Research_Question_1/Figure-5_timeseries_of_aq_articles_delhi.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "29f4dfcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('max_colwidth', 100)\n",
    "from pandas import json_normalize\n",
    "import numpy as np\n",
    "import os\n",
    "import csv\n",
    "import multiprocessing as mp\n",
    "import re\n",
    "from datetime import datetime\n",
    "# import helper as h\n",
    "# import AQDataHelper as aqh\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.dates import DateFormatter\n",
    "import matplotlib.dates as mdates\n",
    "import matplotlib\n",
    "import geopy\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1c885953",
   "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>media</th>\n",
       "      <th>date</th>\n",
       "      <th>url</th>\n",
       "      <th>heading</th>\n",
       "      <th>content</th>\n",
       "      <th>other.author</th>\n",
       "      <th>other.top_image</th>\n",
       "      <th>other.category</th>\n",
       "      <th>city</th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>district</th>\n",
       "      <th>matches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TOI</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>http://timesofindia.indiatimes.com//city/mumbai/Mumbai-celebrates-a-cool-New-Years-Eve-at-13-4C/...</td>\n",
       "      <td>Mumbai celebrates a cool New Year's Eve at 13.4फ्C</td>\n",
       "      <td>MUMBAI: The city witnessed its lowest temperature of the season on New Year ’s Eve when the merc...</td>\n",
       "      <td>TNN /  Jan 1, 2010, 01:47 IST</td>\n",
       "      <td>https://static.toiimg.com/thumb/msid-47529300,width-1070,height-580,imgsize-110164,resizemode-75...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>maharashtra</td>\n",
       "      <td>mumbai suburban</td>\n",
       "      <td>{'smog': 3.0}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TOI</td>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>http://timesofindia.indiatimes.com//city/lucknow/Dense-fog-leads-to-tripping-of-power-lines/arti...</td>\n",
       "      <td>Dense fog leads to tripping of power lines</td>\n",
       "      <td>LUCKNOW: The UP Power Corporation Limited is keeping its fingers crossed for Saturday with the s...</td>\n",
       "      <td>TNN /  Jan 2, 2010, 06:40 IST</td>\n",
       "      <td>https://static.toiimg.com/thumb/msid-47529300,width-1070,height-580,imgsize-110164,resizemode-75...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>lucknow</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>uttar pradesh</td>\n",
       "      <td>lucknow</td>\n",
       "      <td>{'air pollutants': 2.0}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TOI</td>\n",
       "      <td>2010-01-09</td>\n",
       "      <td>http://timesofindia.indiatimes.com//city/chennai/City-has-high-levels-of-smog-forming-ozone/arti...</td>\n",
       "      <td>City has high levels of smog-forming ozone</td>\n",
       "      <td>CHENNAI: Dangerous levels of ground-level ozone that causes respiratory ailments could be envelo...</td>\n",
       "      <td>Bhama Devi Ravi / TNN /  Jan 9, 2010, 00:37 IST</td>\n",
       "      <td>https://static.toiimg.com/thumb/msid-47529300,width-1070,height-580,imgsize-110164,resizemode-75...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chennai</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>tamil nadu</td>\n",
       "      <td>chennai</td>\n",
       "      <td>{'ozone': 9.0, 'smog': 4.0}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TOI</td>\n",
       "      <td>2010-01-11</td>\n",
       "      <td>http://timesofindia.indiatimes.com//city/jaipur/Woman-four-kids-in-hospital-after-inhaling-smoke...</td>\n",
       "      <td>Woman, four kids in hospital after inhaling smoke</td>\n",
       "      <td>JAIPUR: A woman and her four kids were admitted to a city hospital in a serious condition after ...</td>\n",
       "      <td>TNN /  Jan 11, 2010, 01:56 IST</td>\n",
       "      <td>https://static.toiimg.com/thumb/msid-47529300,width-1070,height-580,imgsize-110164,resizemode-75...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jaipur</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>rajasthan</td>\n",
       "      <td>jaipur</td>\n",
       "      <td>{'carbon monoxide': 1.0}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TOI</td>\n",
       "      <td>2010-01-13</td>\n",
       "      <td>http://timesofindia.indiatimes.com//city/chennai/Pollution-high-on-Bhogi-eve-Study/articleshow/5...</td>\n",
       "      <td>Pollution high on Bhogi eve: Study</td>\n",
       "      <td>CHENNAI: By the time you get to read this, the Bhogi pollutants may have enveloped the city, but...</td>\n",
       "      <td>Bhama Devi Ravi / TNN /  Jan 13, 2010, 04:23 IST</td>\n",
       "      <td>https://static.toiimg.com/thumb/msid-47529300,width-1070,height-580,imgsize-110164,resizemode-75...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chennai</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>tamil nadu</td>\n",
       "      <td>chennai</td>\n",
       "      <td>{'air quality': 1.0, 'carbon monoxide': 1.0, 'ozone': 1.0, 'so2': 1.0}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17369</th>\n",
       "      <td>Hindu</td>\n",
       "      <td>2021-05-16</td>\n",
       "      <td>https://www.thehindu.com/news/national/andhra-pradesh/naval-dockyard-teams-repair-two-oxygen-pla...</td>\n",
       "      <td>Naval Dockyard teams repair two oxygen plants in Nellore and Srikalahasti</td>\n",
       "      <td>Oxygen supply gets a big boost with this in the State during the pandemic\\n\\nTeams from the Nava...</td>\n",
       "      <td>SpecialCorrespondent</td>\n",
       "      <td>https://www.thehindu.com/news/national/andhra-pradesh/fvjzld/article34572702.ece/ALTERNATES/LAND...</td>\n",
       "      <td>andhra pradesh</td>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>NaN</td>\n",
       "      <td>andhra pradesh</td>\n",
       "      <td>visakhapatnam</td>\n",
       "      <td>{'carbon monoxide': 2.0}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17370</th>\n",
       "      <td>Hindu</td>\n",
       "      <td>2021-05-24</td>\n",
       "      <td>https://www.thehindu.com/news/national/kerala/greens-call-for-a-relook-into-silverline-implement...</td>\n",
       "      <td>Greens call for a relook into SilverLine implementation</td>\n",
       "      <td>The Kerala Paristhithi Aikya Vedi, a forum of environmentalists, has urged Chief Minister Pinara...</td>\n",
       "      <td>SpecialCorrespondent</td>\n",
       "      <td>https://www.thehindu.com/static/theme/default/base/img/og-image.jpg</td>\n",
       "      <td>kerala</td>\n",
       "      <td>thiruvananthapuram</td>\n",
       "      <td>NaN</td>\n",
       "      <td>kerala</td>\n",
       "      <td>thiruvananthapuram</td>\n",
       "      <td>{'air pollution': 1.0}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17371</th>\n",
       "      <td>Hindu</td>\n",
       "      <td>2021-05-25</td>\n",
       "      <td>https://www.thehindu.com/news/national/karnataka/cftris-ozone-based-air-disinfection-system-to-c...</td>\n",
       "      <td>CFTRI’s ozone-based air disinfection system to combat droplet infections</td>\n",
       "      <td>The CSIR-Central Food Technological Research Institute (CFTRI) in Mysuru has come up with an ozo...</td>\n",
       "      <td>LaiqhAKhan</td>\n",
       "      <td>https://www.thehindu.com/sci-tech/science/wsmdd0/article33283019.ece/ALTERNATES/LANDSCAPE_615/CF...</td>\n",
