Python in a Nutshell | O'Reilly Media

Content preview·Buy PDF of this chapter|Buy reprint rights for this chapter

Python is a general-purpose programming language. It has been around for quite a while: Guido van Rossum, Python's creator, started developing Python back in 1990. This stable and mature language is very high-level, dynamic, object-oriented, and cross-platform—all characteristics that are very attractive to developers. Python runs on all major hardware platforms and operating systems, so it doesn't constrain your platform choices.

Python offers high productivity for all phases of the software life cycle: analysis, design, prototyping, coding, testing, debugging, tuning, documentation, deployment, and, of course, maintenance. Python's popularity has seen steady, unflagging growth over the years. Today, familiarity with Python is an advantage for every programmer, as Python has infiltrated every niche and has useful roles to play as a part of any software solution.

Python provides a unique mix of elegance, simplicity, practicality, and power. You'll quickly become productive with Python, thanks to its consistency and regularity, its rich standard library, and the many third-party modules that are readily available for it. Python is easy to learn, so it is quite suitable if you are new to programming, yet at the same time, it is powerful enough for the most sophisticated expert.

The Python language, while not minimalist, is rather spare for good pragmatic reasons. Once a language offers one good way to express a design idea, adding other ways has only modest benefits, while the cost in terms of language complexity grows more than linearly with the number of features. A complicated language is harder to learn and master (and implement efficiently and without bugs) than a simpler one. Any complications and quirks in a language hamper productivity in software maintenance, particularly in large projects, where many developers cooperate and often maintain code originally written by others.

Additional content appearing in this section has been removed.
Purchase this book now or read it online at Safari to get the whole thing!

Content preview·Buy PDF of this chapter|Buy reprint rights for this chapter

The Python language, while not minimalist, is rather spare for good pragmatic reasons. Once a language offers one good way to express a design idea, adding other ways has only modest benefits, while the cost in terms of language complexity grows more than linearly with the number of features. A complicated language is harder to learn and master (and implement efficiently and without bugs) than a simpler one. Any complications and quirks in a language hamper productivity in software maintenance, particularly in large projects, where many developers cooperate and often maintain code originally written by others.

Python is simple, but not simplistic. It adheres to the idea that if a language behaves a certain way in some contexts, it should ideally work similarly in all contexts. Python also follows the principle that a language should not have "convenient" shortcuts, special cases, ad hoc exceptions, overly subtle distinctions, or mysterious and tricky under-the-covers optimizations. A good language, like any other designed artifact, must balance such general principles with taste, common sense, and a high degree of practicality.

Python is a general-purpose programming language, so Python's traits are useful in just about any area of software development. There is no area where Python cannot be part of an optimal solution. "Part" is an important word here; while many developers find that Python fills all of their needs, Python does not have to stand alone. Python programs can easily cooperate with a variety of other software components, making it an ideal language for gluing together components written in other languages.

Python is a very-high-level language (VHLL). This means that Python uses a higher level of abstraction, conceptually farther from the underlying machine, than do classic compiled languages such as C, C++, and Fortran, which are traditionally called high-level languages. Python is also simpler, faster to process, and more regular than classic high-level languages. This affords high programmer productivity and makes Python an attractive development tool. Good compilers for classic compiled languages can often generate binary machine code that runs much faster than Python code. However, in most cases, the performance of Python-coded applications proves sufficient. When it doesn't, you can apply the optimization techniques covered in "Optimization" on page 474 to enhance your program's performance while keeping the benefits of high programming productivity.

Additional content appearing in this section has been removed.
Purchase this book now or read it online at Safari to get the whole thing!

Content preview·Buy PDF of this chapter|Buy reprint rights for this chapter

There is more to Python programming than just the Python language: the standard Python library and other extension modules are almost as important for effective Python use as the language itself. The Python standard library supplies many well-designed, solid, 100 percent pure Python modules for convenient reuse. It includes modules for such tasks as representing data, string and text processing, interacting with the operating system and filesystem, and web programming. Because these modules are written in Python, they work on all platforms supported by Python.

Extension modules, from the standard library or from elsewhere, let Python code access functionality supplied by the underlying operating system or other software components such as graphical user interfaces (GUIs), databases, and networks. Extensions also afford maximal speed in computationally intensive tasks such as XML parsing and numeric array computations. Extension modules that are not coded in Python, however, do not necessarily enjoy the same cross-platform portability as pure Python code.

You can write special-purpose extension modules in lower-level languages to achieve maximum performance for small, computationally intensive parts that you originally prototyped in Python. You can also use tools such as SWIG to wrap existing C/C++ libraries into Python extension modules, as we'll see in "Extending Python Without Python's C API" on page 645. Finally, you can embed Python in applications coded in other languages, exposing existing application functionality to Python scripts via dedicated Python extension modules.

