What is JSON to Python Dataclass & Pydantic Generator?
This tool converts JSON data into Python type definitions — dataclasses, Pydantic BaseModel, or TypedDict. It automatically infers types from values, converts camelCase to snake_case, detects ISO dates, and generates ready-to-use Python code.
How to Use
- Choose your output format: dataclass, Pydantic, or TypedDict
- Set the root class name and configure options (snake_case, Optional nulls, date detection, Python syntax version)
- Paste your JSON data into the input area
- Click "Generate Python Code" and copy the output into your project
Why Use This Tool?
Tips & Best Practices
- Use realistic sample data — type inference depends on actual values
- Enable snake_case conversion for idiomatic Python field names
- Pydantic models include Field(alias=...) so JSON deserialization works with original camelCase keys
- Modern syntax uses built-in generics (list[str]) while legacy uses typing module (List[str])
Frequently Asked Questions
Should I use dataclass or Pydantic?
Use dataclasses for simple internal data structures with no validation needs. Use Pydantic when you need data validation, serialization, or are building FastAPI applications. Pydantic provides automatic type coercion, validation errors, and JSON schema generation.
How are JSON types mapped to Python?
JSON strings → str, integers → int, floats → float, booleans → bool, null → Optional[T] or None, arrays → List[T], objects → separate nested classes. ISO-8601 date strings can be detected as datetime when the option is enabled.
How are nested objects handled?
Nested JSON objects are converted to separate Python classes. They are ordered children-first, so nested classes appear before the parent class that references them, ensuring valid Python code.
Is my data sent to a server?
No, all processing happens entirely in your browser. Your JSON data never leaves your device.