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Learning Python, Second Edition

By Mark Lutz, David Ascher

Cover | Table of Contents | Colophon

Chapter 1: A Python Q&A Session

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If you've bought this book, you may already know what Python is, and why it's an important tool to learn. If not, you probably won't be sold on Python until you've learned the language by reading the rest of this book and have done a project or two. But before jumping into details, the first few pages briefly introduce some of the main reasons behind Python's popularity. To begin sculpting a definition of Python, this chapter takes the form of a question and answer session, which poses some of the most common non-technical questions asked by beginners.

Because there are many programming languages available today, this is the usual first question of newcomers. Given the hundreds of thousands of Python users out there today, there really is no way to answer this question with complete accuracy. The choice of development tools is sometimes based on unique constraints or personal preference.

But after teaching Python to roughly one thousand students and almost 100 companies in recent years, some common themes have emerged. The primary factors cited by Python users seem to be these:

Software quality: For many, Python's focus on readability, coherence, and software quality in general, sets it apart from "kitchen sink" style languages like Perl. Python code is designed to be readable, and hence maintainable—much more so than traditional scripting languages. In addition, Python has deep support for software reuse mechanisms such as object oriented programming (OOP).
Developer productivity: Python boosts developer productivity many times beyond compiled or statically typed languages such as C, C++, and Java. Python code is typically 1/3 to 1/5 the size of equivalent C++ or Java code. That means there is less to type, less to debug, and less to maintain after the fact. Python programs also run immediately, without the lengthy compile and link steps of some other tools.
Program portability: Most Python programs run unchanged on all major computer platforms. Porting Python code between Unix and Windows, for example, is usually just a matter of copying a script's code between machines. Moreover, Python offers multiple options for coding portable graphical user interfaces.

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Why Do People Use Python?

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But after teaching Python to roughly one thousand students and almost 100 companies in recent years, some common themes have emerged. The primary factors cited by Python users seem to be these:

Software quality: For many, Python's focus on readability, coherence, and software quality in general, sets it apart from "kitchen sink" style languages like Perl. Python code is designed to be readable, and hence maintainable—much more so than traditional scripting languages. In addition, Python has deep support for software reuse mechanisms such as object oriented programming (OOP).
Developer productivity: Python boosts developer productivity many times beyond compiled or statically typed languages such as C, C++, and Java. Python code is typically 1/3 to 1/5 the size of equivalent C++ or Java code. That means there is less to type, less to debug, and less to maintain after the fact. Python programs also run immediately, without the lengthy compile and link steps of some other tools.
Program portability: Most Python programs run unchanged on all major computer platforms. Porting Python code between Unix and Windows, for example, is usually just a matter of copying a script's code between machines. Moreover, Python offers multiple options for coding portable graphical user interfaces.
Support libraries: Python comes with a large collection of prebuilt and portable functionality, known as the standard library. This library supports an array of application-level programming tasks, from text pattern matching, to network scripting. In addition, Python can be extended with both home-grown libraries, as well as a vast collection of third-party application support software.

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Is Python a Scripting Language?

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Python is a general purpose programming language that is often applied in scripting roles. It is commonly defined as an object-oriented scripting language—a definition that blends support for OOP with an overall orientation toward scripting roles. In fact, people often use the word "script" instead of "program" to describe a Python code file. In this book, the terms "script" and "program" are used interchangeably, with a slight preference for "script" to describe a simpler top-level file and "program" to refer to a more sophisticated multifile application.

Because the term "scripting" has so many different meanings to different observers, some would prefer that it not be applied to Python at all. In fact, people tend to think of three very different definitions when they hear Python labeled a "scripting" language, some of which are more useful than others:

Shell tools: Tools for coding operating system-oriented scripts. Such programs are often launched from console command-lines, and perform tasks such as processing text files and launching other programs. Python programs can serve such roles, but this is just one of dozens of common Python application domains. It is not just a better shell script language.
Control language: A "glue" layer used to control and direct (i.e., script) other application components. Python programs are indeed often deployed in the context of a larger application. For instance, to test hardware devices, Python programs may call out to components that give low-level access to a device. Similarly, programs may run bits of Python code at strategic points, to support end-user product customization, without having to ship and recompile the entire system's source code. Python's simplicity makes it a naturally flexible control tool. Technically, though, this is also just a common Python role; many Python programmers code standalone scripts, without ever using or knowing about any integrated components.
Ease of use

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Okay, But What's the Downside?

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Perhaps the only downside to Python is that, as currently implemented, its execution speed may not always be as fast as compiled languages such as C and C++.

