Preamble

About a year ago, a friend and I were discussing closures in programming languages, and how they were treated so specially in many technical circles. To us, the concept seemed weird. Python’s closures were… intuitive, weren’t they? Both of us were not exactly from a standard programming background, we’d both studied Mechanical Engineering, not Computer Science, but we were (and still are) super passionate about tech.

Which is why we were confused. Whatever it is that the world calls closures seems pretty obvious. At least to us they were.

And well, we weren’t wrong. But we were mildly surprised at how Python deals with it.

Intended Audience

This is an intermediate level article, and you might be confused reading it if you don’t understand the following:

1. Writing Python scripts.
2. Using the REPL Interpreter
3. Writing functions
4. Writing classes (knowledge of inheritance is not required)
5. Using the id, help and dir functions.
6. Using the dis module to disassemble Python code.

I have linked to the best articles (IMO) on these topics above, so if you like, you can go ahead and read those first.

Also, most articles deal with an article with respect to nested functions. While I do deal with nested functions, I do not start there. I start earlier, with a simple variable, and build up to nested… let’s say objects.

Introduction

Let’s take a look.

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def some_func():
    something_to_return = 10
    print(f"id(something_to_return) = {id(something_to_return)}")
    return something_to_return

The id function in Python is a built-in that returns the identity of the object that represents the variable. I use this function to sniff around my code to see if I am passing around copies of a variable or if I am actually passing the variable (sic object) itself.

In this case, it is easy to test this theory.

>>> x = some_func()
id(something_to_return) = 10917664
>>> id(x)
10917664

This snippet prints out the id value of these variables, and while of course this number will vary, and it could be the same, but for objects with non-overlapping lifetime. In other words, during one specific scope, these numbers are guaranteed to be representative of a specific object.

Scopes

The scope that I just brought up is, in very simple terms, like a space for your code to run in. In python, variables are shared from an outer scope to an inner scope.

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def test():
    x = 10

print(x) # This will result in a NameError exception since x was defined inside a function.

test()
print(x)
# no, calling the function doesn't add whatever is inside magically to
# the "higher" scope"

A higher scope is indicative of a scope that contains another scope. If that is too wordy for your taste:

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# this is scope 0
def func():
    # this is scope 1
    def func2():
        # this is scope 2
        pass
    pass

In this example, scope 0 contains scope 1. Scope 1 contains scope 2. However, everything in scopes 0 and 1 are accessible in scope 2, and everything in scope 0 is accessible to scope 1.

Scope 0 is the outermost scope. Scope 1 is inside scope 0. Scope 1 is outside of scope 2. Scope 2 is inside scope 1.

This will become clearer in time.

Sniffing around returned values

Now, let’s look back at our closures. We saw that the id value of the integer that was returned is the same. which means, technically, that the same object was returned into the outer scope from the inner scope.

Let’s expand this example.

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def func():
    x = 10
    print(id(10))
    return x

external_x = func()
print(id(external_x))

def func2(inp):
    print(id(inp))
    return inp

external_inp = func2(external_x)
print(id(external_inp))
assert external_x is external_inp, "The item is not the same. This assertion should not have been raised"

When I run the above snippet, here’s what I get:

94093123237024
94093123237024
94093123237024
94093123237024

If you are following along, this means that the application passed around a variable x, created inside func, outside of its scope, and then into the scope of another function that returned it unmodified, and the id was never changed. This means that all through your process, you passed around a single object. You did not change it.

Now, you might wonder if this would work.

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def func():
    x = 10
    print(id(x))
    return x

ext_x = func()
print(id(ext_x))

def func2(inp):
    inp = inp**2
    return inp

ext_x2 = func2(ext_x)
print(id(ext_x2))
assert ext_x is ext_x2, "the objects are not the same"

When I run this, here’s what I get:

94093123237024
94093123237024
94093123239904
---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
<ipython-input-6-bb929bfaec24> in <module>
     13 ext_x2 = func2(ext_x)
     14 print(id(ext_x2))
---> 15 assert ext_x is ext_x2, "the objects are not the same"

AssertionError: the objects are not the same

This doesn’t work. Why? Let’s take it apart using another tool in the python standard library, the dis module.

Disassembling our snippets

The dis module allows us to see the bytecode that Python generates from our source code. This can help us understand what’s happening under the hood:

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import dis

def func2(inp):
    inp = inp**2
    return inp

dis.dis(func2)

When you examine the bytecode, you’ll see that the assignment inp = inp**2 creates a new object (the result of the power operation) and assigns it to the local variable inp. The original object is not modified - instead, a new integer object is created.

Let’s return a dictionary instead!

