Data_scraping_and_processing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COVID-19 DATASET GENERATION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Acknowledgement\n",
    "\n",
    "<p style=\"font-size:16px; text-align:justify;\">Thank you Center for Systems Science and Engineering (CSSE), Johns Hopkins University for open sourcing the updated and compiled global COVID-19 data repository consisting of the total number of confirmed cases, total number of deaths and total number of recovered cases.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import wget"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fetch dataset from <a href=\"https://github.com/CSSEGISandData/COVID-19\">source</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "urls = ['https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv', \n",
    "        'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv',\n",
    "        'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100% [..............................................................................] 77288 / 77288"
     ]
    }
   ],
   "source": [
    "for url in urls:\n",
    "    filename = wget.download(url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CSV to Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "confirmed_df_raw = pd.read_csv('time_series_covid19_confirmed_global.csv')\n",
    "deaths_df_raw = pd.read_csv('time_series_covid19_deaths_global.csv')\n",
    "recovered_df_raw = pd.read_csv('time_series_covid19_recovered_global.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "  Province/State Country/Region      Lat     Long  1/22/20  1/23/20  1/24/20  \\\n",
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       "  Province/State Country/Region      Lat     Long  1/22/20  1/23/20  1/24/20  \\\n",
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  {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>403</td>\n",
       "      <td>410</td>\n",
       "      <td>422</td>\n",
       "      <td>431</td>\n",
       "      <td>455</td>\n",
       "      <td>470</td>\n",
       "      <td>488</td>\n",
       "      <td>519</td>\n",
       "      <td>531</td>\n",
       "      <td>543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.0339</td>\n",
       "      <td>1.6596</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1479</td>\n",
       "      <td>1508</td>\n",
       "      <td>1558</td>\n",
       "      <td>1651</td>\n",
       "      <td>1702</td>\n",
       "      <td>1779</td>\n",
       "      <td>1821</td>\n",
       "      <td>1872</td>\n",
       "      <td>1936</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.5063</td>\n",
       "      <td>1.5218</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>344</td>\n",
       "      <td>344</td>\n",
       "      <td>385</td>\n",
       "      <td>398</td>\n",
       "      <td>423</td>\n",
       "      <td>468</td>\n",
       "      <td>468</td>\n",
       "      <td>472</td>\n",
       "      <td>493</td>\n",
       "      <td>499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.2027</td>\n",
       "      <td>17.8739</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 108 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Province/State Country/Region      Lat     Long  1/22/20  1/23/20  1/24/20  \\\n",
       "0            NaN    Afghanistan  33.0000  65.0000        0        0        0   \n",
       "1            NaN        Albania  41.1533  20.1683        0        0        0   \n",
       "2            NaN        Algeria  28.0339   1.6596        0        0        0   \n",
       "3            NaN        Andorra  42.5063   1.5218        0        0        0   \n",
       "4            NaN         Angola -11.2027  17.8739        0        0        0   \n",
       "\n",
       "   1/25/20  1/26/20  1/27/20  ...  4/25/20  4/26/20  4/27/20  4/28/20  \\\n",
       "0        0        0        0  ...      188      207      220      228   \n",
       "1        0        0        0  ...      403      410      422      431   \n",
       "2        0        0        0  ...     1479     1508     1558     1651   \n",
       "3        0        0        0  ...      344      344      385      398   \n",
       "4        0        0        0  ...        6        6        6        6   \n",
       "\n",
       "   4/29/20  4/30/20  5/1/20  5/2/20  5/3/20  5/4/20  \n",
       "0      252      260     310     331     345     397  \n",
       "1      455      470     488     519     531     543  \n",
       "2     1702     1779    1821    1872    1936    1998  \n",
       "3      423      468     468     472     493     499  \n",
       "4        7        7      11      11      11      11  \n",
       "\n",
       "[5 rows x 108 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recovered_df_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(266, 108)\n",
      "(266, 108)\n",
      "(252, 108)\n"
     ]
    }
   ],
   "source": [
    "print(confirmed_df_raw.shape)\n",
    "print(deaths_df_raw.shape)\n",
    "print(recovered_df_raw.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Province/State', 'Country/Region', 'Lat', 'Long', '1/22/20', '1/23/20',\n",
       "       '1/24/20', '1/25/20', '1/26/20', '1/27/20',\n",
       "       ...\n",
       "       '4/25/20', '4/26/20', '4/27/20', '4/28/20', '4/29/20', '4/30/20',\n",
       "       '5/1/20', '5/2/20', '5/3/20', '5/4/20'],\n",
       "      dtype='object', length=108)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confirmed_df_raw.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Province/State', 'Country/Region', 'Lat', 'Long', '1/22/20', '1/23/20',\n",
