Parents (Total: 24)

The searched file hash is included in 24 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/6.6/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/6.6/packages//sparc64//py3-seaborn-0.9.0.tgz
MD5	87DC20264AAF4E710B148F6EE9C23F99
SHA-1	3A6307E77F972FDFC4CCD7AC7BA2B440E6806FC2
SHA-256	79874B5F4DCE9E27026308E4046911E763D07397BBAF7518AA123AA5951BF6A8
SSDEEP	12288:9bpZ6lC7gQjbtCbEKnYSFsIJ4f6zkXOzMgMea+3:xpZ60cQVNKnXaI7zkXmxta+3
TLSH	T1B9A423A7DDCFBC6A52EE80590DD889750E19F8673A831681C0E5B4964793A870EDF0FC

Key	Value
FileName	https://ftp.lysator.liu.se/pub/OpenBSD/6.6/packages//i386//py3-seaborn-0.9.0.tgz
MD5	D4EB7846183746CCB77A4F3F879CC073
SHA-1	458C5618409336DB4684592E3C69636DD1106EF0
SHA-256	A6CFEC3499E388E2EB2F0B8D419281FD54E12CBB440059804D296C5D1996D04A
SSDEEP	12288:68cSi8D27SC7gQjbtCbEKnYSFsIJ4f6zkXOzMgMea+3:6F3fbcQVNKnXaI7zkXmxta+3
TLSH	T14EA4237B88CFBE62E6CAC04A0C5499741F09F0677D822352D5E8B49F49637436EE51F8

Key	Value
FileName	https://ftp.lysator.liu.se/pub/OpenBSD/6.6/packages//mips64el//py-seaborn-0.9.0.tgz
MD5	CC1CA9F152498DAAF0321385F67A8436
SHA-1	519CB0A9DA8B6C2A9BA93768511790D41035F496
SHA-256	E59681E5255219D6519E6081FEDF506BFD3A9F7D7D68A2461868C1B4FC7BEC33
SSDEEP	12288:+GuGIg9pVhDjcFKgPCUkaP2RnsMHQzozABFoUS:vZpVhD5gPCU9P21ZQzOWG
TLSH	T102B4233C056A411EE5F79AEBA26BA4238D1DD411D2DC2E955BC111BA70F7E8188F03EF

Key	Value
FileName	https://ftp.lysator.liu.se/pub/OpenBSD/6.6/packages//arm//py-seaborn-0.9.0.tgz
MD5	27250E82A1D4393D392FCC0748852822
SHA-1	5FBDAC413520DA2BEA2245391E6CC6518CB47CF2
SHA-256	2345C5FBE14DD40013CAFF70E2AC6FFA02E6EC821627A0C978BFBCE09F2304F9
SSDEEP	12288:xbyRKaPwFLWNg9pVhDjcFKgPCe6qsnsMHQzozABFoUS:MRPoxW6pVhD5gPCe6qkZQzOWG
TLSH	T1C3B4233C199D05039AA35E9FA263F5238D1D9011DAD929876BC217FF713ABE244B036F

Key	Value
FileName	https://ftp.lysator.liu.se/pub/OpenBSD/6.6/packages//powerpc//py-seaborn-0.9.0.tgz
MD5	1FAFF080BAAF2B0E028CBFD384A3CE9A
SHA-1	65F314D7B255623A339B9C8C6873AC0F7698C8AD
SHA-256	95001615CE757176028B7CEB355AF39EE4860B14ED726B84D942E7C4D7B5C1F0
SSDEEP	12288:EWYuGmg9pVhDjcFKgPCe6qsnsMHQzozABFoUS:EWYDpVhD5gPCe6qkZQzOWG
TLSH	T181B4233C0CA6400BA5B3EDEB966764238D1D9021C2C91E845AC215FEB4F7F9598F07EB

Key	Value
MD5	962B8A994AEC73A203CF682A5052B181
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	python2-seaborn
PackageRelease	lp151.4.2
PackageVersion	0.9.0
SHA-1	79C083E1055EBA2C3E8D6B7035A7EF49D9F483AE
SHA-256	AA6450BD4EB6D6D8DDA8A9598CAB909C55178E6430E215E9995C01F1D2508397

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
FileName	https://ftp.lysator.liu.se/pub/OpenBSD/6.6/packages//sparc64//py-seaborn-0.9.0.tgz
MD5	D9D977915E6E532D67D9EB1CA6A40BC0
SHA-1	7F880C2A82787A602E3CAB3B03BA856535B269EB
SHA-256	69F8FC6F8904F2E1A2A4896B960B82C32AECC5FC156EBC518E3BAD128A57AF51
SSDEEP	12288:GxFJw2Ng9pVhDjcFKgPCUkaP2RnsMHQzozABFoUS:GxFJw/pVhD5gPCU9P21ZQzOWG
TLSH	T10EB4232C094A140AFE73999BF5326433892ED851A99D2D552FC106FB71B7F828CE43DB

Key	Value
MD5	5495BB90AC684E369C01652E51C7D496
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	python2-seaborn
PackageRelease	lp150.4.2
PackageVersion	0.9.0
SHA-1	868AF640C0454A1FEB5CAD620840078BAF86C922
SHA-256	E6A373CE3BC96E7635F574466475D7F6EC392A00C08D8B3FF0D9ED27AB41ECD4