covid data visualization matplotlib and seaborn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "import matplotlib.animation as animation\n",
    "from IPython.display import HTML\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('train.csv',index_col = 'Id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province_State</th>\n",
       "      <th>Country_Region</th>\n",
       "      <th>Date</th>\n",
       "      <th>ConfirmedCases</th>\n",
       "      <th>Fatalities</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Province_State Country_Region        Date  ConfirmedCases  Fatalities\n",
       "Id                                                                      \n",
       "1             NaN    Afghanistan  2020-01-22             0.0         0.0\n",
       "2             NaN    Afghanistan  2020-01-23             0.0         0.0\n",
       "3             NaN    Afghanistan  2020-01-24             0.0         0.0\n",
       "4             NaN    Afghanistan  2020-01-25             0.0         0.0\n",
       "5             NaN    Afghanistan  2020-01-26             0.0         0.0\n",
       "6             NaN    Afghanistan  2020-01-27             0.0         0.0\n",
       "7             NaN    Afghanistan  2020-01-28             0.0         0.0\n",
       "8             NaN    Afghanistan  2020-01-29             0.0         0.0\n",
       "9             NaN    Afghanistan  2020-01-30             0.0         0.0\n",
       "10            NaN    Afghanistan  2020-01-31             0.0         0.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>province</th>\n",
       "      <th>country</th>\n",
       "      <th>Date</th>\n",
       "      <th>ConfirmedCases</th>\n",
       "      <th>Fatalities</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35645</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2020-04-07</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35646</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2020-04-08</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35647</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35648</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2020-04-10</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35649</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2020-04-11</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25353 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      province      country        Date  ConfirmedCases  Fatalities\n",
       "Id                                                                 \n",
       "1          NaN  Afghanistan  2020-01-22             0.0         0.0\n",
       "2          NaN  Afghanistan  2020-01-23             0.0         0.0\n",
       "3          NaN  Afghanistan  2020-01-24             0.0         0.0\n",
       "4          NaN  Afghanistan  2020-01-25             0.0         0.0\n",
       "5          NaN  Afghanistan  2020-01-26             0.0         0.0\n",
       "...        ...          ...         ...             ...         ...\n",
       "35645      NaN     Zimbabwe  2020-04-07            11.0         2.0\n",
       "35646      NaN     Zimbabwe  2020-04-08            11.0         3.0\n",
       "35647      NaN     Zimbabwe  2020-04-09            11.0         3.0\n",
       "35648      NaN     Zimbabwe  2020-04-10            13.0         3.0\n",
       "35649      NaN     Zimbabwe  2020-04-11            14.0         3.0\n",
       "\n",
       "[25353 rows x 5 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns={\"Country_Region\": \"country\", \"Province_State\": \"province\"}, inplace=True, errors=\"raise\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>country</th>\n",
       "      <th>ConfirmedCases</th>\n",
       "      <th>Fatalities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "      <td>5135445.0</td>\n",
       "      <td>147545.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>China</td>\n",
       "      <td>5096274.0</td>\n",
       "      <td>182450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2661341.0</td>\n",
       "      <td>297444.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2237252.0</td>\n",
       "      <td>200412.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Germany</td>\n",
       "      <td>1728391.0</td>\n",
       "      <td>24491.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>179</td>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>180</td>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>181</td>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>182</td>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>183</td>\n",
       "      <td>South Sudan</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>184 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   country  ConfirmedCases  Fatalities\n",
       "0                       US       5135445.0    147545.0\n",
       "1                    China       5096274.0    182450.0\n",
