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

Last updated: November 23,2024

1. Install "python39-dask-dataframe" package

Please follow the instructions below to install python39-dask-dataframe on openSuSE Tumbleweed


    
        
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                sudo zypper refresh
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo zypper install
            
                        
                python39-dask-dataframe
            
                    
    
    Copied

2. Uninstall "python39-dask-dataframe" package

Please follow the step by step instructions below to uninstall python39-dask-dataframe on openSuSE Tumbleweed:


    
        
            $
        

        
            
                sudo zypper remove
            
                        
                python39-dask-dataframe
            
                    
    
    Copied

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

Information for package python39-dask-dataframe:
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Repository : openSUSE-Tumbleweed-Oss
Name : python39-dask-dataframe
Version : 2024.2.1-1.1
Arch : noarch
Vendor : openSUSE
Installed Size : 2.5 MiB
Installed : No
Status : not installed
Source package : python-dask-2024.2.1-1.1.src
Upstream URL : https://dask.org
Summary : Pandas-like DataFrame 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 DataFrame class.
A Dask DataFrame is a large parallel dataframe composed of many
smaller Pandas dataframes, split along the index. These pandas
dataframes may live on disk for larger-than-memory computing
on a single machine, or on many different machines in a cluster.