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python-numpy-groupies package

python-numpy-groupies 0.9.20-1 source package in Ubuntu

Changelog

python-numpy-groupies (0.9.20-1) unstable; urgency=medium

  * Team upload.
  * New upstream release.
    (closes: #1026885, #1027196, #1027234)
  * Sort build dependencies.
  * Drop obsolete python3-pytest-runner build dependency.
  * Drop python3-distutils build dependency.
  * Add patch to not use broken Clean class.
  * Build without python3-numba (RC buggy).
  * Bump Standards-Version to 4.6.1, no changes.
  * Add patch to fix versioneer configuration.

 -- Bas Couwenberg <email address hidden>  Sat, 07 Jan 2023 20:03:30 +0100

Upload details

Uploaded by:
Debian Python Team
Uploaded to:
Sid
Original maintainer:
Debian Python Team
Architectures:
all
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Mantic release universe misc
Lunar release universe misc

Builds

Lunar: [FULLYBUILT] amd64

Downloads

File Size SHA-256 Checksum
python-numpy-groupies_0.9.20-1.dsc 2.2 KiB e0ddd272647bc09d1a2df11e9282557cb9db4ff1612f5aa8c3a33ffc7b144898
python-numpy-groupies_0.9.20.orig.tar.gz 169.3 KiB 1c651930de328e88c7cd0685df96a1d8314ec0c9444816f8fbfe6689275e8fd3
python-numpy-groupies_0.9.20-1.debian.tar.xz 4.4 KiB 5a1ce686c4bb4bfcc420634b215649bc51ae9b7756f6b495619ac483196168ad

Available diffs

No changes file available.

Binary packages built by this source

python3-numpy-groupies: performs operations on/with subsets of n-dim arrays

 This package consists of a couple of optimised tools for doing things
 that can roughly be considered "group-indexing operations". The most
 prominent tool is `aggregate`.
 .
 `aggregate` takes an array of values, and an array giving the group
 number for each of those values. It then returns the sum (or mean, or
 std, or any, ...etc.) of the values in each group. You have probably
 come across this idea before, using `matlab` accumarray, `pandas`
 groupby, or generally MapReduce algorithms and histograms.
 .
 There are different implementations of `aggregate` provided, based on
 plain `numpy`, `numba` and `weave`. Performance is a main concern, and
 so far this implementation comfortably beats similar implementations in
 other packages (check the benchmarks).