UdemyProjects-Covid-19.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4b5fcb98",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8a00d083",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('Covid_19_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "36397253",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>State</th>\n",
       "      <th>Region</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>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1939</td>\n",
       "      <td>60</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>766</td>\n",
       "      <td>30</td>\n",
       "      <td>455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>3848</td>\n",
       "      <td>444</td>\n",
       "      <td>1702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>743</td>\n",
       "      <td>42</td>\n",
       "      <td>423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>US</td>\n",
       "      <td>545</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Xinjiang</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>76</td>\n",
       "      <td>3</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Yukon</td>\n",
       "      <td>Canada</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Yunnan</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>185</td>\n",
       "      <td>2</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Zhejiang</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1268</td>\n",
       "      <td>1</td>\n",
       "      <td>1263</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>321 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date     State          Region  Confirmed  Deaths  Recovered\n",
       "0    4/29/2020       NaN     Afghanistan       1939      60        252\n",
       "1    4/29/2020       NaN         Albania        766      30        455\n",
       "2    4/29/2020       NaN         Algeria       3848     444       1702\n",
       "3    4/29/2020       NaN         Andorra        743      42        423\n",
       "4    4/29/2020       NaN          Angola         27       2          7\n",
       "..         ...       ...             ...        ...     ...        ...\n",
       "316  4/29/2020   Wyoming              US        545       7          0\n",
       "317  4/29/2020  Xinjiang  Mainland China         76       3         73\n",
       "318  4/29/2020     Yukon          Canada         11       0          0\n",
       "319  4/29/2020    Yunnan  Mainland China        185       2        181\n",
       "320  4/29/2020  Zhejiang  Mainland China       1268       1       1263\n",
       "\n",
       "[321 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2ea6c1f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(321, 6)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9ea53c34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date         321\n",
       "State        140\n",
       "Region       321\n",
       "Confirmed    321\n",
       "Deaths       321\n",
       "Recovered    321\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bcd06286",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date           0\n",
       "State        181\n",
       "Region         0\n",
       "Confirmed      0\n",
       "Deaths         0\n",
       "Recovered      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c4849b63",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\lib\\site-packages\\scipy\\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "64f3fa02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.isnull())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3290f3c6",
   "metadata": {},
   "source": [
    "# Show the number of confirmed deaths and recovered cases in each region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e81600ee",
   "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>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>1939</td>\n",
       "      <td>60</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>766</td>\n",
       "      <td>30</td>\n",
       "      <td>455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>3848</td>\n",
       "      <td>444</td>\n",
       "      <td>1702</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Confirmed  Deaths  Recovered\n",
       "Region                                   \n",
       "Afghanistan       1939      60        252\n",
       "Albania            766      30        455\n",
       "Algeria           3848     444       1702"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('Region').sum().head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9ceb6bc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Region\n",
       "US        1039909\n",
       "Spain      236899\n",
       "Italy      203591\n",
       "France     166543\n",
       "UK         166441\n",
       "Name: Confirmed, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('Region')['Confirmed'].sum().sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2df572f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Region\n",
       "Afghanistan             252\n",
       "Albania                 455\n",
       "Algeria                1702\n",
       "Andorra                 423\n",
       "Angola                    7\n",
       "Antigua and Barbuda      11\n",
       "Argentina              1192\n",
       "Armenia                 900\n",
       "Australia              5715\n",
       "Name: Recovered, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('Region')['Recovered'].sum().head(9)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8a6cf1a",
   "metadata": {},
   "source": [
    "# Remove all the records where confirmed cases is less than 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "095aebe8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>State</th>\n",
       "      <th>Region</th>\n",
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       "      <th>Recovered</th>\n",
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       "      <td>4/29/2020</td>\n",
