1 - gather-data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 481
    },
    "colab_type": "code",
    "id": "ZixuNIdpPHwa",
    "outputId": "57dfa9c2-9fc8-450b-81e0-fb91f76a1131"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting overpy\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9d/20/31f2e2af0dbc05a358259b9cdf67056d34b002d8041944628d05adfab9ba/overpy-0.4.tar.gz (41kB)\n",
      "\r",
      "\u001b[K     |███████▉                        | 10kB 16.5MB/s eta 0:00:01\r",
      "\u001b[K     |███████████████▊                | 20kB 2.2MB/s eta 0:00:01\r",
      "\u001b[K     |███████████████████████▋        | 30kB 3.2MB/s eta 0:00:01\r",
      "\u001b[K     |███████████████████████████████▌| 40kB 4.2MB/s eta 0:00:01\r",
      "\u001b[K     |████████████████████████████████| 51kB 4.3MB/s \n",
      "\u001b[?25hBuilding wheels for collected packages: overpy\n",
      "  Building wheel for overpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for overpy: filename=overpy-0.4-cp36-none-any.whl size=45771 sha256=1d473ae9c8c880944fc31ae667cb0eb18c968f819ff8061ebeb39b879621764d\n",
      "  Stored in directory: /root/.cache/pip/wheels/10/53/c2/e6b6f97e7bb419193bd3aafbe38628666f0f93a1cec9dc521d\n",
      "Successfully built overpy\n",
      "Installing collected packages: overpy\n",
      "Successfully installed overpy-0.4\n",
      "Collecting gmplot\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e2/b1/e1429c31a40b3ef5840c16f78b506d03be9f27e517d3870a6fd0b356bd46/gmplot-1.2.0.tar.gz (115kB)\n",
      "\u001b[K     |████████████████████████████████| 122kB 3.5MB/s \n",
      "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from gmplot) (2.21.0)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->gmplot) (2019.9.11)\n",
      "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->gmplot) (2.8)\n",
      "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->gmplot) (1.24.3)\n",
      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->gmplot) (3.0.4)\n",
      "Building wheels for collected packages: gmplot\n",
      "  Building wheel for gmplot (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for gmplot: filename=gmplot-1.2.0-cp36-none-any.whl size=143765 sha256=b606aae3e76dfbd32faabf0a4e3069c36c3ee05df94ba82a46e1a06765c97599\n",
      "  Stored in directory: /root/.cache/pip/wheels/81/6a/76/4dd6a7cc310ba765894159ee84871e8cd55221d82ef14b81a1\n",
      "Successfully built gmplot\n",
      "Installing collected packages: gmplot\n",
      "Successfully installed gmplot-1.2.0\n"
     ]
    }
   ],
   "source": [
    "!pip install overpy\n",
    "!pip install gmplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WpywWda7PLx-"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import copy\n",
    "import json\n",
    "import math\n",
    "import overpy\n",
    "import pandas as pd\n",
    "from gmplot import gmplot\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "import scipy.cluster.hierarchy as shc\n",
    "from sklearn.datasets.samples_generator import make_blobs\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage, fcluster\n",
    "from geopy.distance import vincenty\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import DBSCAN\n",
    "import matplotlib.cm as cm\n",
    "from scipy.spatial.distance import cdist, pdist\n",
    "from sklearn import metrics\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from datetime import datetime\n",
    "import numpy as np\n",
    "import mpl_toolkits\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "from collections import OrderedDict\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "i03ogDDHPHwg"
   },
   "source": [
    "# 1. Use Overpass API to get all Point of Interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Oldo9IuCPHwi"
   },
   "outputs": [],
   "source": [
    "#  Jaipur - 26.84, 75.71, 26.98, 75.90\n",
    "#  Delhi - 28.488, 77.103, 28.843, 77.390\n",
    "#  Mumbai - 18.898986, 72.780002, 19.280442, 72.985434\n",
    "#  la 33.820973, -118.387737, 34.167668, -118.085683\n",
    "#  london-1 - 51.3674, -0.3059, 51.6442, 0.089625\n",
    "# delhi-mini - 28.5825, 77.1645, 28.7004, 77.3144"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zTJ6ror9PHwp"
   },
   "outputs": [],
   "source": [
    "city_name = 'delhi-1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WUh6AHWWPHwu"
   },
   "outputs": [],
   "source": [
    "api = overpy.Overpass()\n",
