Chapter 4. Parsers and State Machines

All the techniques presented in the prior chapters of this book have something in common, but something that is easy to overlook. In a sense, every basic string and regular expression operation treats strings as homogeneous. Put another way: String and regex techniques operate on flat texts. While said techniques are largely in keeping with the "Zen of Python" maxim that "Flat is better than nested," sometimes the maxim (and homogeneous operations) cannot solve a problem. Sometimes the data in a text has a deeper structure than the linear sequence of bytes that make up strings.

It is not entirely true that the prior chapters have eschewed data structures. From time to time, the examples presented broke flat texts into lists of lines, or of fields, or of segments matched by patterns. But the structures used have been quite simple and quite regular. Perhaps a text was treated as a list of substrings, with each substring manipulated in some manner or maybe even a list of lists of such substrings, or a list of tuples of data fields. But overall, the data structures have had limited (and mostly fixed) nesting depth and have consisted of sequences of items that are themselves treated similarly. What this chapter introduces is the notion of thinking about texts as trees of nodes, or even still more generally as graphs.

Before jumping too far into the world of nonflat texts, I should repeat a warning this book has issued from time to time. If you do not need to use the techniques in this chapter, you are better off sticking with the simpler and more maintainable techniques discussed in the prior chapters. Solving too general a problem too soon is a pitfall for application development it is almost always better to do less than to do more. Fullscale parsers and state machines fall to the "more" side of such a choice. As we have seen already, the class of problems you can solve using regular expressions or even only string operations is quite broad.

There is another warning that can be mentioned at this point. This book does not attempt to explain parsing theory or the design of parseable languages. There are a lot of intricacies to these matters, about which a reader can consult a specialized text like the so-called "Dragon Book" Aho, Sethi, and Ullman's Compilers: Principle, Techniques and Tools (Addison-Wesley, 1986; ISBN: 0201100886) or Levine, Mason, and Brown's Lex & Yacc (Second Edition, O'Reilly, 1992; ISBN: 1-56592-000-7). When Extended Backus-Naur Form (EBNF) grammars or other parsing descriptions are discussed below, it is in a general fashion that does not delve into algorithmic resolution of ambiguities or big-O efficiencies (at least not in much detail). In practice, everyday Python programmers who are processing texts but who are not designing new programming languages need not worry about those parsing subtleties omitted from this book.



Text Processing in Python
Text Processing in Python
ISBN: 0321112547
EAN: 2147483647
Year: 2005
Pages: 59
Authors: David Mertz

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