How do I convert JSON to Python dataclass?

Open the JSON to Python converter at jsonwebtools.com, paste your JSON, select the output type (dataclass, TypedDict, or Pydantic), and click Convert. The tool generates Python code with proper type annotations for all fields.

What is the difference between Python dataclass and TypedDict for JSON?

TypedDict creates a type-annotated dictionary subclass — the data is still a plain dict at runtime, which is easy to serialize back to JSON. Dataclass creates a proper Python class with attributes, better IDE support, and methods. Use TypedDict when interoperating with JSON frequently; use dataclass when you need methods and immutability.

What is Pydantic and why use it for JSON?

Pydantic is a Python library for data validation using type annotations. It automatically validates JSON data on parse, provides clear error messages for invalid data, and has built-in JSON serialization. It is the standard for FastAPI request/response models.

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Three Output Formats

Given this JSON:

{
  "id": 101,
  "name": "Alice Johnson",
  "email": "alice@example.com",
  "active": true,
  "score": 9.5
}

1. Python Dataclass

from dataclasses import dataclass

@dataclass
class Root:
    id: int
    name: str
    email: str
    active: bool
    score: float

2. TypedDict

from typing import TypedDict

class Root(TypedDict):
    id: int
    name: str
    email: str
    active: bool
    score: float

3. Pydantic Model

from pydantic import BaseModel

class Root(BaseModel):
    id: int
    name: str
    email: str
    active: bool
    score: float

# Usage:
user = Root.model_validate(json_dict)
print(user.name)  # "Alice Johnson"
print(user.model_dump_json())  # Back to JSON

JSON Type to Python Type Mapping

JSON Type	Python Type
`string`	`str`
integer (`42`)	`int`
float (`3.14`)	`float`
`boolean`	`bool`
`null`	`None` / `Optional[T]`
array of strings	`list[str]`
array of objects	`list[ChildClass]`
object	Nested class
mixed array	`list[str \| int]` (union)

Parse JSON into a Python Dataclass

import json
from dataclasses import dataclass

@dataclass
class User:
    id: int
    name: str
    email: str
    active: bool

def from_json(json_str: str) -> User:
    data = json.loads(json_str)
    return User(**data)

user = from_json('{"id":1,"name":"Alice","email":"a@b.com","active":true}')
print(user.name)  # Alice

Parse JSON with Pydantic (Validation)

from pydantic import BaseModel, EmailStr, ValidationError

class User(BaseModel):
    id: int
    name: str
    email: EmailStr
    active: bool = True  # default value

# Valid JSON
user = User.model_validate_json('{"id":1,"name":"Alice","email":"alice@example.com"}')
print(user.id)    # 1
print(user.email) # alice@example.com

# Invalid JSON — Pydantic raises ValidationError with clear messages
try:
    User.model_validate_json('{"id":"not-a-number","name":"Alice"}')
except ValidationError as e:
    print(e)  # id: Input should be a valid integer

Which Format Should I Use?

Format	Best For
TypedDict	Type checking only, stays as dict, easiest JSON round-trip
Dataclass	Structured data with methods, no external dependencies
Pydantic	API validation, FastAPI, automatic JSON serialization, strict typing
Plain dict	Quick scripts, prototyping, when structure doesn't matter

Frequently Asked Questions

How do I convert JSON to Python?

Paste your JSON into the JSON to Python converter and click Convert. The tool generates Python dataclasses or typed dictionaries with correct type annotations matching your JSON structure. Copy the output directly into your Python project.

What Python type does JSON null map to?

JSON null maps to Python None. In type annotations, a nullable field is typed as Optional[str] (or str | None in Python 3.10+). Fields that are always null in the sample data are typed as Optional[Any].

Should I use dataclass or TypedDict for JSON data?

Use dataclass when you want an actual class with methods and a constructor. Use TypedDict when you only need type annotations for dicts (no instance creation, useful with mypy). For simple API response typing, TypedDict is lighter-weight.

How do I parse JSON in Python?

Use the built-in json module: json.loads(json_string) for strings, or json.load(file_obj) for files. For type-safe validation and parsing, use Pydantic: from pydantic import BaseModel; user = User.model_validate_json(json_str).

Does the converter handle nested JSON objects?

Yes. Each nested object generates a separate Python dataclass or TypedDict. The parent class references the child class by name. For example, a user with an address field generates both a User dataclass and an Address dataclass.

How do I serialize a Python object back to JSON?

For regular dicts, use json.dumps(data). For dataclasses, use json.dumps(dataclasses.asdict(obj)). For Pydantic models, use model.model_dump_json(). For custom objects, pass a custom default serializer function to json.dumps().

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