This module provides regular expression matching operations similar to those found in Perl.
Both patterns and strings to be searched can be Unicode strings as well as 8-bit strings. However, Unicode strings and 8-bit strings cannot be mixed: that is, you cannot match an Unicode string with a byte pattern or vice-versa; similarly, when asking for a substitution, the replacement string must be of the same type as both the pattern and the search string.
Regular expressions use the backslash character ('\') to indicate special forms or to allow special characters to be used without invoking their special meaning. This collides with Python’s usage of the same character for the same purpose in string literals; for example, to match a literal backslash, one might have to write '\\\\' as the pattern string, because the regular expression must be \\, and each backslash must be expressed as \\ inside a regular Python string literal.
The solution is to use Python’s raw string notation for regular expression patterns; 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. Usually patterns will be expressed in Python code using this raw string notation.
It is important to note that most regular expression operations are available as module-level functions and RegexObject methods. The functions are shortcuts that don’t require you to compile a regex object first, but miss some fine-tuning parameters.
See also
A regular expression (or RE) specifies a set of strings that matches it; the functions in this module let you check if a particular string matches a given regular expression (or if a given regular expression matches a particular string, which comes down to the same thing).
Regular expressions can be concatenated to form new regular expressions; if A and B are both regular expressions, then AB is also a regular expression. In general, if a string p matches A and another string q matches B, the string pq will match AB. This holds unless A or B contain low precedence operations; boundary conditions between A and B; or have numbered group references. Thus, complex expressions can easily be constructed from simpler primitive expressions like the ones described here. For details of the theory and implementation of regular expressions, consult the Friedl book referenced above, or almost any textbook about compiler construction.
A brief explanation of the format of regular expressions follows. For further information and a gentler presentation, consult the Regular Expression HOWTO.
Regular expressions can contain both special and ordinary characters. Most ordinary characters, like 'A', 'a', or '0', are the simplest regular expressions; they simply match themselves. You can concatenate ordinary characters, so last matches the string 'last'. (In the rest of this section, we’ll write RE’s in this special style, usually without quotes, and strings to be matched 'in single quotes'.)
Some characters, like '|' or '(', are special. Special characters either stand for classes of ordinary characters, or affect how the regular expressions around them are interpreted. Regular expression pattern strings may not contain null bytes, but can specify the null byte using the \number notation, e.g., '\x00'.
The special characters are:
Either escapes special characters (permitting you to match characters like '*', '?', and so forth), or signals a special sequence; special sequences are discussed below.
If you’re not using a raw string to express the pattern, remember that Python also uses the backslash as an escape sequence in string literals; if the escape sequence isn’t recognized by Python’s parser, the backslash and subsequent character are included in the resulting string. However, if Python would recognize the resulting sequence, the backslash should be repeated twice. This is complicated and hard to understand, so it’s highly recommended that you use raw strings for all but the simplest expressions.
Used to indicate a set of characters. Characters can be listed individually, or a range of characters can be indicated by giving two characters and separating them by a '-'. Special characters are not active inside sets. For example, [akm$] will match any of the characters 'a', 'k', 'm', or '$'; [a-z] will match any lowercase letter, and [a-zA-Z0-9] matches any letter or digit. Character classes such as \w or \S (defined below) are also acceptable inside a range, although the characters they match depends on whether ASCII or LOCALE mode is in force. If you want to include a ']' or a '-' inside a set, precede it with a backslash, or place it as the first character. The pattern []] will match ']', for example.
You can match the characters not within a range by complementing the set. This is indicated by including a '^' as the first character of the set; '^' elsewhere will simply match the '^' character. For example, [^5] will match any character except '5', and [^^] will match any character except '^'.
Note that inside [] the special forms and special characters lose their meanings and only the syntaxes described here are valid. For example, +, *, (, ), and so on are treated as literals inside [], and backreferences cannot be used inside [].
(One or more letters from the set 'a', 'i', 'L', 'm', 's', 'u', 'x'.) The group matches the empty string; the letters set the corresponding flags: re.A (ASCII-only matching), re.I (ignore case), re.L (locale dependent), re.M (multi-line), re.S (dot matches all), and re.X (verbose), for the entire regular expression. (The flags are described in Module Contents.) This is useful if you wish to include the flags as part of the regular expression, instead of passing a flag argument to the re.compile() function.
