I wrote a full answer (below the line) before discovering CVXPY, which (like CVX for MATLAB) does all the hard stuff for you and has a very short example almost identical to yours here. You only need to replace the relevant line with
p = program(minimize(norm2(A*x-b)),[equals(sum(x),1),geq(x,0)])
My old answer, doing it the harder way with CVXOPT:
Following Geoff's suggestion to square your objective function gives
$$\|Ax-b\|^2_2 = <x^TA^T - b^T,Ax-b> = x^TA^TAx - b^TAx - x^TAb - b^Tb$$
Of course, all terms are scalars, so you can transpose the third one and drop the last one (as it doesn't depend on $x$ and therefore won't change which $x$ gives you a minimum, though you will need to add it back in after solving in order to get the correct value of your objective) to obtain
$$ x^TA^TAx - b^T(A + A^T)x$$
This (including your constraints) has the form of a quadratic program, as given in the CVXOPT documentation here, where there is also example code for solving such a problem.