How to Install and Uninstall python-dask-doc Package on Ubuntu 21.10 (Impish Indri)

Last updated: January 11,2025

1. Install "python-dask-doc" package

Please follow the guidelines below to install python-dask-doc on Ubuntu 21.10 (Impish Indri)


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                python-dask-doc
            
                    
    
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2. Uninstall "python-dask-doc" package

In this section, we are going to explain the necessary steps to uninstall python-dask-doc on Ubuntu 21.10 (Impish Indri):


    
        
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                sudo apt remove
            
                        
                python-dask-doc
            
                    
    
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                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the python-dask-doc package on Ubuntu 21.10 (Impish Indri)

Package: python-dask-doc
Architecture: all
Version: 2021.01.0+dfsg-1ubuntu1
Built-Using: sphinx (= 3.4.3-1)
Priority: optional
Section: universe/doc
Source: dask
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Python Team
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 19085
Depends: libjs-mathjax, libjs-sphinxdoc (>= 2.4.3-5~), sphinx-rtd-theme-common (>= 0.5.1+dfsg)
Filename: pool/universe/d/dask/python-dask-doc_2021.01.0+dfsg-1ubuntu1_all.deb
Size: 3253872
MD5sum: c8fe808ef6c70a828d474bf97ab67dc9
SHA1: 28540e287b74c6da388246ac2dda3d82b79b3181
SHA256: 1868c9dd368eee2a39ce1430e7a555bc23636256c6ce9ebde5a3e991f371c033
SHA512: 1f17601f157d347a5661cce795248d42052cec14870f1d73b614994c3aa0a2735cbdfcd107a60a885528163e65fb61a62dfe4ab2b327a2898bf73849e4656a50
Homepage: https://github.com/dask/dask
Description-en: Minimal task scheduling abstraction documentation
Dask is a flexible parallel computing library for analytics,
containing two components.
.
1. Dynamic task scheduling optimized for computation. This is similar
to Airflow, Luigi, Celery, or Make, but optimized for interactive
computational workloads.
2. "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 the dynamic task schedulers.
.
This contains the documentation
Description-md5: 249a6cc0e56c6f4be343e8b38a334beb