Profiling Python Fibonacci: From Naive Recursion to lru_cache Speed

1. 1Run the naive recursive Fibonacci function in main.py to understand its performance limitations
2. 2Apply the lru_cache decorator to the Fibonacci function in main_cached.py to observe speed enhancements
3. 3Compare the execution times of main.py and main_cached.py to quantify the impact of caching
4. 4Configure the lru_cache decorator with different parameters to explore its effects on performance
5. 5Test the optimized Fibonacci function with various input values to ensure its correctness and efficiency

Developers and data scientists can benefit from understanding how to optimize recursive functions in Python, leading to improved performance in their applications

💡 Using lru_cache can significantly improve the performance of recursive functions in Python by avoiding redundant computations

⚡️ Speed up your Python Fibonacci function with lru_cache! 🚀