v1.18.4
title: 'NumPy 1.18.4 Release Notes'
This is that last planned release in the 1.18.x series. It reverts the
bool("0")
behavior introduced in 1.18.3 and fixes a bug in
Generator.integers
. There is also improved help in the error message
emitted when numpy import fails due to a link to a new troubleshooting
section in the documentation that is now included.
The Python versions supported in this release are 3.5-3.8. Downstream
developers should use Cython >= 0.29.15 for Python 3.8 support and
OpenBLAS >= 3.7 to avoid errors on the Skylake architecture.
Contributors
A total of 4 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.
- Charles Harris
- Matti Picus
- Sebastian Berg
- Warren Weckesser
Pull requests merged
A total of 6 pull requests were merged for this release.
- #16055 BLD: add i686 for 1.18 builds
- #16090 BUG: random:
Generator.integers(2**32)
always returned 0. - #16091 BLD: fix path to libgfortran on macOS
- #16109 REV: Reverts side-effect changes to casting
- #16114 BLD: put openblas library in local directory on windows
- #16132 DOC: Change import error "howto" to link to new troubleshooting...
Checksums
MD5
1fe09153c9e6da5c9e73f3ed466da50c numpy-1.18.4-cp35-cp35m-macosx_10_9_intel.whl
707b0270ece3e9a16905e756884daa48 numpy-1.18.4-cp35-cp35m-manylinux1_i686.whl
47f90c71c3df80ace2b32d011ed1c240 numpy-1.18.4-cp35-cp35m-manylinux1_x86_64.whl
e0e7d9fd9f4c8cf077ba5cda69833d38 numpy-1.18.4-cp35-cp35m-win32.whl
06e844091463932a0d4da103951ffc2c numpy-1.18.4-cp35-cp35m-win_amd64.whl
32ce3d6d266f1fbfef4a2ff917053718 numpy-1.18.4-cp36-cp36m-macosx_10_9_x86_64.whl
f5d27cca8bf9dc8f603cad5255674bb8 numpy-1.18.4-cp36-cp36m-manylinux1_i686.whl
460bd10297e582f0e061194356990afb numpy-1.18.4-cp36-cp36m-manylinux1_x86_64.whl
160c62c881a5109f3e47813dd0079ab1 numpy-1.18.4-cp36-cp36m-win32.whl
03e2d39bfaaf27993b353b98c75f27cc numpy-1.18.4-cp36-cp36m-win_amd64.whl
672cb3889e7c9285ca260f8d15c2bc9f numpy-1.18.4-cp37-cp37m-macosx_10_9_x86_64.whl
eaebca109ce5346ec1626af476e88edb numpy-1.18.4-cp37-cp37m-manylinux1_i686.whl
bdf6d9bd169e5552284dd366c12e3759 numpy-1.18.4-cp37-cp37m-manylinux1_x86_64.whl
408f8eedcfb8bee6c0d8cb13f4665edd numpy-1.18.4-cp37-cp37m-win32.whl
2d2cc2ccd5c276bde6696856609dee9f numpy-1.18.4-cp37-cp37m-win_amd64.whl
5bdfaa2daf5afd8e6db8c202f58d5ef0 numpy-1.18.4-cp38-cp38-macosx_10_9_x86_64.whl
1aad5b0c4545e206aae7848853633885 numpy-1.18.4-cp38-cp38-manylinux1_i686.whl
f7e78dcee83fb851c97804d7fb987fdb numpy-1.18.4-cp38-cp38-manylinux1_x86_64.whl
