mirror of https://github.com/python/cpython
Second round of updates to the descriptor howto guide (GH-22946)
This commit is contained in:
Raymond Hettinger
GitHub
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@ -29,8 +29,8 @@ This HowTo guide has three major sections:
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Primer
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^^^^^^
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In this primer, we start with most basic possible example and then we'll add
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new capabilities one by one.
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In this primer, we start with the most basic possible example and then we'll
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add new capabilities one by one.
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Simple example: A descriptor that returns a constant
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@ -197,7 +197,7 @@ be recorded, giving each descriptor its own *public_name* and *private_name*::
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import logging
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logging.basicConfig(level=logging.INFO)
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logging.basicConfig(level=logging.INFO, force=True)
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class LoggedAccess:
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@ -258,6 +258,10 @@ Closing thoughts
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A :term:`descriptor` is what we call any object that defines :meth:`__get__`,
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:meth:`__set__`, or :meth:`__delete__`.
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Optionally, descriptors can have a :meth:`__set_name__` method. This is only
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used in cases where a descriptor needs to know either the class where it is
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created or the name of class variable it was assigned to.
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Descriptors get invoked by the dot operator during attribute lookup. If a
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descriptor is accessed indirectly with ``vars(some_class)[descriptor_name]``,
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the descriptor instance is returned without invoking it.
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@ -291,7 +295,7 @@ Validator class
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A validator is a descriptor for managed attribute access. Prior to storing
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any data, it verifies that the new value meets various type and range
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restrictions. If those restrictions aren't met, it raises an exception to
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prevents data corruption at its source.
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prevent data corruption at its source.
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This :class:`Validator` class is both an :term:`abstract base class` and a
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managed attribute descriptor::
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@ -438,12 +442,12 @@ In general, a descriptor is an object attribute with "binding behavior", one
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whose attribute access has been overridden by methods in the descriptor
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protocol. Those methods are :meth:`__get__`, :meth:`__set__`, and
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:meth:`__delete__`. If any of those methods are defined for an object, it is
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said to be a descriptor.
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said to be a :term:`descriptor`.
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The default behavior for attribute access is to get, set, or delete the
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attribute from an object's dictionary. For instance, ``a.x`` has a lookup chain
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starting with ``a.__dict__['x']``, then ``type(a).__dict__['x']``, and
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continuing through the base classes of ``type(a)`` excluding metaclasses. If the
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continuing through the base classes of ``type(a)``. If the
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looked-up value is an object defining one of the descriptor methods, then Python
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may override the default behavior and invoke the descriptor method instead.
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Where this occurs in the precedence chain depends on which descriptor methods
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@ -492,60 +496,76 @@ Invoking Descriptors
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A descriptor can be called directly by its method name. For example,
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``d.__get__(obj)``.
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Alternatively, it is more common for a descriptor to be invoked automatically
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upon attribute access. For example, ``obj.d`` looks up ``d`` in the dictionary
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of ``obj``. If ``d`` defines the method :meth:`__get__`, then ``d.__get__(obj)``
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But it is more common for a descriptor to be invoked automatically from
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attribute access. The expression ``obj.d`` looks up ``d`` in the dictionary of
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``obj``. If ``d`` defines the method :meth:`__get__`, then ``d.__get__(obj)``
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is invoked according to the precedence rules listed below.
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The details of invocation depend on whether ``obj`` is an object or a class.
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The details of invocation depend on whether ``obj`` is an object, class, or
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instance of super.
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For objects, the machinery is in :meth:`object.__getattribute__` which
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transforms ``b.x`` into ``type(b).__dict__['x'].__get__(b, type(b))``. The
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implementation works through a precedence chain that gives data descriptors
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**Objects**: The machinery is in :meth:`object.__getattribute__`.
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It transforms ``b.x`` into ``type(b).__dict__['x'].__get__(b, type(b))``.
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The implementation works through a precedence chain that gives data descriptors
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priority over instance variables, instance variables priority over non-data
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descriptors, and assigns lowest priority to :meth:`__getattr__` if provided.
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The full C implementation can be found in :c:func:`PyObject_GenericGetAttr()` in
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:source:`Objects/object.c`.
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For classes, the machinery is in :meth:`type.__getattribute__` which transforms
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``B.x`` into ``B.__dict__['x'].__get__(None, B)``. In pure Python, it looks
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like::
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**Classes**: The machinery is in :meth:`type.__getattribute__`.
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def __getattribute__(self, key):
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It transforms ``A.x`` into ``A.__dict__['x'].__get__(None, A)``.
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In pure Python, it looks like this::
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def __getattribute__(cls, key):
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"Emulate type_getattro() in Objects/typeobject.c"
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v = object.__getattribute__(self, key)
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v = object.__getattribute__(cls, key)
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if hasattr(v, '__get__'):
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return v.__get__(None, self)
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return v.__get__(None, cls)
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return v
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The important points to remember are:
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**Super**: The machinery is in the custom :meth:`__getattribute__` method for
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object returned by :class:`super()`.
