How to Install and Uninstall python3.11-numpy.x86_64 Package on Red Hat Enterprise Linux 8 (RHEL 8)

Last updated: June 23,2024

1. Install "python3.11-numpy.x86_64" package

Please follow the steps below to install python3.11-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
            
                        
                python3.11-numpy.x86_64
            
                    
    
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2. Uninstall "python3.11-numpy.x86_64" package

In this section, we are going to explain the necessary steps to uninstall python3.11-numpy.x86_64 on Red Hat Enterprise Linux 8 (RHEL 8):


    
        
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                sudo dnf remove
            
                        
                python3.11-numpy.x86_64
            
                    
    
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            $
        

        
            
                sudo dnf autoremove
            
                    
    
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3. Information about the python3.11-numpy.x86_64 package on Red Hat Enterprise Linux 8 (RHEL 8)

Last metadata expiration check: 0:24:17 ago on Mon Feb 26 15:59:38 2024.
Available Packages
Name : python3.11-numpy
Version : 1.23.5
Release : 1.el8
Architecture : x86_64
Size : 6.8 M
Source : python3.11-numpy-1.23.5-1.el8.src.rpm
Repository : ubi-8-appstream-rpms
Summary : A fast multidimensional array facility for Python
URL : http://www.numpy.org/
License : BSD and Python and ASL 2.0
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.