Chapter 1. A Python Q&A Session If you’ve bought this book, you may already know what Python is and why it’s an important tool to learn. If you don’t, 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 we jump into details, the first few pages of this book will 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 questions asked by beginners. Why Do People Use Python?
Because there are many programming languages available today, this is the usual first question of newcomers. Given that there are roughly 1 million Python users out there at the moment, 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 225 groups and over 3,000 students during the last 12 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 other tools in the scripting world.
Python code is designed to be readable, and hence reusable and maintainable—much more so than traditional scripting languages. The uniformity of Python code makes it easy to understand, even if you did not write it. In addition, Python has deep support for more advanced 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 one-third to one-fifth 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 required by some other tools, further boosting programmer speed.
Program portability Most Python programs run unchanged on all major computer platforms. Porting Python code between Linux 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, database access programs, web-based systems, and more. Even operating system interfaces, including program launches and directory processing, are as portable in Python as they can possibly be.
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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 homegrown libraries and a vast collection of third-party application support software. Python’s third-party domain offers tools for website construction, numeric programming, serial port access, game development, and much more. The NumPy extension, for instance, has been described as a free and more powerful equivalent to the Matlab numeric programming system. Component integration Python scripts can easily communicate with other parts of an application, using a variety of integration mechanisms. Such integrations allow Python to be used as a product customization and extension tool.
Today, Python code can invoke C and C++ libraries, can be called from C and C++ programs, can integrate with Java and.NET components, can communicate over frameworks such as COM, can interface with devices over serial ports, and can interact over networks with interfaces like SOAP, XML-RPC, and CORBA. It is not a standalone tool. Enjoyment Because of Python’s ease of use and built-in toolset, it can make the act of programming more pleasure than chore. Although this may be an intangible benefit, its effect on productivity is an important asset. Of these factors, the first two (quality and productivity) are probably the most compelling benefits to most Python users.
Software Quality By design, Python implements a deliberately simple and readable syntax and a highly coherent programming model. As a slogan at a recent Python conference attests, the net result is that Python seems to “fit your brain”—that is, features of the language interact in consistent and limited ways and follow naturally from a small set of core concepts. This makes the language easier to learn, understand, and remember. In practice, Python programmers do not need to constantly refer to manuals when reading or writing code; it’s a consistently designed system that many find yields surprisingly regular-looking code. By philosophy, Python adopts a somewhat minimalist approach.
This means that although there are usually multiple ways to accomplish a coding task, there is usually just one obvious way, a few less obvious alternatives, and a small set of coherent interactions everywhere in the language. Moreover, Python doesn’t make arbitrary decisions for you; when interactions are ambiguous, explicit intervention is preferred over “magic.” In the Python way of thinking, explicit is better than implicit, and simple is better than complex.  Beyond such design themes, Python includes tools such as modules and OOP that naturally promote code reusability. And because Python is focused on quality, so too, naturally, are Python programmers. Developer Productivity During the great Internet boom of the mid-to-late 1990s, it was difficult to find enough programmers to implement software projects; developers were asked to implement systems as fast as the Internet evolved. Today, in an era of layoffs and economic recession, the picture has shifted. Programming staffs are often now asked to accomplish the same tasks with even fewer people.
In both of these scenarios, Python has shined as a tool that allows programmers to get more done with less effort. It is deliberately optimized for speed of development—its simple syntax, dynamic typing, lack of compile steps, and built-in toolset allow programmers to develop programs in a fraction of the time needed when using some other tools.
The net effect is that Python typically boosts developer productivity many times beyond the levels supported by traditional languages. That’s good news in both boom and bust times, and everywhere the software industry goes in between. Is Python a “Scripting Language”? 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 language” 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 make three very different associations, some of which are more useful than others, when they hear Python labeled as such. Shell tools Sometimes when people hear Python described as a scripting language, they think it means that Python is a tool 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 and do 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 To others, scripting refers to a “glue” layer used to control and direct (i.e., script) other application components. Python programs are indeed often deployed in the context of larger applications. 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 the need 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 (perhaps most) Python programmers code standalone scripts without ever using or knowing about any integrated components. It is not just a control language.
Ease of use Probably the best way to think of the term “scripting language” is that it refers to a simple language used for quickly coding tasks. This is especially true when the term is applied to Python, which allows much faster program development than compiled languages like C++. Its rapid development cycle fosters an exploratory, incremental mode of programming that has to be experienced to be appreciated. Don’t be fooled, though—Python is not just for simple tasks. Rather, it makes tasks simple by its ease of use and flexibility. Python has a simple feature set, but it allows programs to scale up in sophistication as needed.
