Book Image

Mastering Python Regular Expressions

Book Image

Mastering Python Regular Expressions

Overview of this book

Regular expressions are used by many text editors, utilities, and programming languages to search and manipulate text based on patterns. They are considered the Swiss army knife of text processing. Powerful search, replacement, extraction and validation of strings, repetitive and complex tasks are reduced to a simple pattern using regular expressions. Mastering Python Regular Expressions will teach you about Regular Expressions, starting from the basics, irrespective of the language being used, and then it will show you how to use them in Python. You will learn the finer details of what Python supports and how to do it, and the differences between Python 2.x and Python 3.x. The book starts with a general review of the theory behind the regular expressions to follow with an overview of the Python regex module implementation, and then moves on to advanced topics like grouping, looking around, and performance. You will explore how to leverage Regular Expressions in Python, some advanced aspects of Regular Expressions and also how to measure and improve their performance. You will get a better understanding of the working of alternators and quantifiers. Also, you will comprehend the importance of grouping before finally moving on to performance optimization techniques like the RegexBuddy Tool and Backtracking. Mastering Python Regular Expressions provides all the information essential for a better understanding of Regular Expressions in Python.
Table of Contents (12 chapters)

Named groups


Remember from the previous chapter when we got a group through an index?

>>>pattern = re.compile(r"(\w+) (\w+)")
>>>match = pattern.search("Hello⇢world")
>>>match.group(1)
  'Hello'
>>>match.group(2)
  'world'

We just learnt how to access the groups using indexes to extract information and to use it as backreferences. Using numbers to refer to groups can be tedious and confusing, and the worst thing is that it doesn't allow you to give meaning or context to the group. That's why we have named groups.

Imagine a regex in which you have several backreferences, let's say 10, and you find out that the third one is invalid, so you remove it from the regex. That means you have to change the index for every backreference starting from that one onwards. In order to solve this problem, in 1997, Guido Van Rossum designed named groups for Python 1.5. This feature was offered to Perl for cross-pollination.

Nowadays, it can be found in almost any flavor. Basically...