George Pornaras is the owner of Content.Voyage, a tech-focused marketing agency. He's currently the lead content marketer for the TEA Project, a startup that's pioneering secure decentralized computing on the blockchain.

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Oct 07 2021

Selenium vs. Beautiful Soup: A Full Comparison

The limitless amounts of data available online can be downloaded and analyzed in a variety of ways. Researchers can take disparate evidence pulled from multiple web sources and draw statistical conclusions. App developers can pull in commercial listings across different sites to help their users make better-informed buying decisions. Developers will often use an API like the one IMDB offers for movies. If an API isn't offered, there still might be CSV downloads like the ones Sports-Reference offers for sports stats


An API is the preferred way of piping information from outside sources as it cuts down on development time by simplifying data retrieval. It offers the recipient pre-structured data that's simple to sort into structured datasets. But what if a site doesn't give up its data easily? Developers who are not offered APIs or CSV downloads can still retrieve the information they need using tools like Beautiful Soup and Selenium. Both of these tools can scrape websites for relevant information, but choosing which one will be the most effective depends on the job. Let's take a closer look at both to see what applications they're best suited for.


Beautiful Soup, the Python Web Scraper

Beautiful Soup is a Python library built explicitly for scraping structured HTML and XML data. Python programmers using Beautiful Soup can ingest a web page's source code and filter through it to find whatever's needed. For example, it can discover HTML elements by ID or class name and output what's found for further processing or reformatting. Filtering a page through CSS selectors is a useful scraping strategy that this library unlocks.


Beautiful Soup requires other Python dependencies to function fully. For example, you'll need the requests library to get the HTML page source into your script before you can start parsing it. Beautiful Soup is very straightforward to get running and relatively simple to use. It's ideal for small projects where you know the structure of the web pages to parse.


Selenium, the Automated Tester That Also Scrapes Data

Selenium is a general-purpose web page rendering tool designed for automated testing. Think of it as a barebones web browser that executes JavaScript and renders HTML back to your script. Its wide support of popular programming languages means that programmers can choose whatever language they're most comfortable with. Python users can import the Selenium webdriver to begin automated scraping through a variety of locators:

  • ID
  • name
  • className
  • tagName
  • linkText
  • partialLinkText
  • CSS selector
  • XPath


Selenium is an excellent scraping option when a page needs to be loaded first before JavaScript can display the dynamic content. It's a flexible tool for edge cases where its automation engine can perform actions like click buttons and select dropdown menus. It's versatile enough to run across multiple browsers, operating systems, and even hardware devices like Blackberry and Android phones. This flexibility is a major draw of Selenium, along with the project's open-source nature that encourages plugin development.


Developers should keep in mind some drawbacks when using Selenium for their web scraping projects. The most noticeable disadvantage is that it's not as fast as Beautiful Soup's HTTPS requests. All web pages have to load first before Selenium jumps into action, and every Selenium command must first go through the JSON wire HTTP protocol. Bandwidth usage is high from loading full web pages, as is CPU usage from repeated JavaScript execution. And web scrapers should be aware that Selenium scripts can often break due to superficial frontend changes.


Comparing Selenium to Beautiful Soup

The choice between using these two scraping technologies will likely reflect the scope of the project. Selenium is at home scraping relatively more complex, dynamic pages at a price of higher computational resource cost. Beautiful Soup is easier to get started with, and although more limited in the websites it can scrape, it's ideal for smaller projects where the source pages are well structured.


Examining the differences between the two will help you decide which is more appropriate for your project.


Scraping Speed

Selenium waits for client-side technologies like JavaScript to load first, essentially waiting for the full page to load. Beautiful Soup is just scraping the page source, which enables faster scraping. 

Ease of Use

Selenium is running as a headless browser. It can function as a comprehensive web automation toolkit that simulates mouse clicks and fills out forms. All that power does mean it has a steeper learning curve for developers. Beautiful Soup can only meaningfully interact with less complex pages, but it's easier to use. A user can start scraping sites using Beautiful Soup with just a few lines of code. 

Scraping Flexibility

Selenium supports interacting with dynamic pages and content. This is both good and bad. Selenium can run in a wider range of scenarios, but superficial frontend website changes could derail scripts that Beautiful Soup can handle. 

Beautiful Soup is essentially limited to extracting data from static pages. But the simplicity is sometimes a benefit as it's more resilient against frontend-design changes as it only looks at the page source.


An Example Use Case Showcasing Both Selenium and Beautiful Soup

Selenium is flexible enough to do just about anything Beautiful Soup can. For example, Selenium can find many of the same structured elements that Beautiful Soup can by using driver.find_element_by_xpath. Even though Selenium is more flexible, it's still considered best practice to only use it where necessary to limit resource usage. We combine the best aspects of both in our code example.


To help you visualize your scraping strategy, it can be useful to use your browser's Developer Tools menu option to see the structure of the site you want to scrape. This view will reveal to you the website's document object model (DOM). Navigating through the DOM will allow you to pick out the HTML and XPath entities to target. 


Our hypothetical scraping target is a web page that loads dynamic content. Additionally, we'll want to interact with the web page before scraping it. Although dynamic content with automated interaction is right in Selenium's wheelhouse, we only want to use it to get the web page to display its source. Having Selenium hand off the actual parsing to Beautiful Soup after the desired page loads and the DOM is revealed allows us to limit resource usage.


# If necessary, install Selenium and Beautiful Soup
pip install bs4 selenium

# Load their libraries into your script
from bs4 import BeautifulSoup
from selenium import webdriver

# Open the page in a browser using Selenium
browser = webdriver.Firefox()

# Perform any required interactions to get full page to display in
the browser. Here we'll execute a search for "unit test"

browser.find_element_by_id("nav-search").send_keys("unit test")

# Now that we've interacted with the page, we can get the page source
HTML = browser.page_source

# The HTML is passed to Beautiful Soup and it parses the input for the search results
soup = BeautifulSoup(html)
for tag in soup.find_all('result'):
print tag.text


The bane of every web scraper is the variability inherent in the web. A scraping strategy that works for one site might not work for the next. And websites themselves can change, making your scripts error out on subsequent runs. These autonomous bots you build will still need regular maintenance. 


You can set up continuous integration to perform scraping tests that make sure your scripts run error-free. BlazeMeter offers automated testing with robust reports showing you how well your scripts performed in different scenarios. To get started with BlazeMeter, sign up for free today

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