JSON to Python Dataclass

Generate Python dataclasses with type hints from JSON. Free, in your browser.

Class name:

JSON

Python Dataclass

from dataclasses import dataclass
from typing import List, Optional

@dataclass
class UserAddress:
    city: str
    zip: str

@dataclass
class User:
    name: str
    age: int
    skills: List[str]
    address: UserAddress

Processed in your browser

What it's for

JSON to Python dataclass with type hints

Compatible with Python 3.7+

Generated code uses the standard library's dataclasses module. No external dependencies — works in any modern Python environment.

100% private

Your JSON is processed in the browser. Never sent to any server. Safe for internal API data or confidential data models.

Correct type hints

Precise type inference: str, int, float, bool, List, Optional, and nested dataclasses generated with appropriate names and types.

Instant

Real-time generation as you type or paste JSON. No waiting, no server calls.

How it works

Three steps, no hassle

Paste your JSON

Enter the JSON you want to convert. It can be an API response, config file, or any valid JSON structure.

Get your Python dataclass

The tool generates a Python class decorated with @dataclass, with inferred type hints (str, int, float, bool, list, nested dataclass) for each field.

Copy and import in your project

Copy the generated code to your Python module. You only need to import dataclass from the standard library's dataclasses module.

FAQ

Got questions?

What are Python dataclasses?

Dataclasses are Python classes defined primarily to store data, introduced in Python 3.7 via PEP 557. The @dataclass decorator automatically generates methods like __init__, __repr__, and __eq__ based on fields declared with type hints. They are the modern alternative to manually defining classes with repetitive __init__ methods, offering cleaner and more readable code without Pydantic's external dependencies.

What is the difference between a dataclass and a dictionary?

A Python dict has no defined types, accepts any key and value, and provides no IDE autocompletion. A dataclass explicitly defines structure with type hints, allows field access with dot notation (`obj.field` instead of `obj['field']`), automatically generates comparison and representation methods, and enables IDEs and static analysis tools (mypy, Pyright) to catch type errors before running the code.

How are type hints inferred from JSON?

The tool analyzes JSON values to infer the most appropriate Python type: text strings become `str`, integers become `int`, decimals become `float`, booleans become `bool`, arrays become `List[T]` where T is the inferred element type, and nested objects become new dataclasses. Null values become `Optional[T]` with a default value of `None`.

Are nested dataclasses generated?

Yes. When JSON contains objects within objects, the tool generates a separate dataclass for each nesting level. Classes are correctly ordered in the code so each dataclass is defined before being referenced. For example, `{"user": {"name": "Alice", "age": 30}}` generates both a `User` dataclass and the parent dataclass that contains it.

How are optional fields handled?

Fields with null values in the sample JSON are generated as `Optional[T] = None` in the dataclass. This signals the field may be absent or None at runtime. To use Optional you need to import it from `typing` (Python < 3.10) or use the `T | None` syntax (Python 3.10+). The tool generates code compatible with Python 3.7+.