2.5.021 Jun 2026Deprecated

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…

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)

Open source →

2.5.0rc12 Jun 2026pre-releaseDeprecated

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…

NumPy 2.5.0 Release Notes

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

(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

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.'

(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`

(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

(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)

Improvements

(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)

Performance improvements and changes

Improved performance of `numpy.searchsorted`

(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

(gh-31274)

Typing improvements and changes

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

(gh-30480)

`numpy.ma` typing annotations

(gh-30566)

Shape-typing support for many functions and methods

(gh-31172)

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

(gh-31226)

Changes

Structured array copies now use `memcpy` for contiguous dtypes

(gh-29270)

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

(gh-30538)

`__array_interface__` changes on scalars

(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

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

(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)

Open source →

2.4.020 Dec 2025withdrawn: Backward compatibility bugDeprecated

…and annotations. There are many expired deprecations and bug fixes as well.

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)

Warning emitted when using `where` without `out`

Ufuncs called with a where mask and without an out positional or kwarg will now emit a warning. This usage tends to trip up users who expect some value in output locations where the mask is False (the ufunc will not touch those locations). The warning can be suppressed by using out=None.

(gh-29813)

DType sorting and argsorting supports the ArrayMethod API

User-defined dtypes can now implement custom sorting and argsorting using the ArrayMethod API. This mechanism can be used in place of the PyArray_ArrFuncs slots which may be deprecated in the future.

The sorting and argsorting methods are registered by passing the arraymethod specs that implement the operations to the new PyUFunc_AddLoopsFromSpecs function. See the ArrayMethod API documentation for details.

(gh-29900)

New `__numpy_dtype__` protocol

NumPy now has a new __numpy_dtype__ protocol. NumPy will check for this attribute when converting to a NumPy dtype via np.dtype(obj) or any dtype= argument.

Downstream projects are encouraged to implement this for all dtype like objects which may previously have used a .dtype attribute that returned a NumPy dtype. We expect to deprecate .dtype in the future to prevent interpreting array-like objects with a .dtype attribute as a dtype. If you wish you can implement __numpy_dtype__ to ensure an earlier warning or error (.dtype is ignored if this is found).

(gh-30179)

Improvements

Fix `flatiter` indexing edge cases

The flatiter object now shares the same index preparation logic as ndarray, ensuring consistent behavior and fixing several issues where invalid indices were previously accepted or misinterpreted.

Key fixes and improvements:

Stricter index validation
- Boolean non-array indices like arr.flat[[True, True]] were incorrectly treated as arr.flat[np.array([1, 1], dtype=int)]. They now raise an index error. Note that indices that match the iterator's shape are expected to not raise in the future and be handled as regular boolean indices. Use np.asarray(<index>) if you want to match that behavior.
- Float non-array indices were also cast to integer and incorrectly treated as arr.flat[np.array([1.0, 1.0], dtype=int)]. This is now deprecated and will be removed in a future version.
- 0-dimensional boolean indices like arr.flat[True] are also deprecated and will be removed in a future version.
Consistent error types:

Certain invalid flatiter indices that previously raised ValueError now correctly raise IndexError, aligning with ndarray behavior.
Improved error messages:

The error message for unsupported index operations now provides more specific details, including explicitly listing the valid index types, instead of the generic IndexError: unsupported index operation.

(gh-28590)

Improved error handling in `np.quantile`

[np.quantile]{.title-ref} now raises errors if:

All weights are zero
At least one weight is np.nan
At least one weight is np.inf

(gh-28595)

Improved error message for `assert_array_compare`

The error message generated by assert_array_compare which is used by functions like assert_allclose, assert_array_less etc. now also includes information about the indices at which the assertion fails.

(gh-29112)

Show unit information in `repr` for `datetime64("NaT")`

When a datetime64 object is "Not a Time" (NaT), its __repr__ method now includes the time unit of the datetime64 type. This makes it consistent with the behavior of a timedelta64 object.

(gh-29396)

Performance increase for scalar calculations

The speed of calculations on scalars has been improved by about a factor 6 for ufuncs that take only one input (like np.sin(scalar)), reducing the speed difference from their math equivalents from a factor 19 to 3 (the speed for arrays is left unchanged).

(gh-29819)

`numpy.finfo` Refactor

The numpy.finfo class has been completely refactored to obtain floating-point constants directly from C compiler macros rather than deriving them at runtime. This provides better accuracy, platform compatibility and corrected several attribute calculations:

Constants like eps, min, max, smallest_normal, and smallest_subnormal now come directly from standard C macros (FLT_EPSILON, DBL_MIN, etc.), ensuring platform-correct values.
The deprecated MachAr runtime discovery mechanism has been removed.
Derived attributes have been corrected to match standard definitions: machep and negep now use int(log2(eps)); nexp accounts for all exponent patterns; nmant excludes the implicit bit; and minexp follows the C standard definition.
longdouble constants, Specifically smallest_normal now follows the C standard definitions as per respecitive platform.
Special handling added for PowerPC's IBM double-double format.
New test suite added in test_finfo.py to validate all finfo properties against expected machine arithmetic values for float16, float32, and float64 types.

(gh-29836)

Multiple axes are now supported in `numpy.trim_zeros`

The axis argument of numpy.trim_zeros now accepts a sequence; for example np.trim_zeros(x, axis=(0, 1)) will trim the zeros from a multi-dimensional array x along axes 0 and 1. This fixes issue gh‑29945 and was implemented in pull request gh‑29947.

(gh-29947)

Runtime signature introspection support has been significantly improved

Many NumPy functions, classes, and methods that previously raised ValueError when passed to inspect.signature() now return meaningful signatures. This improves support for runtime type checking, IDE autocomplete, documentation generation, and runtime introspection capabilities across the NumPy API.

Over three hundred classes and functions have been updated in total, including, but not limited to, core classes such as ndarray, generic, dtype, ufunc, broadcast, nditer, etc., most methods of ndarray and scalar types, array constructor functions (array, empty, arange, fromiter, etc.), all ufuncs, and many other commonly used functions, including dot, concat, where, bincount, can_cast, and numerous others.

(gh-30208)

Performance improvements and changes

Performance improvements to `np.unique` for string dtypes

The hash-based algorithm for unique extraction provides an order-of-magnitude speedup on large string arrays. In an internal benchmark with about 1 billion string elements, the hash-based np.unique completed in roughly 33.5 seconds, compared to 498 seconds with the sort-based method -- about 15× faster for unsorted unique operations on strings. This improvement greatly reduces the time to find unique values in very large string datasets.

(gh-28767)

Rewrite of `np.ndindex` using `itertools.product`

The numpy.ndindex function now uses itertools.product internally, providing significant improvements in performance for large iteration spaces, while maintaining the original behavior and interface. For example, for an array of shape (50, 60, 90) the NumPy ndindex benchmark improves performance by a factor 5.2.

(gh-29165)

Performance improvements to `np.unique` for complex dtypes

The hash-based algorithm for unique extraction now also supports complex dtypes, offering noticeable performance gains.

In our benchmarks on complex128 arrays with 200,000 elements, the hash-based approach was about 1.4--1.5× faster than the sort-based baseline when there were 20% of unique values, and about 5× faster when there were 0.2% of unique values.

(gh-29537)

Changes

Multiplication between a string and integer now raises OverflowError instead of MemoryError if the result of the multiplication would create a string that is too large to be represented. This follows Python's behavior.

(gh-29060)
The accuracy of np.quantile and np.percentile for 16- and 32-bit floating point input data has been improved.

(gh-29105)

`unique_values` for string dtypes may return unsorted data

np.unique now supports hash‐based duplicate removal for string dtypes. This enhancement extends the hash-table algorithm to byte strings ('S'), Unicode strings ('U'), and the experimental string dtype ('T', StringDType). As a result, calling np.unique() on an array of strings will use the faster hash-based method to obtain unique values. Note that this hash-based method does not guarantee that the returned unique values will be sorted. This also works for StringDType arrays containing None (missing values) when using equal_nan=True (treating missing values as equal).

(gh-28767)

Modulate dispatched x86 CPU features

IMPORTANT: The default setting for cpu-baseline on x86 has been raised to x86-64-v2 microarchitecture. This can be changed to none during build time to support older CPUs, though SIMD optimizations for pre-2009 processors are no longer maintained.

NumPy has reorganized x86 CPU features into microarchitecture-based groups instead of individual features, aligning with Linux distribution standards and Google Highway requirements.