    "result = api.query(\"node(  28.488, 77.103, 28.843, 77.390  );out;\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "3Y5DXvEPHiPA",
    "outputId": "dc5e8697-32b8-4d7b-abeb-558c5acd1663"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<overpy.Node id=16173236 lat=28.6141793 lon=77.2022662>"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.nodes[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "rtfeE9vyZkU_",
    "outputId": "e2c2e2c2-4420-421d-c582-ee917a029290"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "314426"
      ]
     },
     "execution_count": 18,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(result.nodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "45lvsgbTPHwz",
    "outputId": "2330b8c2-8213-4504-9a72-c539e4cc30ef"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<overpy.Node id=249237060 lat=28.6743670 lon=77.2168688>, {})"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.nodes[10], result.nodes[113].tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 105
    },
    "colab_type": "code",
    "id": "iKk420YfPHw4",
    "outputId": "abfae071-c16b-41f8-ef35-c826f5e8b43a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5140\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'highway': 'traffic_signals',\n",
       " 'lat': 28.6298938,\n",
       " 'lon': 77.2194853,\n",
       " 'node_id': 249783293}"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags = []\n",
    "for i in result.nodes:\n",
    "    if len(i.tags) is not 0:\n",
    "        i_tags = i.tags\n",
    "        i_tags['node_id'] = i.id\n",
    "        i_tags['lat'] = float(i.lat)\n",
    "        i_tags['lon'] = float(i.lon)\n",
    "        tags.append(i.tags)\n",
    "        \n",
    "        \n",
    "print(len(tags))\n",
    "tags[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 430
    },
    "colab_type": "code",
    "id": "bMZeKIojPHw9",
    "outputId": "901aaeaa-9d3c-4a40-c1f1-9ed4073d2cf6"
   },
   "outputs": [
    {
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       "      <th>bicycle</th>\n",
       "      <th>bicycle_parking</th>\n",
       "      <th>...</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>signal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 390 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  ISO3166-2 access addr:area  ... wikimedia_commons     wikipedia wikipedia:en\n",
       "0       NaN    NaN       NaN  ...               NaN  en:New Delhi          NaN\n",
       "1       NaN    NaN       NaN  ...               NaN           NaN          NaN\n",
       "2       NaN    NaN       NaN  ...               NaN           NaN          NaN\n",
       "3       NaN    NaN       NaN  ...               NaN           NaN          NaN\n",
       "4       NaN    NaN       NaN  ...               NaN           NaN          NaN\n",
       "5       NaN    NaN       NaN  ...               NaN           NaN          NaN\n",
       "6       NaN    NaN       NaN  ...               NaN           NaN          NaN\n",
       "7       NaN    NaN       NaN  ...               NaN           NaN          NaN\n",
       "8       NaN    NaN       NaN  ...               NaN           NaN          NaN\n",
       "9       NaN    NaN       NaN  ...               NaN           NaN          NaN\n",
       "\n",
       "[10 rows x 390 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(tags)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "spalI2YoIxVq",
    "outputId": "7906767f-8a52-403f-cd02-c4f50c4d6267"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5140, 390)"
      ]
     },
     "execution_count": 22,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "BHAyeOk0PHxC",
    "outputId": "a6cb4447-96c3-4636-d4b0-9eae892c89c6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/delhi-1-overpass.csv\n"
     ]
    }
   ],
   "source": [
    "file_name = \"data/\" + city_name + '-overpass.csv'\n",
    "df.to_csv(file_name, index=False)\n",
    "print(file_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Qmdx8hgbQC1L"
   },
   "source": [
    "Aakash Jhawar\n",
    "https://aakashjhawar.github.io"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ABpyMMFtQDn8"
   },
   "source": [
    "# 2. Select columns which are usefull"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 215
    },
    "colab_type": "code",
    "id": "joBrOkl1QLIi",
    "outputId": "18c617cd-2488-4887-efa2-293f9acf6a91"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5140, 5)\n"
     ]
    },
    {
