Regular Expression HOWTO (2023)


A.M. Kuchling <>


This document is an introductory tutorial to using regular expressions in Pythonwith the re module. It provides a gentler introduction than thecorresponding section in the Library Reference.


Regular expressions (called REs, or regexes, or regex patterns) are essentiallya tiny, highly specialized programming language embedded inside Python and madeavailable through the re module. Using this little language, you specifythe rules for the set of possible strings that you want to match; this set mightcontain English sentences, or e-mail addresses, or TeX commands, or anything youlike. You can then ask questions such as “Does this string match the pattern?”,or “Is there a match for the pattern anywhere in this string?”. You can alsouse REs to modify a string or to split it apart in various ways.

Regular expression patterns are compiled into a series of bytecodes which arethen executed by a matching engine written in C. For advanced use, it may benecessary to pay careful attention to how the engine will execute a given RE,and write the RE in a certain way in order to produce bytecode that runs faster.Optimization isn’t covered in this document, because it requires that you have agood understanding of the matching engine’s internals.

The regular expression language is relatively small and restricted, so not allpossible string processing tasks can be done using regular expressions. Thereare also tasks that can be done with regular expressions, but the expressionsturn out to be very complicated. In these cases, you may be better off writingPython code to do the processing; while Python code will be slower than anelaborate regular expression, it will also probably be more understandable.

Simple Patterns

We’ll start by learning about the simplest possible regular expressions. Sinceregular expressions are used to operate on strings, we’ll begin with the mostcommon task: matching characters.

For a detailed explanation of the computer science underlying regularexpressions (deterministic and non-deterministic finite automata), you can referto almost any textbook on writing compilers.

Matching Characters

Most letters and characters will simply match themselves. For example, theregular expression test will match the string test exactly. (You canenable a case-insensitive mode that would let this RE match Test or TESTas well; more about this later.)

There are exceptions to this rule; some characters are specialmetacharacters, and don’t match themselves. Instead, they signal thatsome out-of-the-ordinary thing should be matched, or they affect other portionsof the RE by repeating them or changing their meaning. Much of this document isdevoted to discussing various metacharacters and what they do.

Here’s a complete list of the metacharacters; their meanings will be discussedin the rest of this HOWTO.

. ^ $ * + ? { } [ ] \ | ( )

The first metacharacters we’ll look at are [ and ]. They’re used forspecifying a character class, which is a set of characters that you wish tomatch. Characters can be listed individually, or a range of characters can beindicated by giving two characters and separating them by a '-'. Forexample, [abc] will match any of the characters a, b, or c; thisis the same as [a-c], which uses a range to express the same set ofcharacters. If you wanted to match only lowercase letters, your RE would be[a-z].

Metacharacters (except \) are not active inside classes. For example, [akm$] willmatch any of the characters 'a', 'k', 'm', or '$'; '$' isusually a metacharacter, but inside a character class it’s stripped of itsspecial nature.

You can match the characters not listed within the class by complementingthe set. This is indicated by including a '^' as the first character of theclass. For example, [^5] will match any character except '5'. If thecaret appears elsewhere in a character class, it does not have special meaning.For example: [5^] will match either a '5' or a '^'.

Perhaps the most important metacharacter is the backslash, \. As in Pythonstring literals, the backslash can be followed by various characters to signalvarious special sequences. It’s also used to escape all the metacharacters soyou can still match them in patterns; for example, if you need to match a [or \, you can precede them with a backslash to remove their specialmeaning: \[ or \\.

Some of the special sequences beginning with '\' representpredefined sets of characters that are often useful, such as the setof digits, the set of letters, or the set of anything that isn’twhitespace.

Let’s take an example: \w matches any alphanumeric character. Ifthe regex pattern is expressed in bytes, this is equivalent to theclass [a-zA-Z0-9_]. If the regex pattern is a string, \w willmatch all the characters marked as letters in the Unicode databaseprovided by the unicodedata module. You can use the morerestricted definition of \w in a string pattern by supplying there.ASCII flag when compiling the regular expression.

The following list of special sequences isn’t complete. For a completelist of sequences and expanded class definitions for Unicode stringpatterns, see the last part of Regular Expression Syntax in the Standard Library reference. In general, theUnicode versions match any character that’s in the appropriatecategory in the Unicode database.


Matches any decimal digit; this is equivalent to the class [0-9].


Matches any non-digit character; this is equivalent to the class [^0-9].


Matches any whitespace character; this is equivalent to the class [\t\n\r\f\v].


Matches any non-whitespace character; this is equivalent to the class [^\t\n\r\f\v].


Matches any alphanumeric character; this is equivalent to the class[a-zA-Z0-9_].


Matches any non-alphanumeric character; this is equivalent to the class[^a-zA-Z0-9_].

These sequences can be included inside a character class. For example,[\s,.] is a character class that will match any whitespace character, or',' or '.'.

The final metacharacter in this section is .. It matches anything except anewline character, and there’s an alternate mode (re.DOTALL) where it willmatch even a newline. . is often used where you want to match “anycharacter”.

Repeating Things

Being able to match varying sets of characters is the first thing regularexpressions can do that isn’t already possible with the methods available onstrings. However, if that was the only additional capability of regexes, theywouldn’t be much of an advance. Another capability is that you can specify thatportions of the RE must be repeated a certain number of times.

The first metacharacter for repeating things that we’ll look at is *. *doesn’t match the literal character '*'; instead, it specifies that theprevious character can be matched zero or more times, instead of exactly once.

For example, ca*t will match 'ct' (0 'a' characters), 'cat' (1 'a'),'caaat' (3 'a' characters), and so forth.

Repetitions such as * are greedy; when repeating a RE, the matchingengine will try to repeat it as many times as possible. If later portions of thepattern don’t match, the matching engine will then back up and try again withfewer repetitions.

A step-by-step example will make this more obvious. Let’s consider theexpression a[bcd]*b. This matches the letter 'a', zero or more lettersfrom the class [bcd], and finally ends with a 'b'. Now imagine matchingthis RE against the string 'abcbd'.






