NTCC Project 2021.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd                       # used for data manipulation\n",
    "from matplotlib import pyplot as plt      # library used for visualization\n",
    "import seaborn as sns                     # library used for visualization\n",
    "import datetime as dt                     # used for manipulation date,time\n",
    "import numpy as np                        # library used for working with arrays, matrices \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid=pd.read_csv(\"C:\\\\Users\\\\User\\\\Downloads\\\\covid_19_india.csv\",parse_dates=['Date'],dayfirst=True)\n",
    "\n",
    "# parse_dates=['Date'] , we are writing this to tell to pandas because if we do not tell this to pandas then it will treat \n",
    "# that column  as a string or as a categorical column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sno</th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>State/UnionTerritory</th>\n",
       "      <th>ConfirmedIndianNational</th>\n",
       "      <th>ConfirmedForeignNational</th>\n",
       "      <th>Cured</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>6:00 PM</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>6:00 PM</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>6:00 PM</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2020-02-02</td>\n",
       "      <td>6:00 PM</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>6:00 PM</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sno       Date     Time State/UnionTerritory ConfirmedIndianNational  \\\n",
       "0    1 2020-01-30  6:00 PM               Kerala                       1   \n",
       "1    2 2020-01-31  6:00 PM               Kerala                       1   \n",
       "2    3 2020-02-01  6:00 PM               Kerala                       2   \n",
       "3    4 2020-02-02  6:00 PM               Kerala                       3   \n",
       "4    5 2020-02-03  6:00 PM               Kerala                       3   \n",
       "\n",
       "  ConfirmedForeignNational  Cured  Deaths  Confirmed  \n",
       "0                        0      0       0          1  \n",
       "1                        0      0       0          1  \n",
       "2                        0      0       0          2  \n",
       "3                        0      0       0          3  \n",
       "4                        0      0       0          3  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid.head()\n",
    "# to see the first five enteries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid=covid[['Date','State/UnionTerritory','Cured','Deaths','Confirmed']]  # Extracting these columns and again storing them\n",
    "                                                                           #again in covid object\n",
    "covid.columns=['date','state','cured','deaths','confirmed']                # renaming columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>state</th>\n",
       "      <th>cured</th>\n",
       "      <th>deaths</th>\n",
       "      <th>confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2020-02-02</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "        date   state  cured  deaths  confirmed\n",
       "0 2020-01-30  Kerala      0       0          1\n",
       "1 2020-01-31  Kerala      0       0          1\n",
       "2 2020-02-01  Kerala      0       0          2\n",
       "3 2020-02-02  Kerala      0       0          3\n",
       "4 2020-02-03  Kerala      0       0          3"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>state</th>\n",
       "      <th>cured</th>\n",
       "      <th>deaths</th>\n",
       "      <th>confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>16352</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Chhattisgarh</td>\n",
       "      <td>971057</td>\n",
       "      <td>13407</td>\n",
       "      <td>992074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16353</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Dadra and Nagar Haveli and Daman and Diu</td>\n",
       "      <td>10459</td>\n",
       "      <td>4</td>\n",
       "      <td>10523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16354</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>1406629</td>\n",
       "      <td>24940</td>\n",
       "      <td>1433366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16355</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Goa</td>\n",
       "      <td>159419</td>\n",
       "      <td>3013</td>\n",
       "      <td>165197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16356</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Gujarat</td>\n",
       "      <td>807911</td>\n",
       "      <td>10040</td>\n",
       "      <td>822758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16357</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>756426</td>\n",
       "      <td>9314</td>\n",
       "      <td>767900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16358</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Himachal Pradesh</td>\n",
       "      <td>195301</td>\n",
       "      <td>3461</td>\n",
       "      <td>201049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16359</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Jammu and Kashmir</td>\n",
       "      <td>301973</td>\n",
       "      <td>4273</td>\n",
       "      <td>313028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16360</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Jharkhand</td>\n",
       "      <td>338446</td>\n",
       "      <td>5104</td>\n",
       "      <td>344914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16361</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>2668705</td>\n",
       "      <td>34287</td>\n",
       "      <td>2819465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16362</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>2729967</td>\n",
       "      <td>12445</td>\n",
       "      <td>2842247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16363</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Ladakh</td>\n",
       "      <td>19341</td>\n",
       "      <td>202</td>\n",
       "      <td>19881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16364</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Lakshadweep</td>\n",
       "      <td>9172</td>\n",
       "      <td>47</td>\n",
       "      <td>9559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16365</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Madhya Pradesh</td>\n",
       "      <td>779177</td>\n",
       "      <td>8827</td>\n",
       "      <td>789499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16366</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>5753290</td>\n",
       "      <td>119303</td>\n",
       "      <td>5997587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16367</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Manipur</td>\n",
       "      <td>55257</td>\n",
       "      <td>1074</td>\n",
       "      <td>65622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16368</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Meghalaya</td>\n",
       "      <td>41349</td>\n",
       "      <td>797</td>\n",
       "      <td>46458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16369</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Mizoram</td>\n",
       "      <td>14096</td>\n",
       "      <td>86</td>\n",
       "      <td>18624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16370</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Nagaland</td>\n",
       "      <td>22486</td>\n",
       "      <td>477</td>\n",
       "      <td>24541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16371</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Odisha</td>\n",
       "      <td>853012</td>\n",
       "      <td>3717</td>\n",
       "      <td>886946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16372</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Puducherry</td>\n",
       "      <td>110838</td>\n",
       "      <td>1731</td>\n",
       "      <td>115627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16373</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Punjab</td>\n",
       "      <td>572008</td>\n",
       "      <td>15923</td>\n",
       "      <td>593572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16374</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Rajasthan</td>\n",