Let’s see what happens with mutable objects:

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def create_dict():
    data = {'value': 10}
    print(f"Inside function: id(data) = {id(data)}")
    return data

my_dict = create_dict()
print(f"Outside function: id(my_dict) = {id(my_dict)}")

def modify_dict(d):
    d['value'] = 20
    print(f"Inside modify_dict: id(d) = {id(d)}")
    return d

modified_dict = modify_dict(my_dict)
print(f"After modification: id(modified_dict) = {id(modified_dict)}")
print(f"Original dict: {my_dict}")
print(f"Are they the same object? {my_dict is modified_dict}")

With mutable objects like dictionaries, the same object reference is passed around, and modifications affect the original object.

Nested Functions - Finally

Now let’s get to the heart of closures with nested functions:

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def outer_function(x):
    outer_variable = x

    def inner_function():
        # This inner function has access to outer_variable
        print(f"From inner function: outer_variable = {outer_variable}")
        return outer_variable * 2

    return inner_function

# Create a closure
multiplier = outer_function(10)
result = multiplier()
print(f"Result: {result}")

Here’s where it gets interesting. Even after outer_function has finished executing, the inner_function still remembers the value of outer_variable. This is a closure - the inner function “closes over” the variables from its enclosing scope.

Let’s examine what makes this possible:

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def create_multiplier(factor):
    def multiply(number):
        return number * factor
    return multiply

double = create_multiplier(2)
triple = create_multiplier(3)

print(double(5))  # 10
print(triple(5))  # 15

# Let's see what's in the closure
print(f"double.__closure__: {double.__closure__}")
print(f"Values in closure: {[cell.cell_contents for cell in double.__closure__]}")

__closure__ and what it means

The __closure__ attribute reveals the secret behind closures. It contains a tuple of cell objects that hold the values from the enclosing scope:

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def outer(x, y):
    def inner():
        return x + y
    return inner

closure_func = outer(10, 20)
print(f"Closure cells: {closure_func.__closure__}")
print(f"Cell contents: {[cell.cell_contents for cell in closure_func.__closure__]}")

# Let's see the variable names
print(f"Code object variables: {closure_func.__code__.co_freevars}")

Practical Example: Building a Counter

Here’s a practical example that demonstrates the power of closures:

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def create_counter(initial=0):
    count = initial

    def increment(step=1):
        nonlocal count
        count += step
        return count

    def decrement(step=1):
        nonlocal count
        count -= step
        return count

    def get_count():
        return count

    # Return a dictionary of functions
    return {
        'increment': increment,
        'decrement': decrement,
        'get_count': get_count
    }

counter = create_counter(5)
print(counter['get_count']())  # 5
print(counter['increment']())  # 6
print(counter['increment'](3))  # 9
print(counter['decrement'](2))  # 7

Each counter maintains its own state through closures, providing a clean way to create stateful functions without classes.

Decorators and Closures

Decorators are perhaps the most common use of closures in Python:

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def timer_decorator(func):
    import time

    def wrapper(*args, **kwargs):
        start_time = time.time()
        result = func(*args, **kwargs)
        end_time = time.time()
        print(f"{func.__name__} took {end_time - start_time:.4f} seconds")
        return result

    return wrapper

@timer_decorator
def slow_function():
    import time
    time.sleep(1)
    return "Done!"

slow_function()

The decorator function returns a closure that wraps the original function, maintaining access to the original function reference.

Common Pitfalls: Late Binding

Be careful with closures in loops:

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# This doesn't work as expected
functions = []
for i in range(3):
    functions.append(lambda: i)

# All functions return 2 (the final value of i)
for f in functions:
    print(f())  # 2, 2, 2

# Fix with default argument
functions = []
for i in range(3):
    functions.append(lambda x=i: x)

# Now each function captures its own value
for f in functions:
    print(f())  # 0, 1, 2

A Personal Story

The conversation at the beginning of this article came about because I had just come out of an interview where the interviewer asked me to explain Python closures. I had faltered, not because I didn’t know, but because, oddly enough, I thought there was nothing special about how you can essentially play ping pong with objects in Python. Most languages do this. Python does this only because C does it. You can return pointers, can’t you?

All in all, a confusing interview led me to understand something in depth, and helped me learn.

Conclusion

Closures in Python are indeed intuitive once you understand the underlying concepts of scope and variable lifetime. They provide a powerful mechanism for:

• Creating stateful functions without classes
• Implementing decorators
• Building factory functions
• Maintaining encapsulation

The key insight is that inner functions can capture and remember variables from their enclosing scope, even after the outer function has finished executing. This creates a “closed over” environment - hence the term “closure.”

Understanding closures deeply will make you a better Python programmer and help you write more elegant, functional code.