Note that the (?x) flag changes how the expression is parsed. It should be used first in the expression string, or after one or more whitespace characters. If there are non-whitespace characters before the flag, the results are undefined.
Similar to regular parentheses, but the substring matched by the group is accessible within the rest of the regular expression via the symbolic group name name. Group names must be valid Python identifiers, and each group name must be defined only once within a regular expression. A symbolic group is also a numbered group, just as if the group were not named. So the group named id in the example below can also be referenced as the numbered group 1.
For example, if the pattern is (?P<id>[a-zA-Z_]\w*), the group can be referenced by its name in arguments to methods of match objects, such as m.group('id') or m.end('id'), and also by name in the regular expression itself (using (?P=id)) and replacement text given to .sub() (using \g<id>).
Matches if the current position in the string is preceded by a match for ... that ends at the current position. This is called a positive lookbehind assertion. (?<=abc)def will find a match in abcdef, since the lookbehind will back up 3 characters and check if the contained pattern matches. The contained pattern must only match strings of some fixed length, meaning that abc or a|b are allowed, but a* and a{3,4} are not. Note that patterns which start with positive lookbehind assertions will never match at the beginning of the string being searched; you will most likely want to use the search() function rather than the match() function:
>>> import re
>>> m = re.search('(?<=abc)def', 'abcdef')
>>> m.group(0)
'def'
This example looks for a word following a hyphen:
>>> m = re.search('(?<=-)\w+', 'spam-egg')
>>> m.group(0)
'egg'
The special sequences consist of '\' and a character from the list below. If the ordinary character is not on the list, then the resulting RE will match the second character. For example, \$ matches the character '$'.
Most of the standard escapes supported by Python string literals are also accepted by the regular expression parser:
\a \b \f \n
\r \t \v \x
\\
Octal escapes are included in a limited form: If the first digit is a 0, or if there are three octal digits, it is considered an octal escape. Otherwise, it is a group reference. As for string literals, octal escapes are always at most three digits in length.
Python offers two different primitive operations based on regular expressions: match checks for a match only at the beginning of the string, while search checks for a match anywhere in the string (this is what Perl does by default).
Note that match may differ from search even when using a regular expression beginning with '^': '^' matches only at the start of the string, or in MULTILINE mode also immediately following a newline. The “match” operation succeeds only if the pattern matches at the start of the string regardless of mode, or at the starting position given by the optional pos argument regardless of whether a newline precedes it.
>>> re.match("c", "abcdef") # No match
>>> re.search("c", "abcdef") # Match
<_sre.SRE_Match object at ...>
The module defines several functions, constants, and an exception. Some of the functions are simplified versions of the full featured methods for compiled regular expressions. Most non-trivial applications always use the compiled form.
Compile a regular expression pattern into a regular expression object, which can be used for matching using its match() and search() methods, described below.
The expression’s behaviour can be modified by specifying a flags value. Values can be any of the following variables, combined using bitwise OR (the | operator).
The sequence
prog = re.compile(pattern)
result = prog.match(string)
is equivalent to
result = re.match(pattern, string)
but using re.compile() and saving the resulting regular expression object for reuse is more efficient when the expression will be used several times in a single program.
Note
The compiled versions of the most recent patterns passed to re.match(), re.search() or re.compile() are cached, so programs that use only a few regular expressions at a time needn’t worry about compiling regular expressions.
Make \w, \W, \b, \B, \d, \D, \s and \S perform ASCII-only matching instead of full Unicode matching. This is only meaningful for Unicode patterns, and is ignored for byte patterns.
Note that for backward compatibility, the re.U flag still exists (as well as its synonym re.UNICODE and its embedded counterpart (?u)), but these are redundant in Python 3.0 since matches are Unicode by default for strings (and Unicode matching isn’t allowed for bytes).
This flag allows you to write regular expressions that look nicer. Whitespace within the pattern is ignored, except when in a character class or preceded by an unescaped backslash, and, when a line contains a '#' neither in a character class or preceded by an unescaped backslash, all characters from the leftmost such '#' through the end of the line are ignored.
That means that the two following regular expression objects that match a decimal number are functionally equal:
a = re.compile(r"""\d + # the integral part
\. # the decimal point
\d * # some fractional digits""", re.X)
b = re.compile(r"\d+\.\d*")
If zero or more characters at the beginning of string match the regular expression pattern, return a corresponding MatchObject instance. Return None if the string does not match the pattern; note that this is different from a zero-length match.