Key changes:

Replaced individual x86 features with microarchitecture levels: X86_V2, X86_V3, and X86_V4
Raised the baseline to X86_V2
Improved - operator behavior to properly exclude successor features that imply the excluded feature
Added meson redirections for removed feature names to maintain backward compatibility
Removed compiler compatibility workarounds for partial feature support (e.g., AVX512 without mask operations)
Removed legacy AMD features (XOP, FMA4) and discontinued Intel Xeon Phi support

New Feature Group Hierarchy:

Name Implies Includes

X86_V2 SSE SSE2 SSE3 SSSE3 SSE4_1 SSE4_2 POPCNT CX16 LAHF X86_V3 X86_V2 AVX AVX2 FMA3 BMI BMI2 LZCNT F16C MOVBE X86_V4 X86_V3 AVX512F AVX512CD AVX512VL AVX512BW AVX512DQ AVX512_ICL X86_V4 AVX512VBMI AVX512VBMI2 AVX512VNNI AVX512BITALG AVX512VPOPCNTDQ AVX512IFMA VAES GFNI VPCLMULQDQ AVX512_SPR AVX512_ICL AVX512FP16

These groups correspond to CPU generations:

X86_V2: x86-64-v2 microarchitectures (CPUs since 2009)
X86_V3: x86-64-v3 microarchitectures (CPUs since 2015)
X86_V4: x86-64-v4 microarchitectures (AVX-512 capable CPUs)
AVX512_ICL: Intel Ice Lake and similar CPUs
AVX512_SPR: Intel Sapphire Rapids and newer CPUs

On 32-bit x86, cx16 is excluded from X86_V2.

Documentation has been updated with details on using these new feature groups with the current meson build system.

(gh-28896)

Fix bug in `matmul` for non-contiguous out kwarg parameter

In some cases, if out was non-contiguous, np.matmul would cause memory corruption or a c-level assert. This was new to v2.3.0 and fixed in v2.3.1.

(gh-29179)

`__array_interface__` with NULL pointer changed

The array interface now accepts NULL pointers (NumPy will do its own dummy allocation, though). Previously, these incorrectly triggered an undocumented scalar path. In the unlikely event that the scalar path was actually desired, you can (for now) achieve the previous behavior via the correct scalar path by not providing a data field at all.

(gh-29338)

`unique_values` for complex dtypes may return unsorted data

np.unique now supports hash‐based duplicate removal for complex dtypes. This enhancement extends the hash‐table algorithm to all complex types ('c'), and their extended precision variants. The hash‐based method provides faster extraction of unique values but does not guarantee that the result will be sorted.

(gh-29537)

Sorting `kind='heapsort'` now maps to `kind='quicksort'`

It is unlikely that this change will be noticed, but if you do see a change in execution time or unstable argsort order, that is likely the cause. Please let us know if there is a performance regression. Congratulate us if it is improved :)

(gh-29642)

`numpy.typing.DTypeLike` no longer accepts `None`

The type alias numpy.typing.DTypeLike no longer accepts None. Instead of

dtype: DTypeLike = None

it should now be

dtype: DTypeLike | None = None

instead.

(gh-29739)

The npymath and npyrandom libraries now have a .lib rather than a .a file extension on win-arm64, for compatibility for building with MSVC and setuptools. Please note that using these static libraries is discouraged and for existing projects using it, it's best to use it with a matching compiler toolchain, which is clang-cl on Windows on Arm.

(gh-29750)

Open source →

2.4.0rc13 Dec 2025pre-releaseDeprecated

…and annotations. There are many expired deprecations and bug fixes as well.

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

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()`

(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

(gh-30168)

in-place modification of `ndarray.shape` is pending deprecation

(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

(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`

(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

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

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.

(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

(gh-29129)

`StringDType` fill_value support in `numpy.ma.MaskedArray`

(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.

# 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)

Warning emitted when using `where` without `out`

(gh-29813)

DType sorting and argsorting supports the ArrayMethod API

(gh-29900)

New `__numpy_dtype__` protocol

NumPy now has a new __numpy_dtype__ protocol. NumPy will check for this attribute when converting to a NumPy dtype via np.dtype(obj) or any dtype= argument.

(gh-30179)

Improvements

Fix `flatiter` indexing edge cases

Key fixes and improvements:

Stricter index validation
- Boolean non-array indices like arr.flat[[True, True]] were incorrectly treated as arr.flat[np.array([1, 1], dtype=int)]. They now raise an index error. Note that indices that match the iterator's shape are expected to not raise in the future and be handled as regular boolean indices. Use np.asarray(<index>) if you want to match that behavior.
- Float non-array indices were also cast to integer and incorrectly treated as arr.flat[np.array([1.0, 1.0], dtype=int)]. This is now deprecated and will be removed in a future version.
- 0-dimensional boolean indices like arr.flat[True] are also deprecated and will be removed in a future version.
Consistent error types:

Certain invalid flatiter indices that previously raised ValueError now correctly raise IndexError, aligning with ndarray behavior.
Improved error messages:

The error message for unsupported index operations now provides more specific details, including explicitly listing the valid index types, instead of the generic IndexError: unsupported index operation.

(gh-28590)

Improved error handling in `np.quantile`

[np.quantile]{.title-ref} now raises errors if:

All weights are zero
At least one weight is np.nan
At least one weight is np.inf

(gh-28595)

Improved error message for `assert_array_compare`

(gh-29112)

Show unit information in `repr` for `datetime64("NaT")`

When a datetime64 object is "Not a Time" (NaT), its __repr__ method now includes the time unit of the datetime64 type. This makes it consistent with the behavior of a timedelta64 object.

(gh-29396)

Performance increase for scalar calculations

(gh-29819)

`numpy.finfo` Refactor

Constants like eps, min, max, smallest_normal, and smallest_subnormal now come directly from standard C macros (FLT_EPSILON, DBL_MIN, etc.), ensuring platform-correct values.
The deprecated MachAr runtime discovery mechanism has been removed.
Derived attributes have been corrected to match standard definitions: machep and negep now use int(log2(eps)); nexp accounts for all exponent patterns; nmant excludes the implicit bit; and minexp follows the C standard definition.
longdouble constants, Specifically smallest_normal now follows the C standard definitions as per respecitive platform.
Special handling added for PowerPC's IBM double-double format.
New test suite added in test_finfo.py to validate all finfo properties against expected machine arithmetic values for float16, float32, and float64 types.

(gh-29836)

Multiple axes are now supported in `numpy.trim_zeros`

(gh-29947)

Runtime signature introspection support has been significantly improved

(gh-30208)

Performance improvements and changes

Performance improvements to `np.unique` for string dtypes

(gh-28767)

Rewrite of `np.ndindex` using `itertools.product`

(gh-29165)

Performance improvements to `np.unique` for complex dtypes

The hash-based algorithm for unique extraction now also supports complex dtypes, offering noticeable performance gains.

(gh-29537)

Changes

Multiplication between a string and integer now raises OverflowError instead of MemoryError if the result of the multiplication would create a string that is too large to be represented. This follows Python's behavior.

(gh-29060)
The accuracy of np.quantile and np.percentile for 16- and 32-bit floating point input data has been improved.

(gh-29105)

`unique_values` for string dtypes may return unsorted data

(gh-28767)

Modulate dispatched x86 CPU features

NumPy has reorganized x86 CPU features into microarchitecture-based groups instead of individual features, aligning with Linux distribution standards and Google Highway requirements.

Key changes:

Replaced individual x86 features with microarchitecture levels: X86_V2, X86_V3, and X86_V4
Raised the baseline to X86_V2
Improved - operator behavior to properly exclude successor features that imply the excluded feature
Added meson redirections for removed feature names to maintain backward compatibility
Removed compiler compatibility workarounds for partial feature support (e.g., AVX512 without mask operations)
Removed legacy AMD features (XOP, FMA4) and discontinued Intel Xeon Phi support

New Feature Group Hierarchy:

Name Implies Includes

These groups correspond to CPU generations:

X86_V2: x86-64-v2 microarchitectures (CPUs since 2009)
X86_V3: x86-64-v3 microarchitectures (CPUs since 2015)
X86_V4: x86-64-v4 microarchitectures (AVX-512 capable CPUs)
AVX512_ICL: Intel Ice Lake and similar CPUs
AVX512_SPR: Intel Sapphire Rapids and newer CPUs

On 32-bit x86, cx16 is excluded from X86_V2.

Documentation has been updated with details on using these new feature groups with the current meson build system.

