1.1 Introduction

Computer science is a tremendously broad academic discipline. The areas of globally distributed systems, artificial intelligence, robotics, graphics, security, scientific computing, computer architecture, and dozens of emerging sub-fields each expand with new techniques and discoveries every year. The rapid progress of computer science has left few aspects of human life unaffected. Commerce, communication, science, art, leisure, and politics have all been reinvented as computational domains.

The tremendous productivity of computer science is only possible because it is built upon an elegant and powerful set of fundamental ideas. All computing begins with representing information, specifying logic to process it, and designing abstractions that manage the complexity of that logic. Mastering these fundamentals will require us to understand precisely how computers interpret computer programs and carry out computational processes.

These fundamental ideas have long been taught at Berkeley using the classic textbook Structure and Interpretation of Computer Programs (SICP) by Harold Abelson and Gerald Jay Sussman with Julie Sussman. These lecture notes borrow heavily from that textbook, which the original authors have kindly licensed for adaptation and reuse.

The embarkment of our intellectual journey requires no revision, nor should we expect that it ever will.

> We are about to study the idea of a computational process. Computational processes are abstract beings that inhabit computers. As they evolve, processes manipulate other abstract things called data. The evolution of a process is directed by a pattern of rules called a program. People create programs to direct processes. In effect, we conjure the spirits of the computer with our spells. > > The programs we use to conjure processes are like a sorcerer's spells. They are carefully composed from symbolic expressions in arcane and esoteric programming languages that prescribe the tasks we want our processes to perform. > > A computational process, in a correctly working computer, executes programs precisely and accurately. Thus, like the sorcerer's apprentice, novice programmers must learn to understand and to anticipate the consequences of their conjuring. > > —Abelson and Sussman, SICP (1993)

1.1.1 Programming in Python

> A language isn’t something you learn so much as something you join. > > —Arika Okrent

In order to define computational processes, we need a programming language; preferably one many humans and a great variety of computers can all understand. In this course, we will learn the Python language.

Python is a widely used programming language that has recruited enthusiasts from many professions: web programmers, game engineers, scientists, academics, and even designers of new programming languages. When you learn Python, you join a million-person-strong community of developers. Developer communities are tremendously important institutions: members help each other solve problems, share their code and experiences, and collectively develop software and tools. Dedicated members often achieve celebrity and widespread esteem for their contributions. Perhaps someday you will be named among these elite Pythonistas.

The Python language itself is the product of a large volunteer community that prides itself on the diversity of its contributors. The language was conceived and first implemented by Guido van Rossum in the late 1980's. The first chapter of his Python 3 Tutorial explains why Python is so popular, among the many languages available today.

Python excels as an instructional language because, throughout its history, Python's developers have emphasized the human interpretability of Python code, reinforced by the Zen of Python guiding principles of beauty, simplicity, and readability. Python is particularly appropriate for this course because its broad set of features support a variety of different programming styles, which we will explore. While there is no single way to program in Python, there are a set of conventions shared across the developer community that facilitate the process of reading, understanding, and extending existing programs. Hence, Python's combination of great flexibility and accessibility allows students to explore many programming paradigms, and then apply their newly acquired knowledge to thousands of ongoing projects.

These notes maintain the spirit of SICP by introducing the features of Python in lock step with techniques for abstraction design and a rigorous model of computation. In addition, these notes provide a practical introduction to Python programming, including some advanced language features and illustrative examples. Learning Python will come naturally as you progress through the course.

However, Python is a rich language with many features and uses, and we consciously introduce them slowly as we layer on fundamental computer science concepts. For experienced students who want to inhale all of the details of the language quickly, we recommend reading Mark Pilgrim's book Dive Into Python 3, which is freely available online. The topics in that book differ substantially from the topics of this course, but the book contains very valuable practical information on using the Python language. Be forewarned: unlike these notes, Dive Into Python 3 assumes substantial programming experience.

