The_Battle_Of_Neighborhoods.ipynb

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        "<a href=\"https://colab.research.google.com/github/sidharth178/The-Battle-of-Neighborhoods-Capstone-Project/blob/master/The_Battle_Of_Neighborhoods.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "# <center>**===========The Battle Of Neighborhoods============**</center>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ku7kGHGaRRLa"
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      "source": [
        "<img align=\"center\" src=\"https://drive.google.com/uc?export=view&id=16XgH-aYQ1Uj2AvXC4QS40nBMedl96eff\" alt=\"The Battle Of Neighborhoods\">"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u1xnNGGTccOo"
      },
      "source": [
        "# **1. Introduction**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R_WPdoPJcbn2"
      },
      "source": [
        "### **Objective**\n",
        "The objective of this project is to find the best neighbourhood in Toronto to open a Italian restaurant using Foursquare location data. In this project we’ll go through the solution for this problem for avoiding or considering low risk criteria and high success rate.\n",
        "### **Target Audiance**\n",
        "* Business personnel who wants to invest or open a start up company or restaurant.\n",
        "* Bachelors who want to stay in a good city where they can get each facilities what they want like GYM,Playground,Parlour,Movie theatre etc.\n",
        "* The freelancer who loves to have their own small company or restaurant as a side business.\n",
        "* Marketing companies who want to release a new product on a best place.\n",
        "* Researchers who want to create a camp for Survey.\n",
        "* Torrists who wants to eat italian food.\n",
        "\n",
        "### **Data Description**\n",
        "For this project we need these following data:\n",
        "1. ***Toronto City data that contains Borough, Neighborhoods along with there latitudes and longitudes***\n",
        "* **Data Source:** https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M\n",
        "* **Description:** This Wikipedia page contain all the information we need to explore and cluster the neighborhoods in Toronto. We will be required to scrape the Wikipedia page and wrangle the data, clean it, and then read it into a pandas dataframe so that it is in a structured format like the Toronto dataset.\n",
        "2. ***Geographical Location data using Geocoder Package***\n",
        "* **Data Source:** https://cocl.us/Geospatial_data\n",
        "* **Description:** The second source of data provided us with the Geographical coordinates of the neighbourhoods with the respective Postal Codes.\n",
        "3. ***Venue Data using Foursquare API***\n",
        "* **Data Source:** https://foursquare.com/developers/apps\n",
        "* **Description:** From Foursquare API we can get the name,category,latitude,longitude for each venue.\n",
        "\n",
        "In this capstone project we are going to answer of these following major steps:\n",
        "\n",
        "* **Assignment-1:** Scrape the data from the wikipedia page of Toronto.\n",
        "\n",
        "* **Assignment-2:**  Get the geographical coordinates of the neighborhoods using the Geocoder package.\n",
        "\n",
        "* **Assignment-3:** Explore and cluster the neighborhoods in Toronto.\n",
        "\n",
        "### **Blog Post(Medium)**\n",
        " Check [here](https://sidharth178.medium.com/the-battle-of-neighborhoods-f3f8bff9abf3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hxzPyt_GbETA"
      },
      "source": [
        "# **2. Import Libraries**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "xsI1bLhFSogC"
      },
      "source": [
        "# install geopy to access geocoder package\n",
        "!pip install geopy"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "tgVuB26KSoVH"
      },
      "source": [
        "# install beautifulsoup4 for web scraping\n",
        "!pip install beautifulsoup4"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "uGF1qr_uSoJz"
      },
      "source": [
        "# install requests to gain access to an URL\n",
        "!pip install requests"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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        "id": "KMuqZMUtSn-5"
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      "source": [
        "# install kmeans for clustering\n",
        "!pip install kmeans"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "H2BKr5HgSn0O"
      },
      "source": [
        "# install folium for visualization\n",
        "!pip install folium"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "id": "4kxtYfsyTAkB"
      },
      "source": [
        "# install sklearn\n",
        "!pip install -U scikit-learn"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WmMWNC15kF1W",
        "outputId": "992bd51c-b0ca-422e-dd66-5e85fbba311a"
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      "source": [
        "# import all necessary libraries\n",
        "import numpy as np # library to handle data in a vectorized manner\n",
        "\n",
        "import pandas as pd # library for data analsysis\n",
        "pd.set_option('display.max_columns', None)\n",
        "pd.set_option('display.max_rows', None)\n",
        "\n",
        "import json # library to handle JSON files\n",
        "\n",
        "# !conda install -c conda-forge geopy --yes \n",
        "from geopy.geocoders import Nominatim # convert an address into latitude and longitude values\n",
        "\n",
        "from bs4 import BeautifulSoup\n",
        "import requests # library to handle requests\n",
        "from pandas.io.json import json_normalize # tranform JSON file into a pandas dataframe\n",
        "\n",
        "# Matplotlib and associated plotting modules\n",
        "import matplotlib.cm as cm\n",
        "import matplotlib.colors as colors\n",
        "import matplotlib.pyplot as plt \n",
        "\n",
        "# import k-means from clustering stage\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "# !conda install -c conda-forge folium=0.5.0 --yes \n",
        "import folium # map rendering library\n",
        "\n",
        "print('Libraries imported.')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Libraries imported.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6ZU_AfkIkvzO"
      },
      "source": [
        "# **3. Scrape Neighborhoods Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wEXkY4Aye7HB"
      },
      "source": [
        "# Get the neighborhood data using beautiful soup \n",
        "url='https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M'\n",
        "result = requests.get(url)\n",
        "data_html = BeautifulSoup(result.content)\n",
        "\n",
        "# read the data into a Pandas Dataframe\n",
        "soup = BeautifulSoup(str(data_html))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "outputId": "b7da98fb-40fb-4a28-a7e6-64765b2f5e94"
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      "source": [
        "# loop through table, grab each of the 3 columns shown\n",
        "# Scrape the neighborhood data from the table in the wikipedia page of Toronto\n",
        "table_contents=[]\n",
        "table=soup.find('table')\n",
        "for row in table.findAll('td'):\n",
        "    cell = {}\n",
        "    if row.span.text=='Not assigned':\n",
        "        pass\n",
        "    else:\n",
        "      # Create three columns named as \"PostalCode\",\"Borough\" & \"Neighborhood\"\n",
        "        cell['PostalCode'] = row.p.text[:3] # store only first three letter from the test of <p> tab.(Ex: M3A )\n",
        "        cell['Borough'] = (row.span.text).split('(')[0]\n",
        "        cell['Neighborhood'] = (((((row.span.text).split('(')[1]).strip(')')).replace(' /',',')).replace(')',' ')).strip(' ')\n",
        "        # here we replace some symbols like \"(\" , \")\" , \"/\" from the neighborhood name(Ex: (Parkview Hill / Woodbine Gardens))\n",
        "        table_contents.append(cell)\n",
        "\n",
        "df=pd.DataFrame(table_contents)\n",
        "# compress some big borough name by smaller one\n",
        "df['Borough']=df['Borough'].replace({'Downtown TorontoStn A PO Boxes25 The Esplanade':'Downtown Toronto Stn A',\n",
        "                                             'East TorontoBusiness reply mail Processing Centre969 Eastern':'East Toronto Business',\n",
        "                                             'EtobicokeNorthwest':'Etobicoke Northwest','East YorkEast Toronto':'East York/East Toronto',\n",
        "                                             'MississaugaCanada Post Gateway Processing Centre':'Mississauga'})\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
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              "      <th></th>\n",
              "      <th>PostalCode</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>M3A</td>\n",
              "      <td>North York</td>\n",
              "      <td>Parkwoods</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>M4A</td>\n",
              "      <td>North York</td>\n",
              "      <td>Victoria Village</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>M5A</td>\n",
              "      <td>Downtown Toronto</td>\n",
              "      <td>Regent Park, Harbourfront</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>M6A</td>\n",
              "      <td>North York</td>\n",
              "      <td>Lawrence Manor, Lawrence Heights</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>M7A</td>\n",
              "      <td>Queen's Park</td>\n",
              "      <td>Ontario Provincial Government</td>\n",
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            "text/plain": [
              "  PostalCode           Borough                      Neighborhood\n",
              "0        M3A        North York                         Parkwoods\n",
              "1        M4A        North York                  Victoria Village\n",
              "2        M5A  Downtown Toronto         Regent Park, Harbourfront\n",
              "3        M6A        North York  Lawrence Manor, Lawrence Heights\n",
              "4        M7A      Queen's Park     Ontario Provincial Government"
            ]
          },
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        }
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    {
      "cell_type": "markdown",
      "metadata": {
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      },
      "source": [
        "This is the created dataset that we'r going to use. This dataset have 3 columns i.e \"PostalCode\", \"Borough\", \"Neighborhood\". As the dataset is unstructured and dirty we need some data pre-processing to clean the dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mXpttW3OAAUU"
      },
      "source": [
        "# save this dataframe in a CSV file\n",
        "df.to_csv('Neighborhood Data.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RVZtQMJjs1aU"
      },
      "source": [
        "# **4. Data Pre-processing**\n",
        "In this step we'll do thses following steps\n",
        "- Only process the cells that have an assigned borough. Ignore cells with a borough that is Not assigned.\n",
        "- More than one neighborhood can exist in one postal code area. For example, in the table on the Wikipedia page, you will notice that M5A is listed twice and has two neighborhoods: Harbourfront and Regent Park. These two rows will be combined into one row with the neighborhoods separated with a comma as shown in row 11  in the above table.\n",
        "- If a cell has a borough but a Not assigned  neighborhood, then the neighborhood will be the same as the borough.\n",
        "- Clean your Notebook and add Markdown cells to explain your work and any assumptions you are making.\n",
        "- In the last cell of your notebook, use the .shape method to print the number of rows of your dataframe.\n",
        "\n",
        "\n"
      ]
    },
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      "cell_type": "code",
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      "source": [
        "# drop rows having null value and value assigned as \"Not assigned\"\n",
        "df_dropna = df.dropna()\n",
        "empty = 'Not assigned'\n",
        "df_dropna = df_dropna[(df_dropna.PostalCode != empty ) & (df_dropna.Borough != empty) & (df_dropna.Neighborhood != empty)].reset_index(drop=True)"
      ],
      "execution_count": null,
      "outputs": []
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        "outputId": "61b1c5a9-f4e1-4ae5-9e3c-97211049504d"
      },
      "source": [
        "# check for missing value\n",
        "df_dropna.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PostalCode      0\n",
              "Borough         0\n",
              "Neighborhood    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
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      "source": [
        "# Check if we still have any Neighborhoods that are Not Assigned\n",
        "df_dropna.loc[df_dropna['Borough'].isin([\"Not assigned\"])]"
      ],
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [PostalCode, Borough, Neighborhood]\n",
              "Index: []"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
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          "height": 206
        },
        "outputId": "13b8a78a-c856-4566-d139-cf74940de203"
      },
      "source": [
        "df = df_dropna\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-bcb2318b-004f-4520-8ad4-d3faf40a50ea\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PostalCode</th>\n",
              "      <th>Borough</th>\n",
              "      <th>Neighborhood</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>M3A</td>\n",
              "      <td>North York</td>\n",
              "      <td>Parkwoods</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>M4A</td>\n",
              "      <td>North York</td>\n",
              "      <td>Victoria Village</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>M5A</td>\n",
              "      <td>Downtown Toronto</td>\n",
              "      <td>Regent Park, Harbourfront</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>M6A</td>\n",
              "      <td>North York</td>\n",
              "      <td>Lawrence Manor, Lawrence Heights</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>M7A</td>\n",
              "      <td>Queen's Park</td>\n",
              "      <td>Ontario Provincial Government</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  PostalCode           Borough                      Neighborhood\n",
              "0        M3A        North York                         Parkwoods\n",
              "1        M4A        North York                  Victoria Village\n",
              "2        M5A  Downtown Toronto         Regent Park, Harbourfront\n",
              "3        M6A        North York  Lawrence Manor, Lawrence Heights\n",
              "4        M7A      Queen's Park     Ontario Provincial Government"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mAqdpV2Ie7HG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c0cc4bca-ebd8-49cf-b917-8d950f450a43"
      },
      "source": [
        "# shape of dataframe\n",
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(103, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sWgy_oVoe7HF"
      },
      "source": [
        "Now  data is cleaned and all the requirements are met. So we just have to add the Latitude and Longitudes of each location.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ClQEhh0vzWVb"
      },
      "source": [
        "Now that you have built a dataframe of the postal code of each neighborhood along with the borough name and neighborhood name, in order to utilize the Foursquare location data, we need to get the latitude and the longitude coordinates of each neighborhood. Now we are going to create a new table with the Latitudes and Longitudes corresponding to the different PostalCodes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BTw3GHEce7HH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "d972fced-73be-4e57-9cee-7eb46ecdc8b0"
      },
      "source": [
        "# get the latitude and the longitude coordinates of each Postal code\n",
        "geo_url = \"https://cocl.us/Geospatial_data\"\n",
        "\n",
        "geo_df = pd.read_csv(geo_url)\n",
        "geo_df.rename(columns={'Postal Code': 'PostalCode'}, inplace=True)\n",
        "geo_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-dbf1fd80-7677-4f53-b284-a382ad127b96\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PostalCode</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>M1B</td>\n",
              "      <td>43.806686</td>\n",
              "      <td>-79.194353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>M1C</td>\n",
              "      <td>43.784535</td>\n",
              "      <td>-79.160497</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>M1E</td>\n",
              "      <td>43.763573</td>\n",
              "      <td>-79.188711</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>M1G</td>\n",
              "      <td>43.770992</td>\n",
              "      <td>-79.216917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>M1H</td>\n",
              "      <td>43.773136</td>\n",
              "      <td>-79.239476</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dbf1fd80-7677-4f53-b284-a382ad127b96')\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "  </style>\n",
              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-dbf1fd80-7677-4f53-b284-a382ad127b96');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  PostalCode   Latitude  Longitude\n",
              "0        M1B  43.806686 -79.194353\n",
              "1        M1C  43.784535 -79.160497\n",
              "2        M1E  43.763573 -79.188711\n",
              "3        M1G  43.770992 -79.216917\n",
              "4        M1H  43.773136 -79.239476"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mV8KBInue7HI"
      },
      "source": [
        "Now we'll merge the **geographical dataframe** with **neighborhood dataframe** according to the **Postal Code**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qmreZkdIe7HI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "b6b93761-41ac-447d-b510-1eb1815de515"
      },
      "source": [
        "# Merging the Data\n",
        "df = pd.merge(df, geo_df, on='PostalCode')\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-c388f0ff-d82f-43fe-bd2e-14da626ab8c0\">\n",
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              "      <th></th>\n",
              "      <th>PostalCode</th>\n",
              "      <th>Borough</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>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>Regent Park, Harbourfront</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 Manor, Lawrence Heights</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>Ontario Provincial Government</td>\n",
              "      <td>43.662301</td>\n",
              "      <td>-79.389494</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "  </svg>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  PostalCode           Borough                      Neighborhood   Latitude  \\\n",
              "0        M3A        North York                         Parkwoods  43.753259   \n",
              "1        M4A        North York                  Victoria Village  43.725882   \n",
              "2        M5A  Downtown Toronto         Regent Park, Harbourfront  43.654260   \n",
              "3        M6A        North York  Lawrence Manor, Lawrence Heights  43.718518   \n",
              "4        M7A      Queen's Park     Ontario Provincial Government  43.662301   \n",
              "\n",
              "   Longitude  \n",
              "0 -79.329656  \n",
              "1 -79.315572  \n",
              "2 -79.360636  \n",
              "3 -79.464763  \n",
              "4 -79.389494  "
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gVLFcML0e7HI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b7248747-b579-4272-f0a9-083e6313cae8"
      },
      "source": [
        "# lets find out how many neighborhoods in each borough\n",
        "df.groupby('Borough').count()['Neighborhood']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Borough\n",
              "Central Toronto            9\n",
              "Downtown Toronto          17\n",
              "Downtown Toronto Stn A     1\n",
              "East Toronto               4\n",
              "East Toronto Business      1\n",
              "East York                  4\n",
              "East York/East Toronto     1\n",
              "Etobicoke                 11\n",
              "Etobicoke Northwest        1\n",
              "Mississauga                1\n",
              "North York                24\n",
              "Queen's Park               1\n",
              "Scarborough               17\n",
              "West Toronto               6\n",
              "York                       5\n",
              "Name: Neighborhood, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NV2QqIXEe7HJ"
      },
      "source": [
        "### 4.1. Now we will visualize all the borough present in Toronto"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_yLVuuuCe7HJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "6dd1e728-4740-4e1d-e233-67ad7de2cd08"
      },
      "source": [
        "df_toronto = df\n",
        "df_toronto.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-cf802c7c-9354-4d7f-8b86-761ed86cdfed\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PostalCode</th>\n",
              "      <th>Borough</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>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>Regent Park, Harbourfront</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 Manor, Lawrence Heights</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>Ontario Provincial Government</td>\n",
              "      <td>43.662301</td>\n",
              "      <td>-79.389494</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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            "text/plain": [
              "  PostalCode           Borough                      Neighborhood   Latitude  \\\n",
              "0        M3A        North York                         Parkwoods  43.753259   \n",
              "1        M4A        North York                  Victoria Village  43.725882   \n",
              "2        M5A  Downtown Toronto         Regent Park, Harbourfront  43.654260   \n",
              "3        M6A        North York  Lawrence Manor, Lawrence Heights  43.718518   \n",
              "4        M7A      Queen's Park     Ontario Provincial Government  43.662301   \n",
              "\n",
              "   Longitude  \n",
              "0 -79.329656  \n",
              "1 -79.315572  \n",
              "2 -79.360636  \n",
              "3 -79.464763  \n",
              "4 -79.389494  "
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X9pVPu79e7HJ"
      },
      "source": [
        "# Create a list and store all unique borough names\n",
        "boroughs = df_toronto['Borough'].unique().tolist()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BNkY8bzIe7HJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "12cd6fdf-4d35-418a-dd7d-440cd96be5f3"
      },
      "source": [
        "# Obtain the Latitude and Longitude of Toronto by taking mean of Latitude/Longitude of all postal code\n",
        "lat_toronto = df_toronto['Latitude'].mean()\n",
        "lon_toronto = df_toronto['Longitude'].mean()\n",
        "print('The geographical coordinates of Toronto are {}, {}'.format(lat_toronto, lon_toronto))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The geographical coordinates of Toronto are 43.70460773398059, -79.39715291165048\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zKuAwUMOe7HK"
      },
      "source": [
        "# This will color categorize each borough\n",
        "borough_color = {}\n",
        "for borough in boroughs:\n",
        "    borough_color[borough]= '#%02X%02X%02X' % tuple(np.random.choice(range(256), size=3)) #Random color"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CErBP_Xke7HK"
      },
      "source": [
        "map_toronto = folium.Map(location=[lat_toronto, lon_toronto], zoom_start=10.5)\n",
        "\n",
        "# add markers to map\n",
        "for lat, lng, borough, neighborhood in zip(df_toronto['Latitude'], \n",
        "                                           df_toronto['Longitude'],\n",
        "                                           df_toronto['Borough'], \n",
        "                                           df_toronto['Neighborhood']):\n",
