Mutability, Identity, and Object Semantics

This guide explains how Python compares objects, what makes objects the "same," and how copying works.

== vs is: equality vs identity

Python has two ways to compare objects: == checks if values are equal, while is checks if they're the same object.

== checks equality

The == operator checks if two objects have the same value:

a = [1, 2, 3]
b = [1, 2, 3]
print(a == b)  # True - same values

Even though a and b are different objects in memory, they contain the same values, so == returns True.

is checks identity

The is operator checks if two names reference the same object:

a = [1, 2, 3]
b = [1, 2, 3]
print(a is b)  # False - different objects

c = a
print(a is c)  # True - same object

a and b are different list objects (even though they have the same contents), so a is b is False. But c = a makes c reference the same object as a, so a is c is True.

When to use each

Use == when:

• Comparing values (numbers, strings, list contents, etc.)
• Checking if two objects represent the same data
• Most comparisons in everyday code

Use is when:

• Checking for None: if x is None:
• Checking for sentinel values: if result is MISSING:
• Verifying two names reference the same object
• Performance-critical code (slightly faster than ==)

# Good: use 'is' for None
if user is None:
    print("No user")

# Good: use 'is' for sentinel values
MISSING = object()
if value is MISSING:
    print("Value not provided")

# Good: use '==' for value comparison
if user.age == 18:
    print("User is 18")

note

Never use == to compare with None. Always use is None or is not None. This is both more Pythonic and slightly faster.

Object identity

Every object in Python has a unique identity—think of it as the object's memory address. The id() function returns this identity, and is compares identities.

Understanding id()

a = [1, 2, 3]
b = [1, 2, 3]
c = a

print(id(a))  # 140234567890123 (example)
print(id(b))  # 140234567890456 (different)
print(id(c))  # 140234567890123 (same as a)

print(a is b)  # False - different identities
print(a is c)  # True - same identity

Two objects with the same value can have different identities. Two names referencing the same object will have the same identity.

Identity persists through mutation

When you mutate an object, its identity stays the same:

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

my_list.append(4)
print(id(my_list))  # Same as original_id
print(my_list is my_list)  # True (obviously)

The object's identity doesn't change when you modify it. Only rebinding changes which object a name references.

Identity vs equality summary

Comparison	What it checks	Example
a == b	Do they have the same value?	[1, 2] == [1, 2] → True
a is b	Are they the same object?	[1, 2] is [1, 2] → False
id(a) == id(b)	Same as a is b	Equivalent to identity check

Shallow vs deep copies

Sometimes you want a copy of an object. Python offers two types: shallow copies and deep copies. The difference matters when objects contain other objects.

Shallow copies

A shallow copy creates a new object, but it doesn't recursively copy nested objects. Instead, it copies references to the nested objects.

import copy

original = [[1, 2], [3, 4]]
shallow = copy.copy(original)

print(original == shallow)  # True - same values
print(original is shallow)  # False - different objects

# But the nested lists are the same objects!
print(original[0] is shallow[0])  # True - same nested list

When you modify a nested object in a shallow copy, the original is also affected:

original = [[1, 2], [3, 4]]
shallow = copy.copy(original)

shallow[0].append(5)
print(shallow)   # [[1, 2, 5], [3, 4]]
print(original)  # [[1, 2, 5], [3, 4]] - also changed!

This happens because shallow[0] and original[0] reference the same list object.

Deep copies

A deep copy creates a new object and recursively copies all nested objects:

import copy

original = [[1, 2], [3, 4]]
deep = copy.deepcopy(original)

print(original == deep)  # True - same values
print(original is deep)  # False - different objects
print(original[0] is deep[0])  # False - different nested lists too!

Now modifications to the deep copy don't affect the original:

original = [[1, 2], [3, 4]]
deep = copy.deepcopy(original)

deep[0].append(5)
print(deep)      # [[1, 2, 5], [3, 4]]
print(original)  # [[1, 2], [3, 4]] - unchanged!

