An Overview of Python Web Scraping Libraries
Get acquainted with the main Python web scraping libraries and find the best fit for your scraping project.
When it comes to web scraping, there are vast amounts of tools available for the job. And it can get confusing to find the right one for your project.
In this guide we’ll focus on Python web scraping libraries. You’ll find out which libraries excel in performance but work well only with static pages, and which can deal with dynamic content at the expense of speed.
Let’s look at the 5 most popular libraries in detail.
Python web scraping libraries are tools written in the Python programming language that control one or more aspects of the web scraping process – crawling, downloading the page, or parsing.
Web scraping libraries can be divided into two groups: 1) ones that require other tools to scrape, crawl or parse data and 2) standalone libraries. Although some libraries can function all alone, they’re often still used with others for a better scraping experience.
Since the Python programming language is preferred by many developers, you’ll find hundreds of guides on how to use a specific library. Check out Proxyway’s scraping knowledge base – you’ll find step-by-step tutorials that will help you develop your scraping skills.
The Best Python Web Scraping Libraries
The Requests library is Python’s standard for sending HTTP requests. Unlike other libraries, Requests is easy to use and often requires writing less code to extract data.
Requests is built on top of urllib3. However, developers prefer Requests over urllib3 because it aims for an easier-to-use API. Also, it supports the most common HTTP request methods, such as GET or POST.
The library has an in-built JSON decoder that can retrieve and decode JSON data. In simple words, with just a few lines of code, you can make a request, extract data, and get a JSON response.
Another benefit of Requests is that it can easily interact with APIs. This method is great for smaller projects since you’re directly connecting to the official API. The website simply gives you direct access to specific information.
Among all the functionalities, Requests comes with SSL verification, connection timeouts, and proxy integration. Furthermore, it supports custom headers that allow sending additional information to the server, passing parameters within URLs, detecting errors, and handling redirects.
Requests is easy to use and implement and offers extensive documentation, making it a popular choice for beginners.
Beautiful Soup is another popular Python-based parsing library that extracts information from HTML and XML pages. The way it works is pretty straightforward – Beautiful Soup selects the data points you need and returns the results in a structured format.
Beautiful Soup comes with a package of inbuilt HTML parsers – html.parser, HTML5lib, and lxml – so, you can try out different parsing approaches. Each has its advantages: you can use HTML5lib for flexibility or lxml for speed. And unlike Selenium, Beautiful Soup uses fewer resources, so you’ll need less computing power.
You can use Beautiful Soup to extract lists, paragraphs, or tables, to name a few. It’s a good tool for beginners or developers working on small to medium-sized projects. Beautiful Soup doesn’t have crawling capabilities, and you won’t be able to make GET requests, so you’ll need to install an HTTP client (such as the Requests library) that will fetch a page you want to scrape.
One of Beautiful Soup’s best features – it can automatically detect page encoding. Let’s say a page doesn’t declare encoding or it’s awfully written. With Beautiful Soup, you can get more accurate HTML results in easy-to-read format. Also, the bs4 module helps to navigate elements like links in the parsed page. That’s why Beautiful Soup is your best choice when working with broken pages.
Beautiful Soup is probably the easiest web scraping library to use. With just a few lines of code, you can build a basic scraper. Since it’s so popular, you can find extensive documentation and many discussions that can basically solve any issues you encounter using this library. If you want to pick up some skills, you can start by checking out our Beautiful Soup tutorials.
Another Python-based library used to parse XML and HTML documents. The library provides you with structured results. It has better performance rates than other libraries, but it’s also more likely to break.
lxml is a wrapper of two C libraries: libxml2 and libxalt. These two libraries make lxml greatly extensible; it combines features like speed, XML characteristics, and the simplicity of native Python API.
The key benefit of lxml is that it doesn’t use a lot of memory, making lxml very fast, especially when it comes to parsing large databases or documents. In addition, you can easily convert XML data to Python data types to simplify work with files.
Another advantage of this library is that it can fully implement XPath. This web scraping technique helps to identify elements from an XML document. It supports three schema languages which help to specify the XML structure.
A word of warning: lxml doesn’t work well when parsing poorly designed or broken HTML pages. However, if it fails to deliver results, lxml includes a fallback to Beautiful Soup.
Overall, it’s a good choice if you’re after speed. lxml is easy to set up, and it’s well-documented. But compared to Beautiful Soup or Requests, it’s more difficult to use.
Selenium is an open-source tool that makes it easily accessible to any user; you can find extensive documentation and consult with other community members on sites like StackOverflow.
The library controls a whole headless browser, so it requires more resources than other Python-based web scraping libraries. This makes Selenium significantly slower and more demanding compared to HTTP libraries. So, you should only use it when necessary.
Playwright can handle requests synchronously and asynchronously; it’s ideal for both small and large-scale scraping. Synchronous scrapers deal with a single request at a time, so this technique works well with smaller projects. And if you’re after multiple sites, you should stick to the asynchronous approach.
The library is capable of parsing since it runs a full browser. Unfortunately, this option isn’t ideal – the parser can easily break. If this is the case, use Beautiful Soup, which is more robust and faster.
|Best for||Small to medium-sized projects||Small to medium-sized projects||Continuous large-scale scraping projects||Small to medium-sized projects||Continuous large-scale scraping projects|
First, maintain your web scraper. Custom-built software is of high-maintenance and needs constant supervision. Since there are quite a few challenges when gathering data, each can impact your scraper’s work.
Also, scrape politely since smaller websites don’t usually monitor the traffic and can’t handle the load. Also, don’t scrape during the busiest hours. There are time intervals when millions of users connect and burden the servers. For you, it means slow speed and connection interruptions.