JSON to Python Converter
Convert JSON to Python POJO classes for Python development
JSON Input
Python Output
What Is JSON to Python?
Python uses dict and custom classes for structured data. When you parse JSON with json.loads(), you get dicts. That works for dynamic access, but for typed access, IDE support, and validation you often want dataclasses or regular classes. The JSON specification defines objects, arrays, strings, numbers, and booleans—but Python needs explicit class definitions for each nested structure.
This tool generates Python class definitions from your JSON. Toggle Dataclass, Typing, and Nullable in the config panel. Dataclasses (Python 3.7+) give you __init__, __repr__, and equality for free. Use the output with json.loads() and manual mapping, or adapt it for Pydantic validation.
Conversion runs entirely in your browser. Your JSON is never sent to a server. You can confirm this by opening your browser's Network tab while using the tool.
How to Use This Tool
Paste or Upload JSON
Copy your JSON and paste it into the left editor. You can also click Upload to load a .json or .txt file from your computer. Use the Sample button to load example data. In the config panel, set the class name (e.g. User) and options: Dataclass for @dataclass, Typing for List[Type] annotations, Nullable for Optional[T].
Review the Generated Classes
The right panel shows the generated Python code. Nested objects become nested classes. Arrays get List[Type] from the typing module. Dataclasses use @dataclass; otherwise you get regular classes with __init__. If your JSON has invalid syntax, fix it first using the JSON Formatter or JSON Validator.
Copy or Download
Use Copy to put the result on your clipboard, or Download to save it as a .py file. Paste into your project. For dynamic typing, json.loads() is enough. For validation and parsing, consider Pydantic.
JSON to Python Examples
Here is an example of generating Python dataclasses from a JSON object.
Example: Subscriber record
JSON input:
Generated Python output:
When JSON to Python Helps
Most developers need this when integrating with REST APIs. You send a request through Postman, requests, or httpx, and the response comes back as JSON. Pasting it here gives you typed classes you can use for structured access. For pulling out specific values from large responses, the JSON Path tool works well alongside this.
Config files, webhook payloads, or event streams are often JSON. Running them through here helps you understand the structure and generate Python classes for type hints, validation, or data processing pipelines.
If you're building a FastAPI or Flask app that consumes external APIs, having typed models improves code quality and catches errors early. The generated classes can be adapted for Pydantic with minimal changes.
Frequently Asked Questions
Dataclass vs regular class?
Dataclasses (Python 3.7+) give you __init__, __repr__, __eq__, and more for free. Regular classes need manual __init__. Both work with JSON parsing. Dataclasses are preferred for modern Python.
What about Optional types?
Enable Nullable Types to get Optional[T] or T | None for fields that might be missing or null. The union syntax T | None requires Python 3.10+.
Is my data sent anywhere?
No. Generation runs entirely in your browser using JavaScript. No data is sent to any server. You can confirm this by opening your browser's Network tab while using the tool.
Can I use this with Pydantic?
The output is standard Python. You can adapt it for Pydantic by changing the base class to BaseModel and adding validators. Pydantic also has its own schema generation from JSON.
What about snake_case vs camelCase?
JSON often uses camelCase; Python prefers snake_case. The generator uses the JSON keys as-is. Use Field(alias="camelCase") in Pydantic or a custom decoder for mapping.