This book documents many modules, both from the standard library and from other sources, in areas such as client- and server-side network programming, GUIs, numerical array processing, databases, manipulation of text and binary files, and interaction with the operating system.

Additional content appearing in this section has been removed.
Purchase this book now or read it online at Safari to get the whole thing!

Content preview·Buy PDF of this chapter|Buy reprint rights for this chapter

Python currently has three production-quality implementations, known as CPython, Jython, and IronPython, and several other experimental implementations, such as PyPy. This book primarily addresses CPython, the most widely used implementation, which I refer to as just Python for simplicity. However, the distinction between a language and its implementations is an important one.

Classic Python (a.k.a. CPython, often just called Python) is the fastest, most up-to-date, most solid and complete implementation of Python. Therefore, it can be considered the "reference implementation" of the language. CPython is a compiler, interpreter, and set of built-in and optional extension modules, all coded in standard C. CPython can be used on any platform where the C compiler complies with the ISO/IEC 9899:1990 standard (i.e., all modern, popular platforms). In Chapter 2, I'll explain how to download and install CPython. All of this book, except Chapter 26 and a few sections explicitly marked otherwise, applies to CPython, since CPython is the most widely used version of Python.

Jython is a Python implementation for any Java Virtual Machine (JVM) compliant with Java 1.2 or better. Such JVMs are available for all popular, modern platforms. With Jython, you can use all Java libraries and frameworks. For optimal use of Jython, you need some familiarity with fundamental Java classes. You do not have to code in Java, but documentation and examples for existing Java classes are couched in Java terms, so you need a nodding acquaintance with Java to read and understand them. You also need to use Java supporting tools for tasks such as manipulating .jar files and signing applets. This book deals with Python, not with Java. For Jython usage, you should complement this book with Jython Essentials, by Noel Rappin and Samuele Pedroni (O'Reilly), possibly

Additional content appearing in this section has been removed.
Purchase this book now or read it online at Safari to get the whole thing!

Content preview·Buy PDF of this chapter|Buy reprint rights for this chapter

Python is developed, maintained, and released by a team of core developers headed by Guido van Rossum, Python's inventor, architect, and Benevolent Dictator For Life (BDFL). This title means that Guido has the final say on what becomes part of the Python language and standard libraries. Python's intellectual property is vested in the Python Software Foundation (PSF), a nonprofit corporation devoted to promoting Python, with dozens of individual members (nominated for their contributions to Python, and including all of the Python core team) and corporate sponsors. Most PSF members have commit privileges to Python's SVN repositories (http://svn.python.org/projects/), and most Python SVN committers are members of the PSF.

Proposed changes to Python are detailed in public documents called Python Enhancement Proposals (PEPs), debated (and sometimes advisorily voted on) by Python developers and the wider Python community, and finally approved or rejected by Guido, who takes debates and votes into account but is not bound by them. Many hundreds of people actively contribute to Python development through PEPs, discussion, bug reports, and proposed patches to Python sources, libraries, and documentation.

The Python core team releases minor versions of Python (2.x, for growing values of x), currently at a pace of about once every year or two. Python 2.2 was released in December 2001, 2.3 in July 2003, and 2.4 in November 2004. Python 2.5 is scheduled to be released in the summer of 2006 (at the time of this writing, the first alpha release of 2.5 has just appeared). Each minor release adds features that make Python more powerful and simpler to use, but also takes care to maintain backward compatibility. One day there will be a Python 3.0 release, which will be allowed to break backward compatibility to some extent in order to remove some redundant "legacy" features and simplify the language even further. However, that release is still years in the future, and no specific schedules for it currently exist; the current state of Guido's ideas about Python 3.0 can be studied at

Additional content appearing in this section has been removed.
Purchase this book now or read it online at Safari to get the whole thing!

Content preview·Buy PDF of this chapter|Buy reprint rights for this chapter

The richest of all Python resources is the Internet. The best starting point is Python's site, http://www.python.org, which is full of interesting links to explore. http://www.jython.org is a must if you have any interest in Jython. For IronPython, at the time of writing the most relevant site is http://workspaces.gotdotnet.com/ironpython, but the IronPython team's near-future plans include reviving the site http://ironpython.com; by the time you read this, http://ironpython.com should be back in its role as the primary IronPython web site.