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       "      <td>Afghanistan</td>\n",
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       "      <th>1</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>766</td>\n",
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       "      <td>455</td>\n",
       "    </tr>\n",
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       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>27</td>\n",
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       "      <td>7</td>\n",
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       "        Date State       Region  Confirmed  Deaths  Recovered\n",
       "0  4/29/2020   NaN  Afghanistan       1939      60        252\n",
       "1  4/29/2020   NaN      Albania        766      30        455\n",
       "2  4/29/2020   NaN      Algeria       3848     444       1702\n",
       "3  4/29/2020   NaN      Andorra        743      42        423\n",
       "4  4/29/2020   NaN       Angola         27       2          7"
      ]
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   "id": "9e555a15",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(321, 6)"
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     "execution_count": 13,
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    "data.shape"
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   "execution_count": 14,
   "id": "390c11c8",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1      False\n",
       "2      False\n",
       "3      False\n",
       "4      False\n",
       "       ...  \n",
       "316    False\n",
       "317    False\n",
       "318    False\n",
       "319    False\n",
       "320    False\n",
       "Name: Confirmed, Length: 321, dtype: bool"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Confirmed < 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "05eb949b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>7</td>\n",
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       "      <th>98</th>\n",
       "      <td>4/29/2020</td>\n",
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       "      <td>9</td>\n",
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       "      <th>105</th>\n",
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       "      <th>126</th>\n",
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       "      <td>8</td>\n",
       "      <td>0</td>\n",
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       "      <th>140</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>177</th>\n",
       "      <td>4/29/2020</td>\n",
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       "      <td>6</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>6</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Anguilla</td>\n",
       "      <td>UK</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "      <th>192</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Bonaire, Sint Eustatius and Saba</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>British Virgin Islands</td>\n",
       "      <td>UK</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Diamond Princess cruise ship</td>\n",
       "      <td>Canada</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Northwest Territories</td>\n",
       "      <td>Canada</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Recovered</td>\n",
       "      <td>Canada</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Recovered</td>\n",
       "      <td>US</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Saint Barthelemy</td>\n",
       "      <td>France</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Saint Pierre and Miquelon</td>\n",
       "      <td>France</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Tibet</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date                             State                 Region  \\\n",
       "18   4/29/2020                               NaN                 Bhutan   \n",
       "98   4/29/2020                               NaN             MS Zaandam   \n",
       "105  4/29/2020                               NaN             Mauritania   \n",
       "126  4/29/2020                               NaN       Papua New Guinea   \n",
       "140  4/29/2020                               NaN  Sao Tome and Principe   \n",
       "177  4/29/2020                               NaN         Western Sahara   \n",
       "178  4/29/2020                               NaN                  Yemen   \n",
       "184  4/29/2020                          Anguilla                     UK   \n",
       "192  4/29/2020  Bonaire, Sint Eustatius and Saba            Netherlands   \n",
       "194  4/29/2020            British Virgin Islands                     UK   \n",
       "203  4/29/2020      Diamond Princess cruise ship                 Canada   \n",
       "272  4/29/2020             Northwest Territories                 Canada   \n",
       "284  4/29/2020                         Recovered                 Canada   \n",
       "285  4/29/2020                         Recovered                     US   \n",
       "288  4/29/2020                  Saint Barthelemy                 France   \n",
       "289  4/29/2020         Saint Pierre and Miquelon                 France   \n",
       "305  4/29/2020                             Tibet         Mainland China   \n",
       "\n",
       "     Confirmed  Deaths  Recovered  \n",
       "18           7       0          5  \n",
       "98           9       2          0  \n",
       "105          8       1          6  \n",
       "126          8       0          0  \n",
       "140          8       0          4  \n",
       "177          6       0          5  \n",
       "178          6       0          1  \n",
       "184          3       0          3  \n",
       "192          5       0          0  \n",
       "194          6       1          3  \n",
       "203          0       1          0  \n",
       "272          5       0          0  \n",
