Working with JSON in Python - potatoscript/json GitHub Wiki

🐍 Working with JSON in Python: Unleash the Power of Data! 🎉

Welcome to the Amazing World of JSON and Python! 🐍💡
JSON is the go-to format for exchanging data, and Python makes it super easy to handle! 😎📚

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🎯 What is JSON in Python?

• JSON (JavaScript Object Notation): A lightweight format to store and exchange data.
• Python + JSON: Python provides the json module to parse, encode, and manipulate JSON effortlessly! 🚀

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🔥 1. Importing the json Module

Before working with JSON, import the json module.

import json

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📝 2. Converting JSON to Python (Deserialization/Parsing)

When you receive JSON data from an API or file, it’s in string format. To convert it into a Python object, use:

json.loads()  # Convert JSON string to Python object

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🎯 Example: Convert JSON String to Python Dictionary

import json

# JSON string
json_data = '{"name": "Lucy Berry", "age": 26, "hobbies": ["coding", "reading"]}'

# Parse JSON string to Python dictionary
data = json.loads(json_data)

print("Name:", data["name"])  # Lucy Berry
print("Age:", data["age"])    # 26
print("Hobbies:", data["hobbies"])  # ['coding', 'reading']

✅ Explanation:

• json.loads() parses the JSON string into a Python dictionary.
• You can access data using key-value pairs.

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📩 3. Converting Python to JSON (Serialization/Encoding)

To convert Python objects back to JSON, use:

json.dumps()  # Convert Python object to JSON string

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🎯 Example: Convert Python Dictionary to JSON String

import json

# Python dictionary
user_data = {
    "name": "Lucy Berry",
    "age": 26,
    "hobbies": ["coding", "reading"]
}

# Convert Python dictionary to JSON string
json_string = json.dumps(user_data)

print("JSON Data:", json_string)

✅ Explanation:

• json.dumps() converts the Python dictionary into a JSON string.

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📚 4. Working with JSON Files in Python

📥 a) Reading JSON from a File

To read JSON from a file and parse it into a Python object:

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

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🎯 Example: Reading JSON File

import json

# Open and read JSON file
with open("user_data.json", "r") as file:
    data = json.load(file)

print("User Name:", data["name"])
print("User Age:", data["age"])

✅ Explanation:

• json.load() reads and parses JSON from a file.
• The data is now a Python dictionary.

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📤 b) Writing JSON to a File

To write Python data to a JSON file:

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

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🎯 Example: Writing JSON to File

import json

# Python dictionary
user_data = {
    "name": "Lucy Berry",
    "age": 26,
    "hobbies": ["coding", "reading"]
}

# Write data to JSON file
with open("user_data.json", "w") as file:
    json.dump(user_data, file, indent=4)

print("Data successfully written to file!")

✅ Explanation:

• json.dump() writes JSON data to a file.
• indent=4 formats the output for better readability.

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🕹️ 5. Pretty Print JSON in Python

To make JSON more readable, use indent in json.dumps():

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🎯 Example: Pretty Print JSON

import json

# Python dictionary
user_data = {
    "name": "Lucy Berry",
    "age": 26,
    "hobbies": ["coding", "reading"]
}

# Pretty print JSON
pretty_json = json.dumps(user_data, indent=4)
print(pretty_json)

✅ Explanation:

• indent=4 makes the JSON easy to read with proper spacing.

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🔄 6. Handling Nested JSON in Python

When you have complex JSON with nested objects or arrays, you can parse them easily.

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🎯 Example: Parsing Nested JSON

import json

# Nested JSON string
nested_json = '''
{
  "name": "Lucy Berry",
  "address": {
    "street": "123 Snoopy Lane",
    "city": "Fukuoka",
    "country": "Japan"
  },
  "hobbies": ["coding", "reading"],
  "education": [
    {"degree": "Bachelors", "year": 2015},
    {"degree": "Masters", "year": 2019}
  ]
}
'''

# Parse nested JSON
data = json.loads(nested_json)

# Access nested objects
print("Street:", data["address"]["street"])
print("City:", data["address"]["city"])

# Access list of objects
for edu in data["education"]:
    print(f"{edu['degree']} - {edu['year']}")

✅ Explanation:

• Nested objects and arrays can be accessed using multiple keys.
• Use loops to iterate through lists.

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🧩 7. Converting Python Objects to JSON

json.dumps() works on many Python objects, including:

🐍 Python Object	🔥 JSON Equivalent
dict	Object
list, tuple	Array
str	String
int, float	Number
True	true
False	false
None	null

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🎯 Example: Convert Different Python Objects to JSON

import json

# Data types to JSON
data = {
    "name": "Lucy Berry",
    "age": 26,
    "isAdmin": True,
    "hobbies": ["coding", "reading"],
    "pets": None
}

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

✅ Explanation:

• Different Python objects are automatically converted to valid JSON.

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📡 8. Using JSON in APIs with Python

When interacting with APIs, you often send and receive JSON data.

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🎯 Example: Sending JSON with requests Library

import requests
import json

# API URL
url = "https://api.example.com/users"

# Data to send
data = {
    "name": "Lucy Berry",
    "age": 26
}

# Send POST request with JSON data
response = requests.post(url, json=data)

# Print server response
print("Response:", response.json())

✅ Explanation:

• requests.post() sends a POST request with JSON.
• response.json() parses the response.

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⚡ 9. Error Handling in JSON with Python

When working with JSON, handle errors gracefully.

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🎯 Example: Handling JSON Parsing Errors

import json

# Invalid JSON string
invalid_json = '{"name": "Lucy", "age": 26,}'

try:
    data = json.loads(invalid_json)
    print("Parsed Data:", data)
except json.JSONDecodeError as e:
    print("Error parsing JSON:", e)

✅ Explanation:

• json.JSONDecodeError catches invalid JSON.
• Proper error handling prevents crashes.

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🧠 10. Customizing JSON Encoding in Python

You can define how custom objects are converted to JSON.

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🎯 Example: Custom JSON Encoder

import json

# Custom class
class User:
    def __init__(self, name, age):
        self.name = name
        self.age = age

# Custom encoder
class UserEncoder(json.JSONEncoder):
    def default(self, obj):
        if isinstance(obj, User):
            return {"name": obj.name, "age": obj.age}
        return super().default(obj)

# Create user instance
user = User("Lucy Berry", 26)

# Encode with custom encoder
json_data = json.dumps(user, cls=UserEncoder)
print(json_data)

✅ Explanation:

• UserEncoder defines how to convert User objects to JSON.

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🔥 11. Advanced: JSON Path in Python

To extract specific data from JSON, use the jsonpath library.

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🎯 Example: Using JSONPath

import json
import jsonpath

# JSON data
data = '''
{
  "users": [
    {"name": "Lucy", "age": 26},
    {"name": "Bruce", "age": 35}
  ]
}
'''

# Parse JSON
parsed_data = json.loads(data)

# Extract all names
names = jsonpath.jsonpath(parsed_data, "$.users[*].name")
print(names)  # ['Lucy', 'Bruce']

✅ Explanation:

• $.users[*].name extracts all names from the JSON.

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