How to Install and Uninstall python312-dask-complete Package on openSuSE Tumbleweed
Last updated: December 26,2024
1. Install "python312-dask-complete" package
This guide let you learn how to install python312-dask-complete on openSuSE Tumbleweed
$
sudo zypper refresh
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$
sudo zypper install
python312-dask-complete
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2. Uninstall "python312-dask-complete" package
This guide let you learn how to uninstall python312-dask-complete on openSuSE Tumbleweed:
$
sudo zypper remove
python312-dask-complete
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3. Information about the python312-dask-complete package on openSuSE Tumbleweed
Information for package python312-dask-complete:
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Repository : openSUSE-Tumbleweed-Oss
Name : python312-dask-complete
Version : 2024.2.1-1.1
Arch : noarch
Vendor : openSUSE
Installed Size : 1.5 KiB
Installed : No
Status : not installed
Source package : python-dask-2024.2.1-1.1.src
Upstream URL : https://dask.org
Summary : All dask components
Description :
A flexible library for parallel computing in Python.
Dask is composed of two parts:
- Dynamic task scheduling optimized for computation. This is similar to
Airflow, Luigi, Celery, or Make, but optimized for interactive
computational workloads.
- “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 dynamic task schedulers.
This package pulls in all the optional dask components.
------------------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : python312-dask-complete
Version : 2024.2.1-1.1
Arch : noarch
Vendor : openSUSE
Installed Size : 1.5 KiB
Installed : No
Status : not installed
Source package : python-dask-2024.2.1-1.1.src
Upstream URL : https://dask.org
Summary : All dask components
Description :
A flexible library for parallel computing in Python.
Dask is composed of two parts:
- Dynamic task scheduling optimized for computation. This is similar to
Airflow, Luigi, Celery, or Make, but optimized for interactive
computational workloads.
- “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 dynamic task schedulers.
This package pulls in all the optional dask components.