Contributing to xarray


Large parts of this document came from the Pandas Contributing Guide.

Where to start?

All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.

If you are brand new to xarray or open-source development, we recommend going through the GitHub “issues” tab to find issues that interest you. There are a number of issues listed under Documentation and good first issue where you could start out. Once you’ve found an interesting issue, you can return here to get your development environment setup.

Feel free to ask questions on the mailing list.

Bug reports and enhancement requests

Bug reports are an important part of making xarray more stable. Having a complete bug report will allow others to reproduce the bug and provide insight into fixing. See this stackoverflow article for tips on writing a good bug report.

Trying out the bug-producing code on the master branch is often a worthwhile exercise to confirm that the bug still exists. It is also worth searching existing bug reports and pull requests to see if the issue has already been reported and/or fixed.

Bug reports must:

  1. Include a short, self-contained Python snippet reproducing the problem. You can format the code nicely by using GitHub Flavored Markdown:

    >>> import xarray as xr
    >>> df = xr.Dataset(...)
  2. Include the full version string of xarray and its dependencies. You can use the built in function:

    >>> import xarray as xr
    >>> xr.show_versions()
  3. Explain why the current behavior is wrong/not desired and what you expect instead.

The issue will then show up to the xarray community and be open to comments/ideas from others.

Working with the code

Now that you have an issue you want to fix, enhancement to add, or documentation to improve, you need to learn how to work with GitHub and the xarray code base.

Version control, Git, and GitHub

To the new user, working with Git is one of the more daunting aspects of contributing to xarray. It can very quickly become overwhelming, but sticking to the guidelines below will help keep the process straightforward and mostly trouble free. As always, if you are having difficulties please feel free to ask for help.

The code is hosted on GitHub. To contribute you will need to sign up for a free GitHub account. We use Git for version control to allow many people to work together on the project.

Some great resources for learning Git:

Getting started with Git

GitHub has instructions for installing git, setting up your SSH key, and configuring git. All these steps need to be completed before you can work seamlessly between your local repository and GitHub.


You will need your own fork to work on the code. Go to the xarray project page and hit the Fork button. You will want to clone your fork to your machine:

git clone
cd xarray
git remote add upstream

This creates the directory xarray and connects your repository to the upstream (main project) xarray repository.

Creating a development environment

To test out code changes, you’ll need to build xarray from source, which requires a Python environment. If you’re making documentation changes, you can skip to Contributing to the documentation but you won’t be able to build the documentation locally before pushing your changes.

Creating a Python Environment

Before starting any development, you’ll need to create an isolated xarray development environment:

We’ll now kick off a two-step process:

  1. Install the build dependencies

  2. Build and install xarray

# Create and activate the build environment
# This is for Linux and MacOS. On Windows, use py37-windows.yml instead.
conda env create -f ci/requirements/py37.yml

conda activate xarray-tests

# or with older versions of Anaconda:
source activate xarray-tests

# Build and install xarray
pip install -e .

At this point you should be able to import xarray from your locally built version:

$ python  # start an interpreter
>>> import xarray
>>> xarray.__version__

This will create the new environment, and not touch any of your existing environments, nor any existing Python installation.

To view your environments:

conda info -e

To return to your root environment:

conda deactivate

See the full conda docs here.

Creating a branch

You want your master branch to reflect only production-ready code, so create a feature branch before making your changes. For example:

git branch shiny-new-feature
git checkout shiny-new-feature

The above can be simplified to:

git checkout -b shiny-new-feature

This changes your working directory to the shiny-new-feature branch. Keep any changes in this branch specific to one bug or feature so it is clear what the branch brings to xarray. You can have many “shiny-new-features” and switch in between them using the git checkout command.

To update this branch, you need to retrieve the changes from the master branch:

git fetch upstream
git merge upstream/master

This will combine your commits with the latest xarray git master. If this leads to merge conflicts, you must resolve these before submitting your pull request. If you have uncommitted changes, you will need to git stash them prior to updating. This will effectively store your changes, which can be reapplied after updating.

Contributing to the documentation

If you’re not the developer type, contributing to the documentation is still of huge value. You don’t even have to be an expert on xarray 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.

About the xarray documentation

The documentation is written in reStructuredText, which is almost like writing in plain English, and built using Sphinx. The Sphinx Documentation has an excellent introduction to reST. Review the Sphinx docs to perform more complex changes to the documentation as well.

Some other important things to know about the docs:

  • The xarray documentation consists of two parts: the docstrings in the code itself and the docs in this folder xarray/doc/.

    The docstrings are meant to 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 the Numpy Docstring Standard, which is used widely in the Scientific Python community. This standard specifies the format of the different sections of the docstring. See this document for a detailed explanation, or look at some of the existing functions to extend it in a similar manner.

