Numpy

Latest version: v2.2.4

Safety actively analyzes 726274 Python packages for vulnerabilities to keep your Python projects secure.

Scan your dependencies

Page 11 of 24

1.22.0rc1

1.21.6

Not secure
- Backs out the mistaken backport of C++ code into 1.21.5.
- Provides a 32 bit Windows wheel for Python 3.10.

The provision of the 32 bit wheel is intended to make life easier for
oldest-supported-numpy.

Checksums

MD5

5a3e5d7298056bcfbc3246597af474d4 numpy-1.21.6-cp310-cp310-macosx_10_9_universal2.whl
d981d2859842e7b62dc93e24808c7bac numpy-1.21.6-cp310-cp310-macosx_10_9_x86_64.whl
171313893c26529404d09fadb3537ed3 numpy-1.21.6-cp310-cp310-macosx_11_0_arm64.whl
5a7a6dfdd43069f9b29d3fe6b7f3a2ce numpy-1.21.6-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
a9e25375a72725c5d74442eda53af405 numpy-1.21.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
6f9a782477380b2cdb7606f6f7634c00 numpy-1.21.6-cp310-cp310-win32.whl
32a73a348864700a3fa510d2fc4350b7 numpy-1.21.6-cp310-cp310-win_amd64.whl
0db8941ebeb0a02cd839d9cd3c5c20bb numpy-1.21.6-cp37-cp37m-macosx_10_9_x86_64.whl
67882155be9592850861f4ad8ba36623 numpy-1.21.6-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl
c70e30e1ff9ab49f898c19e7a6492ae6 numpy-1.21.6-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
e32dbd291032c7554a742f1bb9b2f7a3 numpy-1.21.6-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
689bf804c2cd16cb241fd943e3833ffd numpy-1.21.6-cp37-cp37m-win32.whl
0062a7b0231a07cb5b9f3d7c495e6fe4 numpy-1.21.6-cp37-cp37m-win_amd64.whl
0d08809980ab497659e7aa0df9ce120e numpy-1.21.6-cp38-cp38-macosx_10_9_universal2.whl
3c67d14ea2009069844b27bfbf74304d numpy-1.21.6-cp38-cp38-macosx_10_9_x86_64.whl
5f0e773745cb817313232ac1bf4c7eee numpy-1.21.6-cp38-cp38-macosx_11_0_arm64.whl
fa8011e065f1964d3eb870bb3926fc99 numpy-1.21.6-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl
486cf9d4daab59aad253aa5b84a5aa83 numpy-1.21.6-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
88509abab303c076dfb26f00e455180d numpy-1.21.6-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
f7234e2ef837f5f6ddbde8db246fd05b numpy-1.21.6-cp38-cp38-win32.whl
e1063e01fb44ea7a49adea0c33548217 numpy-1.21.6-cp38-cp38-win_amd64.whl
61c4caad729e3e0e688accbc1424ed45 numpy-1.21.6-cp39-cp39-macosx_10_9_universal2.whl
67488d8ccaeff798f2e314aae7c4c3d6 numpy-1.21.6-cp39-cp39-macosx_10_9_x86_64.whl
128c3713b5d1de45a0f522562bac5263 numpy-1.21.6-cp39-cp39-macosx_11_0_arm64.whl
50e79cd0610b4ed726b3bf08c3716dab numpy-1.21.6-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl
bd0c9e3c0e488faac61daf3227fb95af numpy-1.21.6-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
aa5e9baf1dec16b15e481c23f8a23214 numpy-1.21.6-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
a2405b0e5d3f775ad30177296a997092 numpy-1.21.6-cp39-cp39-win32.whl
f0d20eda8c78f957ea70c5527954303e numpy-1.21.6-cp39-cp39-win_amd64.whl
9682abbcc38cccb7f56e48aacca7de23 numpy-1.21.6-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
6aa3c2e8ea2886bf593bd8e0a1425c64 numpy-1.21.6.tar.gz
04aea95dcb1d256d13a45df42173aa1e numpy-1.21.6.zip

