NumPy 2.5.2 Release Notes

The NumPy 2.5.2 is a patch release that fixes bugs discovered after the 2.5.1
release. The big news is that it includes wheels for the newly released
Python 3.15.0rc1.

This release supports Python versions 3.12-3.15

C API changes

PyArray_StringDTypeObject is opaque under the abi3t stable ABI

The PyArray_StringDTypeObject was accidentally exposed in NumPy
2.5 when targeting the free-threading-compatible stable ABI
(Py_TARGET_ABI3T). PyArray_StringDTypeObject is now an opaque
struct: extensions compiled that way cannot access its fields, since
the struct layout depends on the size of the object header. Any code
that accessed PyArray_StringDTypeObject fields in an abi3t build
would have crashed, so we are making this API change in a bugfix
release.

The NpyString allocator API remains usable by passing the
descriptor object pointer, e.g.
NpyString_acquire_allocator((PyArray_StringDTypeObject *)descr).

(gh-31771)

Contributors

A total of 16 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

• Abhijeetsingh Meena +
• Charalampos Stratakis
• Charles Harris
• Chris Ninham +
• David Woods
• Geonho +
• Gopu Yeshwanth Reddy +
• Iason Krommydas
• Ijtihed Kilani
• Jelle Zijlstra +
• Joren Hammudoglu
• Kumar Aditya
• Mike Boyle
• Nathan Goldbaum
• Raghuveer Devulapalli
• Sebastian Berg

Pull requests merged

A total of 28 pull requests were merged for this release.

• #31864: MAINT: Prepare 2.5.x for further development
• #31889: TYP: Backport multiple static typing fixes 1.
• #31900: TST: add tests for stable ABI numpy extensions (#31822)
• #31901: BUG: fix StringDType coerce flag in binary ufunc promotion...
• #31902: BLD: fix meson deprecation warnings (#31892)
• #31921: TYP: Backport multiple typing fixes 2.
• #31947: MAINT: Update x86-simd-sort subproject (5adb334 → fa944ef) (#31908)
• #31949: BUG: fix crash on 32 bit systems using abi3t (#31771)
• #31950: MNT: remove some obsolete string to bool workarounds (#31859)
• #31952: BUG: centralized helper for output coerce and na_object in stringdtype...
• #31953: BUG: fix CPU feature env diagnostic buffer overruns (#31905)
• #31954: BUG: restore ndarray.conjugate() for legacy user-defined dtypes...
• #31955: TYP: Avoid shadowed dtype annotations
• #32077: MAINT: Update verdored-meson/meson to match main.
• #32114: BUG: fix refcount leak on overlapping copyto with where=False
• #32115: BUG: fix swallowed cast error in fancy indexing assignment (#31975)
• #32116: BUG: Fix buffered iterator stride after removing multi-index
• #32117: BUG: fix np.fromiter corruption when reusing a StringDType...
• #32119: BUG: add a special case for StringDType in np.isdtype (#32030)
• #32121: BUG: reference leak in simd_sequence_from_iterable (#32038)
• #32122: BUG: ensure lock is held when accessing or writing to RNG state...
• #32123: BUG: fully reset cached RNG state for non-MT19937 RNGs (#32062)
• #32135: TYP: type capabilities max dimensions
• #32158: BUG: avoid possible stack overflow in arraydescr_dealloc (#32133)
• #32206: MAINT: Update cibuildwheel to v4.2.0
• #32214: MAINT: Skip limited_api tests on some platforms.
• #32220: TYP: isclose shape-typing fix for 2d array-likes (#32205)
• #32221: BUG: avoid segfaults when legacy copyswap slot is not defined...

NumPy 2.5.1 Release Notes

The NumPy 2.5.1 is a patch release that fixes bugs discovered after the 2.5.0
release. The most noticeable is the fix is to the numpy datetime cython API
which should allow downstream to support NumPy versions older than 2.5.
Preparation for Python 3.15 continues along with typing improvements.