Note
If you want to locate a match anywhere in string, use search() instead.
Split string by the occurrences of pattern. If capturing parentheses are used in pattern, then the text of all groups in the pattern are also returned as part of the resulting list. If maxsplit is nonzero, at most maxsplit splits occur, and the remainder of the string is returned as the final element of the list.
>>> re.split('\W+', 'Words, words, words.')
['Words', 'words', 'words', '']
>>> re.split('(\W+)', 'Words, words, words.')
['Words', ', ', 'words', ', ', 'words', '.', '']
>>> re.split('\W+', 'Words, words, words.', 1)
['Words', 'words, words.']
>>> re.split('[a-f]+', '0a3B9', flags=re.IGNORECASE)
['0', '3', '9']
If there are capturing groups in the separator and it matches at the start of the string, the result will start with an empty string. The same holds for the end of the string:
>>> re.split('(\W+)', '...words, words...')
['', '...', 'words', ', ', 'words', '...', '']
That way, separator components are always found at the same relative indices within the result list (e.g., if there’s one capturing group in the separator, the 0th, the 2nd and so forth).
Note that split will never split a string on an empty pattern match. For example:
>>> re.split('x*', 'foo')
['foo']
>>> re.split("(?m)^$", "foo\n\nbar\n")
['foo\n\nbar\n']
Changed in version 3.1: Added the optional flags argument.
Return the string obtained by replacing the leftmost non-overlapping occurrences of pattern in string by the replacement repl. If the pattern isn’t found, string is returned unchanged. repl can be a string or a function; if it is a string, any backslash escapes in it are processed. That is, \n is converted to a single newline character, \r is converted to a linefeed, and so forth. Unknown escapes such as \j are left alone. Backreferences, such as \6, are replaced with the substring matched by group 6 in the pattern. For example:
>>> re.sub(r'def\s+([a-zA-Z_][a-zA-Z_0-9]*)\s*\(\s*\):',
... r'static PyObject*\npy_\1(void)\n{',
... 'def myfunc():')
'static PyObject*\npy_myfunc(void)\n{'
If repl is a function, it is called for every non-overlapping occurrence of pattern. The function takes a single match object argument, and returns the replacement string. For example:
>>> def dashrepl(matchobj):
... if matchobj.group(0) == '-': return ' '
... else: return '-'
>>> re.sub('-{1,2}', dashrepl, 'pro----gram-files')
'pro--gram files'
>>> re.sub(r'\sAND\s', ' & ', 'Baked Beans And Spam', flags=re.IGNORECASE)
'Baked Beans & Spam'
The pattern may be a string or an RE object.
The optional argument count is the maximum number of pattern occurrences to be replaced; count must be a non-negative integer. If omitted or zero, all occurrences will be replaced. Empty matches for the pattern are replaced only when not adjacent to a previous match, so sub('x*', '-', 'abc') returns '-a-b-c-'.
In addition to character escapes and backreferences as described above, \g<name> will use the substring matched by the group named name, as defined by the (?P<name>...) syntax. \g<number> uses the corresponding group number; \g<2> is therefore equivalent to \2, but isn’t ambiguous in a replacement such as \g<2>0. \20 would be interpreted as a reference to group 20, not a reference to group 2 followed by the literal character '0'. The backreference \g<0> substitutes in the entire substring matched by the RE.
Changed in version 3.1: Added the optional flags argument.
Perform the same operation as sub(), but return a tuple (new_string, number_of_subs_made).
Changed in version 3.1: Added the optional flags argument.
Compiled regular expression objects support the following methods and attributes:
If zero or more characters at the beginning of string match this regular expression, return a corresponding MatchObject instance. Return None if the string does not match the pattern; note that this is different from a zero-length match.
Note
If you want to locate a match anywhere in string, use search() instead.
The optional second parameter pos gives an index in the string where the search is to start; it defaults to 0. This is not completely equivalent to slicing the string; the '^' pattern character matches at the real beginning of the string and at positions just after a newline, but not necessarily at the index where the search is to start.
The optional parameter endpos limits how far the string will be searched; it will be as if the string is endpos characters long, so only the characters from pos to endpos - 1 will be searched for a match. If endpos is less than pos, no match will be found, otherwise, if rx is a compiled regular expression object, rx.match(string, 0, 50) is equivalent to rx.match(string[:50], 0).