The a in the RE matches.



The engine matches [bcd]*,going as far as it can, whichis to the end of the string.



The engine tries to matchb, but the current positionis at the end of the string, soit fails.



Back up, so that [bcd]*matches one less character.



Try b again, but thecurrent position is at the lastcharacter, which is a 'd'.



Back up again, so that[bcd]* is only matchingbc.



Try b again. This timethe character at thecurrent position is 'b', soit succeeds.

The end of the RE has now been reached, and it has matched 'abcb'. Thisdemonstrates how the matching engine goes as far as it can at first, and if nomatch is found it will then progressively back up and retry the rest of the REagain and again. It will back up until it has tried zero matches for[bcd]*, and if that subsequently fails, the engine will conclude that thestring doesn’t match the RE at all.

Another repeating metacharacter is +, which matches one or more times. Paycareful attention to the difference between * and +; * matcheszero or more times, so whatever’s being repeated may not be present at all,while + requires at least one occurrence. To use a similar example,ca+t will match 'cat' (1 'a'), 'caaat' (3 'a's), but won’tmatch 'ct'.

There are two more repeating operators or quantifiers. The question mark character, ?,matches either once or zero times; you can think of it as marking something asbeing optional. For example, home-?brew matches either 'homebrew' or'home-brew'.

The most complicated quantifier is {m,n}, where m and n aredecimal integers. This quantifier means there must be at least m repetitions,and at most n. For example, a/{1,3}b will match 'a/b', 'a//b', and'a///b'. It won’t match 'ab', which has no slashes, or 'a////b', whichhas four.

You can omit either m or n; in that case, a reasonable value is assumed forthe missing value. Omitting m is interpreted as a lower limit of 0, whileomitting n results in an upper bound of infinity.

Readers of a reductionist bent may notice that the three other quantifiers canall be expressed using this notation. {0,} is the same as *, {1,}is equivalent to +, and {0,1} is the same as ?. It’s better to use*, +, or ? when you can, simply because they’re shorter and easierto read.

Using Regular Expressions

Now that we’ve looked at some simple regular expressions, how do we actually usethem in Python? The re module provides an interface to the regularexpression engine, allowing you to compile REs into objects and then performmatches with them.

Compiling Regular Expressions

Regular expressions are compiled into pattern objects, which havemethods for various operations such as searching for pattern matches orperforming string substitutions.

>>> import re>>> p = re.compile('ab*')>>> pre.compile('ab*')

re.compile() also accepts an optional flags argument, used to enablevarious special features and syntax variations. We’ll go over the availablesettings later, but for now a single example will do:

>>> p = re.compile('ab*', re.IGNORECASE)

The RE is passed to re.compile() as a string. REs are handled as stringsbecause regular expressions aren’t part of the core Python language, and nospecial syntax was created for expressing them. (There are applications thatdon’t need REs at all, so there’s no need to bloat the language specification byincluding them.) Instead, the re module is simply a C extension moduleincluded with Python, just like the socket or zlib modules.

Putting REs in strings keeps the Python language simpler, but has onedisadvantage which is the topic of the next section.

The Backslash Plague

As stated earlier, regular expressions use the backslash character ('\') toindicate special forms or to allow special characters to be used withoutinvoking their special meaning. This conflicts with Python’s usage of the samecharacter for the same purpose in string literals.

Let’s say you want to write a RE that matches the string \section, whichmight be found in a LaTeX file. To figure out what to write in the programcode, start with the desired string to be matched. Next, you must escape anybackslashes and other metacharacters by preceding them with a backslash,resulting in the string \\section. The resulting string that must be passedto re.compile() must be \\section. However, to express this as aPython string literal, both backslashes must be escaped again.




Text string to be matched


Escaped backslash for re.compile()


Escaped backslashes for a string literal

In short, to match a literal backslash, one has to write '\\\\' as the REstring, because the regular expression must be \\, and each backslash mustbe expressed as \\ inside a regular Python string literal. In REs thatfeature backslashes repeatedly, this leads to lots of repeated backslashes andmakes the resulting strings difficult to understand.

The solution is to use Python’s raw string notation for regular expressions;backslashes are not handled in any special way in a string literal prefixed with'r', so r"\n" is a two-character string containing '\' and 'n',while "\n" is a one-character string containing a newline. Regularexpressions will often be written in Python code using this raw string notation.

In addition, special escape sequences that are valid in regular expressions,but not valid as Python string literals, now result in aDeprecationWarning and will eventually become a SyntaxError,which means the sequences will be invalid if raw string notation or escapingthe backslashes isn’t used.

Regular String

Raw string







Performing Matches

Once you have an object representing a compiled regular expression, what do youdo with it? Pattern objects have several methods and attributes.Only the most significant ones will be covered here; consult the re docsfor a complete listing.




Determine if the RE matches at the beginningof the string.


Scan through a string, looking for anylocation where this RE matches.


Find all substrings where the RE matches, andreturns them as a list.


Find all substrings where the RE matches, andreturns them as an iterator.

match() and search() return None if no match can be found. Ifthey’re successful, a match object instance is returned,containing information about the match: where it starts and ends, the substringit matched, and more.

You can learn about this by interactively experimenting with the remodule. If you have tkinter available, you may also want to look atTools/demo/, a demonstration program included with thePython distribution. It allows you to enter REs and strings, and displayswhether the RE matches or fails. can be quite useful whentrying to debug a complicated RE.

This HOWTO uses the standard Python interpreter for its examples. First, run thePython interpreter, import the re module, and compile a RE:

>>> import re>>> p = re.compile('[a-z]+')>>> pre.compile('[a-z]+')

Now, you can try matching various strings against the RE [a-z]+. An emptystring shouldn’t match at all, since + means ‘one or more repetitions’.match() should return None in this case, which will cause theinterpreter to print no output. You can explicitly print the result ofmatch() to make this clear.

>>> p.match("")>>> print(p.match(""))None

Now, let’s try it on a string that it should match, such as tempo. In thiscase, match() will return a match object, so youshould store the result in a variable for later use.

