How to use functools for higher-order functions in Python?
Aug 02, 2025 am 05:53 AM
@wraps preserves the original function’s metadata when used in decorators, ensuring the wrapper does not obscure the function's name and docstring. 2. partial creates new functions with pre-filled arguments, simplifying repeated calls or adapting functions for APIs expecting argument-free callables. 3. @lru_cache caches expensive function results, significantly speeding up repeated calls, especially in recursive functions like fibonacci, and provides cache statistics via cache_info. 4. @total_ordering automatically generates missing comparison methods when at least eq and one other (e.g., __lt__) are defined, reducing boilerplate in class comparisons. 5. reduce applies a binary function cumulatively to a sequence to produce a single value, useful for aggregation though built-ins like sum are often clearer. These functools tools enhance code reusability, performance, and clarity in functional programming patterns.
The functools module in Python provides tools for working with higher-order functions—functions that take other functions as arguments or return them. It's especially useful when you want to modify or enhance function behavior without changing their actual code. Here’s how to use functools effectively for common higher-order function patterns.
1. functools.wraps – Preserving function metadata in decorators
When creating decorators, the wrapper function can lose the original function’s metadata (like name, docstring, etc.). @wraps fixes this.
from functools import wraps
def my_decorator(func):
@wraps(func)
def wrapper(*args, **kwargs):
print("Before function call")
result = func(*args, **kwargs)
print("After function call")
return result
return wrapper
@my_decorator
def greet(name):
"""Says hello to a person."""
print(f"Hello, {name}!")
greet("Alice")
print(greet.__name__) # Output: greet (not 'wrapper')
print(greet.__doc__) # Output: Says hello to a person.Without @wraps, greet.__name__ would be "wrapper" and the docstring would be lost.
2. functools.partial – Fixing function arguments
partial lets you create a new function with some arguments pre-filled. It's useful for simplifying repeated function calls.
from functools import partial
def power(base, exponent):
return base ** exponent
# Create a function that always squares a number
square = partial(power, exponent=2)
cube = partial(power, exponent=3)
print(square(4)) # Output: 16
print(cube(3)) # Output: 27This is handy when passing functions to APIs that expect callables with no arguments, like callbacks.
3. functools.lru_cache – Memoizing expensive function calls
@lru_cache stores the results of expensive function calls and returns cached results when the same inputs occur again.
from functools import lru_cache
@lru_cache(maxsize=128)
def fibonacci(n):
if n < 2:
return n
return fibonacci(n - 1) fibonacci(n - 2)
print(fibonacci(100)) # Fast, even for large n due to cachingThis dramatically improves performance for recursive functions. You can also inspect cache stats:
print(fibonacci.cache_info())
4. functools.total_ordering – Simplifying class comparisons
If you define one or a few comparison methods, @total_ordering automatically fills in the rest.
from functools import total_ordering
@total_ordering
class Student:
def __init__(self, grade):
self.grade = grade
def __eq__(self, other):
return self.grade == other.grade
def __lt__(self, other):
return self.grade < other.grade
# Now all comparisons work
s1 = Student(85)
s2 = Student(90)
print(s1 < s2) # True
print(s1 <= s2) # True
print(s1 > s2) # FalseYou only need to define __eq__ and one other rich comparison.
5. functools.reduce – Aggregating data with a function
Though not a decorator, reduce is a higher-order function that applies a binary function cumulatively to a sequence.
from functools import reduce numbers = [1, 2, 3, 4, 5] product = reduce(lambda x, y: x * y, numbers) print(product) # Output: 120
Note: For simple cases like sum or product, built-in functions (sum, math.prod) are clearer and preferred.
Summary of key tools
-
@wraps: Use in decorators to keep original function identity. -
partial: Freeze function arguments to create simpler callables. -
@lru_cache: Speed up repeated function calls with caching. -
@total_ordering: Reduce boilerplate in comparison methods. -
reduce: Apply a function cumulatively (use sparingly; often clearer with loops).
These tools help write cleaner, more efficient, and more maintainable code when working with functions as first-class objects. Basically, functools gives you superpowers for functional-style programming in Python.
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