seaborn-0.12.0b2.tar.gz

<img src="doc/_static/logo-wide-lightbg.svg"><br> -------------------------------------- seaborn: statistical data visualization ======================================= [](https://pypi.org/project/seaborn/) [](https://github.com/mwaskom/seaborn/blob/master/LICENSE) [](https://doi.org/10.21105/joss.03021) [](https://github.com/mwaskom/seaborn/actions) [](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. To build the documentation locally, please refer to [`doc/README.md`](doc/README.md). There is also a [FAQ](https://github.com/mwaskom/seaborn/wiki/Frequently-Asked-Questions-(FAQs)) page, currently hosted on GitHub. Dependencies ------------ Seaborn supports Python 3.7+ and no longer supports Python 2. Installation requires [numpy](https://numpy.org/), [pandas](https://pandas.pydata.org/), and [matplotlib](https://matplotlib.org/). Some functions will optionally use [scipy](https://www.scipy.org/) and/or [statsmodels](https://www.statsmodels.org/) if they are available. Installation ------------ The latest stable release (and required dependencies) can be installed from PyPI: pip install seaborn It is also possible to include optional dependencies (only relevant for v0.12+): pip install seaborn[all] Seaborn can also be installed with conda: conda install seaborn Note that the main anaconda repository typically lags PyPI in adding new releases, but conda-forge (`-c conda-forge`) typically updates quickly. Citing ------ A paper describing seaborn has been published in the [Journal of Open Source Software](https://joss.theoj.org/papers/10.21105/joss.03021). The paper provides an introduction to the key features of the library, and it can be used as a citation if seaborn proves integral to a scientific publication. 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 coverage 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).