Hint: You can adjust the default video playback speed in your account settings.
Hint: You can set the default subtitles language in your account settings.
Sorry! Looks like there’s an issue with video playback 🙁 This might be due to a temporary outage or because of a configuration issue with your browser. Please see our video player troubleshooting guide to resolve the issue.

Python Histogram Plotting: NumPy, Matplotlib, Pandas & Seaborn (Overview)

Give Feedback

In this course, you’ll be equipped to make production-quality, presentation-ready Python histogram plots with a range of choices and features.

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:

Histogram of commute times for 1000 commuters

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

Become a Member to join the conversation.