       "      <td>karnataka</td>\n",
       "      <td>mysuru</td>\n",
       "      <td>NaN</td>\n",
       "      <td>karnataka</td>\n",
       "      <td>mysuru</td>\n",
       "      <td>{'ozone': 9.0}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17372</th>\n",
       "      <td>Hindu</td>\n",
       "      <td>2021-05-28</td>\n",
       "      <td>https://www.thehindu.com/news/national/ngt-stays-green-nod-for-ntpcs-karimnagar-plant/article346...</td>\n",
       "      <td>NGT stays green nod for NTPC’s Karimnagar plant</td>\n",
       "      <td>Environmental Clearance put on hold for seven months, bench calls for fresh appraisal.\\n\\nThe so...</td>\n",
       "      <td>T.K.Rohit</td>\n",
       "      <td>https://www.thehindu.com/news/cities/Hyderabad/article23578273.ece/ALTERNATES/LANDSCAPE_615/17HY...</td>\n",
       "      <td>national</td>\n",
       "      <td>chennai</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tamil nadu</td>\n",
       "      <td>chennai</td>\n",
       "      <td>{'air quality': 1.0}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17373</th>\n",
       "      <td>Hindu</td>\n",
       "      <td>2021-05-30</td>\n",
       "      <td>https://www.thehindu.com/news/national/kerala/create-forests-to-stop-global-warming-says-ministe...</td>\n",
       "      <td>Create forests to stop global warming, says Minister</td>\n",
       "      <td>Minister for Local Self-Governments and Excise M.V. Govindan has called for a conscious effort t...</td>\n",
       "      <td>StaffReporter</td>\n",
       "      <td>https://www.thehindu.com/static/theme/default/base/img/og-image.jpg</td>\n",
       "      <td>kerala</td>\n",
       "      <td>kannur</td>\n",
       "      <td>NaN</td>\n",
       "      <td>kerala</td>\n",
       "      <td>kannur</td>\n",
       "      <td>{'air pollution': 1.0}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17374 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       media        date  \\\n",
       "0        TOI  2010-01-01   \n",
       "1        TOI  2010-01-02   \n",
       "2        TOI  2010-01-09   \n",
       "3        TOI  2010-01-11   \n",
       "4        TOI  2010-01-13   \n",
       "...      ...         ...   \n",
       "17369  Hindu  2021-05-16   \n",
       "17370  Hindu  2021-05-24   \n",
       "17371  Hindu  2021-05-25   \n",
       "17372  Hindu  2021-05-28   \n",
       "17373  Hindu  2021-05-30   \n",
       "\n",
       "                                                                                                       url  \\\n",
       "0      http://timesofindia.indiatimes.com//city/mumbai/Mumbai-celebrates-a-cool-New-Years-Eve-at-13-4C/...   \n",
       "1      http://timesofindia.indiatimes.com//city/lucknow/Dense-fog-leads-to-tripping-of-power-lines/arti...   \n",
       "2      http://timesofindia.indiatimes.com//city/chennai/City-has-high-levels-of-smog-forming-ozone/arti...   \n",
       "3      http://timesofindia.indiatimes.com//city/jaipur/Woman-four-kids-in-hospital-after-inhaling-smoke...   \n",
       "4      http://timesofindia.indiatimes.com//city/chennai/Pollution-high-on-Bhogi-eve-Study/articleshow/5...   \n",
       "...                                                                                                    ...   \n",
       "17369  https://www.thehindu.com/news/national/andhra-pradesh/naval-dockyard-teams-repair-two-oxygen-pla...   \n",
       "17370  https://www.thehindu.com/news/national/kerala/greens-call-for-a-relook-into-silverline-implement...   \n",
       "17371  https://www.thehindu.com/news/national/karnataka/cftris-ozone-based-air-disinfection-system-to-c...   \n",
       "17372  https://www.thehindu.com/news/national/ngt-stays-green-nod-for-ntpcs-karimnagar-plant/article346...   \n",
       "17373  https://www.thehindu.com/news/national/kerala/create-forests-to-stop-global-warming-says-ministe...   \n",
       "\n",
       "                                                                         heading  \\\n",
       "0                             Mumbai celebrates a cool New Year's Eve at 13.4फ्C   \n",
       "1                                     Dense fog leads to tripping of power lines   \n",
       "2                                     City has high levels of smog-forming ozone   \n",
       "3                              Woman, four kids in hospital after inhaling smoke   \n",
       "4                                             Pollution high on Bhogi eve: Study   \n",
       "...                                                                          ...   \n",
       "17369  Naval Dockyard teams repair two oxygen plants in Nellore and Srikalahasti   \n",
       "17370                   Greens call for a relook into SilverLine implementation    \n",
       "17371  CFTRI’s ozone-based air disinfection system to combat droplet infections    \n",
       "17372                            NGT stays green nod for NTPC’s Karimnagar plant   \n",
       "17373                      Create forests to stop global warming, says Minister    \n",
       "\n",
       "                                                                                                   content  \\\n",
       "0      MUMBAI: The city witnessed its lowest temperature of the season on New Year ’s Eve when the merc...   \n",
       "1      LUCKNOW: The UP Power Corporation Limited is keeping its fingers crossed for Saturday with the s...   \n",
       "2      CHENNAI: Dangerous levels of ground-level ozone that causes respiratory ailments could be envelo...   \n",
       "3      JAIPUR: A woman and her four kids were admitted to a city hospital in a serious condition after ...   \n",
       "4      CHENNAI: By the time you get to read this, the Bhogi pollutants may have enveloped the city, but...   \n",
       "...                                                                                                    ...   \n",
       "17369  Oxygen supply gets a big boost with this in the State during the pandemic\\n\\nTeams from the Nava...   \n",
       "17370  The Kerala Paristhithi Aikya Vedi, a forum of environmentalists, has urged Chief Minister Pinara...   \n",
       "17371  The CSIR-Central Food Technological Research Institute (CFTRI) in Mysuru has come up with an ozo...   \n",
       "17372  Environmental Clearance put on hold for seven months, bench calls for fresh appraisal.\\n\\nThe so...   \n",
       "17373  Minister for Local Self-Governments and Excise M.V. Govindan has called for a conscious effort t...   \n",
       "\n",
       "                                           other.author  \\\n",
       "0                         TNN /  Jan 1, 2010, 01:47 IST   \n",
       "1                         TNN /  Jan 2, 2010, 06:40 IST   \n",
       "2       Bhama Devi Ravi / TNN /  Jan 9, 2010, 00:37 IST   \n",
       "3                        TNN /  Jan 11, 2010, 01:56 IST   \n",
       "4      Bhama Devi Ravi / TNN /  Jan 13, 2010, 04:23 IST   \n",
       "...                                                 ...   \n",
       "17369                              SpecialCorrespondent   \n",
       "17370                              SpecialCorrespondent   \n",
       "17371                                        LaiqhAKhan   \n",
       "17372                                         T.K.Rohit   \n",
       "17373                                     StaffReporter   \n",
       "\n",
       "                                                                                           other.top_image  \\\n",
