Python provides two ways to work around this issue: threading and multiprocessing. Each approach allows you to break a long-running job into parallel batches, which you can work on side-by-side.

Learn how to use Python’s async functions, threads, and multiprocessing capabilities to juggle tasks and improve the responsiveness of your applications.

Python's "multiprocessing" module feels like threads, but actually launches processes. Many people, when they start to work with Python, are excited to hear that the language supports threading. And, ...

Fortunately for us, the Python developers worked hard to create a multiprocessing module which has an interface that is almost identical to the threading module.

Using multiprocessing and multithreading architectures together helps generate higher performance in a range of applications.

More flexible interactive shell Python 3.13 uses a new interactive shell by default, which has emerged from the PyPy project and offers significantly more convenience than the previous one.

I do this all the time. Post the results for each row to a multiprocessing.Queue, and spawn a single process that gets from the queue and writes to the file. It'll post some code when I get to work.