Why @dataclass instead of pydantic?

Standard library — no extra dependency. If you want pydantic, swap @dataclass for class Foo(BaseModel) at the top; the field syntax matches.

How are nested objects handled?

Each unique object shape becomes its own @dataclass. Names are inferred from property names (singularized for arrays of objects).

What about typing imports?

The output starts with `from typing import Any, List, Union` — covering arrays, mixed-type unions, and unknowns. Trim what you don't need.

Does it preserve key names exactly?

Field names match the JSON keys verbatim. If a key isn't a valid Python identifier, you'll need to rename it manually (or switch to pydantic with Field(alias=)). Most JSON keys ARE valid Python identifiers.

What does mixed-array typing look like?

List[Union[str, int]] — Python's typing system covers it cleanly.

Why is the root class called Root?

A safe placeholder you'll find-and-replace with your domain class name (User, ApiResponse, Config, etc.).

Is the JSON sent to a server?

No. Conversion runs entirely in your browser.

Can I generate Pydantic models instead?

Not directly — but the @dataclass output is structurally identical to a Pydantic model. Swap the @dataclass decorator for `class Foo(BaseModel):` and you're 90% of the way there.

JSON to Python Dataclass — Free Online

Generate Python @dataclass definitions from any JSON sample. Includes typing imports, recursive types, mixed-type unions.

Generate Python @dataclass definitions from any JSON sample. Each unique object shape becomes its own dataclass; arrays infer element types; the typing imports are added automatically. Drop the result into a .py file with no extra dependencies — uses only the standard library.

Need TypeScript instead? JSON to TypeScript. Go? JSON to Go. Rust? JSON to Rust.

JSON

Python @dataclass

How to Generate Python Dataclasses from JSON

Paste a JSON sample
An API response, mock fixture, anything that represents the data shape.
Click Convert to Python
PDFFlare emits @dataclass definitions for every nested object, with proper typing imports at the top.
Drop into your project
Paste into a .py file, rename Root to your domain class, run.

When Do You Need JSON to Python?

Adding type hints to a script: You started with dicts; now you want types. Convert a sample of your data and replace dict[str, Any] with proper dataclasses.

FastAPI / Flask request models: Generate dataclass templates fast, then convert to Pydantic by changing the decorator and base class.

Data pipelines: Pandas-adjacent ETL benefits from typed records. Convert your input JSON to dataclasses for safer transformations.

Test fixtures: Want typed fixtures instead of dicts? Generate dataclasses, swap dict literals for Foo(...) constructors.

Why Use PDFFlare for JSON to Python?

Standard Library Only

@dataclass is built-in — no Pydantic, no marshmallow needed.

Smart Typing

List[T], Union[A, B], Any — proper typing everywhere, ready for mypy / pyright.

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Your data stays on your device.

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