An Overview Of Ironpython

It surprises many developers to discover that computer languages are for humans, not for computers. A computer couldn't care less about which language you use, because it's all bits and bytes in the end anyway. Consequently, when you decide to learn another computer language, it really does pay to know what that language will do for you, the developer. Otherwise, there really isn't a point in making the effort.

One phrase you often hear when discussing Python (and by extension, IronPython) is "batteries included." Python has an immense standard library that addresses everything from working with ZIP files to interacting with the file system. You'll discover the details of working with the Standard Library in Chapter 6. For now, it's important to know that the Standard Library has a lot to offer and you may very well be able to build many of your applications without ever thinking about the .NET Framework.

As previously mentioned, IronPython is a .NET version of the Python language. For a .NET developer, using IronPython has the advantage of letting you create extensions using .NET (see Chapters 16 and 17 for details). In addition, you have full access to the .NET Framework (see Chapter 7 for details). You can work with IronPython and other .NET languages that you already know, which means that you can use the right tool for every job. However, IronPython has a few differences from the CPython implementation that everyone else uses (see Appendix A for details), which means that you can occasionally run into some odd compatibility problems when using IronPython. As with most things in life, advantages usually come with a few disadvantages.

You'll see Python appear in many guises when you begin using it. The original implementation of Python is CPython and that's the implementation that most developers target. In fact, you'll often see IronPython compared and contrasted with CPython throughout this book. It's important to remember that all these implementations attempt to achieve the same goal — full support of the Python standard. In most cases, all you really need to worry about is the IronPython implementation, unless you plan to use third-party libraries written for another Python implementation. This book helps you understand the use of CPython extensions in Appendix B.

There are some basic reasons that you want to use IronPython (or Python for that matter). The most important reason is that IronPython is a dynamic language, which means that it performs many tasks during run time, rather than compile time. Using a dynamic language means that your code has advantages of static languages, such as Visual Basic, in that it can more easily adapt to changing environmental conditions. (You'll discover many other dynamic language advantages as the chapter progresses.) Unfortunately, you often pay for runtime flexibility with poorer performance — there's always a tradeoff between flexibility and performance.

Performance is a combination of three factors: speed, reliability, and security. When an application has a performance hit, it means a decrease in any of these three factors. When working with IronPython, there is a decrease in speed because the interpreter must compile code at run time, rather than at compile time. This speed decrease is partially offset by an improvement in reliability because IronPython applications are so flexible.

Dynamic languages provide a number of benefits such as the ability to enter several statements and execute them immediately to obtain feedback. Using a dynamic language also provides easier refactoring and code modification because you don't have to change static definitions throughout your code. It's even possible to call functions you haven't implemented yet and add an implementation later in the code when it's needed. Don't get the idea that dynamic languages are new. In fact, dynamic languages have been around for a very long time. Examples of other dynamic languages include the following:


LISP (List Processing)






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