https://github.com/zarr-developers/zarr-python

An implementation of chunked, compressed, N-dimensional arrays for Python.
https://github.com/zarr-developers/zarr-python

compressed hacktoberfest ndimensional-arrays python zarr

Last synced: 10 days ago
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An implementation of chunked, compressed, N-dimensional arrays for Python.

• Host: GitHub
• URL: https://github.com/zarr-developers/zarr-python
• Owner: zarr-developers
• License: mit
• Created: 2015-12-15T14:49:40.000Z (almost 9 years ago)
• Default Branch: main
• Last Pushed: 2024-04-22T13:48:10.000Z (7 months ago)
• Last Synced: 2024-04-22T13:52:36.344Z (7 months ago)
• Topics: compressed, hacktoberfest, ndimensional-arrays, python, zarr
• Language: Python
• Homepage: http://zarr.readthedocs.io/
• Size: 14.3 MB
• Stars: 1,336
• Watchers: 44
• Forks: 255
• Open Issues: 331
• Metadata Files:
  • Readme: README.md
  • Contributing: .github/CONTRIBUTING.md
  • Funding: FUNDING.yml
  • License: LICENSE.txt
  • Codeowners: .github/CODEOWNERS

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README

        


  




# 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](https://zarr.readthedocs.io) for more information.

## Main Features

- [**Create**](https://zarr.readthedocs.io/en/stable/tutorial.html#creating-an-array) N-dimensional arrays with any NumPy `dtype`.

- [**Chunk arrays**](https://zarr.readthedocs.io/en/stable/tutorial.html#chunk-optimizations) along any dimension.

- [**Compress**](https://zarr.readthedocs.io/en/stable/tutorial.html#compressors) and/or filter chunks using any NumCodecs codec.

- [**Store arrays**](https://zarr.readthedocs.io/en/stable/tutorial.html#tutorial-storage) in memory, on disk, inside a zip file, on S3, etc...

- [**Read**](https://zarr.readthedocs.io/en/stable/tutorial.html#reading-and-writing-data) an array [**concurrently**](https://zarr.readthedocs.io/en/stable/tutorial.html#parallel-computing-and-synchronization) from multiple threads or processes.

- Write to an array concurrently from multiple threads or processes.

- Organize arrays into hierarchies via [**groups**](https://zarr.readthedocs.io/en/stable/tutorial.html#groups).

## Where to get it

Zarr can be installed from PyPI using `pip`:

```bash

pip install zarr

```

or via `conda`:

```bash

conda install -c conda-forge zarr

```

For more details, including how to install from source, see the [installation documentation](https://zarr.readthedocs.io/en/stable/index.html#installation).