SHA256

8737609c3bbdd48e380d463134a35ffad3b22dc56295eff6f79fd85bd0eeeb25 numpy-1.21.6-cp310-cp310-macosx_10_9_universal2.whl
fdffbfb6832cd0b300995a2b08b8f6fa9f6e856d562800fea9182316d99c4e8e numpy-1.21.6-cp310-cp310-macosx_10_9_x86_64.whl
3820724272f9913b597ccd13a467cc492a0da6b05df26ea09e78b171a0bb9da6 numpy-1.21.6-cp310-cp310-macosx_11_0_arm64.whl
f17e562de9edf691a42ddb1eb4a5541c20dd3f9e65b09ded2beb0799c0cf29bb numpy-1.21.6-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
5f30427731561ce75d7048ac254dbe47a2ba576229250fb60f0fb74db96501a1 numpy-1.21.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
d4bf4d43077db55589ffc9009c0ba0a94fa4908b9586d6ccce2e0b164c86303c numpy-1.21.6-cp310-cp310-win32.whl
d136337ae3cc69aa5e447e78d8e1514be8c3ec9b54264e680cf0b4bd9011574f numpy-1.21.6-cp310-cp310-win_amd64.whl
6aaf96c7f8cebc220cdfc03f1d5a31952f027dda050e5a703a0d1c396075e3e7 numpy-1.21.6-cp37-cp37m-macosx_10_9_x86_64.whl
67c261d6c0a9981820c3a149d255a76918278a6b03b6a036800359aba1256d46 numpy-1.21.6-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl
a6be4cb0ef3b8c9250c19cc122267263093eee7edd4e3fa75395dfda8c17a8e2 numpy-1.21.6-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
7c4068a8c44014b2d55f3c3f574c376b2494ca9cc73d2f1bd692382b6dffe3db numpy-1.21.6-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
7c7e5fa88d9ff656e067876e4736379cc962d185d5cd808014a8a928d529ef4e numpy-1.21.6-cp37-cp37m-win32.whl
bcb238c9c96c00d3085b264e5c1a1207672577b93fa666c3b14a45240b14123a numpy-1.21.6-cp37-cp37m-win_amd64.whl
82691fda7c3f77c90e62da69ae60b5ac08e87e775b09813559f8901a88266552 numpy-1.21.6-cp38-cp38-macosx_10_9_universal2.whl
643843bcc1c50526b3a71cd2ee561cf0d8773f062c8cbaf9ffac9fdf573f83ab numpy-1.21.6-cp38-cp38-macosx_10_9_x86_64.whl
357768c2e4451ac241465157a3e929b265dfac85d9214074985b1786244f2ef3 numpy-1.21.6-cp38-cp38-macosx_11_0_arm64.whl
9f411b2c3f3d76bba0865b35a425157c5dcf54937f82bbeb3d3c180789dd66a6 numpy-1.21.6-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl
4aa48afdce4660b0076a00d80afa54e8a97cd49f457d68a4342d188a09451c1a numpy-1.21.6-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
d6a96eef20f639e6a97d23e57dd0c1b1069a7b4fd7027482a4c5c451cd7732f4 numpy-1.21.6-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
5c3c8def4230e1b959671eb959083661b4a0d2e9af93ee339c7dada6759a9470 numpy-1.21.6-cp38-cp38-win32.whl
bf2ec4b75d0e9356edea834d1de42b31fe11f726a81dfb2c2112bc1eaa508fcf numpy-1.21.6-cp38-cp38-win_amd64.whl
4391bd07606be175aafd267ef9bea87cf1b8210c787666ce82073b05f202add1 numpy-1.21.6-cp39-cp39-macosx_10_9_universal2.whl
67f21981ba2f9d7ba9ade60c9e8cbaa8cf8e9ae51673934480e45cf55e953673 numpy-1.21.6-cp39-cp39-macosx_10_9_x86_64.whl
ee5ec40fdd06d62fe5d4084bef4fd50fd4bb6bfd2bf519365f569dc470163ab0 numpy-1.21.6-cp39-cp39-macosx_11_0_arm64.whl
1dbe1c91269f880e364526649a52eff93ac30035507ae980d2fed33aaee633ac numpy-1.21.6-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl
d9caa9d5e682102453d96a0ee10c7241b72859b01a941a397fd965f23b3e016b numpy-1.21.6-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
58459d3bad03343ac4b1b42ed14d571b8743dc80ccbf27444f266729df1d6f5b numpy-1.21.6-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
7f5ae4f304257569ef3b948810816bc87c9146e8c446053539947eedeaa32786 numpy-1.21.6-cp39-cp39-win32.whl
e31f0bb5928b793169b87e3d1e070f2342b22d5245c755e2b81caa29756246c3 numpy-1.21.6-cp39-cp39-win_amd64.whl
dd1c8f6bd65d07d3810b90d02eba7997e32abbdf1277a481d698969e921a3be0 numpy-1.21.6-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
d4efc6491a1cdc00f9eca9bf2c1aa13671776f6941c7321ddf75b45c862f0c2c numpy-1.21.6.tar.gz
ecb55251139706669fdec2ff073c98ef8e9a84473e51e716211b41aa0f18e656 numpy-1.21.6.zip

1.21.5

Not secure
after the 1.21.4 release and does some maintenance to extend the 1.21.x
lifetime. The Python versions supported in this release are 3.7-3.10. If
you want to compile your own version using gcc-11, you will need to use
gcc-11.2+ to avoid problems.

Contributors

A total of 7 people contributed to this release. People with a \"+\" by
their names contributed a patch for the first time.

- Bas van Beek
- Charles Harris
- Matti Picus
- Rohit Goswami
- Ross Barnowski
- Sayed Adel
- Sebastian Berg

Pull requests merged

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

- [20357](https://github.com/numpy/numpy/pull/20357): MAINT: Do not forward `__(deep)copy__` calls of `_GenericAlias`\...
- [20462](https://github.com/numpy/numpy/pull/20462): BUG: Fix float16 einsum fastpaths using wrong tempvar
- [20463](https://github.com/numpy/numpy/pull/20463): BUG, DIST: Print os error message when the executable not exist
- [20464](https://github.com/numpy/numpy/pull/20464): BLD: Verify the ability to compile C++ sources before initiating\...
- [20465](https://github.com/numpy/numpy/pull/20465): BUG: Force `npymath` to respect `npy_longdouble`
- [20466](https://github.com/numpy/numpy/pull/20466): BUG: Fix failure to create aligned, empty structured dtype
- [20467](https://github.com/numpy/numpy/pull/20467): ENH: provide a convenience function to replace `npy_load_module`
- [20495](https://github.com/numpy/numpy/pull/20495): MAINT: update wheel to version that supports python3.10
- [20497](https://github.com/numpy/numpy/pull/20497): BUG: Clear errors correctly in F2PY conversions
- [20613](https://github.com/numpy/numpy/pull/20613): DEV: add a warningfilter to fix pytest workflow.
- [20618](https://github.com/numpy/numpy/pull/20618): MAINT: Help boost::python libraries at least not crash

Checksums

MD5

e00a3c2e1461dd2920ab4af6b753d3da numpy-1.21.5-cp310-cp310-macosx_10_9_universal2.whl
50e0526fa29110fb6033fa8285fba4e1 numpy-1.21.5-cp310-cp310-macosx_10_9_x86_64.whl
bdbb19e7656d66250aa67bd1c7924764 numpy-1.21.5-cp310-cp310-macosx_11_0_arm64.whl
c5c982a07797c8963b8fec44aae6db09 numpy-1.21.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
8b27b622f58caeeb7f14472651d655e3 numpy-1.21.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
e545f6f85f950f57606efcaeeac2e50a numpy-1.21.5-cp310-cp310-win_amd64.whl
5c36eefdcb039c0d4db8882fddbeb695 numpy-1.21.5-cp37-cp37m-macosx_10_9_x86_64.whl
b5d080e0fd8b658419b3636f1cf5dc3a numpy-1.21.5-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl
ec1a9a1333a2bf61897f105ecd9f212a numpy-1.21.5-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
d5ab050300748f20cdc9c6e17ba8ffd4 numpy-1.21.5-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
b7498a1d0ea7273ef1af56d58e02a550 numpy-1.21.5-cp37-cp37m-win32.whl
f55c7ecfd35769fb3f6a408c0c123372 numpy-1.21.5-cp37-cp37m-win_amd64.whl
843e3431ba4b56d3fc36b7c4cb6fc10c numpy-1.21.5-cp38-cp38-macosx_10_9_universal2.whl
4721e71bdc5697d310cd3a6b6cd60741 numpy-1.21.5-cp38-cp38-macosx_10_9_x86_64.whl
2169fb8ed40046e1e33d187fc85b91bb numpy-1.21.5-cp38-cp38-macosx_11_0_arm64.whl
52de43977749109509ee708a142a7d97 numpy-1.21.5-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl
703c0f54c5ede8cc0c648ef66cafac47 numpy-1.21.5-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
50432f9cf1d5b2278ceb7a96890353ed numpy-1.21.5-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
0c4c5336136e045d02c60ba8115eb6a2 numpy-1.21.5-cp38-cp38-win32.whl
c2e0744164f8255be70725ef42bc3f5b numpy-1.21.5-cp38-cp38-win_amd64.whl
b16dd7103117d051cb6c3b6c4434f7d2 numpy-1.21.5-cp39-cp39-macosx_10_9_universal2.whl
220dd07273aeb0b2ca8f0e4f543e43c3 numpy-1.21.5-cp39-cp39-macosx_10_9_x86_64.whl
1dd09ad75eff93b274f650871e0b9287 numpy-1.21.5-cp39-cp39-macosx_11_0_arm64.whl
6801263f51d3b13420b59ff84c716869 numpy-1.21.5-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl
035bde3955ae2f62ada65084d71a7421 numpy-1.21.5-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
09f202576cbd0ed6121cff10cdea831a numpy-1.21.5-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
c6a44c90c2d5124fea6cedbbf575e252 numpy-1.21.5-cp39-cp39-win32.whl
bbc11e31406a9fc48c18a41259bc8866 numpy-1.21.5-cp39-cp39-win_amd64.whl
5be2b6f6cf6fb3a3d98231e891260624 numpy-1.21.5-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
8bc9ff24bac9bf4268372cefea8f0b6b numpy-1.21.5.tar.gz
88b5438ded7992fa2e6a810d43cd32a1 numpy-1.21.5.zip

