How to contribute

Author: Nicolas Rougier

Make sure to read this Documentation style guide [1] as well as these tips, tricks [2] and conventions about documentation content and workflows.

How to contribute ?

  • If you spot typos, unclear or clumsy wording in the lectures, please help to improve them. Simple text editing can be done by editing files in your GitHub fork of the lectures. On every html page of the lectures, an edit button on the top right links to the editable source of the page (you still need to create a fork of the project). Edit the source and choose “Create a new branch for this commit and start a pull request”.

  • Choose a topic that is not yet covered and write it up !

    First create a new issue on GitHub to explain the topic which you would like to cover, in order to discuss with editors and contributors about the scope of the future tutorial.

    Then create a new directory inside one of the chapters directories (intro, advanced, or packages) and create a file index.rst for the new tutorial. Add the new file in the table of contents of the corresponding chapter (in its index.rst).

Keep in mind that tutorials are to be taught at different places and different parts may be combined into a course on Python for scientific computing. Thus you want them to be interactive and reasonably short (one to two hours).

Last but not least, the goal of this material is to provide a concise text to learn the main features of the scientific Python ecosystem. If you want to contribute to reference material, we suggest that you contribute to the documentation of the specific packages that you are interested in.

Using GitHub

The easiest way to make your own version of this teaching material is to fork it under GitHub, and use the git version control system to maintain your own fork. For this, all you have to do is create an account on GitHub and click on the fork button, on the top right of this page. You can use git to pull from your fork, and push back to it the changes. If you want to contribute the changes back, just fill a pull request, using the button on the top of your fork’s page.

Several resources are available online to learn git and GitHub, such as for complete beginners.

Please refrain from modifying the Makefile unless it is absolutely necessary.

Keeping it concise: collapsing paragraphs

The HTML output is used for displaying on screen while teaching. The goal is to have the same material displayed as in the notes. Thus there needs to be a very concise display, with bullet-lists rather than full-blown paragraphs and sentences. For more elaborate discussions that people can read and refer to, please use the tip sphinx directive. It creates collapsible paragraphs, that can be hidden during an oral presentation:

.. tip::
Here insert a full-blown discussion, that will be collapsible in
the HTML version.
It can span on multiple paragraphs

This renders as:


Here insert a full-blown discussion, that will be collapsible in the HTML version.

It can span on multiple paragraphs

Figures and code examples

We do not check figures in the repository. Any figure must be generated from a python script that needs to be named (xxx can be anything of course) and put into the examples directory. The generated image will be named from the script name.


This is the way to include your image and link it to the code:

.. image::  auto_examples/images/sphx_glr_plot_simple_001.png
:target: auto_examples/plot_simple.html

You can display the corresponding code using the literal-include directive.

A simple example
import numpy as np
import matplotlib.pyplot as plt
X = np.linspace(-np.pi, np.pi, 100)
Y = np.sin(X)
plt.plot(X, Y, linewidth=2)


The transformation of Python scripts into figures and galleries of examples is provided by the sphinx-gallery package.

Using Markup

There are three main kinds of markup that should be used: italics, bold and fixed-font. Italics should be used when introducing a new technical term, bold should be used for emphasis and fixed-font for source code.

In restructured-text markup this is:

when using *object-oriented programming* in Python you **must** use the
``class`` keyword to define your *classes*.

Linking to package documentations

The goal of the Scientific Python Lectures is not to duplicate or replace the documentation of the various packages. You should link as much as possible to the original documentation.

For cross-referencing API documentation we prefer to use the intersphinx extension. This provides the directives :mod:, :class: and :func: to cross-link to modules, classes and functions respectively. For example the :func:`numpy.var` will create a link like numpy.var().

Chapter, section, subsection, paragraph

Try to avoid to go below paragraph granularity or your document might become difficult to read:

Chapter title
Sample content.
And some text.



This is a note


This is a warning

Clearing floats

Figures positioned with :align: right are float. To flush them, use: