COVID-19 Big_Data Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b6bbec06",
   "metadata": {},
   "source": [
    "# COVID -19 BIG_DATA_SET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6613e19a",
   "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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data= pd.read_csv(r\"C:\\Users\\Hariprasad\\Downloads\\file.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5cc30748",
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7f77ffbe",
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()             #this will show the total null values in each column individually"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7fc35455",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import seaborn library and matplotlib library as sns and plt...\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d2ecbf37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.isnull())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67002d9f",
   "metadata": {},
   "source": [
    "|QUERY: 1 |Output showing the number of confirmend deaths and recovered cases region-wise in each State...||"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "09c802e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Region\n",
       "US                       1039909\n",
       "Spain                     236899\n",
       "Italy                     203591\n",
       "France                    166543\n",
       "UK                        166441\n",
       "                          ...   \n",
       "Sao Tome and Principe          8\n",
       "Papua New Guinea               8\n",
       "Bhutan                         7\n",
       "Western Sahara                 6\n",
       "Yemen                          6\n",
       "Name: Confirmed, Length: 187, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('Region')['Confirmed'].sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f0f3f4ae",
   "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>Recovered</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</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>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>766</td>\n",
       "      <td>455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>3848</td>\n",
       "      <td>1702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>743</td>\n",
       "      <td>423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>27</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Bank and Gaza</th>\n",
       "      <td>344</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western Sahara</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yemen</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>97</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>32</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>187 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Confirmed  Recovered\n",
       "Region                                  \n",
       "Afghanistan              1939        252\n",
       "Albania                   766        455\n",
       "Algeria                  3848       1702\n",
       "Andorra                   743        423\n",
       "Angola                     27          7\n",
       "...                       ...        ...\n",
       "West Bank and Gaza        344         71\n",
       "Western Sahara              6          5\n",
       "Yemen                       6          1\n",
       "Zambia                     97         54\n",
       "Zimbabwe                   32          5\n",
       "\n",
       "[187 rows x 2 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('Region')[['Confirmed', 'Recovered']].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3be8eac7",
   "metadata": {},
   "source": [
    "|QUERY: 2 |Filter out the cases where the \"confirmed cases\" is less than \"10\"...||"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3464d8cb",
   "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>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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=data[~(data.Confirmed < 10)]\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34db0e94",
   "metadata": {},
   "source": [
    "|QUERY: 3 |Depict the \"Region\" (atleast to 50) where maximum \"Confirmed cases\" were recorded||"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f896051e",
   "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",
       "Brazil                    79685\n",
       "Canada                    52860\n",
       "Belgium                   47859\n",
       "Netherlands               38993\n",
       "Peru                      33931\n",
       "India                     33062\n",
       "Switzerland               29407\n",
       "Ecuador                   24675\n",
       "Portugal                  24505\n",
       "Saudi Arabia              21402\n",
       "Sweden                    20302\n",
       "Ireland                   20253\n",
       "Mexico                    17799\n",
       "Israel                    15834\n",
       "Singapore                 15641\n",
       "Pakistan                  15525\n",
       "Austria                   15402\n",
       "Chile                     14885\n",
       "Japan                     13895\n",
       "Belarus                   13181\n",
       "Poland                    12640\n",
       "Qatar                     12564\n",
       "Romania                   11978\n",
       "United Arab Emirates      11929\n",
       "South Korea               10765\n",
       "Ukraine                    9866\n",
       "Indonesia                  9771\n",
       "Denmark                    9206\n",
       "Philippines                8212\n",
       "Norway                     7710\n",
       "Czech Republic             7579\n",
       "Bangladesh                 7103\n",
       "Australia                  6752\n",
       "Dominican Republic         6652\n",
       "Serbia                     6630\n",
       "Panama                     6378\n",
       "Colombia                   6207\n",
       "Malaysia                   5945\n",
       "South Africa               5350\n",
       "Egypt                      5268\n",
       "Name: Confirmed, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('Region').Confirmed.sum().sort_values(ascending= False).head(50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27094de6",
