Result for 06896AC7C2D60C46F4CBEEBA8E80C9E2214E3EBE

Query result

Key Value
FileName./usr/lib64/python3.10/site-packages/tables/tests/__pycache__/test_nestedtypes.cpython-310.pyc
FileSize38607
MD58CF283D379F3B7E3016558C34BF5A7C0
SHA-106896AC7C2D60C46F4CBEEBA8E80C9E2214E3EBE
SHA-2563A37671E291A1B0451F1053BDAC0987201B55C50859F9C773C5C5D9723AFEF49
SSDEEP768:iHiaJ9eAW/SvMGLaUWiwyHBKRKrYLm1KhcXPMOglZzwS/Mplc3:miaJ9elSvMsapp2BuK0Lm1KhcXPMnF1h
TLSHT16E03F8997D330D05FF16F6F828574B78E47AD3A663C4E2B1243EE38A2E40595ADF0498
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
MD5469BA2DA942B6AB78CB705F3F7D1BAC0
PackageArchaarch64
PackageDescriptionPyTables is a Python 3 package for managing hierarchical datasets designed to efficiently and easily cope with extremely large amounts of data. It is built on top of the HDF5 library and the NumPy package (numarray and Numeric are also supported). PyTables features an object-oriented interface and performance-critical extensions coded in C (generated using Pyrex) that make it a fast yet extremely easy-to-use tool for interactively processing and searching through very large amounts of data. PyTables also optimizes memory and disk resources so that data occupies much less space than with other solutions such as relational or object-oriented databases (especially when compression is used).
PackageMaintainerneoclust <neoclust>
PackageNamepython3-tables
PackageRelease1.mga9
PackageVersion3.7.0
SHA-18B4E8FE0D62B1914E90B0EB706DB818C4D25FFEE
SHA-2562E709179F16BC5E82AF4802DEAC8C9BA05FEE517D653EFE90F25C91E145813D5