Overwriting NumPy array columns

To show a simple example, let's assume I have a simple function for z-score normalization:

def standardizing(array, columns, ddof=0):

    ary_new = array.copy()
    if len(ary_new.shape) == 1:
        ary_new = ary_new[:, np.newaxis]

    return (ary_new[:, columns] - ary_new[:, columns].mean(axis=0)) /\
                       ary_new[:, columns].std(axis=0, ddof=ddof)

And the results are what I expect:

>>> ary = np.array([[1, 10], [2, 9], [3, 8], [4, 7], [5, 6], [6, 5]])
>>> standardizing(ary, [0, 1])

array([[-1.46385011,  1.46385011],
   [-0.87831007,  0.87831007],
   [-0.29277002,  0.29277002],
   [ 0.29277002, -0.29277002],
   [ 0.87831007, -0.87831007],
   [ 1.46385011, -1.46385011]])

However, let's say I don't want to return this array, but overwrite the values in-place. I am wondering why it doesn't work. For example,

def standardizing(array, columns, ddof=0):

    ary_new = array.copy()
    if len(ary_new.shape) == 1:
        ary_new = ary_new[:, np.newaxis]

    ary_new[:, columns] = (ary_new[:, columns] - ary_new[:, columns].mean(axis=0)) /\
                       ary_new[:, columns].std(axis=0, ddof=ddof)

    # some more processing steps with ary_new
    return ary_new  

>>> ary = np.array([[1, 10], [2, 9], [3, 8], [4, 7], [5, 6], [6, 5]])
>>> standardizing(ary, [0, 1])

array([[-1,  1],
   [ 0,  0],
   [ 0,  0],
   [ 0,  0],
   [ 0,  0],
   [ 1, -1]])