   "metadata": {},
   "source": [
    "|QUERY: 4 |...Publish the \"Region\" minimum number of \"deaths\" ()recorded....||"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1048c163",
   "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",
       "Nepal                               0\n",
       "Namibia                             0\n",
       "Saint Vincent and the Grenadines    0\n",
       "Mozambique                          0\n",
       "Holy See                            0\n",
       "Timor-Leste                         0\n",
       "Mongolia                            0\n",
       "Uganda                              0\n",
       "Laos                                0\n",
       "Eritrea                             0\n",
       "Vietnam                             0\n",
       "Fiji                                0\n",
       "Dominica                            0\n",
       "Gambia                              1\n",
       "Equatorial Guinea                   1\n",
       "Eswatini                            1\n",
       "Cabo Verde                          1\n",
       "Maldives                            1\n",
       "Guinea-Bissau                       1\n",
       "Liechtenstein                       1\n",
       "Brunei                              1\n",
       "Burundi                             1\n",
       "Botswana                            1\n",
       "Suriname                            1\n",
       "Benin                               1\n",
       "Djibouti                            2\n",
       "Angola                              2\n",
       "Libya                               2\n",
       "Chad                                2\n",
       "West Bank and Gaza                  2\n",
       "Belize                              2\n",
       "Zambia                              3\n",
       "Malawi                              3\n",
       "Nicaragua                           3\n",
       "Syria                               3\n",
       "Ethiopia                            3\n",
       "Antigua and Barbuda                 3\n",
       "Gabon                               3\n",
       "Hong Kong                           4\n",
       "Zimbabwe                            4\n",
       "Name: Deaths, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('Region').Deaths.sum().sort_values(ascending= True).head(50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7d14eca",
   "metadata": {},
   "source": [
    "|QUERY: 5 |Address the \"Confirmed\", \"Deaths\", \"Recovered\" cases were reported from India till 29 April 2020||"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "16d415b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date State Region  Confirmed  Deaths  Recovered\n",
       "74  4/29/2020   NaN  India      33062    1079       8437"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.Region==\"India\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10152836",
   "metadata": {},
   "source": [
    "|QUERY: 6 |wrt number of \"Confirmed cases\"...sort the data in ascending order||"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6581f5a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date      State    Region  Confirmed  Deaths  Recovered\n",
       "156  4/29/2020        NaN  Suriname         10       1          8\n",
       "70   4/29/2020        NaN  Holy See         10       0          2\n",
       "59   4/29/2020        NaN    Gambia         10       1          8\n",
       "318  4/29/2020      Yukon    Canada         11       0          0\n",
       "217  4/29/2020  Greenland   Denmark         11       0         11"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sort_values(by=[\"Confirmed\"], ascending = True).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a6df0be",
   "metadata": {},
   "source": [
    "|QUERY: 7 |wrt \"Recovered cases\"....Sort the data in descending order||"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "804cf4a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  State          Region  Confirmed  Deaths  Recovered\n",
       "153  4/29/2020    NaN           Spain     236899   24275     132929\n",
       "61   4/29/2020    NaN         Germany     161539    6467     120400\n",
       "76   4/29/2020    NaN            Iran      93657    5957      73791\n",
       "80   4/29/2020    NaN           Italy     203591   27682      71252\n",
       "229  4/29/2020  Hubei  Mainland China      68128    4512      63616"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sort_values(by= [\"Recovered\"], ascending= False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cee60bb7",
   "metadata": {},
   "source": [
    "|QUERY: 8 |...Gather up the \"Region\" corresponding to \"Confirmed\",\"Deaths\",\"Recovered\"..||"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c3a333d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>Afghanistan</td>\n",
       "      <td>1939</td>\n",
       "      <td>60</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>Algeria</td>\n",
       "      <td>3848</td>\n",
       "      <td>444</td>\n",
       "      <td>1702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>Angola</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Region  Confirmed  Deaths  Recovered\n",
       "0  Afghanistan       1939      60        252\n",
       "1      Albania        766      30        455\n",
       "2      Algeria       3848     444       1702\n",
       "3      Andorra        743      42        423\n",
       "4       Angola         27       2          7"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_by_region = data.groupby('Region').agg({\n",
    "    'Confirmed': 'sum',\n",
    "    'Deaths': 'sum',\n",
    "    'Recovered': 'sum'\n",
    "}).reset_index()\n",
    "\n",
    "grouped_by_region.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11635baa",
   "metadata": {},
   "source": [
    "|QUERY: 9 |Combine the data wrt to date , sum up the outcomes||"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e43c8ba3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Hariprasad\\AppData\\Local\\Temp\\ipykernel_13432\\2699462882.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data['Date'] = pd.to_datetime(data['Date'])\n"
