Python Pydantic Schema Validation Library

A Python Pydantic Schema Validation Library is a Python data validation library (can be used to create python data validation systems to support python data validation tasks).

• AKA: Python Pydantic, Pydantic Schema Validator.
• Context:
  • It can typically validate Python Data Structures through python type annotations.
  • It can typically enforce Python Type Hints at runtime rather than only during static type checking.
  • It can typically convert python input data into python validated models with proper python types.
  • It can typically generate Python JSON Schemas from python model classes.
  • It can typically provide Python Error Messages for python validation failures.
  • ...
  • It can often integrate with Python Web Frameworks such as python FastAPI framework.
  • It can often support Python Configuration Management through python settings classes.
  • It can often serialize python model objects to python JSON output.
  • It can often deserialize python JSON input to python model objects.
  • It can often validate python complex data structures including python nested models.
  • ...
  • It can range from being a Simple Pydantic Model to being a Complex Pydantic Model, depending on its python data structure complexity.
  • It can range from being a Basic Pydantic Validator to being an Advanced Pydantic Validator, depending on its python validation rule complexity.
  • ...
  • It can provide Python Type Coercion for python data type conversion.
  • It can support Python Custom Validators for python domain-specific validation.
  • It can handle Python Field Constraints for python data validation rules.
  • It can generate Python Documentation from python model definitions.
  • ...
• Examples:
  • Pydantic Versions, such as:
    • Pydantic V1 Releases, such as:
      • Pydantic V1.0 Release (2018), introducing python core data validation functionality.
      • Pydantic V1.10 Release (2022), the python last major version 1 release with python enhanced validation capabilities.
    • Pydantic V2 Releases, such as:
      • Pydantic V2.0 Release (2023), a python ground-up rewrite with python rust-based validation core.
      • Pydantic V2.10 Release (2024), adding python partial validation support and python enhanced typed dictionary functionality.
      • Pydantic V2.11 Release (2025), focusing on python schema build performance and python PEP 695 support.
  • Pydantic Model Types, such as:
    • Basic Pydantic Models, such as:
      • User Pydantic Model for python user data validation.
      • Configuration Pydantic Model for python application settings.
    • Nested Pydantic Models, such as:
      • Order Pydantic Model with python nested line item models.
      • Organization Pydantic Model with python nested user models.
  • Pydantic Validator Types, such as:
    • Field-Level Pydantic Validators, such as:
      • Email Pydantic Validator for python email format validation.
      • Numeric Range Pydantic Validator for python numeric constraint validation.
    • Model-Level Pydantic Validators, such as:
      • Cross-Field Pydantic Validator for python interdependent field validation.
      • Conditional Pydantic Validator for python context-dependent validation.
  • ...
• Counter-Examples:
  • Python Dataclass, which lacks python runtime type validation.
  • Python TypedDict, which lacks python complex validation logic.
  • Python Marshmallow Library, which uses python schema class instead of python type annotations.
  • Python Cerberus Library, which uses python dictionary schema instead of python class-based validation.
• See: Python Type System, Python Data Validation, Python Schema Validation, FastAPI Framework, Python Runtime Type Checking.

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References

2023

• GBard
  • Pydantic is a fast and extensible data validation and serialization library for Python 3.7+. It allows you to define data models using standard Python type hints and then validate and serialize data to and from those models. Pydantic is also integrated with many other popular Python libraries, such as FastAPI, SQLAlchemy, and Django.
  • Here are some of the key features of Pydantic:
  • Data validation: Pydantic can validate data against a variety of types and constraints, including:
    • Required and optional fields
    • Minimum and maximum values
    • Regular expressions
    • Enums
    • Nested data structures
  • Data serialization: Pydantic can serialize data to and from a variety of formats, including JSON, YAML, and TOML.
    • Extensibility: Pydantic can be extended with custom validators and serializers to meet your specific needs.
    • Developer experience: Pydantic is designed to be easy to use and understand. It provides helpful error messages and works well with linters and IDEs.

2023

• GBard
  • A Python-based data validation library and serialization library is a library that allows you to validate and serialize Python objects. Validation is the process of checking that data meets certain criteria, such as being of a certain type, within a certain range, or having a certain format. Serialization is the process of converting data into a format that can be stored or transmitted.
  • Python-based data validation and serialization libraries are often used in web development to validate and serialize data that is being sent to or received from a server. They can also be used in other applications, such as data science and machine learning, to validate and serialize data that is being processed or stored.
  • Some popular Python-based data validation and serialization libraries include:
    • Marshmallow Python library is a library that allows you to define schemas for your data. Schemas can be used to validate data, as well as to serialize and deserialize data to and from different formats, such as JSON, XML, and YAML.
    • Pydantic is a library that allows you to define data models using Python type annotations. Pydantic models can be used to validate data, as well as to serialize and deserialize data to and from different formats, such as JSON and YAML.
    • Cerberus Python library is a library that focuses on data validation. It provides a flexible way to define validation rules for your data, and it can be used to validate data from a variety of sources, such as JSON, XML, and YAML files, as well as databases and other Python objects.

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