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| author |   Horea Christian <horea.christ@yandex.com> | 2016-12-05 15:08:37 +0100 |
|---|---|---|
| committer |   Patrice Clement <monsieurp@gentoo.org> | 2016-12-10 22:56:06 +0100 |
| commit | db16d58c33bed67694443113c778ad737c55c29f (patch) | |
| tree | db593105ed6e337344cf9294d40242b2461472ad /dev-python/seaborn | |
| parent | dev-python/seaborn: version bump. (diff) | |
| download | gentoo-db16d58c33bed67694443113c778ad737c55c29f.tar.gz gentoo-db16d58c33bed67694443113c778ad737c55c29f.tar.bz2 gentoo-db16d58c33bed67694443113c778ad737c55c29f.zip | |
dev-python/seaborn: add additional maintainer.
Package-Manager: portage-2.3.2
Closes: https://github.com/gentoo/gentoo/pull/3020
Diffstat (limited to 'dev-python/seaborn')
| -rw-r--r-- | dev-python/seaborn/metadata.xml | 60 |
1 files changed, 32 insertions, 28 deletions
diff --git a/dev-python/seaborn/metadata.xml b/dev-python/seaborn/metadata.xml index 771591a322ef..33fde69a9a12 100644 --- a/dev-python/seaborn/metadata.xml +++ b/dev-python/seaborn/metadata.xml @@ -1,34 +1,38 @@ <?xml version="1.0" encoding="UTF-8"?> <!DOCTYPE pkgmetadata SYSTEM "http://www.gentoo.org/dtd/metadata.dtd"> <pkgmetadata> - <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. + <maintainer type="person"> + <email>horea.christ@gmail.com</email> + <name>Horea Christian</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 + 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> + * 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> |