       "      <td>940465</td>\n",
       "      <td>8905</td>\n",
       "      <td>951548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16375</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Sikkim</td>\n",
       "      <td>16903</td>\n",
       "      <td>296</td>\n",
       "      <td>19589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16376</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>2358785</td>\n",
       "      <td>31746</td>\n",
       "      <td>2443415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16377</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Telangana</td>\n",
       "      <td>596628</td>\n",
       "      <td>3598</td>\n",
       "      <td>616688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16378</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Tripura</td>\n",
       "      <td>58978</td>\n",
       "      <td>660</td>\n",
       "      <td>63499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16379</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Uttarakhand</td>\n",
       "      <td>329182</td>\n",
       "      <td>7068</td>\n",
       "      <td>339127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16380</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>1678788</td>\n",
       "      <td>22336</td>\n",
       "      <td>1704790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16381</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>West Bengal</td>\n",
       "      <td>1447510</td>\n",
       "      <td>17475</td>\n",
       "      <td>1487363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date                                     state    cured  deaths  \\\n",
       "16352 2021-06-24                              Chhattisgarh   971057   13407   \n",
       "16353 2021-06-24  Dadra and Nagar Haveli and Daman and Diu    10459       4   \n",
       "16354 2021-06-24                                     Delhi  1406629   24940   \n",
       "16355 2021-06-24                                       Goa   159419    3013   \n",
       "16356 2021-06-24                                   Gujarat   807911   10040   \n",
       "16357 2021-06-24                                   Haryana   756426    9314   \n",
       "16358 2021-06-24                          Himachal Pradesh   195301    3461   \n",
       "16359 2021-06-24                         Jammu and Kashmir   301973    4273   \n",
       "16360 2021-06-24                                 Jharkhand   338446    5104   \n",
       "16361 2021-06-24                                 Karnataka  2668705   34287   \n",
       "16362 2021-06-24                                    Kerala  2729967   12445   \n",
       "16363 2021-06-24                                    Ladakh    19341     202   \n",
       "16364 2021-06-24                               Lakshadweep     9172      47   \n",
       "16365 2021-06-24                            Madhya Pradesh   779177    8827   \n",
       "16366 2021-06-24                               Maharashtra  5753290  119303   \n",
       "16367 2021-06-24                                   Manipur    55257    1074   \n",
       "16368 2021-06-24                                 Meghalaya    41349     797   \n",
       "16369 2021-06-24                                   Mizoram    14096      86   \n",
       "16370 2021-06-24                                  Nagaland    22486     477   \n",
       "16371 2021-06-24                                    Odisha   853012    3717   \n",
       "16372 2021-06-24                                Puducherry   110838    1731   \n",
       "16373 2021-06-24                                    Punjab   572008   15923   \n",
       "16374 2021-06-24                                 Rajasthan   940465    8905   \n",
       "16375 2021-06-24                                    Sikkim    16903     296   \n",
       "16376 2021-06-24                                Tamil Nadu  2358785   31746   \n",
       "16377 2021-06-24                                 Telangana   596628    3598   \n",
       "16378 2021-06-24                                   Tripura    58978     660   \n",
       "16379 2021-06-24                               Uttarakhand   329182    7068   \n",
       "16380 2021-06-24                             Uttar Pradesh  1678788   22336   \n",
       "16381 2021-06-24                               West Bengal  1447510   17475   \n",
       "\n",
       "       confirmed  \n",
       "16352     992074  \n",
       "16353      10523  \n",
       "16354    1433366  \n",
       "16355     165197  \n",
       "16356     822758  \n",
       "16357     767900  \n",
       "16358     201049  \n",
       "16359     313028  \n",
       "16360     344914  \n",
       "16361    2819465  \n",
       "16362    2842247  \n",
       "16363      19881  \n",
       "16364       9559  \n",
       "16365     789499  \n",
       "16366    5997587  \n",
       "16367      65622  \n",
       "16368      46458  \n",
       "16369      18624  \n",
       "16370      24541  \n",
       "16371     886946  \n",
       "16372     115627  \n",
       "16373     593572  \n",
       "16374     951548  \n",
       "16375      19589  \n",
       "16376    2443415  \n",
       "16377     616688  \n",
       "16378      63499  \n",
       "16379     339127  \n",
       "16380    1704790  \n",
       "16381    1487363  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid.tail(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### covid cases in Delhi \n",
    "\n",
    "https://www.google.com/search?q=total+cases+of+corona+in+delhi&rlz=1C1CHBF_enIN903IN903&oq=total+cases+of+&aqs=chrome.1.0i20i263i433j0i433j0i20i263i433j0i433j69i57j0i433j0l2j0i131i433j0.8313j0j7&sourceid=chrome&ie=UTF-8\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "today = covid[covid.date=='2021-06-24']   # extracting covid data of 2021-06-24 in today object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(36, 5)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "today.shape  # checking that how many records are present for today object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>date</th>\n",
       "      <th>state</th>\n",
       "      <th>cured</th>\n",
       "      <th>deaths</th>\n",
       "      <th>confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>16346</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>7210</td>\n",
       "      <td>127</td>\n",
       "      <td>7438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16347</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>1798380</td>\n",
       "      <td>12452</td>\n",
       "      <td>1862036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16348</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>31189</td>\n",
       "      <td>160</td>\n",
       "      <td>33916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16349</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Assam</td>\n",
       "      <td>454726</td>\n",
       "      <td>4310</td>\n",
       "      <td>490907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16350</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Bihar</td>\n",
       "      <td>708231</td>\n",
       "      <td>9569</td>\n",
       "      <td>720505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16351</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>60446</td>\n",
       "      <td>807</td>\n",
       "      <td>61520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16352</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Chhattisgarh</td>\n",
       "      <td>971057</td>\n",
       "      <td>13407</td>\n",
       "      <td>992074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16353</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Dadra and Nagar Haveli and Daman and Diu</td>\n",
       "      <td>10459</td>\n",
       "      <td>4</td>\n",
       "      <td>10523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16354</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>1406629</td>\n",
       "      <td>24940</td>\n",
       "      <td>1433366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16355</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Goa</td>\n",
       "      <td>159419</td>\n",
       "      <td>3013</td>\n",
       "      <td>165197</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date                                     state    cured  deaths  \\\n",
