Chapter Topics
Our introductory chapter provides some background on what Python is, where it came from, and what some of its “bullet points” are. Once we have stimulated your interest and enthusiasm, we describe how you can obtain Python and get it up and running on your system. Finally, the exercises at the end of the chapter will make you comfortable with using Python, both in the interactive interpreter and also in creating scripts and executing them.
Python is an elegant and robust programming language that delivers both the power and general applicability of traditional compiled languages with the ease of use (and then some) of simpler scripting and interpreted languages. It allows you to get the job done, and then read what you wrote later. You will be amazed at how quickly you will pick up the language as well as what kind of things you can do with Python, not to mention the things that have already been done. Your imagination will be the only limit.
Work on Python began in late 1989 by Guido van Rossum, then at CWI (Centrum voor Wiskunde en Informatica, the National Research Institute for Mathematics and Computer Science) in the Netherlands. It was eventually released for public distribution in early 1991. How did it all begin? Like C, C++, Lisp, Java, and Perl, Python came from a research background where the programmer was having a hard time getting the job done with the existing tools at hand, and envisioned and developed a better way.
At the time, van Rossum was a researcher with considerable language design experience with the interpreted language ABC, also developed at CWI, but he was unsatisfied with its ability to be developed into something more. Having used and partially developed a higher-level language like ABC, falling back to C was not an attractive possibility. Some of the tools he envisioned were for performing general system administration tasks, so he also wanted access to the power of system calls that were available through the Amoeba distributed operating system. Although van Rossum gave some thought to an Amoeba-specific language, a generalized language made more sense, and late in 1989, the seeds of Python were sown.
Although it has been around for well over fifteen years, some feel that Python is still relatively new to the general software development industry. We should, however, use caution with our use of the word “relatively,” as a few years seem like decades when developing on “Internet time.”
When people ask, “What is Python?” it is difficult to say any one thing. The tendency is to want to blurt out all the things that you feel Python is in one breath. Python is (fill-in-the-blanks here). Just what are some of those features? For your sanity, we will elucidate each here ... one at a time.
It seems that with every generation of languages, we move to a higher level. Assembly was a godsend for those who struggled with machine code, then came FORTRAN, C, and Pascal, which took computing to another plane and created the software development industry. Through C came more modern compiled languages, C++ and Java. And further still we climb, with powerful, system-accessible, interpreted scripting languages like Tcl, Perl, and Python.
Each of these languages has higher-level data structures that reduce the “framework” development time that was once required. Useful types like Python’s lists (resizeable arrays) and dictionaries (hash tables) are built into the language. Providing these crucial building blocks in the core language encourages their use and minimizes development time as well as code size, resulting in more readable code.
Because there is no one standard library for heterogeneous arrays (lists in Python) and hash tables (Python dictionaries or “dicts” for short) in C, they are often reimplemented and copied to each new project. This process is messy and error prone. C++ improves the situation with the standard template library, but the STL can hardly compare to the simplicity and readability of Python’s built-in lists and dicts.
Object-oriented programming (OOP) adds another dimension to structured and procedural languages where data and logic are discrete elements of programming. OOP allows for associating specific behaviors, characteristics, and/or capabilities with the data that they execute on or are representative of. Python is an object-oriented (OO) language, all the way down to its core. However, Python is not just an OO language like Java or Ruby. It is actually a pleasant mix of multiple programming paradigms. For instance, it even borrows a few things from functional languages like Lisp and Haskell.
Python is often compared to batch or Unix shell scripting languages. Simple shell scripts handle simple tasks. They may grow (indefinitely) in length, but not truly in depth. There is little code-reusability and you are confined to small projects with shell scripts. In fact, even small projects may lead to large and unwieldy scripts. Not so with Python, where you can grow your code from project to project, add other new or existing Python elements, and reuse code at your whim. Python encourages clean code design, high-level structure, and “packaging” of multiple components, all of which deliver the flexibility, consistency, and faster development time required as projects expand in breadth and scope.
