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: fixNDArray[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-existentCanIndex annotation inndarray.setfield
#28915: MAINT: Avoid dereferencing/strict aliasing warnings
#28916: BUG: Fix missing check for PyErr_Occurred() in _pyarray_correlate.
#28966: TYP: reject complex scalar types in ndarray.__ifloordiv__

Checksums

MD5

259343f056061f6eadb2f4b8999d06d4  numpy-2.2.6-cp310-cp310-macosx_10_9_x86_64.whl16fa85488e149489ce7ee044d7b0d307  numpy-2.2.6-cp310-cp310-macosx_11_0_arm64.whlf01b7aea9d2b76b1eeb49766e615d689  numpy-2.2.6-cp310-cp310-macosx_14_0_arm64.whlf2ddc2b22517f6e31caa1372b12c2499  numpy-2.2.6-cp310-cp310-macosx_14_0_x86_64.whl52190e22869884f0870eb3df7a283ca9  numpy-2.2.6-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl8f382b9ca6770db600edd5ea2447a925  numpy-2.2.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whle604aae2ef6e01fb92ecc39aca0424d9  numpy-2.2.6-cp310-cp310-musllinux_1_2_aarch64.whl3e5626cf6d8bec95d430a7362e71691c  numpy-2.2.6-cp310-cp310-musllinux_1_2_x86_64.whl8f4f1982837618ed7636ebd432234aeb  numpy-2.2.6-cp310-cp310-win32.whl1cfd2ac5609b4800512f0ce304e19acc  numpy-2.2.6-cp310-cp310-win_amd64.whl116203803ceeaa911dd64810b0305b4c  numpy-2.2.6-cp311-cp311-macosx_10_9_x86_64.whl0427961f3a70ed92b1c4d2c5516c5803  numpy-2.2.6-cp311-cp311-macosx_11_0_arm64.whlfeb8104ed864d51c68984ff93f7255b5  numpy-2.2.6-cp311-cp311-macosx_14_0_arm64.whlf640cd91637f1d474947ecdb18d17ee8  numpy-2.2.6-cp311-cp311-macosx_14_0_x86_64.whl2f87d921a50fe50d04bb62125f8638dd  numpy-2.2.6-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl7f986c33f49d5940d6d005ff7039e420  numpy-2.2.6-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl0f7073c78e0aede7179c537f64856db7  numpy-2.2.6-cp311-cp311-musllinux_1_2_aarch64.whl7402bbedcc0b59bd6cef1c483b77dac0  numpy-2.2.6-cp311-cp311-musllinux_1_2_x86_64.whl93c920d40abbc10d5d056b8bfbcdad74  numpy-2.2.6-cp311-cp311-win32.whl9162cb90bff0e4ba322f1e61da9f2fba  numpy-2.2.6-cp311-cp311-win_amd64.whl75e9fa94b0a6ef568b532f6e0773a6a7  numpy-2.2.6-cp312-cp312-macosx_10_13_x86_64.whl79d8f89e82971bb2a2f61d0ef8f1a677  numpy-2.2.6-cp312-cp312-macosx_11_0_arm64.whlfb553e49196bce93af4b0d7e1e8fad1e  numpy-2.2.6-cp312-cp312-macosx_14_0_arm64.whl01a338bc3a5349b5b7db4335fe879810  numpy-2.2.6-cp312-cp312-macosx_14_0_x86_64.whlf37533a7ae4aa95da824b1df2786ac55  numpy-2.2.6-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl2f9ac35f955d9217b6841568ce13d636  numpy-2.2.6-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whldf530a075c04dbef9abcac95d027c8bc  numpy-2.2.6-cp312-cp312-musllinux_1_2_aarch64.whl4edf8f80feec739de3e08fffe97195a3  numpy-2.2.6-cp312-cp312-musllinux_1_2_x86_64.whl3e2664254d9a7bb5c66df2b108aaec2f  numpy-2.2.6-cp312-cp312-win32.whlae2e39f1dba9b91d35edcd8736041df8  numpy-2.2.6-cp312-cp312-win_amd64.whl2faa32e27b81105db53fb2fc25a54e0d  numpy-2.2.6-cp313-cp313-macosx_10_13_x86_64.whl0d05b1bb5af5059c8775a4f10fa0ec3d  numpy-2.2.6-cp313-cp313-macosx_11_0_arm64.whlbb404027de8df58312964e26528ef591  numpy-2.2.6-cp313-cp313-macosx_14_0_arm64.whl1340a90e0f62a31691e475214f773196  numpy-2.2.6-cp313-cp313-macosx_14_0_x86_64.whl954981f2846e6735798fb33c1e6fba76  numpy-2.2.6-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl4e4eccd129b31fbef3ced7fb338e862e  numpy-2.2.6-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whlc704c1c56c777bc0fc0d54bbcf9f2ddb  numpy-2.2.6-cp313-cp313-musllinux_1_2_aarch64.whlfb459919a3433235312673bd5797ab8b  numpy-2.2.6-cp313-cp313-musllinux_1_2_x86_64.whl9998e8ae155872c375ce6c020654176b  numpy-2.2.6-cp313-cp313-win32.whl03df8a78963b318b4dfede10b213dce4  numpy-2.2.6-cp313-cp313-win_amd64.whld1982e582eae2fb076942c0bbedcefe4  numpy-2.2.6-cp313-cp313t-macosx_10_13_x86_64.whlcbc7a48b9ca730a8d40927666651430a  numpy-2.2.6-cp313-cp313t-macosx_11_0_arm64.whlcd1d2271c05ccc502b78827b88ff7670  numpy-2.2.6-cp313-cp313t-macosx_14_0_arm64.whlc2b4fb7464e42af240ad51c8be5fb1ba  numpy-2.2.6-cp313-cp313t-macosx_14_0_x86_64.whl6a96c540b8df291a128bb50dfdad0ba4  numpy-2.2.6-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl113d466026e770badd1061a6e1a8ca92  numpy-2.2.6-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl1fce5d26d8d6d021954f717b4bad483c  numpy-2.2.6-cp313-cp313t-musllinux_1_2_aarch64.whld980d6c4b486ad09dbf62ac5cf1b0b2a  numpy-2.2.6-cp313-cp313t-musllinux_1_2_x86_64.whl21571229d4376f3c0458d8eb1be3ba52  numpy-2.2.6-cp313-cp313t-win32.whl4accc0387feec817565aeaba93c79173  numpy-2.2.6-cp313-cp313t-win_amd64.whl774589ee5f842137322ff19b56a35270  numpy-2.2.6-pp310-pypy310_pp73-macosx_10_15_x86_64.whlf934cef42ac65a2094dd5280aa6bf9a2  numpy-2.2.6-pp310-pypy310_pp73-macosx_14_0_x86_64.whl0e53fbb4195726c62b8f237a4bf545e9  numpy-2.2.6-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl3c96c89609022ecd27d44b12c2349a06  numpy-2.2.6-pp310-pypy310_pp73-win_amd64.whl63d66dc1db9d603df0a84c870e703cfc  numpy-2.2.6.tar.gz

SHA256

b412caa66f72040e6d268491a59f2c43bf03eb6c96dd8f0307829feb7fa2b6fb  numpy-2.2.6-cp310-cp310-macosx_10_9_x86_64.whl8e41fd67c52b86603a91c1a505ebaef50b3314de0213461c7a6e99c9a3beff90  numpy-2.2.6-cp310-cp310-macosx_11_0_arm64.whl37e990a01ae6ec7fe7fa1c26c55ecb672dd98b19c3d0e1d1f326fa13cb38d163  numpy-2.2.6-cp310-cp310-macosx_14_0_arm64.whl5a6429d4be8ca66d889b7cf70f536a397dc45ba6faeb5f8c5427935d9592e9cf  numpy-2.2.6-cp310-cp310-macosx_14_0_x86_64.whlefd28d4e9cd7d7a8d39074a4d44c63eda73401580c5c76acda2ce969e0a38e83  numpy-2.2.6-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whlfc7b73d02efb0e18c000e9ad8b83480dfcd5dfd11065997ed4c6747470ae8915  numpy-2.2.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl74d4531beb257d2c3f4b261bfb0fc09e0f9ebb8842d82a7b4209415896adc680  numpy-2.2.6-cp310-cp310-musllinux_1_2_aarch64.whl8fc377d995680230e83241d8a96def29f204b5782f371c532579b4f20607a289  numpy-2.2.6-cp310-cp310-musllinux_1_2_x86_64.whlb093dd74e50a8cba3e873868d9e93a85b78e0daf2e98c6797566ad8044e8363d  numpy-2.2.6-cp310-cp310-win32.whlf0fd6321b839904e15c46e0d257fdd101dd7f530fe03fd6359c1ea63738703f3  numpy-2.2.6-cp310-cp310-win_amd64.whlf9f1adb22318e121c5c69a09142811a201ef17ab257a1e66ca3025065b7f53ae  numpy-2.2.6-cp311-cp311-macosx_10_9_x86_64.whlc820a93b0255bc360f53eca31a0e676fd1101f673dda8da93454a12e23fc5f7a  numpy-2.2.6-cp311-cp311-macosx_11_0_arm64.whl3d70692235e759f260c3d837193090014aebdf026dfd167834bcba43e30c2a42  numpy-2.2.6-cp311-cp311-macosx_14_0_arm64.whl481b49095335f8eed42e39e8041327c05b0f6f4780488f61286ed3c01368d491  numpy-2.2.6-cp311-cp311-macosx_14_0_x86_64.whlb64d8d4d17135e00c8e346e0a738deb17e754230d7e0810ac5012750bbd85a5a  numpy-2.2.6-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whlba10f8411898fc418a521833e014a77d3ca01c15b0c6cdcce6a0d2897e6dbbdf  numpy-2.2.6-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whlbd48227a919f1bafbdda0583705e547892342c26fb127219d60a5c36882609d1  numpy-2.2.6-cp311-cp311-musllinux_1_2_aarch64.whl9551a499bf125c1d4f9e250377c1ee2eddd02e01eac6644c080162c0c51778ab  numpy-2.2.6-cp311-cp311-musllinux_1_2_x86_64.whl0678000bb9ac1475cd454c6b8c799206af8107e310843532b04d49649c717a47  numpy-2.2.6-cp311-cp311-win32.whle8213002e427c69c45a52bbd94163084025f533a55a59d6f9c5b820774ef3303  numpy-2.2.6-cp311-cp311-win_amd64.whl41c5a21f4a04fa86436124d388f6ed60a9343a6f767fced1a8a71c3fbca038ff  numpy-2.2.6-cp312-cp312-macosx_10_13_x86_64.whlde749064336d37e340f640b05f24e9e3dd678c57318c7289d222a8a2f543e90c  numpy-2.2.6-cp312-cp312-macosx_11_0_arm64.whl894b3a42502226a1cac872f840030665f33326fc3dac8e57c607905773cdcde3  numpy-2.2.6-cp312-cp312-macosx_14_0_arm64.whl71594f7c51a18e728451bb50cc60a3ce4e6538822731b2933209a1f3614e9282  numpy-2.2.6-cp312-cp312-macosx_14_0_x86_64.whlf2618db89be1b4e05f7a1a847a9c1c0abd63e63a1607d892dd54668dd92faf87  numpy-2.2.6-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whlfd83c01228a688733f1ded5201c678f0c53ecc1006ffbc404db9f7a899ac6249  numpy-2.2.6-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64...

Assets5

14 people reacted

v2.2.5 (Apr 19, 2025)

19 Apr 23:36

charris

v2.2.5

This tag was signed with the committer’sverified signature.

charris Charles Harris

GPG key ID:679F228377C5247B

Verified

Learn about vigilant mode.

7be8c1f

This commit was created on GitHub.com and signed with GitHub’sverified signature.

GPG key ID:B5690EEEBB952194

Verified

Learn about vigilant mode.

