Understanding Python multiprocessing

Python's multiprocessing module allows you to create and manage parallel processes, enabling you to take advantage of multi-core processors and improve the performance of CPU-bound tasks. This tutorial will provide an overview of the multiprocessing module and guide you through practical examples.
To get started, you need to import the multiprocessing module.
The Process class from the multiprocessing module is used to create a new process. Here's a simple example:
In this example, the print_numbers function is executed in a separate process.
The Pool class can be used to parallelize the execution of a function across multiple input values.
Here, the square function is applied to each element in the numbers list in parallel.
The Queue class allows multiple processes to share data.
The main process puts a message into the queue, and the worker process retrieves and prints it.
Sharing data using Value and Array is efficient for simple data types.
Handling exceptions in multiprocessing requires a bit of care. Use the try and except blocks in