     "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>node_id</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>name</th>\n",
       "      <th>amenity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16173236</td>\n",
       "      <td>28.614179</td>\n",
       "      <td>77.202266</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>246518180</td>\n",
       "      <td>28.601933</td>\n",
       "      <td>77.180677</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>246518184</td>\n",
       "      <td>28.606088</td>\n",
       "      <td>77.187424</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>249783293</td>\n",
       "      <td>28.629894</td>\n",
       "      <td>77.219485</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>249783302</td>\n",
       "      <td>28.631410</td>\n",
       "      <td>77.216760</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     node_id        lat        lon       name amenity\n",
       "0   16173236  28.614179  77.202266  New Delhi     NaN\n",
       "1  246518180  28.601933  77.180677        NaN     NaN\n",
       "2  246518184  28.606088  77.187424        NaN     NaN\n",
       "3  249783293  28.629894  77.219485        NaN     NaN\n",
       "4  249783302  28.631410  77.216760        NaN     NaN"
      ]
     },
     "execution_count": 24,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[['node_id', 'lat', 'lon', 'name', 'amenity']]\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 215
    },
    "colab_type": "code",
    "id": "FctPLLajQ4VN",
    "outputId": "d6b3e372-b85e-4baf-fcbb-4480c4ce4cb3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1242, 5)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>node_id</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>name</th>\n",
       "      <th>amenity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>280741143</td>\n",
       "      <td>28.647250</td>\n",
       "      <td>77.194235</td>\n",
       "      <td>Axis Bank</td>\n",
       "      <td>bank</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>308894803</td>\n",
       "      <td>28.606946</td>\n",
       "      <td>77.229919</td>\n",
       "      <td>NaN</td>\n",
       "      <td>restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>309861440</td>\n",
       "      <td>28.598375</td>\n",
       "      <td>77.185421</td>\n",
       "      <td>US Embassy School New Delhi</td>\n",
       "      <td>school</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>355436031</td>\n",
       "      <td>28.645835</td>\n",
       "      <td>77.172330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>post_box</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>355436037</td>\n",
       "      <td>28.645887</td>\n",
       "      <td>77.172379</td>\n",
       "      <td>ICICI Bank</td>\n",
       "      <td>atm</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      node_id        lat        lon                         name     amenity\n",
       "29  280741143  28.647250  77.194235                    Axis Bank        bank\n",
       "46  308894803  28.606946  77.229919                          NaN  restaurant\n",
       "58  309861440  28.598375  77.185421  US Embassy School New Delhi      school\n",
       "70  355436031  28.645835  77.172330                          NaN    post_box\n",
       "71  355436037  28.645887  77.172379                   ICICI Bank         atm"
      ]
     },
     "execution_count": 25,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nan = df.copy()\n",
    "df = df.dropna(subset=['node_id', 'lat', 'amenity'])\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "j4XsJrN2RD9n",
    "outputId": "8a857774-5d26-48a3-a6db-be1a672ec37e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/delhi-1-amenity.csv\n"
     ]
    }
   ],
   "source": [
    "file_name = \"data/\" + city_name + '-amenity.csv'\n",
    "df.to_csv(file_name, index=False)\n",
    "print(file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "sqSJiD4XRW4B",
    "outputId": "10dd7d31-cf7b-41fd-9c57-f73559a1d755"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "51\n",
      "['food_court', 'drinking_water', 'ice_cream', 'nursing_home', 'cafe', 'dentist', 'nightclub', 'post_box', 'Drinking_Water', 'fire_station', 'public_building', 'payment_centre', 'public_bath', 'hospital', 'university', 'internet_cafe', 'pub', 'marketplace', 'clinic', 'money_transfer', 'embassy', 'bar', 'restaurant', 'bus_station', 'kindergarten', 'post_office', 'disused', 'courthouse', 'community_centre', 'pharmacy', 'theatre', 'taxi', 'bank', 'school', 'toilets', 'police', 'fuel', 'driving_school', 'place_of_worship', 'parking', 'club', 'fast_food', 'studio', 'atm', 'music_school', 'crematorium', 'library', 'college', 'memorial', 'doctors', 'cinema']\n"
