Data/Schema Validation Projects
Libraries for validating data structures and schemas in Python.
3 projects available
How to Choose the Right Data Validation Library for Python?
Among the data validation tools available for Python, the three most important ones are Pydantic, Marshmallow, and Cerberus. Choose Pydantic for type hint-based validation in modern Python applications, especially when working with FastAPI or when leveraging Python 3.6+ type hints is a priority. Choose Marshmallow if you need a library that's ORM/ODM/framework-agnostic and require a high degree of control over serialization and validation logic. Opt for Cerberus when you're looking for a schema-less or highly flexible validation approach, particularly useful when dealing with dynamic or evolving data structures.
Financial Transaction Validation with Pydantic
intermediateBuild a robust financial transaction validation system using Pydantic's powerful type annotations and custom validators. Learn to validate complex nested data, enforce business rules, handle decimal precision for money, and create type-safe data models perfect for FastAPI applications.
API Data Serialization with Marshmallow
intermediateMaster complex data transformation and validation using Marshmallow schemas. Learn to serialize Python objects to JSON, deserialize and validate incoming data, handle nested relationships, and implement custom validation logic essential for robust API development.
Flexible Data Validation with Cerberus
beginnerExplore lightweight, dictionary-based validation with Cerberus. Perfect for scenarios where you need flexible validation rules without heavy frameworks. Learn to define schemas, create custom validators, and validate complex data structures with minimal overhead.