From 5476e9ed50fe6cc37bb97e2573722d120707ff64 Mon Sep 17 00:00:00 2001 From: Mark Pilgrim Date: Sun, 1 Feb 2009 21:44:40 -0500 Subject: [PATCH] first two sections of "your first python program" chapter --- chardet/python3-conversion-notes.txt | 24 +- dip2 | 6388 ++++++++++------------- dip3.css | 3 +- humansize.py | 15 +- porting-code-to-python-3-with-2to3.html | 2 +- your-first-python-program.html | 117 + 6 files changed, 2770 insertions(+), 3779 deletions(-) create mode 100644 your-first-python-program.html diff --git a/chardet/python3-conversion-notes.txt b/chardet/python3-conversion-notes.txt index d97687f..3f7c7e7 100644 --- a/chardet/python3-conversion-notes.txt +++ b/chardet/python3-conversion-notes.txt @@ -1,26 +1,28 @@ -- python 2to3.py -w test.py (the -w flag makes a backup then overwrites the original file) -- python 2to3.py -w chardet/ directory (passing a directory acts on all .py files in the directory) - (TODO: need log of this step) -- global search-and-replace constants.False --> False, constants.True --> True (unnecessary, Python3 always defines a Boolean type) -- constants.py: remove code for defining True and False -- universaldetector.py, charsetgroupprober.py, charsetprober.py, escprober.py, eucjpprober.py, mbcharsetprober.py, sbcharsetprober.py, sbcsgroupprober.py, sjisprober.py, utf8prober.py: manually fix import statements that 2to3 missed +* python 2to3.py -w test.py (the -w flag makes a backup then overwrites the original file) +* python 2to3.py -w chardet/ directory (passing a directory acts on all .py files in the directory) +* global search-and-replace constants.False --> False, constants.True --> True (unnecessary, Python3 always defines a Boolean type) +* constants.py: remove code for defining True and False +* universaldetector.py, charsetgroupprober.py, charsetprober.py, escprober.py, eucjpprober.py, mbcharsetprober.py, sbcharsetprober.py, sbcsgroupprober.py, sjisprober.py, utf8prober.py: manually fix import statements that 2to3 missed old: import constants, sys new: from . import constants import sys -- test.py: change file() to open() -- universaldetector.py: change r'' strings to b'' byte arrays in self._highBitDetector, self._escDetector regular expressions +* test.py: change file() to open() +* universaldetector.py: change r'' strings to b'' byte arrays in self._highBitDetector, self._escDetector regular expressions +- charsetprober.py: change regular expression-based replace to use b'' byte arrays instead of strings + - universaldetector.py: change self._mLastChar from a r'' string to a b'' byte array +- mbcharsetprober.py: change self._mLastChar from a list of two 1-character strings to a list of two ints - universaldetector.py: getting a single element from a byte array yields an integer, not a byte, so change syntax to make sure we self._mLastChar is always a byte old: self._mLastChar = aBuf[-1] new: self._mLastChar = aBuf[-1:] -- jpcntx.py, chardistribution.py (editorial): global search-and-replace "aStr" --> "aBuf" to make it clear that we're passing around a byte array + - jpcntx.py, chardistribution.py: change 1-character strings to ints and hex ints, since we're just comparing ints to ints anyway - jpcntx.py, chardistribution.py: change ord(aBuf[0]) to aBuf[0] since it's already an int (iterating through a byte array) -- mbcharsetprober.py: change self._mLastChar from a list of two 1-character strings to a list of two ints -- charsetprober.py: change regular expression-based replace to use b'' byte arrays instead of strings +- jpcntx.py, chardistribution.py (editorial): global search-and-replace "aStr" --> "aBuf" to make it clear that we're passing around a byte array - sbcharsetprober.py, latin1prober.py: change ord(c) to c since it's already an int (iterating through a byte array) + - latin1prober.py: refactor reduce(operator.add, ...) to use a for loop instead diff --git a/dip2 b/dip2 index ce33600..dee7d9a 100644 --- a/dip2 +++ b/dip2 @@ -1,376 +1,361 @@ - + - - Dive Into Python - - - - - -

Dive Into Python

-

20 May 2004

-

Copyright © 2000, 2001, 2002, 2003, 2004 Mark Pilgrim

-

This book lives at http://diveintopython3.org/. If you're reading it somewhere else, you may not have the latest version.

+

Dive Into Python

+

20 May 2004 +

Copyright © 2000, 2001, 2002, 2003, 2004 Mark Pilgrim +

This book lives at http://diveintopython3.org/. If you're reading it somewhere else, you may not have the latest version.

-

Table of Contents

+

Table of Contents

- + -
-

Chapter 1. Installing Python

-

Welcome to Python. Let's dive in. In this chapter, you'll install the version of Python that's right for you.

-
-

1.1. Which Python is right for you?

-

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 installation. 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.

+

Chapter 1. Installing Python

+

Welcome to Python. Let's dive in. In this chapter, you'll install the version of Python that's right for you. +

1.1. Which Python is right for you?

+

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 installation. 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 a couple choices for installing Python.

-

ActiveState makes a Windows installer for Python called ActivePython, which 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.

+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 a couple choices for installing Python. +

ActiveState makes a Windows installer for Python called ActivePython, which 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 the latest version of Python and ActivePython is still a version behind as you read this, you'll want to use the second option for installing Python on Windows.

-

The second option is the “official” Python 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.

+several months behind in updating their ActivePython installer when new version of Python are released. If you absolutely need the latest version of Python and ActivePython is still a version behind as you read this, you'll want to use the second option for installing Python on Windows. +

The second option is the “official” Python 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 1.1. Option 1: Installing ActivePython

-

Here is the procedure for installing ActivePython:

+

Procedure 1.1. Option 1: Installing ActivePython

+

Here is the procedure for installing ActivePython:

  1. -

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

    -
    2. +

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

  2. -

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

    -
    3. +

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

  3. -

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

    -
    4. +

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

  4. -

    Step through the installer program.

    -
    5. +

    Step through the installer program. +

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

    -
    6. + absolutely can't spare the 14MB. +
  6. -

    After the installation is complete, close the installer and choose Start->Programs->ActiveState ActivePython 2.2->PythonWin IDE. You'll see something like the following:

    -
    7. +

    After the installation is complete, close the installer and choose Start->Programs->ActiveState ActivePython 2.2->PythonWin IDE. You'll see something like the following: +

-
 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.
 >>> 
-
-
-

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

+
+

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

  1. -

    Download the latest Python Windows installer by going to http://www.python.org/ftp/python/ and selecting the highest version number listed, then downloading the .exe installer.

    -
    2. +

    Download the latest Python Windows installer by going to http://www.python.org/ftp/python/ and selecting the highest version number listed, then downloading the .exe installer. +

  2. -

    Double-click the installer, Python-2.xxx.yyy.exe. The name will depend on the version of Python available when you read this.

    -
    3. +

    Double-click the installer, Python-2.xxx.yyy.exe. The name will depend on the version of Python available when you read this. +

  3. -

    Step through the installer program.

    -
    4. +

    Step through the installer program. +

  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 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, then choose Non-Admin Install. This just affects where Registry entries and Start menu shortcuts are created.

    -
    6. +

    If you do not have administrative rights on your machine, you can select Advanced Options, then choose Non-Admin Install. This just affects where Registry entries and Start menu shortcuts are created. +

  6. -

    After the installation is complete, close the installer and select Start->Programs->Python 2.3->IDLE (Python GUI). You'll see something like the following:

    -
    7. +

    After the installation is complete, close the installer and select Start->Programs->Python 2.3->IDLE (Python GUI). You'll see something like the following: +

-
 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.
@@ -384,34 +369,30 @@ Type "copyright", "credits" or "license()" for more information.
     
 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 install it. You probably want to install it.

+

1.3. Python on Mac OS X

+

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

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

+the preinstalled version does not come with an XML parser, so when you get to the XML chapter, you'll need to install the full version.

Rather than using the preinstalled version, you'll probably want to install the latest version, which also comes with a graphical -interactive shell.

+interactive shell.
-

Procedure 1.3. Running the Preinstalled Version of Python on Mac OS X

-

To use the preinstalled version of Python, follow these steps:

+

Procedure 1.3. Running the Preinstalled Version of Python on Mac OS X

+

To use the preinstalled version of Python, follow these steps:

  1. -

    Open the /Applications folder.

    -
    2. +

    Open the /Applications folder. +

  2. -

    Open the Utilities folder.

    -
    3. +

    Open the Utilities folder. +

  3. -

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

    -
    4. +

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

  4. -

    Type python at the command prompt.

    -
    5. +

    Type python at the command prompt. +

-
-

Try it out:

+

Try it out: 

 Welcome to Darwin!
 [localhost:~] you% python
@@ -420,49 +401,46 @@ Welcome to Darwin!
 Type "help", "copyright", "credits", or "license" for more information.
 >>> [press Ctrl+D to get back to the command prompt]
 [localhost:~] you% 
-
-
-

Procedure 1.4. Installing the Latest Version of Python on Mac OS X

-

Follow these steps to download and install the latest version of Python:

+
+

Procedure 1.4. Installing the Latest Version of Python on Mac OS X

+

Follow these steps to download and install the latest version of Python:

  1. -

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

    -
    2. +

    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. +

    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. +

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

  4. -

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

    -
    5. +

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

  5. -

    Step through the installer program.

    -
    6. +

    Step through the installer program. +

  6. -

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

    -
    7. +

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

  7. -

    Open the MacPython-2.3 folder

    -
    8. +

    Open the MacPython-2.3 folder +

  8. -

    Double-click PythonIDE to launch Python.

    -
    9. +

    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). The opening window will look something like this:

+Window->Python Interactive (Cmd-0). The opening window will look something like this: 
 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 1.1. Two versions of Python

+

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 1.1. 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
@@ -474,53 +452,46 @@ Type "help", "copyright", "credits", or "license" for more information.
 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.

+

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.

-

Follow these steps to install Python on Mac OS 9:

+

Follow these steps to install Python on Mac OS 9:

  1. -

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

    -
    2. +

    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. +

    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. +

    Double-click the installer, MacPython23full. +

  4. -

    Step through the installer program.

    -
    5. +

    Step through the installer program. +

  5. -

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

    -
    6. +

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

  6. -

    Open the MacPython-OS9 2.3 folder.

    -
    7. +

    Open the MacPython-OS9 2.3 folder. +

  7. -

    Double-click Python IDE to launch Python.

    -
    8. +

    Double-click Python IDE to launch Python. +

-

The MacPython IDE should display a splash screen, and then take you to the interactive shell. If the interactive shell does not appear, select -Window->Python Interactive (Cmd-0). You'll see a screen like this:

+Window->Python Interactive (Cmd-0). You'll see a screen like this: 
 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

+

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.

-

Download the latest Python RPM by going to http://www.python.org/ftp/python/ and selecting the highest version number listed, then selecting the rpms/ directory within that. Then download the RPM with the highest version number. You can install it with the rpm command, as shown here:

-

Example 1.2. Installing on RedHat Linux 9

+binary packages are available for most popular Linux distributions.  Or you can always compile from source.
+
Download the latest Python RPM by going to http://www.python.org/ftp/python/ and selecting the highest version number listed, then selecting the rpms/ directory within that.  Then download the RPM with the highest version number.  You can install it with the rpm command, as shown here:
+
Example 1.2. 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
@@ -561,17 +532,13 @@ Type "help", "copyright", "credits", or "license" for more information.
 
 3 
 
-This is the complete path of the newer version of Python that you 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.
+This is the complete path of the newer version of Python that you 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, you install Python through the apt command.

-

Example 1.3. Installing on Debian GNU/Linux

+
1.6. Python on Debian GNU/Linux

+
If you are lucky enough to be running Debian GNU/Linux, you install Python through the apt command.
+
Example 1.3. Installing on Debian GNU/Linux
 localhost:~$ su -
 Password: [enter your root password]
 localhost:~# apt-get install python
@@ -603,12 +570,9 @@ logout
 [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. Python Installation from Source

-

If you prefer to build from source, you can download the Python source code from http://www.python.org/ftp/python/. Select the highest version number listed, download the .tgz file), and then do the usual configure, make, make install dance.

-

Example 1.4. Installing from source

+

1.7. Python Installation from Source

+

If you prefer to build from source, you can download the Python source code from http://www.python.org/ftp/python/. Select the highest version number listed, download the .tgz file), and then do the usual configure, make, make install dance. +

Example 1.4. Installing from source

 localhost:~$ su -
 Password: [enter your root password]
 localhost:~# wget http://www.python.org/ftp/python/2.3/Python-2.3.tgz
@@ -645,16 +609,13 @@ logout
 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 you have Python installed, what's this interactive shell thing you're running?

+

1.8. The Interactive Shell

+

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

It's like this: Python leads a double life. It's an interpreter for scripts that you can run from the command line or run like applications, by double-clicking the scripts. But it's 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 with the steps shown here:

-

Example 1.5. First Steps in the Interactive Shell

+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 with the steps shown here:
+
Example 1.5. First Steps in the Interactive Shell
 >>> 1 + 1               1
 2
 >>> print 'hello world' 2
@@ -685,27 +646,20 @@ hello world
 
 
 
-

-
-
-
-

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 version of Python intsalled. 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.

-

Congratulations, and welcome to Python.

-
-
+

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 version of Python intsalled. 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. +

Congratulations, and welcome to Python.

-

Chapter 2. Your First Python Program

+

Chapter 2. Your First Python Program

You know how other books go on and on about programming fundamentals and finally work up to building a complete, working program? -Let's skip all that.

-
-

2.1. Diving in

-

Here is a complete, working Python program.

+Let's skip all that. +

2.1. Diving in

+

Here is a complete, working Python program.

It probably makes absolutely no sense to you. Don't worry about that, because you'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.

+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.
 
@@ -718,8 +672,7 @@ if __name__ == "__main__":
                 "uid":"sa", \
                 "pwd":"secret" \
                 }
-    print buildConnectionString(myParams)

-
Now run this program and see what happens.

+    print buildConnectionString(myParams)
Now run this program and see what happens.

@@ -745,26 +698,22 @@ Python->Run window... (Cmd-R), but there is an impor
 

 

 Tip
 
On UNIX-compatible systems (including Mac OS X), you can run a Python program from the command line: python odbchelper.py
 

 

-
The id="odbchelper.output" output of odbchelper.py will look like this:
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, like this:

+

The id="odbchelper.output" output of odbchelper.py will look like this:

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, like this: 

-def buildConnectionString(params):
-

Note that 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.

+def buildConnectionString(params):

Note that 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.

Also note that 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 or not they return a value. -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.

+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. +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.
-

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.

+

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.

@@ -774,9 +723,8 @@ In fact, every Python function returns a value; if the function ever executes a
Note
-
-

2.2.1. How Python's Datatypes Compare to Other Programming Languages

-

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

+

2.2.1. How Python's Datatypes Compare to Other Programming Languages

+

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

statically typed language
@@ -790,60 +738,53 @@ In fact, every Python function returns a value; if the function ever executes a
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.
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. +
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 2.2. Defining the buildConnectionString Function's doc string

+
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 2.2. 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.

+   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.
+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 (that is, the first thing after the colon).  You don't technically
-need 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.

+
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 (that is, the first thing after the colon).  You don't technically
+need 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.
+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 on Documenting Functions

+
Further Reading on Documenting Functions

 
-

-
-
-

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.

-

Open your favorite Python IDE and follow along:

-

Example 2.3. Accessing the buildConnectionString Function's doc string

>>> import odbchelper            1
+
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.
+
Open your favorite Python IDE and follow along:
+
Example 2.3. 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
@@ -868,25 +809,23 @@ Returns string.

 
 3 
 
-Instead of calling the function as you would expect to, you asked for one of the function's attributes, __doc__.
+Instead of calling the function as you would expect to, you 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.
+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.
 
 

 

-

-
2.4.1. The Import Search Path

+
2.4.1. The Import Search Path

 
Before you 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.  (You'll learn more about lists
-   later in this chapter.)

-
Example 2.4. Import Search Path
+   later in this chapter.)
+
Example 2.4. Import Search Path
 >>> import sys                 1
 >>> sys.path 2
 ['', '/usr/local/lib/python2.2', '/usr/local/lib/python2.2/plat-linux2', 
@@ -906,13 +845,13 @@ Returns string.

 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.
+               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.)
+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.)
 
 
 
@@ -922,40 +861,31 @@ Returns string.

 
 
 
-

-

-

-

-
2.4.2. What's an Object?

-
Everything in Python is an object, and almost everything has attributes and methods.  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.

+
2.4.2. What's an Object?

+
Everything in Python is an object, and almost everything has attributes and methods.  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.
 
Still, this begs the question.  What is an object?  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 in Chapter 3), and not all objects are subclassable (more on this in Chapter 5).  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 4).

-
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.

+   (more in this in Chapter 4).
+
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 on Objects

+
Further Reading on Objects

 
-

-

-
-
-

2.5. Indenting Code

-

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

-

Example 2.5. Indenting the buildConnectionString Function

+
2.5. Indenting Code

+
Python functions have no explicit begin or end, and no curly braces to mark where the function code starts and stops.  The only delimiter is a colon (:) and the indentation of the code itself.
+
Example 2.5. 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 four spaces.  It doesn't need to be four spaces, it just needs to be consistent.  The first line that is not
-indented is outside the function.

-
Example 2.6, “if Statements” shows an example of code indentation with if statements.

-
Example 2.6. if Statements
+    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 four spaces.  It doesn't need to be four spaces, it just needs to be consistent.  The first line that is not
+indented is outside the function.
+
Example 2.6, “if Statements” shows an example of code indentation with if statements.
+
Example 2.6. if Statements
 def fib(n): 1
     print 'n =', n            2
     if n > 1:                 3
@@ -976,27 +906,25 @@ def fib(n): 
 </td>
 <td valign=Printing 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 you'll
             learn about in the next chapter.  You can even mix and match to print several things on one line by using a comma-separated
-            list of values.  Each value is printed on the same line, separated by spaces (the commas don't print).  So when fib is called with 5, this will print "n = 5".
+            list of values.  Each value is printed 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.
+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 amount.  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
+Of course if and else blocks can contain multiple lines, as long as they are all indented the same amount.  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.

+and understand other people's Python code.

@@ -1006,67 +934,58 @@ and understand other people's Python code.

 

 Note
 

 

 

-
Further Reading on Code Indentation

+
Further Reading on Code Indentation

 
-

-
-
-

2.6. Testing Modules

+

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. - Here's an example that uses the if __name__ trick.

+ Here's an example that uses the if __name__ trick. 
-if __name__ == "__main__":
-

Some quick observations before you 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.

+if __name__ == "__main__":

Some quick observations before you 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. +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 a 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__.

+

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 a 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__. 

>>> 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.

+>>> 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 +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 on Importing Modules

+

Further Reading on Importing Modules

-
-
-
-

Chapter 3. Native Datatypes

+

Chapter 3. Native Datatypes

You'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.

-
-

3.1. Introducing Dictionaries

-

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

+ 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. +

3.1. Introducing Dictionaries

+

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 that store hashes always start with a % character. In Python, variables can be named anything, and Python keeps track of the datatype internally. +A dictionary in Python is like a hash in Perl. In Perl, variables that store hashes always start with a % character. In Python, variables can be named anything, and Python keeps track of the datatype internally.
@@ -1086,9 +1005,8 @@ them into a larger program.

-
-

3.1.1. Defining Dictionaries

-

Example 3.1. Defining a Dictionary

>>> d = {"server":"mpilgrim", "database":"master"} 1
+
3.1.1. Defining Dictionaries

+
Example 3.1. Defining a Dictionary
>>> d = {"server":"mpilgrim", "database":"master"} 1
 >>> d
 {'server': 'mpilgrim', 'database': 'master'}
 >>> d["server"]2
@@ -1109,36 +1027,31 @@ KeyError: mpilgrim

 
 2 
 
-'server' is a key, and its associated value, referenced by d["server"], is 'mpilgrim'.
+'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'.
+'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.
+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.
 
 
 
-

-

-
-
-

3.1.2. Modifying Dictionaries

-

Example 3.2. Modifying a Dictionary

>>> d
+
3.1.2. Modifying Dictionaries

+
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'}

-

+{'server': 'mpilgrim', 'uid': 'sa', 'database': 'pubs'}
1 @@ -1154,9 +1067,8 @@ KeyError: mpilgrim
-
-

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 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.

@@ -1167,8 +1079,8 @@ KeyError: mpilgrim
Note
-

When working with dictionaries, you need to be aware that dictionary keys are case-sensitive.

-

Example 3.3. Dictionary Keys Are Case-Sensitive

+
When working with dictionaries, you need to be aware that dictionary keys are case-sensitive.
+
Example 3.3. Dictionary Keys Are Case-Sensitive
 >>> d = {}
 >>> d["key"] = "value"
 >>> d["key"] = "other value" 1
@@ -1187,13 +1099,11 @@ KeyError: mpilgrim

 
 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.
+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.
 
 
 
-

-

-
Example 3.4. Mixing Datatypes in a Dictionary
>>> d
+
Example 3.4. Mixing Datatypes in a Dictionary
>>> d
 {'server': 'mpilgrim', 'uid': 'sa', 'database': 'pubs'}
 >>> d["retrycount"] = 3 1
 >>> d
@@ -1201,8 +1111,7 @@ KeyError: mpilgrim

 >>> d[42] = "douglas"   2
 >>> d
 {'server': 'mpilgrim', 'uid': 'sa', 'database': 'master',
-42: 'douglas', 'retrycount': 3}

-

+42: 'douglas', 'retrycount': 3}
1 @@ -1220,11 +1129,8 @@ KeyError: mpilgrim
-
-
-
-

3.1.3. Deleting Items From Dictionaries

-

Example 3.5. Deleting Items from a Dictionary

>>> d
+
3.1.3. Deleting Items From Dictionaries

+
Example 3.5. Deleting Items from a Dictionary
>>> d
 {'server': 'mpilgrim', 'uid': 'sa', 'database': 'master',
 42: 'douglas', 'retrycount': 3}
 >>> del d[42] 1
@@ -1247,31 +1153,25 @@ KeyError: mpilgrim

 
 
 
-

-

 

-
Further Reading on Dictionaries

+
Further Reading on Dictionaries

 
-

-
-
-
-

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.

+

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 that store arrays always start with the @ character; in Python, variables can be named anything, and Python keeps track of the datatype internally. +A list in Python is like an array in Perl. In Perl, variables that store arrays always start with the @ character; in Python, variables can be named anything, and Python keeps track of the datatype internally.
@@ -1283,9 +1183,8 @@ KeyError: mpilgrim
-
-

3.2.1. Defining Lists

-

Example 3.6. Defining a List

>>> li = ["a", "b", "mpilgrim", "z", "example"] 1
+
3.2.1. Defining Lists

+
Example 3.6. Defining a List
>>> li = ["a", "b", "mpilgrim", "z", "example"] 1
 >>> li
 ['a', 'b', 'mpilgrim', 'z', 'example']
 >>> li[0]   2
@@ -1303,19 +1202,17 @@ KeyError: mpilgrim

 
 2 
 
-A list can be used like a zero-based array.  The first element of any non-empty list is always li[0].
+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 five-element list is li[4], because lists are always zero-based.
+The last element of this five-element list is li[4], because lists are always zero-based.
 
 
 
-

-

-
Example 3.7. Negative List Indices
>>> li
+
Example 3.7. Negative List Indices
>>> li
 ['a', 'b', 'mpilgrim', 'z', 'example']
 >>> li[-1] 1
 'example'
@@ -1326,19 +1223,17 @@ KeyError: 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].
+               always li[-1].
 
 
 
 2 
 
-If the negative index is 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].
+If the negative index is 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
+
Example 3.8. Slicing a List
>>> li
 ['a', 'b', 'mpilgrim', 'z', 'example']
 >>> li[1:3]  1
 ['b', 'mpilgrim']
@@ -1351,7 +1246,7 @@ KeyError: mpilgrim

 1 
 
 You can get a subset of a list, called a “slice”, by specifying two 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]).
+               the first slice index (in this case li[1]), up to but not including the second slice index (in this case li[3]).
 
 
 
@@ -1365,13 +1260,11 @@ KeyError: mpilgrim

 
 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].
+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
+
Example 3.9. Slicing Shorthand
>>> li
 ['a', 'b', 'mpilgrim', 'z', 'example']
 >>> li[:3] 1
 ['a', 'b', 'mpilgrim']
@@ -1383,34 +1276,30 @@ KeyError: mpilgrim

 
 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 Example 3.8, “Slicing a List”.
+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 Example 3.8, “Slicing a List”.
 
 
 
 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 five elements.
+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 five elements.
 
 
 
 3 
 
-Note the symmetry here.  In this five-element list, li[:3] returns the first 3 elements, and li[3:] returns the last two 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.
+Note the symmetry here.  In this five-element list, li[:3] returns the first 3 elements, and li[3:] returns the last two 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 shorthand for making a complete copy of a list.
+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 shorthand for making a complete copy of a list.
 
 
 
-

-

-

-

-
3.2.2. Adding Elements to Lists

-
Example 3.10. Adding Elements to a List
>>> li
+
3.2.2. Adding Elements to Lists

+
Example 3.10. Adding Elements to a List
>>> li
 ['a', 'b', 'mpilgrim', 'z', 'example']
 >>> li.append("new")               1
 >>> li
@@ -1432,7 +1321,7 @@ KeyError: mpilgrim

 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 need to be unique; there are now two separate elements with the value 'new', li[2] and li[6].
+                Note that list elements do not need to be unique; there are now two separate elements with the value 'new', li[2] and li[6].
 
 
 
@@ -1442,9 +1331,7 @@ KeyError: mpilgrim

 
 
 
-

-

-
Example 3.11. The Difference between extend and append
+
Example 3.11. The Difference between extend and append
 >>> li = ['a', 'b', 'c']
 >>> li.extend(['d', 'e', 'f']) 1
 >>> li
@@ -1472,7 +1359,7 @@ KeyError: mpilgrim

 
 2 
 
-Here you started with a list of three elements ('a', 'b', and 'c'), and you extended the list with a list of another three elements ('d', 'e', and 'f'), so you now have a list of six elements.
+Here you started with a list of three elements ('a', 'b', and 'c'), and you extended the list with a list of another three elements ('d', 'e', and 'f'), so you now have a list of six elements.
 
 
 
@@ -1489,12 +1376,8 @@ KeyError: mpilgrim

 
 
 
-

-

-

-

-
3.2.3. Searching Lists

-
Example 3.12. Searching a List
>>> li
+
3.2.3. Searching Lists

+
Example 3.12. Searching a List
>>> li
 ['a', 'b', 'new', 'mpilgrim', 'z', 'example', 'new', 'two', 'elements']
 >>> li.index("example") 1
 5
@@ -1516,7 +1399,7 @@ False

 
 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 return only the first 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 return only the first index, 2.
 
 
 
@@ -1534,34 +1417,31 @@ False

 
 
 
-

 

-

 

 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:
+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.
-
    • 
+
+
  • 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.
+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.
 		
 

 

-

-

-
3.2.4. Deleting List Elements

-
Example 3.13. Removing Elements from a List
>>> li
+
3.2.4. Deleting List Elements

+
Example 3.13. Removing Elements from a List
>>> li
 ['a', 'b', 'new', 'mpilgrim', 'z', 'example', 'new', 'two', 'elements']
 >>> li.remove("z")   1
 >>> li
@@ -1587,7 +1467,7 @@ ValueError: list.remove(x): x not in list
 
 2 
 
-remove removes only the first occurrence of a value.  In this case, 'new' appeared twice in the list, but li.remove("new") removed only the first occurrence.
+remove removes only the first occurrence of a value.  In this case, 'new' appeared twice in the list, but li.remove("new") removed only the first occurrence.
 
 
 
@@ -1600,16 +1480,12 @@ ValueError: list.remove(x): x not in list
 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.
+                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.
 
 
 
-

-

-

-

-
3.2.5. Using List Operators

-
Example 3.14. List Operators
>>> li = ['a', 'b', 'mpilgrim']
+
3.2.5. Using List Operators

+
Example 3.14. List Operators
>>> li = ['a', 'b', 'mpilgrim']
 >>> li = li + ['example', 'new'] 1
 >>> li
 ['a', 'b', 'mpilgrim', 'example', 'new']
@@ -1623,44 +1499,38 @@ ValueError: list.remove(x): x not in list
 
 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.
+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
+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 5.)
 
 
 
 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.
+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.
 
 
 
-

-

 

-
Further Reading on Lists

+
Further Reading on Lists

 
-

-

-

-

-
3.3. Introducing Tuples

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

-
Example 3.15. Defining a tuple
>>> t = ("a", "b", "mpilgrim", "z", "example") 1
+
3.3. Introducing Tuples

+
A tuple is an immutable list.  A tuple can not be changed in any way once it is created.
+
Example 3.15. Defining a tuple
>>> t = ("a", "b", "mpilgrim", "z", "example") 1
 >>> t
 ('a', 'b', 'mpilgrim', 'z', 'example')
 >>> t[0]   2
@@ -1681,7 +1551,7 @@ ValueError: list.remove(x): x not in list
 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].
+            element of a non-empty tuple is always t[0].
 
 
 
@@ -1697,9 +1567,7 @@ ValueError: list.remove(x): x not in list
 
 
 
-

-

-
Example 3.16. Tuples Have No Methods
>>> t
+
Example 3.16. Tuples Have No Methods
>>> t
 ('a', 'b', 'mpilgrim', 'z', 'example')
 >>> t.append("new")    1
 Traceback (innermost last):
@@ -1741,21 +1609,19 @@ True

 
 
 
-

-

-
So what are tuples good for?

+
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 shows this data is constant, and that special thought (and a specific function) is required to override that.
-
    • 
+
+
  • 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 shows this data is constant, and that special thought (and a specific function) is required to override that.
+
 
  • Remember that 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 be used this way.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.
-
    • 
-
  • Tuples are used in string formatting, as you'll see shortly.
    • 
+
+
  • Tuples are used in string formatting, as you'll see shortly.
 

 

@@ -1767,57 +1633,49 @@ True

 

 

 

 

-
Further Reading on Tuples

+
Further Reading on Tuples

 
-

-

-

-
3.4. Declaring variables

-
Now that you know something about dictionaries, tuples, and lists (oh my!), let's get back to the sample program from Chapter 2, odbchelper.py.

+
3.4. Declaring variables

+
Now that you know something about dictionaries, tuples, and lists (oh my!), let's get back to the sample program from Chapter 2, 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 they are automatically destroyed when they go out of scope.

-
Example 3.17. Defining the myParams Variable
+   into existence by being assigned a value, and they are automatically destroyed when they go out of scope.
+
Example 3.17. Defining the myParams Variable
 if __name__ == "__main__":
     myParams = {"server":"mpilgrim", \
                 "database":"master", \
                 "uid":"sa", \
                 "pwd":"secret" \
-                }

-
Notice the indentation.  An if statement is a code block and needs to be indented just like a function.

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

+                }
Notice the indentation.  An if statement is a code block and needs to be indented just like a function.
+
Also notice that 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.
+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.
 
 

 

-
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.

-

-
3.4.1. Referencing Variables

-
Example 3.18. Referencing an Unbound Variable
>>> x
+
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.
+
3.4.1. Referencing Variables

+
Example 3.18. 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.

-

-

-
3.4.2. Assigning Multiple Values at Once

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

-
Example 3.19. Assigning multiple values at once
>>> v = ('a', 'b', 'e')
+1
You will thank Python for this one day.
+
3.4.2. Assigning Multiple Values at Once

+
One of the cooler programming shortcuts in Python is using sequences to assign multiple values at once.
+
Example 3.19. Assigning multiple values at once
>>> v = ('a', 'b', 'e')
 >>> (x, y, z) = v     1
 >>> x
 'a'
@@ -1829,15 +1687,13 @@ NameError: There is no variable named 'x'
 
 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.
+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 3.20. Assigning Consecutive Values
>>> range(7)              1
+
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 3.20. 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
@@ -1851,7 +1707,7 @@ NameError: There is no variable named 'x'
 1 
 
 The built-in range function returns a list of integers.  In its simplest form, it takes an upper limit and returns a zero-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.)
+               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.)
 
 
 
@@ -1867,25 +1723,19 @@ NameError: There is no variable named 'x'
 
 
 
-

-

 
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 you'll discuss in Chapter 6.

+   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 you'll discuss in Chapter 6.
 

-
Further Reading on Variables

+
Further Reading on Variables

 
-

-

-

-

-
3.5. Formatting Strings

+
3.5. 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.

+   to insert values into a string with the %s placeholder.
 
@@ -1895,7 +1745,7 @@ NameError: There is no variable named 'x'

 

 Note
 
 

 

-
Example 3.21. Introducing String Formatting
>>> k = "uid"
+
Example 3.21. Introducing String Formatting
>>> k = "uid"
 >>> v = "sa"
 >>> "%s=%s" % (k, v) 1
 'uid=sa'

@@ -1903,16 +1753,14 @@ NameError: There is no variable named 'x'
 
 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 equal sign) stay as they are.
+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 equal sign) stay as they are.
 
 
 
-

-

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

+
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 would be right, except that
-string formatting isn't just concatenation.  It's not even just formatting.  It's also type coercion.

-
Example 3.22. String Formatting vs. Concatenating
>>> uid = "sa"
+string formatting isn't just concatenation.  It's not even just formatting.  It's also type coercion.
+
Example 3.22. 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
@@ -1929,7 +1777,7 @@ TypeError: cannot concatenate 'str' and 'int' objects
1 
 
-+ is the string concatenation operator.
++ is the string concatenation operator.
 
 
 
@@ -1940,15 +1788,15 @@ TypeError: cannot concatenate 'str' and 'int' objects
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
+(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.
+            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.
+String formatting works with integers by specifying %d instead of %s.
 
 
 
@@ -1959,10 +1807,8 @@ TypeError: cannot concatenate 'str' and 'int' objects
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 3.23. Formatting Numbers
+
As with 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 3.23. Formatting Numbers
 >>> print "Today's stock price: %f" % 50.4625   1
 50.462500
 >>> print "Today's stock price: %.2f" % 50.4625 2
@@ -1974,40 +1820,35 @@ TypeError: cannot concatenate 'str' and 'int' objects
1 
 
-The %f string formatting option treats the value as a decimal, and prints it to six decimal places.
+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 two decimal places.
+The ".2" modifier of the %f option truncates the value to two 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
+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 two decimal places.
 
 
 
-

-

 

-
Further Reading on String Formatting

+
Further Reading on String Formatting

 
-

-

-

-
3.6. Mapping Lists

+
3.6. 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 3.24. Introducing List Comprehensions
>>> li = [1, 9, 8, 4]
+   of the elements of the list.
+
Example 3.24. Introducing List Comprehensions
>>> li = [1, 9, 8, 4]
 >>> [elem*2 for elem in li]      1
 [2, 18, 16, 8]
 >>> li         2
@@ -2019,7 +1860,7 @@ TypeError: cannot concatenate 'str' and 'int' objects
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.
+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.
 
 
 
@@ -2034,13 +1875,10 @@ TypeError: cannot concatenate 'str' and 'int' objects

-
Here are the list comprehensions in the buildConnectionString function that you declared in Chapter 2:
-["%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. The keys, values, and items Functions
>>> params = {"server":"mpilgrim", "database":"master", "uid":"sa", "pwd":"secret"}
+
Here are the list comprehensions in the buildConnectionString function that you declared in Chapter 2:
+["%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. The keys, values, and items Functions
>>> params = {"server":"mpilgrim", "database":"master", "uid":"sa", "pwd":"secret"}
 >>> params.keys()   1
 ['server', 'uid', 'database', 'pwd']
 >>> params.values() 2
@@ -2058,21 +1896,19 @@ TypeError: cannot concatenate 'str' and 'int' objects
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.
+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.
+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"}
+
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
@@ -2085,13 +1921,13 @@ as params.1 
 
-Note that you'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, you'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().
+Note that you'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, you'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().
 
 
 
 2 
 
-Here you're doing the same thing, but ignoring the value of k, so this list comprehension ends up being equivalent to params.values().
+Here you're doing the same thing, but ignoring the value of k, so this list comprehension ends up being equivalent to params.values().
 
 
 
@@ -2102,28 +1938,22 @@ as params.
-
Further Reading on List Comprehensions

+
Further Reading on List Comprehensions

 
-

-

-

-
3.7. 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.

+
3.7. 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.
 

-
Here is an example of joining a list from the buildConnectionString function:
-    return ";".join(["%s=%s" % (k, v) for k, v in params.items()])

-
One interesting note before you 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.

+
Here is an example of joining a list from the buildConnectionString function:
+    return ";".join(["%s=%s" % (k, v) for k, v in params.items()])
One interesting note before you 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.
 
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
-need to be a semi-colon; it doesn't even need to be a single character.  It can be any string.

+need to be a semi-colon; it doesn't even need to be a single character.  It can be any string.

@@ -2133,16 +1963,15 @@ need to be a semi-colon; it doesn't even need to be a single character.  It can
 

 

 Caution
 

 
 

-
Example 3.27. Output of odbchelper.py
>>> params = {"server":"mpilgrim", "database":"master", "uid":"sa", "pwd":"secret"}
+
Example 3.27. 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.

+'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.
 
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 3.28. Splitting a String
>>> li = ['server=mpilgrim', 'uid=sa', 'database=master', 'pwd=secret']
+called split.
+
Example 3.28. Splitting a String
>>> li = ['server=mpilgrim', 'uid=sa', 'database=master', 'pwd=secret']
 >>> s = ";".join(li)
 >>> s
 'server=mpilgrim;uid=sa;database=master;pwd=secret'
@@ -2154,7 +1983,7 @@ called split.

 
 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.
+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.
 
 
 
@@ -2164,41 +1993,35 @@ called split.

 
 
 
-

 

-

 

 Tip
 

 
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
+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).
 
 

 

 

-
Further Reading on String Methods

+
Further Reading on String Methods

 
-

-

-
3.7.1. Historical Note on String Methods

+
3.7.1. Historical Note on String Methods

 
When I first learned Python, I expected join to be a method of a list, which would take the delimiter as an argument.  Many 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 that 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 a lot 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.

-

-

-

-
3.8. Summary

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

+   the old string module (which still has a lot 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.
+
3.8. Summary

+
The odbchelper.py program and its output should now make perfect sense.
 
 def buildConnectionString(params):
     """Build a connection string from a dictionary of parameters.
@@ -2212,44 +2035,37 @@ if __name__ == "__main__":
                 "uid":"sa", \
                 "pwd":"secret" \
                 }
-    print buildConnectionString(myParams)

-

-
Here is the 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:

+    print buildConnectionString(myParams)

+
Here is the 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:
 

 
-

-

-

-

 

-
Chapter 4. The Power Of Introspection

+
Chapter 4. The Power Of Introspection

 
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, you'll define functions with no name, call functions with arguments out of order, and reference
-functions whose names you don't even know ahead of time.

-

-
4.1. Diving In

+functions whose names you don't even know ahead of time.
+
4.1. Diving In

 
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 Chapter 2, 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.
+   in Chapter 2, 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.
     
@@ -2273,7 +2089,7 @@ if __name__ == "__main__":                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
+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, rather than its value.
 
 
@@ -2286,22 +2102,20 @@ if __name__ == "__main__":                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.
+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.
+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
+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
@@ -2312,9 +2126,8 @@ 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 easy to read.  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
+sort       L.sort([cmpfunc]) -- sort *IN PLACE*; if given, cmpfunc(x, y) -> -1, 0, 1
By default the output is formatted to be easy to read.  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)
@@ -2323,19 +2136,15 @@ buildConnectionString          Build a connection string from a dictionary Retur
 buildConnectionString          Build a connection string from a dictionary
     
     Returns string.
-

-

-

-
4.2. Using Optional and Named Arguments

+
4.2. Using 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, so if you're a SQL Server scripting guru, you can skim this part.

+   value.  Futhermore, arguments can be specified in any order by using named arguments.  Stored procedures in SQL Server Transact/SQL can do this, so if you're a SQL Server scripting guru, you can skim this part.
 

-
Here is an example of 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.

+
Here is an example of 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 need to call the function with three arguments.  But in
-Python, arguments can be specified by name, in any order.

-
Example 4.4. Valid Calls of info
+Python, arguments can be specified by name, in any order.
+
Example 4.4. Valid Calls of info
 info(odbchelper)  1
 info(odbchelper, 12)                2
 info(odbchelper, collapse=0)        3
@@ -2344,7 +2153,7 @@ info(spacing=15, object=odbchelper) 1 
 
-With only one argument, spacing gets its default value of 10 and collapse gets its default value of 1.
+With only one argument, spacing gets its default value of 10 and collapse gets its default value of 1.
 
 
 
@@ -2356,7 +2165,7 @@ info(spacing=15, object=odbchelper) 3 
 
-Here you are naming the collapse argument explicitly and specifying its value.  spacing still gets its default value of 10.
+Here you are naming the collapse argument explicitly and specifying its value.  spacing still gets its default value of 10.
 
 
 
@@ -2366,10 +2175,8 @@ info(spacing=15, object=odbchelper) 
+time, you'll call functions the “normal” way, but you always have the additional flexibility if you need it.
@@ -2380,22 +2187,18 @@ time, you'll call functions the “normal” way, but you always have th
 

 

 Note
 

 

 

-
Further Reading on Optional Arguments

+
Further Reading on Optional Arguments

 
-

-

-

-
4.3. Using type, str, dir, and Other Built-In Functions

+
4.3. Using 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).

-

-
4.3.1. The type Function

-
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 that can handle several types of data.

-
Example 4.5. Introducing type
>>> type(1)           1
+   cough, Visual Basic).
+
4.3.1. The type Function

+
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 that can handle several types of data.
+
Example 4.5. Introducing type
>>> type(1)           1
 <type 'int'>
 >>> li = []
 >>> type(li)          2
@@ -2433,13 +2236,9 @@ True

 
 
 
-

-

-

-

-
4.3.2. The str Function

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

-
Example 4.6. Introducing str
+
4.3.2. The str Function

+
The str coerces data into a string.  Every datatype can be coerced into a string.
+
Example 4.6. Introducing str
 >>> str(1)          1
 '1'
 >>> horsemen = ['war', 'pestilence', 'famine']
@@ -2475,15 +2274,13 @@ True

 
 4 
 
-A subtle but important behavior of str is that it works on None, the Python null value.  It returns the string 'None'.  You'll use this to your advantage in the info function, as you'll see shortly.
+A subtle but important behavior of str is that it works on None, the Python null value.  It returns the string 'None'.  You'll use this to your advantage in the info function, as you'll see shortly.
 
 
 
-

-

 
At the heart of the 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 4.7. Introducing dir
>>> li = []
+   anything.
+
Example 4.7. Introducing dir
>>> li = []
 >>> dir(li)           1
 ['append', 'count', 'extend', 'index', 'insert',
 'pop', 'remove', 'reverse', 'sort']
@@ -2497,27 +2294,25 @@ True

 
 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
+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.
+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__, __doc__, and whatever other attributes and methods you define.  In this case, odbchelper has only one user-defined method, the buildConnectionString function described in Chapter 2.
+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__, __doc__, and whatever other attributes and methods you define.  In this case, odbchelper has only one user-defined method, the buildConnectionString function described in Chapter 2.
 
