_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
 / / /      / / /___/ / /     \ \ \ /_/\__/ / /
/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/
python-dask 2024.4.2
Propagated dependencies: python-click@8.1.7 python-cloudpickle@1.6.0 python-dask-expr@1.0.14 python-fsspec@2023.5.0 python-importlib-metadata@5.2.0 python-numpy@1.23.2 python-packaging@21.3 python-pandas@2.1.1 python-partd@1.4.1 python-toolz@0.11.2 python-pyyaml@6.0.1
Channel: guix
Location: gnu/packages/python-xyz.scm (gnu packages python-xyz)
Home page: https://github.com/dask/dask/
Licenses: Modified BSD
Synopsis: Parallel computing with task scheduling
Description:

Dask is a flexible parallel computing library for analytics. It consists of two components: dynamic task scheduling optimized for computation, and large 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.

python-dask 2024.4.2
Propagated dependencies: python-click@8.1.7 python-cloudpickle@1.6.0 python-fsspec@2023.5.0 python-importlib-metadata@5.2.0 python-numpy@1.23.2 python-packaging@21.3 python-pandas@2.1.1 python-partd@1.4.1 python-toolz@0.11.2 python-pyyaml@6.0.1
Channel: guix
Location: gnu/packages/python-xyz.scm (gnu packages python-xyz)
Home page: https://github.com/dask/dask/
Licenses: Modified BSD
Synopsis: Parallel computing with task scheduling
Description:

Dask is a flexible parallel computing library for analytics. It consists of two components: dynamic task scheduling optimized for computation, and large 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.

Total results: 4