corona visualization proj.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",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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,Other</th>\n",
       "      <th>TotalCases</th>\n",
       "      <th>NewCases</th>\n",
       "      <th>TotalDeaths</th>\n",
       "      <th>NewDeaths</th>\n",
       "      <th>TotalRecovered</th>\n",
       "      <th>ActiveCases</th>\n",
       "      <th>Serious,Critical</th>\n",
       "      <th>Tot Cases/1M pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>China</td>\n",
       "      <td>81054</td>\n",
       "      <td>+46</td>\n",
       "      <td>3261.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>72440.0</td>\n",
       "      <td>5353</td>\n",
       "      <td>1845.0</td>\n",
       "      <td>56.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Italy</td>\n",
       "      <td>59138</td>\n",
       "      <td>+5,560</td>\n",
       "      <td>5476.0</td>\n",
       "      <td>651.0</td>\n",
       "      <td>7024.0</td>\n",
       "      <td>46638</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>978.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>USA</td>\n",
       "      <td>38167</td>\n",
       "      <td>+13,960</td>\n",
       "      <td>396.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>37593</td>\n",
       "      <td>708.0</td>\n",
       "      <td>115.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Spain</td>\n",
       "      <td>28603</td>\n",
       "      <td>+3,107</td>\n",
       "      <td>1756.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>2125.0</td>\n",
       "      <td>24722</td>\n",
       "      <td>1785.0</td>\n",
       "      <td>612.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Germany</td>\n",
       "      <td>24806</td>\n",
       "      <td>+2,442</td>\n",
       "      <td>93.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>24447</td>\n",
       "      <td>2.0</td>\n",
       "      <td>296.00</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>186</td>\n",
       "      <td>Sint Maarten</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>187</td>\n",
       "      <td>Somalia</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>188</td>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>189</td>\n",
       "      <td>Uganda</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>190</td>\n",
       "      <td>Total:</td>\n",
       "      <td>338104</td>\n",
       "      <td>33114</td>\n",
       "      <td>14456.0</td>\n",
       "      <td>1449.0</td>\n",
       "      <td>96958.0</td>\n",
       "      <td>226690</td>\n",
       "      <td>10298.0</td>\n",
       "      <td>43.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>191 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country,Other  TotalCases NewCases  TotalDeaths  NewDeaths  \\\n",
       "0           China       81054      +46       3261.0        6.0   \n",
       "1           Italy       59138   +5,560       5476.0      651.0   \n",
       "2             USA       38167  +13,960        396.0       94.0   \n",
       "3           Spain       28603   +3,107       1756.0      375.0   \n",
       "4         Germany       24806   +2,442         93.0        9.0   \n",
       "..            ...         ...      ...          ...        ...   \n",
       "186  Sint Maarten           1      NaN          NaN        NaN   \n",
       "187       Somalia           1      NaN          NaN        NaN   \n",
       "188   Timor-Leste           1      NaN          NaN        NaN   \n",
       "189        Uganda           1      NaN          NaN        NaN   \n",
       "190        Total:      338104    33114      14456.0     1449.0   \n",
       "\n",
       "     TotalRecovered  ActiveCases  Serious,Critical  Tot Cases/1M pop  \n",
       "0           72440.0         5353            1845.0             56.00  \n",
       "1            7024.0        46638            3000.0            978.00  \n",
       "2             178.0        37593             708.0            115.00  \n",
       "3            2125.0        24722            1785.0            612.00  \n",
       "4             266.0        24447               2.0            296.00  \n",
       "..              ...          ...               ...               ...  \n",
       "186             NaN            1               NaN             23.00  \n",
       "187             NaN            1               NaN              0.06  \n",
       "188             NaN            1               NaN              0.80  \n",
       "189             NaN            1               NaN              0.02  \n",
       "190         96958.0       226690           10298.0             43.40  \n",
       "\n",
       "[191 rows x 9 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df,d = pd.read_html(\"https://www.worldometers.info/coronavirus/\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "copy=df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.fillna(value=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country,Other         0\n",
       "TotalCases            0\n",
       "NewCases             82\n",
       "TotalDeaths          98\n",
       "NewDeaths           152\n",
       "TotalRecovered       84\n",
       "ActiveCases           0\n",
       "Serious,Critical    115\n",
       "Tot Cases/1M pop      1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 191 entries, 0 to 190\n",
      "Data columns (total 9 columns):\n",
      "Country,Other       191 non-null object\n",
      "TotalCases          191 non-null int64\n",
      "NewCases            109 non-null object\n",
      "TotalDeaths         93 non-null float64\n",
      "NewDeaths           39 non-null float64\n",
      "TotalRecovered      107 non-null float64\n",
      "ActiveCases         191 non-null int64\n",
      "Serious,Critical    76 non-null float64\n",
      "Tot Cases/1M pop    190 non-null float64\n",
      "dtypes: float64(5), int64(2), object(2)\n",
      "memory usage: 13.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        46\n",
       "1     5,560\n",
       "2    13,960\n",
       "3     3,107\n",
       "4     2,442\n",
       "Name: NewCases, dtype: object"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"NewCases\"]=df[\"NewCases\"].str[1:]\n",
    "df[\"NewCases\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove(x):\n",
    "    a=\",\"\n",
    "    if (isinstance (x,str)==True):\n",
    "        if a in x:\n",
    "            x=x.split(a)\n",
    "            b=x[0]\n",
    "            c=x[1]\n",
    "            x=b+c\n",
    "            return int(x)\n",
    "        elif(x==\" \"):\n",
    "            return(0)\n",
    "        else:\n",
    "            return(int(x))\n",
    "    else:\n",
    "        return (x)\n",
    "#     x=str(x).split(\",\")\n",
    "#     a=str(x[0])\n",
    "#     b=str(x[1])\n",
    "#     x=a+b\n",
    "#     return int(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       46.0\n",
       "1     5560.0\n",
