Both Python and C++ support object-oriented code through classes and thus it is logical to expose C++ classes as Python ones, including the full inheritance hierarchy.

The C++ code used for the examples below can be found here, and it is assumed that that code is loaded at the start of any session. Download it, save it under the name features.h, and load it:

>>> import cppyy
>>> cppyy.include('features.h')


All bound C++ code starts off from the global C++ namespace, represented in Python by gbl. This namespace, as any other namespace, is treated as a module after it has been loaded. Thus, we can import C++ classes that live underneath it:

>>> from cppyy.gbl import Concrete
>>> Concrete
<class cppyy.gbl.Concrete at 0x2058e30>

Placing classes in the same structure as imposed by C++ guarantees identity, even if multiple Python modules bind the same class. There is, however, no necessity to expose that structure to end-users: when developing a Python package that exposes C++ classes through cppyy, consider cppyy.gbl an “internal” module, and expose the classes in any structure you see fit. The C++ names will continue to follow the C++ structure, however, as is needed for e.g. pickling:

>>> from cppyy.gbl import Namespace
>>> Concrete == Namespace.Concrete
>>> n = Namespace.Concrete.NestedClass()
>>> type(n)
<class cppyy.gbl.Namespace.Concrete.NestedClass at 0x22114c0>
>>> type(n).__name__
>>> type(n).__module__
>>> type(n).__cpp_name__


Python and C++ both make a distinction between allocation (__new__ in Python, operator new in C++) and initialization (__init__ in Python, the constructor call in C++). When binding, however, there comes a subtle semantic difference: the Python __new__ allocates memory for the proxy object only, and __init__ initializes the proxy by creating or binding the C++ object. Thus, no C++ memory is allocated until __init__. The advantages are simple: the proxy can now check whether it is initialized, because the pointer to C++ memory will be NULL if not; it can be a reference to another proxy holding the actual C++ memory; and it can now transparently implement a C++ smart pointer. If __init__ is never called, eg. when a call to the base class __init__ is missing in a derived class override, then accessing the proxy will result in a Python ReferenceError exception.


There should not be a reason to call a destructor directly in CPython, but PyPy uses a garbage collector and that makes it sometimes useful to destruct a C++ object where you want it destroyed. Destructors are accessible through the conventional __destruct__ method. Accessing an object after it has been destroyed will result in a Python ReferenceError exception.


The output of help shows the inheritance hierarchy, constructors, public methods, and public data. For example, Concrete inherits from Abstract and it has a constructor that takes an int argument, with a default value of 42. Consider:

>>> from cppyy.gbl import Abstract
>>> issubclass(Concrete, Abstract)
>>> a = Abstract()
Traceback (most recent call last):
  File "<console>", line 1, in <module>
TypeError: cannot instantiate abstract class 'Abstract'
>>> c = Concrete()
>>> isinstance(c, Concrete)
>>> isinstance(c, Abstract)
>>> d = Concrete(13)

Just like in C++, interface classes that define pure virtual methods, such as Abstract does, can not be instantiated, but their concrete implementations can. As the output of help showed, the Concrete constructor takes an integer argument, that by default is 42.


Python classes that derive from C++ classes can override virtual methods as long as those methods are declared on class instantiation (adding methods to the Python class after the fact will not provide overrides on the C++ side, only on the Python side). Example:

>>> from cppyy.gbl import Abstract, call_abstract_method
>>> class PyConcrete(Abstract):
...     def abstract_method(self):
...         return "Hello, Python World!\n"
...     def concrete_method(self):
...         pass
>>> pc = PyConcrete()
>>> call_abstract_method(pc)
Hello, Python World!

Note that it is not necessary to provide a constructor (__init__), but if you do, you must call the base class constructor through the super mechanism.

