Research_Question_1/Figure-4_Violation_of_India_limit_by_IGP.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 3,
   "id": "1c885953",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "  <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",
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      ],
      "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": 3,
     "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": 4,
   "id": "79beddd7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <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": 4,
     "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": 9,
   "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": 9,
     "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": 6,
   "id": "75fade2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 6,
     "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": 7,
   "id": "c2380828",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</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": 7,
     "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": 10,
   "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": 10,
     "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": 11,
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_daily_city_merged.state.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "07fab5f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#check city wise data start date & end date\n",
    "def get_start_date(city,threshold = 0.8,n = 365):\n",
    "    print(city)\n",
    "    test = df_daily_city_merged[df_daily_city_merged.city == city]\n",
    "    test[\"From Date\"] = pd.to_datetime(test[\"From Date\"])\n",
    "    test_drop_na = test.dropna(subset=[\"PM2_5\"])\n",
    "\n",
    "    for idx,row in test_drop_na.iterrows():\n",
    "        curr_date = row[\"From Date\"]\n",
    "        n = 365\n",
    "        end_date = curr_date+pd.Timedelta(days=n)\n",
    "        date_range = pd.date_range(curr_date,end_date)\n",
    "        temp = test[test[\"From Date\"].isin(date_range)]\n",
    "        frac = len(temp.dropna(subset=[\"PM2_5\"]))/len(temp)\n",
    "        #print(frac)\n",
    "        threshold = 0.8\n",
    "        if frac >threshold:\n",
    "            print(curr_date)\n",
    "            return curr_date\n",
    "def get_unique_cities_from_df(df,col=\"city\"):\n",
    "    '''\n",
    "    returns dataframe of city->articles from dataframe\n",
    "    as city can be list so unique() will not work\n",
    "    '''\n",
    "    unq = {}\n",
    "    for each in df[col].values:\n",
    "        if type(each) == str:\n",
    "            cities = [each]\n",
    "        elif type(each) == list:\n",
    "            cities = each\n",
    "        else:\n",
    "            continue\n",
    "        for city in cities:\n",
    "            try:\n",
    "                unq[city]+=1\n",
    "            except:\n",
    "                unq[city] = 1\n",
    "    df = pd.DataFrame(unq.items(),columns = [\"city\",\"articles\"]).sort_values(\"articles\",ascending=False)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6f8d5498",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11 ['amritsar', 'patna', 'delhi', 'lucknow', 'varanasi', 'gurugram', 'noida', 'kolkata', 'agra', 'kanpur', 'faridabad']\n",
      "amritsar\n",
      "2017-02-27 00:00:00\n",
      "patna\n",
      "2015-10-01 00:00:00\n",
      "delhi\n",
      "2010-02-17 00:00:00\n",
      "lucknow\n",
      "2015-03-18 00:00:00\n",
      "varanasi\n",
      "2014-09-13 00:00:00\n",
      "gurugram\n",
      "2016-01-22 00:00:00\n",
      "noida\n",
      "2017-07-17 00:00:00\n",
      "kolkata\n",
      "2018-04-10 00:00:00\n",
      "agra\n",
      "2015-05-11 00:00:00\n",
      "kanpur\n",
      "2015-05-12 00:00:00\n",
      "faridabad\n",
      "2015-05-20 00:00:00\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>city</th>\n",
       "      <th>agra</th>\n",
       "      <th>amritsar</th>\n",
       "      <th>delhi</th>\n",
       "      <th>faridabad</th>\n",
       "      <th>gurugram</th>\n",
       "      <th>kanpur</th>\n",
       "      <th>kolkata</th>\n",
       "      <th>lucknow</th>\n",
       "      <th>noida</th>\n",
       "      <th>patna</th>\n",
       "      <th>varanasi</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>From Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-02-18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>135.687500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107.047158</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.914687</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.360000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-22</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.640370</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.246377</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-18</th>\n",
       "      <td>23.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.128846</td>\n",
