## 3.1 Objects, values and types 

## DATA MODEL 

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 ‘ is ’ operator compares the identity of two objects; the id() function returns an integer representing its identity. 

CPython implementation detail: For CPython,$_{id(x)}$ is the memory address where$_{x}$ is stored. 

An object’s type determines the operations that the 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). Like its identity, an object’s type is also unchangeable. 1 

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: CPython currently uses a reference-counting scheme with (optional) delayed detection of cyclically linked garbage, which collects most objects as soon as they become unreachable, but is not guaranteed to collect garbage containing circular references. See the documentation of the$_{gc}$module for information on controlling the collection of cyclic garbage. Other implementations act differently and CPython may change. Do not depend on immediate finalization of objects when they become unreachable (so you should always close files explicitly). 

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 and the ‘$_{with}$ ’ statement provide convenient ways 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 

