Segmenting and Clustering Neighbourhoods in Toronto.ipynb

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      "cell_type": "markdown",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/vibhakargupta1/Coursera_Capstone/blob/master/Segmenting%20and%20Clustering%20Neighbourhoods%20in%20Toronto.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",
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        "id": "nm1STtgh3Fl9",
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      "source": [
        "# **Segmenting and Clustering Neighbourhoods in Toronto** "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cbL5hvRK3Jxq",
        "colab_type": "text"
      },
      "source": [
        "#**Problem 1:**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hkrFBni83W8Q",
        "colab_type": "text"
      },
      "source": [
        "Use the Notebook to build the code to scrape the following Wikipedia page, https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M, in order to obtain the data that is in the table of postal codes and to transform the data into a pandas dataframe.\n",
        "\n",
        "To create the above dataframe:\n",
        "\n",
        "\n",
        "\n",
        "1.   The dataframe will consist of three columns: PostalCode, Borough, and Neighborhood\n",
        "2.   Only process the cells that have an assigned borough. Ignore cells with a borough that is Not assigned\n",
        "3.   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",
        "4.   If a cell has a borough but a Not assigned neighborhood, then the neighborhood will be the same as the borough. So for the 9th cell in the table on the Wikipedia page, the value of the Borough and the Neighborhood columns will be Queen's Park.\n",
        "5.   Clean your Notebook and add Markdown cells to explain your work and any assumptions you are making.\n",
        "6.   In the last cell of your notebook, use the .shape method to print the number of rows of your dataframe.\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O6zKKshL4VXI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from bs4 import BeautifulSoup\n",
        "import requests\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aTNDiR9Y4nsY",
        "colab_type": "text"
      },
      "source": [
        "#### Scraping of postal codes of Canada"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zDxFzQ9I4idc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "List_url = \"https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M\"\n",
        "source = requests.get(List_url).text"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vyhRuo4x408M",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "soup = BeautifulSoup(source, 'xml')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qekaa8gy45yl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "table=soup.find('table')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZeF7Tu184_Po",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#dataframe will consist of three columns: PostalCode, Borough, and Neighborhood\n",
        "column_names = ['Postalcode','Borough','Neighborhood']\n",
        "df = pd.DataFrame(columns = column_names)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EvuK2qjh5E7z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Search all the postcode, borough, neighborhood \n",
        "for tr_cell in table.find_all('tr'):\n",
        "    row_data=[]\n",
        "    for td_cell in tr_cell.find_all('td'):\n",
        "        row_data.append(td_cell.text.strip())\n",
        "    if len(row_data)==3:\n",
        "        df.loc[len(df)] = row_data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1byr6jpx5KXm",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "5072d9d5-a832-4332-b8ce-41f6270b60cd"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\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>Postalcode</th>\n",
              "      <th>Borough</th>\n",
              "      <th>Neighborhood</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>M1A</td>\n",
              "      <td>Not assigned</td>\n",
              "      <td>Not assigned</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>M2A</td>\n",
              "      <td>Not assigned</td>\n",
              "      <td>Not assigned</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>M3A</td>\n",
              "      <td>North York</td>\n",
              "      <td>Parkwoods</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>M4A</td>\n",
              "      <td>North York</td>\n",
              "      <td>Victoria Village</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>M5A</td>\n",
              "      <td>Downtown Toronto</td>\n",
              "      <td>Regent Park, Harbourfront</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Postalcode           Borough               Neighborhood\n",
              "0        M1A      Not assigned               Not assigned\n",
              "1        M2A      Not assigned               Not assigned\n",
              "2        M3A        North York                  Parkwoods\n",
              "3        M4A        North York           Victoria Village\n",
              "4        M5A  Downtown Toronto  Regent Park, Harbourfront"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VGg43JMd5SBC",
        "colab_type": "text"
      },
      "source": [
        "##### Data Cleaning\n",
        "Removal of rows where borough=='Not assigned'"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PRXhrB1p5fvZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "08effede-5ada-4192-8e1d-b382b076d859"
      },
      "source": [
        "#Removing Not assigned zip codes and fixing df index\n",
        "df.drop(df[df['Borough'] == 'Not assigned'].index, inplace=True)\n",
        "df.index = range(len(df))\n",
        "df"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\n",
              "\n",
              "    .dataframe thead th {\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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>M3A</td>\n",
              "      <td>North York</td>\n",
              "      <td>Parkwoods</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>M4A</td>\n",
              "      <td>North York</td>\n",
              "      <td>Victoria Village</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>M5A</td>\n",
              "      <td>Downtown Toronto</td>\n",
              "      <td>Regent Park, Harbourfront</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>M6A</td>\n",
              "      <td>North York</td>\n",
              "      <td>Lawrence Manor, Lawrence Heights</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>M7A</td>\n",
              "      <td>Downtown Toronto</td>\n",
              "      <td>Queen's Park, Ontario Provincial Government</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>M8X</td>\n",
              "      <td>Etobicoke</td>\n",
              "      <td>The Kingsway, Montgomery Road, Old Mill North</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>M4Y</td>\n",
              "      <td>Downtown Toronto</td>\n",
              "      <td>Church and Wellesley</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>M7Y</td>\n",
              "      <td>East Toronto</td>\n",
              "      <td>Business reply mail Processing Centre, South C...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>M8Y</td>\n",
              "      <td>Etobicoke</td>\n",
              "      <td>Old Mill South, King's Mill Park, Sunnylea, Hu...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>M8Z</td>\n",
              "      <td>Etobicoke</td>\n",
              "      <td>Mimico NW, The Queensway West, South of Bloor,...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>103 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    Postalcode  ...                                       Neighborhood\n",
              "0          M3A  ...                                          Parkwoods\n",
              "1          M4A  ...                                   Victoria Village\n",
              "2          M5A  ...                          Regent Park, Harbourfront\n",
              "3          M6A  ...                   Lawrence Manor, Lawrence Heights\n",
              "4          M7A  ...        Queen's Park, Ontario Provincial Government\n",
              "..         ...  ...                                                ...\n",
              "98         M8X  ...      The Kingsway, Montgomery Road, Old Mill North\n",
              "99         M4Y  ...                               Church and Wellesley\n",
              "100        M7Y  ...  Business reply mail Processing Centre, South C...\n",
              "101        M8Y  ...  Old Mill South, King's Mill Park, Sunnylea, Hu...\n",
              "102        M8Z  ...  Mimico NW, The Queensway West, South of Bloor,...\n",
              "\n",
              "[103 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uvk4NE_Q5vUU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "3eee9a63-63c8-4e96-83ec-0262fae92fa9"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Postalcode</th>\n",
              "      <th>Borough</th>\n",
              "      <th>Neighborhood</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>M3A</td>\n",
              "      <td>North York</td>\n",
              "      <td>Parkwoods</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>M4A</td>\n",
              "      <td>North York</td>\n",
              "      <td>Victoria Village</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>M5A</td>\n",
              "      <td>Downtown Toronto</td>\n",
              "      <td>Regent Park, Harbourfront</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>M6A</td>\n",
              "      <td>North York</td>\n",
              "      <td>Lawrence Manor, Lawrence Heights</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>M7A</td>\n",
              "      <td>Downtown Toronto</td>\n",
              "      <td>Queen's Park, Ontario Provincial Government</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "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  Downtown Toronto  Queen's Park, Ontario Provincial Government"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MNjnh9An6lXZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "temp_df=df.groupby('Postalcode')['Neighborhood'].apply(lambda x: \"%s\" % ', '.join(x))\n",
        "temp_df=temp_df.reset_index(drop=False)\n",
        "temp_df.rename(columns={'Neighborhood':'Neighborhood_joined'},inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sbOVPlma6nl9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "f507cc27-a6bf-4a9d-8363-d52e0dc25203"
      },
      "source": [
        "df_merge = pd.merge(df, temp_df, on='Postalcode')\n",
        "df_merge.drop(['Neighborhood'],axis=1,inplace=True)\n",
        "\n",
        "df_merge.drop_duplicates(inplace=True)\n",
        "df_merge.rename(columns={'Neighborhood_joined':'Neighborhood'},inplace=True)\n",
