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| author | V3n3RiX <venerix@redcorelinux.org> | 2018-07-14 20:57:42 +0100 |
|---|---|---|
| committer | V3n3RiX <venerix@redcorelinux.org> | 2018-07-14 20:57:42 +0100 |
| commit | 1798c4aeca70ac8d0a243684d6a798fbc65735f8 (patch) | |
| tree | e48e19cb6fa03de18e1c63e1a93371b7ebc4eb56 /dev-python/seaborn/metadata.xml | |
| parent | d87262dd706fec50cd150aab3e93883b6337466d (diff) | |
gentoo resync : 14.07.2018
Diffstat (limited to 'dev-python/seaborn/metadata.xml')
| -rw-r--r-- | dev-python/seaborn/metadata.xml | 36 |
1 files changed, 0 insertions, 36 deletions
diff --git a/dev-python/seaborn/metadata.xml b/dev-python/seaborn/metadata.xml deleted file mode 100644 index fefd180716d0..000000000000 --- a/dev-python/seaborn/metadata.xml +++ /dev/null @@ -1,36 +0,0 @@ -<?xml version="1.0" encoding="UTF-8"?> -<!DOCTYPE pkgmetadata SYSTEM "http://www.gentoo.org/dtd/metadata.dtd"> -<pkgmetadata> - <maintainer type="person"> - <email>horea.christ@gmail.com</email> - <name>Horea Christian</name> - </maintainer> - <maintainer type="project"> - <email>proxy-maint@gentoo.org</email> - <name>Proxy Maintainers</name> - </maintainer> - <maintainer type="project"> - <email>python@gentoo.org</email> - <name>Python</name> - </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 - 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 - </longdescription> - <upstream> - <remote-id type="pypi">seaborne</remote-id> - <remote-id type="github">mwaskom/seaborn</remote-id> - </upstream> -</pkgmetadata> |