I have 2 arrays in 2D, where the column vectors are feature vectors. One array is of size F x A, the other of F x B, where A << B. As an example, for A = 2 and F = 3 (B can be anything):

arr1 = np.array( [[1, 4],
                  [2, 5],
                  [3, 6]] )

arr2 = np.array( [[1, 4, 7, 10, ..],
                  [2, 5, 8, 11, ..],
                  [3, 6, 9, 12, ..]] )

I want to calculate the distance between arr1 and a fragment of arr2 that is of equal size (in this case, 3x2), for each possible fragment of arr2. The column vectors are independent of each other, so I believe I should calculate the distance between each column vector in arr1 and a collection of column vectors ranging from i to i + A from arr2 and take the sum of these distances (not sure though).

Does numpy offer an efficient way of doing this, or will I have to take slices from the second array and, using another loop, calculate the distance between each column vector in arr1 and the corresponding column vector in the slice?