Parents (Total: 3)

The searched file hash is included in 3 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
MD5	479D368D927436F36DC8ADB3C5E5C060
PackageArch	noarch
PackageDescription	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
PackageName	python3-seaborn
PackageRelease	4.2
PackageVersion	0.9.0
SHA-1	7CDD368A02CE9A4467BBC913E8384E37BC4DDF76
SHA-256	35C035ADF9848768106E6C3704BF70BA9C0E8874D7AC5817B37AEA5DB1F758A2

Key	Value
MD5	6B1955DC4744C3208947511A86E1B917
PackageArch	noarch
PackageDescription	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
PackageName	python3-seaborn
PackageRelease	lp151.4.2
PackageVersion	0.9.0
SHA-1	FAEB623C7B20BB9C4CA51CCFF8476D1CA004324D
SHA-256	E199419EFCB620E42038AB1E425021769BE2E6721DF5CB04F084DB865FA0F136

Key	Value
MD5	9C8C6C7086C2D3E83A215E79E893EA99
PackageArch	noarch
PackageDescription	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
PackageName	python3-seaborn
PackageRelease	lp150.4.2
PackageVersion	0.9.0
SHA-1	8C7E6377C8C2DB01FD2894E3459A7F329E77C487
SHA-256	8EF8B6ED3C0B3BCDCBD77085ED4EDB9E8FE10BFC32A66E87C2848F134ACDCC4C