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| author |   David Seifert <soap@gentoo.org> | 2017-11-25 21:09:11 +0100 |
|---|---|---|
| committer | David Seifert <soap@gentoo.org> | 2017-11-25 22:43:13 +0100 |
| commit | 6293c288a57adbd3bc830efabad556a78d424ad4 (patch) | |
| tree | 3ba45b82b26e941e4d41958a14b0c3b53fb22d5a /dev-python/seaborn | |
| parent | dev-python/scrypt: [QA] Consistent whitespace in metadata.xml (diff) | |
| download | gentoo-6293c288a57adbd3bc830efabad556a78d424ad4.tar.gz gentoo-6293c288a57adbd3bc830efabad556a78d424ad4.tar.bz2 gentoo-6293c288a57adbd3bc830efabad556a78d424ad4.zip | |
dev-python/seaborn: [QA] Consistent whitespace in metadata.xml
Diffstat (limited to 'dev-python/seaborn')
| -rw-r--r-- | dev-python/seaborn/metadata.xml | 26 |
1 files changed, 10 insertions, 16 deletions
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 @@ </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> |