Lesson 2.1: Introduction to NumPy for Numerical Operations

The provided source offers an introduction to NumPy, emphasizing its role in numerical operations within Python, particularly for clinical trial data. It distinguishes NumPy arrays from standard Python lists by highlighting their homogeneity and efficiency in terms of speed and memory, attributing these advantages to vectorized operations. The content covers fundamental array manipulations such as creation, slicing, and filtering, alongside common aggregation functions like mean and standard deviation, all illustrated with practical code examples in both Python and SAS for comparison.

The provided source offers an introduction to NumPy, emphasizing its role in numerical operations within Python, particularly for clinical trial data. It distinguishes NumPy arrays from standard Python lists by highlighting their homogeneity and efficiency in terms of speed and memory, attributing these advantages to vectorized operations. The content covers fundamental array manipulations such as creation, slicing, and filtering, alongside common aggregation functions like mean and standard deviation, all illustrated with practical code examples in both Python and SAS for comparison.