We'll talk about implementation concepts later in this book. But in short, the standard implementations of Python today compile (i.e., translate) source code statements to an intermediate format known as byte code, and then interpret the byte code. Byte code provides portability, as it is a platform-independent format. However, because Python is not compiled all the way down to binary machine code (e.g., instructions for an Intel chip), some programs will run more slowly in Python than in a fully compiled language like C.

Whether you will ever care about the execution speed difference depends on what kinds of programs you write. Python has been optimized numerous times, and Python code runs fast enough by itself in most application domains. Furthermore, whenever you do something "real" in a Python script, like process a file or construct a GUI, your program is actually running at C speed since such tasks are immediately dispatched to compiled C code inside the Python interpreter. More fundamentally, Python's speed-of-development gain is often far more important than any speed-of-execution loss, especially given modern computer speeds.

Even at today's CPU speeds there still are some domains that do require optimal execution speed. Numeric programming and animation, for example, often need at least their core number-crunching components to run at C speed (or better). If you work in such a domain, you can still use Python—simply split off the parts of the application that require optimal speed into compiled extensions , and link those into your system for use in Python scripts.

We won't talk about extensions much in this text, but this is really just an instance of the Python-as-control-language role that we discussed earlier. A prime example of this dual language strategy is the NumPy numeric programming extension for Python; by combining compiled and optimized numeric extension libraries with the Python language, NumPy turns Python into a numeric programming tool that is both efficient and easy to use. You may never need to code such extensions in your own Python work, but they provide a powerful optimization mechanism if you ever do.

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Who Uses Python Today?

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At this writing, in 2003, the best estimate anyone can seem to make of the size of the Python user base is that there are between 500,000 and 1 million Python users around the world today (plus or minus a few). This estimate is based on various statistics like downloads and comparative newsgroup traffic. Because Python is open source, a more exact count is difficult—there are no license registrations to tally. Moreover, Python is automatically included with Linux distributions and some products and computer hardware, further clouding the user base picture. In general, though, Python enjoys a large user base, and a very active developer community. Because Python has been around for over a decade and has been widely used, it is also very stable and robust.

Besides individual users, Python is also being applied in real revenue-generating products, by real companies. For instance, Google and Yahoo! currently use Python in Internet services; Hewlett-Packard, Seagate, and IBM use Python for hardware testing; Industrial Light and Magic and other companies use Python in the production of movie animation; and so on. Probably the only common thread behind companies using Python today is that Python is used all over the map, in terms of application domains. Its general purpose nature makes it applicable to almost all fields, not just one. For more details on companies using Python today, see Python's web site at http://www.python.org.

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What Can I Do with Python?

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Besides being a well-designed programming language, Python is also useful for accomplishing real world tasks—the sorts of things developers do day in and day out. It's commonly used in a variety of domains, as a tool for both scripting other components and implementing standalone programs. In fact, as a general purpose language, Python's roles are virtually unlimited.

However, the most common Python roles today seem to fall into a few broad categories. The next few sections describe some of Python's most common applications today, as well as tools used in each domain. We won't be able to describe all the tools mentioned here; if you are interested in any of these topics, see Python online or other resources for more details.

Python's built-in interfaces to operating-system services make it ideal for writing portable, maintainable system-administration tools and utilities (sometimes called shell tools). Python programs can search files and directory trees, launch other programs, do parallel processing with processes and threads, and so on.

Python's standard library comes with POSIX bindings, and support for all the usual OS tools: environment variables, files, sockets, pipes, processes, multiple threads, regular expression pattern matching, command-line arguments, standard stream interfaces, shell-command launchers, filename expansion, and more. In addition, the bulk of Python's system interfaces are designed to be portable; for example, a script that copies directory trees typically runs unchanged on all major Python platforms.

Python's simplicity and rapid turnaround also make it a good match for GUI (graphical user interface) programming. Python comes with a standard object-oriented interface to the Tk GUI API called Tkinter, which allows Python programs to implement portable GUIs with native look and feel. Python/Tkinter GUIs run unchanged on MS Windows, X Windows (on Unix and Linux), and Macs. A free extension package, PMW, adds advanced widgets to the base Tkinter toolkit. In addition, the wxPython GUI API, based on a C++ library, offers an alternative toolkit for constructing portable GUIs in Python.

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What Are Python's Technical Strengths?

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Naturally, this is a developer's question. If you don't already have a programming background, the words in the next few sections may be a bit baffling—don't worry, we'll explain all of these in more detail as we proceed through this book. For de-velopers, though, here is a quick introduction to some of Python's top technical features.

