Result for 015F548AA5955C94A2583E364DBC16E26852F268

Query result

Key Value
FileName./usr/lib64/python3.10/site-packages/tables/__pycache__/table.cpython-310.pyc
FileSize99342
MD51F7F983BD7181C79E3BBFDC6FD3E6015
SHA-1015F548AA5955C94A2583E364DBC16E26852F268
SHA-256FE156B82E4A891DC60742147037228DF8E5C267B9350B0C75E034E3856F5EDE1
SSDEEP1536:mNoyodga9jLlxPRD/o7M74AAwzB4KItIi8R9evprV+TTDYDh13bQHvLBjr1KH3cL:u/aNPRDg7M74uzB/It1KRvLBN
TLSHT144A3F8437BA13B76FC42F1F1099E42A1D766926777551090348EC02A3F01A61EEBEEED
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
MD5B526966DF455CFB075548F8CD20AC233
PackageArchx86_64
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-19973423C3CB1CE0F5ABE7558C0639516FA896D66
SHA-25653F1D43D71702BA4F92989D18CD774F50AE0BE2EC7F1A0C996D34E56F2E4BC63