       "2    13960.0\n",
       "3     3107.0\n",
       "4     2442.0\n",
       "Name: NewCases, dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['NewCases']=df['NewCases'].apply(remove)\n",
    "df['NewCases'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"corona.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>Unnamed: 0</th>\n",
       "      <th>Country,Other</th>\n",
       "      <th>TotalCases</th>\n",
       "      <th>NewCases</th>\n",
       "      <th>TotalDeaths</th>\n",
       "      <th>NewDeaths</th>\n",
       "      <th>TotalRecovered</th>\n",
       "      <th>ActiveCases</th>\n",
       "      <th>Serious,Critical</th>\n",
       "      <th>Tot Cases/1M pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>China</td>\n",
       "      <td>81054</td>\n",
       "      <td>46.0</td>\n",
       "      <td>3261.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>72440.0</td>\n",
       "      <td>5353</td>\n",
       "      <td>1845.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Italy</td>\n",
       "      <td>59138</td>\n",
       "      <td>5560.0</td>\n",
       "      <td>5476.0</td>\n",
       "      <td>651.0</td>\n",
       "      <td>7024.0</td>\n",
       "      <td>46638</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>978.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>USA</td>\n",
       "      <td>38167</td>\n",
       "      <td>13960.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>37593</td>\n",
       "      <td>708.0</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Spain</td>\n",
       "      <td>28603</td>\n",
       "      <td>3107.0</td>\n",
       "      <td>1756.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>2125.0</td>\n",
       "      <td>24722</td>\n",
       "      <td>1785.0</td>\n",
       "      <td>612.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Germany</td>\n",
       "      <td>24806</td>\n",
       "      <td>2442.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>24447</td>\n",
       "      <td>2.0</td>\n",
       "      <td>296.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 Country,Other  TotalCases  NewCases  TotalDeaths  NewDeaths  \\\n",
       "0           0         China       81054      46.0       3261.0        6.0   \n",
       "1           1         Italy       59138    5560.0       5476.0      651.0   \n",
       "2           2           USA       38167   13960.0        396.0       94.0   \n",
       "3           3         Spain       28603    3107.0       1756.0      375.0   \n",
       "4           4       Germany       24806    2442.0         93.0        9.0   \n",
       "\n",
       "   TotalRecovered  ActiveCases  Serious,Critical  Tot Cases/1M pop  \n",
       "0         72440.0         5353            1845.0              56.0  \n",
       "1          7024.0        46638            3000.0             978.0  \n",
       "2           178.0        37593             708.0             115.0  \n",
       "3          2125.0        24722            1785.0             612.0  \n",
       "4           266.0        24447               2.0             296.0  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona=pd.read_csv('corona.csv')\n",
    "corona.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Unnamed: 0', 'Country,Other', 'TotalCases', 'NewCases', 'TotalDeaths',\n",
      "       'NewDeaths', 'TotalRecovered', 'ActiveCases', 'Serious,Critical',\n",
      "       'Tot Cases/1M pop'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(corona.columns)\n",
    "df=corona"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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,Other</th>\n",
       "      <th>TotalCases</th>\n",
       "      <th>NewCases</th>\n",
       "      <th>TotalDeaths</th>\n",
       "      <th>NewDeaths</th>\n",
       "      <th>TotalRecovered</th>\n",
       "      <th>ActiveCases</th>\n",
       "      <th>Serious,Critical</th>\n",
       "      <th>Tot Cases/1M pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>China</td>\n",
       "      <td>81054</td>\n",
       "      <td>46.0</td>\n",
       "      <td>3261.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>72440.0</td>\n",
       "      <td>5353</td>\n",
       "      <td>1845.0</td>\n",
       "      <td>56.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Italy</td>\n",
       "      <td>59138</td>\n",
       "      <td>5560.0</td>\n",
       "      <td>5476.0</td>\n",
       "      <td>651.0</td>\n",
       "      <td>7024.0</td>\n",
       "      <td>46638</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>978.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>USA</td>\n",
       "      <td>38167</td>\n",
       "      <td>13960.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>37593</td>\n",
       "      <td>708.0</td>\n",
       "      <td>115.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Spain</td>\n",
       "      <td>28603</td>\n",
       "      <td>3107.0</td>\n",
       "      <td>1756.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>2125.0</td>\n",
       "      <td>24722</td>\n",
       "      <td>1785.0</td>\n",
       "      <td>612.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Germany</td>\n",
       "      <td>24806</td>\n",
       "      <td>2442.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>24447</td>\n",
       "      <td>2.0</td>\n",
       "      <td>296.00</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>185</td>\n",
       "      <td>St. Vincent Grenadines</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>186</td>\n",
       "      <td>Sint Maarten</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>187</td>\n",
       "      <td>Somalia</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>188</td>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>189</td>\n",
       "      <td>Uganda</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>190 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Country,Other  TotalCases  NewCases  TotalDeaths  NewDeaths  \\\n",
       "0                     China       81054      46.0       3261.0        6.0   \n",
       "1                     Italy       59138    5560.0       5476.0      651.0   \n",
       "2                       USA       38167   13960.0        396.0       94.0   \n",
       "3                     Spain       28603    3107.0       1756.0      375.0   \n",
       "4                   Germany       24806    2442.0         93.0        9.0   \n",
       "..                      ...         ...       ...          ...        ...   \n",
       "185  St. Vincent Grenadines           1       NaN          NaN        NaN   \n",
       "186            Sint Maarten           1       NaN          NaN        NaN   \n",
       "187                 Somalia           1       NaN          NaN        NaN   \n",
       "188             Timor-Leste           1       NaN          NaN        NaN   \n",
       "189                  Uganda           1       NaN          NaN        NaN   \n",
       "\n",