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* descriptors are invoked by the :meth:`__getattribute__` method
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* overriding :meth:`__getattribute__` prevents automatic descriptor calls
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* :meth:`object.__getattribute__` and :meth:`type.__getattribute__` make
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different calls to :meth:`__get__`.
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* data descriptors always override instance dictionaries.
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* non-data descriptors may be overridden by instance dictionaries.
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The attribute lookup ``super(A, obj).m`` searches ``obj.__class__.__mro__`` for
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the base class ``B`` immediately following ``A`` and then returns
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``B.__dict__['m'].__get__(obj, A)``.
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The object returned by ``super()`` also has a custom :meth:`__getattribute__`
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method for invoking descriptors. The attribute lookup ``super(B, obj).m`` searches
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``obj.__class__.__mro__`` for the base class ``A`` immediately following ``B``
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and then returns ``A.__dict__['m'].__get__(obj, B)``. If not a descriptor,
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``m`` is returned unchanged. If not in the dictionary, ``m`` reverts to a
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search using :meth:`object.__getattribute__`.
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If not a descriptor, ``m`` is returned unchanged. If not in the dictionary,
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``m`` reverts to a search using :meth:`object.__getattribute__`.
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The implementation details are in :c:func:`super_getattro()` in
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:source:`Objects/typeobject.c`. and a pure Python equivalent can be found in
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:source:`Objects/typeobject.c`. A pure Python equivalent can be found in
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`Guido's Tutorial`_.
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.. _`Guido's Tutorial`: https://www.python.org/download/releases/2.2.3/descrintro/#cooperation
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The details above show that the mechanism for descriptors is embedded in the
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:meth:`__getattribute__()` methods for :class:`object`, :class:`type`, and
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:func:`super`. Classes inherit this machinery when they derive from
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:class:`object` or if they have a metaclass providing similar functionality.
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Likewise, classes can turn-off descriptor invocation by overriding
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:meth:`__getattribute__()`.
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**Summary**: The details listed above show that the mechanism for descriptors is
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embedded in the :meth:`__getattribute__()` methods for :class:`object`,
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:class:`type`, and :func:`super`.
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The important points to remember are:
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* Descriptors are invoked by the :meth:`__getattribute__` method.
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* Classes inherit this machinery from :class:`object`, :class:`type`, or
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:func:`super`.
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* Overriding :meth:`__getattribute__` prevents automatic descriptor calls
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because all the descriptor logic is in that method.
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* :meth:`object.__getattribute__` and :meth:`type.__getattribute__` make
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different calls to :meth:`__get__`. The first includes the instance and may
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include the class. The second puts in ``None`` for the instance and always
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includes the class.
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* Data descriptors always override instance dictionaries.
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* Non-data descriptors may be overridden by instance dictionaries.
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Automatic Name Notification
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@ -569,47 +589,70 @@ afterwards, :meth:`__set_name__` will need to be called manually.
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Descriptor Example
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------------------
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The following code creates a class whose objects are data descriptors which
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print a message for each get or set. Overriding :meth:`__getattribute__` is
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alternate approach that could do this for every attribute. However, this
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descriptor is useful for monitoring just a few chosen attributes::
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The following code is simplified skeleton showing how data descriptors could
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be used to implement an `object relational mapping
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<https://en.wikipedia.org/wiki/Object%E2%80%93relational_mapping>`_.
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class RevealAccess:
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"""A data descriptor that sets and returns values
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normally and prints a message logging their access.
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"""
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The essential idea is that instances only hold keys to a database table. The
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actual data is stored in an external table that is being dynamically updated::
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def __init__(self, initval=None, name='var'):
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self.val = initval
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self.name = name
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class Field:
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def __set_name__(self, owner, name):
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self.fetch = f'SELECT {name} FROM {owner.table} WHERE {owner.key}=?;'
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self.store = f'UPDATE {owner.table} SET {name}=? WHERE {owner.key}=?;'
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def __get__(self, obj, objtype=None):
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print('Retrieving', self.name)
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return self.val
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return conn.execute(self.fetch, [obj.key]).fetchone()[0]
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def __set__(self, obj, val):
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print('Updating', self.name)
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self.val = val
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def __set__(self, obj, value):
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conn.execute(self.store, [value, obj.key])
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conn.commit()
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class B:
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x = RevealAccess(10, 'var "x"')
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y = 5
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We can use the :class:`Field` to define "models" that describe the schema for
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each table in a database::
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>>> m = B()
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>>> m.x
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Retrieving var "x"
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10
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>>> m.x = 20
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Updating var "x"
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>>> m.x
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Retrieving var "x"
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20
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>>> m.y
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5
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class Movie:
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table = 'Movies' # Table name
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key = 'title' # Primary key
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director = Field()
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year = Field()
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The protocol is simple and offers exciting possibilities. Several use cases are
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so common that they have been packaged into individual function calls.
