Result for 0672BCD1FAFC316DE73E07E5E25C27D393CF0CEE

Query result

Key Value
FileName./usr/lib/python3.9/site-packages/seaborn/__pycache__/relational.cpython-39.pyc
FileSize27211
MD5636151B51CB1EED13D9095AC5E0C1238
SHA-10672BCD1FAFC316DE73E07E5E25C27D393CF0CEE
SHA-2564E4A23507717D695603E71C46B4B3D29BB54A759EADD54125FAFAA55DA18D20D
SSDEEP768:kTWcD5xH2XQOm6NJ6xXRrsZjSQlTLkAqjNidC:AqlsBooQlc1jN6C
TLSHT1C5C23B5EBB110777FFD2F178110C5254A620E25B33E161D3BC68A3AD2E46D6C393A2B5
tar:gnameroot
tar:unameroot
hashlookup:parent-total3
hashlookup:trust65

Network graph view

Parents (Total: 3)

The searched file hash is included in 3 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
MD5C2F839B67B0B08CABD98355C24E6278C
PackageArchnoarch
PackageDescriptionSeaborn 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
PackageMaintainerhttps://bugs.opensuse.org
PackageNamepython39-seaborn
PackageRelease2.6
PackageVersion0.11.1
SHA-19E8A45B569CBF8B195157EB021E8014E6CD61520
SHA-2563970784C530D5898D13F750D33450D435BEFA0C5BC791E9C68E1F95AD0889998
Key Value
FileNamehttp://archlinux.mirror.root.lu//pool//community//python-seaborn-0.11.1-1-any.pkg.tar.zst
MD57903E9BA3192716440D6C3856CF16CB0
SHA-1E519A38E72C48567A94EDA01231D2B26F881C566
SHA-2560315FBA6E4A29915C18338255081B2F7B259D0F9D381903B2F544E6901712F5C
SSDEEP12288:+RC0Gw9nbvCv7mSW83Ci30mlDKnrsUrY4yrU:+RhvCv9W8ZJlDSsUEjrU
TLSHT1CFB423006366B880A2266127FAA50B33772DF7D75FD321DA398278936C4FD710F916E9
Key Value
MD57888AD0C7D4502AED9662937D4279DA0
PackageArchnoarch
PackageDescriptionSeaborn 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
PackageNamepython39-seaborn
PackageRelease2.1
PackageVersion0.11.1
SHA-1A38E930103EBE2D729B6AFF64FDED502CA2AE195
SHA-256DF246FDA0C69E1E6C06C51FD12A68B7E1A565253E021265CCA9D5DE456E0FBB5