# seaborn **Repository Path**: Relph/seaborn ## Basic Information - **Project Name**: seaborn - **Description**: Statistical data visualization using matplotlib - **Primary Language**: Unknown - **License**: BSD-3-Clause - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2020-03-08 - **Last Updated**: 2022-06-06 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README seaborn: statistical data visualization =======================================
-------------------------------------- [](https://pypi.org/project/seaborn/) [](https://github.com/mwaskom/seaborn/blob/master/LICENSE) [](https://doi.org/10.5281/zenodo.592845) [](https://travis-ci.org/mwaskom/seaborn) [](https://codecov.io/gh/mwaskom/seaborn) 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. Dependencies ------------ Seaborn supports Python 3.6+ and no longer supports Python 2. Installation requires [numpy](http://www.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 ------- To test the code, run `make test` in the source directory. This will exercise both the unit tests and docstring examples (using `pytest`). The doctests require a network connection (unless all example datasets are cached), but the unit tests can be run offline with `make unittests`. Run `make coverage` to generate a test coverage report and `make lint` to check code style consistency. 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).