(gh-28896)

Fix bug in `matmul` for non-contiguous out kwarg parameter

In some cases, if out was non-contiguous, np.matmul would cause memory corruption or a c-level assert. This was new to v2.3.0 and fixed in v2.3.1.

(gh-29179)

`__array_interface__` with NULL pointer changed

(gh-29338)

`unique_values` for complex dtypes may return unsorted data

(gh-29537)

Sorting `kind='heapsort'` now maps to `kind='quicksort'`

(gh-29642)

`numpy.typing.DTypeLike` no longer accepts `None`

The type alias numpy.typing.DTypeLike no longer accepts None. Instead of

dtype: DTypeLike = None

it should now be

dtype: DTypeLike | None = None

instead.

(gh-29739)

(gh-29750)

Open source →

2.3.224 Jul 2025

The NumPy 2.3.2 release is a patch release with a number of bug fixes and maintenance updates. The highlights are:

NumPy 2.3.2 Release Notes

The NumPy 2.3.2 release is a patch release with a number of bug fixes and maintenance updates. The highlights are:

Wheels for Python 3.14.0rc1
PyPy updated to the latest stable release
OpenBLAS updated to 0.3.30

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.

!DWesl
Charles Harris
Joren Hammudoglu
Maanas Arora
Marco Edward Gorelli
Matti Picus
Nathan Goldbaum
Sebastian Berg
kostayScr +

Pull requests merged

A total of 16 pull requests were merged for this release.

#29256: MAINT: Prepare 2.3.x for further development
#29283: TYP: Work around a mypy issue with bool arrays (#29248)
#29284: BUG: fix fencepost error in StringDType internals
#29287: BUG: handle case in mapiter where descriptors might get replaced...
#29350: BUG: Fix shape error path in array-interface
#29412: BUG: Allow reading non-npy files in npz and add test
#29413: TST: Avoid uninitialized values in test (#29341)
#29414: BUG: Fix reference leakage for output arrays in reduction functions
#29415: BUG: fix casting issue in center, ljust, rjust, and zfill (#29369)
#29416: TYP: Fix overloads in np.char.array and np.char.asarray...
#29417: BUG: Any dtype should call square on arr \*\* 2 (#29392)
#29424: MAINT: use a stable pypy release in CI
#29425: MAINT: Support python 314rc1
#29429: MAINT: Update highway to match main.
#29430: BLD: use github to build macos-arm64 wheels with OpenBLAS and...
#29437: BUG: fix datetime/timedelta hash memory leak (#29411)

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Open source →

2.3.07 Jun 2025Deprecated

It is unusual in the number of expired deprecations, code modernizations, and style cleanups. The latter may not be visible to users, but is important…

NumPy 2.3.0 Release Notes

The NumPy 2.3.0 release continues the work to improve free threaded Python support and annotations together with the usual set of bug fixes. It is unusual in the number of expired deprecations, code modernizations, and style cleanups. The latter may not be visible to users, but is important for code maintenance over the long term. Note that we have also upgraded from manylinux2014 to manylinux_2_28.

Users running on a Mac having an M4 cpu might see various warnings about invalid values and such. The warnings are a known problem with Accelerate. They are annoying, but otherwise harmless. Apple promises to fix them.

This release supports Python versions 3.11-3.13, Python 3.14 will be supported when it is released.

Highlights

Interactive examples in the NumPy documentation.
Building NumPy with OpenMP Parallelization.
Preliminary support for Windows on ARM.
Improved support for free threaded Python.
Improved annotations.

New functions

New function `numpy.strings.slice`

The new function numpy.strings.slice was added, which implements fast native slicing of string arrays. It supports the full slicing API including negative slice offsets and steps.

(gh-27789)

Deprecations

The numpy.typing.mypy_plugin has been deprecated in favor of platform-agnostic static type inference. Please remove numpy.typing.mypy_plugin from the plugins section of your mypy configuration. If this change results in new errors being reported, kindly open an issue.

(gh-28129)
The numpy.typing.NBitBase type has been deprecated and will be removed in a future version.

This type was previously intended to be used as a generic upper bound for type-parameters, for example:
```
import numpy as np
import numpy.typing as npt

def f[NT: npt.NBitBase](x: np.complexfloating[NT]) -> np.floating[NT]: ...
```
But in NumPy 2.2.0, float64 and complex128 were changed to concrete subtypes, causing static type-checkers to reject x: np.float64 = f(np.complex128(42j)).

So instead, the better approach is to use typing.overload:
```
import numpy as np
from typing import overload

@overload
def f(x: np.complex64) -> np.float32: ...
@overload
def f(x: np.complex128) -> np.float64: ...
@overload
def f(x: np.clongdouble) -> np.longdouble: ...
```
(gh-28884)

Expired deprecations

Remove deprecated macros like NPY_OWNDATA from Cython interfaces in favor of NPY_ARRAY_OWNDATA (deprecated since 1.7)

(gh-28254)
Remove numpy/npy_1_7_deprecated_api.h and C macros like NPY_OWNDATA in favor of NPY_ARRAY_OWNDATA (deprecated since 1.7)

(gh-28254)
Remove alias generate_divbyzero_error to npy_set_floatstatus_divbyzero and generate_overflow_error to npy_set_floatstatus_overflow (deprecated since 1.10)

(gh-28254)
Remove np.tostring (deprecated since 1.19)

(gh-28254)
Raise on np.conjugate of non-numeric types (deprecated since 1.13)

(gh-28254)
Raise when using np.bincount(...minlength=None), use 0 instead (deprecated since 1.14)

(gh-28254)
Passing shape=None to functions with a non-optional shape argument errors, use () instead (deprecated since 1.20)

(gh-28254)
Inexact matches for mode and searchside raise (deprecated since 1.20)

(gh-28254)
Setting __array_finalize__ = None errors (deprecated since 1.23)

(gh-28254)
np.fromfile and np.fromstring error on bad data, previously they would guess (deprecated since 1.18)

(gh-28254)
datetime64 and timedelta64 construction with a tuple no longer accepts an event value, either use a two-tuple of (unit, num) or a 4-tuple of (unit, num, den, 1) (deprecated since 1.14)

(gh-28254)
When constructing a dtype from a class with a dtype attribute, that attribute must be a dtype-instance rather than a thing that can be parsed as a dtype instance (deprecated in 1.19). At some point the whole construct of using a dtype attribute will be deprecated (see #25306)

(gh-28254)
Passing booleans as partition index errors (deprecated since 1.23)

(gh-28254)
Out-of-bounds indexes error even on empty arrays (deprecated since 1.20)

(gh-28254)
np.tostring has been removed, use tobytes instead (deprecated since 1.19)

(gh-28254)
Disallow make a non-writeable array writeable for arrays with a base that do not own their data (deprecated since 1.17)

(gh-28254)
concatenate() with axis=None uses same-kind casting by default, not unsafe (deprecated since 1.20)

(gh-28254)
Unpickling a scalar with object dtype errors (deprecated since 1.20)

(gh-28254)
The binary mode of fromstring now errors, use frombuffer instead (deprecated since 1.14)

(gh-28254)
Converting np.inexact or np.floating to a dtype errors (deprecated since 1.19)

(gh-28254)
Converting np.complex, np.integer, np.signedinteger, np.unsignedinteger, np.generic to a dtype errors (deprecated since 1.19)

(gh-28254)
The Python built-in round errors for complex scalars. Use np.round or scalar.round instead (deprecated since 1.19)

(gh-28254)
'np.bool' scalars can no longer be interpreted as an index (deprecated since 1.19)

(gh-28254)
Parsing an integer via a float string is no longer supported. (deprecated since 1.23) To avoid this error you can
- make sure the original data is stored as integers.
- use the converters=float keyword argument.
- Use np.loadtxt(...).astype(np.int64)
(gh-28254)
The use of a length 1 tuple for the ufunc signature errors. Use dtype or fill the tuple with None (deprecated since 1.19)

(gh-28254)
Special handling of matrix is in np.outer is removed. Convert to a ndarray via matrix.A (deprecated since 1.20)

(gh-28254)
Removed the np.compat package source code (removed in 2.0)

(gh-28961)

C API changes

NpyIter_GetTransferFlags is now available to check if the iterator needs the Python API or if casts may cause floating point errors (FPE). FPEs can for example be set when casting float64(1e300) to float32 (overflow to infinity) or a NaN to an integer (invalid value).

(gh-27883)
NpyIter now has no limit on the number of operands it supports.

