Coding_flight_networks_airportwise.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Unofficial_Coding_flight_networks.ipynb",
      "provenance": [],
      "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/piklu007mallick/project_flight_transportation/blob/main/Coding_flight_networks_airportwise.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pvp0EBg_Yg7e",
        "outputId": "bb94e1ae-90fd-47db-d800-f4cecb4abfd8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Import numpy and pandas packages"
      ],
      "metadata": {
        "id": "nO21h_RvZEM0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "file =('/content/drive/MyDrive/M.Tech_main Project work/flight.xlsx')\n",
        "## Removing Unnecessary Rows and Colums from the Excel Sheet\n",
        "flightData = pd.read_excel(file, usecols=\"C,D,E\", skiprows=0)\n",
        "print(flightData)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zLHuyFR_ZCi3",
        "outputId": "f41df6be-e787-4720-826c-748186df30d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    Airport Names Airport(Arrival)  Frequency per week\n",
            "0        Agartala        Bangalore                   7\n",
            "1             NaN            Delhi                   7\n",
            "2             NaN         Guwahati                  25\n",
            "3             NaN           Imphal                   7\n",
            "4             NaN          Kolkata                  77\n",
            "..            ...              ...                 ...\n",
            "875        Thoise            Delhi                   2\n",
            "876     Passighat         Guwahati                   4\n",
            "877       Pakyong          Kolkata                   7\n",
            "878   Pondicherry        Bangalore                   5\n",
            "879           NaN        Hyderabad                   7\n",
            "\n",
            "[880 rows x 3 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Using PAD Fill to Fill Up the Empty Columns in City Name\n",
        "flightData.fillna(method='pad', inplace=True)\n",
        "print(flightData.to_string())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rDW0iu1vZM6p",
        "outputId": "c3a9473b-a3d7-4c91-e802-1c09aefe8908"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Airport Names Airport(Arrival)  Frequency per week\n",
            "0         Agartala        Bangalore                   7\n",
            "1         Agartala            Delhi                   7\n",
            "2         Agartala         Guwahati                  25\n",
            "3         Agartala           Imphal                   7\n",
            "4         Agartala          Kolkata                  77\n",
            "5           Agatti           Cochin                  14\n",
            "6        Ahmedabad       Aurangabad                  13\n",
            "7        Ahmedabad         Bagdogra                  13\n",
            "8        Ahmedabad        Bangalore                  76\n",
            "9        Ahmedabad          Belgaum                   7\n",
            "10       Ahmedabad        Bhavnagar                   6\n",
            "11       Ahmedabad           Bhopal                  14\n",
            "12       Ahmedabad     Bhubaneshwar                   7\n",
            "13       Ahmedabad       Chandigarh                  27\n",
            "14       Ahmedabad          Chennai                  55\n",
            "15       Ahmedabad           Cochin                  14\n",
            "16       Ahmedabad         Dehradun                  13\n",
            "17       Ahmedabad            Delhi                 172\n",
            "18       Ahmedabad              Diu                   6\n",
            "19       Ahmedabad              Goa                  27\n",
            "20       Ahmedabad         Guwahati                   6\n",
            "21       Ahmedabad            Hubli                   7\n",
            "22       Ahmedabad        Hyderabad                  59\n",
            "23       Ahmedabad           Indore                  40\n",
            "24       Ahmedabad         Jabalpur                   6\n",
            "25       Ahmedabad           Jaipur                  39\n",
            "26       Ahmedabad        Jaisalmer                  12\n",
            "27       Ahmedabad          Jalgaon                   7\n",
            "28       Ahmedabad          Jodhpur                  13\n",
            "29       Ahmedabad           Kandla                  13\n",
            "30       Ahmedabad           Kanpur                   6\n",
            "31       Ahmedabad       Kishangarh                   6\n",
            "32       Ahmedabad          Kolkata                  29\n",
            "33       Ahmedabad          Lucknow                  29\n",
            "34       Ahmedabad           Meerut                   6\n",
            "35       Ahmedabad           Mumbai                  88\n",
            "36       Ahmedabad           Nagpur                  13\n",
            "37       Ahmedabad           Nashik                  13\n",
            "38       Ahmedabad            Patna                   6\n",
            "39       Ahmedabad        Porbandar                   7\n",
            "40       Ahmedabad             Pune                  41\n",
            "41       Ahmedabad         Srinagar                   7\n",
            "42       Ahmedabad          Udaipur                   6\n",
            "43       Ahmedabad        Varanashi                  20\n",
            "44       Bhavnagar        Ahmedabad                   6\n",
            "45       Bhavnagar            Surat                   6\n",
            "46       Porbandar        Ahmedabad                   7\n",
            "47       Porbandar           Mumbai                   7\n",
            "48       Jammnagar           Mumbai                   7\n",
            "49          Rajkot            Delhi                  14\n",
            "50          Rajkot           Mumbai                  21\n",
            "51            Bhuj           Mumbai                   7\n",
            "52          Kandla        Ahmedabad                  12\n",
            "53          Kandla           Mumbai                   7\n",
            "54           Surat        Bangalore                  14\n",
            "55           Surat        Bhavnagar                   6\n",
            "56           Surat          Chennai                  14\n",
            "57           Surat            Delhi                  42\n",
            "58           Surat              Goa                  21\n",
            "59           Surat        Hyderabad                  14\n",
            "60           Surat           Jaipur                  10\n",
            "61           Surat        Jaisalmer                   7\n",
            "62           Surat          Kolkata                  28\n",
            "63           Surat           Mumbai                   7\n",
            "64           Surat           Nagpur                   7\n",
            "65           Surat          Udaipur                   7\n",
            "66        Vadodara        Bangalore                  15\n",
            "67        Vadodara            Delhi                  49\n",
            "68        Vadodara        Hyderabad                   7\n",
            "69        Vadodara           Mumbai                  22\n",
            "70        Vadodara           Nagpur                  14\n",
            "71         Aizawal         Guwahati                  14\n",
            "72         Aizawal           Imphal                   4\n",
            "73         Aizawal          Kolkata                  21\n",
            "74            Agra           Jaipur                   4\n",
            "75       Allahabad           Bhopal                   7\n",
            "76       Allahabad        Bangalore                   7\n",
            "77       Allahabad            Delhi                  28\n",
            "78       Allahabad          Kolkata                  21\n",
            "79       Allahabad           Mumbai                   7\n",
            "80       Allahabad           Raipur                  21\n",
            "81       Gorakhpur        Bangalore                   7\n",
            "82       Gorakhpur            Delhi                  14\n",
            "83       Gorakhpur        Hyderabad                   7\n",
            "84       Gorakhpur          Kolkata                   7\n",
            "85       Gorakhpur           Mumbai                   7\n",
            "86          Hindon            Hubli                   3\n",
            "87          Hindon      Pithoragarh                   7\n",
            "88          Kanpur        Ahmedabad                   6\n",
            "89          Kanpur            Delhi                   7\n",
            "90          Kanpur           Mumbai                  14\n",
            "91         Lucknow        Ahmedabad                  36\n",
            "92         Lucknow        Bangalore                  41\n",
            "93         Lucknow          Chennai                   7\n",
            "94         Lucknow         Dehradun                   6\n",
            "95         Lucknow            Delhi                 147\n",
            "96         Lucknow              Goa                   8\n",
            "97         Lucknow        Hyderabad                  20\n",
            "98         Lucknow          Kolkata                  28\n",
            "99         Lucknow           Mumbai                  63\n",
            "100        Lucknow            Patna                  14\n",
            "101        Lucknow             Pune                   7\n",
            "102        Lucknow         Srinagar                   7\n",
            "103      Varanashi        Ahmedabad                  19\n",
            "104      Varanashi        Ahmedabad                  13\n",
            "105      Varanashi        Bangalore                  27\n",
            "106      Varanashi     Bhubaneshwar                   7\n",
            "107      Varanashi          Chennai                  18\n",
            "108      Varanashi         Dehradun                   3\n",
            "109      Varanashi            Delhi                 127\n",
            "110      Varanashi             Gaya                  15\n",
            "111      Varanashi        Hyderabad                  36\n",
            "112      Varanashi           Jaipur                  11\n",
            "113      Varanashi        Khajuraho                  12\n",
            "114      Varanashi          Kolkata                  33\n",
            "115      Varanashi           Mumbai                  56\n",
            "116        Adampur            Delhi                   7\n",
            "117       Amritsar        Bangalore                   7\n",
            "118       Amritsar         Dehradun                   2\n",
            "119       Amritsar            Delhi                  87\n",
            "120       Amritsar        Hyderabad                   7\n",
            "121       Amritsar           Jaipur                   7\n",
            "122       Amritsar          Kolkata                   7\n",
            "123       Amritsar           Mumbai                  16\n",
            "124       Amritsar            Patna                  11\n",
            "125       Amritsar         Srinagar                  14\n",
            "126       Bathinda            Delhi                   3\n",
            "127     Chandigarh        Ahmedabad                  23\n",
            "128     Chandigarh        Bangalore                  19\n",
            "129     Chandigarh         Dehradun                   2\n",
            "130     Chandigarh            Delhi                 167\n",
            "131     Chandigarh      Dharamshala                   6\n",
            "132     Chandigarh        Hyderabad                   8\n",
            "133     Chandigarh          Kolkata                  23\n",
            "134     Chandigarh            Kullu                  18\n",
            "135     Chandigarh              Leh                   4\n",
            "136     Chandigarh           Mumbai                  41\n",
            "137     Chandigarh             Pune                   3\n",
            "138     Chandigarh           Shimla                   6\n",
            "139     Chandigarh         Srinagar                  13\n",
            "140       Bagdogra        Ahmedabad                  13\n",
            "141       Bagdogra        Bangalore                  21\n",
            "142       Bagdogra          Chennai                   7\n",
            "143       Bagdogra            Delhi                  69\n",
            "144       Bagdogra        Dibrugarh                   7\n",
            "145       Bagdogra         Guwahati                  21\n",
            "146       Bagdogra        Hyderabad                   7\n",
            "147       Bagdogra          Kolkata                  66\n",
            "148       Bagdogra           Mumbai                  14\n",
            "149       Durgapur            Delhi                   7\n",
            "150       Durgapur        Hyderabad                   4\n",
            "151        Kolkata         Agartala                  78\n",
            "152        Kolkata        Ahmedabad                  35\n",
            "153        Kolkata          Aizawal                  14\n",
            "154        Kolkata        Allahabad                  21\n",
            "155        Kolkata         Amritsar                   7\n",
            "156        Kolkata         Bagdogra                  66\n",
            "157        Kolkata        Bangalore                 142\n",
            "158        Kolkata     Bhubaneshwar                  56\n",
            "159        Kolkata       Chandigarh                  14\n",
            "160        Kolkata          Chennai                 120\n",
            "161        Kolkata           Cochin                   8\n",
            "162        Kolkata            Delhi                 278\n",
            "163        Kolkata        Dibrugarh                  26\n",
            "164        Kolkata          Dimapur                  13\n",
            "165        Kolkata             Gaya                  13\n",
            "166        Kolkata              Goa                   7\n",
            "167        Kolkata        Gorakhpur                   7\n",
            "168        Kolkata         Guwahati                 122\n",
            "169        Kolkata          Gwalior                   7\n",
            "170        Kolkata        Hyderabad                  65\n",
            "171        Kolkata           Imphal                  26\n",
            "172        Kolkata           Indore                   7\n",
            "173        Kolkata         Jabalpur                  35\n",
            "174        Kolkata           Jaipur                  43\n",
            "175        Kolkata           Jorhat                   7\n",
            "176        Kolkata         Lilabari                   7\n",
            "177        Kolkata          Lucknow                  30\n",
            "178        Kolkata           Mumbai                 169\n",
            "179        Kolkata           Nagpur                  15\n",
            "180        Kolkata          Pakyong                   7\n",
            "181        Kolkata            Patna                 116\n",
            "182        Kolkata       Port Blair                  61\n",
            "183        Kolkata             Pune                  34\n",
            "184        Kolkata           Raipur                  50\n",
            "185        Kolkata         Shillong                   7\n",
            "186        Kolkata          Silchar                  30\n",
            "187        Kolkata            Surat                  22\n",
            "188        Kolkata        Varanashi                  33\n",
            "189        Kolkata    Visakhapatnam                  14\n",
            "190      Bangalore         Agartala                   7\n",
            "191      Bangalore           Agatti                   7\n",
            "192      Bangalore        Ahmedabad                  89\n",
            "193      Bangalore        Allahabad                   7\n",
            "194      Bangalore         Amritsar                   7\n",
            "195      Bangalore         Bagdogra                  21\n",
            "196      Bangalore         Belagavi                  28\n",
            "197      Bangalore          Belgaum                   2\n",
            "198      Bangalore           Bhopal                   7\n",
            "199      Bangalore     Bhubaneshwar                  52\n",
            "200      Bangalore            Bidar                   7\n",
            "201      Bangalore          Calicut                  14\n",
            "202      Bangalore       Chandigarh                  19\n",
            "203      Bangalore          Chennai                 129\n",
            "204      Bangalore           Cochin                  70\n",
            "205      Bangalore       Coimbatore                  39\n",
            "206      Bangalore         Dehradun                  12\n",
            "207      Bangalore            Delhi                 321\n",
            "208      Bangalore              Goa                  68\n",
            "209      Bangalore        Gorakhpur                   7\n",
            "210      Bangalore         Guwahati                  31\n",
            "211      Bangalore          Gwalior                   6\n",
            "212      Bangalore            Hubli                  21\n",
            "213      Bangalore        Hyderabad                 224\n",
            "214      Bangalore           Imphal                  20\n",
            "215      Bangalore           Indore                  26\n",
            "216      Bangalore           Jaipur                  33\n",
            "217      Bangalore        Jaisalmer                   7\n",
            "218      Bangalore       Jharsuguda                   7\n",
            "219      Bangalore          Jodhpur                   7\n",
            "220      Bangalore           Kannur                  19\n",
            "221      Bangalore         Kolhapur                   7\n",
            "222      Bangalore          Kolkata                 118\n",
            "223      Bangalore          Lucknow                  39\n",
            "224      Bangalore          Madurai                  26\n",
            "225      Bangalore        Mangalore                  68\n",
            "226      Bangalore           Mumbai                 341\n",
            "227      Bangalore           Mysore                   7\n",
            "228      Bangalore           Nagpur                  28\n",
            "229      Bangalore            Patna                  41\n",
            "230      Bangalore      Pondicherry                   6\n",
            "231      Bangalore       Port Blair                  20\n",
            "232      Bangalore             Pune                 116\n",
            "233      Bangalore           Raipur                  14\n",
            "234      Bangalore      Rajahmundry                  14\n",
            "235      Bangalore           Ranchi                  27\n",
            "236      Bangalore           Shirdi                  19\n",
            "237      Bangalore            Surat                  14\n",
