Python has a Global Interpreter Lock (GIL) which has been a bane and a boon. A boon because many basic types are thread-safe as actions happen in lock step. A bane because despite having multiple threads, there's still a master coordinating them all, which means there is no parallelism but concurrency. Python 3.13 allows disabling the GIL, but I cannot say much to that since I haven't tested it myself. Most likely it means nothing is really thread safe anymore and it's up to the developer to handle that.

So, in Python, using multiple threads is not a surefire way to have a performance boost. Small tasks that don't require many operations are OK for threading, but many cycles may be lost to the GIL. Using it for I/O bound stuff is good though as the main python thread won't be stuck waiting on those things to complete (reading or writing files, network access, screen access, ...) . Larger tasks with more operations that are I/O bound or require parallelism (encoding a video file, processing multiple large files at once, reading large amounts of data from the network, ...) are better as separate processes.

As an example: if you have one large file to read then split out into multiple small files, threads are a good option. Splitting happens sequentially, but writing to disk is (comparatively) slow task that one shouldn't wait on and can be dedicated to a thread. Doing these operations on multiple large files is worth doing in parallel using multiple processes. Each process will read a file, split it, and write in threads, while one master process orchestrates the slave processes.

Of course, your mileage may vary. I've run into the issue of requiring parallelism on small tasks and the only thing that worked was moving out that logic to a cython and outside the GIL (terrible experience). For small, highly parallel operations, probably Python isn't the right language and something like Rust should be explored.

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