NumPy 2.2.1 Release Notes

NumPy 2.2.1 is a patch release following 2.2.0. It fixes bugs found
after the 2.2.0 release and has several maintenance pins to work around
upstream changes.

There was some breakage in downstream projects following the 2.2.0
release due to updates to NumPy typing. Because of problems due to MyPy
defects, we recommend using basedpyright for type checking, it can be
installed from PyPI. The Pylance extension for Visual Studio Code is
also based on Pyright. Problems that persist when using basedpyright
should be reported as issues on the NumPy github site.

This release supports Python 3.10-3.13.

Contributors

A total of 9 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.

Charles Harris
Joren Hammudoglu
Matti Picus
Nathan Goldbaum
Peter Hawkins
Simon Altrogge
Thomas A Caswell
Warren Weckesser
Yang Wang +

Pull requests merged

A total of 12 pull requests were merged for this release.

#27935: MAINT: Prepare 2.2.x for further development
#27950: TEST: cleanups
#27958: BUG: fix use-after-free error in npy_hashtable.cpp (#27955)
#27959: BLD: add missing include
#27982: BUG:fix compile error libatomic link test to meson.build
#27990: TYP: Fix falsely rejected value types in ndarray.__setitem__
#27991: MAINT: Don't wrap #include <Python.h> with extern "C"
#27993: BUG: Fix segfault in stringdtype lexsort
#28006: MAINT: random: Tweak module code in mtrand.pyx to fix a Cython...
#28007: BUG: Cython API was missing NPY_UINTP.
#28021: CI: pin scipy-doctest to 1.5.1
#28044: TYP: allow None in operand sequence of nditer

Checksums

MD5

d3032be00b974d44aae687fd78a897b4  numpy-2.2.1-cp310-cp310-macosx_10_9_x86_64.whl
49863a39471cf191402da96512e52cb6  numpy-2.2.1-cp310-cp310-macosx_11_0_arm64.whl
31c912e2fa723b877f2d710c26332927  numpy-2.2.1-cp310-cp310-macosx_14_0_arm64.whl
95af4f6b620c76f9ccb8c5693c99737d  numpy-2.2.1-cp310-cp310-macosx_14_0_x86_64.whl
c1b113ad487a3bece6d7a70e0cf70f17  numpy-2.2.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
e93369ddbb637d9d5a820b2bb79588c4  numpy-2.2.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
b3de0a2c345541d2c9a322df360ca497  numpy-2.2.1-cp310-cp310-musllinux_1_2_aarch64.whl
e3e62b93245d9e37cc03ec3cfaf68118  numpy-2.2.1-cp310-cp310-musllinux_1_2_x86_64.whl
004063642d3c3792a3f5ff0241a3fa0f  numpy-2.2.1-cp310-cp310-win32.whl
462b0704ebfd79120edfe6431adc57f4  numpy-2.2.1-cp310-cp310-win_amd64.whl
a739a2dfbceaa1140e564424b2a57540  numpy-2.2.1-cp311-cp311-macosx_10_9_x86_64.whl
91731d46f4ce4b04db512400f4e76ccb  numpy-2.2.1-cp311-cp311-macosx_11_0_arm64.whl
93f50db664a6986c2ebed3ceb588f7cc  numpy-2.2.1-cp311-cp311-macosx_14_0_arm64.whl
8cc0d82b938d71f45a67c74e07ddc7fd  numpy-2.2.1-cp311-cp311-macosx_14_0_x86_64.whl
fc7b253096fc566bbcbadfdf6b034f1b  numpy-2.2.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
b88238965c708578f2c198d1c6e2cf70  numpy-2.2.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
df20d649bb023f98e487b229f01e9708  numpy-2.2.1-cp311-cp311-musllinux_1_2_aarch64.whl
e23d2bfbdb1bd1b2872c9e6e15f64dca  numpy-2.2.1-cp311-cp311-musllinux_1_2_x86_64.whl
cce4ebb9afc1470db243c2ab4cc6639b  numpy-2.2.1-cp311-cp311-win32.whl
c96783ee8ad6ce1efee94821929a12f5  numpy-2.2.1-cp311-cp311-win_amd64.whl
0b2024655573f96a595c7f5072205e84  numpy-2.2.1-cp312-cp312-macosx_10_13_x86_64.whl
22483d8935f5dc128393ad671fde7d8e  numpy-2.2.1-cp312-cp312-macosx_11_0_arm64.whl
61d38533acaa90fb24657f089d177a6c  numpy-2.2.1-cp312-cp312-macosx_14_0_arm64.whl
ecd4289c703356f5b4fd7e440bf94ce8  numpy-2.2.1-cp312-cp312-macosx_14_0_x86_64.whl
a05208461ea09079ae569414d82a606c  numpy-2.2.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
4c66f10580fa26d1d17b2bdda96a5fc5  numpy-2.2.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
60a01c86b1fc55e4ba8f2b41f690703b  numpy-2.2.1-cp312-cp312-musllinux_1_2_aarch64.whl
4bcac2b7f8510b0a6582b7d8661257be  numpy-2.2.1-cp312-cp312-musllinux_1_2_x86_64.whl
7c24a6a3b5c5b2c53c6807bf06c595c5  numpy-2.2.1-cp312-cp312-win32.whl
dc9f3c1eaade4da63e5f87e878e5805e  numpy-2.2.1-cp312-cp312-win_amd64.whl
9aacdedcb2cb3d6a45dfb823148e01cf  numpy-2.2.1-cp313-cp313-macosx_10_13_x86_64.whl
8a2598b081c8af4ea6f6bbccc8965882  numpy-2.2.1-cp313-cp313-macosx_11_0_arm64.whl
e58b8db1a97599ed02a630eb86616bb9  numpy-2.2.1-cp313-cp313-macosx_14_0_arm64.whl
be6871a4edd2cd92b147421b9290e047  numpy-2.2.1-cp313-cp313-macosx_14_0_x86_64.whl
6d3f141f3a8ecd04e1a1f7c1f89a8ca2  numpy-2.2.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
eba9d71e631521bd1d9882f8bfbc01d2  numpy-2.2.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
07f7ea0a7f9f6ce0ba5e016dff2a91e8  numpy-2.2.1-cp313-cp313-musllinux_1_2_aarch64.whl
a015f42afa15be8b87fc64120c245f18  numpy-2.2.1-cp313-cp313-musllinux_1_2_x86_64.whl
881b9b20e68b317850ad7b6306ac1c51  numpy-2.2.1-cp313-cp313-win32.whl
35bd751636dcea0ca0534ad9dee8057a  numpy-2.2.1-cp313-cp313-win_amd64.whl
7057313b668a4a26b5386203ebc040d9  numpy-2.2.1-cp313-cp313t-macosx_10_13_x86_64.whl
02031b405d028714126c26ffc5772f0e  numpy-2.2.1-cp313-cp313t-macosx_11_0_arm64.whl
73eb35111b027d6771d9a91eb21ad7ef  numpy-2.2.1-cp313-cp313t-macosx_14_0_arm64.whl
01f9a5eb7ec872d9682bb6a174897b35  numpy-2.2.1-cp313-cp313t-macosx_14_0_x86_64.whl
9bc363d2782931efa2648b42ce358a4c  numpy-2.2.1-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
b6492f49b50e892a7134baf2dba9f88d  numpy-2.2.1-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
a1c458a98cd9c7ad63f9c301398f4d63  numpy-2.2.1-cp313-cp313t-musllinux_1_2_aarch64.whl
38d2bf31247d9005c7a0197aa992cf1d  numpy-2.2.1-cp313-cp313t-musllinux_1_2_x86_64.whl
30e6acf4391728d0a3a5e3494bd4a2c8  numpy-2.2.1-cp313-cp313t-win32.whl
2100b60306e75288799fca60bd00b84f  numpy-2.2.1-cp313-cp313t-win_amd64.whl
f975551321147c307bbdff4889061b47  numpy-2.2.1-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
cefbc2de3aa5ef518ce652fdaab00c96  numpy-2.2.1-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
7e379c1d0a5be8e548e35fa7abe1d2c0  numpy-2.2.1-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
3cba151351656a83e4c84c942cf490e7  numpy-2.2.1-pp310-pypy310_pp73-win_amd64.whl
57c5757508a50d1daefa4b689e9701cb  numpy-2.2.1.tar.gz

SHA256

5edb4e4caf751c1518e6a26a83501fda79bff41cc59dac48d70e6d65d4ec4440  numpy-2.2.1-cp310-cp310-macosx_10_9_x86_64.whl
aa3017c40d513ccac9621a2364f939d39e550c542eb2a894b4c8da92b38896ab  numpy-2.2.1-cp310-cp310-macosx_11_0_arm64.whl
61048b4a49b1c93fe13426e04e04fdf5a03f456616f6e98c7576144677598675  numpy-2.2.1-cp310-cp310-macosx_14_0_arm64.whl
7671dc19c7019103ca44e8d94917eba8534c76133523ca8406822efdd19c9308  numpy-2.2.1-cp310-cp310-macosx_14_0_x86_64.whl
4250888bcb96617e00bfa28ac24850a83c9f3a16db471eca2ee1f1714df0f957  numpy-2.2.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
a7746f235c47abc72b102d3bce9977714c2444bdfaea7888d241b4c4bb6a78bf  numpy-2.2.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
059e6a747ae84fce488c3ee397cee7e5f905fd1bda5fb18c66bc41807ff119b2  numpy-2.2.1-cp310-cp310-musllinux_1_2_aarch64.whl
f62aa6ee4eb43b024b0e5a01cf65a0bb078ef8c395e8713c6e8a12a697144528  numpy-2.2.1-cp310-cp310-musllinux_1_2_x86_64.whl
48fd472630715e1c1c89bf1feab55c29098cb403cc184b4859f9c86d4fcb6a95  numpy-2.2.1-cp310-cp310-win32.whl
b541032178a718c165a49638d28272b771053f628382d5e9d1c93df23ff58dbf  numpy-2.2.1-cp310-cp310-win_amd64.whl
40f9e544c1c56ba8f1cf7686a8c9b5bb249e665d40d626a23899ba6d5d9e1484  numpy-2.2.1-cp311-cp311-macosx_10_9_x86_64.whl
f9b57eaa3b0cd8db52049ed0330747b0364e899e8a606a624813452b8203d5f7  numpy-2.2.1-cp311-cp311-macosx_11_0_arm64.whl
bc8a37ad5b22c08e2dbd27df2b3ef7e5c0864235805b1e718a235bcb200cf1cb  numpy-2.2.1-cp311-cp311-macosx_14_0_arm64.whl
9036d6365d13b6cbe8f27a0eaf73ddcc070cae584e5ff94bb45e3e9d729feab5  numpy-2.2.1-cp311-cp311-macosx_14_0_x86_64.whl
51faf345324db860b515d3f364eaa93d0e0551a88d6218a7d61286554d190d73  numpy-2.2.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
38efc1e56b73cc9b182fe55e56e63b044dd26a72128fd2fbd502f75555d92591  numpy-2.2.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
31b89fa67a8042e96715c68e071a1200c4e172f93b0fbe01a14c0ff3ff820fc8  numpy-2.2.1-cp311-cp311-musllinux_1_2_aarch64.whl
4c86e2a209199ead7ee0af65e1d9992d1dce7e1f63c4b9a616500f93820658d0  numpy-2.2.1-cp311-cp311-musllinux_1_2_x86_64.whl
b34d87e8a3090ea626003f87f9392b3929a7bbf4104a05b6667348b6bd4bf1cd  numpy-2.2.1-cp311-cp311-win32.whl
360137f8fb1b753c5cde3ac388597ad680eccbbbb3865ab65efea062c4a1fd16  numpy-2.2.1-cp311-cp311-win_amd64.whl
694f9e921a0c8f252980e85bce61ebbd07ed2b7d4fa72d0e4246f2f8aa6642ab  numpy-2.2.1-cp312-cp312-macosx_10_13_x86_64.whl
3683a8d166f2692664262fd4900f207791d005fb088d7fdb973cc8d663626faa  numpy-2.2.1-cp312-cp312-macosx_11_0_arm64.whl
780077d95eafc2ccc3ced969db22377b3864e5b9a0ea5eb347cc93b3ea900315...