            "238      Bangalore        Tirupathi                   7\n",
            "239      Bangalore           Trichy                   7\n",
            "240      Bangalore       Trivandrum                  51\n",
            "241      Bangalore        Tuticorin                   7\n",
            "242      Bangalore          Udaipur                   8\n",
            "243      Bangalore         Vadodara                  14\n",
            "244      Bangalore        Varanashi                  27\n",
            "245      Bangalore       Vidyanagar                   7\n",
            "246      Bangalore        Vijaywada                  40\n",
            "247      Bangalore    Visakhapatnam                  34\n",
            "248        Belgaum        Ahmedabad                   7\n",
            "249        Belgaum        Bangalore                  29\n",
            "250        Belgaum         Cuddapah                   7\n",
            "251        Belgaum        Hyderabad                  21\n",
            "252        Belgaum           Mumbai                  11\n",
            "253        Belgaum           Mysore                   7\n",
            "254        Belgaum             Pune                   7\n",
            "255        Belgaum        Tirupathi                   7\n",
            "256          Hubli        Ahmedabad                   7\n",
            "257          Hubli        Bangalore                  19\n",
            "258          Hubli          Chennai                   7\n",
            "259          Hubli           Cochin                   7\n",
            "260          Hubli              Goa                   7\n",
            "261          Hubli           Hindon                   3\n",
            "262          Hubli        Hyderabad                   7\n",
            "263          Hubli           Kannur                   7\n",
            "264          Hubli           Mumbai                   5\n",
            "265          Hubli        Tirupathi                   7\n",
            "266      Mangalore        Bangalore                  66\n",
            "267      Mangalore          Chennai                  14\n",
            "268      Mangalore            Delhi                  14\n",
            "269      Mangalore        Hyderabad                  14\n",
            "270      Mangalore           Mumbai                  35\n",
            "271      Mangalore             Pune                   7\n",
            "272     Vidyanagar        Bangalore                   7\n",
            "273     Vidyanagar        Hyderabad                   7\n",
            "274         Mysore        Bangalore                   7\n",
            "275         Mysore         Belagavi                   7\n",
            "276         Mysore          Chennai                  14\n",
            "277         Mysore           Cochin                   7\n",
            "278         Mysore              Goa                   7\n",
            "279         Mysore        Hyderabad                  14\n",
            "280         Bhopal        Ahmedabad                  14\n",
            "281         Bhopal        Allahabad                   7\n",
            "282         Bhopal        Bangalore                   7\n",
            "283         Bhopal            Delhi                  55\n",
            "284         Bhopal        Hyderabad                  14\n",
            "285         Bhopal           Jaipur                  14\n",
            "286         Bhopal           Mumbai                  35\n",
            "287         Bhopal           Nagpur                   7\n",
            "288         Bhopal             Pune                   6\n",
            "289         Bhopal           Raipur                   7\n",
            "290         Bhopal          Udaipur                   7\n",
            "291        Gwalior        Bangalore                   6\n",
            "292        Gwalior            Delhi                   3\n",
            "293        Gwalior        Hyderabad                   7\n",
            "294        Gwalior           Indore                   3\n",
            "295        Gwalior            Jammu                   7\n",
            "296        Gwalior          Kolkata                   7\n",
            "297         Indore        Ahmedabad                  54\n",
            "298         Indore        Bangalore                  28\n",
            "299         Indore          Belgaum                   3\n",
            "300         Indore          Chennai                   7\n",
            "301         Indore            Delhi                  81\n",
            "302         Indore              Goa                  14\n",
            "303         Indore          Gwalior                   3\n",
            "304         Indore        Hyderabad                  28\n",
            "305         Indore           Jaipur                   7\n",
            "306         Indore       Kishangarh                   3\n",
            "307         Indore          Kolkata                   7\n",
            "308         Indore           Mumbai                  37\n",
            "309         Indore           Nagpur                   8\n",
            "310         Indore           Raipur                   7\n",
            "311         Indore           Shirdi                   7\n",
            "312      Khajuraho             Agra                   3\n",
            "313      Khajuraho        Varanashi                   4\n",
            "314       Jabalpur        Ahmedabad                   6\n",
            "315       Jabalpur            Delhi                  14\n",
            "316       Jabalpur        Hyderabad                  14\n",
            "317       Jabalpur          Kolkata                  14\n",
            "318       Jabalpur           Mumbai                   7\n",
            "319   Bhubaneshwar        Ahmedabad                   6\n",
            "320   Bhubaneshwar        Bangalore                  49\n",
            "321   Bhubaneshwar          Chennai                  15\n",
            "322   Bhubaneshwar            Delhi                  58\n",
            "323   Bhubaneshwar         Guwahati                   7\n",
            "324   Bhubaneshwar        Hyderabad                  28\n",
            "325   Bhubaneshwar       Jharsuguda                   7\n",
            "326   Bhubaneshwar          Kolkata                  50\n",
            "327   Bhubaneshwar           Mumbai                  21\n",
            "328   Bhubaneshwar           Nagpur                   7\n",
            "329   Bhubaneshwar           Ranchi                   7\n",
            "330     Jharsuguda     Bhubaneshwar                   7\n",
            "331     Jharsuguda           Raipur                   7\n",
            "332     Jharsuguda            Delhi                   7\n",
            "333     Jharsuguda        Hyderabad                   7\n",
            "334     Jharsuguda          Kolkata                   7\n",
            "335        Calicut        Bangalore                   7\n",
            "336        Calicut           Mumbai                   7\n",
            "337        Calicut          Chennai                   7\n",
            "338        Calicut           Mumbai                   7\n",
            "339        Calicut            Delhi                   7\n",
            "340        Calicut        Bangalore                   7\n",
            "341        Calicut          Chennai                   7\n",
            "342         Cochin           Agatti                   7\n",
            "343         Cochin        Ahmedabad                  13\n",
            "344         Cochin        Bangalore                  77\n",
            "345         Cochin          Chennai                  76\n",
            "346         Cochin            Delhi                  56\n",
            "347         Cochin              Goa                   7\n",
            "348         Cochin            Hubli                   7\n",
            "349         Cochin        Hyderabad                  43\n",
            "350         Cochin           Kannur                  14\n",
            "351         Cochin          Kolkata                  14\n",
            "352         Cochin           Mumbai                  56\n",
            "353         Cochin           Mysore                   7\n",
            "354         Cochin             Pune                  13\n",
            "355         Cochin        Tirupathi                   6\n",
            "356         Cochin       Trivandrum                  14\n",
            "357         Kannur        Bangalore                  26\n",
            "358         Kannur          Calicut                   5\n",
            "359         Kannur          Chennai                  14\n",
            "360         Kannur           Cochin                  14\n",
            "361         Kannur            Delhi                   7\n",
            "362         Kannur              Goa                   7\n",
            "363         Kannur            Hubli                   7\n",
            "364         Kannur        Hyderabad                  21\n",
            "365         Kannur           Mumbai                   7\n",
            "366         Kannur       Trivandrum                   8\n",
            "367     Trivandrum        Bangalore                  39\n",
            "368     Trivandrum          Chennai                  41\n",
            "369     Trivandrum           Cochin                   7\n",
            "370     Trivandrum            Delhi                  14\n",
            "371     Trivandrum        Hyderabad                   7\n",
            "372     Trivandrum           Kannur                   8\n",
            "373     Trivandrum           Mumbai                  28\n",
            "374        Chennai        Ahmedabad                  75\n",
            "375        Chennai            Andal                   7\n",
            "376        Chennai         Bagdogra                   7\n",
            "377        Chennai        Bangalore                 154\n",
            "378        Chennai     Bhubaneshwar                  14\n",
            "379        Chennai          Calicut                  15\n",
            "380        Chennai           Cochin                  78\n",
            "381        Chennai       Coimbatore                 107\n",
            "382        Chennai         Cuddapah                   7\n",
            "383        Chennai            Delhi                 170\n",
            "384        Chennai              Goa                  27\n",
            "385        Chennai         Guwahati                  26\n",
            "386        Chennai            Hubli                   7\n",
            "387        Chennai        Hyderabad                 131\n",
            "388        Chennai           Indore                   8\n",
            "389        Chennai           Jaipur                  14\n",
            "390        Chennai           Kannur                  14\n",
            "391        Chennai          Kolkata                 102\n",
            "392        Chennai          Lucknow                   8\n",
            "393        Chennai          Madurai                  99\n",
            "394        Chennai        Mangalore                  14\n",
            "395        Chennai           Mumbai                 157\n",
            "396        Chennai           Mysore                  14\n",
            "397        Chennai           Nagpur                   7\n",
            "398        Chennai            Patna                   8\n",
            "399        Chennai       Port Blair                  69\n",
            "400        Chennai             Pune                  49\n",
            "401        Chennai           Raipur                   7\n",
            "402        Chennai      Rajahmundry                   7\n",
            "403        Chennai            Salem                   7\n",
            "404        Chennai           Shirdi                  14\n",
            "405        Chennai            Surat                   7\n",
            "406        Chennai           Trichy                  61\n",
            "407        Chennai       Trivandrum                  43\n",
            "408        Chennai        Tuticorin                  35\n",
            "409        Chennai        Varanashi                  18\n",
            "410        Chennai        Vijaywada                  17\n",
            "411        Chennai    Visakhapatnam                  27\n",
            "412     Coimbatore        Bangalore                  39\n",
            "413     Coimbatore          Chennai                 100\n",
            "414     Coimbatore            Delhi                  35\n",
            "415     Coimbatore        Hyderabad                  42\n",
            "416     Coimbatore        Mangalore                   2\n",
            "417     Coimbatore           Mumbai                  40\n",
            "418     Coimbatore             Pune                   7\n",
            "419        Madurai        Bangalore                  26\n",
            "420        Madurai          Chennai                  91\n",
            "421        Madurai            Delhi                   7\n",
            "422        Madurai        Hyderabad                  21\n",
            "423        Madurai           Mumbai                   7\n",
            "424         Trichy        Bangalore                   7\n",
            "425         Trichy          Chennai                  49\n",
            "426         Trichy        Hyderabad                   7\n",
            "427      Tuticorin        Bangalore                   7\n",
            "428      Tuticorin          Chennai                  35\n",
            "429          Salem          Chennai                   7\n",
            "430       Dehradun        Ahmedabad                  14\n",
            "431       Dehradun        Bangalore                  12\n",
            "432       Dehradun            Delhi                  59\n",
            "433       Dehradun        Hyderabad                   7\n",
            "434       Dehradun           Jaipur                   7\n",
            "435       Dehradun          Lucknow                   6\n",
            "436       Dehradun           Mumbai                  24\n",
            "437       Dehradun      Pithoragarh                  14\n",
            "438       Dehradun        Varanashi                   3\n",
            "439      Pantnagar         Dehradun                   4\n",
            "440      Pantnagar        Pantnagar                   4\n",
            "441    Pithoragarh         Dehradun                  14\n",
            "442    Pithoragarh           Hindon                   7\n",
            "443          Delhi          Adampur                   7\n",
            "444          Delhi        Ahmedabad                  74\n",
            "445          Delhi        Allahabad                  21\n",
            "446          Delhi         Amritsar                  39\n",
            "447          Delhi            Andal                   7\n",
            "448          Delhi       Aurangabad                  14\n",
            "449          Delhi         Bagdogra                  48\n",
            "450          Delhi        Bangalore                 191\n",
            "451          Delhi         Bathinda                   3\n",
            "452          Delhi           Bhopal                  34\n",
            "453          Delhi     Bhubaneshwar                  29\n",
            "454          Delhi          Bikaner                   7\n",
            "455          Delhi          Calicut                   3\n",
            "456          Delhi       Chandigarh                  80\n",
            "457          Delhi          Chennai                  77\n",
            "458          Delhi           Raipur                  11\n",
            "459          Delhi           Cochin                  56\n",
            "460          Delhi       Coimbatore                  14\n",
            "461          Delhi         Dehradun                  32\n",
            "462          Delhi      Dharamshala                   8\n",
            "463          Delhi              Goa                  81\n",
            "464          Delhi        Gorakhpur                  14\n",
            "465          Delhi         Guwahati                  72\n",
            "466          Delhi          Gwalior                   3\n",
            "467          Delhi        Hyderabad                 124\n",
            "468          Delhi           Imphal                   7\n",
            "469          Delhi           Indore                  46\n",
            "470          Delhi         Jabalpur                  14\n",
            "471          Delhi           Jaipur                  29\n",
            "472          Delhi        Jaisalmer                   7\n",
            "473          Delhi            Jammu                  50\n",
            "474          Delhi       Jharsuguda                   7\n",
            "475          Delhi          Jodhpur                  21\n",
            "476          Delhi           Kangra                   6\n",
            "477          Delhi           Kannur                   8\n",
            "478          Delhi           Kanpur                   7\n",
            "479          Delhi       Kishangarh                   7\n",
            "480          Delhi          Kolkata                 142\n",
            "481          Delhi            Kullu                  21\n",
            "482          Delhi              Leh                  29\n",
            "483          Delhi          Lucknow                  77\n",
            "484          Delhi         Ludhiana                   4\n",
            "485          Delhi        Mangalore                   7\n",
            "486          Delhi           Mumbai                 328\n",
            "487          Delhi           Nagpur                  42\n",
            "488          Delhi        Pathankot                   3\n",
            "489          Delhi            Patna                 128\n",
            "490          Delhi         Patnagar                   4\n",
            "491          Delhi       Port Blair                   3\n",
            "492          Delhi             Pune                 107\n",
            "493          Delhi           Raipur                  14\n",
            "494          Delhi           Rajkot                  15\n",
            "495          Delhi           Ranchi                  70\n",
            "496          Delhi           Shimla                   7\n",
            "497          Delhi           Shirdi                  14\n",
            "498          Delhi         Srinagar                  74\n",
            "499          Delhi            Surat                  29\n",
            "500          Delhi           Thoise                   2\n",
            "501          Delhi       Trivandrum                  14\n",
            "502          Delhi          Udaipur                  14\n",
            "503          Delhi         Vadodara                  14\n",
            "504          Delhi        Varanashi                  70\n",
            "505          Delhi        Vijaywada                  14\n",
            "506          Delhi    Visakhapatnam                  34\n",
            "507      Dibrugarh         Bagdogra                   7\n",
            "508      Dibrugarh            Delhi                  16\n",
