Many convex and mixed integer constrained optimization problems can be compiled into a canonical form and handed to some off-the-shelf optimization solver for numeric solution. However, the necessary reformulations can be technically challenging and a program written to use one solver cannot easily be made to use another. PICOS is a well-established Python meta-interface to many proprietary and open source optimization solvers that allows problems to be input in a natural algebraic form and that handles solver selection and the required transformations transparently. This enables users to focus more on the high level optimization model and its application and less on technicalities.