zarr3.1.3
zarr3.1.3
Published
An implementation of chunked, compressed, N-dimensional arrays for Python
pip install zarr
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Requires Python
>=3.11
Dependencies
- donfig
>=0.8 - numcodecs
[crc32c]>=0.14 - numpy
>=1.26 - packaging
>=22.0 - typing-extensions
>=4.9 - typer
; extra == "cli" - astroid
<4; extra == "docs" - numcodecs
[msgpack]; extra == "docs" - numpydoc
; extra == "docs" - pydata-sphinx-theme
; extra == "docs" - pytest
; extra == "docs" - rich
; extra == "docs" - s3fs
>=2023.10.0; extra == "docs" - sphinx-autoapi
==3.4.0; extra == "docs" - sphinx-autobuild
>=2021.3.14; extra == "docs" - sphinx-copybutton
; extra == "docs" - sphinx-design
; extra == "docs" - sphinx-issues
; extra == "docs" - sphinx-reredirects
; extra == "docs" - sphinx
==8.1.3; extra == "docs" - towncrier
; extra == "docs" - cupy-cuda12x
; extra == "gpu" - rich
; extra == "optional" - universal-pathlib
; extra == "optional" - fsspec
>=2023.10.0; extra == "remote" - obstore
>=0.5.1; extra == "remote" - botocore
; extra == "remote-tests" - fsspec
>=2023.10.0; extra == "remote-tests" - moto
[s3,server]; extra == "remote-tests" - obstore
>=0.5.1; extra == "remote-tests" - requests
; extra == "remote-tests" - s3fs
>=2023.10.0; extra == "remote-tests" - coverage
>=7.10; extra == "test" - hypothesis
; extra == "test" - mypy
; extra == "test" - numpydoc
; extra == "test" - packaging
; extra == "test" - pytest-accept
; extra == "test" - pytest-asyncio
; extra == "test" - pytest-cov
; extra == "test" - pytest-xdist
; extra == "test" - pytest
<8.4; extra == "test" - rich
; extra == "test" - tomlkit
; extra == "test" - uv
; extra == "test"
Zarr
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What is it?
Zarr is a Python package providing an implementation of compressed, chunked, N-dimensional arrays, designed for use in parallel computing. See the documentation for more information.
Main Features
- Create N-dimensional arrays with any NumPy
dtype. - Chunk arrays along any dimension.
- Compress and/or filter chunks using any NumCodecs codec.
- Store arrays in memory, on disk, inside a zip file, on S3, etc...
- 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.
Where to get it
Zarr can be installed from PyPI using pip:
pip install zarr
or via conda:
conda install -c conda-forge zarr
For more details, including how to install from source, see the installation documentation.