Python - The Data Model

Posted by Shujin Wu on May 8, 2016

Python objects

All Data in a Python program represented by objects or by relations between objects. Every objects has an identity, a type and a value.

  • Identity
    • never changes once it has been created
    • is operation compares the identity of two object. e.g. if obj1 is obj2
    • id(obj) is the memory address where obj is stored (CPython)
  • Type
    • unchangeable
    • type(obj) function returns a object’s type
    • operation on immutable types may return a reference to any existing object with the same type and value.
    • operation on mutable type will always return two different reference;
      • example:
        • a = 1; b =1; a and b may and may not refer to same object with value 1
        • c = [ ]; b = [ ]; c and d are guaranteed to refer two different, newly created empty list.
        • c = d = [ ]; c and d refer to same object.
  • Value
    • an object’s mutability is determined by its type;
      • immutable: numbers,strings, tuples…
      • mutable: dictionaries, lists…
    • immutable container object with mutable object reference; e.g. a tuple contains a reference to a mutable object.
      • containers: objects contain references to other objects; e.g. tuples, list, dictionaries.
      • container value changes when its mutable object is changed
      • a value of a container: imply the value of the contained object

Garbage collection

Objects are never explicitly destroyed; they may be garbage-collected when they become unreachable. (Not guaranteed)

  • (Current CPython Implementation) reference-counting scheme with delayed detection of cyclically linked garbage; not guaranteed to collect garbage with circular references.


As a collection algorithm, reference counting tracks, for each object, a count of the number of references to it held by other objects. If an object’s reference count reaches zero, the object has become inaccessible, and can be destroyed.

Glance of special methods

import collections

Card = collections.namedtuple('Card', ['rank', 'suit'])

class FrenchDeck:
    ranks = [str(n) for n in range(2, 11)] + list('JQKA')
    suits = 'spades diamonds clubs hearts'.split()
    def __init__(self):
        self._cards = [Card(rank, suit) for suit in self.suits
    def __len__(self):
        return len(self._cards)
    def __getitem__(self, position):
        return self._cards[position]

Example usage of special methods

Special methods are meant to be called by the python interpreter. By implementing special methods, your objects can behave like the built-in types.

>>> beer_card = Card('7', 'diamonds')
>>> beer_card
Card(rank='7', suit='diamonds')
# __len__
>>> deck = FrenchDeck()
>>> len(deck)
# __getitem__
>>> deck[0]
Card(rank='2', suit='spades')
>>> deck[-1]
Card(rank='A', suit='hearts')
>>> deck[:3]
[Card(rank='2', suit='spades'),
 Card(rank='3', suit='spades'),
 Card(rank='4', suit='spades')]


  • __repr__: used for debugging and logging
  • __str__: used for presentation to end user; invoked by str()
  • len(): in CPython implementation, what len() do is simply read from a field in a C struct. Therefore, the built-in len() is not called as a method.