Question? Leave a message!




Problem Solving with Algorithms and Data Structures

lecture notes on Algorithms and Data Structures and problem solving with algorithms and data structures using python pdf free download
ShawnPacinocal Profile Pic
ShawnPacinocal,United States,Researcher
Published Date:09-07-2017
Website URL
Comment
Problem Solving with Algorithms and Data Structures Release3.0 Brad Miller, David Ranum September 22, 2013CONTENTS 1 Introduction 3 1.1 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2 Getting Started . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 What Is Computer Science? . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.4 Review of Basic Python . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 1.6 Key Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 1.7 Programming Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 2 Algorithm Analysis 41 2.1 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 2.2 What Is Algorithm Analysis? . . . . . . . . . . . . . . . . . . . . . . . . . . 41 2.3 Performance of Python Data Structures . . . . . . . . . . . . . . . . . . . . . 52 2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 2.5 Key Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 2.6 Discussion Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 2.7 Programming Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 3 Basic Data Structures 61 3.1 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 3.2 What Are Linear Structures? . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 3.3 Stacks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 3.4 The Stack Abstract Data Type . . . . . . . . . . . . . . . . . . . . . . . . . . 64 3.5 Queues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 3.6 Deques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 3.7 Lists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 3.8 The Unordered List Abstract Data Type . . . . . . . . . . . . . . . . . . . . . 98 3.9 Implementing an Unordered List: Linked Lists . . . . . . . . . . . . . . . . . 98 3.10 The Ordered List Abstract Data Type . . . . . . . . . . . . . . . . . . . . . . 108 3.11 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 3.12 Key Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 3.13 Discussion Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 3.14 Programming Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 4 Recursion 117 4.1 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 4.2 What is Recursion? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 i4.3 Stack Frames: Implementing Recursion . . . . . . . . . . . . . . . . . . . . . 123 4.4 Visualising Recursion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 4.5 Complex Recursive Problems . . . . . . . . . . . . . . . . . . . . . . . . . . 133 4.6 Exploring a Maze . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 4.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 4.8 Key Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 4.9 Discussion Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 4.10 Programming Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 5 Sorting and Searching 147 5.1 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 5.2 Searching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 5.3 Sorting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 5.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 5.5 Key Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182 5.6 Discussion Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182 5.7 Programming Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 6 Trees and Tree Algorithms 185 6.1 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 6.2 Examples Of Trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 6.3 Vocabulary and Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . 188 6.4 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190 6.5 Priority Queues with Binary Heaps . . . . . . . . . . . . . . . . . . . . . . . 198 6.6 Binary Tree Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206 6.7 Tree Traversals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212 6.8 Binary Search Trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215 6.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231 6.10 Key Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232 6.11 Discussion Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232 6.12 Programming Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 7 JSON 235 7.1 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 7.2 What is JSON? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 7.3 The JSON Syntax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 iiProblem Solving with Algorithms and Data Structures, Release 3.0 CONTENTS 1Problem Solving with Algorithms and Data Structures, Release 3.0 2 CONTENTSCHAPTER ONE INTRODUCTION 1.1 Objectives • To review the ideas of computer science, programming, and problem-solving. • To understand abstraction and the role it plays in the problem-solving process. • To understand and implement the notion of an abstract data type. • To review the Python programming language. 