Covid_data-turing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "adcf1f26",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas.plotting import scatter_matrix\n",
    "covid_df=pd.read_csv(\"covid_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e86e6dbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>population</th>\n",
       "      <th>aged_65_older_percent</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38928341.0</td>\n",
       "      <td>2.581</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38928341.0</td>\n",
       "      <td>2.581</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38928341.0</td>\n",
       "      <td>2.581</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38928341.0</td>\n",
       "      <td>2.581</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38928341.0</td>\n",
       "      <td>2.581</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38928341.0</td>\n",
       "      <td>2.581</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-06</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38928341.0</td>\n",
       "      <td>2.581</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-07</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38928341.0</td>\n",
       "      <td>2.581</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-08</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38928341.0</td>\n",
       "      <td>2.581</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38928341.0</td>\n",
       "      <td>2.581</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      location        date  new_cases  new_deaths  population  \\\n",
       "0  Afghanistan  2019-12-31          0           0  38928341.0   \n",
       "1  Afghanistan  2020-01-01          0           0  38928341.0   \n",
       "2  Afghanistan  2020-01-02          0           0  38928341.0   \n",
       "3  Afghanistan  2020-01-03          0           0  38928341.0   \n",
       "4  Afghanistan  2020-01-04          0           0  38928341.0   \n",
       "5  Afghanistan  2020-01-05          0           0  38928341.0   \n",
       "6  Afghanistan  2020-01-06          0           0  38928341.0   \n",
       "7  Afghanistan  2020-01-07          0           0  38928341.0   \n",
       "8  Afghanistan  2020-01-08          0           0  38928341.0   \n",
       "9  Afghanistan  2020-01-09          0           0  38928341.0   \n",
       "\n",
       "   aged_65_older_percent  gdp_per_capita  hospital_beds_per_thousand  \n",
       "0                  2.581        1803.987                         0.5  \n",
       "1                  2.581        1803.987                         0.5  \n",
       "2                  2.581        1803.987                         0.5  \n",
       "3                  2.581        1803.987                         0.5  \n",
       "4                  2.581        1803.987                         0.5  \n",
       "5                  2.581        1803.987                         0.5  \n",
       "6                  2.581        1803.987                         0.5  \n",
       "7                  2.581        1803.987                         0.5  \n",
       "8                  2.581        1803.987                         0.5  \n",
       "9                  2.581        1803.987                         0.5  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "646913ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 23082 entries, 0 to 23081\n",
      "Data columns (total 8 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   location                    23082 non-null  object \n",
      " 1   date                        23082 non-null  object \n",
      " 2   new_cases                   23082 non-null  int64  \n",
      " 3   new_deaths                  23082 non-null  int64  \n",
      " 4   population                  23018 non-null  float64\n",
      " 5   aged_65_older_percent       20643 non-null  float64\n",
      " 6   gdp_per_capita              20711 non-null  float64\n",
      " 7   hospital_beds_per_thousand  19144 non-null  float64\n",
      "dtypes: float64(4), int64(2), object(2)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "covid_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8fece413",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\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>new_cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>population</th>\n",
       "      <th>aged_65_older_percent</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>23082.000000</td>\n",
       "      <td>23082.000000</td>\n",
       "      <td>2.301800e+04</td>\n",
       "      <td>20643.000000</td>\n",
       "      <td>20711.000000</td>\n",
       "      <td>19144.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>624.769257</td>\n",
       "      <td>35.629062</td>\n",
       "      <td>1.037003e+08</td>\n",
       "      <td>9.758570</td>\n",
       "      <td>22708.384791</td>\n",
       "      <td>3.204687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5517.309068</td>\n",
       "      <td>330.986941</td>\n",
       "      <td>6.716897e+08</td>\n",
       "      <td>6.445662</td>\n",
       "      <td>21187.826121</td>\n",
       "      <td>2.593700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2461.000000</td>\n",
       "      <td>-1918.000000</td>\n",
       "      <td>8.090000e+02</td>\n",
       "      <td>1.144000</td>\n",
       "      <td>661.240000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.083380e+06</td>\n",
       "      <td>3.853000</td>\n",
       "      <td>6426.674000</td>\n",
       "      <td>1.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.449321e+06</td>\n",
       "      <td>7.646000</td>\n",
       "      <td>15524.995000</td>\n",
       "      <td>2.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.346920e+07</td>\n",
       "      <td>15.322000</td>\n",
       "      <td>35220.084000</td>\n",
       "      <td>4.210000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>133510.000000</td>\n",
       "      <td>10520.000000</td>\n",
       "      <td>7.794799e+09</td>\n",
       "      <td>27.049000</td>\n",
       "      <td>116935.600000</td>\n",
       "      <td>13.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           new_cases    new_deaths    population  aged_65_older_percent  \\\n",
       "count   23082.000000  23082.000000  2.301800e+04           20643.000000   \n",
       "mean      624.769257     35.629062  1.037003e+08               9.758570   \n",
       "std      5517.309068    330.986941  6.716897e+08               6.445662   \n",
       "min     -2461.000000  -1918.000000  8.090000e+02               1.144000   \n",
       "25%         0.000000      0.000000  2.083380e+06               3.853000   \n",
       "50%         3.000000      0.000000  9.449321e+06               7.646000   \n",
       "75%        55.000000      1.000000  3.346920e+07              15.322000   \n",
       "max    133510.000000  10520.000000  7.794799e+09              27.049000   \n",
       "\n",
       "       gdp_per_capita  hospital_beds_per_thousand  \n",
       "count    20711.000000                19144.000000  \n",
       "mean     22708.384791                    3.204687  \n",
       "std      21187.826121                    2.593700  \n",
       "min        661.240000                    0.100000  \n",
       "25%       6426.674000                    1.400000  \n",
       "50%      15524.995000                    2.600000  \n",
       "75%      35220.084000                    4.210000  \n",
       "max     116935.600000                   13.800000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e488ac6",
   "metadata": {},
   "source": [
    "## Determining the dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "32712974",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23082, 8)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "af345603",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "covid_df['new_deaths'].plot.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6974c3bc",