       "284          0       0      20327  \n",
       "285          0       0     120720  \n",
       "288          6       0          6  \n",
       "289          1       0          0  \n",
       "305          1       0          1  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.Confirmed<10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "97eb0b1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>State</th>\n",
       "      <th>Region</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
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       "      <th>0</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1939</td>\n",
       "      <td>60</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>766</td>\n",
       "      <td>30</td>\n",
       "      <td>455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>3848</td>\n",
       "      <td>444</td>\n",
       "      <td>1702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>743</td>\n",
       "      <td>42</td>\n",
       "      <td>423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>US</td>\n",
       "      <td>545</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Xinjiang</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>76</td>\n",
       "      <td>3</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Yukon</td>\n",
       "      <td>Canada</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Yunnan</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>185</td>\n",
       "      <td>2</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Zhejiang</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1268</td>\n",
       "      <td>1</td>\n",
       "      <td>1263</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>304 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date     State          Region  Confirmed  Deaths  Recovered\n",
       "0    4/29/2020       NaN     Afghanistan       1939      60        252\n",
       "1    4/29/2020       NaN         Albania        766      30        455\n",
       "2    4/29/2020       NaN         Algeria       3848     444       1702\n",
       "3    4/29/2020       NaN         Andorra        743      42        423\n",
       "4    4/29/2020       NaN          Angola         27       2          7\n",
       "..         ...       ...             ...        ...     ...        ...\n",
       "316  4/29/2020   Wyoming              US        545       7          0\n",
       "317  4/29/2020  Xinjiang  Mainland China         76       3         73\n",
       "318  4/29/2020     Yukon          Canada         11       0          0\n",
       "319  4/29/2020    Yunnan  Mainland China        185       2        181\n",
       "320  4/29/2020  Zhejiang  Mainland China       1268       1       1263\n",
       "\n",
       "[304 rows x 6 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[~(data.Confirmed<10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "99b30bf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[~(data.Confirmed<10)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e9a7b22",
   "metadata": {},
   "source": [
    "# in which region maximum number of confirmed cases were recorded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f2326640",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Region\n",
       "US                1039909\n",
       "Spain              236899\n",
       "Italy              203591\n",
       "France             166536\n",
       "UK                 166432\n",
       "Germany            161539\n",
       "Turkey             117589\n",
       "Russia              99399\n",
       "Iran                93657\n",
       "Mainland China      82861\n",
       "Name: Confirmed, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('Region')['Confirmed'].sum().sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "008d4d3e",
   "metadata": {},
   "source": [
    "# in which region minimum number of dead cases were recorded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "44adb836",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Region\n",
       "Cambodia                    0\n",
       "Seychelles                  0\n",
       "Saint Lucia                 0\n",
       "Central African Republic    0\n",
       "Saint Kitts and Nevis       0\n",
       "South Sudan                 0\n",
       "Rwanda                      0\n",
       "Grenada                     0\n",
       "Macau                       0\n",
       "Madagascar                  0\n",
       "Name: Deaths, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('Region')['Deaths'].sum().sort_values(ascending=True).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ed21fe2",
   "metadata": {},
   "source": [
    "#  How many Confirmed , Deaths & Recovered cases were reported from India till 29 April 2020 ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "28217e7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>State</th>\n",
       "      <th>Region</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>India</td>\n",
       "      <td>33062</td>\n",
       "      <td>1079</td>\n",
       "      <td>8437</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "         Date State Region  Confirmed  Deaths  Recovered\n",
       "74  4/29/2020   NaN  India      33062    1079       8437"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.Region == 'India']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0424a317",
   "metadata": {},
   "source": [
    "#  Sort the entire data wrt No. of Confirmed cases in ascending order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e1a66a5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>State</th>\n",
       "      <th>Region</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Suriname</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Holy See</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gambia</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Yukon</td>\n",
       "      <td>Canada</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Greenland</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Montserrat</td>\n",