 
 
-

-

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

-
Example 4.8. Introducing callable
+
Finally, the callable function takes any object and returns True if the object can be called, or False otherwise.  Callable objects include functions, class methods, even classes themselves.  (More on classes in the next chapter.)
+
Example 4.8. Introducing callable
 >>> import string
 >>> string.punctuation           1
 '!"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~'
@@ -2563,21 +2358,17 @@ True
 
 5 
 
-Any callable object may have a doc string.  By using the callable function on each of an object's attributes, you can determine which attributes you care about (methods, functions, classes)
+Any callable object may have a doc string.  By using the callable function on each of an object's attributes, you can determine which attributes you care about (methods, functions, classes)
                and which you want to ignore (constants and so on) without knowing anything about the object ahead of time.
 
 
 
-

-

-

-

-
4.3.3. Built-In Functions

-
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.

+
4.3.3. Built-In Functions

+
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, Python has a function 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 4.9. Built-in Attributes and Functions
>>> from apihelper import info
+   built-in error classes, like AttributeError, should already look familiar.)
+
Example 4.9. Built-in Attributes and Functions
>>> from apihelper import info
 >>> import __builtin__
 >>> info(__builtin__, 20)
 ArithmeticError      Base class for arithmetic errors.
@@ -2600,19 +2391,15 @@ IOError              I/O operation failed.
 
 
 

-
Further Reading on Built-In Functions

+
Further Reading on Built-In Functions

 
-

-

-

-

-
4.4. Getting Object References With getattr

+
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, by using the
-getattr function.

-
Example 4.10. Introducing getattr
>>> li = ["Larry", "Curly"]
+getattr function.
+
Example 4.10. Introducing getattr
>>> li = ["Larry", "Curly"]
 >>> li.pop     1
 <built-in method pop of list object at 010DF884>
 >>> getattr(li, "pop")           2
@@ -2630,7 +2417,7 @@ 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.
+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.
 
 
 
@@ -2643,7 +2430,7 @@ AttributeError: 'tuple' object has no attribute 'pop'
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.
+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.
 
 
 
@@ -2659,12 +2446,9 @@ AttributeError: 'tuple' object has no attribute 'pop'

-
4.4.1. getattr with Modules

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

-
Example 4.11. The getattr Function in apihelper.py
>>> import odbchelper
+
4.4.1. getattr with Modules

+
getattr isn't just for built-in datatypes.  It also works on modules.
+
Example 4.11. The getattr Function in apihelper.py
>>> import odbchelper
 >>> odbchelper.buildConnectionString             1
 <function buildConnectionString at 00D18DD4>
 >>> getattr(odbchelper, "buildConnectionString") 2
@@ -2690,7 +2474,7 @@ True

 
 2 
 
-Using getattr, you 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.
+Using getattr, you 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.
 
 
 
@@ -2712,28 +2496,23 @@ True

 
 
 
-

-

-

-

-
4.4.2. getattr As a Dispatcher

+
4.4.2. getattr As a Dispatcher

 
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.

+   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 4.12. Creating a Dispatcher with getattr
+   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 4.12. 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
-

-

+

 
-
@@ -2750,12 +2529,11 @@ def output(data, format="text"):            

 

 1 
 The output function takes one required argument, data, and one optional argument, format.  If format is not specified, it defaults to text, and you will end up calling the plain text output function.
+The output function takes one required argument, data, and one optional argument, format.  If format is not specified, it defaults to text, and you will end up calling the plain text output function.
 
 

 

 

 

-

 
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.  That's
-   bad.

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

-
Example 4.13. getattr Default Values
+   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.  That's
+   bad.
+
Luckily, getattr takes an optional third argument, a default value.
+
Example 4.13. getattr Default Values
 import statsout
 
 def output(data, format="text"):
@@ -2771,20 +2549,14 @@ def output(data, format="text"):
 
 
 
-

-

-
As you can see, getattr is quite powerful.  It is the heart of introspection, and you'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 (Section 3.6, “Mapping Lists”).  This can be combined with a filtering mechanism, where some elements in the list are mapped while others are skipped entirely.

+
As you can see, getattr is quite powerful.  It is the heart of introspection, and you'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 (Section 3.6, “Mapping Lists”).  This can be combined with a filtering mechanism, where some elements in the list are mapped while others are skipped entirely.
 

-
Here is the 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 4.14. Introducing List Filtering
>>> li = ["a", "mpilgrim", "foo", "b", "c", "b", "d", "d"]
+
Here is the 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 4.14. 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
@@ -2804,7 +2576,7 @@ so they are never put through the mapping expression and are not included in the
 
 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.
+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.
 
 
 
@@ -2812,35 +2584,29 @@ so they are never put through the mapping expression and are not included in the
 
 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.
+            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.
 
 
 
-

-

-
Let's id="apihelper.filter.care" get back to this line from 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
+
Let's id="apihelper.filter.care" get back to this line from 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,
-which 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.

+which 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 you don't care about.  You 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 you 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).  You don't care about other attributes, like the __name__ attribute that's built in to every module.

+the pop method of a list) and user-defined (like the buildConnectionString function of the odbchelper module).  You don't care about other attributes, like the __name__ attribute that's built in to every module.
 

-
Further Reading on Filtering Lists

+
Further Reading on Filtering Lists

 
-

-

-

-
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; instead, they return one of the actual
-   values they are comparing.

-
Example 4.15. Introducing and
>>> 'a' and 'b'         1
+
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; instead, they return one of the actual
+   values they are comparing.
+
Example 4.15. Introducing and
>>> 'a' and 'b'         1
 'b'
 >>> '' and 'b'          2
 ''
@@ -2850,27 +2616,25 @@ the pop method of a list) and user-defined (like t
 
 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.  Well, almost everything.  By default, instances of classes are
+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.  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 5.  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'.
+            learn all about classes and special methods in Chapter 5.  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.
+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'.
+All values are true, so and returns the last value, 'c'.
 
 
 
-

-

-
Example 4.16. Introducing or
>>> 'a' or 'b'          1
+
Example 4.16. Introducing or
>>> 'a' or 'b'          1
 'a'
 >>> '' or 'b'           2
 'b'
@@ -2885,35 +2649,32 @@ the pop method of a list) and user-defined (like t
 
 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.
+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'.
+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 {}.
+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 evaluates values only 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.
+Note that or evaluates values only 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.

-

-
4.6.1. Using the and-or Trick

-
Example 4.17. Introducing the and-or Trick
>>> a = "first"
+
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.
+
4.6.1. Using the and-or Trick

+
Example 4.17. Introducing the and-or Trick
>>> a = "first"
 >>> b = "second"
 >>> 1 and a or b 1
 'first'
@@ -2924,21 +2685,19 @@ the pop method of a list) and user-defined (like t
 
 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'.
+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 False, and then 0 or 'second' evaluates to 'second'.
+0 and 'first' evalutes to False, and 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 extremely 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 4.18. When the and-or Trick Fails
>>> a = ""
+   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 4.18. When the and-or Trick Fails
>>> a = ""
 >>> b = "second"
 >>> 1 and a or b         1
 'second'

@@ -2946,15 +2705,13 @@ the pop method of a list) and user-defined (like t
 
 1 
 
-Since a is an empty string, which Python considers false in a boolean context, 1 and '' evalutes to '', and then '' or 'second' evalutes to 'second'.  Oops!  That's not what you wanted.
+Since a is an empty string, which Python considers false in a boolean context, 1 and '' evalutes to '', and then '' or 'second' evalutes to 'second'.  Oops!  That's not what you wanted.
 
 
 
-

-

-
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 4.19. Using the and-or Trick Safely
>>> a = ""
+
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 4.19. Using the and-or Trick Safely
>>> a = ""
 >>> b = "second"
 >>> (1 and [a] or [b])[0] 1
 ''

@@ -2962,28 +2719,22 @@ the pop method of a list) and user-defined (like t
 
 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.
+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
+
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 need to use the more complicated safe form, there are good reasons to do so.
-   For example, there are some cases in Python where if statements are not allowed, such as in lambda functions.

+   For example, there are some cases in Python where if statements are not allowed, such as in lambda functions.
 

-
Further Reading on the and-or Trick

+
Further Reading on the and-or Trick

 
-

-

-

-

-
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 4.20. Introducing lambda Functions
>>> def f(x):
+
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 4.20. Introducing lambda Functions
>>> def f(x):
 ...     return x*2
 ...     
 >>> f(3)
@@ -2997,41 +2748,37 @@ the pop method of a list) and user-defined (like t
 
 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,
+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.  This may not be the most useful thing in the world, but it just goes to
+You can use a lambda function without even assigning it to a variable.  This may not be 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.

+
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
+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.
 
 

 

-

-
4.7.1. Real-World lambda Functions

+
4.7.1. Real-World lambda Functions

 

-
Here are the lambda functions in apihelper.py:
-    processFunc = collapse and (lambda s: " ".join(s.split())) or (lambda s: s)

-
Notice that this uses the simple form of the and-or trick, which is okay, 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.)

-
Also notice that you're using the split function with no arguments.  You've already seen it used with one or two arguments, but without any arguments it splits on whitespace.

-
Example 4.21. split With No Arguments
>>> s = "this   is\na\ttest"  1
+
Here are the lambda functions in apihelper.py:
+    processFunc = collapse and (lambda s: " ".join(s.split())) or (lambda s: s)
Notice that this uses the simple form of the and-or trick, which is okay, 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.)
+
Also notice that you're using the split function with no arguments.  You've already seen it used with one or two arguments, but without any arguments it splits on whitespace.
+
Example 4.21. split With No Arguments
>>> s = "this   is\na\ttest"  1
 >>> print s
 this   is
 a	test
@@ -3043,7 +2790,7 @@ a	test
 
 1 
 
-This is a multiline string, defined by escape characters instead of triple quotes.  \n is a carriage return, and \t is a tab character.
+This is a multiline string, defined by escape characters instead of triple quotes.  \n is a carriage return, and \t is a tab character.
 
 
 
@@ -3055,49 +2802,41 @@ a	test
 
 3 
 
-You can normalize whitespace by splitting a string with split and then rejoining it with join, using a single space as a delimiter.  This is what the info function does to collapse multi-line doc strings into a single line.
+You can normalize whitespace by splitting a string with split and then rejoining it with join, using 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?

+
So what is the info function actually doing with these lambda functions, splits, and and-or tricks?
 
-    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,
+    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 need to handle every possible case.  Every time you called it, it would need 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.

+   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 on lambda Functions

+
Further Reading on lambda Functions

 
-

-

-

-

-
4.8. Putting It All Together

+
4.8. Putting It All Together

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

+   to knock them down.
 

-
This is the meat of apihelper.py:
+
This is 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 that this is a list comprehension.  As you know, methodList is a list of all the methods you care about in object.  So you're looping through that list with method.

-
Example 4.22. Getting a doc string Dynamically
>>> import odbchelper
+   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 that this is a list comprehension.  As you know, methodList is a list of all the methods you care about in object.  So you're looping through that list with method.
+
Example 4.22. Getting a doc string Dynamically
>>> import odbchelper
 >>> object = odbchelper 1
 >>> method = 'buildConnectionString'      2
 >>> getattr(object, method)               3
@@ -3128,14 +2867,12 @@ for method in methodList
shows that this is a 
 4 
 
-Now, printing the actual doc string of the method is easy.
+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 4.23. Why Use str on a doc string?
>>> >>> def foo(): print 2
+
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 4.23. Why Use str on a doc string?
>>> >>> def foo(): print 2
 >>> >>> foo()
 2
 >>> >>> foo.__doc__     1
@@ -3148,35 +2885,34 @@ True
 
 1 
 
-You 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.
+You 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.
+You can verify that the value of the __doc__ attribute is actually None by comparing it directly.
 
 
 
 3 
 
-The str function takes the null value and returns a string representation of it, 'None'.
+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.
+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 you are guaranteed to have a string, you can pass the string to processFunc, which you 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 you passed it None because None doesn't have a split method.

-
Stepping back even further, you see that you'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 you haven't seen before.

-
Example 4.24. Introducing ljust
>>> s = 'buildConnectionString'
+
Now that you are guaranteed to have a string, you can pass the string to processFunc, which you 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 you passed it None because None doesn't have a split method.
+
Stepping back even further, you see that you'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 you haven't seen before.
+
Example 4.24. Introducing ljust
>>> s = 'buildConnectionString'
 >>> s.ljust(30) 1
 'buildConnectionString         '
 >>> s.ljust(20) 2
@@ -3185,7 +2921,7 @@ True
 
 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.
+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.
 
 
 
@@ -3195,10 +2931,8 @@ True
 
 
 
-

-

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

-
Example 4.25. Printing a List
>>> li = ['a', 'b', 'c']
+
You're almost finished.  Given the padded method name from the ljust method and the (possibly collapsed) doc string from the call to processFunc, you concatenate the two and get a single string.  Since you're mapping methodList, you end up with a list of strings.  Using the join method of the string "\n", you join this list into a single string, with each element of the list on a separate line, and print the result.
+
Example 4.25. Printing a List
>>> li = ['a', 'b', 'c']
 >>> print "\n".join(li) 1
 a
 b
@@ -3211,18 +2945,13 @@ c

 
 
 
-

-

-
That's the last piece of the puzzle.  You should now understand this code.

+
That's the last piece of the puzzle.  You should now understand this code.
 
     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.

+   for method in methodList])
4.9. Summary

+
The apihelper.py program and its output should now make perfect sense.
 
 def info(object, spacing=10, collapse=1):
     """Print methods and doc strings.
@@ -3236,9 +2965,8 @@ def info(object, spacing=10, collapse=1):
    for method in methodList])
 
 if __name__ == "__main__":
-    print info.__doc__

-

-
Here is the output of apihelper.py:
>>> from apihelper import info
+    print info.__doc__

+
Here is the output of apihelper.py:
>>> from apihelper import info
 >>> li = []
 >>> info(li)
 append     L.append(object) -- append object to end
@@ -3249,37 +2977,31 @@ 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:

+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:

-
-
-
-
-

Chapter 5. Objects and Object-Orientation

-

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

-
-

5.1. Diving In

-

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.

+
Chapter 5. Objects and Object-Orientation

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

+
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
@@ -3366,11 +3088,9 @@ if __name__ == "__main__":
 
 
 
-

-

This is 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=
+   exact taste in music.
album=
 artist=Ghost in the Machine
 title=A Time Long Forgotten (Concept
 genre=31
@@ -3416,22 +3136,18 @@ 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 Section 2.4, “Everything Is an Object”. The other way accomplishes the same thing, but it has subtle and important differences.

+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 Section 2.4, “Everything Is an Object”. The other way accomplishes the same thing, but it has subtle and important differences.

-

Here is the basic from module import syntax:

+
Here is the 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.

+

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. +from module import * in Python is like use module in Perl; import module in Python is like require module in Perl.
@@ -3439,11 +3155,11 @@ can import individual items or use from Note -
from module import * in Python is like import module.* in Java; import module in Python is like import module in Java. +from module import * in Python is like import module.* in Java; import module in Python is like import module in Java.
-

Example 5.2. import module vs. from module import

>>> import types
+
Example 5.2. import module vs. from module import
>>> import types
 >>> types.FunctionType             1
 <type 'function'>
 >>> FunctionType 2
@@ -3479,46 +3195,40 @@ NameError: There is no variable named 'FunctionType'
 
 
 
-

-
-

When should you use from module import?

+

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. -
    • +
  • 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.

+

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

-
Caution
Use from module import * sparingly, because it makes it difficult to determine where a particular function or attribute came from, and that makes +Use from module import * sparingly, because it makes it difficult to determine where a particular function or attribute came from, and that makes debugging and refactoring more difficult.
-

Further Reading on Module Importing Techniques

+

Further Reading on Module Importing Techniques

-
-
-
-

5.3. Defining Classes

+

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. As with 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 need to inherit from any other -class.

-

Example 5.3. The Simplest Python Class

+   classes you've defined.
+
Defining a class in Python is simple.  As with 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 need to inherit from any other
+class.
+
Example 5.3. The Simplest Python Class
 class Loaf: 1
     pass    2 3

 
@@ -3532,30 +3242,29 @@ class Loaf: 12
-
 
 
 
This class doesn't define any methods or attributes, but syntactically, there needs to be something in the definition, so
-            you 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.
+            you 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.
+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.
+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 5.4. Defining the FileInfo Class
+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 5.4. Defining the FileInfo Class
 from UserDict import UserDict
 
 class FileInfo(UserDict): 1

@@ -3569,23 +3278,21 @@ class FileInfo(UserDict): 
 
 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.
+extends in Java.
 
 
 
 
Python supports multiple inheritance.  In the parentheses following the class name, you can list as many ancestor classes as you
-like, separated by commas.

-

-
5.3.1. Initializing and Coding Classes

-
This example shows the initialization of the FileInfo class using the __init__ method.

-
Example 5.5. Initializing the FileInfo Class
+like, separated by commas.
+
5.3.1. Initializing and Coding Classes

+
This example shows the initialization of the FileInfo class using the __init__ method.
+
Example 5.5. Initializing the FileInfo Class
 class FileInfo(UserDict):
     "store file metadata"              1
     def __init__(self, filename=None): 2 3 4

@@ -3593,7 +3300,7 @@ class FileInfo(UserDict):
 
 1 
 
-Classes can (and should) have doc strings too, just like modules and functions.
+Classes can (and should) have doc strings too, just like modules and functions.
 
 
 
@@ -3607,29 +3314,28 @@ class FileInfo(UserDict):
 
 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.
+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.
+               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 Python 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.
+By convention, the first argument of any Python 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 5.6. Coding the FileInfo Class
+
Example 5.6. Coding the FileInfo Class
 class FileInfo(UserDict):
     "store file metadata"
     def __init__(self, filename=None):
@@ -3647,7 +3353,7 @@ class FileInfo(UserDict):
 
 2 
 
-I told you that this class acts like a dictionary, and here is the first sign of it.  You're assigning the argument filename as the value of this object's name key.
+I told you that this class acts like a dictionary, and here is the first sign of it.  You're assigning the argument filename as the value of this object's name key.
 
 
 
@@ -3657,16 +3363,12 @@ class FileInfo(UserDict):
 
 
 
-

-

-

-

-
5.3.2. Knowing When to Use self and __init__

-
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,
+
5.3.2. Knowing When to Use self and __init__

+
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.

+   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:

+   one thing, because I promise it will trip you up:

@@ -3677,25 +3379,21 @@ class FileInfo(UserDict):
 

 

 Note
 

 

 

-
Further Reading on Python Classes

+
Further Reading on Python Classes

 
-

-

-

-

-
5.4. Instantiating Classes

+
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 5.7. Creating a FileInfo Instance
>>> import fileinfo
+__init__ method defines.  The return value will be the newly created object.
+
Example 5.7. Creating a FileInfo Instance
>>> import fileinfo
 >>> f = fileinfo.FileInfo("/music/_singles/kairo.mp3") 1
 >>> f.__class__    2
 <class fileinfo.FileInfo at 010EC204>
@@ -3707,44 +3405,42 @@ class FileInfo(UserDict):
 
 1 
 
-You are creating an instance of the FileInfo class (defined in the fileinfo module) and assigning the newly created instance to the variable f.  You are passing one parameter, /music/_singles/kairo.mp3, which will end up as the filename argument in FileInfo's __init__ method.
+You are creating an instance of the FileInfo class (defined in the fileinfo module) and assigning the newly created instance to the variable f.  You 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__.
+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 as with a function or a module.  All instances of a class share the same doc string.
+You can access the instance's doc string just as with 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 you pass when you create the class instance get sent right along to the __init__ method (along with the object reference, self, which Python adds for free).
+Remember when the __init__ method assigned its filename argument to self["name"]?  Well, here's the result.  The arguments you pass when you 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.
+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.
 
 

 

-

-
5.4.1. Garbage Collection

+
5.4.1. Garbage Collection

 
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 5.8. Trying to Implement a Memory Leak
>>> def leakmem():
+   because they are freed automatically when the variables assigned to them go out of scope.  Memory leaks are rare in Python.
+
Example 5.8. Trying to Implement a Memory Leak
>>> def leakmem():
 ...     f = fileinfo.FileInfo('/music/_singles/kairo.mp3') 1
 ...     
 >>> for i in range(100):
@@ -3763,28 +3459,22 @@ class FileInfo(UserDict):
 
 
 
-

-

-
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.

+
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.

+   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.

+   what happens in Python.  In general, you can simply forget about memory management and let Python clean up after you.
 

-
Further Reading on Garbage Collection

+
Further Reading on Garbage Collection

 
-

-

-

-

-
5.5. Exploring 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 the FileInfo class.  This is nothing special; the class is written in Python and stored in a .py file, just like any other Python code.  In particular, it's stored in the lib directory in your Python installation.

+
5.5. Exploring 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 the FileInfo class.  This is nothing special; the class is written in Python and stored in a .py file, just like any other Python code.  In particular, it's stored in the lib directory in your Python installation.

@@ -3794,7 +3484,7 @@ File->Locate... (Ctrl-L).
 

 

 Tip
 

 
 

-
Example 5.9. Defining the UserDict Class
+
Example 5.9. Defining the UserDict Class
 class UserDict:              1
     def __init__(self, dict=None):             2
         self.data = {}       3
@@ -3818,8 +3508,8 @@ class UserDict:              3 
 
-Python supports data attributes (called “instance variables” in Java and Powerbuilder, and “member variables” in C++).  Data attributes are pieces of 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 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,
-            you 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.
+Python supports data attributes (called “instance variables” in Java and Powerbuilder, and “member variables” in C++).  Data attributes are pieces of 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 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,
+            you 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.
 
 
 
@@ -3832,13 +3522,12 @@ class UserDict:              5 
 
-This is a syntax you may not have seen before (I haven't used it in the examples in this book).  It's an if statement, but instead of having an indented block starting on the next line, there is just a single statement on the same
+This is a syntax you may not have seen before (I haven't used it in the examples in this book).  It's 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, which is just a shortcut you can use 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.
 
 
 
-

 

@@ -3870,7 +3559,7 @@ class UserDict:              
Example 5.10. UserDict Normal Methods
+
Example 5.10. UserDict Normal Methods
     def clear(self): self.data.clear()          1
     def copy(self):           2
         if self.__class__ is UserDict:          3
@@ -3885,7 +3574,7 @@ class UserDict:              1 
 
-

@@ -3893,19 +3582,19 @@ class UserDict:              2
-
-
@@ -3917,8 +3606,7 @@ class UserDict:              
+

 

 Noteclear is a normal class method; it is publicly available to be called by anyone at any time.  Notice that clear, like all class methods, has self as its first argument.  (Remember that 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
+clear is a normal class method; it is publicly available to be called by anyone at any time.  Notice that clear, like all class methods, has self as its first argument.  (Remember that 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.)
 
 
 
 
 
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 you want is to return a new instance that is the same class as self.
+             But UserDict can't simply redirect to self.data.copy, because that method returns a real dictionary, and what you want is to return a new instance that is the same class as self.
 
 

 

 3 
 You use the __class__ attribute to see if self is a UserDict; if so, you're golden, because you know how to copy a UserDict: just create a new UserDict and give it the real dictionary that you've squirreled away in self.data.  Then you immediately return the new UserDict you don't even get to the import copy on the next line.
+You use the __class__ attribute to see if self is a UserDict; if so, you're golden, because you know how to copy a UserDict: just create a new UserDict and give it the real dictionary that you've squirreled away in self.data.  Then you immediately return the new UserDict you don't even get to the import copy on the next line.
 
 

 

 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
+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 you would need 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 it to say that copy can copy arbitrary Python objects, and that's how you're using it here.
 

@@ -3928,8 +3616,8 @@ class UserDict:              dict built-in datatype, as shown in this example.  There are three differences here compared to the UserDict version.

-
Example 5.11. Inheriting Directly from Built-In Datatype dict
+
In Python, you can inherit directly from the dict built-in datatype, as shown in this example.  There are three differences here compared to the UserDict version.
+
Example 5.11. Inheriting Directly from Built-In Datatype dict
 class FileInfo(dict):1
     "store file metadata"
     def __init__(self, filename=None): 2
@@ -3949,26 +3637,20 @@ class FileInfo(dict):
 
 

 

 Note
 

-

-

 

-
Further Reading on UserDict

+
Further Reading on UserDict

 
-

-

-

-
5.6. Special Class Methods

+
5.6. Special Class Methods

 
In addition to normal class methods, there are a number of special methods that 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.

+   you by Python in particular circumstances or when specific syntax is used.
 
As you saw in the previous section, normal methods go a long way towards wrapping a dictionary in a class.  But normal methods alone are not enough, because
 there are a lot 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.

-

-
5.6.1. Getting and Setting Items

-
Example 5.12. The __getitem__ Special Method
+provide a way to map non-method-calling syntax into method calls.
+
5.6.1. Getting and Setting Items

+
Example 5.12. 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'}
@@ -3987,14 +3669,12 @@ provide a way to map non-method-calling syntax into method calls.

 
 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.
+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.
 
 
 
-

-

-
Of course, Python has a __setitem__ special method to go along with __getitem__, as shown in the next example.

-
Example 5.13. The __setitem__ Special Method
+
Of course, Python has a __setitem__ special method to go along with __getitem__, as shown in the next example.
+
Example 5.13. The __setitem__ Special Method
     def __setitem__(self, key, item): self.data[key] = item
>>> f
 {'name':'/music/_singles/kairo.mp3'}
 >>> f.__setitem__("genre", 31) 1
@@ -4013,19 +3693,17 @@ provide a way to map non-method-calling syntax into method calls.

 
 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.
+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, you can redefine it in the descendant class to override the ancestor method.  This allows you to define classes that act
-   like dictionaries in some ways but define their own behavior above and beyond the built-in dictionary.

+   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 you're studying in this chapter.  Each file type can have a handler class
    that 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.  The next example shows how this works.

-
Example 5.14. Overriding __setitem__ in MP3FileInfo
+   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.  The next example shows how this works.
+
Example 5.14. Overriding __setitem__ in MP3FileInfo
     def __setitem__(self, key, item):         1
         if key == "name" and item:            2
             self.__parse(item)                3
@@ -4041,13 +3719,13 @@ provide a way to map non-method-calling syntax into method calls.

 
 2 
 
-Here's the crux of the entire MP3FileInfo class: if you're assigning a value to the name key, you want to do something extra.
+Here's the crux of the entire MP3FileInfo class: if you're assigning a value to the name key, you want to do something extra.
 
 
 
 3 
 
-The extra processing you do for names is encapsulated in the __parse method.  This is another class method defined in MP3FileInfo, and when you call it, you qualify it with self.  Just calling __parse would look for a normal function defined outside the class, which is not what you 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.
+The extra processing you do for names is encapsulated in the __parse method.  This is another class method defined in MP3FileInfo, and when you call it, you qualify it with self.  Just calling __parse would look for a normal function defined outside the class, which is not what you 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.
 
 
 
@@ -4058,17 +3736,16 @@ provide a way to map non-method-calling syntax into method calls.

 
 
 
-

 

-

 

 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.
+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 5.15. Setting an MP3FileInfo's name
>>> import fileinfo
+
Example 5.15. Setting an MP3FileInfo's name
>>> import fileinfo
 >>> mp3file = fileinfo.MP3FileInfo() 1
 >>> mp3file
 {'name':None}
@@ -4086,34 +3763,29 @@ provide a way to map non-method-calling syntax into method calls.

 
 1 
 
-First, you create an instance of MP3FileInfo, without passing it a filename.  (You 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 you didn't pass a filename.  Thus, mp3file initially looks like a dictionary with one key, name, whose value is None.
+First, you create an instance of MP3FileInfo, without passing it a filename.  (You 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 you 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 you're setting the name key with a real value and calls self.__parse.  Although you 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.
+Now the real fun begins.  Setting the name key of mp3file triggers the __setitem__ method on MP3FileInfo (not UserDict), which notices that you're setting the name key with a real value and calls self.__parse.  Although you 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.
+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

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

-
This example shows some of the other special methods in UserDict.

-
Example 5.16. More Special Methods in UserDict
+
5.7. Advanced Special Class Methods

+
Python has more special methods than just __getitem__ and __setitem__.  Some of them let you emulate functionality that you may not even know about.
+
This example shows some of the other special methods in UserDict.
+
Example 5.16. More Special Methods in UserDict
     def __repr__(self): return repr(self.data)     1
     def __cmp__(self, dict):     2
         if isinstance(dict, UserDict):            
@@ -4126,45 +3798,44 @@ provide a way to map non-method-calling syntax into method calls.

 
 1 
 
-__repr__ is a special method that 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 press the ENTER key, 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.
+__repr__ is a special method that 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 press the ENTER key, 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
+__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.
+             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, and then call len(instance) and Python will call your __len__ special method for you.
+__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, and 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.
+__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”.
+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).

+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).

@@ -4176,19 +3847,16 @@ methods would still be useful, because they can be used in any class, not just w
 
Python has a lot 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; you'll talk more about those in later chapters.

+that allow classes to have read-only and write-only data attributes; you'll talk more about those in later chapters.
 

-
Further Reading on Special Class Methods

+
Further Reading on Special Class Methods

 
-

-
-

-
5.8. Introducing 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 5.17. Introducing Class Attributes
+
5.8. Introducing 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 5.17. Introducing Class Attributes
 class MP3FileInfo(FileInfo):
     "store ID3v1.0 MP3 tags"
     tagDataMap = {"title"   : (  3,  33, stripnulls),
@@ -4233,26 +3901,25 @@ class MP3FileInfo(FileInfo):
 

 

 Note
 
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.
+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 you use them in MP3FileInfo), but they are not really constants.  You can also change them.

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

-

 

 Note
 

 
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.
+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.
 
 

 

-
Example 5.18. Modifying Class Attributes
>>> class counter:
+
Example 5.18. Modifying Class Attributes
>>> class counter:
 ...     count = 0   1
 ...     def __init__(self):
 ...         self.__class__.count += 1 2
@@ -4283,7 +3950,7 @@ class MP3FileInfo(FileInfo):
 
 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).
+__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).
 
 
 
@@ -4306,35 +3973,30 @@ class MP3FileInfo(FileInfo):
 
 
 
-

-

-
-
-

5.9. Private Functions

-

Like most languages, Python has the concept of private elements:

+

5.9. Private Functions

+

Like most languages, Python has the concept of private elements:

    -
  • Private functions, which can't be called from outside their module
    • -
  • Private class methods, which can't be called from outside their class
    • -
  • Private attributes, which can't be accessed from outside their class.
    • +
  • Private functions, which can't be called from outside their module +
  • Private class methods, which can't be called from outside their class +
  • Private attributes, which can't be accessed from outside their class.
-
-

Unlike in most languages, whether a Python function, method, or attribute is private or public is determined entirely by its name.

+

Unlike in most languages, whether a Python function, method, or attribute is private or public is determined entirely by its name.

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. 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).

+only in their own class) or public (accessible from anywhere).

In MP3FileInfo, there are two methods: __parse and __setitem__. As you 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.

+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
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 +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, because it will only confuse you (and others) later.
-

Example 5.19. Trying to Call a Private Method

>>> import fileinfo
+
Example 5.19. Trying to Call a Private Method
>>> import fileinfo
 >>> m = fileinfo.MP3FileInfo()
 >>> m.__parse("/music/_singles/kairo.mp3") 1
 Traceback (innermost last):
@@ -4351,73 +4013,63 @@ AttributeError: 'MP3FileInfo' instance has no attribute '__parse'

 
 
-

-
-

Further Reading on Private Functions

+

Further Reading on Private Functions

-
-
-
-

5.10. Summary

-

That's it for the hard-core object trickery. You'll see a real-world application of special class methods in Chapter 12, which uses getattr to create a proxy to a remote web service.

-

The next chapter will continue using this code sample to explore other Python concepts, such as exceptions, file objects, and for loops.

-

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

+

5.10. Summary

+

That's it for the hard-core object trickery. You'll see a real-world application of special class methods in Chapter 12, which uses getattr to create a proxy to a remote web service. +

The next chapter will continue using this code sample to explore other Python concepts, such as exceptions, file objects, and for loops. +

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

-
-
-
-

Chapter 6. Exceptions and File Handling

-

In this chapter, you will dive into exceptions, file objects, for loops, and the os and sys modules. If you've used exceptions in another programming language, you can skim the first section to get a sense of Python's syntax. Be sure to tune in again for file handling.

-
-

6.1. Handling Exceptions

-

Like many other programming languages, Python has exception handling via try...except blocks.

+

Chapter 6. Exceptions and File Handling

+

In this chapter, you will dive into exceptions, file objects, for loops, and the os and sys modules. If you've used exceptions in another programming language, you can skim the first section to get a sense of Python's syntax. Be sure to tune in again for file handling. +

6.1. Handling Exceptions

+

Like many other programming 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. +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.
-

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

+

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

-

In each of these cases, you were simply playing around in the Python IDE: an error occurred, the exception was printed (depending on your IDE, perhaps 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.

+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.

An exception doesn't need result in 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 anticipate. 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
+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'
@@ -4438,39 +4090,36 @@ This line will always print

 
 2 
 
-You're trying to open the same non-existent file, but this time you're doing it within a try...except block.
+You're trying to open the same non-existent file, but this time you're doing it within a try...except block.
 
 
 
 3 
 
-When the open method raises an IOError exception, you're ready for it.  The except IOError: line catches the exception and executes your own block of code, which in this case just prints a more pleasant error message.
+When the open method raises an IOError exception, you're ready for it.  The except IOError: line catches the exception and executes your 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.
+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 need to check its validity
 somehow anyway, and if you forgot, somewhere down the line, your program would give you strange errors somewhere down the
 line that you would need to trace back to the source.  I'm sure you've experienced this, and you know it's not fun.  With
-exceptions, errors occur immediately, and you can handle them in a standard way at the source of the problem.

-

-
6.1.1. Using Exceptions For Other Purposes

+exceptions, errors occur immediately, and you can handle them in a standard way at the source of the problem.
+
6.1.1. Using Exceptions For Other Purposes

 
There are a lot 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, and 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.  See the further reading section if you're interested in doing this.

+   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.  See the further reading section if you're interested in doing this.
 
The next example demonstrates how to use an exception to support 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.

-
Example 6.2. Supporting Platform-Specific Functionality
+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.
+
Example 6.2. Supporting Platform-Specific Functionality
   # Bind the name getpass to the appropriate function
   try:
       import termios, TERMIOS   1
@@ -4519,33 +4168,27 @@ exceptions, errors occur immediately, and you can handle them in a standard way
 
 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 you should bind getpass to the AskPassword function.  Each of the other try...except blocks has similar else clauses to bind getpass to the appropriate function when you find an import that works.
+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 you should bind getpass to the AskPassword function.  Each of the other try...except blocks has similar else clauses to bind getpass to the appropriate function when you find an import that works.
 
 
 
-

-

 

-
Further Reading on Exception Handling

+
Further Reading on Exception Handling

 
-

-

-
-
-

6.2. Working with 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
+
6.2. Working with 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
@@ -4558,7 +4201,7 @@ exceptions, errors occur immediately, and you can handle them in a standard way
 
 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 you are opening the file for reading in binary mode.
-             (print open.__doc__ displays a great explanation of all the possible modes.)
+             (print open.__doc__ displays a great explanation of all the possible modes.)
 
 
 
@@ -4580,12 +4223,9 @@ exceptions, errors occur immediately, and you can handle them in a standard way
 
 
 
-

-
-
-

6.2.1. Reading Files

-

After you open a file, the first thing you'll want to do is read from it, as shown in the next example.

-

Example 6.4. Reading a File

+
6.2.1. Reading Files

+
After you open a file, the first thing you'll want to do is read from it, as shown in the next example.
+
Example 6.4. Reading a File
 >>> f
 <open file '/music/_singles/kairo.mp3', mode 'rb' at 010E3988>
 >>> f.tell()              1
@@ -4611,7 +4251,7 @@ Rave Mix    2000http://mp3.com/DJMARYJANE     \037'
 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 you're looking for are stored at the end of the file, you use 2 and tell the file object to move to a position 128 bytes from the end of the file.
+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 you're looking for are stored at the end of the file, you use 2 and tell the file object to move to a position 128 bytes from the end of the file.
 
 
 
@@ -4624,7 +4264,7 @@ Rave Mix    2000http://mp3.com/DJMARYJANE     \037'
 4 
 
 The read method reads a specified number of bytes from the open file and returns a string with the data that 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.  (You could have simply said read() here, since you know exactly where you are in the file and you are, in fact, reading the last 128 bytes.)  The read data
+               parameter specifies the maximum number of bytes to read.  If no parameter is specified, read will read until the end of the file.  (You could have simply said read() here, since you know exactly where you are in the file and you 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.
 
 
@@ -4636,14 +4276,10 @@ Rave Mix    2000http://mp3.com/DJMARYJANE     \037'
 
 
 
-

-
-
-
-

6.2.2. Closing Files

+

6.2.2. Closing Files

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 finished with them.

-

Example 6.5. Closing a File

+   important to close files as soon as you're finished with them.
+
Example 6.5. Closing a File
 >>> f
 <open file '/music/_singles/kairo.mp3', mode 'rb' at 010E3988>
 >>> f.closed       1
@@ -4700,14 +4336,10 @@ ValueError: I/O operation on closed file
 
 
 
-

-
-
-
-

6.2.3. Handling I/O Errors

+

6.2.3. Handling I/O Errors

Now you've seen enough to understand the file handling code in the fileinfo.py sample code from teh previous chapter. This example shows how to safely open and read from a file and gracefully handle - errors.

-

Example 6.6. File Objects in MP3FileInfo

+   errors.
+
Example 6.6. File Objects in MP3FileInfo
         try:              1
             fsock = open(filename, "rb", 0) 2
             try:         
@@ -4724,7 +4356,7 @@ ValueError: I/O operation on closed file
 
 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.)
+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.)
 
 
 
@@ -4748,33 +4380,28 @@ ValueError: I/O operation on closed file
 
 5 
 
-This is new: a try...finally block.  Once the file has been opened successfully by the open function, you want to make absolutely sure that you 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 before.
+This is new: a try...finally block.  Once the file has been opened successfully by the open function, you want to make absolutely sure that you 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 before.
 
 
 
 6 
 
-At last, you handle your IOError exception.  This could be the IOError exception raised by the call to open, seek, or read.  Here, you really don't care, because all you'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.
+At last, you handle your IOError exception.  This could be the IOError exception raised by the call to open, seek, or read.  Here, you really don't care, because all you'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.
 
 
 
-

-
-
-
-

6.2.4. Writing to Files

-

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:

+

6.2.4. Writing to Files

+

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:

    -
  • "Append" mode will add data to the end of the file.
    • -
  • "write" mode will overwrite the file.
    • +
  • "Append" mode will add data to the end of the file. +
  • "write" mode will overwrite the file.
-

Either mode 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, create a new empty file just so you can open it for the first time" logic. Just open - it and start writing.

-

Example 6.7. Writing to Files

+   it and start writing.
+
Example 6.7. Writing to Files
 >>> logfile = open('test.log', 'w') 1
 >>> logfile.write('test succeeded') 2
 >>> logfile.close()
@@ -4790,7 +4417,7 @@ test succeededline 2
 
 1 
 
-You 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
+You 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.
 
 
@@ -4809,7 +4436,7 @@ test succeededline 2
 
 4 
 
-You happen to know that test.log exists (since you just finished writing to it), so you can open it and append to it.  (The "a" parameter means open the file for appending.)  Actually you could do this even if the file didn't exist, because opening
+You happen to know that test.log exists (since you just finished writing to it), so you can open it and append to it.  (The "a" parameter means open the file for appending.)  Actually you could do this even if the file didn't exist, because opening
                the file for appending will create the file if necessary.  But appending will never harm the existing contents of the file.
 
 
@@ -4817,33 +4444,27 @@ test succeededline 2
 5 
 
 As you can see, both the original line you wrote and the second line you appended are now in test.log.  Also note that carriage returns are not included.  Since you didn't write them explicitly to the file either time, the
-               file doesn't include them.  You can write a carriage return with the "\n" character.  Since you didn't do this, everything you wrote to the file ended up smooshed together on the same line.
+               file doesn't include them.  You can write a carriage return with the "\n" character.  Since you didn't do this, everything you wrote to the file ended up smooshed together on the same line.
 
 
 
-

-
-

Further Reading on File Handling

+

Further Reading on File Handling

-
-
-
-
-

6.3. Iterating with 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.

+

6.3. Iterating with 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']
+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
@@ -4857,26 +4478,24 @@ 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.
+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.
+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: you 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.
+This is the reason you haven't seen the for loop yet: you 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.
 
 
 
-

-

-
Doing a “normal” (by Visual Basic standards) counter for loop is also simple.

-
Example 6.9. Simple Counters
+
Doing a “normal” (by Visual Basic standards) counter for loop is also simple.
+
Example 6.9. Simple Counters
 >>> for i in range(5):             1
 ...     print i
 0
@@ -4908,10 +4527,8 @@ e
 
 
 
-

-

-
for loops are not just for simple counters.  They can iterate through all kinds of things.  Here is an example of using a for loop to iterate through a dictionary.

-
Example 6.10. Iterating Through a Dictionary
+
for loops are not just for simple counters.  They can iterate through all kinds of things.  Here is an example of using a for loop to iterate through a dictionary.
+
Example 6.10. Iterating Through a Dictionary
 >>> import os
 >>> for k, v in os.environ.items():      1 2
 ...     print "%s=%s" % (k, v)
@@ -4940,22 +4557,20 @@ USERNAME=mpilgrim
 
 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.  In the second round, k gets the second key, OS, and v gets the corresponding value, Windows_NT.
+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.  In 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
+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 I'm 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.  The output is the same in either case, although this version is slightly faster, because there is only one print statement instead of many.
+             Other programmers prefer to write this out as a for loop.  The output is the same in either case, although this version is slightly faster, because there is only one print statement instead of many.
 
 
 
-

-

-
Now we can look at the for loop in MP3FileInfo, from the sample fileinfo.py program introduced in Chapter 5.

-
Example 6.11. for Loop in MP3FileInfo
+
Now we can look at the for loop in MP3FileInfo, from the sample fileinfo.py program introduced in Chapter 5.
+
Example 6.11. for Loop in MP3FileInfo
     tagDataMap = {"title"   : (  3,  33, stripnulls),
 "artist"  : ( 33,  63, stripnulls),
 "album"   : ( 63,  93, stripnulls),
@@ -4980,7 +4595,7 @@ USERNAME=mpilgrim
 
 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 that 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.
+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 that 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.
 
 
 
@@ -4990,13 +4605,9 @@ USERNAME=mpilgrim
 
 
 
-

-

-
-
-

6.4. Using sys.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
+
6.4. Using sys.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
@@ -5015,20 +4626,18 @@ stat

 
 1 
 
-The sys module contains system-level information, such as the version of Python you're running (sys.version or sys.version_info), and system-level options such as the maximum allowed recursion depth (sys.getrecursionlimit() and sys.setrecursionlimit()).
+The sys module contains system-level information, such as the version of Python you're running (sys.version or sys.version_info), and system-level options such as 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 using a Python IDE, sys.modules contains all the modules imported by all the programs you've run within the IDE.
+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 using a Python IDE, sys.modules contains all the modules imported by all the programs you've run within the IDE.
 
 
 
-

-

-
This example demonstrates how to use sys.modules.

-
Example 6.13. Using sys.modules
>>> import fileinfo         1
+
This example demonstrates how to use sys.modules.
+
Example 6.13. Using sys.modules
>>> import fileinfo         1
 >>> print '\n'.join(sys.modules.keys())
 win32api
 os.path
@@ -5052,20 +4661,18 @@ stat
 
 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.
+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.
+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.
 
