Dive Into Python

25 March 2004

This book lives at http://diveintopython.org/. If you're reading it somewhere else, you may not have the latest version.

Permission is granted to copy, distribute, and/or modify this document under the terms of the GNU Free Documentation License, Version 1.1 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license is included in GNU Free Documentation License.

The example programs in this book are free software; you can redistribute and/or modify them under the terms of the Python license as published by the Python Software Foundation. A copy of the license is included in Python 2.1.1 license.

Table of Contents

Chapter 1. Installing Python

1.1. Which Python is right for you?

Welcome to Python. Let's dive in.

The first thing you need to do with Python is install it. Or do you? If you're using an account on a hosted server, your ISP may have already installed Python. Most popular Linux distributions come with Python in the default install. Mac OS X 10.2 and later includes a command-line version of Python, although you'll probably want to install a version that includes a more Mac-like graphical interface.

Windows does not come with any version of Python. But don't despair! There are several ways to point-and-click your way to Python on Windows.

As you can see already, Python runs on a great many operating systems. The full list includes Windows, Mac OS, Mac OS X, and all varieties of free UNIX-compatible systems like Linux. There are also versions that run on Sun Solaris, AS/400, Amiga, OS/2, BeOS, and a plethora of other platforms you've probably never even heard of.

What's more, Python programs written on one platform can, with a little care, run on any supported platform. For instance, I regularly develop Python programs on Windows and later deploy them on Linux.

So back to the question that started this section: “which Python is right for you?” The answer is “whichever one runs on the computer you already have”.

1.2. Python on Windows

On Windows, you have several choices for installing Python.

ActiveState makes a Windows installer for Python that includes a complete version of Python, an IDE with a Python-aware code editor, plus some Windows extensions for Python that allow complete access to Windows-specific services, APIs, and the Windows registry.

ActivePython is freely downloadable, although it is not open source. It is the IDE I used to learn Python, and I recommend you try it unless you have a specific reason not to. (One such reason might be that ActiveState is generally several months behind in updating their ActivePython installer when new version of Python are released. If you absolutely need to latest version of Python and ActivePython is still a version behind as you read this, you'll need to skip to option 2.)

Procedure 1.1. Option 1: Installing ActivePython

  1. Download ActivePython from http://www.activestate.com/Products/ActivePython/.

  2. If you are on Windows 95, Windows 98, or Windows ME, you will also need to download and install the Windows Installer 2.0 before installing ActivePython.

  3. Double-click the installer, ActivePython-2.2.2-224-win32-ix86.msi.

  4. Step through the installer.

  5. If space is tight, you can do a custom install and deselect the documentation, but I don't recommend this unless you absolutely can't spare the 14 megabytes.

  6. After installation is complete, close the installer and open Start->Programs->ActiveState ActivePython 2.2->PythonWin IDE.

Example 1.1. ActivePython IDE


PythonWin 2.2.2 (#37, Nov 26 2002, 10:24:37) [MSC 32 bit (Intel)] on win32.
Portions Copyright 1994-2001 Mark Hammond (mhammond@skippinet.com.au) -
see 'Help/About PythonWin' for further copyright information.
>>> 

The second option is the “officialPython installer, distributed by the people who develop Python itself. It is freely downloadable and open source, and it is always current with the latest version of Python.

Procedure 1.2. Option 2: Installing Python from Python.org

  1. Download the latest Python Windows installer from http://www.python.org/ftp/python/2.3.2/.

  2. Double-click the installer, Python-2.3.2.exe.

  3. Step through the installer.

  4. If disk space is tight, you can deselect the HTMLHelp file, the utility scripts (Tools/), and/or the test suite (Lib/test/).

  5. If you do not have administrative rights on your machine, you can select Advanced Options ... and select Non-Admin Install. This just affects where registry entries and start menu shortcuts are created.

  6. After installation is complete, close the installer and open Start->Programs->Python 2.3->IDLE (Python GUI).

Example 1.2. IDLE (Python GUI)

Python 2.3.2 (#49, Oct  2 2003, 20:02:00) [MSC v.1200 32 bit (Intel)] on win32
Type "copyright", "credits" or "license()" for more information.

    ****************************************************************
    Personal firewall software may warn about the connection IDLE
    makes to its subprocess using this computer's internal loopback
    interface.  This connection is not visible on any external
    interface and no data is sent to or received from the Internet.
    ****************************************************************
    
IDLE 1.0
>>> 

1.3. Python on Mac OS X

On Mac OS X, you have two choices for installing Python: install it, or don't. You probably want to install it.

Mac OS X 10.2 and later comes with a command-line version of Python pre-installed. If you are comfortable with the command line, you can use this version for the first third of the book. However, the pre-installed version does not come with an XML parser, so when you get to the XML chapter, you'll need to install the full version.

Procedure 1.3. Running the pre-installed version of Python on Mac OS X

  1. Open the /Applications folder.

  2. Open the Utilities folder.

  3. Double-click Terminal to open a terminal window and get to a command line.

  4. Type python at the command prompt.

Example 1.3. Using the pre-installed version of Python on Mac OS X

Welcome to Darwin!
[localhost:~] you% python
Python 2.2 (#1, 07/14/02, 23:25:09)
[GCC Apple cpp-precomp 6.14] on darwin
Type "help", "copyright", "credits", or "license" for more information.
>>> [press Ctrl+D to get back to the command prompt]
[localhost:~] you% 

However, you probably want to install the latest version, which also comes with a graphical interactive shell.

Procedure 1.4. Installing on Mac OS X

  1. Download the MacPython-OSX disk image from http://homepages.cwi.nl/~jack/macpython/download.html.

  2. If your browser has not already done so, double-click MacPython-OSX-2.3-1.dmg to mount the disk image on your desktop.

  3. Double-click the installer, MacPython-OSX.pkg.

  4. The installer will prompt you for your administrative username and password.

  5. Step through the installer.

  6. After installation is complete, close the installer and open the /Applications folder.

  7. Open the MacPython-2.3 folder

  8. Double-click PythonIDE to launch Python.

The MacPython IDE should display a splash screen, then take you to the interactive shell. If the interactive shell does not appear, select Window->Python Interactive (Cmd-0).

Example 1.4. The MacPython IDE on Mac OS X

Python 2.3 (#2, Jul 30 2003, 11:45:28)
[GCC 3.1 20020420 (prerelease)]
Type "copyright", "credits" or "license" for more information.
MacPython IDE 1.0.1
>>> 

Note that once you install the latest version, the pre-installed version is still present. If you are running scripts from the command line, you need to be aware which version of Python you are using.

Example 1.5. Two versions of Python

[localhost:~] you% python
Python 2.2 (#1, 07/14/02, 23:25:09)
[GCC Apple cpp-precomp 6.14] on darwin
Type "help", "copyright", "credits", or "license" for more information.
>>> [press Ctrl+D to get back to the command prompt]
[localhost:~] you% /usr/local/bin/python
Python 2.3 (#2, Jul 30 2003, 11:45:28)
[GCC 3.1 20020420 (prerelease)] on darwin
Type "help", "copyright", "credits", or "license" for more information.
>>> [press Ctrl+D to get back to the command prompt]
[localhost:~] you% 

1.4. Python on Mac OS 9

Mac OS 9 does not come with any version of Python, but installation is very simple, and there is only one choice.

Procedure 1.5. Installing on Mac OS 9

  1. Download the MacPython23full.bin file from http://homepages.cwi.nl/~jack/macpython/download.html.

  2. If your browser does not decompress the file automatically, double-click MacPython23full.bin to decompress the file with Stuffit Expander.

  3. Double-click the installer, MacPython23full.

  4. Step through the installer.

  5. AFter installation is complete, close the installer and open the /Applications folder.

  6. Open the MacPython-OS9 2.3 folder.

  7. Double-click Python IDE to launch Python.

The MacPython IDE should display a splash screen, then take you to the interactive shell. If the interactive shell does not appear, select Window->Python Interactive (Cmd-0).

Example 1.6. The MacPython IDE on Mac OS 9

Python 2.3 (#2, Jul 30 2003, 11:45:28)
[GCC 3.1 20020420 (prerelease)]
Type "copyright", "credits" or "license" for more information.
MacPython IDE 1.0.1
>>> 

1.5. Python on RedHat Linux

Installing under UNIX-compatible operating systems such as Linux is easy if you're willing to install a binary package. Pre-built binary packages are available for most popular Linux distributions. Or you can always compile from source.

To install under RedHat Linux, you need to download the RPM from http://www.python.org/ftp/python/2.3.2/rpms/ and install it with the rpm command.

Example 1.7. Installing on RedHat Linux 9

localhost:~$ su -
Password: [enter your root password]
[root@localhost root]# wget http://python.org/ftp/python/2.3/rpms/redhat-9/python2.3-2.3-5pydotorg.i386.rpm
Resolving python.org... done.
Connecting to python.org[194.109.137.226]:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 7,495,111 [application/octet-stream]
...
[root@localhost root]# rpm -Uvh python2.3-2.3-5pydotorg.i386.rpm
Preparing...                ########################################### [100%]
   1:python2.3              ########################################### [100%]
[root@localhost root]# python          1
Python 2.2.2 (#1, Feb 24 2003, 19:13:11)
[GCC 3.2.2 20030222 (Red Hat Linux 3.2.2-4)] on linux2
Type "help", "copyright", "credits", or "license" for more information.
>>> [press Ctrl+D to exit]
[root@localhost root]# python2.3       2
Python 2.3 (#1, Sep 12 2003, 10:53:56)
[GCC 3.2.2 20030222 (Red Hat Linux 3.2.2-5)] on linux2
Type "help", "copyright", "credits", or "license" for more information.
>>> [press Ctrl+D to exit]
[root@localhost root]# which python2.3 3
/usr/bin/python2.3
1 Whoops! Just typing python gives us the older version of Python, the one that was installed by default. That's not the one we wanted.
2 The newer version is called python2.3. You'll probably want to change the path on the first line of the example scripts to point to the newer version.
3 This is the complete path of the newer version of Python that we just installed. Use this on the #! line (the first line of each script) to ensure that scripts are running under the latest version of Python, and be sure to type python2.3 to get into the interactive shell.

1.6. Python on Debian GNU/Linux

If you are lucky enough to be running Debian GNU/Linux, the install is done through the apt command.

Example 1.8. Installing on Debian GNU/Linux

localhost:~$ su -
Password: [enter your root password]
localhost:~# apt-get install python
Reading Package Lists... Done
Building Dependency Tree... Done
The following extra packages will be installed:
  python2.3
Suggested packages:
  python-tk python2.3-doc
The following NEW packages will be installed:
  python python2.3
0 upgraded, 2 newly installed, 0 to remove and 3 not upgraded.
Need to get 0B/2880kB of archives.
After unpacking 9351kB of additional disk space will be used.
Do you want to continue? [Y/n] Y
Selecting previously deselected package python2.3.
(Reading database ... 22848 files and directories currently installed.)
Unpacking python2.3 (from .../python2.3_2.3.1-1_i386.deb) ...
Selecting previously deselected package python.
Unpacking python (from .../python_2.3.1-1_all.deb) ...
Setting up python (2.3.1-1) ...
Setting up python2.3 (2.3.1-1) ...
Compiling python modules in /usr/lib/python2.3 ...
Compiling optimized python modules in /usr/lib/python2.3 ...
localhost:~# exit
logout
localhost:~$ python
Python 2.3.1 (#2, Sep 24 2003, 11:39:14)
[GCC 3.3.2 20030908 (Debian prerelease)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> [press Ctrl+D to exit]

1.7. Installing from source

If you prefer to build from source, you can download the Python source code from http://www.python.org/ftp/python/2.3.2/ and do the usual configure, make, make install dance.

Example 1.9. Installing from source

localhost:~$ su -
Password: [enter your root password]
localhost:~# wget http://www.python.org/ftp/python/2.3/Python-2.3.tgz
Resolving www.python.org... done.
Connecting to www.python.org[194.109.137.226]:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 8,436,880 [application/x-tar]
...
localhost:~# tar xfz Python-2.3.tgz
localhost:~# cd Python-2.3
localhost:~/Python-2.3# ./configure
checking MACHDEP... linux2
checking EXTRAPLATDIR...
checking for --without-gcc... no
...
localhost:~/Python-2.3# make
gcc -pthread -c -fno-strict-aliasing -DNDEBUG -g -O3 -Wall -Wstrict-prototypes
-I. -I./Include  -DPy_BUILD_CORE -o Modules/python.o Modules/python.c
gcc -pthread -c -fno-strict-aliasing -DNDEBUG -g -O3 -Wall -Wstrict-prototypes
-I. -I./Include  -DPy_BUILD_CORE -o Parser/acceler.o Parser/acceler.c
gcc -pthread -c -fno-strict-aliasing -DNDEBUG -g -O3 -Wall -Wstrict-prototypes
-I. -I./Include  -DPy_BUILD_CORE -o Parser/grammar1.o Parser/grammar1.c
...
localhost:~/Python-2.3# make install
/usr/bin/install -c python /usr/local/bin/python2.3
...
localhost:~/Python-2.3# exit
logout
localhost:~$ which python
/usr/local/bin/python
localhost:~$ python
Python 2.3.1 (#2, Sep 24 2003, 11:39:14)
[GCC 3.3.2 20030908 (Debian prerelease)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> [press Ctrl+D to get back to the command prompt]
localhost:~$ 

1.8. The interactive shell

Now that we have Python installed, what's this interactive shell thing we're running?

It's like this: Python leads a double life. It is both an interpreter for scripts that you can run -- either from the command line, or by double-clicking on scripts and running them like applications. But it is also an interactive shell that can evaluate arbitrary statements and expressions. This is extremely useful for debugging, quick hacking, and testing. I even know some people who use the Python interactive shell in lieu of a calculator!

Launch the Python interactive shell in whatever way works on your platform, and let's dive in.

Example 1.10. First steps in the interactive shell

>>> 1 + 1               1
2
>>> print 'hello world' 2
hello world
>>> x = 1               3
>>> y = 2
>>> x + y
3
1 The Python interactive shell can evaluate arbitrary Python expressions, including any basic arithmetic expression.
2 The interactive shell can execute arbitrary Python statements, including the print statement.
3 You can also assign values to variables, and the values will be remembered as long as the shell is open (but not any longer than that).

1.9. Summary

You should now have a version of Python installed that works for you.

Depending on your platform, you may have more than one. If so, you need to be aware of your paths. If simply typing python on the command line doesn't run the version of Python that you want to use, you may need to enter the full pathname of your preferred version.

Other than that, congratulations, and welcome to Python.

Chapter 2. Your first Python program

2.1. Diving in

Here is a complete, working Python program.

It probably makes absolutely no sense to you. Don't worry about that; we're going to dissect it line by line. But read through it first and see what, if anything, you can make of it.

Example 2.1. odbchelper.py

If you have not already done so, you can download this and other examples used in this book.

def buildConnectionString(params):
    """Build a connection string from a dictionary of parameters.

    Returns string."""
    return ";".join(["%s=%s" % (k, v) for k, v in params.items()])

if __name__ == "__main__":
    myParams = {"server":"mpilgrim", \
                "database":"master", \
                "uid":"sa", \
                "pwd":"secret" \
                }
    print buildConnectionString(myParams)

Now run this program and see what happens.

Tip
In the ActivePython IDE on Windows, you can run a module with File->Run... (Ctrl-R). Output is displayed in the interactive window.
Tip
In the Python IDE on Mac OS, you can run a module with Python->Run window... (Cmd-R), but there is an important option you must set first. Open the module in the IDE, pop up the module's options menu by clicking the black triangle in the upper-right corner of the window, and make sure “Run as __main__” is checked. This setting is saved with the module, so you only have to do this once per module.
Tip
On UNIX-compatible systems (including Mac OS X), you can run a module from the command line: python odbchelper.py

Example 2.2. Output of odbchelper.py

server=mpilgrim;uid=sa;database=master;pwd=secret

2.2. Declaring functions

Python has functions like most other languages, but it does not have separate header files like C++ or interface/implementation sections like Pascal. When you need a function, just declare it and code it.

Example 2.3. Declaring the buildConnectionString function

def buildConnectionString(params):

Several things to note here. First, the keyword def starts the function declaration, followed by the function name, followed by the arguments in parentheses. Multiple arguments (not shown here) are separated with commas.

Second, the function doesn't define a return datatype. Python functions do not specify the datatype of their return value; they don't even specify whether they return a value or not. In fact, every Python function returns a value; if the function ever executes a return statement, it will return that value, otherwise it will return None, the Python null value.

Note
In Visual Basic, functions (that return a value) start with function, and subroutines (that do not return a value) start with sub. There are no subroutines in Python. Everything is a function, all functions return a value (even if it's None), and all functions start with def.

Third, the argument, params, doesn't specify a datatype. In Python, variables are never explicitly typed. Python figures out what type a variable is and keeps track of it internally.

Note
In Java, C++, and other statically-typed languages, you must specify the datatype of the function return value and each function argument. In Python, you never explicitly specify the datatype of anything. Based on what value you assign, Python keeps track of the datatype internally.

Addendum. An erudite reader sent me this explanation of how Python compares to other programming languages:

statically typed language
A language in which types are fixed at compile time. Most statically typed languages enforce this by requiring you to declare all variables with their datatypes before using them. Java and C are statically typed languages.
dynamically typed language
A language in which types are discovered at execution time; the opposite of statically typed. VBScript and Python are dynamically typed, because they figure out what type a variable is when you first assign it a value.
strongly typed language
A language in which types are always enforced. Java and Python are strongly typed. If you have an integer, you can't treat it like a string without explicitly converting it (more on how to do this later in this chapter).
weakly typed language
A language in which types may be ignored; the opposite of strongly typed. VBScript is weakly typed. In VBScript, you can concatenate the string '12' and the integer 3 to get the string '123', then treat that as the integer 123, all without any explicit conversion.

So Python is both dynamically typed (because it doesn't use explicit datatype declarations) and strongly typed (because once a variable has a datatype, it actually matters).

2.3. Documenting functions

You can document a Python function by giving it a doc string.

Example 2.4. Defining the buildConnectionString function's doc string

def buildConnectionString(params):
    """Build a connection string from a dictionary of parameters.

    Returns string."""

Triple quotes signify a multi-line string. Everything between the start and end quotes is part of a single string, including carriage returns and other quote characters. You can use them anywhere, but you'll see them most often used when defining a doc string.

Note
Triple quotes are also an easy way to define a string with both single and double quotes, like qq/.../ in Perl.

Everything between the triple quotes is the function's doc string, which documents what the function does. A doc string, if it exists, must be the first thing defined in a function (i.e. the first thing after the colon). You don't technically have to give your function a doc string, but you always should. I know you've heard this in every programming class you've ever taken, but Python gives you an added incentive: the doc string is available at runtime as an attribute of the function.

Note
Many Python IDEs use the doc string to provide context-sensitive documentation, so that when you type a function name, its doc string appears as a tooltip. This can be incredibly helpful, but it's only as good as the doc strings you write.

Further reading

2.4. Everything is an object

In case you missed it, I just said that Python functions have attributes, and that those attributes are available at runtime.

A function, like everything else in Python, is an object.

Example 2.5. Accessing the buildConnectionString function's doc string

>>> import odbchelper                              1
>>> params = {"server":"mpilgrim", "database":"master", "uid":"sa", "pwd":"secret"}
>>> print odbchelper.buildConnectionString(params) 2
server=mpilgrim;uid=sa;database=master;pwd=secret
>>> print odbchelper.buildConnectionString.__doc__ 3
Build a connection string from a dictionary

Returns string.
1 The first line imports the odbchelper program as a module. Once you import a module, you can reference any of its public functions, classes, or attributes. Modules can do this to access functionality in other modules, and you can do it in the IDE too. This is an important concept, and we'll talk more about it later.
2 When you want to use functions defined in imported modules, you have to include the module name. So you can't just say buildConnectionString, it has to be odbchelper.buildConnectionString. If you've used classes in Java, this should feel vaguely familiar.
3 Instead of calling the function like you would expect to, we asked for one of the function's attributes, __doc__.
Note
import in Python is like require in Perl. Once you import a Python module, you access its functions with module.function; once you require a Perl module, you access its functions with module::function.

Before we go any further, I want to briefly mention the library search path. Python looks in several places when you try to import a module. Specifically, it looks in all the directories defined in sys.path. This is just a list, and you can easily view it or modify it with standard list methods. (We'll learn more about lists later in this chapter.)

Example 2.6. Import search path

>>> import sys                 1
>>> sys.path                   2
['', '/usr/local/lib/python2.2', '/usr/local/lib/python2.2/plat-linux2', 
'/usr/local/lib/python2.2/lib-dynload', '/usr/local/lib/python2.2/site-packages', 
'/usr/local/lib/python2.2/site-packages/PIL', '/usr/local/lib/python2.2/site-packages/piddle']
>>> sys                        3
<module 'sys' (built-in)>
>>> sys.path.append('/my/new/path') 4
1 Importing the sys module makes all of its functions and attributes available.
2 sys.path is a list of directory names that constitute the current search path. (Yours will look different, depending on your operating system, what version of Python you're running, and where it was originally installed.) Python will look through these directories (in this order) for a .py file matching the module name you're trying to import.
3 Actually, I lied; the truth is more complicated than that, because not all modules are stored as .py files. Some, like the sys module, are "built-in modules"; they are actually baked right into Python itself. Built-in modules behave just like regular modules, but their Python source code is not available, because they are not written in Python! (The sys module is written in C.)
4 You can add a new directory to Python's search path at runtime by appending the directory name to sys.path, and then Python will look in that directory as well, whenever you try to import a module. The effect lasts as long as Python is running. (We'll talk more about append and other list methods later in this chapter.)

Everything in Python is an object, and almost everything has attributes and methods.[1] All functions have a built-in attribute __doc__, which returns the doc string defined in the function's source code. The sys module is an object which has (among other things) an attribute called path. And so forth.

This is so important that I'm going to repeat it in case you missed it the first few times: everything in Python is an object. Strings are objects. Lists are objects. Functions are objects. Even modules are objects.

Further reading

2.5. Indenting code

Python functions have no explicit begin or end, no curly braces that would mark where the function code starts and stops. The only delimiter is a colon (“:”) and the indentation of the code itself.

Example 2.7. Indenting the buildConnectionString function

def buildConnectionString(params):
    """Build a connection string from a dictionary of parameters.

    Returns string."""
    return ";".join(["%s=%s" % (k, v) for k, v in params.items()])

Code blocks are defined by their indentation. By "code block", I mean functions, if statements, for loops, while loops, and so forth. Indenting starts a block and unindenting ends it. There are no explicit braces, brackets, or keywords. This means that whitespace is significant, and must be consistent. In this example, the function code (including the doc string) is indented 4 spaces. It doesn't have to be 4, it just has to be consistent. The first line that is not indented is outside the function.

Example 2.8. if statements

def fib(n):                   1
    print 'n =', n            2
    if n > 1:                 3
        return n * fib(n - 1)
    else:                     4
        print 'end of the line'
        return 1
1 This is a function named fib that takes one argument, n. All the code within the function is indented.
2 Printing out things to the screen is very easy in Python, just use print. print statements can take any data type, including strings, integers, and other native types like dictionaries and lists that we'll learn about in the next chapter. You can even mix and match and print several things on one line by using a comma-separated list of value. Each value is printed out on the same line, separated by spaces (the commas don't print). So when fib is called with 5, this will print "n = 5".
3 if statements are a type of code block. If the if expression evaluates to true, the indented block is executed, otherwise it falls to the else block.
4 Of course if and else blocks can contain multiple lines, as long as they are all indented the same. This else block has two lines of code in it. There is no other special syntax for multi-line code blocks; just indent and get on with your life.

After some initial protests and several snide analogies to Fortran, you will make peace with this and start seeing its benefits. One major benefit is that all Python programs look similar, since indentation is a language requirement and not a matter of style. This makes it easier to read and understand other people's Python code.

Note
Python uses carriage returns to separate statements and a colon and indentation to separate code blocks. C++ and Java use semicolons to separate statements and curly braces to separate code blocks.

Further reading

2.6. Testing modules

Python modules are objects and have several useful attributes. You can use this to easily test your modules as you write them.

Example 2.9. The if __name__ trick

if __name__ == "__main__":

Some quick observations before we get to the good stuff. First, parentheses are not required around the if expression. Second, the if statement ends with a colon, and is followed by indented code.

Note
Like C, Python uses == for comparison and = for assignment. Unlike C, Python does not support in-line assignment, so there's no chance of accidentally assigning the value you thought you were comparing.

So why is this particular if statement a trick? Modules are objects, and all modules have a built-in attribute __name__. A module's __name__ depends on how you're using the module. If you import the module, then __name__ is the module's filename, without directory path or file extension. But you can also run the module directly as a standalone program, in which case __name__ will be a special default value, __main__.

Example 2.10. An imported module's __name__

>>> import odbchelper
>>> odbchelper.__name__
'odbchelper'

Knowing this, you can design a test suite for your module within the module itself by putting it in this if statement. When you run the module directly, __name__ is __main__, so the test suite executes. When you import the module, __name__ is something else, so the test suite is ignored. This makes it easier to develop and debug new modules before integrating them into a larger program.

Tip
On MacPython, there is an additional step to make the if __name__ trick work. Pop up the module's options menu by clicking the black triangle in the upper-right corner of the window, and make sure Run as __main__ is checked.

Further reading



[1] Different programming languages define “object” in different ways. In some, it means that all objects must have attributes and methods; in others, it means that all objects are subclassable. In Python, the definition is looser; some objects have neither attributes nor methods (more on this later in this chapter), and not all objects are subclassable (more on this in chapter 3). But everything is an object in the sense that it can be assigned to a variable or passed as an argument to a function (more in this in chapter 2).

Chapter 3. Native data types

3.1. Introducing dictionaries

We'll get back to your first Python program in just a minute. But first, a short digression is in order, because you need to know about dictionaries, tuples, and lists (oh my!). If you're a Perl hacker, you can probably skim the bits about dictionaries and lists, but you should still pay attention to tuples.

One of Python's built-in datatypes is the dictionary, which defines one-to-one relationships between keys and values.

Note
A dictionary in Python is like a hash in Perl. In Perl, variables which store hashes always start with a % character; in Python, variables can be named anything, and Python keeps track of the datatype internally.
Note
A dictionary in Python is like an instance of the Hashtable class in Java.
Note
A dictionary in Python is like an instance of the Scripting.Dictionary object in Visual Basic.

Example 3.1. Defining a dictionary

>>> d = {"server":"mpilgrim", "database":"master"} 1
>>> d
{'server': 'mpilgrim', 'database': 'master'}
>>> d["server"]                                    2
'mpilgrim'
>>> d["database"]                                  3
'master'
>>> d["mpilgrim"]                                  4
Traceback (innermost last):
  File "<interactive input>", line 1, in ?
KeyError: mpilgrim
1 First, we create a new dictionary with two elements and assign it to the variable d. Each element is a key-value pair, and the whole set of elements is enclosed in curly braces.
2 'server' is a key, and its associated value, referenced by d["server"], is 'mpilgrim'.
3 'database' is a key, and its associated value, referenced by d["database"], is 'master'.
4 You can get values by key, but you can't get keys by value. So d["server"] is 'mpilgrim', but d["mpilgrim"] raises an exception, because 'mpilgrim' is not a key.

Example 3.2. Modifying a dictionary

>>> d
{'server': 'mpilgrim', 'database': 'master'}
>>> d["database"] = "pubs" 1
>>> d
{'server': 'mpilgrim', 'database': 'pubs'}
>>> d["uid"] = "sa"        2
>>> d
{'server': 'mpilgrim', 'uid': 'sa', 'database': 'pubs'}
1 You can not have duplicate keys in a dictionary. Assigning a value to an existing key will wipe out the old value.
2 You can add new key-value pairs at any time. This syntax is identical to modifying existing values. (Yes, this will annoy you someday when you think you are adding new values but are actually just modifying the same value over and over because your key isn't changing the way you think it is.)

Note that the new element (key 'uid', value 'sa') appears to be in the middle. In fact, it was just a coincidence that the elements appeared to be in order in the first example; it is just as much a coincidence that they appear to be out of order now.

Note
Dictionaries have no concept of order among elements. It is incorrect to say that the elements are “out of order”; they are simply unordered. This is an important distinction which will annoy you when you want to access the elements of a dictionary in a specific, repeatable order (like alphabetical order by key). There are ways of doing this, they're just not built into the dictionary.

Example 3.3. Mixing datatypes in a dictionary

>>> d
{'server': 'mpilgrim', 'uid': 'sa', 'database': 'pubs'}
>>> d["retrycount"] = 3 1
>>> d
{'server': 'mpilgrim', 'uid': 'sa', 'database': 'master', 'retrycount': 3}
>>> d[42] = "douglas"   2
>>> d
{'server': 'mpilgrim', 'uid': 'sa', 'database': 'master', 42: 'douglas', 'retrycount': 3}
1 Dictionaries aren't just for strings. Dictionary values can be any datatype, including strings, integers, objects, or even other dictionaries. And within a single dictionary, the values don't all have to be the same type; you can mix and match as needed.
2 Dictionary keys are more restricted, but they can be strings, integers, and a few other types (more on this later). You can also mix and match key datatypes within a dictionary.

Example 3.4. Deleting items from a dictionary

>>> d
{'server': 'mpilgrim', 'uid': 'sa', 'database': 'master', 42: 'douglas', 'retrycount': 3}
>>> del d[42] 1
>>> d
{'server': 'mpilgrim', 'uid': 'sa', 'database': 'master', 'retrycount': 3}
>>> d.clear() 2
>>> d
{}
1 del lets you delete individual items from a dictionary by key.
2 clear deletes all items from a dictionary. Note that the set of empty curly braces signifies a dictionary with no items.

Example 3.5. Strings are case-sensitive

>>> d = {}
>>> d["key"] = "value"
>>> d["key"] = "other value" 1
>>> d
{'key': 'other value'}
>>> d["Key"] = "third value" 2
>>> d
{'Key': 'third value', 'key': 'other value'}
1 Assigning a value to an existing dictionary key simply replaces the old value with a new one.
2 This is not assigning a value to an existing dictionary key, because strings in Python are case-sensitive, so 'key' is not the same as 'Key'. This creates a new key/value pair in the dictionary; it may look similar to you, but as far as Python is concerned, it's completely different.

3.2. Introducing lists

Lists are Python's workhorse datatype. If your only experience with lists is arrays in Visual Basic or (God forbid) the datastore in Powerbuilder, brace yourself for Python lists.

Note
A list in Python is like an array in Perl. In Perl, variables which store arrays always start with the @ character; in Python, variables can be named anything, and Python keeps track of the datatype internally.
Note
A list in Python is much more than an array in Java (although it can be used as one if that's really all you want out of life). A better analogy would be to the ArrayList class, which can hold arbitrary objects and can expand dynamically as new items are added.

Example 3.6. Defining a list

>>> li = ["a", "b", "mpilgrim", "z", "example"] 1
>>> li
['a', 'b', 'mpilgrim', 'z', 'example']
>>> li[0]                                       2
'a'
>>> li[4]                                       3
'example'
1 First, we define a list of 5 elements. Note that they retain their original order. This is not an accident. A list is an ordered set of elements enclosed in square brackets.
2 A list can be used like a zero-based array. The first element of any non-empty list is always li[0].
3 The last element of this 5-element list is li[4], because lists are always zero-based.

Example 3.7. Negative list indices

>>> li
['a', 'b', 'mpilgrim', 'z', 'example']
>>> li[-1] 1
'example'
>>> li[-3] 2
'mpilgrim'
1 A negative index accesses elements from the end of the list counting backwards. The last element of any non-empty list is always li[-1].
2 If negative indices are confusing to you, think of it this way: li[-n] == li[len(li) - n]. So in this list, li[-3] == li[5 - 3] == li[2].

Example 3.8. Slicing a list

>>> li
['a', 'b', 'mpilgrim', 'z', 'example']
>>> li[1:3]  1
['b', 'mpilgrim']
>>> li[1:-1] 2
['b', 'mpilgrim', 'z']
>>> li[0:3]  3
['a', 'b', 'mpilgrim']
1 You can get a subset of a list, called a “slice”, by specifying 2 indices. The return value is a new list containing all the elements of the list, in order, starting with the first slice index (in this case li[1]), up to but not including the second slice index (in this case li[3]).
2 Slicing works if one or both of the slice indices is negative. If it helps, you can think of it this way: reading the list from left to right, the first slice index specifies the first element you want, and the second slice index specifies the first element you don't want. The return value is everything in between.
3 Lists are zero-based, so li[0:3] returns the first three elements of the list, starting at li[0], up to but not including li[3].

Example 3.9. Slicing shorthand

>>> li
['a', 'b', 'mpilgrim', 'z', 'example']
>>> li[:3] 1
['a', 'b', 'mpilgrim']
>>> li[3:] 2 3
['z', 'example']
>>> li[:]  4
['a', 'b', 'mpilgrim', 'z', 'example']
1 If the left slice index is 0, you can leave it out, and 0 is implied. So li[:3] is the same as li[0:3] from the previous example.
2 Similarly, if the right slice index is the length of the list, you can leave it out. So li[3:] is the same as li[3:5], because this list has 5 elements.
3 Note the symmetry here. In this 5-element list, li[:3] returns the first 3 elements, and li[3:] returns the last 2 elements. In fact, li[:n] will always return the first n elements, and li[n:] will return the rest, regardless of the length of the list.
4 If both slice indices are left out, all elements of the list are included. But this is not the same as the original li list; it is a new list that happens to have all the same elements. li[:] is a shorthand for making a complete copy of a list.

Example 3.10. Adding elements to a list

>>> li
['a', 'b', 'mpilgrim', 'z', 'example']
>>> li.append("new")               1
>>> li
['a', 'b', 'mpilgrim', 'z', 'example', 'new']
>>> li.insert(2, "new")            2
>>> li
['a', 'b', 'new', 'mpilgrim', 'z', 'example', 'new']
>>> li.extend(["two", "elements"]) 3
>>> li
['a', 'b', 'new', 'mpilgrim', 'z', 'example', 'new', 'two', 'elements']
1 append adds a single element to the end of the list.
2 insert inserts a single element into a list. The numeric argument is the index of the first element that gets bumped out of position. Note that list elements do not have to be unique; there are now 2 separate elements with the value 'new', li[2] and li[6].
3 extend concatenates lists. Note that you do not call extend with multiple arguments; you call it with one argument, a list. In this case, that list has two elements.

Example 3.11. Searching a list

>>> li
['a', 'b', 'new', 'mpilgrim', 'z', 'example', 'new', 'two', 'elements']
>>> li.index("example") 1
5
>>> li.index("new")     2
2
>>> li.index("c")       3
Traceback (innermost last):
  File "<interactive input>", line 1, in ?
ValueError: list.index(x): x not in list
>>> "c" in li           4
0
1 index finds the first occurrence of a value in the list and returns the index.
2 index finds the first occurrence of a value in the list. In this case, 'new' occurs twice in the list, in li[2] and li[6], but index will only return the first index, 2.
3 If the value is not found in the list, Python raises an exception. This is notably different from most languages, which will return some invalid index. While this may seem annoying, it is a Good Thing, because it means your program will crash at the source of the problem, rather than later on when you try to use the invalid index.
4 To test whether a value is in the list, use in, which returns 1 if the value is found or 0 if it is not.
Note
Before version 2.2.1, Python had no separate boolean datatype. To compensate for this, Python accepted almost anything in a boolean context (like an if statement), according to the following rules: 0 is false; all other numbers are true. An empty string ("") is false, all other strings are true. An empty list ([]) is false; all other lists are true. An empty tuple (()) is false; all other tuples are true. An empty dictionary ({}) is false; all other dictionaries are true. These rules still apply in Python 2.2.1 and beyond, but now you can also use an actual boolean, which has a value of True or False. Note the capitalization; these values, like everything else in Python, are case-sensitive.

Example 3.12. Removing elements from a list

>>> li
['a', 'b', 'new', 'mpilgrim', 'z', 'example', 'new', 'two', 'elements']
>>> li.remove("z")   1
>>> li
['a', 'b', 'new', 'mpilgrim', 'example', 'new', 'two', 'elements']
>>> li.remove("new") 2
>>> li
['a', 'b', 'mpilgrim', 'example', 'new', 'two', 'elements']
>>> li.remove("c")   3
Traceback (innermost last):
  File "<interactive input>", line 1, in ?
ValueError: list.remove(x): x not in list
>>> li.pop()         4
'elements'
>>> li
['a', 'b', 'mpilgrim', 'example', 'new', 'two']
1 remove removes the first occurrence of a value from a list.
2 remove removes only the first occurrence of a value. In this case, 'new' appeared twice in the list, but li.remove("new") only removed the first occurrence.
3 If the value is not found in the list, Python raises an exception. This mirrors the behavior of the index method.
4 pop is an interesting beast. It does two things: it removes the last element of the list, and it returns the value that it removed. Note that this is different from li[-1], which returns a value but does not change the list, and different from li.remove(value), which changes the list but does not return a value.

Example 3.13. List operators

>>> li = ['a', 'b', 'mpilgrim']
>>> li = li + ['example', 'new'] 1
>>> li
['a', 'b', 'mpilgrim', 'example', 'new']
>>> li += ['two']                2
>>> li
['a', 'b', 'mpilgrim', 'example', 'new', 'two']
>>> li = [1, 2] * 3              3
>>> li
[1, 2, 1, 2, 1, 2]
1 Lists can also be concatenated with the + operator. list = list + otherlist has the same result as list.extend(otherlist). But the + operator returns a new (concatenated) list as a value, whereas extend only alters an existing list. This means that extend is faster, especially for large lists.
2 Python supports the += operator. li += ['two'] is equivalent to li.extend(['two']). The += operator works for lists, strings, and integers, and it can be overloaded to work for user-defined classes as well. (More on classes in chapter 3.)
3 The * operator works on lists as a repeater. li = [1, 2] * 3 is equivalent to li = [1, 2] + [1, 2] + [1, 2], which concatenates the three lists into one.

3.3. Introducing tuples

A tuple is an immutable list. A tuple can not be changed in any way once it is created.

Example 3.14. Defining a tuple

>>> t = ("a", "b", "mpilgrim", "z", "example") 1
>>> t
('a', 'b', 'mpilgrim', 'z', 'example')
>>> t[0]                                       2
'a'
>>> t[-1]                                      3
'example'
>>> t[1:3]                                     4
('b', 'mpilgrim')
1 A tuple is defined in the same way as a list, except that the whole set of elements is enclosed in parentheses instead of square brackets.
2 The elements of a tuple have a defined order, just like a list. Tuples indices are zero-based, just like a list, so the first element of a non-empty tuple is always t[0].
3 Negative indices count from the end of the tuple, just like a list.
4 Slicing works too, just like a list. Note that when you slice a list, you get a new list; when you slice a tuple, you get a new tuple.

Example 3.15. Tuples have no methods

>>> t
('a', 'b', 'mpilgrim', 'z', 'example')
>>> t.append("new")    1
Traceback (innermost last):
  File "<interactive input>", line 1, in ?
AttributeError: 'tuple' object has no attribute 'append'
>>> t.remove("z")      2
Traceback (innermost last):
  File "<interactive input>", line 1, in ?
AttributeError: 'tuple' object has no attribute 'remove'
>>> t.index("example") 3
Traceback (innermost last):
  File "<interactive input>", line 1, in ?
AttributeError: 'tuple' object has no attribute 'index'
>>> "z" in t           4
1
1 You can't add elements to a tuple. Tuples have no append or extend method.
2 You can't remove elements from a tuple. Tuples have no remove or pop method.
3 You can't find elements in a tuple. Tuples have no index method.
4 You can, however, use in to see if an element exists in the tuple.

So what are tuples good for?

  • Tuples are faster than lists. If you're defining a constant set of values and all you're ever going to do with it is iterate through it, use a tuple instead of a list.
  • It makes your code safer if you “write-protect” data that does not need to be changed. Using a tuple instead of a list is like having an implied assert statement that this data is constant, and that special thought (and a specific function) is required to override that.
  • Remember I said that dictionary keys can be integers, strings, and “a few other types”? Tuples are one of those types. Tuples can be used as keys in a dictionary, but lists can't.[2]
  • Tuples are used in string formatting, as we'll see shortly.
Note
Tuples can be converted into lists, and vice-versa. The built-in tuple function takes a list and returns a tuple with the same elements, and the list function takes a tuple and returns a list. In effect, tuple freezes a list, and list thaws a tuple.

Further reading

3.4. Declaring variables

Now that you think you know everything about dictionaries, tuples, and lists (oh my!), let's get back to our example program, odbchelper.py.

Python has local and global variables like most other languages, but it has no explicit variable declarations. Variables spring into existence by being assigned a value, and are automatically destroyed when they go out of scope.

Example 3.16. Defining the myParams variable

if __name__ == "__main__":
    myParams = {"server":"mpilgrim", \
                "database":"master", \
                "uid":"sa", \
                "pwd":"secret" \
                }

Several points of interest here. First, note the indentation. An if statement is a code block and needs to be indented just like a function.

Second, the variable assignment is one command split over several lines, with a backslash (“\”) serving as a line continuation marker.

Note
When a command is split among several lines with the line continuation marker (“\”), the continued lines can be indented in any manner; Python's normally stringent indentation rules do not apply. If your Python IDE auto-indents the continued line, you should probably accept its default unless you have a burning reason not to.
Note
Strictly speaking, expressions in parentheses, straight brackets, or curly braces (like defining a dictionary) can be split into multiple lines with or without the line continuation character (“\”). I like to include the backslash even when it's not required because I think it makes the code easier to read, but that's a matter of style.

Third, you never declared the variable myParams, you just assigned a value to it. This is like VBScript without the option explicit option. Luckily, unlike VBScript, Python will not allow you to reference a variable that has never been assigned a value; trying to do so will raise an exception.

Example 3.17. Referencing an unbound variable

>>> x
Traceback (innermost last):
  File "<interactive input>", line 1, in ?
NameError: There is no variable named 'x'
>>> x = 1
>>> x
1

You will thank Python for this one day.

3.5. Assigning multiple values at once

One of the cooler programming shortcuts in Python is using sequences to assign multiple values at once.

Example 3.18. Assigning multiple values at once

>>> v = ('a', 'b', 'e')
>>> (x, y, z) = v 1
>>> x
'a'
>>> y
'b'
>>> z
'e'
1 v is a tuple of three elements, and (x, y, z) is a tuple of three variables. Assigning one to the other assigns each of the values of v to each of the variables, in order.

This has all sorts of uses. I often want to assign names to a range of values. In C, you would use enum and manually list each constant and its associated value, which seems especially tedious when the values are consecutive. In Python, you can use the built-in range function with multi-variable assignment to quickly assign consecutive values.

Example 3.19. Assigning consecutive values

>>> range(7)                                                                    1
[0, 1, 2, 3, 4, 5, 6]
>>> (MONDAY, TUESDAY, WEDNESDAY, THURSDAY, FRIDAY, SATURDAY, SUNDAY) = range(7) 2
>>> MONDAY                                                                      3
0
>>> TUESDAY
1
>>> SUNDAY
6
1 The built-in range function returns a list of integers. In its simplest form, it takes an upper limit and returns a 0-based list counting up to but not including the upper limit. (If you like, you can pass other parameters to specify a base other than 0 and a step other than 1. You can print range.__doc__ for details.)
2 MONDAY, TUESDAY, WEDNESDAY, THURSDAY, FRIDAY, SATURDAY, and SUNDAY are the variables we're defining. (This example came from the calendar module, a fun little module which prints calendars, like the UNIX program cal. The calendar module defines integer constants for days of the week.)
3 Now each variable has its value: MONDAY is 0, TUESDAY is 1, and so forth.

You can also use multi-variable assignment to build functions that return multiple values, simply by returning a tuple of all the values. The caller can treat it as a tuple, or assign the values to individual variables. Many standard Python libraries do this, including the os module, which we'll discuss in chapter 3.

Further reading

3.6. Formatting strings

Python supports formatting values into strings. Although this can include very complicated expressions, the most basic usage is to insert values into a string with the %s placeholder.

Note
String formatting in Python uses the same syntax as the sprintf function in C.

Example 3.20. Introducing string formatting

>>> k = "uid"
>>> v = "sa"
>>> "%s=%s" % (k, v) 1
'uid=sa'
1 The whole expression evaluates to a string. The first %s is replaced by the value of k; the second %s is replaced by the value of v. All other characters in the string (in this case, the equals sign) stay as they are.

Note that (k, v) is a tuple. I told you they were good for something.

You might be thinking that this is a lot of work just to do simple string concatentation, and you'd be right, except that string formatting isn't just concatenation. It's not even just formatting. It's also type coercion.

Example 3.21. String formatting vs. concatenating

>>> uid = "sa"
>>> pwd = "secret"
>>> print pwd + " is not a good password for " + uid      1
secret is not a good password for sa
>>> print "%s is not a good password for %s" % (pwd, uid) 2
secret is not a good password for sa
>>> userCount = 6
>>> print "Users connected: %d" % (userCount, )           3 4
Users connected: 6
>>> print "Users connected: " + userCount                 5
Traceback (innermost last):
  File "<interactive input>", line 1, in ?
TypeError: cannot add type "int" to string
1 + is the string concatenation operator.
2 In this trivial case, string formatting accomplishes the same result as concatentation.
3 (userCount, ) is a tuple with one element. Yes, the syntax is a little strange, but there's a good reason for it: it's unambiguously a tuple. In fact, you can always include a comma after the last element when defining a list, tuple, or dictionary, but the comma is required when defining a tuple with one element. If the comma weren't required, Python wouldn't know whether (userCount) was a tuple with one element or just the value of userCount.
4 String formatting works with integers by specifying %d instead of %s.
5 Trying to concatenate a string with a non-string raises an exception. Unlike string formatting, string concatenation only works when everything is already a string.

Like printf in C, string formatting in Python is like a Swiss Army knife. There are options galore, and modifier strings to specially format many different types of values.

Example 3.22. Formatting numbers

>>> print "Today's stock price: %f" % 50.4625   1
50.462500
>>> print "Today's stock price: %.2f" % 50.4625 2
50.46
>>> print "Change since yesterday: %+.2f" % 1.5 3
+1.50
1 The %f string formatting option treats the value as a decimal, and prints it to six decimal places.
2 The ".2" modifier of the %f option truncates the value to 2 decimal places.
3 You can even combine modifiers. Adding the "+" modifier displays a plus or minus sign before the value. Note that the ".2" modifier is still in place, and is padding the value to exactly 2 decimal places.

3.7. Mapping lists

One of the most powerful features of Python is the list comprehension, which provides a compact way of mapping a list into another list by applying a function to each of the elements of the list.

Example 3.23. Introducing list comprehensions

>>> li = [1, 9, 8, 4]
>>> [elem*2 for elem in li]      1
[2, 18, 16, 8]
>>> li                           2
[1, 9, 8, 4]
>>> li = [elem*2 for elem in li] 3
>>> li
[2, 18, 16, 8]
1 To make sense of this, look at it from right to left. li is the list you're mapping. Python loops through li one element at a time, temporarily assigning the value of each element to the variable elem. Python then applies the function elem*2 and appends that result to the returned list.
2 Note that list comprehensions do not change the original list.
3 It is safe to assign the result of a list comprehension to the variable that you're mapping. There are no racing conditions or any weirdness to worry about; Python constructs the new list in memory, and when the list comprehension is complete, it assigns the result to the variable.

Example 3.24. List comprehensions in buildConnectionString

["%s=%s" % (k, v) for k, v in params.items()]

First, notice that you're calling the items function of the params dictionary. This function returns a list of tuples of all the data in the dictionary.

Example 3.25. keys, values, and items

>>> params = {"server":"mpilgrim", "database":"master", "uid":"sa", "pwd":"secret"}
>>> params.keys()   1
['server', 'uid', 'database', 'pwd']
>>> params.values() 2
['mpilgrim', 'sa', 'master', 'secret']
>>> params.items()  3
[('server', 'mpilgrim'), ('uid', 'sa'), ('database', 'master'), ('pwd', 'secret')]
1 The keys method of a dictionary returns a list of all the keys. The list is not in the order in which the dictionary was defined (remember, elements in a dictionary are unordered), but it is a list.
2 The values method returns a list of all the values. The list is in the same order as the list returned by keys, so params.values()[n] == params[params.keys()[n]] for all values of n.
3 The items method returns a list of tuples of the form (key, value). The list contains all the data in the dictionary.

Now let's see what buildConnectionString does. It takes a list, params.items(), and maps it to a new list by applying string formatting to each element. The new list will have the same number of elements as params.items(), but each element in the new list will be a string that contains both a key and its associated value from the params dictionary.

Example 3.26. List comprehensions in buildConnectionString, step by step

>>> params = {"server":"mpilgrim", "database":"master", "uid":"sa", "pwd":"secret"}
>>> params.items()
[('server', 'mpilgrim'), ('uid', 'sa'), ('database', 'master'), ('pwd', 'secret')]
>>> [k for k, v in params.items()]                1
['server', 'uid', 'database', 'pwd']
>>> [v for k, v in params.items()]                2
['mpilgrim', 'sa', 'master', 'secret']
>>> ["%s=%s" % (k, v) for k, v in params.items()] 3
['server=mpilgrim', 'uid=sa', 'database=master', 'pwd=secret']
1 Note that we're using two variables to iterate through the params.items() list. This is another use of multi-variable assignment. The first element of params.items() is ('server', 'mpilgrim'), so in the first iteration of the list comprehension, k will get 'server' and v will get 'mpilgrim'. In this case we're ignoring the value of v and only including the value of k in the returned list, so this list comprehension ends up being equivalent to params.keys(). (You wouldn't really use a list comprehension like this in real code; this is an overly simplistic example so you can get your head around what's going on here.)
2 Here we're doing the same thing, but ignoring the value of k, so this list comprehension ends up being equivalent to params.values().
3 Combining the previous two examples with some simple string formatting, we get a list of strings that include both the key and value of each element of the dictionary. This looks suspiciously like the output of the program; all that remains is to join the elements in this list into a single string.

Further reading

3.8. Joining lists and splitting strings

You have a list of key-value pairs in the form key=value, and you want to join them into a single string. To join any list of strings into a single string, use the join method of a string object.

Example 3.27. Joining a list in buildConnectionString

    return ";".join(["%s=%s" % (k, v) for k, v in params.items()])

One interesting note before we continue. I keep repeating that functions are objects, strings are objects, everything is an object. You might have thought I meant that string variables are objects. But no, look closely at this example and you'll see that the string ";" itself is an object, and you are calling its join method.

Anyway, the join method joins the elements of the list into a single string, with each element separated by a semi-colon. The delimiter doesn't have to be a semi-colon; it doesn't even have to be a single character. It can be any string.

Important
join only works on lists of strings; it does not do any type coercion. joining a list that has one or more non-string elements will raise an exception.

Example 3.28. Output of odbchelper.py

>>> params = {"server":"mpilgrim", "database":"master", "uid":"sa", "pwd":"secret"}
>>> ["%s=%s" % (k, v) for k, v in params.items()]
['server=mpilgrim', 'uid=sa', 'database=master', 'pwd=secret']
>>> ";".join(["%s=%s" % (k, v) for k, v in params.items()])
'server=mpilgrim;uid=sa;database=master;pwd=secret'

This string is then returned from the odbchelper function and printed by the calling block, which gives you the output that you marveled at when you started reading this chapter.

Historical note. When I first learned Python, I expected join to be a method of a list, which would take the delimiter as an argument. Lots of people feel the same way, and there's a story behind the join method. Prior to Python 1.6, strings didn't have all these useful methods. There was a separate string module which contained all the string functions; each function took a string as its first argument. The functions were deemed important enough to put onto the strings themselves, which made sense for functions like lower, upper, and split. But many hard-core Python programmers objected to the new join method, arguing that it should be a method of the list instead, or that it shouldn't move at all but simply stay a part of the old string module (which still has lots of useful stuff in it). I use the new join method exclusively, but you will see code written either way, and if it really bothers you, you can use the old string.join function instead.

You're probably wondering if there's an analogous method to split a string into a list. And of course there is, and it's called split.

Example 3.29. Splitting a string

>>> li = ['server=mpilgrim', 'uid=sa', 'database=master', 'pwd=secret']
>>> s = ";".join(li)
>>> s
'server=mpilgrim;uid=sa;database=master;pwd=secret'
>>> s.split(";")    1
['server=mpilgrim', 'uid=sa', 'database=master', 'pwd=secret']
>>> s.split(";", 1) 2
['server=mpilgrim', 'uid=sa;database=master;pwd=secret']
1 split reverses join by splitting a string into a multi-element list. Note that the delimiter (“;”) is stripped out completely; it does not appear in any of the elements of the returned list.
2 split takes an optional second argument, which is the number of times to split. (“"Oooooh, optional arguments...” You'll learn how to do this in your own functions in the next chapter.)
Note
anystring.split(delimiter, 1) is a useful technique when you want to search a string for a substring and then work with everything before the substring (which ends up in the first element of the returned list) and everything after it (which ends up in the second element).

3.9. Summary

The odbchelper.py program and its output should now make perfect sense.

Example 3.30. odbchelper.py

def buildConnectionString(params):
    """Build a connection string from a dictionary of parameters.

    Returns string."""
    return ";".join(["%s=%s" % (k, v) for k, v in params.items()])

if __name__ == "__main__":
    myParams = {"server":"mpilgrim", \
                "database":"master", \
                "uid":"sa", \
                "pwd":"secret" \
                }
    print buildConnectionString(myParams)

Example 3.31. Output of odbchelper.py

server=mpilgrim;uid=sa;database=master;pwd=secret

Before diving into the next chapter, make sure you're comfortable doing all of these things:



[2] Actually, it's more complicated than that. Dictionary keys must be immutable. Tuples themselves are immutable, but if you have a tuple of lists, that counts as mutable and isn't safe to use as a dictionary key. Only tuples of strings, numbers, or other dictionary-safe tuples can be used as dictionary keys.

Chapter 4. The Power Of Introspection

4.1. Diving in

This chapter covers one of Python's strengths: introspection. As you know, everything in Python is an object, and introspection is code looking at other modules and functions in memory as objects, getting information about them, and manipulating them. Along the way, we'll define functions with no name, call functions with arguments out of order, and reference functions whose names we don't even know ahead of time.

Here is a complete, working Python program. You should understand a good deal about it just by looking at it. The numbered lines illustrate concepts covered in Your first Python program. Don't worry if the rest of the code looks intimidating; you'll learn all about it throughout this chapter.

Example 4.1. apihelper.py

If you have not already done so, you can download this and other examples used in this book.

def info(object, spacing=10, collapse=1): 1 2 3
    """Print methods and doc strings.
    
    Takes module, class, list, dictionary, or string."""
    methodList = [method for method in dir(object) if callable(getattr(object, method))]
    processFunc = collapse and (lambda s: " ".join(s.split())) or (lambda s: s)
    print "\n".join(["%s %s" %
                      (method.ljust(spacing),
                       processFunc(str(getattr(object, method).__doc__)))
                     for method in methodList])

if __name__ == "__main__":                4 5
    print info.__doc__
1 This module has one function, info. According to its function declaration, it takes three parameters: object, spacing, and collapse. The last two are actually optional parameters, as we'll see shortly.
2 The info function has a multi-line doc string that succinctly describes the function's purpose. Note that no return value is mentioned; this function will be used solely for its effects, not its value.
3 Code within the function is indented.
4 The if __name__ trick allows this program do something useful when run by itself, without interfering with its use as a module for other programs. In this case, the program simply prints out the doc string of the info function.
5 if statements use == for comparison, and parentheses are not required.

The info function is designed to be used by you, the programmer, while working in the Python IDE. It takes any object that has functions or methods (like a module, which has functions, or a list, which has methods) and prints out the functions and their doc strings.

Example 4.2. Sample usage of apihelper.py

>>> from apihelper import info
>>> li = []
>>> info(li)
append     L.append(object) -- append object to end
count      L.count(value) -> integer -- return number of occurrences of value
extend     L.extend(list) -- extend list by appending list elements
index      L.index(value) -> integer -- return index of first occurrence of value
insert     L.insert(index, object) -- insert object before index
pop        L.pop([index]) -> item -- remove and return item at index (default last)
remove     L.remove(value) -- remove first occurrence of value
reverse    L.reverse() -- reverse *IN PLACE*
sort       L.sort([cmpfunc]) -- sort *IN PLACE*; if given, cmpfunc(x, y) -> -1, 0, 1

By default the output is formatted to be easily readable. Multi-line doc strings are collapsed into a single long line, but this option can be changed by specifying 0 for the collapse argument. If the function names are longer than 10 characters, you can specify a larger value for the spacing argument to make the output easier to read.

Example 4.3. Advanced usage of apihelper.py

>>> import odbchelper
>>> info(odbchelper)
buildConnectionString Build a connection string from a dictionary Returns string.
>>> info(odbchelper, 30)
buildConnectionString          Build a connection string from a dictionary Returns string.
>>> info(odbchelper, 30, 0)
buildConnectionString          Build a connection string from a dictionary
    
    Returns string.

4.2. Optional and named arguments

Python allows function arguments to have default values; if the function is called without the argument, the argument gets its default value. Futhermore, arguments can be specified in any order by using named arguments. Stored procedures in SQL Server Transact/SQL can do this; if you're a SQL Server scripting guru, you can skim this part.

Example 4.4. info, a function with two optional arguments

def info(object, spacing=10, collapse=1):

spacing and collapse are optional, because they have default values defined. object is required, because it has no default value. If info is called with only one argument, spacing defaults to 10 and collapse defaults to 1. If info is called with two arguments, collapse still defaults to 1.

Say you want to specify a value for collapse but want to accept the default value for spacing. In most languages, you would be out of luck, because you would have to call the function with three arguments. But in Python, arguments can be specified by name, in any order.

Example 4.5. Valid calls of info

info(odbchelper)                    1
info(odbchelper, 12)                2
info(odbchelper, collapse=0)        3
info(spacing=15, object=odbchelper) 4
1 With only one argument, spacing gets its default value of 10 and collapse gets its default value of 1.
2 With two arguments, collapse gets its default value of 1.
3 Here you are naming the collapse argument explicitly and specifying its value. spacing still gets its default value of 10.
4 Even required arguments (like object, which has no default value) can be named, and named arguments can appear in any order.

This looks totally whacked until you realize that arguments are simply a dictionary. The “normal” method of calling functions without argument names is actually just a shorthand where Python matches up the values with the argument names in the order they're specified in the function declaration. And most of the time, you'll call functions the “normal” way, but you always have the additional flexibility if you need it.

Note
The only thing you have to do to call a function is specify a value (somehow) for each required argument; the manner and order in which you do that is up to you.

Further reading

4.3. type, str, dir, and other built-in functions

Python has a small set of extremely useful built-in functions. All other functions are partitioned off into modules. This was actually a conscious design decision, to keep the core language from getting bloated like other scripting languages (cough cough, Visual Basic).

The type function returns the datatype of any arbitrary object. The possible types are listed in the types module. This is useful for helper functions which can handle several types of data.

Example 4.6. Introducing type

>>> type(1)           1
<type 'int'>
>>> li = []
>>> type(li)          2
<type 'list'>
>>> import odbchelper
>>> type(odbchelper)  3
<type 'module'>
>>> import types      4
>>> type(odbchelper) == types.ModuleType
1
1 type takes anything and returns its datatype. And I mean anything. Integers, strings, lists, dictionaries, tuples, functions, classes, modules, even types.
2 type can take a variable and return its datatype.
3 type also works on modules.
4 You can use the constants in the types module to compare types of objects. This is what the info function does, as we'll see shortly.

