Data Structure Lec #1

1. Lecture No.01 Data Structures Zaigham Abbas University of Gujrat (Pakistan)

2. Data Structures  Prepares the students for (and is a prerequisite for) the more advanced material students will encounter in later courses.  Cover well-known data structures such as dynamic arrays, linked lists, stacks, queues, tree and graphs.  Implement data structures in C++

5. Need for Data Structures  Data structures organize data ⇒ more efficient programs.  More powerful computers ⇒ more complex applications.  More complex applications demand more calculations.

8. Organizing Data  Any organization for a collection of records that can be searched, processed in any order, or modified.  The choice of data structure and algorithm can make the difference between a program running in a few seconds or many days.

9. Organizing Data  Any organization for a collection of records that can be searched, processed in any order, or modified.  The choice of data structure and algorithm can make the difference between a program running in a few seconds or many days.

10. Efficiency  A solution is said to be efficient if it solves the problem within its resource constraints. – Space – Time  The cost of a solution is the amount of resources that the solution consumes.

11. Efficiency  A solution is said to be efficient if it solves the problem within its resource constraints.  Space  Time  The cost of a solution is the amount of resources that the solution consumes.

12. Selecting a Data Structure Select a data structure as follows: 1. Analyze the problem to determine the resource constraints a solution must meet. 2. Determine the basic operations that must be supported. Quantify the resource constraints for each operation. 3. Select the data structure that best meets these requirements.

15. Some Questions to Ask • Are all data inserted into the data structure at the beginning, or are insertions interspersed with other operations? • Can data be deleted? • Are all data processed in some well- defined order, or is random access allowed?

18. Data Structure Philosophy  Each data structure has costs and benefits.  Rarely is one data structure better than another in all situations.  A data structure requires: – space for each data item it stores, – time to perform each basic operation, – programming effort.

21. Goals of this Course 1. Reinforce the concept that costs and benefits exist for every data structure. 1. Learn the commonly used data structures. – These form a programmer's basic data structure “toolkit.” 1. Understand how to measure the cost of a data structure or program. – These techniques also allow you to judge the merits of new data structures that you or others might invent.

22. Goals of this Course 1. Reinforce the concept that costs and benefits exist for every data structure. 1. Learn the commonly used data structures. – These form a programmer's basic data structure “toolkit”. 1. Understand how to measure the cost of a data structure or program. – These techniques also allow you to judge the merits of new data structures that you or others might invent.

23. Goals of this Course 1. Reinforce the concept that costs and benefits exist for every data structure. 1. Learn the commonly used data structures. – These form a programmer's basic data structure “toolkit”. 1. Understand how to measure the cost of a data structure or program. – These techniques also allow you to judge the merits of new data structures that you or others might invent.

24. Arrays  Elementary data structure that exists as built-in in most programming languages. main( int argc, char** argv ) { int x[6]; int j; for(j=0; j < 6; j++) x[j] = 2*j; }

25. Arrays  Array declaration: int x[6];  An array is collection of cells of the same type.  The collection has the name ‘x’.  The cells are numbered with consecutive integers.  To access a cell, use the array name and an index: x[0], x[1], x[2], x[3], x[4], x[5]

26. Array Layout x[1] x[2] x[3] x[4] x[5] x[0] Array cells are contiguous in computer memory The memory can be thought of as an array

27. What is Array Name?  ‘x’ is an array name but there is no variable x. ‘x’ is not an lvalue.  For example, if we have the code int a, b; then we can write b = 2; a = b; a = 5; But we cannot write 2 = a;

30. Array Name  ‘x’ is not an lvalue int x[6]; int n; x[0] = 5; x[1] = 2; x = 3; // not allowed x = a + b; // not allowed x = &n; // not allowed

31. Array Name  ‘x’ is not an lvalue int x[6]; int n; x[0] = 5; x[1] = 2; x = 3; // not allowed x = a + b; // not allowed x = &n; // not allowed

32. Dynamic Arrays  You would like to use an array data structure but you do not know the size of the array at compile time.  You find out when the program executes that you need an integer array of size n=20.  Allocate an array using the new operator: int* y = new int[20]; // or int* y = new int[n] y[0] = 10; y[1] = 15; // use is the same

35. Dynamic Arrays  ‘y’ is a lvalue; it is a pointer that holds the address of 20 consecutive cells in memory.  It can be assigned a value. The new operator returns as address that is stored in y.  We can write: y = &x[0]; y = x; // x can appear on the right // y gets the address of the // first cell of the x array

38. Dynamic Arrays  We must free the memory we got using the new operator once we are done with the y array. delete[ ] y;  We would not do this to the x array because we did not use new to create it.

39. The LIST Data Structure  The List is among the most generic of data structures.  Real life: a. shopping list, b. groceries list, c. list of people to invite to dinner d. List of presents to get

40. Lists  A list is collection of items that are all of the same type (grocery items, integers, names)  The items, or elements of the list, are stored in some particular order  It is possible to insert new elements into various positions in the list and remove any element of the list

43. Lists  List is a set of elements in a linear order. For example, data values a1, a2, a3, a4 can be arranged in a list: (a3, a1, a2, a4) In this list, a3, is the first element, a1 is the second element, and so on  The order is important here; this is not just a random collection of elements, it is an ordered collection

44. Lists  List is a set of elements in a linear order. For example, data values a1, a2, a3, a4 can be arranged in a list: (a3, a1, a2, a4) In this list, a3, is the first element, a1 is the second element, and so on  The order is important here; this is not just a random collection of elements, it is an ordered collection

45. List Operations Useful operations • createList(): create a new list (presumably empty) • copy(): set one list to be a copy of another • clear(); clear a list (remove all elments) • insert(X, ?): Insert element X at a particular position in the list • remove(?): Remove element at some position in the list • get(?): Get element at a given position • update(X, ?): replace the element at a given position with X • find(X): determine if the element X is in the list • length(): return the length of the list.

46. List Operations  We need to decide what is meant by “particular position”; we have used “?” for this.  There are two possibilities: 1. Use the actual index of element: insert after element 3, get element number 6. This approach is taken by arrays 2. Use a “current” marker or pointer to refer to a particular position in the list.

47. List Operations  We need to decide what is meant by “particular position”; we have used “?” for this.  There are two possibilities: 1. Use the actual index of element: insert after element 3, get element number 6. This approach is taken by arrays 2. Use a “current” marker or pointer to refer to a particular position in the list.

48. List Operations  If we use the “current” marker, the following four methods would be useful:  start(): moves to “current” pointer to the very first element.  tail(): moves to “current” pointer to the very last element.  next(): move the current position forward one element  back(): move the current position backward one element

Data Structure Lec #1

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Data Structure Lec #1