Python is good for developers.
No matter what operating system you or your customers are running, it will work. Unless you are coding platform-specific things, or using a platform-specific library, you can work on Linux and deploy on other systems, for example. However, thats not uncommon anymore. (Ruby, Java, and many other languages work in the same way.) Combined with the other qualities that we will discover throughout this book, Python becomes a smart choice for a company's primary development language.
This chapter gathers everything required to get started with Python, no matter what your environment is. It presents:
How to install Python
How to use and enhance the prompt
How to be ready to extend Python, by installing
How to set up a development environment, using theold school or the new school ways
A book always starts with some appetizers. So if you are already familiar with Python, and have it installed and reachable from your favorite code editor, you can skip the first section of this chapter, and just read other sections quickly. You might find in them interesting points to enhance your environment. Be sure to read the section on
setuptools though, as its installation is mandatory for the rest of the book.
If you are using Windows, make sure you have installed the software described in this chapter, as it will be required to run all the examples this book provides.
The Python programming language runs on almost any system such as Linux, Macintosh, and Windows. The distributions are made available by the core team on the main download page of the Python website at: http://www.python.org/download. Other platforms are maintained by the people from the community, and summarized on a dedicated page. (See http://www.python.org/download/other.) Here, you'll probably find the distributions for operating systems that will remind you of your college years, if you are thirty-years old or more.
If you have a computer, you will be able to use Python no matter what operating system this computer runs on.
If not, ditch it.
Before installing Python, let's have a quick tour of the existing implementations.
The main Python implementation is written in the C language and is called CPython. It is the one that majority of people refer to, when they talk about Python. When the language evolves, the C implementation is changed accordingly. Besides C, Python is available in a few other implementations that are trying to keep up with the mainstream. Most of them are a few milestones behind CPython, but provide a great opportunity to use and promote the language in a specific environment.
Jython is a Java implementation of the language. It compiles the code into Java byte code, and allows the developers to seamlessly use Java classes within their Python modules. (In Python, a file containing code is called a module.) Jython allows people to use Python as the top-level scripting language on complex application systems, for example J2EE. It also brings Java applications into Python applications. Making Apache Jackrabbit (which is a document repository API based on JCR; see http://jackrabbit.apache.org) available in a Python program is a good example of what Jython allows. The current milestone is 2.2.1, but the Jython team is heading over to 2.5. Some Python web frameworks such as Pylons are currently boosting Jython development to make it available in Java world.
IronPython brings Python into .NET. The project is supported by Microsoft, where IronPython's lead developers work. The latest stable version is 1.1 (released in April 2007) and implements Python 2.4.3. It is available in ASP.NET, and lets people use the Python code in their .NET application in the same way as Jython does in Java. It is quite an important implementation for the promotion of a language. Besides Java, the .NET community is one of the biggest developer communities out there. The TIOBE community index also shows that .NET languages are among the rising stars. (For more information, visit http://www.tiobe.com/tpci.htm ).
PyPy is probably the most exciting implementation, as its goal is to rewrite Python into Python. In PyPy, the Python interpreter is itself written in Python. We have a C code layer carrying out the nuts-and-bolts work for the CPython implementation of Python. But in the PyPy implementation, this C code layer is rewritten in pure Python. This means that you can change the interpreter's behavior during execution time, and implement code patterns that couldn't be easily done in CPython. (See http://codespeak.net/pypy/dist/pypy/doc/objspace-proxies.html). PyPy used to be 2000 times slower than CPython, but this has improved a lot in the past years. The introduction of techniques such as the JIT (Just-In-Time) compiler is promising. The current speed factor is between 1.7 and 4, and the current implementation target is Python 2.4. PyPy can be seen as the head of R&D in the compilation matters, and the starting point of many innovations that the mainstream implementation can benefit from later. On the whole though, PyPy is interesting for theoretical reasons, and interests those who enjoy going deep into the internals of the language. It is not generally used in production.
There are other implementations and ports of Python. For example, Nokia has made Python 2.2.2 available in the S60 phone series ( http://opensource.nokia.com/projects/pythonfors60/), and Michael Lauer maintains a port on ARM Linux that makes it available in devices such as Sharp Zaurus ( http://www.vanille-media.de/site/index.php/projects/python-for-arm-linux).