The str coerces data into a string. Every datatype can be coerced into a string.

Example 4.7. Introducing str

>>> str(1)          1
'1'
>>> horsemen = ['war', 'pestilence', 'famine']
>>> horsemen.append('Powerbuilder')
>>> str(horsemen)   2
"['war', 'pestilence', 'famine', 'Powerbuilder']"
>>> str(odbchelper) 3
"<module 'odbchelper' from 'c:\\docbook\\dip\\py\\odbchelper.py'>"
>>> str(None)       4
'None'
1 For simple datatypes like integers, you would expect str to work, because almost every language has a function to convert an integer to a string.
2 However, str works on any object of any type. Here it works on a list which we've constructed in bits and pieces.
3 str also works on modules. Note that the string representation of the module includes the pathname of the module on disk, so yours will be different.
4 A subtle but important behavior of str is that it works on None, the Python null value. It returns the string 'None'. We will use this to our advantage in the info function, as we'll see shortly.

At the heart of our info function is the powerful dir function. dir returns a list of the attributes and methods of any object: modules, functions, strings, lists, dictionaries... pretty much anything.

Example 4.8. Introducing dir

>>> li = []
>>> dir(li)           1
['append', 'count', 'extend', 'index', 'insert', 'pop', 'remove', 'reverse', 'sort']
>>> d = {}
>>> dir(d)            2
['clear', 'copy', 'get', 'has_key', 'items', 'keys', 'setdefault', 'update', 'values']
>>> import odbchelper
>>> dir(odbchelper)   3
['__builtins__', '__doc__', '__file__', '__name__', 'buildConnectionString']
1 li is a list, so dir(li) returns a list of all the methods of a list. Note that the returned list contains the names of the methods as strings, not the methods themselves.
2 d is a dictionary, so dir(d) returns a list of the names of dictionary methods. At least one of these, keys, should look familiar.
3 This is where it really gets interesting. odbchelper is a module, so dir(odbchelper) returns a list of all kinds of stuff defined in the module, including built-in attributes, like __name__ and __doc__, and whatever other attributes and methods you define. In this case, odbchelper has only one user-defined method, the buildConnectionString function we studied in Your first Python program.

Finally, the callable function takes any object and returns 1 if the object can be called, or 0 otherwise. Callable objects include functions, class methods, even classes themselves. (More on classes in chapter 3.)

Example 4.9. Introducing callable

>>> import string
>>> string.punctuation           1
'!"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~'
>>> string.join                  2
<function join at 00C55A7C>
>>> callable(string.punctuation) 3
0
>>> callable(string.join)        4
1
>>> print string.join.__doc__    5
join(list [,sep]) -> string

    Return a string composed of the words in list, with
    intervening occurrences of sep.  The default separator is a
    single space.

    (joinfields and join are synonymous)
1 The functions in the string module are deprecated (although lots of people still use the join function), but the module contains lots of useful constants like this string.punctuation, which contains all the standard punctuation characters.
2 string.join is a function that joins a list of strings.
3 string.punctuation is not callable; it is a string. (A string does have callable methods, but the string itself is not callable.)
4 string.join is callable; it's a function that takes two arguments.
5 Any callable object may have a doc string. Using the callable function on each of an object's attributes, we can determine which attributes we care about (methods, functions, classes) and which we want to ignore (constants, etc.) without knowing anything about the object ahead of time.

type, str, dir, and all the rest of Python's built-in functions are grouped into a special module called __builtin__. (That's two underscores before and after.) If it helps, you can think of Python automatically executing from __builtin__ import * on startup, which imports all the “built-in” functions into the namespace so you can use them directly.

The advantage of thinking like this is that you can access all the built-in functions and attributes as a group by getting information about the __builtin__ module. And guess what, we have a function for that; it's called info. Try it yourself and skim through the list now; we'll dive into some of the more important functions later. (Some of the built-in error classes, like AttributeError, should already look familiar.)

Example 4.10. Built-in attributes and functions

>>> from apihelper import info
>>> import __builtin__
>>> info(__builtin__, 20)
ArithmeticError      Base class for arithmetic errors.
AssertionError       Assertion failed.
AttributeError       Attribute not found.
EOFError             Read beyond end of file.
EnvironmentError     Base class for I/O related errors.
Exception            Common base class for all exceptions.
FloatingPointError   Floating point operation failed.
IOError              I/O operation failed.

[...snip...]
Note
Python comes with excellent reference manuals, which you should peruse thoroughly to learn all the modules Python has to offer. But whereas in most languages you would find yourself referring back to the manuals (or man pages, or, God help you, MSDN) to remind yourself how to use these modules, Python is largely self-documenting.

4.4. Getting object references with getattr

You already know that Python functions are objects. What you don't know is that you can get a reference to a function without knowing its name until run-time, using the getattr function.

Example 4.11. Introducing getattr

>>> li = ["Larry", "Curly"]
>>> li.pop                       1
<built-in method pop of list object at 010DF884>
>>> getattr(li, "pop")           2
<built-in method pop of list object at 010DF884>
>>> getattr(li, "append")("Moe") 3
>>> li
["Larry", "Curly", "Moe"]
>>> getattr({}, "clear")         4
<built-in method clear of dictionary object at 00F113D4>
>>> getattr((), "pop")           5
Traceback (innermost last):
  File "<interactive input>", line 1, in ?
AttributeError: 'tuple' object has no attribute 'pop'
1 This gets a reference to the pop method of the list. Note that this is not calling the pop method; that would be li.pop(). This is the method itself.
2 This also returns a reference to the pop method, but this time, the method name is specified as a string argument to the getattr function. getattr is an incredibly useful built-in function which returns any attribute of any object. In this case, the object is a list, and the attribute is the pop method.
3 In case it hasn't sunk in just how incredibly useful this is, try this: the return value of getattr is the method, which you can then call just as if you had said li.append("Moe") directly. But you didn't call the function directly; you specified the function name as a string instead.
4 getattr also works on dictionaries.
5 In theory, getattr would work on tuples, except that tuples have no methods, so getattr will raise an exception no matter what attribute name you give.

getattr isn't just for built-in datatypes. It also works on modules.

Example 4.12. getattr in apihelper.py

>>> import odbchelper
>>> odbchelper.buildConnectionString             1
<function buildConnectionString at 00D18DD4>
>>> getattr(odbchelper, "buildConnectionString") 2
<function buildConnectionString at 00D18DD4>
>>> object = odbchelper
>>> method = "buildConnectionString"
>>> getattr(object, method)                      3
<function buildConnectionString at 00D18DD4>
>>> type(getattr(object, method))                4
<type 'function'>
>>> import types
>>> type(getattr(object, method)) == types.FunctionType
1
>>> callable(getattr(object, method))            5
1
1 This returns a reference to the buildConnectionString function in the odbchelper module, which we studied in Your first Python program. (The hex address you see is specific to my machine; your output will be different.)
2 Using getattr, we can get the same reference to the same function. In general, getattr(object, "attribute") is equivalent to object.attribute. If object is a module, then attribute can be anything defined in the module: a function, class, or global variable.
3 And this is what we actually use in the info function. object is passed into the function as an argument; method is a string which is the name of a method or function.
4 In this case, method is the name of a function, which we can prove by getting its type.
5 Since method is a function, it is callable.

A common usage pattern of getattr is as a dispatcher. For example, if you had a program that could output data in a variety of different formats, you could define separate functions for each output format and use a single dispatch function to call the right one.

For example, let's imagine a program that prints site statistics in HTML, XML, and plain text formats. The choice of output format could be specified on the command line, or stored in a configuration file. A statsout module defines three functions, output_html, output_xml, and output_text. Then the main program defines a single output function, like this:

Example 4.13. Creating a dispatcher with getattr

import statsout

def output(data, format="text"):                              1
    output_function = getattr(statsout, "output_%s" % format) 2
    return output_function(data)                              3
1 The output function takes one required argument, data, and one optional argument, format. If format is not specified, it defaults to text, and we will end up calling the plain text output function.
2 We concatenate the format argument with "output_" to produce a function name, then go get that function from the statsout module. This allows us to easily extend the program later to support other output formats, without changing this dispatch function. Just add another function to statsout named, for instance, output_pdf, and pass "pdf" as the format into the output function.
3 Now we can simply call the output function like any other. The output_function variable is a reference to the appropriate function from the statsout module.

Did you see the bug in the previous example? This is a very loose coupling of strings and functions, and there is no error checking. What happens if the user passes in a format that doesn't have a corresponding function defined in statsout? Well, getattr will return None, which will be assigned to output_function instead of a valid function, and the next line that attempts to call that function will crash and raise an exception. Bad.

Luckily, getattr takes an optional third argument, a default value.

Example 4.14. getattr default values

import statsout

def output(data, format="text"):
    output_function = getattr(statsout, "output_%s" % format, statsout.output_text)
    return output_function(data) 1
1 This function call is guaranteed to work, because we added a third argument to our call to getattr. The third argument is a default value which is returned if the attribute or method specified by the second argument wasn't found.

As you can see, getattr is quite powerful. It is the heart of introspection, and we'll see even more powerful examples of it in later chapters.

4.5. Filtering lists

As you know, Python has powerful capabilities for mapping lists into other lists, via list comprehensions. This can be combined with a filtering mechanism, where some elements in the list are mapped while others are skipped entirely.

Example 4.15. List filtering syntax

[mapping-expression for element in source-list if filter-expression]

This is an extension of the list comprehensions that you know and love. The first two thirds are the same; the last part, starting with the if, is the filter expression. A filter expression can be any expression that evaluates true or false (which in Python can be almost anything). Any element for which the filter expression evaluates true will be included in the mapping. All other elements are ignored, so they are never put through the mapping expression and are not included in the output list.

Example 4.16. Introducing list filtering

>>> li = ["a", "mpilgrim", "foo", "b", "c", "b", "d", "d"]
>>> [elem for elem in li if len(elem) > 1]       1
['mpilgrim', 'foo']
>>> [elem for elem in li if elem != "b"]         2
['a', 'mpilgrim', 'foo', 'c', 'd', 'd']
>>> [elem for elem in li if li.count(elem) == 1] 3
['a', 'mpilgrim', 'foo', 'c']
1 The mapping expression here is simple (it just returns the value of each element), so concentrate on the filter expression. As Python loops through the list, it runs each element through the filter expression; if the filter expression is true, the element is mapped and the result of the mapping expression is included in the returned list. Here you are filtering out all the one-character strings, so you're left with a list of all the longer strings.
2 Here you are filtering out a specific value, b. Note that this filters all occurrences of b, since each time it comes up, the filter expression will be false.
3 count is a list method that returns the number of times a value occurs in a list. You might think that this filter would eliminate duplicates from a list, returning a list containing only one copy of each value in the original list. But it doesn't, because values that appear twice in the original list (in this case, b and d) are excluded completely. There are ways of eliminating duplicates from a list, but filtering is not the solution.

Example 4.17. Filtering a list in apihelper.py

    methodList = [method for method in dir(object) if callable(getattr(object, method))]

This looks complicated, and it is complicated, but the basic structure is the same. The whole filter expression returns a list, which is assigned to the methodList variable. The first half of the expression is the list mapping part. The mapping expression is an identity expression; it returns the value of each element. dir(object) returns a list of object's attributes and methods; that's the list you're mapping. So the only new part is the filter expression after the if.

The filter expression looks scary, but it's not. You already know about callable, getattr, and in. As you saw in the previous section, the expression getattr(object, method) returns a function object if object is a module and method is the name of a function in that module.

So this expression takes an object (named object). Then it gets a list of the names of the object's attributes, methods, functions, and a few other things. Then it filters that list to weed out all the stuff that we don't care about. We do the weeding out by taking the name of each attribute/method/function and getting a reference to the real thing, via the getattr function. Then we check to see if that object is callable, which will be any methods and functions, both built-in (like the pop method of a list) and user-defined (like the buildConnectionString function of the odbchelper module). We don't care about other attributes, like the __name__ attribute that's built in to every module.

Further reading

4.6. The peculiar nature of and and or

In Python, and and or perform boolean logic as you would expect, but they do not return boolean values; they return one of the actual values they are comparing.

Example 4.18. Introducing and

>>> 'a' and 'b'         1
'b'
>>> '' and 'b'          2
''
>>> 'a' and 'b' and 'c' 3
'c'
1 When using and, values are evaluated in a boolean context from left to right. 0, '', [], (), {}, and None are false in a boolean context; everything else is true.[3] If all values are true in a boolean context, and returns the last value. In this case, and evaluates 'a', which is true, then 'b', which is true, and returns 'b'.
2 If any value is false in a boolean context, and returns the first false value. In this case, '' is the first false value.
3 All values are true, so and returns the last value, 'c'.

Example 4.19. Introducing or

>>> 'a' or 'b'          1
'a'
>>> '' or 'b'           2
'b'
>>> '' or [] or {}      3
{}
>>> def sidefx():
...     print "in sidefx()"
...     return 1
>>> 'a' or sidefx()     4
'a'
1 When using or, values are evaluated in a boolean context from left to right, just like and. If any value is true, or returns that value immediately. In this case, 'a' is the first true value.
2 or evaluates '', which is false, then 'b', which is true, and returns 'b'.
3 If all values are false, or returns the last value. or evaluates '', which is false, then [], which is false, then {}, which is false, and returns {}.
4 Note that or only evaluates values until it finds one that is true in a boolean context, and then it ignores the rest. This distinction is important if some values can have side effects. Here, the function sidefx is never called, because or evaluates 'a', which is true, and returns 'a' immediately.

If you're a C hacker, you are certainly familiar with the bool ? a : b expression, which evaluates to a if bool is true, and b otherwise. Because of the way and and or work in Python, you can accomplish the same thing.

Example 4.20. Introducing the and-or trick

>>> a = "first"
>>> b = "second"
>>> 1 and a or b 1
'first'
>>> 0 and a or b 2
'second'
1 This syntax looks similar to the bool ? a : b expression in C. The entire expression is evaluated from left to right, so the and is evaluated first. 1 and 'first' evalutes to 'first', then 'first' or 'second' evalutes to 'first'.
2 0 and 'first' evalutes to 0, then 0 or 'second' evaluates to 'second'.

However, since this Python expression is simply boolean logic, and not a special construct of the language, there is one very, very, very important difference between this and-or trick in Python and the bool ? a : b syntax in C. If the value of a is false, the expression will not work as you would expect it to. (Can you tell I was bitten by this? More than once?)

Example 4.21. When the and-or trick fails

>>> a = ""
>>> b = "second"
>>> 1 and a or b 1
'second'
1 Since a is an empty string, which Python considers false in a boolean context, 1 and '' evalutes to '', then '' or 'second' evalutes to 'second'. Oops! That's not what we wanted.
Important
The and-or trick, bool and a or b, will not work like the C expression bool ? a : b when a is false in a boolean context.

The real trick behind the and-or trick, then, is to make sure that the value of a is never false. One common way of doing this is to turn a into [a] and b into [b], then taking the first element of the returned list, which will be either a or b.

Example 4.22. Using the and-or trick safely

>>> a = ""
>>> b = "second"
>>> (1 and [a] or [b])[0] 1
''
1 Since [a] is a non-empty list, it is never false. Even if a is 0 or '' or some other false value, the list [a] is true because it has one element.

By now, this trick may seem like more trouble than it's worth. You could, after all, accomplish the same thing with an if statement, so why go through all this fuss? Well, in many cases, you are choosing between two constant values, so you can use the simpler syntax and not worry, because you know that the a value will always be true. And even if you have to use the more complicated safe form, there are good reasons to do so; there are some cases in Python where if statements are not allowed, like lambda functions.

4.7. Using lambda functions

Python supports an interesting syntax that lets you define one-line mini-functions on the fly. Borrowed from Lisp, these so-called lambda functions can be used anywhere a function is required.

Example 4.23. Introducing lambda functions

>>> def f(x):
...     return x*2
...     
>>> f(3)
6
>>> g = lambda x: x*2  1
>>> g(3)
6
>>> (lambda x: x*2)(3) 2
6
1 This is a lambda function that accomplishes the same thing as the normal function above it. Note the abbreviated syntax here: there are no parentheses around the argument list, and the return keyword is missing (it is implied, since the entire function can only be one expression). Also, the function has no name, but it can be called through the variable it is assigned to.
2 You can use a lambda function without even assigning it to a variable. Not the most useful thing in the world, but it just goes to show that a lambda is just an in-line function.

To generalize, a lambda function is a function that takes any number of arguments (including optional arguments) and returns the value of a single expression. lambda functions can not contain commands, and they can not contain more than one expression. Don't try to squeeze too much into a lambda function; if you need something more complex, define a normal function instead and make it as long as you want.

Note
lambda functions are a matter of style. Using them is never required; anywhere you could use them, you could define a separate normal function and use that instead. I use them in places where I want to encapsulate specific, non-reusable code without littering my code with a lot of little one-line functions.

Example 4.24. lambda functions in apihelper.py

    processFunc = collapse and (lambda s: " ".join(s.split())) or (lambda s: s)

Several things to note here in passing. First, we're using the simple form of the and-or trick, which is OK, because a lambda function is always true in a boolean context. (That doesn't mean that a lambda function can't return a false value. The function is always true; its return value could be anything.)

Second, we're using the split function with no arguments. You've already seen it used with 1 or 2 arguments, but with no arguments it splits on whitespace.

Example 4.25. split with no arguments

>>> s = "this   is\na\ttest"  1
>>> print s
this   is
a	test
>>> print s.split()           2
['this', 'is', 'a', 'test']
>>> print " ".join(s.split()) 3
'this is a test'
1 This is a multiline string, defined by escape characters instead of triple quotes. \n is a carriage return; \t is a tab character.
2 split with no arguments splits on whitespace. So three spaces, a carriage return, and a tab character are all the same.
3 You can normalize whitespace by splitting a string and then rejoining it with a single space as a delimiter. This is what the info function does to collapse multi-line doc strings into a single line.

So what is the info function actually doing with these lambda functions, splits, and and-or tricks?

Example 4.26. Assigning a function to a variable

    processFunc = collapse and (lambda s: " ".join(s.split())) or (lambda s: s)

processFunc is now a function, but which function it is depends on the value of the collapse variable. If collapse is true, processFunc(string) will collapse whitespace; otherwise, processFunc(string) will return its argument unchanged.

To do this in a less robust language, like Visual Basic, you would probably create a function that took a string and a collapse argument and used an if statement to decide whether to collapse the whitespace or not, then returned the appropriate value. This would be inefficient, because the function would have to handle every possible case; every time you called it, it would have to decide whether to collapse whitespace before it could give you what you wanted. In Python, you can take that decision logic out of the function and define a lambda function that is custom-tailored to give you exactly (and only) what you want. This is more efficient, more elegant, and less prone to those nasty oh-I-thought-those-arguments-were-reversed kinds of errors.

Further reading

4.8. Putting it all together

The last line of code, the only one we haven't deconstructed yet, is the one that does all the work. But by now the work is easy, because everything we need is already set up just the way we need it. All the dominoes are in place; it's time to knock them down.

Example 4.27. The meat of apihelper.py

    print "\n".join(["%s %s" %
                      (method.ljust(spacing),
                       processFunc(str(getattr(object, method).__doc__)))
                     for method in methodList])

Note that this is one command, split over multiple lines, but it doesn't use the line continuation character (“\”). Remember when I said that some expressions can be split into multiple lines without using a backslash? A list comprehension is one of those expressions, since the entire expression is contained in square brackets.

Now, let's take it from the end and work backwards. The

for method in methodList

shows us that this is a list comprehension. As you know, methodList is a list of all the methods we care about in object. So we're looping through that list with method.

Example 4.28. Getting a doc string dynamically

>>> import odbchelper
>>> object = odbchelper                   1
>>> method = 'buildConnectionString'      2
>>> getattr(object, method)               3
<function buildConnectionString at 010D6D74>
>>> print getattr(object, method).__doc__ 4
Build a connection string from a dictionary of parameters.

    Returns string.
1 In the info function, object is the object we're getting help on, passed in as an argument.
2 As we're looping through methodList, method is the name of the current method.
3 Using the getattr function, we're getting a reference to the method function in the object module.
4 Now, printing the actual doc string of the method is easy.

The next piece of the puzzle is the use of str around the doc string. As you may recall, str is a built-in function that coerces data into a string. But a doc string is always a string, so why bother with the str function? The answer is that not every function has a doc string, and if it doesn't, its __doc__ attribute is None.

Example 4.29. Why use str on a doc string?

>>> >>> def foo(): print 2
>>> >>> foo()
2
>>> >>> foo.__doc__     1
>>> foo.__doc__ == None 2
1
>>> str(foo.__doc__)    3
'None'
1 We can easily define a function that has no doc string, so its __doc__ attribute is None. Confusingly, if you evaluate the __doc__ attribute directly, the Python IDE prints nothing at all, which makes sense if you think about it, but is still unhelpful.
2 You can verify that the value of the __doc__ attribute is actually None by comparing it directly.
3 Using the str function takes the null value and returns a string representation of it, 'None'.
Note
In SQL, you must use IS NULL instead of = NULL to compare a null value. In Python, you can use either == None or is None, but is None is faster.

Now that we are guaranteed to have a string, we can pass the string to processFunc, which we have already defined as a function that either does or doesn't collapse whitespace. Now you see why it was important to use str to convert a None value into a string representation. processFunc is assuming a string argument and calling its split method, which would crash if we passed it None because None doesn't have a split method.

Stepping back even further, we see that we're using string formatting again to concatenate the return value of processFunc with the return value of method's ljust method. This is a new string method that we haven't seen before.

Example 4.30. Introducing the ljust method

>>> s = 'buildConnectionString'
>>> s.ljust(30) 1
'buildConnectionString         '
>>> s.ljust(20) 2
'buildConnectionString'
1 ljust pads the string with spaces to the given length. This is what the info function uses to make two columns of output and line up all the doc strings in the second column.
2 If the given length is smaller than the length of the string, ljust will simply return the string unchanged. It never truncates the string.

We're almost done. Given the padded method name from the ljust method and the (possibly collapsed) doc string from the call to processFunc, we concatenate the two and get a single string. Since we're mapping methodList, we end up with a list of strings. Using the join method of the string "\n", we join this list into a single string, with each element of the list on a separate line, and print the result.

Example 4.31. Printing a list

>>> li = ['a', 'b', 'c']
>>> print "\n".join(li) 1
a
b
c
1 This is also a useful debugging trick when you're working with lists. And in Python, you're always working with lists.

That's the last piece of the puzzle. This code should now make perfect sense.

Example 4.32. The meat of apihelper.py, revisited

    print "\n".join(["%s %s" %
                      (method.ljust(spacing),
                       processFunc(str(getattr(object, method).__doc__)))
                     for method in methodList])

4.9. Summary

The apihelper.py program and its output should now make perfect sense.

Example 4.33. apihelper.py

def info(object, spacing=10, collapse=1):
    """Print methods and doc strings.
    
    Takes module, class, list, dictionary, or string."""
    methodList = [method for method in dir(object) if callable(getattr(object, method))]
    processFunc = collapse and (lambda s: " ".join(s.split())) or (lambda s: s)
    print "\n".join(["%s %s" %
                      (method.ljust(spacing),
                       processFunc(str(getattr(object, method).__doc__)))
                     for method in methodList])

if __name__ == "__main__":
    print info.__doc__

Example 4.34. Output of apihelper.py

>>> from apihelper import info
>>> li = []
>>> info(li)
append     L.append(object) -- append object to end
count      L.count(value) -> integer -- return number of occurrences of value
extend     L.extend(list) -- extend list by appending list elements
index      L.index(value) -> integer -- return index of first occurrence of value
insert     L.insert(index, object) -- insert object before index
pop        L.pop([index]) -> item -- remove and return item at index (default last)
remove     L.remove(value) -- remove first occurrence of value
reverse    L.reverse() -- reverse *IN PLACE*
sort       L.sort([cmpfunc]) -- sort *IN PLACE*; if given, cmpfunc(x, y) -> -1, 0, 1

Before diving into the next chapter, make sure you're comfortable doing all of these things:

  • Defining and calling functions with optional and named arguments
  • Using str to coerce any arbitrary value into a string representation
  • Using getattr to get references to functions and other attributes dynamically
  • Extending the list comprehension syntax to do list filtering
  • Recognizing the and-or trick and using it safely
  • Defining lambda functions
  • Assigning functions to variables and calling the function by referencing the variable. I can't emphasize this enough: this mode of thought is vital to advancing your understanding of Python. You'll see more complex applications of this concept throughout this book.


[3] Well, almost everything. By default, instances of classes are true in a boolean context, but you can define special methods in your class to make an instance evaluate to false. You'll learn all about classes and special methods in chapter 3.

Chapter 5. Objects and Object-Orientation

5.1. Diving in

This chapter, and pretty much every chapter after this, deals with object-oriented Python programming.

Here is a complete, working Python program. Read the doc strings of the module, the classes, and the functions to get an overview of what this program does and how it works. As usual, don't worry about the stuff you don't understand; that's what the rest of the chapter is for.

Example 5.1. fileinfo.py

If you have not already done so, you can download this and other examples used in this book.

"""Framework for getting filetype-specific metadata.

Instantiate appropriate class with filename.  Returned object acts like a
dictionary, with key-value pairs for each piece of metadata.
    import fileinfo
    info = fileinfo.MP3FileInfo("/music/ap/mahadeva.mp3")
    print "\\n".join(["%s=%s" % (k, v) for k, v in info.items()])

Or use listDirectory function to get info on all files in a directory.
    for info in fileinfo.listDirectory("/music/ap/", [".mp3"]):
        ...

Framework can be extended by adding classes for particular file types, e.g.
HTMLFileInfo, MPGFileInfo, DOCFileInfo.  Each class is completely responsible for
parsing its files appropriately; see MP3FileInfo for example.
"""
import os
import sys
from UserDict import UserDict

def stripnulls(data):
    "strip whitespace and nulls"
    return data.replace("\00", "").strip()

class FileInfo(UserDict):
    "store file metadata"
    def __init__(self, filename=None):
        UserDict.__init__(self)
        self["name"] = filename

class MP3FileInfo(FileInfo):
    "store ID3v1.0 MP3 tags"
    tagDataMap = {"title"   : (  3,  33, stripnulls),
                  "artist"  : ( 33,  63, stripnulls),
                  "album"   : ( 63,  93, stripnulls),
                  "year"    : ( 93,  97, stripnulls),
                  "comment" : ( 97, 126, stripnulls),
                  "genre"   : (127, 128, ord)}

    def __parse(self, filename):
        "parse ID3v1.0 tags from MP3 file"
        self.clear()
        try:                               
            fsock = open(filename, "rb", 0)
            try:                           
                fsock.seek(-128, 2)        
                tagdata = fsock.read(128)  
            finally:                       
                fsock.close()              
            if tagdata[:3] == "TAG":
                for tag, (start, end, parseFunc) in self.tagDataMap.items():
                    self[tag] = parseFunc(tagdata[start:end])               
        except IOError:                    
            pass                           

    def __setitem__(self, key, item):
        if key == "name" and item:
            self.__parse(item)
        FileInfo.__setitem__(self, key, item)

def listDirectory(directory, fileExtList):                                        
    "get list of file info objects for files of particular extensions"
    fileList = [os.path.normcase(f) for f in os.listdir(directory)]               
    fileList = [os.path.join(directory, f) for f in fileList \
                if os.path.splitext(f)[1] in fileExtList]                         
    def getFileInfoClass(filename, module=sys.modules[FileInfo.__module__]):      
        "get file info class from filename extension"                             
        subclass = "%sFileInfo" % os.path.splitext(filename)[1].upper()[1:]       
        return hasattr(module, subclass) and getattr(module, subclass) or FileInfo
    return [getFileInfoClass(f)(f) for f in fileList]                             

if __name__ == "__main__":
    for info in listDirectory("/music/_singles/", [".mp3"]): 1
        print "\n".join(["%s=%s" % (k, v) for k, v in info.items()])
        print
1 This program's output depends on the files on your hard drive. To get meaningful output, you'll have to change the directory path to point to a directory of MP3 files on your own machine.

Example 5.2. Output of fileinfo.py

This was the output I got on my machine. Your output will be different, unless, by some startling coincidence, you share my exact taste in music.

album=
artist=Ghost in the Machine
title=A Time Long Forgotten (Concept
genre=31
name=/music/_singles/a_time_long_forgotten_con.mp3
year=1999
comment=http://mp3.com/ghostmachine

album=Rave Mix
artist=***DJ MARY-JANE***
title=HELLRAISER****Trance from Hell
genre=31
name=/music/_singles/hellraiser.mp3
year=2000
comment=http://mp3.com/DJMARYJANE

album=Rave Mix
artist=***DJ MARY-JANE***
title=KAIRO****THE BEST GOA
genre=31
name=/music/_singles/kairo.mp3
year=2000
comment=http://mp3.com/DJMARYJANE

album=Journeys
artist=Masters of Balance
title=Long Way Home
genre=31
name=/music/_singles/long_way_home1.mp3
year=2000
comment=http://mp3.com/MastersofBalan

album=
artist=The Cynic Project
title=Sidewinder
genre=18
name=/music/_singles/sidewinder.mp3
year=2000
comment=http://mp3.com/cynicproject

album=Digitosis@128k
artist=VXpanded
title=Spinning
genre=255
name=/music/_singles/spinning.mp3
year=2000
comment=http://mp3.com/artists/95/vxp

5.2. Importing modules using from module import

Python has two ways of importing modules. Both are useful, and you should know when to use each. One way, import module, you've already seen in chapter 1. The other way accomplishes the same thing but works in subtlely and importantly different ways.

Example 5.3. Basic from module import syntax

from UserDict import UserDict

This is similar to the import module syntax that you know and love, but with an important difference: the attributes and methods of the imported module types are imported directly into the local namespace, so they are available directly, without qualification by module name. You can import individual items or use from module import * to import everything.

Note
from module import * in Python is like use module in Perl; import module in Python is like require module in Perl.
Note
from module import * in Python is like import module.* in Java; import module in Python is like import module in Java.

Example 5.4. import module vs. from module import

>>> import types
>>> types.FunctionType             1
<type 'function'>
>>> FunctionType                   2
Traceback (innermost last):
  File "<interactive input>", line 1, in ?
NameError: There is no variable named 'FunctionType'
>>> from types import FunctionType 3
>>> FunctionType                   4
<type 'function'>
1 The types module contains no methods, just attributes for each Python object type. Note that the attribute, FunctionType, must be qualified by the module name, types.
2 FunctionType by itself has not been defined in this namespace; it only exists in the context of types.
3 This syntax imports the attribute FunctionType from the types module directly into the local namespace.
4 Now FunctionType can be accessed directly, without reference to types.

When should you use from module import?

  • If you will be accessing attributes and methods often and don't want to type the module name over and over, use from module import.
  • If you want to selectively import some attributes and methods but not others, use from module import.
  • If the module contains attributes or functions with the same name as ones in your module, you must use import module to avoid name conflicts.

Other than that, it's just a matter of style, and you will see Python code written both ways.

Further reading

5.3. Defining classes

Python is fully object-oriented: you can define your own classes, inherit from your own or built-in classes, and instantiate the classes you've defined.

Defining a class in Python is simple; like functions, there is no separate interface definition. Just define the class and start coding. A Python class starts with the reserved word class, followed by the class name. Technically, that's all that's required, since a class doesn't have to inherit from any other class.

Example 5.5. The simplest Python class

class foo: 1
    pass   2 3
1 The name of this class is foo, and it doesn't inherit from any other class.
2 This class doesn't define any methods or attributes, but syntactically, there needs to be something in the definition, so we use pass. This is a Python reserved word that just means “move along, nothing to see here”. It's a statement that does nothing, and it's a good placeholder when you're stubbing out functions or classes.
3 You probably guessed this, but everything in a class is indented, just like the code within a function, if statement, for loop, and so forth. The first thing not indented is not in the class.
Note
The pass statement in Python is like an empty set of braces ({}) in Java or C.

Of course, realistically, most classes will be inherited from other classes, and they will define their own class methods and attributes. But as you've just seen, there is nothing that a class absolutely must have, other than a name. In particular, C++ programmers may find it odd that Python classes don't have explicit constructors and destructors. Python classes do have something similar to a constructor: the __init__ method.

Example 5.6. Defining the FileInfo class

from UserDict import UserDict

class FileInfo(UserDict): 1
1 In Python, the ancestor of a class is simply listed in parentheses immediately after the class name. So the FileInfo class is inherited from the UserDict class (which was imported from the UserDict module). UserDict is a class that acts like a dictionary, allowing you to essentially subclass the dictionary datatype and add your own behavior. (There are similar classes UserList and UserString which allow you to subclass lists and strings.) There is a bit of black magic behind this, which we will demystify later in this chapter when we explore the UserDict class in more depth.
Note
In Python, the ancestor of a class is simply listed in parentheses immediately after the class name. There is no special keyword like extends in Java.
Note
Although I won't discuss it in depth in this book, Python supports multiple inheritance. In the parentheses following the class name, you can list as many ancestor classes as you like, separated by commas.

Example 5.7. Initializing the FileInfo class

class FileInfo(UserDict):
    "store file metadata"              1
    def __init__(self, filename=None): 2 3 4
1 Classes can (and should) have doc strings too, just like modules and functions.
2 __init__ is called immediately after an instance of the class is created. It would be tempting but incorrect to call this the constructor of the class. Tempting, because it looks like a constructor (by convention, __init__ is the first method defined for the class), acts like one (it's the first piece of code executed in a newly created instance of the class), and even sounds like one (“init” certainly suggests a constructor-ish nature). Incorrect, because the object has already been constructed by the time __init__ is called, and you already have a valid reference to the new instance of the class. But __init__ is the closest thing you're going to get in Python to a constructor, and it fills much the same role.
3 The first argument of every class method, including __init__, is always a reference to the current instance of the class. By convention, this argument is always named self. In the __init__ method, self refers to the newly created object; in other class methods, it refers to the instance whose method was called. Although you need to specify self explicitly when defining the method, you do not specify it when calling the method; Python will add it for you automatically.
4 __init__ methods can take any number of arguments, and just like functions, the arguments can be defined with default values, making them optional to the caller. In this case, filename has a default value of None, which is the Python null value.
Note
By convention, the first argument of any class method (the reference to the current instance) is called self. This argument fills the role of the reserved word this in C++ or Java, but self is not a reserved word in Python, merely a naming convention. Nonetheless, please don't call it anything but self; this is a very strong convention.

Example 5.8. Coding the FileInfo class

class FileInfo(UserDict):
    "store file metadata"
    def __init__(self, filename=None):
        UserDict.__init__(self)        1
        self["name"] = filename        2
                                       3
1 Some pseudo-object-oriented languages like Powerbuilder have a concept of “extending” constructors and other events, where the ancestor's method is called automatically before the descendant's method is executed. Python does not do this; you must always explicitly call the appropriate method in the ancestor class.
2 I told you that this class acts like a dictionary, and here is the first sign of it. We're assigning the argument filename as the value of this object's name key.
3 Note that the __init__ method never returns a value.
Note
When defining your class methods, you must explicitly list self as the first argument for each method, including __init__. When you call a method of an ancestor class from within your class, you must include the self argument. But when you call your class method from outside, you do not specify anything for the self argument; you skip it entirely, and Python automatically adds the instance reference for you. I am aware that this is confusing at first; it's not really inconsistent, but it may appear inconsistent because it relies on a distinction (between bound and unbound methods) that you don't know about yet.

Whew. I realize that's a lot to absorb, but you'll get the hang of it. All Python classes work the same way, so once you learn one, you've learned them all. If you forget everything else, remember this one thing, because I promise it will trip you up:

Note
__init__ methods are optional, but when you define one, you must remember to explicitly call the ancestor's __init__ method. This is more generally true: whenever a descendant wants to extend the behavior of the ancestor, the descendant method must explicitly call the ancestor method at the proper time, with the proper arguments.

5.4. Instantiating classes

Instantiating classes in Python is straightforward. To instantiate a class, simply call the class as if it were a function, passing the arguments that the __init__ method defines. The return value will be the newly created object.

Example 5.9. Creating a FileInfo instance

>>> import fileinfo
>>> f = fileinfo.FileInfo("/music/_singles/kairo.mp3") 1
>>> f.__class__                                        2
<class fileinfo.FileInfo at 010EC204>
>>> f.__doc__                                          3
'base class for file info'
>>> f                                                  4
{'name': '/music/_singles/kairo.mp3'}
1 We are creating an instance of the FileInfo class (defined in the fileinfo module) and assigning the newly created instance to the variable f. We are passing one parameter, /music/_singles/kairo.mp3, which will end up as the filename argument in FileInfo's __init__ method.
2 Every class instance has a built-in attribute, __class__, which is the object's class. (Note that the representation of this includes the physical address of the instance on my machine; your representation will be different.) Java programmers may be familiar with the Class class, which contains methods like getName and getSuperclass to get metadata information about an object. In Python, this kind of metadata is available directly on the object itself through attributes like __class__, __name__, and __bases__.
3 You can access the instance's doc string just like a function or a module. All instances of a class share the same doc string.
4 Remember when the __init__ method assigned its filename argument to self["name"]? Well, here's the result. The arguments we pass when we create the class instance get sent right along to the __init__ method (along with the object reference, self, which Python adds for free).
Note
In Python, simply call a class as if it were a function to create a new instance of the class. There is no explicit new operator like C++ or Java.

If creating new instances is easy, destroying them is even easier. In general, there is no need to explicitly free instances, because they are freed automatically when the variables assigned to them go out of scope. Memory leaks are rare in Python.

Example 5.10. Trying to implement a memory leak

>>> def leakmem():
...     f = fileinfo.FileInfo('/music/_singles/kairo.mp3') 1
...     
>>> for i in range(100):
...     leakmem()                                          2
1 Every time the leakmem function is called, we are creating an instance of FileInfo and assigning it to the variable f, which is a local variable within the function. Then the function ends without ever freeing f, so you would expect a memory leak, but you would be wrong. When the function ends, the local variable f goes out of scope. At this point, there are no longer any references to the newly created instance of FileInfo (since we never assigned it to anything other than f), so Python destroys the instance for us.
2 No matter how many times we call the leakmem function, it will never leak memory, because every time, Python will destroy the newly created FileInfo class before returning from leakmem.

The technical term for this form of garbage collection is “reference counting”. Python keeps a list of references to every instance created. In the above example, there was only one reference to the FileInfo instance: the local variable f. When the function ends, the variable f goes out of scope, so the reference count drops to 0, and Python destroys the instance automatically.

In previous versions of Python, there were situations where reference counting failed, and Python couldn't clean up after you. If you created two instances that referenced each other (for instance, a doubly-linked list, where each node has a pointer to the previous and next node in the list), neither instance would ever be destroyed automatically because Python (correctly) believed that there is always a reference to each instance. Python 2.0 has an additional form of garbage collection called “mark-and-sweep” which is smart enough to notice this virtual gridlock and clean up circular references correctly.

As a former philosophy major, it disturbs me to think that things disappear when no one is looking at them, but that's exactly what happens in Python. In general, you can simply forget about memory management and let Python clean up after you.

Further reading

5.5. UserDict: a wrapper class

As you've seen, FileInfo is a class that acts like a dictionary. To explore this further, let's look at the UserDict class in the UserDict module, which is the ancestor of our FileInfo class. This is nothing special; the class is written in Python and stored in a .py file, just like our code. In particular, it's stored in the lib directory in your Python installation.

Tip
In the Python IDE on Windows, you can quickly open any module in your library path with File->Locate... (Ctrl-L).

Example 5.11. Defining the UserDict class

class UserDict:                                1
    def __init__(self, dict=None):             2
        self.data = {}                         3
        if dict is not None: self.update(dict) 4 5
1 Note that UserDict is a base class, not inherited from any other class.
2 This is the __init__ method that we overrode in the FileInfo class. Note that the argument list in this ancestor class is different than the descendant. That's okay; each subclass can have its own set of arguments, as long as it calls the ancestor with the correct arguments. Here the ancestor class has a way to define initial values (by passing a dictionary in the dict argument) which our FileInfo does not take advantage of.
3 Python supports data attributes (called “instance variables” in Java and Powerbuilder, “member variables” in C++), which is data held by a specific instance of a class. In this case, each instance of UserDict will have a data attribute data. To reference this attribute from code outside the class, you would qualify it with the instance name, instance.data, in the same way that you qualify a function with its module name. To reference a data attribute from within the class, we use self as the qualifier. By convention, all data attributes are initialized to reasonable values in the __init__ method. However, this is not required, since data attributes, like local variables, spring into existence when they are first assigned a value.
4 The update method is a dictionary duplicator: it copies all the keys and values from one dictionary to another. This does not clear the target dictionary first; if the target dictionary already has some keys, the ones from the source dictionary will be overwritten, but others will be left untouched. Think of update as a merge function, not a copy function.
5 Also, this is a syntax you may not have seen before (I haven't used it in the examples in this book). This is an if statement, but instead of having an indented block starting on the next line, there is just a single statement on the same line, after the colon. This is perfectly legal syntax, and is just a shortcut when you have only one statement in a block. (It's like specifying a single statement without braces in C++.) You can use this syntax, or you can have indented code on subsequent lines, but you can't do both for the same block.
Note
Java and Powerbuilder support function overloading by argument list, i.e. one class can have multiple methods with the same name but a different number of arguments, or arguments of different types. Other languages (most notably PL/SQL) even support function overloading by argument name; i.e. one class can have multiple methods with the same name and the same number of arguments of the same type but different argument names. Python supports neither of these; it has no form of function overloading whatsoever. Methods are defined solely by their name, and there can be only one method per class with a given name. So if a descendant class has an __init__ method, it always overrides the ancestor __init__ method, even if the descendant defines it with a different argument list. And the same rule applies to any other method.
Note
Guido, the original author of Python, explains method overriding this way: "Derived classes may override methods of their base classes. Because methods have no special privileges when calling other methods of the same object, a method of a base class that calls another method defined in the same base class, may in fact end up calling a method of a derived class that overrides it. (For C++ programmers: all methods in Python are effectively virtual.)" If that doesn't make sense to you (it confuses the hell out of me), feel free to ignore it. I just thought I'd pass it along.
Note
Always assign an initial value to all of an instance's data attributes in the __init__ method. It will save you hours of debugging later, tracking down AttributeError exceptions because you're referencing uninitialized (and therefore non-existent) attributes.

Example 5.12. UserDict normal methods

    def clear(self): self.data.clear()          1
    def copy(self):                             2
        if self.__class__ is UserDict:          3
            return UserDict(self.data)         
        import copy                             4
        return copy.copy(self)                 
    def keys(self): return self.data.keys()     5
    def items(self): return self.data.items()  
    def values(self): return self.data.values()
1 clear is a normal class method; it is publicly available to be called by anyone at any time. Note that clear, like all class methods, has self as its first argument. (Remember, you don't include self when you call the method; it's something that Python adds for you.) Also note the basic technique of this wrapper class: store a real dictionary (data) as a data attribute, define all the methods that a real dictionary has, and have each class method redirect to the corresponding method on the real dictionary. (In case you'd forgotten, a dictionary's clear method deletes all of its keys and their associated values.)
2 The copy method of a real dictionary returns a new dictionary that is an exact duplicate of the original (all the same key-value pairs). But UserDict can't simply redirect to self.data.copy, because that method returns a real dictionary, and what we want is to return a new instance that is the same class as self.
3 We use the __class__ attribute to see if self is a UserDict; if so, we're golden, because we know how to copy a UserDict: just create a new UserDict and give it the real dictionary that we've squirreled away in self.data.
4 If self.__class__ is not UserDict, then self must be some subclass of UserDict (like maybe FileInfo), in which case life gets trickier. UserDict doesn't know how to make an exact copy of one of its descendants; there could, for instance, be other data attributes defined in the subclass, so we would have to iterate through them and make sure to copy all of them. Luckily, Python comes with a module to do exactly this, and it's called copy. I won't go into the details here (though it's a wicked cool module, if you're ever inclined to dive into it on your own). Suffice to say that copy can copy arbitrary Python objects, and that's how we're using it here.
5 The rest of the methods are straightforward, redirecting the calls to the built-in methods on self.data.

Historical note. In versions of Python prior to 2.2, you could not directly subclass built-in datatypes like strings, lists, and dictionaries. To compensate for this, Python comes with wrapper classes that mimic the behavior of these built-in datatypes: UserString, UserList, and UserDict. Using a combination of normal and special methods, the UserDict class does an excellent imitation of a dictionary. In Python 2.2 and later, you can inherit classes directly from built-in datatypes like dict. An example of this is given in the examples that come with this book, in fileinfo_fromdict.py.

Example 5.13. Inheriting directly from built-in datatype dict

class FileInfo(dict):                  1
    "store file metadata"
    def __init__(self, filename=None): 2
        self["name"] = filename
1 There are three differences here, compared to the UserDict version. The first is that we don't need to import the UserDict module, since dict is a built-in datatype and is always available. The second is that we are inheriting from dict directly, instead of from UserDict.UserDict.
2 The third difference is subtle but important. Because of the way UserDict worked internally, it required us to manually call its __init__ method to properly initialize its internal data structures. dict does not work like this; it is not a wrapper, and it requires no explicit initialization.

Further reading

5.6. Special class methods

In addition to normal class methods, there are a number of special methods which Python classes can define. Instead of being called directly by your code (like normal methods), special methods are called for you by Python in particular circumstances or when specific syntax is used.

As you saw in the previous section, normal methods went a long way towards wrapping a dictionary in a class. But normal methods alone are not enough, because there are lots of things you can do with dictionaries besides call methods on them. For starters, you can get and set items with a syntax that doesn't include explicitly invoking methods. This is where special class methods come in: they provide a way to map non-method-calling syntax into method calls.

Example 5.14. The __getitem__ special method

    def __getitem__(self, key): return self.data[key]
>>> f = fileinfo.FileInfo("/music/_singles/kairo.mp3")
>>> f
{'name':'/music/_singles/kairo.mp3'}
>>> f.__getitem__("name") 1
'/music/_singles/kairo.mp3'
>>> f["name"]             2
'/music/_singles/kairo.mp3'
1 The __getitem__ special method looks simple enough. Like the normal methods clear, keys, and values, it just redirects to the dictionary to return its value. But how does it get called? Well, you can call __getitem__ directly, but in practice you wouldn't actually do that; I'm just doing it here to show you how it works. The right way to use __getitem__ is to get Python to call it for you.
2 This looks just like the syntax you would use to get a dictionary value, and in fact it returns the value you would expect. But here's the missing link: under the covers, Python has converted this syntax to the method call f.__getitem__("name"). That's why __getitem__ is a special class method; not only can you call it yourself, you can get Python to call it for you by using the right syntax.

Example 5.15. The __setitem__ special method

    def __setitem__(self, key, item): self.data[key] = item
>>> f
{'name':'/music/_singles/kairo.mp3'}
>>> f.__setitem__("genre", 31) 1
>>> f
{'name':'/music/_singles/kairo.mp3', 'genre':31}
>>> f["genre"] = 32            2
>>> f
{'name':'/music/_singles/kairo.mp3', 'genre':32}
1 Like the __getitem__ method, __setitem__ simply redirects to the real dictionary self.data to do its work. And like __getitem__, you wouldn't ordinarily call it directly like this; Python calls __setitem__ for you when you use the right syntax.
2 This looks like regular dictionary syntax, except of course that f is really a class that's trying very hard to masquerade as a dictionary, and __setitem__ is an essential part of that masquerade. This line of code actually calls f.__setitem__("genre", 32) under the covers.

__setitem__ is a special class method because it gets called for you, but it's still a class method. Just as easily as the __setitem__ method was defined in UserDict, we can redefine it in our descendant class to override the ancestor method. This allows us to define classes that act like dictionaries in some ways but define their own behavior above and beyond the built-in dictionary.

This concept is the basis of the entire framework we're studying in this chapter. Each file type can have a handler class which knows how to get metadata from a particular type of file. Once some attributes (like the file's name and location) are known, the handler class knows how to derive other attributes automatically. This is done by overriding the __setitem__ method, checking for particular keys, and adding additional processing when they are found.

For example, MP3FileInfo is a descendant of FileInfo. When an MP3FileInfo's name is set, it doesn't just set the name key (like the ancestor FileInfo does); it also looks in the file itself for MP3 tags and populates a whole set of keys.

Example 5.16. Overriding __setitem__ in MP3FileInfo

    def __setitem__(self, key, item):         1
        if key == "name" and item:            2
            self.__parse(item)                3
        FileInfo.__setitem__(self, key, item) 4
1 Note that our __setitem__ method is defined exactly the same way as the ancestor method. This is important, since Python will be calling the method for us, and it expects it to be defined with a certain number of arguments. (Technically speaking, the names of the arguments don't matter, just the number.)
2 Here's the crux of the entire MP3FileInfo class: if we're assigning a value to the name key, we want to do something extra.
3 The extra processing we do for names is encapsulated in the __parse method. This is another class method defined in MP3FileInfo, and when we call it, we qualify it with self. Just calling __parse would look for a normal function defined outside the class, which is not what we want; calling self.__parse will look for a class method defined within the class. This isn't anything new; you reference data attributes the same way.
4 After doing our extra processing, we want to call the ancestor method. Remember, this is never done for you in Python; you have to do it manually. Note that we're calling the immediate ancestor, FileInfo, even though it doesn't have a __setitem__ method. That's okay, because Python will walk up the ancestor tree until it finds a class with the method we're calling, so this line of code will eventually find and call the __setitem__ defined in UserDict.
Note
When accessing data attributes within a class, you need to qualify the attribute name: self.attribute. When calling other methods within a class, you need to qualify the method name: self.method.

Example 5.17. Setting an MP3FileInfo's name

>>> import fileinfo
>>> mp3file = fileinfo.MP3FileInfo()                   1
>>> mp3file
{'name':None}
>>> mp3file["name"] = "/music/_singles/kairo.mp3"      2
>>> mp3file
{'album': 'Rave Mix', 'artist': '***DJ MARY-JANE***', 'genre': 31,
'title': 'KAIRO****THE BEST GOA', 'name': '/music/_singles/kairo.mp3',
'year': '2000', 'comment': 'http://mp3.com/DJMARYJANE'}
>>> mp3file["name"] = "/music/_singles/sidewinder.mp3" 3
>>> mp3file
{'album': '', 'artist': 'The Cynic Project', 'genre': 18, 'title': 'Sidewinder', 
'name': '/music/_singles/sidewinder.mp3', 'year': '2000', 
'comment': 'http://mp3.com/cynicproject'}
1 First, we create an instance of MP3FileInfo, without passing it a filename. (We can get away with this because the filename argument of the __init__ method is optional.) Since MP3FileInfo has no __init__ method of its own, Python walks up the ancestor tree and finds the __init__ method of FileInfo. This __init__ method manually calls the __init__ method of UserDict and then sets the name key to filename, which is None, since we didn't pass a filename. Thus, mp3file initially looks like a dictionary with one key, name, whose value is None.
2 Now the real fun begins. Setting the name key of mp3file triggers the __setitem__ method on MP3FileInfo (not UserDict), which notices that we're setting the name key with a real value and calls self.__parse. Although we haven't traced through the __parse method yet, you can see from the output that it sets several other keys: album, artist, genre, title, year, and comment.
3 Modifying the name key will go through the same process again: Python calls __setitem__, which calls self.__parse, which sets all the other keys.

5.7. Advanced special class methods

There are more special methods than just __getitem__ and __setitem__. Some of them let you emulate functionality that you may not even know about.

Example 5.18. More special methods in UserDict

    def __repr__(self): return repr(self.data)     1
    def __cmp__(self, dict):                       2
        if isinstance(dict, UserDict):            
            return cmp(self.data, dict.data)      
        else:                                     
            return cmp(self.data, dict)           
    def __len__(self): return len(self.data)       3
    def __delitem__(self, key): del self.data[key] 4
1 __repr__ is a special method which is called when you call repr(instance). The repr function is a built-in function that returns a string representation of an object. It works on any object, not just class instances. You're already intimately familiar with repr and you don't even know it. In the interactive window, when you type just a variable name and hit ENTER, Python uses repr to display the variable's value. Go create a dictionary d with some data and then print repr(d) to see for yourself.
2 __cmp__ is called when you compare class instances. In general, you can compare any two Python objects, not just class instances, by using ==. There are rules that define when built-in datatypes are considered equal; for instance, dictionaries are equal when they have all the same keys and values, and strings are equal when they are the same length and contain the same sequence of characters. For class instances, you can define the __cmp__ method and code the comparison logic yourself, and then you can use == to compare instances of your class and Python will call your __cmp__ special method for you.
3 __len__ is called when you call len(instance). The len function is a built-in function that returns the length of an object. It works on any object that could reasonably be thought of as having a length. The len of a string is its number of characters; the len of a dictionary is its number of keys; the len of a list or tuple is its number of elements. For class instances, define the __len__ method and code the length calculation yourself, then call len(instance) and Python will call your __len__ special method for you.
4 __delitem__ is called when you call del instance[key], which you may remember as the way to delete individual items from a dictionary. When you use del on a class instance, Python calls the __delitem__ special method for you.
Note
In Java, you determine whether two string variables reference the same physical memory location by using str1 == str2. This is called object identity, and it is written in Python as str1 is str2. To compare string values in Java, you would use str1.equals(str2); in Python, you would use str1 == str2. Java programmers who have been taught to believe that the world is a better place because == in Java compares by identity instead of by value may have a difficult time adjusting to Python's lack of such “gotchas”.

At this point, you may be thinking, “all this work just to do something in a class that I can do with a built-in datatype”. And it's true that life would be easier (and the entire UserDict class would be unnecessary) if you could inherit from built-in datatypes like a dictionary. But even if you could, special methods would still be useful, because they can be used in any class, not just wrapper classes like UserDict.

Special methods mean that any class can store key-value pairs like a dictionary, just by defining the __setitem__ method. Any class can act like a sequence, just by defining the __getitem__ method. Any class that defines the __cmp__ method can be compared with ==. And if your class represents something that has a length, don't define a GetLength method; define the __len__ method and use len(instance).

Note
While other object-oriented languages only let you define the physical model of an object (“this object has a GetLength method”), Python's special class methods like __len__ allow you to define the logical model of an object (“this object has a length”).

There are lots of other special methods. There's a whole set of them that let classes act like numbers, allowing you to add, subtract, and do other arithmetic operations on class instances. (The canonical example of this is a class that represents complex numbers, numbers with both real and imaginary components.) The __call__ method lets a class act like a function, allowing you to call a class instance directly. And there are other special methods that allow classes to have read-only and write-only data attributes; we'll talk more about those in later chapters.

5.8. Class attributes

You already know about data attributes, which are variables owned by a specific instance of a class. Python also supports class attributes, which are variables owned by the class itself.

Example 5.19. Introducing class attributes

class MP3FileInfo(FileInfo):
    "store ID3v1.0 MP3 tags"
    tagDataMap = {"title"   : (  3,  33, stripnulls),
                  "artist"  : ( 33,  63, stripnulls),
                  "album"   : ( 63,  93, stripnulls),
                  "year"    : ( 93,  97, stripnulls),
                  "comment" : ( 97, 126, stripnulls),
                  "genre"   : (127, 128, ord)}
>>> import fileinfo
>>> fileinfo.MP3FileInfo            1
<class fileinfo.MP3FileInfo at 01257FDC>
>>> fileinfo.MP3FileInfo.tagDataMap 2
{'title': (3, 33, <function stripnulls at 0260C8D4>), 
'genre': (127, 128, <built-in function ord>), 
'artist': (33, 63, <function stripnulls at 0260C8D4>), 
'year': (93, 97, <function stripnulls at 0260C8D4>), 
'comment': (97, 126, <function stripnulls at 0260C8D4>), 
'album': (63, 93, <function stripnulls at 0260C8D4>)}
>>> m = fileinfo.MP3FileInfo()      3
>>> m.tagDataMap
{'title': (3, 33, <function stripnulls at 0260C8D4>), 
'genre': (127, 128, <built-in function ord>), 
'artist': (33, 63, <function stripnulls at 0260C8D4>), 
'year': (93, 97, <function stripnulls at 0260C8D4>), 
'comment': (97, 126, <function stripnulls at 0260C8D4>), 
'album': (63, 93, <function stripnulls at 0260C8D4>)}
1 MP3FileInfo is the class itself, not any particular instance of the class.
2 tagDataMap is a class attribute: literally, an attribute of the class. It is available before creating any instances of the class.
3 Class attributes are available both through direct reference to the class and through any instance of the class.
Note
In Java, both static variables (called class attributes in Python) and instance variables (called data attributes in Python) are defined immediately after the class definition (one with the static keyword, one without). In Python, only class attributes can be defined here; data attributes are defined in the __init__ method.

Class attributes can be used as class-level constants (which is how we use them in MP3FileInfo), but they are not really constants.[4] You can also change them.

Example 5.20. Modifying class attributes

>>> class counter:
...     count = 0                     1
...     def __init__(self):
...         self.__class__.count += 1 2
...     
>>> counter
<class __main__.counter at 010EAECC>
>>> counter.count                     3
0
>>> c = counter()
>>> c.count                           4
1
>>> counter.count
1
>>> d = counter()                     5
>>> d.count
2
>>> c.count
2
>>> counter.count
2
1 count is a class attribute of the counter class.
2 __class__ is a built-in attribute of every class instance (of every class). It is a reference to the class that self is an instance of (in this case, the counter class).
3 Because count is a class attribute, it is available through direct reference to the class, before we have created any instances of the class.
4 Creating an instance of the class calls the __init__ method, which increments the class attribute count by 1. This affects the class itself, not just the newly created instance.
5 Creating a second instance will increment the class attribute count again. Notice how the class attribute is shared by the class and all instances of the class.

5.9. Private functions

Like most languages, Python has the concept of private functions, which can not be called from outside their module; private class methods, which can not be called from outside their class; and private attributes, which can not be accessed from outside their class. Unlike most languages, whether a Python function, method, or attribute is private or public is determined entirely by its name.

In MP3FileInfo, there are two methods: __parse and __setitem__. As we have already discussed, __setitem__ is a special method; normally, you would call it indirectly by using the dictionary syntax on a class instance, but it is public, and you could call it directly (even from outside the fileinfo module) if you had a really good reason. However, __parse is private, because it has two underscores at the beginning of its name.

Note
If the name of a Python function, class method, or attribute starts with (but doesn't end with) two underscores, it's private; everything else is public.
Note
In Python, all special methods (like __setitem__) and built-in attributes (like __doc__) follow a standard naming convention: they both start with and end with two underscores. Don't name your own methods and attributes this way; it will only confuse you (and others) later.
Note
Python has no concept of protected class methods (accessible only in their own class and descendant classes). Class methods are either private (accessible only in their own class) or public (accessible from anywhere).

Example 5.21. Trying to call a private method

>>> import fileinfo
>>> m = fileinfo.MP3FileInfo()
>>> m.__parse("/music/_singles/kairo.mp3") 1
Traceback (innermost last):
  File "<interactive input>", line 1, in ?
AttributeError: 'MP3FileInfo' instance has no attribute '__parse'
1 If you try to call a private method, Python will raise a slightly misleading exception, saying that the method does not exist. Of course it does exist, but it's private, so it's not accessible outside the class.[5]

Further reading



[4] There are no constants in Python. Everything can be changed if you try hard enough. This fits with one of the core principles of Python: bad behavior should be discouraged but not banned. If you really want to change the value of None, you can do it, but don't come running to me when your code is impossible to debug.

