How does Pandas handle internal expression evaluation optimization (numexpr vs Python engine)?

Pandas can evaluate expressions using NumExpr for speed or Python engine for flexibility.

When evaluating expressions via eval() or query(), Pandas may delegate computation to NumExpr, which splits operations into chunks and executes them in a multi-threaded C-like environment. If NumExpr is unavailable or unsupported, Pandas falls back to the Python engine, which is slower because it interprets expressions in Python. This optimization reduces memory allocation and improves CPU cache usage.