["python","arrays","numpy"]
Create a two-dimensional array with two one-dimensional arrays

My function (name CovexHull(point)) accepts the argument as a two-dimensional array: hull = ConvexHull(points) Session 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 two 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. ]) How can I create a two-dimensional NumPy array effectively (like points mentioned above) with tp and fp?
If you wish to combine two 10 element one-dimensional arrays into a two-dimensional 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 one-dimensional 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)