Phase 4 achieved 3.8% efficiency vs theoretical NumPy limit.

Remaining overhead is likely:
- Python interpreter overhead (function calls)
- Pandas overhead (pd.Series wrapping)
- eval() overhead (string parsing)

Investigation areas:
- Profile _eval_in_namespace()
- Profile column assignment (self.df[col] = values)
- Evaluate Arrow compute backend feasibility

Note: Breaking past ~10% efficiency likely requires compiled backend.

Author: miranov25