fix(deps): update dependency numpy to <=2.5.0

[dreadnode-renovate-bot]

ℹ️ Note

This PR body was truncated due to platform limits.

This PR contains the following updates:

Package	Change	Age	Confidence
numpy (changelog)	<=2.3.5 → <=2.5.0

---

Release Notes

numpy/numpy (numpy)

v2.5.0: (June 21, 2026)

Compare Source

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
truncated. Now, an OverflowError will be raised instead.

This change also applies to the underlying C API function PyArray_Where.

(gh-30803)

Default memory allocator change

NumPy now uses PyMem_RawMalloc and PyMem_RawFree as the default memory
allocator, instead of system's malloc and free directly.

(gh-30846)

from_dlpack raises BufferError instead of RuntimeError

np.from_dlpack now raises BufferError instead of RuntimeError when
the incoming DLPack tensor has an unsupported device, dtype, or exceeds the
maximum number of dimensions. This aligns with the DLPack and Array API
specifications, which recommend BufferError for data that cannot be
imported.

(gh-30937)

Corrections to the BTPE binomial sampler

Two independent errors in the Stirling series of the acceptance/rejection step
of the BTPE algorithm used by numpy.random.Generator.binomial have been
corrected:

• The third and fourth error terms were added rather than subtracted. This sign
  error was inherited from section 5.3 of the original 1988 paper by
  Kachitvichyanukul & Schmeiser, which incorrectly adds all four terms.
• The leading coefficient had a digit-swap typo (13680 instead of
  13860) that was introduced in the initial implementation.

As a result, Generator.binomial and Generator.multinomial, which uses
binomial internally, may now return different samples for the same seed.

The legacy numpy.random.RandomState.binomial and
numpy.random.RandomState.multinomial are not affected: they preserve the
original (incorrect) behavior, so existing streams remain reproducible.

(gh-31238)

datetime64/timedelta64 arithmetic raises on overflow

Addition, subtraction, and integer multiplication of datetime64 and
timedelta64 values now raise OverflowError when the result would
overflow int64 or land on the NaT sentinel value. Previously these
operations silently wrapped, often producing a value that was indistinguishable
from NaT. This matches the overflow checking already performed by
unit-conversion casts.

(gh-31378)

C API changes

• It is now possible to register "real" and "imag" ArrayMethods via
  PyUFunc_AddLoopsFromSpecs. These will be used for imag and real
  and should normally set *view_offset in their resolve_descriptors
  function to allow the array attributes to return views.

  (gh-30984)

• New PyDataType_TYPE, PyDataType_KIND, PyDataType_BYTEORDER and
  PyDataType_TYPEOBJ accessor macros to the C API. Together with the other
  accessor macros added for the NumPy 2.0 transition, these allow accessing the
  fields of PyArray_Descr structs without any direct field accesses.

  (gh-30994)

• NumPy now supports the stable ABI for free-threaded Python as described in
  803{.interpreted-text role="pep"}.

  (gh-31091)

• PyArray_DescrFromScalar now returns the full dtype descriptor for scalars
  of user-defined parametric data types, including any dtype parameters.
  Parameters were previously silently discarded, which could cause incorrect
  results in operations like astype on scalar objects. Internally, the
  function now delegates to discover_descr_from_pyobject, which handles
  parametric dtypes correctly.

  (gh-31067)

New Features

• It is now possible to register user-dtypes for dlpack export and import
  via numpy.dtypes.register_dlpack_dtype. This functionality is meant to
  be used with care by user-dtype authors.

  (gh-31256)

Pixi package definitions

Pixi package definitions have been added for different kinds
of from-source builds of NumPy. These can be used in
downstream Pixi workspaces via the pixi-build feature.

Definitions for both default and AddressSanitizer-instrumented
(asan) builds are available in the source code under the
pixi-packages/ directory.

linux-64 and osx-arm64 platforms are supported.

