What are data classes and how are they different from common classes?

Question

PEP 557 introduces data classes into the Python standard library. It says that by applying the @dataclass decorator shown below, it will generate "among other things, an __init__()".

from dataclasses import dataclass

@dataclass
class InventoryItem:
    """Class for keeping track of an item in inventory."""
    name: str
    unit_price: float
    quantity_on_hand: int = 0

    def total_cost(self) -> float:
        return self.unit_price * self.quantity_on_hand

It also says dataclasses are "mutable namedtuples with default", but I don't understand what this means, nor how data classes are different from common classes.

What are data classes and when is it best to use them?

Answer 1

Data classes are just regular classes that are geared towards storing state, rather than containing a lot of logic. Every time you create a class that mostly consists of attributes, you make a data class.

What the dataclasses module does is to make it easier to create data classes. It takes care of a lot of boilerplate for you.

This is especially useful when your data class must be hashable; because this requires a __hash__ method as well as an __eq__ method. If you add a custom __repr__ method for ease of debugging, that can become quite verbose:

class InventoryItem:
    '''Class for keeping track of an item in inventory.'''
    name: str
    unit_price: float
    quantity_on_hand: int = 0

    def __init__(
            self, 
            name: str, 
            unit_price: float,
            quantity_on_hand: int = 0
        ) -> None:
        self.name = name
        self.unit_price = unit_price
        self.quantity_on_hand = quantity_on_hand

    def total_cost(self) -> float:
        return self.unit_price * self.quantity_on_hand
    
    def __repr__(self) -> str:
        return (
            'InventoryItem('
            f'name={self.name!r}, unit_price={self.unit_price!r}, '
            f'quantity_on_hand={self.quantity_on_hand!r})'
        )

    def __hash__(self) -> int:
        return hash((self.name, self.unit_price, self.quantity_on_hand))

    def __eq__(self, other) -> bool:
        if not isinstance(other, InventoryItem):
            return NotImplemented
        return (
            (self.name, self.unit_price, self.quantity_on_hand) == 
            (other.name, other.unit_price, other.quantity_on_hand))

With dataclasses you can reduce it to:

from dataclasses import dataclass

@dataclass(unsafe_hash=True)
class InventoryItem:
    '''Class for keeping track of an item in inventory.'''
    name: str
    unit_price: float
    quantity_on_hand: int = 0

    def total_cost(self) -> float:
        return self.unit_price * self.quantity_on_hand

(Example based on the PEP example).

The same class decorator can also generate comparison methods (__lt__, __gt__, etc.) and handle immutability.

namedtuple classes are also data classes, but are immutable by default (as well as being sequences). dataclasses are much more flexible in this regard, and can easily be structured such that they can fill the same role as a namedtuple class.

The PEP was inspired by the attrs project, which can do even more (including slots, validators, converters, metadata, etc.).

If you want to see some examples, I recently used dataclasses for several of my Advent of Code solutions, see the solutions for day 7, day 8, day 11 and day 20.

If you want to use dataclasses module in Python versions < 3.7, then you could install the backported module (requires 3.6) or use the attrs project mentioned above.