Covid-19 Impact on Global Economy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0311a1fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load datasets\n",
    "transformed_data = pd.read_csv(\"./datasets/transformed_data.csv\")\n",
    "raw_data = pd.read_csv(\"./datasets/raw_data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8361f4a1",
   "metadata": {},
   "source": [
    "The data we are using contains the data on covid-19 cases and their impact on GDP from December 31, 2019, to October 10, 2020."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "977284f9",
   "metadata": {},
   "source": [
    "# Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56156030",
   "metadata": {},
   "source": [
    "The dataset that we are using here contains two data files. One file contains raw data, and the other file contains transformed one. But we have to use both datasets for this task, as both of them contain equally important information in different columns.\n",
    "\n",
    "Let’s have a look at both datasets one by one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c22f4325",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      CODE      COUNTRY        DATE    HDI        TC        TD       STI  \\\n",
      "0      AFG  Afghanistan  2019-12-31  0.498  0.000000  0.000000  0.000000   \n",
      "1      AFG  Afghanistan  2020-01-01  0.498  0.000000  0.000000  0.000000   \n",
      "2      AFG  Afghanistan  2020-01-02  0.498  0.000000  0.000000  0.000000   \n",
      "3      AFG  Afghanistan  2020-01-03  0.498  0.000000  0.000000  0.000000   \n",
      "4      AFG  Afghanistan  2020-01-04  0.498  0.000000  0.000000  0.000000   \n",
      "...    ...          ...         ...    ...       ...       ...       ...   \n",
      "50413  ZWE     Zimbabwe  2020-10-15  0.535  8.994048  5.442418  4.341855   \n",
      "50414  ZWE     Zimbabwe  2020-10-16  0.535  8.996528  5.442418  4.341855   \n",
      "50415  ZWE     Zimbabwe  2020-10-17  0.535  8.999496  5.442418  4.341855   \n",
      "50416  ZWE     Zimbabwe  2020-10-18  0.535  9.000853  5.442418  4.341855   \n",
      "50417  ZWE     Zimbabwe  2020-10-19  0.535  9.005405  5.442418  4.341855   \n",
      "\n",
      "             POP    GDPCAP  \n",
      "0      17.477233  7.497754  \n",
      "1      17.477233  7.497754  \n",
      "2      17.477233  7.497754  \n",
      "3      17.477233  7.497754  \n",
      "4      17.477233  7.497754  \n",
      "...          ...       ...  \n",
      "50413  16.514381  7.549491  \n",
      "50414  16.514381  7.549491  \n",
      "50415  16.514381  7.549491  \n",
      "50416  16.514381  7.549491  \n",
      "50417  16.514381  7.549491  \n",
      "\n",
      "[50418 rows x 9 columns]       iso_code     location        date  total_cases  total_deaths  \\\n",
      "0          AFG  Afghanistan  2019-12-31          0.0           0.0   \n",
      "1          AFG  Afghanistan  2020-01-01          0.0           0.0   \n",
      "2          AFG  Afghanistan  2020-01-02          0.0           0.0   \n",
      "3          AFG  Afghanistan  2020-01-03          0.0           0.0   \n",
      "4          AFG  Afghanistan  2020-01-04          0.0           0.0   \n",
      "...        ...          ...         ...          ...           ...   \n",
      "50413      ZWE     Zimbabwe  2020-10-15       8055.0         231.0   \n",
      "50414      ZWE     Zimbabwe  2020-10-16       8075.0         231.0   \n",
      "50415      ZWE     Zimbabwe  2020-10-17       8099.0         231.0   \n",
      "50416      ZWE     Zimbabwe  2020-10-18       8110.0         231.0   \n",
      "50417      ZWE     Zimbabwe  2020-10-19       8147.0         231.0   \n",
      "\n",
      "       stringency_index  population  gdp_per_capita  human_development_index  \\\n",
      "0                  0.00    38928341        1803.987                    0.498   \n",
      "1                  0.00    38928341        1803.987                    0.498   \n",
      "2                  0.00    38928341        1803.987                    0.498   \n",
      "3                  0.00    38928341        1803.987                    0.498   \n",
      "4                  0.00    38928341        1803.987                    0.498   \n",
      "...                 ...         ...             ...                      ...   \n",
      "50413             76.85    14862927        1899.775                    0.535   \n",
      "50414             76.85    14862927        1899.775                    0.535   \n",