(gh-28080)

New `NpyIter_GetTransferFlags` and `NpyIter_IterationNeedsAPI` change

NumPy now has the new NpyIter_GetTransferFlags function as a more precise way checking of iterator/buffering needs. I.e. whether the Python API/GIL is required or floating point errors may occur. This function is also faster if you already know your needs without buffering.

The NpyIter_IterationNeedsAPI function now performs all the checks that were previously performed at setup time. While it was never necessary to call it multiple times, doing so will now have a larger cost.

(gh-27998)

New Features

The type parameter of np.dtype now defaults to typing.Any. This way, static type-checkers will infer dtype: np.dtype as dtype: np.dtype[Any], without reporting an error.

(gh-28669)
Static type-checkers now interpret:
- _: np.ndarray as _: npt.NDArray[typing.Any].
- _: np.flatiter as _: np.flatiter[np.ndarray].
This is because their type parameters now have default values.

(gh-28940)

NumPy now registers its pkg-config paths with the pkgconf PyPI package

The pkgconf PyPI package provides an interface for projects like NumPy to register their own paths to be added to the pkg-config search path. This means that when using pkgconf from PyPI, NumPy will be discoverable without needing for any custom environment configuration.

[!NOTE] This only applies when using the pkgconf package from PyPI, or put another way, this only applies when installing pkgconf via a Python package manager.

If you are using pkg-config or pkgconf provided by your system, or any other source that does not use the pkgconf-pypi project, the NumPy pkg-config directory will not be automatically added to the search path. In these situations, you might want to use numpy-config.

(gh-28214)

Allow `out=...` in ufuncs to ensure array result

NumPy has the sometimes difficult behavior that it currently usually returns scalars rather than 0-D arrays (even if the inputs were 0-D arrays). This is especially problematic for non-numerical dtypes (e.g. object).

For ufuncs (i.e. most simple math functions) it is now possible to use out=... (literally `...`, e.g. out=Ellipsis) which is identical in behavior to out not being passed, but will ensure a non-scalar return. This spelling is borrowed from arr1d[0, ...] where the ... also ensures a non-scalar return.

Other functions with an out= kwarg should gain support eventually. Downstream libraries that interoperate via __array_ufunc__ or __array_function__ may need to adapt to support this.

(gh-28576)

Building NumPy with OpenMP Parallelization

NumPy now supports OpenMP parallel processing capabilities when built with the -Denable_openmp=true Meson build flag. This feature is disabled by default. When enabled, np.sort and np.argsort functions can utilize OpenMP for parallel thread execution, improving performance for these operations.

(gh-28619)

Interactive examples in the NumPy documentation

The NumPy documentation includes a number of examples that can now be run interactively in your browser using WebAssembly and Pyodide.

Please note that the examples are currently experimental in nature and may not work as expected for all methods in the public API.

(gh-26745)

Improvements

Scalar comparisons between non-comparable dtypes such as np.array(1) == np.array('s') now return a NumPy bool instead of a Python bool.

(gh-27288)
np.nditer now has no limit on the number of supported operands (C-integer).

(gh-28080)
No-copy pickling is now supported for any array that can be transposed to a C-contiguous array.

(gh-28105)
The __repr__ for user-defined dtypes now prefers the __name__ of the custom dtype over a more generic name constructed from its kind and itemsize.

(gh-28250)
np.dot now reports floating point exceptions.

(gh-28442)
np.dtypes.StringDType is now a generic type which accepts a type argument for na_object that defaults to typing.Never. For example, StringDType(na_object=None) returns a StringDType[None], and StringDType() returns a StringDType[typing.Never].

(gh-28856)

Added warnings to `np.isclose`

Added warning messages if at least one of atol or rtol are either np.nan or np.inf within np.isclose.

Warnings follow the user's np.seterr settings

(gh-28205)

Performance improvements and changes

Performance improvements to `np.unique`

np.unique now tries to use a hash table to find unique values instead of sorting values before finding unique values. This is limited to certain dtypes for now, and the function is now faster for those dtypes. The function now also exposes a sorted parameter to allow returning unique values as they were found, instead of sorting them afterwards.

(gh-26018)

Performance improvements to `np.sort` and `np.argsort`

np.sort and np.argsort functions now can leverage OpenMP for parallel thread execution, resulting in up to 3.5x speedups on x86 architectures with AVX2 or AVX-512 instructions. This opt-in feature requires NumPy to be built with the -Denable_openmp Meson flag. Users can control the number of threads used by setting the OMP_NUM_THREADS environment variable.

(gh-28619)

Performance improvements for `np.float16` casts

Earlier, floating point casts to and from np.float16 types were emulated in software on all platforms.

Now, on ARM devices that support Neon float16 intrinsics (such as recent Apple Silicon), the native float16 path is used to achieve the best performance.

(gh-28769)

Changes

The vector norm ord=inf and the matrix norms ord={1, 2, inf, 'nuc'} now always returns zero for empty arrays. Empty arrays have at least one axis of size zero. This affects np.linalg.norm, np.linalg.vector_norm, and np.linalg.matrix_norm. Previously, NumPy would raises errors or return zero depending on the shape of the array.

(gh-28343)
A spelling error in the error message returned when converting a string to a float with the method np.format_float_positional has been fixed.

(gh-28569)
NumPy's __array_api_version__ was upgraded from 2023.12 to 2024.12.
numpy.count_nonzero for axis=None (default) now returns a NumPy scalar instead of a Python integer.
The parameter axis in numpy.take_along_axis function has now a default value of -1.

(gh-28615)
Printing of np.float16 and np.float32 scalars and arrays have been improved by adjusting the transition to scientific notation based on the floating point precision. A new legacy np.printoptions mode '2.2' has been added for backwards compatibility.

(gh-28703)
Multiplication between a string and integer now raises OverflowError instead of MemoryError if the result of the multiplication would create a string that is too large to be represented. This follows Python's behavior.

(gh-29060)

`unique_values` may return unsorted data

The relatively new function (added in NumPy 2.0) unique_values may now return unsorted results. Just as unique_counts and unique_all these never guaranteed a sorted result, however, the result was sorted until now. In cases where these do return a sorted result, this may change in future releases to improve performance.

(gh-26018)

Changes to the main iterator and potential numerical changes

The main iterator, used in math functions and via np.nditer from Python and NpyIter in C, now behaves differently for some buffered iterations. This means that:

The buffer size used will often be smaller than the maximum buffer sized allowed by the buffersize parameter.
The "growinner" flag is now honored with buffered reductions when no operand requires buffering.

For np.sum() such changes in buffersize may slightly change numerical results of floating point operations. Users who use "growinner" for custom reductions could notice changes in precision (for example, in NumPy we removed it from einsum to avoid most precision changes and improve precision for some 64bit floating point inputs).

(gh-27883)

The minimum supported GCC version is now 9.3.0

The minimum supported version was updated from 8.4.0 to 9.3.0, primarily in order to reduce the chance of platform-specific bugs in old GCC versions from causing issues.

(gh-28102)

Changes to automatic bin selection in numpy.histogram

The automatic bin selection algorithm in numpy.histogram has been modified to avoid out-of-memory errors for samples with low variation. For full control over the selected bins the user can use set the bin or range parameters of numpy.histogram.

(gh-28426)

Build manylinux_2_28 wheels

Wheels for linux systems will use the manylinux_2_28 tag (instead of the manylinux2014 tag), which means dropping support for redhat7/centos7, amazonlinux2, debian9, ubuntu18.04, and other pre-glibc2.28 operating system versions, as per the PEP 600 support table.

(gh-28436)

Remove use of -Wl,-ld_classic on macOS

Remove use of -Wl,-ld_classic on macOS. This hack is no longer needed by Spack, and results in libraries that cannot link to other libraries built with ld (new).

(gh-28713)

Re-enable overriding functions in the `numpy.strings`

Re-enable overriding functions in the numpy.strings module.

(gh-28741)

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Open source →

2.2.617 May 2025

NumPy 2.2.6 is a patch release that fixes bugs found after the 2.2.5 release. It is a mix of typing fixes/improvements as well as the normal bug fixes

NumPy 2.2.6 Release Notes

NumPy 2.2.6 is a patch release that fixes bugs found after the 2.2.5 release. It is a mix of typing fixes/improvements as well as the normal bug fixes and some CI maintenance.

This release supports Python versions 3.10-3.13.

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
Ilhan Polat
Joren Hammudoglu
Marco Gorelli +
Matti Picus
Nathan Goldbaum
Peter Hawkins
Sayed Adel

Pull requests merged

A total of 11 pull requests were merged for this release.