[5] Strictly speaking, private methods are accessible outside their class, just not easily accessible. Nothing in Python is truly private; internally, the names of private methods and attributes are mangled and unmangled on the fly to make them seem inaccessible by their given names. You can access the __parse method of the MP3FileInfo class by the name _MP3FileInfo__parse. Acknowledge that this is interesting, then promise to never, ever do it in real code. Private methods are private for a reason, but like many other things in Python, their privateness is ultimately a matter of convention, not force.

Chapter 6. Exceptions and File Handling

6.1. Handling exceptions

Like many object-oriented languages, Python has exception handling via try...except blocks.

Note
Python uses try...except to handle exceptions and raise to generate them. Java and C++ use try...catch to handle exceptions, and throw to generate them.

If you already know all about exceptions, you can skim this section. If you've been stuck programming in a lesser language that doesn't have exception handling, or you've been using a real language but not using exceptions, this section is very important.

Exceptions are everywhere in Python; virtually every module in the standard Python library uses them, and Python itself will raise them in lots of different circumstances. You've already seen them repeatedly throughout this book.

In each of these cases, we were simply playing around in the Python IDE: an error occurred, the exception was printed (depending on your IDE, in an intentionally jarring shade of red), and that was that. This is called an unhandled exception; when the exception was raised, there was no code to explicitly notice it and deal with it, so it bubbled its way back to the default behavior built in to Python, which is to spit out some debugging information and give up. In the IDE, that's no big deal, but if that happened while your actual Python program was running, the entire program would come to a screeching halt.[6]

An exception doesn't have to be a complete program crash, though. Exceptions, when raised, can be handled. Sometimes an exception is really because you have a bug in your code (like accessing a variable that doesn't exist), but many times, an exception is something you can plan for. If you're opening a file, it might not exist; if you're connecting to a database, it might be unavailable, or you might not have the correct security credentials to access it. If you know a line of code may raise an exception, you should handle the exception using a try...except block.

Example 6.1. Opening a non-existent file

>>> fsock = open("/notthere", "r")      1
Traceback (innermost last):
  File "<interactive input>", line 1, in ?
IOError: [Errno 2] No such file or directory: '/notthere'
>>> try:
...     fsock = open("/notthere")       2
... except IOError:                     3
...     print "The file does not exist, exiting gracefully"
... print "This line will always print" 4
The file does not exist, exiting gracefully
This line will always print
1 Using the built-in open function, we can try to open a file for reading (more on open in the next section). But the file doesn't exist, so this raises the IOError exception. Since we haven't provided any explicit check for an IOError exception, Python just prints out some debugging information about what happened and then gives up.
2 We're trying to open the same non-existent file, but this time we're doing it within a try...except block.
3 When the open method raises an IOError exception, we're ready for it. The except IOError: line catches the exception and executes our own block of code, which in this case just prints a more pleasant error message.
4 Once an exception has been handled, processing continues normally on the first line after the try...except block. Note that this line will always print, whether or not an exception occurs. If you really did have a file called notthere in your root directory, the call to open would succeed, the except clause would be ignored, and this line would still be executed.

Exceptions may seem unfriendly (after all, if you don't catch the exception, your entire program will crash), but consider the alternative. Would you rather get back an unusable file object to a non-existent file? You'd have to check its validity somehow anyway, and if you forgot, your program would give you strange errors somewhere down the line that you would have to trace back to the source. I'm sure you've done this; it's not fun. With exceptions, errors occur immediately, and you can handle them in a standard way at the source of the problem.

There are lots of other uses for exceptions besides handling actual error conditions. A common use in the standard Python library is to try to import a module, then check whether it worked. Importing a module that does not exist will raise an ImportError exception. You can use this to define multiple levels of functionality based on which modules are available at run-time, or to support multiple platforms (where platform-specific code is separated into different modules).

You can also define your own exceptions by creating a class that inherits from the built-in Exception class, and then raise your exceptions with the raise command. This is beyond the scope of this section, but see the further reading section if you're interested.

Example 6.2. Supporting platform-specific functionality

This code comes from the getpass module, a wrapper module for getting a password from the user. Getting a password is accomplished differently on UNIX, Windows, and Mac OS platforms, but this code encapsulates all of those differences.

  # Bind the name getpass to the appropriate function
  try:
      import termios, TERMIOS                     1
  except ImportError:
      try:
          import msvcrt                           2
      except ImportError:
          try:
              from EasyDialogs import AskPassword 3
          except ImportError:
              getpass = default_getpass           4
          else:                                   5
              getpass = AskPassword
      else:
          getpass = win_getpass
  else:
      getpass = unix_getpass
1 termios is a UNIX-specific module that provides low-level control over the input terminal. If this module is not available (because it's not on your system, or your system doesn't support it), the import fails and Python raises an ImportError, which we catch.
2 OK, we didn't have termios, so let's try msvcrt, which is a Windows-specific module that provides an API to lots of useful functions in the Microsoft Visual C++ runtime services. If this import fails, Python will raise an ImportError, which we catch.
3 If the first two didn't work, we try to import a function from EasyDialogs, which is a Mac OS-specific module that provides functions to pop up dialogs of various types. Once again, if this import fails, Python will raise an ImportError, which we catch.
4 None of these platform-specific modules is available (which is possible, since Python has been ported to lots of different platforms), so we have to fall back on a default password input function (which is defined elsewhere in the getpass module). Notice what we're doing here: we're assigning the function default_getpass to the variable getpass. If you read the official getpass documentation, it tells you that the getpass module defines a getpass function. This is how it does it: by binding getpass to the right function for your platform. Then when you call the getpass function, you're really calling a platform-specific function that this code has set up for you. You don't have to know or care what platform your code is running on; just call getpass, and it will always do the right thing.
5 A try...except block can have an else clause, like an if statement. If no exception is raised during the try block, the else clause is executed afterwards. In this case, that means that the from EasyDialogs import AskPassword import worked, so we should bind getpass to the AskPassword function. Each of the other try...except blocks have similar else clauses to bind getpass to the appropriate function when we find an import that works.

Further reading

6.2. File objects

Python has a built-in function, open, for opening a file on disk. open returns a file object, which has methods and attributes for getting information about and manipulating the opened file.

Example 6.3. Opening a file

>>> f = open("/music/_singles/kairo.mp3", "rb") 1
>>> f                                           2
<open file '/music/_singles/kairo.mp3', mode 'rb' at 010E3988>
>>> f.mode                                      3
'rb'
>>> f.name                                      4
'/music/_singles/kairo.mp3'
1 The open method can take up to three parameters: a filename, a mode, and a buffering parameter. Only the first one, the filename, is required; the other two are optional. If not specified, the file is opened for reading in text mode. Here we are opening the file for reading in binary mode. (print open.__doc__ displays a great explanation of all the possible modes.)
2 The open function returns an object (by now, this should not surprise you). A file object has several useful attributes.
3 The mode attribute of a file object tells you what mode the file was opened in.
4 The name attribute of a file object tells you the name of the file that the file object has open.

Example 6.4. Reading a file

>>> f
<open file '/music/_singles/kairo.mp3', mode 'rb' at 010E3988>
>>> f.tell()              1
0
>>> f.seek(-128, 2)       2
>>> f.tell()              3
7542909
>>> tagData = f.read(128) 4
>>> tagData
'TAGKAIRO****THE BEST GOA         ***DJ MARY-JANE***            Rave Mix                      2000http://mp3.com/DJMARYJANE     \037'
>>> f.tell()              5
7543037
1 A file object maintains state about the file it has open. The tell method of a file object tells you your current position in the open file. Since we haven't done anything with this file yet, the current position is 0, which is the beginning of the file.
2 The seek method of a file object moves to another position in the open file. The second parameter specifies what the first one means; 0 means move to an absolute position (counting from the start of the file), 1 means move to a relative position (counting from the current position), and 2 means move to a position relative to the end of the file. Since the MP3 tags we're looking for are stored at the end of the file, we use 2 and tell the file object to move to a position 128 bytes from the end of the file.
3 The tell method confirms that the current file position has moved.
4 The read method reads a specified number of bytes from the open file and returns a string with the data which was read. The optional parameter specifies the maximum number of bytes to read. If no parameter is specified, read will read until the end of the file. (We could have simply said read() here, since we know exactly where we are in the file and we are, in fact, reading the last 128 bytes.) The read data is assigned to the tagData variable, and the current position is updated based on how many bytes were read.
5 The tell method confirms that the current position has moved. If you do the math, you'll see that after reading 128 bytes, the position has been incremented by 128.

Example 6.5. Closing a file

>>> f
<open file '/music/_singles/kairo.mp3', mode 'rb' at 010E3988>
>>> f.closed  1
0
>>> f.close() 2
>>> f
<closed file '/music/_singles/kairo.mp3', mode 'rb' at 010E3988>
>>> f.closed
1
>>> f.seek(0) 3
Traceback (innermost last):
  File "<interactive input>", line 1, in ?
ValueError: I/O operation on closed file
>>> f.tell()
Traceback (innermost last):
  File "<interactive input>", line 1, in ?
ValueError: I/O operation on closed file
>>> f.read()
Traceback (innermost last):
  File "<interactive input>", line 1, in ?
ValueError: I/O operation on closed file
>>> f.close() 4
1 The closed attribute of a file object indicates whether the object has a file open or not. In this case, the file is still open (closed is 0). Open files consume system resources, and depending on the file mode, other programs may not be able to access them. It's important to close files as soon as you're done with them.
2 To close a file, call the close method of the file object. This frees the lock (if any) that you were holding on the file, flushes buffered writes (if any) that the system hadn't gotten around to actually writing yet, and releases the system resources. The closed attribute confirms that the file is closed.
3 Just because a file is closed doesn't mean that the file object ceases to exist. The variable f will continue to exist until it goes out of scope or gets manually deleted. However, none of the methods that manipulate an open file will work once the file has been closed; they all raise an exception.
4 Calling close on a file object whose file is already closed does not raise an exception; it fails silently.

Example 6.6. File objects in MP3FileInfo

        try:                                1
            fsock = open(filename, "rb", 0) 2
            try:                           
                fsock.seek(-128, 2)         3
                tagdata = fsock.read(128)   4
            finally:                        5
                fsock.close()              
            .
            .
            .
        except IOError:                     6
            pass                           
1 Because opening and reading files is risky and may raise an exception, all of this code is wrapped in a try...except block. (Hey, isn't standardized indentation great? This is where you start to appreciate it.)
2 The open function may raise an IOError. (Maybe the file doesn't exist.)
3 The seek method may raise an IOError. (Maybe the file is smaller than 128 bytes.)
4 The read method may raise an IOError. (Maybe the disk has a bad sector, or it's on a network drive and the network just went down.)
5 This is new: a try...finally block. Once the file has been opened successfully by the open function, we want to make absolutely sure that we close it, even if an exception is raised by the seek or read methods. That's what a try...finally block is for: code in the finally block will always be executed, even if something in the try block raises an exception. Think of it as code that gets executed “on the way out”, regardless of what happened on the way.
6 At last, we handle our IOError exception. This could be the IOError exception raised by the call to open, seek, or read. Here, we really don't care, because all we're going to do is ignore it silently and continue. (Remember, pass is a Python statement that does nothing.) That's perfectly legal; “handling” an exception can mean explicitly doing nothing. It still counts as handled, and processing will continue normally on the next line of code after the try...except block.

As you would expect, you can also write to files in much the same way that you read from them. There are two basic file modes, write and append. "append" mode will add data to the end of the file; "write" mode will overwrite the file. Either will create the file automatically if it doesn't already exist, so there's never a need for any sort of fiddly "if the log file doesn't exist yet then create a new empty file just so we can open it for the first time" logic. Just open it and start writing.

Example 6.7. Writing to files

>>> logfile = open('test.log', 'w') 1
>>> logfile.write('test succeeded') 2
>>> logfile.close()
>>> print file('test.log').read()   3
test succeeded
>>> logfile = open('test.log', 'a') 4
>>> logfile.write('line 2')
>>> logfile.close()
>>> print file('test.log').read()   5
test succeededline 2
1 We start boldly by creating either the new file test.log or overwrites the existing file, and opening the file for writing. (The second parameter "w" means open the file for writing.) Yes, that's all as dangerous as it sounds. I hope you didn't care about the previous contents of that file, because it's gone now.
2 You can add data to the newly opened file with the write method of the file object returned by open.
3 file is a synonym for open. This one-liner opens the file, reads its contents, and prints them.
4 We happen to know that test.log exists (since we just finished writing to it), so we can open it back up and append to it. (The "a" parameter means open the file for appending.) Actually we could do this even if the file didn't exist; opening the file for appending will create the file if it doesn't already exist. But it will never harm the existing contents of the file.
5 As you can see, both the original line we wrote and the second line we appended are now in test.log. Also note that carriage returns are not included; we didn't write them explicitly to the file either time, so the file doesn't include them. You can write a carriage return with the "\n" character. Since we didn't do this, everything we wrote to the file ended up smooshed together on the same line.

Further reading

6.3. for loops

Like most other languages, Python has for loops. The only reason you haven't seen them until now is that Python is good at so many other things that you don't need them as often.

Most other languages don't have a powerful list datatype like Python, so you end up doing a lot of manual work, specifying a start, end, and step to define a range of integers or characters or other iteratable entities. But in Python, a for loop simply iterates over a list, the same way list comprehensions work.

Example 6.8. Introducing the for loop

>>> li = ['a', 'b', 'e']
>>> for s in li:         1
...     print s          2
a
b
e
>>> print "\n".join(li)  3
a
b
e
1 The syntax for a for loop is similar to list comprehensions. li is a list, and s will take the value of each element in turn, starting from the first element.
2 Like an if statement or any other indented block, a for loop can have any number of lines of code in it.
3 This is the reason you haven't seen the for loop yet: we haven't needed it yet. It's amazing how often you use for loops in other languages when all you really want is a join or a list comprehension.

Example 6.9. Simple counters

>>> for i in range(5):       1
...     print i
0
1
2
3
4
>>> li = ['a', 'b', 'c', 'd', 'e']
>>> for i in range(len(li)): 2
...     print li[i]
a
b
c
d
e
1 Doing a “normal” (by Visual Basic standards) counter for loop is also simple. As we saw in Example 3.19, “Assigning consecutive values”, range produces a list of integers, which we then loop through. I know it looks a bit odd, but it is occasionally (and I stress occasionally) useful to have a counter loop.
2 Don't ever do this. This is Visual Basic-style thinking. Break out of it. Just iterate through the list, as shown in the previous example.

Example 6.10. Iterating through a dictionary

>>> for k, v in os.environ.items(): 1 2
...     print "%s=%s" % (k, v)
USERPROFILE=C:\Documents and Settings\mpilgrim
OS=Windows_NT
COMPUTERNAME=MPILGRIM
USERNAME=mpilgrim

[...snip...]
>>> print "\n".join(["%s=%s" % (k, v) for k, v in os.environ.items()]) 3
USERPROFILE=C:\Documents and Settings\mpilgrim
OS=Windows_NT
COMPUTERNAME=MPILGRIM
USERNAME=mpilgrim

[...snip...]
1 os.environ is a dictionary of the environment variables defined on your system. In Windows, these are your user and system variables accessible from MS-DOS. In UNIX, they are the variables exported in your shell's startup scripts. In Mac OS, there is no concept of environment variables, so this dictionary is empty.
2 os.environ.items() returns a list of tuples: [(key1, value1), (key2, value2), ...]. The for loop iterates through this list. The first round, it assigns key1 to k and value1 to v, so k = USERPROFILE and v = C:\Documents and Settings\mpilgrim. The second round, k gets the second key, OS, and v gets the corresponding value, Windows_NT.
3 With multi-variable assignment and list comprehensions, you can replace the entire for loop with a single statement. Whether you actually do this in real code is a matter of personal coding style; I like it because it makes it clear that what we're doing is mapping a dictionary into a list, then joining the list into a single string. Other programmers prefer to write this out as a for loop. Note that the output is the same in either case, although this version is slightly faster, because there is only one print statement instead of many.

Example 6.11. for loop in MP3FileInfo

    tagDataMap = {"title"   : (  3,  33, stripnulls),
                  "artist"  : ( 33,  63, stripnulls),
                  "album"   : ( 63,  93, stripnulls),
                  "year"    : ( 93,  97, stripnulls),
                  "comment" : ( 97, 126, stripnulls),
                  "genre"   : (127, 128, ord)} 1
    .
    .
    .
            if tagdata[:3] == "TAG":
                for tag, (start, end, parseFunc) in self.tagDataMap.items(): 2
                    self[tag] = parseFunc(tagdata[start:end])                3
1 tagDataMap is a class attribute that defines the tags we're looking for in an MP3 file. Tags are stored in fixed-length fields; once we read the last 128 bytes of the file, bytes 3 through 32 of those are always the song title, 33-62 are always the artist name, 63-92 the album name, and so forth. Note that tagDataMap is a dictionary of tuples, and each tuple contains two integers and a function reference.
2 This looks complicated, but it's not. The structure of the for variables matches the structure of the elements of the list returned by items. Remember, items returns a list of tuples of the form (key, value). The first element of that list is ("title", (3, 33, <function stripnulls>)), so the first time around the loop, tag gets "title", start gets 3, end gets 33, and parseFunc gets the function stripnulls.
3 Now that we've extracted all the parameters for a single MP3 tag, saving the tag data is easy. We slice tagdata from start to end to get the actual data for this tag, call parseFunc to post-process the data, and assign this as the value for the key tag in the pseudo-dictionary self. After iterating through all the elements in tagDataMap, self has the values for all the tags, and you know what that looks like.

6.4. More on modules

Modules, like everything else in Python, are objects. Once imported, you can always get a reference to a module through the global dictionary sys.modules.

Example 6.12. Introducing sys.modules

>>> import sys                          1
>>> print '\n'.join(sys.modules.keys()) 2
win32api
os.path
os
exceptions
__main__
ntpath
nt
sys
__builtin__
site
signal
UserDict
stat
1 The sys module contains system-level information, like the version of Python you're running (sys.version or sys.version_info), and system-level options like the maximum allowed recursion depth (sys.getrecursionlimit() and sys.setrecursionlimit()).
2 sys.modules is a dictionary containing all the modules that have ever been imported since Python was started; the key is the module name, the value is the module object. Note that this is more than just the modules your program has imported. Python preloads some modules on startup, and if you're in a Python IDE, sys.modules contains all the modules imported by all the programs you've run within the IDE.

Example 6.13. Using sys.modules

>>> import fileinfo         1
>>> print '\n'.join(sys.modules.keys())
win32api
os.path
os
fileinfo
exceptions
__main__
ntpath
nt
sys
__builtin__
site
signal
UserDict
stat
>>> fileinfo
<module 'fileinfo' from 'fileinfo.pyc'>
>>> sys.modules["fileinfo"] 2
<module 'fileinfo' from 'fileinfo.pyc'>
1 As new modules are imported, they are added to sys.modules. This explains why importing the same module twice is very fast: Python has already loaded and cached the module in sys.modules, so importing the second time is simply a dictionary lookup.
2 Given the name (as a string) of any previously-imported module, you can get a reference to the module itself through the sys.modules dictionary.

Example 6.14. The __module__ class attribute

>>> from fileinfo import MP3FileInfo
>>> MP3FileInfo.__module__              1
'fileinfo'
>>> sys.modules[MP3FileInfo.__module__] 2
<module 'fileinfo' from 'fileinfo.pyc'>
1 Every Python class has a built-in class attribute __module__, which is the name of the module in which the class is defined.
2 Combining this with the sys.modules dictionary, you can get a reference to the module in which a class is defined.

Example 6.15. sys.modules in fileinfo.py

    def getFileInfoClass(filename, module=sys.modules[FileInfo.__module__]):       1
        "get file info class from filename extension"                             
        subclass = "%sFileInfo" % os.path.splitext(filename)[1].upper()[1:]        2
        return hasattr(module, subclass) and getattr(module, subclass) or FileInfo 3
1 This is a function with two arguments; filename is required, but module is optional and defaults to the module which contains the FileInfo class. This looks inefficient, because you might expect Python to evaluate the sys.modules expression every time the function is called. In fact, Python only evaluates default expressions once, the first time the module is imported. As we'll see later, we never call this function with a module argument, so module serves as a function-level constant.
2 We'll plough through this line later, after we dive into the os module. For now, take it on faith that subclass ends up as the name of a class, like MP3FileInfo.
3 You already know about getattr, which gets a reference to an object by name. hasattr is a complementary function that checks whether an object has a particular attribute; in this case, whether a module has a particular class (although it works for any object and any attribute, just like getattr). In English, this line of code says “if this module has the class named by subclass then return it, otherwise return the base class FileInfo”.

Further reading

6.5. Working with directories

The os.path module has several functions for manipulating files and directories.

Example 6.16. Constructing pathnames

>>> import os
>>> os.path.join("c:\\music\\ap\\", "mahadeva.mp3") 1 2
'c:\\music\\ap\\mahadeva.mp3'
>>> os.path.join("c:\\music\\ap", "mahadeva.mp3")   3
'c:\\music\\ap\\mahadeva.mp3'
>>> os.path.expanduser("~")                         4
'c:\\Documents and Settings\\mpilgrim\\My Documents'
>>> os.path.join(os.path.expanduser("~"), "Python") 5
'c:\\Documents and Settings\\mpilgrim\\My Documents\\Python'
1 os.path is a reference to a module; which module it is depends on what platform you're running on. Just like getpass encapsulates differences between platforms by setting getpass to a platform-specific function, os encapsulates differences between platforms by setting path to a platform-specific module.
2 The join function of os.path constructs a pathname out of one or more partial pathnames. In this simple case, it simply concatenates strings. (Note that dealing with pathnames on Windows is annoying because the backslash character must be escaped.)
3 In this slightly less trivial case, join will add an extra backslash to the pathname before joining it to the filename. I was overjoyed when I discovered this, since addSlashIfNecessary is always one of the stupid little functions I have to write when building up my toolbox in a new language. Do not write this stupid little function in Python; smart people have already taken care of it for you.
4 expanduser will expand a pathname that uses ~ to represent the current user's home directory. This works on any platform where users have a home directory, like Windows, UNIX, and Mac OS X; it has no effect on Mac OS.
5 Combining these techniques, you can easily construct pathnames for directories and files under the user's home directory.

Example 6.17. Splitting pathnames

>>> os.path.split("c:\\music\\ap\\mahadeva.mp3")                        1
('c:\\music\\ap', 'mahadeva.mp3')
>>> (filepath, filename) = os.path.split("c:\\music\\ap\\mahadeva.mp3") 2
>>> filepath                                                            3
'c:\\music\\ap'
>>> filename                                                            4
'mahadeva.mp3'
>>> (shortname, extension) = os.path.splitext(filename)                 5
>>> shortname
'mahadeva'
>>> extension
'.mp3'
1 The split function splits a full pathname and returns a tuple containing the path and filename. Remember when I said you could use multi-variable assignment to return multiple values from a function? Well, split is such a function.
2 We assign the return value of the split function into a tuple of two variables. Each variable receives the value of the corresponding element of the returned tuple.
3 The first variable, filepath, receives the value of the first element of the tuple returned from split, the file path.
4 The second variable, filename, receives the value of the second element of the tuple returned from split, the filename.
5 os.path also contains a function splitext, which splits a filename and returns a tuple containing the filename and the file extension. We use the same technique to assign each of them to separate variables.

Example 6.18. Listing directories

>>> os.listdir("c:\\music\\_singles\\")                                          1
['a_time_long_forgotten_con.mp3', 'hellraiser.mp3', 'kairo.mp3',
'long_way_home1.mp3', 'sidewinder.mp3', 'spinning.mp3']
>>> dirname = "c:\\"
>>> os.listdir(dirname)                                                          2
['AUTOEXEC.BAT', 'boot.ini', 'CONFIG.SYS', 'cygwin', 'docbook',
'Documents and Settings', 'Incoming', 'Inetpub', 'IO.SYS', 'MSDOS.SYS', 'Music',
'NTDETECT.COM', 'ntldr', 'pagefile.sys', 'Program Files', 'Python20', 'RECYCLER',
'System Volume Information', 'TEMP', 'WINNT']
>>> [f for f in os.listdir(dirname) if os.path.isfile(os.path.join(dirname, f))] 3
['AUTOEXEC.BAT', 'boot.ini', 'CONFIG.SYS', 'IO.SYS', 'MSDOS.SYS',
'NTDETECT.COM', 'ntldr', 'pagefile.sys']
>>> [f for f in os.listdir(dirname) if os.path.isdir(os.path.join(dirname, f))]  4
['cygwin', 'docbook', 'Documents and Settings', 'Incoming',
'Inetpub', 'Music', 'Program Files', 'Python20', 'RECYCLER',
'System Volume Information', 'TEMP', 'WINNT']
1 The listdir function takes a pathname and returns a list of the contents of the directory.
2 listdir returns both files and folders, with no indication of which is which.
3 You can use list filtering and the isfile function of the os.path module to separate the files from the folders. isfile takes a pathname and returns 1 if the path represents a file, and 0 otherwise. Here we're using os.path.join to ensure a full pathname, but isfile also works with a partial path, relative to the current working directory. You can use os.getcwd() to get the current working directory.
4 os.path also has a isdir function which returns 1 if the path represents a directory, and 0 otherwise. You can use this to get a list of the subdirectories within a directory.

Example 6.19. Listing directories in fileinfo.py

def listDirectory(directory, fileExtList):                                        
    "get list of file info objects for files of particular extensions"
    fileList = [os.path.normcase(f) for f in os.listdir(directory)]               
    fileList = [os.path.join(directory, f) for f in fileList \
                if os.path.splitext(f)[1] in fileExtList]                         

These two lines of code combine everything we've learned so far about the os module, and then some.

  1. os.listdir(directory) returns a list of all the files and folders in directory.
  2. Iterating through the list with f, we use os.path.normcase(f) to normalize the case according to operating system defaults. normcase is a useful little function that compensates for case-insensitive operating systems that think that mahadeva.mp3 and mahadeva.MP3 are the same file. For instance, on Windows and Mac OS, normcase will convert the entire filename to lowercase; on UNIX-compatible systems, it will return the filename unchanged.
  3. Iterating through the normalized list with f again, we use os.path.splitext(f) to split each filename into name and extension.
  4. For each file, we see if the extension is in the list of file extensions we care about (fileExtList, which was passed to the listDirectory function).
  5. For each file we care about, we use os.path.join(directory, f) to construct the full pathname of the file, and return a list of the full pathnames.
Note
Whenever possible, you should use the functions in os and os.path for file, directory, and path manipulations. These modules are wrappers for platform-specific modules, so functions like os.path.split work on UNIX, Windows, Mac OS, and any other supported Python platform.

There is one other way to get the contents of a directory. It's very powerful, and it uses the sort of wildcards that you may already be familiar with from working on the command line.

Example 6.20. Listing directories with glob

>>> os.listdir("c:\\music\\_singles\\")               1
['a_time_long_forgotten_con.mp3', 'hellraiser.mp3', 'kairo.mp3',
'long_way_home1.mp3', 'sidewinder.mp3', 'spinning.mp3']
>>> import glob
>>> glob.glob('c:\\music\\_singles\\*.mp3')           2
['c:\\music\\_singles\\a_time_long_forgotten_con.mp3',
'c:\\music\\_singles\\hellraiser.mp3',
'c:\\music\\_singles\\kairo.mp3',
'c:\\music\\_singles\\long_way_home1.mp3',
'c:\\music\\_singles\\sidewinder.mp3',
'c:\\music\\_singles\\spinning.mp3']
>>> glob.glob('c:\\music\\_singles\\s*.mp3')          3
['c:\\music\\_singles\\sidewinder.mp3',
'c:\\music\\_singles\\spinning.mp3']
>>> glob.glob('c:\\music\\*\\*.mp3')                  4
1 As we saw earlier, os.listdir simply takes a directory path and lists all files and directories in that directory.
2 The glob module, on the other hand, takes a wildcard and returns the full path of all files and directories matching the wildcard. Here the wildcard is a directory path plus "*.mp3", which will match all .mp3 files. Note that each element of the returned list already includes the full path of the file.
3 If you want to find all the files in a specific directory that start with "s" and end with ".mp3", you can do that too.
4 Now consider this scenario: you have a music directory, and several subdirectories within it, and .mp3 files within each subdirectory. You can get a list of all of those with a single call to glob, by using two wildcards at once. One wildcard is the "*.mp3" (to match .mp3 files), and one wildcard is within the directory path itself, to match any subdirectory within c:\music. That's a crazy amount of power packed into one deceptively simple-looking function!

Further reading

6.6. Putting it all together

Once again, all the dominoes are in place. We've seen how each line of code works. Now let's step back and see how it all fits together.

Example 6.21. listDirectory

def listDirectory(directory, fileExtList):                                         1
    "get list of file info objects for files of particular extensions"
    fileList = [os.path.normcase(f) for f in os.listdir(directory)]               
    fileList = [os.path.join(directory, f) for f in fileList \
                if os.path.splitext(f)[1] in fileExtList]                          2
    def getFileInfoClass(filename, module=sys.modules[FileInfo.__module__]):       3
        "get file info class from filename extension"                             
        subclass = "%sFileInfo" % os.path.splitext(filename)[1].upper()[1:]        4
        return hasattr(module, subclass) and getattr(module, subclass) or FileInfo 5
    return [getFileInfoClass(f)(f) for f in fileList]                              6
1 listDirectory is the main attraction of this entire module. It takes a directory (like c:\music\_singles\ in my case) and a list of interesting file extensions (like ['.mp3']), and it returns a list of class instances that act like dictionaries that contain metadata about each interesting file in that directory. And it does it in just a few straightforward lines of code.
2 As we saw in the previous section, this line of code gets a list of the full pathnames of all the files in directory that have an interesting file extension (as specified by fileExtList).
3 Old-school Pascal programmers may be familiar with them, but most people give me a blank stare when I tell them that Python supports nested functions -- literally, a function within a function. The nested function getFileInfoClass can only be called from the function in which it is defined, listDirectory. As with any other function, you don't need an interface declaration or anything fancy; just define the function and code it.
4 Now that you've seen the os module, this line should make more sense. It gets the extension of the file (os.path.splitext(filename)[1]), forces it to uppercase (.upper()), slices off the dot ([1:]), and constructs a class name out of it with string formatting. So c:\music\ap\mahadeva.mp3 becomes .mp3 becomes .MP3 becomes MP3 becomes MP3FileInfo.
5 Having constructed the name of the handler class that would handle this file, we check to see if that handler class actually exists in this module. If it does, we return the class, otherwise we return the base class FileInfo. This is a very important point: this function returns a class. Not an instance of a class, but the class itself.
6 For each file in our “interesting files” list (fileList), we call getFileInfoClass with the filename (f). Calling getFileInfoClass(f) returns a class; we don't know exactly which class, but we don't care. We then create an instance of this class (whatever it is) and pass the filename (f again), to the __init__ method. As we saw earlier in this chapter, the __init__ method of FileInfo sets self["name"], which triggers __setitem__, which is overridden in the descendant (MP3FileInfo) to parse the file appropriately to pull out the file's metadata. We do all that for each interesting file and return a list of the resulting instances.

Note that listDirectory is completely generic. It doesn't know ahead of time which types of files it will be getting, or which classes are defined that could potentially handle those files. It inspects the directory for the files to process, then introspects its own module to see what special handler classes (like MP3FileInfo) are defined. You can extend this program to handle other types of files simply by defining an appropriately-named class: HTMLFileInfo for HTML files, DOCFileInfo for Word .doc files, and so forth. listDirectory will handle them all, without modification, by handing the real work off to the appropriate classes and collating the results.

6.7. Summary

The fileinfo.py program should now make perfect sense.

Example 6.22. fileinfo.py

"""Framework for getting filetype-specific metadata.

Instantiate appropriate class with filename.  Returned object acts like a
dictionary, with key-value pairs for each piece of metadata.
    import fileinfo
    info = fileinfo.MP3FileInfo("/music/ap/mahadeva.mp3")
    print "\\n".join(["%s=%s" % (k, v) for k, v in info.items()])

Or use listDirectory function to get info on all files in a directory.
    for info in fileinfo.listDirectory("/music/ap/", [".mp3"]):
        ...

Framework can be extended by adding classes for particular file types, e.g.
HTMLFileInfo, MPGFileInfo, DOCFileInfo.  Each class is completely responsible for
parsing its files appropriately; see MP3FileInfo for example.
"""
import os
import sys
from UserDict import UserDict

def stripnulls(data):
    "strip whitespace and nulls"
    return data.replace("\00", "").strip()

class FileInfo(UserDict):
    "store file metadata"
    def __init__(self, filename=None):
        UserDict.__init__(self)
        self["name"] = filename

class MP3FileInfo(FileInfo):
    "store ID3v1.0 MP3 tags"
    tagDataMap = {"title"   : (  3,  33, stripnulls),
                  "artist"  : ( 33,  63, stripnulls),
                  "album"   : ( 63,  93, stripnulls),
                  "year"    : ( 93,  97, stripnulls),
                  "comment" : ( 97, 126, stripnulls),
                  "genre"   : (127, 128, ord)}

    def __parse(self, filename):
        "parse ID3v1.0 tags from MP3 file"
        self.clear()
        try:                               
            fsock = open(filename, "rb", 0)
            try:                           
                fsock.seek(-128, 2)        
                tagdata = fsock.read(128)  
            finally:                       
                fsock.close()              
            if tagdata[:3] == "TAG":
                for tag, (start, end, parseFunc) in self.tagDataMap.items():
                    self[tag] = parseFunc(tagdata[start:end])               
        except IOError:                    
            pass                           

    def __setitem__(self, key, item):
        if key == "name" and item:
            self.__parse(item)
        FileInfo.__setitem__(self, key, item)

def listDirectory(directory, fileExtList):                                        
    "get list of file info objects for files of particular extensions"
    fileList = [os.path.normcase(f) for f in os.listdir(directory)]               
    fileList = [os.path.join(directory, f) for f in fileList \
                if os.path.splitext(f)[1] in fileExtList]                         
    def getFileInfoClass(filename, module=sys.modules[FileInfo.__module__]):      
        "get file info class from filename extension"                             
        subclass = "%sFileInfo" % os.path.splitext(filename)[1].upper()[1:]       
        return hasattr(module, subclass) and getattr(module, subclass) or FileInfo
    return [getFileInfoClass(f)(f) for f in fileList]                             

if __name__ == "__main__":
    for info in listDirectory("/music/_singles/", [".mp3"]):
        print "\n".join(["%s=%s" % (k, v) for k, v in info.items()])
        print

Before diving into the next chapter, make sure you're comfortable doing all of these things:



[6] Or, as some marketroids would put it, your program would perform an illegal action. Whatever.

Chapter 7. Regular Expressions

7.1. Diving in

Regular expressions are a powerful and standardized way of searching, replacing, and parsing text with complex patterns of characters. If you've used regular expressions in other languages (like Perl), the syntax will be very familiar, and you get by just reading the summary of the re module to get an overview of the available functions and their arguments.

If you've never used regular expressions before, think of it this way. Strings have methods for searching (index, find, and count), replacing (replace), and parsing (split), but they are limited to the simplest of cases. The search methods look for a single, hard-coded substring, and they are always case-sensitive; to do case-insensitive searches of a string s, you must call s.lower() or s.upper() and make sure your search strings are the appropriate case to match. The replace and split methods have the same limitations.

If what you're trying to do can be accomplished with string functions, you should use them. They're fast and simple and easy to read, and there's a lot to be said for fast, simple, readable code. But if you find yourself using lots of different string functions with if statements to handle special cases, or if you're combining them with split and join and list comprehensions in weird unreadable ways, you may need to move up to regular expressions. Although the syntax is tight and unlike normal code, the result can end be being more readable than a hand-rolled solution that uses a long chain of string functions. There are even ways of embedding comments within regular expressions to make them practically self-documenting.

7.2. Case study: Street addresses

This series of examples was inspired by a real-life problem I had in my day job several years ago, scrubbing and standardizing street addresses exported from a legacy system before importing them into a newer system. (See, I don't just make this stuff up; it's actually useful.)

Example 7.1. Matching at the end of a string

>>> s = '100 NORTH MAIN ROAD'
>>> s.replace('ROAD', 'RD.')               1
'100 NORTH MAIN RD.'
>>> s = '100 NORTH BROAD ROAD'
>>> s.replace('ROAD', 'RD.')               2
'100 NORTH BRD. RD.'
>>> s[:-4] + s[-4:].replace('ROAD', 'RD.') 3
'100 NORTH BROAD RD.'
>>> import re                              4
>>> re.sub('ROAD$', 'RD.', s)              5 6
'100 NORTH BROAD RD.'
1 My goal is to standardize a street address so that 'ROAD' is always abbreviated as 'RD.'. At first glance, I thought this was simple enough that I could just use the string method replace. After all, all the data was already uppercase, so case mismatches would not be a problem. And the search string, 'ROAD', was a constant. And in this deceptively simple example, s.replace does indeed work.
2 Life, unfortunately, is full of counterexamples, and I quickly discovered this one. The problem here is that 'ROAD' appears twice in the address, once as part of the street name 'BROAD' and once as its own word. The replace method sees these two occurrences and blindly replaces both of them; meanwhile, I see my addresses getting destroyed.
3 To solve the problem of addresses with more than one 'ROAD' substring, we could resort to something like this: only search and replace 'ROAD' in the last 4 characters of the address (s[-4:]), and leave the string alone (s[:-4]). But you can see that this is already getting unweildy. For example, the pattern is dependent on the length of the string we're replacing (if we were replacing 'STREET' with 'ST.', we would need to use s[:-6] and s[-6:].replace(...)). Would you like to come back in six months and debug this? I know I wouldn't.
4 It's time to move up to regular expressions. In Python, all functionality related to regular expressions is contained in the re module.
5 Take a look at the first parameter: 'ROAD$'. This is a very simple regular expression which matches 'ROAD' only when it occurs at the end of a string. The $ means “end of the string”. (There is a corresponding character, the caret ^, which means “beginning of the string”.)
6 Using the re.sub function, we search the string s for the regular expression 'ROAD$' and replace it with 'RD.'. This matches the ROAD at the end of the string s, but does not match the ROAD that's part of the word BROAD, because that's in the middle of s.

Example 7.2. Matching whole words

>>> s = '100 BROAD'
>>> re.sub('ROAD$', 'RD.', s)     1
'100 BRD.'
>>> re.sub('\\bROAD$', 'RD.', s)  2
'100 BROAD'
>>> re.sub(r'\bROAD$', 'RD.', s)  3
'100 BROAD'
>>> s = '100 BROAD ROAD APT. 3'
>>> re.sub(r'\bROAD$', 'RD.', s)  4
'100 BROAD ROAD APT. 3'
>>> re.sub(r'\bROAD\b', 'RD.', s) 5
'100 BROAD RD. APT 3'
1 Continuing with my story of scrubbing addresses, I soon discovered that the previous example, matching 'ROAD' at the end of the address, was not good enough, because not all addresses included a street designation at all; some just ended with the street name. Most of the time, I got away with it, but if the street name was 'BROAD', then the regular expression would match 'ROAD' at the end of the string as part of the word 'BROAD', which is not what I wanted.
2 What I really wanted was to match 'ROAD' when it was at the end of the string and it was its own whole word, not a part of some larger word. To express this in a regular expression, you use \b, which means “a word boundary must occur right here”. In Python, this is complicated by the fact that the '\' character in a string must itself be escaped. (This is sometimes referred to as the backslash plague, and it is one reason why regular expressions are easier in Perl than in Python. On the down side, Perl mixes regular expressions with other syntax, so if you have a bug, it may be hard to tell whether it's a bug in syntax or a bug in your regular expression.)
3 To work around the backslash plague, you can use what is called a raw string, by prefixing the '...' with the letter r. This tells Python that nothing in this string should be escaped; '\t' is a tab character, but r'\t' is really the backslash character \ followed by the letter t. I recommend always using raw strings when dealing with regular expressions, otherwise things get too confusing too quickly (and regular expressions get confusing quickly enough all by themselves).
4 *sigh* Unfortunately, I soon found more cases that contradicted my logic. In this case, the street address contained the word 'ROAD' as a whole word by itself, but it wasn't at the end, because the address had an apartment number after the street designation. Because 'ROAD' isn't at the very end of the string, it doesn't match, so the entire call to re.sub ends up replacing nothing at all, and we get the original string back, which is not what we want.
5 To solve this problem, I removed the $ character and added another \b. Now the regular expression reads “match 'ROAD' when it's a whole word by itself anywhere in the string”, whether at the end, the beginning, or somewhere in the middle.

7.3. Case study: Roman numerals

You've most likely seen Roman numerals, even if you didn't recognize them. You may have seen them in copyrights of old movies and television shows (“Copyright MCMXLVI” instead of “Copyright 1946”), or on the dedication walls of libraries or universities (“established MDCCCLXXXVIII” instead of “established 1888”). You may also have seen them in outlines and bibliographical references. It's a system of representing numbers that really does date back to the ancient Roman empire (hence the name).

In Roman numerals, there are seven characters which are repeated and combined in various ways to represent numbers.

  1. I = 1
  2. V = 5
  3. X = 10
  4. L = 50
  5. C = 100
  6. D = 500
  7. M = 1000

There are some general rules for constructing Roman numerals:

  1. Characters are additive. I is 1, II is 2, and III is 3. VI is 6 (literally, “5 and 1”), VII is 7, and VIII is 8.
  2. The tens characters (I, X, C, and M) can be repeated up to three times. At 4, you have to subtract from the next highest fives character. You can't represent 4 as IIII; instead, it is represented as IV (“1 less than 5”). 40 is written as XL (“10 less than 50”), 41 as XLI, 42 as XLII, 43 as XLIII, and then 44 as XLIV (“10 less than 50, then 1 less than 5”).
  3. Similarly, at 9, you have to subtract from the next highest tens character: 8 is VIII, but 9 is IX (“1 less than 10”), not VIIII (since the I character can not be repeated four times). 90 is XC, 900 is CM.
  4. The fives characters can not be repeated. 10 is always represented as X, never as VV. 100 is always C, never LL.
  5. Roman numerals are always written highest to lowest, and read left to right, so order of characters matters very much. DC is 600; CD is a completely different number (400, “100 less than 500”). CI is 101; IC is not even a valid Roman numeral (because you can't subtract 1 directly from 100; you would have to write it as XCIX, “10 less than 100, then 1 less than 10”).

Since Roman numerals are always written highest to lowest, let's start with the highest: the thousands place. For numbers 1000 and higher, the thousands are represented by a series of M characters.

Example 7.3. Checking for thousands

>>> import re
>>> pattern = '^M?M?M?$'       1
>>> re.search(pattern, 'M')    2
<SRE_Match object at 0106FB58>
>>> re.search(pattern, 'MM')   3
<SRE_Match object at 0106C290>
>>> re.search(pattern, 'MMM')  4
<SRE_Match object at 0106AA38>
>>> re.search(pattern, 'MMMM') 5
>>> re.search(pattern, '')     6
<SRE_Match object at 0106F4A8>
1 This pattern has three parts:
  1. ^ - match what follows only at the beginning of the string. If this were not specified, the pattern would match no matter where the M characters were, which is not what we want. We want to make sure that the M characters, if they're there, are at the beginning of the string.
  2. M? - optionally match a single M character. Since this is repeated three times, we're matching anywhere from 0 to 3 M characters in a row.
  3. $ - match what precedes only at the end of the string. When combined with the ^ character at the beginning, this means that the pattern must match the entire string, with no other characters before or after the M characters.
2 The essense of the re module is the search function, which takes a regular expression (pattern) and a string ('M') to try to match against the regular expression. If a match is found, search returns an object which has various methods to describe the match; if no match is found, search returns None, the Python null value. We won't go into detail about the object that search returns (although it's very interesting), because all we care about at the moment is whether the pattern matches, which we can tell by just looking at the return value of search. 'M' matches this regular expression, because the first optional M matches and the second and third optional M characters are ignored.
3 'MM' matches because the first and second optional M characters match and the third M is ignored.
4 'MMM' matches because all three M characters match.
5 'MMMM' does not match. All three M characters match, but then the regular expression insists on the string ending (because of the $ character), and the string doesn't end yet (because of the fourth M). So search returns None.
6 Interestingly, an empty string also matches this regular expression, since all the M characters are optional.

The hundreds place is more difficult than the thousands, because there are several mutually exclusive ways it could be expressed, depending on its value.

  • 100 = C
  • 200 = CC
  • 300 = CCC
  • 400 = CD
  • 500 = D
  • 600 = DC
  • 700 = DCC
  • 800 = DCCC
  • 900 = CM

So there are four possible patterns:

  1. CM
  2. CD
  3. 0 to 3 C characters (0 if the hundreds place is 0)
  4. D, followed by 0 to 3 C characters

The last two patterns can be combined:

  • an optional D, followed by 0 to 3 C characters

Example 7.4. Checking for hundreds

>>> import re
>>> pattern = '^M?M?M?(CM|CD|D?C?C?C?)$' 1
>>> re.search(pattern, 'MCM')            2
<SRE_Match object at 01070390>
>>> re.search(pattern, 'MD')             3
<SRE_Match object at 01073A50>
>>> re.search(pattern, 'MMMCCC')         4
<SRE_Match object at 010748A8>
>>> re.search(pattern, 'MCMC')           5
>>> re.search(pattern, '')               6
<SRE_Match object at 01071D98>
1 This pattern starts out the same as our previous one, checking for the beginning of the string (^), then the thousands place (M?M?M?). Then we have the new part, in parentheses, which defines a set of three mutually exclusive patterns, separated by vertical bars: CM, CD, and D?C?C?C? (which is an optional D followed by 0 to 3 optional C characters). The regular expression parser checks for each of these patterns in order (from left to right), takes the first one that matches, and ignores the rest.
2 'MCM' matches because the first M matches, the second and third M characters are ignored, and the CM matches (so the CD and D?C?C?C? patterns are never even considered). MCM is the Roman numeral representation of 1900.
3 'MD' matches because the first M matches, the second and third M characters are ignored, and the D?C?C?C? pattern matches D (each of the 3 C characters are optional and are ignored). MD is the Roman numeral representation of 1500.
4 'MMMCCC' matches because all 3 M characters match, and the D?C?C?C? pattern matches CCC (the D is optional and is ignored). MMMCCC is the Roman numeral representation of 3300.
5 'MCMC' does not match. The first M matches, the second and third M characters are ignored, and the CM matches, but then the $ does not match because we're not at the end of the string yet (we still have an unmatched C character). The C does not match as part of the D?C?C?C? pattern, because the mutually exclusive CM pattern has already matched.
6 Interestingly, an empty string still matches this pattern, because all the M characters are optional and ignored, and the empty string matches the D?C?C?C? pattern where all the characters are optional and ignored.

Whew! See how quickly regular expressions can get nasty? And we've only covered the thousands and hundreds places. Luckily, if you followed all that, the tens and ones places are easy, because they're exactly the same pattern.

7.4. The {n,m} syntax

In the previous section, we were dealing with a pattern where the same character could be repeated up to 3 times. There is another way to express this in regular expressions, which some people find more readable.

Example 7.5. The old way: every character optional

>>> import re
>>> pattern = '^M?M?M?$'
>>> re.search(pattern, 'M')    1
<_sre.SRE_Match object at 0x008EE090>
>>> pattern = '^M?M?M?$'
>>> re.search(pattern, 'MM')   2
<_sre.SRE_Match object at 0x008EEB48>
>>> pattern = '^M?M?M?$'
>>> re.search(pattern, 'MMM')  3
<_sre.SRE_Match object at 0x008EE090>
>>> re.search(pattern, 'MMMM') 4
>>> 
1 This matches the start of the string, then the first optional M, but not the second and third M (but that's OK because they're optional), then end of string.
2 This matches the start of the string, then the first and second optional M, but not the third M (but that's OK because it's optional), then end of string.
3 This matches the start of the string, then all three optional M, then end of string.
4 This matches the start of the string, then all three optional M, but then does not match the end of string (because there is still one unmatched M), so the pattern does not match and returns None.

Example 7.6. The new way: from n to m

>>> pattern = '^M{0,3}$'       1
>>> re.search(pattern, 'M')    2
<_sre.SRE_Match object at 0x008EEB48>
>>> re.search(pattern, 'MM')   3
<_sre.SRE_Match object at 0x008EE090>
>>> re.search(pattern, 'MMM')  4
<_sre.SRE_Match object at 0x008EEDA8>
>>> re.search(pattern, 'MMMM') 5
>>> 
1 This pattern says: “Match the start of the string, then anywhere from 0 to 3 M characters, then end of string.” The 0 and 3 can be any numbers; if you want to match at least 1 but no more than 3 M characters, you could say M{1,3}.
2 This matches the start of the string, then 1 M out of a possible 3, then end of string.
3 This matches the start of the string, then 2 M out of a possible 3, then end of string.
4 This matches the start of the string, then 3 M out of a possible 3, then end of string.
5 This matches the start of the string, then 3 M out of a possible 3, but then does not match the end of string. The regular expression only allows for up to 3 M characters before end of string, but we have 4, so the pattern does not match and returns None.
Note
There is no way to programmatically determine that two regular expressions are equivalent. The best you can do is write lots of test cases to make sure they behave the same way on all relevant inputs. We'll talk more about writing test cases later in this book.

Now let's expand our Roman numeral regular expression to cover the tens and ones place.

Example 7.7. The tens place

>>> pattern = '^M?M?M?M?(CM|CD|D?C?C?C?)(XC|XL|L?X?X?X?)$'
>>> re.search(pattern, 'MCMXL')    1
<_sre.SRE_Match object at 0x008EEB48>
>>> re.search(pattern, 'MCML')     2
<_sre.SRE_Match object at 0x008EEB48>
>>> re.search(pattern, 'MCMLX')    3
<_sre.SRE_Match object at 0x008EEB48>
>>> re.search(pattern, 'MCMLXXX')  4
<_sre.SRE_Match object at 0x008EEB48>
>>> re.search(pattern, 'MCMLXXXX') 5
>>> 
1 This matches the start of the string, then the first optional M, then CM, then XL, then end of string. Remember, the (A|B|C) syntax means “match exactly one of A, B, or C”. We match XL, so we ignore the XC and L?X?X?X? choices, and then move on to end of string.
2 This matches the start of the string, then the first optional M, then CM, then L?X?X?X?. Of the L?X?X?X?, it matches the L and skips all 3 optional X characters. Then end of string.
3 This matches the start of the string, then the first optional M, then CM, then the optional L and the first optional X, skips the second and third optional X, then end of string.
4 This matches the start of the string, then the first optional M, then CM, then the optional L and all 3 optional X characters, then end of string.
5 This matches the start of the string, then the first optional M, then CM, then the optional L and all 3 optional X characters, then fails to match the end of string because there is still one more X unaccounted for. So the entire pattern fails to match, and returns None.

The expression for the ones place follows the same pattern. I'll spare you the details and show you the end result:

Example 7.8. The ones place

>>> pattern = '^M?M?M?M?(CM|CD|D?C?C?C?)(XC|XL|L?X?X?X?)(IX|IV|V?I?I?I?)$'

OK, so what does that look like using this alternate {n,m} syntax?

Example 7.9. Validating Roman numerals with {n,m}

>>> pattern = '^M{0,4}(CM|CD|D?C{0,3})(XC|XL|L?X{0,3})(IX|IV|V?I{0,3})$'
>>> re.search(pattern, 'MDLV')             1
<_sre.SRE_Match object at 0x008EEB48>
>>> re.search(pattern, 'MMDCLXVI')         2
<_sre.SRE_Match object at 0x008EEB48>
>>> re.search(pattern, 'MMMMDCCCLXXXVIII') 3
<_sre.SRE_Match object at 0x008EEB48>
>>> re.search(pattern, 'I')                4
<_sre.SRE_Match object at 0x008EEB48>
1 This matches the start of the string, then 1 of a possible 4 M characters, then D?C{0,3}. Of that, it matches the optional D and 0 of 3 possible C characters. Moving on, it matches L?X{0,3} by matching the optional L and 0 of 3 possible X characters. Then it matches V?I{0,3} by matching the optional V and 0 of 3 possible I characters, and finally end of string. (Are your eyes bleeding yet?)
2 This matches the start of the string, then 2 of a possible 4 M characters, then the D?C{0,3} with a D and 1 of 3 possible C characters. Then L?X{0,3} with an L and 1 of 3 possible X characters. Then V?I{0,3} with a V and 1 of 3 possible I characters. Then end of string.
3 This matches the start of the string, then 4 out of 4 M characters, then D?C{0,3} with a D and 3 out of 3 C characters. Then L?X{0,3} with an L and 3 out of 3 X characters. Then V?I{0,3} with a V and 3 out of 3 I characters. Then end of string.
4 Watch closely. (I feel like a magician. “Watch closely, kids, I'm going to pull a rabbit out of my hat.”) This matches the start of the string, then 0 out of 4 M, then matches D?C{0,3} by skipping the optional D and matching 0 out of 3 C, then matches L?X{0,3} by skipping the optional L and matching 0 out of 3 X, then matches V?I{0,3} by skipping the optional V and matching 1 out of 3 I. Then end of string. Whoa.

If you followed all that and understood it on the first try, you're doing better than I did. Now imagine trying to understand someone else's regular expressions, in the middle of a critical function of a large program. Or even imagine coming back to your own regular expressions a few months later. I've done it, and it's not a pretty sight.

In the next section we'll explore an alternate syntax that can help keep your expressions maintainable.

7.5. Verbose regular expressions

So far we've just been dealing with what I'll call “compact” regular expressions. As you've seen, they are difficult to read, and even if you figure out what one does, that's no guarantee that you'll be able to understand it six months later. What we really need is inline documentation.

Python allows you to do this with something called verbose regular expressions. A verbose regular expression is different from a compact regular expression in 2 ways:

  1. Whitespace is ignored. Spaces, tabs, and carriage returns are not matched as spaces, tabs, and carriage returns. They're not matched at all. (If you want to match a space in a verbose regular expression, you'll need to escape it by putting a backslash in front of it.)
  2. Comments are ignored. A comment in a verbose regular expression is just like a comment in Python code: it starts with a # character and goes until the end of the line. In this case it's a comment within a multi-line string instead of within your source code, but it works the same way.

This will be more clear with an example. Let's revisit the compact regular expression we've been working with, and make it a verbose regular expression.

Example 7.10. Regular expressions with inline comments

>>> pattern = """
    ^                   # beginning of string
    M{0,4}              # thousands - 0 to 4 M's
    (CM|CD|D?C{0,3})    # hundreds - 900 (CM), 400 (CD), 0-300 (0 to 3 C's),
                        #            or 500-800 (D, followed by 0 to 3 C's)
    (XC|XL|L?X{0,3})    # tens - 90 (XC), 40 (XL), 0-30 (0 to 3 X's),
                        #        or 50-80 (L, followed by 0 to 3 X's)
    (IX|IV|V?I{0,3})    # ones - 9 (IX), 4 (IV), 0-3 (0 to 3 I's),
                        #        or 5-8 (V, followed by 0 to 3 I's)
    $                   # end of string
    """
>>> re.search(pattern, 'M', re.VERBOSE)                1
<_sre.SRE_Match object at 0x008EEB48>
>>> re.search(pattern, 'MCMLXXXIX', re.VERBOSE)        2
<_sre.SRE_Match object at 0x008EEB48>
>>> re.search(pattern, 'MMMMDCCCLXXXVIII', re.VERBOSE) 3
<_sre.SRE_Match object at 0x008EEB48>
>>> re.search(pattern, 'M')                            4
1 The most important thing to remember when using verbose regular expressions is that you need to pass an extra argument when working with them: re.VERBOSE is a constant defined in the re module that signals that the pattern should be treated as a verbose regular expression. As you can see, this pattern has quite a bit of whitespace (all of which is ignored), and several comments (all of which are ignored). Once you ignore the whitespace and the comments, this is exactly the same regular expression as we saw in the previous section, but it's just a lot more readable.
2 This matches the start of the string, then 1 of a possible 4 M, then CM, then L and 3 of a possible 3 X, then IX, then end of string.
3 This matches the start of the string, then 4 of a possible 4 M, then D and 3 of a possible 3 C, then L and 3 of a possible 3 X, then V and 3 of a possible 3 I, then end of string.
4 This does not match. Why? Because we forgot to add the re.VERBOSE flag, so the re.search function is treating our pattern as a compact regular expression, with significant whitespace and literal hash marks. Python can't auto-detect whether a regular expression is verbose or not. Python assumes every regular expression is compact unless you explicitly state that it is verbose.

7.6. Case study: parsing phone numbers

So far we've concentrated on matching whole patterns. Either the pattern matches, or it doesn't. But regular expressions are much more powerful than that. When a regular expression does match, you can pick out specific pieces of it. You can find out what matched where.

This example came from another real-world problem I encountered, again from a previous day job. The problem: parsing an American phone number. Of course the client wanted the number to be entered free-form (in a single field), but then wanted to store the area code, trunk, number, and optionally an extension separately in their database. I scoured the web and found many examples of regular expressions that purported to do this, and none of them were permissive enough.

Here are some of the phone numbers I'd like to be able to accept:

  • 800-555-1212
  • 800 555 1212
  • 800.555.1212
  • (800) 555-1212
  • 1-800-555-1212
  • 800-555-1212-1234
  • 800-555-1212x1234
  • 800-555-1212 ext. 1234
  • work 1-(800) 555.1212 #1234

Quite a variety! In each of these cases, I need to know that the area code was 800, the trunk was 555, and the rest of the phone number was 1212. For those with an extension, I need to know that the extension was 1234.

Example 7.11. Finding numbers

>>> phonePattern = re.compile(r'^(\d{3})-(\d{3})-(\d{4})$') 1
>>> phonePattern.search('800-555-1212').groups()            2
('800', '555', '1212')
>>> phonePattern.search('800-555-1212-1234')                3
>>> 
1 Always read regular expressions from left to right. This one matches the beginning of the string, then (\d{3}). We already know that \d{3} means “match exactly 3 numeric digits”. Putting it in parentheses means “match exactly 3 numeric digits, and then remember them as a group that I can ask for later”. Then match a literal hyphen. Then match another group of exactly 3 digits. Then another literal hyphen. Then another group of exactly 4 digits. Then end of string.
2 To get access to the groups that the regular expression parser remembered along the way, use the groups() method on the object that the search function returns. It will return a tuple of however many groups were defined in the regular expression. In this case, we defined three groups, one with 3 digits, one with 3 digits, and one with 4 digits.
3 This regular expression is not our final answer, though, because it doesn't handle a phone number with an extension on the end. For that, we'll need to expand our regular expression.

Example 7.12. Finding the extension

>>> phonePattern = re.compile(r'^(\d{3})-(\d{3})-(\d{4})-(\d+)$') 1
>>> phonePattern.search('800-555-1212-1234').groups()             2
('800', '555', '1212', '1234')
>>> phonePattern.search('800 555 1212 1234')                      3
>>> 
>>> phonePattern.search('800-555-1212')                           4
>>> 
1 OK, this regular expression is almost identical to the previous one. Just as before, we match the beginning of the string, then a remembered group of 3 digits, then a hyphen, then a remembered group of 3 digits, then a hyphen, then a remembered group of 4 digits. What's new is that we then match another hyphen, and a remembered group of 1 or more digits, then end of string.
2 The groups() method now returns a tuple of 4 elements, since our regular expression now defines 4 groups to remember.
3 Unfortunately, this regular expression is not our final answer either, because it assumes that the different parts of the phone number are separated by hyphens. What if they're separated by spaces, or commas, or dots? We need a more general solution to match several different types of separators.
4 Oops! Not only does this regular expression not do everything we want, it's actually a step backwards, because now we can't parse phone numbers without an extension. That's not what we wanted at all; if the extension is there, we want to know what it is, but if it's not there, we still want to know what the different parts of the main number are.

