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 functionnumpy.strings.slice

The new functionnumpy.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

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

  (gh-28129)

• Thenumpy.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:

  importnumpyasnpimportnumpy.typingasnptdeff[NT:npt.NBitBase](x:np.complexfloating[NT])->np.floating[NT]: ...

  But in NumPy 2.2.0,float64 andcomplex128 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 usetyping.overload:

  importnumpyasnpfromtypingimportoverload@overloaddeff(x:np.complex64)->np.float32: ...@overloaddeff(x:np.complex128)->np.float64: ...@overloaddeff(x:np.clongdouble)->np.longdouble: ...

  (gh-28884)

Expired deprecations

• Remove deprecated macros likeNPY_OWNDATA from Cython interfaces
  in favor ofNPY_ARRAY_OWNDATA (deprecated since 1.7)

  (gh-28254)

• Removenumpy/npy_1_7_deprecated_api.h and C macros like
  NPY_OWNDATA in favor ofNPY_ARRAY_OWNDATA (deprecated since 1.7)

  (gh-28254)

• Remove aliasgenerate_divbyzero_error to
  npy_set_floatstatus_divbyzero andgenerate_overflow_error to
  npy_set_floatstatus_overflow (deprecated since 1.10)

  (gh-28254)

• Removenp.tostring (deprecated since 1.19)

  (gh-28254)

• Raise onnp.conjugate of non-numeric types (deprecated since 1.13)

  (gh-28254)

• Raise when usingnp.bincount(...minlength=None), use 0 instead
  (deprecated since 1.14)

  (gh-28254)

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

  (gh-28254)

• Inexact matches formode andsearchside raise (deprecated since
  1.20)

  (gh-28254)

• Setting__array_finalize__ = None errors (deprecated since 1.23)

  (gh-28254)

• np.fromfile andnp.fromstring error on bad data, previously they
  would guess (deprecated since 1.18)

  (gh-28254)

• datetime64 andtimedelta64 construction with a tuple no longer
  accepts anevent 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 adtype from a class with adtype 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, usetobytes 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() withaxis=None usessame-kind casting by
  default, notunsafe (deprecated since 1.20)

  (gh-28254)

• Unpickling a scalar with object dtype errors (deprecated since 1.20)

  (gh-28254)

• The binary mode offromstring now errors, usefrombuffer instead
  (deprecated since 1.14)

  (gh-28254)

• Convertingnp.inexact ornp.floating to a dtype errors
  (deprecated since 1.19)

  (gh-28254)

• Convertingnp.complex,np.integer,np.signedinteger,
  np.unsignedinteger,np.generic to a dtype errors (deprecated
  since 1.19)

  (gh-28254)

• The Python built-inround errors for complex scalars. Use
  np.round orscalar.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 theconverters=float keyword argument.
  • Usenp.loadtxt(...).astype(np.int64)

  (gh-28254)

• The use of a length 1 tuple for the ufuncsignature errors. Use
  dtype or fill the tuple withNone (deprecated since 1.19)

  (gh-28254)

• Special handling of matrix is in np.outer is removed. Convert to a
  ndarray viamatrix.A (deprecated since 1.20)

  (gh-28254)

• Removed thenp.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 castingfloat64(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)

NewNpyIter_GetTransferFlags andNpyIter_IterationNeedsAPI change

NumPy now has the newNpyIter_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.

TheNpyIter_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 ofnp.dtype now defaults totyping.Any. This
  way, static type-checkers will inferdtype: 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 thepkgconf PyPI package

Thepkgconf 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 usingpkgconf
from PyPI, NumPy will be discoverable without needing for any custom
environment configuration.

(gh-28214)

Allowout=... 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 toout not being passed, but will ensure a non-scalar
return. This spelling is borrowed fromarr1d[0, ...] where the...
also ensures a non-scalar return.

Other functions with anout= 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 andnp.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 anditemsize.

  (gh-28250)

• np.dot now reports floating point exceptions.

  (gh-28442)

• np.dtypes.StringDType is now ageneric
  type which
  accepts a type argument forna_object that defaults to
  typing.Never. For example,StringDType(na_object=None) returns a
  StringDType[None], andStringDType() returns a
  StringDType[typing.Never].

  (gh-28856)

Added warnings tonp.isclose

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

• Warnings follow the user'snp.seterr settings

(gh-28205)

Performance improvements and changes

Performance improvements tonp.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 asorted parameter to allow returning
unique values as they were found, instead of sorting them afterwards.

(gh-26018)

Performance improvements tonp.sort andnp.argsort

np.sort andnp.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 fornp.float16 casts

Earlier, floating point casts to and fromnp.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 normord=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 methodnp.format_float_positional has
  been fixed.

  (gh-28569)

• NumPy's__array_api_version__ was upgraded from2023.12 to
  2024.12.

• numpy.count_nonzero foraxis=None (default) now returns a NumPy
  scalar instead of a Python integer.

• The parameteraxis innumpy.take_along_axis function has now a
  default value of-1.

  (gh-28615)

• Printing ofnp.float16 andnp.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 asunique_counts andunique_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 vianp.nditer from
Python andNpyIter 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 thebuffersize parameter.
• The "growinner" flag is now honored with buffered reductions when
  no operand requires buffering.

Fornp.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 fromeinsum 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 innumpy.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 thebin
orrange parameters ofnumpy.histogram.

(gh-28426)

Build manylinux_2_28 wheels

Wheels for linux systems will use themanylinux_2_28 tag (instead of
themanylinux2014 tag), which means dropping support for
redhat7/centos7, amazonlinux2, debian9, ubuntu18.04, and other
pre-glibc2.28 operating system versions, as per thePEP 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 thenumpy.strings

Re-enable overriding functions in thenumpy.strings module.

(gh-28741)

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