When to use each

Use shallow copies when:

• You have a flat structure (no nested mutable objects)
• You want to share nested objects (sometimes intentional)
• Performance matters (shallow copies are faster)

Use deep copies when:

• You have nested mutable objects
• You need complete independence between copies
• You're not sure which you need (safer default)

Copy methods for common types

Some types have built-in copy methods that create shallow copies:

import copy

# Lists
original = [1, 2, 3]
shallow = original.copy()  # or original[:] or list(original)
deep = copy.deepcopy(original)

print(original is shallow)  # False - different objects
print(original is deep)     # False - different objects
print(shallow == deep)      # True - same values

# Dictionaries
original = {"a": 1, "b": 2}
shallow = original.copy()
deep = copy.deepcopy(original)

print(original is shallow)  # False
print(original is deep)     # False
print(shallow == deep)      # True

# Sets
original = {1, 2, 3}
shallow = original.copy()
deep = copy.deepcopy(original)

print(original is shallow)  # False
print(original is deep)     # False
print(shallow == deep)      # True

For nested structures, the difference between shallow and deep copies becomes clear:

import copy

# Nested list example
original = [[1, 2], [3, 4]]
shallow = original.copy()
deep = copy.deepcopy(original)

# Modify nested list
shallow[0].append(5)
deep[1].append(6)

print(original)  # [[1, 2, 5], [3, 4]] - affected by shallow copy
print(shallow)   # [[1, 2, 5], [3, 4]]
print(deep)      # [[1, 2], [3, 4, 6]] - independent

For deep copies, always use copy.deepcopy(). The built-in .copy() methods only create shallow copies.

Why small integers appear "cached"

You might notice something surprising with small integers:

a = 256
b = 256
print(a is b)  # True - same object!

c = 257
d = 257
print(c is d)  # False - different objects (usually)

This is called interning. CPython (the standard Python implementation) caches small integers (typically -5 to 256) as an optimization. These integers are reused, so multiple assignments create references to the same object.

Don't rely on interning

Important: This is an implementation detail. Don't write code that depends on it:

# BAD: Don't do this
a = 100
b = 100
if a is b:  # Might work, but unreliable
    print("Same object")

# GOOD: Use == for value comparison
a = 100
b = 100
if a == b:  # Always correct
    print("Same value")

The range of interned integers can vary between Python versions and implementations. Always use == for value comparisons, never is for integers (except when checking for None).

Why this optimization exists

Small integers are used frequently (loop counters, indices, small calculations). By reusing the same objects, Python:

• Saves memory (no need to create new objects for common values)
• Speeds up comparisons (identity checks are faster than value checks)
• Simplifies some internal operations

But this is purely an optimization. Your code should never depend on it.

Why strings are immutable by design

Strings in Python are immutable—once created, they cannot be changed. This is a deliberate design choice with important benefits.

What immutability means

text = "Hello"
text[0] = "h"  # TypeError: 'str' object does not support item assignment

You cannot modify a string in place. Any operation that appears to change a string actually creates a new string:

text = "Hello"
new_text = text.upper()  # Creates a new string
print(text)      # "Hello" - original unchanged
print(new_text)  # "HELLO" - new object

Benefits of immutable strings

1. Safety and predictability

Since strings can't be changed, you can safely pass them around without worrying about accidental modifications:

def process_name(name):
    # Can't accidentally modify 'name' here
    return name.upper()

my_name = "Alice"
result = process_name(my_name)
print(my_name)  # "Alice" - guaranteed unchanged

2. Hashability

Immutable objects can be used as dictionary keys and set elements:

# Strings can be keys
user_data = {"alice": 25, "bob": 30}

# Lists cannot (they're mutable)
# invalid = {[1, 2]: "value"}  # TypeError: unhashable type: 'list'

3. Memory efficiency (interning)

Like small integers, Python interns some strings (string literals, some computed strings). This allows string identity checks to work for common cases:

a = "hello"
b = "hello"
print(a is b)  # True - same object (usually, but don't rely on it!)

But again, this is an implementation detail. Always use == for string comparisons.

4. Thread safety

Immutable objects are inherently thread-safe. Multiple threads can reference the same string without synchronization issues.

Why not make strings mutable?

If strings were mutable, you'd have to constantly worry about:

• Accidental modifications when passing strings to functions
• Thread safety issues
• Unpredictable behavior when strings are used as keys
• More complex memory management

Immutability makes strings safer and more predictable, which is worth the small performance cost of creating new strings for modifications.

Working with strings efficiently

If you need to build a string from many parts, use .join() instead of concatenation:

# Inefficient: creates many intermediate strings
result = ""
for word in words:
    result += word  # Creates new string each time

# Efficient: builds list, joins once
result = "".join(words)  # Single string creation

The .join() method is faster because it builds the final string in one operation, rather than creating many intermediate strings.

Summary

• == checks equality (same values), is checks identity (same object)
• Use is for None and sentinel values, == for everything else
• Shallow copies share nested objects; deep copies are completely independent
• Small integers are interned as an optimization—don't rely on it
• Strings are immutable by design for safety, hashability, and predictability
• Always use == for value comparisons; is is for identity checks only

Understanding these concepts helps you write more predictable code and avoid subtle bugs related to object identity and mutability.