Nepal_Municipality_Finder.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Nepal Municipality Finder.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOYfpqp2ijvkhTqvCcEllEZ",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/saugatrai33/ML-Projects/blob/main/Nepal_Municipality_Finder.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dependency Installation"
      ],
      "metadata": {
        "id": "GmZCAhOsOlXf"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jg1I-HB-_B3R",
        "outputId": "941e86c6-a4dd-4a31-bb83-26461e62d3a1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting geopandas\n",
            "  Downloading geopandas-0.10.2-py2.py3-none-any.whl (1.0 MB)\n",
            "\u001b[?25l\r\u001b[K     |▎                               | 10 kB 27.9 MB/s eta 0:00:01\r\u001b[K     |▋                               | 20 kB 13.9 MB/s eta 0:00:01\r\u001b[K     |█                               | 30 kB 10.2 MB/s eta 0:00:01\r\u001b[K     |█▎                              | 40 kB 4.8 MB/s eta 0:00:01\r\u001b[K     |█▋                              | 51 kB 4.7 MB/s eta 0:00:01\r\u001b[K     |██                              | 61 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██▎                             | 71 kB 6.0 MB/s eta 0:00:01\r\u001b[K     |██▌                             | 81 kB 5.6 MB/s eta 0:00:01\r\u001b[K     |██▉                             | 92 kB 6.2 MB/s eta 0:00:01\r\u001b[K     |███▏                            | 102 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███▌                            | 112 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███▉                            | 122 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████▏                           | 133 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████▌                           | 143 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████▊                           | 153 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████                           | 163 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████▍                          | 174 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████▊                          | 184 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████                          | 194 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████▍                         | 204 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████▊                         | 215 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████                         | 225 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████▎                        | 235 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████▋                        | 245 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████                        | 256 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████▎                       | 266 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████▋                       | 276 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████                       | 286 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████▏                      | 296 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████▌                      | 307 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████▉                      | 317 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████▏                     | 327 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████▌                     | 337 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████▉                     | 348 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████▏                    | 358 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████▍                    | 368 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████▊                    | 378 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████                    | 389 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████▍                   | 399 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████▊                   | 409 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████████                   | 419 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████████▍                  | 430 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████████▋                  | 440 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████████                  | 450 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████████▎                 | 460 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████████▋                 | 471 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████████                 | 481 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████████▎                | 491 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████████▋                | 501 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████████▉                | 512 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████████▏               | 522 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████████▌               | 532 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████████▉               | 542 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████▏              | 552 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████▌              | 563 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████▉              | 573 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████              | 583 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████▍             | 593 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████▊             | 604 