The term “scalable” is most often applied to measuring hardware throughput and usually refers to additional performance when new hardware is added to a system. We would like to differentiate this comparison with ours here, which tries to reflect the notion that Python provides basic building blocks on which you can build an application, and as those needs expand and grow, Python’s pluggable and modular architecture allows your project to flourish as well as maintain manageability.
As the amount of Python code increases in your project, you will still be able to organize it logically by separating your code into multiple files, or modules, and be able to access code from one module and attributes from another. And what is even better is that Python’s syntax for accessing modules is the same for all modules, whether you access one from the Python standard library, one you created just a minute ago, or even an extension you wrote in another language! Using this feature, you feel like you have just “extended” the language for your own needs, and you actually have.
The most critical portions of code, perhaps those hotspots that always show up in the profiler or areas where performance is absolutely required, are candidates for being rewritten as a Python extension written in C. But again, the interface is exactly the same as for pure Python modules. Access to code and objects occurs in exactly the same way without any code modification whatsoever. The only thing different about the code now is that you should notice an improvement in performance. Naturally, it all depends on your application and how resource-intensive it is. There are times where it is absolutely advantageous to convert application bottlenecks to compiled code because it will decidedly improve overall performance.
This type of extensibility in a language provides engineers with the flexibility to add-on or customize their tools to be more productive, and to develop in a shorter period of time. Although this feature is self-evident in mainstream third-generation languages (3GLs) such as C, C++, and even Java, the ease of writing extensions to Python in C is a real strength of Python. Furthermore, tools like PyRex, which understands a mix of C and Python, make writing extensions even easier as they compile everything to C for you.
Python extensions can be written in C and C++ for the standard implementation of Python in C (also known as CPython). The Java language implementation of Python is called Jython, so extensions would be written using Java. Finally, there is IronPython, the C# implementation for the .NET or Mono platforms. You can extend IronPython in C# or Visual Basic.NET.
Python can be found on a wide variety of systems, contributing to its continued rapid growth in today’s computing domain. Because Python is written in C, and because of C’s portability, Python is available on practically every type of platform that has an ANSI C compiler. Although there are some platform-specific modules, any general Python application written on one system will run with little or no modification on another. Portability applies across multiple architectures as well as operating systems.
Python has relatively few keywords, simple structure, and a clearly defined syntax. This allows the student to pick up the language in a relatively short period of time. What may perhaps be new to beginners is the OO nature of Python. Those who are not fully versed in the ways of OOP may be apprehensive about jumping straight into Python, but OOP is neither necessary nor mandatory. Getting started is easy, and you can pick up OOP and use when you are ready to.
Conspicuously absent from the Python syntax are the usual mandatory symbols found in other languages for accessing variables, code block definition, and pattern-matching. These include dollar signs ( $ ), semicolons ( ; ), tildes ( ~ ), and so on. Without all these distractions, Python code is much more clearly defined and visible to the eye. In addition, much to many programmers’ dismay (and relief), Python does not give as much flexibility to write obfuscated code compared to other languages, making it easier for others to understand your code faster and vice versa. Readability usually helps make a language easy to learn, as we described above. We would even venture to claim that Python code is fairly understandable even to a reader who has never seen a single line of Python before. Take a look at the examples in the next chapter, “Getting Started,” and let us know how well you fare.
Maintaining source code is part of the software development lifecycle. Your software usually continues to evolve until it is replaced or obsoleted. Quite often it lasts longer than a programmer’s stay at a company. Much of Python’s success is that source code is fairly easy to maintain, dependent, of course, on size and complexity. However, this conclusion is not difficult to draw given that Python is easy to learn and easy to read. Another motivating advantage of Python is that upon reviewing a script you wrote six months ago, you are less likely to get lost or pull out a reference book to get reacquainted with your software.