   "metadata": {},
   "source": [
    "## Finding and showing null values in a plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "414520d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location                         0\n",
       "date                             0\n",
       "new_cases                        0\n",
       "new_deaths                       0\n",
       "population                      64\n",
       "aged_65_older_percent         2439\n",
       "gdp_per_capita                2371\n",
       "hospital_beds_per_thousand    3938\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a619feb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "212"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df.location.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "febb6d43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['location', 'date', 'new_cases', 'new_deaths', 'population',\n",
       "       'aged_65_older_percent', 'gdp_per_capita',\n",
       "       'hospital_beds_per_thousand'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6d9f277f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='location'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "covid_df.groupby(\"location\").new_deaths.mean().sort_values(ascending=False)[:5].plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "86470fc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='location'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "covid_df.groupby(\"location\").new_cases.mean().sort_values(ascending=False)[:5].plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3424517a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='location'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "covid_df.groupby(\"location\").population.mean().sort_values(ascending=False)[:5].plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8b0ea1e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='location'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "covid_df.groupby(\"location\").hospital_beds_per_thousand.mean().sort_values(ascending=False)[:5].plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "536410ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='location'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "covid_df.groupby(\"location\").aged_65_older_percent.mean().sort_values(ascending=False)[:5].plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "76146487",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:xlabel='new_cases', ylabel='new_cases'>,\n",
       "        <AxesSubplot:xlabel='new_deaths', ylabel='new_cases'>],\n",
       "       [<AxesSubplot:xlabel='new_cases', ylabel='new_deaths'>,\n",
       "        <AxesSubplot:xlabel='new_deaths', ylabel='new_deaths'>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_matrix(covid_df.loc[:,'new_cases':'new_deaths'],figsize=(12, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c1712291",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(covid_df.corr(), annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7866a89",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8639856f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='location'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "covid_df.groupby(\"location\").new_deaths.max().sort_values(ascending=False)[:5].plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3ba90027",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location                         0\n",
       "date                             0\n",
       "new_cases                        0\n",
       "new_deaths                       0\n",
       "population                      64\n",
       "aged_65_older_percent         2439\n",
       "gdp_per_capita                2371\n",
       "hospital_beds_per_thousand    3938\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "237b887c",
   "metadata": {},
   "source": [
    "### Location wise details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "19c7ffc4",
   "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>new_cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>population</th>\n",
       "      <th>aged_65_older_percent</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>21459</td>\n",
       "      <td>384</td>\n",
       "      <td>5.956036e+09</td>\n",
       "      <td>394.893</td>\n",
       "      <td>276010.011</td>\n",
       "      <td>76.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>1299</td>\n",
       "      <td>34</td>\n",
       "      <td>2.705132e+08</td>\n",
       "      <td>1239.672</td>\n",
       "      <td>1109522.514</td>\n",
       "      <td>271.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>10382</td>\n",
       "      <td>724</td>\n",
       "      <td>6.928465e+09</td>\n",
       "      <td>981.338</td>\n",
       "      <td>2198386.562</td>\n",
       "      <td>300.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>852</td>\n",
       "      <td>51</td>\n",
       "      <td>6.876585e+06</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>96</td>\n",
       "      <td>4</td>\n",
       "      <td>2.662168e+09</td>\n",
       "      <td>194.805</td>\n",
       "      <td>471379.095</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Anguilla</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.140152e+06</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>8.128024e+06</td>\n",
       "      <td>575.439</td>\n",
       "      <td>1783748.269</td>\n",
       "      <td>315.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>24748</td>\n",
       "      <td>717</td>\n",
       "      <td>4.338795e+09</td>\n",
       "      <td>1075.008</td>\n",
       "      <td>1817655.072</td>\n",
       "      <td>480.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>13675</td>\n",
       "      <td>217</td>\n",
       "      <td>4.563380e+08</td>\n",
       "      <td>1729.728</td>\n",
       "      <td>1353287.320</td>\n",
       "      <td>646.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aruba</th>\n",
       "      <td>101</td>\n",
       "      <td>3</td>\n",
       "      <td>8.541280e+06</td>\n",
       "      <td>1046.800</td>\n",
       "      <td>2877902.480</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>7267</td>\n",
       "      <td>102</td>\n",
       "      <td>4.156481e+09</td>\n",
       "      <td>2527.152</td>\n",
       "      <td>7277739.730</td>\n",
       "      <td>625.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>16902</td>\n",
       "      <td>672</td>\n",
       "      <td>1.468043e+09</td>\n",
       "      <td>3129.926</td>\n",
       "      <td>7406179.818</td>\n",
       "      <td>1201.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Azerbaijan</th>\n",
       "      <td>8191</td>\n",
       "      <td>98</td>\n",
       "      <td>1.581711e+09</td>\n",
       "      <td>938.808</td>\n",
       "      <td>2472197.364</td>\n",
       "      <td>733.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bahamas</th>\n",
       "      <td>103</td>\n",
       "      <td>11</td>\n",
       "      <td>3.342608e+07</td>\n",
       "      <td>764.660</td>\n",
       "      <td>2356016.995</td>\n",
       "      <td>246.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bahrain</th>\n",
       "      <td>16200</td>\n",
       "      <td>29</td>\n",
       "      <td>2.756564e+08</td>\n",
       "      <td>384.264</td>\n",
       "      <td>7013094.210</td>\n",
       "      <td>324.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bangladesh</th>\n",
       "      <td>71675</td>\n",
       "      <td>975</td>\n",
       "      <td>1.630425e+10</td>\n",
       "      <td>504.702</td>\n",
       "      <td>348874.416</td>\n",
       "      <td>79.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barbados</th>\n",
