contributing.md

Contributing

Thank you for your interest in contributing to plotly.py! We are actively looking for diverse contributors, with diverse background and skills.

This guide starts with a general description of the different ways to contribute to plotly.py, then we explain some technical aspects of preparing your contribution.

Code of Conduct

Please check out the Code of Conduct. Don't skip it, but the general idea is to be nice.

What are the different ways to contribute?

There are many ways to contribute to plotly.py. To contribute effectively, it is important to first gain an understanding of the structure of the code and of the repository.

• the plotly.graph_objects module (usually imported as go) is generated from the Plotly.js schema, so changes to be made in this package need to be contributed to Plotly.js or to the codegen system in packages/python/plotly/codegen. Most of the codegen code concerns the generation of docstrings from the schema JSON in Plotly.js. Traces and Layout classes have a direct correspondence with their Javascript counterpart. Higher-level methods that work on figures regardless of the current schema (e.g., BaseFigure.for_each_trace) are defined in packages/python/plotly/plotly/basedatatypes.py. Additional helper methods are defined there for the Figure object, such as update_layout, add_trace, etc.

• the plotly.express module (usually imported as px) is a high-level functional API that uses graph_objects under the hood. Its code is in packages/python/plotly/plotly/express. Plotly Express functions are designed to be highly consistent with each other, and to do as little computation in Python as possible, generally concerning themselves with formatting data and creating figures out of plotly.graph_objects instances. Most functions of plotly.express call the same internal _make_figure function in _core.py. More generally, the internals of px consist of general functions taking care of building the figure (defining subplots, traces or frames, for example), with special cases for different traces handled within these functions. There is also subsequent code reuse for px docstrings, in particular for documenting parameters.

• the plotly.figure_factory module (usually imported as ff) provides Python "recipes" for building advanced visualizations with involved computation done in Python, such as Hexbin maps, ternary contour plots, etc. Figure factories are one of the easiest entry points into contributing to plotly.py, since they consist of Python-only code, with standalone, well-separated functions. However, please note that some of the figure factories become less relevant as we are introducing more features into plotly.express. Some issues in the tracker are labeled "figure_factory" and can be good issues to work on. More instructions on figure factories are found here.

• other pure-Python submodules are: plotly.io (low-level interface for displaying, reading and writing figures), plotly.subplots (helper function for layout of multi-plot figures)

• tests are found in packages/python/plotly/plotly/tests. Different directories correspond to different test jobs (with different dependency sets) run in continuous integration. These jobs are configured in packages/python/plotly/tox.ini, which itself is used in the Circle CI configuration file .circleci/config.yml. More is explained about tests in the following "Technical aspects" section.

• the documentation is part of this repository. Its structure and some explanations are described here. The documentation, in particular example-based tutorials, is a great place to start contributing. The contribution process is also more lightweight, since you can modify tutorial notebooks without setting up an environment, etc. We maintain a wishlist of examples to add on plotly#1965. If you have writing skills, the wording of existing examples can also be improved in places.

Contributing code or documentation is not the only way to contribute! You can also contribute to the project by

• reporting bugs (see below).

• submitting feature requests (maybe we'll convince you to contribute it as a pull request!).

• helping other users on the community forum. Join the list of nice people helping other plotly users :-).

We also recommend reading the great how to contribute to open source guide.

Have a Bug Report?

Open an issue! Go to https://github.com/plotly/plotly.py/issues. It's possible that your issue was already addressed. If it wasn't, open it. We also accept pull requests; take a look at the steps below for instructions on how to do this.

Have Questions about Plotly?

Check out our Community Forum: https://community.plot.ly/.

Want to improve the plotly documentation?

Thank you! Instructions on how to contribute to the documentation are given here. Please also read the next section if you need to setup a development environment.

How to contribute - Technical Aspects

Below we explain the technical aspects of contributing. It is not strictly necessary to follow all points (for example, you will not write tests when writing documentation, most of the time), but we want to make sure that you know how to deal with most cases.

Note that if you are modifying a single documentation page, you can do it directly on Github by clicking on the "Edit this page on GitHub" link, without cloning the repository.

Setup a Development Environment

Fork, Clone, Setup Your Version of the Plotly Python API

First, you'll need to get our project. This is the appropriate clone command (if you're unfamiliar with this process, https://help.github.com/articles/fork-a-repo):

DO THIS (in the directory where you want the repo to live)

git clone https://github.com/your_github_username/plotly.py.git
cd plotly.py

Note: if you're just getting started with git, there exist great resources to learn and become confident about git, like http://try.github.io/.

Create a virtual environment for plotly development

You can use either conda or virtualenv to create a virtual environment for plotly development, e.g.

conda create -n plotly-dev python
conda activate plotly-dev

Install requirements - (Non-Windows)

(plotly_dev) $ pip install -r packages/python/plotly/requirements.txt
(plotly_dev) $ pip install -r packages/python/plotly/optional-requirements.txt

Install requirements - (Windows + Conda)

Because Windows requires Visual Studio libraries to compile some of the optional dependencies, follow these steps to complete installation and avoid gdal-config errors.

