Data Validation Toolkit

$29

Pydantic models, custom validators, schema evolution patterns, and data quality frameworks for Python applications.

📁 16 files🏷 v1.0.0

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📁 File Structure 16 files

data-validation-toolkit/ ├── LICENSE ├── README.md ├── configs/ │ ├── rules/ │ │ └── business_rules.yaml │ └── schemas/ │ └── user_schema.yaml ├── examples/ │ └── validate_user_data.py ├── guides/ │ └── data-validation-guide.md ├── src/ │ ├── decorators.py │ ├── pipeline.py │ ├── reporters.py │ └── validators/ │ ├── base.py │ ├── business_rules.py │ ├── file_validator.py │ ├── schema_validator.py │ └── type_validator.py └── tests/ └── test_validators.py

📖 Documentation Preview README excerpt

Data Validation Toolkit — Composable Validation for Python

Type-safe, pipeline-driven validation with schema support, business rules, and detailed error reporting.

What You Get

5 validator types — base, type, schema, business rules, and file validators
Composable pipeline — chain validators in sequence with short-circuit or collect-all modes
YAML-driven schemas — define validation rules in config, not code
Rich error reports — JSON, plain text, or structured output with field paths
Function decorators — validate arguments automatically on function call

File Tree


data-validation-toolkit/
├── README.md
├── manifest.json
├── LICENSE
├── src/
│   ├── validators/
│   │   ├── base.py              # Abstract base + ValidationError
│   │   ├── type_validator.py    # Runtime type checking
│   │   ├── schema_validator.py  # YAML-driven schema validation
│   │   ├── business_rules.py    # Configurable business rules
│   │   └── file_validator.py    # File existence, size, format
│   ├── pipeline.py              # Validation pipeline orchestrator
│   ├── reporters.py             # Error formatting & reporting
│   └── decorators.py            # @validate_args decorator
├── configs/
│   ├── schemas/
│   │   └── user_schema.yaml     # Example schema definition
│   └── rules/
│       └── business_rules.yaml  # Example business rules
├── examples/
│   └── validate_user_data.py    # End-to-end usage example
├── tests/
│   └── test_validators.py       # Comprehensive test suite
└── guides/
    └── data-validation-guide.md

Getting Started

Basic type validation


from src.validators.type_validator import TypeValidator

validator = TypeValidator({"name": str, "age": int, "active": bool})
errors = validator.validate({"name": "Alice", "age": "thirty", "active": True})
# errors[0].field == "age", errors[0].message == "Expected int, got str"

Schema validation from YAML


from src.validators.schema_validator import SchemaValidator

validator = SchemaValidator.from_yaml("configs/schemas/user_schema.yaml")

*... continues with setup instructions, usage examples, and more.*

📄 Code Sample .py preview

src/decorators.py """Validation decorators — validate function arguments automatically. Usage: @validate_args(name=str, age=int) def create_user(name: str, age: int) -> dict: return {"name": name, "age": age} create_user("Alice", 30) # OK create_user("Alice", "thirty") # Raises ValidationException """ from __future__ import annotations import functools import inspect from typing import Any, Callable, TypeVar from .validators.base import Severity, ValidationError, ValidationException F = TypeVar("F", bound=Callable[..., Any]) def validate_args(**type_spec: type | tuple[type, ...]) -> Callable[[F], F]: """Decorator that validates function arguments at call time. Checks that each named argument matches the specified type(s). Raises :class:`ValidationException` on mismatch. Args: **type_spec: Keyword arguments mapping parameter names to expected types. Returns: Decorator. Example:: @validate_args(name=str, count=int) def greet(name: str, count: int) -> None: ... """ def decorator(func: F) -> F: sig = inspect.signature(func) @functools.wraps(func) def wrapper(*args: Any, **kwargs: Any) -> Any: bound = sig.bind(*args, **kwargs) bound.apply_defaults() errors: list[ValidationError] = [] # ... 77 more lines ...

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