Anaconda Enterprise: Documentation

# 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.

## 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 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.

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, the `__cinit__()` method of the base type is automatically called before your `__cinit__()` method is called; you cannot explicitly call the inherited `__cinit__()` method. 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.

[1] | http://docs.python.org/reference/datamodel.html#object.__new__

## Finalization method: `__dealloc__()`

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.

Note

There is no `__del__()` method for extension types.

## Arithmetic methods

Arithmetic operator methods, such as `__add__()`, behave differently from their Python counterparts. There are no separate “reversed” versions of these methods (`__radd__()`, etc.) Instead, if the first operand cannot perform the operation, the same method of the second operand is called, with the operands in the same order.

This means that you can’t rely on the first parameter of these methods being “self” or being the right type, and you should test the types of both operands before deciding what to do. If you can’t handle the combination of types you’ve been given, you should return NotImplemented.

This also applies to the in-place arithmetic method `__ipow__()`. It doesn’t apply to any of the other in-place methods (`__iadd__()`, etc.) which always take self as the first argument.

## Rich comparisons

There are no separate methods for the individual rich comparison operations (`__eq__()`, `__le__()`, etc.) Instead there is a single method `__richcmp__()` which takes an integer indicating which operation is to be performed, as follows:

< | 0
== | 2
> | 4
<= | 1
!= | 3
>= | 5

## 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

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
__richcmp__ | x, y, int op | object | Rich comparison (no direct Python equivalent)
__str__ | self | object | str(self)
__repr__ | self | object | repr(self)
__hash__ | self | int | Hash function
__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

### Arithmetic operators

Name | Parameters | Return type | Description
--- | --- | --- | ---
__add__ | x, y | object | binary + operator
__sub__ | x, y | object | binary - operator
__mul__ | x, y | object | * operator
__div__ | x, y | object | / operator for old-style division
__floordiv__ | x, y | object | // operator
__truediv__ | x, y | object | / operator for new-style division
__mod__ | x, y | object | % operator
__divmod__ | x, y | object | combined div and mod
__pow__ | x, y, z | object | ** operator or pow(x, y, z)
__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__ | x, y | object | << operator
__rshift__ | x, y | object | >> operator
__and__ | x, y | object | & operator
__or__ | x, y | object | | operator
__xor__ | x, y | object | ^ operator

### Numeric conversions

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__ (2.5+ only) | self | object | Convert to sequence index

### In-place arithmetic operators

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__ | x, 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

Name | Parameters | Return type | Description
--- | --- | --- | ---
__len__ | self int |  | 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

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)

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”.