NumPy 2.2.0 Release Notes

The NumPy 2.2.0 release is quick release that brings us back into sync
with the usual twice yearly release cycle. There have been an number of
small cleanups, as well as work bringing the new StringDType to
completion and improving support for free threaded Python. Highlights
are:

New functions matvec and vecmat, see below.
Many improved annotations.
Improved support for the new StringDType.
Improved support for free threaded Python
Fixes for f2py

This release supports Python versions 3.10-3.13.

Deprecations

_add_newdoc_ufunc is now deprecated. ufunc.__doc__ = newdoc
should be used instead.

(gh-27735)

Expired deprecations

bool(np.array([])) and other empty arrays will now raise an error.
Use arr.size > 0 instead to check whether an array has no
elements.

(gh-27160)

Compatibility notes

numpy.cov now properly transposes single-row (2d
array) design matrices when rowvar=False. Previously, single-row
design matrices would return a scalar in this scenario, which is not
correct, so this is a behavior change and an array of the
appropriate shape will now be returned.

(gh-27661)

New Features

New functions for matrix-vector and vector-matrix products

Two new generalized ufuncs were defined:
- numpy.matvec - matrix-vector product, treating the
  arguments as stacks of matrices and column vectors,
  respectively.
- numpy.vecmat - vector-matrix product, treating the
  arguments as stacks of column vectors and matrices,
  respectively. For complex vectors, the conjugate is taken.
These add to the existing numpy.matmul as well as to
numpy.vecdot, which was added in numpy 2.0.

Note that numpy.matmul never takes a complex
conjugate, also not when its left input is a vector, while both
numpy.vecdot and numpy.vecmat do take
the conjugate for complex vectors on the left-hand side (which are
taken to be the ones that are transposed, following the physics
convention).

(gh-25675)
np.complexfloating[T, T] can now also be written as
np.complexfloating[T]

(gh-27420)
UFuncs now support __dict__ attribute and allow overriding
__doc__ (either directly or via ufunc.__dict__["__doc__"]).
__dict__ can be used to also override other properties, such as
__module__ or __qualname__.

(gh-27735)
The "nbit" type parameter of np.number and its subtypes now
defaults to typing.Any. This way, type-checkers will infer
annotations such as x: np.floating as x: np.floating[Any], even
in strict mode.

(gh-27736)

Improvements

The datetime64 and timedelta64 hashes now correctly match the
Pythons builtin datetime and timedelta ones. The hashes now
evaluated equal even for equal values with different time units.

(gh-14622)
Fixed a number of issues around promotion for string ufuncs with
StringDType arguments. Mixing StringDType and the fixed-width DTypes
using the string ufuncs should now generate much more uniform
results.

(gh-27636)
Improved support for empty memmap. Previously an empty
memmap would fail unless a non-zero offset was set.
Now a zero-size memmap is supported even if
offset=0. To achieve this, if a memmap is mapped to
an empty file that file is padded with a single byte.

(gh-27723)
A regression has been fixed which allows F2PY users to expose variables
to Python in modules with only assignments, and also fixes situations
where multiple modules are present within a single source file.

(gh-27695)

Performance improvements and changes

Improved multithreaded scaling on the free-threaded build when many
threads simultaneously call the same ufunc operations.

(gh-27896)
NumPy now uses fast-on-failure attribute lookups for protocols. This
can greatly reduce overheads of function calls or array creation
especially with custom Python objects. The largest improvements will
be seen on Python 3.12 or newer.

(gh-27119)
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.
OpenBLAS on windows is linked without quadmath, simplifying
licensing
Due to a regression in OpenBLAS on windows, the performance
improvements when using multiple threads for OpenBLAS 0.3.26 were
reverted.

(gh-27147)
NumPy now indicates hugepages also for large np.zeros allocations
on linux. Thus should generally improve performance.

(gh-27808)

Changes

numpy.fix now won't perform casting to a floating
data-type for integer and boolean data-type input arrays.

(gh-26766)
The type annotations of numpy.float64 and numpy.complex128 now
reflect that they are also subtypes of the built-in float and
complex types, respectively. This update prevents static
type-checkers from reporting errors in cases such as:
```
x: float = numpy.float64(6.28)  # valid
z: complex = numpy.complex128(-1j)  # valid
```
(gh-27334)
The repr of arrays large enough to be summarized (i.e., where
elements are replaced with ...) now includes the shape of the
array, similar to what already was the case for arrays with zero
size and non-obvious shape. With this change, the shape is always
given when it cannot be inferred from the values. Note that while
written as shape=..., this argument cannot actually be passed in
to the np.array constructor. If you encounter problems, e.g., due
to failing doctests, you can use the print option legacy=2.1 to
get the old behaviour.

(gh-27482)
Calling __array_wrap__ directly on NumPy arrays or scalars now
does the right thing when return_scalar is passed (Added in NumPy
2). It is further safe now to call the scalar __array_wrap__ on a
non-scalar result.

(gh-27807)
Bump the musllinux CI image and wheels to 1_2 from 1_1. This is because
1_1 is end of life.

(gh-27088)
The NEP 50 promotion state settings are now removed. They were always
meant as temporary means for testing. A warning will be given if the
environment variable is set to anything but NPY_PROMOTION_STATE=weak
while _set_promotion_state and _get_promotion_state are removed. In
case code used _no_nep50_warning, a contextlib.nullcontext could be
used to replace it when not available.

(gh-27156)

Checksums

MD5

1b58b9e275e80364cd02dafb3f8daf35  numpy-2.2.0-cp310-cp310-macosx_10_9_x86_64.whl
7d3773d9b665b2d7cfec0cc0b760e69e  numpy-2.2.0-cp310-cp310-macosx_11_0_arm64.whl
8ef666a462d3765ccfd5288f2fdf8e08  numpy-2.2.0-cp310-cp310-macosx_14_0_arm64.whl
e4f9e3117075ffe53d7993253c774158  numpy-2.2.0-cp310-cp310-macosx_14_0_x86_64.whl
fd60e410e5db402a2d0c0cb4dd23281d  numpy-2.2.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
64c083cdbd91eb8670cd72b619f3a039  numpy-2.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
c3c75c2299f5163770e2e42f0dee5276  numpy-2.2.0-cp310-cp310-musllinux_1_2_aarch64.whl
f6ab05f787221bbaf8fb4a9778af5467  numpy-2.2.0-cp310-cp310-musllinux_1_2_x86_64.whl
9b04caec124cadf90005ccdb662aad9f  numpy-2.2.0-cp310-cp310-win32.whl
58934f23b6bc71fb1f984b688c1c6136  numpy-2.2.0-cp310-cp310-win_amd64.whl
769e53438154e53ba490fb4f816c083e  numpy-2.2.0-cp311-cp311-macosx_10_9_x86_64.whl
aa8060c013c04133b63780025eef4451  numpy-2.2.0-cp311-cp311-macosx_11_0_arm64.whl
72c10ef28a0ddffe6bf2495954ab82e0  numpy-2.2.0-cp311-cp311-macosx_14_0_arm64.whl
946b2510c86eb48e374e6987582c9b46  numpy-2.2.0-cp311-cp311-macosx_14_0_x86_64.whl
3f5203ae901ddd78cb298582eda07627  numpy-2.2.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
fd14624d40100a5eb0181bf393394448  numpy-2.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
7c86d51d89dbc5a6860d65641ea131ef  numpy-2.2.0-cp311-cp311-musllinux_1_2_aarch64.whl
895c6588c74019b94fb3c740b9e9a0f5  numpy-2.2.0-cp311-cp311-musllinux_1_2_x86_64.whl
1468ae1cb59a43991b199cfa6f1e5679  numpy-2.2.0-cp311-cp311-win32.whl
48a3792698a81917320b91a30c0bacf4  numpy-2.2.0-cp311-cp311-win_amd64.whl
db4377351f167d82adc66b16965d11bd  numpy-2.2.0-cp312-cp312-macosx_10_13_x86_64.whl
3f3978b5e480ed18d55b1799d9a534ff  numpy-2.2.0-cp312-cp312-macosx_11_0_arm64.whl
584b4063eb66688b607f7e7bdca58011  numpy-2.2.0-cp312-cp312-macosx_14_0...

NumPy 2.2.0 Release Notes

The NumPy 2.2.0 release is a quick release that brings us back into sync
with the usual twice yearly release cycle. There have been an number of
small cleanups, as well as work bringing the new StringDType to
completion and improving support for free threaded Python. Highlights
are:

New functions matvec and vecmat, see below.
Many improved annotations.
Improved support for the new StringDType.
Improved support for free threaded Python
Fixes for f2py

This release supports Python versions 3.10-3.13.

Deprecations

_add_newdoc_ufunc is now deprecated. ufunc.__doc__ = newdoc
should be used instead.

(gh-27735)

Expired deprecations

bool(np.array([])) and other empty arrays will now raise an error.
Use arr.size > 0 instead to check whether an array has no
elements.

(gh-27160)

Compatibility notes

numpy.cov now properly transposes single-row (2d array) design matrices
when rowvar=False. Previously, single-row design matrices would return a
scalar in this scenario, which is not correct, so this is a behavior change
and an array of the appropriate shape will now be returned.

(gh-27661)

New Features

New functions for matrix-vector and vector-matrix products

Two new generalized ufuncs were defined:
- numpy.matvec - matrix-vector product, treating the
  arguments as stacks of matrices and column vectors,
  respectively.
- numpy.vecmat - vector-matrix product, treating the
  arguments as stacks of column vectors and matrices,
  respectively. For complex vectors, the conjugate is taken.
These add to the existing numpy.matmul as well as to
numpy.vecdot, which was added in numpy 2.0.

Note that numpy.matmul never takes a complex conjugate, also not when its
left input is a vector, while both numpy.vecdot and numpy.vecmat do
take the conjugate for complex vectors on the left-hand side (which are
taken to be the ones that are transposed, following the physics
convention).

(gh-25675)
np.complexfloating[T, T] can now also be written as
np.complexfloating[T]

(gh-27420)
UFuncs now support __dict__ attribute and allow overriding
__doc__ (either directly or via ufunc.__dict__["__doc__"]).
__dict__ can be used to also override other properties, such as
__module__ or __qualname__.

(gh-27735)
The "nbit" type parameter of np.number and its subtypes now
defaults to typing.Any. This way, type-checkers will infer
annotations such as x: np.floating as x: np.floating[Any], even
in strict mode.

(gh-27736)

Improvements

The datetime64 and timedelta64 hashes now correctly match the
Pythons builtin datetime and timedelta ones. The hashes now
evaluated equal even for equal values with different time units.

(gh-14622)
Fixed a number of issues around promotion for string ufuncs with
StringDType arguments. Mixing StringDType and the fixed-width DTypes
using the string ufuncs should now generate much more uniform
results.

