Module_6_AP.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tkinter import *\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_excel(\"AirQuality.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>city</th>\n",
       "      <th>place</th>\n",
       "      <th>lastupdate</th>\n",
       "      <th>Avg</th>\n",
       "      <th>Max</th>\n",
       "      <th>Min</th>\n",
       "      <th>Pollutants</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>India</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Amaravati</td>\n",
       "      <td>Secretariat, Amaravati - APPCB</td>\n",
       "      <td>2020-01-28 03:00:00</td>\n",
       "      <td>70.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>PM2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>India</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Amaravati</td>\n",
       "      <td>Secretariat, Amaravati - APPCB</td>\n",
       "      <td>2020-01-28 03:00:00</td>\n",
       "      <td>76.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>PM10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>India</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Amaravati</td>\n",
       "      <td>Secretariat, Amaravati - APPCB</td>\n",
       "      <td>2020-01-28 03:00:00</td>\n",
       "      <td>73.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>NO2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>India</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Amaravati</td>\n",
       "      <td>Secretariat, Amaravati - APPCB</td>\n",
       "      <td>2020-01-28 03:00:00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NH3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>India</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Amaravati</td>\n",
       "      <td>Secretariat, Amaravati - APPCB</td>\n",
       "      <td>2020-01-28 03:00:00</td>\n",
       "      <td>41.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>SO2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country           State       city                           place  \\\n",
       "0   India  Andhra_Pradesh  Amaravati  Secretariat, Amaravati - APPCB   \n",
       "1   India  Andhra_Pradesh  Amaravati  Secretariat, Amaravati - APPCB   \n",
       "2   India  Andhra_Pradesh  Amaravati  Secretariat, Amaravati - APPCB   \n",
       "3   India  Andhra_Pradesh  Amaravati  Secretariat, Amaravati - APPCB   \n",
       "4   India  Andhra_Pradesh  Amaravati  Secretariat, Amaravati - APPCB   \n",
       "\n",
       "           lastupdate   Avg    Max   Min Pollutants  \n",
       "0 2020-01-28 03:00:00  70.0  108.0  42.0      PM2.5  \n",
       "1 2020-01-28 03:00:00  76.0  102.0  43.0       PM10  \n",
       "2 2020-01-28 03:00:00  73.0  118.0  46.0        NO2  \n",
       "3 2020-01-28 03:00:00   5.0    6.0   4.0        NH3  \n",
       "4 2020-01-28 03:00:00  41.0  109.0   2.0        SO2  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(824, 9)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>city</th>\n",
       "      <th>place</th>\n",
       "      <th>lastupdate</th>\n",
       "      <th>Avg</th>\n",
       "      <th>Max</th>\n",
       "      <th>Min</th>\n",
       "      <th>Pollutants</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>India</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Amaravati</td>\n",
       "      <td>Secretariat, Amaravati - APPCB</td>\n",
       "      <td>2020-01-28 03:00:00</td>\n",
       "      <td>70.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>PM2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>India</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Amaravati</td>\n",
       "      <td>Secretariat, Amaravati - APPCB</td>\n",
       "      <td>2020-01-28 03:00:00</td>\n",
       "      <td>76.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>PM10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>India</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Amaravati</td>\n",
       "      <td>Secretariat, Amaravati - APPCB</td>\n",
       "      <td>2020-01-28 03:00:00</td>\n",
       "      <td>73.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>NO2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>India</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Amaravati</td>\n",
       "      <td>Secretariat, Amaravati - APPCB</td>\n",
       "      <td>2020-01-28 03:00:00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NH3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>India</td>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Amaravati</td>\n",
       "      <td>Secretariat, Amaravati - APPCB</td>\n",
       "      <td>2020-01-28 03:00:00</td>\n",
       "      <td>41.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>SO2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country           State       city                           place  \\\n",
       "0   India  Andhra_Pradesh  Amaravati  Secretariat, Amaravati - APPCB   \n",
       "1   India  Andhra_Pradesh  Amaravati  Secretariat, Amaravati - APPCB   \n",
       "2   India  Andhra_Pradesh  Amaravati  Secretariat, Amaravati - APPCB   \n",
       "3   India  Andhra_Pradesh  Amaravati  Secretariat, Amaravati - APPCB   \n",
       "4   India  Andhra_Pradesh  Amaravati  Secretariat, Amaravati - APPCB   \n",
       "\n",
       "           lastupdate   Avg    Max   Min Pollutants  \n",
       "0 2020-01-28 03:00:00  70.0  108.0  42.0      PM2.5  \n",
       "1 2020-01-28 03:00:00  76.0  102.0  43.0       PM10  \n",
       "2 2020-01-28 03:00:00  73.0  118.0  46.0        NO2  \n",
       "3 2020-01-28 03:00:00   5.0    6.0   4.0        NH3  \n",
       "4 2020-01-28 03:00:00  41.0  109.0   2.0        SO2  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Secretariat, Amaravati - APPCB',\n",
       "       'Anand Kala Kshetram, Rajamahendravaram - APPCB',\n",
       "       'Tirumala, Tirupati - APPCB', 'PWD Grounds, Vijayawada - APPCB',\n",
       "       'GVM Corporation, Visakhapatnam - APPCB',\n",
       "       'Collectorate, Gaya - BSPCB',\n",
       "       'Muzaffarpur Collectorate, Muzaffarpur - BSPCB',\n",
       "       'IGSC Planetarium Complex, Patna - BSPCB', 'Alipur, Delhi - DPCC',\n",
       "       'Anand Vihar, Delhi - DPCC', 'Ashok Vihar, Delhi - DPCC',\n",
       "       'Aya Nagar, Delhi - IMD', 'Bawana, Delhi - DPCC',\n",
       "       'Burari Crossing, Delhi - IMD', 'CRRI Mathura Road, Delhi - IMD',\n",
       "       'DTU, Delhi - CPCB',\n",
       "       'Dr. Karni Singh Shooting Range, Delhi - DPCC',\n",
       "       'Dwarka-Sector 8, Delhi - DPCC', 'IGI Airport (T3), Delhi - IMD',\n",
       "       'IHBAS, Dilshad Garden, Delhi - CPCB', 'ITO, Delhi - CPCB',\n",
       "       'Jahangirpuri, Delhi - DPCC',\n",
       "       'Jawaharlal Nehru Stadium, Delhi - DPCC',\n",
       "       'Lodhi Road, Delhi - IMD',\n",
       "       'Major Dhyan Chand National Stadium, Delhi - DPCC',\n",
       "       'Mandir Marg, Delhi - DPCC', 'Mundka, Delhi - DPCC',\n",
       "       'NSIT Dwarka, Delhi - CPCB', 'Najafgarh, Delhi - DPCC',\n",
       "       'Narela, Delhi - DPCC', 'Nehru Nagar, Delhi - DPCC',\n",
       "       'North Campus, DU, Delhi - IMD', 'Okhla Phase-2, Delhi - DPCC',\n",
       "       'Patparganj, Delhi - DPCC', 'Punjabi Bagh, Delhi - DPCC',\n",
       "       'Pusa, Delhi - DPCC', 'Pusa, Delhi - IMD',\n",
       "       'R K Puram, Delhi - DPCC', 'Rohini, Delhi - DPCC',\n",
       "       'Shadipur, Delhi - CPCB', 'Sirifort, Delhi - CPCB',\n",
       "       'Sonia Vihar, Delhi - DPCC', 'Sri Aurobindo Marg, Delhi - DPCC',\n",
       "       'Vivek Vihar, Delhi - DPCC', 'Wazirpur, Delhi - DPCC',\n",
       "       'Maninagar, Ahmedabad - GPCB',\n",
       "       'Sector- 16A, Faridabad, Haryana - HSPCB',\n",
       "       'Vikas Sadan, Gurugram, Haryana - HSPCB',\n",
       "       'Sector-2 IMT, Manesar - HSPCB', 'Sector-6, Panchkula - HSPCB',\n",
       "       'MD University, Rohtak, Haryana - HSPCB',\n",
       "       'Tata Stadium, Jorapokhar - JSPCB', 'BTM Layout, Bengaluru - CPCB',\n",
       "       'Bapuji Nagar, Bengaluru - KSPCB',\n",
       "       'City Railway Station, Bengaluru - KSPCB',\n",
       "       'Hebbal, Bengaluru - KSPCB', 'Hombegowda Nagar, Bengaluru - KSPCB',\n",
       "       'Jayanagar 5th Block, Bengaluru - KSPCB',\n",
       "       'Peenya, Bengaluru - CPCB', 'Sanegurava Halli, Bengaluru - KSPCB',\n",
       "       'Silk Board, Bengaluru - KSPCB',\n",
       "       'Chikkaballapur Rural, Chikkaballapur - KSPCB',\n",
       "       'Deshpande Nagar, Hubballi - KSPCB',\n",
       "       'Plammoodu, Thiruvananthapuram - Kerala PCB',\n",
       "       'Bhopal Chauraha, Dewas - MPPCB',\n",
       "       'Sector-D Industrial Area, Mandideep - MPPCB',\n",
       "       'Sector-2 Industrial Area, Pithampur - MPPCB',\n",
       "       'Bandhavgar Colony, Satna - MPPCB',\n",
       "       'Vindhyachal STPS, Singrauli - MPPCB',\n",
       "       'Mahakaleshwar Temple, Ujjain - MPPCB',\n",
       "       'More Chowk Waluj, Aurangabad - MPCB',\n",
       "       'Chandrapur, Chandrapur - MPCB', 'Bandra, Mumbai - MPCB',\n",
       "       'Opp GPO Civil Lines, Nagpur - MPCB',\n",
       "       'Gangapur Road, Nashik - MPCB', 'Karve Road Pune, Pune - MPCB',\n",
       "       'Solapur, Solapur - MPCB', 'Pimpleshwar Mandir, Thane - MPCB',\n",
       "       'GM Office, Brajrajnagar - OSPCB',\n",
       "       'Talcher Coalfields,Talcher - OSPCB',\n",
       "       'Golden Temple, Amritsar - PPCB', 'Hardev Nagar, Bathinda - PPCB',\n",
       "       'Civil Line, Jalandhar - PPCB', 'Kalal Majra, Khanna - PPCB',\n",
       "       'Punjab Agricultural University, Ludhiana - PPCB',\n",
       "       'RIMT University, Mandi Gobindgarh - PPCB',\n",
       "       'Model Town, Patiala - PPCB', 'Ratanpura, Rupnagar - PPCB',\n",
       "       'Civil Lines,  Ajmer - RSPCB',\n",
       "       'Moti Doongri, Alwar, Rajasthan - RSPCB',\n",
       "       'RIICO Ind. Area III, Bhiwadi, Rajasthan - RSPCB',\n",
       "       'Adarsh Nagar, Jaipur - RSPCB',\n",
       "       'Police Commissionerate, Jaipur - RSPCB',\n",
       "       'Shastri Nagar, Jaipur - RSPCB', 'Collectorate, Jodhpur - RSPCB',\n",
       "       'Shrinath Puram, Kota - RSPCB',\n",
       "       'Indira Colony Vistar, Pali - RSPCB',\n",
       "       'Ashok Nagar, Udaipur - RSPCB',\n",
       "       'Alandur Bus Depot, Chennai - CPCB', 'Manali, Chennai - CPCB',\n",
       "       'Velachery Res. Area, Chennai - CPCB',\n",
       "       'Bollaram Industrial Area, Hyderabad - TSPCB',\n",
       "       'Central University, Hyderabad - TSPCB',\n",
       "       'ICRISAT Patancheru, Hyderabad - TSPCB',\n",
       "       'IDA Pashamylaram, Hyderabad - TSPCB',\n",
       "       'Sanathnagar, Hyderabad - TSPCB', 'Zoo Park, Hyderabad - TSPCB',\n",
       "       'Sanjay Palace, Agra - UPPCB', 'New Collectorate, Baghpat - UPPCB',\n",
       "       'Yamunapuram, Bulandshahr - UPPCB',\n",
       "       'Vasundhara, Ghaziabad, UP - UPPCB',\n",
       "       'Knowledge Park - III, Greater Noida - UPPCB',\n",
       "       'Nehru Nagar, Kanpur - UPPCB', 'Central School, Lucknow - CPCB',\n",
       "       'Lalbagh, Lucknow - CPCB', 'Nishant Ganj, Lucknow - UPPCB',\n",
       "       'Talkatora District Industries Center, Lucknow - CPCB',\n",
       "       'Lajpat Nagar, Moradabad - UPPCB',\n",
       "       'New Mandi, Muzaffarnagar - UPPCB',\n",
       "       'Sector - 125, Noida, UP - UPPCB', 'Sector - 62, Noida, UP - IMD',\n",
       "       'Ardhali Bazar, Varanasi - UPPCB',\n",
       "       'Asansol Court Area, Asansol - WBPCB',\n",
       "       'Sidhu Kanhu Indoor Stadium, Durgapur - WBPCB',\n",
       "       'Haldia, Haldia - WBPCB', 'Ghusuri, Howrah - WBPCB',\n",
       "       'Padmapukur, Howrah - WBPCB',\n",
       "       'Rabindra Bharati University, Kolkata - WBPCB',\n",
       "       'Victoria, Kolkata - WBPCB', 'Ward-32 Bapupara, Siliguri - WBPCB'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['place'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(788, 9)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df[\"Country\"]\n",
    "del df[\"place\"]\n",
    "del df[\"lastupdate\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['State', 'city', 'Avg', 'Max', 'Min', 'Pollutants'], dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['PM2.5', 'PM10', 'NO2', 'NH3', 'SO2', 'CO', 'OZONE'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Pollutants'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Source_of_Pollutants'] = df.Pollutants.map({'PM2.5':'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