      "50415             76.85    14862927        1899.775                    0.535   \n",
      "50416             76.85    14862927        1899.775                    0.535   \n",
      "50417             76.85    14862927        1899.775                    0.535   \n",
      "\n",
      "        Unnamed: 9  Unnamed: 10 Unnamed: 11  Unnamed: 12  Unnamed: 13  \n",
      "0            #NUM!        #NUM!       #NUM!    17.477233  7.497754494  \n",
      "1            #NUM!        #NUM!       #NUM!    17.477233  7.497754494  \n",
      "2            #NUM!        #NUM!       #NUM!    17.477233  7.497754494  \n",
      "3            #NUM!        #NUM!       #NUM!    17.477233  7.497754494  \n",
      "4            #NUM!        #NUM!       #NUM!    17.477233  7.497754494  \n",
      "...            ...          ...         ...          ...          ...  \n",
      "50413  8.994048296  5.442417711  4.34185547    16.514381  7.549490737  \n",
      "50414  8.996528148  5.442417711  4.34185547    16.514381  7.549490737  \n",
      "50415  8.999495876  5.442417711  4.34185547    16.514381  7.549490737  \n",
      "50416  9.000853147  5.442417711  4.34185547    16.514381  7.549490737  \n",
      "50417   9.00540504  5.442417711  4.34185547    16.514381  7.549490737  \n",
      "\n",
      "[50418 rows x 14 columns]\n"
     ]
    }
   ],
   "source": [
    "# Print data sets\n",
    "print(transformed_data,raw_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47c9405d",
   "metadata": {},
   "source": [
    "Data preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "00712407",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>CODE</th>\n",
       "      <th>COUNTRY</th>\n",
       "      <th>DATE</th>\n",
       "      <th>HDI</th>\n",
       "      <th>TC</th>\n",
       "      <th>TD</th>\n",
       "      <th>STI</th>\n",
       "      <th>POP</th>\n",
       "      <th>GDPCAP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>0.498</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.477233</td>\n",
       "      <td>7.497754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>0.498</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.477233</td>\n",
       "      <td>7.497754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>0.498</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.477233</td>\n",
       "      <td>7.497754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>0.498</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.477233</td>\n",
       "      <td>7.497754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>0.498</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.477233</td>\n",
       "      <td>7.497754</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CODE      COUNTRY        DATE    HDI   TC   TD  STI        POP    GDPCAP\n",
       "0  AFG  Afghanistan  2019-12-31  0.498  0.0  0.0  0.0  17.477233  7.497754\n",
       "1  AFG  Afghanistan  2020-01-01  0.498  0.0  0.0  0.0  17.477233  7.497754\n",
       "2  AFG  Afghanistan  2020-01-02  0.498  0.0  0.0  0.0  17.477233  7.497754\n",
       "3  AFG  Afghanistan  2020-01-03  0.498  0.0  0.0  0.0  17.477233  7.497754\n",
       "4  AFG  Afghanistan  2020-01-04  0.498  0.0  0.0  0.0  17.477233  7.497754"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print head\n",
    "transformed_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e4bc8039",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .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>iso_code</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>stringency_index</th>\n",
       "      <th>population</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>human_development_index</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>Unnamed: 10</th>\n",
       "      <th>Unnamed: 11</th>\n",
       "      <th>Unnamed: 12</th>\n",
       "      <th>Unnamed: 13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38928341</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>0.498</td>\n",
       "      <td>#NUM!</td>\n",
       "      <td>#NUM!</td>\n",
       "      <td>#NUM!</td>\n",
       "      <td>17.477233</td>\n",
       "      <td>7.497754494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38928341</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>0.498</td>\n",
       "      <td>#NUM!</td>\n",
       "      <td>#NUM!</td>\n",
       "      <td>#NUM!</td>\n",
       "      <td>17.477233</td>\n",
       "      <td>7.497754494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38928341</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>0.498</td>\n",