>>> m = p.match('tempo')>>> m<re.Match object; span=(0, 5), match='tempo'>

Now you can query the match object for informationabout the matching string. Match object instancesalso have several methods and attributes; the most important ones are:




Return the string matched by the RE


Return the starting position of the match


Return the ending position of the match


Return a tuple containing the (start, end)positions of the match

Trying these methods will soon clarify their meaning:

>>>'tempo'>>> m.start(), m.end()(0, 5)>>> m.span()(0, 5)

group() returns the substring that was matched by the RE. start()and end() return the starting and ending index of the match. span()returns both start and end indexes in a single tuple. Since the match()method only checks if the RE matches at the start of a string, start()will always be zero. However, the search() method of patternsscans through the string, so the match may not start at zero in thatcase.

>>> print(p.match('::: message'))None>>> m ='::: message'); print(m)<re.Match object; span=(4, 11), match='message'>>>>'message'>>> m.span()(4, 11)

In actual programs, the most common style is to store thematch object in a variable, and then check if it wasNone. This usually looks like:

p = re.compile( ... )m = p.match( 'string goes here' )if m: print('Match found: ', print('No match')

Two pattern methods return all of the matches for a pattern.findall() returns a list of matching strings:

>>> p = re.compile(r'\d+')>>> p.findall('12 drummers drumming, 11 pipers piping, 10 lords a-leaping')['12', '11', '10']

The r prefix, making the literal a raw string literal, is needed in thisexample because escape sequences in a normal “cooked” string literal that arenot recognized by Python, as opposed to regular expressions, now result in aDeprecationWarning and will eventually become a SyntaxError. SeeThe Backslash Plague.

findall() has to create the entire list before it can be returned as theresult. The finditer() method returns a sequence ofmatch object instances as an iterator:

>>> iterator = p.finditer('12 drummers drumming, 11 ... 10 ...')>>> iterator <callable_iterator object at 0x...>>>> for match in iterator:...  print(match.span())...(0, 2)(22, 24)(29, 31)

Module-Level Functions

You don’t have to create a pattern object and call its methods; there module also provides top-level functions called match(),search(), findall(), sub(), and so forth. These functionstake the same arguments as the corresponding pattern method withthe RE string added as the first argument, and still return either None or amatch object instance.

>>> print(re.match(r'From\s+', 'Fromage amk'))None>>> re.match(r'From\s+', 'From amk Thu May 14 19:12:10 1998') <re.Match object; span=(0, 5), match='From '>

Under the hood, these functions simply create a pattern object for youand call the appropriate method on it. They also store the compiledobject in a cache, so future calls using the same RE won’t need toparse the pattern again and again.

Should you use these module-level functions, or should you get thepattern and call its methods yourself? If you’re accessing a regexwithin a loop, pre-compiling it will save a few function calls.Outside of loops, there’s not much difference thanks to the internalcache.

Compilation Flags

Compilation flags let you modify some aspects of how regular expressions work.Flags are available in the re module under two names, a long name such asIGNORECASE and a short, one-letter form such as I. (If you’refamiliar with Perl’s pattern modifiers, the one-letter forms use the sameletters; the short form of re.VERBOSE is re.X, for example.)Multiple flags can be specified by bitwise OR-ing them; re.I | re.M setsboth the I and M flags, for example.

Here’s a table of the available flags, followed by a more detailed explanationof each one.




Makes several escapes like \w, \b,\s and \d match only on ASCIIcharacters with the respective property.


Make . match any character, includingnewlines.


Do case-insensitive matches.


Do a locale-aware match.


Multi-line matching, affecting ^ and$.

VERBOSE, X(for ‘extended’)

Enable verbose REs, which can be organizedmore cleanly and understandably.


Perform case-insensitive matching; character class and literal strings willmatch letters by ignoring case. For example, [A-Z] will match lowercaseletters, too. Full Unicode matching also works unless the ASCIIflag is used to disable non-ASCII matches. When the Unicode patterns[a-z] or [A-Z] are used in combination with the IGNORECASEflag, they will match the 52 ASCII letters and 4 additional non-ASCIIletters: ‘İ’ (U+0130, Latin capital letter I with dot above), ‘ı’ (U+0131,Latin small letter dotless i), ‘ſ’ (U+017F, Latin small letter long s) and‘K’ (U+212A, Kelvin sign). Spam will match 'Spam', 'spam','spAM', or 'ſpam' (the latter is matched only in Unicode mode).This lowercasing doesn’t take the current locale into account;it will if you also set the LOCALE flag.


Make \w, \W, \b, \B and case-insensitive matching dependenton the current locale instead of the Unicode database.

Locales are a feature of the C library intended to help in writing programsthat take account of language differences. For example, if you’reprocessing encoded French text, you’d want to be able to write \w+ tomatch words, but \w only matches the character class [A-Za-z] inbytes patterns; it won’t match bytes corresponding to é or ç.If your system is configured properly and a French locale is selected,certain C functions will tell the program that the byte corresponding toé should also be considered a letter.Setting the LOCALE flag when compiling a regular expression will causethe resulting compiled object to use these C functions for \w; this isslower, but also enables \w+ to match French words as you’d expect.The use of this flag is discouraged in Python 3 as the locale mechanismis very unreliable, it only handles one “culture” at a time, and it onlyworks with 8-bit locales. Unicode matching is already enabled by defaultin Python 3 for Unicode (str) patterns, and it is able to handle differentlocales/languages.


(^ and $ haven’t been explained yet; they’ll be introduced in sectionMore Metacharacters.)

Usually ^ matches only at the beginning of the string, and $ matchesonly at the end of the string and immediately before the newline (if any) at theend of the string. When this flag is specified, ^ matches at the beginningof the string and at the beginning of each line within the string, immediatelyfollowing each newline. Similarly, the $ metacharacter matches either atthe end of the string and at the end of each line (immediately preceding eachnewline).


Makes the '.' special character match any character at all, including anewline; without this flag, '.' will match anything except a newline.


