Python Json

1. What is JSON

JSON (JavaScript Object Notation) is a lightweight data-interchange format used for storing and exchanging structured data.

{
  "name": "Alice",
  "age": 30,
  "active": true
}

In Python, JSON is handled using the built-in json module.

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2. Importing the JSON Module

import json

Provides methods to convert between Python objects and JSON format.

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3. Convert Python Object → JSON String (json.dumps)

import json

data = {"name": "Alice", "age": 30, "city": "Toronto"}
json_string = json.dumps(data)

print(json_string)

Serializes Python objects into a JSON-formatted string.

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4. Convert JSON String → Python Object (json.loads)

import json

json_text = '{"name": "Bob", "age": 25}'
python_obj = json.loads(json_text)

print(python_obj)
print(type(python_obj))  # dict

Deserializes JSON string into Python data structures.

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5. Writing JSON to a File (json.dump)

import json

data = {
    "product": "Laptop",
    "price": 1200,
    "in_stock": True
}

with open("product.json", "w") as file:
    json.dump(data, file)

Stores structured data persistently.

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6. Reading JSON from a File (json.load)

import json

with open("product.json", "r") as file:
    data = json.load(file)

print(data)

Loads JSON data into Python objects.

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7. Pretty Printing JSON

import json

data = {"name": "Alice", "skills": ["Python", "AI", "ML"]}

pretty_json = json.dumps(data, indent=4)
print(pretty_json)

Improves readability for logs and debugging.

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8. Handling Complex Data Types

import json
from datetime import datetime

data = {"event": "login", "time": str(datetime.now())}

json_string = json.dumps(data)
print(json_string)

Non-serializable objects must be converted manually.

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9. Sorting JSON Keys

import json

data = {"b": 2, "a": 1, "c": 3}
sorted_json = json.dumps(data, sort_keys=True)

print(sorted_json)

Useful for consistent API responses and hashing.

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10. Enterprise Example: API Response Handling

import json

def parse_api_response(response):
    try:
        data = json.loads(response)
        return data.get("status"), data.get("payload")
    except json.JSONDecodeError:
        return "error", None

response = '{"status": "success", "payload": {"id": 101}}'
print(parse_api_response(response))

Standard pattern for microservices and REST APIs.

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JSON ↔ Python Data Type Mapping

JSON Type	Python Type
Object	dict
Array	list
String	str
Number (int)	int
Number (float)	float
true/false	True/False
null	None

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Common JSON Errors

Error	Cause
JSONDecodeError	Invalid JSON syntax
TypeError	Non-serializable object
UnicodeDecodeError	Encoding mismatch

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Best Practices

• Always validate JSON structure

• Use try-except for decoding

• Avoid storing sensitive data in plain JSON

• Format JSON for logs but compress for production

• Use schema validation (e.g., Pydantic, Marshmallow)

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Enterprise Applications

JSON is fundamental in:

• REST API communication

• Configuration files

• Microservices messaging

• Data interchange between systems

• AI model metadata storage

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