web_scraping.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "# ================\n",
    "\n",
    "# for date and time opeations\n",
    "from datetime import datetime\n",
    "# for file and folder operations\n",
    "import os\n",
    "# for regular expression opeations\n",
    "import re\n",
    "# for listing files in a folder\n",
    "import glob\n",
    "# for getting web contents\n",
    "import requests \n",
    "# storing and analysing data\n",
    "import pandas as pd\n",
    "# for scraping web contents\n",
    "from bs4 import BeautifulSoup\n",
    "# regular expression\n",
    "import re\n",
    "# for numerical analysis\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Web Scrapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get data\n",
    "# ========\n",
    "\n",
    "# link at which web data recides\n",
    "link = 'https://www.mohfw.gov.in/'\n",
    "# get web data\n",
    "req = requests.get(link)\n",
    "# parse web data\n",
    "soup = BeautifulSoup(req.content, \"html.parser\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find the table\n",
    "# ==============\n",
    "# our target table is the last table in the page\n",
    "\n",
    "# get the table head\n",
    "# table head may contain the column names, titles, subtitles\n",
    "thead = soup.find_all('thead')[-1]\n",
    "# print(thead)\n",
    "\n",
    "# get all the rows in table head\n",
    "# it usually have only one row, which has the column names\n",
    "head = thead.find_all('tr')\n",
    "# print(head)\n",
    "\n",
    "# get the table tbody\n",
    "# it contains the contents\n",
    "tbody = soup.find_all('tbody')[-1]\n",
    "# print(tbody)\n",
    "\n",
    "# get all the rows in table body\n",
    "# each row is each state's entry\n",
    "body = tbody.find_all('tr')\n",
    "# print(body)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the table contents\n",
    "# ======================\n",
    "\n",
    "# container for header rows / column title\n",
    "head_rows = []\n",
    "# container for table body / contents\n",
    "body_rows = []\n",
    "\n",
    "# loop through the head and append each row to head\n",
    "for tr in head:\n",
    "    td = tr.find_all(['th', 'td'])\n",
    "    row = [i.text for i in td]\n",
    "    head_rows.append(row)\n",
    "# print(head_rows)\n",
    "\n",
    "# loop through the body and append each row to body\n",
    "for tr in body:\n",
    "    td = tr.find_all(['th', 'td'])\n",
    "    row = [i.text for i in td]\n",
    "    body_rows.append(row)\n",
    "# print(head_rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Name of State / UT</th>\n",
       "      <th>Active Cases*</th>\n",
       "      <th>Cured/Discharged/Migrated*</th>\n",
       "      <th>Deaths**</th>\n",
       "      <th>Total Confirmed cases*</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>57</td>\n",
       "      <td>109</td>\n",
       "      <td>0</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>15144</td>\n",
       "      <td>17467</td>\n",
       "      <td>408</td>\n",
       "      <td>33019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>306</td>\n",
       "      <td>153</td>\n",
       "      <td>3</td>\n",
       "      <td>462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Assam</td>\n",
       "      <td>6351</td>\n",
       "      <td>11416</td>\n",
       "      <td>40</td>\n",
       "      <td>17807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bihar</td>\n",
       "      <td>6261</td>\n",
       "      <td>12849</td>\n",
       "      <td>174</td>\n",
       "      <td>19284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>144</td>\n",
       "      <td>446</td>\n",
       "      <td>10</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Chhattisgarh</td>\n",
       "      <td>1084</td>\n",
       "      <td>3275</td>\n",
       "      <td>20</td>\n",
       "      <td>4379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Dadra and Nagar Haveli and Daman and Diu</td>\n",
       "      <td>209</td>\n",
       "      <td>310</td>\n",
       "      <td>1</td>\n",
       "      <td>520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Delhi</td>\n",
       "      <td>18664</td>\n",
       "      <td>93236</td>\n",
       "      <td>3446</td>\n",
       "      <td>115346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Goa</td>\n",
       "      <td>1128</td>\n",
       "      <td>1607</td>\n",
       "      <td>18</td>\n",
       "      <td>2753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Gujarat</td>\n",
       "      <td>11065</td>\n",
       "      <td>30503</td>\n",
       "      <td>2069</td>\n",
       "      <td>43637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Haryana</td>\n",
       "      <td>5226</td>\n",
       "      <td>17090</td>\n",
       "      <td>312</td>\n",
       "      <td>22628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Himachal Pradesh</td>\n",
       "      <td>347</td>\n",
       "      <td>951</td>\n",
       "      <td>11</td>\n",
       "      <td>1309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Jammu and Kashmir</td>\n",
       "      <td>4755</td>\n",
       "      <td>6223</td>\n",
       "      <td>195</td>\n",
       "      <td>11173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Jharkhand</td>\n",
       "      <td>1628</td>\n",
       "      <td>2427</td>\n",
       "      <td>36</td>\n",
       "      <td>4091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Karnataka</td>\n",
       "      <td>25845</td>\n",
       "      <td>17390</td>\n",
       "      <td>842</td>\n",
       "      <td>44077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Kerala</td>\n",
       "      <td>4458</td>\n",
       "      <td>4438</td>\n",
       "      <td>34</td>\n",
       "      <td>8930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ladakh</td>\n",
       "      <td>146</td>\n",
       "      <td>946</td>\n",
       "      <td>1</td>\n",
       "      <td>1093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Madhya Pradesh</td>\n",
       "      <td>4757</td>\n",
       "      <td>13575</td>\n",
       "      <td>673</td>\n",
       "      <td>19005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>107963</td>\n",
       "      <td>149007</td>\n",
       "      <td>10695</td>\n",
       "      <td>267665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Manipur</td>\n",
       "      <td>702</td>\n",
       "      <td>970</td>\n",
       "      <td>0</td>\n",
       "      <td>1672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Meghalaya</td>\n",
       "      <td>250</td>\n",
       "      <td>66</td>\n",
       "      <td>2</td>\n",
       "      <td>318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Mizoram</td>\n",
       "      <td>79</td>\n",
       "      <td>159</td>\n",
       "      <td>0</td>\n",
       "      <td>238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Nagaland</td>\n",
       "      <td>550</td>\n",
       "      <td>346</td>\n",
       "      <td>0</td>\n",
       "      <td>896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Odisha</td>\n",
       "      <td>4342</td>\n",
       "      <td>9864</td>\n",
       "      <td>74</td>\n",
       "      <td>14280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Puducherry</td>\n",
       "      <td>684</td>\n",
       "      <td>829</td>\n",
       "      <td>18</td>\n",
       "      <td>1531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Punjab</td>\n",
       "      <td>2635</td>\n",
       "      <td>5663</td>\n",
       "      <td>213</td>\n",
       "      <td>8511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Rajasthan</td>\n",
       "      <td>5885</td>\n",
       "      <td>19161</td>\n",
       "      <td>525</td>\n",
       "      <td>25571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Sikkim</td>\n",
       "      <td>122</td>\n",
       "      <td>87</td>\n",
       "      <td>0</td>\n",
       "      <td>209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>47915</td>\n",
       "      <td>97310</td>\n",
       "      <td>2099</td>\n",
       "      <td>147324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Telangana</td>\n",
       "      <td>12530</td>\n",
       "      <td>24840</td>\n",
       "      <td>375</td>\n",
       "      <td>37745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Tripura</td>\n",
       "      <td>630</td>\n",
       "      <td>1538</td>\n",
       "      <td>2</td>\n",