(gh-27636)
Improved support for empty memmap. Previously an empty memmap would
fail unless a non-zero offset was set. Now a zero-size memmap is
supported even if offset=0. To achieve this, if a memmap is mapped to
an empty file that file is padded with a single byte.

(gh-27723)
f2py handles multiple modules and exposes variables again. A regression
has been fixed which allows F2PY users to expose variables to Python in
modules with only assignments, and also fixes situations where multiple
modules are present within a single source file.

(gh-27695)

Performance improvements and changes

NumPy now uses fast-on-failure attribute lookups for protocols. This
can greatly reduce overheads of function calls or array creation
especially with custom Python objects. The largest improvements will
be seen on Python 3.12 or newer.

(gh-27119)
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.
OpenBLAS on windows is linked without quadmath, simplifying
licensing
Due to a regression in OpenBLAS on windows, the performance
improvements when using multiple threads for OpenBLAS 0.3.26 were
reverted.

(gh-27147)
NumPy now indicates hugepages also for large np.zeros allocations
on linux. Thus should generally improve performance.

(gh-27808)

Changes

numpy.fix now won't perform casting to a floating
data-type for integer and boolean data-type input arrays.

(gh-26766)
The type annotations of numpy.float64 and numpy.complex128 now reflect
that they are also subtypes of the built-in float and complex types,
respectively. This update prevents static type-checkers from reporting
errors in cases such as:
```
x: float = numpy.float64(6.28)  # valid
z: complex = numpy.complex128(-1j)  # valid
```
(gh-27334)
The repr of arrays large enough to be summarized (i.e., where
elements are replaced with ...) now includes the shape of the
array, similar to what already was the case for arrays with zero
size and non-obvious shape. With this change, the shape is always
given when it cannot be inferred from the values. Note that while
written as shape=..., this argument cannot actually be passed in
to the np.array constructor. If you encounter problems, e.g., due
to failing doctests, you can use the print option legacy=2.1 to
get the old behaviour.

(gh-27482)
Calling __array_wrap__ directly on NumPy arrays or scalars now
does the right thing when return_scalar is passed (Added in NumPy
2). It is further safe now to call the scalar __array_wrap__ on a
non-scalar result.

(gh-27807)
Bump the musllinux CI image and wheels to 1_2 from 1_1. This is because
1_1 is end of life.

(gh-27088)
NEP 50 promotion state option removed

The NEP 50 promotion state settings are now removed. They were always meant as
temporary means for testing. A warning will be given if the environment
variable is set to anything but NPY_PROMOTION_STATE=weak while
_set_promotion_state and _get_promotion_state are removed. In case code
used _no_nep50_warning, a contextlib.nullcontext could be used to replace
it when not available.

(gh-27156)

Checksums

MD5

83746dfc1b7774a6677a69c705b83afe  numpy-2.2.0rc1-cp310-cp310-macosx_10_9_x86_64.whl
e69c45cf5ea08fdf2a5527190a7d6549  numpy-2.2.0rc1-cp310-cp310-macosx_11_0_arm64.whl
d4f8048977139cb229875c201f605369  numpy-2.2.0rc1-cp310-cp310-macosx_14_0_arm64.whl
8710578b7f4ceef7f73b6d234ad3a82a  numpy-2.2.0rc1-cp310-cp310-macosx_14_0_x86_64.whl
899d1f24d8e5570695a024908d100174  numpy-2.2.0rc1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
cb768ee568bed2e4f55d47f43c655bc2  numpy-2.2.0rc1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
5a40726db153ca1984598323cc59eb9b  numpy-2.2.0rc1-cp310-cp310-musllinux_1_2_aarch64.whl
450e5e05bdc5551c0a4df2a8d7f09925  numpy-2.2.0rc1-cp310-cp310-musllinux_1_2_x86_64.whl
1c34c86b0abaa5d2a75677044a7fca07  numpy-2.2.0rc1-cp310-cp310-win32.whl
d679ad13f3892325fd4542931ee74852  numpy-2.2.0rc1-cp310-cp310-win_amd64.whl
a7a8cf5fa2e3d4bd0131ad48c0215f50  numpy-2.2.0rc1-cp311-cp311-macosx_10_9_x86_64.whl
aa6c629290d8b05b44fbbf805fb39dbe  numpy-2.2.0rc1-cp311-cp311-macosx_11_0_arm64.whl
a04fe8ac96a5226686ec4190db8511d6  numpy-2.2.0rc1-cp311-cp311-macosx_14_0_arm64.whl
50aedb2a570a7867e860d98eb816bec4  numpy-2.2.0rc1-cp311-cp311-macosx_14_0_x86_64.whl
cd034c5179ee4cc5669ae36be0deb6ab  numpy-2.2.0rc1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
67e3336cdcdcf72cd07978a465e61ebd  numpy-2.2.0rc1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
45456522fc3996937f1b1ad8bd7f85b2  numpy-2.2.0rc1-cp311-cp311-musllinux_1_2_aarch64.whl
244dcedc05e96c843853738bc2d37bdb  numpy-2.2.0rc1-cp311-cp311-musllinux_1_2_x86_64.whl
da24dd620b6509740a1d8aebe4d1306c  numpy-2.2.0rc1-cp311-cp311-win32.whl
472e5f997dc437b8115ba4ef70a6a266  numpy-2.2.0rc1-cp311-cp311-win_amd64.whl
6e4ec4f92f8b0768d679419360098a89  numpy-2.2.0rc1-cp312-cp312-macosx_10_13_x86_64.whl
e15a1756fbe98aa61cb8d98de1d516fc  numpy-2.2.0rc1-cp312-cp312-macosx_11_0_arm64.whl
6c58bba6f453ad22a651f6f0f6416899  numpy-2.2.0rc1-cp312-cp312-macosx_14_0_arm64.whl
1a00dd2343f8e...

NumPy 2.1.3 Release Notes

NumPy 2.1.3 is a maintenance release that fixes bugs and regressions
discovered after the 2.1.2 release. This release also adds support
for free threaded Python 3.13 on Windows.

The Python versions supported by this release are 3.10-3.13.

Improvements

Fixed a number of issues around promotion for string ufuncs with
StringDType arguments. Mixing StringDType and the fixed-width DTypes
using the string ufuncs should now generate much more uniform
results.

(gh-27636)

Changes

numpy.fix now won't perform casting to a floating
data-type for integer and boolean data-type input arrays.

(gh-26766)

Contributors

A total of 15 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.

Abhishek Kumar +
Austin +
Benjamin A. Beasley +
Charles Harris
Christian Lorentzen
Marcel Telka +
Matti Picus
Michael Davidsaver +
Nathan Goldbaum
Peter Hawkins
Raghuveer Devulapalli
Ralf Gommers
Sebastian Berg
dependabot[bot]
kp2pml30 +

Pull requests merged

A total of 21 pull requests were merged for this release.

#27512: MAINT: prepare 2.1.x for further development
#27537: MAINT: Bump actions/cache from 4.0.2 to 4.1.1
#27538: MAINT: Bump pypa/cibuildwheel from 2.21.2 to 2.21.3
#27539: MAINT: MSVC does not support #warning directive
#27543: BUG: Fix user dtype can-cast with python scalar during promotion
#27561: DEV: bump python to 3.12 in environment.yml
#27562: BLD: update vendored Meson to 1.5.2
#27563: BUG: weighted quantile for some zero weights (#27549)
#27565: MAINT: Use miniforge for macos conda test.
#27566: BUILD: satisfy gcc-13 pendantic errors
#27569: BUG: handle possible error for PyTraceMallocTrack
#27570: BLD: start building Windows free-threaded wheels [wheel build]
#27571: BUILD: vendor tempita from Cython
#27574: BUG: Fix warning "differs in levels of indirection" in npy_atomic.h...
#27592: MAINT: Update Highway to latest
#27593: BUG: Adjust numpy.i for SWIG 4.3 compatibility
#27616: BUG: Fix Linux QEMU CI workflow
#27668: BLD: Do not set __STDC_VERSION__ to zero during build
#27669: ENH: fix wasm32 runtime type error in numpy._core
#27672: BUG: Fix a reference count leak in npy_find_descr_for_scalar.
#27673: BUG: fixes for StringDType/unicode promoters

Checksums

MD5

3f2f22827dd321ae86b5ab4fa888d0db  numpy-2.1.3-cp310-cp310-macosx_10_9_x86_64.whl
13da2761d1abe71731a2806537369115  numpy-2.1.3-cp310-cp310-macosx_11_0_arm64.whl
5aef4a78b69cd90d0f6fff8f88817991  numpy-2.1.3-cp310-cp310-macosx_14_0_arm64.whl
12da7f09cd5707634878f85845c9de10  numpy-2.1.3-cp310-cp310-macosx_14_0_x86_64.whl
5b999693362815b56855533469aea0ca  numpy-2.1.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
8c49f457127bfb4f167c91583e5167af  numpy-2.1.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
f31c0e80b18afc0c04cada401cbe0358  numpy-2.1.3-cp310-cp310-musllinux_1_1_x86_64.whl
2c0709812e27bcaf74d75ac8ed45614b  numpy-2.1.3-cp310-cp310-musllinux_1_2_aarch64.whl
a65b28800e78942b9e60e03e96cfd0c0  numpy-2.1.3-cp310-cp310-win32.whl
d8358545732fe4ee1ecf407b06567d81  numpy-2.1.3-cp310-cp310-win_amd64.whl
34942f9a1391532e2c3168043c0021d5  numpy-2.1.3-cp311-cp311-macosx_10_9_x86_64.whl
0d69ec06e303b5112788db68a8fdde1b  numpy-2.1.3-cp311-cp311-macosx_11_0_arm64.whl
da1988c8d3a9db5947a2bd51290b8b95  numpy-2.1.3-cp311-cp311-macosx_14_0_arm64.whl
b5eba73c2abaf5a81535f4b1034fe8d2  numpy-2.1.3-cp311-cp311-macosx_14_0_x86_64.whl
63cc090209718aa1d0f0fbd3fd03bc0b  numpy-2.1.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
55f14ca7b55554d4a043369ae5f1837f  numpy-2.1.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
4e58e0645d81ff84c0fb75311d2a97d6  numpy-2.1.3-cp311-cp311-musllinux_1_1_x86_64.whl
30235088a5f86d1f343bfec458f6292d  numpy-2.1.3-cp311-cp311-musllinux_1_2_aarch64.whl
c80a03952b2f4950f1eb9d1656413fec  numpy-2.1.3-cp311-cp311-win32.whl
d8c1a5a441b89591af8f09dfa0b2d4d5  numpy-2.1.3-cp311-cp311-win_amd64.whl
2cebcea71e71e8b09a25179b240ee240  numpy-2.1.3-cp312-cp312-macosx_10_13_x86_64.whl
faf5df4bd35ca362795cda193da49591  numpy-2.1.3-cp312-cp312-macosx_11_0_arm64.whl
573f195910fc3b3e9ac5379816280f89  numpy-2.1.3-cp312-cp312-macosx_14_0_arm64.whl
900548b2acb82ed0e306943fb68de802  numpy-2.1.3-cp312-cp312-macosx_14_0_x86_64.whl
81cded28bb87c4987b1d975fe768c3a1  numpy-2.1.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
2b83cb346bca97475fa5e39e704c45f1  numpy-2.1.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
06d8593cb7a2aae157e028c3d4cb3c96  numpy-2.1.3-cp312-cp312-musllinux_1_1_x86_64.whl
eea8b148a6a2fee37b87291043e00bda  numpy-2.1.3-cp312-cp312-musllinux_1_2_aarch64.whl
d407b7c48457789914f28004f41d6ea2  numpy-2.1.3-cp312-cp312-win32.whl
117574ee1a645e63a6d69e20c8673665  numpy-2.1.3-cp312-cp312-win_amd64.whl
0c9ffd1f1f1e96186f30a578b85da653  numpy-2.1.3-cp313-cp313-macosx_10_13_x86_64.whl
cd430b2caf09d21680616aef5d4a439d  numpy-2.1.3-cp313-cp313-macosx_11_0_arm64.whl
b431935148221b79bda9490b1d069e3c  numpy-2.1.3-cp313-cp313-macosx_14_0_arm64.whl
b3ff577c78097b187bd58f20b6e88642  numpy-2.1.3-cp313-cp313-macosx_14_0_x86_64.whl
8186f86f8d94a5505e6dcebe6c056ab7  numpy-2.1.3-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
2c5b2381a4a4e3d9865ccb346d44a7ed  numpy-2.1.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
85786d12388d60b904c02eb12df55b37  numpy-2.1.3-cp313-cp313-musllinux_1_1_x86_64.whl
da68282c0418a22730643906e5dd58a1  numpy-2.1.3-cp313-cp313-musllinux_1_2_aarch64.whl
fe47e181a70d3e865e5d6a27e5fa71cd  numpy-2.1.3-cp313-cp313-win32.whl
8b7f290784c95cf620e0ac1af5470f1d  numpy-2.1.3-cp313-cp313-win_amd64.whl
4f0c3f8c81cb6bd43a9f1f7bef7db82d  numpy-2.1.3-cp313-cp313t-macosx_10_13_x86_64.whl
133905fd003c9504fc5bb9ce71e4103b  numpy-2.1.3-cp313-cp313t-macosx_11_0_arm64.whl
12fe4f265dbda251309f109cbcd46f07  numpy-2.1.3-cp313-cp313t-macosx_14_0_arm64.whl
b60e418506b969e6df2c0d600bf3c6d4  numpy-2.1.3-cp313-cp313t-macosx_14_0_x86_64.whl
c2b7160b748f4c1c483a7954e5024250  numpy-2.1.3-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
8097ddb45c8c821085c19d940bcbe6de  numpy-2.1.3-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
209f55dc1ed6da23a5ea3e11ca962308  numpy-2.1.3-cp313-cp313t-musllinux_1_1_x86_64.whl
06a1792849b601c7bdd38e39bc5cb5f1  numpy-2.1.3-cp313-cp313t-musllinux_1_2_aarch64.whl
86630bf207e8cbe6933232cb2a47a6c0  numpy-2.1.3-cp313-cp313t-win32.whl
6af9109b82c0acdcf8b0e81dc0e4c517  numpy-2.1.3-cp313-cp313t-win_amd64.whl
c7e821e086346afc0078acb237f30431  numpy-2.1.3-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
5b938b2da78b1c84044df8cdb2e8e63a  numpy-2.1.3-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
ef251f3b6aa022b1c2fac14889d6d9d3  numpy-2.1.3-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
356c7bb6067ae0dccc4a54efc1879e74  numpy-2.1.3-pp310-pypy310_pp73-win_amd64.whl
11096358375945114577a0c82b2c6038  numpy-2.1.3.tar.gz

