Working With JSON in Python

1Working With JSON in Python

JSON, or JavaScript Object Notation, is a lightweight text format for storing and exchanging data, and Python handles it with the built-in json module — no installation required. The module converts between JSON text and native Python objects, so a JSON object becomes a dictionary and a JSON array becomes a list.

This mapping is what makes JSON so pleasant in Python: once parsed, the data behaves exactly like the dictionaries and lists you already know. JSON is the format behind most web APIs, config files, and data-interchange between services, so knowing the json module is essential.

• import json
• text = '{"name": "Ava", "age": 29}'
• data = json.loads(text) # -> a Python dict
• print(data['name']) # Ava

2The Four Core Functions

The json module centers on four functions, split by whether you work with a string or a file. Getting these straight removes almost all JSON confusion: the 's' suffix means 'string'.

• json.loads(s) — parse a JSON string into a Python object.
• json.dumps(obj) — serialize a Python object into a JSON string.
• json.load(file) — read and parse JSON from an open file.
• json.dump(obj, file) — write a Python object as JSON to an open file.

3Parsing JSON Into Python

Parsing turns JSON text into Python objects you can navigate. JSON types map cleanly: objects become dictionaries, arrays become lists, strings and numbers stay as themselves, true/false become True/False, and null becomes None.

• raw = '{"users": [{"id": 1}, {"id": 2}], "active": true}'
• data = json.loads(raw)
• print(data['users'][0]['id']) # 1
• print(data['active']) # True (JSON true -> Python True)

JSON object  -> Python dict
JSON array   -> Python list
JSON true/false -> Python True/False
JSON null    -> Python None

4Creating JSON From Python

Serializing goes the other way, turning Python objects into JSON text. The indent argument produces human-readable, pretty-printed output, and sort_keys makes the result deterministic — useful for config files tracked in version control.

• record = {'name': 'Ava', 'roles': ['dev', 'lead'], 'active': True}
• compact = json.dumps(record) # single line
• pretty = json.dumps(record, indent=2, sort_keys=True)
• print(pretty)

5Reading and Writing JSON Files

For files on disk, use load and dump with an open file object. Combined with the with statement, this reads a config file or saves application state in just a couple of lines.

• with open('config.json', 'r', encoding='utf-8') as f:
• config = json.load(f) # file -> Python object
• config['version'] = 2
• with open('config.json', 'w', encoding='utf-8') as f:
• json.dump(config, f, indent=2) # Python object -> file

6Handling Non-Standard Types

JSON supports only a fixed set of types, so objects like datetime raise a TypeError when you try to serialize them. Supply a default function that tells dumps how to convert unsupported values into something JSON understands, such as an ISO-format string.

• from datetime import datetime
• def encode(obj):
• if isinstance(obj, datetime): return obj.isoformat()
• raise TypeError(f'Cannot serialize {type(obj)}')
• json.dumps({'now': datetime.now()}, default=encode)

7Common Mistakes to Avoid

Most JSON errors come from mixing up the functions or forgetting JSON's rules.

• Confusing load with loads — load reads a file, loads reads a string.
• Forgetting that JSON keys are always strings, so integer keys become strings after a round trip.
• Trying to serialize sets, datetimes, or custom objects without a default function.
• Not handling json.JSONDecodeError when parsing untrusted or user-supplied text.
• Writing JSON without encoding='utf-8', which can corrupt non-ASCII characters.
• Assuming tuples survive — they are serialized as JSON arrays and come back as lists.

8Handling Malformed JSON

Real-world JSON from APIs or user input is not always valid. Wrap parsing in a try/except that catches json.JSONDecodeError so a single bad response does not crash your program.

• try:
• data = json.loads(user_input)
• except json.JSONDecodeError as e:
• print(f'Invalid JSON: {e}')
• data = {}

9Key Takeaways

Working with JSON in Python comes down to a compact toolkit.

• The built-in json module needs no installation.
• loads/dumps handle strings; load/dump handle files.
• JSON objects become dicts and arrays become lists.
• Use indent for readability and a default function for custom types.
• Catch json.JSONDecodeError to handle malformed input safely.

10Frequently Asked Questions

Q: What is the difference between json.load and json.loads? A: json.loads parses a JSON string, while json.load reads and parses JSON from an open file object. The 's' stands for string. The same pattern applies to dumps (to a string) and dump (to a file).

Q: How do I pretty-print JSON in Python? A: Pass the indent argument to dumps or dump, for example json.dumps(data, indent=2). This adds line breaks and indentation for readability. Add sort_keys=True to order the keys consistently.

Q: Why can't I serialize a datetime to JSON? A: JSON only supports strings, numbers, booleans, null, arrays, and objects. A datetime is none of these, so it raises a TypeError. Supply a default function that converts it to a string, such as obj.isoformat().

Q: Do I need to install anything to use JSON in Python? A: No. The json module is part of the Python standard library, so you only need to import json. There is nothing to pip install for basic JSON reading and writing.