Understanding
Complex Data Types

What are Complex Data Types?

Complex data types are used to store multiple values in a single variable. Unlike primitive data types (like integer, float, or boolean), which hold a single value, complex data types can hold collections of values and offer built-in ways to organize, access, and manipulate them efficiently.

Why Use Complex Data Types?

• To manage groups of data with a single identifier.
• To perform operations like sorting, searching, filtering, and aggregating on collections of items.
• To better structure data in real-world applications (like user profiles, orders, etc.).

Types of Complex Data Structures

1. Arrays / Lists

An array (or list) is a collection of elements stored in a sequence. Each item can be accessed by its index.

// Define a list of numbers
numbers = [10, 20, 30, 40]

// Access elements
print(numbers[0])  // First element
print(numbers[3])  // Last element

// Modify elements
numbers[1] = 25

// Add a new element at the end
append(numbers, 50)

// Loop through the list
for num in numbers:
    print(num)

Output:

10
40
10
25
30
40
50

Question:

What happens if you try to access an index that doesn’t exist?

Answer:

You get an error or "out of bounds" exception depending on the programming environment. Always ensure the index is valid (between 0 and length - 1).

2. Sets

A set is a collection of unique, unordered elements. Useful for checking membership and removing duplicates.

// Define a set
unique_numbers = set(10, 20, 20, 30, 10)

// Add elements
add(unique_numbers, 40)

// Check membership
if 20 in unique_numbers:
    print("20 is present")

// Loop through set
for item in unique_numbers:
    print(item)

Output:

20 is present
10
20
30
40

Question:

Why might you use a set instead of a list?

Answer:

If you only care about unique items and fast membership checking, sets are more efficient than lists.

3. Dictionaries / Maps

Dictionaries (or maps) store data as key-value pairs. Each key is unique, and you use keys to access corresponding values.

// Create a dictionary to store student scores
scores = {
    "Alice": 90,
    "Bob": 85,
    "Charlie": 92
}

// Access a value by key
print(scores["Alice"])

// Add a new key-value pair
scores["David"] = 88

// Modify a value
scores["Bob"] = 87

// Loop through dictionary
for key in scores:
    print(key, scores[key])

Output:

90
Alice 90
Bob 87
Charlie 92
David 88

Question:

What is a good real-world use case for a dictionary?

Answer:

Storing student grades, user profiles (name, email, age), or configuration settings where you retrieve data using descriptive keys.

Comparing Complex Data Types

Type	Ordered?	Allows Duplicates?	Access Method	Common Use
List / Array	Yes	Yes	By index	Storing sequence of items
Set	No	No	Membership test	Unique items, filtering
Dictionary / Map	No	Keys: No, Values: Yes	By key	Key-value storage

Summary

• Lists are good for ordered, index-based access.
• Sets help eliminate duplicates and check presence efficiently.
• Dictionaries are ideal when data is naturally paired (like name → value).

Quick Practice

// Practice Task:
// Create a dictionary with city names as keys and population as values.
// Print all cities with a population over 1 million.

cities = {
    "Metropolis": 1500000,
    "Smallville": 450000,
    "Star City": 1200000
}

for city in cities:
    if cities[city] > 1000000:
        print(city)

Output:

Metropolis
Star City