Note
This page uses two different syntax variants:
Cython specific cdef syntax, which was designed to make type declarations concise and easily readable from a C/C++ perspective.
Pure Python syntax which allows static Cython type declarations in pure Python code, following PEP-484 type hints and PEP 526 variable annotations.
To make use of C data types in Python syntax, you need to import the special cython module in the Python module that you want to compile, e.g.
If you use the pure Python syntax we strongly recommend you use a recent Cython 3 release, since significant improvements have been made here compared to the 0.29.x releases.
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 or decorated using @cclass decorator. It doesn’t apply to classes defined with the Python class statement, where the normal Python rules apply.
Special methods of extension types must be declared with def, not cdef/@cfunc. This does not impact their performance–Python uses different calling conventions to invoke these special methods.
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.)
__cinit__() and __init__()¶
There are two methods concerned with initialising the object, the normal Python __init__() method and a special __cinit__() method where basic C level initialisation can be performed.
The main difference between the two is when they are called. The __cinit__() method is guaranteed to be called as part of the object allocation, but before the object is fully initialised. Specifically, methods and object attributes that belong to subclasses or that were overridden by subclasses may not have been initialised at all yet and must not be used by __cinit__() in a base class. Note that the object allocation in Python clears all fields and sets them to zero (or NULL). Cython additionally takes responsibility of setting all object attributes to None, but again, this may not yet have been done for the attributes defined or overridden by subclasses. If your object needs anything more than this basic attribute clearing in order to get into a correct and safe state, __cinit__() may be a good place to do it.
The __init__() method, on the other hand, works exactly like in Python. It is called after allocation and basic initialisation of the object, including the complete inheritance chain. By the time __init__() is called, the object is a fully valid Python object and all operations are safe. Any initialisation which cannot safely be done in the __cinit__() method should be done in the __init__() method. However, as in Python, it is the responsibility of the subclasses to call up the hierarchy and make sure that the __init__() methods in the base class are called correctly. If a subclass forgets (or refuses) to call the __init__() method of one of its base classes, that method will not be called. Also, if the object gets created by calling directly its __new__() method [1] (as opposed to calling the class itself), then none of the __init__() methods will be called.
The __cinit__() method is where you should perform basic safety C-level initialisation of the object, possibly including allocation of any C data structures that your object will own. In contrast to __init__(), 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 arguments passed to the constructor will be passed to both the __cinit__() method and the __init__() method. If you anticipate subclassing your extension type, you may find it useful to give the __cinit__() method * and ** arguments so that it can accept and ignore arbitrary extra arguments, since the arguments that are passed through the hierarchy during allocation cannot be changed by subclasses. 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, neither from Python nor from Cython code. If this is needed, use a factory function or method instead that handles the object initialisation. It often helps to directly call the __new__() method in this function to explicitly bypass the call to the __init__() constructor.
See Instantiation from existing C/C++ pointers for an example.
Note
Implementing a __cinit__() method currently excludes the type from auto-pickling.
__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 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.
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.
If you use the functools.total_ordering decorator on an extension type/cdef class, Cython replaces it with a low-level reimplementation designed specifically for extension types. (On a normal Python classes, the functools decorator continues to work as before.) As a shortcut you can also use cython.total_ordering, which applies the same re-implementation but also transforms the class to an extension type if it isn’t already.
import functools
import cython
@functools.total_ordering
@cython.cclass
class ExtGe:
x: cython.int
def __ge__(self, other):
if not isinstance(other, ExtGe):
return NotImplemented
return self.x >= cython.cast(ExtGe, other).x
def __eq__(self, other):
return isinstance(other, ExtGe) and self.x == cython.cast(ExtGe, other).x
import functools
@functools.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.
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. Cython 0.x also did not support the 2 argument version of __pow__ and __rpow__, or the 3 argument version of __ipow__.
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 (3-arg form only on Python >= 3.8)
__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
Customizing class creation¶https://docs.python.org/3/reference/datamodel.html#customizing-class-creation
Name
Parameters
Return type
Description
__set_name__
self, owner, name
Automatically called at the time the owning class owner is created.
Descriptor objects (see note 2)¶https://docs.python.org/3/reference/datamodel.html#implementing-descriptors
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. For a how-to guide to the new API, see Implementing the buffer protocol. The old Python 2 buffer protocol (`__getreadbuffer__, __getwritebuffer__, __getsegcount__, __getcharbuffer__) is no longer supported from Cython 3.1 since Python 2 is no longer supported.
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”.
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