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

• 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)

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)

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)

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)

Checksums

MD5

8ac48250d6b96fce749fbd0fcf464ff9  numpy-2.1.0rc1-cp310-cp310-macosx_10_9_x86_64.whl
13f92a9f7ed33d71ccfb742de0e3fec9  numpy-2.1.0rc1-cp310-cp310-macosx_11_0_arm64.whl
ba9286f6bd7a238eaead5ae2111d23a8  numpy-2.1.0rc1-cp310-cp310-macosx_14_0_arm64.whl
dc2b6c2f586090bc80268a81afec4c6f  numpy-2.1.0rc1-cp310-cp310-macosx_14_0_x86_64.whl
16a13eb5dfad8008baf937026fa2db62  numpy-2.1.0rc1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
c5d5697af3047b8a3dc7a5d6ca86ec86  numpy-2.1.0rc1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
0e48596167a215333f277ff29ea29c45  numpy-2.1.0rc1-cp310-cp310-musllinux_1_1_x86_64.whl
381957df326f45c0fba0b64a00a043ac  numpy-2.1.0rc1-cp310-cp310-musllinux_1_2_aarch64.whl
676fd27cea96af93142b4b420d9cb8af  numpy-2.1.0rc1-cp310-cp310-win32.whl
b30bff4e8846c52e58fab9564b422ed2  numpy-2.1.0rc1-cp310-cp310-win_amd64.whl
4ee7c88591a445b3b5969999eeb7b0a7  numpy-2.1.0rc1-cp311-cp311-macosx_10_9_x86_64.whl
556393087caa0bb6eec1a76dfe2cad32  numpy-2.1.0rc1-cp311-cp311-macosx_14_0_arm64.whl
4e2b2eb39fc3a6ca28048588fc6a5338  numpy-2.1.0rc1-cp311-cp311-macosx_14_0_x86_64.whl
34f5ab41c4c6a3ecbf0cc0b108a63942  numpy-2.1.0rc1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
689944e33b04a11878aecaf59611341b  numpy-2.1.0rc1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
5d2a53263c7daa9a3b9a89a4dc8ef3ac  numpy-2.1.0rc1-cp311-cp311-musllinux_1_1_x86_64.whl
29e27f96f56d0d1b59f9b261ed6fe438  numpy-2.1.0rc1-cp311-cp311-musllinux_1_2_aarch64.whl
f07177a3b6779e6747137e2173a545de  numpy-2.1.0rc1-cp311-cp311-win32.whl
f2d1f68c8c0455cba32be4aa50f5afed  numpy-2.1.0rc1-cp311-cp311-win_amd64.whl
8500240d88e6e3afc281c562af083fd7  numpy-2.1.0rc1-cp312-cp312-macosx_10_9_x86_64.whl
3280b4ad3a5ceb814d739a9c980d16d6  numpy-2.1.0rc1-cp312-cp312-macosx_11_0_arm64.whl
77a6339def5185efa262658c51d6e44e  numpy-2.1.0rc1-cp312-cp312-macosx_14_0_arm64.whl
2e3a71b9ef1e60ce37949af87475f5f7  numpy-2.1.0rc1-cp312-cp312-macosx_14_0_x86_64.whl
3c1877cd6108cb502ac1df39cfec86d0  numpy-2.1.0rc1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
