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 and the number of
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.

There are known test failures in the rc1 release involving MyPy and
PyPy. The cause of both has been determined and fixes will be applied
before the final release. The current Windows on ARM wheels also lack
OpenBLAS, but they should suffice for initial downstream testing.
OpenBLAS will be incorporated in those wheels when it becomes available.

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)

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)

Checksums

MD5

629f59f0a128a7ff1f3fedac6e2c42da  numpy-2.3.0rc1-cp311-cp311-macosx_10_9_x86_64.whl6b69c1f05cb782104bb4063f79bfb804  numpy-2.3.0rc1-cp311-cp311-macosx_11_0_arm64.whl0a5c651274fcd4912490edf17052302b  numpy-2.3.0rc1-cp311-cp311-macosx_14_0_arm64.whlaac595e422ddf5ab5d87cdce3be66262  numpy-2.3.0rc1-cp311-cp311-macosx_14_0_x86_64.whlf7d4f749f9e85a50adfbafd1b9c70fe8  numpy-2.3.0rc1-cp311-cp311-manylinux_2_28_aarch64.whlec8a0e794ce8fc7e35f90d83b68cd2d6  numpy-2.3.0rc1-cp311-cp311-manylinux_2_28_x86_64.whla8800a3c3fc260b02a36fb9dc8a1a399  numpy-2.3.0rc1-cp311-cp311-musllinux_1_2_aarch64.whl8e96ee3eb6e07e8efdff7754b9d008b6  numpy-2.3.0rc1-cp311-cp311-musllinux_1_2_x86_64.whl4157e803760012f28317495eec5b3310  numpy-2.3.0rc1-cp311-cp311-win32.whl1afc4f1a7adb81d9f2705a4c10f2d808  numpy-2.3.0rc1-cp311-cp311-win_amd64.whl11251ad640d2dd50b2d0a08a1c79e870  numpy-2.3.0rc1-cp311-cp311-win_arm64.whlf723f2e6b1ed4430b5a5c0c05501b55c  numpy-2.3.0rc1-cp312-cp312-macosx_10_13_x86_64.whl6ca0c0e1c2819a04b6c01559ce2d60dd  numpy-2.3.0rc1-cp312-cp312-macosx_11_0_arm64.whl56af103256eac1edcc34382a9c45a5eb  numpy-2.3.0rc1-cp312-cp312-macosx_14_0_arm64.whle3acbafb6da1e75c80361e4f3c791d12  numpy-2.3.0rc1-cp312-cp312-macosx_14_0_x86_64.whl278c4727d8b8d05b6202355cf740cfd4  numpy-2.3.0rc1-cp312-cp312-manylinux_2_28_aarch64.whl3cd5e204141a450301fdcbda83e044f5  numpy-2.3.0rc1-cp312-cp312-manylinux_2_28_x86_64.whlc4369a338a9900fc43c6fe46e5113505  numpy-2.3.0rc1-cp312-cp312-musllinux_1_2_aarch64.whl12c5af8ba96f61d1a5d41f15c1d55721  numpy-2.3.0rc1-cp312-cp312-musllinux_1_2_x86_64.whl81fae7bcfc52b810c75a3c14bc182f12  numpy-2.3.0rc1-cp312-cp312-win32.whl1ce23327d02c79b64c16f54d412e32ae  numpy-2.3.0rc1-cp312-cp312-win_amd64.whl9e538dfe7f2051f63422d4e1edcd23bd  numpy-2.3.0rc1-cp312-cp312-win_arm64.whlca94a64c4ad19bad2fd5baba19bc8578  