JSON in Python

Learning Focus

Python has built-in JSON support via the json module. Learn to parse, serialize, stream, and validate JSON safely in production code.

Parse and Serialize

import json

# Parse string → dict
data = json.loads('{"name": "Alice", "age": 30, "active": true}')
print(data["name"])        # Alice
print(type(data["age"]))   # <class 'int'>

# Parse file → dict
with open("data.json", "r", encoding="utf-8") as f:
    data = json.load(f)

# Serialize dict → string
payload = {"name": "Bob", "scores": [95, 87], "active": True, "notes": None}
print(json.dumps(payload, indent=2, sort_keys=True))

# Serialize dict → file
with open("output.json", "w", encoding="utf-8") as f:
    json.dump(payload, f, indent=2, ensure_ascii=False)

Type Mapping

Python	JSON
dict	object {}
list, tuple	array []
str	string
int, float	number
True	true
False	false
None	null

Custom Serialization (Dates, etc.)

from datetime import datetime

def default_encoder(obj):
    if isinstance(obj, datetime):
        return obj.isoformat()
    raise TypeError(f"Not serializable: {type(obj)}")

data = {"event": "launch", "at": datetime.utcnow()}
print(json.dumps(data, default=default_encoder))

Streaming Large Files with ijson

pip install ijson

import ijson

with open("large.json", "rb") as f:
    for item in ijson.items(f, "item"):
        process(item)  # one record at a time — constant memory

Validation with jsonschema

# pip install jsonschema
import jsonschema

schema = {
    "type": "object",
    "properties": {
        "email": {"type": "string", "format": "email"},
        "age":   {"type": "integer", "minimum": 18}
    },
    "required": ["email"]
}

try:
    jsonschema.validate({"email": "alice@example.com", "age": 25}, schema)
    print("Valid")
except jsonschema.ValidationError as e:
    print(f"Error: {e.message}")

Concept Map

Concept Flow

Raw JSON
  └── json.loads / json.load
        └── Python dict / list
              ├── Business Logic
              │     └── json.dumps / json.dump  →  JSON Output
              └── jsonschema.validate  →  Valid / Invalid

Common Pitfalls

Pitfall	Consequence	Prevention
str() instead of json.dumps()	Python repr, not valid JSON	Always use json.dumps()
datetime not serializable	TypeError at runtime	Use default= encoder
json.load() on huge file	Memory overflow	Use ijson for streaming
Missing encoding="utf-8"	Garbled chars on Windows	Always specify encoding

What's Next

• Next: JSON in JavaScript — Native JSON support in Node.js and the browser.
• Section Overview — Return to the Programming module index.