1.2 Getting Started The way we think about programming has undergone many changes in the years since the first electronic computers required patch cables and switches to convey instructions from human to machine. As is the case with many aspects of society, changes in computing technology provide computer scientists with a growing number of tools and platforms on which to practice their craft. Advances such as faster processors, high-speed networks, and large memory ca- pacities have created a spiral of complexity through which computer scientists must navigate. Throughout all of this rapid evolution, a number of basic principles have remained constant. The science of computing is concerned with using computers to solve problems. You have no doubt spent considerable time learning the basics of problem-solving and hope- fully feel confident in your ability to take a problem statement and develop a solution. You have also learned that writing computer programs is often hard. The complexity of large problems and the corresponding complexity of the solutions can tend to overshadow the fundamental ideas related to the problem-solving process. This chapter emphasizes two important areas for the rest of the text. First, it reviews the frame- work within which computer science and the study of algorithms and data structures must fit, in particular, the reasons why we need to study these topics and how understanding these top- ics helps us to become better problem solvers. Second, we review the Python programming language. Although we cannot provide a detailed, exhaustive reference, we will give examples and explanations for the basic constructs and ideas that will occur throughout the remaining chapters. 3Problem Solving with Algorithms and Data Structures, Release 3.0 1.3 What Is Computer Science? Computer science is often difficult to define. This is probably due to the unfortunate use of the word “computer” in the name. As you are perhaps aware, computer science is not simply the study of computers. Although computers play an important supporting role as a tool in the discipline, they are just that – tools. Computer science is the study of problems, problem-solving, and the solutions that come out of the problem-solving process. Given a problem, a computer scientist’s goal is to develop an algorithm, a step-by-step list of instructions for solving any instance of the problem that might arise. Algorithms are finite processes that if followed will solve the problem. Algorithms are solutions. Computer science can be thought of as the study of algorithms. However, we must be careful to include the fact that some problems may not have a solution. Although proving this statement is beyond the scope of this text, the fact that some problems cannot be solved is important for those who study computer science. We can fully define computer science, then, by including both types of problems and stating that computer science is the study of solutions to problems as well as the study of problems with no solutions. It is also very common to include the word computable when describing problems and solu- tions. We say that a problem is computable if an algorithm exists for solving it. An alternative definition for computer science, then, is to say that computer science is the study of problems that are and that are not computable, the study of the existence and the nonexistence of algo- rithms. In any case, you will note that the word “computer” did not come up at all. Solutions are considered independent from the machine. Computer science, as it pertains to the problem-solving process itself, is also the study of abstraction. Abstraction allows us to view the problem and solution in such a way as to separate the so-called logical and physical perspectives. The basic idea is familiar to us in a common example. Consider the automobile that you may have driven to school or work today. As a driver, a user of the car, you have certain interactions that take place in order to utilize the car for its intended purpose. You get in, insert the key, start the car, shift, brake, accelerate, and steer in order to drive. From an abstraction point of view, we can say that you are seeing the logical perspective of the automobile. You are using the functions provided by the car designers for the purpose of transporting you from one location to another. These functions are sometimes also referred to as the interface. On the other hand, the mechanic who must repair your automobile takes a very different point of view. She not only knows how to drive but must know all of the details necessary to carry out all the functions that we take for granted. She needs to understand how the engine works, how the transmission shifts gears, how temperature is controlled, and so on. This is known as the physical perspective, the details that take place “under the hood.” The same thing happens when we use computers. Most people use computers to write docu- ments, send and receive email, surf the web, play music, store images, and play games without any knowledge of the details that take place to allow those types of