Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Update dependency numpy to v2 #1386

Draft
wants to merge 1 commit into
base: master
Choose a base branch
from
Draft

Update dependency numpy to v2 #1386

wants to merge 1 commit into from

Conversation

renovate[bot]
Copy link
Contributor

@renovate renovate bot commented Oct 20, 2024

This PR contains the following updates:

Package Change Age Adoption Passing Confidence
numpy (changelog) ^1.24.1 -> ^2.0.0 age adoption passing confidence
numpy (changelog) ==1.26.4 -> ==2.2.0 age adoption passing confidence

Release Notes

numpy/numpy (numpy)

v2.2.0

Compare Source

v2.1.3: 2.1.3 (Nov 2, 2024)

Compare Source

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
576a1c1d25e9e02ed7fa5477f30a127fe56debd53b8d2c89d5578f9857d03ca9  numpy-2.1.3-cp311-cp311-macosx_14_0_arm64.whl
973faafebaae4c0aaa1a1ca1ce02434554d67e628b8d805e61f874b84e136b09  numpy-2.1.3-cp311-cp311-macosx_14_0_x86_64.whl
762479be47a4863e261a840e8e01608d124ee1361e48b96916f38b119cfda04a  numpy-2.1.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
bc6f24b3d1ecc1eebfbf5d6051faa49af40b03be1aaa781ebdadcbc090b4539b  numpy-2.1.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
17ee83a1f4fef3c94d16dc1802b998668b5419362c8a4f4e8a491de1b41cc3ee  numpy-2.1.3-cp311-cp311-musllinux_1_1_x86_64.whl
15cb89f39fa6d0bdfb600ea24b250e5f1a3df23f901f51c8debaa6a5d122b2f0  numpy-2.1.3-cp311-cp311-musllinux_1_2_aarch64.whl
d9beb777a78c331580705326d2367488d5bc473b49a9bc3036c154832520aca9  numpy-2.1.3-cp311-cp311-win32.whl
d89dd2b6da69c4fff5e39c28a382199ddedc3a5be5390115608345dec660b9e2  numpy-2.1.3-cp311-cp311-win_amd64.whl
f55ba01150f52b1027829b50d70ef1dafd9821ea82905b63936668403c3b471e  numpy-2.1.3-cp312-cp312-macosx_10_13_x86_64.whl
13138eadd4f4da03074851a698ffa7e405f41a0845a6b1ad135b81596e4e9958  numpy-2.1.3-cp312-cp312-macosx_11_0_arm64.whl
a6b46587b14b888e95e4a24d7b13ae91fa22386c199ee7b418f449032b2fa3b8  numpy-2.1.3-cp312-cp312-macosx_14_0_arm64.whl
0fa14563cc46422e99daef53d725d0c326e99e468a9320a240affffe87852564  numpy-2.1.3-cp312-cp312-macosx_14_0_x86_64.whl
8637dcd2caa676e475503d1f8fdb327bc495554e10838019651b76d17b98e512  numpy-2.1.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
2312b2aa89e1f43ecea6da6ea9a810d06aae08321609d8dc0d0eda6d946a541b  numpy-2.1.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
a38c19106902bb19351b83802531fea19dee18e5b37b36454f27f11ff956f7fc  numpy-2.1.3-cp312-cp312-musllinux_1_1_x86_64.whl
02135ade8b8a84011cbb67dc44e07c58f28575cf9ecf8ab304e51c05528c19f0  numpy-2.1.3-cp312-cp312-musllinux_1_2_aarch64.whl
e6988e90fcf617da2b5c78902fe8e668361b43b4fe26dbf2d7b0f8034d4cafb9  numpy-2.1.3-cp312-cp312-win32.whl
0d30c543f02e84e92c4b1f415b7c6b5326cbe45ee7882b6b77db7195fb971e3a  numpy-2.1.3-cp312-cp312-win_amd64.whl
96fe52fcdb9345b7cd82ecd34547fca4321f7656d500eca497eb7ea5a926692f  numpy-2.1.3-cp313-cp313-macosx_10_13_x86_64.whl