        "    label_text = borough + ' - ' + neighborhood\n",
        "    label = folium.Popup(label_text)\n",
        "    folium.CircleMarker(\n",
        "        [lat, lng],\n",
        "        radius=5,\n",
        "        popup=label,\n",
        "        color=borough_color[borough],\n",
        "        fill_color=borough_color[borough],\n",
        "        fill_opacity=0.8).add_to(map_toronto)  \n",
        "    \n",
        "map_toronto"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5YEg8Mete7HL"
      },
      "source": [
        "### 4.2. Next we will define foursquare Credentials"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wqpfEMuNe7HL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "00adc088-9f70-4657-d146-b038a29aa9bc"
      },
      "source": [
        "\n",
        "CLIENT_ID = 'LVNSPMLTBBDPN2INEYVDDVFWLONMFATTHN1UXOKODIXDTMZA' # your Foursquare ID\n",
        "CLIENT_SECRET = 'UU3FWAT2QOHGJHGYAGR54F5GFB4NJZFNDITCJZUTFCNW2OYI' # your Foursquare Secret\n",
        "VERSION = 20200514 # Foursquare API version\n",
        "\n",
        "print('Credentials Stored')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Credentials Stored\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YnPWUgGSe7HL"
      },
      "source": [
        "### 4.3. Now, let's get the top 100 venues that are in Lawrence Park within a radius of 500 meters.\n",
        "\n",
        "First, let's create the GET request URL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IsP5Ojrbe7HM"
      },
      "source": [
        "def getNearbyVenues(names, latitudes, longitudes, radius=500):\n",
        "    LIMIT = 100 # limit of number of venues returned by Foursquare API\n",
        "    radius = 500 # define radius\n",
        "    venues_list=[]\n",
        "    for name, lat, lng in zip(names, latitudes, longitudes):\n",
        "        print(name)\n",
        "            \n",
        "        # create the API request URL\n",
        "        url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n",
        "            CLIENT_ID, \n",
        "            CLIENT_SECRET, \n",
        "            VERSION, \n",
        "            lat, \n",
        "            lng, \n",
        "            radius, \n",
        "            LIMIT)\n",
        "        \n",
        "        # make the GET request\n",
        "        results = requests.get(url).json()[\"response\"]['groups'][0]['items']\n",
        "        #results = requests.get(url, \"none\").json().get('response', {})('groups', [])\n",
        "\n",
        "        \n",
        "        # return only relevant information for each nearby venue\n",
        "        venues_list.append([(\n",
        "            name, \n",
        "            lat, \n",
        "            lng, \n",
        "            v['venue']['name'], \n",
        "            v['venue']['location']['lat'], \n",
        "            v['venue']['location']['lng'],  \n",
        "            v['venue']['categories'][0]['name']) for v in results])\n",
        "\n",
        "    nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])\n",
        "    nearby_venues.columns = ['Neighborhood', \n",
        "                  'Neighborhood Latitude', \n",
        "                  'Neighborhood Longitude', \n",
        "                  'Venue', \n",
        "                  'Venue Latitude', \n",
        "                  'Venue Longitude', \n",
        "                  'Venue Category']\n",
        "    \n",
        "    return(nearby_venues)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DO6hrH6Ae7HM"
      },
      "source": [
        "#Get venues for all neighborhoods in our dataset\n",
        "toronto_venues = getNearbyVenues(names=df_toronto['Neighborhood'],\n",
        "                                latitudes=df_toronto['Latitude'],\n",
        "                                longitudes=df_toronto['Longitude'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Maqy_gDpe7HN",
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          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "outputId": "80545bb0-f01d-471b-d730-a19314055ca2"
      },
      "source": [
        "toronto_venues.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Neighborhood Latitude</th>\n",
              "      <th>Neighborhood Longitude</th>\n",
              "      <th>Venue</th>\n",
              "      <th>Venue Latitude</th>\n",
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              "      <th>Venue Category</th>\n",
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              "      <th>2149</th>\n",
              "      <td>Mimico NW, The Queensway West, South of Bloor,...</td>\n",
              "      <td>43.628841</td>\n",
              "      <td>-79.520999</td>\n",
              "      <td>Jim &amp; Maria's No Frills</td>\n",
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              "      <td>-79.518617</td>\n",
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              "      <td>-79.518903</td>\n",
              "      <td>Social Club</td>\n",
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              "    <tr>\n",
              "      <th>2151</th>\n",
              "      <td>Mimico NW, The Queensway West, South of Bloor,...</td>\n",
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              "      <td>-79.520999</td>\n",
              "      <td>Once Upon A Child</td>\n",
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              "      <td>-79.518290</td>\n",
              "      <td>Kids Store</td>\n",
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              "    <tr>\n",
              "      <th>2152</th>\n",
              "      <td>Mimico NW, The Queensway West, South of Bloor,...</td>\n",
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              "      <td>-79.520999</td>\n",
              "      <td>Kingsway Boxing Club</td>\n",
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              "      <td>-79.526684</td>\n",
              "      <td>Gym</td>\n",
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              "    <tr>\n",
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              "      <td>43.628841</td>\n",
              "      <td>-79.520999</td>\n",
              "      <td>Burrito Boyz</td>\n",
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              "      <td>-79.526349</td>\n",
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              "                                                     [key], {});\n",
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            "text/plain": [
              "                                           Neighborhood  \\\n",
              "2149  Mimico NW, The Queensway West, South of Bloor,...   \n",
              "2150  Mimico NW, The Queensway West, South of Bloor,...   \n",
              "2151  Mimico NW, The Queensway West, South of Bloor,...   \n",
              "2152  Mimico NW, The Queensway West, South of Bloor,...   \n",
              "2153  Mimico NW, The Queensway West, South of Bloor,...   \n",
              "\n",
              "      Neighborhood Latitude  Neighborhood Longitude  \\\n",
              "2149              43.628841              -79.520999   \n",
              "2150              43.628841              -79.520999   \n",
              "2151              43.628841              -79.520999   \n",
              "2152              43.628841              -79.520999   \n",
              "2153              43.628841              -79.520999   \n",
              "\n",
              "                           Venue  Venue Latitude  Venue Longitude  \\\n",
              "2149     Jim & Maria's No Frills       43.631152       -79.518617   \n",
              "2150  Royal Canadian Legion #210       43.628855       -79.518903   \n",
              "2151           Once Upon A Child       43.631075       -79.518290   \n",
              "2152        Kingsway Boxing Club       43.627254       -79.526684   \n",
              "2153                Burrito Boyz       43.626657       -79.526349   \n",
              "\n",
              "     Venue Category  \n",
              "2149  Grocery Store  \n",
              "2150    Social Club  \n",
              "2151     Kids Store  \n",
              "2152            Gym  \n",
              "2153  Burrito Place  "
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DFYNY2QEe7HN"
      },
      "source": [
        "Lets check how many venues are there per neighborhood"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BRbkM1lYe7HN",
        "colab": {
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        },
        "outputId": "5234fb18-b669-4ee7-9de4-4fe78b152ea7"
      },
      "source": [
        "toronto_venues.groupby('Neighborhood').count().head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-06dfe2d2-c2d9-4933-8178-1e9835734de8\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Neighborhood Latitude</th>\n",
              "      <th>Neighborhood Longitude</th>\n",
              "      <th>Venue</th>\n",
              "      <th>Venue Latitude</th>\n",
              "      <th>Venue Longitude</th>\n",
              "      <th>Venue Category</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neighborhood</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Agincourt</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alderwood, Long Branch</th>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bathurst Manor, Wilson Heights, Downsview North</th>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bayview Village</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bedford Park, Lawrence Manor East</th>\n",
              "      <td>22</td>\n",
              "      <td>22</td>\n",
              "      <td>22</td>\n",
              "      <td>22</td>\n",
              "      <td>22</td>\n",
              "      <td>22</td>\n",
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            "text/plain": [
              "                                                 Neighborhood Latitude  \\\n",
              "Neighborhood                                                             \n",
              "Agincourt                                                            4   \n",
              "Alderwood, Long Branch                                               8   \n",
              "Bathurst Manor, Wilson Heights, Downsview North                     20   \n",
              "Bayview Village                                                      4   \n",
              "Bedford Park, Lawrence Manor East                                   22   \n",
              "\n",
              "                                                 Neighborhood Longitude  \\\n",
              "Neighborhood                                                              \n",
              "Agincourt                                                             4   \n",
              "Alderwood, Long Branch                                                8   \n",
              "Bathurst Manor, Wilson Heights, Downsview North                      20   \n",
              "Bayview Village                                                       4   \n",
              "Bedford Park, Lawrence Manor East                                    22   \n",
              "\n",
              "                                                 Venue  Venue Latitude  \\\n",
              "Neighborhood                                                             \n",
              "Agincourt                                            4               4   \n",
              "Alderwood, Long Branch                               8               8   \n",
              "Bathurst Manor, Wilson Heights, Downsview North     20              20   \n",
              "Bayview Village                                      4               4   \n",
              "Bedford Park, Lawrence Manor East                   22              22   \n",
              "\n",
              "                                                 Venue Longitude  \\\n",
              "Neighborhood                                                       \n",
              "Agincourt                                                      4   \n",
              "Alderwood, Long Branch                                         8   \n",
              "Bathurst Manor, Wilson Heights, Downsview North               20   \n",
              "Bayview Village                                                4   \n",
              "Bedford Park, Lawrence Manor East                             22   \n",
              "\n",
              "                                                 Venue Category  \n",
              "Neighborhood                                                     \n",
              "Agincourt                                                     4  \n",
              "Alderwood, Long Branch                                        8  \n",
              "Bathurst Manor, Wilson Heights, Downsview North              20  \n",
              "Bayview Village                                               4  \n",
              "Bedford Park, Lawrence Manor East                            22  "
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sxsGD4Dbe7HN"
      },
      "source": [
        "### 4.4. How many unique venues are there in all neighborhood ?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9TDffqnTe7HO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7cdcb629-2416-404b-ba62-5f2985b1431c"
      },
      "source": [
        "print('There are {} uniques vanue categories.'.format(len(toronto_venues['Venue Category'].unique())))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 275 uniques vanue categories.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I4XkeleIe7HO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3efe0cfa-3c7c-47ab-fcc2-1bb607d7377c"
      },
      "source": [
        "print(\"The Venue Categories are\", toronto_venues['Venue Category'].unique()[:20]) # 20 unique values shown here"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Venue Categories are ['Park' 'Fast Food Restaurant' 'Pool' 'Food & Drink Shop' 'Hockey Arena'\n",
            " 'Coffee Shop' 'Portuguese Restaurant' 'French Restaurant' 'Bakery'\n",
            " 'Distribution Center' 'Spa' 'New American Restaurant' 'Restaurant'\n",
            " 'Historic Site' 'Pub' 'Chocolate Shop' 'Farmers Market'\n",
            " 'Performing Arts Venue' 'Breakfast Spot' 'Dessert Shop']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VUYg9SH5e7HO"
      },
      "source": [
        "## 4.5. Are there any Italian Restaurants present in the venues?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Q5r6ZYZe7HO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "420207d1-ec26-4906-9737-c1b27c84a71a"
      },
      "source": [
        "\"Italian Restaurant\" in toronto_venues['Venue Category'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "azNWQY7pQe3D"
      },
      "source": [
        "# **5. Data Analysis**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dCbyhox-e7HP"
      },
      "source": [
        "### 5.1. Now we will analyze each neighborhood"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9uQ62XlAUJC1"
      },
      "source": [
        "As the column \"Venue Category\" contain categorical value.So we need to convert it to numerical values by one hot encoding."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vMx1IVPCe7HP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "outputId": "d5e84877-dbd4-484f-c97c-5d9f0e572e38"
      },
      "source": [
        "# one hot encoding\n",
        "to_onehot = pd.get_dummies(toronto_venues[['Venue Category']], prefix=\"\", prefix_sep=\"\")\n",
        "\n",
        "# add neighborhood column back to dataframe\n",
        "to_onehot['Neighborhoods'] = toronto_venues['Neighborhood'] \n",
        "\n",
        "# move neighborhood column to the first column\n",
        "fixed_columns = [to_onehot.columns[-1]] + list(to_onehot.columns[:-1])\n",
        "to_onehot = to_onehot[fixed_columns]\n",
        "\n",
        "print(to_onehot.shape)\n",
        "to_onehot.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2154, 276)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-3616bcac-9cf4-44b5-ac20-07a5d2309af9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Neighborhoods</th>\n",
              "      <th>Accessories Store</th>\n",
              "      <th>Adult Boutique</th>\n",
              "      <th>Afghan Restaurant</th>\n",
              "      <th>Airport</th>\n",
              "      <th>Airport Food Court</th>\n",
              "      <th>Airport Gate</th>\n",
              "      <th>Airport Lounge</th>\n",
              "      <th>Airport Service</th>\n",
              "      <th>Airport Terminal</th>\n",
              "      <th>American Restaurant</th>\n",
              "      <th>Antique Shop</th>\n",
              "      <th>Aquarium</th>\n",
              "      <th>Art Gallery</th>\n",
              "      <th>Art Museum</th>\n",
              "      <th>Arts &amp; Crafts Store</th>\n",
              "      <th>Asian Restaurant</th>\n",
              "      <th>Athletics &amp; Sports</th>\n",
              "      <th>Auto Garage</th>\n",
              "      <th>Auto Workshop</th>\n",
              "      <th>BBQ Joint</th>\n",
              "      <th>Baby Store</th>\n",
              "      <th>Bagel Shop</th>\n",
              "      <th>Bakery</th>\n",
              "      <th>Bank</th>\n",
              "      <th>Bar</th>\n",
              "      <th>Baseball Field</th>\n",
              "      <th>Baseball Stadium</th>\n",
              "      <th>Basketball Stadium</th>\n",
              "      <th>Beach</th>\n",
              "      <th>Bed &amp; Breakfast</th>\n",
              "      <th>Beer Bar</th>\n",
              "      <th>Beer Store</th>\n",
              "      <th>Belgian Restaurant</th>\n",
              "      <th>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>Bridal Shop</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 Station</th>\n",
              "      <th>Butcher</th>\n",
              "      <th>Cable Car</th>\n",
              "      <th>Café</th>\n",
              "      <th>Cajun / Creole Restaurant</th>\n",
              "      <th>Candy Store</th>\n",
              "      <th>Caribbean Restaurant</th>\n",
              "      <th>Cheese Shop</th>\n",
              "      <th>Chinese Restaurant</th>\n",
              "      <th>Chocolate Shop</th>\n",
              "      <th>Church</th>\n",
              "      <th>Climbing Gym</th>\n",
              "      <th>Clothing Store</th>\n",
              "      <th>Cocktail Bar</th>\n",
              "      <th>Coffee Shop</th>\n",
              "      <th>College Arts Building</th>\n",
              "      <th>College Auditorium</th>\n",
              "      <th>College Cafeteria</th>\n",
              "      <th>College Gym</th>\n",
              "      <th>College Rec Center</th>\n",
              "      <th>College Stadium</th>\n",
              "      <th>Colombian Restaurant</th>\n",
              "      <th>Comfort Food Restaurant</th>\n",
              "      <th>Comic Shop</th>\n",
              "      <th>Concert Hall</th>\n",
              "      <th>Construction &amp; Landscaping</th>\n",
              "      <th>Convenience Store</th>\n",
              "      <th>Cosmetics Shop</th>\n",
              "      <th>Coworking Space</th>\n",
              "      <th>Creperie</th>\n",
              "      <th>Cuban Restaurant</th>\n",
              "      <th>Cupcake Shop</th>\n",
              "      <th>Curling Ice</th>\n",
              "      <th>Dance Studio</th>\n",
              "      <th>Deli / Bodega</th>\n",
              "      <th>Department Store</th>\n",
              "      <th>Dessert Shop</th>\n",
              "      <th>Dim Sum Restaurant</th>\n",
              "      <th>Diner</th>\n",
              "      <th>Discount Store</th>\n",
              "      <th>Distribution Center</th>\n",
              "      <th>Dog Run</th>\n",
              "      <th>Doner Restaurant</th>\n",
              "      <th>Donut Shop</th>\n",
              "      <th>Drugstore</th>\n",
              "      <th>Dumpling Restaurant</th>\n",
              "      <th>Eastern European Restaurant</th>\n",
              "      <th>Electronics Store</th>\n",
              "      <th>Ethiopian Restaurant</th>\n",
              "      <th>Event Space</th>\n",
              "      <th>Falafel Restaurant</th>\n",
              "      <th>Farmers Market</th>\n",
              "      <th>Fast Food Restaurant</th>\n",
              "      <th>Field</th>\n",
              "      <th>Filipino Restaurant</th>\n",
              "      <th>Fish &amp; Chips Shop</th>\n",
              "      <th>Fish Market</th>\n",
              "      <th>Flea Market</th>\n",
              "      <th>Flower Shop</th>\n",
              "      <th>Food &amp; Drink Shop</th>\n",
              "      <th>Food Court</th>\n",
              "      <th>Food Truck</th>\n",
              "      <th>Fountain</th>\n",
              "      <th>French Restaurant</th>\n",
              "      <th>Fried Chicken Joint</th>\n",
              "      <th>Frozen Yogurt Shop</th>\n",
              "      <th>Fruit &amp; Vegetable Store</th>\n",
              "      <th>Furniture / Home Store</th>\n",
              "      <th>Gaming Cafe</th>\n",
              "      <th>Garden</th>\n",
              "      <th>Garden Center</th>\n",
              "      <th>Gas Station</th>\n",
              "      <th>Gastropub</th>\n",
              "      <th>Gay Bar</th>\n",
              "      <th>General Entertainment</th>\n",
              "      <th>General Travel</th>\n",
              "      <th>German Restaurant</th>\n",
              "      <th>Gift Shop</th>\n",
              "      <th>Gluten-free Restaurant</th>\n",
              "      <th>Golf Course</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>Hakka Restaurant</th>\n",
              "      <th>Harbor / Marina</th>\n",
              "      <th>Hardware Store</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>Hockey Arena</th>\n",
              "      <th>Home Service</th>\n",
              "      <th>Hookah Bar</th>\n",
              "      <th>Hospital</th>\n",
              "      <th>Hotel</th>\n",
              "      <th>Hotel Bar</th>\n",
              "      <th>Housing Development</th>\n",
              "      <th>IT Services</th>\n",
              "      <th>Ice Cream Shop</th>\n",
              "      <th>Indian Restaurant</th>\n",
              "      <th>Indie Movie Theater</th>\n",
              "      <th>Intersection</th>\n",
              "      <th>Irish Pub</th>\n",
              "      <th>Italian Restaurant</th>\n",
              "      <th>Japanese Restaurant</th>\n",
              "      <th>Jazz Club</th>\n",
              "      <th>Jewelry Store</th>\n",
              "      <th>Juice Bar</th>\n",
              "      <th>Kids Store</th>\n",
              "      <th>Korean BBQ Restaurant</th>\n",
              "      <th>Korean Restaurant</th>\n",
              "      <th>Lake</th>\n",
              "      <th>Latin American Restaurant</th>\n",
              "      <th>Light Rail Station</th>\n",
              "      <th>Lingerie Store</th>\n",
              "      <th>Liquor Store</th>\n",
              "      <th>Locksmith</th>\n",
              "      <th>Lounge</th>\n",
              "      <th>Luggage Store</th>\n",
              "      <th>Market</th>\n",
              "      <th>Martial Arts School</th>\n",
              "      <th>Massage Studio</th>\n",
              "      <th>Medical Center</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>Moroccan Restaurant</th>\n",
              "      <th>Motel</th>\n",
              "      <th>Movie Theater</th>\n",
              "      <th>Museum</th>\n",
              "      <th>Music Store</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 Repair Shop</th>\n",
              "      <th>Park</th>\n",
              "      <th>Pastry Shop</th>\n",
              "      <th>Performing Arts Venue</th>\n",
              "      <th>Pet Store</th>\n",
              "      <th>Pharmacy</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>Portuguese Restaurant</th>\n",
              "      <th>Poutine Place</th>\n",
              "      <th>Print Shop</th>\n",
              "      <th>Pub</th>\n",
              "      <th>Ramen Restaurant</th>\n",
              "      <th>Record Shop</th>\n",
              "      <th>Recording Studio</th>\n",
              "      <th>Rental Car Location</th>\n",
              "      <th>Restaurant</th>\n",
              "      <th>River</th>\n",
              "      <th>Roof Deck</th>\n",
              "      <th>Sake Bar</th>\n",
              "      <th>Salad Place</th>\n",
              "      <th>Salon / Barbershop</th>\n",
              "      <th>Sandwich Place</th>\n",
              "      <th>Scenic Lookout</th>\n",
              "      <th>School</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>South American Restaurant</th>\n",
              "      <th>Spa</th>\n",
              "      <th>Speakeasy</th>\n",
              "      <th>Sporting Goods Shop</th>\n",
              "      <th>Sports Bar</th>\n",
              "      <th>Sri Lankan Restaurant</th>\n",
              "      <th>Stadium</th>\n",
              "      <th>Stationery Store</th>\n",
              "      <th>Steakhouse</th>\n",
              "      <th>Strip Club</th>\n",
              "      <th>Summer Camp</th>\n",
              "      <th>Supermarket</th>\n",
              "      <th>Supplement Shop</th>\n",
              "      <th>Sushi Restaurant</th>\n",
              "      <th>Swim School</th>\n",
              "      <th>Tailor Shop</th>\n",
              "      <th>Taiwanese Restaurant</th>\n",
              "      <th>Tanning Salon</th>\n",
              "      <th>Tea Room</th>\n",
              "      <th>Tennis Court</th>\n",
              "      <th>Thai Restaurant</th>\n",
              "      <th>Theater</th>\n",
              "      <th>Theme Restaurant</th>\n",
              "      <th>Tibetan Restaurant</th>\n",
              "      <th>Toy / Game Store</th>\n",
              "      <th>Trail</th>\n",
              "      <th>Train Station</th>\n",
              "      <th>Truck Stop</th>\n",
              "      <th>Turkish Restaurant</th>\n",
              "      <th>Vegetarian / Vegan Restaurant</th>\n",
              "      <th>Video Game Store</th>\n",
              "      <th>Vietnamese Restaurant</th>\n",
              "      <th>Warehouse Store</th>\n",
              "      <th>Wine Bar</th>\n",
              "      <th>Wings Joint</th>\n",
              "      <th>Women's Store</th>\n",
              "      <th>Yoga Studio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Parkwoods</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Parkwoods</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Parkwoods</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Parkwoods</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Victoria Village</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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            "text/plain": [
              "      Neighborhoods  Accessories Store  Adult Boutique  Afghan Restaurant  \\\n",
              "0         Parkwoods                  0               0                  0   \n",
              "1         Parkwoods                  0               0                  0   \n",
              "2         Parkwoods                  0               0                  0   \n",
              "3         Parkwoods                  0               0                  0   \n",
              "4  Victoria Village                  0               0                  0   \n",
              "\n",
              "   Airport  Airport Food Court  Airport Gate  Airport Lounge  Airport Service  \\\n",
              "0        0                   0             0               0                0   \n",
              "1        0                   0             0               0                0   \n",
              "2        0                   0             0               0                0   \n",
              "3        0                   0             0               0                0   \n",
              "4        0                   0             0               0                0   \n",
              "\n",
              "   Airport Terminal  American Restaurant  Antique Shop  Aquarium  Art Gallery  \\\n",
              "0                 0                    0             0         0            0   \n",
              "1                 0                    0             0         0            0   \n",
              "2                 0                    0             0         0            0   \n",
              "3                 0                    0             0         0            0   \n",
              "4                 0                    0             0         0            0   \n",
              "\n",
              "   Art Museum  Arts & Crafts Store  Asian Restaurant  Athletics & Sports  \\\n",
              "0           0                    0                 0                   0   \n",
              "1           0                    0                 0                   0   \n",
              "2           0                    0                 0                   0   \n",
              "3           0                    0                 0                   0   \n",
              "4           0                    0                 0                   0   \n",
              "\n",
              "   Auto Garage  Auto Workshop  BBQ Joint  Baby Store  Bagel Shop  Bakery  \\\n",
              "0            0              0          0           0           0       0   \n",
              "1            0              0          0           0           0       0   \n",
              "2            0              0          0           0           0       0   \n",
              "3            0              0          0           0           0       0   \n",
              "4            0              0          0           0           0       0   \n",
              "\n",
              "   Bank  Bar  Baseball Field  Baseball Stadium  Basketball Stadium  Beach  \\\n",
              "0     0    0               0                 0                   0      0   \n",
              "1     0    0               0                 0                   0      0   \n",
              "2     0    0               0                 0                   0      0   \n",
              "3     0    0               0                 0                   0      0   \n",
              "4     0    0               0                 0                   0      0   \n",
              "\n",
              "   Bed & Breakfast  Beer Bar  Beer Store  Belgian Restaurant  Bike Shop  \\\n",
              "0                0         0           0                   0          0   \n",
              "1                0         0           0                   0          0   \n",
              "2                0         0           0                   0          0   \n",
              "3                0         0           0                   0          0   \n",
              "4                0         0           0                   0          0   \n",
              "\n",
              "   Bistro  Boat or Ferry  Bookstore  Boutique  Brazilian Restaurant  \\\n",
              "0       0              0          0         0                     0   \n",
              "1       0              0          0         0                     0   \n",
              "2       0              0          0         0                     0   \n",
              "3       0              0          0         0                     0   \n",
              "4       0              0          0         0                     0   \n",
              "\n",
              "   Breakfast Spot  Brewery  Bridal Shop  Bubble Tea Shop  Building  \\\n",
              "0               0        0            0                0         0   \n",
              "1               0        0            0                0         0   \n",
              "2               0        0            0                0         0   \n",
              "3               0        0            0                0         0   \n",
              "4               0        0            0                0         0   \n",
              "\n",
              "   Burger Joint  Burrito Place  Bus Line  Bus Station  Butcher  Cable Car  \\\n",
              "0             0              0         0            0        0          0   \n",
              "1             0              0         0            0        0          0   \n",
              "2             0              0         0            0        0          0   \n",
              "3             0              0         0            0        0          0   \n",
              "4             0              0         0            0        0          0   \n",
              "\n",
              "   Café  Cajun / Creole Restaurant  Candy Store  Caribbean Restaurant  \\\n",
              "0     0                          0            0                     0   \n",
              "1     0                          0            0                     0   \n",
              "2     0                          0            0                     0   \n",
              "3     0                          0            0                     0   \n",
              "4     0                          0            0                     0   \n",
              "\n",
              "   Cheese Shop  Chinese Restaurant  Chocolate Shop  Church  Climbing Gym  \\\n",
              "0            0                   0               0       0             0   \n",
              "1            0                   0               0       0             0   \n",
              "2            0                   0               0       0             0   \n",
              "3            0                   0               0       0             0   \n",
              "4            0                   0               0       0             0   \n",
              "\n",
              "   Clothing Store  Cocktail Bar  Coffee Shop  College Arts Building  \\\n",
              "0               0             0            0                      0   \n",
              "1               0             0            0                      0   \n",
              "2               0             0            0                      0   \n",
              "3               0             0            0                      0   \n",
              "4               0             0            0                      0   \n",
              "\n",
              "   College Auditorium  College Cafeteria  College Gym  College Rec Center  \\\n",
              "0                   0                  0            0                   0   \n",
              "1                   0                  0            0                   0   \n",
              "2                   0                  0            0                   0   \n",
              "3                   0                  0            0                   0   \n",
              "4                   0                  0            0                   0   \n",
              "\n",
              "   College Stadium  Colombian Restaurant  Comfort Food Restaurant  Comic Shop  \\\n",
              "0                0                     0                        0           0   \n",
              "1                0                     0                        0           0   \n",
              "2                0                     0                        0           0   \n",
              "3                0                     0                        0           0   \n",
              "4                0                     0                        0           0   \n",
              "\n",
              "   Concert Hall  Construction & Landscaping  Convenience Store  \\\n",
              "0             0                           0                  0   \n",
              "1             0                           0                  0   \n",
              "2             0                           0                  0   \n",
              "3             0                           0                  0   \n",
              "4             0                           0                  0   \n",
              "\n",
              "   Cosmetics Shop  Coworking Space  Creperie  Cuban Restaurant  Cupcake Shop  \\\n",
              "0               0                0         0                 0             0   \n",
              "1               0                0         0                 0             0   \n",
              "2               0                0         0                 0             0   \n",
              "3               0                0         0                 0             0   \n",
              "4               0                0         0                 0             0   \n",
              "\n",
              "   Curling Ice  Dance Studio  Deli / Bodega  Department Store  Dessert Shop  \\\n",
              "0            0             0              0                 0             0   \n",
              "1            0             0              0                 0             0   \n",
              "2            0             0              0                 0             0   \n",
              "3            0             0              0                 0             0   \n",
              "4            0             0              0                 0             0   \n",
              "\n",
              "   Dim Sum Restaurant  Diner  Discount Store  Distribution Center  Dog Run  \\\n",
              "0                   0      0               0                    0        0   \n",
              "1                   0      0               0                    0        0   \n",
              "2                   0      0               0                    0        0   \n",
              "3                   0      0               0                    0        0   \n",
              "4                   0      0               0                    0        0   \n",
              "\n",
              "   Doner Restaurant  Donut Shop  Drugstore  Dumpling Restaurant  \\\n",
              "0                 0           0          0                    0   \n",
              "1                 0           0          0                    0   \n",
              "2                 0           0          0                    0   \n",
              "3                 0           0          0                    0   \n",
              "4                 0           0          0                    0   \n",
              "\n",
              "   Eastern European Restaurant  Electronics Store  Ethiopian Restaurant  \\\n",
              "0                            0                  0                     0   \n",
              "1                            0                  0                     0   \n",
              "2                            0                  0                     0   \n",
              "3                            0                  0                     0   \n",
              "4                            0                  0                     0   \n",
              "\n",
              "   Event Space  Falafel Restaurant  Farmers Market  Fast Food Restaurant  \\\n",
              "0            0                   0               0                     0   \n",
              "1            0                   0               0                     1   \n",
              "2            0                   0               0                     0   \n",
              "3            0                   0               0                     0   \n",
              "4            0                   0               0                     0   \n",
              "\n",
              "   Field  Filipino Restaurant  Fish & Chips Shop  Fish Market  Flea Market  \\\n",
              "0      0                    0                  0            0            0   \n",
              "1      0                    0                  0            0            0   \n",
              "2      0                    0                  0            0            0   \n",
              "3      0                    0                  0            0            0   \n",
              "4      0                    0                  0            0            0   \n",
              "\n",
              "   Flower Shop  Food & Drink Shop  Food Court  Food Truck  Fountain  \\\n",
              "0            0                  0           0           0         0   \n",
              "1            0                  0           0           0         0   \n",
              "2            0                  0           0           0         0   \n",
              "3            0                  1           0           0         0   \n",
              "4            0                  0           0           0         0   \n",
              "\n",
              "   French Restaurant  Fried Chicken Joint  Frozen Yogurt Shop  \\\n",
              "0                  0                    0                   0   \n",
              "1                  0                    0                   0   \n",
              "2                  0                    0                   0   \n",
              "3                  0                    0                   0   \n",
              "4                  0                    0                   0   \n",
              "\n",
              "   Fruit & Vegetable Store  Furniture / Home Store  Gaming Cafe  Garden  \\\n",
              "0                        0                       0            0       0   \n",
              "1                        0                       0            0       0   \n",
              "2                        0                       0            0       0   \n",
              "3                        0                       0            0       0   \n",
              "4                        0                       0            0       0   \n",
              "\n",
              "   Garden Center  Gas Station  Gastropub  Gay Bar  General Entertainment  \\\n",
              "0              0            0          0        0                      0   \n",
              "1              0            0          0        0                      0   \n",
              "2              0            0          0        0                      0   \n",
              "3              0            0          0        0                      0   \n",
              "4              0            0          0        0                      0   \n",
              "\n",
              "   General Travel  German Restaurant  Gift Shop  Gluten-free Restaurant  \\\n",
              "0               0                  0          0                       0   \n",
              "1               0                  0          0                       0   \n",
              "2               0                  0          0                       0   \n",
              "3               0                  0          0                       0   \n",
              "4               0                  0          0                       0   \n",
              "\n",
              "   Golf Course  Gourmet Shop  Greek Restaurant  Grocery Store  Gym  \\\n",
              "0            0             0                 0              0    0   \n",
              "1            0             0                 0              0    0   \n",
              "2            0             0                 0              0    0   \n",
              "3            0             0                 0              0    0   \n",
              "4            0             0                 0              0    0   \n",
              "\n",
              "   Gym / Fitness Center  Hakka Restaurant  Harbor / Marina  Hardware Store  \\\n",
              "0                     0                 0                0               0   \n",
              "1                     0                 0                0               0   \n",
              "2                     0                 0                0               0   \n",
              "3                     0                 0                0               0   \n",
              "4                     0                 0                0               0   \n",
              "\n",
              "   Health & Beauty Service  Health Food Store  Historic Site  History Museum  \\\n",
              "0                        0                  0              0               0   \n",
              "1                        0                  0              0               0   \n",
              "2                        0                  0              0               0   \n",
              "3                        0                  0              0               0   \n",
              "4                        0                  0              0               0   \n",
              "\n",
              "   Hobby Shop  Hockey Arena  Home Service  Hookah Bar  Hospital  Hotel  \\\n",
              "0           0             0             0           0         0      0   \n",
              "1           0             0             0           0         0      0   \n",
              "2           0             0             0           0         0      0   \n",
              "3           0             0             0           0         0      0   \n",
              "4           0             1             0           0         0      0   \n",
              "\n",
              "   Hotel Bar  Housing Development  IT Services  Ice Cream Shop  \\\n",
              "0          0                    0            0               0   \n",
              "1          0                    0            0               0   \n",
              "2          0                    0            0               0   \n",
              "3          0                    0            0               0   \n",
              "4          0                    0            0               0   \n",
              "\n",
              "   Indian Restaurant  Indie Movie Theater  Intersection  Irish Pub  \\\n",
              "0                  0                    0             0          0   \n",
              "1                  0                    0             0          0   \n",
              "2                  0                    0             0          0   \n",
              "3                  0                    0             0          0   \n",
              "4                  0                    0             0          0   \n",
              "\n",
              "   Italian Restaurant  Japanese Restaurant  Jazz Club  Jewelry Store  \\\n",
              "0                   0                    0          0              0   \n",
              "1                   0                    0          0              0   \n",
              "2                   0                    0          0              0   \n",
              "3                   0                    0          0              0   \n",
              "4                   0                    0          0              0   \n",
              "\n",
              "   Juice Bar  Kids Store  Korean BBQ Restaurant  Korean Restaurant  Lake  \\\n",
              "0          0           0                      0                  0     0   \n",
              "1          0           0                      0                  0     0   \n",
              "2          0           0                      0                  0     0   \n",
              "3          0           0                      0                  0     0   \n",
              "4          0           0                      0                  0     0   \n",
              "\n",
              "   Latin American Restaurant  Light Rail Station  Lingerie Store  \\\n",
              "0                          0                   0               0   \n",
              "1                          0                   0               0   \n",
              "2                          0                   0               0   \n",
              "3                          0                   0               0   \n",
              "4                          0                   0               0   \n",
              "\n",
              "   Liquor Store  Locksmith  Lounge  Luggage Store  Market  \\\n",
              "0             0          0       0              0       0   \n",
              "1             0          0       0              0       0   \n",
              "2             0          0       0              0       0   \n",
              "3             0          0       0              0       0   \n",
              "4             0          0       0              0       0   \n",
              "\n",
              "   Martial Arts School  Massage Studio  Medical Center  \\\n",
              "0                    0               0               0   \n",
              "1                    0               0               0   \n",
              "2                    0               0               0   \n",
              "3                    0               0               0   \n",
              "4                    0               0               0   \n",
              "\n",
