How to concatenate these numpy arrays?
first np.array with a shape (5,4):
[[ 6487 400 489580 0]
[ 6488 401 492994 0]
[ 6491 408 489247 0]
[ 6491 408 489247 0]
[ 6492 402 499013 0]]
second np.array with a shape (5,):
[ 16. 15. 12. 12. 17. ]
final result should be:
[[ 6487 400 489580 0 16]
[ 6488 401 492994 0 15]
[ 6491 408 489247 0 12]
[ 6491 408 489247 0 12]
[ 6492 402 499013 0 17]]
I tried np.concatenate([array1, array2]) but I get this error:
ValueError: all the input arrays must have same number of dimensions
What am I doing wrong?
To use np.concatenate, we need to extend the second array to 2D and then concatenate along axis=1 -
np.concatenate((a,b[:,None]),axis=1)
Alternatively, we can use np.column_stack that takes care of it -
np.column_stack((a,b))
Sample run -
In [84]: a
Out[84]:
array([[54, 30, 55, 12],
[64, 94, 50, 72],
[67, 31, 56, 43],
[26, 58, 35, 14],
[97, 76, 84, 52]])
In [85]: b
Out[85]: array([56, 70, 43, 19, 16])
In [86]: np.concatenate((a,b[:,None]),axis=1)
Out[86]:
array([[54, 30, 55, 12, 56],
[64, 94, 50, 72, 70],
[67, 31, 56, 43, 43],
[26, 58, 35, 14, 19],
[97, 76, 84, 52, 16]])
If b is such that its a 1D array of dtype=object with a shape of (1,), most probably all of the data is contained in the only element in it, we need to flatten it out before concatenating. For that purpose, we can use np.concatenate on it too. Here's a sample run to make the point clear -
In [118]: a
Out[118]:
array([[54, 30, 55, 12],
[64, 94, 50, 72],
[67, 31, 56, 43],
[26, 58, 35, 14],
[97, 76, 84, 52]])
In [119]: b
Out[119]: array([array([30, 41, 76, 13, 69])], dtype=object)
In [120]: b.shape
Out[120]: (1,)
In [121]: np.concatenate((a,np.concatenate(b)[:,None]),axis=1)
Out[121]:
array([[54, 30, 55, 12, 30],
[64, 94, 50, 72, 41],
[67, 31, 56, 43, 76],
[26, 58, 35, 14, 13],
[97, 76, 84, 52, 69]])