I'm surprised no one has mentioned it already: int and uint are reversed in the first table.
I do agree with others that this is a great overview page that should be included in the numpy docs. Thanks Lev! Juan. On Sun, 26 Dec 2021, at 12:59 PM, Lev Maximov wrote: > I've tried to take into account all the suggestions from this thread. > > https://axil.github.io/numpy-data-types.html shows new version now and > > https://github.com/axil/numpy-data-types/commit/14d9da053fd67e5569436faa1f58599c0cc8b380#diff-ed7002b439e9ac845f22357d822bac1444730fbdb6016d3ec9432297b9ec9f73 > displays most of the changes. > > As for the inheritance diagram, I think it is perfectly fine to add it to the > documentation as is, > except that I'd put back the 'void' type I've originally omitted to keep it > simple. > > Btw is anyone aware why 'U' is missing from the np.typecode['Character']? > > On Sun, Dec 26, 2021 at 11:57 PM Lev Maximov <lev.maxi...@gmail.com> wrote: >> Python 3.9.7 (tags/v3.9.7:1016ef3, Aug 30 2021, 20:19:38) [MSC v.1929 64 bit >> (AMD64)] on win32 >> >> ^^ this is relevant ^^^^ this is >> not >> Type "help", "copyright", "credits" or "license" for more information. >> >>> import numpy as np >> >>> np.int_ >> <class 'numpy.int32'> >> >> On Sun, Dec 26, 2021 at 11:42 PM Michael Siebert >> <michael.sieber...@gmail.com> wrote: >>> >>> Okay, little modification to my last mail: many Android smartphones are >>> still 32 bit, but according to >>> >>> https://www.androidauthority.com/arm-32-vs-64-bit-explained-1232065/ >>> >>> from 2023 on, all (or at least many) new ARM processors will be 64 bit only. >>> >>> Apple‘s iPhone 64 bit only since quite a while already (September 2017, iOS >>> 11 release). >>> >>>> On 26. Dec 2021, at 17:31, Lev Maximov <lev.maxi...@gmail.com> wrote: >>>> >>>> Hi Michael, >>>> >>>> > Python on Windows is and is supposed to be, as far as I get it, a dying >>>> > platform. >>>> I would join Matti in thinking that it is a misconception. >>>> >>>> Have you heard of the enormous daily updated unofficial repository >>>> <https://www.lfd.uci.edu/~gohlke/pythonlibs/> of the binary windows >>>> compilations of >>>> almost 600 python libraries by Christoph Gohlke? (numpy and libs depending >>>> on it are built with MKL there) >>>> It is there for a reason. >>>> >>>> If you look at the stats such as this one (Matti already mentioned them >>>> while I was writing this text), >>>> >>>> https://www.jetbrains.com/research/python-developers-survey-2018/ >>>> https://www.jetbrains.com/lp/python-developers-survey-2020/ >>>> >>>> you'll see (in addition to the fact that numpy is the #1 library in data >>>> science ;) ) that in >>>> the recent years the percentage of windows user among the developers is >>>> quite high: >>>> 69% linux - 47% windows - 32% macos (2018) >>>> 68% linux - 48% windows - 29% macos (2020) >>>> So it looks as if it is rather growing than dying. >>>> >>>> This is due to the popularity of the above mentioned data science and AI, >>>> which have skyrocketed in the >>>> last 10 years. And the vast majority of data scientists work on windows. >>>> >>>> Windows as a platform for developers as a whole is also quite flourishing >>>> today. >>>> According to the stackoverflow 2021 developer survey >>>> <https://insights.stackoverflow.com/survey/2021#most-popular-technologies-op-sys> >>>> 45% of the respondents use Windows (25% linux, 25% macos). >>>> Among the professional developers the numbers are 41% for windows, 30% >>>> macos, 26% linux. >>>> >>>> Also the primary audience of the tutorials like mine (as well as of >>>> stackoverflow?) are windows users. >>>> Linux users can easily figure things described there on their own, through >>>> the docstrings, source code >>>> or, as a last resort, through the docs ) >>>> >>>> >The more experienced the Python developers are, the more likely they are >>>> >to use Linux and macOS as development >>>> > environments, and the less likely they are to choose Windows. >>>> (from the same jetbrains survey of 2018) >>>> >>>> I wouldn't like to go into holy wars, though. I'm equally literate in both >>>> unix and windows (somewhat less in macos) >>>> and in my