Python, Jython, and IronPython come with good documentation. The manuals are available in many formats, suitable for viewing, searching, and printing. You can browse the manuals on the Web at http://www.python.org/doc/current/. You can find links to the various formats you can download at http://www.python.org/doc/current/download.html, and http://www.python.org/doc/ has links to a large variety of documents. For Jython, http://www.jython.org/docs/ has links to Jython-specific documents as well as general Python ones. The Python FAQ (Frequently Asked Questions) document is at http://www.python.org/doc/FAQ.html, and the Jython-specific FAQ document is at http://www.jython.org/cgi-bin/faqw.py?req=index.

Most Python documentation (including this book) assumes some software development knowledge. However, Python is quite suitable for first-time programmers, so there are exceptions to this rule. A few good introductory online texts for nonprogrammers are:

Josh Cogliati's "Non-Programmers Tutorial For Python," available at http://www.honors.montana.edu/~jjc/easytut/easytut/
Alan Gauld's "Learning to Program," available at http://www.freenetpages.co.uk/hp/alan.gauld/
Allen Downey and Jeffrey Elkner's "How to Think Like a Computer Scientist (Python Version)," available at http://www.ibiblio.org/obp/thinkCSpy/

Additional content appearing in this section has been removed.
Purchase this book now or read it online at Safari to get the whole thing!

Content preview·Buy PDF of this chapter|Buy reprint rights for this chapter

You can install Python, in classic (CPython), JVM (Jython), and .NET (IronPython) versions, on most platforms. With a suitable development system (C for CPython, Java for Jython, .NET for IronPython), you can install Python from its source code distribution. On popular platforms, you also have the alternative of installing from pre-built binary distributions. If your platform comes with a pre-installed version of Python, you may still want to install another richer or better updated one: if you do, I recommend you do not remove nor overwrite your platform's original version—rather, install the other version "side by side" with the first one. In this way, you can be sure you are not going to disturb any other software that is installed as part of your platform: such software might well rely on the exact Python version that came with the platform itself.

Installing CPython from a binary distribution is faster, saves you substantial work on some platforms, and is the only possibility if you have no suitable C compiler. Installing from sources gives you more control and flexibility and is the only possibility if you can't find a suitable pre-built binary distribution for your platform. Even if you install from binaries, I recommend you also download the source distribution because it includes examples and demos that may be missing from pre-built binary packages.

To install CPython from source code, you need a platform with an ISO-compliant C compiler and ancillary tools such as make. On Windows, the normal way to build Python is with Microsoft Visual Studio (version 7.1, a.k.a. VS2003, for Python 2.4 and 2.5).

To download Python source code, visit http://www.python.org and follow the link labeled Download. The latest version at the time of this writing is:

http://www.python.org/ftp/python/2.4.3/Python-2.4.3.tgz

The .tgz file extension is equivalent to .tar.gz (i.e., a tar archive of files, compressed by the powerful and popular

Additional content appearing in this section has been removed.
Purchase this book now or read it online at Safari to get the whole thing!

Content preview·Buy PDF of this chapter|Buy reprint rights for this chapter

To install CPython from source code, you need a platform with an ISO-compliant C compiler and ancillary tools such as make. On Windows, the normal way to build Python is with Microsoft Visual Studio (version 7.1, a.k.a. VS2003, for Python 2.4 and 2.5).

To download Python source code, visit http://www.python.org and follow the link labeled Download. The latest version at the time of this writing is:

http://www.python.org/ftp/python/2.4.3/Python-2.4.3.tgz

The .tgz file extension is equivalent to .tar.gz (i.e., a tar archive of files, compressed by the powerful and popular gzip compressor). You can also get a version with an extension of .tar.bz2 instead of .tgz, compressed with the even more powerful bzip2 compressor, if you're able to deal with Bzip-2 compression (most popular utilities can nowadays).

To download sources for Python 2.5, see http://www.python.org/download/releases/2.5/. At the same URL, you will also find Python 2.5 documentation and binary releases. At the time of this writing, the first alpha release of 2.5 had just appeared, but by the time you read this book the final release of 2.5 is likely to be available.

On Windows, installing Python from source code can be a chore unless you are already familiar with Microsoft Visual Studio and also used to working at the Windows command line (i.e., in the text-oriented windows known as MS-DOS Prompt or Command Prompt, depending on your version of Windows).

If the following instructions give you trouble, I suggest you skip ahead to "Installing Python from Binaries" on page 18. It may be a good idea to do an installation from binaries anyway, even if you also install from source code. This way, if you notice anything strange while using the version you installed from source code, you can double-check with the installation from binaries. If the strangeness goes away, it must be due to some quirk in your installation from source code, and then you know you must double-check the latter.

Additional content appearing in this section has been removed.
Purchase this book now or read it online at Safari to get the whole thing!

