Movatterモバイル変換


[0]ホーム

URL:


Skip to content

Navigation Menu

Sign in
Appearance settings

Search code, repositories, users, issues, pull requests...

Provide feedback

We read every piece of feedback, and take your input very seriously.

Saved searches

Use saved searches to filter your results more quickly

Sign up
Appearance settings

v2.3.1 (Jun 21, 2025)

Latest
Compare
Choose a tag to compare
Loading
@charrischarris released this 21 Jun 13:02
· 281 commits to main since this release
v2.3.1
This tag was signed with the committer’sverified signature.
charris Charles Harris
GPG key ID:679F228377C5247B
Verified
Learn about vigilant mode.
4d833e5
This commit was created on GitHub.com and signed with GitHub’sverified signature.
GPG key ID:B5690EEEBB952194
Verified
Learn about vigilant mode.

NumPy 2.3.1 Release Notes

The NumPy 2.3.1 release is a patch release with several bug fixes,
annotation improvements, and better support for OpenBSD. Highlights are:

  • Fix bug inmatmul for non-contiguous out kwarg parameter
  • Fix for Accelerate runtime warnings on M4 hardware
  • Fix new in NumPy 2.3.0np.vectorize casting errors
  • Improved support of cpu features for FreeBSD and OpenBSD

This release supports Python versions 3.11-3.13, Python 3.14 will be
supported when it is released.

Contributors

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

  • Brad Smith +
  • Charles Harris
  • Developer-Ecosystem-Engineering
  • François Rozet
  • Joren Hammudoglu
  • Matti Picus
  • Mugundan Selvanayagam
  • Nathan Goldbaum
  • Sebastian Berg

Pull requests merged

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

  • #29140: MAINT: Prepare 2.3.x for further development
  • #29191: BUG: fix matmul with transposed out arg (#29179)
  • #29192: TYP: Backport typing fixes and improvements.
  • #29205: BUG: Revertnp.vectorize casting to legacy behavior (#29196)
  • #29222: TYP: Backport typing fixes
  • #29233: BUG: avoid negating unsigned integers in resize implementation...
  • #29234: TST: Fix test that uses uninitialized memory (#29232)
  • #29235: BUG: Address interaction between SME and FPSR (#29223)
  • #29237: BUG: Enforce integer limitation in concatenate (#29231)
  • #29238: CI: Add support for building NumPy with LLVM for Win-ARM64
  • #29241: ENH: Detect CPU features on OpenBSD ARM and PowerPC64
  • #29242: ENH: Detect CPU features on FreeBSD / OpenBSD RISC-V64.