            "509      Dibrugarh         Guwahati                  14\n",
            "510      Dibrugarh          Kolkata                  33\n",
            "511       Guwahati         Agartala                  25\n",
            "512       Guwahati        Ahmedabad                  21\n",
            "513       Guwahati          Aizawal                   7\n",
            "514       Guwahati         Bagdogra                  21\n",
            "515       Guwahati        Bangalore                  39\n",
            "516       Guwahati     Bhubaneshwar                   7\n",
            "517       Guwahati          Chennai                  26\n",
            "518       Guwahati            Delhi                 107\n",
            "519       Guwahati        Dibrugarh                  23\n",
            "520       Guwahati        Hyderabad                   9\n",
            "521       Guwahati           Imphal                  32\n",
            "522       Guwahati           Jaipur                  14\n",
            "523       Guwahati           Jorhat                   7\n",
            "524       Guwahati          Kolkata                 118\n",
            "525       Guwahati           Mumbai                  28\n",
            "526       Guwahati        Passighat                   4\n",
            "527       Guwahati          Silchar                  14\n",
            "528       Guwahati           Tezpur                   3\n",
            "529         Jorhat         Guwahati                   7\n",
            "530         Jorhat          Kolkata                   7\n",
            "531        Silchar          Aizawal                   1\n",
            "532        Silchar            Delhi                   7\n",
            "533        Silchar         Guwahati                  14\n",
            "534        Silchar          Kolkata                  28\n",
            "535         Tezpur         Guwahati                   3\n",
            "536       Lilabari          Kolkata                   1\n",
            "537       Lilabari          Kolkata                   6\n",
            "538        Dimapur          Kolkata                  13\n",
            "539           Gaya            Delhi                   7\n",
            "540           Gaya          Kolkata                  13\n",
            "541           Gaya        Varanashi                   8\n",
            "542          Patna        Ahmedabad                  13\n",
            "543          Patna         Amritsar                  11\n",
            "544          Patna        Bangalore                  48\n",
            "545          Patna          Chennai                   8\n",
            "546          Patna            Delhi                 204\n",
            "547          Patna        Hyderabad                  22\n",
            "548          Patna          Kolkata                  74\n",
            "549          Patna          Lucknow                  14\n",
            "550          Patna           Mumbai                  67\n",
            "551          Patna           Ranchi                   7\n",
            "552            Goa        Ahmedabad                  26\n",
            "553            Goa        Bangalore                  76\n",
            "554            Goa          Chennai                  27\n",
            "555            Goa           Cochin                   7\n",
            "556            Goa            Delhi                 137\n",
            "557            Goa            Hubli                   7\n",
            "558            Goa        Hyderabad                  64\n",
            "559            Goa           Indore                  14\n",
            "560            Goa           Kannur                   7\n",
            "561            Goa          Kolkata                   7\n",
            "562            Goa          Lucknow                   7\n",
            "563            Goa           Mumbai                 148\n",
            "564            Goa             Pune                  20\n",
            "565            Goa            Surat                  21\n",
            "566      Hyderabad        Ahmedabad                  61\n",
            "567      Hyderabad         Amritsar                   7\n",
            "568      Hyderabad            Andal                   4\n",
            "569      Hyderabad       Aurangabad                  14\n",
            "570      Hyderabad         Bagdogra                   7\n",
            "571      Hyderabad        Bangalore                 199\n",
            "572      Hyderabad         Belagavi                  23\n",
            "573      Hyderabad           Bhopal                  14\n",
            "574      Hyderabad     Bhubaneshwar                  29\n",
            "575      Hyderabad       Chandigarh                  25\n",
            "576      Hyderabad          Chennai                 127\n",
            "577      Hyderabad           Cochin                  44\n",
            "578      Hyderabad       Coimbatore                  36\n",
            "579      Hyderabad         Cuddapah                   7\n",
            "580      Hyderabad         Dehradun                   7\n",
            "581      Hyderabad            Delhi                 212\n",
            "582      Hyderabad              Goa                  69\n",
            "583      Hyderabad        Gorakhpur                   7\n",
            "584      Hyderabad         Guwahati                  10\n",
            "585      Hyderabad          Gwalior                   7\n",
            "586      Hyderabad            Hubli                   8\n",
            "587      Hyderabad           Indore                  29\n",
            "588      Hyderabad         Jabalpur                  16\n",
            "589      Hyderabad        Jagdalpur                   7\n",
            "590      Hyderabad           Jaipur                  46\n",
            "591      Hyderabad       Jharsuguda                   7\n",
            "592      Hyderabad           Kannur                  21\n",
            "593      Hyderabad       Kishangarh                   8\n",
            "594      Hyderabad         Kolhapur                  14\n",
            "595      Hyderabad          Kolkata                  79\n",
            "596      Hyderabad          Lucknow                  22\n",
            "597      Hyderabad          Madurai                  21\n",
            "598      Hyderabad        Mangalore                  15\n",
            "599      Hyderabad           Mumbai                 165\n",
            "600      Hyderabad           Mysore                  15\n",
            "601      Hyderabad           Nagpur                   7\n",
            "602      Hyderabad           Nanded                   7\n",
            "603      Hyderabad           Nashik                   7\n",
            "604      Hyderabad            Patna                  21\n",
            "605      Hyderabad      Pondicherry                   7\n",
            "606      Hyderabad       Port Blair                   7\n",
            "607      Hyderabad             Pune                  34\n",
            "608      Hyderabad           Raipur                  28\n",
            "609      Hyderabad      Rajahmundry                  51\n",
            "610      Hyderabad           Ranchi                   7\n",
            "611      Hyderabad           Shirdi                  32\n",
            "612      Hyderabad            Surat                  15\n",
            "613      Hyderabad        Tirupathi                  63\n",
            "614      Hyderabad           Trichy                   8\n",
            "615      Hyderabad       Trivandrum                  21\n",
            "616      Hyderabad          Udaipur                   7\n",
            "617      Hyderabad         Vadodara                   7\n",
            "618      Hyderabad        Varanashi                  28\n",
            "619      Hyderabad       Vidyanagar                   7\n",
            "620      Hyderabad        Vijaywada                  80\n",
            "621      Hyderabad    Visakhapatnam                  66\n",
            "622         Imphal         Agartala                   6\n",
            "623         Imphal          Aizawal                   4\n",
            "624         Imphal        Bangalore                  13\n",
            "625         Imphal            Delhi                   9\n",
            "626         Imphal         Guwahati                  44\n",
            "627         Imphal          Kolkata                  27\n",
            "628        Bikaner            Delhi                   7\n",
            "629         Jaipur             Agra                   4\n",
            "630         Jaipur        Ahmedabad                  34\n",
            "631         Jaipur         Amritsar                   7\n",
            "632         Jaipur        Bangalore                  47\n",
            "633         Jaipur           Bhopal                  14\n",
            "634         Jaipur          Chennai                  14\n",
            "635         Jaipur         Dehradun                   7\n",
            "636         Jaipur            Delhi                  59\n",
            "637         Jaipur         Guwahati                  14\n",
            "638         Jaipur        Hyderabad                  42\n",
            "639         Jaipur           Indore                   7\n",
            "640         Jaipur        Jaisalmer                   7\n",
            "641         Jaipur          Kolkata                  46\n",
            "642         Jaipur           Mumbai                  64\n",
            "643         Jaipur             Pune                  21\n",
            "644         Jaipur            Surat                  10\n",
            "645         Jaipur          Udaipur                  17\n",
            "646         Jaipur        Varanashi                  11\n",
            "647      Jaisalmer        Ahmedabad                  12\n",
            "648      Jaisalmer        Bangalore                   7\n",
            "649      Jaisalmer           Mumbai                   7\n",
            "650      Jaisalmer            Delhi                   7\n",
            "651      Jaisalmer           Jaipur                   7\n",
            "652      Jaisalmer            Surat                   7\n",
            "653        Jodhpur        Ahmedabad                  13\n",
            "654        Jodhpur        Bangalore                   7\n",
            "655        Jodhpur            Delhi                  35\n",
            "656        Jodhpur           Mumbai                  28\n",
            "657        Jodhpur             Pune                   6\n",
            "658     Kishangarh        Ahmedabad                   6\n",
            "659     Kishangarh            Delhi                   7\n",
            "660     Kishangarh        Hyderabad                   7\n",
            "661        Udaipur        Ahmedabad                   7\n",
            "662        Udaipur       Aurangabad                   3\n",
            "663        Udaipur        Bangalore                   7\n",
            "664        Udaipur           Bhopal                   7\n",
            "665        Udaipur            Delhi                  59\n",
            "666        Udaipur        Hyderabad                   7\n",
            "667        Udaipur           Jaipur                  24\n",
            "668        Udaipur           Mumbai                  35\n",
            "669        Udaipur            Surat                   7\n",
            "670          Jammu         Bathinda                   3\n",
            "671          Jammu            Delhi                  56\n",
            "672          Jammu          Gwalior                   7\n",
            "673          Jammu              Leh                  10\n",
            "674          Jammu          Lucknow                   7\n",
            "675          Jammu           Mumbai                  90\n",
            "676          Jammu         Srinagar                  53\n",
            "677       Srinagar         Amritsar                  14\n",
            "678       Srinagar       Chandigarh                  18\n",
            "679       Srinagar            Delhi                  96\n",
            "680       Srinagar            Jammu                 142\n",
            "681       Srinagar              Leh                   2\n",
            "682       Srinagar           Mumbai                  10\n",
            "683     Aurangabad        Ahmedabad                   7\n",
            "684     Aurangabad            Delhi                  21\n",
            "685     Aurangabad        Hyderabad                  14\n",
            "686     Aurangabad           Mumbai                  17\n",
            "687     Aurangabad          Udaipur                   3\n",
            "688       Kolhapur        Bangalore                   7\n",
            "689       Kolhapur        Hyderabad                  14\n",
            "690       Kolhapur           Mumbai                   5\n",
            "691       Kolhapur        Tirupathi                   7\n",
            "692         Mumbai        Ahmedabad                  90\n",
            "693         Mumbai        Allahabad                   7\n",
            "694         Mumbai         Amritsar                  19\n",
            "695         Mumbai            Andal                   7\n",
            "696         Mumbai       Aurangabad                  10\n",
            "697         Mumbai         Bagdogra                  21\n",
            "698         Mumbai        Bangalore                 261\n",
            "699         Mumbai         Belagavi                   7\n",
            "700         Mumbai          Belgaum                   4\n",
            "701         Mumbai        Bhavnagar                   7\n",
            "702         Mumbai           Bhopal                  28\n",
            "703         Mumbai     Bhubaneshwar                  28\n",
            "704         Mumbai             Bhuj                   7\n",
            "705         Mumbai          Calicut                  14\n",
            "706         Mumbai       Chandigarh                  56\n",
            "707         Mumbai          Chennai                 154\n",
            "708         Mumbai           Cochin                  56\n",
            "709         Mumbai       Coimbatore                  43\n",
            "710         Mumbai         Dehradun                  31\n",
            "711         Mumbai            Delhi                 457\n",
            "712         Mumbai              Diu                   7\n",
            "713         Mumbai              Goa                 137\n",
            "714         Mumbai        Gorakhpur                   7\n",
            "715         Mumbai         Guwahati                  28\n",
            "716         Mumbai            Hubli                   5\n",
            "717         Mumbai        Hyderabad                 157\n",
            "718         Mumbai           Indore                  38\n",
            "719         Mumbai           Kandla                   7\n",
            "720         Mumbai         Jabalpur                   7\n",
            "721         Mumbai           Jaipur                  56\n",
            "722         Mumbai        Jaisalmer                   7\n",
            "723         Mumbai          Jalgaon                   5\n",
            "724         Mumbai        Jammnagar                   7\n",
            "725         Mumbai          Jodhpur                  28\n",
            "726         Mumbai           Kannur                   7\n",
            "727         Mumbai           Kanpur                  28\n",
            "728         Mumbai         Kolhapur                   5\n",
            "729         Mumbai          Kolkata                 178\n",
            "730         Mumbai          Lucknow                  49\n",
            "731         Mumbai          Madurai                   7\n",
            "732         Mumbai        Mangalore                  28\n",
            "733         Mumbai           Nagpur                  58\n",
            "734         Mumbai           Nanded                   7\n",
            "735         Mumbai            Patna                  70\n",
            "736         Mumbai        Porbandar                   7\n",
            "737         Mumbai       Port Blair                   7\n",
            "738         Mumbai           Raipur                  14\n",
            "739         Mumbai           Rajkot                  18\n",
            "740         Mumbai           Ranchi                  29\n",
            "741         Mumbai           Shirdi                   7\n",
            "742         Mumbai         Srinagar                 107\n",
            "743         Mumbai            Surat                   7\n",
            "744         Mumbai       Trivandrum                  30\n",
            "745         Mumbai          Udaipur                  35\n",
            "746         Mumbai         Vadodara                  21\n",
            "747         Mumbai        Varanashi                  43\n",
            "748         Mumbai    Visakhapatnam                  12\n",
            "749         Nagpur        Ahmedabad                   7\n",
            "750         Nagpur         Amritsar                   2\n",
            "751         Nagpur        Bangalore                  28\n",
            "752         Nagpur           Bhopal                   7\n",
            "753         Nagpur       Chandigarh                   2\n",
            "754         Nagpur          Chennai                   7\n",
            "755         Nagpur           Raipur                   3\n",
            "756         Nagpur            Delhi                  84\n",
            "757         Nagpur              Goa                  14\n",
            "758         Nagpur        Hyderabad                   7\n",
            "759         Nagpur           Indore                  15\n",
            "760         Nagpur          Kolkata                  21\n",
            "761         Nagpur          Lucknow                   1\n",
            "762         Nagpur           Mumbai                  57\n",
            "763         Nagpur            Patna                   7\n",
            "764         Nagpur             Pune                  40\n",
            "765         Nagpur           Raipur                   7\n",
            "766           Pune        Ahmedabad                  41\n",
            "767           Pune        Bangalore                 117\n",
            "768           Pune         Belagavi                   6\n",
            "769           Pune           Bhopal                   6\n",
            "770           Pune       Chandigarh                   3\n",
            "771           Pune          Chennai                  42\n",
            "772           Pune           Cochin                  13\n",
            "773           Pune       Coimbatore                   7\n",
            "774           Pune            Delhi                 167\n",
            "775           Pune              Goa                  19\n",
            "776           Pune        Hyderabad                  34\n",
            "777           Pune           Jaipur                  22\n",
            "778           Pune          Jodhpur                   6\n",
            "779           Pune          Kolkata                  47\n",