       "       '1/24/20', '1/25/20', '1/26/20', '1/27/20',\n",
       "       ...\n",
       "       '4/25/20', '4/26/20', '4/27/20', '4/28/20', '4/29/20', '4/30/20',\n",
       "       '5/1/20', '5/2/20', '5/3/20', '5/4/20'],\n",
       "      dtype='object', length=108)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deaths_df_raw.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Province/State', 'Country/Region', 'Lat', 'Long', '1/22/20', '1/23/20',\n",
       "       '1/24/20', '1/25/20', '1/26/20', '1/27/20',\n",
       "       ...\n",
       "       '4/25/20', '4/26/20', '4/27/20', '4/28/20', '4/29/20', '4/30/20',\n",
       "       '5/1/20', '5/2/20', '5/3/20', '5/4/20'],\n",
       "      dtype='object', length=108)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recovered_df_raw.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transforming Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = confirmed_df_raw.columns[4:]\n",
    "\n",
    "confirmed_df = confirmed_df_raw.melt(id_vars=['Province/State', 'Country/Region', 'Lat', 'Long'], \n",
    "                            value_vars=dates, var_name='Date', value_name='Confirmed')\n",
    "\n",
    "deaths_df = deaths_df_raw.melt(id_vars=['Province/State', 'Country/Region', 'Lat', 'Long'], \n",
    "                            value_vars=dates, var_name='Date', value_name='Deaths')\n",
    "\n",
    "recovered_df = recovered_df_raw.melt(id_vars=['Province/State', 'Country/Region', 'Lat', 'Long'], \n",
    "                            value_vars=dates, var_name='Date', value_name='Recovered')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Province/State', 'Country/Region', 'Lat', 'Long', 'Date', 'Confirmed'], dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confirmed_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(27664, 6)\n",
      "(27664, 6)\n",
      "(26208, 6)\n"
     ]
    }
   ],
   "source": [
    "print(confirmed_df.shape)\n",
    "print(deaths_df.shape)\n",
    "print(recovered_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "confirmed_df.rename(columns={'Date': 'date', \n",
    "                     'Province/State':'state',\n",
    "                     'Country/Region':'country',\n",
    "                     'Lat':'lat', 'Long':'long',\n",
    "                     'Confirmed': 'confirmed'\n",
    "                    }, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "deaths_df.rename(columns={'Date': 'date', \n",
    "                     'Province/State':'state',\n",
    "                     'Country/Region':'country',\n",
    "                     'Lat':'lat', 'Long':'long',\n",
    "                     'Deaths':'deaths'\n",
    "                    }, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "recovered_df.rename(columns={'Date': 'date', \n",
    "                     'Province/State':'state',\n",
    "                     'Country/Region':'country',\n",
    "                     'Lat':'lat', 'Long':'long',\n",
    "                     'Recovered':'recovered'\n",
    "                    }, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "confirmed_df['country'] = confirmed_df['country'].replace('Korea, South', 'South Korea')\n",
    "deaths_df['country'] = deaths_df['country'].replace('Korea, South', 'South Korea')\n",
    "recovered_df['country'] = recovered_df['country'].replace('Korea, South', 'South Korea')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state        19136\n",
       "country          0\n",
       "lat              0\n",
       "long             0\n",
       "date             0\n",
       "confirmed        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confirmed_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state      19136\n",
       "country        0\n",
       "lat            0\n",
       "long           0\n",
       "date           0\n",
       "deaths         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deaths_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state        19240\n",
       "country          0\n",
       "lat              0\n",
       "long             0\n",
       "date             0\n",
       "recovered        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recovered_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>date</th>\n",
       "      <th>confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>41.1533</td>\n",
       "      <td>20.1683</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.0339</td>\n",
       "      <td>1.6596</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.5063</td>\n",
       "      <td>1.5218</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.2027</td>\n",
       "      <td>17.8739</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state      country      lat     long     date  confirmed\n",
       "0   NaN  Afghanistan  33.0000  65.0000  1/22/20          0\n",
       "1   NaN      Albania  41.1533  20.1683  1/22/20          0\n",
       "2   NaN      Algeria  28.0339   1.6596  1/22/20          0\n",
       "3   NaN      Andorra  42.5063   1.5218  1/22/20          0\n",
       "4   NaN       Angola -11.2027  17.8739  1/22/20          0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confirmed_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 27664 entries, 0 to 27663\n",
      "Data columns (total 6 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   state      8528 non-null   object \n",
      " 1   country    27664 non-null  object \n",
      " 2   lat        27664 non-null  float64\n",
      " 3   long       27664 non-null  float64\n",
      " 4   date       27664 non-null  object \n",
