Everything is an Object in Python — And Why That Matters

When I started learning Python, I kept running into questions like:

• Why does changing a list inside a function affect the original?
• Why does a is b sometimes return True and sometimes False even when the values are the same?
• Why does a += [4] behave differently from a = a + [4]?

These aren't random quirks — they all come down to one fundamental idea: in Python, everything is an object. Once you understand that, a lot of confusing behavior starts making sense.

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id() and type() — Every Object Has an Identity and a Type

In Python, every object has two things:

• type() — what kind of object it is
• id() — where it lives in memory (its unique identity)

a = 89
print(type(a))   # <class 'int'>
print(id(a))     # 140234567891234  ← memory address

Think of id() like a home address — two people can have the same name (value) but live at different addresses (identity).

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

print(a == b)    # True  ← same values
print(a is b)    # False ← different objects in memory

This is the difference between == (same value?) and is (same object?).

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Mutable Objects — Things That Can Change

A mutable object can be changed after it's created — without creating a new object. Lists, dictionaries, and sets are mutable.

a = [1, 2, 3]
print(id(a))     # 139926795932424

a.append(4)
print(id(a))     # 139926795932424 ← same address!
print(a)         # [1, 2, 3, 4]

The list changed — but the object is still the same one in memory. This is what "mutable" means: the object itself can be modified in place.

The += vs = + Trick

This is where mutability gets interesting. Consider this:

a = [1, 2, 3]

a += [4]         # modifies in place → same address
print(id(a))     # same!

a = a + [4]      # creates a NEW list → new address
print(id(a))     # different!

a += [4] calls a.__iadd__([4]) — the i stands for in-place. It extends the existing list without creating a new one.

a = a + [4] creates a brand new list, then assigns it to a. The old list is gone.

This matters a lot when multiple variables point to the same list:

a = [1, 2, 3]
b = a            # b points to the SAME list

a += [4]         # modifies in place
print(b)         # [1, 2, 3, 4] ← b sees the change!

a = a + [4]      # creates a new list
print(b)         # [1, 2, 3, 4] ← b doesn't see this change

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Immutable Objects — Things That Cannot Change

An immutable object cannot be changed after it's created. Integers, strings, floats, and tuples are immutable.

a = 89
print(id(a))     # some address

a = a + 1        # creates a NEW integer object
print(id(a))     # different address!

When you "change" an integer, Python actually creates a brand new integer and points your variable to it. The original object is untouched.

A Confusing but Important Special Case

You might think (1) is a tuple — but it's not:

type((1))    # <class 'int'>   ← just grouping, like in math
type((1,))   # <class 'tuple'> ← the comma makes it a tuple
type(())     # <class 'tuple'> ← empty tuple, special case

The comma is what makes a tuple, not the parentheses. And () is a special case — there's nothing else empty parentheses could mean, so Python treats it as an empty tuple.

Why Two Tuples with the Same Values Are Different Objects

a = (1, 2)
b = (1, 2)

print(a == b)    # True  ← same values
print(a is b)    # False ← different objects in memory

Even though they look the same, Python creates two separate tuple objects. They live at different addresses.

But with small integers, Python does something clever:

a = 1
b = 1
print(a is b)    # True ← Python caches small integers!

Python caches integers from -5 to 256 and reuses the same objects — so a and b actually point to the same object. This is a performance optimization called integer interning.

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Why Does It Matter? How Python Treats Them Differently

The key difference is what happens in memory:

Mutable (list, dict, set):
a = [1, 2, 3]   → object at address 0x100
a.append(4)     → SAME object at 0x100, now [1, 2, 3, 4]

Immutable (int, str, tuple):
a = 89          → object at address 0x200
a = a + 1       → NEW object at 0x300, value is 90
                   old object at 0x200 still exists (until garbage collected)

This affects how you write code — especially when multiple variables point to the same object:

# mutable — shared reference
a = [1, 2, 3]
b = a
b.append(4)
print(a)    # [1, 2, 3, 4] ← a changed too!

# immutable — no shared state issue
a = 89
b = a
b = b + 1
print(a)    # 89 ← a is unchanged

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How Arguments Are Passed to Functions

This is where everything comes together. Python passes arguments by object reference — sometimes called "pass by assignment".

What this means:

• the function receives a reference to the same object
• whether it can modify the original depends on whether the object is mutable or immutable

Immutable — function cannot change the original:

def add_one(n):
    n = n + 1    # creates a NEW integer, n now points to it
    print(n)     # 90

a = 89
add_one(a)
print(a)         # 89 ← unchanged! function got its own copy

Inside the function, n starts pointing to the same object as a. But when you do n = n + 1, a new integer is created and n now points to that. The original a is untouched.

Mutable — function CAN change the original:

def add_item(my_list):
    my_list.append(4)    # modifies the SAME list object

a = [1, 2, 3]
add_item(a)
print(a)                 # [1, 2, 3, 4] ← changed!

The function receives a reference to the same list. When it calls .append(), it modifies that same object — so the change is visible outside the function too.

The Gotcha — Reassigning Inside a Function:

def replace_list(my_list):
    my_list = [10, 20, 30]   # creates NEW list, local variable only
    print(my_list)            # [10, 20, 30]

a = [1, 2, 3]
replace_list(a)
print(a)                     # [1, 2, 3] ← unchanged!

Reassigning my_list inside the function just makes the local variable point to a new list. The original a still points to the old list.

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Summary

	Mutable	Immutable
Examples	list, dict, set	int, str, tuple
Can change in place?	✅ Yes	❌ No
+= behavior	modifies same object	creates new object
Same address after change?	✅ Yes	❌ No
Function can modify original?	✅ Yes	❌ No

The golden rules:

• == checks if values are equal
• is checks if they are the literally same object in memory
• mutable objects can be changed in place — watch out for shared references
• immutable objects always create new objects when "changed"
• functions can modify mutable arguments but not immutable ones

Once these click, a huge chunk of Python's behavior that seemed random starts making complete sense.