac5dd83f7a4c0bb2a4cbb6b28a39b5.setContent(html_539b783dda75401197b7e35c1e7d3294);
            

            circle_marker_054c3555ef92408cb3dfc948b4b74648.bindPopup(popup_26ac5dd83f7a4c0bb2a4cbb6b28a39b5);

            
        
    
            var circle_marker_4110df3e511a407a82ee83fe620a5a64 = L.circleMarker(
                [43.71452278400005,-79.40695999999997],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_bcb25672bb3940b58e1e07f387074787 = L.popup({maxWidth: '300'});

            
                var html_1baca8e1f8d647d4b580f25cbe002b8b = $('<div id="html_1baca8e1f8d647d4b580f25cbe002b8b" style="width: 100.0%; height: 100.0%;">M4R, Central Toronto</div>')[0];
                popup_bcb25672bb3940b58e1e07f387074787.setContent(html_1baca8e1f8d647d4b580f25cbe002b8b);
            

            circle_marker_4110df3e511a407a82ee83fe620a5a64.bindPopup(popup_bcb25672bb3940b58e1e07f387074787);

            
        
    
            var circle_marker_4a612f5db1d14bb581a70b298e76ea63 = L.circleMarker(
                [43.70339500000006,-79.38596360499997],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_317b8024d4b14d87be9ed78a1273a577 = L.popup({maxWidth: '300'});

            
                var html_02839e3cd7554ee09c634fb5655497c2 = $('<div id="html_02839e3cd7554ee09c634fb5655497c2" style="width: 100.0%; height: 100.0%;">M4S, Central Toronto</div>')[0];
                popup_317b8024d4b14d87be9ed78a1273a577.setContent(html_02839e3cd7554ee09c634fb5655497c2);
            

            circle_marker_4a612f5db1d14bb581a70b298e76ea63.bindPopup(popup_317b8024d4b14d87be9ed78a1273a577);

            
        
    
            var circle_marker_e6d170edcc5b434faf22f5baddec324f = L.circleMarker(
                [43.69065500000005,-79.38356145799997],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_45ab5f7515e7462686a390bfca9ea918 = L.popup({maxWidth: '300'});

            
                var html_b0e63cf12f544820a47d8f26559c0734 = $('<div id="html_b0e63cf12f544820a47d8f26559c0734" style="width: 100.0%; height: 100.0%;">M4T, Central Toronto</div>')[0];
                popup_45ab5f7515e7462686a390bfca9ea918.setContent(html_b0e63cf12f544820a47d8f26559c0734);
            

            circle_marker_e6d170edcc5b434faf22f5baddec324f.bindPopup(popup_45ab5f7515e7462686a390bfca9ea918);

            
        
    
            var circle_marker_326db4e697994e3baf52c7ddc4bf9625 = L.circleMarker(
                [43.68608285400006,-79.40233499999994],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_73fee6879bc94f0d83634fa29dc56ccb = L.popup({maxWidth: '300'});

            
                var html_6773f43dce944add90b465cd70760600 = $('<div id="html_6773f43dce944add90b465cd70760600" style="width: 100.0%; height: 100.0%;">M4V, Central Toronto</div>')[0];
                popup_73fee6879bc94f0d83634fa29dc56ccb.setContent(html_6773f43dce944add90b465cd70760600);
            

            circle_marker_326db4e697994e3baf52c7ddc4bf9625.bindPopup(popup_73fee6879bc94f0d83634fa29dc56ccb);

            
        
    
            var circle_marker_27ec7a9cd6cc4a33848d1ab7ab517635 = L.circleMarker(
                [43.681940000000054,-79.37847416699998],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_e1ef6e9327f641b4b843c0d6b464d4b9 = L.popup({maxWidth: '300'});

            
                var html_70b56915332c49808857ce758f114c00 = $('<div id="html_70b56915332c49808857ce758f114c00" style="width: 100.0%; height: 100.0%;">M4W, Downtown Toronto</div>')[0];
                popup_e1ef6e9327f641b4b843c0d6b464d4b9.setContent(html_70b56915332c49808857ce758f114c00);
            

            circle_marker_27ec7a9cd6cc4a33848d1ab7ab517635.bindPopup(popup_e1ef6e9327f641b4b843c0d6b464d4b9);

            
        
    
            var circle_marker_88a49af6f188495cbc0035f90819a55d = L.circleMarker(
                [43.66816000000006,-79.36660236199998],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_b37cddd13d184279b4d51bc38d886e78 = L.popup({maxWidth: '300'});

            
                var html_9ad4674b5a3440dda4ef0b934734468a = $('<div id="html_9ad4674b5a3440dda4ef0b934734468a" style="width: 100.0%; height: 100.0%;">M4X, Downtown Toronto</div>')[0];
                popup_b37cddd13d184279b4d51bc38d886e78.setContent(html_9ad4674b5a3440dda4ef0b934734468a);
            

            circle_marker_88a49af6f188495cbc0035f90819a55d.bindPopup(popup_b37cddd13d184279b4d51bc38d886e78);

            
        
    
            var circle_marker_26801db17a994b69965fe079d1a410a0 = L.circleMarker(
                [43.666585000000055,-79.38130203699995],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_afb06b20a0a34e0da0b14c1e88bb9cb8 = L.popup({maxWidth: '300'});

            
                var html_a5c5bd142be441f1b9c45cc7c57fefb9 = $('<div id="html_a5c5bd142be441f1b9c45cc7c57fefb9" style="width: 100.0%; height: 100.0%;">M4Y, Downtown Toronto</div>')[0];
                popup_afb06b20a0a34e0da0b14c1e88bb9cb8.setContent(html_a5c5bd142be441f1b9c45cc7c57fefb9);
            

            circle_marker_26801db17a994b69965fe079d1a410a0.bindPopup(popup_afb06b20a0a34e0da0b14c1e88bb9cb8);

            
        
    
            var circle_marker_a7a8da44ea734582a3f6620d8affe9ab = L.circleMarker(
                [43.65512000000007,-79.36263979699999],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_9a2730bbe0084b9e8ce9be01bf667618 = L.popup({maxWidth: '300'});

            
                var html_0913f0689f6640bfbd2e32228c7da1af = $('<div id="html_0913f0689f6640bfbd2e32228c7da1af" style="width: 100.0%; height: 100.0%;">M5A, Downtown Toronto</div>')[0];
                popup_9a2730bbe0084b9e8ce9be01bf667618.setContent(html_0913f0689f6640bfbd2e32228c7da1af);
            

            circle_marker_a7a8da44ea734582a3f6620d8affe9ab.bindPopup(popup_9a2730bbe0084b9e8ce9be01bf667618);

            
        
    
            var circle_marker_c62c6a2c59da4e469c579c692dfc6523 = L.circleMarker(
                [43.65736301100003,-79.37817999999999],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_3c73e1e76e884aaebd659969da6eac7e = L.popup({maxWidth: '300'});

            
                var html_59e326f0cce3498ea552ebb859130255 = $('<div id="html_59e326f0cce3498ea552ebb859130255" style="width: 100.0%; height: 100.0%;">M5B, Downtown Toronto</div>')[0];
                popup_3c73e1e76e884aaebd659969da6eac7e.setContent(html_59e326f0cce3498ea552ebb859130255);
            

            circle_marker_c62c6a2c59da4e469c579c692dfc6523.bindPopup(popup_3c73e1e76e884aaebd659969da6eac7e);

            
        
    
            var circle_marker_398bfbd5d7914b89961b78c636af7ce1 = L.circleMarker(
                [43.65121000000005,-79.37548057699996],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_fc2804b6822a437bb9d13e601933c9f8 = L.popup({maxWidth: '300'});

            
                var html_9ce64cb8ce3a4397bbd1852d463b3aaa = $('<div id="html_9ce64cb8ce3a4397bbd1852d463b3aaa" style="width: 100.0%; height: 100.0%;">M5C, Downtown Toronto</div>')[0];
                popup_fc2804b6822a437bb9d13e601933c9f8.setContent(html_9ce64cb8ce3a4397bbd1852d463b3aaa);
            

            circle_marker_398bfbd5d7914b89961b78c636af7ce1.bindPopup(popup_fc2804b6822a437bb9d13e601933c9f8);

            
        
    
            var circle_marker_6ebb498df90942a1bacad2f9088bcc13 = L.circleMarker(
                [43.64516015600003,-79.37367499999993],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_4688bc5042924318ae098f2c6266c138 = L.popup({maxWidth: '300'});

            
                var html_23885517847441b0802f04fbd82e4505 = $('<div id="html_23885517847441b0802f04fbd82e4505" style="width: 100.0%; height: 100.0%;">M5E, Downtown Toronto</div>')[0];
                popup_4688bc5042924318ae098f2c6266c138.setContent(html_23885517847441b0802f04fbd82e4505);
            

            circle_marker_6ebb498df90942a1bacad2f9088bcc13.bindPopup(popup_4688bc5042924318ae098f2c6266c138);

            
        
    
            var circle_marker_8dd051ccbb9844e996e8ece992a23ce0 = L.circleMarker(
                [43.65609081300005,-79.38492999999994],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_2c7d8adc87e6444c8a0cc0c2f94f4896 = L.popup({maxWidth: '300'});

            
                var html_87be08ba577e4af498100f7b4fcc34f3 = $('<div id="html_87be08ba577e4af498100f7b4fcc34f3" style="width: 100.0%; height: 100.0%;">M5G, Downtown Toronto</div>')[0];
                popup_2c7d8adc87e6444c8a0cc0c2f94f4896.setContent(html_87be08ba577e4af498100f7b4fcc34f3);
            

            circle_marker_8dd051ccbb9844e996e8ece992a23ce0.bindPopup(popup_2c7d8adc87e6444c8a0cc0c2f94f4896);

            
        
    
            var circle_marker_d5081e11b9ba4b43acc7573e23a09c2c = L.circleMarker(
                [43.649515000000065,-79.38250344699998],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_50c782169bcd4d32994e35c57b980de0 = L.popup({maxWidth: '300'});

            
                var html_29b4540cbb3344c9a1d7820cad4873ce = $('<div id="html_29b4540cbb3344c9a1d7820cad4873ce" style="width: 100.0%; height: 100.0%;">M5H, Downtown Toronto</div>')[0];
                popup_50c782169bcd4d32994e35c57b980de0.setContent(html_29b4540cbb3344c9a1d7820cad4873ce);
            

            circle_marker_d5081e11b9ba4b43acc7573e23a09c2c.bindPopup(popup_50c782169bcd4d32994e35c57b980de0);

            
        
    
            var circle_marker_22e59558c037471f8c0d01dd7741fb52 = L.circleMarker(
                [43.623470000000054,-79.39150736399995],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_cebe9903102e4ac7bdb1aa58305d8427 = L.popup({maxWidth: '300'});

            
                var html_2c630e9eb35d4f30908f71fb06b9b8f6 = $('<div id="html_2c630e9eb35d4f30908f71fb06b9b8f6" style="width: 100.0%; height: 100.0%;">M5J, Downtown Toronto</div>')[0];
                popup_cebe9903102e4ac7bdb1aa58305d8427.setContent(html_2c630e9eb35d4f30908f71fb06b9b8f6);
            

            circle_marker_22e59558c037471f8c0d01dd7741fb52.bindPopup(popup_cebe9903102e4ac7bdb1aa58305d8427);

            
        
    
            var circle_marker_5dbf21de2c3e428f873ecfda728c8a3e = L.circleMarker(
                [43.64710000000008,-79.38153109899997],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_aff57799c0354289a459155762ce778d = L.popup({maxWidth: '300'});

            
                var html_8776e5cdbcc947a784b2166f5278c518 = $('<div id="html_8776e5cdbcc947a784b2166f5278c518" style="width: 100.0%; height: 100.0%;">M5K, Downtown Toronto</div>')[0];
                popup_aff57799c0354289a459155762ce778d.setContent(html_8776e5cdbcc947a784b2166f5278c518);
            

            circle_marker_5dbf21de2c3e428f873ecfda728c8a3e.bindPopup(popup_aff57799c0354289a459155762ce778d);

            
        
    
            var circle_marker_55220b6026d44550a3d7337f7a2c7862 = L.circleMarker(
                [43.648205000000075,-79.37879339899996],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_13e4d43e9ac743c78989db5dec406d48 = L.popup({maxWidth: '300'});

            
                var html_00297198cd544f09bfcf483ba98023e7 = $('<div id="html_00297198cd544f09bfcf483ba98023e7" style="width: 100.0%; height: 100.0%;">M5L, Downtown Toronto</div>')[0];
                popup_13e4d43e9ac743c78989db5dec406d48.setContent(html_00297198cd544f09bfcf483ba98023e7);
            

            circle_marker_55220b6026d44550a3d7337f7a2c7862.bindPopup(popup_13e4d43e9ac743c78989db5dec406d48);

            
        
    
            var circle_marker_c80ec4dca26b4baa89b2b136deac0e20 = L.circleMarker(
                [43.711941154000044,-79.41911999999996],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_c4c7d8bb22d945c49bfc0930952887e1 = L.popup({maxWidth: '300'});

            
                var html_95c61514a3bf4f2395a81b1bb376e871 = $('<div id="html_95c61514a3bf4f2395a81b1bb376e871" style="width: 100.0%; height: 100.0%;">M5N, Central Toronto</div>')[0];
                popup_c4c7d8bb22d945c49bfc0930952887e1.setContent(html_95c61514a3bf4f2395a81b1bb376e871);
            

            circle_marker_c80ec4dca26b4baa89b2b136deac0e20.bindPopup(popup_c4c7d8bb22d945c49bfc0930952887e1);

            
        
    
            var circle_marker_ab468038b7054dca8ffe9bed18468767 = L.circleMarker(
                [43.69478500000008,-79.41440483299994],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_7d68d976946a494c9d73a9a1f7babd00 = L.popup({maxWidth: '300'});

            
                var html_b822ff5e5c2a469bbc363a5d9b31dae7 = $('<div id="html_b822ff5e5c2a469bbc363a5d9b31dae7" style="width: 100.0%; height: 100.0%;">M5P, Central Toronto</div>')[0];
                popup_7d68d976946a494c9d73a9a1f7babd00.setContent(html_b822ff5e5c2a469bbc363a5d9b31dae7);
            

            circle_marker_ab468038b7054dca8ffe9bed18468767.bindPopup(popup_7d68d976946a494c9d73a9a1f7babd00);

            
        
    
            var circle_marker_dfd2d05fbcce435abda7d16762c4ae89 = L.circleMarker(
                [43.674840000000074,-79.40369769099993],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_2ca8df3c6df94cc6a202d9a1bb79fddd = L.popup({maxWidth: '300'});

            
                var html_36f9634aac9c4a2893690048a1f19a3e = $('<div id="html_36f9634aac9c4a2893690048a1f19a3e" style="width: 100.0%; height: 100.0%;">M5R, Central Toronto</div>')[0];
                popup_2ca8df3c6df94cc6a202d9a1bb79fddd.setContent(html_36f9634aac9c4a2893690048a1f19a3e);
            

            circle_marker_dfd2d05fbcce435abda7d16762c4ae89.bindPopup(popup_2ca8df3c6df94cc6a202d9a1bb79fddd);

            
        
    
            var circle_marker_ad91732c7de744b9baaa5b962e4f81cb = L.circleMarker(
                [43.663110000000074,-79.40180056699995],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_2a4fbdb0d42b4168bcf6c0d0e0b5f911 = L.popup({maxWidth: '300'});

            
                var html_0c82bc103f7444e5bd7f6943160b9e5e = $('<div id="html_0c82bc103f7444e5bd7f6943160b9e5e" style="width: 100.0%; height: 100.0%;">M5S, Downtown Toronto</div>')[0];
                popup_2a4fbdb0d42b4168bcf6c0d0e0b5f911.setContent(html_0c82bc103f7444e5bd7f6943160b9e5e);
            

            circle_marker_ad91732c7de744b9baaa5b962e4f81cb.bindPopup(popup_2a4fbdb0d42b4168bcf6c0d0e0b5f911);

            
        
    
            var circle_marker_6a15cd58dfa74359964abf9b8b8f2917 = L.circleMarker(
                [43.65357000000006,-79.39724915699998],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_00106eb3f9f541ea9c05862e41990ab4 = L.popup({maxWidth: '300'});

            
                var html_2211836c639746ffab77432e6d47e860 = $('<div id="html_2211836c639746ffab77432e6d47e860" style="width: 100.0%; height: 100.0%;">M5T, Downtown Toronto</div>')[0];
                popup_00106eb3f9f541ea9c05862e41990ab4.setContent(html_2211836c639746ffab77432e6d47e860);
            

            circle_marker_6a15cd58dfa74359964abf9b8b8f2917.bindPopup(popup_00106eb3f9f541ea9c05862e41990ab4);