Make \w, \W, \b, \B, \s and \S perform ASCII-onlymatching instead of full Unicode matching. This is only meaningful forUnicode patterns, and is ignored for byte patterns.


This flag allows you to write regular expressions that are more readable bygranting you more flexibility in how you can format them. When this flag hasbeen specified, whitespace within the RE string is ignored, except when thewhitespace is in a character class or preceded by an unescaped backslash; thislets you organize and indent the RE more clearly. This flag also lets you putcomments within a RE that will be ignored by the engine; comments are marked bya '#' that’s neither in a character class or preceded by an unescapedbackslash.

For example, here’s a RE that uses re.VERBOSE; see how much easier itis to read?

charref = re.compile(r""" &[#] # Start of a numeric entity reference ( 0[0-7]+ # Octal form | [0-9]+ # Decimal form | x[0-9a-fA-F]+ # Hexadecimal form ) ; # Trailing semicolon""", re.VERBOSE)

Without the verbose setting, the RE would look like this:

charref = re.compile("&#(0[0-7]+" "|[0-9]+" "|x[0-9a-fA-F]+);")

In the above example, Python’s automatic concatenation of string literals hasbeen used to break up the RE into smaller pieces, but it’s still more difficultto understand than the version using re.VERBOSE.

More Pattern Power

So far we’ve only covered a part of the features of regular expressions. Inthis section, we’ll cover some new metacharacters, and how to use groups toretrieve portions of the text that was matched.

More Metacharacters

There are some metacharacters that we haven’t covered yet. Most of them will becovered in this section.

Some of the remaining metacharacters to be discussed are zero-widthassertions. They don’t cause the engine to advance through the string;instead, they consume no characters at all, and simply succeed or fail. Forexample, \b is an assertion that the current position is located at a wordboundary; the position isn’t changed by the \b at all. This means thatzero-width assertions should never be repeated, because if they match once at agiven location, they can obviously be matched an infinite number of times.


Alternation, or the “or” operator. If A and B are regular expressions,A|B will match any string that matches either A or B. | has verylow precedence in order to make it work reasonably when you’re alternatingmulti-character strings. Crow|Servo will match either 'Crow' or 'Servo',not 'Cro', a 'w' or an 'S', and 'ervo'.

To match a literal '|', use \|, or enclose it inside a character class,as in [|].


Matches at the beginning of lines. Unless the MULTILINE flag has beenset, this will only match at the beginning of the string. In MULTILINEmode, this also matches immediately after each newline within the string.

For example, if you wish to match the word From only at the beginning of aline, the RE to use is ^From.

>>> print('^From', 'From Here to Eternity')) <re.Match object; span=(0, 4), match='From'>>>> print('^From', 'Reciting From Memory'))None

To match a literal '^', use \^.


Matches at the end of a line, which is defined as either the end of the string,or any location followed by a newline character.

>>> print('}$', '{block}')) <re.Match object; span=(6, 7), match='}'>>>> print('}$', '{block} '))None>>> print('}$', '{block}\n')) <re.Match object; span=(6, 7), match='}'>

To match a literal '$', use \$ or enclose it inside a character class,as in [$].


Matches only at the start of the string. When not in MULTILINE mode,\A and ^ are effectively the same. In MULTILINE mode, they’redifferent: \A still matches only at the beginning of the string, but ^may match at any location inside the string that follows a newline character.


Matches only at the end of the string.


Word boundary. This is a zero-width assertion that matches only at thebeginning or end of a word. A word is defined as a sequence of alphanumericcharacters, so the end of a word is indicated by whitespace or anon-alphanumeric character.

The following example matches class only when it’s a complete word; it won’tmatch when it’s contained inside another word.

>>> p = re.compile(r'\bclass\b')>>> print('no class at all'))<re.Match object; span=(3, 8), match='class'>>>> print('the declassified algorithm'))None>>> print('one subclass is'))None

There are two subtleties you should remember when using this special sequence.First, this is the worst collision between Python’s string literals and regularexpression sequences. In Python’s string literals, \b is the backspacecharacter, ASCII value 8. If you’re not using raw strings, then Python willconvert the \b to a backspace, and your RE won’t match as you expect it to.The following example looks the same as our previous RE, but omits the 'r'in front of the RE string.

>>> p = re.compile('\bclass\b')>>> print('no class at all'))None>>> print('\b' + 'class' + '\b'))<re.Match object; span=(0, 7), match='\x08class\x08'>

Second, inside a character class, where there’s no use for this assertion,\b represents the backspace character, for compatibility with Python’sstring literals.


Another zero-width assertion, this is the opposite of \b, only matching whenthe current position is not at a word boundary.


Frequently you need to obtain more information than just whether the RE matchedor not. Regular expressions are often used to dissect strings by writing a REdivided into several subgroups which match different components of interest.For example, an RFC-822 header line is divided into a header name and a value,separated by a ':', like this:

From: author@example.comUser-Agent: Thunderbird (X11/20061227)MIME-Version: 1.0To:

This can be handled by writing a regular expression which matches an entireheader line, and has one group which matches the header name, and another groupwhich matches the header’s value.

Groups are marked by the '(', ')' metacharacters. '(' and ')'have much the same meaning as they do in mathematical expressions; they grouptogether the expressions contained inside them, and you can repeat the contentsof a group with a quantifier, such as *, +, ?, or{m,n}. For example, (ab)* will match zero or more repetitions ofab.

>>> p = re.compile('(ab)*')>>> print(p.match('ababababab').span())(0, 10)

Groups indicated with '(', ')' also capture the starting and endingindex of the text that they match; this can be retrieved by passing an argumentto group(), start(), end(), andspan(). Groups arenumbered starting with 0. Group 0 is always present; it’s the whole RE, somatch object methods all have group 0 as their defaultargument. Later we’ll see how to express groups that don’t capture the spanof text that they match.

>>> p = re.compile('(a)b')>>> m = p.match('ab')>>>'ab'>>>'ab'

Subgroups are numbered from left to right, from 1 upward. Groups can be nested;to determine the number, just count the opening parenthesis characters, goingfrom left to right.

