16+ Best Python Web Scraping Libraries for 2024

Are you looking to improve your web scraping skills in 2024? Well, you’re in for a treat.

Over the years I’ve tried and tested many Python web scraping libraries. I will list the best ones in this article.

With these tools, web scraping in Python and data extraction would be as simple as using your web browser (well… not really, but close enough).

Why Use These Python Web Scraping Libraries?

Now, you might be thinking, “Why do I need these libraries?”

Well, let’s say you have a data science project idea or web scraping software use case brewing, and you need to extract some valuable data from the web. To get that data, you’ll need to extract it from websites, in other words, you need to “scrape data”, and to do so, you have to use a web scraping tool.

Web scrapers are mainly written in Python, and they rely on the magical libraries you’ll learn about in this article.

1. Beautiful Soup

When talking about the best Python libraries and frameworks, we must never forget Beautiful Soup.

Beautiful Soup Benefits 👍

Alright, let me spill the beans on Beautiful Soup. Imagine you’re in the wild web, surrounded by tangled HTML and XML vines. It’s a jungle out there, right? That’s where Beautiful Soup comes to the rescue like your trusty machete, slashing through the web foliage with finesse using xpath.

Navigating the parse tree becomes a piece of cake – no more feeling lost in the labyrinth of tags and attributes.

Now, let’s talk syntax. Beautiful Soup is not one of those cryptic incantations you need a magic wand for. No, sir! It’s straightforward and user-friendly. The learning curve? Almost non-existent.

Beautiful Soup Problems 👎

The only thing I don’t like about Beautiful Soup is that it’s a bit slow, as it is written in pure Python. If you want to create a fast scraping tool, then lxml might be a better alternative. It also doesn’t support JavaScript rendering, so you will need to use it with other tools, such as the Selenium library, if you want to scrape websites with a lot of dynamic data.

2. Requests-HTML

Requests-HTML Benefits 👍

If simplicity is your game, Requests-HTML is the library for you. It combines the ease of Requests with the flexibility of jQuery, making it a fantastic choice for those who want a straightforward solution to scrape the web. You’ll find yourself fetching URLs, parsing HTML data from the web, and more with just a few lines of code with the help of things like css selectors and the Python requests library.

It renders Javascript, and it also supports async, and you can even use it to fetch multiple sites at the same time!

Additionally, Requests-HTML has an intelligent pagination support, which detects website pages and returns them in a simple python list for you.

Requests-HTML Problems 👎

Requests-HTML may be slow at times.

3. Scrapy

If you’re into data mining and need a framework that does the heavy lifting, Scrapy is the way to go.

Scrapy is a python open-source and collaborative framework designed for large-scale web scraping, making it perfect for ambitious projects. You’ll love its extensibility and the ease with which you can define the data flow.

Benefits of Scrapy 👍

Scrapy provides what it calls “spiders”. These are essentially scripts that know how to navigate websites, pick out specific data, and organize it neatly. The beauty of Scrapy is that you don’t need to dive deep into the complexities of HTTP requests or HTML parsing – it handles that for you.

What sets Scrapy apart is its efficiency, especially when dealing with sizable tasks. Using Scrapy can help you handle big scraping projects smoothly, managing multiple requests simultaneously and dealing well with complex website structures. And if you need to tweak things a bit, you can customize Scrapy using middleware to suit your specific requirements.

Scrapy Problems 👎

Python’s Scrapy, does not have built-in JavaScript rendering functionality. However, you can integrate Scrapy with headless browsers or JavaScript rendering libraries like one of the following:

Scrapy-Selenium: This middleware allows you to use Selenium to render JavaScript and interact with the browser as if it were a real user.

Scrapy-Playwright: This library integrates Scrapy with Playwright, a modern web browser automation library that supports JavaScript rendering.

Scrapy-Splash: Splash is a headless browser that can be used with Scrapy to render JavaScript and interact with web pages.

4. Selenium

When it comes to dynamic web pages and dealing with JavaScript, Selenium is your best friend.

Selenium: The Good 👍

With Selenium, you can automate browser actions and scrape content from pages that rely heavily on client-side rendering.

Selenium is one of the most popular python libraries for web automation.

With Selenium, you can script your browser actions. Instead of clicking through web pages manually, you can instruct Selenium to navigate to a website, click buttons, scroll through pages, and grab the information you need.

Although this post is about Python web scraping libraries, the great thing about Selenium is that it also supports other programming languages like JavaScript, Java, and more. This makes it one of the best web scraping libraries and frameworks in general, not just in the Python world.

Selenium: The Bad 👎

It can be a bit slower compared to other methods since it simulates a browser.