 
 
-

-

-
The next example shows how to use the __module__ class attribute with the sys.modules dictionary to get a reference to the module in which a class is defined.

-
Example 6.14. The __module__ Class Attribute
>>> from fileinfo import MP3FileInfo
+
The next example shows how to use the __module__ class attribute with the sys.modules dictionary to get a reference to the module in which a class is defined.
+
Example 6.14. The __module__ Class Attribute
>>> from fileinfo import MP3FileInfo
 >>> MP3FileInfo.__module__              1
 'fileinfo'
 >>> sys.modules[MP3FileInfo.__module__] 2
@@ -5074,20 +4681,18 @@ stat
 
 1 
 
-Every Python class has a built-in class attribute __module__, which is the name of the module in which the class is defined.
+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.
+Combining this with the sys.modules dictionary, you can get a reference to the module in which a class is defined.
 
 
 
-

-

-
Now you're ready to see how sys.modules is used in fileinfo.py, the sample program introduced in Chapter 5.  This example shows that portion of the code.

-
Example 6.15. sys.modules in fileinfo.py
+
Now you're ready to see how sys.modules is used in fileinfo.py, the sample program introduced in Chapter 5.  This example shows that portion of the code.
+
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
@@ -5096,7 +4701,7 @@ stat
 
 1 
 
-This is a function with two arguments; filename is required, but module is optional and defaults to the module that 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 evaluates default expressions only once, the first time the module is imported.  As you'll see later, you never call this
+This is a function with two arguments; filename is required, but module is optional and defaults to the module that 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 evaluates default expressions only once, the first time the module is imported.  As you'll see later, you never call this
             function with a module argument, so module serves as a function-level constant.
 
 
@@ -5114,23 +4719,18 @@ stat
 
 
 
-

-

 

-
Further Reading on Modules

+
Further Reading on Modules

 
-

-
-
-

6.5. Working with Directories

+

6.5. Working with Directories

The os.path module has several functions for manipulating files and directories. Here, we're looking at handling pathnames and listing - the contents of a directory.

-

Example 6.16. Constructing Pathnames

+   the contents of a directory.
+
Example 6.16. Constructing Pathnames
 >>> import os
 >>> os.path.join("c:\\music\\ap\\", "mahadeva.mp3") 1 2
 'c:\\music\\ap\\mahadeva.mp3'
@@ -5164,7 +4764,7 @@ stat
 
 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,
+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.
 
 
@@ -5174,9 +4774,7 @@ stat
 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
+
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
@@ -5222,9 +4820,7 @@ stat
 
 
 
-

-
-

Example 6.18. Listing Directories

>>> os.listdir("c:\\music\\_singles\\")              1
+
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']
@@ -5260,7 +4856,7 @@ stat
 
 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 you'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.
+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 you'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.
 
 
 
@@ -5271,9 +4867,7 @@ stat
 
 
 
-

-
-

Example 6.19. Listing Directories in fileinfo.py

+
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)
@@ -5285,19 +4879,19 @@ def listDirectory(directory, fileExtList):
 
 1 
 
-os.listdir(directory) returns a list of all the files and folders in directory.
+os.listdir(directory) returns a list of all the files and folders in directory.
 
 
 
 2 
 
-Iterating through the list with f, you 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.
+Iterating through the list with f, you 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, you use os.path.splitext(f) to split each filename into name and extension.
+Iterating through the normalized list with f again, you use os.path.splitext(f) to split each filename into name and extension.
 
 
 
@@ -5309,11 +4903,10 @@ def listDirectory(directory, fileExtList):
 
 5 
 
-For each file you care about, you use os.path.join(directory, f) to construct the full pathname of the file, and return a list of the full pathnames.
+For each file you care about, you use os.path.join(directory, f) to construct the full pathname of the file, and return a list of the full pathnames.
 
 
 
-
@@ -5325,8 +4918,8 @@ def listDirectory(directory, fileExtList):
Note

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

+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',
@@ -5366,27 +4959,22 @@ may already be familiar with from working on the command line.

 
 4 
 
-Now consider this scenario: you have a music directory, with several subdirectories within it, with .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!
+Now consider this scenario: you have a music directory, with several subdirectories within it, with .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 on the os Module

+

Further Reading on the os Module

-
-
-
-

6.6. Putting It All Together

+

6.6. Putting It All Together

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

-

Example 6.21. listDirectory

+   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)
@@ -5403,7 +4991,7 @@ def listDirectory(directory, fileExtList):     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
+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.
 
 
@@ -5423,7 +5011,7 @@ def listDirectory(directory, fileExtList):     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.
+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.
 
 
 
@@ -5436,22 +5024,18 @@ def listDirectory(directory, fileExtList):     6 
 
-For each file in the “interesting files” list (fileList), you call getFileInfoClass with the filename (f).  Calling getFileInfoClass(f) returns a class; you don't know exactly which class, but you don't care.  You then create an instance of this class (whatever
-            it is) and pass the filename (f again), to the __init__ method.  As you 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.  You do all that for each interesting file and return a
+For each file in the “interesting files” list (fileList), you call getFileInfoClass with the filename (f).  Calling getFileInfoClass(f) returns a class; you don't know exactly which class, but you don't care.  You then create an instance of this class (whatever
+            it is) and pass the filename (f again), to the __init__ method.  As you 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.  You 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, and 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 off the real work to the appropriate classes and collating the results.

-
-
-

6.7. Summary

-

The fileinfo.py program introduced in Chapter 5 should now make perfect sense.

+HTMLFileInfo for HTML files, DOCFileInfo for Word .doc files, and so forth. listDirectory will handle them all, without modification, by handing off the real work to the appropriate classes and collating the results. +

6.7. Summary

+

The fileinfo.py program introduced in Chapter 5 should now make perfect sense. 

 """Framework for getting filetype-specific metadata.
 
@@ -5529,44 +5113,36 @@ def listDirectory(directory, fileExtList):
 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 the following things:

+ print
+

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

-
-
-
-
-

Chapter 7. Regular Expressions

+

Chapter 7. Regular Expressions

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.

-
-

7.1. Diving In

+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. +

7.1. Diving In

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.

+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 a lot 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.

+ 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 regular expression syntax is tight and unlike normal code, the result can end up 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

+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, when I needed to scrub and standardize 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.) This example shows how I approached the problem.

-

Example 7.1. Matching at the End of a String

+   make this stuff up; it's actually useful.)  This example shows how I approached the problem.
+
Example 7.1. Matching at the End of a String
 >>> s = '100 NORTH MAIN ROAD'
 >>> s.replace('ROAD', 'RD.')               1
 '100 NORTH MAIN RD.'
@@ -5582,20 +5158,20 @@ within regular expressions to make them practically self-documenting.

 
 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.
+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.
+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, you could resort to something like this: only search and replace 'ROAD' in the last four characters of the address (s[-4:]), and leave the string alone (s[:-4]).  But you can see that this is already getting unwieldy.  For example, the pattern is dependent on the length of the string
-            you're replacing (if you were replacing 'STREET' with 'ST.', you 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.
+To solve the problem of addresses with more than one 'ROAD' substring, you could resort to something like this: only search and replace 'ROAD' in the last four characters of the address (s[-4:]), and leave the string alone (s[:-4]).  But you can see that this is already getting unwieldy.  For example, the pattern is dependent on the length of the string
+            you're replacing (if you were replacing 'STREET' with 'ST.', you 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.
 
 
 
@@ -5607,21 +5183,19 @@ within regular expressions to make them practically self-documenting.

 
 5 
 
-Take a look at the first parameter: 'ROAD$'.  This is a simple regular expression that 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”.)
+Take a look at the first parameter: 'ROAD$'.  This is a simple regular expression that 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, you search the string s for the regular expression 'ROAD$' and replace it with 'RD.'.  This matches the ROAD at the end of the string s, but does not match the ROAD that's part of the word BROAD, because that's in the middle of s.
+Using the re.sub function, you search the string s for the regular expression 'ROAD$' 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.
 
 
 
-

-
-

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.

-

Example 7.2. Matching Whole Words

+
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.
+
Example 7.2. Matching Whole Words
 >>> s = '100 BROAD'
 >>> re.sub('ROAD$', 'RD.', s)
 '100 BRD.'
@@ -5638,7 +5212,7 @@ ended with the street name.  Most of the time, I got away with it, but if the st
 
 1 
 
-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
+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.
 
@@ -5646,7 +5220,7 @@ ended with the street name.  Most of the time, I got away with it, but if the st
 
 2 
 
-To work around the backslash plague, you can use what is called a raw string, by prefixing the string 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
+To work around the backslash plague, you can use what is called a raw string, by prefixing the string 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).
 
 
@@ -5654,59 +5228,52 @@ ended with the street name.  Most of the time, I got away with it, but if the st
 3 
 
 *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 you get the original string back, which is not what you want.
+'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 you get the original string back, which is not what you want.
 
 
 
 4 
 
-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.
+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

+

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 that are repeated and combined in various ways to represent numbers.

+ 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 that are repeated and combined in various ways to represent numbers.

    -
  • I = 1
    • -
  • V = 5
    • -
  • X = 10
    • -
  • L = 50
    • -
  • C = 100
    • -
  • D = 500
    • -
  • M = 1000
    • +
  • I = 1 +
  • V = 5 +
  • X = 10 +
  • L = 50 +
  • C = 100 +
  • D = 500 +
  • M = 1000
-
-

The following are some general rules for constructing Roman numerals:

+

The following are some general rules for constructing Roman numerals:

    -
  • 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. -
    • -
  • The tens characters (I, X, C, and M) can be repeated up to three times. At 4, you need 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”). The number 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). -
    • -
  • Similarly, at 9, you need 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). The number 90 is XC, 900 is CM. -
    • -
  • The fives characters can not be repeated. The number 10 is always represented as X, never as VV. The number 100 is always C, never LL. -
    • +
  • 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. + +
  • The tens characters (I, X, C, and M) can be repeated up to three times. At 4, you need 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”). The number 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). + +
  • Similarly, at 9, you need 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). The number 90 is XC, 900 is CM. + +
  • The fives characters can not be repeated. The number 10 is always represented as X, never as VV. The number 100 is always C, never LL. +
  • Roman numerals are always written highest to lowest, and read left to right, so the order the 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 need to write it as XCIX, for 10 less than 100, then 1 less than 10). -
    • + 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 need to write it as XCIX, for 10 less than 100, then 1 less than 10). +
-
-
-

7.3.1. Checking for Thousands

+

7.3.1. Checking for Thousands

What would it take to validate that an arbitrary string is a valid Roman numeral? Let's take it one digit at a time. 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

+   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
@@ -5725,90 +5292,82 @@ ended with the street name.  Most of the time, I got away with it, but if the st
 This pattern has three parts:
 

 
    
-
  • ^ to 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 you want.  You want to make sure that the M characters, if they're there, are at the beginning of the string.
-
    • 
-
  • M? to optionally match a single M character.  Since this is repeated three times, you're matching anywhere from zero to three M characters in a row.
-
    • 
-
  • $ to 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.
-
    • 
+
  • ^ to 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 you want.  You want to make sure that the M characters, if they're there, are at the beginning of the string.
+
+
  • M? to optionally match a single M character.  Since this is repeated three times, you're matching anywhere from zero to three M characters in a row.
+
+
  • $ to 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 essence of the re module is the search function, that 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.  All you care about at the moment is whether the pattern matches, which you 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.
+The essence of the re module is the search function, that 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.  All you care about at the moment is whether the pattern matches, which you 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.
+'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.
+'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.
+'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.
+Interestingly, an empty string also matches this regular expression, since all the M characters are optional.
 
 
 
-

-
-
-
-

7.3.2. Checking for Hundreds

+

7.3.2. Checking for Hundreds

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

+ depending on its value.
    -
  • 100 = C
    • -
  • 200 = CC
    • -
  • 300 = CCC
    • -
  • 400 = CD
    • -
  • 500 = D
    • -
  • 600 = DC
    • -
  • 700 = DCC
    • -
  • 800 = DCCC
    • -
  • 900 = CM
    • +
  • 100 = C +
  • 200 = CC +
  • 300 = CCC +
  • 400 = CD +
  • 500 = D +
  • 600 = DC +
  • 700 = DCC +
  • 800 = DCCC +
  • 900 = CM
-
-

So there are four possible patterns:

+

So there are four possible patterns:

    -
  • CM
    • -
  • CD
    • -
  • Zero to three C characters (zero if the hundreds place is 0) -
    • -
  • D, followed by zero to three C characters -
    • +
  • CM +
  • CD +
  • Zero to three C characters (zero if the hundreds place is 0) + +
  • D, followed by zero to three C characters +
-
-

The last two patterns can be combined:

+

The last two patterns can be combined:

    -
  • an optional D, followed by zero to three C characters -
    • +
  • an optional D, followed by zero to three C characters +
-
-

This example shows how to validate the hundreds place of a Roman numeral.

-

Example 7.4. Checking for Hundreds

+
This example shows how to validate the hundreds place of a Roman numeral.
+
Example 7.4. Checking for Hundreds
 >>> import re
 >>> pattern = '^M?M?M?(CM|CD|D?C?C?C?)$' 1
 >>> re.search(pattern, 'MCM')            2
@@ -5824,55 +5383,50 @@ ended with the street name.  Most of the time, I got away with it, but if the st
 
 1 
 
-This pattern starts out the same as the previous one, checking for the beginning of the string (^), then the thousands place (M?M?M?).  Then it has 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 zero to three optional C characters).  The regular expression parser checks for each of these patterns in order (from left to right), takes the first
+This pattern starts out the same as the previous one, checking for the beginning of the string (^), then the thousands place (M?M?M?).  Then it has 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 zero to three 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.
+'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 three C characters are optional and are ignored).  MD is the Roman numeral representation of 1500.
+'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 three C characters are optional and are ignored).  MD is the Roman numeral representation of 1500.
 
 
 
 4 
 
-'MMMCCC' matches because all three 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.
+'MMMCCC' matches because all three 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 you're not at the end of the string yet (you 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.
+'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 you're not at the end of the string yet (you 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.
+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 you've only covered the thousands and hundreds places of Roman numerals. But if you followed all that, the tens and ones places are easy, because they're exactly the same pattern. But - let's look at another way to express the pattern.

-
-
-
-

7.4. Using the {n,m} Syntax

+ let's look at another way to express the pattern. +

7.4. Using the {n,m} Syntax

In the previous section, you were dealing with a pattern where the same character could be repeated up to three times. There is another way to express this in regular expressions, which some people find more readable. First look at the method we already used in the previous - example.

-

Example 7.5. The Old Way: Every Character Optional

+   example.
+
Example 7.5. The Old Way: Every Character Optional
 >>> import re
 >>> pattern = '^M?M?M?$'
 >>> re.search(pattern, 'M')    1
@@ -5890,31 +5444,29 @@ ended with the street name.  Most of the time, I got away with it, but if the st
 
 1 
 
-This matches the start of the string, and then the first optional M, but not the second and third M (but that's okay because they're optional), and then the end of the string.
+This matches the start of the string, and then the first optional M, but not the second and third M (but that's okay because they're optional), and then the end of the string.
 
 
 
 2 
 
-This matches the start of the string, and then the first and second optional M, but not the third M (but that's okay because it's optional), and then the end of the string.
+This matches the start of the string, and then the first and second optional M, but not the third M (but that's okay because it's optional), and then the end of the string.
 
 
 
 3 
 
-This matches the start of the string, and then all three optional M, and then the end of the string.
+This matches the start of the string, and then all three optional M, and then the end of the string.
 
 
 
 4 
 
-This matches the start of the string, and then all three optional M, but then does not match the the end of the string (because there is still one unmatched M), so the pattern does not match and returns None.
+This matches the start of the string, and then all three optional M, but then does not match the the end of the 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 o m

+
Example 7.6. The New Way: From n o m
 >>> pattern = '^M{0,3}$'       1
 >>> re.search(pattern, 'M')    2
 <_sre.SRE_Match object at 0x008EEB48>
@@ -5929,36 +5481,35 @@ ended with the street name.  Most of the time, I got away with it, but if the st
 
 1 
 
-This pattern says: “Match the start of the string, then anywhere from zero to three M characters, then the end of the string.”  The 0 and 3 can be any numbers; if you want to match at least one but no more than three M characters, you could say M{1,3}.
+This pattern says: “Match the start of the string, then anywhere from zero to three M characters, then the end of the string.”  The 0 and 3 can be any numbers; if you want to match at least one but no more than three M characters, you could say M{1,3}.
 
 
 
 2 
 
-This matches the start of the string, then one M out of a possible three, then the end of the string.
+This matches the start of the string, then one M out of a possible three, then the end of the string.
 
 
 
 3 
 
-This matches the start of the string, then two M out of a possible three, then the end of the string.
+This matches the start of the string, then two M out of a possible three, then the end of the string.
 
 
 
 4 
 
-This matches the start of the string, then three M out of a possible three, then the end of the string.
+This matches the start of the string, then three M out of a possible three, then the end of the string.
 
 
 
 5 
 
-This matches the start of the string, then three M out of a possible three, but then does not match the end of the string.  The regular expression allows for up to only three M characters before the end of the string, but you have four, so the pattern does not match and returns None.
+This matches the start of the string, then three M out of a possible three, but then does not match the end of the string.  The regular expression allows for up to only three M characters before the end of the string, but you have four, so the pattern does not match and returns None.
 
 
 
-

-
+
@@ -5969,11 +5520,10 @@ ended with the street name. Most of the time, I got away with it, but if the st
Note
-
-

7.4.1. Checking for Tens and Ones

+

7.4.1. Checking for Tens and Ones

Now let's expand the Roman numeral regular expression to cover the tens and ones place. This example shows the check for - tens.

-

Example 7.7. Checking for Tens

+   tens.
+
Example 7.7. Checking for Tens
 >>> 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>
@@ -5990,42 +5540,39 @@ ended with the street name.  Most of the time, I got away with it, but if the st
 
 1 
 
-This matches the start of the string, then the first optional M, then CM, then XL, then the end of the string.  Remember, the (A|B|C) syntax means “match exactly one of A, B, or C”.  You match XL, so you ignore the XC and L?X?X?X? choices, and then move on to the end of the string.  MCML is the Roman numeral representation of 1940.
+This matches the start of the string, then the first optional M, then CM, then XL, then the end of the string.  Remember, the (A|B|C) syntax means “match exactly one of A, B, or C”.  You match XL, so you ignore the XC and L?X?X?X? choices, and then move on to the end of the string.  MCML is the Roman numeral representation of 1940.
 
 
 
 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 three optional X characters.  Then you move to the end of the string.  MCML is the Roman numeral representation of 1950.
+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 three optional X characters.  Then you move to the end of the string.  MCML is the Roman numeral representation of 1950.
 
 
 
 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 the end of the string.  MCMLX is the Roman numeral representation of 1960.
+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 the end of the string.  MCMLX is the Roman numeral representation of 1960.
 
 
 
 4 
 
-This matches the start of the string, then the first optional M, then CM, then the optional L and all three optional X characters, then the end of the string.  MCMLXXX is the Roman numeral representation of 1980.
+This matches the start of the string, then the first optional M, then CM, then the optional L and all three optional X characters, then the end of the string.  MCMLXXX is the Roman numeral representation of 1980.
 
 
 
 5 
 
-This matches the start of the string, then the first optional M, then CM, then the optional L and all three optional X characters, then fails to match the end of the string because there is still one more X unaccounted for.  So the entire pattern fails to match, and returns None.  MCMLXXXX is not a valid Roman numeral.
+This matches the start of the string, then the first optional M, then CM, then the optional L and all three optional X characters, then fails to match the end of the string because there is still one more X unaccounted for.  So the entire pattern fails to match, and returns None.  MCMLXXXX is not a valid Roman numeral.
 
 
 
-

-
-

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

+

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

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

So what does that look like using this alternate {n,m} syntax? This example shows the new syntax.

-

Example 7.8. Validating Roman Numerals with {n,m}

+

So what does that look like using this alternate {n,m} syntax? This example shows the new syntax. +

Example 7.8. 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>
@@ -6040,55 +5587,49 @@ ended with the street name.  Most of the time, I got away with it, but if the st
 
 1 
 
-This matches the start of the string, then one of a possible four M characters, then D?C{0,3}.  Of that, it matches the optional D and zero of three possible C characters.  Moving on, it matches L?X{0,3} by matching the optional L and zero of three possible X characters.  Then it matches V?I{0,3} by matching the optional V and zero of three possible I characters, and finally the end of the string.  MDLV is the Roman numeral representation of 1555.
+This matches the start of the string, then one of a possible four M characters, then D?C{0,3}.  Of that, it matches the optional D and zero of three possible C characters.  Moving on, it matches L?X{0,3} by matching the optional L and zero of three possible X characters.  Then it matches V?I{0,3} by matching the optional V and zero of three possible I characters, and finally the end of the string.  MDLV is the Roman numeral representation of 1555.
 
 
 
 2 
 
-This matches the start of the string, then two of a possible four M characters, then the D?C{0,3} with a D and one of three possible C characters; then L?X{0,3} with an L and one of three possible X characters; then V?I{0,3} with a V and one of three possible I characters; then the end of the string.  MMDCLXVI is the Roman numeral representation of 2666.
+This matches the start of the string, then two of a possible four M characters, then the D?C{0,3} with a D and one of three possible C characters; then L?X{0,3} with an L and one of three possible X characters; then V?I{0,3} with a V and one of three possible I characters; then the end of the string.  MMDCLXVI is the Roman numeral representation of 2666.
 
 
 
 3 
 
-This matches the start of the string, then four out of four M characters, then D?C{0,3} with a D and three out of three C characters; then L?X{0,3} with an L and three out of three X characters; then V?I{0,3} with a V and three out of three I characters; then the end of the string.  MMMMDCCCLXXXVIII is the Roman numeral representation of 3888, and it's the longest Roman numeral you can write without extended syntax.
+This matches the start of the string, then four out of four M characters, then D?C{0,3} with a D and three out of three C characters; then L?X{0,3} with an L and three out of three X characters; then V?I{0,3} with a V and three out of three I characters; then the end of the string.  MMMMDCCCLXXXVIII is the Roman numeral representation of 3888, and it's the longest Roman numeral you can write without extended syntax.
 
 
 
 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 zero out of four M, then matches D?C{0,3} by skipping the optional D and matching zero out of three C, then matches L?X{0,3} by skipping the optional L and matching zero out of three X, then matches V?I{0,3} by skipping the optional V and matching one out of three I.  Then the end of the string.  Whoa.
+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 zero out of four M, then matches D?C{0,3} by skipping the optional D and matching zero out of three C, then matches L?X{0,3} by skipping the optional L and matching zero out of three X, then matches V?I{0,3} by skipping the optional V and matching one out of three I.  Then the end of the 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 you'll explore an alternate syntax that can help keep your expressions maintainable.

-
-
-
-

7.5. Verbose Regular Expressions

+ to your own regular expressions a few months later. I've done it, and it's not a pretty sight. +

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

7.5. Verbose Regular Expressions

So far you'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 you 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 two ways:

+ that you'll be able to understand it six months later. What you 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 two ways:

  • 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.) -
    • -
  • 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 + +
  • 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 you've been working with, and make -it a verbose regular expression. This example shows how.

-

Example 7.9. Regular Expressions with Inline Comments

+it a verbose regular expression.  This example shows how.
+
Example 7.9. Regular Expressions with Inline Comments
 >>> pattern = """
     ^ # beginning of string
     M{0,4}              # thousands - 0 to 4 M's
@@ -6113,7 +5654,7 @@ it a verbose regular expression.  This example shows how.

 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
+            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 you saw in the previous section, but it's a lot more readable.
 
@@ -6121,50 +5662,45 @@ it a verbose regular expression.  This example shows how.

 
 2 
 
-This matches the start of the string, then one of a possible four M, then CM, then L and three of a possible three X, then IX, then the end of the string.
+This matches the start of the string, then one of a possible four M, then CM, then L and three of a possible three X, then IX, then the end of the string.
 
 
 
 3 
 
-This matches the start of the string, then four of a possible four M, then D and three of a possible three C, then L and three of a possible three X, then V and three of a possible three I, then the end of the string.
+This matches the start of the string, then four of a possible four M, then D and three of a possible three C, then L and three of a possible three X, then V and three of a possible three I, then the end of the string.
 
 
 
 4 
 
-This does not match.  Why?  Because it doesn't have the re.VERBOSE flag, so the re.search function is treating the 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.
+This does not match.  Why?  Because it doesn't have the re.VERBOSE flag, so the re.search function is treating the 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

+

7.6. Case study: Parsing Phone Numbers

So far you'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.

+ 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. The client wanted to be able to enter the number free-form (in a single field), but then wanted to store the area code, trunk, number, and optionally an extension separately in the company's database. I scoured the Web and found many -examples of regular expressions that purported to do this, but none of them were permissive enough.

-

Here are the phone numbers I needed to be able to accept:

+examples of regular expressions that purported to do this, but none of them were permissive enough. +

Here are the phone numbers I needed 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
    • +
  • 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.

-

Let's work through developing a solution for phone number parsing. This example shows the first step.

-

Example 7.10. Finding Numbers

+
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.
+
Let's work through developing a solution for phone number parsing.  This example shows the first step.
+
Example 7.10. Finding Numbers
 >>> phonePattern = re.compile(r'^(\d{3})-(\d{3})-(\d{4})$') 1
 >>> phonePattern.search('800-555-1212').groups()            2
 ('800', '555', '1212')
@@ -6175,7 +5711,7 @@ examples of regular expressions that purported to do this, but none of them were
 
 1 
 
-Always read regular expressions from left to right.  This one matches the beginning of the string, and then (\d{3}).  What's \d{3}?  Well, the {3} means “match exactly three numeric digits”; it's a variation on the {n,m} syntax you saw earlier.  \d means “any numeric digit” (0 through 9).  Putting it in parentheses means “match exactly three 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 three digits.  Then another literal hyphen.  Then another
+Always read regular expressions from left to right.  This one matches the beginning of the string, and then (\d{3}).  What's \d{3}?  Well, the {3} means “match exactly three numeric digits”; it's a variation on the {n,m} syntax you saw earlier.  \d means “any numeric digit” (0 through 9).  Putting it in parentheses means “match exactly three 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 three digits.  Then another literal hyphen.  Then another
             group of exactly four digits.  Then match the end of the string.
 
 
@@ -6194,9 +5730,7 @@ examples of regular expressions that purported to do this, but none of them were
 
 
 
-

-
-

Example 7.11. Finding the Extension

+
Example 7.11. 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')
@@ -6238,10 +5772,8 @@ examples of regular expressions that purported to do this, but none of them were
 
 
 
-

-
-

The next example shows the regular expression to handle separators between the different parts of the phone number.

-

Example 7.12. Handling Different Separators

+
The next example shows the regular expression to handle separators between the different parts of the phone number.
+
Example 7.12. 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')
@@ -6256,14 +5788,14 @@ examples of regular expressions that purported to do this, but none of them were
 
 1 
 
-Hang on to your hat.  You're matching the beginning of the string, then a group of three 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 one or more characters that are not digits.  This is what you're using instead of a literal hyphen, to try to match
+Hang on to your hat.  You're matching the beginning of the string, then a group of three 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 one or more characters that are not digits.  This is what you're using instead of a literal hyphen, to try to match
             different separators.
 
 
 
 2 
 
-Using \D+ instead of - means you can now match phone numbers where the parts are separated by spaces instead of hyphens.
+Using \D+ instead of - means you can now match phone numbers where the parts are separated by spaces instead of hyphens.
 
 
 
@@ -6286,10 +5818,8 @@ examples of regular expressions that purported to do this, but none of them were
 
 
 
-

-
-

The next example shows the regular expression for handling phone numbers without separators.

-

Example 7.13. Handling Numbers Without Separators

+
The next example shows the regular expression for handling phone numbers without separators.
+
Example 7.13. Handling Numbers Without 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')
@@ -6304,20 +5834,20 @@ examples of regular expressions that purported to do this, but none of them were
 
 1 
 
-The only change you've made since that last step is changing all the + to *.  Instead of \D+ between the parts of the phone number, you now match on \D*.  Remember that + means “1 or more”?  Well, * means “zero or more”.  So now you should be able to parse phone numbers even when there is no separator character at all.
+The only change you've made since that last step is changing all the + to *.  Instead of \D+ between the parts of the phone number, you now match on \D*.  Remember that + means “1 or more”?  Well, * means “zero or more”.  So now you should be able to parse phone numbers even when there is no separator character at all.
 
 
 
 2 
 
 Lo and behold, it actually works.  Why?  You matched the beginning of the string, then a remembered group of three digits
-            (800), then zero non-numeric characters, then a remembered group of three digits (555), then zero non-numeric characters, then a remembered group of four digits (1212), then zero non-numeric characters, then a remembered group of an arbitrary number of digits (1234), then the end of the string.
+            (800), then zero non-numeric characters, then a remembered group of three digits (555), then zero non-numeric characters, then a remembered group of four digits (1212), then zero non-numeric characters, then a remembered group of an arbitrary number of digits (1234), then the end of the string.
 
 
 
 3 
 
-Other variations work now too: dots instead of hyphens, and both a space and an x before the extension.
+Other variations work now too: dots instead of hyphens, and both a space and an x before the extension.
 
 
 
@@ -6335,10 +5865,8 @@ examples of regular expressions that purported to do this, but none of them were
 
 
 
-

-
-

The next example shows how to handle leading characters in phone numbers.

-

Example 7.14. Handling Leading Characters

+
The next example shows how to handle leading characters in phone numbers.
+
Example 7.14. 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')
@@ -6351,7 +5879,7 @@ examples of regular expressions that purported to do this, but none of them were
 
 1 
 
-This is the same as in the previous example, except now you're matching \D*, zero or more non-numeric characters, before the first remembered group (the area code).  Notice that you're not remembering
+This is the same as in the previous example, except now you're matching \D*, zero or more non-numeric characters, before the first remembered group (the area code).  Notice that you're not remembering
             these non-numeric characters (they're not in parentheses).  If you find them, you'll just skip over them and then start remembering
             the area code whenever you get to it.
 
@@ -6360,7 +5888,7 @@ examples of regular expressions that purported to do this, but none of them were
 2 
 
 You 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.)
+            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.)
 
 
 
@@ -6368,24 +5896,22 @@ examples of regular expressions that purported to do this, but none of them were
 
 Just a sanity check to make sure you haven't broken anything that used to work.  Since the leading characters are entirely
             optional, this matches the beginning of the string, then zero non-numeric characters, then a remembered group of three digits
-            (800), then one non-numeric character (the hyphen), then a remembered group of three digits (555), then one non-numeric character (the hyphen), then a remembered group of four digits (1212), then zero non-numeric characters, then a remembered group of zero digits, then the end of the string.
+            (800), then one non-numeric character (the hyphen), then a remembered group of three digits (555), then one non-numeric character (the hyphen), then a remembered group of four digits (1212), then zero non-numeric characters, then a remembered group of zero digits, then the end of the 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 you assumed that all the leading characters before the area code were non-numeric characters (\D*).  Aargh.
+             Because there's a 1 before the area code, but you 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 the regular expressions have all matched from the beginning of the string. But now you see that there may be an indeterminate amount of stuff at the beginning of the string that you want to ignore. Rather than trying to match it all just so you can skip over it, let's take a different approach: don't explicitly match the beginning -of the string at all. This approach is shown in the next example.

-

Example 7.15. Phone Number, Wherever I May Find Ye

+of the string at all.  This approach is shown in the next example.
+
Example 7.15. 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')
@@ -6398,7 +5924,7 @@ of the string at all.  This approach is shown in the next example.

 
 1 
 
-Note the lack of ^ in this regular expression.  You are not matching the beginning of the string anymore.  There's nothing that says you need
+Note the lack of ^ in this regular expression.  You are not matching the beginning of the string anymore.  There's nothing that says you need
             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.
 
@@ -6421,14 +5947,12 @@ of the string at all.  This approach is shown in the next example.

 That still works too.
 
 
-

-

See how quickly a regular expression can get out of control? Take a quick glance at any of the previous iterations. Can -you tell the difference between one and the next?

+you tell the difference between one and the next?

While you still understand the final answer (and it is the 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 you forget why you made the choices -you made.

-

Example 7.16. Parsing Phone Numbers (Final Version)

+you made.
+
Example 7.16. 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')
@@ -6459,58 +5983,52 @@ you made.

 Final sanity check.  Yes, this still works.  You're done.
 
 
-

-
-

Further Reading on Regular Expressions

+

Further Reading on Regular Expressions

-
-
-
-

7.7. Summary

+

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:

+ 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. -
    • -
  • \d matches any numeric digit. -
    • -
  • \D matches any non-numeric character. -
    • -
  • x? matches an optional x character (in other words, it matches an x zero or one times). -
    • -
  • x* matches x zero or more times. -
    • -
  • x+ matches x one 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. -
    • +
  • ^ matches the beginning of a string. + +
  • $ matches the end of a string. + +
  • \b matches a word boundary. + +
  • \d matches any numeric digit. + +
  • \D matches any non-numeric character. + +
  • x? matches an optional x character (in other words, it matches an x zero or one times). + +
  • x* matches x zero or more times. + +
  • x+ matches x one 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.

+they solve.
@@ -6519,18 +6037,14 @@ they solve.

-

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

+

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

-
-
-
-

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.

+
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
 
@@ -6601,8 +6115,7 @@ class BaseHTMLProcessor(SGMLParser):
 
     def output(self):              
         """Return processed HTML as a single string"""
-        return "".join(self.pieces)

-
Example 8.2. dialect.py
+        return "".join(self.pieces)
Example 8.2. dialect.py
 import re
 from BaseHTMLProcessor import BaseHTMLProcessor
 
@@ -6755,10 +6268,9 @@ def test(url):
         webbrowser.open_new(outfile)
 
 if __name__ == "__main__":
-    test("http://diveintopython3.org/odbchelper_list.html")

-
Example 8.3. Output of dialect.py

+    test("http://diveintopython3.org/odbchelper_list.html")

Example 8.3. Output of dialect.py

Running this script will translate Section 3.2, “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.

+   code listings and screen examples were left untouched.
 <div class="abstract">
 <p>Lists awe <span class="application">Pydon</span>'s wowkhowse datatype.
 If youw onwy expewience wif wists is awways in
@@ -6766,16 +6278,13 @@ If youw onwy expewience wif wists is awways in
 in <span class="application">Powewbuiwdew</span>, bwace youwsewf fow
 <span class="application">Pydon</span> wists.</p>
 </div>
-
-
-
-

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.

+

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.

+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:

+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
@@ -6785,25 +6294,25 @@ it calls a method on itself based on what it found. In order to use the parser,
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. +
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. +
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. +
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. +
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. +
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.
-
+
@@ -6813,7 +6322,7 @@ it calls a method on itself based on what it found. In order to use the parser,
Important

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.

+the SGMLParser class and defining unknown_starttag, unknown_endtag, handle_data and other methods which simply print their arguments.
@@ -6822,10 +6331,10 @@ the SGMLParser class and defining

Example 8.4. Sample test of sgmllib.py

-

Here is a snippet from the table of contents of the HTML version of this book. Of course your paths may vary. (If you haven't downloaded the HTML version of the book, you can do so at http://diveintopython3.org/.

+
Example 8.4. Sample test of sgmllib.py

+
Here is a snippet from the table of contents of the HTML version of this book.  Of course your paths may vary.  (If you haven't downloaded the HTML version of the book, you can do so at http://diveintopython3.org/.
 c:\python23\lib> type "c:\downloads\diveintopython3\html\toc\index.html"
-
+
 <!DOCTYPE html
   PUBLIC "-//W3C//DTD HTML 4.01//EN" "http://www.w3.org/TR/html4/strict.dtd">
 <html>
@@ -6836,7 +6345,7 @@ the SGMLParser class and defining 
 
 ... rest of file omitted for brevity ...
-
Running this through the test suite of sgmllib.py yields this output:
+
Running this through the test suite of sgmllib.py yields this output:
 c:\python23\lib> python sgmllib.py "c:\downloads\diveintopython3\html\toc\index.html"
 data: '\n\n'
 start tag: <html >
@@ -6853,29 +6362,25 @@ start tag: <link rel="stylesheet" href="diveintopython3.css" type="text/css"
 data: '\n      '
 
 ... rest of output omitted for brevity ...
-

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

+

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://diveintopython3.org/ and processes it. -
    • +
-
-

Along the way, you'll also learn about locals, globals, and dictionary-based string formatting.

- -
-

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

+
Along the way, you'll also learn about locals, globals, and dictionary-based string formatting.
+
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://diveintopython3.org/") 2
 >>> htmlSource = sock.read()          3
@@ -6928,10 +6433,8 @@ data: '\n      '
Tip
-
-
-
-

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

+

+
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):
@@ -6954,25 +6457,23 @@ class URLLister(SGMLParser):
 
 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.
+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 
 
-You can find out whether this <a> tag has an href attribute with a simple multi-variable list comprehension.
+You 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.
+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

+
Example 8.7. Using urllister.py
 >>> import urllib, urllister
 >>> usock = urllib.urlopen("http://diveintopython3.org/")
 >>> parser = urllister.URLLister()
@@ -7021,16 +6522,12 @@ download/diveintopython3-common-5.0.zip
 
 
 
-

-
-
-
-

8.4. Introducing BaseHTMLProcessor.py

+

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 you'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

+   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 = []
@@ -7078,21 +6575,21 @@ Python is much more efficient at dealing with lists.[
 3 
 
-Reconstructing end tags is much simpler; just take the tag name and wrap it in the </...> brackets.
+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.
+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.)
+            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.)
 
 
 
@@ -7104,18 +6601,17 @@ Python is much more efficient at dealing with lists.[
 7 
 
-HTML comments are wrapped in <!--...--> characters.
+HTML comments are wrapped in <!--...--> characters.
 
 
 
 8 
 
-Processing instructions are wrapped in <?...> characters.
+Processing instructions are wrapped in <?...> characters.
 
 
 
-

-
+
@@ -7125,7 +6621,7 @@ Python is much more efficient at dealing with lists.[
Important
-

Example 8.9. BaseHTMLProcessor output

+
Example 8.9. BaseHTMLProcessor output
     def output(self):               1
         """Return processed HTML as a single string"""
         return "".join(self.pieces) 2

@@ -7139,60 +6635,53 @@ Python is much more efficient at dealing with lists.[
 2 
 
-If you prefer, you could use the join method of the string module instead: string.join(self.pieces, "")
+If you prefer, you could use the join method of the string module instead: string.join(self.pieces, "")
 
 
-

-

 

-
Further reading

+
Further reading

 
-

-
-
-

8.5. locals and globals

-

Let's digress from HTML processing for a minute and talk about how Python handles variables. Python has two built-in functions, locals and globals, which provide dictionary-based access to local and global variables.

-

Remember locals? You first saw it here:

+

8.5. locals and globals

+

Let's digress from HTML processing for a minute and talk about how Python handles variables. Python has two built-in functions, locals and globals, which provide dictionary-based access to local and global variables. +

Remember locals? You first saw it here: 

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

No, wait, you can't learn about locals yet. First, you need to learn about namespaces. This is dry stuff, but it's important, so pay attention.

+

No, wait, you can't learn about locals yet. First, you need to learn about 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 you'll see in a minute.

+of variables and the dictionary values are the values of those variables. In fact, you can access a namespace as a Python dictionary, as you'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:

+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. +
  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. +
  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. -
    4. +
-
-

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 3.18, “Referencing an Unbound Variable”, but you didn't appreciate how much work Python was doing before giving you that error.

+

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 3.18, “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:
+
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
-

Are you confused yet? Don't despair! This is really cool, I promise. Like many things in Python, namespaces are directly accessible at run-time. How? Well, 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
+
Are you confused yet?  Don't despair!  This is really cool, I promise.  Like many things in Python, namespaces are directly accessible at run-time.  How?  Well, 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()
 ...     
@@ -7211,7 +6700,7 @@ from __future__ import nested_scopes

 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).
+            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).
 
 
 
@@ -7221,15 +6710,13 @@ from __future__ import nested_scopes
-
-

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.[3] 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 need 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

-

Look at the following block of code at the bottom of BaseHTMLProcessor.py:

+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 need 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

+
Look at the following block of code at the bottom of BaseHTMLProcessor.py:
 if __name__ == "__main__":
     for k, v in globals().items():             1
         print k, "=", v

@@ -7241,9 +6728,8 @@ if __name__ == "__main__":
 
 
 
-

 
Now running the script from the command line gives this output (note that your output may be slightly different, depending
-   on your platform and where you installed Python):
c:\docbook\dip\py> python BaseHTMLProcessor.py
+   on your platform and where you installed Python):
c:\docbook\dip\py> python BaseHTMLProcessor.py
 SGMLParser = sgmllib.SGMLParser                1
 htmlentitydefs = <module 'htmlentitydefs' from 'C:\Python23\lib\htmlentitydefs.py'> 2
 BaseHTMLProcessor = __main__.BaseHTMLProcessor 3
@@ -7253,13 +6739,13 @@ __name__ = __main__          1 
 
-SGMLParser was imported from sgmllib, using from module import.  That means that it was imported directly into the module's namespace, and here it is.
+SGMLParser was imported from sgmllib, using from module import.  That means that it was imported directly into the 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 the namespace, but the entitydefs variable defined within htmlentitydefs is not.
+Contrast this with htmlentitydefs, which was imported using import.  That means that the htmlentitydefs module itself is in the namespace, but the entitydefs variable defined within htmlentitydefs is not.
 
 
 
@@ -7271,11 +6757,10 @@ __name__ = __main__          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 you ran this module as a script from the command line, __name__ is __main__, which is why the little test code to print the globals got executed.
+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 you ran this module as a script from the command line, __name__ is __main__, which is why the little test code to print the globals got executed.
 
 
 
-
@@ -7287,8 +6772,8 @@ __name__ = __main__ locals and globals functions, 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

+it.
+
Example 8.12. locals is read-only, globals is not
 def foo(arg):
     x = 1
     print locals()    1
@@ -7305,21 +6790,21 @@ print "z=",z
- - @@ -7331,21 +6816,17 @@ print "z=",z -
Note1 Since foo is called with 3, this will print {'arg': 3, 'x': 1}. This should not be a surprise. +Since foo is called with 3, this will print {'arg': 3, 'x': 1}. This should not be a surprise.
2 locals is a function that returns a dictionary, and here you are setting a value in that dictionary. 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 + 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. +This prints x= 1, not x= 2.
5 This prints z= 8, not z= 7. +This prints z= 8, not z= 7.
-
-
-
-
-

8.6. Dictionary-based string formatting

+

8.6. Dictionary-based string formatting

Why did you learn about locals and globals? So you can learn about dictionary-based string formatting. As you recall, regular 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

+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" % params1
 'secret'
@@ -7357,7 +6838,7 @@ constantly switching between reading the string and reading the tuple of values.
 
 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.
+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.
 
 
 
@@ -7373,12 +6854,10 @@ constantly switching between reading the string and reading the tuple of values.
 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

+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
 

@@ -7387,13 +6866,11 @@ meaningful keys and values already.  Like 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-->'.
+            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-->'.
 
