Need help with seaborn?
Click the “chat” button below for chat support from the developer who created it, or find similar developers for support.

About the developer

mwaskom
8.4K Stars 1.4K Forks BSD 3-Clause "New" or "Revised" License 2.8K Commits 95 Opened issues

Description

Statistical data visualization using matplotlib

Services available

!
?

Need anything else?

Contributors list



seaborn: statistical data visualization

PyPI Version License DOI Tests Code Coverage

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

Documentation

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
.

Dependencies

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.

Installation

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

pip install seaborn

It is also possible to include the optional dependencies:

pip install seaborn[all]

You may instead want to use the development version from Github:

pip install git+https://github.com/mwaskom/seaborn.git

Seaborn is also available from Anaconda and can be installed with conda:

conda install seaborn

Note that the main anaconda repository typically lags PyPI in adding new releases.

Citing

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

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.

Development

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.

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.