I'm using Cython to wrap a C++ library. In the C++ code there is some data that represents a list of 3D vectors. It is stored in the object std::vector< std::array<double, 3> >. My current method to convert this into a python object is to loop over the vector and use the method arrayd3ToNumpy in the answer to my previous question on each element. This is quite slow, however, when the vector is very large. I'm not as worried about making copies of the data (since I believe the auto casting of vector to list creates a copy), but more about the speed of the casting.
This this case the data is a simple C type, continguous in memory. I'd therefore expose it to Python via a wrapper cdef class that has the buffer protocol.
There's a guide (with an example) in the Cython documentation. In your case you don't need to store ncols - it's just 3 defined by the array type.
The key advantage is that you can largely eliminate copying. You could use your type directly with Cython's typed memoryviews to quickly access the underlying data. You could also do np.asarray to create a Numpy array that just wraps the underlying data that already exists.