Demystifying Python’s Memory Model: Mutability, Identity, and Function Arguments

Divine-Favour — Sun, 17 May 2026 22:41:40 +0000

When you first start programming in Python, everything feels intuitive. You assign a value to a variable, use it, and move on. However, underneath Python's clean syntax lies a strict set of rules governing how data is stored, referenced, and manipulated in memory. Understanding these mechanics is the dividing line between writing buggy code and writing highly optimized, predictable Python programs. This post breaks down the core concepts of Python's memory model, exploring object identity, mutability, and how data moves through functions.

Understanding id and type
In Python, absolutely everything is an object—from numbers and strings to functions and lists. Every object created in memory is automatically assigned three things: a value, a data type, and a unique identification number. You can inspect these properties using the built-in type() and id() functions. The type() function tells you the class of the object, while id() returns its unique memory address (in CPython, this corresponds to the object's location in RAM).

a = [1, 2, 3]
type(a)

id(a)
139926795932424

b = a
id(b)
139926795932424
As shown in the example above, when we assign b = a, Python does not duplicate the list. Instead, it copies the memory reference. Both variables now point to the exact same memory address, meaning a is b evaluates to True.

Mutable Objects
Mutable objects are data structures that can be modified in place without changing their identity (memory address). Common examples of mutable objects in Python include lists (list), dictionaries (dict), sets (set), and byte arrays. When you append an element to a list or update a key in a dictionary, you are directly altering the existing object in memory.

Python

l1 = [1, 2, 3]
print(id(l1))
140531824638784

l1.append(4)
print(l1)
[1, 2, 3, 4]
print(id(l1))
140531824638784
Notice how the list's contents changed from [1, 2, 3] to [1, 2, 3, 4], but the id() remained exactly the same. Because mutable objects can change, copying their reference carelessly (e.g., l2 = l1) means updates to one variable will unexpectedly modify the other.

Immutable Objects
In contrast, immutable objects cannot be altered once they are created. Examples include integers (int), floats (float), strings (str), tuples (tuple), and frozen sets (frozenset). If you attempt to alter an immutable object, Python is forced to build a brand new object at a completely different memory address and redirect your variable to it.

Python

a = (1, 2)
print(id(a))
139926795932424

a = a + (3,)
print(a)
(1, 2, 3)
print(id(a))
139926795938112
In this snippet, appending 3 to the tuple looks like a modification, but the changing id() proves that Python secretly created a whole new tuple (1, 2, 3) behind the scenes.

Why It Matters and How Python Treats Them Differently
Understanding mutability is crucial because Python optimizes memory allocation based on whether an object can change. Since immutable objects are safely locked down, Python heavily utilizes memory optimization tricks like string interning and integer caching. For instance, Python pre-loads small integers (from -5 to 256) and empty tuples into a shared global pool.

Python

x = ()
y = ()
x is y
True

num1 = 100
num2 = 100
num1 is num2
True
Because x, y, num1, and num2 are immutable, Python points identical values to the exact same pre-existing object to save RAM. If these were mutable lists, Python would never risk sharing memory addresses because a change to one would break the other.

How Arguments Are Passed to Functions
Python employs a mechanism known as pass-by-assignment (or pass-by-object-reference) when handing variables over to functions. This means the function parameter receives a copy of the memory address of the argument. What happens next depends entirely on whether that object is mutable or immutable.

If you pass a mutable object to a function and mutate it inside (e.g., using .append()), the changes persist outside the function:

Python
def increment_list(n):
n.append(4)

l = [1, 2, 3]
increment_list(l)
print(l) # Output: [1, 2, 3, 4]
However, if you pass an immutable object—or if you completely reassign a mutable variable inside the function using the = operator—you only change the local variable's shortcut. The outer scope remains completely untouched:

Python
def assign_value(n, v):
n = v # Rebinds the local name 'n' to point to 'v'

l1 = [1, 2, 3]
l2 = [4, 5, 6]
assign_value(l1, l2)
print(l1) # Output: [1, 2, 3]
Deep Dive: Advanced Python Optimization Techniques
Moving beyond the basics, diving into Python's implementation details reveals fascinating architectural decisions, specifically regarding the memory layouts of CPython's core types. For instance, NSMALLPOSINTS and NSMALLNEGINTS are specific macros in the C source code that handle the compilation-level caching of integers. Furthermore, looking into special cases like tuple immutability reveals a unique nuance: while a tuple itself is structurally immutable and cannot have its references swapped, it can contain a mutable object (like a list) as an element. If you modify the list inside that tuple, the list changes in place, yet the tuple’s identity remains intact—highlighting that immutability guarantees the integrity of the references the tuple holds, not necessarily the values inside those references.

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Demystifying Python’s Memory Model: Mutability, Identity, and Function Arguments