Introduction to Data Visualization in Python with Pandas



People can rarely look at a raw data and immediately deduce a data-oriented observation like:

People in stores tend to buy diapers and beer in conjunction!

Or even if you as a data scientist can indeed sight read raw data, your investor or boss most likely can’t.

In order for us to properly analyze our data, we need to represent it in a tangible, comprehensive way. Which is exactly why we use data visualization!

The pandas library offers a large array of tools that will help you accomplish this. In this article, we’ll go step by step and cover everything you’ll need to get started with pandas visualization tools, including bar charts, histograms, area plots, density plots, scatter matrices, and bootstrap plots.

Importing Data

First, we’ll need a small dataset to work with and test things out.

I’ll use an Indian food dataset



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