3_data_model_python_3717_documentation_1.txt

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PAGE_URL: https://docs.python.org/3.7/reference/datamodel.html
TITLE: 3. Data model — Python 3.7.17 documentation
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KEYWORDS: objects, object, value, type, numbers, python, implementation, immutable, garbage, point, values, change, types, built, single

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Objectsare 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’sidentitynever
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; theid()function returns an integer representing its identity.

CPython implementation detail:For CPython,id(x)is the memory address wherexis 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.  Thetype()function returns an object’s type (which is an object
itself).  Like its identity, an object’stypeis also unchangeable.1

Thevalueof some objects can change.  Objects whose value can
change are said to bemutable; objects whose value is unchangeable once they
are created are calledimmutable. (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 thegcmodule 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
object
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