First of all let me remember that graphic output currently is available for some NLP and NSP solvers via p.plot=True (or p.plot=1).
Also, you can modify p.graphics.xlabel (or, with latest svn changes, p.xlabel as well): default is 'time', also possible 'cputime', 'nIter' (case-insensitive).
Some solvers (for example ALGENCAN) still don't have convenient ability for iter-to-iter data output via a func that could be connected to OO. So you should use (regardless does user provide gradient or not - if latter, OO finite-difference approximation will be used instead) p.connectIterFcn('df') (you can use other func as well, for example 'f' is good for scipy_fminbound, but bad choise can significantly slow your calculations). BTW matplotlib output of 2 subplots is significantly slower than single plot - I hope it will be either fixed in future matplotlib releases or your hardware is(/will be) adequate for your problems.
So, here's an example of ALGENCAN graphic output:
for unconstrained problems (as well as those solvers that have each iter point feasible, for example some box-bound solvers) graphic output remains same:Some minor problems still remain (plotting several solvers in single window or no Python control return till figure close), but I suspect they are due to pylab native bugs (I consider using MATLAB plotting tool was more convenient). I will try to fix(/avoid) that ones when I'll have enough time, I'm short of the one for now.
See also: NLSP (non-linear solve) graphical output.
See also: LSP (non-linear least squares) graphical output.