clustering.ipynb

{
    "nbformat_minor": 1, 
    "cells": [
        {
            "source": "# Explore and cluster the neighborhoods in Toronto", 
            "cell_type": "markdown", 
            "metadata": {
                "collapsed": true
            }
        }, 
        {
            "source": "# install geopy & folium\n!conda install -c conda-forge geopy --yes\n!conda install -c conda-forge folium=0.5.0 --yes", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "Fetching package metadata .............\nSolving package specifications: .\n\nPackage plan for installation in environment /opt/conda/envs/DSX-Python35:\n\nThe following NEW packages will be INSTALLED:\n\n    geographiclib: 1.49-py_0   conda-forge\n    geopy:         1.18.1-py_0 conda-forge\n\ngeographiclib- 100% |################################| Time: 0:00:00  24.49 MB/s\ngeopy-1.18.1-p 100% |################################| Time: 0:00:00  37.95 MB/s\nFetching package metadata .............\nSolving package specifications: .\n\nPackage plan for installation in environment /opt/conda/envs/DSX-Python35:\n\nThe following NEW packages will be INSTALLED:\n\n    altair:  2.2.2-py35_1 conda-forge\n    branca:  0.3.1-py_0   conda-forge\n    folium:  0.5.0-py_0   conda-forge\n    vincent: 0.4.4-py_1   conda-forge\n\naltair-2.2.2-p 100% |################################| Time: 0:00:00  47.11 MB/s\nbranca-0.3.1-p 100% |################################| Time: 0:00:00  35.24 MB/s\nvincent-0.4.4- 100% |################################| Time: 0:00:00  40.11 MB/s\nfolium-0.5.0-p 100% |################################| Time: 0:00:00  48.14 MB/s\n"
                }
            ], 
            "execution_count": 1
        }, 
        {
            "source": "# import Libraries that we need\nimport requests\nfrom bs4 import BeautifulSoup\nimport pandas as pd\npd.set_option('display.max_columns', None)\npd.set_option('display.max_rows', None)\nimport numpy as np\nfrom geopy.geocoders import Nominatim\nimport matplotlib.cm as cm\nimport matplotlib.colors as colors\nfrom sklearn.cluster import KMeans\nimport folium", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 2
        }, 
        {
            "source": "## Scrap data from wikipedia page & Create dataframe", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "page = requests.get(\"https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M\")\nsoup = BeautifulSoup(page.content, 'html.parser')", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 3
        }, 
        {
            "source": "## Read table", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "table = soup.find('tbody')\nrows = table.select('tr')\nrow = [r.get_text() for r in rows]", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "execution_count": 4
        }, 
        {
            "source": "## Create a clean dataframe", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "df = pd.DataFrame(row)\ndf1 = df[0].str.split('\\n', expand=True)\ndf2 = df1.rename(columns=df1.iloc[0])\ndf3 = df2.drop(df2.index[0])\ndf4 = df3[df3.Borough != 'Not assigned']\ndf5 = df4.groupby(['Postcode', 'Borough'], sort = False).agg(','.join)\ndf5.reset_index(inplace = True)\ndf6 = df5.replace(\"Not assigned\", \"Queen's Park\")\ndf6.head()", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "  Postcode           Borough                    Neighbourhood\n0      M3A        North York                        Parkwoods\n1      M4A        North York                 Victoria Village\n2      M5A  Downtown Toronto         Harbourfront,Regent Park\n3      M6A        North York  Lawrence Heights,Lawrence Manor\n4      M7A      Queen's Park                     Queen's Park", 
                        "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>Neighbourhood</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>M3A</td>\n      <td>North York</td>\n      <td>Parkwoods</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>M4A</td>\n      <td>North York</td>\n      <td>Victoria Village</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>M5A</td>\n      <td>Downtown Toronto</td>\n      <td>Harbourfront,Regent Park</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>M6A</td>\n      <td>North York</td>\n      <td>Lawrence Heights,Lawrence Manor</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>M7A</td>\n      <td>Queen's Park</td>\n      <td>Queen's Park</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
