python multiprocessing pool for loop

python's multiprocessing module provides a way to leverage multiple processor cores to parallelize the execution of code. one of the convenient tools in this module is the pool class, which allows you to distribute the workload across multiple processes. in this tutorial, we will explore how to use the pool class to parallelize a for loop.
imagine you have a computationally intensive task that involves iterating over a large dataset or performing complex calculations within a loop. instead of executing this loop sequentially, you can use the pool class to split the workload among multiple processes, taking advantage of multi-core processors to speed up the execution.
let's start with a basic example where we have a simple loop that squares each element of a list:
in this example:
by default, the pool class creates a pool of worker processes equal to the number of available cpu cores. however, you can customize the pool size based on your requirements. for example, to create a pool with four processes:

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