How to Install and Uninstall python311-holoviews Package on openSuSE Tumbleweed
Last updated: November 27,2024
1. Install "python311-holoviews" package
Please follow the guidance below to install python311-holoviews on openSuSE Tumbleweed
$
sudo zypper refresh
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$
sudo zypper install
python311-holoviews
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2. Uninstall "python311-holoviews" package
Learn how to uninstall python311-holoviews on openSuSE Tumbleweed:
$
sudo zypper remove
python311-holoviews
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3. Information about the python311-holoviews package on openSuSE Tumbleweed
Information for package python311-holoviews:
--------------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : python311-holoviews
Version : 1.18.3-2.1
Arch : noarch
Vendor : openSUSE
Installed Size : 23.0 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 : python311-holoviews
Version : 1.18.3-2.1
Arch : noarch
Vendor : openSUSE
Installed Size : 23.0 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).