Source: seaborn
Section: python
Priority: optional
Maintainer: Debian Science Maintainers
Uploaders: Yaroslav Halchenko , Michael Hanke
Build-Depends: debhelper (>= 8), dh-python,
python-setuptools, python3-setuptools,
python-all (>= 2.6.6-3~), python3-all,
python-nose, python3-nose,
xvfb, xauth,
python-numpy, python-scipy,
python-pandas,
python-matplotlib, python-tk,
python-statsmodels,
python-patsy,
python3-numpy, python3-scipy,
python3-pandas,
python3-matplotlib | python-matplotlib (<< 1.2.0~),
python3-tk,
python3-patsy,
Standards-Version: 3.9.5
Vcs-Browser: https://anonscm.debian.org/cgit/debian-science/packages/seaborn.git
Vcs-Git: https://anonscm.debian.org/git/debian-science/packages/seaborn.git -b debian
Homepage: https://github.com/mwaskom/seaborn
X-Python-Version: >= 2.7
X-Python3-Version: >= 3.2
Package: python-seaborn
Architecture: all
Depends: ${misc:Depends}, ${python:Depends},
python-numpy, python-scipy,
python-pandas,
python-matplotlib,
Recommends:
python-statsmodels,
python-patsy,
python-bs4,
Description: statistical visualization library
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
- 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
.
This is the Python 2 version of the package.
Package: python3-seaborn
Architecture: all
Depends: ${misc:Depends}, ${python3:Depends},
python3-numpy, python3-scipy,
python3-pandas,
python3-matplotlib,
Recommends:
python3-patsy,
python3-bs4
Description: statistical visualization library
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
- 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
.
This is the Python 3 version of the package.