            "780           Pune          Lucknow                   7\n",
            "781           Pune           Nagpur                  27\n",
            "782           Pune           Nashik                   7\n",
            "783         Shirdi        Bangalore                  19\n",
            "784         Shirdi          Chennai                  14\n",
            "785         Shirdi            Delhi                  14\n",
            "786         Shirdi        Hyderabad                  31\n",
            "787         Shirdi           Indore                   7\n",
            "788         Shirdi           Mumbai                   7\n",
            "789         Nashik        Ahmedabad                  12\n",
            "790         Nashik        Hyderabad                   7\n",
            "791         Nashik             Pune                   3\n",
            "792         Nanded        Hyderabad                   7\n",
            "793         Nanded           Mumbai                   7\n",
            "794        Jalgaon        Ahmedabad                   7\n",
            "795        Jalgaon           Mumbai                   5\n",
            "796     Port Blair        Bangalore                   7\n",
            "797     Port Blair          Chennai                  27\n",
            "798     Port Blair            Delhi                   6\n",
            "799     Port Blair        Hyderabad                   7\n",
            "800     Port Blair          Kolkata                  38\n",
            "801     Port Blair    Visakhapatnam                   5\n",
            "802      Jagdalpur        Hyderabad                   7\n",
            "803      Jagdalpur           Raipur                   7\n",
            "804         Raipur        Ahmedabad                   7\n",
            "805         Raipur        Allahabad                  21\n",
            "806         Raipur        Bangalore                  14\n",
            "807         Raipur           Bhopal                   7\n",
            "808         Raipur          Chennai                   7\n",
            "809         Raipur            Delhi                  42\n",
            "810         Raipur        Hyderabad                  28\n",
            "811         Raipur           Indore                  14\n",
            "812         Raipur        Jagdalpur                   7\n",
            "813         Raipur       Jharsuguda                   7\n",
            "814         Raipur          Kolkata                  21\n",
            "815         Raipur           Mumbai                  14\n",
            "816         Raipur           Ranchi                   7\n",
            "817       Cuddapah         Belagavi                   7\n",
            "818       Cuddapah          Chennai                   7\n",
            "819       Cuddapah        Hyderabad                   7\n",
            "820       Cuddapah        Vijaywada                   7\n",
            "821    Rajahmundry        Bangalore                  14\n",
            "822    Rajahmundry          Chennai                   7\n",
            "823    Rajahmundry        Hyderabad                  49\n",
            "824    Rajahmundry    Visakhapatnam                   7\n",
            "825      Tirupathi        Bangalore                   7\n",
            "826      Tirupathi         Belagavi                   7\n",
            "827      Tirupathi           Cochin                   6\n",
            "828      Tirupathi            Delhi                   7\n",
            "829      Tirupathi            Hubli                   7\n",
            "830      Tirupathi        Hyderabad                  62\n",
            "831      Tirupathi         Kolhapur                   7\n",
            "832      Tirupathi        Vijaywada                   9\n",
            "833      Vijaywada        Bangalore                  40\n",
            "834      Vijaywada          Chennai                  14\n",
            "835      Vijaywada            Delhi                  14\n",
            "836      Vijaywada        Hyderabad                  63\n",
            "837      Vijaywada           Kadapa                   7\n",
            "838      Vijaywada        Tirupathi                   9\n",
            "839      Vijaywada    Visakhapatnam                  13\n",
            "840  Visakhapatnam        Bangalore                  34\n",
            "841  Visakhapatnam          Chennai                  27\n",
            "842  Visakhapatnam            Delhi                  47\n",
            "843  Visakhapatnam        Hyderabad                  92\n",
            "844  Visakhapatnam          Kolkata                  19\n",
            "845  Visakhapatnam           Mumbai                   5\n",
            "846  Visakhapatnam       Port Blair                   6\n",
            "847  Visakhapatnam           Raipur                   7\n",
            "848  Visakhapatnam      Rajahmundry                   7\n",
            "849  Visakhapatnam        Vijaywada                  13\n",
            "850         Ranchi        Bangalore                  37\n",
            "851         Ranchi     Bhubaneshwar                   7\n",
            "852         Ranchi            Delhi                  98\n",
            "853         Ranchi        Hyderabad                  14\n",
            "854         Ranchi          Kolkata                  28\n",
            "855         Ranchi           Mumbai                  26\n",
            "856         Ranchi            Patna                   8\n",
            "857         Ranchi           Raipur                   7\n",
            "858       Shillong          Kolkata                   7\n",
            "859    Dharamshala       Chandigarh                   6\n",
            "860    Dharamshala            Delhi                  15\n",
            "861    Dharamshala           Shimla                   3\n",
            "862          Kullu       Chandigarh                  18\n",
            "863          Kullu            Delhi                  21\n",
            "864          Kullu           Shimla                   3\n",
            "865         Shimla       Chandigarh                   6\n",
            "866         Shimla            Delhi                   7\n",
            "867         Shimla      Dharamshala                   3\n",
            "868         Shimla            Kullu                   3\n",
            "869            Diu        Ahmedabad                   6\n",
            "870            Diu           Mumbai                   7\n",
            "871            Leh       Chandigarh                   4\n",
            "872            Leh            Delhi                  30\n",
            "873            Leh            Jammu                   3\n",
            "874            Leh         Srinagar                   2\n",
            "875         Thoise            Delhi                   2\n",
            "876      Passighat         Guwahati                   4\n",
            "877        Pakyong          Kolkata                   7\n",
            "878    Pondicherry        Bangalore                   5\n",
            "879    Pondicherry        Hyderabad                   7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Converting the city names to index based on dictionary\n",
        "\n",
        "cityDict = { \"Delhi\" : 0, \"Mumbai\" : 1, \"Kolkata\" : 2, \"Chennai\" : 3, \"Agartala\" : 4, \"Agatti\" : 5, \"Ahmedabad\" : 6, \"Bhavnagar\" : 7, \"Porbandar\" : 8, \"Jammnagar\" : 9, \"Rajkot\" : 10, \"Bhuj\" : 11, \"Kandla\" : 12, \"Surat\" : 13, \"Vadodara\" : 14, \"Aizawal\" : 15, \"Agra\" : 16, \"Allahabad\" : 17, \"Gorakhpur\" : 18, \"Hindon\" : 19, \"Kanpur\" : 20, \"Lucknow\" : 21, \"Varanashi\" : 22, \"Adampur\" : 23, \"Amritsar\" : 24, \"Bathinda\" : 25, \"Chandigarh\" : 26, \"Bagdogra\" : 27, \"Durgapur\" : 28, \"Bangalore\" : 29, \"Belgaum\" : 30, \"Hubli\" : 31, \"Mangalore\" : 32, \"Vidyanagar\" : 33, \"Mysore\" : 34, \"Bhopal\" : 35, \"Gwalior\" : 36, \"Indore\" : 37, \"Khajuraho\" : 38, \"Jabalpur\" : 39, \"Bhubaneshwar\" : 40, \"Jharsuguda\" : 41, \"Calicut\" : 42, \"Cochin\" : 43, \"Kannur\" : 44, \"Trivandrum\" : 45, \"Coimbatore\" : 46, \"Madurai\" : 47, \"Trichy\" : 48, \"Tuticorin\" : 49, \"Salem\" : 50, \"Dehradun\" : 51, \"Pantnagar\" : 52, \"Pithoragarh\" : 53, \"Dibrugarh\" : 54, \"Guwahati\" : 55, \"Jorhat\" : 56, \"Silchar\" : 57, \"Tezpur\" : 58, \"Lilabari\" : 59, \"Dimapur\" : 60, \"Gaya\" : 61, \"Patna\" : 62, \"Goa\" : 63, \"Hyderabad\" : 64, \"Imphal\" : 65, \"Bikaner\" : 66, \"Jaipur\" : 67, \"Jaisalmer\" : 68, \"Jodhpur\" : 69, \"Kishangarh\" : 70, \"Udaipur\" : 71, \"Jammu\" : 72, \"Srinagar\" : 73, \"Aurangabad\" : 74, \"Kolhapur\" : 75, \"Nagpur\" : 76, \"Pune\" : 77, \"Shirdi\" : 78, \"Nashik\" : 79, \"Nanded\" : 80, \"Jalgaon\" : 81, \"Port Blair\" : 82, \"Jagdalpur\" : 83, \"Raipur\" : 84, \"Cuddapah\" : 85, \"Rajahmundry\" : 86, \"Tirupathi\" : 87, \"Vijaywada\" : 88, \"Visakhapatnam\" : 89, \"Ranchi\" : 90, \"Shillong\" : 91, \"Dharamshala\" : 92, \"Kullu\" : 93, \"Shimla\" : 94, \"Diu\" : 95, \"Leh\" : 96, \"Thoise\" : 97, \"Passighat\" : 98, \"Pakyong\" : 99, \"Pondicherry\" : 100, \"Belagavi\" : 101, \"Kadapa\" : 102, \"Andal\" : 103, \"Ludhiana\" : 104, \"Pathankot\" : 105, \"Patnagar\" : 106, \"Kangra\" : 107, \"Meerut\" : 108, \"Bidar\" :109, }\n",
        "inv_cityDict = {v: k for k, v in cityDict.items()}\n",
        "print(inv_cityDict)\n",
        "print(len(cityDict))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PN4bosnTZSri",
        "outputId": "09da1b77-5116-488a-b3d5-19accbca9f85"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{0: 'Delhi', 1: 'Mumbai', 2: 'Kolkata', 3: 'Chennai', 4: 'Agartala', 5: 'Agatti', 6: 'Ahmedabad', 7: 'Bhavnagar', 8: 'Porbandar', 9: 'Jammnagar', 10: 'Rajkot', 11: 'Bhuj', 12: 'Kandla', 13: 'Surat', 14: 'Vadodara', 15: 'Aizawal', 16: 'Agra', 17: 'Allahabad', 18: 'Gorakhpur', 19: 'Hindon', 20: 'Kanpur', 21: 'Lucknow', 22: 'Varanashi', 23: 'Adampur', 24: 'Amritsar', 25: 'Bathinda', 26: 'Chandigarh', 27: 'Bagdogra', 28: 'Durgapur', 29: 'Bangalore', 30: 'Belgaum', 31: 'Hubli', 32: 'Mangalore', 33: 'Vidyanagar', 34: 'Mysore', 35: 'Bhopal', 36: 'Gwalior', 37: 'Indore', 38: 'Khajuraho', 39: 'Jabalpur', 40: 'Bhubaneshwar', 41: 'Jharsuguda', 42: 'Calicut', 43: 'Cochin', 44: 'Kannur', 45: 'Trivandrum', 46: 'Coimbatore', 47: 'Madurai', 48: 'Trichy', 49: 'Tuticorin', 50: 'Salem', 51: 'Dehradun', 52: 'Pantnagar', 53: 'Pithoragarh', 54: 'Dibrugarh', 55: 'Guwahati', 56: 'Jorhat', 57: 'Silchar', 58: 'Tezpur', 59: 'Lilabari', 60: 'Dimapur', 61: 'Gaya', 62: 'Patna', 63: 'Goa', 64: 'Hyderabad', 65: 'Imphal', 66: 'Bikaner', 67: 'Jaipur', 68: 'Jaisalmer', 69: 'Jodhpur', 70: 'Kishangarh', 71: 'Udaipur', 72: 'Jammu', 73: 'Srinagar', 74: 'Aurangabad', 75: 'Kolhapur', 76: 'Nagpur', 77: 'Pune', 78: 'Shirdi', 79: 'Nashik', 80: 'Nanded', 81: 'Jalgaon', 82: 'Port Blair', 83: 'Jagdalpur', 84: 'Raipur', 85: 'Cuddapah', 86: 'Rajahmundry', 87: 'Tirupathi', 88: 'Vijaywada', 89: 'Visakhapatnam', 90: 'Ranchi', 91: 'Shillong', 92: 'Dharamshala', 93: 'Kullu', 94: 'Shimla', 95: 'Diu', 96: 'Leh', 97: 'Thoise', 98: 'Passighat', 99: 'Pakyong', 100: 'Pondicherry', 101: 'Belagavi', 102: 'Kadapa', 103: 'Andal', 104: 'Ludhiana', 105: 'Pathankot', 106: 'Patnagar', 107: 'Kangra', 108: 'Meerut', 109: 'Bidar'}\n",
            "110\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Replacing the Names with Indices using Dictionary\n",
        "flightData.replace(cityDict, inplace=True)\n",
        "print(flightData.to_string())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NH-JF5RUZogA",
        "outputId": "b9923d42-8261-4d3f-ee60-0dbbf5f21c1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Airport Names  Airport(Arrival)  Frequency per week\n",
            "0                4                29                   7\n",
            "1                4                 0                   7\n",
            "2                4                55                  25\n",
            "3                4                65                   7\n",
            "4                4                 2                  77\n",
            "5                5                43                  14\n",
            "6                6                74                  13\n",
            "7                6                27                  13\n",
            "8                6                29                  76\n",
            "9                6                30                   7\n",
            "10               6                 7                   6\n",
            "11               6                35                  14\n",
            "12               6                40                   7\n",
            "13               6                26                  27\n",
            "14               6                 3                  55\n",
            "15               6                43                  14\n",
            "16               6                51                  13\n",
            "17               6                 0                 172\n",
            "18               6                95                   6\n",
            "19               6                63                  27\n",
            "20               6                55                   6\n",
            "21               6                31                   7\n",
            "22               6                64                  59\n",
            "23               6                37                  40\n",
            "24               6                39                   6\n",
            "25               6                67                  39\n",
            "26               6                68                  12\n",
            "27               6                81                   7\n",
            "28               6                69                  13\n",
            "29               6                12                  13\n",
            "30               6                20                   6\n",
            "31               6                70                   6\n",
            "32               6                 2                  29\n",
            "33               6                21                  29\n",
            "34               6               108                   6\n",
            "35               6                 1                  88\n",
            "36               6                76                  13\n",
            "37               6                79                  13\n",
            "38               6                62                   6\n",
            "39               6                 8                   7\n",
            "40               6                77                  41\n",
            "41               6                73                   7\n",
            "42               6                71                   6\n",
            "43               6                22                  20\n",
            "44               7                 6                   6\n",
            "45               7                13                   6\n",
            "46               8                 6                   7\n",
            "47               8                 1                   7\n",
            "48               9                 1                   7\n",
            "49              10                 0                  14\n",
            "50              10                 1                  21\n",
            "51              11                 1                   7\n",
            "52              12                 6                  12\n",
            "53              12                 1                   7\n",
            "54              13                29                  14\n",
            "55              13                 7                   6\n",
            "56              13                 3                  14\n",
            "57              13                 0                  42\n",
            "58              13                63                  21\n",
            "59              13                64                  14\n",
            "60              13                67                  10\n",
            "61              13                68                   7\n",
            "62              13                 2                  28\n",
            "63              13                 1                   7\n",
            "64              13                76                   7\n",
            "65              13                71                   7\n",
            "66              14                29                  15\n",
            "67              14                 0                  49\n",
            "68              14                64                   7\n",
            "69              14                 1                  22\n",
            "70              14                76                  14\n",
            "71              15                55                  14\n",
            "72              15                65                   4\n",
            "73              15                 2                  21\n",
            "74              16                67                   4\n",
            "75              17                35                   7\n",
            "76              17                29                   7\n",
            "77              17                 0                  28\n",
            "78              17                 2                  21\n",
            "79              17                 1                   7\n",
            "80              17                84                  21\n",
            "81              18                29                   7\n",
            "82              18                 0                  14\n",
            "83              18                64                   7\n",
            "84              18                 2                   7\n",
            "85              18                 1                   7\n",
            "86              19                31                   3\n",
            "87              19                53                   7\n",
            "88              20                 6                   6\n",
            "89              20                 0                   7\n",
            "90              20                 1                  14\n",
            "91              21                 6                  36\n",
            "92              21                29                  41\n",
            "93              21                 3                   7\n",
            "94              21                51                   6\n",
            "95              21                 0                 147\n",
            "96              21                63                   8\n",
            "97              21                64                  20\n",
            "98              21                 2                  28\n",
            "99              21                 1                  63\n",
            "100             21                62                  14\n",
            "101             21                77                   7\n",
            "102             21                73                   7\n",
            "103             22                 6                  19\n",
            "104             22                 6                  13\n",
            "105             22                29                  27\n",
