Regular expressions can also be used to perform search and replace operations, with re.sub(). But if you’re working with arbitrary input that you want to escape automatically - for instance, by searching for some user input - you can use re.escape() to transform a string into its regex-escaped version. If you want to search for those characters, you will need to escape them with backslashes in your expression. Regular expressions use many characters in a way that is specific to the regex syntax, such as the dot (. Here is an example using re.finditer(): import re text = 'a11 b213 a13 x15 c21 a40 a55 m34' for match in re.finditer(r'a\d*\W', text): print (match) If your regular expression is likely to generate many matches, re.finditer() and re.findal() give you two different ways of consuming the results. If you’re looking for a single match, re.match() and re.fullmatch() give you two handy options. The downside is that the list is generated all at once, not incrementally, so may not be ideal for large strings that generate many matches. If you don’t want to bother with all the details of working with match objects, you can just use re.findall() to produce a Python list of all the matches found. re.findall() looks for all matches, like re.finditer(), but returns the matches as a simple list.This is useful if you are working with a large string that might yield a great many matches, and you want to conserve memory by creating and consuming one match object at a time. Each iteration of the generator yields up a match object, one for each match found. re.finditer() looks for all the matches available, and returns them in the form of a generator.Again, this optimizes the matching strategy in cases where you want an all-or-nothing match. re.fullmatch() attempts to match the regex against the entire string, and only the entire string.This is useful if you know you’re not going to be scanning the rest of the string, and you want to optimize the matching method. re.match() is like re.search(), but looks only for matches from the beginning of the string and nowhere else. ![]() These four other methods available in re might better fit your use case: Re.search() is not the only way to find patterns in text with re, and it’s far from the most flexible. The r prefix before the string tells the Python interpreter, “Don't interpret the backslashes as escape codes.” More ways to match using Python regex This is because the syntax of a regex, since it uses backslashes, can conflict with the way ordinary strings are escaped in Python. Most of the time, if you’re just looking for the first match, you can obtain it simply by indexing into the match object as we’ve done above (with the index).įinally, note that we used Python raw strings to construct our regex. When re makes one or more matches, it returns what is called a match object, which is a data structure that contains many details about the match. re.search() takes that regular expression and looks for the first match against it in the provided string text. ![]() ![]() ![]() Here, we use the regular expression 'a\d*\W', which looks for the letter a followed by any number of digits and then whitespace. The easiest way to use re is to make quick-and-dirty searches, using a particular regex against a particular string.
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