Objects are Python's abstraction for data. All data in a Python program is represented by objects or by relations between objects. (In a sense, and in conformance to Von Neumann's model of a ``stored program computer,'' code is also represented by 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 `
compares the identity of two objects; the
id() function returns an integer
representing its identity (currently implemented as its address).
An object's type is
also unchangeable. It determines the operations that an object
supports (e.g., ``does it have a length?'') and also defines the
possible values for objects of that type. The
type() function returns an object's type
(which is an object itself). 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
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. (Implementation note: the current implementation uses a reference-counting scheme with (optional) delayed detection of cyclicly linked garbage, which collects most objects as soon as they become unreachable, but is not guaranteed to collect garbage containing circular references. See the Python Library Reference for information on controlling the collection of cyclic garbage.)
Note that the use of the implementation's tracing or debugging facilities may keep objects alive that would normally be collectable. Also note that catching an exception with a `try...except' statement may keep objects alive.
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 provides a convenient way to do this.
Some objects contain references to other objects; these are called containers. Examples of containers are tuples, lists and dictionaries. The references are part of a container's value. In most cases, when we talk about the value of a container, we imply the values, not the identities of the contained objects; however, when we talk about the mutability of a container, only the identities of the immediately contained objects are implied. So, if an immutable container (like a tuple) contains a reference to a mutable object, its value changes if that mutable object is changed.
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",
b may or may not refer to the same object with the
value one, depending on the implementation, but after
"c = ; d = ",
are guaranteed to refer to two different, unique, newly created empty
(Note that "c = d = " assigns the same object to both
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