       "      <td>40.66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.7</td>\n",
       "      <td>19.250000</td>\n",
       "      <td>8.18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-19</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>53.615</td>\n",
       "      <td>30.945804</td>\n",
       "      <td>31.00</td>\n",
       "      <td>39.6</td>\n",
       "      <td>25.0</td>\n",
       "      <td>29.350000</td>\n",
       "      <td>21.96</td>\n",
       "      <td>18.355000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-20</th>\n",
       "      <td>24.850000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.494222</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.540984</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.835000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-21</th>\n",
       "      <td>19.866667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.096285</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.5</td>\n",
       "      <td>35.5</td>\n",
       "      <td>40.250000</td>\n",
       "      <td>12.90</td>\n",
       "      <td>31.658095</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4080 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "city             agra  amritsar       delhi  faridabad  gurugram  kanpur  \\\n",
       "From Date                                                                  \n",
       "2010-02-18        NaN       NaN  135.687500        NaN       NaN     NaN   \n",
       "2010-02-19        NaN       NaN  107.047158        NaN       NaN     NaN   \n",
       "2010-02-20        NaN       NaN   15.914687        NaN       NaN     NaN   \n",
       "2010-02-21        NaN       NaN    9.360000        NaN       NaN     NaN   \n",
       "2010-02-22        NaN       NaN   17.640370        NaN       NaN     NaN   \n",
       "...               ...       ...         ...        ...       ...     ...   \n",
       "2021-06-17        NaN       NaN   34.246377        NaN       NaN     NaN   \n",
       "2021-06-18  23.200000       NaN   16.128846      40.66       NaN     4.7   \n",
       "2021-06-19  10.000000    53.615   30.945804      31.00      39.6    25.0   \n",
       "2021-06-20  24.850000       NaN   23.494222        NaN      31.0     NaN   \n",
       "2021-06-21  19.866667       NaN   41.096285        NaN      49.5    35.5   \n",
       "\n",
       "city          kolkata  lucknow      noida  patna  varanasi  \n",
       "From Date                                                   \n",
       "2010-02-18        NaN      NaN        NaN    NaN       NaN  \n",
       "2010-02-19        NaN      NaN        NaN    NaN       NaN  \n",
       "2010-02-20        NaN      NaN        NaN    NaN       NaN  \n",
       "2010-02-21        NaN      NaN        NaN    NaN       NaN  \n",
       "2010-02-22        NaN      NaN        NaN    NaN       NaN  \n",
       "...               ...      ...        ...    ...       ...  \n",
       "2021-06-17        NaN      NaN        NaN    NaN       NaN  \n",
       "2021-06-18  19.250000     8.18        NaN    NaN       NaN  \n",
       "2021-06-19  29.350000    21.96  18.355000    NaN     20.00  \n",
       "2021-06-20  29.540984      NaN  15.835000    NaN       NaN  \n",
       "2021-06-21  40.250000    12.90  31.658095    NaN     16.78  \n",
       "\n",
       "[4080 rows x 11 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf = get_unique_cities_from_df(df_articles)\n",
    "igp_cities = [\"delhi\",\"agra\",\"lucknow\",\"patna\",\"kanpur\",\"varanasi\",\"noida\",\"gurugram\",\"faridabad\",\"kolkata\",\"amritsar\"]\n",
    "most_discussed_cities = []\n",
    "#most_discussed_cities = list(cdf.nlargest(11,\"articles\")[\"city\"].values) #most 11 discussed cities in news-media\n",
    "needed_cities = list(set(igp_cities + most_discussed_cities)) #most\n",
    "print(len(needed_cities),needed_cities)\n",
    "\n",
    "\n",
    "#needed_cities = h.get_unique_cities_from_df(df_articles).nlargest(15,\"articles\")[\"city\"].values\n",
    "\n",
    "start_date = datetime(2018,4,1)\n",
    "end_date = datetime(2021,4,1)\n",
    "\n",
    "#df_cross_cities = df_daily_city_merged[(df_daily_city_merged[\"From Date\"] >= start_date) & (df_daily_city_merged[\"From Date\"] <= end_date)]\n",
    "df_cross_cities = df_daily_city_merged.copy()\n",
    "df_cross_cities = df_cross_cities[df_cross_cities.city.isin(needed_cities)]\n",
    "dfs = []\n",
    "for city in needed_cities:\n",
    "    try:\n",
    "        start_date = get_start_date(city)\n",
    "        df = df_cross_cities[(df_cross_cities[\"city\"] == city) & (df_cross_cities[\"From Date\"]>start_date)]\n",
    "        dfs.append(df)\n",
    "    except Exception as e:\n",
    "        print(city,e)\n",
    "    \n",
    "df_cross_cities = pd.concat(dfs)\n",
    "df_cross_cities = pd.crosstab(index=df_cross_cities[\"From Date\"],\n",
