numpy - v2.3.2 (Jul 24, 2025)

NumPy 2.3.2 Release Notes

The NumPy 2.3.2 release is a patch release with a number of bug fixes and maintenance updates. The highlights are:

• Wheels for Python 3.14.0rc1
• PyPy updated to the latest stable release
• OpenBLAS updated to 0.3.30

This release supports Python versions 3.11-3.14

Contributors

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

• !DWesl
• Charles Harris
• Joren Hammudoglu
• Maanas Arora
• Marco Edward Gorelli
• Matti Picus
• Nathan Goldbaum
• Sebastian Berg
• kostayScr +

Pull requests merged

A total of 16 pull requests were merged for this release.

• #29256: MAINT: Prepare 2.3.x for further development
• #29283: TYP: Work around a mypy issue with bool arrays (#29248)
• #29284: BUG: fix fencepost error in StringDType internals
• #29287: BUG: handle case in mapiter where descriptors might get replaced...
• #29350: BUG: Fix shape error path in array-interface
• #29412: BUG: Allow reading non-npy files in npz and add test
• #29413: TST: Avoid uninitialized values in test (#29341)
• #29414: BUG: Fix reference leakage for output arrays in reduction functions
• #29415: BUG: fix casting issue in center, ljust, rjust, and zfill (#29369)
• #29416: TYP: Fix overloads in np.char.array and np.char.asarray...
• #29417: BUG: Any dtype should call square on arr \*\* 2 (#29392)
• #29424: MAINT: use a stable pypy release in CI
• #29425: MAINT: Support python 314rc1
• #29429: MAINT: Update highway to match main.
• #29430: BLD: use github to build macos-arm64 wheels with OpenBLAS and...
• #29437: BUG: fix datetime/timedelta hash memory leak (#29411)

Checksums

MD5

e35c637ea9fba77eabfdf70e26eaa16d  numpy-2.3.2-cp311-cp311-macosx_10_9_x86_64.whl
3dede42d11c843cfacff422f65a80e47  numpy-2.3.2-cp311-cp311-macosx_11_0_arm64.whl
f5c485a43210eb3541b254c8c9d6ac9e  numpy-2.3.2-cp311-cp311-macosx_14_0_arm64.whl
658950eb37e19b42920635ee60830a1d  numpy-2.3.2-cp311-cp311-macosx_14_0_x86_64.whl
9a864a280798829cc522521bc5d9c7e2  numpy-2.3.2-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
085e1ff7746d327a1320672ab86966c3  numpy-2.3.2-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
6acefa06c38bc616352b76174d4f19d2  numpy-2.3.2-cp311-cp311-musllinux_1_2_aarch64.whl
4dd3469970dbfba60dad41b9923c5a5a  numpy-2.3.2-cp311-cp311-musllinux_1_2_x86_64.whl
ad090139b8b872a9157b92c840566c5e  numpy-2.3.2-cp311-cp311-win32.whl
09b023f808432e60633e36a13630dc13  numpy-2.3.2-cp311-cp311-win_amd64.whl
c80f2a1c4c829ccb6745a6d0803b7177  numpy-2.3.2-cp311-cp311-win_arm64.whl
307fc28e0c630dbc5a6ff4051ee9ec6c  numpy-2.3.2-cp312-cp312-macosx_10_13_x86_64.whl
4af1ffb81bdec235aef1b9bdf7c1566d  numpy-2.3.2-cp312-cp312-macosx_11_0_arm64.whl
8003e8df1badaffee163a603bf05656b  numpy-2.3.2-cp312-cp312-macosx_14_0_arm64.whl
e703fab1c371fd27389401caa34a5cbd  numpy-2.3.2-cp312-cp312-macosx_14_0_x86_64.whl
5fdc228f15ec5de78b89c7aa4c137019  numpy-2.3.2-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
f3bc10b89911c09777c4c5d9752f35b0  numpy-2.3.2-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
5d0128aa0f6aa3a5122364a727a72eba  numpy-2.3.2-cp312-cp312-musllinux_1_2_aarch64.whl
ef392070c44709321d7f87ab15bbd674  numpy-2.3.2-cp312-cp312-musllinux_1_2_x86_64.whl
909e05dcd1164cc02d5fccc1cc6c9ca6  numpy-2.3.2-cp312-cp312-win32.whl
3ba0b657682fc54d9433b4d7244c9264  numpy-2.3.2-cp312-cp312-win_amd64.whl
05755e8c591b1ac2fff05a06d76ac414  numpy-2.3.2-cp312-cp312-win_arm64.whl
c1e323fa1986bc99ae96c46126a30f93  numpy-2.3.2-cp313-cp313-macosx_10_13_x86_64.whl
9a89327ef3550581017ea6e2a47c1a8e  numpy-2.3.2-cp313-cp313-macosx_11_0_arm64.whl
3c7236116911c5c19de0091d7ac81f65  numpy-2.3.2-cp313-cp313-macosx_14_0_arm64.whl
1809c7adafae6492741864cf4dda7d1e  numpy-2.3.2-cp313-cp313-macosx_14_0_x86_64.whl
ee68f94ec5f9c0c7f9423d7329bc085e  numpy-2.3.2-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
24c4e95f0a615356787e2920378e5c6f  numpy-2.3.2-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
5c53a2c915d177b7c305c0386ba21b43  numpy-2.3.2-cp313-cp313-musllinux_1_2_aarch64.whl
c4607ea441320a0078d942ca21ef2411  numpy-2.3.2-cp313-cp313-musllinux_1_2_x86_64.whl
09f2fdeb35d952751ba269ca5fa77e7a  numpy-2.3.2-cp313-cp313-win32.whl
47a7326544ce192df844b3e9750c7704  numpy-2.3.2-cp313-cp313-win_amd64.whl
9b5adab8ee4eb97ccf90d73d63671db4  numpy-2.3.2-cp313-cp313-win_arm64.whl
7169baf4160b9a75790650cef23a73e1  numpy-2.3.2-cp313-cp313t-macosx_10_13_x86_64.whl
0338f2a78981d84d84e5f693ed6112d5  numpy-2.3.2-cp313-cp313t-macosx_11_0_arm64.whl
b0c1c28add9716f7cee433d53fb43067  numpy-2.3.2-cp313-cp313t-macosx_14_0_arm64.whl
d2d8d43c535184095550420169858b90  numpy-2.3.2-cp313-cp313t-macosx_14_0_x86_64.whl
745bb6930958f4d7980cd705621abc25  numpy-2.3.2-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
96412f8c9687d468e260aacdfb9cca02  numpy-2.3.2-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
11ce971fe997bf5c0784516db85891ff  numpy-2.3.2-cp313-cp313t-musllinux_1_2_aarch64.whl
e71ba272e9db74bc753ca056e76fdf5b  numpy-2.3.2-cp313-cp313t-musllinux_1_2_x86_64.whl
82feb6822f2cf04a9edf38cf7f7d4806  numpy-2.3.2-cp313-cp313t-win32.whl
c6c8a1a2e94a9fc2dad9d161a6666e54  numpy-2.3.2-cp313-cp313t-win_amd64.whl
29e65f132c4a916214a0e82bca214717  numpy-2.3.2-cp313-cp313t-win_arm64.whl
2b99d343001495b182027843bf2148b2  numpy-2.3.2-cp314-cp314-macosx_10_13_x86_64.whl
40d04ac18cd9db3c380224d3d5607770  numpy-2.3.2-cp314-cp314-macosx_11_0_arm64.whl
871631874c6839719d1c1b3ad81835cd  numpy-2.3.2-cp314-cp314-macosx_14_0_arm64.whl
4d4098888f19de85dd18646c2f955cd2  numpy-2.3.2-cp314-cp314-macosx_14_0_x86_64.whl
813e47e3c07cd28bf0458a1e513d6619  numpy-2.3.2-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
1fe080566baca813e6ac4635011a408a  numpy-2.3.2-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
bd44ab38b53a4b5b6130b6f01ffaf5fa  numpy-2.3.2-cp314-cp314-musllinux_1_2_aarch64.whl
f2fda217bec39ede344b42fef2cbd9e5  numpy-2.3.2-cp314-cp314-musllinux_1_2_x86_64.whl
c02218de0d0666769c91513eafaf251f  numpy-2.3.2-cp314-cp314-win32.whl
d419eb806a6f5debb366d4bcf0f5bde0  numpy-2.3.2-cp314-cp314-win_amd64.whl
851529ffdf2b0d4b66eb1ac99c24da3e  numpy-2.3.2-cp314-cp314-win_arm64.whl
2306e8b73fcd2d46116c6a95034e4d3a  numpy-2.3.2-cp314-cp314t-macosx_10_13_x86_64.whl
b4d4ce3339cb9f0b0f2b339db803f39c  numpy-2.3.2-cp314-cp314t-macosx_11_0_arm64.whl
6ae336ac461d5d89811c8a236b442842  numpy-2.3.2-cp314-cp314t-macosx_14_0_arm64.whl
351f35dd00bfb35e6cad2447a14c7cdf  numpy-2.3.2-cp314-cp314t-macosx_14_0_x86_64.whl
0e0b26b34024f24a5f59809a1778ace0  numpy-2.3.2-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
bc77a7f5826bb0a38154d31d8444abb7  numpy-2.3.2-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
cd1e335e2a8437339475db12ee30f26d  numpy-2.3.2-cp314-cp314t-musllinux_1_2_aarch64.whl
5c8093e713bd7e5f8512458d53fefeed  numpy-2.3.2-cp314-cp314t-musllinux_1_2_x86_64.whl
66125a7e4e311fc2dedfa8c25ee577f2  numpy-2.3.2-cp314-cp314t-win32.whl
97713f41a5d4a08e8ed3d629d07678d3  numpy-2.3.2-cp314-cp314t-win_amd64.whl
848c4c409b643c2b42c431f51b310095  numpy-2.3.2-cp314-cp314t-win_arm64.whl
e240eed2fc098f7a0ae9813abead8a05  numpy-2.3.2-pp311-pypy311_pp73-macosx_10_15_x86_64.whl
7e46ebe46530596019ae6b5db8a7a564  numpy-2.3.2-pp311-pypy311_pp73-macosx_11_0_arm64.whl
82077182e608a0d366eba700902463b5  numpy-2.3.2-pp311-pypy311_pp73-macosx_14_0_arm64.whl
67db17064907cd22a74676b50de1ab6d  numpy-2.3.2-pp311-pypy311_pp73-macosx_14_0_x86_64.whl
6d59903ecd732d53dd230ca59cdc2c34  numpy-2.3.2-pp311-pypy311_pp73-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
baae8d6875e1de409ffef875896c4b4f  numpy-2.3.2-pp311-pypy311_pp73-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
5d92d6c39f2f0b28149ed15437b13cf7  numpy-2.3.2-pp311-pypy311_pp73-win_amd64.whl
f8d3d3b3ecd2b6e98889e88f6bbdc1a3  numpy-2.3.2.tar.gz

SHA256

852ae5bed3478b92f093e30f785c98e0cb62fa0a939ed057c31716e18a7a22b9  numpy-2.3.2-cp311-cp311-macosx_10_9_x86_64.whl
7a0e27186e781a69959d0230dd9909b5e26024f8da10683bd6344baea1885168  numpy-2.3.2-cp311-cp311-macosx_11_0_arm64.whl
f0a1a8476ad77a228e41619af2fa9505cf69df928e9aaa165746584ea17fed2b  numpy-2.3.2-cp311-cp311-macosx_14_0_arm64.whl
cbc95b3813920145032412f7e33d12080f11dc776262df1712e1638207dde9e8  numpy-2.3.2-cp311-cp311-macosx_14_0_x86_64.whl
f75018be4980a7324edc5930fe39aa391d5734531b1926968605416ff58c332d  numpy-2.3.2-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
20b8200721840f5621b7bd03f8dcd78de33ec522fc40dc2641aa09537df010c3  numpy-2.3.2-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
1f91e5c028504660d606340a084db4b216567ded1056ea2b4be4f9d10b67197f  numpy-2.3.2-cp311-cp311-musllinux_1_2_aarch64.whl
fb1752a3bb9a3ad2d6b090b88a9a0ae1cd6f004ef95f75825e2f382c183b2097  numpy-2.3.2-cp311-cp311-musllinux_1_2_x86_64.whl
4ae6863868aaee2f57503c7a5052b3a2807cf7a3914475e637a0ecd366ced220  numpy-2.3.2-cp311-cp311-win32.whl
240259d6564f1c65424bcd10f435145a7644a65a6811cfc3201c4a429ba79170  numpy-2.3.2-cp311-cp311-win_amd64.whl
4209f874d45f921bde2cff1ffcd8a3695f545ad2ffbef6d3d3c6768162efab89  numpy-2.3.2-cp311-cp311-win_arm64.whl
bc3186bea41fae9d8e90c2b4fb5f0a1f5a690682da79b92574d63f56b529080b  numpy-2.3.2-cp312-cp312-macosx_10_13_x86_64.whl
2f4f0215edb189048a3c03bd5b19345bdfa7b45a7a6f72ae5945d2a28272727f  numpy-2.3.2-cp312-cp312-macosx_11_0_arm64.whl
8b1224a734cd509f70816455c3cffe13a4f599b1bf7130f913ba0e2c0b2006c0  numpy-2.3.2-cp312-cp312-macosx_14_0_arm64.whl
3dcf02866b977a38ba3ec10215220609ab9667378a9e2150615673f3ffd6c73b  numpy-2.3.2-cp312-cp312-macosx_14_0_x86_64.whl
572d5512df5470f50ada8d1972c5f1082d9a0b7aa5944db8084077570cf98370  numpy-2.3.2-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
8145dd6d10df13c559d1e4314df29695613575183fa2e2d11fac4c208c8a1f73  numpy-2.3.2-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
103ea7063fa624af04a791c39f97070bf93b96d7af7eb23530cd087dc8dbe9dc  numpy-2.3.2-cp312-cp312-musllinux_1_2_aarch64.whl
fc927d7f289d14f5e037be917539620603294454130b6de200091e23d27dc9be  numpy-2.3.2-cp312-cp312-musllinux_1_2_x86_64.whl
d95f59afe7f808c103be692175008bab926b59309ade3e6d25009e9a171f7036  numpy-2.3.2-cp312-cp312-win32.whl
9e196ade2400c0c737d93465327d1ae7c06c7cb8a1756121ebf54b06ca183c7f  numpy-2.3.2-cp312-cp312-win_amd64.whl
ee807923782faaf60d0d7331f5e86da7d5e3079e28b291973c545476c2b00d07  numpy-2.3.2-cp312-cp312-win_arm64.whl
c8d9727f5316a256425892b043736d63e89ed15bbfe6556c5ff4d9d4448ff3b3  numpy-2.3.2-cp313-cp313-macosx_10_13_x86_64.whl
efc81393f25f14d11c9d161e46e6ee348637c0a1e8a54bf9dedc472a3fae993b  numpy-2.3.2-cp313-cp313-macosx_11_0_arm64.whl
dd937f088a2df683cbb79dda9a772b62a3e5a8a7e76690612c2737f38c6ef1b6  numpy-2.3.2-cp313-cp313-macosx_14_0_arm64.whl
11e58218c0c46c80509186e460d79fbdc9ca1eb8d8aee39d8f2dc768eb781089  numpy-2.3.2-cp313-cp313-macosx_14_0_x86_64.whl
5ad4ebcb683a1f99f4f392cc522ee20a18b2bb12a2c1c42c3d48d5a1adc9d3d2  numpy-2.3.2-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
938065908d1d869c7d75d8ec45f735a034771c6ea07088867f713d1cd3bbbe4f  numpy-2.3.2-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
66459dccc65d8ec98cc7df61307b64bf9e08101f9598755d42d8ae65d9a7a6ee  numpy-2.3.2-cp313-cp313-musllinux_1_2_aarch64.whl
a7af9ed2aa9ec5950daf05bb11abc4076a108bd3c7db9aa7251d5f107079b6a6  numpy-2.3.2-cp313-cp313-musllinux_1_2_x86_64.whl
906a30249315f9c8e17b085cc5f87d3f369b35fedd0051d4a84686967bdbbd0b  numpy-2.3.2-cp313-cp313-win32.whl
c63d95dc9d67b676e9108fe0d2182987ccb0f11933c1e8959f42fa0da8d4fa56  numpy-2.3.2-cp313-cp313-win_amd64.whl
b05a89f2fb84d21235f93de47129dd4f11c16f64c87c33f5e284e6a3a54e43f2  numpy-2.3.2-cp313-cp313-win_arm64.whl
4e6ecfeddfa83b02318f4d84acf15fbdbf9ded18e46989a15a8b6995dfbf85ab  numpy-2.3.2-cp313-cp313t-macosx_10_13_x86_64.whl
508b0eada3eded10a3b55725b40806a4b855961040180028f52580c4729916a2  numpy-2.3.2-cp313-cp313t-macosx_11_0_arm64.whl
754d6755d9a7588bdc6ac47dc4ee97867271b17cee39cb87aef079574366db0a  numpy-2.3.2-cp313-cp313t-macosx_14_0_arm64.whl
a9f66e7d2b2d7712410d3bc5684149040ef5f19856f20277cd17ea83e5006286  numpy-2.3.2-cp313-cp313t-macosx_14_0_x86_64.whl
de6ea4e5a65d5a90c7d286ddff2b87f3f4ad61faa3db8dabe936b34c2275b6f8  numpy-2.3.2-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
a3ef07ec8cbc8fc9e369c8dcd52019510c12da4de81367d8b20bc692aa07573a  numpy-2.3.2-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
27c9f90e7481275c7800dc9c24b7cc40ace3fdb970ae4d21eaff983a32f70c91  numpy-2.3.2-cp313-cp313t-musllinux_1_2_aarch64.whl
07b62978075b67eee4065b166d000d457c82a1efe726cce608b9db9dd66a73a5  numpy-2.3.2-cp313-cp313t-musllinux_1_2_x86_64.whl
c771cfac34a4f2c0de8e8c97312d07d64fd8f8ed45bc9f5726a7e947270152b5  numpy-2.3.2-cp313-cp313t-win32.whl
72dbebb2dcc8305c431b2836bcc66af967df91be793d63a24e3d9b741374c450  numpy-2.3.2-cp313-cp313t-win_amd64.whl
72c6df2267e926a6d5286b0a6d556ebe49eae261062059317837fda12ddf0c1a  numpy-2.3.2-cp313-cp313t-win_arm64.whl
448a66d052d0cf14ce9865d159bfc403282c9bc7bb2a31b03cc18b651eca8b1a  numpy-2.3.2-cp314-cp314-macosx_10_13_x86_64.whl
546aaf78e81b4081b2eba1d105c3b34064783027a06b3ab20b6eba21fb64132b  numpy-2.3.2-cp314-cp314-macosx_11_0_arm64.whl
87c930d52f45df092f7578889711a0768094debf73cfcde105e2d66954358125  numpy-2.3.2-cp314-cp314-macosx_14_0_arm64.whl
8dc082ea901a62edb8f59713c6a7e28a85daddcb67454c839de57656478f5b19  numpy-2.3.2-cp314-cp314-macosx_14_0_x86_64.whl
af58de8745f7fa9ca1c0c7c943616c6fe28e75d0c81f5c295810e3c83b5be92f  numpy-2.3.2-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
fed5527c4cf10f16c6d0b6bee1f89958bccb0ad2522c8cadc2efd318bcd545f5  numpy-2.3.2-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
095737ed986e00393ec18ec0b21b47c22889ae4b0cd2d5e88342e08b01141f58  numpy-2.3.2-cp314-cp314-musllinux_1_2_aarch64.whl
b5e40e80299607f597e1a8a247ff8d71d79c5b52baa11cc1cce30aa92d2da6e0  numpy-2.3.2-cp314-cp314-musllinux_1_2_x86_64.whl
7d6e390423cc1f76e1b8108c9b6889d20a7a1f59d9a60cac4a050fa734d6c1e2  numpy-2.3.2-cp314-cp314-win32.whl
b9d0878b21e3918d76d2209c924ebb272340da1fb51abc00f986c258cd5e957b  numpy-2.3.2-cp314-cp314-win_amd64.whl
2738534837c6a1d0c39340a190177d7d66fdf432894f469728da901f8f6dc910  numpy-2.3.2-cp314-cp314-win_arm64.whl
4d002ecf7c9b53240be3bb69d80f86ddbd34078bae04d87be81c1f58466f264e  numpy-2.3.2-cp314-cp314t-macosx_10_13_x86_64.whl
293b2192c6bcce487dbc6326de5853787f870aeb6c43f8f9c6496db5b1781e45  numpy-2.3.2-cp314-cp314t-macosx_11_0_arm64.whl
0a4f2021a6da53a0d580d6ef5db29947025ae8b35b3250141805ea9a32bbe86b  numpy-2.3.2-cp314-cp314t-macosx_14_0_arm64.whl
9c144440db4bf3bb6372d2c3e49834cc0ff7bb4c24975ab33e01199e645416f2  numpy-2.3.2-cp314-cp314t-macosx_14_0_x86_64.whl
f92d6c2a8535dc4fe4419562294ff957f83a16ebdec66df0805e473ffaad8bd0  numpy-2.3.2-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
cefc2219baa48e468e3db7e706305fcd0c095534a192a08f31e98d83a7d45fb0  numpy-2.3.2-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
76c3e9501ceb50b2ff3824c3589d5d1ab4ac857b0ee3f8f49629d0de55ecf7c2  numpy-2.3.2-cp314-cp314t-musllinux_1_2_aarch64.whl
122bf5ed9a0221b3419672493878ba4967121514b1d7d4656a7580cd11dddcbf  numpy-2.3.2-cp314-cp314t-musllinux_1_2_x86_64.whl
6f1ae3dcb840edccc45af496f312528c15b1f79ac318169d094e85e4bb35fdf1  numpy-2.3.2-cp314-cp314t-win32.whl
087ffc25890d89a43536f75c5fe8770922008758e8eeeef61733957041ed2f9b  numpy-2.3.2-cp314-cp314t-win_amd64.whl
092aeb3449833ea9c0bf0089d70c29ae480685dd2377ec9cdbbb620257f84631  numpy-2.3.2-cp314-cp314t-win_arm64.whl
14a91ebac98813a49bc6aa1a0dfc09513dcec1d97eaf31ca21a87221a1cdcb15  numpy-2.3.2-pp311-pypy311_pp73-macosx_10_15_x86_64.whl
71669b5daae692189540cffc4c439468d35a3f84f0c88b078ecd94337f6cb0ec  numpy-2.3.2-pp311-pypy311_pp73-macosx_11_0_arm64.whl
69779198d9caee6e547adb933941ed7520f896fd9656834c300bdf4dd8642712  numpy-2.3.2-pp311-pypy311_pp73-macosx_14_0_arm64.whl
2c3271cc4097beb5a60f010bcc1cc204b300bb3eafb4399376418a83a1c6373c  numpy-2.3.2-pp311-pypy311_pp73-macosx_14_0_x86_64.whl
8446acd11fe3dc1830568c941d44449fd5cb83068e5c70bd5a470d323d448296  numpy-2.3.2-pp311-pypy311_pp73-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
aa098a5ab53fa407fded5870865c6275a5cd4101cfdef8d6fafc48286a96e981  numpy-2.3.2-pp311-pypy311_pp73-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
6936aff90dda378c09bea075af0d9c675fe3a977a9d2402f95a87f440f59f619  numpy-2.3.2-pp311-pypy311_pp73-win_amd64.whl
e0486a11ec30cdecb53f184d496d1c6a20786c81e55e41640270130056f8ee48  numpy-2.3.2.tar.gz

- Python
Published by charris 10 months ago

numpy - v2.3.1 (Jun 21, 2025)

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 in matmul for non-contiguous out kwarg parameter
• Fix for Accelerate runtime warnings on M4 hardware
• Fix new in NumPy 2.3.0 np.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: Revert np.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.whl
fdb5454e372d399cf570868ea7e2b192  numpy-2.3.1-cp311-cp311-macosx_11_0_arm64.whl
dc0f17823bb1826519d6974c2b95fa90  numpy-2.3.1-cp311-cp311-macosx_14_0_arm64.whl
7e3118fe383af697a8868ba191b9eac0  numpy-2.3.1-cp311-cp311-macosx_14_0_x86_64.whl
705aafad1250aa3e41502c5710a26ed5  numpy-2.3.1-cp311-cp311-manylinux_2_28_aarch64.whl
003d6268344577b804205098e11cdaa0  numpy-2.3.1-cp311-cp311-manylinux_2_28_x86_64.whl
7d0c0fd11c573c510a25dd7513e4ae0a  numpy-2.3.1-cp311-cp311-musllinux_1_2_aarch64.whl
d99f993ef05966ead99df736df18b521  numpy-2.3.1-cp311-cp311-musllinux_1_2_x86_64.whl
96933cac225fb8b60a9cc2c0efa14d36  numpy-2.3.1-cp311-cp311-win32.whl
f777712419f3dd586ac294ddce84b274  numpy-2.3.1-cp311-cp311-win_amd64.whl
1fe2615669de5c271a48b99356fa3528  numpy-2.3.1-cp311-cp311-win_arm64.whl
fccca48846d41d38966cc75395787f79  numpy-2.3.1-cp312-cp312-macosx_10_13_x86_64.whl
fa389e78db43f3c2841ce127c1205422  numpy-2.3.1-cp312-cp312-macosx_11_0_arm64.whl
2554944d786abd284db4a699d4edfe1e  numpy-2.3.1-cp312-cp312-macosx_14_0_arm64.whl
7fec491834803a8ffa3765ef3d03cea5  numpy-2.3.1-cp312-cp312-macosx_14_0_x86_64.whl
7c2d8b4412f12b9b02e98349fb5cd760  numpy-2.3.1-cp312-cp312-manylinux_2_28_aarch64.whl
94dcc636a2f2478666d820e21fc91682  numpy-2.3.1-cp312-cp312-manylinux_2_28_x86_64.whl
404128939d89d1ea26be105fb03b5028  numpy-2.3.1-cp312-cp312-musllinux_1_2_aarch64.whl
e89d8d460060e8315c3ba68b2b649db0  numpy-2.3.1-cp312-cp312-musllinux_1_2_x86_64.whl
a767bd10267ad6baef9655fb08db3fd3  numpy-2.3.1-cp312-cp312-win32.whl
f753b957fcb7f06f043cf9c6114f294c  numpy-2.3.1-cp312-cp312-win_amd64.whl
58ffa7c69587f9bf8f6025794fec7f63  numpy-2.3.1-cp312-cp312-win_arm64.whl
22a2a9a568dd0866b288ad8bd8bb3e90  numpy-2.3.1-cp313-cp313-macosx_10_13_x86_64.whl
5e1593fcc8bb3447e995622f2dca017b  numpy-2.3.1-cp313-cp313-macosx_11_0_arm64.whl
894d56072db9358e0096538710a1a8ce  numpy-2.3.1-cp313-cp313-macosx_14_0_arm64.whl
593cb311f5170cbcfcefb587cdcc70bb  numpy-2.3.1-cp313-cp313-macosx_14_0_x86_64.whl
22935447e75acda4075c57b332c0236a  numpy-2.3.1-cp313-cp313-manylinux_2_28_aarch64.whl
5aa2040f947204e15e95ec87461a7e91  numpy-2.3.1-cp313-cp313-manylinux_2_28_x86_64.whl
6516337f0347974fada21a23a818be64  numpy-2.3.1-cp313-cp313-musllinux_1_2_aarch64.whl
ec956eb37b874b1ec52d6ffccda6ef65  numpy-2.3.1-cp313-cp313-musllinux_1_2_x86_64.whl
0aaed62cb1bae9c1b1a44d1a4eda2db7  numpy-2.3.1-cp313-cp313-win32.whl
57829996fc12f649547f0258443bbb20  numpy-2.3.1-cp313-cp313-win_amd64.whl
a0d0dd68bbf0ab378142b2daff0a8e06  numpy-2.3.1-cp313-cp313-win_arm64.whl
b22dc66970a8017e4d0ce83ef8c938af  numpy-2.3.1-cp313-cp313t-macosx_10_13_x86_64.whl
93c17afb38cf8fd876ca2bd9ea7e9612  numpy-2.3.1-cp313-cp313t-macosx_11_0_arm64.whl
283064dabb434f3dbc1a5e2514b9cb29  numpy-2.3.1-cp313-cp313t-macosx_14_0_arm64.whl
5b8c778033c98b4a0ce6e5bfc7625f05  numpy-2.3.1-cp313-cp313t-macosx_14_0_x86_64.whl
2340bd78962f194bcdbee6531d954acc  numpy-2.3.1-cp313-cp313t-manylinux_2_28_aarch64.whl
43a92ad37dc68d719bdeeeb65b3f4d2f  numpy-2.3.1-cp313-cp313t-manylinux_2_28_x86_64.whl
eb110c4aa0d73558187397ddfba179ad  numpy-2.3.1-cp313-cp313t-musllinux_1_2_aarch64.whl
1f7f0076411ed4afa9c4553eb06564cb  numpy-2.3.1-cp313-cp313t-musllinux_1_2_x86_64.whl
30f30dde6f806070b2164e48a632a350  numpy-2.3.1-cp313-cp313t-win32.whl
2375e2f2a5b75c5f5c908af6bb85d639  numpy-2.3.1-cp313-cp313t-win_amd64.whl
b421530a87bb8e9e3d4dc34c75d5d953  numpy-2.3.1-cp313-cp313t-win_arm64.whl
b1bc3cbf9cd407964b2bb25dfe86ca3d  numpy-2.3.1-pp311-pypy311_pp73-macosx_10_15_x86_64.whl
4c2e234eb4f346f362d6e6c620fa7a56  numpy-2.3.1-pp311-pypy311_pp73-macosx_14_0_arm64.whl
98ec3c19a365d0ae926113bb349e323b  numpy-2.3.1-pp311-pypy311_pp73-macosx_14_0_x86_64.whl
e0c7bcd526cde46489d5a8f12e06cc77  numpy-2.3.1-pp311-pypy311_pp73-manylinux_2_28_aarch64.whl
41f535aa1f1acaf3d8a32a462a4cd4c8  numpy-2.3.1-pp311-pypy311_pp73-manylinux_2_28_x86_64.whl
2abf906a6688c98693045cbbc655d5b7  numpy-2.3.1-pp311-pypy311_pp73-win_amd64.whl
886559a4c541298b37245e389ce8bf10  numpy-2.3.1.tar.gz

SHA256

6ea9e48336a402551f52cd8f593343699003d2353daa4b72ce8d34f66b722070  numpy-2.3.1-cp311-cp311-macosx_10_9_x86_64.whl
5ccb7336eaf0e77c1635b232c141846493a588ec9ea777a7c24d7166bb8533ae  numpy-2.3.1-cp311-cp311-macosx_11_0_arm64.whl
0bb3a4a61e1d327e035275d2a993c96fa786e4913aa089843e6a2d9dd205c66a  numpy-2.3.1-cp311-cp311-macosx_14_0_arm64.whl
e344eb79dab01f1e838ebb67aab09965fb271d6da6b00adda26328ac27d4a66e  numpy-2.3.1-cp311-cp311-macosx_14_0_x86_64.whl
467db865b392168ceb1ef1ffa6f5a86e62468c43e0cfb4ab6da667ede10e58db  numpy-2.3.1-cp311-cp311-manylinux_2_28_aarch64.whl
afed2ce4a84f6b0fc6c1ce734ff368cbf5a5e24e8954a338f3bdffa0718adffb  numpy-2.3.1-cp311-cp311-manylinux_2_28_x86_64.whl
0025048b3c1557a20bc80d06fdeb8cc7fc193721484cca82b2cfa072fec71a93  numpy-2.3.1-cp311-cp311-musllinux_1_2_aarch64.whl
a5ee121b60aa509679b682819c602579e1df14a5b07fe95671c8849aad8f2115  numpy-2.3.1-cp311-cp311-musllinux_1_2_x86_64.whl
a8b740f5579ae4585831b3cf0e3b0425c667274f82a484866d2adf9570539369  numpy-2.3.1-cp311-cp311-win32.whl
d4580adadc53311b163444f877e0789f1c8861e2698f6b2a4ca852fda154f3ff  numpy-2.3.1-cp311-cp311-win_amd64.whl
ec0bdafa906f95adc9a0c6f26a4871fa753f25caaa0e032578a30457bff0af6a  numpy-2.3.1-cp311-cp311-win_arm64.whl
2959d8f268f3d8ee402b04a9ec4bb7604555aeacf78b360dc4ec27f1d508177d  numpy-2.3.1-cp312-cp312-macosx_10_13_x86_64.whl
762e0c0c6b56bdedfef9a8e1d4538556438288c4276901ea008ae44091954e29  numpy-2.3.1-cp312-cp312-macosx_11_0_arm64.whl
867ef172a0976aaa1f1d1b63cf2090de8b636a7674607d514505fb7276ab08fc  numpy-2.3.1-cp312-cp312-macosx_14_0_arm64.whl
4e602e1b8682c2b833af89ba641ad4176053aaa50f5cacda1a27004352dde943  numpy-2.3.1-cp312-cp312-macosx_14_0_x86_64.whl
8e333040d069eba1652fb08962ec5b76af7f2c7bce1df7e1418c8055cf776f25  numpy-2.3.1-cp312-cp312-manylinux_2_28_aarch64.whl
e7cbf5a5eafd8d230a3ce356d892512185230e4781a361229bd902ff403bc660  numpy-2.3.1-cp312-cp312-manylinux_2_28_x86_64.whl
5f1b8f26d1086835f442286c1d9b64bb3974b0b1e41bb105358fd07d20872952  numpy-2.3.1-cp312-cp312-musllinux_1_2_aarch64.whl
ee8340cb48c9b7a5899d1149eece41ca535513a9698098edbade2a8e7a84da77  numpy-2.3.1-cp312-cp312-musllinux_1_2_x86_64.whl
e772dda20a6002ef7061713dc1e2585bc1b534e7909b2030b5a46dae8ff077ab  numpy-2.3.1-cp312-cp312-win32.whl
cfecc7822543abdea6de08758091da655ea2210b8ffa1faf116b940693d3df76  numpy-2.3.1-cp312-cp312-win_amd64.whl
7be91b2239af2658653c5bb6f1b8bccafaf08226a258caf78ce44710a0160d30  numpy-2.3.1-cp312-cp312-win_arm64.whl
25a1992b0a3fdcdaec9f552ef10d8103186f5397ab45e2d25f8ac51b1a6b97e8  numpy-2.3.1-cp313-cp313-macosx_10_13_x86_64.whl
7dea630156d39b02a63c18f508f85010230409db5b2927ba59c8ba4ab3e8272e  numpy-2.3.1-cp313-cp313-macosx_11_0_arm64.whl
bada6058dd886061f10ea15f230ccf7dfff40572e99fef440a4a857c8728c9c0  numpy-2.3.1-cp313-cp313-macosx_14_0_arm64.whl
a894f3816eb17b29e4783e5873f92faf55b710c2519e5c351767c51f79d8526d  numpy-2.3.1-cp313-cp313-macosx_14_0_x86_64.whl
18703df6c4a4fee55fd3d6e5a253d01c5d33a295409b03fda0c86b3ca2ff41a1  numpy-2.3.1-cp313-cp313-manylinux_2_28_aarch64.whl
5902660491bd7a48b2ec16c23ccb9124b8abfd9583c5fdfa123fe6b421e03de1  numpy-2.3.1-cp313-cp313-manylinux_2_28_x86_64.whl
36890eb9e9d2081137bd78d29050ba63b8dab95dff7912eadf1185e80074b2a0  numpy-2.3.1-cp313-cp313-musllinux_1_2_aarch64.whl
a780033466159c2270531e2b8ac063704592a0bc62ec4a1b991c7c40705eb0e8  numpy-2.3.1-cp313-cp313-musllinux_1_2_x86_64.whl
39bff12c076812595c3a306f22bfe49919c5513aa1e0e70fac756a0be7c2a2b8  numpy-2.3.1-cp313-cp313-win32.whl
8d5ee6eec45f08ce507a6570e06f2f879b374a552087a4179ea7838edbcbfa42  numpy-2.3.1-cp313-cp313-win_amd64.whl
0c4d9e0a8368db90f93bd192bfa771ace63137c3488d198ee21dfb8e7771916e  numpy-2.3.1-cp313-cp313-win_arm64.whl
b0b5397374f32ec0649dd98c652a1798192042e715df918c20672c62fb52d4b8  numpy-2.3.1-cp313-cp313t-macosx_10_13_x86_64.whl
c5bdf2015ccfcee8253fb8be695516ac4457c743473a43290fd36eba6a1777eb  numpy-2.3.1-cp313-cp313t-macosx_11_0_arm64.whl
d70f20df7f08b90a2062c1f07737dd340adccf2068d0f1b9b3d56e2038979fee  numpy-2.3.1-cp313-cp313t-macosx_14_0_arm64.whl
2fb86b7e58f9ac50e1e9dd1290154107e47d1eef23a0ae9145ded06ea606f992  numpy-2.3.1-cp313-cp313t-macosx_14_0_x86_64.whl
23ab05b2d241f76cb883ce8b9a93a680752fbfcbd51c50eff0b88b979e471d8c  numpy-2.3.1-cp313-cp313t-manylinux_2_28_aarch64.whl
ce2ce9e5de4703a673e705183f64fd5da5bf36e7beddcb63a25ee2286e71ca48  numpy-2.3.1-cp313-cp313t-manylinux_2_28_x86_64.whl
c4913079974eeb5c16ccfd2b1f09354b8fed7e0d6f2cab933104a09a6419b1ee  numpy-2.3.1-cp313-cp313t-musllinux_1_2_aarch64.whl
010ce9b4f00d5c036053ca684c77441f2f2c934fd23bee058b4d6f196efd8280  numpy-2.3.1-cp313-cp313t-musllinux_1_2_x86_64.whl
6269b9edfe32912584ec496d91b00b6d34282ca1d07eb10e82dfc780907d6c2e  numpy-2.3.1-cp313-cp313t-win32.whl
2a809637460e88a113e186e87f228d74ae2852a2e0c44de275263376f17b5bdc  numpy-2.3.1-cp313-cp313t-win_amd64.whl
eccb9a159db9aed60800187bc47a6d3451553f0e1b08b068d8b277ddfbb9b244  numpy-2.3.1-cp313-cp313t-win_arm64.whl
ad506d4b09e684394c42c966ec1527f6ebc25da7f4da4b1b056606ffe446b8a3  numpy-2.3.1-pp311-pypy311_pp73-macosx_10_15_x86_64.whl
ebb8603d45bc86bbd5edb0d63e52c5fd9e7945d3a503b77e486bd88dde67a19b  numpy-2.3.1-pp311-pypy311_pp73-macosx_14_0_arm64.whl
15aa4c392ac396e2ad3d0a2680c0f0dee420f9fed14eef09bdb9450ee6dcb7b7  numpy-2.3.1-pp311-pypy311_pp73-macosx_14_0_x86_64.whl
c6e0bf9d1a2f50d2b65a7cf56db37c095af17b59f6c132396f7c6d5dd76484df  numpy-2.3.1-pp311-pypy311_pp73-manylinux_2_28_aarch64.whl
eabd7e8740d494ce2b4ea0ff05afa1b7b291e978c0ae075487c51e8bd93c0c68  numpy-2.3.1-pp311-pypy311_pp73-manylinux_2_28_x86_64.whl
e610832418a2bc09d974cc9fecebfa51e9532d6190223bc5ef6a7402ebf3b5cb  numpy-2.3.1-pp311-pypy311_pp73-win_amd64.whl
1ec9ae20a4226da374362cca3c62cd753faf2f951440b0e3b98e93c235441d2b  numpy-2.3.1.tar.gz

- Python
Published by charris 12 months ago

numpy - v2.3.0 (June 7, 2025)

NumPy 2.3.0 Release Notes

The NumPy 2.3.0 release continues the work to improve free threaded Python support and annotations together with the usual set of bug fixes. It is unusual in the number of expired deprecations, code modernizations, and style cleanups. The latter may not be visible to users, but is important for code maintenance over the long term. Note that we have also upgraded from manylinux2014 to manylinux228.

Users running on a Mac having an M4 cpu might see various warnings about invalid values and such. The warnings are a known problem with Accelerate. They are annoying, but otherwise harmless. Apple promises to fix them.

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

Highlights

• Interactive examples in the NumPy documentation.
• Building NumPy with OpenMP Parallelization.
• Preliminary support for Windows on ARM.
• Improved support for free threaded Python.
• Improved annotations.

New functions

New function numpy.strings.slice

The new function numpy.strings.slice was added, which implements fast native slicing of string arrays. It supports the full slicing API including negative slice offsets and steps.

(gh-27789)

Deprecations

• The numpy.typing.mypy_plugin has been deprecated in favor of platform-agnostic static type inference. Please remove numpy.typing.mypy_plugin from the plugins section of your mypy configuration. If this change results in new errors being reported, kindly open an issue.

  (gh-28129)

• The numpy.typing.NBitBase type has been deprecated and will be removed in a future version.

  This type was previously intended to be used as a generic upper bound for type-parameters, for example:

  ``` python import numpy as np import numpy.typing as npt

  def fNT: npt.NBitBase -> np.floating[NT]: ... ```

  But in NumPy 2.2.0, float64 and complex128 were changed to concrete subtypes, causing static type-checkers to reject x: np.float64 = f(np.complex128(42j)).

  So instead, the better approach is to use typing.overload:

  ``` python import numpy as np from typing import overload

  @overload def f(x: np.complex64) -> np.float32: ... @overload def f(x: np.complex128) -> np.float64: ... @overload def f(x: np.clongdouble) -> np.longdouble: ... ```

  (gh-28884)

Expired deprecations

• Remove deprecated macros like NPY_OWNDATA from Cython interfaces in favor of NPY_ARRAY_OWNDATA (deprecated since 1.7)

  (gh-28254)

• Remove numpy/npy_1_7_deprecated_api.h and C macros like NPY_OWNDATA in favor of NPY_ARRAY_OWNDATA (deprecated since 1.7)

  (gh-28254)

• Remove alias generate_divbyzero_error to npy_set_floatstatus_divbyzero and generate_overflow_error to npy_set_floatstatus_overflow (deprecated since 1.10)

  (gh-28254)

• Remove np.tostring (deprecated since 1.19)

  (gh-28254)

• Raise on np.conjugate of non-numeric types (deprecated since 1.13)

  (gh-28254)

• Raise when using np.bincount(...minlength=None), use 0 instead (deprecated since 1.14)

  (gh-28254)

• Passing shape=None to functions with a non-optional shape argument errors, use () instead (deprecated since 1.20)

  (gh-28254)

• Inexact matches for mode and searchside raise (deprecated since 1.20)

  (gh-28254)

• Setting __array_finalize__ = None errors (deprecated since 1.23)

  (gh-28254)

• np.fromfile and np.fromstring error on bad data, previously they would guess (deprecated since 1.18)

  (gh-28254)

• datetime64 and timedelta64 construction with a tuple no longer accepts an event value, either use a two-tuple of (unit, num) or a 4-tuple of (unit, num, den, 1) (deprecated since 1.14)

  (gh-28254)

• When constructing a dtype from a class with a dtype attribute, that attribute must be a dtype-instance rather than a thing that can be parsed as a dtype instance (deprecated in 1.19). At some point the whole construct of using a dtype attribute will be deprecated (see #25306)

  (gh-28254)

• Passing booleans as partition index errors (deprecated since 1.23)

  (gh-28254)

• Out-of-bounds indexes error even on empty arrays (deprecated since 1.20)

  (gh-28254)

• np.tostring has been removed, use tobytes instead (deprecated since 1.19)

  (gh-28254)

• Disallow make a non-writeable array writeable for arrays with a base that do not own their data (deprecated since 1.17)

  (gh-28254)

• concatenate() with axis=None uses same-kind casting by default, not unsafe (deprecated since 1.20)

  (gh-28254)

• Unpickling a scalar with object dtype errors (deprecated since 1.20)

  (gh-28254)

• The binary mode of fromstring now errors, use frombuffer instead (deprecated since 1.14)

  (gh-28254)

• Converting np.inexact or np.floating to a dtype errors (deprecated since 1.19)

  (gh-28254)

• Converting np.complex, np.integer, np.signedinteger, np.unsignedinteger, np.generic to a dtype errors (deprecated since 1.19)

  (gh-28254)

• The Python built-in round errors for complex scalars. Use np.round or scalar.round instead (deprecated since 1.19)

  (gh-28254)

• \'np.bool\' scalars can no longer be interpreted as an index (deprecated since 1.19)

  (gh-28254)

• Parsing an integer via a float string is no longer supported. (deprecated since 1.23) To avoid this error you can

  • make sure the original data is stored as integers.
  • use the converters=float keyword argument.
  • Use np.loadtxt(...).astype(np.int64)

  (gh-28254)

• The use of a length 1 tuple for the ufunc signature errors. Use dtype or fill the tuple with None (deprecated since 1.19)

  (gh-28254)

• Special handling of matrix is in np.outer is removed. Convert to a ndarray via matrix.A (deprecated since 1.20)

  (gh-28254)

• Removed the np.compat package source code (removed in 2.0)

  (gh-28961)

C API changes

• NpyIter_GetTransferFlags is now available to check if the iterator needs the Python API or if casts may cause floating point errors (FPE). FPEs can for example be set when casting float64(1e300) to float32 (overflow to infinity) or a NaN to an integer (invalid value).

  (gh-27883)

• NpyIter now has no limit on the number of operands it supports.

  (gh-28080)

New NpyIter_GetTransferFlags and NpyIter_IterationNeedsAPI change

NumPy now has the new NpyIter_GetTransferFlags function as a more precise way checking of iterator/buffering needs. I.e. whether the Python API/GIL is required or floating point errors may occur. This function is also faster if you already know your needs without buffering.

The NpyIter_IterationNeedsAPI function now performs all the checks that were previously performed at setup time. While it was never necessary to call it multiple times, doing so will now have a larger cost.

(gh-27998)

New Features

• The type parameter of np.dtype now defaults to typing.Any. This way, static type-checkers will infer dtype: np.dtype as dtype: np.dtype[Any], without reporting an error.

  (gh-28669)

• Static type-checkers now interpret:

  • _: np.ndarray as _: npt.NDArray[typing.Any].
  • _: np.flatiter as _: np.flatiter[np.ndarray].

  This is because their type parameters now have default values.

  (gh-28940)

NumPy now registers its pkg-config paths with the pkgconf PyPI package

The pkgconf PyPI package provides an interface for projects like NumPy to register their own paths to be added to the pkg-config search path. This means that when using pkgconf from PyPI, NumPy will be discoverable without needing for any custom environment configuration.

[!NOTE] This only applies when using the pkgconf package from PyPI, or put another way, this only applies when installing pkgconf via a Python package manager.

If you are using pkg-config or pkgconf provided by your system, or any other source that does not use the pkgconf-pypi project, the NumPy pkg-config directory will not be automatically added to the search path. In these situations, you might want to use numpy-config.

(gh-28214)

Allow out=... in ufuncs to ensure array result

NumPy has the sometimes difficult behavior that it currently usually returns scalars rather than 0-D arrays (even if the inputs were 0-D arrays). This is especially problematic for non-numerical dtypes (e.g. object).

For ufuncs (i.e. most simple math functions) it is now possible to use out=... (literally `...`, e.g. out=Ellipsis) which is identical in behavior to out not being passed, but will ensure a non-scalar return. This spelling is borrowed from arr1d[0, ...] where the ... also ensures a non-scalar return.

Other functions with an out= kwarg should gain support eventually. Downstream libraries that interoperate via __array_ufunc__ or __array_function__ may need to adapt to support this.

(gh-28576)

Building NumPy with OpenMP Parallelization

NumPy now supports OpenMP parallel processing capabilities when built with the -Denable_openmp=true Meson build flag. This feature is disabled by default. When enabled, np.sort and np.argsort functions can utilize OpenMP for parallel thread execution, improving performance for these operations.

(gh-28619)

Interactive examples in the NumPy documentation

The NumPy documentation includes a number of examples that can now be run interactively in your browser using WebAssembly and Pyodide.

Please note that the examples are currently experimental in nature and may not work as expected for all methods in the public API.

(gh-26745)

Improvements

• Scalar comparisons between non-comparable dtypes such as np.array(1) == np.array('s') now return a NumPy bool instead of a Python bool.

  (gh-27288)

• np.nditer now has no limit on the number of supported operands (C-integer).

  (gh-28080)

• No-copy pickling is now supported for any array that can be transposed to a C-contiguous array.

  (gh-28105)

• The __repr__ for user-defined dtypes now prefers the __name__ of the custom dtype over a more generic name constructed from its kind and itemsize.

  (gh-28250)

• np.dot now reports floating point exceptions.

  (gh-28442)

• np.dtypes.StringDType is now a generic type which accepts a type argument for na_object that defaults to typing.Never. For example, StringDType(na_object=None) returns a StringDType[None], and StringDType() returns a StringDType[typing.Never].

  (gh-28856)

Added warnings to np.isclose

Added warning messages if at least one of atol or rtol are either np.nan or np.inf within np.isclose.

• Warnings follow the user\'s np.seterr settings

(gh-28205)

Performance improvements and changes

Performance improvements to np.unique

np.unique now tries to use a hash table to find unique values instead of sorting values before finding unique values. This is limited to certain dtypes for now, and the function is now faster for those dtypes. The function now also exposes a sorted parameter to allow returning unique values as they were found, instead of sorting them afterwards.

(gh-26018)

Performance improvements to np.sort and np.argsort

np.sort and np.argsort functions now can leverage OpenMP for parallel thread execution, resulting in up to 3.5x speedups on x86 architectures with AVX2 or AVX-512 instructions. This opt-in feature requires NumPy to be built with the -Denableopenmp Meson flag. Users can control the number of threads used by setting the OMPNUM_THREADS environment variable.

(gh-28619)

Performance improvements for np.float16 casts

Earlier, floating point casts to and from np.float16 types were emulated in software on all platforms.

Now, on ARM devices that support Neon float16 intrinsics (such as recent Apple Silicon), the native float16 path is used to achieve the best performance.

(gh-28769)

Changes

• The vector norm ord=inf and the matrix norms ord={1, 2, inf, 'nuc'} now always returns zero for empty arrays. Empty arrays have at least one axis of size zero. This affects np.linalg.norm, np.linalg.vector_norm, and np.linalg.matrix_norm. Previously, NumPy would raises errors or return zero depending on the shape of the array.

  (gh-28343)

• A spelling error in the error message returned when converting a string to a float with the method np.format_float_positional has been fixed.

  (gh-28569)

• NumPy\'s __array_api_version__ was upgraded from 2023.12 to 2024.12.

• numpy.count_nonzero for axis=None (default) now returns a NumPy scalar instead of a Python integer.

• The parameter axis in numpy.take_along_axis function has now a default value of -1.

  (gh-28615)

• Printing of np.float16 and np.float32 scalars and arrays have been improved by adjusting the transition to scientific notation based on the floating point precision. A new legacy np.printoptions mode '2.2' has been added for backwards compatibility.

  (gh-28703)

• Multiplication between a string and integer now raises OverflowError instead of MemoryError if the result of the multiplication would create a string that is too large to be represented. This follows Python\'s behavior.

  (gh-29060)

unique_values may return unsorted data

The relatively new function (added in NumPy 2.0) unique_values may now return unsorted results. Just as unique_counts and unique_all these never guaranteed a sorted result, however, the result was sorted until now. In cases where these do return a sorted result, this may change in future releases to improve performance.

(gh-26018)

Changes to the main iterator and potential numerical changes

The main iterator, used in math functions and via np.nditer from Python and NpyIter in C, now behaves differently for some buffered iterations. This means that:

• The buffer size used will often be smaller than the maximum buffer sized allowed by the buffersize parameter.
• The \"growinner\" flag is now honored with buffered reductions when no operand requires buffering.

For np.sum() such changes in buffersize may slightly change numerical results of floating point operations. Users who use \"growinner\" for custom reductions could notice changes in precision (for example, in NumPy we removed it from einsum to avoid most precision changes and improve precision for some 64bit floating point inputs).

(gh-27883)

The minimum supported GCC version is now 9.3.0

The minimum supported version was updated from 8.4.0 to 9.3.0, primarily in order to reduce the chance of platform-specific bugs in old GCC versions from causing issues.

(gh-28102)

Changes to automatic bin selection in numpy.histogram

The automatic bin selection algorithm in numpy.histogram has been modified to avoid out-of-memory errors for samples with low variation. For full control over the selected bins the user can use set the bin or range parameters of numpy.histogram.

(gh-28426)

Build manylinux228 wheels

Wheels for linux systems will use the manylinux_2_28 tag (instead of the manylinux2014 tag), which means dropping support for redhat7/centos7, amazonlinux2, debian9, ubuntu18.04, and other pre-glibc2.28 operating system versions, as per the PEP 600 support table.

(gh-28436)

Remove use of -Wl,-ld_classic on macOS

Remove use of -Wl,-ld_classic on macOS. This hack is no longer needed by Spack, and results in libraries that cannot link to other libraries built with ld (new).

(gh-28713)

Re-enable overriding functions in the numpy.strings

Re-enable overriding functions in the numpy.strings module.

