dev-python/seaborn: [QA] Consistent whitespace in metadata.xml

diff --git a/dev-python/seaborn/metadata.xml b/dev-python/seaborn/metadata.xml
index 86ec3a36c731d93c578bab5fb3784ffcd4fdd9c9..fefd180716d06ad35918c5b2166760a85254f082 100644 (file)
--- 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>