numpy-2.3.0rc1-cp313-cp313-macosx_10_13_x86_64.whld964ed57e7b28365a75210e83c396ea5  numpy-2.3.0rc1-cp313-cp313-macosx_11_0_arm64.whl03247ffd1c0a63e86382867df6914cd6  numpy-2.3.0rc1-cp313-cp313-macosx_14_0_arm64.whl65ace2a8b56475a356357091c704a257  numpy-2.3.0rc1-cp313-cp313-macosx_14_0_x86_64.whle3283698c1124b6e60b8361d4884792f  numpy-2.3.0rc1-cp313-cp313-manylinux_2_28_aarch64.whlafa8c55affdc96845ee0f904de86e1dc  numpy-2.3.0rc1-cp313-cp313-manylinux_2_28_x86_64.whlfc296e255a2afb62956642355d1830c2  numpy-2.3.0rc1-cp313-cp313-musllinux_1_2_aarch64.whl32fd94baff245adcd4e12531ec2812be  numpy-2.3.0rc1-cp313-cp313-musllinux_1_2_x86_64.whl8114fe3c90fb01856b089b076a3eaf69  numpy-2.3.0rc1-cp313-cp313-win32.whl3aae3297bd4d94fd628666da9224b871  numpy-2.3.0rc1-cp313-cp313-win_amd64.whl76387f5d0a213f13b1352efc5087ebe2  numpy-2.3.0rc1-cp313-cp313-win_arm64.whlcd6d0165be416093c2229319d9578e22  numpy-2.3.0rc1-cp313-cp313t-macosx_10_13_x86_64.whl783a19b5db79d0080727dcae4c48ce90  numpy-2.3.0rc1-cp313-cp313t-macosx_11_0_arm64.whlaf327355774245aebd2bc790982190b9  numpy-2.3.0rc1-cp313-cp313t-macosx_14_0_arm64.whlfe643d848c382e0c97cb85ce993c7ac9  numpy-2.3.0rc1-cp313-cp313t-macosx_14_0_x86_64.whld4facf10a4628a67f076e230caa8cd7b  numpy-2.3.0rc1-cp313-cp313t-manylinux_2_28_aarch64.whla16b6003b0778caddb708d06a8720c2b  numpy-2.3.0rc1-cp313-cp313t-manylinux_2_28_x86_64.whl27732d00b9124f2866f45602d5c46ef5  numpy-2.3.0rc1-cp313-cp313t-musllinux_1_2_aarch64.whl1c25ee631482975bbeb378d88608b3b5  numpy-2.3.0rc1-cp313-cp313t-musllinux_1_2_x86_64.whl63974df1b36d9486a0a8a4953a53a418  numpy-2.3.0rc1-cp313-cp313t-win32.whl1e67187dd5fbc2663a0922e3434e4838  numpy-2.3.0rc1-cp313-cp313t-win_amd64.whl8ae83e7618c196dfe4e637c5f81692e5  numpy-2.3.0rc1-cp313-cp313t-win_arm64.whla96f68abaa4aa674e49eec4add0b2c0b  numpy-2.3.0rc1-pp311-pypy311_pp73-macosx_10_15_x86_64.whlfe679f10ad4c9d3fce0db85c632967a7  numpy-2.3.0rc1-pp311-pypy311_pp73-macosx_14_0_arm64.whlc9f14388eae0b01b5225e0f8f79ba9f6  numpy-2.3.0rc1-pp311-pypy311_pp73-macosx_14_0_x86_64.whl7cea15ab8d47bc2f3f6377c29a74138f  numpy-2.3.0rc1-pp311-pypy311_pp73-manylinux_2_28_aarch64.whl46d07cd0f0aacce6d4b5ce949a333abc  numpy-2.3.0rc1-pp311-pypy311_pp73-manylinux_2_28_x86_64.whldf12654557fe28806bed04e15d1fe1d7  numpy-2.3.0rc1-pp311-pypy311_pp73-win_amd64.whl5677afac6d3d39fe93a96cab654f38e7  numpy-2.3.0rc1.tar.gz

