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Python Functions → Returning a function from a function

Python Functions

Returning a function from a function

Returning Functions from Functions in Python In Python, functions are first-class citizens. This means they can be treated like any other object: assigned to variables, passed as arguments to other functions, and, importantly, *returned* from functions. This capability opens up powerful possibilities for creating flexible and reusable code. Let's explore this with several examples.

1. The Basic Concept

Imagine you need to generate different mathematical functions based on a certain parameter. Instead of writing separate functions for each variation, you can create a single function that *returns* the specific function you need.
Returning functions from functions example def create_multiplier(factor): """Returns a function that multiplies its input by the given factor.""" def multiplier(x): return x * factor return multiplier double = create_multiplier(2) # double is now a function triple = create_multiplier(3) # triple is now a function print(double(5)) print(triple(5))

Output

10 15
Here, `create_multiplier` takes a `factor` and returns a *new* function (`multiplier`). This inner function, `multiplier`, "remembers" the value of `factor` from its enclosing scope (a concept called *closures*). `double` and `triple` are now independent functions that perform multiplication by 2 and 3 respectively.

2. Closures and State Preservation

The power of returning functions shines when you need to preserve state across function calls. Let's create a counter:
Closures and State Preservation in Python function def create_counter(): count = 0 # This variable is local to create_counter def increment(): nonlocal count # This is crucial! Modifies the outer count. count += 1 return count def decrement(): nonlocal count count -= 1 return count return increment, decrement # Returning multiple functions increment, decrement = create_counter() print(increment()) print(increment()) print(decrement())

Output

1 2 1
`create_counter` returns two functions: `increment` and `decrement`. Crucially, these functions access and modify the `count` variable from their enclosing scope (`create_counter`). This demonstrates a closure: the inner functions "close over" the variables from their surrounding scope. Without `nonlocal count`, you'd get an `UnboundLocalError`.

3. Decorators (Advanced Example)

Decorators are a prime example of functions returning functions. They modify the behavior of other functions without directly changing their code.
Decorators in Python function def my_decorator(func): """A simple decorator that prints a message before and after the decorated function.""" def wrapper(*args, **kwargs): print("Before function call") result = func(*args, **kwargs) print("After function call") return result return wrapper @my_decorator # This is syntactic sugar for: greet = my_decorator(greet) def greet(name): print(f"Hello, {name}!") greet("Alice")

Output

Before function call Hello, Alice! After function call
`my_decorator` takes a function (`func`) as input and returns a new function (`wrapper`). `wrapper` executes code before and after calling the original function. The `@` syntax provides a concise way to apply the decorator.

4. Higher-Order Functions and Functional Programming

Returning functions is a cornerstone of functional programming. Consider a function that applies a given operation to each element of a list:
Higher-Order Functions example def apply_operation(data, operation): """Applies a given operation to each element in a list.""" return [operation(x) for x in data] numbers = [1, 2, 3, 4, 5] squared = apply_operation(numbers, lambda x: x**2) #Using a lambda function cubed = apply_operation(numbers, lambda x: x**3) # Another lambda function print(squared) print(cubed) def add_one(x): return x+1 added = apply_operation(numbers, add_one) print(added)

Output

[1, 4, 9, 16, 25] [1, 8, 27, 64, 125] [2, 3, 4, 5, 6]
`apply_operation` is a higher-order function because it takes another function (`operation`) as an argument. This allows for great flexibility. These examples illustrate the versatility of returning functions in Python. It enables creating dynamic, reusable, and elegant code, especially beneficial in scenarios needing closures, decorators, or functional programming paradigms. Remember the importance of `nonlocal` when modifying variables in nested scopes.

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