(gh-30381)

numpy.ndarray now supports structural pattern matching

numpy.ndarray and its subclasses now have the Py_TPFLAGS_SEQUENCE flag
set, enabling structural pattern matching (PEP 634) with match/case
statements. This also enables Cython to optimize integer indexing operations.
See `arrays.ndarray.pattern-matching{.interpreted-text role="ref"}` for details.

(gh-30653)

Added N-D evaluation functions to the polynomial package

New functions polyvalnd, chebvalnd, legvalnd, hermvalnd,
hermevalnd, and lagvalnd have been added to evaluate polynomials
in arbitrary dimensions, analogous to the existing 2D and 3D evaluators.

(gh-30857)

New "descending" keyword argument for numpy.sort and numpy.argsort

Users can now pass the descending=True keyword argument to numpy.sort
and numpy.argsort to sort and argsort arrays in descending order. NaN
values, if present, are sorted to the end of the array in both ascending and
descending sorts. This feature is available for all built-in dtypes except
void, object, and generic. Note that SIMD optimizations for sorting
are currently not available for descending sorts, so performance may be slower.

(gh-31345)

Improvements

For f2py, the behaviour of intent(inplace) has improved. Previously,
if an input array did not have the right dtype or order, the input array was
modified in-place, changing its dtype and replacing its data by a corrected
copy. Now, instead, the corrected copy is kept a separate array, which, after
being passed and presumably modified by the fortran routine, is copied back to
the input routine. The above means one no longer has the risk that
pre-existing views or slices of the input array start pointing to unallocated
memory (at the price of increased overhead for the write-back copy at the end
of the call).

A potential problem would be that one might get very different results if one,
e.g., previously passed in an integer array where a double array was expected:
the writeback to integer would likely give wrong results. To avoid such
situations, intent(inplace) will now only allow arrays that have equivalent
type to that used in the fortran routine, i.e., dtype.kind is the same. For
instance, a routine expecting double would be able to receive float, but would
raise on integer input.

(gh-29929)

f2py modules now show allocatable arrays in dir()

Allocatable module variables wrapped by f2py now appear in dir()
output, matching their accessibility by name.

(gh-30965)

StringDType comparisons now correctly handle embedded NULL bytes.

(gh-31662)

Performance improvements and changes

Improved performance of numpy.searchsorted

The C++ binary search implementation used by numpy.searchsorted now has a
much better performance when searching for multiple keys. The new
implementation batches binary search steps across all keys to leverage cache
locality and out-of-order execution. Benchmarks show the new implementation can
be up to 20 times faster for hundreds of thousands keys while single-key
performance remains comparable to previous versions.

(gh-30517)

Improved scaling of ufuncs on free-threading

NumPy's ufuncs now scale significantly better on free-threading builds
of CPython due to the following optimizations:

• Lock-free dispatch table: The ufuncs dispatch table is now implemented as
  a lock-free concurrent hash map, allowing multiple threads to call ufuncs
  without contention.
• Immortal shared objects: Certain shared objects, such as global memory
  handlers, have been made immortal. This effectively reduces reference
  counting contention across threads.
• Optimized memory allocation: NumPy now utilizes PyMem_RawMalloc and
  PyMem_RawFree for memory allocation. On Python 3.15 and newer, this
  leverages mimalloc and significantly reduces memory allocation overhead
  in multi-threaded workloads.

(gh-30846)

Faster reductions on small/medium contiguous arrays

numpy.sum, numpy.prod, numpy.any, numpy.all, and other
reductions with an identity value now use a fast path when the input is a
contiguous, aligned, non-object array and the reduction covers all axes
(axis=None) with no special arguments. Typical speedup is ~1.3x on small
arrays; numpy.any / numpy.all on contiguous boolean arrays can see
speedup up to 1.9x.

(gh-31274)

Typing improvements and changes

numpy.linalg typing improvements and preliminary shape-typing support

Input and output dtypes for numpy.linalg functions are now more precise.
Several of these functions also gain preliminary shape-typing support while
remaining backward compatible. For example, the return type of
numpy.linalg.matmul now depends on the shape-type of its inputs, or fall
back to the backward-compatible return type if the shape-types are unknown at
type-checking time. Because of limitations in Python's type system and current
type-checkers, shape-typing cannot cover every situation and is often only
implemented for the most common lower-rank cases.