    "'PM10':'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
    "'NO2':'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.', \n",
    "'NH3':'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.', \n",
    "'SO2':'Burning of fossil fuels by power plants and Other industrial facilities.', \n",
    "'CO':'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.', \n",
    "'OZONE':'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.',\n",
       " 'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
       " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
       " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
       " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
       " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
       " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
       " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df['Source_of_Pollutants'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Andhra_Pradesh', 'Bihar', 'Delhi', 'Gujarat', 'Haryana',\n",
       "       'Jharkhand', 'Karnataka', 'Kerala', 'Madhya Pradesh',\n",
       "       'Maharashtra', 'Odisha', 'Punjab', 'Rajasthan', 'TamilNadu',\n",
       "       'Telangana', 'Uttar_Pradesh', 'West_Bengal'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['State'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Andhra_Pradesh'] = df.State.map({'Andhra_Pradesh':1, 'Bihar':0, 'Delhi':0, 'Gujarat':0, 'Haryana':0,\n",
    "       'Jharkhand':0, 'Karnataka':0, 'Kerala':0, 'Madhya Pradesh':0,\n",
    "       'Maharashtra':0, 'Odisha':0, 'Punjab':0, 'Rajasthan':0, 'TamilNadu':0,\n",
    "       'Telangana':0, 'Uttar_Pradesh':0, 'West_Bengal':0})\n",
    "df['Bihar'] = df.State.map({'Andhra_Pradesh':0, 'Bihar':1, 'Delhi':0, 'Gujarat':0, 'Haryana':0,\n",
    "       'Jharkhand':0, 'Karnataka':0, 'Kerala':0, 'Madhya Pradesh':0,\n",
    "       'Maharashtra':0, 'Odisha':0, 'Punjab':0, 'Rajasthan':0, 'TamilNadu':0,\n",
    "       'Telangana':0, 'Uttar_Pradesh':0, 'West_Bengal':0})\n",
    "df['Delhi'] = df.State.map({'Andhra_Pradesh':0, 'Bihar':0, 'Delhi':1, 'Gujarat':0, 'Haryana':0,\n",
    "       'Jharkhand':0, 'Karnataka':0, 'Kerala':0, 'Madhya Pradesh':0,\n",
    "       'Maharashtra':0, 'Odisha':0, 'Punjab':0, 'Rajasthan':0, 'TamilNadu':0,\n",
    "       'Telangana':0, 'Uttar_Pradesh':0, 'West_Bengal':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Gujarat'] = df.State.map({'Andhra_Pradesh':0, 'Bihar':0, 'Delhi':0, 'Gujarat':1, 'Haryana':0,\n",
    "       'Jharkhand':0, 'Karnataka':0, 'Kerala':0, 'Madhya Pradesh':0,\n",
    "       'Maharashtra':0, 'Odisha':0, 'Punjab':0, 'Rajasthan':0, 'TamilNadu':0,\n",
    "       'Telangana':0, 'Uttar_Pradesh':0, 'West_Bengal':0})\n",
    "df['Haryana'] = df.State.map({'Andhra_Pradesh':0, 'Bihar':0, 'Delhi':0, 'Gujarat':0, 'Haryana':1,\n",
    "       'Jharkhand':0, 'Karnataka':0, 'Kerala':0, 'Madhya Pradesh':0,\n",
    "       'Maharashtra':0, 'Odisha':0, 'Punjab':0, 'Rajasthan':0, 'TamilNadu':0,\n",
    "       'Telangana':0, 'Uttar_Pradesh':0, 'West_Bengal':0})\n",
    "df['Jharkhand'] = df.State.map({'Andhra_Pradesh':0, 'Bihar':0, 'Delhi':0, 'Gujarat':0, 'Haryana':0,\n",
    "       'Jharkhand':1, 'Karnataka':0, 'Kerala':0, 'Madhya Pradesh':0,\n",
    "       'Maharashtra':0, 'Odisha':0, 'Punjab':0, 'Rajasthan':0, 'TamilNadu':0,\n",
    "       'Telangana':0, 'Uttar_Pradesh':0, 'West_Bengal':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Karnataka'] = df.State.map({'Andhra_Pradesh':0, 'Bihar':0, 'Delhi':0, 'Gujarat':0, 'Haryana':0,\n",
    "       'Jharkhand':0, 'Karnataka':1, 'Kerala':0, 'Madhya Pradesh':0,\n",
    "       'Maharashtra':0, 'Odisha':0, 'Punjab':0, 'Rajasthan':0, 'TamilNadu':0,\n",
    "       'Telangana':0, 'Uttar_Pradesh':0, 'West_Bengal':0})\n",
    "df['Kerala'] = df.State.map({'Andhra_Pradesh':0, 'Bihar':0, 'Delhi':0, 'Gujarat':0, 'Haryana':0,\n",
    "       'Jharkhand':0, 'Karnataka':0, 'Kerala':1, 'Madhya Pradesh':0,\n",
    "       'Maharashtra':0, 'Odisha':0, 'Punjab':0, 'Rajasthan':0, 'TamilNadu':0,\n",
    "       'Telangana':0, 'Uttar_Pradesh':0, 'West_Bengal':0})\n",
    "df['Madhya_Pradesh'] = df.State.map({'Andhra_Pradesh':0, 'Bihar':0, 'Delhi':0, 'Gujarat':0, 'Haryana':0,\n",
    "       'Jharkhand':0, 'Karnataka':0, 'Kerala':0, 'Madhya Pradesh':1,\n",
    "       'Maharashtra':0, 'Odisha':0, 'Punjab':0, 'Rajasthan':0, 'TamilNadu':0,\n",
    "       'Telangana':0, 'Uttar_Pradesh':0, 'West_Bengal':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Maharashtra'] = df.State.map({'Andhra_Pradesh':0, 'Bihar':0, 'Delhi':0, 'Gujarat':0, 'Haryana':0,\n",
    "       'Jharkhand':0, 'Karnataka':0, 'Kerala':0, 'Madhya Pradesh':0,\n",
    "       'Maharashtra':1, 'Odisha':0, 'Punjab':0, 'Rajasthan':0, 'TamilNadu':0,\n",
    "       'Telangana':0, 'Uttar_Pradesh':0, 'West_Bengal':0})\n",
    "df['Odisha'] = df.State.map({'Andhra_Pradesh':0, 'Bihar':0, 'Delhi':0, 'Gujarat':0, 'Haryana':0,\n",
    "       'Jharkhand':0, 'Karnataka':0, 'Kerala':0, 'Madhya Pradesh':0,\n",
    "       'Maharashtra':0, 'Odisha':1, 'Punjab':0, 'Rajasthan':0, 'TamilNadu':0,\n",
    "       'Telangana':0, 'Uttar_Pradesh':0, 'West_Bengal':0})\n",
    "df['Punjab'] = df.State.map({'Andhra_Pradesh':0, 'Bihar':0, 'Delhi':0, 'Gujarat':0, 'Haryana':0,\n",
    "       'Jharkhand':0, 'Karnataka':0, 'Kerala':0, 'Madhya Pradesh':0,\n",
    "       'Maharashtra':0, 'Odisha':0, 'Punjab':0, 'Rajasthan':1, 'TamilNadu':0,\n",
    "       'Telangana':0, 'Uttar_Pradesh':0, 'West_Bengal':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Rajasthan'] = df.State.map({'Andhra_Pradesh':0, 'Bihar':0, 'Delhi':0, 'Gujarat':0, 'Haryana':0,\n",
    "       'Jharkhand':0, 'Karnataka':0, 'Kerala':0, 'Madhya Pradesh':0,\n",
    "       'Maharashtra':0, 'Odisha':0, 'Punjab':0, 'Rajasthan':0, 'TamilNadu':0,\n",
    "       'Telangana':0, 'Uttar_Pradesh':0, 'West_Bengal':0})\n",
    "df['TamilNadu'] = df.State.map({'Andhra_Pradesh':0, 'Bihar':0, 'Delhi':0, 'Gujarat':0, 'Haryana':0,\n",
    "       'Jharkhand':0, 'Karnataka':0, 'Kerala':0, 'Madhya Pradesh':0,\n",
    "       'Maharashtra':0, 'Odisha':0, 'Punjab':0, 'Rajasthan':0, 'TamilNadu':1,\n",
    "       'Telangana':0, 'Uttar_Pradesh':0, 'West_Bengal':0})\n",
    "df['Telangana'] = df.State.map({'Andhra_Pradesh':0, 'Bihar':0, 'Delhi':0, 'Gujarat':0, 'Haryana':0,\n",
    "       'Jharkhand':0, 'Karnataka':0, 'Kerala':0, 'Madhya Pradesh':0,\n",
    "       'Maharashtra':0, 'Odisha':0, 'Punjab':0, 'Rajasthan':0, 'TamilNadu':0,\n",
    "       'Telangana':1, 'Uttar_Pradesh':0, 'West_Bengal':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Uttar_Pradesh'] = df.State.map({'Andhra_Pradesh':0, 'Bihar':0, 'Delhi':0, 'Gujarat':0, 'Haryana':0,\n",
    "       'Jharkhand':0, 'Karnataka':0, 'Kerala':0, 'Madhya Pradesh':0,\n",
    "       'Maharashtra':0, 'Odisha':0, 'Punjab':0, 'Rajasthan':0, 'TamilNadu':0,\n",
    "       'Telangana':0, 'Uttar_Pradesh':1, 'West_Bengal':0})\n",
    "df['West_Bengal'] = df.State.map({'Andhra_Pradesh':0, 'Bihar':0, 'Delhi':0, 'Gujarat':0, 'Haryana':0,\n",
    "       'Jharkhand':0, 'Karnataka':0, 'Kerala':0, 'Madhya Pradesh':0,\n",
    "       'Maharashtra':0, 'Odisha':0, 'Punjab':0, 'Rajasthan':0, 'TamilNadu':0,\n",
    "       'Telangana':0, 'Uttar_Pradesh':0, 'West_Bengal':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Amaravati', 'Rajamahendravaram', 'Tirupati', 'Vijayawada',\n",
       "       'Visakhapatnam', 'Gaya', 'Muzaffarpur', 'Patna', 'Delhi',\n",
       "       'Ahmedabad', 'Faridabad', 'Gurugram', 'Manesar', 'Panchkula',\n",
       "       'Rohtak', 'Jorapokhar', 'Bengaluru', 'Chikkaballapur', 'Hubballi',\n",
       "       'Thiruvananthapuram', 'Dewas', 'Mandideep', 'Pithampur', 'Satna',\n",
       "       'Singrauli', 'Ujjain', 'Aurangabad', 'Chandrapur', 'Mumbai',\n",
       "       'Nagpur', 'Nashik', 'Pune', 'Solapur', 'Thane', 'Brajrajnagar',\n",
       "       'Talcher', 'Amritsar', 'Bathinda', 'Jalandhar', 'Khanna',\n",
       "       'Ludhiana', 'Mandi Gobindgarh', 'Patiala', 'Rupnagar', 'Ajmer',\n",
       "       'Alwar', 'Bhiwadi', 'Jaipur', 'Jodhpur', 'Kota', 'Pali', 'Udaipur',\n",
       "       'Chennai', 'Hyderabad', 'Agra', 'Baghpat', 'Bulandshahr',\n",
       "       'Ghaziabad', 'Greater_Noida', 'Kanpur', 'Lucknow', 'Moradabad',\n",
       "       'Muzaffarnagar', 'Noida', 'Varanasi', 'Asanol', 'Durgapur',\n",
       "       'Haldia', 'Howrah', 'Kolkata', 'Siliguri'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['city'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Amaravati'] = df.city.map({'Amaravati':1,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Rajamahendravaram'] = df.city.map({'Amaravati':0,'Rajamahendravaram':1, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Tirupati'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':1, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Vijayawada'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':1,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Visakhapatnam'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':1, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Gaya'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':1, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Muzaffarpur'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':1, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Patna'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':1, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Delhi'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':1,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Ahmedabad'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':1, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Faridabad'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':1, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Gurugram'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':1, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Manesar'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':1, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Panchkula'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':1,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Rohtak'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':1, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Jorapokhar'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':1, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Bengaluru'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':1, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Chikkaballapur'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':1, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Hubballi'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':1,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Thiruvananthapuram'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':1, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Dewas'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':1, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Mandideep'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':1, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Pithampur'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':1, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Satna'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':1,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Singrauli'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':1, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Ujjain'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':1, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Aurangabad'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':1, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Chandrapur'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':1, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Mumbai'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':1,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Nagpur'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':1, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Nashik'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':1, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Pune'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':1, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Solapur'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':1, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Thane'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':1, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Brajrajnagar'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':1,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Talcher'