>>> p = re.compile('(a(b)c)d')>>> m = p.match('abcd')>>>'abcd'>>>'abc'>>>'b'

group() can be passed multiple group numbers at a time, in which case itwill return a tuple containing the corresponding values for those groups.

>>>,1,2)('b', 'abc', 'b')

The groups() method returns a tuple containing the strings for all thesubgroups, from 1 up to however many there are.

>>> m.groups()('abc', 'b')

Backreferences in a pattern allow you to specify that the contents of an earliercapturing group must also be found at the current location in the string. Forexample, \1 will succeed if the exact contents of group 1 can be found atthe current position, and fails otherwise. Remember that Python’s stringliterals also use a backslash followed by numbers to allow including arbitrarycharacters in a string, so be sure to use a raw string when incorporatingbackreferences in a RE.

For example, the following RE detects doubled words in a string.

>>> p = re.compile(r'\b(\w+)\s+\1\b')>>>'Paris in the the spring').group()'the the'

Backreferences like this aren’t often useful for just searching through a string— there are few text formats which repeat data in this way — but you’ll soonfind out that they’re very useful when performing string substitutions.

Non-capturing and Named Groups

Elaborate REs may use many groups, both to capture substrings of interest, andto group and structure the RE itself. In complex REs, it becomes difficult tokeep track of the group numbers. There are two features which help with thisproblem. Both of them use a common syntax for regular expression extensions, sowe’ll look at that first.

Perl 5 is well known for its powerful additions to standard regular expressions.For these new features the Perl developers couldn’t choose new single-keystroke metacharactersor new special sequences beginning with \ without making Perl’s regularexpressions confusingly different from standard REs. If they chose & as anew metacharacter, for example, old expressions would be assuming that & wasa regular character and wouldn’t have escaped it by writing \& or [&].

The solution chosen by the Perl developers was to use (?...) as theextension syntax. ? immediately after a parenthesis was a syntax errorbecause the ? would have nothing to repeat, so this didn’t introduce anycompatibility problems. The characters immediately after the ? indicatewhat extension is being used, so (?=foo) is one thing (a positive lookaheadassertion) and (?:foo) is something else (a non-capturing group containingthe subexpression foo).

Python supports several of Perl’s extensions and adds an extensionsyntax to Perl’s extension syntax. If the first character after thequestion mark is a P, you know that it’s an extension that’sspecific to Python.

Now that we’ve looked at the general extension syntax, we can returnto the features that simplify working with groups in complex REs.

Sometimes you’ll want to use a group to denote a part of a regular expression,but aren’t interested in retrieving the group’s contents. You can make this factexplicit by using a non-capturing group: (?:...), where you can replace the... with any other regular expression.

>>> m = re.match("([abc])+", "abc")>>> m.groups()('c',)>>> m = re.match("(?:[abc])+", "abc")>>> m.groups()()

Except for the fact that you can’t retrieve the contents of what the groupmatched, a non-capturing group behaves exactly the same as a capturing group;you can put anything inside it, repeat it with a repetition metacharacter suchas *, and nest it within other groups (capturing or non-capturing).(?:...) is particularly useful when modifying an existing pattern, since youcan add new groups without changing how all the other groups are numbered. Itshould be mentioned that there’s no performance difference in searching betweencapturing and non-capturing groups; neither form is any faster than the other.

A more significant feature is named groups: instead of referring to them bynumbers, groups can be referenced by a name.

The syntax for a named group is one of the Python-specific extensions:(?P<name>...). name is, obviously, the name of the group. Named groupsbehave exactly like capturing groups, and additionally associate a namewith a group. The match object methods that deal withcapturing groups all accept either integers that refer to the group by numberor strings that contain the desired group’s name. Named groups are stillgiven numbers, so you can retrieve information about a group in two ways:

>>> p = re.compile(r'(?P<word>\b\w+\b)')>>> m = '(((( Lots of punctuation )))' )>>>'word')'Lots'>>>'Lots'

Additionally, you can retrieve named groups as a dictionary withgroupdict():

>>> m = re.match(r'(?P<first>\w+) (?P<last>\w+)', 'Jane Doe')>>> m.groupdict(){'first': 'Jane', 'last': 'Doe'}

Named groups are handy because they let you use easily remembered names, insteadof having to remember numbers. Here’s an example RE from the imaplibmodule:

InternalDate = re.compile(r'INTERNALDATE "' r'(?P<day>[ 123][0-9])-(?P<mon>[A-Z][a-z][a-z])-' r'(?P<year>[0-9][0-9][0-9][0-9])' r' (?P<hour>[0-9][0-9]):(?P<min>[0-9][0-9]):(?P<sec>[0-9][0-9])' r' (?P<zonen>[-+])(?P<zoneh>[0-9][0-9])(?P<zonem>[0-9][0-9])' r'"')

It’s obviously much easier to retrieve'zonem'), instead of havingto remember to retrieve group 9.

The syntax for backreferences in an expression such as (...)\1 refers to thenumber of the group. There’s naturally a variant that uses the group nameinstead of the number. This is another Python extension: (?P=name) indicatesthat the contents of the group called name should again be matched at thecurrent point. The regular expression for finding doubled words,\b(\w+)\s+\1\b can also be written as \b(?P<word>\w+)\s+(?P=word)\b:

>>> p = re.compile(r'\b(?P<word>\w+)\s+(?P=word)\b')>>>'Paris in the the spring').group()'the the'

Lookahead Assertions

Another zero-width assertion is the lookahead assertion. Lookahead assertionsare available in both positive and negative form, and look like this:


Positive lookahead assertion. This succeeds if the contained regularexpression, represented here by ..., successfully matches at the currentlocation, and fails otherwise. But, once the contained expression has beentried, the matching engine doesn’t advance at all; the rest of the pattern istried right where the assertion started.


Negative lookahead assertion. This is the opposite of the positive assertion;it succeeds if the contained expression doesn’t match at the current positionin the string.

