Best Python Web Scraping Libraries: Selenium vs Beautiful Soup

Based on my over a decade of software development experience, including my role as CTO at AIMultiple, where I led data collection from ~80,000 web domains, I have selected the top Python web scraping libraries. You can see my rationale behind each selection by following the links:

8 best Python web scraping libraries

1. Beautiful Soup

BeautifulSoup is a Python library that extracts data from HTML and XML files.¹ It excels at parsing messy, real-world HTML and transforming it into a navigable object tree, which makes finding and extracting specific pieces of information incredibly simple.

It’s important to understand that Beautiful Soup does not fetch web pages. It is a parser. To use it, you must first get the HTML content of a page using another library, most commonly Requests.

Pros:

• Beginner-friendly: It features a simple, Pythonic API that is easy to learn and use, making it an ideal starting point for new scrapers.
• Handles messy HTML: Exceptionally good at parsing broken or poorly structured HTML, which is common on the web.
• Flexible parser options: It works with various parsers, including Python’s built-in HTML parser, the lenient html5lib, and the very fast lxml. This allows you to choose between speed and flexibility.

Cons:

• Requires a separate library for fetching: It cannot fetch web pages on its own. You must always pair it with a library like Requests or urllib3.
• No JavaScript support: It can only parse the static HTML that is returned in the initial server response.

When to use Beautiful Soup:

• You are a beginner just starting with web scraping.
• You need to scrape data from static websites where the content does not depend on JavaScript.

2. Selenium

Selenium offers different open-source extensions and libraries to support web browser automation. ² Its ability to control a real browser makes it an essential tool for scraping dynamic websites that rely on JavaScript. Selenium launches a browser and automates it to perform actions like a human user would, such as clicking buttons and scrolling.

Pros of Selenium:

• Scraping dynamic web pages is Selenium’s most significant advantage. It can access content that is generated dynamically, which is impossible for libraries like Scrapy or the Requests library alone.
• Simulates real user interaction: It can automate virtually any user action, including clicking buttons, filling out forms, scrolling, and dragging and dropping.
• Cross-browser support: It operates in multiple browsers, including Chrome, Firefox, Safari, and Microsoft Edge.
• Offers headless browser testing: You can run the browser in “headless” mode (without a visible UI), which is faster and essential for running scrapers on a server.

Cons of Selenium:

• Resource-intensive: Running a browser instance consumes much more CPU and memory, making it less suitable for very large-scale crawls with thousands of pages.
• Complex setup: Managing the WebDriver executables and ensuring they match your browser version can sometimes be challenging, although tools like webdriver-manager help simplify this process.

When to use Selenium:

• When your web scraping process requires user interaction, such as logging into an account or clicking “Load More” buttons.

3. Requests

Requests is an HTTP library that allows users to make  HTTP calls to collect data from web sources.³ The Requests source code is available on GitHub for integration into your Python package, and it officially supports Python 3.7+.

Pros of Requests:

• User-friendly Syntax: Requests provides built-in Python request modules, including GET, POST, PUT, PATCH, and more, for making HTTP requests to a target web server. This makes the code highly readable.
• Automatic content handling: It automatically decodes web content from the target server. There’s also a built-in JSON decoder if you’re working with JSON data, which greatly simplifies working with APIs.
• Proxy support: It supports Transport Layer Security (TLS) and Secure Sockets Layer (SSL) verification out of the box. Additionally, Requests supports SOCKS and HTTP(S) proxy protocols for more advanced use cases.
• Session management: It handles cookies and sessions automatically across requests, making it easy to interact with sites that require login.

Cons of Requests:

• Fetches source code only, no rendering: It is crucial to understand that Requests only downloads the raw HTML source code as it exists on the server. It does not render JavaScript web pages.
• Purely a fetching tool: As a library focused only on HTTP, it is not intended for data parsing.

When to use Requests:

• As the standard tool for interacting with APIs that return structured data like JSON.
• Any time you need to perform simple HTTP actions (GET or POST) in a Python script where browser interaction isn’t required.

3. Scrapy

Scrapy is an open-source web scraping and web crawling framework written in Python.⁴ Unlike the other tools we’ve discussed, Scrapy is not a single library but a complete framework.

Pros of Scrapy:

• Fast and asynchronous: Scrapy is built on Twisted, an asynchronous networking library. This allows it to handle many requests simultaneously.
• Built-in data extraction and export: This feature enables you to extract data from HTML and XML sources using powerful selectors, such as XPath and CSS. You can then save the retrieved web data directly in formats such as CSV, JSON, or XML.
• Middleware and extensions: This includes built-in extensions and middleware for automatically handling common tasks, such as observing robots.txt rules, user-agent spoofing, managing cookies and sessions, and supporting HTTP proxies.

Cons of Scrapy:

• Steep learning curve: As a full framework, Scrapy has a more complex architecture and a steeper learning curve than a simple Python web scraping library like Beautiful Soup.
• Can be overkill for simple tasks: For just grabbing a few pieces of data from a single page, setting up a full Scrapy project is unnecessary overhead.

When to use Scrapy:

• For large-scale web scraping projects that require crawling multiple pages or entire websites.

