How to Install and Uninstall python38-dask-dot Package on openSuSE Tumbleweed
Last updated: December 25,2024
Deprecated! Installation of this package may no longer be supported.
1. Install "python38-dask-dot" package
Please follow the guidelines below to install python38-dask-dot on openSuSE Tumbleweed
$
sudo zypper refresh
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$
sudo zypper install
python38-dask-dot
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2. Uninstall "python38-dask-dot" package
This guide let you learn how to uninstall python38-dask-dot on openSuSE Tumbleweed:
$
sudo zypper remove
python38-dask-dot
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3. Information about the python38-dask-dot package on openSuSE Tumbleweed
Information for package python38-dask-dot:
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Repository : openSUSE-Tumbleweed-Oss
Name : python38-dask-dot
Version : 2021.9.1-1.1
Arch : noarch
Vendor : openSUSE
Installed Size : 17,5 KiB
Installed : No
Status : not installed
Source package : python-dask-2021.9.1-1.1.src
Summary : Display dask graphs using graphviz
Description :
A flexible library for parallel computing in Python.
Dask is composed of two parts:
- Dynamic task scheduling optimized for computation. This is similar to
Airflow, Luigi, Celery, or Make, but optimized for interactive
computational workloads.
- “Big Data” collections like parallel arrays, dataframes, and lists that
extend common interfaces like NumPy, Pandas, or Python iterators to
larger-than-memory or distributed environments. These parallel collections
run on top of dynamic task schedulers.
This package contains the graphviz dot rendering interface.
------------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : python38-dask-dot
Version : 2021.9.1-1.1
Arch : noarch
Vendor : openSUSE
Installed Size : 17,5 KiB
Installed : No
Status : not installed
Source package : python-dask-2021.9.1-1.1.src
Summary : Display dask graphs using graphviz
Description :
A flexible library for parallel computing in Python.
Dask is composed of two parts:
- Dynamic task scheduling optimized for computation. This is similar to
Airflow, Luigi, Celery, or Make, but optimized for interactive
computational workloads.
- “Big Data” collections like parallel arrays, dataframes, and lists that
extend common interfaces like NumPy, Pandas, or Python iterators to
larger-than-memory or distributed environments. These parallel collections
run on top of dynamic task schedulers.
This package contains the graphviz dot rendering interface.