applications to work. They view computers from a logical or user perspective. Computer scientists, programmers, technol- ogy support staff, and system administrators take a very different view of the computer. They 4 Chapter 1. IntroductionProblem Solving with Algorithms and Data Structures, Release 3.0 Figure 1.1: Procedural Abstraction must know the details of how operating systems work, how network protocols are configured, and how to code various scripts that control function. They must be able to control the low-level details that a user simply assumes. The common point for both of these examples is that the user of the abstraction, sometimes also called the client, does not need to know the details as long as the user is aware of the way the interface works. This interface is the way we as users communicate with the underlying complexities of the implementation. As another example of abstraction, consider the Python math module. Once we import the module, we can perform computations such as import math math.sqrt(16) 4.0 This is an example of procedural abstraction. We do not necessarily know how the square root is being calculated, but we know what the function is called and how to use it. If we perform the import correctly, we can assume that the function will provide us with the correct results. We know that someone implemented a solution to the square root problem but we only need to know how to use it. This is sometimes referred to as a “black box” view of a process. We simply describe the interface: the name of the function, what is needed (the parameters), and what will be returned. The details are hidden inside (see Figure 1.1). 1.3.1 What Is Programming? Programming is the process of taking an algorithm and encoding it into a notation, a pro- gramming language, so that it can be executed by a computer. Although many programming languages and many different types of computers exist, the important first step is the need to have the solution. Without an algorithm there can be no program. Computer science is not the study of programming. Programming, however, is an important part of what a computer scientist does. Programming is often the way that we create a repre- sentation for our solutions. Therefore, this language representation and the process of creating it becomes a fundamental part of the discipline. Algorithms describe the solution to a problem in terms of the data needed to represent the problem instance and the set of steps necessary to produce the intended result. Programming languages must provide a notational way to represent both the process and the data. To this end, languages provide control constructs and data types. 1.3. What Is Computer Science? 5Problem Solving with Algorithms and Data Structures, Release 3.0 Control constructs allow algorithmic steps to be represented in a convenient yet unambiguous way. At a minimum, algorithms require constructs that perform sequential processing, selection for decision-making, and iteration for repetitive control. As long as the language provides these basic statements, it can be used for algorithm representation. All data items in the computer are represented as strings of binary digits. In order to give these strings meaning, we need to have data types. Data types provide an interpretation for this binary data so that we can think about the data in terms that make sense with respect to the problem being solved. These low-level, built-in data types (sometimes called the primitive data types) provide the building blocks for algorithm development. For example, most programming languages provide a data type for integers. Strings of binary digits in the computer’s memory can be interpreted as integers and given the typical meanings that we commonly associate with integers (e.g. 23, 654, and− 19). In addition, a data type also provides a description of the operations that the data items can participate in. With integers, operations such as addition, subtraction, and multiplication are common. We have come to expect that numeric types of data can participate in these arithmetic operations. The difficulty that often arises for us is the fact that problems and their solutions are very complex. These simple, language-provided constructs and data types, although certainly suf- ficient to represent complex solutions, are typically at a disadvantage as we work through the problem-solving process. We need ways to control this complexity and assist with the creation of solutions. 