Lecture
This lecture introduces NumPy, a fundamental library for scientific computing in Python. It covers the creation and manipulation of multi-dimensional arrays, known as ndarrays, which are central to NumPy's functionality. The instructor explains how to construct one-dimensional arrays using functions like np.linspace, which generates evenly spaced values over a specified range. The lecture also discusses the advantages of using NumPy arrays over Python lists, particularly in terms of performance and the ability to perform vectorized operations. Additionally, the lecture touches on basic operations such as arithmetic on arrays, indexing, and slicing. The importance of NumPy in numerical computing is emphasized, showcasing its role in optimizing calculations and enabling efficient data manipulation. The session concludes with a brief overview of the library's capabilities, setting the stage for more advanced topics in subsequent lectures.