  • The tutorials make heavy use of the ipython directive 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 building the docs a bit more complex.

  • Our API documentation in doc/api.rst houses the auto-generated documentation from the docstrings. For classes, there are a few subtleties around controlling which methods and attributes have pages auto-generated.

    Every method should be included in a toctree in api.rst, else Sphinx will emit a warning.

How to build the xarray documentation


Make sure to follow the instructions on creating a development environment above, but to build the docs you need to use the environment file ci/requirements/doc.yml.

# Create and activate the docs environment
conda env create -f ci/requirements/doc.yml
conda activate xarray-docs

# or with older versions of Anaconda:
source activate xarray-docs

# Build and install xarray
pip install -e .

Building the documentation

Navigate to your local xarray/doc/ directory in the console and run:

make html

Then you can find the HTML output in the folder xarray/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:

make clean
make html

Contributing to the code base

Code standards

Writing good code is not just about what you write. It is also about how you write it. During Continuous Integration testing, several tools will be run to check your code for stylistic errors. Generating any warnings will cause the test to fail. Thus, good style is a requirement for submitting code to xarray.

In addition, because a lot of people use our library, it is important that we do not make sudden changes to the code that could have the potential to break a lot of user code as a result, that is, we need it to be as backwards compatible as possible to avoid mass breakages.

Code Formatting

xarray uses several tools to ensure a consistent code format throughout the project:


pip install black flake8 isort mypy blackdoc

and then run from the root of the xarray repository:

isort .
black -t py36 .
blackdoc -t py36 .
mypy .

to auto-format your code. Additionally, many editors have plugins that will apply black as you edit files.

Optionally, you may wish to setup pre-commit hooks to automatically run all the above tools every time you make a git commit. This can be done by installing pre-commit:

pip install pre-commit

and then running:

pre-commit install

from the root of the xarray repository. You can skip the pre-commit checks with git commit --no-verify.

Backwards Compatibility

Please try to maintain backward compatibility. xarray has growing number of users with lots of existing code, so don’t break it if at all possible. If you think breakage is required, clearly state why as part of the pull request. Also, be careful when changing method signatures and add deprecation warnings where needed.

Testing With Continuous Integration

The xarray test suite runs automatically the Azure Pipelines, continuous integration service, once your pull request is submitted. However, if you wish to run the test suite on a branch prior to submitting the pull request, then Azure Pipelines needs to be configured for your GitHub repository.

A pull-request will be considered for merging when you have an all ‘green’ build. If any tests are failing, then you will get a red ‘X’, where you can click through to see the individual failed tests. This is an example of a green build.



Each time you push to your PR branch, a new run of the tests will be triggered on the CI. If they haven’t already finished, tests for any older commits on the same branch will be automatically cancelled.

Test-driven development/code writing

xarray is serious about testing and strongly encourages contributors to embrace test-driven development (TDD). This development process “relies on the repetition of a very short development cycle: first the developer writes an (initially failing) automated test case that defines a desired improvement or new function, then produces the minimum amount of code to pass that test.” So, before actually writing any code, you should write your tests. Often the test can be taken from the original GitHub issue. However, it is always worth considering additional use cases and writing corresponding tests.

Adding tests is one of the most common requests after code is pushed to xarray. Therefore, it is worth getting in the habit of writing tests ahead of time so that this is never an issue.

Like many packages, xarray uses pytest and the convenient extensions in numpy.testing.

Writing tests

All tests should go into the tests subdirectory of the specific package. This folder contains many current examples of tests, and we suggest looking to these for inspiration. If your test requires working with files or network connectivity, there is more information on the testing page of the wiki.

The xarray.testing module has many special assert functions that make it easier to make statements about whether DataArray or Dataset objects are equivalent. The easiest way to verify that your code is correct is to explicitly construct the result you expect, then compare the actual result to the expected correct result:

def test_constructor_from_0d():
    expected = Dataset({None: ([], 0)})[None]
    actual = DataArray(0)
    assert_identical(expected, actual)

Transitioning to pytest

xarray existing test structure is mostly classed based, meaning that you will typically find tests wrapped in a class.

class TestReallyCoolFeature:

Going forward, we are moving to a more functional style using the pytest framework, which offers a richer testing framework that will facilitate testing and developing. Thus, instead of writing test classes, we will write test functions like this:

def test_really_cool_feature():

Using pytest

Here is an example of a self-contained set of tests that illustrate multiple features that we like to use.

  • functional style: tests are like test_* and only take arguments that are either fixtures or parameters

  • pytest.mark can be used to set metadata on test functions, e.g. skip or xfail.

  • using parametrize: allow testing of multiple cases

  • to set a mark on a parameter, pytest.param(..., marks=...) syntax should be used

  • fixture, code for object construction, on a per-test basis

  • using bare assert for scalars and truth-testing

  • assert_equal and assert_identical from the xarray.testing module for xarray object comparisons.

