I think that and string functions that are exposed from an ndarray would have to be guaranteed to work in-place. Requiring casting to objects to use the methods feels more like syntactic sugar than an essential case. I think most of the ones mentioned are low performance and can't take advantage of the storage as a blob of int8 (ascii) or int32 (utf32) that underlay Numpy string arrays.
I also think the existence of these in pandas reduces the case for them being in Numpy. On Sun, Mar 7, 2021, 05:32 Todd <toddr...@gmail.com> wrote: > On Sat, Mar 6, 2021 at 12:57 PM dan_patterson <dan_patter...@outlook.com> > wrote: > >> The are in np.char >> >> mystr = np.array(["test first", "test second", "test third"]) >> >> np.char.title(mystr) >> array(['Test First', 'Test Second', 'Test Third'], dtype='<U11') >> > > I mentioned those in my email, but they are far less convenient to use > than class methods, nor do they relate well to how built-in strings are > used in Python. That is why other projects have started using accessor > methods and why Python removed all the separate string functions in Python > 3. The functions in np.char are also limited in their capabilities, and > fairly poorly documented in my opinion. Some of those limitations are > impossible to overcome, for example they inherently can never support > operators, addition or multiplication, or slicing like Python strings can, > while an accessor could. > > However, putting them as top-level methods for ndarray would pollute the > methods too much. That is why I am suggesting numpy do the same thing that > pandas, xarray, etc. are doing and putting those as methods under a 'str' > attribute for ndarrays rather than as separate functions. > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion >
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