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 ...

When learning Python, one of the most important concepts to understand is the difference between   mutable   and   immutable   data types. To explain this concept, I'm going to break it down   step-by-step   with examples and visuals! 1. What Do "Mutable" and "Immutable" Mean? Mutable  means: You can change the object after it’s created. Immutable  means: Once the object is created, it cannot be changed. Think of it like this: Type Real-life Analogy Mutable A whiteboard: you can erase and write again Immutable A printed photo: once printed, you can’t change it 2. Immutable Data Types in Python These are data types that  cannot be changed  after they are created. Examples of Immutable Types: int float bool str tuple frozenset Example 1: Integers (int) a = 5 print (id(a)) # memory address of a a = a + 1 print (a) # 6 print (id(a)) # different memory address! Even though just added 1 to a, Python  created a new object in memo...