NumPy

NumPy provides homogeneous arrays that enable hardware-level parallelism not available to Python lists. NumPy's C implementation removes Python interpreter overhead from hot loops. NumPy gains speed from SIMD instructions because homogeneous arrays let the CPU apply one arithmetic instruction to multiple elements. Inhomogeneous lists cannot use SIMD because they lack a uniform byte layout. The article recommends using NumPy arrays or libraries built on them for numerical work in quant trading, AI, and machine learning.