There are many other examples, but this book will focus installing the CPython implementation on Linux, Windows, and Mac OS X.
If you are running Linux, you probably have Python installed. So, try to call it from the shell:
tarek@dabox:~$ python Python 2.3.5 (#1, Jul 4 2007, 17:28:59) [GCC 4.1.2 20061115 (prerelease) (Debian 4.1.1-21)] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>>
If the command is found, you will be placed into the interactive shell that comes with Python, represented by the
>>> sign. The information about the compiler used to build Python (here GCC) and the target system (Linux) is displayed. If you are using Windows, you will get Microsoft Visual Studio as the compiler. The Python version is also displayed in the result. Make sure you are running the latest stable release (probably 2.6 by the time this book is printed).
If it is not the case, you can install several versions of Python on your system without any unexpected interaction. Each Python version will be reachable with its full name, or with the Python command, depending on your path environment:
tarek@dabox:~$ which python /usr/bin/python tarek@dabox:~$ python<tab> python python2.3 python2.5 python2.4
If the command is not found, which is very uncommon under Linux, you need to install it using the package-management tools for your Linux system, such as apt for Debian, or rpm for Red Hat, or by compiling the sources.
While it is preferable to stick with a package installation, we will now discuss each of the two installation methods (package-managed installation and source installation) in a little more detail. However, the latest Python version might not always be available in your package-management tools as yet.
Using the Linux package system of the Linux distribution is the common way to install Python, and to make sure that you can easily upgrade it. Depending on your system, you will have to run one of these commands:
apt-get install pythonfor Debian-based distributions, such as Ubuntu
urpmi pythonfor rpm-based ones, such as Fedora or Red Hat series
emerge pythonfor Gentoo
If the latest version does not show up, a manual installation will be needed.
Finally, some extra packages should be installed in order to have a full installation. They are optional and you can work without them. But they are useful if you want to code C extensions, or to profile your programs. The packages that should be installed in order to have a full installation are:
python-dev: It contains Python headers needed when the C modules are compiled.
python-profiler: It contains non-GPL modules (Hotshot profiler) for full GPL distributions such as Debian or Ubuntu.
gcc: It is used to compile extensions that contain C code.
A manual installation is done with the cmmi process (configure, make, make install sequence) that performs a compilation of Python and deploys it on the system. The latest Python archive can be found on http://python.org/download.
Using wget for downloads:
The wget program, from the Gnu project, is a command line utility that can perform downloads. It is available under all platforms. Under Windows, you can get a binary distribution at: http://gnuwin32.sourceforge.net/packages/wget.htm.
On Linux or Mac OS X, it is installable through the package systems such as apt or MacPorts.
To build Python, we will use
makeis a program that is used to read configuration files, usually named
Makefile, and check that all requirements to compile the program are met. It is also used to drive the compilation. It is invoked with the
gccis the GNU C Compiler, an open-source compiler widely used to build programs.
Make sure they are both installed on your system. Under some versions of Linux such as Ubuntu, you can install build tools with the
To build and install Python, run this sequence:
cd /tmp wget http://python.org/ftp/python/2.5.1/Python-2.5.1.tgz tar -xzvf tar -xzvf Python-2.5.1.tgz cd Python-2.5.1 ./configure make sudo make install
This installation will also install the headers provided for binary installations that are usually included in the python-dev package. The Hotshot profiler is also bundled into the source releases. The result should be the same when you are done, that is, Python should be reachable in the shell.
Python can be compiled on Windows in the same way as for Linux. But this can be quite painful because you will need to set up a complicated compilation environment. Standard installers are provided in the python.org download section, and the wizard to achieve the installation is pretty straightforward.
If you leave all the options at default, Python will be installed under
c:\Python25, and not under the usual
Program Files folder. This prevents any space in the path.
The last step is changing your
PATH environment variable, so that we can call Python from the DOS shell.
