- #Python regular expression not matching groups full
- #Python regular expression not matching groups series
The $ means “end of the string.” (There is a corresponding character, the caret ^, which means “beginning of the string.”) Using the re.sub() function, you search the string s for the regular expression 'ROAD$' and replace it with 'RD.'. This is a simple regular expression that matches 'ROAD' only when it occurs at the end of a string. Take a look at the first parameter: 'ROAD$'.In Python, all functionality related to regular expressions is contained in the re module. It’s time to move up to regular expressions.(If you were replacing 'STREET' with 'ST.', you would need to use s and s.replace(.).) Would you like to come back in six months and debug this? I know I wouldn’t. For example, the pattern is dependent on the length of the string you’re replacing. But you can see that this is already getting unwieldy. To solve the problem of addresses with more than one 'ROAD' substring, you could resort to something like this: only search and replace 'ROAD' in the last four characters of the address ( s), and leave the string alone ( s).The replace() method sees these two occurrences and blindly replaces both of them meanwhile, I see my addresses getting destroyed. The problem here is that 'ROAD' appears twice in the address, once as part of the street name 'BROAD' and once as its own word.
#Python regular expression not matching groups full
Life, unfortunately, is full of counterexamples, and I quickly discovered this one.And in this deceptively simple example, s.replace() does indeed work. And the search string, 'ROAD', was a constant. After all, all the data was already uppercase, so case mismatches would not be a problem.
At first glance, I thought this was simple enough that I could just use the string method replace(). My goal is to standardize a street address so that 'ROAD' is always abbreviated as 'RD.'.(See, I don’t just make this stuff up it’s actually useful.) This example shows how I approached the problem.
#Python regular expression not matching groups series
This series of examples was inspired by a real-life problem I had in my day job several years ago, when I needed to scrub and standardize street addresses exported from a legacy system before importing them into a newer system.
Read the summary of the re module to get an overview of the available functions and their arguments. ☞If you’ve used regular expressions in other languages (like Perl, JavaScript, or PHP), Python’s syntax will be very familiar. There are even ways of embedding comments within regular expressions, so you can include fine-grained documentation within them. Although the regular expression syntax is tight and unlike normal code, the result can end up being more readable than a hand-rolled solution that uses a long chain of string functions. Regular expressions are a powerful and (mostly) standardized way of searching, replacing, and parsing text with complex patterns of characters. But if you find yourself using a lot of different string functions with if statements to handle special cases, or if you’re chaining calls to split() and join() to slice-and-dice your strings, you may need to move up to regular expressions. They’re fast and simple and easy to read, and there’s a lot to be said for fast, simple, readable code. If your goal can be accomplished with string methods, you should use them. The replace() and split() methods have the same limitations.
To do case-insensitive searches of a string s, you must call s.lower() or s.upper() and make sure your search strings are the appropriate case to match. For example, the index() method looks for a single, hard-coded substring, and the search is always case-sensitive. But these methods are limited to the simplest of cases. In Python, strings have methods for searching and replacing: index(), find(), split(), count(), replace(), &c. Getting a small bit of text out of a large block of text is a challenge. ❝ Some people, when confronted with a problem, think “I know, I’ll use regular expressions.” Now they have two problems. You are here: Home ‣ Dive Into Python 3 ‣ĭifficulty level: ♦♦♦♢♢ Regular Expressions