            "106             22                40                   7\n",
            "107             22                 3                  18\n",
            "108             22                51                   3\n",
            "109             22                 0                 127\n",
            "110             22                61                  15\n",
            "111             22                64                  36\n",
            "112             22                67                  11\n",
            "113             22                38                  12\n",
            "114             22                 2                  33\n",
            "115             22                 1                  56\n",
            "116             23                 0                   7\n",
            "117             24                29                   7\n",
            "118             24                51                   2\n",
            "119             24                 0                  87\n",
            "120             24                64                   7\n",
            "121             24                67                   7\n",
            "122             24                 2                   7\n",
            "123             24                 1                  16\n",
            "124             24                62                  11\n",
            "125             24                73                  14\n",
            "126             25                 0                   3\n",
            "127             26                 6                  23\n",
            "128             26                29                  19\n",
            "129             26                51                   2\n",
            "130             26                 0                 167\n",
            "131             26                92                   6\n",
            "132             26                64                   8\n",
            "133             26                 2                  23\n",
            "134             26                93                  18\n",
            "135             26                96                   4\n",
            "136             26                 1                  41\n",
            "137             26                77                   3\n",
            "138             26                94                   6\n",
            "139             26                73                  13\n",
            "140             27                 6                  13\n",
            "141             27                29                  21\n",
            "142             27                 3                   7\n",
            "143             27                 0                  69\n",
            "144             27                54                   7\n",
            "145             27                55                  21\n",
            "146             27                64                   7\n",
            "147             27                 2                  66\n",
            "148             27                 1                  14\n",
            "149             28                 0                   7\n",
            "150             28                64                   4\n",
            "151              2                 4                  78\n",
            "152              2                 6                  35\n",
            "153              2                15                  14\n",
            "154              2                17                  21\n",
            "155              2                24                   7\n",
            "156              2                27                  66\n",
            "157              2                29                 142\n",
            "158              2                40                  56\n",
            "159              2                26                  14\n",
            "160              2                 3                 120\n",
            "161              2                43                   8\n",
            "162              2                 0                 278\n",
            "163              2                54                  26\n",
            "164              2                60                  13\n",
            "165              2                61                  13\n",
            "166              2                63                   7\n",
            "167              2                18                   7\n",
            "168              2                55                 122\n",
            "169              2                36                   7\n",
            "170              2                64                  65\n",
            "171              2                65                  26\n",
            "172              2                37                   7\n",
            "173              2                39                  35\n",
            "174              2                67                  43\n",
            "175              2                56                   7\n",
            "176              2                59                   7\n",
            "177              2                21                  30\n",
            "178              2                 1                 169\n",
            "179              2                76                  15\n",
            "180              2                99                   7\n",
            "181              2                62                 116\n",
            "182              2                82                  61\n",
            "183              2                77                  34\n",
            "184              2                84                  50\n",
            "185              2                91                   7\n",
            "186              2                57                  30\n",
            "187              2                13                  22\n",
            "188              2                22                  33\n",
            "189              2                89                  14\n",
            "190             29                 4                   7\n",
            "191             29                 5                   7\n",
            "192             29                 6                  89\n",
            "193             29                17                   7\n",
            "194             29                24                   7\n",
            "195             29                27                  21\n",
            "196             29               101                  28\n",
            "197             29                30                   2\n",
            "198             29                35                   7\n",
            "199             29                40                  52\n",
            "200             29               109                   7\n",
            "201             29                42                  14\n",
            "202             29                26                  19\n",
            "203             29                 3                 129\n",
            "204             29                43                  70\n",
            "205             29                46                  39\n",
            "206             29                51                  12\n",
            "207             29                 0                 321\n",
            "208             29                63                  68\n",
            "209             29                18                   7\n",
            "210             29                55                  31\n",
            "211             29                36                   6\n",
            "212             29                31                  21\n",
            "213             29                64                 224\n",
            "214             29                65                  20\n",
            "215             29                37                  26\n",
            "216             29                67                  33\n",
            "217             29                68                   7\n",
            "218             29                41                   7\n",
            "219             29                69                   7\n",
            "220             29                44                  19\n",
            "221             29                75                   7\n",
            "222             29                 2                 118\n",
            "223             29                21                  39\n",
            "224             29                47                  26\n",
            "225             29                32                  68\n",
            "226             29                 1                 341\n",
            "227             29                34                   7\n",
            "228             29                76                  28\n",
            "229             29                62                  41\n",
            "230             29               100                   6\n",
            "231             29                82                  20\n",
            "232             29                77                 116\n",
            "233             29                84                  14\n",
            "234             29                86                  14\n",
            "235             29                90                  27\n",
            "236             29                78                  19\n",
            "237             29                13                  14\n",
            "238             29                87                   7\n",
            "239             29                48                   7\n",
            "240             29                45                  51\n",
            "241             29                49                   7\n",
            "242             29                71                   8\n",
            "243             29                14                  14\n",
            "244             29                22                  27\n",
            "245             29                33                   7\n",
            "246             29                88                  40\n",
            "247             29                89                  34\n",
            "248             30                 6                   7\n",
            "249             30                29                  29\n",
            "250             30                85                   7\n",
            "251             30                64                  21\n",
            "252             30                 1                  11\n",
            "253             30                34                   7\n",
            "254             30                77                   7\n",
            "255             30                87                   7\n",
            "256             31                 6                   7\n",
            "257             31                29                  19\n",
            "258             31                 3                   7\n",
            "259             31                43                   7\n",
            "260             31                63                   7\n",
            "261             31                19                   3\n",
            "262             31                64                   7\n",
            "263             31                44                   7\n",
            "264             31                 1                   5\n",
            "265             31                87                   7\n",
            "266             32                29                  66\n",
            "267             32                 3                  14\n",
            "268             32                 0                  14\n",
            "269             32                64                  14\n",
            "270             32                 1                  35\n",
            "271             32                77                   7\n",
            "272             33                29                   7\n",
            "273             33                64                   7\n",
            "274             34                29                   7\n",
            "275             34               101                   7\n",
            "276             34                 3                  14\n",
            "277             34                43                   7\n",
            "278             34                63                   7\n",
            "279             34                64                  14\n",
            "280             35                 6                  14\n",
            "281             35                17                   7\n",
            "282             35                29                   7\n",
            "283             35                 0                  55\n",
            "284             35                64                  14\n",
            "285             35                67                  14\n",
            "286             35                 1                  35\n",
            "287             35                76                   7\n",
            "288             35                77                   6\n",
            "289             35                84                   7\n",
            "290             35                71                   7\n",
            "291             36                29                   6\n",
            "292             36                 0                   3\n",
            "293             36                64                   7\n",
            "294             36                37                   3\n",
            "295             36                72                   7\n",
            "296             36                 2                   7\n",
            "297             37                 6                  54\n",
            "298             37                29                  28\n",
            "299             37                30                   3\n",
            "300             37                 3                   7\n",
            "301             37                 0                  81\n",
            "302             37                63                  14\n",
            "303             37                36                   3\n",
            "304             37                64                  28\n",
            "305             37                67                   7\n",
            "306             37                70                   3\n",
            "307             37                 2                   7\n",
            "308             37                 1                  37\n",
            "309             37                76                   8\n",
            "310             37                84                   7\n",
            "311             37                78                   7\n",
            "312             38                16                   3\n",
            "313             38                22                   4\n",
            "314             39                 6                   6\n",
            "315             39                 0                  14\n",
            "316             39                64                  14\n",
            "317             39                 2                  14\n",
            "318             39                 1                   7\n",
            "319             40                 6                   6\n",
            "320             40                29                  49\n",
            "321             40                 3                  15\n",
            "322             40                 0                  58\n",
            "323             40                55                   7\n",
            "324             40                64                  28\n",
            "325             40                41                   7\n",
            "326             40                 2                  50\n",
            "327             40                 1                  21\n",
            "328             40                76                   7\n",
            "329             40                90                   7\n",
            "330             41                40                   7\n",
            "331             41                84                   7\n",
            "332             41                 0                   7\n",
            "333             41                64                   7\n",
            "334             41                 2                   7\n",
            "335             42                29                   7\n",
            "336             42                 1                   7\n",
            "337             42                 3                   7\n",
            "338             42                 1                   7\n",
            "339             42                 0                   7\n",
            "340             42                29                   7\n",
            "341             42                 3                   7\n",
            "342             43                 5                   7\n",
            "343             43                 6                  13\n",
            "344             43                29                  77\n",
            "345             43                 3                  76\n",
            "346             43                 0                  56\n",
            "347             43                63                   7\n",
            "348             43                31                   7\n",
            "349             43                64                  43\n",
            "350             43                44                  14\n",
            "351             43                 2                  14\n",
            "352             43                 1                  56\n",
            "353             43                34                   7\n",
            "354             43                77                  13\n",
            "355             43                87                   6\n",
            "356             43                45                  14\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Creating the Matrix with Zero Values\n",
        "## len function is used to calculate the length of the dictionary\n",
        "cityArray = np.zeros((len(cityDict), len(cityDict)))\n",
        "print(cityArray)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D4HIrwMQZrMw",
        "outputId": "2281d22f-b67b-4333-ecff-db59928ace06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " ...\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]\n",
            " [0. 0. 0. ... 0. 0. 0.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Iterating the DataFrame to insert value at the adjacency matrix\n",
        "for i in range(len(flightData)) :\n",
        "\n",
        "    print(flightData.iloc[i, 0], flightData.iloc[i, 1], flightData.iloc[i, 2])\n",
        "    cityArray[int(flightData.iloc[i, 0]), int(flightData.iloc[i, 1])] = int(flightData.iloc[i, 2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dzcx2L1vZw3u",
        "outputId": "27ead830-13c2-42b9-9c62-5ac73cd50db3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4 29 7\n",
            "4 0 7\n",
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            "87 0 7\n",
            "87 31 7\n",
