Have you ever needed to parse through large amounts of text looking for a specific pattern? Patterns like “one capital letter followed by three numbers” or “dd/mm/yyyy”? This is known as Pattern Matching. Regular Expressions allow easy syntax for pattern matching, and is an invaluable skill to add to one’s toolkit, no matter what your area of expertise/practice is. Whether you’re writing a Compiler, Form Validator, Text Editor, Django Project, or Language Translator, Regular Expressions will always prove to be invaluable. Here is a very basic overview of some syntax: ‘d’ represents a digit. ‘s’ represents whitespace. ‘.’ represents any character. If you have worked with Python for very long, you are probably already familiar with the concept. Take a look at the following code:
print(“Rounded = %05d” % (42))
This makes sure that the digit printed has 5 digits, and will automatically add 0’s to compensate. If you understand this concept, then you shouldn’t have a problem. Perl-style Regular Expressions are a very widely-accepted implementation, and Python has built in support for this mini-language! It’s easily accessible, so let’s get started. The included ‘re’ module will give us everything we need to get started:
Lets give our new module a try! It will enable you to do anything you could ever want with regular expressions. Here’s a quick example of some basic use.
import re string0 = 'Kenneth Reitz is a cool guy!' regExp = r’kenneth[- ]?reitz’ if re.match(regExp, string0, re.IGNORECASE): print “True” else: print “False”
This script takes the string ‘Kenneth Reitz is a cool guy’, and searches for ‘kenneth reitz’ inside of it. If ‘kenneth reitz’ is found within string0 (re.match compares the expression with the string), the script will print “True”, if not, it will print “False”. Additional parameters can be passed to the re.match function when needed. Note the ‘re.IGNORECASE’ flag used here – This tells the function be case-insensitive. Once you master the regular expression syntax, you’ll realize how truly powerful they can be. The options become limitless and the usefulness becomes undeniable. Here’s another example:
import re string0 = '10.03.1988' regExp = r'^dd[./]dd[./]dddd?$' if re.match(regExp, string0): print 'True' else: print 'False/
When run, this script prints out “True”. If we were to change string0 to ‘10.03.88’, it would print “False”. Simple, isn’t it? Now, while a True/False return could be useful in certain applications (i.e. form validation), most of the time, we’re going to want to have a bit more information in order for our checks to be useful. We can tell Python to show us the data that matches our query. To do this, we’re going to have to break our expression up into different groups. In the date we have defined, there are three obvious groups we could separate this into: the day, month, and year. While defining a Regular Expression, you can use parentheses ‘()’ to define groups:
regExp = r’^(dd)././$’
This separates our expression into 3 separate groups. Python also supports turning a Regular Expression string into an heavily-supported object with the re.compile() function. Once you define a string as a Regular Expression object, you can use the built in methods to preform powerful parsing. Now we can ask python what is in those groups:
import restring0 = ‘10.03.1988’ regExp = re.compile(‘^(dd)././$’) regExpMatches = regExp.match(string0) if re.match(regExp, string0): print(“Day: %snMonth: %snYear: %s” % (regExpMatches.group(1), regExpMatches.group(2), regExpMatches.group(3))) else: print(“Invalid Date.”)
When executed, this script parses through our validated date, breaks it down into groups, and prints the following:
> Day: 10 > Month: 03 > Year: 1988
The possibilities are limitless! Here’s a quick run-down of the re module’s functions, strait from the Python documentation for reference:
match: Match a regular expression pattern to the beginning of a string. search: Search a string for the presence of a pattern. sub: Substitute occurrences of a pattern found in a string subn: Same as sub, but also return the number of substitutions made. split: Split a string by the occurrences of a pattern. findall: Find all occurrences of a pattern in a string. compile: Compile a pattern into a RegexObject. purge: Clear the regular expression cache. escape: Backslash all non-alphanumerics in a string.
Remember, you can always type help(re) (after importing the re module) into the Python interpret to take a quick look at the module’s built-in documentation. Good luck and happy coding!