Nothing is more powerful than allowing a programmer to recognize error conditions and provide a software handler when such errors occur. Python provides “safe and sane” exits on errors, allowing the programmer to be in the driver’s seat. When your Python crashes due to errors, the interpreter dumps out a “stack trace” full of useful information such as why your program crashed and where in the code (file name, line number, function call, etc.) the error took place. These errors are known as exceptions. Python even gives you the ability to monitor for errors and take an evasive course of action if such an error does occur during runtime.
These exception handlers can take steps such as defusing the problem, redirecting program flow, perform cleanup or maintenance measures, shutting down the application gracefully, or just ignoring it. In any case, the debugging part of the development cycle is reduced considerably. Python’s robustness is beneficial for both the software designer and the user. There is also some accountability when certain errors occur that are not handled properly. The stack trace that is generated as a result of an error reveals not only the type and location of the error, but also in which module the erroneous code resides.
We’ve mentioned before how Python is easy to learn and easy to read. But, you say, so is a language like BASIC. What more can Python do? Unlike self-contained and less flexible languages, Python has so many different interfaces to other systems that it is powerful enough in features and robust enough that entire systems can be prototyped completely in Python. Obviously, the same systems can be completed in traditional compiled languages, but Python’s simplicity of engineering allows us to do the same thing and still be home in time for supper. Also, numerous external libraries have already been developed for Python, so whatever your application is, someone may have traveled down that road before. All you need to do is “plug-and-play” (some assembly required, as usual). There are Python modules and packages that can do practically anything and everything you can imagine. The Python Standard Library is fairly complete, and if you cannot find what you need there, chances are there is a third-party module or package that can do the job.
The biggest pitfall with programming in C or C++ is that the responsibility of memory management is in the hands of the developer. Even if the application has very little to do with memory access, memory modification, and memory management, the programmer must still perform those duties, in addition to the original task at hand. This places an unnecessary burden and responsibility upon the developer and often provides an extended distraction.
Because memory management is performed by the Python interpreter, the application developer is able to steer clear of memory issues and focus on the immediate goal of just creating the application that was planned in the first place. This leads to fewer bugs, a more robust application, and shorter overall development time.
Python is classified as an interpreted language, meaning that compile-time is no longer a factor during development. Traditionally, purely interpreted languages are almost always slower than compiled languages because execution does not take place in a system’s native binary language. However, like Java, Python is actually byte-compiled, resulting in an intermediate form closer to machine language. This improves Python’s performance, yet allows it to retain all the advantages of interpreted languages.
Core Note: File extensions
Python source files typically end with the .py extension. The source is byte-compiled upon being loaded by the interpreter or by being byte-compiled explicitly. Depending on how you invoke the interpreter, it may leave behind byte-compiled files with a .pyc or .pyo extension. You can find out more about file extensions in Chapter 12, “Modules.”
The most obvious place to get all Python-related software is at the main Web site at http://python.org. For your convenience, you can also go to the book’s Web site at http://corepython.com and click on the “Install Python” link to the left—we have organized a grid with most contemporary versions of Python for the most platforms, with a focus, of course, on the “Big Three.” Unix, Win 32, MacOS X.
As we alluded to earlier in Section 1.3.5, Python is available on a wide variety of platforms. They can be broken down into these basic categories and available platforms:
• All Unix flavors (Linux, MacOS X, Solaris, FreeBSD, etc.)
• Win32 (Windows NT, 2000, XP, etc.)
• Older platforms: MacOS 8/9, Windows 3.x, DOS, OS/2, AIX
• Handhelds (PDAs/phones): Nokia Series 60/SymbianOS, Windows CE/Pocket PC, Sharp Zaurus/arm-linux, PalmOS
• Gaming consoles: Sony PS2, PSP; Nintendo GameCube
• Real-Time platforms: VxWorks, QNX
• Alternative implementations: Jython, IronPython, stackless
• Others
The most recent versions of Python will likely be found only on “the Big Three.” In fact, current versions of Linux and MacOS X already come with Python installed—you’ll have to check to see which Python release it is. Other versions will be older 2.x releases while some have yet to progress beyond 1.5. Some come with binaries to install directly while others require you to build Python manually before installation.
