I have 2 Numpy array <type 'numpy.ndarray'> with shape of (10,) (10, 6) and I would like to concat the first one with the second. The numpy array provided below,
r1
['467c8100-7f13-4244-81ee-5e2a0f8218a8',
'71a4b5b2-80d6-4c12-912f-fc71be8d923e',
'7a3e0168-e47d-4203-98f2-a54a46c62ae0',
'7dfd43e7-ced1-435f-a0f9-80cfd00ae246',
'85dbc70e-c773-43ee-b434-8f458d295d10',
'a56b2bc3-4a81-469e-bc5f-b3aaa520db05',
'a9e8996f-ff35-4bfb-bbd9-ede5ffecd4d8',
'c3037410-0c2e-40f8-a844-ac0664a05783',
'c5618563-10c0-425b-a11b-2fcf931f0ff7',
'f65e6cea-892e-4335-8e86-bf7f083b5f53']
r2
[[1.55000000e+02, 5.74151515e-01, 1.55000000e+02, 5.74151515e-01, 3.49000000e+02, 1.88383585e+00],
[5.00000000e+00, 1.91871554e-01, 1.03000000e+02, 1.22893828e+00, 2.95000000e+02, 3.21148368e+00],
[7.10000000e+01, 1.15231270e-01, 2.42000000e+02, 5.78527276e-01, 4.09000000e+02, 2.67915246e+00],
[3.60000000e+01, 7.10066720e-01, 2.42000000e+02, 1.80213634e+00, 4.12000000e+02, 4.16314391e+00],
[1.15000000e+02, 1.05120284e+00, 1.30000000e+02, 1.71697773e+00, 2.53000000e+02, 2.73640301e+00],
[4.70000000e+01, 2.19434656e-01, 3.23000000e+02, 4.84093786e+00, 5.75000000e+02, 7.00530186e+00],
[5.50000000e+01, 1.22614463e+00, 1.04000000e+02, 1.55392099e+00, 4.34000000e+02, 4.13661261e+00],
[3.90000000e+01, 3.34816889e-02, 1.10000000e+02, 2.54431753e-01, 2.76000000e+02, 1.52322736e+00],
[3.43000000e+02, 2.93550948e+00, 5.84000000e+02, 5.27968165e+00, 7.45000000e+02, 7.57657633e+00],
[1.66000000e+02, 1.01436635e+00, 2.63000000e+02, 2.69197514e+00, 8.13000000e+02, 7.96477735e+00]]
I tried to concatenate with the command np.concatenate((r1, r2)), it returns with the message of ValueError: all the input arrays must have same number of dimensions which I don't understand. Because, the r1 can possibly concat with the r2 and can form a whole new array and make a new array of 10 x 7 as result.
How to solve this problem ?
You can reshape r1 to make it two-dimensional and specify the axis along which the arrays should be joined:
import numpy as np
r1 = np.ones((10,))
r2 = np.zeros((10, 6))
np.concatenate((r1.reshape(10, 1), r2), axis=1)
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