NumPy tutorials¶

This set of tutorials and educational materials is being developed in the numpy-tutorials repository, and is not a part of the NumPy source tree. 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.

To open a live version of the content, click the launch Binder button above. To open each of the .md files, right click and select “Open with -> Notebook”. You can also launch individual tutorials on Binder by clicking on the rocket icon that appears in the upper-right corner of each tutorial. To download a local copy of the .ipynb files, you can either clone this repository or use the download icon in the upper-right corner of each tutorial.

Content¶

Contributing¶

We very much welcome contributions! If you have an idea or proposal for a new tutorial, please open an issue with an outline.

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

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

The NumPy tutorials are a curated collection of MyST-NB notebooks. These notebooks are used to produce static websites and can be opened as notebooks in Jupyter using Jupytext.

Note: You should use CommonMark markdown cells. Jupyter only renders CommonMark.

Why Jupyter Notebooks?¶

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

Jupyter notebooks are a common format for communicating scientific information.
Jupyter notebooks can be launched in Binder, so that users can interact with tutorials
rST may present a barrier for some people who might otherwise be very interested in contributing tutorial material.

Note¶

You may notice our content is in markdown format (.md files). We review and host notebooks in the MyST-NB format. We accept both Jupyter notebooks (.ipynb) and MyST-NB notebooks (.md). If you want to sync your .ipynb to your .md file follow the pairing tutorial.

Adding your own tutorials¶

If you have your own tutorial in the form of a Jupyter notebook (an .ipynb file) and you’d like to try add it out to the repository, follow the steps below.

Create an issue¶

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

Check out our suggested template¶

You can use this template to make your content consistent with our existing tutorials:

Learn to write a NumPy tutorial

Upload your content¶

Remember to clear all outputs on your notebook before uploading it.

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

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

Add your notebook to the content/ directory.

Update the environment.yml file with the dependencies for your tutorial (only if you add new dependencies).

Update this README.md to include your new entry.

Create a pull 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 see this document.

Useful links and resources¶

The following links may be useful:

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

NumPy Tutorials