NumPy 1.26.2 Release Notes

NumPy 1.26.2 is a maintenance release that fixes bugs and regressions
discovered after the 1.26.1 release. The 1.26.release series is the last
planned minor release series before NumPy 2.0. 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.

• @stefan6419846
• @thalassemia +
• Andrew Nelson
• Charles Bousseau +
• Charles Harris
• Marcel Bargull +
• Mark Mentovai +
• Matti Picus
• Nathan Goldbaum
• Ralf Gommers
• Sayed Adel
• Sebastian Berg
• William Ayd +

Pull requests merged

A total of 25 pull requests were merged for this release.

• #24814: MAINT: align test_dispatcher s390x targets with _umath_tests_mtargets
• #24929: MAINT: prepare 1.26.x for further development
• #24955: ENH: Add Cython enumeration for NPY_FR_GENERIC
• #24962: REL: Remove Python upper version from the release branch
• #24971: BLD: Use the correct Python interpreter when running tempita.py
• #24972: MAINT: Remove unhelpful error replacements from import_array()
• #24977: BLD: use classic linker on macOS, the new one in XCode 15 has...
• #25003: BLD: musllinux_aarch64 [wheel build]
• #25043: MAINT: Update mailmap
• #25049: MAINT: Update meson build infrastructure.
• #25071: MAINT: Split up .github/workflows to match main
• #25083: BUG: Backport fix build on ppc64 when the baseline set to Power9...
• #25093: BLD: Fix features.h detection for Meson builds [1.26.x Backport]
• #25095: BUG: Avoid intp conversion regression in Cython 3 (backport)
• #25107: CI: remove obsolete jobs, and move macOS and conda Azure jobs...
• #25108: CI: Add linux_qemu action and remove travis testing.
• #25112: MAINT: Update .spin/cmds.py from main.
• #25113: DOC: Visually divide main license and bundled licenses in wheels
• #25115: MAINT: Add missing noexcept to shuffle helpers
• #25116: DOC: Fix license identifier for OpenBLAS
• #25117: BLD: improve detection of Netlib libblas/libcblas/liblapack
• #25118: MAINT: Make bitfield integers unsigned
• #25119: BUG: Make n a long int for np.random.multinomial
• #25120: BLD: change default of the allow-noblas option to true.
• #25121: BUG: ensure passing np.dtype to itself doesn't crash