(plotly_dev) $ pip install -r packages/python/plotly/requirements.txt
(plotly_dev) $ conda install fiona
(plotly_dev) $ pip install -r packages/python/plotly/optional-requirements.txt

Editable install of plotly packages

(plotly_dev) $ pip install -e packages/python/plotly/
(plotly_dev) $ pip install -e packages/python/chart-studio/
(plotly_dev) $ pip install -e packages/python/plotly-geo/

This will ensure that the installed packages links to your local development directory, meaning that all changes you make reflect directly in your environment (don't forget to restart the Jupyter kernel though!). For more information see the setuptools and pip documentation on development mode.

Configure black code formatting

This repo uses the Black code formatter, and the pre-commit library to manage a git commit hook to run Black prior to each commit. Both pre-commit and black are included in the packages/python/plotly/optional-requirements.txt file, so you should have them installed already if you've been following along.

To enable the Black formatting git hook, run the following from within your virtual environment.

(plotly_dev) $ pre-commit install

Now, whenever you perform a commit, the Black formatter will run. If the formatter makes no changes, then the commit will proceed. But if the formatter does make changes, then the commit will abort. To proceed, stage the files that the formatter modified and commit again.

If you don't want to use pre-commit, then you can run black manually prior to making a PR as follows.

(plotly_dev) $ black .

Making a Development Branch

Third, don't work in the master branch. As soon as you get your master branch ready, run:

DO THIS (but change the branch name)

git checkout -b my-dev-branch

... where you should give your branch a more descriptive name than my-dev-branch

Pull Request When Ready

Once you've made your changes (and hopefully written some tests, see below for more about testing...), make that pull request!

Update to a new version of Plotly.js

First update the version of the plotly.js dependency in packages/javascript/jupyterlab-plotly/package.json.

Then run the updateplotlyjs command with:

$ cd packages/python/plotly
$ python setup.py updateplotlyjs

This will download new versions of plot-schema.json and plotly.min.js from the plotly/plotly.js GitHub repository (and place them in plotly/package_data). It will then regenerate all of the graph_objs classes based on the new schema.

For dev branches, it is also possible to use updateplotlyjsdev --devrepo reponame --devbranch branchname to update to development versions of plotly.js. This will fetch the plotly.js in the CircleCI artifact of the branch branchname of the repo reponame. If --devrepo or --devbranch are omitted, updateplotlyjsdev defaults using plotly/plotly.js and master respectively.

Testing

We take advantage of two tools to run tests:

• tox, which is both a virtualenv management and test tool.
• pytest, a powerful framework for unit testing.

Running Tests with pytest

Since our tests cover all the functionality, to prevent tons of errors from showing up and having to parse through a messy output, you'll need to install optional-requirements.txt as explained above.

After you've done that, go ahead and run the test suite!

pytest  packages/python/plotly/plotly/tests/

Or for more verbose output:

pytest -v  packages/python/plotly/plotly/tests/

Either of those will run every test we've written for the Python API. You can get more granular by running something like:

pytest  packages/python/plotly/plotly/tests/test_core/

... or even more granular by running something like:

pytest plotly/tests/test_plotly/test_plot.py

or for a specific test function

pytest plotly/tests/test_plotly/test_plot.py::test_function

Running tests with tox

Running tests with tox is much more powerful, but requires a bit more setup.

You'll need to export an environment variable for each tox environment you wish to test with. For example, if you want to test with Python 3.9 and Python 3.6, but only care to check the core specs, you would need to ensure that the following variables are exported:

export PLOTLY_TOX_PYTHON_39=<python binary>
export PLOTLY_TOX_PYTHON_36=<python binary>

Where the <python binary is going to be specific to your development setup. As a more complete example, you might have this loaded in a .bash_profile (or equivalent shell loader):

############
# tox envs #
############

export PLOTLY_TOX_PYTHON_39=python3.9
export PLOTLY_TOX_PYTHON_36=python3.6
export TOXENV=py39-core,py36-core

Where TOXENV is the environment list you want to use when invoking tox from the command line. Note that the PLOTLY_TOX_* pattern is used to pass in variables for use in the tox.ini file. Though this is a little setup, intensive, you'll get the following benefits:

• tox will automatically manage a virtual env for each environment you want to test in.
• You only have to run tox and know that the module is working in all included Python versions.

Finally, tox allows you to pass in additional command line arguments that are formatted in (by us) in the tox.ini file, see {posargs}. This is setup to help with our configuration of pytest markers, which are set up in packages/python/plotly/pytest.ini. To run only tests that are not tagged with nodev, you could use the following command:

tox -- -a '!nodev'

Note that anything after -- is substituted in for {posargs} in the tox.ini. For completeness, because it's reasonably confusing, if you want to force a match for multiple pytest marker tags, you comma-separate the tags like so:

tox -- -a '!nodev','!matplotlib'

Writing Tests

You're strongly encouraged to write tests that check your added functionality.

When you write a new test anywhere under the tests directory, if your PR gets accepted, that test will run in a virtual machine to ensure that future changes don't break your contributions!

Test accounts include: PythonTest, PlotlyImageTest, and PlotlyStageTest.