Hi, I know that I'm a little late to be asking about this, but I don't see a comment elsewhere on it (in the NEP, the implementation PR #25347, or this email thread).
As I understand it, the new StringDType implementation distinguishes 3 types of individual strings, any of which can be present in an array: 1. short strings, included inline in the array (at most 15 bytes on a 64-bit system) 2. arena-allocated strings, which are managed by the npy_string_allocator 3. heap-allocated strings, which are pointers anywhere in RAM. Does case 3 include strings that are passed to the array as views, without copying? If so, then the ownership of strings would either need to be tracked on a per-string basis (distinct from the array_owned boolean, which characterizes the whole array), or they need to all be considered stolen references (NumPy will free all of them when the array goes out of scope), or they all need to be considered borrowed references (NumPy will not free any of them when the array goes out of scope). If the array does not accept new strings as views, but always copies any externally provided string, then why distinguish between cases 2 and 3? How would an array end up with some strings being arena-allocated and other strings being heap-allocated? Thanks! -- Jim On Wed, Sep 20, 2023 at 10:25 AM Nathan <nathan.goldb...@gmail.com> wrote: > > > On Wed, Sep 20, 2023 at 4:40 AM Kevin Sheppard <kevin.k.shepp...@gmail.com> > wrote: > >> >> >> On Wed, Sep 20, 2023 at 11:23 AM Ralf Gommers <ralf.gomm...@gmail.com> >> wrote: >> >>> >>> >>> On Wed, Sep 20, 2023 at 8:26 AM Warren Weckesser < >>> warren.weckes...@gmail.com> wrote: >>> >>>> >>>> >>>> On Fri, Sep 15, 2023 at 3:18 PM Warren Weckesser < >>>> warren.weckes...@gmail.com> wrote: >>>> > >>>> > >>>> > >>>> > On Mon, Sep 11, 2023 at 12:25 PM Nathan <nathan.goldb...@gmail.com> >>>> wrote: >>>> >> >>>> >> >>>> >> >>>> >> On Sun, Sep 3, 2023 at 10:54 AM Warren Weckesser < >>>> warren.weckes...@gmail.com> wrote: >>>> >>> >>>> >>> >>>> >>> >>>> >>> On Tue, Aug 29, 2023 at 10:09 AM Nathan <nathan.goldb...@gmail.com> >>>> wrote: >>>> >>> > >>>> >>> > The NEP was merged in draft form, see below. >>>> >>> > >>>> >>> > https://numpy.org/neps/nep-0055-string_dtype.html >>>> >>> > >>>> >>> > On Mon, Aug 21, 2023 at 2:36 PM Nathan <nathan.goldb...@gmail.com> >>>> wrote: >>>> >>> >> >>>> >>> >> Hello all, >>>> >>> >> >>>> >>> >> I just opened a pull request to add NEP 55, see >>>> https://github.com/numpy/numpy/pull/24483. >>>> >>> >> >>>> >>> >> Per NEP 0, I've copied everything up to the "detailed >>>> description" section below. >>>> >>> >> >>>> >>> >> I'm looking forward to your feedback on this. >>>> >>> >> >>>> >>> >> -Nathan Goldbaum >>>> >>> >> >>>> >>> >>>> >>> This will be a nice addition to NumPy, and matches a suggestion by >>>> >>> @rkern (and probably others) made in the 2017 mailing list thread; >>>> >>> see the last bullet of >>>> >>> >>>> >>> >>>> https://mail.python.org/pipermail/numpy-discussion/2017-April/076681.html >>>> >>> >>>> >>> So +1 for the enhancement! >>>> >>> >>>> >>> Now for some nitty-gritty review... >>>> >> >>>> >> >>>> >> Thanks for the nitty-gritty review! I was on vacation last week and >>>> haven't had a chance to look over this in detail yet, but at first glance >>>> this seems like a really nice improvement. >>>> >> >>>> >> I'm going to try to integrate your proposed design into the dtype >>>> prototype this week. If that works, I'd like to include some of the text >>>> from the README in your repo in the NEP and add you as an author, would >>>> that be alright? >>>> > >>>> > >>>> > >>>> > Sure, that would be fine. >>>> > >>>> > I have a few more comments and questions about the NEP that I'll >>>> finish up and send this weekend. >>>> > >>>> >>>> One more comment on the NEP... >>>> >>>> My first impression of the missing data API design is that >>>> it is more complicated than necessary. An alternative that >>>> is simpler--and is consistent with the pattern established for >>>> floats and datetimes--is to define a "not a string" value, say >>>> `np.nastring` or something similar, just like we have `nan` for >>>> floats and `nat` for datetimes. Its behavior could be what >>>> you called "nan-like". >>>> >>> >>> Float `np.nan` and datetime missing value sentinel are not all that >>> similar, and the latter was always a bit questionable (at least partially >>> it's