Dataclass & TypedDictNested Support100% Private
from dataclasses import dataclass

@dataclass
class Root:
    id: int
    name: str
    email: str
    active: bool
    score: float
    address: Address
    tags: list[str]
    orders: list[OrdersItem]

@dataclass
class OrdersItem:
    id: int
    total: float

@dataclass
class Address:
    street: str
    city: str

FAQ

What output formats are supported?
Dataclass (with @dataclass decorator) and TypedDict for static type checking.
Are nested objects handled?
Yes. Nested objects generate separate class definitions with proper type references.
Does it use snake_case?
Yes. CamelCase and kebab-case JSON keys are automatically converted to Python snake_case.
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About JSON to Python

What this tool does

Data converter tools transform structured data between JSON, CSV, YAML, XML, TOML, SQL, TypeScript interfaces, Go structs, and more. They handle nested objects, arrays, and type inference automatically.

Why use this tool

APIs return JSON, spreadsheets export CSV, infrastructure config uses YAML, and legacy systems speak XML. Being able to convert between these formats without writing a custom script saves hours of tedious data wrangling.

How it works

The tool parses your input format into an in-memory object tree, then serializes that tree into the target format. Type information (string, number, boolean) is inferred from values and mapped to the closest equivalent in the output format.

Pro tip

When converting CSV to JSON, the first row is treated as column headers by default. If your CSV has no headers, toggle that option off to get array-of-arrays output instead.

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