Development Guidelines

Dask is a community maintained project. We welcome contributions in the form of bug reports, documentation, code, design proposals, and more. This page provides resources on how best to contribute.

Where to ask for help

Dask conversation happens in the following places:

  1. StackOverflow #dask tag: for usage questions
  2. Github Issue Tracker: for discussions around new features or established bugs
  3. Gitter chat: for real-time discussion

For usage questions and bug reports we strongly prefer the use of StackOverflow and Github issues over gitter chat. Github and StackOverflow are more easily searchable by future users and so is more efficient for everyone’s time. Gitter chat is generally reserved for community discussion.

Separate Code Repositories

Dask maintains code and documentation in a few git repositories hosted on the Github dask organization, This includes the primary repository and several other repositories for different components. A non-exhaustive list follows:

Git and Github can be challenging at first. Fortunately good materials exist on the internet. Rather than repeat these materials here we refer you to Pandas’ documentation and links on this subject at


The community discusses and tracks known bugs and potential features in the Github Issue Tracker. If you have a new idea or have identified a bug then you should raise it there to start public discussion.

If you are looking for an introductory issue to get started with development then check out the introductory label, which contains issues that are good for starting developers. Generally familiarity with Python, NumPy, Pandas, and some parallel computing are assumed.

Development Environment

Download code

Clone the main dask git repository (or whatever repository you’re working on.):

git clone


You may want to install larger dependencies like NumPy and Pandas using a binary package manager, like conda. You can skip this step if you already have these libraries, don’t care to use them, or have sufficient build environment on your computer to compile them when installing with pip:

conda install -y numpy pandas scipy bokeh

Install dask and dependencies:

cd dask
pip install -e .[complete]

For development dask uses the following additional dependencies:

pip install pytest moto mock

Run Tests

Dask uses py.test for testing. You can run tests from the main dask directory as follows:

py.test dask --verbose

Contributing to Code

Dask maintains development standards that are similar to most PyData projects. These standards include language support, testing, documentation, and style.

Python Versions

Dask supports Python versions 2.7, 3.4, 3.5, and 3.6 in a single codebase. Name changes are handled by the dask/ file.


Dask employs extensive unit tests to ensure correctness of code both for today and for the future. Test coverage is expected for all code contributions.

Tests are written in a py.test style with bare functions.

def test_fibonacci():
    assert fib(0) == 0
    assert fib(1) == 0
    assert fib(10) == 55
    assert fib(8) == fib(7) + fib(6)

    for x in [-3, 'cat', 1.5]:
        with pytest.raises(ValueError):

These tests should compromise well between covering all branches and fail cases and running quickly (slow test suites get run less often.)

You can run tests locally by running py.test in the local dask directory:

py.test dask --verbose

You can also test certain modules or individual tests for faster response:

py.test dask/dataframe --verbose

py.test dask/dataframe/tests/

Tests run automatically on the and Appveyor continuous testing frameworks on every push to every pull request on GitHub.

Tests are organized within the various modules’ subdirectories:


For the Dask collections like dask.array and dask.dataframe behavior is typically tested directly against the Numpy or Pandas libraries using the assert_eq functions:

import numpy as np
import dask.array as da
from dask.array.utils import assert_eq

def test_aggregations():
    nx = np.random.random(100)
    dx = da.from_array(x, chunks=(10,))

    assert_eq(nx.sum(), dx.sum())
    assert_eq(nx.min(), dx.min())
    assert_eq(nx.max(), dx.max())

This technique helps to ensure compatibility with upstream libraries, and tends to be simpler than testing correctness directly. Additionally, by passing Dask collections directly to the assert_eq function rather than call compute manually the testing suite is able to run a number of checks on the lazy collections themselves.


User facing functions should roughly follow the numpydoc standard, including sections for Parameters, Examples and general explanatory prose.

By default examples will be doc-tested. Reproducible examples in documentation is valuable both for testing and, more importantly, for communication of common usage to the user. Documentation trumps testing in this case and clear examples should take precedence over using the docstring as testing space. To skip a test in the examples add the comment # doctest: +SKIP directly after the line.

def fib(i):
    """ A single line with a brief explanation

    A more thorough description of the function, consisting of multiple
    lines or paragraphs.

    i: int
         A short description of the argument if not immediately clear

    >>> fib(4)
    >>> fib(5)
    >>> fib(6)
    >>> fib(-1)  # Robust to bad inputs

Docstrings are currently tested under Python 3.6 on You can test docstrings with pytest as follows:

py.test dask --doctest-modules

Docstring testing requires graphviz to be installed. This can be done via:

conda install -y graphviz


Dask verifies style uniformity with the flake8 tool.:

pip install flake8
flake8 dask


Every significative code contribution should be listed in the Changelog under the corresponding version. When submitting a Pull Request in Github please add to that file explaining what was added/modified.

Contributing to Documentation

Dask uses Sphinx for documentation, hosted on . Documentation is maintained in the RestructuredText markup language (.rst files) in dask/docs/source. The documentation consists both of prose and API documentation.

To build the documentation locally, first install requirements:

cd docs/
pip install -r requirements-docs.txt

Then build documentation with make:

make html

The resulting HTML files end up in the build/html directory.

You can now make edits to rst files and run make html again to update the affected pages.