JSON to Python

This tool takes a JSON object or array and generates Python code representing the same data structure. Depending on the output mode you select, it produces one of three things: a plain Python dictionary literal, a Python dataclass with type annotations, or a Pydantic BaseModel with field definitions and aliases. For plain dict output, the tool produces a Python dict literal that mirrors your JSON structure exactly. Nested objects become nested dicts. Arrays become Python lists. JSON strings become Python strings. JSON numbers become Python int or float. JSON booleans become Python True or False (with the capitalisation difference from JSON's true and false handled automatically). JSON null becomes Python None. The result is a Python expression you can assign to a variable and use immediately. For dataclass output, the tool generates a @dataclass class for every nested object level in your JSON. Each field is annotated with its inferred Python type: str for string fields, int for integer fields, float for decimal fields, bool for boolean fields, None-able fields as Optional[str], Optional[int], and so on. Lists of primitives become List[str], List[int], or List[Any] depending on the element type consistency across your sample. Nested objects generate their own dataclass definition, and the parent class references them by name. The output imports dataclasses.dataclass and dataclasses.field, plus typing.Optional, typing.List, and typing.Any as needed. For JSON keys that use camelCase — the JavaScript convention that is standard in REST API responses — the generator produces snake_case field names following Python's PEP 8 naming convention, with a field(default=...) alias that maps the camelCase JSON key to the snake_case Python attribute. This means you deserialise from camelCase JSON using the alias and work with snake_case in all your Python code. For Pydantic output, the generated class inherits from pydantic.BaseModel rather than using the @dataclass decorator. Field types are the same, but the model gains Pydantic's runtime validation, .model_validate() for deserialisation from a dict or JSON string, .model_dump() for serialisation back to a dict, and model_config with populate_by_name and alias generator support for camelCase API compatibility. The output is ready to use as a request or response model in a FastAPI application or any Python service that uses Pydantic for data validation.

Every Python developer who works with APIs has written the same code dozens of times. You get a JSON response, you call json.loads(), you start accessing keys with data["user"]["profile"]["email"], and everything works fine until the API changes a field name, adds a nested level, or starts returning null for a field you assumed was always present. The bug is silent until it hits production. The pattern I have seen most often in Python codebases that started without typed models is what I call the dict chain: deeply nested key access spread across dozens of files, no central definition of what the JSON structure looks like, and no way to know which parts of the codebase break when the API schema changes. Refactoring out of this pattern after the fact is painful. Starting with a typed model from the first API call costs almost nothing — especially when a tool generates it for you. Python dataclasses are the right tool for local or script-level work where you want type checking and editor support but do not need runtime validation. They are zero-dependency (standard library since Python 3.7), lightweight, and integrate directly with mypy for static analysis. For a CLI tool, a data pipeline script, or a batch processing job that consumes a JSON API, a dataclass representation of the response structure is strictly better than a plain dict — same performance, dramatically better maintainability. Pydantic is the right tool for application-level work, particularly in FastAPI and anywhere you are accepting JSON input from external sources. Pydantic validates field types at instantiation time and raises ValidationError with clear field-level messages when the incoming data does not match. This is the difference between a cryptic KeyError at an unexpected place in your application and a structured error at the boundary where the data enters your system. FastAPI uses Pydantic models as its entire request and response schema system — if you are writing FastAPI endpoints, you are already using Pydantic whether you know it or not. The camelCase to snake_case conversion is one of those small things that saves a disproportionate amount of friction. REST APIs almost universally use camelCase because JSON comes from JavaScript. Python code almost universally uses snake_case because PEP 8 requires it. Without aliasing, you either violate PEP 8 by using camelCase Python attributes, or you write manual mapping code in every deserialisation call. A generated model with proper aliases handles this at the class definition level — once, correctly, with no repeated mapping logic throughout your codebase. The browser-based, no-install design has practical value beyond convenience. When you are pair-programming, reviewing a PR, or debugging an API response on a shared screen, reaching for this tool is faster than opening a Python REPL, importing json and dataclasses, and manually writing out type annotations. Paste, generate, review the structure — the whole thing takes fifteen seconds.