zarr-python - Store and process chunked N-D arrays
Store large N-dimensional arrays with chunking and compression across local, memory, ZIP, S3, or GCS storage, with NumPy, Dask, and Xarray compatibility.
Tags
Updated: 2026-10-06Capabilities
Typical Inputs
Typical Outputs
What this skill does
- Create chunked arrays
- Read and write slices
- Resize and append arrays
- Configure compression codecs
- Select storage backends
- Organize hierarchical groups
- Attach array metadata
- Use sharded storage
Inputs
- Array shape and dtype
- Array data
- Storage path or backend
- Chunk configuration
- Compression configuration
- Cloud storage credentials
- Cloud project configuration
Outputs
- Zarr arrays
- Stored array data
- NumPy array reads
- Hierarchical groups
- Array and group metadata
- Storage objects
Requirements
- Python 3.11 or later
- zarr package
- s3fs for S3 storage
- gcsfs for Google Cloud Storage