How to Install and Uninstall python3-holoviews Package on openSUSE Leap
Last updated: November 26,2024
1. Install "python3-holoviews" package
In this section, we are going to explain the necessary steps to install python3-holoviews on openSUSE Leap
$
sudo zypper refresh
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$
sudo zypper install
python3-holoviews
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2. Uninstall "python3-holoviews" package
Learn how to uninstall python3-holoviews on openSUSE Leap:
$
sudo zypper remove
python3-holoviews
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3. Information about the python3-holoviews package on openSUSE Leap
Information for package python3-holoviews:
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Repository : Main Repository
Name : python3-holoviews
Version : 1.13.2-bp155.2.11
Arch : noarch
Vendor : openSUSE
Installed Size : 13.9 MiB
Installed : No
Status : not installed
Source package : python-holoviews-1.13.2-bp155.2.11.src
Upstream URL : https://github.com/ioam/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 : Main Repository
Name : python3-holoviews
Version : 1.13.2-bp155.2.11
Arch : noarch
Vendor : openSUSE
Installed Size : 13.9 MiB
Installed : No
Status : not installed
Source package : python-holoviews-1.13.2-bp155.2.11.src
Upstream URL : https://github.com/ioam/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).