How to Install and Uninstall python3-nilearn.noarch Package on Fedora 36

Last updated: January 16,2025

1. Install "python3-nilearn.noarch" package

Please follow the instructions below to install python3-nilearn.noarch on Fedora 36


    
        
            $
        

        
            
                sudo dnf update
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo dnf install
            
                        
                python3-nilearn.noarch
            
                    
    
    Copied

2. Uninstall "python3-nilearn.noarch" package

Please follow the step by step instructions below to uninstall python3-nilearn.noarch on Fedora 36:


    
        
            $
        

        
            
                sudo dnf remove
            
                        
                python3-nilearn.noarch
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo dnf autoremove
            
                    
    
    Copied

3. Information about the python3-nilearn.noarch package on Fedora 36

Last metadata expiration check: 1:05:34 ago on Thu Sep 8 02:05:26 2022.
Available Packages
Name : python3-nilearn
Version : 0.9.0
Release : 4.fc36
Architecture : noarch
Size : 8.1 M
Source : python-nilearn-0.9.0-4.fc36.src.rpm
Repository : updates
Summary : Python module for fast and easy statistical learning on NeuroImaging data
URL : https://pypi.python.org/pypi/nilearn
License : BSD
Description :
: Nilearn is a Python module for fast and easy statistical learning on
: NeuroImaging data.
:
: It leverages the scikit-learn Python toolbox for multivariate statistics with
: applications such as predictive modelling, classification, decoding, or
: connectivity analysis.
:
: This work is made available by a community of people, amongst which the INRIA
: Parietal Project Team and the scikit-learn folks, in particular P. Gervais, A.
: Abraham, V. Michel, A. Gramfort, G. Varoquaux, F. Pedregosa, B. Thirion, M.
: Eickenberg, C. F. Gorgolewski, D. Bzdok, L. Esteve and B. Cipollini.
:
: Detailed documentation is available at http://nilearn.github.io/.