(gh-30480)

numpy.ma typing annotations

The numpy.ma module is now fully covered by typing annotations. This
includes annotations for masked arrays, masks, and various functions and
methods. With this, NumPy has achieved 100% typing coverage across all its
submodules.

(gh-30566)

Shape-typing support for many functions and methods

Many functions and methods now have shape-aware return type annotations.
Type-checkers can now infer the number of dimensions of the returned array
through common operations. For example, np.linspace(0, 1) is now typed as a
1-d float64 array, and np.sum(x, keepdims=True) has the same number of
dimensions as x.

This covers numpy.linalg functions, array creation functions (like
asarray, from{buffer,string,file,iter,regex}), range functions
(linspace, logspace, geomspace), aggregation functions and methods
(sum, mean, std, var, min, max, all, any,
etc.), sorting (sort, argsort, argpartition), cumulative operations
(cumsum, cumprod, etc.), set operations (unique_values,
intersect1d, union1d, etc.), and various other functions including
nonzero, transpose, diagonal, atleast_{1,2,3}d, clip,
round, inner, bincount, and fft.fftfreq. Several of these also
gained more precise return dtype annotations as part of this work.

Shape-typing is still a work-in-progress, so coverage is not yet complete.
Because of limitations in Python's type system and current type-checkers,
shape-typing is often only implemented for the most common lower-rank cases.

(gh-31172)

numpy.fft typing improvements and preliminary shape-typing support

The numpy.fft functions now support non-float64/complex128 dtypes
and gain preliminary shape-typing support. For example, the return type of
numpy.fft.fft now depends on the shape-type of its inputs, falling back to
the backward-compatible return type when the shape-types are unknown at
type-checking time.

(gh-31226)

Changes

Structured array copies now use memcpy for contiguous dtypes

Copying structured arrays with identical dtypes now uses memcpy instead of
field-by-field transfer when the dtype has a contiguous layout (no gaps between
fields). A new NPY_NOT_TRIVIALLY_COPYABLE dtype flag is set on structured
dtypes that have gaps in their memory layout, such as those created with
explicit offsets or via multi-field indexing. Only these dtypes continue to
use the slower field-by-field copy.

This means that padding bytes in contiguous structured dtypes (e.g. those
created without explicit offsets) may now be copied as part of the
memcpy, whereas previously they were left untouched. Code that relies on
padding bytes being preserved during structured array copies may be affected.

(gh-29270)

numpy.ctypeslib.as_ctypes now does not support scalar types

The function numpy.ctypeslib.as_ctypes has been updated to only accept
numpy.ndarray. Passing a scalar type (e.g., numpy.int32(5)) will now
raise a TypeError. This change was made to avoid the issue
gh-30354 and to enforce the
readonly nature of scalar types in NumPy. The previous behavior relied on
undocumented implicit temporary arrays and was not well-defined. Users who
need to convert scalar types to ctypes should first convert them to an array
(e.g., numpy.asarray) before passing them to numpy.ctypeslib.as_ctypes.

(gh-30538)

__array_interface__ changes on scalars

Scalars now export the __array_interface__ directly rather than including
an array copy as a __ref entry. This means that scalars are now exported as
read-only while they previously exported as writeable. The path via __ref
was undocumented and not consistently used even within NumPy itself.

(gh-30538)

meshgrid now always returns a tuple

np.meshgrid previously used to return a list when sparse was true and
copy was false. Now, it always returns a tuple regardless of the
arguments.

(gh-30707)

numpy.triu_indices now accepts unsigned integers

numpy.triu_indices previously used to error in some cases when unsigned integers
were given as arguments. Now, it accepts them in all cases.

(gh-30869)

object dtype in .real and .imag and related functions

The array attributes .real and .imag now behave differently for object
arrays and return getattr(element, "real", element) or getattr(element, "imag", 0)
elementwise. Additionally, the return for both is now read-only to avoid possible
in-place changes having no effect.

