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seaborn: statistical data visualization
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[](https://pypi.org/project/seaborn/)
[](https://github.com/mwaskom/seaborn/blob/master/LICENSE.md)
[](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), [FAQ](https://seaborn.pydata.org/faq), and other useful information.
To build the documentation locally, please refer to [`doc/README.md`](doc/README.md).
Dependencies
------------
Seaborn supports Python 3.8+.
Installation requires [numpy](https://numpy.org/), [pandas](https://pandas.pydata.org/), and [matplotlib](https://matplotlib.org/). Some advanced statistical functionality requires [scipy](https://www.scipy.org/) and/or [statsmodels](https://www.statsmodels.org/).
Installation
------------
The latest stable release (and required dependencies) can be installed from PyPI:
pip install seaborn
It is also possible to include optional statistical dependencies:
pip install seaborn[stats]
Seaborn can also be installed with conda:
conda install seaborn
Note that the main anaconda repository 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 dependencies; they can be installed with the `dev` extra (e.g., `pip install .[dev]`).
To test the code, run `make test` in the source directory. This will exercise the unit tests (using [pytest](https://docs.pytest.org/)) and generate a coverage report.
Code style is enforced with `flake8` using the settings in the [`setup.cfg`](./setup.cfg) file. Run `make lint` to check. Alternately, you can use `pre-commit` to automatically run lint checks on any files you are committing: just run `pre-commit install` to set it up, and then commit as usual going forward.
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).
seaborn-0.13.2.tar.gz
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seaborn-0.13.2.tar.gz （322个子文件） 
APPDIRS_LICENSE 1KB make.bat 800B CITATION.cff 512B setup.cfg 584B custom.css 2KB .gitignore 156B .gitignore 111B .gitignore 16B .gitkeep 0B
layout.html 1KB version.html 86B HUSL_LICENSE 1KB favicon_old.ico 264KB favicon.ico 15KB objects_interface.ipynb 34KB color_palettes.ipynb 34KB properties.ipynb 34KB distributions.ipynb 28KB relational.ipynb 21KB axis_grids.ipynb 20KB data_structure.ipynb 19KB categorical.ipynb 19KB function_overview.ipynb 19KB introduction.ipynb 18KB regression.ipynb 15KB error_bars.ipynb 15KB aesthetics.ipynb 12KB histplot.ipynb 11KB lineplot.ipynb 10KB FacetGrid.ipynb 8KB violinplot.ipynb 8KB objects.Plot.scale.ipynb 8KB scatterplot.ipynb 7KB stripplot.ipynb 7KB barplot.ipynb 7KB kdeplot.ipynb 7KB boxenplot.ipynb 7KB swarmplot.ipynb 7KB relplot.ipynb 7KB objects.Plot.add.ipynb 7KB PairGrid.ipynb 6KB pointplot.ipynb 6KB objects.KDE.ipynb 6KB JointGrid.ipynb 6KB objects.Plot.label.ipynb 6KB regplot.ipynb 6KB displot.ipynb 5KB objects.Hist.ipynb 5KB objects.Plot.pair.ipynb 5KB color_palette.ipynb 5KB objects.Plot.facet.ipynb 5KB boxplot.ipynb 5KB objects.Plot.on.ipynb 5KB objects.Plot.config.ipynb 5KB pairplot.ipynb 5KB jointplot.ipynb 5KB cubehelix_palette.ipynb 5KB catplot.ipynb 4KB objects.Dodge.ipynb 4KB objects.Dot.ipynb 4KB objects.Text.ipynb 4KB objects.Bar.ipynb 4KB heatmap.ipynb 4KB objects.Plot.theme.ipynb 4KB objects.Jitter.ipynb 4KB objects.Line.ipynb 4KB objects.Bars.ipynb 4KB move_legend.ipynb 4KB clustermap.ipynb 4KB objects.Est.ipynb 4KB diverging_palette.ipynb 4KB objects.Dash.ipynb 4KB objects.Area.ipynb 4KB lmplot.ipynb 4KB set_theme.ipynb 4KB objects.Range.ipynb 3KB objects.Band.ipynb 3KB objects.Agg.ipynb 3KB objects.Dots.ipynb 3KB hls_palette.ipynb 3KB husl_palette.ipynb 3KB ecdfplot.ipynb 3KB rugplot.ipynb 3KB objects.Plot.share.ipynb 3KB mpl_palette.ipynb 3KB objects.Perc.ipynb 3KB light_palette.ipynb 3KB dark_palette.ipynb 3KB objects.Plot.layout.ipynb 3KB objects.Count.ipynb 3KB objects.Plot.limit.ipynb 2KB residplot.ipynb 2KB objects.Paths.ipynb 2KB objects.Lines.ipynb 2KB objects.Shift.ipynb 2KB objects.Norm.ipynb 2KB blend_palette.ipynb 2KB set_context.ipynb 2KB objects.Stack.ipynb 2KB plotting_context.ipynb 2KB共 322 条
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