Chapter 3. Regular Expressions

Regular expressions allow extremely valuable text processing techniques, but ones that warrant careful explanation. Python's re module, in particular, allows numerous enhancements to basic regular expressions (such as named backreferences, lookahead assertions, backreference skipping, non-greedy quantifiers, and others). A solid introduction to the subtleties of regular expressions is valuable to programmers engaged in text processing tasks.

The prequel of this chapter contains a tutorial on regular expressions that allows a reader unfamiliar with regular expressions to move quickly from simple to complex elements of regular expression syntax. This tutorial is aimed primarily at beginners, but programmers familiar with regular expressions in other programming tools can benefit from a quick read of the tutorial, which explicates the particular regular expression dialect in Python.

It is important to note up-front that regular expressions, while very powerful, also have limitations. In brief, regular expressions cannot match patterns that nest to arbitrary depths. If that statement does not make sense, read Chapter 4, which discusses parsers to a large extent, parsing exists to address the limitations of regular expressions. In general, if you have doubts about whether a regular expression is sufficient for your task, try to understand the examples in Chapter 4, particularly the discussion of how you might spell a floating point number.

Section 3.1 examines a number of text processing problems that are solved most naturally using regular expressions. As in other chapters, the solutions presented to problems can generally be adopted directly as little utilities for performing tasks. However, as elsewhere, the larger goal in presenting problems and solutions is to address a style of thinking about a wider class of problems than those whose solutions are presented directly in this book. Readers who are interested in a range of ready utilities and modules will probably want to check additional resources on the Web, such as the Vaults of Parnassus <> and the Python Cookbook <>.

Section 3.2 is a "reference with commentary" on the Python standard library modules for doing regular expression tasks. Several utility modules and backward-compatibility regular expression engines are available, but for most readers, the only important module will be re itself. The discussions interspersed with each module try to give some guidance on why you would want to use a given module or function, and the reference documentation tries to contain more examples of actual typical usage than does a plain reference. In many cases, the examples and discussion of individual functions address common and productive design patterns in Python. The cross-references are intended to contextualize a given function (or other thing) in terms of related ones (and to help a reader decide which is right for her). The actual listing of functions, constants, classes, and the like are in alphabetical order within each category.

Text Processing in Python
Text Processing in Python
ISBN: 0321112547
EAN: 2147483647
Year: 2005
Pages: 59
Authors: David Mertz © 2008-2017.
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