Common libraries like requests or sqlalchemy take 300-500ms to import (eg. try `time python3 -c 'import requests'` and contrast just `time python3 -c ''` which is python startup overhead).
As I said, tests run in sub 10ms, but from issuing pytest to completion it's usually 5-15s.
This is usually the point at which I pull out a 'watch' implementation, since the 5 seconds it's going to take me to switch windows and hit 'up' the right number of times counts too, if we're comparing apples to apples.
That said, one of the last times I had a unit testing mentor, I walked into a project that ran 3800 tests in about 7 seconds, and then started poking around trying to figure out who was materially responsible. (He didn't know much more than me from an implementation standpoint, but boy was he good at selling people on test quality.) If that had been 20 seconds it would have still been lovely, but it wouldn't have grabbed my attention quite as much.
At all my recent jobs, I grow frustrated with how slow running a single unit test is locally on a codebase. We are talking 5+ seconds for even the most trivial of trivial unit tests (say, purely functional arithmetic unit test).
And this is even with dynamic languages like Python (you see pytest reporting how your unit test completed in 0.00s, and wall time is 7s).
And then I get grumpy if they don't let me go and fix it because I am the only one who is that annoyed with this :D