Content preview·Buy PDF of this chapter|Buy reprint rights for this chapter

If your platform is popular and current, you may find pre-built and packaged binary versions of Python ready for installation. Binary packages are typically self-installing, either directly as executable programs, or via appropriate system tools, such as the RedHat Package Manager (RPM) on Linux and the Microsoft Installer (MSI) on Windows. Once you have downloaded a package, install it by running the program and interactively choosing installation parameters, such as the directory where Python is to be installed.

To download Python binaries, visit http://www.python.org and follow the link labeled Download. At the time of this writing, the binary installers directly available from the main Python site are a Windows Installer (MSI) package:

http://www.python.org/ftp/python/2.4.3/Python-2.4.3.msi

and a Mac OS X Disk Image (.dmg) package suitable for Mac OS X 10.3.9 and later on either a PowerPC or Intel processor ("Universal" format):

http://www.python.org/ftp/python/2.4.3/Universal-MacPython-2.4.3.dmg

Many third parties supply free binary Python installers for other platforms. For Linux distributions, see http://rpmfind.net if your distribution is RPM-based (RedHat, Fedora, Mandriva, SUSE, etc.) or http://www.debian.org for Debian and Ubuntu. The site http://www.python.org/download/ provides links to binary distributions for OS/2, Amiga, RISC OS, QNX, VxWorks, IBM AS/400, Sony PlayStation 2, Sharp Zaurus, and Windows CE (also known as "Pocket PC"). Older Python versions, starting from 1.5.2, are also usable and functional, though not as powerful and polished as the current Python 2.4.3. The download page provides links to 1.5.2 and other installers for older or less popular platforms (MS-DOS, Windows 3.1, Psion, BeOS, etc.).

To get Python for Nokia Series 60 cellphones, see http://www.forum.nokia.com/python.

ActivePython (http://www.activestate.com/Products/ActivePython) is a binary package of Python 2.4, with several third-party extensions included, available for AIX, HP-UX, Linux (x86 processors only), Mac OS X, Solaris (SPARC, x64, and x86 processors), and Windows (all versions from Windows 95 to Windows XP and Windows Server 2003).

Additional content appearing in this section has been removed.
Purchase this book now or read it online at Safari to get the whole thing!

Content preview·Buy PDF of this chapter|Buy reprint rights for this chapter

To install Jython, you need a Java Virtual Machine (JVM) that complies with Java 1.1 or higher. See http://www.jython.org/platform.html for advice on JVMs for your platform.

To download Jython, visit http://www.jython.org and follow the link labeled Download. The latest version, which at the time of this writing (supporting some Python 2.3 features, as well as all of Python 2.2) is:

http://prdownloads.sf.net/jython/jython-22.class

In the following section, for clarity, I assume you have created a new directory named C:\Jy and downloaded jython-22.class there. Of course, you can choose to name and place the directory as it best suits you. On Unix-like platforms, in particular, the directory name will probably be something like ~/Jy.

The Jython installer .class file is a self-installing program. Open an MS-DOS Prompt window (or a shell prompt on a Unix-like platform), change directory to C:\Jy, and run your Java interpreter on the Jython installer. Make sure to include directory C:\Jy in the Java CLASSPATH. With most releases of Sun's Java Development Kit (JDK), for example, you can run:

C:\Jy>java -cp
. jython-22

This runs a GUI installer that lets you choose destination directory and options. If you want to avoid the GUI, you can use the -o switch on the command line. The switch lets you specify the installation directory and options on the command line. For example:

C:\Jy>java -cp .
jython-22 -o C:\Jython-2.2
demo lib source

installs Jython, with all optional components (demos, libraries, and source code), in directory C:\Jython-2.2. The Jython installation builds two small, useful command files. One, run as jython (named jython.bat on Windows), runs the interpreter. The other, run as jythonc, compiles Python source into JVM bytecode. You can add the Jython installation directory to your PATH or copy these command files into any directory on your

Additional content appearing in this section has been removed.
Purchase this book now or read it online at Safari to get the whole thing!

Content preview·Buy PDF of this chapter|Buy reprint rights for this chapter

To install IronPython, you need to have a current Common Language Runtime (CLR) implementation installed on your machine. Both the latest version of Mono (see http://www.mono-project.com/Main_Page), and Microsoft .NET Framework 2.0, work fine with IronPython. To download IronPython, visit http://workspaces.gotdotnet.com/ironpython (or http://ironpython.com, which will eventually become IronPython's main site, but is still out of date at the time of this writing) and follow download instructions on that page. The latest version at the time of this writing is 1.0. The same site also provides up-to-date installation instructions. I cannot provide such instructions in this book because they are still in flux at the time of this writing.

Additional content appearing in this section has been removed.
Purchase this book now or read it online at Safari to get the whole thing!

