v1.21.2

NumPy 1.21.2 Release Notes

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

v1.21.1

NumPy 1.21.1 Release Notes

The NumPy 1.21.1 is maintenance release that fixes bugs discovered after
the 1.21.0 release and updates OpenBLAS to v0.3.17 to deal with problems
on arm64.

The Python versions supported for this release are 3.7-3.9. The 1.21.x
series is compatible with development Python 3.10. Python 3.10 will be
officially supported after it is released.

⚠️ There are unresolved problems compiling NumPy 1.20.0 with gcc-11.1.

• Optimization level -O3 results in many incorrect
  warnings when running the tests.
• On some hardware NumPY will hang in an infinite loop.

Contributors

A total of 11 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.

• Bas van Beek
• Charles Harris
• Ganesh Kathiresan
• Gregory R. Lee
• Hugo Defois +
• Kevin Sheppard
• Matti Picus
• Ralf Gommers
• Sayed Adel
• Sebastian Berg
• Thomas J. Fan

Pull requests merged

A total of 26 pull requests were merged for this release.

• #19311: REV,BUG: Replace NotImplemented with typing.Any
• #19324: MAINT: Fixed the return-dtype of ndarray.real and imag
• #19330: MAINT: Replace "dtype[Any]" with dtype in the definiton of...
• #19342: DOC: Fix some docstrings that crash pdf generation.
• #19343: MAINT: bump scipy-mathjax
• #19347: BUG: Fix arr.flat.index for large arrays and big-endian machines
• #19348: ENH: add numpy.f2py.get_include function
• #19349: BUG: Fix reference count leak in ufunc dtype handling
• #19350: MAINT: Annotate missing attributes of np.number subclasses
• #19351: BUG: Fix cast safety and comparisons for zero sized voids
• #19352: BUG: Correct Cython declaration in random
• #19353: BUG: protect against accessing base attribute of a NULL subarray
• #19365: BUG, SIMD: Fix detecting AVX512 features on Darwin
• #19366: MAINT: remove print()'s in distutils template handling
• #19390: ENH: SIMD architectures to show_config
• #19391: BUG: Do not raise deprecation warning for all nans in unique...
• #19392: BUG: Fix NULL special case in object-to-any cast code
• #19430: MAINT: Use arm64-graviton2 for testing on travis
• #19495: BUILD: update OpenBLAS to v0.3.17
• #19496: MAINT: Avoid unicode characters in division SIMD code comments
• #19499: BUG, SIMD: Fix infinite loop during count non-zero on GCC-11
• #19500: BUG: fix a numpy.npiter leak in npyiter_multi_index_set
• #19501: TST: Fix a GenericAlias test failure for python 3.9.0
• #19502: MAINT: Start testing with Python 3.10.0b3.
• #19503: MAINT: Add missing dtype overloads for object- and ctypes-based...
• #19510: REL: Prepare for NumPy 1.21.1 release.

Checksums

MD5

d88af78c155cb92ce5535724ed13ed73  numpy-1.21.1-cp37-cp37m-macosx_10_9_x86_64.whl
946e54ec9d174ec90db8ae07a4c4ae2f  numpy-1.21.1-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl
84d7f8534fa3ce1a8c2e2eab18e514de  numpy-1.21.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
2e256d7862047967f2a7dbff8b8e9d6c  numpy-1.21.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
4887ff09cc0652f3f1d9e0f40d1add63  numpy-1.21.1-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl
bbe00679ce0ae484bb46776f64e00e32  numpy-1.21.1-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl
b8eff5ba6bb920f3e65409abcfe7a55e  numpy-1.21.1-cp37-cp37m-win32.whl
d6ab781ad4537a818663a37392bdf647  numpy-1.21.1-cp37-cp37m-win_amd64.whl
f974f7a90567e082b16817e1218eb059  numpy-1.21.1-cp38-cp38-macosx_10_9_universal2.whl
37fb814042195516db4c5eedc23f65ef  numpy-1.21.1-cp38-cp38-macosx_10_9_x86_64.whl
2840e0ed51c8ebfb6fded7f1acfed810  numpy-1.21.1-cp38-cp38-macosx_11_0_arm64.whl
d87ed548450f324a3a6a3a230991e90a  numpy-1.21.1-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl
e5e0e271fb18986887920f24b9ad8ec3  numpy-1.21.1-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
f060727f195388df3f3c1e2c43a8d247  numpy-1.21.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
26b0cc05d6f59241f401c16a6fe9300e  numpy-1.21.1-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl
dac4489fdaeffd24d402a555e61b4087  numpy-1.21.1-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl
c248a8f07bb458660274eab769dcc1e2  numpy-1.21.1-cp38-cp38-win32.whl
52386872b66b108de80b5447d0e3f6b1  numpy-1.21.1-cp38-cp38-win_amd64.whl
1a730aa7303421f31c2bca5a343010bb  numpy-1.21.1-cp39-cp39-macosx_10_9_universal2.whl
141701393752d472456d4a15f9a554e4  numpy-1.21.1-cp39-cp39-macosx_10_9_x86_64.whl
33a9c001675f708aebc06f0a653378c1  numpy-1.21.1-cp39-cp39-macosx_11_0_arm64.whl
6b9482c5090f532285313ad2cf48d319  numpy-1.21.1-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl
94fa7591ad4e51a85cb17bcec170b986  numpy-1.21.1-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
f580b2ce2fb9cead163bab3f1d88fba7  numpy-1.21.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
238930d877b5d8a012b5b1bbc994ebb1  numpy-1.21.1-cp39-cp39-win32.whl
4014c63ac2a1c3e1df95f76feb14816e  numpy-1.21.1-cp39-cp39-win_amd64.whl
7cff22c1a04fdee710d38bd9468edbf1  numpy-1.21.1-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
033726e7ec59eea6b23307dcec35a37b  numpy-1.21.1.tar.gz
1d016e05851a4ba85307f3246eb569aa  numpy-1.21.1.zip

SHA256

38e8648f9449a549a7dfe8d8755a5979b45b3538520d1e735637ef28e8c2dc50  numpy-1.21.1-cp37-cp37m-macosx_10_9_x86_64.whl
fd7d7409fa643a91d0a05c7554dd68aa9c9bb16e186f6ccfe40d6e003156e33a  numpy-1.21.1-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl
a75b4498b1e93d8b700282dc8e655b8bd559c0904b3910b144646dbbbc03e062  numpy-1.21.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
1412aa0aec3e00bc23fbb8664d76552b4efde98fb71f60737c83efbac24112f1  numpy-1.21.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
e46ceaff65609b5399163de5893d8f2a82d3c77d5e56d976c8b5fb01faa6b671  numpy-1.21.1-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl
c6a2324085dd52f96498419ba95b5777e40b6bcbc20088fddb9e8cbb58885e8e  numpy-1.21.1-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl
73101b2a1fef16602696d133db402a7e7586654682244344b8329cdcbbb82172  numpy-1.21.1-cp37-cp37m-win32.whl
7a708a79c9a9d26904d1cca8d383bf869edf6f8e7650d85dbc77b041e8c5a0f8  numpy-1.21.1-cp37-cp37m-win_amd64.whl
95b995d0c413f5d0428b3f880e8fe1660ff9396dcd1f9eedbc311f37b5652e16  numpy-1.21.1-cp38-cp38-macosx_10_9_universal2.whl
635e6bd31c9fb3d475c8f44a089569070d10a9ef18ed13738b03049280281267  numpy-1.21.1-cp38-cp38-macosx_10_9_x86_64.whl
4a3d5fb89bfe21be2ef47c0614b9c9c707b7362386c9a3ff1feae63e0267ccb6  numpy-1.21.1-cp38-cp38-macosx_11_0_arm64.whl
8a326af80e86d0e9ce92bcc1e65c8ff88297de4fa14ee936cb2293d414c9ec63  numpy-1.21.1-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl
791492091744b0fe390a6ce85cc1bf5149968ac7d5f0477288f78c89b385d9af  numpy-1.21.1-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
0318c465786c1f63ac05d7c4dbcecd4d2d7e13f0959b01b534ea1e92202235c5  numpy-1.21.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
9a513bd9c1551894ee3d31369f9b07460ef223694098cf27d399513415855b68  numpy-1.21.1-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl
91c6f5fc58df1e0a3cc0c3a717bb3308ff850abdaa6d2d802573ee2b11f674a8  numpy-1.21.1-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl
978010b68e17150db8765355d1ccdd450f9fc916824e8c4e35ee620590e234cd  numpy-1.21.1-cp38-cp38-win32.whl
9749a40a5b22333467f02fe11edc98f022133ee1bfa8ab99bda5e5437b831214  numpy-1.21.1-cp38-cp38-win_amd64.whl
d7a4aeac3b94af92a9373d6e77b37691b86411f9745190d2c351f410ab3a791f  numpy-1.21.1-cp39-cp39-macosx_10_9_universal2.whl
d9e7912a56108aba9b31df688a4c4f5cb0d9d3787386b87d504762b6754fbb1b  numpy-1.21.1-cp39-cp39-macosx_10_9_x86_64.whl
25b40b98ebdd272bc3020935427a4530b7d60dfbe1ab9381a39147834e985eac  numpy-1.21.1-cp39-cp39-macosx_11_0_arm64.whl
8a92c5aea763d14ba9d6475803fc7904bda7decc2a0a68153f587ad82941fec1  numpy-1.21.1-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl
05a0f648eb28bae4bcb204e6fd14603de2908de982e761a2fc78efe0f19e96e1  numpy-1.21.1-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
f01f28075a92eede918b965e86e8f0ba7b7797a95aa8d35e1cc8821f5fc3ad6a  numpy-1.21.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
88c0b89ad1cc24a5efbb99ff9ab5db0f9a86e9cc50240177a571fbe9c2860ac2  numpy-1.21.1-cp39-cp39-win32.whl
01721eefe70544d548425a07c80be8377096a54118070b8a62476866d5208e33...

v1.21.0

NumPy 1.21.0 Release Notes

The NumPy 1.21.0 release highlights are

• continued SIMD work covering more functions and platforms,
• initial work on the new dtype infrastructure and casting,
• universal2 wheels for Python 3.8 and Python 3.9 on Mac,
• improved documentation,
• improved annotations,
• new PCG64DXSM bitgenerator for random numbers.