       "      <td>92</td>\n",
       "      <td>7</td>\n",
       "      <td>2.442654e+07</td>\n",
       "      <td>1270.920</td>\n",
       "      <td>1443135.780</td>\n",
       "      <td>493.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belarus</th>\n",
       "      <td>50265</td>\n",
       "      <td>282</td>\n",
       "      <td>1.540239e+09</td>\n",
       "      <td>2412.237</td>\n",
       "      <td>2798378.621</td>\n",
       "      <td>1793.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>59437</td>\n",
       "      <td>9619</td>\n",
       "      <td>1.889107e+09</td>\n",
       "      <td>3027.073</td>\n",
       "      <td>6953347.888</td>\n",
       "      <td>919.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belize</th>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>3.141206e+07</td>\n",
       "      <td>304.387</td>\n",
       "      <td>618124.598</td>\n",
       "      <td>102.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     new_cases  new_deaths    population  \\\n",
       "location                                                   \n",
       "Afghanistan              21459         384  5.956036e+09   \n",
       "Albania                   1299          34  2.705132e+08   \n",
       "Algeria                  10382         724  6.928465e+09   \n",
       "Andorra                    852          51  6.876585e+06   \n",
       "Angola                      96           4  2.662168e+09   \n",
       "Anguilla                     3           0  1.140152e+06   \n",
       "Antigua and Barbuda         26           3  8.128024e+06   \n",
       "Argentina                24748         717  4.338795e+09   \n",
       "Armenia                  13675         217  4.563380e+08   \n",
       "Aruba                      101           3  8.541280e+06   \n",
       "Australia                 7267         102  4.156481e+09   \n",
       "Austria                  16902         672  1.468043e+09   \n",
       "Azerbaijan                8191          98  1.581711e+09   \n",
       "Bahamas                    103          11  3.342608e+07   \n",
       "Bahrain                  16200          29  2.756564e+08   \n",
       "Bangladesh               71675         975  1.630425e+10   \n",
       "Barbados                    92           7  2.442654e+07   \n",
       "Belarus                  50265         282  1.540239e+09   \n",
       "Belgium                  59437        9619  1.889107e+09   \n",
       "Belize                      19           2  3.141206e+07   \n",
       "\n",
       "                     aged_65_older_percent  gdp_per_capita  \\\n",
       "location                                                     \n",
       "Afghanistan                        394.893      276010.011   \n",
       "Albania                           1239.672     1109522.514   \n",
       "Algeria                            981.338     2198386.562   \n",
       "Andorra                              0.000           0.000   \n",
       "Angola                             194.805      471379.095   \n",
       "Anguilla                             0.000           0.000   \n",
       "Antigua and Barbuda                575.439     1783748.269   \n",
       "Argentina                         1075.008     1817655.072   \n",
       "Armenia                           1729.728     1353287.320   \n",
       "Aruba                             1046.800     2877902.480   \n",
       "Australia                         2527.152     7277739.730   \n",
       "Austria                           3129.926     7406179.818   \n",
       "Azerbaijan                         938.808     2472197.364   \n",
       "Bahamas                            764.660     2356016.995   \n",
       "Bahrain                            384.264     7013094.210   \n",
       "Bangladesh                         504.702      348874.416   \n",
       "Barbados                          1270.920     1443135.780   \n",
       "Belarus                           2412.237     2798378.621   \n",
       "Belgium                           3027.073     6953347.888   \n",
       "Belize                             304.387      618124.598   \n",
       "\n",
       "                     hospital_beds_per_thousand  \n",
       "location                                         \n",
       "Afghanistan                               76.50  \n",
       "Albania                                  271.66  \n",
       "Algeria                                  300.20  \n",
       "Andorra                                    0.00  \n",
       "Angola                                     0.00  \n",
       "Anguilla                                   0.00  \n",
       "Antigua and Barbuda                      315.40  \n",
       "Argentina                                480.00  \n",
       "Armenia                                  646.80  \n",
       "Aruba                                      0.00  \n",
       "Australia                                625.92  \n",
       "Austria                                 1201.31  \n",
       "Azerbaijan                               733.20  \n",
       "Bahamas                                  246.50  \n",
       "Bahrain                                  324.00  \n",
       "Bangladesh                                79.20  \n",
       "Barbados                                 493.00  \n",
       "Belarus                                 1793.00  \n",
       "Belgium                                  919.32  \n",
       "Belize                                   102.70  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Location wise details\n",
    "covid_df.groupby('location').sum().head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76c4e1b8",
   "metadata": {},
   "source": [
    "### Location wise confirmed cases in descending order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e312db2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location\n",
       "World             7210462\n",
       "United States     1979850\n",
       "Brazil             739503\n",
       "Russia             485253\n",
       "United Kingdom     289140\n",
       "India              276583\n",
       "Spain              241966\n",
       "Italy              235561\n",
       "Peru               203736\n",
       "Germany            184861\n",
       "Iran               175927\n",
       "Turkey             172114\n",
       "France             154591\n",
       "Chile              142759\n",
       "Mexico             124301\n",
       "Pakistan           113702\n",
       "Saudi Arabia       108571\n",
       "Canada              96642\n",
       "China               84198\n",
       "Qatar               71879\n",
       "Name: new_cases, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Location wise confirmed cases in descending order\n",
    "covid_df.groupby('location')['new_cases'].sum().sort_values(ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95e833ec",
   "metadata": {},
   "source": [
    "### Finding the region with highest confirmed cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8242a150",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location\n",
       "World            7210462\n",
       "United States    1979850\n",
       "Name: new_cases, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Finding the region with highest confirmed cases\n",
    "\n",
    "covid_df.groupby('location').new_cases.sum().sort_values(ascending = False).head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14221a3e",
   "metadata": {},
   "source": [
    "### Finding the Locations with lowest deaths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b25f9b10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location\n",
       "Bonaire Sint Eustatius and Saba     0\n",
       "Timor                               0\n",
       "French Polynesia                    0\n",
       "Namibia                             0\n",