SHA256

301e408a052fdcda5cdcf03021ebafc3c6ea093021bf9d1aa47c54d48bdad166 numpy-1.21.5-cp310-cp310-macosx_10_9_universal2.whl
a7e8f6216f180f3fd4efb73de5d1eaefb5f5a1ee5b645c67333033e39440e63a numpy-1.21.5-cp310-cp310-macosx_10_9_x86_64.whl
fc7a7d7b0ed72589fd8b8486b9b42a564f10b8762be8bd4d9df94b807af4a089 numpy-1.21.5-cp310-cp310-macosx_11_0_arm64.whl
58ca1d7c8aef6e996112d0ce873ac9dfa1eaf4a1196b4ff7ff73880a09923ba7 numpy-1.21.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
dc4b2fb01f1b4ddbe2453468ea0719f4dbb1f5caa712c8b21bb3dd1480cd30d9 numpy-1.21.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
cc1b30205d138d1005adb52087ff45708febbef0e420386f58664f984ef56954 numpy-1.21.5-cp310-cp310-win_amd64.whl
08de8472d9f7571f9d51b27b75e827f5296295fa78817032e84464be8bb905bc numpy-1.21.5-cp37-cp37m-macosx_10_9_x86_64.whl
4fe6a006557b87b352c04596a6e3f12a57d6e5f401d804947bd3188e6b0e0e76 numpy-1.21.5-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl
3d893b0871322eaa2f8c7072cdb552d8e2b27645b7875a70833c31e9274d4611 numpy-1.21.5-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
341dddcfe3b7b6427a28a27baa59af5ad51baa59bfec3264f1ab287aa3b30b13 numpy-1.21.5-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
ca9c23848292c6fe0a19d212790e62f398fd9609aaa838859be8459bfbe558aa numpy-1.21.5-cp37-cp37m-win32.whl
025b497014bc33fc23897859350f284323f32a2fff7654697f5a5fc2a19e9939 numpy-1.21.5-cp37-cp37m-win_amd64.whl
3a5098df115340fb17fc93867317a947e1dcd978c3888c5ddb118366095851f8 numpy-1.21.5-cp38-cp38-macosx_10_9_universal2.whl
311283acf880cfcc20369201bd75da907909afc4666966c7895cbed6f9d2c640 numpy-1.21.5-cp38-cp38-macosx_10_9_x86_64.whl
b545ebadaa2b878c8630e5bcdb97fc4096e779f335fc0f943547c1c91540c815 numpy-1.21.5-cp38-cp38-macosx_11_0_arm64.whl
c5562bcc1a9b61960fc8950ade44d00e3de28f891af0acc96307c73613d18f6e numpy-1.21.5-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl
eed2afaa97ec33b4411995be12f8bdb95c87984eaa28d76cf628970c8a2d689a numpy-1.21.5-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
61bada43d494515d5b122f4532af226fdb5ee08fe5b5918b111279843dc6836a numpy-1.21.5-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
7b9d6b14fc9a4864b08d1ba57d732b248f0e482c7b2ff55c313137e3ed4d8449 numpy-1.21.5-cp38-cp38-win32.whl
dbce7adeb66b895c6aaa1fad796aaefc299ced597f6fbd9ceddb0dd735245354 numpy-1.21.5-cp38-cp38-win_amd64.whl
507c05c7a37b3683eb08a3ff993bd1ee1e6c752f77c2f275260533b265ecdb6c numpy-1.21.5-cp39-cp39-macosx_10_9_universal2.whl
00c9fa73a6989895b8815d98300a20ac993c49ac36c8277e8ffeaa3631c0dbbb numpy-1.21.5-cp39-cp39-macosx_10_9_x86_64.whl
69a5a8d71c308d7ef33ef72371c2388a90e3495dbb7993430e674006f94797d5 numpy-1.21.5-cp39-cp39-macosx_11_0_arm64.whl
2d8adfca843bc46ac199a4645233f13abf2011a0b2f4affc5c37cd552626f27b numpy-1.21.5-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl
c293d3c0321996cd8ffe84215ffe5d269fd9d1d12c6f4ffe2b597a7c30d3e593 numpy-1.21.5-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
3c978544be9e04ed12016dd295a74283773149b48f507d69b36f91aa90a643e5 numpy-1.21.5-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
2a9add27d7fc0fdb572abc3b2486eb3b1395da71e0254c5552b2aad2a18b5441 numpy-1.21.5-cp39-cp39-win32.whl
1964db2d4a00348b7a60ee9d013c8cb0c566644a589eaa80995126eac3b99ced numpy-1.21.5-cp39-cp39-win_amd64.whl
a7c4b701ca418cd39e28ec3b496e6388fe06de83f5f0cb74794fa31cfa384c02 numpy-1.21.5-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
1a7ee0ffb35dc7489aebe5185a483f4c43b0d2cf784c3c9940f975a7dde56506 numpy-1.21.5.tar.gz
6a5928bc6241264dce5ed509e66f33676fc97f464e7a919edc672fb5532221ee numpy-1.21.5.zip

1.21.4

Not secure
==========================

The NumPy 1.21.4 is a maintenance release that fixes a few bugs
discovered after 1.21.3. The most important fix here is a fix for the
NumPy header files to make them work for both x86\_64 and M1 hardware
when included in the Mac universal2 wheels. Previously, the header files
only worked for M1 and this caused problems for folks building x86\_64
extensions. This problem was not seen before Python 3.10 because there
were thin wheels for x86\_64 that had precedence. This release also
provides thin x86\_64 Mac wheels for Python 3.10.

