# zarr-developers/zarr-python

An implementation of chunked, compressed, N-dimensional arrays for Python.

Repository: https://github.com/zarr-developers/zarr-python
Canonical: https://ross.abutalabs.com/products/zarr-python
Homepage: https://zarr.readthedocs.io
Language: Python
License: MIT
License Family: permissive
Topics: zarr, ndimensional-arrays, compressed, python
Last push: 2026-08-26T19:18:48+00:00

## Health v2 (maintenance only)
Score: 98/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 95, longevity 100
- inputs: {"age_days": 3914, "days_push": 7, "days_rel": 34, "gap_med": 14, "n_releases_24m": 26}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2044, forks 445 (observed 2026-08-28T04:06:08.712412+00:00)

## What it is
Zarr is a Python library implementing the Zarr storage format for chunked, compressed, N-dimensional arrays with NumPy-compatible dtypes. It supports storing arrays in memory, on disk, in zip files, or on object stores like S3, with concurrent reads and writes across threads and processes.

## Use cases
- store large numpy arrays on S3 in chunked compressed format
- read and write n-dimensional arrays concurrently from multiple processes
- organize scientific datasets into hierarchical groups of arrays
- stream chunks of a huge array without loading it all into memory
- compress array data on disk for efficient storage
- use zarr arrays as backing store for distributed computing workflows

## When to choose
- you need NumPy-compatible arrays larger than memory with chunked access
- you want cloud/object-storage-friendly scientific array storage
- you need concurrent parallel reads or writes to shared arrays
- you work with large imaging, geospatial, or simulation datasets

## When to avoid
- you only need small in-memory arrays that plain NumPy handles fine
- you need relational or tabular data storage rather than arrays
- you require a single-file format like HDF5 for portability

## Facets
- artifact type: library
- maturity: stable
- function: serialization, compression, file-system, data-science
- domain: data-science, big-data, cloud-computing, developer-tools
- platform: python, cross-platform, cloud
- tags: ndarray, chunked-arrays, numpy, object-storage, parallel-computing, scientific-computing, storage-format

## Member repositories
- zarr-developers/zarr-python (main) score 98

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:08.712412+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:58:05.298887+00:00, confidence not recorded.
  - readme: https://github.com/zarr-developers/zarr-python (fetched 2026-08-28T04:06:08.712412+00:00, sha 8d62d1fb48f8)
- Data as of 2026-08-30T08:39:29.467469+00:00.