Without wishing to offend any Python masters reading this, Python is usually the beginner's choice for web scraping. If you know the language but you’re new to web scraping, you may be wondering whether it’s the best option for whatever cunning data extraction plan you’re concocting. Let’s swiftly go through some of the pros and cons of web scraping in Python and find out which tools you can use for extracting data from the web.
What are the pros of web scraping in Python?
A vast Python community
One great advantage of Python is its enormous community. You’ll get plenty of support from developers, and there are video and blog tutorials aplenty on the world wide web. Python is by far the most used language for web scraping, and there’s a huge amount of documentation and guidance on how to use it for web data extraction.
The world of Python includes many post-processing and visualization tools, including but not limited to Pandas, Matplotlib, and Seaborn. Such tools are great for EDA (exploratory data analysis), data manipulation, and storing unstructured data in a structured format. That makes Python a great choice for adjusting scraped data to your needs.
One reason Python is such a popular language for data scientists is that Python’s packages make data pipelines so easy to create. This feature is no less useful for data acquired through web scraping. If you know Python, you can scrape something with libraries such as Requests and Beautiful Soup and then process it with Pandas and visualize it with Matplotlib.
What are the cons of web scraping in Python?
Scaling scraping tasks
When trying to scale up your scraping operations and run multiple tasks in parallel, other languages allow you to do that much easier than Python. To do it with Python, you need a library like Scrapy that manages the multiple threads for you. Whereas, with Node.js, for example, the built-in event loop allows you to run multiple scraping tasks in parallel easily and with superior performance.
The Python ecosystem contains some pretty powerful scraping tools. The six below are certainly among the most popular.
Requests is a library for Python that lets you easily send HTTP requests. You need to download the HTML with an HTTP client before you can parse it with something like Beautiful Soup or MechanicalSoup. With Requests, you don’t need to manually add query strings to your URLs or form-encode POST data. This tutorial on using Requests for web scraping will help you get started.
Scrapy is among the most popular tools for extracting data from websites. Scrapy is a full-fledged web scraping framework that can use the previously mentioned libraries to extract data from web pages. It allows you to scrape multiple pages in parallel and export the data into a format of your choice. You can learn how to use Scrapy here.
Did you know you can now deploy your Scrapy spiders to Apify? Learn more here.
That being said, given it was not designed for web scraping, it isn’t the most user-friendly option. If you’re new to web scraping, Beautiful Soup is much easier to set up and master. Another notable disadvantage of Selenium is that it isn’t ideal for large-scale extraction, as scraping large amounts of data with Selenium is a slow and inefficient process compared to other options out there.
Although it’s a fairly new library, Playwright is rapidly gaining popularity among developers due to its cross-browser and multi-language support, ease of use, and other cool modern features. While it’s primarily for controlling browsers rather than a web scraping tool, its versatility and auto-awaiting function make it a very popular choice for web scraping.