Checksums

MD5

1a5dc6b5b3bf11ad40a59eedb3b69fa1  numpy-1.26.2-cp310-cp310-macosx_10_9_x86_64.whl
4b741c6dfe4e6e22e34e9c5c788d4f04  numpy-1.26.2-cp310-cp310-macosx_11_0_arm64.whl
2953687fb26e1dd8a2d1bb7109551fcd  numpy-1.26.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
ea9127a3a03f27fd101c62425c661d8d  numpy-1.26.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
7a6be7c6c1cc3e1ff73f64052fe30677  numpy-1.26.2-cp310-cp310-musllinux_1_1_aarch64.whl
4f45d3f69f54fd1638609fde34c33a5c  numpy-1.26.2-cp310-cp310-musllinux_1_1_x86_64.whl
f22f5ea26c86eb126ff502fff75d6c21  numpy-1.26.2-cp310-cp310-win32.whl
49871452488e1a55d15ab54c6f3e546e  numpy-1.26.2-cp310-cp310-win_amd64.whl
676740bf60fb1c8f5a6b31e00b9a4e9b  numpy-1.26.2-cp311-cp311-macosx_10_9_x86_64.whl
7170545dcc2a38a1c2386a6081043b64  numpy-1.26.2-cp311-cp311-macosx_11_0_arm64.whl
feae1190c73d811e2e7ebcad4baf6edf  numpy-1.26.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
03131896abade61b77e0f6e53abb988a  numpy-1.26.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
f160632f128a3fd46787aa02d8731fbb  numpy-1.26.2-cp311-cp311-musllinux_1_1_aarch64.whl
014250db593d589b5533ef7127839c46  numpy-1.26.2-cp311-cp311-musllinux_1_1_x86_64.whl
fb437346dac24d0cb23f5314db043c8b  numpy-1.26.2-cp311-cp311-win32.whl
7359adc233874898ea768cd4aec28bb3  numpy-1.26.2-cp311-cp311-win_amd64.whl
207a678bea75227428e7fb84d4dc457a  numpy-1.26.2-cp312-cp312-macosx_10_9_x86_64.whl
302ff6cc047a408cdf21981bd7b26056  numpy-1.26.2-cp312-cp312-macosx_11_0_arm64.whl
7526faaea58c76aed395c7128dd6e14d  numpy-1.26.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
28d3b1943d3a8ad4bbb2ae9da0a77cb9  numpy-1.26.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
d91f5b2bb2c931e41ae7c80ec7509a31  numpy-1.26.2-cp312-cp312-musllinux_1_1_aarch64.whl
b2504d4239419f012c08fa1eab12f940  numpy-1.26.2-cp312-cp312-musllinux_1_1_x86_64.whl
57944ba30adc07f33e83a9b45f5c625a  numpy-1.26.2-cp312-cp312-win32.whl
fe38cd95bbee405ce0cf51c8753a2676  numpy-1.26.2-cp312-cp312-win_amd64.whl
28e1bc3efaf89cf6f0a2b616c0e16401  numpy-1.26.2-cp39-cp39-macosx_10_9_x86_64.whl
9932ccff54855f12ee24f60528279bf1  numpy-1.26.2-cp39-cp39-macosx_11_0_arm64.whl
b52c1e987074dad100ad234122a397b9  numpy-1.26.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
1d1bd7e0d2a89ce795a9566a38ed9bb5  numpy-1.26.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
01d2abfe8e9b35415efb791ac6c5865e  numpy-1.26.2-cp39-cp39-musllinux_1_1_aarch64.whl
5a6d6ac287ebd93a221e59590329e202  numpy-1.26.2-cp39-cp39-musllinux_1_1_x86_64.whl
4e4e4d8cf661a8d2838ee700fabae87e  numpy-1.26.2-cp39-cp39-win32.whl
b8e52ecac110471502686abbdf774b78  numpy-1.26.2-cp39-cp39-win_amd64.whl
aed2d2914be293f60fedda360b64abf8  numpy-1.26.2-pp39-pypy39_pp73-macosx_10_9_x86_64.whl
6bd88e0f33933445d0e18c1a850f60e0  numpy-1.26.2-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
010aeb2a50af0af1f7ef56f76f8cf463  numpy-1.26.2-pp39-pypy39_pp73-win_amd64.whl
8f6446a32e47953a03f8fe8533e21e98  numpy-1.26.2.tar.gz

