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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallIn Python, decoding JSON objects produces dictionaries (dict), while decoding JSON arrays produces lists (list). JSON is text—not JavaScript code—and its object and array structures describe different shapes of data: named fields and ordered items.
What JSON objects and arrays represent
JSON is a text data-interchange format. It has two compound structures: an object, made of name/value pairs, and an array, an ordered sequence of values. JSON also supports strings, numbers, booleans, and null; objects and arrays can contain one another. The format’s terms describe the data shape, not a required programming-language type. See JSON.org’s introduction to JSON.
| JSON structure | Typical use | Python default after decoding |
|---|---|---|
Object, such as {"name":"Ari"} |
Values addressed by field name | dict |
Array, such as ["Python","JSON"] |
Ordered items addressed by position | list |
Choose an object when a value belongs to a named field, such as a person’s name. Choose an array when the values form a sequence, such as a list of skills where order or position matters. Neither structure is universally better; use the one that matches the data.
Why JSON is not JavaScript syntax
The name stands for JavaScript Object Notation, but JSON is a language-independent text format. It resembles JavaScript object literals, yet a JavaScript expression that looks plausible may not be valid JSON. JSON strings and property names must use double quotes; comments and trailing commas are not allowed. MDN describes JSON as “a syntax for serializing objects, arrays, numbers, strings, booleans, and null.” See MDN’s JSON reference.
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This is valid JSON:
{"name": "Ari", "skills": ["Python", "JSON"]}These JavaScript-like variations are not valid JSON:
{name: 'Ari'} // unquoted name, single-quoted string, and comment
{"name": "Ari",} // trailing commaDecode JSON into Python values
Python’s standard json module converts JSON text to built-in Python values. Use json.loads when the JSON is already in a string, or json.load when reading from a file-like object.
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import json
text = '{"name": "Ari", "skills": ["Python", "JSON"]}'
data = json.loads(text)
print(type(data)) # <class 'dict'>
print(type(data["skills"])) # <class 'list'>The outer value here is an object, so data is a dictionary. Its skills value is an array, so that nested value is a list.
The top-level value can be something other than an object
A JSON document does not have to start with an object. It can have an array or a single value at its root. For example, decoding ["red", "blue"] returns a Python list, not a dictionary; decoding true returns True. A top-level list is not, by itself, evidence that parsing failed. Check the document’s outermost JSON value and inspect the resulting Python type.
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Encode Python values as JSON
Use json.dumps to create a JSON string, or json.dump to write JSON to a file-like object. The encoder maps Python dictionaries to JSON objects and lists or tuples to JSON arrays.
back_to_text = json.dumps(data)
print(back_to_text)
# {"name": "Ari", "skills": ["Python", "JSON"]}The result of json.dumps is a Python str, not bytes. That distinction matters if the destination expects binary data rather than text.
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What changes across the JSON boundary
JSON defines a limited set of values. It does not have built-in representations for every value a programming language can hold, so converting data to JSON is not a universal way to preserve native types exactly.
| JSON value | Python default from json.loads |
JavaScript parsed value |
|---|---|---|
| Object | dict |
ordinary object |
| Array | list |
array |
true / false |
True / False |
true / false |
null |
None |
null |
| Number | int or float, depending on its form |
JavaScript number |
These are common mappings, not a promise that every runtime preserves a number’s original spelling, arbitrary precision, or every language-specific type. Python’s conversion behavior is documented in the Python 3.12 json documentation.
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Unsupported values need an explicit representation
Values such as functions, dates, sets, maps, and JavaScript’s undefined have no direct JSON value equivalent. What happens depends on the language and serializer. In JavaScript, JSON.stringify omits unsupported values such as undefined, functions, and symbols in objects, but converts them to null in arrays; it converts NaN and infinities to null. It throws for circular references and BigInt unless custom handling is provided. See MDN’s JSON.stringify() reference.
Python’s encoder supports its documented native types and can be extended with a custom encoder; decoding can also be customized with hooks. If a value needs a custom representation, define that representation as part of the data contract so another program can interpret it consistently. A JSON round trip should not be assumed to act as a general deep copy or preserve every runtime type.
Python’s non-standard numeric constants
Python’s json module accepts NaN, Infinity, and -Infinity when decoding as an extension, and its encoder permits them by default. These constants are outside the JSON specification. Set allow_nan=False when encoding if you want the encoder to reject them rather than emit non-standard JSON.
Common problems and what to check
- “I expected a dictionary but got a list.” The JSON root may be an array. Inspect the source text’s outermost structure and the decoded value’s type.
- “The parser rejects my file.” Check for single quotes, unquoted property names, comments, or trailing commas; none are valid JSON.
- “This Python value is not JSON serializable.” Identify the value that lacks a direct JSON representation and convert it to a documented JSON-compatible shape, or use a custom encoder for a clearly defined format.
Handle untrusted JSON with care
Parsing JSON from an untrusted source can consume substantial CPU time and memory if the input is malicious or excessively large. Python’s official documentation recommends limiting the size of data you parse. Apply an appropriate input-size limit for your application rather than accepting arbitrarily large text.
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