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.

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, ...

We think it’s awesome that Python manages to keep the same syntax between the threading and multiprocessing modules, when the action taking place under the hood is so different.

Advantages of Python multiprocessing. With threading and coroutines, the Python runtime forces all operations to run serially, the better to manage access to any Python objects.

Python knows that I/O can take a long time, and so whenever a Python thread engages in I/O (that is, the screen, disk or network), it gives up control and hands use of the GIL over to a different ...