---
id: fast-array-utils
version: "1.5"
license: MPL-2.0
license_treatment: copyleft
maintenance: active
---

# fast-array-utils

License: copyleft · Maintenance: active · Downloads: 417.8K/mo

## What it is and what it does

fast-array-utils provides a unified interface to convert and analyze arrays across multiple storage and compute backends—NumPy, SciPy sparse matrices, CuPy GPU arrays, Dask distributed arrays, and file-backed formats like HDF5 and Zarr. It abstracts away the differences between these formats so you can densify sparse data, move arrays between CPU and GPU memory, and compute statistics without rewriting logic for each array type.

The package is built on top of numpy and array-api-compat, keeping its core lightweight. It targets modern Python (3.12+) and is marked Production/Stable. Use it when you need to work with heterogeneous array sources in the same pipeline—for instance, reading sparse matrices from disk, densifying them, computing row statistics, and optionally moving results to GPU for downstream processing.

Use it for:

- Densify sparse matrices before passing to dense-only algorithms.
- Compute row or column statistics across mixed array types without format-specific code.
- Move Dask or CuPy arrays to CPU memory for serialization or downstream libraries.
- Unify array handling in pipelines that mix dense, sparse, and GPU data.
- Calculate aggregate statistics across distributed or GPU-resident arrays.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Converts and computes statistics across sparse, dense, GPU, and distributed array formats with a unified interface.

Yes, if you work with multiple array formats (sparse, GPU, distributed, file-backed) and want a single conversion and stats API. The low install friction, active maintenance, and zero known vulnerabilities make it safe to adopt. Be aware of the MPL-2.0 copyleft license if you redistribute. Verify that optional dependencies align with your environment before relying on GPU or Dask features.

## Install

pip install fast-array-utils
uv add fast-array-utils
poetry add fast-array-utils

## Installing fast-array-utils

Before you install: Low friction: pure Python wheel with two runtime dependencies (array-api-compat and numpy). Active maintenance with recent releases; last commit 2026-08-14. Requires Python 3.12 or later.

License in practice: MPL-2.0 (copyleft): you must disclose source code modifications and distribute under the same license if you redistribute the package or derivative works.

Quickstart:

pip install 'fast-array-utils[accel]'

from fast_array_utils.conv import to_dense
from fast_array_utils import stats

numpy_arr = to_dense(sparse_arr)
col_sums = stats.sum(arr_2d, axis=0)

Requires Python 3.12 or later. Optional dependencies (scipy, cupy, dask, h5py, zarr) must be installed separately or via extras to use those array types.

Verify before relying:

- Whether optional dependencies (scipy, cupy, dask, h5py, zarr, anndata) are automatically installed with extras or must be added separately.
- Performance characteristics and memory overhead compared to calling array libraries directly.
- Whether the testing extra includes test fixtures or only testing utilities.

## Package facts

- License: MPL-2.0 (copyleft)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 417.8K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

sparse array conversion, array format conversion, compute statistics arrays, gpu array utilities, dask array operations, dense conversion sparse, cross-format array tools, array-conversion, sparse-dense, gpu-compute

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