Dear Lev, thank you a lot! Something like this should be part of the Numpy documentation. I like the diagram, looks very nice! Also, I’ve opened an issue regarding data types
https://github.com/numpy/numpy/issues/20662 <https://github.com/numpy/numpy/issues/20662> Some feedback from my side: 1. When calling numpy.array([1,2,3,4]) it gives me an int64 data type most of the time (two x86_64 systems, one arm64 system). The only time I’ve got int32 was on a Raspberry Pi, which is a software limitation, since the CPU is 64 bit and they have even replaced their so-far 32bit only Raspberry Pi Zero with a 64bit version (yes, one day Raspberry OS with 64 bit might actually become the default!). I don’t know what machine you are working on, but int64 should be the default. 2. x64 refers to the obsolete Intel Itanium architecture (mentioned once). Should be x86_64. 3. np.errstate looks nice, I could use that for my pull request as well. Many thanks & best regards, Michael > On 25. Dec 2021, at 10:02, Lev Maximov <lev.maxi...@gmail.com> wrote: > > Hi everyone, > > I'm almost done with the article about numpy types – something I haven't > covered in Numpy Illustrated. > > Would someone please have a look to confirm I haven't written anything > anti-climatic there? > > https://axil.github.io/numpy-data-types.html > <https://axil.github.io/numpy-data-types.html> > > -- > Best regards, > Lev > > PS Earlier today I've mistakenly sent an email with the wrong link. > _______________________________________________ > NumPy-Discussion mailing list -- numpy-discussion@python.org > To unsubscribe send an email to numpy-discussion-le...@python.org > https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ > Member address: michael.sieber...@gmail.com
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