This release supports Python versions 3.12-3.14

Changes

• The minimum supported GCC version has been updated from 9.3.0 to 10.3.0

  (gh-31843)

Contributors

A total of 10 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

• Adhyan Gupta +
• Ankit Ahlawat
• Charles Harris
• Iason Krommydas
• Joren Hammudoglu
• Kumar Aditya
• Nathan Goldbaum
• Sebastian Berg
• Ties Jan Hefting +
• Vineet Kumar

Pull requests merged

A total of 20 pull requests were merged for this release.

• #31707: MAINT: Prepare 2.5.x for further development
• #31721: CI: fix new cython-lint errors (#31711)
• #31723: MAINT: Update meson to match main
• #31729: TST: use setup-sde instead of curl to get SDE binaries (#31727)
• #31829: BUG: Relax finfo to be easier accessible for all user dtypes...
• #31831: TYP: Fix flatiter.__next__ return type for object_ and...
• #31832: BUG: avoid deadlocks using NpyString API (#31682)
• #31833: BUG: fix out array leak in reduceat and accumulate when dtype...
• #31835: BUG: fix numpy datetime cython APIs to be compatible with older...
• #31836: TYP: Fix incorrect dtype inference of asarray([]) (#31732)
• #31837: TYP: Fix np.ma.masked_array 2.5.0 regression
• #31838: FIX: Refactor error handling in array_setstate to prevent typecode...
• #31839: TST: xfail multithreaded BLAS test more generously
• #31840: MAINT: Rename subroutine for crackfortran tests
• #31842: BUG: fix leak in reductions when a ufunc override errors or is...
• #31849: BLD: set minimum required gcc version to 10.3 (#31843)
• #31855: CI: fix hangs on MacOS ASan CI (#31853)
• #31856: BUG: fix several bugs in StringDType operations (#31846)
• #31857: BUG: Fix segfault in MT19937 by preventing recursive seed lists...
• #31858: BUG: Fix signed integer overflow in datetime.c (#31688)

NumPy 2.5.0 Release Notes

Numpy 2.5.0 is a transitional release. It drops support for Python 3.11,
marking the end of distutils, and expires a large number of deprecations made
in the 2.0.x release. It also improves free threading and brings sorting into
compliance with the array-api standard with the addition of descending sorts.
There is also a fair amount of preparation for Python 3.15, which will be
supported starting with the first rc.

This release supports Python versions 3.12-3.14.

Highlights

• Distutils has been removed,
• Many expired deprecations, see below,
• Many new deprecations, see below,
• Many static typing improvements.
• Improved support for free threading,
• Support for descending sorts,

See New Features below for other additions.

Deprecations

• numpy.char.chararray is deprecated. Use an ndarray with a string or bytes dtype instead.

  (gh-30605)

• numpy.take now correctly checks if the result can be cast to the provided
  out=out under the same-kind rule. A DeprecationWarning is given now
  when this check fails. Previously, take incorrectly checked if out
  could be cast to the result (the wrong direction). This deprecation also
  affects compress and possibly other functions. (Future versions of NumPy
  may tighten the casting check further.)

  (gh-30615)

• The numpy.char.[as]array functions are deprecated. Use an
  numpy.[as]array with a string or bytes dtype instead.

  (gh-30802)

• Setting the dtype attribute is deprecated because mutating an array is unsafe
  if an array is shared, especially by multiple threads. As an alternative,
  you can create a view with a new dtype via array.view(dtype=new_dtype).

  (gh-29244)

• Setting the shape attribute is deprecated because mutating an array is
  unsafe if an array is shared, especially by multiple threads. As an
  alternative, you can create a new view via np.reshape or
  np.ndarray.reshape. For example: x = np.arange(15); x = np.reshape(x, (3, 5)).
  To ensure no copy is made from the data, one can use np.reshape(..., copy=False).

