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Update dependency numpy to v2.3.0 #102

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renovate_bot merged 1 commit from renovate/numpy-2.x into main 2025-06-14 17:00:46 -07:00
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This PR contains the following updates:

Package Type Update Change
numpy (changelog) project.dependencies minor ==2.2.6 -> ==2.3.0

Release Notes

numpy/numpy (numpy)

v2.3.0: (June 7, 2025)

Compare Source

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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This PR contains the following updates: | Package | Type | Update | Change | |---|---|---|---| | [numpy](https://github.com/numpy/numpy) ([changelog](https://numpy.org/doc/stable/release)) | project.dependencies | minor | `==2.2.6` -> `==2.3.0` | --- ### Release Notes <details> <summary>numpy/numpy (numpy)</summary> ### [`v2.3.0`](https://github.com/numpy/numpy/releases/tag/v2.3.0): (June 7, 2025) [Compare Source](https://github.com/numpy/numpy/compare/v2.2.6...v2.3.0) ### 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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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: ```python 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`: ```python import numpy as np from typing import overload @&#8203;overload def f(x: np.complex64) -> np.float32: ... @&#8203;overload def f(x: np.complex128) -> np.float64: ... @&#8203;overload def f(x: np.clongdouble) -> np.longdouble: ... ``` ([gh-28884](https://github.com/numpy/numpy/pull/28884)) #### Expired deprecations - Remove deprecated macros like `NPY_OWNDATA` from Cython interfaces in favor of `NPY_ARRAY_OWNDATA` (deprecated since 1.7) ([gh-28254](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/28254)) - Remove `np.tostring` (deprecated since 1.19) ([gh-28254](https://github.com/numpy/numpy/pull/28254)) - Raise on `np.conjugate` of non-numeric types (deprecated since 1.13) ([gh-28254](https://github.com/numpy/numpy/pull/28254)) - Raise when using `np.bincount(...minlength=None)`, use 0 instead (deprecated since 1.14) ([gh-28254](https://github.com/numpy/numpy/pull/28254)) - Passing `shape=None` to functions with a non-optional shape argument errors, use `()` instead (deprecated since 1.20) ([gh-28254](https://github.com/numpy/numpy/pull/28254)) - Inexact matches for `mode` and `searchside` raise (deprecated since 1.20) ([gh-28254](https://github.com/numpy/numpy/pull/28254)) - Setting `__array_finalize__ = None` errors (deprecated since 1.23) ([gh-28254](https://github.com/numpy/numpy/pull/28254)) - `np.fromfile` and `np.fromstring` error on bad data, previously they would guess (deprecated since 1.18) ([gh-28254](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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 [#&#8203;25306](https://github.com/numpy/numpy/issues/25306)) ([gh-28254](https://github.com/numpy/numpy/pull/28254)) - Passing booleans as partition index errors (deprecated since 1.23) ([gh-28254](https://github.com/numpy/numpy/pull/28254)) - Out-of-bounds indexes error even on empty arrays (deprecated since 1.20) ([gh-28254](https://github.com/numpy/numpy/pull/28254)) - `np.tostring` has been removed, use `tobytes` instead (deprecated since 1.19) ([gh-28254](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/28254)) - `concatenate()` with `axis=None` uses `same-kind` casting by default, not `unsafe` (deprecated since 1.20) ([gh-28254](https://github.com/numpy/numpy/pull/28254)) - Unpickling a scalar with object dtype errors (deprecated since 1.20) ([gh-28254](https://github.com/numpy/numpy/pull/28254)) - The binary mode of `fromstring` now errors, use `frombuffer` instead (deprecated since 1.14) ([gh-28254](https://github.com/numpy/numpy/pull/28254)) - Converting `np.inexact` or `np.floating` to a dtype errors (deprecated since 1.19) ([gh-28254](https://github.com/numpy/numpy/pull/28254)) - Converting `np.complex`, `np.integer`, `np.signedinteger`, `np.unsignedinteger`, `np.generic` to a dtype errors (deprecated since 1.19) ([gh-28254](https://github.com/numpy/numpy/pull/28254)) - The Python built-in `round` errors for complex scalars. Use `np.round` or `scalar.round` instead (deprecated since 1.19) ([gh-28254](https://github.com/numpy/numpy/pull/28254)) - 'np.bool' scalars can no longer be interpreted as an index (deprecated since 1.19) ([gh-28254](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/28254)) - Special handling of matrix is in np.outer is removed. Convert to a ndarray via `matrix.A` (deprecated since 1.20) ([gh-28254](https://github.com/numpy/numpy/pull/28254)) - Removed the `np.compat` package source code (removed in 2.0) ([gh-28961](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/27883)) - `NpyIter` now has no limit on the number of operands it supports. ([gh-28080](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/28940)) ##### NumPy now registers its pkg-config paths with the [pkgconf](https://github.com/pypackaging-native/pkgconf-pypi) PyPI package The [pkgconf](https://github.com/pypackaging-native/pkgconf-pypi) 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](https://github.com/pypackaging-native/pkgconf-pypi) from PyPI, NumPy will be discoverable without needing for any custom environment configuration. > \[!NOTE] > This only applies when using the [pkgconf](https://github.com/pypackaging-native/pkgconf-pypi) package from [PyPI](https://pypi.org/), > or put another way, this only applies when installing [pkgconf](https://github.com/pypackaging-native/pkgconf-pypi) 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](https://github.com/pypackaging-native/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/27288)) - `np.nditer` now has no limit on the number of supported operands (C-integer). ([gh-28080](https://github.com/numpy/numpy/pull/28080)) - No-copy pickling is now supported for any array that can be transposed to a C-contiguous array. ([gh-28105](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/28250)) - `np.dot` now reports floating point exceptions. ([gh-28442](https://github.com/numpy/numpy/pull/28442)) - `np.dtypes.StringDType` is now a [generic type](https://typing.python.org/en/latest/spec/generics.html) 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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/mayeut/pep600\_compliance?tab=readme-ov-file#pep600-compliance-check). ([gh-28436](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/28713)) ##### Re-enable overriding functions in the `numpy.strings` Re-enable overriding functions in the `numpy.strings` module. 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renovate_bot added 1 commit 2025-06-14 17:00:34 -07:00
Update dependency numpy to v2.3.0
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cb10886891
renovate_bot scheduled this pull request to auto merge when all checks succeed 2025-06-14 17:00:34 -07:00
renovate_bot merged commit cb10886891 into main 2025-06-14 17:00:46 -07:00
renovate_bot deleted branch renovate/numpy-2.x 2025-06-14 17:00:46 -07:00
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