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

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.

Overview

The question has been addressed. However, this answer adds some practical examples to aid in the basic understanding of dataclasses.

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

1. code generators: generate boilerplate code; you can choose to implement special methods in a regular class or have a dataclass implement them automatically.
2. data containers: structures that hold data (e.g. tuples and dicts), often with dotted, attribute access such as classes, namedtuple and others.

"mutable namedtuples with default[s]"

Here is what the latter phrase means:

• mutable: by default, dataclass attributes can be reassigned. You can optionally make them immutable (see Examples below).
• namedtuple: you have dotted, attribute access like a namedtuple or a regular class.
• default: you can assign default values to attributes.

Compared to common classes, you primarily save on typing boilerplate code.

Features

This is an overview of dataclass features (TL;DR? See the Summary Table in the next section).

What you get

Here are features you get by default from dataclasses.

Attributes + Representation + Comparison

import dataclasses@dataclasses.dataclass#@dataclasses.dataclass()                                       # alternativeclass Color:    r : int = 0    g : int = 0    b : int = 0

These defaults are provided by automatically setting the following keywords to True:

@dataclasses.dataclass(init=True, repr=True, eq=True)

What you can turn on

Additional features are available if the appropriate keywords are set to True.

Order

@dataclasses.dataclass(order=True)class Color:    r : int = 0    g : int = 0    b : int = 0

The ordering methods are now implemented (overloading operators: < > <= >=), similarly to functools.total_ordering with stronger equality tests.

Hashable, Mutable

@dataclasses.dataclass(unsafe_hash=True)                        # override base `__hash__`class Color:    ...

Although the object is potentially mutable (possibly undesired), a hash is implemented.

Hashable, Immutable

@dataclasses.dataclass(frozen=True)                             # `eq=True` (default) to be immutable class Color:    ...

A hash is now implemented and changing the object or assigning to attributes is disallowed.

Overall, the object is hashable if either unsafe_hash=True or frozen=True.

See also the original hashing logic table with more details.

What you don't get

To get the following features, special methods must be manually implemented:

Unpacking

@dataclasses.dataclassclass Color:    r : int = 0    g : int = 0    b : int = 0    def __iter__(self):        yield from dataclasses.astuple(self)

Optimization

@dataclasses.dataclassclass SlottedColor:    __slots__ = ["r", "b", "g"]    r : int    g : int    b : int

The object size is now reduced:

>>> imp sys>>> sys.getsizeof(Color)1056>>> sys.getsizeof(SlottedColor)888

In some circumstances, __slots__ also improves the speed of creating instances and accessing attributes. Also, slots do not allow default assignments; otherwise, a ValueError is raised.

See more on slots in this blog post.

Summary Table

+----------------------+----------------------+----------------------------------------------------+-----------------------------------------+|       Feature        |       Keyword        |                      Example                       |           Implement in a Class          |+----------------------+----------------------+----------------------------------------------------+-----------------------------------------+| Attributes           |  init                |  Color().r -> 0                                    |  __init__                               || Representation       |  repr                |  Color() -> Color(r=0, g=0, b=0)                   |  __repr__                               || Comparision*         |  eq                  |  Color() == Color(0, 0, 0) -> True                 |  __eq__                                 ||                      |                      |                                                    |                                         || Order                |  order               |  sorted([Color(0, 50, 0), Color()]) -> ...         |  __lt__, __le__, __gt__, __ge__         || Hashable             |  unsafe_hash/frozen  |  {Color(), {Color()}} -> {Color(r=0, g=0, b=0)}    |  __hash__                               || Immutable            |  frozen + eq         |  Color().r = 10 -> TypeError                       |  __setattr__, __delattr__               ||                      |                      |                                                    |                                         || Unpacking+           |  -                   |  r, g, b = Color()                                 |   __iter__                              || Optimization+        |  -                   |  sys.getsizeof(SlottedColor) -> 888                |  __slots__                              |+----------------------+----------------------+----------------------------------------------------+-----------------------------------------+

_{⁺These methods are not automatically generated and require manual implementation in a dataclass.}

_{^* __ne__ is not needed and thus not implemented.}

Additional features

Post-initialization

@dataclasses.dataclassclass RGBA:    r : int = 0    g : int = 0    b : int = 0    a : float = 1.0    def __post_init__(self):        self.a : int =  int(self.a * 255)RGBA(127, 0, 255, 0.5)# RGBA(r=127, g=0, b=255, a=127)

Inheritance

@dataclasses.dataclassclass RGBA(Color):    a : int = 0

Conversions

Convert a dataclass to a tuple or a dict, recursively:

>>> dataclasses.astuple(Color(128, 0, 255))(128, 0, 255)>>> dataclasses.asdict(Color(128, 0, 255)){'r': 128, 'g': 0, 'b': 255}

Limitations

• Lacks mechanisms to handle starred arguments
• Working with nested dataclasses can be complicated

References

• R. Hettinger's talk on Dataclasses: The code generator to end all code generators
• T. Hunner's talk on Easier Classes: Python Classes Without All the Cruft
• Python's documentation on hashing details
• Real Python's guide on The Ultimate Guide to Data Classes in Python 3.7
• A. Shaw's blog post on A brief tour of Python 3.7 data classes
• E. Smith's github repository on dataclasses

From the PEP specification:

A class decorator is provided which inspects a class definition for variables with type annotations as defined in PEP 526, "Syntax for Variable Annotations". In this document, such variables are called fields. Using these fields, the decorator adds generated method definitions to the class to support instance initialization, a repr, comparison methods, and optionally other methods as described in the Specification section. Such a class is called a Data Class, but there's really nothing special about the class: the decorator adds generated methods to the class and returns the same class it was given.

The @dataclass generator adds methods to the class that you'd otherwise define yourself like __repr__, __init__, __lt__, and __gt__.