Python list vs. array – when to use?

In Python, lists are versatile, capable of storing elements of varying data types, and can dynamically resize, making them ideal for general-purpose storage. Arrays, available via the array module, require all elements to be of the same type and are more memory-efficient, offering better performance for numerical computations. For advanced numerical operations, NumPy arrays are recommended due to their enhanced functionality and efficiency. In summary, use lists for collections with mixed data types or when frequent resizing is needed, and opt for arrays when handling large, homogeneous datasets where performance and memory optimization are crucial.