       "      <td>#NUM!</td>\n",
       "      <td>#NUM!</td>\n",
       "      <td>#NUM!</td>\n",
       "      <td>17.477233</td>\n",
       "      <td>7.497754494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38928341</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>0.498</td>\n",
       "      <td>#NUM!</td>\n",
       "      <td>#NUM!</td>\n",
       "      <td>#NUM!</td>\n",
       "      <td>17.477233</td>\n",
       "      <td>7.497754494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38928341</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>0.498</td>\n",
       "      <td>#NUM!</td>\n",
       "      <td>#NUM!</td>\n",
       "      <td>#NUM!</td>\n",
       "      <td>17.477233</td>\n",
       "      <td>7.497754494</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  iso_code     location        date  total_cases  total_deaths  \\\n",
       "0      AFG  Afghanistan  2019-12-31          0.0           0.0   \n",
       "1      AFG  Afghanistan  2020-01-01          0.0           0.0   \n",
       "2      AFG  Afghanistan  2020-01-02          0.0           0.0   \n",
       "3      AFG  Afghanistan  2020-01-03          0.0           0.0   \n",
       "4      AFG  Afghanistan  2020-01-04          0.0           0.0   \n",
       "\n",
       "   stringency_index  population  gdp_per_capita  human_development_index  \\\n",
       "0               0.0    38928341        1803.987                    0.498   \n",
       "1               0.0    38928341        1803.987                    0.498   \n",
       "2               0.0    38928341        1803.987                    0.498   \n",
       "3               0.0    38928341        1803.987                    0.498   \n",
       "4               0.0    38928341        1803.987                    0.498   \n",
       "\n",
       "  Unnamed: 9 Unnamed: 10 Unnamed: 11  Unnamed: 12  Unnamed: 13  \n",
       "0      #NUM!       #NUM!       #NUM!    17.477233  7.497754494  \n",
       "1      #NUM!       #NUM!       #NUM!    17.477233  7.497754494  \n",
       "2      #NUM!       #NUM!       #NUM!    17.477233  7.497754494  \n",
       "3      #NUM!       #NUM!       #NUM!    17.477233  7.497754494  \n",
       "4      #NUM!       #NUM!       #NUM!    17.477233  7.497754494  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4f26e0a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Afghanistan        294\n",
       "Indonesia          294\n",
       "Macedonia          294\n",
       "Luxembourg         294\n",
       "Lithuania          294\n",
       "                  ... \n",
       "Tajikistan         172\n",
       "Comoros            171\n",
       "Lesotho            158\n",
       "Hong Kong           51\n",
       "Solomon Islands      4\n",
       "Name: COUNTRY, Length: 210, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformed_data['COUNTRY'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b69d0f05",
   "metadata": {},
   "source": [
    "So we don’t have an equal number of samples of each country in the dataset. Let’s have a look at the mode value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e97210e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    294\n",
       "Name: COUNTRY, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the most frequent value\n",
    "transformed_data['COUNTRY'].value_counts().mode()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10eda248",
   "metadata": {},
   "source": [
    "So 294 is the mode value. We will need to use it for dividing the sum of all the samples related to the human development index, GDP per capita, and the population. Now let’s create a new dataset by combining the necessary columns from both the datasets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d050088f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Country Code      Country       HDI  Total Cases  Total Deaths  \\\n",
      "0          AFG  Afghanistan  0.498000    5126433.0      165875.0   \n",
      "1          ALB      Albania  0.600765    1071951.0       31056.0   \n",
      "2          DZA      Algeria  0.754000    4893999.0      206429.0   \n",
      "3          AND      Andorra  0.659551     223576.0        9850.0   \n",
      "4          AGO       Angola  0.418952     304005.0       11820.0   \n",
      "\n",
      "   Stringency Index  Population  \n",
      "0          3.049673   17.477233  \n",
      "1          3.005624   14.872537  \n",
      "2          3.195168   17.596309  \n",
