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.
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