3.Data model———从python官方文档上摘取

2019-04-22  本文已影响0人  雨浩天

3.1 objects,values and types

Objects are Python’s abstraction for data. All data in a Python program is represented by objects or by relations between objects.

Every object has an identity, a type and a value. An object’s identity never changes once it has been created; you may think of it as the object’s address in memory. The ‘is’ operator compares the identity of two objects; the id() function returns an integer representing its identity.

CPython implementation detail:
id(x) is the memory address where x is stored.

The type() function returns an object’s type (which is an object itself). Like its identity, an object’s type is also unchangeable.

The value of some objects can change. Objects whose value can change are said to be mutable; objects whose value is unchangeable once they are created are called immutable.
(The value of an immutable container object that contains a reference to a mutable object can change when the latter’s value is changed; however the container is still considered immutable, because the collection of objects it contains cannot be changed. So, immutability is not strictly the same as having an unchangeable value, it is more subtle.)
An object’s mutability is determined by its type; for instance, numbers, strings and tuples are immutable, while dictionaries and lists are mutable.

Objects are never explicitly destroyed; however, when they become unreachable they may be garbage-collected. An implementation is allowed to postpone garbage collection or omit it altogether — it is a matter of implementation quality how garbage collection is implemented, as long as no objects are collected that are still reachable.

CPython implementation detail:

Some objects contain references to “external” resources such as open files or windows. It is understood that these resources are freed when the object is garbage-collected, but since garbage collection is not guaranteed to happen, such objects also provide an explicit way to release the external resource, usually a close() method. Programs are strongly recommended to explicitly close such objects. The ‘try…finally’ statement and the ‘with’ statement provide convenient ways to do this.

Types affect almost all aspects of object behavior. Even the importance of object identity is affected in some sense: for immutable types, operations that compute new values may actually return a reference to any existing object with the same type and value, while for mutable objects this is not allowed. E.g., after a = 1; b = 1, a and b may or may not refer to the same object with the value one, depending on the implementation, but after c = []; d = [], c and d are guaranteed to refer to two different, unique, newly created empty lists. (Note that c = d = [] assigns the same object to both c and d.)

3.2. The standard type hierarchy

Below is a list of the types that are built into Python.Extension modules (written in C, Java, or other languages, depending on the implementation) can define additional types. Future versions of Python may add types to the type hierarchy (e.g., rational numbers, efficiently stored arrays of integers, etc.), although such additions will often be provided via the standard library instead.

1 None
This type has a single value. There is a single object with this value. This object is accessed through the built-in name None. It is used to signify the absence of a value in many situations, e.g.,it is returned from functions that don’t explicitly return anything. Its truth value is false.

2 NotImplemented
This type has a single value. There is a single object with this value. This object is accessed through the built-in name NotImplemented. Numeric methods and rich comparison methods should return this value if they do not implement the operation for the operands provided. (The interpreter will then try the reflected operation, or some other fallback, depending on the operator.) Its truth value is true.

For example, fractions.Fraction uses:

def _operator_fallbacks(monomorphic_operator, fallback_operator):
    def forward(a, b):
        if isinstance(b, (int, Fraction)):
            return monomorphic_operator(a, b)
        elif isinstance(b, float):
            return fallback_operator(float(a), b)
        elif isinstance(b, complex):
            return fallback_operator(complex(a), b)
        else:
            return NotImplemented
    forward.__name__ = '__' + fallback_operator.__name__ + '__'
    forward.__doc__ = monomorphic_operator.__doc__

    def reverse(b, a):
        if isinstance(a, Rational):
            # Includes ints.
            return monomorphic_operator(a, b)
        elif isinstance(a, numbers.Real):
            return fallback_operator(float(a), float(b))
        elif isinstance(a, numbers.Complex):
            return fallback_operator(complex(a), complex(b))
        else:
            return NotImplemented
    reverse.__name__ = '__r' + fallback_operator.__name__ + '__'
    reverse.__doc__ = monomorphic_operator.__doc__

    return forward, reverse

def _add(a, b):
    """a + b"""
    return Fraction(a.numerator * b.denominator +
                    b.numerator * a.denominator,
                    a.denominator * b.denominator)

__add__, __radd__ = _operator_fallbacks(_add, operator.add)

3 Ellipsis
This type has a single value. There is a single object with this value. This object is accessed through the literal ... or the built-in name Ellipsis. Its truth value is true.