    "                              columns=df_cross_cities.city,values=df_cross_cities[\"PM2_5\"],aggfunc=lambda x:x)\n",
    "df_cross_cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ae49bf0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "agra           2081\n",
       "amritsar       2783\n",
       "delhi             3\n",
       "faridabad      2037\n",
       "gurugram       2326\n",
       "kanpur         2060\n",
       "kolkata        3001\n",
       "lucknow        1940\n",
       "noida          2815\n",
       "patna          2279\n",
       "varanasi       1913\n",
       "day_of_year       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_cross_cities.isna()).sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "13fd1294",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>city</th>\n",
       "      <th>agra</th>\n",
       "      <th>amritsar</th>\n",
       "      <th>delhi</th>\n",
       "      <th>faridabad</th>\n",
       "      <th>gurugram</th>\n",
       "      <th>kanpur</th>\n",
       "      <th>kolkata</th>\n",
       "      <th>lucknow</th>\n",
       "      <th>noida</th>\n",
       "      <th>patna</th>\n",
       "      <th>varanasi</th>\n",
       "      <th>day_of_year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>From Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-04-01</th>\n",
       "      <td>61.916667</td>\n",
       "      <td>43.980000</td>\n",
       "      <td>74.347176</td>\n",
       "      <td>77.404255</td>\n",
       "      <td>98.030937</td>\n",
       "      <td>53.987342</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74.332864</td>\n",
       "      <td>63.175156</td>\n",
       "      <td>73.619565</td>\n",
       "      <td>62.931034</td>\n",
       "      <td>04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-02</th>\n",
       "      <td>71.232558</td>\n",
       "      <td>34.550000</td>\n",
       "      <td>96.461212</td>\n",
       "      <td>95.125000</td>\n",
       "      <td>64.897879</td>\n",
       "      <td>74.204301</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.093734</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>63.729167</td>\n",
       "      <td>75.811111</td>\n",
       "      <td>04-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-03</th>\n",
       "      <td>88.224719</td>\n",
       "      <td>65.538750</td>\n",
       "      <td>100.474653</td>\n",
       "      <td>109.041667</td>\n",
       "      <td>90.604022</td>\n",
       "      <td>60.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>162.669110</td>\n",
       "      <td>70.564479</td>\n",
       "      <td>97.565217</td>\n",
       "      <td>84.022222</td>\n",
       "      <td>04-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-04</th>\n",
       "      <td>79.239130</td>\n",
       "      <td>58.903846</td>\n",
       "      <td>118.512089</td>\n",
       "      <td>122.125000</td>\n",
       "      <td>101.449886</td>\n",
       "      <td>76.568421</td>\n",
       "      <td>NaN</td>\n",
       "      <td>164.142913</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.623656</td>\n",
       "      <td>85.168539</td>\n",
       "      <td>04-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-05</th>\n",
       "      <td>82.232558</td>\n",
       "      <td>50.727021</td>\n",
       "      <td>144.107495</td>\n",
       "      <td>154.958333</td>\n",
       "      <td>118.452604</td>\n",
       "      <td>51.106383</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82.921629</td>\n",
       "      <td>124.883177</td>\n",
       "      <td>64.576087</td>\n",
       "      <td>47.957447</td>\n",
       "      <td>04-05</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-28</th>\n",
       "      <td>374.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>126.025294</td>\n",
       "      <td>215.116667</td>\n",
       "      <td>114.500000</td>\n",
       "      <td>161.150000</td>\n",
       "      <td>61.071382</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>195.666667</td>\n",
       "      <td>136.792000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>03-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-29</th>\n",
       "      <td>58.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>103.573150</td>\n",
       "      <td>175.566667</td>\n",
       "      <td>59.550000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.847151</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>100.674000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>03-29</td>\n",
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       "    <tr>\n",
       "      <th>2021-03-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95.200427</td>\n",
       "      <td>159.790000</td>\n",
       "      <td>78.996667</td>\n",
       "      <td>54.600000</td>\n",
