@D’aprano I think you’re misleading by what I said, sorry for not being crystal clear.
I just read the link on Julia (which I didn’t do) and I get what you mean now and it’s not quite different from what I said. I proposed introducing a new type : « vector » A few steps have been made in Python for typing and I think the next step is having typed collections. Keeping with nothing checked is better imo. So if we take this next step, we’ll get a vector type with *not-guaranteed* homogeneous data. Whether its type is « object » « int » or anything else doesn’t matter as long as it’s supposed to be the same. This doesn’t change anything in term of usage. Of course we should/could use map and usual operators on collections. What I was then proposing, to complete what you suggested and because I don’t like the dot notation, is using the matrix-multiplication the same way it is used in Julia with the dots. But I have a question. I never coded anything at C-level nor a compiler, is this possible for user defined types to make the vectorieation optimized the same way it’s done with numbers in numpy ? If yes, I think it would benefit the community. If no, it’s less likely, though it’s pursuing the steps made with typing On Sat 2 Feb 2019 at 10:23, Kirill Balunov <kirillbalu...@gmail.com> wrote: > > > сб, 2 февр. 2019 г. в 07:33, Steven D'Aprano <st...@pearwood.info>: > >> >> I didn't say anything about a vector type. >> >> > I agree you did not say. But since you started a new thread from the one > where the vector type was a little discussed, it seemed to me that it is > appropriate to mention it here. Sorry about that. > > >> > Therefore, it allows you to ensure that the method is present for each >> > element in the vector. The first given example is what numpy is all >> about >> > and without some guarantee that L consists of homogeneous data it hardly >> > make sense. >> >> Of course it makes sense. Even numpy supports inhomogeneous data: >> >> py> a = np.array([1, 'spam']) >> py> a >> array(['1', 'spam'], >> dtype='|S4') >> >> > Yes, numpy, at some degree, supports heterogeneous arrays. But not in the > way you brought it. Your example just shows homogeneous array of type > `'|S4'`. In the same way as `np.array([1, 1.234])` will be homogeneous. Of > course you can say - np.array([1, 'spam'], dtype='object'), but in this > case it will also be homogeneous array, but of type `object`. > > >> Inhomogeneous data may rule out some optimizations, but that hardly >> means that it "doesn't make sense" to use it. >> > > I did not say that it "doesn't make sense". I only said that you should > be lucky to call `..method()` on collections of heterogeneous data. And > therefore, usually this kind of operations imply that you are working with > a "homogeneous data". Unfortunately, built-in containers cannot provide > such a guarantee without self-checking. Therefore, in my opinion that at > the moment such an operator is not needed. > > With kind regards, > -gdg > > _______________________________________________ > Python-ideas mailing list > Python-ideas@python.org > https://mail.python.org/mailman/listinfo/python-ideas > Code of Conduct: http://python.org/psf/codeofconduct/ >
_______________________________________________ Python-ideas mailing list Python-ideas@python.org https://mail.python.org/mailman/listinfo/python-ideas Code of Conduct: http://python.org/psf/codeofconduct/
