Python Data Structures – Lists, Tuples, Sets, and Dictionaries

What You’ll Learn:

• Built-in data structures in Python
• Differences between them
• When to use which
• Methods and operations
• Real-life examples
• Practice problems

Why Data Structures?

Data structures help you organize and store data efficiently so that operations like insertion, deletion, searching, and sorting become easier.

Python has four main built-in data structures:

• List
• Tuple
• Set
• Dictionary

1. Lists – Ordered, Mutable Collections

Key Features:

• Ordered
• Mutable (can be changed)
• Allows duplicate elements

Creating a List:

fruits = ["apple", "banana", "cherry"]

Accessing Items:

print(fruits[0])       # Output: apple
print(fruits[-1])      # Output: cherry (last item)

# Add
fruits.append("kiwi")

# Insert at position
fruits.insert(1, "grape")

# Delete
fruits.remove("banana")  # Removes by value
del fruits[2]  # Rmmoves by index

Useful Methods:

• append(), extend(), insert(), remove(), pop(), sort(), reverse(), count(), index()

Modifying a List:

fruits[1] = "blueberry"
print(fruits)  # ['apple', 'blueberry', 'cherry']

List Methods:

numbers = [3, 1, 4]
numbers.append(2)
numbers.sort()
print(numbers)  # [1, 2, 3, 4]

2. Tuples – Ordered, Immutable Collections

Key Features:

• Ordered
• Immutable (cannot be changed)
• Faster than lists

Creating a Tuple:

person = ("Akash", 25, "India")

Accessing Elements:

print(person[0])  # Akash

Use tuples when data shouldn’t change (e.g., coordinates, constants).

3. Sets – Unordered, Unique Elements

Key Features:

• Unordered
• No duplicates
• Mutable (can be changed)
• Useful for mathematical set operations

Creating a Set:

colors = {"red", "green", "blue"}

Set Operations:

colors.add("yellow")
colors.remove("green")

Set Math:

a = {1, 2, 3}
b = {3, 4, 5}

print(a | b)  # Union → {1, 2, 3, 4, 5}
print(a & b)  # Intersection → {3}
print(a - b)  # Difference → {1, 2}

Great for checking membership, uniqueness, and eliminating duplicates.

4. Dictionaries – Key-Value Pairs

Key Features:

• Unordered (as of < Python 3.6; ordered from 3.7+)
• Key-value mapping
• Fast lookups

Creating a Dictionary:

student = {
    "name": "Akash",
    "age": 25,
    "course": "Python"
}

Accessing & Modifying:

print(student["name"])     # Akash
student["age"] = 26        # Update
student["email"] = "a@x.com"  # Add new key

Dictionary Methods:

Summary: When to Use What?

Real-World Examples

List Example – Shopping Cart

cart = ["apple", "banana", "apple"]
print(cart.count("apple"))  # Output: 2

Tuple Example – Coordinates

location = (37.7749, -122.4194)

Set Example – Unique Voters

voters = {"alice", "bob", "alice"}
print(voters)  # Output: {'alice', 'bob'}

Dictionary Example – API Response

response = {
    "status": "success",
    "data": {"user_id": 101, "name": "Akash"}
}

Practice Tasks

✅ Task 1: List Practice

• Create a list of your 5 favorite movies
• Add 2 more
• Sort the list
• Remove the last movie

✅ Task 2: Set Practice

• Create a set of integers with duplicates
• Remove duplicates and print the sorted result

✅ Task 3: Dictionary Practice

• Create a dictionary for a book:
  • title, author, year, price
• Update the price
• Add key "available": True

Mini Quiz

1. Can a dictionary key be a list? Why or why not?
2. What happens if you access a missing key using dict[key] vs dict.get(key)?
3. Which is faster for checking membership: list or set?