In addition there are the usual large number of bug fixes and other
improvements.

The Python versions supported for this release are 3.7-3.9. Official
support for Python 3.10 will be added when it is released.

⚠️ Warning: there are unresolved problems compiling NumPy 1.21.0 with gcc-11.1 .

• Optimization level -O3 results in many wrong warnings when running the tests.
• On some hardware NumPy will hang in an infinite loop.

New functions

Add PCG64DXSM BitGenerator

Uses of the PCG64 BitGenerator in a massively-parallel context have
been shown to have statistical weaknesses that were not apparent at the
first release in numpy 1.17. Most users will never observe this weakness
and are safe to continue to use PCG64. We have introduced a new
PCG64DXSM BitGenerator that will eventually become the new default
BitGenerator implementation used by default_rng in future releases.
PCG64DXSM solves the statistical weakness while preserving the
performance and the features of PCG64.

See upgrading-pcg64 for more details.

(gh-18906)

Expired deprecations

• The shape argument numpy.unravel_index cannot be
  passed as dims keyword argument anymore. (Was deprecated in NumPy
  1.16.)

  (gh-17900)

• The function PyUFunc_GenericFunction has been disabled. It was
  deprecated in NumPy 1.19. Users should call the ufunc directly using
  the Python API.

  (gh-18697)

• The function PyUFunc_SetUsesArraysAsData has been disabled. It was
  deprecated in NumPy 1.19.

  (gh-18697)

• The class PolyBase has been removed (deprecated in numpy 1.9.0).
  Please use the abstract ABCPolyBase class instead.

  (gh-18963)

• The unused PolyError and PolyDomainError exceptions are removed.

  (gh-18963)

Deprecations

The .dtype attribute must return a dtype

A DeprecationWarning is now given if the .dtype attribute of an
object passed into np.dtype or as a dtype=obj argument is not a
dtype. NumPy will stop attempting to recursively coerce the result of
.dtype.

(gh-13578)

Inexact matches for numpy.convolve and numpy.correlate are deprecated

numpy.convolve and numpy.correlate now
emit a warning when there are case insensitive and/or inexact matches
found for mode argument in the functions. Pass full "same",
"valid", "full" strings instead of "s", "v", "f" for the
mode argument.

(gh-17492)

np.typeDict has been formally deprecated

np.typeDict is a deprecated alias for np.sctypeDict and has been so
for over 14 years
(6689502).
A deprecation warning will now be issued whenever getting np.typeDict.

(gh-17586)

Exceptions will be raised during array-like creation

When an object raised an exception during access of the special
attributes __array__ or __array_interface__, this exception was
usually ignored. A warning is now given when the exception is anything
but AttributeError. To silence the warning, the type raising the
exception has to be adapted to raise an AttributeError.

(gh-19001)

Four ndarray.ctypes methods have been deprecated

Four methods of the ndarray.ctypes object have been
deprecated, as they are (undocumentated) implementation artifacts of
their respective properties.

The methods in question are:

• _ctypes.get_data (use _ctypes.data instead)
• _ctypes.get_shape (use _ctypes.shape instead)
• _ctypes.get_strides (use _ctypes.strides instead)
• _ctypes.get_as_parameter (use _ctypes._as_parameter_ instead)

(gh-19031)

Expired deprecations

• The shape argument numpy.unravel_index] cannot be
  passed as dims keyword argument anymore. (Was deprecated in NumPy
  1.16.)

  (gh-17900)

• The function PyUFunc_GenericFunction has been disabled. It was
  deprecated in NumPy 1.19. Users should call the ufunc directly using
  the Python API.

  (gh-18697)

• The function PyUFunc_SetUsesArraysAsData has been disabled. It was
  deprecated in NumPy 1.19.

  (gh-18697)

Remove deprecated PolyBase and unused PolyError and PolyDomainError

The class PolyBase has been removed (deprecated in numpy 1.9.0).
Please use the abstract ABCPolyBase class instead.

Furthermore, the unused PolyError and PolyDomainError exceptions are
removed from the numpy.polynomial.

(gh-18963)

Compatibility notes

Error type changes in universal functions

The universal functions may now raise different errors on invalid input
in some cases. The main changes should be that a RuntimeError was
replaced with a more fitting TypeError. When multiple errors were
present in the same call, NumPy may now raise a different one.

(gh-15271)

__array_ufunc__ argument validation

NumPy will now partially validate arguments before calling
__array_ufunc__. Previously, it was possible to pass on invalid
arguments (such as a non-existing keyword argument) when dispatch was
known to occur.

(gh-15271)

__array_ufunc__ and additional positional arguments

Previously, all positionally passed arguments were checked for
__array_ufunc__ support. In the case of reduce, accumulate, and
reduceat all arguments may be passed by position. This means that when
they were passed by position, they could previously have been asked to
handle the ufunc call via __array_ufunc__. Since this depended on the
way the arguments were passed (by position or by keyword), NumPy will
now only dispatch on the input and output array. For example, NumPy will
never dispatch on the where array in a reduction such as
np.add.reduce.

(gh-15271)

Validate input values in Generator.uniform

Checked that high - low >= 0 in np.random.Generator.uniform. Raises
ValueError if low > high. Previously out-of-order inputs were
accepted and silently swapped, so that if low > high, the value
generated was high + (low - high) * random().

(gh-17921)

/usr/include removed from default include paths

The default include paths when building a package with numpy.distutils
no longer include /usr/include. This path is normally added by the
compiler, and hardcoding it can be problematic. In case this causes a
problem, please open an issue. A workaround is documented in PR 18658.

(gh-18658)

Changes to comparisons with dtype=...

When the dtype= (or signature) arguments to comparison ufuncs
(equal, less, etc.) is used, this will denote the desired output
dtype in the future. This means that:

np.equal(2, 3, dtype=object)

will give a FutureWarning that it will return an object array in the
future, which currently happens for:

np.equal(None, None, dtype=object)

due to the fact that np.array(None) is already an object array. (This
also happens for some other dtypes.)

Since comparisons normally only return boolean arrays, providing any
other dtype will always raise an error in the future and give a
DeprecationWarning now.

(gh-18718)

Changes to dtype and signature arguments in ufuncs

The universal function arguments dtype and signature which are also
valid for reduction such as np.add.reduce (which is the implementation
for np.sum) will now issue a warning when the dtype provided is not
a "basic" dtype.

NumPy almost always ignored metadata, byteorder or time units on these
inputs. NumPy will now always ignore it and raise an error if byteorder
or time unit changed. The following are the most important examples of
changes which will give the error. In some cases previously the
information stored was not ignored, in all of these an error is now
raised:

# Previously ignored the byte-order (affect if non-native)
np.add(3, 5, dtype=">i32")

# The biggest impact is for timedelta or datetimes:
arr = np.arange(10, dtype="m8[s]")

# The examples always ignored the time unit "ns":
np.add(arr, arr, dtype="m8[ns]")
np.maximum.reduce(arr, dtype="m8[ns]")

# The following previously did use "ns" (as opposed to `arr.dtype`)
np.add(3, 5, dtype="m8[ns]")  # Now return generic time units
np.maximum(arr, arr, dtype="m8[ns]")  # Now returns "s" (from `arr`)

The same applies for functions like np.sum which use these internally.
This change is nece...

v1.21.0rc2

NumPy 1.21.0 Release Notes

The NumPy 1.21.0 release highlights are

• continued SIMD work covering more functions and platforms,
• initial work on the new dtype infrastructure and casting,
• universal2 wheels for Python 3.8 and Python 3.9 on Mac,
• improved documentation,
• improved annotations,
• new PCG64DXSM bitgenerator for random numbers.

In addition there are the usual large number of bug fixes and other
improvements.

The Python versions supported for this release are 3.7-3.9. Official
support for Python 3.10 will be added when it is released.

New functions

Add PCG64DXSM BitGenerator

Uses of the PCG64 BitGenerator in a massively-parallel context have
been shown to have statistical weaknesses that were not apparent at the
first release in numpy 1.17. Most users will never observe this weakness
and are safe to continue to use PCG64. We have introduced a new
PCG64DXSM BitGenerator that will eventually become the new default
BitGenerator implementation used by default_rng in future releases.
PCG64DXSM solves the statistical weakness while preserving the
performance and the features of PCG64.

See upgrading-pcg64 for more details.

(gh-18906)

Expired deprecations

• The shape argument numpy.unravel_index cannot be
  passed as dims keyword argument anymore. (Was deprecated in NumPy
  1.16.)

  (gh-17900)

• The function PyUFunc_GenericFunction has been disabled. It was
  deprecated in NumPy 1.19. Users should call the ufunc directly using
  the Python API.

  (gh-18697)

• The function PyUFunc_SetUsesArraysAsData has been disabled. It was
  deprecated in NumPy 1.19.

  (gh-18697)

• The class PolyBase has been removed (deprecated in numpy 1.9.0).
  Please use the abstract ABCPolyBase class instead.

  (gh-18963)

• The unused PolyError and PolyDomainError exceptions are removed.

  (gh-18963)

Deprecations

Inexact matches for numpy.convolve and numpy.correlate are deprecated

numpy.convolve and numpy.correlate now
emit a warning when there are case insensitive and/or inexact matches
found for mode argument in the functions. Pass full "same",
"valid", "full" strings instead of "s", "v", "f" for the
mode argument.

(gh-17492)

np.typeDict has been formally deprecated

np.typeDict is a deprecated alias for np.sctypeDict and has been so
for over 14 years
(6689502).
A deprecation warning will now be issued whenever getting np.typeDict.