1.3.2 Why Study Data Structures and Abstract Data Types? To manage the complexity of problems and the problem-solving process, computer scientists use abstractions to allow them to focus on the “big picture” without getting lost in the details. By creating models of the problem domain, we are able to utilize a better and more efficient problem-solving process. These models allow us to describe the data that our algorithms will manipulate in a much more consistent way with respect to the problem itself. Earlier, we referred to procedural abstraction as a process that hides the details of a particular function to allow the user or client to view it at a very high level. We now turn our attention to a similar idea, that of data abstraction. An abstract data type, sometimes called an ADT, is a logical description of how we view the data and the operations that are allowed without regard to how they will be implemented. This means that we are concerned only with what the data is representing and not with how it will eventually be constructed. By providing this level of abstraction, we are creating an encapsulation around the data. The idea is that by encapsulating the details of the implementation, we are hiding them from the user’s view. This is called information hiding. Figure 1.2 shows a picture of what an abstract data type is and how it operates. The user interacts with the interface, using the operations that have been specified by the abstract data type. The abstract data type is the shell that the user interacts with. The implementation is hidden one level deeper. The user is not concerned with the details of the implementation. The implementation of an abstract data type, often referred to as a data structure, will require that we provide a physical view of the data using some collection of programming constructs and primitive data types. As we discussed earlier, the separation of these two perspectives will 6 Chapter 1. IntroductionProblem Solving with Algorithms and Data Structures, Release 3.0 Figure 1.2: Abstract Data Type allow us to define the complex data models for our problems without giving any indication as to the details of how the model will actually be built. This provides an implementation- independent view of the data. Since there will usually be many different ways to implement an abstract data type, this implementation independence allows the programmer to switch the details of the implementation without changing the way the user of the data interacts with it. The user can remain focused on the problem-solving process. 1.3.3 Why Study Algorithms? Computer scientists learn by experience. We learn by seeing others solve problems and by solving problems by ourselves. Being exposed to different problem-solving techniques and seeing how different algorithms are designed helps us to take on the next challenging problem that we are given. By considering a number of different algorithms, we can begin to develop pattern recognition so that the next time a similar problem arises, we are better able to solve it. Algorithms are often quite different from one another. Consider the example of sqrt seen earlier. It is entirely possible that there are many different ways to implement the details to compute the square root function. One algorithm may use many fewer resources than another. One algorithm might take 10 times as long to return the result as the other. We would like to have some way to compare these two solutions. Even though they both work, one is perhaps “better” than the other. We might suggest that one is more efficient or that one simply works faster or uses less memory. As we study algorithms, we can learn analysis techniques that allow us to compare and contrast solutions based solely on their own characteristics, not the characteristics of the program or computer used to implement them. In the worst case scenario, we may have a problem that is intractable, meaning that there is no algorithm that can solve the problem in a realistic amount of time. It is important to be able to distinguish between those problems that have solutions, those that do not, and those where solutions exist but require too much time or other resources to work reasonably. There will often be trade-offs that we will need to identify and decide upon. As computer scientists, in addition to our ability to solve problems, we will also need to know and understand 1.3. What Is Computer Science? 7Problem Solving with Algorithms and Data Structures, Release 3.0 solution evaluation techniques. In the end, there are often many ways to solve a problem. Finding a solution and then deciding whether it is a good one are tasks that we will do over and over again. 1.4 Review of Basic Python In this section, we will review the programming language Python and also provide some more detailed examples of the ideas from the previous section. If you are new to Python or find that you need more information about any of the topics presented, we recommend that you consult a resource such as the Python Language Reference or a Python Tutorial. Our goal here is to reacquaint you with the language and also reinforce some of the concepts that will be central to later chapters. Python is a modern, easy-to-learn, object-oriented programming language. It has a powerful set of built-in data types and easy-to-use control constructs. Since Python is an interpreted language, it is most easily reviewed by simply looking at and describing interactive sessions. You should recall that the interpreter displays the familiar prompt and then evaluates the Python construct that you provide. For example, print("Algorithms and Data Structures") Algorithms and Data Structures shows the prompt, theprint function, the result, and the next prompt. 