(gh-28741)

Checksums

MD5

cf552b6b6390343c24bf60365950c91c  numpy-2.3.0-cp311-cp311-macosx_10_9_x86_64.whl
d3c377f49f84b36297cfc2fc30c6a288  numpy-2.3.0-cp311-cp311-macosx_11_0_arm64.whl
4e12cd2aea876c09fdc3aaac2d0f4bac  numpy-2.3.0-cp311-cp311-macosx_14_0_arm64.whl
a33af1d4e1f0ee5ed82d7933c5df9f84  numpy-2.3.0-cp311-cp311-macosx_14_0_x86_64.whl
cd5cf04cb8b40e65aac8264c7bf3d7c9  numpy-2.3.0-cp311-cp311-manylinux_2_28_aarch64.whl
6a45424beb8f4f23e7b2b853bc18aefa  numpy-2.3.0-cp311-cp311-manylinux_2_28_x86_64.whl
2dc1c1d1b9deb8c0626af68c0c00660a  numpy-2.3.0-cp311-cp311-musllinux_1_2_aarch64.whl
9ff8ea227afce090dea3b4dac4653fa6  numpy-2.3.0-cp311-cp311-musllinux_1_2_x86_64.whl
a1e9e40a20187e1f5ae2f8ba165e291b  numpy-2.3.0-cp311-cp311-win32.whl
819e4ac62a3449c79818ff5aa0e6b276  numpy-2.3.0-cp311-cp311-win_amd64.whl
347260edfd35535b15b8133280793080  numpy-2.3.0-cp311-cp311-win_arm64.whl
9c1ad46e637b876a0535de60f5b604bc  numpy-2.3.0-cp312-cp312-macosx_10_13_x86_64.whl
5b656fbed339bcac1af6de73b15e5dba  numpy-2.3.0-cp312-cp312-macosx_11_0_arm64.whl
5b86d6d0cab79d0cd381bb2e912e7e23  numpy-2.3.0-cp312-cp312-macosx_14_0_arm64.whl
ea83ef5cd00d5e42bb745eee1ee0ad3f  numpy-2.3.0-cp312-cp312-macosx_14_0_x86_64.whl
15a5f57cb51d3d957c1b387c4bc54830  numpy-2.3.0-cp312-cp312-manylinux_2_28_aarch64.whl
b5fa92d1093dab4c3ca0622c29c4a241  numpy-2.3.0-cp312-cp312-manylinux_2_28_x86_64.whl
666cad26086ee212047e5ea0e8906480  numpy-2.3.0-cp312-cp312-musllinux_1_2_aarch64.whl
6263705622ca89ccadc6f458effde281  numpy-2.3.0-cp312-cp312-musllinux_1_2_x86_64.whl
bf1bf83eca701ff70351c2d7b308e181  numpy-2.3.0-cp312-cp312-win32.whl
0707b427c1102bb904994289e1555c3d  numpy-2.3.0-cp312-cp312-win_amd64.whl
097bd498f8333d383db61105044906dc  numpy-2.3.0-cp312-cp312-win_arm64.whl
54eb5fa0444ff5dd078bb1aa30d9533f  numpy-2.3.0-cp313-cp313-macosx_10_13_x86_64.whl
004b4c3650562bd851e31fb925863acb  numpy-2.3.0-cp313-cp313-macosx_11_0_arm64.whl
cd4e31304e51cc5dacd355730be25e4e  numpy-2.3.0-cp313-cp313-macosx_14_0_arm64.whl
0ed70aa071f35060ee68d6ab407159e5  numpy-2.3.0-cp313-cp313-macosx_14_0_x86_64.whl
a89b304bbb52268b233ab9652fee8142  numpy-2.3.0-cp313-cp313-manylinux_2_28_aarch64.whl
4b55cf791be482e8d8e5aaba0c10b6f2  numpy-2.3.0-cp313-cp313-manylinux_2_28_x86_64.whl
d3a1b81da2f2cba4743d1ee5385cb4d6  numpy-2.3.0-cp313-cp313-musllinux_1_2_aarch64.whl
fcaacdedcd8cfec7a6cb430fba7a5553  numpy-2.3.0-cp313-cp313-musllinux_1_2_x86_64.whl
7d0deec2ad395fda48b80be59612db22  numpy-2.3.0-cp313-cp313-win32.whl
7386a22b0ef219ba043f6e085933dbd6  numpy-2.3.0-cp313-cp313-win_amd64.whl
f4559038276d0e2bfb19601484d4cdff  numpy-2.3.0-cp313-cp313-win_arm64.whl
6c586985db2e888876aa96ceaf99ee66  numpy-2.3.0-cp313-cp313t-macosx_10_13_x86_64.whl
9726de30cce2b36940225a7ea086c824  numpy-2.3.0-cp313-cp313t-macosx_11_0_arm64.whl
ea021092cbb7b1e7d0984dc774bb288d  numpy-2.3.0-cp313-cp313t-macosx_14_0_arm64.whl
6f8261bc789eed1d3f6f7ea9ff3c2a2c  numpy-2.3.0-cp313-cp313t-macosx_14_0_x86_64.whl
ab624ddc1425d44412541aad1f012fd9  numpy-2.3.0-cp313-cp313t-manylinux_2_28_aarch64.whl
af55bc7a8f46ec8d413eb1fbe2c200e9  numpy-2.3.0-cp313-cp313t-manylinux_2_28_x86_64.whl
830eecf7c372aa0d7d746ad031ff0ba1  numpy-2.3.0-cp313-cp313t-musllinux_1_2_aarch64.whl
28870039fde4fec369185e185bf0077e  numpy-2.3.0-cp313-cp313t-musllinux_1_2_x86_64.whl
4510373c08383787c263a4b5a21a24ef  numpy-2.3.0-cp313-cp313t-win32.whl
de883c4313f4dc984045a51b8edb4084  numpy-2.3.0-cp313-cp313t-win_amd64.whl
334f5c275a6aad46e5f46436572d3dc1  numpy-2.3.0-cp313-cp313t-win_arm64.whl
05b86d4a21a832e20e4ebdc6febf298d  numpy-2.3.0-pp311-pypy311_pp73-macosx_10_15_x86_64.whl
4589038edf55f085252f194e880d7454  numpy-2.3.0-pp311-pypy311_pp73-macosx_14_0_arm64.whl
7d8f0554035717dc396de7d77c696377  numpy-2.3.0-pp311-pypy311_pp73-macosx_14_0_x86_64.whl
c0cb89f0dca94446e6aa472ec6874c22  numpy-2.3.0-pp311-pypy311_pp73-manylinux_2_28_aarch64.whl
14e43315dea5eddffe888986e47d8584  numpy-2.3.0-pp311-pypy311_pp73-manylinux_2_28_x86_64.whl
e3688182f8551c3c99b559c1696d41dc  numpy-2.3.0-pp311-pypy311_pp73-win_amd64.whl
19a5470a37d066bd3e9385918d7760e7  numpy-2.3.0.tar.gz

SHA256

c3c9fdde0fa18afa1099d6257eb82890ea4f3102847e692193b54e00312a9ae9  numpy-2.3.0-cp311-cp311-macosx_10_9_x86_64.whl
46d16f72c2192da7b83984aa5455baee640e33a9f1e61e656f29adf55e406c2b  numpy-2.3.0-cp311-cp311-macosx_11_0_arm64.whl
a0be278be9307c4ab06b788f2a077f05e180aea817b3e41cebbd5aaf7bd85ed3  numpy-2.3.0-cp311-cp311-macosx_14_0_arm64.whl
99224862d1412d2562248d4710126355d3a8db7672170a39d6909ac47687a8a4  numpy-2.3.0-cp311-cp311-macosx_14_0_x86_64.whl
2393a914db64b0ead0ab80c962e42d09d5f385802006a6c87835acb1f58adb96  numpy-2.3.0-cp311-cp311-manylinux_2_28_aarch64.whl
7729c8008d55e80784bd113787ce876ca117185c579c0d626f59b87d433ea779  numpy-2.3.0-cp311-cp311-manylinux_2_28_x86_64.whl
06d4fb37a8d383b769281714897420c5cc3545c79dc427df57fc9b852ee0bf58  numpy-2.3.0-cp311-cp311-musllinux_1_2_aarch64.whl
c39ec392b5db5088259c68250e342612db82dc80ce044cf16496cf14cf6bc6f8  numpy-2.3.0-cp311-cp311-musllinux_1_2_x86_64.whl
ee9d3ee70d62827bc91f3ea5eee33153212c41f639918550ac0475e3588da59f  numpy-2.3.0-cp311-cp311-win32.whl
43c55b6a860b0eb44d42341438b03513cf3879cb3617afb749ad49307e164edd  numpy-2.3.0-cp311-cp311-win_amd64.whl
2e6a1409eee0cb0316cb64640a49a49ca44deb1a537e6b1121dc7c458a1299a8  numpy-2.3.0-cp311-cp311-win_arm64.whl
389b85335838155a9076e9ad7f8fdba0827496ec2d2dc32ce69ce7898bde03ba  numpy-2.3.0-cp312-cp312-macosx_10_13_x86_64.whl
9498f60cd6bb8238d8eaf468a3d5bb031d34cd12556af53510f05fcf581c1b7e  numpy-2.3.0-cp312-cp312-macosx_11_0_arm64.whl
622a65d40d8eb427d8e722fd410ac3ad4958002f109230bc714fa551044ebae2  numpy-2.3.0-cp312-cp312-macosx_14_0_arm64.whl
b9446d9d8505aadadb686d51d838f2b6688c9e85636a0c3abaeb55ed54756459  numpy-2.3.0-cp312-cp312-macosx_14_0_x86_64.whl
50080245365d75137a2bf46151e975de63146ae6d79f7e6bd5c0e85c9931d06a  numpy-2.3.0-cp312-cp312-manylinux_2_28_aarch64.whl
c24bb4113c66936eeaa0dc1e47c74770453d34f46ee07ae4efd853a2ed1ad10a  numpy-2.3.0-cp312-cp312-manylinux_2_28_x86_64.whl
4d8d294287fdf685281e671886c6dcdf0291a7c19db3e5cb4178d07ccf6ecc67  numpy-2.3.0-cp312-cp312-musllinux_1_2_aarch64.whl
6295f81f093b7f5769d1728a6bd8bf7466de2adfa771ede944ce6711382b89dc  numpy-2.3.0-cp312-cp312-musllinux_1_2_x86_64.whl
e6648078bdd974ef5d15cecc31b0c410e2e24178a6e10bf511e0557eed0f2570  numpy-2.3.0-cp312-cp312-win32.whl
0898c67a58cdaaf29994bc0e2c65230fd4de0ac40afaf1584ed0b02cd74c6fdd  numpy-2.3.0-cp312-cp312-win_amd64.whl
bd8df082b6c4695753ad6193018c05aac465d634834dca47a3ae06d4bb22d9ea  numpy-2.3.0-cp312-cp312-win_arm64.whl
5754ab5595bfa2c2387d241296e0381c21f44a4b90a776c3c1d39eede13a746a  numpy-2.3.0-cp313-cp313-macosx_10_13_x86_64.whl
d11fa02f77752d8099573d64e5fe33de3229b6632036ec08f7080f46b6649959  numpy-2.3.0-cp313-cp313-macosx_11_0_arm64.whl
aba48d17e87688a765ab1cd557882052f238e2f36545dfa8e29e6a91aef77afe  numpy-2.3.0-cp313-cp313-macosx_14_0_arm64.whl
4dc58865623023b63b10d52f18abaac3729346a7a46a778381e0e3af4b7f3beb  numpy-2.3.0-cp313-cp313-macosx_14_0_x86_64.whl
df470d376f54e052c76517393fa443758fefcdd634645bc9c1f84eafc67087f0  numpy-2.3.0-cp313-cp313-manylinux_2_28_aarch64.whl
87717eb24d4a8a64683b7a4e91ace04e2f5c7c77872f823f02a94feee186168f  numpy-2.3.0-cp313-cp313-manylinux_2_28_x86_64.whl
d8fa264d56882b59dcb5ea4d6ab6f31d0c58a57b41aec605848b6eb2ef4a43e8  numpy-2.3.0-cp313-cp313-musllinux_1_2_aarch64.whl
e651756066a0eaf900916497e20e02fe1ae544187cb0fe88de981671ee7f6270  numpy-2.3.0-cp313-cp313-musllinux_1_2_x86_64.whl
e43c3cce3b6ae5f94696669ff2a6eafd9a6b9332008bafa4117af70f4b88be6f  numpy-2.3.0-cp313-cp313-win32.whl
81ae0bf2564cf475f94be4a27ef7bcf8af0c3e28da46770fc904da9abd5279b5  numpy-2.3.0-cp313-cp313-win_amd64.whl
c8738baa52505fa6e82778580b23f945e3578412554d937093eac9205e845e6e  numpy-2.3.0-cp313-cp313-win_arm64.whl
39b27d8b38942a647f048b675f134dd5a567f95bfff481f9109ec308515c51d8  numpy-2.3.0-cp313-cp313t-macosx_10_13_x86_64.whl
0eba4a1ea88f9a6f30f56fdafdeb8da3774349eacddab9581a21234b8535d3d3  numpy-2.3.0-cp313-cp313t-macosx_11_0_arm64.whl
b0f1f11d0a1da54927436505a5a7670b154eac27f5672afc389661013dfe3d4f  numpy-2.3.0-cp313-cp313t-macosx_14_0_arm64.whl
690d0a5b60a47e1f9dcec7b77750a4854c0d690e9058b7bef3106e3ae9117808  numpy-2.3.0-cp313-cp313t-macosx_14_0_x86_64.whl
8b51ead2b258284458e570942137155978583e407babc22e3d0ed7af33ce06f8  numpy-2.3.0-cp313-cp313t-manylinux_2_28_aarch64.whl
aaf81c7b82c73bd9b45e79cfb9476cb9c29e937494bfe9092c26aece812818ad  numpy-2.3.0-cp313-cp313t-manylinux_2_28_x86_64.whl
f420033a20b4f6a2a11f585f93c843ac40686a7c3fa514060a97d9de93e5e72b  numpy-2.3.0-cp313-cp313t-musllinux_1_2_aarch64.whl
d344ca32ab482bcf8735d8f95091ad081f97120546f3d250240868430ce52555  numpy-2.3.0-cp313-cp313t-musllinux_1_2_x86_64.whl
48a2e8eaf76364c32a1feaa60d6925eaf32ed7a040183b807e02674305beef61  numpy-2.3.0-cp313-cp313t-win32.whl
ba17f93a94e503551f154de210e4d50c5e3ee20f7e7a1b5f6ce3f22d419b93bb  numpy-2.3.0-cp313-cp313t-win_amd64.whl
f14e016d9409680959691c109be98c436c6249eaf7f118b424679793607b5944  numpy-2.3.0-cp313-cp313t-win_arm64.whl
80b46117c7359de8167cc00a2c7d823bdd505e8c7727ae0871025a86d668283b  numpy-2.3.0-pp311-pypy311_pp73-macosx_10_15_x86_64.whl
5814a0f43e70c061f47abd5857d120179609ddc32a613138cbb6c4e9e2dbdda5  numpy-2.3.0-pp311-pypy311_pp73-macosx_14_0_arm64.whl
ef6c1e88fd6b81ac6d215ed71dc8cd027e54d4bf1d2682d362449097156267a2  numpy-2.3.0-pp311-pypy311_pp73-macosx_14_0_x86_64.whl
33a5a12a45bb82d9997e2c0b12adae97507ad7c347546190a18ff14c28bbca12  numpy-2.3.0-pp311-pypy311_pp73-manylinux_2_28_aarch64.whl
54dfc8681c1906d239e95ab1508d0a533c4a9505e52ee2d71a5472b04437ef97  numpy-2.3.0-pp311-pypy311_pp73-manylinux_2_28_x86_64.whl
e017a8a251ff4d18d71f139e28bdc7c31edba7a507f72b1414ed902cbe48c74d  numpy-2.3.0-pp311-pypy311_pp73-win_amd64.whl
581f87f9e9e9db2cba2141400e160e9dd644ee248788d6f90636eeb8fd9260a6  numpy-2.3.0.tar.gz

- Python
Published by charris 12 months ago

numpy - v2.3.0rc1 (May 25, 2025)

NumPy 2.3.0 Release Notes

The NumPy 2.3.0 release continues the work to improve free threaded Python support and annotations together with the usual set of bug fixes. It is unusual in the number of expired deprecations and the number of code modernizations and style cleanups. The latter may not be visible to users, but is important for code maintenance over the long term. Note that we have also upgraded from manylinux2014 to manylinux228.

There are known test failures in the rc1 release involving MyPy and PyPy. The cause of both has been determined and fixes will be applied before the final release. The current Windows on ARM wheels also lack OpenBLAS, but they should suffice for initial downstream testing. OpenBLAS will be incorporated in those wheels when it becomes available.

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

Highlights

• Interactive examples in the NumPy documentation.
• Building NumPy with OpenMP Parallelization.
• Preliminary support for Windows on ARM.
• Improved support for free threaded Python.
• Improved annotations.

New functions

New function numpy.strings.slice

The new function numpy.strings.slice was added, which implements fast native slicing of string arrays. It supports the full slicing API including negative slice offsets and steps.

(gh-27789)

Deprecations

• The numpy.typing.mypy_plugin has been deprecated in favor of platform-agnostic static type inference. Please remove numpy.typing.mypy_plugin from the plugins section of your mypy configuration. If this change results in new errors being reported, kindly open an issue.

  (gh-28129)

• The numpy.typing.NBitBase type has been deprecated and will be removed in a future version.

  This type was previously intended to be used as a generic upper bound for type-parameters, for example:

  ``` python import numpy as np import numpy.typing as npt

  def fNT: npt.NBitBase -> np.floating[NT]: ... ```

  But in NumPy 2.2.0, float64 and complex128 were changed to concrete subtypes, causing static type-checkers to reject x: np.float64 = f(np.complex128(42j)).

  So instead, the better approach is to use typing.overload:

  ``` python import numpy as np from typing import overload

  @overload def f(x: np.complex64) -> np.float32: ... @overload def f(x: np.complex128) -> np.float64: ... @overload def f(x: np.clongdouble) -> np.longdouble: ... ```

  (gh-28884)

Expired deprecations

• Remove deprecated macros like NPY_OWNDATA from Cython interfaces in favor of NPY_ARRAY_OWNDATA (deprecated since 1.7)

  (gh-28254)

• Remove numpy/npy_1_7_deprecated_api.h and C macros like NPY_OWNDATA in favor of NPY_ARRAY_OWNDATA (deprecated since 1.7)

  (gh-28254)

• Remove alias generate_divbyzero_error to npy_set_floatstatus_divbyzero and generate_overflow_error to npy_set_floatstatus_overflow (deprecated since 1.10)

  (gh-28254)

• Remove np.tostring (deprecated since 1.19)

  (gh-28254)

• Raise on np.conjugate of non-numeric types (deprecated since 1.13)

  (gh-28254)

• Raise when using np.bincount(...minlength=None), use 0 instead (deprecated since 1.14)

  (gh-28254)

• Passing shape=None to functions with a non-optional shape argument errors, use () instead (deprecated since 1.20)

  (gh-28254)

• Inexact matches for mode and searchside raise (deprecated since 1.20)

  (gh-28254)

• Setting __array_finalize__ = None errors (deprecated since 1.23)

  (gh-28254)

• np.fromfile and np.fromstring error on bad data, previously they would guess (deprecated since 1.18)

  (gh-28254)

• datetime64 and timedelta64 construction with a tuple no longer accepts an event value, either use a two-tuple of (unit, num) or a 4-tuple of (unit, num, den, 1) (deprecated since 1.14)

  (gh-28254)

• When constructing a dtype from a class with a dtype attribute, that attribute must be a dtype-instance rather than a thing that can be parsed as a dtype instance (deprecated in 1.19). At some point the whole construct of using a dtype attribute will be deprecated (see #25306)

  (gh-28254)

• Passing booleans as partition index errors (deprecated since 1.23)

  (gh-28254)

• Out-of-bounds indexes error even on empty arrays (deprecated since 1.20)

  (gh-28254)

• np.tostring has been removed, use tobytes instead (deprecated since 1.19)

  (gh-28254)

• Disallow make a non-writeable array writeable for arrays with a base that do not own their data (deprecated since 1.17)

  (gh-28254)

• concatenate() with axis=None uses same-kind casting by default, not unsafe (deprecated since 1.20)

  (gh-28254)

• Unpickling a scalar with object dtype errors (deprecated since 1.20)

  (gh-28254)

• The binary mode of fromstring now errors, use frombuffer instead (deprecated since 1.14)

  (gh-28254)

• Converting np.inexact or np.floating to a dtype errors (deprecated since 1.19)

  (gh-28254)

• Converting np.complex, np.integer, np.signedinteger, np.unsignedinteger, np.generic to a dtype errors (deprecated since 1.19)

  (gh-28254)

• The Python built-in round errors for complex scalars. Use np.round or scalar.round instead (deprecated since 1.19)

  (gh-28254)

• \'np.bool\' scalars can no longer be interpreted as an index (deprecated since 1.19)

  (gh-28254)

• Parsing an integer via a float string is no longer supported. (deprecated since 1.23) To avoid this error you can

  • make sure the original data is stored as integers.
  • use the converters=float keyword argument.
  • Use np.loadtxt(...).astype(np.int64)

  (gh-28254)

• The use of a length 1 tuple for the ufunc signature errors. Use dtype or fill the tuple with None (deprecated since 1.19)

  (gh-28254)

• Special handling of matrix is in np.outer is removed. Convert to a ndarray via matrix.A (deprecated since 1.20)

  (gh-28254)

• Removed the np.compat package source code (removed in 2.0)

  (gh-28961)

C API changes

• NpyIter_GetTransferFlags is now available to check if the iterator needs the Python API or if casts may cause floating point errors (FPE). FPEs can for example be set when casting float64(1e300) to float32 (overflow to infinity) or a NaN to an integer (invalid value).

  (gh-27883)

• NpyIter now has no limit on the number of operands it supports.

  (gh-28080)

New NpyIter_GetTransferFlags and NpyIter_IterationNeedsAPI change

NumPy now has the new NpyIter_GetTransferFlags function as a more precise way checking of iterator/buffering needs. I.e. whether the Python API/GIL is required or floating point errors may occur. This function is also faster if you already know your needs without buffering.

The NpyIter_IterationNeedsAPI function now performs all the checks that were previously performed at setup time. While it was never necessary to call it multiple times, doing so will now have a larger cost.

(gh-27998)

New Features

• The type parameter of np.dtype now defaults to typing.Any. This way, static type-checkers will infer dtype: np.dtype as dtype: np.dtype[Any], without reporting an error.

  (gh-28669)

• Static type-checkers now interpret:

  • _: np.ndarray as _: npt.NDArray[typing.Any].
  • _: np.flatiter as _: np.flatiter[np.ndarray].

  This is because their type parameters now have default values.

  (gh-28940)

NumPy now registers its pkg-config paths with the pkgconf PyPI package

The pkgconf PyPI package provides an interface for projects like NumPy to register their own paths to be added to the pkg-config search path. This means that when using pkgconf from PyPI, NumPy will be discoverable without needing for any custom environment configuration.

[!NOTE] This only applies when using the pkgconf package from PyPI, or put another way, this only applies when installing pkgconf via a Python package manager.

If you are using pkg-config or pkgconf provided by your system, or any other source that does not use the pkgconf-pypi project, the NumPy pkg-config directory will not be automatically added to the search path. In these situations, you might want to use numpy-config.

(gh-28214)

Allow out=... in ufuncs to ensure array result

NumPy has the sometimes difficult behavior that it currently usually returns scalars rather than 0-D arrays (even if the inputs were 0-D arrays). This is especially problematic for non-numerical dtypes (e.g. object).

For ufuncs (i.e. most simple math functions) it is now possible to use out=... (literally `...`, e.g. out=Ellipsis) which is identical in behavior to out not being passed, but will ensure a non-scalar return. This spelling is borrowed from arr1d[0, ...] where the ... also ensures a non-scalar return.

Other functions with an out= kwarg should gain support eventually. Downstream libraries that interoperate via __array_ufunc__ or __array_function__ may need to adapt to support this.

(gh-28576)

Building NumPy with OpenMP Parallelization

NumPy now supports OpenMP parallel processing capabilities when built with the -Denable_openmp=true Meson build flag. This feature is disabled by default. When enabled, np.sort and np.argsort functions can utilize OpenMP for parallel thread execution, improving performance for these operations.

(gh-28619)

Interactive examples in the NumPy documentation

The NumPy documentation includes a number of examples that can now be run interactively in your browser using WebAssembly and Pyodide.

Please note that the examples are currently experimental in nature and may not work as expected for all methods in the public API.

(gh-26745)

Improvements

• Scalar comparisons between non-comparable dtypes such as np.array(1) == np.array('s') now return a NumPy bool instead of a Python bool.

  (gh-27288)

• np.nditer now has no limit on the number of supported operands (C-integer).

  (gh-28080)

• No-copy pickling is now supported for any array that can be transposed to a C-contiguous array.

  (gh-28105)

• The __repr__ for user-defined dtypes now prefers the __name__ of the custom dtype over a more generic name constructed from its kind and itemsize.

  (gh-28250)

• np.dot now reports floating point exceptions.

  (gh-28442)

• np.dtypes.StringDType is now a generic type which accepts a type argument for na_object that defaults to typing.Never. For example, StringDType(na_object=None) returns a StringDType[None], and StringDType() returns a StringDType[typing.Never].

  (gh-28856)

Added warnings to np.isclose

Added warning messages if at least one of atol or rtol are either np.nan or np.inf within np.isclose.

• Warnings follow the user\'s np.seterr settings

(gh-28205)

Performance improvements and changes

Performance improvements to np.unique

np.unique now tries to use a hash table to find unique values instead of sorting values before finding unique values. This is limited to certain dtypes for now, and the function is now faster for those dtypes. The function now also exposes a sorted parameter to allow returning unique values as they were found, instead of sorting them afterwards.

(gh-26018)

Performance improvements to np.sort and np.argsort

np.sort and np.argsort functions now can leverage OpenMP for parallel thread execution, resulting in up to 3.5x speedups on x86 architectures with AVX2 or AVX-512 instructions. This opt-in feature requires NumPy to be built with the -Denableopenmp Meson flag. Users can control the number of threads used by setting the OMPNUM_THREADS environment variable.

(gh-28619)

Performance improvements for np.float16 casts

Earlier, floating point casts to and from np.float16 types were emulated in software on all platforms.

Now, on ARM devices that support Neon float16 intrinsics (such as recent Apple Silicon), the native float16 path is used to achieve the best performance.

(gh-28769)

Changes

• The vector norm ord=inf and the matrix norms ord={1, 2, inf, 'nuc'} now always returns zero for empty arrays. Empty arrays have at least one axis of size zero. This affects np.linalg.norm, np.linalg.vector_norm, and np.linalg.matrix_norm. Previously, NumPy would raises errors or return zero depending on the shape of the array.

  (gh-28343)

• A spelling error in the error message returned when converting a string to a float with the method np.format_float_positional has been fixed.

  (gh-28569)

• NumPy\'s __array_api_version__ was upgraded from 2023.12 to 2024.12.

• numpy.count_nonzero for axis=None (default) now returns a NumPy scalar instead of a Python integer.

• The parameter axis in numpy.take_along_axis function has now a default value of -1.

  (gh-28615)

• Printing of np.float16 and np.float32 scalars and arrays have been improved by adjusting the transition to scientific notation based on the floating point precision. A new legacy np.printoptions mode '2.2' has been added for backwards compatibility.

  (gh-28703)

unique_values may return unsorted data

The relatively new function (added in NumPy 2.0) unique_values may now return unsorted results. Just as unique_counts and unique_all these never guaranteed a sorted result, however, the result was sorted until now. In cases where these do return a sorted result, this may change in future releases to improve performance.

(gh-26018)

Changes to the main iterator and potential numerical changes

The main iterator, used in math functions and via np.nditer from Python and NpyIter in C, now behaves differently for some buffered iterations. This means that:

• The buffer size used will often be smaller than the maximum buffer sized allowed by the buffersize parameter.
• The \"growinner\" flag is now honored with buffered reductions when no operand requires buffering.

For np.sum() such changes in buffersize may slightly change numerical results of floating point operations. Users who use \"growinner\" for custom reductions could notice changes in precision (for example, in NumPy we removed it from einsum to avoid most precision changes and improve precision for some 64bit floating point inputs).

(gh-27883)

The minimum supported GCC version is now 9.3.0

The minimum supported version was updated from 8.4.0 to 9.3.0, primarily in order to reduce the chance of platform-specific bugs in old GCC versions from causing issues.

(gh-28102)

Changes to automatic bin selection in numpy.histogram

The automatic bin selection algorithm in numpy.histogram has been modified to avoid out-of-memory errors for samples with low variation. For full control over the selected bins the user can use set the bin or range parameters of numpy.histogram.

(gh-28426)

Build manylinux228 wheels

Wheels for linux systems will use the manylinux_2_28 tag (instead of the manylinux2014 tag), which means dropping support for redhat7/centos7, amazonlinux2, debian9, ubuntu18.04, and other pre-glibc2.28 operating system versions, as per the PEP 600 support table.

(gh-28436)

Remove use of -Wl,-ld_classic on macOS

Remove use of -Wl,-ld_classic on macOS. This hack is no longer needed by Spack, and results in libraries that cannot link to other libraries built with ld (new).

(gh-28713)

Re-enable overriding functions in the numpy.strings

Re-enable overriding functions in the numpy.strings module.

(gh-28741)

Checksums

MD5

629f59f0a128a7ff1f3fedac6e2c42da  numpy-2.3.0rc1-cp311-cp311-macosx_10_9_x86_64.whl
6b69c1f05cb782104bb4063f79bfb804  numpy-2.3.0rc1-cp311-cp311-macosx_11_0_arm64.whl
0a5c651274fcd4912490edf17052302b  numpy-2.3.0rc1-cp311-cp311-macosx_14_0_arm64.whl
aac595e422ddf5ab5d87cdce3be66262  numpy-2.3.0rc1-cp311-cp311-macosx_14_0_x86_64.whl
f7d4f749f9e85a50adfbafd1b9c70fe8  numpy-2.3.0rc1-cp311-cp311-manylinux_2_28_aarch64.whl
ec8a0e794ce8fc7e35f90d83b68cd2d6  numpy-2.3.0rc1-cp311-cp311-manylinux_2_28_x86_64.whl
a8800a3c3fc260b02a36fb9dc8a1a399  numpy-2.3.0rc1-cp311-cp311-musllinux_1_2_aarch64.whl
8e96ee3eb6e07e8efdff7754b9d008b6  numpy-2.3.0rc1-cp311-cp311-musllinux_1_2_x86_64.whl
4157e803760012f28317495eec5b3310  numpy-2.3.0rc1-cp311-cp311-win32.whl
1afc4f1a7adb81d9f2705a4c10f2d808  numpy-2.3.0rc1-cp311-cp311-win_amd64.whl
11251ad640d2dd50b2d0a08a1c79e870  numpy-2.3.0rc1-cp311-cp311-win_arm64.whl
f723f2e6b1ed4430b5a5c0c05501b55c  numpy-2.3.0rc1-cp312-cp312-macosx_10_13_x86_64.whl
6ca0c0e1c2819a04b6c01559ce2d60dd  numpy-2.3.0rc1-cp312-cp312-macosx_11_0_arm64.whl
56af103256eac1edcc34382a9c45a5eb  numpy-2.3.0rc1-cp312-cp312-macosx_14_0_arm64.whl
e3acbafb6da1e75c80361e4f3c791d12  numpy-2.3.0rc1-cp312-cp312-macosx_14_0_x86_64.whl
278c4727d8b8d05b6202355cf740cfd4  numpy-2.3.0rc1-cp312-cp312-manylinux_2_28_aarch64.whl
3cd5e204141a450301fdcbda83e044f5  numpy-2.3.0rc1-cp312-cp312-manylinux_2_28_x86_64.whl
c4369a338a9900fc43c6fe46e5113505  numpy-2.3.0rc1-cp312-cp312-musllinux_1_2_aarch64.whl
12c5af8ba96f61d1a5d41f15c1d55721  numpy-2.3.0rc1-cp312-cp312-musllinux_1_2_x86_64.whl
81fae7bcfc52b810c75a3c14bc182f12  numpy-2.3.0rc1-cp312-cp312-win32.whl
1ce23327d02c79b64c16f54d412e32ae  numpy-2.3.0rc1-cp312-cp312-win_amd64.whl
9e538dfe7f2051f63422d4e1edcd23bd  numpy-2.3.0rc1-cp312-cp312-win_arm64.whl
ca94a64c4ad19bad2fd5baba19bc8578  numpy-2.3.0rc1-cp313-cp313-macosx_10_13_x86_64.whl
d964ed57e7b28365a75210e83c396ea5  numpy-2.3.0rc1-cp313-cp313-macosx_11_0_arm64.whl
03247ffd1c0a63e86382867df6914cd6  numpy-2.3.0rc1-cp313-cp313-macosx_14_0_arm64.whl
65ace2a8b56475a356357091c704a257  numpy-2.3.0rc1-cp313-cp313-macosx_14_0_x86_64.whl
e3283698c1124b6e60b8361d4884792f  numpy-2.3.0rc1-cp313-cp313-manylinux_2_28_aarch64.whl
afa8c55affdc96845ee0f904de86e1dc  numpy-2.3.0rc1-cp313-cp313-manylinux_2_28_x86_64.whl
fc296e255a2afb62956642355d1830c2  numpy-2.3.0rc1-cp313-cp313-musllinux_1_2_aarch64.whl
32fd94baff245adcd4e12531ec2812be  numpy-2.3.0rc1-cp313-cp313-musllinux_1_2_x86_64.whl
8114fe3c90fb01856b089b076a3eaf69  numpy-2.3.0rc1-cp313-cp313-win32.whl
3aae3297bd4d94fd628666da9224b871  numpy-2.3.0rc1-cp313-cp313-win_amd64.whl
76387f5d0a213f13b1352efc5087ebe2  numpy-2.3.0rc1-cp313-cp313-win_arm64.whl
cd6d0165be416093c2229319d9578e22  numpy-2.3.0rc1-cp313-cp313t-macosx_10_13_x86_64.whl
783a19b5db79d0080727dcae4c48ce90  numpy-2.3.0rc1-cp313-cp313t-macosx_11_0_arm64.whl
af327355774245aebd2bc790982190b9  numpy-2.3.0rc1-cp313-cp313t-macosx_14_0_arm64.whl
fe643d848c382e0c97cb85ce993c7ac9  numpy-2.3.0rc1-cp313-cp313t-macosx_14_0_x86_64.whl
d4facf10a4628a67f076e230caa8cd7b  numpy-2.3.0rc1-cp313-cp313t-manylinux_2_28_aarch64.whl
a16b6003b0778caddb708d06a8720c2b  numpy-2.3.0rc1-cp313-cp313t-manylinux_2_28_x86_64.whl
27732d00b9124f2866f45602d5c46ef5  numpy-2.3.0rc1-cp313-cp313t-musllinux_1_2_aarch64.whl
1c25ee631482975bbeb378d88608b3b5  numpy-2.3.0rc1-cp313-cp313t-musllinux_1_2_x86_64.whl
63974df1b36d9486a0a8a4953a53a418  numpy-2.3.0rc1-cp313-cp313t-win32.whl
1e67187dd5fbc2663a0922e3434e4838  numpy-2.3.0rc1-cp313-cp313t-win_amd64.whl
8ae83e7618c196dfe4e637c5f81692e5  numpy-2.3.0rc1-cp313-cp313t-win_arm64.whl
a96f68abaa4aa674e49eec4add0b2c0b  numpy-2.3.0rc1-pp311-pypy311_pp73-macosx_10_15_x86_64.whl
fe679f10ad4c9d3fce0db85c632967a7  numpy-2.3.0rc1-pp311-pypy311_pp73-macosx_14_0_arm64.whl
c9f14388eae0b01b5225e0f8f79ba9f6  numpy-2.3.0rc1-pp311-pypy311_pp73-macosx_14_0_x86_64.whl
7cea15ab8d47bc2f3f6377c29a74138f  numpy-2.3.0rc1-pp311-pypy311_pp73-manylinux_2_28_aarch64.whl
46d07cd0f0aacce6d4b5ce949a333abc  numpy-2.3.0rc1-pp311-pypy311_pp73-manylinux_2_28_x86_64.whl
df12654557fe28806bed04e15d1fe1d7  numpy-2.3.0rc1-pp311-pypy311_pp73-win_amd64.whl
5677afac6d3d39fe93a96cab654f38e7  numpy-2.3.0rc1.tar.gz

SHA256

82b035e631187bee40c1d2c1593e40dcad62cfbaf833c5d3e179c1d7bce87e0c  numpy-2.3.0rc1-cp311-cp311-macosx_10_9_x86_64.whl
2f2f8a82005073929532ed8e09eda78544140996ef283d007945323d27e3ef84  numpy-2.3.0rc1-cp311-cp311-macosx_11_0_arm64.whl
96af406cf156acb52edfa3b38c1fc8efe0f986bff5f99176657ba09606e78fe7  numpy-2.3.0rc1-cp311-cp311-macosx_14_0_arm64.whl
30f11867a70c96e6628f9b5da5cb426efe4ebe5e1f02bc8932c79f4dfa0c1e02  numpy-2.3.0rc1-cp311-cp311-macosx_14_0_x86_64.whl
c54f93b5c0c210e580fd1d114b76b172190bdefcdee3eab865587d4c860067fa  numpy-2.3.0rc1-cp311-cp311-manylinux_2_28_aarch64.whl
6d72740255bcbaa94a66329daffd974e8212eee4b77acc8c3da8dd179adbe7f3  numpy-2.3.0rc1-cp311-cp311-manylinux_2_28_x86_64.whl
44f65efc7f4a62e0624f53cd241761b5a9cdaa302e50dd8f30adc1173e2c1691  numpy-2.3.0rc1-cp311-cp311-musllinux_1_2_aarch64.whl
e9766d1d3b36df82777c7f0a6d248d21e7aceafd40d24b87f9cec6c5916a4908  numpy-2.3.0rc1-cp311-cp311-musllinux_1_2_x86_64.whl
07638479fc90c38168a3dd29a54c750701100dd84622bbaa32783459d2df7c54  numpy-2.3.0rc1-cp311-cp311-win32.whl
7cc39c12551a3b92f9db5dd0faff021043f4f432167f990424a3c9149314f3a3  numpy-2.3.0rc1-cp311-cp311-win_amd64.whl
213b2aa630118690e684a2666c58978a45763fbd4034b9b990d827a25df7b6ae  numpy-2.3.0rc1-cp311-cp311-win_arm64.whl
c9d04e6f6bf7fe48eb25af803de3e2ee5dbaba7786d17764d7bacc242d47d96e  numpy-2.3.0rc1-cp312-cp312-macosx_10_13_x86_64.whl
8cd953239b23dd0f5363553efb2eab4651c33b3772c7bcfe3bc0e1a84a9149e6  numpy-2.3.0rc1-cp312-cp312-macosx_11_0_arm64.whl
66bff8b92964eb21593d787e55668d1800d5cfeb0ccf75691cce80c15b155839  numpy-2.3.0rc1-cp312-cp312-macosx_14_0_arm64.whl
580ce0f8e9e340571a5b0c32b7f3e1489ea018dbc37b22913788be23e8a50559  numpy-2.3.0rc1-cp312-cp312-macosx_14_0_x86_64.whl
93909bcf08b4aa21a74a687342843eef5479c317add9700a66b0122cc1c9d604  numpy-2.3.0rc1-cp312-cp312-manylinux_2_28_aarch64.whl
9efc27c9b25d4387e6153d01cc93a8ae18a6a37d6598cb4da3876874a3aa1283  numpy-2.3.0rc1-cp312-cp312-manylinux_2_28_x86_64.whl
14e86a57107b03e7b31b4d677ded1af52079f05bde89008b4dfd5a3fd7767769  numpy-2.3.0rc1-cp312-cp312-musllinux_1_2_aarch64.whl
c4ac94252a8dec06d6c55126f83775ec90b0d8ceaed9086d7166175455772155  numpy-2.3.0rc1-cp312-cp312-musllinux_1_2_x86_64.whl
c8047609194df241d20b5d3534319df0f2ef57c7163a9e67ceb8a7ab3d3c7413  numpy-2.3.0rc1-cp312-cp312-win32.whl
ea64b01b0b9ee53fd1c05dad508e1ee05ca888579687a94044cdea8ef47f8efc  numpy-2.3.0rc1-cp312-cp312-win_amd64.whl
c0ca78c3c15db1b69b91027c6485291b2ffbdd29dab77bd5d044248569c9ff85  numpy-2.3.0rc1-cp312-cp312-win_arm64.whl
77fc845f90f74b83881330bd3d15c112bdac5e2947e77adec2be141eb5a71aff  numpy-2.3.0rc1-cp313-cp313-macosx_10_13_x86_64.whl
fcaf7cfbe003ea5d6e2f470fbb67001dae39dbddc76a608b10623592f4711c78  numpy-2.3.0rc1-cp313-cp313-macosx_11_0_arm64.whl
167c1b4ad39017d5b0bfae7c5b71ced40025ef26ff8b9fc65e39519533b7c416  numpy-2.3.0rc1-cp313-cp313-macosx_14_0_arm64.whl
f4d15d5ad63ea6760b0ff49cc88a635b02838488ebabb41bc2524f67d9d5b74e  numpy-2.3.0rc1-cp313-cp313-macosx_14_0_x86_64.whl
eb5a7a5d1c28881b1c9536038d9ea224051464b0a033d8bd87ec75634e8ee2db  numpy-2.3.0rc1-cp313-cp313-manylinux_2_28_aarch64.whl
022fa032a8dcabef0771db621a77a9203358e41cb651d8903836783e9708248f  numpy-2.3.0rc1-cp313-cp313-manylinux_2_28_x86_64.whl
b6d9d7ad7666f646bbb831a80c668b61a08d4deb00935ef475a1c81ba0b87bdb  numpy-2.3.0rc1-cp313-cp313-musllinux_1_2_aarch64.whl
172965a57a44ccfbfc67746c721128647a4df3aecec3d3b91c67e75c575548cc  numpy-2.3.0rc1-cp313-cp313-musllinux_1_2_x86_64.whl
b056d511fdb33ee7076586fbb831be0a8030e9565f5ea0ceebe649f29e647ac0  numpy-2.3.0rc1-cp313-cp313-win32.whl
f437b4af30ab36e7fa9fd7856b0482229259fc98aeb5e53a287eda5253e4f1ec  numpy-2.3.0rc1-cp313-cp313-win_amd64.whl
ffbda03577e4f401f4465345ca98c9dc95e3a23df2ec16854f4aa37ae6c1fb56  numpy-2.3.0rc1-cp313-cp313-win_arm64.whl
4de6304a0653a76f6418f4c0aa84048953d5891aa8296681d916f95b0a5eddf8  numpy-2.3.0rc1-cp313-cp313t-macosx_10_13_x86_64.whl
17cb57f5c862904d57a1d1eb2d6e3f0e3a0d7ea738d5281638aef7c45b8c4525  numpy-2.3.0rc1-cp313-cp313t-macosx_11_0_arm64.whl
96d8e7e0666836202e9cff63cb16d9c49bd31222303f3b70dc9144c3ae191cdd  numpy-2.3.0rc1-cp313-cp313t-macosx_14_0_arm64.whl
e967c553e48c14feb3387248cf089c4f7f8ec66f6800cb184effa2da5494d9e8  numpy-2.3.0rc1-cp313-cp313t-macosx_14_0_x86_64.whl
cb18f1e661c4480e7d40bac0172ab456aececa05aec5855d987d6a1f51d51206  numpy-2.3.0rc1-cp313-cp313t-manylinux_2_28_aarch64.whl
eb0876192b685ed3f96049dbd5aa9a1d871b47c553b3d304692bbbe386fa7441  numpy-2.3.0rc1-cp313-cp313t-manylinux_2_28_x86_64.whl
de2e8223ef8e65963976de817419314d7e791d403a9d6448174e3f490559ba8d  numpy-2.3.0rc1-cp313-cp313t-musllinux_1_2_aarch64.whl
b7c0007ae7dd02acaaf198cf1a6b4da805aaa3857a452403c3de1fed29ab158c  numpy-2.3.0rc1-cp313-cp313t-musllinux_1_2_x86_64.whl
875ee6c2865523adb78ebcfbcef186fc99201e9931a49132d97101f57f154ff1  numpy-2.3.0rc1-cp313-cp313t-win32.whl
b59de7a3beeca46f189c53609db7794ccb83a90fa5841111d5ca77bbe990ed21  numpy-2.3.0rc1-cp313-cp313t-win_amd64.whl
7f06a256ce74713eae26c19221b305783d4db5f8cad457e82f9a4f8cc55b9632  numpy-2.3.0rc1-cp313-cp313t-win_arm64.whl
3c8681de7c4fc72f5ab29e5a8343b37fe7bdc465e8cc3c99637ac884e26e41fd  numpy-2.3.0rc1-pp311-pypy311_pp73-macosx_10_15_x86_64.whl
ec2037b6d700b8491a97efcbfa05dec666d8ae7807f374beb9d3320531d42e9b  numpy-2.3.0rc1-pp311-pypy311_pp73-macosx_14_0_arm64.whl
ab6e2b743192e7e9ecef7d22a90c394a6fe3313676e55fcc1c566ee0649b35db  numpy-2.3.0rc1-pp311-pypy311_pp73-macosx_14_0_x86_64.whl
fccea8370d46dcb20012a1e8e54bc70dc7e2db8bfc3e20b95f8f871bb408744f  numpy-2.3.0rc1-pp311-pypy311_pp73-manylinux_2_28_aarch64.whl
477064e29dfac734cb53269f5a4916e38e11ef9d42f6c475e9c695e25f78e868  numpy-2.3.0rc1-pp311-pypy311_pp73-manylinux_2_28_x86_64.whl
95c190db9870e0dc616da2f267a3fc29544a843601251947eadc0e00a0aa8ad6  numpy-2.3.0rc1-pp311-pypy311_pp73-win_amd64.whl
a28686275bf434b4304408c2711832033e3906c50282eb93fc567b8483922625  numpy-2.3.0rc1.tar.gz

- Python
Published by charris about 1 year ago

numpy - v2.2.6 (May 17, 2025)

NumPy 2.2.6 Release Notes

NumPy 2.2.6 is a patch release that fixes bugs found after the 2.2.5 release. It is a mix of typing fixes/improvements as well as the normal bug fixes and some CI maintenance.

This release supports Python versions 3.10-3.13.

Contributors

A total of 8 people contributed to this release. People with a \"+\" by their names contributed a patch for the first time.

• Charles Harris
• Ilhan Polat
• Joren Hammudoglu
• Marco Gorelli +
• Matti Picus
• Nathan Goldbaum
• Peter Hawkins
• Sayed Adel

Pull requests merged

A total of 11 pull requests were merged for this release.

• #28778: MAINT: Prepare 2.2.x for further development
• #28851: BLD: Update vendor-meson to fix module_feature conflicts arguments...
• #28852: BUG: fix heap buffer overflow in np.strings.find
• #28853: TYP: fix NDArray[floating] + float return type
• #28864: BUG: fix stringdtype singleton thread safety
• #28865: MAINT: use OpenBLAS 0.3.29
• #28889: MAINT: from_dlpack thread safety fixes
• #28913: TYP: Fix non-existent CanIndex annotation in ndarray.setfield
• #28915: MAINT: Avoid dereferencing/strict aliasing warnings
• #28916: BUG: Fix missing check for PyErrOccurred() in _pyarraycorrelate.
• #28966: TYP: reject complex scalar types in ndarray.__ifloordiv__

Checksums

MD5

259343f056061f6eadb2f4b8999d06d4  numpy-2.2.6-cp310-cp310-macosx_10_9_x86_64.whl
16fa85488e149489ce7ee044d7b0d307  numpy-2.2.6-cp310-cp310-macosx_11_0_arm64.whl
f01b7aea9d2b76b1eeb49766e615d689  numpy-2.2.6-cp310-cp310-macosx_14_0_arm64.whl
f2ddc2b22517f6e31caa1372b12c2499  numpy-2.2.6-cp310-cp310-macosx_14_0_x86_64.whl
52190e22869884f0870eb3df7a283ca9  numpy-2.2.6-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
8f382b9ca6770db600edd5ea2447a925  numpy-2.2.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
e604aae2ef6e01fb92ecc39aca0424d9  numpy-2.2.6-cp310-cp310-musllinux_1_2_aarch64.whl
3e5626cf6d8bec95d430a7362e71691c  numpy-2.2.6-cp310-cp310-musllinux_1_2_x86_64.whl
8f4f1982837618ed7636ebd432234aeb  numpy-2.2.6-cp310-cp310-win32.whl
1cfd2ac5609b4800512f0ce304e19acc  numpy-2.2.6-cp310-cp310-win_amd64.whl
116203803ceeaa911dd64810b0305b4c  numpy-2.2.6-cp311-cp311-macosx_10_9_x86_64.whl
0427961f3a70ed92b1c4d2c5516c5803  numpy-2.2.6-cp311-cp311-macosx_11_0_arm64.whl
feb8104ed864d51c68984ff93f7255b5  numpy-2.2.6-cp311-cp311-macosx_14_0_arm64.whl
f640cd91637f1d474947ecdb18d17ee8  numpy-2.2.6-cp311-cp311-macosx_14_0_x86_64.whl
2f87d921a50fe50d04bb62125f8638dd  numpy-2.2.6-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
7f986c33f49d5940d6d005ff7039e420  numpy-2.2.6-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
0f7073c78e0aede7179c537f64856db7  numpy-2.2.6-cp311-cp311-musllinux_1_2_aarch64.whl
7402bbedcc0b59bd6cef1c483b77dac0  numpy-2.2.6-cp311-cp311-musllinux_1_2_x86_64.whl
93c920d40abbc10d5d056b8bfbcdad74  numpy-2.2.6-cp311-cp311-win32.whl
9162cb90bff0e4ba322f1e61da9f2fba  numpy-2.2.6-cp311-cp311-win_amd64.whl
75e9fa94b0a6ef568b532f6e0773a6a7  numpy-2.2.6-cp312-cp312-macosx_10_13_x86_64.whl
79d8f89e82971bb2a2f61d0ef8f1a677  numpy-2.2.6-cp312-cp312-macosx_11_0_arm64.whl
fb553e49196bce93af4b0d7e1e8fad1e  numpy-2.2.6-cp312-cp312-macosx_14_0_arm64.whl
01a338bc3a5349b5b7db4335fe879810  numpy-2.2.6-cp312-cp312-macosx_14_0_x86_64.whl
f37533a7ae4aa95da824b1df2786ac55  numpy-2.2.6-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
2f9ac35f955d9217b6841568ce13d636  numpy-2.2.6-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
df530a075c04dbef9abcac95d027c8bc  numpy-2.2.6-cp312-cp312-musllinux_1_2_aarch64.whl
4edf8f80feec739de3e08fffe97195a3  numpy-2.2.6-cp312-cp312-musllinux_1_2_x86_64.whl
3e2664254d9a7bb5c66df2b108aaec2f  numpy-2.2.6-cp312-cp312-win32.whl
ae2e39f1dba9b91d35edcd8736041df8  numpy-2.2.6-cp312-cp312-win_amd64.whl
2faa32e27b81105db53fb2fc25a54e0d  numpy-2.2.6-cp313-cp313-macosx_10_13_x86_64.whl
0d05b1bb5af5059c8775a4f10fa0ec3d  numpy-2.2.6-cp313-cp313-macosx_11_0_arm64.whl
bb404027de8df58312964e26528ef591  numpy-2.2.6-cp313-cp313-macosx_14_0_arm64.whl
1340a90e0f62a31691e475214f773196  numpy-2.2.6-cp313-cp313-macosx_14_0_x86_64.whl
954981f2846e6735798fb33c1e6fba76  numpy-2.2.6-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
4e4eccd129b31fbef3ced7fb338e862e  numpy-2.2.6-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
c704c1c56c777bc0fc0d54bbcf9f2ddb  numpy-2.2.6-cp313-cp313-musllinux_1_2_aarch64.whl
fb459919a3433235312673bd5797ab8b  numpy-2.2.6-cp313-cp313-musllinux_1_2_x86_64.whl
9998e8ae155872c375ce6c020654176b  numpy-2.2.6-cp313-cp313-win32.whl
03df8a78963b318b4dfede10b213dce4  numpy-2.2.6-cp313-cp313-win_amd64.whl
d1982e582eae2fb076942c0bbedcefe4  numpy-2.2.6-cp313-cp313t-macosx_10_13_x86_64.whl
cbc7a48b9ca730a8d40927666651430a  numpy-2.2.6-cp313-cp313t-macosx_11_0_arm64.whl
cd1d2271c05ccc502b78827b88ff7670  numpy-2.2.6-cp313-cp313t-macosx_14_0_arm64.whl
c2b4fb7464e42af240ad51c8be5fb1ba  numpy-2.2.6-cp313-cp313t-macosx_14_0_x86_64.whl
6a96c540b8df291a128bb50dfdad0ba4  numpy-2.2.6-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
113d466026e770badd1061a6e1a8ca92  numpy-2.2.6-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
1fce5d26d8d6d021954f717b4bad483c  numpy-2.2.6-cp313-cp313t-musllinux_1_2_aarch64.whl
d980d6c4b486ad09dbf62ac5cf1b0b2a  numpy-2.2.6-cp313-cp313t-musllinux_1_2_x86_64.whl
21571229d4376f3c0458d8eb1be3ba52  numpy-2.2.6-cp313-cp313t-win32.whl
4accc0387feec817565aeaba93c79173  numpy-2.2.6-cp313-cp313t-win_amd64.whl
774589ee5f842137322ff19b56a35270  numpy-2.2.6-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
f934cef42ac65a2094dd5280aa6bf9a2  numpy-2.2.6-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
0e53fbb4195726c62b8f237a4bf545e9  numpy-2.2.6-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
3c96c89609022ecd27d44b12c2349a06  numpy-2.2.6-pp310-pypy310_pp73-win_amd64.whl
63d66dc1db9d603df0a84c870e703cfc  numpy-2.2.6.tar.gz

SHA256

b412caa66f72040e6d268491a59f2c43bf03eb6c96dd8f0307829feb7fa2b6fb  numpy-2.2.6-cp310-cp310-macosx_10_9_x86_64.whl
8e41fd67c52b86603a91c1a505ebaef50b3314de0213461c7a6e99c9a3beff90  numpy-2.2.6-cp310-cp310-macosx_11_0_arm64.whl
37e990a01ae6ec7fe7fa1c26c55ecb672dd98b19c3d0e1d1f326fa13cb38d163  numpy-2.2.6-cp310-cp310-macosx_14_0_arm64.whl
5a6429d4be8ca66d889b7cf70f536a397dc45ba6faeb5f8c5427935d9592e9cf  numpy-2.2.6-cp310-cp310-macosx_14_0_x86_64.whl
efd28d4e9cd7d7a8d39074a4d44c63eda73401580c5c76acda2ce969e0a38e83  numpy-2.2.6-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
fc7b73d02efb0e18c000e9ad8b83480dfcd5dfd11065997ed4c6747470ae8915  numpy-2.2.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
74d4531beb257d2c3f4b261bfb0fc09e0f9ebb8842d82a7b4209415896adc680  numpy-2.2.6-cp310-cp310-musllinux_1_2_aarch64.whl
8fc377d995680230e83241d8a96def29f204b5782f371c532579b4f20607a289  numpy-2.2.6-cp310-cp310-musllinux_1_2_x86_64.whl
b093dd74e50a8cba3e873868d9e93a85b78e0daf2e98c6797566ad8044e8363d  numpy-2.2.6-cp310-cp310-win32.whl
f0fd6321b839904e15c46e0d257fdd101dd7f530fe03fd6359c1ea63738703f3  numpy-2.2.6-cp310-cp310-win_amd64.whl
f9f1adb22318e121c5c69a09142811a201ef17ab257a1e66ca3025065b7f53ae  numpy-2.2.6-cp311-cp311-macosx_10_9_x86_64.whl
c820a93b0255bc360f53eca31a0e676fd1101f673dda8da93454a12e23fc5f7a  numpy-2.2.6-cp311-cp311-macosx_11_0_arm64.whl
3d70692235e759f260c3d837193090014aebdf026dfd167834bcba43e30c2a42  numpy-2.2.6-cp311-cp311-macosx_14_0_arm64.whl
481b49095335f8eed42e39e8041327c05b0f6f4780488f61286ed3c01368d491  numpy-2.2.6-cp311-cp311-macosx_14_0_x86_64.whl
b64d8d4d17135e00c8e346e0a738deb17e754230d7e0810ac5012750bbd85a5a  numpy-2.2.6-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
ba10f8411898fc418a521833e014a77d3ca01c15b0c6cdcce6a0d2897e6dbbdf  numpy-2.2.6-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
bd48227a919f1bafbdda0583705e547892342c26fb127219d60a5c36882609d1  numpy-2.2.6-cp311-cp311-musllinux_1_2_aarch64.whl
9551a499bf125c1d4f9e250377c1ee2eddd02e01eac6644c080162c0c51778ab  numpy-2.2.6-cp311-cp311-musllinux_1_2_x86_64.whl
0678000bb9ac1475cd454c6b8c799206af8107e310843532b04d49649c717a47  numpy-2.2.6-cp311-cp311-win32.whl
e8213002e427c69c45a52bbd94163084025f533a55a59d6f9c5b820774ef3303  numpy-2.2.6-cp311-cp311-win_amd64.whl
41c5a21f4a04fa86436124d388f6ed60a9343a6f767fced1a8a71c3fbca038ff  numpy-2.2.6-cp312-cp312-macosx_10_13_x86_64.whl
de749064336d37e340f640b05f24e9e3dd678c57318c7289d222a8a2f543e90c  numpy-2.2.6-cp312-cp312-macosx_11_0_arm64.whl
894b3a42502226a1cac872f840030665f33326fc3dac8e57c607905773cdcde3  numpy-2.2.6-cp312-cp312-macosx_14_0_arm64.whl
71594f7c51a18e728451bb50cc60a3ce4e6538822731b2933209a1f3614e9282  numpy-2.2.6-cp312-cp312-macosx_14_0_x86_64.whl
f2618db89be1b4e05f7a1a847a9c1c0abd63e63a1607d892dd54668dd92faf87  numpy-2.2.6-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
fd83c01228a688733f1ded5201c678f0c53ecc1006ffbc404db9f7a899ac6249  numpy-2.2.6-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
37c0ca431f82cd5fa716eca9506aefcabc247fb27ba69c5062a6d3ade8cf8f49  numpy-2.2.6-cp312-cp312-musllinux_1_2_aarch64.whl
fe27749d33bb772c80dcd84ae7e8df2adc920ae8297400dabec45f0dedb3f6de  numpy-2.2.6-cp312-cp312-musllinux_1_2_x86_64.whl
4eeaae00d789f66c7a25ac5f34b71a7035bb474e679f410e5e1a94deb24cf2d4  numpy-2.2.6-cp312-cp312-win32.whl
c1f9540be57940698ed329904db803cf7a402f3fc200bfe599334c9bd84a40b2  numpy-2.2.6-cp312-cp312-win_amd64.whl
0811bb762109d9708cca4d0b13c4f67146e3c3b7cf8d34018c722adb2d957c84  numpy-2.2.6-cp313-cp313-macosx_10_13_x86_64.whl
287cc3162b6f01463ccd86be154f284d0893d2b3ed7292439ea97eafa8170e0b  numpy-2.2.6-cp313-cp313-macosx_11_0_arm64.whl
f1372f041402e37e5e633e586f62aa53de2eac8d98cbfb822806ce4bbefcb74d  numpy-2.2.6-cp313-cp313-macosx_14_0_arm64.whl
55a4d33fa519660d69614a9fad433be87e5252f4b03850642f88993f7b2ca566  numpy-2.2.6-cp313-cp313-macosx_14_0_x86_64.whl
f92729c95468a2f4f15e9bb94c432a9229d0d50de67304399627a943201baa2f  numpy-2.2.6-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
1bc23a79bfabc5d056d106f9befb8d50c31ced2fbc70eedb8155aec74a45798f  numpy-2.2.6-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
e3143e4451880bed956e706a3220b4e5cf6172ef05fcc397f6f36a550b1dd868  numpy-2.2.6-cp313-cp313-musllinux_1_2_aarch64.whl
b4f13750ce79751586ae2eb824ba7e1e8dba64784086c98cdbbcc6a42112ce0d  numpy-2.2.6-cp313-cp313-musllinux_1_2_x86_64.whl
5beb72339d9d4fa36522fc63802f469b13cdbe4fdab4a288f0c441b74272ebfd  numpy-2.2.6-cp313-cp313-win32.whl
b0544343a702fa80c95ad5d3d608ea3599dd54d4632df855e4c8d24eb6ecfa1c  numpy-2.2.6-cp313-cp313-win_amd64.whl
0bca768cd85ae743b2affdc762d617eddf3bcf8724435498a1e80132d04879e6  numpy-2.2.6-cp313-cp313t-macosx_10_13_x86_64.whl
fc0c5673685c508a142ca65209b4e79ed6740a4ed6b2267dbba90f34b0b3cfda  numpy-2.2.6-cp313-cp313t-macosx_11_0_arm64.whl
5bd4fc3ac8926b3819797a7c0e2631eb889b4118a9898c84f585a54d475b7e40  numpy-2.2.6-cp313-cp313t-macosx_14_0_arm64.whl
fee4236c876c4e8369388054d02d0e9bb84821feb1a64dd59e137e6511a551f8  numpy-2.2.6-cp313-cp313t-macosx_14_0_x86_64.whl
e1dda9c7e08dc141e0247a5b8f49cf05984955246a327d4c48bda16821947b2f  numpy-2.2.6-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
f447e6acb680fd307f40d3da4852208af94afdfab89cf850986c3ca00562f4fa  numpy-2.2.6-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
389d771b1623ec92636b0786bc4ae56abafad4a4c513d36a55dce14bd9ce8571  numpy-2.2.6-cp313-cp313t-musllinux_1_2_aarch64.whl
8e9ace4a37db23421249ed236fdcdd457d671e25146786dfc96835cd951aa7c1  numpy-2.2.6-cp313-cp313t-musllinux_1_2_x86_64.whl
038613e9fb8c72b0a41f025a7e4c3f0b7a1b5d768ece4796b674c8f3fe13efff  numpy-2.2.6-cp313-cp313t-win32.whl
6031dd6dfecc0cf9f668681a37648373bddd6421fff6c66ec1624eed0180ee06  numpy-2.2.6-cp313-cp313t-win_amd64.whl
0b605b275d7bd0c640cad4e5d30fa701a8d59302e127e5f79138ad62762c3e3d  numpy-2.2.6-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
7befc596a7dc9da8a337f79802ee8adb30a552a94f792b9c9d18c840055907db  numpy-2.2.6-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
ce47521a4754c8f4593837384bd3424880629f718d87c5d44f8ed763edd63543  numpy-2.2.6-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
d042d24c90c41b54fd506da306759e06e568864df8ec17ccc17e9e884634fd00  numpy-2.2.6-pp310-pypy310_pp73-win_amd64.whl
e29554e2bef54a90aa5cc07da6ce955accb83f21ab5de01a62c8478897b264fd  numpy-2.2.6.tar.gz

- Python
Published by charris about 1 year ago

numpy - v2.2.5 (Apr 19, 2025)

NumPy 2.2.5 Release Notes

NumPy 2.2.5 is a patch release that fixes bugs found after the 2.2.4 release. It has a large number of typing fixes/improvements as well as the normal bug fixes and some CI maintenance.

This release supports Python versions 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.