            
        
    
            var circle_marker_3be03aeb9e6a481ebddbab0f9fa7e396 = L.circleMarker(
                [43.64081500000003,-79.39953781899999],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_f05aa9f4d4ed413c8d46751f20919c25 = L.popup({maxWidth: '300'});

            
                var html_f63c632f387a4a5786e12764d76947d4 = $('<div id="html_f63c632f387a4a5786e12764d76947d4" style="width: 100.0%; height: 100.0%;">M5V, Downtown Toronto</div>')[0];
                popup_f05aa9f4d4ed413c8d46751f20919c25.setContent(html_f63c632f387a4a5786e12764d76947d4);
            

            circle_marker_3be03aeb9e6a481ebddbab0f9fa7e396.bindPopup(popup_f05aa9f4d4ed413c8d46751f20919c25);

            
        
    
            var circle_marker_81f363f9d88c44f38ea2a351f4f5dd38 = L.circleMarker(
                [43.648690000000045,-79.38543999999996],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_68e902066d964b22bdc147cf6c24ada8 = L.popup({maxWidth: '300'});

            
                var html_ec33c4997da041acb3cb6de674de3ef6 = $('<div id="html_ec33c4997da041acb3cb6de674de3ef6" style="width: 100.0%; height: 100.0%;">M5W, Downtown Toronto</div>')[0];
                popup_68e902066d964b22bdc147cf6c24ada8.setContent(html_ec33c4997da041acb3cb6de674de3ef6);
            

            circle_marker_81f363f9d88c44f38ea2a351f4f5dd38.bindPopup(popup_68e902066d964b22bdc147cf6c24ada8);

            
        
    
            var circle_marker_aa0831f01dcc478a9d9c7b0e1d19463d = L.circleMarker(
                [43.64828000000006,-79.38146082599997],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_ae5b42b6e448423c88598f265f18117f = L.popup({maxWidth: '300'});

            
                var html_dfee743304994710b27ac834ca58d3a0 = $('<div id="html_dfee743304994710b27ac834ca58d3a0" style="width: 100.0%; height: 100.0%;">M5X, Downtown Toronto</div>')[0];
                popup_ae5b42b6e448423c88598f265f18117f.setContent(html_dfee743304994710b27ac834ca58d3a0);
            

            circle_marker_aa0831f01dcc478a9d9c7b0e1d19463d.bindPopup(popup_ae5b42b6e448423c88598f265f18117f);

            
        
    
            var circle_marker_766e681b3bf9479db6e28b49bfbb39cf = L.circleMarker(
                [43.66878132800008,-79.42070999999999],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_c54817eae96448548ee309b62c977585 = L.popup({maxWidth: '300'});

            
                var html_6a0b7ff2afe94483a933945ce42c7240 = $('<div id="html_6a0b7ff2afe94483a933945ce42c7240" style="width: 100.0%; height: 100.0%;">M6G, Downtown Toronto</div>')[0];
                popup_c54817eae96448548ee309b62c977585.setContent(html_6a0b7ff2afe94483a933945ce42c7240);
            

            circle_marker_766e681b3bf9479db6e28b49bfbb39cf.bindPopup(popup_c54817eae96448548ee309b62c977585);

            
        
    
            var circle_marker_7e2522a0d9924eb2aa51a9d7a3d81140 = L.circleMarker(
                [43.66508694300006,-79.43870499999997],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_f1f1cf21a38c4eee886e2e42d9f74dfe = L.popup({maxWidth: '300'});

            
                var html_0d5917db45054313b3e05f66623ed74c = $('<div id="html_0d5917db45054313b3e05f66623ed74c" style="width: 100.0%; height: 100.0%;">M6H, West Toronto</div>')[0];
                popup_f1f1cf21a38c4eee886e2e42d9f74dfe.setContent(html_0d5917db45054313b3e05f66623ed74c);
            

            circle_marker_7e2522a0d9924eb2aa51a9d7a3d81140.bindPopup(popup_f1f1cf21a38c4eee886e2e42d9f74dfe);

            
        
    
            var circle_marker_c4900a0afcf240a4aaf0bb51cf131471 = L.circleMarker(
                [43.64848500000005,-79.41774150899994],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_2cdca58448c447edb9594eb673f42f2f = L.popup({maxWidth: '300'});

            
                var html_a8c6513c47c54100adba35fc510ca024 = $('<div id="html_a8c6513c47c54100adba35fc510ca024" style="width: 100.0%; height: 100.0%;">M6J, West Toronto</div>')[0];
                popup_2cdca58448c447edb9594eb673f42f2f.setContent(html_a8c6513c47c54100adba35fc510ca024);
            

            circle_marker_c4900a0afcf240a4aaf0bb51cf131471.bindPopup(popup_2cdca58448c447edb9594eb673f42f2f);

            
        
    
            var circle_marker_3647879aadf047c38b104b49bbed6237 = L.circleMarker(
                [43.639410000000055,-79.42436201999999],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_c3e46a342a874c088291cdb65138e461 = L.popup({maxWidth: '300'});

            
                var html_afce495595204fe88d52ca844d0834b7 = $('<div id="html_afce495595204fe88d52ca844d0834b7" style="width: 100.0%; height: 100.0%;">M6K, West Toronto</div>')[0];
                popup_c3e46a342a874c088291cdb65138e461.setContent(html_afce495595204fe88d52ca844d0834b7);
            

            circle_marker_3647879aadf047c38b104b49bbed6237.bindPopup(popup_c3e46a342a874c088291cdb65138e461);

            
        
    
            var circle_marker_2e0bd113e3744a75874b223d9552b01f = L.circleMarker(
                [43.65997500000003,-79.46287357999995],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_2b49a54cad9e4cac9f0b842bcf3ff85b = L.popup({maxWidth: '300'});

            
                var html_2034ac3f33c04b1290a095868cccd40e = $('<div id="html_2034ac3f33c04b1290a095868cccd40e" style="width: 100.0%; height: 100.0%;">M6P, West Toronto</div>')[0];
                popup_2b49a54cad9e4cac9f0b842bcf3ff85b.setContent(html_2034ac3f33c04b1290a095868cccd40e);
            

            circle_marker_2e0bd113e3744a75874b223d9552b01f.bindPopup(popup_2b49a54cad9e4cac9f0b842bcf3ff85b);

            
        
    
            var circle_marker_955db04b4efd4205abddb49d6e26390e = L.circleMarker(
                [43.64787000000007,-79.44976249999996],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_b66fc59461fe42e1a856ecdb000e7e77 = L.popup({maxWidth: '300'});

            
                var html_d2fecaae0d6f43958c841440e3706dbb = $('<div id="html_d2fecaae0d6f43958c841440e3706dbb" style="width: 100.0%; height: 100.0%;">M6R, West Toronto</div>')[0];
                popup_b66fc59461fe42e1a856ecdb000e7e77.setContent(html_d2fecaae0d6f43958c841440e3706dbb);
            

            circle_marker_955db04b4efd4205abddb49d6e26390e.bindPopup(popup_b66fc59461fe42e1a856ecdb000e7e77);

            
        
    
            var circle_marker_9a733eb7be994a91b51c65815824ffe1 = L.circleMarker(
                [43.64988500000004,-79.47492879599997],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_e4410c725e3c458db4f465248b276e04 = L.popup({maxWidth: '300'});

            
                var html_1f8c872e9ca54c5fb642b45c5e87706d = $('<div id="html_1f8c872e9ca54c5fb642b45c5e87706d" style="width: 100.0%; height: 100.0%;">M6S, West Toronto</div>')[0];
                popup_e4410c725e3c458db4f465248b276e04.setContent(html_1f8c872e9ca54c5fb642b45c5e87706d);
            

            circle_marker_9a733eb7be994a91b51c65815824ffe1.bindPopup(popup_e4410c725e3c458db4f465248b276e04);

            
        
    
            var circle_marker_1803914f370b4351af632f094ab683a6 = L.circleMarker(
                [43.648690000000045,-79.38543999999996],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_73dfb77953b044a6aaf2235fda14a46d);
            
    
            var popup_d530387f38124cb7b4bc99af6c230a0f = L.popup({maxWidth: '300'});

            
                var html_93f4dd402b31449c8a2f758886b0f513 = $('<div id="html_93f4dd402b31449c8a2f758886b0f513" style="width: 100.0%; height: 100.0%;">M7Y, East Toronto</div>')[0];
                popup_d530387f38124cb7b4bc99af6c230a0f.setContent(html_93f4dd402b31449c8a2f758886b0f513);
            

            circle_marker_1803914f370b4351af632f094ab683a6.bindPopup(popup_d530387f38124cb7b4bc99af6c230a0f);

            
        
