7 Python Web Scraping Libraries and Frameworks in 2023

Are you looking to start web scraping using the Python programming language? Then the article below has been written for you as I will describe the best Python web scraping libraries you can use.

Python is arguably the most popular programming language for web scraping. This is so because of its simplicity, easy-to-learn syntax, fast development time, and the huge libraries available. As a Python developer, libraries will make your tasks easier as you do not have to reinvent the wheel and code from scratch. One thing you will come to like about the Python programming language is that there is a library mosslike for most of the general things you will want to do.

Currently, Python provides the highest number of libraries as far as web scraping is concerned. While some of the libraries are included in the Python standard library, some of the libraries are third-party libraries that you need to install in order to make use of it. Some of the libraries have just one function, while some can do more than one. In this article, I will be going over the different libraries available to you as a Python developer for web scraping and crawling.

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Table Of Contents

1. Urllib — Default Library for Web Requests and URL Handling

The Urllib is a Python package that is part of the standard library. It contains 4 major modules each with specific tasks. There is the urllib.request which is the major module here. With this, you can send HTTP web requests and get a response with it, then use a different library to parse the HTML document downloaded with the urllib.request.

This is quite advanced as it can handle authentication, proxy usage, and sessions, and even comes with a password manager. Another module in this package is the urllib.error which is just a module for handling exceptions raised by the urllib.request.

The urllib.parse is for parsing URLs into its different components, while urllib.robotparser is for parsing robots.txt file, which comes in handy if you are building an ethical scraper that wants to check whether it has permission to scrape a page or not. Generally, this should be the first library you learn to make use of in order to start web scraping in Python, but it is quite complex, making requests, a library built on it as the de facto library for sending web requests.

Pros & Cons of Urllib

Pros:

• It comes preinstalled in the python standard library.
• Comes with some helper functions and modules that make sending web requests easy.
• There is extensive documentation for those who want to use it.
• Does come with some advanced features you wouldn’t get in some other libraries

Cons:

• There is a lot for you to learn to effectively know how to make use of this library.
• More lines of code compared to its competitors.

Learning Material for Urllib

The most comprehensive learning material of the Urllib package is available in the standard documentation of Python. You can find the page for Urllib here.

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2. Requests — Best Library for Sending HTTP Requests for Web Scraping

If you read moat beginner tutorials in web scraping using Python, you will notice they advise users to ditch the urllib package discussed above and instead use Requests. The Requests library, dubbed HTTP for Humans, is loved for its simplicity and the few lines of code you will write to achieve a task.

Anytime I need to code a web scraping, and Javascript execution is not a requirement, I use the Requests library. For this library, you will need to install it in order to make use of it. However, just like it is easy to use, it is also easy to install, as you can use the “pip install requests” command. This web scraping library is meant for just sending requests and getting the response.

It does not parse the content of the page, you will need a separate library for that. You can see this as the best alternative to the urllib package. It does have some advanced features ranging from a session with cookie persistence, internationalization of URLs, keep-alive and connection pooling, streaming downloads, connection timeout, and SSL verification, among others.

Pros & Cons of Requests

Pros:

• Easy to use HTTP library with an extensive documentation.
• High-level API makes the number of lines of code smaller

Cons:

• It wasn’t developed specifically for web scraping
• Does not have support for Javascript rendering and execution
• Can be slow

Learning Material for Requests

The Requests for Human library is easy to use. However, you will still need to learn how to use it. You can read the Requests documentation on ReadTheDocs to learn how to make use of this library.

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3. BeautifulSoup — Best for Extracting Data from HTML Pages

The BeautifulSoup library is a data extraction library that can help with parsing HTML and XML documents. It parses a web page, presenting you with high-level APIs that make it easy for you to use CSS selectors and attributes to extract data from the web page. This library is not an actual parser but a parser wrapper with a high-level function. By default, it will use the html.parser library that is available in the Python standard library.

You can also use a third-party parser, such as the much-fast lxml parser. BeautifulSoup is like the Siamese twin of Requests. This is because they go hand in hand. While Requests is for sending and receiving the HTML of a page, BeautifulSoup is meant for extracting the specific data points you want out of a page.

