How to Install and Uninstall python312-numba Package on openSuSE Tumbleweed

Last updated: November 12,2024

1. Install "python312-numba" package

In this section, we are going to explain the necessary steps to install python312-numba on openSuSE Tumbleweed


    
        
            $
        

        
            
                sudo zypper refresh
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo zypper install
            
                        
                python312-numba
            
                    
    
    Copied

2. Uninstall "python312-numba" package

This guide let you learn how to uninstall python312-numba on openSuSE Tumbleweed:


    
        
            $
        

        
            
                sudo zypper remove
            
                        
                python312-numba
            
                    
    
    Copied

3. Information about the python312-numba package on openSuSE Tumbleweed

Information for package python312-numba:
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Repository : openSUSE-Tumbleweed-Oss
Name : python312-numba
Version : 0.59.0-3.1
Arch : x86_64
Vendor : openSUSE
Installed Size : 31.7 MiB
Installed : No
Status : not installed
Source package : python-numba-0.59.0-3.1.src
Upstream URL : https://numba.pydata.org/
Summary : NumPy-aware optimizing compiler for Python using LLVM
Description :
Numba is a NumPy-aware optimizing compiler for Python. It uses the
LLVM compiler infrastructure to compile Python syntax to
machine code.
It is aware of NumPy arrays as typed memory regions and so can speed-up
code using NumPy arrays. Other, less well-typed code will be translated
to Python C-API calls, effectively removing the "interpreter", but not removing
the dynamic indirection.
Numba is also not a tracing JIT. It *compiles* your code before it gets
run, either using run-time type information or type information you provide
in the decorator.
Numba is a mechanism for producing machine code from Python syntax and typed
data structures such as those that exist in NumPy.