If you are doing linear algebra, I agree. Yet linear algebra is not the only thing I want to do with numbers. I think most of the time I do use the elementwise operation, such as:

    x = linspace(0,10, 1000)
    y = (x<5)*4.0
    z = x**2 + y

Of course I can just use matrices and then I have the information at hand that I am doing linear algebra right now:

    x = matrix([[3, 0],
                [9, 5]])

    Out[28]: 
    matrix([[ 9,  0],
            [72, 25]])

    x**2 + x

I think this is not too much boilerplate and gives nice semantic information within the sourcecode. However, if you want to perform this with a 2d array object, you can also use its dot method:

    In [ 1]: a
    Out[ 1]: 
    array([[ 9,  0],
           [72, 25]])

    In [ 2]: a.dot(a)
    Out[ 2]: 
    array([[  81,    0],
           [2448,  625]])

    In [ 3]: a * a
    Out[ 3]: 
    array([[  81,    0],
           [5184,  625]])

The approach of the matrix object nicely takes into account that operands of a "normal" multiplication `*` commute, so the elementwise multiplication fits the picture here. Wheres matrix multiplication - which is non-associative for most matrices - is performed by a different method.