seaborn: statistical data visualization
Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics.
Online documentation is available at seaborn.pydata.org.
To build the documentation locally, please refer to
There is also a FAQ page, currently hosted on GitHub.
Seaborn supports Python 3.7+ and no longer supports Python 2.
The latest stable release (and required dependencies) can be installed from PyPI:
pip install seaborn
It is also possible to include optional statistical dependencies (only relevant for v0.12+):
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 the Journal 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 the
dev extra (e.g.,
pip install .[dev]).
To test the code, run
make test in the source directory. This will exercise the unit tests (using pytest) and generate a coverage report.
Code style is enforced with
flake8 using the settings in the
setup.cfg file. Run
make lint to check. 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.
Seaborn development takes place on Github: https://github.com/mwaskom/seaborn
Please submit bugs that you encounter to the issue tracker with a reproducible example demonstrating the problem. Questions about usage are more at home on StackOverflow, where there is a seaborn tag.