Update dependency numpy to <2.5.2

_renovate commented

2026-06-22 00:32:57 +02:00

(Migrated from edugit.org)

This MR contains the following updates:

Package	Type	Update	Change
numpy (changelog)	dependencies	minor	`<2.4.7` → `<2.5.2`

Release Notes

numpy/numpy (numpy)

`v2.5.1`: (July 4, 2026)

Compare Source

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)

`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)

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This MR contains the following updates: | Package | Type | Update | Change | |---|---|---|---| | [numpy](https://github.com/numpy/numpy) ([changelog](https://numpy.org/doc/stable/release)) | dependencies | minor | `<2.4.7` → `<2.5.2` | --- ### Release Notes <details> <summary>numpy/numpy (numpy)</summary> ### [`v2.5.1`](https://github.com/numpy/numpy/releases/tag/v2.5.1): (July 4, 2026) [Compare Source](https://github.com/numpy/numpy/compare/v2.5.0...v2.5.1) ### 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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/31707): MAINT: Prepare 2.5.x for further development - [#31721](https://github.com/numpy/numpy/pull/31721): CI: fix new `cython-lint` errors ([#31711](https://github.com/numpy/numpy/issues/31711)) - [#31723](https://github.com/numpy/numpy/pull/31723): MAINT: Update meson to match main - [#31729](https://github.com/numpy/numpy/pull/31729): TST: use setup-sde instead of curl to get SDE binaries ([#31727](https://github.com/numpy/numpy/issues/31727)) - [#31829](https://github.com/numpy/numpy/pull/31829): BUG: Relax finfo to be easier accessible for all user dtypes... - [#31831](https://github.com/numpy/numpy/pull/31831): TYP: Fix `flatiter.__next__` return type for `object_` and... - [#31832](https://github.com/numpy/numpy/pull/31832): BUG: avoid deadlocks using NpyString API ([#31682](https://github.com/numpy/numpy/issues/31682)) - [#31833](https://github.com/numpy/numpy/pull/31833): BUG: fix out array leak in reduceat and accumulate when dtype... - [#31835](https://github.com/numpy/numpy/pull/31835): BUG: fix numpy datetime cython APIs to be compatible with older... - [#31836](https://github.com/numpy/numpy/pull/31836): TYP: Fix incorrect dtype inference of `asarray([])` ([#31732](https://github.com/numpy/numpy/issues/31732)) - [#31837](https://github.com/numpy/numpy/pull/31837): TYP: Fix `np.ma.masked_array` 2.5.0 regression - [#31838](https://github.com/numpy/numpy/pull/31838): FIX: Refactor error handling in array\_setstate to prevent typecode... - [#31839](https://github.com/numpy/numpy/pull/31839): TST: xfail multithreaded BLAS test more generously - [#31840](https://github.com/numpy/numpy/pull/31840): MAINT: Rename subroutine for crackfortran tests - [#31842](https://github.com/numpy/numpy/pull/31842): BUG: fix leak in reductions when a ufunc override errors or is... - [#31849](https://github.com/numpy/numpy/pull/31849): BLD: set minimum required gcc version to 10.3 ([#31843](https://github.com/numpy/numpy/issues/31843)) - [#31855](https://github.com/numpy/numpy/pull/31855): CI: fix hangs on MacOS ASan CI ([#31853](https://github.com/numpy/numpy/issues/31853)) - [#31856](https://github.com/numpy/numpy/pull/31856): BUG: fix several bugs in StringDType operations ([#31846](https://github.com/numpy/numpy/issues/31846)) - [#31857](https://github.com/numpy/numpy/pull/31857): BUG: Fix segfault in MT19937 by preventing recursive seed lists... - [#31858](https://github.com/numpy/numpy/pull/31858): BUG: Fix signed integer overflow in datetime.c ([#31688](https://github.com/numpy/numpy/issues/31688)) ### [`v2.5.0`](https://github.com/numpy/numpy/releases/tag/v2.5.0): (June 21, 2026) [Compare Source](https://github.com/numpy/numpy/compare/v2.4.6...v2.5.0) ### 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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/30615)) - The `numpy.char.[as]array` functions are deprecated. Use an `numpy.[as]array` with a string or bytes dtype instead. ([gh-30802](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/issues/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/30644)) - `numpy.ma.round_` is deprecated. `numpy.ma.round` can be used as a replacement. ([gh-30738](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/31293)) #### Expired deprecations - `numpy.distutils` has been removed ([gh-30340](https://github.com/numpy/numpy/pull/30340)) - Passing `None` as dtype to `np.finfo` will now raise a `TypeError` (deprecated since 1.25) ([gh-30460](https://github.com/numpy/numpy/pull/30460)) - `numpy.cross` no longer supports 2-dimensional vectors. (Deprecated since 2.0) ([gh-30461](https://github.com/numpy/numpy/pull/30461)) - `numpy._core.numerictypes.maximum_sctype` has been removed. (deprecated since 2.0) ([gh-30462](https://github.com/numpy/numpy/pull/30462)) - `numpy.row_stack` has been removed in favor of `numpy.vstack`. (deprecated since 2.0) ([gh-30463](https://github.com/numpy/numpy/pull/30463)) - `get_array_wrap` has been removed. (deprecated since 2.0) ([gh-30463](https://github.com/numpy/numpy/pull/30463)) - `recfromtxt` and `recfromcsv` have been removed from `numpy.lib._npyio` in favor of `numpy.genfromtxt`. (deprecated since 2.0) ([gh-30467](https://github.com/numpy/numpy/pull/30467)) - The `numpy.chararray` re-export of `numpy.char.chararray` has been removed. (deprecated since 2.0) ([gh-30604](https://github.com/numpy/numpy/pull/30604)) - `bincount` now raises a `TypeError` for non-integer inputs. (deprecated since 2.1) ([gh-30610](https://github.com/numpy/numpy/pull/30610)) - The `numpy.lib.math` alias for the standard library `math` module has been removed. (deprecated since 1.25) ([gh-30612](https://github.com/numpy/numpy/pull/30612)) - Data type alias `'a'` was removed in favor of `'S'`. (deprecated since 2.0) ([gh-30613](https://github.com/numpy/numpy/pull/30613)) - `_add_newdoc_ufunc(ufunc, newdoc)` has been removed in favor of `ufunc.__doc__ = newdoc`. (deprecated since 2.2) ([gh-30614](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/30994)) - NumPy now supports the stable ABI for free-threaded Python as described in `803`{.interpreted-text role="pep"}. ([gh-31091](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/30965)) ##### `StringDType` comparisons now correctly handle embedded NULL bytes. ([gh-31662](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/issues/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/31503)) </details> --- ### Configuration 📅 **Schedule**: (UTC) - Branch creation - At any time (no schedule defined) - Automerge - At any time (no schedule defined) 🚦 **Automerge**: Enabled. ♻ **Rebasing**: Whenever MR is behind base branch, or you tick the rebase/retry checkbox. 🔕 **Ignore**: Close this MR and you won't be reminded about this update again. --- - [ ] If you want to rebase/retry this MR, check this box --- This MR has been generated by [Mend Renovate](https://github.com/renovatebot/renovate).

