If you have introductory to intermediate knowledge in Python and statistics, then you can use this article as a one-stop shop for building and plotting histograms in Python using libraries from its scientific stack, including NumPy, Matplotlib, Pandas, and Seaborn.

A histogram is a great tool for quickly assessing a probability distribution that is intuitively understood by almost any audience. Python offers a handful of different options for building and plotting histograms. Most people know a histogram by its graphical representation, which is similar to a bar graph:

This course will guide you through creating plots like the one above as well as more complex ones. Here's what you'll cover:

Building histograms in pure Python, without use of third party libraries

Constructing histograms with NumPy to summarize the underlying data

Plotting the resulting histogram with Matplotlib, Pandas, and Seaborn



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