       "16346 2021-06-24               Andaman and Nicobar Islands     7210     127   \n",
       "16347 2021-06-24                            Andhra Pradesh  1798380   12452   \n",
       "16348 2021-06-24                         Arunachal Pradesh    31189     160   \n",
       "16349 2021-06-24                                     Assam   454726    4310   \n",
       "16350 2021-06-24                                     Bihar   708231    9569   \n",
       "16351 2021-06-24                                Chandigarh    60446     807   \n",
       "16352 2021-06-24                              Chhattisgarh   971057   13407   \n",
       "16353 2021-06-24  Dadra and Nagar Haveli and Daman and Diu    10459       4   \n",
       "16354 2021-06-24                                     Delhi  1406629   24940   \n",
       "16355 2021-06-24                                       Goa   159419    3013   \n",
       "\n",
       "       confirmed  \n",
       "16346       7438  \n",
       "16347    1862036  \n",
       "16348      33916  \n",
       "16349     490907  \n",
       "16350     720505  \n",
       "16351      61520  \n",
       "16352     992074  \n",
       "16353      10523  \n",
       "16354    1433366  \n",
       "16355     165197  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "today.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "now we will see the most affected states/Union territories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sorting w.r.t to confirmed cases\n",
    "max_confirmed_cases=today.sort_values(by='confirmed',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>date</th>\n",
       "      <th>state</th>\n",
       "      <th>cured</th>\n",
       "      <th>deaths</th>\n",
       "      <th>confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>16366</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>5753290</td>\n",
       "      <td>119303</td>\n",
       "      <td>5997587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16362</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>2729967</td>\n",
       "      <td>12445</td>\n",
       "      <td>2842247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16361</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>2668705</td>\n",
       "      <td>34287</td>\n",
       "      <td>2819465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16376</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>2358785</td>\n",
       "      <td>31746</td>\n",
       "      <td>2443415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16347</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>1798380</td>\n",
       "      <td>12452</td>\n",
       "      <td>1862036</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date           state    cured  deaths  confirmed\n",
       "16366 2021-06-24     Maharashtra  5753290  119303    5997587\n",
       "16362 2021-06-24          Kerala  2729967   12445    2842247\n",
       "16361 2021-06-24       Karnataka  2668705   34287    2819465\n",
       "16376 2021-06-24      Tamil Nadu  2358785   31746    2443415\n",
       "16347 2021-06-24  Andhra Pradesh  1798380   12452    1862036"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_confirmed_cases.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_states_confirmed = max_confirmed_cases[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(10,10)})\n",
    "sns.barplot(x='state',y='confirmed',data=top_states_confirmed,hue=\"state\")  # hue is used to color bars\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_death_cases=today.sort_values(by=\"deaths\",ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>date</th>\n",
       "      <th>state</th>\n",
       "      <th>cured</th>\n",
       "      <th>deaths</th>\n",
       "      <th>confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>16366</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>5753290</td>\n",
       "      <td>119303</td>\n",
       "      <td>5997587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16361</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>2668705</td>\n",
       "      <td>34287</td>\n",
       "      <td>2819465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16376</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>2358785</td>\n",
       "      <td>31746</td>\n",
       "      <td>2443415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16354</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>1406629</td>\n",
       "      <td>24940</td>\n",
       "      <td>1433366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16380</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>1678788</td>\n",
       "      <td>22336</td>\n",
       "      <td>1704790</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date          state    cured  deaths  confirmed\n",
       "16366 2021-06-24    Maharashtra  5753290  119303    5997587\n",
       "16361 2021-06-24      Karnataka  2668705   34287    2819465\n",
       "16376 2021-06-24     Tamil Nadu  2358785   31746    2443415\n",
       "16354 2021-06-24          Delhi  1406629   24940    1433366\n",
       "16380 2021-06-24  Uttar Pradesh  1678788   22336    1704790"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_death_cases.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_states_death= max_death_cases[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(10,10)})\n",
    "sns.barplot(x='state',y='deaths',data=top_states_death,hue=\"state\")  \n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_cured_cases=today.sort_values(by=\"cured\",ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "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>date</th>\n",
       "      <th>state</th>\n",
       "      <th>cured</th>\n",
       "      <th>deaths</th>\n",
       "      <th>confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>16366</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>5753290</td>\n",
       "      <td>119303</td>\n",
       "      <td>5997587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16362</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>2729967</td>\n",
       "      <td>12445</td>\n",
       "      <td>2842247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16361</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>2668705</td>\n",
       "      <td>34287</td>\n",
       "      <td>2819465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16376</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>2358785</td>\n",
       "      <td>31746</td>\n",
       "      <td>2443415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16347</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>1798380</td>\n",
       "      <td>12452</td>\n",
       "      <td>1862036</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date           state    cured  deaths  confirmed\n",
       "16366 2021-06-24     Maharashtra  5753290  119303    5997587\n",
       "16362 2021-06-24          Kerala  2729967   12445    2842247\n",
       "16361 2021-06-24       Karnataka  2668705   34287    2819465\n",
       "16376 2021-06-24      Tamil Nadu  2358785   31746    2443415\n",
       "16347 2021-06-24  Andhra Pradesh  1798380   12452    1862036"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_cured_cases.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_states_cured=max_cured_cases[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(10,10)})\n",
    "sns.barplot(x=\"state\",y=\"cured\",data=top_states_cured,hue=\"state\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Maharashtra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "MH=covid[covid.state==\"Maharashtra\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(473, 5)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MH.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>date</th>\n",
       "      <th>state</th>\n",
       "      <th>cured</th>\n",
       "      <th>deaths</th>\n",
       "      <th>confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>2020-03-10</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>120</td>\n",
       "      <td>2020-03-12</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>133</td>\n",
       "      <td>2020-03-13</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date        state  cured  deaths  confirmed\n",
       "76  2020-03-09  Maharashtra      0       0          2\n",
       "91  2020-03-10  Maharashtra      0       0          5\n",