       "2                    Italy       2661341.0    297444.0\n",
       "3                    Spain       2237252.0    200412.0\n",
       "4                  Germany       1728391.0     24491.0\n",
       "..                     ...             ...         ...\n",
       "179       Papua New Guinea            29.0         0.0\n",
       "180         Western Sahara            28.0         0.0\n",
       "181  Sao Tome and Principe            24.0         0.0\n",
       "182            Timor-Leste            23.0         0.0\n",
       "183            South Sudan            17.0         0.0\n",
       "\n",
       "[184 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_groupby_country = df.groupby(\"country\")[\"ConfirmedCases\",\"Fatalities\"].sum().sort_values(\"ConfirmedCases\",ascending = False).reset_index()\n",
    "df_groupby_country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_groupby_country[\"Recoveries\"] = df_groupby_country[\"ConfirmedCases\"] - df_groupby_country[\"Fatalities\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>ConfirmedCases</th>\n",
       "      <th>Fatalities</th>\n",
       "      <th>Recoveries</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "      <td>5135445.0</td>\n",
       "      <td>147545.0</td>\n",
       "      <td>4987900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>China</td>\n",
       "      <td>5096274.0</td>\n",
       "      <td>182450.0</td>\n",
       "      <td>4913824.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2661341.0</td>\n",
       "      <td>297444.0</td>\n",
       "      <td>2363897.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2237252.0</td>\n",
       "      <td>200412.0</td>\n",
       "      <td>2036840.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Germany</td>\n",
       "      <td>1728391.0</td>\n",
       "      <td>24491.0</td>\n",
       "      <td>1703900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>France</td>\n",
       "      <td>1476739.0</td>\n",
       "      <td>122427.0</td>\n",
       "      <td>1354312.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Iran</td>\n",
       "      <td>1184893.0</td>\n",
       "      <td>75219.0</td>\n",
       "      <td>1109674.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>749434.0</td>\n",
       "      <td>72805.0</td>\n",
       "      <td>676629.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>408544.0</td>\n",
       "      <td>8310.0</td>\n",
       "      <td>400234.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>393842.0</td>\n",
       "      <td>11029.0</td>\n",
       "      <td>382813.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  ConfirmedCases  Fatalities  Recoveries\n",
       "0              US       5135445.0    147545.0   4987900.0\n",
       "1           China       5096274.0    182450.0   4913824.0\n",
       "2           Italy       2661341.0    297444.0   2363897.0\n",
       "3           Spain       2237252.0    200412.0   2036840.0\n",
       "4         Germany       1728391.0     24491.0   1703900.0\n",
       "5          France       1476739.0    122427.0   1354312.0\n",
       "6            Iran       1184893.0     75219.0   1109674.0\n",
       "7  United Kingdom        749434.0     72805.0    676629.0\n",
       "8          Turkey        408544.0      8310.0    400234.0\n",
       "9     Switzerland        393842.0     11029.0    382813.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_groupby_country.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>ConfirmedCases</th>\n",
       "      <th>Fatalities</th>\n",
       "      <th>Recoveries</th>\n",
       "      <th>Survival Rate</th>\n",
       "      <th>Fatalities Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "      <td>5135445.0</td>\n",
       "      <td>147545.0</td>\n",
       "      <td>4987900.0</td>\n",
       "      <td>97.13</td>\n",
       "      <td>2.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>China</td>\n",
       "      <td>5096274.0</td>\n",
       "      <td>182450.0</td>\n",
       "      <td>4913824.0</td>\n",
       "      <td>96.42</td>\n",
       "      <td>3.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2661341.0</td>\n",
       "      <td>297444.0</td>\n",
       "      <td>2363897.0</td>\n",
       "      <td>88.82</td>\n",
       "      <td>11.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2237252.0</td>\n",
       "      <td>200412.0</td>\n",
       "      <td>2036840.0</td>\n",
       "      <td>91.04</td>\n",
       "      <td>8.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Germany</td>\n",
       "      <td>1728391.0</td>\n",
       "      <td>24491.0</td>\n",
       "      <td>1703900.0</td>\n",
       "      <td>98.58</td>\n",
       "      <td>1.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>France</td>\n",
       "      <td>1476739.0</td>\n",
       "      <td>122427.0</td>\n",
       "      <td>1354312.0</td>\n",