This change also affects np.isreal() which uses arr.imag.

Previously, .imag always returned 0 while .real returned the
original array unmodified. The new behavior now returnes the correct values
for complex Python objects but may also lead to surprises for example if
element.real() is a method and not a property.

(gh-30984)

NumPy's internal memory allocations now use PyMem_RawMalloc

NumPy's internal memory allocations now use PyMem_RawMalloc instead of
malloc and can be tracked by tracemalloc.

(gh-31503)

v2.4.6: (May 18, 2026)

Compare Source

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...

v2.4.5: (May 15, 2026)

Compare Source

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)

v2.4.4: 2.4.4 (Mar 29, 2026)

Compare Source

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...

v2.4.3: 2.4.3 (Mar 9, 2026)

Compare Source

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)

v2.4.2: 2.4.2 (Feb 1, 2026)

Compare Source

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

v2.4.1: 2.4.1 (Jan 10, 2026)

Compare Source

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...

v2.4.0: 2.4.0 (Dec 20, 2025)

Compare Source

NumPy 2.4.0 Release Notes

The NumPy 2.4.0 release continues the work to improve free threaded Python
support, user dtypes implementation, and annotations. There are many expired
deprecations and bug fixes as well.

This release supports Python versions 3.11-3.14

Highlights

Apart from annotations and same_value kwarg, the 2.4 highlights are mostly
of interest to downstream developers. They should help in implementing new user
dtypes.

• Many annotation improvements. In particular, runtime signature introspection.
• New casting kwarg 'same_value' for casting by value.
• New PyUFunc_AddLoopsFromSpec function that can be used to add user sort
  loops using the ArrayMethod API.
• New __numpy_dtype__ protocol.

Deprecations

Setting the strides attribute is deprecated

Setting the strides attribute is now deprecated since mutating
an array is unsafe if an array is shared, especially by multiple
threads. As an alternative, you can create a new view (no copy) via:

• np.lib.stride_tricks.strided_window_view if applicable,
• np.lib.stride_tricks.as_strided for the general case,
• or the np.ndarray constructor (buffer is the original array) for a
  light-weight version.

(gh-28925)

Positional out argument to np.maximum, np.minimum is deprecated

Passing the output array out positionally to numpy.maximum and
numpy.minimum is deprecated. For example, np.maximum(a, b, c) will emit
a deprecation warning, since c is treated as the output buffer rather than
a third input.

Always pass the output with the keyword form, e.g. np.maximum(a, b, out=c).
This makes intent clear and simplifies type annotations.

(gh-29052)

align= must be passed as boolean to np.dtype()

When creating a new dtype a VisibleDeprecationWarning will be given if
align= is not a boolean. This is mainly to prevent accidentally passing a
subarray align flag where it has no effect, such as np.dtype("f8", 3)
instead of np.dtype(("f8", 3)). We strongly suggest to always pass
align= as a keyword argument.

(gh-29301)

Assertion and warning control utilities are deprecated

np.testing.assert_warns and np.testing.suppress_warnings are
deprecated. Use warnings.catch_warnings, warnings.filterwarnings,
pytest.warns, or pytest.filterwarnings instead.

(gh-29550)

np.fix is pending deprecation

The numpy.fix function will be deprecated in a future release. It is
recommended to use numpy.trunc instead, as it provides the same
functionality of truncating decimal values to their integer parts. Static type
checkers might already report a warning for the use of numpy.fix.

(gh-30168)

in-place modification of ndarray.shape is pending deprecation

Setting the ndarray.shape attribute directly will be deprecated in a future
release. Instead of modifying the shape in place, it is recommended to use the
numpy.reshape function. Static type checkers might already report a
warning for assignments to ndarray.shape.

(gh-30282)

Deprecation of numpy.lib.user_array.container

The numpy.lib.user_array.container class is deprecated and will be removed
in a future version.

(gh-30284)

Expired deprecations

Removed deprecated MachAr runtime discovery mechanism.