       "Fiji                                0\n",
       "Cambodia                            0\n",
       "Falkland Islands                    0\n",
       "Bhutan                              0\n",
       "Faeroe Islands                      0\n",
       "Eritrea                             0\n",
       "Papua New Guinea                    0\n",
       "Dominica                            0\n",
       "Saint Kitts and Nevis               0\n",
       "Saint Lucia                         0\n",
       "Seychelles                          0\n",
       "New Caledonia                       0\n",
       "Gibraltar                           0\n",
       "Saint Vincent and the Grenadines    0\n",
       "Vietnam                             0\n",
       "Anguilla                            0\n",
       "Name: new_deaths, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Finding the regions with lowest deaths\n",
    "covid_df.groupby('location').new_deaths.sum().sort_values(ascending = True).head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad68d81b",
   "metadata": {},
   "source": [
    "### Finding details for a particular country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2685f396",
   "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>location</th>\n",
       "      <th>date</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>population</th>\n",
       "      <th>aged_65_older_percent</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15954</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>220892331.0</td>\n",
       "      <td>4.495</td>\n",
       "      <td>5034.708</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15955</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>220892331.0</td>\n",
       "      <td>4.495</td>\n",
       "      <td>5034.708</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15956</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>220892331.0</td>\n",
       "      <td>4.495</td>\n",
       "      <td>5034.708</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15957</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>220892331.0</td>\n",
       "      <td>4.495</td>\n",
       "      <td>5034.708</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15958</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>220892331.0</td>\n",
       "      <td>4.495</td>\n",
       "      <td>5034.708</td>\n",
       "      <td>0.6</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16107</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>2020-06-06</td>\n",
       "      <td>4734</td>\n",
       "      <td>97</td>\n",
       "      <td>220892331.0</td>\n",
       "      <td>4.495</td>\n",
       "      <td>5034.708</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16108</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>2020-06-07</td>\n",
       "      <td>4960</td>\n",
       "      <td>67</td>\n",
       "      <td>220892331.0</td>\n",
       "      <td>4.495</td>\n",
       "      <td>5034.708</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16109</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>2020-06-08</td>\n",
       "      <td>4728</td>\n",
       "      <td>65</td>\n",
       "      <td>220892331.0</td>\n",
       "      <td>4.495</td>\n",
       "      <td>5034.708</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16110</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>2020-06-09</td>\n",
       "      <td>4646</td>\n",
       "      <td>105</td>\n",
       "      <td>220892331.0</td>\n",
       "      <td>4.495</td>\n",
       "      <td>5034.708</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16111</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>2020-06-10</td>\n",
       "      <td>5385</td>\n",
       "      <td>83</td>\n",
       "      <td>220892331.0</td>\n",
       "      <td>4.495</td>\n",
       "      <td>5034.708</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>158 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       location        date  new_cases  new_deaths   population  \\\n",
       "15954  Pakistan  2019-12-31          0           0  220892331.0   \n",
       "15955  Pakistan  2020-01-01          0           0  220892331.0   \n",
       "15956  Pakistan  2020-01-02          0           0  220892331.0   \n",
       "15957  Pakistan  2020-01-03          0           0  220892331.0   \n",
       "15958  Pakistan  2020-01-04          0           0  220892331.0   \n",
       "...         ...         ...        ...         ...          ...   \n",
       "16107  Pakistan  2020-06-06       4734          97  220892331.0   \n",
       "16108  Pakistan  2020-06-07       4960          67  220892331.0   \n",
       "16109  Pakistan  2020-06-08       4728          65  220892331.0   \n",
       "16110  Pakistan  2020-06-09       4646         105  220892331.0   \n",
       "16111  Pakistan  2020-06-10       5385          83  220892331.0   \n",
       "\n",
       "       aged_65_older_percent  gdp_per_capita  hospital_beds_per_thousand  \n",
       "15954                  4.495        5034.708                         0.6  \n",
       "15955                  4.495        5034.708                         0.6  \n",
       "15956                  4.495        5034.708                         0.6  \n",
       "15957                  4.495        5034.708                         0.6  \n",
       "15958                  4.495        5034.708                         0.6  \n",
       "...                      ...             ...                         ...  \n",
       "16107                  4.495        5034.708                         0.6  \n",
       "16108                  4.495        5034.708                         0.6  \n",
       "16109                  4.495        5034.708                         0.6  \n",
       "16110                  4.495        5034.708                         0.6  \n",
       "16111                  4.495        5034.708                         0.6  \n",
       "\n",
       "[158 rows x 8 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Finding details for a particular country\n",
    "covid_df[covid_df.location == 'Pakistan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b0f51970",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>6237</th>\n",
       "      <td>Ecuador</td>\n",
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       "      <td>-2461</td>\n",
       "      <td>49</td>\n",
       "      <td>1.764306e+07</td>\n",
       "      <td>7.104</td>\n",
       "      <td>10581.936</td>\n",
       "      <td>1.500</td>\n",
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       "    <tr>\n",
       "      <th>6239</th>\n",
       "      <td>Ecuador</td>\n",
       "      <td>2020-05-09</td>\n",
       "      <td>-1480</td>\n",
       "      <td>50</td>\n",
       "      <td>1.764306e+07</td>\n",
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       "    <tr>\n",
       "      <th>7488</th>\n",
       "      <td>France</td>\n",
       "      <td>2020-06-03</td>\n",
       "      <td>-766</td>\n",
       "      <td>107</td>\n",
       "      <td>6.527351e+07</td>\n",
       "      <td>19.718</td>\n",
       "      <td>38605.671</td>\n",
       "      <td>5.980</td>\n",
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       "    <tr>\n",
       "      <th>19498</th>\n",
       "      <td>Spain</td>\n",
       "      <td>2020-04-19</td>\n",
       "      <td>-713</td>\n",
       "      <td>410</td>\n",
       "      <td>4.675478e+07</td>\n",
       "      <td>19.436</td>\n",
       "      <td>34272.360</td>\n",
       "      <td>2.970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21805</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2020-05-21</td>\n",
       "      <td>-525</td>\n",
       "      <td>363</td>\n",
       "      <td>6.788600e+07</td>\n",
       "      <td>18.517</td>\n",
