[ Bron: seaborn ]
Pakket: python3-seaborn (0.11.1-1)
Verwijzigingen voor python3-seaborn
Debian bronnen:
Het bronpakket seaborn downloaden:
Beheerders:
- Debian Science Maintainers (QA-pagina, Mailarchief)
- Yaroslav Halchenko (QA-pagina)
- Michael Hanke (QA-pagina)
- Nilesh Patra (QA-pagina)
Externe bronnen:
- Homepage [github.com]
Vergelijkbare pakketten:
statistical visualization library for Python3
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.
Andere aan python3-seaborn gerelateerde pakketten
|
|
|
|
-
- dep: python3
- interactive high-level object-oriented language (default python3 version)
-
- dep: python3-matplotlib
- Python based plotting system in a style similar to Matlab (Python 3)
-
- dep: python3-numpy
- Fast array facility to the Python 3 language
-
- dep: python3-pandas
- data structures for "relational" or "labeled" data
-
- dep: python3-scipy
- scientific tools for Python 3
-
- dep: python3-tk
- Tkinter - Writing Tk applications with Python 3.x
-
- rec: python3-bs4
- error-tolerant HTML parser for Python 3
-
- rec: python3-patsy
- statistical models in Python using symbolic formulas