a left-over of trying to introduce generic missing value support I >>> believe). `nan` is a float and part of C/C++ standards with well-defined >>> numerical behavior. In contrast, there is no `np.nat`; you can retrieve a >>> sentinel value with `np.datetime64("NaT")` only. I'm not sure if it's >>> possible to generate a NaT value with a regular operation on a datetime >>> array a la `np.array([1.5]) / 0.0`. >>> >>> The handling of `np.nastring` would be an intrinsic part of the >>>> dtype, so there would be no need for the `na_object` parameter >>>> of `StringDType`. All `StringDType`s would handle `np.nastring` >>>> in the same consistent manner. >>>> >>>> The use-case for the string sentinel does not seem very >>>> compelling (but maybe I just don't understand the use-cases). >>>> If there is a real need here that is not covered by >>>> `np.nastring`, perhaps just a flag to control the repr of >>>> `np.nastring` for each StringDType instance would be enough? >>>> >>> >>> My understanding is that the NEP provides the necessary but limited >>> support to allow Pandas to adopt the new dtype. The scope section of the >>> NEP says: "Fully agreeing on the semantics of a missing data sentinels or >>> adding a missing data sentinel to NumPy itself.". And then further down: >>> "By only supporting user-provided missing data sentinels, we avoid >>> resolving exactly how NumPy itself should support missing data and the >>> correct semantics of the missing data object, leaving that up to users to >>> decide" >>> >>> That general approach I agree with, it's a large can of worms and not >>> the main purpose of this NEP. Nathan may have more thoughts about what, if >>> anything, from your suggestions could be adopted, but the general "let's >>> introduce a missing value thing" is a path we should not go down here imho. >>> >>> >>> >>>> >>>> If there is an objection to a potential proliferation of >>>> "not a thing" special values, one for each type that can >>>> handle them, then perhaps a generic "not a value" (say >>>> `np.navalue`) could be created that, when assigned to an >>>> element of an array, results in the appropriate "not a thing" >>>> value actually being assigned. In a sense, I guess this NEP is >>>> proposing that, but it is reusing the floating point object >>>> `np.nan` as the generic "not a thing" value >>>> >>> >>> It is explicitly not using `np.nan` but instead allowing the user to >>> provide their preferred sentinel. You're probably referring to the example >>> with `na_object=np.nan`, but that example would work with another sentinel >>> value too. >>> >>> Cheers, >>> Ralf >>> >>> >>> >>>> , and my preference >>>> is that, *if* we go with such a generic object, it is not >>>> the floating point value `nan` but a new thing with a name >>>> that reflects its purpose. (I guess Pandas users might be >>>> accustomed to `nan` being a generic sentinel for missing data, >>>> so its use doesn't feel as incohesive as it might to others. >>>> Passing a string array to `np.isnan()` just feels *wrong* to >>>> me.) >>>> >>>> Any, that's my 2¢. >>>> >>>> Warren >>>> >>>> >>>> >>> >> I was a bit surprised that len was not used as part of the missing >> value. The NEP proposal that 0 is a empty string unless there is a >> sentinal in which case it is a missing value feels pretty limiting, since >> these are distinctly different things. >> >> Would it make sense for len<0 to indicate a missing value. This would >> require using ssize_t instead of size_t, and would then limit the string >> size. In principle this would allow for sizeof(ssize_t) / 2 distinct >> missing value. I think ssize_t is well-defined on all platforms >> targeted by NumPy. >> >> Kevin >> >> > Hey Kevin, > > Thanks for the comment. Right now the current NEP text is a little out of > date compared to the implementation. I've since rewritten it to use > Warren's proposal more or less verbatim, so now the missing value flag is > stored in a bit of the size field > > See https://github.com/numpy/numpy-user-dtypes/pull/86 for the > implementation, which also includes a small string optimization > implementation. > > >> _______________________________________________ >> 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: nathan12...@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: jpivar...@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: arch...@mail-archive.com
