Parents (Total: 13)

The searched file hash is included in 13 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
MD5	39631835CFDCB031BB630C9D65F6E1C8
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	python39-seaborn
PackageRelease	33.27
PackageVersion	0.11.1
SHA-1	21CFAFDBF856872DE3B0125CE48B905A6AF1C721
SHA-256	A3D1BC25D0AECC0E69551F121AAC869E0B2A1E0A860494F5BB531A44577AF12D

Key	Value
MD5	04C6ECE00B60038E0397C1F11455D8E6
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	python38-seaborn
PackageRelease	2.1
PackageVersion	0.11.1
SHA-1	5532D2D71D2F3B58D63AE14EB963B453D9E52E16
SHA-256	354084ED4309FF74DABDA5C5F2A43365DFBCC366D9C0E6C4A408A6CDE91429C9

Key	Value
MD5	7888AD0C7D4502AED9662937D4279DA0
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	python39-seaborn
PackageRelease	2.1
PackageVersion	0.11.1
SHA-1	A38E930103EBE2D729B6AFF64FDED502CA2AE195
SHA-256	DF246FDA0C69E1E6C06C51FD12A68B7E1A565253E021265CCA9D5DE456E0FBB5

Key	Value
MD5	C2F839B67B0B08CABD98355C24E6278C
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
PackageMaintainer	https://bugs.opensuse.org
PackageName	python39-seaborn
PackageRelease	2.6
PackageVersion	0.11.1
SHA-1	9E8A45B569CBF8B195157EB021E8014E6CD61520
SHA-256	3970784C530D5898D13F750D33450D435BEFA0C5BC791E9C68E1F95AD0889998

Key	Value
MD5	0A82BC52B98CEC15D4F1332AF403C1A3
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	python39-seaborn
PackageRelease	33.13
PackageVersion	0.11.1
SHA-1	AA851DB460F9549494B27516427D4EDF2B3AB3B6
SHA-256	F4BAA493C42ED4B7FBD916A7D170002FC80698D08D8D24CA718FBA6684E46735

Key	Value
MD5	8D7D0069B446A5556E87764BC9304C7B
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	python38-seaborn
PackageRelease	33.27
PackageVersion	0.11.1
SHA-1	B7DE7A02FE65B9B85CC03286F5F10F3F30FE8E56
SHA-256	5C3AE53C87B1F421A444E0CB389DE66548E7DCDD0CBCC9126F661D04FC43F277

Key	Value
MD5	CD8574FF26C641B63B736B6A5417C3BD
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	python38-seaborn
PackageRelease	33.13
PackageVersion	0.11.1
SHA-1	BBB01FED02FB48C0221A33F404AEA342B099B79A
SHA-256	30AA757CD7A14E5EE502B1FBE8D5EC4F945F443CE4964C4924B7A8FD98273EAE

Key	Value
MD5	E86E675CF88C7551239C6B07E3FE4F43
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.
PackageMaintainer	Fedora Project
PackageName	python3-seaborn
PackageRelease	2.fc34
PackageVersion	0.11.0
SHA-1	D06A0DFC5D640C3BAD8507A89F7C8B6DAB48573E
SHA-256	122D3439E2C70DD08BC05E3044A4295FD8F02E2EDEDE864DA377786729FB5F1E

Key	Value
MD5	E61BC6CBAFEB5CB96439CCCF670ACFA8
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	lp153.33.1
PackageVersion	0.11.1
SHA-1	7FCE61135A669CE20186F9C8EC550D1AFFF15AAC
SHA-256	2F22FF56B0EDA199D4A6DC28E5BB799F40CE7227FA7CD08B243B8B3057E8549D

Key	Value
MD5	DCA789A0E32BFF13A75D2F72AB05D9B9
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	lp152.29.1
PackageVersion	0.11.1
SHA-1	C0DD94977A096E62C179CCB4CBD536C9A1C5C586
SHA-256	1EAAAA0D9586C7BEBE570F6B14A41DE673BBC002B58581967D6EDC89367EAB4D

Key	Value
MD5	03CCDA36B3F707D49748F0EA74F06DF7
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	lp152.3.2
PackageVersion	0.11.1
SHA-1	F8DC41328A14E8139284F3CC5617F9FA8E3B14C4
SHA-256	5D822F82E9C8214BD239AB368186835AA0118F1685FCDCEE7EC68DBFAD8C8647

Key	Value
MD5	403287316ADE9A91DC3AE0CE084355D4
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	lp153.3.3
PackageVersion	0.11.1
SHA-1	B60E7B0B14809B1069BDE00A91640A08C6D9AB52
SHA-256	895145BD741E35CBB08D36E6C0BAC2D68F4C178514A49B4D7DAFD23D58CEF6BC

Key	Value
MD5	14F363F5AE740C7730F62085329EA233
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
PackageMaintainer	https://bugs.opensuse.org
PackageName	python38-seaborn
PackageRelease	2.6
PackageVersion	0.11.1
SHA-1	AC7C738409FBBB7A600F94F03F165DFC9F40133C
SHA-256	D5F543CAAF236C34383D5223796CAC22453339C1159D1834CAB5C2C6AC42617B