• Charles Harris
• Joren Hammudoglu
• Baskar Gopinath +
• Nathan Goldbaum
• Nicholas Christensen +
• Sayed Adel
• karl +

Pull requests merged

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

• #28545: MAINT: Prepare 2.2.x for further development
• #28582: BUG: Fix return type of NpyIter_GetIterNext in Cython declarations
• #28583: BUG: avoid deadlocks with C++ shared mutex in dispatch cache
• #28585: TYP: fix typing errors in _core.strings
• #28631: MAINT, CI: Update Ubuntu to 22.04 in azure-pipelines
• #28632: BUG: Set writeable flag for writeable dlpacks.
• #28633: BUG: Fix crackfortran parsing error when a division occurs within...
• #28650: TYP: fix ndarray.tolist() and .item() for unknown dtype
• #28654: BUG: fix deepcopying StringDType arrays (#28643)
• #28661: TYP: Accept objects that write() to str in savetxt
• #28663: CI: Replace QEMU armhf with native (32-bit compatibility mode)
• #28682: SIMD: Resolve Highway QSort symbol linking error on aarch32/ASIMD
• #28683: TYP: add missing "b1" literals for dtype[bool]
• #28705: TYP: Fix false rejection of NDArray[object_].__abs__()
• #28706: TYP: Fix inconsistent NDArray[float64].__[r]truediv__ return...
• #28723: TYP: fix string-like ndarray rich comparison operators
• #28758: TYP: some [arg]partition fixes
• #28772: TYP: fix incorrect random.Generator.integers return type
• #28774: TYP: fix count_nonzero signature

Checksums

MD5

3a5d0889d6d7951f44bc6f7a03fa30c6  numpy-2.2.5-cp310-cp310-macosx_10_9_x86_64.whl
bcf9f4e768b070e17b2635f422a6e27d  numpy-2.2.5-cp310-cp310-macosx_11_0_arm64.whl
e82c8fa47a65bb5c2c83295f549dab12  numpy-2.2.5-cp310-cp310-macosx_14_0_arm64.whl
a5511a995c0f79a8b9a81f2b50e9f692  numpy-2.2.5-cp310-cp310-macosx_14_0_x86_64.whl
72bfc1f98238a8e4ba08999e61111e0e  numpy-2.2.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
146c83a5b8099d8d2607392b2ef7fedf  numpy-2.2.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
6ebdc80b54b008a10575e5d7bbb613f5  numpy-2.2.5-cp310-cp310-musllinux_1_2_aarch64.whl
97efde6443da8f9280a5fc2614a087e5  numpy-2.2.5-cp310-cp310-musllinux_1_2_x86_64.whl
c143f352206cec535b41b6b1d34c5898  numpy-2.2.5-cp310-cp310-win32.whl
0b17fbbf584785f675f1c5b24a00ff93  numpy-2.2.5-cp310-cp310-win_amd64.whl
58532622d7eff69a3c71c1ae89dea070  numpy-2.2.5-cp311-cp311-macosx_10_9_x86_64.whl
0d002c733bb02debe0b15de5ba872d1e  numpy-2.2.5-cp311-cp311-macosx_11_0_arm64.whl
ff0c736c60be96506806061ace2251a1  numpy-2.2.5-cp311-cp311-macosx_14_0_arm64.whl
4febdec973c4405fd08ef35e0c130de1  numpy-2.2.5-cp311-cp311-macosx_14_0_x86_64.whl
0bf4e457c612e565420e135458e70fe0  numpy-2.2.5-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
a43b608ad15ebdc0960611497205d598  numpy-2.2.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
7b4b1afd412149a9af7c25d7346fade8  numpy-2.2.5-cp311-cp311-musllinux_1_2_aarch64.whl
a1e70be013820f92dbfd4796fc4044bb  numpy-2.2.5-cp311-cp311-musllinux_1_2_x86_64.whl
73344e05a6fec0b38183363b4a026252  numpy-2.2.5-cp311-cp311-win32.whl
b7d5fdd23057c58d15c84eef6bfedb55  numpy-2.2.5-cp311-cp311-win_amd64.whl
801b11bb546aac2d92d7b3d5d6c90e86  numpy-2.2.5-cp312-cp312-macosx_10_13_x86_64.whl
68dc4298cad9405ad30cfb723be4ae48  numpy-2.2.5-cp312-cp312-macosx_11_0_arm64.whl
c31c872e0fa8df5ed7f91882621a925f  numpy-2.2.5-cp312-cp312-macosx_14_0_arm64.whl
179dfa545c32c44b77cf8db3b973785f  numpy-2.2.5-cp312-cp312-macosx_14_0_x86_64.whl
4562513ff2f1e3f31d66b8e435000141  numpy-2.2.5-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
c80a2d8aab1a4d6a66f3fca2f0744744  numpy-2.2.5-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
e363e0d8c116522d55b0ddd0cbf2de67  numpy-2.2.5-cp312-cp312-musllinux_1_2_aarch64.whl
d31d443270c76b7238ece2f87b048d21  numpy-2.2.5-cp312-cp312-musllinux_1_2_x86_64.whl
bf469fe048fa4ed75a5d8725297e283a  numpy-2.2.5-cp312-cp312-win32.whl
069b832aa15b6a815497135e7fa8cae8  numpy-2.2.5-cp312-cp312-win_amd64.whl
b2cf059c831cbcfdb4044613a1e5bc8d  numpy-2.2.5-cp313-cp313-macosx_10_13_x86_64.whl
70bcb93e55ff0f6602636602e0834607  numpy-2.2.5-cp313-cp313-macosx_11_0_arm64.whl
00c4938d67fd5b658ad92ac26fbe9cab  numpy-2.2.5-cp313-cp313-macosx_14_0_arm64.whl
0ca38aa51874b9252a2c9d85f81dcd07  numpy-2.2.5-cp313-cp313-macosx_14_0_x86_64.whl
6062cf707b8bc07a1600af0991a0a88e  numpy-2.2.5-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
62c1cf7de0327546f3a1e3852de640d3  numpy-2.2.5-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
ab3ad3390396552f76160139cc528784  numpy-2.2.5-cp313-cp313-musllinux_1_2_aarch64.whl
d258ba55c9a3936fa0c113cac8bbc0cc  numpy-2.2.5-cp313-cp313-musllinux_1_2_x86_64.whl
59bb7e1acb81fc4a02c3b791e110f01e  numpy-2.2.5-cp313-cp313-win32.whl
2e5728a9e5c6405d3a22138e4dd7019f  numpy-2.2.5-cp313-cp313-win_amd64.whl
d315521ec7275d0341787f2450e57e55  numpy-2.2.5-cp313-cp313t-macosx_10_13_x86_64.whl
17018c7c259ae81cf2ca4f58523d7d1c  numpy-2.2.5-cp313-cp313t-macosx_11_0_arm64.whl
ef6fd6a9c6a07db004a272b82f0ea710  numpy-2.2.5-cp313-cp313t-macosx_14_0_arm64.whl
07b2baf70b84b44ca6924794d9c7e431  numpy-2.2.5-cp313-cp313t-macosx_14_0_x86_64.whl
a2fb1ed562d2b6da091d980c7486d113  numpy-2.2.5-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
22fa9137283f463436d7b20a220071cd  numpy-2.2.5-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
b0ae924e4834155eb5ac159ae611c292  numpy-2.2.5-cp313-cp313t-musllinux_1_2_aarch64.whl
c7a8351484f2df9a499c68f1ac73121c  numpy-2.2.5-cp313-cp313t-musllinux_1_2_x86_64.whl
1da753e4127a0bdcdfbfa6639568057e  numpy-2.2.5-cp313-cp313t-win32.whl
a8c869efc0888f214239e5c4f0e6acfb  numpy-2.2.5-cp313-cp313t-win_amd64.whl
7255b93f38e7d54a59d6798182f24c6a  numpy-2.2.5-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
6743ce025de6c245b03ca8511b306503  numpy-2.2.5-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
5abbeec4ff2add1c46f8779f730c73fa  numpy-2.2.5-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
8e2e01f02d05e111ef2b104d1b3afad1  numpy-2.2.5-pp310-pypy310_pp73-win_amd64.whl
df2e46b468f9fdf06b13b04eca9a723f  numpy-2.2.5.tar.gz

SHA256

1f4a922da1729f4c40932b2af4fe84909c7a6e167e6e99f71838ce3a29f3fe26  numpy-2.2.5-cp310-cp310-macosx_10_9_x86_64.whl
b6f91524d31b34f4a5fee24f5bc16dcd1491b668798b6d85585d836c1e633a6a  numpy-2.2.5-cp310-cp310-macosx_11_0_arm64.whl
19f4718c9012e3baea91a7dba661dcab2451cda2550678dc30d53acb91a7290f  numpy-2.2.5-cp310-cp310-macosx_14_0_arm64.whl
eb7fd5b184e5d277afa9ec0ad5e4eb562ecff541e7f60e69ee69c8d59e9aeaba  numpy-2.2.5-cp310-cp310-macosx_14_0_x86_64.whl
6413d48a9be53e183eb06495d8e3b006ef8f87c324af68241bbe7a39e8ff54c3  numpy-2.2.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
7451f92eddf8503c9b8aa4fe6aa7e87fd51a29c2cfc5f7dbd72efde6c65acf57  numpy-2.2.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
0bcb1d057b7571334139129b7f941588f69ce7c4ed15a9d6162b2ea54ded700c  numpy-2.2.5-cp310-cp310-musllinux_1_2_aarch64.whl
36ab5b23915887543441efd0417e6a3baa08634308894316f446027611b53bf1  numpy-2.2.5-cp310-cp310-musllinux_1_2_x86_64.whl
422cc684f17bc963da5f59a31530b3936f57c95a29743056ef7a7903a5dbdf88  numpy-2.2.5-cp310-cp310-win32.whl
e4f0b035d9d0ed519c813ee23e0a733db81ec37d2e9503afbb6e54ccfdee0fa7  numpy-2.2.5-cp310-cp310-win_amd64.whl
c42365005c7a6c42436a54d28c43fe0e01ca11eb2ac3cefe796c25a5f98e5e9b  numpy-2.2.5-cp311-cp311-macosx_10_9_x86_64.whl
498815b96f67dc347e03b719ef49c772589fb74b8ee9ea2c37feae915ad6ebda  numpy-2.2.5-cp311-cp311-macosx_11_0_arm64.whl
6411f744f7f20081b1b4e7112e0f4c9c5b08f94b9f086e6f0adf3645f85d3a4d  numpy-2.2.5-cp311-cp311-macosx_14_0_arm64.whl
9de6832228f617c9ef45d948ec1cd8949c482238d68b2477e6f642c33a7b0a54  numpy-2.2.5-cp311-cp311-macosx_14_0_x86_64.whl
369e0d4647c17c9363244f3468f2227d557a74b6781cb62ce57cf3ef5cc7c610  numpy-2.2.5-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
262d23f383170f99cd9191a7c85b9a50970fe9069b2f8ab5d786eca8a675d60b  numpy-2.2.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
aa70fdbdc3b169d69e8c59e65c07a1c9351ceb438e627f0fdcd471015cd956be  numpy-2.2.5-cp311-cp311-musllinux_1_2_aarch64.whl
37e32e985f03c06206582a7323ef926b4e78bdaa6915095ef08070471865b906  numpy-2.2.5-cp311-cp311-musllinux_1_2_x86_64.whl
f5045039100ed58fa817a6227a356240ea1b9a1bc141018864c306c1a16d4175  numpy-2.2.5-cp311-cp311-win32.whl
b13f04968b46ad705f7c8a80122a42ae8f620536ea38cf4bdd374302926424dd  numpy-2.2.5-cp311-cp311-win_amd64.whl
ee461a4eaab4f165b68780a6a1af95fb23a29932be7569b9fab666c407969051  numpy-2.2.5-cp312-cp312-macosx_10_13_x86_64.whl
ec31367fd6a255dc8de4772bd1658c3e926d8e860a0b6e922b615e532d320ddc  numpy-2.2.5-cp312-cp312-macosx_11_0_arm64.whl
47834cde750d3c9f4e52c6ca28a7361859fcaf52695c7dc3cc1a720b8922683e  numpy-2.2.5-cp312-cp312-macosx_14_0_arm64.whl
2c1a1c6ccce4022383583a6ded7bbcda22fc635eb4eb1e0a053336425ed36dfa  numpy-2.2.5-cp312-cp312-macosx_14_0_x86_64.whl
9d75f338f5f79ee23548b03d801d28a505198297534f62416391857ea0479571  numpy-2.2.5-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
3a801fef99668f309b88640e28d261991bfad9617c27beda4a3aec4f217ea073  numpy-2.2.5-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
abe38cd8381245a7f49967a6010e77dbf3680bd3627c0fe4362dd693b404c7f8  numpy-2.2.5-cp312-cp312-musllinux_1_2_aarch64.whl
5a0ac90e46fdb5649ab6369d1ab6104bfe5854ab19b645bf5cda0127a13034ae  numpy-2.2.5-cp312-cp312-musllinux_1_2_x86_64.whl
0cd48122a6b7eab8f06404805b1bd5856200e3ed6f8a1b9a194f9d9054631beb  numpy-2.2.5-cp312-cp312-win32.whl
ced69262a8278547e63409b2653b372bf4baff0870c57efa76c5703fd6543282  numpy-2.2.5-cp312-cp312-win_amd64.whl
059b51b658f4414fff78c6d7b1b4e18283ab5fa56d270ff212d5ba0c561846f4  numpy-2.2.5-cp313-cp313-macosx_10_13_x86_64.whl
47f9ed103af0bc63182609044b0490747e03bd20a67e391192dde119bf43d52f  numpy-2.2.5-cp313-cp313-macosx_11_0_arm64.whl
261a1ef047751bb02f29dfe337230b5882b54521ca121fc7f62668133cb119c9  numpy-2.2.5-cp313-cp313-macosx_14_0_arm64.whl
4520caa3807c1ceb005d125a75e715567806fed67e315cea619d5ec6e75a4191  numpy-2.2.5-cp313-cp313-macosx_14_0_x86_64.whl
3d14b17b9be5f9c9301f43d2e2a4886a33b53f4e6fdf9ca2f4cc60aeeee76372  numpy-2.2.5-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
2ba321813a00e508d5421104464510cc962a6f791aa2fca1c97b1e65027da80d  numpy-2.2.5-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
a4cbdef3ddf777423060c6f81b5694bad2dc9675f110c4b2a60dc0181543fac7  numpy-2.2.5-cp313-cp313-musllinux_1_2_aarch64.whl
54088a5a147ab71a8e7fdfd8c3601972751ded0739c6b696ad9cb0343e21ab73  numpy-2.2.5-cp313-cp313-musllinux_1_2_x86_64.whl
c8b82a55ef86a2d8e81b63da85e55f5537d2157165be1cb2ce7cfa57b6aef38b  numpy-2.2.5-cp313-cp313-win32.whl
d8882a829fd779f0f43998e931c466802a77ca1ee0fe25a3abe50278616b1471  numpy-2.2.5-cp313-cp313-win_amd64.whl
e8b025c351b9f0e8b5436cf28a07fa4ac0204d67b38f01433ac7f9b870fa38c6  numpy-2.2.5-cp313-cp313t-macosx_10_13_x86_64.whl
8dfa94b6a4374e7851bbb6f35e6ded2120b752b063e6acdd3157e4d2bb922eba  numpy-2.2.5-cp313-cp313t-macosx_11_0_arm64.whl
97c8425d4e26437e65e1d189d22dff4a079b747ff9c2788057bfb8114ce1e133  numpy-2.2.5-cp313-cp313t-macosx_14_0_arm64.whl
352d330048c055ea6db701130abc48a21bec690a8d38f8284e00fab256dc1376  numpy-2.2.5-cp313-cp313t-macosx_14_0_x86_64.whl
8b4c0773b6ada798f51f0f8e30c054d32304ccc6e9c5d93d46cb26f3d385ab19  numpy-2.2.5-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
55f09e00d4dccd76b179c0f18a44f041e5332fd0e022886ba1c0bbf3ea4a18d0  numpy-2.2.5-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
02f226baeefa68f7d579e213d0f3493496397d8f1cff5e2b222af274c86a552a  numpy-2.2.5-cp313-cp313t-musllinux_1_2_aarch64.whl
c26843fd58f65da9491165072da2cccc372530681de481ef670dcc8e27cfb066  numpy-2.2.5-cp313-cp313t-musllinux_1_2_x86_64.whl
1a161c2c79ab30fe4501d5a2bbfe8b162490757cf90b7f05be8b80bc02f7bb8e  numpy-2.2.5-cp313-cp313t-win32.whl
d403c84991b5ad291d3809bace5e85f4bbf44a04bdc9a88ed2bb1807b3360bb8  numpy-2.2.5-cp313-cp313t-win_amd64.whl
b4ea7e1cff6784e58fe281ce7e7f05036b3e1c89c6f922a6bfbc0a7e8768adbe  numpy-2.2.5-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
d7543263084a85fbc09c704b515395398d31d6395518446237eac219eab9e55e  numpy-2.2.5-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
0255732338c4fdd00996c0421884ea8a3651eea555c3a56b84892b66f696eb70  numpy-2.2.5-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
d2e3bdadaba0e040d1e7ab39db73e0afe2c74ae277f5614dad53eadbecbbb169  numpy-2.2.5-pp310-pypy310_pp73-win_amd64.whl
a9c0d994680cd991b1cb772e8b297340085466a6fe964bc9d4e80f5e2f43c291  numpy-2.2.5.tar.gz

- Python
Published by charris about 1 year ago

numpy - 2.2.4 (Mar 16, 2025)

NumPy 2.2.4 Release Notes

NumPy 2.2.4 is a patch release that fixes bugs found after the 2.2.3 release. There are a large number of typing improvements, the rest of the changes are the usual mix of bugfixes and platform maintenace.

This release supports Python versions 3.10-3.13.

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
• Andrej Zhilenkov
• Andrew Nelson
• Charles Harris
• Giovanni Del Monte
• Guan Ming(Wesley) Chiu +
• Jonathan Albrecht +
• Joren Hammudoglu
• Mark Harfouche
• Matthieu Darbois
• Nathan Goldbaum
• Pieter Eendebak
• Sebastian Berg
• Tyler Reddy
• lvllvl +

Pull requests merged

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

• #28333: MAINT: Prepare 2.2.x for further development.
• #28348: TYP: fix positional- and keyword-only params in astype, cross...
• #28377: MAINT: Update FreeBSD version and fix test failure
• #28379: BUG: numpy.loadtxt reads only 50000 lines when skiprows >= maxrows
• #28385: BUG: Make np.nonzero threading safe
• #28420: BUG: safer bincount casting (backport to 2.2.x)
• #28422: BUG: Fix building on s390x with clang
• #28423: CI: use QEMU 9.2.2 for Linux Qemu tests
• #28424: BUG: skip legacy dtype multithreaded test on 32 bit runners
• #28435: BUG: Fix searchsorted and CheckFromAny byte-swapping logic
• #28449: BUG: sanity check __array_interface__ number of dimensions
• #28510: MAINT: Hide decorator from pytest traceback
• #28512: TYP: Typing fixes backported from #28452, #28491, #28494
• #28521: TYP: Backport fixes from #28505, #28506, #28508, and #28511
• #28533: TYP: Backport typing fixes from main (2)
• #28534: TYP: Backport typing fixes from main (3)
• #28542: TYP: Backport typing fixes from main (4)

Checksums

MD5

935928cbd2de140da097f6d5f4a01d72  numpy-2.2.4-cp310-cp310-macosx_10_9_x86_64.whl
bf7fd01bb177885e920173b610c195d9  numpy-2.2.4-cp310-cp310-macosx_11_0_arm64.whl
826e52cd898567a0c446113ab7a7b362  numpy-2.2.4-cp310-cp310-macosx_14_0_arm64.whl
9982a91d7327aea541c24aff94d3e462  numpy-2.2.4-cp310-cp310-macosx_14_0_x86_64.whl
5bdf5b63f4ee01fa808d13043b2a2275  numpy-2.2.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
677b3031105e24eaee2e0e57d7c2a306  numpy-2.2.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
d857867787fe1eb236670e7fdb25f414  numpy-2.2.4-cp310-cp310-musllinux_1_2_aarch64.whl
a5aff3a7eb2923878e67fbe1cd04a9e9  numpy-2.2.4-cp310-cp310-musllinux_1_2_x86_64.whl
e00bd3ac85d8f34b46b7f97a8278aeb3  numpy-2.2.4-cp310-cp310-win32.whl
e5cb2a5d14bccee316bb73173be125ec  numpy-2.2.4-cp310-cp310-win_amd64.whl
494f60d8e1c3500413bd093bb3f486ea  numpy-2.2.4-cp311-cp311-macosx_10_9_x86_64.whl
a886a9f3e80a60ce6ba95b431578bbca  numpy-2.2.4-cp311-cp311-macosx_11_0_arm64.whl
889f3b507bab9272d9b549780840a642  numpy-2.2.4-cp311-cp311-macosx_14_0_arm64.whl
059788668d2c4e9aace4858e77c099ed  numpy-2.2.4-cp311-cp311-macosx_14_0_x86_64.whl
db9ae978afb76a4bf79df0657a66aaeb  numpy-2.2.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
e36963a4c177157dc7b0775c309fa5a8  numpy-2.2.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
3603e683878b74f38e5617f04ff6a369  numpy-2.2.4-cp311-cp311-musllinux_1_2_aarch64.whl
afbc410fb9b42b19f4f7c81c21d6777f  numpy-2.2.4-cp311-cp311-musllinux_1_2_x86_64.whl
33ff8081378188894097942f80c33e26  numpy-2.2.4-cp311-cp311-win32.whl
5b11fe8d26318d85e0bc577a654f6643  numpy-2.2.4-cp311-cp311-win_amd64.whl
91121787f396d3e98210de8b617e5d48  numpy-2.2.4-cp312-cp312-macosx_10_13_x86_64.whl
c524d1020b4652aacf4477d1628fa1ba  numpy-2.2.4-cp312-cp312-macosx_11_0_arm64.whl
eb08f551bdd6772155bb39ac0da47479  numpy-2.2.4-cp312-cp312-macosx_14_0_arm64.whl
7cb37fc9145d0ebbea5666b4f9ed1027  numpy-2.2.4-cp312-cp312-macosx_14_0_x86_64.whl
c4452a5dc557c291904b5c51a4148237  numpy-2.2.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
bd23a12ead870759f264160ab38b2c9d  numpy-2.2.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
07b44109381985b48d1eef80feebc5ad  numpy-2.2.4-cp312-cp312-musllinux_1_2_aarch64.whl
95f1a27d33106fa9f40ee0714681c840  numpy-2.2.4-cp312-cp312-musllinux_1_2_x86_64.whl
507e550a55b19dedf267b58a487ba0bc  numpy-2.2.4-cp312-cp312-win32.whl
be21ccbf8931e92ba1fdb2dc1250bf2a  numpy-2.2.4-cp312-cp312-win_amd64.whl
e94003c2b65d81b00203711c5c42fb8e  numpy-2.2.4-cp313-cp313-macosx_10_13_x86_64.whl
cf781fd5412ffd826e0436883452cc17  numpy-2.2.4-cp313-cp313-macosx_11_0_arm64.whl
92c9a30386a64f2deddad1db742bd296  numpy-2.2.4-cp313-cp313-macosx_14_0_arm64.whl
7fd16554fa0a15b7f99b1fabf1c4592c  numpy-2.2.4-cp313-cp313-macosx_14_0_x86_64.whl
9293b0575a902b2d55c35567dee7679e  numpy-2.2.4-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
9970699bd95e8a64a562b1e6328b83d0  numpy-2.2.4-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
e8597c611a919a8e88229d6889c1f86e  numpy-2.2.4-cp313-cp313-musllinux_1_2_aarch64.whl
329288501f012606605bdbed368e58e9  numpy-2.2.4-cp313-cp313-musllinux_1_2_x86_64.whl
04bf8d0f6a9e279ab01df4ed0b4aeee1  numpy-2.2.4-cp313-cp313-win32.whl
66801fe84a436b7ed3be6e0082b86917  numpy-2.2.4-cp313-cp313-win_amd64.whl
3e2f31e01b45cd16a87b794477de3714  numpy-2.2.4-cp313-cp313t-macosx_10_13_x86_64.whl
7504018213a3a8fea7173e2c1d0fcfd1  numpy-2.2.4-cp313-cp313t-macosx_11_0_arm64.whl
e299021397c3cdb941b7ffe77cf0fefe  numpy-2.2.4-cp313-cp313t-macosx_14_0_arm64.whl
1cc2731a246079bcab361179f38e7ccb  numpy-2.2.4-cp313-cp313t-macosx_14_0_x86_64.whl
e6eccf936d25c9eda9df1a4d50ae2fdc  numpy-2.2.4-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
ba825efd05cca6d56c3dca9f7f1f88e7  numpy-2.2.4-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
369eebec47c9c27cb4841a13e9522167  numpy-2.2.4-cp313-cp313t-musllinux_1_2_aarch64.whl
554dbfa52988d01f715cbe8d4da4b409  numpy-2.2.4-cp313-cp313t-musllinux_1_2_x86_64.whl
811d25a008c68086c9382487e9a4127a  numpy-2.2.4-cp313-cp313t-win32.whl
893fd2fdd42f386e300bee885bbb7778  numpy-2.2.4-cp313-cp313t-win_amd64.whl
65e284546c5ee575eca0a3726c0a1d98  numpy-2.2.4-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
e4e73511eac8f1a10c6abbd6fa2fa0aa  numpy-2.2.4-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
a884ed5263b91fa87b5e3d14caf955a5  numpy-2.2.4-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
7330087a6ad1527ae20a495e2fb3b357  numpy-2.2.4-pp310-pypy310_pp73-win_amd64.whl
56232f4a69b03dd7a87a55fffc5f2ebc  numpy-2.2.4.tar.gz

SHA256

8146f3550d627252269ac42ae660281d673eb6f8b32f113538e0cc2a9aed42b9  numpy-2.2.4-cp310-cp310-macosx_10_9_x86_64.whl
e642d86b8f956098b564a45e6f6ce68a22c2c97a04f5acd3f221f57b8cb850ae  numpy-2.2.4-cp310-cp310-macosx_11_0_arm64.whl
a84eda42bd12edc36eb5b53bbcc9b406820d3353f1994b6cfe453a33ff101775  numpy-2.2.4-cp310-cp310-macosx_14_0_arm64.whl
4ba5054787e89c59c593a4169830ab362ac2bee8a969249dc56e5d7d20ff8df9  numpy-2.2.4-cp310-cp310-macosx_14_0_x86_64.whl
7716e4a9b7af82c06a2543c53ca476fa0b57e4d760481273e09da04b74ee6ee2  numpy-2.2.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
adf8c1d66f432ce577d0197dceaac2ac00c0759f573f28516246351c58a85020  numpy-2.2.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
218f061d2faa73621fa23d6359442b0fc658d5b9a70801373625d958259eaca3  numpy-2.2.4-cp310-cp310-musllinux_1_2_aarch64.whl
df2f57871a96bbc1b69733cd4c51dc33bea66146b8c63cacbfed73eec0883017  numpy-2.2.4-cp310-cp310-musllinux_1_2_x86_64.whl
a0258ad1f44f138b791327961caedffbf9612bfa504ab9597157806faa95194a  numpy-2.2.4-cp310-cp310-win32.whl
0d54974f9cf14acf49c60f0f7f4084b6579d24d439453d5fc5805d46a165b542  numpy-2.2.4-cp310-cp310-win_amd64.whl
e9e0a277bb2eb5d8a7407e14688b85fd8ad628ee4e0c7930415687b6564207a4  numpy-2.2.4-cp311-cp311-macosx_10_9_x86_64.whl
9eeea959168ea555e556b8188da5fa7831e21d91ce031e95ce23747b7609f8a4  numpy-2.2.4-cp311-cp311-macosx_11_0_arm64.whl
bd3ad3b0a40e713fc68f99ecfd07124195333f1e689387c180813f0e94309d6f  numpy-2.2.4-cp311-cp311-macosx_14_0_arm64.whl
cf28633d64294969c019c6df4ff37f5698e8326db68cc2b66576a51fad634880  numpy-2.2.4-cp311-cp311-macosx_14_0_x86_64.whl
2fa8fa7697ad1646b5c93de1719965844e004fcad23c91228aca1cf0800044a1  numpy-2.2.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
f4162988a360a29af158aeb4a2f4f09ffed6a969c9776f8f3bdee9b06a8ab7e5  numpy-2.2.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
892c10d6a73e0f14935c31229e03325a7b3093fafd6ce0af704be7f894d95687  numpy-2.2.4-cp311-cp311-musllinux_1_2_aarch64.whl
db1f1c22173ac1c58db249ae48aa7ead29f534b9a948bc56828337aa84a32ed6  numpy-2.2.4-cp311-cp311-musllinux_1_2_x86_64.whl
ea2bb7e2ae9e37d96835b3576a4fa4b3a97592fbea8ef7c3587078b0068b8f09  numpy-2.2.4-cp311-cp311-win32.whl
f7de08cbe5551911886d1ab60de58448c6df0f67d9feb7d1fb21e9875ef95e91  numpy-2.2.4-cp311-cp311-win_amd64.whl
a7b9084668aa0f64e64bd00d27ba5146ef1c3a8835f3bd912e7a9e01326804c4  numpy-2.2.4-cp312-cp312-macosx_10_13_x86_64.whl
dbe512c511956b893d2dacd007d955a3f03d555ae05cfa3ff1c1ff6df8851854  numpy-2.2.4-cp312-cp312-macosx_11_0_arm64.whl
bb649f8b207ab07caebba230d851b579a3c8711a851d29efe15008e31bb4de24  numpy-2.2.4-cp312-cp312-macosx_14_0_arm64.whl
f34dc300df798742b3d06515aa2a0aee20941c13579d7a2f2e10af01ae4901ee  numpy-2.2.4-cp312-cp312-macosx_14_0_x86_64.whl
c3f7ac96b16955634e223b579a3e5798df59007ca43e8d451a0e6a50f6bfdfba  numpy-2.2.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
4f92084defa704deadd4e0a5ab1dc52d8ac9e8a8ef617f3fbb853e79b0ea3592  numpy-2.2.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
7a4e84a6283b36632e2a5b56e121961f6542ab886bc9e12f8f9818b3c266bfbb  numpy-2.2.4-cp312-cp312-musllinux_1_2_aarch64.whl
11c43995255eb4127115956495f43e9343736edb7fcdb0d973defd9de14cd84f  numpy-2.2.4-cp312-cp312-musllinux_1_2_x86_64.whl
65ef3468b53269eb5fdb3a5c09508c032b793da03251d5f8722b1194f1790c00  numpy-2.2.4-cp312-cp312-win32.whl
2aad3c17ed2ff455b8eaafe06bcdae0062a1db77cb99f4b9cbb5f4ecb13c5146  numpy-2.2.4-cp312-cp312-win_amd64.whl
1cf4e5c6a278d620dee9ddeb487dc6a860f9b199eadeecc567f777daace1e9e7  numpy-2.2.4-cp313-cp313-macosx_10_13_x86_64.whl
1974afec0b479e50438fc3648974268f972e2d908ddb6d7fb634598cdb8260a0  numpy-2.2.4-cp313-cp313-macosx_11_0_arm64.whl
79bd5f0a02aa16808fcbc79a9a376a147cc1045f7dfe44c6e7d53fa8b8a79392  numpy-2.2.4-cp313-cp313-macosx_14_0_arm64.whl
3387dd7232804b341165cedcb90694565a6015433ee076c6754775e85d86f1fc  numpy-2.2.4-cp313-cp313-macosx_14_0_x86_64.whl
6f527d8fdb0286fd2fd97a2a96c6be17ba4232da346931d967a0630050dfd298  numpy-2.2.4-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
bce43e386c16898b91e162e5baaad90c4b06f9dcbe36282490032cec98dc8ae7  numpy-2.2.4-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
31504f970f563d99f71a3512d0c01a645b692b12a63630d6aafa0939e52361e6  numpy-2.2.4-cp313-cp313-musllinux_1_2_aarch64.whl
81413336ef121a6ba746892fad881a83351ee3e1e4011f52e97fba79233611fd  numpy-2.2.4-cp313-cp313-musllinux_1_2_x86_64.whl
f486038e44caa08dbd97275a9a35a283a8f1d2f0ee60ac260a1790e76660833c  numpy-2.2.4-cp313-cp313-win32.whl
207a2b8441cc8b6a2a78c9ddc64d00d20c303d79fba08c577752f080c4007ee3  numpy-2.2.4-cp313-cp313-win_amd64.whl
8120575cb4882318c791f839a4fd66161a6fa46f3f0a5e613071aae35b5dd8f8  numpy-2.2.4-cp313-cp313t-macosx_10_13_x86_64.whl
a761ba0fa886a7bb33c6c8f6f20213735cb19642c580a931c625ee377ee8bd39  numpy-2.2.4-cp313-cp313t-macosx_11_0_arm64.whl
ac0280f1ba4a4bfff363a99a6aceed4f8e123f8a9b234c89140f5e894e452ecd  numpy-2.2.4-cp313-cp313t-macosx_14_0_arm64.whl
879cf3a9a2b53a4672a168c21375166171bc3932b7e21f622201811c43cdd3b0  numpy-2.2.4-cp313-cp313t-macosx_14_0_x86_64.whl
f05d4198c1bacc9124018109c5fba2f3201dbe7ab6e92ff100494f236209c960  numpy-2.2.4-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
e2f085ce2e813a50dfd0e01fbfc0c12bbe5d2063d99f8b29da30e544fb6483b8  numpy-2.2.4-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
92bda934a791c01d6d9d8e038363c50918ef7c40601552a58ac84c9613a665bc  numpy-2.2.4-cp313-cp313t-musllinux_1_2_aarch64.whl
ee4d528022f4c5ff67332469e10efe06a267e32f4067dc76bb7e2cddf3cd25ff  numpy-2.2.4-cp313-cp313t-musllinux_1_2_x86_64.whl
05c076d531e9998e7e694c36e8b349969c56eadd2cdcd07242958489d79a7286  numpy-2.2.4-cp313-cp313t-win32.whl
188dcbca89834cc2e14eb2f106c96d6d46f200fe0200310fc29089657379c58d  numpy-2.2.4-cp313-cp313t-win_amd64.whl
7051ee569db5fbac144335e0f3b9c2337e0c8d5c9fee015f259a5bd70772b7e8  numpy-2.2.4-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
ab2939cd5bec30a7430cbdb2287b63151b77cf9624de0532d629c9a1c59b1d5c  numpy-2.2.4-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
d0f35b19894a9e08639fd60a1ec1978cb7f5f7f1eace62f38dd36be8aecdef4d  numpy-2.2.4-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
b4adfbbc64014976d2f91084915ca4e626fbf2057fb81af209c1a6d776d23e3d  numpy-2.2.4-pp310-pypy310_pp73-win_amd64.whl
9ba03692a45d3eef66559efe1d1096c4b9b75c0986b5dff5530c378fb8331d4f  numpy-2.2.4.tar.gz

- Python
Published by charris about 1 year ago

numpy - 2.2.3 (Feb 13, 2025)

NumPy 2.2.3 Release Notes

NumPy 2.2.3 is a patch release that fixes bugs found after the 2.2.2 release. The majority of the changes are typing improvements and fixes for free threaded Python. Both of those areas are still under development, so if you discover new problems, please report them.

This release supports Python versions 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.

• !amotzop
• Charles Harris
• Chris Sidebottom
• Joren Hammudoglu
• Matthew Brett
• Nathan Goldbaum
• Raghuveer Devulapalli
• Sebastian Berg
• Yakov Danishevsky +

Pull requests merged

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

• #28185: MAINT: Prepare 2.2.x for further development
• #28201: BUG: fix data race in a more minimal way on stable branch
• #28208: BUG: Fix from_float_positional errors for huge pads
• #28209: BUG: fix data race in np.repeat
• #28212: MAINT: Use VQSORTCOMPILERCOMPATIBLE to determine if we should...
• #28224: MAINT: update highway to latest
• #28236: BUG: Add cpp atomic support (#28234)
• #28237: BLD: Compile fix for clang-cl on WoA
• #28243: TYP: Avoid upcasting float64 in the set-ops
• #28249: BLD: better fix for clang / ARM compiles
• #28266: TYP: Fix timedelta64.__divmod__ and timedelta64.__mod__...
• #28274: TYP: Fixed missing typing information of set_printoptions
• #28278: BUG: backport resource cleanup bugfix from gh-28273
• #28282: BUG: fix incorrect bytes to stringdtype coercion
• #28283: TYP: Fix scalar constructors
• #28284: TYP: stub numpy.matlib
• #28285: TYP: stub the missing numpy.testing modules
• #28286: CI: Fix the github label for TYP: PR\'s and issues
• #28305: TYP: Backport typing updates from main
• #28321: BUG: fix race initializing legacy dtype casts
• #28324: CI: update testmoderatelysmall_alpha

Checksums

MD5

9cd8b5e358f89016f403a6c1a27e7e87  numpy-2.2.3-cp310-cp310-macosx_10_9_x86_64.whl
2818f5a9efcfc3bb6bf657137df26046  numpy-2.2.3-cp310-cp310-macosx_11_0_arm64.whl
6d65c6a336cfb69fe4ddd756cad73d55  numpy-2.2.3-cp310-cp310-macosx_14_0_arm64.whl
7f4cf33c634b33f633d4bf47f560a86d  numpy-2.2.3-cp310-cp310-macosx_14_0_x86_64.whl
3c04024badd42bfcc68c14f106efa93f  numpy-2.2.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
07658df1de0e1d3721de0aacff4313cd  numpy-2.2.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
3e753fc4b7c879b29442ee9bab25eddd  numpy-2.2.3-cp310-cp310-musllinux_1_2_aarch64.whl
d1811f1988d88b00825bc6e943d8e22d  numpy-2.2.3-cp310-cp310-musllinux_1_2_x86_64.whl
b5fe91363c16001ea30cbd5befbb0555  numpy-2.2.3-cp310-cp310-win32.whl
44dfe1df1640e4fe762bedad57cd7165  numpy-2.2.3-cp310-cp310-win_amd64.whl
6156418f596620b00a3c221baef02476  numpy-2.2.3-cp311-cp311-macosx_10_9_x86_64.whl
97b925bac245aad1297d22ad3cfaa74c  numpy-2.2.3-cp311-cp311-macosx_11_0_arm64.whl
3f05819fcb71df1d3093e5d1c041a4e9  numpy-2.2.3-cp311-cp311-macosx_14_0_arm64.whl
f6763893ba9a5739fefa0929fd152db2  numpy-2.2.3-cp311-cp311-macosx_14_0_x86_64.whl
e93cf6ed4e1a3f9a8009ee7f2fcb0da8  numpy-2.2.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
851dcbcbe90212c385dcdac1614cca83  numpy-2.2.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
9b27cf1d6319f70370f4b0af10c03f5c  numpy-2.2.3-cp311-cp311-musllinux_1_2_aarch64.whl
28d20c95ff23d27ae639b4960df777ec  numpy-2.2.3-cp311-cp311-musllinux_1_2_x86_64.whl
559fefe30c0043a088adeca90231b382  numpy-2.2.3-cp311-cp311-win32.whl
5e32a1cc3dcfe729f675784a53e4d553  numpy-2.2.3-cp311-cp311-win_amd64.whl
12134dcf62b2bca2eeebb7bbc45c2a71  numpy-2.2.3-cp312-cp312-macosx_10_13_x86_64.whl
c72318236531d3ca61d229eaf96f7d04  numpy-2.2.3-cp312-cp312-macosx_11_0_arm64.whl
1b807acc844c2ba5be7bc7586d4a3a6b  numpy-2.2.3-cp312-cp312-macosx_14_0_arm64.whl
810d4908371bb2f08b0c7b16d3f05970  numpy-2.2.3-cp312-cp312-macosx_14_0_x86_64.whl
bb918cedd0931cb68af9e77096dedf54  numpy-2.2.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
92c6c6c5b22b207425b329f061bd18fa  numpy-2.2.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
10d48fb9d86280db1afe7224b15a51af  numpy-2.2.3-cp312-cp312-musllinux_1_2_aarch64.whl
a73da0434a971b21d8a9c0596015d629  numpy-2.2.3-cp312-cp312-musllinux_1_2_x86_64.whl
c5f1e734c7d872e2f9af71d32e62d59c  numpy-2.2.3-cp312-cp312-win32.whl
884c1a89844f539ab15b7016a43d231c  numpy-2.2.3-cp312-cp312-win_amd64.whl
3a2de7f886cb756cf8d0375a36721926  numpy-2.2.3-cp313-cp313-macosx_10_13_x86_64.whl
c1fe5b6a9015c2877647419caa009be0  numpy-2.2.3-cp313-cp313-macosx_11_0_arm64.whl
bb3f3a69219bbcdb719bbe38e4e69f79  numpy-2.2.3-cp313-cp313-macosx_14_0_arm64.whl
8158c2e980a1cbfb4d98ff3a273bb2e9  numpy-2.2.3-cp313-cp313-macosx_14_0_x86_64.whl
4d3d9b0c14db955e4b1aa1a1971d2def  numpy-2.2.3-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
6575308269513900c94803258b89ac83  numpy-2.2.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
945b91c2093fed2a1f34597fc66e5a35  numpy-2.2.3-cp313-cp313-musllinux_1_2_aarch64.whl
c5867508607f75ed23426315a7ad86d7  numpy-2.2.3-cp313-cp313-musllinux_1_2_x86_64.whl
5a1497c262d9aa52ce6859a12a54ebbc  numpy-2.2.3-cp313-cp313-win32.whl
69c98e036d59eb74e4620c7649b5d7fc  numpy-2.2.3-cp313-cp313-win_amd64.whl
2535d7c0f98ad848bcf1f48f7c358e41  numpy-2.2.3-cp313-cp313t-macosx_10_13_x86_64.whl
aea9afa69d510ce905b2b8dbf0e33a11  numpy-2.2.3-cp313-cp313t-macosx_11_0_arm64.whl
cc5aceacd0a44a67cdd2cf8d5a446ca3  numpy-2.2.3-cp313-cp313t-macosx_14_0_arm64.whl
32eb2ed1e734ea26c90f75b1f5616564  numpy-2.2.3-cp313-cp313t-macosx_14_0_x86_64.whl
f1d85f322c3e85ef748c3e5594b94226  numpy-2.2.3-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
7f24ce01ad5c352c76614a12fa5e2319  numpy-2.2.3-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
62841d4b49c5a0cef2c2ba26a16f6959  numpy-2.2.3-cp313-cp313t-musllinux_1_2_aarch64.whl
d7b512f83999d05c47e55b931f2dcdfe  numpy-2.2.3-cp313-cp313t-musllinux_1_2_x86_64.whl
1dca2f20e0accc1741e5fb233ecf7dff  numpy-2.2.3-cp313-cp313t-win32.whl
347b71f0db5b49a25ef1ed677e47999b  numpy-2.2.3-cp313-cp313t-win_amd64.whl
3615d13c8c14c323aeda1c07d5a7fd55  numpy-2.2.3-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
f7d2ba950c5aa11c100bb6bf202d5799  numpy-2.2.3-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
b4336174c843c4943084e17945cd1165  numpy-2.2.3-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
0d856a89e028c393f8125739c56591e0  numpy-2.2.3-pp310-pypy310_pp73-win_amd64.whl
c6ee254bcdf1e2fdb13d87e0ee4166ba  numpy-2.2.3.tar.gz

SHA256

cbc6472e01952d3d1b2772b720428f8b90e2deea8344e854df22b0618e9cce71  numpy-2.2.3-cp310-cp310-macosx_10_9_x86_64.whl
cdfe0c22692a30cd830c0755746473ae66c4a8f2e7bd508b35fb3b6a0813d787  numpy-2.2.3-cp310-cp310-macosx_11_0_arm64.whl
e37242f5324ffd9f7ba5acf96d774f9276aa62a966c0bad8dae692deebec7716  numpy-2.2.3-cp310-cp310-macosx_14_0_arm64.whl
95172a21038c9b423e68be78fd0be6e1b97674cde269b76fe269a5dfa6fadf0b  numpy-2.2.3-cp310-cp310-macosx_14_0_x86_64.whl
d5b47c440210c5d1d67e1cf434124e0b5c395eee1f5806fdd89b553ed1acd0a3  numpy-2.2.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
0391ea3622f5c51a2e29708877d56e3d276827ac5447d7f45e9bc4ade8923c52  numpy-2.2.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
f6b3dfc7661f8842babd8ea07e9897fe3d9b69a1d7e5fbb743e4160f9387833b  numpy-2.2.3-cp310-cp310-musllinux_1_2_aarch64.whl
1ad78ce7f18ce4e7df1b2ea4019b5817a2f6a8a16e34ff2775f646adce0a5027  numpy-2.2.3-cp310-cp310-musllinux_1_2_x86_64.whl
5ebeb7ef54a7be11044c33a17b2624abe4307a75893c001a4800857956b41094  numpy-2.2.3-cp310-cp310-win32.whl
596140185c7fa113563c67c2e894eabe0daea18cf8e33851738c19f70ce86aeb  numpy-2.2.3-cp310-cp310-win_amd64.whl
16372619ee728ed67a2a606a614f56d3eabc5b86f8b615c79d01957062826ca8  numpy-2.2.3-cp311-cp311-macosx_10_9_x86_64.whl
5521a06a3148686d9269c53b09f7d399a5725c47bbb5b35747e1cb76326b714b  numpy-2.2.3-cp311-cp311-macosx_11_0_arm64.whl
7c8dde0ca2f77828815fd1aedfdf52e59071a5bae30dac3b4da2a335c672149a  numpy-2.2.3-cp311-cp311-macosx_14_0_arm64.whl
77974aba6c1bc26e3c205c2214f0d5b4305bdc719268b93e768ddb17e3fdd636  numpy-2.2.3-cp311-cp311-macosx_14_0_x86_64.whl
d42f9c36d06440e34226e8bd65ff065ca0963aeecada587b937011efa02cdc9d  numpy-2.2.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
f2712c5179f40af9ddc8f6727f2bd910ea0eb50206daea75f58ddd9fa3f715bb  numpy-2.2.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
c8b0451d2ec95010d1db8ca733afc41f659f425b7f608af569711097fd6014e2  numpy-2.2.3-cp311-cp311-musllinux_1_2_aarch64.whl
d9b4a8148c57ecac25a16b0e11798cbe88edf5237b0df99973687dd866f05e1b  numpy-2.2.3-cp311-cp311-musllinux_1_2_x86_64.whl
1f45315b2dc58d8a3e7754fe4e38b6fce132dab284a92851e41b2b344f6441c5  numpy-2.2.3-cp311-cp311-win32.whl
9f48ba6f6c13e5e49f3d3efb1b51c8193215c42ac82610a04624906a9270be6f  numpy-2.2.3-cp311-cp311-win_amd64.whl
12c045f43b1d2915eca6b880a7f4a256f59d62df4f044788c8ba67709412128d  numpy-2.2.3-cp312-cp312-macosx_10_13_x86_64.whl
87eed225fd415bbae787f93a457af7f5990b92a334e346f72070bf569b9c9c95  numpy-2.2.3-cp312-cp312-macosx_11_0_arm64.whl
712a64103d97c404e87d4d7c47fb0c7ff9acccc625ca2002848e0d53288b90ea  numpy-2.2.3-cp312-cp312-macosx_14_0_arm64.whl
a5ae282abe60a2db0fd407072aff4599c279bcd6e9a2475500fc35b00a57c532  numpy-2.2.3-cp312-cp312-macosx_14_0_x86_64.whl
5266de33d4c3420973cf9ae3b98b54a2a6d53a559310e3236c4b2b06b9c07d4e  numpy-2.2.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
3b787adbf04b0db1967798dba8da1af07e387908ed1553a0d6e74c084d1ceafe  numpy-2.2.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
34c1b7e83f94f3b564b35f480f5652a47007dd91f7c839f404d03279cc8dd021  numpy-2.2.3-cp312-cp312-musllinux_1_2_aarch64.whl
4d8335b5f1b6e2bce120d55fb17064b0262ff29b459e8493d1785c18ae2553b8  numpy-2.2.3-cp312-cp312-musllinux_1_2_x86_64.whl
4d9828d25fb246bedd31e04c9e75714a4087211ac348cb39c8c5f99dbb6683fe  numpy-2.2.3-cp312-cp312-win32.whl
83807d445817326b4bcdaaaf8e8e9f1753da04341eceec705c001ff342002e5d  numpy-2.2.3-cp312-cp312-win_amd64.whl
7bfdb06b395385ea9b91bf55c1adf1b297c9fdb531552845ff1d3ea6e40d5aba  numpy-2.2.3-cp313-cp313-macosx_10_13_x86_64.whl
23c9f4edbf4c065fddb10a4f6e8b6a244342d95966a48820c614891e5059bb50  numpy-2.2.3-cp313-cp313-macosx_11_0_arm64.whl
a0c03b6be48aaf92525cccf393265e02773be8fd9551a2f9adbe7db1fa2b60f1  numpy-2.2.3-cp313-cp313-macosx_14_0_arm64.whl
2376e317111daa0a6739e50f7ee2a6353f768489102308b0d98fcf4a04f7f3b5  numpy-2.2.3-cp313-cp313-macosx_14_0_x86_64.whl
8fb62fe3d206d72fe1cfe31c4a1106ad2b136fcc1606093aeab314f02930fdf2  numpy-2.2.3-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
52659ad2534427dffcc36aac76bebdd02b67e3b7a619ac67543bc9bfe6b7cdb1  numpy-2.2.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
1b416af7d0ed3271cad0f0a0d0bee0911ed7eba23e66f8424d9f3dfcdcae1304  numpy-2.2.3-cp313-cp313-musllinux_1_2_aarch64.whl
1402da8e0f435991983d0a9708b779f95a8c98c6b18a171b9f1be09005e64d9d  numpy-2.2.3-cp313-cp313-musllinux_1_2_x86_64.whl
136553f123ee2951bfcfbc264acd34a2fc2f29d7cdf610ce7daf672b6fbaa693  numpy-2.2.3-cp313-cp313-win32.whl
5b732c8beef1d7bc2d9e476dbba20aaff6167bf205ad9aa8d30913859e82884b  numpy-2.2.3-cp313-cp313-win_amd64.whl
435e7a933b9fda8126130b046975a968cc2d833b505475e588339e09f7672890  numpy-2.2.3-cp313-cp313t-macosx_10_13_x86_64.whl
7678556eeb0152cbd1522b684dcd215250885993dd00adb93679ec3c0e6e091c  numpy-2.2.3-cp313-cp313t-macosx_11_0_arm64.whl
2e8da03bd561504d9b20e7a12340870dfc206c64ea59b4cfee9fceb95070ee94  numpy-2.2.3-cp313-cp313t-macosx_14_0_arm64.whl
c9aa4496fd0e17e3843399f533d62857cef5900facf93e735ef65aa4bbc90ef0  numpy-2.2.3-cp313-cp313t-macosx_14_0_x86_64.whl
f4ca91d61a4bf61b0f2228f24bbfa6a9facd5f8af03759fe2a655c50ae2c6610  numpy-2.2.3-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
deaa09cd492e24fd9b15296844c0ad1b3c976da7907e1c1ed3a0ad21dded6f76  numpy-2.2.3-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
246535e2f7496b7ac85deffe932896a3577be7af8fb7eebe7146444680297e9a  numpy-2.2.3-cp313-cp313t-musllinux_1_2_aarch64.whl
daf43a3d1ea699402c5a850e5313680ac355b4adc9770cd5cfc2940e7861f1bf  numpy-2.2.3-cp313-cp313t-musllinux_1_2_x86_64.whl
cf802eef1f0134afb81fef94020351be4fe1d6681aadf9c5e862af6602af64ef  numpy-2.2.3-cp313-cp313t-win32.whl
aee2512827ceb6d7f517c8b85aa5d3923afe8fc7a57d028cffcd522f1c6fd082  numpy-2.2.3-cp313-cp313t-win_amd64.whl
3c2ec8a0f51d60f1e9c0c5ab116b7fc104b165ada3f6c58abf881cb2eb16044d  numpy-2.2.3-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
ed2cf9ed4e8ebc3b754d398cba12f24359f018b416c380f577bbae112ca52fc9  numpy-2.2.3-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
39261798d208c3095ae4f7bc8eaeb3481ea8c6e03dc48028057d3cbdbdb8937e  numpy-2.2.3-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
783145835458e60fa97afac25d511d00a1eca94d4a8f3ace9fe2043003c678e4  numpy-2.2.3-pp310-pypy310_pp73-win_amd64.whl
dbdc15f0c81611925f382dfa97b3bd0bc2c1ce19d4fe50482cb0ddc12ba30020  numpy-2.2.3.tar.gz

- Python
Published by charris over 1 year ago

numpy - 2.2.2 (Jan 18, 2025)

NumPy 2.2.2 Release Notes

NumPy 2.2.2 is a patch release that fixes bugs found after the 2.2.1 release. The number of typing fixes/updates is notable. This release supports Python versions 3.10-3.13.

Contributors

A total of 8 people contributed to this release. People with a \"+\" by their names contributed a patch for the first time.

• Alicia Boya García +
• Charles Harris
• Joren Hammudoglu
• Kai Germaschewski +
• Nathan Goldbaum
• PTUsumit +
• Rohit Goswami
• Sebastian Berg

Pull requests merged

A total of 16 pull requests were merged for this release.

• #28050: MAINT: Prepare 2.2.x for further development
• #28055: TYP: fix void arrays not accepting str keys in __setitem__
• #28066: TYP: fix unnecessarily broad integer binop return types (#28065)
• #28112: TYP: Better ndarray binop return types for float64 &...
• #28113: TYP: Return the correct bool from issubdtype
• #28114: TYP: Always accept date[time] in the datetime64 constructor
• #28120: BUG: Fix auxdata initialization in ufunc slow path
• #28131: BUG: move reduction initialization to ufunc initialization
• #28132: TYP: Fix interp to accept and return scalars
• #28137: BUG: call PyType_Ready in f2py to avoid data races
• #28145: BUG: remove unnecessary call to PyArray_UpdateFlags
• #28160: BUG: Avoid data race in PyArrayCheckFromAnyint
• #28175: BUG: Fix f2py directives and --lower casing
• #28176: TYP: Fix overlapping overloads issue in 2->1 ufuncs
• #28177: TYP: preserve shape-type in ndarray.astype()
• #28178: TYP: Fix missing and spurious top-level exports

Checksums

MD5

749cb2adf8043551aae22bbf0ed3130a  numpy-2.2.2-cp310-cp310-macosx_10_9_x86_64.whl
bc79fa2e44316b7ce9bacb48a993ed91  numpy-2.2.2-cp310-cp310-macosx_11_0_arm64.whl
c6b2caa2bbb645b5950dccb77efb1dbb  numpy-2.2.2-cp310-cp310-macosx_14_0_arm64.whl
8c410efac169af880cacbbac8a731658  numpy-2.2.2-cp310-cp310-macosx_14_0_x86_64.whl
21d165669635a9b680d03b0b4e7f5b98  numpy-2.2.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
a34ef5e7c967136fdc59c822e99f87d6  numpy-2.2.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
a81749effc5160ff8dde7eb2ebe868c4  numpy-2.2.2-cp310-cp310-musllinux_1_2_aarch64.whl
546612d82fae082697879aaf2b985b1b  numpy-2.2.2-cp310-cp310-musllinux_1_2_x86_64.whl
d874e626f58175ad603cb68fda2a4e28  numpy-2.2.2-cp310-cp310-win32.whl
20564a5caeb621061267f9d80c1e7ed0  numpy-2.2.2-cp310-cp310-win_amd64.whl
ef5336ddae73feef891844a205f89b15  numpy-2.2.2-cp311-cp311-macosx_10_9_x86_64.whl
7a0c8804cb6ebca82b1cf3063b410687  numpy-2.2.2-cp311-cp311-macosx_11_0_arm64.whl
1682639d0420a532f8894c4a8685b23d  numpy-2.2.2-cp311-cp311-macosx_14_0_arm64.whl
d33d53efc5744b577cb8a6ac9971cfdb  numpy-2.2.2-cp311-cp311-macosx_14_0_x86_64.whl
c85b92e2ed7ef0eaeb15909ad73aea22  numpy-2.2.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
efa1a587f607a37336c477bed977ea64  numpy-2.2.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
e0effe9902e262704a115c6f7095daf7  numpy-2.2.2-cp311-cp311-musllinux_1_2_aarch64.whl
425e0cebeb1c2c91bba42ae195836268  numpy-2.2.2-cp311-cp311-musllinux_1_2_x86_64.whl
57121319a2fbb76eed4b268282ed668e  numpy-2.2.2-cp311-cp311-win32.whl
fdb54e7345ff657d208fbb52469a5861  numpy-2.2.2-cp311-cp311-win_amd64.whl
bdf299e0abc45b5c5113a1cc5505636a  numpy-2.2.2-cp312-cp312-macosx_10_13_x86_64.whl
30c25784c07965592cf88104b6c02508  numpy-2.2.2-cp312-cp312-macosx_11_0_arm64.whl
65e630a0de5403c41a0083198bc14442  numpy-2.2.2-cp312-cp312-macosx_14_0_arm64.whl
6d9f50717e7b40f1ebdf139f83cc7504  numpy-2.2.2-cp312-cp312-macosx_14_0_x86_64.whl
6b092a9280ada70482d44f538752fc0b  numpy-2.2.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
9c273da8438391eab30f6c1c4898be5d  numpy-2.2.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
d619047dcaf041b806a7b59ff0a798d5  numpy-2.2.2-cp312-cp312-musllinux_1_2_aarch64.whl
fa5d0d979104456d7c43a183223c8587  numpy-2.2.2-cp312-cp312-musllinux_1_2_x86_64.whl
3b8689aedff5037cad85b018e2d5e43a  numpy-2.2.2-cp312-cp312-win32.whl
a2340ff05cae7e09f63bfcfd4e75ea87  numpy-2.2.2-cp312-cp312-win_amd64.whl
044e86bd65492af34a59e4109fbeed16  numpy-2.2.2-cp313-cp313-macosx_10_13_x86_64.whl
7ca0f0e8c8d3d80ec473ec33929c2ae3  numpy-2.2.2-cp313-cp313-macosx_11_0_arm64.whl
4b866ad895e007005afe8a29837cf7d6  numpy-2.2.2-cp313-cp313-macosx_14_0_arm64.whl
2e6247faabf6d0ac0fafaca0bb405ff8  numpy-2.2.2-cp313-cp313-macosx_14_0_x86_64.whl
773982551185ae327cdefe416e73acfc  numpy-2.2.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
1c0ecc958a555a8a95c92c1dd7dc2358  numpy-2.2.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
9f662eb58b8f711585550d6fdf8afa4f  numpy-2.2.2-cp313-cp313-musllinux_1_2_aarch64.whl
53471186fc990eb22e82a0512b310438  numpy-2.2.2-cp313-cp313-musllinux_1_2_x86_64.whl
6b4d65349c74dd91853a7cc6b5c5786e  numpy-2.2.2-cp313-cp313-win32.whl
33dc5bab2d3f752ef00f81021d68cb5a  numpy-2.2.2-cp313-cp313-win_amd64.whl
0acc5069c5ab4fe3ea7c35956636c462  numpy-2.2.2-cp313-cp313t-macosx_10_13_x86_64.whl
01e3f727594a12eee6d0677113525b96  numpy-2.2.2-cp313-cp313t-macosx_11_0_arm64.whl
7b1ddabcb187b18caa52055bb2b2dc67  numpy-2.2.2-cp313-cp313t-macosx_14_0_arm64.whl
a09f5c138ad8c87b9692eea99f344a98  numpy-2.2.2-cp313-cp313t-macosx_14_0_x86_64.whl
289ec3155aa21c5a161b2d61d2cf3c2d  numpy-2.2.2-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
6bb3eb03d400ad708942afbfebd07abc  numpy-2.2.2-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
62f8ef2a5c9e76b0e43851a7bb9c0379  numpy-2.2.2-cp313-cp313t-musllinux_1_2_aarch64.whl
59b4b77118f958dd07484686e82b1e7a  numpy-2.2.2-cp313-cp313t-musllinux_1_2_x86_64.whl
726b58ec542581c5e46adfd4c5c0fed0  numpy-2.2.2-cp313-cp313t-win32.whl
f2b4eab55a963e8cd4c6c1e573c9a59f  numpy-2.2.2-cp313-cp313t-win_amd64.whl
f6a93eaebee6f9890a4922571141ecb5  numpy-2.2.2-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
fb457bbe2d231e836d2230b06d4706ca  numpy-2.2.2-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
df4c07a48a24621167c12704ba5ac0de  numpy-2.2.2-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
0d1108b9060469eb28bb4a4cffa7b98f  numpy-2.2.2-pp310-pypy310_pp73-win_amd64.whl
ac108586d3aeab9e2d0134b744763eb9  numpy-2.2.2.tar.gz

SHA256

7079129b64cb78bdc8d611d1fd7e8002c0a2565da6a47c4df8062349fee90e3e  numpy-2.2.2-cp310-cp310-macosx_10_9_x86_64.whl
2ec6c689c61df613b783aeb21f945c4cbe6c51c28cb70aae8430577ab39f163e  numpy-2.2.2-cp310-cp310-macosx_11_0_arm64.whl
40c7ff5da22cd391944a28c6a9c638a5eef77fcf71d6e3a79e1d9d9e82752715  numpy-2.2.2-cp310-cp310-macosx_14_0_arm64.whl
995f9e8181723852ca458e22de5d9b7d3ba4da3f11cc1cb113f093b271d7965a  numpy-2.2.2-cp310-cp310-macosx_14_0_x86_64.whl
b78ea78450fd96a498f50ee096f69c75379af5138f7881a51355ab0e11286c97  numpy-2.2.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
3fbe72d347fbc59f94124125e73fc4976a06927ebc503ec5afbfb35f193cd957  numpy-2.2.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
8e6da5cffbbe571f93588f562ed130ea63ee206d12851b60819512dd3e1ba50d  numpy-2.2.2-cp310-cp310-musllinux_1_2_aarch64.whl
09d6a2032faf25e8d0cadde7fd6145118ac55d2740132c1d845f98721b5ebcfd  numpy-2.2.2-cp310-cp310-musllinux_1_2_x86_64.whl
159ff6ee4c4a36a23fe01b7c3d07bd8c14cc433d9720f977fcd52c13c0098160  numpy-2.2.2-cp310-cp310-win32.whl
64bd6e1762cd7f0986a740fee4dff927b9ec2c5e4d9a28d056eb17d332158014  numpy-2.2.2-cp310-cp310-win_amd64.whl
642199e98af1bd2b6aeb8ecf726972d238c9877b0f6e8221ee5ab945ec8a2189  numpy-2.2.2-cp311-cp311-macosx_10_9_x86_64.whl
6d9fc9d812c81e6168b6d405bf00b8d6739a7f72ef22a9214c4241e0dc70b323  numpy-2.2.2-cp311-cp311-macosx_11_0_arm64.whl
c7d1fd447e33ee20c1f33f2c8e6634211124a9aabde3c617687d8b739aa69eac  numpy-2.2.2-cp311-cp311-macosx_14_0_arm64.whl
451e854cfae0febe723077bd0cf0a4302a5d84ff25f0bfece8f29206c7bed02e  numpy-2.2.2-cp311-cp311-macosx_14_0_x86_64.whl
bd249bc894af67cbd8bad2c22e7cbcd46cf87ddfca1f1289d1e7e54868cc785c  numpy-2.2.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
02935e2c3c0c6cbe9c7955a8efa8908dd4221d7755644c59d1bba28b94fd334f  numpy-2.2.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
a972cec723e0563aa0823ee2ab1df0cb196ed0778f173b381c871a03719d4826  numpy-2.2.2-cp311-cp311-musllinux_1_2_aarch64.whl
d6d6a0910c3b4368d89dde073e630882cdb266755565155bc33520283b2d9df8  numpy-2.2.2-cp311-cp311-musllinux_1_2_x86_64.whl
860fd59990c37c3ef913c3ae390b3929d005243acca1a86facb0773e2d8d9e50  numpy-2.2.2-cp311-cp311-win32.whl
da1eeb460ecce8d5b8608826595c777728cdf28ce7b5a5a8c8ac8d949beadcf2  numpy-2.2.2-cp311-cp311-win_amd64.whl
ac9bea18d6d58a995fac1b2cb4488e17eceeac413af014b1dd26170b766d8467  numpy-2.2.2-cp312-cp312-macosx_10_13_x86_64.whl
23ae9f0c2d889b7b2d88a3791f6c09e2ef827c2446f1c4a3e3e76328ee4afd9a  numpy-2.2.2-cp312-cp312-macosx_11_0_arm64.whl
3074634ea4d6df66be04f6728ee1d173cfded75d002c75fac79503a880bf3825  numpy-2.2.2-cp312-cp312-macosx_14_0_arm64.whl
8ec0636d3f7d68520afc6ac2dc4b8341ddb725039de042faf0e311599f54eb37  numpy-2.2.2-cp312-cp312-macosx_14_0_x86_64.whl
2ffbb1acd69fdf8e89dd60ef6182ca90a743620957afb7066385a7bbe88dc748  numpy-2.2.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
0349b025e15ea9d05c3d63f9657707a4e1d471128a3b1d876c095f328f8ff7f0  numpy-2.2.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
463247edcee4a5537841d5350bc87fe8e92d7dd0e8c71c995d2c6eecb8208278  numpy-2.2.2-cp312-cp312-musllinux_1_2_aarch64.whl
9dd47ff0cb2a656ad69c38da850df3454da88ee9a6fde0ba79acceee0e79daba  numpy-2.2.2-cp312-cp312-musllinux_1_2_x86_64.whl
4525b88c11906d5ab1b0ec1f290996c0020dd318af8b49acaa46f198b1ffc283  numpy-2.2.2-cp312-cp312-win32.whl
5acea83b801e98541619af398cc0109ff48016955cc0818f478ee9ef1c5c3dcb  numpy-2.2.2-cp312-cp312-win_amd64.whl
b208cfd4f5fe34e1535c08983a1a6803fdbc7a1e86cf13dd0c61de0b51a0aadc  numpy-2.2.2-cp313-cp313-macosx_10_13_x86_64.whl
d0bbe7dd86dca64854f4b6ce2ea5c60b51e36dfd597300057cf473d3615f2369  numpy-2.2.2-cp313-cp313-macosx_11_0_arm64.whl
22ea3bb552ade325530e72a0c557cdf2dea8914d3a5e1fecf58fa5dbcc6f43cd  numpy-2.2.2-cp313-cp313-macosx_14_0_arm64.whl
128c41c085cab8a85dc29e66ed88c05613dccf6bc28b3866cd16050a2f5448be  numpy-2.2.2-cp313-cp313-macosx_14_0_x86_64.whl
250c16b277e3b809ac20d1f590716597481061b514223c7badb7a0f9993c7f84  numpy-2.2.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
e0c8854b09bc4de7b041148d8550d3bd712b5c21ff6a8ed308085f190235d7ff  numpy-2.2.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
b6fb9c32a91ec32a689ec6410def76443e3c750e7cfc3fb2206b985ffb2b85f0  numpy-2.2.2-cp313-cp313-musllinux_1_2_aarch64.whl
57b4012e04cc12b78590a334907e01b3a85efb2107df2b8733ff1ed05fce71de  numpy-2.2.2-cp313-cp313-musllinux_1_2_x86_64.whl
4dbd80e453bd34bd003b16bd802fac70ad76bd463f81f0c518d1245b1c55e3d9  numpy-2.2.2-cp313-cp313-win32.whl
5a8c863ceacae696aff37d1fd636121f1a512117652e5dfb86031c8d84836369  numpy-2.2.2-cp313-cp313-win_amd64.whl
b3482cb7b3325faa5f6bc179649406058253d91ceda359c104dac0ad320e1391  numpy-2.2.2-cp313-cp313t-macosx_10_13_x86_64.whl
9491100aba630910489c1d0158034e1c9a6546f0b1340f716d522dc103788e39  numpy-2.2.2-cp313-cp313t-macosx_11_0_arm64.whl
41184c416143defa34cc8eb9d070b0a5ba4f13a0fa96a709e20584638254b317  numpy-2.2.2-cp313-cp313t-macosx_14_0_arm64.whl
7dca87ca328f5ea7dafc907c5ec100d187911f94825f8700caac0b3f4c384b49  numpy-2.2.2-cp313-cp313t-macosx_14_0_x86_64.whl
0bc61b307655d1a7f9f4b043628b9f2b721e80839914ede634e3d485913e1fb2  numpy-2.2.2-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
9fad446ad0bc886855ddf5909cbf8cb5d0faa637aaa6277fb4b19ade134ab3c7  numpy-2.2.2-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
149d1113ac15005652e8d0d3f6fd599360e1a708a4f98e43c9c77834a28238cb  numpy-2.2.2-cp313-cp313t-musllinux_1_2_aarch64.whl
106397dbbb1896f99e044efc90360d098b3335060375c26aa89c0d8a97c5f648  numpy-2.2.2-cp313-cp313t-musllinux_1_2_x86_64.whl
0eec19f8af947a61e968d5429f0bd92fec46d92b0008d0a6685b40d6adf8a4f4  numpy-2.2.2-cp313-cp313t-win32.whl
97b974d3ba0fb4612b77ed35d7627490e8e3dff56ab41454d9e8b23448940576  numpy-2.2.2-cp313-cp313t-win_amd64.whl
b0531f0b0e07643eb089df4c509d30d72c9ef40defa53e41363eca8a8cc61495  numpy-2.2.2-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
e9e82dcb3f2ebbc8cb5ce1102d5f1c5ed236bf8a11730fb45ba82e2841ec21df  numpy-2.2.2-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
e0d4142eb40ca6f94539e4db929410f2a46052a0fe7a2c1c59f6179c39938d2a  numpy-2.2.2-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
356ca982c188acbfa6af0d694284d8cf20e95b1c3d0aefa8929376fea9146f60  numpy-2.2.2-pp310-pypy310_pp73-win_amd64.whl
ed6906f61834d687738d25988ae117683705636936cc605be0bb208b23df4d8f  numpy-2.2.2.tar.gz