       "      <td>2170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Uttarakhand</td>\n",
       "      <td>769</td>\n",
       "      <td>2867</td>\n",
       "      <td>50</td>\n",
       "      <td>3686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>13758</td>\n",
       "      <td>24983</td>\n",
       "      <td>983</td>\n",
       "      <td>39724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>11927</td>\n",
       "      <td>19931</td>\n",
       "      <td>980</td>\n",
       "      <td>32838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Name of State / UT Active Cases*  \\\n",
       "0                Andaman and Nicobar Islands            57   \n",
       "1                             Andhra Pradesh         15144   \n",
       "2                          Arunachal Pradesh           306   \n",
       "3                                      Assam          6351   \n",
       "4                                      Bihar          6261   \n",
       "5                                 Chandigarh           144   \n",
       "6                               Chhattisgarh          1084   \n",
       "7   Dadra and Nagar Haveli and Daman and Diu           209   \n",
       "8                                      Delhi         18664   \n",
       "9                                        Goa          1128   \n",
       "10                                   Gujarat         11065   \n",
       "11                                   Haryana          5226   \n",
       "12                          Himachal Pradesh           347   \n",
       "13                         Jammu and Kashmir          4755   \n",
       "14                                 Jharkhand          1628   \n",
       "15                                 Karnataka         25845   \n",
       "16                                    Kerala          4458   \n",
       "17                                    Ladakh           146   \n",
       "18                            Madhya Pradesh          4757   \n",
       "19                               Maharashtra        107963   \n",
       "20                                   Manipur           702   \n",
       "21                                 Meghalaya           250   \n",
       "22                                   Mizoram            79   \n",
       "23                                  Nagaland           550   \n",
       "24                                    Odisha          4342   \n",
       "25                                Puducherry           684   \n",
       "26                                    Punjab          2635   \n",
       "27                                 Rajasthan          5885   \n",
       "28                                    Sikkim           122   \n",
       "29                                Tamil Nadu         47915   \n",
       "30                                 Telangana         12530   \n",
       "31                                   Tripura           630   \n",
       "32                               Uttarakhand           769   \n",
       "33                             Uttar Pradesh         13758   \n",
       "34                               West Bengal         11927   \n",
       "\n",
       "   Cured/Discharged/Migrated* Deaths** Total Confirmed cases*  \n",
       "0                         109        0                    166  \n",
       "1                       17467      408                  33019  \n",
       "2                         153        3                    462  \n",
       "3                       11416       40                  17807  \n",
       "4                       12849      174                  19284  \n",
       "5                         446       10                    600  \n",
       "6                        3275       20                   4379  \n",
       "7                         310        1                    520  \n",
       "8                       93236     3446                 115346  \n",
       "9                        1607       18                   2753  \n",
       "10                      30503     2069                  43637  \n",
       "11                      17090      312                  22628  \n",
       "12                        951       11                   1309  \n",
       "13                       6223      195                  11173  \n",
       "14                       2427       36                   4091  \n",
       "15                      17390      842                  44077  \n",
       "16                       4438       34                   8930  \n",
       "17                        946        1                   1093  \n",
       "18                      13575      673                  19005  \n",
       "19                     149007    10695                 267665  \n",
       "20                        970        0                   1672  \n",
       "21                         66        2                    318  \n",
       "22                        159        0                    238  \n",
       "23                        346        0                    896  \n",
       "24                       9864       74                  14280  \n",
       "25                        829       18                   1531  \n",
       "26                       5663      213                   8511  \n",
       "27                      19161      525                  25571  \n",
       "28                         87        0                    209  \n",
       "29                      97310     2099                 147324  \n",
       "30                      24840      375                  37745  \n",
       "31                       1538        2                   2170  \n",
       "32                       2867       50                   3686  \n",
       "33                      24983      983                  39724  \n",
       "34                      19931      980                  32838  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# save contents in a dataframe\n",
    "# ============================\n",
    "    \n",
    "# skip last 3 rows, it contains unwanted info\n",
    "# head_rows contains column title\n",
    "df_bs = pd.DataFrame(body_rows[:len(body_rows)-6], \n",
    "                     columns=head_rows[0])         \n",
    "\n",
    "# Drop 'S. No.' column\n",
    "df_bs.drop('S. No.', axis=1, inplace=True)\n",
    "\n",
    "# there are 36 states+UT in India\n",
    "df_bs.head(36)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# date-time information\n",
    "# =====================\n",
    "\n",
    "# today's date\n",
    "now  = datetime.now()\n",
    "# format date to month-day-year\n",
    "df_bs['Date'] = now.strftime(\"%m/%d/%Y\") \n",
    "\n",
    "# add 'Date' column to dataframe\n",
    "df_bs['Date'] = pd.to_datetime(df_bs['Date'], format='%m/%d/%Y')\n",
    "\n",
    "# df_bs.head(36)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove extra characters from 'Name of State/UT' column\n",
    "df_bs['Name of State / UT'] = df_bs['Name of State / UT'].str.replace('#', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# latitude and longitude information\n",
    "# ==================================\n",
    "\n",
    "# latitude of the states\n",
    "lat = {'Delhi':28.7041, 'Haryana':29.0588, 'Kerala':10.8505, 'Rajasthan':27.0238,\n",
    "       'Telengana':18.1124, 'Telangana':18.1124, 'Uttar Pradesh':26.8467, 'Ladakh':34.2996, 'Tamil Nadu':11.1271,\n",
    "       'Jammu and Kashmir':33.7782, 'Punjab':31.1471, 'Karnataka':15.3173, 'Maharashtra':19.7515,\n",
    "       'Andhra Pradesh':15.9129, 'Odisha':20.9517, 'Uttarakhand':30.0668, 'West Bengal':22.9868, \n",
    "       'Puducherry': 11.9416, 'Chandigarh': 30.7333, 'Chhattisgarh':21.2787, 'Gujarat': 22.2587, \n",
    "       'Himachal Pradesh': 31.1048, 'Madhya Pradesh': 22.9734, 'Bihar': 25.0961, 'Manipur':24.6637, \n",
    "       'Mizoram':23.1645, 'Goa': 15.2993, 'Andaman and Nicobar Islands': 11.7401, 'Assam' : 26.2006, \n",
    "       'Jharkhand': 23.6102, 'Arunachal Pradesh': 28.2180, 'Tripura': 23.9408, 'Nagaland': 26.1584, \n",
    "       'Meghalaya' : 25.4670, 'Dadar Nagar Haveli' : 20.1809, 'Sikkim':27.5330, \n",
    "       'Dadra and Nagar Haveli and Daman and Diu': 20.1809}\n",
    "\n",
    "# longitude of the states\n",
    "long = {'Delhi':77.1025, 'Haryana':76.0856, 'Kerala':76.2711, 'Rajasthan':74.2179,\n",
    "        'Telengana':79.0193, 'Telangana':79.0193, 'Uttar Pradesh':80.9462, 'Ladakh':78.2932, 'Tamil Nadu':78.6569,\n",