#28778: MAINT: Prepare 2.2.x for further development
#28851: BLD: Update vendor-meson to fix module_feature conflicts arguments...
#28852: BUG: fix heap buffer overflow in np.strings.find
#28853: TYP: fix NDArray[floating] + float return type
#28864: BUG: fix stringdtype singleton thread safety
#28865: MAINT: use OpenBLAS 0.3.29
#28889: MAINT: from_dlpack thread safety fixes
#28913: TYP: Fix non-existent CanIndex annotation in ndarray.setfield
#28915: MAINT: Avoid dereferencing/strict aliasing warnings
#28916: BUG: Fix missing check for PyErr_Occurred() in _pyarray_correlate.
#28966: TYP: reject complex scalar types in ndarray.__ifloordiv__

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Open source →

2.2.519 Apr 2025

NumPy 2.2.5 is a patch release that fixes bugs found after the 2.2.4 release. It has a large number of typing fixes/improvements as well as the normal

NumPy 2.2.5 Release Notes

NumPy 2.2.5 is a patch release that fixes bugs found after the 2.2.4 release. It has a large number of typing fixes/improvements as well as the normal bug fixes and some CI maintenance.

This release supports Python versions 3.10-3.13.

Contributors

A total of 7 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Charles Harris
Joren Hammudoglu
Baskar Gopinath +
Nathan Goldbaum
Nicholas Christensen +
Sayed Adel
karl +

Pull requests merged

A total of 19 pull requests were merged for this release.

#28545: MAINT: Prepare 2.2.x for further development
#28582: BUG: Fix return type of NpyIter_GetIterNext in Cython declarations
#28583: BUG: avoid deadlocks with C++ shared mutex in dispatch cache
#28585: TYP: fix typing errors in _core.strings
#28631: MAINT, CI: Update Ubuntu to 22.04 in azure-pipelines
#28632: BUG: Set writeable flag for writeable dlpacks.
#28633: BUG: Fix crackfortran parsing error when a division occurs within...
#28650: TYP: fix ndarray.tolist() and .item() for unknown dtype
#28654: BUG: fix deepcopying StringDType arrays (#28643)
#28661: TYP: Accept objects that write() to str in savetxt
#28663: CI: Replace QEMU armhf with native (32-bit compatibility mode)
#28682: SIMD: Resolve Highway QSort symbol linking error on aarch32/ASIMD
#28683: TYP: add missing "b1" literals for dtype[bool]
#28705: TYP: Fix false rejection of NDArray[object_].__abs__()
#28706: TYP: Fix inconsistent NDArray[float64].__[r]truediv__ return...
#28723: TYP: fix string-like ndarray rich comparison operators
#28758: TYP: some [arg]partition fixes
#28772: TYP: fix incorrect random.Generator.integers return type
#28774: TYP: fix count_nonzero signature

Checksums

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Open source →

2.2.416 Mar 2025

NumPy 2.2.4 is a patch release that fixes bugs found after the 2.2.3 release. There are a large number of typing improvements, the rest of the changes

NumPy 2.2.4 Release Notes

NumPy 2.2.4 is a patch release that fixes bugs found after the 2.2.3 release. There are a large number of typing improvements, the rest of the changes are the usual mix of bugfixes and platform maintenace.

This release supports Python versions 3.10-3.13.

Contributors

A total of 15 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Abhishek Kumar
Andrej Zhilenkov
Andrew Nelson
Charles Harris
Giovanni Del Monte
Guan Ming(Wesley) Chiu +
Jonathan Albrecht +
Joren Hammudoglu
Mark Harfouche
Matthieu Darbois
Nathan Goldbaum
Pieter Eendebak
Sebastian Berg
Tyler Reddy
lvllvl +

Pull requests merged

A total of 17 pull requests were merged for this release.

#28333: MAINT: Prepare 2.2.x for further development.
#28348: TYP: fix positional- and keyword-only params in astype, cross...
#28377: MAINT: Update FreeBSD version and fix test failure
#28379: BUG: numpy.loadtxt reads only 50000 lines when skip_rows >= max_rows
#28385: BUG: Make np.nonzero threading safe
#28420: BUG: safer bincount casting (backport to 2.2.x)
#28422: BUG: Fix building on s390x with clang
#28423: CI: use QEMU 9.2.2 for Linux Qemu tests
#28424: BUG: skip legacy dtype multithreaded test on 32 bit runners
#28435: BUG: Fix searchsorted and CheckFromAny byte-swapping logic
#28449: BUG: sanity check __array_interface__ number of dimensions
#28510: MAINT: Hide decorator from pytest traceback
#28512: TYP: Typing fixes backported from #28452, #28491, #28494
#28521: TYP: Backport fixes from #28505, #28506, #28508, and #28511
#28533: TYP: Backport typing fixes from main (2)
#28534: TYP: Backport typing fixes from main (3)
#28542: TYP: Backport typing fixes from main (4)

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Open source →

2.2.313 Feb 2025

NumPy 2.2.3 is a patch release that fixes bugs found after the 2.2.2 release. The majority of the changes are typing improvements and fixes for free t

NumPy 2.2.3 Release Notes

NumPy 2.2.3 is a patch release that fixes bugs found after the 2.2.2 release. The majority of the changes are typing improvements and fixes for free threaded Python. Both of those areas are still under development, so if you discover new problems, please report them.

This release supports Python versions 3.10-3.13.

Contributors

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

!amotzop
Charles Harris
Chris Sidebottom
Joren Hammudoglu
Matthew Brett
Nathan Goldbaum
Raghuveer Devulapalli
Sebastian Berg
Yakov Danishevsky +

Pull requests merged

A total of 21 pull requests were merged for this release.

#28185: MAINT: Prepare 2.2.x for further development
#28201: BUG: fix data race in a more minimal way on stable branch
#28208: BUG: Fix from_float_positional errors for huge pads
#28209: BUG: fix data race in np.repeat
#28212: MAINT: Use VQSORT_COMPILER_COMPATIBLE to determine if we should...
#28224: MAINT: update highway to latest
#28236: BUG: Add cpp atomic support (#28234)
#28237: BLD: Compile fix for clang-cl on WoA
#28243: TYP: Avoid upcasting float64 in the set-ops
#28249: BLD: better fix for clang / ARM compiles
#28266: TYP: Fix timedelta64.__divmod__ and timedelta64.__mod__...
#28274: TYP: Fixed missing typing information of set_printoptions
#28278: BUG: backport resource cleanup bugfix from gh-28273
#28282: BUG: fix incorrect bytes to stringdtype coercion
#28283: TYP: Fix scalar constructors
#28284: TYP: stub numpy.matlib
#28285: TYP: stub the missing numpy.testing modules
#28286: CI: Fix the github label for TYP: PR's and issues
#28305: TYP: Backport typing updates from main
#28321: BUG: fix race initializing legacy dtype casts
#28324: CI: update test_moderately_small_alpha

Checksums

MD5

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Open source →

2.2.218 Jan 2025

NumPy 2.2.2 is a patch release that fixes bugs found after the 2.2.1 release. The number of typing fixes/updates is notable. This release supports Pyt

NumPy 2.2.2 Release Notes

NumPy 2.2.2 is a patch release that fixes bugs found after the 2.2.1 release. The number of typing fixes/updates is notable. This release supports Python versions 3.10-3.13.

Contributors

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

Alicia Boya García +
Charles Harris
Joren Hammudoglu
Kai Germaschewski +
Nathan Goldbaum
PTUsumit +
Rohit Goswami
Sebastian Berg

Pull requests merged

A total of 16 pull requests were merged for this release.

#28050: MAINT: Prepare 2.2.x for further development
#28055: TYP: fix void arrays not accepting str keys in __setitem__
#28066: TYP: fix unnecessarily broad integer binop return types (#28065)
#28112: TYP: Better ndarray binop return types for float64 &...
#28113: TYP: Return the correct bool from issubdtype
#28114: TYP: Always accept date[time] in the datetime64 constructor
#28120: BUG: Fix auxdata initialization in ufunc slow path
#28131: BUG: move reduction initialization to ufunc initialization
#28132: TYP: Fix interp to accept and return scalars
#28137: BUG: call PyType_Ready in f2py to avoid data races
#28145: BUG: remove unnecessary call to PyArray_UpdateFlags
#28160: BUG: Avoid data race in PyArray_CheckFromAny_int
#28175: BUG: Fix f2py directives and --lower casing
#28176: TYP: Fix overlapping overloads issue in 2->1 ufuncs
#28177: TYP: preserve shape-type in ndarray.astype()
#28178: TYP: Fix missing and spurious top-level exports

Checksums

MD5

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SHA256

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Open source →

2.2.121 Dec 2024

NumPy 2.2.1 is a patch release following 2.2.0. It fixes bugs found after the 2.2.0 release and has several maintenance pins to work around upstream c

NumPy 2.2.1 Release Notes

NumPy 2.2.1 is a patch release following 2.2.0. It fixes bugs found after the 2.2.0 release and has several maintenance pins to work around upstream changes.