Basically, you will use BeautifulSoup to retrieve a web page and then feed the web page content to BeautifulSoup for parsing and data extraction. But BeautifulSoup is a standalone library and can be used with other libraries as well. You can decide to use urllib in place of Requests. You can even feed it HTML or XML from any source, and it will parse and extract the data you want.

Pros & Cons of BeautifulSoup

Pros:

• Easy to use with high-level APIs
• Ability to chain element find methods
• Support for CSS selectors
• Allows you to choose the parser of your choice
• Easy to learn with extensive documentation

Cons:

• It can be slow — you can speed it up by using lxml parser
• Does not support the XPATH selector
• Can’t be used for parsing Javascript-heavy pages

Learning Material for BeautifulSoup

If you want to learn more about the BeautifulSoup library and how to make use of it, you can read the official documentation of the BeautifulSoup on the Crummy website.

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4. Lxml — Best for Processing and Parsing XML and HTML Documents

The Lxml library is an HTML and XML processing library that you can use to parse and extract the specific data you need from a web page. Lxml does not have the capability to download an HTML document. You will need to use an HTTP library like Requests.

However, once downloaded, you can use it to manipulate, parse, and extract the data you want from the downloaded HTML document. You can see it more like a parser and a competitor of BeautifulSoup. However, it is much more powerful as BeautifulSoup is not even a parser but wraps specific parsers. You can use this library for cleaning HTML documents and even serialize documents back to strings or files.

It is arguably the fastest parser for Python developers. This is because it is actually a Python binding for the C libraries — libxml2 and libxslt which are known to be super fast. While lxml is quite fast and provides more advanced features compared to BeautifulSoup, you wouldn’t want to use it for smaller projects as it is more complex. Instead of using it, you might as well just install it and then have BeautifulSoup use it as its parser, and that makes BeautifulSoup even faster.

Pros & Cons of Lxml

Pros:

• The speed of processing and parsing is fast
• Supports the use of XPATH
• Offer some advanced features
• Support serialization of XML and HTML back to a string

Cons:

• Does not have support for CSS selectors
• Difficult to master

Learning Material for Lxml

As stated earlier, the Lxml library is not used by many as BeautifulSoup is the preferred option — you can still use lxml in BeautifulSoup. However, complex projects and those that have speed requirements will do well using lxlm. To learn how to make use of this, visit the official Lxml website to learn more.

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5. Scrapy — Best Web Scraping Framework

Scrapy is a different tool compared to the other tools mentioned above. Unlike the other 3 mentioned above, which are libraries meant for a specific task in the web scraping workflow, Scrapy is a full-fledged web scraping framework that has been developed to enable Python developers to take on and handle complex web scraping and crawling projects.

It comes with support for both accessing web pages as well as crawling, but quite opinionated on how you make use of it. One thing you will come to like about this web scraping project is its speed which is unrivalled by any other framework or libraries available to Python programmers. However, it can be quite difficult to learn, which makes most beginners avoid it.

But if you want to develop a complex web crawler, Python and BeautifulSoup can’t be difficult to manage and maintain — Scrapy is the answer here. Aside from being fast and powerful, it also does comes with support for the extension. You can develop a plugin to extend the functionality of Scrapy without impacting the core Scrapy code functionality in any way.

Pros & Cons of Scrapy

Pros:

• Perfect for complex projects
• Fastest web scraping tool in Python
• Advanced features with support for extension
• Easy to maintain even for complex projects

Cons:

• The learning curve is steep
• Does not support scraping content that requires Javascript execution or rendering to display.

Learning Material for Scrapy

Scrapy is a third-party tool, and its developers provide comprehensive documentation for the Scrapy framework. You can read the official documentation of Scrapy to learn how to make use of this framework for web scraping.

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6. Selenium — Best Browser Automation Tool for Web Scraping

Selenium is in a class of its own. This is a web driver that is used to automate web browsers, including PhantomJS, Chrome, and Firefox, among others. It does have support for a good number of programming languages, including Python. This tool is the tool for you if you want to scrape data from web pages that require Javascript execution and rendering.

