How to Install and Uninstall python-dask-doc Package on Kali Linux
Last updated: February 24,2025
1. Install "python-dask-doc" package
In this section, we are going to explain the necessary steps to install python-dask-doc on Kali Linux
$
sudo apt update
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$
sudo apt install
python-dask-doc
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2. Uninstall "python-dask-doc" package
This guide covers the steps necessary to uninstall python-dask-doc on Kali Linux:
$
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 Kali Linux
Package: python-dask-doc
Source: dask
Version: 2023.12.1+dfsg-2
Installed-Size: 67343
Maintainer: Debian Python Team
Architecture: all
Depends: libjs-mathjax, node-js-yaml, libjs-sphinxdoc (>= 7.2.2)
Size: 10713584
SHA256: e76b0646b546dae71cb1284cd5756993bb2f7c9201425d89304370a123332e42
SHA1: 606384a971769c2e6fe8b9fd262478a0982eecc9
MD5sum: e9fc6c3d5e81258b0d73bda629c0041c
Description: 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:
Homepage: https://github.com/dask/dask
Section: doc
Priority: optional
Filename: pool/main/d/dask/python-dask-doc_2023.12.1+dfsg-2_all.deb
Source: dask
Version: 2023.12.1+dfsg-2
Installed-Size: 67343
Maintainer: Debian Python Team
Architecture: all
Depends: libjs-mathjax, node-js-yaml, libjs-sphinxdoc (>= 7.2.2)
Size: 10713584
SHA256: e76b0646b546dae71cb1284cd5756993bb2f7c9201425d89304370a123332e42
SHA1: 606384a971769c2e6fe8b9fd262478a0982eecc9
MD5sum: e9fc6c3d5e81258b0d73bda629c0041c
Description: 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:
Homepage: https://github.com/dask/dask
Section: doc
Priority: optional
Filename: pool/main/d/dask/python-dask-doc_2023.12.1+dfsg-2_all.deb