How to Install and Uninstall python312-holoviews Package on openSuSE Tumbleweed
Last updated: December 28,2024
1. Install "python312-holoviews" package
In this section, we are going to explain the necessary steps to install python312-holoviews on openSuSE Tumbleweed
$
sudo zypper refresh
Copied
$
sudo zypper install
python312-holoviews
Copied
2. Uninstall "python312-holoviews" package
In this section, we are going to explain the necessary steps to uninstall python312-holoviews on openSuSE Tumbleweed:
$
sudo zypper remove
python312-holoviews
Copied
3. Information about the python312-holoviews package on openSuSE Tumbleweed
Information for package python312-holoviews:
--------------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : python312-holoviews
Version : 1.18.3-2.1
Arch : noarch
Vendor : openSUSE
Installed Size : 21.9 MiB
Installed : No
Status : not installed
Source package : python-holoviews-1.18.3-2.1.src
Upstream URL : https://github.com/holoviz/holoviews
Summary : Composable, declarative visualizations for Python
Description :
HoloViews is a Python library for automated plotting of annotated
data.
Instead of building a plot using direct calls to a plotting library,
the developer instead first describes the data with semantic
information and then additional metadata to determine more detailed
aspects of the visualization. This approach provides automatic
visualization that can be requested at any time as the data evolves,
rendered automatically by one of the supported plotting libraries
(such as Bokeh or Matplotlib).
--------------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : python312-holoviews
Version : 1.18.3-2.1
Arch : noarch
Vendor : openSUSE
Installed Size : 21.9 MiB
Installed : No
Status : not installed
Source package : python-holoviews-1.18.3-2.1.src
Upstream URL : https://github.com/holoviz/holoviews
Summary : Composable, declarative visualizations for Python
Description :
HoloViews is a Python library for automated plotting of annotated
data.
Instead of building a plot using direct calls to a plotting library,
the developer instead first describes the data with semantic
information and then additional metadata to determine more detailed
aspects of the visualization. This approach provides automatic
visualization that can be requested at any time as the data evolves,
rendered automatically by one of the supported plotting libraries
(such as Bokeh or Matplotlib).