JSON to Python Converter
Convert JSON to Python POJO classes for Python development
JSON Input
Python Output
What Is JSON to Python?
You've called an API, got a JSON response, and now you're accessing everything with response["data"]["user"]["email"] — no autocomplete, no type checking, and one typo away from a KeyError. json.loads() gives you a dict, which is fine for quick scripts. But for real application code, typed dataclasses or Pydantic models are much better. This tool generates Python classes directly from your JSON — paste it in, get structured Python output, and paste it into your project.
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, jq works well from the command line.
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
How do I convert JSON to a Python dataclass automatically?
Paste your JSON into this tool, enable the Dataclass option, and it generates a @dataclass class for each nested object. Copy it into your project — works with Python 3.7+.
What's the difference between a dataclass and a regular class for JSON?
Dataclasses (Python 3.7+) auto-generate __init__, __repr__, and __eq__ for you. Regular classes need manual __init__. For most API work, dataclasses are cleaner. For validation, consider Pydantic.
Is my data sent anywhere?
No. Everything runs in your browser. Your JSON never leaves your machine — safe to use with production API responses or any sensitive data.
Can I use this with Pydantic?
The output is standard Python. To use with Pydantic, change the base class to BaseModel and remove @dataclass. Pydantic also has a model_validate() method that handles parsing from a dict automatically.
What about snake_case vs camelCase?
JSON typically uses camelCase; Python convention is snake_case. The generator uses JSON keys as-is. For Pydantic, use model_config = ConfigDict(populate_by_name=True) and Field(alias="camelCaseKey") to handle the mapping.
Related Tools
For Python JSON handling, see the Python json module and Pydantic. For JSON, see JSON spec, RFC 8259, and MDN. See also jq and Postman.