What is a JSON to Python converter?
Hand-writing dataclasses for an API response is slow and error-prone. This tool infers Python dataclasses from a JSON sample: nested objects become named types, arrays become typed collections, and null/missing values become optional fields. It runs quicktype's inference engine entirely in your browser, so real API payloads (often containing user data or credentials) never leave your machine.
Python mapping notes
- Field names convert to snake_case.
- Missing/null fields become
Optional[...]. - JSON numbers map to
intorfloatbased on the sample values.
How to use
- Paste a JSON sample (an API response works well) into the Input pane.
- Generated Python dataclasses appear instantly. Nested objects become their own named types.
- Non-standard JSON (single quotes, trailing commas, comments) is auto-repaired first.
- Copy the code into your project.
Examples
JSON → Python dataclasses
{
"id": 42,
"name": "workbench",
"owner": { "email": "[email protected]", "active": true }
}@dataclass
class Owner:
email: str
active: bool
@dataclass
class Root:
id: int
name: str
owner: OwnerFAQ
How do I convert JSON to Python types?
Paste any JSON sample. The dataclasses are inferred from the values and nesting, entirely in your browser. Nothing is uploaded.
How are nested objects handled?
Each distinct nested object becomes its own named type, referenced from the parent, arrays of objects included.
What about optional or null fields?
Fields that are null or missing in parts of the sample are typed as optional/nullable in the generated code.
Can I paste multiple samples?
Paste an array of objects: the type is inferred from the union of all items, which catches optional fields a single sample would miss.
Does this work with Pydantic?
The generated dataclasses translate to Pydantic models almost 1:1. Change the decorator/base class and the field types carry over.
Is my JSON uploaded?
No. Generation runs 100% in your browser via quicktype's engine. Check DevTools: zero network requests.