How does Pandas handle internal optimization of apply vs vectorized operations?

Vectorized operations are executed in compiled C loops, while apply runs Python-level functions.

Vectorized operations leverage NumPy and C extensions, enabling bulk computation. apply executes Python functions row-by-row or column-by-column, which introduces interpreter overhead and reduces performance. Pandas internally tries to use vectorization wherever possible for built-in operations.