Covid19DataAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "03508ee2",
   "metadata": {},
   "source": [
    "# Covid-19 Analysis to visualize and understand the pandemic cases using different Techniques. \n",
    "\n",
    "Coronavirus disease 2019 (COVID-19) time series lists confirmed cases, reported deaths, active cases and comparison with other Epidemics. \n",
    "Data are disaggregated by country (and sometimes subregion). \n",
    "Coronavirus disease (COVID-19) is caused by the\n",
    "[Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2)](https://en.wikipedia.org/wiki/Severe_acute_respiratory_syndrome_coronavirus_2) and has had a worldwide effect.\n",
    "\n",
    "This notebook uses data from various sources to understand, analyze and visualize the changes in the number of cases through different visualization techniques and plots.\n",
    "\n",
    "This dataset includes time-series data tracking the number of people affected by COVID-19 worldwide, including:\n",
    "\n",
    "- confirmed tested cases of Coronavirus infection\n",
    "- number of people who have reportedly died due to Coronavirus\n",
    "- number of people who have reportedly recovered from it\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c6b3428",
   "metadata": {},
   "source": [
    "**Note:** The Data collection for **Recovered cases** isn't quite accurate and has been stopped by a lot of countries and it is also found discrepancies in data if taken from multiple sources. Also a lot of recovery cases aren't reported and it is not possible to analyze them accurately. Yet the data reported till July 2020, is quite accurate to understand. You can select from Jan2020 to Jan 2021 to clearly see the transition in all the graphs.  \n",
    "\n",
    "Therefore the following two scenarios should also be kept in mind:\n",
    "- Active Cases are calculated based on recoveries so this will not be also correct\n",
    "- Anything that requires recoveries in the calculation isn't accurate, like deaths/100recoveries, etc.\n",
    "\n",
    "**Additional References** for the above point:\n",
    "- https://www.dallasnews.com/news/public-health/2020/05/19/why-arent-coronavirus-recoveries-always-reported/\n",
    "- https://abc11.com/nc-coronavirus-recovery-cases-update/6127051/\n",
    "\n",
    "Most of these reports are based on the US but mostly it is true for the rest of the world."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce9a7b27",
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install folium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4596cf8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2b2cecf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#required packages and libraries\n",
    "\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "import plotly.figure_factory as ff\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "import folium\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import math\n",
    "import random\n",
    "from datetime import timedelta\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# color pallette\n",
    "cnf ='#393e46'\n",
    "dth ='#ff2e63'\n",
    "rec ='#21bf73'\n",
    "act ='#fe9801'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7aae2d3",
   "metadata": {},
   "source": [
    "## Dataset Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e5c4bcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly as py\n",
    "py.offline.init_notebook_mode(connected = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98aa72c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39f7a9f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    os.system(\"rm -rf Covid-19-Preprocessed-Dataset\")\n",
    "except:\n",
    "    print('File does not exist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e162c430",
   "metadata": {},
   "outputs": [],
   "source": [
    "# cloning the data \n",
    "!git clone https://github.com/laxmimerit/Covid-19-Preprocessed-Dataset.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf40c86d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Understanding the Data\n",
    "df = pd.read_csv('Covid-19-Preprocessed-Dataset/preprocessed/covid_19_data_cleaned.csv', parse_dates=['Date'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44b41934",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We need to handle the NaN values which are there under the Province/State column\n",