The Python versions supported in this release are 3.7-3.10. If you want
to compile your own version using gcc-11, you will need to use gcc-11.2+
to avoid problems.

Contributors
------------

A total of 7 people contributed to this release. People with a \"+\" by
their names contributed a patch for the first time.

- Bas van Beek
- Charles Harris
- Isuru Fernando
- Matthew Brett
- Sayed Adel
- Sebastian Berg
- 傅立业(Chris Fu) +

Pull requests merged
--------------------

A total of 9 pull requests were merged for this release.

- [\20278](https://github.com/numpy/numpy/pull/20278): BUG: Fix shadowed reference of `dtype` in type stub
- [\20293](https://github.com/numpy/numpy/pull/20293): BUG: Fix headers for universal2 builds
- [\20294](https://github.com/numpy/numpy/pull/20294): BUG: `VOID_nonzero` could sometimes mutate alignment flag
- [\20295](https://github.com/numpy/numpy/pull/20295): BUG: Do not use nonzero fastpath on unaligned arrays
- [\20296](https://github.com/numpy/numpy/pull/20296): BUG: Distutils patch to allow for 2 as a minor version (!)
- [\20297](https://github.com/numpy/numpy/pull/20297): BUG, SIMD: Fix 64-bit/8-bit integer division by a scalar
- [\20298](https://github.com/numpy/numpy/pull/20298): BUG, SIMD: Workaround broadcasting SIMD 64-bit integers on MSVC\...
- [\20300](https://github.com/numpy/numpy/pull/20300): REL: Prepare for the NumPy 1.21.4 release.
- [\20302](https://github.com/numpy/numpy/pull/20302): TST: Fix a `Arrayterator` typing test failure

Checksums
---------

MD5

95486a3ed027c926fb3fc279db6d843e numpy-1.21.4-cp310-cp310-macosx_10_9_universal2.whl
9f57fad74762f7665669af33583a3dc9 numpy-1.21.4-cp310-cp310-macosx_10_9_x86_64.whl
719a9053aef01a067ce44ede2281eef9 numpy-1.21.4-cp310-cp310-macosx_11_0_arm64.whl
72035d101774fd03beff391927f59aa9 numpy-1.21.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
5813e7a378a6e3f5c269c23f61eff4d9 numpy-1.21.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
b88a1bc4f08dfb154d5a07d15e387af6 numpy-1.21.4-cp310-cp310-win_amd64.whl
f0cc946d2f4ab4df7cc7e0cc8cfd429e numpy-1.21.4-cp37-cp37m-macosx_10_9_x86_64.whl
1234643306ce481f0e5f801ddf3f1099 numpy-1.21.4-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl
b9208ce1695ba61ab2932c7ce7285d1d numpy-1.21.4-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
9804fe2011618bf2d7b8d92f6860b2e3 numpy-1.21.4-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
2ad3a06f974acd61326fd66c098df5bc numpy-1.21.4-cp37-cp37m-win32.whl
172301389f1532b2d9130362580e1e22 numpy-1.21.4-cp37-cp37m-win_amd64.whl
a037bf88979ae0d4699a0cdce92bbab3 numpy-1.21.4-cp38-cp38-macosx_10_9_universal2.whl
ba94609688f575cc8dce84f1512db116 numpy-1.21.4-cp38-cp38-macosx_10_9_x86_64.whl
c78edc0ae8c9a5d8d0f9e3eb6dabd0b3 numpy-1.21.4-cp38-cp38-macosx_11_0_arm64.whl
d683b6f6af46806391579d528a040451 numpy-1.21.4-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl
df631f776716aeb3fd705f3659599b9e numpy-1.21.4-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
b1cbca49d24c7ba43d377feb425afdce numpy-1.21.4-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
8b5c214bc0f060dbb0287c15dde4673d numpy-1.21.4-cp38-cp38-win32.whl
2307cf9f3c02f6cdad448a681c272974 numpy-1.21.4-cp38-cp38-win_amd64.whl
fc02b5a068e29b2dd2de19c7ddd69926 numpy-1.21.4-cp39-cp39-macosx_10_9_universal2.whl
f16068540001de8a3d8f096830c97ea2 numpy-1.21.4-cp39-cp39-macosx_10_9_x86_64.whl
80562c39cfbdf1af9bb43b2ea5e45b6d numpy-1.21.4-cp39-cp39-macosx_11_0_arm64.whl
6c103bec3085e5a6ea92cf7f6e4189ab numpy-1.21.4-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl
9d715ba5f7596a39eb631f2dae85d203 numpy-1.21.4-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
8b8cf8c7b093419ff75ed1dd2eaa18ae numpy-1.21.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
404200b858b7addd03f6cdd5a484d30a numpy-1.21.4-cp39-cp39-win32.whl
cdab6a1bf1b86021526d08a60219a6ad numpy-1.21.4-cp39-cp39-win_amd64.whl
70ca6b591e844fdcb8c22175f094d3b4 numpy-1.21.4-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
06019c1116b3e2791bd507f898257e7f numpy-1.21.4.tar.gz
b3c4477a027d5b6fba5e1065064fd076 numpy-1.21.4.zip

