How can I optimize my Python code for parallelism?

How can I optimize my Python code for parallelism?

You can use libraries like multiprocessing or concurrent.futures to run tasks in parallel. What specific task are you trying to optimize?

I'm working on a data processing task that takes too long to run sequentially.

For data processing, consider using pandas with dask for parallel data manipulation, or break your task into smaller chunks and use multiprocessing to handle them concurrently.

Can you give me an example of using multiprocessing?

Sure! Here's a simple example:

from multiprocessing import Pool

def square(n):
    return n * n

if __name__ == '__main__':
    with Pool(4) as p:
        results = p.map(square, range(10))
    print(results)

This will compute the square of numbers from 0 to 9 in parallel.

What are the limitations of using parallelism in Python?

Some limitations include the Global Interpreter Lock (GIL) in CPython, which can hinder true parallel execution of threads, and the overhead of managing multiple processes.