       "      <td>48.878437</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>76.684000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>03-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>109.559939</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59.406667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>03-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.859583</td>\n",
       "      <td>37.770000</td>\n",
       "      <td>69.866667</td>\n",
       "      <td>44.400000</td>\n",
       "      <td>46.632150</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>68.666667</td>\n",
       "      <td>72.516000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>04-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1090 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "city              agra   amritsar       delhi   faridabad    gurugram  \\\n",
       "From Date                                                               \n",
       "2018-04-01   61.916667  43.980000   74.347176   77.404255   98.030937   \n",
       "2018-04-02   71.232558  34.550000   96.461212   95.125000   64.897879   \n",
       "2018-04-03   88.224719  65.538750  100.474653  109.041667   90.604022   \n",
       "2018-04-04   79.239130  58.903846  118.512089  122.125000  101.449886   \n",
       "2018-04-05   82.232558  50.727021  144.107495  154.958333  118.452604   \n",
       "...                ...        ...         ...         ...         ...   \n",
       "2021-03-28  374.000000        NaN  126.025294  215.116667  114.500000   \n",
       "2021-03-29   58.000000        NaN  103.573150  175.566667   59.550000   \n",
       "2021-03-30         NaN        NaN   95.200427  159.790000   78.996667   \n",
       "2021-03-31         NaN        NaN  109.559939         NaN         NaN   \n",
       "2021-04-01         NaN        NaN   78.859583   37.770000   69.866667   \n",
       "\n",
       "city            kanpur    kolkata     lucknow       noida       patna  \\\n",
       "From Date                                                               \n",
       "2018-04-01   53.987342        NaN   74.332864   63.175156   73.619565   \n",
       "2018-04-02   74.204301        NaN  100.093734  164.000000   63.729167   \n",
       "2018-04-03   60.200000        NaN  162.669110   70.564479   97.565217   \n",
       "2018-04-04   76.568421        NaN  164.142913         NaN   86.623656   \n",
       "2018-04-05   51.106383        NaN   82.921629  124.883177   64.576087   \n",
       "...                ...        ...         ...         ...         ...   \n",
       "2021-03-28  161.150000  61.071382   90.000000  195.666667  136.792000   \n",
       "2021-03-29         NaN  55.847151  102.000000   54.000000  100.674000   \n",
       "2021-03-30   54.600000  48.878437   72.000000   49.000000   76.684000   \n",
       "2021-03-31         NaN  59.406667         NaN   84.500000         NaN   \n",
       "2021-04-01   44.400000  46.632150   35.000000   68.666667   72.516000   \n",
       "\n",
       "city         varanasi day_of_year  \n",
       "From Date                          \n",
       "2018-04-01  62.931034       04-01  \n",
       "2018-04-02  75.811111       04-02  \n",
       "2018-04-03  84.022222       04-03  \n",
       "2018-04-04  85.168539       04-04  \n",
       "2018-04-05  47.957447       04-05  \n",
       "...               ...         ...  \n",
       "2021-03-28        NaN       03-28  \n",
       "2021-03-29        NaN       03-29  \n",
       "2021-03-30        NaN       03-30  \n",
       "2021-03-31        NaN       03-31  \n",
       "2021-04-01        NaN       04-01  \n",
       "\n",
       "[1090 rows x 12 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filter based in start & end date\n",
    "df_cross_cities= df_cross_cities[df_cross_cities.index.isin(pd.date_range(start=\"2018-04-01\",end=\"2021-04-01\"))]\n",
    "df_cross_cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1d265cfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2018-04-01 00:00:00')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cross_cities.index.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3874214",
   "metadata": {},
   "source": [
    "### Heatmap like plot for Figure_4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "29d56ba1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>city</th>\n",
       "      <th>agra</th>\n",
       "      <th>amritsar</th>\n",
       "      <th>delhi</th>\n",
       "      <th>faridabad</th>\n",
       "      <th>gurugram</th>\n",
       "      <th>kanpur</th>\n",
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       "      <th>lucknow</th>\n",
       "      <th>noida</th>\n",
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       "      <td>74.332864</td>\n",
       "      <td>63.175156</td>\n",
       "      <td>73.619565</td>\n",
       "      <td>62.931034</td>\n",
       "      <td>04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-02</th>\n",
       "      <td>71.232558</td>\n",
       "      <td>34.550000</td>\n",
       "      <td>96.461212</td>\n",