1.4.1 Getting Started with Data We stated above that Python supports the object-oriented programming paradigm. This means that Python considers data to be the focal point of the problem-solving process. In Python, as well as in any other object-oriented programming language, we define a class to be a description of what the data look like (the state) and what the data can do (the behavior). Classes are analogous to abstract data types because a user of a class only sees the state and behavior of a data item. Data items are called objects in the object-oriented paradigm. An object is an instance of a class. Built-in Atomic Data Types We will begin our review by considering the atomic data types. Python has two main built-in numeric classes that implement the integer and floating point data types. These Python classes are calledint andfloat. The standard arithmetic operations, +,− , ,/, and (exponentia- tion), can be used with parentheses forcing the order of operations away from normal operator precedence. Other very useful operations are the remainder (modulo) operator, %, and integer division, //. Note that when two integers are divided, the result is a floating point. The inte- ger division operator returns the integer portion of the quotient by truncating any fractional part. 8 Chapter 1. IntroductionProblem Solving with Algorithms and Data Structures, Release 3.0 Operation Name Operator Explanation less than Less than operator greater than Greater than operator less than or equal = Less than or equal to operator greater than or equal = Greater than or equal to operator equal == Equality operator not equal = Not equal operator logical and and Both operands True for result to be True logical or or Either operand True for result to be True logical not not Negates the truth value: False becomes True, True becomes False Table 1.1: Relational and Logical Operators print(2+3 4) 14 print((2+3) 4) 20 print(2 10) 1024 print(6/3) 2.0 print(7/3) 2.33333333333 print(7//3) 2 print(7%3) 1 print(3/6) 0.5 print(3//6) 0 print(3%6) 3 print(2 100) 1267650600228229401496703205376 The boolean data type, implemented as the Python bool class, will be quite useful for representing truth values. The possible state values for a boolean object are True and False with the standard boolean operators,and,or, andnot. True True False False False or True True not (False or True) False True and True True Boolean data objects are also used as results for comparison operators such as equality (==) and greater than (). In addition, relational operators and logical operators can be combined together to form complex logical questions. Table 1.1 shows the relational and logical operators with examples shown in the session that follows. print(5 == 10) print(10 5) 1.4. Review of Basic Python 9Problem Solving with Algorithms and Data Structures, Release 3.0 Figure 1.3: Variables Hold References to Data Objects Figure 1.4: Assignment changes the Reference print((5 = 1) and (5 = 10)) Identifiers are used in programming languages as names. In Python, identifiers start with a letter or an underscore (_), are case sensitive, and can be of any length. Remember that it is always a good idea to use names that convey meaning so that your program code is easier to read and understand. A Python variable is created when a name is used for the first time on the left-hand side of an assignment statement. Assignment statements provide a way to associate a name with a value. The variable will hold a reference to a piece of data and not the data itself. Consider the following session: the_sum = 0 the_sum 0 the_sum = the_sum + 1 the_sum 1 the_sum = True the_sum True The assignment statementthe_sum = 0 creates a variable calledthe_sum and lets it hold the reference to the data object 0 (see Figure 1.3). In general, the right-hand side of the assignment statement is evaluated and a reference to the resulting data object is “assigned” to the name on the left-hand side. At this point in our example, the type of the variable is integer as that is the type of the data currently being referred to by “the_sum.” If the type of the data changes (see Figure 1.4), as shown above with the boolean value True, so does the type of the variable (the_sum is now of the type boolean). The assignment statement changes the reference being held by the variable. This is a dynamic characteristic of Python. The same variable can refer to many different types of data. 10 Chapter 1. IntroductionProblem Solving with Algorithms and Data Structures, Release 3.0 Operation Name Operator Explanation indexing Access an element of a sequence concatenation + Combine sequences together repetition Concatenate a repeated number of times membership in Ask whether an item is in a sequence length len Ask the number of items in the sequence slicing : Extract a part of a sequence Table 1.2: Operations on Any Sequence in Python Built-in Collection Data Types In addition to the numeric and boolean classes, Python has a number of very powerful built- in collection classes. Lists, strings, and tuples are ordered collections that are very similar in general structure but have specific differences that must be understood