Content preview·Buy PDF of this chapter|Buy reprint rights for this chapter

To develop software systems in Python, you write text files that contain Python source code and documentation. You can use any text editor, including those in Integrated Development Environments (IDEs). You then process the source files with the Python compiler and interpreter. You can do this directly, implicitly inside an IDE, or via another program that embeds Python. The Python interpreter also lets you execute Python code interactively, as do IDEs.

The Python interpreter program is run as python (it's named python.exe on Windows). python includes both the interpreter itself and the Python compiler, which is implicitly invoked, as needed, on imported modules. Depending on your system, the program may have to be in a directory listed in your PATH environment variable. Alternatively, as with any other program, you can give a complete pathname to it at a command (shell) prompt, or in the shell script (or .BAT file, shortcut target, etc.) that runs it. On Windows, you can also use Start → Programs → Python 2.4 → Python (command line).

Besides PATH, other environment variables affect the python program. Some environment variables have the same effects as options passed to python on the command line, as documented in the next section. A few environment variables provide settings not available via command-line options:

PYTHONHOME: The Python installation directory. A lib subdirectory, containing the standard Python library modules, should exist under this directory. On Unix-like systems, the standard library modules should be in subdirectory lib/python-2.3 for Python 2.3, lib/python-2.4 for Python 2.4, and so on.
PYTHONPATH: A list of directories separated by colons on Unix-like systems and by semicolons on Windows. Modules are imported from these directories. This list extends the initial value for Python's

Additional content appearing in this section has been removed.
Purchase this book now or read it online at Safari to get the whole thing!

Content preview·Buy PDF of this chapter|Buy reprint rights for this chapter

The Python interpreter program is run as python (it's named python.exe on Windows). python includes both the interpreter itself and the Python compiler, which is implicitly invoked, as needed, on imported modules. Depending on your system, the program may have to be in a directory listed in your PATH environment variable. Alternatively, as with any other program, you can give a complete pathname to it at a command (shell) prompt, or in the shell script (or .BAT file, shortcut target, etc.) that runs it. On Windows, you can also use Start → Programs → Python 2.4 → Python (command line).

Besides PATH, other environment variables affect the python program. Some environment variables have the same effects as options passed to python on the command line, as documented in the next section. A few environment variables provide settings not available via command-line options:

PYTHONHOME: The Python installation directory. A lib subdirectory, containing the standard Python library modules, should exist under this directory. On Unix-like systems, the standard library modules should be in subdirectory lib/python-2.3 for Python 2.3, lib/python-2.4 for Python 2.4, and so on.
PYTHONPATH: A list of directories separated by colons on Unix-like systems and by semicolons on Windows. Modules are imported from these directories. This list extends the initial value for Python's sys.path variable. Modules, importing, and the sys.path variable are covered in Chapter 7.
PYTHONSTARTUP: The name of a Python source file that is automatically executed each time an interactive interpreter session starts. No such file is run if this variable is not set or if it is set to the path of a file that is not found. The PYTHONSTARTUP file is not used when you run a Python script; it is used only when you start an interactive session.

Additional content appearing in this section has been removed.
Purchase this book now or read it online at Safari to get the whole thing!

Content preview·Buy PDF of this chapter|Buy reprint rights for this chapter

The Python interpreter's built-in interactive mode is the simplest development environment for Python. It is a bit primitive, but it is lightweight, has a small footprint, and starts fast. Together with an appropriate text editor (as discussed in "Free Text Editors with Python Support" on page 27), and line-editing and history facilities, the interactive interpreter (or, alternatively, IPython) offers a usable and popular development environment. However, there are a number of other development environments that you can also use.

Python's Integrated DeveLopment Environment (IDLE) comes with the standard Python distribution. IDLE is a cross-platform, 100 percent pure Python application based on Tkinter (see Chapter 17). IDLE offers a Python shell similar to interactive Python interpreter sessions but richer in functionality. It also includes a text editor optimized to edit Python source code, an integrated interactive debugger, and several specialized browsers/viewers.

IDLE is mature, stable, easy to use, and fairly rich in functionality. Promising new Python IDEs that share IDLE's free and cross-platform nature are emerging. Red Hat's Source Navigator (http://sources.redhat.com/sourcenav/) supports many languages. It runs on Linux, Solaris, HPUX, and Windows. Boa Constructor (http://boa-constructor.sf.net/) is Python-only and still beta-level, but well worth trying out. Boa Constructor includes a GUI builder for the wxWindows cross-platform GUI toolkit.

eric3 (http://www.die-offenbachs.de/detlev/eric3.html) is a full-featured IDE for Python and Ruby, based on the PyQt 3.1 cross-platform GUI toolkit.