91678301ec0d6e6c20bf7c71bc8665a5 numpy-1.18.4-cp38-cp38-win32.whl
916b27fca6fb780907033067cad175fe numpy-1.18.4-cp38-cp38-win_amd64.whl
70e6c294f8dffa8d630eda1b0d42ae4d numpy-1.18.4.tar.gz
37277c5cbe5a850513fbff5ffdad1caf numpy-1.18.4.zip
SHA256
efdba339fffb0e80fcc19524e4fdbda2e2b5772ea46720c44eaac28096d60720 numpy-1.18.4-cp35-cp35m-macosx_10_9_intel.whl
2b573fcf6f9863ce746e4ad00ac18a948978bb3781cffa4305134d31801f3e26 numpy-1.18.4-cp35-cp35m-manylinux1_i686.whl
3f0dae97e1126f529ebb66f3c63514a0f72a177b90d56e4bce8a0b5def34627a numpy-1.18.4-cp35-cp35m-manylinux1_x86_64.whl
dccd380d8e025c867ddcb2f84b439722cf1f23f3a319381eac45fd077dee7170 numpy-1.18.4-cp35-cp35m-win32.whl
02ec9582808c4e48be4e93cd629c855e644882faf704bc2bd6bbf58c08a2a897 numpy-1.18.4-cp35-cp35m-win_amd64.whl
904b513ab8fbcbdb062bed1ce2f794ab20208a1b01ce9bd90776c6c7e7257032 numpy-1.18.4-cp36-cp36m-macosx_10_9_x86_64.whl
e22cd0f72fc931d6abc69dc7764484ee20c6a60b0d0fee9ce0426029b1c1bdae numpy-1.18.4-cp36-cp36m-manylinux1_i686.whl
2466fbcf23711ebc5daa61d28ced319a6159b260a18839993d871096d66b93f7 numpy-1.18.4-cp36-cp36m-manylinux1_x86_64.whl
00d7b54c025601e28f468953d065b9b121ddca7fff30bed7be082d3656dd798d numpy-1.18.4-cp36-cp36m-win32.whl
7d59f21e43bbfd9a10953a7e26b35b6849d888fc5a331fa84a2d9c37bd9fe2a2 numpy-1.18.4-cp36-cp36m-win_amd64.whl
efb7ac5572c9a57159cf92c508aad9f856f1cb8e8302d7fdb99061dbe52d712c numpy-1.18.4-cp37-cp37m-macosx_10_9_x86_64.whl
0e6f72f7bb08f2f350ed4408bb7acdc0daba637e73bce9f5ea2b207039f3af88 numpy-1.18.4-cp37-cp37m-manylinux1_i686.whl
9933b81fecbe935e6a7dc89cbd2b99fea1bf362f2790daf9422a7bb1dc3c3085 numpy-1.18.4-cp37-cp37m-manylinux1_x86_64.whl
96dd36f5cdde152fd6977d1bbc0f0561bccffecfde63cd397c8e6033eb66baba numpy-1.18.4-cp37-cp37m-win32.whl
57aea170fb23b1fd54fa537359d90d383d9bf5937ee54ae8045a723caa5e0961 numpy-1.18.4-cp37-cp37m-win_amd64.whl
ed722aefb0ebffd10b32e67f48e8ac4c5c4cf5d3a785024fdf0e9eb17529cd9d numpy-1.18.4-cp38-cp38-macosx_10_9_x86_64.whl
50fb72bcbc2cf11e066579cb53c4ca8ac0227abb512b6cbc1faa02d1595a2a5d numpy-1.18.4-cp38-cp38-manylinux1_i686.whl
709c2999b6bd36cdaf85cf888d8512da7433529f14a3689d6e37ab5242e7add5 numpy-1.18.4-cp38-cp38-manylinux1_x86_64.whl
f22273dd6a403ed870207b853a856ff6327d5cbce7a835dfa0645b3fc00273ec numpy-1.18.4-cp38-cp38-win32.whl
1be2e96314a66f5f1ce7764274327fd4fb9da58584eaff00b5a5221edefee7d6 numpy-1.18.4-cp38-cp38-win_amd64.whl
e0781ec6627e85f2a618478ee278893343fb8b40577b4c74b2ec15c7a5b8f698 numpy-1.18.4.tar.gz
bbcc85aaf4cd84ba057decaead058f43191cc0e30d6bc5d44fe336dc3d3f4509 numpy-1.18.4.zip