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For the rendered tutorials, seehttps://numpy.org/numpy-tutorials/.

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

To download a local copy of the.ipynb files, you can eitherclone this repositoryor 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: NumPy deep reinforcement learning with Pong from pixels
7. Tutorial: Masked Arrays
8. Tutorial: Static Equilibrium
9. Tutorial: Plotting Fractals
10. Tutorial: NumPy natural language processing from scratch with a focus on ethics
11. Tutorial: Analysing the impact of the lockdown on air quality in Delhi, India

We very much welcome contributions! If you have an idea or proposal for a newtutorial, pleaseopen an issuewith an outline.

Don’t worry if English is not your first language, or if you can only come upwith a rough draft. Open source is a community effort. Do your best – we’ll helpfix issues.

Images and real-life data make text more engaging and powerful, but be sure whatyou use is appropriately licensed and available. Here again, even a rough ideafor artwork can be polished by others.

The NumPy tutorials are a curated collection ofMyST-NB notebooks. These notebooks are usedto produce static websites and can be opened as notebooks in Jupyter usingJupytext.

Note: You should useCommonMark markdowncells. Jupyter only renders CommonMark.

The choice of Jupyter Notebook in this repo instead of the usual format(reStructuredText, through Sphinx)used in the main NumPy documentation has two reasons:

• Jupyter notebooks are a common format for communicating scientificinformation.
• Jupyter notebooks can be launched inBinder, so that users can interactwith tutorials
• rST may present a barrier for some people who might otherwise be veryinterested in contributing tutorial material.

You may notice our content is in markdown format (.md files). We review andhost notebooks in theMyST-NB format. Weaccept both Jupyter notebooks (.ipynb) and MyST-NB notebooks (.md). If you wantto sync your.ipynb to your.md file follow thepairingtutorial.

If you have your own tutorial in the form of a Jupyter notebook (a.ipynbfile) and you'd like to add it to the repository, follow the steps below.

Go tohttps://github.com/numpy/numpy-tutorials/issuesand create a new issue with your proposal. Give as much detail as you can aboutwhat kind of content you would like to write (tutorial, how-to) and what youplan to cover. We will try to respond as quickly as possible with comments, ifapplicable.

You can use ourTutorial Style Guide to makeyour content consistent with our existing tutorials.

Fork this repository (if you haven't before).

In your own fork, create a new branch for your content.

Add your notebook to thecontent/ directory.


Update theenvironment.yml file with the dependencies for yourtutorial (only if you add new dependencies).

Update thisREADME.md to include your new entry.


Update the attribution section (below) to credit the original tutorialauthor, if applicable.

Create apull request. Make sure the "Allow edits and access to secrets by maintainers" option is selected so we can properly review your submission.


🎉Wait for review!

For more information about GitHub and its workflow, you can seethis document.

Building the tutorials website, which is published athttps://github.com/numpy/numpy-tutorials, locally isn't necessary before makinga contribution, but may be helpful:

conda env create -f environment.ymlconda activate numpy-tutorialscd sitemake html

While we don't have the capacity to translate and maintain translated versionsof 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 themain NumPyrepository (see theDocumentationlabels there).