What are the performance implications of using arrays vs. lists in Python?
In Python, a popular programming language in data science, performance can be greatly influenced by the choice between using arrays and lists. Both are used to store collections of items, but they are implemented differently and serve different purposes. Understanding the differences between these data structures is crucial for writing efficient code. Arrays, typically provided by the NumPy library, are designed for numerical computation and are highly optimized for performance. Lists, on the other hand, are built-in Python data structures that are more flexible but may not offer the same speed for numerical operations.