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DATA STRUCTURES
PRASANTA MANGAR
DEPT OF CSA
ST. JOSEPH’S COLLEGE
DARJEELING
INTRODUCTION
 That means, algorithm is a set of instruction written to carry
out certain tasks & the data structure is the way of
organizing the data with their logical relationship retained.
 To develop a program of an algorithm, we should select an
appropriate data structure for that algorithm.
 Therefore algorithm and its associated data structures from a
program.
CLASSIFICATION OF DATA
STRUCTURE
 Data structure are normally divided into two broad
categories:
 Primitive Data Structure
 Non-Primitive Data Structure
CLASSIFICATION OF DATA
STRUCTURE
Data structure
Primitive DS Non-Primitive DS
Integer Float Character Pointer
Float
Integer Float
CLASSIFICATION OF DATA
STRUCTURE
Non-Primitive DS
Linear List Non-Linear List
Array
Link List Stack
Queue Graph Trees
PRIMITIVE DATA STRUCTURE
 There are basic structures and directly operated upon by
the machine instructions.
 In general, there are different representation on different
computers.
 Integer, Floating-point number, Character constants,
string constants, pointers etc, fall in this category.
NON-PRIMITIVE DATA STRUCTURE
 There are more sophisticated data structures.
 These are derived from the primitive data structures.
 The non-primitive data structures emphasize on
structuring of a group of homogeneous (same type) or
heterogeneous (different type) data items.
NON-PRIMITIVE DATA STRUCTURE
 Lists, Stack, Queue, Tree, Graph are example of non-
primitive data structures.
 The design of an efficient data structure must take
operations to be performed on the data structure.
NON-PRIMITIVE DATA STRUCTURE
 The most commonly used operation on data structure are
broadly categorized into following types:
 Create
 Selection
 Updating
 Searching
 Sorting
 Merging
 Destroy or Delete
DIFFERENT BETWEEN THEM
 A primitive data structure is generally a basic structure
that is usually built into the language, such as an integer,
a float.
 A non-primitive data structure is built out of primitive
data structures linked together in meaningful ways, such
as a or a linked-list, binary search tree, AVL Tree, graph
etc.
DESCRIPTION OF VARIOUS
DATA STRUCTURES : ARRAYS
 An array is defined as a set of finite number of
homogeneous elements or same data items.
 It means an array can contain one type of data only,
either all integer, all float-point number or all character.
ARRAYS
 Simply, declaration of array is as follows:
int arr[10]
 Where int specifies the data type or type of elements arrays
stores.
 “arr” is the name of array & the number specified inside the
square brackets is the number of elements an array can store,
this is also called sized or length of array.
ARRAYS
 Following are some of the concepts to be remembered
about arrays:
 The individual element of an array can
be accessed by specifying name of the
array, following by index or subscript
inside square brackets.
 The first element of the array has index
zero[0]. It means the first element and
last element will be specified as:arr[0] &
arr[9]
Respectively.
ARRAYS
 The elements of array will always be stored
in the consecutive (continues) memory
location.
 The number of elements that can be stored
in an array, that is the size of array or its
length is given by the following equation:
(Upperbound-lowerbound)+1
ARRAYS
 For the above array it would be
(9-0)+1=10,where 0 is the lower bound
of array and 9 is the upper bound of
array.
 Array can always be read or written
through loop. If we read a one-
dimensional array it require one loop for
reading and other for writing the array.
ARRAYS
 For example: Reading an array
For(i=0;i<=9;i++)
scanf(“%d”,&arr[i]);
 For example: Writing an array
For(i=0;i<=9;i++)
printf(“%d”,arr[i]);
ARRAYS
 If we are reading or writing two-
dimensional array it would require two
loops. And similarly the array of a N
dimension would required N loops.
 Some common operation performed on
array are:
Creation of an array
Traversing an array
ARRAYS
 Insertion of new element
 Deletion of required element
 Modification of an element
 Merging of arrays
LISTS
 A lists (Linear linked list) can be defined as a collection of
variable number of data items.
 Lists are the most commonly used non-primitive data
structures.
 An element of list must contain at least two fields, one for
storing data or information and other for storing address of
next element.
 As you know for storing address we have a special data
structure of list the address must be pointer type.
LISTS
 Technically each such element is referred to as a node,
therefore a list can be defined as a collection of nodes as
show bellow:
Head
AAA BBB CCC
Information field Pointer field
[Linear Liked List]
LISTS
 Types of linked lists:
 Single linked list
 Doubly linked list
 Single circular linked list
 Doubly circular linked list
STACK
 A stack is also an ordered collection of elements like
arrays, but it has a special feature that deletion and
insertion of elements can be done only from one end
called the top of the stack (TOP)
 Due to this property it is also called as last in first out
type of data structure (LIFO).
STACK
 It could be through of just like a stack of plates placed on table in
a party, a guest always takes off a fresh plate from the top and the
new plates are placed on to the stack at the top.