       "      <td>91.71</td>\n",
       "      <td>8.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Iran</td>\n",
       "      <td>1184893.0</td>\n",
       "      <td>75219.0</td>\n",
       "      <td>1109674.0</td>\n",
       "      <td>93.65</td>\n",
       "      <td>6.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>749434.0</td>\n",
       "      <td>72805.0</td>\n",
       "      <td>676629.0</td>\n",
       "      <td>90.29</td>\n",
       "      <td>9.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>408544.0</td>\n",
       "      <td>8310.0</td>\n",
       "      <td>400234.0</td>\n",
       "      <td>97.97</td>\n",
       "      <td>2.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>393842.0</td>\n",
       "      <td>11029.0</td>\n",
       "      <td>382813.0</td>\n",
       "      <td>97.20</td>\n",
       "      <td>2.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  ConfirmedCases  Fatalities  Recoveries  Survival Rate  \\\n",
       "0              US       5135445.0    147545.0   4987900.0          97.13   \n",
       "1           China       5096274.0    182450.0   4913824.0          96.42   \n",
       "2           Italy       2661341.0    297444.0   2363897.0          88.82   \n",
       "3           Spain       2237252.0    200412.0   2036840.0          91.04   \n",
       "4         Germany       1728391.0     24491.0   1703900.0          98.58   \n",
       "5          France       1476739.0    122427.0   1354312.0          91.71   \n",
       "6            Iran       1184893.0     75219.0   1109674.0          93.65   \n",
       "7  United Kingdom        749434.0     72805.0    676629.0          90.29   \n",
       "8          Turkey        408544.0      8310.0    400234.0          97.97   \n",
       "9     Switzerland        393842.0     11029.0    382813.0          97.20   \n",
       "\n",
       "   Fatalities Rate  \n",
       "0             2.87  \n",
       "1             3.58  \n",
       "2            11.18  \n",
       "3             8.96  \n",
       "4             1.42  \n",
       "5             8.29  \n",
       "6             6.35  \n",
       "7             9.71  \n",
       "8             2.03  \n",
       "9             2.80  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_groupby_country['Survival Rate'] = round(df_groupby_country['Recoveries']/df_groupby_country['ConfirmedCases']*100,2)\n",
    "df_groupby_country['Fatalities Rate'] = round(df_groupby_country['Fatalities']/df_groupby_country['ConfirmedCases']*100,2)\n",
    "df_groupby_country.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d06d16c3c8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df_groupby_country.head(5)[\"country\"], y=df_groupby_country.head(5)[\"ConfirmedCases\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already up-to-date: cufflinks in c:\\users\\inzim\\anaconda3\\lib\\site-packages (0.17.3)\n",
      "Requirement already satisfied, skipping upgrade: pandas>=0.19.2 in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from cufflinks) (0.25.1)\n",
      "Requirement already satisfied, skipping upgrade: six>=1.9.0 in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from cufflinks) (1.14.0)\n",
      "Requirement already satisfied, skipping upgrade: setuptools>=34.4.1 in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from cufflinks) (46.1.3.post20200330)\n",
      "Requirement already satisfied, skipping upgrade: ipython>=5.3.0 in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from cufflinks) (7.13.0)\n",
      "Requirement already satisfied, skipping upgrade: colorlover>=0.2.1 in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from cufflinks) (0.3.0)\n",
      "Requirement already satisfied, skipping upgrade: plotly>=4.1.1 in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from cufflinks) (4.8.1)\n",
      "Requirement already satisfied, skipping upgrade: ipywidgets>=7.0.0 in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from cufflinks) (7.5.1)\n",
      "Requirement already satisfied, skipping upgrade: numpy>=1.9.2 in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from cufflinks) (1.18.1)\n",
      "Requirement already satisfied, skipping upgrade: pytz>=2017.2 in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from pandas>=0.19.2->cufflinks) (2019.3)\n",
      "Requirement already satisfied, skipping upgrade: python-dateutil>=2.6.1 in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from pandas>=0.19.2->cufflinks) (2.8.1)\n",
      "Requirement already satisfied, skipping upgrade: traitlets>=4.2 in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from ipython>=5.3.0->cufflinks) (4.3.3)\n",
      "Requirement already satisfied, skipping upgrade: jedi>=0.10 in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from ipython>=5.3.0->cufflinks) (0.16.0)\n",
      "Requirement already satisfied, skipping upgrade: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from ipython>=5.3.0->cufflinks) (3.0.4)\n",
