[GuB-42]

And "print items[0]" is actually O(n) where n is the size of the string. Maybe even O(n^2) if the item is a big number and a binary to decimal conversion is required.

But it is doesn't matter here because we have chosen n to be the size of the array, because it is what we are studying.

Here it is big-O "constant time" because the execution time doesn't depend on on the size of the array, assuming it is really big. Not that every call of the function will take the same time to run.

The reality is much more complicated, with things like caches to take into account. As a result, complexities under O(n) often don't mean much in practice. But the higher you go, the more important it becomes. As you get to O(n^2) it is time to take a hard look at your algorithms, or make really sure that n is small and stays small.