Beautiful Soup is a Python library designed for parsing HTML and XML documents. It creates parse trees that make it straightforward to extract data from HTML documents you’ve scraped from the internet. Beautiful Soup is a useful tool in your web scraping toolkit, allowing you to conveniently extract specific information from HTML, even from complex static websites.
In this tutorial, you’ll learn how to build a web scraper using Beautiful Soup along with the Requests library to scrape and parse job listings from a static website.
Static websites provide consistent HTML content, while dynamic sites may require handling JavaScript. For dynamic websites, you’ll need to incorporate additional tools that can execute JavaScript, such as Scrapy or Selenium.
By the end of this tutorial, you’ll understand that:
- You can use Beautiful Soup for parsing HTML and XML documents to extract data from web pages.
- Beautiful Soup is named after a song in Alice’s Adventures in Wonderland by Lewis Carroll, based on its ability to tackle poorly structured HTML known as tag soup.
- You’ll often use Beautiful Soup in your web scraping pipeline when scraping static content, while you’ll need additional tools such as Selenium to handle dynamic, JavaScript-rendered pages.
- Using Beautiful Soup is legal because you only use it for parsing documents. Web scraping in general is also legal if you respect a website’s terms of service and copyright laws.
Working through this project will give you the knowledge and tools that you need to scrape any static website out there on the World Wide Web.
If you like learning with hands-on examples and have a basic understanding of Python and HTML, then this tutorial is for you!
You can download the project source code by clicking on the link below:
Take the Quiz: Test your knowledge with our interactive “Beautiful Soup: Build a Web Scraper With Python” quiz. You’ll receive a score upon completion to help you track your learning progress:
Interactive Quiz
Beautiful Soup: Build a Web Scraper With Python
In this quiz, you'll test your understanding of web scraping using Python. By working through this quiz, you'll revisit how to inspect the HTML structure of a target site, decipher data encoded in URLs, and use Requests and Beautiful Soup for scraping and parsing data.
What Is Web Scraping?
Web scraping is the process of gathering information from the internet. Even copying and pasting the lyrics of your favorite song can be considered a form of web scraping! However, the term “web scraping” usually refers to a process that involves automation. While some websites don’t like it when automatic scrapers gather their data, which can lead to legal issues, others don’t mind it.
If you’re scraping a page respectfully for educational purposes, then you’re unlikely to have any problems. Still, it’s a good idea to do some research on your own to make sure you’re not violating any Terms of Service before you start a large-scale web scraping project.
Reasons for Automated Web Scraping
Say that you like to surf—both in the ocean and online—and you’re looking for employment. It’s clear that you’re not interested in just any job. With a surfer’s mindset, you’re waiting for the perfect opportunity to roll your way!
You know about a job site that offers precisely the kinds of jobs you want. Unfortunately, a new position only pops up once in a blue moon, and the site doesn’t provide an email notification service. You consider checking up on it every day, but that doesn’t sound like the most fun and productive way to spend your time. You’d rather be outside surfing real-life waves!
Thankfully, Python offers a way to apply your surfer’s mindset. Instead of having to check the job site every day, you can use Python to help automate the repetitive parts of your job search. With automated web scraping, you can write the code once, and it’ll get the information that you need many times and from many pages.
Note: In contrast, when you try to get information manually, you might spend a lot of time clicking, scrolling, and searching, especially if you need large amounts of data from websites that are regularly updated with new content. Manual web scraping can take a lot of time and be highly repetitive and error-prone.
There’s so much information on the internet, with new information constantly being added. You’ll probably be interested in some of that data, and much of it is out there for the taking. Whether you’re actually on the job hunt or just want to automatically download all the lyrics of your favorite artist, automated web scraping can help you accomplish your goals.
Challenges of Web Scraping
The internet has grown organically out of many sources. It combines many different technologies, styles, and personalities, and it continues to grow every day. In other words, the internet is a hot mess! Because of this, you’ll run into some challenges when scraping the web:
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Variety: Every website is different. While you’ll encounter general structures that repeat themselves, each website is unique and will need personal treatment if you want to extract the relevant information.
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Durability: Websites constantly change. Say you’ve built a shiny new web scraper that automatically cherry-picks what you want from your resource of interest. The first time you run your script, it works flawlessly. But when you run the same script a while later, you run into a discouraging and lengthy stack of tracebacks!
Unstable scripts are a realistic scenario because many websites are in active development. If a site’s structure changes, then your scraper might not be able to navigate the sitemap correctly or find the relevant information. The good news is that changes to websites are often small and incremental, so you’ll likely be able to update your scraper with minimal adjustments.
Still, keep in mind that the internet is dynamic and keeps on changing. Therefore, the scrapers you build will probably require maintenance. You can set up continuous integration to run scraping tests periodically to ensure that your main script doesn’t break without your knowledge.
An Alternative to Web Scraping: APIs
Some website providers offer application programming interfaces (APIs) that allow you to access their data in a predefined manner. With APIs, you can avoid parsing HTML. Instead, you can access the data directly using formats like JSON and XML. HTML is primarily a way to visually present content to users.
When you use an API, the data collection process is generally more stable than it is through web scraping. That’s because developers create APIs to be consumed by programs rather than by human eyes.
The front-end presentation of a site might change often, but a change in the website’s design doesn’t affect its API structure. The structure of an API is usually more permanent, which means it’s a more reliable source of the site’s data.
