# rust-ndarray/ndarray

ndarray: an N-dimensional array with array views, multidimensional slicing, and efficient operations

Repository: https://github.com/rust-ndarray/ndarray
Canonical: https://ross.abutalabs.com/products/ndarray
Homepage: https://docs.rs/ndarray/
Language: Rust
License: Apache-2.0
License Family: permissive
Topics: rust, numerics, scientific-computing, rust-sci
Last push: 2026-07-18T11:53:04+00:00

## Health v2 (maintenance only)
Score: 85/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 93, release rhythm 65, longevity 100
- inputs: {"age_days": 4412, "days_push": 46, "days_rel": 235, "gap_med": 19, "n_releases_24m": 4}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4314, forks 391 (observed 2026-08-28T04:08:41.814713+00:00)

## What it is
ndarray is a Rust crate providing an n-dimensional array container for general elements and numerics, with owned arrays, lightweight array views, multidimensional slicing, and efficient operations including optional BLAS-backed matrix multiplication. It is a foundational data structure for scientific computing in Rust.

## Use cases
- work with n-dimensional arrays in Rust
- perform matrix multiplication efficiently
- slice and view multidimensional data
- build scientific computing or numerical code in Rust
- process image or tensor-like data with array views
- parallelize array operations with rayon

## When to choose
- you need NumPy-like n-dimensional arrays in Rust
- you want lightweight array views and arbitrary-step slicing without copying data
- you need efficient large matrix multiplication, optionally accelerated by BLAS
- you want a no_std-compatible numeric array library

## When to avoid
- you need a full deep-learning tensor framework with autodiff (use tch-rs, burn, or candle)
- you need GPU-accelerated arrays out of the box
- you want a batteries-included linear algebra library with solvers and decompositions (consider nalgebra or ndarray-linalg)

## Facets
- artifact type: library
- maturity: active
- function: math, data-science, machine-learning
- domain: data-science, machine-learning
- platform: rust, cross-platform
- tags: n-dimensional-array, numerics, slicing, array-views, blas, scientific-computing, matrix, algorithms

## Member repositories
- rust-ndarray/ndarray (main) score 85

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:41.814713+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 2026-08-29T18:21:46.767605+00:00, confidence not recorded.
  - readme: https://github.com/rust-ndarray/ndarray (fetched 2026-08-28T04:08:41.814713+00:00, sha 4a9eb1dc6b35)
  - homepage: https://docs.rs/ndarray/ (fetched 2026-08-29T09:10:59.442213+00:00, sha eeb0d825a10f)
  - site_page: https://docs.rs/ (fetched 2026-08-29T09:10:59.451592+00:00, sha 95ad592be94d)
  - site_page: https://docs.rs/crate/ndarray/latest (fetched 2026-08-29T09:10:59.453486+00:00, sha 89658248bc13)
  - site_page: https://docs.rs/crate/ndarray/latest/features (fetched 2026-08-29T09:10:59.455280+00:00, sha f8504904fd2b)
  - site_page: https://docs.rs/about (fetched 2026-08-29T09:10:59.456794+00:00, sha 29ab7986fbf5)
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- Data as of 2026-08-30T08:39:29.467469+00:00.