    "df['Province/State'] = df['Province/State'].fillna(\"\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23224733",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing the country_daywise, countrywise, daywise dataset\n",
    "country_daywise = pd.read_csv('Covid-19-Preprocessed-Dataset/preprocessed/country_daywise.csv', parse_dates=['Date'])\n",
    "countrywise = pd.read_csv('Covid-19-Preprocessed-Dataset/preprocessed/countrywise.csv')\n",
    "daywise = pd.read_csv('Covid-19-Preprocessed-Dataset/preprocessed/daywise.csv', parse_dates=['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "feee13e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "country_daywise"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a0e1fda",
   "metadata": {},
   "source": [
    "## Worldwide Total Confirmed, Recovered Cases and Deaths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98c75b7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#looking at the number of confirmed cases\n",
    "confirmed = df.groupby('Date').sum()['Confirmed'].reset_index()\n",
    "confirmed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff3cf3d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#looking at the recovered cases\n",
    "recovered = df.groupby('Date').sum()['Recovered'].reset_index()\n",
    "recovered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26d4639e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#looking at the number of death cases\n",
    "deaths = df.groupby('Date').sum()['Deaths'].reset_index()\n",
    "deaths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f04b2fe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a035c043",
   "metadata": {},
   "outputs": [],
   "source": [
    "#check for any null values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e4d95fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.query('Country == \"US\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "514c9ab6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37750b90",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "712e5c87",
   "metadata": {},
   "source": [
    "## Scatterplot Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "268f529d",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter(x = confirmed['Date'], y= confirmed['Confirmed'], mode='lines+markers', name='Confirmed Cases', line=dict(color='Orange',width =2)))\n",
    "fig.add_trace(go.Scatter(x = recovered['Date'], y= recovered['Recovered'], mode='lines+markers', name='Recovered Cases', line=dict(color='Green', width =2)))\n",
    "fig.add_trace(go.Scatter(x = deaths['Date'], y= deaths['Deaths'], mode='lines+markers', name='Deaths', line=dict(color='Red', width=2)))\n",
    "\n",
    "fig.update_layout(title = \"WorldWide Covid-19 Cases\", xaxis_tickfont_size=14, yaxis= dict(title='Number of Cases'))\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a665415d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "73a7fdb9",
   "metadata": {},
   "source": [
    "## Cases Density Animation On World Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9380324a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert date to string\n",
    "df['Date'] = df['Date'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d874f48",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04a1e181",
   "metadata": {},
   "outputs": [],
   "source": [
    "#using plotly express to display a density map\n",
    "fig =px.density_mapbox(df, lat='Lat', lon='Long', hover_name='Country', hover_data=['Confirmed', 'Recovered', 'Deaths'], animation_frame='Date',color_continuous_scale='Portland', radius=7, zoom=0,height=700)\n",
    "fig.update_layout(title='WorldWide Covid-19 Cases with TimeLapse')\n",
    "fig.update_layout(mapbox_style='open-street-map', mapbox_center_lon=0)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ba51576",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "59633385",
   "metadata": {},
   "source": [
    "## Cases over a time period with Area Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4c3722f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Date'] =pd.to_datetime(df['Date'])\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c76f9f86",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = df.groupby('Date')['Confirmed', 'Deaths', 'Recovered', 'Active'].sum().reset_index()\n",