       "0      https://static.toiimg.com/thumb/msid-47529300,width-1070,height-580,imgsize-110164,resizemode-75...   \n",
       "1      https://static.toiimg.com/thumb/msid-47529300,width-1070,height-580,imgsize-110164,resizemode-75...   \n",
       "2      https://static.toiimg.com/thumb/msid-47529300,width-1070,height-580,imgsize-110164,resizemode-75...   \n",
       "3      https://static.toiimg.com/thumb/msid-47529300,width-1070,height-580,imgsize-110164,resizemode-75...   \n",
       "4      https://static.toiimg.com/thumb/msid-47529300,width-1070,height-580,imgsize-110164,resizemode-75...   \n",
       "...                                                                                                    ...   \n",
       "17369  https://www.thehindu.com/news/national/andhra-pradesh/fvjzld/article34572702.ece/ALTERNATES/LAND...   \n",
       "17370                                  https://www.thehindu.com/static/theme/default/base/img/og-image.jpg   \n",
       "17371  https://www.thehindu.com/sci-tech/science/wsmdd0/article33283019.ece/ALTERNATES/LANDSCAPE_615/CF...   \n",
       "17372  https://www.thehindu.com/news/cities/Hyderabad/article23578273.ece/ALTERNATES/LANDSCAPE_615/17HY...   \n",
       "17373                                  https://www.thehindu.com/static/theme/default/base/img/og-image.jpg   \n",
       "\n",
       "       other.category                city    year           state  \\\n",
       "0                 NaN              mumbai  2010.0     maharashtra   \n",
       "1                 NaN             lucknow  2010.0   uttar pradesh   \n",
       "2                 NaN             chennai  2010.0      tamil nadu   \n",
       "3                 NaN              jaipur  2010.0       rajasthan   \n",
       "4                 NaN             chennai  2010.0      tamil nadu   \n",
       "...               ...                 ...     ...             ...   \n",
       "17369  andhra pradesh       visakhapatnam     NaN  andhra pradesh   \n",
       "17370          kerala  thiruvananthapuram     NaN          kerala   \n",
       "17371       karnataka              mysuru     NaN       karnataka   \n",
       "17372        national             chennai     NaN      tamil nadu   \n",
       "17373          kerala              kannur     NaN          kerala   \n",
       "\n",
       "                 district  \\\n",
       "0         mumbai suburban   \n",
       "1                 lucknow   \n",
       "2                 chennai   \n",
       "3                  jaipur   \n",
       "4                 chennai   \n",
       "...                   ...   \n",
       "17369       visakhapatnam   \n",
       "17370  thiruvananthapuram   \n",
       "17371              mysuru   \n",
       "17372             chennai   \n",
       "17373              kannur   \n",
       "\n",
       "                                                                      matches  \n",
       "0                                                               {'smog': 3.0}  \n",
       "1                                                     {'air pollutants': 2.0}  \n",
       "2                                                 {'ozone': 9.0, 'smog': 4.0}  \n",
       "3                                                    {'carbon monoxide': 1.0}  \n",
       "4      {'air quality': 1.0, 'carbon monoxide': 1.0, 'ozone': 1.0, 'so2': 1.0}  \n",
       "...                                                                       ...  \n",
       "17369                                                {'carbon monoxide': 2.0}  \n",
       "17370                                                  {'air pollution': 1.0}  \n",
       "17371                                                          {'ozone': 9.0}  \n",
       "17372                                                    {'air quality': 1.0}  \n",
       "17373                                                  {'air pollution': 1.0}  \n",
       "\n",
       "[17374 rows x 13 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_articles = pd.read_csv(\"../dataset/News_articles_dataset/News_articles_dataset.csv.gz\")\n",
    "df_articles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "79beddd7",
   "metadata": {
    "scrolled": true
   },
   "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>Unnamed: 0</th>\n",
       "      <th>From Date</th>\n",
       "      <th>city</th>\n",
       "      <th>WS</th>\n",
       "      <th>WD</th>\n",
       "      <th>Gust</th>\n",
       "      <th>Variance</th>\n",
       "      <th>AT</th>\n",
       "      <th>RH</th>\n",
       "      <th>SR</th>\n",
       "      <th>BP</th>\n",
       "      <th>PPAccum</th>\n",
       "      <th>Power</th>\n",
       "      <th>PM10</th>\n",
       "      <th>PM2_5</th>\n",
       "      <th>NO</th>\n",
       "      <th>NO2</th>\n",
       "      <th>NOx</th>\n",
       "      <th>CO</th>\n",
       "      <th>CO2</th>\n",
       "      <th>Ozone</th>\n",
       "      <th>BlackCarbon</th>\n",
       "      <th>Benzene</th>\n",
       "      <th>Toluene</th>\n",
       "      <th>Eth-Benzene</th>\n",
       "      <th>Xylene</th>\n",
       "      <th>CH4</th>\n",
       "      <th>NMHC</th>\n",
       "      <th>VWS</th>\n",
       "      <th>THC</th>\n",
       "      <th>NH3</th>\n",
       "      <th>SO2</th>\n",
       "      <th>Temp</th>\n",
       "      <th>MP-Xylene</th>\n",
       "      <th>RF</th>\n",
       "      <th>RackTemp</th>\n",
       "      <th>P_Xylene</th>\n",
       "      <th>O Xylene</th>\n",
       "      <th>ETH</th>\n",
       "      <th>SPM</th>\n",
       "      <th>AP</th>\n",
       "      <th>MH</th>\n",
       "      <th>readings_in_day</th>\n",
       "      <th>total_stations</th>\n",
       "      <th>active_stations</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>Agra</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Gujarat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>Ambala</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Haryana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>Ankleshwar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Gujarat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>Bahadurgarh</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Haryana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202192</th>\n",
       "      <td>202192</td>\n",
       "      <td>2019-04-30</td>\n",
       "      <td>Udaipur</td>\n",
       "      <td>1.017579</td>\n",
       "      <td>253.649263</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.871263</td>\n",
       "      <td>192.898000</td>\n",
       "      <td>757.183158</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87.814737</td>\n",
       "      <td>34.304255</td>\n",
       "      <td>13.917158</td>\n",
       "      <td>19.854526</td>\n",
       "      <td>33.771684</td>\n",
       "      <td>0.734835</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.070213</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.563936</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.092842</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.120947</td>\n",
       "      <td>12.064725</td>\n",
       "      <td>33.071053</td>\n",
       "      <td>1.188842</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.700211</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Rajasthan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202193</th>\n",
       "      <td>202193</td>\n",
       "      <td>2019-04-30</td>\n",
       "      <td>Ujjain</td>\n",
