Contributing to pandas

Where to start?

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

If you are brand new to pandas 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 Docs 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.

When you start working on an issue, it’s a good idea to assign the issue to yourself, so nobody else duplicates the work on it. GitHub restricts assigning issues to maintainers of the project only. In most projects, and until recently in pandas, contributors added a comment letting others know they are working on an issue. While this is ok, you need to check each issue individually, and it’s not possible to find the unassigned ones.

For this reason, we implemented a workaround consisting of adding a comment with the exact text take. When you do it, a GitHub action will automatically assign you the issue (this will take seconds, and may require refreshing the page to see it). By doing this, it’s possible to filter the list of issues and find only the unassigned ones.

So, a good way to find an issue to start contributing to pandas is to check the list of unassigned good first issues and assign yourself one you like by writing a comment with the exact text take.

If for whatever reason you are not able to continue working with the issue, please try to unassign it, so other people know it’s available again. You can check the list of assigned issues, since people may not be working in them anymore. If you want to work on one that is assigned, feel free to kindly ask the current assignee if you can take it (please allow at least a week of inactivity before considering work in the issue discontinued).

We have several contributor community communication channels, which you are welcome to join, and ask questions as you figure things out. Among them are regular meetings for new contributors, dev meetings, a dev mailing list, and a slack for the contributor community. All pandas contributors are welcome to these spaces, where they can connect with each other. Even maintainers who have been with us for a long time felt just like you when they started out, and are happy to welcome you and support you as you get to know how we work, and where things are. Take a look at the next sections to learn more.

Bug reports and enhancement requests

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

Trying the bug-producing code out on the main branch is often a worthwhile exercise to confirm 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:

    ```python
>>> from pandas import DataFrame
>>> df = DataFrame(...)
...
```

  2. Include the full version string of pandas and its dependencies. You can use the built-in function:

    >>> import pandas as pd
>>> pd.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 pandas 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 pandas code base.

Version control, Git, and GitHub

To the new user, working with Git is one of the more daunting aspects of contributing to pandas. 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.

Forking

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

git clone https://github.com/your-user-name/pandas.git pandas-yourname
cd pandas-yourname
git remote add upstream https://github.com/pandas-dev/pandas.git

This creates the directory pandas-yourname and connects your repository to the upstream (main project) pandas repository.

Note that performing a shallow clone (with --depth==N, for some N greater or equal to 1) might break some tests and features as pd.show_versions() as the version number cannot be computed anymore.

Creating a branch

You want your main branch to reflect only production-ready code, so create a feature branch for 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 pandas. You can have many shiny-new-features and switch in between them using the git checkout command.

When creating this branch, make sure your main branch is up to date with the latest upstream main version. To update your local main branch, you can do:

git checkout main
git pull upstream main --ff-only

When you want to update the feature branch with changes in main after you created the branch, check the section on updating a PR.

Contributing your changes to pandas

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/file-to-be-added.py

Doing ‘git status’ again should give something like:

# On branch shiny-new-feature
#
#       modified:   /relative/path/to/file-you-added.py
#

Finally, commit your changes to your local repository with an explanatory message. pandas uses a convention for commit message prefixes and layout. Here are some common prefixes along with general guidelines for when to use them:

  • ENH: Enhancement, new functionality

  • BUG: Bug fix

  • DOC: Additions/updates to documentation

  • TST: Additions/updates to tests

  • BLD: Updates to the build process/scripts

  • PERF: Performance improvement

  • TYP: Type annotations

  • CLN: Code cleanup

The following defines how a commit message should be structured. Please reference the relevant GitHub issues in your commit message using GH1234 or #1234. Either style is fine, but the former is generally preferred:

  • a subject line with < 80 chars.

  • One blank line.

  • Optionally, a commit message body.

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  git@github.com:yourname/pandas.git (fetch)
origin  git@github.com:yourname/pandas.git (push)
upstream        git://github.com/pandas-dev/pandas.git (fetch)
upstream        git://github.com/pandas-dev/pandas.git (push)

Now your code is on GitHub, but it is not yet a part of the pandas 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 – https://github.com/your-user-name/pandas

  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 main 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 main version. This pull request and its associated changes will eventually be committed to the main 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.

Updating your pull request

Based on the review you get on your pull request, you will probably need to make some changes to the code. In that case, you can make them in your branch, add a new commit to that branch, push it to GitHub, and the pull request will be automatically 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.

Another reason you might need to update your pull request is to solve conflicts with changes that have been merged into the main branch since you opened your pull request.

To do this, you need to “merge upstream main” in your branch:

git checkout shiny-new-feature
git fetch upstream
git merge upstream/main

If there are no conflicts (or they could be fixed automatically), a file with a default commit message will open, and you can simply save and quit this file.

If there are merge conflicts, you need to solve those conflicts. See for example at https://help.github.com/articles/resolving-a-merge-conflict-using-the-command-line/ for an explanation on how to do this. Once the conflicts are merged and the files where the conflicts were solved are added, you can run git commit to save those fixes.

If you have uncommitted changes at the moment you want to update the branch with main, you will need to stash them prior to updating (see the stash docs). This will effectively store your changes and they can be reapplied after updating.

After the feature branch has been update locally, you can now update your pull request by pushing to the branch on GitHub:

git push origin shiny-new-feature

Autofixing formatting errors

We use several styling checks (e.g. black, flake8, isort) which are run after you make a pull request.

To automatically fix formatting errors on each commit you make, you can set up pre-commit yourself. First, create a Python environment and then set up pre-commit.

Delete your merged branch (optional)

Once your feature branch is accepted into upstream, you’ll probably want to get rid of the branch. First, merge upstream main into your branch so git knows it is safe to delete your branch:

git fetch upstream
git checkout main
git merge upstream/main

Then you can do:

git branch -d shiny-new-feature

Make sure you use a lower-case -d, or else git won’t warn you if your feature branch has not actually been merged.

The branch will still exist on GitHub, so to delete it there do:

git push origin --delete shiny-new-feature

Tips for a successful pull request

If you have made it to the Review your code phase, one of the core contributors may take a look. Please note however that a handful of people are responsible for reviewing all of the contributions, which can often lead to bottlenecks.

To improve the chances of your pull request being reviewed, you should:

  • Reference an open issue for non-trivial changes to clarify the PR’s purpose

  • Ensure you have appropriate tests. These should be the first part of any PR

  • Keep your pull requests as simple as possible. Larger PRs take longer to review

  • Ensure that CI is in a green state. Reviewers may not even look otherwise

  • Keep Updating your pull request, either by request or every few days

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