Loading JSON to Python Dataclass Converter...
Loading JSON to Python Dataclass Converter...
Learn how to convert JSON into Python dataclasses step-by-step. Includes examples, manual methods, and a free online generator to save hours of work.
See how JSON transforms into Python dataclass:
{
"name": "John Doe",
"email": "john@example.com",
"age": 30,
"is_active": true
}@dataclass
class User:
name: str
email: str
age: int
is_active: boolJSON is the backbone of modern data exchange. REST APIs, webhooks, config files — they all use it. Here's what JSON looks like:
{
"name": "ToolsVIA",
"type": "developer-tools",
"users": 50000,
"features": ["converter", "generator", "validator"],
"is_free": true
}JSON gives you strings, numbers, booleans, arrays, objects, and null. Simple, but everywhere — from localStorage to massive distributed systems.
The catch? Python's json.loads() gives you a dictionary — no autocomplete, no type safety. That's where dataclasses come in.
Before Python 3.7, data containers meant boilerplate:
class User:
def __init__(self, name, email, age):
self.name = name
self.email = email
self.age = age
def __repr__(self):
return f"User(name={self.name}, email={self.email}, age={self.age})"Then dataclasses arrived:
from dataclasses import dataclass
@dataclass
class User:
name: str
email: str
age: intThat's it. Python generates __init__, __repr__, and __eq__ automatically. Plus you get IDE autocomplete for user.name, user.email, user.age.
But manual dataclasses get tedious with 20+ fields or nested objects. That's why I built this converter.
Here's a weather API response:
{
"location": { "city": "Austin", "coordinates": { "lat": 30.2672, "lng": -97.7431 } },
"current": { "temp": 72, "condition": "sunny", "humidity": 45 },
"forecast": [
{ "day": "Monday", "high": 75, "low": 60 },
{ "day": "Tuesday", "high": 78, "low": 62 }
]
}Manually, you'd write 4 dataclasses and ~30 lines of code. For 15 API endpoints? That's hours of tedious, error-prone work.
Great for validation and serialization. Perfect for FastAPI. But it's an external dependency with a learning curve.
The library that inspired dataclasses. More features, but still needs an external package.
Powerful schema definition, but verbose. Schemas are separate from your classes.
This is my go-to. Built-in, no external packages, gives you 80% of what you need. The only missing piece is code generation — which this tool provides.
import json
from dataclasses import dataclass
@dataclass
class User:
name: str
email: str
age: int
data = json.loads('{"name": "John", "email": "john@example.com", "age": 30}')
user = User(**data)Three steps — about 10 seconds
Paste Your JSON Copy your JSON into the editor, upload a file, or click an example.
Hit Convert Click Convert or press Ctrl+Enter (⌘+Enter on Mac).
Copy & Use Copy the code or download as .py. Import and use immediately.
Built from real-world Python development
When you should convert JSON to Python dataclasses
25 fields with 3 nested objects? Generated in 2 seconds. Manual? 15 minutes of error-prone work.
Ever misspelled a field name? Tools don't. Perfect field names every time.
Proper type annotations — str, int, float, bool, List, Dict. Your IDE will thank you.
API changed? Paste new JSON, re-convert, copy. No manual refactoring.
Not sure how to structure a dataclass? Generate one from JSON and see how it's done.
PEP 8 compliant, clean formatting, proper imports. Copy-paste and deploy.
Stop writing boilerplate code. Generate Python dataclasses instantly.
🚀 Go to JSON to Python Dataclass ConverterFree • No signup • Works in browser
Real questions from Python developers
Completely free. No signup, no credit card, no 'free trial'. Built by a developer for developers.
Yes — automatically generates separate dataclasses for each nested object, up to any depth.
Objects in arrays become List[ChildClass]. Primitives become List[str], List[int], etc.
Never. Everything runs in your browser. You could disconnect from the internet after loading.
Python 3.7+ for dataclasses. Python 3.6 is end-of-life — time to upgrade.
Yes — add methods, change types, add default values. Think of it as a starting point.
Currently all fields are required. Add = None and Optional[Type] after generation.
This generates built-in dataclasses, no dependencies. Use Pydantic for validation, dataclasses for lightweight containers.