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':1, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Amritsar'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':1, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Bathinda'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':1, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Jalandhar'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':1, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Khanna'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':1,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Ludhiana'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':1, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Mandi_Gobindgarh'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':1, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Patiala'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':1, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Rupnagar'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':1, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Ajmer'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':1,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Alwar'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':1, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Bhiwadi'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':1, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Jaipur'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':1, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Jodhpur'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':1, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Kota'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':1, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Pali'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':1, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Udaipur'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':1,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Chennai'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':1, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Hyderabad'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':1, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Agra'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':1, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Baghpat'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':1, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Bulandshahr'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':1,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Ghaziabad'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':1, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Greater_Noida'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':1, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Kanpur'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':1, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Lucknow'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':1, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Moradabad'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':1,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Muzaffarnagar'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':1, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Noida'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':1, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Varanasi'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':1, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Asanol'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':1, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Durgapur'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':1,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Haldia'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':1, 'Howrah':0, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Howrah'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':1, 'Kolkata':0, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Kolkata'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':0, 'Howrah':0, 'Kolkata':1, 'Siliguri':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Siliguri'] = df.city.map({'Amaravati':0,'Rajamahendravaram':0, 'Tirupati':0, 'Vijayawada':0,\n",
    "       'Visakhapatnam':0, 'Gaya':0, 'Muzaffarpur':0, 'Patna':0, 'Delhi':0,\n",
    "       'Ahmedabad':0, 'Faridabad':0, 'Gurugram':0, 'Manesar':0, 'Panchkula':0,\n",
    "       'Rohtak':0, 'Jorapokhar':0, 'Bengaluru':0, 'Chikkaballapur':0, 'Hubballi':0,\n",
    "       'Thiruvananthapuram':0, 'Dewas':0, 'Mandideep':0, 'Pithampur':0, 'Satna':0,\n",
    "       'Singrauli':0, 'Ujjain':0, 'Aurangabad':0, 'Chandrapur':0, 'Mumbai':0,\n",
    "       'Nagpur':0, 'Nashik':0, 'Pune':0, 'Solapur':0, 'Thane':0, 'Brajrajnagar':0,\n",
    "       'Talcher':0, 'Amritsar':0, 'Bathinda':0, 'Jalandhar':0, 'Khanna':0,\n",
    "       'Ludhiana':0, 'Mandi Gobindgarh':0, 'Patiala':0, 'Rupnagar':0, 'Ajmer':0,\n",
    "       'Alwar':0, 'Bhiwadi':0, 'Jaipur':0, 'Jodhpur':0, 'Kota':0, 'Pali':0, 'Udaipur':0,\n",
    "       'Chennai':0, 'Hyderabad':0, 'Agra':0, 'Baghpat':0, 'Bulandshahr':0,\n",
    "       'Ghaziabad':0, 'Greater_Noida':0, 'Kanpur':0, 'Lucknow':0, 'Moradabad':0,\n",
    "       'Muzaffarnagar':0, 'Noida':0, 'Varanasi':0, 'Asanol':0, 'Durgapur':0,\n",
    "       'Haldia':1, 'Howrah':0, 'Kolkata':0, 'Siliguri':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 70.,  76.,  73.,   5.,  41.,  44.,  29.,  30., 108.,  46.,  64.,\n",
       "        61.,   2.,  10.,  16.,  36.,  65.,  15.,  14.,  71.,  39.,   3.,\n",
       "         6.,  68., 342.,  12.,  11.,  54.,  56., 436., 457.,  34.,  95.,\n",
       "       389., 314., 131.,  89.,  20., 418., 333.,  40.,  86.,  63., 329.,\n",
       "       257.,  25., 114.,  45., 311., 345.,  17.,  21., 388., 317.,  28.,\n",
       "       106.,  27., 385., 360.,  85.,  84., 401., 350.,  80.,  72., 361.,\n",
       "       278.,  93.,  51., 150., 228., 383.,  59.,  75., 356., 304.,  50.,\n",
       "         7.,   8., 323.,  22.,  52., 396., 272.,  47.,  88., 426., 397.,\n",
       "        19.,  33.,  97., 411., 374., 101.,  35., 274.,  24.,  31., 110.,\n",
       "        18.,  77., 379., 235., 187.,   4., 433., 414.,  74.,  79., 358.,\n",
       "        26., 336., 201.,  55., 392., 307.,  53., 439., 378., 102.,  38.,\n",
       "       349.,  32., 380., 393., 308.,  58., 395., 320., 119., 410., 381.,\n",
       "       354., 269.,  60., 408., 364., 126., 185., 421., 372.,  82.,  81.,\n",
       "        69.,  42.,  37., 305., 103.,  48., 359., 247.,  13.,  49., 390.,\n",
       "       339., 431., 413., 223., 377., 229., 265., 207., 149., 121., 321.,\n",
       "       130.,  67.,  98., 266., 105.,   9., 195., 107.,  66.,   1.,  78.,\n",
       "       139.,  99., 120., 190., 165.,  43., 104.,  92., 148., 100., 337.,\n",
       "       318., 214., 161.,  96., 113.,  83., 141., 180., 177., 142.,  87.,\n",
       "       122., 111., 203., 146.,  90., 118., 124., 240., 232., 157., 154.,\n",
       "       160., 112.,  62., 276., 253., 176., 238., 204., 156., 193.,  91.,\n",
       "       125., 231., 221., 420., 371., 129., 406., 325., 144., 373., 370.,\n",
       "       127., 115., 241., 341., 164., 287., 167., 138., 297., 277., 217.,\n",
       "       163., 302.,  94., 280., 183.,  57., 136.])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Avg'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['APLevels'] = df.Avg.map({70.:'Above_70',  76.:'Above_70',  73.:'Above_70',   5.:'Below_10',  41.:'Above_40',\n",
    "                                  44.:'Above_40',  29.:'Above_20',  30.:'Above_30', 108.:'Above_100',  46.:'Above_40',\n",
    "                                  64.:'Above_60',\n",
    "        61.:'Above_60',   2.:'Below_10',  10.:'Above_10',  16.:'Above_10',  36.:'Above_30',  65.:'Above_60',\n",
    "                                  15.:'Above_10',  14.:'Above_10',  71.:'Above_70',  39.:'Above_30',   3.:'Below_10',\n",
    "         6.:'Below_10',  68.:'Above_60', 342.:'Above_300',  12.:'Above_10',  11.:'Above_10',  54.:'Above_50',\n",
    "                                  56.:'Above_50', 436.:'Above_400', 457.:'Above_450',  34.:'Above_30',  95.:'Above_90',\n",
    "       389.:'Above_350', 314.:'Above_300', 131.:'Above_125',  89.:'Above_80',  20.:'Above_20', 418.:'Above_400',\n",
    "                                  333.:'Above_300',  40.:'Above_40',  86.:'Above_80',  63.:'Above_10', 329.:'Above_300',\n",
    "       257.:'Above_250',  25.:'Above_20', 114.:'Above_100',  45.:'Above_40', 311.:'Above_300', 345.:'Above_300',\n",
    "                                  17.:'Above_10',  21.:'Above_20', 388.:'Above_350', 317.:'Above_300',  28.:'Above_20',\n",
    "       106.:'Above_100',  27.:'Above_20', 385.:'Above_350', 360.:'Above_350',  85.:'Above_80',  84.:'Above_80',\n",
    "                                  401.:'Above_400', 350.:'Above_350',  80.:'Above_80',  72.:'Above_70', 361.:'Above_350',\n",
    "       278.:'Above_250',  93.:'Above_90',  51.:'Above_50', 150.:'Above_150', 228.:'Above_200', 383.:'Above_350',\n",
    "                                  59.:'Above_50',  75.:'Above_70', 356.:'Above_350', 304.:'Above_300',  50.:'Above_50',\n",
    "         7.:'Below_10',   8.:'Below_10', 323.:'Above_300',  22.:'Above_20',  52.:'Above_50', 396.:'Above_350',\n",
    "                                  272.:'Above_250',  47.:'Above_40',  88.:'Above_80', 426.:'Above_400', 397.:'Above_350',\n",
    "        19.:'Above_10',  33.:'Above_30',  97.:'Above_90', 411.:'Above_400', 374.:'Above_350', 101.:'Above_100',\n",
    "                                  35.:'Above_30', 274.:'Above_250',  24.:'Above_20',  31.:'Above_30', 110.:'Above_100',\n",
    "        18.:'Above_10',  77.:'Above_70', 379.:'Above_350', 235.:'Above_200', 187.:'Above_175',\n",
    "                                  4.:'Below_10', 433.:'Above_400', 414.:'Above_400',  74.:'Above_70',  79.:'Above_70',\n",
    "                                  358.:'Above_350',\n",
    "        26.:'Above_20', 336.:'Above_300', 201.:'Above_200',  55.:'Above_50', 392.:'Above_350', 307.:'Above_300',\n",
    "                                  53.:'Above_50', 439.:'Above_400', 378.:'Above_350', 102.:'Above_100',  38.:'Above_30',\n",
    "       349.:'Above_300',  32.:'Above_30', 380.:'Above_350', 393.:'Above_350', 308.:'Above_300',  58.:'Above_50',\n",
    "                                  395.:'Above_350', 320.:'Above_300', 119.:'Above_100', 410.:'Above_400', 381.:'Above_350',\n",
    "       354.:'Above_350', 269.:'Above_250',  60.:'Above_60', 408.:'Above_400', 364.:'Above_350', 126.:'Above_125',\n",
    "                                  185.:'Above_175', 421.:'Above_70', 372.:'Above_350',  82.:'Above_80',  81.:'Above_80',\n",
    "        69.:'Above_60',  42.:'Above_40',  37.:'Above_30', 305.:'Above_300', 103.:'Above_100',  48.:'Above_40',\n",
    "                                  359.:'Above_350', 247.:'Above_200',  13.:'Above_10',  49.:'Above_40', 390.:'Above_350',\n",
    "       339.:'Above_300', 431.:'Above_400', 413.:'Above_400', 223.:'Above_200', 377.:'Above_350', 229.:'Above_200',\n",
    "                                  265.:'Above_250', 207.:'Above_200', 149.:'Above_125', 121.:'Above_100', 321.:'Above_300',\n",
    "       130.:'Above_125',  67.:'Above_60',  98.:'Above_90', 266.:'Above_250', 105.:'Above_100',   9.:'Below_10',\n",
    "                                  195.:'Above_175', 107.:'Above_100',  66.:'Above_60',   1.:'Below_10',  78.:'Above_70',\n",
    "       139.:'Above_125',  99.:'Above_90', 120.:'Above_100', 190.:'Above_175', 165.:'Above_150',  43.:'Above_40',\n",
    "                                  104.:'Above_100',  92.:'Above_90', 148.:'Above_125', 100.:'Above_100', 337.:'Above_300',\n",
    "       318.:'Above_300', 214.:'Above_200', 161.:'Above_150',  96.:'Above_90', 113.:'Above_100',  83.:'Above_80',\n",
    "                                  141.:'Above_125', 180.:'Above_175', 177.:'Above_175', 142.:'Above_125',  87.:'Above_70',\n",
    "       122.:'Above_100', 111.:'Above_100', 203.:'Above_200', 146.:'Above_125',  90.:'Above_90', 118.:'Above_100',\n",
    "                                  124.:'Above_100', 240.:'Above_200', 232.:'Above_200', 157.:'Above_150', 154.:'Above_150',\n",
    "       160.:'Above_150', 112.:'Above_100',  62.:'Above_60', 276.:'Above_250', 253.:'Above_250', 176.:'Above_175',\n",
    "                                  238.:'Above_200', 204.:'Above_200', 156.:'Above_150', 193.:'Above_175',  91.:'Above_90',\n",
    "       125.:'Above_125', 231.:'Above_200', 221.:'Above_200', 420.:'Above_400', 371.:'Above_350', 129.:'Above_125',\n",
