Python Precomputing caching parts of of functions

Certainly! Let's dive into the world of precomputing and caching in Python. Caching is a powerful technique to optimize the performance of your functions by storing previously computed results. This is especially useful when dealing with functions that have expensive computations or repetitive calculations. In this tutorial, we'll explore how to precompute and cache parts of functions using the functools module and a popular caching library called lru_cache.
Caching is the process of storing and reusing previously computed results to avoid redundant calculations. This is particularly beneficial in scenarios where the same inputs may lead to the same output multiple times.
The functools module in Python provides the lru_cache decorator, which stands for "Least Recently Used Cache." This decorator automatically caches the results of a function based on its arguments, and it discards the least recently used entries when the cache reaches its maximum size.
Before we begin, make sure you have the lru_cache library installed. You can install it using:
Let's start with a simple example:
In this example, expensive_function is a simple function that squares its input. The @lru_cache decorator is used to automatically cache the results. The maxsize=None argument means the cache can grow without bounds.
Now, let's consider a more complex scenario where we want to precompute and cache intermediate results within a function.
In this example, complex_function calls expensive_computation and then combines the

In Python, precomputing or caching parts of functions can significantly improve the performance of your code, especially when dealing with computationally expensive operations or repetitive function calls. Caching allows you to store the results of function calls and reuse them when the same inputs occur again, avoiding redundant computations.
In this tutorial, we'll explore how to implement caching in Python using the functools module. We'll use the lru_cache decorator to cache function results and demonstrate how it can enhance the efficiency of your code.
The functools module in Python provides tools for working with functions. We'll be using the lru_cache decorator from this module to implement caching. Import it at the beginning of your script or notebook:
Let's create a simple function that performs a time-consuming computation. We'll use this function as an example