On most Windows installations, this is done by:
Right-clicking on the My Computer icon that is located on the desktop or the start menu, to get to the System Properties dialog box
Getting in the Advanced tab
Clicking on the Environment Variables button
PATHsystem variable to add two new paths, separated by ";" (a semi-colon)
The paths to be added are:
c:\Python25, to be able to call
c:\Python25\Scripts, to be able to call third-party scripts that are installed in your Python by extensions
You should be able to run Python in the Command Prompt. To get there, open the Run shortcut in the Start menu, open cmd, and then call
C:\> python Python 2.5.2 (#71, Oct 18 2006, 08:34:43) [MSC v.1310 32 bit (Intel)] on win32 Type "help", "copyright", "credits" or "license" for more information. >>>
This is enough to run Python. But this environment is not quite complete, when compared to that of a Linux user. To perform everything that is presented in this book, MinGW needs to be installed.
MinGW is a compiler for Windows platforms. It provides the gcc compiler in all flavors, and a set of libraries and headers. MinGW can be used as a full replacement for Microsoft's Visual C++. You could also choose to keep both compilers on your system and use them for different purposes, depending upon your requirements.
To install MinGW, get the distribution from http://sourceforge.net/project/showfiles.php?group_id=2435&package_id=240780. There you will find a link to Sourceforge. (See http://sourceforge.net, the largest developer website for Open Source projects.) The automated installer is the best choice, as everything will be bundled. Get the installer and run it.
Just as for Python, the
PATH environment variable in the system properties needs to be extended with
c:\MinGW\bin, in order to be able to invoke its commands. You should be able to run MinGW commands from the shell after the path is set:
C:\>gcc -v Reading specs from c:/MinGW/bin/../lib/gcc-lib/mingw32/3.2.3/specs Configured with: ../gcc/configure --with-gcc --with-gnu-ld --with-gnu-as --host= mingw32 --target=mingw32 --prefix=/mingw --enable-threads --disable-nls --enable -languages=c++,f77,objc --disable-win32-registry --disable-shared --enable-sjlj- exceptions Thread model: win32 gcc version 3.2.3 (mingw special 20030504-1)
These commands will never be run manually, but are used automatically by Python when a compiler needs to be used.
Another tool that should be installed under Windows is MSYS (Minimal SYStem). It provides a Bourne Shell command-line interpreter environment under Windows that provides all the usual commands Linux or Mac OS X has, such as
cp, rm and so on.
This may sound overkill, since Windows has the same set of tools whether they are graphical or available in an MS-DOS prompt. But this helps the developers who work on several systems to have a universal set of commands to work with.
Get the download link for MSYS from http://sourceforge.net/project/showfiles.php?group_id=2435&package_id=240780 and install it on your system.
If you perform a standard installation, MSYS will be installed in
c:\msys. You must add
C:\msys\1.0\bin in your
PATH variable in the same way as you added MinGW.
The rest of this book uses Bourne Shell commands in its examples. So if you are under Windows, you should install MSYS.
Mac OS X is based on Darwin, which in turn is based on FreeBSD. This makes the platform quite similar and compatible to Linux. Apple, on the top of it, added a graphical engine (Quartz) and a specific file tree.
From the shell point of view, the major difference is how the system tree is organized. You will not find, for example a
/home root folder, but you can find a
/Users folder. The applications are also usually installed in
/Library. /usr/bin is used though, as it is used on Linux.
Just as for Linux and Windows, there are two ways you can install Python on Mac OS X. You can install it using a package installer, or you can compile it from the source. The package installation is the simplest way, but you might want to build Python yourself. However, the latest version might not be available yet, as a binary distribution.
The latest Mac OS X version (Leopard at this time) comes with an installed Python. To install an extra Python, get a universal binary at http://www.pythonmac.org/packages for Python 2.5.x. You will get a
.dmg file that you can mount. It contains a
.pkg file that you can launch.
gcccompiler: It is provided in the Xcode Tools, and is available on the install disk or online at: http://developer.apple.com/tools/xcode.
MacPorts: This is a package system comparable to Debian's package-management system apt that will help you install dependencies, for instance the same way Linux users can with apt. See http://www.macports.org.