      "Requirement already satisfied, skipping upgrade: decorator in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from ipython>=5.3.0->cufflinks) (4.4.2)\n",
      "Requirement already satisfied, skipping upgrade: backcall in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from ipython>=5.3.0->cufflinks) (0.1.0)\n",
      "Requirement already satisfied, skipping upgrade: pickleshare in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from ipython>=5.3.0->cufflinks) (0.7.5)\n",
      "Requirement already satisfied, skipping upgrade: colorama; sys_platform == \"win32\" in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from ipython>=5.3.0->cufflinks) (0.4.3)\n",
      "Requirement already satisfied, skipping upgrade: pygments in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from ipython>=5.3.0->cufflinks) (2.6.1)\n",
      "Requirement already satisfied, skipping upgrade: retrying>=1.3.3 in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from plotly>=4.1.1->cufflinks) (1.3.3)\n",
      "Requirement already satisfied, skipping upgrade: nbformat>=4.2.0 in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from ipywidgets>=7.0.0->cufflinks) (5.0.4)\n",
      "Requirement already satisfied, skipping upgrade: ipykernel>=4.5.1 in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from ipywidgets>=7.0.0->cufflinks) (5.1.4)\n",
      "Requirement already satisfied, skipping upgrade: widgetsnbextension~=3.5.0 in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from ipywidgets>=7.0.0->cufflinks) (3.5.1)\n",
      "Requirement already satisfied, skipping upgrade: ipython-genutils in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from traitlets>=4.2->ipython>=5.3.0->cufflinks) (0.2.0)\n",
      "Requirement already satisfied, skipping upgrade: parso>=0.5.2 in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from jedi>=0.10->ipython>=5.3.0->cufflinks) (0.6.2)\n",
      "Requirement already satisfied, skipping upgrade: wcwidth in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython>=5.3.0->cufflinks) (0.1.9)\n",
      "Requirement already satisfied, skipping upgrade: jupyter-core in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from nbformat>=4.2.0->ipywidgets>=7.0.0->cufflinks) (4.6.3)\n",
      "Requirement already satisfied, skipping upgrade: jsonschema!=2.5.0,>=2.4 in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from nbformat>=4.2.0->ipywidgets>=7.0.0->cufflinks) (3.2.0)\n",
      "Requirement already satisfied, skipping upgrade: tornado>=4.2 in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from ipykernel>=4.5.1->ipywidgets>=7.0.0->cufflinks) (6.0.4)\n",
      "Requirement already satisfied, skipping upgrade: jupyter-client in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from ipykernel>=4.5.1->ipywidgets>=7.0.0->cufflinks) (6.1.2)\n",
      "Requirement already satisfied, skipping upgrade: notebook>=4.4.1 in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (6.0.3)\n",
      "Requirement already satisfied, skipping upgrade: pywin32>=1.0; sys_platform == \"win32\" in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from jupyter-core->nbformat>=4.2.0->ipywidgets>=7.0.0->cufflinks) (227)\n",
      "Requirement already satisfied, skipping upgrade: importlib-metadata; python_version < \"3.8\" in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.2.0->ipywidgets>=7.0.0->cufflinks) (1.5.0)\n",
      "Requirement already satisfied, skipping upgrade: pyrsistent>=0.14.0 in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.2.0->ipywidgets>=7.0.0->cufflinks) (0.16.0)\n",
      "Requirement already satisfied, skipping upgrade: attrs>=17.4.0 in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.2.0->ipywidgets>=7.0.0->cufflinks) (19.3.0)\n",
      "Requirement already satisfied, skipping upgrade: pyzmq>=13 in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from jupyter-client->ipykernel>=4.5.1->ipywidgets>=7.0.0->cufflinks) (18.1.1)\n",
      "Requirement already satisfied, skipping upgrade: prometheus-client in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (0.7.1)\n",
      "Requirement already satisfied, skipping upgrade: Send2Trash in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (1.5.0)\n",
      "Requirement already satisfied, skipping upgrade: jinja2 in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (2.11.1)\n",
      "Requirement already satisfied, skipping upgrade: terminado>=0.8.1 in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (0.8.3)\n",
      "Requirement already satisfied, skipping upgrade: nbconvert in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (5.6.1)\n",
      "Requirement already satisfied, skipping upgrade: zipp>=0.5 in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from importlib-metadata; python_version < \"3.8\"->jsonschema!=2.5.0,>=2.4->nbformat>=4.2.0->ipywidgets>=7.0.0->cufflinks) (2.2.0)\n",