ae1a9945726e7d970ee0b6232d5d9b4d  numpy-2.1.0rc1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
f1a71557d35d8b2f87f277e85c958b2b  numpy-2.1.0rc1-cp312-cp312-musllinux_1_1_x86_64.whl
b1ba7049684a7d674c006325b4606dd1  numpy-2.1.0rc1-cp312-cp312-musllinux_1_2_aarch64.whl
5944d81459d443a72346e7ea767b72a2  numpy-2.1.0rc1-cp312-cp312-win32.whl
f8b17b8f9bddb1c21844ae2475f72389  numpy-2.1.0rc1-cp312-cp312-win_amd64.whl
084ecd080c6871ed034ef69cda7573de  numpy-2.1.0rc1-cp313-cp313-macosx_10_13_x86_64.whl
dbeca273db0240ca7fe395611f0c23c8  numpy-2.1.0rc1-cp313-cp313-macosx_11_0_arm64.whl
242794f34818844e0fe695ec42c62dbe  numpy-2.1.0rc1-cp313-cp313-macosx_14_0_arm64.whl
3f1c04457ce363250ac5d37935172527  numpy-2.1.0rc1-cp313-cp313-macosx_14_0_x86_64.whl
2ce171281092e5f5d9f3d1ce8a615a94  numpy-2.1.0rc1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
95416f883c14a10fca22007594c94a94  numpy-2.1.0rc1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
36c07d317516f84cb376cc475b3ed13d  numpy-2.1.0rc1-cp313-cp313-musllinux_1_1_x86_64.whl
e7c1f9c2964e4d71878a1654194452b2  numpy-2.1.0rc1-cp313-cp313-musllinux_1_2_aarch64.whl
ea27f5a8b6dfa219b630aee52e621c8c  numpy-2.1.0rc1-cp313-cp313-win32.whl
1821d7e0980f297296509090cfd9c288  numpy-2.1.0rc1-cp313-cp313-win_amd64.whl
1b7f8160179aef59822e3eb43cb8a210  numpy-2.1.0rc1-cp313-cp313t-macosx_10_13_x86_64.whl
fed8d00d6819c467ef97e0b7611624cd  numpy-2.1.0rc1-cp313-cp313t-macosx_11_0_arm64.whl
f58df469b6ec5e1755b1572702b56716  numpy-2.1.0rc1-cp313-cp313t-macosx_14_0_arm64.whl
fe13066a540c68598b1180bec61e8e30  numpy-2.1.0rc1-cp313-cp313t-macosx_14_0_x86_64.whl
67d51902daf5bc9de69c6e46dfea9a64  numpy-2.1.0rc1-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
8409acd1916df8f8630260207a5b4eec  numpy-2.1.0rc1-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
e64a5ccac64641cbbbd2caa652ff815a  numpy-2.1.0rc1-cp313-cp313t-musllinux_1_1_x86_64.whl
488776d734d4eddc9c1540bf862106bb  numpy-2.1.0rc1-cp313-cp313t-musllinux_1_2_aarch64.whl
fbc57a82683e2c9697a6992290ebe337  numpy-2.1.0rc1-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
ed26d5d79acc222e107900668edcd01f  numpy-2.1.0rc1-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
c29f8c6a55c1ac9e5c693f63ec17f251  numpy-2.1.0rc1-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
4beab0a7bde06687f699e75cd04ec024  numpy-2.1.0rc1-pp310-pypy310_pp73-win_amd64.whl
88e72b72f2859ff084eb3863fac3ac20  numpy-2.1.0rc1.tar.gz