</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>", 
                        "text/plain": "<folium.folium.Map at 0x7f253cd05518>"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "# Get the latitude and longitude of Toronto\ng = geocoder.arcgis('Toronto, Ontario')\nlat_tor = g.latlng[0]\nlng_tor = g.latlng[1]\n\n# Create a map of Toronto\nmap_toronto = folium.Map(location=[lat_tor, lng_tor], zoom_start=11)\n\n# Add markers to map\nfor lat, lng, bor, postcode in zip(toronto['Latitude'], toronto['Longitude'], toronto['Borough'], toronto['Postcode']):\n    label = '{}, {}'.format(postcode, bor)        # popup labels with postcode and borough\n    label = folium.Popup(label, parse_html=True)\n    folium.CircleMarker([lat, lng],\n                        radius=5,\n                        popup=label,\n                        color='blue',\n                        fill=True,\n                        fill_color='#3186cc',\n                        fill_opacity=0.7,\n                        parse_html=False).add_to(map_toronto)  \n    \nmap_toronto"
        }, 
        {
            "source": "Next, Foursquare API is utilized to explore venues in each neighborhood and use the venues to cluster the neighborhoods. To do that, my Foursquare credentials are defined first.", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 12, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "# The code was removed by Watson Studio for sharing."
        }, 
        {
            "source": "A function wass defined to explore the venues in a given neighborhood. Then, the function wass run with the postcodes, latitudes, and longitudes from the 'toronto' dataframe to get the venues for each postcode. The radius and number of venues were limited to 700 meters and 100, respectively.", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 13, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "def getNearbyVenues(names, latitudes, longitudes, radius=500):    \n    venues_list=[]\n    for name, lat, lng in zip(names, latitudes, longitudes):        \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([(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 = ['Postcode', \n                  'Latitude', \n                  'Longitude', \n                  'Venue', \n                  'Venue Latitude', \n                  'Venue Longitude', \n                  'Venue Category']\n    \n    return(nearby_venues)"
        }, 
        {
            "execution_count": 14, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "# Limit the number of venues to 100\nLIMIT = 100\n\n# Get the venues for each postcode\ntoronto_venues = getNearbyVenues(names=toronto['Postcode'],\n                                 latitudes=toronto['Latitude'],\n                                 longitudes=toronto['Longitude'],\n                                 radius=700) # limit radius to 700 meters"
        }, 
        {
            "execution_count": 15, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "(2405, 7)\n"
                }, 
                {
                    "execution_count": 15, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Postcode</th>\n      <th>Latitude</th>\n      <th>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>M4E</td>\n      <td>43.676845</td>\n      <td>-79.295225</td>\n      <td>Glen Manor Ravine</td>\n      <td>43.676821</td>\n      <td>-79.293942</td>\n      <td>Trail</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>M4E</td>\n      <td>43.676845</td>\n      <td>-79.295225</td>\n      <td>The Big Carrot Natural Food Market</td>\n      <td>43.678879</td>\n      <td>-79.297734</td>\n      <td>Health Food Store</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>M4E</td>\n      <td>43.676845</td>\n      <td>-79.295225</td>\n      <td>Tori's Bakeshop</td>\n      <td>43.672114</td>\n      <td>-79.290331</td>\n      <td>Vegetarian / Vegan Restaurant</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>M4E</td>\n      <td>43.676845</td>\n      <td>-79.295225</td>\n      <td>The Beech Tree</td>\n      <td>43.680493</td>\n      <td>-79.288846</td>\n      <td>Gastropub</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>M4E</td>\n      <td>43.676845</td>\n      <td>-79.295225</td>\n      <td>Beaches Bake Shop</td>\n      <td>43.680363</td>\n      <td>-79.289692</td>\n      <td>Bakery</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>M4E</td>\n      <td>43.676845</td>\n      <td>-79.295225</td>\n      <td>Mastermind Toys</td>\n      <td>43.671453</td>\n      <td>-79.293971</td>\n      <td>Toy / Game Store</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>M4E</td>\n      <td>43.676845</td>\n      <td>-79.295225</td>\n      <td>Beacher Cafe</td>\n      <td>43.671938</td>\n      <td>-79.291238</td>\n      <td>Breakfast Spot</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>M4E</td>\n      <td>43.676845</td>\n      <td>-79.295225</td>\n      <td>Veloute Bistro</td>\n      <td>43.672267</td>\n      <td>-79.289584</td>\n      <td>French Restaurant</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>M4E</td>\n      <td>43.676845</td>\n      <td>-79.295225</td>\n      <td>Castro's Lounge</td>\n      <td>43.671104</td>\n      <td>-79.295107</td>\n      <td>Bar</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>M4E</td>\n      <td>43.676845</td>\n      <td>-79.295225</td>\n      <td>Yumei Sushi</td>\n      <td>43.671108</td>\n      <td>-79.295064</td>\n      <td>Japanese Restaurant</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "  Postcode   Latitude  Longitude                               Venue  \\\n0      M4E  43.676845 -79.295225                   Glen Manor Ravine   \n1      M4E  43.676845 -79.295225  The Big Carrot Natural Food Market   \n2      M4E  43.676845 -79.295225                     Tori's Bakeshop   \n3      M4E  43.676845 -79.295225                      The Beech Tree   \n4      M4E  43.676845 -79.295225                   Beaches Bake Shop   \n5      M4E  43.676845 -79.295225                     Mastermind Toys   \n6      M4E  43.676845 -79.295225                        Beacher Cafe   \n7      M4E  43.676845 -79.295225                      Veloute Bistro   \n8      M4E  43.676845 -79.295225                     Castro's Lounge   \n9      M4E  43.676845 -79.295225                         Yumei Sushi   \n\n   Venue Latitude  Venue Longitude                 Venue Category  \n0       43.676821       -79.293942                          Trail  \n1       43.678879       -79.297734              Health Food Store  \n2       43.672114       -79.290331  Vegetarian / Vegan Restaurant  \n3       43.680493       -79.288846                      Gastropub  \n4       43.680363       -79.289692                         Bakery  \n5       43.671453       -79.293971               Toy / Game Store  \n6       43.671938       -79.291238                 Breakfast Spot  \n7       43.672267       -79.289584              French Restaurant  \n8       43.671104       -79.295107                            Bar  \n9       43.671108       -79.295064            Japanese Restaurant  "
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "# Check the venues dataframe\nprint(toronto_venues.shape)\ntoronto_venues.head(10)"
        }, 
        {
            "execution_count": 16, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 16, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Postcode</th>\n      <th>Venue Count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>M4E</td>\n      <td>26</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>M4K</td>\n      <td>34</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>M4L</td>\n      <td>33</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>M4M</td>\n      <td>78</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>M4N</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>M4P</td>\n      <td>21</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>M4R</td>\n      <td>33</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>M4S</td>\n      <td>45</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>M4T</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>M4V</td>\n      <td>21</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>M4W</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>M4X</td>\n      <td>69</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>M4Y</td>\n      <td>100</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>M5A</td>\n      <td>91</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>M5B</td>\n      <td>100</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>M5C</td>\n      <td>100</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>M5E</td>\n      <td>100</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>M5G</td>\n      <td>100</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>M5H</td>\n      <td>100</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>M5J</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>M5K</td>\n      <td>100</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>M5L</td>\n      <td>100</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>M5N</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>M5P</td>\n      <td>13</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>M5R</td>\n      <td>41</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>M5S</td>\n      <td>86</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>M5T</td>\n      <td>100</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>M5V</td>\n      <td>100</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>M5W</td>\n      <td>100</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>M5X</td>\n      <td>100</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>M6G</td>\n      <td>69</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>M6H</td>\n      <td>49</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>M6J</td>\n      <td>100</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>M6K</td>\n      <td>100</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>M6P</td>\n      <td>42</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>M6R</td>\n      <td>70</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>M6S</td>\n      <td>53</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>M7Y</td>\n      <td>100</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "   Postcode  Venue Count\n0       M4E           26\n1       M4K           34\n2       M4L           33\n3       M4M           78\n4       M4N            7\n5       M4P           21\n6       M4R           33\n7       M4S           45\n8       M4T            7\n9       M4V           21\n10      M4W            7\n11      M4X           69\n12      M4Y          100\n13      M5A           91\n14      M5B          100\n15      M5C          100\n16      M5E          100\n17      M5G          100\n18      M5H          100\n19      M5J            6\n20      M5K          100\n21      M5L          100\n22      M5N            4\n23      M5P           13\n24      M5R           41\n25      M5S           86\n26      M5T          100\n27      M5V          100\n28      M5W          100\n29      M5X          100\n30      M6G           69\n31      M6H           49\n32      M6J          100\n33      M6K          100\n34      M6P           42\n35      M6R           70\n36      M6S           53\n37      M7Y          100"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "# Check the number of venues for each postcode\nnum_venues = pd.DataFrame()\nnum_venues['Postcode'] = toronto_venues.groupby('Postcode').count().reset_index()['Postcode']\nnum_venues['Venue Count'] = toronto_venues.groupby('Postcode').count().reset_index()['Venue']\nnum_venues"
        }, 
        {
            "source": "To analyze each neighborhood and cluster them based on Foursquare venue categories, mean frequency of occurrences for each category was calculated for each neighborhood.", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 17, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 17, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Postcode</th>\n      <th>Accessories Store</th>\n      <th>Afghan Restaurant</th>\n      <th>African Restaurant</th>\n      <th>American Restaurant</th>\n      <th>Animal Shelter</th>\n      <th>Antique Shop</th>\n      <th>Art Gallery</th>\n      <th>Arts &amp; Crafts Store</th>\n      <th>Asian Restaurant</th>\n      <th>Athletics &amp; Sports</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 Field</th>\n      <th>Basketball Stadium</th>\n      <th>Beach</th>\n      <th>Beer Bar</th>\n      <th>Beer Store</th>\n      <th>Belgian Restaurant</th>\n      <th>Bike Shop</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>Bus Stop</th>\n      <th>Business Service</th>\n      <th>Butcher</th>\n      <th>Caf\u00e9</th>\n      <th>Cajun / Creole Restaurant</th>\n      <th>Camera Store</th>\n      <th>Candy Store</th>\n      <th>Caribbean Restaurant</th>\n      <th>Castle</th>\n      <th>Cemetery</th>\n      <th>Cheese Shop</th>\n      <th>Chinese Restaurant</th>\n      <th>Chiropractor</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 Gym</th>\n      <th>College Rec Center</th>\n      <th>Colombian Restaurant</th>\n      <th>Comedy Club</th>\n      <th>Comfort Food Restaurant</th>\n      <th>Comic Shop</th>\n      <th>Concert Hall</th>\n      <th>Convenience Store</th>\n      <th>Cosmetics Shop</th>\n      <th>Costume Shop</th>\n      <th>Creperie</th>\n      <th>Cuban Restaurant</th>\n      <th>Cupcake Shop</th>\n      <th>Curling Ice</th>\n      <th>Cycle Studio</th>\n      <th>Dance Studio</th>\n      <th>Deli / Bodega</th>\n      <th>Department Store</th>\n      <th>Design Studio</th>\n      <th>Dessert Shop</th>\n      <th>Diner</th>\n      <th>Discount Store</th>\n      <th>Dive Bar</th>\n      <th>Dog Run</th>\n      <th>Doner Restaurant</th>\n      <th>Donut Shop</th>\n      <th>Dumpling Restaurant</th>\n      <th>Eastern European Restaurant</th>\n      <th>Electronics Store</th>\n      <th>Elementary School</th>\n      <th>Ethiopian Restaurant</th>\n      <th>Event Space</th>\n      <th>Falafel Restaurant</th>\n      <th>Farm</th>\n      <th>Farmers Market</th>\n      <th>Fast Food 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>Furniture / Home Store</th>\n      <th>Gaming Cafe</th>\n      <th>Garden</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>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>Gym Pool</th>\n      <th>Harbor / Marina</th>\n      <th>Hardware Store</th>\n      <th>Hawaiian Restaurant</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>Hostel</th>\n      <th>Hot Dog Joint</th>\n      <th>Hotel</th>\n      <th>Hotel Bar</th>\n      <th>Ice Cream Shop</th>\n      <th>Indian Restaurant</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>Jewish Restaurant</th>\n      <th>Juice Bar</th>\n      <th>Karaoke Bar</th>\n      <th>Korean Restaurant</th>\n      <th>Latin American Restaurant</th>\n      <th>Library</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>Malay Restaurant</th>\n      <th>Market</th>\n      <th>Martial Arts Dojo</th>\n      <th>Massage Studio</th>\n      <th>Mattress Store</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>Mobile Phone Shop</th>\n      <th>Modern European Restaurant</th>\n      <th>Molecular Gastronomy Restaurant</th>\n      <th>Monument / Landmark</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>Other Nightlife</th>\n      <th>Outdoor Sculpture</th>\n      <th>Paper / Office Supplies Store</th>\n      <th>Park</th>\n      <th>Pastry Shop</th>\n      <th>Performing Arts Venue</th>\n      <th>Persian Restaurant</th>\n      <th>Peruvian Restaurant</th>\n      <th>Pet Store</th>\n      <th>Pharmacy</th>\n      <th>Pilates Studio</th>\n      <th>Pizza Place</th>\n      <th>Playground</th>\n      <th>Plaza</th>\n      <th>Poke Place</th>\n      <th>Pool</th>\n      <th>Pool Hall</th>\n      <th>Portuguese Restaurant</th>\n      <th>Poutine Place</th>\n      <th>Pub</th>\n      <th>Ramen Restaurant</th>\n      <th>Record Shop</th>\n      <th>Rental Car Location</th>\n      <th>Restaurant</th>\n      <th>Rock Club</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 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>Soccer Field</th>\n      <th>Social Club</th>\n      <th>Soup Place</th>\n      <th>Southern / Soul Food Restaurant</th>\n      <th>Spa</th>\n      <th>Spanish Restaurant</th>\n      <th>Speakeasy</th>\n      <th>Sporting Goods Shop</th>\n      <th>Sports Bar</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>Taco Place</th>\n      <th>Tailor Shop</th>\n      <th>Taiwanese Restaurant</th>\n      <th>Tanning Salon</th>\n      <th>Tapas Restaurant</th>\n      <th>Tea Room</th>\n      <th>Tech Startup</th>\n      <th>Tennis Court</th>\n      <th>Thai Restaurant</th>\n      <th>Theater</th>\n      <th>Theme Park Ride / Attraction</th>\n      <th>Theme Restaurant</th>\n      <th>Thrift / Vintage Store</th>\n      <th>Toy / Game Store</th>\n      <th>Track</th>\n      <th>Trail</th>\n      <th>Train Station</th>\n      <th>Tram Station</th>\n      <th>University</th>\n      <th>Vegetarian / Vegan Restaurant</th>\n      <th>Video Game Store</th>\n      <th>Video Store</th>\n      <th>Vietnamese Restaurant</th>\n      <th>Wine Bar</th>\n      <th>Wine Shop</th>\n      <th>Wings Joint</th>\n      <th>Yoga Studio</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>M4E</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.038462</td>\n      <td>0.000000</td>\n      <td>0.076923</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.038462</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.038462</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.038462</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.038462</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.038462</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.038462</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</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>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.038462</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.076923</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.038462</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.038462</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.038462</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.038462</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.038462</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.038462</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.038462</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.038462</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.038462</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.038462</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.038462</td>\n      <td>0.000000</td>\n      <td>0.038462</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.038462</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>M4K</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.029412</td>\n      <td>0.029412</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.029412</td>\n      <td>0.029412</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.029412</td>\n      <td>0.000000</td>\n      <td>0.058824</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.029412</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.029412</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.029412</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.029412</td>\n      <td>0.029412</td>\n      <td>0.029412</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.058824</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.029412</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.117647</td>\n      <td>0.029412</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.058824</td>\n      <td>0.029412</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.029412</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.029412</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.029412</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.029412</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.029412</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.029412</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.029412</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.029412</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.029412</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.029412</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>M4L</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.060606</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.030303</td>\n      <td>0.060606</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.030303</td>\n      <td>0.030303</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.030303</td>\n      <td>0.060606</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.060606</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.030303</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.030303</td>\n      <td>0.060606</td>\n      <td>0.030303</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.030303</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.060606</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.030303</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.030303</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.030303</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.030303</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.030303</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.030303</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.030303</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.030303</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.060606</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.030303</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.030303</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.030303</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>M4M</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.025641</td>\n      <td>0.0</td>\n      <td>0.012821</td>\n      <td>0.0</td>\n      <td>0.025641</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.012821</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.051282</td>\n      <td>0.012821</td>\n      <td>0.038462</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.012821</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.012821</td>\n      <td>0.012821</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.025641</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.051282</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.012821</td>\n      <td>0.012821</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.012821</td>\n      <td>0.0</td>\n      <td>0.051282</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.012821</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.012821</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.051282</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.012821</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.025641</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.012821</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.025641</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.012821</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.025641</td>\n      <td>0.012821</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.025641</td>\n      <td>0.000000</td>\n      <td>0.025641</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.012821</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.012821</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.038462</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.012821</td>\n      <td>0.0</td>\n      <td>0.012821</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.012821</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.012821</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.012821</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.012821</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.025641</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.012821</td>\n      <td>0.0</td>\n      <td>0.012821</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.038462</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.012821</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.012821</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.012821</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.025641</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.012821</td>\n      <td>0.012821</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.012821</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.012821</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.012821</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>M4N</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.142857</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.142857</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.142857</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.142857</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.142857</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.142857</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.142857</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>M4P</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.047619</td>\n      <td>0.047619</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.047619</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.047619</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.047619</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.047619</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.047619</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.047619</td>\n      <td>0.047619</td>\n      <td>0.000000</td>\n      <td>0.095238</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.047619</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.047619</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.047619</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.047619</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.047619</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.047619</td>\n      <td>0.0</td>\n      <td>0.047619</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.047619</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.047619</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.047619</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>M4R</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.030303</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.030303</td>\n      <td>0.030303</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.030303</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.060606</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.030303</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.060606</td>\n      <td>0.0</td>\n      <td>0.090909</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.030303</td>\n      <td>0.030303</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.030303</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.030303</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.030303</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.030303</td>\n      <td>0.030303</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.030303</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.030303</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.030303</td>\n      <td>0.060606</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.030303</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.030303</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.090909</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.030303</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.030303</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.030303</td>\n      <td>0.030303</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>M4S</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.022222</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.022222</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.022222</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.022222</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.044444</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.022222</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.044444</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.022222</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.022222</td>\n      <td>0.022222</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.066667</td>\n      <td>0.022222</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.022222</td>\n      <td>0.022222</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.022222</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.022222</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.022222</td>\n      <td>0.000000</td>\n      <td>0.044444</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.022222</td>\n      <td>0.022222</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.044444</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.022222</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.022222</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.022222</td>\n      <td>0.0</td>\n      <td>0.044444</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.044444</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.066667</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.022222</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.044444</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.022222</td>\n      <td>0.044444</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.022222</td>\n      <td>0.000000</td>\n      <td>0.022222</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>M4T</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.142857</td>\n      <td>0.142857</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.285714</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.142857</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.142857</td>\n      <td>0.142857</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>M4V</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.047619</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.047619</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.095238</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.047619</td>\n      <td>0.047619</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.047619</td>\n      <td>0.0</td>\n      <td>0.047619</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.095238</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.047619</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.095238</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.047619</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.095238</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.047619</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.047619</td>\n      <td>0.047619</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.047619</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.047619</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "  Postcode  Accessories Store  Afghan Restaurant  African Restaurant  \\\n0      M4E                0.0                0.0                 0.0   \n1      M4K                0.0                0.0                 0.0   \n2      M4L                0.0                0.0                 0.0   \n3      M4M                0.0                0.0                 0.0   \n4      M4N                0.0                0.0                 0.0   \n5      M4P                0.0                0.0                 0.0   \n6      M4R                0.0                0.0                 0.0   \n7      M4S                0.0                0.0                 0.0   \n8      M4T                0.0                0.0                 0.0   \n9      M4V                0.0                0.0                 0.0   \n\n   American Restaurant  Animal Shelter  Antique Shop  Art Gallery  \\\n0             0.000000             0.0      0.000000          0.0   \n1             0.000000             0.0      0.000000          0.0   \n2             0.000000             0.0      0.000000          0.0   \n3             0.025641             0.0      0.012821          0.0   \n4             0.000000             0.0      0.000000          0.0   \n5             0.000000             0.0      0.000000          0.0   \n6             0.000000             0.0      0.000000          0.0   \n7             0.022222             0.0      0.000000          0.0   \n8             0.000000             0.0      0.000000          0.0   \n9             0.047619             0.0      0.000000          0.0   \n\n   Arts & Crafts Store  Asian Restaurant  Athletics & Sports  BBQ Joint  \\\n0             0.000000          0.000000            0.000000   0.000000   \n1             0.000000          0.000000            0.000000   0.000000   \n2             0.000000          0.000000            0.000000   0.000000   \n3             0.025641          0.000000            0.000000   0.012821   \n4             0.000000          0.000000            0.000000   0.000000   \n5             0.000000          0.000000            0.000000   0.000000   \n6             0.000000          0.000000            0.030303   0.000000   \n7             0.000000          0.022222            0.000000   0.000000   \n8             0.000000          0.000000            0.000000   0.000000   \n9             0.000000          0.000000            0.000000   0.000000   \n\n   Baby Store  Bagel Shop    Bakery      Bank       Bar  Baseball Field  \\\n0         0.0    0.000000  0.038462  0.000000  0.076923             0.0   \n1         0.0    0.000000  0.029412  0.029412  0.000000             0.0   \n2         0.0    0.000000  0.060606  0.000000  0.000000             0.0   \n3         0.0    0.000000  0.051282  0.012821  0.038462             0.0   \n4         0.0    0.000000  0.000000  0.000000  0.000000             0.0   \n5         0.0    0.000000  0.000000  0.000000  0.000000             0.0   \n6         0.0    0.030303  0.030303  0.000000  0.000000             0.0   \n7         0.0    0.000000  0.000000  0.000000  0.022222             0.0   \n8         0.0    0.000000  0.000000  0.000000  0.000000             0.0   \n9         0.0    0.047619  0.000000  0.000000  0.000000             0.0   \n\n   Basketball Stadium  Beach  Beer Bar  Beer Store  Belgian Restaurant  \\\n0                 0.0    0.0       0.0    0.000000                 0.0   \n1                 0.0    0.0       0.0    0.000000                 0.0   \n2                 0.0    0.0       0.0    0.000000                 0.0   \n3                 0.0    0.0       0.0    0.012821                 0.0   \n4                 0.0    0.0       0.0    0.000000                 0.0   \n5                 0.0    0.0       0.0    0.000000                 0.0   \n6                 0.0    0.0       0.0    0.000000                 0.0   \n7                 0.0    0.0       0.0    0.000000                 0.0   \n8                 0.0    0.0       0.0    0.000000                 0.0   \n9                 0.0    0.0       0.0    0.000000                 0.0   \n\n   Bike Shop  Bistro  Boat or Ferry  Bookstore  Boutique  \\\n0        0.0     0.0            0.0   0.000000  0.000000   \n1        0.0     0.0            0.0   0.000000  0.000000   \n2        0.0     0.0            0.0   0.000000  0.000000   \n3        0.0     0.0            0.0   0.012821  0.012821   \n4        0.0     0.0            0.0   0.142857  0.000000   \n5        0.0     0.0            0.0   0.000000  0.000000   \n6        0.0     0.0            0.0   0.000000  0.000000   \n7        0.0     0.0            0.0   0.000000  0.000000   \n8        0.0     0.0            0.0   0.000000  0.000000   \n9        0.0     0.0            0.0   0.000000  0.000000   \n\n   Brazilian Restaurant  Breakfast Spot   Brewery  Bubble Tea Shop  Building  \\\n0                   0.0        0.038462  0.000000         0.000000       0.0   \n1                   0.0        0.000000  0.029412         0.029412       0.0   \n2                   0.0        0.030303  0.060606         0.000000       0.0   \n3                   0.0        0.000000  0.025641         0.000000       0.0   \n4                   0.0        0.000000  0.000000         0.000000       0.0   \n5                   0.0        0.047619  0.047619         0.000000       0.0   \n6                   0.0        0.000000  0.000000         0.000000       0.0   \n7                   0.0        0.000000  0.022222         0.000000       0.0   \n8                   0.0        0.000000  0.000000         0.000000       0.0   \n9                   0.0        0.000000  0.000000         0.000000       0.0   \n\n   Burger Joint  Burrito Place  Bus Line  Bus Stop  Business Service  \\\n0      0.000000       0.000000  0.000000       0.0          0.000000   \n1      0.000000       0.000000  0.000000       0.0          0.029412   \n2      0.030303       0.030303  0.000000       0.0          0.000000   \n3      0.000000       0.000000  0.000000       0.0          0.000000   \n4      0.000000       0.000000  0.142857       0.0          0.000000   \n5      0.000000       0.000000  0.000000       0.0          0.000000   \n6      0.030303       0.000000  0.000000       0.0          0.000000   \n7      0.000000       0.000000  0.000000       0.0          0.000000   \n8      0.000000       0.000000  0.000000       0.0          0.000000   \n9      0.000000       0.000000  0.000000       0.0          0.000000   \n\n    Butcher      Caf\u00e9  Cajun / Creole Restaurant  Camera Store  Candy Store  \\\n0  0.000000  0.038462                        0.0           0.0          0.0   \n1  0.000000  0.058824                        0.0           0.0          0.0   \n2  0.030303  0.060606                        0.0           0.0          0.0   \n3  0.000000  0.051282                        0.0           0.0          0.0   \n4  0.000000  0.142857                        0.0           0.0          0.0   \n5  0.000000  0.047619                        0.0           0.0          0.0   \n6  0.000000  0.060606                        0.0           0.0          0.0   \n7  0.000000  0.044444                        0.0           0.0          0.0   \n8  0.000000  0.000000                        0.0           0.0          0.0   \n9  0.000000  0.000000                        0.0           0.0          0.0   \n\n   Caribbean Restaurant  Castle  Cemetery  Cheese Shop  Chinese Restaurant  \\\n0                   0.0     0.0  0.000000     0.000000            0.000000   \n1                   0.0     0.0  0.000000     0.000000            0.029412   \n2                   0.0     0.0  0.000000     0.000000            0.000000   \n3                   0.0     0.0  0.000000     0.012821            0.012821   \n4                   0.0     0.0  0.000000     0.000000            0.000000   \n5                   0.0     0.0  0.000000     0.000000            0.000000   \n6                   0.0     0.0  0.000000     0.000000            0.030303   \n7                   0.0     0.0  0.022222     0.000000            0.000000   \n8                   0.0     0.0  0.000000     0.000000            0.000000   \n9                   0.0     0.0  0.000000     0.000000            0.000000   \n\n   Chiropractor  Chocolate Shop    Church  Climbing Gym  Clothing Store  \\\n0           0.0             0.0  0.038462           0.0        0.000000   \n1           0.0             0.0  0.000000           0.0        0.000000   \n2           0.0             0.0  0.000000           0.0        0.000000   \n3           0.0             0.0  0.000000           0.0        0.012821   \n4           0.0             0.0  0.000000           0.0        0.000000   \n5           0.0             0.0  0.000000           0.0        0.047619   \n6           0.0             0.0  0.000000           0.0        0.060606   \n7           0.0             0.0  0.000000           0.0        0.000000   \n8           0.0             0.0  0.000000           0.0        0.000000   \n9           0.0             0.0  0.000000           0.0        0.000000   \n\n   Cocktail Bar  Coffee Shop  College Arts Building  College Gym  \\\n0           0.0     0.000000                    0.0          0.0   \n1           0.0     0.029412                    0.0          0.0   \n2           0.0     0.060606                    0.0          0.0   \n3           0.0     0.051282                    0.0          0.0   \n4           0.0     0.142857                    0.0          0.0   \n5           0.0     0.000000                    0.0          0.0   \n6           0.0     0.090909                    0.0          0.0   \n7           0.0     0.044444                    0.0          0.0   \n8           0.0     0.000000                    0.0          0.0   \n9           0.0     0.095238                    0.0          0.0   \n\n   College Rec Center  Colombian Restaurant  Comedy Club  \\\n0                 0.0                   0.0          0.0   \n1                 0.0                   0.0          0.0   \n2                 0.0                   0.0          0.0   \n3                 0.0                   0.0          0.0   \n4                 0.0                   0.0          0.0   \n5                 0.0                   0.0          0.0   \n6                 0.0                   0.0          0.0   \n7                 0.0                   0.0          0.0   \n8                 0.0                   0.0          0.0   \n9                 0.0                   0.0          0.0   \n\n   Comfort Food Restaurant  Comic Shop  Concert Hall  Convenience Store  \\\n0                 0.000000         0.0           0.0           0.000000   \n1                 0.000000         0.0           0.0           0.000000   \n2                 0.000000         0.0           0.0           0.000000   \n3                 0.012821         0.0           0.0           0.012821   \n4                 0.000000         0.0           0.0           0.000000   \n5                 0.000000         0.0           0.0           0.000000   \n6                 0.000000         0.0           0.0           0.000000   \n7                 0.000000         0.0           0.0           0.000000   \n8                 0.000000         0.0           0.0           0.000000   \n9                 0.000000         0.0           0.0           0.000000   \n\n   Cosmetics Shop  Costume Shop  Creperie  Cuban Restaurant  Cupcake Shop  \\\n0        0.000000      0.000000       0.0               0.0           0.0   \n1        0.029412      0.000000       0.0               0.0           0.0   \n2        0.000000      0.000000       0.0               0.0           0.0   \n3        0.000000      0.000000       0.0               0.0           0.0   \n4        0.000000      0.000000       0.0               0.0           0.0   \n5        0.000000      0.000000       0.0               0.0           0.0   \n6        0.000000      0.000000       0.0               0.0           0.0   \n7        0.000000      0.022222       0.0               0.0           0.0   \n8        0.000000      0.000000       0.0               0.0           0.0   \n9        0.000000      0.000000       0.0               0.0           0.0   \n\n   Curling Ice  Cycle Studio  Dance Studio  Deli / Bodega  Department Store  \\\n0          0.0           0.0      0.000000       0.000000               0.0   \n1          0.0           0.0      0.000000       0.000000               0.0   \n2          0.0           0.0      0.000000       0.000000               0.0   \n3          0.0           0.0      0.000000       0.000000               0.0   \n4          0.0           0.0      0.000000       0.000000               0.0   \n5          0.0           0.0      0.000000       0.000000               0.0   \n6          0.0           0.0      0.000000       0.000000               0.0   \n7          0.0           0.0      0.022222       0.022222               0.0   \n8          0.0           0.0      0.000000       0.000000               0.0   \n9          0.0           0.0      0.000000       0.000000               0.0   \n\n   Design Studio  Dessert Shop     Diner  Discount Store  Dive Bar   Dog Run  \\\n0            0.0      0.000000  0.000000        0.000000       0.0  0.000000   \n1            0.0      0.029412  0.029412        0.029412       0.0  0.000000   \n2            0.0      0.000000  0.030303        0.000000       0.0  0.000000   \n3            0.0      0.000000  0.051282        0.000000       0.0  0.000000   \n4            0.0      0.000000  0.000000        0.000000       0.0  0.000000   \n5            0.0      0.000000  0.000000        0.000000       0.0  0.047619   \n6            0.0      0.030303  0.030303        0.000000       0.0  0.000000   \n7            0.0      0.066667  0.022222        0.000000       0.0  0.000000   \n8            0.0      0.000000  0.000000        0.000000       0.0  0.000000   \n9            0.0      0.000000  0.000000        0.000000       0.0  0.000000   \n\n   Doner Restaurant  Donut Shop  Dumpling Restaurant  \\\n0               0.0         0.0                  0.0   \n1               0.0         0.0                  0.0   \n2               0.0         0.0                  0.0   \n3               0.0         0.0                  0.0   \n4               0.0         0.0                  0.0   \n5               0.0         0.0                  0.0   \n6               0.0         0.0                  0.0   \n7               0.0         0.0                  0.0   \n8               0.0         0.0                  0.0   \n9               0.0         0.0                  0.0   \n\n   Eastern European Restaurant  Electronics Store  Elementary School  \\\n0                          0.0           0.038462                0.0   \n1                          0.0           0.000000                0.0   \n2                          0.0           0.000000                0.0   \n3                          0.0           0.000000                0.0   \n4                          0.0           0.000000                0.0   \n5                          0.0           0.000000                0.0   \n6                          0.0           0.000000                0.0   \n7                          0.0           0.000000                0.0   \n8                          0.0           0.000000                0.0   \n9                          0.0           0.000000                0.0   \n\n   Ethiopian Restaurant  Event Space  Falafel Restaurant  Farm  \\\n0                   0.0          0.0            0.000000   0.0   \n1                   0.0          0.0            0.000000   0.0   \n2                   0.0          0.0            0.000000   0.0   \n3                   0.0          0.0            0.012821   0.0   \n4                   0.0          0.0            0.000000   0.0   \n5                   0.0          0.0            0.000000   0.0   \n6                   0.0          0.0            0.000000   0.0   \n7                   0.0          0.0            0.000000   0.0   \n8                   0.0          0.0            0.000000   0.0   \n9                   0.0          0.0            0.000000   0.0   \n\n   Farmers Market  Fast Food Restaurant  Fish & Chips Shop  Fish Market  \\\n0        0.000000              0.000000           0.000000          0.0   \n1        0.000000              0.058824           0.000000          0.0   \n2        0.030303              0.060606           0.030303          0.0   \n3        0.000000              0.025641           0.000000          0.0   \n4        0.000000              0.000000           0.000000          0.0   \n5        0.000000              0.000000           0.000000          0.0   \n6        0.000000              0.000000           0.000000          0.0   \n7        0.022222              0.022222           0.000000          0.0   \n8        0.000000              0.000000           0.000000          0.0   \n9        0.000000              0.000000           0.000000          0.0   \n\n   Flea Market  Flower Shop  Food  Food & Drink Shop  Food Court  Food Truck  \\\n0     0.000000     0.000000   0.0           0.000000         0.0         0.0   \n1     0.000000     0.029412   0.0           0.000000         0.0         0.0   \n2     0.000000     0.000000   0.0           0.000000         0.0         0.0   \n3     0.012821     0.000000   0.0           0.000000         0.0         0.0   \n4     0.000000     0.000000   0.0           0.000000         0.0         0.0   \n5     0.000000     0.000000   0.0           0.047619         0.0         0.0   \n6     0.000000     0.000000   0.0           0.000000         0.0         0.0   \n7     0.000000     0.022222   0.0           0.000000         0.0         0.0   \n8     0.000000     0.000000   0.0           0.000000         0.0         0.0   \n9     0.000000     0.000000   0.0           0.000000         0.0         0.0   \n\n   Fountain  French Restaurant  Fried Chicken Joint  Frozen Yogurt Shop  \\\n0       0.0           0.038462             0.000000                 0.0   \n1       0.0           0.000000             0.000000                 0.0   \n2       0.0           0.030303             0.000000                 0.0   \n3       0.0           0.025641             0.000000                 0.0   \n4       0.0           0.000000             0.000000                 0.0   \n5       0.0           0.000000             0.047619                 0.0   \n6       0.0           0.000000             0.000000                 0.0   \n7       0.0           0.000000             0.000000                 0.0   \n8       0.0           0.000000             0.000000                 0.0   \n9       0.0           0.047619             0.047619                 0.0   \n\n   Furniture / Home Store  Gaming Cafe  Garden  Gas Station  Gastropub  \\\n0                0.000000          0.0     0.0          0.0   0.038462   \n1                0.000000          0.0     0.0          0.0   0.000000   \n2                0.000000          0.0     0.0          0.0   0.000000   \n3                0.012821          0.0     0.0          0.0   0.025641   \n4                0.000000          0.0     0.0          0.0   0.000000   \n5                0.000000          0.0     0.0          0.0   0.000000   \n6                0.000000          0.0     0.0          0.0   0.000000   \n7                0.000000          0.0     0.0          0.0   0.022222   \n8                0.000000          0.0     0.0          0.0   0.000000   \n9                0.000000          0.0     0.0          0.0   0.000000   \n\n    Gay Bar  General Entertainment  General Travel  German Restaurant  \\\n0  0.000000                    0.0             0.0                0.0   \n1  0.000000                    0.0             0.0                0.0   \n2  0.000000                    0.0             0.0                0.0   \n3  0.012821                    0.0             0.0                0.0   \n4  0.000000                    0.0             0.0                0.0   \n5  0.000000                    0.0             0.0                0.0   \n6  0.000000                    0.0             0.0                0.0   \n7  0.000000                    0.0             0.0                0.0   \n8  0.000000                    0.0             0.0                0.0   \n9  0.000000                    0.0             0.0                0.0   \n\n   Gift Shop  Gourmet Shop  Greek Restaurant  Grocery Store       Gym  \\\n0   0.000000      0.000000          0.038462       0.000000  0.000000   \n1   0.000000      0.000000          0.117647       0.029412  0.000000   \n2   0.000000      0.000000          0.000000       0.000000  0.060606   \n3   0.000000      0.000000          0.000000       0.025641  0.000000   \n4   0.000000      0.000000          0.000000       0.000000  0.000000   \n5   0.000000      0.047619          0.047619       0.000000  0.095238   \n6   0.030303      0.000000          0.000000       0.000000  0.000000   \n7   0.000000      0.000000          0.022222       0.000000  0.044444   \n8   0.000000      0.000000          0.000000       0.142857  0.142857   \n9   0.000000      0.000000          0.000000       0.000000  0.000000   \n\n   Gym / Fitness Center  Gym Pool  Harbor / Marina  Hardware Store  \\\n0              0.000000  0.000000              0.0        0.000000   \n1              0.000000  0.000000              0.0        0.000000   \n2              0.000000  0.000000              0.0        0.000000   \n3              0.025641  0.000000              0.0        0.012821   \n4              0.142857  0.000000              0.0        0.000000   \n5              0.000000  0.000000              0.0        0.000000   \n6              0.000000  0.030303              0.0        0.000000   \n7              0.000000  0.000000              0.0        0.000000   \n8              0.000000  0.000000              0.0        0.000000   \n9              0.000000  0.000000              0.0        0.000000   \n\n   Hawaiian Restaurant  Health & Beauty Service  Health Food Store  \\\n0                  0.0                      0.0           0.038462   \n1                  0.0                      0.0           0.000000   \n2                  0.0                      0.0           0.000000   \n3                  0.0                      0.0           0.000000   \n4                  0.0                      0.0           0.000000   \n5                  0.0                      0.0           0.000000   \n6                  0.0                      0.0           0.000000   \n7                  0.0                      0.0           0.000000   \n8                  0.0                      0.0           0.000000   \n9                  0.0                      0.0           0.000000   \n\n   Historic Site  History Museum  Hobby Shop  Hostel  Hot Dog Joint     Hotel  \\\n0            0.0             0.0         0.0     0.0            0.0  0.000000   \n1            0.0             0.0         0.0     0.0            0.0  0.000000   \n2            0.0             0.0         0.0     0.0            0.0  0.000000   \n3            0.0             0.0         0.0     0.0            0.0  0.000000   \n4            0.0             0.0         0.0     0.0            0.0  0.000000   \n5            0.0             0.0         0.0     0.0            0.0  0.047619   \n6            0.0             0.0         0.0     0.0            0.0  0.000000   \n7            0.0             0.0         0.0     0.0            0.0  0.000000   \n8            0.0             0.0         0.0     0.0            0.0  0.000000   \n9            0.0             0.0         0.0     0.0            0.0  0.000000   \n\n   Hotel Bar  Ice Cream Shop  Indian Restaurant  Indoor Play Area  \\\n0        0.0        0.000000           0.000000          0.000000   \n1        0.0        0.058824           0.029412          0.000000   \n2        0.0        0.030303           0.000000          0.000000   \n3        0.0        0.012821           0.000000          0.000000   \n4        0.0        0.000000           0.000000          0.000000   \n5        0.0        0.000000           0.047619          0.000000   \n6        0.0        0.000000           0.000000          0.000000   \n7        0.0        0.000000           0.022222          0.022222   \n8        0.0        0.000000           0.000000          0.000000   \n9        0.0        0.000000           0.000000          0.000000   \n\n   Intersection  Irish Pub  Italian Restaurant  Japanese Restaurant  \\\n0           0.0        0.0            0.000000             0.076923   \n1           0.0        0.0            0.029412             0.000000   \n2           0.0        0.0            0.030303             0.000000   \n3           0.0        0.0            0.038462             0.000000   \n4           0.0        0.0            0.000000             0.000000   \n5           0.0        0.0            0.000000             0.047619   \n6           0.0        0.0            0.030303             0.000000   \n7           0.0        0.0            0.044444             0.000000   \n8           0.0        0.0            0.000000             0.000000   \n9           0.0        0.0            0.000000             0.000000   \n\n   Jazz Club  Jewelry Store  Jewish Restaurant  Juice Bar  Karaoke Bar  \\\n0        0.0            0.0                0.0   0.038462          0.0   \n1        0.0            0.0                0.0   0.029412          0.0   \n2        0.0            0.0                0.0   0.000000          0.0   \n3        0.0            0.0                0.0   0.000000          0.0   \n4        0.0            0.0                0.0   0.000000          0.0   \n5        0.0            0.0                0.0   0.000000          0.0   \n6        0.0            0.0                0.0   0.000000          0.0   \n7        0.0            0.0                0.0   0.000000          0.0   \n8        0.0            0.0                0.0   0.000000          0.0   \n9        0.0            0.0                0.0   0.000000          0.0   \n\n   Korean Restaurant  Latin American Restaurant  Library  Light Rail Station  \\\n0                0.0                   0.000000      0.0            0.000000   \n1                0.0                   0.000000      0.0            0.000000   \n2                0.0                   0.000000      0.0            0.000000   \n3                0.0                   0.012821      0.0            0.012821   \n4                0.0                   0.000000      0.0            0.000000   \n5                0.0                   0.000000      0.0            0.000000   \n6                0.0                   0.000000      0.0            0.000000   \n7                0.0                   0.000000      0.0            0.000000   \n8                0.0                   0.000000      0.0            0.000000   \n9                0.0                   0.000000      0.0            0.047619   \n\n   Lingerie Store  Liquor Store    Lounge  Mac & Cheese Joint  \\\n0             0.0      0.000000  0.000000                 0.0   \n1             0.0      0.000000  0.000000                 0.0   \n2             0.0      0.030303  0.000000                 0.0   \n3             0.0      0.000000  0.012821                 0.0   \n4             0.0      0.000000  0.000000                 0.0   \n5             0.0      0.000000  0.000000                 0.0   \n6             0.0      0.000000  0.000000                 0.0   \n7             0.0      0.000000  0.000000                 0.0   \n8             0.0      0.000000  0.000000                 0.0   \n9             0.0      0.047619  0.000000                 0.0   \n\n   Malay Restaurant  Market  Martial Arts Dojo  Massage Studio  \\\n0               0.0     0.0                0.0        0.000000   \n1               0.0     0.0                0.0        0.000000   \n2               0.0     0.0                0.0        0.030303   \n3               0.0     0.0                0.0        0.000000   \n4               0.0     0.0                0.0        0.000000   \n5               0.0     0.0                0.0        0.000000   \n6               0.0     0.0                0.0        0.000000   \n7               0.0     0.0                0.0        0.000000   \n8               0.0     0.0                0.0        0.000000   \n9               0.0     0.0                0.0        0.000000   \n\n   Mattress Store  Mediterranean Restaurant  Men's Store  Metro Station  \\\n0             0.0                       0.0          0.0       0.000000   \n1             0.0                       0.0          0.0       0.000000   \n2             0.0                       0.0          0.0       0.000000   \n3             0.0                       0.0          0.0       0.000000   \n4             0.0                       0.0          0.0       0.000000   \n5             0.0                       0.0          0.0       0.000000   \n6             0.0                       0.0          0.0       0.030303   \n7             0.0                       0.0          0.0       0.000000   \n8             0.0                       0.0          0.0       0.000000   \n9             0.0                       0.0          0.0       0.000000   \n\n   Mexican Restaurant  Middle Eastern Restaurant  Miscellaneous Shop  \\\n0            0.000000                   0.000000                 0.0   \n1            0.000000                   0.000000                 0.0   \n2            0.000000                   0.000000                 0.0   \n3            0.000000                   0.000000                 0.0   \n4            0.000000                   0.000000                 0.0   \n5            0.000000                   0.047619                 0.0   \n6            0.030303                   0.000000                 0.0   \n7            0.022222                   0.000000                 0.0   \n8            0.000000                   0.000000                 0.0   \n9            0.000000                   0.000000                 0.0   \n\n   Mobile Phone Shop  Modern European Restaurant  \\\n0           0.038462                         0.0   \n1           0.000000                         0.0   \n2           0.000000                         0.0   \n3           0.000000                         0.0   \n4           0.000000                         0.0   \n5           0.000000                         0.0   \n6           0.000000                         0.0   \n7           0.000000                         0.0   \n8           0.000000                         0.0   \n9           0.000000                         0.0   \n\n   Molecular Gastronomy Restaurant  Monument / Landmark  Movie Theater  \\\n0                              0.0                  0.0       0.000000   \n1                              0.0                  0.0       0.000000   \n2                              0.0                  0.0       0.030303   \n3                              0.0                  0.0       0.000000   \n4                              0.0                  0.0       0.000000   \n5                              0.0                  0.0       0.000000   \n6                              0.0                  0.0       0.000000   \n7                              0.0                  0.0       0.000000   \n8                              0.0                  0.0       0.000000   \n9                              0.0                  0.0       0.000000   \n\n   Museum  Music Venue  Neighborhood  New American Restaurant  Nightclub  \\\n0     0.0          0.0      0.038462                      0.0        0.0   \n1     0.0          0.0      0.000000                      0.0        0.0   \n2     0.0          0.0      0.000000                      0.0        0.0   \n3     0.0          0.0      0.012821                      0.0        0.0   \n4     0.0          0.0      0.000000                      0.0        0.0   \n5     0.0          0.0      0.000000                      0.0        0.0   \n6     0.0          0.0      0.000000                      0.0        0.0   \n7     0.0          0.0      0.000000                      0.0        0.0   \n8     0.0          0.0      0.000000                      0.0        0.0   \n9     0.0          0.0      0.000000                      0.0        0.0   \n\n   Noodle House  Office  Opera House  Optical Shop  Organic Grocery  \\\n0           0.0     0.0          0.0           0.0              0.0   \n1           0.0     0.0          0.0           0.0              0.0   \n2           0.0     0.0          0.0           0.0              0.0   \n3           0.0     0.0          0.0           0.0              0.0   \n4           0.0     0.0          0.0           0.0              0.0   \n5           0.0     0.0          0.0           0.0              0.0   \n6           0.0     0.0          0.0           0.0              0.0   \n7           0.0     0.0          0.0           0.0              0.0   \n8           0.0     0.0          0.0           0.0              0.0   \n9           0.0     0.0          0.0           0.0              0.0   \n\n   Other Great Outdoors  Other Nightlife  Outdoor Sculpture  \\\n0              0.000000              0.0                0.0   \n1              0.029412              0.0                0.0   \n2              0.000000              0.0                0.0   \n3              0.000000              0.0                0.0   \n4              0.000000              0.0                0.0   \n5              0.000000              0.0                0.0   \n6              0.000000              0.0                0.0   \n7              0.000000              0.0                0.0   \n8              0.000000              0.0                0.0   \n9              0.000000              0.0                0.0   \n\n   Paper / Office Supplies Store      Park  Pastry Shop  \\\n0                            0.0  0.000000          0.0   \n1                            0.0  0.000000          0.0   \n2                            0.0  0.030303          0.0   \n3                            0.0  0.012821          0.0   \n4                            0.0  0.142857          0.0   \n5                            0.0  0.047619          0.0   \n6                            0.0  0.030303          0.0   \n7                            0.0  0.022222          0.0   \n8                            0.0  0.285714          0.0   \n9                            0.0  0.095238          0.0   \n\n   Performing Arts Venue  Persian Restaurant  Peruvian Restaurant  Pet Store  \\\n0                    0.0                 0.0                  0.0   0.038462   \n1                    0.0                 0.0                  0.0   0.029412   \n2                    0.0                 0.0                  0.0   0.030303   \n3                    0.0                 0.0                  0.0   0.012821   \n4                    0.0                 0.0                  0.0   0.000000   \n5                    0.0                 0.0                  0.0   0.000000   \n6                    0.0                 0.0                  0.0   0.000000   \n7                    0.0                 0.0                  0.0   0.000000   \n8                    0.0                 0.0                  0.0   0.000000   \n9                    0.0                 0.0                  0.0   0.000000   \n\n   Pharmacy  Pilates Studio  Pizza Place  Playground  Plaza  Poke Place  Pool  \\\n0  0.000000             0.0     0.038462    0.000000    0.0         0.0   0.0   \n1  0.000000             0.0     0.029412    0.000000    0.0         0.0   0.0   \n2  0.000000             0.0     0.000000    0.000000    0.0         0.0   0.0   \n3  0.000000             0.0     0.025641    0.000000    0.0         0.0   0.0   \n4  0.000000             0.0     0.000000    0.000000    0.0         0.0   0.0   \n5  0.047619             0.0     0.047619    0.000000    0.0         0.0   0.0   \n6  0.000000             0.0     0.030303    0.000000    0.0         0.0   0.0   \n7  0.022222             0.0     0.044444    0.000000    0.0         0.0   0.0   \n8  0.000000             0.0     0.000000    0.142857    0.0         0.0   0.0   \n9  0.000000             0.0     0.047619    0.000000    0.0         0.0   0.0   \n\n   Pool Hall  Portuguese Restaurant  Poutine Place       Pub  \\\n0        0.0                    0.0            0.0  0.038462   \n1        0.0                    0.0            0.0  0.029412   \n2        0.0                    0.0            0.0  0.030303   \n3        0.0                    0.0            0.0  0.012821   \n4        0.0                    0.0            0.0  0.000000   \n5        0.0                    0.0            0.0  0.000000   \n6        0.0                    0.0            0.0  0.000000   \n7        0.0                    0.0            0.0  0.000000   \n8        0.0                    0.0            0.0  0.000000   \n9        0.0                    0.0            0.0  0.095238   \n\n   Ramen Restaurant  Record Shop  Rental Car Location  Restaurant  Rock Club  \\\n0               0.0     0.000000             0.000000    0.000000        0.0   \n1               0.0     0.000000             0.000000    0.029412        0.0   \n2               0.0     0.000000             0.000000    0.000000        0.0   \n3               0.0     0.012821             0.000000    0.000000        0.0   \n4               0.0     0.000000             0.000000    0.142857        0.0   \n5               0.0     0.000000             0.000000    0.000000        0.0   \n6               0.0     0.000000             0.030303    0.060606        0.0   \n7               0.0     0.000000             0.000000    0.044444        0.0   \n8               0.0     0.000000             0.000000    0.000000        0.0   \n9               0.0     0.000000             0.000000    0.047619        0.0   \n\n   Sake Bar  Salad Place  Salon / Barbershop  Sandwich Place  Scenic Lookout  \\\n0       0.0          0.0            0.000000        0.038462        0.000000   \n1       0.0          0.0            0.000000        0.000000        0.029412   \n2       0.0          0.0            0.000000        0.060606        0.000000   \n3       0.0          0.0            