Readers like you help support MUO. If you make a purchase through links on our site, we may receive an affiliate commission. Continue reading.
Want to learn web scraping with Python but not sure whether to use Beautiful Soup, Selenium, or Scrapy for your next project? While all of these Python libraries and frameworks are powerful on their own, they don’t meet all web scraping needs, and so it’s important to know which tool to use for a specific job.
Let’s take a look at the differences between Beautiful Soup, Scrapy, and Selenium so you can make a wise decision before starting your next Python web scraping project.
1. Ease of use
If you are a beginner, your first requirement would be a library that is easy to learn and use. Beautiful Soup gives you all the rudimentary tools you need to scrape the web, and is especially useful for people who have minimal Python experience but want to get started with web scraping.
The only caveat is that Beautiful Soup isn’t as strong as Scrapy or Selenium due to its simplicity. Programmers with development experience can easily master both Scrapy and Selenium, but building the first project can take a lot of time for beginners if they decide to use these frameworks instead of Beautiful Soup.
To scrape the title tag content on example.com with Beautiful Soup you would use the following code:
url = "https://example.com/"
res = requests.get(url).text
soup = BeautifulSoup(res, 'html.parser')
title = soup.find("title").text
To get similar results with selenium, you would write:
url = "https://example.com"
driver = webdriver.Chrome("path/to/chromedriver")
title = driver.find_element(By.TAG_NAME, "title").get_attribute('text')
The file structure of a Scrapy project consists of multiple files, which adds to its complexity. The following code removes the title from example.com:
name = 'title'
start_urls = ['https://example.com']
def parse(self, response):
If you want to extract data from a service that offers an official API, it might be a wise decision to use the API instead of developing a web scraper.
2. Scraping Speed and Parallelization
Of the three, Scrapy is the clear winner when it comes to speed. This is because it supports parallelization by default. With Scrapy, you can send multiple HTTP requests at once, and when the script has downloaded the HTML for the first set of requests, it’s ready to send another batch.
Beautiful Soup allows you to use the threading library to send concurrent HTTP requests, but that’s not convenient and you need to learn multithreading to do this. On Selenium it is impossible to achieve parallelization without launching multiple browser instances.
If you were to rank these three web scraping tools in terms of speed, Scrapy is the fastest, followed by Beautiful Soup and Selenium.
3. Storage Usage
Selenium is a browser automation API that has found its applications in the web scraping space. When you use Selenium to scrape a website, a headless browser instance is created that runs in the background. This makes Selenium a resource intensive tool compared to Beautiful Soup and Scrapy.
Since the latter work entirely in the command line, they consume less system resources and offer better performance than Selenium.
4. Dependency Requirements
Beautiful Soup is a collection of parsing tools that you can use to extract data from HTML and XML files. It comes with nothing else. You have to use libraries like Requests or holiday to make HTTP requests, integrated parsers to parse HTML/XML and additional libraries to implement proxies or database support.
Scrapy, on the other hand, brings all the stuff with it. You get tools to send requests, analyze the downloaded code, perform operations on the extracted data, and save the scraped information. You can add more functionality to Scrapy using extensions and middleware, but that comes later.
With Selenium, you download a web driver for the browser you want to automate. To implement other features like data storage and proxy support, you need third-party modules.
5. Documentation quality
Overall, each project documentation is well structured and describes each method using examples. However, the effectiveness of project documentation also depends heavily on the reader.
Beautiful Soup’s documentation is much better for beginners starting out with web scraping. Selenium and Scrapy undoubtedly have detailed documentation, but the technical jargon can surprise many newbies.
If you are familiar with programming concepts and terminology, then any of the three documentations would be a cinch to read through.
6. Support for extensions and middleware
Scrapy is the most extensible web scraping Python framework, period. It supports middleware, extensions, proxies and more, helping you to develop a crawler for large projects.
You can write foolproof and efficient crawlers by implementing middlewares in Scrapy, which are basically hooks that add custom functionality to the framework’s default mechanism. For example, the HttpErrorMiddleware handles HTTP errors so the spiders don’t have to worry about them when processing requests.
Middleware and extensions are exclusive to Scrapy, but you can achieve similar results with Beautiful Soup and Selenium using additional Python libraries.
You use a browser to load a website, interact with it with clicks and keystrokes, and when you have the content you need to scratch onto the screen, you extract it using Selenium’s CSS and XPath selectors.
Web scraping made easy with Python
Scrapy is a full-fledged web scraping framework for all your needs, whether you want to write a small crawler or a large scraper that repeatedly scans the web for updated data.
You can use Beautiful Soup if you are a beginner or need to develop a scraper quickly. Whatever framework or library you use, it’s easy to learn web scraping with Python.
This article was previously published on Source link