Covid19 Data cleansing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# libraries\n",
    "# ----------\n",
    "\n",
    "import pandas as pd\n",
    "import wget\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Download data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "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']\n",
    "\n",
    "for url in urls:\n",
    "    filename = wget.download(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataset\n",
    "# --------\n",
    "\n",
    "conf_df = pd.read_csv('time_series_covid19_confirmed_global (1).csv')\n",
    "deaths_df = pd.read_csv('time_series_covid19_deaths_global (1).csv')\n",
    "recv_df = pd.read_csv('time_series_covid19_Recovered_global.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\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>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>...</th>\n",
       "      <th>4/18/20</th>\n",
       "      <th>4/19/20</th>\n",
       "      <th>4/20/20</th>\n",
       "      <th>4/21/20</th>\n",
       "      <th>4/22/20</th>\n",
       "      <th>4/23/20</th>\n",
       "      <th>4/24/20</th>\n",
       "      <th>4/25/20</th>\n",
       "      <th>4/26/20</th>\n",
       "      <th>4/27/20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>65.0000</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>112</td>\n",
       "      <td>131</td>\n",
       "      <td>135</td>\n",
       "      <td>150</td>\n",
       "      <td>166</td>\n",
       "      <td>179</td>\n",
       "      <td>188</td>\n",
       "      <td>188</td>\n",
       "      <td>207</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>41.1533</td>\n",
       "      <td>20.1683</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>302</td>\n",
       "      <td>314</td>\n",
       "      <td>327</td>\n",
       "      <td>345</td>\n",
       "      <td>356</td>\n",
       "      <td>385</td>\n",
       "      <td>394</td>\n",
       "      <td>403</td>\n",
       "      <td>410</td>\n",
       "      <td>422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\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>894</td>\n",
       "      <td>1047</td>\n",
       "      <td>1099</td>\n",
       "      <td>1152</td>\n",
       "      <td>1204</td>\n",
       "      <td>1355</td>\n",
       "      <td>1408</td>\n",
       "      <td>1479</td>\n",
       "      <td>1508</td>\n",
       "      <td>1558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\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>205</td>\n",
       "      <td>235</td>\n",
       "      <td>248</td>\n",
       "      <td>282</td>\n",
       "      <td>309</td>\n",
       "      <td>333</td>\n",
       "      <td>344</td>\n",
       "      <td>344</td>\n",
       "      <td>344</td>\n",
       "      <td>385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 101 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  ...  4/18/20  4/19/20  4/20/20  4/21/20  \\\n",
       "0        0        0        0  ...      112      131      135      150   \n",
       "1        0        0        0  ...      302      314      327      345   \n",
       "2        0        0        0  ...      894     1047     1099     1152   \n",
       "3        0        0        0  ...      205      235      248      282   \n",
       "4        0        0        0  ...        6        6        6        6   \n",
       "\n",
       "   4/22/20  4/23/20  4/24/20  4/25/20  4/26/20  4/27/20  \n",
       "0      166      179      188      188      207      220  \n",
       "1      356      385      394      403      410      422  \n",
       "2     1204     1355     1408     1479     1508     1558  \n",
       "3      309      333      344      344      344      385  \n",
       "4        6        6        6        6        6        6  \n",
       "\n",
       "[5 rows x 101 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf_df.head()\n",
    "deaths_df.head()\n",
    "recv_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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",
       "       '4/18/20', '4/19/20', '4/20/20', '4/21/20', '4/22/20', '4/23/20',\n",
       "       '4/24/20', '4/25/20', '4/26/20', '4/27/20'],\n",
       "      dtype='object', length=101)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf_df.columns\n",
    "deaths_df.columns\n",
