<img src="https://raw.githubusercontent.com/mwaskom/seaborn/master/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 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 (only relevant for v0.12+):
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 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).
seaborn-0.12.0.tar.gz
140 浏览量
2024-03-19
15:37:10
上传
评论
收藏 1.34MB GZ 举报
seaborn-0.12.0.tar.gz （283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 color_palettes.ipynb 34KB objects_interface.ipynb 34KB properties.ipynb 29KB distributions.ipynb 28KB relational.ipynb 21KB data_structure.ipynb 20KB axis_grids.ipynb 20KB function_overview.ipynb 19KB categorical.ipynb 19KB introduction.ipynb 18KB regression.ipynb 15KB error_bars.ipynb 15KB aesthetics.ipynb 12KB histplot.ipynb 11KB lineplot.ipynb 10KB FacetGrid.ipynb 8KB objects.Plot.scale.ipynb 8KB scatterplot.ipynb 7KB stripplot.ipynb 7KB kdeplot.ipynb 7KB swarmplot.ipynb 7KB relplot.ipynb 7KB PairGrid.ipynb 6KB JointGrid.ipynb 6KB objects.Plot.add.ipynb 5KB displot.ipynb 5KB objects.Plot.pair.ipynb 5KB objects.Plot.facet.ipynb 5KB objects.Plot.on.ipynb 5KB pairplot.ipynb 5KB jointplot.ipynb 5KB catplot.ipynb 4KB objects.Dot.ipynb 4KB color_palette.ipynb 4KB objects.Bar.ipynb 4KB violinplot.ipynb 4KB objects.Line.ipynb 4KB objects.Bars.ipynb 4KB move_legend.ipynb 4KB boxplot.ipynb 4KB objects.Area.ipynb 4KB set_theme.ipynb 4KB objects.Plot.label.ipynb 3KB objects.Plot.theme.ipynb 3KB objects.Dots.ipynb 3KB pointplot.ipynb 3KB objects.Range.ipynb 3KB ecdfplot.ipynb 3KB rugplot.ipynb 3KB objects.Plot.share.ipynb 3KB boxenplot.ipynb 3KB objects.Band.ipynb 3KB barplot.ipynb 3KB objects.Plot.limit.ipynb 2KB objects.Paths.ipynb 2KB objects.Lines.ipynb 2KB objects.Plot.layout.ipynb 2KB set_context.ipynb 2KB countplot.ipynb 2KB plotting_context.ipynb 2KB objects.Path.ipynb 2KB axes_style.ipynb 2KB set_style.ipynb 2KB copybutton.js 3KB Makefile 6KB Makefile 345B Makefile 324B Makefile 195B matplotlibrc 21B README.md 4KB CONTRIBUTING.md 2KB LICENSE.md 1KB README.md 1KB NUMPYDOC_LICENSE 1KB PACKAGING_LICENSE 1KB PKG-INFO 5KB
logo-wide-whitebg.png 136KB logo-wide-lightbg.png 122KB logo-wide-darkbg.png 120KB logo-tall-whitebg.png 88KB logo-tall-lightbg.png 81KB logo-tall-darkbg.png 79KB logo-mark-whitebg.png 74KB logo-mark-lightbg.png 71KB logo-mark-darkbg.png 69KB categorical.py 127KB共 283 条
- 1
- 2
- 3
资源评论