0.000000        0.038462        0.000000   \n4       0.0          0.0            0.000000        0.000000        0.000000   \n5       0.0          0.0            0.000000        0.047619        0.000000   \n6       0.0          0.0            0.030303        0.000000        0.000000   \n7       0.0          0.0            0.000000        0.066667        0.000000   \n8       0.0          0.0            0.000000        0.000000        0.000000   \n9       0.0          0.0            0.000000        0.000000        0.000000   \n\n   Sculpture Garden  Seafood Restaurant  Shoe Store  Shopping Mall  \\\n0               0.0            0.000000         0.0            0.0   \n1               0.0            0.000000         0.0            0.0   \n2               0.0            0.000000         0.0            0.0   \n3               0.0            0.012821         0.0            0.0   \n4               0.0            0.000000         0.0            0.0   \n5               0.0            0.000000         0.0            0.0   \n6               0.0            0.000000         0.0            0.0   \n7               0.0            0.022222         0.0            0.0   \n8               0.0            0.000000         0.0            0.0   \n9               0.0            0.000000         0.0            0.0   \n\n   Skate Park  Skating Rink  Smoke Shop  Smoothie Shop  Snack Place  \\\n0    0.000000      0.000000         0.0            0.0     0.000000   \n1    0.000000      0.000000         0.0            0.0     0.000000   \n2    0.030303      0.000000         0.0            0.0     0.000000   \n3    0.000000      0.000000         0.0            0.0     0.012821   \n4    0.000000      0.000000         0.0            0.0     0.000000   \n5    0.000000      0.000000         0.0            0.0     0.000000   \n6    0.000000      0.000000         0.0            0.0     0.000000   \n7    0.000000      0.000000         0.0            0.0     0.000000   \n8    0.000000      0.000000         0.0            0.0     0.000000   \n9    0.000000      0.095238         0.0            0.0     0.000000   \n\n   Soccer Field  Social Club  Soup Place  Southern / Soul Food Restaurant  \\\n0           0.0          0.0         0.0                              0.0   \n1           0.0          0.0         0.0                              0.0   \n2           0.0          0.0         0.0                              0.0   \n3           0.0          0.0         0.0                              0.0   \n4           0.0          0.0         0.0                              0.0   \n5           0.0          0.0         0.0                              0.0   \n6           0.0          0.0         0.0                              0.0   \n7           0.0          0.0         0.0                              0.0   \n8           0.0          0.0         0.0                              0.0   \n9           0.0          0.0         0.0                              0.0   \n\n        Spa  Spanish Restaurant  Speakeasy  Sporting Goods Shop  Sports Bar  \\\n0  0.038462                 0.0        0.0             0.000000    0.000000   \n1  0.000000                 0.0        0.0             0.000000    0.000000   \n2  0.000000                 0.0        0.0             0.000000    0.000000   \n3  0.000000                 0.0        0.0             0.000000    0.000000   \n4  0.000000                 0.0        0.0             0.000000    0.000000   \n5  0.000000                 0.0        0.0             0.000000    0.000000   \n6  0.030303                 0.0        0.0             0.090909    0.000000   \n7  0.000000                 0.0        0.0             0.000000    0.000000   \n8  0.000000                 0.0        0.0             0.000000    0.000000   \n9  0.000000                 0.0        0.0             0.000000    0.047619   \n\n   Stationery Store  Steakhouse  Strip Club  Supermarket  Sushi Restaurant  \\\n0          0.000000    0.000000         0.0     0.000000          0.000000   \n1          0.000000    0.000000         0.0     0.000000          0.000000   \n2          0.000000    0.030303         0.0     0.000000          0.030303   \n3          0.012821    0.000000         0.0     0.000000          0.025641   \n4          0.000000    0.000000         0.0     0.000000          0.000000   \n5          0.000000    0.000000         0.0     0.000000          0.047619   \n6          0.000000    0.000000         0.0     0.000000          0.000000   \n7          0.000000    0.000000         0.0     0.000000          0.044444   \n8          0.000000    0.000000         0.0     0.000000          0.000000   \n9          0.000000    0.000000         0.0     0.047619          0.047619   \n\n   Taco Place  Tailor Shop  Taiwanese Restaurant  Tanning Salon  \\\n0         0.0          0.0                   0.0            0.0   \n1         0.0          0.0                   0.0            0.0   \n2         0.0          0.0                   0.0            0.0   \n3         0.0          0.0                   0.0            0.0   \n4         0.0          0.0                   0.0            0.0   \n5         0.0          0.0                   0.0            0.0   \n6         0.0          0.0                   0.0            0.0   \n7         0.0          0.0                   0.0            0.0   \n8         0.0          0.0                   0.0            0.0   \n9         0.0          0.0                   0.0            0.0   \n\n   Tapas Restaurant  Tea Room  Tech Startup  Tennis Court  Thai Restaurant  \\\n0               0.0  0.038462           0.0      0.000000         0.038462   \n1               0.0  0.000000           0.0      0.000000         0.000000   \n2               0.0  0.000000           0.0      0.000000         0.000000   \n3               0.0  0.000000           0.0      0.000000         0.012821   \n4               0.0  0.000000           0.0      0.000000         0.000000   \n5               0.0  0.000000           0.0      0.000000         0.000000   \n6               0.0  0.000000           0.0      0.000000         0.030303   \n7               0.0  0.000000           0.0      0.022222         0.044444   \n8               0.0  0.000000           0.0      0.142857         0.142857   \n9               0.0  0.000000           0.0      0.000000         0.000000   \n\n    Theater  Theme Park Ride / Attraction  Theme Restaurant  \\\n0  0.000000                           0.0               0.0   \n1  0.029412                           0.0               0.0   \n2  0.000000                           0.0               0.0   \n3  0.012821                           0.0               0.0   \n4  0.000000                           0.0               0.0   \n5  0.000000                           0.0               0.0   \n6  0.000000                           0.0               0.0   \n7  0.000000                           0.0               0.0   \n8  0.000000                           0.0               0.0   \n9  0.000000                           0.0               0.0   \n\n   Thrift / Vintage Store  Toy / Game Store     Track     Trail  \\\n0                0.000000          0.038462  0.000000  0.038462   \n1                0.000000          0.000000  0.000000  0.000000   \n2                0.000000          0.000000  0.000000  0.000000   \n3                0.012821          0.000000  0.000000  0.000000   \n4                0.000000          0.000000  0.000000  0.000000   \n5                0.000000          0.000000  0.000000  0.000000   \n6                0.000000          0.000000  0.030303  0.000000   \n7                0.000000          0.022222  0.000000  0.022222   \n8                0.000000          0.000000  0.000000  0.000000   \n9                0.000000          0.000000  0.000000  0.000000   \n\n   Train Station  Tram Station  University  Vegetarian / Vegan Restaurant  \\\n0            0.0           0.0         0.0                       0.038462   \n1            0.0           0.0         0.0                       0.000000   \n2            0.0           0.0         0.0                       0.000000   \n3            0.0           0.0         0.0                       0.000000   \n4            0.0           0.0         0.0                       0.000000   \n5            0.0           0.0         0.0                       0.000000   \n6            0.0           0.0         0.0                       0.000000   \n7            0.0           0.0         0.0                       0.000000   \n8            0.0           0.0         0.0                       0.000000   \n9            0.0           0.0         0.0                       0.000000   \n\n   Video Game Store  Video Store  Vietnamese Restaurant  Wine Bar  Wine Shop  \\\n0               0.0          0.0               0.000000       0.0        0.0   \n1               0.0          0.0               0.000000       0.0        0.0   \n2               0.0          0.0               0.000000       0.0        0.0   \n3               0.0          0.0               0.012821       0.0        0.0   \n4               0.0          0.0               0.000000       0.0        0.0   \n5               0.0          0.0               0.000000       0.0        0.0   \n6               0.0          0.0               0.000000       0.0        0.0   \n7               0.0          0.0               0.000000       0.0        0.0   \n8               0.0          0.0               0.000000       0.0        0.0   \n9               0.0          0.0               0.047619       0.0        0.0   \n\n   Wings Joint  Yoga Studio  \n0     0.000000     0.000000  \n1     0.000000     0.029412  \n2     0.000000     0.000000  \n3     0.000000     0.012821  \n4     0.000000     0.000000  \n5     0.000000     0.047619  \n6     0.030303     0.030303  \n7     0.000000     0.000000  \n8     0.000000     0.000000  \n9     0.000000     0.047619  "
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "# One hot encoding\ntoronto_onehot = pd.get_dummies(toronto_venues[['Venue Category']], prefix=\"\", prefix_sep=\"\")\n\n# Add Postcode column\ntoronto_onehot['Postcode'] = toronto_venues['Postcode'] \n\n# Move postcode column to the first column\nfixed_columns = [toronto_onehot.columns[-1]] + list(toronto_onehot.columns[:-1])\ntoronto_onehot = toronto_onehot[fixed_columns]\n\ntoronto_grouped = toronto_onehot.groupby('Postcode').mean().reset_index()\ntoronto_grouped.head(10)"
        }, 
        {
            "source": "Before clustering, a table was constructed with top venues for each neighborhood to show the characteristics of each neighborhood.", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 18, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": "# Define a function that returns the sorted categories\ndef return_most_common_venues(row, num_top_venues):\n    row_categories = row.iloc[1:]                                       # row of the table of interest\n    row_categories_sorted = row_categories.sort_values(ascending=False) # sort the mean frequency of occurrences\n    \n    return row_categories_sorted.index.values[0:num_top_venues]         # return the index (category) of the mena frequency values"
        }, 
        {
            "execution_count": 19, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 19, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Postcode</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>M4E</td>\n      <td>Japanese Restaurant</td>\n      <td>Bar</td>\n      <td>Pizza Place</td>\n      <td>Church</td>\n      <td>Juice Bar</td>\n      <td>Pub</td>\n      <td>Neighborhood</td>\n      <td>Breakfast Spot</td>\n      <td>Caf\u00e9</td>\n      <td>Tea Room</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>M4K</td>\n      <td>Greek Restaurant</td>\n      <td>Caf\u00e9</td>\n      <td>Fast Food Restaurant</td>\n      <td>Ice Cream Shop</td>\n      <td>Pub</td>\n      <td>Bubble Tea Shop</td>\n      <td>Business Service</td>\n      <td>Chinese Restaurant</td>\n      <td>Scenic Lookout</td>\n      <td>Restaurant</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>M4L</td>\n      <td>Sandwich Place</td>\n      <td>Fast Food Restaurant</td>\n      <td>Caf\u00e9</td>\n      <td>Gym</td>\n      <td>Bakery</td>\n      <td>Coffee Shop</td>\n      <td>Brewery</td>\n      <td>Steakhouse</td>\n      <td>Movie Theater</td>\n      <td>French Restaurant</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>M4M</td>\n      <td>Bakery</td>\n      <td>Coffee Shop</td>\n      <td>Caf\u00e9</td>\n      <td>Diner</td>\n      <td>Sandwich Place</td>\n      <td>Bar</td>\n      <td>Italian Restaurant</td>\n      <td>American Restaurant</td>\n      <td>French Restaurant</td>\n      <td>Sushi Restaurant</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>M4N</td>\n      <td>Bookstore</td>\n      <td>Bus Line</td>\n      <td>Caf\u00e9</td>\n      <td>Coffee Shop</td>\n      <td>Park</td>\n      <td>Restaurant</td>\n      <td>Gym / Fitness Center</td>\n      <td>Falafel Restaurant</td>\n      <td>Elementary School</td>\n      <td>Ethiopian Restaurant</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "  Postcode 1st Most Common Venue 2nd Most Common Venue 3rd Most Common Venue  \\\n0      M4E   Japanese Restaurant                   Bar           Pizza Place   \n1      M4K      Greek Restaurant                  Caf\u00e9  Fast Food Restaurant   \n2      M4L        Sandwich Place  Fast Food Restaurant                  Caf\u00e9   \n3      M4M                Bakery           Coffee Shop                  Caf\u00e9   \n4      M4N             Bookstore              Bus Line                  Caf\u00e9   \n\n  4th Most Common Venue 5th Most Common Venue 6th Most Common Venue  \\\n0                Church             Juice Bar                   Pub   \n1        Ice Cream Shop                   Pub       Bubble Tea Shop   \n2                   Gym                Bakery           Coffee Shop   \n3                 Diner        Sandwich Place                   Bar   \n4           Coffee Shop                  Park            Restaurant   \n\n  7th Most Common Venue 8th Most Common Venue 9th Most Common Venue  \\\n0          Neighborhood        Breakfast Spot                  Caf\u00e9   \n1      Business Service    Chinese Restaurant        Scenic Lookout   \n2               Brewery            Steakhouse         Movie Theater   \n3    Italian Restaurant   American Restaurant     French Restaurant   \n4  Gym / Fitness Center    Falafel Restaurant     Elementary School   \n\n  10th Most Common Venue  \n0               Tea Room  \n1             Restaurant  \n2      French Restaurant  \n3       Sushi Restaurant  \n4   Ethiopian Restaurant  "
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "num_top_venues = 10\n\nindicators = ['st', 'nd', 'rd']\n\n# Create columns according to number of top venues\ncolumns = ['Postcode']\nfor ind in np.arange(num_top_venues):\n    try:\n        columns.append('{}{} Most Common Venue'.format(ind+1, indicators[ind])) # 1st, 2nd, 3rd\n    except:\n        columns.append('{}th Most Common Venue'.format(ind+1))\n\n# Create a new dataframe\ntoronto_venues_sorted = pd.DataFrame(columns=columns)\ntoronto_venues_sorted['Postcode'] = toronto_grouped['Postcode']\n\n# Each row of the new dataframe is filled with the sorted categories of each row of toronto_goruped dataframe\nfor ind in np.arange(toronto_grouped.shape[0]):\n    toronto_venues_sorted.iloc[ind, 1:] = return_most_common_venues(toronto_grouped.iloc[ind, :], num_top_venues)\n\ntoronto_venues_sorted.head()"
        }, 
        {
            "source": "Finally, using *k*-means, the neighborhoods were clustered into 3 clusters. A map visualizing the clusters was also created.", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "execution_count": 20, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 20, 
                    "metadata": {}, 
                    "data": {
                        "text/plain": "array([0, 0, 0, 0, 0, 0, 0, 0, 1, 0], dtype=int32)"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "# Set number of clusters\nkclusters = 3\n\ntoronto_grouped_clustering = toronto_grouped.drop('Postcode', 1)\n\n# Run k-means clustering\nkmeans = KMeans(n_clusters=kclusters, random_state=0).fit(toronto_grouped_clustering)\n\n# Check cluster labels generated for each row in the dataframe\nkmeans.labels_[0:10] "
        }, 
        {
            "execution_count": 21, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 21, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Postcode</th>\n      <th>Borough</th>\n      <th>Neighborhood</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n      <th>Cluster Labels</th>\n      <th>1st Most Common Venue</th>\n      <th>2nd Most Common Venue</th>\n      <th>3rd Most Common Venue</th>\n      <th>4th Most Common Venue</th>\n      <th>5th Most Common Venue</th>\n      <th>6th Most Common Venue</th>\n      <th>7th Most Common Venue</th>\n      <th>8th Most Common Venue</th>\n      <th>9th Most Common Venue</th>\n      <th>10th Most Common Venue</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>M4E</td>\n      <td>East Toronto</td>\n      <td>The Beaches</td>\n      <td>43.676845</td>\n      <td>-79.295225</td>\n      <td>0</td>\n      <td>Japanese Restaurant</td>\n      <td>Bar</td>\n      <td>Pizza Place</td>\n      <td>Church</td>\n      <td>Juice Bar</td>\n      <td>Pub</td>\n      <td>Neighborhood</td>\n      <td>Breakfast Spot</td>\n      <td>Caf\u00e9</td>\n      <td>Tea Room</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>M4K</td>\n      <td>East Toronto</td>\n      <td>The Danforth West, Riverdale</td>\n      <td>43.683262</td>\n      <td>-79.355120</td>\n      <td>0</td>\n      <td>Greek Restaurant</td>\n      <td>Caf\u00e9</td>\n      <td>Fast Food Restaurant</td>\n      <td>Ice Cream Shop</td>\n      <td>Pub</td>\n      <td>Bubble Tea Shop</td>\n      <td>Business Service</td>\n      <td>Chinese Restaurant</td>\n      <td>Scenic Lookout</td>\n      <td>Restaurant</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>M4L</td>\n      <td>East Toronto</td>\n      <td>The Beaches West, India Bazaar</td>\n      <td>43.667965</td>\n      <td>-79.314673</td>\n      <td>0</td>\n      <td>Sandwich Place</td>\n      <td>Fast Food Restaurant</td>\n      <td>Caf\u00e9</td>\n      <td>Gym</td>\n      <td>Bakery</td>\n      <td>Coffee Shop</td>\n      <td>Brewery</td>\n      <td>Steakhouse</td>\n      <td>Movie Theater</td>\n      <td>French Restaurant</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>M4M</td>\n      <td>East Toronto</td>\n      <td>Studio District</td>\n      <td>43.662766</td>\n      <td>-79.334830</td>\n      <td>0</td>\n      <td>Bakery</td>\n      <td>Coffee Shop</td>\n      <td>Caf\u00e9</td>\n      <td>Diner</td>\n      <td>Sandwich Place</td>\n      <td>Bar</td>\n      <td>Italian Restaurant</td>\n      <td>American Restaurant</td>\n      <td>French Restaurant</td>\n      <td>Sushi Restaurant</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>M4N</td>\n      <td>Central Toronto</td>\n      <td>Lawrence Park</td>\n      <td>43.728160</td>\n      <td>-79.387085</td>\n      <td>0</td>\n      <td>Bookstore</td>\n      <td>Bus Line</td>\n      <td>Caf\u00e9</td>\n      <td>Coffee Shop</td>\n      <td>Park</td>\n      <td>Restaurant</td>\n      <td>Gym / Fitness Center</td>\n      <td>Falafel Restaurant</td>\n      <td>Elementary School</td>\n      <td>Ethiopian Restaurant</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "  Postcode          Borough                    Neighborhood   Latitude  \\\n0      M4E     East Toronto                     The Beaches  43.676845   \n1      M4K     East Toronto    The Danforth West, Riverdale  43.683262   \n2      M4L     East Toronto  The Beaches West, India Bazaar  43.667965   \n3      M4M     East Toronto                 Studio District  43.662766   \n4      M4N  Central Toronto                   Lawrence Park  43.728160   \n\n   Longitude  Cluster Labels 1st Most Common Venue 2nd Most Common Venue  \\\n0 -79.295225               0   Japanese Restaurant                   Bar   \n1 -79.355120               0      Greek Restaurant                  Caf\u00e9   \n2 -79.314673               0        Sandwich Place  Fast Food Restaurant   \n3 -79.334830               0                Bakery           Coffee Shop   \n4 -79.387085               0             Bookstore              Bus Line   \n\n  3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue  \\\n0           Pizza Place                Church             Juice Bar   \n1  Fast Food Restaurant        Ice Cream Shop                   Pub   \n2                  Caf\u00e9                   Gym                Bakery   \n3                  Caf\u00e9                 Diner        Sandwich Place   \n4                  Caf\u00e9           Coffee Shop                  Park   \n\n  6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n0                   Pub          Neighborhood        Breakfast Spot   \n1       Bubble Tea Shop      Business Service    Chinese Restaurant   \n2           Coffee Shop               Brewery            Steakhouse   \n3                   Bar    Italian Restaurant   American Restaurant   \n4            Restaurant  Gym / Fitness Center    Falafel Restaurant   \n\n  9th Most Common Venue 10th Most Common Venue  \n0                  Caf\u00e9               Tea Room  \n1        Scenic Lookout             Restaurant  \n2         Movie Theater      French Restaurant  \n3     French Restaurant       Sushi Restaurant  \n4     Elementary School   Ethiopian Restaurant  "
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "# Add clustering labels\ntoronto_venues_sorted.insert(0, 'Cluster Labels', kmeans.labels_)\n\ntoronto_merged = toronto\n\n# Merge toronto_grouped with toronto to add latitude/longitude for each neighborhood\ntoronto_merged = toronto_merged.join(toronto_venues_sorted.set_index('Postcode'), on='Postcode')\ntoronto_merged[['Cluster Labels']] = toronto_merged[['Cluster Labels']].astype('int')\n\ntoronto_merged.head()"
        }, 
        {
            "execution_count": 22, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 22, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe src=\"data:text/html;charset=utf-8;base64,<!DOCTYPE html>
<head>    
    <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
    <script>L_PREFER_CANVAS = false; L_NO_TOUCH = false; L_DISABLE_3D = false;</script>
    <script src="https://cdn.jsdelivr.net/npm/leaflet@1.2.0/dist/leaflet.js"></script>
    <script src="https://ajax.googleapis.com/ajax/libs/jquery/1.11.1/jquery.min.js"></script>
    <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>
    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet@1.2.0/dist/leaflet.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css"/>
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css"/>
    <link rel="stylesheet" href="https://rawgit.com/python-visualization/folium/master/folium/templates/leaflet.awesome.rotate.css"/>
    <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>
    <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>
    