Checksums

MD5

c353ac75ea083594a6cb674b5f943d83  numpy-2.3.1-cp311-cp311-macosx_10_9_x86_64.whlfdb5454e372d399cf570868ea7e2b192  numpy-2.3.1-cp311-cp311-macosx_11_0_arm64.whldc0f17823bb1826519d6974c2b95fa90  numpy-2.3.1-cp311-cp311-macosx_14_0_arm64.whl7e3118fe383af697a8868ba191b9eac0  numpy-2.3.1-cp311-cp311-macosx_14_0_x86_64.whl705aafad1250aa3e41502c5710a26ed5  numpy-2.3.1-cp311-cp311-manylinux_2_28_aarch64.whl003d6268344577b804205098e11cdaa0  numpy-2.3.1-cp311-cp311-manylinux_2_28_x86_64.whl7d0c0fd11c573c510a25dd7513e4ae0a  numpy-2.3.1-cp311-cp311-musllinux_1_2_aarch64.whld99f993ef05966ead99df736df18b521  numpy-2.3.1-cp311-cp311-musllinux_1_2_x86_64.whl96933cac225fb8b60a9cc2c0efa14d36  numpy-2.3.1-cp311-cp311-win32.whlf777712419f3dd586ac294ddce84b274  numpy-2.3.1-cp311-cp311-win_amd64.whl1fe2615669de5c271a48b99356fa3528  numpy-2.3.1-cp311-cp311-win_arm64.whlfccca48846d41d38966cc75395787f79  numpy-2.3.1-cp312-cp312-macosx_10_13_x86_64.whlfa389e78db43f3c2841ce127c1205422  numpy-2.3.1-cp312-cp312-macosx_11_0_arm64.whl2554944d786abd284db4a699d4edfe1e  numpy-2.3.1-cp312-cp312-macosx_14_0_arm64.whl7fec491834803a8ffa3765ef3d03cea5  numpy-2.3.1-cp312-cp312-macosx_14_0_x86_64.whl7c2d8b4412f12b9b02e98349fb5cd760  numpy-2.3.1-cp312-cp312-manylinux_2_28_aarch64.whl94dcc636a2f2478666d820e21fc91682  numpy-2.3.1-cp312-cp312-manylinux_2_28_x86_64.whl404128939d89d1ea26be105fb03b5028  numpy-2.3.1-cp312-cp312-musllinux_1_2_aarch64.whle89d8d460060e8315c3ba68b2b649db0  numpy-2.3.1-cp312-cp312-musllinux_1_2_x86_64.whla767bd10267ad6baef9655fb08db3fd3  numpy-2.3.1-cp312-cp312-win32.whlf753b957fcb7f06f043cf9c6114f294c  numpy-2.3.1-cp312-cp312-win_amd64.whl58ffa7c69587f9bf8f6025794fec7f63  numpy-2.3.1-cp312-cp312-win_arm64.whl22a2a9a568dd0866b288ad8bd8bb3e90  numpy-2.3.1-cp313-cp313-macosx_10_13_x86_64.whl5e1593fcc8bb3447e995622f2dca017b  numpy-2.3.1-cp313-cp313-macosx_11_0_arm64.whl894d56072db9358e0096538710a1a8ce  numpy-2.3.1-cp313-cp313-macosx_14_0_arm64.whl593cb311f5170cbcfcefb587cdcc70bb  