Because of that, it is commonly used for quick tactical tasks and longer-term strategic development. So, is Python a scripting language or not?
It depends on whom you ask. In general, the term “scripting” is probably best used to describe the rapid and flexible mode of development that Python supports, rather than a particular application domain.
OK, but What’s the Downside? After using it for 17 years and teaching it for 12, the only downside to Python I’ve found is that, as currently implemented, its execution speed may not always be as fast as that of compiled languages such as C and C++.
We’ll talk about implementation concepts in detail later in this book. 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 processing a file or constructing a graphical user interface (GUI), your program will actually run 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, though, there still are some domains that do require optimal execution speeds. 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 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 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.
Who Uses Python Today? At this writing, the best estimate anyone can seem to make of the size of the Python user base is that there are roughly 1 million Python users around the world today (plus or minus a few).
This estimate is based on various statistics, like download rates and developer surveys. 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, Macintosh computers, and some products and 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 some 19 years and has been widely used, it is also very stable and robust.
Besides being employed by individual users, Python is also being applied in real revenue-generating products by real companies. For instance. • Google makes extensive use of Python in its web search systems, and employs Python’s creator. • The YouTube video sharing service is largely written in Python. • The popular BitTorrent peer-to-peer file sharing system is a Python program. • Google’s popular App Engine web development framework uses Python as its application language.
• EVE Online, a Massively Multiplayer Online Game (MMOG), makes extensive use of Python. • Maya, a powerful integrated 3D modeling and animation system, provides a Python scripting API. • Intel, Cisco, Hewlett-Packard, Seagate, Qualcomm, and IBM use Python for hardware testing. • Industrial Light & Magic, Pixar, and others use Python in the production of animated movies. • JPMorgan Chase, UBS, Getco, and Citadel apply Python for financial market forecasting. • NASA, Los Alamos, Fermilab, JPL, and others use Python for scientific programming tasks. • iRobot uses Python to develop commercial robotic devices.
• ESRI uses Python as an end-user customization tool for its popular GIS mapping products. • The NSA uses Python for cryptography and intelligence analysis. • The IronPort email server product uses more than 1 million lines of Python code to do its job.
• The One Laptop Per Child (OLPC) project builds its user interface and activity model in Python. Probably the only common thread amongst the 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.
In fact, it’s safe to say that virtually every substantial organization writing software is using Python, whether for short-term tactical tasks, such as testing and administration, or for long-term strategic product development. Python has proven to work well in both modes.
For more details on companies using Python today, see Python’s website. What Can I Do with Python? In addition to being a well-designed programming language, Python is 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 scripting other components and implementing standalone programs. In fact, as a general-purpose language, Python’s roles are virtually unlimited: you can use it for everything from website development and gaming to robotics and spacecraft control. However, the most common Python roles currently 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 explore the tools mentioned here in any depth—if you are interested in any of these topics, see the Python website or other resources for more details. Systems Programming 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. The Stackless Python system, used by EVE Online, also offers advanced solutions to multiprocessing requirements. GUIs Python’s simplicity and rapid turnaround also make it a good match for graphical user interface programming. Python comes with a standard object-oriented interface to the Tk GUI API called tkinter ( Tkinter in 2.6) that allows Python programs to implement portable GUIs with a native look and feel.
Python/tkinter GUIs run unchanged on Microsoft Windows, X Windows (on Unix and Linux), and the Mac OS (both Classic and OS X). A free extension package, PMW, adds advanced widgets to the tkinter toolkit. In addition, the wxPython GUI API, based on a C++ library, offers an alternative toolkit for constructing portable GUIs in Python. Higher-level toolkits such as PythonCard and Dabo are built on top of base APIs such as wxPython and tkinter.
With the proper library, you can also use GUI support in other toolkits in Python, such as Qt with PyQt, GTK with PyGTK, MFC with PyWin32,.NET with IronPython, and Swing with Jython (the Java version of Python, described in ) or JPype. For applications that run in web browsers or have simple interface requirements, both Jython and Python web frameworks and server-side CGI scripts, described in the next section, provide additional user interface options. Internet Scripting Python comes with standard Internet modules that allow Python programs to perform a wide variety of networking tasks, in client and server modes. Scripts can communicate over sockets; extract form information sent to server-side CGI scripts; transfer files by FTP; parse, generate, and analyze XML files; send, receive, compose, and parse email; fetch web pages by URLs; parse the HTML and XML of fetched web pages; communicate over XML-RPC, SOAP, and Telnet; and more. Python’s libraries make these tasks remarkably simple. In addition, a large collection of third-party tools are available on the Web for doing Internet programming in Python. For instance, the HTMLGen system generates HTML files from Python class-based descriptions, the mod_python package runs Python efficiently within the Apache web server and supports server-side templating with its Python Server Pages, and the Jython system provides for seamless Python/Java integration and supports coding of server-side applets that run on clients.