        "df_merge.head()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .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>Postalcode</th>\n",
              "      <th>Borough</th>\n",
              "      <th>Neighborhood</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>M3A</td>\n",
              "      <td>North York</td>\n",
              "      <td>Parkwoods</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>M4A</td>\n",
              "      <td>North York</td>\n",
              "      <td>Victoria Village</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>M5A</td>\n",
              "      <td>Downtown Toronto</td>\n",
              "      <td>Regent Park, Harbourfront</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>M6A</td>\n",
              "      <td>North York</td>\n",
              "      <td>Lawrence Manor, Lawrence Heights</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>M7A</td>\n",
              "      <td>Downtown Toronto</td>\n",
              "      <td>Queen's Park, Ontario Provincial Government</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "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  Downtown Toronto  Queen's Park, Ontario Provincial Government"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gEDDXGLn65BR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "912c0990-dc4e-45e0-f64a-96cf3578f71a"
      },
      "source": [
        "df_merge.shape"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(103, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SpzWsx4G7Nx8",
        "colab_type": "text"
      },
      "source": [
        "# Problem 2:\n",
        "We 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."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PaqAhKSP7VDc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_geocode(postal_code):\n",
        "    # initialize your variable to None\n",
        "    lat_lng_coords = None\n",
        "    while(lat_lng_coords is None):\n",
        "        g = geocoder.google('{}, Toronto, Ontario'.format(postal_code))\n",
        "        lat_lng_coords = g.latlng\n",
        "    latitude = lat_lng_coords[0]\n",
        "    longitude = lat_lng_coords[1]\n",
        "    return latitude,longitude"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2itQ_1Ph7cC5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "geo_df=pd.read_csv('http://cocl.us/Geospatial_data')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YnMs0OmM7mza",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "77e12d93-3a80-48ab-ec68-4fa9af18f2e2"
      },
      "source": [
        "geo_df.head()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Postal Code</th>\n",
              "      <th>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>"
            ],
            "text/plain": [
              "  Postal Code   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": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qj3Mn4oT7pLZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "8a6bdd70-83a3-4803-f6ea-83df966384ce"
      },
      "source": [
        "geo_df.rename(columns={'Postal Code':'Postalcode'},inplace=True)\n",
        "geo_merged = pd.merge(geo_df, df_merge, on='Postalcode')\n",
        "geo_data=geo_merged[['Postalcode','Borough','Neighborhood','Latitude','Longitude']]\n",
        "\n",
        "geo_data.head()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Postalcode</th>\n",
              "      <th>Borough</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>M1B</td>\n",
              "      <td>Scarborough</td>\n",
              "      <td>Malvern, Rouge</td>\n",
              "      <td>43.806686</td>\n",
              "      <td>-79.194353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>M1C</td>\n",
              "      <td>Scarborough</td>\n",
              "      <td>Rouge Hill, Port Union, Highland Creek</td>\n",
              "      <td>43.784535</td>\n",
              "      <td>-79.160497</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>M1E</td>\n",
              "      <td>Scarborough</td>\n",
              "      <td>Guildwood, Morningside, West Hill</td>\n",
              "      <td>43.763573</td>\n",
              "      <td>-79.188711</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>M1G</td>\n",
              "      <td>Scarborough</td>\n",
              "      <td>Woburn</td>\n",
              "      <td>43.770992</td>\n",
              "      <td>-79.216917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>M1H</td>\n",
              "      <td>Scarborough</td>\n",
              "      <td>Cedarbrae</td>\n",
              "      <td>43.773136</td>\n",
              "      <td>-79.239476</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Postalcode      Borough  ...   Latitude  Longitude\n",
              "0        M1B  Scarborough  ...  43.806686 -79.194353\n",
              "1        M1C  Scarborough  ...  43.784535 -79.160497\n",
              "2        M1E  Scarborough  ...  43.763573 -79.188711\n",
              "3        M1G  Scarborough  ...  43.770992 -79.216917\n",
              "4        M1H  Scarborough  ...  43.773136 -79.239476\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LNRoiL0Q3SDW",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fV1Pz7JE811X",
        "colab_type": "text"
      },
      "source": [
        "# Problem 3:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SA8J1Xqa85Vx",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "1.   Explore and cluster the neighborhoods in Toronto\n",
        "2.   Generate maps to visualize your neighborhoods and how they cluster together\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vxl0H4v69CUq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2aea337e-0d62-4bb0-c747-03e8713f6324"
      },
      "source": [
        "import os\n",
        "from sklearn.cluster import KMeans\n",
        "import folium \n",
        "from geopy.geocoders import Nominatim \n",
        "import matplotlib.cm as cm\n",
        "import matplotlib.colors as colors\n",
        "\n",
        "\n",
        "print('Libraries imported.')"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Libraries imported.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8OMna9t59MfW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "CLIENT_ID = 'NVUVUWPGRS0KBJPYGIWYNY25SQ0KI5YFTTE0XRGY4OE1LO55' # your Foursquare ID\n",
        "CLIENT_SECRET = 'UCPJJOQ0KEAEN2K2NASJEJYPUZY5VJ4GFGKIDICIUTZQIQ3A' # your Foursquare Secret\n",
        "VERSION = '20200610'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mCIDCOTv9iIz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def getNearbyVenues(names, latitudes, longitudes):\n",
        "    radius=500\n",
        "    LIMIT=100\n",
        "    venues_list=[]\n",
        "    for name, lat, lng in zip(names, latitudes, longitudes):\n",
        "        print(name)\n",
        "            \n",
        "        # create the API request URL\n",
        "        url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n",
        "            CLIENT_ID, \n",
        "            CLIENT_SECRET, \n",
        "            VERSION, \n",
        "            lat, \n",
        "            lng, \n",
        "            radius, \n",
        "            LIMIT)\n",
        "            \n",
        "        # make the GET request\n",
        "        results = requests.get(url).json()[\"response\"]['groups'][0]['items']\n",
        "        \n",
        "        # return only relevant information for each nearby venue\n",
        "        venues_list.append([(\n",
        "            name, \n",
        "            lat, \n",
        "            lng, \n",
        "            v['venue']['name'], \n",
        "            v['venue']['location']['lat'], \n",
        "            v['venue']['location']['lng'],  \n",
        "            v['venue']['categories'][0]['name']) for v in results])\n",
        "\n",
        "    nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])\n",
        "    nearby_venues.columns = ['Neighborhood', \n",
        "                  'Neighborhood Latitude', \n",
        "                  'Neighborhood Longitude', \n",
        "                  'Venue', \n",
        "                  'Venue Latitude', \n",
        "                  'Venue Longitude', \n",
        "                  'Venue Category']\n",
        "    \n",
        "    return(nearby_venues)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jZqL4Jlx9sKt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "da6b144c-e639-41ee-9a88-629e697f11ee"
      },
      "source": [
        "toronto_data=geo_data[geo_data['Borough'].str.contains(\"Toronto\")]\n",
        "toronto_data.head()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Postalcode</th>\n",
              "      <th>Borough</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>M4E</td>\n",
              "      <td>East Toronto</td>\n",
              "      <td>The Beaches</td>\n",
              "      <td>43.676357</td>\n",
              "      <td>-79.293031</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>M4K</td>\n",
              "      <td>East Toronto</td>\n",
              "      <td>The Danforth West, Riverdale</td>\n",
              "      <td>43.679557</td>\n",
              "      <td>-79.352188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>M4L</td>\n",
              "      <td>East Toronto</td>\n",
              "      <td>India Bazaar, The Beaches West</td>\n",
              "      <td>43.668999</td>\n",
              "      <td>-79.315572</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>M4M</td>\n",
              "      <td>East Toronto</td>\n",
              "      <td>Studio District</td>\n",
              "      <td>43.659526</td>\n",
              "      <td>-79.340923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>M4N</td>\n",
              "      <td>Central Toronto</td>\n",
              "      <td>Lawrence Park</td>\n",
              "      <td>43.728020</td>\n",
              "      <td>-79.388790</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Postalcode          Borough  ...   Latitude  Longitude\n",
              "37        M4E     East Toronto  ...  43.676357 -79.293031\n",
              "41        M4K     East Toronto  ...  43.679557 -79.352188\n",
              "42        M4L     East Toronto  ...  43.668999 -79.315572\n",
              "43        M4M     East Toronto  ...  43.659526 -79.340923\n",
              "44        M4N  Central Toronto  ...  43.728020 -79.388790\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U2NWZiz4-Eld",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 672
        },
        "outputId": "f953bc6a-45d7-4e45-884e-43aac1587bd7"
      },
      "source": [
        "toronto_venues = getNearbyVenues(names=toronto_data['Neighborhood'],\n",
        "                                   latitudes=toronto_data['Latitude'],\n",