SHA256

82b035e631187bee40c1d2c1593e40dcad62cfbaf833c5d3e179c1d7bce87e0c  numpy-2.3.0rc1-cp311-cp311-macosx_10_9_x86_64.whl2f2f8a82005073929532ed8e09eda78544140996ef283d007945323d27e3ef84  numpy-2.3.0rc1-cp311-cp311-macosx_11_0_arm64.whl96af406cf156acb52edfa3b38c1fc8efe0f986bff5f99176657ba09606e78fe7  numpy-2.3.0rc1-cp311-cp311-macosx_14_0_arm64.whl30f11867a70c96e6628f9b5da5cb426efe4ebe5e1f02bc8932c79f4dfa0c1e02  numpy-2.3.0rc1-cp311-cp311-macosx_14_0_x86_64.whlc54f93b5c0c210e580fd1d114b76b172190bdefcdee3eab865587d4c860067fa  numpy-2.3.0rc1-cp311-cp311-manylinux_2_28_aarch64.whl6d72740255bcbaa94a66329daffd974e8212eee4b77acc8c3da8dd179adbe7f3  numpy-2.3.0rc1-cp311-cp311-manylinux_2_28_x86_64.whl44f65efc7f4a62e0624f53cd241761b5a9cdaa302e50dd8f30adc1173e2c1691  numpy-2.3.0rc1-cp311-cp311-musllinux_1_2_aarch64.whle9766d1d3b36df82777c7f0a6d248d21e7aceafd40d24b87f9cec6c5916a4908  numpy-2.3.0rc1-cp311-cp311-musllinux_1_2_x86_64.whl07638479fc90c38168a3dd29a54c750701100dd84622bbaa32783459d2df7c54  numpy-2.3.0rc1-cp311-cp311-win32.whl7cc39c12551a3b92f9db5dd0faff021043f4f432167f990424a3c9149314f3a3  numpy-2.3.0rc1-cp311-cp311-win_amd64.whl213b2aa630118690e684a2666c58978a45763fbd4034b9b990d827a25df7b6ae  numpy-2.3.0rc1-cp311-cp311-win_arm64.whlc9d04e6f6bf7fe48eb25af803de3e2ee5dbaba7786d17764d7bacc242d47d96e  numpy-2.3.0rc1-cp312-cp312-macosx_10_13_x86_64.whl8cd953239b23dd0f5363553efb2eab4651c33b3772c7bcfe3bc0e1a84a9149e6  numpy-2.3.0rc1-cp312-cp312-macosx_11_0_arm64.whl66bff8b92964eb21593d787e55668d1800d5cfeb0ccf75691cce80c15b155839  numpy-2.3.0rc1-cp312-cp312-macosx_14_0_arm64.whl580ce0f8e9e340571a5b0c32b7f3e1489ea018dbc37b22913788be23e8a50559  numpy-2.3.0rc1-cp312-cp312-macosx_14_0_x86_64.whl93909bcf08b4aa21a74a687342843eef5479c317add9700a66b0122cc1c9d604  numpy-2.3.0rc1-cp312-cp312-manylinux_2_28_aarch64.whl9efc27c9b25d4387e6153d01cc93a8ae18a6a37d6598cb4da3876874a3aa1283  numpy-2.3.0rc1-cp312-cp312-manylinux_2_28_x86_64.whl14e86a57107b03e7b31b4d677ded1af52079f05bde89008b4dfd5a3fd7767769  numpy-2.3.0rc1-cp312-cp312-musllinux_1_2_aarch64.whlc4ac94252a8dec06d6c55126f83775ec90b0d8ceaed9086d7166175455772155  numpy-2.3.0rc1-cp312-cp312-musllinux_1_2_x86_64.whlc8047609194df241d20b5d3534319df0f2ef57c7163a9e67ceb8a7ab3d3c7413  numpy-2.3.0rc1-cp312-cp312-win32.whlea64b01b0b9ee53fd1c05dad508e1ee05ca888579687a94044cdea8ef47f8efc  numpy-2.3.0rc1-cp312-cp312-win_amd64.whlc0ca78c3c15db1b69b91027c6485291b2ffbdd29dab77bd5d044248569c9ff85  numpy-2.3.0rc1-cp312-cp312-win_arm64.whl77fc845f90f74b83881330bd3d15c112bdac5e2947e77adec2be141eb5a71aff  numpy-2.3.0rc1-cp313-cp313-macosx_10_13_x86_64.whlfcaf7cfbe003ea5d6e2f470fbb67001dae39dbddc76a608b10623592f4711c78  numpy-2.3.0rc1-cp313-cp313-macosx_11_0_arm64.whl167c1b4ad39017d5b0bfae7c5b71ced40025ef26ff8b9fc65e39519533b7c416  numpy-2.3.0rc1-cp313-cp313-macosx_14_0_arm64.whlf4d15d5ad63ea6760b0ff49cc88a635b02838488ebabb41bc2524f67d9d5b74e  