These documents describe the Zarr Python implementation. More information about the Zarr format can be found on the main website.
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, …
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.
Projects using Zarr#
If you are using Zarr, we would love to hear about it.