       "      <td>4.636842</td>\n",
       "      <td>255.042105</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.660000</td>\n",
       "      <td>18.252632</td>\n",
       "      <td>161.242105</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>147.452632</td>\n",
       "      <td>40.126316</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.891011</td>\n",
       "      <td>5.250562</td>\n",
       "      <td>0.819535</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.206522</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.031461</td>\n",
       "      <td>8.647059</td>\n",
       "      <td>31.801075</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Madhya Pradesh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202194</th>\n",
       "      <td>202194</td>\n",
       "      <td>2019-04-30</td>\n",
       "      <td>Varanasi</td>\n",
       "      <td>0.801053</td>\n",
       "      <td>180.757895</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.582105</td>\n",
       "      <td>23.600000</td>\n",
       "      <td>80.157895</td>\n",
       "      <td>745.378947</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>331.126761</td>\n",
       "      <td>131.732394</td>\n",
       "      <td>51.202360</td>\n",
       "      <td>104.323370</td>\n",
       "      <td>74.652174</td>\n",
       "      <td>0.860652</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.047692</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.522105</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.748652</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202195</th>\n",
       "      <td>202195</td>\n",
       "      <td>2019-04-30</td>\n",
       "      <td>Vijayawada</td>\n",
       "      <td>0.519041</td>\n",
       "      <td>221.018904</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.019178</td>\n",
       "      <td>55.307671</td>\n",
       "      <td>228.706154</td>\n",
       "      <td>749.476301</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.119577</td>\n",
       "      <td>20.220000</td>\n",
       "      <td>6.365139</td>\n",
       "      <td>14.738356</td>\n",
       "      <td>21.004658</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.996849</td>\n",
       "      <td>1.870137</td>\n",
       "      <td>0.057397</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.329589</td>\n",
       "      <td>6.234658</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.734110</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.899589</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202196</th>\n",
       "      <td>202196</td>\n",
       "      <td>2019-04-30</td>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.226596</td>\n",
       "      <td>73.127660</td>\n",
       "      <td>163.893617</td>\n",
       "      <td>745.968085</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.734043</td>\n",
       "      <td>13.709302</td>\n",
       "      <td>8.764835</td>\n",
       "      <td>16.689011</td>\n",
       "      <td>15.998901</td>\n",
       "      <td>0.617912</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.763218</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.097872</td>\n",
       "      <td>2.794681</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.318085</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.931111</td>\n",
       "      <td>5.094444</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>202197 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0   From Date           city        WS          WD  Gust  \\\n",
       "0                0  2010-01-01           Agra       NaN         NaN   NaN   \n",
       "1                1  2010-01-01      Ahmedabad       NaN         NaN   NaN   \n",
       "2                2  2010-01-01        Ambala        NaN         NaN   NaN   \n",
       "3                3  2010-01-01     Ankleshwar       NaN         NaN   NaN   \n",
       "4                4  2010-01-01   Bahadurgarh        NaN         NaN   NaN   \n",
       "...            ...         ...            ...       ...         ...   ...   \n",
       "202192      202192  2019-04-30        Udaipur  1.017579  253.649263   NaN   \n",
       "202193      202193  2019-04-30         Ujjain  4.636842  255.042105   NaN   \n",
       "202194      202194  2019-04-30       Varanasi  0.801053  180.757895   NaN   \n",
       "202195      202195  2019-04-30     Vijayawada  0.519041  221.018904   NaN   \n",
       "202196      202196  2019-04-30  Visakhapatnam       NaN         NaN   NaN   \n",
       "\n",
       "        Variance         AT         RH          SR          BP  PPAccum  \\\n",
       "0            NaN        NaN        NaN         NaN         NaN      NaN   \n",
       "1            NaN        NaN        NaN         NaN         NaN      NaN   \n",
       "2            NaN        NaN        NaN         NaN         NaN      NaN   \n",
       "3            NaN        NaN        NaN         NaN         NaN      NaN   \n",
       "4            NaN        NaN        NaN         NaN         NaN      NaN   \n",
       "...          ...        ...        ...         ...         ...      ...   \n",
       "202192       NaN        NaN  17.871263  192.898000  757.183158      NaN   \n",
       "202193       NaN  36.660000  18.252632  161.242105         NaN      NaN   \n",
       "202194       NaN  33.582105  23.600000   80.157895  745.378947      NaN   \n",
       "202195       NaN  37.019178  55.307671  228.706154  749.476301      NaN   \n",
       "202196       NaN  30.226596  73.127660  163.893617  745.968085      NaN   \n",
       "\n",
       "        Power        PM10       PM2_5         NO         NO2        NOx  \\\n",
       "0         NaN         NaN         NaN        NaN         NaN        NaN   \n",
       "1         NaN         NaN         NaN        NaN         NaN        NaN   \n",
       "2         NaN         NaN         NaN        NaN         NaN        NaN   \n",
       "3         NaN         NaN         NaN        NaN         NaN        NaN   \n",
       "4         NaN         NaN         NaN        NaN         NaN        NaN   \n",
       "...       ...         ...         ...        ...         ...        ...   \n",
       "202192    NaN   87.814737   34.304255  13.917158   19.854526  33.771684   \n",
       "202193    NaN  147.452632   40.126316        NaN    8.891011   5.250562   \n",
       "202194    NaN  331.126761  131.732394  51.202360  104.323370  74.652174   \n",
       "202195    NaN   50.119577   20.220000   6.365139   14.738356  21.004658   \n",
       "202196    NaN   52.734043   13.709302   8.764835   16.689011  15.998901   \n",
       "\n",
       "              CO  CO2      Ozone  BlackCarbon   Benzene   Toluene  \\\n",
       "0            NaN  NaN        NaN          NaN       NaN       NaN   \n",
       "1            NaN  NaN        NaN          NaN       NaN       NaN   \n",
       "2            NaN  NaN        NaN          NaN       NaN       NaN   \n",
       "3            NaN  NaN        NaN          NaN       NaN       NaN   \n",
       "4            NaN  NaN        NaN          NaN       NaN       NaN   \n",
       "...          ...  ...        ...          ...       ...       ...   \n",
       "202192  0.734835  NaN  38.070213          NaN  1.563936       NaN   \n",
       "202193  0.819535  NaN  78.206522          NaN  0.000000  0.620000   \n",
       "202194  0.860652  NaN  76.047692          NaN       NaN       NaN   \n",
       "202195       NaN  NaN        NaN          NaN  0.996849  1.870137   \n",