As mentioned above, your Unix-based system may already have Python installed. The best way to check is to run Python from the command line and see if it is both in your path and available. Just type:
If starting Python fails, it doesn’t mean it’s not installed, just that it’s not in your path. Hunt around for it, and if you’re unsuccessful, try building it manually, which isn’t very difficult (see “Build It Yourself” on the next page). If you’re using certain versions of Linux, you can get the binary or source RPMs.
Download the .msi file from python.org or corepython.com as described previously (i.e., python-2.5.msi) and execute it to install Python. If you are planning on doing any kind of Win32 development, such as with COM, MFC, or need any of the Win32 libraries, we also strongly suggest that you download and install the Python for Windows Extensions. You can then run Python from a DOS command window or via one of the IDEs, IDLE, the default Python IDE, or PythonWin, the IDE that comes with the Windows Extensions distribution.
For most other platforms, download the .tgz file,. extract the files, and go to the main directory. Build Python by performing the following:
./configuremakemake install
Python is usually installed in a standard location so you can find it rather easily. It has become quite commonplace for systems today to have multiple versions of Python installed. While it is easy to find the binary executable, you also have to deal with where the libraries are installed.
On Unix machines, the executable is usually installed in /usr/local/bin while the libraries are in /usr/local/lib/python2.x where the 2.x is the version of Python you are using. For MacOS X, Python is installed in /sw/bin and/or /usr/local/bin, and the libraries are in /sw/lib,/usr/local /lib, and/or /Library/Frameworks/Python.framework/Versions.
On Windows, the default installation area is C:Python2x. Try to avoid installing Python in C:Program Files. Yes, we know it’s the general place to put installed programs, but DOS does not support those types of long names; it is usually aliased as Progra~1. This may also lead to problems running some programs, so it’s best to avoid it. So, let’s say you installed Python in C:Python, then the standard library files are typically installed in C:PythonLib.
There are three different ways to start Python. The simplest way is by starting the interpreter interactively, entering one line of Python at a time for execution. Another way to start Python is by running a script written in Python. This is accomplished by invoking the interpreter on your script application. Finally, you can run from a graphical user interface (GUI) from within an integrated development environment (IDE). IDEs typically feature additional tools such as an integrated debugger, text editor, and support for a wide range of source code control tools such as CVS.
You can enter Python and start coding right away in the interactive interpreter by starting it from the command line. You can do this from Unix, DOS, or any other system that provides you a command-line interpreter or shell window. One of the best ways to start learning Python is to run the interpreter interactively. Interactive mode is also very useful later on when you want to experiment with specific features of Python.
To access Python, you will need to type in the full pathname to its location unless you have added the directory where Python resides to your search path. Common places where Python is installed include /usr/bin and /usr/local/bin.
We recommend that you add Python (i.e., the executable file python, or jpython if you wish to use the Java version of the interpreter) to your search path because you do not want to have to type in the full pathname every time you wish to run interactively. Once this is accomplished, you can start the interpreter with just its name.
To add Python to your search path, simply check your login startup scripts and look for a set of directories given to the set path or PATH= directive. Adding the full path to where your Python interpreter is located is all you have to do, followed by refreshing your shell’s path variable. Now at the Unix prompt (% or $, depending on your shell), you can start the interpreter just by invoking the name python (or jpython), as in the following.
$ python
Once Python has started, you’ll see the interpreter startup message indicating version and platform and be given the interpreter prompt “>>>” to enter Python commands. Figure 1-1 is a screen shot of what it looks like when you start Python in a Unix (MacOS X) environment.
To add Python to your search path, you need to edit the C:autoexec.bat file and add the full path to where your interpreter is installed. It is usually either C:Python or C:Program Files Python (or its short DOS name equivalent C:Progra~1Python). From a DOS window (either really running in DOS or started from Windows), the command to start Python is the same as Unix, python (see Figure 1-2). The only difference is the prompt, which is C:>.