    "                                  406.:'Above_400', 325.:'Above_300', 144.:'Above_125', 373.:'Above_350', 370.:'Above_350',\n",
    "       127.:'Above_125', 115.:'Above_100', 241.:'Above_200', 341.:'Above_300', 164.:'Above_150', 287.:'Above_250',\n",
    "                                  167.:'Above_150', 138.:'Above_125', 297.:'Above_250', 277.:'Above_250', 217.:'Above_200',\n",
    "       163.:'Above_150', 302.:'Above_300',  94.:'Above_90', 280.:'Above_250', 183.:'Above_150',  57.:'Above_50', 136.:'Above_125'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Above_70', 'Below_10', 'Above_40', 'Above_20', 'Above_30',\n",
       "       'Above_100', 'Above_60', 'Above_10', 'Above_300', 'Above_50',\n",
       "       'Above_400', 'Above_450', 'Above_90', 'Above_350', 'Above_125',\n",
       "       'Above_80', 'Above_250', 'Above_150', 'Above_200', 'Above_175'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['APLevels'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Above_70'] = df.APLevels.map({'Above_70':1, 'Below_10':0, 'Above_40':0,\n",
    "                             'Above_20':0, 'Above_30':0,\n",
    "       'Above_100':0, 'Above_60':0, 'Above_10':0, 'Above_300':0,\n",
    "                             'Above_50':0,\n",
    "       'Above_400':0, 'Above_450':0, 'Above_90':0, 'Above_350':0, 'Above_125':0,\n",
    "       'Above_80':0, 'Above_250':0, 'Above_150':0, 'Above_200':0, 'Above_175':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Below_10'] = df.APLevels.map({'Above_70':0, 'Below_10':1, 'Above_40':0,\n",
    "                             'Above_20':0, 'Above_30':0,\n",
    "       'Above_100':0, 'Above_60':0, 'Above_10':0, 'Above_300':0,\n",
    "                             'Above_50':0,\n",
    "       'Above_400':0, 'Above_450':0, 'Above_90':0, 'Above_350':0, 'Above_125':0,\n",
    "       'Above_80':0, 'Above_250':0, 'Above_150':0, 'Above_200':0, 'Above_175':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Above_40'] = df.APLevels.map({'Above_70':0, 'Below_10':0, 'Above_40':1,\n",
    "                             'Above_20':0, 'Above_30':0,\n",
    "       'Above_100':0, 'Above_60':0, 'Above_10':0, 'Above_300':0,\n",
    "                             'Above_50':0,\n",
    "       'Above_400':0, 'Above_450':0, 'Above_90':0, 'Above_350':0, 'Above_125':0,\n",
    "       'Above_80':0, 'Above_250':0, 'Above_150':0, 'Above_200':0, 'Above_175':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Above_20'] = df.APLevels.map({'Above_70':0, 'Below_10':0, 'Above_40':0,\n",
    "                             'Above_20':1, 'Above_30':0,\n",
    "       'Above_100':0, 'Above_60':0, 'Above_10':0, 'Above_300':0,\n",
    "                             'Above_50':0,\n",
    "       'Above_400':0, 'Above_450':0, 'Above_90':0, 'Above_350':0, 'Above_125':0,\n",
    "       'Above_80':0, 'Above_250':0, 'Above_150':0, 'Above_200':0, 'Above_175':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Above_30'] = df.APLevels.map({'Above_70':0, 'Below_10':0, 'Above_40':0,\n",
    "                             'Above_20':0, 'Above_30':1,\n",
    "       'Above_100':0, 'Above_60':0, 'Above_10':0, 'Above_300':0,\n",
    "                             'Above_50':0,\n",
    "       'Above_400':0, 'Above_450':0, 'Above_90':0, 'Above_350':0, 'Above_125':0,\n",
    "       'Above_80':0, 'Above_250':0, 'Above_150':0, 'Above_200':0, 'Above_175':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Above_100'] = df.APLevels.map({'Above_70':0, 'Below_10':0, 'Above_40':0,\n",
    "                             'Above_20':0, 'Above_30':0,\n",
    "       'Above_100':1, 'Above_60':0, 'Above_10':0, 'Above_300':0,\n",
    "                             'Above_50':0,\n",
    "       'Above_400':0, 'Above_450':0, 'Above_90':0, 'Above_350':0, 'Above_125':0,\n",
    "       'Above_80':0, 'Above_250':0, 'Above_150':0, 'Above_200':0, 'Above_175':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Above_60'] = df.APLevels.map({'Above_70':0, 'Below_10':0, 'Above_40':0,\n",
    "                             'Above_20':0, 'Above_30':0,\n",
    "       'Above_100':0, 'Above_60':1, 'Above_10':0, 'Above_300':0,\n",
    "                             'Above_50':0,\n",
    "       'Above_400':0, 'Above_450':0, 'Above_90':0, 'Above_350':0, 'Above_125':0,\n",
    "       'Above_80':0, 'Above_250':0, 'Above_150':0, 'Above_200':0, 'Above_175':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Above_10'] = df.APLevels.map({'Above_70':0, 'Below_10':0, 'Above_40':0,\n",
    "                             'Above_20':0, 'Above_30':0,\n",
    "       'Above_100':0, 'Above_60':0, 'Above_10':1, 'Above_300':0,\n",
    "                             'Above_50':0,\n",
    "       'Above_400':0, 'Above_450':0, 'Above_90':0, 'Above_350':0, 'Above_125':0,\n",
    "       'Above_80':0, 'Above_250':0, 'Above_150':0, 'Above_200':0, 'Above_175':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Above_300'] = df.APLevels.map({'Above_70':0, 'Below_10':0, 'Above_40':0,\n",
    "                             'Above_20':0, 'Above_30':0,\n",
    "       'Above_100':0, 'Above_60':0, 'Above_10':0, 'Above_300':1,\n",
    "                             'Above_50':0,\n",
    "       'Above_400':0, 'Above_450':0, 'Above_90':0, 'Above_350':0, 'Above_125':0,\n",
    "       'Above_80':0, 'Above_250':0, 'Above_150':0, 'Above_200':0, 'Above_175':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Above_50'] = df.APLevels.map({'Above_70':0, 'Below_10':0, 'Above_40':0,\n",
    "                             'Above_20':0, 'Above_30':0,\n",
    "       'Above_100':0, 'Above_60':0, 'Above_10':0, 'Above_300':0,\n",
    "                             'Above_50':1,\n",
    "       'Above_400':0, 'Above_450':0, 'Above_90':0, 'Above_350':0, 'Above_125':0,\n",
    "       'Above_80':0, 'Above_250':0, 'Above_150':0, 'Above_200':0, 'Above_175':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Above_400'] = df.APLevels.map({'Above_70':0, 'Below_10':0, 'Above_40':0,\n",
    "                             'Above_20':0, 'Above_30':0,\n",
    "       'Above_100':0, 'Above_60':0, 'Above_10':0, 'Above_300':0,\n",
    "                             'Above_50':0,\n",
    "       'Above_400':1, 'Above_450':0, 'Above_90':0, 'Above_350':0, 'Above_125':0,\n",
    "       'Above_80':0, 'Above_250':0, 'Above_150':0, 'Above_200':0, 'Above_175':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Above_450'] = df.APLevels.map({'Above_70':0, 'Below_10':0, 'Above_40':0,\n",
    "                             'Above_20':0, 'Above_30':0,\n",
    "       'Above_100':0, 'Above_60':0, 'Above_10':0, 'Above_300':0,\n",
    "                             'Above_50':0,\n",
    "       'Above_400':0, 'Above_450':1, 'Above_90':0, 'Above_350':0, 'Above_125':0,\n",
    "       'Above_80':0, 'Above_250':0, 'Above_150':0, 'Above_200':0, 'Above_175':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Above_90'] = df.APLevels.map({'Above_70':0, 'Below_10':0, 'Above_40':0,\n",
    "                             'Above_20':0, 'Above_30':0,\n",
    "       'Above_100':0, 'Above_60':0, 'Above_10':0, 'Above_300':0,\n",
    "                             'Above_50':0,\n",
    "       'Above_400':0, 'Above_450':0, 'Above_90':1, 'Above_350':0, 'Above_125':0,\n",
    "       'Above_80':0, 'Above_250':0, 'Above_150':0, 'Above_200':0, 'Above_175':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Above_350'] = df.APLevels.map({'Above_70':0, 'Below_10':0, 'Above_40':0,\n",
    "                             'Above_20':0, 'Above_30':0,\n",
    "       'Above_100':0, 'Above_60':0, 'Above_10':0, 'Above_300':0,\n",
    "                             'Above_50':0,\n",
    "       'Above_400':0, 'Above_450':0, 'Above_90':0, 'Above_350':1, 'Above_125':0,\n",
    "       'Above_80':0, 'Above_250':0, 'Above_150':0, 'Above_200':0, 'Above_175':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Above_125'] = df.APLevels.map({'Above_70':0, 'Below_10':0, 'Above_40':0,\n",
    "                             'Above_20':0, 'Above_30':0,\n",
    "       'Above_100':0, 'Above_60':0, 'Above_10':0, 'Above_300':0,\n",
    "                             'Above_50':0,\n",
    "       'Above_400':0, 'Above_450':0, 'Above_90':0, 'Above_350':0, 'Above_125':1,\n",
    "       'Above_80':0, 'Above_250':0, 'Above_150':0, 'Above_200':0, 'Above_175':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Above_80'] = df.APLevels.map({'Above_70':0, 'Below_10':0, 'Above_40':0,\n",
    "                             'Above_20':0, 'Above_30':0,\n",
    "       'Above_100':0, 'Above_60':0, 'Above_10':0, 'Above_300':0,\n",
    "                             'Above_50':0,\n",
    "       'Above_400':0, 'Above_450':0, 'Above_90':0, 'Above_350':0, 'Above_125':0,\n",
    "       'Above_80':1, 'Above_250':0, 'Above_150':0, 'Above_200':0, 'Above_175':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Above_250'] = df.APLevels.map({'Above_70':0, 'Below_10':0, 'Above_40':0,\n",
    "                             'Above_20':0, 'Above_30':0,\n",
    "       'Above_100':0, 'Above_60':0, 'Above_10':0, 'Above_300':0,\n",
    "                             'Above_50':0,\n",
    "       'Above_400':0, 'Above_450':0, 'Above_90':0, 'Above_350':0, 'Above_125':0,\n",
    "       'Above_80':0, 'Above_250':1, 'Above_150':0, 'Above_200':0, 'Above_175':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Above_150'] = df.APLevels.map({'Above_70':0, 'Below_10':0, 'Above_40':0,\n",
    "                             'Above_20':0, 'Above_30':0,\n",
    "       'Above_100':0, 'Above_60':0, 'Above_10':0, 'Above_300':0,\n",
    "                             'Above_50':0,\n",
    "       'Above_400':0, 'Above_450':0, 'Above_90':0, 'Above_350':0, 'Above_125':0,\n",
    "       'Above_80':0, 'Above_250':0, 'Above_150':1, 'Above_200':0, 'Above_175':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Above_200'] = df.APLevels.map({'Above_70':0, 'Below_10':0, 'Above_40':0,\n",
    "                             'Above_20':0, 'Above_30':0,\n",
    "       'Above_100':0, 'Above_60':0, 'Above_10':0, 'Above_300':0,\n",
    "                             'Above_50':0,\n",
    "       'Above_400':0, 'Above_450':0, 'Above_90':0, 'Above_350':0, 'Above_125':0,\n",
    "       'Above_80':0, 'Above_250':0, 'Above_150':0, 'Above_200':1, 'Above_175':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Above_175'] = df.APLevels.map({'Above_70':0, 'Below_10':0, 'Above_40':0,\n",
    "                             'Above_20':0, 'Above_30':0,\n",
    "       'Above_100':0, 'Above_60':0, 'Above_10':0, 'Above_300':0,\n",
    "                             'Above_50':0,\n",
    "       'Above_400':0, 'Above_450':0, 'Above_90':0, 'Above_350':0, 'Above_125':0,\n",
    "       'Above_80':0, 'Above_250':0, 'Above_150':0, 'Above_200':0, 'Above_175':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Good (0–50)-Minimal impact\n",
    "#Satisfactory (51–100)-May cause minor breathing discomfort to sensitive people.\n",
    "#Moderately polluted (101–200)-May cause breathing discomfort to people with lung disease such as asthma, and discomfort to people with heart disease, children and older adults.\n",
    "#Poor (201–300)-May cause breathing discomfort to people on prolonged exposure, and discomfort to people with heart disease.\n",
    "#Very poor (301–400)-May cause respiratory illness to the people on prolonged exposure. Effect may be more pronounced in people with lung and heart diseases.\n",
    "#Severe (401–500)-May cause respiratory impact even on healthy people, and serious health impacts on people with lung/heart disease. The health impacts may be experienced even during light physical activity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['AQI'] = df.APLevels.map({'Above_70':'Satisfactory', 'Below_10':'Good', 'Above_40':'Good',\n",
    "                             'Above_20':'Good', 'Above_30':'Good',\n",
    "       'Above_100':'Moderate', 'Above_60':'Satisfactory', 'Above_10':'Good', 'Above_300':'Very_Poor',\n",
    "                             'Above_50':'Satisfactory',\n",
    "       'Above_400':'Severe', 'Above_450':'Severe', 'Above_90':'Satisfactory', 'Above_350':'Very_Poor', 'Above_125':'Moderate',\n",
    "       'Above_80':'Satisfactory', 'Above_250':'Poor', 'Above_150':'Moderate', 'Above_200':'Poor', 'Above_175':'Moderate'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Satisfactory', 'Good', 'Moderate', 'Very_Poor', 'Severe', 'Poor'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['AQI'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['PM2.5', 'PM10', 'NO2', 'NH3', 'SO2', 'CO', 'OZONE'], dtype=object)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Pollutants'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Good (0–50)-Minimal impact\n",