_renovate commented

2026-06-22 00:34:10 +02:00

(Migrated from edugit.org)

added 1 commit

9e68ff3b - Update dependency numpy to <2.5.1

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Update dependency numpy to <2.5.2 #299

Release Notes

v2.5.1: (July 4, 2026)

NumPy 2.5.1 Release Notes

Changes

Contributors

Pull requests merged

v2.5.0: (June 21, 2026)

NumPy 2.5.0 Release Notes

Highlights

Deprecations

1 is implicitly converted to generic timedelta64

Expired deprecations

Compatibility notes

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

MSVC support

Cython support

versions.

See init.cython-30.pxd for the real Cython header

numpy.where no longer truncates Python integers

Default memory allocator change

from_dlpack raises BufferError instead of RuntimeError

Corrections to the BTPE binomial sampler

datetime64/timedelta64 arithmetic raises on overflow

C API changes

New Features

Pixi package definitions

numpy.ndarray now supports structural pattern matching

Added N-D evaluation functions to the polynomial package

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

Improvements

f2py modules now show allocatable arrays in dir()

StringDType comparisons now correctly handle embedded NULL bytes.

Performance improvements and changes

Improved performance of numpy.searchsorted

Improved scaling of ufuncs on free-threading

Faster reductions on small/medium contiguous arrays

Typing improvements and changes

numpy.linalg typing improvements and preliminary shape-typing support

numpy.ma typing annotations

Shape-typing support for many functions and methods

numpy.fft typing improvements and preliminary shape-typing support

Changes

Structured array copies now use memcpy for contiguous dtypes

numpy.ctypeslib.as_ctypes now does not support scalar types

__array_interface__ changes on scalars

meshgrid now always returns a tuple

numpy.triu_indices now accepts unsigned integers

object dtype in .real and .imag and related functions

NumPy's internal memory allocations now use PyMem_RawMalloc

Configuration

Checkout

`v2.5.1`: (July 4, 2026)

`v2.5.0`: (June 21, 2026)

`1` is implicitly converted to generic timedelta64

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

`numpy.where` no longer truncates Python integers

`from_dlpack` raises `BufferError` instead of `RuntimeError`

`datetime64`/`timedelta64` arithmetic raises on overflow

`numpy.ndarray` now supports structural pattern matching

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

`f2py` modules now show allocatable arrays in `dir()`

`StringDType` comparisons now correctly handle embedded NULL bytes.

Improved performance of `numpy.searchsorted`

`numpy.linalg` typing improvements and preliminary shape-typing support

`numpy.ma` typing annotations

`numpy.fft` typing improvements and preliminary shape-typing support

Structured array copies now use `memcpy` for contiguous dtypes

`numpy.ctypeslib.as_ctypes` now does not support scalar types

`__array_interface__` changes on scalars

`meshgrid` now always returns a tuple

`numpy.triu_indices` now accepts `unsigned integers`

`object` dtype in `.real` and `.imag` and related functions

NumPy's internal memory allocations now use `PyMem_RawMalloc`