       "      <td>95.125000</td>\n",
       "      <td>64.897879</td>\n",
       "      <td>74.204301</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.093734</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>63.729167</td>\n",
       "      <td>75.811111</td>\n",
       "      <td>04-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-03</th>\n",
       "      <td>88.224719</td>\n",
       "      <td>65.538750</td>\n",
       "      <td>100.474653</td>\n",
       "      <td>109.041667</td>\n",
       "      <td>90.604022</td>\n",
       "      <td>60.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>162.669110</td>\n",
       "      <td>70.564479</td>\n",
       "      <td>97.565217</td>\n",
       "      <td>84.022222</td>\n",
       "      <td>04-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-04</th>\n",
       "      <td>79.239130</td>\n",
       "      <td>58.903846</td>\n",
       "      <td>118.512089</td>\n",
       "      <td>122.125000</td>\n",
       "      <td>101.449886</td>\n",
       "      <td>76.568421</td>\n",
       "      <td>NaN</td>\n",
       "      <td>164.142913</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.623656</td>\n",
       "      <td>85.168539</td>\n",
       "      <td>04-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-05</th>\n",
       "      <td>82.232558</td>\n",
       "      <td>50.727021</td>\n",
       "      <td>144.107495</td>\n",
       "      <td>154.958333</td>\n",
       "      <td>118.452604</td>\n",
       "      <td>51.106383</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82.921629</td>\n",
       "      <td>124.883177</td>\n",
       "      <td>64.576087</td>\n",
       "      <td>47.957447</td>\n",
       "      <td>04-05</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-28</th>\n",
       "      <td>374.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>126.025294</td>\n",
       "      <td>215.116667</td>\n",
       "      <td>114.500000</td>\n",
       "      <td>161.150000</td>\n",
       "      <td>61.071382</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>195.666667</td>\n",
       "      <td>136.792000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>03-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-29</th>\n",
       "      <td>58.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>103.573150</td>\n",
       "      <td>175.566667</td>\n",
       "      <td>59.550000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.847151</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>100.674000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>03-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95.200427</td>\n",
       "      <td>159.790000</td>\n",
       "      <td>78.996667</td>\n",
       "      <td>54.600000</td>\n",
       "      <td>48.878437</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>76.684000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>03-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>109.559939</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59.406667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>03-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.859583</td>\n",
       "      <td>37.770000</td>\n",
       "      <td>69.866667</td>\n",
       "      <td>44.400000</td>\n",
       "      <td>46.632150</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>68.666667</td>\n",
       "      <td>72.516000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>04-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1090 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "city              agra   amritsar       delhi   faridabad    gurugram  \\\n",
       "From Date                                                               \n",
       "2018-04-01   61.916667  43.980000   74.347176   77.404255   98.030937   \n",
       "2018-04-02   71.232558  34.550000   96.461212   95.125000   64.897879   \n",
       "2018-04-03   88.224719  65.538750  100.474653  109.041667   90.604022   \n",
       "2018-04-04   79.239130  58.903846  118.512089  122.125000  101.449886   \n",
       "2018-04-05   82.232558  50.727021  144.107495  154.958333  118.452604   \n",
       "...                ...        ...         ...         ...         ...   \n",
       "2021-03-28  374.000000        NaN  126.025294  215.116667  114.500000   \n",
       "2021-03-29   58.000000        NaN  103.573150  175.566667   59.550000   \n",
       "2021-03-30         NaN        NaN   95.200427  159.790000   78.996667   \n",
       "2021-03-31         NaN        NaN  109.559939         NaN         NaN   \n",
       "2021-04-01         NaN        NaN   78.859583   37.770000   69.866667   \n",
       "\n",
       "city            kanpur    kolkata     lucknow       noida       patna  \\\n",
       "From Date                                                               \n",
       "2018-04-01   53.987342        NaN   74.332864   63.175156   73.619565   \n",
