Python has formed a very strong ecology in scientific computing, data analysis and visualization. Moreover, as a fashionable scripting language, it is easy to learn and use. Therefore, for practitioners of quantitative analysis and risk management, it is worth trying to convert some historical codes of QuantLib into Python programs.
Python itself has a perfect object-oriented mechanism. With SWIG packaging, Python programs converted from C++ code can basically keep the original class architecture intact. For users, the application-level historical code can be transplanted almost in parallel, with little modification.