| <?xml version="1.0" encoding="UTF-8"?> |
| <!DOCTYPE pkgmetadata SYSTEM "http://www.gentoo.org/dtd/metadata.dtd"> |
| <pkgmetadata> |
| <herd>python</herd> |
| <maintainer> |
| <email>jlec@gentoo.org</email> |
| <name>Justin Lecher</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> |