SHA256

3703fc9258a4a122d17043e57b35e5ef1c5a5837c3db8be396c82e04c1cf9b0f  numpy-1.26.2-cp310-cp310-macosx_10_9_x86_64.whl
cc392fdcbd21d4be6ae1bb4475a03ce3b025cd49a9be5345d76d7585aea69440  numpy-1.26.2-cp310-cp310-macosx_11_0_arm64.whl
36340109af8da8805d8851ef1d74761b3b88e81a9bd80b290bbfed61bd2b4f75  numpy-1.26.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
bcc008217145b3d77abd3e4d5ef586e3bdfba8fe17940769f8aa09b99e856c00  numpy-1.26.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
3ced40d4e9e18242f70dd02d739e44698df3dcb010d31f495ff00a31ef6014fe  numpy-1.26.2-cp310-cp310-musllinux_1_1_aarch64.whl
b272d4cecc32c9e19911891446b72e986157e6a1809b7b56518b4f3755267523  numpy-1.26.2-cp310-cp310-musllinux_1_1_x86_64.whl
22f8fc02fdbc829e7a8c578dd8d2e15a9074b630d4da29cda483337e300e3ee9  numpy-1.26.2-cp310-cp310-win32.whl
26c9d33f8e8b846d5a65dd068c14e04018d05533b348d9eaeef6c1bd787f9919  numpy-1.26.2-cp310-cp310-win_amd64.whl
b96e7b9c624ef3ae2ae0e04fa9b460f6b9f17ad8b4bec6d7756510f1f6c0c841  numpy-1.26.2-cp311-cp311-macosx_10_9_x86_64.whl
aa18428111fb9a591d7a9cc1b48150097ba6a7e8299fb56bdf574df650e7d1f1  numpy-1.26.2-cp311-cp311-macosx_11_0_arm64.whl
06fa1ed84aa60ea6ef9f91ba57b5ed963c3729534e6e54055fc151fad0423f0a  numpy-1.26.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
96ca5482c3dbdd051bcd1fce8034603d6ebfc125a7bd59f55b40d8f5d246832b  numpy-1.26.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
854ab91a2906ef29dc3925a064fcd365c7b4da743f84b123002f6139bcb3f8a7  numpy-1.26.2-cp311-cp311-musllinux_1_1_aarch64.whl
f43740ab089277d403aa07567be138fc2a89d4d9892d113b76153e0e412409f8  numpy-1.26.2-cp311-cp311-musllinux_1_1_x86_64.whl
a2bbc29fcb1771cd7b7425f98b05307776a6baf43035d3b80c4b0f29e9545186  numpy-1.26.2-cp311-cp311-win32.whl
2b3fca8a5b00184828d12b073af4d0fc5fdd94b1632c2477526f6bd7842d700d  numpy-1.26.2-cp311-cp311-win_amd64.whl
a4cd6ed4a339c21f1d1b0fdf13426cb3b284555c27ac2f156dfdaaa7e16bfab0  numpy-1.26.2-cp312-cp312-macosx_10_9_x86_64.whl
5d5244aabd6ed7f312268b9247be47343a654ebea52a60f002dc70c769048e75  numpy-1.26.2-cp312-cp312-macosx_11_0_arm64.whl
6a3cdb4d9c70e6b8c0814239ead47da00934666f668426fc6e94cce869e13fd7  numpy-1.26.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
aa317b2325f7aa0a9471663e6093c210cb2ae9c0ad824732b307d2c51983d5b6  numpy-1.26.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
174a8880739c16c925799c018f3f55b8130c1f7c8e75ab0a6fa9d41cab092fd6  numpy-1.26.2-cp312-cp312-musllinux_1_1_aarch64.whl
f79b231bf5c16b1f39c7f4875e1ded36abee1591e98742b05d8a0fb55d8a3eec  numpy-1.26.2-cp312-cp312-musllinux_1_1_x86_64.whl
4a06263321dfd3598cacb252f51e521a8cb4b6df471bb12a7ee5cbab20ea9167  numpy-1.26.2-cp312-cp312-win32.whl
b04f5dc6b3efdaab541f7857351aac359e6ae3c126e2edb376929bd3b7f92d7e  numpy-1.26.2-cp312-cp312-win_amd64.whl
4eb8df4bf8d3d90d091e0146f6c28492b0be84da3e409ebef54349f71ed271ef  numpy-1.26.2-cp39-cp39-macosx_10_9_x86_64.whl
1a13860fdcd95de7cf58bd6f8bc5a5ef81c0b0625eb2c9a783948847abbef2c2  numpy-1.26.2-cp39-cp39-macosx_11_0_arm64.whl
64308ebc366a8ed63fd0bf426b6a9468060962f1a4339ab1074c228fa6ade8e3  numpy-1.26.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
baf8aab04a2c0e859da118f0b38617e5ee65d75b83795055fb66c0d5e9e9b818  numpy-1.26.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
d73a3abcac238250091b11caef9ad12413dab01669511779bc9b29261dd50210  numpy-1.26.2-cp39-cp39-musllinux_1_1_aarch64.whl
b361d369fc7e5e1714cf827b731ca32bff8d411212fccd29ad98ad622449cc36  numpy-1.26.2-cp39-cp39-musllinux_1_1_x86_64.whl
bd3f0091e845164a20bd5a326860c840fe2af79fa12e0469a12768a3ec578d80  numpy-1.26.2-cp39-cp39-win32.whl
2beef57fb031dcc0dc8fa4fe297a742027b954949cabb52a2a376c144e5e6060  numpy-1.26.2-cp39-cp39-win_amd64.whl
1cc3d5029a30fb5f06704ad6b23b35e11309491c999838c31f124fee32107c79  numpy-1.26.2-pp39-pypy39_pp73-macosx_10_9_x86_64.whl
94cc3c222bb9fb5a12e334d0479b97bb2df446fbe622b470928f5284ffca3f8d  numpy-1.26.2-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
fe6b44fb8fcdf7eda4ef4461b97b3f63c466b27ab151bec2366db8b197387841  numpy-1.26.2-pp39-pypy39_pp73-win_amd64.whl
f65738447676ab5777f11e6bbbdb8ce11b785e105f690bc45966574816b6d3ea  numpy-1.26.2.tar.gz