How to use the JSON to Python Dataclass
Generate Python models in seconds:
1
Paste your JSON
Paste a JSON object into the input panel. Arrays of objects are also accepted — the first element is used for type inference.
2
Choose output type
Select Python dataclass for lightweight stdlib models, or Pydantic BaseModel for runtime validation and FastAPI compatibility.
3
Set class name
Enter the root class name (e.g. User, Product). Nested objects automatically generate their own named classes in PascalCase.
4
Copy into your project
Paste the generated class definitions into your Python file. All imports are included — dataclasses, typing, datetime, and pydantic as needed.
When to use this tool
Use this when working with JSON data in Python projects:
- →Building a FastAPI endpoint and need Pydantic request/response models from existing JSON
- →Adding type annotations to a Python project that processes JSON API responses
- →Generating dataclasses for use with json.loads() and dataclasses.asdict()
- →Creating Pydantic models for data validation in ML pipelines or AI agents
- →Scaffolding Django REST Framework serializers from existing JSON payloads
Frequently asked questions
Q:What's the difference between dataclass and Pydantic output?
Python dataclasses are part of the standard library and are lightweight — ideal for internal data structures and use with json.loads(). Pydantic models add runtime validation, serialization, and are the standard for FastAPI request/response typing. Choose dataclass for simplicity, Pydantic for validation and APIs.
Q:How are nested JSON objects handled?
Nested objects generate separate class definitions above the root class. The parent class references the nested class by name using standard Python type annotations — for example, address: Address. This matches Python conventions and works with both dataclasses and Pydantic.
Q:Are Optional fields supported?
Yes — fields with null values in the JSON are typed as Optional[str], Optional[int], etc. using Python's typing module. In the generated output they default to None, matching standard Python and Pydantic conventions.
Q:How are arrays and lists handled?
JSON arrays become List[T] typed fields. Arrays of primitives become List[str], List[int] etc. Arrays of objects generate a named nested class and type the field as List[NestedClass]. All required List imports are included automatically.
Q:Does Pydantic output work with FastAPI?
Yes — the generated Pydantic models are standard BaseModel subclasses and work directly as FastAPI request bodies, response models, and dependency injection types. If your JSON has email fields, EmailStr is used and requires pip install pydantic[email].