                    }, 
                    "execution_count": 5, 
                    "metadata": {}
                }
            ], 
            "execution_count": 5
        }, 
        {
            "source": "## Read csv file of Geospatial_data", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "url = \"http://cocl.us/Geospatial_data\"\ndf7 = pd.read_csv(url)\ndf7.rename(columns={'Postal Code': 'Postcode'}, inplace=True) # chane the first column's name to be as the first dataframe\ndf7.head()", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "  Postcode   Latitude  Longitude\n0      M1B  43.806686 -79.194353\n1      M1C  43.784535 -79.160497\n2      M1E  43.763573 -79.188711\n3      M1G  43.770992 -79.216917\n4      M1H  43.773136 -79.239476", 
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Postcode</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>M1B</td>\n      <td>43.806686</td>\n      <td>-79.194353</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>M1C</td>\n      <td>43.784535</td>\n      <td>-79.160497</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>M1E</td>\n      <td>43.763573</td>\n      <td>-79.188711</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>M1G</td>\n      <td>43.770992</td>\n      <td>-79.216917</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>M1H</td>\n      <td>43.773136</td>\n      <td>-79.239476</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
                    }, 
                    "execution_count": 6, 
                    "metadata": {}
                }
            ], 
            "execution_count": 6
        }, 
        {
            "source": "## Create a new dataframe to merge first & second dataframe", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "df8 = pd.merge(df6, df7, on='Postcode')\ndf8.head()", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "  Postcode           Borough                    Neighbourhood   Latitude  \\\n0      M3A        North York                        Parkwoods  43.753259   \n1      M4A        North York                 Victoria Village  43.725882   \n2      M5A  Downtown Toronto         Harbourfront,Regent Park  43.654260   \n3      M6A        North York  Lawrence Heights,Lawrence Manor  43.718518   \n4      M7A      Queen's Park                     Queen's Park  43.662301   \n\n   Longitude  \n0 -79.329656  \n1 -79.315572  \n2 -79.360636  \n3 -79.464763  \n4 -79.389494  ", 
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Postcode</th>\n      <th>Borough</th>\n      <th>Neighbourhood</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>M3A</td>\n      <td>North York</td>\n      <td>Parkwoods</td>\n      <td>43.753259</td>\n      <td>-79.329656</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>M4A</td>\n      <td>North York</td>\n      <td>Victoria Village</td>\n      <td>43.725882</td>\n      <td>-79.315572</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>M5A</td>\n      <td>Downtown Toronto</td>\n      <td>Harbourfront,Regent Park</td>\n      <td>43.654260</td>\n      <td>-79.360636</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>M6A</td>\n      <td>North York</td>\n      <td>Lawrence Heights,Lawrence Manor</td>\n      <td>43.718518</td>\n      <td>-79.464763</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>M7A</td>\n      <td>Queen's Park</td>\n      <td>Queen's Park</td>\n      <td>43.662301</td>\n      <td>-79.389494</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
                    }, 
                    "execution_count": 7, 
                    "metadata": {}
                }
            ], 
            "execution_count": 7
        }, 
        {
            "source": "## Show how many Borough & Neighbourhood in the dataframe", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "print('The dataframe has {} Borough and {} Neighbourhood.'.format(\n        len(df8['Borough'].unique()),\n        df8.shape[0]\n    )\n)", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "The dataframe has 11 Borough and 103 Neighbourhood.\n"
                }
            ], 
            "execution_count": 8
        }, 
        {
