How to Install and Uninstall python39-xarray Package on openSuSE Tumbleweed
Last updated: November 23,2024
Deprecated! Installation of this package may no longer be supported.
1. Install "python39-xarray" package
Please follow the guidelines below to install python39-xarray on openSuSE Tumbleweed
$
sudo zypper refresh
Copied
$
sudo zypper install
python39-xarray
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2. Uninstall "python39-xarray" package
Please follow the step by step instructions below to uninstall python39-xarray on openSuSE Tumbleweed:
$
sudo zypper remove
python39-xarray
Copied
3. Information about the python39-xarray package on openSuSE Tumbleweed
Information for package python39-xarray:
----------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : python39-xarray
Version : 2023.12.0-2.3
Arch : noarch
Vendor : openSUSE
Installed Size : 12.1 MiB
Installed : No
Status : not installed
Source package : python-xarray-2023.12.0-2.3.src
Upstream URL : https://github.com/pydata/xarray
Summary : N-D labeled arrays and datasets in Python
Description :
xarray (formerly xray) is a python-pandas-like and pandas-compatible
toolkit for analytics on multi-dimensional arrays. It provides
N-dimensional variants of the python-pandas labeled data structures,
rather than the tabular data that pandas uses.
The Common Data Model for self-describing scientific data is used.
The dataset is an in-memory representation of a netCDF file.
----------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : python39-xarray
Version : 2023.12.0-2.3
Arch : noarch
Vendor : openSUSE
Installed Size : 12.1 MiB
Installed : No
Status : not installed
Source package : python-xarray-2023.12.0-2.3.src
Upstream URL : https://github.com/pydata/xarray
Summary : N-D labeled arrays and datasets in Python
Description :
xarray (formerly xray) is a python-pandas-like and pandas-compatible
toolkit for analytics on multi-dimensional arrays. It provides
N-dimensional variants of the python-pandas labeled data structures,
rather than the tabular data that pandas uses.
The Common Data Model for self-describing scientific data is used.
The dataset is an in-memory representation of a netCDF file.