 It is a non-primitive data structure.
 When an element is inserted into a stack or removed from the
stack, its base remains fixed where the top of stack changes.
STACK
 Insertion of element into stack is called PUSH and
deletion of element from stack is called POP.
 The bellow show figure how the operations take place on
a stack:
PUSH POP
[STACK]
STACK
 The stack can be implemented into two ways:
 Using arrays (Static implementation)
 Using pointer (Dynamic
implementation)
QUEUE
 Queue are first in first out type of data structure (i.e. FIFO)
 In a queue new elements are added to the queue from one end
called REAR end and the element are always removed from
other end called the FRONT end.
 The people standing in a railway reservation row are an
example of queue.
QUEUE
 Each new person comes and stands at the end of the row
and person getting their reservation confirmed get out of
the row from the front end.
 The bellow show figure how the operations take place on
a stack:
10 20 30 40 50
front rear
QUEUE
 The queue can be implemented into two ways:
 Using arrays (Static implementation)
 Using pointer (Dynamic
implementation)
TREES
 A tree can be defined as finite set of data items (nodes).
 Tree is non-linear type of data structure in which data
items are arranged or stored in a sorted sequence.
 Tree represent the hierarchical relationship between
various elements.
TREES
 In trees:
 There is a special data item at the top of hierarchy called the
Root of the tree.
 The remaining data items are partitioned into number of
mutually exclusive subset, each of which is itself, a tree
which is called the sub tree.
 The tree always grows in length towards bottom in data
structures, unlike natural trees which grows upwards.
TREES
 The tree structure organizes the data into branches,
which related the information.
A
B C
D E F G
root
GRAPH
 Graph is a mathematical non-linear data structure
capable of representing many kind of physical structures.
 It has found application in Geography, Chemistry and
Engineering sciences.
 Definition: A graph G(V,E) is a set of vertices V and a set
of edges E.
GRAPH
 An edge connects a pair of vertices and many have
weight such as length, cost and another measuring
instrument for according the graph.
 Vertices on the graph are shown as point or circles and
edges are drawn as arcs or line segment.
GRAPH
 Example of graph:
v2
v1
v4
v5
v3
10
15
8
6
11
9
v4
v1
v2
v4
v3
[a] Directed &
Weighted Graph
[b] Undirected Graph
GRAPH
 Types of Graphs:
 Directed graph
 Undirected graph
 Simple graph
 Weighted graph
 Connected graph
 Non-connected graph
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data structure programing language in c.ppt

  • 1. DATA STRUCTURES PRASANTA MANGAR DEPT OF CSA ST. JOSEPH’S COLLEGE DARJEELING
  • 2. INTRODUCTION  That means, algorithm is a set of instruction written to carry out certain tasks & the data structure is the way of organizing the data with their logical relationship retained.  To develop a program of an algorithm, we should select an appropriate data structure for that algorithm.  Therefore algorithm and its associated data structures from a program.
  • 3. CLASSIFICATION OF DATA STRUCTURE  Data structure are normally divided into two broad categories:  Primitive Data Structure  Non-Primitive Data Structure
  • 4. CLASSIFICATION OF DATA STRUCTURE Data structure Primitive DS Non-Primitive DS Integer Float Character Pointer Float Integer Float
  • 5. CLASSIFICATION OF DATA STRUCTURE Non-Primitive DS Linear List Non-Linear List Array Link List Stack Queue Graph Trees
  • 6. PRIMITIVE DATA STRUCTURE  There are basic structures and directly operated upon by the machine instructions.  In general, there are different representation on different computers.  Integer, Floating-point number, Character constants, string constants, pointers etc, fall in this category.
  • 7. NON-PRIMITIVE DATA STRUCTURE  There are more sophisticated data structures.  These are derived from the primitive data structures.  The non-primitive data structures emphasize on structuring of a group of homogeneous (same type) or heterogeneous (different type) data items.
  • 8. NON-PRIMITIVE DATA STRUCTURE  Lists, Stack, Queue, Tree, Graph are example of non- primitive data structures.  The design of an efficient data structure must take operations to be performed on the data structure.
  • 9. NON-PRIMITIVE DATA STRUCTURE  The most commonly used operation on data structure are broadly categorized into following types:  Create  Selection  Updating  Searching  Sorting  Merging  Destroy or Delete
  • 10. DIFFERENT BETWEEN THEM  A primitive data structure is generally a basic structure that is usually built into the language, such as an integer, a float.  A non-primitive data structure is built out of primitive data structures linked together in meaningful ways, such as a or a linked-list, binary search tree, AVL Tree, graph etc.
  • 11. DESCRIPTION OF VARIOUS DATA STRUCTURES : ARRAYS  An array is defined as a set of finite number of homogeneous elements or same data items.  It means an array can contain one type of data only, either all integer, all float-point number or all character.