     ]
    }
   ],
   "source": [
    "amenities = list(set(df['amenity']))\n",
    "print(len(amenities))\n",
    "print(amenities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ReC2KUozRnDK"
   },
   "outputs": [],
   "source": [
    "remove_amenity =  [\n",
    "    'arts_centre',\n",
    "    \"Ayurvedic Hospital\",\n",
    "    \"baby_hatch\",\n",
    "    \"bench\",\n",
    "    \"bicycle_parking\",\n",
    "    \"bicycle_rental\",\n",
    "    \"bicycle_repair_station\",\n",
    "    \"bureau_de_change\",\n",
    "    \"car_rental\",\n",
    "    \"car_wash\",\n",
    "    \"charging_station\",\n",
    "    \"fountain\",\n",
    "    \"grave_yard\",\n",
    "    \"House\",\n",
    "    \"language_school\",\n",
    "    \"meditation_centre\",\n",
    "    \"motorcycle_parking\",\n",
    "    \"orphanage\",\n",
    "    \"payment_terminal\",\n",
    "    \"photo_booth\",\n",
    "    \"post_depot\",\n",
    "    \"recycling\",\n",
    "    \"shelter\",\n",
    "    \"social_centre\",\n",
    "    \"social_facility\",\n",
    "    \"telephone\",\n",
    "    \"training\",\n",
    "    \"tuition\",\n",
    "    \"vending_machine\",\n",
    "    \"veterinary\",\n",
    "    \"waste_basket\",\n",
    "    \"waste_disposal\",\n",
    "    \"waste_transfer_station\",\n",
    "    \"water_point\",\n",
    "    \"weighbridge\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 215
    },
    "colab_type": "code",
    "id": "AgUegD_KR0Yz",
    "outputId": "2e5abadc-19ac-4a84-d45b-9e0f1e573412"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1138, 5)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>node_id</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>name</th>\n",
       "      <th>amenity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>280741143</td>\n",
       "      <td>28.647250</td>\n",
       "      <td>77.194235</td>\n",
       "      <td>Axis Bank</td>\n",
       "      <td>bank</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>308894803</td>\n",
       "      <td>28.606946</td>\n",
       "      <td>77.229919</td>\n",
       "      <td>NaN</td>\n",
       "      <td>restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>309861440</td>\n",
       "      <td>28.598375</td>\n",
       "      <td>77.185421</td>\n",
       "      <td>US Embassy School New Delhi</td>\n",
       "      <td>school</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>355436031</td>\n",
       "      <td>28.645835</td>\n",
       "      <td>77.172330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>post_box</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>355436037</td>\n",
       "      <td>28.645887</td>\n",
       "      <td>77.172379</td>\n",
       "      <td>ICICI Bank</td>\n",
       "      <td>atm</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      node_id        lat        lon                         name     amenity\n",
       "29  280741143  28.647250  77.194235                    Axis Bank        bank\n",
       "46  308894803  28.606946  77.229919                          NaN  restaurant\n",
       "58  309861440  28.598375  77.185421  US Embassy School New Delhi      school\n",
       "70  355436031  28.645835  77.172330                          NaN    post_box\n",
       "71  355436037  28.645887  77.172379                   ICICI Bank         atm"
      ]
     },
     "execution_count": 31,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nan_all = df.copy()\n",
    "for val in remove_amenity:\n",
    "    df = df[df.amenity != val]\n",
    "\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "PclZ7WviSLoz",
    "outputId": "5c5ddaf8-e11a-4773-a82d-5431c67f601d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/delhi-1-reduced_amenity.csv\n"
     ]
    }
   ],
   "source": [
    "file_name = \"data/\" + city_name + '-reduced_amenity.csv'\n",
    "df.to_csv(file_name, index=False)\n",
    "print(file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FTdHSiAGTUv1"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ShoFSj4MTU6X"
   },
   "source": [
    "# 3. Analyze coordinates using Agglomerative Hierarchical Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 197
