Threads share the same memory space to guarantee that two threads don't share the same memory location so special precautions must be taken the CPython interpreter handles this using a mechanism called GIL, or the Global Interpreter Lock
what is GIL(Just I want to Clarify GIL it's repeated above)?
In CPython, the global interpreter lock, or GIL, is a mutex that protects access to Python objects, preventing multiple threads from executing Python bytecodes at once. This lock is necessary mainly because CPython's memory management is not thread-safe.
For the main question, we can compare using Use Cases, How?
1-Use Cases for Threading: in case of GUI programs threading can be used to make the application responsive For example, in a text editing program, one thread can take care of recording the user inputs, another can be responsible for displaying the text, a third can do spell-checking, and so on. Here, the program has to wait for user interaction. which is the biggest bottleneck. Another use case for threading is programs that are IO bound or network bound, such as web-scrapers.
2-Use Cases for Multiprocessing: Multiprocessing outshines threading in cases where the program is CPU intensive and doesn’t have to do any IO or user interaction.
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