C:> python
From any flavor of Unix, a Python script can be executed by invoking the interpreter on your application from the command line, as in the following:
$ python script.py
Python scripts end with a file extension of .py, as indicated above.
It is also possible in Unix to automatically launch the Python interpreter without explicitly invoking it by name from the command line. If you are using any Unix-flavored system, you can use the shell-launching (“sh-bang”) first line of your program:
#!/usr/local/bin/python
The file path, the part that follows the #!, is the full path location of the Python interpreter. As we mentioned before, it is usually installed in /usr/local/bin or /usr/bin. If not, be sure to get the exact pathname correct so that you can run your Python scripts. Pathnames that are not correct will result in the familiar Command not found error message.
As a preferred alternative, many Unix systems have a command named env, installed in either /bin or /usr/bin, which will look for the Python interpreter in your path. If you have env, your startup line can be changed to something like this:
#!/usr/bin/env python
or, if your env is located in /bin,
#!/bin/env python
env is useful when you either do not know exactly where the Python executable is located, or if it changes location often, yet still remains available via your directory path. Once you add the proper startup directive to the beginning of your script, it becomes directly executable, and when invoked, loads the Python interpreter first, then runs your script. As we mentioned before, Python no longer has to be invoked explicitly from the command. You only need the script name:
$ script.py
Be sure the file permission mode allows execution first. There should be an ‘rwx’ permissions getting for the user in the long listing of your file. Check with your system administrator if you require help in finding where Python is installed or if you need help with file permissions or the chmod (CHange MODe) command.
The DOS command window does not support the auto-launching mechanism; however, at least with WinXP, it is able to do the same thing as Windows: it uses the “file type” interface. This interface allows Windows to recognize file types based on extension names and to invoke a program to handle files of predetermined types. For example, if you install Python with PythonWin, double-clicking on a Python script with the .py extension will invoke Python or PythonWin IDE (if you have it installed) to run your script. Thus, running the following will have the same effect as double-clicking on it:
C:> script.py
So now both Unix-based and Win32 systems can launch Python scripts without naming Python on the command line, but you can always fall back on it if just calling the script leads to an error like “command is not recognized.”
You can run Python from a graphical user interface (GUI) environment as well. All you need is a GUI application on your system that supports Python. If you have found one, chances are that it is also an IDE (integrated development environment). IDEs are more than just graphical interfaces. They typically have source code editors and trace and debugging facilities.
IDLE is the very first Unix IDE for Python. It was also developed by Guido van Rossum and made its debut in Python 1.5.2. IDLE stands for IDE with a raised “L,” as in Integrated DeveLopment Environment. Suspiciously, IDLE also happens to be the name of a Monty Python troupe member. Hmmm.... IDLE is Tkinter-based, thus requiring you to have Tcl/Tk installed on your system. Current distributions of Python include a minimal subset of the Tcl/Tk library so that a full install is no longer required.
Also, if Python was automatically installed on your system or if you have a Python RPM, chances are it does not include IDLE or Tkinter, so look for both before trying to run IDLE. (There is actually a separate Tkinter RPM that you can download along with the Python one if you want it.) If you build Python yourself and Tk libraries are available, then Tkinter will be automatically built along with Python, and both Tkinter and IDLE will be installed when Python is.
If you want to run IDLE, you will find it where your standard library is installed: /usr/local/lib/python2.x/idlelib/idle.py. If you build and install Python yourself, you may find a shortcut script called idle in /usr/local/bin allowing you to just launch IDLE from your shell command-line prompt. A screen shot of IDLE in Unix appears in Figure 1-3.
MacOS X is very Unix-like (based on the Mach kernel with BSD services). Python is now compiled for MacOS X with the traditional Unix build tools. The MacOS X distributions come with a compiled Python interpreter; however, none of the special Mac-oriented tools (i.e., GNU readline, IDE, etc.) are installed. The same applies for Tkinter and IDLE.