>>> pattern = re.compile("o")
>>> pattern.match("dog") # No match as "o" is not at the start of "dog."
>>> pattern.match("dog", 1) # Match as "o" is the 2nd character of "dog".
<_sre.SRE_Match object at ...>
Scan through string looking for a location where this regular expression produces a match, and return a corresponding MatchObject instance. Return None if no position in the string matches the pattern; note that this is different from finding a zero-length match at some point in the string.
The optional pos and endpos parameters have the same meaning as for the match() method.
Match objects always have a boolean value of True, so that you can test whether e.g. match() resulted in a match with a simple if statement. They support the following methods and attributes:
Returns one or more subgroups of the match. If there is a single argument, the result is a single string; if there are multiple arguments, the result is a tuple with one item per argument. Without arguments, group1 defaults to zero (the whole match is returned). If a groupN argument is zero, the corresponding return value is the entire matching string; if it is in the inclusive range [1..99], it is the string matching the corresponding parenthesized group. If a group number is negative or larger than the number of groups defined in the pattern, an IndexError exception is raised. If a group is contained in a part of the pattern that did not match, the corresponding result is None. If a group is contained in a part of the pattern that matched multiple times, the last match is returned.
>>> m = re.match(r"(\w+) (\w+)", "Isaac Newton, physicist")
>>> m.group(0) # The entire match
'Isaac Newton'
>>> m.group(1) # The first parenthesized subgroup.
'Isaac'
>>> m.group(2) # The second parenthesized subgroup.
'Newton'
>>> m.group(1, 2) # Multiple arguments give us a tuple.
('Isaac', 'Newton')
If the regular expression uses the (?P<name>...) syntax, the groupN arguments may also be strings identifying groups by their group name. If a string argument is not used as a group name in the pattern, an IndexError exception is raised.
A moderately complicated example:
>>> m = re.match(r"(?P<first_name>\w+) (?P<last_name>\w+)", "Malcolm Reynolds")
>>> m.group('first_name')
'Malcolm'
>>> m.group('last_name')
'Reynolds'
Named groups can also be referred to by their index:
>>> m.group(1)
'Malcolm'
>>> m.group(2)
'Reynolds'
If a group matches multiple times, only the last match is accessible:
>>> m = re.match(r"(..)+", "a1b2c3") # Matches 3 times.
>>> m.group(1) # Returns only the last match.
'c3'
Return a tuple containing all the subgroups of the match, from 1 up to however many groups are in the pattern. The default argument is used for groups that did not participate in the match; it defaults to None.
For example:
>>> m = re.match(r"(\d+)\.(\d+)", "24.1632")
>>> m.groups()
('24', '1632')
If we make the decimal place and everything after it optional, not all groups might participate in the match. These groups will default to None unless the default argument is given:
>>> m = re.match(r"(\d+)\.?(\d+)?", "24")
>>> m.groups() # Second group defaults to None.
('24', None)
>>> m.groups('0') # Now, the second group defaults to '0'.
('24', '0')
Return a dictionary containing all the named subgroups of the match, keyed by the subgroup name. The default argument is used for groups that did not participate in the match; it defaults to None. For example:
>>> m = re.match(r"(?P<first_name>\w+) (?P<last_name>\w+)", "Malcolm Reynolds")
>>> m.groupdict()
{'first_name': 'Malcolm', 'last_name': 'Reynolds'}
Return the indices of the start and end of the substring matched by group; group defaults to zero (meaning the whole matched substring). Return -1 if group exists but did not contribute to the match. For a match object m, and a group g that did contribute to the match, the substring matched by group g (equivalent to m.group(g)) is
m.string[m.start(g):m.end(g)]
Note that m.start(group) will equal m.end(group) if group matched a null string. For example, after m = re.search('b(c?)', 'cba'), m.start(0) is 1, m.end(0) is 2, m.start(1) and m.end(1) are both 2, and m.start(2) raises an IndexError exception.
An example that will remove remove_this from email addresses:
>>> email = "tony@tiremove_thisger.net"
>>> m = re.search("remove_this", email)
>>> email[:m.start()] + email[m.end():]
'[email protected]'
In this example, we’ll use the following helper function to display match objects a little more gracefully:
def displaymatch(match):
if match is None:
return None
return '<Match: %r, groups=%r>' % (match.group(), match.groups())
Suppose you are writing a poker program where a player’s hand is represented as a 5-character string with each character representing a card, “a” for ace, “k” for king, “q” for queen, j for jack, “0” for 10, and “1” through “9” representing the card with that value.