4 numbers.Number
These are created by numeric literals and returned as results by arithmetic operators and arithmetic built-in functions. Numeric objects are immutable; once created their value never changes. Python numbers are of course strongly related to mathematical numbers, but subject to the limitations of numerical representation in computers.
Python distinguishes between integers, floating point numbers, and complex numbers:
numbers.Integral:Integers (int),Booleans (bool)
numbers.Real (float)
numbers.Complex (complex)

5 Sequences
These represent finite ordered sets indexed by non-negative numbers.The built-in function len() returns the number of items of a sequence. When the length of a sequence is n, the index set contains the numbers 0, 1, …, n-1. Item i of sequence a is selected by a[i].
Sequences also support slicing: a[i:j] selects all items with index k such that i <= k < j.
Some sequences also support “extended slicing” with a third “step” parameter: a[i:j:k] selects all items of a with index x where x = i + n*k, n >= 0 and i <= x < j.

6 Set types
These represent unordered, finite sets of unique, immutable objects.

7 Mappings

8 Callable types
These are the types to which the function call operation (see section Calls) can be applied:

call                 ::=  primary "(" [argument_list [","] | comprehension] ")"
argument_list        ::=  positional_arguments ["," starred_and_keywords]
                            ["," keywords_arguments]
                          | starred_and_keywords ["," keywords_arguments]
                          | keywords_arguments
positional_arguments ::=  ["*"] expression ("," ["*"] expression)*
starred_and_keywords ::=  ("*" expression | keyword_item)
                          ("," "*" expression | "," keyword_item)*
keywords_arguments   ::=  (keyword_item | "**" expression)
                          ("," keyword_item | "," "**" expression)*
keyword_item         ::=  identifier "=" expression
Attribute Meaning able
doc The function’s documentation string, or None if unavailable; not inherited by subclasses. Writable
name The function’s name. Writable
qualname The function’s qualified name. New in version 3.3. Writable
module The name of the module the function was defined in, or None if unavailable. Writable
defaults A tuple containing default argument values for those arguments that have defaults, or None if no arguments have a default value. Writable
code The code object representing the compiled function body. Writable
globals A reference to the dictionary that holds the function’s global variables — the global namespace of the module in which the function was defined. Read-only
dict The namespace supporting arbitrary function attributes. Writable
closure None or a tuple of cells that contain bindings for the function’s free variables. See below for information on the cell_contents attribute. Read-only
annotations A dict containing annotations of parameters. The keys of the dict are the parameter names, and 'return' for the return annotation, if provided. Writable
kwdefaults A dict containing defaults for keyword-only parameters. Writable

9 Modules
Modules are a basic organizational unit of Python code, and are created by the import system as invoked either by the import statement, or by calling functions such as importlib.import_module() and built-in __import__(). A module object has a namespace implemented by a dictionary object (this is the dictionary referenced by the __globals__ attribute of functions defined in the module). Attribute references are translated to lookups in this dictionary, e.g., m.x is equivalent to m.__dict__["x"]. A module object does not contain the code object used to initialize the module (since it isn’t needed once the initialization is done).
Special read-only attribute: __dict__ is the module’s namespace as a dictionary object.
CPython implementation detail: Because of the way CPython clears module dictionaries, the module dictionary will be cleared when the module falls out of scope even if the dictionary still has live references. To avoid this, copy the dictionary or keep the module around while using its dictionary directly. \