(gh-17586)

Exceptions will be raised during array-like creation

When an object raised an exception during access of the special
attributes __array__ or __array_interface__, this exception was
usually ignored. A warning is now given when the exception is anything
but AttributeError. To silence the warning, the type raising the
exception has to be adapted to raise an AttributeError.

(gh-19001)

Four ndarray.ctypes methods have been deprecated

Four methods of the ndarray.ctypes object have been
deprecated, as they are (undocumentated) implementation artifacts of
their respective properties.

The methods in question are:

• _ctypes.get_data (use _ctypes.data instead)
• _ctypes.get_shape (use _ctypes.shape instead)
• _ctypes.get_strides (use _ctypes.strides instead)
• _ctypes.get_as_parameter (use _ctypes._as_parameter_ instead)

(gh-19031)

Expired deprecations

• The shape argument numpy.unravel_index] cannot be
  passed as dims keyword argument anymore. (Was deprecated in NumPy
  1.16.)

  (gh-17900)

• The function PyUFunc_GenericFunction has been disabled. It was
  deprecated in NumPy 1.19. Users should call the ufunc directly using
  the Python API.

  (gh-18697)

• The function PyUFunc_SetUsesArraysAsData has been disabled. It was
  deprecated in NumPy 1.19.

  (gh-18697)

Remove deprecated PolyBase and unused PolyError and PolyDomainError

The class PolyBase has been removed (deprecated in numpy 1.9.0).
Please use the abstract ABCPolyBase class instead.

Furthermore, the unused PolyError and PolyDomainError exceptions are
removed from the numpy.polynomial.

(gh-18963)

Compatibility notes

Error type changes in universal functions

The universal functions may now raise different errors on invalid input
in some cases. The main changes should be that a RuntimeError was
replaced with a more fitting TypeError. When multiple errors were
present in the same call, NumPy may now raise a different one.

(gh-15271)

__array_ufunc__ argument validation

NumPy will now partially validate arguments before calling
__array_ufunc__. Previously, it was possible to pass on invalid
arguments (such as a non-existing keyword argument) when dispatch was
known to occur.

(gh-15271)

__array_ufunc__ and additional positional arguments

Previously, all positionally passed arguments were checked for
__array_ufunc__ support. In the case of reduce, accumulate, and
reduceat all arguments may be passed by position. This means that when
they were passed by position, they could previously have been asked to
handle the ufunc call via __array_ufunc__. Since this depended on the
way the arguments were passed (by position or by keyword), NumPy will
now only dispatch on the input and output array. For example, NumPy will
never dispatch on the where array in a reduction such as
np.add.reduce.

(gh-15271)

Validate input values in Generator.uniform

Checked that high - low >= 0 in np.random.Generator.uniform. Raises
ValueError if low > high. Previously out-of-order inputs were
accepted and silently swapped, so that if low > high, the value
generated was high + (low - high) * random().

(gh-17921)

/usr/include removed from default include paths

The default include paths when building a package with numpy.distutils
no longer include /usr/include. This path is normally added by the
compiler, and hardcoding it can be problematic. In case this causes a
problem, please open an issue. A workaround is documented in PR 18658.

(gh-18658)

Changes to comparisons with dtype=...

When the dtype= (or signature) arguments to comparison ufuncs
(equal, less, etc.) is used, this will denote the desired output
dtype in the future. This means that:

np.equal(2, 3, dtype=object)

will give a FutureWarning that it will return an object array in the
future, which currently happens for:

np.equal(None, None, dtype=object)

due to the fact that np.array(None) is already an object array. (This
also happens for some other dtypes.)

Since comparisons normally only return boolean arrays, providing any
other dtype will always raise an error in the future and give a
DeprecationWarning now.

(gh-18718)

Changes to dtype and signature arguments in ufuncs

The universal function arguments dtype and signature which are also
valid for reduction such as np.add.reduce (which is the implementation
for np.sum) will now issue a warning when the dtype provided is not
a "basic" dtype.

NumPy almost always ignored metadata, byteorder or time units on these
inputs. NumPy will now always ignore it and raise an error if byteorder
or time unit changed. The following are the most important examples of
changes which will give the error. In some cases previously the
information stored was not ignored, in all of these an error is now
raised:

# Previously ignored the byte-order (affect if non-native)
np.add(3, 5, dtype=">i32")

# The biggest impact is for timedelta or datetimes:
arr = np.arange(10, dtype="m8[s]")

# The examples always ignored the time unit "ns":
np.add(arr, arr, dtype="m8[ns]")
np.maximum.reduce(arr, dtype="m8[ns]")

# The following previously did use "ns" (as opposed to `arr.dtype`)
np.add(3, 5, dtype="m8[ns]")  # Now return generic time units
np.maximum(arr, arr, dtype="m8[ns]")  # Now returns "s" (from `arr`)

The same applies for functions like np.sum which use these internally.
This change is necessary to achieve consistent handling within NumPy.

If you run into these, in most cases pass for example
dtype=np.timedelta64 which clearly denotes a general timedelta64
without any unit or byte-order defined. If you need to specify the
output dtype precisely, you may do so by either casting the inputs or
providing an output array using out=.

NumPy may choose to allow providing an exact output dtype here in the
future, which would be preceded by a FutureWarning.

(gh-18718)

Ufunc `signature=.....

v1.21.0rc1

NumPy 1.21.0 Release Notes

The NumPy 1.21.0 release highlights are

• continued SIMD work covering more functions and platforms,
• initial work on the new dtype infrastructure and casting,
• improved documentation,
• improved annotations,
• the new PCG64DXSM bitgenerator for random numbers.

In addition there are the usual large number of bug fixes and other
improvements.

The Python versions supported for this release are 3.7-3.9. Official
support for Python 3.10 will be added when it is released.

New functions

Add PCG64DXSM BitGenerator

Uses of the PCG64 BitGenerator in a massively-parallel context have
been shown to have statistical weaknesses that were not apparent at the
first release in numpy 1.17. Most users will never observe this weakness
and are safe to continue to use PCG64. We have introduced a new
PCG64DXSM BitGenerator that will eventually become the new default
BitGenerator implementation used by default_rng in future releases.
PCG64DXSM solves the statistical weakness while preserving the
performance and the features of PCG64.

See upgrading-pcg64{.interpreted-text role="ref"} for more details.

(gh-18906)

Expired deprecations

• The shape argument of numpy.unravel_index cannot be
  passed as dims keyword argument anymore. (Was deprecated in NumPy
  1.16.)

  (gh-17900)

• The function PyUFunc_GenericFunction has been disabled. It was
  deprecated in NumPy 1.19. Users should call the ufunc directly using
  the Python API.

  (gh-18697)

• The function PyUFunc_SetUsesArraysAsData has been disabled. It was
  deprecated in NumPy 1.19.

  (gh-18697)

• The class PolyBase has been removed (deprecated in numpy 1.9.0).
  Please use the abstract ABCPolyBase class instead.

  (gh-18963)

• The unused PolyError and PolyDomainError exceptions are removed.

  (gh-18963)

Deprecations

Inexact matches for numpy.convolve and numpy.correlate are deprecated

numpy.convolve and numpy.correlate now
emit a warning when there are case insensitive and/or inexact matches
found for mode argument in the functions. Pass full "same",
"valid", "full" strings instead of "s", "v", "f" for the
mode argument.

(gh-17492)

np.typeDict has been formally deprecated

np.typeDict is a deprecated alias for np.sctypeDict and has been so
for over 14 years (6689502).
A deprecation warning will now be issued whenever getting np.typeDict.

(gh-17586)

Exceptions will be raised during array-like creation

When an object raised an exception during access of the special
attributes __array__ or __array_interface__, this exception was
usually ignored. A warning is now given when the exception is anything
but AttributeError. To silence the warning, the type raising the
exception has to be adapted to raise an AttributeError.

(gh-19001)

Four ndarray.ctypes methods have been deprecated

Four methods of the ndarray.ctypes object have been
deprecated, as they are (undocumentated) implementation artifacts of
their respective properties.

The methods in question are:

• _ctypes.get_data (use _ctypes.data instead)
• _ctypes.get_shape (use _ctypes.shape instead)
• _ctypes.get_strides (use _ctypes.strides instead)
• _ctypes.get_as_parameter (use _ctypes._as_parameter_ instead)

(gh-19031)

Future Changes

Promotion of strings with numbers and bools will be deprecated

Any promotion of numbers and strings is deprecated and will give a
FutureWarning the main affected functionalities are:

• numpy.promote_types and
  numpy.result_type which will raise an error in this
  case in the future.
• numpy.concatenate will raise an error when
  concatenating a string and numeric array. You can use dtype="S" to
  explicitly request a string result.
• numpy.array and related functions will start
  returning object arrays because these functions use object as a
  fallback when no common dtype can be found. However, it may happen
  that future releases of NumPy will generally error in these cases.

This will mainly affect code such as:

np.asarray(['string', 0])

and:

np.concatenate((['string'], [0]))

in both cases adding dtype="U" or dtype="S" will give the previous
(string) result, while dtype=object will ensure an array with object
dtype is returned.

Comparisons, universal functions, and casting are not affected by this.

(gh-18116)

Compatibility notes

Error type changes in universal functions

The universal functions may now raise different errors on invalid input
in some cases. The main changes should be that a RuntimeError was
replaced with a more fitting TypeError. When multiple errors were
present in the same call, NumPy may now raise a different one.

(gh-15271)

__array_ufunc__ argument validation

NumPy will now partially validate arguments before calling
__array_ufunc__. Previously, it was possible to pass on invalid
arguments (such as a non-existing keyword argument) when dispatch was
known to occur.

