Paste any valid JSON object or array
Python output appears here
Paste JSON and click Convert// turn json payloads into python dicts in one click
Convert JSON objects and arrays into ready-to-use Python dictionaries and lists. Handles nested structures, null values, booleans, and all data types instantly.
Python output appears here
Paste JSON and click ConvertPaste your JSON object or array into the left panel.
Hit the Convert button or press Ctrl+Enter to run the conversion.
Copy the output and paste directly into your Python script or notebook.
This tool converts JSON syntax into valid Python dictionary or list literals. JSON uses null, true, and false — Python uses None, True, and False. This converter handles all those differences automatically, including deeply nested structures, and produces properly indented Python code ready to paste.
JSON and Python dict syntax are very similar but differ in three key ways: JSON uses null while Python uses None; JSON uses lowercase true/false while Python uses capitalized True/False; and JSON requires double-quoted keys. This tool handles all those conversions for you.
Yes. The converter recursively processes any depth of nesting. Nested JSON objects become nested Python dicts, and JSON arrays become Python lists — with correct indentation at every level.
JSON integers and floats are preserved as-is in the Python output. Strings are kept in double quotes with any special characters properly escaped (backslashes, newlines, tabs, etc.).
Yes. The output is a valid Python assignment statement (data = {...}) that you can copy and paste directly into any Python script, Jupyter notebook, or REPL. No modification needed.
The conversion is processed server-side via a lightweight PHP endpoint, but no data is stored, logged, or retained. Your JSON is processed in-memory and immediately discarded after conversion.
The tool will show a clear error message describing what's wrong with the JSON syntax — for example, a missing comma, unmatched bracket, or unquoted key. Fix the issue and try again.
When working with APIs, configuration files, or data pipelines in Python, you often start with JSON data that needs to live inside your code as a native Python object. While Python's json module can parse JSON at runtime, there are plenty of scenarios where you want a hardcoded dictionary or list literal — in a test fixture, a seed script, a notebook, or a mock object. That's exactly what this converter does.
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JSON and Python dictionary syntax are close cousins — both use curly braces for key-value pairs and square brackets for ordered sequences. However, there are a few critical syntactic differences that make direct copy-paste from JSON invalid Python:
null keyword becomes Python's None singleton.This tool handles all of these automatically, recursing through arbitrarily nested structures to produce a properly indented, syntactically correct Python literal.
There are several workflows where converting JSON to a Python dict literal is genuinely useful:
responses or httpretty, you need Python dict literals to define mock response bodies.When the top-level JSON value is an array (e.g., [{"id": 1}, {"id": 2}]), this tool outputs a Python list of dicts. The output variable will be data = [...] rather than data = {...}. Nested arrays within objects are also recursively converted to Python list syntax.
The converter is careful about a few edge cases that trip up naive implementations:
{} and [] are preserved as empty dict and list literals.1.5e10 are preserved in their JSON representation.Using json.loads() at runtime is perfectly valid and often the right approach. But hardcoded dict literals have advantages in specific contexts: they're faster (no parsing step), they're easier to read and edit directly in source code, and they work without importing the json module. For test fixtures and notebooks especially, having the data inline rather than in a separate file simplifies the development workflow.
Here's a quick reference for how JSON types map to Python:
object → dictarray → liststring → strnumber (integer) → intnumber (float) → floattrue → Truefalse → Falsenull → NoneThis mapping is defined in Python's official json module documentation and is what this tool implements.