To see if a given string is a valid hand, one could do the following:
>>> valid = re.compile(r"[0-9akqj]{5}$")
>>> displaymatch(valid.match("ak05q")) # Valid.
"<Match: 'ak05q', groups=()>"
>>> displaymatch(valid.match("ak05e")) # Invalid.
>>> displaymatch(valid.match("ak0")) # Invalid.
>>> displaymatch(valid.match("727ak")) # Valid.
"<Match: '727ak', groups=()>"
That last hand, "727ak", contained a pair, or two of the same valued cards. To match this with a regular expression, one could use backreferences as such:
>>> pair = re.compile(r".*(.).*\1")
>>> displaymatch(pair.match("717ak")) # Pair of 7s.
"<Match: '717', groups=('7',)>"
>>> displaymatch(pair.match("718ak")) # No pairs.
>>> displaymatch(pair.match("354aa")) # Pair of aces.
"<Match: '354aa', groups=('a',)>"
To find out what card the pair consists of, one could use the group() method of MatchObject in the following manner:
>>> pair.match("717ak").group(1)
'7'
# Error because re.match() returns None, which doesn't have a group() method:
>>> pair.match("718ak").group(1)
Traceback (most recent call last):
File "<pyshell#23>", line 1, in <module>
re.match(r".*(.).*\1", "718ak").group(1)
AttributeError: 'NoneType' object has no attribute 'group'
>>> pair.match("354aa").group(1)
'a'
Python does not currently have an equivalent to scanf(). Regular expressions are generally more powerful, though also more verbose, than scanf() format strings. The table below offers some more-or-less equivalent mappings between scanf() format tokens and regular expressions.
| scanf() Token | Regular Expression |
|---|---|
| %c | . |
| %5c | .{5} |
| %d | [-+]?\d+ |
| %e, %E, %f, %g | [-+]?(\d+(\.\d*)?|\.\d+)([eE][-+]?\d+)? |
| %i | [-+]?(0[xX][\dA-Fa-f]+|0[0-7]*|\d+) |
| %o | 0[0-7]* |
| %s | \S+ |
| %u | \d+ |
| %x, %X | 0[xX][\dA-Fa-f]+ |
To extract the filename and numbers from a string like
/usr/sbin/sendmail - 0 errors, 4 warnings
you would use a scanf() format like
%s - %d errors, %d warnings
The equivalent regular expression would be
(\S+) - (\d+) errors, (\d+) warnings
If you create regular expressions that require the engine to perform a lot of recursion, you may encounter a RuntimeError exception with the message maximum recursion limit exceeded. For example,
>>> s = 'Begin ' + 1000*'a very long string ' + 'end'
>>> re.match('Begin (\w| )*? end', s).end()
Traceback (most recent call last):
File "<stdin>", line 1, in ?
File "/usr/local/lib/python2.5/re.py", line 132, in match
return _compile(pattern, flags).match(string)
RuntimeError: maximum recursion limit exceeded
You can often restructure your regular expression to avoid recursion.
Simple uses of the *? pattern are special-cased to avoid recursion. Thus, the above regular expression can avoid recursion by being recast as Begin [a-zA-Z0-9_ ]*?end. As a further benefit, such regular expressions will run faster than their recursive equivalents.
In a nutshell, match() only attempts to match a pattern at the beginning of a string where search() will match a pattern anywhere in a string. For example:
>>> re.match("o", "dog") # No match as "o" is not the first letter of "dog".
>>> re.search("o", "dog") # Match as search() looks everywhere in the string.
<_sre.SRE_Match object at ...>
Note
The following applies only to regular expression objects like those created with re.compile("pattern"), not the primitives re.match(pattern, string) or re.search(pattern, string).
match() has an optional second parameter that gives an index in the string where the search is to start:
>>> pattern = re.compile("o")
>>> pattern.match("dog") # No match as "o" is not at the start of "dog."