       "      <td>UK</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Seychelles</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Burundi</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Turks and Caicos Islands</td>\n",
       "      <td>UK</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nicaragua</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Grand Princess</td>\n",
       "      <td>Canada</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Falkland Islands (Malvinas)</td>\n",
       "      <td>UK</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Northern Mariana Islands</td>\n",
       "      <td>US</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Saint Kitts and Nevis</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Saint Vincent and the Grenadines</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dominica</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Curacao</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Namibia</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Saint Lucia</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>New Caledonia</td>\n",
       "      <td>France</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Belize</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fiji</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Qinghai</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Laos</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Grenada</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Botswana</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Prince Edward Island</td>\n",
       "      <td>Canada</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Northern Territory</td>\n",
       "      <td>Australia</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>South Sudan</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Malawi</td>\n",
       "      <td>36</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>St Martin</td>\n",
       "      <td>France</td>\n",
       "      <td>38</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mongolia</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Eritrea</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Syria</td>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Macau</td>\n",
       "      <td>Macau</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Diamond Princess cruise ship</td>\n",
       "      <td>US</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chad</td>\n",
       "      <td>52</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Virgin Islands</td>\n",
       "      <td>US</td>\n",
       "      <td>57</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nepal</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>French Polynesia</td>\n",
       "      <td>France</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Libya</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Benin</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Cayman Islands</td>\n",
       "      <td>UK</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Sint Maarten</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>75</td>\n",
       "      <td>13</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Ningxia</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>75</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date                         State  \\\n",
       "156  4/29/2020                           NaN   \n",
       "70   4/29/2020                           NaN   \n",
       "59   4/29/2020                           NaN   \n",
       "318  4/29/2020                         Yukon   \n",
       "217  4/29/2020                     Greenland   \n",
       "256  4/29/2020                    Montserrat   \n",
       "144  4/29/2020                           NaN   \n",
       "27   4/29/2020                           NaN   \n",
       "306  4/29/2020      Turks and Caicos Islands   \n",
       "118  4/29/2020                           NaN   \n",
       "215  4/29/2020                Grand Princess   \n",
       "206  4/29/2020   Falkland Islands (Malvinas)   \n",
       "270  4/29/2020      Northern Mariana Islands   \n",
       "136  4/29/2020                           NaN   \n",
       "138  4/29/2020                           NaN   \n",
       "45   4/29/2020                           NaN   \n",
       "201  4/29/2020                       Curacao   \n",
       "114  4/29/2020                           NaN   \n",
       "137  4/29/2020                           NaN   \n",
       "260  4/29/2020                 New Caledonia   \n",
       "16   4/29/2020                           NaN   \n",
       "55   4/29/2020                           NaN   \n",
       "281  4/29/2020                       Qinghai   \n",
       "90   4/29/2020                           NaN   \n",
       "64   4/29/2020                           NaN   \n",
       "21   4/29/2020                           NaN   \n",
       "5    4/29/2020                           NaN   \n",
       "163  4/29/2020                           NaN   \n",
       "279  4/29/2020          Prince Edward Island   \n",
       "4    4/29/2020                           NaN   \n",
       "271  4/29/2020            Northern Territory   \n",
       "180  4/29/2020                           NaN   \n",
       "152  4/29/2020                           NaN   \n",
       "100  4/29/2020                           NaN   \n",
       "300  4/29/2020                     St Martin   \n",
       "110  4/29/2020                           NaN   \n",
       "51   4/29/2020                           NaN   \n",
       "159  4/29/2020                           NaN   \n",
       "244  4/29/2020                         Macau   \n",
       "204  4/29/2020  Diamond Princess cruise ship   \n",
       "31   4/29/2020                           NaN   \n",
       "32   4/29/2020                           NaN   \n",
       "310  4/29/2020                Virgin Islands   \n",
       "115  4/29/2020                           NaN   \n",
