Special Methods of Extension Types

This page describes the special methods currently supported by Cython extension types. A complete list of all the special methods appears in the table at the bottom. Some of these methods behave differently from their Python counterparts or have no direct Python counterparts, and require special mention.

Note

Everything said on this page applies only to extension types, defined with the cdef class statement. It doesn’t apply to classes defined with the Python class statement, where the normal Python rules apply.

Declaration

Special methods of extension types must be declared with def, not cdef. This does not impact their performance–Python uses different calling conventions to invoke these special methods.

Docstrings

Currently, docstrings are not fully supported in some special methods of extension types. You can place a docstring in the source to serve as a comment, but it won’t show up in the corresponding __doc__ attribute at run time. (This seems to be is a Python limitation – there’s nowhere in the PyTypeObject data structure to put such docstrings.)

Initialisation methods: __cinit__() and __init__()

There are two methods concerned with initialising the object.

The __cinit__() method is where you should perform basic C-level initialisation of the object, including allocation of any C data structures that your object will own. You need to be careful what you do in the __cinit__() method, because the object may not yet be a fully valid Python object when it is called. Therefore, you should be careful invoking any Python operations which might touch the object; in particular, its methods and anything that could be overridden by subtypes (and thus depend on their subtype state being initialised already).

By the time your __cinit__() method is called, memory has been allocated for the object and any C attributes it has have been initialised to 0 or null. (Any Python attributes have also been initialised to None, but you probably shouldn’t rely on that.) Your __cinit__() method is guaranteed to be called exactly once.

If your extension type has a base type, any existing __cinit__() methods in the base type hierarchy are automatically called before your __cinit__() method. You cannot explicitly call the inherited __cinit__() methods, and the base types are free to choose whether they implement __cinit__() at all. If you need to pass a modified argument list to the base type, you will have to do the relevant part of the initialisation in the __init__() method instead, where the normal rules for calling inherited methods apply.

Any initialisation which cannot safely be done in the __cinit__() method should be done in the __init__() method. By the time __init__() is called, the object is a fully valid Python object and all operations are safe. Under some circumstances it is possible for __init__() to be called more than once or not to be called at all, so your other methods should be designed to be robust in such situations.

Any arguments passed to the constructor will be passed to both the __cinit__() method and the __init__() method. If you anticipate subclassing your extension type in Python, you may find it useful to give the __cinit__() method * and ** arguments so that it can accept and ignore extra arguments. Otherwise, any Python subclass which has an __init__() with a different signature will have to override __new__() 1 as well as __init__(), which the writer of a Python class wouldn’t expect to have to do. Alternatively, as a convenience, if you declare your __cinit__`() method to take no arguments (other than self) it will simply ignore any extra arguments passed to the constructor without complaining about the signature mismatch.

Note

All constructor arguments will be passed as Python objects. This implies that non-convertible C types such as pointers or C++ objects cannot be passed into the constructor from Cython code. If this is needed, use a factory function instead that handles the object initialisation. It often helps to directly call __new__() in this function to bypass the call to the __init__() constructor.

See Instantiation from existing C/C++ pointers for an example.

1

https://docs.python.org/reference/datamodel.html#object.__new__

Finalization methods: __dealloc__() and __del__()

The counterpart to the __cinit__() method is the __dealloc__() method, which should perform the inverse of the __cinit__() method. Any C data that you explicitly allocated (e.g. via malloc) in your __cinit__() method should be freed in your __dealloc__() method.

You need to be careful what you do in a __dealloc__() method. By the time your __dealloc__() method is called, the object may already have been partially destroyed and may not be in a valid state as far as Python is concerned, so you should avoid invoking any Python operations which might touch the object. In particular, don’t call any other methods of the object or do anything which might cause the object to be resurrected. It’s best if you stick to just deallocating C data.

You don’t need to worry about deallocating Python attributes of your object, because that will be done for you by Cython after your __dealloc__() method returns.

When subclassing extension types, be aware that the __dealloc__() method of the superclass will always be called, even if it is overridden. This is in contrast to typical Python behavior where superclass methods will not be executed unless they are explicitly called by the subclass.

Python 3.4 made it possible for extension types to safely define finalizers for objects. When running a Cython module on Python 3.4 and higher you can add a __del__() method to extension types in order to perform Python cleanup operations. When the __del__() is called the object is still in a valid state (unlike in the case of __dealloc__()), permitting the use of Python operations on its class members. On Python <3.4 __del__() will not be called.

