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.

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.

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:

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.