            "87 64 62\n",
            "87 75 7\n",
            "87 88 9\n",
            "88 29 40\n",
            "88 3 14\n",
            "88 0 14\n",
            "88 64 63\n",
            "88 102 7\n",
            "88 87 9\n",
            "88 89 13\n",
            "89 29 34\n",
            "89 3 27\n",
            "89 0 47\n",
            "89 64 92\n",
            "89 2 19\n",
            "89 1 5\n",
            "89 82 6\n",
            "89 84 7\n",
            "89 86 7\n",
            "89 88 13\n",
            "90 29 37\n",
            "90 40 7\n",
            "90 0 98\n",
            "90 64 14\n",
            "90 2 28\n",
            "90 1 26\n",
            "90 62 8\n",
            "90 84 7\n",
            "91 2 7\n",
            "92 26 6\n",
            "92 0 15\n",
            "92 94 3\n",
            "93 26 18\n",
            "93 0 21\n",
            "93 94 3\n",
            "94 26 6\n",
            "94 0 7\n",
            "94 92 3\n",
            "94 93 3\n",
            "95 6 6\n",
            "95 1 7\n",
            "96 26 4\n",
            "96 0 30\n",
            "96 72 3\n",
            "96 73 2\n",
            "97 0 2\n",
            "98 55 4\n",
            "99 2 7\n",
            "100 29 5\n",
            "100 64 7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## tolist function converts the values to Float values. Hence specifying the type explicitly \n",
        "#print(np.matrix(cityArray.astype(np.int32).tolist()))\n",
        "output_df = pd.DataFrame(cityArray)\n",
        "output_df.rename(index=dict(inv_cityDict), columns=dict(inv_cityDict), inplace=True)"
      ],
      "metadata": {
        "id": "ICarxgcDZ3HE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Adjacency matrix for a week (7 days)\n",
        "\n",
        "output_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "ihYMxNzpaLYo",
        "outputId": "5ad5c95f-6212-4b6f-94f7-56ef946b6516"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-43ae151f-ab2b-4f3b-b952-95e756c58f52\">\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>Delhi</th>\n",
              "      <th>Mumbai</th>\n",
              "      <th>Kolkata</th>\n",
              "      <th>Chennai</th>\n",
              "      <th>Agartala</th>\n",
              "      <th>Agatti</th>\n",
              "      <th>Ahmedabad</th>\n",
              "      <th>Bhavnagar</th>\n",
              "      <th>Porbandar</th>\n",
              "      <th>Jammnagar</th>\n",
              "      <th>Rajkot</th>\n",
              "      <th>Bhuj</th>\n",
              "      <th>Kandla</th>\n",
              "      <th>Surat</th>\n",
              "      <th>Vadodara</th>\n",
              "      <th>Aizawal</th>\n",
              "      <th>Agra</th>\n",
              "      <th>Allahabad</th>\n",
              "      <th>Gorakhpur</th>\n",
              "      <th>Hindon</th>\n",
              "      <th>Kanpur</th>\n",
              "      <th>Lucknow</th>\n",
              "      <th>Varanashi</th>\n",
              "      <th>Adampur</th>\n",
              "      <th>Amritsar</th>\n",
              "      <th>Bathinda</th>\n",
              "      <th>Chandigarh</th>\n",
              "      <th>Bagdogra</th>\n",
              "      <th>Durgapur</th>\n",
              "      <th>Bangalore</th>\n",
              "      <th>Belgaum</th>\n",
              "      <th>Hubli</th>\n",
              "      <th>Mangalore</th>\n",
              "      <th>Vidyanagar</th>\n",
              "      <th>Mysore</th>\n",
              "      <th>Bhopal</th>\n",
              "      <th>Gwalior</th>\n",
              "      <th>Indore</th>\n",
              "      <th>Khajuraho</th>\n",
              "      <th>Jabalpur</th>\n",
              "      <th>...</th>\n",
              "      <th>Kishangarh</th>\n",
              "      <th>Udaipur</th>\n",
              "      <th>Jammu</th>\n",
              "      <th>Srinagar</th>\n",
              "      <th>Aurangabad</th>\n",
              "      <th>Kolhapur</th>\n",
              "      <th>Nagpur</th>\n",
              "      <th>Pune</th>\n",
              "      <th>Shirdi</th>\n",
              "      <th>Nashik</th>\n",
              "      <th>Nanded</th>\n",
              "      <th>Jalgaon</th>\n",
              "      <th>Port Blair</th>\n",
              "      <th>Jagdalpur</th>\n",
              "      <th>Raipur</th>\n",
              "      <th>Cuddapah</th>\n",
              "      <th>Rajahmundry</th>\n",
              "      <th>Tirupathi</th>\n",
              "      <th>Vijaywada</th>\n",
              "      <th>Visakhapatnam</th>\n",
              "      <th>Ranchi</th>\n",
              "      <th>Shillong</th>\n",
              "      <th>Dharamshala</th>\n",
              "      <th>Kullu</th>\n",
              "      <th>Shimla</th>\n",
              "      <th>Diu</th>\n",
              "      <th>Leh</th>\n",
              "      <th>Thoise</th>\n",
              "      <th>Passighat</th>\n",
              "      <th>Pakyong</th>\n",
              "      <th>Pondicherry</th>\n",
              "      <th>Belagavi</th>\n",
              "      <th>Kadapa</th>\n",
              "      <th>Andal</th>\n",
              "      <th>Ludhiana</th>\n",
              "      <th>Pathankot</th>\n",
              "      <th>Patnagar</th>\n",
              "      <th>Kangra</th>\n",
              "      <th>Meerut</th>\n",
              "      <th>Bidar</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Delhi</th>\n",
              "      <td>0.0</td>\n",
              "      <td>328.0</td>\n",
              "      <td>142.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>191.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>...</td>\n",
              "      <td>7.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>74.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>107.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mumbai</th>\n",
              "      <td>457.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>178.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>261.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>107.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kolkata</th>\n",
              "      <td>278.0</td>\n",
              "      <td>169.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>142.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chennai</th>\n",
              "      <td>170.0</td>\n",
              "      <td>157.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agartala</th>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>77.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pathankot</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Patnagar</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kangra</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Meerut</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bidar</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>110 rows × 110 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-43ae151f-ab2b-4f3b-b952-95e756c58f52')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
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              "                                                     [key], {});\n",
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              "\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",
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              "  "
            ],
            "text/plain": [
              "           Delhi  Mumbai  Kolkata  Chennai  ...  Patnagar  Kangra  Meerut  Bidar\n",
              "Delhi        0.0   328.0    142.0     77.0  ...       4.0     6.0     0.0    0.0\n",
              "Mumbai     457.0     0.0    178.0    154.0  ...       0.0     0.0     0.0    0.0\n",
              "Kolkata    278.0   169.0      0.0    120.0  ...       0.0     0.0     0.0    0.0\n",
              "Chennai    170.0   157.0    102.0      0.0  ...       0.0     0.0     0.0    0.0\n",
              "Agartala     7.0     0.0     77.0      0.0  ...       0.0     0.0     0.0    0.0\n",
              "...          ...     ...      ...      ...  ...       ...     ...     ...    ...\n",
              "Pathankot    0.0     0.0      0.0      0.0  ...       0.0     0.0     0.0    0.0\n",
              "Patnagar     0.0     0.0      0.0      0.0  ...       0.0     0.0     0.0    0.0\n",
              "Kangra       0.0     0.0      0.0      0.0  ...       0.0     0.0     0.0    0.0\n",
              "Meerut       0.0     0.0      0.0      0.0  ...       0.0     0.0     0.0    0.0\n",
              "Bidar        0.0     0.0      0.0      0.0  ...       0.0     0.0     0.0    0.0\n",
              "\n",
              "[110 rows x 110 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "p=output_df"
      ],
      "metadata": {
        "id": "rx-FIUwuaRpB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Creating 110 adjacency matrix for entire duration ( 27th October 2019 to 28th March 2020)\n",
        "\n",
        "n = 22  ## n=22 weeks or 152 days\n",
        "output_df=np.multiply(p,n)\n",
        "output_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "LhnT1K8Zaz8w",
        "outputId": "3d4100b6-0c9c-4bd6-d5a2-e2379c1c5642"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Delhi</th>\n",
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              "      <th>Kolkata</th>\n",
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              "      <th>Bhavnagar</th>\n",
              "      <th>Porbandar</th>\n",
              "      <th>Jammnagar</th>\n",
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              "      <th>Bhuj</th>\n",
              "      <th>Kandla</th>\n",
              "      <th>Surat</th>\n",
              "      <th>Vadodara</th>\n",
              "      <th>Aizawal</th>\n",
              "      <th>Agra</th>\n",
              "      <th>Allahabad</th>\n",
              "      <th>Gorakhpur</th>\n",
              "      <th>Hindon</th>\n",
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              "      <th>Varanashi</th>\n",
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              "      <th>Belgaum</th>\n",
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              "      <th>Vidyanagar</th>\n",
              "      <th>Mysore</th>\n",
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              "      <th>Gwalior</th>\n",
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              "      <th>Jabalpur</th>\n",
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              "      <th>Kolhapur</th>\n",
              "      <th>Nagpur</th>\n",
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              "      <th>Shirdi</th>\n",
              "      <th>Nashik</th>\n",
              "      <th>Nanded</th>\n",
              "      <th>Jalgaon</th>\n",
              "      <th>Port Blair</th>\n",
              "      <th>Jagdalpur</th>\n",
              "      <th>Raipur</th>\n",
              "      <th>Cuddapah</th>\n",
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              "      <th>Visakhapatnam</th>\n",
              "      <th>Ranchi</th>\n",
              "      <th>Shillong</th>\n",
              "      <th>Dharamshala</th>\n",
              "      <th>Kullu</th>\n",
              "      <th>Shimla</th>\n",
              "      <th>Diu</th>\n",
              "      <th>Leh</th>\n",
              "      <th>Thoise</th>\n",
              "      <th>Passighat</th>\n",
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              "      <th>Pondicherry</th>\n",
              "      <th>Belagavi</th>\n",
              "      <th>Kadapa</th>\n",
              "      <th>Andal</th>\n",
              "      <th>Ludhiana</th>\n",
              "      <th>Pathankot</th>\n",
              "      <th>Patnagar</th>\n",
              "      <th>Kangra</th>\n",
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              "      <th>Bidar</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Delhi</th>\n",
              "      <td>0.0</td>\n",
              "      <td>7216.0</td>\n",
              "      <td>3124.0</td>\n",
              "      <td>1694.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1628.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>330.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>638.0</td>\n",
              "      <td>308.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>462.0</td>\n",
              "      <td>308.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>1694.0</td>\n",
              "      <td>1540.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>858.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>1760.0</td>\n",
              "      <td>1056.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4202.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>748.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>1012.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>308.0</td>\n",
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              "      <td>154.0</td>\n",
              "      <td>308.0</td>\n",
              "      <td>1100.0</td>\n",
              "      <td>1628.0</td>\n",
              "      <td>308.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>924.0</td>\n",
              "      <td>2354.0</td>\n",
              "      <td>308.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>308.0</td>\n",
              "      <td>748.0</td>\n",
              "      <td>1540.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>176.0</td>\n",
              "      <td>462.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>638.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>132.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mumbai</th>\n",
              "      <td>10054.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3916.0</td>\n",
              "      <td>3388.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1980.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>396.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>462.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>616.0</td>\n",
              "      <td>1078.0</td>\n",
              "      <td>946.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>418.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1232.0</td>\n",
              "      <td>462.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5742.0</td>\n",
              "      <td>88.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>616.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>616.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>836.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>770.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2354.0</td>\n",
              "      <td>220.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>1276.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>308.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>264.0</td>\n",
              "      <td>638.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kolkata</th>\n",
              "      <td>6116.0</td>\n",
              "      <td>3718.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2640.0</td>\n",
              "      <td>1716.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>770.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>484.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>308.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>462.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>660.0</td>\n",
              "      <td>726.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>308.0</td>\n",
              "      <td>1452.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3124.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>770.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>330.0</td>\n",
              "      <td>748.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1342.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1100.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>308.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chennai</th>\n",
              "      <td>3740.0</td>\n",
              "      <td>3454.0</td>\n",
              "      <td>2244.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1650.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>176.0</td>\n",
              "      <td>396.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3388.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>308.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>308.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>176.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>1078.0</td>\n",
              "      <td>308.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1518.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>374.0</td>\n",
              "      <td>594.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agartala</th>\n",
              "      <td>154.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1694.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pathankot</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Patnagar</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kangra</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
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              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>Bidar</th>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>110 rows × 110 columns</p>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fa412032-f42f-4f42-97d4-813491ae5352');\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",
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              "  "
            ],
            "text/plain": [
              "             Delhi  Mumbai  Kolkata  Chennai  ...  Patnagar  Kangra  Meerut  Bidar\n",
              "Delhi          0.0  7216.0   3124.0   1694.0  ...      88.0   132.0     0.0    0.0\n",
              "Mumbai     10054.0     0.0   3916.0   3388.0  ...       0.0     0.0     0.0    0.0\n",
              "Kolkata     6116.0  3718.0      0.0   2640.0  ...       0.0     0.0     0.0    0.0\n",
              "Chennai     3740.0  3454.0   2244.0      0.0  ...       0.0     0.0     0.0    0.0\n",
              "Agartala     154.0     0.0   1694.0      0.0  ...       0.0     0.0     0.0    0.0\n",
              "...            ...     ...      ...      ...  ...       ...     ...     ...    ...\n",
              "Pathankot      0.0     0.0      0.0      0.0  ...       0.0     0.0     0.0    0.0\n",
              "Patnagar       0.0     0.0      0.0      0.0  ...       0.0     0.0     0.0    0.0\n",
              "Kangra         0.0     0.0      0.0      0.0  ...       0.0     0.0     0.0    0.0\n",
              "Meerut         0.0     0.0      0.0      0.0  ...       0.0     0.0     0.0    0.0\n",
              "Bidar          0.0     0.0      0.0      0.0  ...       0.0     0.0     0.0    0.0\n",
              "\n",
              "[110 rows x 110 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Exporting this output adjacency matrix data for this entire duration (27th October to 28th March = 152 days) into excel sheet\n",
        "\n",
        "filepath = 'Output_adjacency_matrix_entire_duration.xlsx'\n",
        "output_df.to_excel(filepath, index=True)"
      ],
      "metadata": {
        "id": "MtZg0d2Ka6zO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Normalizing the adjacency matrix\n",
        "\n",
        "df=output_df.to_numpy()\n",
        "dfmax, dfmin = df.max(), df.min()\n",
        "df = (df - dfmin)/(dfmax - dfmin)\n",