    },
    "colab_type": "code",
    "id": "02kfZaZlTavl",
    "outputId": "62455f81-aceb-4500-eef4-d5e3ab89c9df"
   },
   "outputs": [
    {
     "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>lat</th>\n",
       "      <th>lon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>28.647250</td>\n",
       "      <td>77.194235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>28.606946</td>\n",
       "      <td>77.229919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>28.598375</td>\n",
       "      <td>77.185421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>28.645835</td>\n",
       "      <td>77.172330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>28.645887</td>\n",
       "      <td>77.172379</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          lat        lon\n",
       "29  28.647250  77.194235\n",
       "46  28.606946  77.229919\n",
       "58  28.598375  77.185421\n",
       "70  28.645835  77.172330\n",
       "71  28.645887  77.172379"
      ]
     },
     "execution_count": 33,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = df[['lat', 'lon']]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 446
    },
    "colab_type": "code",
    "id": "8in3WZozT7_w",
    "outputId": "63bf30bf-e38a-4997-e199-79c633c89752"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hBFYAAACFEFgBAAAUQmAFAABQCIEVAABAIQRWAAAAhRBYAQAAFEJgBQAAUAiBFQAAQCEE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\nrkJggg==\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))  \n",
    "plt.title(\"Dendrograms\")  \n",
    "dend = shc.dendrogram(shc.linkage(data, method='ward'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MFPAkILiURWB"
   },
   "outputs": [],
   "source": [
    "X = data[['lat', 'lon']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KfLkb8CzUZbF"
   },
   "outputs": [],
   "source": [
    "# def plot_elbow(kmean, X):\n",
    "#     centroids = [k.cluster_centers_ for k in kmean]\n",
    "#     D_k = [cdist(X, center, 'euclidean') for center in centroids]\n",
    "#     dist = [np.min(D,axis=1) for D in D_k]\n",
    "\n",
    "#     # Total with-in sum of square\n",
    "#     wcss = [sum(d**2) for d in dist]\n",
    "#     tss = sum(pdist(X)**2)/X.shape[0]\n",
    "#     bss = tss-wcss\n",
    "\n",
    "#     plt.subplots(nrows=1, ncols=1, figsize=(8,8))\n",
    "#     ax = plt.subplot(1, 1, 1)\n",
    "#     ax.plot(Ks, bss/tss*100, 'b*-')\n",
    "#     plt.grid(True)\n",
    "#     plt.xlabel('Number of clusters')\n",
    "#     plt.ylabel('Percentage of variance explained (%)')\n",
    "#     plt.title('Elbow for KMeans clustering')\n",
    "#     plt.show()\n",
    "\n",
    "# plot_elbow(kmean, X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Qj0-BEEhUbQd"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QDRIO3p6VYta"
   },
   "source": [
    "# 4. Plot coordinates on Map using gmplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "sROXJ7sGVeHY",
    "outputId": "2f4d989e-ed43-49c0-d114-d2c69a75f641"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28.5825971, 28.7002715, 77.1652197, 77.3142748)"
      ]
     },
     "execution_count": 39,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_lat, max_lat, min_lon, max_lon = min(data['lat']), max(data['lat']), min(data['lon']), max(data['lon'])\n",
    "## Create empty map with zoom level 16\n",
    "mymap = gmplot.GoogleMapPlotter(\n",
    "                      min_lat + (max_lat - min_lat) / 2, \n",
    "                      min_lon + (max_lon - min_lon) / 2, \n",
    "                      16)\n",
    "min_lat, max_lat, min_lon, max_lon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gre823YsVjwB"
   },
   "outputs": [],
   "source": [
    "for i in range(data.shape[0]):\n",
    "    mymap.scatter([data.iloc[i]['lat']], [data.iloc[i]['lon']], 'red', size=50, marker=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "rRon-LPnVmTU",
    "outputId": "86d00eaa-c00c-4381-8a8d-7d52d96dc94c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/delhi-1-scatter.html\n"
     ]
    }
   ],
   "source": [
    "file_name = \"data/\" + city_name + '-scatter.html'\n",
    "mymap.draw(file_name)\n",
    "print(file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FwmYw3f4V0UH"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aakash Jhawar https://aakashjhawar.github.io"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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