       "202196  0.617912  NaN  10.763218          NaN  1.097872  2.794681   \n",
       "\n",
       "        Eth-Benzene    Xylene  CH4  NMHC       VWS  THC        NH3        SO2  \\\n",
       "0               NaN       NaN  NaN   NaN       NaN  NaN        NaN        NaN   \n",
       "1               NaN       NaN  NaN   NaN       NaN  NaN        NaN        NaN   \n",
       "2               NaN       NaN  NaN   NaN       NaN  NaN        NaN        NaN   \n",
       "3               NaN       NaN  NaN   NaN       NaN  NaN        NaN        NaN   \n",
       "4               NaN       NaN  NaN   NaN       NaN  NaN        NaN        NaN   \n",
       "...             ...       ...  ...   ...       ...  ...        ...        ...   \n",
       "202192     4.092842       NaN  NaN   NaN       NaN  NaN  12.120947  12.064725   \n",
       "202193     0.000000       NaN  NaN   NaN       NaN  NaN  19.031461   8.647059   \n",
       "202194          NaN       NaN  NaN   NaN -0.522105  NaN        NaN  37.748652   \n",
       "202195     0.057397       NaN  NaN   NaN  5.000000  NaN   5.329589   6.234658   \n",
       "202196          NaN  0.318085  NaN   NaN  0.050000  NaN  38.931111   5.094444   \n",
       "\n",
       "             Temp  MP-Xylene   RF  RackTemp  P_Xylene  O Xylene  ETH  SPM  AP  \\\n",
       "0             NaN        NaN  NaN       NaN       NaN       NaN  NaN  NaN NaN   \n",
       "1             NaN        NaN  NaN       NaN       NaN       NaN  NaN  NaN NaN   \n",
       "2             NaN        NaN  NaN       NaN       NaN       NaN  NaN  NaN NaN   \n",
       "3             NaN        NaN  NaN       NaN       NaN       NaN  NaN  NaN NaN   \n",
       "4             NaN        NaN  NaN       NaN       NaN       NaN  NaN  NaN NaN   \n",
       "...           ...        ...  ...       ...       ...       ...  ...  ...  ..   \n",
       "202192  33.071053   1.188842  0.0       NaN       NaN  3.700211  NaN  NaN NaN   \n",
       "202193  31.801075        NaN  0.0       NaN       NaN       NaN  NaN  NaN NaN   \n",
       "202194        NaN        NaN  NaN       NaN       NaN       NaN  NaN  NaN NaN   \n",
       "202195        NaN   0.734110  0.0       NaN       NaN  1.899589  NaN  NaN NaN   \n",
       "202196        NaN        NaN  0.0       NaN       NaN       NaN  NaN  NaN NaN   \n",
       "\n",
       "        MH  readings_in_day  total_stations  active_stations           state  \n",
       "0      NaN              0.0               1                0   Uttar Pradesh  \n",
       "1      NaN              0.0               1                0         Gujarat  \n",
       "2      NaN              0.0               1                0         Haryana  \n",
       "3      NaN              0.0               1                0         Gujarat  \n",
       "4      NaN              0.0               1                0         Haryana  \n",
       "...     ..              ...             ...              ...             ...  \n",
       "202192 NaN             94.0               1                1       Rajasthan  \n",
       "202193 NaN             95.0               1                1  Madhya Pradesh  \n",
       "202194 NaN             71.0               1                1   Uttar Pradesh  \n",
       "202195 NaN             67.0               1                1  Andhra Pradesh  \n",
       "202196 NaN             86.0               1                1  Andhra Pradesh  \n",
       "\n",
       "[202197 rows x 46 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load PM2.5 data 2010-2019 (cpcb)\n",
    "root_path = \"../PMdataset/\"\n",
    "df_daily_city_stn = pd.read_csv(root_path+\"pm2.5_allcities_groupby_date_city_station_2010_19.csv\",index_col=0)\n",
    "df_daily_city = pd.read_csv(root_path+\"pm2.5_allcities_groupby_date_city_2010_19.csv\",index_col=0)\n",
    "#df_daily_city = df_daily_city.groupby([\"From Date\",\"city\"]).mean().reset_index()\n",
    "df_daily_city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "78f146d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(101,\n",
       " {'Agra': 'Uttar Pradesh',\n",
       "  'Ahmedabad': 'Gujarat',\n",
       "  'Ambala ': 'Haryana',\n",
       "  'Ankleshwar': 'Gujarat',\n",
       "  'Bahadurgarh ': 'Haryana',\n",
       "  'Ballabgarh': 'Haryana',\n",
       "  'Bengaluru': 'Karnataka',\n",
       "  'Bhiwani': 'Haryana',\n",
       "  'Chennai': 'Tamil Nadu',\n",
       "  'Damoh': 'Madhya Pradesh',\n",
       "  'Delhi': 'Delhi',\n",
       "  'Dharuhera': 'Haryana',\n",
       "  'Faridabad': 'Haryana',\n",
       "  'Fatehabad ': 'Haryana',\n",
       "  'Gandhinagar': 'Gujarat',\n",
       "  'Ghaziabad': 'Uttar Pradesh',\n",
       "  'Gurugram': 'Haryana',\n",
       "  'Guwahati': 'Assam',\n",
       "  'Hapur': 'Uttar Pradesh',\n",
       "  'Hisar': 'Haryana',\n",
       "  'Hyderabad': 'Telangana',\n",
       "  'Jind': 'Haryana',\n",
       "  'Kaithal': 'Haryana',\n",
       "  'Kanpur': 'Uttar Pradesh',\n",
       "  'Karnal': 'Haryana',\n",
       "  'Kurukshetra ': 'Haryana',\n",
       "  'Loni Dehat': 'Uttar Pradesh',\n",
       "  'Lucknow': 'Uttar Pradesh',\n",
       "  'Maihar': 'Madhya Pradesh',\n",
       "  'Mandikhera': 'Haryana',\n",
       "  'Manesar ': 'Haryana',\n",
       "  'Mumbai': 'Maharashtra',\n",
       "  'Nagpur': 'Maharashtra',\n",
       "  'Narnaul': 'Haryana',\n",
       "  'Navi Mumbai': 'Maharashtra',\n",
       "  'Palwal ': 'Haryana',\n",
       "  'Panipat': 'Haryana',\n",
       "  'Pune': 'Maharashtra',\n",
       "  'Ratlam': 'Madhya Pradesh',\n",
       "  'Sirsa ': 'Haryana',\n",
       "  'Solapur': 'Maharashtra',\n",
       "  'Sonipat': 'Haryana',\n",
       "  'Vapi': 'Gujarat',\n",
       "  'Varanasi': 'Uttar Pradesh',\n",
       "  'Vatva': 'Gujarat',\n",
       "  'Yamuna Nagar': 'Haryana',\n",
       "  'Noida': 'Uttar Pradesh',\n",
       "  'Patna': 'Bihar',\n",
       "  'Chandrapur': 'Maharashtra',\n",
       "  'Gaya': 'Bihar',\n",
       "  'Haldia': 'West Bengal',\n",
       "  'Howrah': 'West Bengal',\n",
       "  'Kolkata': 'West Bengal',\n",
       "  'Muzaffarpur': 'Bihar',\n",
       "  'Durgapur': 'West Bengal',\n",
       "  'Jodhpur': 'Rajasthan',\n",
       "  'Nashik': 'Maharashtra',\n",
       "  'Rohtak': 'Haryana',\n",
       "  'Thane': 'Maharashtra',\n",
       "  'Tirupati': 'Andhra Pradesh',\n",
       "  'Visakhapatnam': 'Andhra Pradesh',\n",
       "  ' Alwar': 'Rajasthan',\n",
       "  'Ajmer': 'Rajasthan',\n",
       "  'Amaravati': 'Andhra Pradesh',\n",
       "  'Amritsar': 'Punjab',\n",
       "  'Aurangabad': 'Maharashtra',\n",
       "  'Bhiwadi': 'Rajasthan',\n",
       "  'Brajrajnagar': 'Odisha',\n",
       "  'Dewas': 'Madhya Pradesh',\n",
       "  'Jaipur': 'Rajasthan',\n",
       "  'Jorapokhar': 'Jharkhand',\n",
       "  'Kota': 'Rajasthan',\n",
       "  'Ludhiana': 'Punjab',\n",
       "  'Mandi Gobindgarh': 'Punjab',\n",
       "  'Moradabad': 'Uttar Pradesh',\n",
       "  'Pali': 'Rajasthan',\n",
       "  'Panchkula': 'Haryana',\n",
       "  'Pithampur': 'Madhya Pradesh',\n",
       "  'Rajamahendravaram': 'Andhra Pradesh',\n",
       "  'Singrauli': 'Madhya Pradesh',\n",
       "  'Talcher': 'Odisha',\n",
       "  'Thiruvananthapuram': 'Kerala',\n",
       "  'Udaipur': 'Rajasthan',\n",
       "  'Ujjain': 'Madhya Pradesh',\n",
       "  'Vijayawada': 'Andhra Pradesh',\n",
       "  'Asansol': 'West Bengal',\n",
       "  'Baghpat': 'Uttar Pradesh',\n",
       "  'Bathinda ': 'Punjab',\n",
       "  'Bulandshahr': 'Uttar Pradesh',\n",