  • the typical pattern of constructing an expected and comparing versus the result

We would name this file and put in an appropriate place in the xarray/tests/ structure.

import pytest
import numpy as np
import xarray as xr
from xarray.testing import assert_equal

@pytest.mark.parametrize("dtype", ["int8", "int16", "int32", "int64"])
def test_dtypes(dtype):
    assert str(np.dtype(dtype)) == dtype

        pytest.param("int16", marks=pytest.mark.skip),
            "int32", marks=pytest.mark.xfail(reason="to show how it works")
def test_mark(dtype):
    assert str(np.dtype(dtype)) == "float32"

def dataarray():
    return xr.DataArray([1, 2, 3])

@pytest.fixture(params=["int8", "int16", "int32", "int64"])
def dtype(request):
    return request.param

def test_series(dataarray, dtype):
    result = dataarray.astype(dtype)
    assert result.dtype == dtype

    expected = xr.DataArray(np.array([1, 2, 3], dtype=dtype))
    assert_equal(result, expected)

A test run of this yields

((xarray) $ pytest -v
 =============================== test session starts ================================
 platform darwin -- Python 3.6.4, pytest-3.2.1, py-1.4.34, pluggy-0.4.0 --
 cachedir: ../../.cache
 plugins: cov-2.5.1, hypothesis-3.23.0
 collected 11 items[int8] PASSED[int16] PASSED[int32] PASSED[int64] PASSED[float32] PASSED[int16] SKIPPED[int32] xfail[int8] PASSED[int16] PASSED[int32] PASSED[int64] PASSED

 ================== 9 passed, 1 skipped, 1 xfailed in 1.83 seconds ==================

Tests that we have parametrized are now accessible via the test name, for example we could run these with -k int8 to sub-select only those tests which match int8.

((xarray) bash-3.2$ pytest  -v -k int8
=========================== test session starts ===========================
platform darwin -- Python 3.6.2, pytest-3.2.1, py-1.4.31, pluggy-0.4.0
collected 11 items[int8] PASSED[int8] PASSED

Running the test suite

The tests can then be run directly inside your Git clone (without having to install xarray) by typing:

pytest xarray

The tests suite is exhaustive and takes a few minutes. Often it is worth running only a subset of tests first around your changes before running the entire suite.

The easiest way to do this is with:

pytest xarray/path/to/ -k regex_matching_test_name

Or with one of the following constructs:

pytest xarray/tests/[test-module].py
pytest xarray/tests/[test-module].py::[TestClass]
pytest xarray/tests/[test-module].py::[TestClass]::[test_method]

Using pytest-xdist, one can speed up local testing on multicore machines. To use this feature, you will need to install pytest-xdist via:

pip install pytest-xdist

Then, run pytest with the optional -n argument:

pytest xarray -n 4

This can significantly reduce the time it takes to locally run tests before submitting a pull request.

For more, see the pytest documentation.

Running the performance test suite

Performance matters and it is worth considering whether your code has introduced performance regressions. xarray is starting to write a suite of benchmarking tests using asv to enable easy monitoring of the performance of critical xarray operations. These benchmarks are all found in the xarray/asv_bench directory. asv supports both python2 and python3.

To use all features of asv, you will need either conda or virtualenv. For more details please check the asv installation webpage.

To install asv:

pip install git+

If you need to run a benchmark, change your directory to asv_bench/ and run:

asv continuous -f 1.1 upstream/master HEAD

You can replace HEAD with the name of the branch you are working on, and report benchmarks that changed by more than 10%. The command uses conda by default for creating the benchmark environments. If you want to use virtualenv instead, write:

asv continuous -f 1.1 -E virtualenv upstream/master HEAD

The -E virtualenv option should be added to all asv commands that run benchmarks. The default value is defined in asv.conf.json.

Running the full benchmark suite can take up to one hour and use up a few GBs of RAM. Usually it is sufficient to paste only a subset of the results into the pull request to show that the committed changes do not cause unexpected performance regressions. You can run specific benchmarks using the -b flag, which takes a regular expression. For example, this will only run tests from a xarray/asv_bench/benchmarks/ file:

asv continuous -f 1.1 upstream/master HEAD -b ^groupby

If you want to only run a specific group of tests from a file, you can do it using . as a separator. For example:

asv continuous -f 1.1 upstream/master HEAD -b groupby.GroupByMethods

will only run the GroupByMethods benchmark defined in

You can also run the benchmark suite using the version of xarray already installed in your current Python environment. This can be useful if you do not have virtualenv or conda, or are using the develop approach discussed above; for the in-place build you need to set PYTHONPATH, e.g. PYTHONPATH="$PWD/.." asv [remaining arguments]. You can run benchmarks using an existing Python environment by:

asv run -e -E existing

or, to use a specific Python interpreter,:

asv run -e -E existing:python3.6

This will display stderr from the benchmarks, and use your local python that comes from your $PATH.