    "#Satisfactory (51–100)-May cause minor breathing discomfort to sensitive people.\n",
    "#Moderately polluted (101–200)-May cause breathing discomfort to people with lung disease such as asthma, and discomfort to people with heart disease, children and older adults.\n",
    "#Poor (201–300)-May cause breathing discomfort to people on prolonged exposure, and discomfort to people with heart disease.\n",
    "#Very poor (301–400)-May cause respiratory illness to the people on prolonged exposure. Effect may be more pronounced in people with lung and heart diseases.\n",
    "#Severe (401–500)-May cause respiratory impact even on healthy people, and serious health impacts on people with lung/heart disease. The health impacts may be experienced even during light physical activity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['AQICause'] = df.AQI.map({'Satisfactory':'(Satisfactory)-May_cause_minor_breathing_discomfort_to_sensitive_people.',\n",
    "'Good':'(Good)-Minimal_impact',\n",
    "'Moderate':'(Moderate)-May_cause_breathing_discomfort_to_people_with_lung_disease_such_as_asthma,_and_discomfort_to_people_with_heart_disease,_children_and_older_adults.',\n",
    "'Very_Poor':'(Very_Poor)-May_cause_respiratory_illness_to_the_people_on_prolonged_exposure._Effect_may_be_more_pronounced-in_people_with_lung_and_heart_diseases.',\n",
    "'Severe':'(Severe)-May_cause_respiratory_impact_even_on_healthy_people,_and_serious_health_impacts_on_people_with_lung/heart_disease._The_health_impacts_may_be_experienced_even_during_light_physical_activity.',\n",
    "'Poor':'(Poor)-May_cause_breathing_discomfort_to_people_on_prolonged_exposure,_and_discomfort_to_people_with_heart_disease.'})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>city</th>\n",
       "      <th>Avg</th>\n",
       "      <th>Max</th>\n",
       "      <th>Min</th>\n",
       "      <th>Pollutants</th>\n",
       "      <th>Source_of_Pollutants</th>\n",
       "      <th>Andhra_Pradesh</th>\n",
       "      <th>Bihar</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>...</th>\n",
       "      <th>Above_90</th>\n",
       "      <th>Above_350</th>\n",
       "      <th>Above_125</th>\n",
       "      <th>Above_80</th>\n",
       "      <th>Above_250</th>\n",
       "      <th>Above_150</th>\n",
       "      <th>Above_200</th>\n",
       "      <th>Above_175</th>\n",
       "      <th>AQI</th>\n",
       "      <th>AQICause</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Amaravati</td>\n",
       "      <td>70.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>Combustion, Fireplaces, Car engines and Coal o...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Satisfactory</td>\n",
       "      <td>(Satisfactory)-May_cause_minor_breathing_disco...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Amaravati</td>\n",
       "      <td>76.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>PM10</td>\n",
       "      <td>Burning of fuels, Wood stoves, Fires, Wind gen...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Satisfactory</td>\n",
       "      <td>(Satisfactory)-May_cause_minor_breathing_disco...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Amaravati</td>\n",
       "      <td>73.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>NO2</td>\n",
       "      <td>Internal combustion engines burning fossil fue...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Satisfactory</td>\n",
       "      <td>(Satisfactory)-May_cause_minor_breathing_disco...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Amaravati</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NH3</td>\n",
       "      <td>Emissions is agriculture, Including animal hus...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Good</td>\n",
       "      <td>(Good)-Minimal_impact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andhra_Pradesh</td>\n",
       "      <td>Amaravati</td>\n",
       "      <td>41.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>SO2</td>\n",
       "      <td>Burning of fossil fuels by power plants and Ot...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Good</td>\n",
       "      <td>(Good)-Minimal_impact</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 117 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            State       city   Avg    Max   Min Pollutants  \\\n",
       "0  Andhra_Pradesh  Amaravati  70.0  108.0  42.0      PM2.5   \n",
       "1  Andhra_Pradesh  Amaravati  76.0  102.0  43.0       PM10   \n",
       "2  Andhra_Pradesh  Amaravati  73.0  118.0  46.0        NO2   \n",
       "3  Andhra_Pradesh  Amaravati   5.0    6.0   4.0        NH3   \n",
       "4  Andhra_Pradesh  Amaravati  41.0  109.0   2.0        SO2   \n",
       "\n",
       "                                Source_of_Pollutants  Andhra_Pradesh  Bihar  \\\n",
       "0  Combustion, Fireplaces, Car engines and Coal o...               1      0   \n",
       "1  Burning of fuels, Wood stoves, Fires, Wind gen...               1      0   \n",
       "2  Internal combustion engines burning fossil fue...               1      0   \n",
       "3  Emissions is agriculture, Including animal hus...               1      0   \n",
       "4  Burning of fossil fuels by power plants and Ot...               1      0   \n",
       "\n",
       "   Delhi  ...  Above_90  Above_350  Above_125  Above_80  Above_250  Above_150  \\\n",
       "0      0  ...         0          0          0         0          0          0   \n",
       "1      0  ...         0          0          0         0          0          0   \n",
       "2      0  ...         0          0          0         0          0          0   \n",
       "3      0  ...         0          0          0         0          0          0   \n",
       "4      0  ...         0          0          0         0          0          0   \n",
       "\n",
       "   Above_200  Above_175           AQI  \\\n",
       "0          0          0  Satisfactory   \n",
       "1          0          0  Satisfactory   \n",
       "2          0          0  Satisfactory   \n",
       "3          0          0          Good   \n",
       "4          0          0          Good   \n",
       "\n",
       "                                            AQICause  \n",
       "0  (Satisfactory)-May_cause_minor_breathing_disco...  \n",
       "1  (Satisfactory)-May_cause_minor_breathing_disco...  \n",
       "2  (Satisfactory)-May_cause_minor_breathing_disco...  \n",
       "3                              (Good)-Minimal_impact  \n",
       "4                              (Good)-Minimal_impact  \n",
       "\n",
       "[5 rows x 117 columns]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df[\"State\"]\n",
    "del df[\"city\"]\n",
    "del df[\"Avg\"]\n",
    "del df[\"Max\"]\n",
    "del df[\"Min\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pollutants</th>\n",
       "      <th>Source_of_Pollutants</th>\n",
       "      <th>Andhra_Pradesh</th>\n",
       "      <th>Bihar</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Gujarat</th>\n",
       "      <th>Haryana</th>\n",
       "      <th>Jharkhand</th>\n",
       "      <th>Karnataka</th>\n",
       "      <th>Kerala</th>\n",
       "      <th>...</th>\n",
       "      <th>Above_90</th>\n",
       "      <th>Above_350</th>\n",
       "      <th>Above_125</th>\n",
       "      <th>Above_80</th>\n",
       "      <th>Above_250</th>\n",
       "      <th>Above_150</th>\n",
       "      <th>Above_200</th>\n",
       "      <th>Above_175</th>\n",
       "      <th>AQI</th>\n",
       "      <th>AQICause</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PM2.5</td>\n",
       "      <td>Combustion, Fireplaces, Car engines and Coal o...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Satisfactory</td>\n",
       "      <td>(Satisfactory)-May_cause_minor_breathing_disco...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PM10</td>\n",
       "      <td>Burning of fuels, Wood stoves, Fires, Wind gen...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Satisfactory</td>\n",
       "      <td>(Satisfactory)-May_cause_minor_breathing_disco...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NO2</td>\n",
       "      <td>Internal combustion engines burning fossil fue...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Satisfactory</td>\n",
       "      <td>(Satisfactory)-May_cause_minor_breathing_disco...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NH3</td>\n",
       "      <td>Emissions is agriculture, Including animal hus...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Good</td>\n",
       "      <td>(Good)-Minimal_impact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SO2</td>\n",
       "      <td>Burning of fossil fuels by power plants and Ot...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Good</td>\n",
       "      <td>(Good)-Minimal_impact</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>819</th>\n",
       "      <td>NO2</td>\n",
       "      <td>Internal combustion engines burning fossil fue...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Good</td>\n",
       "      <td>(Good)-Minimal_impact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820</th>\n",
       "      <td>NH3</td>\n",
       "      <td>Emissions is agriculture, Including animal hus...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Good</td>\n",
       "      <td>(Good)-Minimal_impact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>821</th>\n",
       "      <td>SO2</td>\n",
       "      <td>Burning of fossil fuels by power plants and Ot...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Good</td>\n",
       "      <td>(Good)-Minimal_impact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>822</th>\n",
       "      <td>CO</td>\n",
       "      <td>Clothes dryers, Water heaters, Furnaces or Boi...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Good</td>\n",
       "      <td>(Good)-Minimal_impact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>823</th>\n",
       "      <td>OZONE</td>\n",
       "      <td>Pollutants emitted by cars, Power plants, Indu...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Good</td>\n",
       "      <td>(Good)-Minimal_impact</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>788 rows × 112 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Pollutants                               Source_of_Pollutants  \\\n",
       "0        PM2.5  Combustion, Fireplaces, Car engines and Coal o...   \n",
       "1         PM10  Burning of fuels, Wood stoves, Fires, Wind gen...   \n",
       "2          NO2  Internal combustion engines burning fossil fue...   \n",
       "3          NH3  Emissions is agriculture, Including animal hus...   \n",
       "4          SO2  Burning of fossil fuels by power plants and Ot...   \n",
       "..         ...                                                ...   \n",
       "819        NO2  Internal combustion engines burning fossil fue...   \n",
       "820        NH3  Emissions is agriculture, Including animal hus...   \n",
       "821        SO2  Burning of fossil fuels by power plants and Ot...   \n",
       "822         CO  Clothes dryers, Water heaters, Furnaces or Boi...   \n",
       "823      OZONE  Pollutants emitted by cars, Power plants, Indu...   \n",
       "\n",
       "     Andhra_Pradesh  Bihar  Delhi  Gujarat  Haryana  Jharkhand  Karnataka  \\\n",
       "0                 1      0      0        0        0          0          0   \n",
       "1                 1      0      0        0        0          0          0   \n",
       "2                 1      0      0        0        0          0          0   \n",
       "3                 1      0      0        0        0          0          0   \n",
       "4                 1      0      0        0        0          0          0   \n",
       "..              ...    ...    ...      ...      ...        ...        ...   \n",
       "819               0      0      0        0        0          0          0   \n",
       "820               0      0      0        0        0          0          0   \n",
       "821               0      0      0        0        0          0          0   \n",
       "822               0      0      0        0        0          0          0   \n",
       "823               0      0      0        0        0          0          0   \n",
       "\n",
       "     Kerala  ...  Above_90  Above_350  Above_125  Above_80  Above_250  \\\n",
       "0         0  ...         0          0          0         0          0   \n",
       "1         0  ...         0          0          0         0          0   \n",