Example 7.13. Handling different separators

>>> phonePattern = re.compile(r'^(\d{3})\D+(\d{3})\D+(\d{4})\D+(\d+)$') 1
>>> phonePattern.search('800 555 1212 1234').groups()                   2
('800', '555', '1212', '1234')
>>> phonePattern.search('800-555-1212-1234').groups()                   3
('800', '555', '1212', '1234')
>>> phonePattern.search('80055512121234')                               4
>>> 
>>> phonePattern.search('800-555-1212')                                 5
>>> 
1 OK, hang on to your hat. We're matching the beginning of the string, then a group of 3 digits, then \D+. What the heck is that? Well, \D matches any character except a numeric digit, and + means “1 or more”. So \D+ matches 1 or more characters that are not digits. This is what we're using now instead of a literal hyphen, to try to match different separators.
2 Using \D+ instead of - means we can now match phone numbers where the parts are separated by spaces instead of hyphens.
3 Of course, phone numbers separated by hyphens still work too.
4 Unfortunately, this is still not our final answer, because it assumes that there is a separator at all. What if the phone number is entered without any spaces or hyphens at all?
4 Oops! We still haven't fixed the problem of requiring extensions. Now we have two problems, but we can solve both of them with the same technique.

Example 7.14. Handling no separators

>>> phonePattern = re.compile(r'^(\d{3})\D*(\d{3})\D*(\d{4})\D*(\d*)$') 1
>>> phonePattern.search('80055512121234').groups()                      2
('800', '555', '1212', '1234')
>>> phonePattern.search('800.555.1212 x1234').groups()                  3
('800', '555', '1212', '1234')
>>> phonePattern.search('800-555-1212').groups()                        4
('800', '555', '1212', '')
>>> phonePattern.search('(800)5551212 x1234')                           5
>>> 
1 The only change we've made since that last step is changing all the + to *. Instead of \D+ between the parts of the phone number, we now match on \D*. Remember that + means “1 or more”? Well, * means “0 or more”. So now we should be able to parse phone numbers even when there is no separator character at all.
2 Lo and behold, it actually works. Why? We matched the beginning of the string, then a remembered group of 3 digits (800), then 0 non-numeric characters, then a remembered group of 3 digits (555), then 0 non-numeric characters, then a remembered group of 4 digits (1212), then 0 non-numeric characters, then a remembered group of an arbitrary number of digits (1234), then end of string.
3 Other variations work now too. Dots instead of hyphens, and both a space and an x before the extension.
4 Finally, we've solved our other long-standing problem: extensions are optional again. If no extension is found, the groups() method still returns a tuple of 4 elements, but the fourth element is just an empty string.
5 I hate to be the bearer of bad news, but we're not done yet. What's the problem here? There's an extra character before the area code, but our regular expression assumes that the area code is the first thing at the beginning of the string. No problem, we can use the same technique of “0 or more non-numeric characters” to skip over the leading characters before the area code.

Example 7.15. Handling leading characters

>>> phonePattern = re.compile(r'^\D*(\d{3})\D*(\d{3})\D*(\d{4})\D*(\d*)$') 1
>>> phonePattern.search('(800)5551212 ext. 1234').groups()                 2
('800', '555', '1212', '1234')
>>> phonePattern.search('800-555-1212').groups()                           3
('800', '555', '1212', '')
>>> phonePattern.search('work 1-(800) 555.1212 #1234')                     4
>>> 
1 Same as before, except now we're matching \D*, 0 or more non-numeric characters, before the first remembered group (the area code). Note that we're not remembering these non-numeric characters (they're not in parentheses). If we find them, we'll just skip over them and then start remembering the area code whenever we get to it.
2 OK, we can successfully parse the phone number, even with the leading left parenthesis before the area code. (The right parenthesis after the area code is already handled; it's treated as a non-numeric separator and matched by the \D* after the first remembered group.)
3 Just a sanity check to make sure we haven't broken anything that used to work. Since the leading characters are entirely optional, this matches the beginning of the string, then 0 non-numeric characters, then a remembered group of 3 digits (800), then 1 non-numeric character (the hyphen), then a remembered group of 3 digits (555), then 1 non-numeric character (the hyphen), then a remembered group of 4 digits (1212), then 0 non-numeric characters, then a remembered group of 0 digits, then end of string.
4 This is where regular expressions make me want to gouge my eyes out with a blunt object. Why doesn't this phone number match? Because there's a 1 before the area code, but we assumed that all the leading characters before the area code were non-numeric characters (\D*). Aargh.

Let's back up for a second. So far our regular expressions have all matched from the beginning of the string. But now we see that there may be an indeterminate amount of stuff at the beginning of the string that we want to ignore. Rather than trying to match it all just so we can skip over it, let's take a different approach: don't explicitly match the beginning of the string at all.

Example 7.16. Phone number, wherever I may find ye

>>> phonePattern = re.compile(r'(\d{3})\D*(\d{3})\D*(\d{4})\D*(\d*)$') 1
>>> phonePattern.search('work 1-(800) 555.1212 #1234').groups()        2
('800', '555', '1212', '1234')
>>> phonePattern.search('800-555-1212')                                3
('800', '555', '1212', '')
>>> phonePattern.search('80055512121234')                              4
('800', '555', '1212', '1234')
1 Note the lack of ^ in this regular expression. We are not matching the beginning of the string anymore. There's nothing that says you have to match the entire input with your regular expression. The regular expression engine will do the hard work of figuring out where the input string starts to match, and go from there.
2 Now we can successfully parse a phone number that includes leading characters and a leading digit, plus any number of any kind of separators around each part of the phone number.
3 Sanity check. this still works.
4 That still works too.

See how quickly our regular expression got out of control? Take a quick glance at any of the previous iterations. Can you tell the difference between one and the next? While we still understand our final answer (and it is our final answer, if you've discovered a case it doesn't handle, I don't want to know about it), let's write it out as a verbose regular expression, before we forget why we made the choices we made.

Example 7.17. Parsing phone numbers (final version)

>>> phonePattern = re.compile(r'''
                # don't match beginning of string, number can start anywhere
    (\d{3})     # area code is 3 digits (e.g. '800')
    \D*         # optional separator is any number of non-digits
    (\d{3})     # trunk is 3 digits (e.g. '555')
    \D*         # optional separator
    (\d{4})     # rest of number is 4 digits (e.g. '1212')
    \D*         # optional separator
    (\d*)       # extension is optional and can be any number of digits
    $           # end of string
    ''', re.VERBOSE)
>>> phonePattern.search('work 1-(800) 555.1212 #1234').groups()        1
('800', '555', '1212', '1234')
>>> phonePattern.search('800-555-1212')                                2
('800', '555', '1212', '')
1 Other than being spread out over multiple lines, this is exactly the same regular expression as the last step, so it's no surprise that it parses the same inputs.
2 Final sanity check. Yes, this still works. I think we're done.

7.7. Summary

This is just the tiniest tip of the iceberg of what regular expressions can do. In other words, even though you're completely overwhelmed by them now, believe me, you ain't seen nothing yet.

You should now be familiar with the following techniques:

  • $ matches the beginning of a string.
  • ^ matches the end of a string.
  • \b matches a word boundary.
  • . matches any character.
  • \d matches any numeric digit.
  • \D matches any non-numeric character.
  • x? matches an optional x character (in other words, it matches an x 0 or 1 times).
  • x* matches x 0 or more times.
  • x+ matches x 1 or more times.
  • x{n,m} matches an x character at least n times, but not more than m times.
  • (a|b|c) matches either a or b or c.
  • (x) in general is a remembered group. You can get the value of what matched by using the groups() method of the object returned by re.search.

Regular expressions are extremely powerful, but they are not the correct solution for every problem. You should learn enough about them to know when they are appropriate, when they will solve your problems, and when they will cause more problems than they solve.

 

Some people, when confronted with a problem, think “I know, I'll use regular expressions.” Now they have two problems.

 
--Jamie Zawinski, in comp.emacs.xemacs  

Further reading

Chapter 8. HTML Processing

8.1. Diving in

I often see questions on comp.lang.python like “How can I list all the [headers|images|links] in my HTML document?” “How do I parse/translate/munge the text of my HTML document but leave the tags alone?” “How can I add/remove/quote attributes of all my HTML tags at once?” This chapter will answer all of these questions.

Here is a complete, working Python program in two parts. The first part, BaseHTMLProcessor.py, is a generic tool to help you process HTML files by walking through the tags and text blocks. The second part, dialect.py, is an example of how to use BaseHTMLProcessor.py to translate the text of an HTML document but leave the tags alone. Read the doc strings and comments to get an overview of what's going on. Most of it will seem like black magic, because it's not obvious how any of these class methods ever get called. Don't worry, all will be revealed in due time.

Example 8.1. BaseHTMLProcessor.py

If you have not already done so, you can download this and other examples used in this book.

from sgmllib import SGMLParser
import htmlentitydefs

class BaseHTMLProcessor(SGMLParser):
    def reset(self):                       
        # extend (called by SGMLParser.__init__)
        self.pieces = []
        SGMLParser.reset(self)

    def unknown_starttag(self, tag, attrs):
        # called for each start tag
        # attrs is a list of (attr, value) tuples
        # e.g. for <pre class="screen">, tag="pre", attrs=[("class", "screen")]
        # Ideally we would like to reconstruct original tag and attributes, but
        # we may end up quoting attribute values that weren't quoted in the source
        # document, or we may change the type of quotes around the attribute value
        # (single to double quotes).
        # Note that improperly embedded non-HTML code (like client-side Javascript)
        # may be parsed incorrectly by the ancestor, causing runtime script errors.
        # All non-HTML code must be enclosed in HTML comment tags (<!-- code -->)
        # to ensure that it will pass through this parser unaltered (in handle_comment).
        strattrs = "".join([' %s="%s"' % (key, value) for key, value in attrs])
        self.pieces.append("<%(tag)s%(strattrs)s>" % locals())

    def unknown_endtag(self, tag):         
        # called for each end tag, e.g. for </pre>, tag will be "pre"
        # Reconstruct the original end tag.
        self.pieces.append("</%(tag)s>" % locals())

    def handle_charref(self, ref):         
        # called for each character reference, e.g. for "&#160;", ref will be "160"
        # Reconstruct the original character reference.
        self.pieces.append("&#%(ref)s;" % locals())

    def handle_entityref(self, ref):       
        # called for each entity reference, e.g. for "&copy;", ref will be "copy"
        # Reconstruct the original entity reference.
        self.pieces.append("&%(ref)s" % locals())
        # standard HTML entities are closed with a semicolon; other entities are not
        if htmlentitydefs.entitydefs.has_key(ref):
            self.pieces.append(";")

    def handle_data(self, text):           
        # called for each block of plain text, i.e. outside of any tag and
        # not containing any character or entity references
        # Store the original text verbatim.
        self.pieces.append(text)

    def handle_comment(self, text):        
        # called for each HTML comment, e.g. <!-- insert Javascript code here -->
        # Reconstruct the original comment.
        # It is especially important that the source document enclose client-side
        # code (like Javascript) within comments so it can pass through this
        # processor undisturbed; see comments in unknown_starttag for details.
        self.pieces.append("<!--%(text)s-->" % locals())

    def handle_pi(self, text):             
        # called for each processing instruction, e.g. <?instruction>
        # Reconstruct original processing instruction.
        self.pieces.append("<?%(text)s>" % locals())

    def handle_decl(self, text):
        # called for the DOCTYPE, if present, e.g.
        # <!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"
        #     "http://www.w3.org/TR/html4/loose.dtd">
        # Reconstruct original DOCTYPE
        self.pieces.append("<!%(text)s>" % locals())

    def output(self):              
        """Return processed HTML as a single string"""
        return "".join(self.pieces)

Example 8.2. dialect.py

import re
from BaseHTMLProcessor import BaseHTMLProcessor

class Dialectizer(BaseHTMLProcessor):
    subs = ()

    def reset(self):
        # extend (called from __init__ in ancestor)
        # Reset all data attributes
        self.verbatim = 0
        BaseHTMLProcessor.reset(self)

    def start_pre(self, attrs):            
        # called for every <pre> tag in HTML source
        # Increment verbatim mode count, then handle tag like normal
        self.verbatim += 1                 
        self.unknown_starttag("pre", attrs)

    def end_pre(self):                     
        # called for every </pre> tag in HTML source
        # Decrement verbatim mode count
        self.unknown_endtag("pre")         
        self.verbatim -= 1                 

    def handle_data(self, text):                                        
        # override
        # called for every block of text in HTML source
        # If in verbatim mode, save text unaltered;
        # otherwise process the text with a series of substitutions
        self.pieces.append(self.verbatim and text or self.process(text))

    def process(self, text):
        # called from handle_data
        # Process text block by performing series of regular expression
        # substitutions (actual substitions are defined in descendant)
        for fromPattern, toPattern in self.subs:
            text = re.sub(fromPattern, toPattern, text)
        return text

class ChefDialectizer(Dialectizer):
    """convert HTML to Swedish Chef-speak
    
    based on the classic chef.x, copyright (c) 1992, 1993 John Hagerman
    """
    subs = ((r'a([nu])', r'u\1'),
            (r'A([nu])', r'U\1'),
            (r'a\B', r'e'),
            (r'A\B', r'E'),
            (r'en\b', r'ee'),
            (r'\Bew', r'oo'),
            (r'\Be\b', r'e-a'),
            (r'\be', r'i'),
            (r'\bE', r'I'),
            (r'\Bf', r'ff'),
            (r'\Bir', r'ur'),
            (r'(\w*?)i(\w*?)$', r'\1ee\2'),
            (r'\bow', r'oo'),
            (r'\bo', r'oo'),
            (r'\bO', r'Oo'),
            (r'the', r'zee'),
            (r'The', r'Zee'),
            (r'th\b', r't'),
            (r'\Btion', r'shun'),
            (r'\Bu', r'oo'),
            (r'\BU', r'Oo'),
            (r'v', r'f'),
            (r'V', r'F'),
            (r'w', r'w'),
            (r'W', r'W'),
            (r'([a-z])[.]', r'\1.  Bork Bork Bork!'))

class FuddDialectizer(Dialectizer):
    """convert HTML to Elmer Fudd-speak"""
    subs = ((r'[rl]', r'w'),
            (r'qu', r'qw'),
            (r'th\b', r'f'),
            (r'th', r'd'),
            (r'n[.]', r'n, uh-hah-hah-hah.'))

class OldeDialectizer(Dialectizer):
    """convert HTML to mock Middle English"""
    subs = ((r'i([bcdfghjklmnpqrstvwxyz])e\b', r'y\1'),
            (r'i([bcdfghjklmnpqrstvwxyz])e', r'y\1\1e'),
            (r'ick\b', r'yk'),
            (r'ia([bcdfghjklmnpqrstvwxyz])', r'e\1e'),
            (r'e[ea]([bcdfghjklmnpqrstvwxyz])', r'e\1e'),
            (r'([bcdfghjklmnpqrstvwxyz])y', r'\1ee'),
            (r'([bcdfghjklmnpqrstvwxyz])er', r'\1re'),
            (r'([aeiou])re\b', r'\1r'),
            (r'ia([bcdfghjklmnpqrstvwxyz])', r'i\1e'),
            (r'tion\b', r'cioun'),
            (r'ion\b', r'ioun'),
            (r'aid', r'ayde'),
            (r'ai', r'ey'),
            (r'ay\b', r'y'),
            (r'ay', r'ey'),
            (r'ant', r'aunt'),
            (r'ea', r'ee'),
            (r'oa', r'oo'),
            (r'ue', r'e'),
            (r'oe', r'o'),
            (r'ou', r'ow'),
            (r'ow', r'ou'),
            (r'\bhe', r'hi'),
            (r've\b', r'veth'),
            (r'se\b', r'e'),
            (r"'s\b", r'es'),
            (r'ic\b', r'ick'),
            (r'ics\b', r'icc'),
            (r'ical\b', r'ick'),
            (r'tle\b', r'til'),
            (r'll\b', r'l'),
            (r'ould\b', r'olde'),
            (r'own\b', r'oune'),
            (r'un\b', r'onne'),
            (r'rry\b', r'rye'),
            (r'est\b', r'este'),
            (r'pt\b', r'pte'),
            (r'th\b', r'the'),
            (r'ch\b', r'che'),
            (r'ss\b', r'sse'),
            (r'([wybdp])\b', r'\1e'),
            (r'([rnt])\b', r'\1\1e'),
            (r'from', r'fro'),
            (r'when', r'whan'))

def translate(url, dialectName="chef"):
    """fetch URL and translate using dialect
    
    dialect in ("chef", "fudd", "olde")"""
    import urllib                      
    sock = urllib.urlopen(url)         
    htmlSource = sock.read()           
    sock.close()                       
    parserName = "%sDialectizer" % dialectName.capitalize()
    parserClass = globals()[parserName]                    
    parser = parserClass()                                 
    parser.feed(htmlSource)
    parser.close()         
    return parser.output() 

def test(url):
    """test all dialects against URL"""
    for dialect in ("chef", "fudd", "olde"):
        outfile = "%s.html" % dialect
        fsock = open(outfile, "wb")
        fsock.write(translate(url, dialect))
        fsock.close()
        import webbrowser
        webbrowser.open_new(outfile)

if __name__ == "__main__":
    test("http://diveintopython.org/odbchelper_list.html")

Example 8.3. Output of dialect.py

Running this script will translate Introducing lists into mock Swedish Chef-speak (from The Muppets), mock Elmer Fudd-speak (from Bugs Bunny cartoons), and mock Middle English (loosely based on Chaucer's The Canterbury Tales). If you look at the HTML source of the output pages, you'll see that all the HTML tags and attributes are untouched, but the text between the tags has been “translated” into the mock language. If you look closer, you'll see that, in fact, only the titles and paragraphs were translated; the code listings and screen examples were left untouched.

8.2. Introducing sgmllib.py

HTML processing is broken into three steps: breaking down the HTML into its constituent pieces, fiddling with the pieces, and reconstructing the pieces into HTML again. The first step is done by sgmllib.py, a part of the standard Python library.

The key to understanding this chapter is to realize that HTML is not just text, it is structured text. The structure is derived from the more-or-less-hierarchical sequence of start tags and end tags. Usually you don't work with HTML this way; you work with it textually in a text editor, or visually in a web browser or web authoring tool. sgmllib.py presents HTML structurally.

sgmllib.py contains one important class: SGMLParser. SGMLParser parses HTML into useful pieces, like start tags and end tags. As soon as it succeeds in breaking down some data into a useful piece, it calls a method on itself based on what it found. In order to use the parser, you subclass the SGMLParser class and override these methods. This is what I meant when I said that it presents HTML structurally: the structure of the HTML determines the sequence of method calls and the arguments passed to each method.

SGMLParser parses HTML into 8 kinds of data, and calls a separate method for each of them:

Start tag
An HTML tag that starts a block, like <html>, <head>, <body>, or <pre>, or a standalone tag like <br> or <img>. When it finds a start tag tagname, SGMLParser will look for a method called start_tagname or do_tagname. For instance, when it finds a <pre> tag, it will look for a start_pre or do_pre method. If found, SGMLParser calls this method with a list of the tag's attributes; otherwise, it calls unknown_starttag with the tag name and list of attributes.
End tag
An HTML tag that ends a block, like </html>, </head>, </body>, or </pre>. When it finds an end tag, SGMLParser will look for a method called end_tagname. If found, SGMLParser calls this method, otherwise it calls unknown_endtag with the tag name.
Character reference
An escaped character referenced by its decimal or hexadecimal equivalent, like &#160;. When found, SGMLParser calls handle_charref with the text of the decimal or hexadecimal character equivalent.
Entity reference
An HTML entity, like &copy;. When found, SGMLParser calls handle_entityref with the name of the HTML entity.
Comment
An HTML comment, enclosed in <!-- ... -->. When found, SGMLParser calls handle_comment with the body of the comment.
Processing instruction
An HTML processing instruction, enclosed in <? ... >. When found, SGMLParser calls handle_pi with the body of the processing instruction.
Declaration
An HTML declaration, such as a DOCTYPE, enclosed in <! ... >. When found, SGMLParser calls handle_decl with the body of the declaration.
Text data
A block of text. Anything that doesn't fit into the other 7 categories. When found, SGMLParser calls handle_data with the text.
Important
Python 2.0 had a bug where SGMLParser would not recognize declarations at all (handle_decl would never be called), which meant that DOCTYPEs were silently ignored. This is fixed in Python 2.1.

sgmllib.py comes with a test suite to illustrate this. You can run sgmllib.py, passing the name of an HTML file on the command line, and it will print out the tags and other elements as it parses them. It does this by subclassing the SGMLParser class and defining unknown_starttag, unknown_endtag, handle_data and other methods which simply print their arguments.

Tip
In the Python IDE on Windows, you can specify command line arguments in the “Run script” dialog. Separate multiple arguments with spaces.

Example 8.4. Sample test of sgmllib.py

Here is a snippet from the table of contents of the HTML version of this book, toc.html.

<h1>
  <a name='c40a'></a>
  Dive Into Python
</h1>
<p class='pubdate'>
  28 Feb 2001
</p>
<p class='copyright'>
  Copyright copy 2000, 2001 by 
  <a href='mailto:mark@diveintopython.org' title='send e-mail to the author'>
    Mark Pilgrim
  </a>
</p>
<p>
  <a name='c40ab2b4'></a>
  <b></b>
</p>
<p>
  This book lives at 
  <a href='http://diveintopython.org/'>
    http://diveintopython.org/
  </a>
  .
  If you're reading it somewhere else, you may not have the latest version.
</p>

Running this through the test suite of sgmllib.py yields this output:

start tag: <h1>
start tag: <a name="c40a" >
end tag: </a>
data: 'Dive Into Python'
end tag: </h1>
start tag: <p class="pubdate" >
data: '28 Feb 2001'
end tag: </p>
start tag: <p class="copyright" >
data: 'Copyright '
*** unknown entity ref: &copy;
data: ' 2000, 2001 by '
start tag: <a href="mailto:mark@diveintopython.org" title="send e-mail to the author" >
data: 'Mark Pilgrim'
end tag: </a>
end tag: </p>
start tag: <p>
start tag: <a name="c40ab2b4" >
end tag: </a>
start tag: <b>
end tag: </b>
end tag: </p>
start tag: <p>
data: 'This book lives at '
start tag: <a href="http://diveintopython.org/" >
data: 'http://diveintopython.org/'
end tag: </a>
data: ".\012If you're reading it somewhere else, you may not have the lates"
data: 't version.\012'
end tag: </p>

Here's the roadmap for the rest of the chapter:

  • Subclass SGMLParser to create classes that extract interesting data out of HTML documents.
  • Subclass SGMLParser to create BaseHTMLProcessor, which overrides all 8 handler methods and uses them to reconstruct the original HTML from the pieces.
  • Subclass BaseHTMLProcessor to create Dialectizer, which adds some methods to process specific HTML tags specially, and overrides the handle_data method to provide a framework for processing the text blocks between the HTML tags.
  • Subclass Dialectizer to create classes that define text processing rules used by Dialectizer.handle_data.
  • Write a test suite that grabs a real web page from http://diveintopython.org/ and processes it.

8.3. Extracting data from HTML documents

To extract data from HTML documents, subclass the SGMLParser class and define methods for each tag or entity you want to capture.

The first step to extracting data from an HTML document is getting some HTML. If you have some HTML lying around on your hard drive, you can use file functions to read it, but the real fun begins when you get HTML from live web pages.

Example 8.5. Introducing urllib

>>> import urllib                                       1
>>> sock = urllib.urlopen("http://diveintopython.org/") 2
>>> htmlSource = sock.read()                            3
>>> sock.close()                                        4
>>> print htmlSource                                    5
<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/html4/loose.dtd"><html><head>
      <meta http-equiv='Content-Type' content='text/html; charset=ISO-8859-1'>
   <title>Dive Into Python</title>
<link rel='stylesheet' href='diveintopython.css' type='text/css'>
<link rev='made' href='mailto:mark@diveintopython.org'>
<meta name='keywords' content='Python, Dive Into Python, tutorial, object-oriented, programming, documentation, book, free'>
<meta name='description' content='a free Python tutorial for experienced programmers'>
</head>
<body bgcolor='white' text='black' link='#0000FF' vlink='#840084' alink='#0000FF'>
<table cellpadding='0' cellspacing='0' border='0' width='100%'>
<tr><td class='header' width='1%' valign='top'>diveintopython.org</td>
<td width='99%' align='right'><hr size='1' noshade></td></tr>
<tr><td class='tagline' colspan='2'>Python&nbsp;for&nbsp;experienced&nbsp;programmers</td></tr>

[...snip...]
1 The urllib module is part of the standard Python library. It contains functions for getting information about and actually retrieving data from Internet-based URLs (mainly web pages).
2 The simplest use of urllib is to retrieve the entire text of a web page using the urlopen function. Opening a URL is similar to opening a file. The return value of urlopen is a file-like object, which has some of the same methods as a file object.
3 The simplest thing to do with the file-like object returned by urlopen is read, which reads the entire HTML of the web page into a single string. The object also supports readlines, which reads the text line by line into a list.
4 When you're done with the object, make sure to close it, just like a normal file object.
5 We now have the complete HTML of the home page of http://diveintopython.org/ in a string, and we're ready to parse it.

Example 8.6. Introducing urllister.py

If you have not already done so, you can download this and other examples used in this book.

from sgmllib import SGMLParser

class URLLister(SGMLParser):
    def reset(self):                              1
        SGMLParser.reset(self)
        self.urls = []

    def start_a(self, attrs):                     2
        href = [v for k, v in attrs if k=='href'] 3 4
        if href:
            self.urls.extend(href)
1 reset is called by the __init__ method of SGMLParser, and it can also be called manually once an instance of the parser has been created. So if you need to do any initialization, do it in reset, not in __init__, so that it will be re-initialized properly when someone re-uses a parser instance.
2 start_a is called by SGMLParser whenever it finds an <a> tag. The tag may contain an href attribute, and/or other attributes, like name or title. The attrs parameter is a list of tuples, [(attribute, value), (attribute, value), ...]. Or it may be just an <a>, a valid (if useless) HTML tag, in which case attrs would be an empty list.
3 We can find out whether this <a> tag has an href attribute with a simple multi-variable list comprehension.
4 String comparisons like k=='href' are always case-sensitive, but that's safe in this case, because SGMLParser converts attribute names to lowercase while building attrs.

Example 8.7. Using urllister.py

>>> import urllib, urllister
>>> usock = urllib.urlopen("http://diveintopython.org/")
>>> parser = urllister.URLLister()
>>> parser.feed(usock.read())         1
>>> usock.close()                     2
>>> parser.close()                    3
>>> for url in parser.urls: print url 4
toc.html
#download
toc.html
history.html
download/dip_pdf.zip
download/dip_pdf.tgz
download/dip_pdf.hqx
download/diveintopython.pdf
download/diveintopython.zip
download/diveintopython.tgz
download/diveintopython.hqx

[...snip...]
1 Call the feed method, defined in SGMLParser, to get HTML into the parser.[7] It takes a string, which is what usock.read() returns.
2 Like files, you should close your URL objects as soon as you're done with them.
3 You should close your parser object, too, but for a different reason. The feed method isn't guaranteed to process all the HTML you give it; it may buffer it, waiting for more. Once there isn't any more, call close to flush the buffer and force everything to be fully parsed.
4 Once the parser is closed, the parsing is complete, and parser.urls contains a list of all the linked URLs in the HTML document.

8.4. Introducing BaseHTMLProcessor.py

SGMLParser doesn't produce anything by itself. It parses and parses and parses, and it calls a method for each interesting thing it finds, but the methods don't do anything. SGMLParser is an HTML consumer: it takes HTML and breaks it down into small, structured pieces. As you saw in the previous section, you can subclass SGMLParser to define classes that catch specific tags and produce useful things, like a list of all the links on a web page. Now we'll take this one step further by defining a class that catches everything SGMLParser throws at it and reconstructs the complete HTML document. In technical terms, this class will be an HTML producer.

BaseHTMLProcessor subclasses SGMLParser and provides all 8 essential handler methods: unknown_starttag, unknown_endtag, handle_charref, handle_entityref, handle_comment, handle_pi, handle_decl, and handle_data.

Example 8.8. Introducing BaseHTMLProcessor

class BaseHTMLProcessor(SGMLParser):
    def reset(self):                        1
        self.pieces = []
        SGMLParser.reset(self)

    def unknown_starttag(self, tag, attrs): 2
        strattrs = "".join([' %s="%s"' % (key, value) for key, value in attrs])
        self.pieces.append("<%(tag)s%(strattrs)s>" % locals())

    def unknown_endtag(self, tag):          3
        self.pieces.append("</%(tag)s>" % locals())

    def handle_charref(self, ref):          4
        self.pieces.append("&#%(ref)s;" % locals())

    def handle_entityref(self, ref):        5
        self.pieces.append("&%(ref)s" % locals())
        if htmlentitydefs.entitydefs.has_key(ref):
            self.pieces.append(";")

    def handle_data(self, text):            6
        self.pieces.append(text)

    def handle_comment(self, text):         7
        self.pieces.append("<!--%(text)s-->" % locals())

    def handle_pi(self, text):              8
        self.pieces.append("<?%(text)s>" % locals())

    def handle_decl(self, text):
        self.pieces.append("<!%(text)s>" % locals())
1 reset, called by SGMLParser.__init__, initializes self.pieces as an empty list before calling the ancestor method. self.pieces is a data attribute which will hold the pieces of the HTML document we're constructing. Each handler method will reconstruct the HTML that SGMLParser parsed, and each method will append that string to self.pieces. Note that self.pieces is a list. You might be tempted to define it as a string and just keep appending each piece to it. That would work, but Python is much more efficient at dealing with lists.[8]
2 Since BaseHTMLProcessor does not define any methods for specific tags (like the start_a method in URLLister), SGMLParser will call unknown_starttag for every start tag. This method takes the tag (tag) and the list of attribute name/value pairs (attrs), reconstructs the original HTML, and appends it to self.pieces. The string formatting here is a little strange; we'll untangle that in the next section.
3 Reconstructing end tags is much simpler; just take the tag name and wrap it in the </...> brackets.
4 When SGMLParser finds a character reference, it calls handle_charref with the bare reference. If the HTML document contains the reference &#160;, ref will be 160. Reconstructing the original complete character reference just involves wrapping ref in &#...; characters.
5 Entity references are similar to character references, but without the hash mark. Reconstructing the original entity reference requires wrapping ref in &...; characters. (Actually, as an erudite reader pointed out to me, it's slightly more complicated than this. Only certain standard HTML entites end in a semicolon; other similar-looking entities do not. Luckily for us, the set of standard HTML entities is defined in a dictionary in a Python module called htmlentitydefs. Hence the extra if statement.)
6 Blocks of text are simply appended to self.pieces unaltered.
7 HTML comments are wrapped in <!--...--> characters.
8 Processing instructions are wrapped in <?...> characters.
Important
The HTML specification requires that all non-HTML (like client-side JavaScript) must be enclosed in HTML comments, but not all web pages do this properly (and all modern web browsers are forgiving if they don't). BaseHTMLProcessor is not forgiving; if script is improperly embedded, it will be parsed as if it were HTML. For instance, if the script contains less-than and equals signs, SGMLParser may incorrectly think that it has found tags and attributes. SGMLParser always converts tags and attribute names to lowercase, which may break the script, and BaseHTMLProcessor always encloses attribute values in double quotes (even if the original HTML document used single quotes or no quotes), which will certainly break the script. Always protect your client-side script within HTML comments.

Example 8.9. BaseHTMLProcessor output

    def output(self):               1
        """Return processed HTML as a single string"""
        return "".join(self.pieces) 2
1 This is the one method in BaseHTMLProcessor that is never called by the ancestor SGMLParser. Since the other handler methods store their reconstructed HTML in self.pieces, this function is needed to join all those pieces into one string. As noted before, Python is great at lists and mediocre at strings, so we only create the complete string when somebody explicitly asks for it.
2 If you prefer, you could use the join method of the string module instead: string.join(self.pieces, "")

Further reading

8.5. locals and globals

Python has two built-in functions, locals and globals, which provide dictionary-based access to local and global variables.

First, a word on namespaces. This is dry stuff, but it's important, so pay attention. Python uses what are called namespaces to keep track of variables. A namespace is just like a dictionary where the keys are names of variables and the dictionary values are the values of those variables. In fact, you can access a namespace as a Python dictionary, as we'll see in a minute.

At any particular point in a Python program, there are several namespaces available. Each function has its own namespace, called the local namespace, which keeps track of the function's variables, including function arguments and locally defined variables. Each module has its own namespace, called the global namespace, which keeps track of the module's variables, including functions, classes, any other imported modules, and module-level variables and constants. And there is the built-in namespace, accessible from any module, which holds built-in functions and exceptions.

When a line of code asks for the value of a variable x, Python will search for that variable in all the available namespaces, in order:

  1. local namespace - specific to the current function or class method. If the function defines a local variable x, or has an argument x, Python will use this and stop searching.
  2. global namespace - specific to the current module. If the module has defined a variable, function, or class called x, Python will use that and stop searching.
  3. built-in namespace - global to all modules. As a last resort, Python will assume that x is the name of built-in function or variable.

If Python doesn't find x in any of these namespaces, it gives up and raises a NameError with the message There is no variable named 'x', which you saw back in Example 3.17, “Referencing an unbound variable”, but you didn't appreciate how much work Python was doing before giving you that error.

Important
Python 2.2 introduced a subtle but important change that affects the namespace search order: nested scopes. In versions of Python prior to 2.2, when you reference a variable within a nested function or lambda function, Python will search for that variable in the current (nested or lambda) function's namespace, then in the module's namespace. Python 2.2 will search for the variable in the current (nested or lambda) function's namespace, then in the parent function's namespace, then in the module's namespace. Python 2.1 can work either way; by default, it works like Python 2.0, but you can add the following line of code at the top of your module to make your module work like Python 2.2:
from __future__ import nested_scopes

Like many things in Python, namespaces are directly accessible at run-time. Specifically, the local namespace is accessible via the built-in locals function, and the global (module level) namespace is accessible via the built-in globals function.

Example 8.10. Introducing locals

>>> def foo(arg): 1
...     x = 1
...     print locals()
...     
>>> foo(7)        2
{'arg': 7, 'x': 1}
>>> foo('bar')    3
{'arg': 'bar', 'x': 1}
1 The function foo has two variables in its local namespace: arg, whose value is passed in to the function, and x, which is defined within the function.
2 locals returns a dictionary of name/value pairs. The keys of this dictionary are the names of the variables as strings; the values of the dictionary are the actual values of the variables. So calling foo with 7 prints the dictionary containing the function's two local variables: arg (7) and x (1).
3 Remember, Python has dynamic typing, so you could just as easily pass a string in for arg; the function (and the call to locals) would still work just as well. locals works with all variables of all datatypes.

What locals does for the local (function) namespace, globals does for the global (module) namespace. globals is more exciting, though, because a module's namespace is more exciting.[9] Not only does the module's namespace include module-level variables and constants, it includes all the functions and classes defined in the module. Plus, it includes anything that was imported into the module.

Remember the difference between from module import and import module? With import module, the module itself is imported, but it retains its own namespace, which is why you have to use the module name to access any of its functions or attributes: module.function. But with from module import, you're actually importing specific functions and attributes from another module into your own namespace, which is why you access them directly without referencing the original module they came from. With the globals function, you can actually see this happen.

Example 8.11. Introducing globals

Add the following block to BaseHTMLProcessor.py:

if __name__ == "__main__":
    for k, v in globals().items():             1
        print k, "=", v
1 Just so you don't get intimidated, remember that you've seen all this before. The globals function returns a dictionary, and we're iterating through the dictionary using the items method and multi-variable assignment. The only thing new here is the globals function.

Now running the script from the command line gives this output:

c:\docbook\dip\py>python BaseHTMLProcessor.py
SGMLParser = sgmllib.SGMLParser                1
htmlentitydefs = <module 'htmlentitydefs' from 'C:\Python21\lib\htmlentitydefs.py'> 2
BaseHTMLProcessor = __main__.BaseHTMLProcessor 3
__name__ = __main__                            4
[...snip...]
1 SGMLParser was imported from sgmllib, using from module import. That means that it was imported directly into our module's namespace, and here it is.
2 Contrast this with htmlentitydefs, which was imported using import. That means that the htmlentitydefs module itself is in our namespace, but the entitydefs variable defined within htmlentitydefs is not.
3 This module only defines one class, BaseHTMLProcessor, and here it is. Note that the value here is the class itself, not a specific instance of the class.
4 Remember the if __name__ trick? When running a module (as opposed to importing it from another module), the built-in __name__ attribute is a special value, __main__. Since we ran this module as a script from the command line, __name__ is __main__, which is why our little test code to print the globals got executed.
Note
Using the locals and globals functions, you can get the value of arbitrary variables dynamically, providing the variable name as a string. This mirrors the functionality of the getattr function, which allows you to access arbitrary functions dynamically by providing the function name as a string.

There is one other important difference between locals and globals, which you should learn now before it bites you. It will bite you anyway, but at least then you'll remember learning it.

Example 8.12. locals is read-only, globals is not

def foo(arg):
    x = 1
    print locals()    1
    locals()["x"] = 2 2
    print "x=",x      3

z = 7
print "z=",z
foo(3)
globals()["z"] = 8    4
print "z=",z          5
1 Since foo is called with 3, this will print {'arg': 3, 'x': 1}. This should not be a surprise.
2 You might think that this would change the value of the local variable x to 2, but it doesn't. locals does not actually return the local namespace, it returns a copy. So changing it does nothing to the value of the variables in the local namespace.
3 This prints x= 1, not x= 2.
4 After being burned by locals, you might think that this wouldn't change the value of z, but it does. Due to internal differences in how Python is implemented (which I'd rather not go into, since I don't fully understand them myself), globals returns the actual global namespace, not a copy: the exact opposite behavior of locals. So any changes to the dictionary returned by globals directly affect your global variables.
5 This prints z= 8, not z= 7.

8.6. Dictionary-based string formatting

String formatting provides an easy way to insert values into strings. Values are listed in a tuple and inserted in order into the string in place of each formatting marker. While this is efficient, it is not always the easiest code to read, especially when multiple values are being inserted. You can't simply scan through the string in one pass and understand what the result will be; you're constantly switching between reading the string and reading the tuple of values.

There is an alternative form of string formatting that uses dictionaries instead of tuples of values.

Example 8.13. Introducing dictionary-based string formatting

>>> params = {"server":"mpilgrim", "database":"master", "uid":"sa", "pwd":"secret"}
>>> "%(pwd)s" % params                                    1
'secret'
>>> "%(pwd)s is not a good password for %(uid)s" % params 2
'secret is not a good password for sa'
>>> "%(database)s of mind, %(database)s of body" % params 3
'master of mind, master of body'
1 Instead of a tuple of explicit values, this form of string formatting uses a dictionary, params. And instead of a simple %s marker in the string, the marker contains a name in parentheses. This name is used as a key in the params dictionary and subsitutes the corresponding value, secret, in place of the %(pwd)s marker.
2 Dictionary-based string formatting works with any number of named keys. Each key must exist in the given dictionary, or the formatting will fail with a KeyError.
3 You can even specify the same key twice; each occurrence will be replaced with the same value.

So why would you use dictionary-based string formatting? Well, it does seem like overkill to set up a dictionary of keys and values simply to do string formatting in the next line; it's really most useful when you happen to have a dictionary of meaningful keys and values already. Like locals.

Example 8.14. Dictionary-based string formatting in BaseHTMLProcessor.py

    def handle_comment(self, text):        
        self.pieces.append("<!--%(text)s-->" % locals()) 1
1 Using the built-in locals function is the most common use of dictionary-based string formatting. It means that you can use the names of local variables within your string (in this case, text, which was passed to the class method as an argument) and each named variable will be replaced by its value. If text is 'Begin page footer', the string formatting "<!--%(text)s-->" % locals() will resolve to the string '<!--Begin page footer-->'.
    def unknown_starttag(self, tag, attrs):
        strattrs = "".join([' %s="%s"' % (key, value) for key, value in attrs]) 1
        self.pieces.append("<%(tag)s%(strattrs)s>" % locals())                              2
1 When this method is called, attrs is a list of key/value tuples, just like the items of a dictionary, which means we can use multi-variable assignment to iterate through it. This should be a familiar pattern by now, but there's a lot going on here, so let's break it down:
  1. Suppose attrs is [('href', 'index.html'), ('title', 'Go to home page')].
  2. In the first round of the list comprehension, key will get 'href', and value will get 'index.html'.
  3. The string formatting ' %s="%s"' % (key, value) will resolve to ' href="index.html"'. This string becomes the first element of the list comprehension's return value.
  4. In the second round, key will get 'title', and value will get 'Go to home page'.
  5. The string formatting will resolve to ' title="Go to home page"'.
  6. The list comprehension returns a list of these two resolved strings, and strattrs will join both elements of this list together to form ' href="index.html" title="Go to home page"'.
2 Now, using dictionary-based string formatting, we insert the value of tag and strattrs into a string. So if tag is 'a', the final result would be '<a href="index.html" title="Go to home page">', and that is what gets appended to self.pieces.
Important
Using dictionary-based string formatting with locals is a convenient way of making complex string formatting expressions more readable, but it comes with a price. There is a slight performance hit in making the call to locals, since locals builds a copy of the local namespace.

8.7. Quoting attribute values

A common question on comp.lang.python is “I have a bunch of HTML documents with unquoted attribute values, and I want to properly quote them all. How can I do this?[10] (This is generally precipitated by a project manager who has found the HTML-is-a-standard religion joining a large project and proclaiming that all pages must validate against an HTML validator. Unquoted attribute values are a common violation of the HTML standard.) Whatever the reason, unquoted attribute values are easy to fix by feeding HTML through BaseHTMLProcessor.

BaseHTMLProcessor consumes HTML (since it's descended from SGMLParser) and produces equivalent HTML, but the HTML output is not identical to the input. Tags and attribute names will end up in lowercase, even if they started in uppercase or mixed case, and attribute values will be enclosed in double quotes, even if they started in single quotes or with no quotes at all. It is this last side effect that we can take advantage of.

Example 8.15. Quoting attribute values

>>> htmlSource = """        1
...     <html>
...     <head>
...     <title>Test page</title>
...     </head>
...     <body>
...     <ul>
...     <li><a href=index.html>Home</a></li>
...     <li><a href=toc.html>Table of contents</a></li>
...     <li><a href=history.html>Revision history</a></li>
...     </body>
...     </html>
...     """
>>> from BaseHTMLProcessor import BaseHTMLProcessor
>>> parser = BaseHTMLProcessor()
>>> parser.feed(htmlSource) 2
>>> print parser.output()   3
<html>
<head>
<title>Test page</title>
</head>
<body>
<ul>
<li><a href="index.html">Home</a></li>
<li><a href="toc.html">Table of contents</a></li>
<li><a href="history.html">Revision history</a></li>
</body>
</html>
1 Note that the attribute values of the href attributes in the <a> tags are not properly quoted. (Also note that we're using triple quotes for something other than a doc string. And directly in the IDE, no less. They're very useful.)
2 Feed the parser.
3 Using the output function defined in BaseHTMLProcessor, we get the output as a single string, complete with quoted attribute values. While this may seem anti-climactic, think about how much has actually happened here: SGMLParser parsed the entire HTML document, breaking it down into tags, refs, data, and so forth; BaseHTMLProcessor used those elements to reconstruct pieces of HTML (which are still stored in parser.pieces, if you want to see them); finally, we called parser.output, which joined all the pieces of HTML into one string.

8.8. Introducing dialect.py

Dialectizer is a simple (and silly) descendant of BaseHTMLProcessor. It runs blocks of text through a series of substitutions, but it makes sure that anything within a <pre>...</pre> block passes through unaltered.

To handle the <pre> blocks, we define two methods in Dialectizer: start_pre and end_pre.

Example 8.16. Handling specific tags

    def start_pre(self, attrs):             1
        self.verbatim += 1                  2
        self.unknown_starttag("pre", attrs) 3

    def end_pre(self):                      4
        self.unknown_endtag("pre")          5
        self.verbatim -= 1                  6
1 start_pre is called every time SGMLParser finds a <pre> tag in the HTML source. (In a minute, we'll see exactly how this happens.) The method takes a single parameter, attrs, which contains the attributes of the tag (if any). attrs is a list of key/value tuples, just like unknown_starttag takes.
2 In the reset method, we initialize a data attribute that serves as a counter for <pre> tags. Every time we hit a <pre> tag, we increment the counter; every time we hit a </pre> tag, we'll decrement the counter. (We could just use this as a flag and set it to 1 and reset it to 0, but it's just as easy to do it this way, and this handles the odd (but possible) case of nested <pre> tags.) In a minute, we'll see how this counter is put to good use.
3 That's it, that's the only special processing we do for <pre> tags. Now we pass the list of attributes along to unknown_starttag so it can do the default processing.
4 end_pre is called every time SGMLParser finds a </pre> tag. Since end tags can not contain attributes, the method takes no parameters.
5 First, we want to do the default processing, just like any other end tag.
6 Second, we decrement our counter to signal that this <pre> block has been closed.

At this point, it's worth digging a little further into SGMLParser. I've claimed repeatedly (and you've taken it on faith so far) that SGMLParser looks for and calls specific methods for each tag, if they exist. For instance, we just saw the definition of start_pre and end_pre to handle <pre> and </pre>. But how does this happen? Well, it's not magic, it's just good Python coding.

Example 8.17. SGMLParser

    def finish_starttag(self, tag, attrs):               1
        try:                                            
            method = getattr(self, 'start_' + tag)       2
        except AttributeError:                           3
            try:                                        
                method = getattr(self, 'do_' + tag)      4
            except AttributeError:                      
                self.unknown_starttag(tag, attrs)        5
                return -1                               
            else:                                       
                self.handle_starttag(tag, method, attrs) 6
                return 0                                
        else:                                           
            self.stack.append(tag)                      
            self.handle_starttag(tag, method, attrs)    
            return 1                                     7

    def handle_starttag(self, tag, method, attrs):      
        method(attrs)                                    8
1 At this point, SGMLParser has already found a start tag and parsed the attribute list. The only thing left to do is figure out whether there is a specific handler method for this tag, or whether we should fall back on the default method (unknown_starttag).
2 The “magic” of SGMLParser is nothing more than our old friend, getattr. What you may not have realized before is that getattr will find methods defined in descendants of an object as well as the object itself. Here the object is self, the current instance. So if tag is 'pre', this call to getattr will look for a start_pre method on the current instance, which is an instance of the Dialectizer class.
3 getattr raises an AttributeError if the method it's looking for doesn't exist in the object (or any of its descendants), but that's okay, because we wrapped the call to getattr inside a try...except block and explicitly caught the AttributeError.
4 Since we didn't find a start_xxx method, we'll also look for a do_xxx method before giving up. This alternate naming scheme is generally used for standalone tags, like <br>, which have no corresponding end tag. But you can use either naming scheme; as you can see, SGMLParser tries both for every tag. (You shouldn't define both a start_xxx and do_xxx handler method for the same tag, though; only the start_xxx method will get called.)
5 Another AttributeError, which means that the call to getattr failed with do_xxx. Since we found neither a start_xxx nor a do_xxx method for this tag, we catch the exception and fall back on the default method, unknown_starttag.
6 Remember, try...except blocks can have an else clause, which is called if no exception is raised during the try...except block. Logically, that means that we did find a do_xxx method for this tag, so we're going to call it.
7 By the way, don't worry about these different return values; in theory they mean something, but they're never actually used. Don't worry about the self.stack.append(tag) either; SGMLParser keeps track internally of whether your start tags are balanced by appropriate end tags, but it doesn't do anything with this information either. In theory, you could use this module to validate that your tags were fully balanced, but it's probably not worth it, and it's beyond the scope of this chapter. We have better things to worry about right now.
8 start_xxx and do_xxx methods are not called directly; the tag, method, and attributes are passed to this function, handle_starttag, so that descendants can override it and change the way all start tags are dispatched. We don't need that level of control, so we just let this method do its thing, which is to call the method (start_xxx or do_xxx) with the list of attributes. Remember, method is a function, returned from getattr, and functions are objects. (I know you're getting tired of hearing it, and I promise I'll stop saying it as soon as we stop finding new ways of using it to our advantage.) Here, the function object is passed into this dispatch method as an argument, and this method turns around and calls the function. At this point, we don't have to know what the function is, what it's named, or where it's defined; the only thing we have to know about the function is that it is called with one argument, attrs.

Now back to our regularly scheduled program: Dialectizer. When we left, we were in the process of defining specific handler methods for <pre> and </pre> tags. There's only one thing left to do, and that is to process text blocks with our pre-defined substitutions. For that, we need to override the handle_data method.

Example 8.18. Overriding the handle_data method

    def handle_data(self, text):                                         1
        self.pieces.append(self.verbatim and text or self.process(text)) 2
1 handle_data is called with only one argument, the text to process.
2 In the ancestor BaseHTMLProcessor, the handle_data method simply appended the text to the output buffer, self.pieces. Here the logic is only slightly more complicated. If we're in the middle of a <pre>...</pre> block, self.verbatim will be some value greater than 0, and we want to put the text in the output buffer unaltered. Otherwise, we will call a separate method to process the substitutions, then put the result of that into the output buffer. In Python, this is a one-liner, using the and-or trick.

We're close to completely understanding Dialectizer. The only missing link is the nature of the text substitutions themselves. If you know any Perl, you know that when complex text substitutions are required, the only real solution is regular expressions.

8.9. Putting it all together

It's time to put everything we've learned so far to good use. I hope you were paying attention.

Example 8.19. The translate function, part 1

def translate(url, dialectName="chef"): 1
    import urllib                       2
    sock = urllib.urlopen(url)          3
    htmlSource = sock.read()           
    sock.close()                       
1 The translate function has an optional argument dialectName, which is a string that specifies the dialect we'll be using. We'll see how this is used in a minute.
2 Hey, wait a minute, there's an import statement in this function! That's perfectly legal in Python. You're used to seeing import statements at the top of a program, which means that the imported module is available anywhere in the program. But you can also import modules within a function, which means that the imported module is only available within the function. If you have a module that is only ever used in one function, this is an easy way to make your code more modular. (When you find that your weekend hack has turned into an 800-line work of art and decide to split it up into a dozen reusable modules, you'll appreciate this.)
3 Now we get the source of the given URL.

Example 8.20. The translate function, part 2: curiouser and curiouser

    parserName = "%sDialectizer" % dialectName.capitalize() 1
    parserClass = globals()[parserName]                     2
    parser = parserClass()                                  3
1 capitalize is a string method we haven't seen before; it simply capitalizing the first letter of a string and forces everything else to lowercase. Combined with some string formatting, we've taken the name of a dialect and transformed it into the name of the corresponding Dialectizer class. If dialectName is the string 'chef', parserName will be the string 'ChefDialectizer'.
2 We have the name of a class as a string (parserName), and we have the global namespace as a dictionary (globals()). Combined, we can get a reference to the class which the string names. (Remember, classes are objects, and they can be assigned to variables just like any other object.) If parserName is the string 'ChefDialectizer', parserClass will be the class ChefDialectizer.
3 Finally, we have a class object (parserClass), and we want an instance of the class. Well, we already know how to do that: call the class like a function. The fact that the class is being stored in a local variable makes absolutely no difference; we just call the local variable like a function, and out pops an instance of the class. If parserClass is the class ChefDialectizer, parser will be an instance of the class ChefDialectizer.

Why bother? After all, there are only 3 Dialectizer classes; why not just use a case statement? (Well, there's no case statement in Python, but why not just use a series of if statements?) One reason: extensibility. The translate function has absolutely no idea how many Dialectizer classes we've defined. Imagine if we defined a new FooDialectizer tomorrow; translate would work by passing 'foo' as the dialectName.

Even better, imagine putting FooDialectizer in a separate module, and importing it with from module import. We've already seen that this includes it in globals(), so translate would still work without modification, even though FooDialectizer was in a separate file.

Now imagine that the name of the dialect is coming from somewhere outside the program, maybe from a database or from a user-inputted value on a form. You can use any number of server-side Python scripting architectures to dynamically generate web pages; this function could take a URL and a dialect name (both strings) in the query string of a web page request, and output the “translated” web page.

Finally, imagine a Dialectizer framework with a plug-in architecture. You could put each Dialectizer class in a separate file, leaving only the translate function in dialect.py. Assuming a consistent naming scheme, the translate function could dynamic import the appropiate class from the appropriate file, given nothing but the dialect name. (You haven't seen dynamic importing yet, but I promise to cover in a later chapter.) To add a new dialect, you would simply add an appropriately-named file in the plug-ins directory (like foodialect.py which contains the FooDialectizer class). Calling the translate function with the dialect name 'foo' would find the module foodialect.py, import the class FooDialectizer, and away we go.

Example 8.21. The translate function, part 3

    parser.feed(htmlSource) 1
    parser.close()          2
    return parser.output()  3
1 After all that imagining, this is going to seem pretty boring, but the feed function is what does the entire transformation. We had the entire HTML source in a single string, so we only had to call feed once. However, you can call feed as often as you want, and the parser will just keep parsing. So if we were worried about memory usage (or we knew we were going to be dealing with very large HTML pages), we could set this up in a loop, where we read a few bytes of HTML and fed it to the parser. The result would be the same.
2 Because feed maintains an internal buffer, you should always call the parser's close method when you're done (even if you fed it all at once, like we did). Otherwise you may find that your output is missing the last few bytes.
3 Remember, output is the function we defined on BaseHTMLProcessor that joins all the pieces of output we've buffered and returns them in a single string.

And just like that, we've “translated” a web page, given nothing but a URL and the name of a dialect.

Further reading

  • You thought I was kidding about the server-side scripting idea. So did I, until I found this web-based dialectizer. Unfortunately, source code does not appear to be available.

8.10. Summary

Python provides you with a powerful tool, sgmllib.py, to manipulate HTML by turning its structure into an object model. You can use this tool in many different ways.

  • parsing the HTML looking for something specific
  • aggregating the results, like the URL lister
  • altering the structure along the way, like the attribute quoter
  • transforming the HTML into something else by manipulating the text while leaving the tags alone, like the Dialectizer

Along with these examples, you should be comfortable doing all of the following things:



[7] The technical term for a parser like SGMLParser is a consumer: it consumes HTML and breaks it down. Presumably, the name feed was chosen to fit into the whole “consumer” motif. Personally, it makes me think of an exhibit in the zoo where there's just a dark cage with no trees or plants or evidence of life of any kind, but if you stand perfectly still and look really closely you can make out two beady eyes staring back at you from the far left corner, but you convince yourself that that's just your mind playing tricks on you, and the only way you can tell that the whole thing isn't just an empty cage is a small innocuous sign on the railing that reads, “Do not feed the parser.” But maybe that's just me. In any event, it's an interesting mental image.

[8] The reason Python is better at lists than strings is that lists are mutable but strings are immutable. This means that appending to a list just adds the element and updates the index. Since strings can not be changed after they are created, code like s = s + newpiece will create an entirely new string out of the concatenation of the original and the new piece, then throw away the original string. This involves a lot of expensive memory management, and the amount of effort involved increases as the string gets longer, so doing s = s + newpiece in a loop is deadly. In technical terms, appending n items to a list is O(n), while appending n items to a string is O(n2).

[9] I don't get out much.

[10] All right, it's not that common a question. It's not up there with “What editor should I use to write Python code?” (answer: Emacs) or “Is Python better or worse than Perl?” (answer: “Perl is worse than Python because people wanted it worse.” -Larry Wall, 10/14/1998) But questions about HTML processing pop up in one form or another about once a month, and among those questions, this is a popular one.

Chapter 9. XML Processing

9.1. Diving in

This chapter is about XML processing in Python. It would be helpful if you already knew what an XML document looks like, that it's made up of structured tags to form a hierarchy of elements, and so on. If this doesn't make sense to you, go read an XML tutorial first, then come back.

Being a philosophy major is not required, although if you have ever had the misfortune of being subjected to the writings of Immanuel Kant, you will appreciate the example program a lot more than if you majored in something useful, like computer science.

There are two basic ways to work with XML. One is called SAX (“Simple API for XML”), and it works by reading the XML a little bit at a time and calling a method for each element it finds. (If you read HTML Processing, this should sound familiar, because that's how the sgmllib module works.) The other is called DOM (“Document Object Model”), and it works by reading in the entire XML document at once and creating an internal representation of it using native Python classes linked in a tree structure. Python has standard modules for both kinds of parsing, but this chapter will only deal with using the DOM.

The following is a complete Python program which generates pseudo-random output based on a context-free grammar defined in an XML format. Don't worry yet if you don't understand what that means; we'll examine both the program's input and its output in more depth throughout the chapter.

Example 9.1. kgp.py

If you have not already done so, you can download this and other examples used in this book.

