Seaborn is a library of high-level functions that facilitate making informative and attractive plots of statistical data using matplotlib. It also provides concise control over the aesthetics of the plots, improving on matplotlib's default look.

Online documentation is available here.

There are a few tutorial notebooks that offer some thoughts on visualizing statistical data in a general sense and show how to do it using the tools that are provided in seaborn. They also serve as the primary test suite for the package. The notebooks are meant to be fairly, but not completely comprehensive; hopefully the docstrings for the specific functions will answer any additional questions.

• Controlling figure aesthetics in seaborn

• Plotting complex linear models

• Visualizing distributions of data

• Plotting statistical timeseries data

• Python 2.7 or 3.3

• numpy

• scipy

• matplotlib

• pandas

• statsmodels

• patsy

• husl

• moss

Installing with pip will automatically install aside from numpy, scipy, and matplotlib.

To install the released version, just do

pip install seaborn

You may instead want to use the development version from Github, by running

pip install git+git://github.com/mwaskom/seaborn.git#egg=seaborn

To test seaborn, run make test in the source directory. This will execute the example notebooks and compare the outputs of each cell to the data in the stored versions. There is also a (small) set of unit tests for the utility functions that can be tested separately with nosetests.

https://github.com/mwaskom/seaborn

Please submit any bugs you encounter to the Github issue tracker.

Released under a BSD (3-clause) license

"Those are nice plots" -Hadley Wickham