Update dependency numpy to v2 #97
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This PR contains the following updates:
==1.23.5->==2.3.4Release Notes
numpy/numpy (numpy)
v2.3.4: (Oct 15, 2025)Compare Source
NumPy 2.3.4 Release Notes
The NumPy 2.3.4 release is a patch release split between a number of maintenance
updates and bug fixes. This release supports Python versions 3.11-3.14. This
release is based on Python 3.14.0 final.
Changes
The
npymathandnpyrandomlibraries now have a.librather than a.afile extension on win-arm64, for compatibility for building with MSVC andsetuptools. Please note that using these static libraries is discouragedand for existing projects using it, it's best to use it with a matching
compiler toolchain, which is
clang-clon Windows on Arm.(gh-29750)
Contributors
A total of 17 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.
Pull requests merged
A total of 30 pull requests were merged for this release.
dtyperefcount in__array__(#29715)__slots__(#29901)testing._private(#29902)errstate(#29914)@classmethodarg to clsv2.3.3: 2.3.3 (Sep 9, 2025)Compare Source
NumPy 2.3.3 Release Notes
The NumPy 2.3.3 release is a patch release split between a number of maintenance
updates and bug fixes. This release supports Python versions 3.11-3.14. Note
that the 3.14.0 final is currently expected in Oct, 2025. This release is based
on 3.14.0rc2.
Contributors
A total of 13 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.
Pull requests merged
A total of 23 pull requests were merged for this release.
sortedkwarg touniquev2.3.2: (Jul 24, 2025)Compare Source
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:
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.
Pull requests merged
A total of 16 pull requests were merged for this release.
np.char.arrayandnp.char.asarray...squareonarr \*\* 2(#29392)Checksums
MD5
SHA256
v2.3.1: (Jun 21, 2025)Compare Source
NumPy 2.3.1 Release Notes
The NumPy 2.3.1 release is a patch release with several bug fixes,
annotation improvements, and better support for OpenBSD. Highlights are:
matmulfor non-contiguous out kwarg parameternp.vectorizecasting errorsThis release supports Python versions 3.11-3.13, Python 3.14 will be
supported when it is released.
Contributors
A total of 9 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.
Pull requests merged
A total of 12 pull requests were merged for this release.
np.vectorizecasting to legacy behavior (#29196)Checksums
MD5
SHA256
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
New functions
New function
numpy.strings.sliceThe new function
numpy.strings.slicewas added, which implements fastnative slicing of string arrays. It supports the full slicing API
including negative slice offsets and steps.
(gh-27789)
Deprecations
The
numpy.typing.mypy_pluginhas been deprecated in favor ofplatform-agnostic static type inference. Please remove
numpy.typing.mypy_pluginfrom thepluginssection of your mypyconfiguration. If this change results in new errors being reported,
kindly open an issue.
(gh-28129)
The
numpy.typing.NBitBasetype has been deprecated and will beremoved in a future version.
