Hello Pythonistas, In this tutorial you’re are going to learn out how to track worldwide Coronavirus cases using requests and BeautifulSoup library in Python.
If you’re new to webscraping I would recommend checking out A beginner guide to Webscraping firstly and then come back to complete this tutorial.
To effectively follow through with this tutorial, you need to have the following libraries installed on your machine with exception of CSV Module which comes with Python standard library.
Just use the pip to install the mentioned dependencies just as shown below;
$ pip install requests $ pip install beautifulsoup4
Let’s get started
where do scrap the corona cases ?
As we have stated above we are going to be tracking the number of cases worldwide by scraping the information from the cloud, there many choices of websites to scrap from but in this tutorial, we will with worldometer.
Let’s figure out the structure of website
Let’s firstly understand the structure of the website we are about to scrap, once you open up the worldometer website, scroll down and then you will see a table similar to what shown below.
How table is represented in HTML ?
The table in HTML is usually represented with a table tag whereby tr indicate row and td indicate a specific column in that low just as shown in the example below;
<table border = "1"> <tr> <td>Row 1, Column 1</td> <td>Row 1, Column 2</td> </tr> <tr> <td>Row 2, Column 1</td> <td>Row 2, Column 2</td> </tr> </table>
That’s means every row in the worldometers table is represented by tag tr, therefore we need to filter all rows from the worldometers table then store in CSV file.
Complete Coronavirus Spider
Below is the complete code of the spider that you’re going to build in this tutorial which is capable of scraping the live Coronavirus number from worldometer website and then store them in a CSV file.
import csv import requests from bs4 import BeautifulSoup html = requests.get('https://www.worldometers.info/coronavirus/').text html_soup = BeautifulSoup(html, 'html.parser') rows = html_soup.find_all('tr') def extract_text(row, tag): element = BeautifulSoup(row, 'html.parser').find_all(tag) text = [col.get_text() for col in element] return text heading = rows.pop(0) heading_row = extract_text(str(heading), 'th')[1:9] with open('corona.csv', 'w') as store: Store = csv.writer(store, delimiter=',') Store.writerow(heading_row) for row in rows: test_data = extract_text(str(row), 'td')[1:9] Store.writerow(test_data)
Let’s break the code into pieces
Importing necessary libraries
The first 3 lines of code just import all our necessary modules and libraries that we will be using to scrap the live covid19 cases and storing data in the file.
import csv import requests from bs4 import BeautifulSoup
Getting the web-page
In order for us to extract and filter coronavirus numbers, we need a way to programmatically access the source code of the webpage, in doing this we will use the requests library just as shown below.
html = requests.get('https://www.worldometers.info/coronavirus/').text
Extracting all rows in table
Now that we have the HTML source code of the website, it’s now time to parse all the rows present in that table showing coronavirus stats, in doing this will use beautifulSoup just as shown below.
html_soup = BeautifulSoup(html, 'html.parser') rows = html_soup.find_all('tr')
Making a function do unpack the row columns
After extracting all the rows in the coronavirus table, we need a way to parse all the details on each column in that row, and that’s what the below function does.
def extract_text(row, tag): element = BeautifulSoup(row, 'html.parser').find_all(tag) text = [col.get_text() for col in element] return text
Parsing the the header
Since we don’t wanna confuse the header naming and real stats, we need to pop out the header from the row list as shown below.
heading = rows.pop(0) heading_row = extract_text(str(heading), 'th')[1:9]
Parsing row details and storing to CSV
Now finally our last job is to parse all the individual details of every row in the table and then store in the CSV file using CSV module as shown below;
with open('corona.csv', 'w') as store: Store = csv.writer(store, delimiter=',') Store.writerow(heading_row) for row in rows: test_data = extract_text(str(row), 'td')[1:9] Store.writerow(test_data)
Based on your interest , you might also love these articles;
- A beginner guide to web scraping
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- The basics of requests module in Python
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- How to remove duplicates on your drive using python
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To get the full code for this article you can check out on My Github