numpy-2.5.4 (Python Module)

[Bruce Dubbs]

New point version.

[Joe Locash]

NumPy 2.5.4 Release Notes

Released on 2026-10-10.

NumPy 2.5.4 is a patch release that fixes bugs discovered after the 2.5.3 release. Highlights are:

• Many typing fixes and improvements,
• A workaround for the MSVC 19.51 bug, see below.

This release supports Python versions 3.12-3.15.

Improvements

• numpy.linalg.svd and numpy.linalg.lstsq now raise for inputs containing infinities instead of returning garbage results.

• Fixed hangs in numpy.linalg.svd and numpy.linalg.lstsq for certain inputs containing infinities for recent versions of some LAPACK implementations.

• numpy.linalg.cond now handles non-finite inputs more consistently.

StringDType hashing with NaN sentinels

StringDType instances with equivalent floating-point NaN sentinels now have equal hashes. Previously, distinct NaN objects could produce equal dtype instances with different hashes, causing dictionary lookups and set deduplication to fail.

Changes MaskedArray resets a fill_value that cannot be represented in a new dtype

A fill_value copied from a source array is now reset to the default fill_value for the new dtype when it cannot be represented in that dtype. Previously the copied value could be stale: ufuncs that change dtype left the result holding a fill_value typed for the old dtype, which raised a TypeError only when something later validated it (such as .view()), and a floating point fill_value that overflows an integer dtype was kept as an out-of-range value together with a RuntimeWarning. Both now fall back to the default, including when a masked array is passed to MaskedArray with a different dtype. A fill_value given explicitly by the user is still validated and raises as before. This can now raise a ComplexWarning if the fill_value is complex and the new dtype is real.

Fixed at 19509c9f17.