1. Lists: The Mutable Workhorse

Practical Example: Shopping Cart

# Create shopping cart
shopping_cart = ["apples", "milk", "bread"]

# Modify cart
shopping_cart.append("eggs")
shopping_cart.remove("milk")
shopping_cart[1] = "whole wheat bread"

When to Use: Dynamic data, frequent modifications, ordered collections

2. Tuples: The Immutable Container

Practical Example: GPS Coordinates

# Store location coordinates
office_location = (40.7128, -74.0060)

# Access values
latitude = office_location[0]
longitude = office_location[1]

When to Use: Fixed data, dictionary keys, memory efficiency

3. Dictionaries: Key-Value Storage

Practical Example: User Profile

user_profile = {
    "username": "python_dev",
    "skills": ["Python", "SQL", "AWS"],
    "experience": 5,
    "active": True
}

# Access data
print(user_profile["skills"])  # Output: ['Python', 'SQL', 'AWS']

When to Use: Key-based access, complex objects, fast lookups

Comparison Table

FeatureListTupleDictionary
MutabilityMutableImmutableMutable
OrderOrderedOrderedUnordered
Syntax[](){}
Best Use CaseDynamic collectionsFixed dataKey-value pairs

Real-World Scenario: E-commerce System

# Product tuple (immutable)
PRODUCT = ("Laptop", 999.99, "electronics")

# Cart list (mutable)
cart = [{
    "id": 101,
    "name": PRODUCT[0],
    "price": PRODUCT[1]
}]

# User dictionary
user = {
    "name": "John Doe",
    "cart": cart,
    "discount": 0.15
}

# Calculate total
total = sum(item["price"] for item in user["cart"])
final_price = total * (1 - user["discount"])

When to Choose Which?

  • List: Changing data, multiple same-type items
  • Tuple: Unchanging data, safer data storage
  • Dictionary: Labeled data, complex relationships