  While setting the shape on an array is discouraged, for cases where it is
  difficult to work around, e.g., in __array_finalize__, it is possible
  with the private method np.ndarray._set_shape.

  (gh-29536)

• Using the generic unit in numpy.timedelta64 is deprecated since this
  can lead to unexpected behavior such as non-transitive comparison, see
  gh-28287 for details. As
  an alternative, specify an explicit unit such as 's' (seconds) or 'D'
  (days) when constructing numpy.timedelta64. Due to this change, operations
  that implicitly rely on the generic unit are also deprecated. For
  example:

  arr = np.array([1, 2, 3], dtype="m8[s]")
  
  # `1` is implicitly converted to generic timedelta64
  arr + 1
  

  (gh-29619)

• Resizing a Numpy array in place is deprecated since mutating an array is
  unsafe if an array is shared, especially by multiple threads. As an
  alternative, you can create a resized array via np.resize.

  (gh-30181)

• numpy.fix is deprecated, use numpy.trunc instead. It is faster and
  follows the Array API standard. Both functions provide identical
  functionality: rounding array elements towards zero.

  (gh-30644)

• numpy.ma.round_ is deprecated. numpy.ma.round can be used as a
  replacement.

  (gh-30738)

• numpy.typename is deprecated because the names returned by it were
  outdated and inconsistent. numpy.dtype.name can be used as a
  replacement.

  (gh-30774)

• Inputs other than integers are deprecated for numpy.triu_indices and
  numpy.tril_indices. Non-integer values for the M, k and N
  parameters of numpy.tri are deprecated. Non-integer values for the k
  parameter of both numpy.tril_indices_from and numpy.triu_indices_from
  are deprecated.

  (gh-30869)

• Deprecations in custom dtype property and __array_finalize__.

  Previously arr.view(dtype=new_dtype) called arr.dtype = new_dtype
  also for subclasses, i.e., the attribute setting. That path is now
  deprecated and refined, meaning that even subclasses that do not see this
  DeprecationWarning may wish to update their code.

  A subclass that does any dtype specific logic (i.e. verifying the dtype
  in __array_finalize__ or has a dtype property) should now:

  • Set _set_dtype = None in which case arr.view(dtype=new_dtype)
    will call __array_finalize__ with the new dtype, ensuring that
    any validation __array_finalize__ will run is done.
  • Or, for a quick fix, define _set_dtype as a function (calling
    ndarray._set_dtype() to avoid DeprecationWarnings.
    (Future versions might migrate towards the _set_dtype = None path.)

  Ideally, follow NumPy's deprecation to prevent dtype mutation by users.
  The use of ndarray._set_dtype() may be necessary for some subclass
  finalization patterns, but should otherwise be avoided.

  (gh-31293)

Expired deprecations

• numpy.distutils has been removed

  (gh-30340)

• Passing None as dtype to np.finfo will now raise a TypeError
  (deprecated since 1.25)

  (gh-30460)

• numpy.cross no longer supports 2-dimensional vectors.
  (Deprecated since 2.0)

  (gh-30461)

• numpy._core.numerictypes.maximum_sctype has been removed.
  (deprecated since 2.0)

  (gh-30462)

• numpy.row_stack has been removed in favor of numpy.vstack.
  (deprecated since 2.0)

  (gh-30463)

• get_array_wrap has been removed.
  (deprecated since 2.0)

  (gh-30463)

• recfromtxt and recfromcsv have been removed from numpy.lib._npyio
  in favor of numpy.genfromtxt.
  (deprecated since 2.0)

  (gh-30467)

• The numpy.chararray re-export of numpy.char.chararray has been removed.
  (deprecated since 2.0)

  (gh-30604)

• bincount now raises a TypeError for non-integer inputs.
  (deprecated since 2.1)

  (gh-30610)

• The numpy.lib.math alias for the standard library math module has
  been removed.
  (deprecated since 1.25)

  (gh-30612)

• Data type alias 'a' was removed in favor of 'S'.
  (deprecated since 2.0)

  (gh-30613)

• _add_newdoc_ufunc(ufunc, newdoc) has been removed in favor of
  ufunc.__doc__ = newdoc.
  (deprecated since 2.2)

  (gh-30614)

Compatibility notes

linalg.eig and linalg.eigvals now always return complex arrays

Previously, the return values depended on whether the eigenvalues happen to lie
on the real line (which, for a general, non-symmetric matrix, is not
guaranteed).