    "temp = temp[temp['Date']==max(temp['Date'])].reset_index(drop =True)\n",
    "tm =temp.melt(id_vars = 'Date', value_vars = ['Active', 'Deaths', 'Recovered'])\n",
    "fig = px.treemap(tm , path = ['variable'], values='value', height = 250, width = 800, color_discrete_sequence=[act, dth, rec])\n",
    "fig.data[0].textinfo ='label+text+value'\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31b1376d",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = df.groupby('Date')['Recovered', 'Deaths', 'Active'].sum().reset_index()\n",
    "temp = temp.melt(id_vars = 'Date', value_vars =['Recovered', 'Deaths', 'Active'], var_name = 'Case', value_name = 'Count')\n",
    "\n",
    "fig = px.area(temp, x='Date', y='Count', color='Case', height=500, title='Cases Over Time', color_discrete_sequence=[rec, dth, act])\n",
    "fig.update_layout(xaxis_rangeslider_visible=True)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b93d36d6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "24087642",
   "metadata": {},
   "source": [
    "## Cases visualization using Folium Maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d2776ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Worldwide cases on Folium Maps\n",
    "temp =df[df['Date'] == max(df['Date'])]\n",
    "\n",
    "m =folium.Map(location =[0,0], tiles='cartodbpositron', min_zoom =1, max_zoom= 4, zoom_start=1)\n",
    "\n",
    "for i in range(0, len(temp)):\n",
    "    folium.Circle(location = [temp.iloc[i]['Lat'], temp.iloc[i]['Long']], color='crimson', fill =  'crimson', \n",
    "                tooltip = '<li><bold> Country: ' + str(temp.iloc[i]['Country'])+\n",
    "                          '<li><bold> Province: ' + str(temp.iloc[i]['Province/State'])+\n",
    "                          '<li><bold> Confirmed: ' + str(temp.iloc[i]['Confirmed'])+\n",
    "                          '<li><bold> Deaths: ' + str(temp.iloc[i]['Deaths']),\n",
    "                radius = int(temp.iloc[i]['Confirmed'])**0.5).add_to(m)\n",
    "\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "198a8e88",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "aa122046",
   "metadata": {},
   "source": [
    "## Confirmed Cases with Choropleth Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ab21e95",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.choropleth(country_daywise, locations='Country', locationmode='country names', color=country_daywise['Confirmed'],\n",
    "                   hover_name = 'Country', animation_frame=country_daywise['Date'].dt.strftime('%Y-%m-%d'),\n",
    "                   title = 'Cases Over Time in Different Countries', color_continuous_scale=px.colors.sequential.Inferno)\n",
    "\n",
    "fig.update(layout_coloraxis_showscale=True)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e95244c0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7b0588c0",
   "metadata": {},
   "source": [
    "## Confirmed & Death Cases using Bar plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74ba1c3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig_c =px.bar(daywise, x='Date', y='Confirmed', color_discrete_sequence=[act])\n",
    "fig_d =px.bar(daywise, x='Date', y='Deaths', color_discrete_sequence=[dth])\n",
    "\n",
    "fig = make_subplots(rows =1, cols =2, shared_xaxes=False, horizontal_spacing= 0.1,\n",
    "                   subplot_titles=('Confirmed Cases', 'Death Cases'))\n",
    "\n",
    "fig.add_trace(fig_c['data'][0], row=1, col=1)\n",
    "fig.add_trace(fig_d['data'][0], row=1, col=2)\n",
    "\n",
    "fig.update_layout(height=400)\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ef73219",
   "metadata": {},
   "source": [
    "## Confirmed & Death Cases with Static Colormap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d38469b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig_c = px.choropleth(countrywise, locations='Country', locationmode='country names',\n",
    "                     color=np.log(countrywise['Confirmed']), hover_name='Country', hover_data=['Confirmed'])\n",
    "\n",
    "temp = countrywise[countrywise['Deaths']> 0]\n",