You tend to go download and build your own, but be careful: sometimes it is tricky to decouple your new Python install from the Apple factory version. Do your research carefully first. You can also get Python for MacOS X from Fink/FinkCommander and DarwinPorts:
For the most up-to-date Mac stuff and information for Python, visit:
Another option would be to download a MacOS X Universal binary from the Python Web site. This disk image (DMG) file requires at least version 10.3.9 and will run on both PowerPC- and Intel-based Macs.
PythonWin is the first Windows interface for Python and is an IDE with a GUI. Included with the PythonWin distribution are Windows API, and COM (Component Object Model, a.k.a. OLE [Object Linking and Embedding] and ActiveX) extensions. PythonWin itself was written to the MFC (Microsoft Foundation Class) libraries, and it can be used as a development environment to create your own Windows applications. You can download and install it from the Web sites shown on the next page.
PythonWin is usually installed in the same directory as Python, in its own subdirectory, C:Python2xLibsite-packagespythonwin as the executable pythonwin.exe. PythonWin features a color editor, a new and improved debugger, interactive shell window, COM extensions, and more. A screen snapshot of the PythonWin IDE running on a Windows machine appears in Figure 1-4.
You can find out more about PythonWin and the Python for Windows Extensions (also known as “win32all”) at the following locations organized by Mark Hammond:
IDLE is also available on the Windows platform, due to the portability of Tcl/Tk and Python/Tkinter. It looks similar to its Unix counterpart (Figure 1-5).
From Windows, IDLE can be found in the Libidlelib subdirectory where your Python interpreter is found, usually C:Python2x. To start IDLE from a DOS command window, invoke idle.py. You can also invoke idle.py from a Windows environment, but that starts an unnecessary DOS window. Instead, double-click on idle.pyw. Files ending in .pyw will not open a DOS command window to run the script in. In fact, your author just creates a shortcut to C:Python2xLibidlelibidle.pyw on the desktop that can be double-clicked ... simple!
Many software professionals actually prefer to code in their favorite text editor such as vi(m) or emacs. In addition to these and the IDEs mentioned in the previous section, there are good number of Open Source and commercial IDEs as well. Here is a short list:
Open Source
• IDLE (comes with Python distribution)
• PythonWin + Win32 Extensions
• IPython (enhanced Interactive Python)
• IDE Studio (IDLE+more)
Commercial
• WingIDE Python IDE by WingWare
• Komodo IDE by ActiveState
General overall IDE list
Core Tip: Running the code examples in this book
You will find many example Python scripts and applications in this book, which can be downloaded from the book’s Web site. When you run them, however, bear in mind that they were designed to execute either from the command line (DOS command window or Unix shell) or from an IDE. If you are using a Win32 system and double-click on a Python program, a DOS window opens up but closes when the script completes, so you may miss all of the output. If you encounter this situation, just open up a DOS window normally and run it from the command line or execute the script in an IDE instead. Alternatively, you can add a raw_input() line at the bottom, which keeps the window alive until you press the RETURN key.
Python documentation can be found in numerous places. The fastest way to get to it is by viewing the online docs at the Python Web page. If you are not online and use a Win32 system, an offline compressed help file is located at C:Python2xDocPython2x.chm. It uses an Internet Explorer (IE) interface so that you are actually using a Web browser to view the docs. Other offline options include Adobe Portable Document Format (PDF) or PostScript (PS) files in Letter and A4 sizes. Finally, if you download the Python distribution, you will get the LaTeX source.
At the book’s Web site, we created a page with a grid that has links to the docs for most versions of Python. Just visit http://corepython.com and click on “Documentation” to the left.
Python has been compared with many languages. One reason is that it provides many features found in other languages. Another reason is that Python itself is derived from many other languages, including ABC, Modula-3, C, C++, Algol-68, SmallTalk, and Unix shell and other scripting languages, to name a few. Python is a virtual “greatest hits”: van Rossum combined the features he admired most in the other languages he had studied and brought them together for our programming sanity.
