- Expert Python Programming（Third Edition）
- Micha? Jaworski Tarek Ziadé
- 602字
- 2021-06-24 14:55:17
The protocols of the Python language – dunder methods and attributes
The Python data model specifies a lot of specially named methods that can be overridden in your custom classes to provide them with additional syntax capabilities. You can recognize these methods by their specific naming conventions that wrap the method name with double underscores. Because of this, they are sometimes referred to as dunder. It is simply a speech shorthand for double underscores.
The most common and obvious example of such dunder methods is __init__(), which is used for class instance initialization:
class CustomUserClass:
def __init__(self, initiatization_argument):
...
These methods, either alone or when defined in specific combination, constitute the so-called language protocols. If an object implements specific language protocols, it becomes compatible with specific parts of the Python language syntax. The following is the table of the most important protocols within the Python language:
These are the most important language protocols from the perspective of this chapter. The full list is, of course, a lot longer. For instance, Python provides over 50 dunder methods that allow us to emulate numeric values. Each of these methods is correlated to some specific mathematical operator, and so could be considered a separate language protocol. The full list of all the dunder methods can be found in the official documentation of the Python data model (see https://docs.python.org/3/reference/datamodel.html).
Language protocols are the foundation of the concept of interfaces in Python. One implementation of Python interfaces is in abstract base classes that allow us to define an arbitrary set of attributes and methods as an interface definition. These definitions of interfaces in the form of abstract classes can be later used to test whether or not the given object is compatible with a specific interface. The collections.abc module from the Python standard library provides a collection of abstract base classes that refer to the most common Python language protocol. You'll find more information about interfaces and abstract base classes in the Interfaces section of Chapter 17, Useful Design Patterns.
The same dunder convention is also used for specific attributes of custom user functions and is used to store various metadata about Python objects. These attributes are as follows:
- __doc__: A writable attribute that holds the function's documentation. It is, by default, populated by the docstring function.
- __name__: A writable attribute that holds the function's name.
- __qualname__: A writable attribute that holds the function's qualified name. The qualified name is a full dotted path to the object (with class names) in the global scope of the module where the object is defined.
- __module__: A writable attribute that holds the name of the module that function belongs to.
- __defaults__: A writable attribute that holds the default argument values if the function has any.
- __code__: A writable attribute that holds the function's compile code object.
- __globals__: A read-only attribute that holds the reference to the dictionary of global variables for that function's scope. The global scope for a function is the namespace of the module where this function is defined.
- __dict__: A writable attribute that holds a dictionary of function attributes. Functions in Python are first-class objects, so they can have any arbitrary arguments defined, just like any other object.
- __closure__: A read-only attribute that holds a tuple of cells with the function's free variables. Closure cells allow you to create parametrized function decorators.
- __annotations__: A writable attribute that holds the function's argument and return annotations.
- __kwdefaults__: A writable attribute that holds the default argument values for keyword-only arguments if the function has any.
Let's see how to reduce the boilerplate with data classes.