Scraping a GitHub user’s profile for their daily commit data can be a useful way to track their activity on the platform and potentially even analyze their work habits. In this tutorial, we’ll go through how to use the provided Python script to scrape a user’s profile and export the data to a CSV file.
I got curious as to how the 37% improvement in James Clear’s book “Atomic Habits” was calculated. As such, I went about figuring out how and tried to generalize it to different time periods (rather than just a year) and with variable improvement and regression rates for each day.
In this post, I will show you how to scrape and prepare data for analyzing PCSO lottery results in Excel to take your lottery play to the next level. Discover powerful data-driven insights that can help you make smarter, more informed decisions and maximize your chances of winning.