Python_Data_Analysis_COVID19 .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing Packages\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read csv data file\n",
    "\n",
    "url='https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv'\n",
    "covid_data=pd.read_csv(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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', '1/28/20', '1/29/20',\n",
       "       '1/30/20', '1/31/20', '2/1/20', '2/2/20', '2/3/20', '2/4/20', '2/5/20',\n",
       "       '2/6/20', '2/7/20', '2/8/20', '2/9/20', '2/10/20', '2/11/20', '2/12/20',\n",
       "       '2/13/20', '2/14/20', '2/15/20', '2/16/20', '2/17/20', '2/18/20',\n",
       "       '2/19/20', '2/20/20', '2/21/20', '2/22/20', '2/23/20', '2/24/20',\n",
       "       '2/25/20', '2/26/20', '2/27/20', '2/28/20', '2/29/20', '3/1/20',\n",
       "       '3/2/20', '3/3/20', '3/4/20', '3/5/20', '3/6/20', '3/7/20', '3/8/20',\n",
       "       '3/9/20', '3/10/20', '3/11/20', '3/12/20', '3/13/20', '3/14/20',\n",
       "       '3/15/20', '3/16/20', '3/17/20', '3/18/20', '3/19/20', '3/20/20',\n",
       "       '3/21/20', '3/22/20', '3/23/20', '3/24/20', '3/25/20', '3/26/20',\n",
       "       '3/27/20', '3/28/20', '3/29/20', '3/30/20', '3/31/20', '4/1/20',\n",
       "       '4/2/20', '4/3/20', '4/4/20', '4/5/20', '4/6/20', '4/7/20', '4/8/20'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>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>3/30/20</th>\n",
       "      <th>3/31/20</th>\n",
       "      <th>4/1/20</th>\n",
       "      <th>4/2/20</th>\n",
       "      <th>4/3/20</th>\n",
       "      <th>4/4/20</th>\n",
       "      <th>4/5/20</th>\n",
       "      <th>4/6/20</th>\n",
       "      <th>4/7/20</th>\n",
       "      <th>4/8/20</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>170</td>\n",
       "      <td>174</td>\n",
       "      <td>237</td>\n",
       "      <td>273</td>\n",
       "      <td>281</td>\n",
       "      <td>299</td>\n",
       "      <td>349</td>\n",
       "      <td>367</td>\n",
       "      <td>423</td>\n",
       "      <td>444</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>223</td>\n",
       "      <td>243</td>\n",
       "      <td>259</td>\n",
       "      <td>277</td>\n",
       "      <td>304</td>\n",
       "      <td>333</td>\n",
       "      <td>361</td>\n",
       "      <td>377</td>\n",
       "      <td>383</td>\n",
       "      <td>400</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>584</td>\n",
       "      <td>716</td>\n",
       "      <td>847</td>\n",
       "      <td>986</td>\n",
       "      <td>1171</td>\n",
       "      <td>1251</td>\n",
       "      <td>1320</td>\n",
       "      <td>1423</td>\n",
       "      <td>1468</td>\n",
       "      <td>1572</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>370</td>\n",
       "      <td>376</td>\n",
       "      <td>390</td>\n",
       "      <td>428</td>\n",
       "      <td>439</td>\n",
       "      <td>466</td>\n",
       "      <td>501</td>\n",
       "      <td>525</td>\n",
       "      <td>545</td>\n",
       "      <td>564</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>7</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 82 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  ...  3/30/20  3/31/20  4/1/20  4/2/20  4/3/20  \\\n",
       "0        0        0        0  ...      170      174     237     273     281   \n",
       "1        0        0        0  ...      223      243     259     277     304   \n",
       "2        0        0        0  ...      584      716     847     986    1171   \n",
       "3        0        0        0  ...      370      376     390     428     439   \n",
       "4        0        0        0  ...        7        7       8       8       8   \n",
       "\n",
       "   4/4/20  4/5/20  4/6/20  4/7/20  4/8/20  \n",
       "0     299     349     367     423     444  \n",
       "1     333     361     377     383     400  \n",
       "2    1251    1320    1423    1468    1572  \n",
       "3     466     501     525     545     564  \n",
       "4      10      14      16      17      19  \n",
       "\n",
       "[5 rows x 82 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename Column Name\n",
    "covid_data_label=covid_data.rename(\n",
    "    columns= {\n",
    "        'Province/State':'subregion',\n",
    "        'Country/Region':'country',\n",
    "        'Lat':'lat',\n",
    "        'Long':'long'\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reshape the data\n",
    "\n",
    "# melt the data into 'tidy' shape\n",
    "covid_data_label=(covid_data_label.melt(id_vars=['country','subregion','lat','long'],\n",
    "                                        var_name='date_raw',\n",
    "                                        value_name='confirmed'\n",
    "                                       ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   country  subregion      lat      long date_raw  confirmed\n",
      "49   China      Anhui  31.8257  117.2264  1/22/20          1\n",
      "50   China    Beijing  40.1824  116.4142  1/22/20         14\n",
      "51   China  Chongqing  30.0572  107.8740  1/22/20          6\n",
      "52   China     Fujian  26.0789  117.9874  1/22/20          1\n",
      "54   China  Guangdong  23.3417  113.4244  1/22/20         26\n"
     ]
    }
   ],
   "source": [
    "# to check the first case in the country's subregion\n",
    "print(covid_data_label.loc[covid_data_label['confirmed']>0, : ].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         country subregion      lat      long date_raw  confirmed\n",
      "0    Afghanistan       NaN  33.0000   65.0000  1/22/20          0\n",
      "1        Albania       NaN  41.1533   20.1683  1/22/20          0\n",
      "2        Algeria       NaN  28.0339    1.6596  1/22/20          0\n",
      "3        Andorra       NaN  42.5063    1.5218  1/22/20          0\n",
      "4         Angola       NaN -11.2027   17.8739  1/22/20          0\n",
