Free JSON to Python — dict · dataclass · Pydantic v2 · TypedDict

Convert JSON to Python
dict, dataclass,
Pydantic or TypedDict.

Generate Python dataclasses, Pydantic v2 models, TypedDicts and json.loads() code from any JSON sample. Optional fields, List[T] arrays, nested classes. Runs entirely in your browser.

Your data never leaves your browser
dict · dataclass · Pydantic v2 · TypedDict
Nested objects → separate classes
Optional fields from null values
Always free
JSON to Python Generator   100% client-side
Use a representative JSON sample with non-null values. Null fields become Optional[Any] — refine manually after generating.
JSON input
  Python ready 

      

 
    

  

 
  
  
Dict vs dataclass vs Pydantic v2 vs TypedDict

  
Four Python patterns — choose based on your use case and Python version.

  

    
StylePythonRuntime validationBest for

    
      

        Dict
        Any
        None
        Quick scripts, prototypes. json.loads() returns a dict — access with data["key"]. Zero dependencies.
      

      

        dataclass
        3.7+
        None (type hints only)
        Structured data containers in production code. Works with asdict() for re-serialization. Built-in, no pip.
      

      

        Pydantic v2
        3.8+
        Yes — fast (Rust core)
        APIs, config parsing, CLI tools. Validates types at runtime, generates JSON Schema, OpenAPI compatible.
      

      

        TypedDict
        3.8+
        None (static only)
        Adding types to dict-based code without adding dependencies. Works with mypy, pyright, Pylance.
      

    
  

 
  

 
  
  
How JSON types map to Python types

  

    
JSON valuePython typeNotes

    
      
"Alice"strAll JSON strings become str.

      
42intJSON integers become int.

      
98.5floatJSON floats become float.

      
trueboolDirect mapping.

      
nullOptional[Any]Null → Optional[Any]. Refine the type manually after generating.

      
["a","b"]List[str]Item type inferred from first element. Empty → List[Any].

      
[{…},{…}]List[ChildClass]Array of objects → separate class + typed List.

      
{…}ChildClassNested object → separate named class in PascalCase.

    
  

 
  

 
  
  
When do you need JSON to Python?

  

    

      
 FastAPI and Pydantic v2

      
FastAPI request bodies and response models require Pydantic BaseModel classes. Generate the model from a real API payload JSON sample — then use model_validate_json() to deserialize incoming requests. Pydantic v2 validates types, raises clear errors and generates OpenAPI schema automatically.

    

    

      
 Third-party API consumption

      
When consuming a REST API in Python, json.loads() returns a plain dict — accessing nested fields with string keys is error-prone. Generate a dataclass or TypedDict from a real response sample to get IDE autocomplete and static type checking across the response structure.

    

    

      
 Config file parsing

      
Configuration files in JSON format (pyproject.json, .vscode/settings.json, API config files) need typed Python representations for safe access. Generate a Pydantic model for runtime validation — any misconfigured value raises a clear ValidationError with the field name and expected type.

    

    

      
 Type-safe API testing

      
pytest tests that call API endpoints need typed response objects for assertion. Generate a TypedDict from the expected response structure — then use cast() to tell mypy the json.loads() result has the right shape, getting autocomplete in tests without a runtime dependency.

    

  

 
  

 
  
  

    
      

      
Python → JSON

      
Reverse — mock JSON from a class, json.dumps() snippets, FastAPI and Django patterns.

      
9,540 searches/month

    
    
      

      
JSON → TypeScript

      
Generate TypeScript interfaces for the front-end consuming the same API.

      
14,470 searches/month

    
    
      

      
JSON → JSON Schema

      
Generate a Draft-07 schema — Pydantic v2 can load it with model_validate().

      
7,510 searches/month

    
  

 
  
  

    
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Python generated in
your browser. No upload.

      
The entire Python code generation runs in JavaScript locally. Your JSON is parsed and traversed entirely in your browser — it is never transmitted to any server. The generated code follows Python conventions: snake_case field names, correct Optional syntax, and proper import statements for each style.

      
The Pydantic v2 output uses the new API — model_validate_json(), model_dump_json(), BaseModel with model_config — not the deprecated Pydantic v1 methods (.json(), .dict()) that most generators still produce.

    

    

      

        
4 output styles

        
Dict, dataclass, Pydantic v2 and TypedDict — covering every Python version from 3.7 to 3.12 and every use case from quick scripts to production APIs.

      

      

        
Pydantic v2 — not v1

        
model_validate_json() and model_dump_json() — the current Pydantic v2 API with Rust-based validation, not the deprecated v1 methods most tools still generate.

      

      

        
Nested classes

        
Each nested JSON object generates a separate Python class — no Dict[str, Any] for nested data. Fully typed from root to leaf.

      

      

        
47 tools, always free

        
No file size limits, no watermarks, no account. Funded by non-intrusive display advertising only.

      

    

  

 
  
  

    
Frequently asked questions

    
Common questions about converting JSON to Python.

 
    

      
How do I convert JSON to a Python dict?

      
Use json.loads(): import json; data = json.loads(json_string). For a file: with open('file.json') as f: data = json.load(f). Select the Dict output style for complete code with type hints and error handling. The json module is part of Python's standard library — no pip install needed.

    

    

      
What is the difference between dataclass and Pydantic?

      
dataclasses are built into Python (3.7+), mutable by default, and have no runtime validation — type hints are for IDE and mypy only. Pydantic v2 validates data at runtime, raises clear errors with field names, is significantly faster than v1 (Rust core), and generates JSON Schema automatically. Use dataclass for internal data containers; use Pydantic for API boundaries and config parsing.

    

    

      
When should I use TypedDict instead of dataclass?

      
Use TypedDict when you want static type checking on existing dict-based code without adding a dependency or changing the data structure. TypedDicts are just dicts at runtime — no class instantiation, no overhead. They work perfectly with json.loads() + cast() for type-safe API response handling in mypy-checked codebases.

    

    

      
How do I deserialize JSON into a Pydantic v2 model?

      
Use model_validate_json(): user = User.model_validate_json(json_string). Or parse a dict first: user = User.model_validate(json.loads(json_string)). Pydantic v2 validates all fields, raises ValidationError with details if any field fails, and handles Optional fields correctly.

    

    

      
Is the JSON to Python converter free?

      
Yes, completely free. No file size limits, no account required. JSONshift is funded by non-intrusive display advertising.

    

  

 


				
	
		
	


			
		
				
	

	
		
		



 

	
	

	
	
	
	
			
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