JSON to Python

Python dataclasses are full classes with __init__, __repr__, __eq__, and other dunder methods generated automatically by the @dataclass decorator. They are ideal when you need instances with behavior, validation, or methods like from_dict/to_dict. TypedDict, on the other hand, is a typing construct that describes the shape of a dictionary. It does not create a new class at runtime \u2014 it is used purely for static type checking. Choose dataclass when you need real objects and TypedDict when you want to annotate existing dict data.

Each nested JSON object is extracted into its own named Python class. The class name is derived from the field key using PascalCase conversion. For example, a field called shipping_address produces a ShippingAddress class. If a name collision occurs (e.g. two fields that both normalize to the same name), a numeric suffix is appended automatically (Address, Address2, etc.). This works for arbitrarily deep nesting.

JSON null values are mapped to Optional[None] by default, which simplifies to Optional[Any] since the actual type cannot be inferred from null alone. When the Optional fields toggle is enabled, all fields across all classes are wrapped in Optional[T], making them explicitly nullable. This is useful for partial data or API responses where fields may be missing.

The converter inspects every element in a JSON array and collects all distinct Python types. If all elements share the same type, a simple List[T] annotation is produced (e.g. List[str]). If multiple types are found (e.g. strings and numbers mixed together), the field is typed as List[Any]. Empty arrays also default to List[Any] since the element type cannot be determined.

Yes. All type inference and code generation runs entirely in your browser using JavaScript. No data is sent to any server. You can verify this by checking the Network tab in your browser's developer tools while using the tool.

When generating dataclasses, the tool can optionally include from_dict (a @classmethod that constructs an instance from a plain dictionary) and to_dict (an instance method that serializes the dataclass back to a dictionary). These methods handle nested objects and lists recursively, making it easy to convert between JSON-compatible dicts and typed Python objects without external libraries.

Yes. The generated Python code follows PEP 8 conventions: class names use PascalCase, field names use snake_case (preserved from the JSON keys), imports are grouped at the top of the file, and proper spacing is maintained between class definitions. The code is ready to paste directly into your project.