f2py speed with array ordering
There shouldn't be any slow-down. Since NumPy 1.6, most ufuncs (ie, the basic 'universal' functions) take an optional argument allowing a user to specify the memory layout of her array: by default, it's K, meaning that the 'the element ordering of the inputs (is matched) as closely as possible`. 
So, everything should be taken care of below the hood.
At worst, you could always switch from one order to another with the order parameter of np.array (but that will copy your data and is probably not worth it).
I'm writing some code in fortran (f2py) in order to gain some speed because of a large amount of calculations that would be quite bothering to do in pure Python.
I was wondering if setting NumPy arrays in Python as order=Fortran will kind of slow down
the main python code with respect to the classical C-style order.