      "..           ...       ...      ...       ...      ...        ...\n",
      "995  Switzerland       NaN  46.8182    8.2275  1/25/20          0\n",
      "996      Taiwan*       NaN  23.7000  121.0000  1/25/20          3\n",
      "997     Tanzania       NaN  -6.3690   34.8888  1/25/20          0\n",
      "998     Thailand       NaN  15.0000  101.0000  1/25/20          7\n",
      "999         Togo       NaN   8.6195    0.8248  1/25/20          0\n",
      "\n",
      "[1000 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "print(covid_data_label.head(1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_raw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1/22/20</td>\n",
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       "      <td>1</td>\n",
       "      <td>1/22/20</td>\n",
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       "      <td>2</td>\n",
       "      <td>1/22/20</td>\n",
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       "      <td>3</td>\n",
       "      <td>1/22/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1/22/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20509</td>\n",
       "      <td>4/8/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20510</td>\n",
       "      <td>4/8/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20511</td>\n",
       "      <td>4/8/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20512</td>\n",
       "      <td>4/8/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20513</td>\n",
       "      <td>4/8/20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20514 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      date_raw\n",
       "0      1/22/20\n",
       "1      1/22/20\n",
       "2      1/22/20\n",
       "3      1/22/20\n",
       "4      1/22/20\n",
       "...        ...\n",
       "20509   4/8/20\n",
       "20510   4/8/20\n",
       "20511   4/8/20\n",
       "20512   4/8/20\n",
       "20513   4/8/20\n",
       "\n",
       "[20514 rows x 1 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert dates\n",
    "\n",
    "#Inspect dates\n",
    "covid_data_label.filter(['date_raw'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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></th>\n",
       "      <th>confirmed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th>date_raw</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>1/23/20</td>\n",
       "      <td>654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>1/24/20</td>\n",
       "      <td>941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>1/25/20</td>\n",
       "      <td>1434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>1/26/20</td>\n",
       "      <td>2118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-04-04</td>\n",
       "      <td>4/4/20</td>\n",
       "      <td>1197405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-04-05</td>\n",
       "      <td>4/5/20</td>\n",
       "      <td>1272115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-04-06</td>\n",
       "      <td>4/6/20</td>\n",
       "      <td>1345101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-04-07</td>\n",
       "      <td>4/7/20</td>\n",
       "      <td>1426096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-04-08</td>\n",
       "      <td>4/8/20</td>\n",
       "      <td>1511104</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>78 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     confirmed\n",
       "date       date_raw           \n",
       "2020-01-22 1/22/20         555\n",
       "2020-01-23 1/23/20         654\n",
       "2020-01-24 1/24/20         941\n",
       "2020-01-25 1/25/20        1434\n",
       "2020-01-26 1/26/20        2118\n",
       "...                        ...\n",
       "2020-04-04 4/4/20      1197405\n",
       "2020-04-05 4/5/20      1272115\n",
       "2020-04-06 4/6/20      1345101\n",
       "2020-04-07 4/7/20      1426096\n",
       "2020-04-08 4/8/20      1511104\n",
       "\n",
       "[78 rows x 1 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test date Conversion\n",
    "\n",
    "(covid_data_label.assign(date=pd.to_datetime(covid_data_label.date_raw, format='%m/%d/%y'))\n",
    "                .filter(['date', 'date_raw', 'confirmed'])\n",
    "                .groupby(['date', 'date_raw'])\n",
    "                .agg('sum')\n",
    "                .sort_values('date')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#backup\n",
    "covid_data_Backup=covid_data_label.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_data_label=covid_data_label.assign(date=pd.to_datetime(covid_data_label.date_raw, format=\"%m/%d/%y\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sort and rearrange data\n",
    "covid_data_label = (covid_data_label\n",
    "               .filter(['country', 'subregion', 'date', 'lat', 'long', 'confirmed'])\n",
    "               .sort_values(['country','subregion','lat','long','date'])\n",
    "               )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      country subregion       date      lat     long  confirmed\n",
      "225        US       NaN 2020-01-22  37.0902 -95.7129          1\n",
      "488        US       NaN 2020-01-23  37.0902 -95.7129          1\n",
      "751        US       NaN 2020-01-24  37.0902 -95.7129          2\n",
      "1014       US       NaN 2020-01-25  37.0902 -95.7129          2\n",
      "1277       US       NaN 2020-01-26  37.0902 -95.7129          5\n",
      "...       ...       ...        ...      ...      ...        ...\n",
      "19424      US       NaN 2020-04-04  37.0902 -95.7129     308850\n",
      "19687      US       NaN 2020-04-05  37.0902 -95.7129     337072\n",
      "19950      US       NaN 2020-04-06  37.0902 -95.7129     366667\n",
      "20213      US       NaN 2020-04-07  37.0902 -95.7129     396223\n",
      "20476      US       NaN 2020-04-08  37.0902 -95.7129     429052\n",
      "\n",
      "[78 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "#check the first confirmed case of any country \n",