If your target site depends heavily on Javascript to render content, then the likes of Scrapy, Requests, and BeautifulSoup will not help you as they are meant for the traditional web where you send requests and get responses. For a web page that depends on AJAX and Javascript, you need a tool that renders javascript and only a browser will do that for you.

Selenium will automate a browser to access the web page for you, and then you can use the Selenium API to access the data point of your choice. You can use Selenium in a headless mode, which hides the browser interface. This is done in production. While writing your script, you will use turn off headless mode so you can see the browser window open and do what it is instructed to do.

Pros & Cons of Selenium

Pros:

• Ability to scrape Javascript pages, including Single Page Applications (SAP).
• Help in avoiding blocks from pages with browser fingerprint abilities.
• Mimic human actions like mouse click and scrolling.

Cons:

• Speed is slow compared to the other tools.
• Difficult to set up and use.
• The learning curve for selenium is steep.

Learning Material for Selenium

If you want to learn how to make use of the Selenium web driver to automate browsers and scrape Javascript-heavy pages, read the Selenium documentation on ReadTheDocs.

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7. MechanicalSoup — Best for Automating Browser Interaction

MechanicalSoup is an automation tool meant for automation browser integration with websites. You can also make use of it to scrape data from the Internet. Unlike Selenium, which makes use of an actual browser to access a page, MechanicalSoup uses Requests (the HTTP library mentioned above) to access a website.

And because Requests isn’t built for accessing JavaScript-heavy pages, you can’t use MechanicalSoup for scraping data from websites that require Javascript actions. While it uses Requests for accessing a web page, it uses BeautifulSoup to parse and extract the needed data. In essence, it uses two of the popular Python web scraping library.

This tool can follow redirects, store cookies, and other session details, as well as submit forms. It was built to take the place of Mechanize, a popular web scraping tool for Python 2. Generally, I will recommend MechanicalSoup for simple projects, especially the ones that require you to interact with forms.

Pros & Cons of MechanicalSoup

Pros:

• Simple and easy to use
• Comes with both a parser and an HTTP library
• Good for interacting with form elements

Cons:

• Doesn’t support scraping of Javascript web pages

Learning Material for MechanicalSoup

The MechanicalSoup official website is one of the best places to learn about the MechanicalSoup library and how you can make use of it. It contains a comprehensive tutorial and documentation of the library.

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FAQs

Q. Which Python Libraries are Used for Web Scraping?

There are a good number of libraries for web scraping in Python. The one you make use of depends on your skills, requirements for the project, and your personal preference. Requests and BeautifulSoup are the de facto web scraping libraries for Python.

However, they fail when it comes to scraping sites that require JavaScript rendering and execution. If that is what you need, then Selenium, a web driver for automating browsers, is the option for you. Scrapy is also a good option and meant for big projects — it is also very fast too.

Q. Which is Better Scrapy or BeautifulSoup?

Both Scrapy and BeautifulSoup are all good and popular as far as web scraping in Python is concerned. And to be frank with you, I wouldn’t say one is better than the other — they both have their strengths and weaknesses.

Scrapy is a full-fledged framework for web scraping and is used mostly for big projects. It is also difficult to use. On the other hand, BeautifulSoup is just a data extraction library and needs Requests or its alternative before you are able to web scrape. Generally, BeautifulSoup is used mostly for small to mid-scale projects.

Q. How to Install Web Scraping Libraries in Python

Python comes with a standard library that contains some libraries for web scraping. However, you will most likely not use them. Most web scrapers use third-party libraries as they make the whole task easier. These third-party libraries can be installed the same way you install other libraries.

For all of the libraries mentioned above, you can use the pip command to get it done. However, there are some, such as Selenium, that require you to carry out other tasks in order to get them to work. For each of the libraries mentioned above, I provided a URL to their learning materials. You can use that to know how to install them.

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Conclusion

The list above contains the top and most popular Python web scraping libraries. Aside from the above, there are other tools that you can use as a Python developer for coding a web scraper. There is actually no best tool as far as web scraping is concerned. All of the libraries and frameworks for web scraping in Python have their strengths and weaknesses. The Javascript rendering and execution capability of Selenium that Requests lacks does not make it bad. The request itself is faster and easier to use, and Selenium isn’t too. So it is a thing of what you need and the library that can help you get it done.

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