Peer_Assignment_Part_2.ipynb

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  "metadata": {
    "colab": {
      "name": "Peer Assignment Part 2.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ccD5l1P8Y5fu",
        "colab_type": "text"
      },
      "source": [
        "# Part 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n20pZy340srI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "from bs4 import BeautifulSoup\n",
        "import urllib.request"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EyOfKa3Y1-y5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "url = \"https://en.wikipedia.org/wiki/List_of_postal_codes_of_Canada:_M\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KlePjCq02RNI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "page = urllib.request.urlopen(url)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5vsIIuva2XbA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "soup = BeautifulSoup(page, \"lxml\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cd_Ec_QF2j4Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "PostalCode = []\n",
        "Borough = []\n",
        "Neighborhood = []\n",
        "\n",
        "for row in soup.find('table').find_all('tr'):\n",
        "    cells = row.find_all('td')\n",
        "    if(len(cells) > 0):\n",
        "        PostalCode.append(cells[0].text.rstrip('\\n'))\n",
        "        Borough.append(cells[1].text.rstrip('\\n'))\n",
        "        Neighborhood.append(cells[2].text.rstrip('\\n'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X3z-LQXL6jl2",
        "colab_type": "text"
      },
      "source": [
        "***The dataframe will consist of three columns: PostalCode, Borough, and Neighborhood***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JpreNbNh4deD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.DataFrame({\"Postal Code\":PostalCode,\n",
        "                   \"Borough\":Borough,\n",
        "                   \"Neighborhood\":Neighborhood})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E_DVLLBL4uOk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "fc455711-9feb-4502-a134-355bf3d84838"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Postal Code</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></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>M2A</td>\n",
              "      <td>Not assigned</td>\n",
              "      <td></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": [
              "  Postal Code           Borough               Neighborhood\n",
              "0         M1A      Not assigned                           \n",
              "1         M2A      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": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "51dXaICD6PBF",
        "colab_type": "text"
      },
      "source": [
        "***Only process the cells that have an assigned borough. Ignore cells with a borough that is Not assigned.***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gG3DRVC_42aH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_dropna = df[df.Borough != \"Not assigned\"].reset_index(drop=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ib147sFq55oE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "4bd2ee9f-d4b3-4e6b-f002-758d025ae26d"
      },
      "source": [
        "df_dropna.head()"
      ],
      "execution_count": 11,
      "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 tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Postal Code</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": [
              "  Postal Code           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": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lv4OywA16_P-",
        "colab_type": "text"
      },
      "source": [
        "***More than one neighborhood can exist in one postal code area***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7X538imp56kV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_grp = df_dropna.groupby([\"Postal Code\", \"Borough\"], as_index=False).agg(lambda x: \",\".join(x))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a5yD0APg7heL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "765dfce2-f7c5-4e85-e496-b14709618304"
      },
      "source": [
        "df_grp.head()"
      ],
      "execution_count": 13,
      "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>Postal Code</th>\n",
              "      <th>Borough</th>\n",
              "      <th>Neighborhood</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>M1B</td>\n",
              "      <td>Scarborough</td>\n",
              "      <td>Malvern, Rouge</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>M1E</td>\n",
              "      <td>Scarborough</td>\n",
              "      <td>Guildwood, Morningside, West Hill</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>M1G</td>\n",
              "      <td>Scarborough</td>\n",
              "      <td>Woburn</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>M1H</td>\n",
              "      <td>Scarborough</td>\n",
              "      <td>Cedarbrae</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Postal Code      Borough                            Neighborhood\n",
              "0         M1B  Scarborough                          Malvern, Rouge\n",
              "1         M1C  Scarborough  Rouge Hill, Port Union, Highland Creek\n",
              "2         M1E  Scarborough       Guildwood, Morningside, West Hill\n",
              "3         M1G  Scarborough                                  Woburn\n",
              "4         M1H  Scarborough                               Cedarbrae"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0HU0qo177ryV",
        "colab_type": "text"
      },
      "source": [
        "***If a cell has a borough but a Not assigned neighborhood, then the neighborhood will be the same as the borough.***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BI04lHDt7jvd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for index, row in df_grp.iterrows():\n",
        "  if row[\"Neighborhood\"] == \"Not Assigned\":\n",
        "    row[\"Neighborhood\"] = row[\"Borough\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Dvch2of8Ni1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "7d16fb8b-7932-4cd6-8b5a-56f32316ff29"
      },
      "source": [
        "df_grp.head()"
      ],
      "execution_count": 15,
      "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>Borough</th>\n",
              "      <th>Neighborhood</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>M1B</td>\n",
              "      <td>Scarborough</td>\n",
              "      <td>Malvern, Rouge</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>M1E</td>\n",
              "      <td>Scarborough</td>\n",
              "      <td>Guildwood, Morningside, West Hill</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>M1G</td>\n",
              "      <td>Scarborough</td>\n",
              "      <td>Woburn</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>M1H</td>\n",
              "      <td>Scarborough</td>\n",
              "      <td>Cedarbrae</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Postal Code      Borough                            Neighborhood\n",
              "0         M1B  Scarborough                          Malvern, Rouge\n",
              "1         M1C  Scarborough  Rouge Hill, Port Union, Highland Creek\n",
              "2         M1E  Scarborough       Guildwood, Morningside, West Hill\n",
              "3         M1G  Scarborough                                  Woburn\n",
              "4         M1H  Scarborough                               Cedarbrae"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h72tupBa9dZs",
        "colab_type": "text"
      },
      "source": [
        "***In the last cell of your notebook, use the .shape method to print the number of rows of your dataframe.***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9PdRlivg9Iwe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ddd4ac27-47e8-4405-bc19-e76e6f7b869a"
      },
      "source": [
        "df_grp.shape"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(103, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uzVN1n3k9idT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IvY-OKjsY-vL",
        "colab_type": "text"
      },
      "source": [
        "# Part 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g5bBx_nCZA8J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "geo_url = \"https://cocl.us/Geospatial_data\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MPvk95dwbTVq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "geo_df = pd.read_csv(geo_url)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sRn8mQ-BbZtp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "b1c2645d-041c-42ac-cfb3-015fb72a6401"
      },
      "source": [
        "geo_df.head()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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": "markdown",
      "metadata": {
        "id": "__Q49C5TcZMo",
        "colab_type": "text"
      },
      "source": [
        "***Use the Geocoder package or the csv file to create the dataframe (Latitude and Longitude)***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EsPn7eXlbfHZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_mrg = df_grp.merge(geo_df, on=\"Postal Code\", how=\"left\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zoMX5MxecRjw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "eca0dafc-991d-4ab1-f472-876a3e7f7d43"
      },
      "source": [
        "df_mrg.head()"
      ],
      "execution_count": 26,
      "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>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": [
              "  Postal Code      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": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U7G_G9hvcUVY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}