How to Install and Uninstall python2-networkx Package on openSUSE Leap
Last updated: November 05,2024
Deprecated! Installation of this package may no longer be supported.
1. Install "python2-networkx" package
Please follow the instructions below to install python2-networkx on openSUSE Leap
$
sudo zypper refresh
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$
sudo zypper install
python2-networkx
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2. Uninstall "python2-networkx" package
This is a short guide on how to uninstall python2-networkx on openSUSE Leap:
$
sudo zypper remove
python2-networkx
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3. Information about the python2-networkx package on openSUSE Leap
Information for package python2-networkx:
-----------------------------------------
Repository : Main Repository
Name : python2-networkx
Version : 2.0-3.2.8
Arch : noarch
Vendor : SUSE LLC
Installed Size : 7,6 MiB
Installed : No
Status : not installed
Source package : python-networkx-2.0-3.2.8.src
Summary : Python package for the creation, manipulation,
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
* Easy 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.
* Open source (encourages community input)
* Unit-tested
-----------------------------------------
Repository : Main Repository
Name : python2-networkx
Version : 2.0-3.2.8
Arch : noarch
Vendor : SUSE LLC
Installed Size : 7,6 MiB
Installed : No
Status : not installed
Source package : python-networkx-2.0-3.2.8.src
Summary : Python package for the creation, manipulation,
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
* Easy 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.
* Open source (encourages community input)
* Unit-tested