  • 12. ARRAYS  Simply, declaration of array is as follows: int arr[10]  Where int specifies the data type or type of elements arrays stores.  “arr” is the name of array & the number specified inside the square brackets is the number of elements an array can store, this is also called sized or length of array.
  • 13. ARRAYS  Following are some of the concepts to be remembered about arrays:  The individual element of an array can be accessed by specifying name of the array, following by index or subscript inside square brackets.  The first element of the array has index zero[0]. It means the first element and last element will be specified as:arr[0] & arr[9] Respectively.
  • 14. ARRAYS  The elements of array will always be stored in the consecutive (continues) memory location.  The number of elements that can be stored in an array, that is the size of array or its length is given by the following equation: (Upperbound-lowerbound)+1
  • 15. ARRAYS  For the above array it would be (9-0)+1=10,where 0 is the lower bound of array and 9 is the upper bound of array.  Array can always be read or written through loop. If we read a one- dimensional array it require one loop for reading and other for writing the array.
  • 16. ARRAYS  For example: Reading an array For(i=0;i<=9;i++) scanf(“%d”,&arr[i]);  For example: Writing an array For(i=0;i<=9;i++) printf(“%d”,arr[i]);
  • 17. ARRAYS  If we are reading or writing two- dimensional array it would require two loops. And similarly the array of a N dimension would required N loops.  Some common operation performed on array are: Creation of an array Traversing an array
  • 18. ARRAYS  Insertion of new element  Deletion of required element  Modification of an element  Merging of arrays
  • 19. LISTS  A lists (Linear linked list) can be defined as a collection of variable number of data items.  Lists are the most commonly used non-primitive data structures.  An element of list must contain at least two fields, one for storing data or information and other for storing address of next element.  As you know for storing address we have a special data structure of list the address must be pointer type.
  • 20. LISTS  Technically each such element is referred to as a node, therefore a list can be defined as a collection of nodes as show bellow: Head AAA BBB CCC Information field Pointer field [Linear Liked List]
  • 21. LISTS  Types of linked lists:  Single linked list  Doubly linked list  Single circular linked list  Doubly circular linked list
  • 22. STACK  A stack is also an ordered collection of elements like arrays, but it has a special feature that deletion and insertion of elements can be done only from one end called the top of the stack (TOP)  Due to this property it is also called as last in first out type of data structure (LIFO).
  • 23. STACK  It could be through of just like a stack of plates placed on table in a party, a guest always takes off a fresh plate from the top and the new plates are placed on to the stack at the top.  It is a non-primitive data structure.  When an element is inserted into a stack or removed from the stack, its base remains fixed where the top of stack changes.
  • 24. STACK  Insertion of element into stack is called PUSH and deletion of element from stack is called POP.  The bellow show figure how the operations take place on a stack: PUSH POP [STACK]
  • 25. STACK  The stack can be implemented into two ways:  Using arrays (Static implementation)  Using pointer (Dynamic implementation)
  • 26. QUEUE  Queue are first in first out type of data structure (i.e. FIFO)  In a queue new elements are added to the queue from one end called REAR end and the element are always removed from other end called the FRONT end.  The people standing in a railway reservation row are an example of queue.
  • 27. QUEUE  Each new person comes and stands at the end of the row and person getting their reservation confirmed get out of the row from the front end.  The bellow show figure how the operations take place on a stack: 10 20 30 40 50 front rear
  • 28. QUEUE  The queue can be implemented into two ways:  Using arrays (Static implementation)  Using pointer (Dynamic implementation)
  • 29. TREES  A tree can be defined as finite set of data items (nodes).  Tree is non-linear type of data structure in which data items are arranged or stored in a sorted sequence.  Tree represent the hierarchical relationship between various elements.
  • 30. TREES  In trees:  There is a special data item at the top of hierarchy called the Root of the tree.  The remaining data items are partitioned into number of mutually exclusive subset, each of which is itself, a tree which is called the sub tree.  The tree always grows in length towards bottom in data structures, unlike natural trees which grows upwards.
  • 31. TREES  The tree structure organizes the data into branches, which related the information. A B C D E F G root
  • 32. GRAPH  Graph is a mathematical non-linear data structure capable of representing many kind of physical structures.  It has found application in Geography, Chemistry and Engineering sciences.  Definition: A graph G(V,E) is a set of vertices V and a set of edges E.
  • 33. GRAPH  An edge connects a pair of vertices and many have weight such as length, cost and another measuring instrument for according the graph.  Vertices on the graph are shown as point or circles and edges are drawn as arcs or line segment.
  • 34. GRAPH  Example of graph: v2 v1 v4 v5 v3 10 15 8 6 11 9 v4 v1 v2 v4 v3 [a] Directed & Weighted Graph [b] Undirected Graph
  • 35. GRAPH  Types of Graphs:  Directed graph  Undirected graph  Simple graph  Weighted graph  Connected graph  Non-connected graph