Please submit bugs that you encounter to the issue tracker with a reproducible example demonstrating the problem. Seaborn development takes place on Github: Alternately, you can use pre-commit to automatically run lint checks on any files you are committing: just run pre-commit install to set it up, and then commit as usual going forward. This will exercise the unit tests (using pytest) and generate a coverage report.Ĭode style is enforced with flake8 using the settings in the setup.cfg file. To test the code, run make test in the source directory. Testing seaborn requires installing additional dependencies they can be installed with the dev extra (e.g., pip install. The paper provides an introduction to the key features of the library, and it can be used as a citation if seaborn proves integral to a scientific publication. CitingĪ paper describing seaborn has been published in the Journal of Open Source Software. Note that the main anaconda repository lags PyPI in adding new releases, but conda-forge ( -c conda-forge) typically updates quickly.

Seaborn can also be installed with conda: conda install seaborn It is also possible to include optional statistical dependencies (only relevant for v0.12+): pip install seaborn The latest stable release (and required dependencies) can be installed from PyPI: pip install seaborn

Some advanced statistical functionality requires scipy and/or statsmodels. Installation requires numpy, pandas, and matplotlib. Seaborn supports Python 3.7+ and no longer supports Python 2. To build the documentation locally, please refer to doc/README.md. The docs include a tutorial, example gallery, API reference, FAQ, and other useful information. It provides a high-level interface for drawing attractive statistical graphics.

Seaborn is a Python visualization library based on matplotlib.

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