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Python Libraries for Web Scraping

Since you’re here, let’s assume you’re already planning on a web scraping or crawling project of your own. If you’ve decided that you want full control over your bot or just want to save money, you’ll need to program it yourself. Thankfully, you don’t have to do it completely from scratch. There are Python libraries for web scraping out there to help you.

These web scraping libraries are packages and modules designed for specific tasks. More precisely, they’re for things like: 

  • Making HTTP requests.
  • Handling cookies, authentication, sessions, timeouts, connection pooling, and proxies.
  • Managing headless browsers to interact with JavaScript while also replicating human-like behavior when navigating your target sites.
  • Parsing the results of the pages you’ve scraped.

In addition to libraries, there are also frameworks, such as Scrapy and pyspider, or you could do everything using Nimble. Web scraping frameworks are more robust tools that contain several functions, as opposed to libraries tending to have only one or two functions each. However, frameworks are more challenging to implement and are therefore generally only recommended for more complex projects.

I’ll be going over four libraries shortly. But first…

Why Python?

There are plenty of viable programming languages you could use to build your web scraper. However, Python is a hugely popular language for web scraping and its following requisite data parsing.

This is because Python:

  • Is very easy to learn while being widely distributed. It’s Stack Overflow’s 3rd most popular programming language according to their 2021 survey.
  • Uses much cleaner and more readily readable code than most other programming languages. With how often coders borrow segments of code from each other when working on large projects, this detail helps greatly!
  • Has a lot of multipurpose web scraping modules. Mastery of only one or two is enough for a wide array of projects.
  • Packages are easy to create, use, and distribute. Interaction between the different components of your project will play much more nicely with each other, even when it needs to be converted into other languages.
  • Lambda functions let you create and call them right where you need them, no need to declare every last thing right at the start of it all.
  • Smoothly handles adjacent necessary tasks when web scraping, such as downloading files, coordinating your proxies with your requests, handling strings and regular expressions, and exporting your results to your desired database.

That being said, let’s get to those libraries!

Python Web Scraping Libraries

As mentioned earlier, I’ll be covering four libraries. Let’s start with urllib, which is a package already included in Python’s standard libraries. From there we’ll move up the chain to Requests, then to BeautifulSoup, and then finally to Selenium.


Urllib has four modules for handling URL and HTTP requests. They are:

  • urllib.request to do HTTP requests.
  • urllib.error for dealing with the exceptions from urllib.request.
  • urllib.parse to parse URLs.
  • urllib.robotparser for, well, parsing robots.txt files.

Unfortunately, urllib isn’t easy to use directly. In exchange, it gives you fine control over your requests. Generally, it’s advised to use a higher-level library that handles more of the precision work for you unless you really need that granular level of control.

Since it’s a package already in Python’s standard libraries, there’s no need for you to install anything extra. You just need to import it into your code and it’s good to go. 

All of the following libraries require downloads and installation with a pip command before they’re usable.


The Requests library is built with urllib as its foundation. It makes it much easier to utilize urllib’s core functions with fewer lines of code on your part. There is in-depth documentation for Requests to help you with implementation.

It can handle:

  • International URLs
  • Session cookies
  • Verification
  • Authentication
  • Connection Pooling
  • Timeouts
  • Decompression and decoding
  • HTTP(S) proxy support
  • And more!


Regardless of its creative naming, BeautifulSoup is an open-source library for extracting data from HTML and XML that is very beginner-friendly. Even a complete Python programming newb can put it to use in a short time thanks to BeautifulSoup’s robust documentation.

The vast majority of web scraping programmers start with BeautifulSoup for their early HTML projects before they dip their toes into more complex frameworks like Scrapy.

While BeautifulSoup can handle multitasking, you’ll need to have mastery over Python’s multithread programming to do so. Another disadvantage of BeautifulSoup is its heavy dependence on other libraries in order to fully function.

To send out requests you’ll need to utilize urllib or Requests. For final parsing, you’ll need something such as html.parser, HTML5lib, ElementTree, or others.

Even needing to combine the other dependencies with BeautifulSoup, it is still overall one of the easiest to use tools out there.


Unlike other web scraping tools, Selenium wasn’t originally designed explicitly for scraping. Instead, it was made for automated web testing. It will use your selected browser, including headless, to send web requests while replicating normal user interactions. So, while that was originally intended for testing, you can see how it can also be utilized for scraping. 

One thing that makes Selenium really stand out is its ability to handle JavaScript, which most libraries can’t cope with. Even if you’re using BeautifulSoup for the bulk of your scraper, you may still call Selenium to deal with that pesky JavaScript when needed.

Selenium has different versions per different browsers, such as ChromeDriver for Google Chrome. If you’re using it for a web crawler, that’ll take some more Java modules. Maven will help you out in this regard.


As you can see, there is no single perfect tool to use when building a web scraper. For a simple project, BeautifulSoup is likely your best option. Once JavaScript comes into the picture, you’ll want to consider using Selenium.

For projects at any scale, python libraries for web scraping will give you the tools you need to make a capable web scraper. Just don’t forget that any bot you run needs to utilize a web scraping compatible proxy. Specifically, a rotating residential proxy to greatly reduce your chances of being blocked.