It probably makes more sense if you think of matrices as n-dimentional arrays: sum eliminates the n-th dimention, insert and delete increase or decrease the n-th dimension size.

Very good point. Additionally, with insert and delete we might need to apply the rules of broadcasting [0].

For example, if I have a 2-dimensional array and I insert a scalar value by using axis=0, the scalar value needs to be broadcasted to match the column dimension of the array:

  >>> a = np.array([[1,2],[3,4],[5,6]])
  >>> np.insert(a, 1, 11, axis=0)
  array([[ 1,  2],
         [11, 11],
         [ 3,  4],
         [ 5,  6]])

[0] https://jakevdp.github.io/PythonDataScienceHandbook/02.05-co...