You Will NEVER Use Panda’s Describe Method After Using These 5 Libraries

Generate a comprehensive data summary in seconds.

Probably the first (or second) thing I do when I load any Pandas DataFrame is describe it, using the df.describe() method.

However, I always find its output to be pretty naive and almost of no use. In other words, it hardly highlights any key information about the data.

But some time back, I came across three pretty cool libraries that IMMENSELY supercharge this DataFrame summary.

Since then, I don’t think I have ever used the describe() method.

Lets learn about them!

The first one is Skimpy

It is a Jupyter-based tool that provides a standardized and comprehensive data summary.

This includes data shape, column data types, column summary statistics, distribution charts, missing stats, etc., as shown below:

Beyond the Basics: What Skimpy Offers