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seaborn: statistical data visualization
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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](https://seaborn.pydata.org).
The docs include a [tutorial](https://seaborn.pydata.org/tutorial.html), [example gallery](https://seaborn.pydata.org/examples/index.html), [API reference](https://seaborn.pydata.org/api.html), and other useful information.
To build the documentation locally, please refer to [`doc/README.md`](doc/README.md).
Dependencies
------------
Seaborn supports Python 3.6+ and no longer supports Python 2.
Installation requires [numpy](https://numpy.org/), [scipy](https://www.scipy.org/), [pandas](https://pandas.pydata.org/), and [matplotlib](https://matplotlib.org/). Some functions will optionally use [statsmodels](https://www.statsmodels.org/) if it is installed.
Installation
------------
The latest stable release (and older versions) can be installed from PyPI:
pip install seaborn
You may instead want to use the development version from Github:
pip install git+https://github.com/mwaskom/seaborn.git#egg=seaborn
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](https://docs.pytest.org/)) and generate a coverate 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`](./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](https://github.com/mwaskom/seaborn/issues) with a reproducible example demonstrating the problem. Questions about usage are more at home on StackOverflow, where there is a [seaborn tag](https://stackoverflow.com/questions/tagged/seaborn).
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