There was some breakage in downstream projects following the 2.2.0 release due to updates to NumPy typing. Because of problems due to MyPy defects, we recommend using basedpyright for type checking, it can be installed from PyPI. The Pylance extension for Visual Studio Code is also based on Pyright. Problems that persist when using basedpyright should be reported as issues on the NumPy github site.

This release supports Python 3.10-3.13.

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
Joren Hammudoglu
Matti Picus
Nathan Goldbaum
Peter Hawkins
Simon Altrogge
Thomas A Caswell
Warren Weckesser
Yang Wang +

Pull requests merged

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

#27935: MAINT: Prepare 2.2.x for further development
#27950: TEST: cleanups
#27958: BUG: fix use-after-free error in npy_hashtable.cpp (#27955)
#27959: BLD: add missing include
#27982: BUG:fix compile error libatomic link test to meson.build
#27990: TYP: Fix falsely rejected value types in ndarray.__setitem__
#27991: MAINT: Don't wrap #include <Python.h> with extern "C"
#27993: BUG: Fix segfault in stringdtype lexsort
#28006: MAINT: random: Tweak module code in mtrand.pyx to fix a Cython...
#28007: BUG: Cython API was missing NPY_UINTP.
#28021: CI: pin scipy-doctest to 1.5.1
#28044: TYP: allow None in operand sequence of nditer

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Open source →

2.2.08 Dec 2024Deprecated

_add_newdoc_ufunc is now deprecated. ufunc.__doc__ = newdoc should be used instead.

NumPy 2.2.0 Release Notes

The NumPy 2.2.0 release is quick release that brings us back into sync with the usual twice yearly release cycle. There have been an number of small cleanups, as well as work bringing the new StringDType to completion and improving support for free threaded Python. Highlights are:

New functions matvec and vecmat, see below.
Many improved annotations.
Improved support for the new StringDType.
Improved support for free threaded Python
Fixes for f2py

This release supports Python versions 3.10-3.13.

Deprecations

_add_newdoc_ufunc is now deprecated. ufunc.__doc__ = newdoc should be used instead.

(gh-27735)

Expired deprecations

bool(np.array([])) and other empty arrays will now raise an error. Use arr.size > 0 instead to check whether an array has no elements.

(gh-27160)

Compatibility notes

numpy.cov now properly transposes single-row (2d array) design matrices when rowvar=False. Previously, single-row design matrices would return a scalar in this scenario, which is not correct, so this is a behavior change and an array of the appropriate shape will now be returned.

(gh-27661)

New Features

New functions for matrix-vector and vector-matrix products

Two new generalized ufuncs were defined:
- numpy.matvec - matrix-vector product, treating the arguments as stacks of matrices and column vectors, respectively.
- numpy.vecmat - vector-matrix product, treating the arguments as stacks of column vectors and matrices, respectively. For complex vectors, the conjugate is taken.
These add to the existing numpy.matmul as well as to numpy.vecdot, which was added in numpy 2.0.

Note that numpy.matmul never takes a complex conjugate, also not when its left input is a vector, while both numpy.vecdot and numpy.vecmat do take the conjugate for complex vectors on the left-hand side (which are taken to be the ones that are transposed, following the physics convention).

(gh-25675)
np.complexfloating[T, T] can now also be written as np.complexfloating[T]

(gh-27420)
UFuncs now support __dict__ attribute and allow overriding __doc__ (either directly or via ufunc.__dict__["__doc__"]). __dict__ can be used to also override other properties, such as __module__ or __qualname__.

(gh-27735)
The "nbit" type parameter of np.number and its subtypes now defaults to typing.Any. This way, type-checkers will infer annotations such as x: np.floating as x: np.floating[Any], even in strict mode.

(gh-27736)

Improvements

The datetime64 and timedelta64 hashes now correctly match the Pythons builtin datetime and timedelta ones. The hashes now evaluated equal even for equal values with different time units.

(gh-14622)
Fixed a number of issues around promotion for string ufuncs with StringDType arguments. Mixing StringDType and the fixed-width DTypes using the string ufuncs should now generate much more uniform results.

(gh-27636)
Improved support for empty memmap. Previously an empty memmap would fail unless a non-zero offset was set. Now a zero-size memmap is supported even if offset=0. To achieve this, if a memmap is mapped to an empty file that file is padded with a single byte.

(gh-27723)
A regression has been fixed which allows F2PY users to expose variables to Python in modules with only assignments, and also fixes situations where multiple modules are present within a single source file.

(gh-27695)

Performance improvements and changes

Improved multithreaded scaling on the free-threaded build when many threads simultaneously call the same ufunc operations.

(gh-27896)
NumPy now uses fast-on-failure attribute lookups for protocols. This can greatly reduce overheads of function calls or array creation especially with custom Python objects. The largest improvements will be seen on Python 3.12 or newer.

(gh-27119)
OpenBLAS on x86_64 and i686 is built with fewer kernels. Based on benchmarking, there are 5 clusters of performance around these kernels: PRESCOTT NEHALEM SANDYBRIDGE HASWELL SKYLAKEX.
OpenBLAS on windows is linked without quadmath, simplifying licensing
Due to a regression in OpenBLAS on windows, the performance improvements when using multiple threads for OpenBLAS 0.3.26 were reverted.

(gh-27147)
NumPy now indicates hugepages also for large np.zeros allocations on linux. Thus should generally improve performance.

(gh-27808)

Changes

numpy.fix now won't perform casting to a floating data-type for integer and boolean data-type input arrays.

(gh-26766)
The type annotations of numpy.float64 and numpy.complex128 now reflect that they are also subtypes of the built-in float and complex types, respectively. This update prevents static type-checkers from reporting errors in cases such as:
```
x: float = numpy.float64(6.28)  # valid
z: complex = numpy.complex128(-1j)  # valid
```
(gh-27334)
The repr of arrays large enough to be summarized (i.e., where elements are replaced with ...) now includes the shape of the array, similar to what already was the case for arrays with zero size and non-obvious shape. With this change, the shape is always given when it cannot be inferred from the values. Note that while written as shape=..., this argument cannot actually be passed in to the np.array constructor. If you encounter problems, e.g., due to failing doctests, you can use the print option legacy=2.1 to get the old behaviour.

(gh-27482)
Calling __array_wrap__ directly on NumPy arrays or scalars now does the right thing when return_scalar is passed (Added in NumPy 2). It is further safe now to call the scalar __array_wrap__ on a non-scalar result.

(gh-27807)
Bump the musllinux CI image and wheels to 1_2 from 1_1. This is because 1_1 is end of life.

(gh-27088)
The NEP 50 promotion state settings are now removed. They were always meant as temporary means for testing. A warning will be given if the environment variable is set to anything but NPY_PROMOTION_STATE=weak while _set_promotion_state and _get_promotion_state are removed. In case code used _no_nep50_warning, a contextlib.nullcontext could be used to replace it when not available.

(gh-27156)

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Open source →

2.1.32 Nov 2024

NumPy 2.1.3 is a maintenance release that fixes bugs and regressions discovered after the 2.1.2 release. This release also adds support for free threa

NumPy 2.1.3 Release Notes

NumPy 2.1.3 is a maintenance release that fixes bugs and regressions discovered after the 2.1.2 release. This release also adds support for free threaded Python 3.13 on Windows.

The Python versions supported by this release are 3.10-3.13.

Improvements

Fixed a number of issues around promotion for string ufuncs with StringDType arguments. Mixing StringDType and the fixed-width DTypes using the string ufuncs should now generate much more uniform results.

(gh-27636)

Changes

numpy.fix now won't perform casting to a floating data-type for integer and boolean data-type input arrays.

(gh-26766)

Contributors

A total of 15 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Abhishek Kumar +
Austin +
Benjamin A. Beasley +
Charles Harris
Christian Lorentzen
Marcel Telka +
Matti Picus
Michael Davidsaver +
Nathan Goldbaum
Peter Hawkins
Raghuveer Devulapalli
Ralf Gommers
Sebastian Berg
dependabot[bot]
kp2pml30 +

Pull requests merged

A total of 21 pull requests were merged for this release.