This change makes consistent what was a value-dependent result. To retain the
previous behavior, do:

w = eigvals(a)
if np.any(w.imag == 0):  # this is what NumPy used to do
    w = w.real

If your matrix is symmetrix/hermitian, use eigh and eigvalsh instead of
eig and eigvals. These are guaranteed to return real values. A common
case is covariance matrices, which are symmetric and positive definite by
construction.

(gh-30411)

MSVC support

NumPy now requires minimum MSVC 19.35 toolchain version on Windows platforms.
This corresponds to Visual Studio 2022 version 17.5 Preview 2 or newer.

(gh-30489)

Cython support

NumPy's Cython headers (accessed via cimport numpy) now require Cython 3.0
or newer to build. If you try to compile a project that depends on NumPy's
Cython headers using Cython 0.29 or older, you will see a message like this:

Error compiling Cython file:
------------------------------------------------------------
...
# versions.
#
# See __init__.cython-30.pxd for the real Cython header
#

DEF err = int('Build aborted: the NumPy Cython headers require Cython 3.0.0 or newer.')
  ------------------------------------------------------------

  /path/to/site-packages/numpy/__init__.pxd:11:13: Error in compile-time expression:
  ValueError: invalid literal for int() with base 10: 
  'Build aborted: the NumPy Cython headers require Cython 3.0.0 or newer.'

Note that the invalid integer is not a bug in NumPy - we are intentionally
generating this error to avoid triggering a more obscure error later in the
build when an older Cython version tries to use a Cython feature that was not
available in the old Cython version.

(gh-30770)

numpy.where no longer truncates Python integers

Previously, if the x or y argument of numpy.where was a Python
integer that was out of range of the output type, it would be silently
...

NumPy 2.5.0 Release Notes

Numpy 2.5.0 is a transitional release. It drops support for Python 3.11,
marking the end of distutils, and expires a large number of deprecations made
in the 2.0.x release. It also improves free threading and brings sorting into
compliance with the array-api standard with the addition of descending sorts.
Python 3.15 will be supported when it is released.

This release supports Python versions 3.12-3.14.

Highlights

• Distutils has been removed,
• Many expired deprecations, see below,
• Many new deprecations, see below,
• Many static typing improvements.
• Improved support for free threading,
• Support for descending sorts,

See New Features below for other additions.

Deprecations

• numpy.char.chararray is deprecated. Use an ndarray with a string or bytes dtype instead.

  (gh-30605)

• numpy.take now correctly checks if the result can be cast to the provided
  out=out under the same-kind rule. A DeprecationWarning is given now
  when this check fails. Previously, take incorrectly checked if out
  could be cast to the result (the wrong direction). This deprecation also
  affects compress and possibly other functions. (Future versions of NumPy
  may tighten the casting check further.)

  (gh-30615)

• The numpy.char.[as]array functions are deprecated. Use an
  numpy.[as]array with a string or bytes dtype instead.

  (gh-30802)

• Setting the dtype attribute is deprecated because mutating an array is unsafe
  if an array is shared, especially by multiple threads. As an alternative,
  you can create a view with a new dtype via array.view(dtype=new_dtype).

  (gh-29244)

• Setting the shape attribute is deprecated because mutating an array is
  unsafe if an array is shared, especially by multiple threads. As an
  alternative, you can create a new view via np.reshape or
  np.ndarray.reshape. For example: x = np.arange(15); x = np.reshape(x, (3, 5)).
  To ensure no copy is made from the data, one can use np.reshape(..., copy=False).

