Contributor quickstart guide#

After getting the source code from GitHub, there are three steps to start contributing:

  1. Set up a development environment

    Using mamba, or some flavor of the many virtual environment management tools, you can make sure the development version of SciPy does not interfere with any other local installations of SciPy on your machine.

  2. Build SciPy

    SciPy uses compiled code for speed, which means you might need extra dependencies to complete this step depending on your system - see Building from source.

  3. Perform development tasks

    These can include any changes you want to make to the source code, running tests, building the documentation, running benchmarks, etc.

Basic workflow#


We strongly recommend using a user-activated environment setup, such as a conda or virtual environment.

Since SciPy contains parts written in C, C++, and Fortran that need to be compiled before use, make sure you have the necessary compilers and Python development headers installed. If you are using mamba, these will be installed automatically. If you are using pip, check which system-level dependencies you might need.

First, fork a copy of the main SciPy repository in GitHub onto your own account and then create your local repository via:

git clone scipy
cd scipy
git submodule update --init
git remote add upstream

Next, set up your development environment. With system-level dependencies installed, execute the following commands at the terminal from the base directory of your SciPy clone:

# Create an environment with all development dependencies
mamba env create -f environment.yml  # works with `conda` too
# Activate the environment
mamba activate scipy-dev
# Create the virtual environment
python -m venv $HOME/.venvs/scipy-dev
# Activate the environment
source $HOME/.venvs/scipy-dev/bin/activate
# Install python-level dependencies
python -m pip install numpy pytest cython pythran pybind11 meson ninja pydevtool rich-click hypothesis

Your command prompt now lists the name of your new environment, like so: (scipy-dev)$.

Finally, build SciPy for development and run the test suite with:

python test  # this will always (re)build as needed first

Notice that this will take a few minutes (and some really slow tests are disabled by default), so you might want to test only the part of SciPy you will be working on. For details on how to do that, see the more complete setup walkthrough in Development workflow, or python test --help.

Other workflows#

This is only one possible way to set up your development environment out of many. For more detailed instructions, see the SciPy contributor guide.


If you are having trouble building SciPy from source or setting up your local development environment, you can try to build SciPy with GitHub Codespaces. It allows you to create the correct development environment right in your browser, reducing the need to install local development environments and deal with incompatible dependencies.

If you have good internet connectivity and want a temporary set-up, it is often faster to work on SciPy in a Codespaces environment. For documentation on how to get started with Codespaces, see the Codespaces docs. When creating a codespace for the scipy/scipy repository, the default 2-core machine type works; 4-core will build and work a bit faster (but of course at a cost of halving your number of free usage hours). Once your codespace has started, you can run conda activate scipy-dev and your development environment is completely set up - you can then follow the relevant parts of the SciPy documentation to build, test, develop, write docs, and contribute to SciPy.

Another alternative is to use Gitpod. We do not maintain this solution anymore but some information can be found in previous versions of our docs.