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████             | 614 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████▍            | 624 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████▊            | 634 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████            | 645 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████▎           | 655 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████▋           | 665 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████████           | 675 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████████▎          | 686 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████████▋          | 696 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████          | 706 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▎         | 716 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▋         | 727 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▉         | 737 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▏        | 747 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▌        | 757 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▉        | 768 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▏       | 778 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▌       | 788 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▉       | 798 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████       | 808 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▍      | 819 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▊      | 829 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████      | 839 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▍     | 849 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▊     | 860 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████     | 870 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▎    | 880 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▋    | 890 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████    | 901 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▎   | 911 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▋   | 921 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████   | 931 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▎  | 942 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▌  | 952 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▉  | 962 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▏ | 972 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▌ | 983 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▉ | 993 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▏| 1.0 MB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▌| 1.0 MB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▊| 1.0 MB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 1.0 MB 5.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: shapely>=1.6 in /usr/local/lib/python3.7/dist-packages (from geopandas) (1.8.1.post1)\n",
            "Requirement already satisfied: pandas>=0.25.0 in /usr/local/lib/python3.7/dist-packages (from geopandas) (1.3.5)\n",
            "Collecting fiona>=1.8\n",
            "  Downloading Fiona-1.8.21-cp37-cp37m-manylinux2014_x86_64.whl (16.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 16.7 MB 31.0 MB/s \n",
            "\u001b[?25hCollecting pyproj>=2.2.0\n",
            "  Downloading pyproj-3.2.1-cp37-cp37m-manylinux2010_x86_64.whl (6.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.3 MB 22.7 MB/s \n",
            "\u001b[?25hCollecting click-plugins>=1.0\n",
            "  Downloading click_plugins-1.1.1-py2.py3-none-any.whl (7.5 kB)\n",
            "Collecting munch\n",
            "  Downloading munch-2.5.0-py2.py3-none-any.whl (10 kB)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (57.4.0)\n",
            "Requirement already satisfied: attrs>=17 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (21.4.0)\n",
            "Requirement already satisfied: six>=1.7 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (1.15.0)\n",
            "Requirement already satisfied: click>=4.0 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (7.1.2)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (2021.10.8)\n",
            "Collecting cligj>=0.5\n",
            "  Downloading cligj-0.7.2-py3-none-any.whl (7.1 kB)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas) (1.21.5)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas) (2018.9)\n",
            "Installing collected packages: munch, cligj, click-plugins, pyproj, fiona, geopandas\n",
            "Successfully installed click-plugins-1.1.1 cligj-0.7.2 fiona-1.8.21 geopandas-0.10.2 munch-2.5.0 pyproj-3.2.1\n",
            "CPU times: user 99.1 ms, sys: 26.8 ms, total: 126 ms\n",
            "Wall time: 7.4 s\n"
          ]
        }
      ],
      "source": [
        "%%time \n",
        "\n",
        "!pip install geopandas"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Import dependencies"
      ],
      "metadata": {
        "id": "yDA301CdOtfQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import geopandas as gpd\n",
        "from shapely.geometry import Point\n",
        "from shapely.geometry.polygon import Polygon"
      ],
      "metadata": {
        "id": "TX4aN-2AAzpu"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Read geojson data from github raw"
      ],
      "metadata": {
        "id": "Cpp0zhT3Oxzx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "munis = gpd.read_file('https://raw.githubusercontent.com/mesaugat/geoJSON-Nepal/master/nepal-municipalities.geojson')\n",
        "munis.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "x0rIqkssBEfA",