  While setting the shape on an array is discouraged, for cases where it is
  difficult to work around, e.g., in __array_finalize__, it is possible
  with the private method np.ndarray._set_shape.

  (gh-29536)

• Using the generic unit in numpy.timedelta64 is deprecated since this
  can lead to unexpected behavior such as non-transitive comparison, see
  gh-28287 for details. As
  an alternative, specify an explicit unit such as 's' (seconds) or 'D'
  (days) when constructing numpy.timedelta64. Due to this change, operations
  that implicitly rely on the generic unit are also deprecated. For
  example:

  arr = np.array([1, 2, 3], dtype="m8[s]")
  
  # `1` is implicitly converted to generic timedelta64
  arr + 1
  

  (gh-29619)

• Resizing a Numpy array in place is deprecated since mutating an array is
  unsafe if an array is shared, especially by multiple threads. As an
  alternative, you can create a resized array via np.resize.

  (gh-30181)

• numpy.fix is deprecated, use numpy.trunc instead. It is faster and
  follows the Array API standard. Both functions provide identical
  functionality: rounding array elements towards zero.

  (gh-30644)

• numpy.ma.round_ is deprecated. numpy.ma.round can be used as a
  replacement.

  (gh-30738)

• numpy.typename is deprecated because the names returned by it were
  outdated and inconsistent. numpy.dtype.name can be used as a
  replacement.

  (gh-30774)

• Inputs other than integers are deprecated for numpy.triu_indices and
  numpy.tril_indices. Non-integer values for the M, k and N
  parameters of numpy.tri are deprecated. Non-integer values for the k
  parameter of both numpy.tril_indices_from and numpy.triu_indices_from
  are deprecated.

  (gh-30869)

• Deprecations in custom dtype property and __array_finalize__.

  Previously arr.view(dtype=new_dtype) called arr.dtype = new_dtype
  also for subclasses, i.e., the attribute setting. That path is now
  deprecated and refined, meaning that even subclasses that do not see this
  DeprecationWarning may wish to update their code.

  A subclass that does any dtype specific logic (i.e. verifying the dtype
  in __array_finalize__ or has a dtype property) should now:

  • Set _set_dtype = None in which case arr.view(dtype=new_dtype)
    will call __array_finalize__ with the new dtype, ensuring that
    any validation __array_finalize__ will run is done.
  • Or, for a quick fix, define _set_dtype as a function (calling
    ndarray._set_dtype() to avoid DeprecationWarnings.
    (Future versions might migrate towards the _set_dtype = None path.)

  Ideally, follow NumPy's deprecation to prevent dtype mutation by users.
  The use of ndarray._set_dtype() may be necessary for some subclass
  finalization patterns, but should otherwise be avoided.

  (gh-31293)

Expired deprecations

• numpy.distutils has been removed

  (gh-30340)

• Passing None as dtype to np.finfo will now raise a TypeError
  (deprecated since 1.25)

  (gh-30460)

• numpy.cross no longer supports 2-dimensional vectors.
  (Deprecated since 2.0)

  (gh-30461)

• numpy._core.numerictypes.maximum_sctype has been removed.
  (deprecated since 2.0)

  (gh-30462)

• numpy.row_stack has been removed in favor of numpy.vstack.
  (deprecated since 2.0)

  (gh-30463)

• get_array_wrap has been removed.
  (deprecated since 2.0)

  (gh-30463)

• recfromtxt and recfromcsv have been removed from numpy.lib._npyio
  in favor of numpy.genfromtxt.
  (deprecated since 2.0)

  (gh-30467)

• The numpy.chararray re-export of numpy.char.chararray has been removed.
  (deprecated since 2.0)

  (gh-30604)

• bincount now raises a TypeError for non-integer inputs.
  (deprecated since 2.1)

  (gh-30610)

• The numpy.lib.math alias for the standard library math module has
  been removed.
  (deprecated since 1.25)

  (gh-30612)

• Data type alias 'a' was removed in favor of 'S'.
  (deprecated since 2.0)

  (gh-30613)

• _add_newdoc_ufunc(ufunc, newdoc) has been removed in favor of
  ufunc.__doc__ = newdoc.
  (deprecated since 2.2)

  (gh-30614)

Compatibility notes

linalg.eig and linalg.eigvals now always return complex arrays

Previously, the return values depended on whether the eigenvalues happen to lie
on the real line (which, for a general, non-symmetric matrix, is not
guaranteed).