(gh-15271)

__array_ufunc__ and additional positional arguments

Previously, all positionally passed arguments were checked for
__array_ufunc__ support. In the case of reduce, accumulate, and
reduceat all arguments may be passed by position. This means that when
they were passed by position, they could previously have been asked to
handle the ufunc call via __array_ufunc__. Since this depended on the
way the arguments were passed (by position or by keyword), NumPy will
now only dispatch on the input and output array. For example, NumPy will
never dispatch on the where array in a reduction such as
np.add.reduce.

(gh-15271)

Validate input values in Generator.uniform

Checked that high - low >= 0 in np.random.Generator.uniform. Raises
ValueError if low > high. Previously out-of-order inputs were
accepted and silently swapped, so that if low > high, the value
generated was high + (low - high) * random().

(gh-17921)

/usr/include removed from default include paths

The default include paths when building a package with numpy.distutils
no longer include /usr/include. This path is normally added by the
compiler, and hardcoding it can be problematic. In case this causes a
problem, please open an issue. A workaround is documented in PR 18658.

(gh-18658)

Changes to comparisons with dtype=...

When the dtype= (or signature) arguments to comparison ufuncs
(equal, less, etc.) is used, this will denote the desired output
dtype in the future. This means that:

np.equal(2, 3, dtype=object)

will give a FutureWarning that it will return an object array in the
future, which currently happens for:

np.equal(None, None, dtype=object)

due to the fact that np.array(None) is already an object array. (This
also happens for some other dtypes.)

Since comparisons normally only return boolean arrays, providing any
other dtype will always raise an error in the future and give a
DeprecationWarning now.

(gh-18718)

Changes to dtype and signature arguments in ufuncs

The universal function arguments dtype and signature which are also
valid for reduction such as np.add.reduce (which is the implementation
for np.sum) will now issue a warning when the dtype provided is not
a "basic" dtype.

NumPy almost always ignored metadata, byteorder or time units on these
inputs. NumPy will now always ignore it and raise an error if byteorder
or time unit changed. The following are the most important examples of
changes which will give the error. In some cases previously the
information stored was not ignored, in all of these an error is now
raised:

# Previously ignored the byte-order (affect if non-native)
np.add(3, 5, dtype=">i32")

# The biggest impact is for timedelta or datetimes:
arr = np.arange(10, dtype="m8[s]")
# The examples always ignored the time unit "ns":
np.add(arr, arr, dtype="m8[ns]")
np.maximum.reduce(arr, dtype="m8[ns]")

# The following previously did use "ns" (as opposed to `arr.dtype`)
np.add(3, 5, dtype="m8[ns]")  # Now return generic time units
np.maximum(arr, arr, dtype="m8[ns]")  # Now returns "s" (from `arr`)

The same applies for functions like np.sum which use these internally.
This change is necessary to achieve consistent handling within NumPy.

If you run into these, in most cases pass for example
dtype=np.timedelta64 which clearly denotes a general timedelta64
without any unit or byte-order defined. If you need to specify the
output dtype precisely, you may do so by either casting the inputs or
providing an output array using out=.

NumPy may choose to allow providing an exact out...

v1.20.3

NumPy 1.20.3 Release Notes

NumPy 1.20.3 is a bugfix release containing several fixes merged to the
main branch after the NumPy 1.20.2 release.

Contributors

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

• Anne Archibald
• Bas van Beek
• Charles Harris
• Dong Keun Oh +
• Kamil Choudhury +
• Sayed Adel
• Sebastian Berg

Pull requests merged

A total of 15 pull requests were merged for this release.

• #18763: BUG: Correct datetime64 missing type overload for datetime.date...
• #18764: MAINT: Remove __all__ in favor of explicit re-exports
• #18768: BLD: Strip extra newline when dumping gfortran version on MacOS
• #18769: BUG: fix segfault in object/longdouble operations
• #18794: MAINT: Use towncrier build explicitly
• #18887: MAINT: Relax certain integer-type constraints
• #18915: MAINT: Remove unsafe unions and ABCs from return-annotations
• #18921: MAINT: Allow more recursion depth for scalar tests.
• #18922: BUG: Initialize the full nditer buffer in case of error
• #18923: BLD: remove unnecessary flag -faltivec on macOS
• #18924: MAINT, CI: treats _SIMD module build warnings as errors through...
• #18925: BUG: for MINGW, threads.h existence test requires GLIBC > 2.12
• #18941: BUG: Make changelog recognize gh- as a PR number prefix.
• #18948: REL, DOC: Prepare for the NumPy 1.20.3 release.
• #18953: BUG: Fix failing mypy test in 1.20.x.

Checksums

MD5

702d0185042f1ff9a5d7e72a29f4e1c0  numpy-1.20.3-cp37-cp37m-macosx_10_9_x86_64.whl
3d0284b39b20c243b74f6690ad5ae27f  numpy-1.20.3-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl
d1b42dd67dc228088cf822eaab86d424  numpy-1.20.3-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
126b1a5d46cc7d9b9b426f56d075a1e0  numpy-1.20.3-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
5b0445346f08b610025dbd2064d4b482  numpy-1.20.3-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl
02bd4a2c764882e8af353c16344cb633  numpy-1.20.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl
0f6a36724d5477c8fca6c34e73683db6  numpy-1.20.3-cp37-cp37m-win32.whl
c7d3ae93743d6c0ea2c9dfcad1d42cb4  numpy-1.20.3-cp37-cp37m-win_amd64.whl
445da50ae14b3318170ccf996baca72c  numpy-1.20.3-cp38-cp38-macosx_10_9_x86_64.whl
c651fdb4829703e164bc78613c1a90a8  numpy-1.20.3-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl
c9411ef729b8ebe9ed3b8e9dee3da4ac  numpy-1.20.3-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
ff69ad241598607fdfea24155625a6e3  numpy-1.20.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
9fd8d44d8a5f19e434e9dfb7738d954f  numpy-1.20.3-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl
d144fdfe141442a7f362d498bc9a40c2  numpy-1.20.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl
e7ffa27f1c75cf11529d90967fa15bbc  numpy-1.20.3-cp38-cp38-win32.whl
58c12a54d1b5bc14d36ed2b0d8617fef  numpy-1.20.3-cp38-cp38-win_amd64.whl
18efbadcb513054c765f826fc3bb1645  numpy-1.20.3-cp39-cp39-macosx_10_9_x86_64.whl
319300952bd42455cb2ad98188c74b5f  numpy-1.20.3-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl
1d1451f9a5a2eeef666fc512a101a6ca  numpy-1.20.3-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
cdef3fb002bb5e3036f056ea0528c804  numpy-1.20.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
85e575735877094f3a76106e9d2a9cac  numpy-1.20.3-cp39-cp39-win32.whl
59f1dba95dedc7a1bebc58ee7e7a945a  numpy-1.20.3-cp39-cp39-win_amd64.whl
6abc979843929b41b099e4e6c0253063  numpy-1.20.3-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
802ddf90c7e226ba56ed0ea244f8b53d  numpy-1.20.3.tar.gz
949d9114af9accc25ede1daa359c4227  numpy-1.20.3.zip

SHA256

70eb5808127284c4e5c9e836208e09d685a7978b6a216db85960b1a112eeace8  numpy-1.20.3-cp37-cp37m-macosx_10_9_x86_64.whl
6ca2b85a5997dabc38301a22ee43c82adcb53ff660b89ee88dded6b33687e1d8  numpy-1.20.3-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl
c5bf0e132acf7557fc9bb8ded8b53bbbbea8892f3c9a1738205878ca9434206a  numpy-1.20.3-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
db250fd3e90117e0312b611574cd1b3f78bec046783195075cbd7ba9c3d73f16  numpy-1.20.3-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
637d827248f447e63585ca3f4a7d2dfaa882e094df6cfa177cc9cf9cd6cdf6d2  numpy-1.20.3-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl
8b7bb4b9280da3b2856cb1fc425932f46fba609819ee1c62256f61799e6a51d2  numpy-1.20.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl
67d44acb72c31a97a3d5d33d103ab06d8ac20770e1c5ad81bdb3f0c086a56cf6  numpy-1.20.3-cp37-cp37m-win32.whl
43909c8bb289c382170e0282158a38cf306a8ad2ff6dfadc447e90f9961bef43  numpy-1.20.3-cp37-cp37m-win_amd64.whl
f1452578d0516283c87608a5a5548b0cdde15b99650efdfd85182102ef7a7c17  numpy-1.20.3-cp38-cp38-macosx_10_9_x86_64.whl
6e51534e78d14b4a009a062641f465cfaba4fdcb046c3ac0b1f61dd97c861b1b  numpy-1.20.3-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl
e515c9a93aebe27166ec9593411c58494fa98e5fcc219e47260d9ab8a1cc7f9f  numpy-1.20.3-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
c1c09247ccea742525bdb5f4b5ceeacb34f95731647fe55774aa36557dbb5fa4  numpy-1.20.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
66fbc6fed94a13b9801fb70b96ff30605ab0a123e775a5e7a26938b717c5d71a  numpy-1.20.3-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl
ea9cff01e75a956dbee133fa8e5b68f2f92175233de2f88de3a682dd94deda65  numpy-1.20.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl
f39a995e47cb8649673cfa0579fbdd1cdd33ea497d1728a6cb194d6252268e48  numpy-1.20.3-cp38-cp38-win32.whl
1676b0a292dd3c99e49305a16d7a9f42a4ab60ec522eac0d3dd20cdf362ac010  numpy-1.20.3-cp38-cp38-win_amd64.whl
830b044f4e64a76ba71448fce6e604c0fc47a0e54d8f6467be23749ac2cbd2fb  numpy-1.20.3-cp39-cp39-macosx_10_9_x86_64.whl
55b745fca0a5ab738647d0e4db099bd0a23279c32b31a783ad2ccea729e632df  numpy-1.20.3-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl
5d050e1e4bc9ddb8656d7b4f414557720ddcca23a5b88dd7cff65e847864c400  numpy-1.20.3-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
a9c65473ebc342715cb2d7926ff1e202c26376c0dcaaee85a1fd4b8d8c1d3b2f  numpy-1.20.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
16f221035e8bd19b9dc9a57159e38d2dd060b48e93e1d843c49cb370b0f415fd  numpy-1.20.3-cp39-cp39-win32.whl
6690080810f77485667bfbff4f69d717c3be25e5b11bb2073e76bb3f578d99b4  numpy-1.20.3-cp39-cp39-win_amd64.whl
4e465afc3b96dbc80cf4a5273e5e2b1e3451286361b4af70ce1adb2984d392f9  numpy-1.20.3-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
b7340f0628ce1823c151e3d2a2a8cba2a3ff1357fba4475a24b1816e75c21f90  numpy-1.20.3.tar.gz
e55185e51b18d788e49fe8305fd73ef4470596b33fc2c1ceb304566b99c71a69  numpy-1.20.3.zip

v1.20.2

NumPy 1.20.2 Release Notes

NumPy 1,20.2 is a bugfix release containing several fixes merged to the
main branch after the NumPy 1.20.1 release.