      " 5   confirmed  27664 non-null  int64  \n",
      "dtypes: float64(2), int64(1), object(3)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "confirmed_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>date</th>\n",
       "      <th>deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>41.1533</td>\n",
       "      <td>20.1683</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.0339</td>\n",
       "      <td>1.6596</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.5063</td>\n",
       "      <td>1.5218</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.2027</td>\n",
       "      <td>17.8739</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state      country      lat     long     date  deaths\n",
       "0   NaN  Afghanistan  33.0000  65.0000  1/22/20       0\n",
       "1   NaN      Albania  41.1533  20.1683  1/22/20       0\n",
       "2   NaN      Algeria  28.0339   1.6596  1/22/20       0\n",
       "3   NaN      Andorra  42.5063   1.5218  1/22/20       0\n",
       "4   NaN       Angola -11.2027  17.8739  1/22/20       0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deaths_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 27664 entries, 0 to 27663\n",
      "Data columns (total 6 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   state    8528 non-null   object \n",
      " 1   country  27664 non-null  object \n",
      " 2   lat      27664 non-null  float64\n",
      " 3   long     27664 non-null  float64\n",
      " 4   date     27664 non-null  object \n",
      " 5   deaths   27664 non-null  int64  \n",
      "dtypes: float64(2), int64(1), object(3)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "deaths_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>date</th>\n",
       "      <th>recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>41.1533</td>\n",
       "      <td>20.1683</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.0339</td>\n",
       "      <td>1.6596</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.5063</td>\n",
       "      <td>1.5218</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.2027</td>\n",
       "      <td>17.8739</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state      country      lat     long     date  recovered\n",
       "0   NaN  Afghanistan  33.0000  65.0000  1/22/20          0\n",
       "1   NaN      Albania  41.1533  20.1683  1/22/20          0\n",
       "2   NaN      Algeria  28.0339   1.6596  1/22/20          0\n",
       "3   NaN      Andorra  42.5063   1.5218  1/22/20          0\n",
       "4   NaN       Angola -11.2027  17.8739  1/22/20          0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recovered_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 26208 entries, 0 to 26207\n",
      "Data columns (total 6 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   state      6968 non-null   object \n",
      " 1   country    26208 non-null  object \n",
      " 2   lat        26208 non-null  float64\n",
      " 3   long       26208 non-null  float64\n",
      " 4   date       26208 non-null  object \n",
      " 5   recovered  26208 non-null  int64  \n",
      "dtypes: float64(2), int64(1), object(3)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "recovered_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### !!! Fixing errata/missing data in separate dataframes !!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def change_sep_val(df_name , date, ref_col, val_col, dtnry):\n",
    "    '''\n",
    "        Example: Only for demonstration purpose\n",
    "        1. New values:\n",
    "        feb_12_conf = {'Hubei' : 34874}\n",
    "        2. Change values:\n",
    "        change_val(confirmed_df, '2/12/20', 'state', 'confirmed', feb_12_conf)\n",
    "        3. Check values:\n",
    "        confirmed_df[(confirmed_df['date']=='2/12/20') & (confirmed_df['state']=='Hubei')]\n",
    "    '''\n",
    "    for key, val in dtnry.items():\n",
    "        df_name.loc[(df_name['date']==date) & (df_name[ref_col]==key), val_col] = val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving separate dataframes as CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "confirmed_df.to_csv('covid_19_confirmed_global.csv', index=False)\n",
    "deaths_df.to_csv('covid_19_deaths_global.csv', index=False)\n",
    "recovered_df.to_csv('covid_19_recovered_global.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### !!! Reading separate datasets !!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "confirmed_df = pd.read_csv('covid_19_confirmed_global.csv')\n",
    "deaths_df = pd.read_csv('covid_19_deaths_global.csv')\n",
    "recovered_df = pd.read_csv('covid_19_recovered_global.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "confirmed_df_prune = confirmed_df.drop(['state', 'lat', 'long'], axis=1)\n",
    "deaths_df_prune = deaths_df.drop(['state', 'lat', 'long'], axis=1)\n",
    "recovered_df_prune = recovered_df.drop(['state', 'lat', 'long'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(27664, 3)\n",
      "(27664, 3)\n",
      "(26208, 3)\n"
     ]
    }
   ],
   "source": [
    "print(confirmed_df_prune.shape)\n",
    "print(deaths_df_prune.shape)\n",
    "print(recovered_df_prune.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "confirmed_df_merge=confirmed_df_prune.groupby(['date','country'])['confirmed'].apply(np.sum).reset_index()\n",
    "deaths_df_merge=deaths_df_prune.groupby(['date','country'])['deaths'].apply(np.sum).reset_index()\n",
    "recovered_df_merge=recovered_df_prune.groupby(['date','country'])['recovered'].apply(np.sum).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19448, 3)\n",