Python is an object-oriented language, from the ground up. Its class model supports advanced notions such as polymorphism, operator overloading, and multiple inheritance; yet in the context of Python's simple syntax and typing, OOP is remarkably easy to apply. In fact, if you don't understand these terms, you'll find they are much easier to learn with Python than with just about any other OOP language available.

Besides serving as a powerful code structuring and reuse device, Python's OOP nature makes it ideal as a scripting tool for object-oriented systems languages such as C++ and Java. For example, with the appropriate glue code, Python programs can subclass (specialize) classes implemented in C++ or Java. Of equal significance, OOP is an option in Python; you can go far without having to become an object guru all at once.

Python is free. Just like other open source software, such as Tcl, Perl, Linux, and Apache, you can get the entire Python system for free on the Internet. There are no restrictions on copying it, embedding it in your systems, or shipping it with your products. In fact, you can even sell Python's source code, if you are so inclined.

But don't get the wrong idea: "free" doesn't mean "unsupported." On the contrary, the Python online community responds to user queries with a speed that most commercial software vendors would do well to notice. Moreover, because Python comes with complete source code, it empowers developers, and creates a large team of implementation experts. Although studying or changing a programming language's implementation isn't everyone's idea of fun, it's comforting to know that it's available as a final resort and ultimate documentation source. You're not dependent on a commercial vendor.

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How Does Python Stack Up to Language X?

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Finally, in terms of what you may already know, people sometimes compare Python to languages such as Perl, Tcl, and Java. We talked about performance earlier, so here the focus is on functionality. While other languages are also useful tools to know and use, we think that Python:

Is more powerful than Tcl. Python's support for "programming in the large" makes it applicable to larger systems development.
Has a cleaner syntax and simpler design than Perl, which makes it more readable and maintainable, and helps reduce program bugs.
Is simpler and easier to use than Java. Python is a scripting language, but Java inherits much of the complexity of systems languages such as C++.
Is simpler and easier to use than C++, but often doesn't compete with C++ either; as a scripting language, Python often serves different roles.
Is both more powerful and more cross-platform than Visual Basic. Its open source nature also means it is not controlled by a single company.
Has the dynamic flavor of languages like SmallTalk and Lisp, but also has a simple, traditional syntax accessible to developers and end users.

Especially for programs that do more than scan text files, and that might have to be read in the future by others (or by you!), we think Python fits the bill better than any other scripting language available today. Furthermore, unless your application requires peak performance, Python is often a viable alternative to systems development languages such as C, C++, and Java; Python code will be much less to write, debug, and maintain.

Of course, both of the authors are card-carrying Python evangelists, so take these comments as you may. They do, however, reflect the common experience of many developers who have taken time to explore what Python has to offer.

And that concludes the hype portion of this book. The best way to judge a language is to see it in action, so the next two chapters turn to a strictly technical introduction to the language. There, we explore ways to run Python programs, peek at Python's byte code execution model, and introduce the basics of module files for saving your code. Our goal will be to give you just enough information to run the examples and exercises in the rest of the book. As mentioned earlier, you won't really start programming until Chapter 4, but make sure you have a handle on the startup details before moving on.

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Chapter 2: How Python Runs Programs

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This chapter and the next give a quick look at program execution—how you launch code, and how Python runs it. In this chapter, we explain the Python interpreter. Chapter 3 will show you how to get your own programs up and running.

Startup details are inherently platform-specific, and some of the material in this chapter may not apply to the platform you work on, so you should feel free to skip parts not relevant to your intended use. In fact, more advanced readers who have used similar tools in the past, and prefer to get to the meat of the language quickly, may want to file some of this chapter away for future reference. For the rest of you, let's learn how to run some code.

So far, we've mostly been talking about Python as a programming language. But as currently implemented, it's also a software package called an interpreter. An interpreter is a kind of program that executes other programs. When you write Python programs, the Python interpreter reads your program, and carries out the instructions it contains. In effect, the interpreter is a layer of software logic between your code and the computer hardware on your machine.

When the Python package is installed on your machine, it generates a number of components—minimally, an interpreter and a support library. Depending on how you use it, the Python interpreter may take the form of an executable program, or a set of libraries linked into another program. Depending on which flavor of Python you run, the interpreter itself may be implemented as a C program, a set of Java classes, or other. Whatever form it takes, the Python code you write must always be run by this interpreter. And to do that, you must first install a Python interpreter on your computer.

Python installation details vary per platform, and are covered in depth in Appendix A. In short:

Windows users fetch and run a self-installing executable file, which puts Python on their machine. Simply double-click and say Yes or Next at all prompts.
Linux and Unix users typically either install Python from RPM files, or compile it from its full source-code distribution package.

