How to Install and Uninstall python39-dask-bag Package on openSuSE Tumbleweed

Last updated: November 21,2024

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

1. Install "python39-dask-bag" package

Here is a brief guide to show you how to install python39-dask-bag on openSuSE Tumbleweed


    
        
            $
        

        
            
                sudo zypper refresh
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo zypper install
            
                        
                python39-dask-bag
            
                    
    
    Copied

2. Uninstall "python39-dask-bag" package

This guide let you learn how to uninstall python39-dask-bag on openSuSE Tumbleweed:


    
        
            $
        

        
            
                sudo zypper remove
            
                        
                python39-dask-bag
            
                    
    
    Copied

3. Information about the python39-dask-bag package on openSuSE Tumbleweed

Information for package python39-dask-bag:
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Repository : openSUSE-Tumbleweed-Oss
Name : python39-dask-bag
Version : 2021.9.1-1.1
Arch : noarch
Vendor : openSUSE
Installed Size : 482,1 KiB
Installed : No
Status : not installed
Source package : python-dask-2021.9.1-1.1.src
Summary : Data structure generic python objects in 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 bag class.
Dask.Bag parallelizes computations across a large collection of
generic Python objects. It is particularly useful when dealing
with large quantities of semi-structured data like JSON blobs
or log files.