    "        'Jammu and Kashmir':76.5762, 'Punjab':75.3412, 'Karnataka':75.7139, 'Maharashtra':75.7139,\n",
    "        'Andhra Pradesh':79.7400, 'Odisha':85.0985, 'Uttarakhand':79.0193, 'West Bengal':87.8550, \n",
    "        'Puducherry': 79.8083, 'Chandigarh': 76.7794, 'Chhattisgarh':81.8661, 'Gujarat': 71.1924, \n",
    "        'Himachal Pradesh': 77.1734, 'Madhya Pradesh': 78.6569, 'Bihar': 85.3131, 'Manipur':93.9063, \n",
    "        'Mizoram':92.9376, 'Goa': 74.1240, 'Andaman and Nicobar Islands': 92.6586, 'Assam' : 92.9376, \n",
    "        'Jharkhand': 85.2799, 'Arunachal Pradesh': 94.7278, 'Tripura': 91.9882, 'Nagaland': 94.5624,\n",
    "        'Meghalaya' : 91.3662, 'Dadar Nagar Haveli' : 73.0169, 'Sikkim':88.5122,\n",
    "        'Dadra and Nagar Haveli and Daman and Diu': 73.0169}\n",
    "\n",
    "# add latitude column based on 'Name of State / UT' column\n",
    "df_bs['Latitude'] = df_bs['Name of State / UT'].map(lat)\n",
    "\n",
    "# add longitude column based on 'Name of State / UT' column\n",
    "df_bs['Longitude'] = df_bs['Name of State / UT'].map(long)\n",
    "\n",
    "# df_bs.head(36)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Andaman and Nicobar Islands', 'Andhra Pradesh',\n",
       "       'Arunachal Pradesh', 'Assam', 'Bihar', 'Chandigarh',\n",
       "       'Chhattisgarh', 'Dadra and Nagar Haveli and Daman and Diu',\n",
       "       'Delhi', 'Goa', 'Gujarat', 'Haryana', 'Himachal Pradesh',\n",
       "       'Jammu and Kashmir', 'Jharkhand', 'Karnataka', 'Kerala', 'Ladakh',\n",
       "       'Madhya Pradesh', 'Maharashtra', 'Manipur', 'Meghalaya', 'Mizoram',\n",
       "       'Nagaland', 'Odisha', 'Puducherry', 'Punjab', 'Rajasthan',\n",
       "       'Sikkim', 'Tamil Nadu', 'Telangana', 'Tripura', 'Uttarakhand',\n",
       "       'Uttar Pradesh', 'West Bengal'], dtype=object)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# unique state names\n",
    "df_bs['Name of State / UT'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name of State / UT            0\n",
       "Active Cases*                 0\n",
       "Cured/Discharged/Migrated*    0\n",
       "Deaths**                      0\n",
       "Total Confirmed cases*        0\n",
       "Date                          0\n",
       "Latitude                      0\n",
       "Longitude                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of missing values \n",
    "df_bs.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name of State / UT            35\n",
       "Active Cases*                 35\n",
       "Cured/Discharged/Migrated*    35\n",
       "Deaths**                      28\n",
       "Total Confirmed cases*        35\n",
       "Date                           1\n",
       "Latitude                      35\n",
       "Longitude                     31\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of unique values \n",
    "df_bs.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saving data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Name of State / UT</th>\n",
       "      <th>Active Cases*</th>\n",
       "      <th>Cured/Discharged/Migrated*</th>\n",
       "      <th>Deaths**</th>\n",
       "      <th>Total Confirmed cases*</th>\n",
       "      <th>Date</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>57</td>\n",
       "      <td>109</td>\n",
       "      <td>0</td>\n",
       "      <td>166</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>11.7401</td>\n",
       "      <td>92.6586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>15144</td>\n",
       "      <td>17467</td>\n",
       "      <td>408</td>\n",
       "      <td>33019</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>15.9129</td>\n",
       "      <td>79.7400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>306</td>\n",
       "      <td>153</td>\n",
       "      <td>3</td>\n",
       "      <td>462</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>28.2180</td>\n",
       "      <td>94.7278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Assam</td>\n",
       "      <td>6351</td>\n",
       "      <td>11416</td>\n",
       "      <td>40</td>\n",
       "      <td>17807</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>26.2006</td>\n",
       "      <td>92.9376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bihar</td>\n",
       "      <td>6261</td>\n",
       "      <td>12849</td>\n",
       "      <td>174</td>\n",
       "      <td>19284</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>25.0961</td>\n",
       "      <td>85.3131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>144</td>\n",
       "      <td>446</td>\n",
       "      <td>10</td>\n",
       "      <td>600</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>30.7333</td>\n",
       "      <td>76.7794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Chhattisgarh</td>\n",
       "      <td>1084</td>\n",
       "      <td>3275</td>\n",
       "      <td>20</td>\n",
       "      <td>4379</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>21.2787</td>\n",
       "      <td>81.8661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Dadra and Nagar Haveli and Daman and Diu</td>\n",
       "      <td>209</td>\n",
       "      <td>310</td>\n",
       "      <td>1</td>\n",
       "      <td>520</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>20.1809</td>\n",
       "      <td>73.0169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Delhi</td>\n",
       "      <td>18664</td>\n",
       "      <td>93236</td>\n",
       "      <td>3446</td>\n",
       "      <td>115346</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>28.7041</td>\n",
       "      <td>77.1025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Goa</td>\n",
       "      <td>1128</td>\n",
       "      <td>1607</td>\n",
       "      <td>18</td>\n",
       "      <td>2753</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>15.2993</td>\n",
       "      <td>74.1240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Gujarat</td>\n",
       "      <td>11065</td>\n",
       "      <td>30503</td>\n",
       "      <td>2069</td>\n",
       "      <td>43637</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>22.2587</td>\n",
       "      <td>71.1924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Haryana</td>\n",
       "      <td>5226</td>\n",
       "      <td>17090</td>\n",
       "      <td>312</td>\n",
       "      <td>22628</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>29.0588</td>\n",
       "      <td>76.0856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Himachal Pradesh</td>\n",
       "      <td>347</td>\n",
       "      <td>951</td>\n",
       "      <td>11</td>\n",
       "      <td>1309</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>31.1048</td>\n",
       "      <td>77.1734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Jammu and Kashmir</td>\n",
       "      <td>4755</td>\n",
       "      <td>6223</td>\n",
       "      <td>195</td>\n",
       "      <td>11173</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>33.7782</td>\n",
       "      <td>76.5762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Jharkhand</td>\n",
       "      <td>1628</td>\n",
       "      <td>2427</td>\n",
       "      <td>36</td>\n",
       "      <td>4091</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>23.6102</td>\n",
       "      <td>85.2799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Karnataka</td>\n",
       "      <td>25845</td>\n",
       "      <td>17390</td>\n",
       "      <td>842</td>\n",
       "      <td>44077</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>15.3173</td>\n",
       "      <td>75.7139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Kerala</td>\n",
       "      <td>4458</td>\n",
       "      <td>4438</td>\n",
       "      <td>34</td>\n",
       "      <td>8930</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>10.8505</td>\n",
       "      <td>76.2711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ladakh</td>\n",
       "      <td>146</td>\n",
       "      <td>946</td>\n",
       "      <td>1</td>\n",
       "      <td>1093</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>34.2996</td>\n",
       "      <td>78.2932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Madhya Pradesh</td>\n",
       "      <td>4757</td>\n",
       "      <td>13575</td>\n",
       "      <td>673</td>\n",
       "      <td>19005</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>22.9734</td>\n",
