Convert structured array to regular NumPy array

The simplest method is probably

x.view((float, len(x.dtype.names)))

(float must generally be replaced by the type of the elements in x: x.dtype[0]). This assumes that all the elements have the same type.

This method gives you the regular numpy.ndarray version in a single step (as opposed to the two steps required by the view(…).reshape(…) method.

[~]
|5> x = np.array([(1.0, 4.0,), (2.0, -1.0)], dtype=[('f0', '<f8'), ('f1', '<f8')])

[~]
|6> x.view(np.float64).reshape(x.shape + (-1,))
array([[ 1.,  4.],
       [ 2., -1.]])

np.array(x.tolist())
array([[ 1.,  4.],
      [ 2., -1.]])

but maybe there is a better method...