Contributing to pandas-gbq¶
Table of contents:
All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome.
If you are simply looking to start working with the pandas-gbq codebase, navigate to the GitHub “issues” tab and start looking through interesting issues.
Or maybe through using pandas-gbq you have an idea of your own or are looking for something in the documentation and thinking ‘this can be improved’…you can do something about it!
Feel free to ask questions on the mailing list.
Bug reports are an important part of making pandas-gbq more stable. Having a complete bug report will allow others to reproduce the bug and provide insight into fixing it. Because many versions of pandas-gbq are supported, knowing version information will also identify improvements made since previous versions. Trying the bug-producing code out on the master 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:
Include a short, self-contained Python snippet reproducing the problem. You can format the code nicely by using GitHub Flavored Markdown
>>> from pandas_gbq import gbq >>> df = gbq.read_gbq(...) ...
Include the full version string of pandas-gbq.
>>> import pandas_gbq >>> pandas_gbq.__version__ ...
Explain why the current behavior is wrong/not desired and what you expect instead.
The issue will then show up to the pandas-gbq community and be open to comments/ideas from others.
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-gbq code base.
To the new user, working with Git is one of the more daunting aspects of contributing to pandas-gbq. 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.
Some great resources for learning 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 pandas-gbq project
page and hit the
Fork button. You will
want to clone your fork to your machine:
git clone email@example.com:your-user-name/pandas-gbq.git pandas-gbq-yourname cd pandas-gbq-yourname git remote add upstream git://github.com/googleapis/python-bigquery-pandas.git
This creates the directory pandas-gbq-yourname and connects your repository to the upstream (main project) pandas-gbq repository.
The testing suite will run automatically on CircleCI 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 CircleCI needs to be hooked up to your GitHub repository. Instructions for doing so are here.
You want your master 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-gbq. 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 rebase upstream/master
This will replay your commits on top of the latest pandas-gbq 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
stash them prior
to updating. This will effectively store your changes and they can be reapplied
It’s helpful to install pandas-gbq in development mode so that you can use the library without reinstalling the package after every change.
Create a new conda environment and install the necessary dependencies
$ conda create -n my-env --channel conda-forge \ db-dtypes \ pandas \ pydata-google-auth \ google-cloud-bigquery $ source activate my-env
Install pandas-gbq in development mode
$ python setup.py develop
Skip this section if you already followed the conda instructions.
Create a new virtual environment.
$ virtualenv env $ source env/bin/activate
You can install pandas-gbq and its dependencies in development mode via pip.
$ pip install -e .
Writing good code is not just about what you write. It is also about how you write it. During testing on Travis-CI, 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 pandas-gbq.
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.
pandas-gbq uses the PEP8 standard.
There are several tools to ensure you abide by this standard. Here are some of
the more common
we restrict line-length to 79 characters to promote readability
passing arguments should have spaces after commas, e.g.
foo(arg1, arg2, kw1='bar')
CircleCI will run the ‘black’ code formatting tool and report any stylistic errors in your code. Therefore, it is helpful before submitting code to run the formatter yourself:
pip install black black .
Please try to maintain backward compatibility. 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.
pandas-gbq 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 pandas-gbq. Therefore, it is worth getting in the habit of writing tests ahead of time so this is never an issue.
Like many packages, pandas-gbq uses pytest.
The tests can then be run directly inside your Git clone (without having to install pandas-gbq) by typing:
pytest tests/unit pytest tests/system.py
The tests suite is exhaustive and takes around 20 minutes to run. 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 tests/path/to/test.py -k regex_matching_test_name
Or with one of the following constructs:
pytest tests/[test-module].py pytest tests/[test-module].py::[TestClass] pytest tests/[test-module].py::[TestClass]::[test_method]
For more, see the pytest documentation.
pandas-gbq uses nox to automate testing in multiple Python environments. First, install nox.
$ pip install --upgrade nox-automation
To run tests in all versions of Python, run nox from the repository’s root directory.