However, more often than not, since Python is an interpreted language, you will find that most of the comparisons are with Perl, Java, Tcl, and JavaScript. Perl is another scripting language that goes well beyond the realm of the standard shell scripts. Like Python, Perl gives you the power of a full programming language as well as system call access.
Perl’s greatest strength is in its string pattern matching ability, providing an extremely powerful regular expression matching engine. This has pushed Perl to become the de facto language for string text stream filtering, recognition, and extraction, and it is still the most popular language for developing Internet applications through Web servers’ Common Gateway Interface (CGI). Python’s regular expression engine is based significantly on Perl’s.
However, Perl’s obscure and overly symbolic syntax is much more difficult to decipher, resulting in a steep learning curve that inhibits the beginner, frustrating those for whom grasping concepts is impeded by semantics. This, coupled with Perl’s “feature” of providing many ways of accomplishing the same task, introduces inconsistency and factionization of developers. Finally, all too often the reference book is required reading to decipher a Perl script that was written just a couple of months back.
Python is often compared to Java because of their similar OO nature and syntax. Java’s syntax, although much simpler than C++’s, can still be fairly cumbersome, especially if you want to perform just a small task. Python’s simplicity offers a much more rapid development environment than using just pure Java. One major evolution in Python’s relationship with Java is the development of Jython, a Python interpreter written completely in Java. It is now possible to run Python programs with only the presence of a Java VM (virtual machine). We will mention more of Jython’s advantages briefly in the following section, but for now we can tell you that in the Jython scripting environment, you can manipulate Java objects, Java can interact with Python objects, and you have access to your normal Java class libraries as if Java has always been part of the Python environment.
Python is now often compared to Ruby as well, due to the popularity of the Rails project. As we mentioned above, Python is a wider mix of multiple programming paradigms. It is not purely OO like Ruby and does not have Smalltalk-like blocks, perhaps Ruby’s most distinguishable feature. Python does have a byte-code interpreter, where Ruby does not. Python is perhaps more readable, as Ruby can really be thought of as more of an OO Perl. With regard to Rails, Python has several of its own Web application frameworks, such as Django and Turbogears, to name two.
Tcl is another scripting language that shares similarities with Python. Tcl is one of the first truly easy-to-use scripting languages to provide the programmer extensibility as well as system call access. Tcl is still popular today and perhaps somewhat more restrictive (due to its limited types) than Python, but it shares Python’s ability to extend past its original design. More importantly, Tcl is often used with its graphical toolkit partner, Tk, in developing graphical user interface (GUI) applications. Due to its popularity, Tk has been ported to Perl (Perl/Tk) and Python (Tkinter). Also, it can be argued that Python’s classes, modules, and packages make writing large programs in Python more pleasant than writing them in Tcl.
Python has some light functional programming (FP) constructs, which likens it to languages such as Lisp or Scheme. Although Python cannot be considered a traditional functional language, it continues to borrow features from languages such as Lisp and Haskell. For instance, list comprehensions were a welcome addition from the Haskell world, and Lisp programmers will feel at home with lambda, map, filter, and reduce.
JavaScript is another OO language very similar to Python. Any proficient JavaScript programmer will have little or no difficulty learning Python. The particularly astute reader will note that JavaScript is based on a prototype system, whereas Python follows a more traditional OO system that differentiates objects and classes.
Here is a list of some Web pages that have information on comparing or transitioning between Python and other languages:
Perl
http://www2.linuxjournal.com/article/3882
http://llama.med.harvard.edu/~fgibbons/PerlPythonPhrasebook.html
Java
http://dirtsimple.org/2004/12/python-is-not-java.html
http://twistedmatrix.com/users/glyph/rant/python-vs-java.html
http://netpub.cstudies.ubc.ca/oleary/python/python_java_comparison.php
Lisp
Ruby
Perl, C++
Perl, Java, C++
C++, Java, Ruby
Perl, Java, PHP, Tcl
http://www-128.ibm.com/developerworks/linux/library/l-python101.html
http://www-128.ibm.com/developerworks/linux/library/l-script-survey/
C, C++, Java, Perl, Rexx, Tcl
You can access a number of other comparisons between Python and other languages at:
The “standard” version of Python is C-compiled, aka CPython. There are a few other Python implementations. We will describe some here, but for more on the various Python implementations out there, check out:
As we mentioned in the previous section, a Python interpreter completely written in Java, called Jython, is currently available. Although there are still minor differences between the two interpreters, they are very similar and provide a comparable startup environment.