       "2018-04-02   74.204301        NaN  100.093734  164.000000   63.729167   \n",
       "2018-04-03   60.200000        NaN  162.669110   70.564479   97.565217   \n",
       "2018-04-04   76.568421        NaN  164.142913         NaN   86.623656   \n",
       "2018-04-05   51.106383        NaN   82.921629  124.883177   64.576087   \n",
       "...                ...        ...         ...         ...         ...   \n",
       "2021-03-28  161.150000  61.071382   90.000000  195.666667  136.792000   \n",
       "2021-03-29         NaN  55.847151  102.000000   54.000000  100.674000   \n",
       "2021-03-30   54.600000  48.878437   72.000000   49.000000   76.684000   \n",
       "2021-03-31         NaN  59.406667         NaN   84.500000         NaN   \n",
       "2021-04-01   44.400000  46.632150   35.000000   68.666667   72.516000   \n",
       "\n",
       "city         varanasi day_of_year  \n",
       "From Date                          \n",
       "2018-04-01  62.931034       04-01  \n",
       "2018-04-02  75.811111       04-02  \n",
       "2018-04-03  84.022222       04-03  \n",
       "2018-04-04  85.168539       04-04  \n",
       "2018-04-05  47.957447       04-05  \n",
       "...               ...         ...  \n",
       "2021-03-28        NaN       03-28  \n",
       "2021-03-29        NaN       03-29  \n",
       "2021-03-30        NaN       03-30  \n",
       "2021-03-31        NaN       03-31  \n",
       "2021-04-01        NaN       04-01  \n",
       "\n",
       "[1090 rows x 12 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cross_cities[\"day_of_year\"] = df_cross_cities.apply(lambda x: x.name.date().strftime(\"%m-%d\") ,axis=1)\n",
    "df_cross_cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "38fad6cd",
   "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>city</th>\n",
       "      <th>agra</th>\n",
       "      <th>amritsar</th>\n",
       "      <th>delhi</th>\n",
       "      <th>faridabad</th>\n",
       "      <th>gurugram</th>\n",
       "      <th>kanpur</th>\n",
       "      <th>kolkata</th>\n",
       "      <th>lucknow</th>\n",
       "      <th>noida</th>\n",
       "      <th>patna</th>\n",
       "      <th>varanasi</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_of_year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>01-01</th>\n",
       "      <td>266.698864</td>\n",
       "      <td>83.531003</td>\n",
       "      <td>339.500161</td>\n",
       "      <td>308.607128</td>\n",
       "      <td>296.732793</td>\n",
       "      <td>287.404168</td>\n",
       "      <td>158.835409</td>\n",
       "      <td>268.894621</td>\n",
       "      <td>352.894609</td>\n",
       "      <td>266.542610</td>\n",
       "      <td>290.180489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01-02</th>\n",
       "      <td>217.708791</td>\n",
       "      <td>67.501699</td>\n",
       "      <td>286.541694</td>\n",
       "      <td>272.483981</td>\n",
       "      <td>284.044378</td>\n",
       "      <td>281.060555</td>\n",
       "      <td>158.556038</td>\n",
       "      <td>276.175727</td>\n",
       "      <td>311.942907</td>\n",
       "      <td>293.614419</td>\n",
       "      <td>251.950222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01-03</th>\n",
       "      <td>181.764706</td>\n",
       "      <td>56.305769</td>\n",
       "      <td>213.416915</td>\n",
       "      <td>184.997108</td>\n",
       "      <td>170.000772</td>\n",
       "      <td>189.386701</td>\n",
       "      <td>144.005913</td>\n",
       "      <td>197.238824</td>\n",
       "      <td>214.131900</td>\n",
       "      <td>277.188675</td>\n",
       "      <td>191.683816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01-04</th>\n",
       "      <td>135.981481</td>\n",
       "      <td>48.950430</td>\n",
       "      <td>177.967932</td>\n",
       "      <td>180.672158</td>\n",
       "      <td>121.098769</td>\n",
       "      <td>219.847844</td>\n",
       "      <td>150.226441</td>\n",
       "      <td>179.901643</td>\n",
       "      <td>184.805661</td>\n",
       "      <td>239.647225</td>\n",
       "      <td>134.890120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01-05</th>\n",
       "      <td>168.283809</td>\n",
       "      <td>33.799005</td>\n",
       "      <td>163.549142</td>\n",
       "      <td>145.166978</td>\n",
       "      <td>132.729565</td>\n",
       "      <td>230.228400</td>\n",
       "      <td>159.456713</td>\n",
       "      <td>203.904844</td>\n",
       "      <td>162.422537</td>\n",
       "      <td>230.358128</td>\n",
       "      <td>159.540609</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12-27</th>\n",
       "      <td>163.674157</td>\n",
       "      <td>62.652531</td>\n",
       "      <td>248.279012</td>\n",
       "      <td>263.867595</td>\n",
       "      <td>148.220943</td>\n",
       "      <td>292.720153</td>\n",
       "      <td>215.919662</td>\n",
