np.concatenate a ND tensor/array with a 1D array

Question

I have two arrays a & b

a.shape
(5, 4, 3)
array([[[ 0.        ,  0.        ,  0.        ],
        [ 0.        ,  0.        ,  0.        ],
        [ 0.          


        

Answer 1

You can also use np.insert.

b_broad = np.expand_dims(b, axis=0) # b_broad.shape = (1, 3)
ab = np.insert(a, 4, b_broad, axis=1)
""" 
Because now we are inserting along axis 1
     a'shape without axis 1 = (5, 3) 
     b_broad's shape          (1, 3)  
can be aligned and broadcast b_broad to (5, 3)
"""

In this example, we insert along the axis 1, and will put b_broad before the index given, 4 here. In other words, the b_broad will occupy index 4 at long the axis and make ab.shape equal (5, 5, 3).

Note again that before we do insertion, we turn b into b_broad for safely achieve the right broadcasting you want. The dimension of b is smaller and there will be broadcasting at insertion. We can use expand_dims to achieve this goal.

If a is of shape (3, 4, 5), you will need b_broad to have shape (3, 1) to match up dimensions if inserting along axis 1. This can be achieved by

b_broad = np.expand_dims(b, axis=1)  # shape = (3, 1)

It would be a good practice to make b_broad in a right shape because you might have a.shape = (3, 4, 3) and you really need to specify which way to broadcast in this case!

Timing Results

From OP's dataset: COLDSPEED's answer is 3 times faster.

def Divakar():  # Divakar's answer
    b3D = b.reshape(1, 1, -1).repeat(a.shape[0], axis=0)
    r = np.concatenate((a, b3D), axis=1)
# COLDSPEED's result
%timeit np.concatenate((a, b.reshape(1, 1, -1).repeat(a.shape[0], axis=0)), axis=1)
2.95 µs ± 164 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
# Divakar's result
%timeit Divakar()
3.03 µs ± 173 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
# Mine's
%timeit np.insert(a, 4, b, axis=1)
10.1 µs ± 220 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

Dataset 2 (Borrow the timing experiment from COLDSPEED): nothing can be concluded in this case because they share nearly the same mean and standard deviation.

a = np.random.randn(100, 99, 100)
b = np.random.randn(100)

# COLDSPEED's result
%timeit np.concatenate((a, b.reshape(1, 1, -1).repeat(a.shape[0], axis=0)), axis=1) 
2.37 ms ± 194 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
# Divakar's
%timeit Divakar()
2.31 ms ± 249 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
# Mine's
%timeit np.insert(a, 99, b, axis=1) 
2.34 ms ± 154 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

Speed will depend on data's size, shape, and volume. Please tested on you dataset if speed is your concern.