"""Kant Generator for Python

Generates mock philosophy based on a context-free grammar

Usage: python kgp.py [options] [source]

Options:
  -g ..., --grammar=...   use specified grammar file or URL
  -h, --help              show this help
  -d                      show debugging information while parsing

Examples:
  kgp.py                  generates several paragraphs of Kantian philosophy
  kgp.py -g husserl.xml   generates several paragraphs of Husserl
  kpg.py "<xref id='paragraph'/>"  generates a paragraph of Kant
  kgp.py template.xml     reads from template.xml to decide what to generate
"""
from xml.dom import minidom
import random
import toolbox
import sys
import getopt

_debug = 0

class NoSourceError(Exception): pass

class KantGenerator:
    """generates mock philosophy based on a context-free grammar"""

    def __init__(self, grammar, source=None):
        self.loadGrammar(grammar)
        self.loadSource(source and source or self.getDefaultSource())
        self.refresh()

    def _load(self, source):
        """load XML input source, return parsed XML document

        - a URL of a remote XML file ("http://diveintopython.org/kant.xml")
        - a filename of a local XML file ("~/diveintopython/common/py/kant.xml")
        - standard input ("-")
        - the actual XML document, as a string
        """
        sock = toolbox.openAnything(source)
        xmldoc = minidom.parse(sock).documentElement
        sock.close()
        return xmldoc

    def loadGrammar(self, grammar):                         
        """load context-free grammar"""                     
        self.grammar = self._load(grammar)                  
        self.refs = {}                                      
        for ref in self.grammar.getElementsByTagName("ref"):
            self.refs[ref.attributes["id"].value] = ref     

    def loadSource(self, source):
        """load source"""
        self.source = self._load(source)

    def getDefaultSource(self):
        """guess default source of the current grammar
        
        The default source will be one of the <ref>s that is not
        cross-referenced.  This sounds complicated but it's not.
        Example: The default source for kant.xml is
        "<xref id='section'/>", because 'section' is the one <ref>
        that is not <xref>'d anywhere in the grammar.
        In most grammars, the default source will produce the
        longest (and most interesting) output.
        """
        xrefs = {}
        for xref in self.grammar.getElementsByTagName("xref"):
            xrefs[xref.attributes["id"].value] = 1
        xrefs = xrefs.keys()
        standaloneXrefs = [e for e in self.refs.keys() if e not in xrefs]
        if not standaloneXrefs:
            raise NoSourceError, "can't guess source, and no source specified"
        return '<xref id="%s"/>' % random.choice(standaloneXrefs)
        
    def reset(self):
        """reset parser"""
        self.pieces = []
        self.capitalizeNextWord = 0

    def refresh(self):
        """reset output buffer, re-parse entire source file, and return output
        
        Since parsing involves a good deal of randomness, this is an
        easy way to get new output without having to reload a grammar file
        each time.
        """
        self.reset()
        self.parse(self.source)
        return self.output()

    def output(self):
        """output generated text"""
        return "".join(self.pieces)

    def randomChildElement(self, node):
        """choose a random child element of a node
        
        This is a utility method used by do_xref and do_choice.
        """
        choices = [e for e in node.childNodes
                   if e.nodeType == e.ELEMENT_NODE]
        chosen = random.choice(choices)            
        if _debug:                                 
            sys.stderr.write('%s available choices: %s\n' % \
                (len(choices), [e.toxml() for e in choices]))
            sys.stderr.write('Chosen: %s\n' % chosen.toxml())
        return chosen                              

    def parse(self, node):         
        """parse a single XML node
        
        A parsed XML document (from minidom.parse) is a tree of nodes
        of various types.  Each node is represented by an instance of the
        corresponding Python class (Element for a tag, Text for
        text data, Document for the top-level document).  The following
        statement constructs the name of a class method based on the type
        of node we're parsing ("parse_Element" for an Element node,
        "parse_Text" for a Text node, etc.) and then calls the method.
        """
        parseMethod = getattr(self, "parse_%s" % node.__class__.__name__)
        parseMethod(node)

    def parse_Document(self, node):
        """parse the document node
        
        The document node by itself isn't interesting (to us), but
        its only child, node.documentElement, is: it's the root node
        of the grammar.
        """
        self.parse(node.documentElement)

    def parse_Text(self, node):    
        """parse a text node
        
        The text of a text node is usually added to the output buffer
        verbatim.  The one exception is that <p class='sentence'> sets
        a flag to capitalize the first letter of the next word.  If
        that flag is set, we capitalize the text and reset the flag.
        """
        text = node.data
        if self.capitalizeNextWord:
            self.pieces.append(text[0].upper())
            self.pieces.append(text[1:])
            self.capitalizeNextWord = 0
        else:
            self.pieces.append(text)

    def parse_Element(self, node): 
        """parse an element
        
        An XML element corresponds to an actual tag in the source:
        <xref id='...'>, <p chance='...'>, <choice>, etc.
        Each element type is handled in its own method.  Like we did in
        parse(), we construct a method name based on the name of the
        element ("do_xref" for an <xref> tag, etc.) and
        call the method.
        """
        handlerMethod = getattr(self, "do_%s" % node.tagName)
        handlerMethod(node)

    def parse_Comment(self, node):
        """parse a comment
        
        The grammar can contain XML comments, but we ignore them
        """
        pass
    
    def do_xref(self, node):
        """handle <xref id='...'> tag
        
        An <xref id='...'> tag is a cross-reference to a <ref id='...'>
        tag.  <xref id='sentence'/> evaluates to a randomly chosen child of
        <ref id='sentence'>.
        """
        id = node.attributes["id"].value
        self.parse(self.randomChildElement(self.refs[id]))

    def do_p(self, node):
        """handle <p> tag
        
        The <p> tag is the core of the grammar.  It can contain almost
        anything: freeform text, <choice> tags, <xref> tags, even other
        <p> tags.  If a "class='sentence'" attribute is found, a flag
        is set and the next word will be capitalized.  If a "chance='X'"
        attribute is found, there is an X% chance that the tag will be
        evaluated (and therefore a (100-X)% chance that it will be
        completely ignored)
        """
        keys = node.attributes.keys()
        if "class" in keys:
            if node.attributes["class"].value == "sentence":
                self.capitalizeNextWord = 1
        if "chance" in keys:
            chance = int(node.attributes["chance"].value)
            doit = (chance > random.randrange(100))
        else:
            doit = 1
        if doit:
            for child in node.childNodes: self.parse(child)

    def do_choice(self, node):
        """handle <choice> tag
        
        A <choice> tag contains one or more <p> tags.  One <p> tag
        is chosen at random and evaluated; the rest are ignored.
        """
        self.parse(self.randomChildElement(node))

def usage():
    print __doc__

def main(argv):                         
    grammar = "kant.xml"                
    try:                                
        opts, args = getopt.getopt(argv, "hg:d", ["help", "grammar="])
    except getopt.GetoptError:          
        usage()                         
        sys.exit(2)                     
    for opt, arg in opts:               
        if opt in ("-h", "--help"):     
            usage()                     
            sys.exit()                  
        elif opt == '-d':               
            global _debug               
            _debug = 1                  
        elif opt in ("-g", "--grammar"):
            grammar = arg               
    
    source = "".join(args)              

    k = KantGenerator(grammar, source)
    print k.output()

if __name__ == "__main__":
    main(sys.argv[1:])

Example 9.2. toolbox.py

"""Miscellaneous utility functions"""

def openAnything(source):            
    """URI, filename, or string --> stream

    This function lets you define parsers that take any input source
    (URL, pathname to local or network file, or actual data as a string)
    and deal with it in a uniform manner.  Returned object is guaranteed
    to have all the basic stdio read methods (read, readline, readlines).
    Just .close() the object when you're done with it.
    
    Examples:
    >>> from xml.dom import minidom
    >>> sock = openAnything("http://localhost/kant.xml")
    >>> doc = minidom.parse(sock)
    >>> sock.close()
    >>> sock = openAnything("c:\\inetpub\\wwwroot\\kant.xml")
    >>> doc = minidom.parse(sock)
    >>> sock.close()
    >>> sock = openAnything("<ref id='conjunction'><text>and</text><text>or</text></ref>")
    >>> doc = minidom.parse(sock)
    >>> sock.close()
    """
    if hasattr(source, "read"):
        return source

    if source == '-':
        import sys
        return sys.stdin

    # try to open with urllib (if source is http, ftp, or file URL)
    import urllib                         
    try:                                  
        return urllib.urlopen(source)     
    except (IOError, OSError):            
        pass                              
    
    # try to open with native open function (if source is pathname)
    try:                                  
        return open(source)               
    except (IOError, OSError):            
        pass                              
    
    # treat source as string
    import StringIO                       
    return StringIO.StringIO(str(source)) 

Run the program kgp.py by itself, and it will parse the default XML-based grammar, in kant.xml, and print several paragraphs worth of philosophy in the style of Immanuel Kant.

Example 9.3. Sample output of kgp.py

[you@localhost kgp]$ python kgp.py
     As is shown in the writings of Hume, our a priori concepts, in
reference to ends, abstract from all content of knowledge; in the study
of space, the discipline of human reason, in accordance with the
principles of philosophy, is the clue to the discovery of the
Transcendental Deduction.  The transcendental aesthetic, in all
theoretical sciences, occupies part of the sphere of human reason
concerning the existence of our ideas in general; still, the
never-ending regress in the series of empirical conditions constitutes
the whole content for the transcendental unity of apperception.  What
we have alone been able to show is that, even as this relates to the
architectonic of human reason, the Ideal may not contradict itself, but
it is still possible that it may be in contradictions with the
employment of the pure employment of our hypothetical judgements, but
natural causes (and I assert that this is the case) prove the validity
of the discipline of pure reason.  As we have already seen, time (and
it is obvious that this is true) proves the validity of time, and the
architectonic of human reason, in the full sense of these terms,
abstracts from all content of knowledge.  I assert, in the case of the
discipline of practical reason, that the Antinomies are just as
necessary as natural causes, since knowledge of the phenomena is a
posteriori.
    The discipline of human reason, as I have elsewhere shown, is by
its very nature contradictory, but our ideas exclude the possibility of
the Antinomies.  We can deduce that, on the contrary, the pure
employment of philosophy, on the contrary, is by its very nature
contradictory, but our sense perceptions are a representation of, in
the case of space, metaphysics.  The thing in itself is a
representation of philosophy.  Applied logic is the clue to the
discovery of natural causes.  However, what we have alone been able to
show is that our ideas, in other words, should only be used as a canon
for the Ideal, because of our necessary ignorance of the conditions.

[...snip...]

This is, of course, complete gibberish. Well, not complete gibberish. It is syntactically and grammatically correct (although very verbose -- Kant wasn't what you would call a get-to-the-point kind of guy). Some of it may actually be true (or at least the sort of thing that Kant would have agreed with), some of it is blatantly false, and most of it is simply incoherent. But all of it is in the style of Immanuel Kant.

Let me repeat that this is much, much funnier if you are now or have ever been a philosophy major.

The interesting thing about this program is that there is nothing Kant-specific about it. All the content in the previous example was derived from the grammar file, kant.xml. If we tell the program to use a different grammar file (which we can specify on the command line), the output will be completely different.

Example 9.4. Simpler output from kgp.py

[you@localhost kgp]$ python kgp.py -g binary.xml
00101001
[you@localhost kgp]$ python kgp.py -g binary.xml
10110100

We will take a closer look at the structure of the grammar file later in this chapter. For now, all you have to know is that the grammar file defines the structure of the output, and the kgp.py program reads through the grammar and makes random decisions about which words to plug in where.

9.2. Packages

Actually parsing an XML document is very simple: one line of code. However, before we get to that line of code, we need to take a short detour to talk about packages.

Example 9.5. Loading an XML document (a sneak peek)

>>> from xml.dom import minidom 1
>>> xmldoc = minidom.parse('~/diveintopython/common/py/kgp/binary.xml')
1 This is a syntax we haven't seen before. It looks almost like the from module import we know and love, but the "." gives it away as something above and beyond a simple import. In fact, xml is what is known as a package, dom is a nested package within xml, and minidom is a module within xml.dom.

That sounds complicated, but it's really not. Looking at the actual implementation may help. Packages are little more than directories of modules; nested packages are subdirectories. The modules within a package (or a nested package) are still just .py files, like always, except that they're in a subdirectory instead of the main lib/ directory of your Python installation.

Example 9.6. File layout of a package

Python21/           root Python installation (home of the executable)
|
+--lib/             library directory (home of the standard library modules)
   |
   +-- xml/         xml package (really just a directory with other stuff in it)
       |
       +--sax/      xml.sax package (again, just a directory)
       |
       +--dom/      xml.dom package (contains minidom.py)
       |
       +--parsers/  xml.parsers package (used internally)

So when we say from xml.dom import minidom, Python figures out that that means “look in the xml directory for a dom directory, and look in that for the minidom module, and import it as minidom”. But Python is even smarter than that; not only can you import entire modules contained within a package, you can selectively import specific classes or functions from a module contained within a package. You can also import the package itself as a module. The syntax is all the same; Python figures out what you mean based on the file layout of the package, and automatically does the right thing.

Example 9.7. Packages are modules, too

>>> from xml.dom import minidom         1
>>> minidom
<module 'xml.dom.minidom' from 'C:\Python21\lib\xml\dom\minidom.pyc'>
>>> minidom.Element
<class xml.dom.minidom.Element at 01095744>
>>> from xml.dom.minidom import Element 2
>>> Element
<class xml.dom.minidom.Element at 01095744>
>>> minidom.Element
<class xml.dom.minidom.Element at 01095744>
>>> from xml import dom                 3
>>> dom
<module 'xml.dom' from 'C:\Python21\lib\xml\dom\__init__.pyc'>
>>> import xml                          4
>>> xml
<module 'xml' from 'C:\Python21\lib\xml\__init__.pyc'>
1 Here we're importing a module (minidom) from a nested package (xml.dom). The result is that minidom is imported into our namespace, and in order to reference classes within the minidom module (like Element), we have to preface them with the module name.
2 Here we are importing a class (Element) from a module (minidom) from a nested package (xml.dom). The result is that Element is imported directly into our namespace. Note that this does not interfere with the previous import; the Element class can now be referenced in two ways (but it's all still the same class).
3 Here we are importing the dom package (a nested package of xml) as a module in and of itself. Any level of a package can be treated as a module, as we'll see in a moment. It can even have its own attributes and methods, just the modules we've seen before.
4 Here we are importing the root level xml package as a module.

So how can a package (which is just a directory on disk) be imported and treated as a module (which is always a file on disk)? The answer is the magical __init__.py file. You see, packages are not simply directories; they are directories with a specific file, __init__.py, inside. This file defines the attributes and methods of the package. For instance, xml.dom contains a Node class, which is defined in xml/dom/__init__.py. When you import a package as a module (like dom from xml), you're really importing its __init__.py file.

Note
A package is a directory with the special __init__.py file in it. The __init__.py file defines the attributes and methods of the package. It doesn't have to define anything; it can just be an empty file, but it has to exist. But if __init__.py doesn't exist, the directory is just a directory, not a package, and it can't be imported or contain modules or nested packages.

So why bother with packages? Well, they provide a way to logically group related modules. Instead of having an xml package with sax and dom packages inside, the authors could have chosen to put all the sax functionality in xmlsax.py and all the dom functionality in xmldom.py, or even put all of it in a single module. But that would have been unwieldy (as of this writing, the XML package has over 3000 lines of code) and difficult to manage (separate source files mean multiple people can work on different areas simultaneously).

If you ever find yourself writing a large subsystem in Python (or, more likely, when you realize that your small subsystem has grown into a large one), invest some time designing a good package architecture. It's one of the many things Python is good at, so take advantage of it.

9.3. Parsing XML

As I was saying, actually parsing an XML document is very simple: one line of code. Where you go from there is up to you.

Example 9.8. Loading an XML document (for real this time)

>>> from xml.dom import minidom                                          1
>>> xmldoc = minidom.parse('~/diveintopython/common/py/kgp/binary.xml')  2
>>> xmldoc                                                               3
<xml.dom.minidom.Document instance at 010BE87C>
>>> print xmldoc.toxml()                                                 4
<?xml version="1.0" ?>
<grammar>
<ref id="bit">
  <p>0</p>
  <p>1</p>
</ref>
<ref id="byte">
  <p><xref id="bit"/><xref id="bit"/><xref id="bit"/><xref id="bit"/>\
<xref id="bit"/><xref id="bit"/><xref id="bit"/><xref id="bit"/></p>
</ref>
</grammar>
1 As we saw in the previous section, this imports the minidom module from the xml.dom package.
2 Here is the one line of code that does all the work: minidom.parse takes one argument and returns a parsed representation of the XML document. The argument can be many things; in this case, it's simply a filename of an XML document on my local disk. (To follow along, you'll need to change the path to point to your downloaded examples directory.) But you can also pass a file object, or even a file-like object. We'll take advantage of this flexibility later in this chapter.
3 The object returned from minidom.parse is a Document object, a descendant of the Node class. This Document object is the root level of a complex tree-like structure of interlocking Python objects that completely represent the XML document we passed to minidom.parse.
4 toxml is a method of the Node class (and is therefore available on the Document object we got from minidom.parse). toxml prints out the XML that this Node represents. For the Document node, this prints out the entire XML document.

Now that we have an XML document in memory, we can start traversing through it.

Example 9.9. Getting child nodes

>>> xmldoc.childNodes    1
[<DOM Element: grammar at 17538908>]
>>> xmldoc.childNodes[0] 2
<DOM Element: grammar at 17538908>
>>> xmldoc.firstChild    3
<DOM Element: grammar at 17538908>
1 Every Node has a childNodes attribute, which is a list of the Node objects. A Document always has only one child node, the root element of the XML document (in this case, the grammar element).
2 To get the first (and in this case, the only) child node, just use regular list syntax. Remember, there is nothing special going on here; this is just a regular Python list of regular Python objects.
3 Since getting the first child node of a node is a useful and common activity, the Node class has a firstChild attribute, which is synonymous with childNodes[0]. (There is also a lastChild attribute, which is synonymous with childNodes[-1].)

Example 9.10. toxml works on any node

>>> grammarNode = xmldoc.firstChild
>>> print grammarNode.toxml() 1
<grammar>
<ref id="bit">
  <p>0</p>
  <p>1</p>
</ref>
<ref id="byte">
  <p><xref id="bit"/><xref id="bit"/><xref id="bit"/><xref id="bit"/>\
<xref id="bit"/><xref id="bit"/><xref id="bit"/><xref id="bit"/></p>
</ref>
</grammar>
1 Since the toxml method is defined in the Node class, it is available on any XML node, not just the Document element.

Example 9.11. Child nodes can be text

>>> grammarNode.childNodes                  1
[<DOM Text node "\n">, <DOM Element: ref at 17533332>, \
<DOM Text node "\n">, <DOM Element: ref at 17549660>, <DOM Text node "\n">]
>>> print grammarNode.firstChild.toxml()    2



>>> print grammarNode.childNodes[1].toxml() 3
<ref id="bit">
  <p>0</p>
  <p>1</p>
</ref>
>>> print grammarNode.childNodes[3].toxml() 4
<ref id="byte">
  <p><xref id="bit"/><xref id="bit"/><xref id="bit"/><xref id="bit"/>\
<xref id="bit"/><xref id="bit"/><xref id="bit"/><xref id="bit"/></p>
</ref>
>>> print grammarNode.lastChild.toxml()     5


1 Looking at the XML in binary.xml, you might think that the grammar has only two child nodes, the two ref elements. But you're missing something: the carriage returns! After the '<grammar>' and before the first '<ref>' is a carriage return, and this text counts as a child node of the grammar element. Similarly, there is a carriage return after each '</ref>'; these also count as child nodes. So grammar.childNodes is actually a list of 5 objects: 3 Text objects and 2 Element objects.
2 The first child is a Text object representing the carriage return after the '<grammar>' tag and before the first '<ref>' tag.
3 The second child is an Element object representing the first ref element.
4 The fourth child is an Element object representing the second ref element.
5 The last child is a Text object representing the carriage return after the '</ref>' end tag and before the '</grammar>' end tag.

Example 9.12. Drilling down all the way to text

>>> grammarNode
<DOM Element: grammar at 19167148>
>>> refNode = grammarNode.childNodes[1] 1
>>> refNode
<DOM Element: ref at 17987740>
>>> refNode.childNodes                  2
[<DOM Text node "\n">, <DOM Text node "  ">, <DOM Element: p at 19315844>, \
<DOM Text node "\n">, <DOM Text node "  ">, \
<DOM Element: p at 19462036>, <DOM Text node "\n">]
>>> pNode = refNode.childNodes[2]
>>> pNode
<DOM Element: p at 19315844>
>>> print pNode.toxml()                 3
<p>0</p>
>>> pNode.firstChild                    4
<DOM Text node "0">
>>> pNode.firstChild.data               5
u'0'
1 As we saw in the previous example, the first ref element is grammarNode.childNodes[1], since childNodes[0] is a Text node for the carriage return.
2 The ref element has its own set of child nodes, one for the carriage return, a separate one for the spaces, one for the p element, and so forth.
3 You can even use the toxml method here, deeply nested within the document.
4 The p element has only one child node (you can't tell that from this example, but look at pNode.childNodes if you don't believe me), and it is a Text node for the single character '0'.
5 The .data attribute of a Text node gives you the actual string that the text node represents. But what is that 'u' in front of the string? The answer to that deserves its own section.

9.4. Unicode

Unicode is a system to represent characters from all the world's different languages. When Python parses an XML document, all data is stored in memory as unicode.

We'll get to all that in a minute, but first, some background.

Historical note. Before unicode, there were separate character encoding systems for each language, each using the same numbers (0-255) to represent that language's characters. Some languages (like Russian) have multiple conflicting standards about how to represent the same characters; other languages (like Japanese) have so many characters that they require multiple-byte character sets. Exchanging documents between systems was difficult because there was no way for a computer to tell for certain which character encoding scheme the document author had used; the computer only saw numbers, and the numbers could mean different things. Then think about trying to store these documents in the same place (like in the same database table); you would need to store the character encoding alongside each piece of text, and make sure to pass it around whenever you passed the text around. Then think about multilingual documents, with characters from multiple languages in the same document. (They typically used escape codes to switch modes; poof, we're in Russian koi8-r mode, so character 241 means this; poof, now we're in Mac Greek mode, so character 241 means something else. And so on.) These are the problems which unicode was designed to solve.

To solve these problems, unicode represents each character as a 2-byte number, from 0 to 65535.[11] Each 2-byte number represents a unique character used in at least one of the world's languages. (Characters that are used in multiple languages have the same numeric code.) There is exactly 1 number per character, and exactly 1 character per number. Unicode data is never ambiguous.

Of course, there is still the matter of all these legacy encoding systems. 7-bit ASCII, for instance, which stores English characters as numbers ranging from 0 to 127. (65 is capital “A”, 97 is lowercase “a”, and so forth.) English has a very simple alphabet, so it can be completely expressed in 7-bit ASCII. Western European languages like French, Spanish, and German all use an encoding system called ISO-8859-1 (also called “latin-1”), which uses the 7-bit ASCII characters for the numbers 0 through 127, but then extends into the 128-255 range for characters like n-with-a-tilde-over-it (241), and u-with-two-dots-over-it (252). And unicode uses the same characters as 7-bit ASCII for 0 through 127, and the same characters as ISO-8859-1 for 128 through 255, and then extends from there into characters for other languages with the remaining numbers, 256 through 65535.

When dealing with unicode data, you may at some point need to convert the data back into one of these other legacy encoding systems. For instance, to integrate with some other computer system which expects its data in a specific 1-byte encoding scheme, or to print it to a non-unicode-aware terminal or printer. Or to store it in an XML document which explicitly specifies the encoding scheme.

And on that note, let's get back to Python.

Python has had unicode support throughout the language since version 2.0.[12] The XML package uses unicode to store all parsed XML data, but you can use unicode anywhere.

Example 9.13. Introducing unicode

>>> s = u'Dive in'            1
>>> s
u'Dive in'
>>> print s                   2
Dive in
1 To create a unicode string instead of a regular ASCII string, add the letter “u” before the string. Note that this particular string doesn't have any non-ASCII characters. That's fine; unicode is a superset of ASCII (a very large superset at that), so any regular ASCII string can also be stored as unicode.
2 When printing a string, Python will attempt to convert it to your default encoding, which is usually ASCII. (More on this in a minute.) Since this unicode string is made up of characters that are also ASCII characters, printing it has the same result as printing a normal ASCII string; the conversion is seamless, and if you didn't know that s was a unicode string, you'd never notice the difference.

Example 9.14. Storing non-ASCII characters

>>> s = u'La Pe\xf1a'         1
>>> print s                   2
Traceback (innermost last):
  File "<interactive input>", line 1, in ?
UnicodeError: ASCII encoding error: ordinal not in range(128)
>>> print s.encode('latin-1') 3
La Peña
1 The real advantage of unicode, of course, is its ability to store non-ASCII characters, like the Spanish “ñ” (n with a tilde over it). The unicode character code for the tilde-n is 0xf1 in hexadecimal (241 in decimal), which you can type like this: \xf1.
2 Remember I said that the print function attempts to convert a unicode string to ASCII so it can print it? Well, that's not going to work here, because our unicode string contains non-ASCII characters, so Python raises a UnicodeError error.
3 Here's where the conversion-from-unicode-to-other-encoding-schemes comes in. s is a unicode string, but print can only print a regular string. To solve this problem, we call the encode method, available on every unicode string, to convert the unicode string to a regular string in the given encoding scheme, which we pass as a parameter. In this case, we're using latin-1 (also known as iso-8859-1), which includes the tilde-n (whereas the default ASCII encoding scheme did not, since it only includes characters numbered 0 through 127).

Remember I said Python usually converted unicode to ASCII whenever it needed to make a regular string out of a unicode string? Well, this default encoding scheme is an option which you can customize.

Example 9.15. sitecustomize.py

# sitecustomize.py                   1
# this file can be anywhere in your Python path,
# but it usually goes in ${pythondir}/lib/site-packages/

import sys

sys.setdefaultencoding('iso-8859-1') 2
1 sitecustomize.py is a special script; Python will try to import it on startup, so any code in it will be run automatically. As the comment mentions, it can go anywhere (as long as import can find it), but it usually goes in the site-packages directory within your Python lib directory.
2 setdefaultencoding function sets, well, the default encoding. This is the encoding scheme that Python will try to use whenever it needs to auto-coerce a unicode string into a regular string.

Example 9.16. Effects of setting the default encoding

>>> import sys
>>> sys.getdefaultencoding() 1
'iso-8859-1'
>>> s = u'La Pe\xf1a'
>>> print s                  2
La Peña
1 This example assumes that you have made the changes listed in the previous example to your sitecustomize.py file, and restarted Python. If your default encoding still says 'ascii', you didn't set up your sitecustomize.py properly, or you didn't restart Python. The default encoding can only be changed during Python startup; you can't change it later. (Due to some wacky programming tricks that I won't get into right now, you can't even call sys.setdefaultencoding after Python has started up. Dig into site.py and search for “setdefaultencoding” to find out how.)
2 Now that the default encoding scheme includes all the characters we use in our string, Python has no problem auto-coercing the string and printing it.

Now, what about XML? Well, every XML document is in a specific encoding. Again, ISO-8859-1 is a popular encoding for data in Western European languages. KOI8-R is popular for Russian texts. The encoding, if specified, is in the header of the XML document.

Example 9.17. russiansample.xml


<?xml version="1.0" encoding="koi8-r"?>       1
<preface>
<title>Предисловие</title>                    2
</preface>
1 This is a sample extract from a real Russian XML document; it's part of a Russian translation of this very book. Note the encoding, koi8-r, specified in the header.
2 These are Cyrillic characters which, as far as I know, spell the Russian word for “Preface”. If you open this file in a regular text editor, the characters will most likely like gibberish, because they're encoded using the koi8-r encoding scheme, but they're being displayed in iso-8859-1.

Example 9.18. Parsing russiansample.xml

>>> from xml.dom import minidom
>>> xmldoc = minidom.parse('russiansample.xml') 1
>>> title = xmldoc.getElementsByTagName('title')[0].firstChild.data
>>> title                                       2
u'\u041f\u0440\u0435\u0434\u0438\u0441\u043b\u043e\u0432\u0438\u0435'
>>> print title                                 3
Traceback (innermost last):
  File "<interactive input>", line 1, in ?
UnicodeError: ASCII encoding error: ordinal not in range(128)
>>> convertedtitle = title.encode('koi8-r')     4
>>> convertedtitle
'\xf0\xd2\xc5\xc4\xc9\xd3\xcc\xcf\xd7\xc9\xc5'
>>> print convertedtitle                        5
Предисловие
1 I'm assuming here that you saved the previous example as russiansample.xml in the current directory. I am also, for the sake of completeness, assuming that you've changed your default encoding back to 'ascii' by removing your sitecustomize.py file, or at least commenting out the setdefaultencoding line.
2 Note that the text data of the title tag (now in the title variable, thanks to that long concatenation of Python functions which I hastily skipped over and, annoyingly, won't explain until the next section) -- the text data inside the XML document's title element is stored in unicode.
3 Printing the title is not possible, because this unicode string contains non-ASCII characters, so Python can't convert it to ASCII because that doesn't make sense.
4 We can, however, explicitly convert it to koi8-r, in which case we get a (regular, not unicode) string of single-byte characters (f0, d2, c5, and so forth) that are the koi8-r-encoded versions of the characters in the original unicode string.
5 Printing the koi8-r-encoded string will probably show gibberish on your screen, because your Python IDE is interpreting those characters as iso-8859-1, not koi8-r. But at least they do print. (And, if you look carefully, it's the same gibberish that you saw when you opened the original XML document in a non-unicode-aware text editor. Python converted it from koi8-r into unicode when it parsed the XML document, and we've just converted it back.)

To sum up, unicode itself is a bit intimidating if you've never seen it before, but unicode data is really very easy to handle in Python. If your XML documents are all 7-bit ASCII (like the examples in this chapter), you will literally never think about unicode. Python will convert the ASCII data in the XML documents into unicode while parsing, and auto-coerce it back to ASCII whenever necessary, and you'll never even notice. But if you need to deal with that in other languages, Python is ready.

Further reading

  • Unicode.org is the home page of the unicode standard, including a brief technical introduction.
  • Unicode Tutorial has some more examples of how to use Python's unicode functions, including how to force Python to coerce unicode into ASCII even when it doesn't really want to.
  • Unicode Proposal is the original technical specification for Python's unicode functionality. For advanced unicode hackers only.

9.5. Searching for elements

Traversing XML documents by stepping through each node can be tedious. If you're looking for something in particular, buried deep within your XML document, there is a shortcut you can use to find it quickly: getElementsByTagName.

For this section, we'll be using the binary.xml grammar file, which looks like this:

Example 9.19. binary.xml

<?xml version="1.0"?>
<!DOCTYPE grammar PUBLIC "-//diveintopython.org//DTD Kant Generator Pro v1.0//EN" "kgp.dtd">
<grammar>
<ref id="bit">
  <p>0</p>
  <p>1</p>
</ref>
<ref id="byte">
  <p><xref id="bit"/><xref id="bit"/><xref id="bit"/><xref id="bit"/>\
<xref id="bit"/><xref id="bit"/><xref id="bit"/><xref id="bit"/></p>
</ref>
</grammar>

It has two refs, 'bit' and 'byte'. A bit is either a '0' or '1', and a byte is 8 bits.

Example 9.20. Introducing getElementsByTagName

>>> from xml.dom import minidom
>>> xmldoc = minidom.parse('binary.xml')
>>> reflist = xmldoc.getElementsByTagName('ref') 1
>>> reflist
[<DOM Element: ref at 136138108>, <DOM Element: ref at 136144292>]
>>> print reflist[0].toxml()
<ref id="bit">
  <p>0</p>
  <p>1</p>
</ref>
>>> print reflist[1].toxml()
<ref id="byte">
  <p><xref id="bit"/><xref id="bit"/><xref id="bit"/><xref id="bit"/>\
<xref id="bit"/><xref id="bit"/><xref id="bit"/><xref id="bit"/></p>
</ref>
1 getElementsByTagName takes one argument, the name of the element you wish to find. It returns a list of Element objects, corresponding to the XML elements that have that name. In this case, we find two ref elements.

Example 9.21. Every element is searchable

>>> firstref = reflist[0]                      1
>>> print firstref.toxml()
<ref id="bit">
  <p>0</p>
  <p>1</p>
</ref>
>>> plist = firstref.getElementsByTagName("p") 2
>>> plist
[<DOM Element: p at 136140116>, <DOM Element: p at 136142172>]
>>> print plist[0].toxml()                     3
<p>0</p>
>>> print plist[1].toxml()
<p>1</p>
1 Continuing from the previous example, the first object in our reflist is the 'bit' ref element.
2 We can use the same getElementsByTagName method on this Element to find all the <p> elements within the 'bit' ref element.
3 Just as before, the getElementsByTagName method returns a list of all the elements it found. In this case, we have two, one for each bit.

Example 9.22. Searching is actually recursive

>>> plist = xmldoc.getElementsByTagName("p") 1
>>> plist
[<DOM Element: p at 136140116>, <DOM Element: p at 136142172>, <DOM Element: p at 136146124>]
>>> plist[0].toxml()                         2
'<p>0</p>'
>>> plist[1].toxml()
'<p>1</p>'
>>> plist[2].toxml()                         3
'<p><xref id="bit"/><xref id="bit"/><xref id="bit"/><xref id="bit"/>\
<xref id="bit"/><xref id="bit"/><xref id="bit"/><xref id="bit"/></p>'
1 Note carefully the difference between this and the previous example. Previously, we were searching for p elements within firstref, but here we are searching for p elements within xmldoc, the root-level object that represents the entire XML document. This does find the p elements nested within the ref elements within the root grammar element.
2 The first two p elements are within the first ref (the 'bit' ref).
3 The last p element is the one within the second ref (the 'byte' ref).

9.6. Accessing element attributes

XML elements can have one or more attributes, and it is incredibly simple to access them once you have parsed an XML document.

For this section, we'll be using the binary.xml grammar file that we saw in the previous section.

Note
This section may be a little confusing, because of some overlapping terminology. Elements in an XML document have attributes, and Python objects also have attributes. When we parse an XML document, we get a bunch of Python objects that represent all the pieces of the XML document, and some of these Python objects represent attributes of the XML elements. But the (Python) objects that represent the (XML) attributes also have (Python) attributes, which are used to access various parts of the (XML) attribute that the object represents. I told you it was confusing. I am open to suggestions on how to distinguish these more clearly.

Example 9.23. Accessing element attributes

>>> xmldoc = minidom.parse('binary.xml')
>>> reflist = xmldoc.getElementsByTagName('ref')
>>> bitref = reflist[0]
>>> print bitref.toxml()
<ref id="bit">
  <p>0</p>
  <p>1</p>
</ref>
>>> bitref.attributes          1
<xml.dom.minidom.NamedNodeMap instance at 0x81e0c9c>
>>> bitref.attributes.keys()   2 3
[u'id']
>>> bitref.attributes.values() 4
[<xml.dom.minidom.Attr instance at 0x81d5044>]
>>> bitref.attributes["id"]    5
<xml.dom.minidom.Attr instance at 0x81d5044>
1 Each Element object has an attribute called attributes, which is a NamedNodeMap object. This sounds scary, but it's not, because a NamedNodeMap is an object that acts like a dictionary, so you already know how to use it.
2 Treating the NamedNodeMap as a dictionary, we can get a list of the names of the attributes of this element by using attributes.keys(). This element has only one attribute, 'id'.
3 Attribute names, like all other text in an XML document, are stored in unicode.
4 Again treating the NamedNodeMap as a dictionary, we can get a list of the values of the attributes by using attributes.values(). The values are themselves objects, of type Attr. We'll see how to get useful information out of this object in the next example.
5 Still treating the NamedNodeMap as a dictionary, we can access an individual attribute by name, using normal dictionary syntax. (Readers who have been paying extra-close attention will already know how the NamedNodeMap class accomplishes this neat trick: by defining a __getitem__ special method. Other readers can take comfort in the fact that they don't need to understand how it works in order to use it effectively.)

Example 9.24. Accessing individual attributes

>>> a = bitref.attributes["id"]
>>> a
<xml.dom.minidom.Attr instance at 0x81d5044>
>>> a.name  1
u'id'
>>> a.value 2
u'bit'
1 The Attr object completely represents a single XML attribute of a single XML element. The name of the attribute (the same name as we used to find this object in the bitref.attributes NamedNodeMap pseudo-dictionary) is stored in a.name.
2 The actual text value of this XML attribute is stored in a.value.
Note
Like a dictionary, attributes of an XML element have no ordering. Attributes may happen to be listed in a certain order in the original XML document, and the Attr objects may happen to be listed in a certain order when the XML document is parsed into Python objects, but these orders are arbitrary and should carry no special meaning. You should always access individual attributes by name, like the keys of a dictionary.


[11] This, sadly, is still an oversimplification. Unicode now has been extended to handle ancient Chinese, Korean, and Japanese texts, which had so many different characters that the 2-byte unicode system could not represent them all. But Python doesn't currently support that out of the box, and I don't know if there is a project afoot to add it. You've reached the limits of my expertise, sorry.

[12] Actually, Python has had unicode support since version 1.6, but version 1.6 was a contractual obligation release that nobody likes to talk about, a bastard stepchild of a hippie youth best left forgotten. Even the official Python documentation claims that unicode was “new in version 2.0”. It's a lie, but, like the lies of presidents who say they inhaled but didn't enjoy it, we choose to believe it because we remember our own misspent youths a bit too vividly.

Chapter 10. Scripts and Streams

10.1. Abstracting input sources

One of Python's greatest strengths is its dynamic binding, and one powerful use of dynamic binding is the file-like object.

Many functions which require an input source could simply take a filename, go open the file for reading, read it, and close it when they're done. But they don't. Instead, they take a file-like object.

In the simplest case, a file-like object is any object with a read method with an optional size parameter, which returns a string. When called with no size parameter, it reads everything there is to read from the input source and returns all the data as a single string. When called with a size parameter, it reads that much from the input source and returns that much data; when called again, it picks up where it left off and returns the next chunk of data.

This is how reading from real files works; the difference is that we're not limiting ourselves to real files. The input source could be anything: a file on disk, a web page, even a hard-coded string. As long as we pass a file-like object to the function, and the function simply calls the object's read method, the function can handle any kind of input source without specific code to handle each kind.

In case you were wondering how this relates to XML processing, minidom.parse is one such function which can take a file-like object.

Example 10.1. Parsing XML from a file

>>> from xml.dom import minidom
>>> fsock = open('binary.xml')    1
>>> xmldoc = minidom.parse(fsock) 2
>>> fsock.close()                 3
>>> print xmldoc
<?xml version="1.0" ?>
<grammar>
<ref id="bit">
  <p>0</p>
  <p>1</p>
</ref>
<ref id="byte">
  <p><xref id="bit"/><xref id="bit"/><xref id="bit"/><xref id="bit"/>\
<xref id="bit"/><xref id="bit"/><xref id="bit"/><xref id="bit"/></p>
</ref>
</grammar>
1 First, we open the file on disk. This gives us a file object.
2 We pass the file object to minidom.parse, which calls the read method of fsock and reads the XML document from the file on disk.
3 Be sure to call the close method of the file object after we're done with it. minidom.parse will not do this for you.

Well, that all seems like a colossal waste of time. After all, we've already seen that minidom.parse can simply take the filename and do all the opening and closing nonsense automatically. And it's true that if you know you're just going to be parsing a local file, you can pass the filename and minidom.parse is smart enough to Do The Right Thing™. But notice how similar -- and easy -- it is to parse an XML document straight from the Internet.

Example 10.2. Parsing XML from a URL

>>> import urllib
>>> usock = urllib.urlopen('http://slashdot.org/slashdot.rdf') 1
>>> xmldoc = minidom.parse(usock)                              2
>>> usock.close()                                              3
>>> print xmldoc.toxml()                                       4
<?xml version="1.0" ?>
<rdf:RDF xmlns="http://my.netscape.com/rdf/simple/0.9/"
 xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">

<channel>
<title>Slashdot</title>
<link>http://slashdot.org/</link>
<description>News for nerds, stuff that matters</description>
</channel>

<image>
<title>Slashdot</title>
<url>http://images.slashdot.org/topics/topicslashdot.gif</url>
<link>http://slashdot.org/</link>
</image>

<item>
<title>To HDTV or Not to HDTV?</title>
<link>http://slashdot.org/article.pl?sid=01/12/28/0421241</link>
</item>

[...snip...]
1 As we saw in the previous chapter, urlopen takes a web page URL and returns a file-like object. Most importantly, this object has a read method which returns the HTML source of the web page.
2 Now we pass the file-like object to minidom.parse, which obediently calls the read method of the object and parses the XML data that the read method returns. The fact that this XML data is now coming straight from a web page is completely irrelevant. minidom.parse doesn't know about web pages, and it doesn't care about web pages; it just knows about file-like objects.
3 As soon as you're done with it, be sure to close the file-like object that urlopen gives you.
4 By the way, this URL is real, and it really is XML. It's an XML representation of the current headlines on Slashdot, a technical news and gossip site.

Example 10.3. Parsing XML from a string (the easy but inflexible way)

>>> contents = "<grammar><ref id='bit'><p>0</p><p>1</p></ref></grammar>"
>>> xmldoc = minidom.parseString(contents) 1
>>> print xmldoc.toxml()
<?xml version="1.0" ?>
<grammar><ref id="bit"><p>0</p><p>1</p></ref></grammar>
1 minidom has a method, parseString, which takes an entire XML document as a string and parses it. You can use this instead of minidom.parse if you know you already have your entire XML document in a string.

OK, so we can use the minidom.parse function for parsing both local files and remote URLs, but for parsing strings, we use... a different function. That means that if we want to be able to take input from a file, a URL, or a string, we'll need special logic to check whether it's a string, and call the parseString function instead. How unsatisfying.

If there were a way to turn a string into a file-like object, then we could simply pass this object to minidom.parse. And in fact, there is a module specifically designed for doing just that: StringIO.

Example 10.4. Introducing StringIO

>>> contents = "<grammar><ref id='bit'><p>0</p><p>1</p></ref></grammar>"
>>> import StringIO
>>> ssock = StringIO.StringIO(contents)   1
>>> ssock.read()                          2
"<grammar><ref id='bit'><p>0</p><p>1</p></ref></grammar>"
>>> ssock.read()                          3
''
>>> ssock.seek(0)                         4
>>> ssock.read(15)                        5
'<grammar><ref i'
>>> ssock.read(15)
"d='bit'><p>0</p"
>>> ssock.read()
'><p>1</p></ref></grammar>'
>>> ssock.close()                         6
1 The StringIO module contains a single class, also called StringIO, which allows you to turn a string into a file-like object. The StringIO class takes the string as a parameter when creating an instance.
2 Now we have a file-like object, and we can do all sorts of file-like things with it. Like read, which returns the original string.
3 Calling read again returns an empty string. This is how real file objects work too; once you read the entire file, you can't read any more without explicitly seeking to the beginning of the file. The StringIO object works the same way.
4 You can explicitly seek to the beginning of the string, just like seeking through a file, by using the seek method of the StringIO object.
5 You can also read the string in chunks, by passing a size parameter to the read method.
6 At any time, read will return the rest of the string that you haven't read yet. All of this is exactly how file objects work; hence the term file-like object.

Example 10.5. Parsing XML from a string (the file-like object way)

>>> contents = "<grammar><ref id='bit'><p>0</p><p>1</p></ref></grammar>"
>>> ssock = StringIO.StringIO(contents)
>>> xmldoc = minidom.parse(ssock) 1
>>> print xmldoc.toxml()
<?xml version="1.0" ?>
<grammar><ref id="bit"><p>0</p><p>1</p></ref></grammar>
1 Now we can pass the file-like object (really a StringIO) to minidom.parse, which will call the object's read method and happily parse away, never knowing that its input came from a hard-coded string.

So now we know how to use a single function, minidom.parse, to parse an XML document stored on a web page, in a local file, or in a hard-coded string. For a web page, we use urlopen to get a file-like object; for a local file, we use open; and for a string, we use StringIO. Now let's take it one step further and generalize these differences as well.

Example 10.6. openAnything

def openAnything(source):                  1
    # try to open with urllib (if source is http, ftp, or file URL)
    import urllib                         
    try:                                  
        return urllib.urlopen(source)      2
    except (IOError, OSError):            
        pass                              

    # try to open with native open function (if source is pathname)
    try:                                  
        return open(source)                3
    except (IOError, OSError):            
        pass                              

    # treat source as string
    import StringIO                       
    return StringIO.StringIO(str(source))  4
1 The openAnything function takes a single parameter, source, and returns a file-like object. source is a string of some sort; it can either be a URL (like 'http://slashdot.org/slashdot.rdf'), a full or partial pathname to a local file (like 'binary.xml'), or a string that contains actual XML data to be parsed.
2 First, we see if source is a URL. We do this through brute force: we try to open it as a URL and silently ignore errors caused by trying to open something which is not a URL. This is actually elegant in the sense that, if urllib ever supports new types of URLs in the future, we will also support them without recoding.
3 If urllib yelled at us and told us that source wasn't a valid URL, we assume it's a path to a file on disk and try to open it. Again, we don't do anything fancy to check whether source is a valid filename or not (the rules for valid filenames vary wildly between different platforms anyway, so we'd probably get them wrong anyway). Instead, we just blindly open the file, and silently trap any errors.
4 By this point, we have to assume that source is a string that has hard-coded data in it (since nothing else worked), so we use StringIO to create a file-like object out of it and return that. (In fact, since we're using the str function, source doesn't even need to be a string; it could be any object, and we'll use its string representation, as defined by its __str__ special method.)

Now we can use this openAnything function in conjunction with minidom.parse to make a function that takes a source that refers to an XML document somehow (either as a URL, or a local filename, or a hard-coded XML document in a string) and parses it.

Example 10.7. Using openAnything in kgp.py

class KantGenerator:
    def _load(self, source):
        sock = toolbox.openAnything(source)
        xmldoc = minidom.parse(sock).documentElement
        sock.close()
        return xmldoc

10.2. Standard input, output, and error

UNIX users are already familiar with the concept of standard input, standard output, and standard error. This section is for the rest of you.

Standard output and standard error (commonly abbreviated stdout and stderr) are pipes that are built into every UNIX system. When you print something, it goes to the stdout pipe; when your program crashes and prints out debugging information (like a traceback in Python), it goes to the stderr pipe. Both of these pipes are ordinarily just connected to the terminal window where you are working, so when a program prints, you see the output, and when a program crashes, you see the debugging information. (If you're working on a system with a window-based Python IDE, stdout and stderr default to your “Interactive Window”.)

Example 10.8. Introducing stdout and stderr

>>> for i in range(3):
...     print 'Dive in'             1
Dive in
Dive in
Dive in
>>> import sys
>>> for i in range(3):
...     sys.stdout.write('Dive in') 2
Dive inDive inDive in
>>> for i in range(3):
...     sys.stderr.write('Dive in') 3
Dive inDive inDive in
1 As we saw in Example 6.9, “Simple counters”, we can use Python's built-in range function to build simple counter loops that repeat something a set number of times.
2 stdout is a file-like object; calling its write function will print out whatever string you give it. In fact, this is what the print function really does; it adds a carriage return to the end of the string you're printing, and calls sys.stdout.write.
3 In the simplest case, stdout and stderr send their output to the same place: the Python IDE (if you're in one), or the terminal (if you're running Python from the command line). Like stdout, stderr does not add carriage returns for you; if you want them, add them yourself.

stdout and stderr are both file-like objects, like the ones we discussed in Abstracting input sources, but they are both write-only. They have no read method, only write. Still, they are file-like objects, and you can assign any other file- or file-like object to them to redirect their output.

Example 10.9. Redirecting output

[you@localhost kgp]$ python stdout.py
Dive in
[you@localhost kgp]$ cat out.log
This message will be logged instead of displayed

If you have not already done so, you can download this and other examples used in this book.

#stdout.py
import sys

print 'Dive in'                                          1
saveout = sys.stdout                                     2
fsock = open('out.log', 'w')                             3
sys.stdout = fsock                                       4
print 'This message will be logged instead of displayed' 5
sys.stdout = saveout                                     6
fsock.close()                                            7
1 This will print to the IDEInteractive Window” (or the terminal, if running the script from the command line).
2 Always save stdout before redirecting it, so you can set it back to normal later.
3 Open a new file for writing.
4 Redirect all further output to the new file we just opened.
5 This will be “printed” to the log file only; it will not be visible in the IDE window or on the screen.
6 Set stdout back to the way it was before we mucked with it.
7 Close the log file.

Redirecting stderr works exactly the same way, using sys.stderr instead of sys.stdout.

Example 10.10. Redirecting error information

[you@localhost kgp]$ python stderr.py
[you@localhost kgp]$ cat error.log
Traceback (most recent line last):
  File "stderr.py", line 5, in ?
    raise Exception, 'this error will be logged'
Exception: this error will be logged

If you have not already done so, you can download this and other examples used in this book.

#stderr.py
import sys

fsock = open('error.log', 'w')               1
sys.stderr = fsock                           2
raise Exception, 'this error will be logged' 3 4
1 Open the log file where we want to store debugging information.
2 Redirect standard error by assigning the file object of our newly-opened log file to stderr.
3 Raise an exception. Note from the screen output that this does not print anything on screen. All the normal traceback information has been written to error.log.
4 Also note that we're not explicitly closing our log file, nor are we setting stderr back to its original value. This is fine, since once the program crashes (due to our exception), Python will clean up and close the file for us, and it doesn't make any difference that stderr is never restored, since, as I mentioned, the program crashes and Python ends. Restoring the original is more important for stdout, if you expect to go do other stuff within the same script afterwards.

Since it is so common to write error messages to standard error, there is a shorthand syntax that can be used instead of going through the hassle of redirecting it outright.

Example 10.11. Printing to stderr

>>> print 'entering function'
entering function
>>> print >> sys.stderr, 'entering function' 1
entering function
1 This shorthand syntax of the print statement can be used to write to any open file, or file-like object. In this case, we can redirect a single print statement to stderr without affecting subsequent print statements.

Standard input, on the other hand, is a read-only file object, and it represents the data flowing into the program from some previous program. This will likely not make much sense to classic Mac OS users, or even Windows users unless you were ever fluent on the MS-DOS command line. The way it works is that you can construct a chain of commands in a single line, so that one program's output becomes the input for the next program in the chain. The first program simply outputs to standard output (without doing any special redirecting itself, just doing normal print statements or whatever), and the next program reads from standard input, and the operating system takes care of connecting one program's output to the next program's input.

Example 10.12. Chaining commands

[you@localhost kgp]$ python kgp.py -g binary.xml         1
01100111
[you@localhost kgp]$ cat binary.xml                      2
<?xml version="1.0"?>
<!DOCTYPE grammar PUBLIC "-//diveintopython.org//DTD Kant Generator Pro v1.0//EN" "kgp.dtd">
<grammar>
<ref id="bit">
  <p>0</p>
  <p>1</p>
</ref>
<ref id="byte">
  <p><xref id="bit"/><xref id="bit"/><xref id="bit"/><xref id="bit"/>\
<xref id="bit"/><xref id="bit"/><xref id="bit"/><xref id="bit"/></p>
</ref>
</grammar>
[you@localhost kgp]$ cat binary.xml | python kgp.py -g - 3 4
10110001
1 As we saw in Diving in, this will print a string of eight random bits, 0 or 1.
2 This simply prints out the entire contents of binary.xml. (Windows users should use type instead of cat.)
3 This prints the contents of binary.xml, but the “|” character, called the “pipe” character, means that the contents will not be printed to the screen. Instead, they will become the standard input of the next command, which in this case calls our Python script.
4 Instead of specifying a module (like binary.xml), we specify “-”, which causes our script to load the grammar from standard input instead of from a specific file on disk. (More on how this happens in the next example.) So the effect is the same as the first syntax, where we specified the grammar filename directly, but think of the expansion possibilities here. Instead of simply doing cat binary.xml, we could run a script that dynamically generates the grammar, then we can pipe it into our script. It could come from anywhere: a database, or some grammar-generating meta-script, or whatever. The point is that we don't have to change our kgp.py script at all to incorporate any of this functionality. All we have to do is be able to take grammar files from standard input, and we can separate all the other logic into another program.

So how does our script “know” to read from standard input when the grammar file is “-”? It's not magic; it's just code.

Example 10.13. Reading from standard input in kgp.py

def openAnything(source):
    if source == "-":    1
        import sys
        return sys.stdin

    # try to open with urllib (if source is http, ftp, or file URL)
    import urllib
    try:

[... snip ...]
1 This is the openAnything function from toolbox.py, which we previously examined in Abstracting input sources. All we've done is add three lines of code at the beginning of the function to check if the source is “-”; if so, we return sys.stdin. Really, that's it! Remember, stdin is a file-like object with a read method, so the rest of our code (in kgp.py, where we call openAnything) doesn't change a bit.

10.3. Caching node lookups

kgp.py employs several tricks which may or may not be useful to you in your XML processing. The first one takes advantage of the consistent structure of the input documents to build a cache of nodes.

A grammar file defines a series of ref elements. Each ref contains one or more p elements, which can contain lots of different things, including xrefs. Whenever we encounter an xref, we look for a corresponding ref element with the same id attribute, and choose one of the ref element's children and parse it. (We'll see how this random choice is made in the next section.)

This is how we build up our grammar: define ref elements for the smallest pieces, then define ref elements which "include" the first ref elements by using xref, and so forth. Then we parse the "largest" reference and follow each xref, and eventually output real text. The text we output depends on the (random) decisions we make each time we fill in an xref, so the output is different each time.

This is all very flexible, but there is one downside: performance. When we find an xref and need to find the corresponding ref element, we have a problem. The xref has an id attribute, and we want to find the ref element that has that same id attribute, but there is no easy way to do that. The slow way to do it would be to get the entire list of ref elements each time, then manually loop through and look at each id attribute. The fast way is to do that once and build a cache, in the form of a dictionary.

Example 10.14. loadGrammar

    def loadGrammar(self, grammar):                         
        self.grammar = self._load(grammar)                  
        self.refs = {}                                       1
        for ref in self.grammar.getElementsByTagName("ref"): 2
            self.refs[ref.attributes["id"].value] = ref      3 4
1 Start by creating an empty dictionary, self.refs.
2 As we saw in Searching for elements, getElementsByTagName returns a list of all the elements of a particular name. We easily can get a list of all the ref elements, then simply loop through that list.
3 As we saw in Accessing element attributes, we can access individual attributes of an element by name, using standard dictionary syntax. So the keys of our self.refs dictionary will be the values of the id attribute of each ref element.
4 The values of our self.refs dictionary will be the ref elements themselves. As we saw in Parsing XML, each element, each node, each comment, each piece of text in a parsed XML document is an object.

Once we build this cache, whenever we come across an xref and need to find the ref element with the same id attribute, we can simply look it up in self.refs.

Example 10.15. Using our ref element cache

    def do_xref(self, node):
        id = node.attributes["id"].value
        self.parse(self.randomChildElement(self.refs[id]))

We'll explore the randomChildElement function in the next section.

10.4. Finding direct children of a node

Another useful techique when parsing XML documents is finding all the direct child elements of a particular element. For instance, in our grammar files, a ref element can have several p elements, each of which can contain many things, including other p elements. We want to find just the p elements that are children of the ref, not p elements that are children of other p elements.

You might think we could simply use getElementsByTagName for this, but we can't. getElementsByTagName searches recursively and returns a single list for all the elements it finds. Since p elements can contain other p elements, we can't use getElementsByTagName, because it would return nested p elements that we don't want. To find only direct child elements, we'll need to do it ourselves.

Example 10.16. Finding direct child elements

    def randomChildElement(self, node):
        choices = [e for e in node.childNodes
                   if e.nodeType == e.ELEMENT_NODE] 1 2 3
        chosen = random.choice(choices)             4
        return chosen                              
1 As we saw in Example 9.9, “Getting child nodes”, the childNodes attribute returns a list of all the child nodes of an element.
2 However, as we saw in Example 9.11, “Child nodes can be text”, the list returned by childNodes contains all different types of nodes, including text nodes. That's not what we're looking for here. We only want the children that are elements.
3 Each node has a nodeType attribute, which can be ELEMENT_NODE, TEXT_NODE, COMMENT_NODE, or any number of other values. The complete list of possible values is in the __init__.py file in the xml.dom package. (See Packages for more on packages.) But we're just interested in nodes that are elements, so we can filter the list to only include those nodes whose nodeType is ELEMENT_NODE.
4 Once we have a list of actual elements, choosing a random one is easy. Python comes with a module called random which includes several useful functions. The random.choice function takes a list of any number of items and returns a random item. In this case, the list contains p elements, so chosen is now a p element selected at random from the children of the ref element we were given.

10.5. Creating separate handlers by node type

The third useful XML processing tip involves separating your code into logical functions, based on node types and element names. Parsed XML documents are made up of various types of nodes, each represented by a Python object. The root level of the document itself is represented by a Document object. The Document then contains one or more Element objects (for actual XML tags), each of which may contain other Element objects, Text objects (for bits of text), or Comment objects (for embedded comments). Python makes it easy to write a dispatcher to separate the logic for each node type.

Example 10.17. Class names of parsed XML objects

>>> from xml.dom import minidom
>>> xmldoc = minidom.parse('kant.xml') 1
>>> xmldoc
<xml.dom.minidom.Document instance at 0x01359DE8>
>>> xmldoc.__class__                   2
<class xml.dom.minidom.Document at 0x01105D40>
>>> xmldoc.__class__.__name__          3
'Document'
1 Assume for a moment that kant.xml is in the current directory.
2 As we saw in Packages, the object returned by parsing an XML document is a Document object, as defined in the minidom.py in the xml.dom package. As we saw in Instantiating classes, __class__ is built-in attribute of every Python object.
3 Furthermore, __name__ is a built-in attribute of every Python class, and it is a string. This string is not mysterious; it's the same as the class name you type when you define a class yourself. (See Defining classes.)

Fine, so now we can get the class name of any particular XML node (since each XML node is represented as a Python object). How can we use this to our advantage to separate the logic of parsing each node type? The answer is getattr, which we first saw in Getting object references with getattr.

Example 10.18. parse, a generic XML node dispatcher

    def parse(self, node):          
        parseMethod = getattr(self, "parse_%s" % node.__class__.__name__) 1 2
        parseMethod(node) 3
1 First off, notice that we're constructing a larger string based on the class name of the node we were passed (in the node argument). So if we're passed a Document node, we're constructing the string 'parse_Document', and so forth.
2 Now we can treat that string as a function name, and get a reference to the function itself using getattr
3 Finally, we can call that function and pass the node itself as an argument. The next example shows the definitions of each of these functions.

Example 10.19. Functions called by the parse dispatcher

    def parse_Document(self, node): 1
        self.parse(node.documentElement)

    def parse_Text(self, node):    2
        text = node.data
        if self.capitalizeNextWord:
            self.pieces.append(text[0].upper())
            self.pieces.append(text[1:])
            self.capitalizeNextWord = 0
        else:
            self.pieces.append(text)

    def parse_Comment(self, node): 3
        pass

    def parse_Element(self, node): 4
        handlerMethod = getattr(self, "do_%s" % node.tagName)
        handlerMethod(node)
1 parse_Document is only ever called once, since there is only one Document node in an XML document, and only one Document object in the parsed XML representation. It simply turns around and parses the root element of the grammar file.
2 parse_Text is called on nodes that represent bits of text. The function itself does some special processing to handle automatic capitalization of the first word of a sentence, but otherwise simply appends the represented text to a list.
3 parse_Comment is just a pass, since we don't care about embedded comments in our grammar files. Note, however, that we still need to define the function and explicitly make it do nothing. If the function did not exist, our generic parse function would fail as soon as it stumbled on a comment, because it would try to find the non-existent parse_Comment function. Defining a separate function for every node type, even ones we don't use, allows the generic parse function to stay simple and dumb.
4 The parse_Element method is actually itself a dispatcher, based on the name of the element's tag. The basic idea is the same: take what distinguishes elements from each other (their tag names) and dispatch to a separate function for each of them. We construct a string like 'do_xref' (for an <xref> tag), find a function of that name, and call it. And so forth for each of the other tag names that might be found in the course of parsing a grammar file (<p> tags, <choice> tags).

In this example, the dispatch functions parse and parse_Element simply find other methods in the same class. If your processing is very complex (or you have many different tag names), you could break up your code into separate modules, and use dynamic importing to import each module and call whatever functions you needed. Dynamic importing will be discussed in Regression Testing.

10.6. Handling command line arguments

Python fully supports creating programs that can be run on the command line, complete with command-line arguments and either short- or long-style flags to specify various options. None of this is XML-specific, but this script makes good use of command-line processing, so it seemed like a good time to mention it.

It's difficult to talk about command line processing without understanding how command line arguments are exposed to your Python program, so let's write a simple program to see them.

Example 10.20. Introducing sys.argv

If you have not already done so, you can download this and other examples used in this book.

#argecho.py
import sys

for arg in sys.argv: 1
    print arg
1 Each command line argument passed to the program will be in sys.argv, which is just a list. Here we are printing each argument on a separate line.