This type was previously intended to be used as a generic upper
bound for type-parameters, for example:
But in NumPy 2.2.0,
float64andcomplex128were changed toconcrete subtypes, causing static type-checkers to reject
x: np.float64 = f(np.complex128(42j)).So instead, the better approach is to use
typing.overload:(gh-28884)
Expired deprecations
Remove deprecated macros like
NPY_OWNDATAfrom Cython interfacesin favor of
NPY_ARRAY_OWNDATA(deprecated since 1.7)(gh-28254)
Remove
numpy/npy_1_7_deprecated_api.hand C macros likeNPY_OWNDATAin favor ofNPY_ARRAY_OWNDATA(deprecated since 1.7)(gh-28254)
Remove alias
generate_divbyzero_errortonpy_set_floatstatus_divbyzeroandgenerate_overflow_errortonpy_set_floatstatus_overflow(deprecated since 1.10)(gh-28254)
Remove
np.tostring(deprecated since 1.19)(gh-28254)
Raise on
np.conjugateof 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=Noneto functions with a non-optional shape argumenterrors, use
()instead (deprecated since 1.20)(gh-28254)
Inexact matches for
modeandsearchsideraise (deprecated since1.20)
(gh-28254)
Setting
__array_finalize__ = Noneerrors (deprecated since 1.23)(gh-28254)
np.fromfileandnp.fromstringerror on bad data, previously theywould guess (deprecated since 1.18)
(gh-28254)
datetime64andtimedelta64construction with a tuple no longeraccepts an
eventvalue, either use a two-tuple of (unit, num) or a4-tuple of (unit, num, den, 1) (deprecated since 1.14)
(gh-28254)
When constructing a
dtypefrom a class with adtypeattribute,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.tostringhas been removed, usetobytesinstead (deprecatedsince 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()withaxis=Noneusessame-kindcasting bydefault, 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
fromstringnow errors, usefrombufferinstead(deprecated since 1.14)
(gh-28254)
Converting
np.inexactornp.floatingto a dtype errors(deprecated since 1.19)
(gh-28254)
Converting
np.complex,np.integer,np.signedinteger,np.unsignedinteger,np.genericto a dtype errors (deprecatedsince 1.19)
(gh-28254)
The Python built-in
rounderrors for complex scalars. Usenp.roundorscalar.roundinstead (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
converters=floatkeyword argument.np.loadtxt(...).astype(np.int64)(gh-28254)
The use of a length 1 tuple for the ufunc
signatureerrors. Usedtypeor fill the tuple withNone(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.compatpackage source code (removed in 2.0)(gh-28961)
C API changes
NpyIter_GetTransferFlagsis now available to check if the iteratorneeds the Python API or if casts may cause floating point errors
(FPE). FPEs can for example be set when casting
float64(1e300)tofloat32(overflow to infinity) or a NaN to an integer (invalidvalue).
(gh-27883)
NpyIternow has no limit on the number of operands it supports.(gh-28080)
New
NpyIter_GetTransferFlagsandNpyIter_IterationNeedsAPIchangeNumPy now has the new
NpyIter_GetTransferFlagsfunction as a moreprecise 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_IterationNeedsAPIfunction now performs all the checksthat 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.dtypenow defaults totyping.Any. Thisway, static type-checkers will infer
dtype: np.dtypeasdtype: np.dtype[Any], without reporting an error.(gh-28669)
Static type-checkers now interpret:
_: np.ndarrayas_: npt.NDArray[typing.Any]._: np.flatiteras_: 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.
(gh-28214)
Allow
out=...in ufuncs to ensure array resultNumPy 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 identicalin behavior to
outnot being passed, but will ensure a non-scalarreturn. 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=trueMeson build flag. This feature isdisabled by default. When enabled,
np.sortandnp.argsortfunctionscan 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 aPython bool.
(gh-27288)
np.nditernow 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__ofthe custom dtype over a more generic name constructed from its
kindanditemsize.(gh-28250)
np.dotnow reports floating point exceptions.(gh-28442)
np.dtypes.StringDTypeis now a generictype which
accepts a type argument for
na_objectthat defaults totyping.Never. For example,StringDType(na_object=None)returns aStringDType[None], andStringDType()returns aStringDType[typing.Never].(gh-28856)
Added warnings to
np.iscloseAdded warning messages if at least one of atol or rtol are either
np.nanornp.infwithinnp.isclose.np.seterrsettings(gh-28205)
Performance improvements and changes
Performance improvements to
np.uniquenp.uniquenow tries to use a hash table to find unique values insteadof 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
sortedparameter to allow returningunique values as they were found, instead of sorting them afterwards.
(gh-26018)
Performance improvements to
np.sortandnp.argsortnp.sortandnp.argsortfunctions now can leverage OpenMP forparallel 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.float16castsEarlier, floating point casts to and from
np.float16types wereemulated 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=infand the matrix normsord={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, andnp.linalg.matrix_norm. Previously, NumPy would raises errors orreturn 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_positionalhasbeen fixed.
(gh-28569)
NumPy's
__array_api_version__wasConfiguration
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