JSON to Python Converter

How to use

1. Paste JSON input from your API payload or sample document.
2. Select your output target and model options.
3. Generate, review, and copy the result.

Benefits

• Handles nested objects and arrays from API responses.
• Reduces manual model writing and naming inconsistencies.
• Works instantly in browser with copy-ready output.

Best use cases

• Generate Pydantic models for FastAPI request and response validation.
• Create typed data models for ETL and backend services.
• Speed up schema modeling for nested JSON payloads.

Type mapping notes

• JSON primitives map to Python scalar hints in Pydantic fields.
• Nested objects map to nested BaseModel classes.
• Arrays map to typed List fields using inferred value types.

Implementation tips

• Review optional fields when APIs return sparse payloads.
• Split large generated models into multiple modules where needed.
• Validate edge payloads with model.parse_obj before production use.

Sample JSON

{
  "id": 101,
  "name": "Ada Lovelace",
  "active": true,
  "roles": ["admin", "editor"],
  "profile": {
    "email": "ada@example.com",
    "score": 9.8
  }
}

Sample output

from pydantic import BaseModel
from typing import List

class UserModel(BaseModel):
    id: int
    name: str
    active: bool
    roles: List[str]

FAQ