From here, you can follow the same process explained for compiling under Linux.
macziade:/home/tziade tziade$ python Python 2.5 (r25:51918, Sep 19 2006, 08:49:13) [GCC 4.0.1 (Apple Computer, Inc. build 5341)] on darwin Type "help", "copyright", "credits" or "license" for more information. >>>1 + 3 4 >>>5 * 8 40
When the enter key is hit, the line is interpreted and the result is immediately displayed. This particularity, inherited from the ABC language, affects the way Python the programmers work. In code documentation, all usage examples are shown in small prompt sessions.
Getting out of the prompt:
To get out of the prompt, use
Ctrl+D under Linux or Mac OS X, and
Ctrl+Z under Windows.
Since the prompt interactive mode will play an important role in the coding process, we need to make it very easy to use.
The interactive prompt can be configured with a startup file. When it starts, it looks for the
PYTHONSTARTUP environment variable and executes the code in the file pointed to by this variable. Some Linux distributions provide a default startup script, which is generally located in your home directory. It is called
.pythonstartup. Tab completion and command history are often provided to enhance the prompt, and are based on the the
readline module. (You need the
readline library.) If you don't have such a file, you can easily create one.
Here's an example of the simplest startup file that adds completion with the<Tab> key, and history:
# python startup file import readline import rlcompleter import atexit import os # tab completion readline.parse_and_bind('tab: complete') # history file histfile = os.path.join(os.environ['HOME'], '.pythonhistory') try: readline.read_history_file(histfile) except IOError: pass atexit.register(readline.write_history_file, histfile) del os, histfile, readline, rlcompleter
Create this file in your home directory and call it
.pythonstartup. Then add a
PYTHONSTARTUP variable in your environment using the path of your file.
The python script is available in the pbp.script package under the 'pythonstartup.py' name. You can get this file at http://pypi.python.org/pypi/pbp.scripts and rename it to '
Setting up the PYTHONSTARTUP environment variable:
If you are running Linux or Mac OS X, the simplest way is to create the startup script in your home folder. Then link it with a
PYTHONSTARTUP environment variable set into the system shell startup script. For example, Bash and Korn shell use the
.profile file, where you can insert a line such as:
If you are running Windows, it is easy to set a new environment variable as an administrator in the system preferences, and save the script in a common place instead of using a specific user location.
When the interactive prompt is called for, the
.pythonstartup script should be executed, and the new functionalities made available. For instance, tab completion is really useful to recall module contents:
>>> import md5 >>> md5.<tab> md5.__class__ md5.__file__ md5.__name__ md5.__repr__ md5.digest_size md5.__delattr__ md5.__getattribute__ md5.__new__ md5.__setattr__ md5.md5 md5.__dict__ md5.__hash__ md5.__reduce__ md5.__str__ md5.new md5.__doc__ md5.__init__ md5.__reduce_ex__ md5.blocksize
You can adapt the script for more automation, as Python provides an entry point with a module. Further, a module provides the interpreter base classes. (See the
code module at: http://docs.python.org/lib/module-code.html). But if you want an advanced interactive prompt, you can use an existing tool: iPython.
iPython ( http://ipython.scipy.org) is a project aiming to provide an extended prompt. Among the features provided, the most interesting ones are:
Dynamic object introspection
System shell access from the prompt
Profiling direct support
See the full list at: http://ipython.scipy.org/doc/manual/index.html.
To install iPython, go to the download page http://ipython.scipy.org/moin/Download and follow the instructions in accordance with your platform.
The iPython shell in action looks like this:
tarek@luvdit:~$ ipython Python 2.4.4 (#2, Apr 5 2007, 20:11:18) Type "copyright", "credits" or "license" for more information. IPython 0.7.2 -- An enhanced Interactive Python. ? -> Introduction to IPython's features. %magic -> Information about IPython's 'magic' % functions. help -> Python's own help system. object? -> Details about 'object'. ?object also works, ?? prints more. In :
Perl has a great collection of third-party libraries, and a simple way to install them. The Perl CPAN system lets any developer publish a new library with a simple set of commands. A similar technology has been used in the Python world for the past few years, and is becoming the standard way to install extensions. It is based on:
A centralized repository on Python's official website called the Python Package Index (PyPI), which was formerly the Cheeseshop (with reference to a Monty Python sketch from the BBC)
A packaging system called
setuptoolsthat is based on
distutils, to deliver the code in archives and interact with PyPI
Before installing these extensions, a few explanations are necessary to get the whole picture.
