Segmenting and Clustering Neighborhoods 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%20Neighborhoods%20in%20Toronto%20.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "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": [
              "<div>\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",
              "    <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": [
              "<div>\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Postalcode</th>\n",
              "      <th>Borough</th>\n",
              "      <th>Neighborhood</th>\n",
              "    </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",
              "<style scoped>\n",
              "    .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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              "  <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": "901592db-f42b-4e33-8afc-66b40807b371"
      },
      "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",
        "display(map_clusters)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "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 html_aea498d0cf504f73b405ef6dcc525179 = $(`<div id="html_aea498d0cf504f73b405ef6dcc525179" style="width: 100.0%; height: 100.0%;">St. James Town Cluster 2</div>`)[0];
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            circle_marker_64c4f18fdf9547409e187e02e4bc6e57.bindPopup(popup_6391c5d00b2c4226885c84730a0ea714)
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            var circle_marker_386a95d5f9374776a25bf68f43055662 = L.circleMarker(
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  "dashArray": null,
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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_8a02c94ed95c44ef961537ca6cef93fa);
            
    
            var popup_e3bb47fbf82642a29da0b98c178464ff = L.popup({maxWidth: '100%'
            
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                var html_29374083f069435987dfb3db7e666b20 = $(`<div id="html_29374083f069435987dfb3db7e666b20" style="width: 100.0%; height: 100.0%;">Berczy Park Cluster 2</div>`)[0];
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            circle_marker_386a95d5f9374776a25bf68f43055662.bindPopup(popup_e3bb47fbf82642a29da0b98c178464ff)
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            var circle_marker_59f1fad40d3b4e36879d5c1d260194d2 = L.circleMarker(
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  "dashArray": null,
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  "fill": true,
  "fillColor": "#00b5eb",
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  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
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}
                )
                .addTo(map_8a02c94ed95c44ef961537ca6cef93fa);
            
    
            var popup_976c214fabbd4fa5b04f831c6bd16c6f = L.popup({maxWidth: '100%'
            
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                var html_a40195904c984cd7be951904975d2963 = $(`<div id="html_a40195904c984cd7be951904975d2963" style="width: 100.0%; height: 100.0%;">Central Bay Street Cluster 2</div>`)[0];
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            circle_marker_59f1fad40d3b4e36879d5c1d260194d2.bindPopup(popup_976c214fabbd4fa5b04f831c6bd16c6f)
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  "dashArray": null,
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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_8a02c94ed95c44ef961537ca6cef93fa);
            
    
            var popup_ee4dbc914418495ca2c740007842fb4a = L.popup({maxWidth: '100%'
            
            });

            
                var html_e96fd7f632e94a27b26461a6a308fd3e = $(`<div id="html_e96fd7f632e94a27b26461a6a308fd3e" style="width: 100.0%; height: 100.0%;">Richmond, Adelaide, King Cluster 2</div>`)[0];
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            var circle_marker_636abc370a054aa3961d281173cc9699 = L.circleMarker(
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  "dashArray": null,
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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_8a02c94ed95c44ef961537ca6cef93fa);
            
    
            var popup_522ea9b2e4b8478885a0da9cd054955e = L.popup({maxWidth: '100%'
            
            });

            
                var html_497927a1d05f42028d60b25bbb0048e4 = $(`<div id="html_497927a1d05f42028d60b25bbb0048e4" style="width: 100.0%; height: 100.0%;">Harbourfront East, Union Station, Toronto Islands Cluster 2</div>`)[0];
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            circle_marker_636abc370a054aa3961d281173cc9699.bindPopup(popup_522ea9b2e4b8478885a0da9cd054955e)
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                {
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  "fill": true,
  "fillColor": "#00b5eb",
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  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
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}
                )
                .addTo(map_8a02c94ed95c44ef961537ca6cef93fa);
            
    
            var popup_5465e04a16a84de2bea7c7e7e73d37ff = L.popup({maxWidth: '100%'
            
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                var html_7e747c156e9840afab09a0d363dc134e = $(`<div id="html_7e747c156e9840afab09a0d363dc134e" style="width: 100.0%; height: 100.0%;">Toronto Dominion Centre, Design Exchange Cluster 2</div>`)[0];
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            circle_marker_68f0d4ea7378452ea3f951c639001e24.bindPopup(popup_5465e04a16a84de2bea7c7e7e73d37ff)
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  "color": "#00b5eb",
  "dashArray": null,
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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_8a02c94ed95c44ef961537ca6cef93fa);
            
    
            var popup_e22bba66bbf745bab057115a1877f416 = L.popup({maxWidth: '100%'
            
