numpy-minmax: a fast function for finding the minimum and maximum value in a NumPy array
NumPy lacked an optimized minmax function, so we wrote our own. At Nomono, we use it for audio processing, but it can be applied any kind of float32 ndarray.
- Written in C and takes advantage of AVX/AVX512 for speed
- Roughly 2.3x speedup compared to the numpy amin+amax equivalent (tested on Intel CPU with numpy 1.24-1.26)
- The fast implementation is tailored for float32 arrays that are C-contiguous, F-contiguous or 1D strided. Strided arrays with ndim >= 2 get processed with numpy.amin and numpy.amax, so no perf gain there. There is also a fast implementation for contiguous int16 arrays.
Installation
$ pip install numpy-minmax
Usage
import numpy_minmax
import numpy as np
arr = np.arange(1337, dtype=np.float32)
min_val, max_val = numpy_minmax.minmax(arr) # 0.0, 1336.0
Changelog
[0.6.0] - 2025-12-27
Added
- Add support for Python 3.14
Removed
- Remove support for Python 3.9
For the complete changelog, go to CHANGELOG.md
Development
- Install dev/build/test dependencies as denoted in pyproject.toml
CC=clang pip install -e .pytest
Running benchmarks
- Install diplib
pip install diplib python scripts/perf_benchmark.py
Acknowledgements
This library is maintained/backed by Nomono, a Norwegian audio AI startup.
Metadata
Release files for numpy-minmax 0.6.0
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Source distribution (sdist)
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Release files / numpy_minmax-0.6.0-cp310-cp310-win_amd64.whl
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Release files / numpy_minmax-0.6.0-cp310-cp310-musllinux_1_2_x86_64.whl
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