Python编程入门：理解数据的应用教程（第二版）

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"《使用Python进行计算与编程入门：数据理解应用教程》第二版是John V. Guttag所著的一本专业教材，由麻省理工学院出版社于2016年出版。该书旨在向初学者介绍计算机科学中的基本概念和Python编程语言，特别强调了如何通过Python来理解和分析数据。这本书在教学过程中注重实践，使读者能够掌握编程基础，并将其应用于实际的数据处理项目。全书覆盖了计算和编程的基础知识，包括变量、数据类型、控制结构（如循环和条件语句）、函数和模块的使用，以及面向对象编程的基本概念。作者通过清晰的讲解和实例，引导读者逐步构建对编程逻辑的理解。此外，书中还深入探讨了如何利用Python进行数据处理，如数据清洗、数据可视化和数据分析，这些都是现代IT专业人士必备的技能。本书特别关注Python作为一门易学且功能强大的语言，它的语法简洁直观，对于初学者而言易于上手。同时，它也强调了Python在数据科学领域的重要性，让读者能够快速适应大数据时代的需求。每章都配有练习题和项目，以帮助读者巩固所学知识并提升实际操作能力。版权方面，所有内容未经麻省理工学院出版社书面许可，不得任何形式复制或机械复制，包括影印、录音和信息存储与检索。该书已在美国印刷装订，获得了美国国会图书馆的分类和记录。《使用Python进行计算与编程入门：数据理解应用教程》是一本适合编程初学者和数据科学入门者的实用教材，不仅教授理论知识，更注重培养读者在实际工作中解决问题的能力。无论你是计算机科学专业的学生，还是希望提升数据分析技能的专业人士，这本书都将为你提供坚实的指导。"

PREFACE xv

most students than what is typically covered in the second computer science

course.

We chose not to include problems at the end of chapters. Instead we inserted

“finger exercises” at opportune points within the chapters. Some are quite short,

and are intended to allow readers to confirm that they understood the material

they just read. Some are a bit more challenging, and are suitable for exam ques-

tions. And others are challenging enough to be useful as homework assignments.

The book has three pervasive themes: systematic problem solving, the power

of abstraction, and computation as a way of thinking about the world. When you

have finished this book you should have:

• Learned a language, Python, for expressing computations,

• Learned a systematic approach to organizing, writing, and debugging medi-

um-sized programs,

• Developed an informal understanding of computational complexity,

• Developed some insight into the process of moving from an ambiguous prob-

lem statement to a computational formulation of a method for solving the

problem,

• Learned a useful set of algorithmic and problem reduction techniques,

• Learned how to use randomness and simulations to shed light on problems

that don’t easily succumb to closed-form solutions, and

• Learned how to use computational tools (including simple statistical, visuali-

zation, and machine learning tools) to model and understand data.

Programming is an intrinsically difficult activity. Just as “there is no royal

road to geometry,”

there is no royal road to programming. If you really want to

learn the material, reading the book will not be enough. At the very least you

should try running some of the code in the book. Various versions of the courses

from which this book has been derived have been available on MIT’s Open-

CourseWare (OCW) Web site since 2008. The site includes video recordings of

lectures and a complete set of problem sets and exams. Since the fall of 2012, edX

and MITx have offered online courses that cover much of the material in this

book. We strongly recommend that you do the problem sets associated with one

of the OCW or edX offerings.

This was Euclid’s purported response, circa 300 BCE, to King Ptolemy’s request for an easier way

to learn mathematics.

1 GETTING STARTED

A computer does two things, and two things only: it performs calculations and it

remembers the results of those calculations. But it does those two things ex-

tremely well. The typical computer that sits on a desk or in a briefcase performs a

billion or so calculations a second. It’s hard to image how truly fast that is. Think

about holding a ball a meter above the floor, and letting it go. By the time it

reaches the floor, your computer could have executed over a billion instructions.

As for memory, a small computer might have hundreds of gigabytes of storage.

How big is that? If a byte (the number of bits, typically eight, required to repre-

sent one character) weighed one gram (which it doesn’t), 100 gigabytes would

weigh 10,000 metric tons. For comparison, that’s roughly the combined weight of

15,000 African elephants.

For most of human history, computation was limited by the speed of calcula-

tion of the human brain and the ability to record computational results with the

human hand. This meant that only the smallest problems could be attacked

computationally. Even with the speed of modern computers, there are still prob-

lems that are beyond modern computational models (e.g., understanding climate

change), but more and more problems are proving amenable to computational

solution. It is our hope that by the time you finish this book, you will feel com-

fortable bringing computational thinking to bear on solving many of the prob-

lems you encounter during your studies, work, and even everyday life.

What do we mean by computational thinking?

All knowledge can be thought of as either declarative or imperative.

Declara-

tive knowledge

is composed of statements of fact. For example, “the square root

of x is a number y such that y

y#=#x.” This is a statement of fact. Unfortunately, it

doesn’t tell us anything about how to find a square root.

Imperative knowledge

is “how to” knowledge, or recipes for deducing infor-

mation. Heron of Alexandria was the first to document a way to compute the

square root of a number.

His method for finding the square root of a number,

call it x, can be summarized as:

Many believe that Heron was not the inventor of this method, and indeed there is some evidence

that it was well known to the ancient Babylonians.

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