Convert Python to JSON
Mock data, snippets
or framework guide.
Generate mock JSON from a Python class or dataclass,
get json.dumps()
snippets for every scenario, or get Pydantic v2,
FastAPI and Django serialization patterns.
Each mode targets a different Python workflow.
Paste a Python class, dataclass or Pydantic model and get realistic mock JSON. Works with type annotations, Optional fields and List types. Useful for pytest fixtures, API docs and Postman collections — no Python interpreter needed.
id: int name: str active: bool score: float
{"id":1,"name":"Alice",
"active":true,"score":9.99}
Get complete Python code for every json serialization use case — basic, pretty-print, file I/O, custom encoders for datetime and Decimal, dataclass with asdict(), and Pydantic v2 model_dump_json(). Each snippet is production-ready.
json.dumps(data, indent=2,
default=lambda o:
o.isoformat() if
isinstance(o, datetime)
else str(o))
Production-ready JSON serialization for Pydantic v2, FastAPI, Django REST Framework and Flask. Each snippet shows the recommended approach for the framework — including response models, serializer classes and jsonify() usage.
@app.get("/users/{id}",
response_model=UserSchema)
async def get_user(id: int):
return await get(id)
The right method for each Python data type.
| Data type | Method | Notes |
|---|---|---|
| dict, list, str, int, float, bool, None | json.dumps(obj) |
Built-in, no imports needed beyond json. The default encoder handles all native JSON types. |
| dataclass instance | json.dumps(asdict(obj)) |
dataclasses.asdict() recursively converts nested dataclasses. Import: from dataclasses import asdict. |
| Pydantic v2 model | model.model_dump_json() |
Fastest option — Pydantic v2 uses Rust-based serialization. Returns a str, not bytes. |
| Pydantic v1 model | model.json() |
Pydantic v1 legacy. Prefer model.model_dump_json() after upgrading to v2. |
| Custom class instance | json.dumps(obj.__dict__) |
Quick option for simple classes. Use a custom default= function for full control. |
| datetime, Decimal, UUID | json.dumps(obj, default=encoder) |
Pass a default= function that handles non-serializable types. See Mode 2 for complete example. |
FastAPI uses Pydantic v2 for request validation and response serialization. Mode 3 gives you the complete FastAPI endpoint with response_model, Pydantic schema definition, and the model_dump_json() pattern for manual serialization — the fastest Python JSON library available.
pytest fixtures often need JSON representations of Python objects for API testing. Mode 1 generates realistic mock JSON from your dataclass or Pydantic model — paste it as the expected response in assert response.json() == expected or create it as a JSON fixture file loaded with json.load().
Python data pipelines often need to export processed data as JSON files — for downstream consumers, S3 storage or API responses. Mode 2 gives you the file output pattern with proper encoding, ensure_ascii=False for Unicode and error handling.
DRF serializers control exactly what fields are included in JSON responses. Mode 3 gives you the complete DRF serializer class with nested relationships, the ViewSet that uses it, and the @api_view pattern for function-based views.
Python parsed in your
browser. No interpreter needed.
The mock JSON mode uses a type annotation parser — no need to run Python. It handles dataclass, Pydantic, plain class and TypedDict annotations including Optional[T], List[T] and Union types. The snippets are generated from your selection without any API call.
The framework snippets reflect the actual recommended patterns from each framework's documentation — Pydantic v2's model_dump_json() (not the deprecated .json()), FastAPI's response_model pattern, and DRF's ModelSerializer with nested relationships.