1. Connecting with Computer Science, 2e
Chapter 8
Data Structures 1

2. Objectives In this chapter you will:
Learn what a data structure is and how it’s used
Learn about single-dimensional and multidimensional arrays and how they work
Learn what a pointer is and how it’s used in data structures
Learn that a linked list allows you to work with dynamic information
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3. Objectives (cont’d.) In this chapter you will (cont’d.):
Understand that a stack is a linked list and how it’s used
Learn that a queue is another form of a linked list and how it’s used
Learn that a binary tree is a data structure that stores information in a hierarchical order
See an overview of several sorting routines
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4. Why You Need to Know About…Data Structures
Organize the data in a computer
Efficiently access and process data
All programs use some form of data structure
There are many occasions for using data structures
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5. Data Structures Defined as a way of organizing data
Types of data structures in memory
Arrays, lists, stacks, queues, trees
File structures organize storage media data
Computer memory is organized into cells
Memory cell has a memory address and content
Memory addresses are organized consecutively
Data structures hide memory implementation details
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6. Arrays Set of contiguous memory cells Simplest memory data structure
Used for storing the same type of data
Simplest memory data structure
Consists of a set of contiguous memory cells
Memory cells store the same type of data
Usefulness:
Storing similar kinds of information in memory
Sorted or left as entered
One array name for a number of similar items
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7. Arrays (cont’d.)
Figure 8-2, Arrays make program logic easier to understand and use
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8. How an Array Works Java example
int[ ] aGrades = new int[5];
 “int[ ]” indicates array will hold integers
“aGrades” identifies the array name
“new” keyword specifies new array being created
“int[5]” reserves five memory locations
“=” sign assigns aGrades as “manager” of the array
“;” (semicolon) indicates end of statement reached
Hungarian notation standard is used to name “aGrades”
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9. How an Array Works (cont’d.)
Figure 8-3, Five contiguous memory cells managed by aGrades in a single-dimensional array
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10. How an Array Works (cont’d.)
Element: memory cell in an array
Dimension: levels created to hold array elements
aGrades: single-dimensional array (row of mailboxes)
Offset specifies distance between memory locations
Array’s first position: referenced as position 0
Next array position: referenced as position 1
Found by using starting memory location plus one offset
Third array position: referenced as position 2
Found by using starting memory location plus two offsets
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11. How an Array Works (cont’d.)
Figure 8-5, Arrays start at position 0 and use an offset to know where the next element is located in memory
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12. How an Array Works (cont’d.)
Index (subscript)
Indicates memory cell to access in the array
Looks at element’s position
Placed between square brackets ( [ ] ) after array name
Integer placed in “[ ]” for access
Example: aGrades[0] = 50;
Position 0: first position
Array with positions 0 to 4
Five memory cells or addresses
Upper bound: highest array position
Lower bound: lowest array position
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13. How an Array Works (cont’d.)
Figure 8-6, The array with all elements stored
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14. Multidimensional Arrays
Consists of two or more single-dimensional arrays
Multiple rows stacked on top of each other
Apartment building mailboxes and tic-tac-toe boards
Creating the tic-tac-toe board
char[ ][ ] aTicTacToe = new char[3][3];
Assignment: aTicTacToe[1][1] = ’X’;
Place X in second row of the second column
Arrays beyond three dimensions are difficult to manage
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15. Multidimensional Arrays (cont’d.)
Figure 8-7, A multidimensional array is like apartment mailboxes stacked on top of each other
Figure 8-8, Tic-tac-toe board
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16. Multidimensional Arrays (cont’d.)
Figure 8-9, First row of the tic-tac-toe
board
Figure 8-10, Second and third rows of
the tic-tac-toe board
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17. Multidimensional Arrays (cont’d.)
Figure 8-11, Storing a value in an array location
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18. Multidimensional Arrays (cont’d.)
