NumPy 1.23.3 Release Notes
NumPy 1.23.3 is a maintenance release that fixes bugs discovered after
the 1.23.2 release. There is no major theme for this release, the main
improvements are for some downstream builds and some annotation corner
cases. The Python versions supported for this release are 3.8-3.11.
Note that we will move to MacOS 11 for the NumPy 1.23.4 release, the
10.15 version currently used will no longer be supported by our build
infrastructure at that point.
Contributors
A total of 16 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.
- Aaron Meurer
- Bas van Beek
- Charles Harris
- Ganesh Kathiresan
- Gavin Zhang +
- Iantra Solari+
- Jyn Spring 琴春 +
- Matti Picus
- Rafael Cardoso Fernandes Sousa
- Rafael Sousa +
- Ralf Gommers
- Rin Cat (鈴猫) +
- Saransh Chopra +
- Sayed Adel
- Sebastian Berg
- Serge Guelton
Pull requests merged
A total of 14 pull requests were merged for this release.
- #22136: BLD: Add Python 3.11 wheels to aarch64 build
- #22148: MAINT: Update setup.py for Python 3.11.
- #22155: CI: Test NumPy build against old versions of GCC(6, 7, 8)
- #22156: MAINT: support IBM i system
- #22195: BUG: Fix circleci build
- #22214: BUG: Expose heapsort algorithms in a shared header
- #22215: BUG: Support using libunwind for backtrack
- #22216: MAINT: fix an incorrect pointer type usage in f2py
- #22220: BUG: change overloads to play nice with pyright.
- #22221: TST,BUG: Use fork context to fix MacOS savez test
- #22222: TYP,BUG: Reduce argument validation in C-based
__class_getitem__
- #22223: TST: ensure
np.equal.reduce
raises aTypeError
- #22224: BUG: Fix the implementation of numpy.array_api.vecdot
- #22230: BUG: Better report integer division overflow (backport)
Checksums
MD5
a60bf0b1d440bf18d87c49409036d05a numpy-1.23.3-cp310-cp310-macosx_10_9_x86_64.whl
59b43423a692f5351c6a43b852b210d7 numpy-1.23.3-cp310-cp310-macosx_11_0_arm64.whl
f482a4be6954b1b606320f0ffc1995dd numpy-1.23.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
a82e2ecc4060a37dae5424e624eabfe3 numpy-1.23.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
84916178e5f4d073d0008754cba7f300 numpy-1.23.3-cp310-cp310-win32.whl
605da65b9b66dfce8b62d847cb3841f7 numpy-1.23.3-cp310-cp310-win_amd64.whl
57cf29f781be955a9cd0de8d07fbce56 numpy-1.23.3-cp311-cp311-macosx_10_9_x86_64.whl
f395dcf622dff0ba44777cbae0442189 numpy-1.23.3-cp311-cp311-macosx_11_0_arm64.whl
55d6a6439913ba84ad89268e0ad59fa0 numpy-1.23.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
202bc3a8617f479ebe60ca0dec29964b numpy-1.23.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
a42c3d058bcef47b26841bf9472a89bf numpy-1.23.3-cp311-cp311-win32.whl
237dbd94e5529065c0c5cc4e47ceeb7e numpy-1.23.3-cp311-cp311-win_amd64.whl
d0587d5b28d3fa7e0ec8fd3df76e4bd4 numpy-1.23.3-cp38-cp38-macosx_10_9_x86_64.whl
054234695ed3d955fb01f661db2c14fc numpy-1.23.3-cp38-cp38-macosx_11_0_arm64.whl
4e75ac61e34f1bf23e7cbd6e2bfc7a32 numpy-1.23.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
29ccb3a732027ee1abe23a9562c32d0c numpy-1.23.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
12817838edc1e1bea27df79f3a83da5d numpy-1.23.3-cp38-cp38-win32.whl
ef430e830a9fea7d8db0218b901671f6 numpy-1.23.3-cp38-cp38-win_amd64.whl
b001f7e17df798f9b949bbe259924c77 numpy-1.23.3-cp39-cp39-macosx_10_9_x86_64.whl
bc1782f5d79187d63d14ed69a6a411e9 numpy-1.23.3-cp39-cp39-macosx_11_0_arm64.whl
f8fb0178bc34a198d5ce4e166076e1fc numpy-1.23.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
fb80d38c37aae1e4d416cd4de068ff0a numpy-1.23.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
318d0a2a27b7e361295c0382a0ff4a94 numpy-1.23.3-cp39-cp39-win32.whl