    "print(covid_data_label.loc[(covid_data_label['country']=='US') & (covid_data_label['confirmed']>0) , :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           country subregion       date   lat  long  confirmed\n",
      "0      Afghanistan       NaN 2020-01-22  33.0  65.0          0\n",
      "263    Afghanistan       NaN 2020-01-23  33.0  65.0          0\n",
      "526    Afghanistan       NaN 2020-01-24  33.0  65.0          0\n",
      "789    Afghanistan       NaN 2020-01-25  33.0  65.0          0\n",
      "1052   Afghanistan       NaN 2020-01-26  33.0  65.0          0\n",
      "...            ...       ...        ...   ...   ...        ...\n",
      "19429     Zimbabwe       NaN 2020-04-04 -20.0  30.0          9\n",
      "19692     Zimbabwe       NaN 2020-04-05 -20.0  30.0          9\n",
      "19955     Zimbabwe       NaN 2020-04-06 -20.0  30.0         10\n",
      "20218     Zimbabwe       NaN 2020-04-07 -20.0  30.0         11\n",
      "20481     Zimbabwe       NaN 2020-04-08 -20.0  30.0         11\n",
      "\n",
      "[20514 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "print(covid_data_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set Index\n",
    "covid_data_label.set_index('country', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', 155)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>78</td>\n",
       "      <td>Albania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>156</td>\n",
       "      <td>Algeria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>234</td>\n",
       "      <td>Andorra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>312</td>\n",
       "      <td>Angola</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20124</td>\n",
       "      <td>Vietnam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20202</td>\n",
       "      <td>West Bank and Gaza</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20280</td>\n",
       "      <td>Western Sahara</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20358</td>\n",
       "      <td>Zambia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20436</td>\n",
       "      <td>Zimbabwe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>184 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  country\n",
       "0             Afghanistan\n",
       "78                Albania\n",
       "156               Algeria\n",
       "234               Andorra\n",
       "312                Angola\n",
       "...                   ...\n",
       "20124             Vietnam\n",
       "20202  West Bank and Gaza\n",
       "20280      Western Sahara\n",
       "20358              Zambia\n",
       "20436            Zimbabwe\n",
       "\n",
       "[184 rows x 1 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(covid_data_label\n",
    "                .reset_index()\n",
    "                .filter(['country'])\n",
    "                .drop_duplicates()\n",
    "                .head(n=200))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.reset_option('display.max_rows')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>subregion</th>\n",
       "      <th>date</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>confirmed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>India</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>21.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>India</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>21.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>India</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>21.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>India</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>21.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>India</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>21.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>India</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-04-04</td>\n",
       "      <td>21.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>3082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>India</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-04-05</td>\n",
       "      <td>21.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>3588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>India</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-04-06</td>\n",
       "      <td>21.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>4778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>India</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-04-07</td>\n",
       "      <td>21.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>5311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>India</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-04-08</td>\n",
       "      <td>21.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>5916</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>78 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        subregion       date   lat  long  confirmed\n",
       "country                                            \n",
       "India         NaN 2020-01-22  21.0  78.0          0\n",
       "India         NaN 2020-01-23  21.0  78.0          0\n",
       "India         NaN 2020-01-24  21.0  78.0          0\n",
       "India         NaN 2020-01-25  21.0  78.0          0\n",
       "India         NaN 2020-01-26  21.0  78.0          0\n",
       "...           ...        ...   ...   ...        ...\n",
       "India         NaN 2020-04-04  21.0  78.0       3082\n",
       "India         NaN 2020-04-05  21.0  78.0       3588\n",
       "India         NaN 2020-04-06  21.0  78.0       4778\n",
       "India         NaN 2020-04-07  21.0  78.0       5311\n",
       "India         NaN 2020-04-08  21.0  78.0       5916\n",
       "\n",
       "[78 rows x 5 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pull data for India\n",
    "covid_data_label.loc['India']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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