Figure 8-12, Three-dimensional array
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19. Uses of Arrays Advantages Disadvantages
Allow sequential access of memory cells
Retrieve and store data with element array name and data type
Easy to create
Useful for people who write computer programs
Disadvantages
Require a lot of overhead for insertions
Memory cell data is only accessed sequentially
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20. Lists Hold dynamic lists of data
Lists vary in size
Examples: class enrollment, cars being repaired, in-boxes
Appropriate whenever amount of data is unknown or can change
Three basic list forms:
Linked lists
Queues
Stacks
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21. Linked Lists Use noncontiguous memory locations to store data
Each element points to the next element in line
Does not have to be contiguous with previous element
Used when exact number of items is unknown
Store data noncontiguously
Maintain data and address of next linked cell
Examples: names of students visiting a professor, points scored in a video game, list of spammers
Basic constructs for more advanced data structures
Queues and stacks: pointer based
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22. Linked Lists (cont’d.)
Pointers: memory variable containing the address of a memory cell as its data
Illustration: linked list game
Students sit in a circle with piece of paper
Paper has box in the upper left corner and center
Upper left box indicates a student number
Center box divided into two parts
Students indicate favorite color in left part of center
Professor has a piece of paper with a number only
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23. Linked Lists (cont’d.) Figure 8-14, Structure of a linked list
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24. Linked Lists (cont’d.) Piece of paper represents a two-part node
Data (the first part, the color)
Pointer (the student ID number)
Professor’s piece: head pointer with no data
Last student: pointer’s value is NULL
Inserting new elements
No resizing needed
Create new “piece of paper” with dual node structure
Realign pointers to accommodate new node (paper)
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25. Linked Lists (cont’d.)
Figure 8-15, Inserting an element into a linked list
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26. Linked Lists (cont’d.) Similar procedure for deleting items
Modify pointer of element preceding target item
Students deleted from list without moving elements
Use dynamic memory allocation
More efficient than arrays
Memory cells need not be contiguous
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27. Linked Lists (cont’d.)
Figure 8-16, Deleting an element from a linked list
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28. Stacks
List in which the next item to be removed is the item most recently stored
“Push” items on to the list to store new items
“Pop” items off the list to retrieve current items
Examples
Restaurant spring-loaded plate holder or text editor
Peeking
Looking at the stack’s top item without removing it
LIFO data structure
Last or most recent item pushed (put) onto the stack
Becomes first item popped (removed) from the stack
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29. Stacks (cont’d.) Figure 8-17, The stack concept
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30. Uses of a Stack Processes lines of program source code
Source code is logically organized into procedures
Keep track of procedure calls with a stack
Address of procedure popped off stack 
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31. Back to Pointers Stack pointer
Keeps track of where to remove or add an item in a data structure
Check stack before applying pop or push operations
Stacks
Memory locations organized into logical structures
Facilitates reading from them and writing to them
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32. Back to Pointers (cont’d.)
Figure 8-18, Stack pointer is decremented when the item
is popped off
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33. Queues Another type of linked list
Implements first in, first out (FIFO) storage system
Insertions made at the end
Deletions made at the beginning
Similar to that of a waiting line
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34. Uses of a Queue Printer example
First item printed
Document waiting longest
Current item deleted from queue
Next item printed
New documents
Placed at the end of the queue
Insertions of new data occur at the rear of the queue
Removal of data occurs at the front of the queue
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35. Pointers Again Head pointer tracks beginning of queue
Tail pointer tracks end of queue
Queue containing no items
Both the head and tail pointer point to same location
Dequeue operation
Remove item (oldest entry) from the queue
Head pointer changed to point to the next item in list
Enqueue operation
Item placed at list end
Tail pointer updated
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36. Pointers Again (cont’d.)
Figure 8-19, A queue uses a FIFO structure
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37. Pointers Again (cont’d.)
Figure 8-20, Removing an item
from the queue
Figure 8-21, Inserting an item into
the queue
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38. Trees
Hierarchical data structure similar to organizational or genealogy charts
Node or vertex: position in the tree
Figure 8-22, Tree data structure
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39. Trees (cont’d.) Binary tree Each node has at most two child nodes
Node can have zero, one, or two child nodes
Left child: child node to the left of the parent node
Right child: child node to the right of the parent node
Root: node that begins the tree
Leaf node: node that has no child nodes
Depth (level): distance from root node
Height: longest path length in the tree
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40. Trees (cont’d.) Figure 8-23, Tree nodes
Figure 8-24, The level and height
of a binary tree
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41. Uses of Binary Trees Binary search tree: a type of binary tree
Data value of left child node is less than the value of parent node
Data value of right child node is greater than the value of parent node
Useful for searching through stored data
Storing information in a hierarchical representation
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42. Uses of Binary Trees (cont’d.)