#27512: MAINT: prepare 2.1.x for further development
#27537: MAINT: Bump actions/cache from 4.0.2 to 4.1.1
#27538: MAINT: Bump pypa/cibuildwheel from 2.21.2 to 2.21.3
#27539: MAINT: MSVC does not support #warning directive
#27543: BUG: Fix user dtype can-cast with python scalar during promotion
#27561: DEV: bump python to 3.12 in environment.yml
#27562: BLD: update vendored Meson to 1.5.2
#27563: BUG: weighted quantile for some zero weights (#27549)
#27565: MAINT: Use miniforge for macos conda test.
#27566: BUILD: satisfy gcc-13 pendantic errors
#27569: BUG: handle possible error for PyTraceMallocTrack
#27570: BLD: start building Windows free-threaded wheels [wheel build]
#27571: BUILD: vendor tempita from Cython
#27574: BUG: Fix warning "differs in levels of indirection" in npy_atomic.h...
#27592: MAINT: Update Highway to latest
#27593: BUG: Adjust numpy.i for SWIG 4.3 compatibility
#27616: BUG: Fix Linux QEMU CI workflow
#27668: BLD: Do not set __STDC_VERSION__ to zero during build
#27669: ENH: fix wasm32 runtime type error in numpy._core
#27672: BUG: Fix a reference count leak in npy_find_descr_for_scalar.
#27673: BUG: fixes for StringDType/unicode promoters

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Open source →

2.1.018 Aug 2024Deprecated

The fix_imports keyword argument in numpy.save is deprecated. Since NumPy 1.17, numpy.save uses a pickle protocol that no longer supports Python 2, an…

NumPy 2.1.0 Release Notes

NumPy 2.1.0 provides support for the upcoming Python 3.13 release and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get us back into our usual release cycle after the extended development of 2.0. The highlights for this release are:

Support for the array-api 2023.12 standard.
Support for Python 3.13.
Preliminary support for free threaded Python 3.13.

Python versions 3.10-3.13 are supported in this release.

New functions

New function `numpy.unstack`

A new function np.unstack(array, axis=...) was added, which splits an array into a tuple of arrays along an axis. It serves as the inverse of [numpy.stack]{.title-ref}.

(gh-26579)

Deprecations

The fix_imports keyword argument in numpy.save is deprecated. Since NumPy 1.17, numpy.save uses a pickle protocol that no longer supports Python 2, and ignored fix_imports keyword. This keyword is kept only for backward compatibility. It is now deprecated.

(gh-26452)
Passing non-integer inputs as the first argument of [bincount]{.title-ref} is now deprecated, because such inputs are silently cast to integers with no warning about loss of precision.

(gh-27076)

Expired deprecations

Scalars and 0D arrays are disallowed for numpy.nonzero and numpy.ndarray.nonzero.

(gh-26268)
set_string_function internal function was removed and PyArray_SetStringFunction was stubbed out.

(gh-26611)

C API changes

API symbols now hidden but customizable

NumPy now defaults to hide the API symbols it adds to allow all NumPy API usage. This means that by default you cannot dynamically fetch the NumPy API from another library (this was never possible on windows).

If you are experiencing linking errors related to PyArray_API or PyArray_RUNTIME_VERSION, you can define the NPY_API_SYMBOL_ATTRIBUTE to opt-out of this change.

If you are experiencing problems due to an upstream header including NumPy, the solution is to make sure you #include "numpy/ndarrayobject.h" before their header and import NumPy yourself based on including-the-c-api.

(gh-26103)

Many shims removed from npy_3kcompat.h

Many of the old shims and helper functions were removed from npy_3kcompat.h. If you find yourself in need of these, vendor the previous version of the file into your codebase.

(gh-26842)

New `PyUFuncObject` field `process_core_dims_func`

The field process_core_dims_func was added to the structure PyUFuncObject. For generalized ufuncs, this field can be set to a function of type PyUFunc_ProcessCoreDimsFunc that will be called when the ufunc is called. It allows the ufunc author to check that core dimensions satisfy additional constraints, and to set output core dimension sizes if they have not been provided.

(gh-26908)

New Features

Preliminary Support for Free-Threaded CPython 3.13

CPython 3.13 will be available as an experimental free-threaded build. See https://py-free-threading.github.io, PEP 703 and the CPython 3.13 release notes for more detail about free-threaded Python.

NumPy 2.1 has preliminary support for the free-threaded build of CPython 3.13. This support was enabled by fixing a number of C thread-safety issues in NumPy. Before NumPy 2.1, NumPy used a large number of C global static variables to store runtime caches and other state. We have either refactored to avoid the need for global state, converted the global state to thread-local state, or added locking.

Support for free-threaded Python does not mean that NumPy is thread safe. Read-only shared access to ndarray should be safe. NumPy exposes shared mutable state and we have not added any locking to the array object itself to serialize access to shared state. Care must be taken in user code to avoid races if you would like to mutate the same array in multiple threads. It is certainly possible to crash NumPy by mutating an array simultaneously in multiple threads, for example by calling a ufunc and the resize method simultaneously. For now our guidance is: "don't do that". In the future we would like to provide stronger guarantees.

Object arrays in particular need special care, since the GIL previously provided locking for object array access and no longer does. See Issue #27199 for more information about object arrays in the free-threaded build.

If you are interested in free-threaded Python, for example because you have a multiprocessing-based workflow that you are interested in running with Python threads, we encourage testing and experimentation.

If you run into problems that you suspect are because of NumPy, please open an issue, checking first if the bug also occurs in the "regular" non-free-threaded CPython 3.13 build. Many threading bugs can also occur in code that releases the GIL; disabling the GIL only makes it easier to hit threading bugs.

(gh-26157)

`f2py` can generate freethreading-compatible C extensions

Pass --freethreading-compatible to the f2py CLI tool to produce a C extension marked as compatible with the free threading CPython interpreter. Doing so prevents the interpreter from re-enabling the GIL at runtime when it imports the C extension. Note that f2py does not analyze fortran code for thread safety, so you must verify that the wrapped fortran code is thread safe before marking the extension as compatible.

(gh-26981)

numpy.reshape and numpy.ndarray.reshape now support shape and copy arguments.

(gh-26292)
NumPy now supports DLPack v1, support for older versions will be deprecated in the future.

(gh-26501)
numpy.asanyarray now supports copy and device arguments, matching numpy.asarray.

(gh-26580)
numpy.printoptions, numpy.get_printoptions, and numpy.set_printoptions now support a new option, override_repr, for defining custom repr(array) behavior.

(gh-26611)
numpy.cumulative_sum and numpy.cumulative_prod were added as Array API compatible alternatives for numpy.cumsum and numpy.cumprod. The new functions can include a fixed initial (zeros for sum and ones for prod) in the result.

(gh-26724)
numpy.clip now supports max and min keyword arguments which are meant to replace a_min and a_max. Also, for np.clip(a) or np.clip(a, None, None) a copy of the input array will be returned instead of raising an error.

(gh-26724)
numpy.astype now supports device argument.

(gh-26724)

Improvements

`histogram` auto-binning now returns bin sizes >=1 for integer input data

For integer input data, bin sizes smaller than 1 result in spurious empty bins. This is now avoided when the number of bins is computed using one of the algorithms provided by histogram_bin_edges.

(gh-12150)

`ndarray` shape-type parameter is now covariant and bound to `tuple[int, ...]`

Static typing for ndarray is a long-term effort that continues with this change. It is a generic type with type parameters for the shape and the data type. Previously, the shape type parameter could be any value. This change restricts it to a tuple of ints, as one would expect from using ndarray.shape. Further, the shape-type parameter has been changed from invariant to covariant. This change also applies to the subtypes of ndarray, e.g. numpy.ma.MaskedArray. See the typing docs for more information.

(gh-26081)

`np.quantile` with method `closest_observation` chooses nearest even order statistic

This changes the definition of nearest for border cases from the nearest odd order statistic to nearest even order statistic. The numpy implementation now matches other reference implementations.

(gh-26656)

`lapack_lite` is now thread safe

NumPy provides a minimal low-performance version of LAPACK named lapack_lite that can be used if no BLAS/LAPACK system is detected at build time.

Until now, lapack_lite was not thread safe. Single-threaded use cases did not hit any issues, but running linear algebra operations in multiple threads could lead to errors, incorrect results, or segfaults due to data races.

We have added a global lock, serializing access to lapack_lite in multiple threads.

