Working with JSON in Python (json Module)

🟡 Intermediate

📖 Definition

JSON (JavaScript Object Notation) is a lightweight, human-readable, text-based data format used to exchange structured data between client web applications, mobile apps, and backend servers. Python provides the built-in json module for serializing and deserializing data.

🇮🇳 Hindi

Server aur client ke beech data share karne ke liye JSON format ka use hota hai. Python Dictionary ko JSON string mein convert karne ke liye json.dumps() (Serialization) aur JSON string ko Python Dictionary mein convert karne ke liye json.loads() (Deserialization) ka use hota hai.

🚩 Marathi

JSON format server sobat data share karnyasathi vaparatat. Python Dictionary la JSON string madhye convert karnyasathi json.dumps() aani parat dict madhye sathi json.loads() vaparatat.

📝 1. Python Datatypes vs JSON Equivalent Mapping

Python Datatype	Equivalent JSON Type
dict	Object {}
list, tuple	Array []
str	String ""
int, float	Number
True / False	true / false
None	null

📝 2. Serialization & Deserialization Methods

• json.dumps(obj): Converts Python dict/object -> JSON String in memory.
• json.dump(obj, file): Serializes Python dict -> Writes directly into a File.
• json.loads(json_str): Parses JSON String -> Python dict in memory.
• json.load(file): Reads JSON File -> Parses directly into Python dict.

import json

data_dict = {"name": "Aarav", "age": 25, "active": True}

# Serialization (Python Dict -> JSON String)
json_string = json.dumps(data_dict, indent=4)

# Deserialization (JSON String -> Python Dict)
parsed_dict = json.loads(json_string)

💡 Complete Example: REST API Payload Serialization & File Storage

import json
import os

# Python Nested Dictionary Payload
user_profile = {
    "user_id": 501,
    "full_name": "Rahul Verma",
    "email": "rahul@example.com",
    "is_premium": True,
    "roles": ["DEVELOPER", "ADMIN"],
    "address": {
        "city": "Mumbai",
        "zipcode": "400001"
    }
}

file_path = "user_payload.json"

print("=== JSON SERIALIZATION ENGINE ===")

# 1. Serializing to File with pretty formatting (indent=4)
with open(file_path, "w", encoding="utf-8") as file:
    json.dump(user_profile, file, indent=4)
    
print(f"Successfully serialized data to '{file_path}'.")

# 2. Deserializing back from File
print("\n--- DESERIALIZING JSON FROM FILE ---")

with open(file_path, "r", encoding="utf-8") as file:
    loaded_data = json.load(file)

print(f"Loaded Name   : {loaded_data['full_name']}")
print(f"Is Premium?   : {loaded_data['is_premium']}")
print(f"City          : {loaded_data['address']['city']}")
print(f"Primary Role  : {loaded_data['roles'][0]}")

# Cleanup demo file
if os.path.exists(file_path):
    os.remove(file_path)

👀 Output

=== JSON SERIALIZATION ENGINE ===
Successfully serialized data to 'user_payload.json'.

--- DESERIALIZING JSON FROM FILE ---
Loaded Name   : Rahul Verma
Is Premium?   : True
City          : Mumbai
Primary Role  : DEVELOPER

⚠️ Common Mistakes

• Confusing json.dumps() (string serialization) with json.dump() (file serialization).
• Attempting to serialize non-standard custom Python objects (like custom class instances or datetime objects) without providing a custom default serializer function.

🛡️ Safety / Important Notes

Always pass indent=4 to json.dumps() or json.dump() when creating JSON configuration files to make them human-readable.

🌍 Real-World Usage

REST API endpoints (FastAPI/Django), configuration files (config.json), web scraping output storage, and microservice messaging payloads.

🧪 Try It Yourself

1. Create a dictionary with your name, age, and list of favorite programming languages.
2. Convert it into a formatted JSON string using json.dumps(data, indent=2).

🎯 Mini Challenge

Write a program that reads a JSON string {"items": [10, 20, 30], "status": "ok"}, calculates the sum of items, and outputs {"sum": 60, "status": "ok"} as a new JSON string.

🔗 Related Topics

• File Handling
• Working with CSV

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