      "Requirement already satisfied, skipping upgrade: MarkupSafe>=0.23 in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from jinja2->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (1.1.1)\n",
      "Requirement already satisfied, skipping upgrade: pandocfilters>=1.4.1 in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (1.4.2)\n",
      "Requirement already satisfied, skipping upgrade: mistune<2,>=0.8.1 in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (0.8.4)\n",
      "Requirement already satisfied, skipping upgrade: testpath in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (0.4.4)\n",
      "Requirement already satisfied, skipping upgrade: bleach in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (3.1.0)\n",
      "Requirement already satisfied, skipping upgrade: entrypoints>=0.2.2 in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (0.3)\n",
      "Requirement already satisfied, skipping upgrade: defusedxml in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (0.6.0)\n",
      "Requirement already satisfied, skipping upgrade: webencodings in c:\\users\\inzim\\anaconda3\\lib\\site-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->cufflinks) (0.5.1)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install cufflinks --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cufflinks as cf\n",
    "cf.go_offline()\n",
    "cf.set_config_file(offline=False, world_readable=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": true
       },
       "data": [
        {
         "boxpoints": false,
         "line": {
          "width": 1.3
         },
         "marker": {
          "color": "rgba(255, 153, 51, 1.0)"
         },
         "name": "country",
         "orientation": "v",
         "type": "box",
         "y": [
          "US",
          "China",
          "Italy",
          "Spain",
          "Germany",
          "France",
          "Iran",
          "United Kingdom",
          "Turkey",
          "Switzerland",
          "Korea, South",
          "Belgium",
          "Netherlands",
          "Canada",
          "Austria",
          "Brazil",
          "Portugal",
          "Israel",
          "Sweden",
          "Norway",
          "Australia",
          "Russia",
          "Denmark",
          "Ireland",
          "Czechia",
          "Japan",
          "Chile",
          "Malaysia",
          "Poland",
          "Ecuador",
          "India",
          "Romania",
          "Pakistan",
          "Philippines",
          "Luxembourg",
          "Peru",
          "Saudi Arabia",
          "Indonesia",
          "Diamond Princess",
          "Thailand",
          "Finland",
          "Greece",
          "Mexico",
          "Qatar",
          "Serbia",
          "Singapore",
          "United Arab Emirates",
          "Panama",
          "South Africa",
          "Iceland",
          "Dominican Republic",
          "Colombia",
          "Argentina",
          "Egypt",
          "Algeria",
          "Croatia",
          "Slovenia",
          "Ukraine",
          "Estonia",
          "Iraq",
          "Bahrain",
          "Morocco",
          "New Zealand",
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          "Lithuania",
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          "Tunisia",
          "North Macedonia",
          "Taiwan*",
          "Andorra",
          "Costa Rica",
          "Cameroon",
          "Cyprus",
          "Uruguay",
          "San Marino",
          "Albania",
          "Jordan",
          "Burkina Faso",
          "Uzbekistan",
          "Vietnam",
          "Oman",
          "Afghanistan",
          "Cuba",
          "Cote d'Ivoire",
          "Malta",
          "Ghana",
          "Honduras",
          "Senegal",
          "West Bank and Gaza",
          "Nigeria",
          "Mauritius",
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          "Georgia",
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          "Venezuela",
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          "Niger",
          "Kyrgyzstan",
          "Bolivia",
          "Bangladesh",
          "Cambodia",
          "Congo (Kinshasa)",
          "Kosovo",
          "Kenya",
          "Trinidad and Tobago",
          "Paraguay",
          "Rwanda",
          "Guinea",
          "Liechtenstein",
          "Monaco",
          "Guatemala",
          "Madagascar",
          "Djibouti",
          "Jamaica",
          "El Salvador",
          "Togo",
          "Barbados",
          "Uganda",
          "Ethiopia",
          "Mali",
          "Zambia",
          "Congo (Brazzaville)",
          "Maldives",
          "Guyana",
          "Bahamas",
          "Tanzania",
          "Gabon",
          "Eritrea",
          "Haiti",
          "Benin",
          "Mongolia",
          "Equatorial Guinea",
          "Burma",
          "Guinea-Bissau",
          "Namibia",
          "Liberia",
          "Syria",
          "Dominica",
          "Antigua and Barbuda",
          "Libya",
