Journey to Master Python (WIP)

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01 Journey to Master Python

02 How we argue with functions

03 Collections Module

04 Dynamic Attributes

07 Slots for scaling

08 Functools Module

09 Exec and Eval

11 Introspection

12 Multiple Inheritance

Previous: Slots for scaling Next: Exec and Eval

Functools module

Memoization

Functools module supports memoization through lru_cache decorator. This way you dont need to write memoization code.

In the example adding the decorator @lru_cache does the needful. You can also see the cache details with cache_info

lru_cache Decorator

import time
from functools import lru_cache
def fibonacci(n):
    if n < 2:
        return n
    return fibonacci(n-1) + fibonacci(n-2)
@lru_cache(maxsize=128)
def fibonacci_cached(n):
    if n < 2:
        return n
    return fibonacci_cached(n-1) + fibonacci_cached(n-2)
time1 = time.time()
print(f'35 Fibonocci number is {fibonacci(35)}')
time2 = time.time()
print(f'Total time taken is {(time2-time1)*1000}ms')
time1 = time.time()
print(f'35 Fibonocci number is {fibonacci_cached(35)}')
time2 = time.time()
print(f'Total time taken is {(time2-time1)*1000 }ms')
print(f'{fibonacci_cached.cache_info()}')

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