Result for 08CAE4643C3A3DE5084E31DCB80176765BD57575

Query result

Key Value
FileName./usr/lib64/python3.10/site-packages/tables/tests/__pycache__/test_queries.cpython-310.opt-1.pyc
FileSize33273
MD5C3132DC0655F3AEFD10C60F0698AECEA
SHA-108CAE4643C3A3DE5084E31DCB80176765BD57575
SHA-2568C9FF54D2005FB31A43882E50AA206EABB724E311169973053E79B47545E9443
SSDEEP768:KsxBs/Qg8D6U61pWWIgpl+EN/vd9EA/Nmzypt3hEmn/zoO1S1bufFuc:KsxB2QpD6h1pWWIgpllN/vd9EA/gzypB
TLSHT1F1E2C4F079F37D2AF5E0F5F15C8B421D56E650D72A408E937B5892AD3A002E82D8ACD9
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