(gh-29836)

Raise TypeError on attempt to convert array with ndim > 0 to scalar

Conversion of an array with ndim > 0 to a scalar was deprecated in NumPy
1.25. Now, attempting to do so raises TypeError. Ensure you extract a
single element from your array before performing this operation.

(gh-29841)

Removed numpy.linalg.linalg and numpy.fft.helper

The following were deprecated in NumPy 2.0 and have been moved to private
modules:

• numpy.linalg.linalg
  Use numpy.linalg instead.
• numpy.fft.helper
  Use numpy.fft instead.

(gh-29909)

Removed interpolation parameter from quantile and percentile functions

The interpolation parameter was deprecated in NumPy 1.22.0 and has been
removed from the following functions:

• numpy.percentile
• numpy.nanpercentile
• numpy.quantile
• numpy.nanquantile

Use the method parameter instead.

(gh-29973)

Removed numpy.in1d

numpy.in1d has been deprecated since NumPy 2.0 and is now removed in favor of numpy.isin.

(gh-29978)

Removed numpy.ndindex.ndincr()

The ndindex.ndincr() method has been deprecated since NumPy 1.20 and is now
removed; use next(ndindex) instead.

(gh-29980)

Removed fix_imports parameter from numpy.save

The fix_imports parameter was deprecated in NumPy 2.1.0 and is now removed.
This flag has been ignored since NumPy 1.17 and was only needed to support
loading files in Python 2 that were written in Python 3.

(gh-29984)

Removal of four undocumented ndarray.ctypes methods

Four undocumented methods of the ndarray.ctypes object have been removed:

• _ctypes.get_data() (use _ctypes.data instead)
• _ctypes.get_shape() (use _ctypes.shape instead)
• _ctypes.get_strides() (use _ctypes.strides instead)
• _ctypes.get_as_parameter() (use _ctypes._as_parameter_ instead)

These methods have been deprecated since NumPy 1.21.

(gh-29986)

Removed newshape parameter from numpy.reshape

The newshape parameter was deprecated in NumPy 2.1.0 and has been
removed from numpy.reshape. Pass it positionally or use shape=
on newer NumPy versions.

(gh-29994)

Removal of deprecated functions and arguments

The following long-deprecated APIs have been removed:

• numpy.trapz --- deprecated since NumPy 2.0 (2023-08-18). Use numpy.trapezoid or
  scipy.integrate functions instead.
• disp function --- deprecated from 2.0 release and no longer functional. Use
  your own printing function instead.
• bias and ddof arguments in numpy.corrcoef --- these had no effect
  since NumPy 1.10.

(gh-29997)

Removed delimitor parameter from numpy.ma.mrecords.fromtextfile()

The delimitor parameter was deprecated in NumPy 1.22.0 and has been
removed from numpy.ma.mrecords.fromtextfile(). Use delimiter instead.

(gh-30021)

numpy.array2string and numpy.sum deprecations finalized

The following long-deprecated APIs have been removed or converted to errors:

• The style parameter has been removed from numpy.array2string.
  This argument had no effect since Numpy 1.14.0. Any arguments following
  it, such as formatter have now been made keyword-only.
• Calling np.sum(generator) directly on a generator object now raises a
  TypeError. This behavior was deprecated in NumPy 1.15.0. Use
  np.sum(np.fromiter(generator)) or the python sum builtin instead.

(gh-30068)

Compatibility notes

• NumPy's C extension modules have begun to use multi-phase initialisation, as
  defined by PEP 489. As part of this, a new explicit check has been added that
  each such module is only imported once per Python process. This comes with
  the side-effect that deleting numpy from sys.modules and re-importing
  it will now fail with an ImportError. This has always been unsafe, with
  unexpected side-effects, though did not previously raise an error.

  (gh-29030)

• numpy.round now always returns a copy. Previously, it returned a view
  for integer inputs for decimals >= 0 and a copy in all other cases.
  This change brings round in line with ceil, floor and trunc.

