# seaborn

**Repository Path**: agedcoder/seaborn

## Basic Information

- **Project Name**: seaborn
- **Description**: Seaborn 可实现对统计数据的可视化展示，基于 Python 语言开发，使用 matplotlib 库
- **Primary Language**: Python
- **License**: BSD-3-Clause
- **Default Branch**: master
- **Homepage**: https://www.oschina.net/p/seaborn
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 4
- **Created**: 2021-03-16
- **Last Updated**: 2021-08-30

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

<img src="doc/_static/logo-wide-lightbg.svg"><br>

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seaborn: statistical data visualization
=======================================

[![PyPI Version](https://img.shields.io/pypi/v/seaborn.svg)](https://pypi.org/project/seaborn/)
[![License](https://img.shields.io/pypi/l/seaborn.svg)](https://github.com/mwaskom/seaborn/blob/master/LICENSE)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.592845.svg)](https://doi.org/10.5281/zenodo.592845)
![Tests](https://github.com/mwaskom/seaborn/workflows/CI/badge.svg)
[![Code Coverage](https://codecov.io/gh/mwaskom/seaborn/branch/master/graph/badge.svg)](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).


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 the optional dependencies:

    pip install seaborn[all]

You may instead want to use the development version from Github:

    pip install git+https://github.com/mwaskom/seaborn.git

Seaborn is also available from Anaconda and can be installed with conda:

    conda install seaborn

Note that the main anaconda repository typically lags PyPI in adding new releases.

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).