@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 <kirillbalunov@gmail.com> wrote:


сб, 2 февр. 2019 г. в 07:33, Steven D'Aprano <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.

With kind regards,
-gdg
 
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