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Introducing the Python Interpreter

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Python installation details vary per platform, and are covered in depth in Appendix A. In short:

Windows users fetch and run a self-installing executable file, which puts Python on their machine. Simply double-click and say Yes or Next at all prompts.
Linux and Unix users typically either install Python from RPM files, or compile it from its full source-code distribution package.
Other platforms have installation techniques relevant to that platform. For instance, files are synched on Palm Pilots.

Python itself may be fetched from the downloads page at Python's web site, www.python.org. It may also be found through various other distribution channels. You may have Python already available on your machine, especially on Linux and Unix. If you're working on Windows, you'll usually find Python in the Start menu, as captured in Figure 2-1 (we'll learn what these menu items mean in a moment). On Unix and Linux, Python probably lives in your /usr directory tree.

Figure 2-1: Python on the Windows Start menu

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Program Execution

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What it means to write and run a Python script depends on whether you look at these tasks as a programmer or as a Python interpreter. Both views offer important perspective on Python programming.

In its simplest form, a Python program is just a text file containing Python statements. For example, the following file, named script1.py, is one of the simplest Python scripts we could dream up, but it passes for an official Python program:

print 'hello world'
print 2 ** 100

This file contains two Python print statements, which simply print a string (the text in quotes) and a numeric expression result (2 to the power 100) to the output stream. Don't worry about the syntax of this code yet—for this chapter, we're interested only in getting it to run. We'll explain the print statement, and why you can raise 2 to the power 100 in Python without overflowing, in later parts of this book.

You can create such a file of statements with any text editor you like. By convention, Python program files are given names that end in ".py"; technically, this naming scheme is required only for files that are "imported," as shown later in this book, but most Python files have .py names for consistency.

After you've typed these statements into a text file in one way or another, you must tell Python to execute the file—which simply means to run all the statements from top to bottom in the file, one after another. Python program files may be launched by command lines, by clicking their icons, and with other standard techniques. We'll demonstrate how to invoke this execution in the next chapter. If all goes well, you'll see the results of the two print statements show up somewhere on your computer—by default, usually in the same window you were in when you ran the program:

hello world
1267650600228229401496703205376

For example, here's how this script ran from a DOS command line on a Windows laptop, to make sure it didn't have any silly typos:

D:\temp>python script1.py
hello world
1267650600228229401496703205376

We've just run a Python script that prints a string and a number. We probably won't win any programming awards with this code, but it's enough to capture the basics of program execution.

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Execution Model Variations

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Before moving on, we should point out that the internal execution flow described in the prior section reflects the standard implementation of Python today, and is not really a requirement of the Python language itself. Because of that, the execution model is prone to change with time. In fact, there are already a few systems that modify the picture in Figure 2-2 somewhat. Let's take a few moments to explore the most prominent of these variations.

Really, as this book is being written, there are two primary implementations of the Python language—CPython and Jython—along with a handful of secondary implementations such as Python.net. CPython is the standard implementation; all the others have very specific purposes and roles. All implement the same Python language, but execute programs in different ways.

Section 2.3.1.1: CPython

The original, and standard, implementation of Python is usually called CPython, when you want to contrast it with the other two. Its name comes from the fact that it is coded in portable ANSI C language code. This is the Python that you fetch from www.python.org, get with the ActivePython distribution, and have automatically in most Linux machines. If you've found a preinstalled version of Python on your machine, it's probably CPython as well, unless your company is using Python in very specialized ways.

Unless you want to script Java or .NET applications with Python, you probably want to use the standard CPython system. Because it is the reference implementation of the language, it tends to run fastest, be the most complete, and be more robust than the alternative systems. Figure 2-2 reflects CPython's runtime architecture.

Section 2.3.1.2: Jython

The Jython system (originally known as JPython) is an alternative implementation of the Python language, targeted for integration with the Java programming language. Jython consists of Java classes that compile Python source code to Java byte code, and then route the resulting byte code to the Java Virtual Machine ( JVM). Programmers still code Python statements in .py

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Chapter 3: How You Run Programs

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Okay, it's time to start running some code. Now that you have a handle on program execution, you're finally ready to start some real Python programming. At this point, we'll assume that you have Python installed on your computer; if not, see Appendix A for installation and configuration hints.

There are a variety of ways to tell Python to execute the code you type. This chapter discusses all the program launching techniques in common use today. Along the way, you'll learn both how to type code interactively, and save it in files to be run with command lines, Unix tricks, icon clicks, IDEs, imports, and more.

