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Plotting the Growth Curve of Coronavirus in various Countries using Python
  • Last Updated : 29 Dec, 2020

In this article, we will collect latest updated information about the coronavirus cases across the world and in a particular country. We will plot graphs to visualise the growth of total number of cases and the total deaths for the last 20 days. The latest data is fetched from Our World in Data.

Python modules required

  • requests:
    The requests library is used for making HTTP requests in Python.

    pip install requests
    
  • matplotlib:
    matplotlib is a comprehensive library for creating various types of graphs and interactive visualisations in Python.

    pip install matplotlib
    

Explanation:

The data scraped from the website(using requests), is converted to the csv-like format. Then this data is filtered to get the required statistics for the last 20 days and the visualizations are plotted(using matplotlib).



Below is the implementation.

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from matplotlib import pyplot as plt
import requests
   
  
# function to plot data for country 
# and world
def Plot(country):
   
    # getting request from the url
    req = requests.get('https://covid.ourworldindata.org / data / ecdc / full_data.csv')
    req.raise_for_status()
       
    # converting to text and splitting 
    # the rows of the csv data
    cf = req.text.split('\n')
   
    # converting to 2 dimensional list
    for i in range(len(cf)):
        cf[i]= cf[i].split(', ')  
       
       
    dates =[]
    total =[]
    total_w =[]
    deaths =[]
    deaths_w =[]
    l =[]
    f = 0
   
       
    for i in range(len(cf)-1):
        l = cf[i]
        c = l[1]
          
        # filtering data for a particular country
        if c == country:
            f = 1
              
            # getting the dates, total cases and
            # deaths for the particular country
            dates.append(l[0][5:])      
            total.append(int(l[4]))
            deaths.append(int(l[5]))
               
                  
        # filtering data for the world
        if c =='World':
   
            # getting total cases and deaths for
            # the world
            total_w.append(int(l[4]))
            deaths_w.append(int(l[5]))
               
    if f == 0:
        print("Invalid country name.")
        return
   
        
    # Plotting country data
    total_ax = plt.subplot(2, 2, 1)
    total_ax.set_title(country+' (Total Cases)')
       
    # plotting the curve for total cases
    total_ax.plot(dates[-20:], total[-20:])
       
    # plotting the bars for total cases
    total_ax.bar(dates[-20:], total[-20:], alpha = 0.5)
       
    total_ax.set_xlabel("Date")
    plt.xticks(rotation = 45)
   
   
   
   
   
    death_ax = plt.subplot(2, 2, 2)
    death_ax.set_title(country+' (Total Deaths)')
   
    # plotting the curve for deaths
    death_ax.plot(dates[-20:], deaths[-20:], color ='red')
       
    # plotting the bars for deaths
    death_ax.bar(dates[-20:], deaths[-20:], color ='red', alpha = 0.5)
       
    death_ax.set_xlabel("Date")
    plt.xticks(rotation = 45)
   
   
    
    # Plotting world data
    total_w_ax = plt.subplot(2, 2, 3)
    total_w_ax.set_title('World (Total Cases)')
   
    # plotting the curve for total cases
    total_w_ax.plot(dates[-20:], total_w[-20:])
   
    # plotting the bar for total cases
    total_w_ax.bar(dates[-20:], total_w[-20:], alpha = 0.5)
   
    total_w_ax.set_xlabel("Date")
    plt.xticks(rotation = 45)
   
   
   
       
       
    death_w_ax = plt.subplot(2, 2, 4)
    death_w_ax.set_title('World (Total Deaths)')
   
    # plotting the curve for deaths
    death_w_ax.plot(dates[-20:], deaths_w[-20:], color ='red')
   
    # plotting the curve for deaths
    death_w_ax.bar(dates[-20:], deaths_w[-20:], color ='red', alpha = 0.5)
   
    death_w_ax.set_xlabel("Date")
    plt.xticks(rotation = 45)
       
   
    plt.tight_layout()
         
  
print("Enter country name...")
country = input().title()
Plot(country)
plt.show()

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Input :

Enter country name...
India

Output :

Input :

Enter country name...
Italy

Output :

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