        "# Create the dataframe\n",
        "output_df=pd.DataFrame(df)\n",
        "output_df.rename(index=dict(inv_cityDict), columns=dict(inv_cityDict), inplace=True)"
      ],
      "metadata": {
        "id": "4VIaDlNQbDO3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Normalized adjacency matrix\n",
        "\n",
        "output_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "SRaP9L3WbHhW",
        "outputId": "7437ec96-75bb-458b-dc33-a0a1071d5d57"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Delhi</th>\n",
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              "      <th>Bhavnagar</th>\n",
              "      <th>Porbandar</th>\n",
              "      <th>Jammnagar</th>\n",
              "      <th>Rajkot</th>\n",
              "      <th>Bhuj</th>\n",
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              "      <th>Surat</th>\n",
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              "      <th>Aizawal</th>\n",
              "      <th>Agra</th>\n",
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              "      <th>Gwalior</th>\n",
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              "      <th>Kolhapur</th>\n",
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              "      <th>Shirdi</th>\n",
              "      <th>Nashik</th>\n",
              "      <th>Nanded</th>\n",
              "      <th>Jalgaon</th>\n",
              "      <th>Port Blair</th>\n",
              "      <th>Jagdalpur</th>\n",
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              "      <th>Cuddapah</th>\n",
              "      <th>Rajahmundry</th>\n",
              "      <th>Tirupathi</th>\n",
              "      <th>Vijaywada</th>\n",
              "      <th>Visakhapatnam</th>\n",
              "      <th>Ranchi</th>\n",
              "      <th>Shillong</th>\n",
              "      <th>Dharamshala</th>\n",
              "      <th>Kullu</th>\n",
              "      <th>Shimla</th>\n",
              "      <th>Diu</th>\n",
              "      <th>Leh</th>\n",
              "      <th>Thoise</th>\n",
              "      <th>Passighat</th>\n",
              "      <th>Pakyong</th>\n",
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              "      <th>Belagavi</th>\n",
              "      <th>Kadapa</th>\n",
              "      <th>Andal</th>\n",
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              "      <th>Pathankot</th>\n",
              "      <th>Patnagar</th>\n",
              "      <th>Kangra</th>\n",
              "      <th>Meerut</th>\n",
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              "      <th>Delhi</th>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.032823</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.175055</td>\n",
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              "      <td>0.109409</td>\n",
              "      <td>0.161926</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.091904</td>\n",
              "      <td>0.234136</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.006565</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.074398</td>\n",
              "      <td>0.153173</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.017505</td>\n",
              "      <td>0.045952</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.063457</td>\n",
              "      <td>0.004376</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.008753</td>\n",
              "      <td>0.006565</td>\n",
              "      <td>0.008753</td>\n",
              "      <td>0.013129</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mumbai</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.389497</td>\n",
              "      <td>0.336980</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.196937</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.039387</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.045952</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.061269</td>\n",
              "      <td>0.107221</td>\n",
              "      <td>0.094092</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.041575</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.122538</td>\n",
              "      <td>0.045952</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.571116</td>\n",
              "      <td>0.008753</td>\n",
              "      <td>0.010941</td>\n",
              "      <td>0.061269</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.061269</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.083151</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.076586</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.234136</td>\n",
              "      <td>0.021882</td>\n",
              "      <td>0.010941</td>\n",
              "      <td>0.126915</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.010941</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.026258</td>\n",
              "      <td>0.063457</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kolkata</th>\n",
              "      <td>0.608315</td>\n",
              "      <td>0.369803</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.262582</td>\n",
              "      <td>0.170678</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.076586</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.048140</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.045952</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.065646</td>\n",
              "      <td>0.072210</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.144420</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.310722</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.076586</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.032823</td>\n",
              "      <td>0.074398</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.133479</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.109409</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chennai</th>\n",
              "      <td>0.371991</td>\n",
              "      <td>0.343545</td>\n",
              "      <td>0.223195</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.164114</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.017505</td>\n",
              "      <td>0.039387</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.336980</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.017505</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.107221</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.150985</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.037199</td>\n",
              "      <td>0.059081</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agartala</th>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.168490</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pathankot</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>Patnagar</th>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kangra</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Meerut</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bidar</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>110 rows × 110 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-651e870d-953e-499e-aa9e-89a7098359ef')\"\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-651e870d-953e-499e-aa9e-89a7098359ef 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-651e870d-953e-499e-aa9e-89a7098359ef');\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": [
              "              Delhi    Mumbai   Kolkata  ...    Kangra  Meerut  Bidar\n",
              "Delhi      0.000000  0.717724  0.310722  ...  0.013129     0.0    0.0\n",
              "Mumbai     1.000000  0.000000  0.389497  ...  0.000000     0.0    0.0\n",
              "Kolkata    0.608315  0.369803  0.000000  ...  0.000000     0.0    0.0\n",
              "Chennai    0.371991  0.343545  0.223195  ...  0.000000     0.0    0.0\n",
              "Agartala   0.015317  0.000000  0.168490  ...  0.000000     0.0    0.0\n",
              "...             ...       ...       ...  ...       ...     ...    ...\n",
              "Pathankot  0.000000  0.000000  0.000000  ...  0.000000     0.0    0.0\n",
              "Patnagar   0.000000  0.000000  0.000000  ...  0.000000     0.0    0.0\n",
              "Kangra     0.000000  0.000000  0.000000  ...  0.000000     0.0    0.0\n",
              "Meerut     0.000000  0.000000  0.000000  ...  0.000000     0.0    0.0\n",
              "Bidar      0.000000  0.000000  0.000000  ...  0.000000     0.0    0.0\n",
              "\n",
              "[110 rows x 110 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Exporting this normalized adjacency matrix into excel sheet\n",
        "\n",
        "filepath = 'Normalized_adjacency_matrix.xlsx'\n",
        "output_df.to_excel(filepath, index=True)"
      ],
      "metadata": {
        "id": "PAMzx_SAbKHQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Formation of network graphs from this normalized adjacency matrix \n",
        "\n",
        "import networkx as nx\n",
        "#convert dataframe to numpy array\n",
        "x=output_df.to_numpy()"
      ],
      "metadata": {
        "id": "w9EImK0SbeP6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Representing adjacency matrix in form of network graphs\n",
        "\n",
        "H=nx.from_numpy_matrix(x)\n",
        "nx.draw(H)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "Pvsy54cbbkwt",
        "outputId": "856152c0-877f-4f41-ab29-0b86ddc5a32a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Representing this network graphs in circular form\n",
        "nx.draw_circular(H)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "zHGxhio5bm4O",
        "outputId": "6ff52e34-7fb9-4abf-d93d-a99d7ed8e3fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "output_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "nzA3YMSbc4TP",
        "outputId": "d4481b61-d6c6-4986-b347-8d01021b1ab8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-0adc03fe-2960-4c21-9d5f-4a2c632dc534\">\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>Delhi</th>\n",
              "      <th>Mumbai</th>\n",
              "      <th>Kolkata</th>\n",
              "      <th>Chennai</th>\n",
              "      <th>Agartala</th>\n",
              "      <th>Agatti</th>\n",
              "      <th>Ahmedabad</th>\n",
              "      <th>Bhavnagar</th>\n",
              "      <th>Porbandar</th>\n",
              "      <th>Jammnagar</th>\n",
              "      <th>Rajkot</th>\n",
              "      <th>Bhuj</th>\n",
              "      <th>Kandla</th>\n",
              "      <th>Surat</th>\n",
              "      <th>Vadodara</th>\n",
              "      <th>Aizawal</th>\n",
              "      <th>Agra</th>\n",
              "      <th>Allahabad</th>\n",
              "      <th>Gorakhpur</th>\n",
              "      <th>Hindon</th>\n",
              "      <th>Kanpur</th>\n",
              "      <th>Lucknow</th>\n",
              "      <th>Varanashi</th>\n",
              "      <th>Adampur</th>\n",
              "      <th>Amritsar</th>\n",
              "      <th>Bathinda</th>\n",
              "      <th>Chandigarh</th>\n",
              "      <th>Bagdogra</th>\n",
              "      <th>Durgapur</th>\n",
              "      <th>Bangalore</th>\n",
              "      <th>Belgaum</th>\n",
              "      <th>Hubli</th>\n",
              "      <th>Mangalore</th>\n",
              "      <th>Vidyanagar</th>\n",
              "      <th>Mysore</th>\n",
              "      <th>Bhopal</th>\n",
              "      <th>Gwalior</th>\n",
              "      <th>Indore</th>\n",
              "      <th>Khajuraho</th>\n",
              "      <th>Jabalpur</th>\n",
              "      <th>...</th>\n",
              "      <th>Kishangarh</th>\n",
              "      <th>Udaipur</th>\n",
              "      <th>Jammu</th>\n",
              "      <th>Srinagar</th>\n",
              "      <th>Aurangabad</th>\n",
              "      <th>Kolhapur</th>\n",
              "      <th>Nagpur</th>\n",
              "      <th>Pune</th>\n",
              "      <th>Shirdi</th>\n",
              "      <th>Nashik</th>\n",
              "      <th>Nanded</th>\n",
              "      <th>Jalgaon</th>\n",
              "      <th>Port Blair</th>\n",
              "      <th>Jagdalpur</th>\n",
              "      <th>Raipur</th>\n",
              "      <th>Cuddapah</th>\n",
              "      <th>Rajahmundry</th>\n",
              "      <th>Tirupathi</th>\n",
              "      <th>Vijaywada</th>\n",
              "      <th>Visakhapatnam</th>\n",
              "      <th>Ranchi</th>\n",
              "      <th>Shillong</th>\n",
              "      <th>Dharamshala</th>\n",
              "      <th>Kullu</th>\n",
              "      <th>Shimla</th>\n",
              "      <th>Diu</th>\n",
              "      <th>Leh</th>\n",
              "      <th>Thoise</th>\n",
              "      <th>Passighat</th>\n",
              "      <th>Pakyong</th>\n",
              "      <th>Pondicherry</th>\n",
              "      <th>Belagavi</th>\n",
              "      <th>Kadapa</th>\n",
              "      <th>Andal</th>\n",
              "      <th>Ludhiana</th>\n",
              "      <th>Pathankot</th>\n",
              "      <th>Patnagar</th>\n",
              "      <th>Kangra</th>\n",
              "      <th>Meerut</th>\n",
              "      <th>Bidar</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Delhi</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.717724</td>\n",
              "      <td>0.310722</td>\n",
              "      <td>0.168490</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.161926</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.032823</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.063457</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.045952</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.168490</td>\n",
              "      <td>0.153173</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.085339</td>\n",
              "      <td>0.006565</td>\n",
              "      <td>0.175055</td>\n",
              "      <td>0.105033</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.417943</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.074398</td>\n",
              "      <td>0.006565</td>\n",
              "      <td>0.100656</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>...</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.109409</td>\n",
              "      <td>0.161926</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.091904</td>\n",
              "      <td>0.234136</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.006565</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.074398</td>\n",
              "      <td>0.153173</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.017505</td>\n",
              "      <td>0.045952</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.063457</td>\n",
              "      <td>0.004376</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.008753</td>\n",
              "      <td>0.006565</td>\n",
              "      <td>0.008753</td>\n",
              "      <td>0.013129</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mumbai</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.389497</td>\n",
              "      <td>0.336980</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.196937</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.039387</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.045952</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.061269</td>\n",
              "      <td>0.107221</td>\n",
              "      <td>0.094092</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.041575</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.122538</td>\n",
              "      <td>0.045952</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.571116</td>\n",
              "      <td>0.008753</td>\n",
              "      <td>0.010941</td>\n",
              "      <td>0.061269</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.061269</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.083151</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.076586</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.234136</td>\n",
              "      <td>0.021882</td>\n",
              "      <td>0.010941</td>\n",
              "      <td>0.126915</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.010941</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.026258</td>\n",
              "      <td>0.063457</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kolkata</th>\n",
              "      <td>0.608315</td>\n",
              "      <td>0.369803</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.262582</td>\n",
              "      <td>0.170678</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.076586</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.048140</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.045952</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.065646</td>\n",
              "      <td>0.072210</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.144420</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.310722</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.076586</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.032823</td>\n",
              "      <td>0.074398</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.133479</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.109409</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chennai</th>\n",
              "      <td>0.371991</td>\n",
              "      <td>0.343545</td>\n",
              "      <td>0.223195</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.164114</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.017505</td>\n",
              "      <td>0.039387</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.336980</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.017505</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.107221</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.150985</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.037199</td>\n",
              "      <td>0.059081</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agartala</th>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.168490</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pathankot</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
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              "    <tr>\n",
              "      <th>Bidar</th>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>110 rows × 110 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0adc03fe-2960-4c21-9d5f-4a2c632dc534')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert {\n",
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            ],
            "text/plain": [
              "              Delhi    Mumbai   Kolkata  ...    Kangra  Meerut  Bidar\n",
              "Delhi      0.000000  0.717724  0.310722  ...  0.013129     0.0    0.0\n",
              "Mumbai     1.000000  0.000000  0.389497  ...  0.000000     0.0    0.0\n",
              "Kolkata    0.608315  0.369803  0.000000  ...  0.000000     0.0    0.0\n",
              "Chennai    0.371991  0.343545  0.223195  ...  0.000000     0.0    0.0\n",
              "Agartala   0.015317  0.000000  0.168490  ...  0.000000     0.0    0.0\n",
              "...             ...       ...       ...  ...       ...     ...    ...\n",