Example 10.21. The contents of sys.argv

[you@localhost py]$ python argecho.py             1
argecho.py
[you@localhost py]$ python argecho.py abc def     2
argecho.py
abc
def
[you@localhost py]$ python argecho.py --help      3
argecho.py
--help
[you@localhost py]$ python argecho.py -m kant.xml 4
argecho.py
-m
kant.xml
1 The first thing to know about sys.argv is that it contains the name of the script we're calling. We will actually use this knowledge to our advantage later, in Regression Testing. Don't worry about it for now.
2 Command line arguments are separated by spaces, and each shows up as a separate element in the sys.argv list.
3 Command line flags, like --help, also show up as their own element in the sys.argv list.
4 To make things even more interesting, some command line flags themselves take arguments. For instance, here we have a flag (-m) which takes an argument (kant.xml). Both the flag itself and the flag's argument are simply sequential elements in the sys.argv list. No attempt is made to associate one with the other; all you get is a list.

So as we can see, we certainly have all the information passed on the command line, but then again, it doesn't look like it's going to be all that easy to actually use it. For simple programs that only take a single argument and have no flags, you can simply use sys.argv[1] to access the argument. There's no shame in this; I do it all the time. For more complex programs, you need the getopt module.

Example 10.22. Introducing getopt

def main(argv):                         
    grammar = "kant.xml"                 1
    try:                                
        opts, args = getopt.getopt(argv, "hg:d", ["help", "grammar="]) 2
    except getopt.GetoptError:           3
        usage()                          4
        sys.exit(2)                     

...

if __name__ == "__main__":
    main(sys.argv[1:])
1 First off, look at the bottom of the example and notice that we're calling the main function with sys.argv[1:]. Remember, sys.argv[0] is the name of the script that we're running; we don't care about that for command line processing, so we chop it off and pass the rest of the list.
2 This is where all the interesting processing happens. The getopt function of the getopt module takes three parameters: the argument list (which we got from sys.argv[1:]), a string containing all the possible single-character command line flags that this program accepts, and a list of longer command line flags that are equivalent to the single-character versions. This is quite confusing at first glance, and is explained in more detail below.
3 If anything goes wrong trying to parse these command line flags, getopt will raise an exception, which we catch. We told getopt all the flags we understand, so this probably means that the end user passed some command line flag that we don't understand.
4 As is standard practice in the UNIX world, when our script is passed flags it doesn't understand, we print out a summary of proper usage and exit gracefully. Note that I haven't shown the usage function here. We would still need to code that somewhere and have it print out the appropriate summary; it's not automatic.

So what are all those parameters we pass to the getopt function? Well, the first one is simply the raw list of command line flags and arguments (not including the first element, the script name, which we already chopped off before calling our main function). The second is the list of short command line flags that our script accepts.

"hg:d"

-h
print usage summary
-g ...
use specified grammar file or URL
-d
show debugging information while parsing

The first and third flags are simply standalone flags; you specify them or you don't, and they do things (print help) or change state (turn on debugging). However, the second flag (-g) must be followed by an argument, which is the name of the grammar file to read from. In fact it can be a filename or a web address, and we don't know which yet (we'll figure it out later), but we know it has to be something. So we tell getopt this by putting a colon after the g in that second parameter to the getopt function.

To further complicate things, our script accepts either short flags (like -h) or long flags (like --help), and we want them to do the same thing. This is what the third parameter to getopt is for, to specify a list of the long flags that correspond to the short flags we specified in the second parameter.

["help", "grammar="]

--help
print usage summary
--grammar ...
use specified grammar file or URL

Three things of note here:

  1. All long flags are preceded by two dashes on the command line, but we don't include those dashes when calling getopt. They are understood.
  2. The --grammar flag must always be followed by an additional argument, just like the -g flag. This is notated by an equals sign, "grammar=".
  3. The list of long flags is shorter than the list of short flags, because the -d flag does not have a corresponding long version. This is fine; only -d will turn on debugging. But the order of short and long flags needs to be the same, so you'll need to specify all the short flags that do have corresponding long flags first, then all the rest of the short flags.

Confused yet? Let's look at the actual code and see if it makes sense in context.

Example 10.23. Handling command-line arguments in kgp.py

def main(argv):                          1
    grammar = "kant.xml"                
    try:                                
        opts, args = getopt.getopt(argv, "hg:d", ["help", "grammar="])
    except getopt.GetoptError:          
        usage()                         
        sys.exit(2)                     
    for opt, arg in opts:                2
        if opt in ("-h", "--help"):      3
            usage()                     
            sys.exit()                  
        elif opt == '-d':                4
            global _debug               
            _debug = 1                  
        elif opt in ("-g", "--grammar"): 5
            grammar = arg               

    source = "".join(args)               6

    k = KantGenerator(grammar, source)
    print k.output()
1 The grammar variable will keep track of the grammar file we're using. We initialize it here in case it's not specified on the command line (using either the -g or the --grammar flag).
2 The opts variable that we get back from getopt contains a list of tuples, flag and argument. If the flag doesn't take an argument, then arg will simply be None. This makes it easier to loop through the flags.
3 getopt validates that the command line flags are acceptable, but it doesn't do any sort of conversion between short and long flags. If you specify the -h flag, opt will contain "-h"; if you specify the --help flag, opt will contain "--help". So we need to check for both.
4 Remember, the -d flag didn't have a corresponding long flag, so we only need to check for the short form. If we find it, we set a global variable that we'll refer to later to print out debugging information. (I used this during the development of the script. What, you thought all these examples worked on the first try?)
5 If we find a grammar file, either with a -g flag or a --grammar flag, we save the argument that followed it (stored in arg) into our grammar variable, overwriting the default that we initialized at the top of the main function.
6 That's it. We've looped through and dealt with all the command line flags. That means that anything left must be command line arguments. These come back from the getopt function in the args variable. In this case, we're treating them as source material for our parser. If there are no command line arguments specified, args will be an empty list, and source will end up as the empty string.

10.7. Putting it all together

We've covered a lot of ground. Let's step back and see how all the pieces fit together.

To start with, this is a script that takes its arguments on the command line, using the getopt module.

def main(argv):                         
...
    try:                                
        opts, args = getopt.getopt(argv, "hg:d", ["help", "grammar="])
    except getopt.GetoptError:          
...
    for opt, arg in opts:               
...

We create a new instance of the KantGenerator class, and pass it the grammar file and source that may or may not have been specified on the command line.

    k = KantGenerator(grammar, source)

The KantGenerator instance automatically loads the grammar, which is an XML file. We use our custom openAnything function to open the file (which could be stored in a local file or a remote web server), then use the built-in minidom parsing functions to parse the XML into a tree of Python objects.

    def _load(self, source):
        sock = toolbox.openAnything(source)
        xmldoc = minidom.parse(sock).documentElement
        sock.close()

Oh, and along the way, we take advantage of our knowledge of the structure of the XML document to set up a little cache of references, which are just elements in the XML document.

    def loadGrammar(self, grammar):                         
        for ref in self.grammar.getElementsByTagName("ref"):
            self.refs[ref.attributes["id"].value] = ref     

If we specified some source material on the command line, we use that; otherwise we rip through the grammar looking for the "top-level" reference (that isn't referenced by anything else) and use that as a starting point.

    def getDefaultSource(self):
        xrefs = {}
        for xref in self.grammar.getElementsByTagName("xref"):
            xrefs[xref.attributes["id"].value] = 1
        xrefs = xrefs.keys()
        standaloneXrefs = [e for e in self.refs.keys() if e not in xrefs]
        return '<xref id="%s"/>' % random.choice(standaloneXrefs)

Now we rip through our source material. The source material is also XML, and we parse it one node at a time. To keep our code separated and more maintainable, we use separate handlers for each node type.

    def parse_Element(self, node): 
        handlerMethod = getattr(self, "do_%s" % node.tagName)
        handlerMethod(node)

We bounce through the grammar, parsing all the children of each p element,

    def do_p(self, node):
...
        if doit:
            for child in node.childNodes: self.parse(child)

replacing choice elements with a random child,

    def do_choice(self, node):
        self.parse(self.randomChildElement(node))

and replacing xref elements with a random child of the corresponding ref element, which we previously cached.

    def do_xref(self, node):
        id = node.attributes["id"].value
        self.parse(self.randomChildElement(self.refs[id]))

Eventually, we parse our way down to plain text,

    def parse_Text(self, node):    
        text = node.data
...
            self.pieces.append(text)

which we print out.

def main(argv):                         
...
    k = KantGenerator(grammar, source)
    print k.output()

10.8. Summary

Python comes with powerful libraries for parsing and manipulating XML documents. The minidom takes an XML file and parses it into Python objects, providing for random access to arbitrary elements. Furthermore, this chapter shows how Python can be used to create a "real" standalone command-line script, complete with command-line flags, command-line arguments, error handling, even the ability to take input from the piped result of a previous program.

Before moving on to the next chapter, you should be comfortable doing all of these things:

Chapter 11. Introduction to Unit Testing

11.1. Introduction to Roman numerals

In previous chapters, we “dived in” by immediately looking at code and trying to understand it as quickly as possible. Now that you have some Python under your belt, we're going to step back and look at the steps that happen before the code gets written.

In the next few chapters, we're going to write, debug, and optimize a set of utility functions to convert to and from Roman numerals. We discussed the mechanics of constructing and validating Roman numerals in Case study: Roman numerals, but now let's step back and consider what it would take to expand that into a two-way utility.

The rules for Roman numerals lead to a number of interesting observations:

  1. There is only one correct way to represent a particular number as Roman numerals.
  2. The converse is also true: if a string of characters is a valid Roman numeral, it represents only one number (i.e. it can only be read one way).
  3. There is a limited range of numbers that can be expressed as Roman numerals, specifically 1 through 3999. (The Romans did have several ways of expressing larger numbers, for instance by having a bar over a numeral to represent that its normal value should be multiplied by 1000, but we're not going to deal with that. For our purposes, let's stipulate that Roman numerals go from 1 to 3999.)
  4. There is no way to represent 0 in Roman numerals. (Amazingly, the ancient Romans had no concept of 0 as a number. Numbers were for counting things you had; how can you count what you don't have?)
  5. There is no way to represent negative numbers in Roman numerals.
  6. There is no way to represent decimals or fractions in Roman numerals.

Given all of this, what would we expect out of a set of functions to convert to and from Roman numerals?

roman.py requirements

  1. toRoman should return the Roman numeral representation for all integers 1 to 3999.
  2. toRoman should fail when given an integer outside the range 1 to 3999.
  3. toRoman should fail when given a non-integer decimal.
  4. fromRoman should take a valid Roman numeral and return the number that it represents.
  5. fromRoman should fail when given an invalid Roman numeral.
  6. If you take a number, convert it to Roman numerals, then convert that back to a number, you should end up with the number you started with. So fromRoman(toRoman(n)) == n for all n in 1..3999.
  7. toRoman should always return a Roman numeral using uppercase letters.
  8. fromRoman should only accept uppercase Roman numerals (i.e. it should fail when given lowercase input).

Further reading

  • This site has more on Roman numerals, including a fascinating history of how Romans and other civilizations really used them (short answer: haphazardly and inconsistently).

11.2. Diving in

Now that we've completely defined the behavior we expect from our conversion functions, we're going to do something a little unexpected: we're going to write a test suite that puts these functions through their paces and makes sure that they behave the way we want them to. You read that right: we're going to write code that tests code that we haven't written yet.

This is called unit testing, since the set of two conversion functions can be written and tested as a unit, separate from any larger program they may become part of later. Python has a framework for unit testing, the appropriately-named unittest module.

Note
unittest is included with Python 2.1 and later. Python 2.0 users can download it from pyunit.sourceforge.net.

Unit testing is an important part of an overall testing-centric development strategy. If you write unit tests, it is important to write them early (preferably before writing the code that they test), and to keep them updated as code and requirements change. Unit testing is not a replacement for higher-level functional or system testing, but it is important in all phases of development:

  • Before writing code, it forces you to detail your requirements in a useful fashion.
  • While writing code, it keeps you from over-coding. When all the test cases pass, the function is complete.
  • When refactoring code, it assures you that the new version behaves the same way as the old version.
  • When maintaining code, it helps you cover your ass when someone comes screaming that your latest change broke their old code. (“But sir, all the unit tests passed when I checked it in...”)

11.3. Introducing romantest.py

This is the complete test suite for our Roman numeral conversion functions, which are yet to be written but will eventually be in roman.py. It is not immediately obvious how it all fits together; none of these classes or methods reference any of the others. There are good reasons for this, as we'll see shortly.

Example 11.1. romantest.py

If you have not already done so, you can download this and other examples used in this book.

"""Unit test for roman.py"""

import roman
import unittest

class KnownValues(unittest.TestCase):                          
    knownValues = ( (1, 'I'),
                    (2, 'II'),
                    (3, 'III'),
                    (4, 'IV'),
                    (5, 'V'),
                    (6, 'VI'),
                    (7, 'VII'),
                    (8, 'VIII'),
                    (9, 'IX'),
                    (10, 'X'),
                    (50, 'L'),
                    (100, 'C'),
                    (500, 'D'),
                    (1000, 'M'),
                    (31, 'XXXI'),
                    (148, 'CXLVIII'),
                    (294, 'CCXCIV'),
                    (312, 'CCCXII'),
                    (421, 'CDXXI'),
                    (528, 'DXXVIII'),
                    (621, 'DCXXI'),
                    (782, 'DCCLXXXII'),
                    (870, 'DCCCLXX'),
                    (941, 'CMXLI'),
                    (1043, 'MXLIII'),
                    (1110, 'MCX'),
                    (1226, 'MCCXXVI'),
                    (1301, 'MCCCI'),
                    (1485, 'MCDLXXXV'),
                    (1509, 'MDIX'),
                    (1607, 'MDCVII'),
                    (1754, 'MDCCLIV'),
                    (1832, 'MDCCCXXXII'),
                    (1993, 'MCMXCIII'),
                    (2074, 'MMLXXIV'),
                    (2152, 'MMCLII'),
                    (2212, 'MMCCXII'),
                    (2343, 'MMCCCXLIII'),
                    (2499, 'MMCDXCIX'),
                    (2574, 'MMDLXXIV'),
                    (2646, 'MMDCXLVI'),
                    (2723, 'MMDCCXXIII'),
                    (2892, 'MMDCCCXCII'),
                    (2975, 'MMCMLXXV'),
                    (3051, 'MMMLI'),
                    (3185, 'MMMCLXXXV'),
                    (3250, 'MMMCCL'),
                    (3313, 'MMMCCCXIII'),
                    (3408, 'MMMCDVIII'),
                    (3501, 'MMMDI'),
                    (3610, 'MMMDCX'),
                    (3743, 'MMMDCCXLIII'),
                    (3844, 'MMMDCCCXLIV'),
                    (3888, 'MMMDCCCLXXXVIII'),
                    (3940, 'MMMCMXL'),
                    (3999, 'MMMCMXCIX'))                       

    def testToRomanKnownValues(self):                          
        """toRoman should give known result with known input"""
        for integer, numeral in self.knownValues:              
            result = roman.toRoman(integer)                    
            self.assertEqual(numeral, result)                  

    def testFromRomanKnownValues(self):                          
        """fromRoman should give known result with known input"""
        for integer, numeral in self.knownValues:                
            result = roman.fromRoman(numeral)                    
            self.assertEqual(integer, result)                    

class ToRomanBadInput(unittest.TestCase):                            
    def testTooLarge(self):                                          
        """toRoman should fail with large input"""                   
        self.assertRaises(roman.OutOfRangeError, roman.toRoman, 4000)

    def testZero(self):                                              
        """toRoman should fail with 0 input"""                       
        self.assertRaises(roman.OutOfRangeError, roman.toRoman, 0)   

    def testNegative(self):                                          
        """toRoman should fail with negative input"""                
        self.assertRaises(roman.OutOfRangeError, roman.toRoman, -1)  

    def testDecimal(self):                                           
        """toRoman should fail with non-integer input"""             
        self.assertRaises(roman.NotIntegerError, roman.toRoman, 0.5) 

class FromRomanBadInput(unittest.TestCase):                                      
    def testTooManyRepeatedNumerals(self):                                       
        """fromRoman should fail with too many repeated numerals"""              
        for s in ('MMMM', 'DD', 'CCCC', 'LL', 'XXXX', 'VV', 'IIII'):             
            self.assertRaises(roman.InvalidRomanNumeralError, roman.fromRoman, s)

    def testRepeatedPairs(self):                                                 
        """fromRoman should fail with repeated pairs of numerals"""              
        for s in ('CMCM', 'CDCD', 'XCXC', 'XLXL', 'IXIX', 'IVIV'):               
            self.assertRaises(roman.InvalidRomanNumeralError, roman.fromRoman, s)

    def testMalformedAntecedent(self):                                           
        """fromRoman should fail with malformed antecedents"""                   
        for s in ('IIMXCC', 'VX', 'DCM', 'CMM', 'IXIV',
                  'MCMC', 'XCX', 'IVI', 'LM', 'LD', 'LC'):                       
            self.assertRaises(roman.InvalidRomanNumeralError, roman.fromRoman, s)

class SanityCheck(unittest.TestCase):        
    def testSanity(self):                    
        """fromRoman(toRoman(n))==n for all n"""
        for integer in range(1, 4000):       
            numeral = roman.toRoman(integer) 
            result = roman.fromRoman(numeral)
            self.assertEqual(integer, result)

class CaseCheck(unittest.TestCase):                   
    def testToRomanCase(self):                        
        """toRoman should always return uppercase"""  
        for integer in range(1, 4000):                
            numeral = roman.toRoman(integer)          
            self.assertEqual(numeral, numeral.upper())

    def testFromRomanCase(self):                      
        """fromRoman should only accept uppercase input"""
        for integer in range(1, 4000):                
            numeral = roman.toRoman(integer)          
            roman.fromRoman(numeral.upper())          
            self.assertRaises(roman.InvalidRomanNumeralError,
                              roman.fromRoman, numeral.lower())

if __name__ == "__main__":
    unittest.main()   

Further reading

Chapter 12. Unit Testing: Step by Step

12.1. Testing for success

The most fundamental part of unit testing is constructing individual test cases. A test case answers a single question about the code it is testing.

A test case should be able to...

  • ...run completely by itself, without any human input. Unit testing is about automation.
  • ...determine by itself whether the function it is testing has passed or failed, without a human interpreting the results.
  • ...run in isolation, separate from any other test cases (even if they test the same functions). Each test case is an island.

Given that, let's build our first test case. We have the following requirement:

  1. toRoman should return the Roman numeral representation for all integers 1 to 3999.

Example 12.1. testToRomanKnownValues

class KnownValues(unittest.TestCase):                           1
    knownValues = ( (1, 'I'),
                    (2, 'II'),
                    (3, 'III'),
                    (4, 'IV'),
                    (5, 'V'),
                    (6, 'VI'),
                    (7, 'VII'),
                    (8, 'VIII'),
                    (9, 'IX'),
                    (10, 'X'),
                    (50, 'L'),
                    (100, 'C'),
                    (500, 'D'),
                    (1000, 'M'),
                    (31, 'XXXI'),
                    (148, 'CXLVIII'),
                    (294, 'CCXCIV'),
                    (312, 'CCCXII'),
                    (421, 'CDXXI'),
                    (528, 'DXXVIII'),
                    (621, 'DCXXI'),
                    (782, 'DCCLXXXII'),
                    (870, 'DCCCLXX'),
                    (941, 'CMXLI'),
                    (1043, 'MXLIII'),
                    (1110, 'MCX'),
                    (1226, 'MCCXXVI'),
                    (1301, 'MCCCI'),
                    (1485, 'MCDLXXXV'),
                    (1509, 'MDIX'),
                    (1607, 'MDCVII'),
                    (1754, 'MDCCLIV'),
                    (1832, 'MDCCCXXXII'),
                    (1993, 'MCMXCIII'),
                    (2074, 'MMLXXIV'),
                    (2152, 'MMCLII'),
                    (2212, 'MMCCXII'),
                    (2343, 'MMCCCXLIII'),
                    (2499, 'MMCDXCIX'),
                    (2574, 'MMDLXXIV'),
                    (2646, 'MMDCXLVI'),
                    (2723, 'MMDCCXXIII'),
                    (2892, 'MMDCCCXCII'),
                    (2975, 'MMCMLXXV'),
                    (3051, 'MMMLI'),
                    (3185, 'MMMCLXXXV'),
                    (3250, 'MMMCCL'),
                    (3313, 'MMMCCCXIII'),
                    (3408, 'MMMCDVIII'),
                    (3501, 'MMMDI'),
                    (3610, 'MMMDCX'),
                    (3743, 'MMMDCCXLIII'),
                    (3844, 'MMMDCCCXLIV'),
                    (3888, 'MMMDCCCLXXXVIII'),
                    (3940, 'MMMCMXL'),
                    (3999, 'MMMCMXCIX'))                        2

    def testToRomanKnownValues(self):                           3
        """toRoman should give known result with known input"""
        for integer, numeral in self.knownValues:              
            result = roman.toRoman(integer)                     4 5
            self.assertEqual(numeral, result)                   6
1 To write a test case, first subclass the TestCase class of the unittest module. This class provides many useful methods which you can use in your test case to test specific conditions.
2 This is a list of integer/numeral pairs that I verified manually. It includes the lowest ten numbers, the highest number, every number that translates to a single-character Roman numeral, and a random sampling of other valid numbers. The point of a unit test is not to test every possible input, but to test a representative sample.
3 Every individual test is its own method, which must take no parameters and return no value. If the method exits normally without raising an exception, the test is considered passed; if the method raises an exception, the test is considered failed.
4 Here we call the actual toRoman function. (Well, the function hasn't be written yet, but once it is, this is the line that will call it.) Notice that we have now defined the API for the toRoman function: it must take an integer (the number to convert) and return a string (the Roman numeral representation). If the API is different than that, this test is considered failed.
5 Also notice that we are not trapping any exceptions when we call toRoman. This is intentional. toRoman shouldn't raise an exception when we call it with valid input, and these input values are all valid. If toRoman raises an exception, this test is considered failed.
6 Assuming the toRoman function was defined correctly, called correctly, completed successfully, and returned a value, the last step is to check whether it returned the right value. This is a common question, and the TestCase class provides a method, assertEqual, to check whether two values are equal. If the result returned from toRoman (result) does not match the known value we were expecting (numeral), assertEqual will raise an exception and the test will fail. If the two values are equal, assertEqual will do nothing. If every value returned from toRoman matches the known value we expect, assertEqual never raises an exception, so testToRomanKnownValues eventually exits normally, which means toRoman has passed this test.

12.2. Testing for failure

It is not enough to test that our functions succeed when given good input; we must also test that they fail when given bad input. And not just any sort of failure; they must fail in the way we expect.

Remember our other requirements for toRoman:

  1. toRoman should fail when given an integer outside the range 1 to 3999.
  2. toRoman should fail when given a non-integer decimal.

In Python, functions indicate failure by raising exceptions, and the unittest module provides methods for testing whether a function raises a particular exception when given bad input.

Example 12.2. Testing bad input to toRoman

class ToRomanBadInput(unittest.TestCase):                            
    def testTooLarge(self):                                          
        """toRoman should fail with large input"""                   
        self.assertRaises(roman.OutOfRangeError, roman.toRoman, 4000) 1

    def testZero(self):                                              
        """toRoman should fail with 0 input"""                       
        self.assertRaises(roman.OutOfRangeError, roman.toRoman, 0)    2

    def testNegative(self):                                          
        """toRoman should fail with negative input"""                
        self.assertRaises(roman.OutOfRangeError, roman.toRoman, -1)  

    def testDecimal(self):                                           
        """toRoman should fail with non-integer input"""             
        self.assertRaises(roman.NotIntegerError, roman.toRoman, 0.5)  3
1 The TestCase class of the unittest provides the assertRaises method, which takes the following arguments: the exception we're expecting, the function we're testing, and the arguments we're passing that function. (If the function we're testing takes more than one argument, pass them all to assertRaises, in order, and it will pass them right along to the function we're testing.) Pay close attention to what we're doing here: instead of calling toRoman directly and manually checking that it raises a particular exception (by wrapping it in a try...except block), assertRaises has encapsulated all of that for us. All we do is give it the exception (roman.OutOfRangeError), the function (toRoman), and toRoman's arguments (4000), and assertRaises takes care of calling toRoman and checking to make sure that it raises roman.OutOfRangeError. (Also note that we're passing the toRoman function itself as an argument; we're not calling it, and we're not passing the name of it as a string. Have I mentioned recently how handy it is that everything in Python is an object, including functions and exceptions?)
2 Along with testing numbers that are too large, we need to test numbers that are too small. Remember, Roman numerals cannot express 0 or negative numbers, so we have a test case for each of those (testZero and testNegative). In testZero, we are testing that toRoman raises a roman.OutOfRangeError exception when called with 0; if it does not raise a roman.OutOfRangeError (either because it returns an actual value, or because it raises some other exception), this test is considered failed.
3 Requirement #3 specifies that toRoman cannot accept a non-integer decimal, so here we test to make sure that toRoman raises a roman.NotIntegerError exception when called with a decimal (0.5). If toRoman does not raise a roman.NotIntegerError, this test is considered failed.

The next two requirements are similar to the first three, except they apply to fromRoman instead of toRoman:

  1. fromRoman should take a valid Roman numeral and return the number that it represents.
  2. fromRoman should fail when given an invalid Roman numeral.

Requirement #4 is handled in the same way as requirement #1, iterating through a sampling of known values and testing each in turn. Requirement #5 is handled in the same way as requirements #2 and #3, by testing a series of bad inputs and making sure fromRoman raises the appropriate exception.

Example 12.3. Testing bad input to fromRoman

class FromRomanBadInput(unittest.TestCase):                                      
    def testTooManyRepeatedNumerals(self):                                       
        """fromRoman should fail with too many repeated numerals"""              
        for s in ('MMMM', 'DD', 'CCCC', 'LL', 'XXXX', 'VV', 'IIII'):             
            self.assertRaises(roman.InvalidRomanNumeralError, roman.fromRoman, s) 1

    def testRepeatedPairs(self):                                                 
        """fromRoman should fail with repeated pairs of numerals"""              
        for s in ('CMCM', 'CDCD', 'XCXC', 'XLXL', 'IXIX', 'IVIV'):               
            self.assertRaises(roman.InvalidRomanNumeralError, roman.fromRoman, s)

    def testMalformedAntecedent(self):                                           
        """fromRoman should fail with malformed antecedents"""                   
        for s in ('IIMXCC', 'VX', 'DCM', 'CMM', 'IXIV',
                  'MCMC', 'XCX', 'IVI', 'LM', 'LD', 'LC'):                       
            self.assertRaises(roman.InvalidRomanNumeralError, roman.fromRoman, s)
1 Not much new to say about these; the pattern is exactly the same as the one we used to test bad input to toRoman. I will briefly note that we have another exception: roman.InvalidRomanNumeralError. That makes a total of three custom exceptions that will need to be defined in roman.py (along with roman.OutOfRangeError and roman.NotIntegerError). We'll see how to define these custom exceptions when we actually start writing roman.py, later in this chapter.

12.3. Testing for sanity

Often, you will find that a unit of code contains a set of reciprocal functions, usually in the form of conversion functions where one converts A to B and the other converts B to A. In these cases, it is useful to create a “sanity check” to make sure that you can convert A to B and back to A without losing decimal precision, incurring rounding errors, or triggering any other sort of bug.

Consider this requirement:

  1. If you take a number, convert it to Roman numerals, then convert that back to a number, you should end up with the number you started with. So fromRoman(toRoman(n)) == n for all n in 1..3999.

Example 12.4. Testing toRoman against fromRoman

class SanityCheck(unittest.TestCase):        
    def testSanity(self):                    
        """fromRoman(toRoman(n))==n for all n"""
        for integer in range(1, 4000):        1 2
            numeral = roman.toRoman(integer) 
            result = roman.fromRoman(numeral)
            self.assertEqual(integer, result) 3
1 We've seen the range function before, but here it is called with two arguments, which returns a list of integers starting at the first argument (1) and counting consecutively up to but not including the second argument (4000). Thus, 1..3999, which is the valid range for converting to Roman numerals.
2 I just wanted to mention in passing that integer is not a keyword in Python; here it's just a variable name like any other.
3 The actual testing logic here is straightforward: take a number (integer), convert it to a Roman numeral (numeral), then convert it back to a number (result) and make sure you end up with the same number you started with. If not, assertEqual will raise an exception and the test will immediately be considered failed. If all the numbers match, assertEqual will always return silently, the entire testSanity method will eventually return silently, and the test will be considered passed.

The last two requirements are different from the others because they seem both arbitrary and trivial:

  1. toRoman should always return a Roman numeral using uppercase letters.
  2. fromRoman should only accept uppercase Roman numerals (i.e. it should fail when given lowercase input).

In fact, they are somewhat arbitrary. We could, for instance, have stipulated that fromRoman accept lowercase and mixed case input. But they are not completely arbitrary; if toRoman is always returning uppercase output, then fromRoman must at least accept uppercase input, or our “sanity check” (requirement #6) would fail. The fact that it only accepts uppercase input is arbitrary, but as any systems integrator will tell you, case always matters, so it's worth specifying the behavior up front. And if it's worth specifying, it's worth testing.

Example 12.5. Testing for case

class CaseCheck(unittest.TestCase):                   
    def testToRomanCase(self):                        
        """toRoman should always return uppercase"""  
        for integer in range(1, 4000):                
            numeral = roman.toRoman(integer)          
            self.assertEqual(numeral, numeral.upper()) 1

    def testFromRomanCase(self):                      
        """fromRoman should only accept uppercase input"""
        for integer in range(1, 4000):                
            numeral = roman.toRoman(integer)          
            roman.fromRoman(numeral.upper())           2 3
            self.assertRaises(roman.InvalidRomanNumeralError,
                              roman.fromRoman, numeral.lower())
1 The most interesting thing about this test case is all the things it doesn't test. It doesn't test that the value returned from toRoman is right or even consistent; those questions are answered by separate test cases. We have a whole test case just to test for uppercase-ness. You might be tempted to combine this with the sanity check, since both run through the entire range of values and call toRoman.[13] But that would violate one of our fundamental rules: each test case should answer only a single question. Imagine that you combined this case check with the sanity check, and then that test case failed. You would have to do further analysis to figure out which part of the test case failed to determine what the problem was. If you have to analyze the results of your unit testing just to figure out what they mean, it's a sure sign that you've mis-designed your test cases.
2 There's a similar lesson to be learned here: even though “we know” that toRoman always returns uppercase, we are explicitly converting its return value to uppercase here to test that fromRoman accepts uppercase input. Why? Because the fact that toRoman always returns uppercase is an independent requirement. If we changed that requirement so that, for instance, it always returned lowercase, the testToRomanCase test case would have to change, but this test case would still work. This was another of our fundamental rules: each test case must be able to work in isolation from any of the others. Every test case is an island.
3 Note that we're not assigning the return value of fromRoman to anything. This is legal syntax in Python; if a function returns a value but nobody's listening, Python just throws away the return value. In this case, that's what we want. This test case doesn't test anything about the return value; it just tests that fromRoman accepts the uppercase input without raising an exception.

12.4. roman.py, stage 1

Now that our unit test is complete, it's time to start writing the code that our test cases are attempting to test. We're going to do this in stages, so we can see all the unit tests fail, then watch them pass one by one as we fill in the gaps in roman.py.

Example 12.6. roman1.py

If you have not already done so, you can download this and other examples used in this book.

"""Convert to and from Roman numerals"""

#Define exceptions
class RomanError(Exception): pass                1
class OutOfRangeError(RomanError): pass          2
class NotIntegerError(RomanError): pass
class InvalidRomanNumeralError(RomanError): pass 3

def toRoman(n):
    """convert integer to Roman numeral"""
    pass                                         4

def fromRoman(s):
    """convert Roman numeral to integer"""
    pass
1 This is how you define your own custom exceptions in Python. Exceptions are classes, and you create your own by subclassing existing exceptions. It is strongly recommended (but not required) that you subclass Exception, which is the base class that all built-in exceptions inherit from. Here I am defining RomanError (inherited from Exception) to act as the base class for all my other custom exceptions to follow. This is a matter of style; I could just as easily have inherited each individual exception from the Exception class directly.
2 The OutOfRangeError and NotIntegerError exceptions will eventually be used by toRoman to flag various forms of invalid input, as specified in ToRomanBadInput.
3 The InvalidRomanNumeralError exception will eventually be used by fromRoman to flag invalid input, as specified in FromRomanBadInput.
4 At this stage, we want to define the API of each of our functions, but we don't want to code them yet, so we stub them out using the Python reserved word pass.

Now for the big moment (drum roll please): we're finally going to run our unit test against this stubby little module. At this point, every test case should fail. In fact, if any test case passes in stage 1, we should go back to romantest.py and re-evaluate why we coded a test so useless that it passes with do-nothing functions.

Run romantest1.py with the -v command-line option, which will give more verbose output so we can see exactly what's going on as each test case runs. With any luck, your output should look like this:

Example 12.7. Output of romantest1.py against roman1.py

fromRoman should only accept uppercase input ... ERROR
toRoman should always return uppercase ... ERROR
fromRoman should fail with malformed antecedents ... FAIL
fromRoman should fail with repeated pairs of numerals ... FAIL
fromRoman should fail with too many repeated numerals ... FAIL
fromRoman should give known result with known input ... FAIL
toRoman should give known result with known input ... FAIL
fromRoman(toRoman(n))==n for all n ... FAIL
toRoman should fail with non-integer input ... FAIL
toRoman should fail with negative input ... FAIL
toRoman should fail with large input ... FAIL
toRoman should fail with 0 input ... FAIL

======================================================================
ERROR: fromRoman should only accept uppercase input
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage1\romantest1.py", line 154, in testFromRomanCase
    roman1.fromRoman(numeral.upper())
AttributeError: 'None' object has no attribute 'upper'
======================================================================
ERROR: toRoman should always return uppercase
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage1\romantest1.py", line 148, in testToRomanCase
    self.assertEqual(numeral, numeral.upper())
AttributeError: 'None' object has no attribute 'upper'
======================================================================
FAIL: fromRoman should fail with malformed antecedents
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage1\romantest1.py", line 133, in testMalformedAntecedent
    self.assertRaises(roman1.InvalidRomanNumeralError, roman1.fromRoman, s)
  File "c:\python21\lib\unittest.py", line 266, in failUnlessRaises
    raise self.failureException, excName
AssertionError: InvalidRomanNumeralError
======================================================================
FAIL: fromRoman should fail with repeated pairs of numerals
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage1\romantest1.py", line 127, in testRepeatedPairs
    self.assertRaises(roman1.InvalidRomanNumeralError, roman1.fromRoman, s)
  File "c:\python21\lib\unittest.py", line 266, in failUnlessRaises
    raise self.failureException, excName
AssertionError: InvalidRomanNumeralError
======================================================================
FAIL: fromRoman should fail with too many repeated numerals
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage1\romantest1.py", line 122, in testTooManyRepeatedNumerals
    self.assertRaises(roman1.InvalidRomanNumeralError, roman1.fromRoman, s)
  File "c:\python21\lib\unittest.py", line 266, in failUnlessRaises
    raise self.failureException, excName
AssertionError: InvalidRomanNumeralError
======================================================================
FAIL: fromRoman should give known result with known input
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage1\romantest1.py", line 99, in testFromRomanKnownValues
    self.assertEqual(integer, result)
  File "c:\python21\lib\unittest.py", line 273, in failUnlessEqual
    raise self.failureException, (msg or '%s != %s' % (first, second))
AssertionError: 1 != None
======================================================================
FAIL: toRoman should give known result with known input
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage1\romantest1.py", line 93, in testToRomanKnownValues
    self.assertEqual(numeral, result)
  File "c:\python21\lib\unittest.py", line 273, in failUnlessEqual
    raise self.failureException, (msg or '%s != %s' % (first, second))
AssertionError: I != None
======================================================================
FAIL: fromRoman(toRoman(n))==n for all n
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage1\romantest1.py", line 141, in testSanity
    self.assertEqual(integer, result)
  File "c:\python21\lib\unittest.py", line 273, in failUnlessEqual
    raise self.failureException, (msg or '%s != %s' % (first, second))
AssertionError: 1 != None
======================================================================
FAIL: toRoman should fail with non-integer input
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage1\romantest1.py", line 116, in testDecimal
    self.assertRaises(roman1.NotIntegerError, roman1.toRoman, 0.5)
  File "c:\python21\lib\unittest.py", line 266, in failUnlessRaises
    raise self.failureException, excName
AssertionError: NotIntegerError
======================================================================
FAIL: toRoman should fail with negative input
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage1\romantest1.py", line 112, in testNegative
    self.assertRaises(roman1.OutOfRangeError, roman1.toRoman, -1)
  File "c:\python21\lib\unittest.py", line 266, in failUnlessRaises
    raise self.failureException, excName
AssertionError: OutOfRangeError
======================================================================
FAIL: toRoman should fail with large input
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage1\romantest1.py", line 104, in testTooLarge
    self.assertRaises(roman1.OutOfRangeError, roman1.toRoman, 4000)
  File "c:\python21\lib\unittest.py", line 266, in failUnlessRaises
    raise self.failureException, excName
AssertionError: OutOfRangeError
======================================================================
FAIL: toRoman should fail with 0 input                                 1
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage1\romantest1.py", line 108, in testZero
    self.assertRaises(roman1.OutOfRangeError, roman1.toRoman, 0)
  File "c:\python21\lib\unittest.py", line 266, in failUnlessRaises
    raise self.failureException, excName
AssertionError: OutOfRangeError                                        2
----------------------------------------------------------------------
Ran 12 tests in 0.040s                                                 3

FAILED (failures=10, errors=2)                                         4
1 Running the script runs unittest.main(), which runs each test case, which is to say each method defined in each class within romantest.py. For each test case, it prints out the doc string of the method and whether that test passed or failed. As expected, none of our test cases passed.
2 For each failed test case, unittest displays the trace information showing exactly what happened. In this case, our call to assertRaises (also called failUnlessRaises) raised an AssertionError because it was expecting toRoman to raise an OutOfRangeError and it didn't.
3 After the detail, unittest displays a summary of how many tests were performed and how long it took.
4 Overall, the unit test failed because at least one test case did not pass. When a test case doesn't pass, unittest distinguishes between failures and errors. A failure is a call to an assertXYZ method, like assertEqual or assertRaises, that fails because the asserted condition is not true or the expected exception was not raised. An error is any other sort of exception raised in the code we're testing or the unit test case itself. For instance, the testFromRomanCase method (“fromRoman should only accept uppercase input”) was an error, because the call to numeral.upper() raised an AttributeError exception, because toRoman was supposed to return a string but didn't. But testZero (“toRoman should fail with 0 input”) was a failure, because the call to fromRoman did not raise the InvalidRomanNumeral exception that assertRaises was looking for.

12.5. roman.py, stage 2

Now that we have the framework of our roman module laid out, it's time to start writing code and passing test cases.

Example 12.8. roman2.py

If you have not already done so, you can download this and other examples used in this book.

"""Convert to and from Roman numerals"""

#Define exceptions
class RomanError(Exception): pass
class OutOfRangeError(RomanError): pass
class NotIntegerError(RomanError): pass
class InvalidRomanNumeralError(RomanError): pass

#Define digit mapping
romanNumeralMap = (('M',  1000), 1
                   ('CM', 900),
                   ('D',  500),
                   ('CD', 400),
                   ('C',  100),
                   ('XC', 90),
                   ('L',  50),
                   ('XL', 40),
                   ('X',  10),
                   ('IX', 9),
                   ('V',  5),
                   ('IV', 4),
                   ('I',  1))

def toRoman(n):
    """convert integer to Roman numeral"""
    result = ""
    for numeral, integer in romanNumeralMap:
        while n >= integer:      2
            result += numeral
            n -= integer
    return result

def fromRoman(s):
    """convert Roman numeral to integer"""
    pass
1 romanNumeralMap is a tuple of tuples which defines three things:
  1. The character representations of the most basic Roman numerals. Note that this is not just the single-character Roman numerals; we're also defining two-character pairs like CM (“one hundred less than one thousand”); this will make our toRoman code simpler later.
  2. The order of the Roman numerals. They are listed in descending value order, from M all the way down to I.
  3. The value of each Roman numeral. Each inner tuple is a pair of (numeral, value).
2 Here's where our rich data structure pays off, because we don't need any special logic to handle the subtraction rule. To convert to Roman numerals, we simply iterate through romanNumeralMap looking for the largest integer value less than or equal to our input. Once found, we add the Roman numeral representation to the end of the output, subtract the corresponding integer value from the input, lather, rinse, repeat.

Example 12.9. How toRoman works

If you're not clear how toRoman works, add a print statement to the end of the while loop:

        while n >= integer:
            result += numeral
            n -= integer
            print 'subtracting', integer, 'from input, adding', numeral, 'to output'
>>> import roman2
>>> roman2.toRoman(1424)
subtracting 1000 from input, adding M to output
subtracting 400 from input, adding CD to output
subtracting 10 from input, adding X to output
subtracting 10 from input, adding X to output
subtracting 4 from input, adding IV to output
'MCDXXIV'

So toRoman appears to work, at least in our manual spot check. But will it pass the unit testing? Well no, not entirely.

Example 12.10. Output of romantest2.py against roman2.py

fromRoman should only accept uppercase input ... FAIL
toRoman should always return uppercase ... ok                  1
fromRoman should fail with malformed antecedents ... FAIL
fromRoman should fail with repeated pairs of numerals ... FAIL
fromRoman should fail with too many repeated numerals ... FAIL
fromRoman should give known result with known input ... FAIL
toRoman should give known result with known input ... ok       2
fromRoman(toRoman(n))==n for all n ... FAIL
toRoman should fail with non-integer input ... FAIL            3
toRoman should fail with negative input ... FAIL
toRoman should fail with large input ... FAIL
toRoman should fail with 0 input ... FAIL
1 toRoman does, in fact, always return uppercase, because our romanNumeralMap defines the Roman numeral representations as uppercase. So this test passes already.
2 Here's the big news: this version of the toRoman function passes the known values test. Remember, it's not comprehensive, but it does put the function through its paces with a variety of good inputs, including inputs that produce every single-character Roman numeral, the largest possible input (3999), and the input that produces the longest possible Roman numeral (3888). At this point, we can be reasonably confident that the function works for any good input value you could throw at it.
3 However, the function does not “work” for bad values; it fails every single bad input test. That makes sense, because we didn't include any checks for bad input. Those test cases look for specific exceptions to be raised (via assertRaises), and we're never raising them. We'll do that in the next stage.

Here's the rest of the output of the unit test, listing the details of all the failures. We're down to 10.


======================================================================
FAIL: fromRoman should only accept uppercase input
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage2\romantest2.py", line 156, in testFromRomanCase
    roman2.fromRoman, numeral.lower())
  File "c:\python21\lib\unittest.py", line 266, in failUnlessRaises
    raise self.failureException, excName
AssertionError: InvalidRomanNumeralError
======================================================================
FAIL: fromRoman should fail with malformed antecedents
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage2\romantest2.py", line 133, in testMalformedAntecedent
    self.assertRaises(roman2.InvalidRomanNumeralError, roman2.fromRoman, s)
  File "c:\python21\lib\unittest.py", line 266, in failUnlessRaises
    raise self.failureException, excName
AssertionError: InvalidRomanNumeralError
======================================================================
FAIL: fromRoman should fail with repeated pairs of numerals
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage2\romantest2.py", line 127, in testRepeatedPairs
    self.assertRaises(roman2.InvalidRomanNumeralError, roman2.fromRoman, s)
  File "c:\python21\lib\unittest.py", line 266, in failUnlessRaises
    raise self.failureException, excName
AssertionError: InvalidRomanNumeralError
======================================================================
FAIL: fromRoman should fail with too many repeated numerals
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage2\romantest2.py", line 122, in testTooManyRepeatedNumerals
    self.assertRaises(roman2.InvalidRomanNumeralError, roman2.fromRoman, s)
  File "c:\python21\lib\unittest.py", line 266, in failUnlessRaises
    raise self.failureException, excName
AssertionError: InvalidRomanNumeralError
======================================================================
FAIL: fromRoman should give known result with known input
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage2\romantest2.py", line 99, in testFromRomanKnownValues
    self.assertEqual(integer, result)
  File "c:\python21\lib\unittest.py", line 273, in failUnlessEqual
    raise self.failureException, (msg or '%s != %s' % (first, second))
AssertionError: 1 != None
======================================================================
FAIL: fromRoman(toRoman(n))==n for all n
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage2\romantest2.py", line 141, in testSanity
    self.assertEqual(integer, result)
  File "c:\python21\lib\unittest.py", line 273, in failUnlessEqual
    raise self.failureException, (msg or '%s != %s' % (first, second))
AssertionError: 1 != None
======================================================================
FAIL: toRoman should fail with non-integer input
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage2\romantest2.py", line 116, in testDecimal
    self.assertRaises(roman2.NotIntegerError, roman2.toRoman, 0.5)
  File "c:\python21\lib\unittest.py", line 266, in failUnlessRaises
    raise self.failureException, excName
AssertionError: NotIntegerError
======================================================================
FAIL: toRoman should fail with negative input
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage2\romantest2.py", line 112, in testNegative
    self.assertRaises(roman2.OutOfRangeError, roman2.toRoman, -1)
  File "c:\python21\lib\unittest.py", line 266, in failUnlessRaises
    raise self.failureException, excName
AssertionError: OutOfRangeError
======================================================================
FAIL: toRoman should fail with large input
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage2\romantest2.py", line 104, in testTooLarge
    self.assertRaises(roman2.OutOfRangeError, roman2.toRoman, 4000)
  File "c:\python21\lib\unittest.py", line 266, in failUnlessRaises
    raise self.failureException, excName
AssertionError: OutOfRangeError
======================================================================
FAIL: toRoman should fail with 0 input
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage2\romantest2.py", line 108, in testZero
    self.assertRaises(roman2.OutOfRangeError, roman2.toRoman, 0)
  File "c:\python21\lib\unittest.py", line 266, in failUnlessRaises
    raise self.failureException, excName
AssertionError: OutOfRangeError
----------------------------------------------------------------------
Ran 12 tests in 0.320s

FAILED (failures=10)

12.6. roman.py, stage 3

Now that toRoman behaves correctly with good input (integers from 1 to 3999), it's time to make it behave correctly with bad input (everything else).

Example 12.11. roman3.py

If you have not already done so, you can download this and other examples used in this book.

"""Convert to and from Roman numerals"""

#Define exceptions
class RomanError(Exception): pass
class OutOfRangeError(RomanError): pass
class NotIntegerError(RomanError): pass
class InvalidRomanNumeralError(RomanError): pass

#Define digit mapping
romanNumeralMap = (('M',  1000),
                   ('CM', 900),
                   ('D',  500),
                   ('CD', 400),
                   ('C',  100),
                   ('XC', 90),
                   ('L',  50),
                   ('XL', 40),
                   ('X',  10),
                   ('IX', 9),
                   ('V',  5),
                   ('IV', 4),
                   ('I',  1))

def toRoman(n):
    """convert integer to Roman numeral"""
    if not (0 < n < 4000):                                             1
        raise OutOfRangeError, "number out of range (must be 1..3999)" 2
    if int(n) <> n:                                                    3
        raise NotIntegerError, "decimals can not be converted"

    result = ""                                                        4
    for numeral, integer in romanNumeralMap:
        while n >= integer:
            result += numeral
            n -= integer
    return result

def fromRoman(s):
    """convert Roman numeral to integer"""
    pass
1 This is a nice Pythonic shortcut: multiple comparisons at once. This is equivalent to if not ((0 < n) and (n < 4000)), but it's much easier to read. This is our range check, and it should catch inputs that are too large, negative, or zero.
2 You raise exceptions yourself with the raise statement. You can raise any of the built-in exceptions, or you can raise any of your custom exceptions that you've defined. The second parameter, the error message, is optional; if given, it is displayed in the traceback that is printed if the exception is never handled.
3 This is our decimal check. Decimals can not be converted to Roman numerals.
4 The rest of the function is unchanged.

Example 12.12. Watching toRoman handle bad input

>>> import roman3
>>> roman3.toRoman(4000)
Traceback (most recent call last):
  File "<interactive input>", line 1, in ?
  File "roman3.py", line 27, in toRoman
    raise OutOfRangeError, "number out of range (must be 1..3999)"
OutOfRangeError: number out of range (must be 1..3999)
>>> roman3.toRoman(1.5)
Traceback (most recent call last):
  File "<interactive input>", line 1, in ?
  File "roman3.py", line 29, in toRoman
    raise NotIntegerError, "decimals can not be converted"
NotIntegerError: decimals can not be converted

Example 12.13. Output of romantest3.py against roman3.py

fromRoman should only accept uppercase input ... FAIL
toRoman should always return uppercase ... ok
fromRoman should fail with malformed antecedents ... FAIL
fromRoman should fail with repeated pairs of numerals ... FAIL
fromRoman should fail with too many repeated numerals ... FAIL
fromRoman should give known result with known input ... FAIL
toRoman should give known result with known input ... ok 1
fromRoman(toRoman(n))==n for all n ... FAIL
toRoman should fail with non-integer input ... ok        2
toRoman should fail with negative input ... ok           3
toRoman should fail with large input ... ok
toRoman should fail with 0 input ... ok
1 toRoman still passes the known values test, which is comforting. All the tests that passed in stage 2 still pass, so our latest code hasn't broken anything.
2 More exciting is the fact that all of our bad input tests now pass. This test, testDecimal, passes because of the int(n) <> n check. When a decimal is passed to toRoman, the int(n) <> n check notices it and raises the NotIntegerError exception, which is what testDecimal is looking for.
3 This test, testNegative, passes because of the not (0 < n < 4000) check, which raises an OutOfRangeError exception, which is what testNegative is looking for.

======================================================================
FAIL: fromRoman should only accept uppercase input
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage3\romantest3.py", line 156, in testFromRomanCase
    roman3.fromRoman, numeral.lower())
  File "c:\python21\lib\unittest.py", line 266, in failUnlessRaises
    raise self.failureException, excName
AssertionError: InvalidRomanNumeralError
======================================================================
FAIL: fromRoman should fail with malformed antecedents
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage3\romantest3.py", line 133, in testMalformedAntecedent
    self.assertRaises(roman3.InvalidRomanNumeralError, roman3.fromRoman, s)
  File "c:\python21\lib\unittest.py", line 266, in failUnlessRaises
    raise self.failureException, excName
AssertionError: InvalidRomanNumeralError
======================================================================
FAIL: fromRoman should fail with repeated pairs of numerals
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage3\romantest3.py", line 127, in testRepeatedPairs
    self.assertRaises(roman3.InvalidRomanNumeralError, roman3.fromRoman, s)
  File "c:\python21\lib\unittest.py", line 266, in failUnlessRaises
    raise self.failureException, excName
AssertionError: InvalidRomanNumeralError
======================================================================
FAIL: fromRoman should fail with too many repeated numerals
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage3\romantest3.py", line 122, in testTooManyRepeatedNumerals
    self.assertRaises(roman3.InvalidRomanNumeralError, roman3.fromRoman, s)
  File "c:\python21\lib\unittest.py", line 266, in failUnlessRaises
    raise self.failureException, excName
AssertionError: InvalidRomanNumeralError
======================================================================
FAIL: fromRoman should give known result with known input
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage3\romantest3.py", line 99, in testFromRomanKnownValues
    self.assertEqual(integer, result)
  File "c:\python21\lib\unittest.py", line 273, in failUnlessEqual
    raise self.failureException, (msg or '%s != %s' % (first, second))
AssertionError: 1 != None
======================================================================
FAIL: fromRoman(toRoman(n))==n for all n
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage3\romantest3.py", line 141, in testSanity
    self.assertEqual(integer, result)
  File "c:\python21\lib\unittest.py", line 273, in failUnlessEqual
    raise self.failureException, (msg or '%s != %s' % (first, second))
AssertionError: 1 != None
----------------------------------------------------------------------
Ran 12 tests in 0.401s

FAILED (failures=6) 1
1 We're down to 6 failures, and all of them involve fromRoman: the known values test, the three separate bad input tests, the case check, and the sanity check. That means that toRoman has passed all the tests it can pass by itself. (It's involved in the sanity check, but that also requires that fromRoman be written, which it isn't yet.) Which means that we must stop coding toRoman now. No tweaking, no twiddling, no extra checks “just in case”. Stop. Now. Back away from the keyboard.
Note
The most important thing that comprehensive unit testing can tell you is when to stop coding. When all the unit tests for a function pass, stop coding the function. When all the unit tests for an entire module pass, stop coding the module.

12.7. roman.py, stage 4

Now that toRoman is done, it's time to start coding fromRoman. Thanks to our rich data structure that maps individual Roman numerals to integer values, this is no more difficult than the toRoman function.

Example 12.14. roman4.py

If you have not already done so, you can download this and other examples used in this book.

"""Convert to and from Roman numerals"""

#Define exceptions
class RomanError(Exception): pass
class OutOfRangeError(RomanError): pass
class NotIntegerError(RomanError): pass
class InvalidRomanNumeralError(RomanError): pass

#Define digit mapping
romanNumeralMap = (('M',  1000),
                   ('CM', 900),
                   ('D',  500),
                   ('CD', 400),
                   ('C',  100),
                   ('XC', 90),
                   ('L',  50),
                   ('XL', 40),
                   ('X',  10),
                   ('IX', 9),
                   ('V',  5),
                   ('IV', 4),
                   ('I',  1))

# toRoman function omitted for clarity (it hasn't changed)

def fromRoman(s):
    """convert Roman numeral to integer"""
    result = 0
    index = 0
    for numeral, integer in romanNumeralMap:
        while s[index:index+len(numeral)] == numeral: 1
            result += integer
            index += len(numeral)
    return result
1 The pattern here is the same as toRoman. We iterate through our Roman numeral data structure (a tuple of tuples), and instead of matching the highest integer values as often as possible, we match the “highest” Roman numeral character strings as often as possible.

Example 12.15. How fromRoman works

If you're not clear how fromRoman works, add a print statement to the end of the while loop:

        while s[index:index+len(numeral)] == numeral:
            result += integer
            index += len(numeral)
            print 'found', numeral, ', adding', integer
>>> import roman4
>>> roman4.fromRoman('MCMLXXII')
found M , adding 1000
found CM , adding 900
found L , adding 50
found X , adding 10
found X , adding 10
found I , adding 1
found I , adding 1
1972

Example 12.16. Output of romantest4.py against roman4.py

fromRoman should only accept uppercase input ... FAIL
toRoman should always return uppercase ... ok
fromRoman should fail with malformed antecedents ... FAIL
fromRoman should fail with repeated pairs of numerals ... FAIL
fromRoman should fail with too many repeated numerals ... FAIL
fromRoman should give known result with known input ... ok 1
toRoman should give known result with known input ... ok
fromRoman(toRoman(n))==n for all n ... ok                  2
toRoman should fail with non-integer input ... ok
toRoman should fail with negative input ... ok
toRoman should fail with large input ... ok
toRoman should fail with 0 input ... ok
1 Two pieces of exciting news here. The first is that fromRoman works for good input, at least for all the known values we test.
2 The second is that our sanity check also passed. Combined with the known values tests, we can be reasonably sure that both toRoman and fromRoman work properly for all possible good values. (This is not guaranteed; it is theoretically possible that toRoman has a bug that produces the wrong Roman numeral for some particular set of inputs, and that fromRoman has a reciprocal bug that produces the same wrong integer values for exactly that set of Roman numerals that toRoman generated incorrectly. Depending on your application and your requirements, this possibility may bother you; if so, write more comprehensive test cases until it doesn't bother you.)

======================================================================
FAIL: fromRoman should only accept uppercase input
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage4\romantest4.py", line 156, in testFromRomanCase
    roman4.fromRoman, numeral.lower())
  File "c:\python21\lib\unittest.py", line 266, in failUnlessRaises
    raise self.failureException, excName
AssertionError: InvalidRomanNumeralError
======================================================================
FAIL: fromRoman should fail with malformed antecedents
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage4\romantest4.py", line 133, in testMalformedAntecedent
    self.assertRaises(roman4.InvalidRomanNumeralError, roman4.fromRoman, s)
  File "c:\python21\lib\unittest.py", line 266, in failUnlessRaises
    raise self.failureException, excName
AssertionError: InvalidRomanNumeralError
======================================================================
FAIL: fromRoman should fail with repeated pairs of numerals
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage4\romantest4.py", line 127, in testRepeatedPairs
    self.assertRaises(roman4.InvalidRomanNumeralError, roman4.fromRoman, s)
  File "c:\python21\lib\unittest.py", line 266, in failUnlessRaises
    raise self.failureException, excName
AssertionError: InvalidRomanNumeralError
======================================================================
FAIL: fromRoman should fail with too many repeated numerals
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage4\romantest4.py", line 122, in testTooManyRepeatedNumerals
    self.assertRaises(roman4.InvalidRomanNumeralError, roman4.fromRoman, s)
  File "c:\python21\lib\unittest.py", line 266, in failUnlessRaises
    raise self.failureException, excName
AssertionError: InvalidRomanNumeralError
----------------------------------------------------------------------
Ran 12 tests in 1.222s

FAILED (failures=4)

12.8. roman.py, stage 5

Now that fromRoman works properly with good input, it's time to fit in the last piece of the puzzle: making it work properly with bad input. That means finding a way to look at a string and determine if it's a valid Roman numeral. This is inherently more difficult than validating numeric input in toRoman, but we have a powerful tool at our disposal: regular expressions.

If you're not familiar with regular expressions and didn't read Regular Expressions, now would be a good time.

As we saw in Case study: Roman numerals, there are several simple rules for constructing a Roman numeral, using the letters M, D, C, L, X, V, and I. Let's review the rules:

  1. Characters are additive. I is 1, II is 2, and III is 3. VI is 6 (literally, “5 and 1”), VII is 7, and VIII is 8.
  2. The tens characters (I, X, C, and M) can be repeated up to three times. At 4, you have to subtract from the next highest fives character. You can't represent 4 as IIII; instead, it is represented as IV (“1 less than 5”). 40 is written as XL (“10 less than 50”), 41 as XLI, 42 as XLII, 43 as XLIII, and then 44 as XLIV (“10 less than 50, then 1 less than 5”).
  3. Similarly, at 9, you have to subtract from the next highest tens character: 8 is VIII, but 9 is IX (“1 less than 10”), not VIIII (since the I character can not be repeated four times). 90 is XC, 900 is CM.
  4. The fives characters can not be repeated. 10 is always represented as X, never as VV. 100 is always C, never LL.
  5. Roman numerals are always written highest to lowest, and read left to right, so order of characters matters very much. DC is 600; CD is a completely different number (400, “100 less than 500”). CI is 101; IC is not even a valid Roman numeral (because you can't subtract 1 directly from 100; you would have to write it as XCIX, “10 less than 100, then 1 less than 10”).

Example 12.17. roman5.py

If you have not already done so, you can download this and other examples used in this book.