Arithmetic methods

Arithmetic operator methods, such as __add__(), used to behave differently from their Python counterparts in Cython 0.x, following the low-level semantics of the C-API slot functions. Since Cython 3.0, they are called in the same way as in Python, including the separate “reversed” versions of these methods (__radd__(), etc.).

Previously, if the first operand could not perform the operation, the same method of the second operand was called, with the operands in the same order. This means that you could not rely on the first parameter of these methods being “self” or being the right type, and you needed to test the types of both operands before deciding what to do.

If backwards compatibility is needed, the normal operator method (__add__, etc.) can still be implemented to support both variants, applying a type check to the arguments. The reversed method (__radd__, etc.) can always be implemented with self as first argument and will be ignored by older Cython versions, whereas Cython 3.x and later will only call the normal method with the expected argument order, and otherwise call the reversed method instead.

Alternatively, the old Cython 0.x (or native C-API) behaviour is still available with the directive c_api_binop_methods=True.

If you can’t handle the combination of types you’ve been given, you should return NotImplemented. This will let Python’s operator implementation first try to apply the reversed operator to the second operand, and failing that as well, report an appropriate error to the user.

This change in behaviour also applies to the in-place arithmetic method __ipow__(). It does not apply to any of the other in-place methods (__iadd__(), etc.) which always take self as the first argument.

Rich comparisons

There are a few ways to implement comparison methods. Depending on the application, one way or the other may be better:

• Use the 6 Python special methods __eq__(), __lt__(), etc. This is supported since Cython 0.27 and works exactly as in plain Python classes.

• Use a single special method __richcmp__(). This implements all rich comparison operations in one method. The signature is def __richcmp__(self, other, int op). The integer argument op indicates which operation is to be performed as shown in the table below:

  <	Py_LT
  ==	Py_EQ
  >	Py_GT
  <=	Py_LE
  !=	Py_NE
  >=	Py_GE

  These constants can be cimported from the cpython.object module.

• Use the @cython.total_ordering decorator, which is a low-level re-implementation of the functools.total_ordering decorator specifically for cdef classes. (Normal Python classes can use the original functools decorator.)

  @cython.total_ordering
  cdef class ExtGe:
      cdef int x
  
      def __ge__(self, other):
          if not isinstance(other, ExtGe):
              return NotImplemented
          return self.x >= (<ExtGe>other).x
  
      def __eq__(self, other):
          return isinstance(other, ExtGe) and self.x == (<ExtGe>other).x
  

The __next__() method

Extension types wishing to implement the iterator interface should define a method called __next__(), not next. The Python system will automatically supply a next method which calls your __next__(). Do NOT explicitly give your type a next() method, or bad things could happen.

Special Method Table

This table lists all of the special methods together with their parameter and return types. In the table below, a parameter name of self is used to indicate that the parameter has the type that the method belongs to. Other parameters with no type specified in the table are generic Python objects.

You don’t have to declare your method as taking these parameter types. If you declare different types, conversions will be performed as necessary.

General

https://docs.python.org/3/reference/datamodel.html#special-method-names

Name	Parameters	Return type	Description
__cinit__	self, …		Basic initialisation (no direct Python equivalent)
__init__	self, …		Further initialisation
__dealloc__	self		Basic deallocation (no direct Python equivalent)
__cmp__	x, y	int	3-way comparison (Python 2 only)
__str__	self	object	str(self)
__repr__	self	object	repr(self)
__hash__	self	Py_hash_t	Hash function (returns 32/64 bit integer)
__call__	self, …	object	self(…)
__iter__	self	object	Return iterator for sequence
__getattr__	self, name	object	Get attribute
__getattribute__	self, name	object	Get attribute, unconditionally
__setattr__	self, name, val		Set attribute
__delattr__	self, name		Delete attribute

Rich comparison operators

https://docs.python.org/3/reference/datamodel.html#basic-customization

You can choose to either implement the standard Python special methods like __eq__() or the single special method __richcmp__(). Depending on the application, one way or the other may be better.