If you just want to find out how to run a Python program quickly, you may be tempted to just read the parts that pertain to your platform and move on to Chapter 4. But don't skip the material on module imports, since that's essential to understanding Python's architecture. And we encourage you to at least skim the sections on IDLE and other IDEs, so you know what tools are available once you start developing more sophisticated Python programs.

Perhaps the simplest way to run Python programs is to type them at Python's interactive command line. There are a variety of ways to start this command line—in an IDE, from a system console, and so on. Assuming the interpreter is installed as an executable program on your system, the most platform-neutral way to start an interactive interpreter session is usually to type just "python" at your operating system's prompt, without any arguments. For example:

% python
Python 2.2 (#28, Dec 21 2001, 12:21:22) [MSC 32 bit (Intel)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>>

Here the word "python" is typed at your system shell prompt, to begin an interactive Python session (the "%" character stands for your system's prompt, not your input). The notion of a system shell prompt is generic, but varies per platform:

On Windows, you can type python in a DOS console window (a.k.a. Command Prompt), or the Start/Run... dialog box.
On Unix and Linux, you might type this in a shell window (e.g., in an

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Interactive Coding

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% python
Python 2.2 (#28, Dec 21 2001, 12:21:22) [MSC 32 bit (Intel)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>>

On Windows, you can type python in a DOS console window (a.k.a. Command Prompt), or the Start/Run... dialog box.
On Unix and Linux, you might type this in a shell window (e.g., in an xterm or console, running a shell such as ksh or csh).
Other systems may use similar or platform-specific devices. On PalmPilots, for example, click the Python home icon to launch an interactive session; on a Zaurus PDA, open a Terminal window.

If you have not set your shell's PATH environment variable to include Python, you may need to replace the word "python" with the full path to the Python executable on your machine. For instance, on Windows, try typing C:\Python22\python (or C:\Python23\python for Version 2.3); on Unix and Linux, /usr/local/bin/python (or /usr/bin/python) will often suffice.

Once the Python interactive session starts, it begins by printing two lines of informational text (which we normally omit in our examples to save space), and prompts for input with >>> when it's waiting for you to type a new Python statement or expression. When working interactively, the results of your code are displayed after the >>> lines—here are the results of two Python print statements:

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System Command Lines and Files

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Although the interactive prompt is great for experimenting and testing, it has one big disadvantage: programs you type there go away as soon as the Python interpreter executes them. The code you type interactively is never stored in a file, so you can't run it again without retyping it from scratch. Cut-and-paste and command recall can help some here, but not much, especially when you start writing larger programs. To cut and paste code from an interactive session, you have to edit out Python prompts, program outputs, and so on.

To save programs permanently, you need to write your code in files, usually known as modules. Modules are simply text files containing Python statements. Once coded, you can ask the Python interpreter to execute the statements in such a file any number of times, and in a variety of ways—by system command lines, by file icon clicks, by options in the IDLE user interface, and more. However they are run, Python executes all the code in a module file from top to bottom, each time you run the file. Such files are often referred to as programs in Python—a series of precoded statements.

The next few sections explore ways to run code typed into module files. In this section we run files in the most basic way: by listing their names in a python command line entered at a system prompt. As a first example, suppose we start our favorite text editor (e.g., vi, notepad, or the IDLE editor) and type three Python statements into a text file named spam.py:

print 2 ** 8                              # Raise to a power.
print 'the bright side ' + 'of life'      # + means concatenation.

This file contains two Python print statements and Python comments to the right. Text after a # is simply ignored as a human-readable comment, and is not part of the statement's syntax. Again, ignore the syntax of code in this file for now. The point to notice is that we've typed code into a file, rather than at the interactive prompt. In the process, we've coded a fully-functional Python script.

Once we've saved this text file, we can ask Python to run it by listing its full filename as a first argument on a python command, typed at the system shell's prompt:

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Clicking Windows File Icons

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On Windows, Python automatically registers itself to be the program that opens Python program files when they are clicked. Because of that, it is possible to launch the Python programs you write by simply clicking (or double-clicking) on their file icons with your mouse.

On Windows, icon clicks are made easy by the Windows registry. On non-Windows systems, you will probably be able to perform a similar trick, but the icons, file explorer, navigation schemes, and more may differ slightly. On some Unix systems, for instance, you may need to register the .py extension with your file explorer GUI, make your script executable using the #! trick of the prior section, or associate the file MIME type with an application or command by editing files, installing programs, or using other tools. See your file explorer's documentation for more details, if clicks do not work correctly right off the bat.