    "fig_d = px.choropleth(temp, locations='Country', locationmode='country names',\n",
    "                     color=np.log(temp['Deaths']), hover_name='Country', hover_data=['Deaths'])\n",
    "\n",
    "fig = make_subplots(rows=1, cols=2, subplot_titles=['Confirmed Cases', 'Deaths'], \n",
    "                   specs=[[{'type':'choropleth'},{'type':'choropleth'}]])\n",
    "\n",
    "fig.add_trace(fig_c['data'][0], row=1, col=1)\n",
    "fig.add_trace(fig_d['data'][0], row=1, col=2)\n",
    "\n",
    "fig.update(layout_coloraxis_showscale=False)\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1dc4ec9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "987bb80a",
   "metadata": {},
   "source": [
    "## Deaths & Recoveries per 100 cases"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23cba2b1",
   "metadata": {},
   "source": [
    "**Note:** The Data is only accurate till July 2021, as after that time the data for recovery cases were not reported or were not being tracked by the institutes. And it is difficult to predict the number based on it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39a696b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "daywise.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eed93fa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig1 = px.line(daywise, x='Date', y='Deaths / 100 Cases', color_discrete_sequence=[dth])\n",
    "fig2 = px.line(daywise, x='Date', y='Recovered / 100 Cases', color_discrete_sequence=[rec])\n",
    "fig3 = px.line(daywise, x='Date', y='Deaths / 100 Recovered', color_discrete_sequence=[rec])\n",
    "\n",
    "fig = make_subplots(rows =1, cols=3, shared_xaxes=False, \n",
    "                   subplot_titles=('Deaths / 100 Cases','Recovered / 100 Cases','Deaths / 100 Recovered'))\n",
    "\n",
    "fig.add_trace(fig1['data'][0], row=1,col=1)\n",
    "fig.add_trace(fig2['data'][0], row=1,col=2)\n",
    "fig.add_trace(fig3['data'][0], row=1,col=3)\n",
    "\n",
    "fig.update_layout(height=400)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d03b2c40",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "262ad64f",
   "metadata": {},
   "source": [
    "## Number of new cases per Day & countries affected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8503c44",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig_c = px.bar(daywise, x='Date', y='Confirmed', color_discrete_sequence=[act])\n",
    "fig_d = px.bar(daywise, x='Date', y='No. of Countries', color_discrete_sequence=[dth])\n",
    "\n",
    "fig =make_subplots(rows =1, cols=2, shared_xaxes=False, horizontal_spacing=0.1, subplot_titles=('No. of New Cases per Day', 'No. of Countries'))\n",
    "\n",
    "fig.add_trace(fig_c['data'][0], row=1, col=1)\n",
    "fig.add_trace(fig_d['data'][0], row=1, col=2)\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ace6cdc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "95a0f8ac",
   "metadata": {},
   "source": [
    "## Top 15 Countries Case Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8130b3b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "countrywise.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f82052a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%javascript\n",
    "IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
    "    return false;\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6d8bdc1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "top =15\n",
    "\n",
    "#fig for confirmed cases\n",
    "fig_c = px.bar(countrywise.sort_values('Confirmed').tail(top), x='Confirmed', y='Country',\n",
    "              text = 'Confirmed', orientation='h', color_discrete_sequence=[cnf])\n",
    "\n",
    "#fig for death cases\n",
    "fig_d = px.bar(countrywise.sort_values('Deaths').tail(top), x='Deaths', y='Country',\n",
    "              text = 'Deaths', orientation='h', color_discrete_sequence=[dth])\n",
    "\n",
    "#fig for active cases\n",
    "fig_a = px.bar(countrywise.sort_values('Active').tail(top), x='Active', y='Country',\n",
    "              text = 'Active', orientation='h', color_discrete_sequence=[act])\n",
    "\n",
    "#for recovered cases but due to discrepancy and no data available commented this part\n",
    "# fig_r = px.bar(countrywise.sort_values('Recovered').tail(top), x='Recovered', y='Country',\n",