SHA256

c894b4305373b9c5576d7a12b473702afdf48ce5369c074ba304cc5ad8730dff  numpy-2.1.3-cp310-cp310-macosx_10_9_x86_64.whl
b47fbb433d3260adcd51eb54f92a2ffbc90a4595f8970ee00e064c644ac788f5  numpy-2.1.3-cp310-cp310-macosx_11_0_arm64.whl
825656d0743699c529c5943554d223c021ff0494ff1442152ce887ef4f7561a1  numpy-2.1.3-cp310-cp310-macosx_14_0_arm64.whl
6a4825252fcc430a182ac4dee5a505053d262c807f8a924603d411f6718b88fd  numpy-2.1.3-cp310-cp310-macosx_14_0_x86_64.whl
e711e02f49e176a01d0349d82cb5f05ba4db7d5e7e0defd026328e5cfb3226d3  numpy-2.1.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
78574ac2d1a4a02421f25da9559850d59457bac82f2b8d7a44fe83a64f770098  numpy-2.1.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
c7662f0e3673fe4e832fe07b65c50342ea27d989f92c80355658c7f888fcc83c  numpy-2.1.3-cp310-cp310-musllinux_1_1_x86_64.whl
fa2d1337dc61c8dc417fbccf20f6d1e139896a30721b7f1e832b2bb6ef4eb6c4  numpy-2.1.3-cp310-cp310-musllinux_1_2_aarch64.whl
72dcc4a35a8515d83e76b58fdf8113a5c969ccd505c8a946759b24e3182d1f23  numpy-2.1.3-cp310-cp310-win32.whl
ecc76a9ba2911d8d37ac01de72834d8849e55473457558e12995f4cd53e778e0  numpy-2.1.3-cp310-cp310-win_amd64.whl
4d1167c53b93f1f5d8a139a742b3c6f4d429b54e74e6b57d0eff40045187b15d  numpy-2.1.3-cp311-cp311-macosx_10_9_x86_64.whl
c80e4a09b3d95b4e1cac08643f1152fa71a0a821a2d4277334c88d54b2219a41  numpy-2.1.3-cp311-cp311-macosx_11_0_arm64.whl
576a1c...

NumPy 2.1.2 Release Notes

NumPy 2.1.2 is a maintenance release that fixes bugs and regressions
discovered after the 2.1.1 release.

The Python versions supported by this release are 3.10-3.13.

Contributors

A total of 11 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.

Charles Harris
Chris Sidebottom
Ishan Koradia +
João Eiras +
Katie Rust +
Marten van Kerkwijk
Matti Picus
Nathan Goldbaum
Peter Hawkins
Pieter Eendebak
Slava Gorloff +

Pull requests merged

A total of 14 pull requests were merged for this release.

#27333: MAINT: prepare 2.1.x for further development
#27400: BUG: apply critical sections around populating the dispatch cache
#27406: BUG: Stub out get_build_msvc_version if distutils.msvccompiler...
#27416: BUILD: fix missing include for std::ptrdiff_t for C++23 language...
#27433: BLD: pin setuptools to avoid breaking numpy.distutils
#27437: BUG: Allow unsigned shift argument for np.roll
#27439: BUG: Disable SVE VQSort
#27471: BUG: rfftn axis bug
#27479: BUG: Fix extra decref of PyArray_UInt8DType.
#27480: CI: use PyPI not scientific-python-nightly-wheels for CI doc...
#27481: MAINT: Check for SVE support on demand
#27484: BUG: initialize the promotion state to be weak
#27501: MAINT: Bump pypa/cibuildwheel from 2.20.0 to 2.21.2
#27506: BUG: avoid segfault on bad arguments in ndarray.__array_function__

Checksums

MD5

4aae28b7919b126485c1aaccee37a6ba  numpy-2.1.2-cp310-cp310-macosx_10_9_x86_64.whl
172614423a82ef73d8752ad8a59cbafc  numpy-2.1.2-cp310-cp310-macosx_11_0_arm64.whl
5ee5e7a8a892cbe96ee228ca5fe7546b  numpy-2.1.2-cp310-cp310-macosx_14_0_arm64.whl
9ce6f9222dfabd32e66b883f1fe015aa  numpy-2.1.2-cp310-cp310-macosx_14_0_x86_64.whl
291da8bfeb7c9a3491ec35ecb2596335  numpy-2.1.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
9317d9b049f09c0193f074a6458cf79b  numpy-2.1.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
1f2c121533715d8b099d6498e4498f81  numpy-2.1.2-cp310-cp310-musllinux_1_1_x86_64.whl
2834df46e2cb2e81cbe4fd1ce9b96b4b  numpy-2.1.2-cp310-cp310-musllinux_1_2_aarch64.whl
cbc3ae2c176324fe2a9c04ec0aff181f  numpy-2.1.2-cp310-cp310-win32.whl
e4d74f9d188dc3fe7a65adf8c01e98cc  numpy-2.1.2-cp310-cp310-win_amd64.whl
cbcece9c21ed1daf60f3729a37b32266  numpy-2.1.2-cp311-cp311-macosx_10_9_x86_64.whl
0e62474993ff6faca9c467f68cc16ceb  numpy-2.1.2-cp311-cp311-macosx_11_0_arm64.whl
8747e85e09b2000a0af5a8226740dc92  numpy-2.1.2-cp311-cp311-macosx_14_0_arm64.whl
34e7f3591ce81926518a36c92038a056  numpy-2.1.2-cp311-cp311-macosx_14_0_x86_64.whl
0ec3e617161b42d643aaa4b8d3e477f5  numpy-2.1.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
e2a6a419b4672bfb4f3f6a98c0e575bb  numpy-2.1.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
8c14b4d03fc8672e43eddd3ede89be09  numpy-2.1.2-cp311-cp311-musllinux_1_1_x86_64.whl
dc183e12b24317bf210fb093da598d29  numpy-2.1.2-cp311-cp311-musllinux_1_2_aarch64.whl
4918f2c32ca3be20c7c5d8551e649757  numpy-2.1.2-cp311-cp311-win32.whl
a8991919b6fae3c7a77c260f60a5e2e2  numpy-2.1.2-cp311-cp311-win_amd64.whl
879f307d16f9222c49508be5ea6491fc  numpy-2.1.2-cp312-cp312-macosx_10_13_x86_64.whl
fe9dfac7bee0cff178737e1706aee61a  numpy-2.1.2-cp312-cp312-macosx_11_0_arm64.whl
1f0c671db3294f4df8bffedc41a2e37f  numpy-2.1.2-cp312-cp312-macosx_14_0_arm64.whl
d131c4bd6ba29b05a5b7fa74e87a0506  numpy-2.1.2-cp312-cp312-macosx_14_0_x86_64.whl
8f9cca33590be334d44cc026a3716966  numpy-2.1.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
3692a9290dd430e56e1b15387c25b7af  numpy-2.1.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
3549439284dbb1a05785b535c3de60d9  numpy-2.1.2-cp312-cp312-musllinux_1_1_x86_64.whl
b9934410f20505e5c4b70974cd8fdc26  numpy-2.1.2-cp312-cp312-musllinux_1_2_aarch64.whl
96759e3380e4893b9b88d5d498d856b2  numpy-2.1.2-cp312-cp312-win32.whl
f94c7405ed72a136e374ab82400fefdc  numpy-2.1.2-cp312-cp312-win_amd64.whl
2ea775cb4da02f39edf3089af60bddd5  numpy-2.1.2-cp313-cp313-macosx_10_13_x86_64.whl
354d0970154dd002573f4291e0e9de76  numpy-2.1.2-cp313-cp313-macosx_11_0_arm64.whl
bbfee75640b337e12f894d0b54727d66  numpy-2.1.2-cp313-cp313-macosx_14_0_arm64.whl
a443fff50571df87f687ad55c9060d25  numpy-2.1.2-cp313-cp313-macosx_14_0_x86_64.whl
9f8cd7de5b5aa5ad8ba52608a4b0a3b8  numpy-2.1.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
c198fe3deaa77fb94d15284b4e26b875  numpy-2.1.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
0a59171c983fc2d8ea599bdf382c3d6a  numpy-2.1.2-cp313-cp313-musllinux_1_1_x86_64.whl
5ba974cd59fb8c9fc94787c754a5f636  numpy-2.1.2-cp313-cp313-musllinux_1_2_aarch64.whl
93d5c642606fe8abeff0e6db31ebe88f  numpy-2.1.2-cp313-cp313-win32.whl
f6455bb4311ddde071a5ea2e14016003  numpy-2.1.2-cp313-cp313-win_amd64.whl
d2a21857c924d4b1b3c8ae8a9e9b9bb4  numpy-2.1.2-cp313-cp313t-macosx_10_13_x86_64.whl
cd6afcbd05835255750a2fba6012c565  numpy-2.1.2-cp313-cp313t-macosx_11_0_arm64.whl
d2fab663ea84f1cfe13dfc00dae74fb6  numpy-2.1.2-cp313-cp313t-macosx_14_0_arm64.whl
9477b923000d63617324c487a4ce0e28  numpy-2.1.2-cp313-cp313t-macosx_14_0_x86_64.whl
84b621a2c9a8c077bc9c471abd2b3933  numpy-2.1.2-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
b1c341c7192d03e8f0f5e7c4b9b6f894  numpy-2.1.2-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
b59750ea55cf274854f64109bf67a112  numpy-2.1.2-cp313-cp313t-musllinux_1_1_x86_64.whl
33f4d63f81ad85c1ea873197f2189d89  numpy-2.1.2-cp313-cp313t-musllinux_1_2_aarch64.whl
f26a9ac42953c84c94f8203b2dbc61c0  numpy-2.1.2-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
e7cf2857582d507dfa3e8644dd3562a6  numpy-2.1.2-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
9e3d44cb302c629c00fde8f25809b04d  numpy-2.1.2-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
3f97ee2d9962cf9d84624f725bdd2a8f  numpy-2.1.2-pp310-pypy310_pp73-win_amd64.whl
3d92e07d34f60dbac6b82a0982a98757  numpy-2.1.2.tar.gz

