How to Install and Uninstall python38-dask-array Package on openSuSE Tumbleweed

Last updated: November 26,2024

Deprecated! Installation of this package may no longer be supported.

1. Install "python38-dask-array" package

This guide let you learn how to install python38-dask-array on openSuSE Tumbleweed


    
        
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                sudo zypper refresh
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo zypper install
            
                        
                python38-dask-array
            
                    
    
    Copied

2. Uninstall "python38-dask-array" package

Please follow the steps below to uninstall python38-dask-array on openSuSE Tumbleweed:


    
        
            $
        

        
            
                sudo zypper remove
            
                        
                python38-dask-array
            
                    
    
    Copied

3. Information about the python38-dask-array package on openSuSE Tumbleweed

Information for package python38-dask-array:
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Repository : openSUSE-Tumbleweed-Oss
Name : python38-dask-array
Version : 2021.9.1-1.1
Arch : noarch
Vendor : openSUSE
Installed Size : 3,4 MiB
Installed : No
Status : not installed
Source package : python-dask-2021.9.1-1.1.src
Summary : Numpy-like array data structure for dask
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 contains the dask array class.
Dask arrays implement a subset of the NumPy interface on large
arrays using blocked algorithms and task scheduling.