NumPy is indispensible for manipulating data in python and luckily there are plenty of online resources for learning the ropes. Here are some solid options, recommended by the good people of Twitter and LinkedIn. What some of them lack in polish, they make up for in substance.
- Jay Alammar's Visual Intro to Numpy is a perfect first stop.
- Finxter's NumPy Tutorial provides a complementary approach.
- Nicolas Rougier's collection of 100 NumPy exercises gives an excellent opportunity to learn the mechanics of NumPy. There are problems for learners of all levels. Together with Machine Learning Plus' NumPy Exercises these provide a fantastic companion to the introductory tutorials.
- There's no beating the SciPy lectures for being comprehensive. It's a great follow-up for a deeper survey of NumPy's capabilities.
- Jake VanderPlas' Introduction to NumPy chapter from his Python Data Science Handbook takes NumPy tutorial to the next level.
- Nicolas Rougier makes a second appearance in the list with his From Python to NumPy online book. It goes deeper into how to use NumPy to solve useful scientific problems.
- Chapter 4 of Wes McKinney's Python for Data Analysis is another great introduction to NumPy for those who already have a running start on what scientific computing is all about.
I wasn't planning to include anything too in-depth here, but this paper on the structure and efficiency of the NumPy array was too cool not to. You'll never need to know any of this, but some day you might want to.