5. Playwright

Playwright is an open-source framework designed for web testing and automation. The Microsoft team maintains it.⁵ Like Selenium, it automates browsers, but it does so with a more modern architecture that provides some significant advantages in ease of use.

Pros of Playwright:

• Capable of scraping JavaScript-rendered sites: Just like Selenium, it can handle dynamic content by controlling a real browser.
• Simplified setup: Playwright downloads web browsers automatically with a single command.
• Auto-waiting: Its API is designed to automatically wait for elements to be actionable, which reduces script flakiness and the need for manual sleep calls.
• Features: It provides APIs for monitoring and modifying HTTP/HTTPS network traffic, and can emulate real devices, such as mobile phones and tablets. It can also take a screenshot of either a single element or the entire scrollable page.

Cons of Playwright:

• Resource-intensive: Like Selenium, running a full browser instance is much slower and more memory-intensive.
• Newer ecosystem: Its community and the number of third-party integrations are not yet as vast as Selenium’s.

When to use Playwright:

• When you are looking for a more modern and faster alternative to Selenium with a more straightforward setup process.
• When you need to test on the WebKit engine (Safari) in addition to Chrome and Firefox.

6. Lxml

Lxml is another Python-based library for processing and parsing XML and HTML content. Lxml is a wrapper over the powerful C libraries libxml2 and libxslt.

Pros of Lxml:

• Fast: The key benefit of lxml is that it parses larger and more complex documents faster than other Python libraries.
• Powerful querying: It offers native support for both XPath and CSS selectors, enabling the extraction of data from documents.
• XML support: Lxml provides an API for advanced XML processing. This includes features such as lxml. etree for efficient XML handling and lxml.objectify for a Pythonic object syntax.

Cons of Lxml:

• Steeper learning curve: XPath syntax can be more complex for beginners to learn.
• Strict on encoding: As your original text noted, lxml does not parse Python unicode strings directly. You must provide data that can be parsed in a valid encoding.

When to use Lxml:

• As the backend parser for Beautiful Soup or Scrapy to improve their speed.
• For advanced XML tasks, such as validation or XSLT transformations.

7. Urllib3

While Python has a built-in package named urllib for handling URLs, a more widely used alternative is the third-party library Urllib3.⁶

It focuses on providing a reliable HTTP client with advanced features, such as connection pooling.

Pros of Urllib3:

• Connection pooling: urllib3’s pool manager reuses connections to the same host, which significantly improves performance when making multiple requests to the same server.
• Improved control: It provides client-side TLS/SSL verification and robust support for proxies (HTTP and SOCKS).
• File uploads: It supports multipart file uploads.

Cons of Urllib3:

• Not for beginners: urllib3 is better suited for applications and libraries that need its advanced features.

When to use Urllib3:

• When building a library or application that requires fine-grained control over HTTP requests and connection management.

8. MechanicalSoup

MechanicalSoup is a popular Python library that automates website interaction.⁷

It combines the functionality of two other libraries: Requests (for making HTTP requests) and BeautifulSoup (for parsing the HTML).

Pros of MechanicalSoup:

• Proven libraries: MechanicalSoup uses the BeautifulSoup (BS4) library at its core. This means that after you navigate to a page, you can immediately use familiar find() and find_all() methods to extract data. It also supports CSS and XPath selectors via BeautifulSoup.
• Easy browsing: It automatically stores and sends cookies, which means it can maintain a session and stay “logged in” as it navigates between pages.

Cons of MechanicalSoup:

• Limited to browser-like actions: It cannot perform complex actions that require a real browser engine, like taking screenshots.

When to use MechanicalSoup:

• For scraping tasks that require navigating through a series of linked pages.

Comparison of the web scraping libraries

How do you choose the best web scraping library?

Choosing the best web scraping library is not a one-size-fits-all decision. To make an informed decision, you can consider the following main points:

• How complex is the target website? For sites with clean and straightforward HTML, the combination of the Requests library and Beautiful Soup is often the most efficient approach. Modern websites often utilize JavaScript, which means that the data you want to scrape may not be directly present in the initial HTML source. You’ll need a browser automation tool that can render JavaScript (such as Selenium or Playwright) to simulate user actions, like clicks, and scroll to reveal the desired publicly available web data.
• What is the scale of your project? For single-use scraping tasks, the simplicity of Beautiful Soup can make it an ideal choice. If you need to build a scalable web crawler to scrape large volumes of data, Scrapy is a good choice, as it offers built-in support for asynchronous scraping and data processing pipelines.
• Do you need to handle anti-scraping measures? Many websites have measures in place to block scrapers, such as CAPTCHAs, IP blocking, and rate limiting. While some Python web scraping tools offer basic support for proxy servers, more advanced data collection projects might require rotating proxies and web unblockers to avoid detection.

FAQs about Python web scraping libraries

External Links

• 1. Beautiful Soup: We called him Tortoise because he taught us..
• 2. Selenium
• 3. Requests: HTTP for Humans™ — Requests 2.32.4 documentation.
• 4. Scrapy 2.13 documentation — Scrapy 2.13.3 documentation.
• 5. Fast and reliable end-to-end testing for modern web apps | Playwright.
• 6. urllib — URL handling modules — Python 3.13.7 documentation.
• 7. Welcome to MechanicalSoup’s documentation! — MechanicalSoup 1.4.0 documentation.