Name	Parameters	Return type	Description
__eq__	self, y	object	self == y
__ne__	self, y	object	self != y (falls back to __eq__ if not available)
__lt__	self, y	object	self < y
__gt__	self, y	object	self > y
__le__	self, y	object	self <= y
__ge__	self, y	object	self >= y
__richcmp__	self, y, int op	object	Joined rich comparison method for all of the above (no direct Python equivalent)

Arithmetic operators

https://docs.python.org/3/reference/datamodel.html#emulating-numeric-types

Name	Parameters	Return type	Description
__add__, __radd__	self, other	object	binary + operator
__sub__, __rsub__	self, other	object	binary - operator
__mul__, __rmul__	self, other	object	* operator
__div__, __rdiv__	self, other	object	/ operator for old-style division
__floordiv__, __rfloordiv__	self, other	object	// operator
__truediv__, __rtruediv__	self, other	object	/ operator for new-style division
__mod__, __rmod__	self, other	object	% operator
__divmod__, __rdivmod__	self, other	object	combined div and mod
__pow__, __rpow__	self, other, [mod]	object	** operator or pow(x, y, [mod])
__neg__	self	object	unary - operator
__pos__	self	object	unary + operator
__abs__	self	object	absolute value
__nonzero__	self	int	convert to boolean
__invert__	self	object	~ operator
__lshift__, __rlshift__	self, other	object	<< operator
__rshift__, __rrshift__	self, other	object	>> operator
__and__, __rand__	self, other	object	& operator
__or__, __ror__	self, other	object	| operator
__xor__, __rxor__	self, other	object	^ operator

Note that Cython 0.x did not make use of the __r...__ variants and instead used the bidirectional C slot signature for the regular methods, thus making the first argument ambiguous (not ‘self’ typed). Since Cython 3.0, the operator calls are passed to the respective special methods. See the section on Arithmetic methods above.

Numeric conversions

https://docs.python.org/3/reference/datamodel.html#emulating-numeric-types

Name	Parameters	Return type	Description
__int__	self	object	Convert to integer
__long__	self	object	Convert to long integer
__float__	self	object	Convert to float
__oct__	self	object	Convert to octal
__hex__	self	object	Convert to hexadecimal
__index__	self	object	Convert to sequence index

In-place arithmetic operators

https://docs.python.org/3/reference/datamodel.html#emulating-numeric-types

Name	Parameters	Return type	Description
__iadd__	self, x	object	+= operator
__isub__	self, x	object	-= operator
__imul__	self, x	object	*= operator
__idiv__	self, x	object	/= operator for old-style division
__ifloordiv__	self, x	object	//= operator
__itruediv__	self, x	object	/= operator for new-style division
__imod__	self, x	object	%= operator
__ipow__	self, y, z	object	**= operator
__ilshift__	self, x	object	<<= operator
__irshift__	self, x	object	>>= operator
__iand__	self, x	object	&= operator
__ior__	self, x	object	|= operator
__ixor__	self, x	object	^= operator

Sequences and mappings

https://docs.python.org/3/reference/datamodel.html#emulating-container-types

Name	Parameters	Return type	Description
__len__	self	Py_ssize_t	len(self)
__getitem__	self, x	object	self[x]
__setitem__	self, x, y		self[x] = y
__delitem__	self, x		del self[x]
__getslice__	self, Py_ssize_t i, Py_ssize_t j	object	self[i:j]
__setslice__	self, Py_ssize_t i, Py_ssize_t j, x		self[i:j] = x
__delslice__	self, Py_ssize_t i, Py_ssize_t j		del self[i:j]
__contains__	self, x	int	x in self

Iterators

https://docs.python.org/3/reference/datamodel.html#emulating-container-types

Name	Parameters	Return type	Description
__next__	self	object	Get next item (called next in Python)

Buffer interface [PEP 3118] (no Python equivalents - see note 1)

Name	Parameters	Return type	Description
__getbuffer__	self, Py_buffer *view, int flags
__releasebuffer__	self, Py_buffer *view

Buffer interface [legacy] (no Python equivalents - see note 1)

Name	Parameters	Return type	Description
__getreadbuffer__	self, Py_ssize_t i, void **p
__getwritebuffer__	self, Py_ssize_t i, void **p
__getsegcount__	self, Py_ssize_t *p
__getcharbuffer__	self, Py_ssize_t i, char **p

Descriptor objects (see note 2)

https://docs.python.org/3/reference/datamodel.html#emulating-container-types

Name	Parameters	Return type	Description
__get__	self, instance, class	object	Get value of attribute
__set__	self, instance, value		Set value of attribute
__delete__	self, instance		Delete attribute

Note

(1) The buffer interface was intended for use by C code and is not directly accessible from Python. It is described in the Python/C API Reference Manual of Python 2.x under sections 6.6 and 10.6. It was superseded by the new PEP 3118 buffer protocol in Python 2.6 and is no longer available in Python 3. For a how-to guide to the new API, see Implementing the buffer protocol.

Note

(2) Descriptor objects are part of the support mechanism for new-style Python classes. See the discussion of descriptors in the Python documentation. See also PEP 252, “Making Types Look More Like Classes”, and PEP 253, “Subtyping Built-In Types”.