SHA256

8890b3360f345e8360133bc078d2dacc2843b6ee6059b568781b15b97acbe39f numpy-1.21.4-cp310-cp310-macosx_10_9_universal2.whl
69077388c5a4b997442b843dbdc3a85b420fb693ec8e33020bb24d647c164fa5 numpy-1.21.4-cp310-cp310-macosx_10_9_x86_64.whl
e89717274b41ebd568cd7943fc9418eeb49b1785b66031bc8a7f6300463c5898 numpy-1.21.4-cp310-cp310-macosx_11_0_arm64.whl
0b78ecfa070460104934e2caf51694ccd00f37d5e5dbe76f021b1b0b0d221823 numpy-1.21.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
615d4e328af7204c13ae3d4df7615a13ff60a49cb0d9106fde07f541207883ca numpy-1.21.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
1403b4e2181fc72664737d848b60e65150f272fe5a1c1cbc16145ed43884065a numpy-1.21.4-cp310-cp310-win_amd64.whl
74b85a17528ca60cf98381a5e779fc0264b4a88b46025e6bcbe9621f46bb3e63 numpy-1.21.4-cp37-cp37m-macosx_10_9_x86_64.whl
92aafa03da8658609f59f18722b88f0a73a249101169e28415b4fa148caf7e41 numpy-1.21.4-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl
5d95668e727c75b3f5088ec7700e260f90ec83f488e4c0aaccb941148b2cd377 numpy-1.21.4-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
f5162ec777ba7138906c9c274353ece5603646c6965570d82905546579573f73 numpy-1.21.4-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
81225e58ef5fce7f1d80399575576fc5febec79a8a2742e8ef86d7b03beef49f numpy-1.21.4-cp37-cp37m-win32.whl
32fe5b12061f6446adcbb32cf4060a14741f9c21e15aaee59a207b6ce6423469 numpy-1.21.4-cp37-cp37m-win_amd64.whl
c449eb870616a7b62e097982c622d2577b3dbc800aaf8689254ec6e0197cbf1e numpy-1.21.4-cp38-cp38-macosx_10_9_universal2.whl
2e4ed57f45f0aa38beca2a03b6532e70e548faf2debbeb3291cfc9b315d9be8f numpy-1.21.4-cp38-cp38-macosx_10_9_x86_64.whl
1247ef28387b7bb7f21caf2dbe4767f4f4175df44d30604d42ad9bd701ebb31f numpy-1.21.4-cp38-cp38-macosx_11_0_arm64.whl
34f3456f530ae8b44231c63082c8899fe9c983fd9b108c997c4b1c8c2d435333 numpy-1.21.4-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl
4c9c23158b87ed0e70d9a50c67e5c0b3f75bcf2581a8e34668d4e9d7474d76c6 numpy-1.21.4-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
e4799be6a2d7d3c33699a6f77201836ac975b2e1b98c2a07f66a38f499cb50ce numpy-1.21.4-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
bc988afcea53e6156546e5b2885b7efab089570783d9d82caf1cfd323b0bb3dd numpy-1.21.4-cp38-cp38-win32.whl
170b2a0805c6891ca78c1d96ee72e4c3ed1ae0a992c75444b6ab20ff038ba2cd numpy-1.21.4-cp38-cp38-win_amd64.whl
fde96af889262e85aa033f8ee1d3241e32bf36228318a61f1ace579df4e8170d numpy-1.21.4-cp39-cp39-macosx_10_9_universal2.whl
c885bfc07f77e8fee3dc879152ba993732601f1f11de248d4f357f0ffea6a6d4 numpy-1.21.4-cp39-cp39-macosx_10_9_x86_64.whl
9e6f5f50d1eff2f2f752b3089a118aee1ea0da63d56c44f3865681009b0af162 numpy-1.21.4-cp39-cp39-macosx_11_0_arm64.whl
ad010846cdffe7ec27e3f933397f8a8d6c801a48634f419e3d075db27acf5880 numpy-1.21.4-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl
c74c699b122918a6c4611285cc2cad4a3aafdb135c22a16ec483340ef97d573c numpy-1.21.4-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
9864424631775b0c052f3bd98bc2712d131b3e2cd95d1c0c68b91709170890b0 numpy-1.21.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
b1e2312f5b8843a3e4e8224b2b48fe16119617b8fc0a54df8f50098721b5bed2 numpy-1.21.4-cp39-cp39-win32.whl
e3c3e990274444031482a31280bf48674441e0a5b55ddb168f3a6db3e0c38ec8 numpy-1.21.4-cp39-cp39-win_amd64.whl
a3deb31bc84f2b42584b8c4001c85d1934dbfb4030827110bc36bfd11509b7bf numpy-1.21.4-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
5d412381aa489b8be82ac5c6a9e99c3eb3f754245ad3f90ab5c339d92f25fb47 numpy-1.21.4.tar.gz
e6c76a87633aa3fa16614b61ccedfae45b91df2767cf097aa9c933932a7ed1e0 numpy-1.21.4.zip

1.21.3

Not secure
==========================

The NumPy 1.21.3 is a maintenance release the fixes a few bugs
discovered after 1.21.2. It also provides 64 bit Python 3.10.0 wheels.
Note a few oddities about Python 3.10:

- There are no 32 bit wheels for Windows, Mac, or Linux.
- The Mac Intel builds are only available in universal2 wheels.

The Python versions supported in this release are 3.7-3.10. If you want
to compile your own version using gcc-11 you will need to use gcc-11.2+
to avoid problems.

Contributors
------------

A total of 7 people contributed to this release. People with a \"+\" by
their names contributed a patch for the first time.

- Aaron Meurer
- Bas van Beek
- Charles Harris
- Developer-Ecosystem-Engineering +
- Kevin Sheppard
- Sebastian Berg
- Warren Weckesser

Pull requests merged
--------------------

A total of 8 pull requests were merged for this release.

- [\19745](https://github.com/numpy/numpy/pull/19745): ENH: Add dtype-support to 3 `` `generic ``/`ndarray` methods
- [\19955](https://github.com/numpy/numpy/pull/19955): BUG: Resolve Divide by Zero on Apple silicon + test failures\...
- [\19958](https://github.com/numpy/numpy/pull/19958): MAINT: Mark type-check-only ufunc subclasses as ufunc aliases\...
- [\19994](https://github.com/numpy/numpy/pull/19994): BUG: np.tan(np.inf) test failure
- [\20080](https://github.com/numpy/numpy/pull/20080): BUG: Correct incorrect advance in PCG with emulated int128
- [\20081](https://github.com/numpy/numpy/pull/20081): BUG: Fix NaT handling in the PyArray\_CompareFunc for datetime\...
- [\20082](https://github.com/numpy/numpy/pull/20082): DOC: Ensure that we add documentation also as to the dict for\...
- [\20106](https://github.com/numpy/numpy/pull/20106): BUG: core: result\_type(0, np.timedelta64(4)) would seg. fault.