    "#               text = 'Recovered', orientation='h', color_discrete_sequence=[rec])\n",
    "\n",
    "#note we haven't plotted the fig's for any scenario where recovered cases is being used to calculate the result to avoid irregularity\n",
    "\n",
    "#fig for deaths / 100 cases\n",
    "fig_dc = px.bar(countrywise.sort_values('Deaths / 100 Cases').tail(top), x='Deaths / 100 Cases', y='Country',\n",
    "              text = 'Deaths / 100 Cases', orientation='h', color_discrete_sequence=['#ff0000'])\n",
    "\n",
    "\n",
    "#fig for new cases country wise\n",
    "fig_nc = px.bar(countrywise.sort_values('New Cases').tail(top), x='New Cases', y='Country',\n",
    "              text = 'New Cases', orientation='h', color_discrete_sequence=['#944dff'])\n",
    "\n",
    "temp= countrywise[countrywise['Population']>1000000]\n",
    "#fig for cases per million people\n",
    "fig_p = px.bar(temp.sort_values('Cases / Million People').tail(top), x='Cases / Million People', y='Country',\n",
    "              text = 'Cases / Million People', orientation='h', color_discrete_sequence=['#3366ff'])\n",
    "\n",
    "#fig for 1 week changes\n",
    "fig_ow = px.bar(countrywise.sort_values('1 week change').tail(top), x='1 week change', y='Country',\n",
    "              text = '1 week change', orientation='h', color_discrete_sequence=['#ff6600'])\n",
    "\n",
    "#fig for 1 week % increase\n",
    "tem= countrywise[countrywise['Confirmed']>100]\n",
    "fig_op = px.bar(tem.sort_values('1 week % increase').tail(top), x='1 week % increase', y='Country',\n",
    "              text = '1 week % increase', orientation='h', color_discrete_sequence=['#990033'])\n",
    "\n",
    "\n",
    "\n",
    "fig= make_subplots(rows=4 , cols=2, shared_xaxes=False, horizontal_spacing= 0.2, vertical_spacing=.05,\n",
    "                  subplot_titles=('Confirmed Cases', 'Deaths Reported', 'Active Cases', 'Deaths / 100 Cases'\n",
    "                                 ,'New Cases', 'Cases / Million People', '1 week change', '1 week % increase'))\n",
    "\n",
    "fig.add_trace(fig_c['data'][0], row=1, col=1)\n",
    "fig.add_trace(fig_d['data'][0], row=1, col=2)\n",
    "\n",
    "fig.add_trace(fig_a['data'][0], row=2, col=1)\n",
    "fig.add_trace(fig_dc['data'][0], row=2, col=2)\n",
    "\n",
    "\n",
    "fig.add_trace(fig_nc['data'][0], row=3, col=1)\n",
    "fig.add_trace(fig_p['data'][0], row=3, col=2)\n",
    "\n",
    "\n",
    "fig.add_trace(fig_ow['data'][0], row=4, col=1)\n",
    "fig.add_trace(fig_op['data'][0], row=4, col=2)\n",
    "\n",
    "\n",
    "fig.update_layout(height=3000)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d52ee727",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "54e4dadb",
   "metadata": {},
   "source": [
    "## Scatter Plot for Deaths Vs Confirmed Cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c03b7d19",
   "metadata": {},
   "outputs": [],
   "source": [
    "countrywise.sort_values('Deaths', ascending=False).head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6877f24f",
   "metadata": {},
   "outputs": [],
   "source": [
    "top =15\n",
    "fig = px.scatter(countrywise.sort_values('Deaths', ascending=False).head(top), \n",
    "                x = 'Confirmed', y='Deaths', color='Country', height=600, size='Confirmed',\n",
    "                text='Country', log_x = True, log_y = True, title='Deaths vs Confirmed Cases(Cases are on log10 scale)')\n",
    "\n",
    "fig.update_traces(textposition= 'top center')\n",
    "fig.update_layout(showlegend = True)\n",
    "fig.update_layout(xaxis_rangeslider_visible = True)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41fafe37",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f3d46c69",
   "metadata": {},
   "source": [
    "## Stacked Bar Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "280e62e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.bar(country_daywise, x='Date', y='Confirmed', color='Country', height=600,\n",
    "            title='Confirmed Cases', color_discrete_sequence=px.colors.cyclical.mygbm)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ae90f4a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "495c0535",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.bar(country_daywise, x='Date', y='Deaths', color='Country', height=600,\n",