       "97  2020-03-11  Maharashtra      0       0          2\n",
       "120 2020-03-12  Maharashtra      0       0         11\n",
       "133 2020-03-13  Maharashtra      0       0         14"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MH.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above we can notice that our first case in Maharashtra was reported on 9th march 2020."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>state</th>\n",
       "      <th>cured</th>\n",
       "      <th>deaths</th>\n",
       "      <th>confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>16222</td>\n",
       "      <td>2021-06-20</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>5710356</td>\n",
       "      <td>117356</td>\n",
       "      <td>5963420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16258</td>\n",
       "      <td>2021-06-21</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>5719457</td>\n",
       "      <td>117961</td>\n",
       "      <td>5972781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16294</td>\n",
       "      <td>2021-06-22</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>5733215</td>\n",
       "      <td>118313</td>\n",
       "      <td>5979051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16330</td>\n",
       "      <td>2021-06-23</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>5742258</td>\n",
       "      <td>118795</td>\n",
       "      <td>5987521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16366</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>5753290</td>\n",
       "      <td>119303</td>\n",
       "      <td>5997587</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date        state    cured  deaths  confirmed\n",
       "16222 2021-06-20  Maharashtra  5710356  117356    5963420\n",
       "16258 2021-06-21  Maharashtra  5719457  117961    5972781\n",
       "16294 2021-06-22  Maharashtra  5733215  118313    5979051\n",
       "16330 2021-06-23  Maharashtra  5742258  118795    5987521\n",
       "16366 2021-06-24  Maharashtra  5753290  119303    5997587"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MH.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={\"figure.figsize\":(10,10)})\n",
    "sns.lineplot(x=\"date\",y=\"confirmed\",data=MH,color=\"g\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the curve we can see that due to second wave there was increase in no. of cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fm5Dn7AEAAMSanmPHJEmuyy43OcnQoewBAICk5T9eI0lyjh5tcpKhQ9kDAABJy3+8Rvb84bKmusyOMmQoewAAIGn5a44l9F49ibIHAACSVLirS0GvV84xY82OMqQoewAAICn59r4pKbEvzpAoewAAIEm17aiSPX+4XFdeZXaUIUXZAwAAScd/4ri633tXWcUzZLEmdh1K7NUBAAB8jOatW2RxOJQ1/fNmRxlylD0AAJBUgk1N6nj1FWUVzVCKu39PoYhnlD0AAJBUOna/KkUiypl5s9lRooKyBwAAkopv75tyjhkr+zCP2VGigrIHAACSRqijXd3vv6f0yVPMjhI1lD0AAJA0Oqt3S4Yh9+TPmB0laih7AAAgKUSCATVX/FGpl14m59jEfmpGX5Q9AACQFNp37VSopVnD5t8hi8VidpyooewBAICk0Pn6HtmHj1Da+KvNjhJVlD0AAJDwIj096n7nbbmT6MKM0yh7AAAg4XUdOiAjFFL6NZPNjhJ1lD0AAJDwOvfuldXlkuvyK8yOEnWUPQAAkNCMSES+t/YqbcIkWWw2s+NEHWUPAAAkNH/NMYXb2uSenHyHcCXKHgAASHCde9+ULBalT7zG7CimoOwBAICE5tu3V6mXXqaUjAyzo5iCsgcAABJWqLVF/mNHk/KWK6dR9gAAQMLy7dsnSUl5y5XTKHsAACBhde57U7bcPDlGXWR2FNNQ9gAAQEKKBAPqOnRQ6ZMnJ9WzcD+MsgcAABJS9zvvyPD7lT4peQ/hSpQ9AACQoHz73pTF4VDauPFmRzEVZQ8AACQk3/79Shs3XlaHw+wopqLsAQCAhBNsalKw4ZTSxl9tdhTTUfYAAEDC6Tp0UJIoe6LsAQCABNR16KBSMjKT+pYrp1H2AABAwul+9x25rhqX1LdcOY2yBwAAEkqwqUmhlma5rrjC7CgxgbIHAAASSvfh9yRJrsspexJlDwAAJJie99+TxemU86LRZkeJCZQ9AACQULrff1+pl1wqS0qK2VFiAmUPAAAkDP+J4/LXHFP6xElmR4kZlD0AAJAwWv78J1kcDmV9rsjsKDGDsgcAABKCEQqp49VXlHnjNKW43WbHiRmUPQAAkBD8dbUygkGljZ9gdpSYQtkDAAAJwX/sqCTJOWasuUFiDGUPAAAkhJ5jx2R1uWTPzzc7Skyh7AEAgITgrzkq55ixPCLtQyh7AAAg7hmhkPzHjyuVQ7gfQdkDAABxz/fWPhnBoFzjx5sdJeZQ9gAAQNxr2/miUrKylD6Bmyl/GGUPAADEtXBnp3z79ipz2nQekfYxKHsAACCudX/wvhSJKP2ayWZHiUmUPQAAENf8R49KFgsXZ5wDZQ8AAMS1nqNH5Bg5UtbUVLOjxCTKHgAAiFuGYajn6BGlXnyJ2VFiFmUPAADErVBLs8Lt7XJS9s6JsgcAAOJW97vvSJJcl11ucpLYRdkDAABxq+vQIVnT0+UcPcbsKDGLsgcAAOKSYRjqOnRAaePGy2Kl0pwLkwEAAHEpeOqUQs3NSht/tdlRYhplDwAAxKX2XS9JFovSJ3Ez5U9C2QMAAHEn4verdfsLck+5Vva8PLPjxDTKHgAAiDude99QxOdT9hdnmh0l5lH2AABA3On54ANZHA65rrjS7Cgxj7IHAADijv/YUTlHj5ElJcXsKDGPsgcAAOKKEYmop+aYUsdebHaUuEDZAwAAcSVQf1KG3y8nZa9fKHsAACCu+I8elSSl8jzcfqHsAQCAuNL17tuypqXLMXKk2VHiAmUPAADEDcMw1HXwgNLGjeMRaf3ElAAAQNwINpx+RNoEs6PEDcoeAACIG10HD0qS0q7mebj9RdkDAABxo+vtg7Ll5smeP9zsKHGDsgcAAOKCEYmo69AhpV19tSwWi9lx4gZlDwAAxAV/zTFFunycr3eeTCl7mzdvVmlpqUpLS/XjH/9YknTo0CGVlZVp1qxZeuCBBxQKhSRJdXV1WrRokUpKSnTvvffK5/NJktrb27VkyRLNnj1bixYtktfrlSQFAgEtW7ZMs2fP1vz583X48GEzlggAAAZZ18EDkqS0ceNNThJfol72uru7tWbNGm3cuFGbN29WdXW1du3apWXLlmnlypXaunWrDMPQpk2bJEmrV6/WwoULVVlZqYkTJ2rDhg2SpPXr16uwsFBbtmzRggULtGbNGknSxo0b5XK5tGXLFq1YsUL3339/tJcIAACGQNehg3JcNFq2rCyzo8SVqJe9cDisSCSi7u5uhUIhhUIh2Ww29fT0aMqUKZKksrIyVVZWKhgMavfu3Zo1a9ZZ2yWpqqpKc+fOlSTNmTNHO3bsUDAYVFVVlebNmydJmjp1qpqbm1VXVxftZQIAgEEUCQTU/d67Sh/PVbjnyxbtH+h2u7V06VLNnj1bLpdLU6dOld1ul8fjOfMaj8ejU6dOqaWlRW63Wzab7aztktTQ0HDmPTabTW63W83NzWdtP/2e+vp6FRQU9CtfXp77gtfo8WRc8GckAubADPpiFr2YQ3LPIJnX3tdA5tD65l4ZoZBGTitUToLMMVq/D1Eve2+//bZ+//vf64UXXlBGRobuu+8+7dy586yragzDkMViOfO1r3NdfWMYhqxW60fec3p7fzU1dSoSMc5zVX/j8WTI6+0Y8PsTBXNgBn0xi17MIblnkMxr72ugc/C+XC2lpCiQPzoh5nihvw9Wq6XfO6iifhj3pZde0rRp05SXlyeHw6GysjK9+uqrZy6wkKTGxkbl5+crNzdXHR0dCofDkiSv16v8/HxJUn5+vhobGyVJoVBIPp9P2dnZGj58uBoaGj7yWQAAIH759r4p1xVXypqaanaUuBP1sjdu3Djt2rVLXV1dMgxD27Zt0/XXXy+n06k9e/ZI6r1at6ioSHa7XYWFhaqoqJAklZeXq6ioSJJUXFys8vJySVJFRYUKCwtlt9tVXFyszZs3S5Kqq6vldDr7fQgXAADEHn9drQIn65RxXaHZUeJS1A/jfu5zn9PBgwdVVlYmu92uSZMmacmSJbr55pv14IMPqrOzUxMmTNDdd98tSVq1apWWL1+uxx57TCNHjtRPfvITSdLSpUu1fPlylZaWKiMjQ+vWrZMkLV68WCtXrlRpaakcDocefvjhaC8RAAAMos7q3ZLFIvdnrjM7SlyyGIYx8BPUEhDn7A0O5sAM+mIWvZhDcs8gmdfe10DmcHTVg0pJS9Po/3vFEKWKvoQ+Zw8AAKC/AifrFKg9IXfhVLOjxC3KHgAAiFkde6olSe5rOV9voCh7AAAgZnXu2a3Uyy6XPSfH7Chxi7IHAABiUuBUvfzHjyvjOg7hXgjKHgAAiEmdpw/hcsuVC0LZAwAAMamjerdSL71U9rw8s6PENcoeAACIOQFvg/w1x+TmEO4Fo+wBAICYc/oQbgZX4V4wyh4AAIg5HdW75Rx7sewej9lR4h5lDwAAxJSeIx/If/SIMm+40ewoCYGyBwAAYkrz1i2yulzK/Hyx2VESAmUPAADEjMCpU+rcU62sGV9QistldpyEQNkDAAAxo+VPlbKkpChn5s1mR0kYlD0AABATwj6f2ne+qMzPTpctK9vsOAmDsgcAAGKCb/9bMkIhZU7/vNlREgplDwAAxATfvr1KychQ6iWXmh0loVD2AACA6YxIRL4Dbyl94jWyWKkng4lpAgAA0/V8cFiRzk6lXzPZ7CgJh7IHAABM59u3V7JalTZhgtlREg5lDwAAmM731l65Lr9CKWnpZkdJOJQ9AABgqmBzs/zHj3MId4hQ9gAAgKl8b+2TJMreEKHsAQAAU/ne2ivbsGFyjCwwO0pCouwBAADTRIIBdR08oPRJk2WxWMyOk5AoewAAwDTd77wtIxCQm0O4Q4ayBwAATOPbt1cWh0Ouq8aZHSVhUfYAAIApDMOQb98+pY0bL6vDYXachEXZAwAApgicPKlgo5ercIcYZQ8AAJjC99ZeSVL6JMreUKLsAQAAU/j27ZVj1EWy5+WZHSWhUfYAAEDUhbu61P3+e0qfdI3ZURIeZQ8AAERd18H9UjjM+XpRQNkDAABR17Z9u1KysuW67HKzoyQ8yh4AAIiqnmNH1XXogHJm3iJLSorZcRIeZQ8AAERV61+el8WZqqziGWZHSQqUPQAAEDUhn08d1buVecONSklLMztOUqDsAQCAqGl8caeMQECZnysyO0rSoOwBAICo8e54UY6CAqVeconZUZIGZQ8AAERFqK1N7QcPyX3dVFksFrPjJA3KHgAAiIrON1+XDEMZ1xaaHSWpUPYAAEBUdO6pVurIEXJcdJHZUZIKZQ8AAAy5cGenut55W3nTbuQQbpRR9gAAwJDr3PuGFA4r77PTzI6SdCh7AABgyHXuqZYtN0/uyy8zO0rSoewBAIAhFe7uVtfBA3Jfex2HcE1A2QMAAEPKt2+vjFBIGddNNTtKUqLsAQCAIdX5erVSsrKVehmHcM1A2QMAAEPGCIfVdWC/3JOnyGKldpiBqQMAgCHjrzmmSE+P0saNNztK0qLsAQCAIdP1ztuSJNdVV5mcJHlR9gAAwJDpfudtOUaMlC0r2+woSYuyBwAAhoQRCqnr3Xflumqc2VGSGmUPAAAMie733pXh71H6pGvMjpLUKHsAAGBI+N7aJ4vNxsUZJqPsAQCAQWcYhnz79sp15VWypqaaHSepUfYAAMCg637nbQXqT8p97XVmR0l6lD0AADDomv5QrpSsbGVO/5zZUZIeZQ8AAAwqf12dut99Rzm3zJLV7jA7TtKj7AEAgEHle/N1SVLG9TeanAQSZQ8AAAyyzjdel/PiS2TPyTE7CkTZAwAAgyjY3KyeIx/I/ZlrzY6Cv6LsAQCAQdP+8k5JUsbUG0xOgtMoewAAYFAYkYjaX3pRrqvGyZGfb3Yc/FW/y97x48clSVVVVfr3f/93dXR0DFkoAAAQf7rff09Bb4Oypn/e7Cjoo19lb+XKlfr5z3+uw4cP68EHH9SJEye0YsWKoc4GAADiSGf1a7LY7XJfy/l6saRfZW///v3653/+Zz3//POaP3++1q5dq9ra2qHOBgAA4oQRiahjT7XSJ10ja6rL7Djoo19lzzAMWa1W7dy5Uzfe2HvPnJ6eniENBgAA4kf3O28r3NamjMLrzY6CD+lX2RszZoy++c1v6sSJE7r++uv1gx/8QFddddVQZwMAAHGi5fmtSnFnKH3yFLOj4ENs/XnR2rVr9fzzz+u6666T3W5XYWGhbr/99qHOBgAA4oC/rla+fXuVN+92WZ1Os+PgQ/q1Zy8tLU033nij2tvbdeDAAV1zzTX64IMPhjobAACIAy2VFbI4HMq+6YtmR8HH6NeevUceeURPPPGEhg0bdmabxWLRX/7ylyELBgAAYl+w0av2V15W9hdmKiUjw+w4+Bj9Knv/+7//q23btp1V9gAAAJq3bpEsFuXcUmJ2FJxDvw7j5uTkUPQAAMBZQm2tan9xhzI/O1323Fyz4+AcPnHP3oEDByRJV199tX70ox9p7ty5stn+9pYJEyYMbToAABCzGp99RkY4rNySUrOj4BN8Ytn7zne+c9b327ZtO/PPF3LO3rZt2/Too4+qu7tb06dP14MPPqhdu3Zp7dq18vv9mj17tr7//e9Lkg4dOqQHHnhAPp9PhYWFWr16tWw2m+rq6rRs2TI1NTXpkksu0bp165Senq729nbdd999On78uHJzc7V+/Xp5PJ4B5QQAAB+vo/o1tb+0QzmzS+UYPtzsOPgEn3gYd9u2bdq2bZuefPLJM/98+n//8R//MaAfePz4ca1atUobNmzQH/7wBx08eFDbt2/XihUrtGHDBlVUVGj//v3avn27JGnZsmVauXKltm7dKsMwtGnTJknS6tWrtXDhQlVWVmrixInasGGDJGn9+vUqLCzUli1btGDBAq1Zs2ZAOQEAwMeLBIPybvqtnGPGatht882Og0/xiWWvtbVVra2tWrJkidra2tTa2qq2tjY1NjZ+ZK9ffz3//PO69dZbNWLECNntdj3yyCNyuVwaO3asRo8eLZvNprlz56qyslK1tbXq6enRlCm9N2gsKytTZWWlgsGgdu/erVmzZp21XZKqqqo0d+5cSdKcOXO0Y8cOBYPBAWUFAAAf1f7idmhhI3kAACAASURBVIWamzTsjgWy2Pp1rSdM9In/hX7wgx9o586dkqQbbrjhb2+y2c4UrfN17Ngx2e12ffvb39bJkyc1Y8YMXXHFFWcdas3Pz9epU6fU0NBw1naPx6NTp06ppaVFbrf7zPmDp7dLOus9NptNbrdbzc3NGt7PXcx5ee4Brasvj4dLzyXmIDGDvphFL+aQ3DNIhLUHWtv0wR/KlTlxgsYW3yiLxXLen5EIcxgM0ZrDJ5a9xx9/XJJ0//33a+3atYPyA8PhsKqrq7Vx40alpaXp3nvvVWpq6lm/LIZhyGKxKBKJfOz201/7Otcv2+nn+vZXU1OnIhHjPFf1Nx5PhrzejgG/P1EwB2bQF7PoxRySewaJsvaTP/uZwj09yv3yIjU2dp73+xNlDhfqQudgtVr6vYOq349La21tVXd3twzDUDgcVk1NjaZPn37e4YYNG6Zp06Yp96+XaM+cOVOVlZVKSUk58xqv16v8/HyNGDFCXq/3zPbGxkbl5+crNzdXHR0dCofDSklJOfN6qXevYGNjo0aMGKFQKCSfz6fs7OzzzgkAAM7me2ufOl57RXm3zZdjZIHZcdBP/drl9W//9m+aPn26Zs6cqZKSEt1yyy166KGHBvQDb7rpJr300ktqb29XOBzWiy++qJKSEh05ckTHjh1TOBzWc889p6KiIo0aNUpOp1N79uyRJG3evFlFRUVnns9bUVEhSSovL1dRUZEkqbi4WOXl5ZKkiooKFRYWym63DygrAADoFe7s1Klf/ZccIwuUU3Kr2XFwHvq1Z6+8vFwvvPCCHnroIf3whz/UK6+8cuZq2fM1efJk3XPPPVq4cKGCwaCmT5+uu+66S5deeqm+853vyO/3q7i4WCUlvXfiXrdunR588EF1dnZqwoQJuvvuuyVJq1at0vLly/XYY49p5MiR+slPfiJJWrp0qZYvX67S0lJlZGRo3bp1A8oJAAB6GeGwTv7iZwp3tKvgH78rKztR4orFMIxPPUHtS1/6kp5++mk9+uijmjhxombMmKHS0lL98Y9/jEbGqOKcvcHBHJhBX8yiF3NI7hnE69qNSESnnnhc7S/vVP7irym7+KYL+rx4ncNgi+Y5e/06jGuz2VRTU6NLL71U1dXVCoVC8vv9Aw4IAABin2EYavjNRrW/vFN5t82/4KIHc/Sr7H3rW9/SP/3TP2nGjBl6/vnnNWPGDN14441DnQ0AAJjEMAw1bnpKbdtfUE7JrcqdM8/sSBigfp2zd9NNN+mmm3rbfHl5uY4dO6arrrpqSIMBAADztO98US3Pb1X2F2b23jx5APfTQ2zo1549n8+n1atX62tf+5r8fr+efPJJdXV1DXU2AABggp6aY2p48tdKG3+1PF9ZSNGLc/0qez/60Y+UmZmppqYmOZ1OdXZ2auXKlUOdDQAARFmwpUV1P12vlHS3RtyzRJbzeDABYlO//gseOnRI3//+92Wz2eRyubRu3TodOnRoqLMBAIAoivj9qvvpeoW7ujXqu9+TLYuHEiSCfpW9Dz9uLBwOn9cjyAAAQOzz/vZJ+Y/XaOS3vi3n6DFmx8Eg6dcFGlOnTtW//Mu/qKenRy+++KJ+/etf6/rrrx/qbAAAIEo639ijth3blVNyq9zXTDE7DgZRv3bP3XfffUpLS1NGRobWr1+vcePGafny5UOdDQAARIH/eI1O/uJncl58ifJum292HAyyT9yzt3jx4rOuwElNTZUkvfHGG7rnnnv0q1/9amjTAQCAIRVqa1XtT9crJS1No/5xKY9CS0CfWPa++tWvSpKef/55dXZ26o477lBKSoo2b96szMzMqAQEAABDo6N6txp+/StFAn6NXv6AbNlckJGIPrHszZo1S5L0+OOP66mnnjpzUcaMGTP05S9/eejTAQCAIdFcWaHGpzcp9ZJLNfzur3NBRgLr1wUaLS0t8vv9crlcknpvstzW1jakwQAAwNBofWGbGp/epIyp12v4N77JodsE16+yN2fOHN155526+eabZRiGKisrdeeddw51NgAAMMhCrS3y/u4ppU2cpBH3fEuWlBSzI2GI9avsLV26VBMmTNArr7wiSVq+fLmKi4uHNBgAABhchmGo8ZnfywiHlb9oMUUvSfSr7EnSzJkzNXPmzKHMAgAAhogRici76X/Uvusl5ZTcKocn3+xIiJJ+lz0AABCfgo1eeZ/+nTqrX1P2zFs0rOxLZkdCFFH2AABIUN0ffKCWP21R555qyWrVsLIvKWd26Vn30EXio+wBAJBgAt4GNZU/q45XX5bV5VLOLSXK/uLNsufmmh0NJqDsAQCQAIxQSL6D+9W2Y7t8e9+UrFblzpmn3JLZsqa6zI4HE1H2AACIU4ZhyH+8Ru27dqrj1VcU7mhXSkaGckvnKnvGTbJl55gdETGAsgcAQByJBIPqOfKBut95Wx3VuxWoPSGLzab0yVOUOW260idOksXG/73jb/htAAAghhmGoVBTo3qOHFHn69Xq3LdXht8vSUq97HLlf/VuZUy9QSnp6SYnRayi7AEAEGMMw1DPkSNqf2mHOqp3K9LlkySlZGQo84ZpSp90jVxXXKkUt9vkpIgHlD0AAExgGIZCra0KnDguf+0JBZsaFTh5UqGmJoV9PkW6fLI4HHJfe51cV1wp5+gxSh17MU+9wHmj7AEAMMQiwYA63ntf7Qffl7+uVv5jx9RTc1QRn+/Ma6zp6bLnDVPqJZfK6kpV6qWXyX1toVJcXEmLC0PZAwBgEEUCAQXqT8pfU6Oeo0fUc+QD+U8cl8Lh3hekpMg56iK5P3OdnGPGyHnRaDlHXcQ5dxgylD0AAAYg4vcrUH9Sgbo6+etqFThZp0BtrYKNXskwJElWl0vOsRcr55YS5V8zXv6MPNk9+RyKRVRR9gAA+BiGYSjc3qZgY6OCjd4zX0N9vj9d6pSSIsfwEXKOHavMaZ+Vo2CUnKNGyT58hCxWqyRpmCdDXm+HiStCsqLsAQCSUiQQULCxUaGmxt4i19SoUHOTQi0tCrW1KdTSLCMYPOs9KRmZsnt6z6vLnDZdjoJRchQUyJE/nHvbIWbxmwkASGhhn0/+2hPyH6+R/8RxBerqFPQ2KNzefvYLU1Jkz82TLSdHqRdfLNuUz8g+bJhswzyyDxsme94wWZ1OcxYBXADKHgAgIYQ62uWvqZG/5pgC9fUKNpxSoKFB4bbWM6+xut1yFoxS+jWTZT9T4jyye4YpJTPrzCFXIJFQ9gAAccUwDIWam+SvOaaemhr5jx2V/3iNQi0tZ16TkpUtx/DhSp80qfdcutGj5bxotFKysmWxWExMD0QfZQ8AENPC3d3qOfyeut99Vz1HPlBPzbG/3Z/OYpFj5Ei5rhwn55gxSh0zVs7RY3iyBNAHZQ8AEDNCbW3qfved3nPrTtYpcPKkAvUne696tVrlHD1GGdcVyjl6rJxjx8o56iLOowM+BWUPAGAKIxJRqKlJPcdr1P32IXW9fUiButref2m1yu7Jl2PkSLkLpyrtyquUeullFDtgACh7AIAhZ0QiCtTVqv6NOnn3HZD/2DEF6k/KCAQkSRaHQ64rrlTmtM/KddV4OUePltVuNzk1kBgoewCAQWcYhkJNjep+7z11v/euOt98/cytTqxp6Uode7GyimbIUVAgZ8EoOcdeTLkDhghlDwAwKMJdPvne2iff3r3qfu+dM1fHWl0upV09Qe7Jn1FB4TXqsLu5IhaIIsoeAGBADMNQ4ORJ+d7aK9++vep+/z0pHFZKRqbSxo+X6/Ir5LriKjlGjTpz/zqXJ0OdPDIMiCrKHgCg3yLBgLrfeVu+fXvl27dPwUavJMkx6iLl3FIi95TPKPWSS7k5MRBDKHsAgE8UbG4+s/eu69BBGYGALA6H0saNV07JbKVPmix7Xp7ZMQGcA2UPAHAWIxJRzweHe/fevbVX/uPHJUm2vDxlTv+c0idNVtq48bI6HCYnBdAflD0ASHJGJKJgY6N6jnzQW/AOvKVIZ6dktcp1+RUadsedSr9mshwFBVxYAcQhyh4AJKFQa4taq16Qb/9bCtTVnrnfXYo7Q+mTrpF70mSlTZiolPR0k5MCuFCUPQBIEoZhqOf999X6wl/UsWe3FInIdeVVyiqaIeeoUXKOHiPnmLFcXAEkGMoeACQ4f12tOl55We2vvaJQY6OsLpeyvzBT2Td9UY78fLPjARhilD0ASDC997+rk2/vXnW89or8x2ski0VpV0/QsHnz5b72WllTXWbHBBAllD0AiGNGJKJAfb0CdbUKNTfJX1errgMHFGppliSlXnKpPF9ZpIyp18uWlWVyWgBmoOwBQBwKtbaq8Zmn1fnGHkW6u89st6alK238eKVNmKf0CZO4/x0Ayh4AxJNQa6vadr6oli1/lBEKKePGz8p1+RVyjhkje94wWdPSuD0KgLNQ9gAghkWCAfneeENd7xxS97vvKnCyTpKUPukaeb6yUI7hI0xOCCDWUfYAIAaFu3xqq3pBLX95XuG2NlldLqVedoUyp31W6VOulbOgwOyIAOIEZQ8AYkjQ61XrC39R6/YqGf4epU2YqJxvfFNp46/m/ncABoSyBwAmMyIRdVbvVmvVNnW/+45ktSqj8HrllMxW6pixZscDEOcoewBgoq5331HDbzYqUHtCdk++8m4vU+Znp8uey1W0AAYHZQ8ATGCEw2r4zUa17aiSLTdPI5fcK3fhVA7VAhh0lD0AiDIjElH94z9Tx2uvKmfWbOXNu11Wp9PsWAASFGUPAKIo4vfL+/Rv1fHaqxp2x53KnX2r2ZEAJDjKHgBEQbCpUUeee0b1f/qzIl1dyp55M0UPQFRQ9gBgiBiGoe733lXrn/+kzjdelywWua+9TjlfvEWpl19udjwASYKyBwCDLBIMqOO1V9X6lz/LX3NM1rR05cyarcu+dJvaxbl5AKKLsgcAg8R//LjaX96p9pd3KdzRLkdBgfIXf12ZN06T1emU05MheTvMjgkgyVD2AOAC9Rw9qqY/PCvfvr1SSorSJ12j7C/M7H3qhcVidjwASY6yBwADZEQiaqmsUGP5M7K6XMq7bb6yb/qiUtxus6MBwBmUPQAYgFB7u+p/8Z/qOnhAGVOvV/7iryslLc3sWADwEZQ9ADhPXe++o5P/+ZgiXT7l3/11ZX2+mMO1AGIWZQ8A+ino9arxD8+q45WXZc/P10Xf+4Gco0ebHQsAPhFlDwD6ofv993Ri3Y8li0U5t5Qod848pbhcZscCgE9F2QOAT2FEImp46kmlZGRq9Ip/kj0nx+xIANBvVrMDAECsa33hL/IfPaJhZXdQ9ADEHcoeAHyCrnfelnfTU0q/ZrIybphmdhwAOG+UPQA4h4C3QSf/499l93g04p5vyWLlTyaA+GPaX64f//jHWr58uSTp0KFDKisr06xZs/TAAw8oFApJkurq6rRo0SKVlJTo3nvvlc/nkyS1t7dryZIlmj17thYtWiSv1ytJCgQCWrZsmWbPnq358+fr8OHD5iwOQFwzIhG1Vm3TsX9eKSMU0qh/XMo99ADELVPK3ssvv6xnn332zPfLli3TypUrtXXrVhmGoU2bNkmSVq9erYULF6qyslITJ07Uhg0bJEnr169XYWGhtmzZogULFmjNmjWSpI0bN8rlcmnLli1asWKF7r///ugvDkBcC3gbdGLdj9Xw61/JdellGrvq/5FjxEizYwHAgEW97LW2tuqRRx7Rt7/9bUlSbW2tenp6NGXKFElSWVmZKisrFQwGtXv3bs2aNeus7ZJUVVWluXPnSpLmzJmjHTt2KBgMqqqqSvPmzZMkTZ06Vc3Nzaqrq4v2EgHEqWBLi048vFb+4zUa/rW/06j/6z7Zh3nMjgUAFyTqZW/lypX6/ve/r8zMTElSQ0ODPJ6//TH1eDw6deqUWlpa5Ha7ZbPZztr+4ffYbDa53W41Nzd/7GfV19dHa2kA4ljE71fdT9cr3NWt0T9cwVMxACSMqN5n73e/+51GjhypadOm6ZlnnpEkRSKRs/6gGoYhi8Vy5mtf5/rDaxiGrFbrR95zevv5yMu78AeYezwZF/wZiYA5MIO+YnkWRiSitx/aIP+J4xr/wHLlXnv1kP2sWJ5DtCTzDJJ57X0xh17RmkNUy15FRYW8Xq9uu+02tbW1qaurSxaL5cwFFpLU2Nio/Px85ebmqqOjQ+FwWCkpKfJ6vcrPz5ck5efnq7GxUSNGjFAoFJLP51N2draGDx+uhoYGjRkz5qzPOh9NTZ2KRIwBr9HjyZDX2zHg9ycK5sAM+or1WXif3qSWV1+T5ysLFR575ZBljfU5REMyzyCZ194Xc+h1oXOwWi393kEV1cO4TzzxhJ577jlt3rxZ3/3ud/WFL3xBa9euldPp1J49eyRJmzdvVlFRkex2uwoLC1VRUSFJKi8vV1FRkSSpuLhY5eXlknoLZGFhoex2u4qLi7V582ZJUnV1tZxOpwoKCqK5RABxpnPfm2qprFBW8Qxlf/Fms+MAwKCLiZtGrVu3TmvXrlVJSYm6urp09913S5JWrVqlTZs26dZbb1V1dbW+973vSZKWLl2qN998U6WlpXryySe1cuVKSdLixYsVCARUWlqqNWvW6OGHHzZtTQBiX6i1Vad++bico0fL85VFnKMHICFZDMMY+DHLBMRh3MHBHJhBX7E4CyMSUe2//kTd772rMQ+ukrNg1JD/zFicQ7Ql8wySee19MYdeCXsYFwBiReuf/6SuA/vl+fJdUSl6AGAWyh6ApOM7eEDe3/9O6Z+5VllFM8yOAwBDirIHIKl0f3BYdT9dL8eIkRrxtW9wnh6AhEfZA5A0Qq2tOvnYo7JlZeui+36oFPeF31cTAGIdZQ9AUgj7fDrxyDqFu7pU8A/fkS0j0+xIABAVlD0AScG76SkF6k+q4B++K+foMWbHAYCooewBSHjdh99X+84XlXPzLKVfPcHsOAAQVZQ9AAnNiETU8OtfyZaTo7w588yOAwBRR9kDkNDaqrbJf7xGnjvvkjU11ew4ABB1lD0ACSvU3q7G8meUNv5quQunmh0HAExB2QOQsBp//ztF/H7lL/wq99MDkLQoewASUt+LMhwjC8yOAwCmsZkdAAAGW+feN1X/y5/LlpPLRRkAkh5lD0BCCTY16uTPHpMjf7hGfvv/cFEGgKRH2QOQMAzDUMNvNkqGoYJ//K7secPMjgQApuOcPQAJo3P3a/Lt26tht99B0QOAv6LsAUgIYZ9PDf/zGzkvvkTZM282Ow4AxAzKHoCE4P3dbxX2dWr43V+XxcqfNgA4jb+IAOJez9Ejan9ph3JuvkWpY8aaHQcAYgplD0BcMwxD3k1PKSUjQ7lzbjM7DgDEHMoegLjm2/umut99R3nzbleKy2V2HACIOZQ9AHHLCIXU+PQm2YePUNbni82OAwAxibIHIG41V1YoUH9SngVflsXGbUMB4ONQ9gDEJX9trZr+d7Mypl4v95TPmB0HAGIWZQ9A3DEiEZ361ROyulzyLPyq2XEAIKZR9gDEnbaqbeo5/L7yv3yXbBmZZscBgJhG2QMQV4LNzWp85mmlXT1BGTd+1uw4ABDzKHsA4oZhGGp4cqOMSET5i78mi8VidiQAiHmUPQBxo/ON1+V78w3lzbtdDk++2XEAIC5Q9gDEhUgwIO+m/5Fj1EXKuXmW2XEAIG5Q9gDEhdbn/6RQY6Pyv7JQlpQUs+MAQNyg7AGIeaG2VjX98TmlT/mM0sZfbXYcAIgrlD0AMa9pc7mMUFCeBV82OwoAxB3KHoCYFjhVr7aXdii7eIYcw0eYHQcA4g5lD0BMa9pcLovNptzSeWZHAYC4RNkDELP8x2vU8dorypl5i2xZWWbHAYC4RNkDEJMMw5D3d7+VNS1NObNmmx0HAOIWZQ9ATGrf+ZK6Dh5Q3u1lSklPNzsOAMQtyh6AmBNsbpb3t0/KdeVVyp7xBbPjAEBco+wBiCmGYejUr/5LRjis4V//e1ms/JkCgAvBX1EAMaV910vq2r9Pw+5YIEc+z78FgAtF2QMQM8KdnfL+9n/kuuJKZd/0RbPjAEBCoOwBiBlNz21WpLtb+V/9GodvAWCQ8NcUQEwInKpX6wvblPX5YjlHjTI7DgAkDMoegJjQ+MzTsthsyrvtdrOjAEBCoewBMJ1v/z517qlWbsmtsmVlmx0HABIKZQ+AqcI+n+qf+KUcBaOUU8KTMgBgsFH2AJjKu+kphTvaNeIb35TV7jA7DgAkHMoeANP4Dh5Q+84XlTNrtlIvvtjsOACQkCh7AEwR8fvV8Kv/kn34COXNvc3sOACQsCh7AKKu95FoTyjY1KjhX/s7WR0cvgWAoULZAxB1LVu3qOPVV5R3e5nSrrzK7DgAkNAoewCiyndgvxp//zu5C6cq99Y5ZscBgIRH2QMQNb23WfmFHCNHasTf3SOLxWJ2JABIeJQ9AFHjfepJhdv/epsVp9PsOACQFCh7AKKi843X1f