What is the difference between asynchronous concurrency and parallelism in Python?

Separates overlapping I/O work from simultaneous execution across processing resources.

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Asynchronous concurrency lets one thread make progress on other work while an operation is waiting, usually by suspending at await. Parallelism means work is executing at the same time on multiple cores or processing units. An asyncio event loop normally gives concurrency on one thread; it does not make Python code run in parallel. Threads or processes may add parallel execution, depending on the runtime and workload.

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Separates overlapping I/O work from simultaneous execution across processing resources.

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Separates overlapping I/O work from simultaneous execution across processing resources.
Asynchronous concurrency lets one thread make progress on other work while an operation is waiting, usually by suspending at await. Parallelism means work is executing at the same time on multiple cores or processing units. An asyncio event loop normally gives concurrency on one thread; it does not make Python code run in parallel. Threads or processes may add parallel execution, depending on the runtime and workload.
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For an API making several independent network calls, asyncio can overlap their waits without dedicating one thread to each call. For a CPU-heavy image transform, merely making the function async does not help because it keeps the event-loop thread busy. I choose the model from where time is spent: awaitable I/O favors async concurrency, while sustained CPU work needs measured parallel capacity.

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Calling async parallel by definition
Making CPU work async without moving it
Starting unlimited tasks because they are lightweight

What is the difference between asynchronous concurrency and parallelism in Python?

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