Skip to content

v1.24.3

Compare
Choose a tag to compare
@charris charris released this 22 Apr 21:58
· 5045 commits to main since this release
v1.24.3
14bb214

NumPy 1.24.3 Release Notes

NumPy 1.24.3 is a maintenance release that fixes bugs and regressions
discovered after the 1.24.2 release. The Python versions supported by
this release are 3.8-3.11.

Contributors

A total of 12 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.

  • Aleksei Nikiforov +
  • Alexander Heger
  • Bas van Beek
  • Bob Eldering
  • Brock Mendel
  • Charles Harris
  • Kyle Sunden
  • Peter Hawkins
  • Rohit Goswami
  • Sebastian Berg
  • Warren Weckesser
  • dependabot[bot]

Pull requests merged

A total of 17 pull requests were merged for this release.

  • #23206: BUG: fix for f2py string scalars (#23194)
  • #23207: BUG: datetime64/timedelta64 comparisons return NotImplemented
  • #23208: MAINT: Pin matplotlib to version 3.6.3 for refguide checks
  • #23221: DOC: Fix matplotlib error in documentation
  • #23226: CI: Ensure submodules are initialized in gitpod.
  • #23341: TYP: Replace duplicate reduce in ufunc type signature with reduceat.
  • #23342: TYP: Remove duplicate CLIP/WRAP/RAISE in __init__.pyi.
  • #23343: TYP: Mark d argument to fftfreq and rfftfreq as optional...
  • #23344: TYP: Add type annotations for comparison operators to MaskedArray.
  • #23345: TYP: Remove some stray type-check-only imports of msort
  • #23370: BUG: Ensure like is only stripped for like= dispatched functions
  • #23543: BUG: fix loading and storing big arrays on s390x
  • #23544: MAINT: Bump larsoner/circleci-artifacts-redirector-action
  • #23634: BUG: Ignore invalid and overflow warnings in masked setitem
  • #23635: BUG: Fix masked array raveling when order="A" or order="K"
  • #23636: MAINT: Update conftest for newer hypothesis versions
  • #23637: BUG: Fix bug in parsing F77 style string arrays.

Checksums

MD5

93a3ce07e3773842c54d831f18e3eb8d  numpy-1.24.3-cp310-cp310-macosx_10_9_x86_64.whl
39691ff3d1612438dfcd3266c9765aab  numpy-1.24.3-cp310-cp310-macosx_11_0_arm64.whl
a99234799a239e7e9c6fa15c212996df  numpy-1.24.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
3673aa638746851dd19d5199e1eb3a91  numpy-1.24.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
3c72962360bcd0938a6bddee6cdca766  numpy-1.24.3-cp310-cp310-win32.whl
a3329efa646012fa4ee06ce5e08eadaf  numpy-1.24.3-cp310-cp310-win_amd64.whl
5323fb0323d1ec10ee3c35a2fa79cbcd  numpy-1.24.3-cp311-cp311-macosx_10_9_x86_64.whl
cfa001dcd07cdf6414ced433e88959d4  numpy-1.24.3-cp311-cp311-macosx_11_0_arm64.whl
d75bbfb06ed00d04232dce0e865eb42c  numpy-1.24.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
fe18b810bcf284572467ce585dbc533b  numpy-1.24.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
e97699a4ef96a81e0916bdf15440abe0  numpy-1.24.3-cp311-cp311-win32.whl
e6de5b7d77dc43ed47f516eb10bbe8b6  numpy-1.24.3-cp311-cp311-win_amd64.whl
dd04ebf441a8913f4900b56e7a33a75e  numpy-1.24.3-cp38-cp38-macosx_10_9_x86_64.whl
e47ac5521b0bfc3effb040072d8a7902  numpy-1.24.3-cp38-cp38-macosx_11_0_arm64.whl
7b7dae3309e7ca8a8859633a5d337431  numpy-1.24.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
8cc87b88163ed84e70c48fd0f5f8f20e  numpy-1.24.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
350934bae971d0ebe231a59b640069db  numpy-1.24.3-cp38-cp38-win32.whl
c4708ef009bb5d427ea94a4fc4a10e12  numpy-1.24.3-cp38-cp38-win_amd64.whl
44b08a293a4e12d62c27b8f15ba5664e  numpy-1.24.3-cp39-cp39-macosx_10_9_x86_64.whl
3ae7ac30f86c720e42b2324a0ae1adf5  numpy-1.24.3-cp39-cp39-macosx_11_0_arm64.whl
065464a8d918c670c7863d1e72e3e6dd  numpy-1.24.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
1f163b9ea417c253e84480aa8d99dee6  numpy-1.24.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
c86e648389e333e062bea11c749b9a32  numpy-1.24.3-cp39-cp39-win32.whl
bfe332e577c604d6d62a57381e6aa0a6  numpy-1.24.3-cp39-cp39-win_amd64.whl
374695eeef5aca32a5b7f2f518dd3ba1  numpy-1.24.3-pp38-pypy38_pp73-macosx_10_9_x86_64.whl
6abd9dba54405182e6e7bb32dbe377bb  numpy-1.24.3-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
0848bd41c08dd5ebbc5a7f0788678e0e  numpy-1.24.3-pp38-pypy38_pp73-win_amd64.whl
89e5e2e78407032290ae6acf6dcaea46  numpy-1.24.3.tar.gz