       "     TotalRecovered  ActiveCases  Serious,Critical  Tot Cases/1M pop  \n",
       "0           72440.0         5353            1845.0             56.00  \n",
       "1            7024.0        46638            3000.0            978.00  \n",
       "2             178.0        37593             708.0            115.00  \n",
       "3            2125.0        24722            1785.0            612.00  \n",
       "4             266.0        24447               2.0            296.00  \n",
       "..              ...          ...               ...               ...  \n",
       "185             NaN            1               NaN              9.00  \n",
       "186             NaN            1               NaN             23.00  \n",
       "187             NaN            1               NaN              0.06  \n",
       "188             NaN            1               NaN              0.80  \n",
       "189             NaN            1               NaN              0.02  \n",
       "\n",
       "[190 rows x 9 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dff=df.iloc[0:-1,1:]\n",
    "dff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country,Other        0.0\n",
       "TotalCases           0.0\n",
       "NewCases            43.2\n",
       "TotalDeaths         51.6\n",
       "NewDeaths           80.0\n",
       "TotalRecovered      44.2\n",
       "ActiveCases          0.0\n",
       "Serious,Critical    60.5\n",
       "Tot Cases/1M pop     0.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Percentage of missing values\n",
    "round((dff.isnull().sum()/len(dff))*100,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical columns:  ['Country,Other']\n"
     ]
    }
   ],
   "source": [
    "catcols=list(dff.select_dtypes(include=['object']).columns)\n",
    "print(\"Categorical columns: \",catcols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuous columns: \n",
      " ['TotalCases', 'NewCases', 'TotalDeaths', 'NewDeaths', 'TotalRecovered', 'ActiveCases', 'Serious,Critical', 'Tot\\xa0Cases/1M pop']\n"
     ]
    }
   ],
   "source": [
    "numcols=list(dff.select_dtypes(exclude=['object']).columns)\n",
    "print(\"Continuous columns: \\n\",numcols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Those are null values not missing values, lets impute with 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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,Other</th>\n",
       "      <th>TotalCases</th>\n",
       "      <th>NewCases</th>\n",
       "      <th>TotalDeaths</th>\n",
       "      <th>NewDeaths</th>\n",
       "      <th>TotalRecovered</th>\n",
       "      <th>ActiveCases</th>\n",
       "      <th>Serious,Critical</th>\n",
       "      <th>Tot Cases/1M pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>China</td>\n",
       "      <td>81054</td>\n",
       "      <td>46.0</td>\n",
       "      <td>3261.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>72440.0</td>\n",
       "      <td>5353</td>\n",
       "      <td>1845.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Italy</td>\n",
       "      <td>59138</td>\n",
       "      <td>5560.0</td>\n",
       "      <td>5476.0</td>\n",
       "      <td>651.0</td>\n",
       "      <td>7024.0</td>\n",
       "      <td>46638</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>978.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>USA</td>\n",
       "      <td>38167</td>\n",
       "      <td>13960.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>37593</td>\n",
       "      <td>708.0</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Spain</td>\n",
       "      <td>28603</td>\n",
       "      <td>3107.0</td>\n",
       "      <td>1756.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>2125.0</td>\n",
       "      <td>24722</td>\n",
       "      <td>1785.0</td>\n",
       "      <td>612.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Germany</td>\n",
       "      <td>24806</td>\n",
       "      <td>2442.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>24447</td>\n",
       "      <td>2.0</td>\n",
       "      <td>296.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country,Other  TotalCases  NewCases  TotalDeaths  NewDeaths  TotalRecovered  \\\n",
       "0         China       81054      46.0       3261.0        6.0         72440.0   \n",
       "1         Italy       59138    5560.0       5476.0      651.0          7024.0   \n",
       "2           USA       38167   13960.0        396.0       94.0           178.0   \n",
       "3         Spain       28603    3107.0       1756.0      375.0          2125.0   \n",
       "4       Germany       24806    2442.0         93.0        9.0           266.0   \n",
       "\n",
       "   ActiveCases  Serious,Critical  Tot Cases/1M pop  \n",
       "0         5353            1845.0              56.0  \n",
       "1        46638            3000.0             978.0  \n",
       "2        37593             708.0             115.0  \n",
       "3        24722            1785.0             612.0  \n",
       "4        24447               2.0             296.0  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dff.fillna(0,inplace=True)\n",
    "dff.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 190 entries, 0 to 189\n",
      "Data columns (total 9 columns):\n",
      "Country,Other       190 non-null object\n",
      "TotalCases          190 non-null int64\n",
      "NewCases            190 non-null float64\n",
      "TotalDeaths         190 non-null float64\n",
      "NewDeaths           190 non-null float64\n",
      "TotalRecovered      190 non-null float64\n",
      "ActiveCases         190 non-null int64\n",
      "Serious,Critical    190 non-null float64\n",
      "Tot Cases/1M pop    190 non-null float64\n",
      "dtypes: float64(6), int64(2), object(1)\n",
      "memory usage: 13.5+ KB\n"
     ]
    }
   ],
   "source": [
    "dff.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of missing values: \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Country,Other       0.0\n",
       "TotalCases          0.0\n",
       "NewCases            0.0\n",
       "TotalDeaths         0.0\n",
       "NewDeaths           0.0\n",
       "TotalRecovered      0.0\n",
       "ActiveCases         0.0\n",
       "Serious,Critical    0.0\n",
       "Tot Cases/1M pop    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Percentage of missing values: \")\n",
    "round((dff.isnull().sum()/len(dff))*100,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>TotalCases</th>\n",
       "      <th>NewCases</th>\n",
       "      <th>TotalDeaths</th>\n",
       "      <th>NewDeaths</th>\n",
       "      <th>TotalRecovered</th>\n",
       "      <th>ActiveCases</th>\n",
       "      <th>Serious,Critical</th>\n",
       "      <th>Tot Cases/1M pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>China</td>\n",
       "      <td>81054</td>\n",
       "      <td>46.0</td>\n",
       "      <td>3261.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>72440.0</td>\n",