       "      <td>78.6569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>107963</td>\n",
       "      <td>149007</td>\n",
       "      <td>10695</td>\n",
       "      <td>267665</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>19.7515</td>\n",
       "      <td>75.7139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Manipur</td>\n",
       "      <td>702</td>\n",
       "      <td>970</td>\n",
       "      <td>0</td>\n",
       "      <td>1672</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>24.6637</td>\n",
       "      <td>93.9063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Meghalaya</td>\n",
       "      <td>250</td>\n",
       "      <td>66</td>\n",
       "      <td>2</td>\n",
       "      <td>318</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>25.4670</td>\n",
       "      <td>91.3662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Mizoram</td>\n",
       "      <td>79</td>\n",
       "      <td>159</td>\n",
       "      <td>0</td>\n",
       "      <td>238</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>23.1645</td>\n",
       "      <td>92.9376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Nagaland</td>\n",
       "      <td>550</td>\n",
       "      <td>346</td>\n",
       "      <td>0</td>\n",
       "      <td>896</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>26.1584</td>\n",
       "      <td>94.5624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Odisha</td>\n",
       "      <td>4342</td>\n",
       "      <td>9864</td>\n",
       "      <td>74</td>\n",
       "      <td>14280</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>20.9517</td>\n",
       "      <td>85.0985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Puducherry</td>\n",
       "      <td>684</td>\n",
       "      <td>829</td>\n",
       "      <td>18</td>\n",
       "      <td>1531</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>11.9416</td>\n",
       "      <td>79.8083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Punjab</td>\n",
       "      <td>2635</td>\n",
       "      <td>5663</td>\n",
       "      <td>213</td>\n",
       "      <td>8511</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>31.1471</td>\n",
       "      <td>75.3412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Rajasthan</td>\n",
       "      <td>5885</td>\n",
       "      <td>19161</td>\n",
       "      <td>525</td>\n",
       "      <td>25571</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>27.0238</td>\n",
       "      <td>74.2179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Sikkim</td>\n",
       "      <td>122</td>\n",
       "      <td>87</td>\n",
       "      <td>0</td>\n",
       "      <td>209</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>27.5330</td>\n",
       "      <td>88.5122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>47915</td>\n",
       "      <td>97310</td>\n",
       "      <td>2099</td>\n",
       "      <td>147324</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>11.1271</td>\n",
       "      <td>78.6569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Telangana</td>\n",
       "      <td>12530</td>\n",
       "      <td>24840</td>\n",
       "      <td>375</td>\n",
       "      <td>37745</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>18.1124</td>\n",
       "      <td>79.0193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Tripura</td>\n",
       "      <td>630</td>\n",
       "      <td>1538</td>\n",
       "      <td>2</td>\n",
       "      <td>2170</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>23.9408</td>\n",
       "      <td>91.9882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Uttarakhand</td>\n",
       "      <td>769</td>\n",
       "      <td>2867</td>\n",
       "      <td>50</td>\n",
       "      <td>3686</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>30.0668</td>\n",
       "      <td>79.0193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>13758</td>\n",
       "      <td>24983</td>\n",
       "      <td>983</td>\n",
       "      <td>39724</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>26.8467</td>\n",
       "      <td>80.9462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>11927</td>\n",
       "      <td>19931</td>\n",
       "      <td>980</td>\n",
       "      <td>32838</td>\n",
       "      <td>2020-07-15</td>\n",
       "      <td>22.9868</td>\n",
       "      <td>87.8550</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Name of State / UT Active Cases*  \\\n",
       "0                Andaman and Nicobar Islands            57   \n",
       "1                             Andhra Pradesh         15144   \n",
       "2                          Arunachal Pradesh           306   \n",
       "3                                      Assam          6351   \n",
       "4                                      Bihar          6261   \n",
       "5                                 Chandigarh           144   \n",
       "6                               Chhattisgarh          1084   \n",
       "7   Dadra and Nagar Haveli and Daman and Diu           209   \n",
       "8                                      Delhi         18664   \n",
       "9                                        Goa          1128   \n",
       "10                                   Gujarat         11065   \n",
       "11                                   Haryana          5226   \n",
       "12                          Himachal Pradesh           347   \n",
       "13                         Jammu and Kashmir          4755   \n",
       "14                                 Jharkhand          1628   \n",
       "15                                 Karnataka         25845   \n",
       "16                                    Kerala          4458   \n",
       "17                                    Ladakh           146   \n",
       "18                            Madhya Pradesh          4757   \n",
       "19                               Maharashtra        107963   \n",
       "20                                   Manipur           702   \n",
       "21                                 Meghalaya           250   \n",
       "22                                   Mizoram            79   \n",
       "23                                  Nagaland           550   \n",
       "24                                    Odisha          4342   \n",
       "25                                Puducherry           684   \n",
       "26                                    Punjab          2635   \n",
       "27                                 Rajasthan          5885   \n",
       "28                                    Sikkim           122   \n",
       "29                                Tamil Nadu         47915   \n",
       "30                                 Telangana         12530   \n",
       "31                                   Tripura           630   \n",
       "32                               Uttarakhand           769   \n",
       "33                             Uttar Pradesh         13758   \n",
       "34                               West Bengal         11927   \n",
       "\n",
       "   Cured/Discharged/Migrated* Deaths** Total Confirmed cases*       Date  \\\n",
       "0                         109        0                    166 2020-07-15   \n",
       "1                       17467      408                  33019 2020-07-15   \n",
       "2                         153        3                    462 2020-07-15   \n",
       "3                       11416       40                  17807 2020-07-15   \n",
       "4                       12849      174                  19284 2020-07-15   \n",
       "5                         446       10                    600 2020-07-15   \n",
       "6                        3275       20                   4379 2020-07-15   \n",
       "7                         310        1                    520 2020-07-15   \n",
       "8                       93236     3446                 115346 2020-07-15   \n",
       "9                        1607       18                   2753 2020-07-15   \n",
       "10                      30503     2069                  43637 2020-07-15   \n",
       "11                      17090      312                  22628 2020-07-15   \n",
       "12                        951       11                   1309 2020-07-15   \n",
       "13                       6223      195                  11173 2020-07-15   \n",
       "14                       2427       36                   4091 2020-07-15   \n",
