json-to-go.input
json-to-go.output
from typing import List, Optional, Any
from dataclasses import dataclass
@dataclass
class Profile:
age: int
city: str
@dataclass
class RootObject:
id: int
name: str
active: bool
score: float
tags: List[str]
profile: ProfileAbout JSON to Python
Python's dynamic typing is flexible, but type annotations dramatically reduce bugs in large projects. This tool parses your sample JSON locally and generates Python dataclass or TypedDict: field names become snake_case, nested objects become separate classes, arrays infer to List[T], null values become Optional[Any]. Nothing is uploaded.
How to Use
- Open the JSON to Python tool page
- Enter or paste your data into the input area
- View the real-time results and use the copy button to get the output
Use Cases
- Type API responses — Convert API JSON to dataclass for type validation with httpx/pydantic.
- Model config files — Generate dataclass from JSON/YAML config samples for typed-settings libraries.
- FastAPI request bodies — Quickly generate Pydantic-compatible dataclass as request body models.
- Data pipeline modeling — Model ETL pipeline inputs/outputs for better code maintainability.
FAQ
What is the difference between dataclass and TypedDict?
dataclass is a runtime instantiable class; TypedDict is for type annotations only (a structured dict). Choose as needed.
How are null values handled?
null infers to Optional[Any]. Use a non-null sample for more precise types.
How are field names converted?
JSON camelCase or kebab-case names are automatically converted to Python-style snake_case.
What dependencies are needed?
dataclass mode needs Python 3.7+ (stdlib); TypedDict needs typing_extensions (below 3.8).
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