Figure 8-25, A file system structure
can be stored as a binary search tree
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43. Searching a Binary Tree
Three components in a binary search tree node:
Left child pointer, right child pointer, data
Root pointer contains root node’s address
Provides initial access to the tree
If left or right child pointers contain a null value
Node is not a parent to other nodes down that specific path
If both left and right pointers contain null values
Node is not a parent down either path
Binary tree must be defined properly to be searchable
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44. Searching a Binary Tree (cont’d.)
Figure 8-26, A node in a binary search tree
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45. Searching a Binary Tree (cont’d.)
Search routine
Start at the root position
Determine if path moves to left child or right
Move in direction of data (left or right)
If left pointer NULL
No node to traverse down the left side
If left pointer does have a value
Path continues down that side
If value looking for is found
Stop at that node
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46. Searching a Binary Tree (cont’d.)
Figure 8-27, Searching a binary tree for the value 8
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47. Searching a Binary Tree (cont’d.)
Figure 8-28, Searching a binary tree for the value 1
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48. Sorting Algorithms Sorting: leverages data structures to organize data
Example of data being sorted:
Words in a dictionary
Files in a directory
Index of a book
Course offerings at a university
Many algorithms for sorting
Each has advantages and disadvantages
Focus: selection and bubble sorts
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49. Selection Sort Selection sort: mimics manual sorting
Starts at first value in the list
Processes each element looking for smallest value
After smallest value found, it is placed in first position
Moves first position value to location originally containing smallest value
Sort moves on looking for next smallest value
Continues to “swap places”
Simple to use and implement
Inefficient for large lists
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50. Selection Sort (cont’d.)
Figure 8-29, A selection sort
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51. Bubble Sort Bubble: older and slower sort method Simple to implement
Start with the last element in the list
Compare its value to that of the item just above
If smaller, change positions and continue up list
Continue comparison until smaller item found
If not smaller, next item compared to item above
Check until smallest value “bubbles” to the top
Process repeated for list less first item
Simple to implement
Inefficient for large lists
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52. Bubble Sort (cont’d.) Figure 8-30, A bubble sort
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53. Bubble Sort (cont’d.) Figure 8-31, The bubble sort continues
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54. Other Types of Sorts
Quicksort: incorporates “divide and conquer” logic
Two small lists are easier to sort than one large list
Uses recursion to break down problem
All sorted sub-lists are combined into single sorted set
Fast but difficult to comprehend
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55. Other Type of Sorts (cont’d.)
Merge sort: similar to the quicksort
Continuously halves data sets using recursion
Sorted halves are merged back into one list
Time efficient, but not as space efficient as quicksort
Insertion sort: simulates manual sorting of cards
Requires two lists
Not complex, but inefficient for list size fewer than 1000
Shell sort: uses insertion sort against expanding data set
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56. One Last Thought Algorithms
Used everywhere in the computer industry
Knowing how to work with data structures and sorting algorithms is necessary to begin writing computer programs
Many algorithms are written and available for use
Knowing the tools available and which sort routine will perform best for a situation saves time
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57. Summary Data structures organize data Basic data structures Arrays
Arrays, lists, queues, stacks, trees
Arrays
Store data contiguously
May have one or more dimensions
Linked lists
Store data in dynamic containers
Use pointers for noncontiguous storage
Pointer: contains memory cell address as its data
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58. Summary (cont’d.) Stack Queue Tree
Linked list structured as LIFO container
Queue
Linked list structured as FIFO container
Tree
Hierarchical structure consisting of nodes
Binary tree: nodes have at most two children
Binary search tree: efficient for searching for information
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59. Summary (cont’d.) Sorting algorithms Sorting routines
Organize data within structure
Examples: selection sort, bubble sort, quicksort, merge sort, insertion sort, shell sort
Sorting routines
Analyzed by code, space, and time complexities
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