       "  'Chikkaballapur': 'Karnataka',\n",
       "  'Greater Noida': 'Uttar Pradesh',\n",
       "  'Hubballi': 'Karnataka',\n",
       "  'Jalandhar': 'Punjab',\n",
       "  'Kalaburagi': 'Karnataka',\n",
       "  'Khanna': 'Punjab',\n",
       "  'Mandideep': 'Madhya Pradesh',\n",
       "  'Muzaffarnagar': 'Uttar Pradesh',\n",
       "  'Patiala': 'Punjab',\n",
       "  'Rupnagar': 'Punjab',\n",
       "  'Satna': 'Madhya Pradesh',\n",
       "  'Siliguri': 'West Bengal'})"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ip = df_daily_city\n",
    "city_state_dict = dict(zip(df_ip[\"city\"].values,df_ip[\"state\"].values))\n",
    "len(city_state_dict), city_state_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "75fade2c",
   "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>From Date</th>\n",
       "      <th>city</th>\n",
       "      <th>PM2_5</th>\n",
       "      <th>readings</th>\n",
       "      <th>total_stations</th>\n",
       "      <th>active_stations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>133.960000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-04-30</td>\n",
       "      <td>Agra</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-04-30</td>\n",
       "      <td>Ajmer</td>\n",
       "      <td>105.640000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-04-30</td>\n",
       "      <td>Amaravati</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019-04-30</td>\n",
       "      <td>Amritsar</td>\n",
       "      <td>50.970000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75127</th>\n",
       "      <td>2021-08-01</td>\n",
       "      <td>Solapur</td>\n",
       "      <td>18.020000</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75128</th>\n",
       "      <td>2021-12-03</td>\n",
       "      <td>Solapur</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75129</th>\n",
       "      <td>2021-12-04</td>\n",
       "      <td>Solapur</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75130</th>\n",
       "      <td>2021-12-05</td>\n",
       "      <td>Solapur</td>\n",
       "      <td>14.172069</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75131</th>\n",
       "      <td>2025-06-14</td>\n",
       "      <td>Pune</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1951.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>51927 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        From Date       city       PM2_5  readings  total_stations  \\\n",
       "0      2019-01-18      Delhi  133.960000       2.0               1   \n",
       "2      2019-04-30       Agra   90.000000       1.0               1   \n",
       "3      2019-04-30      Ajmer  105.640000       2.0               1   \n",
       "4      2019-04-30  Amaravati   22.000000       1.0               1   \n",
       "6      2019-04-30   Amritsar   50.970000       1.0               1   \n",
       "...           ...        ...         ...       ...             ...   \n",
       "75127  2021-08-01    Solapur   18.020000      69.0               1   \n",
       "75128  2021-12-03    Solapur         NaN       0.0               1   \n",
       "75129  2021-12-04    Solapur         NaN       0.0               1   \n",
       "75130  2021-12-05    Solapur   14.172069      29.0               1   \n",
       "75131  2025-06-14       Pune    6.000000    1951.0               1   \n",
       "\n",
       "       active_stations  \n",
       "0                    1  \n",
       "2                    1  \n",
       "3                    1  \n",
       "4                    1  \n",
       "6                    1  \n",
       "...                ...  \n",
       "75127                1  \n",
       "75128                0  \n",
       "75129                0  \n",
       "75130                1  \n",
       "75131                1  \n",
       "\n",
       "[51927 rows x 6 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load OpenAQ data 2019-2021\n",
    "root_path = \"../PMdataset/\"\n",
    "df_daily_city_stn_oq = pd.read_csv(root_path+\"pm2.5_allcities_groupby_date_city_station_2019_21_openaq.csv\",index_col=0)\n",
    "df_daily_city_oq = pd.read_csv(root_path+\"pm2.5_allcities_groupby_date_city_2019_21_openaq.csv\",index_col=0)\n",
    "#df_daily_city_oq = df_daily_city_oq.groupby([\"From Date\",\"city\"]).mean().reset_index()\n",
    "df_daily_city_oq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c2380828",
   "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>From Date</th>\n",
       "      <th>city</th>\n",
       "      <th>PM2_5</th>\n",
       "      <th>total_stations</th>\n",
       "      <th>active_stations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>agra</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>ambala</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>ankleshwar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>bahadurgarh</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254058</th>\n",
       "      <td>2021-08-01</td>\n",
       "      <td>solapur</td>\n",
       "      <td>18.020000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254059</th>\n",
       "      <td>2021-12-03</td>\n",
       "      <td>solapur</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254060</th>\n",
       "      <td>2021-12-04</td>\n",
       "      <td>solapur</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254061</th>\n",
       "      <td>2021-12-05</td>\n",
       "      <td>solapur</td>\n",
       "      <td>14.172069</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254062</th>\n",
       "      <td>2025-06-14</td>\n",
       "      <td>pune</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>254063 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        From Date         city      PM2_5  total_stations  active_stations\n",
       "0      2010-01-01         agra        NaN             1.0              0.0\n",
       "1      2010-01-01    ahmedabad        NaN             1.0              0.0\n",
       "2      2010-01-01       ambala        NaN             1.0              0.0\n",
       "3      2010-01-01   ankleshwar        NaN             1.0              0.0\n",
       "4      2010-01-01  bahadurgarh        NaN             1.0              0.0\n",
       "...           ...          ...        ...             ...              ...\n",
       "254058 2021-08-01      solapur  18.020000             1.0              1.0\n",
       "254059 2021-12-03      solapur        NaN             1.0              0.0\n",
       "254060 2021-12-04      solapur        NaN             1.0              0.0\n",
       "254061 2021-12-05      solapur  14.172069             1.0              1.0\n",
       "254062 2025-06-14         pune   6.000000             1.0              1.0\n",
       "\n",
       "[254063 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merge cpcb and openaq \n",
    "df_daily_city_merged = pd.concat([df_daily_city,df_daily_city_oq])[[\"From Date\",\"city\",\n",
    "                                                                    \"PM2_5\",\"total_stations\",\"active_stations\",\"state\"]]\n",
    "\n",
    "df_daily_city_merged.city = df_daily_city_merged.city.apply(lambda x:x.strip().lower())\n",
    "df_daily_city_merged[\"From Date\"] = pd.to_datetime(df_daily_city_merged[\"From Date\"])\n",
    "df_daily_city_merged = df_daily_city_merged.groupby([\"From Date\",\"city\"]).mean().reset_index()\n",
    "df_daily_city_merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2e27a137",
   "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>From Date</th>\n",