       "2         0  ...         0          0          0         0          0   \n",
       "3         0  ...         0          0          0         0          0   \n",
       "4         0  ...         0          0          0         0          0   \n",
       "..      ...  ...       ...        ...        ...       ...        ...   \n",
       "819       0  ...         0          0          0         0          0   \n",
       "820       0  ...         0          0          0         0          0   \n",
       "821       0  ...         0          0          0         0          0   \n",
       "822       0  ...         0          0          0         0          0   \n",
       "823       0  ...         0          0          0         0          0   \n",
       "\n",
       "     Above_150  Above_200  Above_175           AQI  \\\n",
       "0            0          0          0  Satisfactory   \n",
       "1            0          0          0  Satisfactory   \n",
       "2            0          0          0  Satisfactory   \n",
       "3            0          0          0          Good   \n",
       "4            0          0          0          Good   \n",
       "..         ...        ...        ...           ...   \n",
       "819          0          0          0          Good   \n",
       "820          0          0          0          Good   \n",
       "821          0          0          0          Good   \n",
       "822          0          0          0          Good   \n",
       "823          0          0          0          Good   \n",
       "\n",
       "                                              AQICause  \n",
       "0    (Satisfactory)-May_cause_minor_breathing_disco...  \n",
       "1    (Satisfactory)-May_cause_minor_breathing_disco...  \n",
       "2    (Satisfactory)-May_cause_minor_breathing_disco...  \n",
       "3                                (Good)-Minimal_impact  \n",
       "4                                (Good)-Minimal_impact  \n",
       "..                                                 ...  \n",
       "819                              (Good)-Minimal_impact  \n",
       "820                              (Good)-Minimal_impact  \n",
       "821                              (Good)-Minimal_impact  \n",
       "822                              (Good)-Minimal_impact  \n",
       "823                              (Good)-Minimal_impact  \n",
       "\n",
       "[788 rows x 112 columns]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Pollutants', 'Source_of_Pollutants', 'Andhra_Pradesh', 'Bihar',\n",
       "       'Delhi', 'Gujarat', 'Haryana', 'Jharkhand', 'Karnataka', 'Kerala',\n",
       "       ...\n",
       "       'Above_90', 'Above_350', 'Above_125', 'Above_80', 'Above_250',\n",
       "       'Above_150', 'Above_200', 'Above_175', 'AQI', 'AQICause'],\n",
       "      dtype='object', length=112)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "l1=['Andhra_Pradesh', 'Bihar', 'Delhi', 'Gujarat', 'Haryana',\n",
    "       'Jharkhand', 'Karnataka', 'Kerala', 'Madhya_Pradesh', 'Maharashtra',\n",
    "       'Odisha', 'Punjab', 'Rajasthan', 'TamilNadu', 'Telangana',\n",
    "       'Uttar_Pradesh', 'West_Bengal']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "l2=['Amaravati', 'Rajamahendravaram',\n",
    "       'Tirupati', 'Vijayawada', 'Visakhapatnam', 'Gaya', 'Muzaffarpur',\n",
    "       'Patna', 'Ahmedabad', 'Faridabad', 'Gurugram', 'Manesar', 'Panchkula',\n",
    "       'Rohtak', 'Jorapokhar', 'Bengaluru', 'Chikkaballapur', 'Hubballi',\n",
    "       'Thiruvananthapuram', 'Dewas', 'Mandideep', 'Pithampur', 'Satna',\n",
    "       'Singrauli', 'Ujjain', 'Aurangabad', 'Chandrapur', 'Mumbai', 'Nagpur',\n",
    "       'Nashik', 'Pune', 'Solapur', 'Thane', 'Brajrajnagar', 'Talcher',\n",
    "       'Amritsar', 'Bathinda', 'Jalandhar', 'Khanna', 'Ludhiana',\n",
    "       'Mandi_Gobindgarh', 'Patiala', 'Rupnagar', 'Ajmer', 'Alwar', 'Bhiwadi',\n",
    "       'Jaipur', 'Jodhpur', 'Kota', 'Pali', 'Udaipur', 'Chennai', 'Hyderabad',\n",
    "       'Agra', 'Baghpat', 'Bulandshahr', 'Ghaziabad', 'Greater_Noida',\n",
    "       'Kanpur', 'Lucknow', 'Moradabad', 'Muzaffarnagar', 'Noida', 'Varanasi',\n",
    "       'Asanol', 'Durgapur', 'Haldia', 'Howrah', 'Kolkata', 'Siliguri']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "l3=['Above_70', 'Below_10', 'Above_40', 'Above_20', 'Above_30',\n",
    "       'Above_100', 'Above_60', 'Above_10', 'Above_300', 'Above_50',\n",
    "       'Above_400', 'Above_450', 'Above_90', 'Above_350', 'Above_125',\n",
    "       'Above_80', 'Above_250', 'Above_150', 'Above_200', 'Above_175']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "l4=['Andhra_Pradesh', 'Bihar', 'Delhi', 'Gujarat', 'Haryana',\n",
    "       'Jharkhand', 'Karnataka', 'Kerala', 'Madhya_Pradesh', 'Maharashtra',\n",
    "       'Odisha', 'Punjab', 'Rajasthan', 'TamilNadu', 'Telangana',\n",
    "       'Uttar_Pradesh', 'West_Bengal', 'Amaravati', 'Rajamahendravaram',\n",
    "       'Tirupati', 'Vijayawada', 'Visakhapatnam', 'Gaya', 'Muzaffarpur',\n",
    "       'Patna', 'Ahmedabad', 'Faridabad', 'Gurugram', 'Manesar', 'Panchkula',\n",
    "       'Rohtak', 'Jorapokhar', 'Bengaluru', 'Chikkaballapur', 'Hubballi',\n",
    "       'Thiruvananthapuram', 'Dewas', 'Mandideep', 'Pithampur', 'Satna',\n",
    "       'Singrauli', 'Ujjain', 'Aurangabad', 'Chandrapur', 'Mumbai', 'Nagpur',\n",
    "       'Nashik', 'Pune', 'Solapur', 'Thane', 'Brajrajnagar', 'Talcher',\n",
    "       'Amritsar', 'Bathinda', 'Jalandhar', 'Khanna', 'Ludhiana',\n",
    "       'Mandi_Gobindgarh', 'Patiala', 'Rupnagar', 'Ajmer', 'Alwar', 'Bhiwadi',\n",
    "       'Jaipur', 'Jodhpur', 'Kota', 'Pali', 'Udaipur', 'Chennai', 'Hyderabad',\n",
    "       'Agra', 'Baghpat', 'Bulandshahr', 'Ghaziabad', 'Greater_Noida',\n",
    "       'Kanpur', 'Lucknow', 'Moradabad', 'Muzaffarnagar', 'Noida', 'Varanasi',\n",
    "       'Asanol', 'Durgapur', 'Haldia', 'Howrah', 'Kolkata', 'Siliguri', 'Above_70', 'Below_10', 'Above_40', 'Above_20', 'Above_30',\n",
    "       'Above_100', 'Above_60', 'Above_10', 'Above_300', 'Above_50',\n",
    "       'Above_400', 'Above_450', 'Above_90', 'Above_350', 'Above_125',\n",
    "       'Above_80', 'Above_250', 'Above_150', 'Above_200', 'Above_175' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['PM2.5', 'PM10', 'NO2', 'NH3', 'SO2', 'CO', 'OZONE'], dtype=object)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Pollutants'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Satisfactory', 'Good', 'Moderate', 'Very_Poor', 'Severe', 'Poor'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['AQI'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['(Satisfactory)-May_cause_minor_breathing_discomfort_to_sensitive_people.',\n",
       "       '(Good)-Minimal_impact',\n",
       "       '(Moderate)-May_cause_breathing_discomfort_to_people_with_lung_disease_such_as_asthma,_and_discomfort_to_people_with_heart_disease,_children_and_older_adults.',\n",
       "       '(Very_Poor)-May_cause_respiratory_illness_to_the_people_on_prolonged_exposure._Effect_may_be_more_pronounced-in_people_with_lung_and_heart_diseases.',\n",
       "       '(Severe)-May_cause_respiratory_impact_even_on_healthy_people,_and_serious_health_impacts_on_people_with_lung/heart_disease._The_health_impacts_may_be_experienced_even_during_light_physical_activity.',\n",
       "       '(Poor)-May_cause_breathing_discomfort_to_people_on_prolonged_exposure,_and_discomfort_to_people_with_heart_disease.'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['AQICause'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "pol=['PM2.5', 'PM10', 'NO2', 'NH3', 'SO2', 'CO', 'OZONE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "aqi=['Satisfactory', 'Good', 'Moderate', 'Very_Poor', 'Severe', 'Poor']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "sp=['Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.',\n",
    " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.',\n",
    " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.',\n",
    " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.',\n",
    " 'Burning of fossil fuels by power plants and Other industrial facilities.',\n",
    " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.',\n",
    " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "aqica=['(Satisfactory)-May cause minor breathing discomfort to sensitive people.',\n",
    "       '(Good)-Minimal impact',\n",
    "       '(Moderate)-May cause breathing discomfort to people with lung disease such as asthma, and discomfort to people with heart disease, children and older adults.',\n",
    "       '(Very_Poor)-May cause respiratory illness to the people on prolonged exposure. Effect may be more pronounced in people with lung and heart diseases.',\n",
    "       '(Severe)-May cause respiratory impact even on healthy people, and serious health impacts on people with lung/heart disease. The health impacts may be experienced even during light physical activity.',\n",
    "       '(Poor)-May cause breathing discomfort to people on prolonged exposure, and discomfort to people with heart disease.']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "l5=[]\n",
    "for x in range(0,len(l4)):\n",
    "    l5.append(0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "l6=[]\n",
    "for x in range(0,len(l4)):\n",
    "    l6.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "l7=[]\n",
    "for x in range(0,len(l4)):\n",
    "    l7.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.replace({'Pollutants':{'PM2.5':0, 'PM10':1, 'NO2':2, 'NH3':3, 'SO2':4, 'CO':5, 'OZONE':6}},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.replace({'AQI':{'Satisfactory':0, 'Good':1, 'Moderate':2, 'Very_Poor':3, 'Severe':4, 'Poor':5}},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.replace({'Source_of_Pollutants':{'Combustion, Fireplaces, Car engines and Coal or Natural gas fired power plants.':0,\n",
    " 'Burning of fuels, Wood stoves, Fires, Wind generated dust, Brick works, Refineries, Cement Works, Iron and Steel making.':1,\n",
    " 'Internal combustion engines burning fossil fuels, Cigarette smoke, Butane, Kerosene heaters and Stoves.':2,\n",
    " 'Emissions is agriculture, Including animal husbandry and NH-based fertilizer applications.':3,\n",
    " 'Burning of fossil fuels by power plants and Other industrial facilities.':4,\n",
    " 'Clothes dryers, Water heaters, Furnaces or Boilers, Fireplaces, Both gas and wood burning, Gas stoves and ovens, motor vehicles.':5,\n",
    " 'Pollutants emitted by cars, Power plants, Industrial boilers, Refineries, Chemical plants, Other sources renet chemically in th presence of sunlight.':6}},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.replace({'AQICause':{'(Satisfactory)-May_cause_minor_breathing_discomfort_to_sensitive_people.':1,\n",
    "'(Good)-Minimal_impact':0,\n",
    "'(Moderate)-May_cause_breathing_discomfort_to_people_with_lung_disease_such_as_asthma,_and_discomfort_to_people_with_heart_disease,_children_and_older_adults.':2,\n",
    "'(Very_Poor)-May_cause_respiratory_illness_to_the_people_on_prolonged_exposure._Effect_may_be_more_pronounced-in_people_with_lung_and_heart_diseases.':4,\n",
    "'(Severe)-May_cause_respiratory_impact_even_on_healthy_people,_and_serious_health_impacts_on_people_with_lung/heart_disease._The_health_impacts_may_be_experienced_even_during_light_physical_activity.':5,'(Poor)-May_cause_breathing_discomfort_to_people_on_prolonged_exposure,_and_discomfort_to_people_with_heart_disease.':3}},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5,\n",
       "       0, 1, 2, 3, 4, 6, 0, 2, 4, 5, 6, 0, 2, 4, 5, 6, 0, 2, 4, 5, 0, 1,\n",
       "       2, 3, 4, 5, 6, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 5, 6, 0, 1, 2, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n",
       "       1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 5, 6, 0, 2, 3, 4,\n",
       "       5, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6,\n",
       "       0, 1, 2, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 2, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5,\n",
       "       6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1,\n",
       "       2, 3, 4, 5, 6, 0, 1, 2, 3, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 5,\n",
       "       6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 2, 4, 5, 6, 0, 1,\n",
       "       2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 2, 4, 5, 0, 2, 4, 5, 6, 0, 2,\n",
       "       4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 2, 4, 5, 6, 0, 4, 5, 6, 1, 2, 3,\n",
