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graphics/pyseaborn,
Statistical data visualization
Branch: CURRENT,
Version: 0.8.1,
Package name: py27seaborn0.8.1,
Maintainer: pkgsrcusersSeaborn 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 builtin 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
* Highlevel abstractions for structuring grids of plots that let you easily
build complex visualizations
Required to run:[
devel/pysetuptools] [
math/pyscipy] [
math/pynumpy] [
math/pypandas]
Required to build:[
pkgtools/cwrappers]
Master sites:
SHA1: caf15038d9c7e5990121c5eea6358d4f3c124712
RMD160: e1dcea310682e8df80375026100ec6d0945253c1
Filesize: 174.673 KB
Version history: (Expand)
 (20170904) Updated to version: py27seaborn0.8.1
 (20170714) Package added to pkgsrc.se, version py27seaborn0.8 (created)
CVS history: (Expand)
20170904 19:19:38 by Adam Ciarcinski  Files touched by this commit (2)  
Log message:
v0.8.1:
Added a warning in FacetGrid when passing a categorical plot function without \
specifying order (or hue_order when hue is used), which is likely to produce a \
plot that is incorrect.
Improved compatibility between FacetGrid or PairGrid and interactive matplotlib \
backends so that the legend no longer remains inside the figure when using \
legend_out=True.
Changed categorical plot functions with small plot elements to use dark_palette \
instead of light_palette when generating a sequential palette from a specified \
color.
Improved robustness of kdeplot and distplot to data with fewer than two observations.
Fixed a bug in clustermap when using yticklabels=False.
Fixed a bug in pointplot where colors were wrong if exactly three points were \
being drawn.
Fixed a bug inpointplot where legend entries for missing data appeared with \
empty markers.
Fixed a bug in clustermap where an error was raised when annotating the main \
heatmap and showing category colors.
Fixed a bug in clustermap where row labels were not being properly rotated when \
they overlapped.
Fixed a bug in kdeplot where the maximum limit on the density axes was not being \
updated when multiple densities were drawn.
Improved compatibility with future versions of pandas.

20170714 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 builtin 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
* Highlevel abstractions for structuring grids of plots that let you easily
build complex visualizations