       "      <th>city</th>\n",
       "      <th>PM2_5</th>\n",
       "      <th>total_stations</th>\n",
       "      <th>active_stations</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>agra</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>uttar pradesh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>ahmedabad</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>gujarat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>ambala</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>ankleshwar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>gujarat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>bahadurgarh</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254058</th>\n",
       "      <td>2021-08-01</td>\n",
       "      <td>solapur</td>\n",
       "      <td>18.020000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>maharashtra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254059</th>\n",
       "      <td>2021-12-03</td>\n",
       "      <td>solapur</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>maharashtra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254060</th>\n",
       "      <td>2021-12-04</td>\n",
       "      <td>solapur</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>maharashtra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254061</th>\n",
       "      <td>2021-12-05</td>\n",
       "      <td>solapur</td>\n",
       "      <td>14.172069</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>maharashtra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254062</th>\n",
       "      <td>2025-06-14</td>\n",
       "      <td>pune</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>maharashtra</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>254063 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        From Date         city      PM2_5  total_stations  active_stations  \\\n",
       "0      2010-01-01         agra        NaN             1.0              0.0   \n",
       "1      2010-01-01    ahmedabad        NaN             1.0              0.0   \n",
       "2      2010-01-01       ambala        NaN             1.0              0.0   \n",
       "3      2010-01-01   ankleshwar        NaN             1.0              0.0   \n",
       "4      2010-01-01  bahadurgarh        NaN             1.0              0.0   \n",
       "...           ...          ...        ...             ...              ...   \n",
       "254058 2021-08-01      solapur  18.020000             1.0              1.0   \n",
       "254059 2021-12-03      solapur        NaN             1.0              0.0   \n",
       "254060 2021-12-04      solapur        NaN             1.0              0.0   \n",
       "254061 2021-12-05      solapur  14.172069             1.0              1.0   \n",
       "254062 2025-06-14         pune   6.000000             1.0              1.0   \n",
       "\n",
       "                state  \n",
       "0       uttar pradesh  \n",
       "1             gujarat  \n",
       "2                 NaN  \n",
       "3             gujarat  \n",
       "4                 NaN  \n",
       "...               ...  \n",
       "254058    maharashtra  \n",
       "254059    maharashtra  \n",
       "254060    maharashtra  \n",
       "254061    maharashtra  \n",
       "254062    maharashtra  \n",
       "\n",
       "[254063 rows x 6 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_daily_city_merged[\"state\"] = df_daily_city_merged[\"city\"].apply(lambda x:city_state_dict[x.capitalize()].lower() if x.capitalize() in city_state_dict else np.nan)\n",
    "df_daily_city_merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "29a7eec9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['uttar pradesh', 'gujarat', nan, 'haryana', 'karnataka',\n",
       "       'tamil nadu', 'madhya pradesh', 'delhi', 'assam', 'telangana',\n",
       "       'maharashtra', 'bihar', 'west bengal', 'rajasthan',\n",
       "       'andhra pradesh', 'punjab', 'odisha', 'jharkhand', 'kerala'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_daily_city_merged.state.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f0f0174",
   "metadata": {},
   "source": [
    "# Delhi - Articles vs PM2.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "66550e20",
   "metadata": {},
   "outputs": [],
   "source": [
    "def format_axes(ax,twinx=False,SPINE_COLOR = \"gray\"):\n",
    "    \n",
    "    if twinx:\n",
    "        for spine in ['top']:\n",
    "            ax.spines[spine].set_visible(False)\n",
    "\n",
    "        for spine in ['left', 'bottom', 'right']:\n",
    "            ax.spines[spine].set_color(SPINE_COLOR)\n",
    "            ax.spines[spine].set_linewidth(0.5)\n",
    "        \n",
    "    else:\n",
    "        for spine in ['top', 'right']:\n",
    "            ax.spines[spine].set_visible(False)\n",
    "\n",
    "        for spine in ['left', 'bottom']:\n",
    "            ax.spines[spine].set_color(SPINE_COLOR)\n",
    "            ax.spines[spine].set_linewidth(0.5)\n",
    "\n",
    "    return ax\n",
    "\n",
    "def latexify(fig_width=None, fig_height=None, columns=2):\n",
    "    \"\"\"Set up matplotlib's RC params for LaTeX plotting.\n",
    "    Call this before plotting a figure.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    fig_width : float, optional, inches\n",
    "    fig_height : float,  optional, inches\n",
    "    columns : {1, 2}\n",
    "    \"\"\"\n",
    "\n",
    "    # code adapted from http://www.scipy.org/Cookbook/Matplotlib/LaTeX_Examples\n",
    "\n",
    "    # Width and max height in inches for IEEE journals taken from\n",
    "    # computer.org/cms/Computer.org/Journal%20templates/transactions_art_guide.pdf\n",
    "\n",
    "    assert(columns in [1,2])\n",
    "\n",
    "    if fig_width is None:\n",
    "        fig_width = 3.32 if columns==1 else 6.75 # width in inches\n",
    "\n",
    "    if fig_height is None:\n",
    "        golden_mean = (sqrt(5)-1.0)/2.0    # Aesthetic ratio\n",
    "        fig_height = fig_width*golden_mean # height in inches\n",
    "\n",
    "    MAX_HEIGHT_INCHES = 8.0\n",
    "    if fig_height > MAX_HEIGHT_INCHES:\n",
    "        print(\"WARNING: fig_height too large:\" + fig_height + \n",
    "              \"so will reduce to\" + MAX_HEIGHT_INCHES + \"inches.\")\n",
    "        fig_height = MAX_HEIGHT_INCHES\n",
    "\n",
    "    params = {'backend': 'ps',\n",
    "              'text.latex.preamble': [r'\\usepackage{gensymb}'],\n",
    "              'axes.labelsize': 8, # fontsize for x and y labels (was 10)\n",
    "              'axes.titlesize': 8,\n",
    "              'font.size': 8, # was 10\n",
    "              'legend.fontsize': 8, # was 10\n",
    "              'xtick.labelsize': 8,\n",
    "              'ytick.labelsize': 8,\n",
    "              'text.usetex': True,\n",
    "              'figure.figsize': [fig_width,fig_height],\n",
    "              'font.family': 'serif'\n",
    "    }\n",
    "\n",
    "    matplotlib.rcParams.update(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c0e41f21",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_month_year_column(which_df):\n",
    "    #make month column from date\n",
    "    which_df[\"month\"] = which_df.apply(lambda x:f'{x[\"date\"].year}-{x[\"date\"].month}',axis=1)\n",
    "\n",
    "    #make year column from date\n",
    "    which_df[\"year\"] = which_df.apply(lambda x:f'{x[\"date\"].year}',axis=1)\n",
    "    \n",
    "    return which_df\n",
    "\n",
    "\n",
