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Re:Loops in Python – Comparison and Performance by jph00 on Thursday August 08, 2019 @03:01PM (#20647480)
> Turning a little more complex elementwise computation into vectorized numpy calls produces a lot of memory overhead. For each vectorized numpy call, which does a basic computation (like adding two operands, or negating, or sinus of, ...), you allocate a whole new array.

I believe this is no longer correct, within a single expression. In the last couple of years things were fixed so that `a = x+y+z`, for instance, will not create an intermediate array.

Re:Loops in Python – Comparison and Performance by nerdponx on Thursday August 08, 2019 @04:27PM (#20648326)
This is also what the 'numexpr' library does.