Result for 020120303CBF151904644D7040E42E7FCE76AE41

Query result

Key Value
FileName./usr/share/doc/python-dask-doc/html/generated/dask.array.sinc.html
FileSize30823
MD58FCAC3E7972EE1AB57A0D69E6BFBE837
SHA-1020120303CBF151904644D7040E42E7FCE76AE41
SHA-2566D93BA6A2F9D2902E9A4D05EC02F88CF801945E6FB3579EB88A67EFF2B11BF70
SSDEEP768:o4XvX9YHTO8y3CnuR96cNHo1hkh1kUWcwXwl:o4/GcNHo1hkhyUWcwm
TLSHT1BDD2312158F6257341A381C147BD3B28BAF7941BE14A385271FC13298FD6F92BA0776E
hashlookup:parent-total1
hashlookup:trust55

Network graph view

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
FileSize9678540
MD551916ABB5151B40836CC495B2297C5F7
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
PackageVersion2022.12.1+dfsg-2
SHA-1CE9AD860D74774AA01A349FEF116DB92C09698DA
SHA-25681FC4CC36BFC939ED9FF28C2423D921909CFBEF1AF69055131B79AB69A33B615