"""Convert to and from Roman numerals"""
import re

#Define exceptions
class RomanError(Exception): pass
class OutOfRangeError(RomanError): pass
class NotIntegerError(RomanError): pass
class InvalidRomanNumeralError(RomanError): pass

#Define digit mapping
romanNumeralMap = (('M',  1000),
                   ('CM', 900),
                   ('D',  500),
                   ('CD', 400),
                   ('C',  100),
                   ('XC', 90),
                   ('L',  50),
                   ('XL', 40),
                   ('X',  10),
                   ('IX', 9),
                   ('V',  5),
                   ('IV', 4),
                   ('I',  1))

def toRoman(n):
    """convert integer to Roman numeral"""
    if not (0 < n < 4000):
        raise OutOfRangeError, "number out of range (must be 1..3999)"
    if int(n) <> n:
        raise NotIntegerError, "decimals can not be converted"

    result = ""
    for numeral, integer in romanNumeralMap:
        while n >= integer:
            result += numeral
            n -= integer
    return result

#Define pattern to detect valid Roman numerals
romanNumeralPattern = '^M?M?M?(CM|CD|D?C?C?C?)(XC|XL|L?X?X?X?)(IX|IV|V?I?I?I?)$' 1

def fromRoman(s):
    """convert Roman numeral to integer"""
    if not re.search(romanNumeralPattern, s):                                    2
        raise InvalidRomanNumeralError, 'Invalid Roman numeral: %s' % s

    result = 0
    index = 0
    for numeral, integer in romanNumeralMap:
        while s[index:index+len(numeral)] == numeral:
            result += integer
            index += len(numeral)
    return result
1 This is just a continuation of the pattern we discussed in Case study: Roman numerals. The tens places is either XC (90), XL (40), or an optional L followed by 0 to 3 optional X characters. The ones place is either IX (9), IV (4), or an optional V followed by 0 to 3 optional I characters.
2 Having encoded all that logic into our regular expression, the code to check for invalid Roman numerals becomes trivial. If re.search returns an object, then the regular expression matched and our input is valid; otherwise, our input is invalid.

At this point, you are allowed to be skeptical that that big ugly regular expression could possibly catch all the types of invalid Roman numerals. But don't take my word for it, look at the results:

Example 12.18. Output of romantest5.py against roman5.py


fromRoman should only accept uppercase input ... ok          1
toRoman should always return uppercase ... ok
fromRoman should fail with malformed antecedents ... ok      2
fromRoman should fail with repeated pairs of numerals ... ok 3
fromRoman should fail with too many repeated numerals ... ok
fromRoman should give known result with known input ... ok
toRoman should give known result with known input ... ok
fromRoman(toRoman(n))==n for all n ... ok
toRoman should fail with non-integer input ... ok
toRoman should fail with negative input ... ok
toRoman should fail with large input ... ok
toRoman should fail with 0 input ... ok

----------------------------------------------------------------------
Ran 12 tests in 2.864s

OK                                                           4
1 One thing I didn't mention about regular expressions is that, by default, they are case-sensitive. Since our regular expression romanNumeralPattern was expressed in uppercase characters, our re.search check will reject any input that isn't completely uppercase. So our uppercase input test passes.
2 More importantly, our bad input tests pass. For instance, the malformed antecedents test checks cases like MCMC. As we've seen, this does not match our regular expression, so fromRoman raises an InvalidRomanNumeralError exception, which is what the malformed antecedents test case is looking for, so the test passes.
3 In fact, all the bad input tests pass. This regular expression catches everything we could think of when we made our test cases.
4 And the anticlimax award of the year goes to the word “OK”, which is printed by the unittest module when all the tests pass.
Note
When all of your tests pass, stop coding.


[13] I can resist everything except temptation.” --Oscar Wilde

Chapter 13. Refactoring

13.1. Handling bugs

Despite your best efforts to write comprehensive unit tests, bugs happen. What do I mean by “bug”? A bug is a test case you haven't written yet.

Example 13.1. The bug

>>> import roman5
>>> roman5.fromRoman("") 1
0
1 Remember in the previous section when we kept seeing that an empty string would match the regular expression we were using to check for valid Roman numerals? Well, it turns out that this is still true for the final version of the regular expression. And that's a bug; we want an empty string to raise an InvalidRomanNumeralError exception just like any other sequence of characters that don't represent a valid Roman numeral.

After reproducing the bug, and before fixing it, you should write a test case that fails, thus illustrating the bug.

Example 13.2. Testing for the bug (romantest61.py)

class FromRomanBadInput(unittest.TestCase):                                      

    # previous test cases omitted for clarity (they haven't changed)

    def testBlank(self):
        """fromRoman should fail with blank string"""
        self.assertRaises(roman.InvalidRomanNumeralError, roman.fromRoman, "") 1
1 Pretty simple stuff here. Call fromRoman with an empty string and make sure it raises an InvalidRomanNumeralError exception. The hard part was finding the bug; now that we know about it, testing for it is the easy part.

Since our code has a bug, and we now have a test case that tests this bug, the test case will fail:

Example 13.3. Output of romantest61.py against roman61.py

fromRoman should only accept uppercase input ... ok
toRoman should always return uppercase ... ok
fromRoman should fail with blank string ... FAIL
fromRoman should fail with malformed antecedents ... ok
fromRoman should fail with repeated pairs of numerals ... ok
fromRoman should fail with too many repeated numerals ... ok
fromRoman should give known result with known input ... ok
toRoman should give known result with known input ... ok
fromRoman(toRoman(n))==n for all n ... ok
toRoman should fail with non-integer input ... ok
toRoman should fail with negative input ... ok
toRoman should fail with large input ... ok
toRoman should fail with 0 input ... ok

======================================================================
FAIL: fromRoman should fail with blank string
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage6\romantest61.py", line 137, in testBlank
    self.assertRaises(roman61.InvalidRomanNumeralError, roman61.fromRoman, "")
  File "c:\python21\lib\unittest.py", line 266, in failUnlessRaises
    raise self.failureException, excName
AssertionError: InvalidRomanNumeralError
----------------------------------------------------------------------
Ran 13 tests in 2.864s

FAILED (failures=1)

Now we can fix the bug.

Example 13.4. Fixing the bug (roman62.py)

def fromRoman(s):
    """convert Roman numeral to integer"""
    if not s: 1
        raise InvalidRomanNumeralError, 'Input can not be blank'
    if not re.search(romanNumeralPattern, s):
        raise InvalidRomanNumeralError, 'Invalid Roman numeral: %s' % s

    result = 0
    index = 0
    for numeral, integer in romanNumeralMap:
        while s[index:index+len(numeral)] == numeral:
            result += integer
            index += len(numeral)
    return result
1 Only two lines of code are required: an explicit check for an empty string, and a raise statement.

Example 13.5. Output of romantest62.py against roman62.py

fromRoman should only accept uppercase input ... ok
toRoman should always return uppercase ... ok
fromRoman should fail with blank string ... ok 1
fromRoman should fail with malformed antecedents ... ok
fromRoman should fail with repeated pairs of numerals ... ok
fromRoman should fail with too many repeated numerals ... ok
fromRoman should give known result with known input ... ok
toRoman should give known result with known input ... ok
fromRoman(toRoman(n))==n for all n ... ok
toRoman should fail with non-integer input ... ok
toRoman should fail with negative input ... ok
toRoman should fail with large input ... ok
toRoman should fail with 0 input ... ok

----------------------------------------------------------------------
Ran 13 tests in 2.834s

OK 2
1 The blank string test case now passes, so the bug is fixed.
2 All the other test cases still pass, which means that this bug fix didn't break anything else. Stop coding.

Coding this way does not make fixing bugs any easier. Simple bugs (like this one) require simple test cases; complex bugs will require complex test cases. In a testing-centric environment, it may seem like it takes longer to fix a bug, since you have to articulate in code exactly what the bug is (to write the test case), then fix the bug itself. Then if the test case doesn't pass right away, you have to figure out whether the fix was wrong, or whether the test case itself has a bug in it. However, in the long run, this back-and-forth between test code and code tested pays for itself, because it makes it more likely that bugs are fixed correctly the first time. Also, since you can easily re-run all the test cases along with your new one, you are much less likely to break old code when fixing new code. Today's unit test is tomorrow's regression test.

13.2. Handling changing requirements

Despite your best efforts to pin your customers to the ground and extract exact requirements from them on pain of horrible nasty things involving scissors and hot wax, requirements will change. Most customers don't know what they want until they see it, and even if they do, they aren't that good at articulating what they want precisely enough to be useful. And even if they do, they'll want more in the next release anyway. So be prepared to update your test cases as requirements change.

Suppose, for instance, that we wanted to expand the range of our Roman numeral conversion functions. Remember the rule that said that no character could be repeated more than three times? Well, the Romans were willing to make an exception to that rule by having 4 M characters in a row to represent 4000. If we make this change, we'll be able to expand our range of convertible numbers from 1..3999 to 1..4999. But first, we need to make some changes to our test cases.

Example 13.6. Modifying test cases for new requirements (romantest71.py)

If you have not already done so, you can download this and other examples used in this book.

import roman71
import unittest

class KnownValues(unittest.TestCase):
    knownValues = ( (1, 'I'),
                    (2, 'II'),
                    (3, 'III'),
                    (4, 'IV'),
                    (5, 'V'),
                    (6, 'VI'),
                    (7, 'VII'),
                    (8, 'VIII'),
                    (9, 'IX'),
                    (10, 'X'),
                    (50, 'L'),
                    (100, 'C'),
                    (500, 'D'),
                    (1000, 'M'),
                    (31, 'XXXI'),
                    (148, 'CXLVIII'),
                    (294, 'CCXCIV'),
                    (312, 'CCCXII'),
                    (421, 'CDXXI'),
                    (528, 'DXXVIII'),
                    (621, 'DCXXI'),
                    (782, 'DCCLXXXII'),
                    (870, 'DCCCLXX'),
                    (941, 'CMXLI'),
                    (1043, 'MXLIII'),
                    (1110, 'MCX'),
                    (1226, 'MCCXXVI'),
                    (1301, 'MCCCI'),
                    (1485, 'MCDLXXXV'),
                    (1509, 'MDIX'),
                    (1607, 'MDCVII'),
                    (1754, 'MDCCLIV'),
                    (1832, 'MDCCCXXXII'),
                    (1993, 'MCMXCIII'),
                    (2074, 'MMLXXIV'),
                    (2152, 'MMCLII'),
                    (2212, 'MMCCXII'),
                    (2343, 'MMCCCXLIII'),
                    (2499, 'MMCDXCIX'),
                    (2574, 'MMDLXXIV'),
                    (2646, 'MMDCXLVI'),
                    (2723, 'MMDCCXXIII'),
                    (2892, 'MMDCCCXCII'),
                    (2975, 'MMCMLXXV'),
                    (3051, 'MMMLI'),
                    (3185, 'MMMCLXXXV'),
                    (3250, 'MMMCCL'),
                    (3313, 'MMMCCCXIII'),
                    (3408, 'MMMCDVIII'),
                    (3501, 'MMMDI'),
                    (3610, 'MMMDCX'),
                    (3743, 'MMMDCCXLIII'),
                    (3844, 'MMMDCCCXLIV'),
                    (3888, 'MMMDCCCLXXXVIII'),
                    (3940, 'MMMCMXL'),
                    (3999, 'MMMCMXCIX'),
                    (4000, 'MMMM'),                                       1
                    (4500, 'MMMMD'),
                    (4888, 'MMMMDCCCLXXXVIII'),
                    (4999, 'MMMMCMXCIX'))

    def testToRomanKnownValues(self):
        """toRoman should give known result with known input"""
        for integer, numeral in self.knownValues:
            result = roman71.toRoman(integer)
            self.assertEqual(numeral, result)

    def testFromRomanKnownValues(self):
        """fromRoman should give known result with known input"""
        for integer, numeral in self.knownValues:
            result = roman71.fromRoman(numeral)
            self.assertEqual(integer, result)

class ToRomanBadInput(unittest.TestCase):
    def testTooLarge(self):
        """toRoman should fail with large input"""
        self.assertRaises(roman71.OutOfRangeError, roman71.toRoman, 5000) 2

    def testZero(self):
        """toRoman should fail with 0 input"""
        self.assertRaises(roman71.OutOfRangeError, roman71.toRoman, 0)

    def testNegative(self):
        """toRoman should fail with negative input"""
        self.assertRaises(roman71.OutOfRangeError, roman71.toRoman, -1)

    def testDecimal(self):
        """toRoman should fail with non-integer input"""
        self.assertRaises(roman71.NotIntegerError, roman71.toRoman, 0.5)

class FromRomanBadInput(unittest.TestCase):
    def testTooManyRepeatedNumerals(self):
        """fromRoman should fail with too many repeated numerals"""
        for s in ('MMMMM', 'DD', 'CCCC', 'LL', 'XXXX', 'VV', 'IIII'):     3
            self.assertRaises(roman71.InvalidRomanNumeralError, roman71.fromRoman, s)

    def testRepeatedPairs(self):
        """fromRoman should fail with repeated pairs of numerals"""
        for s in ('CMCM', 'CDCD', 'XCXC', 'XLXL', 'IXIX', 'IVIV'):
            self.assertRaises(roman71.InvalidRomanNumeralError, roman71.fromRoman, s)

    def testMalformedAntecedent(self):
        """fromRoman should fail with malformed antecedents"""
        for s in ('IIMXCC', 'VX', 'DCM', 'CMM', 'IXIV',
                  'MCMC', 'XCX', 'IVI', 'LM', 'LD', 'LC'):
            self.assertRaises(roman71.InvalidRomanNumeralError, roman71.fromRoman, s)

    def testBlank(self):
        """fromRoman should fail with blank string"""
        self.assertRaises(roman71.InvalidRomanNumeralError, roman71.fromRoman, "")

class SanityCheck(unittest.TestCase):
    def testSanity(self):
        """fromRoman(toRoman(n))==n for all n"""
        for integer in range(1, 5000):                                    4
            numeral = roman71.toRoman(integer)
            result = roman71.fromRoman(numeral)
            self.assertEqual(integer, result)

class CaseCheck(unittest.TestCase):
    def testToRomanCase(self):
        """toRoman should always return uppercase"""
        for integer in range(1, 5000):
            numeral = roman71.toRoman(integer)
            self.assertEqual(numeral, numeral.upper())

    def testFromRomanCase(self):
        """fromRoman should only accept uppercase input"""
        for integer in range(1, 5000):
            numeral = roman71.toRoman(integer)
            roman71.fromRoman(numeral.upper())
            self.assertRaises(roman71.InvalidRomanNumeralError,
                              roman71.fromRoman, numeral.lower())

if __name__ == "__main__":
    unittest.main()
1 The existing known values don't change (they're all still reasonable values to test), but we need to add a few more in the 4000 range. Here I've included 4000 (the shortest), 4500 (the second shortest), 4888 (the longest), and 4999 (the largest).
2 The definition of “large input” has changed. This test used to call toRoman with 4000 and expect an error; now that 4000-4999 are good values, we need to bump this up to 5000.
3 The definition of “too many repeated numerals” has also changed. This test used to call fromRoman with 'MMMM' and expect an error; now that MMMM is considered a valid Roman numeral, we need to bump this up to 'MMMMM'.
4 The sanity check and case checks loop through every number in the range, from 1 to 3999. Since the range has now expanded, these for loops need to be updated as well to go up to 4999.

Now our test cases are up to date with our new requirements, but our code is not, so we expect several of our test cases to fail.

Example 13.7. Output of romantest71.py against roman71.py


fromRoman should only accept uppercase input ... ERROR        1
toRoman should always return uppercase ... ERROR
fromRoman should fail with blank string ... ok
fromRoman should fail with malformed antecedents ... ok
fromRoman should fail with repeated pairs of numerals ... ok
fromRoman should fail with too many repeated numerals ... ok
fromRoman should give known result with known input ... ERROR 2
toRoman should give known result with known input ... ERROR   3
fromRoman(toRoman(n))==n for all n ... ERROR                  4
toRoman should fail with non-integer input ... ok
toRoman should fail with negative input ... ok
toRoman should fail with large input ... ok
toRoman should fail with 0 input ... ok
1 Our case checks now fail because they loop from 1 to 4999, but toRoman only accepts numbers from 1 to 3999, so it will fail as soon the test case hits 4000.
2 The fromRoman known values test will fail as soon as it hits 'MMMM', because fromRoman still thinks this is an invalid Roman numeral.
3 The toRoman known values test will fail as soon as it hits 4000, because toRoman still thinks this is out of range.
4 The sanity check will also fail as soon as it hits 4000, because toRoman still thinks this is out of range.

======================================================================
ERROR: fromRoman should only accept uppercase input
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage7\romantest71.py", line 161, in testFromRomanCase
    numeral = roman71.toRoman(integer)
  File "roman71.py", line 28, in toRoman
    raise OutOfRangeError, "number out of range (must be 1..3999)"
OutOfRangeError: number out of range (must be 1..3999)
======================================================================
ERROR: toRoman should always return uppercase
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage7\romantest71.py", line 155, in testToRomanCase
    numeral = roman71.toRoman(integer)
  File "roman71.py", line 28, in toRoman
    raise OutOfRangeError, "number out of range (must be 1..3999)"
OutOfRangeError: number out of range (must be 1..3999)
======================================================================
ERROR: fromRoman should give known result with known input
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage7\romantest71.py", line 102, in testFromRomanKnownValues
    result = roman71.fromRoman(numeral)
  File "roman71.py", line 47, in fromRoman
    raise InvalidRomanNumeralError, 'Invalid Roman numeral: %s' % s
InvalidRomanNumeralError: Invalid Roman numeral: MMMM
======================================================================
ERROR: toRoman should give known result with known input
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage7\romantest71.py", line 96, in testToRomanKnownValues
    result = roman71.toRoman(integer)
  File "roman71.py", line 28, in toRoman
    raise OutOfRangeError, "number out of range (must be 1..3999)"
OutOfRangeError: number out of range (must be 1..3999)
======================================================================
ERROR: fromRoman(toRoman(n))==n for all n
----------------------------------------------------------------------
Traceback (most recent call last):
  File "C:\docbook\dip\py\roman\stage7\romantest71.py", line 147, in testSanity
    numeral = roman71.toRoman(integer)
  File "roman71.py", line 28, in toRoman
    raise OutOfRangeError, "number out of range (must be 1..3999)"
OutOfRangeError: number out of range (must be 1..3999)
----------------------------------------------------------------------
Ran 13 tests in 2.213s

FAILED (errors=5)

Now that we have test cases that fail due to the new requirements, we can think about fixing the code to bring it in line with the test cases. (One thing that takes some getting used to when you first start coding unit tests is that the code being tested is never “ahead” of the test cases. While it's behind, you still have some work to do, and as soon as it catches up to the test cases, you stop coding.)

Example 13.8. Coding the new requirements (roman72.py)

"""Convert to and from Roman numerals"""
import re

#Define exceptions
class RomanError(Exception): pass
class OutOfRangeError(RomanError): pass
class NotIntegerError(RomanError): pass
class InvalidRomanNumeralError(RomanError): pass

#Define digit mapping
romanNumeralMap = (('M',  1000),
                   ('CM', 900),
                   ('D',  500),
                   ('CD', 400),
                   ('C',  100),
                   ('XC', 90),
                   ('L',  50),
                   ('XL', 40),
                   ('X',  10),
                   ('IX', 9),
                   ('V',  5),
                   ('IV', 4),
                   ('I',  1))

def toRoman(n):
    """convert integer to Roman numeral"""
    if not (0 < n < 5000):                                                         1
        raise OutOfRangeError, "number out of range (must be 1..4999)"
    if int(n) <> n:
        raise NotIntegerError, "decimals can not be converted"

    result = ""
    for numeral, integer in romanNumeralMap:
        while n >= integer:
            result += numeral
            n -= integer
    return result

#Define pattern to detect valid Roman numerals
romanNumeralPattern = '^M?M?M?M?(CM|CD|D?C?C?C?)(XC|XL|L?X?X?X?)(IX|IV|V?I?I?I?)$' 2

def fromRoman(s):
    """convert Roman numeral to integer"""
    if not s:
        raise InvalidRomanNumeralError, 'Input can not be blank'
    if not re.search(romanNumeralPattern, s):
        raise InvalidRomanNumeralError, 'Invalid Roman numeral: %s' % s

    result = 0
    index = 0
    for numeral, integer in romanNumeralMap:
        while s[index:index+len(numeral)] == numeral:
            result += integer
            index += len(numeral)
    return result
1 toRoman only needs one small change, in the range check. Where we used to check 0 < n < 4000, we now check 0 < n < 5000. And we change the error message that we raise to reflect the new acceptable range (1..4999 instead of 1..3999). We don't need to make any changes to the rest of the function; it handles the new cases already. (It merrily adds 'M' for each thousand that it finds; given 4000, it will spit out 'MMMM'. The only reason it didn't do this before is that we explicitly stopped it with the range check.)
2 We don't need to make any changes to fromRoman at all. The only change is to romanNumeralPattern; if you look closely, you'll notice that we added another optional M in the first section of the regular expression. This will allow up to 4 M characters instead of 3, meaning we will allow the Roman numeral equivalents of 4999 instead of 3999. The actual fromRoman function is completely general; it just looks for repeated Roman numeral characters and adds them up, without caring how many times they repeat. The only reason it didn't handle 'MMMM' before is that we explicitly stopped it with the regular expression pattern matching.

You may be skeptical that these two small changes are all that we need. Hey, don't take my word for it; see for yourself:

Example 13.9. Output of romantest72.py against roman72.py

fromRoman should only accept uppercase input ... ok
toRoman should always return uppercase ... ok
fromRoman should fail with blank string ... ok
fromRoman should fail with malformed antecedents ... ok
fromRoman should fail with repeated pairs of numerals ... ok
fromRoman should fail with too many repeated numerals ... ok
fromRoman should give known result with known input ... ok
toRoman should give known result with known input ... ok
fromRoman(toRoman(n))==n for all n ... ok
toRoman should fail with non-integer input ... ok
toRoman should fail with negative input ... ok
toRoman should fail with large input ... ok
toRoman should fail with 0 input ... ok

----------------------------------------------------------------------
Ran 13 tests in 3.685s

OK 1
1 All the test cases pass. Stop coding.

Comprehensive unit testing means never having to rely on a programmer who says “Trust me.

13.3. Refactoring

The best thing about comprehensive unit testing is not the feeling you get when all your test cases finally pass, or even the feeling you get when someone else blames you for breaking their code and you can actually prove that you didn't. The best thing about unit testing is that it gives you the freedom to refactor mercilessly.

Refactoring is the process of taking working code and making it work better. Usually, “better” means “faster”, although it can also mean “using less memory”, or “using less disk space”, or simply “more elegantly”. Whatever it means to you, to your project, in your environment, refactoring is important to the long-term health of any program.

Here, “better” means “faster”. Specifically, the fromRoman function is slower than it needs to be, because of that big nasty regular expression that we use to validate Roman numerals. It's probably not worth trying to do away with the regular expression altogether (it would be difficult, and it might not end up any faster), but we can speed up the function by precompiling the regular expression.

Example 13.10. Compiling regular expressions

>>> import re
>>> pattern = '^M?M?M?$'
>>> re.search(pattern, 'M')               1
<SRE_Match object at 01090490>
>>> compiledPattern = re.compile(pattern) 2
>>> compiledPattern
<SRE_Pattern object at 00F06E28>
>>> dir(compiledPattern)                  3
['findall', 'match', 'scanner', 'search', 'split', 'sub', 'subn']
>>> compiledPattern.search('M')           4
<SRE_Match object at 01104928>
1 This is the syntax we've seen before: re.search takes a regular expression as a string (pattern) and a string to match against it ('M'). If the pattern matches, the function returns a match object which can be queried to find out exactly what matched and how.
2 This is the new syntax: re.compile takes a regular expression as a string and returns a pattern object. Note there is no string to match here. Compiling a regular expression has nothing to do with matching it against any specific strings (like 'M'); it only involves the regular expression itself.
3 The compiled pattern object returned from re.compile has several useful-looking functions, including several (like search and sub) that are available directly in the re module.
4 Calling the compiled pattern object's search function with the string 'M' accomplishes the same thing as calling re.search with both the regular expression and the string 'M'. Only much, much faster. (In fact, the re.search function simply compiles the regular expression and calls the resulting pattern object's search method for you.)
Note
Whenever you are going to use a regular expression more than once, you should compile it to get a pattern object, then call the methods on the pattern object directly.

Example 13.11. Compiled regular expressions in roman81.py

If you have not already done so, you can download this and other examples used in this book.

# toRoman and rest of module omitted for clarity

romanNumeralPattern = \
    re.compile('^M?M?M?M?(CM|CD|D?C?C?C?)(XC|XL|L?X?X?X?)(IX|IV|V?I?I?I?)$') 1

def fromRoman(s):
    """convert Roman numeral to integer"""
    if not s:
        raise InvalidRomanNumeralError, 'Input can not be blank'
    if not romanNumeralPattern.search(s):                                    2
        raise InvalidRomanNumeralError, 'Invalid Roman numeral: %s' % s

    result = 0
    index = 0
    for numeral, integer in romanNumeralMap:
        while s[index:index+len(numeral)] == numeral:
            result += integer
            index += len(numeral)
    return result
1 This looks very similar, but in fact a lot has changed. romanNumeralPattern is no longer a string; it is a pattern object which was returned from re.compile.
2 That means that we can call methods on romanNumeralPattern directly. This will be much, much faster than calling re.search every time. The regular expression is compiled once and stored in romanNumeralPattern when the module is first imported; then, every time we call fromRoman, we can immediately match the input string against the regular expression, without any intermediate steps occurring under the covers.

So how much faster is it to compile our regular expressions? See for yourself:

Example 13.12. Output of romantest81.py against roman81.py

.............          1
----------------------------------------------------------------------
Ran 13 tests in 3.385s 2

OK                     3
1 Just a note in passing here: this time, I ran the unit test without the -v option, so instead of the full doc string for each test, we only get a dot for each test that passes. (If a test failed, we'd get an F, and if it had an error, we'd get an E. We'd still get complete tracebacks for each failure and error, so we could track down any problems.)
2 We ran 13 tests in 3.385 seconds, compared to 3.685 seconds without precompiling the regular expressions. That's an 8% improvement overall, and remember that most of the time spent during the unit test is spent doing other things. (Separately, I time-tested the regular expressions by themselves, apart from the rest of the unit tests, and found that compiling this regular expression speeds up the search by an average of 54%.) Not bad for such a simple fix.
3 Oh, and in case you were wondering, precompiling our regular expression didn't break anything, and we just proved it.

There is one other performance optimization that I want to try. Given the complexity of regular expression syntax, it should come as no surprise that there is frequently more than one way to write the same expression. After some discussion about this module on comp.lang.python, someone suggested that I try using the {m,n} syntax for the optional repeated characters.

Example 13.13. roman82.py

If you have not already done so, you can download this and other examples used in this book.

# rest of program omitted for clarity

#old version
#romanNumeralPattern = \
#   re.compile('^M?M?M?M?(CM|CD|D?C?C?C?)(XC|XL|L?X?X?X?)(IX|IV|V?I?I?I?)$')

#new version
romanNumeralPattern = \
    re.compile('^M{0,4}(CM|CD|D?C{0,3})(XC|XL|L?X{0,3})(IX|IV|V?I{0,3})$') 1
1 We have replaced M?M?M?M? with M{0,4}. Both mean the same thing: “match 0 to 4 M characters”. Similarly, C?C?C? became C{0,3} (“match 0 to 3 C characters”) and so forth for X and I.

This form of the regular expression is a little shorter (though not any more readable). The big question is, is it any faster?

Example 13.14. Output of romantest82.py against roman82.py

.............
----------------------------------------------------------------------
Ran 13 tests in 3.315s 1

OK                     2
1 Overall, the unit tests run 2% faster with this form of regular expression. That doesn't sound exciting, but remember that the search function is a small part of the overall unit test; most of the time is spent doing other things. (Separately, I time-tested just the regular expressions, and found that the search function is 11% faster with this syntax.) By precompiling the regular expression and rewriting part of it to use this new syntax, we've improved the regular expression performance by over 60%, and improved the overall performance of the entire unit test by over 10%.
2 More important than any performance boost is the fact that the module still works perfectly. This is the freedom I was talking about earlier: the freedom to tweak, change, or rewrite any piece of it and verify that you haven't messed anything up in the process. This is not a license to endlessly tweak your code just for the sake of tweaking it; we had a very specific objective (“make fromRoman faster”), and we we able to accomplish that objective without any lingering doubts about whether we introduced new bugs in the process.

One other tweak I would like to make, and then I promise I'll stop refactoring and put this module to bed. As we've seen repeatedly, regular expressions can get pretty hairy and unreadable pretty quickly. I wouldn't like to come back to this module in six months and try to maintain it. Sure, the test cases pass, so I know that it works, but if I can't figure out how it works, I won't be able to add new features, fix new bugs, or otherwise maintain it. Documentation is critical, and Python provides a way of verbosely documenting your regular expressions.

Example 13.15. roman83.py

If you have not already done so, you can download this and other examples used in this book.

# rest of program omitted for clarity

#old version
#romanNumeralPattern = \
#   re.compile('^M{0,4}(CM|CD|D?C{0,3})(XC|XL|L?X{0,3})(IX|IV|V?I{0,3})$')

#new version
romanNumeralPattern = re.compile('''
    ^                   # beginning of string
    M{0,4}              # thousands - 0 to 4 M's
    (CM|CD|D?C{0,3})    # hundreds - 900 (CM), 400 (CD), 0-300 (0 to 3 C's),
                        #            or 500-800 (D, followed by 0 to 3 C's)
    (XC|XL|L?X{0,3})    # tens - 90 (XC), 40 (XL), 0-30 (0 to 3 X's),
                        #        or 50-80 (L, followed by 0 to 3 X's)
    (IX|IV|V?I{0,3})    # ones - 9 (IX), 4 (IV), 0-3 (0 to 3 I's),
                        #        or 5-8 (V, followed by 0 to 3 I's)
    $                   # end of string
    ''', re.VERBOSE) 1
1 The re.compile function can take an optional second argument, which is a set of one or more flags that control various options about the compiled regular expression. Here we're specifying the re.VERBOSE flag, which tells Python that there are in-line comments within the regular expression itself. The comments and all the whitespace around them are not considered part of the regular expression; the re.compile function simply strips them all out when it compiles the expression. This new, “verbose” version is identical to the old version, but it is infinitely more readable.

Example 13.16. Output of romantest83.py against roman83.py

.............
----------------------------------------------------------------------
Ran 13 tests in 3.315s 1

OK                     2
1 This new, “verbose” version runs at exactly the same speed as the old version. In fact, the compiled pattern objects are the same, since the re.compile function strips out all the stuff we added.
2 This new, “verbose” version passes all the same tests as the old version. Nothing has changed, except that the programmer who comes back to this module in six months stands a fighting chance of understanding how the function works.

13.4. Postscript

A clever reader read the previous section and took it to the next level. The biggest headache (and performance drain) in the program as it is currently written is the regular expression, which is required because we have no other way of breaking down a Roman numeral. But there's only 5000 of them; why don't we just build a lookup table once, then simply read that? This idea gets even better when you realize that you don't need to use regular expressions at all. As you build the lookup table for converting integers to Roman numerals, you can build the reverse lookup table to convert Roman numerals to integers.

And best of all, he already had a complete set of unit tests. He changed over half the code in the module, but the unit tests stayed the same, so he could prove that his code worked just as well as the original.

Example 13.17. roman9.py

If you have not already done so, you can download this and other examples used in this book.

#Define exceptions
class RomanError(Exception): pass
class OutOfRangeError(RomanError): pass
class NotIntegerError(RomanError): pass
class InvalidRomanNumeralError(RomanError): pass

#Roman numerals must be less than 5000
MAX_ROMAN_NUMERAL = 4999

#Define digit mapping
romanNumeralMap = (('M',  1000),
                   ('CM', 900),
                   ('D',  500),
                   ('CD', 400),
                   ('C',  100),
                   ('XC', 90),
                   ('L',  50),
                   ('XL', 40),
                   ('X',  10),
                   ('IX', 9),
                   ('V',  5),
                   ('IV', 4),
                   ('I',  1))

#Create tables for fast conversion of roman numerals.
#See fillLookupTables() below.
toRomanTable = [ None ]  # Skip an index since Roman numerals have no zero
fromRomanTable = {}

def toRoman(n):
    """convert integer to Roman numeral"""
    if not (0 < n <= MAX_ROMAN_NUMERAL):
        raise OutOfRangeError, "number out of range (must be 1..%s)" % MAX_ROMAN_NUMERAL
    if int(n) <> n:
        raise NotIntegerError, "decimals can not be converted"
    return toRomanTable[n]

def fromRoman(s):
    """convert Roman numeral to integer"""
    if not s:
        raise InvalidRomanNumeralError, "Input can not be blank"
    if not fromRomanTable.has_key(s):
        raise InvalidRomanNumeralError, "Invalid Roman numeral: %s" % s
    return fromRomanTable[s]

def toRomanDynamic(n):
    """convert integer to Roman numeral using dynamic programming"""
    result = ""
    for numeral, integer in romanNumeralMap:
        if n >= integer:
            result = numeral
            n -= integer
            break
    if n > 0:
        result += toRomanTable[n]
    return result

def fillLookupTables():
    """compute all the possible roman numerals"""
    #Save the values in two global tables to convert to and from integers.
    for integer in range(1, MAX_ROMAN_NUMERAL + 1):
        romanNumber = toRomanDynamic(integer)
        toRomanTable.append(romanNumber)
        fromRomanTable[romanNumber] = integer

fillLookupTables()

So how fast is it?

Example 13.18. Output of romantest9.py against roman9.py


.............
----------------------------------------------------------------------
Ran 13 tests in 0.791s

OK

Remember, the best performance we ever got in the original version was 13 tests in 3.315 seconds. Of course, it's not entirely a fair comparison, because this version will take longer to import (when it fills the lookup tables). But since import is only done once, this is negligible in the long run.

The moral of the story?

  • Simplicity is a virtue.
  • Especially when regular expressions are involved.
  • And unit tests can give you the confidence to do large-scale refactoring... even if you didn't write the original code.

13.5. Summary

Unit testing is a powerful concept which, if properly implemented, can both reduce maintenance costs and increase flexibility in any long-term project. It is also important to understand that unit testing is not a panacea, a Magic Problem Solver, or a silver bullet. Writing good test cases is hard, and keeping them up to date takes discipline (especially when customers are screaming for critical bug fixes). Unit testing is not a replacement for other forms of testing, including functional testing, integration testing, and user acceptance testing. But it is feasible, and it does work, and once you've seen it work, you'll wonder how you ever got along without it.

This chapter covered a lot of ground, and much of it wasn't even Python-specific. There are unit testing frameworks for many languages, all of which require you to understand the same basic concepts:

  • Designing test cases that are specific, automated, and independent
  • Writing test cases before the code they are testing
  • Writing tests that test good input and check for proper results
  • Writing tests that test bad input and check for proper failures
  • Writing and updating test cases to illustrate bugs or reflect new requirements
  • Refactoring mercilessly to improve performance, scalability, readability, maintainability, or whatever other -ility you're lacking

Additionally, you should be comfortable doing all of the following Python-specific things:

Further reading

Chapter 14. Regression Testing

14.1. Diving in

In Introduction to Unit Testing, we discussed the philosophy of unit testing. In Unit Testing: Step by Step, we stepped through the implementation of basic unit tests in Python. In Refactoring, we saw how unit testing makes large-scale refactoring easier. This chapter will build on those, but focus more on advanced Python-specific techniques with the unittest module, rather than unit testing itself.

The following is a complete Python program that acts as a cheap and simple regression testing framework. It takes unit tests that you've written for individual modules, collects them all into one big test suite, and runs them all at once. I actually use this script as part of the build process for this book; I have unit tests for several of the example programs (not just the roman.py module featured in Introduction to Unit Testing), and the first thing my automated build script does is run this program to make sure all my examples still work. If this regression test fails, the build immediately stops. I don't want to release non-working examples any more than you want to download them and sit around scratching your head and yelling at your monitor and wondering why they don't work.

Example 14.1. regression.py

If you have not already done so, you can download this and other examples used in this book.

"""Regression testing framework

This module will search for scripts in the same directory named
XYZtest.py.  Each such script should be a test suite that tests a
module through PyUnit.  (As of Python 2.1, PyUnit is included in
the standard library as "unittest".)  This script will aggregate all
found test suites into one big test suite and run them all at once.
"""

import sys, os, re, unittest

def regressionTest():
    path = os.path.abspath(os.path.dirname(sys.argv[0]))   
    files = os.listdir(path)                               
    test = re.compile("test\.py$", re.IGNORECASE)          
    files = filter(test.search, files)                     
    filenameToModuleName = lambda f: os.path.splitext(f)[0]
    moduleNames = map(filenameToModuleName, files)         
    modules = map(__import__, moduleNames)                 
    load = unittest.defaultTestLoader.loadTestsFromModule  
    return unittest.TestSuite(map(load, modules))          

if __name__ == "__main__":                   
    unittest.main(defaultTest="regressionTest")

Running this script in the same directory as the rest of the example scripts that come with this book will find all the unit tests, named moduletest.py, run them as a single test, and pass or fail them all at once.

Example 14.2. Sample output of regression.py

[you@localhost py]$ python regression.py -v
help should fail with no object ... ok                             1
help should return known result for apihelper ... ok
help should honor collapse argument ... ok
help should honor spacing argument ... ok
buildConnectionString should fail with list input ... ok           2
buildConnectionString should fail with string input ... ok
buildConnectionString should fail with tuple input ... ok
buildConnectionString handles empty dictionary ... ok
buildConnectionString returns known result with known input ... ok
fromRoman should only accept uppercase input ... ok                3
toRoman should always return uppercase ... ok
fromRoman should fail with blank string ... ok
fromRoman should fail with malformed antecedents ... ok
fromRoman should fail with repeated pairs of numerals ... ok
fromRoman should fail with too many repeated numerals ... ok
fromRoman should give known result with known input ... ok
toRoman should give known result with known input ... ok
fromRoman(toRoman(n))==n for all n ... ok
toRoman should fail with non-integer input ... ok
toRoman should fail with negative input ... ok
toRoman should fail with large input ... ok
toRoman should fail with 0 input ... ok
kgp a ref test ... ok
kgp b ref test ... ok
kgp c ref test ... ok
kgp d ref test ... ok
kgp e ref test ... ok
kgp f ref test ... ok
kgp g ref test ... ok

----------------------------------------------------------------------
Ran 29 tests in 2.799s

OK
1 The first 5 tests are from apihelpertest.py, which tests the example script from The Power Of Introspection.
2 The next 5 tests are from odbchelpertest.py, which tests the example script from Your first Python program.
3 The rest are from romantest.py, which we studied in depth in Introduction to Unit Testing.

14.2. Finding the path

When running Python scripts from the command line, it is sometimes useful to know where the currently running script is located on disk.

This is one of those obscure little tricks that is virtually impossible to figure out on your own, but simple to remember once you see it. The key to it is sys.argv. As we saw in XML Processing, this is a list that holds the list of command-line arguments. However, it also holds the name of the running script, exactly as it was called from the command line, and this is enough information to determine its location.

Example 14.3. fullpath.py

If you have not already done so, you can download this and other examples used in this book.

import sys, os

print 'sys.argv[0] =', sys.argv[0]             1
pathname = os.path.dirname(sys.argv[0])        2
print 'path =', pathname
print 'full path =', os.path.abspath(pathname) 3
1 Regardless of how you run a script, sys.argv[0] will always contain the name of the script, exactly as it appears on the command line. This may or may not include any path information, as we'll see shortly.
2 os.path.dirname takes a filename as a string and returns the directory path portion. If the given filename does not include any path information, os.path.dirname returns an empty string.
3 os.path.abspath is the key here. It takes a pathname, which can be partial or even blank, and returns a fully qualified pathname.

os.path.abspath deserves further explanation. It is very flexible; it can take any kind of pathname.

Example 14.4. Further explanation of os.path.abspath

>>> import os
>>> os.getcwd()                        1
/home/you
>>> os.path.abspath('')                2
/home/you
>>> os.path.abspath('.ssh')            3
/home/you/.ssh
>>> os.path.abspath('/home/you/.ssh') 4
/home/you/.ssh
>>> os.path.abspath('.ssh/../foo/')    5
/home/you/foo
1 os.getcwd() returns the current working directory.
2 Calling os.path.abspath with an empty string returns the current working directory, same as os.getcwd().
3 Calling os.path.abspath with a partial pathname constructs a fully qualified pathname out of it, based on the current working directory.
4 Calling os.path.abspath with a full pathname simply returns it.
5 os.path.abspath also normalizes the pathname it returns. Note that this example worked even though I don't actually have a 'foo' directory. os.path.abspath never checks your actual disk; this is all just string manipulation.
Note
The pathnames and filenames you pass to os.path.abspath do not need to exist.
Note
os.path.abspath not only constructs full path names, it also normalizes them. If you are in the /usr/ directory, os.path.abspath('bin/../local/bin' will return /usr/local/bin. If you just want to normalize a pathname without turning it into a full pathname, use os.path.normpath instead.

Example 14.5. Sample output from fullpath.py

[you@localhost py]$ python /home/you/diveintopython/common/py/fullpath.py 1
sys.argv[0] = /home/you/diveintopython/common/py/fullpath.py
path = /home/you/diveintopython/common/py
full path = /home/you/diveintopython/common/py
[you@localhost diveintopython]$ python common/py/fullpath.py               2
sys.argv[0] = common/py/fullpath.py
path = common/py
full path = /home/you/diveintopython/common/py
[you@localhost diveintopython]$ cd common/py
[you@localhost py]$ python fullpath.py                                     3
sys.argv[0] = fullpath.py
path = 
full path = /home/you/diveintopython/common/py
1 In the first case, sys.argv[0] includes the full path of the script. We can then use the os.path.dirname function to strip off the script name and return the full directory name, and os.path.abspath simply returns what we give it.
2 If the script is run by using a partial pathname, sys.argv[0] will still contain exactly what appears on the command line. os.path.dirname will then give us a partial pathname (relative to the current directory), and os.path.abspath will construct a full pathname from the partial pathname.
3 If the script is run from the current directory without giving any path, os.path.dirname will simply return an empty string. Given an empty string, os.path.abspath returns the current directory, which is what we want, since the script was run from the current directory.
Note
Like the other functions in the os and os.path modules, os.path.abspath is cross-platform. Your results will look slightly different than my examples if you're running on Windows (which uses backslash as a path separator) or Mac OS (which uses colons), but they'll still work. That's the whole point of the os module.

Addendum. One reader was dissatisfied with this solution, and wanted to be able to run all the unit tests in the current directory, not the directory where regression.py is located. He suggests this approach instead:

Example 14.6. Running scripts in the current directory

import sys, os, re, unittest

def regressionTest():
    path = os.getcwd()       1
    sys.path.append(path)    2
    files = os.listdir(path) 3
1 Instead of setting path to the directory where the currently running script is located, we set it to the current working directory instead. This will be whatever directory you were in before you ran the script, which is not necessarily the same as the directory the script is in. (Read that sentence a few times until you get it.)
2 Append this directory to the Python library search path, so that when we dynamically import the unit test modules later, Python can find them. We didn't have to do this when path was the directory of the currently running script, because Python always looks in that directory.
3 The rest of the function is the same.

This technique will allow you to re-use this regression.py script on multiple projects. Just put the script in a common directory, then change to the project's directory before running it. All of that project's unit tests will be found and tested, instead of the unit tests in the common directory where regression.py is located.

14.3. Filtering lists revisited

You're already familiar with using list comprehensions to filter lists. There is another way to accomplish this same thing, which some people feel is more expressive.

Python has a built-in filter function which takes two arguments, a function and a list, and returns a list.[14] The function passed as the first argument to filter must itself take one argument, and the list that filter returns will contain all the elements from the list passed to filter for which the function passed to filter returns true.

Got all that? It's not as difficult as it sounds.

Example 14.7. Introducting filter

>>> def odd(n):           1
...     return n%2
...     
>>> li = [1, 2, 3, 5, 9, 10, 256, -3]
>>> filter(odd, li)       2
[1, 3, 5, 9, -3]
>>> filteredList = []
>>> for n in li:          3
...     if odd(n):
...         filteredList.append(n)
...     
>>> filteredList
[1, 3, 5, 9, -3]
1 odd uses the built-in mod function “%” to return 1 if n is odd and 0 if n is even.
2 filter takes two arguments, a function (odd) and a list (li). It loops through the list and calls odd with each element. If odd returns a true value (remember, any non-zero value is true in Python), then the element is included in the returned list, otherwise it is filtered out. The result is a list of only the odd numbers from the original list, in the same order as they appeared in the original.
3 You could accomplish the same thing with a for loop. Depending on your programming background, this may seem more “straightforward”, but functions like filter are much more expressive. Not only is it easier to write, it's easier to read, too. Reading the for loop is like standing too close to a painting; you see all the details, but it may take a few seconds to be able to step back and see the bigger picture: “Oh, we're just filtering the list!

Example 14.8. filter in regression.py

    files = os.listdir(path)                                1
    test = re.compile("test\.py$", re.IGNORECASE)           2
    files = filter(test.search, files)                      3
1 As we saw in Finding the path, path may contain the full or partial pathname of the directory of the currently running script, or it may contain an empty string if the script is being run from the current directory. Either way, files will end up with the names of the files in the same directory as this script we're running.
2 This is a compiled regular expression. As we saw in Refactoring, if you're going to use the same regular expression over and over, you should compile it for faster performance. The compiled object has a search method which takes a single argument, the string to search. If the regular expression matches the string, the search method returns a Match object containing information about the regular expression match; otherwise it returns None, the Python null value.
3 For each element in the files list, we're going to call the search method of the compiled regular expression object, test. If the regular expression matches, the method will return a Match object, which Python considers to be true, so the element will be included in the list returned by filter. If the regular expression does not match, the search method will return None, which Python considers to be false, so the element will not be included.

Historical note. Versions of Python prior to 2.0 did not have list comprehensions, so you couldn't filter using list comprehensions; the filter function was the only game in town. Even with the introduction of list comprehensions in 2.0, some people still prefer the old-style filter (and its companion function, map, which we'll see later in this chapter). Both techniques work, and neither is going away, so which one you use is a matter of style.

Example 14.9. Filtering using list comprehensions instead

    files = os.listdir(path)                               
    test = re.compile("test\.py$", re.IGNORECASE)          
    files = [f for f in files if test.search(f)] 1
1 This will accomplish exactly the same result as using the filter function. Which way is more expressive? That's up to you.

14.4. Mapping lists revisited

You're already familiar with using list comprehensions to map one list into another. There is another way to accomplish the same thing, using the built-in map function. It works much the same way as the filter function.

Example 14.10. Introducing map

>>> def double(n):
...     return n*2
...     
>>> li = [1, 2, 3, 5, 9, 10, 256, -3]
>>> map(double, li)                       1
[2, 4, 6, 10, 18, 20, 512, -6]
>>> [double(n) for n in li]               2
[2, 4, 6, 10, 18, 20, 512, -6]
>>> newlist = []
>>> for n in li:                          3
...     newlist.append(double(n))
...     
>>> newlist
[2, 4, 6, 10, 18, 20, 512, -6]
1 map takes a function and a list[15] and returns a new list by calling the function with each element of the list in order. In this case, the function simply multiplies each element by 2.
2 You could accomplish the same thing with a list comprehension. List comprehensions were first introduced in Python 2.0; map has been around forever.
3 You could, if you insist on thinking like a Visual Basic programmer, use a for loop to accomplish the same thing.

Example 14.11. map with lists of mixed datatypes

>>> li = [5, 'a', (2, 'b')]
>>> map(double, li)                       1
[10, 'aa', (2, 'b', 2, 'b')]
1 As a side note, I'd like to point out that map works just as well with lists of mixed datatypes, as long as the function you're using correctly handles each type. In this case, our double function simply multiplies the given argument by 2, and Python Does The Right Thing depending on the datatype of the argument. For integers, this means actually multiplying it by 2; for strings, it means concatenating the string with itself; for tuples, it means making a new tuple that has all of the elements of the original, then all of the elements of the original again.

All right, enough play time. Let's look at some real code.

Example 14.12. map in regression.py

    filenameToModuleName = lambda f: os.path.splitext(f)[0] 1
    moduleNames = map(filenameToModuleName, files)          2
1 As we saw in Using lambda functions, lambda defines an inline function. And as we saw in Example 6.17, “Splitting pathnames”, os.path.splitext takes a filename and returns a tuple (name, extension). So filenameToModuleName is a function which will take a filename and strip off the file extension, and return just the name.
2 Calling map takes each filename listed in files, passes it to our function filenameToModuleName, and returns a list of the return values of each of those function calls. In other words, we strip the file extension off of each filename, and store the list of all those stripped filenames in moduleNames.

As we'll see in the rest of the chapter, we can extend this type of data-centric thinking all the way to our final goal, which is to define and execute a single test suite that contains the tests from all of those individual test suites.

14.5. Data-centric programming

By now you're probably scratching your head wondering why this is better than using for loops and straight function calls. And that's a perfectly valid question. Mostly, it's a matter of perspective. Using map and filter forces you to center your thinking around your data.

In this case, we started with no data at all; the first thing we did was get the directory path of the current script, and got a list of files in that directory. That was our bootstrap, and it gave us real data to work with: a list of filenames.

However, we knew we didn't care about all of those files, only the ones that were actually test suites. We had too much data, so we needed to filter it. How did we know which data to keep? We needed a test to decide, so we defined one and passed it to the filter function. In this case we used a regular expression to decide, but the concept would be the same regardless of how we constructed the test.

Now we had the filenames of each of the test suites (and only the test suites, since everything else had been filtered out), but we really wanted module names instead. We had the right amount of data, but it was in the wrong format. So we defined a function that would transform a single filename into a module name, and we mapped that function onto the entire list. From one filename, we can get a module name; from a list of filenames, we can get a list of module names.

Instead of filter, we could have used a for loop with an if statement. Instead of map, we could have used a for loop with a function call. But using for loops like that is busywork. At best, it simply wastes time; at worst, it introduces obscure bugs. For instance, we have to figure out how to test for the condition “is this file a test suite?” anyway; that's our application-specific logic, and no language can write that for us. But once we've figured that out, do we really want go to all the trouble of defining a new empty list and writing a for loop and an if statement and manually calling append to add each element to the new list if it passes the condition and then keeping track of which variable holds the new filtered data and which one holds the old unfiltered data? Why not just define the test condition, then let Python do the rest of that work for us?

Oh sure, you could try to be fancy and delete elements in place without creating a new list. But you've been burned by that before. Trying to modify a data structure that you're looping through can be tricky. You delete an element, then loop to the next element, and suddenly you've skipped one. Is Python one of the languages that works that way? How long would it take you to figure it out? Would you remember for certain whether it was safe the next time you tried? Programmers spend so much time and make so many mistakes dealing with purely technical issues like this, and it's all pointless. It doesn't advance your program at all; it's just busywork.

I resisted list comprehensions when I first learned Python, and I resisted filter and map even longer. I insisted on making my life more difficult, sticking to the familiar way of for loops and if statements and step-by-step code-centric programming. And my Python programs looked a lot like Visual Basic programs, detailing every step of every operation in every function. And they had all the same types of little problems and obscure bugs. And it was all pointless.

Let it all go. Busywork code is not important. Data is important. And data is not difficult. It's only data. If you have too much, filter it. If it's not what you want, map it. Focus on the data; leave the busywork behind.

14.6. Dynamically importing modules

OK, enough philosophizing. Let's talk about dynamically importing modules.

First, let's look at how we normally import modules. The import module syntax looks in the search path for the named module and imports it by name. You can even import multiple modules at once this way, with a comma-separated list. We did this on the very first line of this chapter's script.

Example 14.13. Importing multiple modules at once

import sys, os, re, unittest 1
1 This imports four modules at once: sys (for system functions and access to the command line parameters), os (for operating system functions like directory listings), re (for regular expressions), and unittest (for unit testing).

Now let's do the same thing, but with dynamic imports.

Example 14.14. Importing modules dynamically

>>> sys = __import__('sys')           1
>>> os = __import__('os')
>>> re = __import__('re')
>>> unittest = __import__('unittest')
>>> sys                               2
>>> <module 'sys' (built-in)>
>>> os
>>> <module 'os' from '/usr/local/lib/python2.2/os.pyc'>
1 The built-in __import__ function accomplishes the same goal as using the import statement, but it's an actual function, and it takes a string as an argument.
2 The variable sys is now the sys module, just as if we had said import sys. The variable os is now the os module, and so forth.

So __import__ imports a module, but takes a string argument to do it. In this case the module we imported was just a hard-coded string, but it could just as easily be a variable, or the result of a function call. And the variable that we assign the module to doesn't have to match the module name, either. We could import a series of modules and assign them to a list.

Example 14.15. Importing a list of modules dynamically

>>> moduleNames = ['sys', 'os', 're', 'unittest'] 1
>>> moduleNames
['sys', 'os', 're', 'unittest']
>>> modules = map(__import__, moduleNames)        2
>>> modules                                       3
[<module 'sys' (built-in)>,
<module 'os' from 'c:\Python22\lib\os.pyc'>,
<module 're' from 'c:\Python22\lib\re.pyc'>,
<module 'unittest' from 'c:\Python22\lib\unittest.pyc'>]
>>> modules[0].version                            4
'2.2.2 (#37, Nov 26 2002, 10:24:37) [MSC 32 bit (Intel)]'
>>> import sys
>>> sys.version
'2.2.2 (#37, Nov 26 2002, 10:24:37) [MSC 32 bit (Intel)]'
1 moduleNames is just a list of strings. Nothing fancy, except that the strings happen to be names of modules that we could import, if we wanted to.
2 Surprise, we wanted to import them, and we did, by mapping the __import__ function onto the list. Remember, this takes each element of the list (moduleNames) and calls the function (__import__) over and over, once with each element of the list, builds a list of the return values, and returns the result.
3 So now from a list of strings, we've created a list of actual modules. (Your paths may be different, depending on your operating system, where you installed Python, the phase of the moon, etc.)
4 To drive home the point that these are real modules, let's look at some module attributes. Remember, modules[0] is the sys module, so modules[0].version is sys.version. All the other attributes and methods of these modules are also available. There's nothing magic about the import statement, and there's nothing magic about modules. Modules are objects. Everything is an object.

Now we should be able to put this all together and figure out what most of this chapter's code sample is doing.

14.7. Putting it all together

We've learned enough now to deconstruct the first seven lines of this chapter's code sample: reading a directory and importing selected modules within it.

Example 14.16. The regressionTest function

def regressionTest():
    path = os.path.abspath(os.path.dirname(sys.argv[0]))   
    files = os.listdir(path)                               
    test = re.compile("test\.py$", re.IGNORECASE)          
    files = filter(test.search, files)                     
    filenameToModuleName = lambda f: os.path.splitext(f)[0]
    moduleNames = map(filenameToModuleName, files)         
    modules = map(__import__, moduleNames)                 
load = unittest.defaultTestLoader.loadTestsFromModule  
return unittest.TestSuite(map(load, modules))          

Let's look at it line by line, interactively. Assume that the current directory is c:\diveintopython\py, which contains the examples that come with this book, including this chapter's script. As we saw in Finding the path, the script directory will end up in the path variable, so let's start hard-code that and go from there.

Example 14.17. Step 1: Get all the files

>>> import sys, os, re, unittest
>>> path = r'c:\diveintopython\py'
>>> files = os.listdir(path)                               
>>> files 1
['BaseHTMLProcessor.py', 'LICENSE.txt', 'apihelper.py', 'apihelpertest.py',
'argecho.py', 'autosize.py', 'builddialectexamples.py', 'dialect.py',
'fileinfo.py', 'fullpath.py', 'kgptest.py', 'makerealworddoc.py',
'odbchelper.py', 'odbchelpertest.py', 'parsephone.py', 'piglatin.py',
'plural.py', 'pluraltest.py', 'pyfontify.py', 'regression.py', 'roman.py', 'romantest.py',
'uncurly.py', 'unicode2koi8r.py', 'urllister.py', 'kgp', 'plural', 'roman',
'colorize.py']
1 files is a list of all the files and directories in the script's directory. (If you've been running some of the examples already, you may also see some .pyc files in there as well.)

Example 14.18. Step 2: Filter to find the files we care about

>>> test = re.compile("test\.py$", re.IGNORECASE)           1
>>> files = filter(test.search, files)                      2
>>> files                                                   3
['apihelpertest.py', 'kgptest.py', 'odbchelpertest.py', 'pluraltest.py', 'romantest.py']
1 This regular expression will match any string that ends with test.py. Note that we need to escape the period, since a period in a regular expression usually means “match any single character”, but we actually want to match a literal period instead.
2 The compiled regular expression acts like a function, so we can use it to filter our large list of files and directories, to find the ones that match the regular expression.
3 And we're left with the list of unit testing scripts, because they were the only ones named SOMETHINGtest.py.

Example 14.19. Step 3: Map filenames to module names

>>> filenameToModuleName = lambda f: os.path.splitext(f)[0] 1
>>> filenameToModuleName('romantest.py')                    2
'romantest'
>>> filenameToModuleName('odchelpertest.py')
'odbchelpertest'
>>> moduleNames = map(filenameToModuleName, files)          3
>>> moduleNames                                             4
['apihelpertest', 'kgptest', 'odbchelpertest', 'pluraltest', 'romantest']
1 As we saw in Using lambda functions, lambda is a quick-and-dirty way of creating an inline, one-line function. This one takes a filename with an extension and returns just the filename part, using the standard library function os.path.splitext that we saw in Example 6.17, “Splitting pathnames”.
2 filenameToModuleName is a function. There's nothing magic about lambda functions as opposed to regular functions that we define with a def statement. We can call the filenameToModuleName function like any other, and it does just what we wanted it to do: strips the file extension off of its argument.
3 Now we can apply this function to each file in our list of unit test files, using map.
4 And the result is just what we wanted: a list of modules, as strings.

Example 14.20. Step 4: Mapping module names to modules

>>> modules = map(__import__, moduleNames)                  1
>>> modules                                                 2
[<module 'apihelpertest' from 'apihelpertest.py'>,
<module 'kgptest' from 'kgptest.py'>,
<module 'odbchelpertest' from 'odbchelpertest.py'>,
<module 'pluraltest' from 'pluraltest.py'>,
<module 'romantest' from 'romantest.py'>]
>>> modules[-1]                                             3
<module 'romantest' from 'romantest.py'>
1 As we saw in Dynamically importing modules, we can use a combination of map and __import__ to map a list of module names (as strings) into actual modules (which we can call or access like any other module).
2 modules is now a list of modules, fully accessible like any other module.
3 The last module in the list is the romantest module, just as if we had said import romantest.

Example 14.21. Step 5: Loading the modules into a test suite

>>> load = unittest.defaultTestLoader.loadTestsFromModule  
>>> map(load, modules)                     1
[<unittest.TestSuite tests=[
  <unittest.TestSuite tests=[<apihelpertest.BadInput testMethod=testNoObject>]>,
  <unittest.TestSuite tests=[<apihelpertest.KnownValues testMethod=testApiHelper>]>,
  <unittest.TestSuite tests=[
    <apihelpertest.ParamChecks testMethod=testCollapse>, 
    <apihelpertest.ParamChecks testMethod=testSpacing>]>, 
    ...
  ]
]
>>> unittest.TestSuite(map(load, modules)) 2
1 These are real module objects. Not only can we access them like any other module, instantiate classes and call functions, we can also introspect into the module to figure out which classes and functions it has in the first place. That's what the loadTestsFromModule method does: it introspects into each module and returns a unittest.TestSuite object for each module. Each TestSuite object actually contains a list of TestSuite objects, one for each TestCase class in our module, and each of those TestSuite objects contains a list of tests, one for each test method in our module.
2 Finally, we wrap the list of TestSuite objects into one big test suite. The unittest module has no problem traversing this tree of nested test suites within test suites; eventually it gets down to an individual test method and executes it, verifies that it passes or fails, and moves on to the next one.