SHA256

30d53720b726ec36a7f88dc873f0eec8447fbc93d93a8f079dfac2629598d6ee  numpy-2.1.2-cp310-cp310-macosx_10_9_x86_64.whl
e8d3ca0a72dd8846eb6f7dfe8f19088060fcb76931ed592d29128e0219652884  numpy-2.1.2-cp310-cp310-macosx_11_0_arm64.whl
fc44e3c68ff00fd991b59092a54350e6e4911152682b4782f68070985aa9e648  numpy-2.1.2-cp310-cp310-macosx_14_0_arm64.whl
7c1c60328bd964b53f8b835df69ae8198659e2b9302ff9ebb7de4e5a5994db3d  numpy-2.1.2-cp310-cp310-macosx_14_0_x86_64.whl
6cdb606a7478f9ad91c6283e238544451e3a95f30fb5467fbf715964341a8a86  numpy-2.1.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
d666cb72687559689e9906197e3bec7b736764df6a2e58ee265e360663e9baf7  numpy-2.1.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
c6eef7a2dbd0abfb0d9eaf78b73017dbfd0b54051102ff4e6a7b2980d5ac1a03  numpy-2.1.2-cp310-cp310-musllinux_1_1_x86_64.whl
12edb90831ff481f7ef5f6bc6431a9d74dc0e5ff401559a71e5e4611d4f2d466  numpy-2.1.2-cp310-cp310-musllinux_1_2_aarch64.whl
a65acfdb9c6ebb8368490dbafe83c03c7e277b37e6857f0caeadbbc56e12f4fb  numpy-2.1.2-cp310-cp310-win32.whl
860ec6e63e2c5c2ee5e9121808145c7bf86c96cca9ad396c0bd3e0f2798ccbe2  numpy-2.1.2-cp310-cp310-win_amd64.whl
b42a1a511c81cc78cbc4539675713bbcf9d9c3913386243ceff0e9429ca892fe  numpy-2.1.2-cp311-cp311-macosx_10_9_x86_64.whl
faa88bc527d0f097abdc2c663cddf37c05a1c2f113716601555249805cf573f1  numpy-2.1.2-cp311-cp311-macosx_11_0_arm64.whl
c82af4b2ddd2ee72d1fc0c6695048d457e00b3582ccde72d8a1c991b808bb20f  numpy-2.1.2-cp311-cp311-macosx_14_0_arm64.whl
13602b3174432a35b16c4cfb5de9a12d229727c3dd47a6ce35111f2ebdf66ff4  numpy-2.1.2-cp311-cp311-macosx_14_0_x86_64.whl
1ebec5fd716c5a5b3d8dfcc439be82a8407b7b24b230d0ad28a81b61c2f4659a  numpy-2.1.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
e2b49c3c0804e8ecb05d59af8386ec2f74877f7ca8fd9c1e00be2672e4d399b1  numpy-2.1.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
2cbba4b30bf31ddbe97f1c7205ef976909a93a66bb1583e983adbd155ba72ac2  numpy-2.1.2-cp311-cp311-musllinux_1_1_x86_64.whl
8e00ea6fc82e8a804433d3e9cedaa1051a1422cb6e443011590c14d2dea59146  numpy-2.1.2-cp311-cp311-musllinux_1_2_aarch64.whl
5006b13a06e0b38d561fab5ccc37581f23c9511879be7693bd33c7cd15ca227c  numpy-2.1.2-cp311-cp311-win32.whl
f1eb068ead09f4994dec71c24b2844f1e4e4e013b9629f812f292f04bd1510d9  numpy-2.1.2-cp311-cp311-win_amd64.whl
d7bf0a4f9f15b32b5ba53147369e94296f5fffb783db5aacc1be15b4bf72f43b  numpy-2.1.2-cp312-cp312-macosx_10_13_x86_64.whl
b1d0fcae4f0949f215d4632be684a539859b295e2d0cb14f78ec231915d644db  numpy-2.1.2-cp312-cp312-macosx_11_0_arm64.whl
f751ed0a2f250541e19dfca9f1eafa31a392c71c832b6bb9e113b10d050cb0f1  numpy-2.1.2-cp312-cp312-macosx_14_0_arm64.whl
bd33f82e95ba7ad632bc57837ee99dba3d7e006536200c4e9124089e1bf42426  numpy-2.1.2-cp312-cp312-macosx_14_0_x86_64.whl
1b8cde4f11f0a975d1fd59373b32e2f5a562ade7cde4f85b7137f3de8fbb29a0  numpy-2.1.2-cp312-cp312-manylinux_2_17_...

NumPy 2.1.1 Release Notes

NumPy 2.1.1 is a maintenance release that fixes bugs and regressions
discovered after the 2.1.0 release.

The Python versions supported by this release are 3.10-3.13.

Contributors

A total of 7 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.

Andrew Nelson
Charles Harris
Mateusz Sokół
Maximilian Weigand +
Nathan Goldbaum
Pieter Eendebak
Sebastian Berg

Pull requests merged

A total of 10 pull requests were merged for this release.

#27236: REL: Prepare for the NumPy 2.1.0 release [wheel build]
#27252: MAINT: prepare 2.1.x for further development
#27259: BUG: revert unintended change in the return value of set_printoptions
#27266: BUG: fix reference counting bug in __array_interface__ implementation...
#27267: TST: Add regression test for missing descr in array-interface
#27276: BUG: Fix #27256 and #27257
#27278: BUG: Fix array_equal for numeric and non-numeric scalar types
#27287: MAINT: Update maintenance/2.1.x after the 2.0.2 release
#27303: BLD: cp311- macosx_arm64 wheels [wheel build]
#27304: BUG: f2py: better handle filtering of public/private subroutines

Checksums

MD5

3053a97400db800b7377749e691eb39e  numpy-2.1.1-cp310-cp310-macosx_10_9_x86_64.whl
84b752a2220dce7c96ff89eef4f4aec3  numpy-2.1.1-cp310-cp310-macosx_11_0_arm64.whl
47ed4f704a64261f07ca24ef2e674524  numpy-2.1.1-cp310-cp310-macosx_14_0_arm64.whl
b8a45caa870aee980c298053cf064d28  numpy-2.1.1-cp310-cp310-macosx_14_0_x86_64.whl
e097ad5eee572b791b4a25eedad6df4a  numpy-2.1.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
ae502c99315884cda7f0236a07c035c4  numpy-2.1.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
841a859d975c55090c0b60b72aab93a3  numpy-2.1.1-cp310-cp310-musllinux_1_1_x86_64.whl
d51be2b17f5b87aac64ab80fdfafc85e  numpy-2.1.1-cp310-cp310-musllinux_1_2_aarch64.whl
1f8249bd725397c6233fe6a0e8ad18b1  numpy-2.1.1-cp310-cp310-win32.whl
d38d6f06589c1ec104a6a31ff6035781  numpy-2.1.1-cp310-cp310-win_amd64.whl
6a18fe3029aae00986975250313bf16f  numpy-2.1.1-cp311-cp311-macosx_10_9_x86_64.whl
5b0b3aa01fbd0b5a8b0f354bb878351e  numpy-2.1.1-cp311-cp311-macosx_11_0_arm64.whl
1c492dad399abe7b97274b4c6c12ae53  numpy-2.1.1-cp311-cp311-macosx_14_0_arm64.whl
4d55d91e71b62eb5fa6561c606524f60  numpy-2.1.1-cp311-cp311-macosx_14_0_x86_64.whl
88e99ecd063c178f25bc08d20792a9bf  numpy-2.1.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
f3c8b0e4fb059b9219e8ec86d9fda861  numpy-2.1.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
df632b5fed7eb78d39e7194d2475c19b  numpy-2.1.1-cp311-cp311-musllinux_1_1_x86_64.whl
65499daccdb178d26e322d9f359cf146  numpy-2.1.1-cp311-cp311-musllinux_1_2_aarch64.whl
eb97327fd7aa6027e2409d0dcca1129a  numpy-2.1.1-cp311-cp311-win32.whl
9e4b05b38cbff22c2bdfead528b9d2bc  numpy-2.1.1-cp311-cp311-win_amd64.whl
6b8a359bb865b5c624fd9ffc848393e1  numpy-2.1.1-cp312-cp312-macosx_10_9_x86_64.whl
eaf8dce312efa2b0f17ad46612fb1681  numpy-2.1.1-cp312-cp312-macosx_11_0_arm64.whl
c861ff048b336284fe7c0791b1a6b0b4  numpy-2.1.1-cp312-cp312-macosx_14_0_arm64.whl
7e1befccfe729dc5d6c450a5fb6b801c  numpy-2.1.1-cp312-cp312-macosx_14_0_x86_64.whl
ea0a401ef653a167221987a10cbef260  numpy-2.1.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
97326ac792d26f2e536a519c82f2d6bc  numpy-2.1.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
fdd2a82232c03d11bbc7cec0a8e01ab0  numpy-2.1.1-cp312-cp312-musllinux_1_1_x86_64.whl
0d6716e9a7b2c0d6e5ace9c01b9bca01  numpy-2.1.1-cp312-cp312-musllinux_1_2_aarch64.whl
ba589ed2a79c88187c3b8574ae72a1c7  numpy-2.1.1-cp312-cp312-win32.whl
806ca7c1e2a2013b786edbb619f6da47  numpy-2.1.1-cp312-cp312-win_amd64.whl
647665353e5af5884df4e51610990c22  numpy-2.1.1-cp313-cp313-macosx_10_13_x86_64.whl
bfd3b3c5c4616ef99d917bd94d39114a  numpy-2.1.1-cp313-cp313-macosx_11_0_arm64.whl
cb989095f9c74e3b32250a984390faeb  numpy-2.1.1-cp313-cp313-macosx_14_0_arm64.whl
55ad7548e58f61b9a4f91749e36d237f  numpy-2.1.1-cp313-cp313-macosx_14_0_x86_64.whl
5bc73d67dd1032524bfd36ef877b09e4  numpy-2.1.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
c7dfb09db8284cb75296f708c3f77ea3  numpy-2.1.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
7cf90ce1b844a97aeea1a5b8c71fb49b  numpy-2.1.1-cp313-cp313-musllinux_1_1_x86_64.whl
6ec8baeac5f979a3b98017679d457bbc  numpy-2.1.1-cp313-cp313-musllinux_1_2_aarch64.whl
1f198cb5210c76faae81359a83d58230  numpy-2.1.1-cp313-cp313-win32.whl
1766258213ad41f7e36f2209ee6d2a30  numpy-2.1.1-cp313-cp313-win_amd64.whl
f0a7a0456308dbeb739ad886f1632f16  numpy-2.1.1-cp313-cp313t-macosx_10_13_x86_64.whl
302c9cf7b4aa695974500ee1935a92c9  numpy-2.1.1-cp313-cp313t-macosx_11_0_arm64.whl
f4aa7d784992abb9bd9fe9db09c01c06  numpy-2.1.1-cp313-cp313t-macosx_14_0_arm64.whl
3bb4ae9906499609769f1774438149a5  numpy-2.1.1-cp313-cp313t-macosx_14_0_x86_64.whl
ff6b9e1993d3d540074736014b1d13af  numpy-2.1.1-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
749489c091ee9c00abf1ad1ef822c3ca  numpy-2.1.1-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
32d2daf4064031f365ced5036757ad8b  numpy-2.1.1-cp313-cp313t-musllinux_1_1_x86_64.whl
603dfe4ef56c01e1fc0dcc9d5e3090ed  numpy-2.1.1-cp313-cp313t-musllinux_1_2_aarch64.whl
70fa2d3b78633bb6061c90e17364f27f  numpy-2.1.1-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
9a430be5d14b689ed051eccc540dfbdc  numpy-2.1.1-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
7291ff124e471d32c03464da18ff108d  numpy-2.1.1-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
e56ce141724af119c7c647a8705827a5  numpy-2.1.1-pp310-pypy310_pp73-win_amd64.whl
f63b4750618bfa5490f10cae37fde998  numpy-2.1.1.tar.gz