f653490b33e9c3a4c1c01d41bc2aef08f9475af51146e4a7710c450cf9761598  numpy-2.1.3-cp313-cp313-macosx_11_0_arm64.whl
dc258a761a16daa791081d026f0ed4399b582712e6fc887a95af09df10c5ca57  numpy-2.1.3-cp313-cp313-macosx_14_0_arm64.whl
016d0f6f5e77b0f0d45d77387ffa4bb89816b57c835580c3ce8e099ef830befe  numpy-2.1.3-cp313-cp313-macosx_14_0_x86_64.whl
c181ba05ce8299c7aa3125c27b9c2167bca4a4445b7ce73d5febc411ca692e43  numpy-2.1.3-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
5641516794ca9e5f8a4d17bb45446998c6554704d888f86df9b200e66bdcce56  numpy-2.1.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
ea4dedd6e394a9c180b33c2c872b92f7ce0f8e7ad93e9585312b0c5a04777a4a  numpy-2.1.3-cp313-cp313-musllinux_1_1_x86_64.whl
b0df3635b9c8ef48bd3be5f862cf71b0a4716fa0e702155c45067c6b711ddcef  numpy-2.1.3-cp313-cp313-musllinux_1_2_aarch64.whl
50ca6aba6e163363f132b5c101ba078b8cbd3fa92c7865fd7d4d62d9779ac29f  numpy-2.1.3-cp313-cp313-win32.whl
747641635d3d44bcb380d950679462fae44f54b131be347d5ec2bce47d3df9ed  numpy-2.1.3-cp313-cp313-win_amd64.whl
996bb9399059c5b82f76b53ff8bb686069c05acc94656bb259b1d63d04a9506f  numpy-2.1.3-cp313-cp313t-macosx_10_13_x86_64.whl
45966d859916ad02b779706bb43b954281db43e185015df6eb3323120188f9e4  numpy-2.1.3-cp313-cp313t-macosx_11_0_arm64.whl
baed7e8d7481bfe0874b566850cb0b85243e982388b7b23348c6db2ee2b2ae8e  numpy-2.1.3-cp313-cp313t-macosx_14_0_arm64.whl
a9f7f672a3388133335589cfca93ed468509cb7b93ba3105fce780d04a6576a0  numpy-2.1.3-cp313-cp313t-macosx_14_0_x86_64.whl
d7aac50327da5d208db2eec22eb11e491e3fe13d22653dce51b0f4109101b408  numpy-2.1.3-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
4394bc0dbd074b7f9b52024832d16e019decebf86caf909d94f6b3f77a8ee3b6  numpy-2.1.3-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
50d18c4358a0a8a53f12a8ba9d772ab2d460321e6a93d6064fc22443d189853f  numpy-2.1.3-cp313-cp313t-musllinux_1_1_x86_64.whl
14e253bd43fc6b37af4921b10f6add6925878a42a0c5fe83daee390bca80bc17  numpy-2.1.3-cp313-cp313t-musllinux_1_2_aarch64.whl
08788d27a5fd867a663f6fc753fd7c3ad7e92747efc73c53bca2f19f8bc06f48  numpy-2.1.3-cp313-cp313t-win32.whl
2564fbdf2b99b3f815f2107c1bbc93e2de8ee655a69c261363a1172a79a257d4  numpy-2.1.3-cp313-cp313t-win_amd64.whl
4f2015dfe437dfebbfce7c85c7b53d81ba49e71ba7eadbf1df40c915af75979f  numpy-2.1.3-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
3522b0dfe983a575e6a9ab3a4a4dfe156c3e428468ff08ce582b9bb6bd1d71d4  numpy-2.1.3-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
c006b607a865b07cd981ccb218a04fc86b600411d83d6fc261357f1c0966755d  numpy-2.1.3-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
e14e26956e6f1696070788252dcdff11b4aca4c3e8bd166e0df1bb8f315a67cb  numpy-2.1.3-pp310-pypy310_pp73-win_amd64.whl
aa08e04e08aaf974d4458def539dece0d28146d866a39da5639596f4921fd761  numpy-2.1.3.tar.gz

v2.1.2

Compare Source

v2.1.1: 2.1.1 (Sep 3, 2024)

