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| author | V3n3RiX <venerix@redcorelinux.org> | 2018-07-14 21:03:06 +0100 |
|---|---|---|
| committer | V3n3RiX <venerix@redcorelinux.org> | 2018-07-14 21:03:06 +0100 |
| commit | 8376ef56580626e9c0f796d5b85b53a0a1c7d5f5 (patch) | |
| tree | 7681bbd4e8b05407772df40a4bf04cbbc8afc3fa /dev-python/seaborn/metadata.xml | |
| parent | 30a9caf154332f12ca60756e1b75d2f0e3e1822d (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, 36 insertions, 0 deletions
diff --git a/dev-python/seaborn/metadata.xml b/dev-python/seaborn/metadata.xml new file mode 100644 index 000000000000..fefd180716d0 --- /dev/null +++ b/dev-python/seaborn/metadata.xml @@ -0,0 +1,36 @@ +<?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> |