JSON to Python
Convert between data formats instantly with this json to python. Transform your data for different applications and programming languages.
Features
- Instant Conversion: Transform data formats in milliseconds
- Preserve Structure: Maintains data relationships and hierarchy
- Error Handling: Clear feedback on conversion issues
- Copy Output: One-click copying of converted data
Example
Input:
``
{"user": {"name": "John", "scores": [95, 87, 92]}}
`
Output:
`
from dataclasses import dataclass
from typing import List
@dataclass
class User:
name: str
scores: List[int]
@dataclass
class Root:
user: User
`
Tips
Uses Python 3.7+ dataclasses- Type hints are generated
- Nested objects become separate classes
How to Use
1. Paste your JSON in the input area
2. Click convert to transform the data
3. Review the converted output
4. Copy or download the result
Best Practices
- Validate your source data before conversion
- Check the output for any formatting issues
- Test with a small sample before converting large files
- Keep backups of original data
JSON to Python
Python's json module maps cleanly onto built-in types:
| JSON | Python |
|---|---|
| object | dict |
| array | list |
| string | str |
| number | int or float |
| true / false | True / False |
| null | None |
`python
import json
data = json.loads(text)
`For anything structured, parse into a dataclass or a Pydantic model rather than passing dicts
around — you get validation, type hints and an actual error message when the payload changes.
Two traps: json.dumps cannot serialise datetime or Decimal without a custom encoder,
and Python dicts allow non-string keys that JSON does not (they are coerced to strings, which
does not round-trip).
JSON Types in Python
| JSON | Python |
|---|---|
| object | dict |
| array | list |
| string | str |
| number | int or float |
| true / false | True / False |
| null | None |
json.dumps cannot serialise datetime, Decimal or set without a custom encoder.Generate the Type, Do Not Hand-Write It
Deriving a type from a sample payload is a starting point and not a specification. A sample
cannot tell you which fields are optional, which strings are really enums, what the bounds
are, or which numbers are money. Treat generated types as a first draft, then:
Mark optional fields explicitly. A field absent from your sample is not necessarily
required.
- Narrow strings to unions where the API documents a fixed set.
- Widen numbers where precision matters — decimals for money, strings for large IDs.
- Decide on unknown fields. Ignoring them is forgiving; rejecting them catches API
changes early. Pick deliberately.Numbers Are the Recurring Problem
A JSON number is an IEEE 754 double. Three consequences that bite in production:
Twitter hit the first one publicly: 64-bit tweet IDs arrived in JavaScript rounded, so the
API began sending an Value What happens Integers above 2⁵³ Silently lose precision — send IDs as strings Money as a float 0.1 + 0.2 = 0.30000000000000004 Leading zeros 007 is invalid JSON; "007" is a stringNaN and InfinityNot valid JSON at all alongside every id.
Keys, Order and Duplicates
Objects are formally unordered, though every JavaScript engine preserves insertion order for
string keys — with one exception: integer-like keys sort numerically and come first.
`javascript
JSON.stringify({ b: 1, 2: 2, a: 3 }); // {"2":2,"b":1,"a":3}
`
Duplicate keys are not an error in the spec, and JSON.parse` keeps the last one. Two
parsers can legitimately disagree about which value wins, which has been the basis of real
request-smuggling attacks.