            "source": "## Create a new dataframe for only boroughs that contain the word Toronto ", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "Toronto=df8[df8['Borough'].str.contains('Toronto')]\nToronto", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "    Postcode           Borough  \\\n2        M5A  Downtown Toronto   \n9        M5B  Downtown Toronto   \n15       M5C  Downtown Toronto   \n19       M4E      East Toronto   \n20       M5E  Downtown Toronto   \n24       M5G  Downtown Toronto   \n25       M6G  Downtown Toronto   \n30       M5H  Downtown Toronto   \n31       M6H      West Toronto   \n36       M5J  Downtown Toronto   \n37       M6J      West Toronto   \n41       M4K      East Toronto   \n42       M5K  Downtown Toronto   \n43       M6K      West Toronto   \n47       M4L      East Toronto   \n48       M5L  Downtown Toronto   \n54       M4M      East Toronto   \n61       M4N   Central Toronto   \n62       M5N   Central Toronto   \n67       M4P   Central Toronto   \n68       M5P   Central Toronto   \n69       M6P      West Toronto   \n73       M4R   Central Toronto   \n74       M5R   Central Toronto   \n75       M6R      West Toronto   \n79       M4S   Central Toronto   \n80       M5S  Downtown Toronto   \n81       M6S      West Toronto   \n83       M4T   Central Toronto   \n84       M5T  Downtown Toronto   \n86       M4V   Central Toronto   \n87       M5V  Downtown Toronto   \n91       M4W  Downtown Toronto   \n92       M5W  Downtown Toronto   \n96       M4X  Downtown Toronto   \n97       M5X  Downtown Toronto   \n99       M4Y  Downtown Toronto   \n100      M7Y      East Toronto   \n\n                                         Neighbourhood   Latitude  Longitude  \n2                             Harbourfront,Regent Park  43.654260 -79.360636  \n9                              Ryerson,Garden District  43.657162 -79.378937  \n15                                      St. James Town  43.651494 -79.375418  \n19                                         The Beaches  43.676357 -79.293031  \n20                                         Berczy Park  43.644771 -79.373306  \n24                                  Central Bay Street  43.657952 -79.387383  \n25                                            Christie  43.669542 -79.422564  \n30                              Adelaide,King,Richmond  43.650571 -79.384568  \n31                         Dovercourt Village,Dufferin  43.669005 -79.442259  \n36     Harbourfront East,Toronto Islands,Union Station  43.640816 -79.381752  \n37                             Little Portugal,Trinity  43.647927 -79.419750  \n41                         The Danforth West,Riverdale  43.679557 -79.352188  \n42             Design Exchange,Toronto Dominion Centre  43.647177 -79.381576  \n43          Brockton,Exhibition Place,Parkdale Village  43.636847 -79.428191  \n47                       The Beaches West,India Bazaar  43.668999 -79.315572  \n48                       Commerce Court,Victoria Hotel  43.648198 -79.379817  \n54                                     Studio District  43.659526 -79.340923  \n61                                       Lawrence Park  43.728020 -79.388790  \n62                                            Roselawn  43.711695 -79.416936  \n67                                    Davisville North  43.712751 -79.390197  \n68                  Forest Hill North,Forest Hill West  43.696948 -79.411307  \n69                        High Park,The Junction South  43.661608 -79.464763  \n73                                  North Toronto West  43.715383 -79.405678  \n74                   The Annex,North Midtown,Yorkville  43.672710 -79.405678  \n75                               Parkdale,Roncesvalles  43.648960 -79.456325  \n79                                          Davisville  43.704324 -79.388790  \n80                       Harbord,University of Toronto  43.662696 -79.400049  \n81                                   Runnymede,Swansea  43.651571 -79.484450  \n83                          Moore Park,Summerhill East  43.689574 -79.383160  \n84             Chinatown,Grange Park,Kensington Market  43.653206 -79.400049  \n86   Deer Park,Forest Hill SE,Rathnelly,South Hill,...  43.686412 -79.400049  \n87   CN Tower,Bathurst Quay,Island airport,Harbourf...  43.628947 -79.394420  \n91                                            Rosedale  43.679563 -79.377529  \n92                     Stn A PO Boxes 25 The Esplanade  43.646435 -79.374846  \n96                          Cabbagetown,St. James Town  43.667967 -79.367675  \n97               First Canadian Place,Underground city  43.648429 -79.382280  \n99                                Church and Wellesley  43.665860 -79.383160  \n100  Business Reply Mail Processing Centre 969 Eastern  43.662744 -79.321558  ", 