numpy-2.3.1-cp313-cp313-macosx_14_0_x86_64.whl22935447e75acda4075c57b332c0236a  numpy-2.3.1-cp313-cp313-manylinux_2_28_aarch64.whl5aa2040f947204e15e95ec87461a7e91  numpy-2.3.1-cp313-cp313-manylinux_2_28_x86_64.whl6516337f0347974fada21a23a818be64  numpy-2.3.1-cp313-cp313-musllinux_1_2_aarch64.whlec956eb37b874b1ec52d6ffccda6ef65  numpy-2.3.1-cp313-cp313-musllinux_1_2_x86_64.whl0aaed62cb1bae9c1b1a44d1a4eda2db7  numpy-2.3.1-cp313-cp313-win32.whl57829996fc12f649547f0258443bbb20  numpy-2.3.1-cp313-cp313-win_amd64.whla0d0dd68bbf0ab378142b2daff0a8e06  numpy-2.3.1-cp313-cp313-win_arm64.whlb22dc66970a8017e4d0ce83ef8c938af  numpy-2.3.1-cp313-cp313t-macosx_10_13_x86_64.whl93c17afb38cf8fd876ca2bd9ea7e9612  numpy-2.3.1-cp313-cp313t-macosx_11_0_arm64.whl283064dabb434f3dbc1a5e2514b9cb29  numpy-2.3.1-cp313-cp313t-macosx_14_0_arm64.whl5b8c778033c98b4a0ce6e5bfc7625f05  numpy-2.3.1-cp313-cp313t-macosx_14_0_x86_64.whl2340bd78962f194bcdbee6531d954acc  numpy-2.3.1-cp313-cp313t-manylinux_2_28_aarch64.whl43a92ad37dc68d719bdeeeb65b3f4d2f  numpy-2.3.1-cp313-cp313t-manylinux_2_28_x86_64.whleb110c4aa0d73558187397ddfba179ad  numpy-2.3.1-cp313-cp313t-musllinux_1_2_aarch64.whl1f7f0076411ed4afa9c4553eb06564cb  numpy-2.3.1-cp313-cp313t-musllinux_1_2_x86_64.whl30f30dde6f806070b2164e48a632a350  numpy-2.3.1-cp313-cp313t-win32.whl2375e2f2a5b75c5f5c908af6bb85d639  numpy-2.3.1-cp313-cp313t-win_amd64.whlb421530a87bb8e9e3d4dc34c75d5d953  numpy-2.3.1-cp313-cp313t-win_arm64.whlb1bc3cbf9cd407964b2bb25dfe86ca3d  numpy-2.3.1-pp311-pypy311_pp73-macosx_10_15_x86_64.whl4c2e234eb4f346f362d6e6c620fa7a56  numpy-2.3.1-pp311-pypy311_pp73-macosx_14_0_arm64.whl98ec3c19a365d0ae926113bb349e323b  numpy-2.3.1-pp311-pypy311_pp73-macosx_14_0_x86_64.whle0c7bcd526cde46489d5a8f12e06cc77  numpy-2.3.1-pp311-pypy311_pp73-manylinux_2_28_aarch64.whl41f535aa1f1acaf3d8a32a462a4cd4c8  numpy-2.3.1-pp311-pypy311_pp73-manylinux_2_28_x86_64.whl2abf906a6688c98693045cbbc655d5b7  numpy-2.3.1-pp311-pypy311_pp73-win_amd64.whl886559a4c541298b37245e389ce8bf10  numpy-2.3.1.tar.gz