        "                                   longitudes=toronto_data['Longitude']\n",
        "                                  )"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Beaches\n",
            "The Danforth West, Riverdale\n",
            "India Bazaar, The Beaches West\n",
            "Studio District\n",
            "Lawrence Park\n",
            "Davisville North\n",
            "North Toronto West,  Lawrence Park\n",
            "Davisville\n",
            "Moore Park, Summerhill East\n",
            "Summerhill West, Rathnelly, South Hill, Forest Hill SE, Deer Park\n",
            "Rosedale\n",
            "St. James Town, Cabbagetown\n",
            "Church and Wellesley\n",
            "Regent Park, Harbourfront\n",
            "Garden District, Ryerson\n",
            "St. James Town\n",
            "Berczy Park\n",
            "Central Bay Street\n",
            "Richmond, Adelaide, King\n",
            "Harbourfront East, Union Station, Toronto Islands\n",
            "Toronto Dominion Centre, Design Exchange\n",
            "Commerce Court, Victoria Hotel\n",
            "Roselawn\n",
            "Forest Hill North & West, Forest Hill Road Park\n",
            "The Annex, North Midtown, Yorkville\n",
            "University of Toronto, Harbord\n",
            "Kensington Market, Chinatown, Grange Park\n",
            "CN Tower, King and Spadina, Railway Lands, Harbourfront West, Bathurst Quay, South Niagara, Island airport\n",
            "Stn A PO Boxes\n",
            "First Canadian Place, Underground city\n",
            "Christie\n",
            "Dufferin, Dovercourt Village\n",
            "Little Portugal, Trinity\n",
            "Brockton, Parkdale Village, Exhibition Place\n",
            "High Park, The Junction South\n",
            "Parkdale, Roncesvalles\n",
            "Runnymede, Swansea\n",
            "Queen's Park, Ontario Provincial Government\n",
            "Business reply mail Processing Centre, South Central Letter Processing Plant Toronto\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PdUZxcOl-S5D",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "41cb739b-02eb-4eb5-d8ba-6d4c2b8527a2"
      },
      "source": [
        "toronto_venues.head()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Neighborhood Latitude</th>\n",
              "      <th>Neighborhood Longitude</th>\n",
              "      <th>Venue</th>\n",
              "      <th>Venue Latitude</th>\n",
              "      <th>Venue Longitude</th>\n",
              "      <th>Venue Category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>The Beaches</td>\n",
              "      <td>43.676357</td>\n",
              "      <td>-79.293031</td>\n",
              "      <td>Glen Manor Ravine</td>\n",
              "      <td>43.676821</td>\n",
              "      <td>-79.293942</td>\n",
              "      <td>Trail</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>The Beaches</td>\n",
              "      <td>43.676357</td>\n",
              "      <td>-79.293031</td>\n",
              "      <td>The Big Carrot Natural Food Market</td>\n",
              "      <td>43.678879</td>\n",
              "      <td>-79.297734</td>\n",
              "      <td>Health Food Store</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>The Beaches</td>\n",
              "      <td>43.676357</td>\n",
              "      <td>-79.293031</td>\n",
              "      <td>Grover Pub and Grub</td>\n",
              "      <td>43.679181</td>\n",
              "      <td>-79.297215</td>\n",
              "      <td>Pub</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>The Beaches</td>\n",
              "      <td>43.676357</td>\n",
              "      <td>-79.293031</td>\n",
              "      <td>Upper Beaches</td>\n",
              "      <td>43.680563</td>\n",
              "      <td>-79.292869</td>\n",
              "      <td>Neighborhood</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>The Danforth West, Riverdale</td>\n",
              "      <td>43.679557</td>\n",
              "      <td>-79.352188</td>\n",
              "      <td>MenEssentials</td>\n",
              "      <td>43.677820</td>\n",
              "      <td>-79.351265</td>\n",
              "      <td>Cosmetics Shop</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   Neighborhood  ...     Venue Category\n",
              "0                   The Beaches  ...              Trail\n",
              "1                   The Beaches  ...  Health Food Store\n",
              "2                   The Beaches  ...                Pub\n",
              "3                   The Beaches  ...       Neighborhood\n",
              "4  The Danforth West, Riverdale  ...     Cosmetics Shop\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cdaPuUKq-U16",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "289b7a47-40ea-4a36-ea85-7719a3a2163d"
      },
      "source": [
        "toronto_venues.groupby('Neighborhood').count()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Neighborhood Latitude</th>\n",
              "      <th>Neighborhood Longitude</th>\n",
              "      <th>Venue</th>\n",
              "      <th>Venue Latitude</th>\n",
              "      <th>Venue Longitude</th>\n",
              "      <th>Venue Category</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neighborhood</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Berczy Park</th>\n",
              "      <td>56</td>\n",
              "      <td>56</td>\n",
              "      <td>56</td>\n",
              "      <td>56</td>\n",
              "      <td>56</td>\n",
              "      <td>56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brockton, Parkdale Village, Exhibition Place</th>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Business reply mail Processing Centre, South Central Letter Processing Plant Toronto</th>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CN Tower, King and Spadina, Railway Lands, Harbourfront West, Bathurst Quay, South Niagara, Island airport</th>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Central Bay Street</th>\n",
              "      <td>64</td>\n",
              "      <td>64</td>\n",
              "      <td>64</td>\n",
              "      <td>64</td>\n",
              "      <td>64</td>\n",
              "      <td>64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Christie</th>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Church and Wellesley</th>\n",
              "      <td>80</td>\n",
              "      <td>80</td>\n",
              "      <td>80</td>\n",
              "      <td>80</td>\n",
              "      <td>80</td>\n",
              "      <td>80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Commerce Court, Victoria Hotel</th>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Davisville</th>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Davisville North</th>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dufferin, Dovercourt Village</th>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>First Canadian Place, Underground city</th>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Forest Hill North &amp; West, Forest Hill Road Park</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Garden District, Ryerson</th>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Harbourfront East, Union Station, Toronto Islands</th>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>High Park, The Junction South</th>\n",
              "      <td>23</td>\n",
              "      <td>23</td>\n",
              "      <td>23</td>\n",
              "      <td>23</td>\n",
              "      <td>23</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>India Bazaar, The Beaches West</th>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kensington Market, Chinatown, Grange Park</th>\n",
              "      <td>61</td>\n",
              "      <td>61</td>\n",
              "      <td>61</td>\n",
              "      <td>61</td>\n",
              "      <td>61</td>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lawrence Park</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Little Portugal, Trinity</th>\n",
              "      <td>45</td>\n",
              "      <td>45</td>\n",
              "      <td>45</td>\n",
              "      <td>45</td>\n",
              "      <td>45</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Moore Park, Summerhill East</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>North Toronto West,  Lawrence Park</th>\n",
              "      <td>21</td>\n",
              "      <td>21</td>\n",
              "      <td>21</td>\n",
              "      <td>21</td>\n",
              "      <td>21</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Parkdale, Roncesvalles</th>\n",
              "      <td>14</td>\n",
              "      <td>14</td>\n",
              "      <td>14</td>\n",
              "      <td>14</td>\n",
              "      <td>14</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Queen's Park, Ontario Provincial Government</th>\n",
              "      <td>31</td>\n",
              "      <td>31</td>\n",
              "      <td>31</td>\n",
              "      <td>31</td>\n",
              "      <td>31</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Regent Park, Harbourfront</th>\n",
              "      <td>46</td>\n",
              "      <td>46</td>\n",
              "      <td>46</td>\n",
              "      <td>46</td>\n",
              "      <td>46</td>\n",
              "      <td>46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Richmond, Adelaide, King</th>\n",
              "      <td>95</td>\n",
              "      <td>95</td>\n",
              "      <td>95</td>\n",
              "      <td>95</td>\n",
              "      <td>95</td>\n",
              "      <td>95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rosedale</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Roselawn</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Runnymede, Swansea</th>\n",
              "      <td>38</td>\n",
              "      <td>38</td>\n",
              "      <td>38</td>\n",
              "      <td>38</td>\n",
              "      <td>38</td>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>St. James Town</th>\n",
              "      <td>79</td>\n",
              "      <td>79</td>\n",
              "      <td>79</td>\n",
              "      <td>79</td>\n",
              "      <td>79</td>\n",
              "      <td>79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>St. James Town, Cabbagetown</th>\n",
              "      <td>45</td>\n",
              "      <td>45</td>\n",
              "      <td>45</td>\n",
              "      <td>45</td>\n",
              "      <td>45</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Stn A PO Boxes</th>\n",
              "      <td>94</td>\n",
              "      <td>94</td>\n",
              "      <td>94</td>\n",
              "      <td>94</td>\n",
              "      <td>94</td>\n",
