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Building Robust Object-Oriented Python Applications and Libraries

Dataclasses

Explore how dataclasses in Python 3.7 and later allow you to define objects with less boilerplate, offering benefits like automatic __init__, string representation, equality checking, and optional ordering or immutability. Understand when to use frozen dataclasses versus other structures and how to leverage these features in your coding.

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Since Python 3.7, dataclasses let us define ordinary objects with a clean syntax for specifying attributes. They look very similar to named tuples. This is a pleasant approach that makes it easy to understand how they work.

Example

Here’s a dataclass version of our Stock example:

Python 3.10.4
from dataclasses import dataclass 
@dataclass
class Stock:
    symbol: str
    current: float
    high: float
    low: float

For this case, the definition is nearly identical to the NamedTuple definition. The dataclass function is applied as a class decorator, using the @ operator. This class definition syntax isn’t much less verbose than an ordinary class with __init__(), but it gives us access to several additional dataclass features.

It’s important to recognize that the names are provided at the class level but are not actually creating class-level attributes. The class-level names are used to build several methods, including the __init__() method; each instance will have the expected attributes. The ...