Compare

Choose a tag to compare

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: fixndarray.tolist() and.item() for unknown dtype
#28654: BUG: fix deepcopying StringDType arrays (#28643)
#28661: TYP: Accept objects thatwrite() tostr insavetxt
#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 fordtype[bool]
#28705: TYP: Fix false rejection ofNDArray[object_].__abs__()
#28706: TYP: Fix inconsistentNDArray[float64].__[r]truediv__ return...
#28723: TYP: fix string-likendarray rich comparison operators
#28758: TYP: some[arg]partition fixes
#28772: TYP: fix incorrectrandom.Generator.integers return type
#28774: TYP: fixcount_nonzero signature

Checksums

MD5

3a5d0889d6d7951f44bc6f7a03fa30c6  numpy-2.2.5-cp310-cp310-macosx_10_9_x86_64.whlbcf9f4e768b070e17b2635f422a6e27d  numpy-2.2.5-cp310-cp310-macosx_11_0_arm64.whle82c8fa47a65bb5c2c83295f549dab12  numpy-2.2.5-cp310-cp310-macosx_14_0_arm64.whla5511a995c0f79a8b9a81f2b50e9f692  numpy-2.2.5-cp310-cp310-macosx_14_0_x86_64.whl72bfc1f98238a8e4ba08999e61111e0e  numpy-2.2.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl146c83a5b8099d8d2607392b2ef7fedf  numpy-2.2.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl6ebdc80b54b008a10575e5d7bbb613f5  numpy-2.2.5-cp310-cp310-musllinux_1_2_aarch64.whl97efde6443da8f9280a5fc2614a087e5  numpy-2.2.5-cp310-cp310-musllinux_1_2_x86_64.whlc143f352206cec535b41b6b1d34c5898  numpy-2.2.5-cp310-cp310-win32.whl0b17fbbf584785f675f1c5b24a00ff93  numpy-2.2.5-cp310-cp310-win_amd64.whl58532622d7eff69a3c71c1ae89dea070  numpy-2.2.5-cp311-cp311-macosx_10_9_x86_64.whl0d002c733bb02debe0b15de5ba872d1e  numpy-2.2.5-cp311-cp311-macosx_11_0_arm64.whlff0c736c60be96506806061ace2251a1  numpy-2.2.5-cp311-cp311-macosx_14_0_arm64.whl4febdec973c4405fd08ef35e0c130de1  numpy-2.2.5-cp311-cp311-macosx_14_0_x86_64.whl0bf4e457c612e565420e135458e70fe0  numpy-2.2.5-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whla43b608ad15ebdc0960611497205d598  numpy-2.2.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl7b4b1afd412149a9af7c25d7346fade8  numpy-2.2.5-cp311-cp311-musllinux_1_2_aarch64.whla1e70be013820f92dbfd4796fc4044bb  numpy-2.2.5-cp311-cp311-musllinux_1_2_x86_64.whl73344e05a6fec0b38183363b4a026252  numpy-2.2.5-cp311-cp311-win32.whlb7d5fdd23057c58d15c84eef6bfedb55  numpy-2.2.5-cp311-cp311-win_amd64.whl801b11bb546aac2d92d7b3d5d6c90e86  numpy-2.2.5-cp312-cp312-macosx_10_13_x86_64.whl68dc4298cad9405ad30cfb723be4ae48  numpy-2.2.5-cp312-cp312-macosx_11_0_arm64.whlc31c872e0fa8df5ed7f91882621a925f  numpy-2.2.5-cp312-cp312-macosx_14_0_arm64.whl179dfa545c32c44b77cf8db3b973785f  numpy-2.2.5-cp312-cp312-macosx_14_0_x86_64.whl4562513ff2f1e3f31d66b8e435000141  numpy-2.2.5-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whlc80a2d8aab1a4d6a66f3fca2f0744744  numpy-2.2.5-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whle363e0d8c116522d55b0ddd0cbf2de67  numpy-2.2.5-cp312-cp312-musllinux_1_2_aarch64.whld31d443270c76b7238ece2f87b048d21  numpy-2.2.5-cp312-cp312-musllinux_1_2_x86_64.whlbf469fe048fa4ed75a5d8725297e283a  numpy-2.2.5-cp312-cp312-win32.whl069b832aa15b6a815497135e7fa8cae8  numpy-2.2.5-cp312-cp312-win_amd64.whlb2cf059c831cbcfdb4044613a1e5bc8d  numpy-2.2.5-cp313-cp313-macosx_10_13_x86_64.whl70bcb93e55ff0f6602636602e0834607  numpy-2.2.5-cp313-cp313-macosx_11_0_arm64.whl00c4938d67fd5b658ad92ac26fbe9cab  numpy-2.2.5-cp313-cp313-macosx_14_0_arm64.whl0ca38aa51874b9252a2c9d85f81dcd07  numpy-2.2.5-cp313-cp313-macosx_14_0_x86_64.whl6062cf707b8bc07a1600af0991a0a88e  numpy-2.2.5-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl62c1cf7de0327546f3a1e3852de640d3  numpy-2.2.5-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whlab3ad3390396552f76160139cc528784  numpy-2.2.5-cp313-cp313-musllinux_1_2_aarch64.whld258ba55c9a3936fa0c113cac8bbc0cc  numpy-2.2.5-cp313-cp313-musllinux_1_2_x86_64.whl59bb7e1acb81fc4a02c3b791e110f01e  numpy-2.2.5-cp313-cp313-win32.whl2e5728a9e5c6405d3a22138e4dd7019f  numpy-2.2.5-cp313-cp313-win_amd64.whld315521ec7275d0341787f2450e57e55  numpy-2.2.5-cp313-cp313t-macosx_10_13_x86_64.whl17018c7c259ae81cf2ca4f58523d7d1c  numpy-2.2.5-cp313-cp313t-macosx_11_0_arm64.whlef6fd6a9c6a07db004a272b82f0ea710  numpy-2.2.5-cp313-cp313t-macosx_14_0_arm64.whl07b2baf70b84b44ca6924794d9c7e431  numpy-2.2.5-cp313-cp313t-macosx_14_0_x86_64.whla2fb1ed562d2b6da091d980c7486d113  numpy-2.2.5-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl22fa9137283f463436d7b20a220071cd  numpy-2.2.5-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whlb0ae924e4834155eb5ac159ae611c292  numpy-2.2.5-cp313-cp313t-musllinux_1_2_aarch64.whlc7a8351484f2df9a499c68f1ac73121c  numpy-2.2.5-cp313-cp313t-musllinux_1_2_x86_64.whl1da753e4127a0bdcdfbfa6639568057e  numpy-2.2.5-cp313-cp313t-win32.whla8c869efc0888f214239e5c4f0e6acfb  numpy-2.2.5-cp313-cp313t-win_amd64.whl7255b93f38e7d54a59d6798182f24c6a  numpy-2.2.5-pp310-pypy310_pp73-macosx_10_15_x86_64.whl6743ce025de6c245b03ca8511b306503  numpy-2.2.5-pp310-pypy310_pp73-macosx_14_0_x86_64.whl5abbeec4ff2add1c46f8779f730c73fa  numpy-2.2.5-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl8e2e01f02d05e111ef2b104d1b3afad1  numpy-2.2.5-pp310-pypy310_pp73-win_amd64.whldf2e46b468f9fdf06b13b04eca9a723f  numpy-2.2.5.tar.gz

SHA256

1f4a922da1729f4c40932b2af4fe84909c7a6e167e6e99f71838ce3a29f3fe26  numpy-2.2.5-cp310-cp310-macosx_10_9_x86_64.whlb6f91524d31b34f4a5fee24f5bc16dcd1491b668798b6d85585d836c1e633a6a  numpy-2.2.5-cp310-cp310-macosx_11_0_arm64.whl19f4718c9012e3baea91a7dba661dcab2451cda2550678dc30d53acb91a7290f  numpy-2.2.5-cp310-cp310-macosx_14_0_arm64.whleb7fd5b184e5d277afa9ec0ad5e4eb562ecff541e7f60e69ee69c8d59e9aeaba  numpy-2.2.5-cp310-cp310-macosx_14_0_x86_64.whl6413d48a9be53e183eb06495d8e3b006ef8f87c324af68241bbe7a39e8ff54c3  numpy-2.2.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl7451f92eddf8503c9b8aa4fe6aa7e87fd51a29c2cfc5f7dbd72efde6c65acf57  numpy-2.2.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl0bcb1d057b7571334139129b7f941588f69ce7c4ed15a9d6162b2ea54ded700c  numpy-2.2.5-cp310-cp310-musllinux_1_2_aarch64.whl36ab5b23915887543441efd0417e6a3baa08634308894316f446027611b53bf1  numpy-2.2.5-cp310-cp310-musllinux_1_2_x86_64.whl422cc684f17bc963da5f59a31530b3936f57c95a29743056ef7a7903a5dbdf88  numpy-2.2.5-cp310-cp310-win32.whle4f0b035d9d0ed519c813ee23e0a733db81ec37d2e9503afbb6e54ccfdee0fa7  numpy-2.2.5-cp310-cp310-win_amd64.whlc42365005c7a6c42436a54d28c43fe0e01ca11eb2ac3cefe796c25a5f98e5e9b  numpy-2.2.5-cp311-cp311-macosx_10_9_x86_64.whl498815b96f67dc347e03b719ef49c772589fb74b8ee9ea2c37feae915ad6ebda  numpy-2.2.5-cp311-cp311-macosx_11_0_arm64.whl6411f744f7f20081b1b4e7112e0f4c9c5b08f94b9f086e6f0adf3645f85d3a4d  numpy-2.2.5-cp311-cp311-macosx_14_0_arm64.whl9de6832228f617c9ef45d948ec1cd8949c482238d68b2477e6f642c33a7b0a54  numpy-2.2.5-cp311-cp311-macosx_14_0_x86_64.whl369e0d4647c17c9363244f3468f2227d557a74b6781cb62ce57cf3ef5cc7c610  numpy-2.2.5-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl262d23f383170f99cd9191a7c85b9a50970fe9069b2f8ab5d786eca8a675d60b  numpy-2.2.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whlaa70fdbdc3b169d69e8c59e65c07a1c9351ceb438e627f0fdcd471015cd956be  numpy-2.2.5-cp311-cp311-musllinux_1_2_aarch64.whl37e32e985f03c06206582a7323ef926b4e78bdaa6915095ef08070471865b906  numpy-2.2.5-cp311-cp311-musllinux_1_2_x86_64.whlf5045039100ed58fa817a6227a356240...

Assets5

14 people reacted

2.2.4 (Mar 16, 2025)

16 Mar 18:35

charris

v2.2.4

This tag was signed with the committer’sverified signature.

charris Charles Harris

GPG key ID:679F228377C5247B

Verified

Learn about vigilant mode.

3b37785

This commit was created on GitHub.com and signed with GitHub’sverified signature.

GPG key ID:B5690EEEBB952194

Verified

Learn about vigilant mode.

