.. _contributing_documentation:

{{ header }}

=================================
Contributing to the documentation
=================================

Contributing to the documentation benefits everyone who uses pandas.
We encourage you to help us improve the documentation, and
you don't have to be an expert on pandas to do so! In fact,
there are sections of the docs that are worse off after being written by
experts. If something in the docs doesn't make sense to you, updating the
relevant section after you figure it out is a great way to ensure it will help
the next person. Please visit the `issues page <https://github.com/pandas-dev/pandas/issues?page=1&q=is%3Aopen+sort%3Aupdated-desc+label%3ADocs>`__
for a full list of issues that are currently open regarding the
pandas documentation.



.. contents:: Documentation:
   :local:


About the pandas documentation
--------------------------------

The documentation is written in **reStructuredText**, which is almost like writing
in plain English, and built using `Sphinx <https://www.sphinx-doc.org/en/master/>`__. The
Sphinx Documentation has an excellent `introduction to reST
<https://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html>`__. Review the Sphinx docs to perform more
complex changes to the documentation as well.

Some other important things to know about the docs:

* The pandas documentation consists of two parts: the docstrings in the code
  itself and the docs in this folder ``doc/``.

  The docstrings provide a clear explanation of the usage of the individual
  functions, while the documentation in this folder consists of tutorial-like
  overviews per topic together with some other information (what's new,
  installation, etc).

* The docstrings follow a pandas convention, based on the **Numpy Docstring
  Standard**. Follow the :ref:`pandas docstring guide <docstring>` for detailed
  instructions on how to write a correct docstring.

  .. toctree::
     :maxdepth: 1

     contributing_docstring.rst

* The tutorials make heavy use of the `IPython directive
  <https://matplotlib.org/sampledoc/ipython_directive.html>`_ sphinx extension.
  This directive lets you put code in the documentation which will be run
  during the doc build. For example::

      .. ipython:: python

          x = 2
          x**3

  will be rendered as::

      In [1]: x = 2

      In [2]: x**3
      Out[2]: 8

  Almost all code examples in the docs are run (and the output saved) during the
  doc build. This approach means that code examples will always be up to date,
  but it does make the doc building a bit more complex.

* Our API documentation files in ``doc/source/reference`` house the auto-generated
  documentation from the docstrings. For classes, there are a few subtleties
  around controlling which methods and attributes have pages auto-generated.

  We have two autosummary templates for classes.

  1. ``_templates/autosummary/class.rst``. Use this when you want to
     automatically generate a page for every public method and attribute on the
     class. The ``Attributes`` and ``Methods`` sections will be automatically
     added to the class' rendered documentation by numpydoc. See ``DataFrame``
     for an example.

  2. ``_templates/autosummary/class_without_autosummary``. Use this when you
     want to pick a subset of methods / attributes to auto-generate pages for.
     When using this template, you should include an ``Attributes`` and
     ``Methods`` section in the class docstring. See ``CategoricalIndex`` for an
     example.

  Every method should be included in a ``toctree`` in one of the documentation files in
  ``doc/source/reference``, else Sphinx
  will emit a warning.

Docstring validation — both standard numpydoc checks and pandas-specific conventions
— is enforced automatically during the Sphinx
documentation build via ``numpydoc_validation_checks`` in ``doc/source/conf.py``.


Updating a pandas docstring
-----------------------------

When improving a single function or method's docstring, it is not necessarily
needed to build the full documentation (see next section).
You can validate a single docstring — including both standard numpydoc checks and
pandas-specific conventions (GL04, PD01, SA05, EX04) — by building a single-page
doc::

    python doc/make.py --warnings-are-errors --no-browser --single pandas.DataFrame.mean

Check the :ref:`pandas docstring guide <docstring>` for a detailed guide
on how to format the docstring.

The examples in the docstring ('doctests') must be valid Python code,
that in a deterministic way returns the presented output, and that can be
copied and run by users. This can be checked with the script above, and is
also tested on Travis. A failing doctest will be a blocker for merging a PR.
Check the :ref:`examples <docstring.examples>` section in the docstring guide
for some tips and tricks to get the doctests passing.

When doing a PR with a docstring update, it is good to post the
output of the validation script in a comment on github.

.. _contributing.howto-build-docs:

How to build the pandas documentation
---------------------------------------

Requirements
~~~~~~~~~~~~

First, you need to have a development environment to be able to build pandas
(see the docs on :ref:`creating a development environment <contributing_environment>`).

Building the documentation
~~~~~~~~~~~~~~~~~~~~~~~~~~

So how do you build the docs? Navigate to your local
``doc/`` directory in the console and run::

    python make.py html

Then you can find the HTML output in the folder ``doc/build/html/``.

The first time you build the docs, it will take quite a while because it has to run
all the code examples and build all the generated docstring pages. In subsequent
evocations, sphinx will try to only build the pages that have been modified.

If you want to do a full clean build, do::

    python make.py clean
    python make.py html

.. tip::
  If ``python make.py html`` exits with an error status,
  try running the command ``python make.py html --num-jobs=1``
  to identify the cause of the error.

You can tell ``make.py`` to compile only a single section of the docs, greatly
reducing the turn-around time for checking your changes.

::

    # omit autosummary and API section
    python make.py clean
    python make.py --no-api

    # compile the docs with only a single section, relative to the "source" folder.
    # For example, compiling only this guide (doc/source/development/contributing.rst)
    python make.py clean
    python make.py --single development/contributing.rst

    # compile the reference docs for a single function
    python make.py clean
    python make.py --single pandas.DataFrame.join

    # compile whatsnew and API section (to resolve links in the whatsnew)
    python make.py clean
    python make.py --whatsnew

For comparison, a full documentation build may take 15 minutes, but a single
section may take 15 seconds. Subsequent builds, which only process portions
you have changed, will be faster.

The build will automatically use the number of cores available on your machine
to speed up the documentation build. You can override this::

    python make.py html --num-jobs 4

Open the following file in a web browser to see the full documentation you
just built ``doc/build/html/index.html``.

And you'll have the satisfaction of seeing your new and improved documentation!

.. _contributing.dev_docs:

Building main branch documentation
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

When pull requests are merged into the pandas ``main`` branch, the main parts of
the documentation are also built by Github Actions. These docs are then hosted `here
<https://pandas.pydata.org/docs/dev/>`__, see also
the :any:`Continuous Integration <contributing.ci>` section.

Previewing changes
------------------

Once, the pull request is submitted, GitHub Actions will automatically build the
documentation. To view the built site:

#. Wait for the ``CI / Web and docs`` check to complete.
#. Click ``Details`` next to it.
#. From the ``Artifacts`` drop-down, click ``docs`` or ``website`` to download
   the site as a ZIP file.