              "      <td>94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Studio District</th>\n",
              "      <td>40</td>\n",
              "      <td>40</td>\n",
              "      <td>40</td>\n",
              "      <td>40</td>\n",
              "      <td>40</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Summerhill West, Rathnelly, South Hill, Forest Hill SE, Deer Park</th>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>The Annex, North Midtown, Yorkville</th>\n",
              "      <td>21</td>\n",
              "      <td>21</td>\n",
              "      <td>21</td>\n",
              "      <td>21</td>\n",
              "      <td>21</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>The Beaches</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>The Danforth West, Riverdale</th>\n",
              "      <td>43</td>\n",
              "      <td>43</td>\n",
              "      <td>43</td>\n",
              "      <td>43</td>\n",
              "      <td>43</td>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Toronto Dominion Centre, Design Exchange</th>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>University of Toronto, Harbord</th>\n",
              "      <td>34</td>\n",
              "      <td>34</td>\n",
              "      <td>34</td>\n",
              "      <td>34</td>\n",
              "      <td>34</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                    Neighborhood Latitude  ...  Venue Category\n",
              "Neighborhood                                                               ...                \n",
              "Berczy Park                                                            56  ...              56\n",
              "Brockton, Parkdale Village, Exhibition Place                           24  ...              24\n",
              "Business reply mail Processing Centre, South Ce...                     18  ...              18\n",
              "CN Tower, King and Spadina, Railway Lands, Harb...                     18  ...              18\n",
              "Central Bay Street                                                     64  ...              64\n",
              "Christie                                                               17  ...              17\n",
              "Church and Wellesley                                                   80  ...              80\n",
              "Commerce Court, Victoria Hotel                                        100  ...             100\n",
              "Davisville                                                             33  ...              33\n",
              "Davisville North                                                        8  ...               8\n",
              "Dufferin, Dovercourt Village                                           15  ...              15\n",
              "First Canadian Place, Underground city                                100  ...             100\n",
              "Forest Hill North & West, Forest Hill Road Park                         5  ...               5\n",
              "Garden District, Ryerson                                              100  ...             100\n",
              "Harbourfront East, Union Station, Toronto Islands                     100  ...             100\n",
              "High Park, The Junction South                                          23  ...              23\n",
              "India Bazaar, The Beaches West                                         19  ...              19\n",
              "Kensington Market, Chinatown, Grange Park                              61  ...              61\n",
              "Lawrence Park                                                           3  ...               3\n",
              "Little Portugal, Trinity                                               45  ...              45\n",
              "Moore Park, Summerhill East                                             4  ...               4\n",
              "North Toronto West,  Lawrence Park                                     21  ...              21\n",
              "Parkdale, Roncesvalles                                                 14  ...              14\n",
              "Queen's Park, Ontario Provincial Government                            31  ...              31\n",
              "Regent Park, Harbourfront                                              46  ...              46\n",
              "Richmond, Adelaide, King                                               95  ...              95\n",
              "Rosedale                                                                4  ...               4\n",
              "Roselawn                                                                1  ...               1\n",
              "Runnymede, Swansea                                                     38  ...              38\n",
              "St. James Town                                                         79  ...              79\n",
              "St. James Town, Cabbagetown                                            45  ...              45\n",
              "Stn A PO Boxes                                                         94  ...              94\n",
              "Studio District                                                        40  ...              40\n",
              "Summerhill West, Rathnelly, South Hill, Forest ...                     17  ...              17\n",
              "The Annex, North Midtown, Yorkville                                    21  ...              21\n",
              "The Beaches                                                             4  ...               4\n",
              "The Danforth West, Riverdale                                           43  ...              43\n",
              "Toronto Dominion Centre, Design Exchange                              100  ...             100\n",
              "University of Toronto, Harbord                                         34  ...              34\n",
              "\n",
              "[39 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WWjzn6Y7-ar6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b81612e3-b72a-4697-d4b0-f84e5fea1fca"
      },
      "source": [
        "# one hot encoding\n",
        "toronto_onehot = pd.get_dummies(toronto_venues[['Venue Category']], prefix=\"\", prefix_sep=\"\")\n",
        "toronto_onehot.drop(['Neighborhood'],axis=1,inplace=True) \n",
        "toronto_onehot.insert(loc=0, column='Neighborhood', value=toronto_venues['Neighborhood'] )\n",
        "toronto_onehot.shape"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1620, 238)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MuCUiTyN-gPT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "outputId": "3cbbeef9-9ba6-4fa7-d87e-e38ea4889f4d"
      },
      "source": [
        "toronto_grouped = toronto_onehot.groupby('Neighborhood').mean().reset_index()\n",
        "toronto_grouped.head()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Afghan Restaurant</th>\n",
              "      <th>Airport</th>\n",
              "      <th>Airport Food Court</th>\n",
              "      <th>Airport Gate</th>\n",
              "      <th>Airport Lounge</th>\n",
              "      <th>Airport Service</th>\n",
              "      <th>Airport Terminal</th>\n",
              "      <th>American Restaurant</th>\n",
              "      <th>Antique Shop</th>\n",
              "      <th>Aquarium</th>\n",
              "      <th>Art Gallery</th>\n",
              "      <th>Art Museum</th>\n",
              "      <th>Arts &amp; Crafts Store</th>\n",
              "      <th>Asian Restaurant</th>\n",
              "      <th>Athletics &amp; Sports</th>\n",
              "      <th>Auto Workshop</th>\n",
              "      <th>BBQ Joint</th>\n",
              "      <th>Baby Store</th>\n",
              "      <th>Bagel Shop</th>\n",
              "      <th>Bakery</th>\n",
              "      <th>Bank</th>\n",
              "      <th>Bar</th>\n",
              "      <th>Baseball Stadium</th>\n",
              "      <th>Basketball Stadium</th>\n",
              "      <th>Beach</th>\n",
              "      <th>Bed &amp; Breakfast</th>\n",
              "      <th>Beer Bar</th>\n",
              "      <th>Beer Store</th>\n",
              "      <th>Belgian Restaurant</th>\n",
              "      <th>Bistro</th>\n",
              "      <th>Boat or Ferry</th>\n",
              "      <th>Bookstore</th>\n",
              "      <th>Boutique</th>\n",
              "      <th>Brazilian Restaurant</th>\n",
              "      <th>Breakfast Spot</th>\n",
              "      <th>Brewery</th>\n",
              "      <th>Bubble Tea Shop</th>\n",
              "      <th>Building</th>\n",
              "      <th>Burger Joint</th>\n",
              "      <th>...</th>\n",
              "      <th>Shoe Store</th>\n",
              "      <th>Shopping Mall</th>\n",
              "      <th>Skate Park</th>\n",
              "      <th>Skating Rink</th>\n",
              "      <th>Smoke Shop</th>\n",
              "      <th>Smoothie Shop</th>\n",
              "      <th>Snack Place</th>\n",
              "      <th>Soup Place</th>\n",
              "      <th>Spa</th>\n",
              "      <th>Speakeasy</th>\n",
              "      <th>Sporting Goods Shop</th>\n",
              "      <th>Sports Bar</th>\n",
              "      <th>Sri Lankan Restaurant</th>\n",
              "      <th>Stadium</th>\n",
              "      <th>Stationery Store</th>\n",
              "      <th>Steakhouse</th>\n",
              "      <th>Strip Club</th>\n",
              "      <th>Supermarket</th>\n",
              "      <th>Sushi Restaurant</th>\n",
              "      <th>Swim School</th>\n",
              "      <th>Taco Place</th>\n",
              "      <th>Tailor Shop</th>\n",
              "      <th>Taiwanese Restaurant</th>\n",
              "      <th>Tanning Salon</th>\n",
              "      <th>Tapas Restaurant</th>\n",
              "      <th>Tea Room</th>\n",
              "      <th>Thai Restaurant</th>\n",
              "      <th>Theater</th>\n",
              "      <th>Theme Restaurant</th>\n",
              "      <th>Toy / Game Store</th>\n",
              "      <th>Trail</th>\n",
              "      <th>Train Station</th>\n",
              "      <th>Vegetarian / Vegan Restaurant</th>\n",
              "      <th>Video Game Store</th>\n",
              "      <th>Vietnamese Restaurant</th>\n",
              "      <th>Wine Bar</th>\n",
              "      <th>Wine Shop</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>Berczy Park</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.017857</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>0.017857</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.017857</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.017857</td>\n",