Checksums
---------

MD5

9acea9630856659ba48fdb582ecc37b4 numpy-1.21.3-cp310-cp310-macosx_10_9_universal2.whl
a70f80a4e74a3153a8307c4f0ea8d13d numpy-1.21.3-cp310-cp310-macosx_11_0_arm64.whl
13cfe83efd261ea1c3d1eb02c1d3af83 numpy-1.21.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
8576bfd867834182269f72abbaa2e81e numpy-1.21.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
8ac48f503f1e22c0c2b5d056772aca27 numpy-1.21.3-cp310-cp310-win_amd64.whl
cbe0d0d7623de3c2c7593f673d1a880a numpy-1.21.3-cp37-cp37m-macosx_10_9_x86_64.whl
0967b18baba13e511c7eb48902a62b39 numpy-1.21.3-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl
da54c9566f3e3f8c7d60efebfdf7e1ae numpy-1.21.3-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
0aa000f3c10cf74bf47770577384b5c8 numpy-1.21.3-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
5683501bf91be25c53c52e3b083098c3 numpy-1.21.3-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl
89e15d979533f8a314e0ab0648ee7153 numpy-1.21.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl
a093fea475b5ed18bd21b3c79e68e388 numpy-1.21.3-cp37-cp37m-win32.whl
f906001213ed0902b1aecfaa12224e94 numpy-1.21.3-cp37-cp37m-win_amd64.whl
88a2cd378412220d618473dd273baf04 numpy-1.21.3-cp38-cp38-macosx_10_9_universal2.whl
1bc55202f604e30f338bc2ed27b561bc numpy-1.21.3-cp38-cp38-macosx_10_9_x86_64.whl
9555dc6de8748958434e8f2feba98494 numpy-1.21.3-cp38-cp38-macosx_11_0_arm64.whl
93ad32cc87866e9242156bdadc61e5f5 numpy-1.21.3-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl
7cb0b7dd6aee667ecdccae1829260186 numpy-1.21.3-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
34e6f5f9e9534ef8772f024170c2bd2d numpy-1.21.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
54e6abfb8f600de2ccd1649b1fca820b numpy-1.21.3-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl
260ba58f2dc64e779eac7318ec92f36c numpy-1.21.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl
889202c6bdaf8c1ae0803925e9e1a8f7 numpy-1.21.3-cp38-cp38-win32.whl
980303a7e6317faf9a56ba8fc80795d9 numpy-1.21.3-cp38-cp38-win_amd64.whl
44d6bd26fb910710ab4002d0028c9020 numpy-1.21.3-cp39-cp39-macosx_10_9_universal2.whl
6f5b02152bd0b08a77b79657788ce59c numpy-1.21.3-cp39-cp39-macosx_10_9_x86_64.whl
ad05d5c412d15e7880cd65cc6cdd4aac numpy-1.21.3-cp39-cp39-macosx_11_0_arm64.whl
5b61a91221931af4a78c3bd20925a91f numpy-1.21.3-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl
df7344ae04c5a54249fa1b63a256ce61 numpy-1.21.3-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
c653a096da47b64b42e8f1536a21f7d4 numpy-1.21.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
e0d35451ba1c37f96e032bc6f75ccdf7 numpy-1.21.3-cp39-cp39-win32.whl
b2e1dc59b6fa224ce11728d94be740a6 numpy-1.21.3-cp39-cp39-win_amd64.whl
8ce925a0fcbc1062985026215d369276 numpy-1.21.3-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
b8e6b7165f105bde0b45cd9ae34bfe20 numpy-1.21.3.tar.gz
59d986f5ccf3edfb7d4d14949c6666ed numpy-1.21.3.zip

SHA256

508b0b513fa1266875524ba8a9ecc27b02ad771fe1704a16314dc1a816a68737 numpy-1.21.3-cp310-cp310-macosx_10_9_universal2.whl
5dfe9d6a4c39b8b6edd7990091fea4f852888e41919d0e6722fe78dd421db0eb numpy-1.21.3-cp310-cp310-macosx_11_0_arm64.whl
8a10968963640e75cc0193e1847616ab4c718e83b6938ae74dea44953950f6b7 numpy-1.21.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
49c6249260890e05b8111ebfc391ed58b3cb4b33e63197b2ec7f776e45330721 numpy-1.21.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
f8f4625536926a155b80ad2bbff44f8cc59e9f2ad14cdda7acf4c135b4dc8ff2 numpy-1.21.3-cp310-cp310-win_amd64.whl
e54af82d68ef8255535a6cdb353f55d6b8cf418a83e2be3569243787a4f4866f numpy-1.21.3-cp37-cp37m-macosx_10_9_x86_64.whl
f41b018f126aac18583956c54544db437f25c7ee4794bcb23eb38bef8e5e192a numpy-1.21.3-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl
50cd26b0cf6664cb3b3dd161ba0a09c9c1343db064e7c69f9f8b551f5104d654 numpy-1.21.3-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
4cc9b512e9fb590797474f58b7f6d1f1b654b3a94f4fa8558b48ca8b3cfc97cf numpy-1.21.3-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
88a5d6b268e9ad18f3533e184744acdaa2e913b13148160b1152300c949bbb5f numpy-1.21.3-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl
3c09418a14471c7ae69ba682e2428cae5b4420a766659605566c0fa6987f6b7e numpy-1.21.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl
90bec6a86b348b4559b6482e2b684db4a9a7eed1fa054b86115a48d58fbbf62a numpy-1.21.3-cp37-cp37m-win32.whl
043e83bfc274649c82a6f09836943e4a4aebe5e33656271c7dbf9621dd58b8ec numpy-1.21.3-cp37-cp37m-win_amd64.whl
75621882d2230ab77fb6a03d4cbccd2038511491076e7964ef87306623aa5272 numpy-1.21.3-cp38-cp38-macosx_10_9_universal2.whl
188031f833bbb623637e66006cf75e933e00e7231f67e2b45cf8189612bb5dc3 numpy-1.21.3-cp38-cp38-macosx_10_9_x86_64.whl
160ccc1bed3a8371bf0d760971f09bfe80a3e18646620e9ded0ad159d9749baa numpy-1.21.3-cp38-cp38-macosx_11_0_arm64.whl
29fb3dcd0468b7715f8ce2c0c2d9bbbaf5ae686334951343a41bd8d155c6ea27 numpy-1.21.3-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl
32437f0b275c1d09d9c3add782516413e98cd7c09e6baf4715cbce781fc29912 numpy-1.21.3-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
e606e6316911471c8d9b4618e082635cfe98876007556e89ce03d52ff5e8fcf0 numpy-1.21.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
a99a6b067e5190ac6d12005a4d85aa6227c5606fa93211f86b1dafb16233e57d numpy-1.21.3-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl
dde972a1e11bb7b702ed0e447953e7617723760f420decb97305e66fb4afc54f numpy-1.21.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl
fe52dbe47d9deb69b05084abd4b0df7abb39a3c51957c09f635520abd49b29dd numpy-1.21.3-cp38-cp38-win32.whl
75eb7cadc8da49302f5b659d40ba4f6d94d5045fbd9569c9d058e77b0514c9e4 numpy-1.21.3-cp38-cp38-win_amd64.whl
2a6ee9620061b2a722749b391c0d80a0e2ae97290f1b32e28d5a362e21941ee4 numpy-1.21.3-cp39-cp39-macosx_10_9_universal2.whl
5c4193f70f8069550a1788bd0cd3268ab7d3a2b70583dfe3b2e7f421e9aace06 numpy-1.21.3-cp39-cp39-macosx_10_9_x86_64.whl
28f15209fb535dd4c504a7762d3bc440779b0e37d50ed810ced209e5cea60d96 numpy-1.21.3-cp39-cp39-macosx_11_0_arm64.whl
c6c2d535a7beb1f8790aaa98fd089ceab2e3dd7ca48aca0af7dc60e6ef93ffe1 numpy-1.21.3-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl
bffa2eee3b87376cc6b31eee36d05349571c236d1de1175b804b348dc0941e3f numpy-1.21.3-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
cc14e7519fab2a4ed87d31f99c31a3796e4e1fe63a86ebdd1c5a1ea78ebd5896 numpy-1.21.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
dd0482f3fc547f1b1b5d6a8b8e08f63fdc250c58ce688dedd8851e6e26cff0f3 numpy-1.21.3-cp39-cp39-win32.whl
300321e3985c968e3ae7fbda187237b225f3ffe6528395a5b7a5407f73cf093e numpy-1.21.3-cp39-cp39-win_amd64.whl
98339aa9911853f131de11010f6dd94c8cec254d3d1f7261528c3b3e3219f139 numpy-1.21.3-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
d0bba24083c01ae43457514d875f10d9ce4c1125d55b1e2573277b2410f2d068 numpy-1.21.3.tar.gz
63571bb7897a584ca3249c86dd01c10bcb5fe4296e3568b2e9c1a55356b6410e numpy-1.21.3.zip

