What's interesting there is that numpy is inspired (more than a little) by APL and aims to bring that 'array' thinking to python. I agree that thinking in this 'array' way helps to better construct a solution in any language, so I'm leaning towards 'designing' with APL glyphs, even if that's not the language I'm implementing the thing in.

If it takes any inspiration from APL, it would be mostly indirect, via Matlab.

I've seen the connection made here https://dev.to/bakerjd99/numpy-another-iverson-ghost-9mc though the link to the source quote is broken. In all fairness, Matlab is itself inspired by APL.

Analogous code in J,

  /:~ s -. p [ p =: s*/s [ s=: 2+i.48

An exercise for numpy, test that GCD(x,y) * LCM(x,y) = x*y using 1000 random numbers in the range 0..99 for x e y.

  test =:  (* = *. * +.) & ?
  *./ test~  1000 # 100

Thanks, that was fun.

    def d(x): N = arange(1, x + 1); return N[x % N == 0]
    def m(x, n): return x * arange(1, n + 1)
    def gcd(x, y): return max(set(d(x)) & set(d(y)))
    def lcm(x, y): return min(set(m(x, y)) & set(m(y, x)))
    def test(x, y): return gcd(x, y) * lcm(x, y) == x * y
    all([test(x, y) for (x, y) in randint(1, 100, (1000, 2))])  # True

I am not a good golfer. Now I want to look at the codegolf stackexchange for this...

Equivalently in APL:

  ⎕io←0               ⍝ to match J
  test ← (× = ∨ × ∧) ∘ ?
  ∧⌿ test⍨ 1000⍴100