880dc73de09fccda0650e9404fa83608 numpy-1.23.3-cp39-cp39-win_amd64.whl
3b5a51f78718a1a82d2750ec159f9acf numpy-1.23.3-pp38-pypy38_pp73-macosx_10_9_x86_64.whl
56a0c90a303979d5bf8fc57e86e57ccb numpy-1.23.3-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
5338d997a3178750834e742a257dfa4a numpy-1.23.3-pp38-pypy38_pp73-win_amd64.whl
6efc60a3f6c1b74c849d53fbcc07807b numpy-1.23.3.tar.gz
SHA256
c9f707b5bb73bf277d812ded9896f9512a43edff72712f31667d0a8c2f8e71ee numpy-1.23.3-cp310-cp310-macosx_10_9_x86_64.whl
ffcf105ecdd9396e05a8e58e81faaaf34d3f9875f137c7372450baa5d77c9a54 numpy-1.23.3-cp310-cp310-macosx_11_0_arm64.whl
0ea3f98a0ffce3f8f57675eb9119f3f4edb81888b6874bc1953f91e0b1d4f440 numpy-1.23.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
004f0efcb2fe1c0bd6ae1fcfc69cc8b6bf2407e0f18be308612007a0762b4089 numpy-1.23.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
98dcbc02e39b1658dc4b4508442a560fe3ca5ca0d989f0df062534e5ca3a5c1a numpy-1.23.3-cp310-cp310-win32.whl
39a664e3d26ea854211867d20ebcc8023257c1800ae89773cbba9f9e97bae036 numpy-1.23.3-cp310-cp310-win_amd64.whl
1f27b5322ac4067e67c8f9378b41c746d8feac8bdd0e0ffede5324667b8a075c numpy-1.23.3-cp311-cp311-macosx_10_9_x86_64.whl
2ad3ec9a748a8943e6eb4358201f7e1c12ede35f510b1a2221b70af4bb64295c numpy-1.23.3-cp311-cp311-macosx_11_0_arm64.whl
bdc9febce3e68b697d931941b263c59e0c74e8f18861f4064c1f712562903411 numpy-1.23.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
301c00cf5e60e08e04d842fc47df641d4a181e651c7135c50dc2762ffe293dbd numpy-1.23.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
7cd1328e5bdf0dee621912f5833648e2daca72e3839ec1d6695e91089625f0b4 numpy-1.23.3-cp311-cp311-win32.whl
8355fc10fd33a5a70981a5b8a0de51d10af3688d7a9e4a34fcc8fa0d7467bb7f numpy-1.23.3-cp311-cp311-win_amd64.whl
bc6e8da415f359b578b00bcfb1d08411c96e9a97f9e6c7adada554a0812a6cc6 numpy-1.23.3-cp38-cp38-macosx_10_9_x86_64.whl
22d43376ee0acd547f3149b9ec12eec2f0ca4a6ab2f61753c5b29bb3e795ac4d numpy-1.23.3-cp38-cp38-macosx_11_0_arm64.whl
a64403f634e5ffdcd85e0b12c08f04b3080d3e840aef118721021f9b48fc1460 numpy-1.23.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
efd9d3abe5774404becdb0748178b48a218f1d8c44e0375475732211ea47c67e numpy-1.23.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
f8c02ec3c4c4fcb718fdf89a6c6f709b14949408e8cf2a2be5bfa9c49548fd85 numpy-1.23.3-cp38-cp38-win32.whl
e868b0389c5ccfc092031a861d4e158ea164d8b7fdbb10e3b5689b4fc6498df6 numpy-1.23.3-cp38-cp38-win_amd64.whl
09f6b7bdffe57fc61d869a22f506049825d707b288039d30f26a0d0d8ea05164 numpy-1.23.3-cp39-cp39-macosx_10_9_x86_64.whl
8c79d7cf86d049d0c5089231a5bcd31edb03555bd93d81a16870aa98c6cfb79d numpy-1.23.3-cp39-cp39-macosx_11_0_arm64.whl
e5d5420053bbb3dd64c30e58f9363d7a9c27444c3648e61460c1237f9ec3fa14 numpy-1.23.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
d5422d6a1ea9b15577a9432e26608c73a78faf0b9039437b075cf322c92e98e7 numpy-1.23.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
c1ba66c48b19cc9c2975c0d354f24058888cdc674bebadceb3cdc9ec403fb5d1 numpy-1.23.3-cp39-cp39-win32.whl
78a63d2df1d947bd9d1b11d35564c2f9e4b57898aae4626638056ec1a231c40c numpy-1.23.3-cp39-cp39-win_amd64.whl
17c0e467ade9bda685d5ac7f5fa729d8d3e76b23195471adae2d6a6941bd2c18 numpy-1.23.3-pp38-pypy38_pp73-macosx_10_9_x86_64.whl
91b8d6768a75247026e951dce3b2aac79dc7e78622fc148329135ba189813584 numpy-1.23.3-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
94c15ca4e52671a59219146ff584488907b1f9b3fc232622b47e2cf832e94fb8 numpy-1.23.3-pp38-pypy38_pp73-win_amd64.whl
51bf49c0cd1d52be0a240aa66f3458afc4b95d8993d2d04f0d91fa60c10af6cd numpy-1.23.3.tar.gz