The popular cross-platform, cross-language modular IDE Eclipse has plug-ins that support CPython and Jython; see http://pydev.sourceforge.net/ for more information.

Another new but very popular cross-platform Python editor and IDE is SPE, "Stani's Python Editor" (

Additional content appearing in this section has been removed.
Purchase this book now or read it online at Safari to get the whole thing!

Content preview·Buy PDF of this chapter|Buy reprint rights for this chapter

Whatever tools you use to produce your Python application, you can see your application as a set of Python source files, which are normal text files. A script is a file that you can run directly. A module is a file that you can import (as covered in Chapter 7) to provide functionality to other files or to interactive sessions. A Python file can be both a module and a script, exposing functionality when imported, but is also suitable for being run directly. A useful and widespread convention is that Python files that are primarily intended to be imported as modules, when run directly, should execute some simple self-test operations, as covered in "Testing" on page 452.

The Python interpreter automatically compiles Python source files as needed. Python source files normally have extension .py. Python saves the compiled bytecode file for each module in the same directory as the module's source, with the same basename and extension .pyc (or .pyo if Python is run with option -O). Python does not save the compiled bytecode form of a script when you run the script directly; rather, Python recompiles the script each time you run it. Python saves bytecode files only for modules you import. It automatically rebuilds each module's bytecode file whenever necessary—for example, when you edit the module's source. Eventually, for deployment, you may package Python modules using tools covered in Chapter 27.

You can run Python code interactively with the Python interpreter or an IDE. Normally, however, you initiate execution by running a top-level script. To run a script, give its path as an argument to python, as covered earlier in "The python Program" on page 22. Depending on your operating system, you can invoke python directly from a shell script or in a command file. On Unix-like systems, you can make a Python script directly executable by setting the file's permission bits

Additional content appearing in this section has been removed.
Purchase this book now or read it online at Safari to get the whole thing!

Content preview·Buy PDF of this chapter|Buy reprint rights for this chapter

The jython interpreter built during installation (see "Installing Jython" on page 20) is run similarly to the python program:

[path]jython {options} [ -j
jar | -c command |
file | - ] {arguments}

-j jar tells jython that the main script to run is _ _run_ _.py in the .jar file. Options -i, -S, and -v are the same as for python. --help is like python's -h, and --version is like python's --V. Instead of environment variables, jython uses a text file named registry in the installation directory to record properties with structured names. Property python.path, for example, is the Jython equivalent of Python's environment variable PYTHONPATH. You can also set properties with jython command-line options in the form -D name = value.

Additional content appearing in this section has been removed.
Purchase this book now or read it online at Safari to get the whole thing!

Content preview·Buy PDF of this chapter|Buy reprint rights for this chapter

IronPython may be run similarly to the python program:

[path]IronPythonConsole {options}
[-c command | file | - ]
{arguments}

Unfortunately, details are still in flux at the time of this writing, so I cannot provide them in this book. See http://ironpython.com for up-to-date information.

Additional content appearing in this section has been removed.
Purchase this book now or read it online at Safari to get the whole thing!

Content preview·Buy PDF of this chapter|Buy reprint rights for this chapter

This chapter is a quick guide to the Python language. To learn Python from scratch, I suggest you start with Learning Python, by Mark Lutz and David Ascher (O'Reilly). If you already know other programming languages and just want to learn the specific differences of Python, this chapter is for you. However, I'm not trying to teach Python here, so we're going to cover a lot of ground at a pretty fast pace. I focus on teaching the rules, and only secondarily on pointing out best practices and recommended style; for a standard Python style guide, see http://python.org/doc/peps/pep-0008/.

The lexical structure of a programming language is the set of basic rules that govern how you write programs in that language. It is the lowest-level syntax of the language and specifies such things as what variable names look like and which characters denote comments. Each Python source file, like any other text file, is a sequence of characters. You can also usefully consider it as a sequence of lines, tokens, or statements. These different lexical views complement and reinforce each other. Python is very particular about program layout, especially with regard to lines and indentation, so you'll want to pay attention to this information if you are coming to Python from another language.

A Python program is composed of a sequence of logical lines, each made up of one or more physical lines. Each physical line may end with a comment. A hash sign (#) that is not inside a string literal begins a comment. All characters after the # and up to the physical line end are part of the comment, and the Python interpreter ignores them. A line containing only whitespace, possibly with a comment, is known as a blank line, and Python totally ignores it. In an interactive interpreter session, you must enter an empty physical line (without any whitespace or comment) to terminate a multiline statement.

Additional content appearing in this section has been removed.
Purchase this book now or read it online at Safari to get the whole thing!

