Parents (Total: 11)

The searched file hash is included in 11 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
SHA-1	BE01F22369CDCC5851671A12B3A6AA601F7E9D57
snap-authority	canonical
snap-filename	YZQF6inA19lP49wUDLLuvVPpGqqJMAY2_34.snap
snap-id	YZQF6inA19lP49wUDLLuvVPpGqqJMAY2_34
snap-name	easyvizar-detect
snap-publisher-id	2E7LCuuHN2jNM6Itr2vX0HK5a02oHnrs
snap-signkey	BWDEoaqyr25nF5SNCvEv2v7QnM9QsfCc0PBMYD_i2NGSQ32EF2d4D0hqUel3m8ul
snap-timestamp	2022-07-07T18:12:13.540043Z
source-url	https://api.snapcraft.io/api/v1/snaps/download/YZQF6inA19lP49wUDLLuvVPpGqqJMAY2_34.snap

Key	Value
MD5	6FC21A1F5C3A951E04C86D837553F14A
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	33.1
PackageVersion	0.11.2
SHA-1	F9561ED5573340F5F03CE5FE735C296D6624371E
SHA-256	2D6AB40EA671680EFBA784FFD3B69BE6E2B399DD04973FF071E2C4E266CAD221

Key	Value
MD5	F3D237ECE65BFC51C3642802885FC747
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	33.1
PackageVersion	0.11.2
SHA-1	E574B016B6308008993C99E1AE6D9638191CD1ED
SHA-256	8205BB553903092CAB21640134980C741EF1FCE232F7BE33FBE861EE2E28E052

Key	Value
MD5	5C08C3325020B07714498BBC25C5D07C
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	33.1
PackageVersion	0.11.2
SHA-1	DFDFA54E1CB9B44CCF46ADD3488FA020C636A5AA
SHA-256	5DF92169F19E33792111B9898C75B259EE619D8A6EAD3A6D3504A842EF83DFD7

Key	Value
MD5	22DC86AE0A2DEE85292A857E69394E09
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	33.1
PackageVersion	0.11.2
SHA-1	DBA61D470A066E02D8B859CF2D47A91759868C25
SHA-256	750F25738587AA9C781CBC915C7D5CEA7CD70E48C89F2C6D557DAE3A19AD5E6B

Key	Value
MD5	A0B9D0D4E3E26497C60372D820528C7A
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	python310-seaborn
PackageRelease	33.1
PackageVersion	0.11.2
SHA-1	31A1CC3990A233566AC968A9672951243BF7C248
SHA-256	F978A313AA70BF578EF7303541CFE4D9FC1F7A8E7F203F4FEBD1E39417E9A3CB

Key	Value
MD5	BD9B092F464C2E40B0EE8520886F43EB
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	33.1
PackageVersion	0.11.2
SHA-1	4C5453CDDBBD6A3E927152F8F141906090F3D9F4
SHA-256	A10F0D4A26BF931A985B2B511974206414495169B88C00C492C46B68EBE6343C

Key	Value
MD5	B3B1C6EF85D9BBCEA1769E7BC80E17D5
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	33.1
PackageVersion	0.11.2
SHA-1	9833D8BCD60E858C85387ADF406DDE6204EACF3E
SHA-256	32BE3639D80DE3112C2958677E61ACC95D2691BD7EBD91D20452E2F6A513EB5C

Key	Value
MD5	6523EA65AA08E14082E6106DCDBD551C
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	python310-seaborn
PackageRelease	33.1
PackageVersion	0.11.2
SHA-1	6E3E23A673F255682DA6B6DCF518679E624DB829
SHA-256	7E956DD6106C2AA43C9628E72FB69CCAB1FE4F69AC09E1BFE7E71840C2A29D40

Key	Value
MD5	8C0E9C24728389B0D0E7D97EFA8D32C6
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	python310-seaborn
PackageRelease	33.1
PackageVersion	0.11.2
SHA-1	5A25E1F9614CDD6489D8E355CFC535120BF173C4
SHA-256	0CC2DA4A133F1FDF364ADAE04D702C04A141287FEDD94BE103C2EF5223646EDA

Key	Value
FileSize	211812
MD5	3738EE62CE452D232D656409A3C5E7E1
PackageDescription	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.
PackageMaintainer	Debian Science Maintainers <debian-science-maintainers@lists.alioth.debian.org>
PackageName	python3-seaborn
PackageSection	python
PackageVersion	0.11.2-1
SHA-1	9ED5D363ACBAE5927E2326EBF70F3F6674475BCC
SHA-256	E1F44F06D2F520418F0BA697938D6D7E5BDBFDB38E479B22723DCF7589E241D2