Contributors

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

• Allan Haldane
• Bas van Beek
• Charles Harris
• Christoph Gohlke
• Mateusz Sokół +
• Michael Lamparski
• Sebastian Berg

Pull requests merged

A total of 20 pull requests were merged for this release.

• #18382: MAINT: Update f2py from master.
• #18459: BUG: diagflat could overflow on windows or 32-bit platforms
• #18460: BUG: Fix refcount leak in f2py complex_double_from_pyobj.
• #18461: BUG: Fix tiny memory leaks when like= overrides are used
• #18462: BUG: Remove temporary change of descr/flags in VOID functions
• #18469: BUG: Segfault in nditer buffer dealloc for Object arrays
• #18485: BUG: Remove suspicious type casting
• #18486: BUG: remove nonsensical comparison of pointer < 0
• #18487: BUG: verify pointer against NULL before using it
• #18488: BUG: check if PyArray_malloc succeeded
• #18546: BUG: incorrect error fallthrough in nditer
• #18559: CI: Backport CI fixes from main.
• #18599: MAINT: Add annotations for __getitem__, __mul__ and...
• #18611: BUG: NameError in numpy.distutils.fcompiler.compaq
• #18612: BUG: Fixed where keyword for np.mean & np.var methods
• #18617: CI: Update apt package list before Python install
• #18636: MAINT: Ensure that re-exported sub-modules are properly annotated
• #18638: BUG: Fix ma coercion list-of-ma-arrays if they do not cast to...
• #18661: BUG: Fix small valgrind-found issues
• #18671: BUG: Fix small issues found with pytest-leaks

Checksums

MD5

a95718df123e0726a7dac5043050b251  numpy-1.20.2-cp37-cp37m-macosx_10_9_x86_64.whl
4cacfe903c60827c0e44d0bed7e3a760  numpy-1.20.2-cp37-cp37m-manylinux1_i686.whl
2879728d4f815f07c7d133347deefe45  numpy-1.20.2-cp37-cp37m-manylinux1_x86_64.whl
97546a3cf4ddcc9fcc7eb41b9558f1de  numpy-1.20.2-cp37-cp37m-manylinux2010_i686.whl
65ffbc38abe1c1b92eb3bebf3484f679  numpy-1.20.2-cp37-cp37m-manylinux2010_x86_64.whl
5746efbd42db03518a51adbacbc70fa7  numpy-1.20.2-cp37-cp37m-manylinux2014_aarch64.whl
e9b8e30a5c62f003835b374dbc1c9031  numpy-1.20.2-cp37-cp37m-win32.whl
b2d0fa9383776ab68a1bbefc84331fc1  numpy-1.20.2-cp37-cp37m-win_amd64.whl
321aa118fbd40fe53a7c82557f3f2772  numpy-1.20.2-cp38-cp38-macosx_10_9_x86_64.whl
518013677b05371bbe7e1d6fa4ef61aa  numpy-1.20.2-cp38-cp38-manylinux1_i686.whl
58c61ea025646c391788f7bc7f681fa5  numpy-1.20.2-cp38-cp38-manylinux1_x86_64.whl
e8ce1857f017bffeed46b003a0385b11  numpy-1.20.2-cp38-cp38-manylinux2010_i686.whl
8ed52b7194b0953d0b04b88fbabea1ac  numpy-1.20.2-cp38-cp38-manylinux2010_x86_64.whl
0a9202dfd47fb02c8eab9f71f084633c  numpy-1.20.2-cp38-cp38-manylinux2014_aarch64.whl
8c70e309be1ae43d2938895b56ffbdb7  numpy-1.20.2-cp38-cp38-win32.whl
8aaa91a51b79556643ad93cb1d55b7d3  numpy-1.20.2-cp38-cp38-win_amd64.whl
b1b03999df657ccd4e65ff6abcf7e042  numpy-1.20.2-cp39-cp39-macosx_10_9_x86_64.whl
139fef5109539031e570aee9aa3090bf  numpy-1.20.2-cp39-cp39-manylinux2010_i686.whl
2c9463187e6a1a0245ed4a2db8e8e656  numpy-1.20.2-cp39-cp39-manylinux2010_x86_64.whl
b6cb08e8f56accedc4fdc29720ffb380  numpy-1.20.2-cp39-cp39-manylinux2014_aarch64.whl
a3024059b52e7688d3c98b82e2f2688e  numpy-1.20.2-cp39-cp39-win32.whl
abcd17ffd3b29014ff15e93a74c2c3d6  numpy-1.20.2-cp39-cp39-win_amd64.whl
67704047e60c2b280f7e9f42400cca91  numpy-1.20.2-pp37-pypy37_pp73-manylinux2010_x86_64.whl
6fe93791438f9c1f69c9352680151002  numpy-1.20.2.tar.gz
5e1b381630af4d18db0fedd56b6d8da2  numpy-1.20.2.zip

SHA256

e9459f40244bb02b2f14f6af0cd0732791d72232bbb0dc4bab57ef88e75f6935  numpy-1.20.2-cp37-cp37m-macosx_10_9_x86_64.whl
a8e6859913ec8eeef3dbe9aed3bf475347642d1cdd6217c30f28dee8903528e6  numpy-1.20.2-cp37-cp37m-manylinux1_i686.whl
9cab23439eb1ebfed1aaec9cd42b7dc50fc96d5cd3147da348d9161f0501ada5  numpy-1.20.2-cp37-cp37m-manylinux1_x86_64.whl
9c0fab855ae790ca74b27e55240fe4f2a36a364a3f1ebcfd1fb5ac4088f1cec3  numpy-1.20.2-cp37-cp37m-manylinux2010_i686.whl
61d5b4cf73622e4d0c6b83408a16631b670fc045afd6540679aa35591a17fe6d  numpy-1.20.2-cp37-cp37m-manylinux2010_x86_64.whl
d15007f857d6995db15195217afdbddfcd203dfaa0ba6878a2f580eaf810ecd6  numpy-1.20.2-cp37-cp37m-manylinux2014_aarch64.whl
d76061ae5cab49b83a8cf3feacefc2053fac672728802ac137dd8c4123397677  numpy-1.20.2-cp37-cp37m-win32.whl
bad70051de2c50b1a6259a6df1daaafe8c480ca98132da98976d8591c412e737  numpy-1.20.2-cp37-cp37m-win_amd64.whl
719656636c48be22c23641859ff2419b27b6bdf844b36a2447cb39caceb00935  numpy-1.20.2-cp38-cp38-macosx_10_9_x86_64.whl
aa046527c04688af680217fffac61eec2350ef3f3d7320c07fd33f5c6e7b4d5f  numpy-1.20.2-cp38-cp38-manylinux1_i686.whl
2428b109306075d89d21135bdd6b785f132a1f5a3260c371cee1fae427e12727  numpy-1.20.2-cp38-cp38-manylinux1_x86_64.whl
e8e4fbbb7e7634f263c5b0150a629342cc19b47c5eba8d1cd4363ab3455ab576  numpy-1.20.2-cp38-cp38-manylinux2010_i686.whl
edb1f041a9146dcf02cd7df7187db46ab524b9af2515f392f337c7cbbf5b52cd  numpy-1.20.2-cp38-cp38-manylinux2010_x86_64.whl
c73a7975d77f15f7f68dacfb2bca3d3f479f158313642e8ea9058eea06637931  numpy-1.20.2-cp38-cp38-manylinux2014_aarch64.whl
6c915ee7dba1071554e70a3664a839fbc033e1d6528199d4621eeaaa5487ccd2  numpy-1.20.2-cp38-cp38-win32.whl
471c0571d0895c68da309dacee4e95a0811d0a9f9f532a48dc1bea5f3b7ad2b7  numpy-1.20.2-cp38-cp38-win_amd64.whl
4703b9e937df83f5b6b7447ca5912b5f5f297aba45f91dbbbc63ff9278c7aa98  numpy-1.20.2-cp39-cp39-macosx_10_9_x86_64.whl
abc81829c4039e7e4c30f7897938fa5d4916a09c2c7eb9b244b7a35ddc9656f4  numpy-1.20.2-cp39-cp39-manylinux2010_i686.whl
377751954da04d4a6950191b20539066b4e19e3b559d4695399c5e8e3e683bf6  numpy-1.20.2-cp39-cp39-manylinux2010_x86_64.whl
6e51e417d9ae2e7848314994e6fc3832c9d426abce9328cf7571eefceb43e6c9  numpy-1.20.2-cp39-cp39-manylinux2014_aarch64.whl
780ae5284cb770ade51d4b4a7dce4faa554eb1d88a56d0e8b9f35fca9b0270ff  numpy-1.20.2-cp39-cp39-win32.whl
924dc3f83de20437de95a73516f36e09918e9c9c18d5eac520062c49191025fb  numpy-1.20.2-cp39-cp39-win_amd64.whl
97ce8b8ace7d3b9288d88177e66ee75480fb79b9cf745e91ecfe65d91a856042  numpy-1.20.2-pp37-pypy37_pp73-manylinux2010_x86_64.whl
c049f410c78e76ffb0af830a8afbdf8baac09897b4152b97b1a3b8345ee338ff  numpy-1.20.2.tar.gz
878922bf5ad7550aa044aa9301d417e2d3ae50f0f577de92051d739ac6096cee  numpy-1.20.2.zip

v1.20.1

NumPy 1.20.1 Release Notes

NumPy 1.20.1 is a rapid bugfix release fixing several bugs and
regressions reported after the 1.20.0 release.