1.21.2

Not secure
==========================

The NumPy 1.21.2 is maintenance release that fixes bugs discovered after
1.21.1. It also provides 64 bit manylinux Python 3.10.0rc1 wheels for
downstream testing. Note that Python 3.10 is not yet final. There is
also preliminary support for Windows on ARM64 builds, but there is no
OpenBLAS for that platform and no wheels are available.

The Python versions supported for this release are 3.7-3.9. The 1.21.x
series is compatible with Python 3.10.0rc1 and Python 3.10 will be
officially supported after it is released. The previous problems with
gcc-11.1 have been fixed by gcc-11.2, check your version if you are
using gcc-11.

Contributors
------------

A total of 10 people contributed to this release. People with a \"+\" by
their names contributed a patch for the first time.

- Bas van Beek
- Carl Johnsen +
- Charles Harris
- Gwyn Ciesla +
- Matthieu Dartiailh
- Matti Picus
- Niyas Sait +
- Ralf Gommers
- Sayed Adel
- Sebastian Berg

Pull requests merged
--------------------

A total of 18 pull requests were merged for this release.

- [\19497](https://github.com/numpy/numpy/pull/19497): MAINT: set Python version for 1.21.x to `<3.11`
- [\19533](https://github.com/numpy/numpy/pull/19533): BUG: Fix an issue wherein importing `numpy.typing` could raise
- [\19646](https://github.com/numpy/numpy/pull/19646): MAINT: Update Cython version for Python 3.10.
- [\19648](https://github.com/numpy/numpy/pull/19648): TST: Bump the python 3.10 test version from beta4 to rc1
- [\19651](https://github.com/numpy/numpy/pull/19651): TST: avoid distutils.sysconfig in runtests.py
- [\19652](https://github.com/numpy/numpy/pull/19652): MAINT: add missing dunder method to nditer type hints
- [\19656](https://github.com/numpy/numpy/pull/19656): BLD, SIMD: Fix testing extra checks when `-Werror` isn\'t applicable\...
- [\19657](https://github.com/numpy/numpy/pull/19657): BUG: Remove logical object ufuncs with bool output
- [\19658](https://github.com/numpy/numpy/pull/19658): MAINT: Include .coveragerc in source distributions to support\...
- [\19659](https://github.com/numpy/numpy/pull/19659): BUG: Fix bad write in masked iterator output copy paths
- [\19660](https://github.com/numpy/numpy/pull/19660): ENH: Add support for windows on arm targets
- [\19661](https://github.com/numpy/numpy/pull/19661): BUG: add base to templated arguments for platlib
- [\19662](https://github.com/numpy/numpy/pull/19662): BUG,DEP: Non-default UFunc signature/dtype usage should be deprecated
- [\19666](https://github.com/numpy/numpy/pull/19666): MAINT: Add Python 3.10 to supported versions.
- [\19668](https://github.com/numpy/numpy/pull/19668): TST,BUG: Sanitize path-separators when running `runtest.py`
- [\19671](https://github.com/numpy/numpy/pull/19671): BLD: load extra flags when checking for libflame
- [\19676](https://github.com/numpy/numpy/pull/19676): BLD: update circleCI docker image
- [\19677](https://github.com/numpy/numpy/pull/19677): REL: Prepare for 1.21.2 release.

