Performance/standard using 1d vs 2d vectors in numpy
In matlab (for historical reason I would argue) the basic type is an M-by-N array (matrix) so that scalars are 1-by-1 arrays and vectors either N-by-1 or 1-by-N arrays. (Memory layout is always Fortran style).
This "limitation" is not present in numpy: you have true scalars and ndarray's can have as many dimensions you like. (Memory layout can be C or Fortran-contigous.) For this reason there is no preferred (standard) practice. It is up to you, according to your application, to choose the one which better suits your needs.
Is there a standard practice for representing vectors as 1d or 2d ndarrays in NumPy?  I'm moving from MATLAB which represents vectors as 2d arrays.