    "            title='Deaths', color_discrete_sequence=px.colors.cyclical.mygbm)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6348f8d3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9fa2a02",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.bar(country_daywise, x='Date', y='New Cases', color='Country', height=600,\n",
    "            title='New Cases', color_discrete_sequence=px.colors.cyclical.mygbm)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "055d60b9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2cad1d55",
   "metadata": {},
   "source": [
    "## Line Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e80d8b4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.line(country_daywise, x ='Date', y='Confirmed', color='Country', height=600, title='Confirmed Cases',\n",
    "             color_discrete_sequence=px.colors.cyclical.mygbm)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4cfa7bfc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d710e40f",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.line(country_daywise, x ='Date', y='Deaths', color='Country', height=600, title='Deaths',\n",
    "             color_discrete_sequence=px.colors.cyclical.mygbm)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9de824ce",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92ca2547",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = px.line(country_daywise, x ='Date', y='New Cases', color='Country', height=600, title='New Cases',\n",
    "             color_discrete_sequence=px.colors.cyclical.mygbm)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b16b49b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7e5dbdf5",
   "metadata": {},
   "source": [
    "## Growth rate after 100 Cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6f1861b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Date'] =pd.to_datetime(df['Date'])\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e0c550e",
   "metadata": {},
   "outputs": [],
   "source": [
    "gt_100 = country_daywise[country_daywise['Confirmed']>100]['Country'].unique()\n",
    "\n",
    "temp = df[df['Country'].isin(gt_100)]\n",
    "\n",
    "temp = temp.groupby(['Country', 'Date'])['Confirmed'].sum().reset_index()\n",
    "temp =temp[temp['Confirmed']>100]\n",
    "\n",
    "min_date = temp.groupby('Country')['Date'].min().reset_index()\n",
    "min_date.columns = ['Country', 'Min Date']\n",
    "\n",
    "from_100th_case = pd.merge(temp, min_date, on ='Country')\n",
    "from_100th_case['N days'] = (from_100th_case['Date'] - from_100th_case['Min Date']).dt.days\n",
    "\n",
    "fig = px.line(from_100th_case, x = 'N days', y='Confirmed', color='Country', title='N Days From 100 Cases', height=600)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13800959",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8e879549",
   "metadata": {},
   "source": [
    "### Growth rate after 1000 Cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d682916",
   "metadata": {},
   "outputs": [],
   "source": [
    "gt_1000 = country_daywise[country_daywise['Confirmed']>1000]['Country'].unique()\n",
    "temp = df[df['Country'].isin(gt_1000)]\n",
    "\n",
    "temp = temp.groupby(['Country', 'Date'])['Confirmed'].sum().reset_index()\n",
    "temp =temp[temp['Confirmed']>1000]\n",
    "\n",
    "min_date = temp.groupby('Country')['Date'].min().reset_index()\n",
    "min_date.columns = ['Country', 'Min Date']\n",
    "\n",
    "from_1000th_case = pd.merge(temp, min_date, on ='Country')\n",
    "from_1000th_case['N days'] = (from_1000th_case['Date'] - from_1000th_case['Min Date']).dt.days\n",
    "\n",
    "fig = px.line(from_1000th_case, x = 'N days', y='Confirmed', color='Country', title='N Days From 1000 Cases', height=600)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c9b6d6e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f8a64f9e",
   "metadata": {},
   "source": [
    "### Growth rate after 100K Cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8c3d7a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "gt_100000 = country_daywise[country_daywise['Confirmed']>100000]['Country'].unique()\n",
    "temp = df[df['Country'].isin(gt_100000)]\n",
    "\n",
