Concatenating two 1d numpy to create a Cartesian product of 2d numpy

Question

I want to create a Cartesian product of two numpy so that the first numpy will be the rows and the second will be the columns. For example get these two numpys:

a = np.array([0,0])
b = np.array([0,1,2,3])

The expected result should be 2d numpy like this:

[[0 0 0]
 [0 0 1]
 [0 0 2]
 [0 0 3]]

The following code does not produce the requested result:

a = np.array([0,0])
b = np.array([0,1,2,3])

_new_ = []
for idx in range(len(a)):
    for i in a:
        newArr = np.append(a[idx], b)
        _new_.append(newArr)

print(np.stack(_new_))

What needs to be changed to produce the desired result?

I don't get the logic. a has 2 elements, but the result has 3 columns. If appending lists, I'd suggest sticking with lists, and don't try to use np.append (that's a nasty function). — hpaulj
– hpaulj, Commented Nov 14, 2020 at 19:22

Ch3steR · Accepted Answer · 2020-11-14 19:07:49Z

2

You can use np.tile with np.column_stack

np.column_stack([np.tile(a, (len(b), 1)), b])

array([[0, 0, 0],
       [0, 0, 1],
       [0, 0, 2],
       [0, 0, 3]])

If you have a a as 2D array

a = np.array([[0, 0], [1, 1]])
b = np.array([0,1,2,3])

np.c_[np.tile(a, (len(b), 1)), np.repeat(b, len(a), axis=0)]

array([[0, 0, 0],
       [1, 1, 0],
       [0, 0, 1],
       [1, 1, 1],
       [0, 0, 2],
       [1, 1, 2],
       [0, 0, 3],
       [1, 1, 3]])

edited Nov 14, 2020 at 19:07

answered Nov 14, 2020 at 19:02

Ch3steR

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