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
Representing variability in timeseries plots
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Python 2.7
To install the released version, just do
pip install seaborn
However, I update the code pretty frequently, so you may want to clone the github repository and install with
python setup.py install
from within the source directory.
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.
"Those are nice plots" -Hadley Wickham