Python comes with a module called
distutils that provides a set of tools to distribute your Python applications. It provides the following:
A skeleton to provide standard metadata fields such as the author name, the license type, and many others
A set of helpers who know how to build a distribution over the code of a package (in Python, a package is a system folder containing one or more modules) and let you create either a set of pre-compiled python files, or a real installer for Windows.
distutils is limited to the package, and doesn't provide a way to define its dependencies over other packages.
setuptools enhances this by adding a basic dependency system and a lot of other features. It also provides an automatic package finder that knows how to fetch dependencies, and install them automatically. In other words,
setuptools is to Python what
apt is to Debian.
Preparing a setuptools wrapper in Python is becoming the standard way to deploy it. Chapter 5 will cover it extensively.
This tool has become very popular, and is now almost mandatory when writing Python applications that are meant to be distributed to others. It will hopefully be integrated in the standard library that comes with Python within the next few years. Until then, if you want a fully-enabled Python system for yourself with all the power of
setuptools, you will need to separately install
setuptools. This is because it is not yet a part of the standard Python install.
setuptools, you need to install EasyInstall, which is a package downloader and installer. This program is complementary to
setuptools because it knows how to handle packages built with it. Installing it will also install
Download and run the
ez_setup.py script provided on Peak's website. You can find it on http://peak.telecommunity.com/DevCenter/EasyInstall, and its location is usually http://peak.telecommunity.com/dist/ez_setup.py:
macziade:~ tziade$ wget http://peak.telecommunity.com/dist/ez_setup.py 08:31:40 (29.26 KB/s) - " ez_setup.py " saved [8960/8960] macziade:~ tziade$ python ez_setup.py setuptools Searching for setuptools Reading http://pypi.python.org/simple/setuptools/ Best match: setuptools 0.6c7 ... Processing dependencies for setuptools Finished processing dependencies for setuptools
If you have a previous installation, you will get a warning, and you will need to use the upgrade option (
macziade:~ tziade$ python ez_setup.py Setuptools version 0.6c7 or greater has been installed. (Run "ez_setup.py -U setuptools" to reinstall or upgrade.) macziade:~ tziade$ python ez_setup.py -U setuptools Searching for setuptools Reading http://pypi.python.org/simple/setuptools/ Best match: setuptools 0.6c7 ... Processing dependencies for setuptools Finished processing dependencies for setuptools
When everything is installed, a new command is available on your system called
easy_install. Any installation or upgrade of an extension will be done through this command. For example, if the
py.test extension (which is a set of tools to practice agile development; see http://codespeak.net/py/dist needs to be installed, you can run the following code:
tarek@luvdit:/tmp$ sudo easy_install py Searching for py Reading http://cheeseshop.python.org/pypi/py/ Reading http://codespeak.net/py Reading http://cheeseshop.python.org/pypi/py/0.9.0 Best match: py 0.9.0 Downloading http://codespeak.net/download/py/py-0.9.0.tar.gz ... Installing pytest.cmd script to /usr/local/bin Installed /usr/local/lib/python2.3/site-packages/py-0.9.0-py2.3.egg Processing dependencies for py Finished processing dependencies for py
If you are under Windows, the script is called
easy_install.exe, and is located in the
Scripts folder of your Python installation. So as long as this folder, similar to the one configured in the Windows installation section, is in your
PATH, you will be able to call it with
easy_install as well (without the
sudo prefix that is used to have root privileges under Linux and Mac OS X).
This tool makes it really easy to extend Python, as every dependency is automatically installed. If an extension needs to be compiled when you are under Windows, an extra step is needed for MinGW to be automatically called.
When a compilation is needed, a compiler can be indicated to Python with a configuration file. This has to be done explicitly under Windows. Create a new file called
distutils.cfg, in the
python-installation-path\lib\distutils folder (
Lib folder comes with a capital
L under Windows) with the following content:
[build] compiler = mingw32
This will link MinGW and Python, so that every time Python builds a package that has some C code inside, it will use MinGW transparently.
