Using caching and memoization to optimize python performance

caching and memoization are techniques used to optimize the performance of python programs by storing the results of expensive function calls and reusing them when the same inputs occur again. this helps in reducing the computational time for repetitive calculations and enhances the overall efficiency of the program.

caching involves storing the results of function calls in a data structure like a dictionary, while memoization involves storing the results of function calls in a cache specifically designed for that purpose.

here is an example demonstrating how to implement caching and memoization in python:

1. caching example:

2. memoization example:

in the above examples, caching and memoization are used to store the results of function calls for factorial and fibonacci calculations, respectively. by storing the results in a cache or memo, the functions can quickly retrieve the result when the same inputs occur again, thereby optimizing the performance of the program.

remember to be cautious when using caching and memoization as they can consume memory if not managed properly. it's important to clear the cache if it grows too large or becomes irrelevant to avoid memory overflow issues.

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