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       "      <td>125862</td>\n",
       "      <td>3822</td>\n",
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       "      <td>8.696</td>\n",
       "      <td>15469.207</td>\n",
       "      <td>2.705</td>\n",
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       "    <tr>\n",
       "      <th>23011</th>\n",
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       "      <td>2020-06-04</td>\n",
       "      <td>126684</td>\n",
       "      <td>5746</td>\n",
       "      <td>7.794799e+09</td>\n",
       "      <td>8.696</td>\n",
       "      <td>15469.207</td>\n",
       "      <td>2.705</td>\n",
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       "      <th>23012</th>\n",
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       "      <td>127662</td>\n",
       "      <td>5189</td>\n",
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       "      <td>15469.207</td>\n",
       "      <td>2.705</td>\n",
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       "      <th>23007</th>\n",
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       "      <td>127796</td>\n",
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       "      <td>World</td>\n",
       "      <td>2020-06-06</td>\n",
       "      <td>133510</td>\n",
       "      <td>4784</td>\n",
       "      <td>7.794799e+09</td>\n",
       "      <td>8.696</td>\n",
       "      <td>15469.207</td>\n",
       "      <td>2.705</td>\n",
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       "<p>23082 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             location        date  new_cases  new_deaths    population  \\\n",
       "6237          Ecuador  2020-05-07      -2461          49  1.764306e+07   \n",
       "6239          Ecuador  2020-05-09      -1480          50  1.764306e+07   \n",
       "7488           France  2020-06-03       -766         107  6.527351e+07   \n",
       "19498           Spain  2020-04-19       -713         410  4.675478e+07   \n",
       "21805  United Kingdom  2020-05-21       -525         363  6.788600e+07   \n",
       "...               ...         ...        ...         ...           ...   \n",
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       "23012           World  2020-06-05     127662        5189  7.794799e+09   \n",
       "23007           World  2020-05-31     127796        4050  7.794799e+09   \n",
       "23013           World  2020-06-06     133510        4784  7.794799e+09   \n",
       "\n",
       "       aged_65_older_percent  gdp_per_capita  hospital_beds_per_thousand  \n",
       "6237                   7.104       10581.936                       1.500  \n",
       "6239                   7.104       10581.936                       1.500  \n",
       "7488                  19.718       38605.671                       5.980  \n",
       "19498                 19.436       34272.360                       2.970  \n",
       "21805                 18.517       39753.244                       2.540  \n",
       "...                      ...             ...                         ...  \n",
       "23014                  8.696       15469.207                       2.705  \n",
       "23011                  8.696       15469.207                       2.705  \n",
       "23012                  8.696       15469.207                       2.705  \n",
       "23007                  8.696       15469.207                       2.705  \n",
       "23013                  8.696       15469.207                       2.705  \n",
       "\n",
       "[23082 rows x 8 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Sorting the dataset by according to confirmed cases\n",
    "covid_df.sort_values(by = ['new_cases'],ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3703cdc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_df['date'] = pd.to_datetime(covid_df.date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "61c45e02",
   "metadata": {},
   "outputs": [],
   "source": [
    "germany_covid_df = covid_df[covid_df.location == \"Germany\"]\n",
    "italy_covid_df = covid_df[covid_df.location == \"Italy\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "eba45d4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Muntaha\\AppData\\Local\\Temp\\ipykernel_13292\\205320774.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",
      "  germany_covid_df[\"cumsum\"] = germany_covid_df.new_cases.cumsum()\n",
      "C:\\Users\\Muntaha\\AppData\\Local\\Temp\\ipykernel_13292\\205320774.py:2: 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",
      "  italy_covid_df[\"cumsum\"] = italy_covid_df.new_cases.cumsum()\n"
     ]
    },
    {
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       "      <th>159</th>\n",
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       "      <td>234801</td>\n",
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       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>Germany</td>\n",
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       "      <td>214</td>\n",
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       "      <td>53</td>\n",
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       "      <td>234998</td>\n",
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       "      <th>161</th>\n",
       "      <td>Germany</td>\n",
       "      <td>2020-06-09</td>\n",
       "      <td>350</td>\n",
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       "      <td>184543</td>\n",
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       "      <td>65</td>\n",
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       "    <tr>\n",
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       "      <td>Germany</td>\n",
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       "      <td>318</td>\n",
       "      <td>18</td>\n",
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       "      <td>79</td>\n",
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       "<p>163 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    location_x       date  new_cases_x  new_deaths_x  population_x  \\\n",
       "0      Germany 2019-12-31            0             0    83783945.0   \n",
       "1      Germany 2020-01-01            0             0    83783945.0   \n",
       "2      Germany 2020-01-02            0             0    83783945.0   \n",
       "3      Germany 2020-01-03            0             0    83783945.0   \n",
       "4      Germany 2020-01-04            0             0    83783945.0   \n",
       "..         ...        ...          ...           ...           ...   \n",
       "158    Germany 2020-06-06          407            33    83783945.0   \n",
       "159    Germany 2020-06-07          301            22    83783945.0   \n",
       "160    Germany 2020-06-08          214             6    83783945.0   \n",
       "161    Germany 2020-06-09          350            37    83783945.0   \n",
       "162    Germany 2020-06-10          318            18    83783945.0   \n",
       "\n",
       "     aged_65_older_percent_x  gdp_per_capita_x  hospital_beds_per_thousand_x  \\\n",
       "0                     21.453         45229.245                           8.0   \n",
       "1                     21.453         45229.245                           8.0   \n",
       "2                     21.453         45229.245                           8.0   \n",
       "3                     21.453         45229.245                           8.0   \n",
       "4                     21.453         45229.245                           8.0   \n",
       "..                       ...               ...                           ...   \n",
       "158                   21.453         45229.245                           8.0   \n",
       "159                   21.453         45229.245                           8.0   \n",
       "160                   21.453         45229.245                           8.0   \n",