SHA256

590acae9e4b0baa895850c0edab988c329a196bacc7326f3249fa5fe7b94e5a8  numpy-2.1.0rc1-cp310-cp310-macosx_10_9_x86_64.whl
61cf71f62033987ed49b78a19465f40fcbf6f7e94674eda21096ebde6935c2e0  numpy-2.1.0rc1-cp310-cp310-macosx_11_0_arm64.whl
0c489f6c47bbed44918c9c8036a679614920da2a45f481d0eca2ad168ca5327f  numpy-2.1.0rc1-cp310-cp310-macosx_14_0_arm64.whl
4c33387be8eadc07d0834e0b9e2ead53117fe76ab2dadd37ee80d1df80be4c05  numpy-2.1.0rc1-cp310-cp310-macosx_14_0_x86_64.whl
f412923d4ce1ec29aa3cf7752598e5eb154f549cfbf62d7c6f3cc76cb25b32e0  numpy-2.1.0rc1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
06156c55771da4952a2432aa457cd96159675dcab4336f5307bff042535cb6ea  numpy-2.1.0rc1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
be3ddd26a22d032914cfca5ef7db74f31adbd6c9d88a6f4e21ebd8e057d9474c  numpy-2.1.0rc1-cp310-cp310-musllinux_1_1_x86_64.whl
12b38b0f3ddc1342863a6849f4fcb3f506e1d21179ebd34b7aa55a30cb50899f  numpy-2.1.0rc1-cp310-cp310-musllinux_1_2_aarch64.whl
17581a2080012afe603c43005c9d050570e54683dde0d395e3edb4fa9c25f328  numpy-2.1.0rc1-cp310-cp310-win32.whl
8ee3ab33c02a0bd7d219a184c9bc43811de373551529981035673ca2a1ba7b93  numpy-2.1.0rc1-cp310-cp310-win_amd64.whl
2d3d1e61191e408a11658a64e9f9bb61741ad28c160576c95dac9df6f74713b4  numpy-2.1.0rc1-cp311-cp311-macosx_10_9_x86_64.whl
4e08e733600647242a9046b6aff888e72fe8a846b00855e5136e7641b08d25d8  numpy-2.1.0rc1-cp311-cp311-macosx_14_0_arm64.whl
2b0e379a15c6b8eb69bb8170d10cfbb8a0dc9126b5402ee8860a2646f4111c3d  numpy-2.1.0rc1-cp311-cp311-macosx_14_0_x86_64.whl
fea6d6939d9bf098d96c6d22bb3e4ff39f8eb3f0f26b52c8c69ba06845490095  numpy-2.1.0rc1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
9a6bdc19830703eee91e7eb2d671b165febefbf5eec6a4f163d1833d23be17af  numpy-2.1.0rc1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
58a07f2947aa06ca03d922a86ac30e403ce8282cd15904606bac852bf29ea2ad  numpy-2.1.0rc1-cp311-cp311-musllinux_1_1_x86_64.whl
1a4f960e2e5c1084cf6b1d15482a5556ecc122855d631a2395063ab703d62fdd  numpy-2.1.0rc1-cp311-cp311-musllinux_1_2_aarch64.whl
f38fabd7b8d14fb7d63fbb2d07971d6edd518d2a43542c63c29164c901d2a758  numpy-2.1.0rc1-cp311-cp311-win32.whl
e82b8e0b88d493d4e882f18de30f679bf1197c82d8c799acb5fdb4068cadb945  numpy-2.1.0rc1-cp311-cp311-win_amd64.whl
dc2af0135139bbb26b1ea5bdc430e049edb745ae643cb898afb32549ce4801de  numpy-2.1.0rc1-cp312-cp312-macosx_10_9_x86_64.whl
47f11bf152d8707217feb46e9662a8b1aa3554a8ee56b64d2aa99c3e9914f101  numpy-2.1.0rc1-cp312-cp312-macosx_11_0_arm64.whl
3b534c62b1887b4bfa80f633485f2a9338f5d46d720b6cc695d2ba8b38d98987  numpy-2.1.0rc1-cp312-cp312-macosx_14_0_arm64.whl
f4e07df8476545da7cf23f75811f4fc334b06fc50d8e945e897cfc00c8f89690  numpy-2.1.0rc1-cp312-cp312-macosx_14_0_x86_64.whl
c8458becc562ee35b30b5e53173933414cf42e56b3f4f3d80997bf0dda7308d1  numpy-2.1.0rc1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
524b5311d21741f0b3f48efcdd3442546be3b38507a4e3b0f5138e4340f5dee0  numpy-2.1.0rc1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
7bcb4f360dc9e29b4f5f9fb36b35b429e731373843ccf39a22105bd809ef3138  numpy-2.1.0rc1-cp312-cp312-musllinux_1_1_x86_64.whl
