From: David Seifert Date: Sat, 25 Nov 2017 20:09:11 +0000 (+0100) Subject: dev-python/seaborn: [QA] Consistent whitespace in metadata.xml X-Git-Url: http://git.tremily.us/gitweb.cgi?a=commitdiff_plain;h=6293c288a57adbd3bc830efabad556a78d424ad4;p=gentoo.git dev-python/seaborn: [QA] Consistent whitespace in metadata.xml --- diff --git a/dev-python/seaborn/metadata.xml b/dev-python/seaborn/metadata.xml index 86ec3a36c731..fefd180716d0 100644 --- a/dev-python/seaborn/metadata.xml +++ b/dev-python/seaborn/metadata.xml @@ -15,25 +15,19 @@ 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 + 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 + * 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 seaborne