       "161                   21.453         45229.245                           8.0   \n",
       "162                   21.453         45229.245                           8.0   \n",
       "\n",
       "     cumsum_x location_y  new_cases_y  new_deaths_y  population_y  \\\n",
       "0           0      Italy            0             0    60461828.0   \n",
       "1           0      Italy            0             0    60461828.0   \n",
       "2           0      Italy            0             0    60461828.0   \n",
       "3           0      Italy            0             0    60461828.0   \n",
       "4           0      Italy            0             0    60461828.0   \n",
       "..        ...        ...          ...           ...           ...   \n",
       "158    183678      Italy          518            85    60461828.0   \n",
       "159    183979      Italy          270            72    60461828.0   \n",
       "160    184193      Italy          197            53    60461828.0   \n",
       "161    184543      Italy          280            65    60461828.0   \n",
       "162    184861      Italy          283            79    60461828.0   \n",
       "\n",
       "     aged_65_older_percent_y  gdp_per_capita_y  hospital_beds_per_thousand_y  \\\n",
       "0                     23.021         35220.084                          3.18   \n",
       "1                     23.021         35220.084                          3.18   \n",
       "2                     23.021         35220.084                          3.18   \n",
       "3                     23.021         35220.084                          3.18   \n",
       "4                     23.021         35220.084                          3.18   \n",
       "..                       ...               ...                           ...   \n",
       "158                   23.021         35220.084                          3.18   \n",
       "159                   23.021         35220.084                          3.18   \n",
       "160                   23.021         35220.084                          3.18   \n",
       "161                   23.021         35220.084                          3.18   \n",
       "162                   23.021         35220.084                          3.18   \n",
       "\n",
       "     cumsum_y  \n",
       "0           0  \n",
       "1           0  \n",
       "2           0  \n",
       "3           0  \n",
       "4           0  \n",
       "..        ...  \n",
       "158    234531  \n",
       "159    234801  \n",
       "160    234998  \n",
       "161    235278  \n",
       "162    235561  \n",
       "\n",
       "[163 rows x 17 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "germany_covid_df[\"cumsum\"] = germany_covid_df.new_cases.cumsum()\n",
    "italy_covid_df[\"cumsum\"] = italy_covid_df.new_cases.cumsum()\n",
    "gr_it = pd.merge(germany_covid_df, italy_covid_df, on='date')\n",
    "gr_it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1c8a91b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2020-03-12 00:00:00')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gr_it[abs(gr_it.cumsum_x - gr_it.cumsum_y) > 10000]['date'].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4da68540",
   "metadata": {},
   "source": [
    "### What is the probability that a country has GDP over $10000, if we known that they have at least 5 hospital beds per 1000 inhabitants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "aa96015f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Muntaha\\AppData\\Local\\Temp\\ipykernel_13292\\1523145638.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  gdp_beds_by_country_df = covid_df.groupby('location')['gdp_per_capita', 'hospital_beds_per_thousand'].agg('first').dropna()\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>gdp_per_capita</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>1803.987</td>\n",
       "      <td>0.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>11803.431</td>\n",
       "      <td>2.890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>13913.839</td>\n",
       "      <td>1.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <td>21490.943</td>\n",
       "      <td>3.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>18933.907</td>\n",
       "      <td>5.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <td>6171.884</td>\n",
       "      <td>2.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>World</th>\n",
       "      <td>15469.207</td>\n",
       "      <td>2.705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yemen</th>\n",
       "      <td>1479.147</td>\n",
       "      <td>0.700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>3689.251</td>\n",
       "      <td>2.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>1899.775</td>\n",
       "      <td>1.700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>160 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     gdp_per_capita  hospital_beds_per_thousand\n",
       "location                                                       \n",
       "Afghanistan                1803.987                       0.500\n",
       "Albania                   11803.431                       2.890\n",
       "Algeria                   13913.839                       1.900\n",
       "Antigua and Barbuda       21490.943                       3.800\n",
       "Argentina                 18933.907                       5.000\n",
       "...                             ...                         ...\n",
       "Vietnam                    6171.884                       2.600\n",
       "World                     15469.207                       2.705\n",
       "Yemen                      1479.147                       0.700\n",
       "Zambia                     3689.251                       2.000\n",
       "Zimbabwe                   1899.775                       1.700\n",
       "\n",
       "[160 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp_beds_by_country_df = covid_df.groupby('location')['gdp_per_capita', 'hospital_beds_per_thousand'].agg('first').dropna()\n",
    "gdp_beds_by_country_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "78451ed7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8846153846153846"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_at_least_5_beds_per_thousand_hospitals = gdp_beds_by_country_df[gdp_beds_by_country_df.hospital_beds_per_thousand >= 5]\n",
    "country_at_least_5_beds_gdp_over_10000 = country_at_least_5_beds_per_thousand_hospitals[country_at_least_5_beds_per_thousand_hospitals.gdp_per_capita > 10000]\n",
    "# country_at_least_5_beds_per_thousand_hospitals\n",
    "len(country_at_least_5_beds_gdp_over_10000)/len(country_at_least_5_beds_per_thousand_hospitals)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a40317f",
   "metadata": {},
   "source": [
    "### Q6.Which country has the 3rd highest death rate? Death rate: total number of death per million inhabitants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4ce5ea92",
   "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>new_deaths</th>\n",
       "      <th>population</th>\n",
       "      <th>aged_65_older_percent</th>\n",
       "      <th>death_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>San Marino</th>\n",
       "      <td>42</td>\n",
       "      <td>33938.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1237.550828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>9619</td>\n",
       "      <td>11589616.0</td>\n",
       "      <td>18.571</td>\n",
       "      <td>829.967102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>51</td>\n",
       "      <td>77265.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>660.066007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>40883</td>\n",
       "      <td>67886004.0</td>\n",
       "      <td>18.517</td>\n",
