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 ndimensional-arrays python zarr
Last synced: 14 days ago
JSON representation
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 (over 9 years ago)
- Default Branch: main
- Last Pushed: 2025-05-08T14:45:30.000Z (17 days ago)
- Last Synced: 2025-05-11T05:47:43.365Z (14 days ago)
- Topics: compressed, ndimensional-arrays, python, zarr
- Language: Python
- Homepage: https://zarr.readthedocs.io
- Size: 17.4 MB
- Stars: 1,687
- Watchers: 42
- Forks: 328
- Open Issues: 404
-
Metadata Files:
- Readme: README.md
- Changelog: changes/.gitignore
- Contributing: .github/CONTRIBUTING.md
- Funding: FUNDING.yml
- License: LICENSE.txt
- Codeowners: .github/CODEOWNERS
Awesome Lists containing this project
- awesome-scientific-computing - GitHub
- awesome-list - Zarr - An implementation of chunked, compressed, N-dimensional arrays for Python. (Data Processing / Data Representation)
- awesome-python-machine-learning-resources - GitHub - 38% open · ⏱️ 15.08.2022): (数据容器和结构)
- awesome-production-machine-learning - Zarr - developers/zarr-python.svg?style=social) - Python implementation of chunked, compressed, N-dimensional arrays designed for use in parallel computing. (Data Storage Optimisation)
- best-of-python - GitHub - 42% open · ⏱️ 04.06.2024): (Data Containers & Dataframes)
README
# Zarr
Latest Release
Package Status
License
Build Status
Pre-commit Status
Coverage
Downloads
Developer Chat
Funding
Citation
## 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/user-guide/arrays.html#creating-an-array) N-dimensional arrays with any NumPy `dtype`.
- [**Chunk arrays**](https://zarr.readthedocs.io/en/stable/user-guide/performance.html#chunk-optimizations) along any dimension.
- [**Compress**](https://zarr.readthedocs.io/en/stable/user-guide/arrays.html#compressors) and/or filter chunks using any NumCodecs codec.
- [**Store arrays**](https://zarr.readthedocs.io/en/stable/user-guide/storage.html) in memory, on disk, inside a zip file, on S3, etc...
- [**Read**](https://zarr.readthedocs.io/en/stable/user-guide/arrays.html#reading-and-writing-data) an array [**concurrently**](https://zarr.readthedocs.io/en/stable/user-guide/performance.html#parallel-computing-and-synchronization) from multiple threads or processes.
- [**Write**](https://zarr.readthedocs.io/en/stable/user-guide/arrays.html#reading-and-writing-data) to an array concurrently from multiple threads or processes.
- Organize arrays into hierarchies via [**groups**](https://zarr.readthedocs.io/en/stable/quickstart.html#hierarchical-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).