Checksums
---------

MD5

c4d72c5f8aff59b5e48face558441e9f numpy-1.21.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
eb09d0bfc0bc39ce3e323182ae779fcb numpy-1.21.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
e0bb19ea8cc13a5152085aa42d850077 numpy-1.21.2-cp37-cp37m-macosx_10_9_x86_64.whl
af7d21992179dfa3669a2a238b94a980 numpy-1.21.2-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl
9acbaf0074af75d66ca8676b16cec03a numpy-1.21.2-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
86b755c7ece248e5586a6a58259aa432 numpy-1.21.2-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
b45fbbb0ffabcabcc6dc4cf957713d45 numpy-1.21.2-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl
6f23a3050b1482f9708d36928348d75d numpy-1.21.2-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl
ee45e263e6700b745c43511297385fe1 numpy-1.21.2-cp37-cp37m-win32.whl
6f587dc9ee9ec8700e77df4f3f987911 numpy-1.21.2-cp37-cp37m-win_amd64.whl
e500c1eae3903b7498886721b835d086 numpy-1.21.2-cp38-cp38-macosx_10_9_universal2.whl
ddef2b45ff5526e6314205108f2e3524 numpy-1.21.2-cp38-cp38-macosx_10_9_x86_64.whl
66b5a212ee2fe747cfc19f13dbfc2d15 numpy-1.21.2-cp38-cp38-macosx_11_0_arm64.whl
3ebfe9bcd744c57d3d189394fbbf04de numpy-1.21.2-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl
155a35f990b2e673cb7b361c83fa2313 numpy-1.21.2-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
89e2268d8607b6b363337fafde9fe6c9 numpy-1.21.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
e13968b5f61a3b2f33d4053da8ceaaf1 numpy-1.21.2-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl
5bede1a84624d538d97513006f97fc06 numpy-1.21.2-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl
351b5115ee56f1b598bfa9b479a2492c numpy-1.21.2-cp38-cp38-win32.whl
8a36334d9d183b1ef3e4d3d23b7d0cb8 numpy-1.21.2-cp38-cp38-win_amd64.whl
b6aee8cf57f84da10b38566bde93056c numpy-1.21.2-cp39-cp39-macosx_10_9_universal2.whl
20beaff42d793cb148621e0230d1b650 numpy-1.21.2-cp39-cp39-macosx_10_9_x86_64.whl
6e348361f3b8b75267dc27f3a6530944 numpy-1.21.2-cp39-cp39-macosx_11_0_arm64.whl
809bcd25dc485f31e2c13903d6ac748e numpy-1.21.2-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl
ff4256d8940c6bdce48364af37f99072 numpy-1.21.2-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
b8b19e6667e39feef9f7f2e030945199 numpy-1.21.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
eedae53f1929779387476e7842dc5cb3 numpy-1.21.2-cp39-cp39-win32.whl
704f66b7ede6778283c33eea7a5b8b95 numpy-1.21.2-cp39-cp39-win_amd64.whl
8c5d2a0172f6f6861833a355b1bc57b0 numpy-1.21.2-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
55c11984b0a0ae28baa118052983f355 numpy-1.21.2.tar.gz
5638d5dae3ca387be562912312db842e numpy-1.21.2.zip

SHA256

52a664323273c08f3b473548bf87c8145b7513afd63e4ebba8496ecd3853df13 numpy-1.21.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
51a7b9db0a2941434cd930dacaafe0fc9da8f3d6157f9d12f761bbde93f46218 numpy-1.21.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
9f2dc79c093f6c5113718d3d90c283f11463d77daa4e83aeeac088ec6a0bda52 numpy-1.21.2-cp37-cp37m-macosx_10_9_x86_64.whl
a55e4d81c4260386f71d22294795c87609164e22b28ba0d435850fbdf82fc0c5 numpy-1.21.2-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl
426a00b68b0d21f2deb2ace3c6d677e611ad5a612d2c76494e24a562a930c254 numpy-1.21.2-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
298156f4d3d46815eaf0fcf0a03f9625fc7631692bd1ad851517ab93c3168fc6 numpy-1.21.2-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
09858463db6dd9f78b2a1a05c93f3b33d4f65975771e90d2cf7aadb7c2f66edf numpy-1.21.2-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl
805459ad8baaf815883d0d6f86e45b3b0b67d823a8f3fa39b1ed9c45eaf5edf1 numpy-1.21.2-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl
f545c082eeb09ae678dd451a1b1dbf17babd8a0d7adea02897a76e639afca310 numpy-1.21.2-cp37-cp37m-win32.whl
b160b9a99ecc6559d9e6d461b95c8eec21461b332f80267ad2c10394b9503496 numpy-1.21.2-cp37-cp37m-win_amd64.whl
a5109345f5ce7ddb3840f5970de71c34a0ff7fceb133c9441283bb8250f532a3 numpy-1.21.2-cp38-cp38-macosx_10_9_universal2.whl
209666ce9d4a817e8a4597cd475b71b4878a85fa4b8db41d79fdb4fdee01dde2 numpy-1.21.2-cp38-cp38-macosx_10_9_x86_64.whl
c01b59b33c7c3ba90744f2c695be571a3bd40ab2ba7f3d169ffa6db3cfba614f numpy-1.21.2-cp38-cp38-macosx_11_0_arm64.whl
e42029e184008a5fd3d819323345e25e2337b0ac7f5c135b7623308530209d57 numpy-1.21.2-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl
7fdc7689daf3b845934d67cb221ba8d250fdca20ac0334fea32f7091b93f00d3 numpy-1.21.2-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
550564024dc5ceee9421a86fc0fb378aa9d222d4d0f858f6669eff7410c89bef numpy-1.21.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
bf75d5825ef47aa51d669b03ce635ecb84d69311e05eccea083f31c7570c9931 numpy-1.21.2-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl
a9da45b748caad72ea4a4ed57e9cd382089f33c5ec330a804eb420a496fa760f numpy-1.21.2-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl
e167b9805de54367dcb2043519382be541117503ce99e3291cc9b41ca0a83557 numpy-1.21.2-cp38-cp38-win32.whl
466e682264b14982012887e90346d33435c984b7fead7b85e634903795c8fdb0 numpy-1.21.2-cp38-cp38-win_amd64.whl
dd0e3651d210068d13e18503d75aaa45656eef51ef0b261f891788589db2cc38 numpy-1.21.2-cp39-cp39-macosx_10_9_universal2.whl
92a0ab128b07799dd5b9077a9af075a63467d03ebac6f8a93e6440abfea4120d numpy-1.21.2-cp39-cp39-macosx_10_9_x86_64.whl
fde50062d67d805bc96f1a9ecc0d37bfc2a8f02b937d2c50824d186aa91f2419 numpy-1.21.2-cp39-cp39-macosx_11_0_arm64.whl
640c1ccfd56724f2955c237b6ccce2e5b8607c3bc1cc51d3933b8c48d1da3723 numpy-1.21.2-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl
5de64950137f3a50b76ce93556db392e8f1f954c2d8207f78a92d1f79aa9f737 numpy-1.21.2-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
b342064e647d099ca765f19672696ad50c953cac95b566af1492fd142283580f numpy-1.21.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
30fc68307c0155d2a75ad19844224be0f2c6f06572d958db4e2053f816b859ad numpy-1.21.2-cp39-cp39-win32.whl
b5e8590b9245803c849e09bae070a8e1ff444f45e3f0bed558dd722119eea724 numpy-1.21.2-cp39-cp39-win_amd64.whl
d96a6a7d74af56feb11e9a443150216578ea07b7450f7c05df40eec90af7f4a7 numpy-1.21.2-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
76af194fbc117934ec5bbe2ff15177adbd05aeed23f18ee209ed88edcd777e05 numpy-1.21.2.tar.gz
423216d8afc5923b15df86037c6053bf030d15cc9e3224206ef868c2d63dd6dc numpy-1.21.2.zip

Page 11 of 24

© 2025 Safety CLI Cybersecurity Inc. All Rights Reserved.