       "      <td>182.642965</td>\n",
       "      <td>266.438827</td>\n",
       "      <td>230.902937</td>\n",
       "      <td>176.031373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12-28</th>\n",
       "      <td>133.322387</td>\n",
       "      <td>67.809740</td>\n",
       "      <td>212.078000</td>\n",
       "      <td>220.034782</td>\n",
       "      <td>156.472487</td>\n",
       "      <td>224.959311</td>\n",
       "      <td>184.463257</td>\n",
       "      <td>190.058697</td>\n",
       "      <td>224.455215</td>\n",
       "      <td>204.349044</td>\n",
       "      <td>172.563389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12-29</th>\n",
       "      <td>130.258070</td>\n",
       "      <td>82.956465</td>\n",
       "      <td>239.274173</td>\n",
       "      <td>231.359545</td>\n",
       "      <td>198.970062</td>\n",
       "      <td>235.908719</td>\n",
       "      <td>190.776002</td>\n",
       "      <td>175.776517</td>\n",
       "      <td>249.059945</td>\n",
       "      <td>194.340802</td>\n",
       "      <td>188.031313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12-30</th>\n",
       "      <td>164.281153</td>\n",
       "      <td>79.075383</td>\n",
       "      <td>254.231857</td>\n",
       "      <td>231.774556</td>\n",
       "      <td>202.344618</td>\n",
       "      <td>258.153284</td>\n",
       "      <td>167.308175</td>\n",
       "      <td>203.587983</td>\n",
       "      <td>266.358440</td>\n",
       "      <td>230.255112</td>\n",
       "      <td>168.078947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12-31</th>\n",
       "      <td>157.616757</td>\n",
       "      <td>69.791762</td>\n",
       "      <td>281.103719</td>\n",
       "      <td>261.053448</td>\n",
       "      <td>234.414848</td>\n",
       "      <td>284.201073</td>\n",
       "      <td>146.108409</td>\n",
       "      <td>243.238760</td>\n",
       "      <td>296.132247</td>\n",
       "      <td>232.294232</td>\n",
       "      <td>202.502717</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>366 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "city               agra   amritsar       delhi   faridabad    gurugram  \\\n",
       "day_of_year                                                              \n",
       "01-01        266.698864  83.531003  339.500161  308.607128  296.732793   \n",
       "01-02        217.708791  67.501699  286.541694  272.483981  284.044378   \n",
       "01-03        181.764706  56.305769  213.416915  184.997108  170.000772   \n",
       "01-04        135.981481  48.950430  177.967932  180.672158  121.098769   \n",
       "01-05        168.283809  33.799005  163.549142  145.166978  132.729565   \n",
       "...                 ...        ...         ...         ...         ...   \n",
       "12-27        163.674157  62.652531  248.279012  263.867595  148.220943   \n",
       "12-28        133.322387  67.809740  212.078000  220.034782  156.472487   \n",
       "12-29        130.258070  82.956465  239.274173  231.359545  198.970062   \n",
       "12-30        164.281153  79.075383  254.231857  231.774556  202.344618   \n",
       "12-31        157.616757  69.791762  281.103719  261.053448  234.414848   \n",
       "\n",
       "city             kanpur     kolkata     lucknow       noida       patna  \\\n",
       "day_of_year                                                               \n",
       "01-01        287.404168  158.835409  268.894621  352.894609  266.542610   \n",
       "01-02        281.060555  158.556038  276.175727  311.942907  293.614419   \n",
       "01-03        189.386701  144.005913  197.238824  214.131900  277.188675   \n",
       "01-04        219.847844  150.226441  179.901643  184.805661  239.647225   \n",
       "01-05        230.228400  159.456713  203.904844  162.422537  230.358128   \n",
       "...                 ...         ...         ...         ...         ...   \n",
       "12-27        292.720153  215.919662  182.642965  266.438827  230.902937   \n",
       "12-28        224.959311  184.463257  190.058697  224.455215  204.349044   \n",
       "12-29        235.908719  190.776002  175.776517  249.059945  194.340802   \n",
       "12-30        258.153284  167.308175  203.587983  266.358440  230.255112   \n",
       "12-31        284.201073  146.108409  243.238760  296.132247  232.294232   \n",
       "\n",
       "city           varanasi  \n",
       "day_of_year              \n",
       "01-01        290.180489  \n",
       "01-02        251.950222  \n",
       "01-03        191.683816  \n",
       "01-04        134.890120  \n",
       "01-05        159.540609  \n",
       "...                 ...  \n",
       "12-27        176.031373  \n",
       "12-28        172.563389  \n",
       "12-29        188.031313  \n",
       "12-30        168.078947  \n",
       "12-31        202.502717  \n",
       "\n",
