ExploratoryAnalysis_COVID.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis of COVID-19 Dataset\n",
    "\n",
    "This notebook is created for the purpose of exploratory data analysis on COVID-19 data repository operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) and also, Supported by ESRI Living Atlas Team and the Johns Hopkins University Applied Physics Lab (JHU APL).\n",
    "\n",
    "The repository is hosted [here](https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_daily_reports)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import date, datetime, timedelta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decide on the range of data we will use for analysis\n",
    "\n",
    "Observing the repository we notice that the files are available from 1/22/2020. All timestamps are in UTC (GMT+0)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_date = datetime(2020, 1, 22)\n",
    "current_date = datetime.today()\n",
    "\n",
    "delta_days = current_date - start_date\n",
    "periods = delta_days.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = pd.date_range(start_date, periods=periods, freq='D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, ssl\n",
    "if (not os.environ.get('PYTHONHTTPSVERIFY', '') and getattr(ssl, '_create_unverified_context', None)):\n",
    "    ssl._create_default_https_context = ssl._create_unverified_context"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download the data from Git or Extract from Pickle Serialized object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/01-22-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/01-23-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/01-24-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/01-25-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/01-26-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/01-27-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/01-28-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/01-29-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/01-30-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/01-31-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-01-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-02-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-03-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-04-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-05-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-06-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-07-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-08-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-09-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-10-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-11-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-12-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-13-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-14-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-15-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-16-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-17-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-18-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-19-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-20-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-21-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-22-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-23-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-24-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-25-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-26-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-27-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-28-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/02-29-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-01-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-02-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-03-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-04-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-05-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-06-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-07-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-08-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-09-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-10-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-11-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-12-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-13-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-14-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-15-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-16-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-17-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-18-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-19-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-20-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-21-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-22-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-23-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-24-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-25-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-26-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-27-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-28-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-29-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-30-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-31-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/04-01-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/04-02-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/04-03-2020.csv\nhttps://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/04-04-2020.csv\n"