Compare

Choose a tag to compare

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 skip_rows >= max_rows
#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.whlbf7fd01bb177885e920173b610c195d9  numpy-2.2.4-cp310-cp310-macosx_11_0_arm64.whl826e52cd898567a0c446113ab7a7b362  numpy-2.2.4-cp310-cp310-macosx_14_0_arm64.whl9982a91d7327aea541c24aff94d3e462  numpy-2.2.4-cp310-cp310-macosx_14_0_x86_64.whl5bdf5b63f4ee01fa808d13043b2a2275  numpy-2.2.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl677b3031105e24eaee2e0e57d7c2a306  numpy-2.2.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whld857867787fe1eb236670e7fdb25f414  numpy-2.2.4-cp310-cp310-musllinux_1_2_aarch64.whla5aff3a7eb2923878e67fbe1cd04a9e9  numpy-2.2.4-cp310-cp310-musllinux_1_2_x86_64.whle00bd3ac85d8f34b46b7f97a8278aeb3  numpy-2.2.4-cp310-cp310-win32.whle5cb2a5d14bccee316bb73173be125ec  numpy-2.2.4-cp310-cp310-win_amd64.whl494f60d8e1c3500413bd093bb3f486ea  numpy-2.2.4-cp311-cp311-macosx_10_9_x86_64.whla886a9f3e80a60ce6ba95b431578bbca  numpy-2.2.4-cp311-cp311-macosx_11_0_arm64.whl889f3b507bab9272d9b549780840a642  numpy-2.2.4-cp311-cp311-macosx_14_0_arm64.whl059788668d2c4e9aace4858e77c099ed  numpy-2.2.4-cp311-cp311-macosx_14_0_x86_64.whldb9ae978afb76a4bf79df0657a66aaeb  numpy-2.2.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whle36963a4c177157dc7b0775c309fa5a8  numpy-2.2.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl3603e683878b74f38e5617f04ff6a369  numpy-2.2.4-cp311-cp311-musllinux_1_2_aarch64.whlafbc410fb9b42b19f4f7c81c21d6777f  numpy-2.2.4-cp311-cp311-musllinux_1_2_x86_64.whl33ff8081378188894097942f80c33e26  numpy-2.2.4-cp311-cp311-win32.whl5b11fe8d26318d85e0bc577a654f6643  numpy-2.2.4-cp311-cp311-win_amd64.whl91121787f396d3e98210de8b617e5d48  numpy-2.2.4-cp312-cp312-macosx_10_13_x86_64.whlc524d1020b4652aacf4477d1628fa1ba  numpy-2.2.4-cp312-cp312-macosx_11_0_arm64.whleb08f551bdd6772155bb39ac0da47479  numpy-2.2.4-cp312-cp312-macosx_14_0_arm64.whl7cb37fc9145d0ebbea5666b4f9ed1027  numpy-2.2.4-cp312-cp312-macosx_14_0_x86_64.whlc4452a5dc557c291904b5c51a4148237  numpy-2.2.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whlbd23a12ead870759f264160ab38b2c9d  numpy-2.2.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl07b44109381985b48d1eef80feebc5ad  numpy-2.2.4-cp312-cp312-musllinux_1_2_aarch64.whl95f1a27d33106fa9f40ee0714681c840  numpy-2.2.4-cp312-cp312-musllinux_1_2_x86_64.whl507e550a55b19dedf267b58a487ba0bc  numpy-2.2.4-cp312-cp312-win32.whlbe21ccbf8931e92ba1fdb2dc1250bf2a  numpy-2.2.4-cp312-cp312-win_amd64.whle94003c2b65d81b00203711c5c42fb8e  numpy-2.2.4-cp313-cp313-macosx_10_13_x86_64.whlcf781fd5412ffd826e0436883452cc17  numpy-2.2.4-cp313-cp313-macosx_11_0_arm64.whl92c9a30386a64f2deddad1db742bd296  numpy-2.2.4-cp313-cp313-macosx_14_0_arm64.whl7fd16554fa0a15b7f99b1fabf1c4592c  numpy-2.2.4-cp313-cp313-macosx_14_0_x86_64.whl9293b0575a902b2d55c35567dee7679e  numpy-2.2.4-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl9970699bd95e8a64a562b1e6328b83d0  numpy-2.2.4-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whle8597c611a919a8e88229d6889c1f86e  numpy-2.2.4-cp313-cp313-musllinux_1_2_aarch64.whl329288501f012606605bdbed368e58e9  numpy-2.2.4-cp313-cp313-musllinux_1_2_x86_64.whl04bf8d0f6a9e279ab01df4ed0b4aeee1  numpy-2.2.4-cp313-cp313-win32.whl66801fe84a436b7ed3be6e0082b86917  numpy-2.2.4-cp313-cp313-win_amd64.whl3e2f31e01b45cd16a87b794477de3714  numpy-2.2.4-cp313-cp313t-macosx_10_13_x86_64.whl7504018213a3a8fea7173e2c1d0fcfd1  numpy-2.2.4-cp313-cp313t-macosx_11_0_arm64.whle299021397c3cdb941b7ffe77cf0fefe  numpy-2.2.4-cp313-cp313t-macosx_14_0_arm64.whl1cc2731a246079bcab361179f38e7ccb  numpy-2.2.4-cp313-cp313t-macosx_14_0_x86_64.whle6eccf936d25c9eda9df1a4d50ae2fdc  numpy-2.2.4-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whlba825efd05cca6d56c3dca9f7f1f88e7  numpy-2.2.4-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl369eebec47c9c27cb4841a13e9522167  numpy-2.2.4-cp313-cp313t-musllinux_1_2_aarch64.whl554dbfa52988d01f715cbe8d4da4b409  numpy-2.2.4-cp313-cp313t-musllinux_1_2_x86_64.whl811d25a008c68086c9382487e9a4127a  numpy-2.2.4-cp313-cp313t-win32.whl893fd2fdd42f386e300bee885bbb7778  numpy-2.2.4-cp313-cp313t-win_amd64.whl65e284546c5ee575eca0a3726c0a1d98  numpy-2.2.4-pp310-pypy310_pp73-macosx_10_15_x86_64.whle4e73511eac8f1a10c6abbd6fa2fa0aa  numpy-2.2.4-pp310-pypy310_pp73-macosx_14_0_x86_64.whla884ed5263b91fa87b5e3d14caf955a5  numpy-2.2.4-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl7330087a6ad1527ae20a495e2fb3b357  numpy-2.2.4-pp310-pypy310_pp73-win_amd64.whl56232f4a69b03dd7a87a55fffc5f2ebc  numpy-2.2.4.tar.gz

SHA256

8146f3550d627252269ac42ae660281d673eb6f8b32f113538e0cc2a9aed42b9  numpy-2.2.4-cp310-cp310-macosx_10_9_x86_64.whle642d86b8f956098b564a45e6f6ce68a22c2c97a04f5acd3f221f57b8cb850ae  numpy-2.2.4-cp310-cp310-macosx_11_0_arm64.whla84eda42bd12edc36eb5b53bbcc9b406820d3353f1994b6cfe453a33ff101775  numpy-2.2.4-cp310-cp310-macosx_14_0_arm64.whl4ba5054787e89c59c593a4169830ab362ac2bee8a969249dc56e5d7d20ff8df9  numpy-2.2.4-cp310-cp310-macosx_14_0_x86_64.whl7716e4a9b7af82c06a2543c53ca476fa0b57e4d760481273e09da04b74ee6ee2  numpy-2.2.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whladf8c1d66f432ce577d0197dceaac2ac00c0759f573f28516246351c58a85020  numpy-2.2.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl218f061d2faa73621fa23d6359442b0fc658d5b9a70801373625d958259eaca3  numpy-2.2.4-cp310-cp310-musllinux_1_2_aarch64.whldf2f57871a96bbc1b69733cd4c51dc33bea66146b8c63cacbfed73eec0883017  numpy-2.2.4-cp310-cp310-musllinux_1_2_x86_64.whla0258ad1f44f138b791327961caedffbf9612bfa504ab9597157806faa95194a  numpy-2.2.4-cp310-cp310-win32.whl0d54974f9cf14acf49c60f0f7f4084b6579d24d439453d5fc5805d46a165b542  numpy-2.2.4-cp310-cp310-win_amd64.whle9e0a277bb2eb5d8a7407e14688b85fd8ad628ee4e0c7930415687b6564207a4  numpy-2.2.4-cp311-cp311-macosx_10_9_x86_64.whl9eeea959168ea555e556b8188da5fa7831e21d91ce031e95ce23747b7609f8a4  numpy-2.2.4-cp311-cp311-macosx_11_0_arm64.whlbd3ad3b0a40e713fc68f99ecfd07124195333f1e689387c180813f0e94309d6f  numpy-2.2.4-cp311-cp311-macosx_14_0_arm64.whlcf28633d64294969c019c6df4ff37f5698e8326db68cc2b66576a51fad634880  numpy-2.2.4-cp311-cp311-macosx_14_0_x86_64.whl2fa8fa7697ad1646b5c93de1719965844e004fcad23c91228aca1cf0800044a1  numpy-2.2.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whlf4162988a360a29af158aeb4a2f4f09ffed6a969c9776f8f3bdee9b06a8ab7e5  numpy-2.2.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl892c10d6a73e0f14935c31229e03325a7b3093fafd6ce0af704be7f894d95687  numpy-2.2.4-cp311-cp311-musllinux_1_2_aarch64.whldb1f1c22173ac1c58db249ae48aa7ead29f534b9a948bc56828337aa84a32ed6  numpy-2.2.4-cp311-cp311-musllinux_1_2_x86_64.whlea2bb7e2ae9e37d96835b3576a4fa4b3a97592fbea8ef7c3587078b0068b8f09  numpy-2.2.4-cp311-cp311-win32.whlf7de08cbe5551911886d1ab60de...

Assets5

20 people reacted

2.2.3 (Feb 13, 2025)

13 Feb 17:26

charris

v2.2.3

This tag was signed with the committer’sverified signature.

charris Charles Harris

GPG key ID:679F228377C5247B

Verified

Learn about vigilant mode.

a274561

This commit was created on GitHub.com and signed with GitHub’sverified signature.

GPG key ID:B5690EEEBB952194

Verified

Learn about vigilant mode.

Compare

Choose a tag to compare

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: Fixfrom_float_positional errors for huge pads
#28209: BUG: fix data race in np.repeat
#28212: MAINT: Use VQSORT_COMPILER_COMPATIBLE 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 upcastingfloat64 in the set-ops
#28249: BLD: better fix for clang / ARM compiles
#28266: TYP: Fixtimedelta64.__divmod__ andtimedelta64.__mod__...
#28274: TYP: Fixed missing typing information of set_printoptions
#28278: BUG: backport resource cleanup bugfix fromgh-28273
#28282: BUG: fix incorrect bytes to stringdtype coercion
#28283: TYP: Fix scalar constructors
#28284: TYP: stubnumpy.matlib
#28285: TYP: stub the missingnumpy.testing modules
#28286: CI: Fix the github label forTYP: PR's and issues
#28305: TYP: Backport typing updates from main
#28321: BUG: fix race initializing legacy dtype casts
#28324: CI: update test_moderately_small_alpha

Checksums

MD5

9cd8b5e358f89016f403a6c1a27e7e87  numpy-2.2.3-cp310-cp310-macosx_10_9_x86_64.whl2818f5a9efcfc3bb6bf657137df26046  numpy-2.2.3-cp310-cp310-macosx_11_0_arm64.whl6d65c6a336cfb69fe4ddd756cad73d55  numpy-2.2.3-cp310-cp310-macosx_14_0_arm64.whl7f4cf33c634b33f633d4bf47f560a86d  numpy-2.2.3-cp310-cp310-macosx_14_0_x86_64.whl3c04024badd42bfcc68c14f106efa93f  numpy-2.2.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl07658df1de0e1d3721de0aacff4313cd  numpy-2.2.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl3e753fc4b7c879b29442ee9bab25eddd  numpy-2.2.3-cp310-cp310-musllinux_1_2_aarch64.whld1811f1988d88b00825bc6e943d8e22d  numpy-2.2.3-cp310-cp310-musllinux_1_2_x86_64.whlb5fe91363c16001ea30cbd5befbb0555  numpy-2.2.3-cp310-cp310-win32.whl44dfe1df1640e4fe762bedad57cd7165  numpy-2.2.3-cp310-cp310-win_amd64.whl6156418f596620b00a3c221baef02476  numpy-2.2.3-cp311-cp311-macosx_10_9_x86_64.whl97b925bac245aad1297d22ad3cfaa74c  numpy-2.2.3-cp311-cp311-macosx_11_0_arm64.whl3f05819fcb71df1d3093e5d1c041a4e9  numpy-2.2.3-cp311-cp311-macosx_14_0_arm64.whlf6763893ba9a5739fefa0929fd152db2  numpy-2.2.3-cp311-cp311-macosx_14_0_x86_64.whle93cf6ed4e1a3f9a8009ee7f2fcb0da8  numpy-2.2.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl851dcbcbe90212c385dcdac1614cca83  numpy-2.2.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl9b27cf1d6319f70370f4b0af10c03f5c  numpy-2.2.3-cp311-cp311-musllinux_1_2_aarch64.whl28d20c95ff23d27ae639b4960df777ec  numpy-2.2.3-cp311-cp311-musllinux_1_2_x86_64.whl559fefe30c0043a088adeca90231b382  numpy-2.2.3-cp311-cp311-win32.whl5e32a1cc3dcfe729f675784a53e4d553  numpy-2.2.3-cp311-cp311-win_amd64.whl12134dcf62b2bca2eeebb7bbc45c2a71  numpy-2.2.3-cp312-cp312-macosx_10_13_x86_64.whlc72318236531d3ca61d229eaf96f7d04  numpy-2.2.3-cp312-cp312-macosx_11_0_arm64.whl1b807acc844c2ba5be7bc7586d4a3a6b  numpy-2.2.3-cp312-cp312-macosx_14_0_arm64.whl810d4908371bb2f08b0c7b16d3f05970  numpy-2.2.3-cp312-cp312-macosx_14_0_x86_64.whlbb918cedd0931cb68af9e77096dedf54  numpy-2.2.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl92c6c6c5b22b207425b329f061bd18fa  numpy-2.2.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl10d48fb9d86280db1afe7224b15a51af  numpy-2.2.3-cp312-cp312-musllinux_1_2_aarch64.whla73da0434a971b21d8a9c0596015d629  numpy-2.2.3-cp312-cp312-musllinux_1_2_x86_64.whlc5f1e734c7d872e2f9af71d32e62d59c  numpy-2.2.3-cp312-cp312-win32.whl884c1a89844f539ab15b7016a43d231c  numpy-2.2.3-cp312-cp312-win_amd64.whl3a2de7f886cb756cf8d0375a36721926  numpy-2.2.3-cp313-cp313-macosx_10_13_x86_64.whlc1fe5b6a9015c2877647419caa009be0  numpy-2.2.3-cp313-cp313-macosx_11_0_arm64.whlbb3f3a69219bbcdb719bbe38e4e69f79  numpy-2.2.3-cp313-cp313-macosx_14_0_arm64.whl8158c2e980a1cbfb4d98ff3a273bb2e9  numpy-2.2.3-cp313-cp313-macosx_14_0_x86_64.whl4d3d9b0c14db955e4b1aa1a1971d2def  numpy-2.2.3-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl6575308269513900c94803258b89ac83  numpy-2.2.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl945b91c2093fed2a1f34597fc66e5a35  numpy-2.2.3-cp313-cp313-musllinux_1_2_aarch64.whlc5867508607f75ed23426315a7ad86d7  numpy-2.2.3-cp313-cp313-musllinux_1_2_x86_64.whl5a1497c262d9aa52ce6859a12a54ebbc  numpy-2.2.3-cp313-cp313-win32.whl69c98e036d59eb74e4620c7649b5d7fc  numpy-2.2.3-cp313-cp313-win_amd64.whl2535d7c0f98ad848bcf1f48f7c358e41  numpy-2.2.3-cp313-cp313t-macosx_10_13_x86_64.whlaea9afa69d510ce905b2b8dbf0e33a11  numpy-2.2.3-cp313-cp313t-macosx_11_0_arm64.whlcc5aceacd0a44a67cdd2cf8d5a446ca3  numpy-2.2.3-cp313-cp313t-macosx_14_0_arm64.whl32eb2ed1e734ea26c90f75b1f5616564  numpy-2.2.3-cp313-cp313t-macosx_14_0_x86_64.whlf1d85f322c3e85ef748c3e5594b94226  numpy-2.2.3-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl7f24ce01ad5c352c76614a12fa5e2319  numpy-2.2.3-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl62841d4b49c5a0cef2c2ba26a16f6959  numpy-2.2.3-cp313-cp313t-musllinux_1_2_aarch64.whld7b512f83999d05c47e55b931f2dcdfe  numpy-2.2.3-cp313-cp313t-musllinux_1_2_x86_64.whl1dca2f20e0accc1741e5fb233ecf7dff  numpy-2.2.3-cp313-cp313t-win32.whl347b71f0db5b49a25ef1ed677e47999b  numpy-2.2.3-cp313-cp313t-win_amd64.whl3615d13c8c14c323aeda1c07d5a7fd55  numpy-2.2.3-pp310-pypy310_pp73-macosx_10_15_x86_64.whlf7d2ba950c5aa11c100bb6bf202d5799  numpy-2.2.3-pp310-pypy310_pp73-macosx_14_0_x86_64.whlb4336174c843c4943084e17945cd1165  numpy-2.2.3-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl0d856a89e028c393f8125739c56591e0  numpy-2.2.3-pp310-pypy310_pp73-win_amd64.whlc6ee254bcdf1e2fdb13d87e0ee4166ba  numpy-2.2.3.tar.gz

