How to Install and Uninstall python39-scipy.x86_64 Package on Red Hat Enterprise Linux 8 (RHEL 8)

Last updated: January 15,2025

1. Install "python39-scipy.x86_64" package

Learn how to install python39-scipy.x86_64 on Red Hat Enterprise Linux 8 (RHEL 8)


    
        
            $
        

        
            
                sudo dnf update
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo dnf install
            
                        
                python39-scipy.x86_64
            
                    
    
    Copied

2. Uninstall "python39-scipy.x86_64" package

This guide let you learn how to uninstall python39-scipy.x86_64 on Red Hat Enterprise Linux 8 (RHEL 8):


    
        
            $
        

        
            
                sudo dnf remove
            
                        
                python39-scipy.x86_64
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo dnf autoremove
            
                    
    
    Copied

3. Information about the python39-scipy.x86_64 package on Red Hat Enterprise Linux 8 (RHEL 8)

Last metadata expiration check: 0:55:26 ago on Mon Feb 26 15:59:38 2024.
Available Packages
Name : python39-scipy
Version : 1.5.4
Release : 5.module+el8.9.0+19644+d68f775d
Architecture : x86_64
Size : 16 M
Source : scipy-1.5.4-5.module+el8.9.0+19644+d68f775d.src.rpm
Repository : ubi-8-appstream-rpms
Summary : Scientific Tools for Python
URL : http://www.scipy.org/scipylib/index.html
License : BSD and Boost and Public Domain
Description : Scipy is open-source software for mathematics, science, and
: engineering. The core library is NumPy which provides convenient and
: fast N-dimensional array manipulation. The SciPy library is built to
: work with NumPy arrays, and provides many user-friendly and efficient
: numerical routines such as routines for numerical integration and
: optimization. Together, they run on all popular operating systems, are
: quick to install, and are free of charge. NumPy and SciPy are easy to
: use, but powerful enough to be depended upon by some of the world's
: leading scientists and engineers.