 
 
-

-

-
Example 8.15. More dictionary-based string formatting
+
Example 8.15. More dictionary-based string formatting
     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
@@ -7405,31 +6882,29 @@ meaningful keys and values already.  Like When this method is called, attrs is a list of key/value tuples, just like the items of a dictionary, which means you 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. 
-
  2. In the first round of the list comprehension, key will get 'href', and value will get 'index.html'.
-
    3. 
-
  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. 
-
  4. In the second round, key will get 'title', and value will get 'Go to home page'.
-
    5. 
-
  5. The string formatting will resolve to ' title="Go to home page"'.
-
    6. 
-
  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"'.
-
    7. 
+
  7. Suppose attrs is [('href', 'index.html'), ('title', 'Go to home page')].
+
+
  8. In the first round of the list comprehension, key will get 'href', and value will get 'index.html'.
+
+
  9. 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.
+
+
  10. In the second round, key will get 'title', and value will get 'Go to home page'.
+
+
  11. The string formatting will resolve to ' title="Go to home page"'.
+
+
  12. 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, you 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.
+Now, using dictionary-based string formatting, you 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.
 
 
 
-

-

+

@@ -7439,14 +6914,12 @@ meaningful keys and values already.  Like 
-
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?”[4]  (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.

+
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?”[4]  (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 you can take advantage of.

-
Example 8.16. Quoting attribute values
+at all.  It is this last side effect that you can take advantage of.
+
Example 8.16. Quoting attribute values
 >>> htmlSource = """        1
 ...     <html>
 ...     <head>
@@ -7479,7 +6952,7 @@ at all.  It is this last side effect that you can take advantage of.

 

-
@@ -7495,14 +6968,10 @@ at all.  It is this last side effect that you can take advantage of.

 

 Important
 

 1 
 Note that the attribute values of the href attributes in the <a> tags are not properly quoted.  (Also note that you're using triple quotes for something other than a doc string.  And directly in the IDE, no less.  They're very useful.)
+Note that the attribute values of the href attributes in the <a> tags are not properly quoted.  (Also note that you're using triple quotes for something other than a doc string.  And directly in the IDE, no less.  They're very useful.)
 
 

 

 
 

 

-
-
-
-
-

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, you define two methods in Dialectizer: start_pre and end_pre.

-

Example 8.17. Handling specific tags

+
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, you define two methods in Dialectizer: start_pre and end_pre.
+
Example 8.17. Handling specific tags
     def start_pre(self, attrs):             1
         self.verbatim += 12
         self.unknown_starttag("pre", attrs) 3
@@ -7547,10 +7016,8 @@ at all.  It is this last side effect that you can take advantage of.

 
 
 
-

-
-

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, you 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 8.18. SGMLParser

+
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, you 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 8.18. SGMLParser
     def finish_starttag(self, tag, attrs):               1
         try:        
             method = getattr(self, 'start_' + tag)       2
@@ -7581,14 +7048,14 @@ at all.  It is this last side effect that you can take advantage of.

 
 2 
 
-The “magic” of SGMLParser is nothing more than your 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.
+The “magic” of SGMLParser is nothing more than your 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 you wrapped
-            the call to getattr inside a try...except block and explicitly caught the AttributeError.
+            the call to getattr inside a try...except block and explicitly caught the AttributeError.
 
 
 
@@ -7606,14 +7073,14 @@ at all.  It is this last side effect that you can take advantage of.

 
 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 you did find a do_xxx method for this tag, so you're going to call it.
+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 you did find a do_xxx method for this tag, so you'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
+             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.  You have better things to worry about right now.
 
@@ -7629,11 +7096,9 @@ at all.  It is this last side effect that you can take advantage of.

 
 
 
-

-

Now back to our regularly scheduled program: Dialectizer. When you left, you 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 the pre-defined substitutions. For that, -you need to override the handle_data method.

-

Example 8.19. Overriding the handle_data method

+you need to override the handle_data method.
+
Example 8.19. Overriding the handle_data method
     def handle_data(self, text):     1
         self.pieces.append(self.verbatim and text or self.process(text)) 2

 
@@ -7646,21 +7111,17 @@ you need to override the handle_data method.

-

 

 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 you're in the middle of a <pre>...</pre> block, self.verbatim will be some value greater than 0, and you want to put the text in the output buffer unaltered.  Otherwise, you 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.
+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 you're in the middle of a <pre>...</pre> block, self.verbatim will be some value greater than 0, and you want to put the text in the output buffer unaltered.  Otherwise, you 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.
 
 

 

-

-

 
You'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.  The classes
 later in dialect.py define a series of regular expressions that operate on the text between the HTML tags.  But you just had a whole chapter on regular expressions.  You don't really want to slog through regular expressions again, do you?  God knows I don't.  I think you've learned enough
-for one chapter.

-
-
-

8.9. Putting it all together

-

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

-

Example 8.20. The translate function, part 1

+for one chapter.
+
8.9. Putting it all together

+
It's time to put everything you've learned so far to good use.  I hope you were paying attention.
+
Example 8.20. The translate function, part 1
 def translate(url, dialectName="chef"): 1
     import urllib     2
     sock = urllib.urlopen(url)          3
@@ -7677,7 +7138,7 @@ def translate(url, dialectName="chef"): 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
+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
@@ -7691,9 +7152,7 @@ def translate(url, dialectName="chef"): 
Example 8.21. The translate function, part 2: curiouser and curiouser
+
Example 8.21. The translate function, part 2: curiouser and curiouser
     parserName = "%sDialectizer" % dialectName.capitalize() 1
     parserClass = globals()[parserName]   2
     parser = parserClass()                3
@@ -7703,33 +7162,31 @@ def translate(url, dialectName="chef"): 1 
 
 capitalize is a string method you haven't seen before; it simply capitalizes the first letter of a string and forces everything else
-            to lowercase.  Combined with some string formatting, you'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'.
+            to lowercase.  Combined with some string formatting, you'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 
 
-You have the name of a class as a string (parserName), and you have the global namespace as a dictionary (globals()).  Combined, you 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.
+You have the name of a class as a string (parserName), and you have the global namespace as a dictionary (globals()).  Combined, you 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, you have a class object (parserClass), and you want an instance of the class.  Well, you 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; you 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.
+            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 you've defined.  Imagine if you 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.  You've already seen that this includes it in globals(), so translate would still work without modification, even though FooDialectizer was in a separate file.

+
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 you've defined.  Imagine if you 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.  You'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.

+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 it 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 you go.

-
Example 8.22. The translate function, part 3
+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 you go.
+
Example 8.22. The translate function, part 3
     parser.feed(htmlSource) 1
     parser.close()          2
     return parser.output()  3
@@ -7756,75 +7213,60 @@ appropriately-named file in the plug-ins directory (like 
 
 
 
-

-

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

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

-
Further reading

+
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.

+

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
    • + +
  • 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:

+

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

-
-

[1] 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.

-
+ 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.

[2] 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 + 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).

-
+ 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).
-

[3] I don't get out much.

-
+

[3] I don't get out much.

-

[4] 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.

-
-
-
+

[4] 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

+

Chapter 9. XML Processing

+

9.1. Diving in

These next two chapters are 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, there are many XML tutorials that can explain the basics.

-

If you're not particularly interested in XML, you should still read these chapters, which cover important topics like Python packages, Unicode, command line arguments, and how to use getattr for method dispatching.

+sense to you, there are many XML tutorials that can explain the basics. +

If you're not particularly interested in XML, you should still read these chapters, which cover important topics like Python packages, Unicode, command line arguments, and how to use getattr for method dispatching.

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 Chapter 8, 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.

+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 Chapter 8, 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; you'll examine both the program's input and its output -in more depth throughout these next two chapters.

-

Example 9.1. kgp.py

-

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

+in more depth throughout these next two chapters.
+
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
@@ -8065,8 +7507,7 @@ def main(argv):
 
 if __name__ == "__main__":
     main(sys.argv[1:])
-

-
Example 9.2. toolbox.py
+
Example 9.2. toolbox.py
 """Miscellaneous utility functions"""
 
 def openAnything(source):            
@@ -8113,9 +7554,8 @@ def openAnything(source):
     # 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
+
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
@@ -8148,43 +7588,37 @@ 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
+[...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.

+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 you tell the program to use a different grammar file (which you 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
+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

-
You will take a closer look at the structure of the grammar file later in this chapter.  For now, all you need 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

+10110100

You will take a closer look at the structure of the grammar file later in this chapter. For now, all you need 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 you get to that line of code, you need to take a short detour - to talk about packages.

-

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

+   to talk about packages.
+
Example 9.5. Loading an XML document (a sneak peek)
 >>> from xml.dom import minidom 1
 >>> xmldoc = minidom.parse('~/diveintopython3/common/py/kgp/binary.xml')

 
-

 

 1 
 This is a syntax you haven't seen before.  It looks almost like the from module import you 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.
+This is a syntax you haven't seen before.  It looks almost like the from module import you 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)
+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)
    |
@@ -8194,11 +7628,10 @@ just .py files, like always, except that they're i
        |
        +--dom/      xml.dom package (contains minidom.py)
        |
-       +--parsers/  xml.parsers package (used internally)

-
So when you 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
+       +--parsers/  xml.parsers package (used internally)
So when you 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
+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
@@ -8241,10 +7674,8 @@ The syntax is all the same; Python figures out what you mean based on the file l
 
 
 
-

-

 
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.

+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.

@@ -8255,14 +7686,12 @@ The answer is the magical __init__.py file.  You s
 

 

 Note
 

 

 
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).

+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)
+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('~/diveintopython3/common/py/kgp/binary.xml')  2
 >>> xmldoc         3
@@ -8306,10 +7735,8 @@ package architecture.  It's one of the many things Python is good at, so take ad
 
 
 
-

-

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

-
Example 9.9. Getting child nodes
+
Now that you have an XML document in memory, you can start traversing through it.
+
Example 9.9. Getting child nodes
 >>> xmldoc.childNodes    1
 [<DOM Element: grammar at 17538908>]
 >>> xmldoc.childNodes[0] 2
@@ -8333,13 +7760,11 @@ package architecture.  It's one of the many things Python is good at, so take ad
 
 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].)
+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
+
Example 9.10. toxml works on any node
 >>> grammarNode = xmldoc.firstChild
 >>> print grammarNode.toxml() 1
 <grammar>
@@ -8360,9 +7785,7 @@ package architecture.  It's one of the many things Python is good at, so take ad
 
 
 
-

-

-
Example 9.11. Child nodes can be text
+
Example 9.11. Child nodes can be text
 >>> grammarNode.childNodes1
 [<DOM Text node "\n">, <DOM Element: ref at 17533332>, \
 <DOM Text node "\n">, <DOM Element: ref at 17549660>, <DOM Text node "\n">]
@@ -8388,13 +7811,13 @@ package architecture.  It's one of the many things Python is good at, so take ad
 
 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.
+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.
+The first child is a Text object representing the carriage return after the '<grammar>' tag and before the first '<ref>' tag.
 
 
 
@@ -8412,13 +7835,11 @@ package architecture.  It's one of the many things Python is good at, so take ad
 
 5 
 
-The last child is a Text object representing the carriage return after the '</ref>' end tag and before the '</grammar>' end tag.
+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
+
Example 9.12. Drilling down all the way to text
 >>> grammarNode
 <DOM Element: grammar at 19167148>
 >>> refNode = grammarNode.childNodes[1] 1
@@ -8441,7 +7862,7 @@ u'0'

 
 1 
 
-As you saw in the previous example, the first ref element is grammarNode.childNodes[1], since childNodes[0] is a Text node for the carriage return.
+As you saw in the previous example, the first ref element is grammarNode.childNodes[1], since childNodes[0] is a Text node for the carriage return.
 
 
 
@@ -8459,23 +7880,19 @@ u'0'

 
 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'.
+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.
+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.

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

+
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.
+
You'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. 
@@ -8485,19 +7902,19 @@ Then think about trying to store these documents in the same place (like in the
 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, you're in Russian koi8-r mode, so character 241 means this; poof, now you're in Mac Greek
-mode, so character 241 means something else.  And so on.)  These are the problems which unicode was designed to solve.

+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.[5]  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
+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.

+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.  The XML package uses unicode to store all parsed XML data, but you can use unicode anywhere.

-
Example 9.13. Introducing unicode
+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.  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'
@@ -8507,7 +7924,7 @@ 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.
+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.
 
 
 
@@ -8517,9 +7934,7 @@ Dive in

 
 
 
-

-

-
Example 9.14. Storing non-ASCII characters
+
Example 9.14. Storing non-ASCII characters
 >>> s = u'La Pe\xf1a'         1
 >>> print s 2
 Traceback (innermost last):
@@ -8531,7 +7946,7 @@ 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.
+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.
 
 
 
@@ -8544,15 +7959,13 @@ La Peña

 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, you call the encode method, available on every unicode string, to convert the unicode string to a regular string in the given encoding scheme,
-            which you pass as a parameter.  In this case, you'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).
+            which you pass as a parameter.  In this case, you'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
+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/
@@ -8564,7 +7977,7 @@ sys.setdefaultencoding('iso-8859-1') 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.
+            (as long as import can find it), but it usually goes in the site-packages directory within your Python lib directory.
 
 
 
@@ -8574,9 +7987,7 @@ sys.setdefaultencoding('iso-8859-1') 
Example 9.16. Effects of setting the default encoding
+
Example 9.16. Effects of setting the default encoding
 >>> import sys
 >>> sys.getdefaultencoding() 1
 'iso-8859-1'
@@ -8587,8 +7998,8 @@ 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.)
+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.)
 
 
 
@@ -8598,16 +8009,13 @@ La Peña

 
 
 
-

-

-
Example 9.17. Specifying encoding in .py files

-
If you are going to be storing non-ASCII strings within your Python code, you'll need to specify the encoding of each individual .py file by putting an encoding declaration at the top of each file.  This declaration defines the .py file to be UTF-8:
+
Example 9.17. Specifying encoding in .py files

+
If you are going to be storing non-ASCII strings within your Python code, you'll need to specify the encoding of each individual .py file by putting an encoding declaration at the top of each file.  This declaration defines the .py file to be UTF-8:
 #!/usr/bin/env python
 # -*- coding: UTF-8 -*-
-

-
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 9.18. russiansample.xml

+
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 9.18. russiansample.xml

 <?xml version="1.0" encoding="koi8-r"?>       1
 <preface>
 <title>Предисловие</title>  2
@@ -8616,20 +8024,18 @@ is popular for Russian texts.  The encoding, if specified, is in the header of t
 
 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.
+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.
+            using the koi8-r encoding scheme, but they're being displayed in iso-8859-1.
 
 
 
-

-

-
Example 9.19. Parsing russiansample.xml
+
Example 9.19. Parsing russiansample.xml
 >>> from xml.dom import minidom
 >>> xmldoc = minidom.parse('russiansample.xml') 1
 >>> title = xmldoc.getElementsByTagName('title')[0].firstChild.data
@@ -8649,7 +8055,7 @@ UnicodeError: ASCII encoding error: ordinal not in range(128)
 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.
+            to 'ascii' by removing your sitecustomize.py file, or at least commenting out the setdefaultencoding line.
 
 
 
@@ -8668,39 +8074,34 @@ UnicodeError: ASCII encoding error: ordinal not in range(128)
 
 4 
 
-You can, however, explicitly convert it to koi8-r, in which case you 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.
+You can, however, explicitly convert it to koi8-r, in which case you 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 you've just converted it back.)
+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 you'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.

+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

+
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.
-
    • 
+
 
  • PEP 263 goes into more detail about how and when to define a character encoding in your .py files.
-
    • 
+
 

-

-

-

-
9.5. Searching for elements

+
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, you'll be using the binary.xml grammar file, which looks like this:

-
Example 9.20. binary.xml
<?xml version="1.0"?>
+   your XML document, there is a shortcut you can use to find it quickly: getElementsByTagName.
+
For this section, you'll be using the binary.xml grammar file, which looks like this:
+
Example 9.20. binary.xml
<?xml version="1.0"?>
 <!DOCTYPE grammar PUBLIC "-//diveintopython3.org//DTD Kant Generator Pro v1.0//EN" "kgp.dtd">
 <grammar>
 <ref id="bit">
@@ -8711,9 +8112,8 @@ in Python.  If your XML documents are all 7-bit ASCI
   <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 9.21. Introducing getElementsByTagName
+</grammar>
It has two refs, 'bit' and 'byte'.  A bit is either a '0' or '1', and a byte is 8 bits.
+
Example 9.21. Introducing getElementsByTagName
 >>> from xml.dom import minidom
 >>> xmldoc = minidom.parse('binary.xml')
 >>> reflist = xmldoc.getElementsByTagName('ref') 1
@@ -8734,13 +8134,11 @@ in Python.  If your XML documents are all 7-bit ASCI
 
 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, you find two ref elements.
+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, you find two ref elements.
 
 
 
-

-

-
Example 9.22. Every element is searchable
+
Example 9.22. Every element is searchable
 >>> firstref = reflist[0]    1
 >>> print firstref.toxml()
 <ref id="bit">
@@ -8758,13 +8156,13 @@ in Python.  If your XML documents are all 7-bit ASCI
 
 1 
 
-Continuing from the previous example, the first object in your reflist is the 'bit' ref element.
+Continuing from the previous example, the first object in your reflist is the 'bit' ref element.
 
 
 
 2 
 
-You can use the same getElementsByTagName method on this Element to find all the <p> elements within the 'bit' ref element.
+You can use the same getElementsByTagName method on this Element to find all the <p> elements within the 'bit' ref element.
 
 
 
@@ -8774,9 +8172,7 @@ in Python.  If your XML documents are all 7-bit ASCI
 
 
 
-

-

-
Example 9.23. Searching is actually recursive
+
Example 9.23. 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>]
@@ -8797,23 +8193,19 @@ in Python.  If your XML documents are all 7-bit ASCI
 
 2 
 
-The first two p elements are within the first ref (the 'bit' ref).
+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).
+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, you'll be using the binary.xml grammar file that you saw in the previous section.

+
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, you'll be using the binary.xml grammar file that you saw in the previous section.

@@ -8823,7 +8215,7 @@ in Python.  If your XML documents are all 7-bit ASCI
 
 
 

 

 Note
 

-
Example 9.24. Accessing element attributes
+
Example 9.24. Accessing element attributes
 >>> xmldoc = minidom.parse('binary.xml')
 >>> reflist = xmldoc.getElementsByTagName('ref')
 >>> bitref = reflist[0]
@@ -8844,13 +8236,13 @@ in Python.  If your XML documents are all 7-bit ASCI
 
 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.
+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, you can get a list of the names of the attributes of this element by using attributes.keys().  This element has only one attribute, 'id'.
+Treating the NamedNodeMap as a dictionary, you can get a list of the names of the attributes of this element by using attributes.keys().  This element has only one attribute, 'id'.
 
 
 
@@ -8873,9 +8265,7 @@ in Python.  If your XML documents are all 7-bit ASCI
 
 
 
-

-

-
Example 9.25. Accessing individual attributes
+
Example 9.25. Accessing individual attributes
 >>> a = bitref.attributes["id"]
 >>> a
 <xml.dom.minidom.Attr instance at 0x81d5044>
@@ -8887,18 +8277,17 @@ 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 you used to find this object in the bitref.attributes NamedNodeMap pseudo-dictionary) is stored in a.name.
+The Attr object completely represents a single XML attribute of a single XML element.  The name of the attribute (the same name as you 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.
+The actual text value of this XML attribute is stored in a.value.
 
 
 
-

-

+

@@ -8908,44 +8297,36 @@ u'bit'

 
 
 

 

 Note
 

-

-

-
9.7. Segue

+
9.7. Segue

 
OK, that's it for the hard-core XML stuff.  The next chapter will continue to use these same example programs, but focus on
-   other aspects that make the program more flexible: using streams for input processing, using getattr for method dispatching, and using command-line flags to allow users to reconfigure the program without changing the code.

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

+   other aspects that make the program more flexible: using streams for input processing, using getattr for method dispatching, and using command-line flags to allow users to reconfigure the program without changing the code.
+
Before moving on to the next chapter, you should be comfortable doing all of these things:
 

 
-

-

 


 

 
[5] 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.

-

-

-

+   limits of my expertise, sorry.
 

-
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.

+
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.

+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.

+off and returns the next chunk of data.
 
This is how reading from real files works; the difference is that you're not limiting yourself to real files.  The input source could be anything: a file on
 disk, a web page, even a hard-coded string.  As long as you 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
+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
@@ -8988,11 +8369,9 @@ calls the object's read method, the function can h
 
 
 
-

-

 
Well, that all seems like a colossal waste of time.  After all, you'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
+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
@@ -9046,9 +8425,7 @@ just going to be parsing a local file, you can pass the filename and 
 
 
-

-

-
Example 10.3. Parsing XML from a string (the easy but inflexible way)
+
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()
@@ -9062,12 +8439,10 @@ just going to be parsing a local file, you can pass the filename and 
 
 
-

-

 
OK, so you can use the minidom.parse function for parsing both local files and remote URLs, but for parsing strings, you use... a different function.  That means that if you want to be able to take input from a
-file, a URL, or a string, you'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 you 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
+file, a URL, or a string, you'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 you 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
@@ -9123,9 +8498,7 @@ file, a URL, or a string, you'll need special logic to check
 
 
 
-

-

-
Example 10.5. Parsing XML from a string (the file-like object way)
+
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
@@ -9141,10 +8514,8 @@ file, a URL, or a string, you'll need special logic to check
 
 
 
-

-

-
So now you 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, you use urlopen to get a file-like object; for a local file, you use open; and for a string, you use StringIO.  Now let's take it one step further and generalize these differences as well.

-
Example 10.6. openAnything
+
So now you 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, you use urlopen to get a file-like object; for a local file, you use open; and for a string, you 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       
@@ -9166,13 +8537,13 @@ def openAnything(source):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.
+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, you see if source is a URL.  You do this through brute force: you 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, you will also support them without recoding.  If urllib is able to open source, then the return kicks you out of the function immediately and the following try statements never execute.
+First, you see if source is a URL.  You do this through brute force: you 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, you will also support them without recoding.  If urllib is able to open source, then the return kicks you out of the function immediately and the following try statements never execute.
 
 
 
@@ -9189,25 +8560,20 @@ def openAnything(source):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
+
Now you 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

+        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
+   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
+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
@@ -9230,25 +8596,23 @@ Dive inDive inDive in

 
 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.
+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.
+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 you discussed in Section 10.1, “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
+
stdout and stderr are both file-like objects, like the ones you discussed in Section 10.1, “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
(On Windows, you can use type instead of cat to display the contents of a file.)

-
If you have not already done so, you can download this and other examples used in this book.
+This message will be logged instead of displayed
(On Windows, you can use type instead of cat to display the contents of a file.)
+
If you have not already done so, you can download this and other examples used in this book.
 #stdout.py
 import sys
 
@@ -9270,7 +8634,7 @@ fsock.close()        7
 2 
 
-Always save stdout before redirecting it, so you can set it back to normal later.
+Always save stdout before redirecting it, so you can set it back to normal later.
 
 
 
@@ -9292,7 +8656,7 @@ fsock.close()        7
 6 
 
-Set stdout back to the way it was before you mucked with it.
+Set stdout back to the way it was before you mucked with it.
 
 
 
@@ -9301,16 +8665,14 @@ fsock.close()        7Close the log file.
 
 
-

-

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

-
Example 10.10. Redirecting error information
+
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.
+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
 
@@ -9327,7 +8689,7 @@ raise Exception, 'this error will be logged' 2 
 
-Redirect standard error by assigning the file object of the newly-opened log file to stderr.
+Redirect standard error by assigning the file object of the newly-opened log file to stderr.
 
 
 
@@ -9339,15 +8701,13 @@ raise Exception, 'this error will be logged' 4 
 
-Also note that you're not explicitly closing your log file, nor are you setting stderr back to its original value.  This is fine, since once the program crashes (because of the 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.
+Also note that you're not explicitly closing your log file, nor are you setting stderr back to its original value.  This is fine, since once the program crashes (because of the 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
+through the hassle of redirecting it outright.
+
Example 10.11. Printing to stderr
 >>> print 'entering function'
 entering function
 >>> import sys
@@ -9358,18 +8718,16 @@ 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, you can redirect a single print statement to stderr without affecting subsequent print statements.
+This shorthand syntax of the print statement can be used to write to any open file, or file-like object.  In this case, you 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
+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
@@ -9397,32 +8755,30 @@ one program's output to the next program's input.

 
 2 
 
-This simply prints out the entire contents of binary.xml.  (Windows users should use type instead of cat.)
+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
+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 your Python script.
 
 
 
 4 
 
-Instead of specifying a module (like binary.xml), you specify “-”, which causes your script to load the grammar from standard input instead of from a specific file on disk.  (More on how
+Instead of specifying a module (like binary.xml), you specify “-”, which causes your 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 you specified the grammar filename
-            directly, but think of the expansion possibilities here.  Instead of simply doing cat binary.xml, you could run a script that dynamically generates the grammar, then you can pipe it into your script.  It could come from
+            directly, but think of the expansion possibilities here.  Instead of simply doing cat binary.xml, you could run a script that dynamically generates the grammar, then you can pipe it into your script.  It could come from
             anywhere: a database, or some grammar-generating meta-script, or whatever.  The point is that you don't need to change your
 kgp.py script at all to incorporate any of this functionality.  All you need to do is be able to take grammar files from standard
             input, and you can separate all the other logic into another program.
 
 
 
-

-

-
So how does the 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
+
So how does the 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
@@ -9437,21 +8793,17 @@ def openAnything(source):
 
 1 
 
-This is the openAnything function from toolbox.py, which you previously examined in Section 10.1, “Abstracting input sources”.  All you've done is add three lines of code at the beginning of the function to check if the source is “-”; if so, you return sys.stdin.  Really, that's it!  Remember, stdin is a file-like object with a read method, so the rest of the code (in kgp.py, where you call openAnything) doesn't change a bit.
+This is the openAnything function from toolbox.py, which you previously examined in Section 10.1, “Abstracting input sources”.  All you've done is add three lines of code at the beginning of the function to check if the source is “-”; if so, you return sys.stdin.  Really, that's it!  Remember, stdin is a file-like object with a read method, so the rest of the code (in kgp.py, where you 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 a lot of different things, including xrefs.  Whenever you encounter an xref, you look for a corresponding ref element with the same id attribute, and choose one of the ref element's children and parse it.  (You'll see how this random choice is made in the next section.)

+
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 a lot of different things, including xrefs.  Whenever you encounter an xref, you look for a corresponding ref element with the same id attribute, and choose one of the ref element's children and parse it.  (You'll see how this random choice is made in the next section.)
 
This is how you build up the 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 you parse the "largest" reference and follow each xref, and eventually output real text.  The text you output depends on the (random) decisions you make each time you fill in an
-xref, so the output is different each time.

-
This is all very flexible, but there is one downside: performance.  When you find an xref and need to find the corresponding ref element, you have a problem.  The xref has an id attribute, and you 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
+xref, so the output is different each time.
+
This is all very flexible, but there is one downside: performance.  When you find an xref and need to find the corresponding ref element, you have a problem.  The xref has an id attribute, and you 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
@@ -9483,20 +8835,15 @@ def openAnything(source):
 
 
 
-

-

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

-
Example 10.15. Using the ref element cache
+
Once you build this cache, whenever you come across an xref and need to find the ref element with the same id attribute, you can simply look it up in self.refs.
+
Example 10.15. Using the ref element cache
     def do_xref(self, node):
         id = node.attributes["id"].value
-        self.parse(self.randomChildElement(self.refs[id]))

-
You'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 the grammar files, a ref element can have several p elements, each of which can contain many things, including other p elements.  You 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 you could simply use getElementsByTagName for this, but you can't.  getElementsByTagName searches recursively and returns a single list for all the elements it finds.  Since p elements can contain other p elements, you can't use getElementsByTagName, because it would return nested p elements that you don't want.  To find only direct child elements, you'll need to do it yourself.

-
Example 10.16. Finding direct child elements
+        self.parse(self.randomChildElement(self.refs[id]))
You'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 the grammar files, a ref element can have several p elements, each of which can contain many things, including other p elements.  You 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 you could simply use getElementsByTagName for this, but you can't.  getElementsByTagName searches recursively and returns a single list for all the elements it finds.  Since p elements can contain other p elements, you can't use getElementsByTagName, because it would return nested p elements that you don't want.  To find only direct child elements, you'll need to do it yourself.
+
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
@@ -9519,8 +8866,8 @@ def openAnything(source):
 
 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 Section 9.2, “Packages” for more on packages.)  But you're just interested in nodes that are elements, so you can filter the list to only include
-            those nodes whose nodeType is ELEMENT_NODE.
+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 Section 9.2, “Packages” for more on packages.)  But you're just interested in nodes that are elements, so you can filter the list to only include
+            those nodes whose nodeType is ELEMENT_NODE.
 
 
 
@@ -9530,13 +8877,9 @@ def openAnything(source):
 
 
 
-

-

-

-

-
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
+
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
@@ -9555,21 +8898,19 @@ def openAnything(source):
 
 2 
 
-As you saw in Section 9.2, “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 you saw in Section 5.4, “Instantiating Classes”, __class__ is built-in attribute of every Python object.
+As you saw in Section 9.2, “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 you saw in Section 5.4, “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
+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 Section 5.3, “Defining Classes”.)
 
 
 
-

-

-
Fine, so now you can get the class name of any particular XML node (since each XML node is represented as a Python object).  How can you use this to your advantage to separate the logic of parsing each node type?  The answer is getattr, which you first saw in Section 4.4, “Getting Object References With getattr”.

-
Example 10.18. parse, a generic XML node dispatcher
+
Fine, so now you can get the class name of any particular XML node (since each XML node is represented as a Python object).  How can you use this to your advantage to separate the logic of parsing each node type?  The answer is getattr, which you first saw in Section 4.4, “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

@@ -9577,7 +8918,7 @@ def openAnything(source):
 
 1 
 
-First off, notice that you're constructing a larger string based on the class name of the node you were passed (in the node argument).  So if you're passed a Document node, you're constructing the string 'parse_Document', and so forth.
+First off, notice that you're constructing a larger string based on the class name of the node you were passed (in the node argument).  So if you're passed a Document node, you're constructing the string 'parse_Document', and so forth.
 
 
 
@@ -9593,9 +8934,7 @@ def openAnything(source):
 
 
 
-

-

-
Example 10.19. Functions called by the parse dispatcher
+
Example 10.19. Functions called by the parse dispatcher
     def parse_Document(self, node): 1
         self.parse(node.documentElement)
 
@@ -9631,7 +8970,7 @@ def openAnything(source):
 
 3 
 
-parse_Comment is just a pass, since you don't care about embedded comments in the grammar files.  Note, however, that you still need to define the function
+parse_Comment is just a pass, since you don't care about embedded comments in the grammar files.  Note, however, that you still need to define the function
             and explicitly make it do nothing.  If the function did not exist, the 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 you don't use, allows the generic parse function to stay simple and dumb.
 
 
@@ -9640,25 +8979,21 @@ def openAnything(source):
 
 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.  You 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
+'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 Chapter 16, Functional Programming.

-

-

-
10.6. Handling command-line arguments

+you needed.  Dynamic importing will be discussed in Chapter 16, Functional Programming.
+
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.

+   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.
+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
 
@@ -9672,9 +9007,7 @@ for arg in sys.argv: 
 
 
-

-

-
Example 10.21. The contents of sys.argv
+
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
@@ -9705,23 +9038,21 @@ kant.xml

 
 3 
 
-Command-line flags, like --help, also show up as their own element in the sys.argv list.
+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 you 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.
+            (-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 you can see, you 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
+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:              
@@ -9738,14 +9069,14 @@ if __name__ == "__main__":
 
 1 
 
-First off, look at the bottom of the example and notice that you're calling the main function with sys.argv[1:].  Remember, sys.argv[0] is the name of the script that you're running; you don't care about that for command-line processing, so you chop it off
+First off, look at the bottom of the example and notice that you're calling the main function with sys.argv[1:].  Remember, sys.argv[0] is the name of the script that you're running; you don't care about that for command-line processing, so you 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 you 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
+This is where all the interesting processing happens.  The getopt function of the getopt module takes three parameters: the argument list (which you 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.
 
@@ -9764,48 +9095,43 @@ if __name__ == "__main__":
 
 
 
-

-

 
So what are all those parameters you 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 you already chopped off before calling the main function).  The second is the list of short command-line flags that the script accepts.

+the script name, which you already chopped off before calling the main function).  The second is the list of short command-line flags that the script accepts.
 

-
"hg:d"

+
"hg:d"

 

-
-h

+
-h

 
print usage summary

-
-g ...

+
-g ...

 
use specified grammar file or URL

-
-d

+
-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 you don't know which yet (you'll figure it out later), but you know it has to be something.  So you tell getopt this by putting a colon after the g in that second parameter to the getopt function.

-
To further complicate things, the script accepts either short flags (like -h) or long flags (like --help), and you 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 you specified in the second parameter.

+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 you don't know which yet (you'll figure it out later), but you know it has to be something.  So you tell getopt this by putting a colon after the g in that second parameter to the getopt function.
+
To further complicate things, the script accepts either short flags (like -h) or long flags (like --help), and you 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 you specified in the second parameter.
 

-
["help", "grammar="]

+
["help", "grammar="]

 

-
--help

+
--help

 
print usage summary

-
--grammar ...

+
--grammar ...

 
use specified grammar file or URL

 

-

-
Three things of note here:

+
Three things of note here:
 

 
    
 
  1. All long flags are preceded by two dashes on the command line, but you don't include those dashes when calling getopt.  They are understood.
-
    2. 
-
  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. 
-
  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
+
+
  4. The --grammar flag must always be followed by an additional argument, just like the -g flag.  This is notated by an equals sign, "grammar=".
+
+
  5. 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.
-
    6. 
+
 

-

-
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
+
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:              
@@ -9832,26 +9158,26 @@ def main(argv):        1 
 
 The grammar variable will keep track of the grammar file you're using.  You initialize it here in case it's not specified on the command
-            line (using either the -g or the --grammar flag).
+            line (using either the -g or the --grammar flag).
 
 
 
 2 
 
-The opts variable that you 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.
+The opts variable that you 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 you need to check for both.
+             If you specify the -h flag, opt will contain "-h"; if you specify the --help flag, opt will contain "--help".  So you need to check for both.
 
 
 
 4 
 
-Remember, the -d flag didn't have a corresponding long flag, so you only need to check for the short form.  If you find it, you set a global
+Remember, the -d flag didn't have a corresponding long flag, so you only need to check for the short form.  If you find it, you set a global
             variable that you'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?)
 
@@ -9859,7 +9185,7 @@ def main(argv):        
 5 
 
-If you find a grammar file, either with a -g flag or a --grammar flag, you save the argument that followed it (stored in arg) into the grammar variable, overwriting the default that you initialized at the top of the main function.
+If you find a grammar file, either with a -g flag or a --grammar flag, you save the argument that followed it (stored in arg) into the grammar variable, overwriting the default that you initialized at the top of the main function.
 
 
 
@@ -9871,13 +9197,9 @@ def main(argv):        
 
 
-

-

-

-

-
10.7. Putting it all together

-
You'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.

+
10.7. Putting it all together

+
You'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):       
 ...
@@ -9886,23 +9208,19 @@ def main(argv):
     except getopt.GetoptError:          
 ...
     for opt, arg in opts:               
-...

-
You 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.

+...
You 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.  You use your 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.

+    k = KantGenerator(grammar, source)
The KantGenerator instance automatically loads the grammar, which is an XML file.  You use your 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, you take advantage of your knowledge of the structure of the XML document to set up a little cache of references, which are just elements in the XML document.

+        sock.close()

Oh, and along the way, you take advantage of your 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 you specified some source material on the command line, you use that; otherwise you rip through the grammar looking for -the "top-level" reference (that isn't referenced by anything else) and use that as a starting point.

+ self.refs[ref.attributes["id"].value] = ref

If you specified some source material on the command line, you use that; otherwise you 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 = {}
@@ -9910,91 +9228,77 @@ the "top-level" reference (that isn't referenced by anything else) and use that
             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 you rip through the source material. The source material is also XML, and you parse it one node at a time. To keep the code separated and more maintainable, you use separate handlers for each node type.

+ return '<xref id="%s"/>' % random.choice(standaloneXrefs)

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

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

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

+ handlerMethod(node)

You 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,

+ 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 you previously cached.

+ self.parse(self.randomChildElement(node))

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

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

Eventually, you parse your way down to plain text,

+ self.parse(self.randomChildElement(self.refs[id]))

Eventually, you parse your way down to plain text, 

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

which you print out.

+ self.pieces.append(text)

which you print out. 

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

10.8. Summary

+ 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:

+ 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. HTTP Web Services

-
-

11.1. Diving in

-

You've learned about HTML processing and XML processing, and along the way you saw how to download a web page and how to parse XML from a URL, but let's dive into the more general topic of HTTP web services.

+

Chapter 11. HTTP Web Services

+

11.1. Diving in

+

You've learned about HTML processing and XML processing, and along the way you saw how to download a web page and how to parse XML from a URL, but let's dive into the more general topic of HTTP web services.

Simply stated, HTTP web services are programmatic ways of sending and receiving data from remote servers using the operations of HTTP directly. If you want to get data from the server, use a straight HTTP GET; if you want to send new data to the server, use HTTP POST. (Some more advanced HTTP web service APIs also define ways of modifying existing data and deleting data, using HTTP PUT and HTTP DELETE.) In other words, the “verbs” built into the HTTP protocol (GET, POST, PUT, and DELETE) map directly to application-level operations for receiving, sending, -modifying, and deleting data.

+modifying, and deleting data.

The main advantage of this approach is simplicity, and its simplicity has proven popular with a lot of different sites. Data -- usually XML data -- can be built and stored statically, or generated dynamically by a server-side script, and all major languages include an HTTP library for downloading it. Debugging is also easier, because you can load up the web service in any web browser and see the raw data. Modern browsers will even nicely format and pretty-print XML data for you, to allow -you to quickly navigate through it.

-

Examples of pure XML-over-HTTP web services:

+you to quickly navigate through it. +

Examples of pure XML-over-HTTP web services:

-

In later chapters, you'll explore APIs which use HTTP as a transport for sending and receiving data, but don't map application semantics to the underlying HTTP semantics. (They tunnel everything over HTTP POST.) But this chapter will concentrate on using HTTP GET to get data from a remote server, and you'll explore several HTTP features you can use to get the maximum benefit -out of pure HTTP web services.

-

Here is a more advanced version of the openanything module that you saw in the previous chapter:

-

Example 11.1. openanything.py

-

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

+out of pure HTTP web services.
+
Here is a more advanced version of the openanything module that you saw in the previous chapter:
+
Example 11.1. openanything.py

+
If you have not already done so, you can download this and other examples used in this book.
 import urllib2, urlparse, gzip
 from StringIO import StringIO
 
@@ -10088,21 +9392,17 @@ def fetch(source, etag=None, last_modified=None, agent=USER_AGENT):
         result['status'] = f.status                
     f.close()  
     return result                
-

-

-
Further reading

+
+

Further reading

-
-
-
-

11.2. How not to fetch data over HTTP

+

11.2. How not to fetch data over HTTP

Let's say you want to download a resource over HTTP, such as a syndicated Atom feed. But you don't just want to download it once; you want to download it over and over again, every hour, to get the latest news from the site that's offering the - news feed. Let's do it the quick-and-dirty way first, and then see how you can do better.

-

Example 11.2. Downloading a feed the quick-and-dirty way

+   news feed.  Let's do it the quick-and-dirty way first, and then see how you can do better.
+
Example 11.2. Downloading a feed the quick-and-dirty way
 >>> import urllib
 >>> data = urllib.urlopen('http://diveintomark.org/xml/atom.xml').read()    1
 >>> print data
@@ -10123,82 +9423,66 @@ def fetch(source, etag=None, last_modified=None, agent=USER_AGENT):
 
 
 
-

-

So what's wrong with this? Well, for a quick one-off during testing or development, there's nothing wrong with it. I do it all the time. I wanted the contents of the feed, and I got the contents of the feed. The same technique works for any web page. But once you start thinking in terms of a web service that you want to access on a regular basis -- and remember, you said you were planning on retrieving this syndicated feed once an hour -- then you're being inefficient, and you're being -rude.

-

Let's talk about some of the basic features of HTTP.

-
-
-

11.3. Features of HTTP

-

There are five important features of HTTP which you should support.

-
-

11.3.1. User-Agent

-

The User-Agent is simply a way for a client to tell a server who it is when it requests a web page, a syndicated feed, or any sort of web +rude. +

Let's talk about some of the basic features of HTTP. +

11.3. Features of HTTP

+

There are five important features of HTTP which you should support. +

11.3.1. User-Agent

+

The User-Agent is simply a way for a client to tell a server who it is when it requests a web page, a syndicated feed, or any sort of web service over HTTP. When the client requests a resource, it should always announce who it is, as specifically as possible. This allows the server-side administrator to get in touch with the client-side developer if anything is going fantastically - wrong.

-

By default, Python sends a generic User-Agent: Python-urllib/1.15. In the next section, you'll see how to change this to something more specific.

-
-
-

11.3.2. Redirects

+ wrong. +

By default, Python sends a generic User-Agent: Python-urllib/1.15. In the next section, you'll see how to change this to something more specific. +

11.3.2. Redirects

Sometimes resources move around. Web sites get reorganized, pages move to new addresses. Even web services can reorganize. - A syndicated feed at http://example.com/index.xml might be moved to http://example.com/xml/atom.xml. Or an entire domain might move, as an organization expands and reorganizes; for instance, http://www.example.com/index.xml might be redirected to http://server-farm-1.example.com/index.xml.

+ A syndicated feed at http://example.com/index.xml might be moved to http://example.com/xml/atom.xml. Or an entire domain might move, as an organization expands and reorganizes; for instance, http://www.example.com/index.xml might be redirected to http://server-farm-1.example.com/index.xml.

Every time you request any kind of resource from an HTTP server, the server includes a status code in its response. Status - code 200 means “everything's normal, here's the page you asked for”. Status code 404 means “page not found”. (You've probably seen 404 errors while browsing the web.)

-

HTTP has two different ways of signifying that a resource has moved. Status code 302 is a temporary redirect; it means “oops, that got moved over here temporarily” (and then gives the temporary address in a Location: header). Status code 301 is a permanent redirect; it means “oops, that got moved permanently” (and then gives the new address in a Location: header). If you get a 302 status code and a new address, the HTTP specification says you should use the new address to get what you asked for, but - the next time you want to access the same resource, you should retry the old address. But if you get a 301 status code and a new address, you're supposed to use the new address from then on.

+ code 200 means “everything's normal, here's the page you asked for”. Status code 404 means “page not found”. (You've probably seen 404 errors while browsing the web.) +

HTTP has two different ways of signifying that a resource has moved. Status code 302 is a temporary redirect; it means “oops, that got moved over here temporarily” (and then gives the temporary address in a Location: header). Status code 301 is a permanent redirect; it means “oops, that got moved permanently” (and then gives the new address in a Location: header). If you get a 302 status code and a new address, the HTTP specification says you should use the new address to get what you asked for, but + the next time you want to access the same resource, you should retry the old address. But if you get a 301 status code and a new address, you're supposed to use the new address from then on.

urllib.urlopen will automatically “follow” redirects when it receives the appropriate status code from the HTTP server, but unfortunately, it doesn't tell you when it does so. You'll end up getting data you asked for, but you'll never know that the underlying library “helpfully” followed a redirect for you. So you'll continue pounding away at the old address, and each time you'll get redirected to the new address. That's two round trips instead of one: not very efficient! Later in this chapter, you'll see how to work - around this so you can deal with permanent redirects properly and efficiently.