SHA256

c8a0e34993b510fc19b9a2ce7f31cb8e94ecf6e924a40c0c9dd4f62d0aac47d9  numpy-2.1.1-cp310-cp310-macosx_10_9_x86_64.whl
7dd86dfaf7c900c0bbdcb8b16e2f6ddf1eb1fe39c6c8cca6e94844ed3152a8fd  numpy-2.1.1-cp310-cp310-macosx_11_0_arm64.whl
5889dd24f03ca5a5b1e8a90a33b5a0846d8977565e4ae003a63d22ecddf6782f  numpy-2.1.1-cp310-cp310-macosx_14_0_arm64.whl
59ca673ad11d4b84ceb385290ed0ebe60266e356641428c845b39cd9df6713ab  numpy-2.1.1-cp310-cp310-macosx_14_0_x86_64.whl
13ce49a34c44b6de5241f0b38b07e44c1b2dcacd9e36c30f9c2fcb1bb5135db7  numpy-2.1.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
913cc1d311060b1d409e609947fa1b9753701dac96e6581b58afc36b7ee35af6  numpy-2.1.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
caf5d284ddea7462c32b8d4a6b8af030b6c9fd5332afb70e7414d7fdded4bfd0  numpy-2.1.1-cp310-cp310-musllinux_1_1_x86_64.whl
57eb525e7c2a8fdee02d731f647146ff54ea8c973364f3b850069ffb42799647  numpy-2.1.1-cp310-cp310-musllinux_1_2_aarch64.whl
9a8e06c7a980869ea67bbf551283bbed2856915f0a792dc32dd0f9dd2fb56728  numpy-2.1.1-cp310-cp310-win32.whl
d10c39947a2d351d6d466b4ae83dad4c37cd6c3cdd6d5d0fa797da56f710a6ae  numpy-2.1.1-cp310-cp310-win_amd64.whl
0d07841fd284718feffe7dd17a63a2e6c78679b2d386d3e82f44f0108c905550  numpy-2.1.1-cp311-cp311-macosx_10_9_x86_64.whl
b5613cfeb1adfe791e8e681128f5f49f22f3fcaa942255a6124d58ca59d9528f  numpy-2.1.1-cp311-cp311-macosx_11_0_arm64.whl
0b8cc2715a84b7c3b161f9ebbd942740aaed913584cae9cdc7f8ad5ad41943d0  numpy-2.1.1-cp311-cp311-macosx_14_0_arm64.whl
b49742cdb85f1f81e4dc1b39dcf328244f4d8d1ded95dea725b316bd2cf18c95  numpy-2.1.1-cp311-cp311-macosx_14_0_x86_64.whl
e8d5f8a8e3bc87334f025194c6193e408903d21ebaeb10952264943a985066ca  numpy-2.1.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
d51fc141ddbe3f919e91a096ec739f49d686df8af254b2053ba21a910ae518bf  numpy-2.1.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
98ce7fb5b8063cfdd86596b9c762bf2b5e35a2cdd7e967494ab78a1fa7f8b86e  numpy-2.1.1-cp311-cp311-musllinux_1_1_x86_64.whl
24c2ad697bd8593887b019817ddd9974a7f429c14a5469d7fad413f28340a6d2  numpy-2.1.1-cp311-cp311-musllinux_1_2_aarch64.whl
397bc5ce62d3fb73f304bec332171535c187e0643e176a6e9421a6e3eacef06d  numpy-2.1.1-cp311-cp311-win32.whl
ae8ce252404cdd4de56dcfce8b11eac3c594a9c16c231d081fb705cf23bd4d9e  numpy-2.1.1-cp311-cp311-win_amd64.whl
7c803b7934a7f59563db459292e6aa078bb38b7ab1446ca38dd138646a38203e  numpy-2.1.1-cp312-cp312-macosx_10_9_x86_64.whl
6435c48250c12f001920f0751fe50c0348f5f240852cfddc5e2f97e007544cbe  numpy-2.1.1-cp312-cp312-macosx_11_0_arm64.whl
3269c9eb8745e8d975980b3a7411a98976824e1fdef11f0aacf76147f662b15f  numpy-2.1.1-cp312-cp312-macosx_14_0_arm64.whl
fac6e277a41163d27dfab5f4ec1f7a83fac94e170665a4a50191b545721c6521  numpy-2.1.1-cp312-cp312-macosx_14_0_x86_64.whl
fcd8f556cdc8cfe35e70efb92463082b7f43dd7e547eb071ffc36abc0ca4699b  numpy-2.1.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
d2b9cd92c8f8e7b313b80e93cedc12c0112088541dcedd9197b5dee3738c1201  numpy-2.1.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
afd9c680df4de71cd58582b51e88a61feed4abcc7530bcd3d48483f20fc76f2a  numpy-2.1.1-cp312-cp312-musllinux_1_1_x86_64.whl
8661c94e3aad18e1ea17a11f60f843a4933ccaf1a25a7c6a9182af70610b2313  numpy-2.1.1-cp312-cp312-musllinux_1_2_aarch64.whl
950802d17a33c07cba7fd7c3dcfa7d64...

NumPy 2.0.2 Release Notes

NumPy 2.0.2 is a maintenance release that fixes bugs and regressions
discovered after the 2.0.1 release.

The Python versions supported by this release are 3.9-3.12.

Contributors

A total of 13 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.

Bruno Oliveira +
Charles Harris
Chris Sidebottom
Christian Heimes +
Christopher Sidebottom
Mateusz Sokół
Matti Picus
Nathan Goldbaum
Pieter Eendebak
Raghuveer Devulapalli
Ralf Gommers
Sebastian Berg
Yair Chuchem +

Pull requests merged

A total of 19 pull requests were merged for this release.

#27000: REL: Prepare for the NumPy 2.0.1 release [wheel build]
#27001: MAINT: prepare 2.0.x for further development
#27021: BUG: cfuncs.py: fix crash when sys.stderr is not available
#27022: DOC: Fix migration note for alltrue and sometrue
#27061: BUG: use proper input and output descriptor in array_assign_subscript...
#27073: BUG: Mirror VQSORT_ENABLED logic in Quicksort
#27074: BUG: Bump Highway to latest master
#27077: BUG: Off by one in memory overlap check
#27122: BUG: Use the new npyv_loadable_stride_ functions for ldexp and...
#27126: BUG: Bump Highway to latest
#27128: BUG: add missing error handling in public_dtype_api.c
#27129: BUG: fix another cast setup in array_assign_subscript
#27130: BUG: Fix building NumPy in FIPS mode
#27131: BLD: update vendored Meson for cross-compilation patches
#27146: MAINT: Scipy openblas 0.3.27.44.4
#27151: BUG: Do not accidentally store dtype metadata in np.save
#27195: REV: Revert undef I and document it
#27213: BUG: Fix NPY_RAVEL_AXIS on backwards compatible NumPy 2 builds
#27279: BUG: Fix array_equal for numeric and non-numeric scalar types

