How can I optimize performance in Python using multithreading?

How can I optimize performance in Python using multithreading?

You can optimize performance in Python using multithreading by leveraging the threading module to run tasks concurrently.

What types of tasks benefit the most from multithreading?

I/O-bound tasks, like network requests or file operations, benefit the most from multithreading as they can run while waiting for external resources.

Are there any limitations I should be aware of?

Yes, Python's Global Interpreter Lock (GIL) can limit the effectiveness of multithreading for CPU-bound tasks, where multiprocessing might be a better option.

Can you provide a simple code example?

Sure! Here's a basic example:

import threading

def task():
    print('Task running')

threads = []
for i in range(5):
    t = threading.Thread(target=task)
    threads.append(t)
    t.start()

for t in threads:
    t.join()