functools-and-itertools-part1.md

functools and itertools — Part 1: functools

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The functools module gives you tools to transform and optimize functions. These solve problems you will hit repeatedly: caching expensive computations, pre-filling arguments, folding sequences, and preserving metadata in decorators.

Why This Matters

These tools save you from reinventing the wheel and produce code that is both faster and more readable. lru_cache alone can turn a function that takes minutes into one that returns instantly.

functools.lru_cache — automatic memoization

Caches the results of a function so repeated calls with the same arguments are instant:

from functools import lru_cache

@lru_cache(maxsize=128)
def fibonacci(n):
    if n < 2:
        return n
    return fibonacci(n - 1) + fibonacci(n - 2)

fibonacci(100)    # Returns instantly — without cache, this would take forever

maxsize=128 keeps the 128 most recent results. Use maxsize=None for unlimited cache (careful with memory). Arguments must be hashable (no lists or dicts).

functools.partial — pre-fill function arguments

Creates a new function with some arguments already filled in:

from functools import partial

def power(base, exponent):
    return base ** exponent

square = partial(power, exponent=2)
cube = partial(power, exponent=3)

square(5)    # 25
cube(3)      # 27

Useful for callbacks and configuration:

import json
from functools import partial

# Create a pre-configured JSON dumper:
pretty_json = partial(json.dumps, indent=2, sort_keys=True)
print(pretty_json({"b": 2, "a": 1}))
# {
#   "a": 1,
#   "b": 2
# }

functools.reduce — fold a sequence into one value

Applies a function cumulatively to items in a sequence, reducing it to a single value:

from functools import reduce

# Sum (just to illustrate — use sum() in practice):
numbers = [1, 2, 3, 4, 5]
total = reduce(lambda a, b: a + b, numbers)    # 15

# Find the longest string:
words = ["cat", "elephant", "dog"]
longest = reduce(lambda a, b: a if len(a) >= len(b) else b, words)
# "elephant"

# Flatten nested lists:
nested = [[1, 2], [3, 4], [5]]
flat = reduce(lambda a, b: a + b, nested)    # [1, 2, 3, 4, 5]

For simple operations, prefer built-in functions (sum, max, min). Use reduce when no built-in fits.

functools.wraps — preserve function metadata in decorators

from functools import wraps

def my_decorator(func):
    @wraps(func)    # Copies name, docstring, etc. from the original
    def wrapper(*args, **kwargs):
        print(f"Calling {func.__name__}")
        return func(*args, **kwargs)
    return wrapper

@my_decorator
def greet(name):
    """Say hello."""
    return f"Hello, {name}"

print(greet.__name__)    # "greet" (without @wraps, this would be "wrapper")
print(greet.__doc__)     # "Say hello."

See Decorators Explained for more on this pattern.

Common Mistakes (functools)

Caching functions with unhashable arguments:

@lru_cache
def process(data):    # TypeError if data is a list!
    return sum(data)

# Fix: convert to tuple
process(tuple([1, 2, 3]))

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← Overview	Part 2: itertools →