How does NumPy handle performance bottlenecks in Python-to-C transitions?

NumPy minimizes Python overhead by moving loops into compiled C code.

Performance bottlenecks arise when Python loops interact with NumPy arrays. NumPy solves this by pushing computation into C-based ufuncs, reducing interpreter overhead. However, transitions still occur when mixing Python objects or using object dtype arrays, which reintroduces Python-level execution costs.