3 posts tagged with "data-structures"

When structuring immutable, simple data in Python, developers often choose between several tools. While Dataclasses and Pydantic models dominate modern usage, older structures like namedtuple and simpler tools like tuple and dict still have niche uses.

This article compares these common data structures based on their primary function, mutability, and performance characteristics to help you choose the best tool for the job.

While both Pydantic models and Python dataclasses serve to structure data, their performance characteristics are significantly different. The key distinction lies in when and how validation occurs. Dataclasses rely on simple Python object initialization, while Pydantic executes a comprehensive validation and coercion pipeline on every instantiation.

The clear winner in terms of raw execution speed is the Python Dataclass.

The modern Python landscape offers two excellent tools for defining structured data: Dataclasses (introduced in Python 3.7) and Pydantic (a third-party library). While both help define classes for data, their core purpose, performance characteristics, and feature sets are fundamentally different.

Choosing between them depends on whether your primary need is simple data structuring (Dataclasses) or input validation and parsing (Pydantic).