How does Pandas optimize filtering using boolean masks internally?

Boolean masks are computed using vectorized NumPy operations in compiled code.

Filtering uses NumPy vectorized comparisons that generate boolean arrays. These arrays are then applied to index DataFrames. This avoids Python loops and ensures high performance. However, mask creation still incurs memory overhead for large datasets.