Concurrency is the ability to perform other tasks while you are waiting on the current task. Imagine that you are cooking a lot of dishes for some guests. While waiting for something to cook, you are free to do other things like peeling onions or cutting vegetables. To make an analogy in the world of superheroes, a superhero might battle several bad guys at one place because most would be either recovering from a blow, arriving (or ahem waiting for their turn), which leaves our hero to deliver blows one at a time.

Parallelism is when two or more execution engines are performing a task. Continuing on our analogy, this is when two or more superheroes battle enemies as a team. This is not only a great cinema franchise opportunity, but also more productive than a single hero working at maximum efficiency.

It is very easy to confuse concurrency and parallelism because they can happen at the same time. You could be concurrently running tasks without parallelism or vice versa, but they refer to two different things. Concurrency is a way of structuring your programs, while parallelism refers to how it is executed.

Due to the global interpreter lock (GIL), we cannot run more than one thread of the Python interpreter (to be specific, the standard CPython interpreter) at a time, even in multicore systems. This limits the amount of parallelism that we can achieve with a single instance of the Python process.

Optimal usage of your computing resources requires both concurrency and parallelism. Concurrency will help you avoid blocking the processor core while waiting for, say, I/O events, while parallelism will help to distribute work among all the available cores.

In both cases, you are not executing synchronously, that is, waiting for a task to finish before moving on to another task. Asynchronous systems might seem to be the most optimal; however, they are harder to build and reason about.