Content preview·Buy PDF of this chapter|Buy reprint rights for this chapter

The lexical structure of a programming language is the set of basic rules that govern how you write programs in that language. It is the lowest-level syntax of the language and specifies such things as what variable names look like and which characters denote comments. Each Python source file, like any other text file, is a sequence of characters. You can also usefully consider it as a sequence of lines, tokens, or statements. These different lexical views complement and reinforce each other. Python is very particular about program layout, especially with regard to lines and indentation, so you'll want to pay attention to this information if you are coming to Python from another language.

A Python program is composed of a sequence of logical lines, each made up of one or more physical lines. Each physical line may end with a comment. A hash sign (#) that is not inside a string literal begins a comment. All characters after the # and up to the physical line end are part of the comment, and the Python interpreter ignores them. A line containing only whitespace, possibly with a comment, is known as a blank line, and Python totally ignores it. In an interactive interpreter session, you must enter an empty physical line (without any whitespace or comment) to terminate a multiline statement.

In Python, the end of a physical line marks the end of most statements. Unlike in other languages, you don't normally terminate Python statements with a delimiter, such as a semicolon (;). When a statement is too long to fit on a single physical line, you can join two adjacent physical lines into a logical line by ensuring that the first physical line has no comment and ends with a backslash (\). However, Python automatically joins adjacent physical lines into one logical line if an open parenthesis ((), bracket ([), or brace ({) has not yet been closed, and taking advantage of this mechanism, generally produces more readable code instead of explicitly inserting backslashes at physical line ends. Triple-quoted string literals can also span physical lines. Physical lines after the first one in a logical line are known as

Additional content appearing in this section has been removed.
Purchase this book now or read it online at Safari to get the whole thing!

Content preview·Buy PDF of this chapter|Buy reprint rights for this chapter

The operation of a Python program hinges on the data it handles. All data values in Python are objects, and each object, or value, has a type. An object's type determines which operations the object supports, or, in other words, which operations you can perform on the data value. The type also determines the object's attributes and items (if any) and whether the object can be altered. An object that can be altered is known as a mutable object, while one that cannot be altered is an immutable object. I cover object attributes and items in detail in "Object attributes and items" on page 46.

The built-in type( obj ) accepts any object as its argument and returns the type object that is the type of obj. Built-in function isinstance( obj , type ) returns True if object obj has type type (or any subclass thereof); otherwise, it returns False.

Python has built-in types for fundamental data types such as numbers, strings, tuples, lists, and dictionaries, as covered in the following sections. You can also create user-defined types, known as classes, as discussed in "Classes and Instances" on page 82.

The built-in number objects in Python support integers (plain and long), floating-point numbers, and complex numbers. In Python 2.4, the standard library also offers decimal floating-point numbers, covered in "The decimal Module" on page 372. All numbers in Python are immutable objects, meaning that when you perform any operation on a number object, you always produce a new number object. Operations on numbers, also known as arithmetic operations, are covered in "Numeric Operations" on page 52.

Note that numeric literals do not include a sign: a leading + or -, if present, is a separate operator, as discussed in "Arithmetic Operations" on page 52.

Section 4.2.1.1: Integer numbers

Integer literals can be decimal, octal, or hexadecimal. A decimal literal is represented by a sequence of digits in which the first digit is nonzero. To denote an octal literal, use

Additional content appearing in this section has been removed.
Purchase this book now or read it online at Safari to get the whole thing!

Content preview·Buy PDF of this chapter|Buy reprint rights for this chapter

A Python program accesses data values through references. A reference is a name that refers to the location in memory of a value (object). References take the form of variables, attributes, and items. In Python, a variable or other reference has no intrinsic type. The object to which a reference is bound at a given time always has a type, but a given reference may be bound to objects of various types during the program's execution.

In Python there are no declarations. The existence of a variable begins with a statement that binds the variable, or, in other words, sets a name to hold a reference to some object. You can also unbind a variable, resetting the name so it no longer holds a reference. Assignment statements are the most common way to bind variables and other references. The del statement unbinds references.

Binding a reference that was already bound is also known as rebinding it. Whenever I mention binding in this book, I implicitly include rebinding except where I explicitly exclude it. Rebinding or unbinding a reference has no effect on the object to which the reference was bound, except that an object disappears when nothing refers to it. The automatic cleanup of objects bereft of references is known as garbage collection.

You can name a variable with any identifier except the 30 that are reserved as Python's keywords (see "Keywords" on page 35). A variable can be global or local. A global variable is an attribute of a module object (Chapter 7 covers modules). A local variable lives in a function's local namespace (see "Namespaces" on page 76).

Section 4.3.1.1: Object attributes and items

The main distinction between the attributes and items of an object is in the syntax you use to access them. An attribute of an object is denoted by a reference to the object, followed by a period (

Additional content appearing in this section has been removed.
Purchase this book now or read it online at Safari to get the whole thing!