numpy-2.3.0rc1-cp313-cp313-macosx_14_0_x86_64.whleb5a7a5d1c28881b1c9536038d9ea224051464b0a033d8bd87ec75634e8ee2db  numpy-2.3.0rc1-cp313-cp313-manylinux_2_28_aarch64.whl022fa032a8dcabef0771db621a77a9203358e41cb651d8903836783e9708248f  numpy-2.3.0rc1-cp313-cp313-manylinux_2_28_x86_64.whlb6d9d7ad7666f646bbb831a80c668b61a08d4deb00935ef475a1c81ba0b87bdb  numpy-2.3.0rc1-cp313-cp313-musllinux_1_2_aarch64.whl172965a57a44ccfbfc67746c721128647a4df3aecec3d3b91c67e75c575548cc  numpy-2.3.0rc1-cp313-cp313-musllinux_1_2_x86_64.whlb056d511fdb33ee7076586fbb831be0a8030e9565f5ea0ceebe649f29e647ac0  numpy-2.3.0rc1-cp313-cp313-win32.whlf437b4af30ab36e7fa9fd7856b0482229259fc98aeb5e53a287eda5253e4f1ec  numpy-2.3.0rc1-cp313-cp313-win_amd64.whlffbda03577e4f401f4465345ca98c9dc95e3a23df2ec16854f4aa37ae6c1fb56  numpy-2.3.0rc1-cp313-cp313-win_arm64.whl4de6304a0653a76f6418f4c0aa84048953d5891aa8296681d916f95b0a5eddf8  numpy-2.3.0rc1-cp313-cp313t-macosx_10_13_x86_64.whl17cb57f5c862904d57a1d1eb2d6e3f0e3a0d7ea738d5281638aef7c45b8c4525  numpy-2.3.0rc1-cp313-cp313t-macosx_11_0_arm64.whl96d8e7e0666836202e9cff63cb16d9c49bd31222303f3b70dc9144c3ae191cdd  numpy-2.3.0rc1-cp313-cp313t-macosx_14_0_arm64.whle967c553e48c14feb3387248cf089c4f7f8ec66f6800cb184effa2da5494d9e8  numpy-2.3.0rc1-cp313-cp313t-macosx_14_0_x86_64.whlcb18f1e661c4480e7d40bac0172ab456aececa05aec5855d987d6a1f51d51206  numpy-2.3.0rc1-cp313-cp313t-manylinux_2_28_aarch64.whleb0876192b685ed3f96049dbd5aa9a1d871b47c553b3d304692bbbe386fa7441  numpy-2.3.0rc1-cp313-cp313t-manylinux_2_28_x86_64.whlde2e8223ef8e65963976de817419314d7e791d403a9d6448174e3f490559ba8d  numpy-2.3.0rc1-cp313-cp313t-musllinux_1_2_aarch64.whlb7c0007ae7dd02acaaf198cf1a6b4da805aaa3857a452403c3de1fed29ab158c  numpy-2.3.0rc1-cp313-cp313t-musllinux_1_2_x86_64.whl875ee6c2865523adb78ebcfbcef186fc99201e9931a49132d97101f57f154ff1  numpy-2.3.0rc1-cp313-cp313t-win32.whlb59de7a3beeca46f189c53609db7794ccb83a90fa5841111d5ca77bbe990ed21  numpy-2.3.0rc1-cp313-cp313t-win_amd64.whl7f06a256ce74713eae26c19221b305783d4db5f8cad457e82f9a4f8cc55b9632  numpy-2.3.0rc1-cp313-cp313t-win_arm64.whl3c8681de7c4fc72f5ab29e5a8343b37fe7bdc465e8cc3c99637ac884e26e41fd  numpy-2.3.0rc1-pp311-pypy311_pp73-macosx_10_15_x86_64.whlec2037b6d700b8491a97efcbfa05dec666d8ae7807f374beb9d3320531d42e9b  numpy-2.3.0rc1-pp311-pypy311_pp73-macosx_14_0_arm64.whlab6e2b743192e7e9ecef7d22a90c394a6fe3313676e55fcc1c566ee0649b35db  numpy-2.3.0rc1-pp311-pypy311_pp73-macosx_14_0_x86_64.whlfccea8370d46dcb20012a1e8e54bc70dc7e2db8bfc3e20b95f8f871bb408744f  numpy-2.3.0rc1-pp311-pypy311_pp73-manylinux_2_28_aarch64.whl477064e29dfac734cb53269f5a4916e38e11ef9d42f6c475e9c695e25f78e868  numpy-2.3.0rc1-pp311-pypy311_pp73-manylinux_2_28_x86_64.whl95c190db9870e0dc616da2f267a3fc29544a843601251947eadc0e00a0aa8ad6  numpy-2.3.0rc1-pp311-pypy311_pp73-win_amd64.whla28686275bf434b4304408c2711832033e3906c50282eb93fc567b8483922625  numpy-2.3.0rc1.tar.gz