You will need to create a Google BigQuery private key in JSON format in order to run Google BigQuery integration tests on your local machine and on CircleCI. The first step is to create a service account. Grant the service account permissions to run BigQuery queries and to create datasets and tables.
To run the integration tests locally, set the following environment variables
GBQ_PROJECT_IDwith the value being the ID of your BigQuery project.
GBQ_GOOGLE_APPLICATION_CREDENTIALSwith the value being the path to the JSON key that you downloaded for your service account.
Integration tests are skipped in pull requests because the credentials that are required for running Google BigQuery integration tests are configured in the CircleCI web interface and are only accessible from the googleapis/python-bigquery-pandas repository. The credentials won’t be available on forks of pandas-gbq. Here are the steps to run gbq integration tests on a forked repository:
Go to CircleCI and sign in with your GitHub account.
Switch to your personal account in the top-left organization switcher.
Use the “Add projects” tab to enable CircleCI for your fork.
Click on the gear icon to edit your CircleCI build, and add two environment variables:
GBQ_PROJECT_IDwith the value being the ID of your BigQuery project.
SERVICE_ACCOUNT_KEYwith the value being the base64-encoded contents of the JSON key that you downloaded for your service account.
Keep the contents of these variables confidential. These variables contain sensitive data and you do not want their contents being exposed in build logs.
Your branch should be tested automatically once it is pushed. You can check the status by visiting your Circle CI branches page which exists at the following location: https://circleci.com/gh/your-username/python-bigquery-pandas. Click on a build job for your branch.
Changes should follow convential commits. The release-please bot uses the commit message to create an ongoing change log.
If your code is an enhancement, it is most likely necessary to add usage
examples to the existing documentation. Further, to let users know when
this feature was added, the
versionadded directive is used. The sphinx
syntax for that is:
.. versionadded:: 0.1.3
This will put the text New in version 0.1.3 wherever you put the sphinx directive. This should also be put in the docstring when adding a new function or method.
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:
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-gbq uses conventional commit message prefixes and layout. Here are some common prefixes along with general guidelines for when to use them:
feat: Enhancement, new functionality
fix: Bug fix, performance improvement
doc: Additions/updates to documentation
deps: Change to package dependencies
test: Additions/updates to tests
chore: Updates to the build process/scripts
refactor: 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
If you have multiple commits, you may want to combine them into one commit, often referred to as “squashing” or “rebasing”. This is a common request by package maintainers when submitting a pull request as it maintains a more compact commit history. To rebase your commits:
git rebase -i HEAD~#
Where # is the number of commits you want to combine. Then you can pick the relevant commit message and discard others.
To squash to the master branch do:
git rebase -i master
s option on a commit to
squash, meaning to keep the commit messages,
fixup, meaning to merge the commit messages.
Then you will need to push the branch (see below) forcefully to replace the current commits with the new ones:
git push origin shiny-new-feature -f
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
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 firstname.lastname@example.org:yourname/pandas-gbq.git (fetch) origin email@example.com:yourname/pandas-gbq.git (push) upstream git://github.com/googleapis/python-bigquery-pandas.git (fetch) upstream git://github.com/googleapis/python-bigquery-pandas.git (push)
Now your code is on GitHub, but it is not yet a part of the pandas-gbq project. For that to happen, a pull request needs to be submitted on GitHub.
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:
Navigate to your repository on GitHub – https://github.com/your-user-name/pandas-gbq
Click on the
Comparebutton for your feature branch
comparebranches, if necessary. This will be
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:
Navigate to your repository on GitHub
Click on the
You can then click on
Files Changedto make sure everything looks okay one last time
Write a description of your changes in the
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, push them to GitHub, and the pull request will be automatically updated. Pushing them to GitHub again is done by:
git push -f origin shiny-new-feature
This will automatically update your pull request with the latest code and restart the Travis-CI tests.
Once your feature branch is accepted into upstream, you’ll probably want to get rid of the branch. First, merge upstream master into your branch so git knows it is safe to delete your branch:
git fetch upstream git checkout master git merge upstream/master
Then you can just 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