Text Processing in Python describes techniques for manipulation of text using the
Python programming language. At the broadest level, text processing is simply
taking textual information and doing something with it. This might be
restructuring or reformatting it, extracting smaller bits of information from it,
or performing calculations that depend on the text. Text processing is arguably
what most programmers spend most of their time doing. Because Python is
clear, expressive, and object-oriented it is a perfect language for doing text
processing, even better than Perl. As the amount of data everywhere continues
to increase, this is more and more of a challenge for programmers. This book is
not a tutorial on Python. It has two other goals: helping the programmer get
the job done pragmatically and efficiently; and giving the reader an
understanding - both theoretically and conceptually - of why what works works
and what doesn't work doesn't work. Mertz provides practical pointers and tips
that emphasize efficent, flexible, and maintainable approaches to the textprocessing
tasks that working programmers face daily.
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David Mertz came to writing about programming via the unlikely route of first being a humanities professor. Along the way, he was a senior software developer, and now runs his own development company, Gnosis Software ("We know stuff!"). David writes regular columns and articles for IBM developerWorks, Intel Developer Network, O'Reilly ONLamp, and other publications.
Text Processing in Python is an example-driven, hands-on tutorial that carefully teaches programmers how to accomplish numerous text processing tasks using the Python language. Filled with concrete examples, this book provides efficient and effective solutions to specific text processing problems and practical strategies for dealing with all types of text processing challenges.
Text Processing in Python begins with an introduction to text processing and contains a quick Python tutorial to get you up to speed. It then delves into essential text processing subject areas, including string operations, regular expressions, parsers and state machines, and Internet tools and techniques. Appendixes cover such important topics as data compression and Unicode. A comprehensive index and plentiful cross-referencing offer easy access to available information. In addition, exercises throughout the book provide readers with further opportunity to hone their skills either on their own or in the classroom. A companion Web site (http://gnosis.cx/TPiP) contains source code and examples from the book.
Here is some of what you will find in thie book:
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