Python functools recipes: lru_cache, partial, reduce

Need memoization, partial application, and other functional programming utilities in Python.

Essential functools patterns:

from functools import lru_cache, cache, partial, reduce, wraps

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

# Unlimited cache (Python 3.9+)
@cache
def expensive_lookup(key):
    return database.query(key)

# Check cache stats
print(fibonacci.cache_info())
# CacheInfo(hits=46, misses=50, maxsize=128, currsize=50)
fibonacci.cache_clear()  # Reset cache

# Partial application
def power(base, exponent):
    return base ** exponent

square = partial(power, exponent=2)
cube = partial(power, exponent=3)
print(square(5))  # 25

# Partial with logging
import logging
debug = partial(print, '[DEBUG]')
warn = partial(print, '[WARN]', flush=True)

# Reduce for accumulation
numbers = [1, 2, 3, 4, 5]
total = reduce(lambda acc, x: acc + x, numbers, 0)  # 15

# Compose functions
def compose(*funcs):
    return reduce(lambda f, g: lambda x: f(g(x)), funcs)

process = compose(str.upper, str.strip, str.lower)
print(process('  Hello  '))  # 'HELLO'

functools provides optimized implementations of common functional patterns. lru_cache alone can dramatically speed up recursive or repetitive computations.

Python code needing caching, partial application, or functional patterns