mirror of
https://github.com/gentoo-mirror/gentoo.git
synced 2026-11-04 04:57:07 -08:00
Robin H. Johnson
56bd759df1
This commit represents a new era for Gentoo: Storing the gentoo-x86 tree in Git, as converted from CVS. This commit is the start of the NEW history. Any historical data is intended to be grafted onto this point. Creation process: 1. Take final CVS checkout snapshot 2. Remove ALL ChangeLog* files 3. Transform all Manifests to thin 4. Remove empty Manifests 5. Convert all stale $Header$/$Id$ CVS keywords to non-expanded Git $Id$ 5.1. Do not touch files with -kb/-ko keyword flags. Signed-off-by: Robin H. Johnson <robbat2@gentoo.org> X-Thanks: Alec Warner <antarus@gentoo.org> - did the GSoC 2006 migration tests X-Thanks: Robin H. Johnson <robbat2@gentoo.org> - infra guy, herding this project X-Thanks: Nguyen Thai Ngoc Duy <pclouds@gentoo.org> - Former Gentoo developer, wrote Git features for the migration X-Thanks: Brian Harring <ferringb@gentoo.org> - wrote much python to improve cvs2svn X-Thanks: Rich Freeman <rich0@gentoo.org> - validation scripts X-Thanks: Patrick Lauer <patrick@gentoo.org> - Gentoo dev, running new 2014 work in migration X-Thanks: Michał Górny <mgorny@gentoo.org> - scripts, QA, nagging X-Thanks: All of other Gentoo developers - many ideas and lots of paint on the bikeshed
36 lines
1.5 KiB
XML
36 lines
1.5 KiB
XML
<?xml version="1.0" encoding="UTF-8"?>
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<!DOCTYPE pkgmetadata SYSTEM "http://www.gentoo.org/dtd/metadata.dtd">
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<pkgmetadata>
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<herd>python</herd>
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<maintainer>
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<email>jlec@gentoo.org</email>
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<name>Justin Lecher</name>
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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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statistical routines from scipy and statsmodels.
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Some of the features that seaborn offers are
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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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</longdescription>
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<upstream>
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<remote-id type="pypi">seaborne</remote-id>
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<remote-id type="github">mwaskom/seaborn</remote-id>
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</upstream>
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</pkgmetadata>
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