      "(19448, 3)\n",
      "(19448, 3)\n"
     ]
    }
   ],
   "source": [
    "print(confirmed_df_merge.shape)\n",
    "print(deaths_df_merge.shape)\n",
    "print(recovered_df_merge.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>country</th>\n",
       "      <th>confirmed</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/22/20</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/22/20</td>\n",
       "      <td>Albania</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/22/20</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/22/20</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/22/20</td>\n",
       "      <td>Angola</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19443</th>\n",
       "      <td>5/4/20</td>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19444</th>\n",
       "      <td>5/4/20</td>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19445</th>\n",
       "      <td>5/4/20</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19446</th>\n",
       "      <td>5/4/20</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19447</th>\n",
       "      <td>5/4/20</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19448 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date             country  confirmed\n",
       "0      1/22/20         Afghanistan          0\n",
       "1      1/22/20             Albania          0\n",
       "2      1/22/20             Algeria          0\n",
       "3      1/22/20             Andorra          0\n",
       "4      1/22/20              Angola          0\n",
       "...        ...                 ...        ...\n",
       "19443   5/4/20  West Bank and Gaza        362\n",
       "19444   5/4/20      Western Sahara          6\n",
       "19445   5/4/20               Yemen         12\n",
       "19446   5/4/20              Zambia        137\n",
       "19447   5/4/20            Zimbabwe         34\n",
       "\n",
       "[19448 rows x 3 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confirmed_df_merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>country</th>\n",
       "      <th>deaths</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/22/20</td>\n",
       "      <td>Afghanistan</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/22/20</td>\n",
       "      <td>Albania</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/22/20</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/22/20</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/22/20</td>\n",
       "      <td>Angola</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19443</th>\n",
       "      <td>5/4/20</td>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19444</th>\n",
       "      <td>5/4/20</td>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19445</th>\n",
       "      <td>5/4/20</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19446</th>\n",
       "      <td>5/4/20</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19447</th>\n",
       "      <td>5/4/20</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19448 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date             country  deaths\n",
       "0      1/22/20         Afghanistan       0\n",
       "1      1/22/20             Albania       0\n",
       "2      1/22/20             Algeria       0\n",
       "3      1/22/20             Andorra       0\n",
       "4      1/22/20              Angola       0\n",
       "...        ...                 ...     ...\n",
       "19443   5/4/20  West Bank and Gaza       2\n",
       "19444   5/4/20      Western Sahara       0\n",
       "19445   5/4/20               Yemen       2\n",
       "19446   5/4/20              Zambia       3\n",
       "19447   5/4/20            Zimbabwe       4\n",
       "\n",
       "[19448 rows x 3 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deaths_df_merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "      <th>recovered</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/22/20</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/22/20</td>\n",
       "      <td>Albania</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/22/20</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/22/20</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/22/20</td>\n",
       "      <td>Angola</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19443</th>\n",
       "      <td>5/4/20</td>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19444</th>\n",
       "      <td>5/4/20</td>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19445</th>\n",
       "      <td>5/4/20</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19446</th>\n",
       "      <td>5/4/20</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19447</th>\n",
       "      <td>5/4/20</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19448 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date             country  recovered\n",
       "0      1/22/20         Afghanistan          0\n",
       "1      1/22/20             Albania          0\n",
       "2      1/22/20             Algeria          0\n",
       "3      1/22/20             Andorra          0\n",
       "4      1/22/20              Angola          0\n",
       "...        ...                 ...        ...\n",
       "19443   5/4/20  West Bank and Gaza        102\n",
       "19444   5/4/20      Western Sahara          5\n",
       "19445   5/4/20               Yemen          1\n",
       "19446   5/4/20              Zambia         78\n",
       "19447   5/4/20            Zimbabwe          5\n",
       "\n",