Checksums

MD5

ae4bc199b56d20305984b7465d6fbdf1  numpy-2.0.2-cp310-cp310-macosx_10_9_x86_64.whl
ecce0a682c2ccaaa14500b87ffb69f63  numpy-2.0.2-cp310-cp310-macosx_11_0_arm64.whl
a94f34bec8a62dab95ce9883a87a82a6  numpy-2.0.2-cp310-cp310-macosx_14_0_arm64.whl
a0a26dadf73264d31b7a6952b816d7c8  numpy-2.0.2-cp310-cp310-macosx_14_0_x86_64.whl
972f4366651a1a2ef00f630595104d15  numpy-2.0.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
6cffef937fe67a3879abefd3d2c40fb8  numpy-2.0.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
3717a5deda20f465720717a1a7a293a6  numpy-2.0.2-cp310-cp310-musllinux_1_1_x86_64.whl
e31136ecc97bb76b3cb7e86bfc9471ac  numpy-2.0.2-cp310-cp310-musllinux_1_2_aarch64.whl
9703a02ca6b63ca53f83660d089f4294  numpy-2.0.2-cp310-cp310-win32.whl
12c097ef2c7492282a5514b5c4b68784  numpy-2.0.2-cp310-cp310-win_amd64.whl
f11d11bfa3aaf371d2e7fa0160e3208b  numpy-2.0.2-cp311-cp311-macosx_10_9_x86_64.whl
86fc67666fc6e27740fde7dacb19c484  numpy-2.0.2-cp311-cp311-macosx_11_0_arm64.whl
5fd12e0dd7162ea9599c49bbb6e6730e  numpy-2.0.2-cp311-cp311-macosx_14_0_arm64.whl
a40f473db729ea10ae401ce71899120a  numpy-2.0.2-cp311-cp311-macosx_14_0_x86_64.whl
36ea96e0be954896597543d726157eda  numpy-2.0.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
cfa726b6d5445687020fc4d4f7191e42  numpy-2.0.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
dfb9a7b7fe218e931b0dfb885a8250d6  numpy-2.0.2-cp311-cp311-musllinux_1_1_x86_64.whl
d8bf100186e6cd1b2f27eb617ba9e581  numpy-2.0.2-cp311-cp311-musllinux_1_2_aarch64.whl
4fe937eba0fc4d28a65c0ba571c809fc  numpy-2.0.2-cp311-cp311-win32.whl
a9a0f8e1bc4d825272514896e3b17f15  numpy-2.0.2-cp311-cp311-win_amd64.whl
5ef80ec3b2db487d89c590eb301a7aa4  numpy-2.0.2-cp312-cp312-macosx_10_9_x86_64.whl
1bb398d93422bb9baf63c958ed1aa492  numpy-2.0.2-cp312-cp312-macosx_11_0_arm64.whl
cc8d990a1ad3f4d66d0143ea709ccc99  numpy-2.0.2-cp312-cp312-macosx_14_0_arm64.whl
4fee57e854bc3e9a267e865740438d53  numpy-2.0.2-cp312-cp312-macosx_14_0_x86_64.whl
c2c18eef5118607c0b023f6267ee9774  numpy-2.0.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
2928ed26d7153a488bfb126424d86c8f  numpy-2.0.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
e32167073981b0a1a419aaaec741773e  numpy-2.0.2-cp312-cp312-musllinux_1_1_x86_64.whl
80a10803a3122472c1bf6c4617d0d1c5  numpy-2.0.2-cp312-cp312-musllinux_1_2_aarch64.whl
39724e27a003b6ce9b1bcbf251e50b4b  numpy-2.0.2-cp312-cp312-win32.whl
8319d0b3d23285d4698cbece73b23fde  numpy-2.0.2-cp312-cp312-win_amd64.whl
da0f655880bbcb53094816b77cd493d1  numpy-2.0.2-cp39-cp39-macosx_10_9_x86_64.whl
47347c028f6ccf47d6a22724111fc96f  numpy-2.0.2-cp39-cp39-macosx_11_0_arm64.whl
26a5c8dec993258522fcef84ef0c040e  numpy-2.0.2-cp39-cp39-macosx_14_0_arm64.whl
fe447af86983ef2262e605a941bd46af  numpy-2.0.2-cp39-cp39-macosx_14_0_x86_64.whl
96477b8563e6d4e2db710f4915a4c5e0  numpy-2.0.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
4e8255cdff60de62944aed1f4235ff68  numpy-2.0.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
05d8465b87ca983eee044b66bc725391  numpy-2.0.2-cp39-cp39-musllinux_1_1_x86_64.whl
dcf448ef80720bae7de6724f92499754  numpy-2.0.2-cp39-cp39-musllinux_1_2_aarch64.whl
71557f67f24d39db709cc4ccb85ae5b5  numpy-2.0.2-cp39-cp39-win32.whl
f5dc31c5530037c4d1d990696b1d041c  numpy-2.0.2-cp39-cp39-win_amd64.whl
a8f814da1a4509724346c14cd838b5dc  numpy-2.0.2-pp39-pypy39_pp73-macosx_10_9_x86_64.whl
918f072481d014229dd5f0f5ba75306f  numpy-2.0.2-pp39-pypy39_pp73-macosx_14_0_x86_64.whl
fcbe2e38506fbbbeda509a89063563d3  numpy-2.0.2-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
b99eff795ca26f8a513aace76a45a356  numpy-2.0.2-pp39-pypy39_pp73-win_amd64.whl
d517a3be706295c4a4c8f75f5ee7b261  numpy-2.0.2.tar.gz

SHA256

51129a29dbe56f9ca83438b706e2e69a39892b5eda6cedcb6b0c9fdc9b0d3ece  numpy-2.0.2-cp310-cp310-macosx_10_9_x86_64.whl
f15975dfec0cf2239224d80e32c3170b1d168335eaedee69da84fbe9f1f9cd04  numpy-2.0.2-cp310-cp310-macosx_11_0_arm64.whl
8c5713284ce4e282544c68d1c3b2c7161d38c256d2eefc93c1d683cf47683e66  numpy-2.0.2-cp310-cp310-macosx_14_0_arm64.whl
becfae3ddd30736fe1889a37f1f580e245ba79a5855bff5f2a29cb3ccc22dd7b  numpy-2.0.2-cp310-cp310-macosx_14_0_x86_64.whl
2da5960c3cf0df7eafefd806d4e612c5e19358de82cb3c343631188991566ccd  numpy-2.0.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
496f71341824ed9f3d2fd36cf3ac57ae2e0165c143b55c3a035ee219413f3318  numpy-2.0.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
a61ec659f68ae254e4d237816e33171497e978140353c0c2038d46e63282d0c8  numpy-2.0.2-cp310-cp310-musllinux_1_1_x86_64.whl
d731a1c6116ba289c1e9ee714b08a8ff882944d4ad631fd411106a30f083c326  numpy-2.0.2-cp310-cp310-musllinux_1_2_aarch64.whl
984d96121c9f9616cd33fbd0618b7f08e0cfc9600a7ee1d6fd9b239186d19d97  numpy-2.0.2-cp310-cp310-win32.whl
c7b0be4ef08607dd04da4092faee0b86607f111d5ae68036f16cc787e250a131  numpy-2.0.2-cp310-cp310-win_amd64.whl
49ca4decb342d66018b01932139c0961a8f9ddc7589611158cb3c27cbcf76448  numpy-2.0.2-cp311-cp311-macosx_10_9_x86_64.whl
11a76c372d1d37437857280aa142086476136a8c0f373b2e648ab2c8f18fb195  numpy-2.0.2-cp311-cp311-macosx_11_0_arm64.whl
807ec44583fd708a21d4a11d94aedf2f4f3c3719035c76a2bbe1fe8e217bdc57  numpy-2.0.2-cp311-cp311-macosx_14_0_arm64.whl
8cafab480740e22f8d833acefed5cc87ce276f4ece12fdaa2e8903db2f82897a  numpy-2.0.2-cp311-cp311-macosx_14_0_x86_64.whl
a15f476a45e6e5a3a79d8a14e62161d27ad897381fecfa4a09ed5322f2085669  numpy-2.0.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
13e689d772146140a252c3a28501da66dfecd77490b498b168b501835041f951  numpy-2.0.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
9ea91dfb7c3d1c56a0e55657c0afb38cf1eeae4544c208dc465c3c9f3a7c09f9  numpy-2.0.2-cp311-cp311-musllinux_1_1_x86_64.whl
c1c9307701fec8f3f7a1e6711f9089c06e6284b3afbbcd259f7791282d660a15  numpy-2.0.2-cp311-cp311-musllinux_1_2_aarch64.whl
a392a68bd329eafac5817e5aefeb39038c48b671afd242710b451e76090e81f4  numpy-2.0.2-cp311-cp311-win32.whl
286cd40ce2b7d652a6f22efdfc6d1edf879440e53e76a75955bc0c826c7e64dc  numpy-2.0.2-cp311-cp311-win_amd64.whl
df55d490dea7934f330006d0f81e8551ba6010a5bf035a249ef61a94f21c500b  numpy-2.0.2-cp312-cp312-macosx_10_9_x86_64.whl
8df823f570d9adf0978347d1f926b2a867d5608f434a7cff7f7908c6570dcf5e  numpy-2.0.2-cp312-cp312-macosx_11_0_arm64.whl
9a92ae5c14811e390f3767053ff54eaee3bf84576d99a2456391401323f4ec2c  numpy-2.0.2-cp312-cp312-macosx_14_0_arm64.whl
a842d573724391493a97a62ebbb8e731f8a5dcc5d285dfc99141ca15a3302d0c  numpy-2.0.2-cp312-cp312-macosx_14_0_x86_64.whl
c05e238064fc0610c840d1cf6a13bf63d7e391717d247f1bf0318172e759e692  numpy-2.0.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
0123ffdaa88fa4ab64835dcbde75dcdf89c453c922f18dced6e27c90d1d0ec5a  numpy-2.0.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
96a55...

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

Preliminary Support for Free-Threaded CPython 3.13

CPython 3.13 will be available as an experimental free-threaded build.
See https://py-free-threading.github.io, PEP 703 and the
CPython 3.13 release notes for more detail about free-threaded Python.

NumPy 2.1 has preliminary support for the free-threaded build of CPython
3.13. This support was enabled by fixing a number of C thread-safety
issues in NumPy. Before NumPy 2.1, NumPy used a large number of C global
static variables to store runtime caches and other state. We have either
refactored to avoid the need for global state, converted the global
state to thread-local state, or added locking.

Support for free-threaded Python does not mean that NumPy is thread
safe. Read-only shared access to ndarray should be safe. NumPy exposes
shared mutable state and we have not added any locking to the array
object itself to serialize access to shared state. Care must be taken in
user code to avoid races if you would like to mutate the same array in
multiple threads. It is certainly possible to crash NumPy by mutating an
array simultaneously in multiple threads, for example by calling a ufunc
and the resize method simultaneously. For now our guidance is:
"don't do that". In the future we would like to provide stronger
guarantees.

Object arrays in particular need special care, since the GIL previously
provided locking for object array access and no longer does. See
Issue #27199 for more information about object
arrays in the free-threaded build.

If you are interested in free-threaded Python, for example because you
have a multiprocessing-based workflow that you are interested in running
with Python threads, we encourage testing and experimentation.

If you run into problems that you suspect are because of NumPy, please
open an issue,
checking first if the bug also occurs in the "regular" non-free-threaded CPython 3.13
build. Many threading bugs can also occur in code that releases
the GIL; disabling the GIL only makes it easier to hit threading bugs.

(gh-26157)

`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)

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)

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
comb...

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 ...

@vahidmech

NumPy 2.0.1 Release Notes

NumPy 2.0.1 is a maintenance release that fixes bugs and regressions
discovered after the 2.0.0 release. NumPy 2.0.1 is the last planned
release in the 2.0.x series, 2.1.0rc1 should be out shortly.

The Python versions supported by this release are 3.9-3.12.

NOTE: Do not use the GitHub generated "Source code" files listed in the "Assets", they are garbage.

Improvements

`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)

Contributors

A total of 15 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.

@vahidmech +
Alex Herbert +
Charles Harris
Giovanni Del Monte +
Leo Singer
Lysandros Nikolaou
Matti Picus
Nathan Goldbaum
Patrick J. Roddy +
Raghuveer Devulapalli
Ralf Gommers
Rostan Tabet +
Sebastian Berg
Tyler Reddy
Yannik Wicke +

Pull requests merged

A total of 24 pull requests were merged for this release.

