How does NumPy handle internal vectorization vs Python loop execution trade-offs?

NumPy prefers vectorized C-level execution via ufuncs over Python loops to reduce interpreter overhead.

Vectorization in NumPy replaces explicit Python loops with operations executed in compiled C code. Each ufunc processes entire memory buffers using optimized loops, reducing function-call overhead per element. Python loops are orders of magnitude slower due to bytecode interpretation, dynamic typing, and function dispatch costs. However, vectorization may introduce temporary arrays and memory overhead, so there is a trade-off between speed and memory usage.