       "[19448 rows x 3 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recovered_df_merge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating country-wise dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "cw_df_1 = pd.merge(confirmed_df_merge, deaths_df_merge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>country</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/22/20</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/22/20</td>\n",
       "      <td>Albania</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/22/20</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/22/20</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/22/20</td>\n",
       "      <td>Angola</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19443</th>\n",
       "      <td>5/4/20</td>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>362</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19444</th>\n",
       "      <td>5/4/20</td>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19445</th>\n",
       "      <td>5/4/20</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19446</th>\n",
       "      <td>5/4/20</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>137</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19447</th>\n",
       "      <td>5/4/20</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19448 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date             country  confirmed  deaths\n",
       "0      1/22/20         Afghanistan          0       0\n",
       "1      1/22/20             Albania          0       0\n",
       "2      1/22/20             Algeria          0       0\n",
       "3      1/22/20             Andorra          0       0\n",
       "4      1/22/20              Angola          0       0\n",
       "...        ...                 ...        ...     ...\n",
       "19443   5/4/20  West Bank and Gaza        362       2\n",
       "19444   5/4/20      Western Sahara          6       0\n",
       "19445   5/4/20               Yemen         12       2\n",
       "19446   5/4/20              Zambia        137       3\n",
       "19447   5/4/20            Zimbabwe         34       4\n",
       "\n",
       "[19448 rows x 4 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cw_df_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "cw_df = pd.merge(cw_df_1, recovered_df_merge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>country</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>deaths</th>\n",
       "      <th>recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/22/20</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/22/20</td>\n",
       "      <td>Albania</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/22/20</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/22/20</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/22/20</td>\n",
       "      <td>Angola</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19443</th>\n",
       "      <td>5/4/20</td>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>362</td>\n",
       "      <td>2</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19444</th>\n",
       "      <td>5/4/20</td>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19445</th>\n",
       "      <td>5/4/20</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19446</th>\n",
       "      <td>5/4/20</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>137</td>\n",
       "      <td>3</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19447</th>\n",
       "      <td>5/4/20</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19448 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date             country  confirmed  deaths  recovered\n",
       "0      1/22/20         Afghanistan          0       0          0\n",
       "1      1/22/20             Albania          0       0          0\n",
       "2      1/22/20             Algeria          0       0          0\n",
       "3      1/22/20             Andorra          0       0          0\n",
       "4      1/22/20              Angola          0       0          0\n",
       "...        ...                 ...        ...     ...        ...\n",
       "19443   5/4/20  West Bank and Gaza        362       2        102\n",
       "19444   5/4/20      Western Sahara          6       0          5\n",
       "19445   5/4/20               Yemen         12       2          1\n",
       "19446   5/4/20              Zambia        137       3         78\n",
       "19447   5/4/20            Zimbabwe         34       4          5\n",
       "\n",
       "[19448 rows x 5 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cw_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### !!! Fixing errata in country-wise/merge dataframes !!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def change_cw_val(df_name, date, val_col, dtnry):\n",
    "    '''\n",
    "        Example: Only for demonstration purpose\n",
    "        1. New values:\n",
    "        feb_12_conf = {'China' : 348743}\n",
    "        2. Change values:\n",
    "        change_val(cw_df, '2/12/20', 'China', 'confirmed' ,feb_12_conf)\n",
    "        3. Check values:\n",
    "        cw_df[(cw_df['date']=='2/12/20') & (cw_df['country']=='China')]\n",
    "    '''\n",
    "    for key, val in dtnry.items():\n",
    "        df_name.loc[(df_name['date']==date) & (df_name['country']==key), val_col] = val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving country-wise dataframe as CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "cw_df.to_csv('covid_19_country_global.csv', index=False)"
   ]
  }
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