Learn

For the official NumPy documentation visit numpy.org/doc/stable.

Below is a curated collection of educational resources, both for self-learning and teaching others, developed by NumPy contributors and vetted by the community.

Beginners#

There’s a ton of information about NumPy out there. If you are just starting, we’d strongly recommend the following:

Tutorials

• NumPy Quickstart Tutorial
• NumPy Tutorials A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the numpy-tutorials repository on GitHub.
• NumPy Illustrated: The Visual Guide to NumPy by Lev Maximov
• Scientific Python Lectures Besides covering NumPy, these lectures offer a broader introduction to the scientific Python ecosystem.
• NumPy: the absolute basics for beginners
• NumPy tutorial by Nicolas Rougier
• Stanford CS231 by Justin Johnson
• NumPy User Guide

Books

• Guide to NumPy by Travis E. Oliphant This is a free version 1 from 2006. For the latest copy (2015) see here.
• From Python to NumPy by Nicolas P. Rougier
• Elegant SciPy by Juan Nunez-Iglesias, Stefan van der Walt, and Harriet Dashnow

You may also want to check out the Goodreads list on the subject of “Python+SciPy.” Most books there are about the “SciPy ecosystem,” which has NumPy at its core.

Videos

• Introduction to Numerical Computing with NumPy by Alex Chabot-Leclerc

Advanced#

Try these advanced resources for a better understanding of NumPy concepts like advanced indexing, splitting, stacking, linear algebra, and more.

Tutorials

• 100 NumPy Exercises by Nicolas P. Rougier
• An Introduction to NumPy and Scipy by M. Scott Shell
• Numpy Medkits by Stéfan van der Walt
• NumPy Tutorials A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the numpy-tutorials repository on GitHub.

Books

• Python Data Science Handbook by Jake Vanderplas
• Python for Data Analysis by Wes McKinney
• Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy, and Matplotlib by Robert Johansson

Videos

• Advanced NumPy - broadcasting rules, strides, and advanced indexing by Juan Nunez-Iglesias

NumPy Talks#

• The Future of NumPy Indexing by Jaime Fernández (2016)
• Evolution of Array Computing in Python by Ralf Gommers (2019)
• NumPy: what has changed and what is going to change? by Matti Picus (2019)
• Inside NumPy by Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harris (2019)
• Brief Review of Array Computing in Python by Travis Oliphant (2019)

Citing NumPy#

If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, please see this citation information.