JSON to Python Converter
Generate typed Python models from a JSON payload — pick dataclasses, TypedDicts or Pydantic depending on what your project uses.
Your data stays on your device
Input — JSON
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- size
- 0 B
Output — Python
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Options
About this tool
The three common ways to model JSON in Python solve different problems. Dataclasses give you a plain object with a generated __init__. TypedDicts type a dictionary without changing its runtime form, which suits code that keeps working with dicts. Pydantic adds parsing and validation at the boundary.
All three are generated from the same recursive type inference, with nested objects extracted into their own models and referenced by name. Keys that are not valid Python identifiers get a field alias so the model still maps onto the original JSON.
How to use this tool
- Paste the JSON you want to model.
- Choose dataclass, TypedDict or Pydantic BaseModel.
- Set the root class name and choose whether to convert keys to snake_case.
- Press Generate and copy the code into your project.
Key features
- dataclass, TypedDict or Pydantic BaseModel output
- Optional[...] for nullable and inconsistently present fields
- Nested objects extracted into their own models, ordered so definitions precede use
- PEP 8 snake_case conversion with aliases preserving the original key
- from __future__ import annotations emitted where it helps forward references
- Correct int and float distinction, and list element type unification
Example
Input — JSON
[{"id":1,"name":"Ada"},{"id":2}]Output — Python
from __future__ import annotations
from dataclasses import dataclass
from typing import Optional
@dataclass
class Root:
id: int
name: Optional[str] = NoneGood to know
- Dataclasses and TypedDicts do not validate at runtime; only Pydantic parses and enforces types.
- Field ordering follows the JSON. Dataclasses require non-default fields first, so optional fields are moved to the end.
- Recursive structures are not detected; a self-referencing shape produces separately named models.
Frequently asked questions
Which output should I pick?
Pydantic when the data crosses a trust boundary and must be validated. Dataclasses for internal domain objects. TypedDict when your code already passes dictionaries around and you only want static checking.
How are keys like "user-id" handled?
They become user_id with an alias back to the original key, so serialisation still produces the correct JSON.
Why is from __future__ import annotations included?
It defers annotation evaluation, which lets models reference types defined later in the file and keeps the output valid on older Python versions.
Are int and float distinguished?
Yes. A JSON number without a fractional part is typed int; one with a decimal point or exponent is typed float.
Your data stays on your device
Your data is processed locally in your browser and is not uploaded to our server. This page keeps working after you go offline, because there is nothing to send.