       "[366 rows x 11 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df_cross_cities.groupby(\"day_of_year\").aggregate(lambda x: sum(x.dropna())/len(x.dropna()) if len(x.dropna()) else np.nan)\n",
    "df_cross_cities_yday_avg = df_cross_cities.groupby(\"day_of_year\").mean()\n",
    "df_cross_cities_yday_avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ff0c0070",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "agra         0\n",
       "amritsar     0\n",
       "delhi        0\n",
       "faridabad    0\n",
       "gurugram     0\n",
       "kanpur       0\n",
       "kolkata      0\n",
       "lucknow      0\n",
       "noida        0\n",
       "patna        0\n",
       "varanasi     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check if some entries has null values\n",
    "df_cross_cities_yday_avg.isna().sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "587f680a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "       1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1,\n",
       "       1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "violation_df = (df_cross_cities_yday_avg>60).astype(int)\n",
    "np.array(violation_df[\"amritsar\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1c6c8fad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "agra         0.578082\n",
       "amritsar     0.252055\n",
       "delhi        0.660274\n",
       "faridabad    0.668493\n",
       "gurugram     0.745205\n",
       "kanpur       0.526027\n",
       "kolkata      0.400000\n",
       "lucknow      0.750685\n",
       "noida        0.657534\n",
       "patna        0.616438\n",
       "varanasi     0.646575\n",
       "dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "violation_df.sum(axis=0)/365"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "94a41055",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['delhi',\n",
       " 'gurugram',\n",
       " 'patna',\n",
       " 'varanasi',\n",
       " 'faridabad',\n",
       " 'noida',\n",
       " 'lucknow',\n",
       " 'kanpur',\n",
       " 'agra',\n",
       " 'kolkata',\n",
       " 'amritsar']"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_city = [\"delhi\",\"gurugram\",\"patna\",\"varanasi\",\"faridabad\",\"noida\",\"lucknow\",\"kanpur\",\"agra\",\"kolkata\",\"amritsar\"]\n",
    "sorted_city_cap = [city.capitalize() for city in sorted_city]\n",
    "sorted_city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b1c79eaa",
   "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": 45,
   "id": "e06f89c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f4ef1aa86d0>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.lines import Line2D\n",
    "import calendar as cal\n",
    "fig,ax = plt.subplots()\n",
    "latexify(columns=2, fig_width=5,fig_height=2)\n",
    "\n",
    "days = len(violation_df)\n",
    "ncities = 11\n",
    "y_cord = np.linspace(0,1,num = ncities)\n",
    "x_cord = np.linspace(0,1,num = days)\n",
    "color = [\"green\",\"red\"]\n",
    "month_names = [each[:3].capitalize() for each in list(cal.month_name)[1:]] + [\"\"]\n",
    "\n",
    "ax.set_facecolor('white')\n",
    "ax.set_xticks(np.linspace(0,1,13) + (1/26))\n",
    "ax.set_xticklabels(month_names)\n",
    "ax.set_yticks(np.linspace(0,1,ncities))\n",
    "ax.set_yticklabels(sorted_city_cap[::-1])\n",
    "\n",
    "for ind in range(ncities):\n",
    "    test = np.array(violation_df[sorted_city[len(sorted_city)-ind-1]])\n",
    "    color_arr = [color[each] for each in test]\n",
    "    ax.scatter(x=x_cord,y=np.array([y_cord[ind]]*days),marker=\"s\",c=color_arr,label=\"k\",s=3)\n",
    "\n",
    "    \n",
    "plt.tick_params(\n",
    "    labelsize = 8,\n",
    "    axis='both',          # changes apply to the x-axis\n",
    "    which='both',      # both major and minor ticks are affected\n",
    "    bottom=False,      # ticks along the bottom edge are off\n",
    "    top=False,         # ticks along the top edge are off\n",
    "    labelbottom=True, # labels along the bottom edge are off\n",
    "    left=False\n",
    ")\n",
    "\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.tick_params(axis='y', which='major', pad=-12) \n",
    "ax.set_xlabel(\"Day\")\n",
    "\n",
    "legend_elements = [Line2D([0], [0], marker='o', color='g', label='PM$_{2.5}$ $\\leq 60 \\mu g/m^3$',\n",
    "                          markerfacecolor='green', markersize=5),\n",
    "                  Line2D([0], [0], marker='o', color='r', label='PM$_{2.5}$ $> 60 \\mu g/m^3$',\n",
    "                          markerfacecolor='r', markersize=5)]\n",
    "\n",
    "ax.legend(handles=legend_elements, bbox_to_anchor=(0.955,0.9))\n",
    "\n",
    "#plt.title(\"Violation of Indian limit(60 ug/m^3) in APR-2018 to APR-2021\");\n",
    "#h.format_axes(ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed8999e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig(\"../figures/fig4_pm25_viloation_igp.pdf\",bbox_inches=\"tight\")"
   ]
  }
 ],
 "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
}