SHA256

6ea9e48336a402551f52cd8f593343699003d2353daa4b72ce8d34f66b722070  numpy-2.3.1-cp311-cp311-macosx_10_9_x86_64.whl5ccb7336eaf0e77c1635b232c141846493a588ec9ea777a7c24d7166bb8533ae  numpy-2.3.1-cp311-cp311-macosx_11_0_arm64.whl0bb3a4a61e1d327e035275d2a993c96fa786e4913aa089843e6a2d9dd205c66a  numpy-2.3.1-cp311-cp311-macosx_14_0_arm64.whle344eb79dab01f1e838ebb67aab09965fb271d6da6b00adda26328ac27d4a66e  numpy-2.3.1-cp311-cp311-macosx_14_0_x86_64.whl467db865b392168ceb1ef1ffa6f5a86e62468c43e0cfb4ab6da667ede10e58db  numpy-2.3.1-cp311-cp311-manylinux_2_28_aarch64.whlafed2ce4a84f6b0fc6c1ce734ff368cbf5a5e24e8954a338f3bdffa0718adffb  numpy-2.3.1-cp311-cp311-manylinux_2_28_x86_64.whl0025048b3c1557a20bc80d06fdeb8cc7fc193721484cca82b2cfa072fec71a93  numpy-2.3.1-cp311-cp311-musllinux_1_2_aarch64.whla5ee121b60aa509679b682819c602579e1df14a5b07fe95671c8849aad8f2115  numpy-2.3.1-cp311-cp311-musllinux_1_2_x86_64.whla8b740f5579ae4585831b3cf0e3b0425c667274f82a484866d2adf9570539369  numpy-2.3.1-cp311-cp311-win32.whld4580adadc53311b163444f877e0789f1c8861e2698f6b2a4ca852fda154f3ff  numpy-2.3.1-cp311-cp311-win_amd64.whlec0bdafa906f95adc9a0c6f26a4871fa753f25caaa0e032578a30457bff0af6a  numpy-2.3.1-cp311-cp311-win_arm64.whl2959d8f268f3d8ee402b04a9ec4bb7604555aeacf78b360dc4ec27f1d508177d  numpy-2.3.1-cp312-cp312-macosx_10_13_x86_64.whl762e0c0c6b56bdedfef9a8e1d4538556438288c4276901ea008ae44091954e29  numpy-2.3.1-cp312-cp312-macosx_11_0_arm64.whl867ef172a0976aaa1f1d1b63cf2090de8b636a7674607d514505fb7276ab08fc  numpy-2.3.1-cp312-cp312-macosx_14_0_arm64.whl4e602e1b8682c2b833af89ba641ad4176053aaa50f5cacda1a27004352dde943  numpy-2.3.1-cp312-cp312-macosx_14_0_x86_64.whl8e333040d069eba1652fb08962ec5b76af7f2c7bce1df7e1418c8055cf776f25  numpy-2.3.1-cp312-cp312-manylinux_2_28_aarch64.whle7cbf5a5eafd8d230a3ce356d892512185230e4781a361229bd902ff403bc660  numpy-2.3.1-cp312-cp312-manylinux_2_28_x86_64.whl5f1b8f26d1086835f442286c1d9b64bb3974b0b1e41bb105358fd07d20872952  numpy-2.3.1-cp312-cp312-musllinux_1_2_aarch64.whlee8340cb48c9b7a5899d1149eece41ca535513a9698098edbade2a8e7a84da77  numpy-2.3.1-cp312-cp312-musllinux_1_2_x86_64.whle772dda20a6002ef7061713dc1e2585bc1b534e7909b2030b5a46dae8ff077ab  numpy-2.3.1-cp312-cp312-win32.whlcfecc7822543abdea6de08758091da655ea2210b8ffa1faf116b940693d3df76  numpy-2.3.1-cp312-cp312-win_amd64.whl7be91b2239af2658653c5bb6f1b8bccafaf08226a258caf78ce44710a0160d30  numpy-2.3.1-cp312-cp312-win_arm64.whl25a1992b0a3fdcdaec9f552ef10d8103186f5397ab45e2d25f8ac51b1a6b97e8  numpy-2.3.1-cp313-cp313-macosx_10_13_x86_64.whl7dea630156d39b02a63c18f508f85010230409db5b2927ba59c8ba4ab3e8272e  numpy-2.3.1-cp313-cp313-macosx_11_0_arm64.whlbada6058dd886061f10ea15f230ccf7dfff40572e99fef440a4a857c8728c9c0  numpy-2.3.1-cp313-cp313-macosx_14_0_arm64.whla894f3816eb17b29e4783e5873f92faf55b710c2519e5c351767c51f79d8526d  numpy-2.3.1-cp313-cp313-macosx_14_0_x86_64.whl18703df6c4a4fee55fd3d6e5a253d01c5d33a295409b03fda0c86b3ca2ff41a1  numpy-2.3.1-cp313-cp313-manylinux_2_28_aarch64.whl5902660491bd7a48b2ec16c23ccb9124b8abfd9583c5fdfa123fe6b421e03de1  numpy-2.3.1-cp313-cp313-manylinux_2_28_x86_64.whl36890eb9e9d2081137bd78d29050ba63b8dab95dff7912eadf1185e80074b2a0  