5821c9831fad20cd1a8621a9ed483322ca97a9da9832690a4050ffedcb3e766b  numpy-2.1.0rc1-cp312-cp312-musllinux_1_2_aarch64.whl
1d9e0ddfb33a7a78fe92d49aaa2992a78ed5aff4cef7a21d8b1057cca075cc85  numpy-2.1.0rc1-cp312-cp312-win32.whl
86cc61c5479ed3b324011aa69484cae8f491b7f58dc0e54acf0894bdb4fae879  numpy-2.1.0rc1-cp312-cp312-win_amd64.whl
64e8de086d2e4dac41fa286412321469b4535677184e78cc78e5061b44f0e4bf  numpy-2.1.0rc1-cp313-cp313-macosx_10_13_x86_64.whl
e74dc488a27b90f31ab307b4cf3f07a45bb78a0e91cfb36d69c6eced4f36089b  numpy-2.1.0rc1-cp313-cp313-macosx_11_0_arm64.whl
f73e4fcf7455d3b734e6ecbafdbc12d3c1dd8f2146fd186e003ae1c8f00e5eed  numpy-2.1.0rc1-cp313-cp313-macosx_14_0_arm64.whl
e5a64ac6016839fd906b3d7cc1f7ecb145c7d44a310234b6843f3b23b8ec0651  numpy-2.1.0rc1-cp313-cp313-macosx_14_0_x86_64.whl
ccc68ee27362f8d3516deecffa124d1488ae20347628e357264e7e66dbdaba08  numpy-2.1.0rc1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
d3d59479b98cc364b8a08ddd854c7817b5c578a521b56af5a96b3a9db18cc9b7  numpy-2.1.0rc1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
15c6bde88f242747258cfee803f3161b7a2c1ffead0817e409d95444a79b4029  numpy-2.1.0rc1-cp313-cp313-musllinux_1_1_x86_64.whl
3e9276bff9a57100b53e5f9c44469baca1e58ec612e5143db568d69ec27b65ea  numpy-2.1.0rc1-cp313-cp313-musllinux_1_2_aarch64.whl
53979581e6acdd75b7ce94e6d3b70994f9f8cf1021316d388a159f7f388bdc7f  numpy-2.1.0rc1-cp313-cp313-win32.whl
ca195cd9d1d84b3498532968237774a6e06e2a4afe706b87172f1d033b95e230  numpy-2.1.0rc1-cp313-cp313-win_amd64.whl
77fa9826cbc7273e4bc3b7aa289b86936c942fe2c91bc35617c2417e14421592  numpy-2.1.0rc1-cp313-cp313t-macosx_10_13_x86_64.whl
140c5ce21f1eccb254e550c8431825cb716eb76e896202cffa7a0d2a843506da  numpy-2.1.0rc1-cp313-cp313t-macosx_11_0_arm64.whl
713cb46d266514db773de52af677aa931cc896a4f5e52f494449c4ff53ce6051  numpy-2.1.0rc1-cp313-cp313t-macosx_14_0_arm64.whl
3f79d241e4833a2a570b6e6639d2114d497011e48a351798bba81fda51560ab7  numpy-2.1.0rc1-cp313-cp313t-macosx_14_0_x86_64.whl
48a724dbfad6f4933e2c8a22239980e1b5bc16868df3450cc4ebeb9522b7902f  numpy-2.1.0rc1-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
06d14d20b7e98c8c06bb62e56f2b64747dd10c422bb8adbf1e6dd82cd8442e12  numpy-2.1.0rc1-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
98a1486861fa3c603a5a3ccd56fc45b9756372bb30f6fb559b898fc2fad82e0d  numpy-2.1.0rc1-cp313-cp313t-musllinux_1_1_x86_64.whl
50b3dab872001b87052532bd4da3879fda856a2cf6c9418c19bfc94dc290e259  numpy-2.1.0rc1-cp313-cp313t-musllinux_1_2_aarch64.whl
14dea4f0d62ddd1a7f9d7b0003b35a537ac41a2b6205deec8c9c25a8e01748b4  numpy-2.1.0rc1-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
4f9317da3aa64d0ee93950d3f319b3fe0169500e25c18223715cba39e89808bd  numpy-2.1.0rc1-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
0a5a25ab780b8c29e443824abefc6ca79047ceeb889a6f76d7b1953649498e93  numpy-2.1.0rc1-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
0816fd52956e14551d8d71319d4b4fcfa1bcb21641f2c603f4eb64c65b1e1009  numpy-2.1.0rc1-pp310-pypy310_pp73-win_amd64.whl
dc7ce867d277aa74555c67b93ef2a6f78bd7bd73e6c2bbafeb96f8bccd05b9d9  numpy-2.1.0rc1.tar.gz