            <style> #map_b876d16bf09c40dfbf9220b5b8c979c4 {
                position : relative;
                width : 100.0%;
                height: 100.0%;
                left: 0.0%;
                top: 0.0%;
                }
            </style>
        
</head>
<body>    
    
            <div class="folium-map" id="map_b876d16bf09c40dfbf9220b5b8c979c4" ></div>
        
</body>
<script>    
    

            
                var bounds = null;
            

            var map_b876d16bf09c40dfbf9220b5b8c979c4 = L.map(
                                  'map_b876d16bf09c40dfbf9220b5b8c979c4',
                                  {center: [43.648690000000045,-79.38543999999996],
                                  zoom: 11,
                                  maxBounds: bounds,
                                  layers: [],
                                  worldCopyJump: false,
                                  crs: L.CRS.EPSG3857
                                 });
            
        
    
            var tile_layer_dd200fd5dc5a4348a34863d3be83364f = L.tileLayer(
                'https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png',
                {
  "attribution": null,
  "detectRetina": false,
  "maxZoom": 18,
  "minZoom": 1,
  "noWrap": false,
  "subdomains": "abc"
}
                ).addTo(map_b876d16bf09c40dfbf9220b5b8c979c4);
        
    
            var circle_marker_7c0faf7cd0d24714ab54b12a1f0654d7 = L.circleMarker(
                [43.67684518300007,-79.29522499999996],
                {
  "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_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_b6513a7943944d4689d0217bac940fc3 = L.popup({maxWidth: '300'});

            
                var html_a9d000ba7e714996ad69fdf5ec589069 = $('<div id="html_a9d000ba7e714996ad69fdf5ec589069" style="width: 100.0%; height: 100.0%;">M4E,  Cluster 0</div>')[0];
                popup_b6513a7943944d4689d0217bac940fc3.setContent(html_a9d000ba7e714996ad69fdf5ec589069);
            

            circle_marker_7c0faf7cd0d24714ab54b12a1f0654d7.bindPopup(popup_b6513a7943944d4689d0217bac940fc3);

            
        
    
            var circle_marker_e2981f9964b446448ce0a522bf194467 = L.circleMarker(
                [43.68326150000007,-79.35511999999994],
                {
  "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_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_a9732e1f13484014b3eb62de572b80b5 = L.popup({maxWidth: '300'});

            
                var html_7789219bf29a4a3499202b025b841f94 = $('<div id="html_7789219bf29a4a3499202b025b841f94" style="width: 100.0%; height: 100.0%;">M4K,  Cluster 0</div>')[0];
                popup_a9732e1f13484014b3eb62de572b80b5.setContent(html_7789219bf29a4a3499202b025b841f94);
            

            circle_marker_e2981f9964b446448ce0a522bf194467.bindPopup(popup_a9732e1f13484014b3eb62de572b80b5);

            
        
    
            var circle_marker_73f3ccb63da0477fb61a89e6449d7da4 = L.circleMarker(
                [43.66796500000004,-79.31467251099997],
                {
  "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_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_2be1dc108a2a4f268d802a224eda4a43 = L.popup({maxWidth: '300'});

            
                var html_2784d6ad382c4388ad61d0d0517e3bc3 = $('<div id="html_2784d6ad382c4388ad61d0d0517e3bc3" style="width: 100.0%; height: 100.0%;">M4L,  Cluster 0</div>')[0];
                popup_2be1dc108a2a4f268d802a224eda4a43.setContent(html_2784d6ad382c4388ad61d0d0517e3bc3);
            

            circle_marker_73f3ccb63da0477fb61a89e6449d7da4.bindPopup(popup_2be1dc108a2a4f268d802a224eda4a43);

            
        
    
            var circle_marker_13191568d140454a93e48749dc53cbd1 = L.circleMarker(
                [43.662765652000076,-79.33482999999995],
                {
  "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_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_0695fbc43fb4428fae4ae3c51e49ca1e = L.popup({maxWidth: '300'});

            
                var html_30c34eb4b73b43ee98c2fa6f410267e9 = $('<div id="html_30c34eb4b73b43ee98c2fa6f410267e9" style="width: 100.0%; height: 100.0%;">M4M,  Cluster 0</div>')[0];
                popup_0695fbc43fb4428fae4ae3c51e49ca1e.setContent(html_30c34eb4b73b43ee98c2fa6f410267e9);
            

            circle_marker_13191568d140454a93e48749dc53cbd1.bindPopup(popup_0695fbc43fb4428fae4ae3c51e49ca1e);

            
        
    
            var circle_marker_2a8d284b63df487fa47901533de392b5 = L.circleMarker(
                [43.72816000000006,-79.38708518799996],
                {
  "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_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_6adf04fa803845ad972fd9686d7f8fd9 = L.popup({maxWidth: '300'});

            
                var html_1ae291adf9a54bb8aa451ec39e6b30fe = $('<div id="html_1ae291adf9a54bb8aa451ec39e6b30fe" style="width: 100.0%; height: 100.0%;">M4N,  Cluster 0</div>')[0];
                popup_6adf04fa803845ad972fd9686d7f8fd9.setContent(html_1ae291adf9a54bb8aa451ec39e6b30fe);
            

            circle_marker_2a8d284b63df487fa47901533de392b5.bindPopup(popup_6adf04fa803845ad972fd9686d7f8fd9);

            
        
    
            var circle_marker_9b653f525221420eb06d8ee6b08260c9 = L.circleMarker(
                [43.712815000000035,-79.38852582199996],
                {
  "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_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_abd71fab88514a3188e011eb74b4e806 = L.popup({maxWidth: '300'});

            
                var html_a26ac77715df40d188e7c729f36fff54 = $('<div id="html_a26ac77715df40d188e7c729f36fff54" style="width: 100.0%; height: 100.0%;">M4P,  Cluster 0</div>')[0];
                popup_abd71fab88514a3188e011eb74b4e806.setContent(html_a26ac77715df40d188e7c729f36fff54);
            

            circle_marker_9b653f525221420eb06d8ee6b08260c9.bindPopup(popup_abd71fab88514a3188e011eb74b4e806);

            
        
    
            var circle_marker_def50987e2e4424aa7763a3f9d9d5a87 = L.circleMarker(
                [43.71452278400005,-79.40695999999997],
                {
  "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_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_3040bfc77e714b1ba2c5f858f311df7d = L.popup({maxWidth: '300'});

            
                var html_cfcfe6fee99140178461bc0b1fe663ed = $('<div id="html_cfcfe6fee99140178461bc0b1fe663ed" style="width: 100.0%; height: 100.0%;">M4R,  Cluster 0</div>')[0];
                popup_3040bfc77e714b1ba2c5f858f311df7d.setContent(html_cfcfe6fee99140178461bc0b1fe663ed);
            

            circle_marker_def50987e2e4424aa7763a3f9d9d5a87.bindPopup(popup_3040bfc77e714b1ba2c5f858f311df7d);

            
        
    
            var circle_marker_62c6d61b1eb74a739dda32b5fe2d861b = L.circleMarker(
                [43.70339500000006,-79.38596360499997],
                {
  "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_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_55ca75c6e488456193cfea913a751953 = L.popup({maxWidth: '300'});

            
                var html_841ad8aa5fec4360808b43d1c2400bbd = $('<div id="html_841ad8aa5fec4360808b43d1c2400bbd" style="width: 100.0%; height: 100.0%;">M4S,  Cluster 0</div>')[0];
                popup_55ca75c6e488456193cfea913a751953.setContent(html_841ad8aa5fec4360808b43d1c2400bbd);
            

            circle_marker_62c6d61b1eb74a739dda32b5fe2d861b.bindPopup(popup_55ca75c6e488456193cfea913a751953);

            
        
    
            var circle_marker_621035c9a9234acdb09e2b3d2322aeba = L.circleMarker(
                [43.69065500000005,-79.38356145799997],
                {
  "bubblingMouseEvents": true,
  "color": "#8000ff",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#8000ff",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_a0ccb168fa124078b1e91df0f8464043 = L.popup({maxWidth: '300'});

            
                var html_f39deaff907f4040b9231d2a59786b1a = $('<div id="html_f39deaff907f4040b9231d2a59786b1a" style="width: 100.0%; height: 100.0%;">M4T,  Cluster 1</div>')[0];
                popup_a0ccb168fa124078b1e91df0f8464043.setContent(html_f39deaff907f4040b9231d2a59786b1a);
            

            circle_marker_621035c9a9234acdb09e2b3d2322aeba.bindPopup(popup_a0ccb168fa124078b1e91df0f8464043);

            
        
    
            var circle_marker_c79ccf61b5e045bb837b55d80174e7b1 = L.circleMarker(
                [43.68608285400006,-79.40233499999994],
                {
  "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_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_248885f858d5493eb6260ca120a8e7ea = L.popup({maxWidth: '300'});

            
                var html_5c28f1d224a64c878b314759fb319473 = $('<div id="html_5c28f1d224a64c878b314759fb319473" style="width: 100.0%; height: 100.0%;">M4V,  Cluster 0</div>')[0];
                popup_248885f858d5493eb6260ca120a8e7ea.setContent(html_5c28f1d224a64c878b314759fb319473);
            

            circle_marker_c79ccf61b5e045bb837b55d80174e7b1.bindPopup(popup_248885f858d5493eb6260ca120a8e7ea);

            
        
    
            var circle_marker_6f897ade156a48be9988bd9349baf845 = L.circleMarker(
                [43.681940000000054,-79.37847416699998],
                {
  "bubblingMouseEvents": true,
  "color": "#8000ff",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#8000ff",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_96f8c4cade194c32b759ad0fbefa7989 = L.popup({maxWidth: '300'});

            
                var html_d7304d20df72428ab9561ee681511f41 = $('<div id="html_d7304d20df72428ab9561ee681511f41" style="width: 100.0%; height: 100.0%;">M4W,  Cluster 1</div>')[0];
                popup_96f8c4cade194c32b759ad0fbefa7989.setContent(html_d7304d20df72428ab9561ee681511f41);
            

            circle_marker_6f897ade156a48be9988bd9349baf845.bindPopup(popup_96f8c4cade194c32b759ad0fbefa7989);

            
        
    
            var circle_marker_421651b397704f4e8e81ecc2dc3b5a14 = L.circleMarker(
                [43.66816000000006,-79.36660236199998],
                {
  "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_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_0f48dee760ab4cc5b9d4c7ab45b6de8d = L.popup({maxWidth: '300'});

            
                var html_d110abbeaa504eae88cb4c60bcf4fe6f = $('<div id="html_d110abbeaa504eae88cb4c60bcf4fe6f" style="width: 100.0%; height: 100.0%;">M4X,  Cluster 0</div>')[0];
                popup_0f48dee760ab4cc5b9d4c7ab45b6de8d.setContent(html_d110abbeaa504eae88cb4c60bcf4fe6f);
            

            circle_marker_421651b397704f4e8e81ecc2dc3b5a14.bindPopup(popup_0f48dee760ab4cc5b9d4c7ab45b6de8d);

            
        
    
            var circle_marker_bdbffeeb331f46ba85c895402c52929b = L.circleMarker(
                [43.666585000000055,-79.38130203699995],
                {
  "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_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_30efa03ddb154f71a2ea3c9fddf7a7a6 = L.popup({maxWidth: '300'});

            
                var html_5cc4a8d2c9664971aacf541a10987209 = $('<div id="html_5cc4a8d2c9664971aacf541a10987209" style="width: 100.0%; height: 100.0%;">M4Y,  Cluster 0</div>')[0];
                popup_30efa03ddb154f71a2ea3c9fddf7a7a6.setContent(html_5cc4a8d2c9664971aacf541a10987209);
            

            circle_marker_bdbffeeb331f46ba85c895402c52929b.bindPopup(popup_30efa03ddb154f71a2ea3c9fddf7a7a6);

            
        
    
            var circle_marker_55723f7437894b629483267979764ba6 = L.circleMarker(
                [43.65512000000007,-79.36263979699999],
                {
  "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_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_8946db9486e24c949fc13f3d7fbd4b1c = L.popup({maxWidth: '300'});

            
                var html_948cc9bcd1764f6e83049cea4d43db83 = $('<div id="html_948cc9bcd1764f6e83049cea4d43db83" style="width: 100.0%; height: 100.0%;">M5A,  Cluster 0</div>')[0];
                popup_8946db9486e24c949fc13f3d7fbd4b1c.setContent(html_948cc9bcd1764f6e83049cea4d43db83);
            

            circle_marker_55723f7437894b629483267979764ba6.bindPopup(popup_8946db9486e24c949fc13f3d7fbd4b1c);

            
        
    
            var circle_marker_abe3c80659d24c8bac4d2fd81cd9cf69 = L.circleMarker(
                [43.65736301100003,-79.37817999999999],
                {
  "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_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_1629fb8c82fc41f9a5f2b1da2adafcc4 = L.popup({maxWidth: '300'});

            
                var html_8e4a45c53c72498f8de2d3cb71e42d03 = $('<div id="html_8e4a45c53c72498f8de2d3cb71e42d03" style="width: 100.0%; height: 100.0%;">M5B,  Cluster 0</div>')[0];
                popup_1629fb8c82fc41f9a5f2b1da2adafcc4.setContent(html_8e4a45c53c72498f8de2d3cb71e42d03);
            

            circle_marker_abe3c80659d24c8bac4d2fd81cd9cf69.bindPopup(popup_1629fb8c82fc41f9a5f2b1da2adafcc4);

            
        
    
            var circle_marker_4b9b8400ad2f40f68214ea725cdcc1ed = L.circleMarker(
                [43.65121000000005,-79.37548057699996],
                {
  "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_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_5b79b425365c4ae6864faec87131c7cd = L.popup({maxWidth: '300'});

            
                var html_560e6ca6057649d0b8be503473fb8103 = $('<div id="html_560e6ca6057649d0b8be503473fb8103" style="width: 100.0%; height: 100.0%;">M5C,  Cluster 0</div>')[0];
                popup_5b79b425365c4ae6864faec87131c7cd.setContent(html_560e6ca6057649d0b8be503473fb8103);
            

            circle_marker_4b9b8400ad2f40f68214ea725cdcc1ed.bindPopup(popup_5b79b425365c4ae6864faec87131c7cd);

            
        
    
            var circle_marker_80325a2a80dc40dab343f9705d01e84b = L.circleMarker(
                [43.64516015600003,-79.37367499999993],
                {
  "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_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_96f3c6902caa4e439f74d284d5ea6225 = L.popup({maxWidth: '300'});

            
                var html_9f334b25fa874611b561df4cad45f7f7 = $('<div id="html_9f334b25fa874611b561df4cad45f7f7" style="width: 100.0%; height: 100.0%;">M5E,  Cluster 0</div>')[0];
                popup_96f3c6902caa4e439f74d284d5ea6225.setContent(html_9f334b25fa874611b561df4cad45f7f7);
            

            circle_marker_80325a2a80dc40dab343f9705d01e84b.bindPopup(popup_96f3c6902caa4e439f74d284d5ea6225);

            
        
    
            var circle_marker_3d42abc14b704a7580a68d59e6332972 = L.circleMarker(
                [43.65609081300005,-79.38492999999994],
                {
  "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_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_790a30256be54114811ad4ddd1eb0675 = L.popup({maxWidth: '300'});

            
                var html_68741ccb28d246f3b3933f2c7ba3d81b = $('<div id="html_68741ccb28d246f3b3933f2c7ba3d81b" style="width: 100.0%; height: 100.0%;">M5G,  Cluster 0</div>')[0];
                popup_790a30256be54114811ad4ddd1eb0675.setContent(html_68741ccb28d246f3b3933f2c7ba3d81b);
            

            circle_marker_3d42abc14b704a7580a68d59e6332972.bindPopup(popup_790a30256be54114811ad4ddd1eb0675);

            
        
    
            var circle_marker_a32822c5a7374f1ea463ee3ff94369be = L.circleMarker(
                [43.649515000000065,-79.38250344699998],
                {
  "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_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_956f93fc8dc84b7794975d122ad279c4 = L.popup({maxWidth: '300'});

            
                var html_5689401289d64a05bb79d3c852b792a7 = $('<div id="html_5689401289d64a05bb79d3c852b792a7" style="width: 100.0%; height: 100.0%;">M5H,  Cluster 0</div>')[0];
                popup_956f93fc8dc84b7794975d122ad279c4.setContent(html_5689401289d64a05bb79d3c852b792a7);
            

            circle_marker_a32822c5a7374f1ea463ee3ff94369be.bindPopup(popup_956f93fc8dc84b7794975d122ad279c4);

            
        
    
            var circle_marker_9e1a6f70bf8e455a8283367e5d314815 = L.circleMarker(
                [43.623470000000054,-79.39150736399995],
                {
  "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_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_1f9bfcbd6d724c8fa6d27242b588bdcb = L.popup({maxWidth: '300'});

            
                var html_7b6d081440ad4b78b54bf5fe82d602e0 = $('<div id="html_7b6d081440ad4b78b54bf5fe82d602e0" style="width: 100.0%; height: 100.0%;">M5J,  Cluster 0</div>')[0];
                popup_1f9bfcbd6d724c8fa6d27242b588bdcb.setContent(html_7b6d081440ad4b78b54bf5fe82d602e0);
            

            circle_marker_9e1a6f70bf8e455a8283367e5d314815.bindPopup(popup_1f9bfcbd6d724c8fa6d27242b588bdcb);

            
        
    
            var circle_marker_f3b584c3fe0645ef8d7db16719f4d1e7 = L.circleMarker(
                [43.64710000000008,-79.38153109899997],
                {
  "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_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_308fa8f3b56740ed87849f628640fdfb = L.popup({maxWidth: '300'});

