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 Java in a Nutshell, by David Flanagan (O’Reilly), and, if needed, some of the many other Java resources available.
IronPython is a Python implementation for the Microsoft-designed Common Language Runtime (CLR), most commonly known as .NET. With IronPython, you can use all CLR libraries and frameworks. In addition to Microsoft’s own implementation, a cross-platform implementation of the CLR (known as Mono) works with other, non-Microsoft operating systems, as well as with Windows. For optimal use of IronPython, you need some familiarity with fundamental CLR libraries. You do not have to code in C#, but documentation and examples for existing CLR libraries are often couched in C# terms, so you need a nodding acquaintance with C# to read and understand them. You also need to use CLR supporting tools for tasks such as making CLR assemblies. This book deals with Python, not with the CLR. For IronPython usage, you should complement this book with IronPython’s own online documentation, and, if needed, some of the many other resources available about .NET, the CLR, C#, Mono, and so on.
If your platform is able to run CPython, Jython, and IronPython, how do you choose among them? First of all, don’t choose; download and install them all. They coexist without problems, and they’re free. Having them all on your machine costs only some download time and a little extra disk space.
The primary difference between the implementations is the environment in which they run and the libraries and frameworks they can use. If you need to work in a JVM environment, then Jython is an excellent choice. If you need to work in a CLR environment, you can take advantage of IronPython. If you’re mainly working in a traditional environment, then CPython is an excellent fit. If you don’t have a strong preference for one or the other, then you should start with the standard CPython reference implementation.
In other words, when you’re just experimenting, learning, and trying things out, you will most often use CPython, since it’s faster. To develop and deploy, your best choice depends on the extension modules you want to use and how you want to distribute your programs. CPython applications are often faster, particularly if they can use suitable extension modules, such as
Numeric (covered in Chapter 16). CPython is more mature: it has been around longer, while Jython, and especially IronPython, are newer and less field-proven. The development of CPython versions tends to proceed faster than that of Jython and IronPython versions: at the time of writing, for example, the current language level supported is 2.2 for Jython, 2.4 for IronPython, and 2.4 rapidly progressing towards 2.5 for CPython (2.5 should be out by the time you read this).
However, as you’ll see in Chapter 26, Jython can use any Java class as an extension module, whether the class comes from a standard Java library, a third-party library, or a library you develop yourself. Similarly, IronPython can use any CLR class, whether from the standard CLR libraries, or coded in C#, Visual Basic .NET, or other CLR-compliant languages. A Jython-coded application is a 100 percent pure Java application, with all of Java’s deployment advantages and issues, and runs on any target machine having a suitable JVM. Packaging opportunities are also identical to Java’s. Similarly, an IronPython-coded application is entirely compliant with .NET’s specifications.
Jython, IronPython, and CPython are all good, faithful implementations of Python, and are reasonably close in terms of usability and performance. Since each of the JVM and CLR platforms carries a lot of baggage, but also supplies large amounts of useful libraries, frameworks, and tools, any of the implementations may enjoy decisive practical advantages in a specific deployment scenario. It is wise to become familiar with the strengths and weaknesses of each, and then choose optimally for each development task.
There are several interesting implementations of Python that are not yet suitable for production use at the time of this writing, but may well be worth looking into for intrinsic interest and high future potential. Two such experimental implementations that are being actively worked on are Pirate (http://pirate.tangentcode.com), a Python implementation on top of the Parrot virtual machine, which also supports Perl 6 and other VHLs, and PyPy (http://codespeak.net/pypy/), a fast and flexible implementation of Python coded in Python itself, which is able to target several lower-level languages and virtual machines using advanced techniques such as type inferencing.
CPython is covered by the CNRI Open Source GPL-Compatible License, allowing free use of Python for both commercial and free-software development (http://www.python.org/2.4.2/license.html). Jython’s and IronPython’s licenses are similarly liberal. Anything you download from the main Python, Jython, and IronPython sites will not cost you a penny. These licenses do not constrain what licensing and pricing conditions you can use for software you develop using the tools, libraries, and documentation they cover.
However, not everything Python-related is totally free from licensing costs or hassles. Many third-party Python sources, tools, and extension modules that you can freely download have liberal licenses, similar to that of Python itself. Others, however, are covered by the GNU Public License (GPL) or Lesser GPL (LGPL), constraining the licensing conditions you are allowed to place on derived works. Some commercially developed modules and tools may require you to pay a fee, either unconditionally or if you use them for profit.
There is no substitute for careful examination of licensing conditions and prices. Before you invest time and energy into any software component, check that you can live with its license. Often, especially in a corporate environment, such legal matters may involve consulting lawyers. Modules and tools covered in this book, unless I explicitly say otherwise, can be taken to be, at the time of this writing, freely downloadable, open source, and covered by a liberal license akin to Python’s. However, I claim no legal expertise, and licenses can change over time, so double-checking is always prudent.