How do I memoize function results in Python?

Memoization is an optimization technique used to speed up function calls by storing previously computed results. In Python, you can achieve memoization using decorators or by utilizing the built-in `functools.lru_cache`. Here's an example of how to memoize function results using both methods:

memoization, optimization, Python decorators, functools, lru_cache

This content provides an overview of how to efficiently cache function results in Python, improving performance for recursive functions or expensive calculations.

from functools import lru_cache

# Method 1: Using lru_cache decorator
@lru_cache(maxsize=None)
def fibonacci(n):
    if n < 2:
        return n
    return fibonacci(n - 1) + fibonacci(n - 2)

# Method 2: Manual Memoization
memo = {}
def fibonacci_memo(n):
    if n in memo:
        return memo[n]
    if n < 2:
        return n
    memo[n] = fibonacci_memo(n - 1) + fibonacci_memo(n - 2)
    return memo[n]

# Example usage
print(fibonacci(10))        # Output: 55
print(fibonacci_memo(10))   # Output: 55

memoization optimization Python decorators functools lru_cache

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