#26711: MAINT: prepare 2.0.x for further development
#26792: TYP: fix incorrect import in ma/extras.pyi stub
#26793: DOC: Mention '1.25' legacy printing mode in set_printoptions
#26794: DOC: Remove mention of NaN and NAN aliases from constants
#26821: BLD: Fix x86-simd-sort build failure on openBSD
#26822: BUG: Ensure output order follows input in numpy.fft
#26823: TYP: fix missing sys import in numeric.pyi
#26832: DOC: remove hack to override _add_newdocs_scalars
#26835: BUG: avoid side-effect of 'include complex.h'
#26836: BUG: fix max_rows and chunked string/datetime reading in loadtxt
#26837: BUG: fix PyArray_ImportNumPyAPI under -Werror=strict-prototypes
#26856: DOC: Update some documentation
#26868: BUG: fancy indexing copy
#26869: BUG: Mismatched allocation domains in PyArray_FillWithScalar
#26870: BUG: Handle --f77flags and --f90flags for meson [wheel build]
#26887: BUG: Fix new DTypes and new string promotion when signature is...
#26888: BUG: remove numpy.f2py from excludedimports
#26959: BUG: Quantile closest_observation to round to nearest even order
#26960: BUG: Fix off-by-one error in amount of characters in strip
#26961: API: Partially revert unique with return_inverse
#26962: BUG,MAINT: Fix utf-8 character stripping memory access
#26963: BUG: Fix out-of-bound minimum offset for in1d table method
#26971: BUG: fix f2py tests to work with v2 API
#26995: BUG: Add object cast to avoid warning with limited API

Checksums

MD5

a3e7d0f361ee7302448cae3c10844dd3  numpy-2.0.1-cp310-cp310-macosx_10_9_x86_64.whl
cff8546b69e43ae7b5050f05bdc25df2  numpy-2.0.1-cp310-cp310-macosx_11_0_arm64.whl
1713d23342528f4f8f4027970f010068  numpy-2.0.1-cp310-cp310-macosx_14_0_arm64.whl
20020d28606ea58f986a262daa6018f1  numpy-2.0.1-cp310-cp310-macosx_14_0_x86_64.whl
db22154ea943a707917aebc79e449bc5  numpy-2.0.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
fe86cd85f240216f64eb076a62a229d2  numpy-2.0.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
e0ca08f85150af3cc6050d64e8c0bd27  numpy-2.0.1-cp310-cp310-musllinux_1_1_x86_64.whl
b76f432906f62e31f0e09c41f3f08b4c  numpy-2.0.1-cp310-cp310-musllinux_1_2_aarch64.whl
28e8109e4ef524fa5c272d6faec870ae  numpy-2.0.1-cp310-cp310-win32.whl
874beffaefdc73da42300ce691c2419c  numpy-2.0.1-cp310-cp310-win_amd64.whl
7bbe029f650c924e952da117842d456d  numpy-2.0.1-cp311-cp311-macosx_10_9_x86_64.whl
6d3d6ae26c520e93cef7f11ba3951f57  numpy-2.0.1-cp311-cp311-macosx_11_0_arm64.whl
de6082d719437eb7468ae31c407c503e  numpy-2.0.1-cp311-cp311-macosx_14_0_arm64.whl
d15a8d95661f8a1dfcc4eb089f9b46e8  numpy-2.0.1-cp311-cp311-macosx_14_0_x86_64.whl
c181105e074ee575ccf2c992e40f947a  numpy-2.0.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
00d22b299343fcdc78fbb0716ead6243  numpy-2.0.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
d9c4f49dbedb3f3d0158f00db459bd25  numpy-2.0.1-cp311-cp311-musllinux_1_1_x86_64.whl
63caa03e0625327ad3a756e01c83a6ca  numpy-2.0.1-cp311-cp311-musllinux_1_2_aarch64.whl
99d01d768a115d448ca2b4680de15191  numpy-2.0.1-cp311-cp311-win32.whl
8d1a31eccc8b9f077312095b11f62cb2  numpy-2.0.1-cp311-cp311-win_amd64.whl
6cc86f7761a33941d8c1c552186e774b  numpy-2.0.1-cp312-cp312-macosx_10_9_x86_64.whl
67c48f352afff5f41108f1b9561d1d5c  numpy-2.0.1-cp312-cp312-macosx_11_0_arm64.whl
1068d4eadcac6a869e0e457853b7e611  numpy-2.0.1-cp312-cp312-macosx_14_0_arm64.whl
dfb667450315fddcf84381fc8ef16892  numpy-2.0.1-cp312-cp312-macosx_14_0_x86_64.whl
69822bbbbb65d8a7d00ae32b435f61cc  numpy-2.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
883ed6c41395fb2def6cc0d64dcb817f  numpy-2.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
4b1e9fd464821a7d1de3a8ddf911311e  numpy-2.0.1-cp312-cp312-musllinux_1_1_x86_64.whl
79e6557f40b8ed8f5973b404d98eab3d  numpy-2.0.1-cp312-cp312-musllinux_1_2_aarch64.whl
85596f15d4cf85c2f78b4cc12c2cad1e  numpy-2.0.1-cp312-cp312-win32.whl
487c7c2944306f62b3770576ce903a91  numpy-2.0.1-cp312-cp312-win_amd64.whl
491093641afa21e65d6e629eb70571fc  numpy-2.0.1-cp39-cp39-macosx_10_9_x86_64.whl
5008b16c20f3d7e5a0c7764712f8908e  numpy-2.0.1-cp39-cp39-macosx_11_0_arm64.whl
14633b898f863ea797c40ba1cf226c29  numpy-2.0.1-cp39-cp39-macosx_14_0_arm64.whl
9054ecb69d21b364e59e94aab24247cb  numpy-2.0.1-cp39-cp39-macosx_14_0_x86_64.whl
be028cf4bb691921943939de17593dd7  numpy-2.0.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
9c440ad02ff0a954f696637de37aab2d  numpy-2.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
27aec0d286eabe26d8e9149f4572dba1  numpy-2.0.1-cp39-cp39-musllinux_1_1_x86_64.whl
b02eda82ee511ee27185c8a4073ea35c  numpy-2.0.1-cp39-cp39-musllinux_1_2_aarch64.whl
cf579b902325e023b2dc444692eb5991  numpy-2.0.1-cp39-cp39-win32.whl
302c8c3118a5f55d9ef35ed8e517f6b1  numpy-2.0.1-cp39-cp39-win_amd64.whl
34c17fe980accfb76c6f348f85b3cfef  numpy-2.0.1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl
02676eb84379b0a223288d6fd9d76942  numpy-2.0.1-pp39-pypy39_pp73-macosx_14_0_x86_64.whl
b5300e6fe110bf69e1a8901c5c09e3f8  numpy-2.0.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
204a3ea7fb851e08d166c74f73f9b8a3  numpy-2.0.1-pp39-pypy39_pp73-win_amd64.whl
5df3c50fc124c3167404d396115898d0  numpy-2.0.1.tar.gz

SHA256

0fbb536eac80e27a2793ffd787895242b7f18ef792563d742c2d673bfcb75134  numpy-2.0.1-cp310-cp310-macosx_10_9_x86_64.whl
69ff563d43c69b1baba77af455dd0a839df8d25e8590e79c90fcbe1499ebde42  numpy-2.0.1-cp310-cp310-macosx_11_0_arm64.whl
1b902ce0e0a5bb7704556a217c4f63a7974f8f43e090aff03fcf262e0b135e02  numpy-2.0.1-cp310-cp310-macosx_14_0_arm64.whl
f1659887361a7151f89e79b276ed8dff3d75877df906328f14d8bb40bb4f5101  numpy-2.0.1-cp310-cp310-macosx_14_0_x86_64.whl
4658c398d65d1b25e1760de3157011a80375da861709abd7cef3bad65d6543f9  numpy-2.0.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
4127d4303b9ac9f94ca0441138acead39928938660ca58329fe156f84b9f3015  numpy-2.0.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
e5eeca8067ad04bc8a2a8731183d51d7cbaac66d86085d5f4766ee6bf19c7f87  numpy-2.0.1-cp310-cp310-musllinux_1_1_x86_64.whl
9adbd9bb520c866e1bfd7e10e1880a1f7749f1f6e5017686a5fbb9b72cf69f82  numpy-2.0.1-cp310-cp310-musllinux_1_2_aarch64.whl
7b9853803278db3bdcc6cd5beca37815b133e9e77ff3d4733c247414e78eb8d1  numpy-2.0.1-cp310-cp310-win32.whl
81b0893a39bc5b865b8bf89e9ad7807e16717f19868e9d234bdaf9b1f1393868  numpy-2.0.1-cp310-cp310-win_amd64.whl
75b4e316c5902d8163ef9d423b1c3f2f6252226d1aa5cd8a0a03a7d01ffc6268  numpy-2.0.1-cp311-cp311-macosx_10_9_x86_64.whl
6e4eeb6eb2fced786e32e6d8df9e755ce5be920d17f7ce00bc38fcde8ccdbf9e  numpy-2.0.1-cp311-cp311-macosx_11_0_arm64.whl
a1e01dcaab205fbece13c1410253a9eea1b1c9b61d237b6fa59bcc46e8e89343  numpy-2.0.1-cp311-cp311-macosx_14_0_arm64.whl
a8fc2de81ad835d999113ddf87d1ea2b0f4704cbd947c948d2f5513deafe5a7b  numpy-2.0.1-cp311-cp311-macosx_14_0_x86_64.whl
5a3d94942c331dd4e0e1147f7a8699a4aa47dffc11bf8a1523c12af8b2e91bbe  numpy-2.0.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
15eb4eca47d36ec3f78cde0a3a2ee24cf05ca7396ef808dda2c0ddad7c2bde67  numpy-2.0.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
b83e16a5511d1b...

Releases: numpy/numpy

2.2.1 (DEC 21, 2024)

NumPy 2.2.1 Release Notes

Contributors

Pull requests merged

Checksums

MD5

SHA256

2.2.0 (Dec 8, 2024)

NumPy 2.2.0 Release Notes

Deprecations

Expired deprecations

Compatibility notes

New Features

Improvements

Performance improvements and changes

Changes

Checksums

MD5

2.2.0rc1 (Nov 26, 2024)

NumPy 2.2.0 Release Notes

Deprecations

Expired deprecations

Compatibility notes

New Features

Improvements

Performance improvements and changes

Changes

Checksums

MD5

2.1.3 (Nov 2, 2024)

NumPy 2.1.3 Release Notes

Improvements

Changes

Contributors

Pull requests merged

Checksums

MD5

SHA256

2.1.2 (Oct 5, 2024)

NumPy 2.1.2 Release Notes

Contributors

Pull requests merged

Checksums

MD5

SHA256

2.1.1 (Sep 3, 2024)

NumPy 2.1.1 Release Notes

Contributors

Pull requests merged

Checksums

MD5

SHA256

NumPy 2.0.2 release (Aug 26, 2024)

NumPy 2.0.2 Release Notes

Contributors

Pull requests merged

Checksums

MD5

SHA256

2.1.0 (Aug 18, 2024)

NumPy 2.1.0 Release Notes

New functions

New function numpy.unstack

Deprecations

Expired deprecations

C API changes

API symbols now hidden but customizable

Many shims removed from npy_3kcompat.h

New PyUFuncObject field process_core_dims_func

New Features

Preliminary Support for Free-Threaded CPython 3.13

f2py can generate freethreading-compatible C extensions

Improvements

histogram auto-binning now returns bin sizes >=1 for integer input data

ndarray shape-type parameter is now covariant and bound to tuple[int, ...]

np.quantile with method closest_observation chooses nearest even order statistic

lapack_lite is now thread safe

The numpy.printoptions context manager is now thread and async-safe

2.1.0rc1 (Aug 11, 2024)

New function `numpy.unstack`

New `PyUFuncObject` field `process_core_dims_func`

`f2py` can generate freethreading-compatible C extensions

`histogram` auto-binning now returns bin sizes >=1 for integer input data

`ndarray` shape-type parameter is now covariant and bound to `tuple[int, ...]`

`np.quantile` with method `closest_observation` chooses nearest even order statistic

`lapack_lite` is now thread safe

The `numpy.printoptions` context manager is now thread and async-safe

New function `numpy.unstack`

New `PyUFuncObject` field `process_core_dims_func`

`f2py` can generate freethreading-compatible C extensions

`histogram` auto-binning now returns bin sizes >=1 for integer input data

`ndarray` shape-type parameter is now covariant and bound to `tuple[int, ...]`

`np.quantile` with method `closest_observation` chooses nearest even order statistic

`lapack_lite` is now thread safe

The `numpy.printoptions` context manager is now thread and async-safe

`ma.cov` and `ma.corrcoef` are now significantly faster

`ma.corrcoef` may return a slightly different result

`np.quantile` with method `closest_observation` chooses nearest even order statistic