Python Tools and Libraries for Web Scraping

In the era of machine learning and big data, searching for publicly available data on the Internet has become popular.

However, if you type 'how to create a scraper in python' into a search engine, you will get various answers on how best to develop a python scraping project.

To help resolve some of the confusion about scraping tools, in this guide we will compare four of the most common open-source python libraries and frameworks used for scraping, so you can decide which option is best for your scraping project.

• Requests
• BeautifulSoup
• Selenium
• Scrapy

Requests

Some of them are libraries that can solve specific tasks within scraping. However, other solutions like Scrapy are full-fledged scraping platforms designed specifically for scraping.

Requests is a python library designed to simplify the process of making HTTP requests. This is very important for scraping, since the first step in any scraping workflow is sending an HTTP request to the site's server to get the data displayed on the target web page.

The out-of-the-box Python comes with two built-in modules, urllib and urllib2, intended for handling HTTP requests. However, most developers prefer to use the Requests library over urllib or urllib2 because often both urllib and urllib2 need to be used together, and the documentation can be confusing, often requiring developers to write a lot of code even for a simple HTTP request.

Using the Requests library is well suited for the first part of the python scraping process (fetching web page data). However, to create a fully functional scraping crawler, you will need to write your own scheduling and parallelization logic, and use other python libraries such as BeautifulSoup to implement other aspects of the scraping process, which brings us to the next scraping library we'll discuss.

BeautifulSoup

Unlike Requests, BeautifulSoup is a python library designed for data parsing, i.e., extracting data from HTML or XML documents. To better understand how to organize this process, we recommend checking out the step-by-step guide to designing a web scraping solution.

Since BeautifulSoup can only parse data and cannot fetch web pages themselves, it is often used together with the Requests library. In such situations, Requests makes an HTTP request to the website to get the web page, and after receiving it, BeautifulSoup can be used to parse the target data from the HTML page.

One of the big advantages of using BeautifulSoup is its simplicity and ability to automate some repetitive parts of data parsing during scraping. With just a few lines of code, you can set up BeautifulSoup to navigate the entire parsed document and find all instances of the data you need (e.g., find all links in the document) or automatically detect encodings such as special characters.

Selenium

Selenium is another library that can be useful when searching for information on the Internet.

Unlike other libraries, Selenium was not originally intended for scraping.

First and foremost, Selenium is a web driver designed to display web pages like your browser, for the purpose of automated testing of web applications.

This functionality is useful for scraping, because many modern web pages use JavaScript to dynamically populate the page. The problem this creates for regular crawlers is that most of them do not execute this JavaScript code. This prevents them from accessing all available data, limiting their ability to extract all available data.

In contrast, when a crawler built with Selenium visits a page, it first executes all the JavaScript available on the page before providing it to the parser for data parsing. The advantage of this approach is that it allows scraping data that is not available without JS or a full browser. However, the scraping process is much slower than a simple HTTP request to a web browser, because the crawler will execute all scripts present on the web page.

If speed is not very important or the scale of scraping is small, then using Selenium for scraping will work, but it is not ideal. However, if speed is crucial or you plan large-scale scraping, then executing JavaScript on every visited page will be completely impractical. In such cases, it is worth considering professional scraping of websites and marketplaces.

We have looked at each of the main python libraries used for searching information on the Internet. As you can see, each was designed to perform one aspect of the scraping process, resulting in having to combine multiple libraries to create a fully functional scraping crawler.

However, there is a simpler approach – use a specifically designed scraping framework like Scrapy, which includes all the core components for building a scraper out of the box and has a huge set of plugins designed to handle non-standard situations.

Scrapy

Scrapy is an open-source python framework created specifically for scraping by Scrapinghub co-founders Pablo Hoffman and Shane Evans. You may ask yourself: 'What does that mean?'

It means that Scrapy is a full-fledged scraping solution that takes most of the work out of building and configuring your crawlers, and most importantly, it easily handles non-standard situations that you probably haven't thought of yet.

Within minutes of installing the framework, you can have a fully functional crawler running on the Internet.

Out of the box, Scrapy crawlers are designed to download HTML, parse and process data, and save it in CSV, JSON, or XML formats.

Additionally, there is a wide range of built-in extensions and middleware designed to handle cookies and sessions, as well as HTTP features like compression, authentication, caching, user-agents, robots.txt, and crawl depth limitation. Scrapy is also very easy to extend by developing custom middleware or pipelines for your scraping projects. To create a robust infrastructure, professional software development may be needed.

One of the biggest advantages of using the Scrapy framework is that it is built on Twisted, an asynchronous networking library.

This means that Scrapy spiders do not need to wait to make requests one by one.

Instead, they can make multiple HTTP requests in parallel and parse data as it is returned by the server.

This greatly increases the speed and efficiency of the crawler.

One minor drawback of Scrapy is that it does not handle JavaScript right out of the box like Selenium.

However, the Scrapinghub team created Splash, an easy-to-integrate, lightweight, headless browser specifically designed for scraping.

The learning curve for Scrapy is slightly steeper than, for instance, learning to use BeautifulSoup.

However, the Scrapy project has excellent documentation and an extremely active developer ecosystem on GitHub and StackOverflow, constantly releasing new plugins and helping you troubleshoot issues.

Comparison of Libraries and Frameworks for Web Scraping

To help you understand the difference between various scraping libraries and frameworks, we have created a simple comparison table.

Which scraping software is the best?

So, we've covered some of the most popular python libraries and frameworks for scraping, but which one is best for your specific project?

There is no one-size-fits-all answer, as it depends on the scale and volume of your scraping project.

However, as a general recommendation, we would suggest the following:

Small one-off scraping tasks (Up to 1,000 pages)

If you need to collect a small amount of data for a one-time project, using a combination of BeautifulSoup and Requests (possibly Selenium if you need JavaScript) may be the fastest way to get the data you need, if you don't already have experience with Scrapy.

However, if there's a chance that this scraper will grow or you'll need to write more crawlers in the future, it's better to use Scrapy.

Recurring or large-scale scraping projects

However, if your scraping needs are something more substantial than a one-time data extraction task, you should seriously consider using the Scrapy framework.

Scrapy was designed as a comprehensive scraping solution (and continues to improve), so it's the best option if you want to build a powerful and flexible scraper.

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ESK Solutions has been working in the field of web scraping for 5 years. During this time, we have gained extensive experience and knowledge in data extraction using python and beyond.