Create 2 dimensional array with 2 one dimensional array
PythonArraysNumpyPython Problem Overview
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.
Python Solutions
Solution 1 - Python
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)
Solution 2 - Python
You can use numpy's column_stack
np.column_stack((tp, fp))
Solution 3 - Python
Another way is to use np.transpose. It seems to be used occasionally, but it is not readable, so it is a good idea to use the above answer. But I hope it will be helpful when you come across it somewhere.
import numpy as np
tp = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
fp = np.array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19])
combined = np.transpose((tp, fp))
combined
# Out[3]:
# array([[ 0, 10],
# [ 1, 11],
# [ 2, 12],
# [ 3, 13],
# [ 4, 14],
# [ 5, 15],
# [ 6, 16],
# [ 7, 17],
# [ 8, 18],
# [ 9, 19]])
combined.ndim
# Out[4]: 2
combined.shape
# Out[5]: (10, 2)