This change makes consistent what was a value-dependent result. To retain the
previous behavior, do:

w = eigvals(a)
if np.any(w.imag == 0):  # this is what NumPy used to do
    w = w.real

If your matrix is symmetrix/hermitian, use eigh and eigvalsh instead of
eig and eigvals. These are guaranteed to return real values. A common
case is covariance matrices, which are symmetric and positive definite by
construction.

(gh-30411)

MSVC support

NumPy now requires minimum MSVC 19.35 toolchain version on Windows platforms.
This corresponds to Visual Studio 2022 version 17.5 Preview 2 or newer.

(gh-30489)

Cython support

NumPy's Cython headers (accessed via cimport numpy) now require Cython 3.0
or newer to build. If you try to compile a project that depends on NumPy's
Cython headers using Cython 0.29 or older, you will see a message like this:

Error compiling Cython file:
------------------------------------------------------------
...
# versions.
#
# See __init__.cython-30.pxd for the real Cython header
#

DEF err = int('Build aborted: the NumPy Cython headers require Cython 3.0.0 or newer.')
  ------------------------------------------------------------

  /path/to/site-packages/numpy/__init__.pxd:11:13: Error in compile-time expression:
  ValueError: invalid literal for int() with base 10: 
  'Build aborted: the NumPy Cython headers require Cython 3.0.0 or newer.'

Note that the invalid integer is not a bug in NumPy - we are intentionally
generating this error to avoid triggering a more obscure error later in the
build when an older Cython version tries to use a Cython feature that was not
available in the old Cython version.

(gh-30770)

numpy.where no longer truncates Python integers

Previously, if the x or y argument of numpy.where was a Python
integer that was out of range of the output type, it would be silently
truncated. Now, an OverflowError will be raised instead.

T...

NumPy 2.4.6 Release Notes

NumPy 2.4.6 is a quick release that fixes a regression discovered in the 2.4.5
release.

This release supports Python versions 3.11-3.14

Contributors

A total of 4 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

• !EarlMilktea
• Charles Harris
• Sebastian Berg
• Warren Weckesser

Pull requests merged

A total of 4 pull requests were merged for this release.

• #31444: MAINT: Prepare 2.4.x for further development
• #31453: BUG: Fix regression in arr.conj()
• #31459: BUG: np.linalg.svd(..., hermitian=True) returns non-unitary...
• #31460: BUG: Don't call INCREF/DECREF on descr in NpyStringAcquireAllocator...

NumPy 2.4.5 Release Notes

NumPy 2.4.5 is a patch release that fixes bugs discovered after the 2.4.4
release, has some typing improvements, and maintains infrastructure.

This release supports Python versions 3.11-3.14

Contributors

A total of 17 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

• Aleksei Nikiforov
• Anarion Zuo +
• Ankit Ahlawat
• Breno Favaretto +
• Charles Harris
• Igor Krivenko +
• Ijtihed Kilani +
• Joren Hammudoglu
• Maarten Baert +
• Matti Picus
• Nathan Goldbaum
• Praneeth Kodumagulla +
• Ralf Gommers
• RoomWithOutRoof +
• Sebastian Berg
• Warren Weckesser
• div +

Pull requests merged

A total of 28 pull requests were merged for this release.