The best way to get started programming in Python is to interact with the interpreter directly. This section describes how to install Python 3, initiate an interactive session with the interpreter, and start programming.

1.1.2 Installing Python 3

As with all great software, Python has many versions. This course will use the most recent stable version of Python 3 (currently Python 3.2). Many computers have older versions of Python installed already, but those will not suffice for this course. You should be able to use any computer for this course, but expect to install Python 3. Don't worry, Python is free.

Dive Into Python 3 has detailed installation instructions for all major platforms. These instructions mention Python 3.1 several times, but you're better off with Python 3.2 (although the differences are insignificant for this course). All instructional machines in the EECS department have Python 3.2 already installed.

1.1.3 Interactive Sessions

In an interactive Python session, you type some Python code after the prompt, >>>. The Python interpreter reads and evaluates what you type, carrying out your various commands.

There are several ways to start an interactive session, and they differ in their properties. Try them all to find out what you prefer. They all use exactly the same interpreter behind the scenes.

  • The simplest and most common way is to run the Python 3 application. Type python3 at a terminal prompt (Mac/Unix/Linux) or open the Python 3 application in Windows.

  • A more user-friendly application for those learning the language is called Idle 3 (idle3). Idle colorizes your code (called syntax highlighting), pops up usage hints, and marks the source of some errors. Idle is always bundled with Python, so you have already installed it.

  • The Emacs editor can run an interactive session inside one of its buffers. While slightly more challenging to learn, Emacs is a powerful and versatile editor for any programming language. Read the 61A Emacs Tutorial to get started. Many programmers who invest the time to learn Emacs never switch editors again.

In any case, if you see the Python prompt, >>>, then you have successfully started an interactive session. These notes depict example interactions using the prompt, followed by some input.

>>> 2 + 2

Controls: Each session keeps a history of what you have typed. To access that history, press <Control>-P (previous) and <Control>-N (next). <Control>-D exits a session, which discards this history.

1.1.4 First Example

> And, as imagination bodies forthThe forms of things to unknown, and the poet's penTurns them to shapes, and gives to airy nothingA local habitation and a name. > > —William Shakespeare, A Midsummer-Night's Dream

To give Python the introduction it deserves, we will begin with an example that uses several language features. In the next section, we will have to start from scratch and build up the language piece by piece. Think of this section as a sneak preview of powerful features to come.

Python has built-in support for a wide range of common programming activities, like manipulating text, displaying graphics, and communicating over the Internet. The import statement

>>> from urllib.request import urlopen

loads functionality for accessing data on the Internet. In particular, it makes available a function called urlopen, which can access the content at a uniform resource locator (URL), which is a location of something on the Internet.

Statements & Expressions. Python code consists of statements and expressions. Broadly, computer programs consist of instructions to either

  1. Compute some value
  2. Carry out some action

Statements typically describe actions. When the Python interpreter executes a statement, it carries out the corresponding action. On the other hand, expressions typically describe computations that yield values. When Python evaluates an expression, it computes its value. This chapter introduces several types of statements and expressions.

The assignment statement

>>> shakespeare = urlopen('http://inst.eecs.berkeley.edu/~cs61a/fa11/shakespeare.txt')

associates the name shakespeare with the value of the expression that follows. That expression applies the urlopen function to a URL that contains the complete text of William Shakespeare's 37 plays, all in a single text document.

Functions. Functions encapsulate logic that manipulates data. A web address is a piece of data, and the text of Shakespeare's plays is another. The process by which the former leads to the latter may be complex, but we can apply that process using only a simple expression because that complexity is tucked away within a function. Functions are the primary topic of this chapter.

Another assignment statement

>>> words = set(shakespeare.read().decode().split())

associates the name words to the set of all unique words that appear in Shakespeare's plays, all 33,721 of them. The chain of commands to read, decode, and split, each operate on an intermediate computational entity: data is read from the opened URL, that data is decoded into text, and that text is split into words. All of those words are placed in a set.