-
-
-

11.3.3. Last-Modified/If-Modified-Since

+ around this so you can deal with permanent redirects properly and efficiently. +

11.3.3. Last-Modified/If-Modified-Since

Some data changes all the time. The home page of CNN.com is constantly updating every few minutes. On the other hand, the home page of Google.com only changes once every few weeks (when they put up a special holiday logo, or advertise a new service). Web services are no different; usually the server knows when the data you requested last changed, and HTTP provides a way - for the server to include this last-modified date along with the data you requested.

+ for the server to include this last-modified date along with the data you requested.

If you ask for the same data a second time (or third, or fourth), you can tell the server the last-modified date that you - got last time: you send an If-Modified-Since header with your request, with the date you got back from the server last time. If the data hasn't changed since then, the - server sends back a special HTTP status code 304, which means “this data hasn't changed since the last time you asked for it”. Why is this an improvement? Because when the server sends a 304, it doesn't re-send the data. All you get is the status code. So you don't need to download the same data over and over again if it hasn't changed; - the server assumes you have the data cached locally.

+ got last time: you send an If-Modified-Since header with your request, with the date you got back from the server last time. If the data hasn't changed since then, the + server sends back a special HTTP status code 304, which means “this data hasn't changed since the last time you asked for it”. Why is this an improvement? Because when the server sends a 304, it doesn't re-send the data. All you get is the status code. So you don't need to download the same data over and over again if it hasn't changed; + the server assumes you have the data cached locally.

All modern web browsers support last-modified date checking. If you've ever visited a page, re-visited the same page a day later and found that it hadn't changed, and wondered why it loaded so quickly the second time -- this could be why. Your web browser cached the contents of the page locally the first time, and when you visited the second time, your browser automatically - sent the last-modified date it got from the server the first time. The server simply says 304: Not Modified, so your browser knows to load the page from its cache. Web services can be this smart too.

+ sent the last-modified date it got from the server the first time. The server simply says 304: Not Modified, so your browser knows to load the page from its cache. Web services can be this smart too.

Python's URL library has no built-in support for last-modified date checking, but since you can add arbitrary headers to each request - and read arbitrary headers in each response, you can add support for it yourself.

-
-
-

11.3.4. ETag/If-None-Match

+ and read arbitrary headers in each response, you can add support for it yourself. +

11.3.4. ETag/If-None-Match

ETags are an alternate way to accomplish the same thing as the last-modified date checking: don't re-download data that hasn't - changed. The way it works is, the server sends some sort of hash of the data (in an ETag header) along with the data you requested. Exactly how this hash is determined is entirely up to the server. The second - time you request the same data, you include the ETag hash in an If-None-Match: header, and if the data hasn't changed, the server will send you back a 304 status code. As with the last-modified date checking, the server just sends the 304; it doesn't send you the same data a second time. By including the ETag hash in your second request, you're telling the + changed. The way it works is, the server sends some sort of hash of the data (in an ETag header) along with the data you requested. Exactly how this hash is determined is entirely up to the server. The second + time you request the same data, you include the ETag hash in an If-None-Match: header, and if the data hasn't changed, the server will send you back a 304 status code. As with the last-modified date checking, the server just sends the 304; it doesn't send you the same data a second time. By including the ETag hash in your second request, you're telling the server that there's no need to re-send the same data if it still matches this hash, since you still have the data from the - last time.

-

Python's URL library has no built-in support for ETags, but you'll see how to add it later in this chapter.

-
-
-

11.3.5. Compression

+ last time. +

Python's URL library has no built-in support for ETags, but you'll see how to add it later in this chapter. +

11.3.5. Compression

The last important HTTP feature is gzip compression. When you talk about HTTP web services, you're almost always talking about moving XML back and forth over the wire. XML is text, and quite verbose text at that, and text generally compresses well. When you request a resource over HTTP, you can ask the server that, if it has any new data to send you, to please send - it in compressed format. You include the Accept-encoding: gzip header in your request, and if the server supports compression, it will send you back gzip-compressed data and mark it with - a Content-encoding: gzip header.

+ it in compressed format. You include the Accept-encoding: gzip header in your request, and if the server supports compression, it will send you back gzip-compressed data and mark it with + a Content-encoding: gzip header.

Python's URL library has no built-in support for gzip compression per se, but you can add arbitrary headers to the request. And -Python comes with a separate gzip module, which has functions you can use to decompress the data yourself.

-

Note that our little one-line script to download a syndicated feed did not support any of these HTTP features. Let's see how you can improve it.

-
-
-
-

11.4. Debugging HTTP web services

+Python comes with a separate gzip module, which has functions you can use to decompress the data yourself. +

Note that our little one-line script to download a syndicated feed did not support any of these HTTP features. Let's see how you can improve it. +

11.4. Debugging HTTP web services

First, let's turn on the debugging features of Python's HTTP library and see what's being sent over the wire. This will be useful throughout the chapter, as you add more and - more features.

-

Example 11.3. Debugging HTTP

+   more features.
+
Example 11.3. Debugging HTTP
 >>> import httplib
 >>> httplib.HTTPConnection.debuglevel = 1             1
 >>> import urllib
@@ -10218,13 +9502,12 @@ header: ETag: "e8284-68e0-4de30f80" 
+

 
-
@@ -10232,35 +9515,35 @@ header: Connection: close
 
-
-
-
@@ -10273,17 +9556,14 @@ header: Connection: close
 
-

 

 1 
 urllib relies on another standard Python library, httplib.  Normally you don't need to import httplib directly (urllib does that automatically), but you will here so you can set the debugging flag on the HTTPConnection class that urllib uses internally to connect to the HTTP server.  This is an incredibly useful technique.  Some other Python libraries have similar debug flags, but there's no particular standard for naming them or turning them on; you need to read
+urllib relies on another standard Python library, httplib.  Normally you don't need to import httplib directly (urllib does that automatically), but you will here so you can set the debugging flag on the HTTPConnection class that urllib uses internally to connect to the HTTP server.  This is an incredibly useful technique.  Some other Python libraries have similar debug flags, but there's no particular standard for naming them or turning them on; you need to read
          the documentation of each library to see if such a feature is available.
 
 
2 
 
 Now that the debugging flag is set, information on the the HTTP request and response is printed out in real time.  The first
-         thing it tells you is that you're connecting to the server diveintomark.org on port 80, which is the standard port for HTTP.
+         thing it tells you is that you're connecting to the server diveintomark.org on port 80, which is the standard port for HTTP.
 
 

 

 3 
 
 When you request the Atom feed, urllib sends three lines to the server.  The first line specifies the HTTP verb you're using, and the path of the resource (minus
-         the domain name).  All the requests in this chapter will use GET, but in the next chapter on SOAP, you'll see that it uses POST for everything.  The basic syntax is the same, regardless of the verb.
+         the domain name).  All the requests in this chapter will use GET, but in the next chapter on SOAP, you'll see that it uses POST for everything.  The basic syntax is the same, regardless of the verb.
 
 

 

 4 
 The second line is the Host header, which specifies the domain name of the service you're accessing.  This is important, because a single HTTP server
+The second line is the Host header, which specifies the domain name of the service you're accessing.  This is important, because a single HTTP server
          can host multiple separate domains.  My server currently hosts 12 domains; other servers can host hundreds or even thousands.
 
 

 

 5 
 The third line is the User-Agent header.  What you see here is the generic User-Agent that the urllib library adds by default.  In the next section, you'll see how to customize this to be more specific.
+The third line is the User-Agent header.  What you see here is the generic User-Agent that the urllib library adds by default.  In the next section, you'll see how to customize this to be more specific.
 
 

 

 6 
 The server replies with a status code and a bunch of headers (and possibly some data, which got stored in the feeddata variable).  The status code here is 200, meaning “everything's normal, here's the data you requested”.  The server also tells you the date it responded to your request, some information about the server itself, and the content
+The server replies with a status code and a bunch of headers (and possibly some data, which got stored in the feeddata variable).  The status code here is 200, meaning “everything's normal, here's the data you requested”.  The server also tells you the date it responded to your request, some information about the server itself, and the content
          type of the data it's giving you.  Depending on your application, this might be useful, or not.  It's certainly reassuring
-         that you thought you were asking for an Atom feed, and lo and behold, you're getting an Atom feed (application/atom+xml, which is the registered content type for Atom feeds).
+         that you thought you were asking for an Atom feed, and lo and behold, you're getting an Atom feed (application/atom+xml, which is the registered content type for Atom feeds).
 
 

 

 8 
 The server also tells you that this Atom feed has an ETag hash of "e8284-68e0-4de30f80".  The hash doesn't mean anything by itself; there's nothing you can do with it, except send it back to the server the next
+The server also tells you that this Atom feed has an ETag hash of "e8284-68e0-4de30f80".  The hash doesn't mean anything by itself; there's nothing you can do with it, except send it back to the server the next
          time you request this same feed.  Then the server can use it to tell you if the data has changed or not.
 
 

 

-

-

-

-
11.5. Setting the User-Agent

-
The first step to improving your HTTP web services client is to identify yourself properly with a User-Agent.  To do that, you need to move beyond the basic urllib and dive into urllib2.

-
Example 11.4. Introducing urllib2
+
11.5. Setting the User-Agent

+
The first step to improving your HTTP web services client is to identify yourself properly with a User-Agent.  To do that, you need to move beyond the basic urllib and dive into urllib2.
+
Example 11.4. Introducing urllib2
 >>> import httplib
 >>> httplib.HTTPConnection.debuglevel = 1           1
 >>> import urllib2
@@ -10336,9 +9616,7 @@ header: Connection: close
 
 
 
-

-

-
Example 11.5. Adding headers with the Request
+
Example 11.5. Adding headers with the Request
 >>> request            1
 <urllib2.Request instance at 0x00250AA8>
 >>> request.get_full_url()
@@ -10373,34 +9651,30 @@ header: Connection: close
 2 
 
 Using the add_header method on the Request object, you can add arbitrary HTTP headers to the request.  The first argument is the header, the second is the value you're
-            providing for that header.  Convention dictates that a User-Agent should be in this specific format: an application name, followed by a slash, followed by a version number.  The rest is free-form,
-            and you'll see a lot of variations in the wild, but somewhere it should include a URL of your application.  The User-Agent is usually logged by the server along with other details of your request, and including a URL of your application allows
+            providing for that header.  Convention dictates that a User-Agent should be in this specific format: an application name, followed by a slash, followed by a version number.  The rest is free-form,
+            and you'll see a lot of variations in the wild, but somewhere it should include a URL of your application.  The User-Agent is usually logged by the server along with other details of your request, and including a URL of your application allows
             server administrators looking through their access logs to contact you if something is wrong.
 
 
 
 3 
 
-The opener object you created before can be reused too, and it will retrieve the same feed again, but with your custom User-Agent header.
+The opener object you created before can be reused too, and it will retrieve the same feed again, but with your custom User-Agent header.
 
 
 
 4 
 
-And here's you sending your custom User-Agent, in place of the generic one that Python sends by default.  If you look closely, you'll notice that you defined a User-Agent header, but you actually sent a User-agent header.  See the difference?  urllib2 changed the case so that only the first letter was capitalized.  It doesn't really matter; HTTP specifies that header field
+And here's you sending your custom User-Agent, in place of the generic one that Python sends by default.  If you look closely, you'll notice that you defined a User-Agent header, but you actually sent a User-agent header.  See the difference?  urllib2 changed the case so that only the first letter was capitalized.  It doesn't really matter; HTTP specifies that header field
             names are completely case-insensitive.
 
 
 
-

-

-

-

-
11.6. Handling Last-Modified and ETag

-
Now that you know how to add custom HTTP headers to your web service requests, let's look at adding support for Last-Modified and ETag headers.

+
11.6. Handling Last-Modified and ETag

+
Now that you know how to add custom HTTP headers to your web service requests, let's look at adding support for Last-Modified and ETag headers.
 
These examples show the output with debugging turned off.  If you still have it turned on from the previous section, you can
-turn it off by setting httplib.HTTPConnection.debuglevel = 0.  Or you can just leave debugging on, if that helps you.

-
Example 11.6. Testing Last-Modified
+turn it off by setting httplib.HTTPConnection.debuglevel = 0.  Or you can just leave debugging on, if that helps you.
+
Example 11.6. Testing Last-Modified
 >>> import urllib2
 >>> request = urllib2.Request('http://diveintomark.org/xml/atom.xml')
 >>> opener = urllib2.build_opener()
@@ -10446,24 +9720,22 @@ urllib2.HTTPError: HTTP Error 304: Not Modified
 
 2 
 
-On the second request, you add the If-Modified-Since header with the last-modified date from the first request.  If the data hasn't changed, the server should return a 304 status code.
+On the second request, you add the If-Modified-Since header with the last-modified date from the first request.  If the data hasn't changed, the server should return a 304 status code.
 
 
 
 3 
 
-Sure enough, the data hasn't changed.  You can see from the traceback that urllib2 throws a special exception, HTTPError, in response to the 304 status code.  This is a little unusual, and not entirely helpful.  After all, it's not an error; you specifically asked the
+Sure enough, the data hasn't changed.  You can see from the traceback that urllib2 throws a special exception, HTTPError, in response to the 304 status code.  This is a little unusual, and not entirely helpful.  After all, it's not an error; you specifically asked the
             server not to send you any data if it hadn't changed, and the data didn't change, so the server told you it wasn't sending
             you any data.  That's not an error; that's exactly what you were hoping for.
 
 
 
-

-

-
urllib2 also raises an HTTPError exception for conditions that you would think of as errors, such as 404 (page not found).  In fact, it will raise HTTPError for any status code other than 200 (OK), 301 (permanent redirect), or 302 (temporary redirect).  It would be more helpful for your purposes to capture the status code and simply return it, without
-throwing an exception.  To do that, you'll need to define a custom URL handler.

-
Example 11.7. Defining URL handlers

-
This custom URL handler is part of openanything.py.
+
urllib2 also raises an HTTPError exception for conditions that you would think of as errors, such as 404 (page not found).  In fact, it will raise HTTPError for any status code other than 200 (OK), 301 (permanent redirect), or 302 (temporary redirect).  It would be more helpful for your purposes to capture the status code and simply return it, without
+throwing an exception.  To do that, you'll need to define a custom URL handler.
+
Example 11.7. Defining URL handlers

+
This custom URL handler is part of openanything.py.
 class DefaultErrorHandler(urllib2.HTTPDefaultErrorHandler):    1
     def http_error_default(self, req, fp, code, msg, headers): 2
         result = urllib2.HTTPError(         
@@ -10476,13 +9748,13 @@ class DefaultErrorHandler(urllib2.HTTPDefaultErrorHandler):    1 
 
 urllib2 is designed around URL handlers.  Each handler is just a class that can define any number of methods.  When something happens
-            -- like an HTTP error, or even a 304 code -- urllib2 introspects into the list of defined handlers for a method that can handle it.  You used a similar introspection in Chapter 9, XML Processing to define handlers for different node types, but urllib2 is more flexible, and introspects over as many handlers as are defined for the current request.
+            -- like an HTTP error, or even a 304 code -- urllib2 introspects into the list of defined handlers for a method that can handle it.  You used a similar introspection in Chapter 9, XML Processing to define handlers for different node types, but urllib2 is more flexible, and introspects over as many handlers as are defined for the current request.
 
 
 
 2 
 
-urllib2 searches through the defined handlers and calls the http_error_default method when it encounters a 304 status code from the server. By defining a custom error handler, you can prevent urllib2 from raising an exception.  Instead, you create the HTTPError object, but return it instead of raising it.
+urllib2 searches through the defined handlers and calls the http_error_default method when it encounters a 304 status code from the server. By defining a custom error handler, you can prevent urllib2 from raising an exception.  Instead, you create the HTTPError object, but return it instead of raising it.
 
 
 
@@ -10493,9 +9765,7 @@ class DefaultErrorHandler(urllib2.HTTPDefaultErrorHandler):    
Example 11.8. Using custom URL handlers
+
Example 11.8. Using custom URL handlers
 >>> request.headers         1
 {'If-modified-since': 'Thu, 15 Apr 2004 19:45:21 GMT'}
 >>> import openanything
@@ -10511,7 +9781,7 @@ class DefaultErrorHandler(urllib2.HTTPDefaultErrorHandler):    1 
 
-You're continuing the previous example, so the Request object is already set up, and you've already added the If-Modified-Since header.
+You're continuing the previous example, so the Request object is already set up, and you've already added the If-Modified-Since header.
 
 
 
@@ -10524,21 +9794,19 @@ class DefaultErrorHandler(urllib2.HTTPDefaultErrorHandler):    3 
 
-Now you can quietly open the resource, and what you get back is an object that, along with the usual headers (use seconddatastream.headers.dict to acess them), also contains the HTTP status code.  In this case, as you expected, the status is 304, meaning this data hasn't changed since the last time you asked for it.
+Now you can quietly open the resource, and what you get back is an object that, along with the usual headers (use seconddatastream.headers.dict to acess them), also contains the HTTP status code.  In this case, as you expected, the status is 304, meaning this data hasn't changed since the last time you asked for it.
 
 
 
 4 
 
-Note that when the server sends back a 304 status code, it doesn't re-send the data.  That's the whole point: to save bandwidth by not re-downloading data that hasn't
+Note that when the server sends back a 304 status code, it doesn't re-send the data.  That's the whole point: to save bandwidth by not re-downloading data that hasn't
             changed.  So if you actually want that data, you'll need to cache it locally the first time you get it.
 
 
 
-

-

-
Handling ETag works much the same way, but instead of checking for Last-Modified and sending If-Modified-Since, you check for ETag and send If-None-Match.  Let's start with a fresh IDE session.

-
Example 11.9. Supporting ETag/If-None-Match
+
Handling ETag works much the same way, but instead of checking for Last-Modified and sending If-Modified-Since, you check for ETag and send If-None-Match.  Let's start with a fresh IDE session.
+
Example 11.9. Supporting ETag/If-None-Match
 >>> import urllib2, openanything
 >>> request = urllib2.Request('http://diveintomark.org/xml/atom.xml')
 >>> opener = urllib2.build_opener(
@@ -10568,7 +9836,7 @@ class DefaultErrorHandler(urllib2.HTTPDefaultErrorHandler):    1 
 
-Using the firstdatastream.headers pseudo-dictionary, you can get the ETag returned from the server.  (What happens if the server didn't send back an ETag?  Then this line would return None.)
+Using the firstdatastream.headers pseudo-dictionary, you can get the ETag returned from the server.  (What happens if the server didn't send back an ETag?  Then this line would return None.)
 
 
 
@@ -10579,39 +9847,36 @@ class DefaultErrorHandler(urllib2.HTTPDefaultErrorHandler):    3 
 
-Now set up the second call by setting the If-None-Match header to the ETag you got from the first call.
+Now set up the second call by setting the If-None-Match header to the ETag you got from the first call.
 
 
 
 4 
 
-The second call succeeds quietly (without throwing an exception), and once again you see that the server has sent back a 304 status code.  Based on the ETag you sent the second time, it knows that the data hasn't changed.
+The second call succeeds quietly (without throwing an exception), and once again you see that the server has sent back a 304 status code.  Based on the ETag you sent the second time, it knows that the data hasn't changed.
 
 
 
 5 
 
-Regardless of whether the 304 is triggered by Last-Modified date checking or ETag hash matching, you'll never get the data along with the 304.  That's the whole point.
+Regardless of whether the 304 is triggered by Last-Modified date checking or ETag hash matching, you'll never get the data along with the 304.  That's the whole point.
 
 
 
-

 

-

 

 Note
 

 
In these examples, the HTTP server has supported both Last-Modified and ETag headers, but not all servers do.  As a web services client, you should be prepared to support both, but you must code defensively
+In these examples, the HTTP server has supported both Last-Modified and ETag headers, but not all servers do.  As a web services client, you should be prepared to support both, but you must code defensively
       in case a server only supports one or the other, or neither.
 
 

 

-

-

-
11.7. Handling redirects

-
You can support permanent and temporary redirects using a different kind of custom URL handler.

-
First, let's see why a redirect handler is necessary in the first place.

-
Example 11.10. Accessing web services without a redirect handler
+
11.7. Handling redirects

+
You can support permanent and temporary redirects using a different kind of custom URL handler.
+
First, let's see why a redirect handler is necessary in the first place.
+
Example 11.10. Accessing web services without a redirect handler
 >>> import urllib2, httplib
 >>> httplib.HTTPConnection.debuglevel = 1           1
 >>> request = urllib2.Request(
@@ -10671,25 +9936,25 @@ AttributeError: addinfourl instance has no attribute 'status'
 
 2 
 
-This is a URL which I have set up to permanently redirect to my Atom feed at http://diveintomark.org/xml/atom.xml.
+This is a URL which I have set up to permanently redirect to my Atom feed at http://diveintomark.org/xml/atom.xml.
 
 
 
 3 
 
-Sure enough, when you try to download the data at that address, the server sends back a 301 status code, telling you that the resource has moved permanently.
+Sure enough, when you try to download the data at that address, the server sends back a 301 status code, telling you that the resource has moved permanently.
 
 
 
 4 
 
-The server also sends back a Location: header that gives the new address of this data.
+The server also sends back a Location: header that gives the new address of this data.
 
 
 
 5 
 
-urllib2 notices the redirect status code and automatically tries to retrieve the data at the new location specified in the Location: header.
+urllib2 notices the redirect status code and automatically tries to retrieve the data at the new location specified in the Location: header.
 
 
 
@@ -10703,11 +9968,9 @@ AttributeError: addinfourl instance has no attribute 'status'
 
 
 
-

-

-
This is suboptimal, but easy to fix.  urllib2 doesn't behave exactly as you want it to when it encounters a 301 or 302, so let's override its behavior.  How?  With a custom URL handler, just like you did to handle 304 codes.

-
Example 11.11. Defining the redirect handler

-
This class is defined in openanything.py.
+
This is suboptimal, but easy to fix.  urllib2 doesn't behave exactly as you want it to when it encounters a 301 or 302, so let's override its behavior.  How?  With a custom URL handler, just like you did to handle 304 codes.
+
Example 11.11. Defining the redirect handler

+
This class is defined in openanything.py.
 class SmartRedirectHandler(urllib2.HTTPRedirectHandler):     1
     def http_error_301(self, req, fp, code, msg, headers):  
         result = urllib2.HTTPRedirectHandler.http_error_301( 2
@@ -10731,27 +9994,25 @@ class SmartRedirectHandler(urllib2.HTTPRedirectHandler):     2 
 
-When it encounters a 301 status code from the server, urllib2 will search through its handlers and call the http_error_301 method.   The first thing ours does is just call the http_error_301 method in the ancestor, which handles the grunt work of looking for the Location: header and following the redirect to the new address.
+When it encounters a 301 status code from the server, urllib2 will search through its handlers and call the http_error_301 method.   The first thing ours does is just call the http_error_301 method in the ancestor, which handles the grunt work of looking for the Location: header and following the redirect to the new address.
 
 
 
 3 
 
-Here's the key: before you return, you store the status code (301), so that the calling program can access it later.
+Here's the key: before you return, you store the status code (301), so that the calling program can access it later.
 
 
 
 4 
 
-Temporary redirects (status code 302) work the same way: override the http_error_302 method, call the ancestor, and save the status code before returning.
+Temporary redirects (status code 302) work the same way: override the http_error_302 method, call the ancestor, and save the status code before returning.
 
 
 
-

-

 
So what has this bought us?  You can now build a URL opener with the custom redirect handler, and it will still automatically
-follow redirects, but now it will also expose the redirect status code.

-
Example 11.12. Using the redirect handler to detect permanent redirects
+follow redirects, but now it will also expose the redirect status code.
+
Example 11.12. Using the redirect handler to detect permanent redirects
 >>> request = urllib2.Request('http://diveintomark.org/redir/example301.xml')
 >>> import openanything, httplib
 >>> httplib.HTTPConnection.debuglevel = 1
@@ -10800,7 +10061,7 @@ header: Content-Type: application/atom+xml
 
 2 
 
-You sent off a request, and you got a 301 status code in response.  At this point, the http_error_301 method gets called.  You call the ancestor method, which follows the redirect and sends a request at the new location (http://diveintomark.org/xml/atom.xml).
+You sent off a request, and you got a 301 status code in response.  At this point, the http_error_301 method gets called.  You call the ancestor method, which follows the redirect and sends a request at the new location (http://diveintomark.org/xml/atom.xml).
 
 
 
@@ -10808,15 +10069,13 @@ header: Content-Type: application/atom+xml
 
 This is the payoff: now, not only do you have access to the new URL, but you have access to the redirect status code, so you
             can tell that this was a permanent redirect.  The next time you request this data, you should request it from the new location
-            (http://diveintomark.org/xml/atom.xml, as specified in f.url).  If you had stored the location in a configuration file or a database, you need to update that so you don't keep pounding
+            (http://diveintomark.org/xml/atom.xml, as specified in f.url).  If you had stored the location in a configuration file or a database, you need to update that so you don't keep pounding
             the server with requests at the old address.  It's time to update your address book.
 
 
 
-

-

-
The same redirect handler can also tell you that you shouldn't update your address book.

-
Example 11.13. Using the redirect handler to detect temporary redirects
+
The same redirect handler can also tell you that you shouldn't update your address book.
+
Example 11.13. Using the redirect handler to detect temporary redirects
 >>> request = urllib2.Request(
 ...     'http://diveintomark.org/redir/example302.xml')   1
 >>> f = opener.open(request)
@@ -10857,41 +10116,37 @@ http://diveintomark.org/xml/atom.xml
 
 1 
 
-This is a sample URL I've set up that is configured to tell clients to temporarily redirect to http://diveintomark.org/xml/atom.xml.
+This is a sample URL I've set up that is configured to tell clients to temporarily redirect to http://diveintomark.org/xml/atom.xml.
 
 
 
 2 
 
-The server sends back a 302 status code, indicating a temporary redirect.  The temporary new location of the data is given in the Location: header.
+The server sends back a 302 status code, indicating a temporary redirect.  The temporary new location of the data is given in the Location: header.
 
 
 
 3 
 
-urllib2 calls your http_error_302 method, which calls the ancestor method of the same name in urllib2.HTTPRedirectHandler, which follows the redirect to the new location.  Then your http_error_302 method stores the status code (302) so the calling application can get it later.
+urllib2 calls your http_error_302 method, which calls the ancestor method of the same name in urllib2.HTTPRedirectHandler, which follows the redirect to the new location.  Then your http_error_302 method stores the status code (302) so the calling application can get it later.
 
 
 
 4 
 
-And here you are, having successfully followed the redirect to http://diveintomark.org/xml/atom.xml.  f.status tells you that this was a temporary redirect, which means that you should continue to request data from the original address
-            (http://diveintomark.org/redir/example302.xml).  Maybe it will redirect next time too, but maybe not.  Maybe it will redirect to a different address.  It's not for you
+And here you are, having successfully followed the redirect to http://diveintomark.org/xml/atom.xml.  f.status tells you that this was a temporary redirect, which means that you should continue to request data from the original address
+            (http://diveintomark.org/redir/example302.xml).  Maybe it will redirect next time too, but maybe not.  Maybe it will redirect to a different address.  It's not for you
             to say.  The server said this redirect was only temporary, so you should respect that.  And now you're exposing enough information
             that the calling application can respect that.
 
 
 
-

-

-

-

-
11.8. Handling compressed data

+
11.8. Handling compressed data

 
The last important HTTP feature you want to support is compression.  Many web services have the ability to send data compressed,
    which can cut down the amount of data sent over the wire by 60% or more.  This is especially true of XML web services, since
-   XML data compresses very well.

-
Servers won't give you compressed data unless you tell them you can handle it.

-
Example 11.14. Telling the server you would like compressed data
+   XML data compresses very well.
+
Servers won't give you compressed data unless you tell them you can handle it.
+
Example 11.14. Telling the server you would like compressed data
 >>> import urllib2, httplib
 >>> httplib.HTTPConnection.debuglevel = 1
 >>> request = urllib2.Request('http://diveintomark.org/xml/atom.xml')
@@ -10921,7 +10176,7 @@ header: Content-Type: application/atom+xml
 
 1 
 
-This is the key: once you've created your Request object, add an Accept-encoding header to tell the server you can accept gzip-encoded data.  gzip is the name of the compression algorithm you're using.  In theory there could be other compression algorithms, but gzip is the compression algorithm used by 99% of web servers.
+This is the key: once you've created your Request object, add an Accept-encoding header to tell the server you can accept gzip-encoded data.  gzip is the name of the compression algorithm you're using.  In theory there could be other compression algorithms, but gzip is the compression algorithm used by 99% of web servers.
 
 
 
@@ -10932,20 +10187,18 @@ header: Content-Type: application/atom+xml
 
 3 
 
-And here's what the server sends back: the Content-Encoding: gzip header means that the data you're about to receive has been gzip-compressed.
+And here's what the server sends back: the Content-Encoding: gzip header means that the data you're about to receive has been gzip-compressed.
 
 
 
 4 
 
-The Content-Length header is the length of the compressed data, not the uncompressed data.  As you'll see in a minute, the actual length of
+The Content-Length header is the length of the compressed data, not the uncompressed data.  As you'll see in a minute, the actual length of
             the uncompressed data was 15955, so gzip compression cut your bandwidth by over 60%!
 
 
 
-

-

-
Example 11.15. Decompressing the data
+
Example 11.15. Decompressing the data
 >>> compresseddata = f.read()            1
 >>> len(compresseddata)
 6289
@@ -11000,10 +10253,8 @@ header: Content-Type: application/atom+xml
 Look ma, real data. (15955 bytes of it, in fact.)
 
 
-

-

-
“But wait!” I hear you cry.  “This could be even easier!”  I know what you're thinking.  You're thinking that opener.open returns a file-like object, so why not cut out the StringIO middleman and just pass f directly to GzipFile?  OK, maybe you weren't thinking that, but don't worry about it, because it doesn't work.

-
Example 11.16. Decompressing the data directly from the server
+
“But wait!” I hear you cry.  “This could be even easier!”  I know what you're thinking.  You're thinking that opener.open returns a file-like object, so why not cut out the StringIO middleman and just pass f directly to GzipFile?  OK, maybe you weren't thinking that, but don't worry about it, because it doesn't work.
+
Example 11.16. Decompressing the data directly from the server
 >>> f = opener.open(request)1
 >>> f.headers.get('Content-Encoding')         2
 'gzip'
@@ -11020,14 +10271,14 @@ AttributeError: addinfourl instance has no attribute 'tell'
 
 1 
 
-Continuing from the previous example, you already have a Request object set up with an Accept-encoding: gzip header.
+Continuing from the previous example, you already have a Request object set up with an Accept-encoding: gzip header.
 
 
 
 2 
 
 Simply opening the request will get you the headers (though not download any data yet).  As you can see from the returned
-Content-Encoding header, this data has been sent gzip-compressed.
+Content-Encoding header, this data has been sent gzip-compressed.
 
 
 
@@ -11040,14 +10291,10 @@ AttributeError: addinfourl instance has no attribute 'tell'
 
 
 
-

-

-

-

-
11.9. Putting it all together

-
You've seen all the pieces for building an intelligent HTTP web services client.  Now let's see how they all fit together.

-
Example 11.17. The openanything function

-
This function is defined in openanything.py.
+
11.9. Putting it all together

+
You've seen all the pieces for building an intelligent HTTP web services client.  Now let's see how they all fit together.
+
Example 11.17. The openanything function

+
This function is defined in openanything.py.
 def openAnything(source, etag=None, lastmodified=None, agent=USER_AGENT):
     # non-HTTP code omitted for brevity
     if urlparse.urlparse(source)[0] == 'http':   1
@@ -11073,19 +10320,19 @@ def openAnything(source, etag=None, lastmodified=None, agent=USER_AGENT):
 
 2 
 
-You identify yourself to the HTTP server with the User-Agent passed in by the calling function.  If no User-Agent was specified, you use a default one defined earlier in the openanything.py module.  You never use the default one defined by urllib2.
+You identify yourself to the HTTP server with the User-Agent passed in by the calling function.  If no User-Agent was specified, you use a default one defined earlier in the openanything.py module.  You never use the default one defined by urllib2.
 
 
 
 3 
 
-If an ETag hash was given, send it in the If-None-Match header.
+If an ETag hash was given, send it in the If-None-Match header.
 
 
 
 4 
 
-If a last-modified date was given, send it in the If-Modified-Since header.
+If a last-modified date was given, send it in the If-Modified-Since header.
 
 
 
@@ -11096,7 +10343,7 @@ def openAnything(source, etag=None, lastmodified=None, agent=USER_AGENT):
 
 6 
 
-Build a URL opener that uses both of the custom URL handlers: SmartRedirectHandler for handling 301 and 302 redirects, and DefaultErrorHandler for handling 304, 404, and other error conditions gracefully.
+Build a URL opener that uses both of the custom URL handlers: SmartRedirectHandler for handling 301 and 302 redirects, and DefaultErrorHandler for handling 304, 404, and other error conditions gracefully.
 
 
 
@@ -11105,10 +10352,8 @@ def openAnything(source, etag=None, lastmodified=None, agent=USER_AGENT):
 That's it!  Open the URL and return a file-like object to the caller.
 
 
-

-

-
Example 11.18. The fetch function

-
This function is defined in openanything.py.
+
Example 11.18. The fetch function

+
This function is defined in openanything.py.
 def fetch(source, etag=None, last_modified=None, agent=USER_AGENT):  
     '''Fetch data and metadata from a URL, file, stream, or string'''
     result = {}
@@ -11134,7 +10379,7 @@ def fetch(source, etag=None, last_modified=None, agent=USER_AGENT):
 
 1 
 
-First, you call the openAnything function with a URL, ETag hash, Last-Modified date, and User-Agent.
+First, you call the openAnything function with a URL, ETag hash, Last-Modified date, and User-Agent.
 
 
 
@@ -11145,13 +10390,13 @@ def fetch(source, etag=None, last_modified=None, agent=USER_AGENT):
 
 3 
 
-Save the ETag hash returned from the server, so the calling application can pass it back to you next time, and you can pass it on to openAnything, which can stick it in the If-None-Match header and send it to the remote server.
+Save the ETag hash returned from the server, so the calling application can pass it back to you next time, and you can pass it on to openAnything, which can stick it in the If-None-Match header and send it to the remote server.
 
 
 
 4 
 
-Save the Last-Modified date too.
+Save the Last-Modified date too.
 
 
 
@@ -11162,7 +10407,7 @@ def fetch(source, etag=None, last_modified=None, agent=USER_AGENT):
 
 6 
 
-If you got a URL back from the server, save it, and assume that the status code is 200 until you find out otherwise.
+If you got a URL back from the server, save it, and assume that the status code is 200 until you find out otherwise.
 
 
 
@@ -11171,9 +10416,7 @@ def fetch(source, etag=None, last_modified=None, agent=USER_AGENT):
 If one of the custom URL handlers captured a status code, then save that too.
 
 
-

-

-
Example 11.19. Using openanything.py
+
Example 11.19. Using openanything.py
 >>> import openanything
 >>> useragent = 'MyHTTPWebServicesApp/1.0'
 >>> url = 'http://diveintopython3.org/redir/example301.xml'
@@ -11201,7 +10444,7 @@ def fetch(source, etag=None, last_modified=None, agent=USER_AGENT):
 
 1 
 
-The very first time you fetch a resource, you don't have an ETag hash or Last-Modified date, so you'll leave those out.  (They're optional parameters.)
+The very first time you fetch a resource, you don't have an ETag hash or Last-Modified date, so you'll leave those out.  (They're optional parameters.)
 
 
 
@@ -11214,59 +10457,51 @@ def fetch(source, etag=None, last_modified=None, agent=USER_AGENT):
 
 3 
 
-If you ever get a 301 status code, that's a permanent redirect, and you need to update your URL to the new address.
+If you ever get a 301 status code, that's a permanent redirect, and you need to update your URL to the new address.
 
 
 
 4 
 
 The second time you fetch the same resource, you have all sorts of information to pass back: a (possibly updated) URL, the
-ETag from the last time, the Last-Modified date from the last time, and of course your User-Agent.
+ETag from the last time, the Last-Modified date from the last time, and of course your User-Agent.
 
 
 
 5 
 
-What you get back is again a dictionary, but the data hasn't changed, so all you got was a 304 status code and no data.
+What you get back is again a dictionary, but the data hasn't changed, so all you got was a 304 status code and no data.
 
 
 
-

-

-

-

-
11.10. Summary

-
The openanything.py and its functions should now make perfect sense.

-
There are 5 important features of HTTP web services that every client should support:

+
11.10. Summary

+
The openanything.py and its functions should now make perfect sense.
+
There are 5 important features of HTTP web services that every client should support:
 

 
-

-

-

 

-
Chapter 12. SOAP Web Services

-
Chapter 11 focused on document-oriented web services over HTTP.  The “input parameter” was the URL, and the “return value” was an actual XML document which it was your responsibility to parse.

+
Chapter 12. SOAP Web Services

+
Chapter 11 focused on document-oriented web services over HTTP.  The “input parameter” was the URL, and the “return value” was an actual XML document which it was your responsibility to parse.
 
This chapter will focus on SOAP web services, which take a more structured approach.  Rather than dealing with HTTP requests and XML documents directly,
 SOAP allows you to simulate calling functions that return native data types.  As you will see, the illusion is almost perfect;
-you can “call” a function through a SOAP library, with the standard Python calling syntax, and the function appears to return Python objects and values.  But under the covers, the SOAP library has actually performed a complex transaction involving multiple XML documents and a remote server.

+you can “call” a function through a SOAP library, with the standard Python calling syntax, and the function appears to return Python objects and values.  But under the covers, the SOAP library has actually performed a complex transaction involving multiple XML documents and a remote server.
 
SOAP is a complex specification, and it is somewhat misleading to say that SOAP is all about calling remote functions.  Some people would pipe up to add that SOAP allows for one-way asynchronous message passing, and document-oriented web services.  And those people would be correct;
-SOAP can be used that way, and in many different ways.  But this chapter will focus on so-called “RPC-style” SOAP -- calling a remote function and getting results back.

-

-
12.1. Diving In

+SOAP can be used that way, and in many different ways.  But this chapter will focus on so-called “RPC-style” SOAP -- calling a remote function and getting results back.
+
12.1. Diving In

 
You use Google, right?  It's a popular search engine.  Have you ever wished you could programmatically access Google search
-   results?  Now you can.  Here is a program to search Google from Python.

-
Example 12.1. search.py
from SOAPpy import WSDL
+   results?  Now you can.  Here is a program to search Google from Python.
+
Example 12.1. search.py
from SOAPpy import WSDL
 
 # you'll need to configure these two values;
 # see http://www.google.com/apis/
@@ -11289,12 +10524,11 @@ if __name__ == '__main__':
         print r['title']
         print r['link']
         print r['description']
-        print

-
You can import this as a module and use it from a larger program, or you can run the script from the command line.  On the
+        print
You can import this as a module and use it from a larger program, or you can run the script from the command line.  On the
 command line, you give the search query as a command-line argument, and it prints out the URL, title, and description of the
-top five Google search results.

-
Here is the sample output for a search for the word “python”.

-
Example 12.2. Sample Usage of search.py
+top five Google search results.
+
Here is the sample output for a search for the word “python”.
+
Example 12.2. Sample Usage of search.py
 C:\diveintopython3\common\py> python search.py "python"
 <b>Python</b> Programming Language
 http://www.python.org/
@@ -11323,127 +10557,109 @@ http://diveintopython3.org/
 Dive Into <b>Python</b>. <b>Python</b> from novice to pro. Find:
 <b>...</b> It is also available in multiple<br> languages. Read
 Dive Into <b>Python</b>. This book is still being written. <b>...</b>
-

-

-
Further Reading on SOAP

+

+
Further Reading on SOAP

 
-

-

-

-
12.2. Installing the SOAP Libraries

-
Unlike the other code in this book, this chapter relies on libraries that do not come pre-installed with Python.

-
Before you can dive into SOAP web services, you'll need to install three libraries: PyXML, fpconst, and SOAPpy.

-

-
12.2.1. Installing PyXML

-
The first library you need is PyXML, an advanced set of XML libraries that provide more functionality than the built-in XML libraries we studied in Chapter 9.

+
12.2. Installing the SOAP Libraries

+
Unlike the other code in this book, this chapter relies on libraries that do not come pre-installed with Python.
+
Before you can dive into SOAP web services, you'll need to install three libraries: PyXML, fpconst, and SOAPpy.
+
12.2.1. Installing PyXML

+
The first library you need is PyXML, an advanced set of XML libraries that provide more functionality than the built-in XML libraries we studied in Chapter 9.
 

-
Procedure 12.1. 

-
Here is the procedure for installing PyXML:

+
Procedure 12.1. 

+
Here is the procedure for installing PyXML:
 
    
 
  1. 
-
    Go to http://pyxml.sourceforge.net/, click Downloads, and download the latest version for your operating system.
    
-
    2. 
+
    Go to http://pyxml.sourceforge.net/, click Downloads, and download the latest version for your operating system.
+
 
  2. 
-
    If you are using Windows, there are several choices.  Make sure to download the version of PyXML that matches the version of Python you are using.
    
-
    3. 
+
    If you are using Windows, there are several choices.  Make sure to download the version of PyXML that matches the version of Python you are using.
+
 
  3. 
-
    Double-click the installer.  If you download PyXML 0.8.3 for Windows and Python 2.3, the installer program will be PyXML-0.8.3.win32-py2.3.exe.
    
-
    4. 
+
    Double-click the installer.  If you download PyXML 0.8.3 for Windows and Python 2.3, the installer program will be PyXML-0.8.3.win32-py2.3.exe.
+
 
  4. 
-
    Step through the installer program.
    
-
    5. 
+
    Step through the installer program.
+
 
  5. 
 
    After the installation is complete, close the installer.  There will not be any visible indication of success (no programs
-            installed on the Start Menu or shortcuts installed on the desktop).  PyXML is simply a collection of XML libraries used by other programs.
    
-
    6. 
+            installed on the Start Menu or shortcuts installed on the desktop).  PyXML is simply a collection of XML libraries used by other programs.
+
 

-

-
To verify that you installed PyXML correctly, run your Python IDE and check the version of the XML libraries you have installed, as shown here.

-
Example 12.3. Verifying PyXML Installation
+
To verify that you installed PyXML correctly, run your Python IDE and check the version of the XML libraries you have installed, as shown here.
+
Example 12.3. Verifying PyXML Installation
 >>> import xml
 >>> xml.__version__
 '0.8.3'
-
This version number should match the version number of the PyXML installer program you downloaded and ran.

-

-

-

-
12.2.2. Installing fpconst

+

This version number should match the version number of the PyXML installer program you downloaded and ran. +

12.2.2. Installing fpconst

The second library you need is fpconst, a set of constants and functions for working with IEEE754 double-precision special values. This provides support for the - special values Not-a-Number (NaN), Positive Infinity (Inf), and Negative Infinity (-Inf), which are part of the SOAP datatype specification.

+ special values Not-a-Number (NaN), Positive Infinity (Inf), and Negative Infinity (-Inf), which are part of the SOAP datatype specification.
-

Procedure 12.2.

-

Here is the procedure for installing fpconst:

+

Procedure 12.2.

+

Here is the procedure for installing fpconst:

  1. -

    Download the latest version of fpconst from http://www.analytics.washington.edu/statcomp/projects/rzope/fpconst/.

    -
    2. +

    Download the latest version of fpconst from http://www.analytics.washington.edu/statcomp/projects/rzope/fpconst/. +

  2. -

    There are two downloads available, one in .tar.gz format, the other in .zip format. If you are using Windows, download the .zip file; otherwise, download the .tar.gz file.