                        "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>Neighbourhood</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td>M5A</td>\n      <td>Downtown Toronto</td>\n      <td>Harbourfront,Regent Park</td>\n      <td>43.654260</td>\n      <td>-79.360636</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>M5B</td>\n      <td>Downtown Toronto</td>\n      <td>Ryerson,Garden District</td>\n      <td>43.657162</td>\n      <td>-79.378937</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>M5C</td>\n      <td>Downtown Toronto</td>\n      <td>St. James Town</td>\n      <td>43.651494</td>\n      <td>-79.375418</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>M4E</td>\n      <td>East Toronto</td>\n      <td>The Beaches</td>\n      <td>43.676357</td>\n      <td>-79.293031</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>M5E</td>\n      <td>Downtown Toronto</td>\n      <td>Berczy Park</td>\n      <td>43.644771</td>\n      <td>-79.373306</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>M5G</td>\n      <td>Downtown Toronto</td>\n      <td>Central Bay Street</td>\n      <td>43.657952</td>\n      <td>-79.387383</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>M6G</td>\n      <td>Downtown Toronto</td>\n      <td>Christie</td>\n      <td>43.669542</td>\n      <td>-79.422564</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>M5H</td>\n      <td>Downtown Toronto</td>\n      <td>Adelaide,King,Richmond</td>\n      <td>43.650571</td>\n      <td>-79.384568</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>M6H</td>\n      <td>West Toronto</td>\n      <td>Dovercourt Village,Dufferin</td>\n      <td>43.669005</td>\n      <td>-79.442259</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>M5J</td>\n      <td>Downtown Toronto</td>\n      <td>Harbourfront East,Toronto Islands,Union Station</td>\n      <td>43.640816</td>\n      <td>-79.381752</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>M6J</td>\n      <td>West Toronto</td>\n      <td>Little Portugal,Trinity</td>\n      <td>43.647927</td>\n      <td>-79.419750</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>M4K</td>\n      <td>East Toronto</td>\n      <td>The Danforth West,Riverdale</td>\n      <td>43.679557</td>\n      <td>-79.352188</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>M5K</td>\n      <td>Downtown Toronto</td>\n      <td>Design Exchange,Toronto Dominion Centre</td>\n      <td>43.647177</td>\n      <td>-79.381576</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>M6K</td>\n      <td>West Toronto</td>\n      <td>Brockton,Exhibition Place,Parkdale Village</td>\n      <td>43.636847</td>\n      <td>-79.428191</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>M4L</td>\n      <td>East Toronto</td>\n      <td>The Beaches West,India Bazaar</td>\n      <td>43.668999</td>\n      <td>-79.315572</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>M5L</td>\n      <td>Downtown Toronto</td>\n      <td>Commerce Court,Victoria Hotel</td>\n      <td>43.648198</td>\n      <td>-79.379817</td>\n    </tr>\n    <tr>\n      <th>54</th>\n      <td>M4M</td>\n      <td>East Toronto</td>\n      <td>Studio District</td>\n      <td>43.659526</td>\n      <td>-79.340923</td>\n    </tr>\n    <tr>\n      <th>61</th>\n      <td>M4N</td>\n      <td>Central Toronto</td>\n      <td>Lawrence Park</td>\n      <td>43.728020</td>\n      <td>-79.388790</td>\n    </tr>\n    <tr>\n      <th>62</th>\n      <td>M5N</td>\n      <td>Central Toronto</td>\n      <td>Roselawn</td>\n      <td>43.711695</td>\n      <td>-79.416936</td>\n    </tr>\n    <tr>\n      <th>67</th>\n      <td>M4P</td>\n      <td>Central Toronto</td>\n      <td>Davisville North</td>\n      <td>43.712751</td>\n      <td>-79.390197</td>\n    </tr>\n    <tr>\n      <th>68</th>\n      <td>M5P</td>\n      <td>Central Toronto</td>\n      <td>Forest Hill North,Forest Hill West</td>\n      <td>43.696948</td>\n      <td>-79.411307</td>\n    </tr>\n    <tr>\n      <th>69</th>\n      <td>M6P</td>\n      <td>West Toronto</td>\n      <td>High Park,The Junction South</td>\n      <td>43.661608</td>\n      <td>-79.464763</td>\n    </tr>\n    <tr>\n      <th>73</th>\n      <td>M4R</td>\n      <td>Central Toronto</td>\n      <td>North Toronto West</td>\n      <td>43.715383</td>\n      <td>-79.405678</td>\n    </tr>\n    <tr>\n      <th>74</th>\n      <td>M5R</td>\n      <td>Central Toronto</td>\n      <td>The Annex,North Midtown,Yorkville</td>\n      <td>43.672710</td>\n      <td>-79.405678</td>\n    </tr>\n    <tr>\n      <th>75</th>\n      <td>M6R</td>\n      <td>West Toronto</td>\n      <td>Parkdale,Roncesvalles</td>\n      <td>43.648960</td>\n      <td>-79.456325</td>\n    </tr>\n    <tr>\n      <th>79</th>\n      <td>M4S</td>\n      <td>Central Toronto</td>\n      <td>Davisville</td>\n      <td>43.704324</td>\n      <td>-79.388790</td>\n    </tr>\n    <tr>\n      <th>80</th>\n      <td>M5S</td>\n      <td>Downtown Toronto</td>\n      <td>Harbord,University of Toronto</td>\n      <td>43.662696</td>\n      <td>-79.400049</td>\n    </tr>\n    <tr>\n      <th>81</th>\n      <td>M6S</td>\n      <td>West Toronto</td>\n      <td>Runnymede,Swansea</td>\n      <td>43.651571</td>\n      <td>-79.484450</td>\n    </tr>\n    <tr>\n      <th>83</th>\n      <td>M4T</td>\n      <td>Central Toronto</td>\n      <td>Moore Park,Summerhill East</td>\n      <td>43.689574</td>\n      <td>-79.383160</td>\n    </tr>\n    <tr>\n      <th>84</th>\n      <td>M5T</td>\n      <td>Downtown Toronto</td>\n      <td>Chinatown,Grange Park,Kensington Market</td>\n      <td>43.653206</td>\n      <td>-79.400049</td>\n    </tr>\n    <tr>\n      <th>86</th>\n      <td>M4V</td>\n      <td>Central Toronto</td>\n      <td>Deer Park,Forest Hill SE,Rathnelly,South Hill,...</td>\n      <td>43.686412</td>\n      <td>-79.400049</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>M5V</td>\n      <td>Downtown Toronto</td>\n      <td>CN Tower,Bathurst Quay,Island airport,Harbourf...</td>\n      <td>43.628947</td>\n      <td>-79.394420</td>\n    </tr>\n    <tr>\n      <th>91</th>\n      <td>M4W</td>\n      <td>Downtown Toronto</td>\n      <td>Rosedale</td>\n      <td>43.679563</td>\n      <td>-79.377529</td>\n    </tr>\n    <tr>\n      <th>92</th>\n      <td>M5W</td>\n      <td>Downtown Toronto</td>\n      <td>Stn A PO Boxes 25 The Esplanade</td>\n      <td>43.646435</td>\n      <td>-79.374846</td>\n    </tr>\n    <tr>\n      <th>96</th>\n      <td>M4X</td>\n      <td>Downtown Toronto</td>\n      <td>Cabbagetown,St. James Town</td>\n      <td>43.667967</td>\n      <td>-79.367675</td>\n    </tr>\n    <tr>\n      <th>97</th>\n      <td>M5X</td>\n      <td>Downtown Toronto</td>\n      <td>First Canadian Place,Underground city</td>\n      <td>43.648429</td>\n      <td>-79.382280</td>\n    </tr>\n    <tr>\n      <th>99</th>\n      <td>M4Y</td>\n      <td>Downtown Toronto</td>\n      <td>Church and Wellesley</td>\n      <td>43.665860</td>\n      <td>-79.383160</td>\n    </tr>\n    <tr>\n      <th>100</th>\n      <td>M7Y</td>\n      <td>East Toronto</td>\n      <td>Business Reply Mail Processing Centre 969 Eastern</td>\n      <td>43.662744</td>\n      <td>-79.321558</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
                    }, 
                    "execution_count": 9, 
                    "metadata": {}
                }
            ], 
            "execution_count": 9
        }, 
        {
            "source": "## Generate map to visualize neighborhoods and how they cluster together", 
            "cell_type": "markdown", 
            "metadata": {}
        }, 
        {
            "source": "address = 'Toronto'\ngeolocator = Nominatim(user_agent=\"Toronto_explorer\")\nlocation = geolocator.geocode(address)\nlatitude = location.latitude\nlongitude = location.longitude\n\nToronto_map = folium.Map(location=[latitude, longitude], zoom_start=10)\n\nfor lat, lng, borough, neighborhood in zip(Toronto['Latitude'], Toronto['Longitude'], \n                                           Toronto['Borough'], Toronto['Neighbourhood']):\n    label = '{}, {}'.format(neighborhood, borough)\n    label = folium.Popup(label, parse_html=True)\n    folium.CircleMarker(\n        [lat, lng],\n        radius=5,\n        popup=label,\n        color='blue',\n        fill=True,\n        fill_color='#3186cc',\n        fill_opacity=0.7,\n        parse_html=False).add_to(Toronto_map)  \n    \nToronto_map", 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "execute_result", 
                    "data": {
                        "text/plain": "<folium.folium.Map at 0x7f178bfec8d0>", 
                        "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_ee07ca92caf745d38c0d709956fda13e {
                position : relative;
                width : 100.0%;
                height: 100.0%;
                left: 0.0%;
                top: 0.0%;
                }
            </style>
        
