Create 2 dimensional array with 2 one dimensional array

Question

My function (name CovexHull(point)) accepts argument as 2 dimensional array.

hull = ConvexHull(points)

In [1]: points.ndim Out[1]: 2 In [2]: points.shape Out[2]: (10, 2) In [3]: points Out[3]:  array([[ 0. ,  0. ],        [ 1. ,  0.8],        [ 0.9,  0.8],        [ 0.9,  0.7],        [ 0.9,  0.6],        [ 0.8,  0.5],        [ 0.8,  0.5],        [ 0.7,  0.5],        [ 0.1,  0. ],        [ 0. ,  0. ]]) 

points is a numpy array with ndim 2.

I have 2 different numpy arrays (tp and fp) like below

In [4]: fp.ndim Out[4]: 1 In [5]: fp.shape Out[5]: (10,) In [6]: fp Out[6]:  array([ 0. ,  0.1,  0.2,  0.3,  0.4,  0.4,         0.5, 0.6,  0.9,  1. ]) 

I want to know how can I create a 2 dimensional numpy array effectively (like points mentioned above) with tp and fp.

Answer 1

If you wish to combine two 10 element 1-d arrays into a 2-d array np.vstack((tp, fp)).T will do it. np.vstack((tp, fp)) will return an array of shape (2, 10), and the T attribute returns the transposed array with shape (10, 2) (i.e. with the two 1-d arrays forming columns rather than rows).

>>> tp = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) >>> tp.ndim 1 >>> tp.shape (10,)  >>> fp = np.array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19]) >>> fp.ndim 1 >>> fp.shape (10,)  >>> combined = np.vstack((tp, fp)).T >>> combined array([[ 0, 10],        [ 1, 11],        [ 2, 12],        [ 3, 13],        [ 4, 14],        [ 5, 15],        [ 6, 16],        [ 7, 17],        [ 8, 18],        [ 9, 19]])  >>> combined.ndim 2 >>> combined.shape (10, 2) 

Answer 2

You can use numpy's column_stack

np.column_stack((tp, fp))