Text processing has been one of the most relevant topics since computer science took its very first baby steps. After a few decades of investigation, we now have one of the most versatile and pervasive tools that exist: regular expressions. Validation, search, extraction, and replacement of text are operations that have been simplified thanks to Regular Expressions.
This book will initially cover regular expressions from a bird's-eye view, proceeding step-by-step to more advanced topics such as regular expression specifics on Python or grouping, workaround, and performance. All the topics will be covered with Python-specific examples that can be used straightaway in the Python console.
Chapter 1, Introducing Regular Expressions, will introduce the basics of the regular expression syntax from a non-Python-specific point of view.
Chapter 2, Regular Expressions with Python, will cover the Python's API for regular expressions and its quirks from a Python-specific point of view.
Chapter 3, Grouping, covers the regular expression functionality to extract portions of information, apply quantifiers to specific parts, and perform correct alternation.
Chapter 4, Look Around, explains the concept of zero-width assertions and the different types of look-around mechanisms.
Chapter 5, Performance of Regular Expressions, will cover different tools to measure the speed of a regular expression, the details of the regular expression module of Python, and different recommendations to improve the performance of regular expressions.
To understand this book, a basic knowledge of Python in any of the supported platforms is required. It is important to be able to make use of a console with access to the Python command line.
Previous knowledge of regular expressions is not required as it will be covered from scratch.
This book is intended for Python developers who wish to understand regular expressions in general and also how to leverage them specifically in Python.
In this book, you will find a number of styles of text that distinguish between different kinds of information. Here are some examples of these styles, and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "We can include other contexts through the use of the include directive."
A block of code is set as follows:
>>> import re
>>> pattern = re.compile(r'<HTML>')
>>> pattern.match("<HTML>")When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:
>>> import re
>>> pattern = re.compile(r'<HTML>')
>>> pattern.match("<HTML>")New terms and important words are shown in bold. Words that you see on the screen, in menus or dialog boxes for example, appear in the text like this: "clicking the Next button moves you to the next screen".
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