commit: 6293c288a57adbd3bc830efabad556a78d424ad4
Author: David Seifert <soap <AT> gentoo <DOT> org>
AuthorDate: Sat Nov 25 20:09:11 2017 +0000
Commit: David Seifert <soap <AT> gentoo <DOT> org>
CommitDate: Sat Nov 25 21:43:13 2017 +0000
URL: https://gitweb.gentoo.org/repo/gentoo.git/commit/?id=6293c288
dev-python/seaborn: [QA] Consistent whitespace in metadata.xml
dev-python/seaborn/metadata.xml | 26 ++++++++++----------------
1 file changed, 10 insertions(+), 16 deletions(-)
diff --git a/dev-python/seaborn/metadata.xml b/dev-python/seaborn/metadata.xml
index 86ec3a36c73..fefd180716d 100644
--- a/dev-python/seaborn/metadata.xml
+++ b/dev-python/seaborn/metadata.xml
@@ -15,25 +15,19 @@
</maintainer>
<longdescription lang="en">
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
</longdescription>
<upstream>
<remote-id type="pypi">seaborne</remote-id>
