JSON to Python Converter

Convert your JSON data to Python dataclass or TypedDict definitions with type hints, nested class support, and auto imports.

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What is a JSON to Python Converter?

Generate Python dataclasses or TypedDict definitions from a JSON sample. Pick the mode that fits your project: @dataclass for full classes, TypedDict for type-safe dictionaries.

Python dataclasses provide a concise way to create classes that primarily store data, with automatic __init__, __repr__, and __eq__ methods. TypedDict allows you to define dictionary types with specific key-value type pairs for static type checking.

Our converter automatically infers Python types (str, int, float, bool, List, Optional) and generates properly typed class definitions. Nested JSON objects create separate classes with correct references. All processing happens client-side.

How to Use Our JSON to Python Converter

Select your preferred output mode: @dataclass or TypedDict
Paste your JSON data into the left editor or load a file
The converter generates Python class definitions in real-time
Nested JSON objects are automatically converted into separate classes
Copy the generated Python code or download it as a .py file

Examples

API response to @dataclass model

Paste a sample payload from your REST endpoint and get a typed dataclass you can pass straight into your service layer.

Input

{
  "id": 42,
  "email": "alice@acme.io",
  "is_admin": false,
  "tags": ["beta", "internal"]
}

Output

from dataclasses import dataclass
from typing import List

@dataclass
class User:
    id: int
    email: str
    is_admin: bool
    tags: List[str]

Nested JSON with address block

Nested objects are lifted into their own dataclasses so the parent holds a typed reference.

Input

{
  "name": "Acme Corp",
  "address": {
    "city": "Seoul",
    "zip": "04524"
  }
}

Output

from dataclasses import dataclass

@dataclass
class Address:
    city: str
    zip: str

@dataclass
class Company:
    name: str
    address: Address

TypedDict mode for dict-shaped payloads

Pick TypedDict when the rest of your codebase already passes plain dicts around — you keep the interface and gain mypy type checking.

Input

{
  "event": "checkout.completed",
  "amount": 39.99,
  "currency": "USD"
}

Output

from typing import TypedDict

class CheckoutEvent(TypedDict):
    event: str
    amount: float
    currency: str

JSON specification reference

Quick reference from RFC 8259. The converter enforces these rules on input; anything outside them is rejected or normalized.

Element	Meaning	Example
Object	Unordered set of key/value pairs	{"a": 1}
Array	Ordered list of values	[1, 2, "x"]
String	Unicode, double-quoted only	"hello"
Number	IEEE 754 double, no NaN / Infinity	3.14
Literal	true / false / null only	null

Common invalid forms

{'a': 1}  // single quotes not allowed
{a: 1}    // unquoted keys
{"a": 1,} // trailing comma
NaN       // not permitted by RFC 8259

Key Features

Dual Mode: Generate @dataclass or TypedDict classes depending on your project needs and coding style
Type Hints: Full Python type annotations including str, int, float, bool, List, Dict, and Optional
Auto Imports: Automatically generates necessary import statements from dataclasses and typing modules
Nested Class Support: Nested JSON objects generate separate Python classes with proper dependency ordering
100% Client-Side: All code generation happens in your browser — your data never leaves your device

Frequently Asked Questions

Q: What is the difference between dataclass and TypedDict?

@dataclass creates full Python classes with auto-generated __init__, __repr__, and __eq__ methods. TypedDict defines dictionary types for static type checking with mypy, keeping the dict interface while adding type safety.

Q: What is the difference between dataclass and TypedDict output?

@dataclass creates full Python classes with auto-generated __init__, __repr__, and __eq__ methods that you instantiate as objects. TypedDict defines dictionary type annotations for static type checking with mypy while keeping the standard dict interface.

Q: How are null values handled in the output?

Null values in JSON are converted to Optional[Any] type in Python, correctly indicating that the field may contain None. This ensures proper type safety when working with nullable JSON fields.

Q: Which Python version is required?

The generated @dataclass code requires Python 3.7+ and TypedDict requires Python 3.8+. Both features are part of the Python standard library with no additional dependencies needed.

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JSON to Code: Generating Type-Safe Models in Any Language10 min read

JSON to Python Converter

Convert your JSON data to Python dataclass or TypedDict definitions with type hints, nested class support, and auto imports.

Loading editors...

Auto Sync

Automatically sync from left

Loading editors...

What is a JSON to Python Converter?

Generate Python dataclasses or TypedDict definitions from a JSON sample. Pick the mode that fits your project: @dataclass for full classes, TypedDict for type-safe dictionaries.

How to Use Our JSON to Python Converter

Select your preferred output mode: @dataclass or TypedDict
Paste your JSON data into the left editor or load a file
The converter generates Python class definitions in real-time
Nested JSON objects are automatically converted into separate classes
Copy the generated Python code or download it as a .py file

Examples

API response to @dataclass model

Paste a sample payload from your REST endpoint and get a typed dataclass you can pass straight into your service layer.

Input

{
  "id": 42,
  "email": "alice@acme.io",
  "is_admin": false,
  "tags": ["beta", "internal"]
}

Output

from dataclasses import dataclass
from typing import List

@dataclass
class User:
    id: int
    email: str
    is_admin: bool
    tags: List[str]

Nested JSON with address block

Nested objects are lifted into their own dataclasses so the parent holds a typed reference.

Input

{
  "name": "Acme Corp",
  "address": {
    "city": "Seoul",
    "zip": "04524"
  }
}

Output

from dataclasses import dataclass

@dataclass
class Address:
    city: str
    zip: str

@dataclass
class Company:
    name: str
    address: Address

TypedDict mode for dict-shaped payloads

Pick TypedDict when the rest of your codebase already passes plain dicts around — you keep the interface and gain mypy type checking.

Input

{
  "event": "checkout.completed",
  "amount": 39.99,
  "currency": "USD"
}

Output

from typing import TypedDict

class CheckoutEvent(TypedDict):
    event: str
    amount: float
    currency: str

JSON specification reference

Quick reference from RFC 8259. The converter enforces these rules on input; anything outside them is rejected or normalized.

Element	Meaning	Example
Object	Unordered set of key/value pairs	{"a": 1}
Array	Ordered list of values	[1, 2, "x"]
String	Unicode, double-quoted only	"hello"
Number	IEEE 754 double, no NaN / Infinity	3.14
Literal	true / false / null only	null

Common invalid forms

{'a': 1}  // single quotes not allowed
{a: 1}    // unquoted keys
{"a": 1,} // trailing comma
NaN       // not permitted by RFC 8259

Key Features

Dual Mode: Generate @dataclass or TypedDict classes depending on your project needs and coding style
Type Hints: Full Python type annotations including str, int, float, bool, List, Dict, and Optional
Auto Imports: Automatically generates necessary import statements from dataclasses and typing modules
Nested Class Support: Nested JSON objects generate separate Python classes with proper dependency ordering
100% Client-Side: All code generation happens in your browser — your data never leaves your device

Frequently Asked Questions

Q: What is the difference between dataclass and TypedDict?

Q: What is the difference between dataclass and TypedDict output?

Q: How are null values handled in the output?

Null values in JSON are converted to Optional[Any] type in Python, correctly indicating that the field may contain None. This ensures proper type safety when working with nullable JSON fields.

Q: Which Python version is required?

The generated @dataclass code requires Python 3.7+ and TypedDict requires Python 3.8+. Both features are part of the Python standard library with no additional dependencies needed.