              "      <td>0.017857</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.035714</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.017857</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.017857</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.017857</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.017857</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.017857</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.017857</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.017857</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.017857</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.017857</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Brockton, Parkdale Village, Exhibition Place</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.041667</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.041667</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</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>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.041667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Business reply mail Processing Centre, South C...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.055556</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.055556</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.055556</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.055556</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.055556</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.055556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CN Tower, King and Spadina, Railway Lands, Har...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.055556</td>\n",
              "      <td>0.055556</td>\n",
              "      <td>0.055556</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.055556</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.055556</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.055556</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Central Bay Street</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015625</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015625</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.03125</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.03125</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015625</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015625</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>0.015625</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015625</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015625</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015625</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 238 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                        Neighborhood  ...  Yoga Studio\n",
              "0                                        Berczy Park  ...     0.000000\n",
              "1       Brockton, Parkdale Village, Exhibition Place  ...     0.041667\n",
              "2  Business reply mail Processing Centre, South C...  ...     0.055556\n",
              "3  CN Tower, King and Spadina, Railway Lands, Har...  ...     0.000000\n",
              "4                                 Central Bay Street  ...     0.015625\n",
              "\n",
              "[5 rows x 238 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QAZx6U0Q-px6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def return_most_common_venues(row, num_top_venues):\n",
        "    row_categories = row.iloc[1:]\n",
        "    row_categories_sorted = row_categories.sort_values(ascending=False)\n",
        "    \n",
        "    return row_categories_sorted.index.values[0:num_top_venues]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "scLeSSh3-t9b",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "be87945f-edee-4719-ce6b-a41925b7bd12"
      },
      "source": [
        "import numpy as np\n",
        "num_top_venues = 10\n",
        "\n",
        "indicators = ['st', 'nd', 'rd']\n",
        "\n",
        "# create columns according to number of top venues\n",
        "columns = ['Neighborhood']\n",
        "for ind in np.arange(num_top_venues):\n",
        "    try:\n",
        "        columns.append('{}{} Most Common Venue'.format(ind+1, indicators[ind]))\n",
        "    except:\n",
        "        columns.append('{}th Most Common Venue'.format(ind+1))\n",
        "\n",
        "# create a new dataframe\n",
        "neighborhoods_venues_sorted = pd.DataFrame(columns=columns)\n",
        "neighborhoods_venues_sorted['Neighborhood'] = toronto_grouped['Neighborhood']\n",
        "\n",
        "for ind in np.arange(toronto_grouped.shape[0]):\n",
        "    neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(toronto_grouped.iloc[ind, :], num_top_venues)\n",
        "\n",
        "neighborhoods_venues_sorted.head()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>1st Most Common Venue</th>\n",
              "      <th>2nd Most Common Venue</th>\n",
              "      <th>3rd Most Common Venue</th>\n",
              "      <th>4th Most Common Venue</th>\n",
              "      <th>5th Most Common Venue</th>\n",
              "      <th>6th Most Common Venue</th>\n",
              "      <th>7th Most Common Venue</th>\n",
              "      <th>8th Most Common Venue</th>\n",
              "      <th>9th Most Common Venue</th>\n",
              "      <th>10th Most Common Venue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Berczy Park</td>\n",
              "      <td>Coffee Shop</td>\n",
              "      <td>Cocktail Bar</td>\n",
              "      <td>Café</td>\n",
              "      <td>Beer Bar</td>\n",
              "      <td>Bakery</td>\n",
              "      <td>Seafood Restaurant</td>\n",
              "      <td>Cheese Shop</td>\n",
              "      <td>Restaurant</td>\n",
              "      <td>Eastern European Restaurant</td>\n",
              "      <td>Hotel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Brockton, Parkdale Village, Exhibition Place</td>\n",
              "      <td>Café</td>\n",
              "      <td>Bakery</td>\n",
              "      <td>Breakfast Spot</td>\n",
              "      <td>Coffee Shop</td>\n",
              "      <td>Yoga Studio</td>\n",
              "      <td>Stadium</td>\n",
              "      <td>Burrito Place</td>\n",
              "      <td>Restaurant</td>\n",
              "      <td>Climbing Gym</td>\n",
              "      <td>Pet Store</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Business reply mail Processing Centre, South C...</td>\n",
              "      <td>Light Rail Station</td>\n",
              "      <td>Yoga Studio</td>\n",
              "      <td>Spa</td>\n",
              "      <td>Auto Workshop</td>\n",
              "      <td>Brewery</td>\n",
              "      <td>Burrito Place</td>\n",
              "      <td>Comic Shop</td>\n",
              "      <td>Farmers Market</td>\n",
              "      <td>Fast Food Restaurant</td>\n",
              "      <td>Garden</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CN Tower, King and Spadina, Railway Lands, Har...</td>\n",
              "      <td>Airport Service</td>\n",
              "      <td>Airport Lounge</td>\n",
              "      <td>Airport Terminal</td>\n",
              "      <td>Boutique</td>\n",
              "      <td>Sculpture Garden</td>\n",
              "      <td>Rental Car Location</td>\n",
              "      <td>Plane</td>\n",
              "      <td>Coffee Shop</td>\n",
              "      <td>Boat or Ferry</td>\n",
              "      <td>Bar</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Central Bay Street</td>\n",
              "      <td>Coffee Shop</td>\n",
              "      <td>Café</td>\n",
              "      <td>Italian Restaurant</td>\n",
              "      <td>Sandwich Place</td>\n",
              "      <td>Salad Place</td>\n",
              "      <td>Bubble Tea Shop</td>\n",
              "      <td>Burger Joint</td>\n",
              "      <td>Japanese Restaurant</td>\n",
              "      <td>Department Store</td>\n",
              "      <td>Modern European Restaurant</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                        Neighborhood  ...      10th Most Common Venue\n",
              "0                                        Berczy Park  ...                       Hotel\n",
              "1       Brockton, Parkdale Village, Exhibition Place  ...                   Pet Store\n",
              "2  Business reply mail Processing Centre, South C...  ...                      Garden\n",
              "3  CN Tower, King and Spadina, Railway Lands, Har...  ...                         Bar\n",
              "4                                 Central Bay Street  ...  Modern European Restaurant\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sxi8QTRz_TP6",
        "colab_type": "text"
      },
      "source": [
        "##### **Making Clusters for Neighborhood**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yrZygXYJ_EDF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e94d5f37-b503-4291-8a22-0ecc764198c8"
      },
      "source": [
        "# set number of clusters\n",
        "kclusters = 5\n",
        "\n",
        "toronto_grouped_clustering = toronto_grouped.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": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E11IV9an_bcl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "outputId": "1fe1ed2d-19dc-4376-e608-5e9cd1a190c1"
      },
      "source": [
        "# add clustering labels\n",
        "neighborhoods_venues_sorted.insert(0, 'Cluster Labels', kmeans.labels_)\n",
        "\n",
        "toronto_merged = toronto_data\n",
        "\n",
        "# merge toronto_grouped with toronto_data to add latitude/longitude for each neighborhood\n",
        "toronto_merged = toronto_merged.join(neighborhoods_venues_sorted.set_index('Neighborhood'), on='Neighborhood')\n",
        "\n",
        "toronto_merged.head()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Postalcode</th>\n",
              "      <th>Borough</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "      <th>Cluster Labels</th>\n",
              "      <th>1st Most Common Venue</th>\n",
              "      <th>2nd Most Common Venue</th>\n",
              "      <th>3rd Most Common Venue</th>\n",
              "      <th>4th Most Common Venue</th>\n",
              "      <th>5th Most Common Venue</th>\n",
              "      <th>6th Most Common Venue</th>\n",
              "      <th>7th Most Common Venue</th>\n",
              "      <th>8th Most Common Venue</th>\n",
              "      <th>9th Most Common Venue</th>\n",
              "      <th>10th Most Common Venue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>M4E</td>\n",
              "      <td>East Toronto</td>\n",
              "      <td>The Beaches</td>\n",
              "      <td>43.676357</td>\n",
              "      <td>-79.293031</td>\n",
              "      <td>0</td>\n",
              "      <td>Trail</td>\n",