    -
    3. +

    There are two downloads available, one in .tar.gz format, the other in .zip format. If you are using Windows, download the .zip file; otherwise, download the .tar.gz file. +

  3. Decompress the downloaded file. On Windows XP, you can right-click on the file and choose Extract All; on earlier versions - of Windows, you will need a third-party program such as WinZip. On Mac OS X, you can double-click the compressed file to decompress it with Stuffit Expander.

    -
    4. + of Windows, you will need a third-party program such as WinZip. On Mac OS X, you can double-click the compressed file to decompress it with Stuffit Expander. +
  4. -

    Open a command prompt and navigate to the directory where you decompressed the fpconst files.

    -
    5. +

    Open a command prompt and navigate to the directory where you decompressed the fpconst files. +

  5. -

    Type python setup.py install to run the installation program.

    -
    6. +

    Type python setup.py install to run the installation program. +

-
-

To verify that you installed fpconst correctly, run your Python IDE and check the version number.

-

Example 12.4. Verifying fpconst Installation

+
To verify that you installed fpconst correctly, run your Python IDE and check the version number.
+
Example 12.4. Verifying fpconst Installation
 >>> import fpconst
 >>> fpconst.__version__
 '0.6.0'
-
This version number should match the version number of the fpconst archive you downloaded and installed.

-

-
-
-

12.2.3. Installing SOAPpy

-

The third and final requirement is the SOAP library itself: SOAPpy.

+

This version number should match the version number of the fpconst archive you downloaded and installed. +

12.2.3. Installing SOAPpy

+

The third and final requirement is the SOAP library itself: SOAPpy.

-

Procedure 12.3.

-

Here is the procedure for installing SOAPpy:

+

Procedure 12.3.

+

Here is the procedure for installing SOAPpy:

  1. -

    Go to http://pywebsvcs.sourceforge.net/ and select Latest Official Release under the SOAPpy section.

    -
    2. +

    Go to http://pywebsvcs.sourceforge.net/ and select Latest Official Release under the SOAPpy section. +

  2. -

    There are two downloads available. If you are using Windows, download the .zip file; otherwise, download the .tar.gz file.

    -
    3. +

    There are two downloads available. If you are using Windows, download the .zip file; otherwise, download the .tar.gz file. +

  3. -

    Decompress the downloaded file, just as you did with fpconst.

    -
    4. +

    Decompress the downloaded file, just as you did with fpconst. +

  4. -

    Open a command prompt and navigate to the directory where you decompressed the SOAPpy files.

    -
    5. +

    Open a command prompt and navigate to the directory where you decompressed the SOAPpy files. +

  5. -

    Type python setup.py install to run the installation program.

    -
    6. +

    Type python setup.py install to run the installation program. +

-
-

To verify that you installed SOAPpy correctly, run your Python IDE and check the version number.

-

Example 12.5. Verifying SOAPpy Installation

+
To verify that you installed SOAPpy correctly, run your Python IDE and check the version number.
+
Example 12.5. Verifying SOAPpy Installation
 >>> import SOAPpy
 >>> SOAPpy.__version__
 '0.11.4'
-
This version number should match the version number of the SOAPpy archive you downloaded and installed.

-

-
-
-
-

12.3. First Steps with SOAP

-

The heart of SOAP is the ability to call remote functions. There are a number of public access SOAP servers that provide simple functions for demonstration purposes.

+

This version number should match the version number of the SOAPpy archive you downloaded and installed. +

12.3. First Steps with SOAP

+

The heart of SOAP is the ability to call remote functions. There are a number of public access SOAP servers that provide simple functions for demonstration purposes.

The most popular public access SOAP server is http://www.xmethods.net/. This example uses a demonstration function that takes a United States zip code and returns the current temperature in that -region.

-

Example 12.6. Getting the Current Temperature

+region.
+
Example 12.6. Getting the Current Temperature
 >>> from SOAPpy import SOAPProxy            1
 >>> url = 'http://services.xmethods.net:80/soap/servlet/rpcrouter'
 >>> namespace = 'urn:xmethods-Temperature'  2
@@ -11481,15 +10697,11 @@ region.

 
 
 
-

-
-

Let's peek under those covers.

-
-
-

12.4. Debugging SOAP Web Services

-

The SOAP libraries provide an easy way to see what's going on behind the scenes.

-

Turning on debugging is a simple matter of setting two flags in the SOAPProxy's configuration.

-

Example 12.7. Debugging SOAP Web Services

+
Let's peek under those covers.
+
12.4. Debugging SOAP Web Services

+
The SOAP libraries provide an easy way to see what's going on behind the scenes.
+
Turning on debugging is a simple matter of setting two flags in the SOAPProxy's configuration.
+
Example 12.7. Debugging SOAP Web Services
 >>> from SOAPpy import SOAPProxy
 >>> url = 'http://services.xmethods.net:80/soap/servlet/rpcrouter'
 >>> n = 'urn:xmethods-Temperature'
@@ -11550,10 +10762,8 @@ region.

 
 
 
-

-

Most of the XML request document that gets sent to the server is just boilerplate. Ignore all the namespace declarations; -they're going to be the same (or similar) for all SOAP calls. The heart of the “function call” is this fragment within the <Body> element:

+they're going to be the same (or similar) for all SOAP calls. The heart of the “function call” is this fragment within the <Body> element: 
 <ns1:getTemp               1
   xmlns:ns1="urn:xmethods-Temperature"       2
@@ -11573,19 +10783,17 @@ they're going to be the same (or similar) for all SOAP calls.
 2 
 
 The function's XML element is contained in a specific namespace, which is the namespace you specified when you created the
-SOAPProxy object.  Don't worry about the SOAP-ENC:root; that's boilerplate too.
+SOAPProxy object.  Don't worry about the SOAP-ENC:root; that's boilerplate too.
 
 
 
 3 
 
-The arguments of the function also got translated into XML.  SOAPProxy introspects each argument to determine its datatype (in this case it's a string).  The argument datatype goes into the xsi:type attribute, followed by the actual string value.
+The arguments of the function also got translated into XML.  SOAPProxy introspects each argument to determine its datatype (in this case it's a string).  The argument datatype goes into the xsi:type attribute, followed by the actual string value.
 
 
 
-
-
-

The XML return document is equally easy to understand, once you know what to ignore. Focus on this fragment within the <Body>:

+

The XML return document is equally easy to understand, once you know what to ignore. Focus on this fragment within the <Body>: 

 <ns1:getTempResponse           1
   xmlns:ns1="urn:xmethods-Temperature"           2
@@ -11597,7 +10805,7 @@ they're going to be the same (or similar) for all SOAP calls.
 
 1 
 
-The server wraps the function return value within a <getTempResponse> element.  By convention, this wrapper element is the name of the function, plus Response.  But it could really be almost anything; the important thing that SOAPProxy notices is not the element name, but the namespace.
+The server wraps the function return value within a <getTempResponse> element.  By convention, this wrapper element is the name of the function, plus Response.  But it could really be almost anything; the important thing that SOAPProxy notices is not the element name, but the namespace.
 
 
 
@@ -11614,44 +10822,37 @@ they're going to be the same (or similar) for all SOAP calls.
 
 
 
-
-
-
-
-

12.5. Introducing WSDL

-

The SOAPProxy class proxies local method calls and transparently turns then into invocations of remote SOAP methods. As you've seen, this is a lot of work, and SOAPProxy does it quickly and transparently. What it doesn't do is provide any means of method introspection.

+

12.5. Introducing WSDL

+

The SOAPProxy class proxies local method calls and transparently turns then into invocations of remote SOAP methods. As you've seen, this is a lot of work, and SOAPProxy does it quickly and transparently. What it doesn't do is provide any means of method introspection.

Consider this: the previous two sections showed an example of calling a simple remote SOAP method with one argument and one return value, both of simple data types. This required knowing, and keeping track of, the service URL, the service namespace, the function name, the number of arguments, and the datatype of each argument. If any of these is -missing or wrong, the whole thing falls apart.

+missing or wrong, the whole thing falls apart.

That shouldn't come as a big surprise. If I wanted to call a local function, I would need to know what package or module it was in (the equivalent of service URL and namespace). I would need to know the correct function name and the correct number of arguments. Python deftly handles datatyping without explicit types, but I would still need to know how many argument to pass, and how many -return values to expect.

+return values to expect.

The big difference is introspection. As you saw in Chapter 4, Python excels at letting you discover things about modules and functions at runtime. You can list the available functions within -a module, and with a little work, drill down to individual function declarations and arguments.

-

WSDL lets you do that with SOAP web services. WSDL stands for “Web Services Description Language”. Although designed to be flexible enough to describe many types of web services, it is most often used to describe SOAP web services.

+a module, and with a little work, drill down to individual function declarations and arguments. +

WSDL lets you do that with SOAP web services. WSDL stands for “Web Services Description Language”. Although designed to be flexible enough to describe many types of web services, it is most often used to describe SOAP web services.

A WSDL file is just that: a file. More specifically, it's an XML file. It usually lives on the same server you use to access the -SOAP web services it describes, although there's nothing special about it. Later in this chapter, we'll download the WSDL file for the Google API and use it locally. That doesn't mean we're calling Google locally; the WSDL file still describes the remote functions sitting on Google's server.

-

A WSDL file contains a description of everything involved in calling a SOAP web service:

+SOAP web services it describes, although there's nothing special about it. Later in this chapter, we'll download the WSDL file for the Google API and use it locally. That doesn't mean we're calling Google locally; the WSDL file still describes the remote functions sitting on Google's server. +

A WSDL file contains a description of everything involved in calling a SOAP web service:

  • The service URL and namespace -
    • +
  • The type of web service (probably function calls using SOAP, although as I mentioned, WSDL is flexible enough to describe a wide variety of web services) -
    • -
  • The list of available functions
    • -
  • The arguments for each function
    • -
  • The datatype of each argument
    • -
  • The return values of each function, and the datatype of each return value
    • + +
  • The list of available functions +
  • The arguments for each function +
  • The datatype of each argument +
  • The return values of each function, and the datatype of each return value
-
-

In other words, a WSDL file tells you everything you need to know to be able to call a SOAP web service.

-
-
-

12.6. Introspecting SOAP Web Services with WSDL

+

In other words, a WSDL file tells you everything you need to know to be able to call a SOAP web service. +

12.6. Introspecting SOAP Web Services with WSDL

Like many things in the web services arena, WSDL has a long and checkered history, full of political strife and intrigue. I will skip over this history entirely, since it - bores me to tears. There were other standards that tried to do similar things, but WSDL won, so let's learn how to use it.

-

The most fundamental thing that WSDL allows you to do is discover the available methods offered by a SOAP server.

-

Example 12.8. Discovering The Available Methods

+   bores me to tears.  There were other standards that tried to do similar things, but WSDL won, so let's learn how to use it.
+
The most fundamental thing that WSDL allows you to do is discover the available methods offered by a SOAP server.
+
Example 12.8. Discovering The Available Methods
 >>> from SOAPpy import WSDL          1
 >>> wsdlFile = 'http://www.xmethods.net/sd/2001/TemperatureService.wsdl')
 >>> server = WSDL.Proxy(wsdlFile)    2
@@ -11679,10 +10880,8 @@ a module, and with a little work, drill down to individual function declarations
 
 
 
-

-
-

Okay, so you know that this SOAP server offers a single method: getTemp. But how do you call it? The WSDL proxy object can tell you that too.

-

Example 12.9. Discovering A Method's Arguments

+
Okay, so you know that this SOAP server offers a single method: getTemp.  But how do you call it?  The WSDL proxy object can tell you that too.
+
Example 12.9. Discovering A Method's Arguments
 >>> callInfo = server.methods['getTemp']  1
 >>> callInfo.inparams   2
 [<SOAPpy.wstools.WSDLTools.ParameterInfo instance at 0x00CF3AD0>]
@@ -11718,10 +10917,8 @@ u'zipcode'
 
 
 
-

-
-

WSDL also lets you introspect into a function's return values.

-

Example 12.10. Discovering A Method's Return Values

+
WSDL also lets you introspect into a function's return values.
+
Example 12.10. Discovering A Method's Return Values
 >>> callInfo.outparams            1
 [<SOAPpy.wstools.WSDLTools.ParameterInfo instance at 0x00CF3AF8>]
 >>> callInfo.outparams[0].name    2
@@ -11743,10 +10940,8 @@ u'return'
 
 
 
-

-
-

Let's put it all together, and call a SOAP web service through a WSDL proxy.

-

Example 12.11. Calling A Web Service Through A WSDL Proxy

+
Let's put it all together, and call a SOAP web service through a WSDL proxy.
+
Example 12.11. Calling A Web Service Through A WSDL Proxy
 >>> from SOAPpy import WSDL
 >>> wsdlFile = 'http://www.xmethods.net/sd/2001/TemperatureService.wsdl')
 >>> server = WSDL.Proxy(wsdlFile)               1
@@ -11807,31 +11002,26 @@ u'return'
 
 
 
-

-
-
-
-

12.7. Searching Google

+

12.7. Searching Google

Let's finally turn to the sample code that you saw that the beginning of this chapter, which does something more useful and - exciting than get the current temperature.

-

Google provides a SOAP API for programmatically accessing Google search results. To use it, you will need to sign up for Google Web Services.

+ exciting than get the current temperature. +

Google provides a SOAP API for programmatically accessing Google search results. To use it, you will need to sign up for Google Web Services.

-

Procedure 12.4. Signing Up for Google Web Services

+

Procedure 12.4. Signing Up for Google Web Services

  1. Go to http://www.google.com/apis/ and create a Google account. This requires only an email address. After you sign up you will receive your Google API license - key by email. You will need this key to pass as a parameter whenever you call Google's search functions.

    -
    2. + key by email. You will need this key to pass as a parameter whenever you call Google's search functions. +
  2. -

    Also on http://www.google.com/apis/, download the Google Web APIs developer kit. This includes some sample code in several programming languages (but not Python), and more importantly, it includes the WSDL file.

    -
    3. +

    Also on http://www.google.com/apis/, download the Google Web APIs developer kit. This includes some sample code in several programming languages (but not Python), and more importantly, it includes the WSDL file. +

  3. -

    Decompress the developer kit file and find GoogleSearch.wsdl. Copy this file to some permanent location on your local drive. You will need it later in this chapter.

    -
    4. +

    Decompress the developer kit file and find GoogleSearch.wsdl. Copy this file to some permanent location on your local drive. You will need it later in this chapter. +

-
-

Once you have your developer key and your Google WSDL file in a known place, you can start poking around with Google Web Services.

-

Example 12.12. Introspecting Google Web Services

+
Once you have your developer key and your Google WSDL file in a known place, you can start poking around with Google Web Services.
+
Example 12.12. Introspecting Google Web Services
 >>> from SOAPpy import WSDL
 >>> server = WSDL.Proxy('/path/to/your/GoogleSearch.wsdl') 1
 >>> server.methods.keys()                2
@@ -11873,35 +11063,32 @@ oe              (u'http://www.w3.org/2001/XMLSchema', u'string')
 
 
 
-

-
-

Here is a brief synopsis of all the parameters to the doGoogleSearch function:

+

Here is a brief synopsis of all the parameters to the doGoogleSearch function:

  • key - Your Google API key, which you received when you signed up for Google web services. -
    • +
  • q - The search word or phrase you're looking for. The syntax is exactly the same as Google's web form, so if you know any advanced search syntax or tricks, they all work here as well. -
    • +
  • start - The index of the result to start on. Like the interactive web version of Google, this function returns 10 results at a time. If you wanted to get the second “page” of results, you would set start to 10. -
    • +
  • maxResults - The number of results to return. Currently capped at 10, although you can specify fewer if you are only interested in a few results and want to save a little bandwidth. -
    • +
  • filter - If True, Google will filter out duplicate pages from the results. -
    • -
  • restrict - Set this to country plus a country code to get results only from a particular country. Example: countryUK to search pages in the United Kingdom. You can also specify linux, mac, or bsd to search a Google-defined set of technical sites, or unclesam to search sites about the United States government. -
    • + +
  • restrict - Set this to country plus a country code to get results only from a particular country. Example: countryUK to search pages in the United Kingdom. You can also specify linux, mac, or bsd to search a Google-defined set of technical sites, or unclesam to search sites about the United States government. +
  • safeSearch - If True, Google will filter out porn sites. -
    • +
  • lr (“language restrict”) - Set this to a language code to get results only in a particular language. -
    • -
  • ie and oe (“input encoding” and “output encoding”) - Deprecated, both must be utf-8. -
    • + +
  • ie and oe (“input encoding” and “output encoding”) - Deprecated, both must be utf-8. +
-
-

Example 12.13. Searching Google

+
Example 12.13. Searching Google
 >>> from SOAPpy import WSDL
 >>> server = WSDL.Proxy('/path/to/your/GoogleSearch.wsdl')
 >>> key = 'YOUR_GOOGLE_API_KEY'
@@ -11934,12 +11121,10 @@ oe              (u'http://www.w3.org/2001/XMLSchema', u'string')
 
 
 
-

-

The results object contains more than the actual search results. It also contains information about the search itself, such as how long it took and how many results were found (even though only 10 were returned). The Google web interface shows this information, -and you can access it programmatically too.

-

Example 12.14. Accessing Secondary Information From Google

+and you can access it programmatically too.
+
Example 12.14. Accessing Secondary Information From Google
 >>> results.searchTime   1
 0.224919
 >>> results.estimatedTotalResultsCount     2
@@ -11972,17 +11157,13 @@ and you can access it programmatically too.

 
 
 
-

-
-
-
-

12.8. Troubleshooting SOAP Web Services

-

Of course, the world of SOAP web services is not all happiness and light. Sometimes things go wrong.

+

12.8. Troubleshooting SOAP Web Services

+

Of course, the world of SOAP web services is not all happiness and light. Sometimes things go wrong.

As you've seen throughout this chapter, SOAP involves several layers. There's the HTTP layer, since SOAP is sending XML documents to, and receiving XML documents from, an HTTP server. So all the debugging techniques you learned -in Chapter 11, HTTP Web Services come into play here. You can import httplib and then set httplib.HTTPConnection.debuglevel = 1 to see the underlying HTTP traffic.

-

Beyond the underlying HTTP layer, there are a number of things that can go wrong. SOAPpy does an admirable job hiding the SOAP syntax from you, but that also means it can be difficult to determine where the problem is when things don't work.

-

Here are a few examples of common mistakes that I've made in using SOAP web services, and the errors they generated.

-

Example 12.15. Calling a Method With an Incorrectly Configured Proxy

+in Chapter 11, HTTP Web Services come into play here.  You can import httplib and then set httplib.HTTPConnection.debuglevel = 1 to see the underlying HTTP traffic.
+
Beyond the underlying HTTP layer, there are a number of things that can go wrong.  SOAPpy does an admirable job hiding the SOAP syntax from you, but that also means it can be difficult to determine where the problem is when things don't work.
+
Here are a few examples of common mistakes that I've made in using SOAP web services, and the errors they generated.
+
Example 12.15. Calling a Method With an Incorrectly Configured Proxy
 >>> from SOAPpy import SOAPProxy
 >>> url = 'http://services.xmethods.net:80/soap/servlet/rpcrouter'
 >>> server = SOAPProxy(url)    1
@@ -12015,10 +11196,8 @@ Unable to determine object id from call: is the method element namespaced?>
 
 
-

-
-

Misconfiguring the basic elements of the SOAP service is one of the problems that WSDL aims to solve. The WSDL file contains the service URL and namespace, so you can't get it wrong. Of course, there are still other things you can get wrong.

-

Example 12.16. Calling a Method With the Wrong Arguments

+
Misconfiguring the basic elements of the SOAP service is one of the problems that WSDL aims to solve.  The WSDL file contains the service URL and namespace, so you can't get it wrong.  Of course, there are still other things you can get wrong.
+
Example 12.16. Calling a Method With the Wrong Arguments
 >>> wsdlFile = 'http://www.xmethods.net/sd/2001/TemperatureService.wsdl'
 >>> server = WSDL.Proxy(wsdlFile)
 >>> temperature = server.getTemp(27502)              1
@@ -12051,10 +11230,8 @@ services.temperature.TempService.getTemp(int) -- no signature match>
 
 
 
-

-
-

It's also possible to write Python code that expects a different number of return values than the remote function actually returns.

-

Example 12.17. Calling a Method and Expecting the Wrong Number of Return Values

+
It's also possible to write Python code that expects a different number of return values than the remote function actually returns.
+
Example 12.17. Calling a Method and Expecting the Wrong Number of Return Values
 >>> wsdlFile = 'http://www.xmethods.net/sd/2001/TemperatureService.wsdl'
 >>> server = WSDL.Proxy(wsdlFile)
 >>> (city, temperature) = server.getTemp(27502)  1
@@ -12072,10 +11249,8 @@ TypeError: unpack non-sequence
 
 
 
-

-
-

What about Google's web service? The most common problem I've had with it is that I forget to set the application key properly.

-

Example 12.18. Calling a Method With An Application-Specific Error

+
What about Google's web service?  The most common problem I've had with it is that I forget to set the application key properly.
+
Example 12.18. Calling a Method With An Application-Specific Error
 >>> from SOAPpy import WSDL
 >>> server = WSDL.Proxy(r'/path/to/local/GoogleSearch.wsdl')
 >>> results = server.doGoogleSearch('foo', 'mark', 0, 10, False, "", 1
@@ -12156,108 +11331,93 @@ Caused by: com.google.soap.search.UserKeyInvalidException: Key was of wrong size
 1 
 
 Can you spot the mistake?  There's nothing wrong with the calling syntax, or the number of arguments, or the datatypes.  The
-            problem is application-specific: the first argument is supposed to be my application key, but foo is not a valid Google key.
+            problem is application-specific: the first argument is supposed to be my application key, but foo is not a valid Google key.
 
 
 
 2 
 
 The Google server responds with a SOAP Fault and an incredibly long error message, which includes a complete Java stack trace.  Remember that all SOAP errors are signified by SOAP Faults: errors in configuration, errors in function arguments, and application-specific errors like this.  Buried in there
-            somewhere is the crucial piece of information: Invalid authorization key: foo.
+            somewhere is the crucial piece of information: Invalid authorization key: foo.
 
 
 
-

-
-

Further Reading on Troubleshooting SOAP

+

Further Reading on Troubleshooting SOAP

-
-
-
-

12.9. Summary

+

12.9. Summary

SOAP web services are very complicated. The specification is very ambitious and tries to cover many different use cases for web - services. This chapter has touched on some of the simpler use cases.

+ services. This chapter has touched on some of the simpler use cases.
-

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

+

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

  • Connecting to a SOAP server and calling remote methods -
    • +
  • Loading a WSDL file and introspecting remote methods -
    • +
  • Debugging SOAP calls with wire traces -
    • +
  • Troubleshooting common SOAP-related errors -
    • +
-
-
-
-
-

Chapter 13. Unit Testing

-
-

13.1. Introduction to Roman numerals

-

In previous chapters, you “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, you're going to step back and look at the steps that happen before the code gets written.

+

Chapter 13. Unit Testing

+

13.1. Introduction to Roman numerals

+

In previous chapters, you “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, you're going to step back and look at the steps that happen before the code gets written.

In the next few chapters, you're going to write, debug, and optimize a set of utility functions to convert to and from Roman -numerals. You saw the mechanics of constructing and validating Roman numerals in Section 7.3, “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:

+numerals. You saw the mechanics of constructing and validating Roman numerals in Section 7.3, “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. +
  2. There is only one correct way to represent a particular number as Roman numerals.
  3. 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). -
    4. -
  4. 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 you're not going to deal with that. For the purposes of this chapter, let's stipulate that Roman numerals go from 1 to 3999.) -
    5. + +
  5. 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 you're not going to deal with that. For the purposes of this chapter, let's stipulate that Roman numerals go from 1 to 3999.) +
  6. 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?) -
    7. -
  7. There is no way to represent negative numbers in Roman numerals.
    8. -
  8. There is no way to represent fractions or non-integer numbers in Roman numerals.
    9. + +
  9. There is no way to represent negative numbers in Roman numerals. +
  10. There is no way to represent fractions or non-integer numbers in Roman numerals.
-
-

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

-

roman.py requirements

+

Given all of this, what would you 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. -
  2. toRoman should fail when given an integer outside the range 1 to 3999. -
    3. +
  3. toRoman should return the Roman numeral representation for all integers 1 to 3999. + +
  4. toRoman should fail when given an integer outside the range 1 to 3999. +
  5. toRoman should fail when given a non-integer number. -
    6. +
  6. fromRoman should take a valid Roman numeral and return the number that it represents. -
    7. +
  7. fromRoman should fail when given an invalid Roman numeral. -
    8. +
  8. 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. -
    9. + you started with. So fromRoman(toRoman(n)) == n for all n in 1..3999. +
  9. toRoman should always return a Roman numeral using uppercase letters. -
    10. +
  10. fromRoman should only accept uppercase Roman numerals (i.e. it should fail when given lowercase input). -
    11. +
-
-

Further reading

+

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). -
    • +
-
-
-
-

13.2. Diving in

+

13.2. Diving in

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

+ 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.

+any larger program they may become part of later. Python has a framework for unit testing, the appropriately-named unittest module.
@@ -12269,30 +11429,27 @@ any larger program they may become part of later. Python has a framework for un

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:

+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.
    • +
  • 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...”) -
    • +
  • When writing code in a team, it increases confidence that the code you're about to commit isn't going to break other peoples' code, because you can run their unittests first. (I've seen this sort of thing in code sprints. A team breaks up the assignment, everybody takes the specs for their task, writes unit tests for it, then shares their unit tests with the rest of the team. That way, nobody goes off too far into developing code that won't play well with others.) -
    • +
-
- -
-

13.3. Introducing romantest.py

+

13.3. Introducing romantest.py

This is the complete test suite for your 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 you'll see shortly.

-

Example 13.1. romantest.py

-

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

+   There are good reasons for this, as you'll see shortly.
+
Example 13.1. 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
@@ -12426,43 +11583,37 @@ class CaseCheck(unittest.TestCase):
             roman.fromRoman, numeral.lower())
 
 if __name__ == "__main__":
-    unittest.main()   

-

-
Further reading

+    unittest.main()
+

Further reading

-
-
-
-

13.4. Testing for success

+

13.4. 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...

+ 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.
    • +
  • ...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 the first test case. You have the following requirement:

+

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

    -
  1. toRoman should return the Roman numeral representation for all integers 1 to 3999. -
    2. +
  2. toRoman should return the Roman numeral representation for all integers 1 to 3999. +
-
-

Example 13.2. testToRomanKnownValues

+
Example 13.2. testToRomanKnownValues
 class KnownValues(unittest.TestCase):         1
     knownValues = ( (1, 'I'),
   (2, 'II'),
@@ -12570,24 +11721,19 @@ class KnownValues(unittest.TestCase):         
-
13.5. Testing for failure

+
13.5. Testing for failure

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

-
Remember the other requirements for toRoman:

+   And not just any sort of failure; they must fail in the way you expect.
+
Remember the other requirements for toRoman:
 

 
    
-
  1. toRoman should fail when given an integer outside the range 1 to 3999.
-
    2. 
+
  2. toRoman should fail when given an integer outside the range 1 to 3999.
+
 
  3. toRoman should fail when given a non-integer number.
-
    4. 
+
 

-

-
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 13.3. Testing bad input to toRoman
+
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 13.3. Testing bad input to toRoman
 class ToRomanBadInput(unittest.TestCase):          
     def testTooLarge(self):      
         """toRoman should fail with large input""" 
@@ -12610,7 +11756,7 @@ class ToRomanBadInput(unittest.TestCase):
@@ -12624,24 +11770,21 @@ class ToRomanBadInput(unittest.TestCase): -
Note
The TestCase class of the unittest provides the assertRaises method, which takes the following arguments: the exception you're expecting, the function you're testing, and the arguments you're passing that function. (If the function you're testing takes more than one argument, pass them all to assertRaises, in order, and it will pass them right along to the function you're testing.) Pay close attention to what you'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 you 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 you're passing the toRoman function itself as an argument; you're not calling it, and you're not passing the name of it as a string. Have I mentioned + 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 you 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 you're passing the toRoman function itself as an argument; you're not calling it, and you'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?)
3 Requirement #3 specifies that toRoman cannot accept a non-integer number, so here you test to make sure that toRoman raises a roman.NotIntegerError exception when called with 0.5. If toRoman does not raise a roman.NotIntegerError, this test is considered failed. +Requirement #3 specifies that toRoman cannot accept a non-integer number, so here you test to make sure that toRoman raises a roman.NotIntegerError exception when called with 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:

+

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. +
  2. fromRoman should fail when given an invalid Roman numeral. -
    3. +
-

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 13.4. Testing bad input to fromRoman

+#2 and #3, by testing a series of bad inputs and making sure fromRoman raises the appropriate exception.
+
Example 13.4. Testing bad input to fromRoman
 class FromRomanBadInput(unittest.TestCase):  
     def testTooManyRepeatedNumerals(self):   
         """fromRoman should fail with too many repeated numerals"""              
@@ -12666,23 +11809,18 @@ class FromRomanBadInput(unittest.TestCase):
 
 
 
-

-
-
-
-

13.6. Testing for sanity

+

13.6. 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 precision, incurring rounding errors, or triggering - any other sort of bug.

-

Consider this requirement:

+ 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. -
    2. + you started with. So fromRoman(toRoman(n)) == n for all n in 1..3999. +
-
-

Example 13.5. Testing toRoman against fromRoman

+
Example 13.5. Testing toRoman against fromRoman
 class SanityCheck(unittest.TestCase):        
     def testSanity(self):  
         """fromRoman(toRoman(n))==n for all n"""
@@ -12694,7 +11832,7 @@ class SanityCheck(unittest.TestCase):
 
 1 
 
-You'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.
+You'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.
 
 
 
@@ -12710,20 +11848,17 @@ class SanityCheck(unittest.TestCase):
 
 
 
-

-
-

The last two requirements are different from the others because they seem both arbitrary and trivial:

+

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. +
  2. fromRoman should only accept uppercase Roman numerals (i.e. it should fail when given lowercase input). -
    3. +
-

In fact, they are somewhat arbitrary. You 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 the “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 13.6. Testing for case

+the behavior up front.  And if it's worth specifying, it's worth testing.
+
Example 13.6. Testing for case
 class CaseCheck(unittest.TestCase): 
     def testToRomanCase(self):      
         """toRoman should always return uppercase"""  
@@ -12767,31 +11902,24 @@ class CaseCheck(unittest.TestCase):
 
 4 
 
-This is a complicated line, but it's very similar to what you did in the ToRomanBadInput and FromRomanBadInput tests.  You are testing to make sure that calling a particular function (roman.fromRoman) with a particular value (numeral.lower(), the lowercase version of the current Roman numeral in the loop) raises a particular exception (roman.InvalidRomanNumeralError).  If it does (each time through the loop), the test passes; if even one time it does something else (like raises a different
+This is a complicated line, but it's very similar to what you did in the ToRomanBadInput and FromRomanBadInput tests.  You are testing to make sure that calling a particular function (roman.fromRoman) with a particular value (numeral.lower(), the lowercase version of the current Roman numeral in the loop) raises a particular exception (roman.InvalidRomanNumeralError).  If it does (each time through the loop), the test passes; if even one time it does something else (like raises a different
             exception, or returning a value without raising an exception at all), the test fails.
 
 
 
-

-
-

In the next chapter, you'll see how to write code that passes these tests.

-
+

In the next chapter, you'll see how to write code that passes these tests.


-

[6] “I can resist everything except temptation.” --Oscar Wilde

-
-
-
+

[6] “I can resist everything except temptation.” --Oscar Wilde

-

Chapter 14. Test-First Programming

-
-

14.1. roman.py, stage 1

+

Chapter 14. Test-First Programming

+

14.1. roman.py, stage 1

Now that the unit tests are complete, it's time to start writing the code that the test cases are attempting to test. You're going to do this in stages, so you can see all the unit tests fail, then watch them pass one by one as you fill in the gaps - in roman.py.

-

Example 14.1. roman1.py

-

This file is available in py/roman/stage1/ in the examples directory.

-

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

+   in roman.py.
+
Example 14.1. roman1.py

+
This file is available in py/roman/stage1/ in the examples directory.
+
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
@@ -12832,17 +11960,15 @@ def fromRoman(s):
 
 4 
 
-At this stage, you want to define the API of each of your functions, but you don't want to code them yet, so you stub them out using the Python reserved word pass.
+At this stage, you want to define the API of each of your functions, but you don't want to code them yet, so you stub them out using the Python reserved word pass.
 
 
 
-

-

Now for the big moment (drum roll please): you're finally going to run the 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, you should go back to romantest.py and re-evaluate why you coded a test so useless that it passes with do-nothing functions.

+this point, every test case should fail. In fact, if any test case passes in stage 1, you should go back to romantest.py and re-evaluate why you 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 you can see exactly what's going on as each test case runs. -With any luck, your output should look like this:

-

Example 14.2. Output of romantest1.py against roman1.py

fromRoman should only accept uppercase input ... ERROR
+With any luck, your output should look like this:
+
Example 14.2. 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
@@ -12967,7 +12093,7 @@ FAILED (failures=10, errors=2)     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 the test cases passed.
+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 the test cases passed.
 
 
 
@@ -12990,15 +12116,11 @@ FAILED (failures=10, errors=2)     
-
14.2. roman.py, stage 2

-
Now that you have the framework of the roman module laid out, it's time to start writing code and passing test cases.

-
Example 14.3. roman2.py

-
This file is available in py/roman/stage2/ in the examples directory.

-
If you have not already done so, you can download this and other examples used in this book.
+
14.2. roman.py, stage 2

+
Now that you have the framework of the roman module laid out, it's time to start writing code and passing test cases.
+
Example 14.3. roman2.py

+
This file is available in py/roman/stage2/ in the examples directory.
+
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
@@ -13043,14 +12165,13 @@ def fromRoman(s):
 

 
    
 
  1. The character representations of the most basic Roman numerals.  Note that this is not just the single-character Roman numerals;
-   you're also defining two-character pairs like CM (“one hundred less than one thousand”); this will make the toRoman code simpler later.
-
    2. 
-
  2. The order of the Roman numerals.  They are listed in descending value order, from M all the way down to I.
-
    3. 
-
  3. The value of each Roman numeral.  Each inner tuple is a pair of (numeral, value).
-
    4. 
+   you're also defining two-character pairs like CM (“one hundred less than one thousand”); this will make the toRoman code simpler later.
+
+
  4. The order of the Roman numerals.  They are listed in descending value order, from M all the way down to I.
+
+
  5. The value of each Roman numeral.  Each inner tuple is a pair of (numeral, value).
+
 

-

 
 
 
@@ -13062,10 +12183,8 @@ def fromRoman(s):
 
 
 
-

-

-
Example 14.4. How toRoman works

-
If you're not clear how toRoman works, add a print statement to the end of the while loop:
+
Example 14.4. 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
@@ -13078,10 +12197,9 @@ 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 this manual spot check.  But will it pass the unit testing?  Well no, not entirely.

-
Example 14.5. Output of romantest2.py against roman2.py

-
Remember to run romantest2.py with the -v command-line flag to enable verbose mode.
fromRoman should only accept uppercase input ... FAIL
+
So toRoman appears to work, at least in this manual spot check.  But will it pass the unit testing?  Well no, not entirely.
+
Example 14.5. Output of romantest2.py against roman2.py

+
Remember to run romantest2.py with the -v command-line flag to enable verbose mode.
fromRoman should only accept uppercase input ... FAIL
 toRoman should always return uppercase ... ok1
 fromRoman should fail with malformed antecedents ... FAIL
 fromRoman should fail with repeated pairs of numerals ... FAIL
@@ -13104,7 +12222,7 @@ toRoman should fail with 0 input ... FAIL

 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, you can be reasonably confident that the function works for any good input value you could throw at it.
+            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, you can be reasonably confident that the function works for any good input value you could throw at it.
 
 
 
@@ -13115,8 +12233,7 @@ toRoman should fail with 0 input ... FAIL

 
 
 
-

-
Here's the rest of the output of the unit test, listing the details of all the failures.  You're down to 10.

+
Here's the rest of the output of the unit test, listing the details of all the failures.  You're down to 10.

 ======================================================================
 FAIL: fromRoman should only accept uppercase input
 ----------------------------------------------------------------------
@@ -13210,14 +12327,11 @@ AssertionError: OutOfRangeError
 ----------------------------------------------------------------------
 Ran 12 tests in 0.320s
 
-FAILED (failures=10)

-

-

-
14.3. 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 14.6. roman3.py

-
This file is available in py/roman/stage3/ in the examples directory.

-
If you have not already done so, you can download this and other examples used in this book.
+FAILED (failures=10)
14.3. 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 14.6. roman3.py

+
This file is available in py/roman/stage3/ in the examples directory.
+
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
@@ -13263,13 +12377,13 @@ def fromRoman(s):
 
 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 the range check, and it should catch inputs that are too large, negative, or zero.
+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 the 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.
+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.
 
@@ -13285,9 +12399,7 @@ def fromRoman(s):
 The rest of the function is unchanged.
 
 
-

-

-
Example 14.7. Watching toRoman handle bad input
+
Example 14.7. Watching toRoman handle bad input
 >>> import roman3
 >>> roman3.toRoman(4000)
 Traceback (most recent call last):
@@ -13301,8 +12413,7 @@ OutOfRangeError: number out of range (must be 1..3999)
   File "roman3.py", line 29, in toRoman
     raise NotIntegerError, "non-integers can not be converted"
 NotIntegerError: non-integers can not be converted
-

-
Example 14.8. Output of romantest3.py against roman3.py
fromRoman should only accept uppercase input ... FAIL
+
Example 14.8. 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
@@ -13324,13 +12435,13 @@ toRoman should fail with 0 input ... ok

 
 2 
 
-More exciting is the fact that all of the bad input tests now pass.  This test, testNonInteger, passes because of the int(n) <> n check.  When a non-integer is passed to toRoman, the int(n) <> n check notices it and raises the NotIntegerError exception, which is what testNonInteger is looking for.
+More exciting is the fact that all of the bad input tests now pass.  This test, testNonInteger, passes because of the int(n) <> n check.  When a non-integer is passed to toRoman, the int(n) <> n check notices it and raises the NotIntegerError exception, which is what testNonInteger 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.
+This test, testNegative, passes because of the not (0 < n < 4000) check, which raises an OutOfRangeError exception, which is what testNegative is looking for.
 
 
 
@@ -13401,8 +12512,7 @@ FAILED (failures=6) 
+

@@ -13412,14 +12522,12 @@ FAILED (failures=6)
-
14.4. roman.py, stage 4

+
14.4. roman.py, stage 4
Now that toRoman is done, it's time to start coding fromRoman.  Thanks to the rich data structure that maps individual Roman numerals to integer values, this is no more difficult than
-   the toRoman function.

-
Example 14.9. roman4.py

-
This file is available in py/roman/stage4/ in the examples directory.

-
If you have not already done so, you can download this and other examples used in this book.
+   the toRoman function.
+
Example 14.9. roman4.py

+
This file is available in py/roman/stage4/ in the examples directory.
+
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
@@ -13464,10 +12572,8 @@ def fromRoman(s):
 
 
 

 

 Note
 
 
 

-

-

-
Example 14.10. How fromRoman works

-
If you're not clear how fromRoman works, add a print statement to the end of the while loop:
+
Example 14.10. 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)
@@ -13481,8 +12587,7 @@ found X , of length 1, adding 10
 found X , of length 1, adding 10
 found I , of length 1, adding 1
 found I , of length 1, adding 1
-1972

-
Example 14.11. Output of romantest4.py against roman4.py
fromRoman should only accept uppercase input ... FAIL
+1972
Example 14.11. 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
@@ -13549,32 +12654,28 @@ AssertionError: InvalidRomanNumeralError
 ----------------------------------------------------------------------
 Ran 12 tests in 1.222s
 
-FAILED (failures=4)

-

-

-
14.5. roman.py, stage 5

+FAILED (failures=4)
14.5. 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 you have a powerful tool at your disposal: regular expressions.

-
If you're not familiar with regular expressions and didn't read Chapter 7, Regular Expressions, now would be a good time.

-
As you saw in Section 7.3, “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:

+   than validating numeric input in toRoman, but you have a powerful tool at your disposal: regular expressions.
+
If you're not familiar with regular expressions and didn't read Chapter 7, Regular Expressions, now would be a good time.
+
As you saw in Section 7.3, “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. 
-
  2. The tens characters (I, X, C, and M) can be repeated up to three times.  At 4, you need 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. 
-
  3. Similarly, at 9, you need 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. 
-
  4. The fives characters can not be repeated.  10 is always represented as X, never as VV.  100 is always C, never LL.
-
    5. 
-
  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 need to write it as XCIX, “10 less than 100, then 1 less than 10”).
-
    6. 
+
  6. 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.
+
+
  7. The tens characters (I, X, C, and M) can be repeated up to three times.  At 4, you need 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”).
+
+
  8. Similarly, at 9, you need 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.
+
+
  9. The fives characters can not be repeated.  10 is always represented as X, never as VV.  100 is always C, never LL.
+
+
  10. 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 need to write it as XCIX, “10 less than 100, then 1 less than 10”).
+
 

-

-
Example 14.12. roman5.py

-
This file is available in py/roman/stage5/ in the examples directory.

-
If you have not already done so, you can download this and other examples used in this book.
+
Example 14.12. roman5.py

+
This file is available in py/roman/stage5/ in the examples directory.
+
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
 
@@ -13633,7 +12734,7 @@ def fromRoman(s):
 
 1 
 
-This is just a continuation of the pattern you discussed in Section 7.3, “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.
+This is just a continuation of the pattern you discussed in Section 7.3, “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.
 
 
 
@@ -13644,11 +12745,9 @@ def fromRoman(s):
 
 
 
-

-

 
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 14.13. Output of romantest5.py against roman5.py

+invalid Roman numerals.  But don't take my word for it, look at the results:
+
Example 14.13. 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
@@ -13677,7 +12776,7 @@ OK     4
 2 
 
-More importantly, the bad input tests pass.  For instance, the malformed antecedents test checks cases like MCMC.  As you've seen, this does not match the regular expression, so fromRoman raises an InvalidRomanNumeralError exception, which is what the malformed antecedents test case is looking for, so the test passes.
+More importantly, the bad input tests pass.  For instance, the malformed antecedents test checks cases like MCMC.  As you've seen, this does not match the regular expression, so fromRoman raises an InvalidRomanNumeralError exception, which is what the malformed antecedents test case is looking for, so the test passes.
 
 
 
@@ -13690,12 +12789,11 @@ OK     4
 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.
+And the anticlimax award of the year goes to the word “OK”, which is printed by the unittest module when all the tests pass.
 
 
 
-

-

+

@@ -13703,14 +12801,11 @@ OK     4When all of your tests pass, stop coding.

 

 Note
 

 
 

-

-

 

-
Chapter 15. Refactoring

-

-
15.1. 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 15.1. The bug
>>> import roman5
+
Chapter 15. Refactoring

+
15.1. 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 15.1. The bug
>>> import roman5
 >>> roman5.fromRoman("") 1
 0

 
@@ -13723,10 +12818,8 @@ OK     4

 
 

-

-

-
After reproducing the bug, and before fixing it, you should write a test case that fails, thus illustrating the bug.