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Properties, bound methods, static methods, and class methods are all
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def __init__(self, key):
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self.key = key
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class Song:
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table = 'Music'
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key = 'title'
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artist = Field()
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year = Field()
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genre = Field()
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def __init__(self, key):
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self.key = key
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An interactive session shows how data is retrieved from the database and how
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it can be updated::
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>>> import sqlite3
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>>> conn = sqlite3.connect('entertainment.db')
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>>> Movie('Star Wars').director
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'George Lucas'
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>>> jaws = Movie('Jaws')
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>>> f'Released in {jaws.year} by {jaws.director}'
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'Released in 1975 by Steven Spielberg'
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>>> Song('Country Roads').artist
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'John Denver'
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>>> Movie('Star Wars').director = 'J.J. Abrams'
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>>> Movie('Star Wars').director
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'J.J. Abrams'
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The descriptor protocol is simple and offers exciting possibilities. Several
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use cases are so common that they have been packaged into individual function
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calls. Properties, bound methods, static methods, and class methods are all
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based on the descriptor protocol.
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@ -619,7 +662,7 @@ Properties
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Calling :func:`property` is a succinct way of building a data descriptor that
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triggers function calls upon access to an attribute. Its signature is::
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property(fget=None, fset=None, fdel=None, doc=None) -> property attribute
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property(fget=None, fset=None, fdel=None, doc=None) -> property
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The documentation shows a typical use to define a managed attribute ``x``::
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@ -695,17 +738,30 @@ Functions and Methods
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Python's object oriented features are built upon a function based environment.
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Using non-data descriptors, the two are merged seamlessly.
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Class dictionaries store methods as functions. In a class definition, methods
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are written using :keyword:`def` or :keyword:`lambda`, the usual tools for
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creating functions. Methods only differ from regular functions in that the
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first argument is reserved for the object instance. By Python convention, the
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instance reference is called *self* but may be called *this* or any other
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variable name.
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Functions stored in class dictionaries get turned into methods when invoked.
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Methods only differ from regular functions in that the object instance is
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prepended to the other arguments. By convention, the instance is called
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*self* but could be called *this* or any other variable name.
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To support method calls, functions include the :meth:`__get__` method for
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binding methods during attribute access. This means that all functions are
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non-data descriptors which return bound methods when they are invoked from an
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object. In pure Python, it works like this::
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Methods can be created manually with :class:`types.MethodType` which is
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roughly equivalent to::
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class Method:
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"Emulate Py_MethodType in Objects/classobject.c"
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def __init__(self, func, obj):
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self.__func__ = func
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self.__self__ = obj
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def __call__(self, *args, **kwargs):
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func = self.__func__
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obj = self.__self__
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return func(obj, *args, **kwargs)
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To support automatic creation of methods, functions include the
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:meth:`__get__` method for binding methods during attribute access. This
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means that functions are non-data descriptors which return bound methods
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during dotted lookup from an instance. Here's how it works::
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class Function:
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...
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@ -716,15 +772,20 @@ object. In pure Python, it works like this::
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return self
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return types.MethodType(self, obj)
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Running the following in class in the interpreter shows how the function
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Running the following class in the interpreter shows how the function
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descriptor works in practice::
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class D:
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def f(self, x):
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return x
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Access through the class dictionary does not invoke :meth:`__get__`. Instead,
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it just returns the underlying function object::
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The function has a :term:`qualified name` attribute to support introspection::
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>>> D.f.__qualname__
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'D.f'
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Accessing the function through the class dictionary does not invoke
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:meth:`__get__`. Instead, it just returns the underlying function object::
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>>> D.__dict__['f']
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<function D.f at 0x00C45070>
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@ -735,13 +796,8 @@ underlying function unchanged::
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>>> D.f
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<function D.f at 0x00C45070>
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The function has a :term:`qualified name` attribute to support introspection::
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>>> D.f.__qualname__
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'D.f'
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Dotted access from an instance calls :meth:`__get__` which returns a bound
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method object::
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The interesting behavior occurs during dotted access from an instance. The
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dotted lookup calls :meth:`__get__` which returns a bound method object::
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>>> d = D()
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>>> d.f
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@ -752,9 +808,13 @@ instance::
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>>> d.f.__func__
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<function D.f at 0x1012e5ae8>
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>>> d.f.__self__
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<__main__.D object at 0x1012e1f98>
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If you have ever wondered where *self* comes from in regular methods or where
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*cls* comes from in class methods, this is it!
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Static Methods and Class Methods
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--------------------------------
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@ -798,8 +858,8 @@ in statistical work but does not directly depend on a particular dataset.
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It can be called either from an object or the class: ``s.erf(1.5) --> .9332`` or
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``Sample.erf(1.5) --> .9332``.
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Since staticmethods return the underlying function with no changes, the example
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calls are unexciting::
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Since static methods return the underlying function with no changes, the
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example calls are unexciting::
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class E:
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@staticmethod
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@ -840,7 +900,7 @@ for whether the caller is an object or a class::
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This behavior is useful whenever the function only needs to have a class
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reference and does not care about any underlying data. One use for
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classmethods is to create alternate class constructors. The classmethod
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class methods is to create alternate class constructors. The classmethod
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:func:`dict.fromkeys` creates a new dictionary from a list of keys. The pure
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Python equivalent is::
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