Optimizing the performance of your Python code is crucial for ensuring fast and responsive applications. Decorators offer a practical way to optimize code execution without sacrificing readability. Let’s explore how decorators can help you improve the performance of your Python programs through straightforward examples.
Understanding Performance Optimization
Performance optimization involves reducing the time and resources required for code execution while maintaining or improving functionality. By optimizing code, you can enhance the user experience and reduce operating costs.
Implementing Memoization for Function Optimization
Memoization is a technique used to cache the results of expensive function calls, preventing redundant computations. Decorators make it easy to implement memoization:
from functools import lru_cache
@lru_cache(maxsize=None)
def fibonacci(n):
if n <= 1:
return n
return fibonacci(n-1) + fibonacci(n-2)
result = fibonacci(10)
print(result) # Output: 55
