Many people in different fields have employed Python to do web scraping. The most common purposes for this are data science and mining large amounts of structured or unstructured information from the Internet, which can be difficult without appropriate software tools.
Python is excellent for web scraping because Python allows programmers to write a simple scraping script containing 1000 or more lines of code in 10 to 15 minutes. So you do not need to be a super experienced developer to do this. If you don’t know Python, read this guide to see why you should!
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Dynamic Front-End And Static Front End
Traditionally, static sites meant the website displayed the same content to each user. There was no user-specific database filtering. They were mostly HTML, CSS, and some JavaScript for responsiveness or reactivity. Although, nowadays, most websites are dynamic and serve specialized content for different users and allow the users to modify displayed information from an admin panel. However, the front-end where the information is displayed varies based on how it is built.
The front-end may be built using simple HTML/CSS and JS with the dynamic content controlled from the back end. But websites may also use a JavaScript framework on the front end to fetch data from the back end. Some famous front-end JavaScript frameworks include React, Angular, and Vuejs. A post or story on Facebook or Instagram is an example of one of these websites where the front-end is built using the ReactJS framework. On the other hand, dynamic Front-ends rely solely on JavaScript to control and manage data on the front end. The way dynamic front-end function under the hood is as follows:
- The user requests the front end, e.g., clicking the read more button.
- JavaScript captures the event and sends it to the backend server.
- The backend processes the request and serves the data.
- JavaScript, which is already waiting on the front-end (client-side, which is the browser), receives the data.
- JavaScript injects the data into HTML.
Dynamic front-ends
The approach to extracting data from dynamic front-ends may differ slightly from the approach for static front-ends. A well-known Go-To method involves using either the Selenium or Splash. These two technologies can automate the browser and mimic human behavior. In addition, Selenium is generally considered much easier to learn and use than Splash or other technology.
Static websites front-ends
Static websites act almost like a text file, i.e., they can be parsed and analyzed for relevant content. We can use almost any Python scraping package for websites with static front-ends, such as beautifulsoup4, scrappy, Selenium, and Splash. Of course, they are dependent on various factors, such as the scraper’s experience, the scope of the project, the client’s time and budget, and so on.
Best Python web scraping tool
There is a plethora of information available on the Internet to begin your Data Science project. It is possible to obtain that data by simply copying and pasting it. Still, web scraping is the best option for large amounts of data. This article will look at the three main web scraping tools in Python for your better understanding.
Beautiful Soup
It retrieves data from HTML and XML files. Furthermore, it is the simplest of the three alternatives to understand. Beautifulsoup can read HTML and XML files and extract data from them. Furthermore, it is the easiest of the three options to comprehend.
Beautifulsoup is a fast and reliable way of parsing a web page. However, you cannot use it for dynamic front-end websites. Thus you cannot use it on sites that use JavaScript. This type of scraping would require interacting with a webpage in a browser-like environment. Beautifulsoup only acts as an XML/HTML parser. It can not interact with the webpage or the contents of the page.
Selenium
Selenium was never destined to extract data. In reality, It is a kind of web driver designed to display web pages for automated web app testing. But Selenium is ideal for web scraping in websites that rely heavily on JavaScript to adjust website content dynamically. Other web data extraction tools, such as Beautifulsoup, lack these features, making data extraction from most websites difficult. In contrast, it is a helpful tool for allowing code to mimic human behavior, such as clicking a button, selecting navigation bar menus, maximizing window frames, etc. Selenium can be slow when trying to scrape a large amount of data, such as from an online shop.
Selenium is ideal for websites that use front-end JavaScript libraries like React, Vue, and Angular.
Scrapy and Scrapy-Splash
Scrapy is a Python-based open-source data mining framework explicitly designed for web scraping. It is built on Twisted, an adaptive network framework that allows application forms to adapt to changing network connections without relying on traditional fastener models.
One of Scrapy’s most significant advantages is its speed. Scrapy spiders do not need to queue for requests to be made one at a time because they are asynchronous and can create multiple requests simultaneously. In addition, scrapy increases performance by allowing its memory and CPU to be more useful when opposed to prior web scraping methods.
While scrapy also has the limitations of not being able to interact with a webpage, it overcomes this limitation by working with Splash, which provides a bowerlike environment to interact with the web page. However, both Splash and Scrapy have a learning curve and can take some time to master.
Wrapping Up
Beautifulsoup is ideal for beginners who want to get started with simple web scraping projects. Scrapy works, especially for large projects in which performance and bandwidth are critical. Scrapy, while having a steep learning curve, caters to the needs of a wide variety of projects. With its wide range of features and a gradual learning curve, Selenium can be an excellent tool for working with dynamic front-ends.
Useful Links
- Selenium python official documentation
- Scrapy official documentation
- Scrapy-Splash GitHub repo
- Splash official documentation
- BeautifulSoup documentation