Highlights

• The distutils bug that caused problems with downstream projects is
  fixed.
• The random.shuffle regression is fixed.

Contributors

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

• Bas van Beek
• Charles Harris
• Nicholas McKibben +
• Pearu Peterson
• Ralf Gommers
• Sebastian Berg
• Tyler Reddy
• @Aerysv +

Pull requests merged

A total of 15 pull requests were merged for this release.

• #18306: MAINT: Add missing placeholder annotations
• #18310: BUG: Fix typo in numpy.__init__.py
• #18326: BUG: don't mutate list of fake libraries while iterating over...
• #18327: MAINT: gracefully shuffle memoryviews
• #18328: BUG: Use C linkage for random distributions
• #18336: CI: fix when GitHub Actions builds trigger, and allow ci skips
• #18337: BUG: Allow unmodified use of isclose, allclose, etc. with timedelta
• #18345: BUG: Allow pickling all relevant DType types/classes
• #18351: BUG: Fix missing signed_char dependency. Closes #18335.
• #18352: DOC: Change license date 2020 -> 2021
• #18353: CI: CircleCI seems to occasionally time out, increase the limit
• #18354: BUG: Fix f2py bugs when wrapping F90 subroutines.
• #18356: MAINT: crackfortran regex simplify
• #18357: BUG: threads.h existence test requires GLIBC > 2.12.
• #18359: REL: Prepare for the NumPy 1.20.1 release.

Checksums

MD5

c4748f4f8f703c5e96027407eca02b08  numpy-1.20.1-cp37-cp37m-macosx_10_9_x86_64.whl
f0bf3a78d6b3a169e5a7fb2637f7fd87  numpy-1.20.1-cp37-cp37m-manylinux1_i686.whl
493c17647c05ca5043bcbab1ac266a74  numpy-1.20.1-cp37-cp37m-manylinux1_x86_64.whl
55ec954fc598c72b2bbf57bfa8b2a701  numpy-1.20.1-cp37-cp37m-manylinux2010_i686.whl
8cee88f9683d208686081522609a8726  numpy-1.20.1-cp37-cp37m-manylinux2010_x86_64.whl
26399d3ededc53b354de78f977a6197e  numpy-1.20.1-cp37-cp37m-manylinux2014_aarch64.whl
81051f1e7a79eea8a5aaf5718114ce3a  numpy-1.20.1-cp37-cp37m-win32.whl
899488c55824f02a7a6f0451fc86f63f  numpy-1.20.1-cp37-cp37m-win_amd64.whl
17f4dae5a0d143b46345a9cf1a8c8dec  numpy-1.20.1-cp38-cp38-macosx_10_9_x86_64.whl
f254e98e92b3054c567b6220b37b81d3  numpy-1.20.1-cp38-cp38-manylinux1_i686.whl
483f43a62c7e32ae991990786da90de1  numpy-1.20.1-cp38-cp38-manylinux1_x86_64.whl
bf578b783e36d3feb3344973306a9f96  numpy-1.20.1-cp38-cp38-manylinux2010_i686.whl
f5d6c77c898537017e64ee30b243fdca  numpy-1.20.1-cp38-cp38-manylinux2010_x86_64.whl
5cf541a0d5af3d5812d2970a427075fb  numpy-1.20.1-cp38-cp38-manylinux2014_aarch64.whl
178315c579c0a70285b8ee502eb498af  numpy-1.20.1-cp38-cp38-win32.whl
5164a32e7a00a2b285302b563eb58afe  numpy-1.20.1-cp38-cp38-win_amd64.whl
c123dd10788ea9ff788d735cbee444c5  numpy-1.20.1-cp39-cp39-macosx_10_9_x86_64.whl
72282fefe58650c6e7cc41f5b37b8662  numpy-1.20.1-cp39-cp39-manylinux2010_i686.whl
234d57c1a7b1f8b99c054a7a71a51cbe  numpy-1.20.1-cp39-cp39-manylinux2010_x86_64.whl
352243d4285970e45d825024ca566d47  numpy-1.20.1-cp39-cp39-manylinux2014_aarch64.whl
a78c863323e0f56210c2e1acaad1bc22  numpy-1.20.1-cp39-cp39-win32.whl
86f9d3f358e7d7896e713bce99f17fdd  numpy-1.20.1-cp39-cp39-win_amd64.whl
ed2c81132119fb3c7f73c6a2de306058  numpy-1.20.1-pp37-pypy37_pp73-manylinux2010_x86_64.whl
60a5e2517be19394a7df24f6d4add3f2  numpy-1.20.1.tar.gz
30ea1c7868e73eeff2c86ac465311220  numpy-1.20.1.zip

SHA256

ae61f02b84a0211abb56462a3b6cd1e7ec39d466d3160eb4e1da8bf6717cdbeb  numpy-1.20.1-cp37-cp37m-macosx_10_9_x86_64.whl
65410c7f4398a0047eea5cca9b74009ea61178efd78d1be9847fac1d6716ec1e  numpy-1.20.1-cp37-cp37m-manylinux1_i686.whl
2d7e27442599104ee08f4faed56bb87c55f8b10a5494ac2ead5c98a4b289e61f  numpy-1.20.1-cp37-cp37m-manylinux1_x86_64.whl
4ed8e96dc146e12c1c5cdd6fb9fd0757f2ba66048bf94c5126b7efebd12d0090  numpy-1.20.1-cp37-cp37m-manylinux2010_i686.whl
ecb5b74c702358cdc21268ff4c37f7466357871f53a30e6f84c686952bef16a9  numpy-1.20.1-cp37-cp37m-manylinux2010_x86_64.whl
b9410c0b6fed4a22554f072a86c361e417f0258838957b78bd063bde2c7f841f  numpy-1.20.1-cp37-cp37m-manylinux2014_aarch64.whl
3d3087e24e354c18fb35c454026af3ed8997cfd4997765266897c68d724e4845  numpy-1.20.1-cp37-cp37m-win32.whl
89f937b13b8dd17b0099c7c2e22066883c86ca1575a975f754babc8fbf8d69a9  numpy-1.20.1-cp37-cp37m-win_amd64.whl
a1d7995d1023335e67fb070b2fae6f5968f5be3802b15ad6d79d81ecaa014fe0  numpy-1.20.1-cp38-cp38-macosx_10_9_x86_64.whl
60759ab15c94dd0e1ed88241fd4fa3312db4e91d2c8f5a2d4cf3863fad83d65b  numpy-1.20.1-cp38-cp38-manylinux1_i686.whl
125a0e10ddd99a874fd357bfa1b636cd58deb78ba4a30b5ddb09f645c3512e04  numpy-1.20.1-cp38-cp38-manylinux1_x86_64.whl
c26287dfc888cf1e65181f39ea75e11f42ffc4f4529e5bd19add57ad458996e2  numpy-1.20.1-cp38-cp38-manylinux2010_i686.whl
7199109fa46277be503393be9250b983f325880766f847885607d9b13848f257  numpy-1.20.1-cp38-cp38-manylinux2010_x86_64.whl
72251e43ac426ff98ea802a931922c79b8d7596480300eb9f1b1e45e0543571e  numpy-1.20.1-cp38-cp38-manylinux2014_aarch64.whl
c91ec9569facd4757ade0888371eced2ecf49e7982ce5634cc2cf4e7331a4b14  numpy-1.20.1-cp38-cp38-win32.whl
13adf545732bb23a796914fe5f891a12bd74cf3d2986eed7b7eba2941eea1590  numpy-1.20.1-cp38-cp38-win_amd64.whl
104f5e90b143dbf298361a99ac1af4cf59131218a045ebf4ee5990b83cff5fab  numpy-1.20.1-cp39-cp39-macosx_10_9_x86_64.whl
89e5336f2bec0c726ac7e7cdae181b325a9c0ee24e604704ed830d241c5e47ff  numpy-1.20.1-cp39-cp39-manylinux2010_i686.whl
032be656d89bbf786d743fee11d01ef318b0781281241997558fa7950028dd29  numpy-1.20.1-cp39-cp39-manylinux2010_x86_64.whl
66b467adfcf628f66ea4ac6430ded0614f5cc06ba530d09571ea404789064adc  numpy-1.20.1-cp39-cp39-manylinux2014_aarch64.whl
12e4ba5c6420917571f1a5becc9338abbde71dd811ce40b37ba62dec7b39af6d  numpy-1.20.1-cp39-cp39-win32.whl
9c94cab5054bad82a70b2e77741271790304651d584e2cdfe2041488e753863b  numpy-1.20.1-cp39-cp39-win_amd64.whl
9eb551d122fadca7774b97db8a112b77231dcccda8e91a5bc99e79890797175e  numpy-1.20.1-pp37-pypy37_pp73-manylinux2010_x86_64.whl
9bf51d69ebb4ca9239e55bedc2185fe2c0ec222da0adee7ece4125414676846d  numpy-1.20.1.tar.gz
3bc63486a870294683980d76ec1e3efc786295ae00128f9ea38e2c6e74d5a60a  numpy-1.20.1.zip

v1.20.0

NumPy 1.20.0 Release Notes

This NumPy release is the largest so made to date, some 684 PRs
contributed by 184 people have been merged. See the list of highlights
below for more details. The Python versions supported for this release
are 3.7-3.9, support for Python 3.6 has been dropped. Highlights are

• Annotations for NumPy functions. This work is ongoing and
  improvements can be expected pending feedback from users.
• Wider use of SIMD to increase execution speed of ufuncs. Much work
  has been done in introducing universal functions that will ease use
  of modern features across different hardware platforms. This work is
  ongoing.
• Preliminary work in changing the dtype and casting implementations
  in order to provide an easier path to extending dtypes. This work is
  ongoing but enough has been done to allow experimentation and
  feedback.
• Extensive documentation improvements comprising some 185 PR merges.
  This work is ongoing and part of the larger project to improve
  NumPy's online presence and usefulness to new users.
• Further cleanups related to removing Python 2.7. This improves code
  readability and removes technical debt.
• Preliminary support for the upcoming Cython 3.0.