In addition, full-blown web development framework packages for Python, such as Django, TurboGears, web2py, Pylons, Zope, and WebWare, support quick construction of full-featured and production-quality websites with Python. Many of these include features such as object-relational mappers, a Model/View/Controller architecture, server-side scripting and templating, and AJAX support, to provide complete and enterprise-level web development solutions. Component Integration We discussed the component integration role earlier when describing Python as a control language. Python’s ability to be extended by and embedded in C and C++ systems makes it useful as a flexible glue language for scripting the behavior of other systems and components. For instance, integrating a C library into Python enables Python to test and launch the library’s components, and embedding Python in a product enables onsite customizations to be coded without having to recompile the entire product (or ship its source code at all). Tools such as the SWIG and SIP code generators can automate much of the work needed to link compiled components into Python for use in scripts, and the Cython system allows coders to mix Python and C-like code.
Larger frameworks, such as Python’s COM support on Windows, the Jython Java-based implementation, the IronPython.NET-based implementation, and various CORBA toolkits for Python, provide alternative ways to script components. On Windows, for example, Python scripts can use frameworks to script Word and Excel.
Database Programming For traditional database demands, there are Python interfaces to all commonly used relational database systems—Sybase, Oracle, Informix, ODBC, MySQL, PostgreSQL, SQLite, and more. The Python world has also defined a portable database API for accessing SQL database systems from Python scripts, which looks the same on a variety of underlying database systems.
For instance, because the vendor interfaces implement the portable API, a script written to work with the free MySQL system will work largely unchanged on other systems (such as Oracle); all you have to do is replace the underlying vendor interface. Python’s standard pickle module provides a simple object persistence system—it allows programs to easily save and restore entire Python objects to files and file-like objects. On the Web, you’ll also find a third-party open source system named ZODB that provides a complete object-oriented database system for Python scripts, and others (such as SQLObject and SQLAlchemy) that map relational tables onto Python’s class model. Furthermore, as of Python 2.5, the in-process SQLite embedded SQL database engine is a standard part of Python itself. Numeric and Scientific Programming The NumPy numeric programming extension for Python mentioned earlier includes such advanced tools as an array object, interfaces to standard mathematical libraries, and much more.
By integrating Python with numeric routines coded in a compiled language for speed, NumPy turns Python into a sophisticated yet easy-to-use numeric programming tool that can often replace existing code written in traditional compiled languages such as FORTRAN or C++. Additional numeric tools for Python support animation, 3D visualization, parallel processing, and so on.
The popular SciPy and ScientificPython extensions, for example, provide additional libraries of scientific programming tools and use NumPy code. • Game programming and multimedia in Python with the pygame system • Serial port communication on Windows, Linux, and more with the PySerial extension • Image processing with PIL, PyOpenGL, Blender, Maya, and others • Robot control programming with the PyRo toolkit • XML parsing with the xml library package, the xmlrpclib module, and third-party extensions • Artificial intelligence programming with neural network simulators and expert system shells • Natural language analysis with the NLTK package You can even play solitaire with the PySol program. You’ll find support for many such fields at the PyPI websites, and via web searches (search Google or for links). Many of these specific domains are largely just instances of Python’s component integration role in action again. Adding it as a frontend to libraries of components written in a compiled language such as C makes Python useful for scripting in a wide variety of domains. As a general-purpose language that supports integration, Python is widely applicable. How Is Python Supported?
As a popular open source system, Python enjoys a large and active development community that responds to issues and develops enhancements with a speed that many commercial software developers would find remarkable (if not downright shocking). Python developers coordinate work online with a source-control system. Changes follow a formal PEP (Python Enhancement Proposal) protocol and must be accompanied by extensions to Python’s extensive regression testing system. In fact, modifying Python today is roughly as involved as changing commercial software—a far cry from Python’s early days, when an email to its creator would suffice, but a good thing given its current large user base. The PSF (Python Software Foundation), a formal nonprofit group, organizes conferences and deals with intellectual property issues. Numerous Python conferences are held around the world; O’Reilly’s OSCON and the PSF’s PyCon are the largest. The former of these addresses multiple open source projects, and the latter is a Python-only event that has experienced strong growth in recent years.
