Storage guide Zarr-Python supports multiple storage backends, including: local file systems, Zip files, remote stores via fsspec (S3, HTTP, etc.), and in-memory stores. In Zarr-Python 3, stores must . The reference guide contains a detailed description of the functions, modules, and objects included in Zarr. The reference describes how the methods work and which parameters can be used. Quick start This section will help you get up and running with the Zarr library in Python to efficiently manage and analyze multi-dimensional arrays. Creating an Array To get started, you can create a .
Welcome to the user guide, where you can learn more about using Zarr-Python! Getting Started New to Zarr-Python? Start here: Installation - Install Zarr-Python Quick-start - Quick overview of core . API Variants Zarr-Python provides both synchronous and asynchronous APIs: Async API - Asynchronous operations for concurrent access Sync API - Synchronous operations for simple . Installation Required dependencies Required dependencies include: Python (3.11 or later) packaging (22.0 or later) numpy (1.26 or later) numcodecs (0.14 or later) google-crc32c (1.5 or later) .
Compressors A number of different compressors can be used with Zarr. Zarr includes Blosc, Zstandard and Gzip compressors. Additional compressors are available through a separate package called . Does the store support partial writes? Partial writes are no longer used by Zarr, so this is always false. When using Zarr with Dask, it's important to consider the interaction between Dask's thread pool and Zarr's concurrency settings. Important: When using many Dask threads, you may need to reduce .
By default, consolidated metadata is used if it's present in the store (in the zarr.json for Zarr format 3 and in the .zmetadata file for Zarr format 2). To explicitly require consolidated metadata, set .
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