Mutable vs Immutable Data Types in Python Explained

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 memory for the result.

**Key point: Integers can't be changed in-place. When you "change" them, you're actually creating a new integer object.

Example 2: Strings (str)

s = "hello"
print(id(s))

s = s + " world"
print(s)        # "hello world"
print(id(s))    # memory address has changed

Even though it looks like we modified the string, Python made a brand new one.

3. Mutable Data Types in Python

These are data types that can be changed without creating a new object.

Examples of Mutable Types:

• list
• dict
• set
• bytearray
• Custom objects (most of them)

Example 3: Lists (list)

my_list = [1, 2, 3]
print(id(my_list))

my_list.append(4)
print(my_list)     # [1, 2, 3, 4]
print(id(my_list)) # Same memory address!

Added an item to the list and the memory address did not change. That means the same object was updated in-place.

Example 4: Dictionaries (dict)

my_dict = {"name": "Alice"}
print(id(my_dict))

my_dict["age"] = 30
print(my_dict)      # {'name': 'Alice', 'age': 30}
print(id(my_dict))  # Same memory address

Just like lists, dictionaries can also be changed in-place.

4. Visual Illustration

Here’s a simple diagram to help visualize this:

5. Why Does This Matter?

Understanding mutability helps you:

• Avoid unexpected bugs when passing data to functions.
• Use Python's data types more effectively.
• Understand how Python handles memory and performance.