Array API standard compatibility

NumPy’s main namespace as well as the numpy.fft and numpy.linalg namespaces are compatible [1] with the 2022.12 version of the Python array API standard.

NumPy aims to implement support for the 2023.12 version and future versions of the standard - assuming that those future versions can be upgraded to given NumPy’s backwards compatibility policy.

For usage guidelines for downstream libraries and end users who want to write code that will work with both NumPy and other array libraries, we refer to the documentation of the array API standard itself and to code and developer-focused documention in SciPy and scikit-learn.

Note that in order to use standard-complaint code with older NumPy versions (< 2.0), the array-api-compat package may be useful. For testing whether NumPy-using code is only using standard-compliant features rather than anything NumPy-specific, the array-api-strict package can be used.

History

NumPy 1.22.0 was the first version to include support for the array API standard, via a separate numpy.array_api submodule. This module was marked as experimental (it emitted a warning on import) and removed in NumPy 2.0 because full support was included in the main namespace. NEP 47 and NEP 56 describe the motivation and scope for implementing the array API standard in NumPy.

Entry point

NumPy installs an entry point that can be used for discovery purposes:

>>> from importlib.metadata import entry_points
>>> entry_points(group='array_api', name='numpy')
[EntryPoint(name='numpy', value='numpy', group='array_api')]

Note that leaving out name='numpy' will cause a list of entry points to be returned for all array API standard compatible implementations that installed an entry point.

Footnotes

[1]

With a few very minor exceptions, as documented in NEP 56. The sum, prod and trace behavior adheres to the 2023.12 version instead, as do function signatures; the only known incompatibility that may remain is that the standard forbids unsafe casts for in-place operators while NumPy supports those.