JSON to Python Converter
Generate Python dataclasses from a JSON sample — typed fields, nested classes, Optional detection, snake_case naming.
dataclasses instead of dict juggling
response["items"][0]["price"] works until a typo silently returns KeyError in production. Generating dataclasses from a JSON sample gives you attribute access (item.price), IDE autocomplete, and type hints that mypy and pyright can actually check. Each nested object becomes its own @dataclass; lists become List[Item]; values that appear as null become Optional[…].
Pythonic names, JSON reality
JSON APIs love camelCase, Python wants snake_case — the generator renames fields accordingly (lastLogin → last_login). Keep that mapping in mind when you deserialize: pair the classes with a loader that translates keys, or adjust the generated names where you prefer a 1:1 match to the wire format. The class structure itself — which is the tedious part — is done for you.
Where this fits in a Python workflow
Typical uses: typing the response layer of an API client, giving structure to JSON configs, or turning an exploratory notebook into maintainable code. If you validate at runtime (pydantic), the generated dataclasses translate nearly 1:1 into pydantic models — the field names and types are the work; swapping the decorator is trivial. Check your sample first in the JSON Validator if generation reports a syntax error.
Frequently asked questions
Which Python version does the output target?
Modern Python 3 with standard-library dataclasses and typing (3.7+). No third-party imports are required to use the generated classes.
Can I get pydantic models instead of dataclasses?
The generator emits stdlib dataclasses — the least opinionated form. Converting to pydantic is usually a mechanical swap of the decorator/base class, since the field names and type hints (the real work) carry over unchanged.
Why did my camelCase keys become snake_case attributes?
PEP 8 naming — Python code with camelCase attributes fails most linters. Remember to map keys when loading raw JSON into the classes, or rename fields back where you want wire-format fidelity.
How is null handled?
A null in the sample makes the field Optional[...] with the inferred inner type where possible. If a field is ALWAYS null in your sample, the generator cannot know the real type — feed it a richer sample.