- Python
Published by charris over 1 year ago

numpy - 2.2.1 (DEC 21, 2024)

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-2.2.1-cp312-cp312-macosx_14_0_arm64.whl
55ba24ebe208344aa7a00e4482f65742969a039c2acfcb910bc6fcd776eb4355  numpy-2.2.1-cp312-cp312-macosx_14_0_x86_64.whl
9b1d07b53b78bf84a96898c1bc139ad7f10fda7423f5fd158fd0f47ec5e01ac7  numpy-2.2.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
5062dc1a4e32a10dc2b8b13cedd58988261416e811c1dc4dbdea4f57eea61b0d  numpy-2.2.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
fce4f615f8ca31b2e61aa0eb5865a21e14f5629515c9151850aa936c02a1ee51  numpy-2.2.1-cp312-cp312-musllinux_1_2_aarch64.whl
67d4cda6fa6ffa073b08c8372aa5fa767ceb10c9a0587c707505a6d426f4e046  numpy-2.2.1-cp312-cp312-musllinux_1_2_x86_64.whl
32cb94448be47c500d2c7a95f93e2f21a01f1fd05dd2beea1ccd049bb6001cd2  numpy-2.2.1-cp312-cp312-win32.whl
ba5511d8f31c033a5fcbda22dd5c813630af98c70b2661f2d2c654ae3cdfcfc8  numpy-2.2.1-cp312-cp312-win_amd64.whl
f1d09e520217618e76396377c81fba6f290d5f926f50c35f3a5f72b01a0da780  numpy-2.2.1-cp313-cp313-macosx_10_13_x86_64.whl
3ecc47cd7f6ea0336042be87d9e7da378e5c7e9b3c8ad0f7c966f714fc10d821  numpy-2.2.1-cp313-cp313-macosx_11_0_arm64.whl
f419290bc8968a46c4933158c91a0012b7a99bb2e465d5ef5293879742f8797e  numpy-2.2.1-cp313-cp313-macosx_14_0_arm64.whl
5b6c390bfaef8c45a260554888966618328d30e72173697e5cabe6b285fb2348  numpy-2.2.1-cp313-cp313-macosx_14_0_x86_64.whl
526fc406ab991a340744aad7e25251dd47a6720a685fa3331e5c59fef5282a59  numpy-2.2.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
f74e6fdeb9a265624ec3a3918430205dff1df7e95a230779746a6af78bc615af  numpy-2.2.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
53c09385ff0b72ba79d8715683c1168c12e0b6e84fb0372e97553d1ea91efe51  numpy-2.2.1-cp313-cp313-musllinux_1_2_aarch64.whl
f3eac17d9ec51be534685ba877b6ab5edc3ab7ec95c8f163e5d7b39859524716  numpy-2.2.1-cp313-cp313-musllinux_1_2_x86_64.whl
9ad014faa93dbb52c80d8f4d3dcf855865c876c9660cb9bd7553843dd03a4b1e  numpy-2.2.1-cp313-cp313-win32.whl
164a829b6aacf79ca47ba4814b130c4020b202522a93d7bff2202bfb33b61c60  numpy-2.2.1-cp313-cp313-win_amd64.whl
4dfda918a13cc4f81e9118dea249e192ab167a0bb1966272d5503e39234d694e  numpy-2.2.1-cp313-cp313t-macosx_10_13_x86_64.whl
733585f9f4b62e9b3528dd1070ec4f52b8acf64215b60a845fa13ebd73cd0712  numpy-2.2.1-cp313-cp313t-macosx_11_0_arm64.whl
89b16a18e7bba224ce5114db863e7029803c179979e1af6ad6a6b11f70545008  numpy-2.2.1-cp313-cp313t-macosx_14_0_arm64.whl
676f4eebf6b2d430300f1f4f4c2461685f8269f94c89698d832cdf9277f30b84  numpy-2.2.1-cp313-cp313t-macosx_14_0_x86_64.whl
27f5cdf9f493b35f7e41e8368e7d7b4bbafaf9660cba53fb21d2cd174ec09631  numpy-2.2.1-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
c1ad395cf254c4fbb5b2132fee391f361a6e8c1adbd28f2cd8e79308a615fe9d  numpy-2.2.1-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
08ef779aed40dbc52729d6ffe7dd51df85796a702afbf68a4f4e41fafdc8bda5  numpy-2.2.1-cp313-cp313t-musllinux_1_2_aarch64.whl
26c9c4382b19fcfbbed3238a14abf7ff223890ea1936b8890f058e7ba35e8d71  numpy-2.2.1-cp313-cp313t-musllinux_1_2_x86_64.whl
93cf4e045bae74c90ca833cba583c14b62cb4ba2cba0abd2b141ab52548247e2  numpy-2.2.1-cp313-cp313t-win32.whl
bff7d8ec20f5f42607599f9994770fa65d76edca264a87b5e4ea5629bce12268  numpy-2.2.1-cp313-cp313t-win_amd64.whl
7ba9cc93a91d86365a5d270dee221fdc04fb68d7478e6bf6af650de78a8339e3  numpy-2.2.1-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
3d03883435a19794e41f147612a77a8f56d4e52822337844fff3d4040a142964  numpy-2.2.1-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
4511d9e6071452b944207c8ce46ad2f897307910b402ea5fa975da32e0102800  numpy-2.2.1-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
5c5cc0cbabe9452038ed984d05ac87910f89370b9242371bd9079cb4af61811e  numpy-2.2.1-pp310-pypy310_pp73-win_amd64.whl
45681fd7128c8ad1c379f0ca0776a8b0c6583d2f69889ddac01559dfe4390918  numpy-2.2.1.tar.gz

- Python
Published by charris over 1 year ago

numpy - 2.2.0 (Dec 8, 2024)

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:

  python 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 12 from 11. 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_arm64.whl
dfacf4ee4842d845f5e12f912ebc2055  numpy-2.2.0-cp312-cp312-macosx_14_0_x86_64.whl
2f10d9bc44635078a6222ed50c9fd0f2  numpy-2.2.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
bb69665b4b68b7af82939132972e8261  numpy-2.2.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
92e9b03039ab737e6799dbd9d057fce2  numpy-2.2.0-cp312-cp312-musllinux_1_2_aarch64.whl
a4198f53e3906394012ab88dffff4077  numpy-2.2.0-cp312-cp312-musllinux_1_2_x86_64.whl
f9cb2f38ae8272ab5eb4b209c59be45f  numpy-2.2.0-cp312-cp312-win32.whl
6ffe9e1abf69bb8c4ee2e913b1badfde  numpy-2.2.0-cp312-cp312-win_amd64.whl
330838b6e06481341b35f8d4b6b50bbb  numpy-2.2.0-cp313-cp313-macosx_10_13_x86_64.whl
4fb63be6d2b92acfba0c2eaedce82e06  numpy-2.2.0-cp313-cp313-macosx_11_0_arm64.whl
e4086f43f9ce29b5adf939e1b5150f03  numpy-2.2.0-cp313-cp313-macosx_14_0_arm64.whl
ba74e1f7142954127fb2aef544a6b05e  numpy-2.2.0-cp313-cp313-macosx_14_0_x86_64.whl
83f51806139d5adc70a2e309d0300ad1  numpy-2.2.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
bf9af9abb1e30521b3491f0748ba6199  numpy-2.2.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
4d30c57bd7103b36a4bf7a4a9e25e10d  numpy-2.2.0-cp313-cp313-musllinux_1_2_aarch64.whl
5b12557dde538f99fd87d1682638a212  numpy-2.2.0-cp313-cp313-musllinux_1_2_x86_64.whl
ee7689d2ecbcbd7c883f69099bd41892  numpy-2.2.0-cp313-cp313-win32.whl
370a2b14681c6dc4af8596f62d96bb20  numpy-2.2.0-cp313-cp313-win_amd64.whl
e6fd32fb42fd89c7659e799d65c19557  numpy-2.2.0-cp313-cp313t-macosx_10_13_x86_64.whl
8e9b0579f2e22f38f57399ab18d6e651  numpy-2.2.0-cp313-cp313t-macosx_11_0_arm64.whl
71a7ee75208a633a8c36d4ed5b551c33  numpy-2.2.0-cp313-cp313t-macosx_14_0_arm64.whl
3d76dfe8c2e26dc42b97fdf086ce1fa5  numpy-2.2.0-cp313-cp313t-macosx_14_0_x86_64.whl
b70ea00b40fb7d1b5d84868d8a073afb  numpy-2.2.0-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
a6bc7090a4e2b1e9deabd0671c691129  numpy-2.2.0-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
9789193a075e8d494851fe7955a23af7  numpy-2.2.0-cp313-cp313t-musllinux_1_2_aarch64.whl
8c199d25bd806f737950428232b0dfa5  numpy-2.2.0-cp313-cp313t-musllinux_1_2_x86_64.whl
1f566cfe4880ca30a591a828634da429  numpy-2.2.0-cp313-cp313t-win32.whl
eb9ea9f8fba78e7ae5b1415de1ee5c2c  numpy-2.2.0-cp313-cp313t-win_amd64.whl
9fbe2640c73e4f391019db5af6e854f5  numpy-2.2.0-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
fc89986fda7ba19c7c50712f1fc1e342  numpy-2.2.0-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
d76c5769a3fdfb9e25b3c1a951bd021f  numpy-2.2.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
c138d726fee19ec6ba85eaa2ba9a3390  numpy-2.2.0-pp310-pypy310_pp73-win_amd64.whl
81a58d5fd26fe983162dc3cea2989b7a  numpy-2.2.0.tar.gz

SHA256

1e25507d85da11ff5066269d0bd25d06e0a0f2e908415534f3e603d2a78e4ffa  numpy-2.2.0-cp310-cp310-macosx_10_9_x86_64.whl
a62eb442011776e4036af5c8b1a00b706c5bc02dc15eb5344b0c750428c94219  numpy-2.2.0-cp310-cp310-macosx_11_0_arm64.whl
b606b1aaf802e6468c2608c65ff7ece53eae1a6874b3765f69b8ceb20c5fa78e  numpy-2.2.0-cp310-cp310-macosx_14_0_arm64.whl
36b2b43146f646642b425dd2027730f99bac962618ec2052932157e213a040e9  numpy-2.2.0-cp310-cp310-macosx_14_0_x86_64.whl
7fe8f3583e0607ad4e43a954e35c1748b553bfe9fdac8635c02058023277d1b3  numpy-2.2.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
122fd2fcfafdefc889c64ad99c228d5a1f9692c3a83f56c292618a59aa60ae83  numpy-2.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
3f2f5cddeaa4424a0a118924b988746db6ffa8565e5829b1841a8a3bd73eb59a  numpy-2.2.0-cp310-cp310-musllinux_1_2_aarch64.whl
7fe4bb0695fe986a9e4deec3b6857003b4cfe5c5e4aac0b95f6a658c14635e31  numpy-2.2.0-cp310-cp310-musllinux_1_2_x86_64.whl
b30042fe92dbd79f1ba7f6898fada10bdaad1847c44f2dff9a16147e00a93661  numpy-2.2.0-cp310-cp310-win32.whl
54dc1d6d66f8d37843ed281773c7174f03bf7ad826523f73435deb88ba60d2d4  numpy-2.2.0-cp310-cp310-win_amd64.whl
9874bc2ff574c40ab7a5cbb7464bf9b045d617e36754a7bc93f933d52bd9ffc6  numpy-2.2.0-cp311-cp311-macosx_10_9_x86_64.whl
0da8495970f6b101ddd0c38ace92edea30e7e12b9a926b57f5fabb1ecc25bb90  numpy-2.2.0-cp311-cp311-macosx_11_0_arm64.whl
0557eebc699c1c34cccdd8c3778c9294e8196df27d713706895edc6f57d29608  numpy-2.2.0-cp311-cp311-macosx_14_0_arm64.whl
3579eaeb5e07f3ded59298ce22b65f877a86ba8e9fe701f5576c99bb17c283da  numpy-2.2.0-cp311-cp311-macosx_14_0_x86_64.whl
40deb10198bbaa531509aad0cd2f9fadb26c8b94070831e2208e7df543562b74  numpy-2.2.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
c2aed8fcf8abc3020d6a9ccb31dbc9e7d7819c56a348cc88fd44be269b37427e  numpy-2.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
a222d764352c773aa5ebde02dd84dba3279c81c6db2e482d62a3fa54e5ece69b  numpy-2.2.0-cp311-cp311-musllinux_1_2_aarch64.whl
4e58666988605e251d42c2818c7d3d8991555381be26399303053b58a5bbf30d  numpy-2.2.0-cp311-cp311-musllinux_1_2_x86_64.whl
4723a50e1523e1de4fccd1b9a6dcea750c2102461e9a02b2ac55ffeae09a4410  numpy-2.2.0-cp311-cp311-win32.whl
16757cf28621e43e252c560d25b15f18a2f11da94fea344bf26c599b9cf54b73  numpy-2.2.0-cp311-cp311-win_amd64.whl
cff210198bb4cae3f3c100444c5eaa573a823f05c253e7188e1362a5555235b3  numpy-2.2.0-cp312-cp312-macosx_10_13_x86_64.whl
58b92a5828bd4d9aa0952492b7de803135038de47343b2aa3cc23f3b71a3dc4e  numpy-2.2.0-cp312-cp312-macosx_11_0_arm64.whl
ebe5e59545401fbb1b24da76f006ab19734ae71e703cdb4a8b347e84a0cece67  numpy-2.2.0-cp312-cp312-macosx_14_0_arm64.whl
e2b8cd48a9942ed3f85b95ca4105c45758438c7ed28fff1e4ce3e57c3b589d8e  numpy-2.2.0-cp312-cp312-macosx_14_0_x86_64.whl
57fcc997ffc0bef234b8875a54d4058afa92b0b0c4223fc1f62f24b3b5e86038  numpy-2.2.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
85ad7d11b309bd132d74397fcf2920933c9d1dc865487128f5c03d580f2c3d03  numpy-2.2.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
cb24cca1968b21355cc6f3da1a20cd1cebd8a023e3c5b09b432444617949085a  numpy-2.2.0-cp312-cp312-musllinux_1_2_aarch64.whl
0798b138c291d792f8ea40fe3768610f3c7dd2574389e37c3f26573757c8f7ef  numpy-2.2.0-cp312-cp312-musllinux_1_2_x86_64.whl
afe8fb968743d40435c3827632fd36c5fbde633b0423da7692e426529b1759b1  numpy-2.2.0-cp312-cp312-win32.whl
3a4199f519e57d517ebd48cb76b36c82da0360781c6a0353e64c0cac30ecaad3  numpy-2.2.0-cp312-cp312-win_amd64.whl
f8c8b141ef9699ae777c6278b52c706b653bf15d135d302754f6b2e90eb30367  numpy-2.2.0-cp313-cp313-macosx_10_13_x86_64.whl
0f0986e917aca18f7a567b812ef7ca9391288e2acb7a4308aa9d265bd724bdae  numpy-2.2.0-cp313-cp313-macosx_11_0_arm64.whl
1c92113619f7b272838b8d6702a7f8ebe5edea0df48166c47929611d0b4dea69  numpy-2.2.0-cp313-cp313-macosx_14_0_arm64.whl
5a145e956b374e72ad1dff82779177d4a3c62bc8248f41b80cb5122e68f22d13  numpy-2.2.0-cp313-cp313-macosx_14_0_x86_64.whl
18142b497d70a34b01642b9feabb70156311b326fdddd875a9981f34a369b671  numpy-2.2.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
a7d41d1612c1a82b64697e894b75db6758d4f21c3ec069d841e60ebe54b5b571  numpy-2.2.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
a98f6f20465e7618c83252c02041517bd2f7ea29be5378f09667a8f654a5918d  numpy-2.2.0-cp313-cp313-musllinux_1_2_aarch64.whl
e09d40edfdb4e260cb1567d8ae770ccf3b8b7e9f0d9b5c2a9992696b30ce2742  numpy-2.2.0-cp313-cp313-musllinux_1_2_x86_64.whl
3905a5fffcc23e597ee4d9fb3fcd209bd658c352657548db7316e810ca80458e  numpy-2.2.0-cp313-cp313-win32.whl
a184288538e6ad699cbe6b24859206e38ce5fba28f3bcfa51c90d0502c1582b2  numpy-2.2.0-cp313-cp313-win_amd64.whl
7832f9e8eb00be32f15fdfb9a981d6955ea9adc8574c521d48710171b6c55e95  numpy-2.2.0-cp313-cp313t-macosx_10_13_x86_64.whl
f0dd071b95bbca244f4cb7f70b77d2ff3aaaba7fa16dc41f58d14854a6204e6c  numpy-2.2.0-cp313-cp313t-macosx_11_0_arm64.whl
b0b227dcff8cdc3efbce66d4e50891f04d0a387cce282fe1e66199146a6a8fca  numpy-2.2.0-cp313-cp313t-macosx_14_0_arm64.whl
6ab153263a7c5ccaf6dfe7e53447b74f77789f28ecb278c3b5d49db7ece10d6d  numpy-2.2.0-cp313-cp313t-macosx_14_0_x86_64.whl
e500aba968a48e9019e42c0c199b7ec0696a97fa69037bea163b55398e390529  numpy-2.2.0-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
440cfb3db4c5029775803794f8638fbdbf71ec702caf32735f53b008e1eaece3  numpy-2.2.0-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
a55dc7a7f0b6198b07ec0cd445fbb98b05234e8b00c5ac4874a63372ba98d4ab  numpy-2.2.0-cp313-cp313t-musllinux_1_2_aarch64.whl
4bddbaa30d78c86329b26bd6aaaea06b1e47444da99eddac7bf1e2fab717bd72  numpy-2.2.0-cp313-cp313t-musllinux_1_2_x86_64.whl
30bf971c12e4365153afb31fc73f441d4da157153f3400b82db32d04de1e4066  numpy-2.2.0-cp313-cp313t-win32.whl
d35717333b39d1b6bb8433fa758a55f1081543de527171543a2b710551d40881  numpy-2.2.0-cp313-cp313t-win_amd64.whl
e12c6c1ce84628c52d6367863773f7c8c8241be554e8b79686e91a43f1733773  numpy-2.2.0-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
b6207dc8fb3c8cb5668e885cef9ec7f70189bec4e276f0ff70d5aa078d32c88e  numpy-2.2.0-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
a50aeff71d0f97b6450d33940c7181b08be1441c6c193e678211bff11aa725e7  numpy-2.2.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
df12a1f99b99f569a7c2ae59aa2d31724e8d835fc7f33e14f4792e3071d11221  numpy-2.2.0-pp310-pypy310_pp73-win_amd64.whl
140dd80ff8981a583a60980be1a655068f8adebf7a45a06a6858c873fcdcd4a0  numpy-2.2.0.tar.gz

- Python
Published by charris over 1 year ago

numpy - 2.2.0rc1 (Nov 26, 2024)

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:

  python 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 12 from 11. 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
1a00dd2343f8ec48350b39f72e2c4fa1  numpy-2.2.0rc1-cp312-cp312-macosx_14_0_x86_64.whl
cbe9b6d14530bdfb75ef61f4328f6b9e  numpy-2.2.0rc1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
a4f14055b4cfafab7035f35e61c6cebb  numpy-2.2.0rc1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
8c3c80295b92ae839fcb1fc2ab2edf0e  numpy-2.2.0rc1-cp312-cp312-musllinux_1_2_aarch64.whl
1a5aac9894d1959e1cbbcf58e3aa98d1  numpy-2.2.0rc1-cp312-cp312-musllinux_1_2_x86_64.whl
03577c58315ae4b28c3111be0af0c18a  numpy-2.2.0rc1-cp312-cp312-win32.whl
c8ed06acb7e1b885081e682a391524d8  numpy-2.2.0rc1-cp312-cp312-win_amd64.whl
53955ed28cb43f004ccd9f2f1e07b0d4  numpy-2.2.0rc1-cp313-cp313-macosx_10_13_x86_64.whl
dffe0e20843d5e331358206b535c47f7  numpy-2.2.0rc1-cp313-cp313-macosx_11_0_arm64.whl
1f22dc1bc3dd3bf645a35a8c58e07ac3  numpy-2.2.0rc1-cp313-cp313-macosx_14_0_arm64.whl
57bb0a9d61444162269751eb861bef75  numpy-2.2.0rc1-cp313-cp313-macosx_14_0_x86_64.whl
b38fd53f8f162a833b89e32b52d6f0b5  numpy-2.2.0rc1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
f8975385402dfa988efe0121adcb3b83  numpy-2.2.0rc1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
8b739c89e3c67210467ac0855623da47  numpy-2.2.0rc1-cp313-cp313-musllinux_1_2_aarch64.whl
902e1f704a187a85f02f71877ed69baf  numpy-2.2.0rc1-cp313-cp313-musllinux_1_2_x86_64.whl
fc33a9a4c895b2463672d01e75431a8f  numpy-2.2.0rc1-cp313-cp313-win32.whl
f57eb3377cf0acf5ce165034e5d3d061  numpy-2.2.0rc1-cp313-cp313-win_amd64.whl
4dff6567391c376daf27f2a144a4142d  numpy-2.2.0rc1-cp313-cp313t-macosx_10_13_x86_64.whl
5195eeac3d355592ec97db04cea7fb43  numpy-2.2.0rc1-cp313-cp313t-macosx_11_0_arm64.whl
9a5e6fb707b1bc448d6f5eb226757581  numpy-2.2.0rc1-cp313-cp313t-macosx_14_0_arm64.whl
455ef245987926bb966565de0f68d00f  numpy-2.2.0rc1-cp313-cp313t-macosx_14_0_x86_64.whl
f10882cf7238a03896903b337bce2b05  numpy-2.2.0rc1-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
8889da4b211ca3edba34518306115a81  numpy-2.2.0rc1-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
1d29f0a150c39b500b4f0b1e4c625e9b  numpy-2.2.0rc1-cp313-cp313t-musllinux_1_2_aarch64.whl
dcf499ab9d350e3414368a106c714256  numpy-2.2.0rc1-cp313-cp313t-musllinux_1_2_x86_64.whl
af48c02a9130ad93e93a55ebf87b5c78  numpy-2.2.0rc1-cp313-cp313t-win32.whl
290c12deaff6df2e54569563a8f1316a  numpy-2.2.0rc1-cp313-cp313t-win_amd64.whl
fce62da0e31ae09237cf241c77e54498  numpy-2.2.0rc1-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
85acaaaa495d92bc52631a6a0654fd8e  numpy-2.2.0rc1-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
cb0482e5c60d706b9b0e9ce8dac9d8a6  numpy-2.2.0rc1-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
67390891e461b1983aadab51bc96a78b  numpy-2.2.0rc1-pp310-pypy310_pp73-win_amd64.whl
4836fdb3009f043287f011b5f6d18208  numpy-2.2.0rc1.tar.gz

SHA256

acd4f4e9f8c3c04c9a695333d4f475ec2f7a577342b469b411f7ffb2a2888fdc  numpy-2.2.0rc1-cp310-cp310-macosx_10_9_x86_64.whl
8c3cd769a38a363fe21077ad137ee43be639464e5f257821a4cc4d4e2016deea  numpy-2.2.0rc1-cp310-cp310-macosx_11_0_arm64.whl
72fa15a5f801faf598e6633a6efcb5661085f509f8f6631a0c2c86be06631b78  numpy-2.2.0rc1-cp310-cp310-macosx_14_0_arm64.whl
44d55304a7397d6e89707af99ea8e980a101a7ff01dd768aaaca16b2312c799b  numpy-2.2.0rc1-cp310-cp310-macosx_14_0_x86_64.whl
8a25595d5951ad46bec827dfee09328b8da041fc3f7f13f63880274ed4ec215e  numpy-2.2.0rc1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
c335bd4e3395b8209a011b97e5f9876092fb2dc283933d39620a30c1fa82dfab  numpy-2.2.0rc1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
5ac124ab756ad56a14cdfcdc69cc220befbfb1162fdf3ca4f6eb1a0ace634c56  numpy-2.2.0rc1-cp310-cp310-musllinux_1_2_aarch64.whl
2f7861ff2b862e2536f2256acf5dcf1909e927a5f5e940dfd488eecd178a96b6  numpy-2.2.0rc1-cp310-cp310-musllinux_1_2_x86_64.whl
e2d4b5a37cf5df43ffdabe0ebea150d5ec0a1796ad7122b3a780f1ab646708c8  numpy-2.2.0rc1-cp310-cp310-win32.whl
7a3261b3b7d1403a65112dbad568eee7de596cebd0267e27e7daaa9e08dd396a  numpy-2.2.0rc1-cp310-cp310-win_amd64.whl
61915861927b8e20223b7ccbe40ebf3f52220c0fca43be8423087348c7c00418  numpy-2.2.0rc1-cp311-cp311-macosx_10_9_x86_64.whl
8815f7e6d48dbcf4f14704d79b90c8fee1a68a42886d42e9c8209092e684bd99  numpy-2.2.0rc1-cp311-cp311-macosx_11_0_arm64.whl
3e80348e6d187573dc2bb6b1d862fc32353db371ae063d25b2199f65adc96ff1  numpy-2.2.0rc1-cp311-cp311-macosx_14_0_arm64.whl
8fb79fe9bfefb2b43f701090f70413fb535f10bfdfab1981b7c02bd406cc39dd  numpy-2.2.0rc1-cp311-cp311-macosx_14_0_x86_64.whl
042b6a87c48307955049b338981ff9278fa5e7ff3166bbd0d3294f40726d22d5  numpy-2.2.0rc1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
94251286fd3cec5552f217030af4cae68f7a1db4f1791765e597b6d9c0a7647a  numpy-2.2.0rc1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
ffaa01305af250d733d9940c694d206a0c7d1ea2bd5a01bcb5ff7e48c3e6adac  numpy-2.2.0rc1-cp311-cp311-musllinux_1_2_aarch64.whl
37e6413ed8f66df534631058771ca362939e243da725b5e8537d8c64b664e9b2  numpy-2.2.0rc1-cp311-cp311-musllinux_1_2_x86_64.whl
7bd86cdae85da5fa8763fbe9acfdb4748e1f10bef5e6524bffdfdd2b21bfd56f  numpy-2.2.0rc1-cp311-cp311-win32.whl
27f2593fe479dff6f4398563ca2fbf7a416fd8d3a8ad7a35fecbc8ba959000ab  numpy-2.2.0rc1-cp311-cp311-win_amd64.whl
f721298f4c39b4619b16ba0d341ff5e043d4123dfb796bd84835538bf8abad2b  numpy-2.2.0rc1-cp312-cp312-macosx_10_13_x86_64.whl
aed72fe759ada921342b4a8ae0893cc7778b07d2f36a78445c70d5ea633c3b25  numpy-2.2.0rc1-cp312-cp312-macosx_11_0_arm64.whl
c940b9623e29db06b7d0d3c93c560d42bbd73a76f6d27c41d3fd09c0a15f7773  numpy-2.2.0rc1-cp312-cp312-macosx_14_0_arm64.whl
a783f561c34be98eb25f8cce029b63434d2dfe79702a1d53e9a0fd63c0391dc8  numpy-2.2.0rc1-cp312-cp312-macosx_14_0_x86_64.whl
d0db426baa0d9547d9ac3ea08110e9bba400fab7a036235d9baddf61fd931af8  numpy-2.2.0rc1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
7925618745531971be54a87e0b85dfe83c69dac9dfd8e46c8aaae520af05792b  numpy-2.2.0rc1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
5d7a819d4d31bf9998c907105d97a082919b659ff8d44cef2c4f78d0ac16af47  numpy-2.2.0rc1-cp312-cp312-musllinux_1_2_aarch64.whl
0b6cb83ab76b101b87211ab6227e010789adf4a98ee4af07a2480d1d2f61d195  numpy-2.2.0rc1-cp312-cp312-musllinux_1_2_x86_64.whl
dc86f8502db8dfbe3474a34395e453849d03f0717227f7bda57a235cbbee3575  numpy-2.2.0rc1-cp312-cp312-win32.whl
a87c1a4d808de26157440153bb9c51d7dc4778c6cd730026406298b75fa5c2df  numpy-2.2.0rc1-cp312-cp312-win_amd64.whl
c2ef440fc343cc11e8e1591bf77b0f4f21b0684feabdf7b3ec3d768b8cce7a05  numpy-2.2.0rc1-cp313-cp313-macosx_10_13_x86_64.whl
4332ddb4f40e85f6cdf1594279b35e847a20054c3269f7f2e848b6075cb8f4b3  numpy-2.2.0rc1-cp313-cp313-macosx_11_0_arm64.whl
dc532dd1c767864614f383cad63edf864f78df3533b6444d94af099583c8fb39  numpy-2.2.0rc1-cp313-cp313-macosx_14_0_arm64.whl
ecc601c633667ea5eed0c16f987e4c715ee951d0bfa3658f76b690e8dceaddfd  numpy-2.2.0rc1-cp313-cp313-macosx_14_0_x86_64.whl
38405f26748e7ed4c7b31e5f8c24f385e1daf4954628f6143f5a09047e220ca9  numpy-2.2.0rc1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
e515a7d5f5e1b32eb9e761de4f0327aceee27ec07cc655d26424a5e86d3c8d0d  numpy-2.2.0rc1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
fd3981aa01428eef69fe5ff2e97e3ca8e65e677ffacc7c447e164ae2aaf521fb  numpy-2.2.0rc1-cp313-cp313-musllinux_1_2_aarch64.whl
61a04f035bd4f87d6c0592eaa06061f9f16bf0e11d546e3b9252ccf83f0917a6  numpy-2.2.0rc1-cp313-cp313-musllinux_1_2_x86_64.whl
1b18bf71975be1728042ba232d7406ae2f6fed8431684851fda4b909ab6e20ce  numpy-2.2.0rc1-cp313-cp313-win32.whl
5776d7b395dcf180bc807a9374aca05b6569e5e5e4bdcbf112aa452a471405e0  numpy-2.2.0rc1-cp313-cp313-win_amd64.whl
3f0d900e60e783fa9965729fa2a17021add82d769bf298cdb407abcbbf316e28  numpy-2.2.0rc1-cp313-cp313t-macosx_10_13_x86_64.whl
def9537da892cd995f81646df94021fbf0dce690d518daaabc0902bc8ce42cd9  numpy-2.2.0rc1-cp313-cp313t-macosx_11_0_arm64.whl
f2b59a4e85367107dced5b3c7374a5e828ddb7c5c4e1d98176d09b177e23edd0  numpy-2.2.0rc1-cp313-cp313t-macosx_14_0_arm64.whl
9c3bdfe13209bf4f81aea5f8dd2843ab17c9a9273133d491c220636bfd51432d  numpy-2.2.0rc1-cp313-cp313t-macosx_14_0_x86_64.whl
b0b742731c2721445a03e469f286c9ddf15dd80e52622ea4487ddc10a7869fe9  numpy-2.2.0rc1-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
8c43d7beaab6509f1467175cc7cfdcc048581b91ba55e149cc39af758209b166  numpy-2.2.0rc1-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
598b88170e0f361d2f6d8cc9ec18d798af07a2e9b30b95ba2d76415b7c3cc433  numpy-2.2.0rc1-cp313-cp313t-musllinux_1_2_aarch64.whl
ddb4720b057048d7ac3ce973256e89e1e7481f71b5a214a0a3be936aeda014e7  numpy-2.2.0rc1-cp313-cp313t-musllinux_1_2_x86_64.whl
64b994b9054ab051d137fff61bb6244aa1e7a80defa42c507355b562cc44a561  numpy-2.2.0rc1-cp313-cp313t-win32.whl
67d2f5c34f231e7ed59189c20f8b7472b77cff85277bcd80537417eee61977db  numpy-2.2.0rc1-cp313-cp313t-win_amd64.whl
d4bbc95647ce01252827d4c6ea5de42460ea66d75831333f2b92f088b60e1b43  numpy-2.2.0rc1-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
d8d13dd7b6f1f14c43ff68e81c8edcb035f572d87507b5f629e78a7d8c61e9f4  numpy-2.2.0rc1-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
d12bf735dc4e7dfa8c66b2fd47547bcf91c9996585324959e2c5a2f5360e1c8f  numpy-2.2.0rc1-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
8d7de626a5e554b074890258e63d0b06eff2af48da034fe5ffef8743578b1e0b  numpy-2.2.0rc1-pp310-pypy310_pp73-win_amd64.whl
d3c343e027351fbb3f7ddb0024857cd10837d6a77b40b33e39ff6706ed7ceec1  numpy-2.2.0rc1.tar.gz

- Python
Published by charris over 1 year ago

numpy - 2.1.3 (Nov 2, 2024)

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 npyfinddescrforscalar.
• #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

- Python
Published by charris over 1 year ago

numpy - 2.1.2 (Oct 5, 2024)

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 getbuildmsvc_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_aarch64.manylinux2014_aarch64.whl
6d95f286b8244b3649b477ac066c6906fbb2905f8ac19b170e2175d3d799f4df  numpy-2.1.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
ab4754d432e3ac42d33a269c8567413bdb541689b02d93788af4131018cbf366  numpy-2.1.2-cp312-cp312-musllinux_1_1_x86_64.whl
e585c8ae871fd38ac50598f4763d73ec5497b0de9a0ab4ef5b69f01c6a046142  numpy-2.1.2-cp312-cp312-musllinux_1_2_aarch64.whl
9c6c754df29ce6a89ed23afb25550d1c2d5fdb9901d9c67a16e0b16eaf7e2550  numpy-2.1.2-cp312-cp312-win32.whl
456e3b11cb79ac9946c822a56346ec80275eaf2950314b249b512896c0d2505e  numpy-2.1.2-cp312-cp312-win_amd64.whl
a84498e0d0a1174f2b3ed769b67b656aa5460c92c9554039e11f20a05650f00d  numpy-2.1.2-cp313-cp313-macosx_10_13_x86_64.whl
4d6ec0d4222e8ffdab1744da2560f07856421b367928026fb540e1945f2eeeaf  numpy-2.1.2-cp313-cp313-macosx_11_0_arm64.whl
259ec80d54999cc34cd1eb8ded513cb053c3bf4829152a2e00de2371bd406f5e  numpy-2.1.2-cp313-cp313-macosx_14_0_arm64.whl
675c741d4739af2dc20cd6c6a5c4b7355c728167845e3c6b0e824e4e5d36a6c3  numpy-2.1.2-cp313-cp313-macosx_14_0_x86_64.whl
05b2d4e667895cc55e3ff2b56077e4c8a5604361fc21a042845ea3ad67465aa8  numpy-2.1.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
43cca367bf94a14aca50b89e9bc2061683116cfe864e56740e083392f533ce7a  numpy-2.1.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
76322dcdb16fccf2ac56f99048af32259dcc488d9b7e25b51e5eca5147a3fb98  numpy-2.1.2-cp313-cp313-musllinux_1_1_x86_64.whl
32e16a03138cabe0cb28e1007ee82264296ac0983714094380b408097a418cfe  numpy-2.1.2-cp313-cp313-musllinux_1_2_aarch64.whl
242b39d00e4944431a3cd2db2f5377e15b5785920421993770cddb89992c3f3a  numpy-2.1.2-cp313-cp313-win32.whl
f2ded8d9b6f68cc26f8425eda5d3877b47343e68ca23d0d0846f4d312ecaa445  numpy-2.1.2-cp313-cp313-win_amd64.whl
2ffef621c14ebb0188a8633348504a35c13680d6da93ab5cb86f4e54b7e922b5  numpy-2.1.2-cp313-cp313t-macosx_10_13_x86_64.whl
ad369ed238b1959dfbade9018a740fb9392c5ac4f9b5173f420bd4f37ba1f7a0  numpy-2.1.2-cp313-cp313t-macosx_11_0_arm64.whl
d82075752f40c0ddf57e6e02673a17f6cb0f8eb3f587f63ca1eaab5594da5b17  numpy-2.1.2-cp313-cp313t-macosx_14_0_arm64.whl
1600068c262af1ca9580a527d43dc9d959b0b1d8e56f8a05d830eea39b7c8af6  numpy-2.1.2-cp313-cp313t-macosx_14_0_x86_64.whl
a26ae94658d3ba3781d5e103ac07a876b3e9b29db53f68ed7df432fd033358a8  numpy-2.1.2-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
13311c2db4c5f7609b462bc0f43d3c465424d25c626d95040f073e30f7570e35  numpy-2.1.2-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
2abbf905a0b568706391ec6fa15161fad0fb5d8b68d73c461b3c1bab6064dd62  numpy-2.1.2-cp313-cp313t-musllinux_1_1_x86_64.whl
ef444c57d664d35cac4e18c298c47d7b504c66b17c2ea91312e979fcfbdfb08a  numpy-2.1.2-cp313-cp313t-musllinux_1_2_aarch64.whl
bdd407c40483463898b84490770199d5714dcc9dd9b792f6c6caccc523c00952  numpy-2.1.2-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
da65fb46d4cbb75cb417cddf6ba5e7582eb7bb0b47db4b99c9fe5787ce5d91f5  numpy-2.1.2-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
1c193d0b0238638e6fc5f10f1b074a6993cb13b0b431f64079a509d63d3aa8b7  numpy-2.1.2-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
a7d80b2e904faa63068ead63107189164ca443b42dd1930299e0d1cb041cec2e  numpy-2.1.2-pp310-pypy310_pp73-win_amd64.whl
13532a088217fa624c99b843eeb54640de23b3414b14aa66d023805eb731066c  numpy-2.1.2.tar.gz

- Python
Published by charris over 1 year ago

numpy - 2.1.1 (Sep 3, 2024)

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

- Python
Published by charris over 1 year ago

numpy - NumPy 2.0.2 release (Aug 26, 2024)

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 arrayassignsubscript...
• #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 publicdtypeapi.c
• #27129: BUG: fix another cast setup in arrayassignsubscript
• #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 NPYRAVELAXIS 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

- Python
Published by charris almost 2 years ago

numpy - 2.1.0 (Aug 18, 2024)

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 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)

Type hinting numpy.polynomial

Starting from the 2.1 release, PEP 484 type annotations have been included for the functions and convenience classes in numpy.polynomial and its sub-packages.

(gh-26897)

Improved numpy.dtypes type hints

The type annotations for numpy.dtypes are now a better reflection of the runtime: The numpy.dtype type-aliases have been replaced with specialized dtype subtypes, and the previously missing annotations for numpy.dtypes.StringDType have been added.

(gh-27008)

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 significantly reduces the wall time, but will return slightly different estimates of the correlation coefficients in cases where the observations between a pair of variables are not aligned. However, it will return the same estimates in all other cases, including returning the same correlation matrix as corrcoef when using a masked array with no masked values.

(gh-26285)

Cast-safety fixes in copyto and full

copyto now uses NEP 50 correctly and applies this to its cast safety. Python integer to NumPy integer casts and Python float to NumPy float casts are now considered \"safe\" even if assignment may fail or precision may be lost. This means the following examples change slightly:

• np.copyto(int8_arr, 1000) previously performed an unsafe/same-kind cast of the Python integer. It will now always raise, to achieve an unsafe cast you must pass an array or NumPy scalar.

• np.copyto(uint8_arr, 1000, casting="safe") will raise an OverflowError rather than a TypeError due to same-kind casting.

• np.copyto(float32_arr, 1e300, casting="safe") will overflow to inf (float32 cannot hold 1e300) rather raising a TypeError.

Further, only the dtype is used when assigning NumPy scalars (or 0-d arrays), meaning that the following behaves differently:

• np.copyto(float32_arr, np.float64(3.0), casting="safe") raises.
• np.coptyo(int8_arr, np.int64(100), casting="safe") raises. Previously, NumPy checked whether the 100 fits the int8_arr.

This aligns copyto, full, and full_like with the correct NumPy 2 behavior.

(gh-27091)

Checksums

MD5

2323404663c0b2a86362319d7526eb80  numpy-2.1.0-cp310-cp310-macosx_10_9_x86_64.whl
3d4bca8d05eb1eba859e77ff8f91d843  numpy-2.1.0-cp310-cp310-macosx_11_0_arm64.whl
9bd065f147dbf3f2d59ab57bff4f0074  numpy-2.1.0-cp310-cp310-macosx_14_0_arm64.whl
47d177533511901cd6bf77f72cbd3d6e  numpy-2.1.0-cp310-cp310-macosx_14_0_x86_64.whl
530b7f38f64216f1322b39bc50f36c0c  numpy-2.1.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
d2a3161a10811a675a29a63e25636d83  numpy-2.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
4e9fb20b080f7931791da71708740b83  numpy-2.1.0-cp310-cp310-musllinux_1_1_x86_64.whl
776eb610795d63217980a36eb23bf268  numpy-2.1.0-cp310-cp310-musllinux_1_2_aarch64.whl
8328b9e2afa4013aaf3e4963349445e2  numpy-2.1.0-cp310-cp310-win32.whl
e3184b9979192c8d7b80deb2af16d6bb  numpy-2.1.0-cp310-cp310-win_amd64.whl
54571aef9d9081e35bebef10f8d64e75  numpy-2.1.0-cp311-cp311-macosx_10_9_x86_64.whl
841dac2386c1da870a384b64cd31e32b  numpy-2.1.0-cp311-cp311-macosx_14_0_arm64.whl
0fe85239ebe336d2baaddcb0ed001dc7  numpy-2.1.0-cp311-cp311-macosx_14_0_x86_64.whl
772a55a6c46f7b643af4640c2ca68d70  numpy-2.1.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
64fefbc527229521cf2a516b778b8aa7  numpy-2.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
5cdb3d262d8c513b0f08cd1b6ba48512  numpy-2.1.0-cp311-cp311-musllinux_1_1_x86_64.whl
16140f5de42e87d84b80c350fd014893  numpy-2.1.0-cp311-cp311-musllinux_1_2_aarch64.whl
5e37df534d167af1966e099e0be9d94a  numpy-2.1.0-cp311-cp311-win32.whl
ee443aa000621bed8bb2d6a94afd89b5  numpy-2.1.0-cp311-cp311-win_amd64.whl
d8c911fc34a8dad4ed821036563b5758  numpy-2.1.0-cp312-cp312-macosx_10_9_x86_64.whl
ec25d637c43ae8229052e62a4f40f2d2  numpy-2.1.0-cp312-cp312-macosx_11_0_arm64.whl
67c7abca3d0339f17a8543abc0e7bf11  numpy-2.1.0-cp312-cp312-macosx_14_0_arm64.whl
0d36ec6a64cbef1d727eb608a236ad2c  numpy-2.1.0-cp312-cp312-macosx_14_0_x86_64.whl
0eedab574a3b75ec237be910e9717153  numpy-2.1.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
73dd2a5d0c85007bf5fdb4b7f66b8451  numpy-2.1.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
94fb0cfbc647a34177c766570fad752b  numpy-2.1.0-cp312-cp312-musllinux_1_1_x86_64.whl
de3efbbcd792a1f82d0e3e175ea02ca9  numpy-2.1.0-cp312-cp312-musllinux_1_2_aarch64.whl
9a63ebbfb3c4c6eba77ef0723a5dc86f  numpy-2.1.0-cp312-cp312-win32.whl
c68bc27545ac68c54935a1d0278b18f6  numpy-2.1.0-cp312-cp312-win_amd64.whl
f2795bb974af42e2723e32af9b14b66d  numpy-2.1.0-cp313-cp313-macosx_10_13_x86_64.whl
2f7426b06a332ea7a20159f3c06d67d1  numpy-2.1.0-cp313-cp313-macosx_11_0_arm64.whl
fcef18e031fc8588227023bac55d9636  numpy-2.1.0-cp313-cp313-macosx_14_0_arm64.whl
cbb5ca4dc798ea397344c93a2549e73e  numpy-2.1.0-cp313-cp313-macosx_14_0_x86_64.whl
573213bea3a67452a310355adc7c6aa1  numpy-2.1.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
24f8c8a1235aeaedb8f154a984b3c78b  numpy-2.1.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
b2ef762c0ebb02b58a339c1e38f032b2  numpy-2.1.0-cp313-cp313-musllinux_1_1_x86_64.whl
50e68cbfeb330aff607969c30251632d  numpy-2.1.0-cp313-cp313-musllinux_1_2_aarch64.whl
21228342cd1b4ff8c7ec1aea45c07186  numpy-2.1.0-cp313-cp313-win32.whl
8d234b05f0c4faf7b9884a1f0f19c23d  numpy-2.1.0-cp313-cp313-win_amd64.whl
e0c19ca29fa8e8e051107cd36b978f05  numpy-2.1.0-cp313-cp313t-macosx_10_13_x86_64.whl
98756f2ff9adc2cf374c28db77e28312  numpy-2.1.0-cp313-cp313t-macosx_11_0_arm64.whl
69786349d1f392dc6ac3fe00271e941b  numpy-2.1.0-cp313-cp313t-macosx_14_0_arm64.whl
4d1481bcb17aaebfc785e005455da223  numpy-2.1.0-cp313-cp313t-macosx_14_0_x86_64.whl
1d403eda14369ab023d5ae1c15dce25c  numpy-2.1.0-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
cdeece2cd6508eeee5a4c3150b58ec59  numpy-2.1.0-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
85347b754d8324c508f7aeb7de243feb  numpy-2.1.0-cp313-cp313t-musllinux_1_1_x86_64.whl
6ff18d36d0940de6c1cc962a61b44bd5  numpy-2.1.0-cp313-cp313t-musllinux_1_2_aarch64.whl
2f7d60a99c236a8f909bd86b8ed1e3a4  numpy-2.1.0-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
dc610133d9f09e5b3d396859e75c5593  numpy-2.1.0-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
6a2883ee5b16ab5c031037cc63c20e9b  numpy-2.1.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
c914ba2fe3fcdcd04c8fe6a8374ea5fb  numpy-2.1.0-pp310-pypy310_pp73-win_amd64.whl
4cb2230ffa1cc41329ae29bd69ee08de  numpy-2.1.0.tar.gz

SHA256

6326ab99b52fafdcdeccf602d6286191a79fe2fda0ae90573c5814cd2b0bc1b8  numpy-2.1.0-cp310-cp310-macosx_10_9_x86_64.whl
0937e54c09f7a9a68da6889362ddd2ff584c02d015ec92672c099b61555f8911  numpy-2.1.0-cp310-cp310-macosx_11_0_arm64.whl
30014b234f07b5fec20f4146f69e13cfb1e33ee9a18a1879a0142fbb00d47673  numpy-2.1.0-cp310-cp310-macosx_14_0_arm64.whl
899da829b362ade41e1e7eccad2cf274035e1cb36ba73034946fccd4afd8606b  numpy-2.1.0-cp310-cp310-macosx_14_0_x86_64.whl
08801848a40aea24ce16c2ecde3b756f9ad756586fb2d13210939eb69b023f5b  numpy-2.1.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
398049e237d1aae53d82a416dade04defed1a47f87d18d5bd615b6e7d7e41d1f  numpy-2.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
0abb3916a35d9090088a748636b2c06dc9a6542f99cd476979fb156a18192b84  numpy-2.1.0-cp310-cp310-musllinux_1_1_x86_64.whl
10e2350aea18d04832319aac0f887d5fcec1b36abd485d14f173e3e900b83e33  numpy-2.1.0-cp310-cp310-musllinux_1_2_aarch64.whl
f6b26e6c3b98adb648243670fddc8cab6ae17473f9dc58c51574af3e64d61211  numpy-2.1.0-cp310-cp310-win32.whl
f505264735ee074250a9c78247ee8618292091d9d1fcc023290e9ac67e8f1afa  numpy-2.1.0-cp310-cp310-win_amd64.whl
76368c788ccb4f4782cf9c842b316140142b4cbf22ff8db82724e82fe1205dce  numpy-2.1.0-cp311-cp311-macosx_10_9_x86_64.whl
f8e93a01a35be08d31ae33021e5268f157a2d60ebd643cfc15de6ab8e4722eb1  numpy-2.1.0-cp311-cp311-macosx_14_0_arm64.whl
9523f8b46485db6939bd069b28b642fec86c30909cea90ef550373787f79530e  numpy-2.1.0-cp311-cp311-macosx_14_0_x86_64.whl
54139e0eb219f52f60656d163cbe67c31ede51d13236c950145473504fa208cb  numpy-2.1.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
f5ebbf9fbdabed208d4ecd2e1dfd2c0741af2f876e7ae522c2537d404ca895c3  numpy-2.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
378cb4f24c7d93066ee4103204f73ed046eb88f9ad5bb2275bb9fa0f6a02bd36  numpy-2.1.0-cp311-cp311-musllinux_1_1_x86_64.whl
d8f699a709120b220dfe173f79c73cb2a2cab2c0b88dd59d7b49407d032b8ebd  numpy-2.1.0-cp311-cp311-musllinux_1_2_aarch64.whl
ffbd6faeb190aaf2b5e9024bac9622d2ee549b7ec89ef3a9373fa35313d44e0e  numpy-2.1.0-cp311-cp311-win32.whl
0af3a5987f59d9c529c022c8c2a64805b339b7ef506509fba7d0556649b9714b  numpy-2.1.0-cp311-cp311-win_amd64.whl
fe76d75b345dc045acdbc006adcb197cc680754afd6c259de60d358d60c93736  numpy-2.1.0-cp312-cp312-macosx_10_9_x86_64.whl
f358ea9e47eb3c2d6eba121ab512dfff38a88db719c38d1e67349af210bc7529  numpy-2.1.0-cp312-cp312-macosx_11_0_arm64.whl
dd94ce596bda40a9618324547cfaaf6650b1a24f5390350142499aa4e34e53d1  numpy-2.1.0-cp312-cp312-macosx_14_0_arm64.whl
b47c551c6724960479cefd7353656498b86e7232429e3a41ab83be4da1b109e8  numpy-2.1.0-cp312-cp312-macosx_14_0_x86_64.whl
a0756a179afa766ad7cb6f036de622e8a8f16ffdd55aa31f296c870b5679d745  numpy-2.1.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
24003ba8ff22ea29a8c306e61d316ac74111cebf942afbf692df65509a05f111  numpy-2.1.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
b34fa5e3b5d6dc7e0a4243fa0f81367027cb6f4a7215a17852979634b5544ee0  numpy-2.1.0-cp312-cp312-musllinux_1_1_x86_64.whl
c4f982715e65036c34897eb598d64aef15150c447be2cfc6643ec7a11af06574  numpy-2.1.0-cp312-cp312-musllinux_1_2_aarch64.whl
c4cd94dfefbefec3f8b544f61286584292d740e6e9d4677769bc76b8f41deb02  numpy-2.1.0-cp312-cp312-win32.whl
a0cdef204199278f5c461a0bed6ed2e052998276e6d8ab2963d5b5c39a0500bc  numpy-2.1.0-cp312-cp312-win_amd64.whl
8ab81ccd753859ab89e67199b9da62c543850f819993761c1e94a75a814ed667  numpy-2.1.0-cp313-cp313-macosx_10_13_x86_64.whl
442596f01913656d579309edcd179a2a2f9977d9a14ff41d042475280fc7f34e  numpy-2.1.0-cp313-cp313-macosx_11_0_arm64.whl
848c6b5cad9898e4b9ef251b6f934fa34630371f2e916261070a4eb9092ffd33  numpy-2.1.0-cp313-cp313-macosx_14_0_arm64.whl
54c6a63e9d81efe64bfb7bcb0ec64332a87d0b87575f6009c8ba67ea6374770b  numpy-2.1.0-cp313-cp313-macosx_14_0_x86_64.whl
652e92fc409e278abdd61e9505649e3938f6d04ce7ef1953f2ec598a50e7c195  numpy-2.1.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
0ab32eb9170bf8ffcbb14f11613f4a0b108d3ffee0832457c5d4808233ba8977  numpy-2.1.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
8fb49a0ba4d8f41198ae2d52118b050fd34dace4b8f3fb0ee34e23eb4ae775b1  numpy-2.1.0-cp313-cp313-musllinux_1_1_x86_64.whl
44e44973262dc3ae79e9063a1284a73e09d01b894b534a769732ccd46c28cc62  numpy-2.1.0-cp313-cp313-musllinux_1_2_aarch64.whl
ab83adc099ec62e044b1fbb3a05499fa1e99f6d53a1dde102b2d85eff66ed324  numpy-2.1.0-cp313-cp313-win32.whl
de844aaa4815b78f6023832590d77da0e3b6805c644c33ce94a1e449f16d6ab5  numpy-2.1.0-cp313-cp313-win_amd64.whl
343e3e152bf5a087511cd325e3b7ecfd5b92d369e80e74c12cd87826e263ec06  numpy-2.1.0-cp313-cp313t-macosx_10_13_x86_64.whl
f07fa2f15dabe91259828ce7d71b5ca9e2eb7c8c26baa822c825ce43552f4883  numpy-2.1.0-cp313-cp313t-macosx_11_0_arm64.whl
5474dad8c86ee9ba9bb776f4b99ef2d41b3b8f4e0d199d4f7304728ed34d0300  numpy-2.1.0-cp313-cp313t-macosx_14_0_arm64.whl
1f817c71683fd1bb5cff1529a1d085a57f02ccd2ebc5cd2c566f9a01118e3b7d  numpy-2.1.0-cp313-cp313t-macosx_14_0_x86_64.whl
3a3336fbfa0d38d3deacd3fe7f3d07e13597f29c13abf4d15c3b6dc2291cbbdd  numpy-2.1.0-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
7a894c51fd8c4e834f00ac742abad73fc485df1062f1b875661a3c1e1fb1c2f6  numpy-2.1.0-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
9156ca1f79fc4acc226696e95bfcc2b486f165a6a59ebe22b2c1f82ab190384a  numpy-2.1.0-cp313-cp313t-musllinux_1_1_x86_64.whl
624884b572dff8ca8f60fab591413f077471de64e376b17d291b19f56504b2bb  numpy-2.1.0-cp313-cp313t-musllinux_1_2_aarch64.whl
15ef8b2177eeb7e37dd5ef4016f30b7659c57c2c0b57a779f1d537ff33a72c7b  numpy-2.1.0-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
e5f0642cdf4636198a4990de7a71b693d824c56a757862230454629cf62e323d  numpy-2.1.0-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
f15976718c004466406342789f31b6673776360f3b1e3c575f25302d7e789575  numpy-2.1.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
6c1de77ded79fef664d5098a66810d4d27ca0224e9051906e634b3f7ead134c2  numpy-2.1.0-pp310-pypy310_pp73-win_amd64.whl
7dc90da0081f7e1da49ec4e398ede6a8e9cc4f5ebe5f9e06b443ed889ee9aaa2  numpy-2.1.0.tar.gz

- Python
Published by charris almost 2 years ago

numpy - 2.1.0rc1 (Aug 11, 2024)

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 significantly reduces the wall time, but will return slightly different estimates of the correlation coefficients in cases where the observations between a pair of variables are not aligned. However, it will return the same estimates in all other cases, including returning the same correlation matrix as corrcoef when using a masked array with no masked values.

(gh-26285)

Cast-safety fixes in copyto and full

copyto now uses NEP 50 correctly and applies this to its cast safety. Python integer to NumPy integer casts and Python float to NumPy float casts are now considered \"safe\" even if assignment may fail or precision may be lost. This means the following examples change slightly:

• np.copyto(int8_arr, 1000) previously performed an unsafe/same-kind cast

  : of the Python integer. It will now always raise, to achieve an unsafe cast you must pass an array or NumPy scalar.

• np.copyto(uint8_arr, 1000, casting="safe") will raise an OverflowError rather than a TypeError due to same-kind casting.

• np.copyto(float32_arr, 1e300, casting="safe") will overflow to inf (float32 cannot hold 1e300) rather raising a TypeError.

Further, only the dtype is used when assigning NumPy scalars (or 0-d arrays), meaning that the following behaves differently:

• np.copyto(float32_arr, np.float64(3.0), casting="safe") raises.
• np.coptyo(int8_arr, np.int64(100), casting="safe") raises. Previously, NumPy checked whether the 100 fits the int8_arr.

This aligns copyto, full, and full_like with the correct NumPy 2 behavior.