To make this concrete, let’s look at a case where a lookahead is useful.Consider a simple pattern to match a filename and split it apart into a basename and an extension, separated by a .. For example, in news.rc,news is the base name, and rc is the filename’s extension.

The pattern to match this is quite simple:


Notice that the . needs to be treated specially because it’s ametacharacter, so it’s inside a character class to only match thatspecific character. Also notice the trailing $; this is added toensure that all the rest of the string must be included in theextension. This regular expression matches andautoexec.bat and and printers.conf.

Now, consider complicating the problem a bit; what if you want to matchfilenames where the extension is not bat? Some incorrect attempts:

.*[.][^b].*$ The first attempt above tries to exclude bat by requiringthat the first character of the extension is not a b. This is wrong,because the pattern also doesn’t match


The expression gets messier when you try to patch up the first solution byrequiring one of the following cases to match: the first character of theextension isn’t b; the second character isn’t a; or the third characterisn’t t. This accepts and rejects autoexec.bat, but itrequires a three-letter extension and won’t accept a filename with a two-letterextension such as We’ll complicate the pattern again in aneffort to fix it.


In the third attempt, the second and third letters are all made optional inorder to allow matching extensions shorter than three characters, such

The pattern’s getting really complicated now, which makes it hard to read andunderstand. Worse, if the problem changes and you want to exclude both batand exe as extensions, the pattern would get even more complicated andconfusing.

A negative lookahead cuts through all this confusion:

.*[.](?!bat$)[^.]*$ The negative lookahead means: if the expression batdoesn’t match at this point, try the rest of the pattern; if bat$ doesmatch, the whole pattern will fail. The trailing $ is required to ensurethat something like sample.batch, where the extension only starts withbat, will be allowed. The [^.]* makes sure that the pattern workswhen there are multiple dots in the filename.

Excluding another filename extension is now easy; simply add it as analternative inside the assertion. The following pattern excludes filenames thatend in either bat or exe:


Modifying Strings

Up to this point, we’ve simply performed searches against a static string.Regular expressions are also commonly used to modify strings in various ways,using the following pattern methods:




Split the string into a list, splitting itwherever the RE matches


Find all substrings where the RE matches, andreplace them with a different string


Does the same thing as sub(), butreturns the new string and the number ofreplacements

Splitting Strings

The split() method of a pattern splits a string apartwherever the RE matches, returning a list of the pieces. It’s similar to thesplit() method of strings but provides much more generality in thedelimiters that you can split by; string split() only supports splitting bywhitespace or by a fixed string. As you’d expect, there’s a module-levelre.split() function, too.

.split(string[, maxsplit=0])

Split string by the matches of the regular expression. If capturingparentheses are used in the RE, then their contents will also be returned aspart of the resulting list. If maxsplit is nonzero, at most maxsplit splitsare performed.

You can limit the number of splits made, by passing a value for maxsplit.When maxsplit is nonzero, at most maxsplit splits will be made, and theremainder of the string is returned as the final element of the list. In thefollowing example, the delimiter is any sequence of non-alphanumeric characters.

>>> p = re.compile(r'\W+')>>> p.split('This is a test, short and sweet, of split().')['This', 'is', 'a', 'test', 'short', 'and', 'sweet', 'of', 'split', '']>>> p.split('This is a test, short and sweet, of split().', 3)['This', 'is', 'a', 'test, short and sweet, of split().']

Sometimes you’re not only interested in what the text between delimiters is, butalso need to know what the delimiter was. If capturing parentheses are used inthe RE, then their values are also returned as part of the list. Compare thefollowing calls:

>>> p = re.compile(r'\W+')>>> p2 = re.compile(r'(\W+)')>>> p.split('This... is a test.')['This', 'is', 'a', 'test', '']>>> p2.split('This... is a test.')['This', '... ', 'is', ' ', 'a', ' ', 'test', '.', '']

The module-level function re.split() adds the RE to be used as the firstargument, but is otherwise the same.

>>> re.split(r'[\W]+', 'Words, words, words.')['Words', 'words', 'words', '']>>> re.split(r'([\W]+)', 'Words, words, words.')['Words', ', ', 'words', ', ', 'words', '.', '']>>> re.split(r'[\W]+', 'Words, words, words.', 1)['Words', 'words, words.']

Search and Replace

Another common task is to find all the matches for a pattern, and replace themwith a different string. The sub() method takes a replacement value,which can be either a string or a function, and the string to be processed.

.sub(replacement, string[, count=0])

Returns the string obtained by replacing the leftmost non-overlappingoccurrences of the RE in string by the replacement replacement. If thepattern isn’t found, string is returned unchanged.

The optional argument count is the maximum number of pattern occurrences to bereplaced; count must be a non-negative integer. The default value of 0 meansto replace all occurrences.

Here’s a simple example of using the sub() method. It replaces colournames with the word colour:

>>> p = re.compile('(blue|white|red)')>>> p.sub('colour', 'blue socks and red shoes')'colour socks and colour shoes'>>> p.sub('colour', 'blue socks and red shoes', count=1)'colour socks and red shoes'

The subn() method does the same work, but returns a 2-tuple containing thenew string value and the number of replacements that were performed:

>>> p = re.compile('(blue|white|red)')>>> p.subn('colour', 'blue socks and red shoes')('colour socks and colour shoes', 2)>>> p.subn('colour', 'no colours at all')('no colours at all', 0)

Empty matches are replaced only when they’re not adjacent to a previous empty match.

>>> p = re.compile('x*')>>> p.sub('-', 'abxd')'-a-b--d-'

If replacement is a string, any backslash escapes in it are processed. Thatis, \n is converted to a single newline character, \r is converted to acarriage return, and so forth. Unknown escapes such as \& are left alone.Backreferences, such as \6, are replaced with the substring matched by thecorresponding group in the RE. This lets you incorporate portions of theoriginal text in the resulting replacement string.