              "   Mediterranean Restaurant  Men's Store  Metro Station  Mexican Restaurant  \\\n",
              "0                         0            0              0                   0   \n",
              "1                         0            0              0                   0   \n",
              "2                         0            0              0                   0   \n",
              "3                         0            0              0                   0   \n",
              "4                         0            0              0                   0   \n",
              "\n",
              "   Middle Eastern Restaurant  Miscellaneous Shop  Mobile Phone Shop  \\\n",
              "0                          0                   0                  0   \n",
              "1                          0                   0                  0   \n",
              "2                          0                   0                  0   \n",
              "3                          0                   0                  0   \n",
              "4                          0                   0                  0   \n",
              "\n",
              "   Modern European Restaurant  Molecular Gastronomy Restaurant  \\\n",
              "0                           0                                0   \n",
              "1                           0                                0   \n",
              "2                           0                                0   \n",
              "3                           0                                0   \n",
              "4                           0                                0   \n",
              "\n",
              "   Monument / Landmark  Moroccan Restaurant  Motel  Movie Theater  Museum  \\\n",
              "0                    0                    0      0              0       0   \n",
              "1                    0                    0      0              0       0   \n",
              "2                    0                    0      0              0       0   \n",
              "3                    0                    0      0              0       0   \n",
              "4                    0                    0      0              0       0   \n",
              "\n",
              "   Music Store  Music Venue  Neighborhood  New American Restaurant  Nightclub  \\\n",
              "0            0            0             0                        0          0   \n",
              "1            0            0             0                        0          0   \n",
              "2            0            0             0                        0          0   \n",
              "3            0            0             0                        0          0   \n",
              "4            0            0             0                        0          0   \n",
              "\n",
              "   Noodle House  Office  Opera House  Optical Shop  Organic Grocery  \\\n",
              "0             0       0            0             0                0   \n",
              "1             0       0            0             0                0   \n",
              "2             0       0            0             0                0   \n",
              "3             0       0            0             0                0   \n",
              "4             0       0            0             0                0   \n",
              "\n",
              "   Other Great Outdoors  Other Repair Shop  Park  Pastry Shop  \\\n",
              "0                     0                  0     1            0   \n",
              "1                     0                  0     0            0   \n",
              "2                     0                  0     0            0   \n",
              "3                     0                  0     0            0   \n",
              "4                     0                  0     0            0   \n",
              "\n",
              "   Performing Arts Venue  Pet Store  Pharmacy  Pizza Place  Playground  Plaza  \\\n",
              "0                      0          0         0            0           0      0   \n",
              "1                      0          0         0            0           0      0   \n",
              "2                      0          0         0            0           0      0   \n",
              "3                      0          0         0            0           0      0   \n",
              "4                      0          0         0            0           0      0   \n",
              "\n",
              "   Poke Place  Pool  Portuguese Restaurant  Poutine Place  Print Shop  Pub  \\\n",
              "0           0     0                      0              0           0    0   \n",
              "1           0     0                      0              0           0    0   \n",
              "2           0     1                      0              0           0    0   \n",
              "3           0     0                      0              0           0    0   \n",
              "4           0     0                      0              0           0    0   \n",
              "\n",
              "   Ramen Restaurant  Record Shop  Recording Studio  Rental Car Location  \\\n",
              "0                 0            0                 0                    0   \n",
              "1                 0            0                 0                    0   \n",
              "2                 0            0                 0                    0   \n",
              "3                 0            0                 0                    0   \n",
              "4                 0            0                 0                    0   \n",
              "\n",
              "   Restaurant  River  Roof Deck  Sake Bar  Salad Place  Salon / Barbershop  \\\n",
              "0           0      0          0         0            0                   0   \n",
              "1           0      0          0         0            0                   0   \n",
              "2           0      0          0         0            0                   0   \n",
              "3           0      0          0         0            0                   0   \n",
              "4           0      0          0         0            0                   0   \n",
              "\n",
              "   Sandwich Place  Scenic Lookout  School  Sculpture Garden  \\\n",
              "0               0               0       0                 0   \n",
              "1               0               0       0                 0   \n",
              "2               0               0       0                 0   \n",
              "3               0               0       0                 0   \n",
              "4               0               0       0                 0   \n",
              "\n",
              "   Seafood Restaurant  Shoe Store  Shopping Mall  Skate Park  Skating Rink  \\\n",
              "0                   0           0              0           0             0   \n",
              "1                   0           0              0           0             0   \n",
              "2                   0           0              0           0             0   \n",
              "3                   0           0              0           0             0   \n",
              "4                   0           0              0           0             0   \n",
              "\n",
              "   Smoke Shop  Smoothie Shop  Snack Place  Soccer Field  Social Club  \\\n",
              "0           0              0            0             0            0   \n",
              "1           0              0            0             0            0   \n",
              "2           0              0            0             0            0   \n",
              "3           0              0            0             0            0   \n",
              "4           0              0            0             0            0   \n",
              "\n",
              "   Soup Place  South American Restaurant  Spa  Speakeasy  Sporting Goods Shop  \\\n",
              "0           0                          0    0          0                    0   \n",
              "1           0                          0    0          0                    0   \n",
              "2           0                          0    0          0                    0   \n",
              "3           0                          0    0          0                    0   \n",
              "4           0                          0    0          0                    0   \n",
              "\n",
              "   Sports Bar  Sri Lankan Restaurant  Stadium  Stationery Store  Steakhouse  \\\n",
              "0           0                      0        0                 0           0   \n",
              "1           0                      0        0                 0           0   \n",
              "2           0                      0        0                 0           0   \n",
              "3           0                      0        0                 0           0   \n",
              "4           0                      0        0                 0           0   \n",
              "\n",
              "   Strip Club  Summer Camp  Supermarket  Supplement Shop  Sushi Restaurant  \\\n",
              "0           0            0            0                0                 0   \n",
              "1           0            0            0                0                 0   \n",
              "2           0            0            0                0                 0   \n",
              "3           0            0            0                0                 0   \n",
              "4           0            0            0                0                 0   \n",
              "\n",
              "   Swim School  Tailor Shop  Taiwanese Restaurant  Tanning Salon  Tea Room  \\\n",
              "0            0            0                     0              0         0   \n",
              "1            0            0                     0              0         0   \n",
              "2            0            0                     0              0         0   \n",
              "3            0            0                     0              0         0   \n",
              "4            0            0                     0              0         0   \n",
              "\n",
              "   Tennis Court  Thai Restaurant  Theater  Theme Restaurant  \\\n",
              "0             0                0        0                 0   \n",
              "1             0                0        0                 0   \n",
              "2             0                0        0                 0   \n",
              "3             0                0        0                 0   \n",
              "4             0                0        0                 0   \n",
              "\n",
              "   Tibetan Restaurant  Toy / Game Store  Trail  Train Station  Truck Stop  \\\n",
              "0                   0                 0      0              0           0   \n",
              "1                   0                 0      0              0           0   \n",
              "2                   0                 0      0              0           0   \n",
              "3                   0                 0      0              0           0   \n",
              "4                   0                 0      0              0           0   \n",
              "\n",
              "   Turkish Restaurant  Vegetarian / Vegan Restaurant  Video Game Store  \\\n",
              "0                   0                              0                 0   \n",
              "1                   0                              0                 0   \n",
              "2                   0                              0                 0   \n",
              "3                   0                              0                 0   \n",
              "4                   0                              0                 0   \n",
              "\n",
              "   Vietnamese Restaurant  Warehouse Store  Wine Bar  Wings Joint  \\\n",
              "0                      0                0         0            0   \n",
              "1                      0                0         0            0   \n",
              "2                      0                0         0            0   \n",
              "3                      0                0         0            0   \n",
              "4                      0                0         0            0   \n",
              "\n",
              "   Women's Store  Yoga Studio  \n",
              "0              0            0  \n",
              "1              0            0  \n",
              "2              0            0  \n",
              "3              0            0  \n",
              "4              0            0  "
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N_oar0FNe7HP"
      },
      "source": [
        "Next, let's group rows by neighborhood and by taking the mean of the frequency of occurrence of each category"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1nDD0Um8e7HP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "outputId": "2ec14071-013b-4c9e-a0b6-20052224de09"
      },
      "source": [
        "to_grouped = to_onehot.groupby([\"Neighborhoods\"]).mean().reset_index() \n",
        "\n",
        "print(to_grouped.shape)\n",
        "to_grouped.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(100, 276)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-b88c71c7-c290-4544-a84c-c585efc81428\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Neighborhoods</th>\n",
              "      <th>Accessories Store</th>\n",
              "      <th>Adult Boutique</th>\n",
              "      <th>Afghan Restaurant</th>\n",
              "      <th>Airport</th>\n",
              "      <th>Airport Food Court</th>\n",
              "      <th>Airport Gate</th>\n",
              "      <th>Airport Lounge</th>\n",
              "      <th>Airport Service</th>\n",
              "      <th>Airport Terminal</th>\n",
              "      <th>American Restaurant</th>\n",
              "      <th>Antique Shop</th>\n",
              "      <th>Aquarium</th>\n",
              "      <th>Art Gallery</th>\n",
              "      <th>Art Museum</th>\n",
              "      <th>Arts &amp; Crafts Store</th>\n",
              "      <th>Asian Restaurant</th>\n",
              "      <th>Athletics &amp; Sports</th>\n",
              "      <th>Auto Garage</th>\n",
              "      <th>Auto Workshop</th>\n",
              "      <th>BBQ Joint</th>\n",
              "      <th>Baby Store</th>\n",
              "      <th>Bagel Shop</th>\n",
              "      <th>Bakery</th>\n",
              "      <th>Bank</th>\n",
              "      <th>Bar</th>\n",
              "      <th>Baseball Field</th>\n",
              "      <th>Baseball Stadium</th>\n",
              "      <th>Basketball Stadium</th>\n",
              "      <th>Beach</th>\n",
              "      <th>Bed &amp; Breakfast</th>\n",
              "      <th>Beer Bar</th>\n",
              "      <th>Beer Store</th>\n",
              "      <th>Belgian Restaurant</th>\n",
              "      <th>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>Bridal Shop</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 Station</th>\n",
              "      <th>Butcher</th>\n",
              "      <th>Cable Car</th>\n",
              "      <th>Café</th>\n",
              "      <th>Cajun / Creole Restaurant</th>\n",
              "      <th>Candy Store</th>\n",
              "      <th>Caribbean Restaurant</th>\n",
              "      <th>Cheese Shop</th>\n",
              "      <th>Chinese Restaurant</th>\n",
              "      <th>Chocolate Shop</th>\n",
              "      <th>Church</th>\n",
              "      <th>Climbing Gym</th>\n",
              "      <th>Clothing Store</th>\n",
              "      <th>Cocktail Bar</th>\n",
              "      <th>Coffee Shop</th>\n",
              "      <th>College Arts Building</th>\n",
              "      <th>College Auditorium</th>\n",
              "      <th>College Cafeteria</th>\n",
              "      <th>College Gym</th>\n",
              "      <th>College Rec Center</th>\n",
              "      <th>College Stadium</th>\n",
              "      <th>Colombian Restaurant</th>\n",
              "      <th>Comfort Food Restaurant</th>\n",
              "      <th>Comic Shop</th>\n",
              "      <th>Concert Hall</th>\n",
              "      <th>Construction &amp; Landscaping</th>\n",
              "      <th>Convenience Store</th>\n",
              "      <th>Cosmetics Shop</th>\n",
              "      <th>Coworking Space</th>\n",
              "      <th>Creperie</th>\n",
              "      <th>Cuban Restaurant</th>\n",
              "      <th>Cupcake Shop</th>\n",
              "      <th>Curling Ice</th>\n",
              "      <th>Dance Studio</th>\n",
              "      <th>Deli / Bodega</th>\n",
              "      <th>Department Store</th>\n",
              "      <th>Dessert Shop</th>\n",
              "      <th>Dim Sum Restaurant</th>\n",
              "      <th>Diner</th>\n",
              "      <th>Discount Store</th>\n",
              "      <th>Distribution Center</th>\n",
              "      <th>Dog Run</th>\n",
              "      <th>Doner Restaurant</th>\n",
              "      <th>Donut Shop</th>\n",
              "      <th>Drugstore</th>\n",
              "      <th>Dumpling Restaurant</th>\n",
              "      <th>Eastern European Restaurant</th>\n",
              "      <th>Electronics Store</th>\n",
              "      <th>Ethiopian Restaurant</th>\n",
              "      <th>Event Space</th>\n",
              "      <th>Falafel Restaurant</th>\n",
              "      <th>Farmers Market</th>\n",
              "      <th>Fast Food Restaurant</th>\n",
              "      <th>Field</th>\n",
              "      <th>Filipino Restaurant</th>\n",
              "      <th>Fish &amp; Chips Shop</th>\n",
              "      <th>Fish Market</th>\n",
              "      <th>Flea Market</th>\n",
              "      <th>Flower Shop</th>\n",
              "      <th>Food &amp; Drink Shop</th>\n",
              "      <th>Food Court</th>\n",
              "      <th>Food Truck</th>\n",
              "      <th>Fountain</th>\n",
              "      <th>French Restaurant</th>\n",
              "      <th>Fried Chicken Joint</th>\n",
              "      <th>Frozen Yogurt Shop</th>\n",
              "      <th>Fruit &amp; Vegetable Store</th>\n",
              "      <th>Furniture / Home Store</th>\n",
              "      <th>Gaming Cafe</th>\n",
              "      <th>Garden</th>\n",
              "      <th>Garden Center</th>\n",
              "      <th>Gas Station</th>\n",
              "      <th>Gastropub</th>\n",
              "      <th>Gay Bar</th>\n",
              "      <th>General Entertainment</th>\n",
              "      <th>General Travel</th>\n",
              "      <th>German Restaurant</th>\n",
              "      <th>Gift Shop</th>\n",
              "      <th>Gluten-free Restaurant</th>\n",
              "      <th>Golf Course</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>Hakka Restaurant</th>\n",
              "      <th>Harbor / Marina</th>\n",
              "      <th>Hardware Store</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>Hockey Arena</th>\n",
              "      <th>Home Service</th>\n",
              "      <th>Hookah Bar</th>\n",
              "      <th>Hospital</th>\n",
              "      <th>Hotel</th>\n",
              "      <th>Hotel Bar</th>\n",
              "      <th>Housing Development</th>\n",
              "      <th>IT Services</th>\n",
              "      <th>Ice Cream Shop</th>\n",
              "      <th>Indian Restaurant</th>\n",
              "      <th>Indie Movie Theater</th>\n",
              "      <th>Intersection</th>\n",
              "      <th>Irish Pub</th>\n",
              "      <th>Italian Restaurant</th>\n",
              "      <th>Japanese Restaurant</th>\n",
              "      <th>Jazz Club</th>\n",
              "      <th>Jewelry Store</th>\n",
              "      <th>Juice Bar</th>\n",
              "      <th>Kids Store</th>\n",
              "      <th>Korean BBQ Restaurant</th>\n",
              "      <th>Korean Restaurant</th>\n",
              "      <th>Lake</th>\n",
              "      <th>Latin American Restaurant</th>\n",
              "      <th>Light Rail Station</th>\n",
              "      <th>Lingerie Store</th>\n",
              "      <th>Liquor Store</th>\n",
              "      <th>Locksmith</th>\n",
              "      <th>Lounge</th>\n",
              "      <th>Luggage Store</th>\n",
              "      <th>Market</th>\n",
              "      <th>Martial Arts School</th>\n",
              "      <th>Massage Studio</th>\n",
              "      <th>Medical Center</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>Moroccan Restaurant</th>\n",
              "      <th>Motel</th>\n",
              "      <th>Movie Theater</th>\n",
              "      <th>Museum</th>\n",
              "      <th>Music Store</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 Repair Shop</th>\n",
              "      <th>Park</th>\n",
              "      <th>Pastry Shop</th>\n",
              "      <th>Performing Arts Venue</th>\n",
              "      <th>Pet Store</th>\n",
              "      <th>Pharmacy</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>Portuguese Restaurant</th>\n",
              "      <th>Poutine Place</th>\n",
              "      <th>Print Shop</th>\n",
              "      <th>Pub</th>\n",
              "      <th>Ramen Restaurant</th>\n",
              "      <th>Record Shop</th>\n",
              "      <th>Recording Studio</th>\n",
              "      <th>Rental Car Location</th>\n",
              "      <th>Restaurant</th>\n",
              "      <th>River</th>\n",
              "      <th>Roof Deck</th>\n",
              "      <th>Sake Bar</th>\n",
              "      <th>Salad Place</th>\n",
              "      <th>Salon / Barbershop</th>\n",
              "      <th>Sandwich Place</th>\n",
              "      <th>Scenic Lookout</th>\n",
              "      <th>School</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>South American Restaurant</th>\n",
              "      <th>Spa</th>\n",
              "      <th>Speakeasy</th>\n",
              "      <th>Sporting Goods Shop</th>\n",
              "      <th>Sports Bar</th>\n",
              "      <th>Sri Lankan Restaurant</th>\n",
              "      <th>Stadium</th>\n",
              "      <th>Stationery Store</th>\n",
              "      <th>Steakhouse</th>\n",
              "      <th>Strip Club</th>\n",
              "      <th>Summer Camp</th>\n",
              "      <th>Supermarket</th>\n",
              "      <th>Supplement Shop</th>\n",
              "      <th>Sushi Restaurant</th>\n",
              "      <th>Swim School</th>\n",
              "      <th>Tailor Shop</th>\n",
              "      <th>Taiwanese Restaurant</th>\n",
              "      <th>Tanning Salon</th>\n",
              "      <th>Tea Room</th>\n",
              "      <th>Tennis Court</th>\n",
              "      <th>Thai Restaurant</th>\n",
              "      <th>Theater</th>\n",
              "      <th>Theme Restaurant</th>\n",
              "      <th>Tibetan Restaurant</th>\n",
              "      <th>Toy / Game Store</th>\n",
              "      <th>Trail</th>\n",
              "      <th>Train Station</th>\n",
              "      <th>Truck Stop</th>\n",
              "      <th>Turkish Restaurant</th>\n",
              "      <th>Vegetarian / Vegan Restaurant</th>\n",
              "      <th>Video Game Store</th>\n",
              "      <th>Vietnamese Restaurant</th>\n",
              "      <th>Warehouse Store</th>\n",
              "      <th>Wine Bar</th>\n",
              "      <th>Wings Joint</th>\n",
              "      <th>Women's Store</th>\n",
