Scraping Made Easy: How to Scrape Dynamic Websites with Python?

Web scraping is challenging—especially with dynamic websites. These sites display real-time updates and interactive features, giving users a better browsing experience. However, such qualities make it difficult for scrapers to collect data. 

The good thing is Python can help. This programming language allows you to create scripts for automated website control. With Python and its libraries, you can easily scrape data even from dynamic websites.

Keep reading to learn how to scrape a dynamic website with Python.

🔑 Key Takeaways

• Dynamic websites offer real-time, interactive content, while static websites have stable, unchanging content.
• Python is recommended for web scraping due to its simplicity and compatibility with various libraries. 
• Complex content, IP blocking, element detection, and slow performance can make dynamic web scraping more complex. 
• Consider JavaScript as an alternative for scraping highly interactive dynamic web pages.

What is a Dynamic Website? 

A dynamic website refers to a webpage collection with interactive content. This type of website displays real-time data or presents updates relevant to the user—like their location, age, language, and browsing activity. 

The most common examples of dynamic websites are social media and e-commerce platforms. Your Twitter feed immediately shows the latest posts of the accounts you follow. Also, the products you see on Amazon are usually based on your recent purchases and search history. 

Check out the photo below to see how Amazon updates its homepage to present results that match the season and holiday related to the user. 

Static vs. Dynamic Websites

Contrasting dynamic websites, there’s another type known as static websites. Unlike dynamic sites that are known for real-time data, static websites have stable content. Every user will see the same thing each time they access the site. Brochure and read-only sites are the typical static websites we see daily. 

Most static websites do not require excessive back-end processing. The content in the webpages is already pre-built with HTML and CSS, which means any static site will not take time to load what the user needs.

A static site is easier and cheaper to build, as you will need at least $1,000 to create a dynamic website of your own. However, dynamic websites are better in terms of user experience and functionality. Site visitors get more personalized and interactive browsing with dynamic websites. 

It is easier to scrape static websites since the content is constant, while the interactive nature of dynamic content makes scraping challenging. 

However, scrapers still enjoy extracting information from dynamic websites due to the valuable data that they possess. 

Before 

Requirements to Scrape Dynamic Websites

Knowing the right tools to use when scraping dynamic websites is crucial. Here are the things that you will need to do this task:

1. Code Editor

A code editor is where you’ll create a script to automate the scraping process. You can use any code editor, but Visual Studio Code and Sublime Text are highly recommended.

2. Python

Python is ideal for web scraping since it has a simple syntax that even beginners can understand. It is also compatible with most scraping libraries and modules.

3. Selenium

Selenium is a Python library best used for web scraping dynamic content. This module lets you automate browser actions, saving some of your time and effort. 

4. WebDriver

You will need a WebDriver for this task. This tool offers APIs allowing you to run commands to interact with your target dynamic site. 

With a WebDriver, you can load and edit the content for scraping. You can even transform your collected data into a more readable format. 

5. BeautifulSoup

BeautifulSoup is another Python library that parses HTML and XML. With Selenium, BeautifulSoup can parse and navigate the DOM structures of dynamic websites.

6. Proxy Server

Using a proxy while scraping is beneficial, especially when working with dynamic websites. Proxies mask your actual IP address by letting you use another. This lets you avoid potential IP blocking.

Once you have secured the prerequisites, you can start scraping web pages with Python. Find out how to do that in the next section. 

 7. Dynamic Web Scraping with Python Using Selenium

Whether a beginner or an expert, anyone can scrape dynamic web pages with Python using Selenium and BeautifulSoup. Follow the steps below: 

Step 1: Install the Selenium module for Python. You can use this command in your computer’s terminal or command prompt:

pip install selenium

Step 2: Download the executable file for WebDriver.

Step 3: In your code editor, create a Python file. Import the modules and create a new browser. 

Step 4: Put the path to your driver tool in the ‘<path-to-driver>.’

from selenium import webdriver

driver = webdriver.Chrome(executable_path= ‘<path-to-driver>’)

Step 5: Navigate to the website you want to scrape. Change ‘<websites-url>’ to the URL of the webpage that you want to scrape.

driver .get(‘<websites-url>’)

Step 6: Use the browser to locate things on the page. To find a table, you can use its HTML tag or one of its attributes.

For example, if a table has an id named “table-data,” find it through this command: 

table = driver.find-element-by-id(‘table-data’)

Step 7: Once you have located the table, you can start scraping the data. Use BeautifulSoup to read the data in the table. 

Install BeautifulSoup in your terminal or command prompt using this script:

pip install beautifulsoup

Step 8: Import the tool and parse the HTML of the table.

from bs4 import BeautifulSoup

soup = BeautifulSoup(table.get.attribute(‘outerHTML’), ‘html.parser’)

Step 9: Get the information from the table’s rows and cells.

rows = soup.find-all(‘tr’)

data = [ ]

for row in rows:

     cells = row.find-all( ‘td’ )

     row-data = [ ]

     for cell in cells:

           row-data.append(cell.text.strip())

     data.append(row-data)

Step 10: Print the information you have extracted.

for row-data in data:

      print(row-data)

Feel free to try the steps above with different dynamic content and websites. Check out the video below to better understand how the whole process works:

Challenges of Scraping Dynamic Websites

Besides the regular content shifts, here are the main challenges of scraping dynamic websites:

Complex Dynamic Content Scraping

Dynamic websites can only generate content after loading the page, making it difficult to scrape data. The information you need may be unavailable when you first load the content.

Potential IP Blocking from Websites

Websites use CAPTCHAs or block IP addresses to prevent excessive scraping. Some sites even enforce geo-blocking. Such safety measures can limit your ability to access content. 

Specific Elements Detection

Finding and scraping particular elements on dynamic websites can be challenging due to the constantly changing content.

Slow Performance

Web scraping dynamic content can be slow since you must wait for the website to render the information you intend to scrape. The process is further delayed when you work on huge datasets. 

Conclusion

Scraping data from dynamic websites can be challenging due to their interactive and real-time nature. However, Python helps to make it easier with its tools and libraries.

With coding skills, special tools, and some knowledge on website structures, you can scrape dynamic websites and collect real-time data.

FAQs

By Muninder Adavelli

Muninder Adavelli is a core team member and Digital Growth Strategist at Techjury. With a strong background in marketing and a deep understanding of technology's role in digital marketing, he brings immense value to the TechJury team.