If you are using a pandas.DataFrame
this is achieved by performing a join operation between the two arrays, where each has been given a "key" that is constant for all of the entries.
You example would work like this:
In [271]: A=linspace(-2,0,6)
In [272]: B=linspace(0,2,6)
In [273]: A
Out[273]: array([-2. , -1.6, -1.2, -0.8, -0.4, 0. ])
In [274]: A = pandas.DataFrame({'A':linspace(-2,0,6)})
In [275]: B = pandas.DataFrame({'B':linspace(0,2,6)})
In [276]: A['key'] = 1
In [277]: B['key'] = 1
In [278]: pandas.merge(A, B, on='key')
Out[278]:
A key B
0 -2.0 1 0.0
1 -2.0 1 0.4
2 -2.0 1 0.8
3 -2.0 1 1.2
4 -2.0 1 1.6
5 -2.0 1 2.0
6 -1.6 1 0.0
7 -1.6 1 0.4
8 -1.6 1 0.8
9 -1.6 1 1.2
10 -1.6 1 1.6
11 -1.6 1 2.0
12 -1.2 1 0.0
13 -1.2 1 0.4
14 -1.2 1 0.8
15 -1.2 1 1.2
16 -1.2 1 1.6
17 -1.2 1 2.0
18 -0.8 1 0.0
19 -0.8 1 0.4
20 -0.8 1 0.8
21 -0.8 1 1.2
22 -0.8 1 1.6
23 -0.8 1 2.0
24 -0.4 1 0.0
25 -0.4 1 0.4
26 -0.4 1 0.8
27 -0.4 1 1.2
28 -0.4 1 1.6
29 -0.4 1 2.0
30 0.0 1 0.0
31 0.0 1 0.4
32 0.0 1 0.8
33 0.0 1 1.2
34 0.0 1 1.6
35 0.0 1 2.0
In [279]: pandas.merge(A, B, on='key')[['A','B']].values
Out[279]:
array([[-2. , 0. ],
[-2. , 0.4],
[-2. , 0.8],
[-2. , 1.2],
[-2. , 1.6],
[-2. , 2. ],
[-1.6, 0. ],
[-1.6, 0.4],
[-1.6, 0.8],
[-1.6, 1.2],
[-1.6, 1.6],
[-1.6, 2. ],
[-1.2, 0. ],
[-1.2, 0.4],
[-1.2, 0.8],
[-1.2, 1.2],
[-1.2, 1.6],
[-1.2, 2. ],
[-0.8, 0. ],
[-0.8, 0.4],
[-0.8, 0.8],
[-0.8, 1.2],
[-0.8, 1.6],
[-0.8, 2. ],
[-0.4, 0. ],
[-0.4, 0.4],
[-0.4, 0.8],
[-0.4, 1.2],
[-0.4, 1.6],
[-0.4, 2. ],
[ 0. , 0. ],
[ 0. , 0.4],
[ 0. , 0.8],
[ 0. , 1.2],
[ 0. , 1.6],
[ 0. , 2. ]])