Result for 0A405F767074F51B0409D8E143CE54D868DFE0FF

Query result

Key Value
FileName./usr/lib/python3.10/site-packages/tables/__pycache__/carray.cpython-310.pyc
FileSize7204
MD5F2CAD73273CE816F584A07EAECA3DA2F
SHA-10A405F767074F51B0409D8E143CE54D868DFE0FF
SHA-256467E4F202CD28E70918F0E44476368325B95A76B403299AC05BFE8662F251BEC
SSDEEP96:WnTqaxm/k3g5jTLeVEeSjU489r4IzGWzhOTrjxkpF8+Uykmo3EYndl87XSCYUr5C:6Tzs/r3LeVETQ4IUDaF5BoIJYi51ef
TLSHT1D3E129027790977AFC23F932597E6186F32405773315A205304C8A943F4BAF8A6BE76E
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