SHA256

3c1104d3c036fb81ab923f507536daedc718d0ad5a8707c6061cdfd6d184e570  numpy-1.24.3-cp310-cp310-macosx_10_9_x86_64.whl
202de8f38fc4a45a3eea4b63e2f376e5f2dc64ef0fa692838e31a808520efaf7  numpy-1.24.3-cp310-cp310-macosx_11_0_arm64.whl
8535303847b89aa6b0f00aa1dc62867b5a32923e4d1681a35b5eef2d9591a463  numpy-1.24.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
2d926b52ba1367f9acb76b0df6ed21f0b16a1ad87c6720a1121674e5cf63e2b6  numpy-1.24.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
f21c442fdd2805e91799fbe044a7b999b8571bb0ab0f7850d0cb9641a687092b  numpy-1.24.3-cp310-cp310-win32.whl
ab5f23af8c16022663a652d3b25dcdc272ac3f83c3af4c02eb8b824e6b3ab9d7  numpy-1.24.3-cp310-cp310-win_amd64.whl
9a7721ec204d3a237225db3e194c25268faf92e19338a35f3a224469cb6039a3  numpy-1.24.3-cp311-cp311-macosx_10_9_x86_64.whl
d6cc757de514c00b24ae8cf5c876af2a7c3df189028d68c0cb4eaa9cd5afc2bf  numpy-1.24.3-cp311-cp311-macosx_11_0_arm64.whl
76e3f4e85fc5d4fd311f6e9b794d0c00e7002ec122be271f2019d63376f1d385  numpy-1.24.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
a1d3c026f57ceaad42f8231305d4653d5f05dc6332a730ae5c0bea3513de0950  numpy-1.24.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
c91c4afd8abc3908e00a44b2672718905b8611503f7ff87390cc0ac3423fb096  numpy-1.24.3-cp311-cp311-win32.whl
5342cf6aad47943286afa6f1609cad9b4266a05e7f2ec408e2cf7aea7ff69d80  numpy-1.24.3-cp311-cp311-win_amd64.whl
7776ea65423ca6a15255ba1872d82d207bd1e09f6d0894ee4a64678dd2204078  numpy-1.24.3-cp38-cp38-macosx_10_9_x86_64.whl
ae8d0be48d1b6ed82588934aaaa179875e7dc4f3d84da18d7eae6eb3f06c242c  numpy-1.24.3-cp38-cp38-macosx_11_0_arm64.whl
ecde0f8adef7dfdec993fd54b0f78183051b6580f606111a6d789cd14c61ea0c  numpy-1.24.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
4749e053a29364d3452c034827102ee100986903263e89884922ef01a0a6fd2f  numpy-1.24.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
d933fabd8f6a319e8530d0de4fcc2e6a61917e0b0c271fded460032db42a0fe4  numpy-1.24.3-cp38-cp38-win32.whl
56e48aec79ae238f6e4395886b5eaed058abb7231fb3361ddd7bfdf4eed54289  numpy-1.24.3-cp38-cp38-win_amd64.whl
4719d5aefb5189f50887773699eaf94e7d1e02bf36c1a9d353d9f46703758ca4  numpy-1.24.3-cp39-cp39-macosx_10_9_x86_64.whl
0ec87a7084caa559c36e0a2309e4ecb1baa03b687201d0a847c8b0ed476a7187  numpy-1.24.3-cp39-cp39-macosx_11_0_arm64.whl
ea8282b9bcfe2b5e7d491d0bf7f3e2da29700cec05b49e64d6246923329f2b02  numpy-1.24.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
210461d87fb02a84ef243cac5e814aad2b7f4be953b32cb53327bb49fd77fbb4  numpy-1.24.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
784c6da1a07818491b0ffd63c6bbe5a33deaa0e25a20e1b3ea20cf0e43f8046c  numpy-1.24.3-cp39-cp39-win32.whl
d5036197ecae68d7f491fcdb4df90082b0d4960ca6599ba2659957aafced7c17  numpy-1.24.3-cp39-cp39-win_amd64.whl
352ee00c7f8387b44d19f4cada524586f07379c0d49270f87233983bc5087ca0  numpy-1.24.3-pp38-pypy38_pp73-macosx_10_9_x86_64.whl
1a7d6acc2e7524c9955e5c903160aa4ea083736fde7e91276b0e5d98e6332812  numpy-1.24.3-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
35400e6a8d102fd07c71ed7dcadd9eb62ee9a6e84ec159bd48c28235bbb0f8e4  numpy-1.24.3-pp38-pypy38_pp73-win_amd64.whl
ab344f1bf21f140adab8e47fdbc7c35a477dc01408791f8ba00d018dd0bc5155  numpy-1.24.3.tar.gz