<commit_before>Seaborn: statistical data visualization
=======================================

<div class="row">
<a href=http://stanford.edu/~mwaskom/software/seaborn/examples/anscombes_quartet.html>
<img src="http://stanford.edu/~mwaskom/software/seaborn/_static/anscombes_quartet_thumb.png" height="135" width="135">
</a>

<a href=http://stanford.edu/~mwaskom/software/seaborn/examples/timeseries_from_dataframe.html>
<img src="http://stanford.edu/~mwaskom/software/seaborn/_static/timeseries_from_dataframe_thumb.png" height="135" width="135">
</a>

<a href=http://stanford.edu/~mwaskom/software/seaborn/examples/distplot_options.html>
<img src="http://stanford.edu/~mwaskom/software/seaborn/_static/distplot_options_thumb.png" height="135" width="135">
</a>    

<a href=http://stanford.edu/~mwaskom/software/seaborn/examples/regression_marginals.html>
<img src="http://stanford.edu/~mwaskom/software/seaborn/_static/regression_marginals_thumb.png" height="135" width="135">
</a>

<a href=http://stanford.edu/~mwaskom/software/seaborn/examples/violinplots.html>
<img src="http://stanford.edu/~mwaskom/software/seaborn/_static/violinplots_thumb.png" height="135" width="135">
</a>

</div>

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 [here](http://stanford.edu/~mwaskom/software/seaborn/).

Examples
--------

The documentation has an [example gallery](http://stanford.edu/~mwaskom/software/seaborn/examples/index.html) with short scripts showing how to use different parts of the package. You can also check out the example notebooks:

- [Controlling figure aesthetics in seaborn](http://nbviewer.ipython.org/github/mwaskom/seaborn/blob/master/examples/aesthetics.ipynb)

- [Graphical representations of linear models](http://nbviewer.ipython.org/github/mwaskom/seaborn/blob/master/examples/linear_models.ipynb)

- [Visualizing distributions of data](http://nbviewer.ipython.org/github/mwaskom/seaborn/blob/master/examples/plotting_distributions.ipynb)

- [Plotting statistical timeseries data](http://nbviewer.ipython.org/github/mwaskom/seaborn/blob/master/examples/timeseries_plots.ipynb)

Citing
------

Seaborn can be cited using a DOI provided through Zenodo: [![DOI](https://zenodo.org/badge/doi/10.5072/zenodo.12710.png)](http://dx.doi.org/10.5072/zenodo.12710)


Dependencies
------------

- Python 2.7 or 3.3+

### Mandatory

- [numpy](http://www.numpy.org/)

- [scipy](http://www.scipy.org/)

- [matplotlib](http://matplotlib.sourceforge.net)

- [pandas](http://pandas.pydata.org/)

### Recommended

- [statsmodels](http://statsmodels.sourceforge.net/)

- [patsy](http://patsy.readthedocs.org/en/latest/)


Installation
------------

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


Testing
-------

[![Build Status](https://travis-ci.org/mwaskom/seaborn.png?branch=master)](https://travis-ci.org/mwaskom/seaborn)

To test seaborn, run `make test` in the source directory. This will run the
unit-test suite (using `nose`). It will also execute the example notebooks and
compare the outputs of each cell to the data in the stored versions. 


Development
-----------

https://github.com/mwaskom/seaborn

Please [submit](https://github.com/mwaskom/seaborn/issues/new) any bugs you encounter to the Github issue tracker.

License
-------

Released under a BSD (3-clause) license


Celebrity Endorsements
----------------------

"Those are nice plots" -<NAME>
<commit_msg>Add link to development docs<commit_after>Seaborn: statistical data visualization
=======================================

<div class="row">
<a href=http://stanford.edu/~mwaskom/software/seaborn/examples/anscombes_quartet.html>
<img src="http://stanford.edu/~mwaskom/software/seaborn/_static/anscombes_quartet_thumb.png" height="135" width="135">
</a>

<a href=http://stanford.edu/~mwaskom/software/seaborn/examples/timeseries_from_dataframe.html>
<img src="http://stanford.edu/~mwaskom/software/seaborn/_static/timeseries_from_dataframe_thumb.png" height="135" width="135">
</a>

<a href=http://stanford.edu/~mwaskom/software/seaborn/examples/distplot_options.html>
<img src="http://stanford.edu/~mwaskom/software/seaborn/_static/distplot_options_thumb.png" height="135" width="135">
</a>    

<a href=http://stanford.edu/~mwaskom/software/seaborn/examples/regression_marginals.html>
<img src="http://stanford.edu/~mwaskom/software/seaborn/_static/regression_marginals_thumb.png" height="135" width="135">
</a>

<a href=http://stanford.edu/~mwaskom/software/seaborn/examples/violinplots.html>
<img src="http://stanford.edu/~mwaskom/software/seaborn/_static/violinplots_thumb.png" height="135" width="135">
</a>

</div>

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 [here](http://stanford.edu/~mwaskom/software/seaborn/).

There are docs for the development version [here](http://stanford.edu/~mwaskom/software/seaborn-dev/). These should more or less correspond with the github master branch, but they're not currently built automatically and thus may fall out of sync at times. Also, note that the API docs should always stay up to date, but the tutorials may lag behind.

Examples
--------

The documentation has an [example gallery](http://stanford.edu/~mwaskom/software/seaborn/examples/index.html) with short scripts showing how to use different parts of the package. You can also check out the example notebooks:

- [Controlling figure aesthetics in seaborn](http://nbviewer.ipython.org/github/mwaskom/seaborn/blob/master/examples/aesthetics.ipynb)

- [Graphical representations of linear models](http://nbviewer.ipython.org/github/mwaskom/seaborn/blob/master/examples/linear_models.ipynb)

- [Visualizing distributions of data](http://nbviewer.ipython.org/github/mwaskom/seaborn/blob/master/examples/plotting_distributions.ipynb)

- [Plotting statistical timeseries data](http://nbviewer.ipython.org/github/mwaskom/seaborn/blob/master/examples/timeseries_plots.ipynb)

Citing
------

Seaborn can be cited using a DOI provided through Zenodo: [![DOI](https://zenodo.org/badge/doi/10.5072/zenodo.12710.png)](http://dx.doi.org/10.5072/zenodo.12710)


Dependencies
------------

- Python 2.7 or 3.3+

### Mandatory

- [numpy](http://www.numpy.org/)

- [scipy](http://www.scipy.org/)

- [matplotlib](http://matplotlib.sourceforge.net)

- [pandas](http://pandas.pydata.org/)

### Recommended

- [statsmodels](http://statsmodels.sourceforge.net/)

- [patsy](http://patsy.readthedocs.org/en/latest/)


Installation
------------

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


Testing
-------

[![Build Status](https://travis-ci.org/mwaskom/seaborn.png?branch=master)](https://travis-ci.org/mwaskom/seaborn)

To test seaborn, run `make test` in the source directory. This will run the
unit-test suite (using `nose`). It will also execute the example notebooks and
compare the outputs of each cell to the data in the stored versions. 


Development
-----------

https://github.com/mwaskom/seaborn

Please [submit](https://github.com/mwaskom/seaborn/issues/new) any bugs you encounter to the Github issue tracker.

License
-------

Released under a BSD (3-clause) license


Celebrity Endorsements
----------------------

"Those are nice plots" -<NAME>

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