       "      <td>5353</td>\n",
       "      <td>1845.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Italy</td>\n",
       "      <td>59138</td>\n",
       "      <td>5560.0</td>\n",
       "      <td>5476.0</td>\n",
       "      <td>651.0</td>\n",
       "      <td>7024.0</td>\n",
       "      <td>46638</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>978.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>USA</td>\n",
       "      <td>38167</td>\n",
       "      <td>13960.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>37593</td>\n",
       "      <td>708.0</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Spain</td>\n",
       "      <td>28603</td>\n",
       "      <td>3107.0</td>\n",
       "      <td>1756.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>2125.0</td>\n",
       "      <td>24722</td>\n",
       "      <td>1785.0</td>\n",
       "      <td>612.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Germany</td>\n",
       "      <td>24806</td>\n",
       "      <td>2442.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>24447</td>\n",
       "      <td>2.0</td>\n",
       "      <td>296.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country  TotalCases  NewCases  TotalDeaths  NewDeaths  TotalRecovered  \\\n",
       "0    China       81054      46.0       3261.0        6.0         72440.0   \n",
       "1    Italy       59138    5560.0       5476.0      651.0          7024.0   \n",
       "2      USA       38167   13960.0        396.0       94.0           178.0   \n",
       "3    Spain       28603    3107.0       1756.0      375.0          2125.0   \n",
       "4  Germany       24806    2442.0         93.0        9.0           266.0   \n",
       "\n",
       "   ActiveCases  Serious,Critical  Tot Cases/1M pop  \n",
       "0         5353            1845.0              56.0  \n",
       "1        46638            3000.0             978.0  \n",
       "2        37593             708.0             115.0  \n",
       "3        24722            1785.0             612.0  \n",
       "4        24447               2.0             296.0  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dff.rename(columns={\"Country,Other\":\"Country\"},inplace=True)\n",
    "dff.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>TotalCases</td>\n",
       "      <td>190.0</td>\n",
       "      <td>1779.494737</td>\n",
       "      <td>8380.310393</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>57.5</td>\n",
       "      <td>323.75</td>\n",
       "      <td>81054.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>NewCases</td>\n",
       "      <td>190.0</td>\n",
       "      <td>174.284211</td>\n",
       "      <td>1124.058211</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>31.00</td>\n",
       "      <td>13960.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TotalDeaths</td>\n",
       "      <td>190.0</td>\n",
       "      <td>76.084211</td>\n",
       "      <td>493.720958</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5476.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>NewDeaths</td>\n",
       "      <td>190.0</td>\n",
       "      <td>7.626316</td>\n",
       "      <td>55.578938</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>651.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TotalRecovered</td>\n",
       "      <td>190.0</td>\n",
       "      <td>510.305263</td>\n",
       "      <td>5306.418802</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.75</td>\n",
       "      <td>72440.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ActiveCases</td>\n",
       "      <td>190.0</td>\n",
       "      <td>1193.105263</td>\n",
       "      <td>5164.145628</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>244.25</td>\n",
       "      <td>46638.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Serious,Critical</td>\n",
       "      <td>190.0</td>\n",
       "      <td>54.200000</td>\n",
       "      <td>309.516438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Tot Cases/1M pop</td>\n",
       "      <td>190.0</td>\n",
       "      <td>136.449947</td>\n",
       "      <td>469.210124</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>64.25</td>\n",
       "      <td>5158.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count         mean          std  min  25%   50%     75%  \\\n",
       "TotalCases        190.0  1779.494737  8380.310393  1.0  6.0  57.5  323.75   \n",
       "NewCases          190.0   174.284211  1124.058211  0.0  0.0   3.0   31.00   \n",
       "TotalDeaths       190.0    76.084211   493.720958  0.0  0.0   0.0    3.00   \n",
       "NewDeaths         190.0     7.626316    55.578938  0.0  0.0   0.0    0.00   \n",
       "TotalRecovered    190.0   510.305263  5306.418802  0.0  0.0   1.0    9.75   \n",
       "ActiveCases       190.0  1193.105263  5164.145628  0.0  6.0  53.0  244.25   \n",
       "Serious,Critical  190.0    54.200000   309.516438  0.0  0.0   0.0    3.00   \n",
       "Tot Cases/1M pop  190.0   136.449947   469.210124  0.0  2.0  14.0   64.25   \n",
       "\n",
       "                      max  \n",
       "TotalCases        81054.0  \n",
       "NewCases          13960.0  \n",
       "TotalDeaths        5476.0  \n",
       "NewDeaths           651.0  \n",
       "TotalRecovered    72440.0  \n",
       "ActiveCases       46638.0  \n",
       "Serious,Critical   3000.0  \n",
       "Tot Cases/1M pop   5158.0  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dff.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "import chart_studio.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "from plotly.offline import iplot, init_notebook_mode\n",
    "import cufflinks\n",
    "from plotly.offline import iplot\n",
    "# cufflinks.go_offline(connected=True)\n",
    "# init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Top 5 cities with total no. of cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
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       "    .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>TotalCases</th>\n",
       "      <th>NewCases</th>\n",
       "      <th>TotalDeaths</th>\n",
       "      <th>NewDeaths</th>\n",
       "      <th>TotalRecovered</th>\n",
       "      <th>ActiveCases</th>\n",
       "      <th>Serious,Critical</th>\n",
       "      <th>Tot Cases/1M pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>China</td>\n",
       "      <td>81054</td>\n",
       "      <td>46.0</td>\n",
       "      <td>3261.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>72440.0</td>\n",
       "      <td>5353</td>\n",
       "      <td>1845.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Italy</td>\n",
       "      <td>59138</td>\n",
       "      <td>5560.0</td>\n",
       "      <td>5476.0</td>\n",
       "      <td>651.0</td>\n",
       "      <td>7024.0</td>\n",
       "      <td>46638</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>978.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>USA</td>\n",
       "      <td>38167</td>\n",