    }
   ],
   "source": [
    "from urllib.request import urlopen\n",
    "\n",
    "data = pd.DataFrame()\n",
    "url = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/'\n",
    "\n",
    "i = 0\n",
    "while i < periods:\n",
    "    new_url = url + dates[i].strftime('%m-%d-%Y') + '.csv'\n",
    "    print(new_url)\n",
    "    temp = urlopen(new_url)\n",
    "    temp_data = pd.read_csv(temp)\n",
    "    data = data.append(temp_data)\n",
    "    i = i+1\n",
    ""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the below piece of code was written as reading multiple raw CSV files form Github is throwing below error intermittently\n",
    "# \"URLError: <urlopen error [SSL: KRB5_S_TKT_NYV] unexpected eof while reading (_ssl.c:1076)>\"\n",
    "# which I guess is used to do with session. I need to do a bit of research on this further to understand what is causing it.\n",
    "\n",
    "#############################################\n",
    "\n",
    "#import pickle\n",
    "\n",
    "#with open('data_frame_pkl','wb') as f:\n",
    "#    pickle.dump(data, f)\n",
    "    \n",
    "#with open('data_frame_pkl', 'rb') as f:\n",
    "#    data = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "(51231, 17)"
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "  Province/State  Country/Region      Last Update  Confirmed  Deaths  \\\n0          Anhui  Mainland China  1/22/2020 17:00        1.0     NaN   \n1        Beijing  Mainland China  1/22/2020 17:00       14.0     NaN   \n2      Chongqing  Mainland China  1/22/2020 17:00        6.0     NaN   \n3         Fujian  Mainland China  1/22/2020 17:00        1.0     NaN   \n4          Gansu  Mainland China  1/22/2020 17:00        NaN     NaN   \n\n   Recovered  Latitude  Longitude  FIPS Admin2 Province_State Country_Region  \\\n0        NaN       NaN        NaN   NaN    NaN            NaN            NaN   \n1        NaN       NaN        NaN   NaN    NaN            NaN            NaN   \n2        NaN       NaN        NaN   NaN    NaN            NaN            NaN   \n3        NaN       NaN        NaN   NaN    NaN            NaN            NaN   \n4        NaN       NaN        NaN   NaN    NaN            NaN            NaN   \n\n  Last_Update  Lat  Long_  Active Combined_Key  \n0         NaN  NaN    NaN     NaN          NaN  \n1         NaN  NaN    NaN     NaN          NaN  \n2         NaN  NaN    NaN     NaN          NaN  \n3         NaN  NaN    NaN     NaN          NaN  \n4         NaN  NaN    NaN     NaN          NaN  ",
      "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>Province/State</th>\n      <th>Country/Region</th>\n      <th>Last Update</th>\n      <th>Confirmed</th>\n      <th>Deaths</th>\n      <th>Recovered</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n      <th>FIPS</th>\n      <th>Admin2</th>\n      <th>Province_State</th>\n      <th>Country_Region</th>\n      <th>Last_Update</th>\n      <th>Lat</th>\n      <th>Long_</th>\n      <th>Active</th>\n      <th>Combined_Key</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Anhui</td>\n      <td>Mainland China</td>\n      <td>1/22/2020 17:00</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Beijing</td>\n      <td>Mainland China</td>\n      <td>1/22/2020 17:00</td>\n      <td>14.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Chongqing</td>\n      <td>Mainland China</td>\n      <td>1/22/2020 17:00</td>\n      <td>6.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Fujian</td>\n      <td>Mainland China</td>\n      <td>1/22/2020 17:00</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Gansu</td>\n      <td>Mainland China</td>\n      <td>1/22/2020 17:00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see some of the columns are same across multiple CSV files. So let us combine them.\n",
    "\n",
    "* Province/State and Province_State\n",
    "* Last Update and Last_Update\n",
    "* Latitude and Lat\n",
    "* Longitude and Long"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Country/Region'] = data['Country/Region'].combine_first(data['Country_Region'])\n",
    "data['Last Update'] = data['Last Update'].combine_first(data['Last_Update'])\n",
    "data['Latitude'] = data['Latitude'].combine_first(data['Lat'])\n",
    "data['Longitude'] = data['Longitude'].combine_first(data['Long_'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us convert the \"Last Update\" column to date format for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Last Update'] = pd.to_datetime(data['Last Update']).dt.strftime('%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Upon a careful observation of CSV files from the beginning, only Country/Region, Province/State, Last Update, Confirmed, Deaths and Recovered are the only columns reported out consistently. So let us keep only these columns and throw away others. In future, we will try to source datasets from Mid March so we can consider Longitude and Latitude."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   Country/Region Province/State Last Update  Confirmed  Deaths  Recovered\n0  Mainland China          Anhui  2020-01-22        1.0     NaN        NaN\n1  Mainland China        Beijing  2020-01-22       14.0     NaN        NaN\n2  Mainland China      Chongqing  2020-01-22        6.0     NaN        NaN\n3  Mainland China         Fujian  2020-01-22        1.0     NaN        NaN\n4  Mainland China          Gansu  2020-01-22        NaN     NaN        NaN",