SHA256

cbc6472e01952d3d1b2772b720428f8b90e2deea8344e854df22b0618e9cce71  numpy-2.2.3-cp310-cp310-macosx_10_9_x86_64.whlcdfe0c22692a30cd830c0755746473ae66c4a8f2e7bd508b35fb3b6a0813d787  numpy-2.2.3-cp310-cp310-macosx_11_0_arm64.whle37242f5324ffd9f7ba5acf96d774f9276aa62a966c0bad8dae692deebec7716  numpy-2.2.3-cp310-cp310-macosx_14_0_arm64.whl95172a21038c9b423e68be78fd0be6e1b97674cde269b76fe269a5dfa6fadf0b  numpy-2.2.3-cp310-cp310-macosx_14_0_x86_64.whld5b47c440210c5d1d67e1cf434124e0b5c395eee1f5806fdd89b553ed1acd0a3  numpy-2.2.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl0391ea3622f5c51a2e29708877d56e3d276827ac5447d7f45e9bc4ade8923c52  numpy-2.2.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whlf6b3dfc7661f8842babd8ea07e9897fe3d9b69a1d7e5fbb743e4160f9387833b  numpy-2.2.3-cp310-cp310-musllinux_1_2_aarch64.whl1ad78ce7f18ce4e7df1b2ea4019b5817a2f6a8a16e34ff2775f646adce0a5027  numpy-2.2.3-cp310-cp310-musllinux_1_2_x86_64.whl5ebeb7ef54a7be11044c33a17b2624abe4307a75893c001a4800857956b41094  numpy-2.2.3-cp310-cp310-win32.whl596140185c7fa113563c67c2e894eabe0daea18cf8e33851738c19f70ce86aeb  numpy-2.2.3-cp310-cp310-win_amd64.whl16372619ee728ed67a2a606a614f56d3eabc5b86f8b615c79d01957062826ca8  numpy-2.2.3-cp311-cp311-macosx_10_9_x86_64.whl5521a06a3148686d9269c53b09f7d399a5725c47bbb5b35747e1cb76326b714b  numpy-2.2.3-cp311-cp311-macosx_11_0_arm64.whl7c8dde0ca2f77828815fd1aedfdf52e59071a5bae30dac3b4da2a335c672149a  numpy-2.2.3-cp311-cp311-macosx_14_0_arm64.whl77974aba6c1bc26e3c205c2214f0d5b4305bdc719268b93e768ddb17e3fdd636  numpy-2.2.3-cp311-cp311-macosx_14_0_x86_64.whld42f9c36d06440e34226e8bd65ff065ca0963aeecada587b937011efa02cdc9d  numpy-2.2.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whlf2712c5179f40af9ddc8f6727f2bd910ea0eb50206daea75f58ddd9fa3f715bb  numpy-2.2.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whlc8b0451d2ec95010d1db8ca733afc41f659f425b7f608af569711097fd6014e2  numpy-2.2.3-cp311-cp311-musllinux_1_2_aarch64.whl...

Assets5

14 people reacted

2.2.2 (Jan 18, 2025)

19 Jan 00:15

charris

v2.2.2

This tag was signed with the committer’sverified signature.

charris Charles Harris

GPG key ID:679F228377C5247B

Verified

Learn about vigilant mode.

fd8a68e

This commit was created on GitHub.com and signed with GitHub’sverified signature.

GPG key ID:B5690EEEBB952194

Verified

Learn about vigilant mode.

Compare

Choose a tag to compare

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: fixvoid arrays not acceptingstr keys in__setitem__
#28066: TYP: fix unnecessarily broadinteger binop return types (#28065)
#28112: TYP: Betterndarray binop return types forfloat64 &...
#28113: TYP: Return the correctbool fromissubdtype
#28114: TYP: Always acceptdate[time] in thedatetime64 constructor
#28120: BUG: Fix auxdata initialization in ufunc slow path
#28131: BUG: move reduction initialization to ufunc initialization
#28132: TYP: Fixinterp 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 PyArray_CheckFromAny_int
#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.whlbc79fa2e44316b7ce9bacb48a993ed91  numpy-2.2.2-cp310-cp310-macosx_11_0_arm64.whlc6b2caa2bbb645b5950dccb77efb1dbb  numpy-2.2.2-cp310-cp310-macosx_14_0_arm64.whl8c410efac169af880cacbbac8a731658  numpy-2.2.2-cp310-cp310-macosx_14_0_x86_64.whl21d165669635a9b680d03b0b4e7f5b98  numpy-2.2.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whla34ef5e7c967136fdc59c822e99f87d6  numpy-2.2.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whla81749effc5160ff8dde7eb2ebe868c4  numpy-2.2.2-cp310-cp310-musllinux_1_2_aarch64.whl546612d82fae082697879aaf2b985b1b  numpy-2.2.2-cp310-cp310-musllinux_1_2_x86_64.whld874e626f58175ad603cb68fda2a4e28  numpy-2.2.2-cp310-cp310-win32.whl20564a5caeb621061267f9d80c1e7ed0  numpy-2.2.2-cp310-cp310-win_amd64.whlef5336ddae73feef891844a205f89b15  numpy-2.2.2-cp311-cp311-macosx_10_9_x86_64.whl7a0c8804cb6ebca82b1cf3063b410687  numpy-2.2.2-cp311-cp311-macosx_11_0_arm64.whl1682639d0420a532f8894c4a8685b23d  numpy-2.2.2-cp311-cp311-macosx_14_0_arm64.whld33d53efc5744b577cb8a6ac9971cfdb  numpy-2.2.2-cp311-cp311-macosx_14_0_x86_64.whlc85b92e2ed7ef0eaeb15909ad73aea22  numpy-2.2.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whlefa1a587f607a37336c477bed977ea64  numpy-2.2.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whle0effe9902e262704a115c6f7095daf7  numpy-2.2.2-cp311-cp311-musllinux_1_2_aarch64.whl425e0cebeb1c2c91bba42ae195836268  numpy-2.2.2-cp311-cp311-musllinux_1_2_x86_64.whl57121319a2fbb76eed4b268282ed668e  numpy-2.2.2-cp311-cp311-win32.whlfdb54e7345ff657d208fbb52469a5861  numpy-2.2.2-cp311-cp311-win_amd64.whlbdf299e0abc45b5c5113a1cc5505636a  numpy-2.2.2-cp312-cp312-macosx_10_13_x86_64.whl30c25784c07965592cf88104b6c02508  numpy-2.2.2-cp312-cp312-macosx_11_0_arm64.whl65e630a0de5403c41a0083198bc14442  numpy-2.2.2-cp312-cp312-macosx_14_0_arm64.whl6d9f50717e7b40f1ebdf139f83cc7504  numpy-2.2.2-cp312-cp312-macosx_14_0_x86_64.whl6b092a9280ada70482d44f538752fc0b  numpy-2.2.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl9c273da8438391eab30f6c1c4898be5d  numpy-2.2.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whld619047dcaf041b806a7b59ff0a798d5  numpy-2.2.2-cp312-cp312-musllinux_1_2_aarch64.whlfa5d0d979104456d7c43a183223c8587  numpy-2.2.2-cp312-cp312-musllinux_1_2_x86_64.whl3b8689aedff5037cad85b018e2d5e43a  numpy-2.2.2-cp312-cp312-win32.whla2340ff05cae7e09f63bfcfd4e75ea87  numpy-2.2.2-cp312-cp312-win_amd64.whl044e86bd65492af34a59e4109fbeed16  numpy-2.2.2-cp313-cp313-macosx_10_13_x86_64.whl7ca0f0e8c8d3d80ec473ec33929c2ae3  numpy-2.2.2-cp313-cp313-macosx_11_0_arm64.whl4b866ad895e007005afe8a29837cf7d6  numpy-2.2.2-cp313-cp313-macosx_14_0_arm64.whl2e6247faabf6d0ac0fafaca0bb405ff8  numpy-2.2.2-cp313-cp313-macosx_14_0_x86_64.whl773982551185ae327cdefe416e73acfc  numpy-2.2.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl1c0ecc958a555a8a95c92c1dd7dc2358  numpy-2.2.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl9f662eb58b8f711585550d6fdf8afa4f  numpy-2.2.2-cp313-cp313-musllinux_1_2_aarch64.whl53471186fc990eb22e82a0512b310438  numpy-2.2.2-cp313-cp313-musllinux_1_2_x86_64.whl6b4d65349c74dd91853a7cc6b5c5786e  numpy-2.2.2-cp313-cp313-win32.whl33dc5bab2d3f752ef00f81021d68cb5a  numpy-2.2.2-cp313-cp313-win_amd64.whl0acc5069c5ab4fe3ea7c35956636c462  numpy-2.2.2-cp313-cp313t-macosx_10_13_x86_64.whl01e3f727594a12eee6d0677113525b96  numpy-2.2.2-cp313-cp313t-macosx_11_0_arm64.whl7b1ddabcb187b18caa52055bb2b2dc67  numpy-2.2.2-cp313-cp313t-macosx_14_0_arm64.whla09f5c138ad8c87b9692eea99f344a98  numpy-2.2.2-cp313-cp313t-macosx_14_0_x86_64.whl289ec3155aa21c5a161b2d61d2cf3c2d  numpy-2.2.2-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl6bb3eb03d400ad708942afbfebd07abc  numpy-2.2.2-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl62f8ef2a5c9e76b0e43851a7bb9c0379  numpy-2.2.2-cp313-cp313t-musllinux_1_2_aarch64.whl59b4b77118f958dd07484686e82b1e7a  numpy-2.2.2-cp313-cp313t-musllinux_1_2_x86_64.whl726b58ec542581c5e46adfd4c5c0fed0  numpy-2.2.2-cp313-cp313t-win32.whlf2b4eab55a963e8cd4c6c1e573c9a59f  numpy-2.2.2-cp313-cp313t-win_amd64.whlf6a93eaebee6f9890a4922571141ecb5  numpy-2.2.2-pp310-pypy310_pp73-macosx_10_15_x86_64.whlfb457bbe2d231e836d2230b06d4706ca  numpy-2.2.2-pp310-pypy310_pp73-macosx_14_0_x86_64.whldf4c07a48a24621167c12704ba5ac0de  numpy-2.2.2-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl0d1108b9060469eb28bb4a4cffa7b98f  numpy-2.2.2-pp310-pypy310_pp73-win_amd64.whlac108586d3aeab9e2d0134b744763eb9  numpy-2.2.2.tar.gz