10 Custom classes
Custom class types are typically created by class definitions
When the attribute name is not found there, the attribute search continues in the base classes. This search of the base classes uses the C3 method resolution order which behaves correctly even in the presence of ‘diamond’ inheritance structures where there are multiple inheritance paths leading back to a common ancestor.
A class object can be called (see above) to yield a class instance (see below).
Special attributes: __name__ is the class name; __module__ is the module name in which the class was defined; __dict__ is the dictionary containing the class’s namespace; __bases__ is a tuple containing the base classes, in the order of their occurrence in the base class list; __doc__ is the class’s documentation string, or None if undefined; __annotations__ (optional) is a dictionary containing variable annotations collected during class body execution.
11 Class instances
A class instance is created by calling a class object (see above)
If a class attribute is found that is a user-defined function object, it is transformed into an instance method object whose __self__ attribute is the instance. Static method and class method objects are also transformed;
If no class attribute is found, and the object’s class has a __getattr__() method, that is called to satisfy the lookup.
If the class has a __setattr__() or __delattr__() method, this is called instead of updating the instance dictionary directly.
Class instances can pretend to be numbers, sequences, or mappings if they have methods with certain special names. See section Special method names.
Special attributes: __dict__ is the attribute dictionary; __class__ is the instance’s class.
12 I/O objects (also known as file objects)
A file object represents an open file. Various shortcuts are available to create file objects: the open() built-in function, and also os.popen(), os.fdopen(), and the makefile() method of socket objects (and perhaps by other functions or methods provided by extension modules).
The objects sys.stdin, sys.stdout and sys.stderr are initialized to file objects corresponding to the interpreter’s standard input, output and error streams; they are all open in text mode and therefore follow the interface defined by the io.TextIOBase abstract class.
13 Internal types
A few types used internally by the interpreter are exposed to the user. Their definitions may change with future versions of the interpreter, but they are mentioned here for completeness.

3.3 Special method names

A class can implement certain operations that are invoked by special syntax (such as arithmetic operations or subscripting and slicing) by defining methods with special names. This is Python’s approach to operator overloading, allowing classes to define their own behavior with respect to language operators. For instance, if a class defines a method named __getitem__(), and x is an instance of this class, then x[i] is roughly equivalent to type(x).__getitem__(x, i). Except where mentioned, attempts to execute an operation raise an exception when no appropriate method is defined (typically AttributeError or TypeError).
Setting a special method to None indicates that the corresponding operation is not available. For example, if a class sets __iter__() to None, the class is not iterable, so calling iter() on its instances will raise a TypeError (without falling back to __getitem__()). [2]

3.3.1Basic customization

3.3.2. Customizing attribute access

3.3.2.1. Customizing module attribute access

For a more fine grained customization of the module behavior (setting attributes, properties, etc.), one can set the __class__ attribute of a module object to a subclass of types.ModuleType. For example:

import sys
from types import ModuleType

class VerboseModule(ModuleType):
    def __repr__(self):
        return f'Verbose {self.__name__}'

    def __setattr__(self, attr, value):
        print(f'Setting {attr}...')
        super().__setattr__(attr, value)

sys.modules[__name__].__class__ = VerboseModule

3.3.2.2. Implementing Descriptors

3.3.2.3. Invoking Descriptors

In general, a descriptor is an object attribute with “binding behavior”, one whose attribute access has been overridden by methods in the descriptor protocol: __get__(), __set__(), and __delete__(). If any of those methods are defined for an object, it is said to be a descriptor.

The starting point for descriptor invocation is a binding, a.x. How the arguments are assembled depends on a:

Direct Call
The simplest and least common call is when user code directly invokes a descriptor method: x.__get__(a).
Instance Binding
If binding to an object instance, a.x is transformed into the call: type(a).__dict__['x'].__get__(a, type(a)).
Class Binding
If binding to a class, A.x is transformed into the call: A.__dict__['x'].__get__(None, A).
Super Binding
If a is an instance of super, then the binding super(B, obj).m() searches obj.__class__.__mro__ for the base class A immediately preceding B and then invokes the descriptor with the call: A.__dict__['m'].__get__(obj, obj.__class__).

3.3.2.4. __slots__
__slots__ allow us to explicitly declare data members (like properties) and deny the creation of __dict__ and __weakref__ (unless explicitly declared in __slots__ or available in a parent.)

The space saved over using __dict__ can be significant. Attribute lookup speed can be significantly improved as well.

3.3.3. Customizing class creation

Whenever a class inherits from another class, __init_subclass__ is called on that class. This way, it is possible to write classes which change the behavior of subclasses. This is closely related to class decorators, but where class decorators only affect the specific class they’re applied to, __init_subclass__ solely applies to future subclasses of the class defining the method.

3.3.4. Customizing instance and subclass checks

The following methods are used to override the default behavior of the isinstance() and issubclass() built-in functions.

In particular, the metaclass abc.ABCMeta implements these methods in order to allow the addition of Abstract Base Classes (ABCs) as “virtual base classes” to any class or type (including built-in types), including other ABCs.

