For the rendered tutorials, see https://numpy.org/numpy-tutorials/.

The goal of this repository is to provide high-quality resources by the NumPy project, both for self-learning and for teaching classes with. If you're interested in adding your own content, check the Contributing section. This set of tutorials and educational materials is not a part of the NumPy source tree.

To download a local copy of the .ipynb files, you can either clone this repository or navigate to any of the documents listed below and download it individually.

0. Learn to write a NumPy tutorial: our style guide for writing tutorials.
1. Tutorial: Linear algebra on n-dimensional arrays
2. Tutorial: Determining Moore's Law with real data in NumPy
3. Tutorial: Saving and sharing your NumPy arrays
4. Tutorial: NumPy deep learning on MNIST from scratch
5. Tutorial: X-ray image processing
6. Tutorial: Masked Arrays
7. Tutorial: Static Equilibrium
8. Tutorial: Plotting Fractals
9. Tutorial: Analysing the impact of the lockdown on air quality in Delhi, India

While we don't have the capacity to translate and maintain translated versions of these tutorials, you are free to use and translate them to other languages.

The following links may be useful:

• NumPy Code of Conduct
• Main NumPy documentation
• NumPy documentation team meeting notes
• NEP 44 - Restructuring the NumPy documentation
• Blog post - Documentation as a way to build Community

Note that regular documentation issues for NumPy can be found in the main NumPy repository (see the Documentation labels there).