• #31093: MAINT: Prepare 2.4.x for further development
• #31182: TYP: fix np.shape assignability issue for python lists (#31171)
• #31197: ENH: Return rank 0 for empty matrices in matrix_rank (#30422)
• #31198: CI/BUG: add native jobs for s390x, fix bug in pack_inner...
• #31199: BUG: f2py map complex_long_double to NPY_CLONGDOUBLE
• #31205: MAINT: f2py: Stop setting re._MAXCACHE to 50.
• #31206: BUG: fix heap buffer overflow in timedelta to string casts
• #31207: MAINT: Rename ppc64le and s390x workflow (#31121)
• #31208: BUG: Fix matvec/vecmat in-place aliasing (out=input produces...
• #31209: TYP: tile: accept numpy scalars and arrays as second argument...
• #31211: DEP: Undo deprecation for np.dtype() signature used by old pickles...
• #31212: REV: Manual revert of float16 svml use (#31178)
• #31222: TYP: ix_ fix for boolean and non-1d input (#31218)
• #31329: BUG: incorrect temp elision for new-style (NEP 43) user-defined...
• #31330: TYP: fix sliding_window_view axis parameter typing
• #31335: BUG: Prevent deadlock due to downstream importing NumPy in dlopen...
• #31336: BUG: Fix segfault in nditer.multi_index when __getitem__ raises...
• #31338: TYP: Fix ruff lint error
• #31357: BUG: fix memory leak in np.zeros when fill-zero loop raises (#31320)
• #31358: BUG: np.einsum() fails with a 0-dimensional out argument and...
• #31379: BUG: Fix signed overflow issue in npy_gcd for INT_MIN on s390x...
• #31383: CI: remove Cirrus CI FreeBSD job (#31380)
• #31390: BUILD: newer MKL uses so.3
• #31391: BLD/MAINT: improve support for Intel LLVM compilers
• #31401: BUG: Avoid UB in safe[add,sub,mul] helpers (#31396)
• #31402: BUG: exclude __pycache__ directories from wheels (#31397)
• #31404: TYP: _NestedSequence type parameter default to work around...
• #31426: TYP: Fix DTypeLike runtime type-checker support (#31425)

NumPy 2.4.4 Release Notes

The NumPy 2.4.4 is a patch release that fixes bugs discovered after the 2.4.3
release. It should finally close issue #30816, the OpenBLAS threading problem
on ARM.

This release supports Python versions 3.11-3.14

Contributors

A total of 8 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

• Charles Harris
• Daniel Haag +
• Denis Prokopenko +
• Harshith J +
• Koki Watanabe
• Marten van Kerkwijk
• Matti Picus
• Nathan Goldbaum

Pull requests merged

A total of 7 pull requests were merged for this release.

• #30978: MAINT: Prepare 2.4.x for further development
• #31049: BUG: Add test to reproduce problem described in #30816 (#30818)
• #31052: BUG: fix FNV-1a 64-bit selection by using NPY_SIZEOF_UINTP (#31035)
• #31053: BUG: avoid warning on ufunc with where=True and no output
• #31058: DOC: document caveats of ndarray.resize on 3.14 and newer
• #31079: TST: fix POWER VSX feature mapping (#30801)
• #31084: MAINT: numpy.i: Replace deprecated sprintf with snprintf...

NumPy 2.4.3 Release Notes

The NumPy 2.4.3 is a patch release that fixes bugs discovered after the
2.4.2 release. The most user visible fix may be a threading fix for
OpenBLAS on ARM, closing issue #30816.

This release supports Python versions 3.11-3.14

Contributors

A total of 11 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

• Antareep Sarkar +
• Charles Harris
• Joren Hammudoglu
• Matthieu Darbois
• Matti Picus
• Nathan Goldbaum
• Peter Hawkins
• Pieter Eendebak
• Sebastian Berg
• Warren Weckesser
• stratakis +

Pull requests merged

A total of 14 pull requests were merged for this release.

