On 2019-02-02 09:22, Kirill Balunov wrote:
сб, 2 февр. 2019 г. в 07:33, Steven D'Aprano
mailto:steve@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.
Here's a question: when you use a subscript on a vector, does it apply to the vector itself, or its members? For example, given:
my_strings = Vector(['one', 'two', 'three'])
what is:
my_strings[1 : ]
? Is it: Vector(['ne', 'wo', 'hree']) or: Vector(['two', 'three']) ?