Web scraping is today’s best method to extract, organize, and present data from any website, such as product details, sports stats, stock market information, and more.
While some developers and marketers prefer extracting data manually, others use automated tools, the best of which are Python-based. Put simply, you press a few buttons, and the web scraper collects every bit of data and exports it into a common format.
Let’s check out the seven best Python libraries used for web scraping today!
Top Python Libraries Used for Web Scraping
Do you need to generate leads, compare shopping sites, or get real estate listings? You can easily do all that and more with the following Python web scraping libraries, which offer a range of features and capabilities for specific scraping preferences.
1. Beautiful Soup
Most top Python developers prefer using Beautiful Soup for scraping websites as it parses HTML and XML documents into a tree structure and offers a simple Pythonic interface that lets you organize and extract all the data you need.
BeautifulSoup supports various parsers that are perfect for your first web scraping project. You can set them up within minutes and have them process all the data you have collected. Afterward, you can easily explore and alter the parse tree too!
Its encoding detector, which yields much better output, and its robust documentation and community support are also some of its best features.
However, before getting to the point of organizing your scraped data, you have to be able to extract it easily. That’s where Requests comes in—helps you generate multiple HTTP requests, which is the first step in the web scraping process, and supports all kinds of HTTP requests, including GET, POST, PATCH, and DELETE.
Requests supports authentication modules, handles separate sessions and cookies, and reduces the need to enter your query strings in your URLs manually. You can even control every aspect of your requests, including the headers and responses.
However, since it lacks the capability to process the obtained data further, Requests is typically paired with Beautiful Soup to complete the web scraping process fully.
As another Python library for processing and organizing XML and HTML data, lxml is a combination of libxml2 and libxsit (C libraries) and the Python API. Therefore, you are effectively getting Python’s simplicity and the processing speed of C.
In addition to being rather quick to parse complex documents, lxml also lets you manipulate your files efficiently since you can easily convert them to Python data types. You can even generate various XML and HTML elements and their children.
The fast and efficient lxml parsing engine, coupled with its data conversion feature, makes this library suitable for handling large-scale data scraping tasks. However, it must be noted that it does not work well when crawling through ill-designed HTML elements.
If you are looking for the complete package that requests URLs, gets the data, and successfully parses it, then urllib3 is the choice for you—over 165 million users think so, at least.
Thanks to all the different modules it contains, urllib3 helps you quickly extract information from any HTML document and URL via several protocols. The best part is that it allows you to use a proxy server to access HTTP and SOCKS5 destinations.
Another big urllib3 advantage is the inclusion of a PoolManager feature that tracks your pooled connections and thread safety, which is helpful when submitting future requests. On the other hand, it’s not exactly feature-rich and comes with cumbersome documentation.
However, to work with Selenium, you must integrate it with Python via APIs and use its web driver with any major browser. The typical actions you can do with this advanced library include submitting forms, automatic logins, adding data, and handling alerts.
With the web driver, Selenium opens a browser instance and uses CSS and XPath locators to find and get the data you need from all elements on the page. It can even capture screenshots and headless browser testing for faster processing.
This open-source web scraping Python framework uses all the common selectors to pull data from HTML and XML sources quickly. Moreover, it has a telnet console that can monitor and debug your crawler and supports additional extensions and middlewares.
What makes Scrapy one of the best Python libraries for web scraping, though? Well, it supports HTTP proxy servers, saves your data in all kinds of file formats, from CSV to JSON and XML, and even implements a robust encoder for non-standard declarations.
So, if you are looking for the Swiss army knife of web scraper libraries, Scrapy is probably the best choice since it has all the speed, efficiency, and features you may need.
Why Should You Use These Python Libraries?
There are numerous reasons why these Python libraries are the ideal tools for building web scrapers, including but not limited to the following:
- Reduced development time—web scraping is meant to save you time getting the data you need, so you shouldn’t waste time developing a web scraper from scratch, which is where these Python web scraping libraries come in;
- Simple to learn and understand—not to mention that Python is one of the simplest coding languages to grasp and master, as you can clearly read its indented syntax as if you would a statement in English;
- Vast resources and community support—we all need assistance in our ventures, and you’d be glad to know Python is supported by one of the largest software communities in the world, so you know you’ll find the answer sooner or later;
- Flexible and reusable code—your Python web scraper can be tuned to do more than just extract data—you parse, import, and visualize it—and you only have to execute your code once to have it automatically scrape data each day.
In addition to all of the above, Python also comes with a huge library of tools and services that will further help you in your web data retrieval projects.
Before You Go
Python is an excellent programming language for developing web scraping software as it includes native libraries designed for that purpose exactly. All of the ones listed above are versatile, functional, and easy to understand. Best of all, they help you extract your data in very comprehensive formats that even a layperson could read.