[dtnewman]

I believe that the optimize package within Scipy has a linear programming feature (http://docs.scipy.org/doc/scipy/reference/optimize.html), although I have never used it because I have found the documentation lacking.

However, for Python I would actually recommend the PuLP package (http://pythonhosted.org/PuLP/), which has pretty good documentation and let's you use various solvers (CBC, GLPK, CPLEX, Gurobi, etc.) on the backend. This package definitely has it's advantages over Excel, especially for large or complex LP problems where I have found Excel to be much slower and/or incapable of finding as food of a solution. However, for someone learning about LP (linear programming) or integer programming, it's unlikely that they will be doing anything that can't be done efficiently enough in Excel, particularly with an add-on such as OpenSolver. I think that Excel is just easier to use than any of the Python packages that I've come across for this (hopefully that will change though!). But once someone knows what they're doing, learning how to use something such as PuLP can be very valuable in my opinion.