How to Install and Uninstall python311-numpy Package on openSuSE Tumbleweed
Last updated: November 24,2024
1. Install "python311-numpy" package
This tutorial shows how to install python311-numpy on openSuSE Tumbleweed
$
sudo zypper refresh
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$
sudo zypper install
python311-numpy
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2. Uninstall "python311-numpy" package
Please follow the guidelines below to uninstall python311-numpy on openSuSE Tumbleweed:
$
sudo zypper remove
python311-numpy
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3. Information about the python311-numpy package on openSuSE Tumbleweed
Information for package python311-numpy:
----------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : python311-numpy
Version : 1.26.2-4.3
Arch : x86_64
Vendor : openSUSE
Installed Size : 43.1 MiB
Installed : No
Status : not installed
Source package : python-numpy-1.26.2-4.3.src
Upstream URL : http://www.numpy.org/
Summary : NumPy array processing for numbers, strings, records and objects
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 which also makes NumPy suitable for
interfacing with general-purpose data-base applications.
There are also basic facilities for discrete fourier transform,
basic linear algebra and random number generation.
----------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : python311-numpy
Version : 1.26.2-4.3
Arch : x86_64
Vendor : openSUSE
Installed Size : 43.1 MiB
Installed : No
Status : not installed
Source package : python-numpy-1.26.2-4.3.src
Upstream URL : http://www.numpy.org/
Summary : NumPy array processing for numbers, strings, records and objects
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 which also makes NumPy suitable for
interfacing with general-purpose data-base applications.
There are also basic facilities for discrete fourier transform,
basic linear algebra and random number generation.