Day 15: Python's functools Module - Power of Function Utilities

The functools module provides powerful tools to optimize and enhance functions, making our code more efficient and readable

1?? lru_cache() - Speed Up Function Calls with Caching

If a function performs expensive calculations, caching results can significantly improve performance

from functools import lru_cache

@lru_cache(maxsize=3)  # Stores up to 3 previous results
def fib(n):
    if n<=1:
        return n
    return fib(n-1) + fib(n-2)

print(fib(10))   # Faster due to caching!        

?? Best for: Avoiding redundant calculations, optimizing recursive functions

2?? reduce() - Reduce a List to a Single Value

Applies a function cumulatively to elements in an iterable

from functools import reduce

nums = [1, 2, 3, 4]
result = reduce(lambda x, y: x*y, nums)  # 1*2*3*4 = 24
print(result)        

?? Best for: Aggregating values (sum, product, max, etc.)

3?? partial() - Create Preconfigured Functions

Fixes certain arguments in a function, creating a new specialized function

from functools import partial

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

square = partial(power, exponent=2)  # Predefine exponent =2
print(square(4)) # 16        

?? Best for: Preconfiguring function arguments for reuse

4?? cmp_to_key() - Custom Sorting with Comparison Functions

Converts old-style comparison functions to key-based sorting

from functools import cmp_to_key

def compare(x,y):
    return x-y   # Sort in ascending order

nums = [3, 1, 4, 2]
sorted_nums = sorted(nums, key=cmp_to_key(compare))
print(sorted_nums)  # [1, 2, 3, 4]        

?? Best for: Custom sorting logic without modifying the sorted() function

?? Key Takeaways

?? lru_cache() - Speeds up expensive function calls

?? reduce() - Aggregates iterable values

?? partial() - Creates preconfigured functions

?? cmp_to_key() - Custom sorting with comparison functions

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