Title: Web Scraping Inflation Rates with Python (Includes Video Tutorial)
After conducting a poll on various platforms, it’s clear that using Python for web scraping is a highly sought-after topic. In my latest blog post, I’ve included a video demonstrating how to achieve this.
I chose to focus on inflation rates as they provide significant insights into financial risks. The technique I demonstrate can be applied to any webpage featuring data in a table format. I utilized the pandas read_html function to extract the data and also showed how to export it to Excel.
Check out the full tutorial here: Web Scraping Tables with Python
I’m also planning to create a tutorial on conflict of interest for my next post. If you have any questions or suggestions, feel free to reach out!
Don’t forget to follow me on LinkedIn for more updates on auditing with Python: Connect with me on LinkedIn
One response
Your post sounds great! It’s fantastic that you’ve combined both a practical application of web scraping with a relevant topic like inflation rates. Using
pandasread_html()makes it accessible for many, especially those who might not be as familiar with web scraping techniques.I’m particularly interested in your upcoming tutorial on conflicts of interest—sounds like a valuable topic! If you have any tips on best practices for identifying potential conflicts in data sets or how to handle them in a professional setting, that would be awesome.
I’ll definitely check out your blog and follow you on LinkedIn to keep up with your insights on auditing and Python. Keep up the good work!