C++ functions are first-class objects in Python and can be used wherever Python functions can be used, including for dynamically constructing classes.

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')

Function argument type conversions follow the expected rules, with implicit conversions allowed, including between Python builtin types and STL types, but it is rather more efficient to make conversions explicit.

Free functions

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 directly import C++ functions from it and other namespaces that themselves may contain more functions. All lookups on namespaces are done lazily, thus if loading more headers bring in more functions (incl. new overloads), these become available dynamically.

>>> from cppyy.gbl import global_function, Namespace
>>> global_function == Namespace.global_function
>>> from cppyy.gbl.Namespace import global_function
>>> global_function == Namespace.global_function
>>> from cppyy.gbl import global_function

Free functions can be bound to a class, following the same rules as apply to Python functions: unless marked as static, they will turn into member functions when bound to an instance, but act as static functions when called through the class. Consider this example:

>>> from cppyy.gbl import Concrete, call_abstract_method
>>> c = Concrete()
>>> Concrete.callit = call_abstract_method
>>> Concrete.callit(c)
called Concrete::abstract_method
>>> c.callit()
called Concrete::abstract_method
>>> Concrete.callit = staticmethod(call_abstract_method)
>>> c.callit()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: void ::call_abstract_method(Abstract* a) =>
    TypeError: takes at least 1 arguments (0 given)
>>> c.callit(c)
called Concrete::abstract_method

Static methods

Class static functions are treated the same way as free functions, except that they are accessible either through the class or through an instance, just like Python’s staticmethod.


For class methods, see the methods section under the classes heading.


Globally defined operators are found lazily (ie. can resolve after the class definition by loading the global definition or by defining them interactively) and are mapped onto a Python equivalent when possible. See the operators section under the classes heading for more details.


Templated functions (and class methods) can either be called using square brackets ([]) to provide the template arguments explicitly, or called directly, through automatic lookup. The template arguments may either be a string of type names (this results in faster code, as it needs no further lookup/verification) or a list of the actual types to use (which tends to be more convenient).

Note: the Python type float maps to the C++ type float, even as Python uses a C double as its internal representation. The motivation is that doing so makes the Python code more readable (and Python may anyway change its internal representation in the future). The same has been true for Python int, which used to be a C long internally.

Examples, using multiply from features.h:

>>> mul = cppyy.gbl.multiply
>>> mul(1, 2)
>>> mul(1., 5)
>>> mul[int](1, 1)
>>> mul[int, int](1, 1)
>>> mul[int, int, float](1, 1)
>>> mul[int, int](1, 'a')
 TypeError: Template method resolution failed:
 none of the 6 overloaded methods succeeded. Full details:
 int ::multiply(int a, int b) =>
   TypeError: could not convert argument 2 (int/long conversion expects an integer object)
 Failed to instantiate "multiply(int,std::string)"
>>> mul['double, double, double'](1., 5)


C++ supports overloading, whereas Python supports “duck typing”, thus C++ overloads have to be selected dynamically in response to the available “ducks”. This may lead to additional lookups or template instantiations. However, pre-existing methods (incl. auto-instantiated methods) are always preferred over new template instantiations:

>>> global_function(1.)        # selects 'double' overload
>>> global_function(1)         # selects 'int' overload

C++ does a static dispatch at compile time based on the argument types. The dispatch is a selection among overloads (incl. templates) visible at that point in the translation unit. Bound C++ in Python does a dynamic dispatch: it considers all overloads visible globally at that point in the execution. Because the dispatch is fundamentally different (albeit in line with the expectation of the respective languages), differences can occur. Especially if overloads live in different header files and are only an implicit conversion apart, or if types that have no direct equivalent in Python, such as e.g. unsigned short, are used.

There are two rounds to finding an overload. If all overloads fail argument conversion during the first round, where implicit conversions are not allowed, _and_ at least one converter has indicated that it can do implicit conversions, a second round is tried. In this second round, implicit conversions are allowed, including class instantiation of temporaries. During some template calls, implicit conversions are not allowed at all, to make sure new instantiations happen instead.

In the rare occasion where the automatic overload selection fails, the __overload__ function can be called to access a specific overload matching a specific function signature:

>>> global_function.__overload__('double')(1)   # int implicitly converted

An optional boolean second parameter can be used to restrict the selected method to be const (if True) or non-const (if False).

Note that __overload__ only does a lookup; it performs no (implicit) conversions and the types in the signature to match should be the fully resolved ones (no typedefs). To see all available overloads, use help() or look at the __doc__ string of the function:

>>> print(global_function.__doc__)
int ::global_function(int)
double ::global_function(double)

For convenience, the :any: signature, allows matching any signature, for example to reduce the general method to the const (or non-const) overload only, use:

MyClass.some_method = MyClass.some_method.__overload__(':any:', True)

Return values

Most return types are readily amenable to automatic memory management: builtin returns, by-value returns, (const-)reference returns to internal data, smart pointers, etc. The important exception is pointer returns.

A function that returns a pointer to an object over which Python should claim ownership, should have its __creates__ flag set through its pythonization. Well-written APIs will have clear clues in their naming convention about the ownership rules. For example, functions called New..., Clone..., etc. can be expected to return freshly allocated objects. A simple name-matching in the pythonization then makes it simple to mark all these functions as creators.

The return values are auto-casted.

*args and **kwds

C++ default arguments work as expected. Keywords, however, are a Python language feature that does not exist in C++. Many C++ function declarations do have formal arguments, but these are not part of the C++ interface (the argument names are repeated in the definition, making the names in the declaration irrelevant: they do not even need to be provided). Thus, although cppyy will map keyword argument names to formal argument names from the C++ declaration, use of this feature is not recommended unless you have a guarantee that the names in C++ the interface are maintained. Example:

>>> from cppyy.gbl import Concrete
>>> c = Concrete()       # uses default argument
>>> c.m_int
>>> c = Concrete(13)     # uses provided argument
>>> c.m_int
>>> args = (27,)
>>> c = Concrete(*args)  # argument pack
>>> c.m_int
>>> c = Concrete(n=17)
>>> c.m_int
>>> kwds = {'n' : 18}
>>> c = Concrete(**kwds)
>>> c.m_int


Python callables (functions/lambdas/instances) can be passed to C++ through function pointers and/or std::function. This involves creation of a temporary wrapper, which has the same life time as the Python callable it wraps, so the callable needs to be kept alive on the Python side if the C++ side stores the callback. Example:

>>> from cppyy.gbl import call_int_int
>>> print(call_int_int.__doc__)
int ::call_int_int(int(*)(int,int) f, int i1, int i2)
>>> def add(a, b):
...    return a+b
>>> call_int_int(add, 3, 7)
>>> call_int_int(lambda x, y: x*y, 3, 7)