The Overview page for Pydantic offers the following Rationale <https://pydantic-docs.helpmanual.io/#rationale>, which makes me think that it might be of interest to Leo's users and Leo's developers:
«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. If you know how to use python type hints, you know how to use *pydantic*. Data structures are just instances of classes you define with type annotations, so auto-completion, linting, mypy <https://pydantic-docs.helpmanual.io/usage/mypy/>, IDEs (especially PyCharm <https://pydantic-docs.helpmanual.io/pycharm_plugin/>), and your intuition should all work properly with your validated data. *dual use **pydantic's* BaseSettings <https://pydantic-docs.helpmanual.io/usage/settings/> class allows *pydantic* to be used in both a "validate this request data" context and in a "load my system settings" context. The main differences are that system settings can be read from environment variables, and more complex objects like DSNs and python objects are often required.*fast *In benchmarks <https://pydantic-docs.helpmanual.io/benchmarks/> *pydantic* is faster than all other tested libraries. *validate complex structures *use of recursive *pydantic* models <https://pydantic-docs.helpmanual.io/usage/models/#recursive-models> , typing's standard types <https://pydantic-docs.helpmanual.io/usage/types/#standard-library-types> (e.g. List, Tuple, Dictetc.) and validators <https://pydantic-docs.helpmanual.io/usage/validators/> allow complex data schemas to be clearly and easily defined, validated, and parsed. *extensible**pydantic* allows custom data types <https://pydantic-docs.helpmanual.io/usage/types/#custom-data-types> to be defined or you can extend validation with methods on a model decorated with the validator <https://pydantic-docs.helpmanual.io/usage/validators/> decorator. *dataclasses integration *As well as BaseModel, *pydantic* provides a dataclass <https://pydantic-docs.helpmanual.io/usage/dataclasses/> decorator which creates (almost) vanilla python dataclasses with input data parsing and validation.» On Sunday, April 25, 2021 at 8:18:57 AM UTC-4 David Szent-Györgyi wrote: > These links might be of interest to Edward and others developing Python > code, including Leo. > > First, a praise of property-based testing > <https://increment.com/testing/in-praise-of-property-based-testing/>, > with examples that differentiate it from example-based testing. > > Second, Hypothesis, «a Python library for creating unit tests which are > simpler to write and more powerful when run, finding edge cases in your > code you wouldn’t have thought to look for. It is stable, powerful and easy > to add to any existing test suite. > > It works by letting you write tests that assert that something should be > true for every case, not just the ones you happen to think of.» > > Third, Pydantic, «Data validation and settings management using python > type annotations. *pydantic* enforces type hints at runtime, and provides > user friendly errors when data is invalid. Define how data should be in > pure, canonical python; validate it with *pydantic*.» This includes > support for "custom" (user-defined) types. Pydantic uses type hinting from > Versions 3.6 and later of Python. > > A plug-in for Pydantic supports Hypothesis. Used together, Pydantic 1.8 > and Hypothesis v5.29.0 allow Hypothesis to automatically load support for > custom types like PaymentCardNumber (a credit-card number) and > PositiveFloat. Current releases are Pydantic 1.8.1 and Hypothesis 6.10.0. > > Documentation for Hypothesis on Read the Docs > <https://hypothesis.readthedocs.io/en/latest/> > Hypothesis on PyPi <https://pypi.org/project/hypothesis/> > Installing Hypothesis from source > <https://github.com/HypothesisWorks/hypothesis/blob/master/CONTRIBUTING.rst> > > Documentation for Pydantic on Help Manual > <https://pydantic-docs.helpmanual.io> > Documentation for Pydantic's Hypothesis plug-in > <https://pydantic-docs.helpmanual.io/hypothesis_plugin/> > Pydantic on PyPi <https://pypi.org/project/pydantic/> > Pydantic on GitHub <https://github.com/samuelcolvin/pydantic> > -- You received this message because you are subscribed to the Google Groups "leo-editor" group. To unsubscribe from this group and stop receiving emails from it, send an email to leo-editor+unsubscr...@googlegroups.com. To view this discussion on the web visit https://groups.google.com/d/msgid/leo-editor/f1d2a257-151b-403b-aaeb-2a5397a6835fn%40googlegroups.com.
