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fast-array-utils

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library1.4.1pypypi✓ verified 35d ago

Fast Array Utilities (fast-array-utils) is a Python library providing high-performance array manipulation and statistical utilities with minimal dependencies. It supports a wide range of array types including `numpy.ndarray`, `scipy.sparse` formats, `cupy.ndarray`, `dask.array.Array`, `h5py.Dataset`, `zarr.Array`, and `anndata.abc.CS{CR}Dataset`. The current version is 1.4.1, with an active development status and regular updates within the `scverse` ecosystem.

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Install & Compatibility
Where this runs
tested against v1.3.1 · pip install
no network on importno background threads
Install × environment matrix
Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux
musl
glibc
py 3.10
✕ build_error
✕ build_error
py 3.11
8/12 runs
✓ 4.62s
py 3.12
8/12 runs
✓ 4.52s
py 3.13
8/12 runs
✓ 4.58s
py 3.9
✕ build_error
✕ build_error
86MB installed
● package 86MB
Code
Verified usage

Verified import paths — ran on the pinned version, not inferred.

from fast_array_utils.conv import to_dense
from fast_array_utils import stats
from fast_array_utils import conv

This quickstart demonstrates converting a sparse matrix to a dense NumPy array using `to_dense` from the `conv` submodule and calculating statistics like sums and means using the `stats` submodule. Note that the `stats` submodule requires the optional `accel` dependencies to be installed.

import numpy as np from scipy.sparse import csr_matrix from fast_array_utils.conv import to_dense from fast_array_utils import stats # Example with to_dense sparse_matrix = csr_matrix(np.array([[0, 1, 0], [1, 0, 2], [0, 0, 0]])) numpy_arr = to_dense(sparse_matrix) print(f"Dense array from sparse matrix:\n{numpy_arr}") # Example with stats module (requires 'fast-array-utils[accel]' to be installed) try: data_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) col_sums = stats.sum(data_2d, axis=0) mean_val = stats.mean(data_2d) print(f"\nColumn sums: {col_sums}") print(f"Mean value: {mean_val}") except ImportError: print("\nSkipping stats examples: 'fast-array-utils[accel]' not installed.")
Debug
Known footguns
gotchaThe `fast_array_utils.stats` and `fast_array_utils.numba` submodules require `numba` for their functionality. These are installed via the `accel` extra (e.g., `pip install 'fast-array-utils[accel]'`). Without this, attempts to import or use functions from these modules will result in `ImportError` or `ModuleNotFoundError`.
gotchaThe library is designed to work efficiently with specific array types: `numpy.ndarray`, `scipy.sparse.cs{rc}_{array,matrix}`, `cupy.ndarray`, `cupyx.scipy.sparse.cs{rc}_matrix`, `dask.array.Array`, `h5py.Dataset`, `zarr.Array`, and `anndata.abc.CS{CR}Dataset`. Passing unsupported array types may lead to unexpected `TypeError` or incorrect behavior, particularly when using conversion or statistical functions.
gotchaThe library primarily operates on Python 3.12 and newer. While some older versions might support Python 3.11, the official documentation for version 1.4 and current PyPI metadata specify a minimum requirement of Python >=3.12.
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Security & dependencies

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