     ]
    },
    {
     "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>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-04-29</td>\n",
       "      <td>3193807</td>\n",
       "      <td>227633</td>\n",
       "      <td>831638</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Confirmed  Deaths  Recovered\n",
       "0 2020-04-29    3193807  227633     831638"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Date'] = pd.to_datetime(data['Date'])\n",
    "trend_by_date = data.groupby('Date').agg({\n",
    "    'Confirmed': 'sum',\n",
    "    'Deaths': 'sum',\n",
    "    'Recovered': 'sum'\n",
    "}).reset_index()\n",
    "trend_by_date.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc7171e5",
   "metadata": {},
   "source": [
    "|QUERY: 10 |Plot \"Confirmed cases bars\" vs corresponding \"Deaths\" wrt to particular region as listed||\n",
    "\n",
    "['Belgium', 'Brazil', 'Canada','France', 'Germany', 'India', 'Russia', 'Iran', 'Italy', 'US', 'Spain', 'UK']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "861cfad3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# Filter specific regions\n",
    "regions_of_interest = ['Belgium', 'Brazil', 'Canada', 'France', 'Germany',\n",
    "                       'India','Russia', 'Iran', 'Italy', 'US', 'Spain', 'UK']\n",
    "filtered_data = data[data['Region'].isin(regions_of_interest)]\n",
    "\n",
    "# Group the filtered data by Region and aggregate sums for Confirmed and Deaths\n",
    "filtered_region_data = filtered_data.groupby('Region')[['Confirmed', 'Deaths']].sum().reset_index()\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "# Define bar width and positions\n",
    "bar_width = 0.35\n",
    "r1 = np.arange(len(filtered_region_data['Region']))\n",
    "r2 = [x + bar_width for x in r1]\n",
    "\n",
    "plt.figure()\n",
    "plt.bar(r1, filtered_region_data['Confirmed'], color='blue', width=bar_width, edgecolor='grey', label='Confirmed')\n",
    "plt.bar(r2, filtered_region_data['Deaths'], color='red', width=bar_width, edgecolor='grey', label='Deaths')\n",
    "\n",
    "plt.xlabel('Region')\n",
    "plt.ylabel('Cases')\n",
    "plt.title('Confirmed and Deaths Cases by Region')\n",
    "plt.xticks([r + bar_width/2 for r in range(len(filtered_region_data['Region']))], filtered_region_data['Region'], rotation=90)\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed249c37",
   "metadata": {},
   "source": [
    "|QUERY: 11 |Percentage distribution bar plot for only one single \"Region\" i.e. \"India\" ||\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fa1499db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "# Filter the data for only single region India\n",
    "india_data = data[data['Region'] == 'India'][['Confirmed', 'Deaths', 'Recovered']].sum()\n",
    "\n",
    "india_total = india_data.sum()\n",
    "india_percentage = (india_data / india_total) * 100\n",
    "\n",
    "india_percentage_df = india_percentage.reset_index()\n",
    "india_percentage_df.columns = ['Category', 'Percentage']\n",
    "\n",
    "plt.figure()\n",
    "sns.barplot(x='Category', y='Percentage', data=india_percentage_df, palette='viridis')\n",
    "\n",
    "plt.xlabel('Category')\n",
    "plt.ylabel('Percentage')\n",
    "plt.title('Percentage Distribution of Total Confirmed, Deaths, and Recovered Cases in India')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8baf1ff",
   "metadata": {},
   "source": [
    "|QUERY: 13 |Address the \"Confirmed\", \"Deaths\", \"Recovered\" cases were reported from \"Rusia\" till 29 April 2020||\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9fb175fc",
   "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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date State  Region  Confirmed  Deaths  Recovered\n",
       "134  4/29/2020   NaN  Russia      99399     972      10286"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.Region==\"Russia\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "562b8323",
   "metadata": {},
   "source": [
    "|QUERY: 14 |Percentage distribution bar plot comapring \"India\" vs \"Russia\" ||"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6c8b0c8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Filter data as per 2 Region ie. \"India\" and \"Russia\"\n",
    "countries_of_interest = ['India', 'Russia']\n",
    "filtered_data = data[data['Region'].isin(countries_of_interest)]\n",
    "\n",
    "# Grouping filtered data by Region and aggregate sums for Confirmed, Deaths, and Recovered cases.\n",
    "grouped_data = filtered_data.groupby('Region')[['Confirmed', 'Deaths', 'Recovered']].sum().reset_index()\n",
    "\n",
    "# Calculate the percentage.\n",
    "grouped_data['Total'] = grouped_data['Confirmed'] + grouped_data['Deaths'] + grouped_data['Recovered']\n",
    "grouped_data['Confirmed_Percentage'] = (grouped_data['Confirmed'] / grouped_data['Total']) * 100\n",
    "grouped_data['Deaths_Percentage'] = (grouped_data['Deaths'] / grouped_data['Total']) * 100\n",
    "grouped_data['Recovered_Percentage'] = (grouped_data['Recovered'] / grouped_data['Total']) * 100\n",
    "\n",
    "plot_data = grouped_data.melt(id_vars='Region', \n",
    "                              value_vars=['Confirmed_Percentage', 'Deaths_Percentage', 'Recovered_Percentage'],\n",
    "                              var_name='Category', value_name='Percentage')\n",
    "\n",
    "# \"%\" distribution plot.\n",
    "plt.figure(figsize=(12, 8))\n",
    "bar_plot = sns.barplot(x='Region', y='Percentage', hue='Category', data=plot_data, palette='viridis')\n",
    "\n",
    "plt.xlabel('Country')\n",
    "plt.ylabel('Percentage')\n",
    "plt.title('Percentage Distribution of Total Confirmed, Deaths, and Recovered Cases in India vs Russia')\n",
    "plt.legend(title='Category', loc='upper right')\n",
    "\n",
    "for p in bar_plot.patches:\n",
    "    bar_plot.annotate(format(p.get_height(), '.2f') + '%', \n",
    "                      (p.get_x() + p.get_width() / 2., p.get_height()), \n",
    "                      ha = 'center', va = 'center', \n",
    "                      xytext = (0, 9), \n",
    "                      textcoords = 'offset points')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf0a41fc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}