3 ms·

You have made a very verbose numpy version :) Here is a slightly better one: a = np.random.randint(-100, 100, size=100) a[(a & 2 == 0) & (a > 0)] Pyth

You have made a very verbose numpy version :) Here is a slightly better one: a = np.random.randint(-100, 100, size=100) a[(a & 2 == 0) & (a > 0)] Python does indeed have logical boolean operators. I think this is clearer than your Julia example because: - The numpy version makes it clear that you are using random integers, not floating point values. - The keyword argument for "size" makes it clear what that second 100 is for. (The use of the first two numbers, -100 and 100, is pretty clear from context.) - In numpy, most operations are element-wise by default, because the result would be ambiguous or not useful otherwise. This removes the line noise of the extra "." before operations. Don't get me wrong, I think Julia is awesome. I just think you've constructed a very poor example for numpy.

timholy 11y ago
> 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 11y ago
  > 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 11y ago
    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.

timholy 11y ago

> 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 11y ago
> 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 11y ago
  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.