numpy/numpy-tutorials

This is an official set of tutorials and educational materials produced by the NumPy project, stored in a separate repository from the main NumPy code.

What it is: A collection of high-quality learning resources for NumPy, meant both for self-study and for teaching classes. It is intentionally kept apart from the main NumPy codebase, so its contents do not affect the core library.

What it contains: A list of tutorial documents, each in Jupyter Notebook (.ipynb) or Markdown format, covering various topics: linear algebra on n‑dimensional arrays, using real data to determine Moore's Law, saving and sharing arrays, deep learning from scratch on the MNIST dataset, X‑ray image processing, masked arrays, static equilibrium, plotting fractals, and analysing Delhi air quality during lockdown. There is also a style guide for anyone who wants to write their own NumPy tutorial.

How you can use it: You can clone the whole repository from GitHub, or download any individual tutorial file directly from the list. The tutorials are freely available, and you are allowed to translate them into other languages (though the NumPy team does not maintain those translations).

For contributors: The README points to a contributing section (which isn't shown here) and provides a style guide link for writing new tutorials. It also gives useful links to the main NumPy documentation, the code of conduct, documentation team meeting notes, a proposal about restructuring NumPy docs, and a blog post about documentation as community building.