      "3          2.677654   11.254996  \n",
      "4          2.965560   17.307957  \n"
     ]
    }
   ],
   "source": [
    "# Create a data frame from both datasets\n",
    "code = transformed_data[\"CODE\"].unique().tolist()\n",
    "country = transformed_data[\"COUNTRY\"].unique().tolist()\n",
    "hdi = []\n",
    "tc = []\n",
    "td = []\n",
    "sti = []\n",
    "population = transformed_data['POP'].unique().tolist()\n",
    "gdp = []\n",
    "\n",
    "for i in country:\n",
    "    hdi.append((transformed_data.loc[transformed_data['COUNTRY'] == i, 'HDI']).sum()/294)\n",
    "    tc.append((raw_data.loc[raw_data['location'] == i, 'total_cases']).sum())\n",
    "    td.append((raw_data.loc[raw_data['location'] == i, 'total_deaths']).sum())\n",
    "    sti.append((transformed_data.loc[transformed_data['COUNTRY'] == i, 'STI']).sum()/294)\n",
    "    population.append((raw_data.loc[raw_data['location'] == i, 'population']).sum()/294)\n",
    "\n",
    "aggregated_data = pd.DataFrame(list(zip(code, country, hdi, tc, td, sti, population)), \n",
    "                               columns = [\"Country Code\", \"Country\", \"HDI\", \n",
    "                                          \"Total Cases\", \"Total Deaths\", \n",
    "                                          \"Stringency Index\", \"Population\"])\n",
    "print(aggregated_data.head())\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15aa8a4d",
   "metadata": {},
   "source": [
    "I have not included the GDP per capita column yet. I didn’t find the correct figures for GDP per capita in the dataset. So it will be better to manually collect the data about the GDP per capita of the countries.\n",
    "\n",
    "As we have so many countries in this data, it will not be easy to manually collect the data about the GDP per capita of all the countries. So let’s select a subsample from this dataset. To create a subsample from this dataset, I will be selecting the top 10 countries with the highest number of covid-19 cases. It will be a perfect sample to study the economic impacts of covid-19. So let’s sort the data according to the total cases of Covid-19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "579d1395",
   "metadata": {},
   "outputs": [
    {
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     "output_type": "stream",
     "text": [
      "    Country Code          Country       HDI  Total Cases  Total Deaths  \\\n",
      "200          USA    United States  0.924000  746014098.0    26477574.0   \n",
      "27           BRA           Brazil  0.759000  425704517.0    14340567.0   \n",
      "90           IND            India  0.640000  407771615.0     7247327.0   \n",
      "157          RUS           Russia  0.816000  132888951.0     2131571.0   \n",
      "150          PER             Peru  0.599490   74882695.0     3020038.0   \n",
      "..           ...              ...       ...          ...           ...   \n",
      "204          VAT          Vatican  0.000000       2415.0           0.0   \n",
      "130          MSR       Montserrat  0.000000       2383.0         177.0   \n",
      "5            AIA         Anguilla  0.000000        614.0           0.0   \n",
      "173          SLB  Solomon Islands  0.007429         11.0           0.0   \n",
      "87           HKG        Hong Kong  0.161847          0.0           0.0   \n",
      "\n",
      "     Stringency Index  Population  \n",
      "200          3.350949   19.617637  \n",
      "27           3.136028   19.174732  \n",
      "90           3.610552   21.045353  \n",
      "157          3.380088   18.798668  \n",
      "150          3.430126   17.311165  \n",
      "..                ...         ...  \n",
      "204          0.000000    6.695799  \n",
      "130          2.477077    8.516993  \n",
      "5            2.326181    9.615939  \n",
      "173          0.000000   13.439912  \n",
      "87           0.643980   15.830012  \n",
      "\n",
      "[210 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "# Sorting Data According to Total Cases\n",
    "data = aggregated_data.sort_values(by=['Total Cases'], ascending=False)\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c56f6fdb",
   "metadata": {},
   "outputs": [
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      "    Country Code         Country       HDI  Total Cases  Total Deaths  \\\n",
      "200          USA   United States  0.924000  746014098.0    26477574.0   \n",
      "27           BRA          Brazil  0.759000  425704517.0    14340567.0   \n",