This example matches the word section followed by a string enclosed in{, }, and changes section to subsection:

>>> p = re.compile('section{ ( [^}]* ) }', re.VERBOSE)>>> p.sub(r'subsection{\1}','section{First} section{second}')'subsection{First} subsection{second}'

There’s also a syntax for referring to named groups as defined by the(?P<name>...) syntax. \g<name> will use the substring matched by thegroup named name, and \g<number> uses the corresponding group number.\g<2> is therefore equivalent to \2, but isn’t ambiguous in areplacement string such as \g<2>0. (\20 would be interpreted as areference to group 20, not a reference to group 2 followed by the literalcharacter '0'.) The following substitutions are all equivalent, but use allthree variations of the replacement string.

>>> p = re.compile('section{ (?P<name> [^}]* ) }', re.VERBOSE)>>> p.sub(r'subsection{\1}','section{First}')'subsection{First}'>>> p.sub(r'subsection{\g<1>}','section{First}')'subsection{First}'>>> p.sub(r'subsection{\g<name>}','section{First}')'subsection{First}'

replacement can also be a function, which gives you even more control. Ifreplacement is a function, the function is called for every non-overlappingoccurrence of pattern. On each call, the function is passed amatch object argument for the match and can use thisinformation to compute the desired replacement string and return it.

In the following example, the replacement function translates decimals intohexadecimal:

>>> def hexrepl(match):...  "Return the hex string for a decimal number"...  value = int(  return hex(value)...>>> p = re.compile(r'\d+')>>> p.sub(hexrepl, 'Call 65490 for printing, 49152 for user code.')'Call 0xffd2 for printing, 0xc000 for user code.'

When using the module-level re.sub() function, the pattern is passed asthe first argument. The pattern may be provided as an object or as a string; ifyou need to specify regular expression flags, you must either use apattern object as the first parameter, or use embedded modifiers in thepattern string, e.g. sub("(?i)b+", "x", "bbbb BBBB") returns 'x x'.

Common Problems

Regular expressions are a powerful tool for some applications, but in some waystheir behaviour isn’t intuitive and at times they don’t behave the way you mayexpect them to. This section will point out some of the most common pitfalls.

Use String Methods

Sometimes using the re module is a mistake. If you’re matching a fixedstring, or a single character class, and you’re not using any re featuressuch as the IGNORECASE flag, then the full power of regular expressionsmay not be required. Strings have several methods for performing operations withfixed strings and they’re usually much faster, because the implementation is asingle small C loop that’s been optimized for the purpose, instead of the large,more generalized regular expression engine.

One example might be replacing a single fixed string with another one; forexample, you might replace word with deed. re.sub() seems like thefunction to use for this, but consider the replace() method. Note thatreplace() will also replace word inside words, turning swordfishinto sdeedfish, but the naive RE word would have done that, too. (Toavoid performing the substitution on parts of words, the pattern would have tobe \bword\b, in order to require that word have a word boundary oneither side. This takes the job beyond replace()’s abilities.)

Another common task is deleting every occurrence of a single character from astring or replacing it with another single character. You might do this withsomething like re.sub('\n', ' ', S), but translate() is capable ofdoing both tasks and will be faster than any regular expression operation canbe.

In short, before turning to the re module, consider whether your problemcan be solved with a faster and simpler string method.

match() versus search()

The match() function only checks if the RE matches at the beginning of thestring while search() will scan forward through the string for a match.It’s important to keep this distinction in mind. Remember, match() willonly report a successful match which will start at 0; if the match wouldn’tstart at zero, match() will not report it.

>>> print(re.match('super', 'superstition').span())(0, 5)>>> print(re.match('super', 'insuperable'))None

On the other hand, search() will scan forward through the string,reporting the first match it finds.

>>> print('super', 'superstition').span())(0, 5)>>> print('super', 'insuperable').span())(2, 7)

Sometimes you’ll be tempted to keep using re.match(), and just add .*to the front of your RE. Resist this temptation and use The regular expression compiler does some analysis of REs in order tospeed up the process of looking for a match. One such analysis figures out whatthe first character of a match must be; for example, a pattern starting withCrow must match starting with a 'C'. The analysis lets the enginequickly scan through the string looking for the starting character, only tryingthe full match if a 'C' is found.

Adding .* defeats this optimization, requiring scanning to the end of thestring and then backtracking to find a match for the rest of the RE. instead.

Greedy versus Non-Greedy

When repeating a regular expression, as in a*, the resulting action is toconsume as much of the pattern as possible. This fact often bites you whenyou’re trying to match a pair of balanced delimiters, such as the angle bracketssurrounding an HTML tag. The naive pattern for matching a single HTML tagdoesn’t work because of the greedy nature of .*.

>>> s = '<html><head><title>Title</title>'>>> len(s)32>>> print(re.match('<.*>', s).span())(0, 32)>>> print(re.match('<.*>', s).group())<html><head><title>Title</title>

The RE matches the '<' in '<html>', and the .* consumes the rest ofthe string. There’s still more left in the RE, though, and the > can’tmatch at the end of the string, so the regular expression engine has tobacktrack character by character until it finds a match for the >. Thefinal match extends from the '<' in '<html>' to the '>' in'</title>', which isn’t what you want.

In this case, the solution is to use the non-greedy quantifiers *?, +?,??, or {m,n}?, which match as little text as possible. In the aboveexample, the '>' is tried immediately after the first '<' matches, andwhen it fails, the engine advances a character at a time, retrying the '>'at every step. This produces just the right result:

>>> print(re.match('<.*?>', s).group())<html>

(Note that parsing HTML or XML with regular expressions is painful.Quick-and-dirty patterns will handle common cases, but HTML and XML have specialcases that will break the obvious regular expression; by the time you’ve writtena regular expression that handles all of the possible cases, the patterns willbe very complicated. Use an HTML or XML parser module for such tasks.)

Using re.VERBOSE

By now you’ve probably noticed that regular expressions are a very compactnotation, but they’re not terribly readable. REs of moderate complexity canbecome lengthy collections of backslashes, parentheses, and metacharacters,making them difficult to read and understand.

