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.
The docs include a tutorial, example gallery, API reference, and other useful information.
To build the documentation locally, please refer to doc/README.md.
There is also a FAQ page, currently hosted on GitHub.
Seaborn supports Python 3.7+ and no longer supports Python 2.
Installation requires numpy, pandas, and matplotlib. Some functions will optionally use scipy and/or statsmodels if they are available.
The latest stable release (and required dependencies) can be installed from PyPI:
pip install seaborn
It is also possible to include optional dependencies (only relevant for v0.12+):
pip install seaborn[all]
Seaborn can also be installed with conda:
conda install seaborn
Note that the main anaconda repository typically 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 packages listed in ci/utils.txt.
To test the code, run make test in the source directory. This will exercise both the unit tests and docstring examples (using pytest) and generate a coverage report.
The doctests require a network connection (unless all example datasets are cached), but the unit tests can be run offline with make unittests.
Code style is enforced with flake8 using the settings in the setup.cfg file. Run make lint to check.
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.