          "Seychelles",
          "Saint Lucia",
          "Angola",
          "Mozambique",
          "Suriname",
          "Laos",
          "Sudan",
          "Eswatini",
          "Grenada",
          "Fiji",
          "Nepal",
          "Zimbabwe",
          "Somalia",
          "Chad",
          "Holy See",
          "Saint Kitts and Nevis",
          "Cabo Verde",
          "Central African Republic",
          "Mauritania",
          "MS Zaandam",
          "Bhutan",
          "Nicaragua",
          "Saint Vincent and the Grenadines",
          "Belize",
          "Botswana",
          "Gambia",
          "Malawi",
          "Sierra Leone",
          "Burundi",
          "Papua New Guinea",
          "Western Sahara",
          "Sao Tome and Principe",
          "Timor-Leste",
          "South Sudan"
         ]
        },
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         "boxpoints": false,
         "line": {
          "width": 1.3
         },
         "marker": {
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   "source": [
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   "execution_count": 15,
   "metadata": {},
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       "      <td>mean</td>\n",
       "      <td>1.383150e+05</td>\n",
       "      <td>6943.532609</td>\n",
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       "      <td>96.569891</td>\n",
       "      <td>3.430109</td>\n",
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       "      <td>std</td>\n",
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       "      <td>76.190000</td>\n",
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       "      <td>95.607500</td>\n",
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       "      <td>23.810000</td>\n",
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      "text/plain": [
       "       ConfirmedCases     Fatalities    Recoveries  Survival Rate  \\\n",
       "count    1.840000e+02     184.000000  1.840000e+02     184.000000   \n",
       "mean     1.383150e+05    6943.532609  1.313715e+05      96.569891   \n",
       "std      6.141391e+05   33315.452596  5.863846e+05       4.398191   \n",
       "min      1.700000e+01       0.000000  1.700000e+01      76.190000   \n",
       "25%      3.727500e+02      11.500000  3.702500e+02      95.607500   \n",
       "50%      5.351000e+03      76.000000  5.209000e+03      98.010000   \n",
       "75%      2.943100e+04     623.250000  2.913725e+04      99.512500   \n",
       "max      5.135445e+06  297444.000000  4.987900e+06     100.000000   \n",
       "\n",
       "       Fatalities Rate  \n",
       "count       184.000000  \n",
       "mean          3.430109  \n",
       "std           4.398191  \n",
       "min           0.000000  \n",
       "25%           0.487500  \n",
       "50%           1.990000  \n",
       "75%           4.392500  \n",
       "max          23.810000  "
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     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df_groupby_country.describe()"
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  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "      <th>country</th>\n",
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       "      <td>top</td>\n",
       "      <td>Cameroon</td>\n",
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       "      <td>1</td>\n",
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       "         country\n",
       "count        184\n",
       "unique       184\n",
       "top     Cameroon\n",
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   "metadata": {},
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   "source": [
    "df_groupby_country_10 = df_groupby_country.head(5)"
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  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = sns.scatterplot(data=df_groupby_country_10, x = df_groupby_country_10['country'], y = df_groupby_country_10['ConfirmedCases'], label = 'Confirmed')\n",
    "p = sns.scatterplot(data=df_groupby_country_10, x = df_groupby_country_10['country'], y = df_groupby_country_10['Fatalities'], label = 'Death')\n",
    "p = sns.scatterplot(data=df_groupby_country_10, x = df_groupby_country_10['country'], y = df_groupby_country_10['Recoveries'], label = 'Cure')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "domain": {
          "x": [
           0,
           1
          ],
          "y": [
           0,
           1
          ]
         },
         "hovertemplate": "country=%{label}<br>Survival Rate=%{value}<extra></extra>",
         "labels": [
          "US",
          "China",
          "Italy",
          "Spain",
          "Germany",
          "France",
          "Iran",
          "United Kingdom",
          "Turkey",
          "Switzerland",
          "Korea, South",
          "Belgium",
          "Netherlands",
          "Canada",
          "Austria",
          "Brazil",
