JSON to Python Converter
Convert any JSON object or array into fully annotated Python dataclass definitions, with field types inferred from your data and typing imports for List, Dict, Optional, and more. Nested objects are extracted into their own dataclasses automatically, and you can toggle the @dataclass decorator, type hints, and a ready-to-run __main__ usage example independently. The entire conversion happens client-side — no data is sent to any server.
JSON
Class Name
Code Style
Python
What is JSON to Python Converter?
Python's type annotations and dataclasses were built for exactly this use case — structured data coming from an external source that you want to work with in a type-safe way. Paste your JSON here and get Python dataclasses, TypedDicts, or Pydantic BaseModel classes in the style that fits your project. For FastAPI and Pydantic users, the generated models include proper Optional annotations for nullable fields and are immediately usable as request body types and response models with full JSON schema generation. For data science workflows, the dataclass output integrates cleanly with pandas via asdict() and DataFrame(). Python reserved words that clash with JSON key names are renamed with a trailing underscore and paired with a field alias so serialisation still uses the original key. The generated code includes all necessary imports at the top of the file.
How to Use
- 1
Paste Your JSON
Paste a JSON object or array into the editor. For best results with arrays, include multiple items so the tool can detect field types accurately. Nested objects and arrays are fully supported.
- 2
Configure Python Output Style
Choose between dataclass, TypedDict, Pydantic BaseModel, or plain class output. Set the root class name and select whether to generate Optional[] types for nullable fields and List[] for array fields.
- 3
Generate Python Classes
Click "Generate Python". The tool inspects field values to infer int, float, str, bool, and List types, generates separate classes for nested objects, and adds the appropriate imports at the top of the output.
- 4
Copy into Your Python Project
Copy the generated classes and paste them into your Python module. For Pydantic models, use model_validate(data) to parse JSON directly; for dataclasses, use dacite or cattrs for deserialisation.
Common Use Cases
Data Science & ML Pipelines
Convert JSON API responses or configuration payloads into Python dataclasses to use as typed inputs in pandas workflows, scikit-learn pipelines, or FastAPI endpoints with Pydantic validation.
Rapid Backend Prototyping
Generate Python class definitions from a JSON schema or sample payload to quickly scaffold Flask or Django models without writing boilerplate data structures from scratch.
API Client Generation
Paste a REST API response JSON to generate typed Python dataclasses or TypedDicts that serve as the response model for your API client, enabling IDE autocomplete and type checking.
Config Object Modeling
Convert JSON configuration files into Python dataclasses with default values and type annotations, replacing untyped dict access with attribute-based access and IDE support across your codebase.
Conversion Examples
JSON Object → Python Dataclass
A flat JSON object generates a typed Python dataclass with annotated fields.
Input JSON
{
"id": 1,
"username": "alice",
"email": "alice@example.com",
"is_active": true,
"score": 98.5
}Output CSV
from dataclasses import dataclass
@dataclass
class Root:
id: int
username: str
email: str
is_active: bool
score: floatNested JSON → Nested Dataclasses
Nested objects produce separate dataclasses with typed cross-references.
Input JSON
{
"user": {"id": 1, "name": "Alice"},
"orders": [{"id": 101, "total": 49.99, "paid": true}]
}Output CSV
from dataclasses import dataclass
from typing import List
@dataclass
class User:
id: int
name: str
@dataclass
class Order:
id: int
total: float
paid: bool
@dataclass
class Root:
user: User
orders: List[Order]