        "outputId": "a5160162-ff19-46bd-8f00-25f5b27c6c3b"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-da802e7e-f3a4-49a6-9d87-14435c743e57\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>F_ID</th>\n",
              "      <th>N_ID</th>\n",
              "      <th>NAME</th>\n",
              "      <th>LEVEL</th>\n",
              "      <th>DISTRICT</th>\n",
              "      <th>geometry</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>None</td>\n",
              "      <td>104</td>\n",
              "      <td>bhaktapur-bhaktapur</td>\n",
              "      <td>Bhaktapur</td>\n",
              "      <td>Nagarpalika</td>\n",
              "      <td>Bhaktapur</td>\n",
              "      <td>POLYGON ((85.44459 27.66441, 85.44378 27.66359...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>None</td>\n",
              "      <td>105</td>\n",
              "      <td>madhyapur -thimi-bhaktap</td>\n",
              "      <td>Madhyapur Thimi</td>\n",
              "      <td>Nagarpalika</td>\n",
              "      <td>Bhaktapur</td>\n",
              "      <td>POLYGON ((85.36314 27.66892, 85.35419 27.66769...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>None</td>\n",
              "      <td>343</td>\n",
              "      <td>kathmandu-kathmandu</td>\n",
              "      <td>Kathmandu</td>\n",
              "      <td>Mahanagarpalika</td>\n",
              "      <td>Kathmandu</td>\n",
              "      <td>POLYGON ((85.28577 27.68983, 85.28414 27.69270...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>None</td>\n",
              "      <td>344</td>\n",
              "      <td>kirtipur-kathmandu</td>\n",
              "      <td>Kirtipur</td>\n",
              "      <td>Nagarpalika</td>\n",
              "      <td>Kathmandu</td>\n",
              "      <td>POLYGON ((85.25319 27.64555, 85.25238 27.65416...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>None</td>\n",
              "      <td>396</td>\n",
              "      <td>chitwan -national -park-</td>\n",
              "      <td>Chitwan National Park</td>\n",
              "      <td>national-park</td>\n",
              "      <td>Makwanpur</td>\n",
              "      <td>POLYGON ((84.71727 27.56355, 84.71727 27.55945...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-da802e7e-f3a4-49a6-9d87-14435c743e57')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-da802e7e-f3a4-49a6-9d87-14435c743e57 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-da802e7e-f3a4-49a6-9d87-14435c743e57');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     id  F_ID                      N_ID                   NAME  \\\n",
              "0  None   104       bhaktapur-bhaktapur              Bhaktapur   \n",
              "1  None   105  madhyapur -thimi-bhaktap        Madhyapur Thimi   \n",
              "2  None   343       kathmandu-kathmandu              Kathmandu   \n",
              "3  None   344        kirtipur-kathmandu               Kirtipur   \n",
              "4  None   396  chitwan -national -park-  Chitwan National Park   \n",
              "\n",
              "             LEVEL   DISTRICT  \\\n",
              "0      Nagarpalika  Bhaktapur   \n",
              "1      Nagarpalika  Bhaktapur   \n",
              "2  Mahanagarpalika  Kathmandu   \n",
              "3      Nagarpalika  Kathmandu   \n",
              "4    national-park  Makwanpur   \n",
              "\n",
              "                                            geometry  \n",
              "0  POLYGON ((85.44459 27.66441, 85.44378 27.66359...  \n",
              "1  POLYGON ((85.36314 27.66892, 85.35419 27.66769...  \n",
              "2  POLYGON ((85.28577 27.68983, 85.28414 27.69270...  \n",
              "3  POLYGON ((85.25319 27.64555, 85.25238 27.65416...  \n",
              "4  POLYGON ((84.71727 27.56355, 84.71727 27.55945...  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "munis.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EqKYCwYkC7G4",
        "outputId": "3f9cb89a-5c59-4e72-aa0f-06d8eb2a06e2"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(766, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "munis.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "X-K5rPyaE7Vh",
        "outputId": "cb5a9ddf-20c5-4e7e-d675-20dfb7ba5f99"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-dca6355e-8b9b-4344-9d56-7fa8ea720efa\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>F_ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>766.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>381.873368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>219.031358</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>192.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>383.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>572.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>756.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dca6355e-8b9b-4344-9d56-7fa8ea720efa')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-dca6355e-8b9b-4344-9d56-7fa8ea720efa button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-dca6355e-8b9b-4344-9d56-7fa8ea720efa');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "             F_ID\n",
              "count  766.000000\n",
              "mean   381.873368\n",
              "std    219.031358\n",
              "min      1.000000\n",
              "25%    192.250000\n",
              "50%    383.500000\n",
              "75%    572.750000\n",
              "max    756.000000"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Nepal Municipality plotting"
      ],
