NumPy Fundamentals: Arrays and Vectorized Operations

As you embark on your journey into data analysis and AI/ML using Python within the SAP ecosystem, you'll quickly discover the need for efficient numerical computation. While standard Python lists are versatile, they are not optimized for large-scale mathematical operations. This is where NumPy, short for Numerical Python, becomes indispensable. NumPy is the foundational library for numerical computing in Python, providing powerful tools for working with arrays and performing operations at high speed.

The core data structure in NumPy is the `ndarray`, which stands for N-dimensional array. Unlike standard Python lists that can hold elements of different data types, a NumPy array is a grid of elements of the same type. This homogeneity is what allows NumPy to perform operations much faster and more efficiently than Python lists, especially on large datasets common in SAP environments.