Compare Source

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
950802d17a33c07cba7fd7c3dcfa7d64705509206be1606f196d179e539111ed  numpy-2.1.1-cp312-cp312-win32.whl
3fc5eabfc720db95d68e6646e88f8b399bfedd235994016351b1d9e062c4b270  numpy-2.1.1-cp312-cp312-win_amd64.whl
046356b19d7ad1890c751b99acad5e82dc4a02232013bd9a9a712fddf8eb60f5  numpy-2.1.1-cp313-cp313-macosx_10_13_x86_64.whl
6e5a9cb2be39350ae6c8f79410744e80154df658d5bea06e06e0ac5bb75480d5  numpy-2.1.1-cp313-cp313-macosx_11_0_arm64.whl
d4c57b68c8ef5e1ebf47238e99bf27657511ec3f071c465f6b1bccbef12d4136  numpy-2.1.1-cp313-cp313-macosx_14_0_arm64.whl
8ae0fd135e0b157365ac7cc31fff27f07a5572bdfc38f9c2d43b2aff416cc8b0  numpy-2.1.1-cp313-cp313-macosx_14_0_x86_64.whl
981707f6b31b59c0c24bcda52e5605f9701cb46da4b86c2e8023656ad3e833cb  numpy-2.1.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
2ca4b53e1e0b279142113b8c5eb7d7a877e967c306edc34f3b58e9be12fda8df  numpy-2.1.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
e097507396c0be4e547ff15b13dc3866f45f3680f789c1a1301b07dadd3fbc78  numpy-2.1.1-cp313-cp313-musllinux_1_1_x86_64.whl
f7506387e191fe8cdb267f912469a3cccc538ab108471291636a96a54e599556  numpy-2.1.1-cp313-cp313-musllinux_1_2_aarch64.whl
251105b7c42abe40e3a689881e1793370cc9724ad50d64b30b358bbb3a97553b  numpy-2.1.1-cp313-cp313-win32.whl
f212d4f46b67ff604d11fff7cc62d36b3e8714edf68e44e9760e19be38c03eb0  numpy-2.1.1-cp313-cp313-win_amd64.whl
920b0911bb2e4414c50e55bd658baeb78281a47feeb064ab40c2b66ecba85553  numpy-2.1.1-cp313-cp313t-macosx_10_13_x86_64.whl
bab7c09454460a487e631ffc0c42057e3d8f2a9ddccd1e60c7bb8ed774992480  numpy-2.1.1-cp313-cp313t-macosx_11_0_arm64.whl
cea427d1350f3fd0d2818ce7350095c1a2ee33e30961d2f0fef48576ddbbe90f  numpy-2.1.1-cp313-cp313t-macosx_14_0_arm64.whl
e30356d530528a42eeba51420ae8bf6c6c09559051887196599d96ee5f536468  numpy-2.1.1-cp313-cp313t-macosx_14_0_x86_64.whl
e8dfa9e94fc127c40979c3eacbae1e61fda4fe71d84869cc129e2721973231ef  numpy-2.1.1-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
910b47a6d0635ec1bd53b88f86120a52bf56dcc27b51f18c7b4a2e2224c29f0f  numpy-2.1.1-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
13cc11c00000848702322af4de0147ced365c81d66053a67c2e962a485b3717c  numpy-2.1.1-cp313-cp313t-musllinux_1_1_x86_64.whl
53e27293b3a2b661c03f79aa51c3987492bd4641ef933e366e0f9f6c9bf257ec  numpy-2.1.1-cp313-cp313t-musllinux_1_2_aarch64.whl
7be6a07520b88214ea85d8ac8b7d6d8a1839b0b5cb87412ac9f49fa934eb15d5  numpy-2.1.1-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
52ac2e48f5ad847cd43c4755520a2317f3380213493b9d8a4c5e37f3b87df504  numpy-2.1.1-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
50a95ca3560a6058d6ea91d4629a83a897ee27c00630aed9d933dff191f170cd  numpy-2.1.1-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
99f4a9ee60eed1385a86e82288971a51e71df052ed0b2900ed30bc840c0f2e39  numpy-2.1.1-pp310-pypy310_pp73-win_amd64.whl
d0cf7d55b1051387807405b3898efafa862997b4cba8aa5dbe657be794afeafd  numpy-2.1.1.tar.gz