Class.__instancecheck__(self, instance)
Return true if instance should be considered a (direct or indirect) instance of class. If defined, called to implement isinstance(instance, class).

class.__subclasscheck__(self, subclass)
Return true if subclass should be considered a (direct or indirect) subclass of class. If defined, called to implement issubclass(subclass, class).

Note that these methods are looked up on the type (metaclass) of a class. They cannot be defined as class methods in the actual class. This is consistent with the lookup of special methods that are called on instances, only in this case the instance is itself a class.

3.3.5. Emulating generic types

classmethod object.__class_getitem__(cls, key)
Return an object representing the specialization of a generic class by type arguments found in key.

This method is looked up on the class object itself, and when defined in the class body, this method is implicitly a class method. Note, this mechanism is primarily reserved for use with static type hints, other usage is discouraged.

3.3.6.Emulating callable objects

object.__call__(self[, args...])
Called when the instance is “called” as a function; if this method is defined, x(arg1, arg2, ...) is a shorthand for x.__call__(arg1, arg2, ...).

3.3.7. Emulating container types

The following methods can be defined to implement container objects.
It is also recommended that mappings provide the methods keys(), values(), items(), get(), clear(), setdefault(), pop(), popitem(), copy(), and update() behaving similar to those for Python’s standard dictionary objects
Mutable sequences should provide methods append(), count(), index(), extend(), insert(), pop(), remove(), reverse() and sort(), like Python standard list objects.
It is recommended that both mappings and sequences implement the __contains__() method to allow efficient use of the in operator; for mappings, in should search the mapping’s keys; for sequences, it should search through the values. It is further recommended that both mappings and sequences implement the __iter__() method to allow efficient iteration through the container; for mappings, __iter__() should be the same as keys(); for sequences, it should iterate through the values.

3.3.8. Emulating numeric types

The following methods can be defined to emulate numeric objects.

These methods are called to implement the binary arithmetic operations (+, -, *, @, /, //, %, divmod(), pow(), **, <<, >>, &, ^, |).
x + y, where x is an instance of a class that has an __add__() method, x.__add__(y)

If one of those methods does not support the operation with the supplied arguments, it should return NotImplemented.

3.3.9. With Statement Context Managers

A context manager is an object that defines the runtime context to be established when executing a with statement. The context manager handles the entry into, and the exit from, the desired runtime context for the execution of the block of code. Context managers are normally invoked using the with statement (described in section The with statement), but can also be used by directly invoking their methods.

Typical uses of context managers include saving and restoring various kinds of global state, locking and unlocking resources, closing opened files, etc.

If an exception is supplied, and the method wishes to suppress the exception (i.e., prevent it from being propagated), it should return a true value. Otherwise, the exception will be processed normally upon exit from this method.

Note: that __exit__() methods should not reraise the passed-in exception; this is the caller’s responsibility.

3.3.10. Special method lookup

>>> class Meta(type):
...     def __getattribute__(*args):
...         print("Metaclass getattribute invoked")
...         return type.__getattribute__(*args)
...
>>> class C(object, metaclass=Meta):
...     def __len__(self):
...         return 10
...     def __getattribute__(*args):
...         print("Class getattribute invoked")
...         return object.__getattribute__(*args)
...
>>> c = C()
>>> c.__len__()                 # Explicit lookup via instance
Class getattribute invoked
10
>>> type(c).__len__(c)          # Explicit lookup via type
Metaclass getattribute invoked
10
>>> len(c)                      # Implicit lookup
10

3.4. Coroutines

3.4.1. Awaitable Objects

An awaitable object generally implements an __await__() method. Coroutine objects returned from async def functions are awaitable.

Note: The generator iterator objects returned from generators decorated with types.coroutine() or asyncio.coroutine() are also awaitable, but they do not implement __await__().

3.4.2. Coroutine Objects

Coroutine objects are awaitable objects. A coroutine’s execution can be controlled by calling __await__() and iterating over the result. When the coroutine has finished executing and returns, the iterator raises StopIteration, and the exception’s value attribute holds the return value. If the coroutine raises an exception, it is propagated by the iterator. Coroutines should not directly raise unhandled StopIteration exceptions.

Coroutine objects are automatically closed using the above process when they are about to be destroyed.

3.4.3. Asynchronous Iterators

An asynchronous iterator can call asynchronous code in its __anext__ method.

3.4.4. Asynchronous Context Managers

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