              "      <th>Yoga Studio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <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>0.000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</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>0.0</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>0.0</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>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</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>0.00</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000</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>0.0</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</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>0.00</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>0.25</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.25</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</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>0.0</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>0.0</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000</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>0.0</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.25</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>0.0</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</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>0.000000</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>0.0</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Alderwood, Long Branch</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>0.0</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>0.0</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>0.00</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>0.0</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>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</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>0.125000</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>0.00</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>0.125</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</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>0.0</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>0.0</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>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</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>0.00</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.125</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>0.0</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</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>0.00</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>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</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>0.0</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>0.0</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.125</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>0.125000</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>0.0</td>\n",
              "      <td>0.125000</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>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</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>0.0</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</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>0.000000</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>0.0</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Bathurst Manor, Wilson Heights, Downsview North</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>0.0</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>0.0</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>0.10</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>0.0</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>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.05</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</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>0.100000</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>0.05</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>0.000</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.05</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>0.0</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>0.0</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>0.0</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.05</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>0.05</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000</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>0.0</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.05</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>0.00</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>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.05</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>0.0</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>0.0</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.050000</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.000</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>0.0</td>\n",
              "      <td>0.050000</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>0.050000</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>0.05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</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>0.0</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.050000</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>0.0</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>0.0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Bayview Village</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>0.0</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>0.0</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>0.25</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>0.0</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>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</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>0.250000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.25</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>0.0</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>0.00</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>0.000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</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>0.0</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>0.0</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>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</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>0.00</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000</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>0.0</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>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</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>0.25</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>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</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>0.0</td>\n",
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              "      <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>0.0</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",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000</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>0.0</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",
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              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <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",
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              "    <tr>\n",
              "      <th>4</th>\n",
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            "text/plain": [
              "                                     Neighborhoods  Accessories Store  \\\n",
              "0                                        Agincourt                0.0   \n",
              "1                           Alderwood, Long Branch                0.0   \n",
              "2  Bathurst Manor, Wilson Heights, Downsview North                0.0   \n",
              "3                                  Bayview Village                0.0   \n",
              "4                Bedford Park, Lawrence Manor East                0.0   \n",
              "\n",
              "   Adult Boutique  Afghan Restaurant  Airport  Airport Food Court  \\\n",
              "0             0.0                0.0      0.0                 0.0   \n",
              "1             0.0                0.0      0.0                 0.0   \n",
              "2             0.0                0.0      0.0                 0.0   \n",
              "3             0.0                0.0      0.0                 0.0   \n",
              "4             0.0                0.0      0.0                 0.0   \n",
              "\n",
              "   Airport Gate  Airport Lounge  Airport Service  Airport Terminal  \\\n",
              "0           0.0             0.0              0.0               0.0   \n",
              "1           0.0             0.0              0.0               0.0   \n",
              "2           0.0             0.0              0.0               0.0   \n",
              "3           0.0             0.0              0.0               0.0   \n",
              "4           0.0             0.0              0.0               0.0   \n",
              "\n",
              "   American Restaurant  Antique Shop  Aquarium  Art Gallery  Art Museum  \\\n",
              "0             0.000000           0.0       0.0          0.0         0.0   \n",
              "1             0.000000           0.0       0.0          0.0         0.0   \n",
              "2             0.000000           0.0       0.0          0.0         0.0   \n",
              "3             0.000000           0.0       0.0          0.0         0.0   \n",
              "4             0.045455           0.0       0.0          0.0         0.0   \n",
              "\n",
              "   Arts & Crafts Store  Asian Restaurant  Athletics & Sports  Auto Garage  \\\n",
              "0                  0.0               0.0                 0.0          0.0   \n",
              "1                  0.0               0.0                 0.0          0.0   \n",
              "2                  0.0               0.0                 0.0          0.0   \n",
              "3                  0.0               0.0                 0.0          0.0   \n",
              "4                  0.0               0.0                 0.0          0.0   \n",
              "\n",
              "   Auto Workshop  BBQ Joint  Baby Store  Bagel Shop  Bakery  Bank  Bar  \\\n",
              "0            0.0        0.0         0.0         0.0     0.0  0.00  0.0   \n",
              "1            0.0        0.0         0.0         0.0     0.0  0.00  0.0   \n",
              "2            0.0        0.0         0.0         0.0     0.0  0.10  0.0   \n",
              "3            0.0        0.0         0.0         0.0     0.0  0.25  0.0   \n",
              "4            0.0        0.0         0.0         0.0     0.0  0.00  0.0   \n",
              "\n",
              "   Baseball Field  Baseball Stadium  Basketball Stadium  Beach  \\\n",
              "0             0.0               0.0                 0.0    0.0   \n",
              "1             0.0               0.0                 0.0    0.0   \n",
              "2             0.0               0.0                 0.0    0.0   \n",
              "3             0.0               0.0                 0.0    0.0   \n",
              "4             0.0               0.0                 0.0    0.0   \n",
              "\n",
              "   Bed & Breakfast  Beer Bar  Beer Store  Belgian Restaurant  Bike Shop  \\\n",
              "0              0.0       0.0         0.0                 0.0        0.0   \n",
              "1              0.0       0.0         0.0                 0.0        0.0   \n",
              "2              0.0       0.0         0.0                 0.0        0.0   \n",
              "3              0.0       0.0         0.0                 0.0        0.0   \n",
              "4              0.0       0.0         0.0                 0.0        0.0   \n",
              "\n",
              "   Bistro  Boat or Ferry  Bookstore  Boutique  Brazilian Restaurant  \\\n",
              "0     0.0            0.0        0.0       0.0                   0.0   \n",
              "1     0.0            0.0        0.0       0.0                   0.0   \n",
              "2     0.0            0.0        0.0       0.0                   0.0   \n",
              "3     0.0            0.0        0.0       0.0                   0.0   \n",
              "4     0.0            0.0        0.0       0.0                   0.0   \n",
              "\n",
              "   Breakfast Spot  Brewery  Bridal Shop  Bubble Tea Shop  Building  \\\n",
              "0            0.25      0.0         0.00              0.0       0.0   \n",
              "1            0.00      0.0         0.00              0.0       0.0   \n",
              "2            0.00      0.0         0.05              0.0       0.0   \n",
              "3            0.00      0.0         0.00              0.0       0.0   \n",
              "4            0.00      0.0         0.00              0.0       0.0   \n",
              "\n",
              "   Burger Joint  Burrito Place  Bus Line  Bus Station   Butcher  Cable Car  \\\n",
              "0           0.0            0.0       0.0          0.0  0.000000        0.0   \n",
              "1           0.0            0.0       0.0          0.0  0.000000        0.0   \n",
              "2           0.0            0.0       0.0          0.0  0.000000        0.0   \n",
              "3           0.0            0.0       0.0          0.0  0.000000        0.0   \n",
              "4           0.0            0.0       0.0          0.0  0.045455        0.0   \n",
              "\n",
              "       Café  Cajun / Creole Restaurant  Candy Store  Caribbean Restaurant  \\\n",
              "0  0.000000                        0.0          0.0                   0.0   \n",
              "1  0.000000                        0.0          0.0                   0.0   \n",
              "2  0.000000                        0.0          0.0                   0.0   \n",
              "3  0.250000                        0.0          0.0                   0.0   \n",
              "4  0.045455                        0.0          0.0                   0.0   \n",
              "\n",
              "   Cheese Shop  Chinese Restaurant  Chocolate Shop  Church  Climbing Gym  \\\n",
              "0          0.0                0.00             0.0     0.0           0.0   \n",
              "1          0.0                0.00             0.0     0.0           0.0   \n",
              "2          0.0                0.00             0.0     0.0           0.0   \n",
              "3          0.0                0.25             0.0     0.0           0.0   \n",
              "4          0.0                0.00             0.0     0.0           0.0   \n",
              "\n",
              "   Clothing Store  Cocktail Bar  Coffee Shop  College Arts Building  \\\n",
              "0             0.0           0.0     0.000000                    0.0   \n",
              "1             0.0           0.0     0.125000                    0.0   \n",
              "2             0.0           0.0     0.100000                    0.0   \n",
              "3             0.0           0.0     0.000000                    0.0   \n",
              "4             0.0           0.0     0.090909                    0.0   \n",
              "\n",
              "   College Auditorium  College Cafeteria  College Gym  College Rec Center  \\\n",
              "0                 0.0                0.0          0.0                 0.0   \n",
              "1                 0.0                0.0          0.0                 0.0   \n",
              "2                 0.0                0.0          0.0                 0.0   \n",
              "3                 0.0                0.0          0.0                 0.0   \n",
              "4                 0.0                0.0          0.0                 0.0   \n",
              "\n",
              "   College Stadium  Colombian Restaurant  Comfort Food Restaurant  Comic Shop  \\\n",
              "0              0.0                   0.0                 0.000000         0.0   \n",
              "1              0.0                   0.0                 0.000000         0.0   \n",
              "2              0.0                   0.0                 0.000000         0.0   \n",
              "3              0.0                   0.0                 0.000000         0.0   \n",
              "4              0.0                   0.0                 0.045455         0.0   \n",
              "\n",
              "   Concert Hall  Construction & Landscaping  Convenience Store  \\\n",
              "0           0.0                         0.0               0.00   \n",
              "1           0.0                         0.0               0.00   \n",
              "2           0.0                         0.0               0.05   \n",
              "3           0.0                         0.0               0.00   \n",
              "4           0.0                         0.0               0.00   \n",
              "\n",
              "   Cosmetics Shop  Coworking Space  Creperie  Cuban Restaurant  Cupcake Shop  \\\n",
              "0             0.0              0.0       0.0               0.0           0.0   \n",
              "1             0.0              0.0       0.0               0.0           0.0   \n",
              "2             0.0              0.0       0.0               0.0           0.0   \n",
              "3             0.0              0.0       0.0               0.0           0.0   \n",
              "4             0.0              0.0       0.0               0.0           0.0   \n",
              "\n",
              "   Curling Ice  Dance Studio  Deli / Bodega  Department Store  Dessert Shop  \\\n",
              "0          0.0         0.000           0.00               0.0           0.0   \n",
              "1          0.0         0.125           0.00               0.0           0.0   \n",
              "2          0.0         0.000           0.05               0.0           0.0   \n",
              "3          0.0         0.000           0.00               0.0           0.0   \n",
              "4          0.0         0.000           0.00               0.0           0.0   \n",
              "\n",
              "   Dim Sum Restaurant  Diner  Discount Store  Distribution Center  Dog Run  \\\n",
              "0                 0.0   0.00             0.0                  0.0      0.0   \n",
              "1                 0.0   0.00             0.0                  0.0      0.0   \n",
              "2                 0.0   0.05             0.0                  0.0      0.0   \n",
              "3                 0.0   0.00             0.0                  0.0      0.0   \n",
              "4                 0.0   0.00             0.0                  0.0      0.0   \n",
              "\n",
              "   Doner Restaurant  Donut Shop  Drugstore  Dumpling Restaurant  \\\n",
              "0               0.0         0.0        0.0                  0.0   \n",
              "1               0.0         0.0        0.0                  0.0   \n",
              "2               0.0         0.0        0.0                  0.0   \n",
              "3               0.0         0.0        0.0                  0.0   \n",
              "4               0.0         0.0        0.0                  0.0   \n",
              "\n",
              "   Eastern European Restaurant  Electronics Store  Ethiopian Restaurant  \\\n",
              "0                          0.0                0.0                   0.0   \n",
              "1                          0.0                0.0                   0.0   \n",
              "2                          0.0                0.0                   0.0   \n",
              "3                          0.0                0.0                   0.0   \n",
              "4                          0.0                0.0                   0.0   \n",
              "\n",
              "   Event Space  Falafel Restaurant  Farmers Market  Fast Food Restaurant  \\\n",
              "0          0.0                 0.0             0.0              0.000000   \n",
              "1          0.0                 0.0             0.0              0.000000   \n",
              "2          0.0                 0.0             0.0              0.000000   \n",
              "3          0.0                 0.0             0.0              0.000000   \n",
              "4          0.0                 0.0             0.0              0.045455   \n",
              "\n",
              "   Field  Filipino Restaurant  Fish & Chips Shop  Fish Market  Flea Market  \\\n",
              "0    0.0                  0.0                0.0          0.0          0.0   \n",
              "1    0.0                  0.0                0.0          0.0          0.0   \n",
              "2    0.0                  0.0                0.0          0.0          0.0   \n",
              "3    0.0                  0.0                0.0          0.0          0.0   \n",
              "4    0.0                  0.0                0.0          0.0          0.0   \n",
              "\n",
              "   Flower Shop  Food & Drink Shop  Food Court  Food Truck  Fountain  \\\n",
              "0          0.0                0.0         0.0         0.0       0.0   \n",
              "1          0.0                0.0         0.0         0.0       0.0   \n",
              "2          0.0                0.0         0.0         0.0       0.0   \n",
              "3          0.0                0.0         0.0         0.0       0.0   \n",
              "4          0.0                0.0         0.0         0.0       0.0   \n",
              "\n",
              "   French Restaurant  Fried Chicken Joint  Frozen Yogurt Shop  \\\n",
              "0                0.0                 0.00                0.00   \n",
              "1                0.0                 0.00                0.00   \n",
              "2                0.0                 0.05                0.05   \n",
              "3                0.0                 0.00                0.00   \n",
              "4                0.0                 0.00                0.00   \n",
              "\n",
              "   Fruit & Vegetable Store  Furniture / Home Store  Gaming Cafe  Garden  \\\n",
              "0                      0.0                     0.0          0.0     0.0   \n",
              "1                      0.0                     0.0          0.0     0.0   \n",
              "2                      0.0                     0.0          0.0     0.0   \n",
              "3                      0.0                     0.0          0.0     0.0   \n",
              "4                      0.0                     0.0          0.0     0.0   \n",
              "\n",
              "   Garden Center  Gas Station  Gastropub  Gay Bar  General Entertainment  \\\n",
              "0            0.0         0.00        0.0      0.0                    0.0   \n",
              "1            0.0         0.00        0.0      0.0                    0.0   \n",
              "2            0.0         0.05        0.0      0.0                    0.0   \n",
              "3            0.0         0.00        0.0      0.0                    0.0   \n",
              "4            0.0         0.00        0.0      0.0                    0.0   \n",
              "\n",
              "   General Travel  German Restaurant  Gift Shop  Gluten-free Restaurant  \\\n",
              "0             0.0                0.0        0.0                     0.0   \n",
              "1             0.0                0.0        0.0                     0.0   \n",
              "2             0.0                0.0        0.0                     0.0   \n",
              "3             0.0                0.0        0.0                     0.0   \n",
              "4             0.0                0.0        0.0                     0.0   \n",
              "\n",
              "   Golf Course  Gourmet Shop  Greek Restaurant  Grocery Store    Gym  \\\n",
              "0          0.0           0.0          0.000000       0.000000  0.000   \n",
              "1          0.0           0.0          0.000000       0.000000  0.125   \n",
              "2          0.0           0.0          0.000000       0.000000  0.000   \n",
              "3          0.0           0.0          0.000000       0.000000  0.000   \n",
              "4          0.0           0.0          0.045455       0.045455  0.000   \n",
              "\n",
              "   Gym / Fitness Center  Hakka Restaurant  Harbor / Marina  Hardware Store  \\\n",
              "0                   0.0               0.0              0.0             0.0   \n",
              "1                   0.0               0.0              0.0             0.0   \n",
              "2                   0.0               0.0              0.0             0.0   \n",
              "3                   0.0               0.0              0.0             0.0   \n",
              "4                   0.0               0.0              0.0             0.0   \n",
              "\n",
              "   Health & Beauty Service  Health Food Store  Historic Site  History Museum  \\\n",