       "      <td>602.230174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>27136</td>\n",
       "      <td>46754783.0</td>\n",
       "      <td>19.436</td>\n",
       "      <td>580.389818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>French Polynesia</th>\n",
       "      <td>0</td>\n",
       "      <td>280904.0</td>\n",
       "      <td>7.775</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fiji</th>\n",
       "      <td>0</td>\n",
       "      <td>896444.0</td>\n",
       "      <td>6.224</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mongolia</th>\n",
       "      <td>0</td>\n",
       "      <td>3278292.0</td>\n",
       "      <td>4.031</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Papua New Guinea</th>\n",
       "      <td>0</td>\n",
       "      <td>8947027.0</td>\n",
       "      <td>3.808</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>International</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>212 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  new_deaths  population  aged_65_older_percent   death_rate\n",
       "location                                                                    \n",
       "San Marino                42     33938.0                    NaN  1237.550828\n",
       "Belgium                 9619  11589616.0                 18.571   829.967102\n",
       "Andorra                   51     77265.0                    NaN   660.066007\n",
       "United Kingdom         40883  67886004.0                 18.517   602.230174\n",
       "Spain                  27136  46754783.0                 19.436   580.389818\n",
       "...                      ...         ...                    ...          ...\n",
       "French Polynesia           0    280904.0                  7.775     0.000000\n",
       "Fiji                       0    896444.0                  6.224     0.000000\n",
       "Mongolia                   0   3278292.0                  4.031     0.000000\n",
       "Papua New Guinea           0   8947027.0                  3.808     0.000000\n",
       "International              7         NaN                    NaN          NaN\n",
       "\n",
       "[212 rows x 4 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_death_rate_df = covid_df.groupby('location').agg({'new_deaths': 'sum', 'population': 'mean', 'aged_65_older_percent': 'first'})\n",
    "country_death_rate_df['death_rate'] = country_death_rate_df['new_deaths'] / country_death_rate_df['population'] * 1000000\n",
    "country_death_rate_df.sort_values(by='death_rate', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6890c675",
   "metadata": {},
   "source": [
    "### Q7.Look at the cumulative number of confirmed cases in Italy between 2020-02-28 and 2020-03-20. Fit an exponential function (y = Ae^(Bx)) to this set to express cumulative cases as a function of days passed, by minimizing squared loss. What is the difference between the exponential curve and the total number of real cases on 2020-03-20?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6fd418cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date', ylabel='cumsum'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "italy_cumsum_case_in_time_df = italy_covid_df[(italy_covid_df.date >= \"2020-02-28\") & (italy_covid_df.date <= \"2020-03-20\")]\n",
    "sns.lineplot(data=italy_cumsum_case_in_time_df, x='date', y='cumsum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "78f287e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.24520424e-06 6.80040490e+00]\n",
      "898.2109026485551 2.2452042412439063e-06\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Muntaha\\AppData\\Local\\Temp\\ipykernel_13292\\2959374382.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",
      "  italy_cumsum_case_in_time_df['ts'] = italy_cumsum_case_in_time_df.date.astype('int64') // 10**9\n",
      "C:\\Users\\Muntaha\\AppData\\Local\\Temp\\ipykernel_13292\\2959374382.py:2: 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",
      "  italy_cumsum_case_in_time_df['ts'] -= italy_cumsum_case_in_time_df['ts'].min()\n"
     ]
    }
   ],
   "source": [
    "italy_cumsum_case_in_time_df['ts'] = italy_cumsum_case_in_time_df.date.astype('int64') // 10**9\n",
    "italy_cumsum_case_in_time_df['ts'] -= italy_cumsum_case_in_time_df['ts'].min()\n",
    "x = italy_cumsum_case_in_time_df['ts']\n",
    "y = italy_cumsum_case_in_time_df['cumsum']\n",
    "p = np.polyfit(x, np.log(y), 1)\n",
    "print(p)\n",
    "# Convert the polynomial back into an exponential\n",
    "a = np.exp(p[1])\n",
    "b = p[0]\n",
    "print(a, b)\n",
    "y_fitted = a * np.exp(b * x)\n",
    "# y_fitted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "eb9aa990",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1746b40aa90>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.axes()\n",
    "ax.scatter(x, y, label='Raw data')\n",
    "ax.plot(x, y_fitted, 'k', label='Fitted curve')\n",
    "ax.set_title('Using polyfit() to fit an exponential function')\n",
    "ax.set_ylabel('y-Values')\n",
    "ax.set_xlabel('x-Values')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "79221450",
   "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>location</th>\n",
       "      <th>date</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>population</th>\n",
       "      <th>aged_65_older_percent</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>cumsum</th>\n",
       "      <th>ts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10768</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2020-03-20</td>\n",
       "      <td>5322</td>\n",
       "      <td>429</td>\n",
       "      <td>60461828.0</td>\n",
       "      <td>23.021</td>\n",
       "      <td>35220.084</td>\n",
       "      <td>3.18</td>\n",
       "      <td>41035</td>\n",
       "      <td>1814400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      location       date  new_cases  new_deaths  population  \\\n",
       "10768    Italy 2020-03-20       5322         429  60461828.0   \n",
       "\n",
       "       aged_65_older_percent  gdp_per_capita  hospital_beds_per_thousand  \\\n",
       "10768                 23.021       35220.084                        3.18   \n",
       "\n",
       "       cumsum       ts  \n",
       "10768   41035  1814400  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "italy_data_2020_03_20 = italy_cumsum_case_in_time_df[italy_cumsum_case_in_time_df['date'] == '2020-03-20']\n",
    "italy_data_2020_03_20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d53f9389",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1814400 41035 52791.39454048472\n",
      "11756.394540484718\n"
     ]
    }
   ],
   "source": [
    "x_test = italy_data_2020_03_20['ts'].item()\n",
    "y_test = italy_data_2020_03_20['cumsum'].item()\n",
    "y_pred = a * np.exp(b * x_test)\n",
    "print(x_test, y_test, y_pred)\n",
    "print(y_pred - y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51bebab0",
   "metadata": {},
   "source": [
    "### Q9. What is the F1 score of the following statement: Countries, where more than 20% of the population is over 65 years old, have death rates over 50 per million inhabitants. Ignore countries, where any of the neccessary information is missing!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "cb224b2d",
   "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>new_deaths</th>\n",
       "      <th>population</th>\n",
       "      <th>aged_65_older_percent</th>\n",
       "      <th>death_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>384</td>\n",
       "      <td>3.892834e+07</td>\n",
       "      <td>2.581</td>\n",
       "      <td>9.864279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>34</td>\n",
       "      <td>2.877800e+06</td>\n",
       "      <td>13.188</td>\n",
       "      <td>11.814581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>724</td>\n",
       "      <td>4.385104e+07</td>\n",
