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 ...

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 ...

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.

Many people, when they start to work with Python, are excited to hear that the language supports threading. And, as I've discussed in previous articles, Python does indeed support native-level threads ...

Python can’t thread across cores. Python apps can do a multithreading ... It’s unlikely that libraries written for the standard GIL would work when integrated into a multi-threaded system. Removing ...

In the main thread, I occasionally need to ask the user to take some action. Which means I need to suppress the logger from actually printing until the user has a chance to make a decision.