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
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