              "0                      0.0                0.0            0.0             0.0   \n",
              "1                      0.0                0.0            0.0             0.0   \n",
              "2                      0.0                0.0            0.0             0.0   \n",
              "3                      0.0                0.0            0.0             0.0   \n",
              "4                      0.0                0.0            0.0             0.0   \n",
              "\n",
              "   Hobby Shop  Hockey Arena  Home Service  Hookah Bar  Hospital  Hotel  \\\n",
              "0         0.0           0.0           0.0         0.0       0.0    0.0   \n",
              "1         0.0           0.0           0.0         0.0       0.0    0.0   \n",
              "2         0.0           0.0           0.0         0.0       0.0    0.0   \n",
              "3         0.0           0.0           0.0         0.0       0.0    0.0   \n",
              "4         0.0           0.0           0.0         0.0       0.0    0.0   \n",
              "\n",
              "   Hotel Bar  Housing Development  IT Services  Ice Cream Shop  \\\n",
              "0        0.0                  0.0          0.0            0.00   \n",
              "1        0.0                  0.0          0.0            0.00   \n",
              "2        0.0                  0.0          0.0            0.05   \n",
              "3        0.0                  0.0          0.0            0.00   \n",
              "4        0.0                  0.0          0.0            0.00   \n",
              "\n",
              "   Indian Restaurant  Indie Movie Theater  Intersection  Irish Pub  \\\n",
              "0           0.000000                  0.0           0.0        0.0   \n",
              "1           0.000000                  0.0           0.0        0.0   \n",
              "2           0.000000                  0.0           0.0        0.0   \n",
              "3           0.000000                  0.0           0.0        0.0   \n",
              "4           0.045455                  0.0           0.0        0.0   \n",
              "\n",
              "   Italian Restaurant  Japanese Restaurant  Jazz Club  Jewelry Store  \\\n",
              "0            0.000000                 0.00        0.0            0.0   \n",
              "1            0.000000                 0.00        0.0            0.0   \n",
              "2            0.000000                 0.00        0.0            0.0   \n",
              "3            0.000000                 0.25        0.0            0.0   \n",
              "4            0.136364                 0.00        0.0            0.0   \n",
              "\n",
              "   Juice Bar  Kids Store  Korean BBQ Restaurant  Korean Restaurant  Lake  \\\n",
              "0   0.000000         0.0                    0.0                0.0   0.0   \n",
              "1   0.000000         0.0                    0.0                0.0   0.0   \n",
              "2   0.000000         0.0                    0.0                0.0   0.0   \n",
              "3   0.000000         0.0                    0.0                0.0   0.0   \n",
              "4   0.045455         0.0                    0.0                0.0   0.0   \n",
              "\n",
              "   Latin American Restaurant  Light Rail Station  Lingerie Store  \\\n",
              "0                       0.25                 0.0             0.0   \n",
              "1                       0.00                 0.0             0.0   \n",
              "2                       0.00                 0.0             0.0   \n",
              "3                       0.00                 0.0             0.0   \n",
              "4                       0.00                 0.0             0.0   \n",
              "\n",
              "   Liquor Store  Locksmith  Lounge  Luggage Store  Market  \\\n",
              "0      0.000000        0.0    0.25            0.0     0.0   \n",
              "1      0.000000        0.0    0.00            0.0     0.0   \n",
              "2      0.000000        0.0    0.00            0.0     0.0   \n",
              "3      0.000000        0.0    0.00            0.0     0.0   \n",
              "4      0.045455        0.0    0.00            0.0     0.0   \n",
              "\n",
              "   Martial Arts School  Massage Studio  Medical Center  \\\n",
              "0                  0.0             0.0             0.0   \n",
              "1                  0.0             0.0             0.0   \n",
              "2                  0.0             0.0             0.0   \n",
              "3                  0.0             0.0             0.0   \n",
              "4                  0.0             0.0             0.0   \n",
              "\n",
              "   Mediterranean Restaurant  Men's Store  Metro Station  Mexican Restaurant  \\\n",
              "0                       0.0          0.0            0.0                 0.0   \n",
              "1                       0.0          0.0            0.0                 0.0   \n",
              "2                       0.0          0.0            0.0                 0.0   \n",
              "3                       0.0          0.0            0.0                 0.0   \n",
              "4                       0.0          0.0            0.0                 0.0   \n",
              "\n",
              "   Middle Eastern Restaurant  Miscellaneous Shop  Mobile Phone Shop  \\\n",
              "0                       0.00                 0.0                0.0   \n",
              "1                       0.00                 0.0                0.0   \n",
              "2                       0.05                 0.0                0.0   \n",
              "3                       0.00                 0.0                0.0   \n",
              "4                       0.00                 0.0                0.0   \n",
              "\n",
              "   Modern European Restaurant  Molecular Gastronomy Restaurant  \\\n",
              "0                         0.0                              0.0   \n",
              "1                         0.0                              0.0   \n",
              "2                         0.0                              0.0   \n",
              "3                         0.0                              0.0   \n",
              "4                         0.0                              0.0   \n",
              "\n",
              "   Monument / Landmark  Moroccan Restaurant  Motel  Movie Theater  Museum  \\\n",
              "0                  0.0                  0.0    0.0            0.0     0.0   \n",
              "1                  0.0                  0.0    0.0            0.0     0.0   \n",
              "2                  0.0                  0.0    0.0            0.0     0.0   \n",
              "3                  0.0                  0.0    0.0            0.0     0.0   \n",
              "4                  0.0                  0.0    0.0            0.0     0.0   \n",
              "\n",
              "   Music Store  Music Venue  Neighborhood  New American Restaurant  Nightclub  \\\n",
              "0          0.0          0.0           0.0                      0.0        0.0   \n",
              "1          0.0          0.0           0.0                      0.0        0.0   \n",
              "2          0.0          0.0           0.0                      0.0        0.0   \n",
              "3          0.0          0.0           0.0                      0.0        0.0   \n",
              "4          0.0          0.0           0.0                      0.0        0.0   \n",
              "\n",
              "   Noodle House  Office  Opera House  Optical Shop  Organic Grocery  \\\n",
              "0           0.0     0.0          0.0           0.0              0.0   \n",
              "1           0.0     0.0          0.0           0.0              0.0   \n",
              "2           0.0     0.0          0.0           0.0              0.0   \n",
              "3           0.0     0.0          0.0           0.0              0.0   \n",
              "4           0.0     0.0          0.0           0.0              0.0   \n",
              "\n",
              "   Other Great Outdoors  Other Repair Shop  Park  Pastry Shop  \\\n",
              "0                   0.0                0.0   0.0          0.0   \n",
              "1                   0.0                0.0   0.0          0.0   \n",
              "2                   0.0                0.0   0.0          0.0   \n",
              "3                   0.0                0.0   0.0          0.0   \n",
              "4                   0.0                0.0   0.0          0.0   \n",
              "\n",
              "   Performing Arts Venue  Pet Store  Pharmacy  Pizza Place  Playground  Plaza  \\\n",
              "0                    0.0        0.0  0.000000         0.00       0.000    0.0   \n",
              "1                    0.0        0.0  0.000000         0.25       0.125    0.0   \n",
              "2                    0.0        0.0  0.050000         0.05       0.000    0.0   \n",
              "3                    0.0        0.0  0.000000         0.00       0.000    0.0   \n",
              "4                    0.0        0.0  0.045455         0.00       0.000    0.0   \n",
              "\n",
              "   Poke Place  Pool  Portuguese Restaurant  Poutine Place  Print Shop  \\\n",
              "0         0.0   0.0                    0.0            0.0         0.0   \n",
              "1         0.0   0.0                    0.0            0.0         0.0   \n",
              "2         0.0   0.0                    0.0            0.0         0.0   \n",
              "3         0.0   0.0                    0.0            0.0         0.0   \n",
              "4         0.0   0.0                    0.0            0.0         0.0   \n",
              "\n",
              "        Pub  Ramen Restaurant  Record Shop  Recording Studio  \\\n",
              "0  0.000000               0.0          0.0               0.0   \n",
              "1  0.125000               0.0          0.0               0.0   \n",
              "2  0.000000               0.0          0.0               0.0   \n",
              "3  0.000000               0.0          0.0               0.0   \n",
              "4  0.045455               0.0          0.0               0.0   \n",
              "\n",
              "   Rental Car Location  Restaurant  River  Roof Deck  Sake Bar  Salad Place  \\\n",
              "0                  0.0    0.000000    0.0        0.0       0.0          0.0   \n",
              "1                  0.0    0.000000    0.0        0.0       0.0          0.0   \n",
              "2                  0.0    0.050000    0.0        0.0       0.0          0.0   \n",
              "3                  0.0    0.000000    0.0        0.0       0.0          0.0   \n",
              "4                  0.0    0.045455    0.0        0.0       0.0          0.0   \n",
              "\n",
              "   Salon / Barbershop  Sandwich Place  Scenic Lookout  School  \\\n",
              "0                 0.0        0.000000             0.0     0.0   \n",
              "1                 0.0        0.125000             0.0     0.0   \n",
              "2                 0.0        0.050000             0.0     0.0   \n",
              "3                 0.0        0.000000             0.0     0.0   \n",
              "4                 0.0        0.045455             0.0     0.0   \n",
              "\n",
              "   Sculpture Garden  Seafood Restaurant  Shoe Store  Shopping Mall  \\\n",
              "0               0.0                 0.0         0.0           0.00   \n",
              "1               0.0                 0.0         0.0           0.00   \n",
              "2               0.0                 0.0         0.0           0.05   \n",
              "3               0.0                 0.0         0.0           0.00   \n",
              "4               0.0                 0.0         0.0           0.00   \n",
              "\n",
              "   Skate Park  Skating Rink  Smoke Shop  Smoothie Shop  Snack Place  \\\n",
              "0         0.0          0.25         0.0            0.0          0.0   \n",
              "1         0.0          0.00         0.0            0.0          0.0   \n",
              "2         0.0          0.00         0.0            0.0          0.0   \n",
              "3         0.0          0.00         0.0            0.0          0.0   \n",
              "4         0.0          0.00         0.0            0.0          0.0   \n",
              "\n",
              "   Soccer Field  Social Club  Soup Place  South American Restaurant  Spa  \\\n",
              "0           0.0          0.0         0.0                        0.0  0.0   \n",
              "1           0.0          0.0         0.0                        0.0  0.0   \n",
              "2           0.0          0.0         0.0                        0.0  0.0   \n",
              "3           0.0          0.0         0.0                        0.0  0.0   \n",
              "4           0.0          0.0         0.0                        0.0  0.0   \n",
              "\n",
              "   Speakeasy  Sporting Goods Shop  Sports Bar  Sri Lankan Restaurant  Stadium  \\\n",
              "0        0.0                  0.0         0.0                    0.0      0.0   \n",
              "1        0.0                  0.0         0.0                    0.0      0.0   \n",
              "2        0.0                  0.0         0.0                    0.0      0.0   \n",
              "3        0.0                  0.0         0.0                    0.0      0.0   \n",
              "4        0.0                  0.0         0.0                    0.0      0.0   \n",
              "\n",
              "   Stationery Store  Steakhouse  Strip Club  Summer Camp  Supermarket  \\\n",
              "0               0.0         0.0         0.0          0.0         0.00   \n",
              "1               0.0         0.0         0.0          0.0         0.00   \n",
              "2               0.0         0.0         0.0          0.0         0.05   \n",
              "3               0.0         0.0         0.0          0.0         0.00   \n",
              "4               0.0         0.0         0.0          0.0         0.00   \n",
              "\n",
              "   Supplement Shop  Sushi Restaurant  Swim School  Tailor Shop  \\\n",
              "0              0.0          0.000000          0.0          0.0   \n",
              "1              0.0          0.000000          0.0          0.0   \n",
              "2              0.0          0.050000          0.0          0.0   \n",
              "3              0.0          0.000000          0.0          0.0   \n",
              "4              0.0          0.090909          0.0          0.0   \n",
              "\n",
              "   Taiwanese Restaurant  Tanning Salon  Tea Room  Tennis Court  \\\n",
              "0                   0.0            0.0       0.0           0.0   \n",
              "1                   0.0            0.0       0.0           0.0   \n",
              "2                   0.0            0.0       0.0           0.0   \n",
              "3                   0.0            0.0       0.0           0.0   \n",
              "4                   0.0            0.0       0.0           0.0   \n",
              "\n",
              "   Thai Restaurant  Theater  Theme Restaurant  Tibetan Restaurant  \\\n",
              "0         0.000000      0.0               0.0                 0.0   \n",
              "1         0.000000      0.0               0.0                 0.0   \n",
              "2         0.000000      0.0               0.0                 0.0   \n",
              "3         0.000000      0.0               0.0                 0.0   \n",
              "4         0.045455      0.0               0.0                 0.0   \n",
              "\n",
              "   Toy / Game Store  Trail  Train Station  Truck Stop  Turkish Restaurant  \\\n",
              "0               0.0    0.0            0.0         0.0                 0.0   \n",
              "1               0.0    0.0            0.0         0.0                 0.0   \n",
              "2               0.0    0.0            0.0         0.0                 0.0   \n",
              "3               0.0    0.0            0.0         0.0                 0.0   \n",
              "4               0.0    0.0            0.0         0.0                 0.0   \n",
              "\n",
              "   Vegetarian / Vegan Restaurant  Video Game Store  Vietnamese Restaurant  \\\n",
              "0                            0.0               0.0                    0.0   \n",
              "1                            0.0               0.0                    0.0   \n",
              "2                            0.0               0.0                    0.0   \n",
              "3                            0.0               0.0                    0.0   \n",
              "4                            0.0               0.0                    0.0   \n",
              "\n",
              "   Warehouse Store  Wine Bar  Wings Joint  Women's Store  Yoga Studio  \n",
              "0              0.0       0.0          0.0            0.0          0.0  \n",
              "1              0.0       0.0          0.0            0.0          0.0  \n",
              "2              0.0       0.0          0.0            0.0          0.0  \n",
              "3              0.0       0.0          0.0            0.0          0.0  \n",
              "4              0.0       0.0          0.0            0.0          0.0  "
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SFqrS0tQg7_Z"
      },
      "source": [
        "Here we only require the \"Neighborhoods\" and \"Italian Restaurant\" columns for the clustering. So we'll group these two columns."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zuDufVbRe7HQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "2bf57d52-5d83-452c-f56e-0f4d508c8409"
      },
      "source": [
        "ita = to_grouped[[\"Neighborhoods\",\"Italian Restaurant\"]]\n",
        "ita.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-ee97f6b9-289c-491d-b929-3ad3879a36c6\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "    }\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>Neighborhoods</th>\n",
              "      <th>Italian Restaurant</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Agincourt</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Alderwood, Long Branch</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Bathurst Manor, Wilson Heights, Downsview North</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Bayview Village</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bedford Park, Lawrence Manor East</td>\n",
              "      <td>0.136364</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "          document.querySelector('#df-ee97f6b9-289c-491d-b929-3ad3879a36c6 button.colab-df-convert');\n",
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              "          const element = document.querySelector('#df-ee97f6b9-289c-491d-b929-3ad3879a36c6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            ],
            "text/plain": [
              "                                     Neighborhoods  Italian Restaurant\n",
              "0                                        Agincourt            0.000000\n",
              "1                           Alderwood, Long Branch            0.000000\n",
              "2  Bathurst Manor, Wilson Heights, Downsview North            0.000000\n",
              "3                                  Bayview Village            0.000000\n",
              "4                Bedford Park, Lawrence Manor East            0.136364"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vhKaoBnBe7HQ"
      },
      "source": [
        "# rename column \"Neighborhoods\" to \"Neighborhood\"\n",
        "ita = ita.rename(columns={'Neighborhoods':'Neighborhood'})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zti5sdzse7HQ"
      },
      "source": [
        "\n",
        "# **6. Clustering**\n",
        "We will use k-means clustering. But first we will find the best _K_ value using the **Elbow Point** method."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G019I-qfmzX1"
      },
      "source": [
        "### 6.1. Elbow Method"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4kmWb1LkMhyw"
      },
      "source": [
        "# drop \"Neighborhood\" column from the dataframe\n",
        "X = ita.drop(['Neighborhood'], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "ugBAfPZxL_ij",
        "outputId": "272cf812-6d9f-4c4b-d58d-d08c67828fbf"
      },
      "source": [
        "# find 'k' value by Elbow Method\n",
        "plt.figure(figsize=[10, 8])\n",
        "inertia=[]\n",
        "range_val=range(2,20)\n",
        "for i in range_val:\n",
        "  kmean=KMeans(n_clusters=i)\n",
        "  kmean.fit_predict(X)\n",
        "  inertia.append(kmean.inertia_)\n",
        "plt.plot(range_val,inertia,'bx-')\n",
        "plt.xlabel('Values of K') \n",
        "plt.ylabel('Inertia') \n",
        "plt.title('The Elbow Method using Inertia') \n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HFB2V2Hde7HS"
      },
      "source": [
        "Here,We saw that the optimum K value is 4 so we will have a resulting of 4 clusters."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CKHNi4bie7HS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "48a57173-663e-4da6-e220-6cb72efa6d20"
      },
      "source": [
        "kclusters = 4\n",
        "\n",
        "toronto_grouped_clustering = ita.drop('Neighborhood', 1)\n",
        "\n",
        "# run k-means clustering\n",
        "kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(toronto_grouped_clustering)\n",
        "\n",
        "# check cluster labels generated for each row in the dataframe\n",
        "kmeans.labels_[0:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 3, 1, 1, 0, 1, 1], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AuoPYquMngr4",
        "outputId": "278f4cd8-a935-4384-ff80-900d24436fb1"
      },
      "source": [
        "# unique value in target column\n",
        "np.unique(kmeans.labels_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E-s-lXiMUYNN"
      },
      "source": [
        " Now create a new dataframe that includes the cluster as well as the top 10 venues for each neighborhood."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "26-oUK_-e7HT"
      },
      "source": [
        "# create a new dataframe that includes the cluster as well as the top 10 venues for each neighborhood.\n",
        "to_merged = ita.copy()\n",
        "\n",
        "# add clustering labels\n",
        "to_merged[\"Cluster Labels\"] = kmeans.labels_"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JL82T5nHe7HT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "05dc8825-43da-4054-a4fd-89051c9d0374"
      },
      "source": [
        "to_merged.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-06bb6bde-5d8b-4da9-bed1-3cadcd9b4d07\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Italian Restaurant</th>\n",
              "      <th>Cluster Labels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Agincourt</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Alderwood, Long Branch</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Bathurst Manor, Wilson Heights, Downsview North</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Bayview Village</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bedford Park, Lawrence Manor East</td>\n",
              "      <td>0.136364</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-06bb6bde-5d8b-4da9-bed1-3cadcd9b4d07')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "\n",
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              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-06bb6bde-5d8b-4da9-bed1-3cadcd9b4d07 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-06bb6bde-5d8b-4da9-bed1-3cadcd9b4d07');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                      Neighborhood  Italian Restaurant  \\\n",
              "0                                        Agincourt            0.000000   \n",
              "1                           Alderwood, Long Branch            0.000000   \n",
              "2  Bathurst Manor, Wilson Heights, Downsview North            0.000000   \n",
              "3                                  Bayview Village            0.000000   \n",
              "4                Bedford Park, Lawrence Manor East            0.136364   \n",