       "210  4/29/2020              French Polynesia   \n",
       "94   4/29/2020                           NaN   \n",
       "17   4/29/2020                           NaN   \n",
       "196  4/29/2020                Cayman Islands   \n",
       "296  4/29/2020                  Sint Maarten   \n",
       "267  4/29/2020                       Ningxia   \n",
       "\n",
       "                               Region  Confirmed  Deaths  Recovered  \n",
       "156                          Suriname         10       1          8  \n",
       "70                           Holy See         10       0          2  \n",
       "59                             Gambia         10       1          8  \n",
       "318                            Canada         11       0          0  \n",
       "217                           Denmark         11       0         11  \n",
       "256                                UK         11       1          2  \n",
       "144                        Seychelles         11       0          6  \n",
       "27                            Burundi         11       1          4  \n",
       "306                                UK         12       1          5  \n",
       "118                         Nicaragua         13       3          7  \n",
       "215                            Canada         13       0          0  \n",
       "206                                UK         13       0         11  \n",
       "270                                US         14       2          0  \n",
       "136             Saint Kitts and Nevis         15       0          4  \n",
       "138  Saint Vincent and the Grenadines         16       0          8  \n",
       "45                           Dominica         16       0         13  \n",
       "201                       Netherlands         16       1         13  \n",
       "114                           Namibia         16       0          8  \n",
       "137                       Saint Lucia         17       0         15  \n",
       "260                            France         18       0         17  \n",
       "16                             Belize         18       2          9  \n",
       "55                               Fiji         18       0         12  \n",
       "281                    Mainland China         18       0         18  \n",
       "90                               Laos         19       0          7  \n",
       "64                            Grenada         20       0         13  \n",
       "21                           Botswana         23       1          5  \n",
       "5                 Antigua and Barbuda         24       3         11  \n",
       "163                       Timor-Leste         24       0          6  \n",
       "279                            Canada         27       0          0  \n",
       "4                              Angola         27       2          7  \n",
       "271                         Australia         28       0         25  \n",
       "180                          Zimbabwe         32       4          5  \n",
       "152                       South Sudan         34       0          0  \n",
       "100                            Malawi         36       3          7  \n",
       "300                            France         38       3         24  \n",
       "110                          Mongolia         38       0         10  \n",
       "51                            Eritrea         39       0         19  \n",
       "159                             Syria         43       3         21  \n",
       "244                             Macau         45       0         34  \n",
       "204                                US         49       0          0  \n",
       "31           Central African Republic         50       0         10  \n",
       "32                               Chad         52       2         19  \n",
       "310                                US         57       4          0  \n",
       "115                             Nepal         57       0         16  \n",
       "210                            France         58       0         50  \n",
       "94                              Libya         61       2         18  \n",
       "17                              Benin         64       1         33  \n",
       "196                                UK         73       1         10  \n",
       "296                       Netherlands         75      13         33  \n",
       "267                    Mainland China         75       0         75  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sort_values( by = ['Confirmed'] , ascending = True).head(50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53b79509",
   "metadata": {},
   "source": [
    "# Sort the entire data wrt No. of Recovered cases in descending order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "84688e76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>State</th>\n",
       "      <th>Region</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Spain</td>\n",
       "      <td>236899</td>\n",
       "      <td>24275</td>\n",
       "      <td>132929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Germany</td>\n",
       "      <td>161539</td>\n",
       "      <td>6467</td>\n",
       "      <td>120400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Iran</td>\n",
       "      <td>93657</td>\n",
       "      <td>5957</td>\n",
       "      <td>73791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "      <td>203591</td>\n",
       "      <td>27682</td>\n",
       "      <td>71252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Hubei</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>68128</td>\n",
       "      <td>4512</td>\n",
       "      <td>63616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>165093</td>\n",
       "      <td>24087</td>\n",
       "      <td>48228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>117589</td>\n",
       "      <td>3081</td>\n",
       "      <td>44040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>79685</td>\n",
