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Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics.

Online documentation is available atseaborn.pydata.org.

The docs include atutorial,example gallery,API reference,FAQ, and other useful information.

To build the documentation locally, please refer todoc/README.md.

Seaborn supports Python 3.8+.

Installation requiresnumpy,pandas, andmatplotlib. Some advanced statistical functionality requiresscipy and/orstatsmodels.

The latest stable release (and required dependencies) can be installed from PyPI:

pip install seaborn

It is also possible to include optional statistical dependencies:

pip install seaborn[stats]

Seaborn can also be installed with conda:

conda install seaborn

Note that the main anaconda repository lags PyPI in adding new releases, but conda-forge (-c conda-forge) typically updates quickly.

A paper describing seaborn has been published in theJournal of Open Source Software. 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.

Testing seaborn requires installing additional dependencies; they can be installed with thedev extra (e.g.,pip install .[dev]).

To test the code, runmake test in the source directory. This will exercise the unit tests (usingpytest) and generate a coverage report.

Code style is enforced withflake8 using the settings in thesetup.cfg file. Runmake lint to check. Alternately, you can usepre-commit to automatically run lint checks on any files you are committing: just runpre-commit install to set it up, and then commit as usual going forward.

Seaborn development takes place on Github:https://github.com/mwaskom/seaborn

Please submit bugs that you encounter to theissue tracker with a reproducible example demonstrating the problem. Questions about usage are more at home on StackOverflow, where there is aseaborn tag.