              "\n",
              "   Cluster Labels  \n",
              "0               1  \n",
              "1               1  \n",
              "2               1  \n",
              "3               1  \n",
              "4               3  "
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yXJULZBMe7HT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "outputId": "a294444d-ff42-4482-c8ba-c2256823c86d"
      },
      "source": [
        "# merge toronto_grouped with toronto_data to add latitude/longitude for each neighborhood\n",
        "to_merged = to_merged.join(toronto_venues.set_index(\"Neighborhood\"), on=\"Neighborhood\")\n",
        "\n",
        "print(to_merged.shape)\n",
        "to_merged.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2154, 9)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-12bf2470-fafc-4cbf-b72d-ee38d459015d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Italian Restaurant</th>\n",
              "      <th>Cluster Labels</th>\n",
              "      <th>Neighborhood Latitude</th>\n",
              "      <th>Neighborhood Longitude</th>\n",
              "      <th>Venue</th>\n",
              "      <th>Venue Latitude</th>\n",
              "      <th>Venue Longitude</th>\n",
              "      <th>Venue Category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Agincourt</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>43.794200</td>\n",
              "      <td>-79.262029</td>\n",
              "      <td>Panagio's Breakfast &amp; Lunch</td>\n",
              "      <td>43.792370</td>\n",
              "      <td>-79.260203</td>\n",
              "      <td>Breakfast Spot</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>43.794200</td>\n",
              "      <td>-79.262029</td>\n",
              "      <td>Twilight</td>\n",
              "      <td>43.791999</td>\n",
              "      <td>-79.258584</td>\n",
              "      <td>Lounge</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Agincourt</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>43.794200</td>\n",
              "      <td>-79.262029</td>\n",
              "      <td>El Pulgarcito</td>\n",
              "      <td>43.792648</td>\n",
              "      <td>-79.259208</td>\n",
              "      <td>Latin American Restaurant</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Agincourt</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>43.794200</td>\n",
              "      <td>-79.262029</td>\n",
              "      <td>Commander Arena</td>\n",
              "      <td>43.794867</td>\n",
              "      <td>-79.267989</td>\n",
              "      <td>Skating Rink</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Alderwood, Long Branch</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>43.602414</td>\n",
              "      <td>-79.543484</td>\n",
              "      <td>Il Paesano Pizzeria &amp; Restaurant</td>\n",
              "      <td>43.601280</td>\n",
              "      <td>-79.545028</td>\n",
              "      <td>Pizza Place</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-12bf2470-fafc-4cbf-b72d-ee38d459015d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-12bf2470-fafc-4cbf-b72d-ee38d459015d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-12bf2470-fafc-4cbf-b72d-ee38d459015d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "             Neighborhood  Italian Restaurant  Cluster Labels  \\\n",
              "0               Agincourt                 0.0               1   \n",
              "0               Agincourt                 0.0               1   \n",
              "0               Agincourt                 0.0               1   \n",
              "0               Agincourt                 0.0               1   \n",
              "1  Alderwood, Long Branch                 0.0               1   \n",
              "\n",
              "   Neighborhood Latitude  Neighborhood Longitude  \\\n",
              "0              43.794200              -79.262029   \n",
              "0              43.794200              -79.262029   \n",
              "0              43.794200              -79.262029   \n",
              "0              43.794200              -79.262029   \n",
              "1              43.602414              -79.543484   \n",
              "\n",
              "                              Venue  Venue Latitude  Venue Longitude  \\\n",
              "0       Panagio's Breakfast & Lunch       43.792370       -79.260203   \n",
              "0                          Twilight       43.791999       -79.258584   \n",
              "0                     El Pulgarcito       43.792648       -79.259208   \n",
              "0                   Commander Arena       43.794867       -79.267989   \n",
              "1  Il Paesano Pizzeria & Restaurant       43.601280       -79.545028   \n",
              "\n",
              "              Venue Category  \n",
              "0             Breakfast Spot  \n",
              "0                     Lounge  \n",
              "0  Latin American Restaurant  \n",
              "0               Skating Rink  \n",
              "1                Pizza Place  "
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xj2PIq3ge7HU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        },
        "outputId": "de491303-fda3-4175-a148-6ad4304be0ba"
      },
      "source": [
        "# sort the results by Cluster Labels\n",
        "print(to_merged.shape)\n",
        "to_merged.sort_values([\"Cluster Labels\"], inplace=True)\n",
        "to_merged.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2154, 9)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-ccc5b834-e33b-4420-a0af-db2d313583e8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Italian Restaurant</th>\n",
              "      <th>Cluster Labels</th>\n",
              "      <th>Neighborhood Latitude</th>\n",
              "      <th>Neighborhood Longitude</th>\n",
              "      <th>Venue</th>\n",
              "      <th>Venue Latitude</th>\n",
              "      <th>Venue Longitude</th>\n",
              "      <th>Venue Category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bedford Park, Lawrence Manor East</td>\n",
              "      <td>0.136364</td>\n",
              "      <td>3</td>\n",
              "      <td>43.733283</td>\n",
              "      <td>-79.41975</td>\n",
              "      <td>Drums N Flats</td>\n",
              "      <td>43.735035</td>\n",
              "      <td>-79.420040</td>\n",
              "      <td>Comfort Food Restaurant</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bedford Park, Lawrence Manor East</td>\n",
              "      <td>0.136364</td>\n",
              "      <td>3</td>\n",
              "      <td>43.733283</td>\n",
              "      <td>-79.41975</td>\n",
              "      <td>Darbar Persian Grill</td>\n",
              "      <td>43.735484</td>\n",
              "      <td>-79.420006</td>\n",
              "      <td>Restaurant</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bedford Park, Lawrence Manor East</td>\n",
              "      <td>0.136364</td>\n",
              "      <td>3</td>\n",
              "      <td>43.733283</td>\n",
              "      <td>-79.41975</td>\n",
              "      <td>Aroma Espresso Bar</td>\n",
              "      <td>43.735975</td>\n",
              "      <td>-79.420391</td>\n",
              "      <td>Café</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bedford Park, Lawrence Manor East</td>\n",
              "      <td>0.136364</td>\n",
              "      <td>3</td>\n",
              "      <td>43.733283</td>\n",
              "      <td>-79.41975</td>\n",
              "      <td>Dino's No Frills</td>\n",
              "      <td>43.730699</td>\n",
              "      <td>-79.418535</td>\n",
              "      <td>Grocery Store</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bedford Park, Lawrence Manor East</td>\n",
              "      <td>0.136364</td>\n",
              "      <td>3</td>\n",
              "      <td>43.733283</td>\n",
              "      <td>-79.41975</td>\n",
              "      <td>Issmi Sushi</td>\n",
              "      <td>43.735269</td>\n",
              "      <td>-79.419983</td>\n",
              "      <td>Sushi Restaurant</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ccc5b834-e33b-4420-a0af-db2d313583e8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ccc5b834-e33b-4420-a0af-db2d313583e8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                        Neighborhood  Italian Restaurant  Cluster Labels  \\\n",
              "4  Bedford Park, Lawrence Manor East            0.136364               3   \n",
              "4  Bedford Park, Lawrence Manor East            0.136364               3   \n",
              "4  Bedford Park, Lawrence Manor East            0.136364               3   \n",
              "4  Bedford Park, Lawrence Manor East            0.136364               3   \n",
              "4  Bedford Park, Lawrence Manor East            0.136364               3   \n",
              "\n",
              "   Neighborhood Latitude  Neighborhood Longitude                 Venue  \\\n",
              "4              43.733283               -79.41975         Drums N Flats   \n",
              "4              43.733283               -79.41975  Darbar Persian Grill   \n",
              "4              43.733283               -79.41975    Aroma Espresso Bar   \n",
              "4              43.733283               -79.41975      Dino's No Frills   \n",
              "4              43.733283               -79.41975           Issmi Sushi   \n",
              "\n",
              "   Venue Latitude  Venue Longitude           Venue Category  \n",
              "4       43.735035       -79.420040  Comfort Food Restaurant  \n",
              "4       43.735484       -79.420006               Restaurant  \n",
              "4       43.735975       -79.420391                     Café  \n",
              "4       43.730699       -79.418535            Grocery Store  \n",
              "4       43.735269       -79.419983         Sushi Restaurant  "
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nuRgmZlhe7HU"
      },
      "source": [
        "Lets check how many Italian Restaurant are there"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UdxVdo1Pe7HU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c8fe5815-f138-427b-9d0a-1dbf2b6f4eda"
      },
      "source": [
        "to_merged['Venue Category'].value_counts()['Italian Restaurant']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "39"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s2AKRnJBe7HV"
      },
      "source": [
        "We see that there are a total of **46** locations with Italian Restaurants in Toronto  \n",
        "We will create a new dataframe with the Neighborhood and Italian Restaurants"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o31esXQcUjk8"
      },
      "source": [
        "## 6.2. Visualize Clustering on Google Map"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6oV1VK-oe7HV"
      },
      "source": [
        "# create map\n",
        "map_clusters = folium.Map(location=[lat_toronto, lon_toronto], zoom_start=11)\n",
        "\n",
        "# set color scheme for the clusters\n",
        "x = np.arange(kclusters)\n",
        "ys = [i+x+(i*x)**2 for i in range(kclusters)]\n",
        "colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))\n",
        "rainbow = [colors.rgb2hex(i) for i in colors_array]\n",
        "\n",
        "# add markers to the map\n",
        "markers_colors = []\n",
        "for lat, lon, poi, cluster in zip(to_merged['Neighborhood Latitude'], to_merged['Neighborhood Longitude'], to_merged['Neighborhood'], to_merged['Cluster Labels']):\n",
        "    label = folium.Popup(str(poi) + ' - Cluster ' + str(cluster))\n",
        "    folium.CircleMarker(\n",
        "        [lat, lon],\n",
        "        radius=5,\n",
        "        popup=label,\n",
        "        color=rainbow[cluster-1],\n",
        "        fill_color=rainbow[cluster-1],\n",
        "        fill_opacity=0.8).add_to(map_clusters)\n",
        "       \n",
        "map_clusters"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KTgjCuAQUoSI"
      },
      "source": [
        "### ***Warning :***\n",
        "*If we run the above cell,we can see the visualization on Google Map but when we'll upload this notebook on **Github** the Map visualization will not show. As Github doesn't support Google Map Visualization.*\n",
        "*So i've uploaded the Map visualization image on next cell from my drive.* "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ofcv-D-uUtNs"
      },
      "source": [
        "\n",
        "<img src=\"https://drive.google.com/uc?export=view&id=1Q_kkL6SA_VysraN1kDWfRL2EL45NoAwy\" alt=\"Google Map Visualization\">\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_kw67sEBe7HV"
      },
      "source": [
        "## 6.2. How many Neighborhoods per Cluster?\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-_1LJ-3Je7HV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "6c9655de-aa7b-4d61-a3e4-cb1a0ea0b422"
      },
      "source": [
        "ita[\"Cluster Labels\"] = kmeans.labels_\n",
        "ita.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-45fe86fa-a707-4901-a284-cfe7b74c7225\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Italian Restaurant</th>\n",
              "      <th>Cluster Labels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Agincourt</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Alderwood, Long Branch</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Bathurst Manor, Wilson Heights, Downsview North</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Bayview Village</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bedford Park, Lawrence Manor East</td>\n",
              "      <td>0.136364</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-45fe86fa-a707-4901-a284-cfe7b74c7225')\"\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-45fe86fa-a707-4901-a284-cfe7b74c7225 button.colab-df-convert');\n",
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              "\n",
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              "          const element = document.querySelector('#df-45fe86fa-a707-4901-a284-cfe7b74c7225');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
            ],
            "text/plain": [
              "                                      Neighborhood  Italian Restaurant  \\\n",
              "0                                        Agincourt            0.000000   \n",
              "1                           Alderwood, Long Branch            0.000000   \n",
              "2  Bathurst Manor, Wilson Heights, Downsview North            0.000000   \n",
              "3                                  Bayview Village            0.000000   \n",
              "4                Bedford Park, Lawrence Manor East            0.136364   \n",
              "\n",
              "   Cluster Labels  \n",
              "0               1  \n",
              "1               1  \n",
              "2               1  \n",
              "3               1  \n",
              "4               3  "
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oO_oSrppe7HW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "8792cd5f-fbbf-4705-a28b-460b94dd78ea"
      },
      "source": [
        "objects = (1,2,3,4)\n",
        "y_pos = np.arange(len(objects))\n",
        "performance = ita['Cluster Labels'].value_counts().to_frame().sort_index(ascending=True)\n",
        "perf = performance['Cluster Labels'].tolist()\n",
        "plt.bar(y_pos, perf, align='center', alpha=0.8, color=['red', 'purple','aquamarine', 'darkkhaki'])\n",
        "plt.xticks(y_pos, objects)\n",
        "plt.ylabel('No of Neighborhoods')\n",
        "plt.xlabel('Cluster')\n",
        "plt.title('How many Neighborhoods per Cluster')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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P4/J8hjTEMqTa/48j4uVSIzMzs1IUaer5ETAS+GGePyyXfbqsoMzMrDxFEv97I2L7mvkbJd1eVkBmZlauIr16XpG0Zc9M7uHzSnkhmZlZmYrU+L8MzOnVq+eIUqMyM7PSFOnVMzv36ukZV/8+9+oxMxu63KvHzKxi3KvHzKxi3KvHzKxi3KvHzKxiSu3VI+lhYAXpi2JlRHRK2gj4OTAReBg4OCKe6XfkZmY2IK3o1fOBiHiyZv54YHZEnCrp+Dx/XD/2Z2Zmq6FIjR9gJ1INfQSwgyQi4qcDPOYUYHKengXMxYnfzKxlinTnPB/YEljA6237ARRJ/AFcJymAH0fEDGBsRCzJy58AxjY47nTy8M+bbbZZgUOZmVkRRWr8ncC2EdHw/rtN7B4Rj0n6G+B6SffWLoyIyF8Kq8hfEjMAOjs7B3JsMzOro0ivnrtI99ztt4h4LD8vAy4HdgaWShoHkJ+XDWTfZmY2MA1r/JKuIjXVjAbulnQr8NpJ3YjYv9mOJY0ChkXEijz9P4GTgSuBqcCp+fmK1X0RZmZWXLOmntPzc5C6cfbXWOByST3H+Y98N68/ABdLmgY8Ahw8gH2bmdkANUz8ETFP0nBgYUS8o9F6TbZfBGxfp/wpYM/+7s/MzAZH0zb+iHgFuE+Su9WYma0livTq2RBYmNv4n+8p7KuN38zM1kxFEv/XS4/CzMxapsiQDfMkjQXem4tuzd0zzcxsCOqzH7+kg4FbgY+ReuDcIumgsgMzM7NyFGnq+RfSmPzLACR1ADcAl5QZmJmZlaPIlbvDejXtPFVwOzMzWwMVqfH/StK1wIV5/uPANeWFZGZmZSpycvfLkg4Eds9FMyLi8nLDMjOzshQdj/93pCGZXwX+UF44ZmZWtiK9ej5N6tXzEeAg4GZJR5YdmJmZlaPoPXd3zGPsIGlj0i+Ac8sMzMzMylGkd85TpBum91iRy8zMbAhqNh7/MXnyAdJFW1eQhmieAtzRgtjMzKwEzZp6RufnB/Ojh2+cYmY2hDUbj/+kVgZiZmat0efJXUlvB44FJtauHxF7lBeWmZmVpUivnl8AZwFnk/rym5nZEFYk8a+MiB8N9AD59o1dwGMR8WFJk4CLgI2B+cBhEfHSQPdvZmb907A7p6SNJG0EXCXpnySN6ynL5UUdBdxTM38a8O8RsRXwDDBtQJGbmdmANKvxzyd131Se/3LNsgC26GvnkiYAHwK+BRwjScAewKfyKrOAE4EB/6IwM7P+adarZ9Ig7P9M4Cu83jV0Y2B5RKzM84uB8fU2lDQdmA6w2Wa+17uZ2WAp0qvnwDrFfwbubHYLRkkfBpZFxHxJk/sbWETMAGYAdHZ2Rn+3NzOz+oqc3J0G7ArMyfOTSc1AkySdHBHnN9huN2B/SfsB6wJvBb4HbCBpRK71TwAeW434zcysn4qM1TMCeGdEfDQiPgpsS2rj/zvguEYbRcQJETEhIiYCnwBujIhDSF8gPffsnYqvBDYza6kiiX/TiFhaM78slz0NvDyAYx5HOtH7AKnN/5wB7MPMzAaoSFPPXElXky7kAvhoLhsFLC9ykIiYC8zN04uAnfsdqZmZDYoiif9zpGS/W57/KXBpRATwgbICMzOzchS5524Al+SHmZkNcc3G478pInaXtIJ0Mve1RaTvg7eWHp2ZmQ26Zhdw7Z6fRzdax8zMhp4ivXqQtLukI/L0mDzQmpmZDUF9Jn5J3yR1wTwhF60DXFBmUGZmVp4iNf6PAPsDzwNExOO8PvaOmZkNMUUS/0u5Z08A5P77ZmY2RBVJ/BdL+jFpjJ3/DdwA/KTcsMzMrCxF+vGfLmlv4FlgG+AbEXF96ZGZmVkpily5S070TvZmZmuBZhdwPcQbL9yqFRGxZTkhmZlZmZrV+Dt7zQ8DDgaOBf5YWkRmZlaqZlfuPgUgaRhwGOmeuwuAD0XE3a0Jz8zMBluzpp6RwJHAF4GbgAMi4oFWBWZmZuVo1tTzELCSdMP0PwHvlvTunoURcVnJsZmZWQmaJf4bSCd3t8+PWgE48ZuZDUHN2vgPb2EcZmbWIoVG5xwISetKulXS7ZIWSjopl0+SdIukByT9XNI6ZcVgZmarKi3xAy8Ce0TE9sAOwD6SdgFOA/49IrYCngGmlRiDmZn10jDxS/pYfh7Q2PuRPJdnR+ZHAHvw+m0cZwEHDGT/ZmY2MM1q/D3j71860J1LGi5pAbCMNOTDg8DyiFiZV1kMjG+w7XRJXZK6uru7BxqCmZn10qxXz1OSrgMmSbqy98KI2L+vnUfEK8AOkjYALgfeUTSwiJgBzADo7OxsNHSEmZn1U7PE/yHgPcD5wBmrc5CIWC5pDrAraXjnEbnWPwF4bHX2bWZm/dOsO+dLwM2S3hcR3ZLeksufa7RNLUkdwMs56a8H7E06sTsHOAi4CJgKXLGar8HMzPqhyLDMY3OTz0aAJHUDUyPirj62GwfMkjScdC7h4oi4WtLdwEWSTiEN9nbOasRvZmb9VCTxzwCOiYg5AJIm57L3NdsoIu4AdqxTvgjYud+RmpnZoCjSj39UT9IHiIi5gO+7a2Y2RBWp8S+S9HXSSV6AQ4FF5YVkZmZlKlLjPxLoIA3KdikwJpeZmdkQVORm688AX2hBLGZm1gJljtVjZmZrICd+M7OKceI3M6uYPhO/pAmSLpfULWmZpEslTWhFcGZmNviK1PjPA64kXYm7CXBVLjMzsyGoSOLviIjzImJlfswkde80M7MhqEjif0rSoXls/eGSDgWeKjswMzMrR9ELuA4GngCWkEbWPKLMoMzMrDxFLuB6BOjzpitmZjY0NEz8kr7RZLuIiH8tIR4zMytZsxr/83XKRgHTgI0BJ34zsyGo2R24XrvdoqTRwFGktv2LWM1bMZqZWfs0beOXtBFwDHAIMAt4Tx60zczMhqhmbfzfAQ4k3W3rb4vea9fMzNZszbpzfol0pe7XgMclPZsfKyQ929eOJW0qaY6kuyUtlHRULt9I0vWS7s/PGw7OSzEzsyIaJv6IGBYR60XE6Ih4a81jdES8tcC+VwJfiohtgV2Az0naFjgemB0RWwOz87yZmbVIaaNzRsSSiLgtT68A7gHGA1NI5wvIzweUFYOZma2qJcMyS5oI7AjcAoyNiCV50RPA2AbbTJfUJamru7u7FWGamVVC6Ylf0ltI9+o9OiLecG4gIgKIettFxIyI6IyIzo4OjwlnZjZYSk38kkaSkv7PIuKyXLxU0ri8fBywrMwYzMzsjUpL/JIEnAPcExHfrVl0JTA1T08FrigrBjMzW1Wfg7Stht2Aw4A7JS3IZV8FTgUuljQNeIQ08qeZmbVIaYk/Im4C1GDxnmUd18zMmvPN1s3MKsaJ38ysYpz4zcwqxonfzKxinPjNzCrGid/MrGKc+M3MKqbMC7jWDJ2d7Y6gvbq62h2Bma1hXOM3M6sYJ34zs4px4jczqxgnfjOzinHiNzOrGCd+M7OKceI3M6sYJ34zs4px4jczqxgnfjOziinzZuvnSlom6a6aso0kXS/p/vy8YVnHNzOz+sqs8c8E9ulVdjwwOyK2BmbneTMza6HSEn9E/Bp4ulfxFGBWnp4FHFDW8c3MrL5Wt/GPjYglefoJYGyjFSVNl9Qlqau7u7s10ZmZVUDbTu5GRADRZPmMiOiMiM6Ojo4WRmZmtnZrdeJfKmkcQH5e1uLjm5lVXqsT/5XA1Dw9Fbiixcc3M6u8MrtzXgj8HthG0mJJ04BTgb0l3Q/slefNzKyFSrv1YkR8ssGiPcs6ppmZ9c1X7pqZVYwTv5lZxZTW1GNm8GMebHcIbfUZtmx3CFaHa/xmZhXjxG9mVjFO/GZmFePEb2ZWMU78ZmYV48RvZlYxTvxmZhXjxG9mVjFO/GZmFePEb2ZWMU78ZmYV48RvZlYxTvxmZhXjxG9mVjFO/GZmFdOW8fgl7QN8DxgOnB0Rvveuma3iD787rd0htNV733dcKftteY1f0nDgB8C+wLbAJyVt2+o4zMyqqh1NPTsDD0TEooh4CbgImNKGOMzMKqkdTT3jgUdr5hcDf9d7JUnTgel59jlJ97UgtjKMAZ5s29Glth16kLT1/fuMPtOuQw+Wtr5/n23XgQdPe/9/OX51d7B5vcI19p67ETEDmNHuOFaXpK6I6Gx3HEOV37/V4/dv9ayt7187mnoeAzatmZ+Qy8zMrAXakfj/AGwtaZKkdYBPAFe2IQ4zs0pqeVNPRKyU9HngWlJ3znMjYmGr42ihId9c1WZ+/1aP37/Vs1a+f4qIdsdgZmYt5Ct3zcwqxonfzKxinPhLIulcScsk3dXuWIYiSZtKmiPpbkkLJR3V7piGEknrSrpV0u35/Tup3TENRZKGS/qjpKvbHctgcuIvz0xgn3YHMYStBL4UEdsCuwCf89Ae/fIisEdEbA/sAOwjaZc2xzQUHQXc0+4gBpsTf0ki4tfA0+2OY6iKiCURcVueXkH65xvf3qiGjkiey7Mj88M9OfpB0gTgQ8DZ7Y5lsDnx2xpP0kRgR+CW9kYytORmigXAMuD6iPD71z9nAl8BXm13IIPNid/WaJLeAlwKHB0Rz7Y7nqEkIl6JiB1IV8fvLGm7dsc0VEj6MLAsIua3O5YyOPHbGkvSSFLS/1lEXNbueIaqiFgOzMHnnPpjN2B/SQ+TRhDeQ9IF7Q1p8Djx2xpJkoBzgHsi4rvtjmeokdQhaYM8vR6wN3Bve6MaOiLihIiYEBETScPK3BgRh7Y5rEHjxF8SSRcCvwe2kbRY0rR2xzTE7AYcRqppLciP/dod1BAyDpgj6Q7S+FjXR8Ra1SXRBs5DNpiZVYxr/GZmFePEb2ZWMU78ZmYV48RvZlYxTvxmZhXjxG+VJultki6S9KCk+ZKukfT2gY6qKulwSZsMdpxmg8mJ3yorXyR2OTA3IraMiJ2AE4Cxq7Hbw4F+JX5JLb8FqlWbE79V2QeAlyPirJ6CiLgdeLRnPtfg/1/N/NWSJucB0GZKukvSnZK+KOkgoBP4Wb7gbD1JO0mal39NXCtpXN7PXElnSuoiDf1r1jKuaViVbQcMdBCuHYDxEbEdgKQNImK5pM8Dx0ZEVx5r6PvAlIjolvRx4FvAkXkf60RE52q+BrN+c+I3G5hFwBaSvg/8J3BdnXW2IX25XJ9alRgOLKlZ/vOygzSrx4nfqmwhcFAf66zkjU2i6wJExDOStgc+CHwWOJjXa/I9BCyMiF0b7Pv5fkdsNgjcxm9VdiPwJknTewokvRvYtGadh4EdJA2TtCmwc15vDDAsIi4Fvga8J6+/Ahidp+8DOiTtmrcZKeldJb4es0Jc47fKioiQ9BHgTEnHAX8lJfqja1b7LfAQcDfp9o+35fLxwHmSeipPJ+TnmcBZkv4C7Er6RfF/Ja1P+n87k/RLw6xtPDqnmVnFuKnHzKxinPjNzCrGid/MrGKc+M3MKsaJ38ysYpz4zcwqxonfzKxi/hvj/J7YbFffOQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vao92SCse7HW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6f5d49c5-b871-4b16-df7c-854752066555"
      },
      "source": [
        "# How many neighborhoods in each cluster\n",
        "ita['Cluster Labels'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    77\n",
              "0    15\n",
              "2     7\n",
              "3     1\n",
              "Name: Cluster Labels, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OxplzbuTe7HW"
      },
      "source": [
        "## 6.3. Analysis of each Cluster"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EtW7Ow-6e7HX"
      },
      "source": [
        "### Cluster 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gWQ1d1xTe7HX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "e441a1da-2df0-4ca5-a368-f7c4bb539782"
      },
      "source": [
        "# This will create a dataframe with borough of each neighborhood which we will merge with each cluster dataframe\n",
        "df_new = df[['Borough', 'Neighborhood']]\n",
        "df_new.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-5cb21029-23a3-4284-9c72-aa54863fbc45\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Borough</th>\n",
              "      <th>Neighborhood</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>North York</td>\n",
              "      <td>Parkwoods</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>North York</td>\n",
              "      <td>Victoria Village</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Downtown Toronto</td>\n",
              "      <td>Regent Park, Harbourfront</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>North York</td>\n",
              "      <td>Lawrence Manor, Lawrence Heights</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Queen's Park</td>\n",
              "      <td>Ontario Provincial Government</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5cb21029-23a3-4284-9c72-aa54863fbc45');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "            Borough                      Neighborhood\n",
              "0        North York                         Parkwoods\n",
              "1        North York                  Victoria Village\n",
              "2  Downtown Toronto         Regent Park, Harbourfront\n",
              "3        North York  Lawrence Manor, Lawrence Heights\n",
              "4      Queen's Park     Ontario Provincial Government"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XMq3DHUUe7HX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389
        },
        "outputId": "b6606f99-fe84-4069-e0a0-857542a91199"
      },
      "source": [
        "# Red \n",
        "\n",
        "cluster1 = to_merged.loc[to_merged['Cluster Labels'] == 0]\n",
        "df_cluster1 = pd.merge(df_new, cluster1, on='Neighborhood')\n",
        "df_cluster1.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-1bd8872b-4d90-4c23-8373-2d27b8c10962\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Borough</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Italian Restaurant</th>\n",
              "      <th>Cluster Labels</th>\n",
              "      <th>Neighborhood Latitude</th>\n",
              "      <th>Neighborhood Longitude</th>\n",
              "      <th>Venue</th>\n",
              "      <th>Venue Latitude</th>\n",
