Create a two-dimensional array with two one-dimensional arrays

arrays, numpy, python

Solution

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)

Problem

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?

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