NumSharp 0.80.0

NumSharp

NumPy for .NET — a native .NET array library with a NumPy-shaped API: NDArray, broadcasting, slicing views, dtype-aware np.* functions, unmanaged storage, and runtime-generated SIMD kernels. The compatibility target is NumPy 2.x; where behavior differs, NumPy is treated as the source of truth.

NumSharp.Core is 100% managed C# with no native dependency and no P/Invoke — every kernel is its own managed code. An optional OpenBLAS matrix-product backend ships separately as NumSharp.Interop.OpenBLAS.

Install

dotnet add package NumSharp

Quick start

using NumSharp;

var a = np.arange(12).reshape(3, 4);
var window = a[":, 1::2"];

Console.WriteLine(window);
Console.WriteLine(np.sum(window, axis: 0));

For Python readers, the shape is deliberately close:

import numpy as np

a = np.arange(12).reshape(3, 4)
print(a[:, 1::2].sum(axis=0))

Features

• NumPy-style NDArray — N-dimensional arrays with shape, strides, offsets, slicing, and view semantics (slices return views that share memory).
• Broadcasting — NumPy-style shape expansion without materializing repeated values.
• Dtype-aware operations — 15 core dtypes with NumPy-oriented promotion (NEP50) and conversion behavior.
• Broad np.* surface — creation, manipulation, math, reductions, comparisons, logic, linear algebra, FFT, random sampling, and .npy/.npz I/O.
• Generated IL + SIMD kernels — runtime-specialized kernels (V128/V256/V512) for supported dtype and layout combinations.

Links

• Documentation: https://scisharp.github.io/NumSharp/
• Getting started: https://scisharp.github.io/NumSharp/docs/NDArray.html
• API reference: https://scisharp.github.io/NumSharp/api/
• Coverage & support dashboard: https://scisharp.github.io/NumSharp/docs/coverage-support-dashboard.html
• Source: https://github.com/SciSharp/NumSharp

Licensed under the Apache License 2.0.

What's new in 0.80.0

We are at 95.2% NumPy API coverage, and everything except datetime, complex64 and other exotic dtypes matches NumPy byte for byte. This release brings NumPy's text dtypes with numpy.strings and numpy.char, numpy.ma, numpy.polynomial, np.emath and 48 new np.* functions. Integration rather than competition goes on, this time inside .NET: an NDArray now runs through ONNX Runtime models, serves as an ML.NET IDataView and shares its memory with System.Numerics.Tensors.

🧭 Overview

• NumPy 2.x API coverage around 95.2%, everything except datetime, complex64 and other exotic dtypes is fully supported, with byte-to-byte equality and compatibility to NumPy.
  • Text - NumPy's fixed-width bytes_ (S) and str_ (U) dtypes and NEP 55's variable-width StringDType, with every computation NumPy runs on text, all 46 numpy.strings functions and numpy.char with chararray.
  • np.polynomial.* - the whole numpy.polynomial package, all 193 functions and the six series classes for all six bases, bit-exact.
  • np.* - +48 functions, the ten most used on GitHub: np.hypot, np.histogram, np.logspace, np.gradient, np.vectorize, np.hanning, np.geomspace, np.apply_along_axis, np.trapezoid and np.histogram2d.
  • np.random.* - every NumPy bit generator, the whole Generator distribution surface, and a legacy RandomState that is NumPy's own sampler code line for line, all byte-identical streams.
  • np.ma.* - the masked-array module; np.emath.* - the automatic-domain math module; np.matrix - numpy.matrix as the new NDMatrix type.
• DType is NumPy's two-level dtype model - NDArray.dtype returns a numpy.dtype-shaped descriptor, with np.dtypes, byte order and datetime64 / timedelta64 descriptors, and NumPy's scalar classes (np.integer, np.floating, np.number, ...) with np.typing.
• Reductions on NumPy's own schedules - max, min, ptp, nanmax, nanmin, argmax, the flat sum / mean and the fused np.evaluate reductions match NumPy bit for bit, down to the sign of a ±0 tie and a NaN's payload; mean, var, std and their NaN-aware forms are NumPy's own statements; and most of them run faster.
• NumPy's structure for the engine - every computing np.* function is a TensorEngine member named after its NumPy source file, numpy.linalg and numpy.fft are replaceable sub-engines, and the default engine is settable.
• The 2026-11-02 parity QA - 53 of its 55 findings fixed on the owner's decisions (one kept as a documented divergence, one deferred), and NumPy's own dot functions behind np.dot / np.vdot / np.vecdot.
• 86 NumPy-parity, memory-safety and tooling fixes, 40 measured speedups and 44 breaking changes, across the release's 1,670 commits.
• 3 new NuGet packages - NumSharp.Interop.System.Numerics.Tensors, NumSharp.Interop.OnnxRuntime and NumSharp.Interop.MLNet.
• 1,119,305 NumPy 2.4.2 oracle test cases (0.70.0: 119,517) and 20,506 test methods (0.70.0: 14,416).

📦 New NuGet Packages

• NumSharp.Interop.System.Numerics.Tensors - zero-copy interop between NDArray and the BCL's Tensor<T> / TensorSpan<T> (System.Numerics.Tensors), all 15 dtypes.
• NumSharp.Interop.OnnxRuntime - run ONNX Runtime models on NDArrays, fed from the arrays' own memory, with the outputs read back as NDArrays.
• NumSharp.Interop.MLNet - an NDArray as an ML.NET IDataView, and a transformer's output column back as an NDArray.

Full NumSharp 0.80.0 release notes