       "15                      17390      842                  44077 2020-07-15   \n",
       "16                       4438       34                   8930 2020-07-15   \n",
       "17                        946        1                   1093 2020-07-15   \n",
       "18                      13575      673                  19005 2020-07-15   \n",
       "19                     149007    10695                 267665 2020-07-15   \n",
       "20                        970        0                   1672 2020-07-15   \n",
       "21                         66        2                    318 2020-07-15   \n",
       "22                        159        0                    238 2020-07-15   \n",
       "23                        346        0                    896 2020-07-15   \n",
       "24                       9864       74                  14280 2020-07-15   \n",
       "25                        829       18                   1531 2020-07-15   \n",
       "26                       5663      213                   8511 2020-07-15   \n",
       "27                      19161      525                  25571 2020-07-15   \n",
       "28                         87        0                    209 2020-07-15   \n",
       "29                      97310     2099                 147324 2020-07-15   \n",
       "30                      24840      375                  37745 2020-07-15   \n",
       "31                       1538        2                   2170 2020-07-15   \n",
       "32                       2867       50                   3686 2020-07-15   \n",
       "33                      24983      983                  39724 2020-07-15   \n",
       "34                      19931      980                  32838 2020-07-15   \n",
       "\n",
       "    Latitude  Longitude  \n",
       "0    11.7401    92.6586  \n",
       "1    15.9129    79.7400  \n",
       "2    28.2180    94.7278  \n",
       "3    26.2006    92.9376  \n",
       "4    25.0961    85.3131  \n",
       "5    30.7333    76.7794  \n",
       "6    21.2787    81.8661  \n",
       "7    20.1809    73.0169  \n",
       "8    28.7041    77.1025  \n",
       "9    15.2993    74.1240  \n",
       "10   22.2587    71.1924  \n",
       "11   29.0588    76.0856  \n",
       "12   31.1048    77.1734  \n",
       "13   33.7782    76.5762  \n",
       "14   23.6102    85.2799  \n",
       "15   15.3173    75.7139  \n",
       "16   10.8505    76.2711  \n",
       "17   34.2996    78.2932  \n",
       "18   22.9734    78.6569  \n",
       "19   19.7515    75.7139  \n",
       "20   24.6637    93.9063  \n",
       "21   25.4670    91.3662  \n",
       "22   23.1645    92.9376  \n",
       "23   26.1584    94.5624  \n",
       "24   20.9517    85.0985  \n",
       "25   11.9416    79.8083  \n",
       "26   31.1471    75.3412  \n",
       "27   27.0238    74.2179  \n",
       "28   27.5330    88.5122  \n",
       "29   11.1271    78.6569  \n",
       "30   18.1124    79.0193  \n",
       "31   23.9408    91.9882  \n",
       "32   30.0668    79.0193  \n",
       "33   26.8467    80.9462  \n",
       "34   22.9868    87.8550  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# saving data\n",
    "# ===========\n",
    "\n",
    "# file names as year-month-day.csv format\n",
    "file_name = now.strftime(\"%Y_%m_%d\")+'.csv'\n",
    "\n",
    "# location for saving the file\n",
    "file_loc = 'D:\\\\Projects\\\\Sentiment\\\\final_proj\\\\data\\\\.day_by_day_data'\n",
    "\n",
    "# save file as a scv file\n",
    "df_bs.to_csv(file_loc + file_name, index=False)\n",
    "\n",
    "df_bs.head(36)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# column names \n",
    "# df_bs.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Combining data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "No objects to concatenate",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-38-754c0a505434>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     44\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     45\u001b[0m \u001b[1;31m# concat dataframes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 46\u001b[1;33m \u001b[0mcomplete_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdfs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mignore_index\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Date'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mascending\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     47\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     48\u001b[0m \u001b[1;31m# get just numbers\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Python\\Python37\\lib\\site-packages\\pandas\\core\\reshape\\concat.py\u001b[0m in \u001b[0;36mconcat\u001b[1;34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001b[0m\n\u001b[0;32m    279\u001b[0m         \u001b[0mverify_integrity\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mverify_integrity\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    280\u001b[0m         \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 281\u001b[1;33m         \u001b[0msort\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    282\u001b[0m     )\n\u001b[0;32m    283\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Python\\Python37\\lib\\site-packages\\pandas\\core\\reshape\\concat.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001b[0m\n\u001b[0;32m    327\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    328\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobjs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 329\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"No objects to concatenate\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    330\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    331\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mkeys\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: No objects to concatenate"
     ]
    }
   ],
   "source": [
    "# location of the file\n",
    "loc = \"D:\\\\Projects\\\\Sentiment\\\\final_proj\\\\.day_by_day_data\"\n",
    "\n",
    "# list of all files\n",
    "files = glob.glob(loc+'2020*.csv')\n",
    "   \n",
    "# container for each day's data's dataframe\n",
    "dfs = []\n",
    "\n",
    "# loop through the files and append to the dfs list\n",
    "for i in files:\n",
    "    # read data\n",
    "    df_temp = pd.read_csv(i)\n",
    "    \n",
    "    # rename columns\n",
    "    \n",
    "    try:\n",
    "        df_temp = df_temp.drop(['Total Confirmed cases (Indian National)', \n",
    "                                'Total Confirmed cases ( Foreign National )'], axis=1)\n",
    "    except:\n",
    "        pass\n",
    "        \n",
    "    d = {'^Cured.*': 'Cured/Discharged/Migrated', \n",
    "         'Total Confirmed cases.*': 'Total Confirmed cases', \n",
    "         'Death.*': 'Death'}\n",
    "    \n",
    "    df_temp.columns = df_temp.columns.to_series().replace(d, regex=True)\n",
    "\n",
    "\n",
    "#     df_temp = df_temp.rename(columns={'Cured':'Cured/Discharged'})\n",
    "#     df_temp = df_temp.rename(columns={'Cured/Discharged':'Cured/Discharged/Migrated', \n",
    "#                                       'Total Confirmed cases *': 'Total Confirmed cases', \n",
    "#                                       'Total Confirmed cases ': 'Total Confirmed cases', \n",
    "#                                       'Total Confirmed cases* ': 'Total Confirmed cases'})\n",
    "#     df_temp = df_temp.rename(columns=lambda x: re.sub('Total Confirmed cases \\(Including .* foreign Nationals\\) ',\n",
    "#                                                       'Total Confirmed cases',x))\n",
    "#     df_temp = df_temp.rename(columns=lambda x: re.sub(\"Death.*\", \"Death\", x))\n",
    "\n",
    "    \n",
    "    # append to the df_s\n",
    "    dfs.append(df_temp)\n",
    "    \n",
    "# print(dfs)\n",
    "\n",
    "# concat dataframes\n",
    "complete_data = pd.concat(dfs, ignore_index=True).sort_values(['Date'], ascending=True).reset_index(drop=True)\n",
    "\n",
    "# get just numbers\n",
    "complete_data['Death'] = complete_data['Death'].astype('str').str.extract('(\\d+)').astype('int')\n",
    "\n",
    "# few sample rows\n",
    "complete_data[70:105]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# complete_data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fix datatype\n",
    "complete_data['Date'] = pd.to_datetime(complete_data['Date'])\n",
    "\n",
    "# sort rows\n",
