Web scraping for daily COVID-19 stats

Table of Contents


As part of my aim to learn Python, I decided to teach myself by completing a bunch of projects created in Python. I started off by creating a Twitter bot, and now I want to expand it to web scraping daily stats of a website and tweeting about it. I am specifically interested in the daily stats of the COVID-19 virus (Coronavirus) and what the daily new cases are as well as the global growth factor. The idea to look at the daily stats of the Coronavirus was sparked after watching a video by 3Blue1Brown where he discusses exponential growth of epidemics.

Right! Let’s get started!

What is web scraping?

Web scraping, web harvesting, or web data extraction is used for extracting all kinds of data from websites. Web scraping can be done manually by the user, but web scraping typically includes automated processes implemented using a bot or web crawler. It is a form of copying, in which specific data is gathered and copied from the internet, typically into a database or spreadsheet, for later retrieval or analysis.

The data source

We are interested in the data contained in a table at Worldometer’s website, where it lists all the countries together with their current reported coronavirus cases, new cases for the day, total deaths, new deaths for the day, etc. The idea is to get these daily figures, calculate the global growth factor and create a tweet that can be tweeted daily reporting the stats of:

  • United Kingdom and
  • South Africa.

Coding the web scraper

Get the HTML data

First thing you need to do is to get the HTML data of the site you are interested in, into your Python script. We can do this using the Python library, requests. You can install it using

pip install requests

Once it is installed you can import it to your Python script:

import requests

URL = 'https://www.worldometers.info/coronavirus/#countries'
page = requests(get.URL)

The code retrieves the HTML data that the server sends back and stores that data in a Python object.

We have scraped some HTML from this webpage, but when you look at it, it just seems like a huge mess. There are tons of HTML elements and thousands of attributes scattered around. We can now parse this lengthy code response with Beautiful Soup to make it more accessible and pick out the data that we are interested in.

What is BeautifulSoup

Beautiful Soup is a Python library used for pulling data out of HTML and XML files. It provides ways of navigating, searching, and modifying the parse tree of a website. It can save programmers hours or days of work when gatering data.

Installing BeautifulSoup

If you have not done so already, you need to install BeautifulSoup which is the Python library that you will use to scrape data from the webpage. You can install it using the command prompt:

pip install beautifulsoup4

Once that is done, we can start using it in our Python script.

from bs4 import BeautifulSoup

soup = BeautifulSoup(page.content, 'html.parser

Extracting the data

Find table ID

The Beautiful Soup object has been created in our Python script and the HTML data of the website has been scraped off of the page. Next we need to get the data that we are interested in, out of the HTML code. We first find the id attribute of the table by using the inspect fuctionality of the Chrome web browser. You right-click anywhere on the webpage and at the bottom of the dropdown list that appears, you select inspect.

The ID of the table is main_table_countries_today. We can use this to focus only on the table in the scraped HTML code:

results = soup.find(id='main_table_countries_today')

With a website like this, it is possible that the structure of the website can change in the future, and this can include changing the id attribute of the table which can cause an error in your code. You will just have to go back to the webpage and update your python code with the new table ID.

Cleaning up the scraped data

If we go and print out the data contained in results, we get:

<tr style="">
<td style="font-weight: bold; font-size:15px; text-align:left;"><a class="mt_a" href="country/uk/">UK</a></td>
<td style="font-weight: bold; text-align:right">14,543</td>
<td style="font-weight: bold; text-align:right;"></td>
<td style="font-weight: bold; text-align:right;">759 </td>
<td style="font-weight: bold; text-align:right;"></td>
<td style="font-weight: bold; text-align:right">135</td>
<td style="text-align:right;font-weight:bold;">13,649</td>
<td style="font-weight: bold; text-align:right">163</td>
<td style="font-weight: bold; text-align:right">214</td>
<td style="font-weight: bold; text-align:right">11</td>
<td style="text-align:right;font-size:13px;">
Jan 30 </td>

The data is the entry for the United Kingdom, but there are still a lot of HTML code that we do not want. All the data entries of the table for the given country is wrapped in the HTML element <td ... <\td>. We can use that knowledge to further clean up the scraped data:

content = results.find_all('td')

If we print out the data contained in content we get:

<td style="font-weight: bold; font-size:15px; text-align:left;"><a class="mt_a" href="country/uk/">UK</a></td>, <td style="font-weight: bold; text-align:right">14,543</td>, <td style="font-weight: bold; text-align:right;"></td>, <td style="font-weight: bold; text-align:right;">759 </td>, <td style="font-weight: bold; text-align:right;"></td>, <td style="font-weight: bold; text-align:right">135</td>, <td style="text-align:right;font-weight:bold;">13,649</td>, <td style="font-weight: bold; text-align:right">163</td>, <td style="font-weight: bold; text-align:right">214</td>, <td style="font-weight: bold; text-align:right">11</td>, <td style="text-align:right;font-size:13px;">

This is still every chaotic. Luckily all the data that we need, it text and can be extracted from the above as:

for data in content:

This will contain the following data:




Each line above is a column entry for the United Kingdom. Next, this data needs to be entered into a dictionary so that we can use it to contruct a tweet.

Creating a dict variable

Creating seperate lists

Before we can create a tweet reporting on the daily stats, we need to save the scraped data in some form that can be used effectively. For this project, all the data will be safed in a Python dictionary. To achieve this we will:

  • Save each column in a list and
  • Create a dictionary with all the populated lists.

