How to Install and Uninstall python3-zarr Package on Ubuntu 20.10 (Groovy Gorilla)

Last updated: December 23,2024

1. Install "python3-zarr" package

Learn how to install python3-zarr on Ubuntu 20.10 (Groovy Gorilla)


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                python3-zarr
            
                    
    
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2. Uninstall "python3-zarr" package

This guide covers the steps necessary to uninstall python3-zarr on Ubuntu 20.10 (Groovy Gorilla):


    
        
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                sudo apt remove
            
                        
                python3-zarr
            
                    
    
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                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the python3-zarr package on Ubuntu 20.10 (Groovy Gorilla)

Package: python3-zarr
Architecture: all
Version: 2.4.0+ds-1
Priority: optional
Section: universe/python
Source: zarr
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Science Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 2846
Depends: python3-asciitree, python3-fasteners, python3-numcodecs, python3-numpy, python3:any, libjs-sphinxdoc (>= 1.0), sphinx-rtd-theme-common (>= 0.4.3+dfsg)
Suggests: python3-h5py
Filename: pool/universe/z/zarr/python3-zarr_2.4.0+ds-1_all.deb
Size: 233868
MD5sum: e938f04c32104a1ec556a026ff65fabb
SHA1: 0ce95241d7eb6681c5267ac650c16ed215a319ee
SHA256: 545fa978d81bbe9ae6ba147a0471ab68fc148854ecd829d2747d397085eb7fee
SHA512: 4ca7f37d1d6e520b903f0d92c55dacc5eae6be3752f9fc77f43fb9853c3510a0ddb64f650062410ba711baad43ec4ca6b67e6a9682ce36be9bf62ec49a8fa15c
Homepage: https://github.com/zarr-developers/zarr-python
Description-en: chunked, compressed, N-dimensional arrays for Python
Zarr is a Python package providing an implementation of compressed,
chunked, N-dimensional arrays, designed for use in parallel
computing. Some highlights:
.
- Create N-dimensional arrays with any NumPy dtype.
- Chunk arrays along any dimension.
- Compress chunks using the fast Blosc meta-compressor or
alternatively using zlib, BZ2 or LZMA.
- Store arrays in memory, on disk, inside a Zip file, on S3, ...
- Read an array concurrently from multiple threads or processes.
- Write to an array concurrently from multiple threads or processes.
- Organize arrays into hierarchies via groups.
- Use filters to preprocess data and improve compression.
Description-md5: c64a8435acccdbe74e5e892f033e70f0