Statistical data visualization
Project description
seaborn: statistical data visualization
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.
There is also a FAQ page, currently hosted on GitHub.
Dependencies
Seaborn supports Python 3.7+ and no longer supports Python 2.
Installation requires numpy, pandas, and matplotlib. Some advanced statistical functionality requires scipy and/or statsmodels.
Installation
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.
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.
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