4 Ways to Quantify Fat Tails with Python

This is the third article in a series on <a href="https://towardsdatascience.com/pareto-power-laws-and-fat-tails-0355a187ee6a" rel="noopener" target="_blank">Power Laws and Fat Tails</a>. In the <a href="https://medium.com/towards-data-science/detecting-power-laws-in-real-world-data-with-python-b464190fade6" rel="noopener">previous post</a>, we explored how to detect power laws from empirical data. While this technique can be handy, fat tails go beyond simply fitting data to a power law distribution. In this article, I will break down 4 ways we can quantify fat tails and share example Python code analyzing real-world data. Note: If you are unfamiliar with terms like Power Law distribution or Fat Tail, review <a href="https://medium.com/towards-data-science/pareto-power-laws-and-fat-tails-0355a187ee6a" rel="noopener">this article</a> as a primer. <a href="https://towardsdatascience.com/4-ways-to-quantify-fat-tails-with-python-10ce62c0ada1">Website</a>