During the formative years of the World Wide Web (WWW), a service that enables the access of information over the internet, static HTML pages were the order of the day. This was sometime in the early 1990s. Since then, however, a lot has changed, with the introduction of new server-side processing technologies, programming languages, and frameworks giving rise to dynamic websites.
This evolution has had a few implications for the web scraping industry. In this article, we detail the challenges that have arisen and the different web scraping technologies that have been improved to deal with the impediments.
Static vs. Dynamic Websites
A static website is the most basic type of website, which means it’s the easiest to create. As the name suggests, it’s characterized by non-changing components and layouts. As a result, the website looks the same to everyone who accesses it, even the site administrator.
On the other hand, dynamic websites are characterized by content that’s fine-tuned to every visitor’s individual characteristics. These characteristics are generated from previous browsing history (e.g., shopping habits), location, local time, settings and preferences, and much more. This way, the dynamic sites make for an individualized and interactive experience.
Notably, if your browser doesn’t have a cache of a particular web page, the server will construct and render that particular web page every time it receives a request. And while it’s taxing to the server, it offers excellent benefits regarding Search Engine Optimization (SEO). This is because search engines can easily crawl and index the rendered web pages without individually rendering them.
Web Scraping Static and Dynamic Websites with Python
Python is an easy-to-learn language that boasts many web scraping libraries to ease extracting data from static and dynamic websites. Given the differences associated with static and dynamic websites, however, the approach to extracting data from each of them using Python is markedly different. However, in both cases, it’s important to use the Python web scraping library known as Requests, which sends HTTP/HTTPS requests to initiate the data collection process.
How to Scrape Static Websites with Python
Static websites are generally made up of HTML files. Thus, creating a web scraper that can extract data from such sites requires you to use parsing libraries such as Beautiful Soup and lxml. Beautiful Soup is designed to parse HTML data, while lxml is designed to parse data from either an HTML or XML file. Notably, both libraries can save the structured data in a JSON or CSV file.
How to Scrape Dynamic Websites with Python
The approach to extracting data from dynamic websites, particularly those that contain CSR scripts, differs from the procedure detailed above. For these websites, it’s important to use a Python web scraping library called Selenium.
In a bid to improve the user experience, web developers are increasingly creating dynamic websites. This can impact web scraping by making it more complicated. However, with Python web scraping libraries, you can easily scrape data from static and dynamic websites.