How to Install and Uninstall python38-numpy.x86_64 Package on Red Hat Enterprise Linux 8 (RHEL 8)

Last updated: November 28,2024

1. Install "python38-numpy.x86_64" package

Please follow the guidance below to install python38-numpy.x86_64 on Red Hat Enterprise Linux 8 (RHEL 8)


    
        
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                sudo dnf update
            
                    
    
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                sudo dnf install
            
                        
                python38-numpy.x86_64
            
                    
    
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2. Uninstall "python38-numpy.x86_64" package

This is a short guide on how to uninstall python38-numpy.x86_64 on Red Hat Enterprise Linux 8 (RHEL 8):


    
        
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                sudo dnf remove
            
                        
                python38-numpy.x86_64
            
                    
    
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            $
        

        
            
                sudo dnf autoremove
            
                    
    
    Copied

3. Information about the python38-numpy.x86_64 package on Red Hat Enterprise Linux 8 (RHEL 8)

Last metadata expiration check: 1:16:20 ago on Mon Feb 26 15:59:38 2024.
Available Packages
Name : python38-numpy
Version : 1.17.3
Release : 7.module+el8.9.0+19598+4a60c7b7
Architecture : x86_64
Size : 4.7 M
Source : numpy-1.17.3-7.module+el8.9.0+19598+4a60c7b7.src.rpm
Repository : ubi-8-appstream-rpms
Summary : A fast multidimensional array facility for Python
URL : http://www.numpy.org/
License : BSD
Description : NumPy is a general-purpose array-processing package designed to
: efficiently manipulate large multi-dimensional arrays of arbitrary
: records without sacrificing too much speed for small multi-dimensional
: arrays. NumPy is built on the Numeric code base and adds features
: introduced by numarray as well as an extended C-API and the ability to
: create arrays of arbitrary type.
:
: There are also basic facilities for discrete fourier transform,
: basic linear algebra and random number generation. Also included in
: this package is a version of f2py that works properly with NumPy.