zarr-python

Zarr Python

Overview

Zarr is a Python library for storing large N-dimensional arrays with chunking and compression. Apply this skill for efficient parallel I/O, cloud-native workflows, and seamless integration with NumPy, Dask, and Xarray.

Current upstream: zarr 3.2.1 (PyPI, May 2026). Docs: zarr.readthedocs.io. New arrays default to Zarr format 3; set zarr_format=2 for legacy interop. This skill is a community guide maintained by K-Dense Inc., not an official zarr-developers package.

Quick Start

Installation

uv pip install "zarr>=3.2,<4"

Requires Python 3.12+ (per PyPI metadata for zarr 3.2.x). For remote stores (S3, GCS, HTTP):

uv pip install "zarr[remote]"
uv pip install s3fs   # AWS S3
uv pip install gcsfs  # Google Cloud Storage

Pin zarr>=3,<4 in application dependencies. Use uv pip install "zarr==2.*" only when you must stay on Zarr-Python 2 / Python 3.10–3.11.

Basic Array Creation

import zarr
import numpy as np

# Create a 2D array with chunking and compression
z = zarr.create_array(
    store="data/my_array.zarr",
    shape=(10000, 10000),
    chunks=(1000, 1000),
    dtype="f4"
)

# Write data using NumPy-style indexing
z[:, :] = np.random.random((10000, 10000))

# Read data
data = z[0:100, 0:100]  # Returns NumPy array

Core Operations

Creating Arrays

Zarr provides multiple convenience functions for array creation:

# Create empty array
z = zarr.zeros(shape=(10000, 10000), chunks=(1000, 1000), dtype='f4',
               store='data.zarr')

# Create filled arrays
z = zarr.ones((5000, 5000), chunks=(500, 500))
z = zarr.full((1000, 1000), fill_value=42, chunks=(100, 100))

# Create from existing data
data = np.arange(10000).reshape(100, 100)
z = zarr.array(data, chunks=(10, 10), store='data.zarr')

# Create like another array
z2 = zarr.zeros_like(z)  # Matches shape, chunks, dtype of z

Opening Existing Arrays