(gh-27091)

Checksums

MD5

8ac48250d6b96fce749fbd0fcf464ff9  numpy-2.1.0rc1-cp310-cp310-macosx_10_9_x86_64.whl
13f92a9f7ed33d71ccfb742de0e3fec9  numpy-2.1.0rc1-cp310-cp310-macosx_11_0_arm64.whl
ba9286f6bd7a238eaead5ae2111d23a8  numpy-2.1.0rc1-cp310-cp310-macosx_14_0_arm64.whl
dc2b6c2f586090bc80268a81afec4c6f  numpy-2.1.0rc1-cp310-cp310-macosx_14_0_x86_64.whl
16a13eb5dfad8008baf937026fa2db62  numpy-2.1.0rc1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
c5d5697af3047b8a3dc7a5d6ca86ec86  numpy-2.1.0rc1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
0e48596167a215333f277ff29ea29c45  numpy-2.1.0rc1-cp310-cp310-musllinux_1_1_x86_64.whl
381957df326f45c0fba0b64a00a043ac  numpy-2.1.0rc1-cp310-cp310-musllinux_1_2_aarch64.whl
676fd27cea96af93142b4b420d9cb8af  numpy-2.1.0rc1-cp310-cp310-win32.whl
b30bff4e8846c52e58fab9564b422ed2  numpy-2.1.0rc1-cp310-cp310-win_amd64.whl
4ee7c88591a445b3b5969999eeb7b0a7  numpy-2.1.0rc1-cp311-cp311-macosx_10_9_x86_64.whl
556393087caa0bb6eec1a76dfe2cad32  numpy-2.1.0rc1-cp311-cp311-macosx_14_0_arm64.whl
4e2b2eb39fc3a6ca28048588fc6a5338  numpy-2.1.0rc1-cp311-cp311-macosx_14_0_x86_64.whl
34f5ab41c4c6a3ecbf0cc0b108a63942  numpy-2.1.0rc1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
689944e33b04a11878aecaf59611341b  numpy-2.1.0rc1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
5d2a53263c7daa9a3b9a89a4dc8ef3ac  numpy-2.1.0rc1-cp311-cp311-musllinux_1_1_x86_64.whl
29e27f96f56d0d1b59f9b261ed6fe438  numpy-2.1.0rc1-cp311-cp311-musllinux_1_2_aarch64.whl
f07177a3b6779e6747137e2173a545de  numpy-2.1.0rc1-cp311-cp311-win32.whl
f2d1f68c8c0455cba32be4aa50f5afed  numpy-2.1.0rc1-cp311-cp311-win_amd64.whl
8500240d88e6e3afc281c562af083fd7  numpy-2.1.0rc1-cp312-cp312-macosx_10_9_x86_64.whl
3280b4ad3a5ceb814d739a9c980d16d6  numpy-2.1.0rc1-cp312-cp312-macosx_11_0_arm64.whl
77a6339def5185efa262658c51d6e44e  numpy-2.1.0rc1-cp312-cp312-macosx_14_0_arm64.whl
2e3a71b9ef1e60ce37949af87475f5f7  numpy-2.1.0rc1-cp312-cp312-macosx_14_0_x86_64.whl
3c1877cd6108cb502ac1df39cfec86d0  numpy-2.1.0rc1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
ae1a9945726e7d970ee0b6232d5d9b4d  numpy-2.1.0rc1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
f1a71557d35d8b2f87f277e85c958b2b  numpy-2.1.0rc1-cp312-cp312-musllinux_1_1_x86_64.whl
b1ba7049684a7d674c006325b4606dd1  numpy-2.1.0rc1-cp312-cp312-musllinux_1_2_aarch64.whl
5944d81459d443a72346e7ea767b72a2  numpy-2.1.0rc1-cp312-cp312-win32.whl
f8b17b8f9bddb1c21844ae2475f72389  numpy-2.1.0rc1-cp312-cp312-win_amd64.whl
084ecd080c6871ed034ef69cda7573de  numpy-2.1.0rc1-cp313-cp313-macosx_10_13_x86_64.whl
dbeca273db0240ca7fe395611f0c23c8  numpy-2.1.0rc1-cp313-cp313-macosx_11_0_arm64.whl
242794f34818844e0fe695ec42c62dbe  numpy-2.1.0rc1-cp313-cp313-macosx_14_0_arm64.whl
3f1c04457ce363250ac5d37935172527  numpy-2.1.0rc1-cp313-cp313-macosx_14_0_x86_64.whl
2ce171281092e5f5d9f3d1ce8a615a94  numpy-2.1.0rc1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
95416f883c14a10fca22007594c94a94  numpy-2.1.0rc1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
36c07d317516f84cb376cc475b3ed13d  numpy-2.1.0rc1-cp313-cp313-musllinux_1_1_x86_64.whl
e7c1f9c2964e4d71878a1654194452b2  numpy-2.1.0rc1-cp313-cp313-musllinux_1_2_aarch64.whl
ea27f5a8b6dfa219b630aee52e621c8c  numpy-2.1.0rc1-cp313-cp313-win32.whl
1821d7e0980f297296509090cfd9c288  numpy-2.1.0rc1-cp313-cp313-win_amd64.whl
1b7f8160179aef59822e3eb43cb8a210  numpy-2.1.0rc1-cp313-cp313t-macosx_10_13_x86_64.whl
fed8d00d6819c467ef97e0b7611624cd  numpy-2.1.0rc1-cp313-cp313t-macosx_11_0_arm64.whl
f58df469b6ec5e1755b1572702b56716  numpy-2.1.0rc1-cp313-cp313t-macosx_14_0_arm64.whl
fe13066a540c68598b1180bec61e8e30  numpy-2.1.0rc1-cp313-cp313t-macosx_14_0_x86_64.whl
67d51902daf5bc9de69c6e46dfea9a64  numpy-2.1.0rc1-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
8409acd1916df8f8630260207a5b4eec  numpy-2.1.0rc1-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
e64a5ccac64641cbbbd2caa652ff815a  numpy-2.1.0rc1-cp313-cp313t-musllinux_1_1_x86_64.whl
488776d734d4eddc9c1540bf862106bb  numpy-2.1.0rc1-cp313-cp313t-musllinux_1_2_aarch64.whl
fbc57a82683e2c9697a6992290ebe337  numpy-2.1.0rc1-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
ed26d5d79acc222e107900668edcd01f  numpy-2.1.0rc1-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
c29f8c6a55c1ac9e5c693f63ec17f251  numpy-2.1.0rc1-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
4beab0a7bde06687f699e75cd04ec024  numpy-2.1.0rc1-pp310-pypy310_pp73-win_amd64.whl
88e72b72f2859ff084eb3863fac3ac20  numpy-2.1.0rc1.tar.gz

SHA256

590acae9e4b0baa895850c0edab988c329a196bacc7326f3249fa5fe7b94e5a8  numpy-2.1.0rc1-cp310-cp310-macosx_10_9_x86_64.whl
61cf71f62033987ed49b78a19465f40fcbf6f7e94674eda21096ebde6935c2e0  numpy-2.1.0rc1-cp310-cp310-macosx_11_0_arm64.whl
0c489f6c47bbed44918c9c8036a679614920da2a45f481d0eca2ad168ca5327f  numpy-2.1.0rc1-cp310-cp310-macosx_14_0_arm64.whl
4c33387be8eadc07d0834e0b9e2ead53117fe76ab2dadd37ee80d1df80be4c05  numpy-2.1.0rc1-cp310-cp310-macosx_14_0_x86_64.whl
f412923d4ce1ec29aa3cf7752598e5eb154f549cfbf62d7c6f3cc76cb25b32e0  numpy-2.1.0rc1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
06156c55771da4952a2432aa457cd96159675dcab4336f5307bff042535cb6ea  numpy-2.1.0rc1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
be3ddd26a22d032914cfca5ef7db74f31adbd6c9d88a6f4e21ebd8e057d9474c  numpy-2.1.0rc1-cp310-cp310-musllinux_1_1_x86_64.whl
12b38b0f3ddc1342863a6849f4fcb3f506e1d21179ebd34b7aa55a30cb50899f  numpy-2.1.0rc1-cp310-cp310-musllinux_1_2_aarch64.whl
17581a2080012afe603c43005c9d050570e54683dde0d395e3edb4fa9c25f328  numpy-2.1.0rc1-cp310-cp310-win32.whl
8ee3ab33c02a0bd7d219a184c9bc43811de373551529981035673ca2a1ba7b93  numpy-2.1.0rc1-cp310-cp310-win_amd64.whl
2d3d1e61191e408a11658a64e9f9bb61741ad28c160576c95dac9df6f74713b4  numpy-2.1.0rc1-cp311-cp311-macosx_10_9_x86_64.whl
4e08e733600647242a9046b6aff888e72fe8a846b00855e5136e7641b08d25d8  numpy-2.1.0rc1-cp311-cp311-macosx_14_0_arm64.whl
2b0e379a15c6b8eb69bb8170d10cfbb8a0dc9126b5402ee8860a2646f4111c3d  numpy-2.1.0rc1-cp311-cp311-macosx_14_0_x86_64.whl
fea6d6939d9bf098d96c6d22bb3e4ff39f8eb3f0f26b52c8c69ba06845490095  numpy-2.1.0rc1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
9a6bdc19830703eee91e7eb2d671b165febefbf5eec6a4f163d1833d23be17af  numpy-2.1.0rc1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
58a07f2947aa06ca03d922a86ac30e403ce8282cd15904606bac852bf29ea2ad  numpy-2.1.0rc1-cp311-cp311-musllinux_1_1_x86_64.whl
1a4f960e2e5c1084cf6b1d15482a5556ecc122855d631a2395063ab703d62fdd  numpy-2.1.0rc1-cp311-cp311-musllinux_1_2_aarch64.whl
f38fabd7b8d14fb7d63fbb2d07971d6edd518d2a43542c63c29164c901d2a758  numpy-2.1.0rc1-cp311-cp311-win32.whl
e82b8e0b88d493d4e882f18de30f679bf1197c82d8c799acb5fdb4068cadb945  numpy-2.1.0rc1-cp311-cp311-win_amd64.whl
dc2af0135139bbb26b1ea5bdc430e049edb745ae643cb898afb32549ce4801de  numpy-2.1.0rc1-cp312-cp312-macosx_10_9_x86_64.whl
47f11bf152d8707217feb46e9662a8b1aa3554a8ee56b64d2aa99c3e9914f101  numpy-2.1.0rc1-cp312-cp312-macosx_11_0_arm64.whl
3b534c62b1887b4bfa80f633485f2a9338f5d46d720b6cc695d2ba8b38d98987  numpy-2.1.0rc1-cp312-cp312-macosx_14_0_arm64.whl
f4e07df8476545da7cf23f75811f4fc334b06fc50d8e945e897cfc00c8f89690  numpy-2.1.0rc1-cp312-cp312-macosx_14_0_x86_64.whl
c8458becc562ee35b30b5e53173933414cf42e56b3f4f3d80997bf0dda7308d1  numpy-2.1.0rc1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
524b5311d21741f0b3f48efcdd3442546be3b38507a4e3b0f5138e4340f5dee0  numpy-2.1.0rc1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
7bcb4f360dc9e29b4f5f9fb36b35b429e731373843ccf39a22105bd809ef3138  numpy-2.1.0rc1-cp312-cp312-musllinux_1_1_x86_64.whl
5821c9831fad20cd1a8621a9ed483322ca97a9da9832690a4050ffedcb3e766b  numpy-2.1.0rc1-cp312-cp312-musllinux_1_2_aarch64.whl
1d9e0ddfb33a7a78fe92d49aaa2992a78ed5aff4cef7a21d8b1057cca075cc85  numpy-2.1.0rc1-cp312-cp312-win32.whl
86cc61c5479ed3b324011aa69484cae8f491b7f58dc0e54acf0894bdb4fae879  numpy-2.1.0rc1-cp312-cp312-win_amd64.whl
64e8de086d2e4dac41fa286412321469b4535677184e78cc78e5061b44f0e4bf  numpy-2.1.0rc1-cp313-cp313-macosx_10_13_x86_64.whl
e74dc488a27b90f31ab307b4cf3f07a45bb78a0e91cfb36d69c6eced4f36089b  numpy-2.1.0rc1-cp313-cp313-macosx_11_0_arm64.whl
f73e4fcf7455d3b734e6ecbafdbc12d3c1dd8f2146fd186e003ae1c8f00e5eed  numpy-2.1.0rc1-cp313-cp313-macosx_14_0_arm64.whl
e5a64ac6016839fd906b3d7cc1f7ecb145c7d44a310234b6843f3b23b8ec0651  numpy-2.1.0rc1-cp313-cp313-macosx_14_0_x86_64.whl
ccc68ee27362f8d3516deecffa124d1488ae20347628e357264e7e66dbdaba08  numpy-2.1.0rc1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
d3d59479b98cc364b8a08ddd854c7817b5c578a521b56af5a96b3a9db18cc9b7  numpy-2.1.0rc1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
15c6bde88f242747258cfee803f3161b7a2c1ffead0817e409d95444a79b4029  numpy-2.1.0rc1-cp313-cp313-musllinux_1_1_x86_64.whl
3e9276bff9a57100b53e5f9c44469baca1e58ec612e5143db568d69ec27b65ea  numpy-2.1.0rc1-cp313-cp313-musllinux_1_2_aarch64.whl
53979581e6acdd75b7ce94e6d3b70994f9f8cf1021316d388a159f7f388bdc7f  numpy-2.1.0rc1-cp313-cp313-win32.whl
ca195cd9d1d84b3498532968237774a6e06e2a4afe706b87172f1d033b95e230  numpy-2.1.0rc1-cp313-cp313-win_amd64.whl
77fa9826cbc7273e4bc3b7aa289b86936c942fe2c91bc35617c2417e14421592  numpy-2.1.0rc1-cp313-cp313t-macosx_10_13_x86_64.whl
140c5ce21f1eccb254e550c8431825cb716eb76e896202cffa7a0d2a843506da  numpy-2.1.0rc1-cp313-cp313t-macosx_11_0_arm64.whl
713cb46d266514db773de52af677aa931cc896a4f5e52f494449c4ff53ce6051  numpy-2.1.0rc1-cp313-cp313t-macosx_14_0_arm64.whl
3f79d241e4833a2a570b6e6639d2114d497011e48a351798bba81fda51560ab7  numpy-2.1.0rc1-cp313-cp313t-macosx_14_0_x86_64.whl
48a724dbfad6f4933e2c8a22239980e1b5bc16868df3450cc4ebeb9522b7902f  numpy-2.1.0rc1-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
06d14d20b7e98c8c06bb62e56f2b64747dd10c422bb8adbf1e6dd82cd8442e12  numpy-2.1.0rc1-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
98a1486861fa3c603a5a3ccd56fc45b9756372bb30f6fb559b898fc2fad82e0d  numpy-2.1.0rc1-cp313-cp313t-musllinux_1_1_x86_64.whl
50b3dab872001b87052532bd4da3879fda856a2cf6c9418c19bfc94dc290e259  numpy-2.1.0rc1-cp313-cp313t-musllinux_1_2_aarch64.whl
14dea4f0d62ddd1a7f9d7b0003b35a537ac41a2b6205deec8c9c25a8e01748b4  numpy-2.1.0rc1-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
4f9317da3aa64d0ee93950d3f319b3fe0169500e25c18223715cba39e89808bd  numpy-2.1.0rc1-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
0a5a25ab780b8c29e443824abefc6ca79047ceeb889a6f76d7b1953649498e93  numpy-2.1.0rc1-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
0816fd52956e14551d8d71319d4b4fcfa1bcb21641f2c603f4eb64c65b1e1009  numpy-2.1.0rc1-pp310-pypy310_pp73-win_amd64.whl
dc7ce867d277aa74555c67b93ef2a6f78bd7bd73e6c2bbafeb96f8bccd05b9d9  numpy-2.1.0rc1.tar.gz

- Python
Published by charris almost 2 years ago

numpy -

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 _addnewdocsscalars
• #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

- Python
Published by charris almost 2 years ago

numpy -

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:

• The numpy-2-migration-guide
• The Numpy 2.0-specific advice in for downstream package authors

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 and Google Highway libraries, and may see large (hardware-specific) speedups,
  • macOS Accelerate support and binary wheels for macOS >=14, with significant performance improvements for linear algebra operations on macOS, and wheels that are about 3 times smaller,
  • numpy.char fixed-length string operations have been accelerated by implementing ufuncs that also support numpy.dtypes.StringDType in addition to the fixed-length string dtypes,
  • A new tracing and introspection API, numpy.lib.introspect.opt_func_info, to determine which hardware-specific kernels are available and will be dispatched to.
  • 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.
• Python API improvements:
  • A clear split between public and private API, with a new module structure and each public function now available in a single place.
  • Many removals of non-recommended functions and aliases. This should make it easier to learn and use NumPy. The number of objects in the main namespace decreased by ~10% and in numpy.lib by ~80%.
  • Canonical dtype names and a newnumpy.isdtype` introspection function,
• C API improvements:
  • A new public C API for creating custom dtypes,
  • Many outdated functions and macros removed, and private internals hidden to ease future extensibility,
  • New, easier to use, initialization functions: PyArray_ImportNumPyAPI and PyUFunc_ImportUFuncAPI.
• Improved behavior:
  • Improvements to type promotion behavior was changed by adopting NEP 50. This fixes many user surprises about promotions which previously often depended on data values of input arrays rather than only their dtypes. Please see the NEP and the numpy-2-migration-guide for details as this change can lead to changes in output dtypes and lower precision results for mixed-dtype operations.
  • The default integer type on Windows is now int64 rather than int32, matching the behavior on other platforms,
  • The maximum number of array dimensions is changed from 32 to 64
• Documentation:
  • The reference guide navigation was significantly improved, and there is now documentation on NumPy\'s module structure,
  • The building from source documentation was completely rewritten,

Furthermore there are many changes to NumPy internals, including continuing to migrate code from C to C++, that will make it easier to improve and maintain NumPy in the future.

The \"no free lunch\" theorem dictates that there is a price to pay for all these API and behavior improvements and better future extensibility. This price is:

1. Backwards compatibility. There are a significant number of breaking changes to both the Python and C APIs. In the majority of cases, there are clear error messages that will inform the user how to adapt their code. However, there are also changes in behavior for which it was not possible to give such an error message - these cases are all covered in the Deprecation and Compatibility sections below, and in the numpy-2-migration-guide.

   Note that there is a ruff mode to auto-fix many things in Python code.

2. Breaking changes to the NumPy ABI. As a result, binaries of packages that use the NumPy C API and were built against a NumPy 1.xx release will not work with NumPy 2.0. On import, such packages will see an ImportError with a message about binary incompatibility.

   It is possible to build binaries against NumPy 2.0 that will work at runtime with both NumPy 2.0 and 1.x. See numpy-2-abi-handling for more details.

   All downstream packages that depend on the NumPy ABI are advised to do a new release built against NumPy 2.0 and verify that that release works with both 2.0 and 1.26 - ideally in the period between 2.0.0rc1 (which will be ABI-stable) and the final 2.0.0 release to avoid problems for their users.

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

NumPy 2.0 Python API removals

• np.geterrobj, np.seterrobj and the related ufunc keyword argument extobj= have been removed. The preferred replacement for all of these is using the context manager with np.errstate():.

  (gh-23922)

• np.cast has been removed. The literal replacement for np.cast[dtype](arg) is np.asarray(arg, dtype=dtype).

• np.source has been removed. The preferred replacement is inspect.getsource.

• np.lookfor has been removed.

  (gh-24144)

• numpy.who has been removed. As an alternative for the removed functionality, one can use a variable explorer that is available in IDEs such as Spyder or Jupyter Notebook.

  (gh-24321)

• Warnings and exceptions present in numpy.exceptions, e.g, numpy.exceptions.ComplexWarning, numpy.exceptions.VisibleDeprecationWarning, are no longer exposed in the main namespace.

• Multiple niche enums, expired members and functions have been removed from the main namespace, such as: ERR_*, SHIFT_*, np.fastCopyAndTranspose, np.kernel_version, np.numarray, np.oldnumeric and np.set_numeric_ops.

  (gh-24316)

• Replaced from ... import * in the numpy/__init__.py with explicit imports. As a result, these main namespace members got removed: np.FLOATING_POINT_SUPPORT, np.FPE_*, np.NINF, np.PINF, np.NZERO, np.PZERO, np.CLIP, np.WRAP, np.WRAP, np.RAISE, np.BUFSIZE, np.UFUNC_BUFSIZE_DEFAULT, np.UFUNC_PYVALS_NAME, np.ALLOW_THREADS, np.MAXDIMS, np.MAY_SHARE_EXACT, np.MAY_SHARE_BOUNDS, add_newdoc, np.add_docstring and np.add_newdoc_ufunc.

  (gh-24357)

• Alias np.float_ has been removed. Use np.float64 instead.

• Alias np.complex_ has been removed. Use np.complex128 instead.

• Alias np.longfloat has been removed. Use np.longdouble instead.

• Alias np.singlecomplex has been removed. Use np.complex64 instead.

• Alias np.cfloat has been removed. Use np.complex128 instead.

• Alias np.longcomplex has been removed. Use np.clongdouble instead.

• Alias np.clongfloat has been removed. Use np.clongdouble instead.

• Alias np.string_ has been removed. Use np.bytes_ instead.

• Alias np.unicode_ has been removed. Use np.str_ instead.

• Alias np.Inf has been removed. Use np.inf instead.

• Alias np.Infinity has been removed. Use np.inf instead.

• Alias np.NaN has been removed. Use np.nan instead.

• Alias np.infty has been removed. Use np.inf instead.

• Alias np.mat has been removed. Use np.asmatrix instead.

• np.issubclass_ has been removed. Use the issubclass builtin instead.

• np.asfarray has been removed. Use np.asarray with a proper dtype instead.

• np.set_string_function has been removed. Use np.set_printoptions instead with a formatter for custom printing of NumPy objects.

• np.tracemalloc_domain is now only available from np.lib.

• np.recfromcsv and recfromtxt are now only available from np.lib.npyio.

• np.issctype, np.maximum_sctype, np.obj2sctype, np.sctype2char, np.sctypes, np.issubsctype were all removed from the main namespace without replacement, as they where niche members.

• Deprecated np.deprecate and np.deprecate_with_doc has been removed from the main namespace. Use DeprecationWarning instead.

• Deprecated np.safe_eval has been removed from the main namespace. Use ast.literal_eval instead.

  (gh-24376)

• np.find_common_type has been removed. Use numpy.promote_types or numpy.result_type instead. To achieve semantics for the scalar_types argument, use numpy.result_type and pass 0, 0.0, or 0j as a Python scalar instead.

• np.round_ has been removed. Use np.round instead.

• np.nbytes has been removed. Use np.dtype(<dtype>).itemsize instead.

  (gh-24477)

• np.compare_chararrays has been removed from the main namespace. Use np.char.compare_chararrays instead.

• The charrarray in the main namespace has been deprecated. It can be imported without a deprecation warning from np.char.chararray for now, but we are planning to fully deprecate and remove chararray in the future.

• np.format_parser has been removed from the main namespace. Use np.rec.format_parser instead.

  (gh-24587)

• Support for seven data type string aliases has been removed from np.dtype: int0, uint0, void0, object0, str0, bytes0 and bool8.

  (gh-24807)

• The experimental numpy.array_api submodule has been removed. Use the main numpy namespace for regular usage instead, or the separate array-api-strict package for the compliance testing use case for which numpy.array_api was mostly used.

  (gh-25911)

__array_prepare__ is removed

UFuncs called __array_prepare__ before running computations for normal ufunc calls (not generalized ufuncs, reductions, etc.). The function was also called instead of __array_wrap__ on the results of some linear algebra functions.

It is now removed. If you use it, migrate to __array_ufunc__ or rely on __array_wrap__ which is called with a context in all cases, although only after the result array is filled. In those code paths, __array_wrap__ will now be passed a base class, rather than a subclass array.

(gh-25105)

Deprecations

• np.compat has been deprecated, as Python 2 is no longer supported.

• numpy.int8 and similar classes will no longer support conversion of out of bounds python integers to integer arrays. For example, conversion of 255 to int8 will not return -1. numpy.iinfo(dtype) can be used to check the machine limits for data types. For example, np.iinfo(np.uint16) returns min = 0 and max = 65535.

  np.array(value).astype(dtype) will give the desired result.

• np.safe_eval has been deprecated. ast.literal_eval should be used instead.

  (gh-23830)

• np.recfromcsv, np.recfromtxt, np.disp, np.get_array_wrap, np.maximum_sctype, np.deprecate and np.deprecate_with_doc have been deprecated.

  (gh-24154)

• np.trapz has been deprecated. Use np.trapezoid or a scipy.integrate function instead.

• np.in1d has been deprecated. Use np.isin instead.

• Alias np.row_stack has been deprecated. Use np.vstack directly.

  (gh-24445)

• __array_wrap__ is now passed arr, context, return_scalar and support for implementations not accepting all three are deprecated. Its signature should be __array_wrap__(self, arr, context=None, return_scalar=False)

  (gh-25409)

• Arrays of 2-dimensional vectors for np.cross have been deprecated. Use arrays of 3-dimensional vectors instead.

  (gh-24818)

• np.dtype("a") alias for np.dtype(np.bytes_) was deprecated. Use np.dtype("S") alias instead.

  (gh-24854)

• Use of keyword arguments x and y with functions assert_array_equal and assert_array_almost_equal has been deprecated. Pass the first two arguments as positional arguments instead.

  (gh-24978)

numpy.fft deprecations for n-D transforms with None values in arguments

Using fftn, ifftn, rfftn, irfftn, fft2, ifft2, rfft2 or irfft2 with the s parameter set to a value that is not None and the axes parameter set to None has been deprecated, in line with the array API standard. To retain current behaviour, pass a sequence [0, ..., k-1] to axes for an array of dimension k.

Furthermore, passing an array to s which contains None values is deprecated as the parameter is documented to accept a sequence of integers in both the NumPy docs and the array API specification. To use the default behaviour of the corresponding 1-D transform, pass the value matching the default for its n parameter. To use the default behaviour for every axis, the s argument can be omitted.

(gh-25495)

np.linalg.lstsq now defaults to a new rcond value

numpy.linalg.lstsq now uses the new rcond value of the machine precision times max(M, N). Previously, the machine precision was used but a FutureWarning was given to notify that this change will happen eventually. That old behavior can still be achieved by passing rcond=-1.

(gh-25721)

Expired deprecations

• The np.core.umath_tests submodule has been removed from the public API. (Deprecated in NumPy 1.15)

  (gh-23809)

• The PyDataMem_SetEventHook deprecation has expired and it is removed. Use tracemalloc and the np.lib.tracemalloc_domain domain. (Deprecated in NumPy 1.23)

  (gh-23921)

• The deprecation of set_numeric_ops and the C functions PyArray_SetNumericOps and PyArray_GetNumericOps has been expired and the functions removed. (Deprecated in NumPy 1.16)

  (gh-23998)

• The fasttake, fastclip, and fastputmask ArrFuncs deprecation is now finalized.

• The deprecated function fastCopyAndTranspose and its C counterpart are now removed.

• The deprecation of PyArray_ScalarFromObject is now finalized.

  (gh-24312)

• np.msort has been removed. For a replacement, np.sort(a, axis=0) should be used instead.

  (gh-24494)

• np.dtype(("f8", 1) will now return a shape 1 subarray dtype rather than a non-subarray one.

  (gh-25761)

• Assigning to the .data attribute of an ndarray is disallowed and will raise.

• np.binary_repr(a, width) will raise if width is too small.

• Using NPY_CHAR in PyArray_DescrFromType() will raise, use NPY_STRING NPY_UNICODE, or NPY_VSTRING instead.

  (gh-25794)

Compatibility notes

loadtxt and genfromtxt default encoding changed

loadtxt and genfromtxt now both default to encoding=None which may mainly modify how converters work. These will now be passed str rather than bytes. Pass the encoding explicitly to always get the new or old behavior. For genfromtxt the change also means that returned values will now be unicode strings rather than bytes.

(gh-25158)

f2py compatibility notes

• f2py will no longer accept ambiguous -m and .pyf CLI combinations. When more than one .pyf file is passed, an error is raised. When both -m and a .pyf is passed, a warning is emitted and the -m provided name is ignored.

  (gh-25181)

• The f2py.compile() helper has been removed because it leaked memory, has been marked as experimental for several years now, and was implemented as a thin subprocess.run wrapper. It was also one of the test bottlenecks. See gh-25122 for the full rationale. It also used several np.distutils features which are too fragile to be ported to work with meson.

• Users are urged to replace calls to f2py.compile with calls to subprocess.run("python", "-m", "numpy.f2py",... instead, and to use environment variables to interact with meson. Native files are also an option.

  (gh-25193)

Minor changes in behavior of sorting functions

Due to algorithmic changes and use of SIMD code, sorting functions with methods that aren\'t stable may return slightly different results in 2.0.0 compared to 1.26.x. This includes the default method of numpy.argsort and numpy.argpartition.

Removed ambiguity when broadcasting in np.solve

The broadcasting rules for np.solve(a, b) were ambiguous when b had 1 fewer dimensions than a. This has been resolved in a backward-incompatible way and is now compliant with the Array API. The old behaviour can be reconstructed by using np.solve(a, b[..., None])[..., 0].

(gh-25914)

Modified representation for Polynomial

The representation method for numpy.polynomial.polynomial.Polynomial was updated to include the domain in the representation. The plain text and latex representations are now consistent. For example the output of str(np.polynomial.Polynomial([1, 1], domain=[.1, .2])) used to be 1.0 + 1.0 x, but now is 1.0 + 1.0 (-3.0000000000000004 + 20.0 x).

(gh-21760)

C API changes

• The PyArray_CGT, PyArray_CLT, PyArray_CGE, PyArray_CLE, PyArray_CEQ, PyArray_CNE macros have been removed.

• PyArray_MIN and PyArray_MAX have been moved from ndarraytypes.h to npy_math.h.

  (gh-24258)

• A C API for working with numpy.dtypes.StringDType arrays has been exposed. This includes functions for acquiring and releasing mutexes which lock access to the string data, as well as packing and unpacking UTF-8 bytestreams from array entries.

• NPY_NTYPES has been renamed to NPY_NTYPES_LEGACY as it does not include new NumPy built-in DTypes. In particular the new string DType will likely not work correctly with code that handles legacy DTypes.

  (gh-25347)

• The C-API now only exports the static inline function versions of the array accessors (previously this depended on using \"deprecated API\"). While we discourage it, the struct fields can still be used directly.

  (gh-25789)

• NumPy now defines PyArray_Pack to set an individual memory address. Unlike PyArray_SETITEM this function is equivalent to setting an individual array item and does not require a NumPy array input.

  (gh-25954)

• The ->f slot has been removed from PyArray_Descr. If you use this slot, replace accessing it with PyDataType_GetArrFuncs (see its documentation and the numpy-2-migration-guide). In some cases using other functions like PyArray_GETITEM may be an alternatives.

• PyArray_GETITEM and PyArray_SETITEM now require the import of the NumPy API table to be used and are no longer defined in ndarraytypes.h.

  (gh-25812)

• Due to runtime dependencies, the definition for functionality accessing the dtype flags was moved from numpy/ndarraytypes.h and is only available after including numpy/ndarrayobject.h as it requires import_array(). This includes PyDataType_FLAGCHK, PyDataType_REFCHK and NPY_BEGIN_THREADS_DESCR.

• The dtype flags on PyArray_Descr must now be accessed through the PyDataType_FLAGS inline function to be compatible with both 1.x and 2.x. This function is defined in npy_2_compat.h to allow backporting. Most or all users should use PyDataType_FLAGCHK which is available on 1.x and does not require backporting. Cython users should use Cython 3. Otherwise access will go through Python unless they use PyDataType_FLAGCHK instead.

  (gh-25816)

Datetime functionality exposed in the C API and Cython bindings

The functions NpyDatetime_ConvertDatetime64ToDatetimeStruct, NpyDatetime_ConvertDatetimeStructToDatetime64, NpyDatetime_ConvertPyDateTimeToDatetimeStruct, NpyDatetime_GetDatetimeISO8601StrLen, NpyDatetime_MakeISO8601Datetime, and NpyDatetime_ParseISO8601Datetime have been added to the C API to facilitate converting between strings, Python datetimes, and NumPy datetimes in external libraries.

(gh-21199)

Const correctness for the generalized ufunc C API

The NumPy C API\'s functions for constructing generalized ufuncs (PyUFunc_FromFuncAndData, PyUFunc_FromFuncAndDataAndSignature, PyUFunc_FromFuncAndDataAndSignatureAndIdentity) take types and data arguments that are not modified by NumPy\'s internals. Like the name and doc arguments, third-party Python extension modules are likely to supply these arguments from static constants. The types and data arguments are now const-correct: they are declared as const char *types and void *const *data, respectively. C code should not be affected, but C++ code may be.

(gh-23847)

Larger NPY_MAXDIMS and NPY_MAXARGS, NPY_RAVEL_AXIS introduced

NPY_MAXDIMS is now 64, you may want to review its use. This is usually used in a stack allocation, where the increase should be safe. However, we do encourage generally to remove any use of NPY_MAXDIMS and NPY_MAXARGS to eventually allow removing the constraint completely. For the conversion helper and C-API functions mirroring Python ones such as take, NPY_MAXDIMS was used to mean axis=None. Such usage must be replaced with NPY_RAVEL_AXIS. See also migration_maxdims.

(gh-25149)

NPY_MAXARGS not constant and PyArrayMultiIterObject size change

Since NPY_MAXARGS was increased, it is now a runtime constant and not compile-time constant anymore. We expect almost no users to notice this. But if used for stack allocations it now must be replaced with a custom constant using NPY_MAXARGS as an additional runtime check.

The sizeof(PyArrayMultiIterObject) no longer includes the full size of the object. We expect nobody to notice this change. It was necessary to avoid issues with Cython.

(gh-25271)

Required changes for custom legacy user dtypes

In order to improve our DTypes it is unfortunately necessary to break the ABI, which requires some changes for dtypes registered with PyArray_RegisterDataType. Please see the documentation of PyArray_RegisterDataType for how to adapt your code and achieve compatibility with both 1.x and 2.x.

(gh-25792)

New Public DType API

The C implementation of the NEP 42 DType API is now public. While the DType API has shipped in NumPy for a few versions, it was only usable in sessions with a special environment variable set. It is now possible to write custom DTypes outside of NumPy using the new DType API and the normal import_array() mechanism for importing the numpy C API.

See dtype-api for more details about the API. As always with a new feature, please report any bugs you run into implementing or using a new DType. It is likely that downstream C code that works with dtypes will need to be updated to work correctly with new DTypes.

(gh-25754)

New C-API import functions

We have now added PyArray_ImportNumPyAPI and PyUFunc_ImportUFuncAPI as static inline functions to import the NumPy C-API tables. The new functions have two advantages over import_array and import_ufunc:

• They check whether the import was already performed and are light-weight if not, allowing to add them judiciously (although this is not preferable in most cases).
• The old mechanisms were macros rather than functions which included a return statement.

The PyArray_ImportNumPyAPI() function is included in npy_2_compat.h for simpler backporting.

(gh-25866)

Structured dtype information access through functions

The dtype structures fields c_metadata, names, fields, and subarray must now be accessed through new functions following the same names, such as PyDataType_NAMES. Direct access of the fields is not valid as they do not exist for all PyArray_Descr instances. The metadata field is kept, but the macro version should also be preferred.

(gh-25802)

Descriptor elsize and alignment access

Unless compiling only with NumPy 2 support, the elsize and aligment fields must now be accessed via PyDataType_ELSIZE, PyDataType_SET_ELSIZE, and PyDataType_ALIGNMENT. In cases where the descriptor is attached to an array, we advise using PyArray_ITEMSIZE as it exists on all NumPy versions. Please see migration_c_descr for more information.

(gh-25943)

NumPy 2.0 C API removals

• npy_interrupt.h and the corresponding macros like NPY_SIGINT_ON have been removed. We recommend querying PyErr_CheckSignals() or PyOS_InterruptOccurred() periodically (these do currently require holding the GIL though).

• The noprefix.h header has been removed. Replace missing symbols with their prefixed counterparts (usually an added NPY_ or npy_).

  (gh-23919)

• PyUFunc_GetPyVals, PyUFunc_handlefperr, and PyUFunc_checkfperr have been removed. If needed, a new backwards compatible function to raise floating point errors could be restored. Reason for removal: there are no known users and the functions would have made with np.errstate() fixes much more difficult).

  (gh-23922)

• The numpy/old_defines.h which was part of the API deprecated since NumPy 1.7 has been removed. This removes macros of the form PyArray_CONSTANT. The replaceoldmacros.sed script may be useful to convert them to the NPY_CONSTANT version.

  (gh-24011)

• The legacy_inner_loop_selector member of the ufunc struct is removed to simplify improvements to the dispatching system. There are no known users overriding or directly accessing this member.

  (gh-24271)

• NPY_INTPLTR has been removed to avoid confusion (see intp redefinition).

  (gh-24888)

• The advanced indexing MapIter and related API has been removed. The (truly) public part of it was not well tested and had only one known user (Theano). Making it private will simplify improvements to speed up ufunc.at, make advanced indexing more maintainable, and was important for increasing the maximum number of dimensions of arrays to 64. Please let us know if this API is important to you so we can find a solution together.

  (gh-25138)

• The NPY_MAX_ELSIZE macro has been removed, as it only ever reflected builtin numeric types and served no internal purpose.

  (gh-25149)

• PyArray_REFCNT and NPY_REFCOUNT are removed. Use Py_REFCNT instead.

  (gh-25156)

• PyArrayFlags_Type and PyArray_NewFlagsObject as well as PyArrayFlagsObject are private now. There is no known use-case; use the Python API if needed.

• PyArray_MoveInto, PyArray_CastTo, PyArray_CastAnyTo are removed use PyArray_CopyInto and if absolutely needed PyArray_CopyAnyInto (the latter does a flat copy).

• PyArray_FillObjectArray is removed, its only true use was for implementing np.empty. Create a new empty array or use PyArray_FillWithScalar() (decrefs existing objects).

• PyArray_CompareUCS4 and PyArray_CompareString are removed. Use the standard C string comparison functions.

• PyArray_ISPYTHON is removed as it is misleading, has no known use-cases, and is easy to replace.

• PyArray_FieldNames is removed, as it is unclear what it would be useful for. It also has incorrect semantics in some possible use-cases.

• PyArray_TypestrConvert is removed, since it seems a misnomer and unlikely to be used by anyone. If you know the size or are limited to few types, just use it explicitly, otherwise go via Python strings.

  (gh-25292)

• PyDataType_GetDatetimeMetaData is removed, it did not actually do anything since at least NumPy 1.7.

  (gh-25802)

• PyArray_GetCastFunc is removed. Note that custom legacy user dtypes can still provide a castfunc as their implementation, but any access to them is now removed. The reason for this is that NumPy never used these internally for many years. If you use simple numeric types, please just use C casts directly. In case you require an alternative, please let us know so we can create new API such as PyArray_CastBuffer() which could use old or new cast functions depending on the NumPy version.

  (gh-25161)

New Features

np.add was extended to work with unicode and bytes dtypes.

(gh-24858)