    "recv_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['1/22/20', '1/23/20', '1/24/20', '1/25/20', '1/26/20', '1/27/20',\n",
       "       '1/28/20', '1/29/20', '1/30/20', '1/31/20', '2/1/20', '2/2/20',\n",
       "       '2/3/20', '2/4/20', '2/5/20', '2/6/20', '2/7/20', '2/8/20', '2/9/20',\n",
       "       '2/10/20', '2/11/20', '2/12/20', '2/13/20', '2/14/20', '2/15/20',\n",
       "       '2/16/20', '2/17/20', '2/18/20', '2/19/20', '2/20/20', '2/21/20',\n",
       "       '2/22/20', '2/23/20', '2/24/20', '2/25/20', '2/26/20', '2/27/20',\n",
       "       '2/28/20', '2/29/20', '3/1/20', '3/2/20', '3/3/20', '3/4/20', '3/5/20',\n",
       "       '3/6/20', '3/7/20', '3/8/20', '3/9/20', '3/10/20', '3/11/20', '3/12/20',\n",
       "       '3/13/20', '3/14/20', '3/15/20', '3/16/20', '3/17/20', '3/18/20',\n",
       "       '3/19/20', '3/20/20', '3/21/20', '3/22/20', '3/23/20', '3/24/20',\n",
       "       '3/25/20', '3/26/20', '3/27/20', '3/28/20', '3/29/20', '3/30/20',\n",
       "       '3/31/20', '4/1/20', '4/2/20', '4/3/20', '4/4/20', '4/5/20', '4/6/20',\n",
       "       '4/7/20', '4/8/20', '4/9/20', '4/10/20', '4/11/20', '4/12/20',\n",
       "       '4/13/20', '4/14/20', '4/15/20', '4/16/20', '4/17/20', '4/18/20',\n",
       "       '4/19/20', '4/20/20', '4/21/20', '4/22/20', '4/23/20', '4/24/20',\n",
       "       '4/25/20', '4/26/20', '4/27/20'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " conf_df.columns[4:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merge Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Date</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\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",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\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",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\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",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Province/State Country/Region      Lat     Long     Date  Confirmed  Deaths  \\\n",
       "0            NaN    Afghanistan  33.0000  65.0000  1/22/20          0       0   \n",
       "1            NaN        Albania  41.1533  20.1683  1/22/20          0       0   \n",
       "2            NaN        Algeria  28.0339   1.6596  1/22/20          0       0   \n",
       "3            NaN        Andorra  42.5063   1.5218  1/22/20          0       0   \n",
       "4            NaN         Angola -11.2027  17.8739  1/22/20          0       0   \n",
       "\n",
       "   Recovered  \n",
       "0        0.0  \n",
       "1        0.0  \n",
       "2        0.0  \n",
       "3        0.0  \n",
       "4        0.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = conf_df.columns[4:]\n",
    "\n",
    "conf_df_long = conf_df.melt(id_vars=['Province/State', 'Country/Region', 'Lat', 'Long'], \n",
    "                            value_vars=dates, var_name='Date', value_name='Confirmed')\n",
    "\n",
    "deaths_df_long = deaths_df.melt(id_vars=['Province/State', 'Country/Region', 'Lat', 'Long'], \n",
    "                            value_vars=dates, var_name='Date', value_name='Deaths')\n",
    "\n",
    "recv_df_long = recv_df.melt(id_vars=['Province/State', 'Country/Region', 'Lat', 'Long'], \n",
    "                            value_vars=dates, var_name='Date', value_name='Recovered')\n",
    "\n",
    "full_table = pd.concat([conf_df_long, deaths_df_long['Deaths'], recv_df_long['Recovered']], axis=1, sort=False)\n",
    "\n",
    "full_table.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# removing\n",
    "# =======\n",
    "full_table = full_table[full_table['Province/State'].str.contains('Recovered')!=True]\n",
    "# removing county wise data to avoid double counting\n",
    "full_table = full_table[full_table['Province/State'].str.contains(',')!=True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# renaming\n",
    "# ========\n",
    "\n",
    "# renaming countries, regions, provinces\n",
    "full_table['Country/Region'] = full_table['Country/Region'].replace('Korea, South', 'South Korea')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fixing off data from WHO data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# new values\n",
    "feb_12_conf = {'Hubei' : 34874}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to change value\n",
    "def change_val(date, ref_col, val_col, dtnry):\n",
    "    for key, val in dtnry.items():\n",
