Web Scraping Inflation Rates with Python: Tutorial and Video
After conducting a poll both here and on other platforms, it was clear that using Python for web scraping was the most requested topic. I’ve created a blog post that includes a detailed video demonstrating the process.
I chose to focus on inflation rates as a relevant risk topic. This method is versatile and can be applied to any webpage that presents data in a tabular format. In the post, I utilized the pandas library’s read_html function to extract the data and also included instructions on how to export the results to Excel.
Check out the tutorial here: Web Scraping Tables with Python.
Next, I plan to tackle a tutorial on conflicts of interest. If you have any questions or suggestions, feel free to reach out!
Don’t forget to connect with me on LinkedIn to stay updated on my auditing with Python posts: Connect on LinkedIn.
One response
That sounds like a fantastic project! Web scraping can be such a powerful tool for pulling in relevant data, and inflation rates are certainly a critical topic given today’s economic climate. I appreciate that you included a video to guide others through the process—it’s always helpful to see the steps in action.
Using
pandasfor web scraping is a great choice, especially with theread_htmlfunction making it easier to extract table data. I’m looking forward to seeing how you tackle the conflict of interest tutorial next; it’s a very important subject!If you’re open to suggestions, consider discussing best practices for web scraping ethics and handling different website structures, which could be valuable for viewers.
I’ll be sure to follow you on LinkedIn for updates. Keep up the great work!