              "      <td>Health Food Store</td>\n",
              "      <td>Pub</td>\n",
              "      <td>Yoga Studio</td>\n",
              "      <td>Dim Sum Restaurant</td>\n",
              "      <td>Diner</td>\n",
              "      <td>Discount Store</td>\n",
              "      <td>Distribution Center</td>\n",
              "      <td>Dog Run</td>\n",
              "      <td>Doner Restaurant</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>M4K</td>\n",
              "      <td>East Toronto</td>\n",
              "      <td>The Danforth West, Riverdale</td>\n",
              "      <td>43.679557</td>\n",
              "      <td>-79.352188</td>\n",
              "      <td>2</td>\n",
              "      <td>Greek Restaurant</td>\n",
              "      <td>Italian Restaurant</td>\n",
              "      <td>Coffee Shop</td>\n",
              "      <td>Bookstore</td>\n",
              "      <td>Restaurant</td>\n",
              "      <td>Ice Cream Shop</td>\n",
              "      <td>Furniture / Home Store</td>\n",
              "      <td>Liquor Store</td>\n",
              "      <td>Japanese Restaurant</td>\n",
              "      <td>Spa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>M4L</td>\n",
              "      <td>East Toronto</td>\n",
              "      <td>India Bazaar, The Beaches West</td>\n",
              "      <td>43.668999</td>\n",
              "      <td>-79.315572</td>\n",
              "      <td>2</td>\n",
              "      <td>Park</td>\n",
              "      <td>Fast Food Restaurant</td>\n",
              "      <td>Pub</td>\n",
              "      <td>Liquor Store</td>\n",
              "      <td>Burrito Place</td>\n",
              "      <td>Sandwich Place</td>\n",
              "      <td>Italian Restaurant</td>\n",
              "      <td>Fish &amp; Chips Shop</td>\n",
              "      <td>Steakhouse</td>\n",
              "      <td>Restaurant</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>M4M</td>\n",
              "      <td>East Toronto</td>\n",
              "      <td>Studio District</td>\n",
              "      <td>43.659526</td>\n",
              "      <td>-79.340923</td>\n",
              "      <td>2</td>\n",
              "      <td>Café</td>\n",
              "      <td>Coffee Shop</td>\n",
              "      <td>American Restaurant</td>\n",
              "      <td>Bakery</td>\n",
              "      <td>Brewery</td>\n",
              "      <td>Gastropub</td>\n",
              "      <td>Yoga Studio</td>\n",
              "      <td>Fish Market</td>\n",
              "      <td>Pet Store</td>\n",
              "      <td>Park</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>M4N</td>\n",
              "      <td>Central Toronto</td>\n",
              "      <td>Lawrence Park</td>\n",
              "      <td>43.728020</td>\n",
              "      <td>-79.388790</td>\n",
              "      <td>1</td>\n",
              "      <td>Park</td>\n",
              "      <td>Swim School</td>\n",
              "      <td>Bus Line</td>\n",
              "      <td>Falafel Restaurant</td>\n",
              "      <td>Ethiopian Restaurant</td>\n",
              "      <td>Electronics Store</td>\n",
              "      <td>Eastern European Restaurant</td>\n",
              "      <td>Dumpling Restaurant</td>\n",
              "      <td>Donut Shop</td>\n",
              "      <td>Doner Restaurant</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Postalcode          Borough  ... 9th Most Common Venue  10th Most Common Venue\n",
              "37        M4E     East Toronto  ...               Dog Run        Doner Restaurant\n",
              "41        M4K     East Toronto  ...   Japanese Restaurant                     Spa\n",
              "42        M4L     East Toronto  ...            Steakhouse              Restaurant\n",
              "43        M4M     East Toronto  ...             Pet Store                    Park\n",
              "44        M4N  Central Toronto  ...            Donut Shop        Doner Restaurant\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zwnDsNlx_jzh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "outputId": "8672cf14-aeb4-48c0-b086-bf5b8852d207"
      },
      "source": [
        "neighborhoods_venues_sorted.head()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Cluster Labels</th>\n",
              "      <th>Neighborhood</th>\n",
              "      <th>1st Most Common Venue</th>\n",
              "      <th>2nd Most Common Venue</th>\n",
              "      <th>3rd Most Common Venue</th>\n",
              "      <th>4th Most Common Venue</th>\n",
              "      <th>5th Most Common Venue</th>\n",
              "      <th>6th Most Common Venue</th>\n",
              "      <th>7th Most Common Venue</th>\n",
              "      <th>8th Most Common Venue</th>\n",
              "      <th>9th Most Common Venue</th>\n",
              "      <th>10th Most Common Venue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>Berczy Park</td>\n",
              "      <td>Coffee Shop</td>\n",
              "      <td>Cocktail Bar</td>\n",
              "      <td>Café</td>\n",
              "      <td>Beer Bar</td>\n",
              "      <td>Bakery</td>\n",
              "      <td>Seafood Restaurant</td>\n",
              "      <td>Cheese Shop</td>\n",
              "      <td>Restaurant</td>\n",
              "      <td>Eastern European Restaurant</td>\n",
              "      <td>Hotel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Brockton, Parkdale Village, Exhibition Place</td>\n",
              "      <td>Café</td>\n",
              "      <td>Bakery</td>\n",
              "      <td>Breakfast Spot</td>\n",
              "      <td>Coffee Shop</td>\n",
              "      <td>Yoga Studio</td>\n",
              "      <td>Stadium</td>\n",
              "      <td>Burrito Place</td>\n",
              "      <td>Restaurant</td>\n",
              "      <td>Climbing Gym</td>\n",
              "      <td>Pet Store</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>Business reply mail Processing Centre, South C...</td>\n",
              "      <td>Light Rail Station</td>\n",
              "      <td>Yoga Studio</td>\n",
              "      <td>Spa</td>\n",
              "      <td>Auto Workshop</td>\n",
              "      <td>Brewery</td>\n",
              "      <td>Burrito Place</td>\n",
              "      <td>Comic Shop</td>\n",
              "      <td>Farmers Market</td>\n",
              "      <td>Fast Food Restaurant</td>\n",
              "      <td>Garden</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>CN Tower, King and Spadina, Railway Lands, Har...</td>\n",
              "      <td>Airport Service</td>\n",
              "      <td>Airport Lounge</td>\n",
              "      <td>Airport Terminal</td>\n",
              "      <td>Boutique</td>\n",
              "      <td>Sculpture Garden</td>\n",
              "      <td>Rental Car Location</td>\n",
              "      <td>Plane</td>\n",
              "      <td>Coffee Shop</td>\n",
              "      <td>Boat or Ferry</td>\n",
              "      <td>Bar</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>Central Bay Street</td>\n",
              "      <td>Coffee Shop</td>\n",
              "      <td>Café</td>\n",
              "      <td>Italian Restaurant</td>\n",
              "      <td>Sandwich Place</td>\n",
              "      <td>Salad Place</td>\n",
              "      <td>Bubble Tea Shop</td>\n",
              "      <td>Burger Joint</td>\n",
              "      <td>Japanese Restaurant</td>\n",
              "      <td>Department Store</td>\n",
              "      <td>Modern European Restaurant</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Cluster Labels  ...      10th Most Common Venue\n",
              "0               2  ...                       Hotel\n",
              "1               2  ...                   Pet Store\n",
              "2               2  ...                      Garden\n",
              "3               2  ...                         Bar\n",
              "4               2  ...  Modern European Restaurant\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_r5unAb8_okP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c4d5b670-6539-4f9f-9dd0-c9f74a560bd1"
      },
      "source": [
        "address = 'Toronto, CA'\n",
        "\n",
        "geolocator = Nominatim(user_agent=\"ny_explorer\")\n",
        "location = geolocator.geocode(address)\n",
        "latitude = location.latitude\n",
        "longitude = location.longitude\n",
        "print('The geograpical coordinate of Manhattan are {}, {}.'.format(latitude, longitude))"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The geograpical coordinate of Manhattan are 43.6534817, -79.3839347.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wfRwJGrq_tfI",
        "colab_type": "text"
      },
      "source": [
        "##### **Creating Map**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0S-8exDn_xrJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 866
        },
        "outputId": "8a6013d5-0d8b-4053-bab1-68a6c7fb2c3c"
      },
      "source": [
        "map_clusters = folium.Map(location=[latitude, longitude], zoom_start=11)\n",
        "\n",
        "# set color scheme for the clusters\n",
        "x = np.arange(kclusters)\n",
        "ys = [i + x + (i*x)**2 for i in range(kclusters)]\n",
        "colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))\n",
        "rainbow = [colors.rgb2hex(i) for i in colors_array]\n",
        "\n",
        "# add markers to the map\n",
        "markers_colors = []\n",
        "for lat, lon, poi, cluster in zip(toronto_merged['Latitude'], toronto_merged['Longitude'], toronto_merged['Neighborhood'], toronto_merged['Cluster Labels']):\n",
        "    label = folium.Popup(str(poi) + ' Cluster ' + str(cluster), parse_html=True)\n",
        "    folium.CircleMarker(\n",
        "        [lat, lon],\n",
        "        radius=5,\n",
        "        popup=label,\n",
        "        color=rainbow[cluster-1],\n",
        "        fill=True,\n",
        "        fill_color=rainbow[cluster-1],\n",
        "        fill_opacity=0.7).add_to(map_clusters)\n",
        "       \n",
        "map_clusters"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=<!DOCTYPE html>
<head>    
    <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
    <script>L_PREFER_CANVAS=false; L_NO_TOUCH=false; L_DISABLE_3D=false;</script>
    <script src="https://cdn.jsdelivr.net/npm/leaflet@1.4.0/dist/leaflet.js"></script>
    <script src="https://code.jquery.com/jquery-1.12.4.min.js"></script>
    <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>
    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet@1.4.0/dist/leaflet.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css"/>
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            var popup_0944207060f940c2be0e8913ab6a422b = L.popup({maxWidth: '100%'
            