    "temp = temp.groupby(['Country', 'Date'])['Confirmed'].sum().reset_index()\n",
    "temp =temp[temp['Confirmed']>100000]\n",
    "\n",
    "min_date = temp.groupby('Country')['Date'].min().reset_index()\n",
    "min_date.columns = ['Country', 'Min Date']\n",
    "\n",
    "from_100000th_case = pd.merge(temp, min_date,on ='Country')\n",
    "from_100000th_case['N days'] = (from_100000th_case['Date'] - from_100000th_case['Min Date']).dt.days\n",
    "\n",
    "fig = px.line(from_100000th_case, x = 'N days', y='Confirmed', color='Country', title='N Days From 100K Cases', height=600)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d9a3fb8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d96cbb02",
   "metadata": {},
   "source": [
    "### Growth rate after 1Million Cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "416e3e28",
   "metadata": {},
   "outputs": [],
   "source": [
    "gt_1000000 = country_daywise[country_daywise['Confirmed']>1000000]['Country'].unique()\n",
    "temp = df[df['Country'].isin(gt_1000000)]\n",
    "\n",
    "temp = temp.groupby(['Country', 'Date'])['Confirmed'].sum().reset_index()\n",
    "temp =temp[temp['Confirmed']>1000000]\n",
    "\n",
    "min_date = temp.groupby('Country')['Date'].min().reset_index()\n",
    "min_date.columns = ['Country', 'Min Date']\n",
    "\n",
    "from_1000000th_case = pd.merge(temp, min_date,on ='Country')\n",
    "from_1000000th_case['N days'] = (from_1000000th_case['Date'] - from_1000000th_case['Min Date']).dt.days\n",
    "\n",
    "fig = px.line(from_1000000th_case, x = 'N days', y='Confirmed', color='Country', title='N Days From 1Million Cases', height=600)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bffa1eb0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b186ca34",
   "metadata": {},
   "source": [
    "### Growth rate after 10Million Cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74337583",
   "metadata": {},
   "outputs": [],
   "source": [
    "gt_10000000 = country_daywise[country_daywise['Confirmed']>10000000]['Country'].unique()\n",
    "temp = df[df['Country'].isin(gt_10000000)]\n",
    "\n",
    "temp = temp.groupby(['Country', 'Date'])['Confirmed'].sum().reset_index()\n",
    "temp =temp[temp['Confirmed']>10000000]\n",
    "\n",
    "min_date = temp.groupby('Country')['Date'].min().reset_index()\n",
    "min_date.columns = ['Country', 'Min Date']\n",
    "\n",
    "from_10000000th_case = pd.merge(temp, min_date,on ='Country')\n",
    "from_10000000th_case['N days'] = (from_10000000th_case['Date'] - from_10000000th_case['Min Date']).dt.days\n",
    "\n",
    "fig = px.line(from_10000000th_case, x = 'N days', y='Confirmed', color='Country', title='N Days From 10Million Cases', height=600)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f75896d2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ab4c281e",
   "metadata": {},
   "source": [
    "## Tree Map Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf5f0678",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Confirmed Cases\n",
    "full_latest = df[df['Date'] == max(df['Date'])]\n",
    "\n",
    "fig = px.treemap(full_latest.sort_values(by='Confirmed', ascending=False).reset_index(drop=True),\n",
    "                path = ['Country', 'Province/State'], values='Confirmed', height=700,\n",
    "                title='Number of Confirmed Cases',\n",
    "                color_discrete_sequence=px.colors.qualitative.Dark2)\n",
    "\n",
    "fig.data[0].textinfo= 'label+text+value'\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2679ab9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#deaths\n",
    "full_latest = df[df['Date'] == max(df['Date'])]\n",
    "\n",
    "fig = px.treemap(full_latest.sort_values(by='Deaths', ascending=False).reset_index(drop=True),\n",
    "                path = ['Country', 'Province/State'], values='Deaths', height=700,\n",
    "                title='Number of Deaths',\n",
    "                color_discrete_sequence=px.colors.qualitative.Dark2)\n",
    "\n",
    "fig.data[0].textinfo= 'label+text+value'\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "513c5d5c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7b2762d7",
   "metadata": {},
   "source": [
    "## First & Last Case Report Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11bec444",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "first_date= df[df['Confirmed']>0]\n",
