Result for 02BD1FA7BD74D7A34765D7B43AD3974D5974E8C3

Query result

Key Value
FileName./usr/lib/python3.8/site-packages/seaborn/__pycache__/matrix.cpython-38.opt-1.pyc
FileSize37178
MD57180238F43ED3565C51410031A74E759
SHA-102BD1FA7BD74D7A34765D7B43AD3974D5974E8C3
SHA-2566264085876D3F7BD4A8498CA2AAAB5E1441EF0F5EA0B1187BC6BE37FF82807EA
SSDEEP768:hlSEpG6nCoQDh4mdnSA7Y7r9rRbhafiO8hSO7b9k/yPJVGLLKL:hEECZdF+rZg8hSW+yPJ6KL
TLSHT113F21B6BAA400F57FF93F0F664DD8691C624D12F331A854F385D825D0F42AB4A8AE75C
hashlookup:parent-total1
hashlookup:trust55

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Parents (Total: 1)

The searched file hash is included in 1 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
MD5B3B1C6EF85D9BBCEA1769E7BC80E17D5
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
PackageNamepython38-seaborn
PackageRelease33.1
PackageVersion0.11.2
SHA-19833D8BCD60E858C85387ADF406DDE6204EACF3E
SHA-25632BE3639D80DE3112C2958677E61ACC95D2691BD7EBD91D20452E2F6A513EB5C