In contrast to dynamic websites, we can observe static websites containing all the requested content on the page load.
A great example of a static website is
The whole content of this website is loaded as a plain HTML while the initial page load.
To demonstrate the basic idea of a dynamic website, we can create a web page that contains dynamically rendered text. It will not include any request to get information, just a render of a different HTML after the page load:
All we have here is an HTML file with a single
<div> in the body that contains text -
To prove this, let's open this page in the browser and observe a dynamically replaced text:
BeautifulSoup is one of the most popular Python libraries across the Internet for HTML parsing. Almost 80% of web scraping Python tutorials use this library to extract required content from the HTML.
Let's use BeautifulSoup for extracting the text inside
<div> from our sample above.
This code snippet uses
os library to open our test HTML file (
test.html) from the local directory and creates an instance of the BeautifulSoup library stored in
soup variable. Using the
soup we find the tag with id
test and extracts text from it.
In the screenshot from the first article part, we've seen that the content of the test page is
I ❤️ ScrapingAnt, but the code snippet output is the following:
We need the HTML to be run in a browser to see the correct values and then be able to capture those values programmatically.
Selenium is one of the most popular web browser automation tools for Python. It allows communication with different web browsers by using a special connector - a webdriver.
Selenium instantiating and scraping flow is the following:
- define and setup Chrome path variable
- define and setup Chrome webdriver path variable
- define browser launch arguments (to use headless mode, proxy, etc.)
- instantiate a webdriver with defined above options
- load a webpage via instantiated webdriver
In the code perspective, it looks the following:
And finally, we'll receive the required result:
Selenium usage for dynamic website scraping with Python is not complicated and allows you to choose a specific browser with its version but consists of several moving components that should be maintained. The code itself contains some boilerplate parts like the setup of the browser, webdriver, etc.
I like to use Selenium for my web scraping project, but you can find easier ways to extract data from dynamic web pages below.
Puppeteer is a high-level API to control headless Chrome, so it allows you to automate actions you're doing manually with the browser: copy page's text, download images, save page as HTML, PDF, etc.
To install Pyppeteer you can execute the following command:
The usage of Pyppeteer for our needs is much simpler than Selenium:
I've tried to comment on every atomic part of the code for a better understanding. However, generally, we've just opened a browser page, loaded a local HTML file into it, and extracted the final rendered HTML for further BeautifulSoup processing.
As we can expect, the result is the following:
We did it again and not worried about finding, downloading, and connecting webdriver to a browser. Though, Pyppeteer looks abandoned and not properly maintained. This situation may change in the nearest future, but I'd suggest looking at the more powerful library.
The API is almost the same as for Pyppeteer, but have sync and async version both.
Installation is simple as always:
Let's rewrite the previous example using Playwright.
As a good tradition, we can observe our beloved output:
We've gone through several different data extraction methods with Python, but is there any more straightforward way to implement this job? How can we scale our solution and scrape data with several threads?
Meet the web scraping API!
Usage of web scraping API is the simplest option and requires only basic programming skills.
You do not need to maintain the browser, library, proxies, webdrivers, or every other aspect of web scraper and focus on the most exciting part of the work - data analysis.
As the web scraping API runs on the cloud servers, we have to serve our file somewhere to test it. I've created a repository with a single file: https://github.com/kami4ka/dynamic-website-example/blob/main/index.html
The final test URL to scrape a dynamic web data has a following look: https://kami4ka.github.io/dynamic-website-example/
The scraping code itself is the simplest one across all four described libraries. We'll use ScrapingAntClient library to access the web scraping API.
Let's install in first:
And use the installed library:
And the result is still the required one.
All the headless browser magic happens in the cloud, so you need to make an API call to get the result.
Check out the documentation for more info about ScrapingAnt API.
- Top 5 Popular Python Libraries for Web Scraping in 2020
- Web browser automation with Python and Playwright
- Selenium Documentation
- Pyppeteer Documentation
- Playwright Documentation
- ScrapingAnt Documentation
Happy web scraping, and don't forget to use proxies to avoid blocking 🚀