              "Pathankot  0.000000  0.000000  0.000000  ...  0.000000     0.0    0.0\n",
              "Patnagar   0.000000  0.000000  0.000000  ...  0.000000     0.0    0.0\n",
              "Kangra     0.000000  0.000000  0.000000  ...  0.000000     0.0    0.0\n",
              "Meerut     0.000000  0.000000  0.000000  ...  0.000000     0.0    0.0\n",
              "Bidar      0.000000  0.000000  0.000000  ...  0.000000     0.0    0.0\n",
              "\n",
              "[110 rows x 110 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Calculating the row wise summation\n",
        "\n",
        "A=output_df\n",
        "\n",
        "A[\"sum\"] = A.sum(axis=1)\n",
        "\n",
        "A"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "QuAN2sXseOLl",
        "outputId": "10416287-afaa-41f4-d538-ec2f10632093"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-0ae0af50-910c-4d78-abff-a7cff2ca4303\">\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>Delhi</th>\n",
              "      <th>Mumbai</th>\n",
              "      <th>Kolkata</th>\n",
              "      <th>Chennai</th>\n",
              "      <th>Agartala</th>\n",
              "      <th>Agatti</th>\n",
              "      <th>Ahmedabad</th>\n",
              "      <th>Bhavnagar</th>\n",
              "      <th>Porbandar</th>\n",
              "      <th>Jammnagar</th>\n",
              "      <th>Rajkot</th>\n",
              "      <th>Bhuj</th>\n",
              "      <th>Kandla</th>\n",
              "      <th>Surat</th>\n",
              "      <th>Vadodara</th>\n",
              "      <th>Aizawal</th>\n",
              "      <th>Agra</th>\n",
              "      <th>Allahabad</th>\n",
              "      <th>Gorakhpur</th>\n",
              "      <th>Hindon</th>\n",
              "      <th>Kanpur</th>\n",
              "      <th>Lucknow</th>\n",
              "      <th>Varanashi</th>\n",
              "      <th>Adampur</th>\n",
              "      <th>Amritsar</th>\n",
              "      <th>Bathinda</th>\n",
              "      <th>Chandigarh</th>\n",
              "      <th>Bagdogra</th>\n",
              "      <th>Durgapur</th>\n",
              "      <th>Bangalore</th>\n",
              "      <th>Belgaum</th>\n",
              "      <th>Hubli</th>\n",
              "      <th>Mangalore</th>\n",
              "      <th>Vidyanagar</th>\n",
              "      <th>Mysore</th>\n",
              "      <th>Bhopal</th>\n",
              "      <th>Gwalior</th>\n",
              "      <th>Indore</th>\n",
              "      <th>Khajuraho</th>\n",
              "      <th>Jabalpur</th>\n",
              "      <th>...</th>\n",
              "      <th>Udaipur</th>\n",
              "      <th>Jammu</th>\n",
              "      <th>Srinagar</th>\n",
              "      <th>Aurangabad</th>\n",
              "      <th>Kolhapur</th>\n",
              "      <th>Nagpur</th>\n",
              "      <th>Pune</th>\n",
              "      <th>Shirdi</th>\n",
              "      <th>Nashik</th>\n",
              "      <th>Nanded</th>\n",
              "      <th>Jalgaon</th>\n",
              "      <th>Port Blair</th>\n",
              "      <th>Jagdalpur</th>\n",
              "      <th>Raipur</th>\n",
              "      <th>Cuddapah</th>\n",
              "      <th>Rajahmundry</th>\n",
              "      <th>Tirupathi</th>\n",
              "      <th>Vijaywada</th>\n",
              "      <th>Visakhapatnam</th>\n",
              "      <th>Ranchi</th>\n",
              "      <th>Shillong</th>\n",
              "      <th>Dharamshala</th>\n",
              "      <th>Kullu</th>\n",
              "      <th>Shimla</th>\n",
              "      <th>Diu</th>\n",
              "      <th>Leh</th>\n",
              "      <th>Thoise</th>\n",
              "      <th>Passighat</th>\n",
              "      <th>Pakyong</th>\n",
              "      <th>Pondicherry</th>\n",
              "      <th>Belagavi</th>\n",
              "      <th>Kadapa</th>\n",
              "      <th>Andal</th>\n",
              "      <th>Ludhiana</th>\n",
              "      <th>Pathankot</th>\n",
              "      <th>Patnagar</th>\n",
              "      <th>Kangra</th>\n",
              "      <th>Meerut</th>\n",
              "      <th>Bidar</th>\n",
              "      <th>sum</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Delhi</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.717724</td>\n",
              "      <td>0.310722</td>\n",
              "      <td>0.168490</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.161926</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.032823</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.063457</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.045952</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.168490</td>\n",
              "      <td>0.153173</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.085339</td>\n",
              "      <td>0.006565</td>\n",
              "      <td>0.175055</td>\n",
              "      <td>0.105033</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.417943</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.074398</td>\n",
              "      <td>0.006565</td>\n",
              "      <td>0.100656</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>...</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.109409</td>\n",
              "      <td>0.161926</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.091904</td>\n",
              "      <td>0.234136</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.006565</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.074398</td>\n",
              "      <td>0.153173</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.017505</td>\n",
              "      <td>0.045952</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.063457</td>\n",
              "      <td>0.004376</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.008753</td>\n",
              "      <td>0.006565</td>\n",
              "      <td>0.008753</td>\n",
              "      <td>0.013129</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.529540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mumbai</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.389497</td>\n",
              "      <td>0.336980</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.196937</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.039387</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.045952</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.061269</td>\n",
              "      <td>0.107221</td>\n",
              "      <td>0.094092</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.041575</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.122538</td>\n",
              "      <td>0.045952</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.571116</td>\n",
              "      <td>0.008753</td>\n",
              "      <td>0.010941</td>\n",
              "      <td>0.061269</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.061269</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.083151</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>...</td>\n",
              "      <td>0.076586</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.234136</td>\n",
              "      <td>0.021882</td>\n",
              "      <td>0.010941</td>\n",
              "      <td>0.126915</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.010941</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.026258</td>\n",
              "      <td>0.063457</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.638950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kolkata</th>\n",
              "      <td>0.608315</td>\n",
              "      <td>0.369803</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.262582</td>\n",
              "      <td>0.170678</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.076586</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.048140</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.045952</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.065646</td>\n",
              "      <td>0.072210</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.144420</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.310722</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.076586</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.032823</td>\n",
              "      <td>0.074398</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.133479</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.109409</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.964989</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chennai</th>\n",
              "      <td>0.371991</td>\n",
              "      <td>0.343545</td>\n",
              "      <td>0.223195</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.164114</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.017505</td>\n",
              "      <td>0.039387</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.336980</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.017505</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.107221</td>\n",
              "      <td>0.030635</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.150985</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.037199</td>\n",
              "      <td>0.059081</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.568928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agartala</th>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.168490</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.015317</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.269147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pathankot</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Patnagar</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kangra</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Meerut</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bidar</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>110 rows × 111 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0ae0af50-910c-4d78-abff-a7cff2ca4303')\"\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-0ae0af50-910c-4d78-abff-a7cff2ca4303 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-0ae0af50-910c-4d78-abff-a7cff2ca4303');\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": [
              "              Delhi    Mumbai   Kolkata  ...  Meerut  Bidar       sum\n",
              "Delhi      0.000000  0.717724  0.310722  ...     0.0    0.0  5.529540\n",
              "Mumbai     1.000000  0.000000  0.389497  ...     0.0    0.0  5.638950\n",
              "Kolkata    0.608315  0.369803  0.000000  ...     0.0    0.0  3.964989\n",
              "Chennai    0.371991  0.343545  0.223195  ...     0.0    0.0  3.568928\n",
              "Agartala   0.015317  0.000000  0.168490  ...     0.0    0.0  0.269147\n",
              "...             ...       ...       ...  ...     ...    ...       ...\n",
              "Pathankot  0.000000  0.000000  0.000000  ...     0.0    0.0  0.000000\n",
              "Patnagar   0.000000  0.000000  0.000000  ...     0.0    0.0  0.000000\n",
              "Kangra     0.000000  0.000000  0.000000  ...     0.0    0.0  0.000000\n",
              "Meerut     0.000000  0.000000  0.000000  ...     0.0    0.0  0.000000\n",
              "Bidar      0.000000  0.000000  0.000000  ...     0.0    0.0  0.000000\n",
              "\n",
              "[110 rows x 111 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Finding the in-degree centrality value for all nodes or airports\n",
        "\n",
        "centrality=A[\"sum\"]\n",
        "centrality"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4x4mDIDlenl0",
        "outputId": "991d1882-ba00-4f44-9a70-1a5198918b30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Delhi        5.529540\n",
              "Mumbai       5.638950\n",
              "Kolkata      3.964989\n",
              "Chennai      3.568928\n",
              "Agartala     0.269147\n",
              "               ...   \n",
              "Pathankot    0.000000\n",
              "Patnagar     0.000000\n",
              "Kangra       0.000000\n",
              "Meerut       0.000000\n",
              "Bidar        0.000000\n",
              "Name: sum, Length: 110, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Exporting in-degree centrality for all nodes in excel sheet\n",
        "\n",
        "filepath = 'Centrality_values_nodes.xlsx'\n",
        "centrality.to_excel(filepath, index=True)"
      ],
      "metadata": {
        "id": "Kj8cgkEze3ta"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Quantifying the nodes or airports with environmental(E) and emergency factors(e), where E=0.5, e=0.3\n",
        "\n",
        "centrality=centrality+0.5+0.3\n",
        "centrality"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5VfLa0qlgsg0",
        "outputId": "bd00c0eb-21c2-4601-bff1-f007f5279d64"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Delhi        6.329540\n",
              "Mumbai       6.438950\n",
              "Kolkata      4.764989\n",
              "Chennai      4.368928\n",
              "Agartala     1.069147\n",
              "               ...   \n",
              "Pathankot    0.800000\n",
              "Patnagar     0.800000\n",
              "Kangra       0.800000\n",
              "Meerut       0.800000\n",
              "Bidar        0.800000\n",
              "Name: sum, Length: 110, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Exporting all the quantified nodes in excel sheet\n",
        "\n",
        "filepath = 'Quantified_Centrality_values_nodes.xlsx'\n",
        "centrality.to_excel(filepath, index=True)"
      ],
      "metadata": {
        "id": "1yPlmalehQhY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df=centrality.to_numpy()\n",
        "\n",
        "# Create the dataframe\n",
        "\n",
        "centrality=pd.DataFrame(df)\n",
        "\n",
        "## Converting the city names to index based on dictionary\n",
        "\n",
        "cityDict = { \"Delhi\" : 0, \"Mumbai\" : 1, \"Kolkata\" : 2, \"Chennai\" : 3, \"Agartala\" : 4, \"Agatti\" : 5, \"Ahmedabad\" : 6, \"Bhavnagar\" : 7, \"Porbandar\" : 8, \"Jammnagar\" : 9, \"Rajkot\" : 10, \"Bhuj\" : 11, \"Kandla\" : 12, \"Surat\" : 13, \"Vadodara\" : 14, \"Aizawal\" : 15, \"Agra\" : 16, \"Allahabad\" : 17, \"Gorakhpur\" : 18, \"Hindon\" : 19, \"Kanpur\" : 20, \"Lucknow\" : 21, \"Varanashi\" : 22, \"Adampur\" : 23, \"Amritsar\" : 24, \"Bathinda\" : 25, \"Chandigarh\" : 26, \"Bagdogra\" : 27, \"Durgapur\" : 28, \"Bangalore\" : 29, \"Belgaum\" : 30, \"Hubli\" : 31, \"Mangalore\" : 32, \"Vidyanagar\" : 33, \"Mysore\" : 34, \"Bhopal\" : 35, \"Gwalior\" : 36, \"Indore\" : 37, \"Khajuraho\" : 38, \"Jabalpur\" : 39, \"Bhubaneshwar\" : 40, \"Jharsuguda\" : 41, \"Calicut\" : 42, \"Cochin\" : 43, \"Kannur\" : 44, \"Trivandrum\" : 45, \"Coimbatore\" : 46, \"Madurai\" : 47, \"Trichy\" : 48, \"Tuticorin\" : 49, \"Salem\" : 50, \"Dehradun\" : 51, \"Pantnagar\" : 52, \"Pithoragarh\" : 53, \"Dibrugarh\" : 54, \"Guwahati\" : 55, \"Jorhat\" : 56, \"Silchar\" : 57, \"Tezpur\" : 58, \"Lilabari\" : 59, \"Dimapur\" : 60, \"Gaya\" : 61, \"Patna\" : 62, \"Goa\" : 63, \"Hyderabad\" : 64, \"Imphal\" : 65, \"Bikaner\" : 66, \"Jaipur\" : 67, \"Jaisalmer\" : 68, \"Jodhpur\" : 69, \"Kishangarh\" : 70, \"Udaipur\" : 71, \"Jammu\" : 72, \"Srinagar\" : 73, \"Aurangabad\" : 74, \"Kolhapur\" : 75, \"Nagpur\" : 76, \"Pune\" : 77, \"Shirdi\" : 78, \"Nashik\" : 79, \"Nanded\" : 80, \"Jalgaon\" : 81, \"Port Blair\" : 82, \"Jagdalpur\" : 83, \"Raipur\" : 84, \"Cuddapah\" : 85, \"Rajahmundry\" : 86, \"Tirupathi\" : 87, \"Vijaywada\" : 88, \"Visakhapatnam\" : 89, \"Ranchi\" : 90, \"Shillong\" : 91, \"Dharamshala\" : 92, \"Kullu\" : 93, \"Shimla\" : 94, \"Diu\" : 95, \"Leh\" : 96, \"Thoise\" : 97, \"Passighat\" : 98, \"Pakyong\" : 99, \"Pondicherry\" : 100, \"Belagavi\" : 101, \"Kadapa\" : 102, \"Andal\" : 103, \"Ludhiana\" : 104, \"Pathankot\" : 105, \"Patnagar\" : 106, \"Kangra\" : 107, \"Meerut\" : 108, \"Bidar\" :109, }\n",
        "inv_cityDict = {v: k for k, v in cityDict.items()}\n",
        "cityDictp = { \"sum\" : 0, }\n",
        "inv_cityDictp = {v: k for k, v in cityDictp.items()}\n",
        "centrality.rename(index=dict(inv_cityDict), columns=dict(inv_cityDictp), inplace=True)\n",
        "centrality\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "gypvD1GlhxI5",
        "outputId": "c5c01a11-b4ee-4f10-f0ef-e148c1e6eec3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-59dff520-d258-4f74-9749-46df79b41884\">\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>sum</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Delhi</th>\n",
              "      <td>6.329540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mumbai</th>\n",
              "      <td>6.438950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kolkata</th>\n",
              "      <td>4.764989</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chennai</th>\n",
              "      <td>4.368928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Agartala</th>\n",
              "      <td>1.069147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pathankot</th>\n",
              "      <td>0.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Patnagar</th>\n",
              "      <td>0.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kangra</th>\n",
              "      <td>0.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Meerut</th>\n",
              "      <td>0.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bidar</th>\n",
              "      <td>0.800000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>110 rows × 1 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-59dff520-d258-4f74-9749-46df79b41884')\"\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-59dff520-d258-4f74-9749-46df79b41884 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-59dff520-d258-4f74-9749-46df79b41884');\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": [
              "                sum\n",
              "Delhi      6.329540\n",
              "Mumbai     6.438950\n",
              "Kolkata    4.764989\n",
              "Chennai    4.368928\n",
              "Agartala   1.069147\n",
              "...             ...\n",
              "Pathankot  0.800000\n",
              "Patnagar   0.800000\n",
              "Kangra     0.800000\n",
              "Meerut     0.800000\n",
              "Bidar      0.800000\n",
              "\n",
              "[110 rows x 1 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding the critical nodes \n",
        "\n",
        "rslt_df = centrality[centrality['sum'] > 4.2]\n",
        "  \n",
        "print('\\nResult dataframe :\\n', rslt_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RIo7fdY7jlp4",
        "outputId": "99c8a46e-b428-4034-a1d2-3ccbe3ebb82b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Result dataframe :\n",
            "                 sum\n",
            "Delhi      6.329540\n",
            "Mumbai     6.438950\n",
            "Kolkata    4.764989\n",
            "Chennai    4.368928\n",
            "Bangalore  6.097593\n",
            "Hyderabad  4.940044\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Exporting the critical nodes\n",
        "\n",
        "filepath = 'Critical_nodes_with_values.xlsx'\n",
        "rslt_df.to_excel(filepath, index=True)"
      ],
      "metadata": {
        "id": "fqIyq2kJj3FD"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}