pydantic-validation

name	pydantic-validation
description	Record-level data validation using Pydantic models. Field validators, model validators, and batch validation patterns.
allowed-tools	Read Write Edit Bash

Pydantic Validation

Audience: Data engineers validating records in ETL pipelines.

Goal: Provide reusable Pydantic patterns for record-level validation.

Scripts

Execute validation functions from scripts/validators.py:

from scripts.validators import (
    UserRecord,
    Customer,
    Order,
    Address,
    validate_records,
    print_validation_errors,
    PositiveInt,
    Email
)

Usage Examples

Basic Model Validation

from scripts.validators import UserRecord

# Validate single record
user = UserRecord(
    id=1,
    email="[e-mail]",
    status="active",
    created_at="2024-01-15",
    age=25
)
print(user.email)  # [e-mail] (lowercased)

Batch Validation

from scripts.validators import validate_records, print_validation_errors

raw_data = [
    {"id": 1, "email": "[e-mail]", "status": "active", "created_at": "2024-01-01", "age": 25},
    {"id": -1, "email": "invalid", "status": "bad", "created_at": "2024-01-01", "age": 200},
]

valid, invalid = validate_records(raw_data)
if invalid:
    print_validation_errors(invalid)

Nested Models

from scripts.validators import Customer, Address

customer = Customer(
    id=1,
    name="John Doe",
    billing_address=Address(
        street="123 Main St",
        city="NYC",
        postal_code="10001"
    )
)
# shipping_address defaults to billing_address

Field Constraints Reference

Constraint	Example	Description
gt, ge	Field(gt=0)	Greater than / greater-equal
lt, le	Field(le=100)	Less than / less-equal
pattern	Field(pattern=r'^\d+$')	Regex match
min_length, max_length	Field(min_length=1)	String length

JSON/Dict Conversion

# Parse from dict
customer = Customer(**data_dict)

# Parse from JSON
customer = Customer.model_validate_json(json_string)

# Export to dict/JSON
data = customer.model_dump()
json_str = customer.model_dump_json()

When to Use Pydantic

Use Case	Pydantic	Alternative
API request/response	✓	FastAPI integration
Record-by-record ETL	✓	-
Full DataFrame validation	-	pandera
Pipeline expectations	-	Great Expectations

Dependencies

pydantic>=2.0
pydantic-settings  # For config validation