Pydantic

Abstract

---

• A data validation and parsing library that uses Python type hints to define structured models. It ensures that incoming data (like JSON, APIs, forms) is clean, consistent, and typed correctly before your application uses it.

Type Enforcement & Automatic Conversion

---

• When data is passed into a Pydantic model, it:
  • Checks that each field matches the declared type
  • Automatically converts compatible values when possible: ("20" → int, "true" → bool)
• This helps catch bugs early and reduces manual parsing.

JSON Serialization

---

# Construct models directly from dictionaries
User(**dictionary_object)

• This feels conceptually similar to C++ reflection because Pydantic introspects field definitions, though the underlying mechanism is different.

• Pydantic models can be serialized easily:

  • model_dump() → Python dictionaries
  • model_dump_json() → JSON strings

Built-in Format Validation

---

• Pydantic includes rich field types such as:
  • EmailStr
  • AnyUrl
  • IPvAnyAddress
  • PaymentCardNumber
  • FilePath
• These ensure the value follows a valid format ( like checking the structure of an email).

Custom Validators

---

• You can define your own validation logic using decorators:
  • @field_validator for single fields
  • @model_validator for cross-field constraints
• This is useful for enforcing business rules or complex checks.

References

---

• Pydantic Tutorial • Solving Python’s Biggest Problem - YouTube