Taking time to set up the working environment is important for productivity. The time used to sharpen the tools is never wasted. It is a bad idea to force the usage of a specific set of tools on all developers when you lead a project. It is better to let each person take care of his or her desk as long as a common set of standards is adopted.
Working on a Python project means writing code, but it also means interacting with data files and third-party servers such as code repositories.
A developer spends most of his or her time doing something else on his computer, other than writing code.
There are two paths to set such an environment: either by building it with a composition of small tools (the old school way), or by using an all-in-one tool (the new school way). Of course, there are various blends between these, and every developer should build his or her environment the way he or she likes it.
This kind of environment is the longest one to prepare, but probably the most productive one. This is because you will be able to tweak it to make it fit with the way you are working. If you always use the same computer, it is easier to install and configure a set of chosen tools. But preparing a portable environment is even better. You can bundle it, for example, in a USB key and use it on any computer. It is also a good practice to use the same tools no matter what the platform is. This will help you in working efficiently anywhere.
Portable Python and similar projects:
Portable Python is a project that provides such a feature for Windows, by offering a ready-to-use embedded version of Python and a code editor. We will not create such an exhaustive environment if the target already has Python installed. But this project has an interesting approach and should be looked over. See http://www.portablepython.com.
Damn Small Linux (DSL) is also an interesting solution to embed a set of tools in a USB drive. It knows how to run a Linux embed into a system emulator called Qemu, which runs on any platform. So having a tweaked DSL with Python installed can provide the same features. See http://www.damnsmalllinux.org/usb-qemu.html.
Dragon technology provides a live Ubuntu system that can be used to build a portable Python environment. See http://www.dragontechnology.com/ubuntu_usb.php.
Starting from there, a working environment will be composed of:
A code editor that can be found on all platforms, preferably open-source and free
A few extra binaries that provide some features we do not want to rewrite in Python
Many editors are available that are compatible with Python. In a working environment composed of multiple tools, the best pick is an editor that is focused on editing the code and nothing else. That said, the boundary between a simple code editor and an Integrated Development Environment (IDE) will always be a bit fuzzy. Even simple editors provide ways to extend or interact with the system. But a well-configured code editor will not bother you with superfluous features.
For many years, the best choices in this area have been Vim ( http://www.vim.org) or Emacs ( http://www.gnu.org/software/emacs). They seem unfriendly at first because they have their own standards based on specific keyboard shortcuts, and it takes quite a while to get familiar with them. But when the commands are under control, they are the most productive tools a developer can have. They provide Python-specific modes, and know how to edit other files with a dedicated mode on each format.
Vim is a Python-friendly editor, and lately, the community has shown a lot of interest in it. It can be easily extended with Python. As an example, look up this Pycon 2007 talk: http://www.tummy.com/Community/Presentations/vimpython-20070225/vim.html.
A big advantage of Vim is that it has been installed on all Linux systems for years, so if you have to work on someone else's system or on a server, it will be available.
The next section presents Vim installation and configuration. If you are more likely to use Emacs, a good starting point is this page: http://www.python.org/emacs.
The latest version is 7.1 and comes with nice features such as a bundled code completer.
If you are under Linux, a version of Vim should already be installed, but probably a version older than 7.1. Check this with the
vim --version command. If your version is below 7.0, you should upgrade it either by using the package system of your distribution, or by compiling Vim.
On other systems, Vim needs to be installed. Windows users can get the self-installing executable that provides gvim (a version that comes with a graphical user interface) and also a console version. Mac OS X users need to compile the 7.1 version because binaries for the latest version are not currently available.
Get the right version from the download page here: http://www.vim.org/download.php, and compile if necessary.
If you need to compile Vim while working with multi-byte characters (such as accented letters in French), you need to call
configure with the
--enable-multibyte command. The compilation sequence will look like this:
./configure --enable-multibyte make sudo make install
This will install Vim in
/usr/local, and the binary will be available at:
The last thing to do is to create a
.vimrc file in your home directory if you are under Linux or Mac OS X, and a
_vimrc file under Windows. In this last case, you should save it in the installation folder, and add an environment variable called
VIM containing this path, so Vim will know where to get it.
vimrc file content is as follows:
set encoding=utf8 set paste set expandtab set textwidth=0 set tabstop=4 set softtabstop=4 set shiftwidth=4 set autoindent set backspace=indent,eol,start set incsearch set ignorecase set ruler set wildmenu set commentstring=\ #\ %s set foldlevel=0 set clipboard+=unnamed syntax on
For instance, the
tabstop option will transform a<Tab> stroke into four spaces.