v2.1.0

Compare Source

v2.0.2: NumPy 2.0.2 release (Aug 26, 2024)

Compare Source

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
96a55f64139912d61de9137f11bf39a55ec8faec288c75a54f93dfd39f7eb40c  numpy-2.0.2-cp312-cp312-musllinux_1_1_x86_64.whl
ec9852fb39354b5a45a80bdab5ac02dd02b15f44b3804e9f00c556bf24b4bded  numpy-2.0.2-cp312-cp312-musllinux_1_2_aarch64.whl
671bec6496f83202ed2d3c8fdc486a8fc86942f2e69ff0e986140339a63bcbe5  numpy-2.0.2-cp312-cp312-win32.whl
cfd41e13fdc257aa5778496b8caa5e856dc4896d4ccf01841daee1d96465467a  numpy-2.0.2-cp312-cp312-win_amd64.whl
9059e10581ce4093f735ed23f3b9d283b9d517ff46009ddd485f1747eb22653c  numpy-2.0.2-cp39-cp39-macosx_10_9_x86_64.whl
423e89b23490805d2a5a96fe40ec507407b8ee786d66f7328be214f9679df6dd  numpy-2.0.2-cp39-cp39-macosx_11_0_arm64.whl
2b2955fa6f11907cf7a70dab0d0755159bca87755e831e47932367fc8f2f2d0b  numpy-2.0.2-cp39-cp39-macosx_14_0_arm64.whl
97032a27bd9d8988b9a97a8c4d2c9f2c15a81f61e2f21404d7e8ef00cb5be729  numpy-2.0.2-cp39-cp39-macosx_14_0_x86_64.whl
1e795a8be3ddbac43274f18588329c72939870a16cae810c2b73461c40718ab1  numpy-2.0.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
f26b258c385842546006213344c50655ff1555a9338e2e5e02a0756dc3e803dd  numpy-2.0.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
5fec9451a7789926bcf7c2b8d187292c9f93ea30284802a0ab3f5be8ab36865d  numpy-2.0.2-cp39-cp39-musllinux_1_1_x86_64.whl
9189427407d88ff25ecf8f12469d4d39d35bee1db5d39fc5c168c6f088a6956d  numpy-2.0.2-cp39-cp39-musllinux_1_2_aarch64.whl
905d16e0c60200656500c95b6b8dca5d109e23cb24abc701d41c02d74c6b3afa  numpy-2.0.2-cp39-cp39-win32.whl
a3f4ab0caa7f053f6797fcd4e1e25caee367db3112ef2b6ef82d749530768c73  numpy-2.0.2-cp39-cp39-win_amd64.whl
7f0a0c6f12e07fa94133c8a67404322845220c06a9e80e85999afe727f7438b8  numpy-2.0.2-pp39-pypy39_pp73-macosx_10_9_x86_64.whl
312950fdd060354350ed123c0e25a71327d3711584beaef30cdaa93320c392d4  numpy-2.0.2-pp39-pypy39_pp73-macosx_14_0_x86_64.whl
26df23238872200f63518dd2aa984cfca675d82469535dc7162dc2ee52d9dd5c  numpy-2.0.2-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
a46288ec55ebbd58947d31d72be2c63cbf839f0a63b49cb755022310792a3385  numpy-2.0.2-pp39-pypy39_pp73-win_amd64.whl
883c987dee1880e2a864ab0dc9892292582510604156762362d9326444636e78  numpy-2.0.2.tar.gz