Objects. A set is a type of object, one that supports set operations like computing intersections and testing membership. An object seamlessly bundles together data and the logic that manipulates that data, in a way that hides the complexity of both. Objects are the primary topic of Chapter 2.

The expression

>>> {w for w in words if len(w) >= 5 and w[::-1] in words}
{'madam', 'stink', 'leets', 'rever', 'drawer', 'stops', 'sessa',
'repaid', 'speed', 'redder', 'devil', 'minim', 'spots', 'asses',
'refer', 'lived', 'keels', 'diaper', 'sleek', 'steel', 'leper',
'level', 'deeps', 'repel', 'reward', 'knits'}

is a compound expression that evaluates to the set of Shakespearian words that appear both forward and in reverse. The cryptic notation w[::-1] enumerates each letter in a word, but the -1 says to step backwards (:: here means that the positions of the first and last characters to enumerate are defaulted.) When you enter an expression in an interactive session, Python prints its value on the following line, as shown.

Interpreters. Evaluating compound expressions requires a precise procedure that interprets code in a predictable way. A program that implements such a procedure, evaluating compound expressions and statements, is called an interpreter. The design and implementation of interpreters is the primary topic of Chapter 3.

When compared with other computer programs, interpreters for programming languages are unique in their generality. Python was not designed with Shakespeare or palindromes in mind. However, its great flexibility allowed us to process a large amount of text with only a few lines of code.

In the end, we will find that all of these core concepts are closely related: functions are objects, objects are functions, and interpreters are instances of both. However, developing a clear understanding of each of these concepts and their role in organizing code is critical to mastering the art of programming.

1.1.5 Practical Guidance: Errors

Python is waiting for your command. You are encouraged to experiment with the language, even though you may not yet know its full vocabulary and structure. However, be prepared for errors. While computers are tremendously fast and flexible, they are also extremely rigid. The nature of computers is described in Stanford's introductory course as

> The fundamental equation of computers is: computer = powerful + stupid > > Computers are very powerful, looking at volumes of data very quickly. Computers can perform billions of operations per second, where each operation is pretty simple. > > Computers are also shockingly stupid and fragile. The operations that they can do are extremely rigid, simple, and mechanical. The computer lacks anything like real insight .. it's nothing like the HAL 9000 from the movies. If nothing else, you should not be intimidated by the computer as if it's some sort of brain. It's very mechanical underneath it all. > > Programming is about a person using their real insight to build something useful, constructed out of these teeny, simple little operations that the computer can do. > > —Francisco Cai and Nick Parlante, Stanford CS101

The rigidity of computers will immediately become apparent as you experiment with the Python interpreter: even the smallest spelling and formatting changes will cause unexpected outputs and errors.

Learning to interpret errors and diagnose the cause of unexpected errors is called debugging. Some guiding principles of debugging are:

  1. Test incrementally: Every well-written program is composed of small, modular components that can be tested individually. Test everything you write as soon as possible to catch errors early and gain confidence in your components.
  2. Isolate errors: An error in the output of a compound program, expression, or statement can typically be attributed to a particular modular component. When trying to diagnose a problem, trace the error to the smallest fragment of code you can before trying to correct it.
  3. Check your assumptions: Interpreters do carry out your instructions to the letter --- no more and no less. Their output is unexpected when the behavior of some code does not match what the programmer believes (or assumes) that behavior to be. Know your assumptions, then focus your debugging effort on verifying that your assumptions actually hold.
  4. Consult others: You are not alone! If you don't understand an error message, ask a friend, instructor, or search engine. If you have isolated an error, but can't figure out how to correct it, ask someone else to take a look. A lot of valuable programming knowledge is shared in the context of team problem solving.

Incremental testing, modular design, precise assumptions, and teamwork are themes that persist throughout this course. Hopefully, they will also persist throughout your computer science career.