Parents (Total: 31)

The searched file hash is included in 31 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
FileName	https://ftp.lysator.liu.se/pub/OpenBSD/6.9/packages//sparc64//py3-seaborn-0.11.1.tgz
MD5	40661AE586D4CE5EF73A08AB3F40EB8F
SHA-1	088673F9592B271673557080000D8EAAF2E0D33A
SHA-256	CCF627C7E17DF4A09F251D870A6B8A12EAC59A44CE859AEB492E91A806E1342F
SSDEEP	12288:Ua/G7aFVRQvBQjwe5GVClSXT7j7KC8Qd5fx21jrpAnyy9Xox+UF:Ua/G7u8msmGVClS3BtxyY9a5F
TLSH	T1A7D423746CF4F258E13770AB9D373EC6A1004ECBEDE529050DDAE0E493AAC42419DADE

Key	Value
FileName	https://ftp.lysator.liu.se/pub/OpenBSD/6.8/packages//amd64//py3-seaborn-0.11.0.tgz
MD5	323FFB3E1338C94D0884C9D7BC1DD6FB
SHA-1	0B28BAC12EAE21DF40501A93384391BC50355219
SHA-256	EB025FDC4FACE5AD42D3288417C0377FF193A7E2E93AB8522FCA2F7D4B7265BA
SSDEEP	12288:pA6Z3L6/No6sqrOlSjktarQsGiA9NwUt7OI8VGpXqTifniylx:Oe6rOlSAtarQLoUt7DpXcfwx
TLSH	T1C9D423F4D44A4F099B781E9F3971F03DD92AD5AA1A633C9208FD7325C93A05DED4A883

Key	Value
FileName	https://ftp.lysator.liu.se/pub/OpenBSD/6.9/packages//mips64el//py3-seaborn-0.11.1.tgz
MD5	FED05D66B1ED6E11514F83414F7BAC66
SHA-1	0C1D50B7A919F6E4367079B6493FC5F4EEB998C7
SHA-256	3657339E039A0DB635130226CF94FEC61945976772820D448908F9AB67A01D6C
SSDEEP	12288:QIeiG7aFVRQvBQjwe5GVClSXT7j7KC8Qd5fx21jrpAnyy9Xox+UF:QViG7u8msmGVClS3BtxyY9a5F
TLSH	T16CD423346DE4F248F13B74EB9D273DC6A1004ECBEDA429060DEAE0E597AAC4142DD9DD

Key	Value
FileName	https://ftp.lysator.liu.se/pub/OpenBSD/6.8/packages//mips64//py3-seaborn-0.11.0.tgz
MD5	52A5D8B2BB1A7C360C3998C798EC3783
SHA-1	191D721F56F1BD5289027C0E7DD8866F4370DFB5
SHA-256	1C212DA993A6297DF395DA93E8205BD396756D3C248D201F1E388382393AF775
SSDEEP	12288:ZqZ3L6/No6sqrOlSjktarQsGiA9NwUt7OI8VGpXqTifniylx:Xe6rOlSAtarQLoUt7DpXcfwx
TLSH	T1E4D423B4D44A8F05ABB81E9F39B1F03CD826D5AB19633C9205FD7329C93B09DAD45893

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
FileSize	205760
MD5	E1895E1BE0C3319D32D0C131AA9EB6F0
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	Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageName	python3-seaborn
PackageSection	python
PackageVersion	0.11.1-1
SHA-1	22A01F183EA134F7F7E9A89D595BF125390F1C1B
SHA-256	FFFC71E019C2B68D90E40C818E6CCEA1BE365A50F1C592C782AACAEE8FA3D7B3

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	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
FileName	https://ftp.lysator.liu.se/pub/OpenBSD/6.9/packages//powerpc//py3-seaborn-0.11.1.tgz
MD5	E18402B2EF470C1441D91E67CAB9648D
SHA-1	8B42966D5668E39F592BB774837425EFB3244544
SHA-256	04C7384E3866D70D764DCF229D93281F9BB197E3DFADF3259FAAA9FCDBDCD743
SSDEEP	12288:9fG7aFVRQvBQjwe5XPp+QXT7+b+Cd0dFQd5fYWw55z6XiFXix+UF:9fG7u8msmXPgQ3S7Rt1Xm05F
TLSH	T162D423241EF0F3147427A977A8332FC3E2148ED7EAE1A71214DDE4B1CC0E947459AAAD

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