      "90           IND           India  0.640000  407771615.0     7247327.0   \n",
      "157          RUS          Russia  0.816000  132888951.0     2131571.0   \n",
      "150          PER            Peru  0.599490   74882695.0     3020038.0   \n",
      "125          MEX          Mexico  0.774000   74347548.0     7295850.0   \n",
      "178          ESP           Spain  0.887969   73717676.0     5510624.0   \n",
      "175          ZAF    South Africa  0.608653   63027659.0     1357682.0   \n",
      "42           COL        Colombia  0.581847   60543682.0     1936134.0   \n",
      "199          GBR  United Kingdom  0.922000   59475032.0     7249573.0   \n",
      "\n",
      "     Stringency Index  Population  \n",
      "200          3.350949   19.617637  \n",
      "27           3.136028   19.174732  \n",
      "90           3.610552   21.045353  \n",
      "157          3.380088   18.798668  \n",
      "150          3.430126   17.311165  \n",
      "125          3.019289   18.674802  \n",
      "178          3.393922   17.660427  \n",
      "175          3.364333   17.898266  \n",
      "42           3.357923   17.745037  \n",
      "199          3.353883   18.033340  \n"
     ]
    }
   ],
   "source": [
    "# Top 10 Countries with Highest Covid Cases\n",
    "data = data.head(10)\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5d307bd",
   "metadata": {},
   "source": [
    "Now I will add two more columns (GDP per capita before Covid-19, GDP per capita during Covid-19) to this dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7ac916af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Country Code         Country       HDI  Total Cases  Total Deaths  \\\n",
      "200          USA   United States  0.924000  746014098.0    26477574.0   \n",
      "27           BRA          Brazil  0.759000  425704517.0    14340567.0   \n",
      "90           IND           India  0.640000  407771615.0     7247327.0   \n",
      "157          RUS          Russia  0.816000  132888951.0     2131571.0   \n",
      "150          PER            Peru  0.599490   74882695.0     3020038.0   \n",
      "125          MEX          Mexico  0.774000   74347548.0     7295850.0   \n",
      "178          ESP           Spain  0.887969   73717676.0     5510624.0   \n",
      "175          ZAF    South Africa  0.608653   63027659.0     1357682.0   \n",
      "42           COL        Colombia  0.581847   60543682.0     1936134.0   \n",
      "199          GBR  United Kingdom  0.922000   59475032.0     7249573.0   \n",
      "\n",
      "     Stringency Index  Population  GDP Before Covid  GDP During Covid  \n",
      "200          3.350949   19.617637          65279.53          63543.58  \n",
      "27           3.136028   19.174732           8897.49           6796.84  \n",
      "90           3.610552   21.045353           2100.75           1900.71  \n",
      "157          3.380088   18.798668          11497.65          10126.72  \n",
      "150          3.430126   17.311165           7027.61           6126.87  \n",
      "125          3.019289   18.674802           9946.03           8346.70  \n",
      "178          3.393922   17.660427          29564.74          27057.16  \n",
      "175          3.364333   17.898266           6001.40           5090.72  \n",
      "42           3.357923   17.745037           6424.98           5332.77  \n",
      "199          3.353883   18.033340          42354.41          40284.64  \n"
     ]
    }
   ],
   "source": [
    "# Now I will add two more columns (GDP per capita before Covid-19, GDP per capita during Covid-19) to this dataset\n",
    "# Note: The data about the GDP per capita is collected manually.\n",
    "\n",
    "data[\"GDP Before Covid\"] = [65279.53, 8897.49, 2100.75, \n",
    "                            11497.65, 7027.61, 9946.03, \n",
    "                            29564.74, 6001.40, 6424.98, 42354.41]\n",
    "data[\"GDP During Covid\"] = [63543.58, 6796.84, 1900.71, \n",
    "                            10126.72, 6126.87, 8346.70, \n",
    "                            27057.16, 5090.72, 5332.77, 40284.64]\n",
    "print(data)"
   ]
  },
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   "source": [
    "Analyzing the Spread of Covid-19"
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    "figure = go.Figure()\n",
    "\n",
    "figure.add_trace(go.Bar(x=data['Country'],y=data['Total Cases'], name='Total Cases', marker_color='indianred'))\n",
    "figure.add_trace(go.Bar(x=data['Country'],y=data['Total Deaths'], name='Total Deaths', marker_color='lightsalmon'))\n",
