Microsoft are using pydantic (via FastAPI) for Project Jupyter developers of the Jupyter notebook are using pydantic for subprojects, through the FastAPI-based Jupyter serverĬonfiguration management. Hundreds of organisations and packages are using pydantic, including: FastAPI a high performance API framework, easy to learn,įast to code and ready for production, based on pydantic and Starlette. dataclasses integration As well as BaseModel, pydantic providesĪ dataclass decorator which creates (almost) vanilla Python dataclasses with inputĭata parsing and validation. With methods on a model decorated with the validator decorator. extensible pydantic allows custom data types to be defined or you can extend validation List, Tuple, Dict etc.) andĬomplex data schemas to be clearly and easily defined, validated, and parsed.
validate complex structures use of recursive pydantic models, typing's It's generally as fast or faster than most similar libraries. fast pydantic has always taken performance seriously, most of the library is compiled with cython giving a ~50% speedup, The main differences are that system settings canīe read from environment variables, and more complex objects like DSNs and Python objects are often required. dual use pydantic's BaseSettings class allows pydantic to be used in both a "validate this requestĭata" context and in a "load my system settings" context. So auto-completion, linting, mypy, IDEs (especially P圜harm),Īnd your intuition should all work properly with your validated data. Data structures are just instances of classes you define with type annotations, If you know how to use Python type hints, So pydantic uses some cool new language features, but why should I actually go and use it? plays nicely with your IDE/linter/brain There's no new schema definition micro-language to learn. From datetime import datetime from pydantic import BaseModel class User ( BaseModel ): id : int name = 'John Doe' signup_ts : datetime | None = None friends : list = external_data = ] Rationale ¶
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