for them to be used properly. Sets and dictionaries are unordered collections. A list is an ordered collection of zero or more references to Python data objects. Lists are written as comma-delimited values enclosed in square brackets. The empty list is simply . Lists are heterogeneous, meaning that the data objects need not all be from the same class and the collection can be assigned to a variable as below. The following fragment shows a variety of Python data objects in a list. 1,3,True,6.5 1, 3, True, 6.5 my_list = 1,3,True,6.5 my_list 1, 3, True, 6.5 Note that when Python evaluates a list, the list itself is returned. However, in order to remember the list for later processing, its reference needs to be assigned to a variable. Since lists are considered to be sequentially ordered, they support a number of operations that can be applied to any Python sequence. Table 1.2 reviews these operations and the following session gives examples of their use. Note that the indices for lists (sequences) start counting with 0. The slice operation, my_list1 : 3, returns a list of items starting with the item indexed by 1 up to but not including the item indexed by 3. Sometimes, you will want to initialize a list. This can quickly be accomplished by using repetition. For example, my_list = 0 6 my_list 0, 0, 0, 0, 0, 0 One very important aside relating to the repetition operator is that the result is a repetition of references to the data objects in the sequence. This can best be seen by considering the 1.4. Review of Basic Python 11Problem Solving with Algorithms and Data Structures, Release 3.0 Method Name Use Explanation append a_list.append(item) Adds a new item to the end of a list th insert a_list.insert(i,item) Inserts an item at the𝑖 position in a list pop a_list.pop() Removes and returns the last item in a list th pop a_list.pop(i) Removes and returns the𝑖 item in a list sort a_list.sort() Modifies a list to be sorted reverse a_list.reverse() Modifies a list to be in reverse order th del del a_listi Deletes the item in the𝑖 position index a_list.index(item) Returns the index of the first occurrence of item count a_list.count(item) Returns the number of occurrences of item remove a_list.remove(item) Removes the first occurrence of item Table 1.3: Methods Provided by Lists in Python following session: my_list = 1,2,3,4 A = my_list 3 print(A) my_list2=45 print(A) The variableA holds a collection of three references to the original list calledmy_list. Note that a change to one element ofmy_list shows up in all three occurrences inA. Lists support a number of methods that will be used to build data structures. Table 1.3 provides a summary. Examples of their use follow. my_list = 1024, 3, True, 6.5 my_list.append(False) print(my_list) my_list.insert(2,4.5) print(my_list) print(my_list.pop()) print(my_list) print(my_list.pop(1)) print(my_list) my_list.pop(2) print(my_list) my_list.sort() print(my_list) my_list.reverse() print(my_list) print(my_list.count(6.5)) print(my_list.index(4.5)) my_list.remove(6.5) print(my_list) del my_list0 print(my_list) 12 Chapter 1. IntroductionProblem Solving with Algorithms and Data Structures, Release 3.0 You can see that some of the methods, such as pop, return a value and also modify the list. Others, such as reverse, simply modify the list with no return value. pop will default to the end of the list but can also remove and return a specific item. The index range starting from 0 is again used for these methods. You should also notice the familiar “dot” notation for asking an object to invoke a method. my_list.append(False) can be read as “ask the object my_list to perform its append method and send it the value False.” Even simple data objects such as integers can invoke methods in this way. (54).__add__(21) 75 In this fragment we are asking the integer object54 to execute itsadd method (called__add__ in Python) and passing it21 as the value to add. The result is the sum,75. Of course, we usually write this as54 + 21. We will say much more about these methods later in this section. One common Python function that is often discussed in conjunction with lists is the range function. range produces a range object that represents a sequence of values. By using the list function, it is possible to see the value of the range object as a list. This is illustrated below. range(10) range(0, 10) list(range(10)) 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 range(5,10) range(5, 10) list(range(5,10)) 5, 6, 7, 8, 9 list(range(5,10,2)) 5, 7, 9 list(range(10,1,-1)) 10, 9, 8, 7, 6, 5, 4, 3, 2 The range object represents a sequence of integers. By default, it will start with 0. If you provide more parameters, it will start and end at particular points and can even skip items. In our first example, range(10), the sequence starts with 0 and goes up to but does not include 10. In our second example, range(5, 10) starts at 5 and goes up to but not including 10. range(5, 10, 2) performs similarly but skips by twos (again, 10 is not included). Strings are sequential collections of zero or more letters, numbers and other symbols. We call these letters, numbers and other symbols characters. Literal string values are differentiated from identifiers by using quotation marks (either single or double). "David" 'David' my_name = "David" my_name3 1.4. Review of Basic Python 13Problem Solving with Algorithms and Data Structures, Release 3.0 Method Name Use Explanation center a_string.center(w) Returns a string centered in a field of size𝑤 count a_string.count(item) Returns the number of occurrences of item in the string ljust a_string.ljust(w) Returns a string left-justified in a field of size 𝑤 lower a_string.lower() Returns a string in all lowercase rjust a_string.rjust(w) Returns a string right-justified in a field of size𝑤 find a_string.find(item) Returns the index of the first occurrence of item split a_string.split(s_char) Splits a string into substrings ats_char Table 1.4: Methods Provided by Strings in Python 'i' my_name 2 'DavidDavid' len(my_name) 5 Since strings are sequences, all of the sequence operations described above work as you would expect. In addition, strings have a number of methods, some of which are shown in Table 1.4. For example, my_name 'David' my_name.upper() 'DAVID' my_name.center(10) ' David ' my_name.find('v') 2 my_name.split('v') 'Da', 'id' Of these, split will be very useful for processing data. split will take a string and return a list of strings using the split character as a division point. In the example, v is the division point. If no division is specified, the split method looks for whitespace characters such as tab, newline and space. A major difference between lists and strings is that lists can be modified while strings cannot. This is referred to as mutability. Lists are mutable; strings are immutable. For example, you can change an item in a list by using indexing and assignment. With a string that change is not allowed. 14 Chapter 1. IntroductionProblem Solving with Algorithms and Data Structures, Release 3.0 my_list 1, 3, True, 6.5 my_list0=2 10 my_list 1024, 3, True, 6.5 my_name 'David' my_name0='X' Traceback (most recent call last): File "pyshell84", line 1, in module my_name0='X' TypeError: 'str' object does not support item assignment Note that the error (or traceback) message displayed above is obtained on a Mac OS X machine. If you are running the above code snippet on a Windows machine, your error output will more likely be as follows. my_name0='X' Traceback (most recent call last): File "pyshell84", line 1, in -toplevel- my_name0='X' TypeError: object doesn't support item assignment Depending on your operating system, or version of Python, the output may slightly vary. How- ever it will still indicate where and what the error is. You may want to experiment for yourself and get acquainted with the error message for easier and faster debugging. For the remainder of this work, we will only display the Mac OS X error messages. Tuples are very similar to lists in that they are heterogeneous sequences of data. The difference is that a tuple is immutable, like a string. A tuple cannot be changed. Tuples are written as comma-delimited values enclosed in parentheses. As sequences, they can use any operation described above. For example, my_tuple = (2,True,4.96) my_tuple (2, True, 4.96) len(my_tuple) 3 my_tuple0 2 my_tuple 3 (2, True, 4.96, 2, True, 4.96, 2, True, 4.96) my_tuple0:2 (2, True) However, if you try to change an item in a tuple, you will get an error. Note that the error message provides location and reason for the problem. 1.4. Review of Basic Python 15Problem Solving with Algorithms and Data Structures, Release 3.0 Operator Use Explanation in x.in(set) Set membership len len(set) Returns the cardinality (i.e. the length) of the set set1 set2 Returns a new set with all elements from both sets & set1 & set2 Returns a new set with only the elements common to both sets - set1 - set2 Returns a new set with all items from the first set not in second = set1 = set2 Asks whether all elements of the first set are in the second Table 1.5: Operations on a Set in Python my_tuple1=False Traceback (most recent call last): File "pyshell137", line 1, in module my_tuple1=False TypeError: 'tuple' object does not support item assignment A set is an unordered collection of zero or more immutable Python data objects. Sets do not allow duplicates and are written as comma-delimited values enclosed in curly braces. The empty set is represented byset(). Sets are heterogeneous, and the collection can be assigned to a variable as below. 3,6,"cat",4.5,False False, 4.5, 3, 6, 'cat' my_set = 3,6,"cat",4.5,False my_set False, 3, 4.5, 6, 'cat' Even though sets are not considered to be sequential, they do support a few of the familiar operations presented earlier. Table 1.5 reviews these operations and the following session gives examples of their use. my_set False, 3, 4.5, 6, 'cat' len(my_set) 5 False in my_set True "dog" in my_set False Sets support a number of methods that should be familiar to those who have worked with them in a mathematics setting. Table 1.6 provides a summary. Examples of their use follow. Note that union, intersection, issubset, and difference all have operators that can be used as well. 16 Chapter 1. Introduction