       "      <td>5513</td>\n",
       "      <td>34132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>29407</td>\n",
       "      <td>1716</td>\n",
       "      <td>22600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>20253</td>\n",
       "      <td>1190</td>\n",
       "      <td>13386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Austria</td>\n",
       "      <td>15402</td>\n",
       "      <td>580</td>\n",
       "      <td>12779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>17799</td>\n",
       "      <td>1732</td>\n",
       "      <td>11423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>47859</td>\n",
       "      <td>7501</td>\n",
       "      <td>11283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Russia</td>\n",
       "      <td>99399</td>\n",
       "      <td>972</td>\n",
       "      <td>10286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Peru</td>\n",
       "      <td>33931</td>\n",
       "      <td>943</td>\n",
       "      <td>10037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>10765</td>\n",
       "      <td>247</td>\n",
       "      <td>9059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>India</td>\n",
       "      <td>33062</td>\n",
       "      <td>1079</td>\n",
       "      <td>8437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Israel</td>\n",
       "      <td>15834</td>\n",
       "      <td>215</td>\n",
       "      <td>8233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Chile</td>\n",
       "      <td>14885</td>\n",
       "      <td>216</td>\n",
       "      <td>8057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>9008</td>\n",
       "      <td>443</td>\n",
       "      <td>6366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>5945</td>\n",
       "      <td>100</td>\n",
       "      <td>4087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Romania</td>\n",
       "      <td>11978</td>\n",
       "      <td>693</td>\n",
       "      <td>3569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>15525</td>\n",
       "      <td>343</td>\n",
       "      <td>3425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>3769</td>\n",
       "      <td>89</td>\n",
       "      <td>3134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>7579</td>\n",
       "      <td>227</td>\n",
       "      <td>3108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Poland</td>\n",
       "      <td>12640</td>\n",
       "      <td>624</td>\n",
       "      <td>3025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>21402</td>\n",
       "      <td>157</td>\n",
       "      <td>2953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Finland</td>\n",
       "      <td>4906</td>\n",
       "      <td>206</td>\n",
       "      <td>2800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>2947</td>\n",
       "      <td>54</td>\n",
       "      <td>2665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>13895</td>\n",
       "      <td>413</td>\n",
       "      <td>2368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>11929</td>\n",
       "      <td>98</td>\n",
       "      <td>2329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>3016</td>\n",
       "      <td>40</td>\n",
       "      <td>2284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>5350</td>\n",
       "      <td>103</td>\n",
       "      <td>2073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Belarus</td>\n",
       "      <td>13181</td>\n",
       "      <td>84</td>\n",
       "      <td>2072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>3848</td>\n",
       "      <td>444</td>\n",
       "      <td>1702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>1797</td>\n",
       "      <td>10</td>\n",
       "      <td>1656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>24675</td>\n",
       "      <td>883</td>\n",
       "      <td>1557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Guangdong</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1588</td>\n",
       "      <td>8</td>\n",
       "      <td>1557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>24505</td>\n",
       "      <td>973</td>\n",
       "      <td>1470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahrain</td>\n",
       "      <td>2921</td>\n",
       "      <td>8</td>\n",
       "      <td>1455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>6207</td>\n",
       "      <td>278</td>\n",
       "      <td>1411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>9771</td>\n",
       "      <td>784</td>\n",
       "      <td>1391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kuwait</td>\n",
       "      <td>3740</td>\n",
       "      <td>24</td>\n",
       "      <td>1389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Iraq</td>\n",
       "      <td>2003</td>\n",
       "      <td>92</td>\n",
       "      <td>1346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>5268</td>\n",
       "      <td>380</td>\n",
       "      <td>1335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1361</td>\n",
       "      <td>18</td>\n",
       "      <td>1291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Croatia</td>\n",
       "      <td>2062</td>\n",
       "      <td>67</td>\n",
       "      <td>1288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>1766</td>\n",
       "      <td>23</td>\n",
       "      <td>1267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Zhejiang</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1268</td>\n",
       "      <td>1</td>\n",
       "      <td>1263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>4/29/2020</td>\n",
       "      <td>Henan</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1276</td>\n",
       "      <td>22</td>\n",
       "      <td>1254</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date            State                Region  Confirmed  Deaths  \\\n",
       "153  4/29/2020              NaN                 Spain     236899   24275   \n",