SHA256

7079129b64cb78bdc8d611d1fd7e8002c0a2565da6a47c4df8062349fee90e3e  numpy-2.2.2-cp310-cp310-macosx_10_9_x86_64.whl2ec6c689c61df613b783aeb21f945c4cbe6c51c28cb70aae8430577ab39f163e  numpy-2.2.2-cp310-cp310-macosx_11_0_arm64.whl40c7ff5da22cd391944a28c6a9c638a5eef77fcf71d6e3a79e1d9d9e82752715  numpy-2.2.2-cp310-cp310-macosx_14_0_arm64.whl995f9e8181723852ca458e22de5d9b7d3ba4da3f11cc1cb113f093b271d7965a  numpy-2.2.2-cp310-cp310-macosx_14_0_x86_64.whlb78ea78450fd96a498f50ee096f69c75379af5138f7881a51355ab0e11286c97  numpy-2.2.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl3fbe72d347fbc59f94124125e73fc4976a06927ebc503ec5afbfb35f193cd957  numpy-2.2.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl8e6da5cffbbe571f93588f562ed130ea63ee206d12851b60819512dd3e1ba50d  numpy-2.2.2-cp310-cp310-musllinux_1_2_aarch64.whl09d6a2032faf25e8d0cadde7fd6145118ac55d2740132c1d845f98721b5ebcfd  numpy-2.2.2-cp310-cp310-musllinux_1_2_x86_64.whl159ff6ee4c4a36a23fe01b7c3d07bd8c14cc433d9720f977fcd52c13c0098160  numpy-2.2.2-cp310-cp310-win32.whl64bd6e1762cd7f0986a740fee4dff927b9ec2c5e4d9a28d056eb17d332158014  numpy-2.2.2-cp310-cp310-win_amd64.whl642199e98af1bd2b6aeb8ecf726972d238c9877b0f6e8221ee5ab945ec8a2189  numpy-2.2.2-cp311-cp311-macosx_10_9_x86_64.whl6d9fc9d812c81e6168b6d405bf00b8d6739a7f72ef22a9214c4241e0dc70b323  numpy-2.2.2-cp311-cp311-macosx_11_0_arm64.whlc7d1fd447e33ee20c1f33f2c8e6634211124a9aabde3c617687d8b739aa69eac  numpy-2.2.2-cp311-cp311-macosx_14_0_arm64.whl451e854cfae0febe723077bd0cf0a4302a5d84ff25f0bfece8f29206c7bed02e  numpy-2.2.2-cp311-cp311-macosx_14_0_x86_64.whlbd249bc894af67cbd8bad2c22e7cbcd46cf87ddfca1f1289d1e7e54868cc785c  numpy-2.2.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl02935e2c3c0c6cbe9c7955a8efa8908dd4221d7755644c59d1bba28b94fd334f  numpy-2.2.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whla972cec723e0563aa0823ee2ab1df0cb196ed0778f173b381c871a03719d4826  numpy-2.2.2-cp311-cp311-musllinux_1_2_aarch64.whld6d6a0910c3b4368d89dde073e630882cdb266755565155bc33520283b2d9df8  numpy-2.2.2-cp311-cp311-musllinux_1_2_x86_64.whl860fd59990c37c3ef913c3ae390b3929d005243acca1a86facb0773e2d8d9e50  numpy-2.2.2-cp311-cp311-win32.whlda1eeb460ecce8d5b8608826595c777728cdf28ce7b5a5a8c8ac8d949beadcf2  numpy-2.2.2-cp311-cp311-win_amd64.whlac9bea18d6d58a995fac1b2cb4488e17eceeac413af014b1dd26170b766d8467  numpy-2.2.2-cp312-cp312-macosx_10_13_x86_64.whl23ae9f0c2d889b7b2d88a3791f6c09e2ef827c2446f1c4a3e3e76328ee4afd9a  numpy-2.2.2-cp312-cp312-macosx_1...

Assets5

33 people reacted

2.2.1 (DEC 21, 2024)

21 Dec 23:03

charris

v2.2.1

This tag was signed with the committer’sverified signature.

charris Charles Harris

GPG key ID:679F228377C5247B

Verified

Learn about vigilant mode.

7469245

This commit was created on GitHub.com and signed with GitHub’sverified signature.

GPG key ID:B5690EEEBB952194

Verified

Learn about vigilant mode.

Compare

Choose a tag to compare

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 inndarray.__setitem__
#27991: MAINT: Don't wrap#include <Python.h> withextern "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: allowNone in operand sequence of nditer

Checksums

MD5

d3032be00b974d44aae687fd78a897b4  numpy-2.2.1-cp310-cp310-macosx_10_9_x86_64.whl49863a39471cf191402da96512e52cb6  numpy-2.2.1-cp310-cp310-macosx_11_0_arm64.whl31c912e2fa723b877f2d710c26332927  numpy-2.2.1-cp310-cp310-macosx_14_0_arm64.whl95af4f6b620c76f9ccb8c5693c99737d  numpy-2.2.1-cp310-cp310-macosx_14_0_x86_64.whlc1b113ad487a3bece6d7a70e0cf70f17  numpy-2.2.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whle93369ddbb637d9d5a820b2bb79588c4  numpy-2.2.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whlb3de0a2c345541d2c9a322df360ca497  numpy-2.2.1-cp310-cp310-musllinux_1_2_aarch64.whle3e62b93245d9e37cc03ec3cfaf68118  numpy-2.2.1-cp310-cp310-musllinux_1_2_x86_64.whl004063642d3c3792a3f5ff0241a3fa0f  numpy-2.2.1-cp310-cp310-win32.whl462b0704ebfd79120edfe6431adc57f4  numpy-2.2.1-cp310-cp310-win_amd64.whla739a2dfbceaa1140e564424b2a57540  numpy-2.2.1-cp311-cp311-macosx_10_9_x86_64.whl91731d46f4ce4b04db512400f4e76ccb  numpy-2.2.1-cp311-cp311-macosx_11_0_arm64.whl93f50db664a6986c2ebed3ceb588f7cc  numpy-2.2.1-cp311-cp311-macosx_14_0_arm64.whl8cc0d82b938d71f45a67c74e07ddc7fd  numpy-2.2.1-cp311-cp311-macosx_14_0_x86_64.whlfc7b253096fc566bbcbadfdf6b034f1b  numpy-2.2.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whlb88238965c708578f2c198d1c6e2cf70  numpy-2.2.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whldf20d649bb023f98e487b229f01e9708  numpy-2.2.1-cp311-cp311-musllinux_1_2_aarch64.whle23d2bfbdb1bd1b2872c9e6e15f64dca  numpy-2.2.1-cp311-cp311-musllinux_1_2_x86_64.whlcce4ebb9afc1470db243c2ab4cc6639b  numpy-2.2.1-cp311-cp311-win32.whlc96783ee8ad6ce1efee94821929a12f5  numpy-2.2.1-cp311-cp311-win_amd64.whl0b2024655573f96a595c7f5072205e84  numpy-2.2.1-cp312-cp312-macosx_10_13_x86_64.whl22483d8935f5dc128393ad671fde7d8e  numpy-2.2.1-cp312-cp312-macosx_11_0_arm64.whl61d38533acaa90fb24657f089d177a6c  numpy-2.2.1-cp312-cp312-macosx_14_0_arm64.whlecd4289c703356f5b4fd7e440bf94ce8  numpy-2.2.1-cp312-cp312-macosx_14_0_x86_64.whla05208461ea09079ae569414d82a606c  numpy-2.2.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl4c66f10580fa26d1d17b2bdda96a5fc5  numpy-2.2.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl60a01c86b1fc55e4ba8f2b41f690703b  numpy-2.2.1-cp312-cp312-musllinux_1_2_aarch64.whl4bcac2b7f8510b0a6582b7d8661257be  numpy-2.2.1-cp312-cp312-musllinux_1_2_x86_64.whl7c24a6a3b5c5b2c53c6807bf06c595c5  numpy-2.2.1-cp312-cp312-win32.whldc9f3c1eaade4da63e5f87e878e5805e  numpy-2.2.1-cp312-cp312-win_amd64.whl9aacdedcb2cb3d6a45dfb823148e01cf  numpy-2.2.1-cp313-cp313-macosx_10_13_x86_64.whl8a2598b081c8af4ea6f6bbccc8965882  numpy-2.2.1-cp313-cp313-macosx_11_0_arm64.whle58b8db1a97599ed02a630eb86616bb9  numpy-2.2.1-cp313-cp313-macosx_14_0_arm64.whlbe6871a4edd2cd92b147421b9290e047  numpy-2.2.1-cp313-cp313-macosx_14_0_x86_64.whl6d3f141f3a8ecd04e1a1f7c1f89a8ca2  numpy-2.2.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whleba9d71e631521bd1d9882f8bfbc01d2  numpy-2.2.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl07f7ea0a7f9f6ce0ba5e016dff2a91e8  numpy-2.2.1-cp313-cp313-musllinux_1_2_aarch64.whla015f42afa15be8b87fc64120c245f18  numpy-2.2.1-cp313-cp313-musllinux_1_2_x86_64.whl881b9b20e68b317850ad7b6306ac1c51  numpy-2.2.1-cp313-cp313-win32.whl35bd751636dcea0ca0534ad9dee8057a  numpy-2.2.1-cp313-cp313-win_amd64.whl7057313b668a4a26b5386203ebc040d9  numpy-2.2.1-cp313-cp313t-macosx_10_13_x86_64.whl02031b405d028714126c26ffc5772f0e  numpy-2.2.1-cp313-cp313t-macosx_11_0_arm64.whl73eb35111b027d6771d9a91eb21ad7ef  numpy-2.2.1-cp313-cp313t-macosx_14_0_arm64.whl01f9a5eb7ec872d9682bb6a174897b35  numpy-2.2.1-cp313-cp313t-macosx_14_0_x86_64.whl9bc363d2782931efa2648b42ce358a4c  numpy-2.2.1-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whlb6492f49b50e892a7134baf2dba9f88d  numpy-2.2.1-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whla1c458a98cd9c7ad63f9c301398f4d63  numpy-2.2.1-cp313-cp313t-musllinux_1_2_aarch64.whl38d2bf31247d9005c7a0197aa992cf1d  numpy-2.2.1-cp313-cp313t-musllinux_1_2_x86_64.whl30e6acf4391728d0a3a5e3494bd4a2c8  numpy-2.2.1-cp313-cp313t-win32.whl2100b60306e75288799fca60bd00b84f  numpy-2.2.1-cp313-cp313t-win_amd64.whlf975551321147c307bbdff4889061b47  numpy-2.2.1-pp310-pypy310_pp73-macosx_10_15_x86_64.whlcefbc2de3aa5ef518ce652fdaab00c96  numpy-2.2.1-pp310-pypy310_pp73-macosx_14_0_x86_64.whl7e379c1d0a5be8e548e35fa7abe1d2c0  numpy-2.2.1-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl3cba151351656a83e4c84c942cf490e7  numpy-2.2.1-pp310-pypy310_pp73-win_amd64.whl57c5757508a50d1daefa4b689e9701cb  numpy-2.2.1.tar.gz