A new bitwise_count function

This new function counts the number of 1-bits in a number. numpy.bitwise_count works on all the numpy integer types and integer-like objects.

``` python

a = np.array([2**i - 1 for i in range(16)]) np.bitwise_count(a) array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], dtype=uint8) ```

(gh-19355)

macOS Accelerate support, including the ILP64

Support for the updated Accelerate BLAS/LAPACK library, including ILP64 (64-bit integer) support, in macOS 13.3 has been added. This brings arm64 support, and significant performance improvements of up to 10x for commonly used linear algebra operations. When Accelerate is selected at build time, or if no explicit BLAS library selection is done, the 13.3+ version will automatically be used if available.

(gh-24053)

Binary wheels are also available. On macOS >=14.0, users who install NumPy from PyPI will get wheels built against Accelerate rather than OpenBLAS.

(gh-25255)

Option to use weights for quantile and percentile functions

A weights keyword is now available for numpy.quantile, numpy.percentile, numpy.nanquantile and numpy.nanpercentile. Only method="inverted_cdf" supports weights.

(gh-24254)

Improved CPU optimization tracking

A new tracer mechanism is available which enables tracking of the enabled targets for each optimized function (i.e., that uses hardware-specific SIMD instructions) in the NumPy library. With this enhancement, it becomes possible to precisely monitor the enabled CPU dispatch targets for the dispatched functions.

A new function named opt_func_info has been added to the new namespace numpy.lib.introspect, offering this tracing capability. This function allows you to retrieve information about the enabled targets based on function names and data type signatures.

(gh-24420)

A new Meson backend for f2py

f2py in compile mode (i.e. f2py -c) now accepts the --backend meson option. This is the default option for Python >=3.12. For older Python versions, f2py will still default to --backend distutils.

To support this in realistic use-cases, in compile mode f2py takes a --dep flag one or many times which maps to dependency() calls in the meson backend, and does nothing in the distutils backend.

There are no changes for users of f2py only as a code generator, i.e. without -c.

(gh-24532)

bind(c) support for f2py

Both functions and subroutines can be annotated with bind(c). f2py will handle both the correct type mapping, and preserve the unique label for other C interfaces.

Note: bind(c, name = 'routine_name_other_than_fortran_routine') is not honored by the f2py bindings by design, since bind(c) with the name is meant to guarantee only the same name in C and Fortran, not in Python and Fortran.

(gh-24555)

A new strict option for several testing functions

The strict keyword is now available for numpy.testing.assert_allclose, numpy.testing.assert_equal, and numpy.testing.assert_array_less. Setting strict=True will disable the broadcasting behaviour for scalars and ensure that input arrays have the same data type.

(gh-24680, gh-24770, gh-24775)

Add np.core.umath.find and np.core.umath.rfind UFuncs

Add two find and rfind UFuncs that operate on unicode or byte strings and are used in np.char. They operate similar to str.find and str.rfind.

(gh-24868)

diagonal and trace for numpy.linalg

numpy.linalg.diagonal and numpy.linalg.trace have been added, which are array API standard-compatible variants of numpy.diagonal and numpy.trace. They differ in the default axis selection which define 2-D sub-arrays.

(gh-24887)

New long and ulong dtypes

numpy.long and numpy.ulong have been added as NumPy integers mapping to C\'s long and unsigned long. Prior to NumPy 1.24, numpy.long was an alias to Python\'s int.

(gh-24922)

svdvals for numpy.linalg

numpy.linalg.svdvals has been added. It computes singular values for (a stack of) matrices. Executing np.svdvals(x) is the same as calling np.svd(x, compute_uv=False, hermitian=False). This function is compatible with the array API standard.

(gh-24940)

A new isdtype function

numpy.isdtype was added to provide a canonical way to classify NumPy\'s dtypes in compliance with the array API standard.

(gh-25054)

A new astype function

numpy.astype was added to provide an array API standard-compatible alternative to the numpy.ndarray.astype method.

(gh-25079)

Array API compatible functions\' aliases

13 aliases for existing functions were added to improve compatibility with the array API standard:

• Trigonometry: acos, acosh, asin, asinh, atan, atanh, atan2.
• Bitwise: bitwise_left_shift, bitwise_invert, bitwise_right_shift.
• Misc: concat, permute_dims, pow.
• In numpy.linalg: tensordot, matmul.

(gh-25086)

New unique_* functions

The numpy.unique_all, numpy.unique_counts, numpy.unique_inverse, and numpy.unique_values functions have been added. They provide functionality of numpy.unique with different sets of flags. They are array API standard-compatible, and because the number of arrays they return does not depend on the values of input arguments, they are easier to target for JIT compilation.

(gh-25088)

Matrix transpose support for ndarrays

NumPy now offers support for calculating the matrix transpose of an array (or stack of arrays). The matrix transpose is equivalent to swapping the last two axes of an array. Both np.ndarray and np.ma.MaskedArray now expose a .mT attribute, and there is a matching new numpy.matrix_transpose function.

(gh-23762)

Array API compatible functions for numpy.linalg

Six new functions and two aliases were added to improve compatibility with the Array API standard for `numpy.linalg`:

• numpy.linalg.matrix_norm - Computes the matrix norm of a matrix (or a stack of matrices).

• numpy.linalg.vector_norm - Computes the vector norm of a vector (or batch of vectors).

• numpy.vecdot - Computes the (vector) dot product of two arrays.

• numpy.linalg.vecdot - An alias for numpy.vecdot.

• numpy.linalg.matrix_transpose - An alias for numpy.matrix_transpose.

  (gh-25155)

• numpy.linalg.outer has been added. It computes the outer product of two vectors. It differs from numpy.outer by accepting one-dimensional arrays only. This function is compatible with the array API standard.

  (gh-25101)

• numpy.linalg.cross has been added. It computes the cross product of two (arrays of) 3-dimensional vectors. It differs from numpy.cross by accepting three-dimensional vectors only. This function is compatible with the array API standard.

  (gh-25145)

A correction argument for var and std

A correction argument was added to numpy.var and numpy.std, which is an array API standard compatible alternative to ddof. As both arguments serve a similar purpose, only one of them can be provided at the same time.

(gh-25169)

ndarray.device and ndarray.to_device

An ndarray.device attribute and ndarray.to_device method were added to numpy.ndarray for array API standard compatibility.

Additionally, device keyword-only arguments were added to: numpy.asarray, numpy.arange, numpy.empty, numpy.empty_like, numpy.eye, numpy.full, numpy.full_like, numpy.linspace, numpy.ones, numpy.ones_like, numpy.zeros, and numpy.zeros_like.

For all these new arguments, only device="cpu" is supported.

(gh-25233)

StringDType has been added to NumPy

We have added a new variable-width UTF-8 encoded string data type, implementing a \"NumPy array of Python strings\", including support for a user-provided missing data sentinel. It is intended as a drop-in replacement for arrays of Python strings and missing data sentinels using the object dtype. See NEP 55 and the documentation of stringdtype for more details.

(gh-25347)

New keywords for cholesky and pinv

The upper and rtol keywords were added to numpy.linalg.cholesky and numpy.linalg.pinv, respectively, to improve array API standard compatibility.

For numpy.linalg.pinv, if neither rcond nor rtol is specified, the rcond\'s default is used. We plan to deprecate and remove rcond in the future.

(gh-25388)

New keywords for sort, argsort and linalg.matrix_rank

New keyword parameters were added to improve array API standard compatibility:

• rtol was added to numpy.linalg.matrix_rank.
• stable was added to numpy.sort and numpy.argsort.

(gh-25437)

New numpy.strings namespace for string ufuncs

NumPy now implements some string operations as ufuncs. The old np.char namespace is still available, and where possible the string manipulation functions in that namespace have been updated to use the new ufuncs, substantially improving their performance.

Where possible, we suggest updating code to use functions in np.strings instead of np.char. In the future we may deprecate np.char in favor of np.strings.

(gh-25463)

numpy.fft support for different precisions and in-place calculations

The various FFT routines in numpy.fft now do their calculations natively in float, double, or long double precision, depending on the input precision, instead of always calculating in double precision. Hence, the calculation will now be less precise for single and more precise for long double precision. The data type of the output array will now be adjusted accordingly.

Furthermore, all FFT routines have gained an out argument that can be used for in-place calculations.

(gh-25536)

configtool and pkg-config support

A new numpy-config CLI script is available that can be queried for the NumPy version and for compile flags needed to use the NumPy C API. This will allow build systems to better support the use of NumPy as a dependency. Also, a numpy.pc pkg-config file is now included with Numpy. In order to find its location for use with PKG_CONFIG_PATH, use numpy-config --pkgconfigdir.

(gh-25730)

Array API standard support in the main namespace

The main numpy namespace now supports the array API standard. See array-api-standard-compatibility for details.

(gh-25911)

Improvements

Strings are now supported by any, all, and the logical ufuncs.

(gh-25651)

Integer sequences as the shape argument for memmap

numpy.memmap can now be created with any integer sequence as the shape argument, such as a list or numpy array of integers. Previously, only the types of tuple and int could be used without raising an error.

(gh-23729)

errstate is now faster and context safe

The numpy.errstate context manager/decorator is now faster and safer. Previously, it was not context safe and had (rare) issues with thread-safety.

(gh-23936)

AArch64 quicksort speed improved by using Highway\'s VQSort

The first introduction of the Google Highway library, using VQSort on AArch64. Execution time is improved by up to 16x in some cases, see the PR for benchmark results. Extensions to other platforms will be done in the future.

(gh-24018)

Complex types - underlying C type changes

• The underlying C types for all of NumPy\'s complex types have been changed to use C99 complex types.

• While this change does not affect the memory layout of complex types, it changes the API to be used to directly retrieve or write the real or complex part of the complex number, since direct field access (as in c.real or c.imag) is no longer an option. You can now use utilities provided in numpy/npy_math.h to do these operations, like this:

  c npy_cdouble c; npy_csetreal(&c, 1.0); npy_csetimag(&c, 0.0); printf("%d + %di\n", npy_creal(c), npy_cimag(c));

• To ease cross-version compatibility, equivalent macros and a compatibility layer have been added which can be used by downstream packages to continue to support both NumPy 1.x and 2.x. See complex-numbers for more info.

• numpy/npy_common.h now includes complex.h, which means that complex is now a reserved keyword.

(gh-24085)

iso_c_binding support and improved common blocks for f2py

Previously, users would have to define their own custom f2cmap file to use type mappings defined by the Fortran2003 iso_c_binding intrinsic module. These type maps are now natively supported by f2py

(gh-24555)

f2py now handles common blocks which have kind specifications from modules. This further expands the usability of intrinsics like iso_fortran_env and iso_c_binding.

(gh-25186)

Call str automatically on third argument to functions like assert_equal

The third argument to functions like numpy.testing.assert_equal now has str called on it automatically. This way it mimics the built-in assert statement, where assert_equal(a, b, obj) works like assert a == b, obj.

(gh-24877)

Support for array-like atol/rtol in isclose, allclose

The keywords atol and rtol in numpy.isclose and numpy.allclose now accept both scalars and arrays. An array, if given, must broadcast to the shapes of the first two array arguments.

(gh-24878)

Consistent failure messages in test functions

Previously, some numpy.testing assertions printed messages that referred to the actual and desired results as x and y. Now, these values are consistently referred to as ACTUAL and DESIRED.

(gh-24931)

n-D FFT transforms allow s[i] == -1

The numpy.fft.fftn, numpy.fft.ifftn, numpy.fft.rfftn, numpy.fft.irfftn, numpy.fft.fft2, numpy.fft.ifft2, numpy.fft.rfft2 and numpy.fft.irfft2 functions now use the whole input array along the axis i if s[i] == -1, in line with the array API standard.

(gh-25495)

Guard PyArrayScalar_VAL and PyUnicodeScalarObject for the limited API

PyUnicodeScalarObject holds a PyUnicodeObject, which is not available when using Py_LIMITED_API. Add guards to hide it and consequently also make the PyArrayScalar_VAL macro hidden.

(gh-25531)

Changes

• np.gradient() now returns a tuple rather than a list making the return value immutable.

  (gh-23861)

• Being fully context and thread-safe, np.errstate can only be entered once now.

• np.setbufsize is now tied to np.errstate(): leaving an np.errstate context will also reset the bufsize.

  (gh-23936)

• A new public np.lib.array_utils submodule has been introduced and it currently contains three functions: byte_bounds (moved from np.lib.utils), normalize_axis_tuple and normalize_axis_index.

  (gh-24540)

• Introduce numpy.bool as the new canonical name for NumPy\'s boolean dtype, and make numpy.bool\_ an alias to it. Note that until NumPy 1.24, np.bool was an alias to Python\'s builtin bool. The new name helps with array API standard compatibility and is a more intuitive name.

  (gh-25080)

• The dtype.flags value was previously stored as a signed integer. This means that the aligned dtype struct flag lead to negative flags being set (-128 rather than 128). This flag is now stored unsigned (positive). Code which checks flags manually may need to adapt. This may include code compiled with Cython 0.29.x.

  (gh-25816)

Representation of NumPy scalars changed

As per NEP 51, the scalar representation has been updated to include the type information to avoid confusion with Python scalars.

Scalars are now printed as np.float64(3.0) rather than just 3.0. This may disrupt workflows that store representations of numbers (e.g., to files) making it harder to read them. They should be stored as explicit strings, for example by using str() or f"{scalar!s}". For the time being, affected users can use np.set_printoptions(legacy="1.25") to get the old behavior (with possibly a few exceptions). Documentation of downstream projects may require larger updates, if code snippets are tested. We are working on tooling for doctest-plus to facilitate updates.

(gh-22449)

Truthiness of NumPy strings changed

NumPy strings previously were inconsistent about how they defined if the string is True or False and the definition did not match the one used by Python. Strings are now considered True when they are non-empty and False when they are empty. This changes the following distinct cases:

• Casts from string to boolean were previously roughly equivalent to string_array.astype(np.int64).astype(bool), meaning that only valid integers could be cast. Now a string of "0" will be considered True since it is not empty. If you need the old behavior, you may use the above step (casting to integer first) or string_array == "0" (if the input is only ever 0 or 1). To get the new result on old NumPy versions use string_array != "".
• np.nonzero(string_array) previously ignored whitespace so that a string only containing whitespace was considered False. Whitespace is now considered True.

This change does not affect np.loadtxt, np.fromstring, or np.genfromtxt. The first two still use the integer definition, while genfromtxt continues to match for "true" (ignoring case). However, if np.bool_ is used as a converter the result will change.

The change does affect np.fromregex as it uses direct assignments.

(gh-23871)

A mean keyword was added to var and std function

Often when the standard deviation is needed the mean is also needed. The same holds for the variance and the mean. Until now the mean is then calculated twice, the change introduced here for the numpy.var and numpy.std functions allows for passing in a precalculated mean as an keyword argument. See the docstrings for details and an example illustrating the speed-up.

(gh-24126)

Remove datetime64 deprecation warning when constructing with timezone

The numpy.datetime64 method now issues a UserWarning rather than a DeprecationWarning whenever a timezone is included in the datetime string that is provided.

(gh-24193)

Default integer dtype is now 64-bit on 64-bit Windows

The default NumPy integer is now 64-bit on all 64-bit systems as the historic 32-bit default on Windows was a common source of issues. Most users should not notice this. The main issues may occur with code interfacing with libraries written in a compiled language like C. For more information see migration_windows_int64.

(gh-24224)

Renamed numpy.core to numpy._core

Accessing numpy.core now emits a DeprecationWarning. In practice we have found that most downstream usage of numpy.core was to access functionality that is available in the main numpy namespace. If for some reason you are using functionality in numpy.core that is not available in the main numpy namespace, this means you are likely using private NumPy internals. You can still access these internals via numpy._core without a deprecation warning but we do not provide any backward compatibility guarantees for NumPy internals. Please open an issue if you think a mistake was made and something needs to be made public.

(gh-24634)

The \"relaxed strides\" debug build option, which was previously enabled through the NPY_RELAXED_STRIDES_DEBUG environment variable or the -Drelaxed-strides-debug config-settings flag has been removed.

(gh-24717)

Redefinition of np.intp/np.uintp (almost never a change)

Due to the actual use of these types almost always matching the use of size_t/Py_ssize_t this is now the definition in C. Previously, it matched intptr_t and uintptr_t which would often have been subtly incorrect. This has no effect on the vast majority of machines since the size of these types only differ on extremely niche platforms.

However, it means that:

• Pointers may not necessarily fit into an intp typed array anymore. The p and P character codes can still be used, however.
• Creating intptr_t or uintptr_t typed arrays in C remains possible in a cross-platform way via PyArray_DescrFromType('p').
• The new character codes nN were introduced.
• It is now correct to use the Python C-API functions when parsing to npy_intp typed arguments.

(gh-24888)

numpy.fft.helper made private

numpy.fft.helper was renamed to numpy.fft._helper to indicate that it is a private submodule. All public functions exported by it should be accessed from numpy.fft.

(gh-24945)

numpy.linalg.linalg made private

numpy.linalg.linalg was renamed to numpy.linalg._linalg to indicate that it is a private submodule. All public functions exported by it should be accessed from numpy.linalg.

(gh-24946)

Out-of-bound axis not the same as axis=None

In some cases axis=32 or for concatenate any large value was the same as axis=None. Except for concatenate this was deprecate. Any out of bound axis value will now error, make sure to use axis=None.

(gh-25149)

New copy keyword meaning for array and asarray constructors

Now numpy.array and numpy.asarray support three values for copy parameter:

• None - A copy will only be made if it is necessary.
• True - Always make a copy.
• False - Never make a copy. If a copy is required a ValueError is raised.

The meaning of False changed as it now raises an exception if a copy is needed.

(gh-25168)

The __array__ special method now takes a copy keyword argument.

NumPy will pass copy to the __array__ special method in situations where it would be set to a non-default value (e.g. in a call to np.asarray(some_object, copy=False)). Currently, if an unexpected keyword argument error is raised after this, NumPy will print a warning and re-try without the copy keyword argument. Implementations of objects implementing the __array__ protocol should accept a copy keyword argument with the same meaning as when passed to numpy.array or numpy.asarray.

(gh-25168)

Cleanup of initialization of numpy.dtype with strings with commas

The interpretation of strings with commas is changed slightly, in that a trailing comma will now always create a structured dtype. E.g., where previously np.dtype("i") and np.dtype("i,") were treated as identical, now np.dtype("i,") will create a structured dtype, with a single field. This is analogous to np.dtype("i,i") creating a structured dtype with two fields, and makes the behaviour consistent with that expected of tuples.

At the same time, the use of single number surrounded by parenthesis to indicate a sub-array shape, like in np.dtype("(2)i,"), is deprecated. Instead; one should use np.dtype("(2,)i") or np.dtype("2i"). Eventually, using a number in parentheses will raise an exception, like is the case for initializations without a comma, like np.dtype("(2)i").

(gh-25434)

Change in how complex sign is calculated

Following the array API standard, the complex sign is now calculated as z / |z| (instead of the rather less logical case where the sign of the real part was taken, unless the real part was zero, in which case the sign of the imaginary part was returned). Like for real numbers, zero is returned if z==0.

(gh-25441)

Return types of functions that returned a list of arrays

Functions that returned a list of ndarrays have been changed to return a tuple of ndarrays instead. Returning tuples consistently whenever a sequence of arrays is returned makes it easier for JIT compilers like Numba, as well as for static type checkers in some cases, to support these functions. Changed functions are: numpy.atleast_1d, numpy.atleast_2d, numpy.atleast_3d, numpy.broadcast_arrays, numpy.meshgrid, numpy.ogrid, numpy.histogramdd.

np.unique return_inverse shape for multi-dimensional inputs

When multi-dimensional inputs are passed to np.unique with return_inverse=True, the unique_inverse output is now shaped such that the input can be reconstructed directly using np.take(unique, unique_inverse) when axis=None, and np.take_along_axis(unique, unique_inverse, axis=axis) otherwise.

(gh-25553, gh-25570)

any and all return booleans for object arrays

The any and all functions and methods now return booleans also for object arrays. Previously, they did a reduction which behaved like the Python or and and operators which evaluates to one of the arguments. You can use np.logical_or.reduce and np.logical_and.reduce to achieve the previous behavior.

(gh-25712)

np.can_cast cannot be called on Python int, float, or complex

np.can_cast cannot be called with Python int, float, or complex instances anymore. This is because NEP 50 means that the result of can_cast must not depend on the value passed in. Unfortunately, for Python scalars whether a cast should be considered "same_kind" or "safe" may depend on the context and value so that this is currently not implemented. In some cases, this means you may have to add a specific path for: if type(obj) in (int, float, complex): ....

(gh-26393)

Checksums

MD5

fcda027f9735771088e607161c913094  numpy-2.0.0-cp310-cp310-macosx_10_9_x86_64.whl
1c381a5af3e6b945c6937ab3c6e2de09  numpy-2.0.0-cp310-cp310-macosx_11_0_arm64.whl
6258de3c0599f8e3674e11898f2dd71c  numpy-2.0.0-cp310-cp310-macosx_14_0_arm64.whl
aa4d28b404566dc9f5c34a31c6cd7b23  numpy-2.0.0-cp310-cp310-macosx_14_0_x86_64.whl
6b83ba81bdc750ef9924e3dc6f7c93be  numpy-2.0.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
3d129fe67d99e0aad451742abb963ffa  numpy-2.0.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
24a060577965bd2a573ed87cbd207b4c  numpy-2.0.0-cp310-cp310-musllinux_1_1_x86_64.whl
b00832f558669aacf855c4f5e9cf31d1  numpy-2.0.0-cp310-cp310-musllinux_1_2_aarch64.whl
cfe7420d294c583b90cfe07b730136dc  numpy-2.0.0-cp310-cp310-win32.whl
cff9da6b9fe5ad3b05dd3526dff00ac2  numpy-2.0.0-cp310-cp310-win_amd64.whl
f390e03564df5ea37a97ac10cf0cbb00  numpy-2.0.0-cp311-cp311-macosx_10_9_x86_64.whl
a006b081decba286a321de67a1abe246  numpy-2.0.0-cp311-cp311-macosx_11_0_arm64.whl
6aea3e8589e33349b8170524af5a2e44  numpy-2.0.0-cp311-cp311-macosx_14_0_arm64.whl
eea8146c5dc2a306333bfea1f01f7a37  numpy-2.0.0-cp311-cp311-macosx_14_0_x86_64.whl
e96c2af477c970c8ff50ecb5d1cf754f  numpy-2.0.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
d065256e02a1d410d0db2577bb8fd9a4  numpy-2.0.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
98c570b79459342c219590c5af38d527  numpy-2.0.0-cp311-cp311-musllinux_1_1_x86_64.whl
dc435751cb926f53a9fc457f35146527  numpy-2.0.0-cp311-cp311-musllinux_1_2_aarch64.whl
aaa4b435d29022ceacb4e3dcbd43d11a  numpy-2.0.0-cp311-cp311-win32.whl
9ff8be4f581d86b2f181fe905491b19b  numpy-2.0.0-cp311-cp311-win_amd64.whl
1c9519c5e6a0c5a99715e51ac3b7c932  numpy-2.0.0-cp312-cp312-macosx_10_9_x86_64.whl
b0f26e8728523d716f5165953b35244f  numpy-2.0.0-cp312-cp312-macosx_11_0_arm64.whl
029703d0ff0e96c603c91f611926ef17  numpy-2.0.0-cp312-cp312-macosx_14_0_arm64.whl
2231ecbb380c70ddf462e9671d06612c  numpy-2.0.0-cp312-cp312-macosx_14_0_x86_64.whl
4153b50c1a3647ca58f1084fcaf3e4c6  numpy-2.0.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
82cba3915234f8018fd754ffc45e95b0  numpy-2.0.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
249047dd7255a5fcf5c45614ba211e10  numpy-2.0.0-cp312-cp312-musllinux_1_1_x86_64.whl
f7581ebfe0c9d4ae4f3b6ea09c19eea7  numpy-2.0.0-cp312-cp312-musllinux_1_2_aarch64.whl
8a0dbcd919d1d959f1846a00ebb05162  numpy-2.0.0-cp312-cp312-win32.whl
22aabdfd85ed34f02a7cdacff399c5d9  numpy-2.0.0-cp312-cp312-win_amd64.whl
1fce84122c393e05b69e2ec53ecd1137  numpy-2.0.0-cp39-cp39-macosx_10_9_x86_64.whl
81e4c1152274d85813bf14814ad4d359  numpy-2.0.0-cp39-cp39-macosx_11_0_arm64.whl
5eab1a2b427b590d2bc9d8ecd330fc21  numpy-2.0.0-cp39-cp39-macosx_14_0_arm64.whl
ab967929693baf2d2bfb00c53413ad2b  numpy-2.0.0-cp39-cp39-macosx_14_0_x86_64.whl
85d2971cd78800663766f46ba312d356  numpy-2.0.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
7e831fcf9cff5317429786a3bd123671  numpy-2.0.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
03a6426ca86ad53567e3ef61bc766013  numpy-2.0.0-cp39-cp39-musllinux_1_1_x86_64.whl
b30af2d2b99468538f45e6769f9fee2b  numpy-2.0.0-cp39-cp39-musllinux_1_2_aarch64.whl
cc9a8db8d131fb5a387e2c1342ab0065  numpy-2.0.0-cp39-cp39-win32.whl
9843951308fa31c5e36c4c6a0b090308  numpy-2.0.0-cp39-cp39-win_amd64.whl
5021eb5e225bff3e05a38a565daf8852  numpy-2.0.0-pp39-pypy39_pp73-macosx_10_9_x86_64.whl
99186fe49ac7931d3e92e8993c2faa92  numpy-2.0.0-pp39-pypy39_pp73-macosx_14_0_x86_64.whl
c39f0ab6e07d42708550899951b852b8  numpy-2.0.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
cbf151633948e90c93dd988777750961  numpy-2.0.0-pp39-pypy39_pp73-win_amd64.whl
a180aaba9982c6e15da6db62dab5eb4e  numpy-2.0.0.tar.gz

SHA256

04494f6ec467ccb5369d1808570ae55f6ed9b5809d7f035059000a37b8d7e86f  numpy-2.0.0-cp310-cp310-macosx_10_9_x86_64.whl
2635dbd200c2d6faf2ef9a0d04f0ecc6b13b3cad54f7c67c61155138835515d2  numpy-2.0.0-cp310-cp310-macosx_11_0_arm64.whl
0a43f0974d501842866cc83471bdb0116ba0dffdbaac33ec05e6afed5b615238  numpy-2.0.0-cp310-cp310-macosx_14_0_arm64.whl
8d83bb187fb647643bd56e1ae43f273c7f4dbcdf94550d7938cfc32566756514  numpy-2.0.0-cp310-cp310-macosx_14_0_x86_64.whl
79e843d186c8fb1b102bef3e2bc35ef81160ffef3194646a7fdd6a73c6b97196  numpy-2.0.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
6d7696c615765091cc5093f76fd1fa069870304beaccfd58b5dcc69e55ef49c1  numpy-2.0.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
b4c76e3d4c56f145d41b7b6751255feefae92edbc9a61e1758a98204200f30fc  numpy-2.0.0-cp310-cp310-musllinux_1_1_x86_64.whl
acd3a644e4807e73b4e1867b769fbf1ce8c5d80e7caaef0d90dcdc640dfc9787  numpy-2.0.0-cp310-cp310-musllinux_1_2_aarch64.whl
cee6cc0584f71adefe2c908856ccc98702baf95ff80092e4ca46061538a2ba98  numpy-2.0.0-cp310-cp310-win32.whl
ed08d2703b5972ec736451b818c2eb9da80d66c3e84aed1deeb0c345fefe461b  numpy-2.0.0-cp310-cp310-win_amd64.whl
ad0c86f3455fbd0de6c31a3056eb822fc939f81b1618f10ff3406971893b62a5  numpy-2.0.0-cp311-cp311-macosx_10_9_x86_64.whl
e7f387600d424f91576af20518334df3d97bc76a300a755f9a8d6e4f5cadd289  numpy-2.0.0-cp311-cp311-macosx_11_0_arm64.whl
34f003cb88b1ba38cb9a9a4a3161c1604973d7f9d5552c38bc2f04f829536609  numpy-2.0.0-cp311-cp311-macosx_14_0_arm64.whl
b6f6a8f45d0313db07d6d1d37bd0b112f887e1369758a5419c0370ba915b3871  numpy-2.0.0-cp311-cp311-macosx_14_0_x86_64.whl
5f64641b42b2429f56ee08b4f427a4d2daf916ec59686061de751a55aafa22e4  numpy-2.0.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
a7039a136017eaa92c1848152827e1424701532ca8e8967fe480fe1569dae581  numpy-2.0.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
46e161722e0f619749d1cd892167039015b2c2817296104487cd03ed4a955995  numpy-2.0.0-cp311-cp311-musllinux_1_1_x86_64.whl
0e50842b2295ba8414c8c1d9d957083d5dfe9e16828b37de883f51fc53c4016f  numpy-2.0.0-cp311-cp311-musllinux_1_2_aarch64.whl
2ce46fd0b8a0c947ae047d222f7136fc4d55538741373107574271bc00e20e8f  numpy-2.0.0-cp311-cp311-win32.whl
fbd6acc766814ea6443628f4e6751d0da6593dae29c08c0b2606164db026970c  numpy-2.0.0-cp311-cp311-win_amd64.whl
354f373279768fa5a584bac997de6a6c9bc535c482592d7a813bb0c09be6c76f  numpy-2.0.0-cp312-cp312-macosx_10_9_x86_64.whl
4d2f62e55a4cd9c58c1d9a1c9edaedcd857a73cb6fda875bf79093f9d9086f85  numpy-2.0.0-cp312-cp312-macosx_11_0_arm64.whl
1e72728e7501a450288fc8e1f9ebc73d90cfd4671ebbd631f3e7857c39bd16f2  numpy-2.0.0-cp312-cp312-macosx_14_0_arm64.whl
84554fc53daa8f6abf8e8a66e076aff6ece62de68523d9f665f32d2fc50fd66e  numpy-2.0.0-cp312-cp312-macosx_14_0_x86_64.whl
c73aafd1afca80afecb22718f8700b40ac7cab927b8abab3c3e337d70e10e5a2  numpy-2.0.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
49d9f7d256fbc804391a7f72d4a617302b1afac1112fac19b6c6cec63fe7fe8a  numpy-2.0.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
0ec84b9ba0654f3b962802edc91424331f423dcf5d5f926676e0150789cb3d95  numpy-2.0.0-cp312-cp312-musllinux_1_1_x86_64.whl
feff59f27338135776f6d4e2ec7aeeac5d5f7a08a83e80869121ef8164b74af9  numpy-2.0.0-cp312-cp312-musllinux_1_2_aarch64.whl
c5a59996dc61835133b56a32ebe4ef3740ea5bc19b3983ac60cc32be5a665d54  numpy-2.0.0-cp312-cp312-win32.whl
a356364941fb0593bb899a1076b92dfa2029f6f5b8ba88a14fd0984aaf76d0df  numpy-2.0.0-cp312-cp312-win_amd64.whl
e61155fae27570692ad1d327e81c6cf27d535a5d7ef97648a17d922224b216de  numpy-2.0.0-cp39-cp39-macosx_10_9_x86_64.whl
4554eb96f0fd263041baf16cf0881b3f5dafae7a59b1049acb9540c4d57bc8cb  numpy-2.0.0-cp39-cp39-macosx_11_0_arm64.whl
903703372d46bce88b6920a0cd86c3ad82dae2dbef157b5fc01b70ea1cfc430f  numpy-2.0.0-cp39-cp39-macosx_14_0_arm64.whl
3e8e01233d57639b2e30966c63d36fcea099d17c53bf424d77f088b0f4babd86  numpy-2.0.0-cp39-cp39-macosx_14_0_x86_64.whl
1cde1753efe513705a0c6d28f5884e22bdc30438bf0085c5c486cdaff40cd67a  numpy-2.0.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
821eedb7165ead9eebdb569986968b541f9908979c2da8a4967ecac4439bae3d  numpy-2.0.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
9a1712c015831da583b21c5bfe15e8684137097969c6d22e8316ba66b5baabe4  numpy-2.0.0-cp39-cp39-musllinux_1_1_x86_64.whl
9c27f0946a3536403efb0e1c28def1ae6730a72cd0d5878db38824855e3afc44  numpy-2.0.0-cp39-cp39-musllinux_1_2_aarch64.whl
63b92c512d9dbcc37f9d81b123dec99fdb318ba38c8059afc78086fe73820275  numpy-2.0.0-cp39-cp39-win32.whl
3f6bed7f840d44c08ebdb73b1825282b801799e325bcbdfa6bc5c370e5aecc65  numpy-2.0.0-cp39-cp39-win_amd64.whl
9416a5c2e92ace094e9f0082c5fd473502c91651fb896bc17690d6fc475128d6  numpy-2.0.0-pp39-pypy39_pp73-macosx_10_9_x86_64.whl
17067d097ed036636fa79f6a869ac26df7db1ba22039d962422506640314933a  numpy-2.0.0-pp39-pypy39_pp73-macosx_14_0_x86_64.whl
38ecb5b0582cd125f67a629072fed6f83562d9dd04d7e03256c9829bdec027ad  numpy-2.0.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
cef04d068f5fb0518a77857953193b6bb94809a806bd0a14983a8f12ada060c9  numpy-2.0.0-pp39-pypy39_pp73-win_amd64.whl
cf5d1c9e6837f8af9f92b6bd3e86d513cdc11f60fd62185cc49ec7d1aba34864  numpy-2.0.0.tar.gz

- Python
Published by charris almost 2 years ago

numpy -

NumPy 2.0.0 Release Notes

Note

The release of 2.0 is in progress and the current release overview and highlights are still in a draft state. However, the highlights should already list the most significant changes detailed in the full notes below, and those full notes should be complete (if not copy-edited well enough yet).

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 198 contributors spread over 1041 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:

• The numpy-2-migration-guide
• The Numpy 2.0-specific advice in for dpwmstream package authors

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 and Google Highway libraries, and may see large (hardware-specific) speedups,
  • macOS Accelerate support and binary wheels for macOS >=14, with significant performance improvements for linear algebra operations on macOS, and wheels that are about 3 times smaller,
  • numpy.char fixed-length string operations have been accelerated by implementing ufuncs that also support numpy.dtypes.StringDType in addition to the fixed-length string dtypes,
  • A new tracing and introspection API, numpy.lib.introspect.opt_func_info, to determine which hardware-specific kernels are available and will be dispatched to.
• Python API improvements:
  • A clear split between public and private API, with a new module structure and each public function now available in a single place.
  • Many removals of non-recommended functions and aliases. This should make it easier to learn and use NumPy. The number of objects in the main namespace decreased by ~10% and in numpy.lib by ~80%.
  • Canonical dtype names and a newnumpy.isdtype` introspection function,
• C API improvements:
  • A new public C API for creating custom dtypes,
  • Many outdated functions and macros removed, and private internals hidden to ease future extensibility,
  • New, easier to use, initialization functions: PyArray_ImportNumPyAPI and PyUFunc_ImportUFuncAPI.
• Improved behavior:
  • Improvements to type promotion behavior was changed by adopting NEP 50. This fixes many user surprises about promotions which previously often depended on data values of input arrays rather than only their dtypes. Please see the NEP and the numpy-2-migration-guide for details as this change can lead to changes in output dtypes and lower precision results for mixed-dtype operations.
  • The default integer type on Windows is now int64 rather than int32, matching the behavior on other platforms,
  • The maximum number of array dimensions is changed from 32 to 64
• Documentation:
  • The reference guide navigation was significantly improved, and there is now documentation on NumPy\'s module structure,
  • The building from source documentation was completely rewritten,

Furthermore there are many changes to NumPy internals, including continuing to migrate code from C to C++, that will make it easier to improve and maintain NumPy in the future.

The \"no free lunch\" theorem dictates that there is a price to pay for all these API and behavior improvements and better future extensibility. This price is:

1. Backwards compatibility. There are a significant number of breaking changes to both the Python and C APIs. In the majority of cases, there are clear error messages that will inform the user how to adapt their code. However, there are also changes in behavior for which it was not possible to give such an error message - these cases are all covered in the Deprecation and Compatibility sections below, and in the numpy-2-migration-guide.

   Note that there is a ruff mode to auto-fix many things in Python code.

2. Breaking changes to the NumPy ABI. As a result, binaries of packages that use the NumPy C API and were built against a NumPy 1.xx release will not work with NumPy 2.0. On import, such packages will see an ImportError with a message about binary incompatibility.

   It is possible to build binaries against NumPy 2.0 that will work at runtime with both NumPy 2.0 and 1.x. See numpy-2-abi-handling for more details.

   All downstream packages that depend on the NumPy ABI are advised to do a new release built against NumPy 2.0 and verify that that release works with both 2.0 and 1.26 - ideally in the period between 2.0.0rc1 (which will be ABI-stable) and the final 2.0.0 release to avoid problems for their users.

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

NumPy 2.0 Python API removals

• np.geterrobj, np.seterrobj and the related ufunc keyword argument extobj= have been removed. The preferred replacement for all of these is using the context manager with np.errstate():.

  (gh-23922)

• np.cast has been removed. The literal replacement for np.cast[dtype](arg) is np.asarray(arg, dtype=dtype).

• np.source has been removed. The preferred replacement is inspect.getsource.

• np.lookfor has been removed.

  (gh-24144)

• numpy.who has been removed. As an alternative for the removed functionality, one can use a variable explorer that is available in IDEs such as Spyder or Jupyter Notebook.

  (gh-24321)

• Multiple niche enums, expired members and functions have been removed from the main namespace, such as: ERR_*, SHIFT_*, np.fastCopyAndTranspose, np.kernel_version, np.numarray, np.oldnumeric and np.set_numeric_ops.

  (gh-24316)

• Replaced from ... import * in the numpy/__init__.py with explicit imports. As a result, these main namespace members got removed: np.FLOATING_POINT_SUPPORT, np.FPE_*, np.NINF, np.PINF, np.NZERO, np.PZERO, np.CLIP, np.WRAP, np.WRAP, np.RAISE, np.BUFSIZE, np.UFUNC_BUFSIZE_DEFAULT, np.UFUNC_PYVALS_NAME, np.ALLOW_THREADS, np.MAXDIMS, np.MAY_SHARE_EXACT, np.MAY_SHARE_BOUNDS, add_newdoc, np.add_docstring and np.add_newdoc_ufunc.

  (gh-24357)

• Alias np.float_ has been removed. Use np.float64 instead.

• Alias np.complex_ has been removed. Use np.complex128 instead.

• Alias np.longfloat has been removed. Use np.longdouble instead.

• Alias np.singlecomplex has been removed. Use np.complex64 instead.

• Alias np.cfloat has been removed. Use np.complex128 instead.

• Alias np.longcomplex has been removed. Use np.clongdouble instead.

• Alias np.clongfloat has been removed. Use np.clongdouble instead.

• Alias np.string_ has been removed. Use np.bytes_ instead.

• Alias np.unicode_ has been removed. Use np.str_ instead.

• Alias np.Inf has been removed. Use np.inf instead.

• Alias np.Infinity has been removed. Use np.inf instead.

• Alias np.NaN has been removed. Use np.nan instead.

• Alias np.infty has been removed. Use np.inf instead.

• Alias np.mat has been removed. Use np.asmatrix instead.

• np.issubclass_ has been removed. Use the issubclass builtin instead.

• np.asfarray has been removed. Use np.asarray with a proper dtype instead.

• np.set_string_function has been removed. Use np.set_printoptions instead with a formatter for custom printing of NumPy objects.

• np.tracemalloc_domain is now only available from np.lib.

• np.recfromcsv and recfromtxt are now only available from np.lib.npyio.

• np.issctype, np.maximum_sctype, np.obj2sctype, np.sctype2char, np.sctypes, np.issubsctype were all removed from the main namespace without replacement, as they where niche members.

• Deprecated np.deprecate and np.deprecate_with_doc has been removed from the main namespace. Use DeprecationWarning instead.

• Deprecated np.safe_eval has been removed from the main namespace. Use ast.literal_eval instead.

  (gh-24376)

• np.find_common_type has been removed. Use numpy.promote_types or numpy.result_type instead. To achieve semantics for the scalar_types argument, use numpy.result_type and pass 0, 0.0, or 0j as a Python scalar instead.

• np.round_ has been removed. Use np.round instead.

• np.nbytes has been removed. Use np.dtype(<dtype>).itemsize instead.

  (gh-24477)

• np.compare_chararrays has been removed from the main namespace. Use np.char.compare_chararrays instead.

• The charrarray in the main namespace has been deprecated. It can be imported without a deprecation warning from np.char.chararray for now, but we are planning to fully deprecate and remove chararray in the future.

• np.format_parser has been removed from the main namespace. Use np.rec.format_parser instead.

  (gh-24587)

• Support for seven data type string aliases has been removed from np.dtype: int0, uint0, void0, object0, str0, bytes0 and bool8.

  (gh-24807)

• The experimental numpy.array_api submodule has been removed. Use the main numpy namespace for regular usage instead, or the separate array-api-strict package for the compliance testing use case for which numpy.array_api was mostly used.

  (gh-25911)

__array_prepare__ is removed

UFuncs called __array_prepare__ before running computations for normal ufunc calls (not generalized ufuncs, reductions, etc.). The function was also called instead of __array_wrap__ on the results of some linear algebra functions.

It is now removed. If you use it, migrate to __array_ufunc__ or rely on __array_wrap__ which is called with a context in all cases, although only after the result array is filled. In those code paths, __array_wrap__ will now be passed a base class, rather than a subclass array.

(gh-25105)

Deprecations

• np.compat has been deprecated, as Python 2 is no longer supported.

• np.safe_eval has been deprecated. ast.literal_eval should be used instead.

  (gh-23830)

• np.recfromcsv, np.recfromtxt, np.disp, np.get_array_wrap, np.maximum_sctype, np.deprecate and np.deprecate_with_doc have been deprecated.

  (gh-24154)

• np.trapz has been deprecated. Use np.trapezoid or a scipy.integrate function instead.

• np.in1d has been deprecated. Use np.isin instead.

• Alias np.row_stack has been deprecated. Use np.vstack directly.

  (gh-24445)

• __array_wrap__ is now passed arr, context, return_scalar and support for implementations not accepting all three are deprecated. Its signature should be __array_wrap__(self, arr, context=None, return_scalar=False)

  (gh-25408)

• Arrays of 2-dimensional vectors for np.cross have been deprecated. Use arrays of 3-dimensional vectors instead.

  (gh-24818)

• np.dtype("a") alias for np.dtype(np.bytes_) was deprecated. Use np.dtype("S") alias instead.

  (gh-24854)

• Use of keyword arguments x and y with functions assert_array_equal and assert_array_almost_equal has been deprecated. Pass the first two arguments as positional arguments instead.

  (gh-24978)

numpy.fft deprecations for n-D transforms with None values in arguments

Using fftn, ifftn, rfftn, irfftn, fft2, ifft2, rfft2 or irfft2 with the s parameter set to a value that is not None and the axes parameter set to None has been deprecated, in line with the array API standard. To retain current behaviour, pass a sequence [0, ..., k-1] to axes for an array of dimension k.

Furthermore, passing an array to s which contains None values is deprecated as the parameter is documented to accept a sequence of integers in both the NumPy docs and the array API specification. To use the default behaviour of the corresponding 1-D transform, pass the value matching the default for its n parameter. To use the default behaviour for every axis, the s argument can be omitted.

(gh-25495)

np.linalg.lstsq now defaults to a new rcond value

numpy.linalg.lstsq now uses the new rcond value of the machine precision times max(M, N). Previously, the machine precision was used but a FutureWarning was given to notify that this change will happen eventually. That old behavior can still be achieved by passing rcond=-1.

(gh-25721)

Expired deprecations

• The np.core.umath_tests submodule has been removed from the public API. (Deprecated in NumPy 1.15)

  (gh-23809)

• The PyDataMem_SetEventHook deprecation has expired and it is removed. Use tracemalloc and the np.lib.tracemalloc_domain domain. (Deprecated in NumPy 1.23)

  (gh-23921)

• The deprecation of set_numeric_ops and the C functions PyArray_SetNumericOps and PyArray_GetNumericOps has been expired and the functions removed. (Deprecated in NumPy 1.16)

  (gh-23998)

• The fasttake, fastclip, and fastputmask ArrFuncs deprecation is now finalized.

• The deprecated function fastCopyAndTranspose and its C counterpart are now removed.

• The deprecation of PyArray_ScalarFromObject is now finalized.

  (gh-24312)

• np.msort has been removed. For a replacement, np.sort(a, axis=0) should be used instead.

  (gh-24494)

• np.dtype(("f8", 1) will now return a shape 1 subarray dtype rather than a non-subarray one.

  (gh-25761)

• Assigning to the .data attribute of an ndarray is disallowed and will raise.

• np.binary_repr(a, width) will raise if width is too small.

• Using NPY_CHAR in PyArray_DescrFromType() will raise, use NPY_STRING NPY_UNICODE, or NPY_VSTRING instead.

  (gh-25794)

Compatibility notes

loadtxt and genfromtxt default encoding changed

loadtxt and genfromtxt now both default to encoding=None which may mainly modify how converters work. These will now be passed str rather than bytes. Pass the encoding explicitly to always get the new or old behavior. For genfromtxt the change also means that returned values will now be unicode strings rather than bytes.

(gh-25158)

f2py compatibility notes

• f2py will no longer accept ambiguous -m and .pyf CLI combinations. When more than one .pyf file is passed, an error is raised. When both -m and a .pyf is passed, a warning is emitted and the -m provided name is ignored.

  (gh-25181)

• The f2py.compile() helper has been removed because it leaked memory, has been marked as experimental for several years now, and was implemented as a thin subprocess.run wrapper. It was also one of the test bottlenecks. See gh-25122 for the full rationale. It also used several np.distutils features which are too fragile to be ported to work with meson.

• Users are urged to replace calls to f2py.compile with calls to subprocess.run("python", "-m", "numpy.f2py",... instead, and to use environment variables to interact with meson. Native files are also an option.

  (gh-25193)

Minor changes in behavior of sorting functions

Due to algorithmic changes and use of SIMD code, sorting functions with methods that aren\'t stable may return slightly different results in 2.0.0 compared to 1.26.x. This includes the default method of numpy.argsort and numpy.argpartition.

Removed ambiguity when broadcasting in np.solve

The broadcasting rules for np.solve(a, b) were ambiguous when b had 1 fewer dimensions than a. This has been resolved in a backward-incompatible way and is now compliant with the Array API. The old behaviour can be reconstructed by using np.solve(a, b[..., None])[..., 0].

(gh-25914)

Modified representation for Polynomial

The representation method for numpy.polynomial.polynomial.Polynomial was updated to include the domain in the representation. The plain text and latex representations are now consistent. For example the output of str(np.polynomial.Polynomial([1, 1], domain=[.1, .2])) used to be 1.0 + 1.0 x, but now is 1.0 + 1.0 (-3.0000000000000004 + 20.0 x).

(gh-21760)

C API changes

• The PyArray_CGT, PyArray_CLT, PyArray_CGE, PyArray_CLE, PyArray_CEQ, PyArray_CNE macros have been removed.

• PyArray_MIN and PyArray_MAX have been moved from ndarraytypes.h to npy_math.h.

  (gh-24258)

• A C API for working with numpy.dtypes.StringDType arrays has been exposed. This includes functions for acquiring and releasing mutexes which lock access to the string data, as well as packing and unpacking UTF-8 bytestreams from array entries.

• NPY_NTYPES has been renamed to NPY_NTYPES_LEGACY as it does not include new NumPy built-in DTypes. In particular the new string DType will likely not work correctly with code that handles legacy DTypes.

  (gh-25347)

• The C-API now only exports the static inline function versions of the array accessors (previously this depended on using \"deprecated API\"). While we discourage it, the struct fields can still be used directly.

  (gh-25789)

• NumPy now defines PyArray_Pack to set an individual memory address. Unlike PyArray_SETITEM this function is equivalent to setting an individual array item and does not require a NumPy array input.

  (gh-25954)

• The ->f slot has been removed from PyArray_Descr. If you use this slot, replace accessing it with PyDataType_GetArrFuncs (see its documentation and the numpy-2-migration-guide). In some cases using other functions like PyArray_GETITEM may be an alternatives.

• PyArray_GETITEM and PyArray_SETITEM now require the import of the NumPy API table to be used and are no longer defined in ndarraytypes.h.

  (gh-25812)

• Due to runtime dependencies, the definition for functionality accessing the dtype flags was moved from numpy/ndarraytypes.h and is only available after including numpy/ndarrayobject.h as it requires import_array(). This includes PyDataType_FLAGCHK, PyDataType_REFCHK and NPY_BEGIN_THREADS_DESCR.

• The dtype flags on PyArray_Descr must now be accessed through the PyDataType_FLAGS inline function to be compatible with both 1.x and 2.x. This function is defined in npy_2_compat.h to allow backporting. Most or all users should use PyDataType_FLAGCHK which is available on 1.x and does not require backporting. Cython users should use Cython 3. Otherwise access will go through Python unless they use PyDataType_FLAGCHK instead.

  (gh-25816)

Datetime functionality exposed in the C API and Cython bindings

The functions NpyDatetime_ConvertDatetime64ToDatetimeStruct, NpyDatetime_ConvertDatetimeStructToDatetime64, NpyDatetime_ConvertPyDateTimeToDatetimeStruct, NpyDatetime_GetDatetimeISO8601StrLen, NpyDatetime_MakeISO8601Datetime, and NpyDatetime_ParseISO8601Datetime have been added to the C API to facilitate converting between strings, Python datetimes, and NumPy datetimes in external libraries.

(gh-21199)

Const correctness for the generalized ufunc C API

The NumPy C API\'s functions for constructing generalized ufuncs (PyUFunc_FromFuncAndData, PyUFunc_FromFuncAndDataAndSignature, PyUFunc_FromFuncAndDataAndSignatureAndIdentity) take types and data arguments that are not modified by NumPy\'s internals. Like the name and doc arguments, third-party Python extension modules are likely to supply these arguments from static constants. The types and data arguments are now const-correct: they are declared as const char *types and void *const *data, respectively. C code should not be affected, but C++ code may be.

(gh-23847)

Larger NPY_MAXDIMS and NPY_MAXARGS, NPY_RAVEL_AXIS introduced

NPY_MAXDIMS is now 64, you may want to review its use. This is usually used in a stack allocation, where the increase should be safe. However, we do encourage generally to remove any use of NPY_MAXDIMS and NPY_MAXARGS to eventually allow removing the constraint completely. For the conversion helper and C-API functions mirroring Python ones such as take, NPY_MAXDIMS was used to mean axis=None. Such usage must be replaced with NPY_RAVEL_AXIS. See also migration_maxdims.

(gh-25149)

NPY_MAXARGS not constant and PyArrayMultiIterObject size change

Since NPY_MAXARGS was increased, it is now a runtime constant and not compile-time constant anymore. We expect almost no users to notice this. But if used for stack allocations it now must be replaced with a custom constant using NPY_MAXARGS as an additional runtime check.

The sizeof(PyArrayMultiIterObject) no longer includes the full size of the object. We expect nobody to notice this change. It was necessary to avoid issues with Cython.

(gh-25271)

Required changes for custom legacy user dtypes

In order to improve our DTypes it is unfortunately necessary to break the ABI, which requires some changes for dtypes registered with PyArray_RegisterDataType. Please see the documentation of PyArray_RegisterDataType for how to adapt your code and achieve compatibility with both 1.x and 2.x.

(gh-25792)

New Public DType API

The C implementation of the NEP 42 DType API is now public. While the DType API has shipped in NumPy for a few versions, it was only usable in sessions with a special environment variable set. It is now possible to write custom DTypes outside of NumPy using the new DType API and the normal import_array() mechanism for importing the numpy C API.

See dtype-api for more details about the API. As always with a new feature, please report any bugs you run into implementing or using a new DType. It is likely that downstream C code that works with dtypes will need to be updated to work correctly with new DTypes.

(gh-25754)

New C-API import functions

We have now added PyArray_ImportNumPyAPI and PyUFunc_ImportUFuncAPI as static inline functions to import the NumPy C-API tables. The new functions have two advantages over import_array and import_ufunc:

• They check whether the import was already performed and are light-weight if not, allowing to add them judiciously (although this is not preferable in most cases).
• The old mechanisms were macros rather than functions which included a return statement.

The PyArray_ImportNumPyAPI() function is included in npy_2_compat.h for simpler backporting.

(gh-25866)

Structured dtype information access through functions

The dtype structures fields c_metadata, names, fields, and subarray must now be accessed through new functions following the same names, such as PyDataType_NAMES. Direct access of the fields is not valid as they do not exist for all PyArray_Descr instances. The metadata field is kept, but the macro version should also be preferred.

(gh-25802)

Descriptor elsize and alignment access

Unless compiling only with NumPy 2 support, the elsize and aligment fields must now be accessed via PyDataType_ELSIZE, PyDataType_SET_ELSIZE, and PyDataType_ALIGNMENT. In cases where the descriptor is attached to an array, we advise using PyArray_ITEMSIZE as it exists on all NumPy versions. Please see migration_c_descr for more information.

(gh-25943)

NumPy 2.0 C API removals

• npy_interrupt.h and the corresponding macros like NPY_SIGINT_ON have been removed. We recommend querying PyErr_CheckSignals() or PyOS_InterruptOccurred() periodically (these do currently require holding the GIL though).

• The noprefix.h header has been removed. Replace missing symbols with their prefixed counterparts (usually an added NPY_ or npy_).

  (gh-23919)

• PyUFunc_GetPyVals, PyUFunc_handlefperr, and PyUFunc_checkfperr have been removed. If needed, a new backwards compatible function to raise floating point errors could be restored. Reason for removal: there are no known users and the functions would have made with np.errstate() fixes much more difficult).

  (gh-23922)

• The numpy/old_defines.h which was part of the API deprecated since NumPy 1.7 has been removed. This removes macros of the form PyArray_CONSTANT. The replaceoldmacros.sed script may be useful to convert them to the NPY_CONSTANT version.

  (gh-24011)

• The legacy_inner_loop_selector member of the ufunc struct is removed to simplify improvements to the dispatching system. There are no known users overriding or directly accessing this member.

  (gh-24271)

• NPY_INTPLTR has been removed to avoid confusion (see intp redefinition).

  (gh-24888)

• The advanced indexing MapIter and related API has been removed. The (truly) public part of it was not well tested and had only one known user (Theano). Making it private will simplify improvements to speed up ufunc.at, make advanced indexing more maintainable, and was important for increasing the maximum number of dimensions of arrays to 64. Please let us know if this API is important to you so we can find a solution together.

  (gh-25138)

• The NPY_MAX_ELSIZE macro has been removed, as it only ever reflected builtin numeric types and served no internal purpose.

  (gh-25149)

• PyArray_REFCNT and NPY_REFCOUNT are removed. Use Py_REFCNT instead.

  (gh-25156)

• PyArrayFlags_Type and PyArray_NewFlagsObject as well as PyArrayFlagsObject are private now. There is no known use-case; use the Python API if needed.

• PyArray_MoveInto, PyArray_CastTo, PyArray_CastAnyTo are removed use PyArray_CopyInto and if absolutely needed PyArray_CopyAnyInto (the latter does a flat copy).

• PyArray_FillObjectArray is removed, its only true use was for implementing np.empty. Create a new empty array or use PyArray_FillWithScalar() (decrefs existing objects).

• PyArray_CompareUCS4 and PyArray_CompareString are removed. Use the standard C string comparison functions.

• PyArray_ISPYTHON is removed as it is misleading, has no known use-cases, and is easy to replace.

• PyArray_FieldNames is removed, as it is unclear what it would be useful for. It also has incorrect semantics in some possible use-cases.

• PyArray_TypestrConvert is removed, since it seems a misnomer and unlikely to be used by anyone. If you know the size or are limited to few types, just use it explicitly, otherwise go via Python strings.

  (gh-25292)

• PyDataType_GetDatetimeMetaData is removed, it did not actually do anything since at least NumPy 1.7.

  (gh-25802)

• PyArray_GetCastFunc is removed. Note that custom legacy user dtypes can still provide a castfunc as their implementation, but any access to them is now removed. The reason for this is that NumPy never used these internally for many years. If you use simple numeric types, please just use C casts directly. In case you require an alternative, please let us know so we can create new API such as PyArray_CastBuffer() which could use old or new cast functions depending on the NumPy version.

  (gh-25161)

New Features

np.add was extended to work with unicode and bytes dtypes.

(gh-24858)

A new bitwise_count function

This new function counts the number of 1-bits in a number. numpy.bitwise_count works on all the numpy integer types and integer-like objects.

``` python

a = np.array([2**i - 1 for i in range(16)]) np.bitwise_count(a) array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], dtype=uint8) ```

(gh-19355)

macOS Accelerate support, including the ILP64

Support for the updated Accelerate BLAS/LAPACK library, including ILP64 (64-bit integer) support, in macOS 13.3 has been added. This brings arm64 support, and significant performance improvements of up to 10x for commonly used linear algebra operations. When Accelerate is selected at build time, or if no explicit BLAS library selection is done, the 13.3+ version will automatically be used if available.

(gh-24053)

Binary wheels are also available. On macOS >=14.0, users who install NumPy from PyPI will get wheels built against Accelerate rather than OpenBLAS.

(gh-25255)

Option to use weights for quantile and percentile functions

A weights keyword is now available for numpy.quantile, numpy.percentile, numpy.nanquantile and numpy.nanpercentile. Only method="inverted_cdf" supports weights.

(gh-24254)

Improved CPU optimization tracking

A new tracer mechanism is available which enables tracking of the enabled targets for each optimized function (i.e., that uses hardware-specific SIMD instructions) in the NumPy library. With this enhancement, it becomes possible to precisely monitor the enabled CPU dispatch targets for the dispatched functions.

A new function named opt_func_info has been added to the new namespace numpy.lib.introspect, offering this tracing capability. This function allows you to retrieve information about the enabled targets based on function names and data type signatures.

(gh-24420)

A new Meson backend for f2py

f2py in compile mode (i.e. f2py -c) now accepts the --backend meson option. This is the default option for Python >=3.12. For older Python versions, f2py will still default to --backend distutils.

To support this in realistic use-cases, in compile mode f2py takes a --dep flag one or many times which maps to dependency() calls in the meson backend, and does nothing in the distutils backend.

There are no changes for users of f2py only as a code generator, i.e. without -c.

(gh-24532)

bind(c) support for f2py

Both functions and subroutines can be annotated with bind(c). f2py will handle both the correct type mapping, and preserve the unique label for other C interfaces.

Note: bind(c, name = 'routine_name_other_than_fortran_routine') is not honored by the f2py bindings by design, since bind(c) with the name is meant to guarantee only the same name in C and Fortran, not in Python and Fortran.

(gh-24555)

A new strict option for several testing functions

The strict keyword is now available for numpy.testing.assert_allclose, numpy.testing.assert_equal, and numpy.testing.assert_array_less. Setting strict=True will disable the broadcasting behaviour for scalars and ensure that input arrays have the same data type.

(gh-24680, gh-24770, gh-24775)

Add np.core.umath.find and np.core.umath.rfind UFuncs

Add two find and rfind UFuncs that operate on unicode or byte strings and are used in np.char. They operate similar to str.find and str.rfind.

(gh-24868)

diagonal and trace for numpy.linalg

numpy.linalg.diagonal and numpy.linalg.trace have been added, which are array API standard-compatible variants of numpy.diagonal and numpy.trace. They differ in the default axis selection which define 2-D sub-arrays.

(gh-24887)

New long and ulong dtypes

numpy.long and numpy.ulong have been added as NumPy integers mapping to C\'s long and unsigned long. Prior to NumPy 1.24, numpy.long was an alias to Python\'s int.

(gh-24922)

svdvals for numpy.linalg

numpy.linalg.svdvals has been added. It computes singular values for (a stack of) matrices. Executing np.svdvals(x) is the same as calling np.svd(x, compute_uv=False, hermitian=False). This function is compatible with the array API standard.

(gh-24940)

A new isdtype function

numpy.isdtype was added to provide a canonical way to classify NumPy\'s dtypes in compliance with the array API standard.

(gh-25054)

A new astype function

numpy.astype was added to provide an array API standard-compatible alternative to the numpy.ndarray.astype method.

(gh-25079)

Array API compatible functions\' aliases

13 aliases for existing functions were added to improve compatibility with the array API standard:

• Trigonometry: acos, acosh, asin, asinh, atan, atanh, atan2.
• Bitwise: bitwise_left_shift, bitwise_invert, bitwise_right_shift.
• Misc: concat, permute_dims, pow.
• In numpy.linalg: tensordot, matmul.

(gh-25086)

New unique_* functions

The numpy.unique_all, numpy.unique_counts, numpy.unique_inverse, and numpy.unique_values functions have been added. They provide functionality of numpy.unique with different sets of flags. They are array API standard-compatible, and because the number of arrays they return does not depend on the values of input arguments, they are easier to target for JIT compilation.

(gh-25088)

Matrix transpose support for ndarrays

NumPy now offers support for calculating the matrix transpose of an array (or stack of arrays). The matrix transpose is equivalent to swapping the last two axes of an array. Both np.ndarray and np.ma.MaskedArray now expose a .mT attribute, and there is a matching new numpy.matrix_transpose function.

(gh-23762)

Array API compatible functions for numpy.linalg

Six new functions and two aliases were added to improve compatibility with the Array API standard for `numpy.linalg`:

• numpy.linalg.matrix_norm - Computes the matrix norm of a matrix (or a stack of matrices).

• numpy.linalg.vector_norm - Computes the vector norm of a vector (or batch of vectors).

• numpy.vecdot - Computes the (vector) dot product of two arrays.

• numpy.linalg.vecdot - An alias for numpy.vecdot.

• numpy.linalg.matrix_transpose - An alias for numpy.matrix_transpose.

  (gh-25155)

• numpy.linalg.outer has been added. It computes the outer product of two vectors. It differs from numpy.outer by accepting one-dimensional arrays only. This function is compatible with the array API standard.

  (gh-25101)

• numpy.linalg.cross has been added. It computes the cross product of two (arrays of) 3-dimensional vectors. It differs from numpy.cross by accepting three-dimensional vectors only. This function is compatible with the array API standard.

  (gh-25145)

A correction argument for var and std

A correction argument was added to numpy.var and numpy.std, which is an array API standard compatible alternative to ddof. As both arguments serve a similar purpose, only one of them can be provided at the same time.

(gh-25169)

ndarray.device and ndarray.to_device

An ndarray.device attribute and ndarray.to_device method were added to numpy.ndarray for array API standard compatibility.

Additionally, device keyword-only arguments were added to: numpy.asarray, numpy.arange, numpy.empty, numpy.empty_like, numpy.eye, numpy.full, numpy.full_like, numpy.linspace, numpy.ones, numpy.ones_like, numpy.zeros, and numpy.zeros_like.

For all these new arguments, only device="cpu" is supported.

(gh-25233)

StringDType has been added to NumPy

We have added a new variable-width UTF-8 encoded string data type, implementing a \"NumPy array of Python strings\", including support for a user-provided missing data sentinel. It is intended as a drop-in replacement for arrays of Python strings and missing data sentinels using the object dtype. See NEP 55 and the documentation of stringdtype for more details.

(gh-25347)

New keywords for cholesky and pinv

The upper and rtol keywords were added to numpy.linalg.cholesky and numpy.linalg.pinv, respectively, to improve array API standard compatibility.

For numpy.linalg.pinv, if neither rcond nor rtol is specified, the rcond\'s default is used. We plan to deprecate and remove rcond in the future.

(gh-25388)

New keywords for sort, argsort and linalg.matrix_rank

New keyword parameters were added to improve array API standard compatibility:

• rtol was added to numpy.linalg.matrix_rank.
• stable was added to numpy.sort and numpy.argsort.

(gh-25437)

New numpy.strings namespace for string ufuncs

NumPy now implements some string operations as ufuncs. The old np.char namespace is still available, and where possible the string manipulation functions in that namespace have been updated to use the new ufuncs, substantially improving their performance.

Where possible, we suggest updating code to use functions in np.strings instead of np.char. In the future we may deprecate np.char in favor of np.strings.

(gh-25463)

numpy.fft support for different precisions and in-place calculations

The various FFT routines in numpy.fft now do their calculations natively in float, double, or long double precision, depending on the input precision, instead of always calculating in double precision. Hence, the calculation will now be less precise for single and more precise for long double precision. The data type of the output array will now be adjusted accordingly.

Furthermore, all FFT routines have gained an out argument that can be used for in-place calculations.

(gh-25536)

configtool and pkg-config support

A new numpy-config CLI script is available that can be queried for the NumPy version and for compile flags needed to use the NumPy C API. This will allow build systems to better support the use of NumPy as a dependency. Also, a numpy.pc pkg-config file is now included with Numpy. In order to find its location for use with PKG_CONFIG_PATH, use numpy-config --pkgconfigdir.

(gh-25730)

Array API standard support in the main namespace

The main numpy namespace now supports the array API standard. See array-api-standard-compatibility for details.

(gh-25911)

Improvements

Strings are now supported by any, all, and the logical ufuncs.

(gh-25651)

Integer sequences as the shape argument for memmap

numpy.memmap can now be created with any integer sequence as the shape argument, such as a list or numpy array of integers. Previously, only the types of tuple and int could be used without raising an error.

(gh-23729)

errstate is now faster and context safe

The numpy.errstate context manager/decorator is now faster and safer. Previously, it was not context safe and had (rare) issues with thread-safety.

(gh-23936)

AArch64 quicksort speed improved by using Highway\'s VQSort

The first introduction of the Google Highway library, using VQSort on AArch64. Execution time is improved by up to 16x in some cases, see the PR for benchmark results. Extensions to other platforms will be done in the future.

(gh-24018)

Complex types - underlying C type changes

• The underlying C types for all of NumPy\'s complex types have been changed to use C99 complex types.

• While this change does not affect the memory layout of complex types, it changes the API to be used to directly retrieve or write the real or complex part of the complex number, since direct field access (as in c.real or c.imag) is no longer an option. You can now use utilities provided in numpy/npy_math.h to do these operations, like this:

  c npy_cdouble c; npy_csetreal(&c, 1.0); npy_csetimag(&c, 0.0); printf("%d + %di\n", npy_creal(c), npy_cimag(c));

• To ease cross-version compatibility, equivalent macros and a compatibility layer have been added which can be used by downstream packages to continue to support both NumPy 1.x and 2.x. See complex-numbers for more info.

• numpy/npy_common.h now includes complex.h, which means that complex is now a reserved keyword.

(gh-24085)

iso_c_binding support and improved common blocks for f2py

Previously, users would have to define their own custom f2cmap file to use type mappings defined by the Fortran2003 iso_c_binding intrinsic module. These type maps are now natively supported by f2py

(gh-24555)

f2py now handles common blocks which have kind specifications from modules. This further expands the usability of intrinsics like iso_fortran_env and iso_c_binding.

(gh-25186)

Call str automatically on third argument to functions like assert_equal

The third argument to functions like numpy.testing.assert_equal now has str called on it automatically. This way it mimics the built-in assert statement, where assert_equal(a, b, obj) works like assert a == b, obj.

(gh-24877)

Support for array-like atol/rtol in isclose, allclose

The keywords atol and rtol in numpy.isclose and numpy.allclose now accept both scalars and arrays. An array, if given, must broadcast to the shapes of the first two array arguments.

(gh-24878)

Consistent failure messages in test functions

Previously, some numpy.testing assertions printed messages that referred to the actual and desired results as x and y. Now, these values are consistently referred to as ACTUAL and DESIRED.

(gh-24931)

n-D FFT transforms allow s[i] == -1

The numpy.fft.fftn, numpy.fft.ifftn, numpy.fft.rfftn, numpy.fft.irfftn, numpy.fft.fft2, numpy.fft.ifft2, numpy.fft.rfft2 and numpy.fft.irfft2 functions now use the whole input array along the axis i if s[i] == -1, in line with the array API standard.

(gh-25495)

Guard PyArrayScalar_VAL and PyUnicodeScalarObject for the limited API

PyUnicodeScalarObject holds a PyUnicodeObject, which is not available when using Py_LIMITED_API. Add guards to hide it and consequently also make the PyArrayScalar_VAL macro hidden.

(gh-25531)

Changes

• np.gradient() now returns a tuple rather than a list making the return value immutable.

  (gh-23861)

• Being fully context and thread-safe, np.errstate can only be entered once now.

• np.setbufsize is now tied to np.errstate(): leaving an np.errstate context will also reset the bufsize.

  (gh-23936)

• A new public np.lib.array_utils submodule has been introduced and it currently contains three functions: byte_bounds (moved from np.lib.utils), normalize_axis_tuple and normalize_axis_index.

  (gh-24540)

• Introduce numpy.bool as the new canonical name for NumPy\'s boolean dtype, and make numpy.bool\_ an alias to it. Note that until NumPy 1.24, np.bool was an alias to Python\'s builtin bool. The new name helps with array API standard compatibility and is a more intuitive name.

  (gh-25080)

• The dtype.flags value was previously stored as a signed integer. This means that the aligned dtype struct flag lead to negative flags being set (-128 rather than 128). This flag is now stored unsigned (positive). Code which checks flags manually may need to adapt. This may include code compiled with Cython 0.29.x.

  (gh-25816)

Representation of NumPy scalars changed

As per NEP 51, the scalar representation has been updated to include the type information to avoid confusion with Python scalars.

Scalars are now printed as np.float64(3.0) rather than just 3.0. This may disrupt workflows that store representations of numbers (e.g., to files) making it harder to read them. They should be stored as explicit strings, for example by using str() or f"{scalar!s}". For the time being, affected users can use np.set_printoptions(legacy="1.25") to get the old behavior (with possibly a few exceptions). Documentation of downstream projects may require larger updates, if code snippets are tested. We are working on tooling for doctest-plus to facilitate updates.

(gh-22449)

Truthiness of NumPy strings changed

NumPy strings previously were inconsistent about how they defined if the string is True or False and the definition did not match the one used by Python. Strings are now considered True when they are non-empty and False when they are empty. This changes the following distinct cases:

• Casts from string to boolean were previously roughly equivalent to string_array.astype(np.int64).astype(bool), meaning that only valid integers could be cast. Now a string of "0" will be considered True since it is not empty. If you need the old behavior, you may use the above step (casting to integer first) or string_array == "0" (if the input is only ever 0 or 1). To get the new result on old NumPy versions use string_array != "".
• np.nonzero(string_array) previously ignored whitespace so that a string only containing whitespace was considered False. Whitespace is now considered True.

This change does not affect np.loadtxt, np.fromstring, or np.genfromtxt. The first two still use the integer definition, while genfromtxt continues to match for "true" (ignoring case). However, if np.bool_ is used as a converter the result will change.

The change does affect np.fromregex as it uses direct assignments.

(gh-23871)

A mean keyword was added to var and std function

Often when the standard deviation is needed the mean is also needed. The same holds for the variance and the mean. Until now the mean is then calculated twice, the change introduced here for the numpy.var and numpy.std functions allows for passing in a precalculated mean as an keyword argument. See the docstrings for details and an example illustrating the speed-up.

(gh-24126)

Remove datetime64 deprecation warning when constructing with timezone

The numpy.datetime64 method now issues a UserWarning rather than a DeprecationWarning whenever a timezone is included in the datetime string that is provided.

(gh-24193)

Default integer dtype is now 64-bit on 64-bit Windows

The default NumPy integer is now 64-bit on all 64-bit systems as the historic 32-bit default on Windows was a common source of issues. Most users should not notice this. The main issues may occur with code interfacing with libraries written in a compiled language like C. For more information see migration_windows_int64.

(gh-24224)

Renamed numpy.core to numpy._core

Accessing numpy.core now emits a DeprecationWarning. In practice we have found that most downstream usage of numpy.core was to access functionality that is available in the main numpy namespace. If for some reason you are using functionality in numpy.core that is not available in the main numpy namespace, this means you are likely using private NumPy internals. You can still access these internals via numpy._core without a deprecation warning but we do not provide any backward compatibility guarantees for NumPy internals. Please open an issue if you think a mistake was made and something needs to be made public.

(gh-24634)

The \"relaxed strides\" debug build option, which was previously enabled through the NPY_RELAXED_STRIDES_DEBUG environment variable or the -Drelaxed-strides-debug config-settings flag has been removed.

(gh-24717)

Redefinition of np.intp/np.uintp (almost never a change)

Due to the actual use of these types almost always matching the use of size_t/Py_ssize_t this is now the definition in C. Previously, it matched intptr_t and uintptr_t which would often have been subtly incorrect. This has no effect on the vast majority of machines since the size of these types only differ on extremely niche platforms.

However, it means that:

• Pointers may not necessarily fit into an intp typed array anymore. The p and P character codes can still be used, however.
• Creating intptr_t or uintptr_t typed arrays in C remains possible in a cross-platform way via PyArray_DescrFromType('p').
• The new character codes nN were introduced.
• It is now correct to use the Python C-API functions when parsing to npy_intp typed arguments.

(gh-24888)

numpy.fft.helper made private

numpy.fft.helper was renamed to numpy.fft._helper to indicate that it is a private submodule. All public functions exported by it should be accessed from numpy.fft.

(gh-24945)

numpy.linalg.linalg made private

numpy.linalg.linalg was renamed to numpy.linalg._linalg to indicate that it is a private submodule. All public functions exported by it should be accessed from numpy.linalg.

(gh-24946)

Out-of-bound axis not the same as axis=None

In some cases axis=32 or for concatenate any large value was the same as axis=None. Except for concatenate this was deprecate. Any out of bound axis value will now error, make sure to use axis=None.

(gh-25149)

New copy keyword meaning for array and asarray constructors

Now numpy.array and numpy.asarray support three values for copy parameter:

• None - A copy will only be made if it is necessary.
• True - Always make a copy.
• False - Never make a copy. If a copy is required a ValueError is raised.

The meaning of False changed as it now raises an exception if a copy is needed.

(gh-25168)

The __array__ special method now takes a copy keyword argument.

NumPy will pass copy to the __array__ special method in situations where it would be set to a non-default value (e.g. in a call to np.asarray(some_object, copy=False)). Currently, if an unexpected keyword argument error is raised after this, NumPy will print a warning and re-try without the copy keyword argument. Implementations of objects implementing the __array__ protocol should accept a copy keyword argument with the same meaning as when passed to numpy.array or numpy.asarray.

(gh-25168)

Cleanup of initialization of numpy.dtype with strings with commas

The interpretation of strings with commas is changed slightly, in that a trailing comma will now always create a structured dtype. E.g., where previously np.dtype("i") and np.dtype("i,") were treated as identical, now np.dtype("i,") will create a structured dtype, with a single field. This is analogous to np.dtype("i,i") creating a structured dtype with two fields, and makes the behaviour consistent with that expected of tuples.

At the same time, the use of single number surrounded by parenthesis to indicate a sub-array shape, like in np.dtype("(2)i,"), is deprecated. Instead; one should use np.dtype("(2,)i") or np.dtype("2i"). Eventually, using a number in parentheses will raise an exception, like is the case for initializations without a comma, like np.dtype("(2)i").

(gh-25434)

Change in how complex sign is calculated

Following the array API standard, the complex sign is now calculated as z / |z| (instead of the rather less logical case where the sign of the real part was taken, unless the real part was zero, in which case the sign of the imaginary part was returned). Like for real numbers, zero is returned if z==0.

(gh-25441)

Return types of functions that returned a list of arrays

Functions that returned a list of ndarrays have been changed to return a tuple of ndarrays instead. Returning tuples consistently whenever a sequence of arrays is returned makes it easier for JIT compilers like Numba, as well as for static type checkers in some cases, to support these functions. Changed functions are: numpy.atleast_1d, numpy.atleast_2d, numpy.atleast_3d, numpy.broadcast_arrays, numpy.meshgrid, numpy.ogrid, numpy.histogramdd.

np.unique return_inverse shape for multi-dimensional inputs

When multi-dimensional inputs are passed to np.unique with return_inverse=True, the unique_inverse output is now shaped such that the input can be reconstructed directly using np.take(unique, unique_inverse) when axis=None, and np.take_along_axis(unique, unique_inverse, axis=axis) otherwise.

(gh-25553, gh-25570)

any and all return booleans for object arrays

The any and all functions and methods now return booleans also for object arrays. Previously, they did a reduction which behaved like the Python or and and operators which evaluates to one of the arguments. You can use np.logical_or.reduce and np.logical_and.reduce to achieve the previous behavior.

(gh-25712)

np.can_cast cannot be called on Python int, float, or complex

np.can_cast cannot be called with Python int, float, or complex instances anymore. This is because NEP 50 means that the result of can_cast must not depend on the value passed in. Unfortunately, for Python scalars whether a cast should be considered "same_kind" or "safe" may depend on the context and value so that this is currently not implemented. In some cases, this means you may have to add a specific path for: if type(obj) in (int, float, complex): ....