              "      <th>Venue Longitude</th>\n",
              "      <th>Venue Category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Downtown Toronto</td>\n",
              "      <td>Regent Park, Harbourfront</td>\n",
              "      <td>0.021739</td>\n",
              "      <td>0</td>\n",
              "      <td>43.65426</td>\n",
              "      <td>-79.360636</td>\n",
              "      <td>GW General</td>\n",
              "      <td>43.650495</td>\n",
              "      <td>-79.357538</td>\n",
              "      <td>Antique Shop</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Downtown Toronto</td>\n",
              "      <td>Regent Park, Harbourfront</td>\n",
              "      <td>0.021739</td>\n",
              "      <td>0</td>\n",
              "      <td>43.65426</td>\n",
              "      <td>-79.360636</td>\n",
              "      <td>Nava Social</td>\n",
              "      <td>43.656678</td>\n",
              "      <td>-79.356516</td>\n",
              "      <td>Café</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Downtown Toronto</td>\n",
              "      <td>Regent Park, Harbourfront</td>\n",
              "      <td>0.021739</td>\n",
              "      <td>0</td>\n",
              "      <td>43.65426</td>\n",
              "      <td>-79.360636</td>\n",
              "      <td>Copper Branch</td>\n",
              "      <td>43.653475</td>\n",
              "      <td>-79.355458</td>\n",
              "      <td>Vegetarian / Vegan Restaurant</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Downtown Toronto</td>\n",
              "      <td>Regent Park, Harbourfront</td>\n",
              "      <td>0.021739</td>\n",
              "      <td>0</td>\n",
              "      <td>43.65426</td>\n",
              "      <td>-79.360636</td>\n",
              "      <td>The Healthy Road</td>\n",
              "      <td>43.656265</td>\n",
              "      <td>-79.357119</td>\n",
              "      <td>Health Food Store</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Downtown Toronto</td>\n",
              "      <td>Regent Park, Harbourfront</td>\n",
              "      <td>0.021739</td>\n",
              "      <td>0</td>\n",
              "      <td>43.65426</td>\n",
              "      <td>-79.360636</td>\n",
              "      <td>Residence &amp; Conference Centre</td>\n",
              "      <td>43.653040</td>\n",
              "      <td>-79.357040</td>\n",
              "      <td>Hotel</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "  </style>\n",
              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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            "text/plain": [
              "            Borough               Neighborhood  Italian Restaurant  \\\n",
              "0  Downtown Toronto  Regent Park, Harbourfront            0.021739   \n",
              "1  Downtown Toronto  Regent Park, Harbourfront            0.021739   \n",
              "2  Downtown Toronto  Regent Park, Harbourfront            0.021739   \n",
              "3  Downtown Toronto  Regent Park, Harbourfront            0.021739   \n",
              "4  Downtown Toronto  Regent Park, Harbourfront            0.021739   \n",
              "\n",
              "   Cluster Labels  Neighborhood Latitude  Neighborhood Longitude  \\\n",
              "0               0               43.65426              -79.360636   \n",
              "1               0               43.65426              -79.360636   \n",
              "2               0               43.65426              -79.360636   \n",
              "3               0               43.65426              -79.360636   \n",
              "4               0               43.65426              -79.360636   \n",
              "\n",
              "                           Venue  Venue Latitude  Venue Longitude  \\\n",
              "0                     GW General       43.650495       -79.357538   \n",
              "1                    Nava Social       43.656678       -79.356516   \n",
              "2                  Copper Branch       43.653475       -79.355458   \n",
              "3               The Healthy Road       43.656265       -79.357119   \n",
              "4  Residence & Conference Centre       43.653040       -79.357040   \n",
              "\n",
              "                  Venue Category  \n",
              "0                   Antique Shop  \n",
              "1                           Café  \n",
              "2  Vegetarian / Vegan Restaurant  \n",
              "3              Health Food Store  \n",
              "4                          Hotel  "
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-xTXY4aqe7HX"
      },
      "source": [
        "### Cluster 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RKAhKWDQe7HY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "outputId": "2644040c-4449-4f0b-af3c-2e71845d5afe"
      },
      "source": [
        "# Purple \n",
        "cluster2=to_merged.loc[to_merged['Cluster Labels'] == 1]\n",
        "df_cluster2 = pd.merge(df_new, cluster2, on='Neighborhood')\n",
        "df_cluster2.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-89d6ff92-0ed9-4291-a0db-8c65ef434b63\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Borough</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Italian Restaurant</th>\n",
              "      <th>Cluster Labels</th>\n",
              "      <th>Neighborhood Latitude</th>\n",
              "      <th>Neighborhood Longitude</th>\n",
              "      <th>Venue</th>\n",
              "      <th>Venue Latitude</th>\n",
              "      <th>Venue Longitude</th>\n",
              "      <th>Venue Category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>North York</td>\n",
              "      <td>Parkwoods</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>43.753259</td>\n",
              "      <td>-79.329656</td>\n",
              "      <td>Brookbanks Park</td>\n",
              "      <td>43.751976</td>\n",
              "      <td>-79.332140</td>\n",
              "      <td>Park</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>North York</td>\n",
              "      <td>Parkwoods</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>43.753259</td>\n",
              "      <td>-79.329656</td>\n",
              "      <td>KFC</td>\n",
              "      <td>43.754387</td>\n",
              "      <td>-79.333021</td>\n",
              "      <td>Fast Food Restaurant</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>North York</td>\n",
              "      <td>Parkwoods</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>43.753259</td>\n",
              "      <td>-79.329656</td>\n",
              "      <td>Brookbanks Pool</td>\n",
              "      <td>43.751389</td>\n",
              "      <td>-79.332184</td>\n",
              "      <td>Pool</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>North York</td>\n",
              "      <td>Parkwoods</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>43.753259</td>\n",
              "      <td>-79.329656</td>\n",
              "      <td>Variety Store</td>\n",
              "      <td>43.751974</td>\n",
              "      <td>-79.333114</td>\n",
              "      <td>Food &amp; Drink Shop</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>North York</td>\n",
              "      <td>Victoria Village</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>43.725882</td>\n",
              "      <td>-79.315572</td>\n",
              "      <td>The Frig</td>\n",
              "      <td>43.727051</td>\n",
              "      <td>-79.317418</td>\n",
              "      <td>French Restaurant</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-89d6ff92-0ed9-4291-a0db-8c65ef434b63')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-89d6ff92-0ed9-4291-a0db-8c65ef434b63 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-89d6ff92-0ed9-4291-a0db-8c65ef434b63');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      Borough      Neighborhood  Italian Restaurant  Cluster Labels  \\\n",
              "0  North York         Parkwoods                 0.0               1   \n",
              "1  North York         Parkwoods                 0.0               1   \n",
              "2  North York         Parkwoods                 0.0               1   \n",
              "3  North York         Parkwoods                 0.0               1   \n",
              "4  North York  Victoria Village                 0.0               1   \n",
              "\n",
              "   Neighborhood Latitude  Neighborhood Longitude            Venue  \\\n",
              "0              43.753259              -79.329656  Brookbanks Park   \n",
              "1              43.753259              -79.329656              KFC   \n",
              "2              43.753259              -79.329656  Brookbanks Pool   \n",
              "3              43.753259              -79.329656    Variety Store   \n",
              "4              43.725882              -79.315572         The Frig   \n",
              "\n",
              "   Venue Latitude  Venue Longitude        Venue Category  \n",
              "0       43.751976       -79.332140                  Park  \n",
              "1       43.754387       -79.333021  Fast Food Restaurant  \n",
              "2       43.751389       -79.332184                  Pool  \n",
              "3       43.751974       -79.333114     Food & Drink Shop  \n",
              "4       43.727051       -79.317418     French Restaurant  "
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g890D6bme7HZ"
      },
      "source": [
        "### Cluster 3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oz9uniide7HZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "outputId": "0d4a4564-3c95-4d3e-ca68-5aab5f61592f"
      },
      "source": [
        "# Blue\n",
        "cluster3 = to_merged.loc[to_merged['Cluster Labels'] == 2]\n",
        "df_cluster3 = pd.merge(df_new, cluster3, on='Neighborhood')\n",
        "df_cluster3.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-925b94cf-7c57-4231-aa11-c677129e4005\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Borough</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Italian Restaurant</th>\n",
              "      <th>Cluster Labels</th>\n",
              "      <th>Neighborhood Latitude</th>\n",
              "      <th>Neighborhood Longitude</th>\n",
              "      <th>Venue</th>\n",
              "      <th>Venue Latitude</th>\n",
              "      <th>Venue Longitude</th>\n",
              "      <th>Venue Category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>North York</td>\n",
              "      <td>Don Mills South</td>\n",
              "      <td>0.052632</td>\n",
              "      <td>2</td>\n",
              "      <td>43.7259</td>\n",
              "      <td>-79.340923</td>\n",
              "      <td>C&amp;C Supermarket</td>\n",
              "      <td>43.725556</td>\n",
              "      <td>-79.341667</td>\n",
              "      <td>Grocery Store</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>North York</td>\n",
              "      <td>Don Mills South</td>\n",
              "      <td>0.052632</td>\n",
              "      <td>2</td>\n",
              "      <td>43.7259</td>\n",
              "      <td>-79.340923</td>\n",
              "      <td>Japanese Canadian Cultural Centre</td>\n",
              "      <td>43.726429</td>\n",
              "      <td>-79.334971</td>\n",
              "      <td>Art Gallery</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>North York</td>\n",
              "      <td>Don Mills South</td>\n",
              "      <td>0.052632</td>\n",
              "      <td>2</td>\n",
              "      <td>43.7259</td>\n",
              "      <td>-79.340923</td>\n",
              "      <td>Tim Hortons</td>\n",
              "      <td>43.722897</td>\n",
              "      <td>-79.339117</td>\n",
              "      <td>Coffee Shop</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>North York</td>\n",
              "      <td>Don Mills South</td>\n",
              "      <td>0.052632</td>\n",
              "      <td>2</td>\n",
              "      <td>43.7259</td>\n",
              "      <td>-79.340923</td>\n",
              "      <td>Asian Legend</td>\n",
              "      <td>43.726591</td>\n",
              "      <td>-79.342188</td>\n",
              "      <td>Dim Sum Restaurant</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>North York</td>\n",
              "      <td>Don Mills South</td>\n",
              "      <td>0.052632</td>\n",
              "      <td>2</td>\n",
              "      <td>43.7259</td>\n",
              "      <td>-79.340923</td>\n",
              "      <td>Real Canadian Superstore</td>\n",
              "      <td>43.722704</td>\n",
              "      <td>-79.337508</td>\n",
              "      <td>Supermarket</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-925b94cf-7c57-4231-aa11-c677129e4005')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-925b94cf-7c57-4231-aa11-c677129e4005 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-925b94cf-7c57-4231-aa11-c677129e4005');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      Borough     Neighborhood  Italian Restaurant  Cluster Labels  \\\n",
              "0  North York  Don Mills South            0.052632               2   \n",
              "1  North York  Don Mills South            0.052632               2   \n",
              "2  North York  Don Mills South            0.052632               2   \n",
              "3  North York  Don Mills South            0.052632               2   \n",
              "4  North York  Don Mills South            0.052632               2   \n",
              "\n",
              "   Neighborhood Latitude  Neighborhood Longitude  \\\n",
              "0                43.7259              -79.340923   \n",
              "1                43.7259              -79.340923   \n",
              "2                43.7259              -79.340923   \n",
              "3                43.7259              -79.340923   \n",
              "4                43.7259              -79.340923   \n",
              "\n",
              "                               Venue  Venue Latitude  Venue Longitude  \\\n",
              "0                    C&C Supermarket       43.725556       -79.341667   \n",
              "1  Japanese Canadian Cultural Centre       43.726429       -79.334971   \n",
              "2                        Tim Hortons       43.722897       -79.339117   \n",
              "3                       Asian Legend       43.726591       -79.342188   \n",
              "4           Real Canadian Superstore       43.722704       -79.337508   \n",
              "\n",
              "       Venue Category  \n",
              "0       Grocery Store  \n",
              "1         Art Gallery  \n",
              "2         Coffee Shop  \n",
              "3  Dim Sum Restaurant  \n",
              "4         Supermarket  "
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0e_ue7Kge7HZ"
      },
      "source": [
        "### Cluster 4"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y-lMCV4Le7HZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "outputId": "860f2099-afc6-4e28-a311-8b05ec1d69ec"
      },
      "source": [
        "# Turquoise\n",
        "cluster4 = to_merged.loc[to_merged['Cluster Labels'] == 3]\n",
        "df_cluster4 = pd.merge(df_new, cluster4, on='Neighborhood')\n",
        "df_cluster4.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-ff834e54-e3c3-495e-b499-31b66a813a19\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Borough</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Italian Restaurant</th>\n",
              "      <th>Cluster Labels</th>\n",
              "      <th>Neighborhood Latitude</th>\n",
              "      <th>Neighborhood Longitude</th>\n",
              "      <th>Venue</th>\n",
              "      <th>Venue Latitude</th>\n",
              "      <th>Venue Longitude</th>\n",
              "      <th>Venue Category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>North York</td>\n",
              "      <td>Bedford Park, Lawrence Manor East</td>\n",
              "      <td>0.136364</td>\n",
              "      <td>3</td>\n",
              "      <td>43.733283</td>\n",
              "      <td>-79.41975</td>\n",
              "      <td>Subway</td>\n",
              "      <td>43.731485</td>\n",
              "      <td>-79.419550</td>\n",
              "      <td>Sandwich Place</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>North York</td>\n",
              "      <td>Bedford Park, Lawrence Manor East</td>\n",
              "      <td>0.136364</td>\n",
              "      <td>3</td>\n",
              "      <td>43.733283</td>\n",
              "      <td>-79.41975</td>\n",
              "      <td>Rossini Restaurant</td>\n",
              "      <td>43.735309</td>\n",
              "      <td>-79.420187</td>\n",
              "      <td>Italian Restaurant</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>North York</td>\n",
              "      <td>Bedford Park, Lawrence Manor East</td>\n",
              "      <td>0.136364</td>\n",
              "      <td>3</td>\n",
              "      <td>43.733283</td>\n",
              "      <td>-79.41975</td>\n",
              "      <td>McDonald's</td>\n",
              "      <td>43.732540</td>\n",
              "      <td>-79.419796</td>\n",
              "      <td>Fast Food Restaurant</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>North York</td>\n",
              "      <td>Bedford Park, Lawrence Manor East</td>\n",
              "      <td>0.136364</td>\n",
              "      <td>3</td>\n",
              "      <td>43.733283</td>\n",
              "      <td>-79.41975</td>\n",
              "      <td>Bruno's Fine Food</td>\n",
              "      <td>43.736642</td>\n",
              "      <td>-79.419870</td>\n",
              "      <td>Butcher</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>North York</td>\n",
              "      <td>Bedford Park, Lawrence Manor East</td>\n",
              "      <td>0.136364</td>\n",
              "      <td>3</td>\n",
              "      <td>43.733283</td>\n",
              "      <td>-79.41975</td>\n",
              "      <td>Safari Bar and Grill</td>\n",
              "      <td>43.729051</td>\n",
              "      <td>-79.418109</td>\n",
              "      <td>American Restaurant</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ff834e54-e3c3-495e-b499-31b66a813a19')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-ff834e54-e3c3-495e-b499-31b66a813a19');\n",
              "          const dataTable =\n",
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              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ],
            "text/plain": [
              "      Borough                       Neighborhood  Italian Restaurant  \\\n",
              "0  North York  Bedford Park, Lawrence Manor East            0.136364   \n",
              "1  North York  Bedford Park, Lawrence Manor East            0.136364   \n",
              "2  North York  Bedford Park, Lawrence Manor East            0.136364   \n",
              "3  North York  Bedford Park, Lawrence Manor East            0.136364   \n",
              "4  North York  Bedford Park, Lawrence Manor East            0.136364   \n",
              "\n",
              "   Cluster Labels  Neighborhood Latitude  Neighborhood Longitude  \\\n",
              "0               3              43.733283               -79.41975   \n",
              "1               3              43.733283               -79.41975   \n",
              "2               3              43.733283               -79.41975   \n",
              "3               3              43.733283               -79.41975   \n",
              "4               3              43.733283               -79.41975   \n",
              "\n",
              "                  Venue  Venue Latitude  Venue Longitude        Venue Category  \n",
              "0                Subway       43.731485       -79.419550        Sandwich Place  \n",
              "1    Rossini Restaurant       43.735309       -79.420187    Italian Restaurant  \n",
              "2            McDonald's       43.732540       -79.419796  Fast Food Restaurant  \n",
              "3     Bruno's Fine Food       43.736642       -79.419870               Butcher  \n",
              "4  Safari Bar and Grill       43.729051       -79.418109   American Restaurant  "
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XJj0IXf2L_3H"
      },
      "source": [
        "## 6.4. Number of neighborhoods per cluster *vs* Average number of Italian Restaurants in each Cluster"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "RQknZwf1S5DG",
        "outputId": "d3a7f8d4-7d5d-410a-b19e-609a1b9a8d16"
      },
      "source": [
        "plt.figure(figsize=(15,5))\n",
        "\n",
        "# Plot-1 ( Number of Neighborhoods per Cluster )\n",
        "\n",
        "plt.subplot(1,2,1)\n",
        "objects = (1,2,3,4)\n",
        "y_pos = np.arange(len(objects))\n",
        "performance = ita['Cluster Labels'].value_counts().to_frame().sort_index(ascending=True)\n",
        "perf_1 = performance['Cluster Labels'].tolist()\n",
        "plt.bar(y_pos, perf_1, align='center', alpha=0.8, color=['red', 'purple','aquamarine', 'darkkhaki'])\n",
        "plt.xticks(y_pos, objects)\n",
        "plt.ylabel('No of Neighborhoods')\n",
        "plt.xlabel('Cluster')\n",
        "plt.title('Number of Neighborhoods per Cluster')\n",
        "\n",
        "# Plot-2 ( Average number of Italian Restaurants per Cluster )\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "clusters_mean = [df_cluster1['Italian Restaurant'].mean(),df_cluster2['Italian Restaurant'].mean(),df_cluster3['Italian Restaurant'].mean(),\n",
        "                df_cluster4['Italian Restaurant'].mean()]\n",
        "y_pos = np.arange(len(objects))\n",
        "perf_2 = clusters_mean\n",
        "plt.bar(y_pos, perf_2, align='center', alpha=0.8, color=['red', 'purple','aquamarine', 'darkkhaki'])\n",
        "plt.xticks(y_pos, objects)\n",
        "plt.ylabel('Mean')\n",
        "plt.xlabel('Cluster')\n",
        "plt.title('Average number of Italian Restaurants per Cluster')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Average number of Italian Restaurants per Cluster')"
            ]
          },
          "metadata": {},
          "execution_count": 108
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d5L_ten2e7Ha"
      },
      "source": [
        "# **7. Conclusion**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0p8rOl96e7Ha"
      },
      "source": [
        "The Neighborhoods located in the East Toronto area(cluster-3) have the highest average of Italian Restaurants which is represented by aquamarine colour. North York has second heighest number of Italian restaurants present. Looking at the nearby venues, the optimum place to put a new Italian Restaurant is in Victoria village,North York(cluster-1) as their are many Neighborhoods in that area but a little number of Italian Restaurants therefore, eliminating any competition.The second best Neighborhoods that have a great oppurtunity would be in areas such as Queen's Park which is in Cluster 4.Having 70 neighborhoods in the area with no Italian Restaurants gives a good oppurtunity for opening up a new restaurant. This concludes the optimal findings for this project and recommends the entrepreneur to open an authentic Italian restaurant in these locations with little to no competition. Nonetheless, if the food is authentic, affordable and good taste, I am confident that it will have great following everywhere.\n",
        "\n",
        "**Here we take an Italian Restaurant as an example. We can do the same process to find the best place or neighborhood**\n",
        "- to open a start up company \n",
        "- to stay on rent for bachelors\n",
        "- to start a side business for middle class people\n",
        "- to open a camp for any kind of servey\n",
        "- to release a new product for checking the success rate\n",
        "  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pSVIKmEK0UD0"
      },
      "source": [
        "# **8. Future Work**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YQSwu1d20Zwr"
      },
      "source": [
        "* Apply different types of clustering algorithms to cluster the \n",
        "neighborhoods.\n",
        "* Consider other food vanues,market area etc. as features for clustering.\n",
        "* Consider more then 100 vanues in a neighborhood for analysis using Foursquare api.\n"
      ]
    }
  ]
}