pydantic-validation
Record-level data validation using Pydantic models. Field validators, model validators, and batch validation patterns.
What this skill does
# 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`:
```python
from scripts.validators import (
UserRecord,
Customer,
Order,
Address,
validate_records,
print_validation_errors,
PositiveInt,
Email
)
```
## Usage Examples
### Basic Model Validation
```python
from scripts.validators import UserRecord
# Validate single record
user = UserRecord(
id=1,
email="[email protected]",
status="active",
created_at="2024-01-15",
age=25
)
print(user.email) # [email protected] (lowercased)
```
### Batch Validation
```python
from scripts.validators import validate_records, print_validation_errors
raw_data = [
{"id": 1, "email": "[email protected]", "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
```python
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
```python
# 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
```
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