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            circle_marker_6be64df14f7f4a82829a386aa8d1119a.bindPopup(popup_0944207060f940c2be0e8913ab6a422b)
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            var circle_marker_90667fd0a3eb490398502375647a9f7b = L.circleMarker(
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  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
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                )
                .addTo(map_aa541ac6e8e6413498cfee33524a1ee9);
            
    
            var popup_360cbcf5b2454bf3819245641bf9b071 = L.popup({maxWidth: '100%'
            
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                var html_0f0a5e541ffe433faf48e12fdc5d2c80 = $(`<div id="html_0f0a5e541ffe433faf48e12fdc5d2c80" style="width: 100.0%; height: 100.0%;">Berczy Park Cluster 2</div>`)[0];
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            circle_marker_90667fd0a3eb490398502375647a9f7b.bindPopup(popup_360cbcf5b2454bf3819245641bf9b071)
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            var circle_marker_09c87cdf78884e508a18899fea332a84 = L.circleMarker(
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  "fill": true,
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  "fillRule": "evenodd",
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                .addTo(map_aa541ac6e8e6413498cfee33524a1ee9);
            
    
            var popup_a37ec4c8c5884283a285d1cdf28fa416 = L.popup({maxWidth: '100%'
            
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  "fill": true,
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  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
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  "stroke": true,
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}
                )
                .addTo(map_aa541ac6e8e6413498cfee33524a1ee9);
            
    
            var popup_5ae86a88d602411cb55425286d70279a = L.popup({maxWidth: '100%'
            
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  "fillRule": "evenodd",
  "lineCap": "round",
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                .addTo(map_aa541ac6e8e6413498cfee33524a1ee9);
            
    
            var popup_fee8be4986044272b62bfb8a6e9c26ab = L.popup({maxWidth: '100%'
            
