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.
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.
100% Browser-Based
Your data stays on your device.
No Signup Required
No account, no rate limits.
Frequently Asked Questions About JSON to Python
Standard library — no extra dependency. If you want pydantic, swap @dataclass for class Foo(BaseModel) at the top; the field syntax matches.
Each unique object shape becomes its own @dataclass. Names are inferred from property names (singularized for arrays of objects).
The output starts with `from typing import Any, List, Union` — covering arrays, mixed-type unions, and unknowns. Trim what you don't need.
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.
List[Union[str, int]] — Python's typing system covers it cleanly.
A safe placeholder you'll find-and-replace with your domain class name (User, ApiResponse, Config, etc.).
No. Conversion runs entirely in your browser.
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.