To parse JSON text held in a Python string, use json.loads(text). It returns the Python value represented by the JSON—such as a dictionary, list, string, number, boolean, or None. To go the other direction, from a Python value to JSON text, use json.dumps(value).
Parse a JSON string with json.loads
Import Python’s standard-library json module, then pass the JSON text to loads:
import json
text = '{"name": "Ada", "active": true, "items": [1, 2, 3]}'
value = json.loads(text)
print(value)
# {'name': 'Ada', 'active': True, 'items': [1, 2, 3]}The JSON booleans true and false become Python’s True and False; JSON null becomes None. The operation parses the text—it does not turn the string into a dictionary in every case.
Choose the function for the input and direction
| What you have | What you want | Function |
|---|---|---|
| JSON text in a string, bytes, or bytearray | A Python value | json.loads(text) |
An open file or other object with a .read() method containing JSON |
A Python value | json.load(file_obj) |
| A Python value | A JSON-formatted string | json.dumps(value) |
| A Python value and a file-like object | JSON written to that object | json.dump(value, file_obj) |
The similar names are easy to mix up: loads accepts JSON text already in a variable, while load reads from a file-like object. Passing a string to json.load usually causes an error because a plain string is not a file object.
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Know what type loads returns
The result depends on the top-level JSON value. Python’s documented conversions are:
- JSON object → Python
dict - JSON array → Python
list - JSON string → Python
str - JSON integer → Python
int - JSON real number → Python
float - JSON
trueorfalse→ PythonTrueorFalse - JSON
null→ PythonNone
json.loads('{"language": "Python"}') # dict
json.loads('[1, 2, 3]') # list
json.loads('42') # int
json.loads('true') # True
json.loads('null') # NoneIf your code specifically needs an object, check the result before using dictionary operations:
value = json.loads(text)
if isinstance(value, dict):
print(value.get("name"))
else:
print("Expected a JSON object")Fix invalid JSON instead of disguising it
Malformed JSON raises json.JSONDecodeError. Catch that exception when invalid input is an expected possibility, and use its message and location to find the syntax problem:
import json
text = '{"name": "Ada",}' # trailing comma is invalid JSON
try:
value = json.loads(text)
except json.JSONDecodeError as exc:
print(f"Invalid JSON at line {exc.lineno}, column {exc.colno}: {exc.msg}")Common causes include single quotes around strings or keys, unquoted object keys, trailing commas, Python’s True, False, and None instead of JSON’s lowercase true, false, and null, and literal newlines or control characters inside a JSON string. JSON strings and object keys use double quotes.
If the input is a Python literal rather than JSON, it is a different format; json.loads is not the right parser. Do not use eval to parse input. Fix or safely handle the actual format rather than catching every error and silently substituting an empty dictionary, which can hide corrupted data.
Handle content after a JSON document deliberately
json.loads is the right choice when the input is one complete JSON document. If a protocol intentionally places additional content after a JSON document, use JSONDecoder.raw_decode to obtain both the decoded value and the character index where that JSON ended:
decoder = json.JSONDecoder()
value, end = decoder.raw_decode(text)
remaining = text[end:]raw_decode does not decide what the remaining text means. Your code must handle, validate, or reject it according to the protocol; do not use this method to overlook unexpected trailing content.
Be careful with untrusted or non-standard values
Python’s JSON decoder accepts NaN, Infinity, and -Infinity as extensions, although those values are outside the JSON specification. If strict interoperability matters, reject them with parse_constant:
import json
def reject_constant(value):
raise ValueError(f"Non-standard JSON constant: {value}")
value = json.loads(text, parse_constant=reject_constant)Successful parsing is not application-level validation: check that required fields exist and have the expected types and values. The Python 3.14 library documentation also cautions that malicious JSON may consume considerable CPU and memory and recommends limiting the size of data to be parsed. Apply an appropriate size limit before parsing untrusted input.
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