          "Portugal",
          "Israel",
          "Sweden",
          "Norway"
         ],
         "legendgroup": "",
         "name": "",
         "pull": 0.05,
         "rotation": 180,
         "showlegend": true,
         "textinfo": "value+label",
         "type": "pie",
         "values": [
          97.13,
          96.42,
          88.82,
          91.04,
          98.58,
          91.71,
          93.65,
          90.29,
          97.97,
          97.2,
          98.71,
          92.59,
          91.26,
          98.17,
          98.56,
          95.62,
          97.57,
          99.4,
          94.51,
          99.02
         ]
        }
       ],
       "layout": {
        "legend": {
         "tracegroupgap": 0
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "rgb(36,36,36)"
            },
            "error_y": {
             "color": "rgb(36,36,36)"
            },
            "marker": {
             "line": {
              "color": "rgb(234,234,242)",
              "width": 0.5
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "rgb(234,234,242)",
              "width": 0.5
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "rgb(36,36,36)",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "rgb(36,36,36)"
            },
            "baxis": {
             "endlinecolor": "rgb(36,36,36)",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "rgb(36,36,36)"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "tickcolor": "rgb(36,36,36)",
             "ticklen": 8,
             "ticks": "outside",
             "tickwidth": 2
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "tickcolor": "rgb(36,36,36)",
             "ticklen": 8,
             "ticks": "outside",
             "tickwidth": 2
            },
            "colorscale": [
             [
              0,
              "rgb(2,4,25)"
             ],
             [
              0.06274509803921569,
              "rgb(24,15,41)"
             ],
             [
              0.12549019607843137,
              "rgb(47,23,57)"
             ],
             [
              0.18823529411764706,
              "rgb(71,28,72)"
             ],
             [
              0.25098039215686274,
              "rgb(97,30,82)"
             ],
             [
              0.3137254901960784,
              "rgb(123,30,89)"
             ],
             [
              0.3764705882352941,
              "rgb(150,27,91)"
             ],
             [
              0.4392156862745098,
              "rgb(177,22,88)"
             ],
             [
              0.5019607843137255,
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     "metadata": {},
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   ],
   "source": [
    "fig = px.pie(df_groupby_country.head(20),values=\"Survival Rate\",names=\"country\",title=\"Survival Rate\",template=\"seaborn\")\n",
    "fig.update_traces(rotation=180, pull=0.05, textinfo='value+label')\n",
    "fig.show()"
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  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
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       "                        {\"responsive\": true}\n",
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       "var gd = document.getElementById('6aef6ef2-e246-4980-9192-b445cf713da2');\n",
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       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.pie(df_groupby_country.head(20),values=\"Fatalities Rate\",names=\"country\",title=\"Mortality Rate\",template=\"seaborn\")\n",
    "fig.update_traces(rotation=180, pull=0.05, textinfo='value+label')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "corr = df_groupby_country.apply(lambda x: pd.factorize(x)[0]).corr()\n",
    "ax = sns.heatmap(corr, xticklabels=corr.columns, yticklabels=corr.columns, \n",
    "                 linewidths=.2, cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_groupby_country['Survival Rate'], rug=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_groupby_country['Fatalities Rate'], kde=True, color = 'y')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1d0706a35c8>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 725.76x518.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x='country', y='ConfirmedCases',kind = 'line', data=df_groupby_country.head(10), height = 7.2, aspect = 1.4, marker = 'o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_groupby_country['log_value_survival'] = np.log(df_groupby_country['Survival Rate'])\n",
    "df_groupby_country['log_value_deaths'] = np.log(df_groupby_country['Fatalities Rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='log_value_survival',y='log_value_deaths',data=df_groupby_country.head(50), kind='kde', color='b')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='log_value_survival',y='log_value_deaths',data=df_groupby_country.head(50), kind='hex', color='b')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}