SHA256

5edb4e4caf751c1518e6a26a83501fda79bff41cc59dac48d70e6d65d4ec4440  numpy-2.2.1-cp310-cp310-macosx_10_9_x86_64.whlaa3017c40d513ccac9621a2364f939d39e550c542eb2a894b4c8da92b38896ab  numpy-2.2.1-cp310-cp310-macosx_11_0_arm64.whl61048b4a49b1c93fe13426e04e04fdf5a03f456616f6e98c7576144677598675  numpy-2.2.1-cp310-cp310-macosx_14_0_arm64.whl7671dc19c7019103ca44e8d94917eba8534c76133523ca8406822efdd19c9308  numpy-2.2.1-cp310-cp310-macosx_14_0_x86_64.whl4250888bcb96617e00bfa28ac24850a83c9f3a16db471eca2ee1f1714df0f957  numpy-2.2.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whla7746f235c47abc72b102d3bce9977714c2444bdfaea7888d241b4c4bb6a78bf  numpy-2.2.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl059e6a747ae84fce488c3ee397cee7e5f905fd1bda5fb18c66bc41807ff119b2  numpy-2.2.1-cp310-cp310-musllinux_1_2_aarch64.whlf62aa6ee4eb43b024b0e5a01cf65a0bb078ef8c395e8713c6e8a12a697144528  numpy-2.2.1-cp310-cp310-musllinux_1_2_x86_64.whl48fd472630715e1c1c89bf1feab55c29098cb403cc184b4859f9c86d4fcb6a95  numpy-2.2.1-cp310-cp310-win32.whlb541032178a718c165a49638d28272b771053f628382d5e9d1c93df23ff58dbf  numpy-2.2.1-cp310-cp310-win_amd64.whl40f9e544c1c56ba8f1cf7686a8c9b5bb249e665d40d626a23899ba6d5d9e1484  numpy-2.2.1-cp311-cp311-macosx_10_9_x86_64.whlf9b57eaa3b0cd8db52049ed0330747b0364e899e8a606a624813452b8203d5f7  numpy-2.2.1-cp311-cp311-macosx_11_0_arm64.whlbc8a37ad5b22c08e2dbd27df2b3ef7e5c0864235805b1e718a235bcb200cf1cb  numpy-2.2.1-cp311-cp311-macosx_14_0_arm64.whl9036d6365d13b6cbe8f27a0eaf73ddcc070cae584e5ff94bb45e3e9d729feab5  numpy-2.2.1-cp311-cp311-macosx_14_0_x86_64.whl51faf345324db860b515d3f364eaa93d0e0551a88d6218a7d61286554d190d73  numpy-2.2.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl38efc1e56b73cc9b182fe55e56e63b044dd26a72128fd2fbd502f75555d92591  numpy-2.2.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl31b89fa67a8042e96715c68e071a1200c4e172f93b0fbe01a14c0ff3ff820fc8  numpy-2.2.1-cp311-cp311-musllinux_1_2_aarch64.whl4c86e2a209199ead7ee0af65e1d9992d1dce7e1f63c4b9a616500f93820658d0  numpy-2.2.1-cp311-cp311-musllinux_1_2_x86_64.whlb34d87e8a3090ea626003f87f9392b3929a7bbf4104a05b6667348b6bd4bf1cd  numpy-2.2.1-cp311-cp311-win32.whl360137f8fb1b753c5cde3ac388597ad680eccbbbb3865ab65efea062c4a1fd16  numpy-2.2.1-cp311-cp311-win_amd64.whl694f9e921a0c8f252980e85bce61ebbd07ed2b7d4fa72d0e4246f2f8aa6642ab  numpy-2.2.1-cp312-cp312-macosx_10_13_x86_64.whl3683a8d166f2692664262fd4900f207791d005fb088d7fdb973cc8d663626faa  numpy-2.2.1-cp312-cp312-macosx_11_0_arm64.whl780077d95eafc2ccc3ced969db22377b3864e5b9a0ea5eb347cc93b3ea900315...

Assets5

2 people reacted

2.2.0 (Dec 8, 2024)

08 Dec 16:03

charris

v2.2.0

This tag was signed with the committer’sverified signature.

charris Charles Harris

GPG key ID:679F228377C5247B

Verified

Learn about vigilant mode.

e7a123b

This commit was created on GitHub.com and signed with GitHub’sverified signature.

GPG key ID:B5690EEEBB952194

Verified

Learn about vigilant mode.

Compare

Choose a tag to compare

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 functionsmatvec andvecmat, 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.
Usearr.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 whenrowvar=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 existingnumpy.matmul as well as to
numpy.vecdot, which was added in numpy 2.0.
Note thatnumpy.matmul never takes a complex
conjugate, also not when its left input is a vector, while both
numpy.vecdot andnumpy.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 viaufunc.__dict__["__doc__"]).
__dict__ can be used to also override other properties, such as
__module__ or__qualname__.
(gh-27735)
The "nbit" type parameter ofnp.number and its subtypes now
defaults totyping.Any. This way, type-checkers will infer
annotations such asx: np.floating asx: np.floating[Any], even
in strict mode.
(gh-27736)

Improvements

Thedatetime64 andtimedelta64 hashes now correctly match the
Pythons builtindatetime andtimedelta 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 emptymemmap. Previously an empty
memmap would fail unless a non-zerooffset was set.
Now a zero-sizememmap is supported even if
offset=0. To achieve this, if amemmap 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 largenp.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 ofnumpy.float64 andnumpy.complex128 now
reflect that they are also subtypes of the built-infloat and
complex types, respectively. This update prevents static
type-checkers from reporting errors in cases such as:
```
x:float=numpy.float64(6.28)# validz:complex=numpy.complex128(-1j)# valid
```
(gh-27334)
Therepr of arrays large enough to be summarized (i.e., where
elements are replaced with...) now includes theshape 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 asshape=..., this argument cannot actually be passed in
to thenp.array constructor. If you encounter problems, e.g., due
to failing doctests, you can use the print optionlegacy=2.1 to
get the old behaviour.
(gh-27482)
Calling__array_wrap__ directly on NumPy arrays or scalars now
does the right thing whenreturn_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 1_2 from 1_1. This is because
1_1 isend 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 butNPY_PROMOTION_STATE=weak
while_set_promotion_state and_get_promotion_state are removed. In
case code used_no_nep50_warning, acontextlib.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.whl7d3773d9b665b2d7cfec0cc0b760e69e  numpy-2.2.0-cp310-cp310-macosx_11_0_arm64.whl8ef666a462d3765ccfd5288f2fdf8e08  numpy-2.2.0-cp310-cp310-macosx_14_0_arm64.whle4f9e3117075ffe53d7993253c774158  numpy-2.2.0-cp310-cp310-macosx_14_0_x86_64.whlfd60e410e5db402a2d0c0cb4dd23281d  numpy-2.2.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl64c083cdbd91eb8670cd72b619f3a039  numpy-2.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whlc3c75c2299f5163770e2e42f0dee5276  numpy-2.2.0-cp310-cp310-musllinux_1_2_aarch64.whlf6ab05f787221bbaf8fb4a9778af5467  numpy-2.2.0-cp310-cp310-musllinux_1_2_x86_64.whl9b04caec124cadf90005ccdb662aad9f  numpy-2.2.0-cp310-cp310-win32.whl58934f23b6bc71fb1f984b688c1c6136  numpy-2.2.0-cp310-cp310-win_amd64.whl769e53438154e53ba490fb4f816c083e  numpy-2.2.0-cp311-cp311-macosx_10_9_x86_64.whlaa8060c013c04133b63780025eef4451  numpy-2.2.0-cp311-cp311-macosx_11_0_arm64.whl72c10ef28a0ddffe6bf2495954ab82e0  numpy-2.2.0-cp311-cp311-macosx_14_0_arm64.whl946b2510c86eb48e374e6987582c9b46  numpy-2.2.0-cp311-cp311-macosx_14_0_x86_64.whl3f5203ae901ddd78cb298582eda07627  numpy-2.2.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whlfd14624d40100a5eb0181bf393394448  numpy-2.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl7c86d51d89dbc5a6860d65641ea131ef  numpy-2.2.0-cp311-cp311-musllinux_1_2_aarch64.whl895c6588c74019b94fb3c740b9e9a0f5  numpy-2.2.0-cp311-cp311-musllinux_1_2_x86_64.whl1468ae1cb59a43991b199cfa6f1e5679  numpy-2.2.0-cp311-cp311-win32.whl48a3792698a81917320b91a30c0bacf4  numpy-2.2.0-cp311-cp311-win_amd64.whldb4377351f167d82adc66b16965d11bd  numpy-2.2.0-cp312-cp312-macosx_10_13_x86_64.whl3f3978b5e480ed18d55b1799d9a534ff  numpy-2.2.0-cp312-cp312-macosx_11_0_arm64.whl584b4063eb66688b607f7e7bdca58011  numpy-2.2.0-cp312-cp312-macosx_14_0...

Assets5

19 people reacted

2.2.0rc1 (Nov 26, 2024)

26 Nov 17:43

charris

v2.2.0rc1

This tag was signed with the committer’sverified signature.

charris Charles Harris

GPG key ID:679F228377C5247B

Verified

Learn about vigilant mode.

de271f1

This commit was created on GitHub.com and signed with GitHub’sverified signature.

GPG key ID:B5690EEEBB952194

Verified

Learn about vigilant mode.

Compare

Choose a tag to compare

2.2.0rc1 (Nov 26, 2024)Pre-release

Pre-release

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 functionsmatvec andvecmat, 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.
Usearr.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
whenrowvar=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 existingnumpy.matmul as well as to
numpy.vecdot, which was added in numpy 2.0.
Note thatnumpy.matmul never takes a complex conjugate, also not when its
left input is a vector, while bothnumpy.vecdot andnumpy.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 viaufunc.__dict__["__doc__"]).
__dict__ can be used to also override other properties, such as
__module__ or__qualname__.
(gh-27735)
The "nbit" type parameter ofnp.number and its subtypes now
defaults totyping.Any. This way, type-checkers will infer
annotations such asx: np.floating asx: np.floating[Any], even
in strict mode.
(gh-27736)

Improvements

Thedatetime64 andtimedelta64 hashes now correctly match the
Pythons builtindatetime andtimedelta 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 emptymemmap. Previously an emptymemmap would
fail unless a non-zerooffset was set. Now a zero-sizememmap is
supported even ifoffset=0. To achieve this, if amemmap 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 largenp.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 ofnumpy.float64 andnumpy.complex128 now reflect
that they are also subtypes of the built-infloat andcomplex types,
respectively. This update prevents static type-checkers from reporting
errors in cases such as:
```
x:float=numpy.float64(6.28)# validz:complex=numpy.complex128(-1j)# valid
```
(gh-27334)
Therepr of arrays large enough to be summarized (i.e., where
elements are replaced with...) now includes theshape 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 asshape=..., this argument cannot actually be passed in
to thenp.array constructor. If you encounter problems, e.g., due
to failing doctests, you can use the print optionlegacy=2.1 to
get the old behaviour.
(gh-27482)
Calling__array_wrap__ directly on NumPy arrays or scalars now
does the right thing whenreturn_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 1_2 from 1_1. This is because
1_1 isend 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 butNPY_PROMOTION_STATE=weak while
_set_promotion_state and_get_promotion_state are removed. In case code
used_no_nep50_warning, acontextlib.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.whle69c45cf5ea08fdf2a5527190a7d6549  numpy-2.2.0rc1-cp310-cp310-macosx_11_0_arm64.whld4f8048977139cb229875c201f605369  numpy-2.2.0rc1-cp310-cp310-macosx_14_0_arm64.whl8710578b7f4ceef7f73b6d234ad3a82a  numpy-2.2.0rc1-cp310-cp310-macosx_14_0_x86_64.whl899d1f24d8e5570695a024908d100174  numpy-2.2.0rc1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whlcb768ee568bed2e4f55d47f43c655bc2  numpy-2.2.0rc1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl5a40726db153ca1984598323cc59eb9b  numpy-2.2.0rc1-cp310-cp310-musllinux_1_2_aarch64.whl450e5e05bdc5551c0a4df2a8d7f09925  numpy-2.2.0rc1-cp310-cp310-musllinux_1_2_x86_64.whl1c34c86b0abaa5d2a75677044a7fca07  numpy-2.2.0rc1-cp310-cp310-win32.whld679ad13f3892325fd4542931ee74852  numpy-2.2.0rc1-cp310-cp310-win_amd64.whla7a8cf5fa2e3d4bd0131ad48c0215f50  numpy-2.2.0rc1-cp311-cp311-macosx_10_9_x86_64.whlaa6c629290d8b05b44fbbf805fb39dbe  numpy-2.2.0rc1-cp311-cp311-macosx_11_0_arm64.whla04fe8ac96a5226686ec4190db8511d6  numpy-2.2.0rc1-cp311-cp311-macosx_14_0_arm64.whl50aedb2a570a7867e860d98eb816bec4  numpy-2.2.0rc1-cp311-cp311-macosx_14_0_x86_64.whlcd034c5179ee4cc5669ae36be0deb6ab  numpy-2.2.0rc1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl67e3336cdcdcf72cd07978a465e61ebd  numpy-2.2.0rc1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl45456522fc3996937f1b1ad8bd7f85b2  numpy-2.2.0rc1-cp311-cp311-musllinux_1_2_aarch64.whl244dcedc05e96c843853738bc2d37bdb  numpy-2.2.0rc1-cp311-cp311-musllinux_1_2_x86_64.whlda24dd620b6509740a1d8aebe4d1306c  numpy-2.2.0rc1-cp311-cp311-win32.whl472e5f997dc437b8115ba4ef70a6a266  numpy-2.2.0rc1-cp311-cp311-win_amd64.whl6e4ec4f92f8b0768d679419360098a89  numpy-2.2.0rc1-cp312-cp312-macosx_10_13_x86_64.whle15a1756fbe98aa61cb8d98de1d516fc  numpy-2.2.0rc1-cp312-cp312-macosx_11_0_arm64.whl6c58bba6f453ad22a651f6f0f6416899  numpy-2.2.0rc1-cp312-cp312-macosx_14_0_arm64.whl1a00dd2343f8e...