       "      <td>13960.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>37593</td>\n",
       "      <td>708.0</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Spain</td>\n",
       "      <td>28603</td>\n",
       "      <td>3107.0</td>\n",
       "      <td>1756.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>2125.0</td>\n",
       "      <td>24722</td>\n",
       "      <td>1785.0</td>\n",
       "      <td>612.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Germany</td>\n",
       "      <td>24806</td>\n",
       "      <td>2442.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>24447</td>\n",
       "      <td>2.0</td>\n",
       "      <td>296.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country  TotalCases  NewCases  TotalDeaths  NewDeaths  TotalRecovered  \\\n",
       "0    China       81054      46.0       3261.0        6.0         72440.0   \n",
       "1    Italy       59138    5560.0       5476.0      651.0          7024.0   \n",
       "2      USA       38167   13960.0        396.0       94.0           178.0   \n",
       "3    Spain       28603    3107.0       1756.0      375.0          2125.0   \n",
       "4  Germany       24806    2442.0         93.0        9.0           266.0   \n",
       "\n",
       "   ActiveCases  Serious,Critical  Tot Cases/1M pop  \n",
       "0         5353            1845.0              56.0  \n",
       "1        46638            3000.0             978.0  \n",
       "2        37593             708.0             115.0  \n",
       "3        24722            1785.0             612.0  \n",
       "4        24447               2.0             296.0  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dff.sort_values(by=['TotalCases'],ascending=False,axis=0,inplace=True)\n",
    "d=dff.head()\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
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       "\n",
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       "var notebookContainer = gd.closest('#notebook-container');\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "\n",
    "colors = ['lightslategray',] * 5\n",
    "colors[1] = 'crimson'\n",
    "\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Bar(\n",
    "    x=d[\"Country\"],\n",
    "    y=d.TotalDeaths,\n",
    "    \n",
    "    marker_color=colors,\n",
    "    text=df.TotalDeaths,\n",
    "    textposition='auto'\n",
    "))\n",
    "fig.update_layout(title_text='Top 5 Countries having Total no. of Deaths')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.patches as patches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,5), facecolor='white')\n",
    "ax.vlines(x=d['Country'], ymin=0, ymax=d['TotalCases'], color='#7B68EE', alpha=0.9, linewidth=20,label='TotalCases')\n",
    "ax.vlines(x=d['Country'], ymin=0, ymax=d['TotalDeaths'], color='#FF0000', alpha=0.9, linewidth=20+10,label='TotalDeaths')\n",
    "\n",
    "# Annotate Text\n",
    "for i, cty in enumerate(d['TotalCases']):\n",
    "    ax.text(i, int(cty)+0.1, round(cty, 1), horizontalalignment='center')\n",
    "for i, cty in enumerate(d['TotalDeaths']):\n",
    "    ax.text(i, int(cty)+0.1, round(cty, 1), horizontalalignment='left')\n",
    "\n",
    "\n",
    "\n",
    "# Title, Label, Ticks and Ylim\n",
    "ax.set_title('Bar Chart for Top 5 Countries having TotalCases', fontdict={'size':15})\n",
    "ax.set(ylabel='Count')\n",
    "plt.xticks(d['Country'], d['Country'].str.upper(), rotation=0, horizontalalignment='center', fontsize=10)\n",
    "\n",
    "# Add patches to color the X axis labels\n",
    "p1 = patches.Rectangle((.57, -0.005), width=.33, height=.13, alpha=.1, facecolor='green', transform=fig.transFigure)\n",
    "p2 = patches.Rectangle((.124, -0.005), width=.446, height=.13, alpha=.1, facecolor='red', transform=fig.transFigure)\n",
    "fig.add_artist(p1)\n",
    "fig.add_artist(p2)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Top 10 Cities suffering with Corona Virus severely"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                1\n",
      "Italy        5476\n",
      "China        3261\n",
      "Spain        1756\n",
      "Iran         1685\n",
      "France        562\n",
      "USA           396\n",
      "UK            281\n",
      "Netherlands   179\n",
      "S. Korea      104\n"
     ]
    },
    {
     "data": {
      "image/png": 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NGjWg1/lErplZxfjx45k9ezZz587tdCh9atw5ayCc9M3MKkaOHDmgO1ENNW7eMTOrkX4lfUm3S/q3pKslzSxlq0iaLunm8n/lUi5Jx0maJelaSZtXljO5zH+zpMmteUtmZtaTgdT03x4Rm0bExPL8MODCiNgAuLA8B3gnsEH5mwL8CHInARwBvAHYAjiisaMwM7P2WJzmnV2AaeXxNGDXSvkpkS4HRktaA9gemB4R8yLiIWA6sMNirN/MzAaov0k/gD9LulLSlFK2WkTcC1D+N4Z/Wwu4q/La2aWsp/IXkTRF0kxJM4fC2XMzs6Gkv713toyIeySNA6ZLurGXebsbBi56KX9xQcQJwAkAEydOHNwdZc3Mhph+1fQj4p7yfw7wO7JN/v7SbEP537ily2xg7crLxwP39FJuZmZt0mfSl7ScpBUaj4HtgOuAc4FGD5zJwDnl8bnAvqUXzyRgfmn++ROwnaSVywnc7UqZmZm1SX+ad1YDflcG7x8BnBYRf5Q0AzhD0n7AncDuZf7zgR2BWcATwIcAImKepKOAGWW+r0TEvKa9EzMz61OfST8ibgVe2035g8A23ZQHcFAPyzoZOHngYZqZWTP4ilwzsxpx0jczqxEnfTOzGnHSNzOrESd9M7MacdI3M6sRJ30zsxpx0jczqxEnfTOzGnHSNzOrESd9M7MacdI3M6sRJ30zsxpx0jczqxEnfTOzGnHSNzOrESd9M7MacdI3M6sRJ30zsxpx0jczqxEnfTOzGnHSNzOrESd9M7MacdI3M6sRJ30zsxpx0jczqxEnfTOzGnHSNzOrkX4nfUnDJV0l6bzyfF1J/5R0s6RfS1qqlC9dns8q0ydUlnF4Kb9J0vbNfjNmZta7gdT0Pw7cUHn+deDYiNgAeAjYr5TvBzwUES8Hji3zIWlDYE9gI2AH4IeShi9e+GZmNhD9SvqSxgM7AT8tzwVsDZxVZpkG7Foe71KeU6ZvU+bfBTg9Ip6OiNuAWcAWzXgTZmbWP/2t6X8X+AywsDwfAzwcEc+V57OBtcrjtYC7AMr0+WX+58u7eY2ZmbVBn0lf0ruAORFxZbW4m1mjj2m9vaa6vimSZkqaOXfu3L7CMzOzAehPTX9LYGdJtwOnk8063wVGSxpR5hkP3FMezwbWBijTVwLmVcu7ec3zIuKEiJgYERPHjh074DdkZmY96zPpR8ThETE+IiaQJ2Ivioj3A38BdiuzTQbOKY/PLc8p0y+KiCjle5bePesCGwBXNO2dmJlZn0b0PUuPPgucLulo4CrgpFJ+EvALSbPIGv6eABFxvaQzgP8AzwEHRcSCxVi/mZkN0ICSfkRcDFxcHt9KN71vIuIpYPceXn8McMxAgzQzs+bwFblmZjXipG9mViNO+mZmNeKkb2ZWI076ZmY14qRvZlYjTvpmZjXipG9mViNO+mZmNeKkb2ZWI076ZmY14qRvZlYjTvpmZjXipG9mViNO+mZmNeKkb2ZWI076ZmY14qRvZlYjTvpmZjXipG9mViNO+mZmNeKkb2ZWI076ZmY1MqLTASypJhz2+6Ys5/apOzVlOWZm4Jq+mVmtOOmbmdWIk76ZWY046ZuZ1YiTvplZjfSZ9CWNknSFpGskXS/pyFK+rqR/SrpZ0q8lLVXKly7PZ5XpEyrLOryU3yRp+1a9KTMz615/avpPA1tHxGuBTYEdJE0Cvg4cGxEbAA8B+5X59wMeioiXA8eW+ZC0IbAnsBGwA/BDScOb+WbMzKx3fSb9SI+VpyPLXwBbA2eV8mnAruXxLuU5Zfo2klTKT4+IpyPiNmAWsEVT3oWZmfVLv9r0JQ2XdDUwB5gO3AI8HBHPlVlmA2uVx2sBdwGU6fOBMdXybl5TXdcUSTMlzZw7d+7A35GZmfWoX0k/IhZExKbAeLJ2/uruZiv/1cO0nsq7ruuEiJgYERPHjh3bn/DMzKyfBtR7JyIeBi4GJgGjJTWGcRgP3FMezwbWBijTVwLmVcu7eY2ZmbVBf3rvjJU0ujxeBtgWuAH4C7BbmW0ycE55fG55Tpl+UUREKd+z9O5ZF9gAuKJZb8TMzPrWnwHX1gCmlZ42w4AzIuI8Sf8BTpd0NHAVcFKZ/yTgF5JmkTX8PQEi4npJZwD/AZ4DDoqIBc19O2Zm1ps+k35EXAts1k35rXTT+yYingJ272FZxwDHDDxMMzNrBl+Ra2ZWI076ZmY14qRvZlYjTvpmZjXipG9mViNO+mZmNeKkb2ZWI076ZmY14qRvZlYjTvpmZjXipG9mViNO+mZmNeKkb2ZWI076ZmY14qRvZlYjTvpmZjXipG9mViNO+mZmNeKkb2ZWI076ZmY14qRvZlYjTvpmZjXipG9mViNO+mZmNeKkb2ZWI076ZmY14qRvZlYjTvpmZjXipG9mViN9Jn1Ja0v6i6QbJF0v6eOlfBVJ0yXdXP6vXMol6ThJsyRdK2nzyrIml/lvljS5dW/LzMy605+a/nPApyLi1cAk4CBJGwKHARdGxAbAheU5wDuBDcrfFOBHkDsJ4AjgDcAWwBGNHYWZmbVHn0k/Iu6NiH+Vx48CNwBrAbsA08ps04Bdy+NdgFMiXQ6MlrQGsD0wPSLmRcRDwHRgh6a+GzMz69WA2vQlTQA2A/4JrBYR90LuGIBxZba1gLsqL5tdynoq77qOKZJmSpo5d+7cgYRnZmZ96HfSl7Q88BvgkIh4pLdZuymLXspfXBBxQkRMjIiJY8eO7W94ZmbWD/1K+pJGkgn/1Ij4bSm+vzTbUP7PKeWzgbUrLx8P3NNLuZmZtUl/eu8IOAm4ISK+U5l0LtDogTMZOKdSvm/pxTMJmF+af/4EbCdp5XICd7tSZmZmbTKiH/NsCewD/FvS1aXsc8BU4AxJ+wF3AruXaecDOwKzgCeADwFExDxJRwEzynxfiYh5TXkXZmbWL30m/Yi4lO7b4wG26Wb+AA7qYVknAycPJEAzM2seX5FrZlYjTvpmZjXipG9mViP9OZFrS4gJh/2+acu6fepOTVuWmbWPa/pmZjXipG9mViNu3rGOcpOTWXu5pm9mViNO+mZmNeKkb2ZWI076ZmY14qRvZlYjTvpmZjXipG9mViNO+mZmNeKkb2ZWI076ZmY14qRvZlYjTvpmZjXipG9mViNO+mZmNeKkb2ZWI076ZmY14qRvZlYjTvpmZjXipG9mViNO+mZmNeKkb2ZWI076ZmY10mfSl3SypDmSrquUrSJpuqSby/+VS7kkHSdplqRrJW1eec3kMv/Nkia35u2YmVlv+lPT/zmwQ5eyw4ALI2ID4MLyHOCdwAblbwrwI8idBHAE8AZgC+CIxo7CzMzap8+kHxGXAPO6FO8CTCuPpwG7VspPiXQ5MFrSGsD2wPSImBcRDwHTeemOxMzMWmxR2/RXi4h7Acr/caV8LeCuynyzS1lP5S8haYqkmZJmzp07dxHDMzOz7jT7RK66KYteyl9aGHFCREyMiIljx45tanBmZnW3qEn//tJsQ/k/p5TPBtauzDceuKeXcjMza6NFTfrnAo0eOJOBcyrl+5ZePJOA+aX550/AdpJWLidwtytlZmbWRiP6mkHSr4CtgFUlzSZ74UwFzpC0H3AnsHuZ/XxgR2AW8ATwIYCImCfpKGBGme8rEdH15LCZmbVYn0k/IvbqYdI23cwbwEE9LOdk4OQBRWdmZk3lK3LNzGrESd/MrEac9M3MasRJ38ysRpz0zcxqxEnfzKxGnPTNzGrESd/MrEac9M3MasRJ38ysRpz0zcxqpM+xd8zqZsJhv2/asm6fulPTlmXWDK7pm5nViJO+mVmNOOmbmdWIk76ZWY046ZuZ1Yh775gNEc3qVeQeRfXmmr6ZWY046ZuZ1YiTvplZjbhN38wWmc8zDD2u6ZuZ1YiTvplZjTjpm5nViNv0zWyJ4lFSe+eavplZjTjpm5nViJt3zMxabDA1ObW9pi9pB0k3SZol6bB2r9/MrM7amvQlDQd+ALwT2BDYS9KG7YzBzKzO2l3T3wKYFRG3RsQzwOnALm2OwcysthQR7VuZtBuwQ0TsX57vA7whIg6uzDMFmFKevhK4qUmrXxV4oEnLahbH1H+DMS7H1D+Oqf+aFdfLImJsdxPafSJX3ZS9aK8TEScAJzR9xdLMiJjY7OUuDsfUf4MxLsfUP46p/9oRV7ubd2YDa1eejwfuaXMMZma11e6kPwPYQNK6kpYC9gTObXMMZma11dbmnYh4TtLBwJ+A4cDJEXF9m1bf9CajJnBM/TcY43JM/eOY+q/lcbX1RK6ZmXWWh2EwM6sRJ30zsxpx0jczq5HaJH1Jw8r/ZavPByNJ3V3PYNajxjZT2c47vg1JeoWk5QZDLENZsz+/QZv4mi0iFkoaCRwvab2IWNjpmLqSNBYgImKw7ZT8w+2/MsZUuy0NuZ2X/8/30OjEdydpOeBnwNgYwr1FKjtTdeo32fj8GvlhcdVmaGVJiohnJc0DjpP08Yi4RdKwTu0AJA2PiAVleIo3AStLuhmYFhF3V+Ju24+mEtPywGuAkRHx17IjGg4s7FA8K5Hb64iIuL9d6++PSozvBV6XRbo4Iv7UxjC+J2kccAawDnAB8F9geEQ83MY4KL+pxyXNBtYHbu8yvfF5tXXbXkwfAy4HrmzXCiuf02RgIrBQ0p3AeRGxyMPTLPFdNrtL6pK+BDwWEd/pUFjVWIaRG9PhwCeAkWRyOwv4SQd3SGcDzwGTgFnAlIj4b4diWQb4C3AjsID8vE6NiCc6EU9VI3FJWp2M8SDgl8CBEXGupJUiYn6LY1gJ+DWwHfAN4KnyeCHwIPAM8KOIuKiVcVTiWSoinpH0OWCZiPiipJER8Ww71t8slaS7BfBzYPOIeErS5sCwiJjZwnU3tqsVgb+RO53vkDvQBcAlwGkR8chAlz2omhBaoZE0JW0paStJ6wGnAFMkHSppqQ43pXyYrJVdA6wJ7E1+sf8LbN/OQCSNKP83ApaNiN0iYjzwT+Afkk6XNKxdzQWV72Ufsob1DWA6sClwbKlZd1SlproXcBxwA/DfRsIHDilHTa2MYT7wHuDrwD0R8WXgg8Cryc/sbmCpVsbQxS6S3gRcD7xG0ipkHnuDpMMknSVpKI2uexjwTWCYpC8DPwL2l7Ryq1ZY2a4+DEwjh6sJ4DPAcsAewCsWZdlLdNKX9D5Jq5TkcQSwI3A++YO4BTgA2LTD7fszgOOBfcnDtgeBi4ETI+IP7QwkIp4rD3cD5kuaUMo/C2wELA+s0a5D8nIeZn3gXcDPIuI/wO+Bn5Kjrw6mezFcQ8bzZ+DoUvYRYLOIeKzVKy9HPRcA20vaHvgscHxEXBYRh0TEH1sdA4Dy/hi7l7/lgfXImupvgW8BLwMui4hz2hHPopI0NiIWlKfTye/278BcMhGvBLy9xTGorPNUMj+cHBF3lHjOX+QjjYhYIv/Ims0pwLXAnl3KtwM2AY4ik/+GgyDeLcnhKd4J3Ay8vc3r/x/yRzoK+CLwR+ALwBuB0V3mHdbGuN4APEo2MW1YKR8LrNDp762yTa0ETC2f23uA9wL/AV7dwvUuC+xKNqE0ynYE7gTu7ND39QmyqfKbwMnAv8gbJ23bzjgW8z2sSzaNfZ8cGXgl8mhzr8r0meTRcKtjWa7834k8ity95KzNFnWZdWjT35rcCEcC346I/+sy/VvA7RHx/TbF02gnfD3ZfBPAdRFxjqRPAS8HboiI49oRT4lpdfI+Bz+XtDN5NLQ+cCCwAtm0cnFE3NCmeF5ygq8cVn8COBs4KNpQe+5N5Xv8IHkU9BmyVvsBYEVgGeDCiPhNC2P4MTAfmBoRD1XK30UezX40Iua2av3dxDOCbHZ4JfAs8AQwjhwj/hayAnZFO2NaVJJGk0eV44FDI+LMUj4G+DZwZUQc34L1NrarHYA3k5/l3yPi2JIfNgQujYifLfI6lvSk3yDpQGA/ssZ4ZETcWMqPBc6MiL+3OZ7LgH8DtwEbkDdOODEibmlnHJV4RgKvAj4P3AucERH/kLQdmfy/Gi08cVWJ4/mEL2lbciju8yPi/nJS6yRga2B8RDzZ6nh6U5oN/wEcEhH/qJQvFxGPt3jdmwEnkjchWqC8/mRP8qjjZ8CngQ8BG0XEU62MpZvY1iKPOJbTO74AAA92SURBVNYF5gFzgAnk0fWBkU2Yg1Il6S5DNpFtSHZmeATYPyIul7RRtHigyJIfvkLeUOq6iDiiaT0NO30o1aJDosbObA3ysHKZ8nwMeQh+D7BxKVu5jXENK/+3Aj5fiWlLcgM7n/Y366jyeGlgY7JGfTLZzLM+2W2zXfEML/+/QJ7AugS4jkxiq5Rpq3V6G6t8j78rj0cCS5XHewGjWrzuL1a2odeRTSgzy99NZO26ZU1L3cTzkqY24LUlztPIpsMJnf7OBvB+Tm98vpXP+0myV93wFq97Z/JoYhTZPLZCKZ8KvHyxl9/pD7cFH1gjaUwE/krWDO8FPlOZZ/0OxNVI+MPI/tP3A6+qTF8b2LqDn9vLgNcDq5fnbyLPefyavMCmnbGMLslLJZn9iDy5fSPwrk5vY5U4VwIurMZEnhu5vMXrXZU8Z3AuWXv+N3mS9LVl+o+B7dr4OUwgj1g/08P0k4BdO/19DfA9fZVyLpC8NgTga8DXW7S+RkV1ePktngRcVIlhR2BmM9a1xPXeiRfOuB8FHEP+KK8HPijpFknbR2lCaVfXw+IASUtHHp5tQrZNXynpqyXuu6JN/agbysVWSNqJrNnvDfxa0qfJGsZXgW9E+9tgVyf7ur+S7Bv9UfIE1iNkv/NBIbKr5I+Bb0v6raQPk0coh7d41QeT3fXGlfWfV9Z7bZm+CVlLbIuIuJ1sOp0k6e+Sdu0yy0rAKu2Kp0kuA46UtFfkfUDWI7sKT23FyqJkduCT5HU6s8jzexuU7q9fInsgLrYlqk2/ckHDOmTf2o+TF/JsSbZ1XkKekDyknTGRh7bvBP5AXrxzYkQ8KOlVwA/JHjK7Rnuv4KzGeAXZvfCDZHPTUmRCOT4iftemGIZFdtHclDwR+jdgc/JH9mnyBzcpIg5sRzw9xNho730N8BbgYfIahrnk9wpwVbSwq205Wbo7mfQfIXuZTI/szto4d7VjROzcqhj6iG1P8hzQI2QTxQZkbXWrdsczEJXvdgXyCPMxsi2/cbJ8HnB9RHyuheseD3wrIvYs5W8lf5fPAH+NiFOasr4lJel3OQH4GuAusnbxTTJpLAV8FPh0RDzdtJMiA4txQ7Ib2Hzg1xFxeinfg7yg5+p2xlPWPZFsQ5xKJrAtyaadQ4BTIuK0NsdzEtnD4ydlh3ko2YthPeD9EXFtrwtoA0mXkueFHiB7qdwAnBURD7QxhvHkIf8rgKfJniZLk0e3B3bycyon3D9Kdl09B7goIi7vVDz9JWlpsknlFvJ7PSsi/iDpFeRFby3tMSbpz+R3+Z6oXL3c7Fy1JCX9Lcma17ZkG9y3S9I4BliZrE3/MiK+1V2XwBbG1d0wEHsA+5M7ptMi4sJ2xNKdcrXoSPKE7UcjYr9yOPmhiPhImadln1eXnfUksg3/wxFxTSkbR7ZzLl2aETqiciSyA/C2iDi8HFG+hWxOGQd8PyL+1cIYVoiIR7uUbQzsQp7DmkxeU3Fnq2IYiEYNttNx9KXy3R4AbEZug5uQF189S/YeO69Vv4NKTX9fskn1PmDvKMOeNL2C2oqTEu3844Ud19vJ9vsHgW0q08eTbdWLfDFDk+L8UvnblmxvXRr4HDley3Id+syWI7tpjirPLwLOBK4qGx20+IIaYJ3K4zeSV5VeB+zb6W2rm1iHl+/resoJ71K+KbmTbOW6J9D7ydKfMsROlg6mP7J76W/Jc0iQ5yFeR15/cUSL1qkeyj9X8tg0yknkZv4N+Zp+6Su9akTMkfRtsh3ufvKy+GMj4hFJhwI/jfaPNviqiLhR0sfI3hY3ksn+HuBPEXGZpBVjEQZNWoyYGrWazcjhH+4jr3o9mkwc7yUvVruiTfF8hGzy+nBEnFrK3g+8m2zLPC3aNIRAfyjHJTqabKs+KSKObeO6tyZP4q5OnmA/uzLtLLJGenK74lmSlKPbC8mrmd8VETeX8tWAJ6LLEVaT1lkdRXMHcqC8O8nzfM+SvbN+GhE/b+p6l4Ck/2qyp8nXIweaQtKbySsj1yGveH00ysmRNsb1SvIimT+RV2zuX3ZAW5AJbT3ypPKJ7YyrEt9x5JW/P5L0WrKL2J+jcqKqXc1gyqscTyGbSA6IiH8prxL+MLB2ZO+djqjsJIcBKzYqDpK2ITsKrEs2i13apniG7MnSwaa77VvSMcD/I494D4oWXdhW2a7WJcfSOYAcWmQc2WvtkGjVqKSdPqxq0mHSSODLZBv5PqVsBDlQ1zFk4oD2jkFyaInpu2S//O9U10/WqF/T5s9pxfJ/a/KCmYmVaWvQgT75XeLblOyqdhqwUilbvtPbV4njmyWuvwFbNbYn8oTlRh2IZ0Xygr5/ks0Bkzr9GQ3VP7Jp+APAmPJ8FfIE9IO0eHwdcgd+bOX5BLKZaUp53m0T0GKts9MfeJM/wDFkL4YZdGnDb8WH10scnydHxluH3HPvDvyGvMpvhw59NpPIYXbXKInqfuB3ZQMfQdYWb+t0kiW7y+1FNu28r8OxNC6oezdZG1ue7Hl1a/nsNu5kfCW2ll4duqT+8cJFnIcDvyD75V9L9pNvVDjWbNG6D2hUFEpF5w6yZ1pj+ueBo1r13od88053SnPFH8j+y5PbvO7VyQ1oUpSLmsoh+Zrk1Zo7k8n1oIh4uo1x/Rk4OyJ+WJ6PJ28MsSrZ5fA24JKI+KOkEfHCMMsdUbrPrRBt7AbZSyx/Ao4kTzSvRB7B3UTuAN4cbR7bxpqjdC29kDyn9T2yg8Uryd/EoRFxfgvWuRx5RH1slF575WK2rckK4jXkmEnbR8RtzV4/LKG3S4yIa5SDPq0H7WubLnYk+yXPlbRMRDxZEuidki4ha4vXtDnh7wEQET8sV+FuRdb2byD75K9LdlW7rMzX0YRfYnia7LPcEZU217HkWOaPkz05vl/KzyAvxHLCH7peQx59vwJ4fURMKiduf092/26qkocelzSD0tuwnCu6gByUbkPyyPuDrUr4sATfRCXSLY3HbVz15cBqytvDPQk0bvcHOf75RPJGG+20FNljCLIv9wFkLfVMcnyifckBpX5RruistfLjXKi8M9J5ZNPgY2Qy+GDpJbZtRJzV0UBtwCQdIOmzAJEn379LDh/+SOmZtS05bHIrRt1dSzma7dnkOFdExMKIeKys7xzgVy1a9/OW2KTfCeVisNvIe1j+QNImAPHCEMCfAv4SEc+0ObTzgRGSzifb9c8hewdcQp4Q3D1yvPAxZI8jS3uT3SDvK9/tJeRAcA+SJ09t6PkPsJtyjKAdI2JBRMwgB/SbSn6vP27Ruj9Edk/eCHhM0m6S3iPph5L+TTZJv7VF637eEtmm32nlSs2Pkk0588iLnTYC3h0RkzoU09pkc9fIiLiglC1L3o5t93ihX3I7m8IGHUnLRsQTpRvpd8gT4FMi4q9lesfPd9iiaXx3pbY9mexhdx2Z6G8nr9xfISJmtWDdw8nhRDYjR9SdQl4c+TVy7KYZwK0RcW+z1/2SWGr8+26p0jTwDrI5503kqJGXRcS/OxpYobwz0A+BuyPi002/1HsIkrQGeULvg+TVt68lT7yPJruS/iY6dJMba45yxPYX8mKoZ8iuuDuT4+QfE60fX2ccmfjfQR5Z/4Pcrto2eqyTfg2Vk0cvJ+8V/JOIeLbuNXygcfvBxyLiUEmrkidvR5O1/YnkZ/bdqNwly4aGyon53YDXRcThlWnrk82aJ0eTr37tJZ4JZG+wzcihYo6LNg1K56RfY5UfghN+DmP79Yh4Y3n+B+CEKENLK0dIfR15G8mO9SqyRSdpKbLdfGNyqIUZHYihOsDgcHJgtzcAp7b6KOP5GGr+WzcDQNIHyKEg3kL2xT8wInauNnu5CWzoK10yP0GOTPp/wJcj4okOxzQyWjXkQjfce8cMiIhfkmOfHE520/xjKV8oaaQT/tCkF+4ON7aMe7UGcCywG9kn/kZJO3YoNgG0M+GDa/pmL6Eco/5sciC/AyLioQ6HZItJ0u/IEWXfDkyNiJ+XXjzvAK6NiNktWOeynT6K6I5r+mZdRMR15FhEvwXul7R3h0OyRdCoSZcr0p+KHK11GDn0AuTY+ee3KOF/HNi79JKrljeOPCaUHn5t56Rv1o1yRffp5NWa7b6C2pqg0jlhRXLIg6nAmRFxl/LeBN8u42I1VRl/awrwz3hhKO4VJY2LF+4ktj85OnDbuXnHzJZo5WLJb/FCr51bJf0fcF5E/KQF6/sxef3LUaVf/tbAQeSV3JdG3rL15a24CKw/lsgB18ysvvTCHalEDhV+p6SfkjflOaJchPdQixL+SOBRoNH98gvklb5/Ie99sLuk5TuV8ME1fTNbQkk6ghz+ZBg5yOAC8h4XI4BbImJ+i9a7BTn89grkVbf7NwZRk3Qx8JWIuKgV6+4P1/TNbImhvJfGbDLZv4+8h8Uh5ECI342IL7UhjBnkMNxrkfebvqnE9g7y5i0dS/jgmr6ZLSFKT5kfk7cnBbg/In5Qpq1HDrWwEfCyiHi8jXEJeBnZDfjwiPhDu9bdbTxO+ma2pChj4u8NvIoc5fYosh/+I2X6Bo0RZdsY09LkbRE3i4hWDdvc/3ic9M1sqGvcpa48fhvZjr8bec/ly8nhzW/o5LDYg2WMKyd9MxvyJE0iR6vcE7itDBc+kqz1bwU8RQ6d3PQLsYYaX5xlZkNaGSp8AbArOazCbZJWj4hnI2IaeTOcmU74yTV9M1siSHoPsA3wCBDkXeFmkH3lv9zOG5UMZk76ZjZkVS7EWgYYB9xN9pR5H7AaOV79AxGxRwfDHFSc9M1syJN0HnAnsB95s5ubyO6ZAcxqZxfNwc5t+mY2JJW2/MaIlncAny3/ZwNLAQ9GxDVO+C/mpG9mQ1LjBjfAK4HjgG8DPysjW76LHArBunDSN7MhR9Ib4fm7Tv2RTPjrRcTXyiwfK+XWhdv0zWxIkfQWcjz6q4HpwM1krf5jwPeA9YFhEbFXp2IczJz0zWxIKTcp2ZrsmbMccBlwVnm+HXkF7o0RMadjQQ5iTvpmNmRIGtEYSkHSKuQFWeuXyWcDV0XEc4NlyIPByEnfzIYESZuSd6D6D3kF7gjgWnKMnYXA24BTgG844ffM4+mb2VCxGdkPf275vz15V6pVgaXJwdWedMLvnWv6ZjZkSFoJ+CmwJjC5cdtBSStHxEMdDW6IcNI3syFH0mbkydurgCkRMa/DIQ0Z7qdvZkNORFwFvJxM/PdJ2rvDIQ0Zrumb2ZBW7ky1QkQ80OlYhgInfTOzGnHzjplZjTjpm5nViJO+mVmNOOmbmdWIk76ZWY38fzoCZa2C5EwfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stdeaths=dff.sort_values(by=[\"TotalDeaths\"],axis=0,ascending=False)\n",
    "d=stdeaths[['Country','TotalDeaths']].values[0:9]\n",
    "d=pd.DataFrame(d)\n",
    "d=d.rename({'1':'count'},axis=1)\n",
    "d.set_index(0,inplace=True)\n",
    "del d.index.name\n",
    "d.plot.bar(rot=60)\n",
    "plt.title('Top 10 Countries suffering with CORONA severely')\n",
    "print(d)\n",
    "plt.legend('Deaths')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
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         "type": "bar",
         "x": [
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          "France",
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          "Switzerland",
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          "Netherlands",
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          "Sweden",
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          "Denmark",
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        },
        {
         "marker": {
          "color": "#2ca02c"
         },
         "name": "TotalRecovered",
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         ],
         "textposition": "auto",
         "type": "bar",
         "x": [
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          "Netherlands",
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        },
        {
         "marker": {
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         "name": "TotalDeaths",
         "text": [
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        }
       ],
       "layout": {
        "template": {
         "data": {
          "bar": [
           {
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             "color": "#2a3f5f"
            },
            "error_y": {
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          "contourcarpet": [
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            "type": "contourcarpet"
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          "heatmap": [
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          "heatmapgl": [
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\"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Corona virus TotalCase greater than 1000\"}, \"xaxis\": {\"tickfont\": {\"size\": 14}}, \"yaxis\": {\"tickfont\": {\"size\": 14}, \"title\": {\"font\": {\"size\": 16}, \"text\": \"COUNT\"}}},\n",
       "                        {\"responsive\": true}\n",
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       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = go.Figure()\n",
    "fig.add_trace(go.Bar(\n",
    "    x=dff[\"Country\"][dff.TotalCases>1000],\n",
    "    y=dff.TotalCases[dff.TotalCases>1000],\n",
    "    name='TotalCases',\n",
    "    marker_color='#FAA460',\n",
    "    text=df.TotalCases[df.TotalCases>1000],\n",
    "    textposition='auto'\n",
    "))\n",
    "fig.add_trace(go.Bar(\n",
    "    x=dff[\"Country\"][dff.TotalCases>1000],\n",
    "    y=dff[\"TotalRecovered\"][dff.TotalCases>1000],\n",
    "    name='TotalRecovered',\n",
    "    marker_color='#2ca02c',\n",
    "    text=dff.TotalRecovered[dff.TotalCases>1000],\n",
    "    textposition='auto'\n",
    "))\n",
    "\n",
    "fig.add_trace(go.Bar(\n",
    "    x=dff[\"Country\"][dff.TotalCases>1000],\n",
    "    y=dff.TotalDeaths[dff.TotalCases>1000],\n",
    "    name='TotalDeaths',\n",
    "    marker_color='#EF553B',\n",
    "    text=dff.TotalDeaths[dff.TotalCases>1000],\n",
    "    textposition='auto'\n",
    "))\n",
    "fig.update_layout(title_text='Corona virus TotalCase greater than 1000',xaxis_tickfont_size=14,\n",
    "    yaxis=dict(\n",
    "        title='COUNT',\n",
    "        titlefont_size=16,\n",
    "        tickfont_size=14))\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The countries whose Total Cases are less than 100: \n",
      "\n",
      "['Cyprus', 'Bosnia and Herzegovina', 'Moldova', 'Malta', 'Albania', 'Brunei', 'Cambodia', 'Sri Lanka', 'Belarus', 'Burkina Faso', 'Tunisia', 'Venezuela', 'New Zealand', 'Azerbaijan', 'Kazakhstan', 'Palestine', 'Senegal', 'Guadeloupe', 'Oman', 'Georgia', 'Trinidad and Tobago', 'Ukraine', 'Réunion', 'Uzbekistan', 'Cameroon', 'Martinique', 'Liechtenstein', 'Cuba', 'Afghanistan', 'Channel Islands', 'Nigeria', 'DRC', 'Bangladesh', 'Guam', 'Honduras', 'Mauritius', 'Bolivia', 'Puerto Rico', 'Paraguay', 'Macao', 'Ghana', 'Jamaica', 'French Guiana', 'Guyana', 'Monaco', 'Guatemala', 'Rwanda', 'Montenegro', 'Togo', 'French Polynesia', 'Gibraltar', 'Kenya', 'Barbados', 'Ivory Coast', 'Kyrgyzstan', 'Maldives', 'Tanzania', 'Ethiopia', 'Mayotte', 'Mongolia', 'Aruba', 'Seychelles', 'Equatorial Guinea', 'U.S. Virgin Islands', 'Saint Martin', 'Suriname', 'Gabon', 'Isle of Man', 'New Caledonia', 'Eswatini', 'Bahamas', 'Liberia', 'Zimbabwe', 'Zambia', 'St. Barth', 'Namibia', 'Madagascar', 'El Salvador', 'Congo', 'CAR', 'Cabo Verde', 'Curaçao', 'Cayman Islands', 'Saint Lucia', 'Niger', 'Nicaragua', 'Mauritania', 'Haiti', 'Guinea', 'Greenland', 'Fiji', 'Bermuda', 'Benin', 'Angola', 'Sudan', 'Bhutan', 'Montserrat', 'Timor-Leste', 'Somalia', 'Sint Maarten', 'St. Vincent Grenadines', 'Papua New Guinea', 'Mozambique', 'Eritrea', 'Vatican City', 'Gambia', 'Djibouti', 'Chad', 'Antigua and Barbuda', 'Nepal', 'Uganda']\n"
     ]
    }
   ],
   "source": [
    "d=dff[dff['TotalCases']<=100].Country.count()\n",