-
Example 15.2. Testing for the bug (romantest61.py)
+
After reproducing the bug, and before fixing it, you should write a test case that fails, thus illustrating the bug.
+
Example 15.2. Testing for the bug (romantest61.py)
 class FromRomanBadInput(unittest.TestCase):  
 
     # previous test cases omitted for clarity (they haven't changed)
@@ -13743,10 +12836,8 @@ class FromRomanBadInput(unittest.TestCase):
 
 
 
-

-

-
Since your code has a bug, and you now have a test case that tests this bug, the test case will fail:

-
Example 15.3. Output of romantest61.py against roman61.py
fromRoman should only accept uppercase input ... ok
+
Since your code has a bug, and you now have a test case that tests this bug, the test case will fail:
+
Example 15.3. 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
@@ -13772,10 +12863,9 @@ AssertionError: InvalidRomanNumeralError
 ----------------------------------------------------------------------
 Ran 13 tests in 2.864s
 
-FAILED (failures=1)

-
Now you can fix the bug.

-
Example 15.4. Fixing the bug (roman62.py)

-
This file is available in py/roman/stage6/ in the examples directory.
+FAILED (failures=1)
Now you can fix the bug.
+
Example 15.4. Fixing the bug (roman62.py)

+
This file is available in py/roman/stage6/ in the examples directory.
 def fromRoman(s):
     """convert Roman numeral to integer"""
     if not s: 1
@@ -13795,13 +12885,11 @@ def fromRoman(s):
 
 1 
 
-Only two lines of code are required: an explicit check for an empty string, and a raise statement.
+Only two lines of code are required: an explicit check for an empty string, and a raise statement.
 
 
 
-

-

-
Example 15.5. Output of romantest62.py against roman62.py
fromRoman should only accept uppercase input ... ok
+
Example 15.5. 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
@@ -13831,27 +12919,23 @@ OK 2All 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 need 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 need 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.

-

-

-
15.2. Handling changing requirements

+is tomorrow's regression test.
+
15.2. 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.

+   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 you wanted to expand the range of the 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 you make this change, you'll be able to expand the range of convertible numbers from 1..3999 to 1..4999.  But first, you need to make some changes to the test cases.

-
Example 15.6. Modifying test cases for new requirements (romantest71.py)

-
This file is available in py/roman/stage7/ in the examples directory.

-
If you have not already done so, you can download this and other examples used in this book.
+to that rule by having 4 M characters in a row to represent 4000.  If you make this change, you'll be able to expand the range of convertible numbers from 1..3999 to 1..4999.  But first, you need to make some changes to the test cases.
+
Example 15.6. Modifying test cases for new requirements (romantest71.py)

+
This file is available in py/roman/stage7/ in the examples directory.
+
If you have not already done so, you can download this and other examples used in this book.
 import roman71
 import unittest
 
@@ -13998,33 +13082,31 @@ if __name__ == "__main__":
 1 
 
 The existing known values don't change (they're all still reasonable values to test), but you 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).
+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, you need to bump this up to 5000.
+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, you 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, you need to bump this up to 'MMMMM'.
+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, you 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.
+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 your test cases are up to date with the new requirements, but your code is not, so you expect several of the test cases
-to fail.

-
Example 15.7. Output of romantest71.py against roman71.py

+to fail.
+
Example 15.7. 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
@@ -14043,25 +13125,25 @@ 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.
+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.
+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.
+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.
+The sanity check will also fail as soon as it hits 4000, because toRoman still thinks this is out of range.
 
 
 
@@ -14114,13 +13196,12 @@ OutOfRangeError: number out of range (must be 1..3999)
Example 15.8. Coding the new requirements (roman72.py)

-
This file is available in py/roman/stage7/ in the examples directory.
+stop coding.)
+
Example 15.8. Coding the new requirements (roman72.py)

+
This file is available in py/roman/stage7/ in the examples directory.
 """Convert to and from Roman numerals"""
 import re
 
@@ -14176,26 +13257,24 @@ def fromRoman(s):
             result += integer
             index += len(numeral)
     return result
-

-

+

 
-
-

 

 1 
 toRoman only needs one small change, in the range check.  Where you used to check 0 < n < 4000, you now check 0 < n < 5000.  And you change the error message that you raise to reflect the new acceptable range (1..4999 instead of 1..3999).  You 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 you explicitly stopped it with the range check.)
+toRoman only needs one small change, in the range check.  Where you used to check 0 < n < 4000, you now check 0 < n < 5000.  And you change the error message that you raise to reflect the new acceptable range (1..4999 instead of 1..3999).  You 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 you explicitly stopped it with the range check.)
 
 

 

 2 
 You 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 you added another optional M in the first section of the regular expression.  This will allow up to 4 M characters instead of 3, meaning you 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 you explicitly stopped it with the regular expression pattern matching.
+You 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 you added another optional M in the first section of the regular expression.  This will allow up to 4 M characters instead of 3, meaning you 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 you explicitly stopped it with the regular expression pattern matching.
 
 

 

-

-
You may be skeptical that these two small changes are all that you need.  Hey, don't take my word for it; see for yourself:

-
Example 15.9. Output of romantest72.py against roman72.py
fromRoman should only accept uppercase input ... ok
+
You may be skeptical that these two small changes are all that you need.  Hey, don't take my word for it; see for yourself:
+
Example 15.9. 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
@@ -14220,20 +13299,16 @@ OK 1All the test cases pass.  Stop coding.
 
 
-

-

-
Comprehensive unit testing means never having to rely on a programmer who says “Trust me.”

-

-

-
15.3. Refactoring

+
Comprehensive unit testing means never having to rely on a programmer who says “Trust me.”
+
15.3. 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.

+   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.

+program.
 
Here, “better” means “faster”.  Specifically, the fromRoman function is slower than it needs to be, because of that big nasty regular expression that you 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 you can speed up the function by precompiling the regular expression.

-
Example 15.10. Compiling regular expressions
+end up any faster), but you can speed up the function by precompiling the regular expression.
+
Example 15.10. Compiling regular expressions
 >>> import re
 >>> pattern = '^M?M?M?$'
 >>> re.search(pattern, 'M')               1
@@ -14249,7 +13324,7 @@ end up any faster), but you can speed up the function by precompiling the regula
 
 1 
 
-This is the syntax you'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
+This is the syntax you'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.
 
 
@@ -14257,7 +13332,7 @@ end up any faster), but you can speed up the function by precompiling the regula
 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.
+            regular expression has nothing to do with matching it against any specific strings (like 'M'); it only involves the regular expression itself.
 
 
 
@@ -14269,12 +13344,11 @@ end up any faster), but you can speed up the function by precompiling the regula
 
 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.)
+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.)
 
 
 
-

-

+

@@ -14284,9 +13358,9 @@ end up any faster), but you can speed up the function by precompiling the regula
 

 

 Note
 

 
 

-
Example 15.11. Compiled regular expressions in roman81.py

-
This file is available in py/roman/stage8/ in the examples directory.

-
If you have not already done so, you can download this and other examples used in this book.
+
Example 15.11. Compiled regular expressions in roman81.py

+
This file is available in py/roman/stage8/ in the examples directory.
+
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 = \
@@ -14322,10 +13396,8 @@ def fromRoman(s):
 
 
 
-

-

-
So how much faster is it to compile regular expressions?  See for yourself:

-
Example 15.12. Output of romantest81.py against roman81.py
.............          1
+
So how much faster is it to compile regular expressions?  See for yourself:
+
Example 15.12. Output of romantest81.py against roman81.py
.............          1
 ----------------------------------------------------------------------
 Ran 13 tests in 3.385s 2
 
@@ -14334,15 +13406,15 @@ 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, you only get a dot for each test that passes.  (If a test failed, you'd get an F, and if it had an error, you'd get an E.  You'd still get complete tracebacks for each failure and error, so you could track down any problems.)
+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, you only get a dot for each test that passes.  (If a test failed, you'd get an F, and if it had an error, you'd get an E.  You'd still get complete tracebacks for each failure and error, so you could track down any problems.)
 
 
 
 2 
 
-You 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,
+You 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.
+            regular expression speeds up the search by an average of 54%.)  Not bad for such a simple fix.
 
 
 
@@ -14351,14 +13423,12 @@ OK   3Oh, and in case you were wondering, precompiling the regular expression didn't break anything, and you 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 15.13. roman82.py

-
This file is available in py/roman/stage8/ in the examples directory.

-
If you have not already done so, you can download this and other examples used in this book.
+this module on comp.lang.python, someone suggested that I try using the {m,n} syntax for the optional repeated characters.
+
Example 15.13. roman82.py

+
This file is available in py/roman/stage8/ in the examples directory.
+
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
@@ -14373,14 +13443,12 @@ romanNumeralPattern = \
 
 1 
 
-You 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.
+You 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 15.14. Output of romantest82.py against roman82.py
.............
+
This form of the regular expression is a little shorter (though not any more readable).  The big question is, is it any faster?
+
Example 15.14. Output of romantest82.py against roman82.py
.............
 ----------------------------------------------------------------------
 Ran 13 tests in 3.315s 1
 
@@ -14391,8 +13459,8 @@ OK   2
 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, you've
-            improved the regular expression performance by over 60%, and improved the overall performance of the entire unit test by over 10%.
+            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, you've
+            improved the regular expression performance by over 60%, and improved the overall performance of the entire unit test by over 10%.
 
 
 
@@ -14406,15 +13474,13 @@ OK   2
 
 
-

-

 
One other tweak I would like to make, and then I promise I'll stop refactoring and put this module to bed.  As you'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, it's still going to be difficult to add new features, fix new bugs, or otherwise maintain it.  As you saw in Section 7.5, “Verbose Regular Expressions”, Python provides a way to document your logic line-by-line.

-
Example 15.15. roman83.py

-
This file is available in py/roman/stage8/ in the examples directory.

-
If you have not already done so, you can download this and other examples used in this book.
+how it works, it's still going to be difficult to add new features, fix new bugs, or otherwise maintain it.  As you saw in Section 7.5, “Verbose Regular Expressions”, Python provides a way to document your logic line-by-line.
+
Example 15.15. roman83.py

+
This file is available in py/roman/stage8/ in the examples directory.
+
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
@@ -14439,14 +13505,12 @@ romanNumeralPattern = re.compile('''
 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 you'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
+            compiled regular expression.  Here you'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 15.16. Output of romantest83.py against roman83.py
.............
+
Example 15.16. Output of romantest83.py against roman83.py
.............
 ----------------------------------------------------------------------
 Ran 13 tests in 3.315s 1
 
@@ -14467,21 +13531,17 @@ OK   2
 
 
-

-

-

-

-
15.4. Postscript

+
15.4. 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 you have no other way of breaking down a Roman numeral.  But there's only
    5000 of them; why don't you 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.

+   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 15.17. roman9.py

-
This file is available in py/roman/stage9/ in the examples directory.

-
If you have not already done so, you can download this and other examples used in this book.
+stayed the same, so he could prove that his code worked just as well as the original.
+
Example 15.17. roman9.py

+
This file is available in py/roman/stage9/ in the examples directory.
+
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
@@ -14548,9 +13608,8 @@ def fillLookupTables():
         fromRomanTable[romanNumber] = integer
 
 fillLookupTables()
-

-
So how fast is it?

-
Example 15.18. Output of romantest9.py against roman9.py
+
So how fast is it?
+
Example 15.18. Output of romantest9.py against roman9.py
 
 .............
 ----------------------------------------------------------------------
@@ -14558,83 +13617,71 @@ Ran 13 tests in 0.791s
 
 OK
 
-

-
Remember, the best performance you ever got in the original version was 13 tests in 3.315 seconds.  Of course, it's not entirely
+
Remember, the best performance you 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?

+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.
    • 
+
  • 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.
 

-

-

-

-
15.5. Summary

+
15.5. 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:

+   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
    • 
+
  • 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
    • 
+
+
  • 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:

+
Additionally, you should be comfortable doing all of the following Python-specific things:
 

 

 
-

-

 

-
Further reading

+
Further reading

 
-

-

-

 

-
Chapter 16. Functional Programming

-

-
16.1. Diving in

+
Chapter 16. Functional Programming

+
16.1. Diving in

 
In Chapter 13, Unit Testing, you learned about the philosophy of unit testing.  In Chapter 14, Test-First Programming, you stepped through the implementation of basic unit tests in Python.  In Chapter 15, Refactoring, you saw how unit testing makes large-scale refactoring easier.  This chapter will build on those sample programs, but here
-   we will focus more on advanced Python-specific techniques, rather than on unit testing itself.

+   we will focus more on advanced Python-specific techniques, rather than on 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 Chapter 13, 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 16.1. regression.py

-
If you have not already done so, you can download this and other examples used in this book.
+download them and sit around scratching your head and yelling at your monitor and wondering why they don't work.
+
Example 16.1. 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
@@ -14659,10 +13706,9 @@ def regressionTest():
 
 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 16.2. Sample output of regression.py
+
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 16.2. 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
@@ -14718,17 +13764,13 @@ OK

 
 
 
-

-

-

-

-
16.2. 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.

+
16.2. 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 you saw in Chapter 9, 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 16.3. fullpath.py

-
If you have not already done so, you can download this and other examples used in this book.
+once you see it.  The key to it is sys.argv.  As you saw in Chapter 9, 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 16.3. 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
@@ -14739,7 +13781,7 @@ print 'full path =', os.path.abspath(pathname) 
 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
+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 you'll see shortly.
 
 
@@ -14757,10 +13799,8 @@ print 'full path =', os.path.abspath(pathname) 
 
 
-

-

-
os.path.abspath deserves further explanation.  It is very flexible; it can take any kind of pathname.

-
Example 16.4. Further explanation of os.path.abspath
+
os.path.abspath deserves further explanation.  It is very flexible; it can take any kind of pathname.
+
Example 16.4. Further explanation of os.path.abspath
 >>> import os
 >>> os.getcwd()      1
 /home/you
@@ -14804,7 +13844,6 @@ print 'full path =', os.path.abspath(pathname) 
 
 
-

 

@@ -14818,12 +13857,12 @@ print 'full path =', os.path.abspath(pathname) Note
-

 

 Note
 

 
os.path.abspath not only constructs full path names, it also normalizes them.  That means that if you are in the /usr/ directory, os.path.abspath('bin/../local/bin') will return /usr/local/bin.  It normalizes the path by making it as simple as possible.  If you just want to normalize a pathname like this without
+os.path.abspath not only constructs full path names, it also normalizes them.  That means that if you are in the /usr/ directory, os.path.abspath('bin/../local/bin') will return /usr/local/bin.  It normalizes the path by making it as simple as possible.  If you just want to normalize a pathname like this without
       turning it into a full pathname, use os.path.normpath instead.
 
 

 

-
Example 16.5. Sample output from fullpath.py
+
Example 16.5. Sample output from fullpath.py
 [you@localhost py]$ python /home/you/diveintopython3/common/py/fullpath.py 1
 sys.argv[0] = /home/you/diveintopython3/common/py/fullpath.py
 path = /home/you/diveintopython3/common/py
@@ -14841,13 +13880,13 @@ full path = /home/you/diveintopython3/common/py
1 
 
-In the first case, sys.argv[0] includes the full path of the script.  You 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 you give it.
+In the first case, sys.argv[0] includes the full path of the script.  You 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 you 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 you a partial pathname (relative to the current directory), and os.path.abspath will construct a full pathname from the partial pathname.
+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 you a partial pathname (relative to the current directory), and os.path.abspath will construct a full pathname from the partial pathname.
 
 
 
@@ -14857,7 +13896,6 @@ full path = /home/you/diveintopython3/common/py

 
 Note
@@ -14869,8 +13907,8 @@ full path = /home/you/diveintopython3/common/py
regression.py is located.  He suggests this approach instead:

-
Example 16.6. Running scripts in the current directory
import sys, os, re, unittest
+not the directory where regression.py is located.  He suggests this approach instead:
+
Example 16.6. Running scripts in the current directory
import sys, os, re, unittest
 
 def regressionTest():
     path = os.getcwd()       1
@@ -14898,17 +13936,13 @@ def regressionTest():
 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.

-

-

-

-
16.3. 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.[7]  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 16.7. Introducing filter
+   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.
+
16.3. 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.[7]  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 16.7. Introducing filter
 >>> def odd(n):                 1
 ...     return n % 2
 ...     
@@ -14927,7 +13961,7 @@ def regressionTest():
 
 1 
 
-odd uses the built-in mod function “%” to return True if n is odd and False if n is even.
+odd uses the built-in mod function “%” to return True if n is odd and False if n is even.
 
 
 
@@ -14946,14 +13980,12 @@ def regressionTest():
 
 4 
 
-You could also 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
+You could also 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, you're just filtering the list!”
 
 
 
-

-

-
Example 16.8. filter in regression.py
+
Example 16.8. filter in regression.py
     files = os.listdir(path)              1
     test = re.compile("test\.py$", re.IGNORECASE)           2
     files = filter(test.search, files)    3

@@ -14969,22 +14001,20 @@ def regressionTest():
 2 
 
 This is a compiled regular expression.  As you saw in Section 15.3, “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.
+            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, you'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.
+For each element in the files list, you'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 you'll see later in this chapter).  Both techniques work at the moment, so which one you use is a matter of style.
-There is discussion that map and filter might be deprecated in a future version of Python, but no decision has been made.

-
Example 16.9. Filtering using list comprehensions instead
+There is discussion that map and filter might be deprecated in a future version of Python, but no decision has been made.
+
Example 16.9. 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

@@ -14996,13 +14026,9 @@ There is discussion that map and 
-
16.4. 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 16.10. Introducing map
+
16.4. 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 16.10. Introducing map
 >>> def double(n):
 ...     return n*2
 ...     
@@ -15034,13 +14060,11 @@ There is discussion that map and 3 
 
-You could, if you insist on thinking like a Visual Basic programmer, use a for loop to accomplish the same thing.
+You could, if you insist on thinking like a Visual Basic programmer, use a for loop to accomplish the same thing.
 
 
 
-

-

-
Example 16.11. map with lists of mixed datatypes
+
Example 16.11. map with lists of mixed datatypes
 >>> li = [5, 'a', (2, 'b')]
 >>> map(double, li)     1
 [10, 'aa', (2, 'b', 2, 'b')]

@@ -15055,17 +14079,15 @@ There is discussion that map and 
Example 16.12. map in regression.py
+
All right, enough play time.  Let's look at some real code.
+
Example 16.12. map in regression.py
     filenameToModuleName = lambda f: os.path.splitext(f)[0] 1
     moduleNames = map(filenameToModuleName, files)          2

 
-
@@ -15076,42 +14098,36 @@ There is discussion that map and 
-
16.5. 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.

+which is to define and execute a single test suite that contains the tests from all of those individual test suites.
+
16.5. 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, you started with no data at all; the first thing you did was get the directory path of the current script, and got a list of files in that directory.  That was the bootstrap, and it gave you real data to work
-with: a list of filenames.

+with: a list of filenames.
 
However, you knew you didn't care about all of those files, only the ones that were actually test suites.  You had too much data, so you needed to filter it.  How did you know which data to keep?  You needed a test to decide, so you defined one and passed it to the filter function.  In this case you used a regular expression to decide, but the concept would be the same regardless of how you
-constructed the test.

+constructed the test.
 
Now you had the filenames of each of the test suites (and only the test suites, since everything else had been filtered out),
 but you really wanted module names instead.  You had the right amount of data, but it was in the wrong format.  So you defined a function that would transform a single filename into a module name, and you mapped that function onto
-the entire list.  From one filename, you can get a module name; from a list of filenames, you can get a list of module names.

-
Instead of filter, you could have used a for loop with an if statement.  Instead of map, you 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, you need
+the entire list.  From one filename, you can get a module name; from a list of filenames, you can get a list of module names.
+
Instead of filter, you could have used a for loop with an if statement.  Instead of map, you 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, you need
 to figure out how to test for the condition “is this file a test suite?” anyway; that's the application-specific logic, and no language can write that for us.  But once you've figured that out,
-do you 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?

+do you 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.

+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.

-
-

-
16.6. Dynamically importing modules

-
OK, enough philosophizing.  Let's talk about dynamically importing modules.

-
First, let's look at how you 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.  You did this on the very first line of this chapter's script.

-
Example 16.13. Importing multiple modules at once
+too much, filter it.  If it's not what you want, map it.  Focus on the data; leave the busywork behind.
+
16.6. Dynamically importing modules

+
OK, enough philosophizing.  Let's talk about dynamically importing modules.
+
First, let's look at how you 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.  You did this on the very first line of this chapter's script.
+
Example 16.13. Importing multiple modules at once
 import sys, os, re, unittest 1
 

 

 

 1 
 As you saw in Section 4.7, “Using lambda Functions”, lambda defines an inline function.  And as you 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.
+As you saw in Section 4.7, “Using lambda Functions”, lambda defines an inline function.  And as you 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.
 
 

 

@@ -15122,10 +14138,8 @@ import sys, os, re, unittest 
Example 16.14. Importing modules dynamically
+
Now let's do the same thing, but with dynamic imports.
+
Example 16.14. Importing modules dynamically
 >>> sys = __import__('sys')           1
 >>> os = __import__('os')
 >>> re = __import__('re')
@@ -15139,22 +14153,20 @@ import sys, os, re, unittest 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.
+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 you had said import sys.  The variable os is now the os module, and so forth.
+The variable sys is now the sys module, just as if you 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 you 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 you assign the module
-to doesn't need to match the module name, either.  You could import a series of modules and assign them to a list.

-
Example 16.15. Importing a list of modules dynamically
+to doesn't need to match the module name, either.  You could import a series of modules and assign them to a list.
+
Example 16.15. Importing a list of modules dynamically
 >>> moduleNames = ['sys', 'os', 're', 'unittest'] 1
 >>> moduleNames
 ['sys', 'os', 're', 'unittest']
@@ -15194,19 +14206,15 @@ to doesn't need to match the module name, either.  You could import a series of
 
 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.
+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 you should be able to put this all together and figure out what most of this chapter's code sample is doing.

-

-

-
16.7. Putting it all together

+
Now you should be able to put this all together and figure out what most of this chapter's code sample is doing.
+
16.7. Putting it all together

 
You'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 16.16. The regressionTest function
+   selected modules within it.
+
Example 16.16. The regressionTest function
 def regressionTest():
     path = os.path.abspath(os.path.dirname(sys.argv[0]))   
     files = os.listdir(path)             
@@ -15217,9 +14225,8 @@ def regressionTest():
     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:\diveintopython3\py, which contains the examples that come with this book, including this chapter's script.  As you saw in Section 16.2, “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 16.17. Step 1: Get all the files
+
Let's look at it line by line, interactively.  Assume that the current directory is c:\diveintopython3\py, which contains the examples that come with this book, including this chapter's script.  As you saw in Section 16.2, “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 16.17. Step 1: Get all the files
 >>> import sys, os, re, unittest
 >>> path = r'c:\diveintopython3\py'
 >>> files = os.listdir(path)             
@@ -15241,9 +14248,7 @@ return unittest.TestSuite(map(load, modules))
 
 
 
-

-

-
Example 16.18. Step 2: Filter to find the files you care about
+
Example 16.18. Step 2: Filter to find the files you care about
 >>> test = re.compile("test\.py$", re.IGNORECASE)           1
 >>> files = filter(test.search, files)    2
 >>> files               3
@@ -15253,7 +14258,7 @@ return unittest.TestSuite(map(load, modules))
 
 1 
 
-This regular expression will match any string that ends with test.py.  Note that you need to escape the period, since a period in a regular expression usually means “match any single character”, but you actually want to match a literal period instead.
+This regular expression will match any string that ends with test.py.  Note that you need to escape the period, since a period in a regular expression usually means “match any single character”, but you actually want to match a literal period instead.
 
 
 
@@ -15270,9 +14275,7 @@ return unittest.TestSuite(map(load, modules))
 
 
 
-

-

-
Example 16.19. Step 3: Map filenames to module names
+
Example 16.19. Step 3: Map filenames to module names
 >>> filenameToModuleName = lambda f: os.path.splitext(f)[0] 1
 >>> filenameToModuleName('romantest.py')  2
 'romantest'
@@ -15286,14 +14289,14 @@ return unittest.TestSuite(map(load, modules))
 
 1 
 
-As you saw in Section 4.7, “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
+As you saw in Section 4.7, “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 you 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 you define with a def statement.  You can call the filenameToModuleName function like any other, and it does just what you wanted it to do: strips the file extension off of its argument.
+filenameToModuleName is a function.  There's nothing magic about lambda functions as opposed to regular functions that you define with a def statement.  You can call the filenameToModuleName function like any other, and it does just what you wanted it to do: strips the file extension off of its argument.
 
 
 
@@ -15308,9 +14311,7 @@ return unittest.TestSuite(map(load, modules))
 And the result is just what you wanted: a list of modules, as strings.
 
 
-

-

-
Example 16.20. Step 4: Mapping module names to modules
+
Example 16.20. Step 4: Mapping module names to modules
 >>> modules = map(__import__, moduleNames)1
 >>> modules             2
 [<module 'apihelpertest' from 'apihelpertest.py'>,
@@ -15338,13 +14339,11 @@ return unittest.TestSuite(map(load, modules))
 
 3 
 
-The last module in the list is the romantest module, just as if you had said import romantest.
+The last module in the list is the romantest module, just as if you had said import romantest.
 
 
 
-

-

-
Example 16.21. Step 5: Loading the modules into a test suite
+
Example 16.21. Step 5: Loading the modules into a test suite
 >>> load = unittest.defaultTestLoader.loadTestsFromModule  
 >>> map(load, modules)   1
 [<unittest.TestSuite tests=[
@@ -15364,99 +14363,83 @@ return unittest.TestSuite(map(load, modules))
 
 These are real module objects.  Not only can you access them like any other module, instantiate classes and call functions,
             you 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 your module, and each of those TestSuite objects contains a list of tests, one for each test method in your module.
+            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 your module, and each of those TestSuite objects contains a list of tests, one for each test method in your module.
 
 
 
 2 
 
-Finally, you 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
+Finally, you 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 you 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 16.22. Step 6: Telling unittest to use your test suite
+
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 you 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 16.22. Step 6: Telling unittest to use your test suite
 if __name__ == "__main__": 
     unittest.main(defaultTest="regressionTest") 1
-

-

+

 
-

 

 1 
 Instead of letting the unittest module do all its magic for us, you've done most of it yourself.  You've created a function (regressionTest) that imports the modules yourself, calls unittest.defaultTestLoader yourself, and wraps it all up in a test suite.  Now all you need to do is tell unittest that, instead of looking for tests and building a test suite in the usual way, it should just call the regressionTest function, which returns a ready-to-use TestSuite.
+Instead of letting the unittest module do all its magic for us, you've done most of it yourself.  You've created a function (regressionTest) that imports the modules yourself, calls unittest.defaultTestLoader yourself, and wraps it all up in a test suite.  Now all you need to do is tell unittest that, instead of looking for tests and building a test suite in the usual way, it should just call the regressionTest function, which returns a ready-to-use TestSuite.
 
 

 

-

-

-

-
16.8. Summary

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

-
You should now feel comfortable doing all of these things:

+
16.8. Summary

+
The regression.py program and its output should now make perfect sense.
+
You should now feel comfortable doing all of these things:
 

 
-

-

 


 

-
[7] 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.

-

+
[7] 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.
 

-
[8] 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.

-

-

-

+
[8] 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 17. Dynamic functions

-

-
17.1. Diving in

+
Chapter 17. Dynamic functions

+
17.1. 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.

+   Generators are new in Python 2.3.  But first, let's talk about how to make plural nouns.
 
If you haven't read Chapter 7, 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.

+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.

+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:

+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. 
+
 
  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. 
+
 
  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. 
-
  4. If all else fails, just add S and hope for the best.
    5. 
+
+
  5. If all else fails, just add S and hope for the best.
 

-

-
(I know, there are a lot 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.

+
(I know, there are a lot 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.  Start with just English nouns, and just these four rules, but keep in mind that
-you'll inevitably need to add more rules, and you may eventually need to add more languages.

-

-

-
17.2. plural.py, stage 1

+you'll inevitably need to add more rules, and you may eventually need to add more languages.
+
17.2. plural.py, stage 1

 
So you're looking at words, which at least in English are strings of characters.  And you have rules that say you need to
-   find different combinations of characters, and then do different things to them.  This sounds like a job for regular expressions.

-
Example 17.1. plural1.py
+   find different combinations of characters, and then do different things to them.  This sounds like a job for regular expressions.
+
Example 17.1. plural1.py
 import re
 
 def plural(noun):          
@@ -15473,7 +14456,7 @@ def plural(noun):
 
 1 
 
-OK, this is a regular expression, but it uses a syntax you didn't see in Chapter 7, 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 you're checking to see if noun ends with s, x, or z.
+OK, this is a regular expression, but it uses a syntax you didn't see in Chapter 7, 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 you're checking to see if noun ends with s, x, or z.
 
 
 
@@ -15483,9 +14466,7 @@ def plural(noun):
 
 
 
-

-

-
Example 17.2. Introducing re.sub
+
Example 17.2. Introducing re.sub
 >>> import re
 >>> re.search('[abc]', 'Mark')   1
 <_sre.SRE_Match object at 0x001C1FA8>
@@ -15500,31 +14481,29 @@ def plural(noun):
 
 1 
 
-Does the string Mark contain a, b, or c?  Yes, it contains a.
+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.
+OK, now find a, b, or c, and replace it with o.  Mark becomes Mork.
 
 
 
 3 
 
-The same function turns rock into rook.
+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.
+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 17.3. Back to plural1.py
+
Example 17.3. Back to plural1.py
 import re
 
 def plural(noun):          
@@ -15541,25 +14520,23 @@ def plural(noun):
 
 1 
 
-Back to the plural function.  What are you doing?  You're replacing the end of string with es.  In other words, adding es to the string.  You 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.
+Back to the plural function.  What are you doing?  You're replacing the end of string with es.  In other words, adding es to the string.  You 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.  You're looking for words that end in H where the H can be heard.
+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.  You'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.  You're looking for words that end in Y that sounds like I.
+Same pattern here: match words that end in Y, where the character before the Y is not a, e, i, o, or u.  You're looking for words that end in Y that sounds like I.
 
 
 
-

-

-
Example 17.4. More on negation regular expressions
+
Example 17.4. More on negation regular expressions
 >>> import re
 >>> re.search('[^aeiou]y$', 'vacancy') 1
 <_sre.SRE_Match object at 0x001C1FA8>
@@ -15574,25 +14551,23 @@ def plural(noun):
 
 1 
 
-vacancy matches this regular expression, because it ends in cy, and c is not a, e, i, o, or u.
+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 you specifically said that the character before the y could not be o.  day does not match, because it ends in ay.
+boy does not match, because it ends in oy, and you 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.
+pita does not match, because it does not end in y.
 
 
 
-

-

-
Example 17.5. More on re.sub
+
Example 17.5. More on re.sub
 >>> re.sub('y$', 'ies', 'vacancy')              1
 'vacancies'
 >>> re.sub('y$', 'ies', 'agency')
@@ -15604,7 +14579,7 @@ def plural(noun):
 
 1 
 
-This regular expression turns vacancy into vacancies and agency into agencies, which is what you wanted.  Note that it would also turn boy into boies, but that will never happen in the function because you did that re.search first to find out whether you should do this re.sub.
+This regular expression turns vacancy into vacancies and agency into agencies, which is what you wanted.  Note that it would also turn boy into boies, but that will never happen in the function because you did that re.search first to find out whether you should do this re.sub.
 
 
 
@@ -15612,21 +14587,17 @@ def plural(noun):
 
 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: you're using a remembered group, which you learned in Section 7.6, “Case study: Parsing Phone Numbers”, to remember the character before the y.  Then in the substitution string, you use a new syntax, \1, which means “hey, that first group you remembered?  put it here”.  In this case, you remember the c before the y, and then when you do the substitution, you 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.)
+            of it should look familiar: you're using a remembered group, which you learned in Section 7.6, “Case study: Parsing Phone Numbers”, to remember the character before the y.  Then in the substitution string, you use a new syntax, \1, which means “hey, that first group you remembered?  put it here”.  In this case, you remember the c before the y, and then when you do the substitution, you 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
+
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 you first described the pluralizing rules.  You originally laid out rules
-like “if the word ends in S, X, or Z, then add ES”.  And if you look at this function, you 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.

-

-

-
17.3. plural.py, stage 2

+like “if the word ends in S, X, or Z, then add ES”.  And if you look at this function, you 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.
+
17.3. plural.py, stage 2

 
Now you're going to add a level of abstraction.  You started by defining a list of rules: if this, then do that, otherwise
-   go to the next rule.  Let's temporarily complicate part of the program so you can simplify another part.

-
Example 17.6. plural2.py
+   go to the next rule.  Let's temporarily complicate part of the program so you can simplify another part.
+
Example 17.6. plural2.py
 import re
 
 def match_sxz(noun):        
@@ -15676,26 +14647,24 @@ def plural(noun):
 
 2 
 
-Using a for loop, you 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 you get that far), matchesRule will be assigned match_h, and applyRule will be assigned apply_h.
+Using a for loop, you 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 you 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 you can call.  So on the first iteration of the for loop, this is equivalent to calling matches_sxz(noun).
+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 you 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.
+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 17.7. Unrolling the plural function
+
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 17.7. Unrolling the plural function
 def plural(noun):
     if match_sxz(noun):
         return apply_sxz(noun)
@@ -15705,18 +14674,15 @@ def plural(noun):
         return apply_y(noun)
     if match_default(noun):
         return apply_default(noun)
-

-
The benefit here is that that 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.

+
The benefit here is that that 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
-the function.  Well, in the 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.

-

-

-
17.4. plural.py, stage 3

+the function.  Well, in the 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.
+
17.4. plural.py, stage 3

 
Defining separate named functions for each match and apply rule isn't really necessary.  You never call them directly; you
-   define them in the rules list and call them through there.  Let's streamline the rules definition by anonymizing those functions.

-
Example 17.8. plural3.py
+   define them in the rules list and call them through there.  Let's streamline the rules definition by anonymizing those functions.
+
Example 17.8. plural3.py
 import re
 
 rules = \
@@ -15761,16 +14727,12 @@ def plural(noun):
 
 
 
-

-

 
Now to add a new rule, all you need 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
 you have some unnecessary duplication here.  You 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.

-

-

-
17.5. plural.py, stage 4

-
Let's factor out the duplication in the code so that defining new rules can be easier.

-
Example 17.9. plural4.py
+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.
+
17.5. plural.py, stage 4

+
Let's factor out the duplication in the code so that defining new rules can be easier.
+
Example 17.9. plural4.py
 import re
 
 def buildMatchAndApplyFunctions((pattern, search, replace)):  
@@ -15782,7 +14744,7 @@ def buildMatchAndApplyFunctions((pattern, search, replace)):
 
 1 
 
-buildMatchAndApplyFunctions is a function that builds other functions dynamically.  It takes pattern, search and replace (actually it takes a tuple, but more on that in a minute), and you can build the match function using the lambda 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 you're building.  Whoa.
+buildMatchAndApplyFunctions is a function that builds other functions dynamically.  It takes pattern, search and replace (actually it takes a tuple, but more on that in a minute), and you can build the match function using the lambda 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 you're building.  Whoa.
 
 
 
@@ -15800,10 +14762,8 @@ def buildMatchAndApplyFunctions((pattern, search, replace)):
 
 
 
-

-

-
If this is incredibly confusing (and it should be, this is weird stuff), it may become clearer when you see how to use it.

-
Example 17.10. plural4.py continued
+
If this is incredibly confusing (and it should be, this is weird stuff), it may become clearer when you see how to use it.
+
Example 17.10. plural4.py continued
 patterns = \
   (
     ('[sxz]$', '$', 'es'),
@@ -15829,10 +14789,8 @@ rules = map(buildMatchAndApplyFunctions, patterns)  
 
 
-

-

-
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 17.11. Unrolling the rules definition
+
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 17.11. Unrolling the rules definition
 rules = \
   (
     (
@@ -15852,8 +14810,7 @@ rules = \
      lambda word: re.sub('$', 's', word)
     )
    )      
-

-
Example 17.12. plural4.py, finishing up
+
Example 17.12. plural4.py, finishing up
 def plural(noun):                
     for matchesRule, applyRule in rules:            1
         if matchesRule(noun):    
@@ -15864,14 +14821,12 @@ def plural(noun):
 1 
 
 Since the rules list is the same as the previous example, it should come as no surprise that the 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 lambda 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.
+            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 lambda 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 must confess that there was a subtlety in the definition of buildMatchAndApplyFunctions that I skipped over.  Let's go back and take another look.

-
Example 17.13. Another look at buildMatchAndApplyFunctions
+
Just in case that wasn't mind-blowing enough, I must confess that there was a subtlety in the definition of buildMatchAndApplyFunctions that I skipped over.  Let's go back and take another look.
+
Example 17.13. Another look at buildMatchAndApplyFunctions
 def buildMatchAndApplyFunctions((pattern, search, replace)):   1
 

 
@@ -15884,9 +14839,7 @@ def buildMatchAndApplyFunctions((pattern, search, replace)):   
Example 17.14. Expanding tuples when calling functions
+
Example 17.14. Expanding tuples when calling functions
 >>> def foo((a, b, c)):
 ...     print c
 ...     print b
@@ -15906,27 +14859,22 @@ apple
 
 
 

-

-
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 you 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 you use the auto-tuple-expansion trick in the definition of buildMatchAndApplyFunctions to assign the elements of that tuple to named variables that you can work with.

-

-

-

-
17.6. plural.py, stage 5

+
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 you 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 you use the auto-tuple-expansion trick in the definition of buildMatchAndApplyFunctions to assign the elements of that tuple to named variables that you can work with.
+
17.6. plural.py, stage 5

 
You've factored out all the duplicate code and added enough abstractions so that the 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.

+   separately from the code that uses them.
 
First, let's create a text file that contains the rules you want.  No fancy data structures, just space- (or tab-)delimited
 strings in three columns.  You'll call it rules.en; “en” stands for English.  These are the rules for pluralizing English nouns.  You could add other rule files for other languages
-later.

-
Example 17.15. rules.en
+later.
+
Example 17.15. rules.en
 [sxz]$$               es
 [^aeioudgkprt]h$        $               es
 [^aeiou]y$              y$              ies
 $     $               s
-

-
Now let's see how you can use this rules file.

-
Example 17.16. plural5.py
+
Now let's see how you can use this rules file.
+
Example 17.16. plural5.py
 import re
 import string               
 
@@ -15954,13 +14902,13 @@ def plural(noun, language='en'):           2 
 
-Our plural function now takes an optional second parameter, language, which defaults to en.
+Our plural function now takes an optional second parameter, language, which defaults to en.
 
 
 
 3 
 
-You use the language parameter to construct a filename, then open the file and read the contents into a list.  If language is en, then you'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
+You use the language parameter to construct a filename, then open the file and read the contents into a list.  If language is en, then you'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.
 
 
@@ -15968,34 +14916,30 @@ def plural(noun, language='en'):           4 
 
 As you saw, each line in the 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 you hard-coded it in stage 4.
+            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 you 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).
+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 you're now building a combined match-and-apply function, you 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 you need to try another rule.
+            and if it returns something, then that's the plural; if it returns nothing (None), then the rule didn't match and you need to try another rule.
 
 
 
-

-

 
So the improvement here is that you've completely separated the pluralization rules into an external file.  Not only can the
-file be maintained separately from the code, but you've set up a naming scheme where the same plural function can use different rule files, based on the language parameter.

+file be maintained separately from the code, but you've set up a naming scheme where the same plural function can use different rule files, based on the language parameter.
 
The downside here is that you're reading that file every time you call the plural function.  I thought I could get through this entire book without using the phrase “left as an exercise for the reader”, but here you go: building a caching mechanism for the language-specific rule files that auto-refreshes itself if the rule
-files change between calls is left as an exercise for the reader.  Have fun.

-

-

-
17.7. plural.py, stage 6

-
Now you're ready to talk about generators.

-
Example 17.17. plural6.py
+files change between calls is left as an exercise for the reader.  Have fun.
+
17.7. plural.py, stage 6

+
Now you're ready to talk about generators.
+
Example 17.17. plural6.py
 import re
 
 def rules(language):           
@@ -16007,9 +14951,8 @@ 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 you look at a simpler example first.

-
Example 17.18. Introducing generators
+
This uses a technique called generators, which I'm not even going to try to explain until you look at a simpler example first.
+
Example 17.18. Introducing generators
 >>> def make_counter(x):
 ...     print 'entering make_counter'
 ...     while 1:
@@ -16034,7 +14977,7 @@ def plural(noun, language='en'):
 
 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
+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.
 
@@ -16055,25 +14998,23 @@ def plural(noun, language='en'):
 
 4 
 
-The first time you 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 you originally created the generator by calling make_counter(2).
+The first time you 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 you originally created the generator by calling make_counter(2).
 
 
 
 5 
 
-Repeatedly calling next() on the generator object resumes where you left off and continues until you 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 you loop through the while loop again, and the first thing you do is yield x, which returns the current value of x (now 3).
+Repeatedly calling next() on the generator object resumes where you left off and continues until you 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 you loop through the while loop again, and the first thing you do is yield x, which returns the current value of x (now 3).
 
 
 
 6 
 
-The second time you call counter.next(), you do all the same things again, but this time x is now 4.  And so forth.  Since make_counter sets up an infinite loop, you 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.
+The second time you call counter.next(), you do all the same things again, but this time x is now 4.  And so forth.  Since make_counter sets up an infinite loop, you 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 17.19. Using generators instead of recursion
+
Example 17.19. Using generators instead of recursion
 def fibonacci(max):
     a, b = 0, 1       1
     while a < max:
@@ -16097,15 +15038,13 @@ def fibonacci(max):
 
 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, then a, b = b, a+b will set a to 5 (the previous value of b) and b to 8 (the sum of the previous values of a and b).
+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, then a, b = b, a+b will set a to 5 (the previous value of b) and b to 8 (the sum of the previous values of a and b).
 
 
 
-

-

 
So you 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 17.20. Generators in for loops
+is easier to read.  Also, it works well with for loops.
+
Example 17.20. 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
@@ -16114,20 +15053,18 @@ is easier to read.  Also, it works well with for lo
 
 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).
+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 you 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.
+Each time through the for loop, n gets a new value from the yield statement in fibonacci, and all you 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 the plural function and see how you're using this.

-
Example 17.21. Generators that generate dynamic functions
+
OK, let's go back to the plural function and see how you're using this.
+
Example 17.21. Generators that generate dynamic functions
 def rules(language):           
     for line in file('rules.%s' % language):      1
         pattern, search, replace = line.split()   2
@@ -16142,116 +15079,105 @@ def plural(noun, language='en'):
 
 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.
+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 the rules file have three values separated by whitespace, so line.split() returns a tuple of 3 values, and you assign those values to 3 local variables.
+No magic here.  Remember that the lines of the rules file have three values separated by whitespace, so line.split() returns a tuple of 3 values, and you assign those values to 3 local variables.
 
 
 
 3 
 
-And then you yield.  What do you yield?  A function, built dynamically with lambda, 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 out rule functions.
+And then you yield.  What do you yield?  A function, built dynamically with lambda, 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 out rule functions.
 
 
 
 4 
 
-Since rules is a generator, you can use it directly in a for loop.  The first time through the for loop, you 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, you will pick up where you 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
+Since rules is a generator, you can use it directly in a for loop.  The first time through the for loop, you 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, you will pick up where you 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 you gained over stage 5?  In stage 5, you read the entire rules file and built a list of all the possible rules before you even tried the first one.
 Now with generators, you can do everything lazily: you open the first and read the first rule and create a function to try
-it, but if that works you don't ever read the rest of the file or create any other functions.

