Python currently has two production-quality implementations, CPython and Jython, and one experimental implementation, Python .NET. This book primarily addresses CPython, 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. CPython is a compiler, interpreter, and set of built-in and optional extension modules, 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 24 and a few sections explicitly marked otherwise, applies to CPython.
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. To use Jython well, 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
Essentials, by Noel
Rappin and Samuele Pedroni (O’Reilly), possibly
Nutshell, by David
Flanagan (O’Reilly), and, if needed, some of the
many other Java resources available.
If your platform is able to run both CPython and Jython, how do you choose between them? First of all, don’t choose—download and install them both. They coexist without problems, and they’re free. Having them both on your machine costs only some download time and a little extra disk space.
To experiment, learn, and
try things out, you will most often use CPython, as
it’s faster. To develop and deploy, your best choice
depends on what extension modules you want to use and how you want to
distribute your programs. CPython applications are generally faster,
particularly if they can make good use of suitable extension modules,
Numeric (covered in Chapter 15). The development of CPython versions is
faster than that of Jython versions: at the time of writing, for
example, the next scheduled release is 2.2 for Jython, but 2.3 for
However, as you’ll see in Chapter 25, 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. A Jython-coded application is a 100% 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.
Jython and CPython are both good, faithful implementations of Python, reasonably close in terms of usability and performance. Given these pragmatic issues, either one may enjoy decisive practical advantages in a specific scenario. Thus, it is wise to become familiar with the strengths and weaknesses of each, to be able to choose optimally for each development task.
The experimental Python .NET is a Python implementation for the Microsoft .NET platform, with an architecture similar to Jython’s, but targeting Microsoft Intermediate Language (MSIL) rather than JVM bytecode. Python .NET is not as mature as CPython or Jython, but when it is ready for production use, Python .NET may become a great way to develop for .NET, like Jython is for the JVM. For information on Python .NET and links to download it, see http://www.activestate.com/Initiatives/NET/Research.html.
Current releases of CPython are 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/2.2.1/license.html). Jython’s license is similarly liberal. Anything you download from the main Python and Jython 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. 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 a lawyer. 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.