Combine two dimensional vs one dimensional array in numpy-CodePudding

I have two numpy arrays like this,

data1 = np.array([[17, 48],
                  [17, 53],
                  [17, 55]])
data2 = np.array([100,200])

I want to combine those arrays by adding 1 at the beginning of the each one.

I tried,

out = []
for i in data1:
    for j in data2:
        out.append([1,i,j])
np.array(out)

#array([[1, array([17, 48]), 100],
#       [1, array([17, 48]), 200],
#       [1, array([17, 53]), 100],
#       [1, array([17, 53]), 200],
#       [1, array([17, 55]), 100],
#       [1, array([17, 55]), 200]], dtype=object)

But the output is not the one I desire. The expected one is like below.

data_desired = np.array([[1,17,48,100],
                         [1,17,48,200],
                         [1,17,53,100],
                         [1,17,53,200],
                         [1,17,55,100],
                         [1,17,55,200]])

By the way, using loop seems also wrong to me. Maybe there exist some other alternatives like broadcasting, newaxis etc?

Thanks in advance!

CodePudding user response：

You can use the unpacking operator * to unpack i. This is probably the easiest fix for your situation.

out = []
for i in data1:
    for j in data2:
        out.append([1,*i,j])
np.array(out)

CodePudding user response：

If speed is relevant and you are going to use large arrays, you shouldn't use loops or growing data structures:

>>> n1 = len(data1)
>>> n2 = len(data2)
>>> result = np.empty_like(data1, shape=(n1*n2, 4))
>>> result[:, 0] = 1
>>> result[:, 1:3] = np.repeat(data1, n2, axis=0)
>>> result[:, 3] = np.repeat(data2[None, :], m1, axis=0).ravel()
>>> result
array([[  1,  17,  48, 100],
       [  1,  17,  48, 200],
       [  1,  17,  53, 100],
       [  1,  17,  53, 200],
       [  1,  17,  55, 100],
       [  1,  17,  55, 200]])

CodePudding user response：

A variant on @aerobiomat's answer, using broadcasting instead of repeat to fill in the data. reshape produces a view, so assignment to it affects res itself. Working this out took some experimenting, so it's not necessarily superior.

In [141]: res = np.ones((6,4),data1.dtype)
In [142]: res.reshape((3,2,4))[:,:,1:3]=data1[:,None,:]
In [143]: res.reshape((3,2,4))[:,:,3]=data2
In [144]: res
Out[144]: 
array([[  1,  17,  48, 100],
       [  1,  17,  48, 200],
       [  1,  17,  53, 100],
       [  1,  17,  53, 200],
       [  1,  17,  55, 100],
       [  1,  17,  55, 200]])

The reshaped array looks like:

In [145]: res.reshape((3,2,4))
Out[145]: 
array([[[  1,  17,  48, 100],
        [  1,  17,  48, 200]],

       [[  1,  17,  53, 100],
        [  1,  17,  53, 200]],

       [[  1,  17,  55, 100],
        [  1,  17,  55, 200]]])