Python JSON Tutorial
Parse, Read, Write & Handle JSON

Introduction to JSON in Python

JSON, short for JavaScript Object Notation, is a lightweight format for storing and transporting data. In Python, it plays a pivotal role when working with APIs, configuration files, or structured data exchange. The good news? Python makes JSON handling intuitive through its built-in json module.

What Is JSON?

JSON is a string-based format that represents data as key-value pairs. It's similar to Python dictionaries, which makes it easier for developers to interact with.

Why Use JSON in Python?

• Interchange data between frontend and backend (especially via APIs)
• Save application settings or configurations
• Serialize Python objects for storage or transmission

Importing the JSON Module

Start by importing Python's built-in module:

import json

Converting Python Objects to JSON (Serialization)

Serialization means converting a Python object (like a dict) into a JSON string using json.dumps().

import json

data = {"name": "Alice", "age": 30, "is_admin": False}
json_string = json.dumps(data)
print(json_string)

{"name": "Alice", "age": 30, "is_admin": false}

Explanation

json.dumps() converts Python data into a JSON-formatted string. Note how Python False becomes false in JSON, matching JSON syntax rules.

Converting JSON to Python Objects (Deserialization)

To parse a JSON string into Python data types, use json.loads():

json_data = '{"name": "Bob", "age": 25, "is_admin": true}'
parsed_data = json.loads(json_data)
print(parsed_data)
print(type(parsed_data))

{'name': 'Bob', 'age': 25, 'is_admin': True}
<class 'dict'>

Explanation

The JSON string is turned into a Python dictionary. JSON's true becomes Python's True, and strings are decoded as Python strings.

Reading JSON from a File

with open('data.json', 'r') as file:
    data = json.load(file)
    print(data)

Writing JSON to a File

data = {"language": "Python", "version": 3.10}
with open('data.json', 'w') as file:
    json.dump(data, file)

Tip

Always use a context manager (with statement) to ensure the file is properly closed after reading/writing.

Pretty-Printing JSON

To make your JSON output more readable (e.g., for logging or debugging), use indent and sort_keys parameters:

data = {"name": "Zara", "age": 22, "city": "Delhi"}
print(json.dumps(data, indent=4, sort_keys=True))

{
    "age": 22,
    "city": "Delhi",
    "name": "Zara"
}

Handling Errors While Parsing JSON

Incorrect JSON will throw an exception. Always wrap your parsing logic in a try-except block:

json_string = '{"name": "Alex", "age": 28'  # Missing closing brace

try:
    data = json.loads(json_string)
except json.JSONDecodeError as e:
    print("JSON decoding failed:", e)

JSON decoding failed: Expecting ',' delimiter: line 1 column 29 (char 28)

Common Checks and Best Practices

• Ensure the JSON string is valid before parsing
• Use try-except to handle malformed data
• Use json.dump() and json.load() for file operations
• Validate encoding types when reading non-UTF-8 files

Advanced: Custom Encoding with json.JSONEncoder

Suppose we want to encode a custom object:

import json
from datetime import datetime

class User:
    def __init__(self, name):
        self.name = name
        self.timestamp = datetime.now()

def custom_encoder(obj):
    if isinstance(obj, User):
        return {"name": obj.name, "timestamp": obj.timestamp.isoformat()}
    raise TypeError("Object not serializable")

user = User("Sam")
print(json.dumps(user, default=custom_encoder))

Summary

Whether you're building APIs, consuming web services, or simply storing structured data, JSON is a must-know skill in Python. With just a few functions from the json module, you can serialize, deserialize, and safely manipulate structured data. Remember to handle exceptions gracefully, validate data, and use pretty-printing to inspect JSON when debugging.