./graphics/py-seaborn, Statistical data visualization

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Branch: CURRENT, Version: 0.8, Package name: py27-seaborn-0.8, Maintainer: pkgsrc-users

Seaborn is a library for making attractive and informative statistical graphics
in Python. It is built on top of matplotlib and tightly integrated with the
PyData stack, including support for numpy and pandas data structures and
statistical routines from scipy and statsmodels.

Some of the features that seaborn offers are
* Several built-in themes that improve on the default matplotlib aesthetics
* Tools for choosing color palettes to make beautiful plots that reveal
patterns in your data
* Functions for visualizing univariate and bivariate distributions or for
comparing them between subsets of data
* Tools that fit and visualize linear regression models for different kinds
of independent and dependent variables
* Functions that visualize matrices of data and use clustering algorithms to
discover structure in those matrices
* A function to plot statistical timeseries data with flexible estimation and
representation of uncertainty around the estimate
* High-level abstractions for structuring grids of plots that let you easily
build complex visualizations


Required to run:
[graphics/py-matplotlib] [devel/py-setuptools] [math/py-scipy] [math/py-numpy] [math/py-pandas]

Required to build:
[pkgtools/cwrappers]

Master sites:

SHA1: db50e9c5171270650f7c302356d5b6c5c120e1df
RMD160: ba255e32a65441710edb6cd6c1223d36ed499e1b
Filesize: 173.903 KB

Version history: (Expand)


CVS history: (Expand)


   2017-07-14 17:00:01 by Adam Ciarcinski | Files touched by this commit (4)
Log message:
Seaborn is a library for making attractive and informative statistical graphics
in Python. It is built on top of matplotlib and tightly integrated with the
PyData stack, including support for numpy and pandas data structures and
statistical routines from scipy and statsmodels.

Some of the features that seaborn offers are
* Several built-in themes that improve on the default matplotlib aesthetics
* Tools for choosing color palettes to make beautiful plots that reveal
   patterns in your data
* Functions for visualizing univariate and bivariate distributions or for
  comparing them between subsets of data
* Tools that fit and visualize linear regression models for different kinds
  of independent and dependent variables
* Functions that visualize matrices of data and use clustering algorithms to
  discover structure in those matrices
* A function to plot statistical timeseries data with flexible estimation and
  representation of uncertainty around the estimate
* High-level abstractions for structuring grids of plots that let you easily
  build complex visualizations