First, we initialise empty lists for each column:

countries = []
total_cases = []
new_cases = []
total_deaths = []
new_deaths = []
total_recovered = []
active_cases = []
critical = []
total_per_mil_pop = []

Then we iterate through the content variable and place each corresponding entry into the correct list. There are 10 columns and as such a new country’s data is shown after every 10 iterations:

i = 1
for data in content:
    if i%10 == 1:
    if i%10 == 2:
    if i%10 == 3:
    if i%10 == 4:
    if i%10 == 5:
    if i%10 == 6:
    if i%10 == 7:
    if i%10 == 8:
    if i%10 == 0:
    i += 1

If we assume the first column on the left (Country) can be numbered as 1, then the mathematical operater %10 returns the modulus which corresponds to each column. This can then be used to ensure the correct data is appended to the correct list.

Combining lists into a dictionary

After all the lists have been populated with the data scraped from the webpage, we can combine it into a dictionary:

covid19_table = {
    "columns": column_names,
    "country": countries,
    "total_cases": total_cases,
    "new_cases": new_cases,
    "total_deaths": total_deaths,
    "new_deaths": new_deaths,
    "total_recovered": total_recovered,
    "active_cases": active_cases,
    "critical": critical,
    "total_1M_ pop": total_per_mil_pop}

The column_names have been generated seperately:

column_names = ["Country", 
    "Total Cases", 
    "New Cases", 
    "Total Deaths", 
    "New Deaths", 
    "Total Recovered", 
    "Active Cases", 
    "Total Cases/1M pop"]

We are now ready to start using this data to contruct a tweet that will be sent out daily.

Compiling the tweet

Growth factor

In order to calculate the growth factor of the coronavirus, the following equation can be used:

$$Gf = \frac{N_i}{N_{i-1}}$$

where $N_i$ is the amount of new cases for today and $N_{i-1}$ refers to the amount of new cases for the previous day.

A .csv file will be used to store each day’s stats so that a new growth factor can be calculated each day:


A Python libary pandas will be used to read from, and write to the .csv file. You can install pandas using:

pip install pandas

The following was added to the python script to read the current content of the .csv file and put in a dictionary new_table:

import pandas

df = pandas.read_csv('Total.csv', parse_dates = ["Date"], dayfirst = True)
new_table = df.to_dict()

The current date is added to the new_table dictionary as it is the first entry required for the .csv file:

today = str(date.today())
new_table["Date"][len(new_table["Date"])] = today

Now we calculate the growth factor. We first search for the position in the country label of our covid19_table dictionary for the total numbers for the whole world for the current day. This is under the *country * entry Total:.

search_position = covid19_table["country"].index("Total:")

Next, we retrieve yesterday’s data from the new_table dictionary which was created from the .csv file:

growth_yesterday = new_table["New_cases"][len(new_table["New_cases"])-1]
if type(growth_yesterday) == str:
        growth_yesterday = new_table["New_cases"][len(new_table["New_cases"])-1].replace(',','')

The amount of new cases globally is retrieved from the covid19_table that we created from our scraped data using the search_position which indicates where the corresponding data is in the dictionary.

growth_today = covid19_table["new_cases"][search_position].replace(',','')

The growth factor can now be calculated:

Gf = round(float(growth_today)/float(growth_yesterday),2)

The data needed for the current day’s entry for the .csv file is then added to the new_table dictionary:

new_table["Total_cases"][len(new_table["Total_cases"])] = covid19_table["total_cases"][search_position]
new_table["New_cases"][len(new_table["New_cases"])] = covid19_table["new_cases"][search_position]
new_table["Growth_factor"][len(new_table["Growth_factor"])] = str(Gf)

Lastly, we convert the new_table dictionary back to a .csv file that can be used the next day again:

df = pandas.DataFrame.from_dict(new_table)

Getting stats for the tweet

We create a dictionary that can be returned when the method is called with all the necessary data required for the tweet:

position_UK = covid19_table["country"].index("UK")
position_RSA = covid19_table["country"].index("South Africa")
new_UK = covid19_table["new_cases"][position_UK]
new_RSA = covid19_table["new_cases"][position_RSA]
new_total = covid19_table["new_cases"][search_position].replace(',','')
total_UK = covid19_table["total_cases"][position_UK]
total_RSA = covid19_table["total_cases"][position_RSA]
total_total =     covid19_table["total_cases"][search_position].replace(',','')

tweet_data = {
    "UK": {
    "Total": total_UK,
    "New": new_UK
    "RSA": {
    "Total": {
    "Total": total_total,
    "New": new_total
    "Gf": Gf

The tweet_data dictionary can now be used to construct the tweet.

Constructing the tweet

The best way to create a multi-line tweet is to use a .txt file that can be fed to the twitter API. The content of the .txt file for the tweet was written together with the data in tweet_data:

with open('tweet.txt', 'w',encoding='utf-8') as f:
   f.write('#COVID19 stats '+str(date.today())+':\n\
   \nTotal cases for:\nUK: '+str(tweet_data["UK"]["Total"])+' ('+str(tweet_data["UK"]["New"])+')'+'\
   \nRSA: '+str(tweet_data["RSA"]["Total"])+' ('+str(tweet_data["RSA"]["New"])+')'+'\
   \nOverall: '+str(tweet_data["Total"]["Total"])+' (+'+str(tweet_data["Total"]["New"])+')'+'\
   \n\nGrowth factor: '+str(tweet_data["Gf"])+'\
   \n\n#CoronaVirusSA \n#CoronaVirusUK')

The last thing left is to tweet it!

with open('tweet.txt','r') as f:
Adriaan van Niekerk
Lecturer in Design and Dynamics

A Mechanical Engineer passionate about creating robust and innovative products and teaching others to do the same.

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