(gh-26750)

The `numpy.printoptions` context manager is now thread and async-safe

In prior versions of NumPy, the printoptions were defined using a combination of Python and C global variables. We have refactored so the state is stored in a python ContextVar, making the context manager thread and async-safe.

(gh-26846)

Type hinting `numpy.polynomial`

Starting from the 2.1 release, PEP 484 type annotations have been included for the functions and convenience classes in numpy.polynomial and its sub-packages.

(gh-26897)

Improved `numpy.dtypes` type hints

The type annotations for numpy.dtypes are now a better reflection of the runtime: The numpy.dtype type-aliases have been replaced with specialized dtype subtypes, and the previously missing annotations for numpy.dtypes.StringDType have been added.

(gh-27008)

Performance improvements and changes

numpy.save now uses pickle protocol version 4 for saving arrays with object dtype, which allows for pickle objects larger than 4GB and improves saving speed by about 5% for large arrays.

(gh-26388)
OpenBLAS on x86_64 and i686 is built with fewer kernels. Based on benchmarking, there are 5 clusters of performance around these kernels: PRESCOTT NEHALEM SANDYBRIDGE HASWELL SKYLAKEX.

(gh-27147)
OpenBLAS on windows is linked without quadmath, simplifying licensing

(gh-27147)
Due to a regression in OpenBLAS on windows, the performance improvements when using multiple threads for OpenBLAS 0.3.26 were reverted.

(gh-27147)

`ma.cov` and `ma.corrcoef` are now significantly faster

The private function has been refactored along with ma.cov and ma.corrcoef. They are now significantly faster, particularly on large, masked arrays.

(gh-26285)

Changes

As numpy.vecdot is now a ufunc it has a less precise signature. This is due to the limitations of ufunc's typing stub.

(gh-26313)
numpy.floor, numpy.ceil, and numpy.trunc now won't perform casting to a floating dtype for integer and boolean dtype input arrays.

(gh-26766)

`ma.corrcoef` may return a slightly different result

A pairwise observation approach is currently used in ma.corrcoef to calculate the standard deviations for each pair of variables. This has been changed as it is being used to normalise the covariance, estimated using ma.cov, which does not consider the observations for each variable in a pairwise manner, rendering it unnecessary. The normalisation has been replaced by the more appropriate standard deviation for each variable, which significantly reduces the wall time, but will return slightly different estimates of the correlation coefficients in cases where the observations between a pair of variables are not aligned. However, it will return the same estimates in all other cases, including returning the same correlation matrix as corrcoef when using a masked array with no masked values.

(gh-26285)

Cast-safety fixes in `copyto` and `full`

copyto now uses NEP 50 correctly and applies this to its cast safety. Python integer to NumPy integer casts and Python float to NumPy float casts are now considered "safe" even if assignment may fail or precision may be lost. This means the following examples change slightly:

np.copyto(int8_arr, 1000) previously performed an unsafe/same-kind cast of the Python integer. It will now always raise, to achieve an unsafe cast you must pass an array or NumPy scalar.
np.copyto(uint8_arr, 1000, casting="safe") will raise an OverflowError rather than a TypeError due to same-kind casting.
np.copyto(float32_arr, 1e300, casting="safe") will overflow to inf (float32 cannot hold 1e300) rather raising a TypeError.

Further, only the dtype is used when assigning NumPy scalars (or 0-d arrays), meaning that the following behaves differently:

np.copyto(float32_arr, np.float64(3.0), casting="safe") raises.
np.coptyo(int8_arr, np.int64(100), casting="safe") raises. Previously, NumPy checked whether the 100 fits the int8_arr.

This aligns copyto, full, and full_like with the correct NumPy 2 behavior.

(gh-27091)

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Open source →

2.0.121 Jul 2024

NumPy 2.0.1 is a maintenance release that fixes bugs and regressions discovered after the 2.0.0 release. NumPy 2.0.1 is the last planned release in th

NumPy 2.0.1 Release Notes

NumPy 2.0.1 is a maintenance release that fixes bugs and regressions discovered after the 2.0.0 release. NumPy 2.0.1 is the last planned release in the 2.0.x series, 2.1.0rc1 should be out shortly.

The Python versions supported by this release are 3.9-3.12.

NOTE: Do not use the GitHub generated "Source code" files listed in the "Assets", they are garbage.

Improvements

`np.quantile` with method `closest_observation` chooses nearest even order statistic

This changes the definition of nearest for border cases from the nearest odd order statistic to nearest even order statistic. The numpy implementation now matches other reference implementations.

(gh-26656)

Contributors

A total of 15 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

@vahidmech +
Alex Herbert +
Charles Harris
Giovanni Del Monte +
Leo Singer
Lysandros Nikolaou
Matti Picus
Nathan Goldbaum
Patrick J. Roddy +
Raghuveer Devulapalli
Ralf Gommers
Rostan Tabet +
Sebastian Berg
Tyler Reddy
Yannik Wicke +

Pull requests merged

A total of 24 pull requests were merged for this release.

#26711: MAINT: prepare 2.0.x for further development
#26792: TYP: fix incorrect import in ma/extras.pyi stub
#26793: DOC: Mention '1.25' legacy printing mode in set_printoptions
#26794: DOC: Remove mention of NaN and NAN aliases from constants
#26821: BLD: Fix x86-simd-sort build failure on openBSD
#26822: BUG: Ensure output order follows input in numpy.fft
#26823: TYP: fix missing sys import in numeric.pyi
#26832: DOC: remove hack to override _add_newdocs_scalars
#26835: BUG: avoid side-effect of 'include complex.h'
#26836: BUG: fix max_rows and chunked string/datetime reading in loadtxt
#26837: BUG: fix PyArray_ImportNumPyAPI under -Werror=strict-prototypes
#26856: DOC: Update some documentation
#26868: BUG: fancy indexing copy
#26869: BUG: Mismatched allocation domains in PyArray_FillWithScalar
#26870: BUG: Handle --f77flags and --f90flags for meson [wheel build]
#26887: BUG: Fix new DTypes and new string promotion when signature is...
#26888: BUG: remove numpy.f2py from excludedimports
#26959: BUG: Quantile closest_observation to round to nearest even order
#26960: BUG: Fix off-by-one error in amount of characters in strip
#26961: API: Partially revert unique with return_inverse
#26962: BUG,MAINT: Fix utf-8 character stripping memory access
#26963: BUG: Fix out-of-bound minimum offset for in1d table method
#26971: BUG: fix f2py tests to work with v2 API
#26995: BUG: Add object cast to avoid warning with limited API

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Open source →

numpy

Release timeline

Releases

NumPy 2.5.3 Release Notes

Changes

Contributors

Pull requests merged

NumPy 2.5.2 Release Notes

C API changes

PyArray_StringDTypeObject is opaque under the abi3t stable ABI

Contributors

Pull requests merged

NumPy 2.5.1 Release Notes

Changes

Contributors

Pull requests merged

NumPy 2.5.0 Release Notes

Highlights

Deprecations

Expired deprecations

Compatibility notes

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

MSVC support

Cython support

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

NumPy 2.5.0 Release Notes

Highlights

Deprecations

Expired deprecations

Compatibility notes

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

MSVC support

Cython support

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()

Performance improvements and changes

Improved performance of numpy.searchsorted

Improved scaling of ufuncs on free-threading

Faster reductions on small/medium contiguous arrays

`PyArray_StringDTypeObject` is opaque under the abi3t stable ABI

`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`

`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()`

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`

Setting the `strides` attribute is deprecated

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

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

`np.fix` is pending deprecation

in-place modification of `ndarray.shape` is pending deprecation

Deprecation of `numpy.lib.user_array.container`

Removed deprecated `MachAr` runtime discovery mechanism.

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

Removed `interpolation` parameter from quantile and percentile functions

Removed `numpy.in1d`

Removed `numpy.ndindex.ndincr()`

Removed `fix_imports` parameter from `numpy.save`

Removal of four undocumented `ndarray.ctypes` methods

Removed `newshape` parameter from `numpy.reshape`

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

`numpy.array2string` and `numpy.sum` deprecations finalized

New `NPY_DT_get_constant` slot for DType constant retrieval

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

`'same_value'` for casting by value

`StringDType` fill_value support in `numpy.ma.MaskedArray`

`ndmax` option for `numpy.array`

Warning emitted when using `where` without `out`

New `__numpy_dtype__` protocol

Fix `flatiter` indexing edge cases

Improved error handling in `np.quantile`

Improved error message for `assert_array_compare`