Area 2.3

Data Structures

Master the fundamental data structures: lists, arrays, and dictionaries. Learn when to use each structure and how to perform basic operations on collections of data.

Structures

Operations

~2hrs

Study Time

Learning Objectives

Understand lists, arrays, and dictionary data structures
Know when to use each data structure based on data organization needs
Perform basic operations on collections of data
Apply appropriate data structure selection for different scenarios
Implement adding, removing, and accessing elements in data structures

Core Data Structures

Lists

Ordered collections of items that can be modified (mutable)

Characteristics:

Ordered sequence
Mutable (can change)
Allow duplicates
Indexed by position

When to Use:

Shopping lists, items, student grades, any ordered collection that may change

Python Example:

# Creating lists
fruits = ["apple", "banana", "orange"]
numbers = [1, 2, 3, 4, 5]
mixed = ["hello", 42, True, 3.14]

# Basic operations
fruits.append("grape")        # Add to end
fruits.insert(1, "kiwi")      # Insert at position
first_fruit = fruits[0]       # Access by index
fruits.remove("banana")       # Remove by value
length = len(fruits)          # Get length

Common Operations:

Add item

list.append(item)fruits.append("mango")

Insert item

list.insert(index, item)fruits.insert(0, "berry")

Remove item

list.remove(item)fruits.remove("apple")

Access item

list[index]first = fruits[0]

Find length

len(list)count = len(fruits)

Arrays

Fixed-size collections of items of the same data type

Characteristics:

Fixed size
Same data type
Memory efficient
Fast access by index

When to Use:

Mathematical calculations, image data, sensor readings, performance-critical applications

Python Example:

# Arrays in Python (using array module)

# Create typed arrays
integers = array.array('i', [1, 2, 3, 4, 5])    # 'i' = signed int
floats = array.array('f', [1.1, 2.2, 3.3])      # 'f' = float

# Basic operations
integers.append(6)            # Add element
value = integers[0]           # Access by index
integers[1] = 10             # Modify element
length = len(integers)        # Get length

Common Operations:

Create array

array.array(type, values)arr = array.array("i", [1,2,3])

Add element

array.append(value)arr.append(4)

Access element

array[index]value = arr[0]

Modify element

array[index] = valuearr[1] = 10

Get length

len(array)size = len(arr)

Dictionaries

Collections of key-value pairs for mapping relationships

Characteristics:

Key-value pairs
Unordered (Python 3.7+ maintains insertion order)
Keys must be unique
Fast lookup by key

When to Use:

Student records, configuration settings, word definitions, any key-to-value mapping

Python Example:

# Creating dictionaries
student = {"name": "Alice", "age": 20, "grade": "A"}
prices = {"apple": 1.50, "banana": 0.80, "orange": 2.00}

# Basic operations
student["email"] = "alice@school.com"  # Add new key-value
name = student["name"]                 # Access by key
student["age"] = 21                    # Modify value
del student["grade"]                   # Remove key-value pair
keys = list(student.keys())            # Get all keys

Common Operations:

Add/Update

dict[key] = valuestudent["age"] = 21

Access value

dict[key]name = student["name"]

Remove item

del dict[key]del student["grade"]

Get keys

dict.keys()keys = student.keys()

Check key exists

key in dictif "name" in student:

Data Structure Comparison

Feature	List	Array	Dictionary
Access Method	By index (0, 1, 2...)	By index (0, 1, 2...)	By key ("name", "age"...)
Order	Maintains order	Maintains order	Insertion order (Python 3.7+)
Data Types	Mixed types allowed	Single type only	Keys: hashable, Values: any
Size	Dynamic (can grow/shrink)	Fixed size	Dynamic (can grow/shrink)
Performance	Good for most uses	Fastest for numeric data	Fastest key-based lookup
Memory Usage	Moderate	Most efficient	Higher overhead

Learning Activities

Data Structure Selection Challenge

Decision

30 minutes

Choose the best data structure for various real-world scenarios

Collection Operations Practice

Practice

45 minutes

Implement adding, removing, and searching operations on different structures

Performance Comparison Experiment

Experiment

35 minutes

Compare access times and memory usage of lists, arrays, and dictionaries

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