(Contribution guidelines largely copied from pandas)
Contributions to GeoPandas are very welcome. They are likely to
be accepted more quickly if they follow these guidelines.
At this stage of GeoPandas development, the priorities are to define a
simple, usable, and stable API and to have clean, maintainable,
readable code. Performance matters, but not at the expense of those
In general, GeoPandas follows the conventions of the pandas project
In particular, when submitting a pull request:
All existing tests should pass. Please make sure that the test
suite passes, both locally and on
GitHub Actions. Status on
GHA will be visible on a pull request. GHA are automatically enabled
on your own fork as well. To trigger a check, make a PR to your own fork.
New functionality should include tests. Please write reasonable
tests for your code and make sure that they pass on your pull request.
Classes, methods, functions, etc. should have docstrings. The first
line of a docstring should be a standalone summary. Parameters and
return values should be documented explicitly.
Follow PEP 8 when possible. We use Black and Flake8 to ensure a consistent code
format throughout the project. For more details see
Imports should be grouped with standard library imports first,
3rd-party libraries next, and GeoPandas imports third. Within each
grouping, imports should be alphabetized. Always use absolute
imports when possible, and explicit relative imports for local
imports when necessary in tests.
GeoPandas supports Python 3.6+ only. The last version of GeoPandas
supporting Python 2 is 0.6.
There are seven basic steps to contributing to GeoPandas:
Fork the GeoPandas git repository
Create a development environment
Install GeoPandas dependencies
Make a development build of GeoPandas
Make changes to code and add tests
Update the documentation
Submit a Pull Request
Each of these 7 steps is detailed below.
To the new user, working with Git is one of the more daunting aspects of contributing to GeoPandas*.
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:
Software Carpentry’s Git Tutorial
the GitHub help pages.
Matthew Brett’s Pydagogue.
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 GeoPandas 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/geopandas.git geopandas-yourname
git remote add upstream git://github.com/geopandas/geopandas.git
This creates the directory geopandas-yourname and connects your repository to
the upstream (main project) GeoPandas repository.
The testing suite will run automatically on GitHub Actions once your pull request is
submitted. The test suite will also autmatically run on your branch so you can
check it prior to submitting the pull request.
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 GeoPandas. 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 GeoPandas 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
A development environment is a virtual space where you can keep an independent installation of GeoPandas.
This makes it easy to keep both a stable version of python in one place you use for work, and a development
version (which you may break while playing with code) in another.
An easy way to create a GeoPandas development environment is as follows:
Install either Anaconda or
Make sure that you have cloned the repository
cd to the geopandas* source directory
Tell conda to create a new environment, named geopandas_dev, or any other name you would like
for this environment, by running:
conda create -n geopandas_dev python
This will create the new environment, and not touch any of your existing environments,
nor any existing python installation.
To work in this environment, you need to activate it. The instructions below
should work for both Windows, Mac and Linux:
conda activate geopandas_dev
Once your environment is activated, you will see a confirmation message to
indicate you are in the new development environment.
To view your environments:
conda info -e
To return to you home root environment:
See the full conda docs here.
At this point you can easily do a development install, as detailed in the next sections.
To run GeoPandas in an development environment, you must first install
GeoPandas’s dependencies. We suggest doing so using the following commands
(executed after your development environment has been activated):
conda install -c conda-forge pandas fiona shapely pyproj rtree pytest
This should install all necessary dependencies.
Once dependencies are in place, make an in-place build by navigating to the git
clone of the GeoPandas repository and running:
python setup.py develop
GeoPandas 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 GeoPandas. Therefore,
it is worth getting in the habit of writing tests ahead of time so this is never an issue.
GeoPandas uses the pytest testing system and the convenient
extensions in numpy.testing.
All tests should go into the tests directory. This folder contains many
current examples of tests, and we suggest looking to these for inspiration.
The .util module has some special assert functions that
make it easier to make statements about whether GeoSeries or GeoDataFrame
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, using eg the function assert_geoseries_equal.
The tests can then be run directly inside your Git clone (without having to
install GeoPandas) by typing:
GeoPandas documentation resides in the doc folder. Changes to the docs are make by
modifying the appropriate file in the source folder within doc. GeoPandas docs use
mixture of reStructuredText syntax for rst files, which is explained here and MyST syntax for md
files explained here.
The docstrings follow the Numpy Docstring standard. Some pages
and examples are Jupyter notebooks converted to docs using nbsphinx. Jupyter notebooks should be stored without the output.
Once you have made your changes, you may try if they render correctly by
building the docs using sphinx. To do so, you can navigate to the doc folder
The resulting html pages will be located in doc/build/html. In case of any errors, you
can try to use make html within a new environment based on environment.yml
specification in the doc folder. You may need to register Jupyter kernel as
geopandas_docs. Using conda:
conda env create -f environment.yml
conda activate geopandas_docs
python -m ipykernel install --user --name geopandas_docs
For minor updates, you can skip whole make html part as reStructuredText and MyST
syntax are usually quite straightforward.
Once you’ve made changes and pushed them to your forked repository, you then
submit a pull request to have them integrated into the GeoPandas code base.
You can find a pull request (or PR) tutorial in the GitHub’s Help Docs.
GeoPandas follows the PEP8 standard
and uses Black and
Flake8 to ensure a consistent code
format throughout the project.
Continuous Integration (GitHub Actions) will run those tools and
report any stylistic errors in your code. Therefore, it is helpful before
submitting code to run the check yourself:
git diff upstream/master -u -- "*.py" | flake8 --diff
to auto-format your code. Additionally, many editors have plugins that will
apply black as you edit files.
Optionally (but recommended), you can setup pre-commit hooks
to automatically run black and flake8 when you make a git commit. This
can be done by installing pre-commit:
$ python -m pip install pre-commit
From the root of the geopandas repository, you should then install the
pre-commit included in GeoPandas:
$ pre-commit install
Then black and flake8 will be run automatically
each time you commit changes. You can skip these checks with
git commit --no-verify.
git commit --no-verify
Commit your changes to your local repository with an explanatory message. GeoPandas
uses the pandas 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 refer to 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