Developer tools
JSON to Python Dataclass
Generate Python dataclasses with type annotations from JSON. Nothing you enter leaves your browser.
Your result will appear here.
What this tool does
About the JSON to Python Dataclass
This converter transforms supported JSON input into python dataclass. Review the supported scope below, especially when the target format cannot represent all source features.
It is intended for development and debugging workflows where a small, inspectable transformation is faster than setting up a script.
How to use it
- Paste JSON in the input pane. Use the example button if you want to see the expected syntax.
- Review the input for the structure shown in the example. Then select “Generate Python.”
- Inspect the result for warnings or structural differences, then copy it into the destination system.
Practical example
Start with a known input
Try this sample input:
{"name":"Avery","active":true,"roles":["admin"]}
Select “Generate Python” and compare the result with the source before using it elsewhere. What to know about the result
- Numbers that cannot be represented safely are rejected. Keep large IDs and high-precision decimals in quotes.
- Types are inferred from this sample, not a complete schema. Nested values may use generic types. Check generated names and validate against your target compiler or schema.
- JSON preserves nested objects, arrays, numbers, booleans, and null values, but it requires quoted property names and does not allow comments.
- Processing happens in this browser. Your entered text and selected files are not sent to FormatCalc for conversion.
Common questions
Using the JSON to Python Dataclass
Is the JSON to Python Dataclass free to use?
Yes. FormatCalc's JSON to Python Dataclass is free to use with no account required.
Is my data private when I use the JSON to Python Dataclass?
Yes. This tool processes your input locally in your browser. FormatCalc does not upload or store what you enter.
How do I use the JSON to Python Dataclass?
Paste or type your input, select any available options, and choose Generate Python. You can then copy the result.
What should I check before using the result?
Numbers that cannot be represented safely are rejected. Keep large IDs and high-precision decimals in quotes. Types are inferred from this sample, not a complete schema. Nested values may use generic types. Check generated names and validate against your target compiler or schema. JSON preserves nested objects, arrays, numbers, booleans, and null values, but it requires quoted property names and does not allow comments. Processing happens in this browser. Your entered text and selected files are not sent to FormatCalc for conversion.