To illustrate, suppose we create the following program file with our text editor, and save it as filename script4.py:

# A comment
import sys
print sys.platform
print 2 ** 100

There's not much new here—just an import and two prints again (sys.platform is just a string that identifies the kind of computer you're working on; it lives in a module called sys, which we must import to load). In fact, we can run this file from a system command line:

D:\OldVaio\LP-2ndEd\Examples>c:\python22\python script4.py
win32
1267650600228229401496703205376

Icon clicks allow us to run this file without any typing at all. If we find this file's icon—for instance, by selecting My Computer, and working our way down on the D drive—we will get the file explorer picture captured in Figure 3-1 and shown on Windows XP. Python source files show up as snakes on Windows, and byte code files as snakes with eyes closed (or with a reddish color in Version 2.3). You will normally want to click (or otherwise run) the source code file, in order to pick up your most recent changes. To launch the file here, simply click on the icon for script4.py.

Figure 3-1: Python file icons on Windows

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Module Imports and Reloads

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So far, we've been calling files of code "modules," and using the word "import," without explaining what these terms mean. We'll study modules and larger program architecture in depth in Part V. Because imports are also a way to launch programs, this section introduces enough module basics to get you started.

In simple terms, every file of Python code whose name ends in a .py extension is a module. Other files can access the items defined by a module by importing that module; import operations essentially load another file, and grant access to the file's contents. Furthermore, the contents of a module are made available to the outside world through its attributes , a term we'll define next.

This module-based services model turns out to be the core idea behind program architecture in Python. Larger programs usually take the form of multiple module files, which import tools from other module files. One of the modules is designated as the main or top-level file, and is the one launched to start the entire program.

We'll delve into such architecture issues in more detail later in this book. This chapter is mostly interested in the fact that import operations run the code in a file that is being loaded, as a final step. Because of this, importing a file is yet another way to launch it.

For instance, if we start an interactive session (in IDLE, from a command line, or otherwise), we can run the original script4.py file that appeared earlier with a simple import:

D:\LP-2ndEd\Examples>c:\python22\python
>>> import script4
win32
1267650600228229401496703205376

This works, but only once per session (really, process), by default. After the first import, later imports do nothing, even if we change and save the module's source file again in another window:

>>> import script4
>>> import script4

This is by design; imports are too expensive an operation to repeat more than once per program run. As we'll learn in Chapter 15, imports must find files, compile to byte code, and run the code. If we really want to force Python to rerun the file again in the same session (without stopping and restarting the session), we need to instead call the built-in

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The IDLE User Interface

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IDLE is a graphical user interface for doing Python development, and is a standard and free part of the Python system. It is usually referred to as an Integrated Development Environment (IDE), because it binds together various development tasks into a single view.

In short, IDLE is a GUI that lets you edit, run, browse, and debug Python programs, all from a single interface. Moreover, because IDLE is a Python program that uses the Tkinter GUI toolkit, it runs portably on most Python platforms: MS Windows, X Windows (Unix, Linux), and Macs. For many, IDLE is an easy-to-use alternative to typing command lines, and a less problem-prone alternative to clicking on icons.

Let's jump right into an example. IDLE is easy to start under Windows—it has an entry in the Start button menu for Python (see Figure 2-1); it can also be selected by right-clicking on a Python program icon. On some Unix-like systems, you may need to launch IDLE's top-level script from a command line or icon click—start file idle.pyw in the idle subdirectory of Python's Tools directory.

Figure 3-3 shows the scene after starting IDLE on Windows. The Python Shell window at the bottom is the main window, which runs an interactive session (notice the >>> prompt). This works like all interactive sessions—code you type here is run immediately after you type it—and serves as a testing tool.

Figure 3-3: IDLE main window and text edit window

IDLE uses familiar menus with keyboard shortcuts for most of its operations. To make (or edit) a script under IDLE, open text edit windows—in the main window, select the File menu pulldown, and pick New window to open a text edit window (or Open... to edit an existing file). The window at the top of Figure 3-3 is an IDLE text edit window, where the code for file script3.py was entered.

Although this may not show up fully in this book, IDLE uses syntax-directed colorization for the code typed in both the main window, and all text edit windows—keywords are one color, literals are another, and so on. This helps give you a better picture of the components in your code.