7yTuXeWqrUiy8xOw4AJA3KHoAh11VzXPW//LmcY8Yqbw63WQGAaLKZHQBAYgt1tOvgQ/+fLHa7Cv7hu7LY+LMDANHEX10AQyZwsk51//mYQi2tGnXfctnz8syOBABJh7IHYEgEvV7VPLRGFotV4+7/oUKjLzU7EgAkJc7ZAzDoIn6/av/93yTD0Oj7H1TOtZ8xOxIAJC3KHoBBZRiG6p/4hQK1JzRyyb1yDB9udiQASGqUPQCDqmXLH9VZvVvD7lig9ImTzI4DAEmPsgdg0Pje2qfGZ3+vjOtvUM6s2WbHAQCIsgdgkARO1evkzx6T86LRGv61b/AoNACIEZQ9ABcs3N2tukf/TZYUmwr+8bs8Cg0AYghlD8AFMSIR1T/+MwVO1Wvkt/+P7HnDzI4EAOiD++wBGLCI36/6J34h35tvyPOVRUobN97sSACAD6HsARgQIxRS3YafquvgAQ370p3K/uJMsyMBAD4GZQ/AeTMiEZ38+X+o68B+Df/6N5T1uSKzIwEAzoFz9gCcN++m/1Hnnmp57vwKRQ8AYhxlD8B56XjtVbX++Xllf/Fm5dxSYnYcAMCnoOwB6Dd/XZ3q//uXSr3scnkWfNnsOACAfqDsAeiXSE+PTj72qKwOh0Z++x9ksXHKLwDEA1PK3qOPPqrS0lKVlpbq4YcfliTt2rVLc+fO1S233KJHHnnkzGsPHTqksrIyzZo1Sw888IBCoZAkqa6uTosWLVJJSYnuvfde+Xw+SVJ7e7uWLFmi2bNna9GiRfJ6vdFfIJCAGp7cqED9SY1ccq/sOTlmxwEA9FPUy96uXbv00ksv6dlnn1V5ebkOHDig5557TitWrNCGDRtUUVGh/fv3a/v27ZKkZcuWaeXKldq6dasMw9CmTZskSatXr9bChQtVWVmpiRMnasOGDZKk9evXq7CwUFu2bNGCBQu0Zs2aaC8RSDhdhw6qfddO5d46R2njrzY7DgDgPES97Hk8Hi1fvlwOh0N2u12XXXaZjh49qrFjx2r06NGy2WyaO3euKisrVVtbq56eHk2ZMkWSVFZWpsrKSgWDQe3evVuzZs06a7skVVVVae7cuZKkOXPmaMeOHQoGg9FeJpAw/LW1qv/lz2X35Cu3dK7ZcQAA5ynqZe+K/7+9ew+Psr7zPv6ZTCYnEgiHyQGkoKCguKjViqs8SasiaBqOtmVFUVFAwUMRi2ixuHa9HtQUEFBUHrzcUnddVxFFMWVjSwtCQVgVD4jYEjmEHAkwOcxkDr/nj5iYyCkhydyTe96vS68w99wz8/1+L4bfJ/c9h3PPbQxvhYWFeu+99+RwOOR2uxv3SUtLU0lJiUpLS5ttd7vdKikpUWVlpZKTkxX77WuGGrZLanab2NhYJScn6/Dhw+FqD7CVupIS7X/6/8qEjHrPuEcxcXFWlwQAaCXLXmG9Z88eTZ8+XXPmzJHT6VRhYWHjdcYYORwOhUIhORyO47Y3/Gzq+5eb3iYmpuWZtmfP5NY1cgJud0qb78MOmEPnnoHf49HOZ59RjMOhoU/+mxIzM9t0f515Fu2JOUT3DKK596aYQ71wzcGSsLdjxw7dd999euSRR5STk6Nt27Y1eyNFWVmZ0tLSlJGR0Wx7eXm50tLS1KNHD3k8HgWDQTmdzsb9pfqjguXl5crIyFAgEFB1dbVSU1NbXFtFRZVCIXPGvbndKSor85zx7e2COXTuGZhgUAcW5clXWqo+D/xKVbHJqmpDL515Fu2JOUT3DKK596aYQ722ziEmxtHiA1RhP4176NAhzZw5U3l5ecrJyZEkXXTRRdq7d6+++eYbBYNBvfPOO8rKylKfPn0UHx+vHTt2SJLeeustZWVlyeVy6bLLLtO6deskSWvWrFFWVv2n+GdnZ2vNmjWSpHXr1umyyy6Ty+UKd5tAp1a5Pl+1X+5S2s23Kum8QVaXAwBog7Af2Vu5cqV8Pp8WLFjQuG3ixIlasGCB7r33Xvl8PmVnZ2vUqPpP5s/Ly9O8efNUVVWlIUOGaPLkyZKk+fPna+7cuVq+fLkyMzO1cOFCSdL999+vuXPnKicnRykpKcrLywt3i0Cn5is6qIq33lTyJZeq61XDrS4HANBGDmPMmZ+ztCFO47YP5tA5Z2CCQe1f8ITqykrV/1+fUGy3bu1yv51xFh2BOUT3DKK596aYQz1bn8YFEJmMMSr9j1Xy7v2H0m+6pd2CHgDAWoQ9AJKkyvx1OvqXDep+fY5SLh9mdTkAgHZC2AOgY9v+pvI3/lsplw9Tr3ETrC4HANCOCHtAlKv9+9cqeen/KfG8QUq//U45WvG5lACAyMe/6kAUC/l8Kl65Qs7UVPWeca9i+JgiALAdwh4QpUwopJLfvyx/aYkybrtDzuS2f3sMACDyEPaAKFX22qvybN2inmPHK2nw+VaXAwDoIIQ9IAod+fOfdKRgvVKvGaEeOblWlwMA6ECEPSDKVH/+mUr/8w/qMvQiuX/xL3I4HFaXBADo138SHwAAFOdJREFUQIQ9IIrUlRTr0AvPKS6ztzKm3sU7bwEgCvAvPRAlgjU1Klr6jBQToz733C9nYqLVJQEAwoCwB0QBEwjo0IvLVVdWqt53zZTL7ba6JABAmBD2AJszgYCKnn9WNZ99qvRJk3nnLQBEGcIeYGMmFNKhFc+r+uOPlHbTzeqWlW11SQCAMCPsATZWsWa1qnZsl/vnE5V69bVWlwMAsABhD7Cpyj8V6PC6d9QtK1vdrxtldTkAAIsQ9gAbOrppo8r+4w/qcskPlXbTLVaXAwCwEGEPsJmqj3ao5N9fUtKQC9V7+gw5YmOtLgkAYCHCHmAj1V98rkMvLFfC2Wer9933EPQAAGIlAGyi+vPPVLTsGbnSM9TnvgcUk5BgdUkAgAjAkT3ABhqDXlq6znpwjpzJyVaXBACIEBzZAzq56s8/U9HSxXJlZKrv7DlypqRYXRIAIIIQ9oBOrCHoxWVm6qwHCHoAgOMR9oBOyrv3Hyp6bqlcGZk6a/ZDnLoFAJwQr9kDOqG64mIdfGaRYlO66qxZswl6AICTIuwBnYy/vEwHFj4tOaQ+s2Yrtluq1SUBACIYYQ/oRPwV5dqf96RC3lr1mfWg4tIzrC4JABDheM0e0EnUfLlLxStXKOSt1VmzH1LCD/pZXRIAoBMg7AGdwLFtf1PxihfkSkvTWffcp4R+/a0uCQDQSRD2gAgW8npVsurf5dm6RYnnDVKf+2bxzRgAgFYh7AERKuT16uAzC1X796/V44afqkdOrmLi460uCwDQyRD2gAgU8np1cMki1X69R5lT71LK5cOsLgkA0EkR9oAIEzhSqYNLFsu3f58y7pxO0AMAtAlhD4gg9d+KsUzBmhr1vveXSh56kdUlAQA6OcIeEAFMKKTD695Rxdq3FNutm/o+9DAfrQIAaBeEPcBiwZoaHXrhOdV8/pmSL7tc6bfcKmeXLlaXBQCwCcIeYCH/4QodfGaR6ooPKW3yber2f7LlcDisLgsAYCOEPcAitV/vUdFzS2X8fvW5/wF1uWCI1SUBAGyIsAdY4OjGv6jkD7+Xq0dP9X7wIcX37mN1SQAAmyLsAWFkAgGVvfaqjvypQEkXDFHmtLvlTE62uiwAgI0R9oAwqSsuVsmql1W7+0t1HzFSvW78uRxOp9VlAQBsjrAHhEHN7i91cFGeHLGxypgyVV2vvMrqkgAAUYKwB3Swml1fqGj5MrncaTpr9hzFpqZaXRIAIIoQ9oAO4quo0KEVL8mzbaviMjLV55cPEPQAAGFH2AM6QO2er7T3+WUK1HrV/bpR6pk7RjEJCVaXBQCIQoQ9oB0ZY1T14TYVv7xS8b16qc+DcxWX2dvqsgAAUYywB7STwJEjKnnl96r+6H+VcM45+qf583TUH2N1WQCAKEfYA9rIGKNjmzep7L/+U8bvV68bf67uI0YqLrWbVOaxujwAQJQj7AFtEKyqUvFLK1S98xMlnnue0m+doriMDKvLAgCgEWEPOEPewr0qWr5MwaNH5Z54k1KvvlaOGE7bAgAiC2EPOAPVn+1U0bNL5UzpqrPmPKLEc86xuiQAAE6IsAe0gr+8TKWvrFL1pzsV3/cH6jPrQcV27Wp1WQAAnBRhD2gBf1mZjvz5fR396wbJ4VDPseOVes0IORMTrS4NAIBTIuwBJ2GMUe2Xu1T5/v+o+pOPpZgYpfzwUvUa/zO53G6rywMAoEUIe8BJeLZsVvFLK+RMTlGPG36qbj++Wq7u3a0uCwCAViHsASdRvetzObt109kLnlaMK87qcgAAOCN8TgRwEr79+xXftx9BDwDQqRH2gBMI+f2qO1Sk+L59rS4FAIA2IewBJ1B3qEgKBpXQ9wdWlwIAQJsQ9oAT8O3fL0kc2QMAdHq8QQNowoRCqv7kI1W89aZikrrIlc733AIAOjfCHiDJBIM6+sFGVea/J39piVzpGepz1wy+6xYA0OkR9hDVjDGq+t/tKl/9hvwlxYrvf7Yyp89Q8g8vlcPptLo8AADajLCHqGSMUfWnO1WxZrV8+75RXO/e6n3P/epy0cVyOBxWlwcAQLsh7CGqmEBAx7b+TZXr81V38IBcvdzKmDJVKVf8M6dsAQC2RNhDVAhWV+voX/6syj8VKHjkiOL6nKX02+9U12FXyBHL0wAAYF+scrA1X1GRjv71zzq68a8yPp+SLhii7rfdoaQhF3K6FgAQFQh7sB1/eZk827bq2LatqjuwX3I6lXL5MPW4bpTi+ZBkAECUIezBFkJ+v6o//khHN/5FNbu+kIxRwoCBcv/LJKVc9iPFdku1ukQAACxB2EOnFaypUe1Xu1Xzxec6tu1vClVVKbZHT/XMHaOuV14lVy+31SUCAGA5wh46jZC3VjVffaXa3V+qZveX8n1TKBkjR2ysulx8iboNz1LSBUN4Vy0AAE0Q9hCxQj6far/eo5ovd6l29y55CwulUEiO2FglnDNAPX46WkmDBithwADFuOKsLhcAgIhE2ENEMKGQ/KWl8u3fJ+8//q7av38t7zeFUjAoOZ1KOPsc9bg+R0mDz1fCOQMUEx9vdckAAHQKhD2ElQkE5D98WP7yMvlLSuQ7sE++/fvkO3BApq5OkuRwuZTQ/2x1v26Ukgafr8SB5xLuAAA4Q7YMe2vXrtXy5csVCAR06623atKkSVaXFDWMMQpVV8tzpESerwrlLy9TXVmp/GVl8peXKVBRIRnTuH9MUhfF9+2rblnZiu/7g/r/e/fhg44BAGgntltRS0pKtGjRIq1evVpxcXGaOHGihg0bpoEDB1pdWqdjQiEZv18hr1ehOp+M16dgbY2CHo+CVZ76n03+D3iOKVBRrlBtbbP7caZ0lcvtVuKAgXJd8c9y9UqTy+2Wy52m2O7d+XBjAAA6kO3C3ubNm3XFFVcoNbX+c9VGjhyp/Px83XPPPRZXVi9YW6vA4cPfXjL1R7lM/Z9NkyNeTbfLmG8PhpnvrvvePsfftvm+obq6+tDm9SrkrVWotkahmhqFar0KemsVqq1tcl39T+PznbafmMREOZNT5ExJkatnTyWdN0gut1s9B/RTbVwXuXq5FZOQcIbTAgAAbWW7sFdaWiq3+7vPV0tLS9POnTstrKi5A3lP1n9kiNUcDsUkJikmMUExiUlyfhva4txuxSQmKiY+QY6EBMXExSsmIV6OuHjFxMfXh7uUFDlTusqZnKwYl+uEd9/TnaKyMk+YmwIAAN9nu7AXCoWanRY0xrTqNGHPnsltrsHtTjnpdV0e/KVq9u2TGmty1P/ncDT+uel1jobL325r7KXJzxNta9zXUX+fMXHxciYm1Ie6xCQ5ExM6/PTpqeYQLZjBd5hFPeYQ3TOI5t6bYg71wjUH24W9jIwMbd++vfFyWVmZ0tLSWnz7iooqhULm9DuehPt0R7QSU6VBx39115k/YiuFJFUHpOqqDn2Y084hCjCD7zCLeswhumcQzb03xRzqtXUOMTGOFh+gst1XDVx55ZXasmWLDh8+rNraWq1fv15ZWVlWlwUAAGAJ2x3ZS09P16xZszR58mT5/X7deOONGjp0qNVlAQAAWMJ2YU+ScnNzlZuba3UZAAAAlrPdaVwAAAB8h7AHAABgY4Q9AAAAGyPsAQAA2BhhDwAAwMYIewAAADZG2AMAALAxwh4AAICNEfYAAABsjLAHAABgY4Q9AAAAGyPsAQAA2BhhDwAAwMYIewAAADZG2AMAALAxwh4AAICNEfYAAABsjLAHAABgY7FWFxBpYmIcEXEfdsAcmEFTzKIec4juGURz700xh3ptmUNrbuswxpgzfiQAAABENE7jAgAA2BhhDwAAwMYIewAAADZG2AMAALAxwh4AAICNEfYAAABsjLAHAABgY4Q9AAAAGyPsAQAA2BhhT9KyZcuUk5OjnJwcPfXUU5KkzZs3Kzc3V9ddd50WLVrUuG9BQYHGjBmj0aNHa8aMGTp69KgkqaioSJMmTdKoUaN09913q7q6+oSPtWvXLo0fP14jR47Ur3/9awUCAUnS9u3bNX78eOXm5uquu+5qvN9wioQ5vPnmmxo+fLjGjBmjMWPGNHvMcLB6BhUVFY29jxkzRldffbUuueSSjm/8BKyehSTt3LlTEyZMUG5urqZPn66ysrIO7vp44ZxDgzlz5mj16tXHbV+8eLGWLl3ajt21THvMoMHpejjZ34WW3r4jREL/Vq8RkTADq9cHyfo5nPEaYaLcBx98YH7xi18Yn89n6urqzOTJk83atWtNdna22bdvn/H7/WbKlClmw4YNxuPxmKuuusoUFxcbY4xZvHix+e1vf2uMMWbatGnmnXfeMcYYs2zZMvPUU0+d8PFycnLMRx99ZIwx5uGHHzavvPKKMcaYa6+91uzZs8cYY8zTTz9tfve733Vo398XKXN4/PHHzdq1azu63ROKlBk0CAaD5uabbzZvv/12R7V8UpEwi1AoZLKzs82WLVuMMca8++67Zvr06R3dejPhnkNxcbGZPn26GTp0qHnjjTcatx87dsw8/PDDZujQoWbJkiUd3HVz7TWDlvZwsueFVTOIlP6tXCMiZQZWrg/GRM4cGrRmjYj6I3tut1tz585VXFycXC6XBgwYoMLCQvXr1099+/ZVbGyscnNzlZ+fL7/fr/nz5ys9PV2SNGjQIB06dEh+v18ffvihRo4cKUkaP3688vPzj3usgwcPyuv16uKLLz5uv3Xr1mngwIHy+/0qKSlR165dwzSBepEyh08//VRvvvmmcnNz9eCDD4b1t9dImUGDN954Q4mJicrNze3gzo8XCbOorKyU1+vVFVdcIUn6yU9+ok2bNqmuri5MUwjvHCRp7dq1uuaaa3T99dc32/7++++rf//+uv322zu24RNojxm0tIdTPS+smkGk9G/lGhEpM7ByfZAiZw4NWrNGRH3YO/fccxuHWVhYqPfee08Oh0Nut7txn7S0NJWUlKh79+4aMWKEJMnr9erFF1/Utddeq8rKSiUnJys2NlZS/V+IkpKS4x6rtLS02f023c/lcmn37t3Kzs7W1q1blZOT02E9n0ikzMHtdmvGjBl6++23lZmZqccff7zDev6+SJmBJAWDQT3//POaPXt2h/R6OpEwi+7duyspKUmbNm2SJL377rvy+/2qrKzssL6/L5xzkKQ777xTP/vZz47bPnbsWE2bNk1Op7O9Wzyt9phBS3s41fPCqhlESv9WrhGRMgMr1wcpcuYgtX6NiPqw12DPnj2aMmWK5syZo759+8rhcDReZ4xpdtnj8WjatGkaPHiwxo0bd9z1ko67LEmhUOiU9zto0CBt3rxZM2bM0KxZs9qzvRazeg7PPvusLr30UjkcDt15553auHFje7d4WlbPQJI2btyo/v37a9CgQe3ZWqtZOQuHw6ElS5bohRde0NixY+XxeJSamiqXy9UBnZ5aOOYQ6doyg5Y63fPCSpHQv9VrhNUziIT1QbJ+DlLr1wjCnqQdO3botttu0+zZszVu3DhlZGQ0eyF4WVmZ0tLSJNWn7ZtuukmDBg3SE088IUnq0aOHPB6PgsFgs/1LSkoaX0Q5derU4+63vLxcaWlp8vl8KigoaNw+evRo7d69OxytN2P1HDwej15++eXG7caYsP8Wb/UMGhQUFOiGG24IR8snFQmziI2N1apVq7RmzRqNHj1aoVBIqamp4RqBpPDNIZK1dQYn09rnhVWs7j8S1girZxAJ64Nk/RwatHqNOO2r+myuqKjIDBs2zGzevLlxm9frNVlZWaawsNAEAgFzxx13mHXr1plAIGDGjRtnnn322ePuZ+rUqY0vknzuuefMY489dsLHy8nJMdu3bzfGGDNv3jyzYsUKEwgEzPDhw82nn35qjDHm9ddfN1OmTGnvVk8pUuZw1VVXmY8//tgYY8zSpUvNo48+2t6tnlQkzKDpdYWFhe3ZXqtEyizGjRtnPvnkE2NM/YvS582b1659nk6459DgoYceavYGjQZLliwJ+xs02msGDU7Xw6meFy25fXuLhP6tXiMiZQZWrg/GRMYcml7XmjUituWx0J5Wrlwpn8+nBQsWNG6bOHGiFixYoHvvvVc+n0/Z2dkaNWqUCgoK9MUXXygYDOqPf/yjJOnCCy/UE088ofnz52vu3Llavny5MjMztXDhwhM+Xl5enubNm6eqqioNGTJEkydPltPp1KJFi/Sb3/xGwWBQ6enpp/0toL1FyhwWL16sxx57TF6vV/379298a3s4RMIMGuzfv18ZGRkd2/ApRMosHnvsMc2fP1+1tbUt+u24vYV7DpGovWbQUqd6XlghEvq3eo2IlBlYuT5IkTGHBq1dIxzGGNPivQEAANCp8Jo9AAAAGyPsAQAA2BhhDwAAwMYIewAAADZG2AMAALAxwh4AtIP8/Hzdcsstp9xn2bJlzT4cFwDCgbAHAGGydetWBQIBq8sAEGWi/kOVAeBMPfPMM1q7dq1SU1PVr18/SdLevXv1+OOPq7q6WmVlZRo8eLAWL16s119/XZ999pmeeuopOZ1OZWdnKy8vTx9++KGCwaAuuOACzZs3T8nJyRZ3BcBuOLIHAGegoKBA69ev15o1a/Tqq6+qqqpKkvTaa69p7Nixeu2117R+/XodOHBAGzZs0KRJk3ThhRdqzpw5GjFihF588UU5nU6tXr1ab7/9ttLS0pSXl2dxVwDsiCN7AHAGtmzZohEjRjQeiZswYYJWrVqlX/3qV/rggw+0YsUKFRYWqrS0VDU1NcfdfsOGDfJ4PNq8ebMkye/3q2fPnmHtAUB0IOwBwBlq+m2TTqdTkvTAAw8oGAzq+uuv149//GMdOnRIJ/pWylAopEceeUTZ2dmSpOrqavl8vvAUDiCqcBoXAM5AVlaW8vPzdezYMYVCIb311luSpE2bNmnmzJm64YYbJEmffPKJgsGgpPpA2PAGjeHDh+uVV15RXV2dQqGQHn30US1cuNCaZgDYGkf2AOAMZGdna/fu3ZowYYK6du2qwYMHq7KyUrNmzdLMmTOVlJSk5ORk/ehHP9K+ffskSVdffbUWLlwov9+vGTNm6Mknn9S4ceMUDAZ1/vnna+7cuRZ3BcCOHOZE5xcAAABgC5zGBQAAsDHCHgAAgI0R9gAAAGyMsAcAAGBjhD0AAAAbI+wBAADYGGEPAADAxgh7AAAANvb/AQnDRydmKta2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={\"figure.figsize\":(10,10)})\n",
