![]() ![]() Customisation - Pydantic allows custom validators and serializers to alter how data is processed in many powerful ways.Dataclasses, TypedDicts and more - Pydantic supports validation of many standard library types including dataclass and TypedDict. ![]() ![]() Strict and Lax mode - Pydantic can run in either strict=True mode (where data is not converted) or strict=False mode where Pydantic tries to coerce data to the correct type where appropriate.JSON Schema - Pydantic models can emit JSON Schema, allowing for easy integration with other tools.As a result, Pydantic is among the fastest data validation libraries for Python. Speed - Pydantic's core validation logic is written in Rust.Powered by type hints - with Pydantic, schema validation and serialization are controlled by type annotations less to learn, less code to write, and integration with your IDE and static analysis tools.dimensions ) #> (10, 20) Why use Pydantic? ΒΆ Pydantic Example from datetime import datetime from typing import Tuple from pydantic import BaseModel class Delivery ( BaseModel ): timestamp : datetime dimensions : Tuple m = Delivery ( timestamp = '', dimensions = ) print ( repr ( m. ![]()
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