Assets5

22 people reacted

2.1.3 (Nov 2, 2024)

02 Nov 18:02

charris

v2.1.3

This tag was signed with the committer’sverified signature.

charris Charles Harris

GPG key ID:679F228377C5247B

Verified

Learn about vigilant mode.

98464cc

This commit was created on GitHub.com and signed with GitHub’sverified signature.

GPG key ID:B5690EEEBB952194

Verified

Learn about vigilant mode.

Compare

Choose a tag to compare

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: bumppython to 3.12 in environment.yml
#27562: BLD: update vendored Meson to 1.5.2
#27563: BUG: weighted quantile for some zero weights (#27549)
#27565: MAINT: Use miniforge for macos conda test.
#27566: BUILD: satisfy gcc-13 pendantic errors
#27569: BUG: handle possible error for PyTraceMallocTrack
#27570: BLD: start building Windows free-threaded wheels [wheel build]
#27571: BUILD: vendor tempita from Cython
#27574: BUG: Fix warning "differs in levels of indirection" in npy_atomic.h...
#27592: MAINT: Update Highway to latest
#27593: BUG: Adjust numpy.i for SWIG 4.3 compatibility
#27616: BUG: Fix Linux QEMU CI workflow
#27668: BLD: Do not set __STDC_VERSION__ to zero during build
#27669: ENH: fix wasm32 runtime type error in numpy._core
#27672: BUG: Fix a reference count leak in npy_find_descr_for_scalar.
#27673: BUG: fixes for StringDType/unicode promoters

Checksums

MD5

3f2f22827dd321ae86b5ab4fa888d0db  numpy-2.1.3-cp310-cp310-macosx_10_9_x86_64.whl13da2761d1abe71731a2806537369115  numpy-2.1.3-cp310-cp310-macosx_11_0_arm64.whl5aef4a78b69cd90d0f6fff8f88817991  numpy-2.1.3-cp310-cp310-macosx_14_0_arm64.whl12da7f09cd5707634878f85845c9de10  numpy-2.1.3-cp310-cp310-macosx_14_0_x86_64.whl5b999693362815b56855533469aea0ca  numpy-2.1.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl8c49f457127bfb4f167c91583e5167af  numpy-2.1.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whlf31c0e80b18afc0c04cada401cbe0358  numpy-2.1.3-cp310-cp310-musllinux_1_1_x86_64.whl2c0709812e27bcaf74d75ac8ed45614b  numpy-2.1.3-cp310-cp310-musllinux_1_2_aarch64.whla65b28800e78942b9e60e03e96cfd0c0  numpy-2.1.3-cp310-cp310-win32.whld8358545732fe4ee1ecf407b06567d81  numpy-2.1.3-cp310-cp310-win_amd64.whl34942f9a1391532e2c3168043c0021d5  numpy-2.1.3-cp311-cp311-macosx_10_9_x86_64.whl0d69ec06e303b5112788db68a8fdde1b  numpy-2.1.3-cp311-cp311-macosx_11_0_arm64.whlda1988c8d3a9db5947a2bd51290b8b95  numpy-2.1.3-cp311-cp311-macosx_14_0_arm64.whlb5eba73c2abaf5a81535f4b1034fe8d2  numpy-2.1.3-cp311-cp311-macosx_14_0_x86_64.whl63cc090209718aa1d0f0fbd3fd03bc0b  numpy-2.1.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl55f14ca7b55554d4a043369ae5f1837f  numpy-2.1.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl4e58e0645d81ff84c0fb75311d2a97d6  numpy-2.1.3-cp311-cp311-musllinux_1_1_x86_64.whl30235088a5f86d1f343bfec458f6292d  numpy-2.1.3-cp311-cp311-musllinux_1_2_aarch64.whlc80a03952b2f4950f1eb9d1656413fec  numpy-2.1.3-cp311-cp311-win32.whld8c1a5a441b89591af8f09dfa0b2d4d5  numpy-2.1.3-cp311-cp311-win_amd64.whl2cebcea71e71e8b09a25179b240ee240  numpy-2.1.3-cp312-cp312-macosx_10_13_x86_64.whlfaf5df4bd35ca362795cda193da49591  numpy-2.1.3-cp312-cp312-macosx_11_0_arm64.whl573f195910fc3b3e9ac5379816280f89  numpy-2.1.3-cp312-cp312-macosx_14_0_arm64.whl900548b2acb82ed0e306943fb68de802  numpy-2.1.3-cp312-cp312-macosx_14_0_x86_64.whl81cded28bb87c4987b1d975fe768c3a1  numpy-2.1.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl2b83cb346bca97475fa5e39e704c45f1  numpy-2.1.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl06d8593cb7a2aae157e028c3d4cb3c96  numpy-2.1.3-cp312-cp312-musllinux_1_1_x86_64.whleea8b148a6a2fee37b87291043e00bda  numpy-2.1.3-cp312-cp312-musllinux_1_2_aarch64.whld407b7c48457789914f28004f41d6ea2  numpy-2.1.3-cp312-cp312-win32.whl117574ee1a645e63a6d69e20c8673665  numpy-2.1.3-cp312-cp312-win_amd64.whl0c9ffd1f1f1e96186f30a578b85da653  numpy-2.1.3-cp313-cp313-macosx_10_13_x86_64.whlcd430b2caf09d21680616aef5d4a439d  numpy-2.1.3-cp313-cp313-macosx_11_0_arm64.whlb431935148221b79bda9490b1d069e3c  numpy-2.1.3-cp313-cp313-macosx_14_0_arm64.whlb3ff577c78097b187bd58f20b6e88642  numpy-2.1.3-cp313-cp313-macosx_14_0_x86_64.whl8186f86f8d94a5505e6dcebe6c056ab7  numpy-2.1.3-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl2c5b2381a4a4e3d9865ccb346d44a7ed  numpy-2.1.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl85786d12388d60b904c02eb12df55b37  numpy-2.1.3-cp313-cp313-musllinux_1_1_x86_64.whlda68282c0418a22730643906e5dd58a1  numpy-2.1.3-cp313-cp313-musllinux_1_2_aarch64.whlfe47e181a70d3e865e5d6a27e5fa71cd  numpy-2.1.3-cp313-cp313-win32.whl8b7f290784c95cf620e0ac1af5470f1d  numpy-2.1.3-cp313-cp313-win_amd64.whl4f0c3f8c81cb6bd43a9f1f7bef7db82d  numpy-2.1.3-cp313-cp313t-macosx_10_13_x86_64.whl133905fd003c9504fc5bb9ce71e4103b  numpy-2.1.3-cp313-cp313t-macosx_11_0_arm64.whl12fe4f265dbda251309f109cbcd46f07  numpy-2.1.3-cp313-cp313t-macosx_14_0_arm64.whlb60e418506b969e6df2c0d600bf3c6d4  numpy-2.1.3-cp313-cp313t-macosx_14_0_x86_64.whlc2b7160b748f4c1c483a7954e5024250  numpy-2.1.3-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl8097ddb45c8c821085c19d940bcbe6de  numpy-2.1.3-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl209f55dc1ed6da23a5ea3e11ca962308  numpy-2.1.3-cp313-cp313t-musllinux_1_1_x86_64.whl06a1792849b601c7bdd38e39bc5cb5f1  numpy-2.1.3-cp313-cp313t-musllinux_1_2_aarch64.whl86630bf207e8cbe6933232cb2a47a6c0  numpy-2.1.3-cp313-cp313t-win32.whl6af9109b82c0acdcf8b0e81dc0e4c517  numpy-2.1.3-cp313-cp313t-win_amd64.whlc7e821e086346afc0078acb237f30431  numpy-2.1.3-pp310-pypy310_pp73-macosx_10_15_x86_64.whl5b938b2da78b1c84044df8cdb2e8e63a  numpy-2.1.3-pp310-pypy310_pp73-macosx_14_0_x86_64.whlef251f3b6aa022b1c2fac14889d6d9d3  numpy-2.1.3-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl356c7bb6067ae0dccc4a54efc1879e74  numpy-2.1.3-pp310-pypy310_pp73-win_amd64.whl11096358375945114577a0c82b2c6038  numpy-2.1.3.tar.gz

SHA256

c894b4305373b9c5576d7a12b473702afdf48ce5369c074ba304cc5ad8730dff  numpy-2.1.3-cp310-cp310-macosx_10_9_x86_64.whlb47fbb433d3260adcd51eb54f92a2ffbc90a4595f8970ee00e064c644ac788f5  numpy-2.1.3-cp310-cp310-macosx_11_0_arm64.whl825656d0743699c529c5943554d223c021ff0494ff1442152ce887ef4f7561a1  numpy-2.1.3-cp310-cp310-macosx_14_0_arm64.whl6a4825252fcc430a182ac4dee5a505053d262c807f8a924603d411f6718b88fd  numpy-2.1.3-cp310-cp310-macosx_14_0_x86_64.whle711e02f49e176a01d0349d82cb5f05ba4db7d5e7e0defd026328e5cfb3226d3  numpy-2.1.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl78574ac2d1a4a02421f25da9559850d59457bac82f2b8d7a44fe83a64f770098  numpy-2.1.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whlc7662f0e3673fe4e832fe07b65c50342ea27d989f92c80355658c7f888fcc83c  numpy-2.1.3-cp310-cp310-musllinux_1_1_x86_64.whlfa2d1337dc61c8dc417fbccf20f6d1e139896a30721b7f1e832b2bb6ef4eb6c4  numpy-2.1.3-cp310-cp310-musllinux_1_2_aarch64.whl72dcc4a35a8515d83e76b58fdf8113a5c969ccd505c8a946759b24e3182d1f23  numpy-2.1.3-cp310-cp310-win32.whlecc76a9ba2911d8d37ac01de72834d8849e55473457558e12995f4cd53e778e0  numpy-2.1.3-cp310-cp310-win_amd64.whl4d1167c53b93f1f5d8a139a742b3c6f4d429b54e74e6b57d0eff40045187b15d  numpy-2.1.3-cp311-cp311-macosx_10_9_x86_64.whlc80e4a09b3d95b4e1cac08643f1152fa71a0a821a2d4277334c88d54b2219a41  numpy-2.1.3-cp311-cp311-macosx_11_0_arm64.whl576a1c...

Assets5

1 person reacted

2.1.2 (Oct 5, 2024)

05 Oct 18:52

charris

v2.1.2

This tag was signed with the committer’sverified signature.

charris Charles Harris

GPG key ID:679F228377C5247B

Verified

Learn about vigilant mode.

f5afe3d

This commit was created on GitHub.com and signed with GitHub’sverified signature.

GPG key ID:B5690EEEBB952194

Verified

Learn about vigilant mode.

