As discussed earlier on the post on 'Circular Indexing', considering interpreting indices that lie outside the range 0 to n-1 modulo n for list indexing. Thus element n corresponds to element 0, n+1 to element 1 and so on. This has two consequences:

1.) Makes the Python convention of interpreting index -1 as the last element more logical since -1 is the same as n-1 when taken modulo n. If -1 is the first element then to be logically consistent we have to interpret index n as the element 0 as well!

Obviously this does allow certain errors to slip but if you are going to allow an index of -1 (indices that occur before 0) then you might just as well allow indices above n-1 to be logically consistent.

2.) If circular indexing is used then instead of using a double FOR loop to go through a loop twice we can iterate from 0 to 2n !

Although trivial for the case of one dimensional lists/arrays, cylindrical indexing (for 2D arrays) and toroidal  indexing (2D, 3D and nD arrays) schemes might be worth exploring for n-dimensional NumPy arrays.

Circular and in general cylindrical or toroidal indexing schemes might simplify coding by reducing the number of FOR loops when iterating over nD arrays in NumPy.