Python Web Scraping

Web scraping is a useful technique for extracting data from websites. It’s a handy tool in the world of data science, where large and complex datasets are the norm. Today, we’ll delve into the beautiful world of web scraping using Python, making use of the Beautiful Soup and Requests libraries.

The Basics Link to heading

Python is a high-level programming language that has gained massive popularity due to its simplicity and versatility. It has a rich ecosystem of libraries that make it easier to perform complex tasks. In this guide, we’ll focus on two of these libraries: Beautiful Soup and Requests.

The Libraries Link to heading

Requests is a Python library for sending HTTP requests. It abstracts the complexities of making requests behind a beautiful, simple API, allowing you to send HTTP/1.1 requests with various methods like GET, POST, PUT, DELETE and others. With it, you can send HTTP requests in Python in just a couple of lines of code.

Beautiful Soup is a Python library designed for web-scraping HTML and XML files. It creates a parse tree from page source code that can be used to extract data in a hierarchical and more readable manner.

Here’s a simple example of how you can use these two libraries to scrape a website.

from bs4 import BeautifulSoup
import requests

URL = ''
response = requests.get(URL)

soup = BeautifulSoup(response.content, 'html.parser')
titles = soup.find_all('title')


In the above script, we are sending a GET request to the website and then we are parsing the content of the page using Beautiful Soup. Finally, we are extracting all the titles from the page and printing them.

Handling Pagination Link to heading

When you’re scraping a website, it’s not uncommon for the data to be spread across multiple pages. This is where handling pagination becomes important.

base_url = '{}/'

for page in range(1, 10):
    scrape_url = base_url.format(page)
    response = requests.get(scrape_url)
    soup = BeautifulSoup(response.content, 'html.parser')

    quotes = soup.find_all('span', class_='text')
    for quote in quotes:

In the above script, we are scraping quotes from the first 10 pages of

Web scraping with Python is a powerful tool with many applications, from data science to web development and SEO. By mastering the usage of libraries such as Beautiful Soup and Requests, you can start to harness the full power of this technique.

Note: Be mindful of the legal and ethical implications of web scraping. Always make sure to read and understand a website’s robots.txt file and terms of service before you start scraping.

Disclaimer: The code snippets provided in this article are for educational purposes only. Always obtain the necessary permissions before scraping a website.