v2.0.1

Compare Source

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
b83e16a5511d1b1f8a88cbabb1a6f6a499f82c062a4251892d9ad5d609863fb7  numpy-2.0.1-cp311-cp311-musllinux_1_1_x86_64.whl
1f87fec1f9bc1efd23f4227becff04bd0e979e23ca50cc92ec88b38489db3b55  numpy-2.0.1-cp311-cp311-musllinux_1_2_aarch64.whl
36d3a9405fd7c511804dc56fc32974fa5533bdeb3cd1604d6b8ff1d292b819c4  numpy-2.0.1-cp311-cp311-win32.whl
08458fbf403bff5e2b45f08eda195d4b0c9b35682311da5a5a0a0925b11b9bd8  numpy-2.0.1-cp311-cp311-win_amd64.whl
6bf4e6f4a2a2e26655717a1983ef6324f2664d7011f6ef7482e8c0b3d51e82ac  numpy-2.0.1-cp312-cp312-macosx_10_9_x86_64.whl
7d6fddc5fe258d3328cd8e3d7d3e02234c5d70e01ebe377a6ab92adb14039cb4  numpy-2.0.1-cp312-cp312-macosx_11_0_arm64.whl
5daab361be6ddeb299a918a7c0864fa8618af66019138263247af405018b04e1  numpy-2.0.1-cp312-cp312-macosx_14_0_arm64.whl
ea2326a4dca88e4a274ba3a4405eb6c6467d3ffbd8c7d38632502eaae3820587  numpy-2.0.1-cp312-cp312-macosx_14_0_x86_64.whl
529af13c5f4b7a932fb0e1911d3a75da204eff023ee5e0e79c1751564221a5c8  numpy-2.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
6790654cb13eab303d8402354fabd47472b24635700f631f041bd0b65e37298a  numpy-2.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
cbab9fc9c391700e3e1287666dfd82d8666d10e69a6c4a09ab97574c0b7ee0a7  numpy-2.0.1-cp312-cp312-musllinux_1_1_x86_64.whl
99d0d92a5e3613c33a5f01db206a33f8fdf3d71f2912b0de1739894668b7a93b  numpy-2.0.1-cp312-cp312-musllinux_1_2_aarch64.whl
173a00b9995f73b79eb0191129f2455f1e34c203f559dd118636858cc452a1bf  numpy-2.0.1-cp312-cp312-win32.whl
bb2124fdc6e62baae159ebcfa368708867eb56806804d005860b6007388df171  numpy-2.0.1-cp312-cp312-win_amd64.whl
bfc085b28d62ff4009364e7ca34b80a9a080cbd97c2c0630bb5f7f770dae9414  numpy-2.0.1-cp39-cp39-macosx_10_9_x86_64.whl
8fae4ebbf95a179c1156fab0b142b74e4ba4204c87bde8d3d8b6f9c34c5825ef  numpy-2.0.1-cp39-cp39-macosx_11_0_arm64.whl
72dc22e9ec8f6eaa206deb1b1355eb2e253899d7347f5e2fae5f0af613741d06  numpy-2.0.1-cp39-cp39-macosx_14_0_arm64.whl
ec87f5f8aca726117a1c9b7083e7656a9d0d606eec7299cc067bb83d26f16e0c  numpy-2.0.1-cp39-cp39-macosx_14_0_x86_64.whl
1f682ea61a88479d9498bf2091fdcd722b090724b08b31d63e022adc063bad59  numpy-2.0.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
8efc84f01c1cd7e34b3fb310183e72fcdf55293ee736d679b6d35b35d80bba26  numpy-2.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
3fdabe3e2a52bc4eff8dc7a5044342f8bd9f11ef0934fcd3289a788c0eb10018  numpy-2.0.1-cp39-cp39-musllinux_1_1_x86_64.whl
24a0e1befbfa14615b49ba9659d3d8818a0f4d8a1c5822af8696706fbda7310c  numpy-2.0.1-cp39-cp39-musllinux_1_2_aarch64.whl
f9cf5ea551aec449206954b075db819f52adc1638d46a6738253a712d553c7b4  numpy-2.0.1-cp39-cp39-win32.whl
e9e81fa9017eaa416c056e5d9e71be93d05e2c3c2ab308d23307a8bc4443c368  numpy-2.0.1-cp39-cp39-win_amd64.whl
61728fba1e464f789b11deb78a57805c70b2ed02343560456190d0501ba37b0f  numpy-2.0.1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl
12f5d865d60fb9734e60a60f1d5afa6d962d8d4467c120a1c0cda6eb2964437d  numpy-2.0.1-pp39-pypy39_pp73-macosx_14_0_x86_64.whl
eacf3291e263d5a67d8c1a581a8ebbcfd6447204ef58828caf69a5e3e8c75990  numpy-2.0.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
2c3a346ae20cfd80b6cfd3e60dc179963ef2ea58da5ec074fd3d9e7a1e7ba97f  numpy-2.0.1-pp39-pypy39_pp73-win_amd64.whl
485b87235796410c3519a699cfe1faab097e509e90ebb05dcd098db2ae87e7b3  numpy-2.0.1.tar.gz

