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JSON → Python Dataclass

Generate Python @dataclass types from JSON (typing supported)

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Generation Options
Input JSONJSON
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Generated resultPython
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Overview: The JSON → Python Dataclass generator inspects sample JSON and produces Python classes decorated with @dataclass and annotated using typing. It is a productive helper for Python 3.7+ projects, and the generated classes can be used directly in FastAPI, Pydantic (with light tweaks), SQLAlchemy DTOs and data-analysis scripts, moving code from dynamic dictionaries to strongly-typed object models.

How to use: Step 1 — paste sample JSON on the left, or click "Sample" to load an example quickly. Step 2 — specify the root class name (default RootObject). Step 3 — the right panel produces the corresponding @dataclass code live; nested objects become separate classes and are declared in dependency order. Step 4 — copy the code to your project or download it as a .py file. The generated code automatically imports from dataclasses import dataclass and from typing import List, Optional when needed.

Typical scenarios: Quickly generate request / response models when building a FastAPI service (migrate to Pydantic later); data analysts use dataclasses to improve readability when handling deeply nested JSON logs; batch-generate type definitions when integrating with ML services; Python client SDK developers build models directly from API documentation; migrate an untyped legacy codebase to typed code by generating skeletons.

Technical notes: Strings map to str, integers to int, floats to float, booleans to bool, arrays to List[T], and null maps to Optional[Any]. Class names are PascalCased while field names keep their original JSON spelling (switch to snake_case manually if needed). Default indentation is 4 spaces, matching PEP 8. The dataclass output does not include __post_init__ or JSON serialisation helpers; combine with dataclasses-json or marshmallow if you need those.

Privacy: Type inference runs entirely in your browser, so no JSON data is uploaded. You can safely process sample payloads that contain personal information or API-key placeholders.

Frequently Asked Questions

How do I use JSON → Python Dataclass?
Step 2 — specify the root class name (default RootObject ). Step 3 — the right panel produces the corresponding @dataclass code live; nested objects become separate classes and are declared in dependency order.
What can JSON → Python Dataclass do?
Typical scenarios: Quickly generate request / response models when building a FastAPI service (migrate to Pydantic later); data analysts use dataclasses to improve readability when handling deeply nested JSON logs; batch-generate type…
Is my data private when I use JSON → Python Dataclass?
You can safely process sample payloads that contain personal information or API-key placeholders. You can safely process sample payloads that contain personal information or API-key placeholders.
What technical details should I know about JSON → Python Dataclass?
Class names are PascalCased while field names keep their original JSON spelling (switch to snake_case manually if needed). Default indentation is 4 spaces, matching PEP 8.