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Kind of new to python and I need to use numpy to append a column, I have an ndarray a with [[1 2 3] [4 5 6]] and another ndarray with b [1 7] so the end result should be [[1 2 3 1] [4 5 6 7] . I have tried

array = np.append(a , b, axis=1)

but I get

all the input arrays must have same number of dimensions

(makes sense). I was also trying to insert it in a for loop but based on what i have seen with python these libraries have an easy way to do things and I was wondering if there is a more efficient way?

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Try numpy.hstack with added axis to b -

a = np.array([[1,2,3],[4,5,6]])
b = np.array([1,7])

np.hstack([a,b[:,None]])

array([[1, 2, 3, 1],
       [4, 5, 6, 7]])

Notes:

  1. b[:,None] adds an axis to turn b from 1D (2,) to 2D (2,1) array (its the same as b.reshape(-1,1))
  2. np.hstack is now able to horizontally stack (2,3) and (2,1) to give (2,4) shaped array
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3 Comments

Ah I see thank you, and the resulting is another ndarray ? . and does it work with any array size and dimension? (for a forexample, b will always be 1D)
Depends. for np.hstack, you will need the first dimension to be common. as long as the number of rows in a and the number of elements in b are equal .. yes.
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