Various different ways to do it in Python
Using multiple processes running on multiple cores to create true parallelism. Each process has a separate memory space. Requires a larger memory footprint but no sync primitives are needed. Best for CPU-bound use cases.
Using multiple threads running on the same core to create a sense of concurrent operation. Threads run in the same memory space so has lower memory footprint but is notoriously hard to get right and furthermore CPython suffers from the GIL limitation. Best for IO-bound use cases.
3. asyncio
Using cooperative multitasking in event loop to provide the feeling of concurrency without incurring the costs. Builds on Python coroutines. Best for IO-bound use cases.
From notes here, the syntax async def introduces either a native coroutine or an asynchronous generator. The expressions async with and async for are also valid. A function that you introduce with async def is a coroutine. It may use await, return, or yield, but all of these are optional. The keyword await passes function control back to the event loop. It suspends the execution of the surrounding coroutine.
If Python encounters an await f() expression in the scope of g(), await tells the event loop, "Suspend execution of g() until whatever I'm waiting on - the result of f() - is returned. In the meantime, go let something else run."
Using await and/or return creates a coroutine function. To call a coroutine function, you must await it to get its results. Note that generator-based coroutine support has been outdated since the async/await syntax was put in place in Python 3.5.