      "metadata": {
        "id": "4VhasbjqO4kd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "munis.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257
        },
        "id": "CDmvS-cuFLp0",
        "outputId": "4e624ffb-4f37-4ac3-b93a-5fd967627df0"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f817a70ab10>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Selecting required columns"
      ],
      "metadata": {
        "id": "uJJ8lA7lO9_D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "municipalities = munis[[\"NAME\", \"LEVEL\", \"DISTRICT\", \"geometry\"]].set_index(\"DISTRICT\")"
      ],
      "metadata": {
        "id": "BcbmBGwqJ57t"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "municipalities.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "v9sVhKe7Kkq1",
        "outputId": "229ddf41-ab0f-40a0-e142-98d161c94188"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-0e1c7b90-cd77-4f52-83f4-24acbfd36ebf\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>NAME</th>\n",
              "      <th>LEVEL</th>\n",
              "      <th>geometry</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DISTRICT</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Bhaktapur</th>\n",
              "      <td>Bhaktapur</td>\n",
              "      <td>Nagarpalika</td>\n",
              "      <td>POLYGON ((85.44459 27.66441, 85.44378 27.66359...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bhaktapur</th>\n",
              "      <td>Madhyapur Thimi</td>\n",
              "      <td>Nagarpalika</td>\n",
              "      <td>POLYGON ((85.36314 27.66892, 85.35419 27.66769...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kathmandu</th>\n",
              "      <td>Kathmandu</td>\n",
              "      <td>Mahanagarpalika</td>\n",
              "      <td>POLYGON ((85.28577 27.68983, 85.28414 27.69270...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kathmandu</th>\n",
              "      <td>Kirtipur</td>\n",
              "      <td>Nagarpalika</td>\n",
              "      <td>POLYGON ((85.25319 27.64555, 85.25238 27.65416...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Makwanpur</th>\n",
              "      <td>Chitwan National Park</td>\n",
              "      <td>national-park</td>\n",
              "      <td>POLYGON ((84.71727 27.56355, 84.71727 27.55945...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0e1c7b90-cd77-4f52-83f4-24acbfd36ebf')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0e1c7b90-cd77-4f52-83f4-24acbfd36ebf button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0e1c7b90-cd77-4f52-83f4-24acbfd36ebf');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                            NAME            LEVEL  \\\n",
              "DISTRICT                                            \n",
              "Bhaktapur              Bhaktapur      Nagarpalika   \n",
              "Bhaktapur        Madhyapur Thimi      Nagarpalika   \n",
              "Kathmandu              Kathmandu  Mahanagarpalika   \n",
              "Kathmandu               Kirtipur      Nagarpalika   \n",
              "Makwanpur  Chitwan National Park    national-park   \n",
              "\n",
              "                                                    geometry  \n",
              "DISTRICT                                                      \n",
              "Bhaktapur  POLYGON ((85.44459 27.66441, 85.44378 27.66359...  \n",
              "Bhaktapur  POLYGON ((85.36314 27.66892, 85.35419 27.66769...  \n",
              "Kathmandu  POLYGON ((85.28577 27.68983, 85.28414 27.69270...  \n",
              "Kathmandu  POLYGON ((85.25319 27.64555, 85.25238 27.65416...  \n",
              "Makwanpur  POLYGON ((84.71727 27.56355, 84.71727 27.55945...  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Finding municipality of Nepal from given latitude and longitude"
      ],
      "metadata": {
        "id": "v8DFPTHjPAbr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "poly = municipalities.geometry[:]\n",
        "chandragiri = Point(85.44459, 27.66441)\n",
        "municipality = municipalities.loc[poly.contains(chandragiri)]\n",
        "municipality"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "c1fLMXriLF0R",
        "outputId": "ee8c02b7-330e-4eec-d117-8086806895b0"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-cdfa3d6e-3245-43ce-bfa9-ce08b35ce0bb\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>NAME</th>\n",
              "      <th>LEVEL</th>\n",
              "      <th>geometry</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DISTRICT</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Bhaktapur</th>\n",
              "      <td>Bhaktapur</td>\n",
              "      <td>Nagarpalika</td>\n",
              "      <td>POLYGON ((85.44459 27.66441, 85.44378 27.66359...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cdfa3d6e-3245-43ce-bfa9-ce08b35ce0bb')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cdfa3d6e-3245-43ce-bfa9-ce08b35ce0bb button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-cdfa3d6e-3245-43ce-bfa9-ce08b35ce0bb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                NAME        LEVEL  \\\n",
              "DISTRICT                            \n",
              "Bhaktapur  Bhaktapur  Nagarpalika   \n",
              "\n",
              "                                                    geometry  \n",
              "DISTRICT                                                      \n",
              "Bhaktapur  POLYGON ((85.44459 27.66441, 85.44378 27.66359...  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    }
  ]
}