    "first_date = first_date.groupby('Country')['Date'].agg(['min']).reset_index()\n",
    "\n",
    "\n",
    "last_date= df.groupby(['Country', 'Date'])['Confirmed', 'Deaths']\n",
    "last_date = last_date.sum().diff().reset_index()\n",
    "\n",
    "\n",
    "mask = (last_date['Country'] != last_date['Country'].shift(1))\n",
    "\n",
    "last_date.loc[mask, 'Confirmed'] = np.nan\n",
    "last_date.loc[mask, 'Deaths'] = np.nan\n",
    "\n",
    "last_date = last_date[last_date['Confirmed']>0]\n",
    "last_date = last_date.groupby('Country')['Date'].agg(['max']).reset_index()\n",
    "\n",
    "first_last = pd.concat([first_date, last_date['max']], axis=1)\n",
    "first_last['max'] = first_last['max'] + timedelta(days=1)\n",
    "\n",
    "first_last['Days'] = first_last['max'] - first_last['min']\n",
    "first_last['Task'] = first_last['Country'] \n",
    "\n",
    "first_last.columns = ['Country', 'Start', 'Finish', 'Days', 'Task']\n",
    "\n",
    "first_last = first_last.sort_values('Days')\n",
    "\n",
    "colors = ['#' + ''.join([random.choice('0123456789ABCDEF') for j in range(6)]) for i in range(len(first_last))]\n",
    "\n",
    "\n",
    "fig = ff.create_gantt(first_last, index_col ='Country', colors=colors, show_colorbar=False,\n",
    "                     bar_width =0.2, showgrid_x = True, showgrid_y = True, height= 2500)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be5d3b0c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7c450aca",
   "metadata": {},
   "source": [
    "## Confirmed Cases Country & Day Wise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53f680f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = country_daywise.groupby(['Country', 'Date'])['Confirmed'].sum().reset_index()\n",
    "temp = temp[temp['Country'].isin(gt_10000000)]\n",
    "\n",
    "countries= temp['Country'].unique()\n",
    "\n",
    "\n",
    "ncols = 3\n",
    "nrows = math.ceil(len(countries)/ncols)\n",
    "\n",
    "fig = make_subplots(rows = nrows, cols= ncols, shared_xaxes= False, subplot_titles= countries)\n",
    "\n",
    "for ind, country in enumerate(countries):\n",
    "    row = int((ind/ncols)+1)\n",
    "    col = int((ind%ncols)+1)\n",
    "    fig.add_trace(go.Bar(x= temp['Date'], y=temp.loc[temp['Country']== country, 'Confirmed'], name=country), row= row, col=col)\n",
    "    \n",
    "fig.update_layout(height=4000, title_text='Confirmed Cases in each Country')    \n",
    "fig.update_layout(showlegend=False)\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df843261",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f5dbaf9f",
   "metadata": {},
   "source": [
    "## Covid-19 Vs Other Similar Epidemics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f48afdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#source for the number wikipedia\n",
    "epidemics = pd.DataFrame({\n",
    "    'epidemic' :['COVID-19', 'SARS', 'EBOLA', 'MERS', 'H1N1'],\n",
    "    'start_year' : [2019, 2002, 2013, 2012, 2009],\n",
    "    'end_year' :[2020, 2004, 2016, 2020, 2010],\n",
    "    'confirmed' :[full_latest['Confirmed'].sum(), 8422, 28646, 2519, 6724149],\n",
    "    'deaths': [full_latest['Deaths'].sum(), 813, 11323, 866, 19654]\n",
    "})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4c4b0de",
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculating mortality rate\n",
    "epidemics['mortality'] =round((epidemics['deaths']/epidemics['confirmed'])*100, 2)\n",
    "\n",
    "temp = epidemics.melt(id_vars='epidemic', value_vars=['confirmed', 'deaths', 'mortality'], \n",
    "                     var_name='Case', value_name='Value')\n",
    "\n",
    "fig= px.bar(temp, x='epidemic', y='Value', color= 'epidemic', text='Value', facet_col='Case',\n",
    "           color_discrete_sequence=px.colors.qualitative.Bold)\n",
    "\n",
    "fig.update_traces(textposition='outside')\n",
    "fig.update_layout(uniformtext_minsize=8, uniformtext_mode= 'hide')\n",
    "fig.update_yaxes(showticklabels= False)\n",
    "fig.layout.yaxis2.update(matches = None)\n",
    "fig.layout.yaxis3.update(matches = None)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20ea4a26",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}