Content preview·Buy PDF of this chapter|Buy reprint rights for this chapter

An expression is a phrase of code that Python evaluates to produce a value. The simplest expressions are literals and identifiers. You build other expressions by joining subexpressions with the operators and/or delimiters in Table 4-2. This table lists operators in decreasing order of precedence, higher precedence before lower. Operators listed together have the same precedence. The third column lists the associativity of the operator: L (left-to-right), R (right-to-left), or NA (nonassociative).

Table 4-2: Operator precedence in expressions
Operator	Description	Associativity
`'` expr `,...'`	String conversion	NA
`{` key `:` expr `,...}`	Dictionary creation	NA
`[` expr `,...]`	List creation	NA
`(` expr `,...)`	Tuple creation or just parentheses	NA
f `(` expr `,...)`	Function call	L
x `[` index `:` index `]`	Slicing	L
x `[` index `]`	Indexing	L
x.attr	Attribute reference	L
x `**` y	Exponentiation (x to yth power)	R
`~` x	Bitwise NOT	NA
`+` x, `-` x	Unary plus and minus	NA
x `*` y, x `/` y, x `//` y, x `%` y	Multiplication, division, truncating division, remainder	L
x `+` y, x `-` y	Addition, subtraction	L
x `<<` y, x `>>` y	Left-shift, right-shift	L
x `&` y	Bitwise AND	L
x `^` y	Bitwise XOR	L
x `\|` y	Bitwise OR	L
x `<` y, x `<=` y, x `>` y, x `>=` y, x `<>` y, x `!=` y, x `==` y	Comparisons (less than, less than or equal, greater than, greater than or equal, inequality, equality)

Additional content appearing in this section has been removed.
Purchase this book now or read it online at Safari to get the whole thing!

Content preview·Buy PDF of this chapter|Buy reprint rights for this chapter

Python supplies the usual numeric operations, as we've just seen in Table 4-2. Numbers are immutable objects: when you perform numeric operations on number objects, you always produce a new number object and never modify existing ones. You can access the parts of a complex object z as read-only attributes z.real and z.imag. Trying to rebind these attributes on a complex object raises an exception.

A number's optional + or - sign, and the + that joins a floating-point literal to an imaginary one to make a complex number, are not part of the literals' syntax. They are ordinary operators, subject to normal operator precedence rules (see Table 4-2). For example, -2**2 evaluates to -4: exponentiation has higher precedence than unary minus, so the whole expression parses as -(2**2), not as (-2)**2.

You can perform arithmetic operations and comparisons between any two numbers of Python built-in types. If the operands' types differ, coercion applies: Python converts the operand with the "smaller" type to the "larger" type. The types, in order from smallest to largest, are integers, long integers, floating-point numbers, and complex numbers.

You can request an explicit conversion by passing a noncomplex numeric argument to any of the built-in number types: int, long, float, and complex. int and long drop their argument's fractional part, if any (e.g., int(9.8) is 9). You can also call complex with two numeric arguments, giving real and imaginary parts. You cannot convert a complex to another numeric type in this way, because there is no single unambiguous way to convert a complex number into, e.g., a float.

Each built-in numeric type can also take a string argument with the syntax of an appropriate numeric literal, with small extensions: the argument string may have leading and/or trailing whitespace, may start with a sign, and, for complex numbers, may sum or subtract a real part and an imaginary one.

Additional content appearing in this section has been removed.
Purchase this book now or read it online at Safari to get the whole thing!

Content preview·Buy PDF of this chapter|Buy reprint rights for this chapter

Python supports a variety of operations applicable to all sequences, including strings, lists, and tuples. Some sequence operations apply to all containers (including, for example, sets and dictionaries, which are not sequences), and some apply to all iterables (meaning "any object on which you can loop," as covered in "Iterables" on page 40; all containers, be they sequences or otherwise, are iterable, and so are many objects that are not containers, such as files, covered in "File Objects" on page 216, and generators, covered in "Generators" on page 78). In the following, I use the terms sequence, container, and iterable, quite precisely and specifically, to indicate exactly which operations apply to each category.

Sequences are containers with items that are accessible by indexing or slicing. The built-in len function takes any container as an argument and returns the number of items in the container. The built-in min and max functions take one argument, a nonempty iterable whose items are comparable, and return the smallest and largest items, respectively. You can also call min and max with multiple arguments, in which case they return the smallest and largest arguments, respectively. The built-in sum function takes one argument, an iterable whose items are numbers, and returns the sum of the numbers.

Table of Contents

Section 4.2.1.1: Integer numbers

Section 4.3.1.1: Object attributes and items

Se