    "sns.lineplot(x=\"date\",y=\"deaths\",data=MH,color=\"r\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Delhi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "DEL=covid[covid.state=='Delhi']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>date</th>\n",
       "      <th>state</th>\n",
       "      <th>cured</th>\n",
       "      <th>deaths</th>\n",
       "      <th>confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>2020-03-06</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  state  cured  deaths  confirmed\n",
       "34 2020-03-02  Delhi      0       0          1\n",
       "38 2020-03-03  Delhi      0       0          1\n",
       "42 2020-03-04  Delhi      0       0          1\n",
       "45 2020-03-05  Delhi      0       0          2\n",
       "51 2020-03-06  Delhi      0       0          3"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DEL.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>date</th>\n",
       "      <th>state</th>\n",
       "      <th>cured</th>\n",
       "      <th>deaths</th>\n",
       "      <th>confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>16210</td>\n",
       "      <td>2021-06-20</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>1404889</td>\n",
       "      <td>24907</td>\n",
       "      <td>1432168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16246</td>\n",
       "      <td>2021-06-21</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>1405287</td>\n",
       "      <td>24914</td>\n",
       "      <td>1432292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16282</td>\n",
       "      <td>2021-06-22</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>1405460</td>\n",
       "      <td>24925</td>\n",
       "      <td>1432381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16318</td>\n",
       "      <td>2021-06-23</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>1405927</td>\n",
       "      <td>24933</td>\n",
       "      <td>1432778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16354</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>1406629</td>\n",
       "      <td>24940</td>\n",
       "      <td>1433366</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date  state    cured  deaths  confirmed\n",
       "16210 2021-06-20  Delhi  1404889   24907    1432168\n",
       "16246 2021-06-21  Delhi  1405287   24914    1432292\n",
       "16282 2021-06-22  Delhi  1405460   24925    1432381\n",
       "16318 2021-06-23  Delhi  1405927   24933    1432778\n",
       "16354 2021-06-24  Delhi  1406629   24940    1433366"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DEL.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={\"figure.figsize\":(10,10)})\n",
    "sns.lineplot(x=\"date\",y=\"confirmed\",data=DEL,color=\"g\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={\"figure.figsize\":(10,10)})\n",
    "sns.lineplot(x=\"date\",y=\"deaths\",data=MH,color=\"r\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kerala"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "Kerala=covid[covid.state==\"Kerala\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>date</th>\n",
       "      <th>state</th>\n",
       "      <th>cured</th>\n",
       "      <th>deaths</th>\n",
       "      <th>confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2020-02-02</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date   state  cured  deaths  confirmed\n",
       "0 2020-01-30  Kerala      0       0          1\n",
       "1 2020-01-31  Kerala      0       0          1\n",
       "2 2020-02-01  Kerala      0       0          2\n",
       "3 2020-02-02  Kerala      0       0          3\n",
       "4 2020-02-03  Kerala      0       0          3"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Kerala.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>date</th>\n",
       "      <th>state</th>\n",
       "      <th>cured</th>\n",
       "      <th>deaths</th>\n",
       "      <th>confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>16218</td>\n",
       "      <td>2021-06-20</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>2678499</td>\n",
       "      <td>11948</td>\n",
       "      <td>2797747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16254</td>\n",
       "      <td>2021-06-21</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>2690958</td>\n",
       "      <td>12060</td>\n",
       "      <td>2809394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16290</td>\n",
       "      <td>2021-06-22</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>2704554</td>\n",
       "      <td>12154</td>\n",
       "      <td>2816843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16326</td>\n",
       "      <td>2021-06-23</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>2716284</td>\n",
       "      <td>12295</td>\n",
       "      <td>2829460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16362</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>2729967</td>\n",
       "      <td>12445</td>\n",
       "      <td>2842247</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date   state    cured  deaths  confirmed\n",
       "16218 2021-06-20  Kerala  2678499   11948    2797747\n",
       "16254 2021-06-21  Kerala  2690958   12060    2809394\n",
       "16290 2021-06-22  Kerala  2704554   12154    2816843\n",
       "16326 2021-06-23  Kerala  2716284   12295    2829460\n",
       "16362 2021-06-24  Kerala  2729967   12445    2842247"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Kerala.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={\"figure.figsize\":(10,10)})\n",
    "sns.lineplot(x=\"date\",y=\"confirmed\",data=Kerala,color=\"g\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={\"figure.figsize\":(10,10)})\n",
    "sns.lineplot(x=\"date\",y=\"deaths\",data=Kerala,color=\"r\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Andaman and Nicobar Islands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "AN=covid[covid.state==\"Andaman and Nicobar Islands\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>date</th>\n",
       "      <th>state</th>\n",
       "      <th>cured</th>\n",
       "      <th>deaths</th>\n",
       "      <th>confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>365</td>\n",
       "      <td>2020-03-26</td>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>392</td>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>420</td>\n",
       "      <td>2020-03-28</td>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>447</td>\n",
       "      <td>2020-03-29</td>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>474</td>\n",
       "      <td>2020-03-30</td>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date                        state  cured  deaths  confirmed\n",
       "365 2020-03-26  Andaman and Nicobar Islands      0       0          1\n",
       "392 2020-03-27  Andaman and Nicobar Islands      0       0          1\n",
       "420 2020-03-28  Andaman and Nicobar Islands      0       0          6\n",
       "447 2020-03-29  Andaman and Nicobar Islands      0       0          9\n",
       "474 2020-03-30  Andaman and Nicobar Islands      0       0          9"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AN.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>date</th>\n",