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    "# Percentage of Total Cases and Deaths\n",
    "cases = data['Total Cases'].sum()\n",
    "deceased = data['Total Deaths'].sum()\n",
    "pie_fig = px.pie(data,names=[\"Total Cases\", \"Total Deaths\"],values=[cases,deceased],title=\"Percentage of Total Cases and Deaths\", hole=0.5)\n",
    "pie_fig.show()"
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   "id": "bd28e964",
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      "DetDeath Rate =  3.6144212045653767\n"
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   "source": [
    "# Deth rate\n",
    "\n",
    "death_rate = (data['Total Deaths'].sum() / data['Total Cases'].sum())*100\n",
    "print(\"DetDeath Rate = \", death_rate)"
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    "# Stringency Index\n",
    "Another important column in this dataset is the stringency index. It is a composite measure of response indicators, including school closures, workplace closures, and travel bans. It shows how strictly countries are following these measures to control the spread of covid-19:"
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    "# Analyzing Covid-19 Impacts on Economy"
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    "GDP per capita is the primary factor for analyzing the economic slowdowns caused due to the outbreak of covid-19. Let’s have a look at the GDP per capita before the outbreak of covid-19 among the countries with the highest number of covid-19 cases."
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    "gdp_during_fig = px.bar(data, x='Country', y='Total Cases',\n",
    "             hover_data=['Population', 'Total Deaths'], \n",
    "             color='GDP During Covid', height=400, \n",
    "             title=\"GDP Per Capita During Covid-19\")\n",
    "gdp_during_fig.show()"
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  {
   "cell_type": "markdown",
   "id": "29f75f48",
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   "source": [
    "Let’s compare the GDP per capita before covid-19 and during covid-19 to have a look at the impact of covid-19 on the GDP per capita:"
   ]
  },
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   "source": [
    "fig_compare = go.Figure()\n",
    "fig_compare.add_trace(go.Bar(x=data[\"Country\"],\n",
    "    y=data[\"GDP Before Covid\"],\n",
    "    name='GDP Per Capita Before Covid-19',\n",
    "    marker_color='indianred'\n",
    "    ))\n",
    "fig_compare.add_trace(go.Bar(\n",
    "    x=data[\"Country\"],\n",
    "    y=data[\"GDP During Covid\"],\n",
    "    name='GDP Per Capita During Covid-19',\n",
    "    marker_color='lightsalmon'\n",
    "))\n",
    "fig_compare.update_layout(barmode='group', xaxis_tickangle=-45)\n",
    "fig_compare.show()"
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  {
   "cell_type": "markdown",
   "id": "335de03f",
   "metadata": {},
   "source": [
    "One other important economic factor is Human Development Index. It is a statistic composite index of life expectancy, education, and per capita indicators. Let’s have a look at how many countries were spending their budget on the human development:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b1882857",
   "metadata": {},
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           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Human Development Index during Covid-19"
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "Country"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "Total Cases"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hdi_fig = px.bar(data, x='Country', y='Total Cases',\n",
    "             hover_data=['Population', 'Total Deaths'], \n",
    "             color='HDI', height=400, \n",
    "             title=\"Human Development Index during Covid-19\")\n",
    "hdi_fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b03ca91c",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "In this task, we studied the spread of covid-19 among the countries and its impact on the global economy. We saw that the outbreak of covid-19 resulted in the highest number of covid-19 cases and deaths in the united states. One major reason behind this is the stringency index of the United States. It is comparatively low according to the population. We also analyzed how the GDP per capita of every country was affected during the outbreak of covid-19."
   ]
  }
 ],
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