numpy-2.3.1-cp313-cp313-musllinux_1_2_aarch64.whla780033466159c2270531e2b8ac063704592a0bc62ec4a1b991c7c40705eb0e8  numpy-2.3.1-cp313-cp313-musllinux_1_2_x86_64.whl39bff12c076812595c3a306f22bfe49919c5513aa1e0e70fac756a0be7c2a2b8  numpy-2.3.1-cp313-cp313-win32.whl8d5ee6eec45f08ce507a6570e06f2f879b374a552087a4179ea7838edbcbfa42  numpy-2.3.1-cp313-cp313-win_amd64.whl0c4d9e0a8368db90f93bd192bfa771ace63137c3488d198ee21dfb8e7771916e  numpy-2.3.1-cp313-cp313-win_arm64.whlb0b5397374f32ec0649dd98c652a1798192042e715df918c20672c62fb52d4b8  numpy-2.3.1-cp313-cp313t-macosx_10_13_x86_64.whlc5bdf2015ccfcee8253fb8be695516ac4457c743473a43290fd36eba6a1777eb  numpy-2.3.1-cp313-cp313t-macosx_11_0_arm64.whld70f20df7f08b90a2062c1f07737dd340adccf2068d0f1b9b3d56e2038979fee  numpy-2.3.1-cp313-cp313t-macosx_14_0_arm64.whl2fb86b7e58f9ac50e1e9dd1290154107e47d1eef23a0ae9145ded06ea606f992  numpy-2.3.1-cp313-cp313t-macosx_14_0_x86_64.whl23ab05b2d241f76cb883ce8b9a93a680752fbfcbd51c50eff0b88b979e471d8c  numpy-2.3.1-cp313-cp313t-manylinux_2_28_aarch64.whlce2ce9e5de4703a673e705183f64fd5da5bf36e7beddcb63a25ee2286e71ca48  numpy-2.3.1-cp313-cp313t-manylinux_2_28_x86_64.whlc4913079974eeb5c16ccfd2b1f09354b8fed7e0d6f2cab933104a09a6419b1ee  numpy-2.3.1-cp313-cp313t-musllinux_1_2_aarch64.whl010ce9b4f00d5c036053ca684c77441f2f2c934fd23bee058b4d6f196efd8280  numpy-2.3.1-cp313-cp313t-musllinux_1_2_x86_64.whl6269b9edfe32912584ec496d91b00b6d34282ca1d07eb10e82dfc780907d6c2e  numpy-2.3.1-cp313-cp313t-win32.whl2a809637460e88a113e186e87f228d74ae2852a2e0c44de275263376f17b5bdc  numpy-2.3.1-cp313-cp313t-win_amd64.whleccb9a159db9aed60800187bc47a6d3451553f0e1b08b068d8b277ddfbb9b244  numpy-2.3.1-cp313-cp313t-win_arm64.whlad506d4b09e684394c42c966ec1527f6ebc25da7f4da4b1b056606ffe446b8a3  numpy-2.3.1-pp311-pypy311_pp73-macosx_10_15_x86_64.whlebb8603d45bc86bbd5edb0d63e52c5fd9e7945d3a503b77e486bd88dde67a19b  numpy-2.3.1-pp311-pypy311_pp73-macosx_14_0_arm64.whl15aa4c392ac396e2ad3d0a2680c0f0dee420f9fed14eef09bdb9450ee6dcb7b7  numpy-2.3.1-pp311-pypy311_pp73-macosx_14_0_x86_64.whlc6e0bf9d1a2f50d2b65a7cf56db37c095af17b59f6c132396f7c6d5dd76484df  numpy-2.3.1-pp311-pypy311_pp73-manylinux_2_28_aarch64.whleabd7e8740d494ce2b4ea0ff05afa1b7b291e978c0ae075487c51e8bd93c0c68  numpy-2.3.1-pp311-pypy311_pp73-manylinux_2_28_x86_64.whle610832418a2bc09d974cc9fecebfa51e9532d6190223bc5ef6a7402ebf3b5cb  numpy-2.3.1-pp311-pypy311_pp73-win_amd64.whl1ec9ae20a4226da374362cca3c62cd753faf2f951440b0e3b98e93c235441d2b  numpy-2.3.1.tar.gz
Assets5
Loading
leo-smi, WLM1ke, wanderingeek, github-actions[bot], Molkree, chfly2000, agriyakhetarpal, bargavigowda, Mugundanmcw, Christopher-K-Long, and 3 more reacted with thumbs up emojigithub-actions[bot], agriyakhetarpal, wanderingeek, Mugundanmcw, kikocorreoso, cindytsai, and mattyhosseini reacted with hooray emojiagriyakhetarpal, Mugundanmcw, and mattyhosseini reacted with heart emojiaaravind100, leo-smi, wanderingeek, github-actions[bot], agriyakhetarpal, Mugundanmcw, Lucas-Peterson, hugoesb, mattyhosseini, and Eliastheking reacted with rocket emoji
19 people reacted

[8]ページ先頭

©2009-2025 Movatter.jp