       "       4, 6, 0, 2, 4, 5, 6, 0, 1, 2, 3, 4, 6, 1, 2, 4, 5, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 2, 3, 4, 5, 1, 2, 4, 5, 0, 1,\n",
       "       2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3,\n",
       "       4, 5, 6, 0, 1, 4, 5, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n",
       "       1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 4, 5, 6, 0, 1, 2, 4, 5,\n",
       "       6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 3, 4, 5, 6, 0, 1, 2,\n",
       "       3, 6, 0, 1, 2, 4, 5, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n",
       "       1, 2, 3, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
       "       4, 5, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5,\n",
       "       6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n",
       "       1, 2, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 2, 4, 5, 6, 0, 2, 3, 4, 5, 0, 2, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3,\n",
       "       4, 5, 6, 0, 2, 4, 5, 6, 5, 6, 0, 2, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6,\n",
       "       0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n",
       "       2, 4, 5, 6, 0, 2, 4, 5, 6, 0, 2, 4, 5, 6, 0, 2, 4, 5, 6, 0, 2, 3,\n",
       "       4, 5, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5,\n",
       "       6, 0, 1, 2, 5, 6, 0, 1, 2, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 1, 4, 5,\n",
       "       1, 2, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6], dtype=int64)"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X= df[l4]\n",
    "y = df[['Pollutants']]\n",
    "np.ravel(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 0, 0, 0, 0, 0, 2, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       1, 1, 0, 0, 0, 1, 4, 0, 0, 1, 1, 5, 0, 0, 1, 1, 5, 0, 0, 1, 4, 4,\n",
       "       2, 0, 0, 1, 0, 1, 0, 5, 4, 0, 0, 0, 1, 0, 4, 3, 0, 2, 0, 4, 4, 1,\n",
       "       0, 0, 1, 1, 4, 4, 0, 2, 0, 4, 4, 1, 1, 0, 5, 4, 1, 0, 0, 1, 1, 4,\n",
       "       3, 1, 0, 0, 1, 2, 3, 4, 1, 0, 0, 1, 0, 4, 4, 1, 0, 0, 4, 1, 0, 0,\n",
       "       1, 4, 3, 0, 0, 0, 1, 0, 5, 4, 1, 0, 0, 1, 0, 5, 4, 2, 0, 0, 0, 0,\n",
       "       4, 3, 1, 0, 0, 4, 4, 2, 0, 0, 1, 0, 4, 3, 2, 0, 0, 1, 0, 5, 5, 1,\n",
       "       0, 0, 1, 0, 4, 0, 0, 0, 0, 4, 3, 1, 0, 0, 1, 1, 4, 4, 1, 0, 0, 1,\n",
       "       1, 5, 4, 2, 0, 0, 1, 0, 4, 5, 1, 1, 0, 4, 4, 1, 0, 0, 1, 0, 4, 4,\n",
       "       0, 0, 0, 1, 0, 4, 4, 2, 0, 1, 0, 5, 4, 2, 0, 0, 2, 0, 4, 3, 1, 0,\n",
       "       0, 5, 4, 2, 0, 0, 0, 2, 1, 4, 1, 0, 0, 1, 1, 4, 1, 0, 1, 0, 4, 4,\n",
       "       1, 0, 0, 0, 0, 5, 4, 1, 0, 0, 2, 0, 4, 3, 1, 0, 0, 1, 0, 4, 4, 1,\n",
       "       0, 0, 1, 1, 5, 5, 2, 0, 0, 1, 0, 3, 1, 1, 0, 4, 2, 0, 1, 1, 3, 2,\n",
       "       0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 1, 0, 4, 0, 0,\n",
       "       1, 0, 2, 0, 0, 0, 1, 1, 1, 1, 0, 0, 3, 2, 0, 0, 1, 0, 0, 1, 1, 0,\n",
       "       0, 0, 0, 2, 2, 2, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "       0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 1, 0,\n",
       "       0, 0, 2, 1, 1, 1, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 1, 2, 2, 0, 0,\n",
       "       0, 0, 1, 0, 2, 1, 2, 4, 4, 0, 0, 0, 1, 1, 3, 2, 1, 0, 0, 1, 1, 1,\n",
       "       2, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 1, 2, 1, 0, 1, 0, 2, 2, 1,\n",
       "       0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 2, 2, 2, 0, 1, 2, 0, 2, 2, 1, 0, 1,\n",
       "       0, 1, 2, 2, 0, 0, 0, 0, 3, 2, 0, 0, 0, 1, 0, 0, 0, 2, 0, 1, 2, 0,\n",
       "       0, 0, 1, 2, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 2,\n",
       "       2, 0, 0, 0, 0, 3, 3, 0, 0, 0, 2, 0, 2, 2, 0, 0, 0, 0, 0, 2, 2, 0,\n",
       "       0, 0, 2, 2, 1, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 4, 4, 1, 0, 0, 0,\n",
       "       0, 2, 1, 1, 0, 0, 1, 1, 3, 3, 1, 0, 0, 0, 3, 2, 1, 0, 0, 1, 0, 3,\n",
       "       3, 1, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 1, 0, 2, 2, 0,\n",
       "       0, 0, 1, 0, 1, 0, 0, 1, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0,\n",
       "       0, 0, 0, 0, 1, 2, 1, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 2, 2, 1, 0,\n",
       "       0, 0, 0, 2, 1, 0, 0, 0, 0, 1, 4, 1, 0, 0, 0, 4, 3, 0, 0, 0, 0, 3,\n",
       "       5, 4, 2, 0, 0, 1, 2, 5, 4, 2, 0, 0, 1, 0, 5, 4, 2, 0, 0, 1, 0, 4,\n",
       "       2, 0, 1, 1, 4, 0, 0, 0, 0, 4, 1, 0, 1, 1, 4, 0, 0, 0, 0, 4, 1, 0,\n",
       "       0, 1, 4, 4, 2, 0, 0, 1, 1, 4, 3, 1, 0, 0, 1, 1, 5, 5, 2, 0, 0, 1,\n",
       "       0, 4, 4, 0, 1, 0, 4, 4, 2, 0, 0, 0, 3, 2, 1, 0, 0, 1, 0, 2, 0, 1,\n",
       "       2, 0, 0, 0, 0, 3, 3, 2, 0, 1, 1, 0, 4, 3, 0, 0, 0, 0, 2, 4, 4, 1,\n",
       "       0, 0, 0, 2, 3, 2, 1, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GO= df[l4]\n",
    "A = df[['AQICause']]\n",
    "np.ravel(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5,\n",
       "       0, 1, 2, 3, 4, 6, 0, 2, 4, 5, 6, 0, 2, 4, 5, 6, 0, 2, 4, 5, 0, 1,\n",
       "       2, 3, 4, 5, 6, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 5, 6, 0, 1, 2, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n",
       "       1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 5, 6, 0, 2, 3, 4,\n",
       "       5, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6,\n",
       "       0, 1, 2, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 2, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5,\n",
       "       6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1,\n",
       "       2, 3, 4, 5, 6, 0, 1, 2, 3, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 5,\n",
       "       6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 2, 4, 5, 6, 0, 1,\n",
       "       2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 2, 4, 5, 0, 2, 4, 5, 6, 0, 2,\n",
       "       4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 2, 4, 5, 6, 0, 4, 5, 6, 1, 2, 3,\n",
       "       4, 6, 0, 2, 4, 5, 6, 0, 1, 2, 3, 4, 6, 1, 2, 4, 5, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 2, 3, 4, 5, 1, 2, 4, 5, 0, 1,\n",
       "       2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3,\n",
       "       4, 5, 6, 0, 1, 4, 5, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n",
       "       1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 4, 5, 6, 0, 1, 2, 4, 5,\n",
       "       6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 3, 4, 5, 6, 0, 1, 2,\n",
       "       3, 6, 0, 1, 2, 4, 5, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n",
       "       1, 2, 3, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
       "       4, 5, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5,\n",
       "       6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n",
       "       1, 2, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 2, 4, 5, 6, 0, 2, 3, 4, 5, 0, 2, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3,\n",
       "       4, 5, 6, 0, 2, 4, 5, 6, 5, 6, 0, 2, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6,\n",
       "       0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n",
       "       2, 4, 5, 6, 0, 2, 4, 5, 6, 0, 2, 4, 5, 6, 0, 2, 4, 5, 6, 0, 2, 3,\n",
       "       4, 5, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5,\n",
       "       6, 0, 1, 2, 5, 6, 0, 1, 2, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 1, 4, 5,\n",
       "       1, 2, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6], dtype=int64)"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xsp= df[l4]\n",
    "ysp = df[['Source_of_Pollutants']]\n",
    "np.ravel(ysp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5,\n",
       "       0, 1, 2, 3, 4, 6, 0, 2, 4, 5, 6, 0, 2, 4, 5, 6, 0, 2, 4, 5, 0, 1,\n",
       "       2, 3, 4, 5, 6, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 5, 6, 0, 1, 2, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n",
       "       1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 5, 6, 0, 2, 3, 4,\n",
       "       5, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6,\n",
       "       0, 1, 2, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 2, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5,\n",
       "       6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1,\n",
       "       2, 3, 4, 5, 6, 0, 1, 2, 3, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 5,\n",
       "       6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 2, 4, 5, 6, 0, 1,\n",
       "       2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 2, 4, 5, 0, 2, 4, 5, 6, 0, 2,\n",
       "       4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 2, 4, 5, 6, 0, 4, 5, 6, 1, 2, 3,\n",
       "       4, 6, 0, 2, 4, 5, 6, 0, 1, 2, 3, 4, 6, 1, 2, 4, 5, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 2, 3, 4, 5, 1, 2, 4, 5, 0, 1,\n",
       "       2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3,\n",
       "       4, 5, 6, 0, 1, 4, 5, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n",
       "       1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 4, 5, 6, 0, 1, 2, 4, 5,\n",
       "       6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 3, 4, 5, 6, 0, 1, 2,\n",
       "       3, 6, 0, 1, 2, 4, 5, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n",
       "       1, 2, 3, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
       "       4, 5, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5,\n",
       "       6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n",
       "       1, 2, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 2, 4, 5, 6, 0, 2, 3, 4, 5, 0, 2, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3,\n",
       "       4, 5, 6, 0, 2, 4, 5, 6, 5, 6, 0, 2, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6,\n",
       "       0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n",
       "       2, 4, 5, 6, 0, 2, 4, 5, 6, 0, 2, 4, 5, 6, 0, 2, 4, 5, 6, 0, 2, 3,\n",
       "       4, 5, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5,\n",
       "       6, 0, 1, 2, 5, 6, 0, 1, 2, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 1, 4, 5,\n",
       "       1, 2, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6], dtype=int64)"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test= df[l4]\n",
    "y_test = df[['Pollutants']]\n",
    "np.ravel(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 0, 0, 0, 0, 0, 2, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       1, 1, 0, 0, 0, 1, 4, 0, 0, 1, 1, 5, 0, 0, 1, 1, 5, 0, 0, 1, 4, 4,\n",
       "       2, 0, 0, 1, 0, 1, 0, 5, 4, 0, 0, 0, 1, 0, 4, 3, 0, 2, 0, 4, 4, 1,\n",
       "       0, 0, 1, 1, 4, 4, 0, 2, 0, 4, 4, 1, 1, 0, 5, 4, 1, 0, 0, 1, 1, 4,\n",
       "       3, 1, 0, 0, 1, 2, 3, 4, 1, 0, 0, 1, 0, 4, 4, 1, 0, 0, 4, 1, 0, 0,\n",
       "       1, 4, 3, 0, 0, 0, 1, 0, 5, 4, 1, 0, 0, 1, 0, 5, 4, 2, 0, 0, 0, 0,\n",
       "       4, 3, 1, 0, 0, 4, 4, 2, 0, 0, 1, 0, 4, 3, 2, 0, 0, 1, 0, 5, 5, 1,\n",
       "       0, 0, 1, 0, 4, 0, 0, 0, 0, 4, 3, 1, 0, 0, 1, 1, 4, 4, 1, 0, 0, 1,\n",
       "       1, 5, 4, 2, 0, 0, 1, 0, 4, 5, 1, 1, 0, 4, 4, 1, 0, 0, 1, 0, 4, 4,\n",
       "       0, 0, 0, 1, 0, 4, 4, 2, 0, 1, 0, 5, 4, 2, 0, 0, 2, 0, 4, 3, 1, 0,\n",
       "       0, 5, 4, 2, 0, 0, 0, 2, 1, 4, 1, 0, 0, 1, 1, 4, 1, 0, 1, 0, 4, 4,\n",
       "       1, 0, 0, 0, 0, 5, 4, 1, 0, 0, 2, 0, 4, 3, 1, 0, 0, 1, 0, 4, 4, 1,\n",
       "       0, 0, 1, 1, 5, 5, 2, 0, 0, 1, 0, 3, 1, 1, 0, 4, 2, 0, 1, 1, 3, 2,\n",
       "       0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2, 0, 1, 0, 4, 0, 0,\n",
       "       1, 0, 2, 0, 0, 0, 1, 1, 1, 1, 0, 0, 3, 2, 0, 0, 1, 0, 0, 1, 1, 0,\n",
       "       0, 0, 0, 2, 2, 2, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "       0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 1, 0,\n",
       "       0, 0, 2, 1, 1, 1, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 1, 2, 2, 0, 0,\n",
       "       0, 0, 1, 0, 2, 1, 2, 4, 4, 0, 0, 0, 1, 1, 3, 2, 1, 0, 0, 1, 1, 1,\n",
       "       2, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 1, 2, 1, 0, 1, 0, 2, 2, 1,\n",
       "       0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 2, 2, 2, 0, 1, 2, 0, 2, 2, 1, 0, 1,\n",
       "       0, 1, 2, 2, 0, 0, 0, 0, 3, 2, 0, 0, 0, 1, 0, 0, 0, 2, 0, 1, 2, 0,\n",
       "       0, 0, 1, 2, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 2,\n",
       "       2, 0, 0, 0, 0, 3, 3, 0, 0, 0, 2, 0, 2, 2, 0, 0, 0, 0, 0, 2, 2, 0,\n",
       "       0, 0, 2, 2, 1, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 4, 4, 1, 0, 0, 0,\n",
       "       0, 2, 1, 1, 0, 0, 1, 1, 3, 3, 1, 0, 0, 0, 3, 2, 1, 0, 0, 1, 0, 3,\n",
