Have any of the numpy devs weighed in on this? If an efficient version of this were available in numpy there is a lot of pandas code I would enjoy ripping out.
On Sun, Jan 2, 2022 at 11:16 AM Joseph Fox-Rabinovitz < jfoxrabinov...@gmail.com> wrote: > Is there a guide on how to pacakage non-ufunc functions with multiple > loops? Something like sort? It looks like there is no way of adding > additional arguments to a ufunc as of yet. > > On a related note, would it be more useful to have a function that returns > the number of bits required to store a number, or -1 if it has a fractional > part? Then you could just test something like ``(k := integer_bits(a)) < 64 > & k > 0``. > > - Joe > > > On Sat, Jan 1, 2022 at 5:55 AM Joseph Fox-Rabinovitz < > jfoxrabinov...@gmail.com> wrote: > >> Stefano, >> >> That is an excellent point. Just to make sure I understand, would an >> interface like `is_integer(a, int_dtype=None)` be satisfactory? That way, >> there are no bounds by default (call it python integer bounds), but the >> user can specify a limited type at will. An alternative would be something >> like `is_integer(a, bits=None, unsigned=False)`. This would have the >> advantage of testing against hypothetical types, which might be useful >> sometimes, or just annoying. I could always allow a two-element tuple in as >> an argument to the first version. >> >> While I completely agree with the idea behind adding this test, one big >> question remains: can I add arbirary arguments to a ufunc? >> >> - Joe >> >> On Sat, Jan 1, 2022 at 5:41 AM Stefano Miccoli <stefano.micc...@polimi.it> >> wrote: >> >>> I would rather suggest .is_integer(integer_dtype) signature because >>> knowing that 1e300 is an integer is not very useful in the numpy world, >>> since this integer number is not representable as a numpy.integer dtype. >>> >>> Note that in python >>> >>> assert not f.is_integer() or int(f) == f >>> >>> never fails because integers have unlimited precision but this does >>> would not map into >>> >>> assert ( ~f_arr.is_integer() | (np.int64(f_arr) == f.arr) ).all() >>> >>> because of possible OverflowErrors. >>> >>> Stefano >>> >>> On 31 Dec 2021, at 04:46, numpy-discussion-requ...@python.org wrote: >>> >>> Is adding arbitrary optional parameters a thing with ufuncs? I could >>> easily add upper and lower bounds checks. >>> >>> On Thu, Dec 30, 2021, 20:56 Brock Mendel <jbrockmen...@gmail.com> wrote: >>> >>>> At least some of the commenters on that StackOverflow page need a >>>> slightly stronger check: not only is_integer(x), but also >>>> "np.iinfo(dtype).min <= x <= np.info(dtype).max" for some particular >>>> dtype. i.e. "Can I losslessly set these values into the array I already >>>> have?" >>>> >>>> >>>> >>> >>> _______________________________________________ >>> 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: jfoxrabinov...@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: jbrockmen...@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