    "def get_article_count_df(article_df, start_date = datetime(2010,1,1).date(), end_date = datetime(2021,5,30).date()):\n",
    "    '''\n",
    "    count articles per day by applying group by date\n",
    "    returns dataframe\n",
    "    '''\n",
    "    \n",
    "    article_count = article_df.groupby(by=\"date\")[\"heading\"].count()\n",
    "    \n",
    "    #range of all date\n",
    "    date_idx = pd.date_range(start_date, end_date)\n",
    "    \n",
    "    #get articles from specific range    \n",
    "    article_count = article_count[start_date:end_date]\n",
    "\n",
    "    #fill missing dates\n",
    "    article_count = article_count.reindex(date_idx,fill_value=0)\n",
    "\n",
    "    article_count =  article_count.reset_index()\n",
    "    article_count.columns = [\"date\",\"count\"]\n",
    "    \n",
    "    #add month & year columns\n",
    "    article_count = add_month_year_column(article_count)\n",
    "    return article_count\n",
    "\n",
    "def filter_cities(which_df,allowed_cities):\n",
    "    '''\n",
    "    gives only those rows of dataframe which \"city\" columns\n",
    "    contains given cities\n",
    "    allowed cities can be one city or list of cities\n",
    "    '''\n",
    "    if type(allowed_cities) == str:\n",
    "        allowed_cities = [allowed_cities]\n",
    "    #remove NA values in city\n",
    "    which_df = which_df.dropna(subset=[\"city\"])\n",
    "\n",
    "    #city can be list or str\n",
    "    filtered_df = which_df[which_df[\"city\"].apply(lambda city: True in [each.lower().strip() in city for each in allowed_cities] if type(city) == str or type(city) == list else False)]\n",
    "    return filtered_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "618aac91",
   "metadata": {},
   "outputs": [],
   "source": [
    "%config Completer.use_jedi = False\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8db0d999",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5296\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 324x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "latexify(fig_width=4.5,fig_height=1)\n",
    "\n",
    "fig,ax = plt.subplots()\n",
    "plts = []\n",
    "\n",
    "pm_color = \"tab:red\"\n",
    "article_color = \"tab:green\"\n",
    "#trend_color = \"tab:green\"\n",
    "\n",
    "############################################################################################\n",
    "city = \"delhi\"\n",
    "\n",
    "start_date = datetime(2010,2,1)\n",
    "end_date = datetime(2021,6,21)\n",
    "\n",
    "df_articles[\"date\"] = pd.to_datetime(df_articles[\"date\"])\n",
    "df_a = filter_cities(df_articles,[city])\n",
    "print(len(df_a))\n",
    "df_a = get_article_count_df(df_a,start_date=start_date,end_date=end_date)\n",
    "#df_a = df_a.resample(\"7D\",on=\"date\").reset_index()\n",
    "\n",
    "df_a = df_a.resample(\"7D\",on=\"date\").sum().reset_index()\n",
    "\n",
    "x2 = df_a[\"date\"]\n",
    "y2 = df_a[\"count\"]\n",
    "    \n",
    "twin = []\n",
    "\n",
    "plt2 = ax.plot(x2,y2,label = \"Weekly AQ\\narticles\",linewidth=1.2,color=article_color)\n",
    "ax.set_ylabel(\"Number of Articles\")\n",
    "ax.set_xlabel(\"Week\")\n",
    "\n",
    "ax2 = ax.twinx()\n",
    "twin.append(ax2)\n",
    "format_axes(ax,twinx=True)\n",
    "#ax.set_yticks(np.arange(0,111,55))\n",
    "#format_axes(ax_t,twinx=True);\n",
    "\n",
    "\n",
    "plts.append(plt2[0])\n",
    "ax_t = ax2\n",
    "\n",
    "#plot PM2.5 data\n",
    "city_df = df_daily_city_merged[df_daily_city_merged.city == city]\n",
    "city_df[\"From Date\"] = pd.to_datetime(city_df[\"From Date\"])\n",
    "\n",
    "which_df = city_df\n",
    "which_df = which_df[(which_df[\"From Date\"] >= start_date) & (which_df[\"From Date\"] <= end_date)]\n",
    "which_df = which_df.resample(\"7D\",on=\"From Date\").mean().reset_index()\n",
    "keyword = \"PM2_5\"\n",
    "\n",
    "#total_count = which_df[keyword].sum()\n",
    "x1, y1 = which_df[\"From Date\"], which_df[keyword]\n",
    "\n",
    "plt1 = ax_t.plot(x1,y1,\"--\",label = \"Weekly average\\nPM$_{2.5}$\",color=pm_color,linewidth=1)\n",
    "ax_t.set_ylabel(\"PM$_{2.5}$ in $\\mu g/m^3$\")\n",
    "\n",
    "\n",
    "\n",
    "#set india limit ***************************************\n",
    "plt_hline = ax_t.hlines(60,datetime(2009,11,1),datetime(2021,11,1),\n",
    "            linestyles=\"dashed\",alpha=0.7,color=\"k\",label=\"Daily PM$_{2.5}$\\nIndia limit\\n(60 $\\mu g/m^3 $)\",linewidth=1)\n",
    "\n",
    "#ax_t.set_xlabel(\"Day\")\n",
    "#ax_t.set_title(\"Delhi\")\n",
    "\n",
    "plts.append(plt1[0])\n",
    "plts.append(plt_hline)\n",
    "#india limit ***************************************\n",
    "\n",
    "\n",
    "y_indicator = 90\n",
    "lineWidth = 0.8\n",
    "\n",
    "#Us embassy started posting India air pollution data\n",
    "openaq = datetime(2013,1,1)\n",
    "indicator_1 = ax.axvline(x=openaq, linestyle='dashed', alpha=0.8,  color='#0037ff',linewidth = lineWidth, label=\"A\") #: US embassy\\reporting\n",
    "delta = pd.Timedelta(days=130)\n",
    "#ax.annotate(\"OpenAQ\\nwebsite\\nlauched\",xy = (openaq+delta,100),)\n",
    "ax.annotate(\"A\",xy = (openaq-delta,y_indicator))\n",
    "#plts.append(indicator_1)\n",
    "\n",
    "\n",
    "#WHO updated database\n",
    "openaq = datetime(2014,5,7)\n",
    "indicator_2 = ax.axvline(x=openaq, linestyle='dashed', alpha=0.8,  color='#0037ff',linewidth = lineWidth, label=\"B\") #Delhi became \\nmost polluted \\ncity in the world\n",
    "delta = pd.Timedelta(days=130)\n",
    "#ax.annotate(\"New Delhi became\\nmost polluted \\ncity worldwise\",xy = (openaq-delta,100))\n",
    "ax.annotate(\"B\",xy = (openaq-delta,y_indicator))\n",
    "#plts.append(indicator_2)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#OpenAQ started posting air pollution data\n",
    "openaq = datetime(2015,9,1)\n",
    "indicator_1 = ax.axvline(x=openaq, linestyle='dashed', alpha=0.8,  color='#0037ff',linewidth = lineWidth ,label=\"C\")# OpenAQ\\nwebsite lauched\"\n",
    "delta = pd.Timedelta(days=120)\n",
    "#ax.annotate(\"OpenAQ\\nwebsite\\nlauched\",xy = (openaq+delta,100),)\n",
    "ax.annotate(\"C\",xy = (openaq-delta,y_indicator))\n",
    "#plts.append(indicator_1)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#set format of date\n",
    "date_form = DateFormatter(\"%b\\n%Y\")\n",
    "\n",
    "\n",
    "ax_t.xaxis.set_major_formatter(date_form)\n",
    "\n",
    "#make interval of n months\n",
    "ax_t.xaxis.set_major_locator(mdates.MonthLocator(interval=12))\n",
    "ax_t.tick_params(axis=\"x\",rotation=0)\n",
    "ax.set_xlim(datetime(2009,11,1),datetime(2021,11,1))\n",
    "\n",
    "\n",
    "\n",
    "labs = [l.get_label() for l in plts] + [indicator_1.get_label()]\n",
    "plt.legend(plts, labs, bbox_to_anchor=(1.12, 1.1))\n",
    "\n",
    "\n",
    "format_axes(ax_t,twinx=True);\n",
    "ax_t.spines['right'].set_color(pm_color)\n",
    "ax_t.spines['right'].set_linewidth(1.5)\n",
    "\n",
    "ax_t.spines['left'].set_color(article_color)\n",
    "ax_t.spines['left'].set_linewidth(1.5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "afa55614",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig(\"../figures/fig5_timeseries_of_aq_articles_delhi_with_two_indicators_embassy_added.pdf\",bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5c651de",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:NewsAQ38] *",
   "language": "python",
   "name": "conda-env-NewsAQ38-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}