What are the advantages of Jython? Jython ...
• Can run anywhere a Java virtual machine (JVM) can be found
• Provides access to Java packages and class libraries
• Furnishes a scripting environment for Java development
• Enables ease of testing for Java class libraries
• Offers access to Java’s native exception handling ability
• Delivers JavaBeans property and introspection ability
• Encourages Python-to-Java development (and vice versa)
• Gives GUI developers access to Java AWT/Swing libraries
• Utilizes Java’s native garbage collector (so CPython’s was not implemented)
A full treatment of Jython is beyond the scope of this text, but there is a good amount of information online. Jython is still an ongoing development project, so keep an eye out for new features. You can get more information at the Jython Web site at:
There is now a Python implementation completely in C#, called IronPython. It is targeted at the .NET and Mono environments. You can integrate an IronPython interpreter in a .NET application that can interact with .NET objects. Extensions to IronPython can be implemented in C# or VisualBasic.NET. In addition, there is another .NET/Mono language that is Python-inspired, and it is called Boo. You can find out more information about IronPython and Boo at:
One of the limitations of CPython is that for each Python function call, it results in a C function call. (For the computer science-oriented, we are talking about stack frames here.) This implies restrictions on CPython, most notably a limitation on the total number of concurrent function calls. This can make it difficult to implement effective user-level threading libraries or highly recursive applications in Python. If this total is exceeded, then your program will crash. By using a “stackless” implementation, you are freed from this restriction and can have any number of Python stack frames for the one C stack frame. This allows you to have many function calls and supports a very large number of threads. The main stackless implementation of Python is called ... Stackless (surprise!).
The only problem with Stackless is that it requires significant changes to the existing CPython interpreter, so it is seen as an independent fork. Another project called Greenlets that also supports microthreads is available as a standard C extension and can be used with an unmodified version of Python. You can read about both of these projects at:
1-1. Python Installation. Check if Python is installed on your system. If not, download and install it!
1-2. Executing Python. How many different ways are there to run Python? Which do you prefer and why?
1-3. Python Standard Library.
(a) Find where the Python executables and standard library modules are installed on your system.
(b) Take a look at some of the standard library files, for example, string.py. It will help you get acclimated to looking at Python scripts.
1-4. Interactive Execution. Start your Python interactive interpreter. You can invoke it by typing in its full pathname or just its name (python or python.exe) if you have installed its location in your search path. (You can use any version or implementation of Python that is convenient to you, e.g., command line, GUI/IDE, Jython, IronPython, or Stackless.) The startup screen should look like the ones depicted in this chapter. When you see the >>>, that means the interpreter is ready to accept your Python commands.
Try entering the command for the famous Hello World! program by typing print ‘Hello World!’ (and press RETURN), then exit the interpreter. On Unix systems, ^D will send the EOF signal to terminate the Python interpreter, and on DOS systems, the keypress is ^Z. Exiting from windows in graphical user environments like the Macintosh, PythonWin or IDLE on Windows, or IDLE on Unix can be accomplished by simply closing their respective windows.
1-5. Scripting. As a follow-up to Exercise 1-4, create “Hello World!” as a Python script that does exactly the same thing as the interactive exercise above. If you are using the Unix system, try setting up the automatic startup line so that you can run the program without invoking the Python interpreter.
1-6. Scripting. Create a script that displays your name, age, favorite color, and a bit about you (background, interests, hobbies, etc.) to the screen using the print statement.