New functions

The random.Generator class has a new permuted function.

The new function differs from shuffle and permutation in that the
subarrays indexed by an axis are permuted rather than the axis being
treated as a separate 1-D array for every combination of the other
indexes. For example, it is now possible to permute the rows or columns
of a 2-D array.

(gh-15121)

sliding_window_view provides a sliding window view for numpy arrays

numpy.lib.stride\_tricks.sliding\_window\_view constructs
views on numpy arrays that offer a sliding or moving window access to
the array. This allows for the simple implementation of certain
algorithms, such as running means.

(gh-17394)

[numpy.broadcast_shapes]{.title-ref} is a new user-facing function

numpy.broadcast\_shapes gets the resulting shape from
broadcasting the given shape tuples against each other.

>>> np.broadcast_shapes((1, 2), (3, 1))
(3, 2)

>>> np.broadcast_shapes(2, (3, 1))
(3, 2)

>>> np.broadcast_shapes((6, 7), (5, 6, 1), (7,), (5, 1, 7))
(5, 6, 7)

(gh-17535)

Deprecations

Using the aliases of builtin types like np.int is deprecated

For a long time, np.int has been an alias of the builtin int. This
is repeatedly a cause of confusion for newcomers, and existed mainly for
historic reasons.

These aliases have been deprecated. The table below shows the full list
of deprecated aliases, along with their exact meaning. Replacing uses of
items in the first column with the contents of the second column will
work identically and silence the deprecation warning.

The third column lists alternative NumPy names which may occasionally be
preferential. See also basics.types{.interpreted-text role="ref"} for
additional details.

Deprecated name	Identical to	NumPy scalar type names
numpy.bool	bool	numpy.bool\_
numpy.int	int	numpy.int\_ (default), numpy.int64, or numpy.int32
numpy.float	float	numpy.float64, numpy.float\_, numpy.double (equivalent)
numpy.complex	complex	numpy.complex128, numpy.complex\_, numpy.cdouble (equivalent)
numpy.object	object	numpy.object\_
numpy.str	str	numpy.str\_
numpy.long	int	numpy.int\_ (C long), numpy.longlong (largest integer type)
numpy.unicode	str	numpy.unicode\_

To give a clear guideline for the vast majority of cases, for the types
bool, object, str (and unicode) using the plain version is
shorter and clear, and generally a good replacement. For float and
complex you can use float64 and complex128 if you wish to be more
explicit about the precision.

For np.int a direct replacement with np.int_ or int is also good
and will not change behavior, but the precision will continue to depend
on the computer and operating system. If you want to be more explicit
and review the current use, you have the following alternatives:

• np.int64 or np.int32 to specify the precision exactly. This
  ensures that results cannot depend on the computer or operating
  system.
• np.int_ or int (the default), but be aware that it depends on
  the computer and operating system.
• The C types: np.cint (int), np.int_ (long), np.longlong.
• np.intp which is 32bit on 32bit machines 64bit on 64bit machines.
  This can be the best type to use for indexing.

When used with np.dtype(...) or dtype=... changing it to the NumPy
name as mentioned above will have no effect on the output. If used as a
scalar with:

np.float(123)

changing it can subtly change the result. In this case, the Python
version float(123) or int(12.) is normally preferable, although the
NumPy version may be useful for consistency with NumPy arrays (for
example, NumPy behaves differently for things like division by zero).

(gh-14882)

Passing shape=None to functions with a non-optional shape argument is deprecated

Previously, this was an alias for passing shape=(). This deprecation
is emitted by PyArray\_IntpConverter in the C API. If your
API is intended to support passing None, then you should check for
None prior to invoking the converter, so as to be able to distinguish
None and ().

(gh-15886)

Indexing errors will be reported even when index result is empty

In the future, NumPy will raise an IndexError when an integer array
index contains out of bound values even if a non-indexed dimension is of
length 0. This will now emit a DeprecationWarning. This can happen when
the array is previously empty, or an empty slice is involved:

arr1 = np.zeros((5, 0))
arr1[[20]]
arr2 = np.zeros((5, 5))
arr2[[20], :0]

Previously the non-empty index [20] was not checked for correctness.
It will now be checked causing a deprecation warning which will be
turned into an error. This also applies to assignments.

(gh-15900)

Inexact matches for mode and searchside are deprecated

Inexact and case insensitive matches for mode and searchside were
valid inputs earlier and will give a DeprecationWarning now. For
example, below are some example usages which are now deprecated and will
give a DeprecationWarning:

import numpy as np
arr = np.array([[3, 6, 6], [4, 5, 1]])
# mode: inexact match
np.ravel_multi_index(arr, (7, 6), mode="clap")  # should be "clip"
# searchside: inexact match
np.searchsorted(arr[0], 4, side='random')  # should be "right"

(gh-16056)

Deprecation of [numpy.dual]{.title-ref}

The module numpy.dual is deprecated. Instead of importing
functions from numpy.dual, the functions should be
imported directly from NumPy or SciPy.

(gh-16156)

outer and ufunc.outer deprecated for matrix

np.matrix use with \~numpy.outer or generic ufunc outer
calls such as numpy.add.outer. Previously, matrix was converted to an
array here. This will not be done in the future requiring a manual
conversion to arrays.

(gh-16232)

Further Numeric Style types Deprecated

The remaining numeric-style type codes Bytes0, Str0, Uint32,
Uint64, and Datetime64 have been deprecated. The lower-case variants
should be used instead. For bytes and string "S" and "U" are further
alternatives.

(gh-16554)

The ndincr method of ndindex is deprecated

The documentation has warned against using this function since NumPy
1.8. Use next(it) instead of it.ndincr().

(gh-17233)

ArrayLike objects which do not define __len__ and __getitem__

Objects which define one of the protocols __array__,
__array_interface__, or __array_struct__ but are not sequences
(usually defined by having a __len__ and __getitem__) will behave
differently during array-coercion in the future.

When nested inside sequences, such as np.array([array_like]), these
were handled as a single Python object rather than an array. In the
future they will behave identically to:

np.array([np.array(array_like)])

This change should only have an effect if np.array(array_like) is not
0-D. The solution to this warning may depend on the object:

• Some array-likes may expect the new behaviour, and users can ignore
  the warning. The object can choose to expose the sequence protocol
  to opt-in to the new behaviour.
• For example, shapely will allow conversion to an array-like using
  line.coords rather than np.asarray(line). Users may work around
  the warning, or use the new convention when it becomes available.

Unfortunately, using the new behaviour can only be achieved by calling
np.array(array_like).

If you wish to ensure that the old behaviour remains unchanged, please
create an object array and then fill it explicitly, for example:

arr = np.empty(3, dtype=object)
arr[:] = [array_like1, array_like2, array_like3...

v1.19.5

NumPy 1.19.5 Release Notes

NumPy 1.19.5 is a short bugfix release. Apart from fixing several bugs,
the main improvement is the update to OpenBLAS 0.3.13 that works around
the windows 2004 bug while not breaking execution on other platforms.
This release supports Python 3.6-3.9 and is planned to be the last
release in the 1.19.x cycle.

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
• Christoph Gohlke
• Matti Picus
• Raghuveer Devulapalli
• Sebastian Berg
• Simon Graham +
• Veniamin Petrenko +
• Bernie Gray +

Pull requests merged

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

• #17756: BUG: Fix segfault due to out of bound pointer in floatstatus...
• #17774: BUG: fix np.timedelta64('nat').__format__ throwing an exception
• #17775: BUG: Fixed file handle leak in array_tofile.
• #17786: BUG: Raise recursion error during dimension discovery
• #17917: BUG: Fix subarray dtype used with too large count in fromfile
• #17918: BUG: 'bool' object has no attribute 'ndim'
• #17919: BUG: ensure _UFuncNoLoopError can be pickled
• #17924: BLD: use BUFFERSIZE=20 in OpenBLAS
• #18026: BLD: update to OpenBLAS 0.3.13
• #18036: BUG: make a variable volatile to work around clang compiler bug
• #18114: REL: Prepare for the NumPy 1.19.5 release.

