Parents (Total: 120)

The searched file hash is included in 120 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
MD5	49967476B366C7617EEECFE954DCD47C
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	python3-seaborn
PackageRelease	bp154.1.20
PackageVersion	0.8.1
SHA-1	09F67E0B72DA43AAEA39FF833A8FF62D342E251E
SHA-256	3A220E7B5EA8C9F018A4F2686ED40DC746EFD1F25A8B522A523BBB1B0AA5BA10

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
MD5	E6F0903A783883CA5BAE74AC86450CEE
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	5.fc23
PackageVersion	0.5.1
SHA-1	0B4D4AF5909A86F25BA33C6EBAA8C4CB9FB1DA12
SHA-256	BD120AE30A0C74B0CB9C395D7C9437866F3FBD9ECB193735B461EAD664A2059A

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/6.6/packages//mips64//py-seaborn-0.9.0.tgz
MD5	F51D1B8C753FD0D24211AD935869498D
SHA-1	11AEB070029A8189B309A3B54B5F0EA48798A9FA
SHA-256	835B5000BB99CF36AC31D0C1412D3AC5499877DACD872DFFE6B892D9BCC79040
SSDEEP	12288:gZL+Cwn99g9pVhDjcFKgPCUkaP2RnsMHQzozABFoUS:gzpVhD5gPCU9P21ZQzOWG
TLSH	T1F3B4236C1419410AF9B7AA97EA677473492F8821E4D90C811EC215BBB0F7B86CCD47EF

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
SHA-1	193D4897A223717A11ACAB916F35D7F7421D95D1
snap-authority	canonical
snap-filename	wBEQd0pf939OMkQxQzjc5edUUJ7UTdoO_22.snap
snap-id	wBEQd0pf939OMkQxQzjc5edUUJ7UTdoO_22
snap-name	rapunzel
snap-publisher-id	iF39jGin6mL9yavM0mp1QbryMUfExcYE
snap-signkey	BWDEoaqyr25nF5SNCvEv2v7QnM9QsfCc0PBMYD_i2NGSQ32EF2d4D0hqUel3m8ul
snap-timestamp	2021-03-04T12:32:37.365593Z
source-url	https://api.snapcraft.io/api/v1/snaps/download/wBEQd0pf939OMkQxQzjc5edUUJ7UTdoO_22.snap

Key	Value
FileSize	75258
MD5	57B995B560724CD100104BC0CD78B3C1
PackageDescription	statistical visualization library 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	NeuroDebian Team <team@neuro.debian.net>
PackageName	python3-seaborn
PackageSection	python
PackageVersion	0.4.0-3
SHA-1	1976FCD03BB705F3A80A9CAB64CC86EB4A24BA82
SHA-256	DDF0B82808EB93FC3FD99AABEE1EA809F544F949EE396FE151F73940C3DCEED4

Key	Value
FileSize	75186
MD5	7B6830A3D1439FDD611EDB6ED6EDC14C
PackageDescription	statistical visualization library 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 2 version of the package.
PackageMaintainer	NeuroDebian Team <team@neuro.debian.net>
PackageName	python-seaborn
PackageSection	python
PackageVersion	0.4.0-3
SHA-1	1A57AF2AF6075B1F949D1DE5B558801B2CA9E365
SHA-256	1313126D78A29E06B2DC1B48C275CA25344D76EEDCC0131137D6F4A2667C4BF8