How to Install and Uninstall python3-astroML.noarch Package on Fedora 36
Last updated: November 27,2024
1. Install "python3-astroML.noarch" package
Please follow the guidelines below to install python3-astroML.noarch on Fedora 36
$
sudo dnf update
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$
sudo dnf install
python3-astroML.noarch
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2. Uninstall "python3-astroML.noarch" package
Please follow the instructions below to uninstall python3-astroML.noarch on Fedora 36:
$
sudo dnf remove
python3-astroML.noarch
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$
sudo dnf autoremove
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3. Information about the python3-astroML.noarch package on Fedora 36
Last metadata expiration check: 4:02:41 ago on Thu Sep 8 02:05:26 2022.
Available Packages
Name : python3-astroML
Version : 0.4.1
Release : 8.fc36
Architecture : noarch
Size : 206 k
Source : python-astroML-0.4.1-8.fc36.src.rpm
Repository : fedora
Summary : Python tools for machine learning and data mining in Astronomy
URL : http://www.astroml.org/
License : BSD
Description : AstroML is a Python module for machine learning and data mining built on
: numpy, scipy, scikit-learn, and matplotlib, and distributed under the
: 3-clause BSD license. It contains a growing library of statistical and
: machine learning routines for analyzing astronomical data in python,
: loaders for several open astronomical datasets, and a large suite of
: examples of analyzing and visualizing astronomical datasets.
Available Packages
Name : python3-astroML
Version : 0.4.1
Release : 8.fc36
Architecture : noarch
Size : 206 k
Source : python-astroML-0.4.1-8.fc36.src.rpm
Repository : fedora
Summary : Python tools for machine learning and data mining in Astronomy
URL : http://www.astroml.org/
License : BSD
Description : AstroML is a Python module for machine learning and data mining built on
: numpy, scipy, scikit-learn, and matplotlib, and distributed under the
: 3-clause BSD license. It contains a growing library of statistical and
: machine learning routines for analyzing astronomical data in python,
: loaders for several open astronomical datasets, and a large suite of
: examples of analyzing and visualizing astronomical datasets.