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                var html_ab72de7b1c014cb6aa249caa4d6ee1ef = $(`<div id="html_ab72de7b1c014cb6aa249caa4d6ee1ef" style="width: 100.0%; height: 100.0%;">Harbourfront East, Union Station, Toronto Islands Cluster 2</div>`)[0];
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            circle_marker_3115d1e878e747398ac93be33cc4617e.bindPopup(popup_fee8be4986044272b62bfb8a6e9c26ab)
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  "fill": true,
  "fillColor": "#00b5eb",
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  "fillRule": "evenodd",
  "lineCap": "round",
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  "opacity": 1.0,
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  "stroke": true,
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                .addTo(map_aa541ac6e8e6413498cfee33524a1ee9);
            
    
            var popup_b7e944f03a374266a3c6b3bb116f2391 = L.popup({maxWidth: '100%'
            
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            circle_marker_1eec57f3e5e244e395ca86012af5c706.bindPopup(popup_b7e944f03a374266a3c6b3bb116f2391)
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  "fill": true,
  "fillColor": "#00b5eb",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
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  "stroke": true,
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                .addTo(map_aa541ac6e8e6413498cfee33524a1ee9);
            
    
            var popup_84ea5309d28f46d9b74181627e6c9c0e = L.popup({maxWidth: '100%'
            
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                var html_1701c4913dd94efaa881e658cf22b074 = $(`<div id="html_1701c4913dd94efaa881e658cf22b074" style="width: 100.0%; height: 100.0%;">Commerce Court, Victoria Hotel Cluster 2</div>`)[0];
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            circle_marker_4e2facbba083400f9dc11d3455d59d2b.bindPopup(popup_84ea5309d28f46d9b74181627e6c9c0e)
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}
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            var popup_43a835a192a4442a9ac6e748d5af1b7f = L.popup({maxWidth: '100%'
            
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                var html_35eb16767d6647138f56fec8a647c988 = $(`<div id="html_35eb16767d6647138f56fec8a647c988" style="width: 100.0%; height: 100.0%;">Roselawn Cluster 3</div>`)[0];
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            circle_marker_2686e228b1694558a05d6e4d9418ab35.bindPopup(popup_43a835a192a4442a9ac6e748d5af1b7f)
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  "lineJoin": "round",
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  "stroke": true,
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}
                )
                .addTo(map_aa541ac6e8e6413498cfee33524a1ee9);
            
    
            var popup_745aa6982cdf4aaf8f4bbad9c2422339 = L.popup({maxWidth: '100%'
            
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            circle_marker_b37a146ee78f41afb7f6d8000562b224.bindPopup(popup_745aa6982cdf4aaf8f4bbad9c2422339)
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  "fill": true,
  "fillColor": "#00b5eb",
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  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
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  "stroke": true,
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}
                )
                .addTo(map_aa541ac6e8e6413498cfee33524a1ee9);
            
    
            var popup_3e06a0df14704250b2a171cac8c4473c = L.popup({maxWidth: '100%'
            
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                var html_5566fa1b3cb34d80aaad2366e1167d4c = $(`<div id="html_5566fa1b3cb34d80aaad2366e1167d4c" style="width: 100.0%; height: 100.0%;">The Annex, North Midtown, Yorkville Cluster 2</div>`)[0];
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            circle_marker_0df5cd6402904b299cdd00e928e480af.bindPopup(popup_3e06a0df14704250b2a171cac8c4473c)
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  "fillColor": "#00b5eb",
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}
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            var popup_e3a7ca8b72d0442b92df47d1248faea7 = L.popup({maxWidth: '100%'
            
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            circle_marker_e5d5d4486b68440f974c836c10545a3a.bindPopup(popup_e3a7ca8b72d0442b92df47d1248faea7)
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  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
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                .addTo(map_aa541ac6e8e6413498cfee33524a1ee9);
            
    
            var popup_17120ad2c15c45caa8e1a6b89d4bac63 = L.popup({maxWidth: '100%'
            
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                var html_e60f4719abd54a818a993739b8f150d7 = $(`<div id="html_e60f4719abd54a818a993739b8f150d7" style="width: 100.0%; height: 100.0%;">Kensington Market, Chinatown, Grange Park Cluster 2</div>`)[0];
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  "fill": true,
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  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
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  "stroke": true,
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}
                )
                .addTo(map_aa541ac6e8e6413498cfee33524a1ee9);
            
    
            var popup_faca5990ccd64b8fb09682a3c321fd2e = L.popup({maxWidth: '100%'
            
            });

            
                var html_538698fd25cb43e692a2630a60ff7a33 = $(`<div id="html_538698fd25cb43e692a2630a60ff7a33" style="width: 100.0%; height: 100.0%;">CN Tower, King and Spadina, Railway Lands, Harbourfront West, Bathurst Quay, South Niagara, Island airport Cluster 2</div>`)[0];
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            circle_marker_26188849cc2c4829bc831751dc3df7df.bindPopup(popup_faca5990ccd64b8fb09682a3c321fd2e)
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            var circle_marker_e375ce08ab684e8785749b542c30040e = L.circleMarker(
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  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#00b5eb",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
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}
                )
                .addTo(map_aa541ac6e8e6413498cfee33524a1ee9);
            
    
            var popup_e9e8309a7aeb424fa54b753852c7ff7c = L.popup({maxWidth: '100%'
            
            });

            
                var html_259e90d982314ecdb08c6e45e4ed8a1a = $(`<div id="html_259e90d982314ecdb08c6e45e4ed8a1a" style="width: 100.0%; height: 100.0%;">Stn A PO Boxes Cluster 2</div>`)[0];
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            circle_marker_e375ce08ab684e8785749b542c30040e.bindPopup(popup_e9e8309a7aeb424fa54b753852c7ff7c)
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            var circle_marker_d57d55a8338d497b8987b1a4390d3171 = L.circleMarker(
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                {
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  "color": "#00b5eb",
  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#00b5eb",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                )
                .addTo(map_aa541ac6e8e6413498cfee33524a1ee9);
            
    
            var popup_641657000a904e8c94e36d0a9ed083ae = L.popup({maxWidth: '100%'
            
            });

            
                var html_e5088c7c318e4699b613a3689850d7bb = $(`<div id="html_e5088c7c318e4699b613a3689850d7bb" style="width: 100.0%; height: 100.0%;">First Canadian Place, Underground city Cluster 2</div>`)[0];
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            circle_marker_d57d55a8338d497b8987b1a4390d3171.bindPopup(popup_641657000a904e8c94e36d0a9ed083ae)
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            var circle_marker_268539672a774122a48fdd0edf88f83f = L.circleMarker(
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  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#00b5eb",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                )
                .addTo(map_aa541ac6e8e6413498cfee33524a1ee9);
            
    
            var popup_16cd2f63e42c4e26a27d0595fb334e14 = L.popup({maxWidth: '100%'
            
            });

            
                var html_47179a35bc8c4c48a619714cfd8675d2 = $(`<div id="html_47179a35bc8c4c48a619714cfd8675d2" style="width: 100.0%; height: 100.0%;">Christie Cluster 2</div>`)[0];
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            circle_marker_268539672a774122a48fdd0edf88f83f.bindPopup(popup_16cd2f63e42c4e26a27d0595fb334e14)
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  "fill": true,
  "fillColor": "#00b5eb",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
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                )
                .addTo(map_aa541ac6e8e6413498cfee33524a1ee9);
            
    
            var popup_4747a02380234c01a0f423ce828d4c2c = L.popup({maxWidth: '100%'
            
            });

            
                var html_27e66990fd3f401a93aa3e36397b4d30 = $(`<div id="html_27e66990fd3f401a93aa3e36397b4d30" style="width: 100.0%; height: 100.0%;">Dufferin, Dovercourt Village Cluster 2</div>`)[0];
                popup_4747a02380234c01a0f423ce828d4c2c.setContent(html_27e66990fd3f401a93aa3e36397b4d30);
            

            circle_marker_31f50508381645ecb6f2c3db88c8f651.bindPopup(popup_4747a02380234c01a0f423ce828d4c2c)
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  "fill": true,
  "fillColor": "#00b5eb",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
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