How does NumPy handle internal dtype object fallback execution?

Object dtype forces NumPy to fall back to Python-level execution for element-wise operations.

When arrays use dtype=object, NumPy cannot use vectorized C loops. Instead, each operation is executed via Python function calls per element. This removes SIMD optimizations and significantly reduces performance, making object arrays unsuitable for numerical workloads.