    "complete_data = complete_data.sort_values(['Date', 'Name of State / UT']).reset_index(drop=True)\n",
    "\n",
    "# fill missing values with 0\n",
    "cols = ['Cured/Discharged/Migrated', 'Death']\n",
    "complete_data[cols] = complete_data[cols].fillna(0).astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rename state/UT names\n",
    "complete_data['Name of State / UT'].replace('Chattisgarh', 'Chhattisgarh', inplace=True)\n",
    "complete_data['Name of State / UT'].replace('Pondicherry', 'Puducherry', inplace=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select only rows with more than 1 case\n",
    "complete_data = complete_data[complete_data['Total Confirmed cases']>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop extra columns\n",
    "# complete_data = complete_data.drop(['Active Cases*'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "complete_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rearrange columns\n",
    "complete_data = complete_data[['Date', 'Name of State / UT', 'Active Cases*', 'Latitude', 'Longitude', \n",
    "                               'Total Confirmed cases', 'Death', 'Cured/Discharged/Migrated']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# New cases\n",
    "# =========\n",
    "\n",
    "# temp dataset\n",
    "temp = complete_data.groupby(['Name of State / UT', 'Date', ])['Total Confirmed cases', 'Death', 'Cured/Discharged/Migrated']\n",
    "temp = temp.sum().diff().reset_index()\n",
    "\n",
    "mask = temp['Name of State / UT'] != temp['Name of State / UT'].shift(1)\n",
    "\n",
    "temp.loc[mask, 'Total Confirmed cases'] = np.nan\n",
    "temp.loc[mask, 'Death'] = np.nan\n",
    "temp.loc[mask, 'Cured/Discharged/Migrated'] = np.nan\n",
    "\n",
    "temp = temp[['Date', 'Name of State / UT', 'Total Confirmed cases', 'Death', 'Cured/Discharged/Migrated']]\n",
    "temp.columns = ['Date', 'Name of State / UT', 'New cases', 'New deaths', 'New recovered']\n",
    "\n",
    "# merging new values\n",
    "complete_data = pd.merge(complete_data, temp, on=['Name of State / UT', 'Date'])\n",
    "\n",
    "# filling na with 0\n",
    "complete_data = complete_data.fillna(0)\n",
    "\n",
    "# fixing data types\n",
    "cols = ['New cases', 'New deaths', 'New recovered']\n",
    "complete_data[cols] = complete_data[cols].astype('int')\n",
    "\n",
    "# remove negative values\n",
    "complete_data['New cases'] = complete_data['New cases'].apply(lambda x: 0 if x<0 else x)\n",
    "\n",
    "# final data\n",
    "complete_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "complete_data['Name of State / UT'] = complete_data['Name of State / UT'].replace('Dadar Nagar Haveli', 'Dadra and Nagar Haveli and Daman and Diu')\n",
    "complete_data['Name of State / UT'] = complete_data['Name of State / UT'].replace('Daman & Diu', 'Dadra and Nagar Haveli and Daman and Diu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted(complete_data['Name of State / UT'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# random rows\n",
    "complete_data.sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# complete data info\n",
    "complete_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save as .csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'complete_data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-23-6fbe7fff87aa>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# save data in a csv file\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mcomplete_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'data/complete.csv'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'complete_data' is not defined"
     ]
    }
   ],
   "source": [
    "# save data in a csv file\n",
    "complete_data.to_csv('data/complete.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'complete_data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-24-dfd01cb15cf7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcomplete_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Date'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'complete_data' is not defined"
     ]
    }
   ],
   "source": [
    "complete_data.groupby('Date').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'complete_data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-22-1dc2e876412d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcomplete_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Death'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mascending\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'complete_data' is not defined"
     ]
    }
   ],
   "source": [
    "complete_data.sort_values('Death', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'complete_data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-20-da4e1f3d0352>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcomplete_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcomplete_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Date'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m==\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcomplete_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Date'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'complete_data' is not defined"
     ]
    }
   ],
   "source": [
    "df = complete_data[complete_data['Date']==max(complete_data['Date'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-19-9143b9a5d82d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'data/latest.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    }
   ],
   "source": [
    "df = df.to_csv('data/latest.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Date</th>\n",
       "      <th>Name of State / UT</th>\n",
       "      <th>Active Cases*</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Total Confirmed cases</th>\n",
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       "      <th>New recovered</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>57</td>\n",
       "      <td>11.7401</td>\n",
       "      <td>92.6586</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>14274</td>\n",
       "      <td>15.9129</td>\n",
       "      <td>79.7400</td>\n",
       "      <td>31103</td>\n",
       "      <td>365</td>\n",
       "      <td>16464</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>240</td>\n",
       "      <td>28.2180</td>\n",
       "      <td>94.7278</td>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>145</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Assam</td>\n",
       "      <td>5876</td>\n",
       "      <td>26.2006</td>\n",
       "      <td>92.9376</td>\n",
       "      <td>16806</td>\n",
       "      <td>36</td>\n",
       "      <td>10894</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Bihar</td>\n",
       "      <td>5482</td>\n",
       "      <td>25.0961</td>\n",
       "      <td>85.3131</td>\n",
       "      <td>17959</td>\n",
       "      <td>160</td>\n",
       "      <td>12317</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0        Date           Name of State / UT  Active Cases*  \\\n",
       "0           0  2020-07-14  Andaman and Nicobar Islands             57   \n",
       "1           1  2020-07-14               Andhra Pradesh          14274   \n",
       "2           2  2020-07-14            Arunachal Pradesh            240   \n",
       "3           3  2020-07-14                        Assam           5876   \n",
       "4           4  2020-07-14                        Bihar           5482   \n",
       "\n",
       "   Latitude  Longitude  Total Confirmed cases  Death  \\\n",
       "0   11.7401    92.6586                    166      0   \n",
       "1   15.9129    79.7400                  31103    365   \n",
       "2   28.2180    94.7278                    387      2   \n",
       "3   26.2006    92.9376                  16806     36   \n",
       "4   25.0961    85.3131                  17959    160   \n",
       "\n",
       "   Cured/Discharged/Migrated  New cases  New deaths  New recovered  \n",
       "0                        109          0           0              0  \n",
       "1                      16464          0           0              0  \n",
       "2                        145          0           0              0  \n",
       "3                      10894          0           0              0  \n",
       "4                      12317          0           0              0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_csv('data/latest.csv')\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df2.drop(['Unnamed: 0', 'Latitude', 'Longitude'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.to_csv('data/final.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>Name of State / UT</th>\n",