            
                var html_5c3c89d9afa34c869fd902bce07cc154 = $('<div id="html_5c3c89d9afa34c869fd902bce07cc154" style="width: 100.0%; height: 100.0%;">M5K,  Cluster 0</div>')[0];
                popup_308fa8f3b56740ed87849f628640fdfb.setContent(html_5c3c89d9afa34c869fd902bce07cc154);
            

            circle_marker_f3b584c3fe0645ef8d7db16719f4d1e7.bindPopup(popup_308fa8f3b56740ed87849f628640fdfb);

            
        
    
            var circle_marker_23a598ccf2714104a1fe0a7042d62dc0 = L.circleMarker(
                [43.648205000000075,-79.37879339899996],
                {
  "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_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_716fd3ae029048f6b6245b63cf33da67 = L.popup({maxWidth: '300'});

            
                var html_1e21853acc384ee789cab60a4bfe76e4 = $('<div id="html_1e21853acc384ee789cab60a4bfe76e4" style="width: 100.0%; height: 100.0%;">M5L,  Cluster 0</div>')[0];
                popup_716fd3ae029048f6b6245b63cf33da67.setContent(html_1e21853acc384ee789cab60a4bfe76e4);
            

            circle_marker_23a598ccf2714104a1fe0a7042d62dc0.bindPopup(popup_716fd3ae029048f6b6245b63cf33da67);

            
        
    
            var circle_marker_962d8f692a7b4be18a6e95f98294063d = L.circleMarker(
                [43.711941154000044,-79.41911999999996],
                {
  "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_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_74ec4ad7c4ac4addb9f2e267e2259b06 = L.popup({maxWidth: '300'});

            
                var html_32d0a7ee66a74eecb7b988ed04ad4f63 = $('<div id="html_32d0a7ee66a74eecb7b988ed04ad4f63" style="width: 100.0%; height: 100.0%;">M5N,  Cluster 2</div>')[0];
                popup_74ec4ad7c4ac4addb9f2e267e2259b06.setContent(html_32d0a7ee66a74eecb7b988ed04ad4f63);
            

            circle_marker_962d8f692a7b4be18a6e95f98294063d.bindPopup(popup_74ec4ad7c4ac4addb9f2e267e2259b06);

            
        
    
            var circle_marker_4f209a0d63c04b748ace271622cc5f8a = L.circleMarker(
                [43.69478500000008,-79.41440483299994],
                {
  "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_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_013aa729b6c24deda7866a78fd95bac1 = L.popup({maxWidth: '300'});

            
                var html_afc99c545b394b698d903ef71b196fee = $('<div id="html_afc99c545b394b698d903ef71b196fee" style="width: 100.0%; height: 100.0%;">M5P,  Cluster 0</div>')[0];
                popup_013aa729b6c24deda7866a78fd95bac1.setContent(html_afc99c545b394b698d903ef71b196fee);
            

            circle_marker_4f209a0d63c04b748ace271622cc5f8a.bindPopup(popup_013aa729b6c24deda7866a78fd95bac1);

            
        
    
            var circle_marker_35c627c613ba4bd5a5e4d453e27cfdfc = L.circleMarker(
                [43.674840000000074,-79.40369769099993],
                {
  "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_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_dae2866af43b4e209a358a7377fa71f4 = L.popup({maxWidth: '300'});

            
                var html_429a3a4debce40419af9709ac9797ac3 = $('<div id="html_429a3a4debce40419af9709ac9797ac3" style="width: 100.0%; height: 100.0%;">M5R,  Cluster 0</div>')[0];
                popup_dae2866af43b4e209a358a7377fa71f4.setContent(html_429a3a4debce40419af9709ac9797ac3);
            

            circle_marker_35c627c613ba4bd5a5e4d453e27cfdfc.bindPopup(popup_dae2866af43b4e209a358a7377fa71f4);

            
        
    
            var circle_marker_4f72e6367d484ab8ba2c819b12115077 = L.circleMarker(
                [43.663110000000074,-79.40180056699995],
                {
  "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_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_c1cd8ac8021c47fa9a2ed3df28f161f9 = L.popup({maxWidth: '300'});

            
                var html_f6d293ebf49541818cb8185a7f580e19 = $('<div id="html_f6d293ebf49541818cb8185a7f580e19" style="width: 100.0%; height: 100.0%;">M5S,  Cluster 0</div>')[0];
                popup_c1cd8ac8021c47fa9a2ed3df28f161f9.setContent(html_f6d293ebf49541818cb8185a7f580e19);
            

            circle_marker_4f72e6367d484ab8ba2c819b12115077.bindPopup(popup_c1cd8ac8021c47fa9a2ed3df28f161f9);

            
        
    
            var circle_marker_e958d6ff840145fda3226c7113648453 = L.circleMarker(
                [43.65357000000006,-79.39724915699998],
                {
  "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_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_8e87c14b003b420ea82cc948fcd45801 = L.popup({maxWidth: '300'});

            
                var html_b04fe66cae284401838de7f1af1435fb = $('<div id="html_b04fe66cae284401838de7f1af1435fb" style="width: 100.0%; height: 100.0%;">M5T,  Cluster 0</div>')[0];
                popup_8e87c14b003b420ea82cc948fcd45801.setContent(html_b04fe66cae284401838de7f1af1435fb);
            

            circle_marker_e958d6ff840145fda3226c7113648453.bindPopup(popup_8e87c14b003b420ea82cc948fcd45801);

            
        
    
            var circle_marker_84d4618b504e4888b4f5758e29508817 = L.circleMarker(
                [43.64081500000003,-79.39953781899999],
                {
  "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_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_eaba9364deb84abf90b87dda079144cc = L.popup({maxWidth: '300'});

            
                var html_5051007563e14665b644446ebc828ca1 = $('<div id="html_5051007563e14665b644446ebc828ca1" style="width: 100.0%; height: 100.0%;">M5V,  Cluster 0</div>')[0];
                popup_eaba9364deb84abf90b87dda079144cc.setContent(html_5051007563e14665b644446ebc828ca1);
            

            circle_marker_84d4618b504e4888b4f5758e29508817.bindPopup(popup_eaba9364deb84abf90b87dda079144cc);

            
        
    
            var circle_marker_63353fd57afb425d91d5083766007b6d = L.circleMarker(
                [43.648690000000045,-79.38543999999996],
                {
  "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_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_319d402910484d14806fdf79d4bb965a = L.popup({maxWidth: '300'});

            
                var html_3711d0d0d8b146f49c809aafb2d4aebe = $('<div id="html_3711d0d0d8b146f49c809aafb2d4aebe" style="width: 100.0%; height: 100.0%;">M5W,  Cluster 0</div>')[0];
                popup_319d402910484d14806fdf79d4bb965a.setContent(html_3711d0d0d8b146f49c809aafb2d4aebe);
            

            circle_marker_63353fd57afb425d91d5083766007b6d.bindPopup(popup_319d402910484d14806fdf79d4bb965a);

            
        
    
            var circle_marker_7c1368c0b7174c3ca4d72fd8938a5595 = L.circleMarker(
                [43.64828000000006,-79.38146082599997],
                {
  "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_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_7fe37f14827b432b815865ea19d70c3a = L.popup({maxWidth: '300'});

            
                var html_f9ac502928844b498c7142f86c2e49bd = $('<div id="html_f9ac502928844b498c7142f86c2e49bd" style="width: 100.0%; height: 100.0%;">M5X,  Cluster 0</div>')[0];
                popup_7fe37f14827b432b815865ea19d70c3a.setContent(html_f9ac502928844b498c7142f86c2e49bd);
            

            circle_marker_7c1368c0b7174c3ca4d72fd8938a5595.bindPopup(popup_7fe37f14827b432b815865ea19d70c3a);

            
        
    
            var circle_marker_6620389e2f654c4eb4cf15c3456722ef = L.circleMarker(
                [43.66878132800008,-79.42070999999999],
                {
  "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_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_bc9dfd4ad78647e5bc24cf60120fbb8f = L.popup({maxWidth: '300'});

            
                var html_4a694afc10e04973a8a25321fc8e0124 = $('<div id="html_4a694afc10e04973a8a25321fc8e0124" style="width: 100.0%; height: 100.0%;">M6G,  Cluster 0</div>')[0];
                popup_bc9dfd4ad78647e5bc24cf60120fbb8f.setContent(html_4a694afc10e04973a8a25321fc8e0124);
            

            circle_marker_6620389e2f654c4eb4cf15c3456722ef.bindPopup(popup_bc9dfd4ad78647e5bc24cf60120fbb8f);

            
        
    
            var circle_marker_40ee83f226c048e08a3789e4f68594a8 = L.circleMarker(
                [43.66508694300006,-79.43870499999997],
                {
  "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_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_c499e5c86aad4fa2ba4355b1a571582e = L.popup({maxWidth: '300'});

            
                var html_96067625a3864ea5884c0e6812f5dc66 = $('<div id="html_96067625a3864ea5884c0e6812f5dc66" style="width: 100.0%; height: 100.0%;">M6H,  Cluster 0</div>')[0];
                popup_c499e5c86aad4fa2ba4355b1a571582e.setContent(html_96067625a3864ea5884c0e6812f5dc66);
            

            circle_marker_40ee83f226c048e08a3789e4f68594a8.bindPopup(popup_c499e5c86aad4fa2ba4355b1a571582e);

            
        
    
            var circle_marker_b7c85e7e92884aa5b6be43e9f1fb0637 = L.circleMarker(
                [43.64848500000005,-79.41774150899994],
                {
  "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_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_0a4bafabb4fb4118b96c574abb202ead = L.popup({maxWidth: '300'});

            
                var html_bb7165170a1045a39fc84de2ab3fc555 = $('<div id="html_bb7165170a1045a39fc84de2ab3fc555" style="width: 100.0%; height: 100.0%;">M6J,  Cluster 0</div>')[0];
                popup_0a4bafabb4fb4118b96c574abb202ead.setContent(html_bb7165170a1045a39fc84de2ab3fc555);
            

            circle_marker_b7c85e7e92884aa5b6be43e9f1fb0637.bindPopup(popup_0a4bafabb4fb4118b96c574abb202ead);

            
        
    
            var circle_marker_7ef78845e64b4a65b31784049bfc2935 = L.circleMarker(
                [43.639410000000055,-79.42436201999999],
                {
  "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_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_58855f269b304e9eb8fac146502171b4 = L.popup({maxWidth: '300'});

            
                var html_b8d17b5d375e46c2811ce47b7f7aa487 = $('<div id="html_b8d17b5d375e46c2811ce47b7f7aa487" style="width: 100.0%; height: 100.0%;">M6K,  Cluster 0</div>')[0];
                popup_58855f269b304e9eb8fac146502171b4.setContent(html_b8d17b5d375e46c2811ce47b7f7aa487);
            

            circle_marker_7ef78845e64b4a65b31784049bfc2935.bindPopup(popup_58855f269b304e9eb8fac146502171b4);

            
        
    
            var circle_marker_7096c130cd1640608403039f2f9f3993 = L.circleMarker(
                [43.65997500000003,-79.46287357999995],
                {
  "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_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_d58261c0b23642538bc163ebef234f3c = L.popup({maxWidth: '300'});

            
                var html_ece17f35e78a47ada20c1a7e257d636e = $('<div id="html_ece17f35e78a47ada20c1a7e257d636e" style="width: 100.0%; height: 100.0%;">M6P,  Cluster 0</div>')[0];
                popup_d58261c0b23642538bc163ebef234f3c.setContent(html_ece17f35e78a47ada20c1a7e257d636e);
            

            circle_marker_7096c130cd1640608403039f2f9f3993.bindPopup(popup_d58261c0b23642538bc163ebef234f3c);

            
        
    
            var circle_marker_3e05ab7eb92e442c85069c897315b885 = L.circleMarker(
                [43.64787000000007,-79.44976249999996],
                {
  "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_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_54de89cb770941fe8be0210ad923683f = L.popup({maxWidth: '300'});

            
                var html_75f785aa37644622be585ce1751620a3 = $('<div id="html_75f785aa37644622be585ce1751620a3" style="width: 100.0%; height: 100.0%;">M6R,  Cluster 0</div>')[0];
                popup_54de89cb770941fe8be0210ad923683f.setContent(html_75f785aa37644622be585ce1751620a3);
            

            circle_marker_3e05ab7eb92e442c85069c897315b885.bindPopup(popup_54de89cb770941fe8be0210ad923683f);

            
        
    
            var circle_marker_4f89e9d323ec4f4dbaca1792dca223ad = L.circleMarker(
                [43.64988500000004,-79.47492879599997],
                {
  "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_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_d97cf078542e42aeb7e6bff9379b1e8d = L.popup({maxWidth: '300'});

            
                var html_dcf859d2105141558a92b8a57ff342ca = $('<div id="html_dcf859d2105141558a92b8a57ff342ca" style="width: 100.0%; height: 100.0%;">M6S,  Cluster 0</div>')[0];
                popup_d97cf078542e42aeb7e6bff9379b1e8d.setContent(html_dcf859d2105141558a92b8a57ff342ca);
            

            circle_marker_4f89e9d323ec4f4dbaca1792dca223ad.bindPopup(popup_d97cf078542e42aeb7e6bff9379b1e8d);

            
        
    
            var circle_marker_28885ca2fe8b4263910cfa97ac970c70 = L.circleMarker(
                [43.648690000000045,-79.38543999999996],
                {
  "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_b876d16bf09c40dfbf9220b5b8c979c4);
            
    
            var popup_ecf855fb26264b809c05fd6bb0c76eeb = L.popup({maxWidth: '300'});

            
                var html_1fe3bacbd0034f69a3182f69f0253638 = $('<div id="html_1fe3bacbd0034f69a3182f69f0253638" style="width: 100.0%; height: 100.0%;">M7Y,  Cluster 0</div>')[0];
                popup_ecf855fb26264b809c05fd6bb0c76eeb.setContent(html_1fe3bacbd0034f69a3182f69f0253638);
            

            circle_marker_28885ca2fe8b4263910cfa97ac970c70.bindPopup(popup_ecf855fb26264b809c05fd6bb0c76eeb);

            
        
</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>", 
                        "text/plain": "<folium.folium.Map at 0x7f253cb55550>"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "# Create map\nmap_clusters = folium.Map(location=[lat_tor, lng_tor], zoom_start=11)\n\n# Set color scheme for the clusters\nx = np.arange(kclusters)\nys = [i + x + (i * x) ** 2 for i in range(kclusters)]\ncolors_array = cm.rainbow(np.linspace(0, 1, len(ys)))\nrainbow = [colors.rgb2hex(i) for i in colors_array]\n\n# Add markers to the map\nmarkers_colors = []\nfor lat, lon, pc, cluster in zip(toronto_merged['Latitude'], toronto_merged['Longitude'], toronto_merged['Postcode'], toronto_merged['Cluster Labels']):\n    label = folium.Popup(str(pc) + ', ' + ' Cluster ' + str(cluster), parse_html=True)\n    folium.CircleMarker([lat, lon],\n                        radius=5,\n                        popup=label,\n                        color=rainbow[cluster-1],\n                        fill=True,\n                        fill_color=rainbow[cluster-1],\n                        fill_opacity=0.7).add_to(map_clusters)\n       \nmap_clusters"
        }, 
        {
            "source": "Looking at the map, it seems that most of the neighborhoods (postcodes) are similar according to *k*-means clusters with the exception of three neighborhoods, M4T and M4W in cluster 1 and M5N in cluster 2.", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "To view the notebook including the maps, please click this link. [https://dataplatform.cloud.ibm.com/analytics/notebooks/v2/a4505f3d-526a-44b9-baa7-ad66b0c621b7/view?access_token=09855a0dd70d63ac65f8b23343396b8d0c2af823428566b98b142c38320d8579](https://dataplatform.cloud.ibm.com/analytics/notebooks/v2/a4505f3d-526a-44b9-baa7-ad66b0c621b7/view?access_token=09855a0dd70d63ac65f8b23343396b8d0c2af823428566b98b142c38320d8579)", 
            "cell_type": "markdown", 
            "metadata": {}
        }
    ], 
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3.6", 
            "name": "python3", 
            "language": "python"
        }, 
        "language_info": {
            "mimetype": "text/x-python", 
            "nbconvert_exporter": "python", 
            "version": "3.6.8", 
            "name": "python", 
            "file_extension": ".py", 
            "pygments_lexer": "ipython3", 
            "codemirror_mode": {
                "version": 3, 
                "name": "ipython"
            }
        }
    }, 
    "nbformat": 4
}