Result for 06D8E00E2DFFF3402640BEFC24D122FBA934D7E9

Query result

Key Value
FileName./usr/lib/python3.10/site-packages/tables/tests/__pycache__/test_enum.cpython-310.pyc
FileSize19336
MD5F375F5C758662BA6350A0DA05AFC57DA
SHA-106D8E00E2DFFF3402640BEFC24D122FBA934D7E9
SHA-2566E5E339D72F5223A2CA9C3F4B01108F98F94C7D846EF394AB65C35C3305D9E16
SSDEEP384:trY+ZUwZKUPMLscYlYyFc73Q77xWzkt9Gpz1dK9lcXtTw7nC2+Jz/g3VYCWI:trY+uwZzPQscYlW7AZWAt9GphVlXxJzE
TLSHT1EB92B608EC439B06FE59F7FD0C7157A4D7B092F79B1CA245244D521F2E89E8828F9A9C
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