v2.0.0

Compare Source

NumPy 2.0.0 Release Notes

NumPy 2.0.0 is the first major release since 2006. It is the result of
11 months of development since the last feature release and is the work
of 212 contributors spread over 1078 pull requests. It contains a large
number of exciting new features as well as changes to both the Python
and C APIs.

This major release includes breaking changes that could not happen in a
regular minor (feature) release - including an ABI break, changes to
type promotion rules, and API changes which may not have been emitting
deprecation warnings in 1.26.x. Key documents related to how to adapt to
changes in NumPy 2.0, in addition to these release notes, include:

Highlights

Highlights of this release include:

  • New features:
    • A new variable-length string dtype, numpy.dtypes.StringDType and a new
      numpy.strings namespace with performant ufuncs for string operations,
    • Support for float32 and longdouble in all
      numpy.fft functions,
    • Support for the array API standard in the main numpy
      namespace.
  • Performance improvements:
    • Sorting functions sort, argsort,
      partition, argpartition have been
      accelerated through the use of the Intel x86-simd-sort

Configuration

📅 Schedule: Branch creation - At any time (no schedule defined), Automerge - At any time (no schedule defined).

🚦 Automerge: Disabled by config. Please merge this manually once you are satisfied.

Rebasing: Whenever PR becomes conflicted, or you tick the rebase/retry checkbox.

🔕 Ignore: Close this PR and you won't be reminded about these updates again.


  • If you want to rebase/retry this PR, check this box

This PR was generated by Mend Renovate. View the repository job log.

@renovate renovate bot force-pushed the renovate/numpy-2.x branch 3 times, most recently from 831b6db to 62f803d Compare October 20, 2024 05:39
@OasisAkari OasisAkari marked this pull request as draft October 21, 2024 07:05
@renovate renovate bot force-pushed the renovate/numpy-2.x branch 2 times, most recently from 668a61f to 457cf98 Compare October 28, 2024 16:48
@renovate renovate bot force-pushed the renovate/numpy-2.x branch 3 times, most recently from 960ba12 to 2e943d3 Compare November 5, 2024 17:53
@renovate renovate bot force-pushed the renovate/numpy-2.x branch 2 times, most recently from 712002c to afdf712 Compare November 29, 2024 06:41
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

Successfully merging this pull request may close these issues.

0 participants