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JSON to Python

Generate Python classes/structs automatically from a JSON object.

100% Client-Side Local Execution
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What is JSON to Python?

This free online tool turns a sample JSON payload into a ready-to-use dataclass for Python, so you can use this json to python dataclass tool to go straight from raw JSON to a typed dataclass (or Pydantic model) without writing the boilerplate yourself. It is built around Python's actual idiom — a dataclass — rather than a generic one-size-fits-all class, and pairs naturally with the standard library's dataclasses module or Pydantic for real-world (de)serialization. Use it for json to python class online or any time you need json2dataclass, whether you're scaffolding a new Python project or mapping an existing API response.

Technical Execution

Paste or upload a sample JSON object or array and the tool walks its structure field by field, inferring a concrete Python type for each value — mapping basic JSON types to `str`, `int`, `bool`, and `float` as appropriate. For nested data, it generates one dataclass (or `BaseModel` subclass, if you choose the Pydantic output mode) per nested object, and types arrays as `List[T]` using Python's `typing` module. On the naming side, it keeps snake_case keys as-is since that already matches PEP 8 field naming, and only adds an alias (via `field(metadata=...)` or Pydantic's `Field(alias=...)`) when a key uses camelCase or contains characters that aren't valid Python identifiers. For optional data, fields seen as `null`, or absent in some sample objects, are typed as `Optional[str]` (or the inferred type) with a `None` default, matching how both dataclasses and Pydantic express optional fields.

Zero Data Storage Guarantee

Everything runs client-side in your browser: the sample JSON you paste in — which may contain real field names, endpoint shapes, or structure from a private API or project — is parsed and converted entirely on your device and is never uploaded to any server. Nothing you enter is logged, stored, or transmitted anywhere, so you can safely paste a real payload from an internal or unreleased API to generate an accurate dataclass. Closing or refreshing the tab clears everything, since no copy of your data ever left your machine in the first place.

How to Use JSON to Python (Step-by-Step Guide)

  1. Copy a representative sample of your JSON (the more fields and nested objects it includes, the more complete the generated dataclass (or Pydantic model) will be).
  2. Paste it into the input panel of the JSON to Python tool, or click "Upload" to load it from a json file.
  3. Review the auto-detected root name, and rename it if you want the generated dataclass to use a specific class or type name instead of the default.
  4. Click "Generate" to produce the Python dataclass (or Pydantic model), which appears instantly in the output panel with syntax highlighting.
  5. Click "Copy" to copy the generated code to your clipboard, or "Download" to save it as a `.py` file.
  6. Paste the result into your Python project and adjust field names or types if your data has edge cases the sample didn't cover.

Frequently Asked Questions (FAQ)

Q: How does this tool handle nested objects and arrays when converting JSON to Python?
A: The tool generates one dataclass (or `BaseModel` subclass, if you choose the Pydantic output mode) per nested object, and types arrays as `List[T]` using Python's `typing` module That keeps the generated code organized the way a Python developer would structure it by hand, instead of flattening everything into one giant dataclass (or Pydantic model).
Q: What happens to fields that are nullable or missing from some records?
A: For optional data, fields seen as `null`, or absent in some sample objects, are typed as `Optional[str]` (or the inferred type) with a `None` default, matching how both dataclasses and Pydantic express optional fields. If your sample only has one example object, fields that could be null in other records might not be detected as optional — paste a few varied samples (or an array of objects) for the most accurate result.
Q: Is this better than writing the dataclass (or Pydantic model) by hand?
A: For anything beyond a trivial, flat payload, yes — hand-rolling a dataclass for a nested JSON payload means manually working out every field's type, remembering `Optional` for anything nullable, and keeping the whole tree in sync every time the API response shape changes. The generator produces the same result in seconds and gives you a correct starting point to refine, rather than a blank page.
Q: Is my JSON data safe to paste into this tool, especially if it's from a private API?
A: Yes. Everything runs client-side in your browser: the sample JSON you paste in — which may contain real field names, endpoint shapes, or structure from a private API or project — is parsed and converted entirely on your device and is never uploaded to any server. Nothing you enter is logged, stored, or transmitted anywhere, so you can safely paste a real payload from an internal or unreleased API to generate an accurate dataclass. Closing or refreshing the tab clears everything, since no copy of your data ever left your machine in the first place.
Q: Can I use this as a json to pydantic model?
A: Yes — the tool is commonly used as a json to pydantic model, and works the same way whether you're converting a one-off response for a prototype or generating model code for a production project.
Q: Does the tool rename fields to match Python naming conventions?
A: Yes. It keeps snake_case keys as-is since that already matches PEP 8 field naming, and only adds an alias (via `field(metadata=...)` or Pydantic's `Field(alias=...)`) when a key uses camelCase or contains characters that aren't valid Python identifiers, so the generated code reads naturally in Python while still deserializing the original JSON correctly.
Q: What Python library does the generated code work with?
A: The output is written to pair cleanly with the standard library's dataclasses module or Pydantic, the most common choice for Python developers handling JSON, so you can drop it into an existing project without changing your serialization setup.
Q: Does this tool work for deeply nested or large JSON samples?
A: Yes. The tool recurses through every level of nesting it finds and generates a complete set of linked dataclass (or Pydantic model) definitions, not just the top level — though for very large samples, trimming it down to one representative record per array is usually enough to get an accurate result.
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