Other answers have focused more on the multithreading vs multiprocessing aspect, but in python Global Interpreter Lock (GIL) has to be taken into account. When more number (say k) of threads are created, generally they will not increase the performance by k times, as it will still be running as a single threaded application. GIL is a global lock which locks everything out and allows only single thread execution utilizing only a single core. The performance does increase in places where C extensions like numpy, Network, I/O are being used, where a lot of background work is done and GIL is released.
So when threading is used, there is only a single operating system level thread while python creates pseudo-threads which are completely managed by threading itself but are essentially running as a single process. Preemption takes place between these pseudo threads. If the CPU runs at maximum capacity, you may want to switch to multiprocessing.
Now in case of self-contained instances of execution, you can instead opt for pool. But in case of overlapping data, where you may want processes communicating you should use multiprocessing.Process.
