Python and C++ are in many ways as different as two languages could be: while C++ is usually compiled to machine-code, Python is interpreted. Python's dynamic type system is often cited as the foundation of its flexibility, while in C++ static typing is the cornerstone of its efficiency. C++ has an intricate and difficult compile-time meta-language, while in Python, practically everything happens at runtime.
Yet for many programmers, these very differences mean that Python and C++ complement one another perfectly. Performance bottlenecks in Python programs can be rewritten in C++ for maximal speed, and authors of powerful C++ libraries choose Python as a middleware language for its flexible system integration capabilities. Furthermore, the surface differences mask some strong similarities:
'C'-family control structures (if, while, for...)
Support for object-orientation, functional programming, and generic programming (these are both multi-paradigm programming languages.)
Comprehensive operator overloading facilities, recognizing the importance of syntactic variability for readability and expressivity.
High-level concepts such as collections and iterators.
High-level encapsulation facilities (C++: namespaces, Python: modules) to support the design of re-usable libraries.
Exception-handling for effective management of error conditions.
C++ idioms in common use, such as handle/body classes and reference-counted smart pointers mirror Python reference semantics.
Given Python's rich 'C' interoperability API, it should in principle be possible to expose C++ type and function interfaces to Python with an analogous interface to their C++ counterparts. However, the facilities provided by Python alone for integration with C++ are relatively meager. Compared to C++ and Python, 'C' has only very rudimentary abstraction facilities, and support for exception-handling is completely missing. 'C' extension module writers are required to manually manage Python reference counts, which is both annoyingly tedious and extremely error-prone. Traditional extension modules also tend to contain a great deal of boilerplate code repetition which makes them difficult to maintain, especially when wrapping an evolving API.
These limitations have lead to the development of a variety of wrapping systems. SWIG is probably the most popular package for the integration of C/C++ and Python. A more recent development is SIP, which was specifically designed for interfacing Python with the Qt graphical user interface library. Both SWIG and SIP introduce their own specialized languages for customizing inter-language bindings. This has certain advantages, but having to deal with three different languages (Python, C/C++ and the interface language) also introduces practical and mental difficulties. The CXX package demonstrates an interesting alternative. It shows that at least some parts of Python's 'C' API can be wrapped and presented through a much more user-friendly C++ interface. However, unlike SWIG and SIP, CXX does not include support for wrapping C++ classes as new Python types.
The features and goals of Boost.Python overlap significantly with many of these other systems. That said, Boost.Python attempts to maximize convenience and flexibility without introducing a separate wrapping language. Instead, it presents the user with a high-level C++ interface for wrapping C++ classes and functions, managing much of the complexity behind-the-scenes with static metaprogramming. Boost.Python also goes beyond the scope of earlier systems by providing:
Support for C++ virtual functions that can be overridden in Python.
Comprehensive lifetime management facilities for low-level C++ pointers and references.
Support for organizing extensions as Python packages, with a central registry for inter-language type conversions.
A safe and convenient mechanism for tying into Python's powerful serialization engine (pickle).
Coherence with the rules for handling C++ lvalues and rvalues that can only come from a deep understanding of both the Python and C++ type systems.
The key insight that sparked the development of Boost.Python is that much of the boilerplate code in traditional extension modules could be eliminated using C++ compile-time introspection. Each argument of a wrapped C++ function must be extracted from a Python object using a procedure that depends on the argument type. Similarly the function's return type determines how the return value will be converted from C++ to Python. Of course argument and return types are part of each function's type, and this is exactly the source from which Boost.Python deduces most of the information required.
This approach leads to user guided wrapping: as much information is extracted directly from the source code to be wrapped as is possible within the framework of pure C++, and some additional information is supplied explicitly by the user. Mostly the guidance is mechanical and little real intervention is required. Because the interface specification is written in the same full-featured language as the code being exposed, the user has unprecedented power available when she does need to take control.
