In a way, a Python’s data model can be viewed as a framework. It formalizes the interfaces of the building blocks of the language itself.
The Python interpreter invokes special methods to perform basic object operations, often triggered by special syntax. That provides a consistency in usage of objects from standard library, as well as custom ones.
Example
__len__special method will be called by thelen()function__getitem__- used to read items from the sequence.
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
for rank in self.ranks]
def __len__(self):
return len(self._cards)
def __getitem__(self, position):
return self._cards[position]>>> deck = FrenchDeck()
>>> len(deck)
52>>> deck[0]
Card(rank='2', suit='spades')
>>> deck[-1]
Card(rank='A', suit='hearts')Benefits
- Users don’t have to memorize arbitrary methods names for standard operations (“How to get the number of items? Is it
.size(),.length(), or what?”) - It’s easier to benefit from the rich Python standard library and avoid reinventing the wheel, like the
random.choicefunction or the fact that with__getitem__method we can read specific item in the sequence, slice or iterate it.