opinion the interests of all the users of the the three >>>> operating systems should be taken into account >>>> in both the code of the library and the docs. >>>> >>>> The documentation is sometimes pretty ignorant of mac/windows users, btw: >>>> > Alias on this platform (Linux x86_64) >>>> https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.int_** >>>> And what about the other platforms? >>>> >>>> As for the particular issue of the difference in the default integer >>>> types, in my opinion the default choice of int32 on windows for >>>> array [1,2,3] fits the description >>>> >>>> >" If not given, then the type will be determined as the minimum type >>>> >required to hold the objects in the sequence." >>>> https://numpy.org/doc/stable/reference/generated/numpy.array.html >>>> >>>> better than int64 on linux/macos. >>>> >>>> Best regards, >>>> Lev >>>> >>>> >>>> On Sun, Dec 26, 2021 at 8:45 PM Michael Siebert >>>> <michael.sieber...@gmail.com> wrote: >>>>> >>>>> Hey Lev, >>>>> >>>>> I‘ve forgotten to mention my MacBook M1, >>>>> it‘s also int64 there. >>>>> >>>>> Python on Windows is and is supposed to be, as far as I get it, a dying >>>>> platform. A billion things are broken there (HDF comes to my mind) and it >>>>> seems even Microsoft wants developers to move away from native Windows >>>>> with their introduction of WSL (Windows Subsystem for Linux). Its latest >>>>> version, WSL2 even comes with an actual Linux kernel and since Windows >>>>> 11, it has support for graphical applications (Xorg) out of the box. With >>>>> Visual Studio Code (also Microsoft) and it’s remote capabilities, one >>>>> does not even feel a difference between developing in an Ubuntu in a WSL >>>>> in Windows and an actual Ubuntu. >>>>> >>>>> Considering the „traditional“ C datatypes, fixed types and prioritizing >>>>> them in Numpy documentation, that‘s what my issue (see below) is about. I >>>>> think they have summarized it nicely in >>>>> >>>>> https://matt.sh/howto-c >>>>> >>>>> Best regards, Michael >>>>> >>>>>> On 26. Dec 2021, at 13:49, Lev Maximov <lev.maxi...@gmail.com> wrote: >>>>>> >>>>>> Dear Michael, >>>>>> >>>>>> Thank you for your feedback! >>>>>> >>>>>> I've fixed the x86_64 typo. >>>>>> >>>>>> I'll think how to reformulate the int32 part. I work on debian x86_64 >>>>>> and windows 10 64bit. Constructing an array with np.array([1,2,3]) as >>>>>> well as np.array([1,2,3], dtype=np.int_) gives me int64 dtype on linux, >>>>>> and int32 on windows. >>>>>> >>>>>> As suggested by Matti, I've put the rst source (and images) into a >>>>>> separate github repository >>>>>> >>>>>> https://github.com/axil/numpy-data-types >>>>>> >>>>>> PRs are welcome. My primary concern is to exclude serious typos/mistakes >>>>>> that might mislead/harm the readers if used. >>>>>> >>>>>> My personal preference is towards explicit width types like np.int32, >>>>>> but from reading the docs I have a feeling there's a trend of migrating >>>>>> towards the c-style notation. >>>>>> >>>>>> Best regards, >>>>>> Lev >>>>>> >>>>>> On Sun, Dec 26, 2021 at 7:05 PM Michael Siebert >>>>>> <michael.sieber...@gmail.com> wrote: >>>>>>> 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 >>>>>>> >>>>>>> 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 >>>>>>>> >>>>>>>> -- >>>>>>>> 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 >>>>>>> >>>>>>> _______________________________________________ >>>>>>> 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: lev.maxi...@gmail.com >>>>>> _______________________________________________ >>>>>> 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 >>>>> _______________________________________________ >>>>> 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: lev.maxi...@gmail.com >>>> _______________________________________________ >>>> 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 >>> _______________________________________________ >>> 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: lev.maxi...@gmail.com > _______________________________________________ > 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: j...@fastmail.com >
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