  (gh-29137)

• Type-checkers will no longer accept calls to numpy.arange with
  start as a keyword argument. This was done for compatibility with
  the Array API standard. At runtime it is still possible to use
  numpy.arange with start as a keyword argument.

  (gh-30147)

• The Macro NPY_ALIGNMENT_REQUIRED has been removed The macro was defined in
  the npy_cpu.h file, so might be regarded as semi public. As it turns out,
  with modern compilers and hardware it is almost always the case that
  alignment is required, so numpy no longer uses the macro. It is unlikely
  anyone uses it, but you might want to compile with the -Wundef flag or
  equivalent to be sure.

  (gh-29094)

C API changes

The NPY_SORTKIND enum has been enhanced with new variables

This is of interest if you are using PyArray_Sort or PyArray_ArgSort.
We have changed the semantics of the old names in the NPY_SORTKIND enum and
added new ones. The changes are backward compatible, and no recompilation is
needed. The new names of interest are:

• NPY_SORT_DEFAULT -- default sort (same value as NPY_QUICKSORT)
• NPY_SORT_STABLE -- the sort must be stable (same value as NPY_MERGESORT)
• NPY_SORT_DESCENDING -- the sort must be descending

The semantic change is that NPY_HEAPSORT is mapped to NPY_QUICKSORT when used.
Note that NPY_SORT_DESCENDING is not yet implemented.

(gh-29642)

New NPY_DT_get_constant slot for DType constant retrieval

A new slot NPY_DT_get_constant has been added to the DType API, allowing
dtype implementations to provide constant values such as machine limits and
special values. The slot function has the signature:

int get_constant(PyArray_Descr *descr, int constant_id, void *ptr)

It returns 1 on success, 0 if the constant is not available, or -1 on error.
The function is always called with the GIL held and may write to unaligned memory.

Integer constants (marked with the 1 << 16 bit) return npy_intp values,
while floating-point constants return values of the dtype's native type.

Implementing this can be used by user DTypes to provide numpy.finfo values.

(gh-29836)

A new PyUFunc_AddLoopsFromSpecs convenience function has been added to the C API.

This function allows adding multiple ufunc loops from their specs in one call
using a NULL-terminated array of PyUFunc_LoopSlot structs. It allows
registering sorting and argsorting loops using the new ArrayMethod API.

(gh-29900)

New Features

• Let np.size accept multiple axes.

  (gh-29240)

• Extend numpy.pad to accept a dictionary for the pad_width argument.

  (gh-29273)

'same_value' for casting by value

The casting kwarg now has a 'same_value' option that checks the actual
values can be round-trip cast without changing value. Currently it is only
implemented in ndarray.astype. This will raise a ValueError if any of the
values in the array would change as a result of the cast, including rounding of
floats or overflowing of ints.

(gh-29129)

StringDType fill_value support in numpy.ma.MaskedArray

Masked arrays now accept and preserve a Python str as their fill_value
when using the variable‑width StringDType (kind 'T'), including through
slicing and views. The default is 'N/A' and may be overridden by any valid
string. This fixes issue gh‑29421
and was implemented in pull request gh‑29423.

(gh-29423)

ndmax option for numpy.array

The ndmax option is now available for numpy.array.
It explicitly limits the maximum number of dimensions created from nested sequences.

This is particularly useful when creating arrays of list-like objects with dtype=object.
By default, NumPy recurses through all nesting levels to create the highest possible
dimensional array, but this behavior may not be desired when the intent is to preserve
nested structures as objects. The ndmax parameter provides explicit control over
this recursion depth.

# Default behavior: Creates a 2D array
>>> a = np.array([[1, 2], [3, 4]], dtype=object)
>>> a
array([[1, 2],
       [3, 4]], dtype=object)
>>> a.shape
(2, 2)

# With ndmax=1: Creates a 1D array
>>> b = np.array([[1, 2], [3, 4]], dtype=object, ndmax=1)
>>> b
array([list([1, 2]), list([3, 4])], dtype=object)
>>> b.shape
(2,)

([gh-29569]

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