    "        full_table.loc[(full_table['Date']==date) & (full_table[ref_col]==key), val_col] = val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# changing values\n",
    "change_val('2/12/20', 'Province/State', 'Confirmed', feb_12_conf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Date</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>5606</td>\n",
       "      <td>Hubei</td>\n",
       "      <td>China</td>\n",
       "      <td>30.9756</td>\n",
       "      <td>112.2707</td>\n",
       "      <td>2/12/20</td>\n",
       "      <td>34874</td>\n",
       "      <td>1068</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Province/State Country/Region      Lat      Long     Date  Confirmed  \\\n",
       "5606          Hubei          China  30.9756  112.2707  2/12/20      34874   \n",
       "\n",
       "      Deaths  Recovered  \n",
       "5606    1068        0.0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking values\n",
    "full_table[(full_table['Date']=='2/12/20') & (full_table['Province/State']=='Hubei')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saving full data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_table.to_csv('complete_data_new_format.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Country wise data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# china\n",
    "# =====\n",
    "\n",
    "china_province_wise = full_table[full_table['Country/Region']=='China']\n",
    "china_province_wise['Province/State'].unique()\n",
    "china_province_wise.to_csv('china_province_wise.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Australia\n",
    "# =========\n",
    "\n",
    "australia_state_wise = full_table[full_table['Country/Region']=='Australia']\n",
    "australia_state_wise['Province/State'].unique()\n",
    "australia_state_wise.to_csv('australia_state_wise.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Canada\n",
    "# ======\n",
    "\n",
    "canada_state_wise = full_table[full_table['Country/Region']=='Canada']\n",
    "canada_state_wise['Province/State'].unique()\n",
    "canada_state_wise.to_csv('canada_state_wise.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Nigeria \n",
    "\n",
    "nigeria_state_wise = full_table[full_table['Country/Region']=='Nigeria']\n",
    "nigeria_state_wise['Province/State'].unique()\n",
    "nigeria_state_wise.to_csv('nigeria_state_wise.csv', index=False)"
   ]
  },
  {
   "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>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Date</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>9.082</td>\n",
       "      <td>8.6753</td>\n",
       "      <td>4/23/20</td>\n",
       "      <td>981</td>\n",
       "      <td>31</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>9.082</td>\n",
       "      <td>8.6753</td>\n",
       "      <td>4/24/20</td>\n",
       "      <td>1095</td>\n",
       "      <td>32</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>9.082</td>\n",
       "      <td>8.6753</td>\n",
       "      <td>4/25/20</td>\n",
       "      <td>1182</td>\n",
       "      <td>35</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>9.082</td>\n",
       "      <td>8.6753</td>\n",
       "      <td>4/26/20</td>\n",
       "      <td>1273</td>\n",
       "      <td>40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>9.082</td>\n",
       "      <td>8.6753</td>\n",
       "      <td>4/27/20</td>\n",
       "      <td>1337</td>\n",
       "      <td>40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Province/State Country/Region    Lat    Long     Date  Confirmed  Deaths  \\\n",
       "92             NaN        Nigeria  9.082  8.6753  4/23/20        981      31   \n",
       "93             NaN        Nigeria  9.082  8.6753  4/24/20       1095      32   \n",
       "94             NaN        Nigeria  9.082  8.6753  4/25/20       1182      35   \n",
       "95             NaN        Nigeria  9.082  8.6753  4/26/20       1273      40   \n",
       "96             NaN        Nigeria  9.082  8.6753  4/27/20       1337      40   \n",
       "\n",
       "    Recovered  \n",
       "92        NaN  \n",
       "93        NaN  \n",
       "94        NaN  \n",
       "95        NaN  \n",
       "96        NaN  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nig = pd.read_csv('nigeria_state_wise.csv')\n",
    "nig.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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