(gh-26393)

Checksums

MD5

68918d8a47bdb8032d3c29ba0b7aa922  numpy-2.0.0rc2-cp310-cp310-macosx_10_9_x86_64.whl
28ef21e0db45a843901d5ecb203004f3  numpy-2.0.0rc2-cp310-cp310-macosx_11_0_arm64.whl
b0fbed65410612a81da2610887fcce7a  numpy-2.0.0rc2-cp310-cp310-macosx_14_0_arm64.whl
0e263a8bb87d32496e6de1555b6de0d7  numpy-2.0.0rc2-cp310-cp310-macosx_14_0_x86_64.whl
3700463b63c17514a42540f75f97e109  numpy-2.0.0rc2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
69c0e5b2a6054cee4cef3cfadf5bfc68  numpy-2.0.0rc2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
8b6547f02d1fb76a3436900146509476  numpy-2.0.0rc2-cp310-cp310-musllinux_1_1_aarch64.whl
85008275bc2ebb5d540ec89d20f95d72  numpy-2.0.0rc2-cp310-cp310-musllinux_1_1_x86_64.whl
46d1b93d2f2a6985d6440e010fe231a6  numpy-2.0.0rc2-cp310-cp310-win32.whl
df23c9ab080e695009e6c9dfc5c9dfee  numpy-2.0.0rc2-cp310-cp310-win_amd64.whl
4f4ac0bbff2152b95851f720be6e04ff  numpy-2.0.0rc2-cp311-cp311-macosx_10_9_x86_64.whl
df05a737d40018381c465e083fda65ba  numpy-2.0.0rc2-cp311-cp311-macosx_11_0_arm64.whl
9f3bbbce699559418b713f24bac5fd12  numpy-2.0.0rc2-cp311-cp311-macosx_14_0_arm64.whl
ced25c7ad09b414f941584582c52ce54  numpy-2.0.0rc2-cp311-cp311-macosx_14_0_x86_64.whl
b3af5409624e6aafb31107bd52bb8448  numpy-2.0.0rc2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
2859530bd0234983dadaa51b44a09daf  numpy-2.0.0rc2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
de6b8fe89c20bd85652679bb4a4671ef  numpy-2.0.0rc2-cp311-cp311-musllinux_1_1_aarch64.whl
2f0f3fed01479ee14a05aa1fc960d33a  numpy-2.0.0rc2-cp311-cp311-musllinux_1_1_x86_64.whl
0542e8d87c48155e1c8534e2a39f5e7a  numpy-2.0.0rc2-cp311-cp311-win32.whl
b7f451c340e92eb3d8b31ea5390f7340  numpy-2.0.0rc2-cp311-cp311-win_amd64.whl
f41d4e26c72340801d6f2bb609edba46  numpy-2.0.0rc2-cp312-cp312-macosx_10_9_x86_64.whl
1f8b24cc5b3006d778651e9ca9f7ed8c  numpy-2.0.0rc2-cp312-cp312-macosx_11_0_arm64.whl
5fd09e2e0fcb38fddac7b3ef1807ed82  numpy-2.0.0rc2-cp312-cp312-macosx_14_0_arm64.whl
f6eca0f2a9c770f6c6a4ba551ad7e237  numpy-2.0.0rc2-cp312-cp312-macosx_14_0_x86_64.whl
21b03eca64e88b952d843a70185e4144  numpy-2.0.0rc2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
f7631a072b83fc080662c3f435ba6fd4  numpy-2.0.0rc2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
2a62e6cffd78fd3d35c9142d4c214c00  numpy-2.0.0rc2-cp312-cp312-musllinux_1_1_aarch64.whl
25467c8e576bbc140bafe38f3b1fdd60  numpy-2.0.0rc2-cp312-cp312-musllinux_1_1_x86_64.whl
b7d74f54199d7fa355ec4abc3f2cafff  numpy-2.0.0rc2-cp312-cp312-win32.whl
351eb69f9e0e69b06d13a8c03733e5e6  numpy-2.0.0rc2-cp312-cp312-win_amd64.whl
c774b6e40b62f0503788cd7042bf85a1  numpy-2.0.0rc2-cp39-cp39-macosx_10_9_x86_64.whl
ef664eaadd0796fbc37fc3cc0c498c2e  numpy-2.0.0rc2-cp39-cp39-macosx_11_0_arm64.whl
f9a1d24dbeaa6433b827d82b9622b04b  numpy-2.0.0rc2-cp39-cp39-macosx_14_0_arm64.whl
8ea2a873c471a5180d8a316c14abb09f  numpy-2.0.0rc2-cp39-cp39-macosx_14_0_x86_64.whl
dce70c212e9c321ca189867f7ebc8e03  numpy-2.0.0rc2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
feacd9a21c6de648e8117bc1cc36fcc5  numpy-2.0.0rc2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
cd34c2b0f9e25d80d2c4bd70717d565b  numpy-2.0.0rc2-cp39-cp39-musllinux_1_1_aarch64.whl
c1efda069b8c8826a6ba6d7a49ada148  numpy-2.0.0rc2-cp39-cp39-musllinux_1_1_x86_64.whl
4ef25fd7e575fa64d4c4e9dfdc1c9174  numpy-2.0.0rc2-cp39-cp39-win32.whl
d35cd476de68374a10d56737a882e735  numpy-2.0.0rc2-cp39-cp39-win_amd64.whl
b83c7ccda03ec1a1fec900c149bdc90d  numpy-2.0.0rc2-pp39-pypy39_pp73-macosx_10_9_x86_64.whl
0aff6e5f70e2d9fa299e3895294bd7a8  numpy-2.0.0rc2-pp39-pypy39_pp73-macosx_14_0_x86_64.whl
dd1c5bf6559fe41b21c81097e97232c3  numpy-2.0.0rc2-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
ecab2eae88f6ba9421787655909045b7  numpy-2.0.0rc2-pp39-pypy39_pp73-win_amd64.whl
884d0fe28f77f07a3d51ba905ffdf0d3  numpy-2.0.0rc2.tar.gz

SHA256

53286933bf3be7a13459c7a7885ce0935aff56fe0baf280f0e6d80e75cc3ee3c  numpy-2.0.0rc2-cp310-cp310-macosx_10_9_x86_64.whl
2bc615498fce8e15b99c1b4d7e018ffebf7bd1a288665b3b916357bdf6725d6a  numpy-2.0.0rc2-cp310-cp310-macosx_11_0_arm64.whl
159d9c21a2989afdfebb638f60268becbc3da07eb224d9221a7c37255216feb6  numpy-2.0.0rc2-cp310-cp310-macosx_14_0_arm64.whl
c58bc6aac83175dcfa02a0ef92b7a7fff5a0420014202f052a9af6214684e6ac  numpy-2.0.0rc2-cp310-cp310-macosx_14_0_x86_64.whl
9b07a5c460941ae5ef8cde51c04b635af58abbbd55387ad6257dbdfda043290a  numpy-2.0.0rc2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
0a49e876be11b4409eb3120841c7d2dba1f63549224f85fa4ab7ee83288c3b41  numpy-2.0.0rc2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
951164e9919664a3e5e605715809173b47f14329b586e24ec05e66ae516ce11b  numpy-2.0.0rc2-cp310-cp310-musllinux_1_1_aarch64.whl
201c0e05854d25f16b15851380c07d61aab34eef76a2acf1c3fcc4bda0879b0b  numpy-2.0.0rc2-cp310-cp310-musllinux_1_1_x86_64.whl
800ff28d0da25fca3f843c19035005b73c76350be7c6fa6061c8fcdd248aced9  numpy-2.0.0rc2-cp310-cp310-win32.whl
2a9a5ee4b090af548a1019bb76b53b02cb37f09dc002386349ee5e79ff54c40e  numpy-2.0.0rc2-cp310-cp310-win_amd64.whl
6d23b0db1fd4ad8225fd32f39036b07a5052398929a5af5291379bceac49d95a  numpy-2.0.0rc2-cp311-cp311-macosx_10_9_x86_64.whl
a99ac361ddb0ef14894c3e7405aa98ffdfe6d0101b9f4a2e931f3912f3b43085  numpy-2.0.0rc2-cp311-cp311-macosx_11_0_arm64.whl
6aba1c147f69ee1fb8afb44e93178e92d2aa9a3bf5374b6f1cb53ee1cae1376d  numpy-2.0.0rc2-cp311-cp311-macosx_14_0_arm64.whl
4f3a4c676ab4ce211e5886cb16cc282e9e18b352b2b1427bbb4c104f9d80f12a  numpy-2.0.0rc2-cp311-cp311-macosx_14_0_x86_64.whl
12d3bf0cac2aec23e10b6927ee063aa6cf7ca8deba1d3c5702faa0ea5cfb8049  numpy-2.0.0rc2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
a44b0ebf7ef61c289a33c76247874177c446083c5236c7e7e0595350883e0424  numpy-2.0.0rc2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
0e146557fdede5a7434a788648e62a9e87db8c6e05136a92483e2c2180ad4bab  numpy-2.0.0rc2-cp311-cp311-musllinux_1_1_aarch64.whl
01ac116e2f053f283ac5292fcd146f8f223d4b6cd343beab341748818692a2de  numpy-2.0.0rc2-cp311-cp311-musllinux_1_1_x86_64.whl
74dcc392725837896532ec7d65506cbeaecee237871b36ae813521bc3e2c40ed  numpy-2.0.0rc2-cp311-cp311-win32.whl
225c2b3303eb2ebf745ab954ef8723cd60f64d926edd73dc963141538ddc48ed  numpy-2.0.0rc2-cp311-cp311-win_amd64.whl
32207294f21331ae0d7fd33dc9324447a8117d5af15a0895f39af3441d4af70e  numpy-2.0.0rc2-cp312-cp312-macosx_10_9_x86_64.whl
a666cc3d55f301b86edc7f1eaef10ffa1f79206c4b196a1f2649f91c8a1b49b6  numpy-2.0.0rc2-cp312-cp312-macosx_11_0_arm64.whl
fa5485c565ca222ba69c5fe04ebd8a89f884615466d74e0856e03fff873bcc43  numpy-2.0.0rc2-cp312-cp312-macosx_14_0_arm64.whl
2202abe3e8afb2b88102a75f1beb888f380c09d40898db0f1df5d847623701d5  numpy-2.0.0rc2-cp312-cp312-macosx_14_0_x86_64.whl
6b93d6b401db67948a4997e2c45e958df65b98b1a9183e96e96e491f9fb3c2fe  numpy-2.0.0rc2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
1691e64c838d33fdba59ac7043144194f8f847b5fec6f47ecd9e117418cc9bdc  numpy-2.0.0rc2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
7288d8ac70be23ff29df8da51840aad8f7acd9120d27cd7a61488b96bc5ad68b  numpy-2.0.0rc2-cp312-cp312-musllinux_1_1_aarch64.whl
9dd61b79856aed44f818fffe1555fa7ef8f6ffa5b5211cde473e2e33f7a5bd92  numpy-2.0.0rc2-cp312-cp312-musllinux_1_1_x86_64.whl
83c76a11c5e5a343fb1cb87afec147d6bebac91758c9c9f01d2c692ae4750e27  numpy-2.0.0rc2-cp312-cp312-win32.whl
24bcf0cdd31debdcb80e1f3bb7dba224c9a93a66f48ff1b1df2cb9a53eede944  numpy-2.0.0rc2-cp312-cp312-win_amd64.whl
87172a69d7eafb00ea1b734dba9ffebb474505082078ec2d95b99918f14a0a0e  numpy-2.0.0rc2-cp39-cp39-macosx_10_9_x86_64.whl
e13a1fa60a471b79a53de8abb87e1e0ad53e6899edee8a29b4db3edccee53d65  numpy-2.0.0rc2-cp39-cp39-macosx_11_0_arm64.whl
32725b717f902e7243d270e50ff9487a499820233b57c3e71b33f65a84707e38  numpy-2.0.0rc2-cp39-cp39-macosx_14_0_arm64.whl
f8c7012dd6779f078e3f42e19a2204275abe4d68a80dc807a97caf42e825d9c3  numpy-2.0.0rc2-cp39-cp39-macosx_14_0_x86_64.whl
a0202e282ec9d45fc6ddb85777fddeea1107fe4555be50dd22d044e7fe01860c  numpy-2.0.0rc2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
5262d69981502ded9b397c3fd5a20a1f2c91a66b21325ddff5e6d88486eee6fa  numpy-2.0.0rc2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
6fe254c271f8ce4c2e60250f8ee80684abd2be748af84312a05b7614c3ae3b8d  numpy-2.0.0rc2-cp39-cp39-musllinux_1_1_aarch64.whl
0a2cf839a7d6cc0b854ba81cdfee96aad2c7e4d558c7e23ca82d08e4f7d7daa7  numpy-2.0.0rc2-cp39-cp39-musllinux_1_1_x86_64.whl
9ea90fb601d5ac32ff7f9f0a3bf7ccab5971a0196364b9429734bd270cd2fa67  numpy-2.0.0rc2-cp39-cp39-win32.whl
9e00367261ee0347208a8bcc355b6470b084cb777c45141e098328b67b02c98b  numpy-2.0.0rc2-cp39-cp39-win_amd64.whl
da6ab9dab471668155e0b208ab710417a7407397794a88b3ccbece5bcf10091d  numpy-2.0.0rc2-pp39-pypy39_pp73-macosx_10_9_x86_64.whl
b3ba5f436c6de9b8829f231e9eb9e394aa819efce9eab697cd4e558b0b8c6cc8  numpy-2.0.0rc2-pp39-pypy39_pp73-macosx_14_0_x86_64.whl
d5211fd4e126699b16b8573eef007f25afb9459d966b35430908798b24298e3b  numpy-2.0.0rc2-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
0d5cfbf693408cf1ee72d79d36d51f7b63f5e46a5e9cf12f63d4ed07c0f876e0  numpy-2.0.0rc2-pp39-pypy39_pp73-win_amd64.whl
868e9edbee689d6fdb7957c0b790de2b2123e6feff5d66045d10760c521f2c00  numpy-2.0.0rc2.tar.gz

- Python
Published by charris about 2 years ago

numpy -

NumPy 2.0.0 Release Notes

Note

The release of 2.0 is in progress and the current release overview and highlights are still in a draft state. However, the highlights should already list the most significant changes detailed in the full notes below, and those full notes should be complete (if not copy-edited well enough yet).

NumPy 2.0.0 is the first major release since 2006. It is the result of 10 months of development since the last feature release and is the work of 193 contributors spread over 1006 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:

• The numpy-2-migration-guide
• The Numpy 2.0-specific advice in for dpwmstream package authors

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 and Google Highway libraries, and may see large (hardware-specific) speedups,
  • macOS Accelerate support and binary wheels for macOS >=14, with significant performance improvements for linear algebra operations on macOS, and wheels that are about 3 times smaller,
  • numpy.char fixed-length string operations have been accelerated by implementing ufuncs that also support numpy.dtypes.StringDType in addition to the fixed-length string dtypes,
  • A new tracing and introspection API, numpy.lib.introspect.opt_func_info, to determine which hardware-specific kernels are available and will be dispatched to.
• Python API improvements:
  • A clear split between public and private API, with a new module structure and each public function now available in a single place.
  • Many removals of non-recommended functions and aliases. This should make it easier to learn and use NumPy. The number of objects in the main namespace decreased by ~10% and in numpy.lib by ~80%.
  • Canonical dtype names and a newnumpy.isdtype` introspection function,
• C API improvements:
  • A new public C API for creating custom dtypes,
  • Many outdated functions and macros removed, and private internals hidden to ease future extensibility,
  • New, easier to use, initialization functions: PyArray_ImportNumPyAPI and PyUFunc_ImportUFuncAPI.
• Improved behavior:
  • Improvements to type promotion behavior was changed by adopting NEP 50. This fixes many user surprises about promotions which previously often depended on data values of input arrays rather than only their dtypes. Please see the NEP and the numpy-2-migration-guide for details as this change can lead to changes in output dtypes and lower precision results for mixed-dtype operations.
  • The default integer type on Windows is now int64 rather than int32, matching the behavior on other platforms,
  • The maximum number of array dimensions is changed from 32 to 64
• Documentation:
  • The reference guide navigation was significantly improved, and there is now documentation on NumPy\'s module structure,
  • The building from source documentation was completely rewritten,

Furthermore there are many changes to NumPy internals, including continuing to migrate code from C to C++, that will make it easier to improve and maintain NumPy in the future.

The \"no free lunch\" theorem dictates that there is a price to pay for all these API and behavior improvements and better future extensibility. This price is:

1. Backwards compatibility. There are a significant number of breaking changes to both the Python and C APIs. In the majority of cases, there are clear error messages that will inform the user how to adapt their code. However, there are also changes in behavior for which it was not possible to give such an error message - these cases are all covered in the Deprecation and Compatibility sections below, and in the numpy-2-migration-guide.

   Note that there is a ruff mode to auto-fix many things in Python code.

2. Breaking changes to the NumPy ABI. As a result, binaries of packages that use the NumPy C API and were built against a NumPy 1.xx release will not work with NumPy 2.0. On import, such packages will see an ImportError with a message about binary incompatibility.

   It is possible to build binaries against NumPy 2.0 that will work at runtime with both NumPy 2.0 and 1.x. See numpy-2-abi-handling for more details.

   All downstream packages that depend on the NumPy ABI are advised to do a new release built against NumPy 2.0 and verify that that release works with both 2.0 and 1.26 - ideally in the period between 2.0.0rc1 (which will be ABI-stable) and the final 2.0.0 release to avoid problems for their users.

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

NumPy 2.0 Python API removals

• np.geterrobj, np.seterrobj and the related ufunc keyword argument extobj= have been removed. The preferred replacement for all of these is using the context manager with np.errstate():.

  (gh-23922)

• np.cast has been removed. The literal replacement for np.cast[dtype](arg) is np.asarray(arg, dtype=dtype).

• np.source has been removed. The preferred replacement is inspect.getsource.

• np.lookfor has been removed.

  (gh-24144)

• numpy.who has been removed. As an alternative for the removed functionality, one can use a variable explorer that is available in IDEs such as Spyder or Jupyter Notebook.

  (gh-24321)

• Multiple niche enums, expired members and functions have been removed from the main namespace, such as: ERR_*, SHIFT_*, np.fastCopyAndTranspose, np.kernel_version, np.numarray, np.oldnumeric and np.set_numeric_ops.

  (gh-24316)

• Replaced from ... import * in the numpy/__init__.py with explicit imports. As a result, these main namespace members got removed: np.FLOATING_POINT_SUPPORT, np.FPE_*, np.NINF, np.PINF, np.NZERO, np.PZERO, np.CLIP, np.WRAP, np.WRAP, np.RAISE, np.BUFSIZE, np.UFUNC_BUFSIZE_DEFAULT, np.UFUNC_PYVALS_NAME, np.ALLOW_THREADS, np.MAXDIMS, np.MAY_SHARE_EXACT, np.MAY_SHARE_BOUNDS, add_newdoc, np.add_docstring and np.add_newdoc_ufunc.

  (gh-24357)

• Alias np.float_ has been removed. Use np.float64 instead.

• Alias np.complex_ has been removed. Use np.complex128 instead.

• Alias np.longfloat has been removed. Use np.longdouble instead.

• Alias np.singlecomplex has been removed. Use np.complex64 instead.

• Alias np.cfloat has been removed. Use np.complex128 instead.

• Alias np.longcomplex has been removed. Use np.clongdouble instead.

• Alias np.clongfloat has been removed. Use np.clongdouble instead.

• Alias np.string_ has been removed. Use np.bytes_ instead.

• Alias np.unicode_ has been removed. Use np.str_ instead.

• Alias np.Inf has been removed. Use np.inf instead.

• Alias np.Infinity has been removed. Use np.inf instead.

• Alias np.NaN has been removed. Use np.nan instead.

• Alias np.infty has been removed. Use np.inf instead.

• Alias np.mat has been removed. Use np.asmatrix instead.

• np.issubclass_ has been removed. Use the issubclass builtin instead.

• np.asfarray has been removed. Use np.asarray with a proper dtype instead.

• np.set_string_function has been removed. Use np.set_printoptions instead with a formatter for custom printing of NumPy objects.

• np.tracemalloc_domain is now only available from np.lib.

• np.recfromcsv and recfromtxt are now only available from np.lib.npyio.

• np.issctype, np.maximum_sctype, np.obj2sctype, np.sctype2char, np.sctypes, np.issubsctype were all removed from the main namespace without replacement, as they where niche members.

• Deprecated np.deprecate and np.deprecate_with_doc has been removed from the main namespace. Use DeprecationWarning instead.

• Deprecated np.safe_eval has been removed from the main namespace. Use ast.literal_eval instead.

  (gh-24376)

• np.find_common_type has been removed. Use numpy.promote_types or numpy.result_type instead. To achieve semantics for the scalar_types argument, use numpy.result_type and pass 0, 0.0, or 0j as a Python scalar instead.

• np.round_ has been removed. Use np.round instead.

• np.nbytes has been removed. Use np.dtype(<dtype>).itemsize instead.

  (gh-24477)

• np.compare_chararrays has been removed from the main namespace. Use np.char.compare_chararrays instead.

• The charrarray in the main namespace has been deprecated. It can be imported without a deprecation warning from np.char.chararray for now, but we are planning to fully deprecate and remove chararray in the future.

• np.format_parser has been removed from the main namespace. Use np.rec.format_parser instead.

  (gh-24587)

• Support for seven data type string aliases has been removed from np.dtype: int0, uint0, void0, object0, str0, bytes0 and bool8.

  (gh-24807)

• The experimental numpy.array_api submodule has been removed. Use the main numpy namespace for regular usage instead, or the separate array-api-strict package for the compliance testing use case for which numpy.array_api was mostly used.

  (gh-25911)

__array_prepare__ is removed

UFuncs called __array_prepare__ before running computations for normal ufunc calls (not generalized ufuncs, reductions, etc.). The function was also called instead of __array_wrap__ on the results of some linear algebra functions.

It is now removed. If you use it, migrate to __array_ufunc__ or rely on __array_wrap__ which is called with a context in all cases, although only after the result array is filled. In those code paths, __array_wrap__ will now be passed a base class, rather than a subclass array.

(gh-25105)

Deprecations

• np.compat has been deprecated, as Python 2 is no longer supported.

• np.safe_eval has been deprecated. ast.literal_eval should be used instead.

  (gh-23830)

• np.recfromcsv, np.recfromtxt, np.disp, np.get_array_wrap, np.maximum_sctype, np.deprecate and np.deprecate_with_doc have been deprecated.

  (gh-24154)

• np.trapz has been deprecated. Use np.trapezoid or a scipy.integrate function instead.

• np.in1d has been deprecated. Use np.isin instead.

• Alias np.row_stack has been deprecated. Use np.vstack directly.

  (gh-24445)

• __array_wrap__ is now passed arr, context, return_scalar and support for implementations not accepting all three are deprecated. Its signature should be __array_wrap__(self, arr, context=None, return_scalar=False)

  (gh-25408)

• Arrays of 2-dimensional vectors for np.cross have been deprecated. Use arrays of 3-dimensional vectors instead.

  (gh-24818)

• np.dtype("a") alias for np.dtype(np.bytes_) was deprecated. Use np.dtype("S") alias instead.

  (gh-24854)

• Use of keyword arguments x and y with functions assert_array_equal and assert_array_almost_equal has been deprecated. Pass the first two arguments as positional arguments instead.

  (gh-24978)

numpy.fft deprecations for n-D transforms with None values in arguments

Using fftn, ifftn, rfftn, irfftn, fft2, ifft2, rfft2 or irfft2 with the s parameter set to a value that is not None and the axes parameter set to None has been deprecated, in line with the array API standard. To retain current behaviour, pass a sequence [0, ..., k-1] to axes for an array of dimension k.

Furthermore, passing an array to s which contains None values is deprecated as the parameter is documented to accept a sequence of integers in both the NumPy docs and the array API specification. To use the default behaviour of the corresponding 1-D transform, pass the value matching the default for its n parameter. To use the default behaviour for every axis, the s argument can be omitted.

(gh-25495)

np.linalg.lstsq now defaults to a new rcond value

numpy.linalg.lstsq now uses the new rcond value of the machine precision times max(M, N). Previously, the machine precision was used but a FutureWarning was given to notify that this change will happen eventually. That old behavior can still be achieved by passing rcond=-1.

(gh-25721)

Expired deprecations

• The np.core.umath_tests submodule has been removed from the public API. (Deprecated in NumPy 1.15)

  (gh-23809)

• The PyDataMem_SetEventHook deprecation has expired and it is removed. Use tracemalloc and the np.lib.tracemalloc_domain domain. (Deprecated in NumPy 1.23)

  (gh-23921)

• The deprecation of set_numeric_ops and the C functions PyArray_SetNumericOps and PyArray_GetNumericOps has been expired and the functions removed. (Deprecated in NumPy 1.16)

  (gh-23998)

• The fasttake, fastclip, and fastputmask ArrFuncs deprecation is now finalized.

• The deprecated function fastCopyAndTranspose and its C counterpart are now removed.

• The deprecation of PyArray_ScalarFromObject is now finalized.

  (gh-24312)

• np.msort has been removed. For a replacement, np.sort(a, axis=0) should be used instead.

  (gh-24494)

• np.dtype(("f8", 1) will now return a shape 1 subarray dtype rather than a non-subarray one.

  (gh-25761)

• Assigning to the .data attribute of an ndarray is disallowed and will raise.

• np.binary_repr(a, width) will raise if width is too small.

• Using NPY_CHAR in PyArray_DescrFromType() will raise, use NPY_STRING NPY_UNICODE, or NPY_VSTRING instead.

  (gh-25794)

Compatibility notes

loadtxt and genfromtxt default encoding changed

loadtxt and genfromtxt now both default to encoding=None which may mainly modify how converters work. These will now be passed str rather than bytes. Pass the encoding explicitly to always get the new or old behavior. For genfromtxt the change also means that returned values will now be unicode strings rather than bytes.

(gh-25158)

f2py compatibility notes

• f2py will no longer accept ambiguous -m and .pyf CLI combinations. When more than one .pyf file is passed, an error is raised. When both -m and a .pyf is passed, a warning is emitted and the -m provided name is ignored.

  (gh-25181)

• The f2py.compile() helper has been removed because it leaked memory, has been marked as experimental for several years now, and was implemented as a thin subprocess.run wrapper. It was also one of the test bottlenecks. See gh-25122 for the full rationale. It also used several np.distutils features which are too fragile to be ported to work with meson.

• Users are urged to replace calls to f2py.compile with calls to subprocess.run("python", "-m", "numpy.f2py",... instead, and to use environment variables to interact with meson. Native files are also an option.

  (gh-25193)

Minor changes in behavior of sorting functions

Due to algorithmic changes and use of SIMD code, sorting functions with methods that aren\'t stable may return slightly different results in 2.0.0 compared to 1.26.x. This includes the default method of numpy.argsort and numpy.argpartition.

Removed ambiguity when broadcasting in np.solve

The broadcasting rules for np.solve(a, b) were ambiguous when b had 1 fewer dimensions than a. This has been resolved in a backward-incompatible way and is now compliant with the Array API. The old behaviour can be reconstructed by using np.solve(a, b[..., None])[..., 0].

(gh-25914)

Modified representation for Polynomial

The representation method for numpy.polynomial.polynomial.Polynomial was updated to include the domain in the representation. The plain text and latex representations are now consistent. For example the output of str(np.polynomial.Polynomial([1, 1], domain=[.1, .2])) used to be 1.0 + 1.0 x, but now is 1.0 + 1.0 (-3.0000000000000004 + 20.0 x).

(gh-21760)

C API changes

• The PyArray_CGT, PyArray_CLT, PyArray_CGE, PyArray_CLE, PyArray_CEQ, PyArray_CNE macros have been removed.

• PyArray_MIN and PyArray_MAX have been moved from ndarraytypes.h to npy_math.h.

  (gh-24258)

• A C API for working with numpy.dtypes.StringDType arrays has been exposed. This includes functions for acquiring and releasing mutexes which lock access to the string data, as well as packing and unpacking UTF-8 bytestreams from array entries.

• NPY_NTYPES has been renamed to NPY_NTYPES_LEGACY as it does not include new NumPy built-in DTypes. In particular the new string DType will likely not work correctly with code that handles legacy DTypes.

  (gh-25347)

• The C-API now only exports the static inline function versions of the array accessors (previously this depended on using \"deprecated API\"). While we discourage it, the struct fields can still be used directly.

  (gh-25789)

• NumPy now defines PyArray_Pack to set an individual memory address. Unlike PyArray_SETITEM this function is equivalent to setting an individual array item and does not require a NumPy array input.

  (gh-25954)

• The ->f slot has been removed from PyArray_Descr. If you use this slot, replace accessing it with PyDataType_GetArrFuncs (see its documentation and the numpy-2-migration-guide). In some cases using other functions like PyArray_GETITEM may be an alternatives.

• PyArray_GETITEM and PyArray_SETITEM now require the import of the NumPy API table to be used and are no longer defined in ndarraytypes.h.

  (gh-25812)

• Due to runtime dependencies, the definition for functionality accessing the dtype flags was moved from numpy/ndarraytypes.h and is only available after including numpy/ndarrayobject.h as it requires import_array(). This includes PyDataType_FLAGCHK, PyDataType_REFCHK and NPY_BEGIN_THREADS_DESCR.

• The dtype flags on PyArray_Descr must now be accessed through the PyDataType_FLAGS inline function to be compatible with both 1.x and 2.x. This function is defined in npy_2_compat.h to allow backporting. Most or all users should use PyDataType_FLAGCHK which is available on 1.x and does not require backporting. Cython users should use Cython 3. Otherwise access will go through Python unless they use PyDataType_FLAGCHK instead.

  (gh-25816)

Datetime functionality exposed in the C API and Cython bindings

The functions NpyDatetime_ConvertDatetime64ToDatetimeStruct, NpyDatetime_ConvertDatetimeStructToDatetime64, NpyDatetime_ConvertPyDateTimeToDatetimeStruct, NpyDatetime_GetDatetimeISO8601StrLen, NpyDatetime_MakeISO8601Datetime, and NpyDatetime_ParseISO8601Datetime have been added to the C API to facilitate converting between strings, Python datetimes, and NumPy datetimes in external libraries.

(gh-21199)

Const correctness for the generalized ufunc C API

The NumPy C API\'s functions for constructing generalized ufuncs (PyUFunc_FromFuncAndData, PyUFunc_FromFuncAndDataAndSignature, PyUFunc_FromFuncAndDataAndSignatureAndIdentity) take types and data arguments that are not modified by NumPy\'s internals. Like the name and doc arguments, third-party Python extension modules are likely to supply these arguments from static constants. The types and data arguments are now const-correct: they are declared as const char *types and void *const *data, respectively. C code should not be affected, but C++ code may be.

(gh-23847)

Larger NPY_MAXDIMS and NPY_MAXARGS, NPY_RAVEL_AXIS introduced

NPY_MAXDIMS is now 64, you may want to review its use. This is usually used in a stack allocation, where the increase should be safe. However, we do encourage generally to remove any use of NPY_MAXDIMS and NPY_MAXARGS to eventually allow removing the constraint completely. For the conversion helper and C-API functions mirroring Python ones such as take, NPY_MAXDIMS was used to mean axis=None. Such usage must be replaced with NPY_RAVEL_AXIS. See also migration_maxdims.

(gh-25149)

NPY_MAXARGS not constant and PyArrayMultiIterObject size change

Since NPY_MAXARGS was increased, it is now a runtime constant and not compile-time constant anymore. We expect almost no users to notice this. But if used for stack allocations it now must be replaced with a custom constant using NPY_MAXARGS as an additional runtime check.

The sizeof(PyArrayMultiIterObject) no longer includes the full size of the object. We expect nobody to notice this change. It was necessary to avoid issues with Cython.

(gh-25271)

Required changes for custom legacy user dtypes

In order to improve our DTypes it is unfortunately necessary to break the ABI, which requires some changes for dtypes registered with PyArray_RegisterDataType. Please see the documentation of PyArray_RegisterDataType for how to adapt your code and achieve compatibility with both 1.x and 2.x.

(gh-25792)

New Public DType API

The C implementation of the NEP 42 DType API is now public. While the DType API has shipped in NumPy for a few versions, it was only usable in sessions with a special environment variable set. It is now possible to write custom DTypes outside of NumPy using the new DType API and the normal import_array() mechanism for importing the numpy C API.

See dtype-api for more details about the API. As always with a new feature, please report any bugs you run into implementing or using a new DType. It is likely that downstream C code that works with dtypes will need to be updated to work correctly with new DTypes.

(gh-25754)

New C-API import functions

We have now added PyArray_ImportNumPyAPI and PyUFunc_ImportUFuncAPI as static inline functions to import the NumPy C-API tables. The new functions have two advantages over import_array and import_ufunc:

• They check whether the import was already performed and are light-weight if not, allowing to add them judiciously (although this is not preferable in most cases).
• The old mechanisms were macros rather than functions which included a return statement.

The PyArray_ImportNumPyAPI() function is included in npy_2_compat.h for simpler backporting.

(gh-25866)

Structured dtype information access through functions

The dtype structures fields c_metadata, names, fields, and subarray must now be accessed through new functions following the same names, such as PyDataType_NAMES. Direct access of the fields is not valid as they do not exist for all PyArray_Descr instances. The metadata field is kept, but the macro version should also be preferred.

(gh-25802)

Descriptor elsize and alignment access

Unless compiling only with NumPy 2 support, the elsize and aligment fields must now be accessed via PyDataType_ELSIZE, PyDataType_SET_ELSIZE, and PyDataType_ALIGNMENT. In cases where the descriptor is attached to an array, we advise using PyArray_ITEMSIZE as it exists on all NumPy versions. Please see migration_c_descr for more information.

(gh-25943)

NumPy 2.0 C API removals

• npy_interrupt.h and the corresponding macros like NPY_SIGINT_ON have been removed. We recommend querying PyErr_CheckSignals() or PyOS_InterruptOccurred() periodically (these do currently require holding the GIL though).

• The noprefix.h header has been removed. Replace missing symbols with their prefixed counterparts (usually an added NPY_ or npy_).

  (gh-23919)

• PyUFunc_GetPyVals, PyUFunc_handlefperr, and PyUFunc_checkfperr have been removed. If needed, a new backwards compatible function to raise floating point errors could be restored. Reason for removal: there are no known users and the functions would have made with np.errstate() fixes much more difficult).

  (gh-23922)

• The numpy/old_defines.h which was part of the API deprecated since NumPy 1.7 has been removed. This removes macros of the form PyArray_CONSTANT. The replaceoldmacros.sed script may be useful to convert them to the NPY_CONSTANT version.

  (gh-24011)

• The legacy_inner_loop_selector member of the ufunc struct is removed to simplify improvements to the dispatching system. There are no known users overriding or directly accessing this member.

  (gh-24271)

• NPY_INTPLTR has been removed to avoid confusion (see intp redefinition).

  (gh-24888)

• The advanced indexing MapIter and related API has been removed. The (truly) public part of it was not well tested and had only one known user (Theano). Making it private will simplify improvements to speed up ufunc.at, make advanced indexing more maintainable, and was important for increasing the maximum number of dimensions of arrays to 64. Please let us know if this API is important to you so we can find a solution together.

  (gh-25138)

• The NPY_MAX_ELSIZE macro has been removed, as it only ever reflected builtin numeric types and served no internal purpose.

  (gh-25149)

• PyArray_REFCNT and NPY_REFCOUNT are removed. Use Py_REFCNT instead.

  (gh-25156)

• PyArrayFlags_Type and PyArray_NewFlagsObject as well as PyArrayFlagsObject are private now. There is no known use-case; use the Python API if needed.

• PyArray_MoveInto, PyArray_CastTo, PyArray_CastAnyTo are removed use PyArray_CopyInto and if absolutely needed PyArray_CopyAnyInto (the latter does a flat copy).

• PyArray_FillObjectArray is removed, its only true use was for implementing np.empty. Create a new empty array or use PyArray_FillWithScalar() (decrefs existing objects).

• PyArray_CompareUCS4 and PyArray_CompareString are removed. Use the standard C string comparison functions.

• PyArray_ISPYTHON is removed as it is misleading, has no known use-cases, and is easy to replace.

• PyArray_FieldNames is removed, as it is unclear what it would be useful for. It also has incorrect semantics in some possible use-cases.

• PyArray_TypestrConvert is removed, since it seems a misnomer and unlikely to be used by anyone. If you know the size or are limited to few types, just use it explicitly, otherwise go via Python strings.

  (gh-25292)

• PyDataType_GetDatetimeMetaData is removed, it did not actually do anything since at least NumPy 1.7.

  (gh-25802)

• PyArray_GetCastFunc is removed. Note that custom legacy user dtypes can still provide a castfunc as their implementation, but any access to them is now removed. The reason for this is that NumPy never used these internally for many years. If you use simple numeric types, please just use C casts directly. In case you require an alternative, please let us know so we can create new API such as PyArray_CastBuffer() which could use old or new cast functions depending on the NumPy version.

  (gh-25161)

New Features

np.add was extended to work with unicode and bytes dtypes.

(gh-24858)

A new bitwise_count function

This new function counts the number of 1-bits in a number. numpy.bitwise_count works on all the numpy integer types and integer-like objects.

``` python

a = np.array([2**i - 1 for i in range(16)]) np.bitwise_count(a) array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], dtype=uint8) ```

(gh-19355)

macOS Accelerate support, including the ILP64

Support for the updated Accelerate BLAS/LAPACK library, including ILP64 (64-bit integer) support, in macOS 13.3 has been added. This brings arm64 support, and significant performance improvements of up to 10x for commonly used linear algebra operations. When Accelerate is selected at build time, or if no explicit BLAS library selection is done, the 13.3+ version will automatically be used if available.

(gh-24053)

Binary wheels are also available. On macOS >=14.0, users who install NumPy from PyPI will get wheels built against Accelerate rather than OpenBLAS.

(gh-25255)

Option to use weights for quantile and percentile functions

A weights keyword is now available for numpy.quantile, numpy.percentile, numpy.nanquantile and numpy.nanpercentile. Only method="inverted_cdf" supports weights.

(gh-24254)

Improved CPU optimization tracking

A new tracer mechanism is available which enables tracking of the enabled targets for each optimized function (i.e., that uses hardware-specific SIMD instructions) in the NumPy library. With this enhancement, it becomes possible to precisely monitor the enabled CPU dispatch targets for the dispatched functions.

A new function named opt_func_info has been added to the new namespace numpy.lib.introspect, offering this tracing capability. This function allows you to retrieve information about the enabled targets based on function names and data type signatures.

(gh-24420)

A new Meson backend for f2py

f2py in compile mode (i.e. f2py -c) now accepts the --backend meson option. This is the default option for Python >=3.12. For older Python versions, f2py will still default to --backend distutils.

To support this in realistic use-cases, in compile mode f2py takes a --dep flag one or many times which maps to dependency() calls in the meson backend, and does nothing in the distutils backend.

There are no changes for users of f2py only as a code generator, i.e. without -c.

(gh-24532)

bind(c) support for f2py

Both functions and subroutines can be annotated with bind(c). f2py will handle both the correct type mapping, and preserve the unique label for other C interfaces.

Note: bind(c, name = 'routine_name_other_than_fortran_routine') is not honored by the f2py bindings by design, since bind(c) with the name is meant to guarantee only the same name in C and Fortran, not in Python and Fortran.

(gh-24555)

A new strict option for several testing functions

The strict keyword is now available for numpy.testing.assert_allclose, numpy.testing.assert_equal, and numpy.testing.assert_array_less. Setting strict=True will disable the broadcasting behaviour for scalars and ensure that input arrays have the same data type.

(gh-24680, gh-24770, gh-24775)

Add np.core.umath.find and np.core.umath.rfind UFuncs

Add two find and rfind UFuncs that operate on unicode or byte strings and are used in np.char. They operate similar to str.find and str.rfind.

(gh-24868)

diagonal and trace for numpy.linalg

numpy.linalg.diagonal and numpy.linalg.trace have been added, which are array API standard-compatible variants of numpy.diagonal and numpy.trace. They differ in the default axis selection which define 2-D sub-arrays.

(gh-24887)

New long and ulong dtypes

numpy.long and numpy.ulong have been added as NumPy integers mapping to C\'s long and unsigned long. Prior to NumPy 1.24, numpy.long was an alias to Python\'s int.

(gh-24922)

svdvals for numpy.linalg

numpy.linalg.svdvals has been added. It computes singular values for (a stack of) matrices. Executing np.svdvals(x) is the same as calling np.svd(x, compute_uv=False, hermitian=False). This function is compatible with the array API standard.

(gh-24940)

A new isdtype function

numpy.isdtype was added to provide a canonical way to classify NumPy\'s dtypes in compliance with the array API standard.

(gh-25054)

A new astype function

numpy.astype was added to provide an array API standard-compatible alternative to the numpy.ndarray.astype method.

(gh-25079)

Array API compatible functions\' aliases

13 aliases for existing functions were added to improve compatibility with the array API standard:

• Trigonometry: acos, acosh, asin, asinh, atan, atanh, atan2.
• Bitwise: bitwise_left_shift, bitwise_invert, bitwise_right_shift.
• Misc: concat, permute_dims, pow.
• In numpy.linalg: tensordot, matmul.

(gh-25086)

New unique_* functions

The numpy.unique_all, numpy.unique_counts, numpy.unique_inverse, and numpy.unique_values functions have been added. They provide functionality of numpy.unique with different sets of flags. They are array API standard-compatible, and because the number of arrays they return does not depend on the values of input arguments, they are easier to target for JIT compilation.

(gh-25088)

Matrix transpose support for ndarrays

NumPy now offers support for calculating the matrix transpose of an array (or stack of arrays). The matrix transpose is equivalent to swapping the last two axes of an array. Both np.ndarray and np.ma.MaskedArray now expose a .mT attribute, and there is a matching new numpy.matrix_transpose function.

(gh-23762)

Array API compatible functions for numpy.linalg

Six new functions and two aliases were added to improve compatibility with the Array API standard for `numpy.linalg`:

• numpy.linalg.matrix_norm - Computes the matrix norm of a matrix (or a stack of matrices).

• numpy.linalg.vector_norm - Computes the vector norm of a vector (or batch of vectors).

• numpy.vecdot - Computes the (vector) dot product of two arrays.

• numpy.linalg.vecdot - An alias for numpy.vecdot.

• numpy.linalg.matrix_transpose - An alias for numpy.matrix_transpose.

  (gh-25155)

• numpy.linalg.outer has been added. It computes the outer product of two vectors. It differs from numpy.outer by accepting one-dimensional arrays only. This function is compatible with the array API standard.

  (gh-25101)

• numpy.linalg.cross has been added. It computes the cross product of two (arrays of) 3-dimensional vectors. It differs from numpy.cross by accepting three-dimensional vectors only. This function is compatible with the array API standard.

  (gh-25145)

A correction argument for var and std

A correction argument was added to numpy.var and numpy.std, which is an array API standard compatible alternative to ddof. As both arguments serve a similar purpose, only one of them can be provided at the same time.

(gh-25169)

ndarray.device and ndarray.to_device

An ndarray.device attribute and ndarray.to_device method were added to numpy.ndarray for array API standard compatibility.

Additionally, device keyword-only arguments were added to: numpy.asarray, numpy.arange, numpy.empty, numpy.empty_like, numpy.eye, numpy.full, numpy.full_like, numpy.linspace, numpy.ones, numpy.ones_like, numpy.zeros, and numpy.zeros_like.

For all these new arguments, only device="cpu" is supported.

(gh-25233)

StringDType has been added to NumPy

We have added a new variable-width UTF-8 encoded string data type, implementing a \"NumPy array of Python strings\", including support for a user-provided missing data sentinel. It is intended as a drop-in replacement for arrays of Python strings and missing data sentinels using the object dtype. See NEP 55 and the documentation of stringdtype for more details.

(gh-25347)

New keywords for cholesky and pinv

The upper and rtol keywords were added to numpy.linalg.cholesky and numpy.linalg.pinv, respectively, to improve array API standard compatibility.

For numpy.linalg.pinv, if neither rcond nor rtol is specified, the rcond\'s default is used. We plan to deprecate and remove rcond in the future.

(gh-25388)

New keywords for sort, argsort and linalg.matrix_rank

New keyword parameters were added to improve array API standard compatibility:

• rtol was added to numpy.linalg.matrix_rank.
• stable was added to numpy.sort and numpy.argsort.

(gh-25437)

New numpy.strings namespace for string ufuncs

NumPy now implements some string operations as ufuncs. The old np.char namespace is still available, and where possible the string manipulation functions in that namespace have been updated to use the new ufuncs, substantially improving their performance.

Where possible, we suggest updating code to use functions in np.strings instead of np.char. In the future we may deprecate np.char in favor of np.strings.

(gh-25463)

numpy.fft support for different precisions and in-place calculations

The various FFT routines in numpy.fft now do their calculations natively in float, double, or long double precision, depending on the input precision, instead of always calculating in double precision. Hence, the calculation will now be less precise for single and more precise for long double precision. The data type of the output array will now be adjusted accordingly.

Furthermore, all FFT routines have gained an out argument that can be used for in-place calculations.

(gh-25536)

configtool and pkg-config support

A new numpy-config CLI script is available that can be queried for the NumPy version and for compile flags needed to use the NumPy C API. This will allow build systems to better support the use of NumPy as a dependency. Also, a numpy.pc pkg-config file is now included with Numpy. In order to find its location for use with PKG_CONFIG_PATH, use numpy-config --pkgconfigdir.

(gh-25730)

Array API standard support in the main namespace

The main numpy namespace now supports the array API standard. See array-api-standard-compatibility for details.

(gh-25911)

Improvements

Strings are now supported by any, all, and the logical ufuncs.

(gh-25651)

Integer sequences as the shape argument for memmap

numpy.memmap can now be created with any integer sequence as the shape argument, such as a list or numpy array of integers. Previously, only the types of tuple and int could be used without raising an error.

(gh-23729)

errstate is now faster and context safe

The numpy.errstate context manager/decorator is now faster and safer. Previously, it was not context safe and had (rare) issues with thread-safety.

(gh-23936)

AArch64 quicksort speed improved by using Highway\'s VQSort

The first introduction of the Google Highway library, using VQSort on AArch64. Execution time is improved by up to 16x in some cases, see the PR for benchmark results. Extensions to other platforms will be done in the future.

(gh-24018)

Complex types - underlying C type changes

• The underlying C types for all of NumPy\'s complex types have been changed to use C99 complex types.

• While this change does not affect the memory layout of complex types, it changes the API to be used to directly retrieve or write the real or complex part of the complex number, since direct field access (as in c.real or c.imag) is no longer an option. You can now use utilities provided in numpy/npy_math.h to do these operations, like this:

  c npy_cdouble c; npy_csetreal(&c, 1.0); npy_csetimag(&c, 0.0); printf("%d + %di\n", npy_creal(c), npy_cimag(c));

• To ease cross-version compatibility, equivalent macros and a compatibility layer have been added which can be used by downstream packages to continue to support both NumPy 1.x and 2.x. See complex-numbers for more info.

• numpy/npy_common.h now includes complex.h, which means that complex is now a reserved keyword.

(gh-24085)

iso_c_binding support and improved common blocks for f2py

Previously, users would have to define their own custom f2cmap file to use type mappings defined by the Fortran2003 iso_c_binding intrinsic module. These type maps are now natively supported by f2py

(gh-24555)

f2py now handles common blocks which have kind specifications from modules. This further expands the usability of intrinsics like iso_fortran_env and iso_c_binding.

(gh-25186)

Call str automatically on third argument to functions like assert_equal

The third argument to functions like numpy.testing.assert_equal now has str called on it automatically. This way it mimics the built-in assert statement, where assert_equal(a, b, obj) works like assert a == b, obj.

(gh-24877)

Support for array-like atol/rtol in isclose, allclose

The keywords atol and rtol in numpy.isclose and numpy.allclose now accept both scalars and arrays. An array, if given, must broadcast to the shapes of the first two array arguments.

(gh-24878)

Consistent failure messages in test functions

Previously, some numpy.testing assertions printed messages that referred to the actual and desired results as x and y. Now, these values are consistently referred to as ACTUAL and DESIRED.

(gh-24931)

n-D FFT transforms allow s[i] == -1

The numpy.fft.fftn, numpy.fft.ifftn, numpy.fft.rfftn, numpy.fft.irfftn, numpy.fft.fft2, numpy.fft.ifft2, numpy.fft.rfft2 and numpy.fft.irfft2 functions now use the whole input array along the axis i if s[i] == -1, in line with the array API standard.

(gh-25495)

Guard PyArrayScalar_VAL and PyUnicodeScalarObject for the limited API

PyUnicodeScalarObject holds a PyUnicodeObject, which is not available when using Py_LIMITED_API. Add guards to hide it and consequently also make the PyArrayScalar_VAL macro hidden.

(gh-25531)

Changes

• np.gradient() now returns a tuple rather than a list making the return value immutable.

  (gh-23861)

• Being fully context and thread-safe, np.errstate can only be entered once now.

• np.setbufsize is now tied to np.errstate(): leaving an np.errstate context will also reset the bufsize.

  (gh-23936)

• A new public np.lib.array_utils submodule has been introduced and it currently contains three functions: byte_bounds (moved from np.lib.utils), normalize_axis_tuple and normalize_axis_index.

  (gh-24540)

• Introduce numpy.bool as the new canonical name for NumPy\'s boolean dtype, and make numpy.bool\_ an alias to it. Note that until NumPy 1.24, np.bool was an alias to Python\'s builtin bool. The new name helps with array API standard compatibility and is a more intuitive name.

  (gh-25080)

• The dtype.flags value was previously stored as a signed integer. This means that the aligned dtype struct flag lead to negative flags being set (-128 rather than 128). This flag is now stored unsigned (positive). Code which checks flags manually may need to adapt. This may include code compiled with Cython 0.29.x.

  (gh-25816)

Representation of NumPy scalars changed

As per NEP 51, the scalar representation has been updated to include the type information to avoid confusion with Python scalars.

Scalars are now printed as np.float64(3.0) rather than just 3.0. This may disrupt workflows that store representations of numbers (e.g., to files) making it harder to read them. They should be stored as explicit strings, for example by using str() or f"{scalar!s}". For the time being, affected users can use np.set_printoptions(legacy="1.25") to get the old behavior (with possibly a few exceptions). Documentation of downstream projects may require larger updates, if code snippets are tested. We are working on tooling for doctest-plus to facilitate updates.

(gh-22449)

Truthiness of NumPy strings changed

NumPy strings previously were inconsistent about how they defined if the string is True or False and the definition did not match the one used by Python. Strings are now considered True when they are non-empty and False when they are empty. This changes the following distinct cases:

• Casts from string to boolean were previously roughly equivalent to string_array.astype(np.int64).astype(bool), meaning that only valid integers could be cast. Now a string of "0" will be considered True since it is not empty. If you need the old behavior, you may use the above step (casting to integer first) or string_array == "0" (if the input is only ever 0 or 1). To get the new result on old NumPy versions use string_array != "".
• np.nonzero(string_array) previously ignored whitespace so that a string only containing whitespace was considered False. Whitespace is now considered True.

This change does not affect np.loadtxt, np.fromstring, or np.genfromtxt. The first two still use the integer definition, while genfromtxt continues to match for "true" (ignoring case). However, if np.bool_ is used as a converter the result will change.

The change does affect np.fromregex as it uses direct assignments.

(gh-23871)

A mean keyword was added to var and std function

Often when the standard deviation is needed the mean is also needed. The same holds for the variance and the mean. Until now the mean is then calculated twice, the change introduced here for the numpy.var and numpy.std functions allows for passing in a precalculated mean as an keyword argument. See the docstrings for details and an example illustrating the speed-up.

(gh-24126)

Remove datetime64 deprecation warning when constructing with timezone

The numpy.datetime64 method now issues a UserWarning rather than a DeprecationWarning whenever a timezone is included in the datetime string that is provided.

(gh-24193)

Default integer dtype is now 64-bit on 64-bit Windows

The default NumPy integer is now 64-bit on all 64-bit systems as the historic 32-bit default on Windows was a common source of issues. Most users should not notice this. The main issues may occur with code interfacing with libraries written in a compiled language like C. For more information see migration_windows_int64.

(gh-24224)

Renamed numpy.core to numpy._core

Accessing numpy.core now emits a DeprecationWarning. In practice we have found that most downstream usage of numpy.core was to access functionality that is available in the main numpy namespace. If for some reason you are using functionality in numpy.core that is not available in the main numpy namespace, this means you are likely using private NumPy internals. You can still access these internals via numpy._core without a deprecation warning but we do not provide any backward compatibility guarantees for NumPy internals. Please open an issue if you think a mistake was made and something needs to be made public.

(gh-24634)

The \"relaxed strides\" debug build option, which was previously enabled through the NPY_RELAXED_STRIDES_DEBUG environment variable or the -Drelaxed-strides-debug config-settings flag has been removed.

(gh-24717)

Redefinition of np.intp/np.uintp (almost never a change)

Due to the actual use of these types almost always matching the use of size_t/Py_ssize_t this is now the definition in C. Previously, it matched intptr_t and uintptr_t which would often have been subtly incorrect. This has no effect on the vast majority of machines since the size of these types only differ on extremely niche platforms.

However, it means that:

• Pointers may not necessarily fit into an intp typed array anymore. The p and P character codes can still be used, however.
• Creating intptr_t or uintptr_t typed arrays in C remains possible in a cross-platform way via PyArray_DescrFromType('p').
• The new character codes nN were introduced.
• It is now correct to use the Python C-API functions when parsing to npy_intp typed arguments.

(gh-24888)

numpy.fft.helper made private

numpy.fft.helper was renamed to numpy.fft._helper to indicate that it is a private submodule. All public functions exported by it should be accessed from numpy.fft.

(gh-24945)

numpy.linalg.linalg made private

numpy.linalg.linalg was renamed to numpy.linalg._linalg to indicate that it is a private submodule. All public functions exported by it should be accessed from numpy.linalg.

(gh-24946)

Out-of-bound axis not the same as axis=None

In some cases axis=32 or for concatenate any large value was the same as axis=None. Except for concatenate this was deprecate. Any out of bound axis value will now error, make sure to use axis=None.

(gh-25149)

New copy keyword meaning for array and asarray constructors

Now numpy.array and numpy.asarray support three values for copy parameter:

• None - A copy will only be made if it is necessary.
• True - Always make a copy.
• False - Never make a copy. If a copy is required a ValueError is raised.

The meaning of False changed as it now raises an exception if a copy is needed.

(gh-25168)

The __array__ special method now takes a copy keyword argument.

NumPy will pass copy to the __array__ special method in situations where it would be set to a non-default value (e.g. in a call to np.asarray(some_object, copy=False)). Currently, if an unexpected keyword argument error is raised after this, NumPy will print a warning and re-try without the copy keyword argument. Implementations of objects implementing the __array__ protocol should accept a copy keyword argument with the same meaning as when passed to numpy.array or numpy.asarray.

(gh-25168)

Cleanup of initialization of numpy.dtype with strings with commas

The interpretation of strings with commas is changed slightly, in that a trailing comma will now always create a structured dtype. E.g., where previously np.dtype("i") and np.dtype("i,") were treated as identical, now np.dtype("i,") will create a structured dtype, with a single field. This is analogous to np.dtype("i,i") creating a structured dtype with two fields, and makes the behaviour consistent with that expected of tuples.

At the same time, the use of single number surrounded by parenthesis to indicate a sub-array shape, like in np.dtype("(2)i,"), is deprecated. Instead; one should use np.dtype("(2,)i") or np.dtype("2i"). Eventually, using a number in parentheses will raise an exception, like is the case for initializations without a comma, like np.dtype("(2)i").

(gh-25434)

Change in how complex sign is calculated

Following the array API standard, the complex sign is now calculated as z / |z| (instead of the rather less logical case where the sign of the real part was taken, unless the real part was zero, in which case the sign of the imaginary part was returned). Like for real numbers, zero is returned if z==0.

(gh-25441)

Return types of functions that returned a list of arrays

Functions that returned a list of ndarrays have been changed to return a tuple of ndarrays instead. Returning tuples consistently whenever a sequence of arrays is returned makes it easier for JIT compilers like Numba, as well as for static type checkers in some cases, to support these functions. Changed functions are: numpy.atleast_1d, numpy.atleast_2d, numpy.atleast_3d, numpy.broadcast_arrays, numpy.meshgrid, numpy.ogrid, numpy.histogramdd.

np.unique return_inverse shape for multi-dimensional inputs

When multi-dimensional inputs are passed to np.unique with return_inverse=True, the unique_inverse output is now shaped such that the input can be reconstructed directly using np.take(unique, unique_inverse) when axis=None, and np.take_along_axis(unique, unique_inverse, axis=axis) otherwise.

(gh-25553, gh-25570)

any and all return booleans for object arrays

The any and all functions and methods now return booleans also for object arrays. Previously, they did a reduction which behaved like the Python or and and operators which evaluates to one of the arguments. You can use np.logical_or.reduce and np.logical_and.reduce to achieve the previous behavior.

(gh-25712)

Checksums

MD5

097c3c7e3d5159bee4fd60fabb6f7c15  numpy-2.0.0rc1-cp310-cp310-macosx_10_9_x86_64.whl
dcf4a04f08be737a3224a3fe0f408857  numpy-2.0.0rc1-cp310-cp310-macosx_11_0_arm64.whl
ceee44fdc825abe8945030cc74d78340  numpy-2.0.0rc1-cp310-cp310-macosx_14_0_arm64.whl
0d77ca403f1c03ea0cf3005136d0adbe  numpy-2.0.0rc1-cp310-cp310-macosx_14_0_x86_64.whl
e1a620f15fd797e0e6fe0d3af7dfac6f  numpy-2.0.0rc1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
c356e0c2c7afde1c1e208bab64541e78  numpy-2.0.0rc1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
106c02ca9f2dd023d3d1fa67c00de71f  numpy-2.0.0rc1-cp310-cp310-musllinux_1_1_aarch64.whl
ebbb64f3bf69a58cc2a410d4e9265e7f  numpy-2.0.0rc1-cp310-cp310-musllinux_1_1_x86_64.whl
a9cbb955a541f4e9fd55a779b35e986e  numpy-2.0.0rc1-cp310-cp310-win32.whl
e54e931ca6b7d51919952f8bb5dc46ee  numpy-2.0.0rc1-cp310-cp310-win_amd64.whl
dafd6406b44c5675b002456a4ceb6a26  numpy-2.0.0rc1-cp311-cp311-macosx_10_9_x86_64.whl
4f8b3c8f5cfcb69c78ce41e41cc33085  numpy-2.0.0rc1-cp311-cp311-macosx_11_0_arm64.whl
921260667355fc407dbcf1ec11b75bd2  numpy-2.0.0rc1-cp311-cp311-macosx_14_0_arm64.whl
9554e7e501b7751d144aacc8658e9a45  numpy-2.0.0rc1-cp311-cp311-macosx_14_0_x86_64.whl
683df12eba035788df3092869f6003f0  numpy-2.0.0rc1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
3df4b3fb1e4d865c393e2938991481d2  numpy-2.0.0rc1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
f1a8fdcb957b9f04aa2d621c3f8c3ece  numpy-2.0.0rc1-cp311-cp311-musllinux_1_1_aarch64.whl
0a12d7d664d450efb24c801df03d58ac  numpy-2.0.0rc1-cp311-cp311-musllinux_1_1_x86_64.whl
272659018425cca4123bb46985b48fbd  numpy-2.0.0rc1-cp311-cp311-win32.whl
fc6707777a4d21c554cbd1ee40d02efa  numpy-2.0.0rc1-cp311-cp311-win_amd64.whl
1ab256609e0eb2b86f72e238becd9b49  numpy-2.0.0rc1-cp312-cp312-macosx_10_9_x86_64.whl
71e858d89823dfc3a005218092af49a6  numpy-2.0.0rc1-cp312-cp312-macosx_11_0_arm64.whl
77bd5f87b628b0e6f39e0df7166f3ae1  numpy-2.0.0rc1-cp312-cp312-macosx_14_0_arm64.whl
efda4b10dc0fe086e1d33747d9102cd7  numpy-2.0.0rc1-cp312-cp312-macosx_14_0_x86_64.whl
d2e813366f0ab4fd6ec6077390fc9de2  numpy-2.0.0rc1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
616efe037e2538681f5ab223b6428e69  numpy-2.0.0rc1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
46da92334bd791c759bf414ed61e4a7a  numpy-2.0.0rc1-cp312-cp312-musllinux_1_1_aarch64.whl
2f45fe8fa2648093ba0578416fc3f7a9  numpy-2.0.0rc1-cp312-cp312-musllinux_1_1_x86_64.whl
edc8cf6127019ed7773ed8e5b27a52e8  numpy-2.0.0rc1-cp312-cp312-win32.whl
47b8fcfe22189f9bbc97d30be3f6ba29  numpy-2.0.0rc1-cp312-cp312-win_amd64.whl
3578edf376be61c3173f9eecf4bdeb08  numpy-2.0.0rc1-cp39-cp39-macosx_10_9_x86_64.whl
1571b789456e859b9fa1550e9fcca88a  numpy-2.0.0rc1-cp39-cp39-macosx_11_0_arm64.whl
d09858e54060d0bd38b30f59488b6e9f  numpy-2.0.0rc1-cp39-cp39-macosx_14_0_arm64.whl
2cb1770fb5f426b9c542672d6a39253c  numpy-2.0.0rc1-cp39-cp39-macosx_14_0_x86_64.whl
21f7304c982fb92c4d6c165fc6e9c672  numpy-2.0.0rc1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
96101f85cc666c5666fcb9b8eebd7cbc  numpy-2.0.0rc1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
1469682f758821e6657cbb7469bad713  numpy-2.0.0rc1-cp39-cp39-musllinux_1_1_aarch64.whl
8664fc00fed8e741265dce45b9b13054  numpy-2.0.0rc1-cp39-cp39-musllinux_1_1_x86_64.whl
2098687405b0e05a952ad72121d26365  numpy-2.0.0rc1-cp39-cp39-win32.whl
b54376b8422df10f4bbed2fa7070395f  numpy-2.0.0rc1-cp39-cp39-win_amd64.whl
fef9749bcacad7b4fdece4d0b5b8bb47  numpy-2.0.0rc1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl
3f8303f1768278acb0fcc8ed8fb919f2  numpy-2.0.0rc1-pp39-pypy39_pp73-macosx_14_0_x86_64.whl
227fc258d8b40361ddc1ecbd8b95b6bc  numpy-2.0.0rc1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
466e180fd734cccf2ea3170cc3a97d53  numpy-2.0.0rc1-pp39-pypy39_pp73-win_amd64.whl
04b5c7de86e2883a5aed5a9f1a7f414c  numpy-2.0.0rc1.tar.gz

SHA256

9d96878db0d4f267e62e21f6feb7d0e7f07ec02784e705f37b7f6493a935c7fd  numpy-2.0.0rc1-cp310-cp310-macosx_10_9_x86_64.whl
f9e566457284cb55447eab7566fad2b59e17f01776bb1b76828a6a931d111c72  numpy-2.0.0rc1-cp310-cp310-macosx_11_0_arm64.whl
6e0438e248b5e7e46e80a686868d36d6a4ce875cedce87122d1616ffd8e2a669  numpy-2.0.0rc1-cp310-cp310-macosx_14_0_arm64.whl
015df68fd97bc00e1b7719e80cea401b23a601b639c6d6545922f7a21876b771  numpy-2.0.0rc1-cp310-cp310-macosx_14_0_x86_64.whl
cdea89bba67157bd8ec2ba9613d9f5ba2d18deab113171ca106953fdf8f7f314  numpy-2.0.0rc1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
cfd4e2f1605e3a607674dd3173c03b2e2f8520fa3ec2db04f2da2a3d5339df1b  numpy-2.0.0rc1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
25d43c681fefb4d7e0ffa949097b20eacbad4be9af7c136b1f69dc4c34c1f6d4  numpy-2.0.0rc1-cp310-cp310-musllinux_1_1_aarch64.whl
9da7cddeaf312a3645325a7da3b18bfad345cae5005cb4d6fcf24796bedaf239  numpy-2.0.0rc1-cp310-cp310-musllinux_1_1_x86_64.whl
d4b56e9abe2c3cec5615725320e002396c1e4b78011831a78427c7ff7b185816  numpy-2.0.0rc1-cp310-cp310-win32.whl
5c62c0d071681391b9c73ba09b35cb46477659012fd88af2c877a2a9da84aa2f  numpy-2.0.0rc1-cp310-cp310-win_amd64.whl
060635ab843ea0e2aa6ad153d5656193014eedd90ec4ef6e2b738d81bfe28170  numpy-2.0.0rc1-cp311-cp311-macosx_10_9_x86_64.whl
91103edc14b5b70bc25af26ea5d75a45b6490bed5f1da9478f5bbe82542ba1b5  numpy-2.0.0rc1-cp311-cp311-macosx_11_0_arm64.whl
7d990411f2821bf2812ec66ae85e8f351103fe7c3a229152ab6f8c9a620e82eb  numpy-2.0.0rc1-cp311-cp311-macosx_14_0_arm64.whl
5e289dafe89a0dd756430fa03332c428c897c41cc3143230c38d7d2bb9ad475e  numpy-2.0.0rc1-cp311-cp311-macosx_14_0_x86_64.whl
2b5f87d88212e54263f64257b28daa04f3fde627c204abd7557a80b582de4a63  numpy-2.0.0rc1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
67f9707c3df26ca5bce34162fe0721646504c5961ccfca94c294fbeaf42cfa5b  numpy-2.0.0rc1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
7511694264a1219458a4e77d185a7ee350506b4e1e3b2b82845a5e9db044b6f5  numpy-2.0.0rc1-cp311-cp311-musllinux_1_1_aarch64.whl
09e7a6cab5eac8aca0f17ad29b42ee1cd357e09a76076d5f4cb90ca62a0229b8  numpy-2.0.0rc1-cp311-cp311-musllinux_1_1_x86_64.whl
dfcd76a018c728ce7a3e6e09717e7a3dfbffdf87a57118dbc5ddc2167a678258  numpy-2.0.0rc1-cp311-cp311-win32.whl
afa4679bcbade6a4197c27874c0dacf5d45470d56cee8b1e2398e80859ab797c  numpy-2.0.0rc1-cp311-cp311-win_amd64.whl
1e2478ca8b4b0c5a7146fc316c83843bc47b2d73cf6c02000561794ae5dba537  numpy-2.0.0rc1-cp312-cp312-macosx_10_9_x86_64.whl
09bedcb99b9ac5472d2e63cd18be861750acc7570ae3661be7cb6018ce376694  numpy-2.0.0rc1-cp312-cp312-macosx_11_0_arm64.whl
8a7c01e9c14216e386e42a0c75c76a015a002dd5ed833ffbdaa6a7f2aeed9258  numpy-2.0.0rc1-cp312-cp312-macosx_14_0_arm64.whl
00236e0e8a588fef8f70e0535b898bcebd97becc0b27686d2fc7cb35b5d1ab91  numpy-2.0.0rc1-cp312-cp312-macosx_14_0_x86_64.whl
f36b7ccac6a3bfb342a61dd08be73fbe0286d2cb64c976bb1ed22feda0deb16f  numpy-2.0.0rc1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
fb009d69b3a362240acc5155e3de8f90311eb7f9f3958803af866945b8c9ee43  numpy-2.0.0rc1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
684eef178a2039cba72bce740cdf2f592e67a41885a0f09d5622380fc59af0f8  numpy-2.0.0rc1-cp312-cp312-musllinux_1_1_aarch64.whl
8b510bab996ad7b7fa59ca14fdaae4c68a36ff0f71ccd9ddec769b58f9d19258  numpy-2.0.0rc1-cp312-cp312-musllinux_1_1_x86_64.whl
08d7d73d5b7d97decfb6584f41492f5584f81a3147514b67ac21ccccb3418b35  numpy-2.0.0rc1-cp312-cp312-win32.whl
39a65e8c127d51419942a9e0ec467273536acd373507ce64e63451690ed47bfc  numpy-2.0.0rc1-cp312-cp312-win_amd64.whl
5be315e916e7d4d372acf62dcc86900eb47b2f76c185d835634dd0503f441e35  numpy-2.0.0rc1-cp39-cp39-macosx_10_9_x86_64.whl
b1bfbde0e9221920d02735ced823e53be46786589a5e8db91824bccd5115e5c8  numpy-2.0.0rc1-cp39-cp39-macosx_11_0_arm64.whl
f6539759d26e9b60dd9691732528dda7fe46a8c82be6294d109203dce4a8b89c  numpy-2.0.0rc1-cp39-cp39-macosx_14_0_arm64.whl
7517f752cad3d8bf297ed6421c63be769a03b8e3c34282eec803bae693dae67a  numpy-2.0.0rc1-cp39-cp39-macosx_14_0_x86_64.whl
8798ee3db69d2f531b12897929583021206feb4d45234d035e5511a5bd0cee38  numpy-2.0.0rc1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
070a8b1c93b0bf21c1a3c51514145acbba612e9f3fd86870c1ca37a36cebbfce  numpy-2.0.0rc1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
706f66648712385f5ca5e22ad4f32d1a1a93c143882969d951122b5cf9e40a24  numpy-2.0.0rc1-cp39-cp39-musllinux_1_1_aarch64.whl
cf1b08d8ee6d24576c0552dee71f36859de157481ed283e839d630b50242bbe1  numpy-2.0.0rc1-cp39-cp39-musllinux_1_1_x86_64.whl
c0af260d6818eab709b65953e1e5ce31a34d68230f488589b4bb96b13a28d18f  numpy-2.0.0rc1-cp39-cp39-win32.whl
1860507cb082ee8d9920db806d74d8a3936081b9ecf274b0fdb6d99b664680a1  numpy-2.0.0rc1-cp39-cp39-win_amd64.whl
9085f9a3e4f994ee8027db503627ae34aa867dc5f00ee7fe2b930608534a9293  numpy-2.0.0rc1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl
fbee730ae5265735e2c9b006a0d3fe1443d08d9399d0103245b99ecba10ddff0  numpy-2.0.0rc1-pp39-pypy39_pp73-macosx_14_0_x86_64.whl
d93d29d07b2da78869793ec30321adda61a5a48b9e00d12160d0cd658f5f2e0b  numpy-2.0.0rc1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
fe19044006aeaf783c64f22ee03330caccb4d3e54fe605b57444f448954b022d  numpy-2.0.0rc1-pp39-pypy39_pp73-win_amd64.whl
f0e169ec6cbc1b8e5f6a235845a80961f76f88352082213a1728a0967a761ad2  numpy-2.0.0rc1.tar.gz

- Python
Published by charris about 2 years ago

numpy -

NumPy 2.0.0 Release Notes

Note

The release of 2.0 is in progress and the current release overview and highlights are still in a draft state. However, the highlights should already list the most significant changes detailed in the full notes below, and those full notes should be complete (if not copy-edited well enough yet).

NumPy 2.0.0 is the first major release since 2006. It is the result of 10 months of development since the last feature release and is the work of 190 contributors spread over 968 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:

• The numpy-2-migration-guide
• The Numpy 2.0-specific advice in for dpwmstream package authors

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 and Google Highway libraries, and may see large (hardware-specific) speedups,
  • macOS Accelerate support and binary wheels for macOS >=14, with significant performance improvements for linear algebra operations on macOS, and wheels that are about 3 times smaller,
  • numpy.char fixed-length string operations have been accelerated by implementing ufuncs that also support numpy.dtypes.StringDType in addition to the fixed-length string dtypes,
  • A new tracing and introspection API, numpy.lib.introspect.opt_func_info, to determine which hardware-specific kernels are available and will be dispatched to.
• Python API improvements:
  • A clear split between public and private API, with a new module structure and each public function now available in a single place.
  • Many removals of non-recommended functions and aliases. This should make it easier to learn and use NumPy. The number of objects in the main namespace decreased by ~10% and in numpy.lib by ~80%.
  • Canonical dtype names and a newnumpy.isdtype` introspection function,
• C API improvements:
  • A new public C API for creating custom dtypes,
  • Many outdated functions and macros removed, and private internals hidden to ease future extensibility,
  • New, easier to use, initialization functions: PyArray_ImportNumPyAPI and PyUFunc_ImportUFuncAPI.
• Improved behavior:
  • Improvements to type promotion behavior was changed by adopting NEP 50. This fixes many user surprises about promotions which previously often depended on data values of input arrays rather than only their dtypes. Please see the NEP and the numpy-2-migration-guide for details as this change can lead to changes in output dtypes and lower precision results for mixed-dtype operations.
  • The default integer type on Windows is now int64 rather than int32, matching the behavior on other platforms,
  • The maximum number of array dimensions is changed from 32 to 64
• Documentation:
  • The reference guide navigation was significantly improved, and there is now documentation on NumPy\'s module structure,
  • The building from source documentation was completely rewritten,

Furthermore there are many changes to NumPy internals, including continuing to migrate code from C to C++, that will make it easier to improve and maintain NumPy in the future.

The \"no free lunch\" theorem dictates that there is a price to pay for all these API and behavior improvements and better future extensibility. This price is:

1. Backwards compatibility. There are a significant number of breaking changes to both the Python and C APIs. In the majority of cases, there are clear error messages that will inform the user how to adapt their code. However, there are also changes in behavior for which it was not possible to give such an error message - these cases are all covered in the Deprecation and Compatibility sections below, and in the numpy-2-migration-guide.

   Note that there is a ruff mode to auto-fix many things in Python code.

2. Breaking changes to the NumPy ABI. As a result, binaries of packages that use the NumPy C API and were built against a NumPy 1.xx release will not work with NumPy 2.0. On import, such packages will see an ImportError with a message about binary incompatibility.

   It is possible to build binaries against NumPy 2.0 that will work at runtime with both NumPy 2.0 and 1.x. See numpy-2-abi-handling for more details.

   All downstream packages that depend on the NumPy ABI are advised to do a new release built against NumPy 2.0 and verify that that release works with both 2.0 and 1.26 - ideally in the period between 2.0.0rc1 (which will be ABI-stable) and the final 2.0.0 release to avoid problems for their users.

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

NumPy 2.0 Python API removals

• np.geterrobj, np.seterrobj and the related ufunc keyword argument extobj= have been removed. The preferred replacement for all of these is using the context manager with np.errstate():.

  (gh-23922)

• np.cast has been removed. The literal replacement for np.cast[dtype](arg) is np.asarray(arg, dtype=dtype).

• np.source has been removed. The preferred replacement is inspect.getsource.

• np.lookfor has been removed.

  (gh-24144)

• numpy.who has been removed. As an alternative for the removed functionality, one can use a variable explorer that is available in IDEs such as Spyder or Jupyter Notebook.

  (gh-24321)

• Multiple niche enums, expired members and functions have been removed from the main namespace, such as: ERR_*, SHIFT_*, np.fastCopyAndTranspose, np.kernel_version, np.numarray, np.oldnumeric and np.set_numeric_ops.

  (gh-24316)

• Replaced from ... import * in the numpy/__init__.py with explicit imports. As a result, these main namespace members got removed: np.FLOATING_POINT_SUPPORT, np.FPE_*, np.NINF, np.PINF, np.NZERO, np.PZERO, np.CLIP, np.WRAP, np.WRAP, np.RAISE, np.BUFSIZE, np.UFUNC_BUFSIZE_DEFAULT, np.UFUNC_PYVALS_NAME, np.ALLOW_THREADS, np.MAXDIMS, np.MAY_SHARE_EXACT, np.MAY_SHARE_BOUNDS, add_newdoc, np.add_docstring and np.add_newdoc_ufunc.

  (gh-24357)

• Alias np.float_ has been removed. Use np.float64 instead.

• Alias np.complex_ has been removed. Use np.complex128 instead.

• Alias np.longfloat has been removed. Use np.longdouble instead.

• Alias np.singlecomplex has been removed. Use np.complex64 instead.

• Alias np.cfloat has been removed. Use np.complex128 instead.

• Alias np.longcomplex has been removed. Use np.clongdouble instead.

• Alias np.clongfloat has been removed. Use np.clongdouble instead.

• Alias np.string_ has been removed. Use np.bytes_ instead.

• Alias np.unicode_ has been removed. Use np.str_ instead.

• Alias np.Inf has been removed. Use np.inf instead.

• Alias np.Infinity has been removed. Use np.inf instead.

• Alias np.NaN has been removed. Use np.nan instead.

• Alias np.infty has been removed. Use np.inf instead.

• Alias np.mat has been removed. Use np.asmatrix instead.

• np.issubclass_ has been removed. Use the issubclass builtin instead.

• np.asfarray has been removed. Use np.asarray with a proper dtype instead.

• np.set_string_function has been removed. Use np.set_printoptions instead with a formatter for custom printing of NumPy objects.

• np.tracemalloc_domain is now only available from np.lib.

• np.recfromcsv and recfromtxt are now only available from np.lib.npyio.

• np.issctype, np.maximum_sctype, np.obj2sctype, np.sctype2char, np.sctypes, np.issubsctype were all removed from the main namespace without replacement, as they where niche members.

• Deprecated np.deprecate and np.deprecate_with_doc has been removed from the main namespace. Use DeprecationWarning instead.

• Deprecated np.safe_eval has been removed from the main namespace. Use ast.literal_eval instead.

  (gh-24376)

• np.find_common_type has been removed. Use numpy.promote_types or numpy.result_type instead. To achieve semantics for the scalar_types argument, use numpy.result_type and pass 0, 0.0, or 0j as a Python scalar instead.

• np.round_ has been removed. Use np.round instead.

• np.nbytes has been removed. Use np.dtype(<dtype>).itemsize instead.

  (gh-24477)

• np.compare_chararrays has been removed from the main namespace. Use np.char.compare_chararrays instead.

• The charrarray in the main namespace has been deprecated. It can be imported without a deprecation warning from np.char.chararray for now, but we are planning to fully deprecate and remove chararray in the future.

• np.format_parser has been removed from the main namespace. Use np.rec.format_parser instead.

  (gh-24587)

• Support for seven data type string aliases has been removed from np.dtype: int0, uint0, void0, object0, str0, bytes0 and bool8.

  (gh-24807)

• The experimental numpy.array_api submodule has been removed. Use the main numpy namespace for regular usage instead, or the separate array-api-strict package for the compliance testing use case for which numpy.array_api was mostly used.

  (gh-25911)

__array_prepare__ is removed

UFuncs called __array_prepare__ before running computations for normal ufunc calls (not generalized ufuncs, reductions, etc.). The function was also called instead of __array_wrap__ on the results of some linear algebra functions.

It is now removed. If you use it, migrate to __array_ufunc__ or rely on __array_wrap__ which is called with a context in all cases, although only after the result array is filled. In those code paths, __array_wrap__ will now be passed a base class, rather than a subclass array.

(gh-25105)

Deprecations

• np.compat has been deprecated, as Python 2 is no longer supported.

• np.safe_eval has been deprecated. ast.literal_eval should be used instead.

  (gh-23830)

• np.recfromcsv, np.recfromtxt, np.disp, np.get_array_wrap, np.maximum_sctype, np.deprecate and np.deprecate_with_doc have been deprecated.

  (gh-24154)

• np.trapz has been deprecated. Use np.trapezoid or a scipy.integrate function instead.

• np.in1d has been deprecated. Use np.isin instead.

• Alias np.row_stack has been deprecated. Use np.vstack directly.

  (gh-24445)

• __array_wrap__ is now passed arr, context, return_scalar and support for implementations not accepting all three are deprecated. Its signature should be __array_wrap__(self, arr, context=None, return_scalar=False)

  (gh-25408)

• Arrays of 2-dimensional vectors for np.cross have been deprecated. Use arrays of 3-dimensional vectors instead.

  (gh-24818)

• np.dtype("a") alias for np.dtype(np.bytes_) was deprecated. Use np.dtype("S") alias instead.

  (gh-24854)

• Use of keyword arguments x and y with functions assert_array_equal and assert_array_almost_equal has been deprecated. Pass the first two arguments as positional arguments instead.

  (gh-24978)

numpy.fft deprecations for n-D transforms with None values in arguments

Using fftn, ifftn, rfftn, irfftn, fft2, ifft2, rfft2 or irfft2 with the s parameter set to a value that is not None and the axes parameter set to None has been deprecated, in line with the array API standard. To retain current behaviour, pass a sequence [0, ..., k-1] to axes for an array of dimension k.

Furthermore, passing an array to s which contains None values is deprecated as the parameter is documented to accept a sequence of integers in both the NumPy docs and the array API specification. To use the default behaviour of the corresponding 1-D transform, pass the value matching the default for its n parameter. To use the default behaviour for every axis, the s argument can be omitted.

(gh-25495)

np.linalg.lstsq now defaults to a new rcond value

numpy.linalg.lstsq now uses the new rcond value of the machine precision times max(M, N). Previously, the machine precision was used but a FutureWarning was given to notify that this change will happen eventually. That old behavior can still be achieved by passing rcond=-1.

(gh-25721)

Expired deprecations

• The np.core.umath_tests submodule has been removed from the public API. (Deprecated in NumPy 1.15)

  (gh-23809)

• The PyDataMem_SetEventHook deprecation has expired and it is removed. Use tracemalloc and the np.lib.tracemalloc_domain domain. (Deprecated in NumPy 1.23)

  (gh-23921)

• The deprecation of set_numeric_ops and the C functions PyArray_SetNumericOps and PyArray_GetNumericOps has been expired and the functions removed. (Deprecated in NumPy 1.16)

  (gh-23998)

• The fasttake, fastclip, and fastputmask ArrFuncs deprecation is now finalized.

• The deprecated function fastCopyAndTranspose and its C counterpart are now removed.

• The deprecation of PyArray_ScalarFromObject is now finalized.

  (gh-24312)

• np.msort has been removed. For a replacement, np.sort(a, axis=0) should be used instead.

  (gh-24494)

• np.dtype(("f8", 1) will now return a shape 1 subarray dtype rather than a non-subarray one.

  (gh-25761)

• Assigning to the .data attribute of an ndarray is disallowed and will raise.

• np.binary_repr(a, width) will raise if width is too small.

• Using NPY_CHAR in PyArray_DescrFromType() will raise, use NPY_STRING NPY_UNICODE, or NPY_VSTRING instead.

  (gh-25794)

Compatibility notes

loadtxt and genfromtxt default encoding changed

loadtxt and genfromtxt now both default to encoding=None which may mainly modify how converters work. These will now be passed str rather than bytes. Pass the encoding explicitly to always get the new or old behavior. For genfromtxt the change also means that returned values will now be unicode strings rather than bytes.

(gh-25158)

f2py compatibility notes

• f2py will no longer accept ambiguous -m and .pyf CLI combinations. When more than one .pyf file is passed, an error is raised. When both -m and a .pyf is passed, a warning is emitted and the -m provided name is ignored.

  (gh-25181)

• The f2py.compile() helper has been removed because it leaked memory, has been marked as experimental for several years now, and was implemented as a thin subprocess.run wrapper. It was also one of the test bottlenecks. See gh-25122 for the full rationale. It also used several np.distutils features which are too fragile to be ported to work with meson.

• Users are urged to replace calls to f2py.compile with calls to subprocess.run("python", "-m", "numpy.f2py",... instead, and to use environment variables to interact with meson. Native files are also an option.

  (gh-25193)

Minor changes in behavior of sorting functions

Due to algorithmic changes and use of SIMD code, sorting functions with methods that aren\'t stable may return slightly different results in 2.0.0 compared to 1.26.x. This includes the default method of numpy.argsort and numpy.argpartition.

Removed ambiguity when broadcasting in np.solve

The broadcasting rules for np.solve(a, b) were ambiguous when b had 1 fewer dimensions than a. This has been resolved in a backward-incompatible way and is now compliant with the Array API. The old behaviour can be reconstructed by using np.solve(a, b[..., None])[..., 0].

(gh-25914)

Modified representation for Polynomial

The representation method for numpy.polynomial.polynomial.Polynomial was updated to include the domain in the representation. The plain text and latex representations are now consistent. For example the output of str(np.polynomial.Polynomial([1, 1], domain=[.1, .2])) used to be 1.0 + 1.0 x, but now is 1.0 + 1.0 (-3.0000000000000004 + 20.0 x).

(gh-21760)

C API changes

• The PyArray_CGT, PyArray_CLT, PyArray_CGE, PyArray_CLE, PyArray_CEQ, PyArray_CNE macros have been removed.

• PyArray_MIN and PyArray_MAX have been moved from ndarraytypes.h to npy_math.h.

  (gh-24258)

• A C API for working with numpy.dtypes.StringDType arrays has been exposed. This includes functions for acquiring and releasing mutexes which lock access to the string data, as well as packing and unpacking UTF-8 bytestreams from array entries.

• NPY_NTYPES has been renamed to NPY_NTYPES_LEGACY as it does not include new NumPy built-in DTypes. In particular the new string DType will likely not work correctly with code that handles legacy DTypes.

  (gh-25347)

• The C-API now only exports the static inline function versions of the array accessors (previously this depended on using \"deprecated API\"). While we discourage it, the struct fields can still be used directly.

  (gh-25789)

• NumPy now defines PyArray_Pack to set an individual memory address. Unlike PyArray_SETITEM this function is equivalent to setting an individual array item and does not require a NumPy array input.

  (gh-25954)

• The ->f slot has been removed from PyArray_Descr. If you use this slot, replace accessing it with PyDataType_GetArrFuncs (see its documentation and the numpy-2-migration-guide). In some cases using other functions like PyArray_GETITEM may be an alternatives.

• PyArray_GETITEM and PyArray_SETITEM now require the import of the NumPy API table to be used and are no longer defined in ndarraytypes.h.

  (gh-25812)

• Due to runtime dependencies, the definition for functionality accessing the dtype flags was moved from numpy/ndarraytypes.h and is only available after including numpy/ndarrayobject.h as it requires import_array(). This includes PyDataType_FLAGCHK, PyDataType_REFCHK and NPY_BEGIN_THREADS_DESCR.

• The dtype flags on PyArray_Descr must now be accessed through the PyDataType_FLAGS inline function to be compatible with both 1.x and 2.x. This function is defined in npy_2_compat.h to allow backporting. Most or all users should use PyDataType_FLAGCHK which is available on 1.x and does not require backporting. Cython users should use Cython 3. Otherwise access will go through Python unless they use PyDataType_FLAGCHK instead.

  (gh-25816)

Datetime functionality exposed in the C API and Cython bindings

The functions NpyDatetime_ConvertDatetime64ToDatetimeStruct, NpyDatetime_ConvertDatetimeStructToDatetime64, NpyDatetime_ConvertPyDateTimeToDatetimeStruct, NpyDatetime_GetDatetimeISO8601StrLen, NpyDatetime_MakeISO8601Datetime, and NpyDatetime_ParseISO8601Datetime have been added to the C API to facilitate converting between strings, Python datetimes, and NumPy datetimes in external libraries.

(gh-21199)

Const correctness for the generalized ufunc C API

The NumPy C API\'s functions for constructing generalized ufuncs (PyUFunc_FromFuncAndData, PyUFunc_FromFuncAndDataAndSignature, PyUFunc_FromFuncAndDataAndSignatureAndIdentity) take types and data arguments that are not modified by NumPy\'s internals. Like the name and doc arguments, third-party Python extension modules are likely to supply these arguments from static constants. The types and data arguments are now const-correct: they are declared as const char *types and void *const *data, respectively. C code should not be affected, but C++ code may be.

(gh-23847)

Larger NPY_MAXDIMS and NPY_MAXARGS, NPY_RAVEL_AXIS introduced

NPY_MAXDIMS is now 64, you may want to review its use. This is usually used in a stack allocation, where the increase should be safe. However, we do encourage generally to remove any use of NPY_MAXDIMS and NPY_MAXARGS to eventually allow removing the constraint completely. For the conversion helper and C-API functions mirroring Python ones such as take, NPY_MAXDIMS was used to mean axis=None. Such usage must be replaced with NPY_RAVEL_AXIS. See also migration_maxdims.

(gh-25149)

NPY_MAXARGS not constant and PyArrayMultiIterObject size change

Since NPY_MAXARGS was increased, it is now a runtime constant and not compile-time constant anymore. We expect almost no users to notice this. But if used for stack allocations it now must be replaced with a custom constant using NPY_MAXARGS as an additional runtime check.

The sizeof(PyArrayMultiIterObject) no longer includes the full size of the object. We expect nobody to notice this change. It was necessary to avoid issues with Cython.

(gh-25271)

Required changes for custom legacy user dtypes

In order to improve our DTypes it is unfortunately necessary to break the ABI, which requires some changes for dtypes registered with PyArray_RegisterDataType. Please see the documentation of PyArray_RegisterDataType for how to adapt your code and achieve compatibility with both 1.x and 2.x.

(gh-25792)

New Public DType API

The C implementation of the NEP 42 DType API is now public. While the DType API has shipped in NumPy for a few versions, it was only usable in sessions with a special environment variable set. It is now possible to write custom DTypes outside of NumPy using the new DType API and the normal import_array() mechanism for importing the numpy C API.

See dtype-api for more details about the API. As always with a new feature, please report any bugs you run into implementing or using a new DType. It is likely that downstream C code that works with dtypes will need to be updated to work correctly with new DTypes.

(gh-25754)

New C-API import functions

We have now added PyArray_ImportNumPyAPI and PyUFunc_ImportUFuncAPI as static inline functions to import the NumPy C-API tables. The new functions have two advantages over import_array and import_ufunc:

• They check whether the import was already performed and are light-weight if not, allowing to add them judiciously (although this is not preferable in most cases).
• The old mechanisms were macros rather than functions which included a return statement.

The PyArray_ImportNumPyAPI() function is included in npy_2_compat.h for simpler backporting.

(gh-25866)

Structured dtype information access through functions

The dtype structures fields c_metadata, names, fields, and subarray must now be accessed through new functions following the same names, such as PyDataType_NAMES. Direct access of the fields is not valid as they do not exist for all PyArray_Descr instances. The metadata field is kept, but the macro version should also be preferred.

(gh-25802)

Descriptor elsize and alignment access

Unless compiling only with NumPy 2 support, the elsize and aligment fields must now be accessed via PyDataType_ELSIZE, PyDataType_SET_ELSIZE, and PyDataType_ALIGNMENT. In cases where the descriptor is attached to an array, we advise using PyArray_ITEMSIZE as it exists on all NumPy versions. Please see migration_c_descr for more information.

(gh-25943)

NumPy 2.0 C API removals

• npy_interrupt.h and the corresponding macros like NPY_SIGINT_ON have been removed. We recommend querying PyErr_CheckSignals() or PyOS_InterruptOccurred() periodically (these do currently require holding the GIL though).

• The noprefix.h header has been removed. Replace missing symbols with their prefixed counterparts (usually an added NPY_ or npy_).

  (gh-23919)

• PyUFunc_GetPyVals, PyUFunc_handlefperr, and PyUFunc_checkfperr have been removed. If needed, a new backwards compatible function to raise floating point errors could be restored. Reason for removal: there are no known users and the functions would have made with np.errstate() fixes much more difficult).

  (gh-23922)

• The numpy/old_defines.h which was part of the API deprecated since NumPy 1.7 has been removed. This removes macros of the form PyArray_CONSTANT. The replaceoldmacros.sed script may be useful to convert them to the NPY_CONSTANT version.

  (gh-24011)

• The legacy_inner_loop_selector member of the ufunc struct is removed to simplify improvements to the dispatching system. There are no known users overriding or directly accessing this member.

  (gh-24271)

• NPY_INTPLTR has been removed to avoid confusion (see intp redefinition).

  (gh-24888)

• The advanced indexing MapIter and related API has been removed. The (truly) public part of it was not well tested and had only one known user (Theano). Making it private will simplify improvements to speed up ufunc.at, make advanced indexing more maintainable, and was important for increasing the maximum number of dimensions of arrays to 64. Please let us know if this API is important to you so we can find a solution together.

  (gh-25138)

• The NPY_MAX_ELSIZE macro has been removed, as it only ever reflected builtin numeric types and served no internal purpose.

  (gh-25149)

• PyArray_REFCNT and NPY_REFCOUNT are removed. Use Py_REFCNT instead.

  (gh-25156)

• PyArrayFlags_Type and PyArray_NewFlagsObject as well as PyArrayFlagsObject are private now. There is no known use-case; use the Python API if needed.

• PyArray_MoveInto, PyArray_CastTo, PyArray_CastAnyTo are removed use PyArray_CopyInto and if absolutely needed PyArray_CopyAnyInto (the latter does a flat copy).

• PyArray_FillObjectArray is removed, its only true use was for implementing np.empty. Create a new empty array or use PyArray_FillWithScalar() (decrefs existing objects).

• PyArray_CompareUCS4 and PyArray_CompareString are removed. Use the standard C string comparison functions.

• PyArray_ISPYTHON is removed as it is misleading, has no known use-cases, and is easy to replace.

• PyArray_FieldNames is removed, as it is unclear what it would be useful for. It also has incorrect semantics in some possible use-cases.

• PyArray_TypestrConvert is removed, since it seems a misnomer and unlikely to be used by anyone. If you know the size or are limited to few types, just use it explicitly, otherwise go via Python strings.

  (gh-25292)

• PyDataType_GetDatetimeMetaData is removed, it did not actually do anything since at least NumPy 1.7.

  (gh-25802)

• PyArray_GetCastFunc is removed. Note that custom legacy user dtypes can still provide a castfunc as their implementation, but any access to them is now removed. The reason for this is that NumPy never used these internally for many years. If you use simple numeric types, please just use C casts directly. In case you require an alternative, please let us know so we can create new API such as PyArray_CastBuffer() which could use old or new cast functions depending on the NumPy version.

  (gh-25161)

New Features

np.add was extended to work with unicode and bytes dtypes.

(gh-24858)

A new bitwise_count function

This new function counts the number of 1-bits in a number. numpy.bitwise_count works on all the numpy integer types and integer-like objects.