</head>
<body>    
    
            <div class="folium-map" id="map_ee07ca92caf745d38c0d709956fda13e" ></div>
        
</body>
<script>    
    

            
                var bounds = null;
            

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                                  {center: [43.653963,-79.387207],
                                  zoom: 10,
                                  maxBounds: bounds,
                                  layers: [],
                                  worldCopyJump: false,
                                  crs: L.CRS.EPSG3857
                                 });
            
        
    
            var tile_layer_e207f5a5dac944a2afe7a3d80a9354c0 = L.tileLayer(
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  "attribution": null,
  "detectRetina": false,
  "maxZoom": 18,
  "minZoom": 1,
  "noWrap": false,
  "subdomains": "abc"
}
                ).addTo(map_ee07ca92caf745d38c0d709956fda13e);
        
    
            var circle_marker_0a5a1f8919c94cb19eb297a34ba15561 = L.circleMarker(
                [43.6542599,-79.3606359],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_ee07ca92caf745d38c0d709956fda13e);
            
    
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            var circle_marker_bf07e1e8f1674b21abedaf7432e17d5f = L.circleMarker(
                [43.6571618,-79.37893709999999],
                {
  "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_ee07ca92caf745d38c0d709956fda13e);
            
    
            var popup_a9b7b9d952624271b78d36be0ea29430 = L.popup({maxWidth: '300'});

            
                var html_7c7ceaa1917446e9b18d5151cf462cd3 = $('<div id="html_7c7ceaa1917446e9b18d5151cf462cd3" style="width: 100.0%; height: 100.0%;">Ryerson,Garden District, Downtown Toronto</div>')[0];
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            circle_marker_bf07e1e8f1674b21abedaf7432e17d5f.bindPopup(popup_a9b7b9d952624271b78d36be0ea29430);

            
        
    
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                [43.6514939,-79.3754179],
                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
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            var popup_eb45d503baa94f6da1d361fd5363d0f0 = L.popup({maxWidth: '300'});

            
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                {
  "bubblingMouseEvents": true,
  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_ee07ca92caf745d38c0d709956fda13e);
            
    
            var popup_7795d146eb4c49e880d0dcd4789424f6 = L.popup({maxWidth: '300'});

            
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                {
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  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
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}
                ).addTo(map_ee07ca92caf745d38c0d709956fda13e);
            
    
            var popup_fbcc08f305574ea684e221ef49aea7c3 = L.popup({maxWidth: '300'});

            
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                popup_fbcc08f305574ea684e221ef49aea7c3.setContent(html_b2d27eb3cac2483eb17c4402e8af3505);
            

            circle_marker_87f35a288cb645158648ba4347b010a6.bindPopup(popup_fbcc08f305574ea684e221ef49aea7c3);

            
        
    
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  "lineCap": "round",
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}
                ).addTo(map_ee07ca92caf745d38c0d709956fda13e);
            
    
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            circle_marker_5d6e22a07e32407d91f01efc107d4762.bindPopup(popup_75b9ad4530484672ac1384ac72191cc4);

            
        
    
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  "lineCap": "round",
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  "opacity": 1.0,
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}
                ).addTo(map_ee07ca92caf745d38c0d709956fda13e);
            
    
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}
                ).addTo(map_ee07ca92caf745d38c0d709956fda13e);
            
    
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}
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                {
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  "color": "blue",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#3186cc",
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  "lineCap": "round",
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}
                ).addTo(map_ee07ca92caf745d38c0d709956fda13e);
            
    
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  "fill": true,
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  "fillRule": "evenodd",
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}
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            var popup_cd46ffdbffd045a0a12ac6e11bd799bf = L.popup({maxWidth: '300'});

            
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            circle_marker_edd0633318c94e038fd775ac53058e04.bindPopup(popup_cd46ffdbffd045a0a12ac6e11bd799bf);

            
        
    
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