Selenium: The Ugly 🤮

If you want to perform web scraping on a large scale project in a fast manner, Selenium might eat up all your resources and shamelessly ask for more.

5. Lxml

Lxml’s Good Points 👍

If speed is of the essence, Lxml is your friend. This library is a high-performance solution for parsing XML and HTML documents. It’s known for its blazing-fast processing speed, making it a top choice for projects where every millisecond counts.

Lxml extracts data from html and xml documents using xpath, and CSS selectors. It is perfect if you want a library in python to help you build a fast web crawler or an efficient scraper.

Lxml’s Bad News 👎

lxml, as a Python library, does not directly support JavaScript rendering. It is primarily designed for parsing and extracting data from XML and HTML documents. So you may have to integrate it with other tools that render JavaScript to scrape dynamic websites.

6. PyQuery

PyQuery is a Pythonic way to make jQuery queries.

PyQuery Benefits 👍

PyQuery is a lightweight library that allows you to make sense of HTML documents using the familiar syntax of jQuery. If you’re a jQuery fan, you’ll feel right at home with PyQuery. It uses lxml for fast xml and html manipulation.

PyQuery Problems 👎

The PyQuery library is not actively maintained and lacks many jQuery features.

7. MechanicalSoup

Now, time to meet MechanicalSoup.

MechanicalSoup Benefits 👍

What makes MechanicalSoup great is its simplicity and ease of use. It combines the capabilities of the BeautifulSoup and Requests libraries, streamlining the process of navigating and parsing HTML content. With MechanicalSoup, users can easily automate form submissions, handle cookies, and navigate through web pages, making web scraping tasks more accessible for both beginners and experienced developers. Its intuitive API allows for quick implementation of web scraping scripts.

MechanicalSoup Problems 👎

Sadly, it doesn’t speak JavaScript. For scraping web applications that rely on JavaScript tricks, consider using MechanicalSoup with other libraries or tools, like Selenium, or Playwright, which can handle JavaScript.

8. Playwright

Playwright is a an open-source Node.js library for browser automation, with a set of APIs to control web browsers, similar to Selenium. It provides a Python library that can automate Chromium, Firefox and WebKit browsers with a single API.

Playwright Benefits 👍

Unlike traditional scraping libraries, Playwright supports headless browsers, JavaScript rendering, and can handle dynamic content generated by JavaScript. This makes it effective for scraping modern, dynamic websites.

Playwright also has some neat features such as auto-waits, and retries.

Playwright auto-waits wait for elements to be actionable prior to performing actions.

Playwright retries, on the other hand, are the core of Playwright’s retry logic, which, combined with powerful locators, simplifies web scraping by automagically re-running the scraping process until the necessary conditions are met. This feature helps in handling dynamic web content and intermittent loading issues.

Playwright Problems👎

Performance issues when using many workers: Users have reported confusing performance issues when running tests with multiple workers, such as elements not being found or assertions failing.

Auto-waiting limitations: While Playwright’s auto-waiting feature is useful, it does not always work as expected, requiring manual intervention in some cases

9. Grab

Grab is a Python web scraping framework that provides a set of helpful methods to perform network requests, scrape websites, and process the scraped content.

Grab Benefits 👍

With Grab, you can build web scrapers of various complexity, from simple 5-line scripts to complex asynchronous website crawlers processing millions of web pages. Grab provides an API for performing network requests and for handling the received content, such as interacting with the DOM tree of the HTML document.

Grab Problems👎

The project has not been actively maintained, with the last significant update dating back to 2018. This lack of ongoing development may lead to compatibility issues and a lack of support for modern web technologies.

10. Pyppeteer

Pyppeteer is a Python port of the popular Node library Puppeteer.

Pyppeteer Benefits👍

Pyppeteer is your gateway to controlling headless browsers, perfect for scraping dynamic websites that rely on JavaScript. With Pyppeteer, you can take screenshots, generate PDFs, and interact with web pages like never before.

Pyppeteer Problems👎

Compatibility issues: Pyppeteer may have compatibility issues with newer versions of Python, as reported by users experiencing hangs when running Pyppeteer with Python 3.10.

Dependency issues: Users have reported issues with dependencies and headless Chromium when using Pyppeteer, particularly on Linux systems.

11. Splash

The Splash library is a JavaScript rendering service with an HTTP API, implemented in Python 3 using Twisted and QT5.

Splash Benefits 👍

Splash is fast, as it acts as a lightweight web browser with an HTTP API, allowing users to process multiple web pages in parallel, retrieve HTML results and take screenshots, execute custom JavaScript in the page context, and more.

