The 7 Best Python Libraries and Tools for Web Scraping

There are several Python libraries and frameworks to extract data from the web. Everyone starts with a particular tool until they realize it might not be the best fit for their next project. Although it’s highly unlikely that you’ll use all the Python tools in a single project, you should know which ones to keep handy in your web scraping toolbox.

Here are the best Python libraries, frameworks, and other tools that will help you scrape data from the web, effortlessly.

1. Beautiful Soup

Starting off the list with the best web scraping library for beginners: Beautiful Soup. It’s essentially a tool that extracts data from parsed HTML and XML files by transforming the document into a Python object.

The “beauty” of Beautiful Soup lies in its simplicity. It’s easy to set up and you can get started with your first web scraping project within minutes. Beautiful Soup uses a hierarchical approach to extracting data from an HTML document. You can extract elements using tags, classes, IDs, names, and other HTML attributes.

Expecting more from Beautiful Soup would be taking it too far, though. There’s no built-in support for middlewares and other advanced functionalities such as proxy rotation or multi-threading. With Beautiful Soup, you need libraries to send HTTP requests, parse the downloaded document, and export the scraped information to an output file.

2. requests

requests is undoubtedly the most used Python library for handling HTTP requests. The tool stands up to its tagline: HTTP for Humans™. It supports multiple HTTP request types, ranging from GET and POST to PATCH and DELETE. Not only this, you can control almost every aspect of a request, including headers and responses.

If that sounds easy, rest assured as requests also caters to advanced users with its multitude of features. You can play around with a request and customize its headers, upload a file to a server using POST, and handle timeouts, redirects, and sessions, among other things.

requests is usually associated with Beautiful Soup when it comes to web scraping as other Python frameworks have built-in support for handling HTTP requests. To get the HTML for a web page, you’d use requests to send a GET request to the server, then extract the text data from the response and pass it on to Beautiful Soup.

3. Scrapy

As the name suggests, Scrapy is a Python framework for developing large-scale web scrapers. It’s the swiss-army-knife to extract data from the web. Scrapy handles everything from sending requests and implementing proxies to data extraction and export.

Unlike Beautiful Soup, the true power of Scrapy is its sophisticated mechanism. But don’t let that complexity intimidate you. Scrapy is the most efficient web scraping framework on this list, in terms of speed, efficiency, and features. It comes with selectors that let you select data from an HTML document using XPath or CSS elements.

An added advantage is the speed at which Scrapy sends requests and extracts the data. It sends and processes requests asynchronously, and this is what sets it apart from other web scraping tools.

Apart from the basic features, you also get support for middlewares, which is a framework of hooks that injects additional functionality to the default Scrapy mechanism. You can’t scrape JavaScript-driven websites with Scrapy out of the box, but you can use middlewares like scrapy-selenium, scrapy-splash, and scrapy-scrapingbee to implement that functionality into your project.

Finally, when you’re done extracting the data, you can export it in various file formats; CSV, JSON, and XML, to name a few.

Scrapy is one of the many reasons why Python is the best programming language for anyone into web scraping. Setting up your first Scrapy project can take some time, especially if you don’t have experience with Python classes and frameworks. Scrapy’s workflow is segregated into multiple files and for beginners, that might come off as unsolicited complexity.

4. Selenium

If you’re looking to scrape dynamic, JavaScript-rendered content, then Selenium is what you need. As a cross-platform web testing framework, Selenium helps you render HTML, CSS, and JavaScript and extract what’s required. You can also mimic real user interactions by hard-coding keyboard and mouse actions, which is a complete game-changer.

Selenium spawns a browser instance using the web driver and loads the page. Some popular browsers supported by Selenium are Google Chrome, Mozilla Firefox, Opera, Microsoft Edge, Apple Safari, and Internet Explorer. It employs CSS and XPath locators, similar to Scrapy selectors, to find and extract content from HTML elements on the page.

If you’re not experienced with Python but know other programming languages, you can use Selenium with C#, JavaScript, PHP, Perl, Ruby, and Java.

The only limitation is since Selenium launches a web browser in the background, the resources required to execute the scraper increase significantly, in comparison to Scrapy or Beautiful Soup. But given the additional features Selenium brings to the table, it’s completely justified.

5. urllib

The Python urllib library is a simple yet essential tool to have in your web scraping arsenal. It lets you handle and process URLs in your Python scripts.

An apt practical application of urllib is URL modification. Consider you’re scraping a website with multiple pages and need to modify a part of the URL to get to the next page.

urllib can help you parse the URL and divide it into multiple parts, which you can then modify and unparse to create a new URL. While using a library to parse strings might seem like an overkill, urllib is a lifesaver for people who code web scrapers for fun and don’t want to get into the nitty gritty of data structures.

Also, if you want to examine a website’s robots.txt, which is a text file containing access rules for the Google crawler and other scrapers, urllib can help you with that too. It’s recommended that you follow a website’s robots.txt and only scrape the pages that are allowed.

6. JSON, CSV, and XML Libraries

Since Beautiful Soup or Selenium don’t have built-in features to export the data, you’d need a Python library to export the data into a JSON, CSV, or XML file. Luckily, there are a plethora of libraries you can do to achieve this, and the most basic ones are recommended, namely json, csv, and xml for JSON, CSV, and XML files, respectively.

Such libraries allow you to create a file, add data to it, and then finally, export the file to your local storage or remote server.

7. MechanicalSoup

MechanicalSoup? Is this a cheap Beautiful Soup ripoff? No. Inspired by Mechanize and based on Python requests and Beautiful Soup, MechanicalSoup helps you automate human behavior and extract data from a web page. You can consider it halfway between Beautiful Soup and Selenium. The only catch? It doesn’t handle JavaScript.

While the names are similar, MechanicalSoup’s syntax and workflow are extremely different. You create a browser session using MechanicalSoup and when the page is downloaded, you use Beautiful Soup’s methods like find() and find_all() to extract data from the HTML document.

Another impressive feature of MechanicalSoup is that it lets you fill out forms using a script. This is especially helpful when you need to enter something in a field (a search bar, for instance) to get to the page you want to scrape. MechanicalSoup’s request handling is magnificent as it can automatically handle redirects and follow links on a page, saving you the effort of manually coding a section to do that.

Since it’s based on Beautiful Soup, there’s a significant overlap in the drawbacks of both these libraries. For example, no built-in method to handle data output, proxy rotation, and JavaScript rendering. The only Beautiful Soup issue MechanicalSoup has remedied is support for handling requests, which has been solved by coding a wrapper for the Python requests library.

Web Scraping in Python Made Easier

Python is a powerful programming language for scraping the web, no doubt, but the tools used are only part of the problem. The most prominent issue people face when coding a scraper is learning HTML document hierarchy.

Understanding the structure of a web page and knowing how to locate an element quickly is a must if you want to develop advanced web scrapers.

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