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.

If you’re familiar with threading in general, threading in Python won’t be a big step. Threads in Python are units of work where you can take one or more functions and execute them ...

Well, Python provides threading. Many people think of Python's threads as fatally ... In my next article, I plan to look at how such processes can work and interact. Reuven Lerner teaches Python, data ...

Also see: https://pypi.python.org/pypi/processing/ (multi-processing instead of threading, bypassing the GIL, albeit losing shared resources and forcing IPC ...

However, they work quite differently under the hood ... that can run in parallel on a multi-core processor, threading can be beneficial. Python's Global Interpreter Lock (GIL) can limit the ...

The GIL is controversial because it only allows one thread at a time to access the Python interpreter. This means that it’s often not possible for threads to take advantage of multi-core systems.

Unlock concurrency with Python threads (and run 100s or 1,000s of tasks simultaneously). The threading module provides easy-to-use thread-based concurrency in Python. Unlike Python multiprocessing, ...

Ruby and Python's standard implementations ... access to the GIL for a bit of time, does some work, and releases it. Meanwhile, every other thread is on hold, waiting to get a chance to access ...