For such REs, specifying the re.VERBOSE flag when compiling the regularexpression can be helpful, because it allows you to format the regularexpression more clearly.

The re.VERBOSE flag has several effects. Whitespace in the regularexpression that isn’t inside a character class is ignored. This means that anexpression such as dog | cat is equivalent to the less readable dog|cat,but [a b] will still match the characters 'a', 'b', or a space. Inaddition, you can also put comments inside a RE; comments extend from a #character to the next newline. When used with triple-quoted strings, thisenables REs to be formatted more neatly:

pat = re.compile(r""" \s* # Skip leading whitespace (?P<header>[^:]+) # Header name \s* : # Whitespace, and a colon (?P<value>.*?) # The header's value -- *? used to # lose the following trailing whitespace \s*$ # Trailing whitespace to end-of-line""", re.VERBOSE)

This is far more readable than:

pat = re.compile(r"\s*(?P<header>[^:]+)\s*:(?P<value>.*?)\s*$")


Regular expressions are a complicated topic. Did this document help youunderstand them? Were there parts that were unclear, or Problems youencountered that weren’t covered here? If so, please send suggestions forimprovements to the author.

The most complete book on regular expressions is almost certainly JeffreyFriedl’s Mastering Regular Expressions, published by O’Reilly. Unfortunately,it exclusively concentrates on Perl and Java’s flavours of regular expressions,and doesn’t contain any Python material at all, so it won’t be useful as areference for programming in Python. (The first edition covered Python’snow-removed regex module, which won’t help you much.) Consider checkingit out from your library.


What is \\ W+ in regex? ›

\w+ matches one or more alphanumeric characters. Pattern matching is greedy by default, so the \w+ means “find the first alphanumeric character and then check the next character and if it is alphanumeric then include it in the match too, repeat until you run into a non-alphanumeric character.”

Why is regular expression so hard? ›

Regular expressions are dense. This makes them hard to read, but not in proportion to the information they carry. Certainly 100 characters of regular expression syntax is harder to read than 100 consecutive characters of ordinary prose or 100 characters of C code.

How do you escape in regex? ›

Escape Sequences (\char): To match a character having special meaning in regex, you need to use a escape sequence prefix with a backslash ( \ ). E.g., \. matches "." ; regex \+ matches "+" ; and regex \( matches "(" .

What does {} do in regex? ›

A string within square brackets specifies any of the characters in string. Thus [abc], if compared to other strings, would match any that contained a, b, or c. Integer values enclosed in {} indicate the number of times to apply the preceding regular expression.

What does \\ W+ mean in R? ›

'\\w' denotes word character that may include lower case letters, upper case letters, and digits. The '+' sign indicates that there might be one or more of those characters. After domain, there is one more dot and then more characters. We will get them using: “\\.\\w+”

What does the plus character [+] do in regex? ›

The plus ( + ) is a quantifier that matches one or more occurrences of the preceding element. The plus is similar to the asterisk ( * ) in that many occurrences are acceptable, but unlike the asterisk in that at least one occurrence is required.

Do people still use regex? ›

Despite being hard to read, hard to validate, hard to document and notoriously hard to master, regexes are still widely used today. Supported by all modern programming languages, text processing programs and advanced text editors, regexes are now used in more than a third of both Python and JavaScript projects.

Do I need to memorize regex? ›

Regex are way simpler than one may think. If you have to write a few of them, you will memorize the syntax; unless you have some decease. So, it is not necessary at all, your brain will do it for you.

Is regular expression a skill? ›

For someone to become a master at text analytics, being proficient with regular expressions is a must-have skill.

What does the 0 9 ]+ match in a regular expression? ›

Both [0-9]+ and [0-9]++ match one or more ASCII digits, but the second one will not allow the regex engine to backtrack into the match if that should become necessary for the overall regex to succeed. matches the string 00 , whereas [0-9]++0 doesn't.

How do I ignore lines in regex? ›

To ignore insertions and deletions of lines that match a grep -style regular expression, use the --ignore-matching-lines= regexp ( -I regexp ) option.

What are the safe characters in regex? ›

According to the spec, the set of safe characters for anything other than a domain name are as follows: A-Z a-z 0-9 - . _ ~ ( ) ' ! * : @ , ; + ? And actually + and ? are only safe when used within a URL, not quite otherwise.

What is '\ D +'? ›

This is a Regular Expression pattern \d is a regex pattern for digit + is a regex pattern for at least (one or more) since they are enclosed in a ( ) that means the group that you want to capture.

Why is regex powerful? ›

Regular Expression or (Regex) is one of the most powerful, flexible, and efficient text processing approaches. Regex has its own terminologies, conditions, and syntax; it is, in a sense, a mini programming language. Regex can be used to add, remove, isolate, and manipulate all kinds of text and data.

Which regex accepts everything? ›

3. Matching Multiple Characters
.*Matches any number of characters including special characters.
[0-9]*Matches any number of digits.
[a-zA-Z]*Matches any number of alphabets.
[a-zA-Z0-9]*Matches any number of alphanumeric characters.
Sep 21, 2022

What does \\ W+ mean in Java? ›

\\w+ matches all alphanumeric characters and _ . \\W+ matches all characters except alphanumeric characters and _ . They are opposite.

What are R+ and W+ modes? ›

Python - Understanding File Modes r+, w+ and a+ in open()
ModeDescriptionCreates File if Not Exists
r+Read and write (updating)No
wWrite-only (overwrite or create)Yes
w+Write and read (overwrite or create)Yes
aAppend-only (append or create)Yes
2 more rows

What is difference between R+ and W+ modes? ›

Opening a file: r+ mode opens the file if it exists, while w+ mode also opens the file, but it deletes all the content present in the file. The pointer in both cases is present at the start of the file.

What is the W and W in RegEx? ›

The \w and \W meta characters are used to match the characters of a given string. \w and \W are different from each other. \w (character) is equivalent to [a-zA-Z0-9_] that means it matches any single letter, number, or underscore. The characters that are not matched by the \w are matched by the \W.

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