Python编程入门：理解数据的应用教程（第二版）

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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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