How does a Python list differ from a NumPy array?

Python list is a built-in data type that can contain elements of different types, supports dynamic resizing, and stores references to objects. It is convenient for general programming but not optimized for numerical computations.

NumPy array is a specialized data structure for storing homogeneous (uniform) numerical data in contiguous memory space. It provides efficient operations on large arrays thanks to vectorization and optimized algorithms.

Main differences:

• Data types: list can contain different types, NumPy array has a single type.
• Performance: NumPy is faster for numerical operations.
• Memory: NumPy arrays use less memory.
• Functionality: NumPy offers many functions for mathematics and linear algebra.

Example:

import numpy as np

py_list = [1, 2, 3, 4]
np_array = np.array([1, 2, 3, 4])

# Summing elements
print(sum(py_list))        # 10
print(np_array.sum())       # 10