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Other IDEs

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Because IDLE is free, portable, and a standard part of Python, it's a nice first development tool to become familiar with if you want to use an IDE at all. Use IDLE for this book's exercises if you're just starting out. There are, however, a handful of alternative IDEs for Python developers, some of which are substantially more powerful and robust than IDLE. Among the most commonly used today are these four:

Komodo: A full-featured development environment GUI for Python (and other languages). Komodo includes standard syntax-coloring text editing, debugging, and so on. In addition, Komodo offers many advanced features that IDLE does not, including project files, source-control integration, regular expression debugging, and a drag-and-drop GUI builder which generates Python/Tkinter code to implement the GUIs you design interactively. Komodo is not free as we write this; it is available at http://www.activestate.com.
PythonWorks: Another full-featured development environment GUI. PythonWorks also has standard IDE tools, and provides a Python/Tkinter GUI builder that generates Python code. In addition, it supports unique features such as automatic code refactoring, for optimal maintenance and reuse. This is also a commercial product; see http://www.pythonware.com for details.
PythonWin: A free IDE that ships as part of ActiveState's ActivePython distribution (and may also be fetchable separately from http://www.python.org resources). PythonWin is a Windows-only IDE for Python; it is roughly like IDLE, with a handful of useful Windows-specific extensions added in. For instance, PythonWin has support for COM objects. It also adds basic user interface features beyond IDLE, such as object attribute list popups. Further, PythonWin serves as an example of using the Windows extension package's GUI library. See http://www.activestate.com.
Visual Python: ActiveState also sells a system called Visual Python, which is a plug-in that adds Python support to Microsoft's Visual Studio development environment. This is also a Windows-only solution, but is appealing to developers with a prior intellectual investment in Visual Studio. See ActiveState's web site for details.

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Embedding Calls

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So far, we've seen how to run code typed interactively, and how to launch code saved in files with command lines, icon clicks, IDEs, module imports, and Unix executable scripts. That covers most of the cases we'll see in this book.

But in some specialized domains, Python code may also be run by an enclosing system. In such cases, we say that Python programs are embedded in (i.e., run by) another program. The Python code itself may be entered into a text file, stored in a database, fetched from an HTML page, parsed from an XML document, and so on. But from an operational perspective, another system—not you—may tell Python to run the code you've created.

For example, it's possible to create and run strings of Python code from a C program by calling functions in the Python runtime API (a set of services exported by the libraries created when Python is compiled on your machine):

#include <Python.h>
...
Py_Initialize(  );
PyRun_SimpleString("x = brave + sir + robin");

In this C code snippet, a program coded in the C language embeds the Python interpreter by linking in its libraries, and passes it a Python assignment statement string to run. C programs may also gain access to Python objects, and process or execute them using other Python API tools.

This book isn't about Python/C integration, but you should be aware that, depending on how your organization plans to use Python, you may or may not be the one who actually starts the Python programs you create. Regardless, you can still likely use the interactive and file-based launching techniques described here, to test code in isolation from those enclosing systems that may eventually use it.

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Frozen Binary Executables

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Frozen binary executables are packages that combine your program's byte code and the Python interpreter into a single executable program. With these, programs can be launched in the same ways that you would launch any other executable program (icon clicks, command lines, etc.). While this option works well for delivery of products, it is not really intended for use during program development. You normally freeze just before shipping, and after development is finished.

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Text Editor Launch Options

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Many programmer-friendly text editors have support for editing, and possibly running, Python programs. Such support may be either built-in, or fetchable on the web. For instance, if you are familiar with the emacs text editor, you can do all your Python editing and launching from inside the text editor itself. See the text editor resources page at http://www.python.org/editors for more details.

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Other Launch Options

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Depending on your platform, there may be additional ways that you can start Python programs. For instance, on some Macintosh systems, you may be able to drag Python program file icons onto the Python interpreter icon, to make them execute. And on Windows, you can always start Python scripts with the Run... option in the Start menu. Finally, the Python standard library has utilities that allow Python programs to be started by other Python programs (e.g., execfile, os.popen, os.system); however, these tools are beyond the scope of the present chapter.

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Future Possibilities?

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Although this chapter reflects current practice, much of it has been both platform- and time-specific. Indeed, many of the execution and launch details presented arose between this book's first and second editions. As for program execution, it's not impossible that new program launch options may arise over time.

New operating systems, and new versions of them, may also provide execution techniques beyond those outlined here. In general, because Python keeps pace with such changes, you should be able to launch it in whatever way makes sense for the machines you use, both now and in the future—be that drawing on tablet PCs or PDAs, grabbing icons in a virtual reality, or shouting a script's name over your coworkers' conversations.

Implementation changes may also impact launch schemes somewhat (e.g., a full compiler could produce normal executables, launched much like frozen binaries today). If we knew what the future truly held, though, we would probably be talking to a stock broker instead of writing these words.

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Which Option Should I Use?

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With all these options, the question naturally arises: Which one is best for me? In general, use the IDLE interface for development, if you are just getting started with Python. It provides a user-friendly GUI environment, and can hide some of the underlying configuration details. It also comes with a platform-neutral text editor for coding your scripts, and is a standard and free part of the Python system.