Multiple cross-inheritance

Python requires that any multiple inheritance (also in pure Python) has an unambiguous method resolution order (mro), including for classes and thus also for meta-classes. In Python2, it was possible to resolve any mro conflicts automatically, but meta-classes in Python3, although syntactically richer, have functionally become far more limited. In particular, the mro is checked in the builtin class builder, instead of in the meta-class of the meta-class (which in Python3 is the builtin type rather than the meta-class itself as in Python2, another limitation, and which actually checks the mro a second time for no reason). The upshot is that a helper is required (cppyy.multi) to resolve the mro to support Python3. The helper is written to also work in Python2. Example:

>>> class PyConcrete(cppyy.multi(cppyy.gbl.Abstract1, cppyy.gbl.Abstract2)):
...     def abstract_method1(self):
...         return "first message"
...     def abstract_method2(self):
...         return "second message"
>>> pc = PyConcrete()
>>> cppyy.gbl.call_abstract_method1(pc)
first message
>>> cppyy.gbl/call_abstract_method2(pc)
second message

Contrary to multiple inheritance in Python, in C++ there are no two separate instances representing the base classes. Thus, a single __init__ call needs to construct and initialize all bases, rather than calling __init__ on each base independently. To support this syntax, the arguments to each base class should be grouped together in a tuple. If there are no arguments, provide an empty tuple (or omit them altogether, if these arguments apply to the right-most base(s)).


C++ methods are represented as Python ones: these are first-class objects and can be bound to an instance. If a method is virtual in C++, the proper concrete method is called, whether or not the concrete class is bound. Similarly, if all classes are bound, the normal Python rules apply:

>>> c.abstract_method()
called Concrete::abstract_method
>>> c.concrete_method()
called Concrete::concrete_method
>>> m = c.abstract_method
>>> m()
called Concrete::abstract_method

Data members

Data members are implemented as properties, using descriptors. For example, The Concrete instances have a public data member m_int:

>>> c.m_int, d.m_int
(42, 13)

Note however, that the data members are typed: setting them results in a memory write on the C++ side. This is different in Python, where references are replaced, and thus any type will do:

>>> c.m_int = 3.14   # a float does not fit in an int
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: int/long conversion expects an integer object
>>> c.m_int = int(3.14)
>>> c.m_int, d.m_int
(3, 13)

Private and protected data members are not accessible, contrary to Python data members, and C++ const-ness is respected:

>>> c.m_const_int = 71    # declared 'const int' in class definition
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: assignment to const data not allowed

Static C++ data members act like Python class-level data members. They are also represented by property objects and both read and write access behave as expected:

>>> Concrete.s_int       # access through class
>>> c.s_int = 123        # access through instance
>>> Concrete.s_int


Many C++ operators can be mapped to their Python equivalent. When the operators are part of the C++ class definition, this is done directly. If they are defined globally, the lookup is done lazily (ie. can resolve after the class definition by loading the global definition or by defining them interactively). Some operators have no Python equivalent and are instead made available by mapping them onto the following conventional functions:

C++ Python
operator= __assign__
operator++(int) __postinc__
operator++() __preinc__
operator--(int) __postdec__
operator--() __predec__
unary operator* __deref__
operator-> __follow__
operator&& __dand__
operator|| __dor__
operator, __comma__

Here is an example of operator usage, using STL iterators directly (note that this is not necessary in practice as STL and STL-like containers work transparently in Python for-loops):

>>> v = cppyy.gbl.std.vector[int](range(3))
>>> i = v.begin()
>>> while (i != v.end()):
...    print(i.__deref__())
...    _ = i.__preinc__()

Overridden operator new and operator delete, as well as their array equivalents, are not accessible but will be called as appropriate.


Templated classes are instantiated using square brackets. (For backwards compatibility reasons, parentheses work as well.) The instantiation of a templated class yields a class, which can then be used to create instances.

Templated classes need not pre-exist in the bound code, just their declaration needs to be available. This is true for e.g. all of STL:

>>> cppyy.gbl.std.vector                # template metatype
<cppyy.Template 'std::vector' object at 0x7fffed2674d0>
>>> cppyy.gbl.std.vector(int)           # instantiates template -> class
<class cppyy.gbl.std.vector<int> at 0x1532190>
cppyy.gbl.std.vector[int]()             # instantiates class -> object
<cppyy.gbl.std.vector<int> object at 0x2341ec0>

The template arguments may be actual types or their names as a string, whichever is more convenient. Thus, the following are equivalent:

>>> from cppyy.gbl.std import vector
>>> type1 = vector[Concrete]
>>> type2 = vector['Concrete']
>>> type1 == type2


Typedefs are simple python references to the actual classes to which they refer.

>>> from cppyy.gbl import Concrete_t
>>> Concrete is Concrete_t