``` python

a = np.array([2**i - 1 for i in range(16)]) np.bitwise_count(a) array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], dtype=uint8) ```

(gh-19355)

macOS Accelerate support, including the ILP64

Support for the updated Accelerate BLAS/LAPACK library, including ILP64 (64-bit integer) support, in macOS 13.3 has been added. This brings arm64 support, and significant performance improvements of up to 10x for commonly used linear algebra operations. When Accelerate is selected at build time, or if no explicit BLAS library selection is done, the 13.3+ version will automatically be used if available.

(gh-24053)

Binary wheels are also available. On macOS >=14.0, users who install NumPy from PyPI will get wheels built against Accelerate rather than OpenBLAS.

(gh-25255)

Option to use weights for quantile and percentile functions

A weights keyword is now available for numpy.quantile, numpy.percentile, numpy.nanquantile and numpy.nanpercentile. Only method="inverted_cdf" supports weights.

(gh-24254)

Improved CPU optimization tracking

A new tracer mechanism is available which enables tracking of the enabled targets for each optimized function (i.e., that uses hardware-specific SIMD instructions) in the NumPy library. With this enhancement, it becomes possible to precisely monitor the enabled CPU dispatch targets for the dispatched functions.

A new function named opt_func_info has been added to the new namespace numpy.lib.introspect, offering this tracing capability. This function allows you to retrieve information about the enabled targets based on function names and data type signatures.

(gh-24420)

A new Meson backend for f2py

f2py in compile mode (i.e. f2py -c) now accepts the --backend meson option. This is the default option for Python >=3.12. For older Python versions, f2py will still default to --backend distutils.

To support this in realistic use-cases, in compile mode f2py takes a --dep flag one or many times which maps to dependency() calls in the meson backend, and does nothing in the distutils backend.

There are no changes for users of f2py only as a code generator, i.e. without -c.

(gh-24532)

bind(c) support for f2py

Both functions and subroutines can be annotated with bind(c). f2py will handle both the correct type mapping, and preserve the unique label for other C interfaces.

Note: bind(c, name = 'routine_name_other_than_fortran_routine') is not honored by the f2py bindings by design, since bind(c) with the name is meant to guarantee only the same name in C and Fortran, not in Python and Fortran.

(gh-24555)

A new strict option for several testing functions

The strict keyword is now available for numpy.testing.assert_allclose, numpy.testing.assert_equal, and numpy.testing.assert_array_less. Setting strict=True will disable the broadcasting behaviour for scalars and ensure that input arrays have the same data type.

(gh-24680, gh-24770, gh-24775)

Add np.core.umath.find and np.core.umath.rfind UFuncs

Add two find and rfind UFuncs that operate on unicode or byte strings and are used in np.char. They operate similar to str.find and str.rfind.

(gh-24868)

diagonal and trace for numpy.linalg

numpy.linalg.diagonal and numpy.linalg.trace have been added, which are array API standard-compatible variants of numpy.diagonal and numpy.trace. They differ in the default axis selection which define 2-D sub-arrays.

(gh-24887)

New long and ulong dtypes

numpy.long and numpy.ulong have been added as NumPy integers mapping to C\'s long and unsigned long. Prior to NumPy 1.24, numpy.long was an alias to Python\'s int.

(gh-24922)

svdvals for numpy.linalg

numpy.linalg.svdvals has been added. It computes singular values for (a stack of) matrices. Executing np.svdvals(x) is the same as calling np.svd(x, compute_uv=False, hermitian=False). This function is compatible with the array API standard.

(gh-24940)

A new isdtype function

numpy.isdtype was added to provide a canonical way to classify NumPy\'s dtypes in compliance with the array API standard.

(gh-25054)

A new astype function

numpy.astype was added to provide an array API standard-compatible alternative to the numpy.ndarray.astype method.

(gh-25079)

Array API compatible functions\' aliases

13 aliases for existing functions were added to improve compatibility with the array API standard:

• Trigonometry: acos, acosh, asin, asinh, atan, atanh, atan2.
• Bitwise: bitwise_left_shift, bitwise_invert, bitwise_right_shift.
• Misc: concat, permute_dims, pow.
• In numpy.linalg: tensordot, matmul.

(gh-25086)

New unique_* functions

The numpy.unique_all, numpy.unique_counts, numpy.unique_inverse, and numpy.unique_values functions have been added. They provide functionality of numpy.unique with different sets of flags. They are array API standard-compatible, and because the number of arrays they return does not depend on the values of input arguments, they are easier to target for JIT compilation.

(gh-25088)

Matrix transpose support for ndarrays

NumPy now offers support for calculating the matrix transpose of an array (or stack of arrays). The matrix transpose is equivalent to swapping the last two axes of an array. Both np.ndarray and np.ma.MaskedArray now expose a .mT attribute, and there is a matching new numpy.matrix_transpose function.

(gh-23762)

Array API compatible functions for numpy.linalg

Six new functions and two aliases were added to improve compatibility with the Array API standard for `numpy.linalg`:

• numpy.linalg.matrix_norm - Computes the matrix norm of a matrix (or a stack of matrices).

• numpy.linalg.vector_norm - Computes the vector norm of a vector (or batch of vectors).

• numpy.vecdot - Computes the (vector) dot product of two arrays.

• numpy.linalg.vecdot - An alias for numpy.vecdot.

• numpy.linalg.matrix_transpose - An alias for numpy.matrix_transpose.

  (gh-25155)

• numpy.linalg.outer has been added. It computes the outer product of two vectors. It differs from numpy.outer by accepting one-dimensional arrays only. This function is compatible with the array API standard.

  (gh-25101)

• numpy.linalg.cross has been added. It computes the cross product of two (arrays of) 3-dimensional vectors. It differs from numpy.cross by accepting three-dimensional vectors only. This function is compatible with the array API standard.

  (gh-25145)

A correction argument for var and std

A correction argument was added to numpy.var and numpy.std, which is an array API standard compatible alternative to ddof. As both arguments serve a similar purpose, only one of them can be provided at the same time.

(gh-25169)

ndarray.device and ndarray.to_device

An ndarray.device attribute and ndarray.to_device method were added to numpy.ndarray for array API standard compatibility.

Additionally, device keyword-only arguments were added to: numpy.asarray, numpy.arange, numpy.empty, numpy.empty_like, numpy.eye, numpy.full, numpy.full_like, numpy.linspace, numpy.ones, numpy.ones_like, numpy.zeros, and numpy.zeros_like.

For all these new arguments, only device="cpu" is supported.

(gh-25233)

StringDType has been added to NumPy

We have added a new variable-width UTF-8 encoded string data type, implementing a \"NumPy array of Python strings\", including support for a user-provided missing data sentinel. It is intended as a drop-in replacement for arrays of Python strings and missing data sentinels using the object dtype. See NEP 55 and the documentation of stringdtype for more details.

(gh-25347)

New keywords for cholesky and pinv

The upper and rtol keywords were added to numpy.linalg.cholesky and numpy.linalg.pinv, respectively, to improve array API standard compatibility.

For numpy.linalg.pinv, if neither rcond nor rtol is specified, the rcond\'s default is used. We plan to deprecate and remove rcond in the future.

(gh-25388)

New keywords for sort, argsort and linalg.matrix_rank

New keyword parameters were added to improve array API standard compatibility:

• rtol was added to numpy.linalg.matrix_rank.
• stable was added to numpy.sort and numpy.argsort.

(gh-25437)

New numpy.strings namespace for string ufuncs

NumPy now implements some string operations as ufuncs. The old np.char namespace is still available, and where possible the string manipulation functions in that namespace have been updated to use the new ufuncs, substantially improving their performance.

Where possible, we suggest updating code to use functions in np.strings instead of np.char. In the future we may deprecate np.char in favor of np.strings.

(gh-25463)

numpy.fft support for different precisions and in-place calculations

The various FFT routines in numpy.fft now do their calculations natively in float, double, or long double precision, depending on the input precision, instead of always calculating in double precision. Hence, the calculation will now be less precise for single and more precise for long double precision. The data type of the output array will now be adjusted accordingly.

Furthermore, all FFT routines have gained an out argument that can be used for in-place calculations.

(gh-25536)

configtool and pkg-config support

A new numpy-config CLI script is available that can be queried for the NumPy version and for compile flags needed to use the NumPy C API. This will allow build systems to better support the use of NumPy as a dependency. Also, a numpy.pc pkg-config file is now included with Numpy. In order to find its location for use with PKG_CONFIG_PATH, use numpy-config --pkgconfigdir.

(gh-25730)

Array API standard support in the main namespace

The main numpy namespace now supports the array API standard. See array-api-standard-compatibility for details.

(gh-25911)

Improvements

Strings are now supported by any, all, and the logical ufuncs.

(gh-25651)

Integer sequences as the shape argument for memmap

numpy.memmap can now be created with any integer sequence as the shape argument, such as a list or numpy array of integers. Previously, only the types of tuple and int could be used without raising an error.

(gh-23729)

errstate is now faster and context safe

The numpy.errstate context manager/decorator is now faster and safer. Previously, it was not context safe and had (rare) issues with thread-safety.

(gh-23936)

AArch64 quicksort speed improved by using Highway\'s VQSort

The first introduction of the Google Highway library, using VQSort on AArch64. Execution time is improved by up to 16x in some cases, see the PR for benchmark results. Extensions to other platforms will be done in the future.

(gh-24018)

Complex types - underlying C type changes

• The underlying C types for all of NumPy\'s complex types have been changed to use C99 complex types.

• While this change does not affect the memory layout of complex types, it changes the API to be used to directly retrieve or write the real or complex part of the complex number, since direct field access (as in c.real or c.imag) is no longer an option. You can now use utilities provided in numpy/npy_math.h to do these operations, like this:

  c npy_cdouble c; npy_csetreal(&c, 1.0); npy_csetimag(&c, 0.0); printf("%d + %di\n", npy_creal(c), npy_cimag(c));

• To ease cross-version compatibility, equivalent macros and a compatibility layer have been added which can be used by downstream packages to continue to support both NumPy 1.x and 2.x. See complex-numbers for more info.

• numpy/npy_common.h now includes complex.h, which means that complex is now a reserved keyword.

(gh-24085)

iso_c_binding support and improved common blocks for f2py

Previously, users would have to define their own custom f2cmap file to use type mappings defined by the Fortran2003 iso_c_binding intrinsic module. These type maps are now natively supported by f2py

(gh-24555)

f2py now handles common blocks which have kind specifications from modules. This further expands the usability of intrinsics like iso_fortran_env and iso_c_binding.

(gh-25186)

Call str automatically on third argument to functions like assert_equal

The third argument to functions like numpy.testing.assert_equal now has str called on it automatically. This way it mimics the built-in assert statement, where assert_equal(a, b, obj) works like assert a == b, obj.

(gh-24877)

Support for array-like atol/rtol in isclose, allclose

The keywords atol and rtol in numpy.isclose and numpy.allclose now accept both scalars and arrays. An array, if given, must broadcast to the shapes of the first two array arguments.

(gh-24878)

Consistent failure messages in test functions

Previously, some numpy.testing assertions printed messages that referred to the actual and desired results as x and y. Now, these values are consistently referred to as ACTUAL and DESIRED.

(gh-24931)

n-D FFT transforms allow s[i] == -1

The numpy.fft.fftn, numpy.fft.ifftn, numpy.fft.rfftn, numpy.fft.irfftn, numpy.fft.fft2, numpy.fft.ifft2, numpy.fft.rfft2 and numpy.fft.irfft2 functions now use the whole input array along the axis i if s[i] == -1, in line with the array API standard.

(gh-25495)

Guard PyArrayScalar_VAL and PyUnicodeScalarObject for the limited API

PyUnicodeScalarObject holds a PyUnicodeObject, which is not available when using Py_LIMITED_API. Add guards to hide it and consequently also make the PyArrayScalar_VAL macro hidden.

(gh-25531)

Changes

• np.gradient() now returns a tuple rather than a list making the return value immutable.

  (gh-23861)

• Being fully context and thread-safe, np.errstate can only be entered once now.

• np.setbufsize is now tied to np.errstate(): leaving an np.errstate context will also reset the bufsize.

  (gh-23936)

• A new public np.lib.array_utils submodule has been introduced and it currently contains three functions: byte_bounds (moved from np.lib.utils), normalize_axis_tuple and normalize_axis_index.

  (gh-24540)

• Introduce numpy.bool as the new canonical name for NumPy\'s boolean dtype, and make numpy.bool\_ an alias to it. Note that until NumPy 1.24, np.bool was an alias to Python\'s builtin bool. The new name helps with array API standard compatibility and is a more intuitive name.

  (gh-25080)

• The dtype.flags value was previously stored as a signed integer. This means that the aligned dtype struct flag lead to negative flags being set (-128 rather than 128). This flag is now stored unsigned (positive). Code which checks flags manually may need to adapt. This may include code compiled with Cython 0.29.x.

  (gh-25816)

Representation of NumPy scalars changed

As per NEP 51, the scalar representation has been updated to include the type information to avoid confusion with Python scalars.

Scalars are now printed as np.float64(3.0) rather than just 3.0. This may disrupt workflows that store representations of numbers (e.g., to files) making it harder to read them. They should be stored as explicit strings, for example by using str() or f"{scalar!s}". For the time being, affected users can use np.set_printoptions(legacy="1.25") to get the old behavior (with possibly a few exceptions). Documentation of downstream projects may require larger updates, if code snippets are tested. We are working on tooling for doctest-plus to facilitate updates.

(gh-22449)

Truthiness of NumPy strings changed

NumPy strings previously were inconsistent about how they defined if the string is True or False and the definition did not match the one used by Python. Strings are now considered True when they are non-empty and False when they are empty. This changes the following distinct cases:

• Casts from string to boolean were previously roughly equivalent to string_array.astype(np.int64).astype(bool), meaning that only valid integers could be cast. Now a string of "0" will be considered True since it is not empty. If you need the old behavior, you may use the above step (casting to integer first) or string_array == "0" (if the input is only ever 0 or 1). To get the new result on old NumPy versions use string_array != "".
• np.nonzero(string_array) previously ignored whitespace so that a string only containing whitespace was considered False. Whitespace is now considered True.

This change does not affect np.loadtxt, np.fromstring, or np.genfromtxt. The first two still use the integer definition, while genfromtxt continues to match for "true" (ignoring case). However, if np.bool_ is used as a converter the result will change.

The change does affect np.fromregex as it uses direct assignments.

(gh-23871)

A mean keyword was added to var and std function

Often when the standard deviation is needed the mean is also needed. The same holds for the variance and the mean. Until now the mean is then calculated twice, the change introduced here for the numpy.var and numpy.std functions allows for passing in a precalculated mean as an keyword argument. See the docstrings for details and an example illustrating the speed-up.

(gh-24126)

Remove datetime64 deprecation warning when constructing with timezone

The numpy.datetime64 method now issues a UserWarning rather than a DeprecationWarning whenever a timezone is included in the datetime string that is provided.

(gh-24193)

Default integer dtype is now 64-bit on 64-bit Windows

The default NumPy integer is now 64-bit on all 64-bit systems as the historic 32-bit default on Windows was a common source of issues. Most users should not notice this. The main issues may occur with code interfacing with libraries written in a compiled language like C. For more information see migration_windows_int64.

(gh-24224)

Renamed numpy.core to numpy._core

Accessing numpy.core now emits a DeprecationWarning. In practice we have found that most downstream usage of numpy.core was to access functionality that is available in the main numpy namespace. If for some reason you are using functionality in numpy.core that is not available in the main numpy namespace, this means you are likely using private NumPy internals. You can still access these internals via numpy._core without a deprecation warning but we do not provide any backward compatibility guarantees for NumPy internals. Please open an issue if you think a mistake was made and something needs to be made public.

(gh-24634)

The \"relaxed strides\" debug build option, which was previously enabled through the NPY_RELAXED_STRIDES_DEBUG environment variable or the -Drelaxed-strides-debug config-settings flag has been removed.

(gh-24717)

Redefinition of np.intp/np.uintp (almost never a change)

Due to the actual use of these types almost always matching the use of size_t/Py_ssize_t this is now the definition in C. Previously, it matched intptr_t and uintptr_t which would often have been subtly incorrect. This has no effect on the vast majority of machines since the size of these types only differ on extremely niche platforms.

However, it means that:

• Pointers may not necessarily fit into an intp typed array anymore. The p and P character codes can still be used, however.
• Creating intptr_t or uintptr_t typed arrays in C remains possible in a cross-platform way via PyArray_DescrFromType('p').
• The new character codes nN were introduced.
• It is now correct to use the Python C-API functions when parsing to npy_intp typed arguments.

(gh-24888)

numpy.fft.helper made private

numpy.fft.helper was renamed to numpy.fft._helper to indicate that it is a private submodule. All public functions exported by it should be accessed from numpy.fft.

(gh-24945)

numpy.linalg.linalg made private

numpy.linalg.linalg was renamed to numpy.linalg._linalg to indicate that it is a private submodule. All public functions exported by it should be accessed from numpy.linalg.

(gh-24946)

Out-of-bound axis not the same as axis=None

In some cases axis=32 or for concatenate any large value was the same as axis=None. Except for concatenate this was deprecate. Any out of bound axis value will now error, make sure to use axis=None.

(gh-25149)

New copy keyword meaning for array and asarray constructors

Now numpy.array and numpy.asarray support three values for copy parameter:

• None - A copy will only be made if it is necessary.
• True - Always make a copy.
• False - Never make a copy. If a copy is required a ValueError is raised.

The meaning of False changed as it now raises an exception if a copy is needed.

(gh-25168)

The __array__ special method now takes a copy keyword argument.

NumPy will pass copy to the __array__ special method in situations where it would be set to a non-default value (e.g. in a call to np.asarray(some_object, copy=False)). Currently, if an unexpected keyword argument error is raised after this, NumPy will print a warning and re-try without the copy keyword argument. Implementations of objects implementing the __array__ protocol should accept a copy keyword argument with the same meaning as when passed to numpy.array or numpy.asarray.

(gh-25168)

Cleanup of initialization of numpy.dtype with strings with commas

The interpretation of strings with commas is changed slightly, in that a trailing comma will now always create a structured dtype. E.g., where previously np.dtype("i") and np.dtype("i,") were treated as identical, now np.dtype("i,") will create a structured dtype, with a single field. This is analogous to np.dtype("i,i") creating a structured dtype with two fields, and makes the behaviour consistent with that expected of tuples.

At the same time, the use of single number surrounded by parenthesis to indicate a sub-array shape, like in np.dtype("(2)i,"), is deprecated. Instead; one should use np.dtype("(2,)i") or np.dtype("2i"). Eventually, using a number in parentheses will raise an exception, like is the case for initializations without a comma, like np.dtype("(2)i").

(gh-25434)

Change in how complex sign is calculated

Following the array API standard, the complex sign is now calculated as z / |z| (instead of the rather less logical case where the sign of the real part was taken, unless the real part was zero, in which case the sign of the imaginary part was returned). Like for real numbers, zero is returned if z==0.

(gh-25441)

Return types of functions that returned a list of arrays

Functions that returned a list of ndarrays have been changed to return a tuple of ndarrays instead. Returning tuples consistently whenever a sequence of arrays is returned makes it easier for JIT compilers like Numba, as well as for static type checkers in some cases, to support these functions. Changed functions are: numpy.atleast_1d, numpy.atleast_2d, numpy.atleast_3d, numpy.broadcast_arrays, numpy.meshgrid, numpy.ogrid, numpy.histogramdd.

np.unique return_inverse shape for multi-dimensional inputs

When multi-dimensional inputs are passed to np.unique with return_inverse=True, the unique_inverse output is now shaped such that the input can be reconstructed directly using np.take(unique, unique_inverse) when axis=None, and np.take_along_axis(unique, unique_inverse, axis=axis) otherwise.

(gh-25553, gh-25570)

any and all return booleans for object arrays

The any and all functions and methods now return booleans also for object arrays. Previously, they did a reduction which behaved like the Python or and and operators which evaluates to one of the arguments. You can use np.logical_or.reduce and np.logical_and.reduce to achieve the previous behavior.

(gh-25712)

Content from release note snippets in doc/release/upcoming_changes:

Checksums

MD5

b2f97f907cc640f5f619ea4ebd1231d3  numpy-2.0.0b1-cp310-cp310-macosx_10_9_x86_64.whl
db158043b6fad6e523e23b3eb2de5d88  numpy-2.0.0b1-cp310-cp310-macosx_11_0_arm64.whl
39086961c062d97c5b42da057b9b1947  numpy-2.0.0b1-cp310-cp310-macosx_14_0_arm64.whl
3362d35bf69b852b98b41b8373253a0f  numpy-2.0.0b1-cp310-cp310-macosx_14_0_x86_64.whl
66e907969e32ec43e887cabcc1884763  numpy-2.0.0b1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
b8d1bece144e3b6aae641d44821f815f  numpy-2.0.0b1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
96ab156ec312bb451e8c5e19de4a28b7  numpy-2.0.0b1-cp310-cp310-musllinux_1_1_aarch64.whl
c04819a4f3395b81d124ffc6330925e9  numpy-2.0.0b1-cp310-cp310-musllinux_1_1_x86_64.whl
6af68b8eb8fe583ffabab9bd7da1c620  numpy-2.0.0b1-cp310-cp310-win32.whl
3b8a9514e5795985bcba20e213d55b54  numpy-2.0.0b1-cp310-cp310-win_amd64.whl
0128ad9249f70d97a057a23e0cef1515  numpy-2.0.0b1-cp311-cp311-macosx_10_9_x86_64.whl
612c018a7676ce3747cb863762750e1d  numpy-2.0.0b1-cp311-cp311-macosx_11_0_arm64.whl
6b1480446aff53c71c903fc1248bca94  numpy-2.0.0b1-cp311-cp311-macosx_14_0_arm64.whl
8d66a0af99edf30dc9de487b3f8c1639  numpy-2.0.0b1-cp311-cp311-macosx_14_0_x86_64.whl
f9154a0885b2647d7e81f32900390ebb  numpy-2.0.0b1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
9dd14e2b594a2d47eb25ecc759d5adaa  numpy-2.0.0b1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
8434d07fc4eb80c5df9ae5ebf95546eb  numpy-2.0.0b1-cp311-cp311-musllinux_1_1_aarch64.whl
a0402697c93a9d6bc8d979fabd6bf179  numpy-2.0.0b1-cp311-cp311-musllinux_1_1_x86_64.whl
2ba67ffb4b92b54394b6929b3a899cb2  numpy-2.0.0b1-cp311-cp311-win32.whl
d75e2f02c698e492b7b07f0659f9bbe4  numpy-2.0.0b1-cp311-cp311-win_amd64.whl
558fefd135de6fcebe2b94d857a84c32  numpy-2.0.0b1-cp312-cp312-macosx_10_9_x86_64.whl
d684790e4509e7daa99a1aef1d0be536  numpy-2.0.0b1-cp312-cp312-macosx_11_0_arm64.whl
fd5d4f1d1da0cc685c54e9abd2f9dceb  numpy-2.0.0b1-cp312-cp312-macosx_14_0_arm64.whl
65183c1302348d3db60eaf3b62c1e577  numpy-2.0.0b1-cp312-cp312-macosx_14_0_x86_64.whl
305eaf68e214011557303988f4635271  numpy-2.0.0b1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
e3a84e27effd888cf93eb2c1aad759e7  numpy-2.0.0b1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
88eef47ecdd11ac0939291abe0c74b6f  numpy-2.0.0b1-cp312-cp312-musllinux_1_1_aarch64.whl
fd390078c0046c20a659035c1826185f  numpy-2.0.0b1-cp312-cp312-musllinux_1_1_x86_64.whl
23db11989d2d0086ff12655355245a2a  numpy-2.0.0b1-cp312-cp312-win32.whl
323d05ef29a9c8166d865ab221faf7dc  numpy-2.0.0b1-cp312-cp312-win_amd64.whl
f5ad7adf599b65050ccd116802f0265d  numpy-2.0.0b1-cp39-cp39-macosx_10_9_x86_64.whl
89a94dddb18e4210e01ee6ca24012fcb  numpy-2.0.0b1-cp39-cp39-macosx_11_0_arm64.whl
409a537dc5ea249b3e6868dd37932342  numpy-2.0.0b1-cp39-cp39-macosx_14_0_arm64.whl
0db893de846425d58b90f05c1db3d191  numpy-2.0.0b1-cp39-cp39-macosx_14_0_x86_64.whl
c73ba41d166a5f2e72cdc48b8554c6e6  numpy-2.0.0b1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
786236fc9099283255133273535b8de0  numpy-2.0.0b1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
f2e8717957a6b3b37f881e8939a2af37  numpy-2.0.0b1-cp39-cp39-musllinux_1_1_aarch64.whl
dad671b45f6e13c28ead06064b03eaee  numpy-2.0.0b1-cp39-cp39-musllinux_1_1_x86_64.whl
76f8f89ff91d06df684cf47d7ea6d8ab  numpy-2.0.0b1-cp39-cp39-win32.whl
d4dcbd6157783aa0e78710549f13876f  numpy-2.0.0b1-cp39-cp39-win_amd64.whl
41a13de3afff77390b0d1ea3c7e407db  numpy-2.0.0b1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl
fc2ff82233376f853161c7f9bc6d44b7  numpy-2.0.0b1-pp39-pypy39_pp73-macosx_14_0_arm64.whl
860609ee9f1f24d4f28fbbcf3d31cdc9  numpy-2.0.0b1-pp39-pypy39_pp73-macosx_14_0_x86_64.whl
2a97175cec7a5b1280ed2a991fea23ff  numpy-2.0.0b1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
1656013175e650e053c15fd886be58f1  numpy-2.0.0b1-pp39-pypy39_pp73-win_amd64.whl
c06e95d7cadfa33a1f4549c9a5dcba05  numpy-2.0.0b1.tar.gz

SHA256

411ed8eb48eb679fc732f22e90c9adb994ec6ad2d9c2f53593325a975f9fa501  numpy-2.0.0b1-cp310-cp310-macosx_10_9_x86_64.whl
f8aca0561166702070ea9abcafd70da44df48be70d16f0a886e359127436fdcc  numpy-2.0.0b1-cp310-cp310-macosx_11_0_arm64.whl
0d217dae0f20a3400c1d80aa8401af9de93b9bb4ea7518b8ba200ff8ff62529e  numpy-2.0.0b1-cp310-cp310-macosx_14_0_arm64.whl
824351cb4cce66c1f8e16c1698c01de8d5e4197461f78197c327281f107fc1b2  numpy-2.0.0b1-cp310-cp310-macosx_14_0_x86_64.whl
cae0959a4f5a9c16896a87a43c9e81384f48b69f835f55050948071488820486  numpy-2.0.0b1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
3d47a42c1e48e46dbbe32e0395f8aa6e8ddd251771ed9ec47fc07aa89b8aac89  numpy-2.0.0b1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
909024f923c019d1b9dca16871844f1c313c422bd430a0b7e4a24a3acb766483  numpy-2.0.0b1-cp310-cp310-musllinux_1_1_aarch64.whl
fc6e82bea99727aeed964808f26bed95323825a75e94c015eb913fb6ec3dbdf8  numpy-2.0.0b1-cp310-cp310-musllinux_1_1_x86_64.whl
36862cad55650afbcb3f0e3a5edc07ba4c1090eb649208a41fadcf82cf1b2966  numpy-2.0.0b1-cp310-cp310-win32.whl
0e6a63c725143a6be0e48effcf01b8361b80ab20e2444704356f9d9db48ba429  numpy-2.0.0b1-cp310-cp310-win_amd64.whl
e6c3ba4bcb6cf3fd4ace244075fa214b4f0c090f12437378200a2de68144c166  numpy-2.0.0b1-cp311-cp311-macosx_10_9_x86_64.whl
89bbb14534e53c6175aabc8449a8bdf83f02da62f13d1b5facbb2fd1fecae2e2  numpy-2.0.0b1-cp311-cp311-macosx_11_0_arm64.whl
b14b6e6ca51afdcfc589cb9d6fb73aedf38009a1a0ecab15f77e3d0e0754cac0  numpy-2.0.0b1-cp311-cp311-macosx_14_0_arm64.whl
ffef68423c1edc5d10321f9787fb9d8c20a36fc08ffdba863d103924d02dadce  numpy-2.0.0b1-cp311-cp311-macosx_14_0_x86_64.whl
7e8725313b8a8aaa9cfac450713b1a74a8d79ae010ee0d0dd97505abf54d247b  numpy-2.0.0b1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
d83e18f1c4164dbcaa01adc8f4a3aebc3c5fa635d2009d8dc1bf53dd7eab0063  numpy-2.0.0b1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
91e37a5bb38c11bde547aefeb79dd382b5d9d1d140931927bca46c9d198e08f3  numpy-2.0.0b1-cp311-cp311-musllinux_1_1_aarch64.whl
d39f1005a627c5960f67b02c1f76f265e0d4219b6d7948a7809dc14443fcbeb6  numpy-2.0.0b1-cp311-cp311-musllinux_1_1_x86_64.whl
3ed4afbcdb8db622b90ef33bf0c0d080f287ec590032f9033be5cbc51e005b66  numpy-2.0.0b1-cp311-cp311-win32.whl
941382abe21d26222310275a91f053386450b5364f1307641d03babfec5b1931  numpy-2.0.0b1-cp311-cp311-win_amd64.whl
a78a38ff86aa651534979d597fdb178c7ae2c9934d95bcc921971ceea14ef54a  numpy-2.0.0b1-cp312-cp312-macosx_10_9_x86_64.whl
e5222fb05011c310d294c40e2b8640c9351aaf3238c0605486a3f041a7befabd  numpy-2.0.0b1-cp312-cp312-macosx_11_0_arm64.whl
0f69c008a8533879ea0480fe11b28154c0dc12567522406f2c887bc549a98865  numpy-2.0.0b1-cp312-cp312-macosx_14_0_arm64.whl
a5b47099876fceefb5ac4d2cfe4ee7337de22253aafe6f2e545b84d100bf9e22  numpy-2.0.0b1-cp312-cp312-macosx_14_0_x86_64.whl
a81816e4dc75351dd1ce2d84f381856b8962eef1757ddfe13007d2a8bb966fda  numpy-2.0.0b1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
3ecb219af16b0dbf58bbe1fdb4d074582f9a99567d85c630cf82c3b40168a15d  numpy-2.0.0b1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
7479d8f43dd78a3bd1c8a3c7c9b06e71639c480a0223c31a4aeb2c7e8fd62151  numpy-2.0.0b1-cp312-cp312-musllinux_1_1_aarch64.whl
1665b832541449c7079ee9d41f334ab832a1d84511cc834c0bc8d98bf96d1df5  numpy-2.0.0b1-cp312-cp312-musllinux_1_1_x86_64.whl
585471edf1f205fb589632581cc7b30c6c0e78d79b3c754739bb62ff568fa587  numpy-2.0.0b1-cp312-cp312-win32.whl
881df25d857873947d54dbed01d98c417f3feb5df86ece719eebf1edbbb2095c  numpy-2.0.0b1-cp312-cp312-win_amd64.whl
797dc478feed31f78bca1c69d9a167c6294599927c184f4e9b569ad8895ca6e5  numpy-2.0.0b1-cp39-cp39-macosx_10_9_x86_64.whl
49cb06682f4588c2553a63445b7e37aec731452fe380c3bd142377783a9ba014  numpy-2.0.0b1-cp39-cp39-macosx_11_0_arm64.whl
5fd7ec50b9650ac0aa4fd318eceb9059ed3c0ab3aa79d5f260a10158521f9770  numpy-2.0.0b1-cp39-cp39-macosx_14_0_arm64.whl
72526252a5d1da5067181bfd3df9cc6d7dcd024b757f5d35e8f1d0c08cb729c1  numpy-2.0.0b1-cp39-cp39-macosx_14_0_x86_64.whl
7870b854823217f34e6258328f46e40f68784f61408deb37a29ca64762c60c10  numpy-2.0.0b1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
26a0978595ac2e8160d27f7537ff94402eaaf3ea7a768e7f99170ed91453d1bf  numpy-2.0.0b1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
5801a93e424c12366d8b0b411dfeb7102f7429f0934059a39b1529f02ea2606b  numpy-2.0.0b1-cp39-cp39-musllinux_1_1_aarch64.whl
2f67038ecdf4b372d81fa00530547a5d04b77da5b1e4fc55f58021f3135331ea  numpy-2.0.0b1-cp39-cp39-musllinux_1_1_x86_64.whl
f32b6ec16518b3ba1a2d3a100d9b413cf24aaeeefdec19f1cddec55cb4a31dac  numpy-2.0.0b1-cp39-cp39-win32.whl
70a22408ed088725fe44a6f55a077d1f704977b262e53d30ba485a01229028a3  numpy-2.0.0b1-cp39-cp39-win_amd64.whl
a69f1624d036953f3f2795f22e6be452ee6d24937ae14f77c2e536589e20caa2  numpy-2.0.0b1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl
c27970540ee6b4b8325779cd22eee0283cb9dc6511130ff54e774fcd0a261d4b  numpy-2.0.0b1-pp39-pypy39_pp73-macosx_14_0_arm64.whl
393adcc241ff7010b43e4660710a43c322189ff67461afba18bbaf9f5581b221  numpy-2.0.0b1-pp39-pypy39_pp73-macosx_14_0_x86_64.whl
4205b3efa27b74cb096443bdda178f5032ffc6b41306a7d4a0b903b4b614b146  numpy-2.0.0b1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
52f9cd632f9f5e179e98769d76702ce9a307439f36191607d5ee06cb8a986d01  numpy-2.0.0b1-pp39-pypy39_pp73-win_amd64.whl
e0bb33a37d0d0b9a19cd41a093877f830e06bd4d989341b9792896cf08e629f7  numpy-2.0.0b1.tar.gz

- Python
Published by charris about 2 years ago