Information on how to write a benchmark and how to use asv can be found in the asv documentation.

The xarray benchmarking suite is run remotely and the results are available here.

Documenting your code

Changes should be reflected in the release notes located in doc/whats-new.rst. This file contains an ongoing change log for each release. Add an entry to this file to document your fix, enhancement or (unavoidable) breaking change. Make sure to include the GitHub issue number when adding your entry (using :issue:`1234`, where 1234 is the issue/pull request number).

If your code is an enhancement, it is most likely necessary to add usage examples to the existing documentation. This can be done following the section regarding documentation above.

Contributing your changes to xarray

Committing your code

Keep style fixes to a separate commit to make your pull request more readable.

Once you’ve made changes, you can see them by typing:

git status

If you have created a new file, it is not being tracked by git. Add it by typing:

git add path/to/

Doing ‘git status’ again should give something like:

# On branch shiny-new-feature
#       modified:   /relative/path/to/

The following defines how a commit message should be structured:

  • A subject line with < 72 chars.

  • One blank line.

  • Optionally, a commit message body.

Please reference the relevant GitHub issues in your commit message using GH1234 or #1234. Either style is fine, but the former is generally preferred.

Now you can commit your changes in your local repository:

git commit -m

Pushing your changes

When you want your changes to appear publicly on your GitHub page, push your forked feature branch’s commits:

git push origin shiny-new-feature

Here origin is the default name given to your remote repository on GitHub. You can see the remote repositories:

git remote -v

If you added the upstream repository as described above you will see something like:

origin (fetch)
origin (push)
upstream        git:// (fetch)
upstream        git:// (push)

Now your code is on GitHub, but it is not yet a part of the xarray project. For that to happen, a pull request needs to be submitted on GitHub.

Review your code

When you’re ready to ask for a code review, file a pull request. Before you do, once again make sure that you have followed all the guidelines outlined in this document regarding code style, tests, performance tests, and documentation. You should also double check your branch changes against the branch it was based on:

  1. Navigate to your repository on GitHub –

  2. Click on Branches

  3. Click on the Compare button for your feature branch

  4. Select the base and compare branches, if necessary. This will be master and shiny-new-feature, respectively.

Finally, make the pull request

If everything looks good, you are ready to make a pull request. A pull request is how code from a local repository becomes available to the GitHub community and can be looked at and eventually merged into the master version. This pull request and its associated changes will eventually be committed to the master branch and available in the next release. To submit a pull request:

  1. Navigate to your repository on GitHub

  2. Click on the Pull Request button

  3. You can then click on Commits and Files Changed to make sure everything looks okay one last time

  4. Write a description of your changes in the Preview Discussion tab

  5. Click Send Pull Request.

This request then goes to the repository maintainers, and they will review the code. If you need to make more changes, you can make them in your branch, add them to a new commit, push them to GitHub, and the pull request will automatically be updated. Pushing them to GitHub again is done by:

git push origin shiny-new-feature

This will automatically update your pull request with the latest code and restart the Continuous Integration tests.

Delete your merged branch (optional)

Once your feature branch is accepted into upstream, you’ll probably want to get rid of the branch. First, update your master branch to check that the merge was successful:

git fetch upstream
git checkout master
git merge upstream/master

Then you can do:

git branch -D shiny-new-feature

You need to use a upper-case -D because the branch was squashed into a single commit before merging. Be careful with this because git won’t warn you if you accidentally delete an unmerged branch.

If you didn’t delete your branch using GitHub’s interface, then it will still exist on GitHub. To delete it there do:

git push origin --delete shiny-new-feature

PR checklist

  • Properly comment and document your code. See “Documenting your code”.

  • Test that the documentation builds correctly by typing make html in the doc directory. This is not strictly necessary, but this may be easier than waiting for CI to catch a mistake. See “Contributing to the documentation”.

  • Test your code.

    • Write new tests if needed. See “Test-driven development/code writing”.

    • Test the code using Pytest. Running all tests (type pytest in the root directory) takes a while, so feel free to only run the tests you think are needed based on your PR (example: pytest xarray/tests/ CI will catch any failing tests.

  • Properly format your code and verify that it passes the formatting guidelines set by Black and Flake8. See “Code formatting”. You can use pre-commit to run these automatically on each commit.

    • Run black . in the root directory. This may modify some files. Confirm and commit any formatting changes.

    • Run flake8 in the root directory. If this fails, it will log an error message.

  • Push your code and create a PR on GitHub.

  • Use a helpful title for your pull request by summarizing the main contributions rather than using the latest commit message. If the PR addresses an issue, please reference it.