If instead, you are an experienced programmer, you might be more comfortable with simply the text editor of your choice in one window, and another window for launching the programs you edit, by system command lines or icon clicks. Because development environments are a very subjective choice, we can't offer much more in the way of universal guidelines; in general, the environment you like to use is usually the best to use.

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Part I Exercises

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It's time to start doing a little coding on your own. This first exercise session is fairly simple, but a few of these questions hint at topics to come in later chapters. Remember, check Section B.1 for the answers; the exercises and their solutions sometimes contain supplemental information not discussed in the main part of the chapter. In other words, you should peek, even if you can manage to get all the answers on your own.

Interaction. Using a system command line, IDLE, or other, start the Python interactive command line (>>> prompt), and type the expression: "Hello World!" (including the quotes). The string should be echoed back to you. The purpose of this exercise is to get your environment configured to run Python. In some scenarios, you may need to first run a cd shell command, type the full path to the python executable, or add its path to your PATH environment variable. If desired, you can set it in your .cshrc or .kshrc file to make Python permanently available on Unix systems; on Windows use a setup.bat, autoexec.bat, or the environment variable GUI. See Appendix A for help with environment variable settings.
Programs. With the text editor of your choice, write a simple module file—a file containing the single statement: print 'Hello module world!'. Store this statement in a file named module1.py. Now, run this file by using any launch option you like: running it in IDLE, clicking on its file icon, passing it to the Python interpreter program on the system shell's command line, and so on. In fact, experiment by running your file with as many of the launch techniques seen in this chapter as you can. Which technique seems easiest? (There is no right answer to this one.)
Modules. Next, start the Python interactive command line (>>> prompt) and import the module you wrote in Exercise 2. Try moving the file to a different directory and importing it again from its original directory (i.e., run Python in the original directory when you import); what happens? (Hint: is there still a file named

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Chapter 4: Numbers

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This chapter begins our tour of the Python language. In Python, data takes the form of objects—either built-in objects that Python provides, or objects we create using Python and C tools. Since objects are the most fundamental notion in Python programming, we'll start this chapter with a survey of Python's built-in object types before concentrating on numbers.

By way of introduction, let's first get a clear picture of how this chapter fits into the overall Python picture. From a more concrete perspective, Python programs can be decomposed into modules, statements, expressions, and objects, as follows:

Programs are composed of modules.
Modules contain statements.
Statements contain expressions.
Expressions create and process objects.

We introduced the highest level of this hierarchy when we learned about modules in Chapter 3. This part's chapters begin at the bottom, exploring both built-in objects, and the expressions you can code to use them.

If you've used lower-level languages such as C or C++, you know that much of your work centers on implementing objects—also known as data structures—to represent the components in your application's domain. You need to lay out memory structures, manage memory allocation, implement search and access routines, and so on. These chores are about as tedious (and error prone) as they sound, and usually distract from your programs' real goals.

In typical Python programs, most of this grunt work goes away. Because Python provides powerful object types as an intrinsic part of the language, there's no need to code object implementations before you start solving problems. In fact, unless you have a need for special processing that built-in types don't provide, you're almost always better off using a built-in object instead of implementing your own. Here are some reasons why:

Built-in objects make simple programs easy to write. For simple tasks, built-in types are often all you need to represent the structure of problem domains. Because you get things such as collections (lists) and search tables (dictionaries) for free, you can use them immediately. You can get a lot of work done with Python's built-in object types alone.

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Python Program Structure

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Programs are composed of modules.
Modules contain statements.
Statements contain expressions.
Expressions create and process objects.

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Why Use Built-in Types?

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Built-in objects make simple programs easy to write. For simple tasks, built-in types are often all you need to represent the structure of problem domains. Because you get things such as collections (lists) and search tables (dictionaries) for free, you can use them immediately. You can get a lot of work done with Python's built-in object types alone.
Python provides objects and supports extensions. In some ways, Python borrows both from languages that rely on built-in tools (e.g., LISP), and languages that rely on the programmer to provide tool implementations or frameworks of their own (e.g., C++). Although you can implement unique object types in Python, you don't need to do so just to get started. Moreover, because Python's built-ins are standard, they're always the same; frameworks tend to differ from site to site.
Built-in objects are components of extensions. For more complex tasks you still may need to provide your own objects, using Python statements or C language interfaces. But as we'll see in later parts, objects implemented manually are often built on top of built-in types such as lists and dictionaries. For instance, a stack data structure may be implemented as a class that manages a built-in list.

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Numbers

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