       "       3, 1, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 1, 0, 2, 2, 0,\n",
       "       0, 0, 1, 0, 1, 0, 0, 1, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0,\n",
       "       0, 0, 0, 0, 1, 2, 1, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 2, 2, 1, 0,\n",
       "       0, 0, 0, 2, 1, 0, 0, 0, 0, 1, 4, 1, 0, 0, 0, 4, 3, 0, 0, 0, 0, 3,\n",
       "       5, 4, 2, 0, 0, 1, 2, 5, 4, 2, 0, 0, 1, 0, 5, 4, 2, 0, 0, 1, 0, 4,\n",
       "       2, 0, 1, 1, 4, 0, 0, 0, 0, 4, 1, 0, 1, 1, 4, 0, 0, 0, 0, 4, 1, 0,\n",
       "       0, 1, 4, 4, 2, 0, 0, 1, 1, 4, 3, 1, 0, 0, 1, 1, 5, 5, 2, 0, 0, 1,\n",
       "       0, 4, 4, 0, 1, 0, 4, 4, 2, 0, 0, 0, 3, 2, 1, 0, 0, 1, 0, 2, 0, 1,\n",
       "       2, 0, 0, 0, 0, 3, 3, 2, 0, 1, 1, 0, 4, 3, 0, 0, 0, 0, 2, 4, 4, 1,\n",
       "       0, 0, 0, 2, 3, 2, 1, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GO_test= df[l4]\n",
    "A_test=df[['AQICause']]\n",
    "np.ravel(A_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5,\n",
       "       0, 1, 2, 3, 4, 6, 0, 2, 4, 5, 6, 0, 2, 4, 5, 6, 0, 2, 4, 5, 0, 1,\n",
       "       2, 3, 4, 5, 6, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 5, 6, 0, 1, 2, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n",
       "       1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 5, 6, 0, 2, 3, 4,\n",
       "       5, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6,\n",
       "       0, 1, 2, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 2, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5,\n",
       "       6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1,\n",
       "       2, 3, 4, 5, 6, 0, 1, 2, 3, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 5,\n",
       "       6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 2, 4, 5, 6, 0, 1,\n",
       "       2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 2, 4, 5, 0, 2, 4, 5, 6, 0, 2,\n",
       "       4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 2, 4, 5, 6, 0, 4, 5, 6, 1, 2, 3,\n",
       "       4, 6, 0, 2, 4, 5, 6, 0, 1, 2, 3, 4, 6, 1, 2, 4, 5, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 2, 3, 4, 5, 1, 2, 4, 5, 0, 1,\n",
       "       2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3,\n",
       "       4, 5, 6, 0, 1, 4, 5, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n",
       "       1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 4, 5, 6, 0, 1, 2, 4, 5,\n",
       "       6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 3, 4, 5, 6, 0, 1, 2,\n",
       "       3, 6, 0, 1, 2, 4, 5, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n",
       "       1, 2, 3, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
       "       4, 5, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5,\n",
       "       6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n",
       "       1, 2, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 2, 4, 5, 6, 0, 2, 3, 4, 5, 0, 2, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3,\n",
       "       4, 5, 6, 0, 2, 4, 5, 6, 5, 6, 0, 2, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6,\n",
       "       0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n",
       "       2, 4, 5, 6, 0, 2, 4, 5, 6, 0, 2, 4, 5, 6, 0, 2, 4, 5, 6, 0, 2, 3,\n",
       "       4, 5, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5,\n",
       "       6, 0, 1, 2, 5, 6, 0, 1, 2, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 1, 4, 5,\n",
       "       1, 2, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
       "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6], dtype=int64)"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xsp_test= df[l4]\n",
    "ysp_test = df[['Source_of_Pollutants']]\n",
    "np.ravel(ysp_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "def NBpol():\n",
    "    \n",
    "    from sklearn.tree import DecisionTreeClassifier\n",
    "    \n",
    "    gnb = DecisionTreeClassifier()\n",
    "    \n",
    "\n",
    "    gnb=gnb.fit(X,np.ravel(y))\n",
    "\n",
    "    # calculating accuracy-------------------------------------------------------------------\n",
    "    from sklearn.metrics import accuracy_score\n",
    "    y_pred=gnb.predict(X_test)\n",
    "    print(accuracy_score(y_test, y_pred))\n",
    "    print(accuracy_score(y_test, y_pred,normalize=False))\n",
    "    # -----------------------------------------------------\n",
    "\n",
    "    terms = [st.get(),ct.get(),aplev.get()]\n",
    "    for k in range(0,len(l4)):\n",
    "        for z in terms:\n",
    "            if(z==l4[k]):\n",
    "                l5[k]=1\n",
    "\n",
    "    inputtest = [l5]\n",
    "    predict = gnb.predict(inputtest)\n",
    "    predicted=predict[0]\n",
    "\n",
    "    h='no'\n",
    "    for a in range(0,len(pol)):\n",
    "        if(predicted == a):\n",
    "            h='yes'\n",
    "            break\n",
    "\n",
    "    if (h=='yes'):\n",
    "        t3.delete(\"1.0\", END)\n",
    "        t3.insert(END, pol[a])\n",
    "    else:\n",
    "        t3.delete(\"1.0\", END)\n",
    "        t3.insert(END, \"Not Found\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "def NBaqi():\n",
    "    \n",
    "    from sklearn.tree import DecisionTreeClassifier\n",
    "    \n",
    "    gnb = DecisionTreeClassifier()\n",
    "    \n",
    "    \n",
    "\n",
    "    gnb=gnb.fit(GO,np.ravel(A))\n",
    "\n",
    "    # calculating accuracy-------------------------------------------------------------------\n",
    "    from sklearn.metrics import accuracy_score\n",
    "    t_pred=gnb.predict(GO_test)\n",
    "    print(accuracy_score(A_test, t_pred))\n",
    "    print(accuracy_score(A_test, t_pred,normalize=False))\n",
    "    # -----------------------------------------------------\n",
    "\n",
    "    terms = [st.get(),ct.get(),aplev.get()]\n",
    "    for m in range(0,len(l4)):\n",
    "        for n in terms:\n",
    "            if(n==l4[m]):\n",
    "                l6[m]=1\n",
    "\n",
    "    inputtest = [l6]\n",
    "    predict = gnb.predict(inputtest)\n",
    "    predicted=predict[0]\n",
    "\n",
    "    o='no'\n",
    "    for p in range(0,len(aqica)):\n",
    "        if(predicted == p):\n",
    "            o='yes'\n",
    "            break\n",
    "\n",
    "    if (o=='yes'):\n",
    "        t4.delete(\"1.0\", END)\n",
    "        t4.insert(END, aqica[p])\n",
    "       \n",
    "    else:\n",
    "        t4.delete(\"1.0\", END)\n",
    "        t4.insert(END, \"Not Found\")\n",
    "     \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "def NBsp():\n",
    "    \n",
    "    from sklearn.tree import DecisionTreeClassifier\n",
    "    \n",
    "    gnb = DecisionTreeClassifier()\n",
    "    \n",
    "\n",
    "    gnb=gnb.fit(Xsp,np.ravel(ysp))\n",
    "\n",
    "    # calculating accuracy-------------------------------------------------------------------\n",
    "    from sklearn.metrics import accuracy_score\n",
    "    ysp_pred=gnb.predict(Xsp_test)\n",
    "    print(accuracy_score(ysp_test, ysp_pred))\n",
    "    print(accuracy_score(ysp_test, ysp_pred,normalize=False))\n",
    "    # -----------------------------------------------------\n",
    "\n",
    "    terms = [st.get(),ct.get(),aplev.get()]\n",
    "    for k in range(0,len(l4)):\n",
    "        for z in terms:\n",
    "            if(z==l4[k]):\n",
    "                l7[k]=1\n",
    "\n",
    "    inputtest = [l7]\n",
    "    predict = gnb.predict(inputtest)\n",
    "    predicted=predict[0]\n",
    "\n",
    "    h='no'\n",
    "    for a in range(0,len(sp)):\n",
    "        if(predicted == a):\n",
    "            h='yes'\n",
    "            break\n",
    "\n",
    "    if (h=='yes'):\n",
    "        t5.delete(\"1.0\", END)\n",
    "        t5.insert(END, sp[a])\n",
    "    else:\n",
    "        t5.delete(\"1.0\", END)\n",
    "        t5.insert(END, \"Not Found\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "root = Tk()\n",
    "root.title(\"AQI-INDIA PREDICTION\")\n",
    "#root.configure(background='black')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "canvas = Canvas(root,width=675,height=419)\n",
    "canvas.pack()\n",
    "photo = PhotoImage(file ='im7.png')\n",
    "canvas.create_image(0,0,image=photo,anchor=NW)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gui_stuff------------------------------------------------------------------------------------\n",
    "\n",
    "\n",
    "# entry variables\n",
    "st= StringVar()\n",
    "st.set(None)\n",
    "ct= StringVar()\n",
    "ct.set(None)\n",
    "\n",
    "aplev= StringVar()\n",
    "aplev.set(None)\n",
    "\n",
    "\n",
    "Name= StringVar()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Heading\n",
    "w2 = Label(canvas, justify=CENTER, text=\"AIR QUALITY PREDICTION - INDIA\", fg=\"yellow\",bg=\"gray\")\n",
    "w2.config(font=(\"Elephant\", 15))\n",
    "w2.grid(row=1, column=0, columnspan=2, padx=100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "# labels\n",
    "NameLb = Label(canvas, text=\"NAME:\",fg=\"yellow\", bg=\"black\")\n",
    "NameLb.grid(row=3, column=0, pady=15, sticky=W)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "stLb = Label(canvas, text=\"STATE NAME:\",fg=\"yellow\", bg=\"black\")\n",
    "stLb.grid(row=4, column=0, pady=10, sticky=W)\n",
    "\n",
    "ctLb = Label(canvas, text=\"CITY NAME:\",fg=\"yellow\", bg=\"black\")\n",
    "ctLb.grid(row=5, column=0, pady=10, sticky=W)\n",
    "\n",
    "aplevLb = Label(canvas, text=\"AQI VALUE:\",fg=\"yellow\", bg=\"black\")\n",
    "aplevLb.grid(row=6, column=0, pady=10, sticky=W)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "nbpolLb = Label(canvas, text=\"Pollution Prediction:\", fg=\"yellow\", bg=\"black\")\n",
    "nbpolLb.grid(row=3, column=2, pady=10, sticky=W)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "nbALb = Label(canvas, text=\"AQI-Cause:\", fg=\"yellow\", bg=\"black\")\n",
    "nbALb.grid(row=4, column=2, pady=10, sticky=W)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "nbSPLb = Label(canvas, text=\"Sources of Pollutants:\", fg=\"yellow\", bg=\"black\")\n",
    "nbSPLb.grid(row=7, column=2, pady=10, sticky=W)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "# entries\n",
    "OPTIONS1 = sorted(l1)\n",
    "OPTIONS2 = sorted(l2)\n",
    "OPTIONS3 = sorted(l3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "NameEn = Entry(canvas, textvariable=Name)\n",
    "NameEn.grid(row=3, column=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "stEn = OptionMenu(canvas, st,*OPTIONS1)\n",
    "stEn.grid(row=4, column=0)\n",
    "\n",
    "ctEn = OptionMenu(canvas, ct,*OPTIONS2)\n",
    "ctEn.grid(row=5, column=0)\n",
    "\n",
    "aplevEn = OptionMenu(canvas, aplev,*OPTIONS3)\n",
    "aplevEn.grid(row=6, column=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "nbpol = Button(canvas, text=\"Check:Prediction of Pollutants\", command=NBpol,bg=\"green\",fg=\"white\")\n",
    "nbpol.grid(row=3, column=1,padx=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "nbA = Button(canvas, text=\"Check:AQI-Stages\", command=NBaqi,bg=\"green\",fg=\"white\")\n",
    "nbA.grid(row=5, column=1,padx=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "nbSP = Button(canvas, text=\"Check:Sources\", command=NBsp,bg=\"green\",fg=\"white\")\n",
    "nbSP.grid(row=8, column=1,padx=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "t3 = Text(canvas, height=1, width=10,bg=\"black\",fg=\"white\")\n",
    "t3.grid(row=3, column=2 , padx=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "t4 = Text(canvas, height=5, width=50,bg=\"black\",fg=\"red\")\n",
    "t4.grid(row=5, column=2 , padx=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "t5 = Text(canvas, height=5, width=50,bg=\"black\",fg=\"green\")\n",
    "t5.grid(row=8, column=2 , padx=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clear entry widget\n",
    "def clear_text():\n",
    "\troot.delete('1.0',END)    \n",
    "def clear_display_result():\n",
    "\tt3.delete('1.0',END)\n",
    "\tt4.delete('1.0',END)\n",
    "\tt5.delete('1.0',END) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clear Text  with position 1.0\n",
    "def clear_text_file():\n",
    "\troot.destroy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "button3=Button(canvas,text=\"Reset\", command=clear_display_result,width=12,bg=\"red\",fg=\"white\")\n",
    "button3.grid(row=8,column=0,padx=10,pady=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7106598984771574\n",
      "560\n",
      "1.0\n",
      "788\n",
      "0.7106598984771574\n",
      "560\n"
     ]
    }
   ],
   "source": [
    "root.mainloop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}