[timholy]

> The numpy version makes it clear that you are using random integers, not floating point values.

That's also completely clear in the Julia version, if you learn a little Julia.

> In numpy, most operations are element-wise by default, because the result would be ambiguous or not useful otherwise.

This is why I think the Julia approach is better. If I write `a == 7`, am I testing whether `a` is 7 or whether any of the elements of `a` are 7?

[jdreaver]

> That's also completely clear in the Julia version, if you learn a little Julia.

In every language I've used, the default is for a "rand" function to return random floats between 0 and 1, and given arguments it returns floats between the arguments. I don't think it has to do with learning Julia, it is just that including "integer" in the function name makes it clear the function returns integers.

> This is why I think the Julia approach is better. If I write `a == 7`, am I testing whether `a` is 7 or whether any of the elements of `a` are 7?

I think this is more of a comment about mixing arrays and scalars in a dynamic language. I made my comment assuming you are performing operations on arrays. If you are comparing two arrays, I think the default of element-wise operations makes more sense.

[KenoFischer]

In julia the rand function is more general in that it samples from a distribution, which you can pass as the first argument (defaulting to uniform on [0,1]). Since the first argument is an integer range, you get an integer value.