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                var html_b45fcf16bbbf4577b67a2c8f37566bec = $(`<div id="html_b45fcf16bbbf4577b67a2c8f37566bec" style="width: 100.0%; height: 100.0%;">Commerce Court, Victoria Hotel Cluster 2</div>`)[0];
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            circle_marker_f82add10b5b84958aff54417eae0a4a5.bindPopup(popup_e22bba66bbf745bab057115a1877f416)
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            var circle_marker_d5297c9bc98f4f6391f0d09f958f0321 = L.circleMarker(
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                {
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  "color": "#80ffb4",
  "dashArray": null,
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  "fill": true,
  "fillColor": "#80ffb4",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
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  "lineJoin": "round",
  "opacity": 1.0,
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  "stroke": true,
  "weight": 3
}
                )
                .addTo(map_8a02c94ed95c44ef961537ca6cef93fa);
            
    
            var popup_0834dae77c5843648d217f65cf01d146 = L.popup({maxWidth: '100%'
            
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                var html_aaf13ce740cf4a3c98280e555f687912 = $(`<div id="html_aaf13ce740cf4a3c98280e555f687912" style="width: 100.0%; height: 100.0%;">Roselawn Cluster 3</div>`)[0];
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            circle_marker_d5297c9bc98f4f6391f0d09f958f0321.bindPopup(popup_0834dae77c5843648d217f65cf01d146)
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  "dashArray": null,
  "dashOffset": null,
  "fill": true,
  "fillColor": "#8000ff",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                )
                .addTo(map_8a02c94ed95c44ef961537ca6cef93fa);
            
    
            var popup_42c568fcddbc4ee3a7ad12f8a11b09ec = L.popup({maxWidth: '100%'
            
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                var html_cd33c2481e6c4259b1f0d71c13d50197 = $(`<div id="html_cd33c2481e6c4259b1f0d71c13d50197" style="width: 100.0%; height: 100.0%;">Forest Hill North &amp; West, Forest Hill Road Park Cluster 1</div>`)[0];
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            circle_marker_d4be3e21ed4d4626950742facda6450d.bindPopup(popup_42c568fcddbc4ee3a7ad12f8a11b09ec)
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                {
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  "dashArray": null,
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  "fill": true,
  "fillColor": "#00b5eb",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                )
                .addTo(map_8a02c94ed95c44ef961537ca6cef93fa);
            
    
            var popup_0af44f8851e24a378e9d83f0e250b944 = L.popup({maxWidth: '100%'
            
            });

            
                var html_96d7765d878845b6ad9e11f4a9c68a4a = $(`<div id="html_96d7765d878845b6ad9e11f4a9c68a4a" style="width: 100.0%; height: 100.0%;">The Annex, North Midtown, Yorkville Cluster 2</div>`)[0];
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  "dashOffset": null,
  "fill": true,
  "fillColor": "#00b5eb",
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  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
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}
                )
                .addTo(map_8a02c94ed95c44ef961537ca6cef93fa);
            
    
            var popup_a16a2a390a76400aa872b91b5fcbb44b = L.popup({maxWidth: '100%'
            
            });

            
                var html_9f2b44bf65f94a11a7d6a3b1a917ebaa = $(`<div id="html_9f2b44bf65f94a11a7d6a3b1a917ebaa" style="width: 100.0%; height: 100.0%;">University of Toronto, Harbord Cluster 2</div>`)[0];
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  "color": "#00b5eb",
  "dashArray": null,
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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_8a02c94ed95c44ef961537ca6cef93fa);
            
    
            var popup_e310bcb59c034a96886018c270014b55 = L.popup({maxWidth: '100%'
            
            });

            
                var html_9cd7f098d14447ddbffb2398aa40a531 = $(`<div id="html_9cd7f098d14447ddbffb2398aa40a531" style="width: 100.0%; height: 100.0%;">Kensington Market, Chinatown, Grange Park Cluster 2</div>`)[0];
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            circle_marker_a9d904ff981e4505a96588110056f9a1.bindPopup(popup_e310bcb59c034a96886018c270014b55)
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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_8a02c94ed95c44ef961537ca6cef93fa);
            
    
            var popup_329b8879d5e1444b978d8989f53a4090 = L.popup({maxWidth: '100%'
            
