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commit: 6293c288a57adbd3bc830efabad556a78d424ad4 |
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Author: David Seifert <soap <AT> gentoo <DOT> org> |
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AuthorDate: Sat Nov 25 20:09:11 2017 +0000 |
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Commit: David Seifert <soap <AT> gentoo <DOT> org> |
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CommitDate: Sat Nov 25 21:43:13 2017 +0000 |
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URL: https://gitweb.gentoo.org/repo/gentoo.git/commit/?id=6293c288 |
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|
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dev-python/seaborn: [QA] Consistent whitespace in metadata.xml |
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|
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dev-python/seaborn/metadata.xml | 26 ++++++++++---------------- |
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1 file changed, 10 insertions(+), 16 deletions(-) |
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diff --git a/dev-python/seaborn/metadata.xml b/dev-python/seaborn/metadata.xml |
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index 86ec3a36c73..fefd180716d 100644 |
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--- a/dev-python/seaborn/metadata.xml |
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+++ b/dev-python/seaborn/metadata.xml |
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@@ -15,25 +15,19 @@ |
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</maintainer> |
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<longdescription lang="en"> |
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Seaborn is a library for making attractive and informative statistical graphics |
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- in Python. It is built on top of matplotlib and tightly integrated with the |
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- PyData stack, including support for numpy and pandas data structures and |
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+ in Python. It is built on top of matplotlib and tightly integrated with the |
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+ PyData stack, including support for numpy and pandas data structures and |
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statistical routines from scipy and statsmodels. |
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- |
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+ |
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Some of the features that seaborn offers are |
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- |
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+ |
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* Several built-in themes that improve on the default matplotlib aesthetics |
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- * Tools for choosing color palettes to make beautiful plots that reveal |
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- patterns in your data |
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- * Functions for visualizing univariate and bivariate distributions or for |
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- comparing them between subsets of data |
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- * Tools that fit and visualize linear regression models for different kinds |
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- of independent and dependent variables |
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- * Functions that visualize matrices of data and use clustering algorithms to |
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- discover structure in those matrices |
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- * A function to plot statistical timeseries data with flexible estimation and |
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- representation of uncertainty around the estimate |
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- * High-level abstractions for structuring grids of plots that let you easily |
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- build complex visualizations |
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+ * Tools for choosing color palettes to make beautiful plots that reveal patterns in your data |
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+ * Functions for visualizing univariate and bivariate distributions or for comparing them between subsets of data |
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+ * Tools that fit and visualize linear regression models for different kinds of independent and dependent variables |
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+ * Functions that visualize matrices of data and use clustering algorithms to discover structure in those matrices |
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+ * A function to plot statistical timeseries data with flexible estimation and representation of uncertainty around the estimate |
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+ * High-level abstractions for structuring grids of plots that let you easily build complex visualizations |
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</longdescription> |
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<upstream> |
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<remote-id type="pypi">seaborne</remote-id> |
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