How does NumPy handle large datasets efficiently?

NumPy handles large datasets efficiently by using fixed-type arrays that consume significantly less memory compared to Python lists. It performs operations directly on contiguous blocks of memory, avoiding the overhead of dynamic type checking. NumPy’s vectorized operations enable computations on entire arrays without explicit loops, leveraging optimized C and Fortran libraries. Additionally, NumPy supports memory-mapped files, allowing large datasets to be loaded into memory only as needed, reducing RAM usage for massive arrays.