Result for 037F18FA08A8D114ABB871BD4F90A0E6E4515A7D

Query result

Key Value
FileName./usr/lib64/python3.10/site-packages/tables/tests/__pycache__/test_indexvalues.cpython-310.pyc
FileSize70249
MD5D47E8B58533DA4432F268C08E90A7B99
SHA-1037F18FA08A8D114ABB871BD4F90A0E6E4515A7D
SHA-25616E66064DB22B66F34F12FF7AE94BE32CB7EF8A19E60890E5F3C83A5A48C0C1C
SSDEEP768:AWU/CqLJuZNscx1FiQixCRGOEkg27cYO9fniHFbTb4YUwThvq0nJjaPly7wykCRJ:BU/Bcx1FiVxuEXyEq
TLSHT1916361185625DF0FFA69F1FEF6AC6319FB2A938D1704F392701292293F1098D1E6485B
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