Python编程数据结构与算法指南

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《数据结构与算法：Python版》是一本针对计算机科学本科学生的教材，由经验丰富的专家Kent D. Lee和Steve Hubbard共同编撰。该书将理论知识与实践操作紧密结合，旨在帮助学生理解和掌握处理大量数据时所需的高效编程技术。书中以项目为导向，包含了诸如Python编程基础、计算复杂性分析、递归、序列、集合与映射、树、图、成员结构、堆、平衡二叉搜索树、B树以及启发式搜索等一系列核心数据结构和算法主题。作者们从Python语言的基础入手，对不同类型的运算符（如整数、浮点数、字符串、列表和字典）进行了详细讲解，并通过Turtle模块提供图形演示。此外，每一章都设有明确的学习目标，包含丰富的复习问题和编程练习，确保学生能够逐步深化理解。全书还配有大量示例，有助于读者将理论应用于实际问题。书中特别强调了计算复杂性的概念，使读者明白什么可以高效计算，什么不能，以便他们在选择算法时能做出明智判断。对于课程设置，书中提供了适用于初级和高级课程的不同章节顺序建议，方便教师根据学生水平进行教学。《数据结构与算法：Python版》作为Undergraduate Topics in Computer Science系列的一部分，符合Springer出版社对高质量教育资源的要求，受到国际顾问团的审阅，并且提供了配套的在线资源和教师材料，便于课堂使用和自学者学习。无论是作为独立学习资料还是课程教材，这本书都为学习者提供了一个全面而现代的数据结构与算法学习平台。

Python Programming 101

This Computer Science text further develops the skills you learned in your ﬁrst CS

text or course and adds to your bag of tricks by teaching you how to use efﬁcient

algorithms for dealing with large amounts of data. Without the proper understanding

of efﬁciency, it is possible to bring even the fastest computers to a grinding halt when

working with large data sets. This has happened before, and soon you will understand

just how easy it can occur. But ﬁrst, we’ll review some patterns for programming and

look at the Python programming language to make sure you understand the basic

structure and syntax of the language.

To begin writing programs using Python you need to install Python on your com-

puter. The examples in this text use Python 3. Python 2 is not compatible with Python

3 so you’ll want to be sure you have Python 3 or later installed on your computer.

When writing programs in any language a good Integrated Development Environ-

ment (IDE) is a valuable tool so you’ll want to install an IDE, too. Examples within

this text will use Wing IDE 101 as pictured in Fig. 1.1, although other acceptable

IDEs are available as well. The Wing IDE is well maintained, simple to use, and has

a nice debugger which will be useful as you write Python programs. If you want to

get Wing IDE 101 then go to http://wingware.com. The website http://cs.luther.edu/

~leekent/CS1 has directions for installing both Python 3 and Wing IDE 101. Wing

IDE 101 is the free version of Wing for educational use.

There are some general concepts about Python that you should know when reading

the text. Python is an interpreted language. That means that you don’t have to go

through any extra steps after writing Python code before you can run it. You can

simply press the debug button in the Wing IDE (it looks like an insect) and it will

ask you to save your program if you haven’t already done so at least once. Then

it will run your program. Python is also dynamically typed. This means that you

will not get any type errors before you run your program as you would with some

programming languages. It is especially important for you to understand the types of

data you are using in your program. More on this in just a bit. Finally, your Python

programs are interpreted by the Python interpreter. The shell is another name for

the Python interpreter and Wing IDE 101 gives you access to a shell within the IDE

K.D. Lee and S. Hubbard, Data Structures and Algorithms with Python,

Undergraduate Topics in Computer Science, DOI 10.1007/978-3-319-13072-9_1

1 Python Programming 101 3

To motivate learning or reviewing Python in this chapter, the text will develop

a simple drawing application using turtle graphics and a Graphical User Interface

(GUI) framework called Tkinter. Along the way, you’ll discover some patterns for

programming including the accumulator pattern and the loop and a half pattern for

reading records from a ﬁle. You’ll also see functions in Python and begin to learn

how to implement your own datatypes by designing and writing a class deﬁnition.

1.1 Chapter Goals

By the end of this chapter, you should be able to answer these questions.

• What two parts are needed for the accumulator pattern?

• When do you need to use the loop and a half pattern for reading from a ﬁle?

• What is the purpose of a class deﬁnition?

• What is an object and how do we create one?

• What is a mutator method?

• What is an accessor method?

• What is a widget and how does one use widgets in GUI programming?

1.2 Creating Objects

Python is an object-oriented language. All data items in Python are objects. In Python,

data items that could be thought of as similar are named by a type or class. The term

type and class in Python are synonymous: they are two names for the same thing. So

when you read about types in Python you can think of classes or vice versa.

There are several built-in types of data in Python including int, ﬂoat, str, list, and

dict which is short for dictionary. These types of data and their associated operations

are included in the appendices at the end of the text so you have a quick reference

if you need to refer to it while programming. You can also get help for any type

by typing help(typename) in the Python shell, where typename is a type or class in

Python. A very good language reference can be found at http://python.org/doc, the

ofﬁcial Python documentation website.

1.2.1 Literal Values

There are two ways to create objects in Python. In a few cases, you can use a literal

value to create an object. Literal values are used when we want to set some variable

to a speciﬁc value within our program. For example, the literal 6 denotes any object

with the integer value of 6.

x = 6

This creates an int object containing the value 6. It also points the reference called

x at this object as pictured in Fig. 1.2. All assignments in Python point references

1.2 Creating Objects 5

In this short piece of code, y is a reference to the str object created from the string

literal. The variable x is a reference to an object that is created by using the object

that y refers to. In general, when we want to create an object based on other object

values we write the following:

variable = type(other_object_values)

The type is any type or class name in Python, like int, ﬂoat, str or any other

type. The other_object_values is a comma-separated sequence of references to other

objects that are needed by the class or type to create an instance (i.e. an object) of

that type. Here are some examples of creating objects from non-literal values.

z = float(’6.3’)

w = str(z)

u = list(w)

this

results

the

list

[’6’,

’.’,

’3’]

1.3 Calling Methods on Objects

Objects are useful because they allow us to collect related information and group

them with behavior that act on this data. These behaviors are called methods in

Python. There are two kinds of methods in any object-oriented language: mutator

and accessor methods. Accessor methods access the current state of an object but

don’t change the object. Accessor methods return new object references when called.

x = ’how are you’

y = x.upper()

print(y)

Here, the method upper is called on the object that x refers to. The upper accessor

method returns a new object, a str object, that is an upper-cased version of the

original string. Note that x is not changed by calling the upper method on it. The

upper method is an accessor method. There are many accessor methods available on

the str type which you can learn about in the appendices.

Some methods are mutator methods. These methods actually change the existing

object. One good example of this is the reverse method on the list type.

myList = [1, 2, 3]

myList.reverse()

print(myList)

This

prints

[3,

the

screen

The reverse method mutates the existing object, in this case the list that myList

refers to. Once called, a mutator method can’t be undone. The change or mutation

is permanent until mutated again by some other mutator method.

All classes contain accessor methods. Without accessor methods, the class would

be pretty uninteresting. We use accessor methods to retrieve a value that is stored

in an object or to retrieve a value that depends on the value stored in an object.

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