Checksums

MD5

2651049b70d2ec07d8afd7637f198807  numpy-1.19.5-cp36-cp36m-macosx_10_9_x86_64.whl
71cc7869a54cf55df4699aebe27e9344  numpy-1.19.5-cp36-cp36m-manylinux1_i686.whl
28d23e25c6e6654b2f65218c6e9b3825  numpy-1.19.5-cp36-cp36m-manylinux1_x86_64.whl
fb4128d719d72130cbf24baf308761c9  numpy-1.19.5-cp36-cp36m-manylinux2010_i686.whl
0c8edfbbb26823b7495b5371558b1ae5  numpy-1.19.5-cp36-cp36m-manylinux2010_x86_64.whl
ad8e6247a175f3a9786eedb4baff7c06  numpy-1.19.5-cp36-cp36m-manylinux2014_aarch64.whl
2a3e121d4f242cef4ef00d5e6e3cebc9  numpy-1.19.5-cp36-cp36m-win32.whl
baf1bd7e3a8c19367103483d1fd61cfc  numpy-1.19.5-cp36-cp36m-win_amd64.whl
0086e5551c22e62244781e4179a013c9  numpy-1.19.5-cp37-cp37m-macosx_10_9_x86_64.whl
538fe864a8809a8d9b6b5c102ac8de1f  numpy-1.19.5-cp37-cp37m-manylinux1_i686.whl
5323920ec3e1953078cfa0560ae53867  numpy-1.19.5-cp37-cp37m-manylinux1_x86_64.whl
464f0f6284ede3cb2ea3070fee729048  numpy-1.19.5-cp37-cp37m-manylinux2010_i686.whl
9aa2656bab43993cc99f9cd996c71997  numpy-1.19.5-cp37-cp37m-manylinux2010_x86_64.whl
bcd1e59d57515d2f7be107266cab4f00  numpy-1.19.5-cp37-cp37m-manylinux2014_aarch64.whl
4e87ab21f30016ea5b9a981e3ecd733a  numpy-1.19.5-cp37-cp37m-win32.whl
c50b11de3b82163e6e75d17762368425  numpy-1.19.5-cp37-cp37m-win_amd64.whl
2beca0d3718c5b355f3c78d9f4f1fe87  numpy-1.19.5-cp38-cp38-macosx_10_9_x86_64.whl
8302aaa77a0978df894f9f62caac7ee7  numpy-1.19.5-cp38-cp38-manylinux1_i686.whl
6875515a35558ac17d3cdc8e8578debd  numpy-1.19.5-cp38-cp38-manylinux1_x86_64.whl
2c72ca182bc4b4904b6c87f7d4312036  numpy-1.19.5-cp38-cp38-manylinux2010_i686.whl
1b334aad7bdfa96dc3eb10f55f8c44dd  numpy-1.19.5-cp38-cp38-manylinux2010_x86_64.whl
f4e63f368fc230f482205e3b65b8f5c7  numpy-1.19.5-cp38-cp38-manylinux2014_aarch64.whl
d5a97ef684d53b04bf14e0b6cca7e8a1  numpy-1.19.5-cp38-cp38-win32.whl
abed55a50177d54a10d8e89ccde971ca  numpy-1.19.5-cp38-cp38-win_amd64.whl
3c3fc07aeb311677975a58d1ab1f3e5e  numpy-1.19.5-cp39-cp39-macosx_10_9_x86_64.whl
c7c070e284f49f9915ecbcec847760a5  numpy-1.19.5-cp39-cp39-manylinux1_i686.whl
2613261149a32771243bb71f53e3bc3a  numpy-1.19.5-cp39-cp39-manylinux1_x86_64.whl
5f84721a5e286e383bf6ba251c8add31  numpy-1.19.5-cp39-cp39-manylinux2010_i686.whl
9a0ac6f630de2081302df9bbffe1b555  numpy-1.19.5-cp39-cp39-manylinux2010_x86_64.whl
b48e31d316e4803b5e463dd5e38c8339  numpy-1.19.5-cp39-cp39-manylinux2014_aarch64.whl
15589af64e734aa1ecc7e04767ccc63d  numpy-1.19.5-cp39-cp39-win32.whl
cca2b2301f11a89329727ea5302d9b12  numpy-1.19.5-cp39-cp39-win_amd64.whl
c9b5c30dc035aa7bd9c1ebf6771939c3  numpy-1.19.5-pp36-pypy36_pp73-manylinux2010_x86_64.whl
e67564b7dfedf213fda112ee078c67bf  numpy-1.19.5.tar.gz
f6a1b48717c552bbc18f1adc3cc1fe0e  numpy-1.19.5.zip

SHA256

cc6bd4fd593cb261332568485e20a0712883cf631f6f5e8e86a52caa8b2b50ff  numpy-1.19.5-cp36-cp36m-macosx_10_9_x86_64.whl
aeb9ed923be74e659984e321f609b9ba54a48354bfd168d21a2b072ed1e833ea  numpy-1.19.5-cp36-cp36m-manylinux1_i686.whl
8b5e972b43c8fc27d56550b4120fe6257fdc15f9301914380b27f74856299fea  numpy-1.19.5-cp36-cp36m-manylinux1_x86_64.whl
43d4c81d5ffdff6bae58d66a3cd7f54a7acd9a0e7b18d97abb255defc09e3140  numpy-1.19.5-cp36-cp36m-manylinux2010_i686.whl
a4646724fba402aa7504cd48b4b50e783296b5e10a524c7a6da62e4a8ac9698d  numpy-1.19.5-cp36-cp36m-manylinux2010_x86_64.whl
2e55195bc1c6b705bfd8ad6f288b38b11b1af32f3c8289d6c50d47f950c12e76  numpy-1.19.5-cp36-cp36m-manylinux2014_aarch64.whl
39b70c19ec771805081578cc936bbe95336798b7edf4732ed102e7a43ec5c07a  numpy-1.19.5-cp36-cp36m-win32.whl
dbd18bcf4889b720ba13a27ec2f2aac1981bd41203b3a3b27ba7a33f88ae4827  numpy-1.19.5-cp36-cp36m-win_amd64.whl
603aa0706be710eea8884af807b1b3bc9fb2e49b9f4da439e76000f3b3c6ff0f  numpy-1.19.5-cp37-cp37m-macosx_10_9_x86_64.whl
cae865b1cae1ec2663d8ea56ef6ff185bad091a5e33ebbadd98de2cfa3fa668f  numpy-1.19.5-cp37-cp37m-manylinux1_i686.whl
36674959eed6957e61f11c912f71e78857a8d0604171dfd9ce9ad5cbf41c511c  numpy-1.19.5-cp37-cp37m-manylinux1_x86_64.whl
06fab248a088e439402141ea04f0fffb203723148f6ee791e9c75b3e9e82f080  numpy-1.19.5-cp37-cp37m-manylinux2010_i686.whl
6149a185cece5ee78d1d196938b2a8f9d09f5a5ebfbba66969302a778d5ddd1d  numpy-1.19.5-cp37-cp37m-manylinux2010_x86_64.whl
50a4a0ad0111cc1b71fa32dedd05fa239f7fb5a43a40663269bb5dc7877cfd28  numpy-1.19.5-cp37-cp37m-manylinux2014_aarch64.whl
d051ec1c64b85ecc69531e1137bb9751c6830772ee5c1c426dbcfe98ef5788d7  numpy-1.19.5-cp37-cp37m-win32.whl
a12ff4c8ddfee61f90a1633a4c4afd3f7bcb32b11c52026c92a12e1325922d0d  numpy-1.19.5-cp37-cp37m-win_amd64.whl
cf2402002d3d9f91c8b01e66fbb436a4ed01c6498fffed0e4c7566da1d40ee1e  numpy-1.19.5-cp38-cp38-macosx_10_9_x86_64.whl
1ded4fce9cfaaf24e7a0ab51b7a87be9038ea1ace7f34b841fe3b6894c721d1c  numpy-1.19.5-cp38-cp38-manylinux1_i686.whl
012426a41bc9ab63bb158635aecccc7610e3eff5d31d1eb43bc099debc979d94  numpy-1.19.5-cp38-cp38-manylinux1_x86_64.whl
759e4095edc3c1b3ac031f34d9459fa781777a93ccc633a472a5468587a190ff  numpy-1.19.5-cp38-cp38-manylinux2010_i686.whl
a9d17f2be3b427fbb2bce61e596cf555d6f8a56c222bd2ca148baeeb5e5c783c  numpy-1.19.5-cp38-cp38-manylinux2010_x86_64.whl
99abf4f353c3d1a0c7a5f27699482c987cf663b1eac20db59b8c7b061eabd7fc  numpy-1.19.5-cp38-cp38-manylinux2014_aarch64.whl
384ec0463d1c2671170901994aeb6dce126de0a95ccc3976c43b0038a37329c2  numpy-1.19.5-cp38-cp38-win32.whl
811daee36a58dc79cf3d8bdd4a490e4277d0e4b7d103a001a4e73ddb48e7e6aa  numpy-1.19.5-cp38-cp38-win_amd64.whl
c843b3f50d1ab7361ca4f0b3639bf691569493a56808a0b0c54a051d260b7dbd  numpy-1.19.5-cp39-cp39-macosx_10_9_x86_64.whl
d6631f2e867676b13026e2846180e2c13c1e11289d67da08d71cacb2cd93d4aa  numpy-1.19.5-cp39-cp39-manylinux1_i686.whl
7fb43004bce0ca31d8f13a6eb5e943fa73371381e53f7074ed21a4cb786c32f8  numpy-1.19.5-cp39-cp39-manylinux1_x86_64.whl
2ea52bd92ab9f768cc64a4c3ef8f4b2580a17af0a5436f6126b08efbd1838371  numpy-1.19.5-cp39-cp39-manylinux2010_i686.whl
400580cbd3cff6ffa6293df2278c75aef2d58d8d93d3c5614cd67981dae68ceb  numpy-1.19.5-cp39-cp39-manylinux2010_x86_64.whl
df609c82f18c5b9f6cb97271f03315ff0dbe481a2a02e56aeb1b1a985ce38e60  numpy-1.19.5-cp39-cp39-manylinux2014_aarch64.whl
ab83f24d5c52d60dbc8cd0528759532736b56db58adaa7b5f1f76ad551416a1e  numpy-1.19.5-cp39-cp39-win32.whl
0eef32ca3132a48e43f6a0f5a82cb508f22ce5a3d6f67a8329c81c8e226d3f6e  numpy-1.19.5-cp39-cp39-win_amd64.whl
a0d53e51a6cb6f0d9082decb7a4cb6dfb33055308c4c44f53103c073f649af73  numpy-1.19.5-pp36-pypy36_pp73-manylinux2010_x86_64.whl
d1654047d75fb9d55cc3d46f312d5247eec5f4999039874d2f571bb8021d8f0b  numpy-1.19.5.tar.gz
a76f502430dd98d7546e1ea2250a7360c065a5fdea52b2dffe8ae7180909b6f4  numpy-1.19.5.zip