    "print(\"The countries whose Total Cases are less than 100: \\n\")\n",
    "print(list(dff[dff['TotalCases']<=100].Country))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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>TotalCases</th>\n",
       "      <th>NewCases</th>\n",
       "      <th>TotalDeaths</th>\n",
       "      <th>NewDeaths</th>\n",
       "      <th>TotalRecovered</th>\n",
       "      <th>ActiveCases</th>\n",
       "      <th>Serious,Critical</th>\n",
       "      <th>Tot Cases/1M pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>India</td>\n",
       "      <td>396</td>\n",
       "      <td>64.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>365</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country  TotalCases  NewCases  TotalDeaths  NewDeaths  TotalRecovered  \\\n",
       "42   India         396      64.0          7.0        2.0            24.0   \n",
       "\n",
       "    ActiveCases  Serious,Critical  Tot Cases/1M pop  \n",
       "42          365               0.0               0.3  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dff[dff['Country']=='India']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Countries Ranks in their Deaths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>TotalCases</th>\n",
       "      <th>NewCases</th>\n",
       "      <th>TotalDeaths</th>\n",
       "      <th>NewDeaths</th>\n",
       "      <th>TotalRecovered</th>\n",
       "      <th>ActiveCases</th>\n",
       "      <th>Serious,Critical</th>\n",
       "      <th>Tot Cases/1M pop</th>\n",
       "      <th>Rank_Deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>China</td>\n",
       "      <td>81054</td>\n",
       "      <td>46.0</td>\n",
       "      <td>3261.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>72440.0</td>\n",
       "      <td>5353</td>\n",
       "      <td>1845.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Italy</td>\n",
       "      <td>59138</td>\n",
       "      <td>5560.0</td>\n",
       "      <td>5476.0</td>\n",
       "      <td>651.0</td>\n",
       "      <td>7024.0</td>\n",
       "      <td>46638</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>978.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>USA</td>\n",
       "      <td>38167</td>\n",
       "      <td>13960.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>37593</td>\n",
       "      <td>708.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Spain</td>\n",
       "      <td>28603</td>\n",
       "      <td>3107.0</td>\n",
       "      <td>1756.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>2125.0</td>\n",
       "      <td>24722</td>\n",
       "      <td>1785.0</td>\n",
       "      <td>612.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Germany</td>\n",
       "      <td>24806</td>\n",
       "      <td>2442.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>24447</td>\n",
       "      <td>2.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country  TotalCases  NewCases  TotalDeaths  NewDeaths  TotalRecovered  \\\n",
       "0    China       81054      46.0       3261.0        6.0         72440.0   \n",
       "1    Italy       59138    5560.0       5476.0      651.0          7024.0   \n",
       "2      USA       38167   13960.0        396.0       94.0           178.0   \n",
       "3    Spain       28603    3107.0       1756.0      375.0          2125.0   \n",
       "4  Germany       24806    2442.0         93.0        9.0           266.0   \n",
       "\n",
       "   ActiveCases  Serious,Critical  Tot Cases/1M pop  Rank_Deaths  \n",
       "0         5353            1845.0              56.0          2.0  \n",
       "1        46638            3000.0             978.0          1.0  \n",
       "2        37593             708.0             115.0          6.0  \n",
       "3        24722            1785.0             612.0          3.0  \n",
       "4        24447               2.0             296.0         11.0  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dff['Rank_Deaths']=dff[\"TotalDeaths\"].rank(ascending=False,method='dense',axis=0)\n",
    "dff.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The rank of INDIA in their Death Cases 26 out of 33\n"
     ]
    }
   ],
   "source": [
    "f=dff[dff['Country']=='India'].Rank_Deaths\n",
    "print(\"The rank of INDIA in their Death Cases %d out of %d\" %(f,dff['Rank_Deaths'].max()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The countries whose death rate is low: \n",
      "\n",
      "['Norway', 'Australia', 'Poland', 'India']\n"
     ]
    }
   ],
   "source": [
    "print('The countries whose death rate is low: ')\n",
    "print()\n",
    "g=dff[\"Country\"][dff[\"Rank_Deaths\"]==26]\n",
    "print(list(g.unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
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          "China",
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          "Spain",
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          "France",
          "S. Korea",
          "Switzerland",
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          "Saudi Arabia",
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          "Singapore",
          "Romania",
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          "India",
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          "Russia",
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          "Croatia",
          "Mexico",
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          "Latvia",
          "Uruguay",
          "Hungary",
          "Lithuania",
          "Costa Rica",
          "North Macedonia",
          "Faeroe Islands",
          "Andorra",
          "Vietnam",
          "Jordan",
          "Morocco",
          "Cyprus",
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    "fig.add_trace(go.Choropleth(\n",
    "        locationmode = 'country names',\n",
    "        locations = dff[\"Country\"],\n",
    "        z = dff.TotalCases,\n",
    "        text = dff[\"Country\"],\n",
    "        colorscale='sunset',\n",
    "        autocolorscale = False,\n",
    "        showscale = True,\n",
    "        geo = 'geo'\n",
    "    ))\n",
    "fig.update_layout(title_text='Corona virus affected countries')"
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  {
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       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = go.Figure()\n",
    "fig.add_trace(go.Choropleth(\n",
    "        locationmode = 'country names',\n",
    "        locations = dff[\"Country\"],\n",
    "        z = dff.TotalDeaths,\n",
    "        text = dff[\"Country\"],\n",
    "        colorscale='sunset',\n",
    "        autocolorscale = False,\n",
    "        showscale = True,\n",
    "        geo = 'geo'\n",
    "    ))\n",
    "fig.update_layout(title_text='Corona virus affected countries')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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