What are the differences between threading and parallel libraries in Python?

What are the differences between threading and parallel libraries in Python?

Threading in Python allows multiple threads to run concurrently, sharing the same memory space, while parallel libraries like multiprocessing run separate processes, each with its own memory space. This means threading is more lightweight but can be limited by Python's Global Interpreter Lock (GIL), whereas parallel processing can utilize multiple CPU cores.

When should I use threading instead of parallel processing?

You should use threading when your tasks are I/O-bound, such as network requests or file operations, where waiting for external resources is the bottleneck. For CPU-bound tasks, parallel processing is usually more efficient.

Can you give me an example of using the multiprocessing library?

Sure! Here's a simple example:

from multiprocessing import Process

def square(n):
    print(f'Square of {n} is {n * n}')

if __name__ == '__main__':
    processes = []
    for i in range(5):
        p = Process(target=square, args=(i,))
        processes.append(p)
        p.start()

    for p in processes:
        p.join()

This code creates multiple processes to calculate the square of numbers.

What are some common libraries for threading and parallel processing?

For threading, the threading module is commonly used. For parallel processing, the multiprocessing module is popular, along with libraries like concurrent.futures and joblib.