       "      <th>Active Cases*</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>57</td>\n",
       "      <td>166</td>\n",
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       "      <th>1</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>14274</td>\n",
       "      <td>31103</td>\n",
       "      <td>365</td>\n",
       "      <td>16464</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>240</td>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>145</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Assam</td>\n",
       "      <td>5876</td>\n",
       "      <td>16806</td>\n",
       "      <td>36</td>\n",
       "      <td>10894</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Bihar</td>\n",
       "      <td>5482</td>\n",
       "      <td>17959</td>\n",
       "      <td>160</td>\n",
       "      <td>12317</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>157</td>\n",
       "      <td>588</td>\n",
       "      <td>8</td>\n",
       "      <td>423</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Chhattisgarh</td>\n",
       "      <td>996</td>\n",
       "      <td>4217</td>\n",
       "      <td>19</td>\n",
       "      <td>3202</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Dadra and Nagar Haveli and Daman and Diu</td>\n",
       "      <td>226</td>\n",
       "      <td>495</td>\n",
       "      <td>1</td>\n",
       "      <td>268</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>19017</td>\n",
       "      <td>113740</td>\n",
       "      <td>3411</td>\n",
       "      <td>91312</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Goa</td>\n",
       "      <td>1026</td>\n",
       "      <td>2583</td>\n",
       "      <td>17</td>\n",
       "      <td>1540</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Gujarat</td>\n",
       "      <td>10897</td>\n",
       "      <td>42722</td>\n",
       "      <td>2055</td>\n",
       "      <td>29770</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>4984</td>\n",
       "      <td>21894</td>\n",
       "      <td>308</td>\n",
       "      <td>16602</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Himachal Pradesh</td>\n",
       "      <td>292</td>\n",
       "      <td>1243</td>\n",
       "      <td>11</td>\n",
       "      <td>940</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Jammu and Kashmir</td>\n",
       "      <td>4545</td>\n",
       "      <td>10827</td>\n",
       "      <td>187</td>\n",
       "      <td>6095</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Jharkhand</td>\n",
       "      <td>1514</td>\n",
       "      <td>3898</td>\n",
       "      <td>33</td>\n",
       "      <td>2351</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>24576</td>\n",
       "      <td>41581</td>\n",
       "      <td>757</td>\n",
       "      <td>16248</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>4032</td>\n",
       "      <td>8322</td>\n",
       "      <td>33</td>\n",
       "      <td>4257</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Ladakh</td>\n",
       "      <td>146</td>\n",
       "      <td>1093</td>\n",
       "      <td>1</td>\n",
       "      <td>946</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Madhya Pradesh</td>\n",
       "      <td>4336</td>\n",
       "      <td>18207</td>\n",
       "      <td>663</td>\n",
       "      <td>13208</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>105935</td>\n",
       "      <td>260924</td>\n",
       "      <td>10482</td>\n",
       "      <td>144507</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Manipur</td>\n",
       "      <td>656</td>\n",
       "      <td>1626</td>\n",
       "      <td>0</td>\n",
       "      <td>970</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Meghalaya</td>\n",
       "      <td>250</td>\n",
       "      <td>318</td>\n",
       "      <td>2</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Mizoram</td>\n",
       "      <td>82</td>\n",
       "      <td>233</td>\n",
       "      <td>0</td>\n",
       "      <td>151</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Nagaland</td>\n",
       "      <td>505</td>\n",
       "      <td>845</td>\n",
       "      <td>0</td>\n",
       "      <td>340</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Odisha</td>\n",
       "      <td>4412</td>\n",
       "      <td>13737</td>\n",
       "      <td>70</td>\n",
       "      <td>9255</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Puducherry</td>\n",
       "      <td>665</td>\n",
       "      <td>1468</td>\n",
       "      <td>18</td>\n",
       "      <td>785</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Punjab</td>\n",
       "      <td>2388</td>\n",
       "      <td>8178</td>\n",
       "      <td>204</td>\n",
       "      <td>5586</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Rajasthan</td>\n",
       "      <td>5781</td>\n",
       "      <td>24936</td>\n",
       "      <td>525</td>\n",
       "      <td>18630</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Sikkim</td>\n",
       "      <td>106</td>\n",
       "      <td>192</td>\n",
       "      <td>0</td>\n",
       "      <td>86</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>48199</td>\n",
       "      <td>142798</td>\n",
       "      <td>2032</td>\n",
       "      <td>92567</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Telangana</td>\n",
       "      <td>12177</td>\n",
       "      <td>36221</td>\n",
       "      <td>365</td>\n",
       "      <td>23679</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Tripura</td>\n",
       "      <td>603</td>\n",
       "      <td>2080</td>\n",
       "      <td>2</td>\n",
       "      <td>1475</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>12972</td>\n",
       "      <td>38130</td>\n",
       "      <td>955</td>\n",
       "      <td>24203</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Uttarakhand</td>\n",
       "      <td>703</td>\n",
       "      <td>3608</td>\n",
       "      <td>49</td>\n",
       "      <td>2856</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>West Bengal</td>\n",
       "      <td>11279</td>\n",
       "      <td>31448</td>\n",
       "      <td>956</td>\n",
       "      <td>19213</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date                        Name of State / UT  Active Cases*  \\\n",
       "0   2020-07-14               Andaman and Nicobar Islands             57   \n",
       "1   2020-07-14                            Andhra Pradesh          14274   \n",
       "2   2020-07-14                         Arunachal Pradesh            240   \n",
       "3   2020-07-14                                     Assam           5876   \n",
       "4   2020-07-14                                     Bihar           5482   \n",
       "5   2020-07-14                                Chandigarh            157   \n",
       "6   2020-07-14                              Chhattisgarh            996   \n",
       "7   2020-07-14  Dadra and Nagar Haveli and Daman and Diu            226   \n",
       "8   2020-07-14                                     Delhi          19017   \n",
       "9   2020-07-14                                       Goa           1026   \n",
       "10  2020-07-14                                   Gujarat          10897   \n",
       "11  2020-07-14                                   Haryana           4984   \n",
       "12  2020-07-14                          Himachal Pradesh            292   \n",
       "13  2020-07-14                         Jammu and Kashmir           4545   \n",
       "14  2020-07-14                                 Jharkhand           1514   \n",
       "15  2020-07-14                                 Karnataka          24576   \n",
       "16  2020-07-14                                    Kerala           4032   \n",
       "17  2020-07-14                                    Ladakh            146   \n",
       "18  2020-07-14                            Madhya Pradesh           4336   \n",
       "19  2020-07-14                               Maharashtra         105935   \n",
       "20  2020-07-14                                   Manipur            656   \n",
       "21  2020-07-14                                 Meghalaya            250   \n",
       "22  2020-07-14                                   Mizoram             82   \n",
