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Quick start - zarr-python 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 simple Zarr array:
Storage guide - zarr-python 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 implement the abstract store API from zarr abc store Store
Working with arrays - zarr-python 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 NumCodecs which provides various compressor libraries including LZ4, Zlib, BZ2 and LZMA
API Reference - zarr-python API Variants Zarr-Python provides both synchronous and asynchronous APIs: Async API - Asynchronous operations for concurrent access Sync API - Synchronous operations for simple usage Abstract Base Classes The ABC module defines interfaces for extending Zarr: Codec ABC - Interface for custom compression codecs Metadata ABC - Interface for
Installation - zarr-python 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) typing_extensions (4 9 or later) donfig (0 8 or later) pip Zarr is available on PyPI Install it using pip:
storage - zarr-python Does the store support partial writes? Partial writes are no longer used by Zarr, so this is always false
Optimizing performance - zarr-python 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 both Zarr's async concurrency and threading max_workers settings to avoid creating too many concurrent operations