Remember that the
:help command under Vim can be called on each option, to understand what it does.
:help ruler will display a help screen on the
If you cannot get used to Vim or Emacs and want a visual mode editor that interacts a little more with the mouse, you can pick another editor. But it should provide a Python mode and respect the following criteria:
Replacing the<Tab> keystroke by four spaces: This is the most important feature and is now handled correctly by most editors. If the editor you try does not have it, just drop it. Otherwise, you will end up with mixing the tab and spaces in your code, which is a mess for the compiler.
Removing the trailing spaces on save
Offering smart cursor placement on new lines, to speed up the writing
Providing a standard color-code highlighting
Offering simple code completion
There are a lot of other criteria that can be looked over to compare the code editors. Some are a bit unnecessary such as the code folding, whereas others are quite useful such as API searching. But having the Python interactive prompt, besides the editor, covers enough features to be efficient with the five criteria just mentioned.
To complete the editor, a few binaries can be installed to cover common needs:
diff, from GNU
diffutils, helps comparing the content of two folders or files. This program is available by default on all Linux distributions and Mac OS X. It has to be installed on Windows, and an installer can be found here: http://gnuwin32.sourceforge.net/packages/diffutils.htm. When it is installed, the diff command is available in the prompt.
grep provides a command-line utility to search for strings from files. It is more powerful than the system tools, and works in the same way on all platforms. It is available by default on Linux and Mac OS X. It has to be installed on Windows, and can be found here: http://gnuwin32.sourceforge.net/packages/grep.htm.
Notice that both grep Under Windows, these are available with MSYS.
A very good commercial alternative is Wingware IDE. See http://wingware.com.
Quality Assurance (QA) tools such as PyLint and Bicycle Repair Man
An integrated debugger
Eclipse is written in Java, so the first step is to install the Java Runtime Environment (JRE). If you are running Mac OS X, JRE is already installed. The latest version of JRE can be found on Sun's website at: http://java.sun.com/javase/downloads/index.jsp. Download the correct installer and follow the instructions to deploy it on your system.
Eclipse does not provide an installer, since it is just a folder with Java scripts. So its installation is just a matter of getting an archive and uncompressing it on the system. The plug-ins can then be added through the Eclipse interface with a neat package system. But it can be really painful to install the correct set of plug-ins as the latest Eclipse version might not be compatible with them.
Since the extra plug-ins can be bundled in an archive, the simplest way is to get a custom distribution of Eclipse. There are no specialized distributions for Python, but you can create them online on your own.
Yoxos provides this feature through an AJAX installer located at: http://ondemand.yoxos.com/geteclipse/W4TDelegate. This web page lets you pick the plug-ins you need and prepares a downloadable archive. Search PyDev for an Eclipse plug-in, and double-click on it in the plug-in list tree on the left. This will add it with all its dependencies. You can then click on the Download button on the top right corner to get your archive.
Uncompress the archive on your system, for example in
c:\Program Files\Eclipse under Windows, and in your home directory under Linux or Mac OS X. You will find a shortcut in this folder to launch the application. Eclipse will then be ready to use.
This chapter covered four points:
Python installation: Python comes in many flavors, but this book focuses on CPython. It can be installed on Linux, Mac OS X, and Windows, but can also be compiled. Using available binaries is simple, though.
setuptools installation: To complete Python-based installation, setuptools has to be deployed as well.
Prompt customization: Python comes with an interactive prompt that can be customized using a startup file. It plays an important role when writing code because small sequences of code can be tested in it.
Working environment: Lastly, to complete the prompt, the developers can use:
A classical code editor such as Vim or Emacs, or any other can be used as long as it provides a friendly mode for Python code. This editor has to be completed with a set of tools.
An Integrated Development Environment that integrates everything can be used. Eclipse with PyDev is the best pick at this time.
The next chapter covers the syntax best-practices below the class level.