       "23  2020-07-14                                  Nagaland            505   \n",
       "24  2020-07-14                                    Odisha           4412   \n",
       "25  2020-07-14                                Puducherry            665   \n",
       "26  2020-07-14                                    Punjab           2388   \n",
       "27  2020-07-14                                 Rajasthan           5781   \n",
       "28  2020-07-14                                    Sikkim            106   \n",
       "29  2020-07-14                                Tamil Nadu          48199   \n",
       "30  2020-07-14                                 Telangana          12177   \n",
       "31  2020-07-14                                   Tripura            603   \n",
       "32  2020-07-14                             Uttar Pradesh          12972   \n",
       "33  2020-07-14                               Uttarakhand            703   \n",
       "34  2020-07-14                               West Bengal          11279   \n",
       "\n",
       "    Total Confirmed cases  Death  Cured/Discharged/Migrated  New cases  \\\n",
       "0                     166      0                        109          0   \n",
       "1                   31103    365                      16464          0   \n",
       "2                     387      2                        145          0   \n",
       "3                   16806     36                      10894          0   \n",
       "4                   17959    160                      12317          0   \n",
       "5                     588      8                        423          0   \n",
       "6                    4217     19                       3202          0   \n",
       "7                     495      1                        268          0   \n",
       "8                  113740   3411                      91312          0   \n",
       "9                    2583     17                       1540          0   \n",
       "10                  42722   2055                      29770          0   \n",
       "11                  21894    308                      16602          0   \n",
       "12                   1243     11                        940          0   \n",
       "13                  10827    187                       6095          0   \n",
       "14                   3898     33                       2351          0   \n",
       "15                  41581    757                      16248          0   \n",
       "16                   8322     33                       4257          0   \n",
       "17                   1093      1                        946          0   \n",
       "18                  18207    663                      13208          0   \n",
       "19                 260924  10482                     144507          0   \n",
       "20                   1626      0                        970          0   \n",
       "21                    318      2                         66          0   \n",
       "22                    233      0                        151          0   \n",
       "23                    845      0                        340          0   \n",
       "24                  13737     70                       9255          0   \n",
       "25                   1468     18                        785          0   \n",
       "26                   8178    204                       5586          0   \n",
       "27                  24936    525                      18630          0   \n",
       "28                    192      0                         86          0   \n",
       "29                 142798   2032                      92567          0   \n",
       "30                  36221    365                      23679          0   \n",
       "31                   2080      2                       1475          0   \n",
       "32                  38130    955                      24203          0   \n",
       "33                   3608     49                       2856          0   \n",
       "34                  31448    956                      19213          0   \n",
       "\n",
       "    New deaths  New recovered  \n",
       "0            0              0  \n",
       "1            0              0  \n",
       "2            0              0  \n",
       "3            0              0  \n",
       "4            0              0  \n",
       "5            0              0  \n",
       "6            0              0  \n",
       "7            0              0  \n",
       "8            0              0  \n",
       "9            0              0  \n",
       "10           0              0  \n",
       "11           0              0  \n",
       "12           0              0  \n",
       "13           0              0  \n",
       "14           0              0  \n",
       "15           0              0  \n",
       "16           0              0  \n",
       "17           0              0  \n",
       "18           0              0  \n",
       "19           0              0  \n",
       "20           0              0  \n",
       "21           0              0  \n",
       "22           0              0  \n",
       "23           0              0  \n",
       "24           0              0  \n",
       "25           0              0  \n",
       "26           0              0  \n",
       "27           0              0  \n",
       "28           0              0  \n",
       "29           0              0  \n",
       "30           0              0  \n",
       "31           0              0  \n",
       "32           0              0  \n",
       "33           0              0  \n",
       "34           0              0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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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>Name of State / UT</th>\n",
       "      <th>Active Cases*</th>\n",
       "      <th>Total Confirmed cases</th>\n",
       "      <th>Death</th>\n",
       "      <th>Cured/Discharged/Migrated</th>\n",
       "      <th>New cases</th>\n",
       "      <th>New deaths</th>\n",
       "      <th>New recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Andaman and Nicobar Islands</td>\n",
       "      <td>57</td>\n",
       "      <td>166</td>\n",
       "      <td>0</td>\n",
       "      <td>109</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>14274</td>\n",
       "      <td>31103</td>\n",
       "      <td>365</td>\n",
       "      <td>16464</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>240</td>\n",
       "      <td>387</td>\n",
       "      <td>2</td>\n",
       "      <td>145</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Assam</td>\n",
       "      <td>5876</td>\n",
       "      <td>16806</td>\n",
       "      <td>36</td>\n",
       "      <td>10894</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>Bihar</td>\n",
       "      <td>5482</td>\n",
       "      <td>17959</td>\n",
       "      <td>160</td>\n",
       "      <td>12317</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date           Name of State / UT  Active Cases*  \\\n",
       "0  2020-07-14  Andaman and Nicobar Islands             57   \n",
       "1  2020-07-14               Andhra Pradesh          14274   \n",
       "2  2020-07-14            Arunachal Pradesh            240   \n",
       "3  2020-07-14                        Assam           5876   \n",
       "4  2020-07-14                        Bihar           5482   \n",
       "\n",
       "   Total Confirmed cases  Death  Cured/Discharged/Migrated  New cases  \\\n",
       "0                    166      0                        109          0   \n",
       "1                  31103    365                      16464          0   \n",
       "2                    387      2                        145          0   \n",
       "3                  16806     36                      10894          0   \n",
       "4                  17959    160                      12317          0   \n",
       "\n",
       "   New deaths  New recovered  \n",
       "0           0              0  \n",
       "1           0              0  \n",
       "2           0              0  \n",
       "3           0              0  \n",
       "4           0              0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df3 = pd.read_csv(\"data/final.csv\")\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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