Alterlab zarr

Skill AlterLab-IEU/AlterLab-Academic-Skills/skills/data-science/alterlab-zarr

Chunked, compressed N-dimensional arrays for cloud storage with Zarr — parallel I/O, S3/GCS integration, and NumPy/Dask/Xarray compatibility. Use when storing or reading large N-D scientific arrays, streaming chunked data to/from cloud object stores, or building large-scale scientific computing pipelines. Part of the AlterLab Academic Skills suite.From its SKILL.md

Install

npx -y skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-zarr

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SKILL.md

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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.

Quick Start

Installation

uv pip install zarr

Requires Python 3.11+ and Zarr v3 (zarr>=3). For cloud storage support, install the matching fsspec backend:

uv pip install s3fs   # For S3
uv pip install gcsfs  # For Google Cloud Storage

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 Workflow

Create or open an array/group, picking a store appropriate to the environment (local, in-memory, ZIP, S3/GCS).
Choose chunking aligned to your access pattern (aim for 1-10 MB chunks; rows-first → chunks span columns, and vice versa). This is the single biggest performance lever.
Pick compression via compressors= based on workload — Zstandard (the default), Blosc+LZ4 (fast), Gzip (max ratio); compressors=None to disable.
Read/write with NumPy-style indexing; resize/append as data grows.
Scale out with Dask (lazy, out-of-core, parallel) or label with Xarray for climate/geospatial data.
For cloud and many-array stores, consolidate metadata and consider sharding to cut object/file count.

# Minimal end-to-end
import zarr, numpy as np
z = zarr.create_array(store="data/my_array.zarr", shape=(10000, 10000),
                      chunks=(1000, 1000), dtype="f4")
z[:, :] = np.random.random((10000, 10000))
sub = z[0:100, 0:100]            # returns a NumPy array

Routing — where to look

You need…	Go to
Array create/open, read/write, resize/append, attributes, groups & hierarchies, consolidated metadata	`references/array_operations.md`
Chunk-size guidelines, aligning chunks to access patterns, sharding, compression codecs & tips	`references/chunking_compression.md`
Local / in-memory / ZIP / S3 / GCS stores and cloud best practices	`references/storage_backends.md`
NumPy / Dask / Xarray integration, thread- and process-safe parallel writes	`references/integration.md`
Performance checklist, profiling, common patterns (time series, large matrices, cloud-native, format conversion), troubleshooting	`references/patterns_performance.md`
Full API surface	`references/api_reference.md`

Additional Resources

Official Documentation: https://zarr.readthedocs.io/
Zarr Specifications: https://zarr-specs.readthedocs.io/
GitHub Repository: https://github.com/zarr-developers/zarr-python
Related: Xarray (labeled arrays), Dask (parallel computing), NumCodecs (compression codecs)