You can use Splash in your web scraping process, particularly in combination with the Scrapy framework. Splash is also known for its ability to handle JavaScript-heavy websites.

The library is fast, lightweight, and stateless, making it easy to distribute and use in various web scraping and automation tasks.

Splash Problems 👎

Crashes and bugs: Splash may crash or encounter bugs when rendering certain web pages, particularly those with complex JavaScript or media content.

Limited documentation: While Splash provides documentation, some users have reported that it can be difficult to find answers to specific questions or issues.

12. Pandas

Pandas is a powerful Python library used for data manipulation and analysis, particularly for working with data sets. It offers functions for analyzing, cleaning, exploring, and manipulating data, making it a popular choice for data scientists, analysts, and engineers working with structured data in Python.

Pandas Benefits 👍

Pandas can be used for web scraping. It provides a convenient method for extracting tables from web pages and saving the data as a DataFrame, which can then be processed and analyzed. The pandas.read_html() function, along with the lxml, html5lib, and beautifulsoup4 modules, allows for easy extraction of tabular data from web pages. This makes Pandas a valuable tool for web scraping and data extraction tasks.

Pandas Problems👎

Pandas is one of the most widely used Python tools in data science. However, it is often not considered to be among python web scraping tools, as web scraping is not its main purpose. This means it has minimal features for complex scraping and may not be suitable for efficient web scraping.

13. Fake User-Agent

Don’t let websites detect your scraper too easily. Fake User-Agent is the library that makes convenient fake IDs for you.

Fake User-Agent Benefits 👍

Fake User-Agent generates and manages fake user-agent strings, which are used to mimic different web browsers and devices when making HTTP requests.

This can be useful in web scraping and automation to avoid being blocked by websites that check the user-agent string to detect bot traffic.

The library provides an up-to-date database of real user-agent strings and allows for easy retrieval of random or specific user-agent strings for use in HTTP requests.

Fake User-Agent Problems👎

The library’s documentation may be limited or unclear in some areas, making it difficult for python developers to understand how to use the library effectively.

14. Feedparser

When dealing with RSS feeds and Atom, Feedparser is your best friend.

Feedparser Benefits 👍

Feedparser simplifies the parsing of web feeds, allowing you to extract relevant information effortlessly. If you’re building a news aggregator or need to keep up with updates, Feedparser has got your back.

Feedparser Problems👎

Not a full-featured scraping library, as it focuses mainly on RSS and Atom feeds.

15. Spidy

While this is not a library per say, but a command line tool for web crawling. It deserves to be on this list for its simplicity and ease of use.

Spidy Benefits 👍

Fast and easy to use. You can use it to crawl and scrape data from the web for simple tasks without having to write your own scraper.

Spidy Problems 👎

Just a command line tool and not a complete scraping library.

16. Requests

Requests is the most fundamental of the python libraries for web scraping, as it is the backbone of most scraping tools. It is depended upon by over 1,000,000 repositories!

Requests is a widely used python library for HTTP requests.

Requests Benefits 👍

An easy-to-use API for sending HTTP requests and handling responses, including support for various HTTP methods, custom headers, authentication, and more.

Requests Problems 👎

Only handles HTTP requests, which means you’ll need an additional library to parse the HTML contents you fetch.

That’s it! So… Did you find the Best Python Scraping Library?

As you can see, there is a diverse set of Python libraries for web scraping, and finding the best one for you depends on many factors:

Looking for an easy to use HTML parser? Then go for Beautiful Soup, or Requests-HTML.
Need to handle complex scraping tasks? Go with Scrapy.
Browser automation with good JavaScript rendering? Try Selenium or Playwright.
For fast HTML and XML parsing, check out lxml.

FAQ: Questions on Python Web Scraping Libraries

To scrape websites that require login using Python, you need to follow a few steps:

First, figure out the target domain and its type of security measures. Some websites require only a username and password, while others use more advanced security measures such as client-side validations, CSRF tokens, and Web Application Firewalls (WAFs).
Once you have identified the security measures used by the website, you can use Python libraries such as Requests, Beautiful Soup, and Selenium to scrape the website.

What is a User-Agent and how do I use it in web scraping with Python?

A User-Agent in web scraping is a string that allows the website you are scraping to identify the application, operating system, and browser of the user sending a request to their website. It helps mimic the behavior of a web browser, enabling access to a website as a human user and avoiding being identified as a bot.

How do I handle file downloads during web scraping with Python?

To handle file downloads during web scraping with Python, you can use several libraries and methods, such as Requests, urllib, and Selenium. Many other libraries provide functions you can use to download files into a specific directory.