How to Install and Uninstall python38-networkx Package on openSuSE Tumbleweed
Last updated: November 07,2024
Deprecated! Installation of this package may no longer be supported.
1. Install "python38-networkx" package
Here is a brief guide to show you how to install python38-networkx on openSuSE Tumbleweed
$
sudo zypper refresh
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$
sudo zypper install
python38-networkx
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2. Uninstall "python38-networkx" package
This is a short guide on how to uninstall python38-networkx on openSuSE Tumbleweed:
$
sudo zypper remove
python38-networkx
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3. Information about the python38-networkx package on openSuSE Tumbleweed
Information for package python38-networkx:
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Repository : openSUSE-Tumbleweed-Oss
Name : python38-networkx
Version : 2.6.3-2.1
Arch : noarch
Vendor : openSUSE
Installed Size : 13,3 MiB
Installed : No
Status : not installed
Source package : python-networkx-2.6.3-2.1.src
Summary : Python package for the study of complex networks
Description :
NetworkX (NX) is a Python package for the creation, manipulation, and study of the structure, dynamics,
and functions of complex networks.
Features:
* Includes standard graph-theoretic and statistical physics functions
* Exchange of network algorithms between applications, disciplines, and platforms
* Includes many classic graphs and synthetic networks
* Nodes and edges can be "anything" (e.g. time-series, text, images, XML records)
* Exploits existing code from high-quality legacy software in C, C++, Fortran, etc.
* Unit-tested
------------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : python38-networkx
Version : 2.6.3-2.1
Arch : noarch
Vendor : openSUSE
Installed Size : 13,3 MiB
Installed : No
Status : not installed
Source package : python-networkx-2.6.3-2.1.src
Summary : Python package for the study of complex networks
Description :
NetworkX (NX) is a Python package for the creation, manipulation, and study of the structure, dynamics,
and functions of complex networks.
Features:
* Includes standard graph-theoretic and statistical physics functions
* Exchange of network algorithms between applications, disciplines, and platforms
* Includes many classic graphs and synthetic networks
* Nodes and edges can be "anything" (e.g. time-series, text, images, XML records)
* Exploits existing code from high-quality legacy software in C, C++, Fortran, etc.
* Unit-tested