Attendance at PyCon 2008 nearly doubled from the prior year, growing from 586 attendees in 2007 to over 1,000 in 2008. This was on the heels of a 40% attendance increase in 2007, from 410 in 2006. PyCon 2009 had 943 attendees, a slight decrease from 2008, but a still very strong showing during a global recession. It’s Object-Oriented 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++, Java, and C#. Of equal significance, OOP is an option in Python; you can go far without having to become an object guru all at once. Much like C++, Python supports both procedural and object-oriented programming modes. Its object-oriented tools can be applied if and when constraints allow. This is especially useful in tactical development modes, which preclude design phases.
It’s Free Python is completely free to use and distribute. As with other open source software, such as Tcl, Perl, Linux, and Apache, you can fetch the entire Python system’s source code 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 help desks would do well to try to emulate. Moreover, because Python comes with complete source code, it empowers developers, leading to the creation of 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 you can do so if you need to.
You’re not dependent on the whims of a commercial vendor; the ultimate documentation source is at your disposal. As mentioned earlier, Python development is performed by a community that largely coordinates its efforts over the Internet. It consists of Python’s creator— Guido van Rossum, the officially anointed Benevolent Dictator for Life (BDFL) of Python—plus a supporting cast of thousands. Language changes must follow a formal enhancement procedure and be scrutinized by both other developers and the BDFL. Happily, this tends to make Python more conservative with changes than some other languages. • Linux and Unix systems • Microsoft Windows and DOS (all modern flavors) • Mac OS (both OS X and Classic) • BeOS, OS/2, VMS, and QNX • Real-time systems such as VxWorks • Cray supercomputers and IBM mainframes • PDAs running Palm OS, PocketPC, and Linux • Cell phones running Symbian OS and Windows Mobile • Gaming consoles and iPods • And more Like the language interpreter itself, the standard library modules that ship with Python are implemented to be as portable across platform boundaries as possible.
Further, Python programs are automatically compiled to portable byte code, which runs the same on any platform with a compatible version of Python installed (more on this in the next chapter). What that means is that Python programs using the core language and standard libraries run the same on Linux, Windows, and most other systems with a Python interpreter. Most Python ports also contain platform-specific extensions (e.g., COM support on Windows), but the core Python language and libraries work the same everywhere. As mentioned earlier, Python also includes an interface to the Tk GUI toolkit called tkinter (Tkinter in 2.6), which allows Python programs to implement full-featured graphical user interfaces that run on all major GUI platforms without program changes.
It’s Powerful From a features perspective, Python is something of a hybrid. Its toolset places it between traditional scripting languages (such as Tcl, Scheme, and Perl) and systems development languages (such as C, C++, and Java). Python provides all the simplicity and ease of use of a scripting language, along with more advanced software-engineering tools typically found in compiled languages. Unlike some scripting languages, this combination makes Python useful for large-scale development projects. Dvd Cracker Software. As a preview, here are some of the main things you’ll find in Python’s toolbox.
Dynamic typing Python keeps track of the kinds of objects your program uses when it runs; it doesn’t require complicated type and size declarations in your code. In fact, as you’ll see in, there is no such thing as a type or variable declaration anywhere in Python. Because Python code does not constrain data types, it is also usually automatically applicable to a whole range of objects. Automatic memory management Python automatically allocates objects and reclaims (“garbage collects”) them when they are no longer used, and most can grow and shrink on demand. As you’ll learn, Python keeps track of low-level memory details so you don’t have to. Programming-in-the-large support For building larger systems, Python includes tools such as modules, classes, and exceptions.
These tools allow you to organize systems into components, use OOP to reuse and customize code, and handle events and errors gracefully. Built-in object types Python provides commonly used data structures such as lists, dictionaries, and strings as intrinsic parts of the language; as you’ll see, they’re both flexible and easy to use. For instance, built-in objects can grow and shrink on demand, can be arbitrarily nested to represent complex information, and more.
Built-in tools To process all those object types, Python comes with powerful and standard operations, including concatenation (joining collections), slicing (extracting sections), sorting, mapping, and more. Library utilities For more specific tasks, Python also comes with a large collection of precoded library tools that support everything from regular expression matching to networking. Once you learn the language itself, Python’s library tools are where much of the application-level action occurs. Third-party utilities Because Python is open source, developers are encouraged to contribute precoded tools that support tasks beyond those supported by its built-ins; on the Web, you’ll find free support for COM, imaging, CORBA ORBs, XML, database access, and much more. Despite the array of tools in Python, it retains a remarkably simple syntax and design. The result is a powerful programming tool with all the usability of a scripting language.
