Result for 0226192C224ADE296ED6C5175D45FCC47E0E7EE1

Query result

Key Value
FileName./usr/lib/python3.10/site-packages/tables/tests/__pycache__/create_backcompat_indexes.cpython-310.pyc
FileSize1179
MD5C19A3F24A3B75F1B797F82719640AABE
SHA-10226192C224ADE296ED6C5175D45FCC47E0E7EE1
SHA-2566D642B0C293E9C9B2AC03619AC64678B5AE80549536227BB44ED8AF7262FD510
SSDEEP24:aeO7T+LPtnmQRzDm7ZnrzX08OM6DYZ6Mg4EqkGJ+R1qxh+2ILKkP27fAqJsf+BhX:d1LPdDkrzXJz6DvMjFQPGVf
TLSHT1332126444D762E6EF4B9F177905DC231C09922EDDF44B353A55C62FD845A38413B5D06
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
MD59E65A7E425EA5B2EA644CC941589B36E
PackageArchi586
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-1A9358EA92C760EFD97F289CD9C539BD43105EF94
SHA-256AE978244F890C930897BBEA6503860B91BFB74859910BEAD3EAE9CBBEF4870B4