       "      <th>state</th>\n",
       "      <th>cured</th>\n",
       "      <th>deaths</th>\n",
       "      <th>confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>16202</td>\n",
       "      <td>2021-06-20</td>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>7162</td>\n",
       "      <td>127</td>\n",
       "      <td>7394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16238</td>\n",
       "      <td>2021-06-21</td>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>7177</td>\n",
       "      <td>127</td>\n",
       "      <td>7406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16274</td>\n",
       "      <td>2021-06-22</td>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>7191</td>\n",
       "      <td>127</td>\n",
       "      <td>7415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16310</td>\n",
       "      <td>2021-06-23</td>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>7195</td>\n",
       "      <td>127</td>\n",
       "      <td>7425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16346</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>7210</td>\n",
       "      <td>127</td>\n",
       "      <td>7438</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date                        state  cured  deaths  confirmed\n",
       "16202 2021-06-20  Andaman and Nicobar Islands   7162     127       7394\n",
       "16238 2021-06-21  Andaman and Nicobar Islands   7177     127       7406\n",
       "16274 2021-06-22  Andaman and Nicobar Islands   7191     127       7415\n",
       "16310 2021-06-23  Andaman and Nicobar Islands   7195     127       7425\n",
       "16346 2021-06-24  Andaman and Nicobar Islands   7210     127       7438"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AN.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={\"figure.figsize\":(10,10)})\n",
    "sns.lineplot(x=\"date\",y=\"confirmed\",data=AN,color=\"g\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={\"figure.figsize\":(10,10)})\n",
    "sns.lineplot(x=\"date\",y=\"deaths\",data=AN,color=\"r\")\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "tests=pd.read_csv(\"C:\\\\Users\\\\User\\\\Downloads\\\\StatewiseTestingDetails.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>Date</th>\n",
       "      <th>State</th>\n",
       "      <th>TotalSamples</th>\n",
       "      <th>Negative</th>\n",
       "      <th>Positive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2020-04-17</td>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>1403.0</td>\n",
       "      <td>1210</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2020-04-24</td>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>2679.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2020-04-27</td>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>2848.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>3754.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2020-05-16</td>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>6677.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date                        State  TotalSamples Negative  Positive\n",
       "0  2020-04-17  Andaman and Nicobar Islands        1403.0     1210      12.0\n",
       "1  2020-04-24  Andaman and Nicobar Islands        2679.0      NaN      27.0\n",
       "2  2020-04-27  Andaman and Nicobar Islands        2848.0      NaN      33.0\n",
       "3  2020-05-01  Andaman and Nicobar Islands        3754.0      NaN      33.0\n",
       "4  2020-05-16  Andaman and Nicobar Islands        6677.0      NaN      33.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tests.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>Date</th>\n",
       "      <th>State</th>\n",
       "      <th>TotalSamples</th>\n",
       "      <th>Negative</th>\n",
       "      <th>Positive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>14653</td>\n",
       "      <td>2021-06-19</td>\n",
       "      <td>West Bengal</td>\n",
       "      <td>13631865.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14654</td>\n",
       "      <td>2021-06-20</td>\n",
       "      <td>West Bengal</td>\n",
       "      <td>13684862.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14655</td>\n",
       "      <td>2021-06-21</td>\n",
       "      <td>West Bengal</td>\n",
       "      <td>13732633.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14656</td>\n",
       "      <td>2021-06-22</td>\n",
       "      <td>West Bengal</td>\n",
       "      <td>13783866.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14657</td>\n",
       "      <td>2021-06-23</td>\n",
       "      <td>West Bengal</td>\n",
       "      <td>13839455.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date        State  TotalSamples Negative  Positive\n",
       "14653  2021-06-19  West Bengal    13631865.0      NaN       NaN\n",
       "14654  2021-06-20  West Bengal    13684862.0      NaN       NaN\n",
       "14655  2021-06-21  West Bengal    13732633.0      NaN       NaN\n",
       "14656  2021-06-22  West Bengal    13783866.0      NaN       NaN\n",
       "14657  2021-06-23  West Bengal    13839455.0      NaN       NaN"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tests.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building Machine Learning Model to predict the no. of coronavirus cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "MH['date']=MH['date'].map(dt.datetime.toordinal)   #converting dates into linear values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>state</th>\n",
       "      <th>cured</th>\n",
       "      <th>deaths</th>\n",
       "      <th>confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>737493</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>737494</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>737495</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>120</td>\n",
       "      <td>737496</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>133</td>\n",
       "      <td>737497</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date        state  cured  deaths  confirmed\n",
       "76   737493  Maharashtra      0       0          2\n",
       "91   737494  Maharashtra      0       0          5\n",
       "97   737495  Maharashtra      0       0          2\n",
       "120  737496  Maharashtra      0       0         11\n",
       "133  737497  Maharashtra      0       0         14"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MH.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=MH['date']\n",
    "y=MH['confirmed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf=RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=10,\n",
       "                      n_jobs=None, oob_score=False, random_state=None,\n",
       "                      verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(np.array(x_train).reshape(-1,1),np.array(y_train).reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>date</th>\n",
       "      <th>state</th>\n",
       "      <th>cured</th>\n",
       "      <th>deaths</th>\n",
       "      <th>confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>737493</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>737494</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>737495</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>120</td>\n",
       "      <td>737496</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>133</td>\n",
       "      <td>737497</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146</td>\n",
       "      <td>737498</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date        state  cured  deaths  confirmed\n",
       "76   737493  Maharashtra      0       0          2\n",
       "91   737494  Maharashtra      0       0          5\n",
       "97   737495  Maharashtra      0       0          2\n",
       "120  737496  Maharashtra      0       0         11\n",
       "133  737497  Maharashtra      0       0         14\n",
       "146  737498  Maharashtra      0       0         14"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MH.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([13.7])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.predict([[737498]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On numerical value of date= '737498' no. of cases were 14 and our model predicted 15.8 which is equal to 15."
   ]
  }
 ],
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