• #30759: MAINT: Prepare 2.4.x for further development
• #30827: BUG: Fix some leaks found via LeakSanitizer (#30756)
• #30841: MAINT: Synchronize 2.4.x submodules with main
• #30849: TYP: matlib: missing extended precision imports
• #30850: BUG: Fix weak hash function in np.isin(). (#30840)
• #30921: BUG: fix infinite recursion in np.ma.flatten_structured_array...
• #30922: BUG: Fix buffer overrun in CPU baseline validation (#30877)
• #30923: BUG: Fix busdaycalendar's handling of a bool array weekmask....
• #30924: BUG: Fix reference leaks and NULL pointer dereferences (#30908)
• #30925: MAINT: fix two minor issues noticed when touching the C API setup
• #30955: ENH: Test .kind not .char in np.testing.assert_equal (#30879)
• #30957: BUG: fix type issues in uses if PyDataType macros
• #30958: MAINT: Don't use vulture 2.15, it has false positives
• #30973: MAINT: update openblas (#30961)

NumPy 2.4.2 Release Notes

The NumPy 2.4.2 is a patch release that fixes bugs discovered after the
2.4.1 release. Highlights are:

• Fixes memory leaks
• Updates OpenBLAS to fix hangs

This release supports Python versions 3.11-3.14

Contributors

A total of 9 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

• Charles Harris
• Daniel Tang +
• Joren Hammudoglu
• Kumar Aditya
• Matti Picus
• Nathan Goldbaum
• Ralf Gommers
• Sebastian Berg
• Vikram Kumar +

Pull requests merged

A total of 12 pull requests were merged for this release.

• #30629: MAINT: Prepare 2.4.x for further development
• #30636: TYP: arange: accept datetime strings
• #30657: MAINT: avoid possible race condition by not touching os.environ...
• #30700: BUG: validate contraction axes in tensordot (#30521)
• #30701: DOC: __array_namespace__info__: set_module not __module__ (#30679)
• #30702: BUG: fix free-threaded PyObject layout in replace_scalar_type_names...
• #30703: TST: fix limited API example in tests for latest Cython
• #30709: BUG: Fix some bugs found via valgrind (#30680)
• #30712: MAINT: replace ob_type access with Py_TYPE in PyArray_CheckExact
• #30713: BUG: Fixup the quantile promotion fixup
• #30736: BUG: fix thread safety of array_getbuffer (#30667)
• #30737: backport scipy-openblas version change

NumPy 2.4.1 Release Notes

The NumPy 2.4.1 is a patch release that fixes bugs discoved after the
2.4.0 release. In particular, the typo SeedlessSequence is preserved to
enable wheels using the random Cython API and built against NumPy < 2.4.0
to run without errors.

This release supports Python versions 3.11-3.14

Contributors

A total of 9 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

• Alexander Shadchin
• Bill Tompkins +
• Charles Harris
• Joren Hammudoglu
• Marten van Kerkwijk
• Nathan Goldbaum
• Raghuveer Devulapalli
• Ralf Gommers
• Sebastian Berg

Pull requests merged

A total of 15 pull requests were merged for this release.

• #30490: MAINT: Prepare 2.4.x for further development
• #30503: DOC: numpy.select: fix default parameter docstring...
• #30504: REV: Revert part of #30164 (#30500)
• #30506: TYP: numpy.select: allow passing array-like default...
• #30507: MNT: use if constexpr for compile-time branch selection
• #30513: BUG: Fix leak in flat assignment iterator
• #30516: BUG: fix heap overflow in fixed-width string multiply (#30511)
• #30523: BUG: Ensure summed weights returned by np.average always are...
• #30527: TYP: Fix return type of histogram2d
• #30594: MAINT: avoid passing ints to random functions that take double...
• #30595: BLD: Avoiding conflict with pygit2 for static build
• #30596: MAINT: Fix msvccompiler missing error on FreeBSD
• #30608: BLD: update vendored Meson to 1.9.2
• #30620: ENH: use more fine-grained critical sections in array coercion...
• #30623: BUG: Undo result type change of quantile/percentile but keep...