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Have you ever found yourself writing a Python function that performs complex calculations or queries, only to find out later that it’s taking longer than expected? Or maybe you have a function that calls another function multiple times with the same arguments, but you don’t want to modify the original function. Either way, there’s a technique called function caching that can help you solve these problems and more!
In this article, we’ll explore what function caching is, why it’s useful, and how to implement it in Python using both built-in decorators and third-party libraries. By the end of this article, you’ll be able to use function caching to speed up your Python code and make it more efficient.
What is Function Caching?
Function caching, also known as memoization, is a technique where you store the results of expensive function calls so that if the same inputs are used again, the result can be retrieved from memory instead of recalculating it. This can significantly reduce execution time, especially when dealing with large datasets or computationally intensive operations.
