How to Install and Uninstall python2-scipy.x86_64 Package on Red Hat Enterprise Linux 8 (RHEL 8)

Last updated: January 11,2025

1. Install "python2-scipy.x86_64" package

Here is a brief guide to show you how to install python2-scipy.x86_64 on Red Hat Enterprise Linux 8 (RHEL 8)


    
        
            $
        

        
            
                sudo dnf update
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo dnf install
            
                        
                python2-scipy.x86_64
            
                    
    
    Copied

2. Uninstall "python2-scipy.x86_64" package

Here is a brief guide to show you how to uninstall python2-scipy.x86_64 on Red Hat Enterprise Linux 8 (RHEL 8):


    
        
            $
        

        
            
                sudo dnf remove
            
                        
                python2-scipy.x86_64
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo dnf autoremove
            
                    
    
    Copied

3. Information about the python2-scipy.x86_64 package on Red Hat Enterprise Linux 8 (RHEL 8)

Last metadata expiration check: 0:52:31 ago on Mon Feb 26 15:59:38 2024.
Available Packages
Name : python2-scipy
Version : 1.0.0
Release : 22.module+el8.9.0+19487+7dc18407
Architecture : x86_64
Size : 14 M
Source : scipy-1.0.0-22.module+el8.9.0+19487+7dc18407.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.