+it, but if that works you don't ever read the rest of the file or create any other functions.
 

-
Further reading

+
Further reading

 
-

-

-

-
17.8. Summary

-
You 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:

+
17.8. Summary

+
You 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.

-

-

+the right balance between simplicity and power.
 

-
Chapter 18. Performance Tuning

-
Performance tuning is a many-splendored thing.  Just because Python is an interpreted language doesn't mean you shouldn't worry about code optimization.  But don't worry about it too much.

-

-
18.1. Diving in

-
There are so many pitfalls involved in optimizing your code, it's hard to know where to start.

+
Chapter 18. Performance Tuning

+
Performance tuning is a many-splendored thing.  Just because Python is an interpreted language doesn't mean you shouldn't worry about code optimization.  But don't worry about it too much.
+
18.1. Diving in

+
There are so many pitfalls involved in optimizing your code, it's hard to know where to start.
 
Let's start here: are you sure you need to do it at all?  Is your code really so bad?  Is it worth the time to tune it?  Over the lifetime of your application, how much time is going
-to be spent running that code, compared to the time spent waiting for a remote database server, or waiting for user input?

+to be spent running that code, compared to the time spent waiting for a remote database server, or waiting for user input?
 
Second, are you sure you're done coding?  Premature optimization is like spreading frosting on a half-baked cake.  You spend hours or days (or more) optimizing your
-code for performance, only to discover it doesn't do what you need it to do.  That's time down the drain.

+code for performance, only to discover it doesn't do what you need it to do.  That's time down the drain.
 
This is not to say that code optimization is worthless, but you need to look at the whole system and decide whether it's the
 best use of your time.  Every minute you spend optimizing code is a minute you're not spending adding new features, or writing
-documentation, or playing with your kids, or writing unit tests.

+documentation, or playing with your kids, or writing unit tests.
 
Oh yes, unit tests.  It should go without saying that you need a complete set of unit tests before you begin performance tuning.
-The last thing you need is to introduce new bugs while fiddling with your algorithms.

+The last thing you need is to introduce new bugs while fiddling with your algorithms.
 
With these caveats in place, let's look at some techniques for optimizing Python code.  The code in question is an implementation of the Soundex algorithm.  Soundex was a method used in the early 20th century
 for categorizing surnames in the United States census.  It grouped similar-sounding names together, so even if a name was
 misspelled, researchers had a chance of finding it.  Soundex is still used today for much the same reason, although of course
-we use computerized database servers now.  Most database servers include a Soundex function.

-
There are several subtle variations of the Soundex algorithm.  This is the one used in this chapter:

+we use computerized database servers now.  Most database servers include a Soundex function.
+
There are several subtle variations of the Soundex algorithm.  This is the one used in this chapter:
 

 
    
-
  1. Keep the first letter of the name as-is.
    2. 
+
  2. Keep the first letter of the name as-is.
 
  3. Convert the remaining letters to digits, according to a specific table:
 
    
 
      
-
    • B, F, P, and V become 1.
      • 
-
    • C, G, J, K, Q, S, X, and Z become 2.
      • 
-
    • D and T become 3.
      • 
-
    • L becomes 4.
      • 
-
    • M and N become 5.
      • 
-
    • R becomes 6.
      • 
-
    • All other letters become 9.
      • 
+
    • B, F, P, and V become 1.
+
    • C, G, J, K, Q, S, X, and Z become 2.
+
    • D and T become 3.
+
    • L becomes 4.
+
    • M and N become 5.
+
    • R becomes 6.
+
    • All other letters become 9.
 
    
-
    
-
    4. 
-
  4. Remove consecutive duplicates.
    5. 
-
  5. Remove all 9s altogether.
    6. 
-
  6. If the result is shorter than four characters (the first letter plus three digits), pad the result with trailing zeros.
    7. 
-
  7. if the result is longer than four characters, discard everything after the fourth character.
    8. 
+
+
  8. Remove consecutive duplicates.
+
  9. Remove all 9s altogether.
+
  10. If the result is shorter than four characters (the first letter plus three digits), pad the result with trailing zeros.
+
  11. if the result is longer than four characters, discard everything after the fourth character.
 

-

-
For example, my name, Pilgrim, becomes P942695.  That has no consecutive duplicates, so nothing to do there.  Then you remove the 9s, leaving P4265.  That's
-too long, so you discard the excess character, leaving P426.

-
Another example: Woo becomes W99, which becomes W9, which becomes W, which gets padded with zeros to become W000.

-
Here's a first attempt at a Soundex function:

-
Example 18.1. soundex/stage1/soundex1a.py

-
If you have not already done so, you can download this and other examples used in this book.
+
For example, my name, Pilgrim, becomes P942695.  That has no consecutive duplicates, so nothing to do there.  Then you remove the 9s, leaving P4265.  That's
+too long, so you discard the excess character, leaving P426.
+
Another example: Woo becomes W99, which becomes W9, which becomes W, which gets padded with zeros to become W000.
+
Here's a first attempt at a Soundex function:
+
Example 18.1. soundex/stage1/soundex1a.py

+
If you have not already done so, you can download this and other examples used in this book.
 import string, re
 
 charToSoundex = {"A": "9",
@@ -16324,30 +15250,26 @@ if __name__ == '__main__':
         statement = "soundex('%s')" % name
         t = Timer(statement, "from __main__ import soundex")
         print name.ljust(15), soundex(name), min(t.repeat())
-

-

-
Further Reading on Soundex

+

+
Further Reading on Soundex

 
-

-

-

-
18.2. Using the timeit Module

-
The most important thing you need to know about optimizing Python code is that you shouldn't write your own timing function.

+
18.2. Using the timeit Module

+
The most important thing you need to know about optimizing Python code is that you shouldn't write your own timing function.
 
Timing short pieces of code is incredibly complex.  How much processor time is your computer devoting to running this code?
 Are there things running in the background?  Are you sure?  Every modern computer has background processes running, some all
 the time, some intermittently.  Cron jobs fire off at consistent intervals; background services occasionally “wake up” to do useful things like check for new mail, connect to instant messaging servers, check for application updates, scan for
 viruses, check whether a disk has been inserted into your CD drive in the last 100 nanoseconds, and so on.  Before you start
 your timing tests, turn everything off and disconnect from the network.  Then turn off all the things you forgot to turn off
-the first time, then turn off the service that's incessantly checking whether the network has come back yet, then ...

+the first time, then turn off the service that's incessantly checking whether the network has come back yet, then ...
 
And then there's the matter of the variations introduced by the timing framework itself.  Does the Python interpreter cache method name lookups?  Does it cache code block compilations?  Regular expressions?  Will your code have
 side effects if run more than once?  Don't forget that you're dealing with small fractions of a second, so small mistakes
-in your timing framework will irreparably skew your results.

-
The Python community has a saying: “Python comes with batteries included.”  Don't write your own timing framework.  Python 2.3 comes with a perfectly good one called timeit.

-
Example 18.2. Introducing timeit

-
If you have not already done so, you can download this and other examples used in this book.
+in your timing framework will irreparably skew your results.
+
The Python community has a saying: “Python comes with batteries included.”  Don't write your own timing framework.  Python 2.3 comes with a perfectly good one called timeit.
+
Example 18.2. Introducing timeit

+
If you have not already done so, you can download this and other examples used in this book.
 >>> import timeit
 >>> t = timeit.Timer("soundex.soundex('Pilgrim')",
 ...     "import soundex")   1
@@ -16361,7 +15283,7 @@ in your timing framework will irreparably skew your results.

 1 
 
 The timeit module defines one class, Timer, which takes two arguments.  Both arguments are strings.  The first argument is the statement you wish to time; in this case,
-            you are timing a call to the Soundex function within the soundex with an argument of 'Pilgrim'.  The second argument to the Timer class is the import statement that sets up the environment for the statement.  Internally, timeit sets up an isolated virtual environment, manually executes the setup statement (importing the soundex module), then manually compiles and executes the timed statement (calling the Soundex function).
+            you are timing a call to the Soundex function within the soundex with an argument of 'Pilgrim'.  The second argument to the Timer class is the import statement that sets up the environment for the statement.  Internally, timeit sets up an isolated virtual environment, manually executes the setup statement (importing the soundex module), then manually compiles and executes the timed statement (calling the Soundex function).
 
 
 
@@ -16375,12 +15297,11 @@ in your timing framework will irreparably skew your results.

 
 The other major method of the Timer object is repeat(), which takes two optional arguments.  The first argument is the number of times to repeat the entire test, and the second
             argument is the number of times to call the timed statement within each test.  Both arguments are optional, and they default
-            to 3 and 1000000 respectively.  The repeat() method returns a list of the times each test cycle took, in seconds.
+            to 3 and 1000000 respectively.  The repeat() method returns a list of the times each test cycle took, in seconds.
 
 
 
-

-

+

@@ -16391,15 +15312,15 @@ in your timing framework will irreparably skew your results.

 

 Tip
 

 

 
Note that repeat() returns a list of times.  The times will almost never be identical, due to slight variations in how much processor time the
 Python interpreter is getting (and those pesky background processes that you can't get rid of).  Your first thought might be to
-say “Let's take the average and call that The True Number.”

+say “Let's take the average and call that The True Number.”
 
In fact, that's almost certainly wrong.  The tests that took longer didn't take longer because of variations in your code
 or in the Python interpreter; they took longer because of those pesky background processes, or other factors outside of the Python interpreter that you can't fully eliminate.  If the different timing results differ by more than a few percent, you still
-have too much variability to trust the results.  Otherwise, take the minimum time and discard the rest.

-
Python has a handy min function that takes a list and returns the smallest value:

+have too much variability to trust the results.  Otherwise, take the minimum time and discard the rest.
+
Python has a handy min function that takes a list and returns the smallest value:
 
 >>> min(t.repeat(3, 1000000))
 8.22203948912
-

+

@@ -16407,22 +15328,19 @@ have too much variability to trust the results.  Otherwise, take the minimum tim
 

 

 Tip
 
The timeit module only works if you already know what piece of code you need to optimize.  If you have a larger Python program and don't know where your performance problems are, check out the hotshot module.
 

 

-

-

-
18.3. Optimizing Regular Expressions

+
18.3. Optimizing Regular Expressions

 
The first thing the Soundex function checks is whether the input is a non-empty string of letters.  What's the best way to
-   do this?

+   do this?
 
If you answered “regular expressions”, go sit in the corner and contemplate your bad instincts.  Regular expressions are almost never the right answer; they should
 be avoided whenever possible.  Not only for performance reasons, but simply because they're difficult to debug and maintain.
-Also for performance reasons.

-
This code fragment from soundex/stage1/soundex1a.py checks whether the function argument source is a word made entirely of letters, with at least one letter (not the empty string):

+Also for performance reasons.
+
This code fragment from soundex/stage1/soundex1a.py checks whether the function argument source is a word made entirely of letters, with at least one letter (not the empty string):
 
     allChars = string.uppercase + string.lowercase
     if not re.search('^[%s]+$' % allChars, source):
         return "0000"
-

-
How does soundex1a.py perform?  For convenience, the __main__ section of the script contains this code that calls the timeit module, sets up a timing test with three different names, tests each name three times, and displays the minimum time for
-each:

+
How does soundex1a.py perform?  For convenience, the __main__ section of the script contains this code that calls the timeit module, sets up a timing test with three different names, tests each name three times, and displays the minimum time for
+each:
 
 if __name__ == '__main__':
     from timeit import Timer
@@ -16431,82 +15349,72 @@ if __name__ == '__main__':
         statement = "soundex('%s')" % name
         t = Timer(statement, "from __main__ import soundex")
         print name.ljust(15), soundex(name), min(t.repeat())
-

-
So how does soundex1a.py perform with this regular expression?

+
So how does soundex1a.py perform with this regular expression?
 
 C:\samples\soundex\stage1>python soundex1a.py
 Woo             W000 19.3356647283
 Pilgrim         P426 24.0772053431
 Flingjingwaller F452 35.0463220884
-

-
As you might expect, the algorithm takes significantly longer when called with longer names.  There will be a few things we
+
As you might expect, the algorithm takes significantly longer when called with longer names.  There will be a few things we
 can do to narrow that gap (make the function take less relative time for longer input), but the nature of the algorithm dictates
-that it will never run in constant time.

-
The other thing to keep in mind is that we are testing a representative sample of names.  Woo is a kind of trivial case, in that it gets shorted down to a single letter and then padded with zeros.  Pilgrim is a normal case, of average length and a mixture of significant and ignored letters.  Flingjingwaller is extraordinarily long and contains consecutive duplicates.  Other tests might also be helpful, but this hits a good range
-of different cases.

+that it will never run in constant time.
+
The other thing to keep in mind is that we are testing a representative sample of names.  Woo is a kind of trivial case, in that it gets shorted down to a single letter and then padded with zeros.  Pilgrim is a normal case, of average length and a mixture of significant and ignored letters.  Flingjingwaller is extraordinarily long and contains consecutive duplicates.  Other tests might also be helpful, but this hits a good range
+of different cases.
 
So what about that regular expression?  Well, it's inefficient.  Since the expression is testing for ranges of characters
-(A-Z in uppercase, and a-z in lowercase), we can use a shorthand regular expression syntax.  Here is soundex/stage1/soundex1b.py:

+(A-Z in uppercase, and a-z in lowercase), we can use a shorthand regular expression syntax.  Here is soundex/stage1/soundex1b.py:
 
     if not re.search('^[A-Za-z]+$', source):
         return "0000"
-

-
timeit says soundex1b.py is slightly faster than soundex1a.py, but nothing to get terribly excited about:

+
timeit says soundex1b.py is slightly faster than soundex1a.py, but nothing to get terribly excited about:
 
 C:\samples\soundex\stage1>python soundex1b.py
 Woo             W000 17.1361133887
 Pilgrim         P426 21.8201693232
 Flingjingwaller F452 32.7262294509
-

-
We saw in Section 15.3, “Refactoring” that regular expressions can be compiled and reused for faster results.  Since this regular expression never changes across
-function calls, we can compile it once and use the compiled version.  Here is soundex/stage1/soundex1c.py:

+
We saw in Section 15.3, “Refactoring” that regular expressions can be compiled and reused for faster results.  Since this regular expression never changes across
+function calls, we can compile it once and use the compiled version.  Here is soundex/stage1/soundex1c.py:
 
 isOnlyChars = re.compile('^[A-Za-z]+$').search
 def soundex(source):
     if not isOnlyChars(source):
         return "0000"
-

-
Using a compiled regular expression in soundex1c.py is significantly faster:

+
Using a compiled regular expression in soundex1c.py is significantly faster:
 
 C:\samples\soundex\stage1>python soundex1c.py
 Woo             W000 14.5348347346
 Pilgrim         P426 19.2784703084
 Flingjingwaller F452 30.0893873383
-

-
But is this the wrong path?  The logic here is simple: the input source needs to be non-empty, and it needs to be composed entirely of letters.  Wouldn't it be faster to write a loop checking each
-character, and do away with regular expressions altogether?

-
Here is soundex/stage1/soundex1d.py:

+

But is this the wrong path? The logic here is simple: the input source needs to be non-empty, and it needs to be composed entirely of letters. Wouldn't it be faster to write a loop checking each +character, and do away with regular expressions altogether? +

Here is soundex/stage1/soundex1d.py: 

     if not source:
         return "0000"
     for c in source:
         if not ('A' <= c <= 'Z') and not ('a' <= c <= 'z'):
             return "0000"
-
-

It turns out that this technique in soundex1d.py is not faster than using a compiled regular expression (although it is faster than using a non-compiled regular expression):

+

It turns out that this technique in soundex1d.py is not faster than using a compiled regular expression (although it is faster than using a non-compiled regular expression): 

 C:\samples\soundex\stage1>python soundex1d.py
 Woo             W000 15.4065058548
 Pilgrim         P426 22.2753567842
 Flingjingwaller F452 37.5845122774
-
-

Why isn't soundex1d.py faster? The answer lies in the interpreted nature of Python. The regular expression engine is written in C, and compiled to run natively on your computer. On the other hand, this +

Why isn't soundex1d.py faster? The answer lies in the interpreted nature of Python. The regular expression engine is written in C, and compiled to run natively on your computer. On the other hand, this loop is written in Python, and runs through the Python interpreter. Even though the loop is relatively simple, it's not simple enough to make up for the overhead of being interpreted. -Regular expressions are never the right answer... except when they are.

+Regular expressions are never the right answer... except when they are.

It turns out that Python offers an obscure string method. You can be excused for not knowing about it, since it's never been mentioned in this book. -The method is called isalpha(), and it checks whether a string contains only letters.

-

This is soundex/stage1/soundex1e.py:

+The method is called isalpha(), and it checks whether a string contains only letters. +

This is soundex/stage1/soundex1e.py: 

     if (not source) and (not source.isalpha()):
         return "0000"
-
-

How much did we gain by using this specific method in soundex1e.py? Quite a bit.

+

How much did we gain by using this specific method in soundex1e.py? Quite a bit. 

 C:\samples\soundex\stage1>python soundex1e.py
 Woo             W000 13.5069504644
 Pilgrim         P426 18.2199394057
 Flingjingwaller F452 28.9975225902
-
-

Example 18.3. Best Result So Far: soundex/stage1/soundex1e.py

+

Example 18.3. Best Result So Far: soundex/stage1/soundex1e.py

 import string, re
 
 charToSoundex = {"A": "9",
@@ -16560,14 +15468,11 @@ if __name__ == '__main__':
         statement = "soundex('%s')" % name
         t = Timer(statement, "from __main__ import soundex")
         print name.ljust(15), soundex(name), min(t.repeat())
-
-
-
-

18.4. Optimizing Dictionary Lookups

+

18.4. Optimizing Dictionary Lookups

The second step of the Soundex algorithm is to convert characters to digits in a specific pattern. What's the best way to - do this?

+ do this?

The most obvious solution is to define a dictionary with individual characters as keys and their corresponding digits as values, -and do dictionary lookups on each character. This is what we have in soundex/stage1/soundex1c.py (the current best result so far):

+and do dictionary lookups on each character. This is what we have in soundex/stage1/soundex1c.py (the current best result so far): 
 charToSoundex = {"A": "9",
                  "B": "1",
@@ -16603,68 +15508,60 @@ def soundex(source):
     for s in source[1:]:
         s = s.upper()
         digits += charToSoundex[s]
-
-

You timed soundex1c.py already; this is how it performs:

+

You timed soundex1c.py already; this is how it performs: 

 C:\samples\soundex\stage1>python soundex1c.py
 Woo             W000 14.5341678901
 Pilgrim         P426 19.2650071448
 Flingjingwaller F452 30.1003563302
-
-

This code is straightforward, but is it the best solution? Calling upper() on each individual character seems inefficient; it would probably be better to call upper() once on the entire string.

-

Then there's the matter of incrementally building the digits string. Incrementally building strings like this is horribly inefficient; internally, the Python interpreter needs to create a new string each time through the loop, then discard the old one.

+

This code is straightforward, but is it the best solution? Calling upper() on each individual character seems inefficient; it would probably be better to call upper() once on the entire string. +

Then there's the matter of incrementally building the digits string. Incrementally building strings like this is horribly inefficient; internally, the Python interpreter needs to create a new string each time through the loop, then discard the old one.

Python is good at lists, though. It can treat a string as a list of characters automatically. And lists are easy to combine into -strings again, using the string method join().

-

Here is soundex/stage2/soundex2a.py, which converts letters to digits by using ↦ and lambda:

+strings again, using the string method join(). +

Here is soundex/stage2/soundex2a.py, which converts letters to digits by using ↦ and lambda: 

 def soundex(source):
     # ...
     source = source.upper()
     digits = source[0] + "".join(map(lambda c: charToSoundex[c], source[1:]))
-
-

Surprisingly, soundex2a.py is not faster:

+

Surprisingly, soundex2a.py is not faster: 

 C:\samples\soundex\stage2>python soundex2a.py
 Woo             W000 15.0097526362
 Pilgrim         P426 19.254806407
 Flingjingwaller F452 29.3790847719
-
-

The overhead of the anonymous lambda function kills any performance you gain by dealing with the string as a list of characters.

-

soundex/stage2/soundex2b.py uses a list comprehension instead of ↦ and lambda:

+

The overhead of the anonymous lambda function kills any performance you gain by dealing with the string as a list of characters. +

soundex/stage2/soundex2b.py uses a list comprehension instead of ↦ and lambda: 

     source = source.upper()
     digits = source[0] + "".join([charToSoundex[c] for c in source[1:]])
-
-

Using a list comprehension in soundex2b.py is faster than using ↦ and lambda in soundex2a.py, but still not faster than the original code (incrementally building a string in soundex1c.py):

+

Using a list comprehension in soundex2b.py is faster than using ↦ and lambda in soundex2a.py, but still not faster than the original code (incrementally building a string in soundex1c.py): 

 C:\samples\soundex\stage2>python soundex2b.py
 Woo             W000 13.4221324219
 Pilgrim         P426 16.4901234654
 Flingjingwaller F452 25.8186157738
-
-

It's time for a radically different approach. Dictionary lookups are a general purpose tool. Dictionary keys can be any -length string (or many other data types), but in this case we are only dealing with single-character keys and single-character values. It turns out that Python has a specialized function for handling exactly this situation: the string.maketrans function.

-

This is soundex/stage2/soundex2c.py:

+

It's time for a radically different approach. Dictionary lookups are a general purpose tool. Dictionary keys can be any +length string (or many other data types), but in this case we are only dealing with single-character keys and single-character values. It turns out that Python has a specialized function for handling exactly this situation: the string.maketrans function. +

This is soundex/stage2/soundex2c.py: 

 allChar = string.uppercase + string.lowercase
 charToSoundex = string.maketrans(allChar, "91239129922455912623919292" * 2)
 def soundex(source):
     # ...
     digits = source[0].upper() + source[1:].translate(charToSoundex)
-
-

What the heck is going on here? string.maketrans creates a translation matrix between two strings: the first argument and the second argument. In this case, the first argument -is the string ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz, and the second argument is the string 9123912992245591262391929291239129922455912623919292. See the pattern? It's the same conversion pattern we were setting up longhand with a dictionary. A maps to 9, B maps +

What the heck is going on here? string.maketrans creates a translation matrix between two strings: the first argument and the second argument. In this case, the first argument +is the string ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz, and the second argument is the string 9123912992245591262391929291239129922455912623919292. See the pattern? It's the same conversion pattern we were setting up longhand with a dictionary. A maps to 9, B maps to 1, C maps to 2, and so forth. But it's not a dictionary; it's a specialized data structure that you can access using the -string method translate, which translates each character into the corresponding digit, according to the matrix defined by string.maketrans.

-

timeit shows that soundex2c.py is significantly faster than defining a dictionary and looping through the input and building the output incrementally:

+string method translate, which translates each character into the corresponding digit, according to the matrix defined by string.maketrans. +

timeit shows that soundex2c.py is significantly faster than defining a dictionary and looping through the input and building the output incrementally: 

 C:\samples\soundex\stage2>python soundex2c.py
 Woo             W000 11.437645008
 Pilgrim         P426 13.2825062962
 Flingjingwaller F452 18.5570110168
-
-

You're not going to get much better than that. Python has a specialized function that does exactly what you want to do; use it and move on.

-

Example 18.4. Best Result So Far: soundex/stage2/soundex2c.py

+

You're not going to get much better than that. Python has a specialized function that does exactly what you want to do; use it and move on. +

Example 18.4. Best Result So Far: soundex/stage2/soundex2c.py

 import string, re
 
 allChar = string.uppercase + string.lowercase
@@ -16691,28 +15588,23 @@ if __name__ == '__main__':
         statement = "soundex('%s')" % name
         t = Timer(statement, "from __main__ import soundex")
         print name.ljust(15), soundex(name), min(t.repeat())
-
-
-
-

18.5. Optimizing List Operations

-

The third step in the Soundex algorithm is eliminating consecutive duplicate digits. What's the best way to do this?

-

Here's the code we have so far, in soundex/stage2/soundex2c.py:

+

18.5. Optimizing List Operations

+

The third step in the Soundex algorithm is eliminating consecutive duplicate digits. What's the best way to do this? +

Here's the code we have so far, in soundex/stage2/soundex2c.py: 

     digits2 = digits[0]
     for d in digits[1:]:
         if digits2[-1] != d:
             digits2 += d
-
-

Here are the performance results for soundex2c.py:

+

Here are the performance results for soundex2c.py: 

 C:\samples\soundex\stage2>python soundex2c.py
 Woo             W000 12.6070768771
 Pilgrim         P426 14.4033353401
 Flingjingwaller F452 19.7774882003
-
-

The first thing to consider is whether it's efficient to check digits[-1] each time through the loop. Are list indexes expensive? Would we be better off maintaining the last digit in a separate -variable, and checking that instead?

-

To answer this question, here is soundex/stage3/soundex3a.py:

+

The first thing to consider is whether it's efficient to check digits[-1] each time through the loop. Are list indexes expensive? Would we be better off maintaining the last digit in a separate +variable, and checking that instead? +

To answer this question, here is soundex/stage3/soundex3a.py: 

     digits2 = ''
     last_digit = ''
@@ -16720,36 +15612,32 @@ variable, and checking that instead?

         if d != last_digit:
             digits2 += d
             last_digit = d
-
-

soundex3a.py does not run any faster than soundex2c.py, and may even be slightly slower (although it's not enough of a difference to say for sure):

+

soundex3a.py does not run any faster than soundex2c.py, and may even be slightly slower (although it's not enough of a difference to say for sure): 

 C:\samples\soundex\stage3>python soundex3a.py
 Woo             W000 11.5346048171
 Pilgrim         P426 13.3950636184
 Flingjingwaller F452 18.6108927252
-
-

Why isn't soundex3a.py faster? It turns out that list indexes in Python are extremely efficient. Repeatedly accessing digits2[-1] is no problem at all. On the other hand, manually maintaining the last seen digit in a separate variable means we have two variable assignments for each digit we're storing, which wipes out any small gains we might have gotten from eliminating -the list lookup.

+

Why isn't soundex3a.py faster? It turns out that list indexes in Python are extremely efficient. Repeatedly accessing digits2[-1] is no problem at all. On the other hand, manually maintaining the last seen digit in a separate variable means we have two variable assignments for each digit we're storing, which wipes out any small gains we might have gotten from eliminating +the list lookup.

Let's try something radically different. If it's possible to treat a string as a list of characters, it should be possible to use a list comprehension to iterate through the list. The problem is, the code needs access to the previous character -in the list, and that's not easy to do with a straightforward list comprehension.

+in the list, and that's not easy to do with a straightforward list comprehension.

However, it is possible to create a list of index numbers using the built-in range() function, and use those index numbers to progressively search through the list and pull out each character that is different -from the previous character. That will give you a list of characters, and you can use the string method join() to reconstruct a string from that.

-

Here is soundex/stage3/soundex3b.py:

+from the previous character. That will give you a list of characters, and you can use the string method join() to reconstruct a string from that. +

Here is soundex/stage3/soundex3b.py: 

     digits2 = "".join([digits[i] for i in range(len(digits))
      if i == 0 or digits[i-1] != digits[i]])
-
-

Is this faster? In a word, no.

+

Is this faster? In a word, no. 

 C:\samples\soundex\stage3>python soundex3b.py
 Woo             W000 14.2245271396
 Pilgrim         P426 17.8337165757
 Flingjingwaller F452 25.9954005327
-
-

It's possible that the techniques so far as have been “string-centric”. Python can convert a string into a list of characters with a single command: list('abc') returns ['a', 'b', 'c']. Furthermore, lists can be modified in place very quickly. Instead of incrementally building a new list (or string) out of the source string, why not move elements around -within a single list?

-

Here is soundex/stage3/soundex3c.py, which modifies a list in place to remove consecutive duplicate elements:

+

It's possible that the techniques so far as have been “string-centric”. Python can convert a string into a list of characters with a single command: list('abc') returns ['a', 'b', 'c']. Furthermore, lists can be modified in place very quickly. Instead of incrementally building a new list (or string) out of the source string, why not move elements around +within a single list? +

Here is soundex/stage3/soundex3c.py, which modifies a list in place to remove consecutive duplicate elements: 

     digits = list(source[0].upper() + source[1:].translate(charToSoundex))
     i=0
@@ -16759,16 +15647,14 @@ within a single list?

         digits[i]=item
     del digits[i+1:]
     digits2 = "".join(digits)
-
-

Is this faster than soundex3a.py or soundex3b.py? No, in fact it's the slowest method yet:

+

Is this faster than soundex3a.py or soundex3b.py? No, in fact it's the slowest method yet: 

 C:\samples\soundex\stage3>python soundex3c.py
 Woo             W000 14.1662554878
 Pilgrim         P426 16.0397885765
 Flingjingwaller F452 22.1789341942
-
-

We haven't made any progress here at all, except to try and rule out several “clever” techniques. The fastest code we've seen so far was the original, most straightforward method (soundex2c.py). Sometimes it doesn't pay to be clever.

-

Example 18.5. Best Result So Far: soundex/stage2/soundex2c.py

+

We haven't made any progress here at all, except to try and rule out several “clever” techniques. The fastest code we've seen so far was the original, most straightforward method (soundex2c.py). Sometimes it doesn't pay to be clever. +

Example 18.5. Best Result So Far: soundex/stage2/soundex2c.py

 import string, re
 
 allChar = string.uppercase + string.lowercase
@@ -16795,109 +15681,91 @@ if __name__ == '__main__':
         statement = "soundex('%s')" % name
         t = Timer(statement, "from __main__ import soundex")
         print name.ljust(15), soundex(name), min(t.repeat())
-
-
-
-

18.6. Optimizing String Manipulation

+

18.6. Optimizing String Manipulation

The final step of the Soundex algorithm is padding short results with zeros, and truncating long results. What is the best - way to do this?

-

This is what we have so far, taken from soundex/stage2/soundex2c.py:

+ way to do this? +

This is what we have so far, taken from soundex/stage2/soundex2c.py: 

     digits3 = re.sub('9', '', digits2)
     while len(digits3) < 4:
         digits3 += "0"
     return digits3[:4]
-
-

These are the results for soundex2c.py:

+

These are the results for soundex2c.py: 

 C:\samples\soundex\stage2>python soundex2c.py
 Woo             W000 12.6070768771
 Pilgrim         P426 14.4033353401
 Flingjingwaller F452 19.7774882003
-
-

The first thing to consider is replacing that regular expression with a loop. This code is from soundex/stage4/soundex4a.py:

+

The first thing to consider is replacing that regular expression with a loop. This code is from soundex/stage4/soundex4a.py: 

     digits3 = ''
     for d in digits2:
         if d != '9':
             digits3 += d
-
-

Is soundex4a.py faster? Yes it is:

+

Is soundex4a.py faster? Yes it is: 

 C:\samples\soundex\stage4>python soundex4a.py
 Woo             W000 6.62865531792
 Pilgrim         P426 9.02247576158
 Flingjingwaller F452 13.6328416042
-
-

But wait a minute. A loop to remove characters from a string? We can use a simple string method for that. Here's soundex/stage4/soundex4b.py:

+

But wait a minute. A loop to remove characters from a string? We can use a simple string method for that. Here's soundex/stage4/soundex4b.py: 

     digits3 = digits2.replace('9', '')
-
-

Is soundex4b.py faster? That's an interesting question. It depends on the input:

+

Is soundex4b.py faster? That's an interesting question. It depends on the input: 

 C:\samples\soundex\stage4>python soundex4b.py
 Woo             W000 6.75477414029
 Pilgrim         P426 7.56652144337
 Flingjingwaller F452 10.8727729362
-
-

The string method in soundex4b.py is faster than the loop for most names, but it's actually slightly slower than soundex4a.py in the trivial case (of a very short name). Performance optimizations aren't always uniform; tuning that makes one case +

The string method in soundex4b.py is faster than the loop for most names, but it's actually slightly slower than soundex4a.py in the trivial case (of a very short name). Performance optimizations aren't always uniform; tuning that makes one case faster can sometimes make other cases slower. In this case, the majority of cases will benefit from the change, so let's -leave it at that, but the principle is an important one to remember.

+leave it at that, but the principle is an important one to remember.

Last but not least, let's examine the final two steps of the algorithm: padding short results with zeros, and truncating long -results to four characters. The code you see in soundex4b.py does just that, but it's horribly inefficient. Take a look at soundex/stage4/soundex4c.py to see why:

+results to four characters. The code you see in soundex4b.py does just that, but it's horribly inefficient. Take a look at soundex/stage4/soundex4c.py to see why: 
     digits3 += '000'
     return digits3[:4]
-
-

Why do we need a while loop to pad out the result? We know in advance that we're going to truncate the result to four characters, and we know that +

Why do we need a while loop to pad out the result? We know in advance that we're going to truncate the result to four characters, and we know that we already have at least one character (the initial letter, which is passed unchanged from the original source variable). That means we can simply add three zeros to the output, then truncate it. Don't get stuck in a rut over the -exact wording of the problem; looking at the problem slightly differently can lead to a simpler solution.

-

How much speed do we gain in soundex4c.py by dropping the while loop? It's significant:

+exact wording of the problem; looking at the problem slightly differently can lead to a simpler solution. +

How much speed do we gain in soundex4c.py by dropping the while loop? It's significant: 

 C:\samples\soundex\stage4>python soundex4c.py
 Woo             W000 4.89129791636
 Pilgrim         P426 7.30642134685
 Flingjingwaller F452 10.689832367
-
-

Finally, there is still one more thing you can do to these three lines of code to make them faster: you can combine them into -one line. Take a look at soundex/stage4/soundex4d.py:

+

Finally, there is still one more thing you can do to these three lines of code to make them faster: you can combine them into +one line. Take a look at soundex/stage4/soundex4d.py: 

     return (digits2.replace('9', '') + '000')[:4]
-
-

Putting all this code on one line in soundex4d.py is barely faster than soundex4c.py:

+

Putting all this code on one line in soundex4d.py is barely faster than soundex4c.py: 

 C:\samples\soundex\stage4>python soundex4d.py
 Woo             W000 4.93624105857
 Pilgrim         P426 7.19747593619
 Flingjingwaller F452 10.5490700634
-
-

It is also significantly less readable, and for not much performance gain. Is that worth it? I hope you have good comments. +

It is also significantly less readable, and for not much performance gain. Is that worth it? I hope you have good comments. Performance isn't everything. Your optimization efforts must always be balanced against threats to your program's readability -and maintainability.

-
-
-

18.7. Summary

-

This chapter has illustrated several important aspects of performance tuning in Python, and performance tuning in general.

+and maintainability. +

18.7. Summary

+

This chapter has illustrated several important aspects of performance tuning in Python, and performance tuning in general.

  • If you need to choose between regular expressions and writing a loop, choose regular expressions. The regular expression engine is compiled in C and runs natively on your computer; your loop is written in Python and runs through the Python interpreter. -
    • +
  • If you need to choose between regular expressions and string methods, choose string methods. Both are compiled in C, so choose the simpler one. -
    • +
  • General-purpose dictionary lookups are fast, but specialtiy functions such as string.maketrans and string methods such as isalpha() are faster. If Python has a custom-tailored function for you, use it. -
    • -
  • Don't be too clever. Sometimes the most obvious algorithm is also the fastest.
    • -
  • Don't sweat it too much. Performance isn't everything.
    • + +
  • Don't be too clever. Sometimes the most obvious algorithm is also the fastest. +
  • Don't sweat it too much. Performance isn't everything.
-

I can't emphasize that last point strongly enough. Over the course of this chapter, you made this function three times faster and saved 20 seconds over 1 million function calls. Great. Now think: over the course of those million function calls, how many seconds will your surrounding application wait for a database connection? Or wait for disk I/O? Or wait for user input? Don't spend too much time over-optimizing one algorithm, or you'll ignore obvious improvements somewhere else. Develop an -instinct for the sort of code that Python runs well, correct obvious blunders if you find them, and leave the rest alone.

-
-
+instinct for the sort of code that Python runs well, correct obvious blunders if you find them, and leave the rest alone. diff --git a/dip3.css b/dip3.css index 39cc4d9..88f7468 100644 --- a/dip3.css +++ b/dip3.css @@ -25,7 +25,8 @@ th{text-align:left;padding:0 0.5em;vertical-align:baseline;border:1px dotted} th,td{width:45%;vertical-align:top} td{border:1px dotted;padding:0 0.5em} th:first-child{width:10%;text-align:center} -.note p:first-child,tr + tr th:first-child,span{font-family:'Arial Unicode MS',sans-serif;font-style:normal} +span,.note p:first-child,tr + tr th:first-child{font-family:'Arial Unicode MS',sans-serif;font-style:normal} +table.simple th{font-family:inherit !important} .note p:first-child{float:left;font-size:xx-large;line-height:0.875em;margin:0 0.22em 0 0} .q span{font-size:large} body{counter-reset:h1} diff --git a/humansize.py b/humansize.py index ef94c93..e41db15 100644 --- a/humansize.py +++ b/humansize.py @@ -1,20 +1,21 @@ """Convert file sizes to human-readable form. Available functions: -human_size(size, a_kilobyte_is_1024_bytes) +approximate_size(size, a_kilobyte_is_1024_bytes) takes a file size and returns a human-readable string Examples: ->>> human_size(1024) +>>> approximate_size(1024) '1.0 KiB' ->>> human_size(1000, False) +>>> approximate_size(1000, False) '1.0 KB' + """ SUFFIXES = {1000: ('KB', 'MB', 'GB', 'TB', 'PB', 'EB', 'ZB', 'YB'), 1024: ('KiB', 'MiB', 'GiB', 'TiB', 'PiB', 'EiB', 'ZiB', 'YiB')} -def human_size(size, a_kilobyte_is_1024_bytes=True): +def approximate_size(size, a_kilobyte_is_1024_bytes=True): """Convert a file size to human-readable form. Keyword arguments: @@ -27,13 +28,15 @@ def human_size(size, a_kilobyte_is_1024_bytes=True): """ if size < 0: raise ValueError('number must be non-negative') + multiple = 1024 if a_kilobyte_is_1024_bytes else 1000 for suffix in SUFFIXES[multiple]: size /= multiple if size < multiple: return "{0:.1f} {1}".format(size, suffix) + raise ValueError('number too large') if __name__ == "__main__": - print(human_size(1000000000000, False)) - print(human_size(1000000000000)) + print(approximate_size(1000000000000, False)) + print(approximate_size(1000000000000)) diff --git a/porting-code-to-python-3-with-2to3.html b/porting-code-to-python-3-with-2to3.html index e0837f2..3fff815 100644 --- a/porting-code-to-python-3-with-2to3.html +++ b/porting-code-to-python-3-with-2to3.html @@ -799,7 +799,7 @@ except:
  • The sum() function will also work with an iterator, so 2to3 makes no changes here either. Like dictionary methods that return views instead of lists, this applies to min(), max(), sum(), list(), tuple(), set(), sorted(), any(), and all().

    raw_input() and input() global functions

    -

    Python 2 had two global functions for asking the user for input on the command line. The first, called input(), expected the user to enter a Python expression (and returned the result). The second, called raw_input(), just returned whatever the user typed. This was wildly confusing for beginners and wildly regarded as a “wart” in the language. Python 3 excises this wart by renaming raw_input() to input(), so it works the way everyone naively expects it to work. +

    Python 2 had two global functions for asking the user for input on the command line. The first, called input(), expected the user to enter a Python expression (and returned the result). The second, called raw_input(), just returned whatever the user typed. This was wildly confusing for beginners and widely regarded as a “wart” in the language. Python 3 excises this wart by renaming raw_input() to input(), so it works the way everyone naively expects it to work.

    skip over this table diff --git a/your-first-python-program.html b/your-first-python-program.html new file mode 100644 index 0000000..72a300c --- /dev/null +++ b/your-first-python-program.html @@ -0,0 +1,117 @@ + + + + +Your first Python program - Dive into Python 3 + + + + + +

    Your first Python program

    +
    +

    ❝ FIXME ❞
    — FIXME +

    +
      +
    1. Diving in +
    2. Declaring functions +
    +

    Diving in

    +

    You know how other books go on and on about programming fundamentals and finally work up to building a complete, working program? Let's skip all that. +

    Here is a complete, working Python program. It probably makes absolutely no sense to you. Don't worry about that, because you're going to dissect it line by line. But read through it first and see what, if anything, you can make of it. +

    """Convert file sizes to human-readable form.
+
+Available functions:
+approximate_size(size, a_kilobyte_is_1024_bytes)
+    takes a file size and returns a human-readable string
+
+Examples:
+>>> approximate_size(1024)
+'1.0 KiB'
+>>> approximate_size(1000, False)
+'1.0 KB'
+
+"""
+
+SUFFIXES = {1000: ('KB', 'MB', 'GB', 'TB', 'PB', 'EB', 'ZB', 'YB'),
+            1024: ('KiB', 'MiB', 'GiB', 'TiB', 'PiB', 'EiB', 'ZiB', 'YiB')}
+
+def approximate_size(size, a_kilobyte_is_1024_bytes=True):
+    """Convert a file size to human-readable form.
+
+    Keyword arguments:
+    size -- file size in bytes
+    a_kilobyte_is_1024_bytes -- if True (default), use multiples of 1024
+                                if False, use multiples of 1000
+
+    Returns: string
+
+    """
+    if size < 0:
+        raise ValueError('number must be non-negative')
+
+    multiple = 1024 if a_kilobyte_is_1024_bytes else 1000
+    for suffix in SUFFIXES[multiple]:
+        size /= multiple
+        if size < multiple:
+            return "{0:.1f} {1}".format(size, suffix)
+
+    raise ValueError('number too large')
+
+if __name__ == "__main__":
+    print(approximate_size(1000000000000, False))
+    print(approximate_size(1000000000000))
    +

    Now let's run this program on the command line. On Windows, it will look something like this: +

    c:\home\diveintopython3> c:\python30\python.exe humansize.py
+1.0 TB
+931.3 GiB
    +

    On Mac OS X or Linux, it would look something like this: +

    you@localhost:~$ python3 humansize.py
+1.0 TB
+931.3 GiB
    +

    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, like this: +

    def approximate_size(size, a_kilobyte_is_1024_bytes=True):
    +

    Note that the keyword def starts the function declaration, followed by the function name, followed by the arguments in parentheses. Multiple arguments are separated with commas. +

    Also note that 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 or not they return a value. (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.) +

    +

    ☞ +

    In some languages, 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. +

    +

    The approximate_size function takes the two arguments — size and a_kilobyte_is_1024_bytes — but neither argument specifies a datatype. (As you might guess from the =True syntax, the second argument is a boolean. You'll learn what that syntax does in [FIXME xref].) In Python, variables are never explicitly typed. Python figures out what type a variable is and keeps track of it internally. +

    +

    ☞ +

    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. +

    +

    How Python's Datatypes Compare to Other Programming Languages

    +

    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. JavaScript 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. +
    +
    weakly typed language
    +
    A language in which types are “automagically” coerced to other types as needed; the opposite of strongly typed. PHP is weakly typed. In PHP, 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. [FIXME double-check this] +
    +
    +

    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). +

    If you have experience in other programming languages, this table may help you visualize how Python compares to them: +

    Notes
    + + + +
    Statically typedDynamically typed
    Weakly typedC, Objective-CJavaScript, Perl 5, PHP
    Strongly typedPascal, JavaPython, Ruby
    + + + + +

    © 2001-4, 2009 ℳark Pilgrim, CC-BY-3.0 + +