JSON to Python
ConverterGenerate Python dataclass or TypedDict from sample JSON with nested types, List generics and Optional fields for API response typing and config parsing.
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: ProfileRelated Tools
About 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[...]. Output works with mypy or pyright for static checking and is a natural starting point for FastAPI models and Pydantic schemas. After generation, review optional fields and nested naming, since auto-inference of empty values may not match your business expectations; running the definitions through a static checker surfaces type mismatches early. For very deep nesting or many nullable fields, paste a minimal sample first to avoid verbose, hard-to-maintain output.
How to Use
- Open the JSON to Python tool
- Select the source and target formats
- Adjust the output options as needed
- Click the Convert button; results appear in real time
- Copy or export the result
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.
- Static type checks — Generate annotated class definitions to plug into mypy/pyright for static validation.
FAQ
What is the difference between dataclass and TypedDict?
dataclass is a runtime instantiable class; TypedDict is for type annotations only (a structured dict) and does not enforce at runtime. Use dataclass for instance methods, TypedDict for pure type hints.
How are field names converted?
JSON camelCase or kebab-case names are automatically converted to Python-style snake_case following PEP 8.
What dependencies are needed?
dataclass mode needs Python 3.7+ (stdlib); TypedDict needs typing_extensions (below 3.8) or is built in from 3.8+. Neither requires a third-party framework.
How are null fields handled?
null values are marked Optional[...] to indicate the field may be None. If some fields are never null in practice, tighten the types after generation for stronger checking.
Can the output be used with Pydantic?
Yes. Adjust the generated dataclass slightly to fit Pydantic models, or reuse its field types as a reference, giving FastAPI request bodies stronger validation.