            });

            
                var html_9e5050ef1b414f3c916b90f3462ed20c = $(`<div id="html_9e5050ef1b414f3c916b90f3462ed20c" 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_18ffcf0190394bc5967da941f2bf1e3f.bindPopup(popup_329b8879d5e1444b978d8989f53a4090)
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            var circle_marker_9a76a7d0dae34f1bb5e3c73da46abb1b = 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,
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  "stroke": true,
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}
                )
                .addTo(map_8a02c94ed95c44ef961537ca6cef93fa);
            
    
            var popup_d3a9d0612f7144a0861c3316a895d5f8 = L.popup({maxWidth: '100%'
            
            });

            
                var html_952c5fae805d464e807836e390832e79 = $(`<div id="html_952c5fae805d464e807836e390832e79" style="width: 100.0%; height: 100.0%;">Stn A PO Boxes Cluster 2</div>`)[0];
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            circle_marker_9a76a7d0dae34f1bb5e3c73da46abb1b.bindPopup(popup_d3a9d0612f7144a0861c3316a895d5f8)
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            var circle_marker_72f6e10f7ca24cf298746d5638e3a368 = 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_8a02c94ed95c44ef961537ca6cef93fa);
            
    
            var popup_d40fbe2a8a524e59a40a25a1160a8d6d = L.popup({maxWidth: '100%'
            
            });

            
                var html_53169306d1714d51a9ec533eefc085b4 = $(`<div id="html_53169306d1714d51a9ec533eefc085b4" style="width: 100.0%; height: 100.0%;">First Canadian Place, Underground city Cluster 2</div>`)[0];
                popup_d40fbe2a8a524e59a40a25a1160a8d6d.setContent(html_53169306d1714d51a9ec533eefc085b4);
            

            circle_marker_72f6e10f7ca24cf298746d5638e3a368.bindPopup(popup_d40fbe2a8a524e59a40a25a1160a8d6d)
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            var circle_marker_d90da78398cd484d8be95a9d740cc1dd = L.circleMarker(
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                {
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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_8a02c94ed95c44ef961537ca6cef93fa);
            
    
            var popup_85b37d3825fd41a1988e78e67740dbd9 = L.popup({maxWidth: '100%'
            
            });

            
                var html_002641b27a6f4b79a99d2dd964a25548 = $(`<div id="html_002641b27a6f4b79a99d2dd964a25548" style="width: 100.0%; height: 100.0%;">Christie Cluster 2</div>`)[0];
                popup_85b37d3825fd41a1988e78e67740dbd9.setContent(html_002641b27a6f4b79a99d2dd964a25548);
            

            circle_marker_d90da78398cd484d8be95a9d740cc1dd.bindPopup(popup_85b37d3825fd41a1988e78e67740dbd9)
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            var circle_marker_f474fc22741741f4a07a749f3dc0bc30 = L.circleMarker(
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                {
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  "dashArray": null,
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  "fill": true,
  "fillColor": "#00b5eb",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                )
                .addTo(map_8a02c94ed95c44ef961537ca6cef93fa);
            
    
            var popup_1deffacf685e458bb2c1ae87d7098cdc = L.popup({maxWidth: '100%'
            
            });

            
                var html_7cc71cbc172143f9a6ceeec4fd162efa = $(`<div id="html_7cc71cbc172143f9a6ceeec4fd162efa" style="width: 100.0%; height: 100.0%;">Dufferin, Dovercourt Village Cluster 2</div>`)[0];
                popup_1deffacf685e458bb2c1ae87d7098cdc.setContent(html_7cc71cbc172143f9a6ceeec4fd162efa);
            

            circle_marker_f474fc22741741f4a07a749f3dc0bc30.bindPopup(popup_1deffacf685e458bb2c1ae87d7098cdc)
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            var circle_marker_cc7cfff31ceb4509b25b19fe685511b8 = L.circleMarker(
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                {
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  "dashArray": null,
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  "fill": true,
  "fillColor": "#00b5eb",
  "fillOpacity": 0.7,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
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