Contributing Guide

Types of Contributions

The largest amount of work consists simply of implementing the pandas API using Spark’s built-in functions, which is usually straightforward. But there are many different forms of contributions in addition to writing code:

  1. Use the project and provide feedback, by creating new tickets or commenting on existing relevant tickets.

  2. Review existing pull requests.

  3. Improve the project’s documentation.

  4. Write blog posts or tutorial articles evangelizing Koalas and help new users learn Koalas.

  5. Give a talk about Koalas at your local meetup or a conference.

Step-by-step Guide For Code Contributions

  1. Read and understand the Design Principles for the project. Contributions should follow these principles.

  2. Signaling your work: If you are working on something, comment on the relevant ticket that you are doing so to avoid multiple people taking on the same work at the same time. It is also a good practice to signal that your work has stalled or you have moved on and want somebody else to take over.

  3. Understand what the functionality is in pandas or in Spark.

  4. Implement the functionality, with test cases providing close to 100% statement coverage. Document the functionality.

  5. Run existing and new test cases to make sure they still pass. Also run the linter dev/lint-python.

  6. Build the docs (make html in docs directory) and verify the docs related to your change look OK.

  7. Submit a pull request, and be responsive to code review feedback from other community members.

That’s it. Your contribution, once merged, will be available in the next release.

Environment Setup

Conda

We recommend setting up a Conda environment for development:

# Python 3.6+ is required
conda create --name koalas-dev-env python=3.6
conda activate koalas-dev-env
conda install -c conda-forge pyspark=2.4
conda install -c conda-forge --yes --file requirements-dev.txt
pip install -e .  # installs koalas from current checkout

Once setup, make sure you switch to koalas-dev-env before development:

conda activate koalas-dev-env

pip

You can use pip alternatively if your Python is 3.5+.

pip install pyspark==2.4
pip install -r requirements-dev.txt
pip install -e .  # installs koalas from current checkout

Running Tests

There is a script ./dev/pytest which is exactly same as pytest but with some default settings to run Koalas tests easily.

To run all the tests, similar to our CI pipeline:

# Run all unittest and doctest
./dev/pytest

To run a specific test file:

# Run unittest
./dev/pytest -k test_dataframe.py

# Run doctest
./dev/pytest -k series.py --doctest-modules databricks

To run a specific doctest/unittest:

# Run unittest
./dev/pytest -k "DataFrameTest and test_Dataframe"

# Run doctest
./dev/pytest -k DataFrame.corr --doctest-modules databricks

Note that -k is used for simplicity although it takes an expression. You can use –verbose to check what to filter. See pytest –help for more details.

Building Documentation

To build documentation via Sphinx:

cd docs && make clean html

It generates HTMLs under docs/build/html directory. Open docs/build/html/index.html to check if documentation is built properly.

Coding Conventions

We follow PEP 8 with one exception: lines can be up to 100 characters in length, not 79.

Doctest Conventions

When writing doctests, usually the doctests in pandas are converted into Koalas to make sure the same codes work in Koalas. In general, doctests should be grouped logically by separating a newline.

For instance, the first block is for the statements for preparation, the second block is for using the function with a specific argument, and third block is for another argument. As a example, please refer DataFrame.rsub in pandas.

These blocks should be consistently separated in Koalas, and more doctests should be added if the coverage of the doctests or the number of examples to show is not enough even though they are different from pandas’.

Release Instructions

Only project maintainers can do the following.

  1. Make sure version is set correctly in databricks/koalas/version.py.

  2. Make sure the build is green.

  3. Create a new release on GitHub. Tag it as the same version as the setup.py. If the version is “0.1.0”, tag the commit as “v0.1.0”.

  4. Upload the package to PyPi:

rm -rf dist/koalas*
python setup.py sdist bdist_wheel
export package_version=$(python setup.py --version)
echo $package_version

python3 -m pip install --user --upgrade twine

# for test
python3 -m twine upload --repository-url https://test.pypi.org/legacy/ dist/koalas-$package_version-py3-none-any.whl dist/koalas-$package_version.tar.gz

# for release
python3 -m twine upload --repository-url https://upload.pypi.org/legacy/ dist/koalas-$package_version-py3-none-any.whl dist/koalas-$package_version.tar.gz
  1. Verify the uploaded package can be installed and executed. One unofficial tip is to run the doctests of Koalas within a Python interpreter after installing it.

import os

from pytest import main
import databricks

test_path = os.path.abspath(os.path.dirname(databricks.__file__))
main(['-k', '-to_delta -read_delta', '--verbose', '--showlocals', '--doctest-modules', test_path])

Note that this way might require additional settings, for instance, environment variables.

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