Compare

Choose a tag to compare

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 get_build_msvc_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.whl172614423a82ef73d8752ad8a59cbafc  numpy-2.1.2-cp310-cp310-macosx_11_0_arm64.whl5ee5e7a8a892cbe96ee228ca5fe7546b  numpy-2.1.2-cp310-cp310-macosx_14_0_arm64.whl9ce6f9222dfabd32e66b883f1fe015aa  numpy-2.1.2-cp310-cp310-macosx_14_0_x86_64.whl291da8bfeb7c9a3491ec35ecb2596335  numpy-2.1.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl9317d9b049f09c0193f074a6458cf79b  numpy-2.1.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl1f2c121533715d8b099d6498e4498f81  numpy-2.1.2-cp310-cp310-musllinux_1_1_x86_64.whl2834df46e2cb2e81cbe4fd1ce9b96b4b  numpy-2.1.2-cp310-cp310-musllinux_1_2_aarch64.whlcbc3ae2c176324fe2a9c04ec0aff181f  numpy-2.1.2-cp310-cp310-win32.whle4d74f9d188dc3fe7a65adf8c01e98cc  numpy-2.1.2-cp310-cp310-win_amd64.whlcbcece9c21ed1daf60f3729a37b32266  numpy-2.1.2-cp311-cp311-macosx_10_9_x86_64.whl0e62474993ff6faca9c467f68cc16ceb  numpy-2.1.2-cp311-cp311-macosx_11_0_arm64.whl8747e85e09b2000a0af5a8226740dc92  numpy-2.1.2-cp311-cp311-macosx_14_0_arm64.whl34e7f3591ce81926518a36c92038a056  numpy-2.1.2-cp311-cp311-macosx_14_0_x86_64.whl0ec3e617161b42d643aaa4b8d3e477f5  numpy-2.1.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whle2a6a419b4672bfb4f3f6a98c0e575bb  numpy-2.1.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl8c14b4d03fc8672e43eddd3ede89be09  numpy-2.1.2-cp311-cp311-musllinux_1_1_x86_64.whldc183e12b24317bf210fb093da598d29  numpy-2.1.2-cp311-cp311-musllinux_1_2_aarch64.whl4918f2c32ca3be20c7c5d8551e649757  numpy-2.1.2-cp311-cp311-win32.whla8991919b6fae3c7a77c260f60a5e2e2  numpy-2.1.2-cp311-cp311-win_amd64.whl879f307d16f9222c49508be5ea6491fc  numpy-2.1.2-cp312-cp312-macosx_10_13_x86_64.whlfe9dfac7bee0cff178737e1706aee61a  numpy-2.1.2-cp312-cp312-macosx_11_0_arm64.whl1f0c671db3294f4df8bffedc41a2e37f  numpy-2.1.2-cp312-cp312-macosx_14_0_arm64.whld131c4bd6ba29b05a5b7fa74e87a0506  numpy-2.1.2-cp312-cp312-macosx_14_0_x86_64.whl8f9cca33590be334d44cc026a3716966  numpy-2.1.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl3692a9290dd430e56e1b15387c25b7af  numpy-2.1.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl3549439284dbb1a05785b535c3de60d9  numpy-2.1.2-cp312-cp312-musllinux_1_1_x86_64.whlb9934410f20505e5c4b70974cd8fdc26  numpy-2.1.2-cp312-cp312-musllinux_1_2_aarch64.whl96759e3380e4893b9b88d5d498d856b2  numpy-2.1.2-cp312-cp312-win32.whlf94c7405ed72a136e374ab82400fefdc  numpy-2.1.2-cp312-cp312-win_amd64.whl2ea775cb4da02f39edf3089af60bddd5  numpy-2.1.2-cp313-cp313-macosx_10_13_x86_64.whl354d0970154dd002573f4291e0e9de76  numpy-2.1.2-cp313-cp313-macosx_11_0_arm64.whlbbfee75640b337e12f894d0b54727d66  numpy-2.1.2-cp313-cp313-macosx_14_0_arm64.whla443fff50571df87f687ad55c9060d25  numpy-2.1.2-cp313-cp313-macosx_14_0_x86_64.whl9f8cd7de5b5aa5ad8ba52608a4b0a3b8  numpy-2.1.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whlc198fe3deaa77fb94d15284b4e26b875  numpy-2.1.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl0a59171c983fc2d8ea599bdf382c3d6a  numpy-2.1.2-cp313-cp313-musllinux_1_1_x86_64.whl5ba974cd59fb8c9fc94787c754a5f636  numpy-2.1.2-cp313-cp313-musllinux_1_2_aarch64.whl93d5c642606fe8abeff0e6db31ebe88f  numpy-2.1.2-cp313-cp313-win32.whlf6455bb4311ddde071a5ea2e14016003  numpy-2.1.2-cp313-cp313-win_amd64.whld2a21857c924d4b1b3c8ae8a9e9b9bb4  numpy-2.1.2-cp313-cp313t-macosx_10_13_x86_64.whlcd6afcbd05835255750a2fba6012c565  numpy-2.1.2-cp313-cp313t-macosx_11_0_arm64.whld2fab663ea84f1cfe13dfc00dae74fb6  numpy-2.1.2-cp313-cp313t-macosx_14_0_arm64.whl9477b923000d63617324c487a4ce0e28  numpy-2.1.2-cp313-cp313t-macosx_14_0_x86_64.whl84b621a2c9a8c077bc9c471abd2b3933  numpy-2.1.2-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whlb1c341c7192d03e8f0f5e7c4b9b6f894  numpy-2.1.2-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whlb59750ea55cf274854f64109bf67a112  numpy-2.1.2-cp313-cp313t-musllinux_1_1_x86_64.whl33f4d63f81ad85c1ea873197f2189d89  numpy-2.1.2-cp313-cp313t-musllinux_1_2_aarch64.whlf26a9ac42953c84c94f8203b2dbc61c0  numpy-2.1.2-pp310-pypy310_pp73-macosx_10_15_x86_64.whle7cf2857582d507dfa3e8644dd3562a6  numpy-2.1.2-pp310-pypy310_pp73-macosx_14_0_x86_64.whl9e3d44cb302c629c00fde8f25809b04d  numpy-2.1.2-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl3f97ee2d9962cf9d84624f725bdd2a8f  numpy-2.1.2-pp310-pypy310_pp73-win_amd64.whl3d92e07d34f60dbac6b82a0982a98757  numpy-2.1.2.tar.gz

SHA256

30d53720b726ec36a7f88dc873f0eec8447fbc93d93a8f079dfac2629598d6ee  numpy-2.1.2-cp310-cp310-macosx_10_9_x86_64.whle8d3ca0a72dd8846eb6f7dfe8f19088060fcb76931ed592d29128e0219652884  numpy-2.1.2-cp310-cp310-macosx_11_0_arm64.whlfc44e3c68ff00fd991b59092a54350e6e4911152682b4782f68070985aa9e648  numpy-2.1.2-cp310-cp310-macosx_14_0_arm64.whl7c1c60328bd964b53f8b835df69ae8198659e2b9302ff9ebb7de4e5a5994db3d  numpy-2.1.2-cp310-cp310-macosx_14_0_x86_64.whl6cdb606a7478f9ad91c6283e238544451e3a95f30fb5467fbf715964341a8a86  numpy-2.1.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whld666cb72687559689e9906197e3bec7b736764df6a2e58ee265e360663e9baf7  numpy-2.1.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whlc6eef7a2dbd0abfb0d9eaf78b73017dbfd0b54051102ff4e6a7b2980d5ac1a03  numpy-2.1.2-cp310-cp310-musllinux_1_1_x86_64.whl12edb90831ff481f7ef5f6bc6431a9d74dc0e5ff401559a71e5e4611d4f2d466  numpy-2.1.2-cp310-cp310-musllinux_1_2_aarch64.whla65acfdb9c6ebb8368490dbafe83c03c7e277b37e6857f0caeadbbc56e12f4fb  numpy-2.1.2-cp310-cp310-win32.whl860ec6e63e2c5c2ee5e9121808145c7bf86c96cca9ad396c0bd3e0f2798ccbe2  numpy-2.1.2-cp310-cp310-win_amd64.whlb42a1a511c81cc78cbc4539675713bbcf9d9c3913386243ceff0e9429ca892fe  numpy-2.1.2-cp311-cp311-macosx_10_9_x86_64.whlfaa88bc527d0f097abdc2c663cddf37c05a1c2f113716601555249805cf573f1  numpy-2.1.2-cp311-cp311-macosx_11_0_arm64.whlc82af4b2ddd2ee72d1fc0c6695048d457e00b3582ccde72d8a1c991b808bb20f  numpy-2.1.2-cp311-cp311-macosx_14_0_arm64.whl13602b3174432a35b16c4cfb5de9a12d229727c3dd47a6ce35111f2ebdf66ff4  numpy-2.1.2-cp311-cp311-macosx_14_0_x86_64.whl1ebec5fd716c5a5b3d8dfcc439be82a8407b7b24b230d0ad28a81b61c2f4659a  numpy-2.1.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whle2b49c3c0804e8ecb05d59af8386ec2f74877f7ca8fd9c1e00be2672e4d399b1  numpy-2.1.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl2cbba4b30bf31ddbe97f1c7205ef976909a93a66bb1583e983adbd155ba72ac2  numpy-2.1.2-cp311-cp311-musllinux_1_1_x86_64.whl8e00ea6fc82e8a804433d3e9cedaa1051a1422cb6e443011590c14d2dea59146  numpy-2.1.2-cp311-cp311-musllinux_1_2_aarch64.whl5006b13a06e0b38d561fab5ccc37581f23c9511879be7693bd33c7cd15ca227c  numpy-2.1.2-cp311-cp311-win32.whlf1eb068ead09f4994dec71c24b2844f1e4e4e013b9629f812f292f04bd1510d9  numpy-2.1.2-cp311-cp311-win_amd64.whld7bf0a4f9f15b32b5ba53147369e94296f5fffb783db5aacc1be15b4bf72f43b  numpy-2.1.2-cp312-cp312-macosx_10_13_x86_64.whlb1d0fcae4f0949f215d4632be684a539859b295e2d0cb14f78ec231915d644db  numpy-2.1.2-cp312-cp312-macosx_11_0_arm64.whlf751ed0a2f250541e19dfca9f1eafa31a392c71c832b6bb9e113b10d050cb0f1  numpy-2.1.2-cp312-cp312-macosx_14_0_arm64.whlbd33f82e95ba7ad632bc57837ee99dba3d7e006536200c4e9124089e1bf42426  numpy-2.1.2-cp312-cp312-macosx_14_0_x86_64.whl1b8cde4f11f0a975d1fd59373b32e2f5a562ade7cde4f85b7137f3de8fbb29a0  numpy-2.1.2-cp312-cp312-manylinux_2_17_...

Assets5

31 people reacted

Movatterモバイル変換

Uh oh!

Releases: numpy/numpy

v2.2.6 (May 17, 2025)

NumPy 2.2.6 Release Notes

Contributors

Pull requests merged

Checksums

MD5

SHA256

Uh oh!

v2.2.5 (Apr 19, 2025)

NumPy 2.2.5 Release Notes

Contributors

Pull requests merged

Checksums

MD5

SHA256

Uh oh!

2.2.4 (Mar 16, 2025)

NumPy 2.2.4 Release Notes

Contributors

Pull requests merged

Checksums

MD5

SHA256

Uh oh!

2.2.3 (Feb 13, 2025)

NumPy 2.2.3 Release Notes

Contributors

Pull requests merged

Checksums

MD5

SHA256

Uh oh!

2.2.2 (Jan 18, 2025)

NumPy 2.2.2 Release Notes

Contributors

Pull requests merged

Checksums

MD5

SHA256

Uh oh!

2.2.1 (DEC 21, 2024)

NumPy 2.2.1 Release Notes

Contributors

Pull requests merged

Checksums

MD5

SHA256

Uh oh!

2.2.0 (Dec 8, 2024)

NumPy 2.2.0 Release Notes

Deprecations

Expired deprecations

Compatibility notes

New Features

Improvements

Performance improvements and changes

Changes

Checksums

MD5

Uh oh!

2.2.0rc1 (Nov 26, 2024)

NumPy 2.2.0 Release Notes

Deprecations

Expired deprecations

Compatibility notes

New Features

Improvements

Performance improvements and changes

Changes

Checksums

MD5

Uh oh!

2.1.3 (Nov 2, 2024)

NumPy 2.1.3 Release Notes

Improvements

Changes

Contributors