       "      <td>6.211</td>\n",
       "      <td>16.510440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>4</td>\n",
       "      <td>3.286627e+07</td>\n",
       "      <td>2.405</td>\n",
       "      <td>0.121705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <td>3</td>\n",
       "      <td>9.792800e+04</td>\n",
       "      <td>6.933</td>\n",
       "      <td>30.634752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <td>0</td>\n",
       "      <td>9.733858e+07</td>\n",
       "      <td>7.150</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>World</th>\n",
       "      <td>411195</td>\n",
       "      <td>7.794799e+09</td>\n",
       "      <td>8.696</td>\n",
       "      <td>52.752485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yemen</th>\n",
       "      <td>127</td>\n",
       "      <td>2.982597e+07</td>\n",
       "      <td>2.922</td>\n",
       "      <td>4.258034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>10</td>\n",
       "      <td>1.838396e+07</td>\n",
       "      <td>2.480</td>\n",
       "      <td>0.543953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>4</td>\n",
       "      <td>1.486293e+07</td>\n",
       "      <td>2.822</td>\n",
       "      <td>0.269126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>184 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     new_deaths    population  aged_65_older_percent  \\\n",
       "location                                                               \n",
       "Afghanistan                 384  3.892834e+07                  2.581   \n",
       "Albania                      34  2.877800e+06                 13.188   \n",
       "Algeria                     724  4.385104e+07                  6.211   \n",
       "Angola                        4  3.286627e+07                  2.405   \n",
       "Antigua and Barbuda           3  9.792800e+04                  6.933   \n",
       "...                         ...           ...                    ...   \n",
       "Vietnam                       0  9.733858e+07                  7.150   \n",
       "World                    411195  7.794799e+09                  8.696   \n",
       "Yemen                       127  2.982597e+07                  2.922   \n",
       "Zambia                       10  1.838396e+07                  2.480   \n",
       "Zimbabwe                      4  1.486293e+07                  2.822   \n",
       "\n",
       "                     death_rate  \n",
       "location                         \n",
       "Afghanistan            9.864279  \n",
       "Albania               11.814581  \n",
       "Algeria               16.510440  \n",
       "Angola                 0.121705  \n",
       "Antigua and Barbuda   30.634752  \n",
       "...                         ...  \n",
       "Vietnam                0.000000  \n",
       "World                 52.752485  \n",
       "Yemen                  4.258034  \n",
       "Zambia                 0.543953  \n",
       "Zimbabwe               0.269126  \n",
       "\n",
       "[184 rows x 4 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_death_rate_dropna_df = country_death_rate_df.dropna()\n",
    "country_death_rate_dropna_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5af0ae73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>new_deaths</th>\n",
       "      <th>population</th>\n",
       "      <th>aged_65_older_percent</th>\n",
       "      <th>death_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bulgaria</th>\n",
       "      <td>167</td>\n",
       "      <td>6948445.0</td>\n",
       "      <td>20.801</td>\n",
       "      <td>24.034154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>324</td>\n",
       "      <td>5540718.0</td>\n",
       "      <td>21.228</td>\n",
       "      <td>58.476176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>8729</td>\n",
       "      <td>83783945.0</td>\n",
       "      <td>21.453</td>\n",
       "      <td>104.184638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>183</td>\n",
       "      <td>10423056.0</td>\n",
       "      <td>20.396</td>\n",
       "      <td>17.557231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>34043</td>\n",
       "      <td>60461828.0</td>\n",
       "      <td>23.021</td>\n",
       "      <td>563.049467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>919</td>\n",
       "      <td>126476458.0</td>\n",
       "      <td>27.049</td>\n",
       "      <td>7.266174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>1492</td>\n",
       "      <td>10196707.0</td>\n",
       "      <td>21.502</td>\n",
       "      <td>146.321749</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          new_deaths   population  aged_65_older_percent  death_rate\n",
       "location                                                            \n",
       "Bulgaria         167    6948445.0                 20.801   24.034154\n",
       "Finland          324    5540718.0                 21.228   58.476176\n",
       "Germany         8729   83783945.0                 21.453  104.184638\n",
       "Greece           183   10423056.0                 20.396   17.557231\n",
       "Italy          34043   60461828.0                 23.021  563.049467\n",
       "Japan            919  126476458.0                 27.049    7.266174\n",
       "Portugal        1492   10196707.0                 21.502  146.321749"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_aged_65_over_20_percent = country_death_rate_dropna_df[country_death_rate_dropna_df.aged_65_older_percent > 20]\n",
    "country_aged_65_over_20_percent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "7e78f10c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>new_deaths</th>\n",
       "      <th>population</th>\n",
       "      <th>aged_65_older_percent</th>\n",
       "      <th>death_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>324</td>\n",
       "      <td>5540718.0</td>\n",
       "      <td>21.228</td>\n",
       "      <td>58.476176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>8729</td>\n",
       "      <td>83783945.0</td>\n",
       "      <td>21.453</td>\n",
       "      <td>104.184638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>34043</td>\n",
       "      <td>60461828.0</td>\n",
       "      <td>23.021</td>\n",
       "      <td>563.049467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>1492</td>\n",
       "      <td>10196707.0</td>\n",
       "      <td>21.502</td>\n",
       "      <td>146.321749</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          new_deaths  population  aged_65_older_percent  death_rate\n",
       "location                                                           \n",
       "Finland          324   5540718.0                 21.228   58.476176\n",
       "Germany         8729  83783945.0                 21.453  104.184638\n",
       "Italy          34043  60461828.0                 23.021  563.049467\n",
       "Portugal        1492  10196707.0                 21.502  146.321749"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_aged_65_over_20_percent_death_rate_over_50_per_million = country_aged_65_over_20_percent[country_aged_65_over_20_percent.death_rate > 50]\n",
    "country_aged_65_over_20_percent_death_rate_over_50_per_million"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4173ade5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7272727272727273"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "y_pred = [1] * len(country_aged_65_over_20_percent)\n",
    "y_true = [1] * len(country_aged_65_over_20_percent_death_rate_over_50_per_million) + [0] * (len(country_aged_65_over_20_percent) - len(country_aged_65_over_20_percent_death_rate_over_50_per_million))\n",
    "f1_score(y_true, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2af23e06",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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