Скачать или смотреть How to Use pd.read_html() with Selenium to Scrape Dynamic Tables

Learn how to effectively use `pd.read_html()` to scrape dynamic tables from web pages using Selenium after interacting with page elements.
---
This video is based on the question https://stackoverflow.com/q/74210184/ asked by the user 'user19795989' ( https://stackoverflow.com/u/19795989/ ) and on the answer https://stackoverflow.com/a/74211475/ provided by the user 'Vitalizzare' ( https://stackoverflow.com/u/14909621/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: using pd.read_html to read current page

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Introduction: Scraping Dynamic Tables with Selenium

Scraping data from web pages can be a daunting task, especially when dealing with dynamic content. If you've ever encountered a situation where a table only appears after clicking certain buttons, you're not alone. Many users face challenges when attempting to extract such data using Python's Pandas library, particularly with the pd.read_html() function.

In this guide, we'll tackle the problem of scraping a dynamically generated table from a webpage utilizing Selenium for interaction and Pandas to read the HTML content into a DataFrame. By the end, you'll have a clear understanding of how to efficiently extract the data you need.

Understanding the Problem

When working with pd.read_html(), the function is designed to read HTML tables directly from a string or a URL. However, if a table doesn't exist when the page initially loads and requires user interaction (like clicking a button) to be displayed, pd.read_html() will not function as expected.

The challenge, in this case, is:

You need Selenium to first click buttons or perform actions that load the table.

Then, you want to capture the updated HTML of the page once the content is dynamically rendered.

Let’s delve into how we can solve this problem effectively.

Solution: Steps to Capture Updated Page Content

To successfully scrape the dynamic table from a web page after user interactions, we need to follow several steps. Here’s how you can accomplish this:

Step 1: Set Up Selenium

Make sure you have the necessary libraries installed:

[[See Video to Reveal this Text or Code Snippet]]

Import the libraries in your Python script:

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Navigate to the Web Page

You will need to specify the URL of the webpage you wish to scrape. Use Selenium to open the webpage.

[[See Video to Reveal this Text or Code Snippet]]

Step 3: Interact with the Page

Identify the button(s) you need to click to display the table. You can find it by inspecting the web page. Use Selenium to click the button:

[[See Video to Reveal this Text or Code Snippet]]

Step 4: Wait for the Table to Load

Depending on the website, you might need to wait for the table to load after clicking the button. You can incorporate a wait time using Selenium's WebDriverWait. For instance:

[[See Video to Reveal this Text or Code Snippet]]

Step 5: Capture the Page Source

Now that the page has been updated to include the table, use driver.page_source to grab the current HTML.

[[See Video to Reveal this Text or Code Snippet]]

Step 6: Use pd.read_html()

Finally, you can pass the updated HTML string to pd.read_html() to extract the table data into a DataFrame:

[[See Video to Reveal this Text or Code Snippet]]

You can now access the data as a Pandas DataFrame.

Full Example Code

Here’s the complete code for reference:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

By following these steps, you can effectively scrape tables that are dynamically generated on web pages using Selenium and Pandas. Always ensure to respect the website’s robots.txt file and scrape ethically. With practice, you'll become proficient in extracting data from even the most challenging sites.

Remember, adapting your scraping strategy based on the structure of the target website is key to success. Happy scraping!