      "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/Region</th>\n      <th>Province/State</th>\n      <th>Last Update</th>\n      <th>Confirmed</th>\n      <th>Deaths</th>\n      <th>Recovered</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Mainland China</td>\n      <td>Anhui</td>\n      <td>2020-01-22</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Mainland China</td>\n      <td>Beijing</td>\n      <td>2020-01-22</td>\n      <td>14.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Mainland China</td>\n      <td>Chongqing</td>\n      <td>2020-01-22</td>\n      <td>6.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Mainland China</td>\n      <td>Fujian</td>\n      <td>2020-01-22</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Mainland China</td>\n      <td>Gansu</td>\n      <td>2020-01-22</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "required_cols = ['Country/Region','Province/State','Last Update','Confirmed','Deaths','Recovered']\n",
    "covid_data = data[required_cols]\n",
    "covid_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have got the data in required format, let us plot some graphs.\n",
    "\n",
    "### Plotting\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Total number of casee across countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Confirmed    1190310\nDeaths         64544\nRecovered     239250\ndtype: int64"
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "source": [
    "cdate = date.today()-timedelta(days=1)\n",
    "cols = ['Confirmed','Deaths','Recovered']\n",
    "s = covid_data[covid_data[\"Last Update\"]==cdate.strftime('%Y-%m-%d')][cols].sum(axis = 0).astype(int)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "totals = [s['Confirmed'], s['Deaths'], s['Recovered']]\n",
    "category = ('Confirmed', 'Deaths', 'Recovered')\n",
    "y_pos = np.arange(len(category))\n",
    "\n",
    "plt.figure(figsize=(15, 8))\n",
    "plt.bar(y_pos, totals, color=['red', 'black', 'green'])\n",
    "plt.xticks(y_pos, category)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Cases by Country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                    Confirmed  Deaths  Recovered\nCountry/Region                                  \nAfghanistan             299.0     7.0       10.0\nAlbania                 333.0    20.0       99.0\nAlgeria                1251.0   130.0       90.0\nAndorra                 466.0    17.0       21.0\nAngola                   10.0     2.0        2.0\n...                       ...     ...        ...\nVenezuela               155.0     7.0       52.0\nVietnam                 240.0     0.0       90.0\nWest Bank and Gaza      217.0     1.0       21.0\nZambia                   39.0     1.0        2.0\nZimbabwe                  9.0     1.0        0.0\n\n[181 rows x 3 columns]",
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     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "s = covid_data[covid_data[\"Last Update\"]==cdate.strftime('%Y-%m-%d')].groupby(['Country/Region']).sum()\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see above almost 177 countries have been infected with the virus. To make it visually better let us plot using top most infected countries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x1a2083e590>"
     },
     "metadata": {},
     "execution_count": 16
    },
    {
     "output_type": "display_data",
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xlink:href=\"#ArialMT-67\"/>\n       <use x=\"72.216797\" xlink:href=\"#ArialMT-97\"/>\n       <use x=\"127.832031\" xlink:href=\"#ArialMT-110\"/>\n       <use x=\"183.447266\" xlink:href=\"#ArialMT-97\"/>\n       <use x=\"239.0625\" xlink:href=\"#ArialMT-100\"/>\n       <use x=\"294.677734\" xlink:href=\"#ArialMT-97\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"ytick_9\">\n     <g id=\"line2d_16\">\n      <path clip-path=\"url(#pc0084a5c54)\" d=\"M 110.224531 476.055 \nL 947.224531 476.055 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;\"/>\n     </g>\n     <g id=\"text_16\">\n      <!-- Netherlands -->\n      <defs>\n       <path d=\"M 7.625 0 \nL 7.625 71.578125 \nL 17.328125 71.578125 \nL 54.9375 15.375 \nL 54.9375 71.578125 \nL 64.015625 71.578125 \nL 64.015625 0 \nL 54.296875 0 \nL 16.703125 56.25 \nL 16.703125 0 \nz\n\" id=\"ArialMT-78\"/>\n      </defs>\n      <g style=\"fill:#262626;\" transform=\"translate(41.415625 479.991797)scale(0.11 -0.11)\">\n       <use 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     },
     "metadata": {}
    }
   ],
   "source": [
    "s = s.sort_values(by = ['Confirmed'], ascending = True)[-20:]\n",
    "s.plot(kind='barh', figsize=(15, 15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see above\n",
    "\n",
    "* US is the most infected country as there are more number of confirmed cases\n",
    "* Italy is the country affected by more number of deaths due to virus\n",
    "* China has most recovered cases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Day Wise Confirmed Cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x1a20c34b10>"
     },
     "metadata": {},
     "execution_count": 17
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "s = covid_data.groupby(['Last Update']).sum().sort_values(by=['Confirmed'], ascending=True)[-30:]\n",
    "s.plot.area(stacked=False, figsize=(15,8))"
   ]
  },
  {
   "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": "3.7.7-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}