Metadata-Version: 2.1
Name: seaborn
Version: 0.9.0
Summary: seaborn: statistical data visualization
Home-page: https://seaborn.pydata.org
Author: Michael Waskom
Author-email: mwaskom@nyu.edu
Maintainer: Michael Waskom
Maintainer-email: mwaskom@nyu.edu
License: BSD (3-clause)
Download-URL: https://github.com/mwaskom/seaborn/
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: License :: OSI Approved :: BSD License
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Topic :: Multimedia :: Graphics
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Requires-Dist: numpy (>=1.9.3)
Requires-Dist: scipy (>=0.14.0)
Requires-Dist: pandas (>=0.15.2)
Requires-Dist: matplotlib (>=1.4.3)

Seaborn is a library for making statistical graphics in Python. It is built on top of `matplotlib <https://matplotlib.org/>`_ and closely integrated with `pandas <https://pandas.pydata.org/>`_ data structures.

Here is some of the functionality that seaborn offers:

- A dataset-oriented API for examining relationships between multiple variables
- Specialized support for using categorical variables to show observations or aggregate statistics
- Options for visualizing univariate or bivariate distributions and for comparing them between subsets of data
- Automatic estimation and plotting of linear regression models for different kinds dependent variables
- Convenient views onto the overall structure of complex datasets
- High-level abstractions for structuring multi-plot grids that let you easily build complex visualizations
- Concise control over matplotlib figure styling with several built-in themes
- Tools for choosing color palettes that faithfully reveal patterns in your data

Seaborn aims to make visualization a central part of exploring and understanding data. Its dataset-oriented plotting functions operate on dataframes and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots.