Result for 02019E7DD5BFC37EE90FE604464723C3976B13B1

Query result

Key Value
FileName./usr/share/doc/python-dask-doc/html/delayed-best-practices.html
FileSize45690
MD59D1A3B92168493D979AFB51B8D23B09F
SHA-102019E7DD5BFC37EE90FE604464723C3976B13B1
SHA-25659875D9DBC0D7EDE24931EFC2587167C2D85FBC3E3107BE0B911C7B95C9A67B0
SSDEEP768:KnNFeEOb5eb87qBQjq67xIrl8x0CRP1AG2N+1OJHRc:Kn7587qBQjq67xIrl8xHPr2E1WHa
TLSHT1C02320E1A1FA8137013395C766BF1B39B0F2442AE5960501B2FD837C4BECE55781B9AE
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
FileSize3964340
MD5870AF1465DB0E84ED8B879B3411947CF
PackageDescriptionMinimal task scheduling abstraction documentation Dask is a flexible parallel computing library for analytics, containing two components. . 1. Dynamic task scheduling optimized for computation. This is similar to Airflow, Luigi, Celery, or Make, but optimized for interactive computational workloads. 2. "Big Data" collections like parallel arrays, dataframes, and lists that extend common interfaces like NumPy, Pandas, or Python iterators to larger-than-memory or distributed environments. These parallel collections run on top of the dynamic task schedulers. . This contains the documentation
PackageMaintainerDebian Python Team <team+python@tracker.debian.org>
PackageNamepython-dask-doc
PackageSectiondoc
PackageVersion2021.09.1+dfsg-2
SHA-1827B35900A1D0A2736A0ACDABE77B5FCEC4ABAB2
SHA-2562CDF1DB821BDF373D8AF9D4C6E962696974AF9893708D9CBC31DB5FAA8DF6370