It’s Mixable Python programs can easily be “glued” to components written in other languages in a variety of ways. For example, Python’s C API lets C programs call and be called by Python programs flexibly. That means you can add functionality to the Python system as needed, and use Python programs within other environments or systems.
Mixing Python with libraries coded in languages such as C or C++, for instance, makes it an easy-to-use frontend language and customization tool. As mentioned earlier, this also makes Python good at rapid prototyping; systems may be implemented in Python first, to leverage its speed of development, and later moved to C for delivery, one piece at a time, according to performance demands. It’s Easy to Use To run a Python program, you simply type it and run it. There are no intermediate compile and link steps, like there are for languages such as C or C++. Python executes programs immediately, which makes for an interactive programming experience and rapid turnaround after program changes—in many cases, you can witness the effect of a program change as fast as you can type it. Of course, development cycle turnaround is only one aspect of Python’s ease of use. It also provides a deliberately simple syntax and powerful built-in tools.
In fact, some have gone so far as to call Python “executable pseudocode.” Because it eliminates much of the complexity in other tools, Python programs are simpler, smaller, and more flexible than equivalent programs in languages like C, C++, and Java. It’s Easy to Learn This brings us to a key point of this book: compared to other programming languages, the core Python language is remarkably easy to learn. In fact, you can expect to be coding significant Python programs in a matter of days (or perhaps in just hours, if you’re already an experienced programmer). That’s good news for professional developers seeking to learn the language to use on the job, as well as for end users of systems that expose a Python layer for customization or control. Today, many systems rely on the fact that end users can quickly learn enough Python to tailor their Python customizations’ code onsite, with little or no support.
Although Python does have advanced programming tools, its core language will still seem simple to beginners and gurus alike. It’s Named After Monty Python OK, this isn’t quite a technical strength, but it does seem to be a surprisingly well-kept secret that I wish to expose up front. Despite all the reptile icons in the Python world, the truth is that Python creator Guido van Rossum named it after the BBC comedy series Monty Python’s Flying Circus. He is a big fan of Monty Python, as are many software developers (indeed, there seems to almost be a symmetry between the two fields). This legacy inevitably adds a humorous quality to Python code examples. For instance, the traditional “foo” and “bar” for generic variable names become “spam” and “eggs” in the Python world.
The occasional “Brian,” “ni,” and “shrubbery” likewise owe their appearances to this namesake. It even impacts the Python community at large: talks at Python conferences are regularly billed as “The Spanish Inquisition.” All of this is, of course, very funny if you are familiar wit.
Obtaining a license file A license manager is required to administer the ArcGIS floating installations of ArcInfo Desktop, ArcInfo Workstation, ArcEditor, ArcView floating, and their extensions. The license manager can run either on a Windows or UNIX server regardless of where you install the software. If you intend to run the license manager on a Windows server, you need a SentinelPro hardware key on that server. You only need one hardware key per license manager and you need a minimum of one license manager per network. You can use your existing hardware key if you have one, see Existing ArcGIS Desktop users for additional information. You must have 9.0 9.1, 9.2, or 9.3 keycodes to install and use the ArcGIS 9.3 License Manager. Note The 9.3 License Manager can manage ArcGIS Desktop 9.0, 9.1, 9.2 and 9.3 licenses.
However, the 9.0, 9.1 and 9.2 License Manager can not manage licenses for ArcGIS Desktop 9.3 software. Requesting a License File Request a new or updated license file by visiting the ESRI Customer Service site,, click the License Management tab and follow the instructions to request your license file. ArcGIS 9.0, 9.1 and 9.2 users You do not require a new hardware key or new keycodes (license file). Your existing ArcGIS License Manager will not work with your 9.3 ArcGIS Desktop software. You must install the 9.3 License Manager..
Versions previous to 9.0 You do not require a new hardware key. However, A new license file is required to install the ArcGIS 9.3 license manager. All versions You must define a valid ArcGIS 9.3 License Manager during the ArcGIS Desktop installation.
License Managers installed previous to ArcGIS 9.2 will not be able to manage ArcGIS Desktop 9.3 licenses. Evaluation keycodes At any time, you can order evaluation keycodes for extensions that you have not purchased. The evaluation is free of charge for a fixed period of time. In the United States, request new license files (keycodes) on the Internet. Outside the United States, contact your local ESRI distributor.
For the number of your distributor, call ESRI at 909-793-2853, ext. 1-1235, or visit our Website. Click on 'Outside the United States' in the Contact Us section. Tip The license file will be sent as an attachment to an e-mail. Save the attachment on your computer without opening it.
Opening the attachment with Microsoft Word may corrupt your license file and adversely affect the license manager installation and operation.