       "61   4/29/2020              NaN               Germany     161539    6467   \n",
       "76   4/29/2020              NaN                  Iran      93657    5957   \n",
       "80   4/29/2020              NaN                 Italy     203591   27682   \n",
       "229  4/29/2020            Hubei        Mainland China      68128    4512   \n",
       "57   4/29/2020              NaN                France     165093   24087   \n",
       "167  4/29/2020              NaN                Turkey     117589    3081   \n",
       "22   4/29/2020              NaN                Brazil      79685    5513   \n",
       "158  4/29/2020              NaN           Switzerland      29407    1716   \n",
       "78   4/29/2020              NaN               Ireland      20253    1190   \n",
       "8    4/29/2020              NaN               Austria      15402     580   \n",
       "107  4/29/2020              NaN                Mexico      17799    1732   \n",
       "15   4/29/2020              NaN               Belgium      47859    7501   \n",
       "134  4/29/2020              NaN                Russia      99399     972   \n",
       "128  4/29/2020              NaN                  Peru      33931     943   \n",
       "151  4/29/2020              NaN           South Korea      10765     247   \n",
       "74   4/29/2020              NaN                 India      33062    1079   \n",
       "79   4/29/2020              NaN                Israel      15834     215   \n",
       "33   4/29/2020              NaN                 Chile      14885     216   \n",
       "42   4/29/2020              NaN               Denmark       9008     443   \n",
       "101  4/29/2020              NaN              Malaysia       5945     100   \n",
       "133  4/29/2020              NaN               Romania      11978     693   \n",
       "124  4/29/2020              NaN              Pakistan      15525     343   \n",
       "97   4/29/2020              NaN            Luxembourg       3769      89   \n",
       "41   4/29/2020              NaN        Czech Republic       7579     227   \n",
       "130  4/29/2020              NaN                Poland      12640     624   \n",
       "141  4/29/2020              NaN          Saudi Arabia      21402     157   \n",
       "56   4/29/2020              NaN               Finland       4906     206   \n",
       "162  4/29/2020              NaN              Thailand       2947      54   \n",
       "83   4/29/2020              NaN                 Japan      13895     413   \n",
       "171  4/29/2020              NaN  United Arab Emirates      11929      98   \n",
       "264  4/29/2020  New South Wales             Australia       3016      40   \n",
       "150  4/29/2020              NaN          South Africa       5350     103   \n",
       "14   4/29/2020              NaN               Belarus      13181      84   \n",
       "2    4/29/2020              NaN               Algeria       3848     444   \n",
       "73   4/29/2020              NaN               Iceland       1797      10   \n",
       "47   4/29/2020              NaN               Ecuador      24675     883   \n",
       "220  4/29/2020        Guangdong        Mainland China       1588       8   \n",
       "131  4/29/2020              NaN              Portugal      24505     973   \n",
       "11   4/29/2020              NaN               Bahrain       2921       8   \n",
       "34   4/29/2020              NaN              Colombia       6207     278   \n",
       "75   4/29/2020              NaN             Indonesia       9771     784   \n",
       "88   4/29/2020              NaN                Kuwait       3740      24   \n",
       "77   4/29/2020              NaN                  Iraq       2003      92   \n",
       "48   4/29/2020              NaN                 Egypt       5268     380   \n",
       "309  4/29/2020         Victoria             Australia       1361      18   \n",
       "38   4/29/2020              NaN               Croatia       2062      67   \n",
       "9    4/29/2020              NaN            Azerbaijan       1766      23   \n",
       "320  4/29/2020         Zhejiang        Mainland China       1268       1   \n",
       "227  4/29/2020            Henan        Mainland China       1276      22   \n",
       "\n",
       "     Recovered  \n",
       "153     132929  \n",
       "61      120400  \n",
       "76       73791  \n",
       "80       71252  \n",
       "229      63616  \n",
       "57       48228  \n",
       "167      44040  \n",
       "22       34132  \n",
       "158      22600  \n",
       "78       13386  \n",
       "8        12779  \n",
       "107      11423  \n",
       "15       11283  \n",
       "134      10286  \n",
       "128      10037  \n",
       "151       9059  \n",
       "74        8437  \n",
       "79        8233  \n",
       "33        8057  \n",
       "42        6366  \n",
       "101       4087  \n",
       "133       3569  \n",
       "124       3425  \n",
       "97        3134  \n",
       "41        3108  \n",
       "130       3025  \n",
       "141       2953  \n",
       "56        2800  \n",
       "162       2665  \n",
       "83        2368  \n",
       "171       2329  \n",
       "264       2284  \n",
       "150       2073  \n",
       "14        2072  \n",
       "2         1702  \n",
       "73        1656  \n",
       "47        1557  \n",
       "220       1557  \n",
       "131       1470  \n",
       "11        1455  \n",
       "34        1411  \n",
       "75        1391  \n",
       "88        1389  \n",
       "77        1346  \n",
       "48        1335  \n",
       "309       1291  \n",
       "38        1288  \n",
       "9         1267  \n",
       "320       1263  \n",
       "227       1254  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sort_values( by = ['Recovered'] , ascending = False).head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d96b6c9e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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