This introspection process is what the unittest module usually does for us. Remember that magic-looking unittest.main() function that our individual test modules called to kick the whole thing off? unittest.main() actually creates an instance of unittest.TestProgram, which in turn creates an instance of a unittest.defaultTestLoader and loads it up with the module that called it. (How does it get a reference to the module that called it if we don't give it one? By using the equally-magic __import__('__main__') command, which dynamically imports the currently-running module. I could write a book on all the tricks and techniques used in the unittest module, but then I'd never finish this one.)

Example 14.22. Step 6: Telling unittest to use our test suite

if __name__ == "__main__":                   
    unittest.main(defaultTest="regressionTest") 1
1 Instead of letting the unittest module do all its magic for us, we've done most of it ourselves. We've created a function (regressionTest) that imports the modules ourselves, calls unittest.defaultTestLoader ourselves, and wraps it all up in a test suite. Now all we have to do is tell unittest that, instead of looking for tests and building a test suite in the usual way, it should just call our regressionTest function, which returns a ready-to-use TestSuite.

14.8. Summary

The regression.py program and its output should now make perfect sense.

You should now feel comfortable doing all of these things:



[14] Technically, the second argument to filter can be any sequence, including lists, tuples, and custom classes that act like lists by defining the __getitem__ special method. If possible, filter will return the same datatype as you give it, so filtering a list returns a list, but filtering a tuple returns a tuple.

[15] Again, I should point out that map can take a list, a tuple, or any object that acts like a sequence. See previous footnote about filter.

Chapter 15. Dynamic functions

15.1. Diving in

I want to talk about plural nouns. Also, functions that return other functions, advanced regular expressions, and generators. Generators are new in Python 2.3. But first, let's talk about how to make plural nouns.

If you haven't read Regular Expressions, now would be a good time. This chapter assumes you understand the basics of regular expressions, and quickly descends into more advanced uses.

English is a schizophrenic language that borrows from a lot of other languages, and the rules for making singular nouns into plural nouns are varied and complex. There are rules, and then there are exceptions to those rules, and then there are exceptions to the exceptions.

If you grew up in an English-speaking country or learned English in a formal school setting, you're probably familiar with the basic rules:

  1. If a word ends in S, X, or Z, add ES. “Bass” becomes “basses”, “fax” becomes “faxes”, and “waltz” becomes “waltzes”.
  2. If a word ends in a noisy H, add ES; if it ends in a silent H, just add S. What's a noisy H? One that gets combined with other letters to make a sound that you can hear. So “coach” becomes “coaches” and “rash” becomes “rashes”, because you can hear the CH and SH sounds when you say them. But “cheetah” becomes “cheetahs”, because the H is silent.
  3. If a word ends in Y that sounds like I, change the Y to IES; if the Y is combined with a vowel to sound like something else, just add S. So “vacancy” becomes “vacancies”, but “day” becomes “days”.
  4. If all else fails, just add S and hope for the best.

(I know, there are lots of exceptions. “Man” becomes “men” and “woman” becomes “women”, but “human” becomes “humans”. “Mouse” becomes “mice” and “louse” becomes “lice”, but “house” becomes “houses”. “Knife” becomes “knives” and “wife” becomes “wives”, but “lowlife” becomes “lowlifes”. And don't even get me started on words that are their own plural, like “sheep”, “deer”, and “haiku”.)

Other languages are, of course, completely different.

Let's design a module that pluralizes nouns. We'll start with just English nouns, and just these four rules, but keep in mind that we'll inevitably need to add more rules, and we may eventually need to add more languages.

15.2. plural.py, stage 1

So we're looking at words, which at least in English are strings of characters. And we have rules that say we need to find different combinations of characters, and then do different things to them. This sounds like a job for regular expressions.

Example 15.1. plural1.py

import re

def plural(noun):                            
    if re.search('[sxz]$', noun):             1
        return re.sub('$', 'es', noun)        2
    elif re.search('[^aeioudgkprt]h$', noun):
        return re.sub('$', 'es', noun)       
    elif re.search('[^aeiou]y$', noun):      
        return re.sub('y$', 'ies', noun)     
    else:                                    
        return noun + 's'                    
1 OK, this is a regular expression, but it uses a syntax we didn't see in Regular Expressions. The square brackets mean “match exactly one of these characters”. So [sxz] means “s, or x, or z”, but only one of them. The $ should be familiar; it matches the end of string. So we're checking to see if noun ends with s, x, or z.
2 This re.sub function is totally new, in the sense that we never covered it in the regular expressions chapter. It performs regular expression-based string substitutions. Let's look at it in more detail.

Example 15.2. Introducing re.sub

>>> import re
>>> re.search('[abc]', 'Mark')   1
<_sre.SRE_Match object at 0x001C1FA8>
>>> re.sub('[abc]', 'o', 'Mark') 2
'Mork'
>>> re.sub('[abc]', 'o', 'rock') 3
'rook'
>>> re.sub('[abc]', 'o', 'caps')  4
'oops'
1 Does the string Mark contain a, b, or c? Yes, it contains a.
2 OK, now find a, b, or c, and replace it with o. Mark becomes Mork.
3 The same function turns rock into rook.
4 You might think this would turn caps into oaps, but it doesn't. re.sub replaces all of the matches, not just the first one. So this regular expression turns caps into oops, because both the c and the a get turned into o.

Example 15.3. Back to plural1.py

import re

def plural(noun):                            
    if re.search('[sxz]$', noun):            
        return re.sub('$', 'es', noun)        1
    elif re.search('[^aeioudgkprt]h$', noun): 2
        return re.sub('$', 'es', noun)        3
    elif re.search('[^aeiou]y$', noun):      
        return re.sub('y$', 'ies', noun)     
    else:                                    
        return noun + 's'                    
1 Back to our plural function. What are we doing? We're replacing the end of string with es. In other words, adding es to the string. We could accomplish the same thing with string concatenation, for example noun + 'es', but I'm using regular expressions for everything, for consistency, for reasons that will become clear later in the chapter.
2 Look closely, this is another new variation. The ^ as the first character inside the square brackets means something special: negation. [^abc] means “any single character except a, b, or c”. So [^aeioudgkprt] means any character except a, e, i, o, u, d, g, k, p, r, or t. Then that character needs to be followed by h, followed by end of string. We're looking for words that end in H where the H can be heard.
3 Same pattern here: match words that end in Y, where the character before the Y is not a, e, i, o, or u. We're looking for words that end in Y that sounds like I.

Example 15.4. More on negation regular expressions

>>> import re
>>> re.search('[^aeiou]y$', 'vacancy') 1
<_sre.SRE_Match object at 0x001C1FA8>
>>> re.search('[^aeiou]y$', 'boy')     2
>>> 
>>> re.search('[^aeiou]y$', 'day')
>>> 
>>> re.search('[^aeiou]y$', 'pita')    3
>>> 
1 vacancy matches this regular expression, because it ends in cy, and c is not a, e, i, o, or u.
2 boy does not match, because it ends in oy, and we specifically said that the character before the y could not be o. day does not match, because it ends in ay.
3 pita does not match, because it does not end in y.

Example 15.5. More on re.sub

>>> re.sub('y$', 'ies', 'vacancy')              1
'vacancies'
>>> re.sub('y$', 'ies', 'agency')
'agencies'
>>> re.sub('([^aeiou])y$', r'\1ies', 'vacancy') 2
'vacancies'
1 This regular expression turns vacancy into vacancies and agency into agencies, which is what we wanted. Note that it would also turn boy into boies, but that will never happen in our function because we did that re.search first to find out whether we should do this re.sub.
2 Just in passing, I want to point out that it is possible to combine these two regular expressions (one to find out if the rule applies, and another to actually apply it) into a single regular expression. Here's what that would look like. Most of it should look familiar: we're using a remembered group, which we learned in Case study: parsing phone numbers, to remember the character before the y. Then in the substitution string, we use a new syntax, \1, which means “hey, that first group you remembered? put it here”. In this case, we remember the c before the y, and then when we do the substitution, we substitute c in place of c, and ies in place of y. (If you have more than one remembered group, you can use \2 and \3 and so on.)

Regular expression substitutions are extremely powerful, and the \1 syntax makes them even more powerful. But combining the entire operation into one regular expression is also much harder to read, and it doesn't directly map to the way we first described the pluralizing rules. We originally laid out rules like “if the word ends in S, X, or Z, then add ES”. And if you look at this function, we have two lines of code that say “if the word ends in S, X, or Z, then add ES”. It doesn't get much more direct than that.

15.3. plural.py, stage 2

Now we're going to add a level of abstraction. We started by defining a list of rules: if this, then do that, otherwise go to the next rule. Let's temporarily complicate part of our program so we can simplify another part.

Example 15.6. plural2.py

import re

def match_sxz(noun):                          
    return re.search('[sxz]$', noun)          

def apply_sxz(noun):                          
    return re.sub('$', 'es', noun)            

def match_h(noun):                            
    return re.search('[^aeioudgkprt]h$', noun)

def apply_h(noun):                            
    return re.sub('$', 'es', noun)            

def match_y(noun):                            
    return re.search('[^aeiou]y$', noun)      
        
def apply_y(noun):                            
    return re.sub('y$', 'ies', noun)          

def match_default(noun):                      
    return 1                                  
        
def apply_default(noun):                      
    return noun + 's'                         

rules = ((match_sxz, apply_sxz),
         (match_h, apply_h),
         (match_y, apply_y),
         (match_default, apply_default)
         )                                     1

def plural(noun):                             
    for matchesRule, applyRule in rules:       2
        if matchesRule(noun):                  3
            return applyRule(noun)             4
1 This version looks more complicated (it's certainly longer), but it does exactly the same thing: try to match four different rules, in order, and apply the appropriate regular expression when a match is found. The difference is that each individual match and apply rule is defined in its own function, and the functions are then listed in this rules variable, which is a tuple of tuples.
2 Using a for loop, we can pull out the match and apply rules two at a time (one match, one apply) from the rules tuple. On the first iteration of the for loop, matchesRule will get match_sxz, and applyRule will get apply_sxz. On the second iteration (assuming we get that far), matchesRule will be assigned match_h, and applyRule will be assigned apply_h.
3 Remember that everything in Python is an object, including functions. rules contains actual functions; not names of functions, but actual functions. When they get assigned in the for loop, then matchesRule and applyRule are actual functions that we can call. So on the first iteration of the for loop, this is equivalent to calling matches_sxz(noun).
4 On the first iteration of the for loop, this is equivalent to calling apply_sxz(noun), and so forth.

If this additional level of abstraction is confusing, try unrolling the function to see the equivalence. This for loop is equivalent to the following:

Example 15.7. Unrolling the plural function

def plural(noun):
    if match_sxz(noun):
        return apply_sxz(noun)
    if match_h(noun):
        return apply_h(noun)
    if match_y(noun):
        return apply_y(noun)
    if match_default(noun):
        return apply_default(noun)

The benefit here is that our plural function is now simplified. It takes a list of rules, defined elsewhere, and iterates through them in a generic fashion. Get a match rule; does it match? Then call the apply rule. The rules could be defined anywhere, in any way. The plural function doesn't care.

Now, was adding this level of abstraction worth it? Well, not yet. Let's consider what it would take to add a new rule to our function. Well, in our previous example, it would require adding an if statement to the plural function. In this example, it would require adding two functions, match_foo and apply_foo, and then updating the rules list to specify where in the order the new match and apply functions should be called relative to the other rules.

This is really just a stepping stone to the next section. Let's move on.

15.4. plural.py, stage 3

Defining separate named functions for each match and apply rule isn't really necessary. We never call them directly; we define them in the rules list and call them through there. Let's streamline our rules definition by anonymizing those functions.

Example 15.8. plural3.py

import re

rules = \
  (
    (
     lambda word: re.search('[sxz]$', word),
     lambda word: re.sub('$', 'es', word)
    ),
    (
     lambda word: re.search('[^aeioudgkprt]h$', word),
     lambda word: re.sub('$', 'es', word)
    ),
    (
     lambda word: re.search('[^aeiou]y$', word),
     lambda word: re.sub('y$', 'ies', word)
    ),
    (
     lambda word: re.search('$', word),
     lambda word: re.sub('$', 's', word)
    )
   )                                           1

def plural(noun):                             
    for matchesRule, applyRule in rules:       2
        if matchesRule(noun):                 
            return applyRule(noun)            
1 This is the same set of rules as we defined in stage 2. The only difference is that instead of defining named functions like match_sxz and apply_sxz, we have “inlined” those function definitions directly into the rules list itself, using lambda functions.
2 Note that our plural function hasn't changed at all. It iterates through a set of rule functions, checks the first rule, and if it returns a true value, calls the second rule and returns the value. Same as above, word for word. The only difference is that the rule functions were defined inline, anonymously, using lambda functions. But our plural function doesn't care how they were defined; it just gets a list of rules and blindly works through them.

Now to add a new rule, all we have to do is define the functions directly in the rules list itself: one match rule, and one apply rule. But defining the rule functions inline like this makes it very clear that we have some unnecessary duplication here. We have four pairs of functions, and they all follow the same pattern. The match function is a single call to re.search, and the apply function is a single call to re.sub. Let's factor out these similarities.

15.5. plural.py, stage 4

Let's factor out the duplication in our code so that defining new rules can be easier.

Example 15.9. plural4.py

import re

def buildMatchAndApplyFunctions((pattern, search, replace)):  
    matchFunction = lambda word: re.search(pattern, word)      1
    applyFunction = lambda word: re.sub(search, replace, word) 2
    return (matchFunction, applyFunction)                      3
1 buildMatchAndApplyFunctions is a function that builds other functions dynamically. It takes pattern, search and replace, and we can build the match function using the λ syntax to be a function that takes one parameter (word) and calls re.search with the pattern that was passed to the buildMatchAndApplyFunctions function, and the word that was passed to the match function we're building. Whoa.
2 Building the apply function works the same way. The apply function is a function that takes one parameter, and calls re.search with the search and replace parameters that were passed to the buildMatchAndApplyFunctions function, and the word that was passed to the apply function we're building. This technique of using the values of outside parameters within a dynamic function is called closures. We're essentially defining constants within the apply function we're building: it takes one parameter (word), but it then acts on that plus two other values (search and replace) which were set when we defined the apply function.
3 Finally, the buildMatchAndApplyFunctions function returns a tuple of two values: the two functions we just created. The constants we defined within those functions (pattern within matchFunction, and search and replace within applyFunction) stay with those functions, even after we return from buildMatchAndApplyFunctions. That's insanely cool.

If this is incredibly confusing (and it should be, this is weird stuff), it may become clearer when we see how to use it.

Example 15.10. plural4.py continued

patterns = \
  (
    ('[sxz]$', '$', 'es'),
    ('[^aeioudgkprt]h$', '$', 'es'),
    ('(qu|[^aeiou])y$', 'y$', 'ies'),
    ('$', '$', 's')
  )                                                 1
rules = map(buildMatchAndApplyFunctions, patterns)  2
1 Our pluralization rules are now defined as a series of strings (not functions). The first string is the regular expression that we would use in re.search to see if this rule matches; the second and third are the search and replace expressions we would use in re.sub to actually apply the rule to turn a noun into its plural.
2 This line is magic. It takes the list of strings in patterns and turns them into a list of functions. How? By mapping the strings to the buildMatchAndApplyFunctions function, which just happens to take three strings as parameters and return a tuple of two functions. This means that rules ends up being exactly the same as the previous example: a list of tuples, where each tuple is a pair of functions, where the first function is our match function that calls re.search, and the second function is our apply function that calls re.sub.

I swear I am not making this up: rules ends up with exactly the same list of functions as the previous example. Unroll the rules definition, and you'll get this:

Example 15.11. Unrolling the rules definition

rules = \
  (
    (
     lambda word: re.search('[sxz]$', word),
     lambda word: re.sub('$', 'es', word)
    ),
    (
     lambda word: re.search('[^aeioudgkprt]h$', word),
     lambda word: re.sub('$', 'es', word)
    ),
    (
     lambda word: re.search('[^aeiou]y$', word),
     lambda word: re.sub('y$', 'ies', word)
    ),
    (
     lambda word: re.search('$', word),
     lambda word: re.sub('$', 's', word)
    )
   )                                          

Example 15.12. plural4.py, finishing up

def plural(noun):                                  
    for matchesRule, applyRule in rules:            1
        if matchesRule(noun):                      
            return applyRule(noun)                 
1 Since the rules list is the same as the previous example, it should come as no surprise that our plural function hasn't changed. Remember, it's completely generic; it takes a list of rule functions and calls them in order. It doesn't care how the rules are defined. In stage 2, they were defined as seperate named functions. In stage 3, they were defined as anonymous λ functions. Now in stage 4, they are built dynamically by mapping the buildMatchAndApplyFunctions function onto a list of raw strings. Doesn't matter; the plural function still works the same way.

Just in case that wasn't mind-blowing enough, I have to confess that there was a subtlety in the definition of buildMatchAndApplyFunctions that I skipped over. Let's go back and take another look.

Example 15.13. Another look at buildMatchAndApplyFunctions

def buildMatchAndApplyFunctions((pattern, search, replace)):   1
1 Notice the double parentheses? This function doesn't actually take three parameters; it actually takes one parameter, a tuple of three elements. But the tuple is expanded when the function is called, and the three elements of the tuple are each assigned to different variables: pattern, search, and replace. Confused yet? Let's see it in action.

Example 15.14. Expanding tuples when calling functions

>>> def foo((a, b, c)):
...     print c
...     print b
...     print a
>>> parameters = ('apple', 'bear', 'catnap')
>>> foo(parameters) 1
catnap
bear
apple
1 The proper way to call the function foo is with a tuple of three elements. When the function is called, the elements are assigned to different local variables within foo.

Now let's go back and see why this auto-tuple-expansion trick was necessary. patterns was a list of tuples, and each tuple had three elements. When we called map(buildMatchAndApplyFunctions, patterns), that means that buildMatchAndApplyFunctions is not getting called with three parameters. Using map to map a single list onto a function always calls the function with a single parameter: each element of the list. In the case of patterns, each element of the list is a tuple, so buildMatchAndApplyFunctions always gets called with the tuple, and we use the auto-tuple-expansion trick in the definition of buildMatchAndApplyFunctions to assign the elements of that tuple to named variables that we can work with.

15.6. plural.py, stage 5

We've factored out all the duplicate code and added enough abstractions so that our pluralization rules are defined in a list of strings. The next logical step is to take these strings and put them in a separate file, where they can be maintained separately from the code that uses them.

First, let's create a text file that contains the rules we want. No fancy data structures, just space- (or tab-)delimited strings in three columns. We'll call it rules.en; “en” stands for English. These are the rules for pluralizing English nouns. We could add other rule files for other languages later.

Example 15.15. rules.en

[sxz]$                  $               es
[^aeioudgkprt]h$        $               es
[^aeiou]y$              y$              ies
$                       $               s

Now let's see how we can use this rules file.

Example 15.16. plural5.py

import re
import string                                                                     

def buildRule((pattern, search, replace)):                                        
    return lambda word: re.search(pattern, word) and re.sub(search, replace, word) 1

def plural(noun, language='en'):                             2
    lines = file('rules.%s' % language).readlines()          3
    patterns = map(string.split, lines)                      4
    rules = map(buildRule, patterns)                         5
    for rule in rules:                                      
        result = rule(noun)                                  6
        if result: return result                            
1 We're still using the closures technique here (building a function dynamically that uses variables defined outside the function), but now we've combined the separate match and apply functions into one. (The reason for this change will become clear in the next section.) This will let us accomplish the same thing as having two functions, but we'll need to call it differently, as we'll see in a minute.
2 Our plural function now takes an optional second parameter, language, which defaults to en.
3 We use the language parameter to construct a filename, then open the file and read the contents into a list. If language is en, then we'll open the rules.en file, read the entire thing, break it up by carriage returns, and return a list. Each line of the file will be one element in the list.
4 As we saw, each line in our file really has three values, but they're separated by whitespace (tabs or spaces, it makes no difference). Mapping the string.split function onto this list will create a new list where each element is a tuple of three strings. So a line like [sxz]$ $ es will be broken up into the tuple ('[sxz]$', '$', 'es'). This means that patterns will end up as a list of tuples, just like we hard-coded it in stage 4.
5 If patterns is a list of tuples, then rules will be a list of the functions created dynamically by each call to buildRule. Calling buildRule(('[sxz]$', '$', 'es')) returns a function that takes a single parameter, word. When this returned function is called, it will execute re.search('[sxz]$', word) and re.sub('$', 'es', word).
6 Because we're now building a combined match-and-apply function, we need to call it differently. Just call the function, and if it returns something, then that's the plural; if it returns nothing (None), then the rule didn't match and we need to try another rule.

So the improvement here is that we've completely separated our pluralization rules into an external file. Not only can the file be maintained separately from the code, but we've set up a naming scheme where the same plural function can use different rule files, based on the language parameter.

15.7. plural.py, stage 6

Now we're ready to talk about generators.

Example 15.17. plural6.py

import re

def rules(language):                                                                 
    for line in file('rules.%s' % language):                                         
        pattern, search, replace = line.split()                                      
        yield lambda word: re.search(pattern, word) and re.sub(search, replace, word)

def plural(noun, language='en'):      
    for applyRule in rules(language): 
        result = applyRule(noun)      
        if result: return result      

This uses a technique called generators, which I'm not even going to try to explain until we look at a simpler example first.

Example 15.18. Introducing generators

>>> def make_counter(x):
...     print 'entering make_counter'
...     while 1:
...         yield x               1
...         print 'incrementing x'
...         x = x + 1
...     
>>> counter = make_counter(2) 2
>>> counter                   3
<generator object at 0x001C9C10>
>>> counter.next()            4
entering make_counter
2
>>> counter.next()            5
incrementing x
3
>>> counter.next()            6
incrementing x
4
1 The presence of the yield keyword in make_counter means that this is not a normal function. It is a special kind of function which generates values one at a time. You can think of it as a resumable function. Calling it will return a generator that can be used to generate successive values of x.
2 To create an instance of the make_counter generator, just call it like any other function. Note that this does not actually execute the function code. You can tell this because the first line of make_counter is a print statement, but nothing has been printed yet.
3 The make_counter function returns a generator object.
4 The first time we call the next() method on the generator object, it executes the code in make_counter up to the first yield statement, and then returns the value that was yielded. In this case, that will be 2, because we originally created the generator by calling make_counter(2).
5 Repeatedly calling next() on the generator object resumes where we left off and continues until we hit the next yield statement. The next line of code waiting to be executed is the print statement that prints incrementing x, and then after that the x = x + 1 statement that actually increments it. Then we loop through the while loop again, and the first thing we do is yield x, which returns the current value of x (now 3).
6 The second time we call counter.next(), we do all the same things again, but this time x is now 4. And so forth. Since make_counter sets up an infinite loop, we could theoretically do this forever, and it would just keep incrementing x and spitting out values. But let's look at more productive uses of generators instead.

Example 15.19. Using generators instead of recursion

def fibonacci(max):
    a, b = 0, 1       1
    while a < max:
        yield a       2
        a, b = b, a+b 3
1 The Fibonacci sequence is a sequence of numbers where each number is the sum of the two numbers before it. It starts with 0 and 1, goes up slowly at first, then more and more rapidly. To start the sequence, we need two variables: a starts at 0, and b starts at 1.
2 a is the current number in the sequence, so yield it.
3 b is the next number in the sequence, so assign that to a, but also calculate the next value (a+b) and assign that to b for later use. Note that this happens in parallel; if a is 3 and b is 5, a, b = a, a+b will set a to 8 (the previous value of b) and b to 13 (the sum of the previous values of a and b).

So we have a function that spits out successive Fibonacci numbers. Sure, you could do that with recursion, but this way is easier to read. Also, it works well with for loops.

Example 15.20. Generators in for loops

>>> for n in fibonacci(1000): 1
...     print n,              2
0 1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987
1 You can use a generator like fibonacci in a for loop directly. The for loop will create the generator object and successively call the next() method to get values to assign to the for loop index variable (n).
2 Each time through the for loop, n gets a new value from the yield statement in fibonacci, and all we do is print it out. Once fibonacci runs out of numbers (a gets bigger than max, which in this case is 1000), then the for loop exits gracefully.

OK, let's go back to our plural function and see how we're using this.

Example 15.21. Generators that generate dynamic functions

def rules(language):                                                                 
    for line in file('rules.%s' % language):                                          1
        pattern, search, replace = line.split()                                       2
        yield lambda word: re.search(pattern, word) and re.sub(search, replace, word) 3

def plural(noun, language='en'):      
    for applyRule in rules(language):  4
        result = applyRule(noun)      
        if result: return result      
1 for line in file(...) is a common idiom for reading lines from a file, one line at a time. It works because file actually returns a generator whose next() method returns the next line of the file. That is so insanely cool, I wet myself just thinking about it.
2 No magic here. Remember that the lines of our rules file have three values separated by whitespace, so line.split() returns a tuple of 3 values, and we assign those values to 3 local variables.
3 And then we yield. What do we yield? A function, built dynamically with λ, that is actually a closure (it uses the local variables pattern, search, and replace as constants). In other words, rules is a generator that spits our rule functions.
4 Since rules is a generator, we can use it directly in a for loop. The first time through the for loop, we will call the rules function, which will open the rules file, read the first line out of it, dynamically build a function that matches and applies the first rule defined in the rules file, and yields the dynamically built function. The second time through the for loop, we will pick up where we left off in rules (which was in the middle of the for line in file(...) loop), read the second line of the rules file, dynamically build another function that matches and applies the second rule defined in the rules file, and yields it. And so forth.

What have we gained over stage 5? In stage 5, we read the entire rules file and built a list of all the possible rules before we even tried the first one. Now with generators, we can do everything lazily: we open the first and read the first rule and create a function to try it, but if that works we don't ever read the rest of the file or create any other functions.

Further reading

15.8. Summary

We talked about several different advanced techniques in this chapter. Not all of them are appropriate for every situation.

You should now be comfortable with all of these techniques:

Adding abstractions, building functions dynamically, building closures, and using generators can all make your code simpler, more readable, and more flexible. But they can also end up making it more difficult to debug later. It's up to you to find the right balance between simplicity and power.

Appendix A. Further reading

Chapter 1. Installing Python

Chapter 2. Your first Python program

Chapter 3. Native data types

Chapter 4. The Power Of Introspection

Chapter 5. Objects and Object-Orientation

Chapter 6. Exceptions and File Handling

Chapter 7. Regular Expressions

Chapter 8. HTML Processing

Chapter 9. XML Processing

Chapter 10. Scripts and Streams

Chapter 11. Introduction to Unit Testing

Chapter 12. Unit Testing: Step by Step

Chapter 13. Refactoring

Chapter 14. Regression Testing

Chapter 15. Dynamic functions

Appendix B. A 5-minute review

Chapter 1. Installing Python

Chapter 2. Your first Python program

Chapter 3. Native data types

Chapter 4. The Power Of Introspection

Chapter 5. Objects and Object-Orientation

Chapter 6. Exceptions and File Handling

Chapter 7. Regular Expressions

Chapter 8. HTML Processing

Chapter 9. XML Processing

Chapter 10. Scripts and Streams

Chapter 11. Introduction to Unit Testing

Chapter 12. Unit Testing: Step by Step

Chapter 13. Refactoring

Chapter 14. Regression Testing

Chapter 15. Dynamic functions

Appendix C. Tips and tricks

Chapter 1. Installing Python

Chapter 2. Your first Python program

Chapter 3. Native data types

Chapter 4. The Power Of Introspection

Chapter 5. Objects and Object-Orientation

Chapter 6. Exceptions and File Handling

Chapter 7. Regular Expressions

Chapter 8. HTML Processing

Chapter 9. XML Processing

Chapter 10. Scripts and Streams

Chapter 11. Introduction to Unit Testing

Chapter 12. Unit Testing: Step by Step

Chapter 13. Refactoring

Chapter 14. Regression Testing

Chapter 15. Dynamic functions

Appendix D. List of examples

Chapter 1. Installing Python

Chapter 2. Your first Python program

Chapter 3. Native data types

Chapter 4. The Power Of Introspection

Chapter 5. Objects and Object-Orientation

Chapter 6. Exceptions and File Handling

Chapter 7. Regular Expressions

Chapter 8. HTML Processing

Chapter 9. XML Processing

Chapter 10. Scripts and Streams

Chapter 11. Introduction to Unit Testing

Chapter 12. Unit Testing: Step by Step

Chapter 13. Refactoring

Chapter 14. Regression Testing

Chapter 15. Dynamic functions

Appendix E. Revision history

Revision History
Revision 4.9 2004-03-25
Revision 4.8 2004-03-25
Revision 4.7 2004-03-21
Revision 4.6 2004-03-14
Revision 4.5 2004-03-07
Revision 4.4 2003-10-08
Revision 4.3 2003-09-28
Revision 4.2.1 2003-09-17
  • Fixed links on index page.
  • Fixed syntax highlighting.
Revision 4.2 2003-09-12
Revision 4.1 2002-07-28
Revision 4.0-2 2002-04-26
Revision 4.0 2002-04-19
Revision 3.9 2002-01-01
Revision 3.8 2001-11-18
Revision 3.7 2001-09-30
  • Added Packages.
  • Added Parsing XML.
  • Cleaned up introductory paragraph in Diving in. Thanks to Matt for this suggestion.
  • Added Java tip in Importing modules using from module import. Thanks to Ori for this suggestion.
  • Fixed mistake in Putting it all together where I implied that you could not use is None to compare to a null value in Python. In fact, you can, and it's faster than == None. Thanks to Ori pointing this out.
  • Clarified in Introducing lists where I said that li = li + other was equivalent to li.extend(other). The result is the same, but extend is faster because it doesn't create a new list. Thanks to Denis pointing this out.
  • Fixed mistake in Introducing lists where I said that li += other was equivalent to li = li + other. In fact, it's equivalent to li.extend(other), since it doesn't create a new list. Thanks to Denis pointing this out.
  • Fixed typographical laziness in Your first Python program; when I was writing it, I had not yet standardized on putting string literals in single quotes within the text. They were set off by typography, but this is lost in some renditions of the book (like plain text), making it difficult to read. Thanks to Denis for this suggestion.
  • Fixed mistake in Declaring functions where I said that statically typed languages always use explicit variable + datatype declarations to enforce static typing. Most do, but there are some statically typed languages where the compiler figures out what type the variable is based on usage within the code. Thanks to Tony for pointing this out.
  • Added link to Spanish translation.
Revision 3.6.4 2001-09-06
Revision 3.6.3 2001-09-04
Revision 3.6.2 2001-08-31
Revision 3.6 2001-08-31
  • Finished HTML Processing with Putting it all together and Summary.
  • Added Postscript.
  • Started XML Processing with Diving in.
  • Started Regression Testing with Diving in.
  • Fixed long-standing bug in colorizing script that improperly colorized the examples in HTML Processing.
  • Added link to French translation. They did the right thing and translated the source XML, so they can re-use all my build scripts and make their work available in six different formats.
  • Upgraded to version 1.43 of the DocBook XSL stylesheets.
  • Upgraded to version 6.43 of the SAXON XSLT processor from Michael Kay.
  • Massive stylesheet changes, moving away from a table-based layout and towards more appropriate use of cascading style sheets. Unfortunately, CSS has as many compatibility problems as anything else, so there are still some tables used in the header and footer. The resulting HTML version looks worse in Netscape 4, but better in modern browsers, including Netscape 6, Mozilla, Internet Explorer 5, Opera 5, Konqueror, and iCab. And it's still completely readable in Lynx. I love Lynx. It was my first web browser. You never forget your first.
  • Moved to Ant to have better control over the build process, which is especially important now that I'm juggling six output formats and two languages.
  • Consolidated the available downloadable archives; previously, I had different files for each platform, because the .zip files that Python's zipfile module creates are non-standard and can't be opened by Aladdin Expander on Mac OS. But the .zip files that Ant creates are completely standard and cross-platform. Go Ant!
  • Now hosting the complete XML source, XSL stylesheets, and associated scripts and libraries on SourceForge. There's also CVS access for the really adventurous.
  • Re-licensed the example code under the new-and-improved GPL-compatible Python 2.1.1 license. Thanks, Guido; people really do care, and it really does matter.
Revision 3.5 2001-06-26
Revision 3.4 2001-05-31
Revision 3.3 2001-05-24
Revision 3.2 2001-05-03
  • Added Introducing dialect.py.
  • Added Case study: Street addresses.
  • Fixed bug in handle_decl method that would produce incorrect declarations (adding a space where it couldn't be).
  • Fixed bug in CSS (introduced in 2.9) where body background color was missing.
Revision 3.1 2001-04-18
  • Added code in BaseHTMLProcessor.py to handle declarations, now that Python 2.1 supports them.
  • Added note about nested scopes in locals and globals.
  • Fixed obscure bug in Example 8.1, “BaseHTMLProcessor.py” where attribute values with character entities would not be properly escaped.
  • Now recommending (but not requiring) Python 2.1, due to its support of declarations in sgmllib.py.
  • Updated download links on the home page to point to Python 2.1, where available.
  • Moved to versioned filenames, to help people who redistribute the book.
Revision 3.0 2001-04-16
Revision 2.9 2001-04-13
  • Added locals and globals.
  • Added Dictionary-based string formatting.
  • Tightened code in HTML Processing, specifically ChefDialectizer, to use fewer and simpler regular expressions.
  • Fixed a stylesheet bug that was inserting blank pages between chapters in the PDF version.
  • Fixed a script bug that was stripping the DOCTYPE from the home page.
  • Added link to Python Cookbook, and added a few links to individual recipes in Further reading.
  • Switched to Google for searching on http://diveintopython.org/.
  • Upgraded to version 1.36 of the DocBook XSL stylesheets, which was much more difficult than it sounds. There may still be lingering bugs.
Revision 2.8 2001-03-26
Revision 2.7 2001-03-16
Revision 2.6 2001-02-28
  • The PDF and Word versions now have colorized examples, an improved table of contents, and properly indented tips and notes.
  • The Word version is now in native Word format, compatible with Word 97.
  • The PDF and text versions now have fewer problems with improperly converted special characters (like trademark symbols and curly quotes).
  • Added link to download Word version for UNIX, in case some twisted soul wants to import it into StarOffice or something.
  • Fixed several notes which were missing titles.
  • Fixed stylesheets to work around bug in Internet Explorer 5 for Mac OS which caused colorized words in the examples to be displayed in the wrong font. (Hello?!? Microsoft? Which part of <pre> don't you understand?)
  • Fixed archive corruption in Mac OS downloads.
  • In first section of each chapter, added link to download examples. (My access logs show that people skim or skip the two pages where they could have downloaded them (the home page and preface), then scramble to find a download link once they actually start reading.)
  • Tightened the home page and preface even more, in the hopes that someday someone will read them.
  • Soon I hope to get back to actually writing this book instead of debugging it.
Revision 2.5 2001-02-23
Revision 2.4.1 2001-02-12
  • Changed newsgroup links to use “news:” protocol, now that deja.com is defunct.
  • Added file sizes to download links.
Revision 2.4 2001-02-12
  • Added “further reading” links in most sections, and collated them in Further reading.
  • Added URLs in parentheses next to external links in text version.
Revision 2.3 2001-02-09
Revision 2.2 2001-02-02
  • Edited Getting object references with getattr.
  • Added titles to xref tags, so they can have their cute little tooltips too.
  • Changed the look of the revision history page.
  • Fixed problem I introduced yesterday in my HTML post-processing script that was causing invalid HTML character references and breaking some browsers.
  • Upgraded to version 1.29 of the DocBook XSL stylesheets.
Revision 2.1 2001-02-01
  • Rewrote the example code of The Power Of Introspection to use getattr instead of exec and eval, and rewrote explanatory text to match.
  • Added example of list operators in Introducing lists.
  • Added links to relevant sections in the summary lists at the end of each chapter (Summary and Summary).
Revision 2.0 2001-01-31
Revision 1.9 2001-01-15
Revision 1.8 2001-01-12
Revision 1.71 2001-01-03
  • Made several modifications to stylesheets to improve browser compatibility.
Revision 1.7 2001-01-02
  • Added introduction to Your first Python program.
  • Added introduction to The Power Of Introspection.
  • Added review section to Objects and Object-Orientation [later removed]
  • Added Private functions.
  • Added for loops.
  • Added Assigning multiple values at once.
  • Wrote scripts to convert book to new output formats: one single HTML file, PDF, Microsoft Word 97, and plain text.
  • Registered the diveintopython.org domain and moved the book there, along with links to download the book in all available output formats for offline reading.
  • Modified the XSL stylesheets to change the header and footer navigation that displays on each page. The top of each page is branded with the domain name and book version, followed by a breadcrumb trail to jump back to the chapter table of contents, the main table of contents, or the site home page.
Revision 1.6 2000-12-11
Revision 1.5 2000-11-22
Revision 1.4 2000-11-14
  • Added Filtering lists.
  • Added dir documentation to type, str, dir, and other built-in functions.
  • Added in example in Introducing tuples.
  • Added additional note about if __name__ trick under MacPython.
  • Switched to the SAXON XSLT processor from Michael Kay.
  • Upgraded to version 1.24 of the DocBook XSL stylesheets.
  • Added db-html processing instructions with explicit filenames of each chapter and section, to allow deep links to content even if I add or re-arrange sections later.
  • Made several common phrases into entities for easier reuse.
  • Changed several literal tags to constant.
Revision 1.3 2000-11-09
  • Added section on dynamic code execution.
  • Added links to relevant section/example wherever I refer to previously covered concepts.
  • Expanded introduction of chapter 2 to explain what the function actually does.
  • Explicitly placed example code under the GNU General Public License and added appendix to display license. [Note 8/16/2001: code has been re-licensed under GPL-compatible Python license]
  • Changed links to licenses to use xref tags, now that I know how to use them.
Revision 1.2 2000-11-06
  • Added first four sections of chapter 2.
  • Tightened up preface even more, and added link to Mac OS version of Python.
  • Filled out examples in "Mapping lists" and "Joining strings" to show logical progression.
  • Added output in chapter 1 summary.
Revision 1.1 2000-10-31
  • Finished chapter 1 with sections on mapping and joining, and a chapter summary.
  • Toned down the preface, added links to introductions for non-programmers.
  • Fixed several typos.
Revision 1.0 2000-10-30
  • Initial publication

Appendix F. About the book

This book was written in DocBook XML using Emacs, and converted to HTML using the SAXON XSLT processor from Michael Kay with a customized version of Norman Walsh's XSL stylesheets. From there, it was converted to PDF using HTMLDoc, and to plain text using w3m. Program listings and examples were colorized using an updated version of Just van Rossum's pyfontify.py, which is included in the example scripts.

If you're interested in learning more about DocBook for technical writing, you can download the XML source and the build scripts, which include the customized XSL stylesheets used to create all the different formats of the book. You should also read the canonical book, DocBook: The Definitive Guide. If you're going to do any serious writing in DocBook, I would recommend subscribing to the DocBook mailing lists.

Appendix G. GNU Free Documentation License

Version 1.1, March 2000

Copyright (C) 2000 Free Software Foundation, Inc. 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed.

G.0. Preamble

The purpose of this License is to make a manual, textbook, or other written document "free" in the sense of freedom: to assure everyone the effective freedom to copy and redistribute it, with or without modifying it, either commercially or noncommercially. Secondarily, this License preserves for the author and publisher a way to get credit for their work, while not being considered responsible for modifications made by others.

This License is a kind of "copyleft", which means that derivative works of the document must themselves be free in the same sense. It complements the GNU General Public License, which is a copyleft license designed for free software.

We have designed this License in order to use it for manuals for free software, because free software needs free documentation: a free program should come with manuals providing the same freedoms that the software does. But this License is not limited to software manuals; it can be used for any textual work, regardless of subject matter or whether it is published as a printed book. We recommend this License principally for works whose purpose is instruction or reference.

G.1. Applicability and definitions

This License applies to any manual or other work that contains a notice placed by the copyright holder saying it can be distributed under the terms of this License. The "Document", below, refers to any such manual or work. Any member of the public is a licensee, and is addressed as "you".

A "Modified Version" of the Document means any work containing the Document or a portion of it, either copied verbatim, or with modifications and/or translated into another language.

A "Secondary Section" is a named appendix or a front-matter section of the Document that deals exclusively with the relationship of the publishers or authors of the Document to the Document's overall subject (or to related matters) and contains nothing that could fall directly within that overall subject. (For example, if the Document is in part a textbook of mathematics, a Secondary Section may not explain any mathematics.) The relationship could be a matter of historical connection with the subject or with related matters, or of legal, commercial, philosophical, ethical or political position regarding them.

The "Invariant Sections" are certain Secondary Sections whose titles are designated, as being those of Invariant Sections, in the notice that says that the Document is released under this License.

The "Cover Texts" are certain short passages of text that are listed, as Front-Cover Texts or Back-Cover Texts, in the notice that says that the Document is released under this License.

A "Transparent" copy of the Document means a machine-readable copy, represented in a format whose specification is available to the general public, whose contents can be viewed and edited directly and straightforwardly with generic text editors or (for images composed of pixels) generic paint programs or (for drawings) some widely available drawing editor, and that is suitable for input to text formatters or for automatic translation to a variety of formats suitable for input to text formatters. A copy made in an otherwise Transparent file format whose markup has been designed to thwart or discourage subsequent modification by readers is not Transparent. A copy that is not "Transparent" is called "Opaque".

Examples of suitable formats for Transparent copies include plain ASCII without markup, Texinfo input format, LaTeX input format, SGML or XML using a publicly available DTD, and standard-conforming simple HTML designed for human modification. Opaque formats include PostScript, PDF, proprietary formats that can be read and edited only by proprietary word processors, SGML or XML for which the DTD and/or processing tools are not generally available, and the machine-generated HTML produced by some word processors for output purposes only.

The "Title Page" means, for a printed book, the title page itself, plus such following pages as are needed to hold, legibly, the material this License requires to appear in the title page. For works in formats which do not have any title page as such, "Title Page" means the text near the most prominent appearance of the work's title, preceding the beginning of the body of the text.

G.2. Verbatim copying

You may copy and distribute the Document in any medium, either commercially or noncommercially, provided that this License, the copyright notices, and the license notice saying this License applies to the Document are reproduced in all copies, and that you add no other conditions whatsoever to those of this License. You may not use technical measures to obstruct or control the reading or further copying of the copies you make or distribute. However, you may accept compensation in exchange for copies. If you distribute a large enough number of copies you must also follow the conditions in section 3.

You may also lend copies, under the same conditions stated above, and you may publicly display copies.

G.3. Copying in quantity

If you publish printed copies of the Document numbering more than 100, and the Document's license notice requires Cover Texts, you must enclose the copies in covers that carry, clearly and legibly, all these Cover Texts: Front-Cover Texts on the front cover, and Back-Cover Texts on the back cover. Both covers must also clearly and legibly identify you as the publisher of these copies. The front cover must present the full title with all words of the title equally prominent and visible. You may add other material on the covers in addition. Copying with changes limited to the covers, as long as they preserve the title of the Document and satisfy these conditions, can be treated as verbatim copying in other respects.

If the required texts for either cover are too voluminous to fit legibly, you should put the first ones listed (as many as fit reasonably) on the actual cover, and continue the rest onto adjacent pages.

If you publish or distribute Opaque copies of the Document numbering more than 100, you must either include a machine-readable Transparent copy along with each Opaque copy, or state in or with each Opaque copy a publicly-accessible computer-network location containing a complete Transparent copy of the Document, free of added material, which the general network-using public has access to download anonymously at no charge using public-standard network protocols. If you use the latter option, you must take reasonably prudent steps, when you begin distribution of Opaque copies in quantity, to ensure that this Transparent copy will remain thus accessible at the stated location until at least one year after the last time you distribute an Opaque copy (directly or through your agents or retailers) of that edition to the public.

It is requested, but not required, that you contact the authors of the Document well before redistributing any large number of copies, to give them a chance to provide you with an updated version of the Document.

G.4. Modifications

You may copy and distribute a Modified Version of the Document under the conditions of sections 2 and 3 above, provided that you release the Modified Version under precisely this License, with the Modified Version filling the role of the Document, thus licensing distribution and modification of the Modified Version to whoever possesses a copy of it. In addition, you must do these things in the Modified Version:

  1. Use in the Title Page (and on the covers, if any) a title distinct from that of the Document, and from those of previous versions (which should, if there were any, be listed in the History section of the Document). You may use the same title as a previous version if the original publisher of that version gives permission.
  2. List on the Title Page, as authors, one or more persons or entities responsible for authorship of the modifications in the Modified Version, together with at least five of the principal authors of the Document (all of its principal authors, if it has less than five).
  3. State on the Title page the name of the publisher of the Modified Version, as the publisher.
  4. Preserve all the copyright notices of the Document.
  5. Add an appropriate copyright notice for your modifications adjacent to the other copyright notices.
  6. Include, immediately after the copyright notices, a license notice giving the public permission to use the Modified Version under the terms of this License, in the form shown in the Addendum below.
  7. Preserve in that license notice the full lists of Invariant Sections and required Cover Texts given in the Document's license notice.
  8. Include an unaltered copy of this License.
  9. Preserve the section entitled "History", and its title, and add to it an item stating at least the title, year, new authors, and publisher of the Modified Version as given on the Title Page. If there is no section entitled "History" in the Document, create one stating the title, year, authors, and publisher of the Document as given on its Title Page, then add an item describing the Modified Version as stated in the previous sentence.
  10. Preserve the network location, if any, given in the Document for public access to a Transparent copy of the Document, and likewise the network locations given in the Document for previous versions it was based on. These may be placed in the "History" section. You may omit a network location for a work that was published at least four years before the Document itself, or if the original publisher of the version it refers to gives permission.
  11. In any section entitled "Acknowledgements" or "Dedications", preserve the section's title, and preserve in the section all the substance and tone of each of the contributor acknowledgements and/or dedications given therein.
  12. Preserve all the Invariant Sections of the Document, unaltered in their text and in their titles. Section numbers or the equivalent are not considered part of the section titles.
  13. Delete any section entitled "Endorsements". Such a section may not be included in the Modified Version.
  14. Do not retitle any existing section as "Endorsements" or to conflict in title with any Invariant Section.

If the Modified Version includes new front-matter sections or appendices that qualify as Secondary Sections and contain no material copied from the Document, you may at your option designate some or all of these sections as invariant. To do this, add their titles to the list of Invariant Sections in the Modified Version's license notice. These titles must be distinct from any other section titles.

You may add a section entitled "Endorsements", provided it contains nothing but endorsements of your Modified Version by various parties--for example, statements of peer review or that the text has been approved by an organization as the authoritative definition of a standard.

You may add a passage of up to five words as a Front-Cover Text, and a passage of up to 25 words as a Back-Cover Text, to the end of the list of Cover Texts in the Modified Version. Only one passage of Front-Cover Text and one of Back-Cover Text may be added by (or through arrangements made by) any one entity. If the Document already includes a cover text for the same cover, previously added by you or by arrangement made by the same entity you are acting on behalf of, you may not add another; but you may replace the old one, on explicit permission from the previous publisher that added the old one.

The author(s) and publisher(s) of the Document do not by this License give permission to use their names for publicity for or to assert or imply endorsement of any Modified Version.

G.5. Combining documents

You may combine the Document with other documents released under this License, under the terms defined in section 4 above for modified versions, provided that you include in the combination all of the Invariant Sections of all of the original documents, unmodified, and list them all as Invariant Sections of your combined work in its license notice.

The combined work need only contain one copy of this License, and multiple identical Invariant Sections may be replaced with a single copy. If there are multiple Invariant Sections with the same name but different contents, make the title of each such section unique by adding at the end of it, in parentheses, the name of the original author or publisher of that section if known, or else a unique number. Make the same adjustment to the section titles in the list of Invariant Sections in the license notice of the combined work.

In the combination, you must combine any sections entitled "History" in the various original documents, forming one section entitled "History"; likewise combine any sections entitled "Acknowledgements", and any sections entitled "Dedications". You must delete all sections entitled "Endorsements."

G.6. Collections of documents

You may make a collection consisting of the Document and other documents released under this License, and replace the individual copies of this License in the various documents with a single copy that is included in the collection, provided that you follow the rules of this License for verbatim copying of each of the documents in all other respects.

You may extract a single document from such a collection, and distribute it individually under this License, provided you insert a copy of this License into the extracted document, and follow this License in all other respects regarding verbatim copying of that document.

G.7. Aggregation with independent works

A compilation of the Document or its derivatives with other separate and independent documents or works, in or on a volume of a storage or distribution medium, does not as a whole count as a Modified Version of the Document, provided no compilation copyright is claimed for the compilation. Such a compilation is called an "aggregate", and this License does not apply to the other self-contained works thus compiled with the Document, on account of their being thus compiled, if they are not themselves derivative works of the Document.

If the Cover Text requirement of section 3 is applicable to these copies of the Document, then if the Document is less than one quarter of the entire aggregate, the Document's Cover Texts may be placed on covers that surround only the Document within the aggregate. Otherwise they must appear on covers around the whole aggregate.

G.8. Translation

Translation is considered a kind of modification, so you may distribute translations of the Document under the terms of section 4. Replacing Invariant Sections with translations requires special permission from their copyright holders, but you may include translations of some or all Invariant Sections in addition to the original versions of these Invariant Sections. You may include a translation of this License provided that you also include the original English version of this License. In case of a disagreement between the translation and the original English version of this License, the original English version will prevail.

G.9. Termination

You may not copy, modify, sublicense, or distribute the Document except as expressly provided for under this License. Any other attempt to copy, modify, sublicense or distribute the Document is void, and will automatically terminate your rights under this License. However, parties who have received copies, or rights, from you under this License will not have their licenses terminated so long as such parties remain in full compliance.

G.10. Future revisions of this license

The Free Software Foundation may publish new, revised versions of the GNU Free Documentation License from time to time. Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns. See http://www.gnu.org/copyleft/.

Each version of the License is given a distinguishing version number. If the Document specifies that a particular numbered version of this License "or any later version" applies to it, you have the option of following the terms and conditions either of that specified version or of any later version that has been published (not as a draft) by the Free Software Foundation. If the Document does not specify a version number of this License, you may choose any version ever published (not as a draft) by the Free Software Foundation.

G.11. How to use this License for your documents

To use this License in a document you have written, include a copy of the License in the document and put the following copyright and license notices just after the title page:

Copyright (c) YEAR YOUR NAME. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.1 or any later version published by the Free Software Foundation; with the Invariant Sections being LIST THEIR TITLES, with the Front-Cover Texts being LIST, and with the Back-Cover Texts being LIST. A copy of the license is included in the section entitled "GNU Free Documentation License".

If you have no Invariant Sections, write "with no Invariant Sections" instead of saying which ones are invariant. If you have no Front-Cover Texts, write "no Front-Cover Texts" instead of "Front-Cover Texts being LIST"; likewise for Back-Cover Texts.

If your document contains nontrivial examples of program code, we recommend releasing these examples in parallel under your choice of free software license, such as the GNU General Public License, to permit their use in free software.

Appendix H. Python 2.1.1 license

H.A. History of the software

Python was created in the early 1990s by Guido van Rossum at Stichting Mathematisch Centrum (CWI) in the Netherlands as a successor of a language called ABC. Guido is Python's principal author, although it includes many contributions from others. The last version released from CWI was Python 1.2. In 1995, Guido continued his work on Python at the Corporation for National Research Initiatives (CNRI) in Reston, Virginia where he released several versions of the software. Python 1.6 was the last of the versions released by CNRI. In 2000, Guido and the Python core development team moved to BeOpen.com to form the BeOpen PythonLabs team. Python 2.0 was the first and only release from BeOpen.com.

Following the release of Python 1.6, and after Guido van Rossum left CNRI to work with commercial software developers, it became clear that the ability to use Python with software available under the GNU Public License (GPL) was very desirable. CNRI and the Free Software Foundation (FSF) interacted to develop enabling wording changes to the Python license. Python 1.6.1 is essentially the same as Python 1.6, with a few minor bug fixes, and with a different license that enables later versions to be GPL-compatible. Python 2.1 is a derivative work of Python 1.6.1, as well as of Python 2.0.

After Python 2.0 was released by BeOpen.com, Guido van Rossum and the other PythonLabs developers joined Digital Creations. All intellectual property added from this point on, starting with Python 2.1 and its alpha and beta releases, is owned by the Python Software Foundation (PSF), a non-profit modeled after the Apache Software Foundation. See http://www.python.org/psf/ for more information about the PSF.

Thanks to the many outside volunteers who have worked under Guido's direction to make these releases possible.

H.B. Terms and conditions for accessing or otherwise using Python

H.B.1. PSF license agreement

  1. This LICENSE AGREEMENT is between the Python Software Foundation ("PSF"), and the Individual or Organization ("Licensee") accessing and otherwise using Python 2.1.1 software in source or binary form and its associated documentation.
  2. Subject to the terms and conditions of this License Agreement, PSF hereby grants Licensee a nonexclusive, royalty-free, world-wide license to reproduce, analyze, test, perform and/or display publicly, prepare derivative works, distribute, and otherwise use Python 2.1.1 alone or in any derivative version, provided, however, that PSF's License Agreement and PSF's notice of copyright, i.e., "Copyright (c) 2001 Python Software Foundation; All Rights Reserved" are retained in Python 2.1.1 alone or in any derivative version prepared by Licensee.
  3. In the event Licensee prepares a derivative work that is based on or incorporates Python 2.1.1 or any part thereof, and wants to make the derivative work available to others as provided herein, then Licensee hereby agrees to include in any such work a brief summary of the changes made to Python 2.1.1.
  4. PSF is making Python 2.1.1 available to Licensee on an "AS IS" basis. PSF MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, PSF MAKES NO AND DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF PYTHON 2.1.1 WILL NOT INFRINGE ANY THIRD PARTY RIGHTS.
  5. PSF SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON 2.1.1 FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON 2.1.1, OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.
  6. This License Agreement will automatically terminate upon a material breach of its terms and conditions.
  7. Nothing in this License Agreement shall be deemed to create any relationship of agency, partnership, or joint venture between PSF and Licensee. This License Agreement does not grant permission to use PSF trademarks or trade name in a trademark sense to endorse or promote products or services of Licensee, or any third party.
  8. By copying, installing or otherwise using Python 2.1.1, Licensee agrees to be bound by the terms and conditions of this License Agreement.

H.B.2. BeOpen Python open source license agreement version 1

  1. This LICENSE AGREEMENT is between BeOpen.com ("BeOpen"), having an office at 160 Saratoga Avenue, Santa Clara, CA 95051, and the Individual or Organization ("Licensee") accessing and otherwise using this software in source or binary form and its associated documentation ("the Software").
  2. Subject to the terms and conditions of this BeOpen Python License Agreement, BeOpen hereby grants Licensee a non-exclusive, royalty-free, world-wide license to reproduce, analyze, test, perform and/or display publicly, prepare derivative works, distribute, and otherwise use the Software alone or in any derivative version, provided, however, that the BeOpen Python License is retained in the Software, alone or in any derivative version prepared by Licensee.
  3. BeOpen is making the Software available to Licensee on an "AS IS" basis. BEOPEN MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, BEOPEN MAKES NO AND DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF THE SOFTWARE WILL NOT INFRINGE ANY THIRD PARTY RIGHTS.
  4. BEOPEN SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF THE SOFTWARE FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS A RESULT OF USING, MODIFYING OR DISTRIBUTING THE SOFTWARE, OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.
  5. This License Agreement will automatically terminate upon a material breach of its terms and conditions.
  6. This License Agreement shall be governed by and interpreted in all respects by the law of the State of California, excluding conflict of law provisions. Nothing in this License Agreement shall be deemed to create any relationship of agency, partnership, or joint venture between BeOpen and Licensee. This License Agreement does not grant permission to use BeOpen trademarks or trade names in a trademark sense to endorse or promote products or services of Licensee, or any third party. As an exception, the "BeOpen Python" logos available at http://www.pythonlabs.com/logos.html may be used according to the permissions granted on that web page.
  7. By copying, installing or otherwise using the software, Licensee agrees to be bound by the terms and conditions of this License Agreement.

H.B.3. CNRI open source GPL-compatible license agreement

  1. This LICENSE AGREEMENT is between the Corporation for National Research Initiatives, having an office at 1895 Preston White Drive, Reston, VA 20191 ("CNRI"), and the Individual or Organization ("Licensee") accessing and otherwise using Python 1.6.1 software in source or binary form and its associated documentation.
  2. Subject to the terms and conditions of this License Agreement, CNRI hereby grants Licensee a nonexclusive, royalty-free, world-wide license to reproduce, analyze, test, perform and/or display publicly, prepare derivative works, distribute, and otherwise use Python 1.6.1 alone or in any derivative version, provided, however, that CNRI's License Agreement and CNRI's notice of copyright, i.e., "Copyright (c) 1995-2001 Corporation for National Research Initiatives; All Rights Reserved" are retained in Python 1.6.1 alone or in any derivative version prepared by Licensee. Alternately, in lieu of CNRI's License Agreement, Licensee may substitute the following text (omitting the quotes): "Python 1.6.1 is made available subject to the terms and conditions in CNRI's License Agreement. This Agreement together with Python 1.6.1 may be located on the Internet using the following unique, persistent identifier (known as a handle): 1895.22/1013. This Agreement may also be obtained from a proxy server on the Internet using the following URL: http://hdl.handle.net/1895.22/1013".
  3. In the event Licensee prepares a derivative work that is based on or incorporates Python 1.6.1 or any part thereof, and wants to make the derivative work available to others as provided herein, then Licensee hereby agrees to include in any such work a brief summary of the changes made to Python 1.6.1.
  4. CNRI is making Python 1.6.1 available to Licensee on an "AS IS" basis. CNRI MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, CNRI MAKES NO AND DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF PYTHON 1.6.1 WILL NOT INFRINGE ANY THIRD PARTY RIGHTS.
  5. CNRI SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON 1.6.1 FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON 1.6.1, OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.
  6. This License Agreement will automatically terminate upon a material breach of its terms and conditions.
  7. This License Agreement shall be governed by the federal intellectual property law of the United States, including without limitation the federal copyright law, and, to the extent such U.S. federal law does not apply, by the law of the Commonwealth of Virginia, excluding Virginia's conflict of law provisions. Notwithstanding the foregoing, with regard to derivative works based on Python 1.6.1 that incorporate non-separable material that was previously distributed under the GNU General Public License (GPL), the law of the Commonwealth of Virginia shall govern this License Agreement only as to issues arising under or with respect to Paragraphs 4, 5, and 7 of this License Agreement. Nothing in this License Agreement shall be deemed to create any relationship of agency, partnership, or joint venture between CNRI and Licensee. This License Agreement does not grant permission to use CNRI trademarks or trade name in a trademark sense to endorse or promote products or services of Licensee, or any third party.
  8. By clicking on the "ACCEPT" button where indicated, or by copying, installing or otherwise using Python 1.6.1, Licensee agrees to be bound by the terms and conditions of this License Agreement.

H.B.4. CWI permissions statement and disclaimer

Copyright (c) 1991 - 1995, Stichting Mathematisch Centrum Amsterdam, The Netherlands. All rights reserved.

Permission to use, copy, modify, and distribute this software and its documentation for any purpose and without fee is hereby granted, provided that the above copyright notice appear in all copies and that both that copyright notice and this permission notice appear in supporting documentation, and that the name of Stichting Mathematisch Centrum or CWI not be used in advertising or publicity pertaining to distribution of the software without specific, written prior permission.

STICHTING MATHEMATISCH CENTRUM DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS, IN NO EVENT SHALL STICHTING MATHEMATISCH CENTRUM BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.