In web scraping and data extraction tasks, it is important to ensure that the retrieved data is valid and relevant to the desired purpose. One way to achieve this is by performing data validation or data integrity checks. In this article, we will explore how to perform data validation using the requests-html library in Python.

What is Requests-HTML?

Requests-HTML is a Python library that allows us to easily scrape and parse HTML web pages. It is built on top of the requests library and provides a convenient interface for interacting with HTML content. With requests-html, we can extract data from websites, perform web scraping, and even render JavaScript-based web pages.

Data Validation with Requests-HTML

To perform data validation with requests-html, we will use the HTML parsing capabilities provided by the library. The basic idea is to retrieve the desired HTML content from a web page and then validate specific elements or data points within that content.

Let’s consider a scenario where we want to scrape a website that lists books and validate the book titles. Here’s an example code snippet that demonstrates how to accomplish this:

from requests_html import HTMLSession

# Create an HTML session
session = HTMLSession()

# Send a GET request to the website
response = session.get('https://example.com/books')

# Parse the HTML content
response.html.render()

# Extract and validate book titles
book_titles = response.html.find('.book-title')
valid_titles = []

# Iterate through each book title
for title in book_titles:
    # Perform data validation
    if is_valid(title.text):
        valid_titles.append(title.text)

# Print the valid book titles
for title in valid_titles:
    print(title)

In the above code, we first create an HTMLSession object to establish a session for sending requests. We then use the get method to send a GET request to the desired website.

After retrieving the HTML content, we render the page using the render method, which executes any JavaScript present on the page. This step is necessary if the website relies on JavaScript to load or display content.

Next, we use the find method to locate all the elements with the CSS selector .book-title, which represents the book titles on the website. We then iterate through each title, calling a custom is_valid function to perform the data validation. If the title passes validation, we add it to the valid_titles list.

Finally, we print out the valid book titles.

Conclusion

Performing data validation is an essential step in ensuring the integrity and reliability of the extracted data. By using the requests-html library, we can easily scrape web pages and perform data validation on specific elements within the HTML content. This allows us to filter out irrelevant or erroneous data and extract only the desired information.

Remember to adapt the code snippets to your specific use case and requirements. Happy scraping and data validation!