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Pattern Matching

One point worth noting before we move on is that none of the string object’s methods support pattern-based text processing. Text pattern matching is an advanced tool outside this book’s scope, but readers with backgrounds in other scripting languages may be interested to know that to do pattern matching in Python, we import a module called re. This module has analogous calls for searching, splitting, and replacement, but because we can use patterns to specify substrings, we can be much more general:

>>> import re
>>> match = re.match('Hello[ \t]*(.*)world', 'Hello    Python world')
>>> match.group(1)
'Python '

This example searches for a substring that begins with the word “Hello,” followed by zero or more tabs or spaces, followed by arbitrary characters to be saved as a matched group, terminated by the word “world.” If such a substring is found, portions of the substring matched by parts of the pattern enclosed in parentheses are available as groups. The following pattern, for example, picks out three groups separated by slashes:

>>> match = re.match('/(.*)/(.*)/(.*)', '/usr/home/lumberjack')
>>> match.groups()
('usr', 'home', 'lumberjack')

Pattern matching is a fairly advanced text-processing tool by itself, but there is also support in Python for even more advanced text and language processing, including XML parsing and natural language analysis. I’ve already said enough about strings for this tutorial, though, so let’s move on to the next type.