AI Practitioner

In Python-based numerical computing and data processing, two essential constructs dominate: the native Python list and the NumPy array. While similar in some basic functionality, they are vastly different in performance, flexibility, and internal implementation. This guide walks you through their usage, provides code examples, and compares their technical underpinnings for performance-critical applications. Python List Python lists are mutable, ordered collections capable of holding elements of heterogeneous data types. Basic Usage: # Creating a list py_list = [1, 2, 3, 4, 5] # Accessing and modifying elements py_list[0] = 10 # Appending and extending py_list.append(6) py_list.extend([7, 8]) # List comprehension squared = [x**2 for x in py_list] # Heterogeneous types mixed_list = [1, 'two' , 3.0, [4]] Limitations: No built-in support for vectorized operations. Poor performance with large numerical computations. Higher memory overhead due to dynamic typing and object ...