# Equivalent to the above expression as 0 is the default starting index:
>>> pattern.match("dog", 0)
# Match as "o" is the 2nd character of "dog" (index 0 is the first):
>>> pattern.match("dog", 1)
<_sre.SRE_Match object at ...>
>>> pattern.match("dog", 2) # No match as "o" is not the 3rd character of "dog."
split() splits a string into a list delimited by the passed pattern. The method is invaluable for converting textual data into data structures that can be easily read and modified by Python as demonstrated in the following example that creates a phonebook.
First, here is the input. Normally it may come from a file, here we are using triple-quoted string syntax:
>>> input = """Ross McFluff: 834.345.1254 155 Elm Street
...
... Ronald Heathmore: 892.345.3428 436 Finley Avenue
... Frank Burger: 925.541.7625 662 South Dogwood Way
...
...
... Heather Albrecht: 548.326.4584 919 Park Place"""
The entries are separated by one or more newlines. Now we convert the string into a list with each nonempty line having its own entry:
>>> entries = re.split("\n+", input)
>>> entries
['Ross McFluff: 834.345.1254 155 Elm Street',
'Ronald Heathmore: 892.345.3428 436 Finley Avenue',
'Frank Burger: 925.541.7625 662 South Dogwood Way',
'Heather Albrecht: 548.326.4584 919 Park Place']
Finally, split each entry into a list with first name, last name, telephone number, and address. We use the maxsplit parameter of split() because the address has spaces, our splitting pattern, in it:
>>> [re.split(":? ", entry, 3) for entry in entries]
[['Ross', 'McFluff', '834.345.1254', '155 Elm Street'],
['Ronald', 'Heathmore', '892.345.3428', '436 Finley Avenue'],
['Frank', 'Burger', '925.541.7625', '662 South Dogwood Way'],
['Heather', 'Albrecht', '548.326.4584', '919 Park Place']]
The :? pattern matches the colon after the last name, so that it does not occur in the result list. With a maxsplit of 4, we could separate the house number from the street name:
>>> [re.split(":? ", entry, 4) for entry in entries]
[['Ross', 'McFluff', '834.345.1254', '155', 'Elm Street'],
['Ronald', 'Heathmore', '892.345.3428', '436', 'Finley Avenue'],
['Frank', 'Burger', '925.541.7625', '662', 'South Dogwood Way'],
['Heather', 'Albrecht', '548.326.4584', '919', 'Park Place']]
sub() replaces every occurrence of a pattern with a string or the result of a function. This example demonstrates using sub() with a function to “munge” text, or randomize the order of all the characters in each word of a sentence except for the first and last characters:
>>> def repl(m):
... inner_word = list(m.group(2))
... random.shuffle(inner_word)
... return m.group(1) + "".join(inner_word) + m.group(3)
>>> text = "Professor Abdolmalek, please report your absences promptly."
>>> re.sub("(\w)(\w+)(\w)", repl, text)
'Poefsrosr Aealmlobdk, pslaee reorpt your abnseces plmrptoy.'
>>> re.sub("(\w)(\w+)(\w)", repl, text)
'Pofsroser Aodlambelk, plasee reoprt yuor asnebces potlmrpy.'
findall() matches all occurrences of a pattern, not just the first one as search() does. For example, if one was a writer and wanted to find all of the adverbs in some text, he or she might use findall() in the following manner:
>>> text = "He was carefully disguised but captured quickly by police."
>>> re.findall(r"\w+ly", text)
['carefully', 'quickly']
If one wants more information about all matches of a pattern than the matched text, finditer() is useful as it provides instances of MatchObject instead of strings. Continuing with the previous example, if one was a writer who wanted to find all of the adverbs and their positions in some text, he or she would use finditer() in the following manner:
>>> text = "He was carefully disguised but captured quickly by police."
>>> for m in re.finditer(r"\w+ly", text):
... print('%02d-%02d: %s' % (m.start(), m.end(), m.group(0)))
07-16: carefully
40-47: quickly
Raw string notation (r"text") keeps regular expressions sane. Without it, every backslash ('\') in a regular expression would have to be prefixed with another one to escape it. For example, the two following lines of code are functionally identical:
>>> re.match(r"\W(.)\1\W", " ff ")
<_sre.SRE_Match object at ...>
>>> re.match("\\W(.)\\1\\W", " ff ")
<_sre.SRE_Match object at ...>
When one wants to match a literal backslash, it must be escaped in the regular expression. With raw string notation, this means r"\\". Without raw string notation, one must use "\\\\", making the following lines of code functionally identical:
>>> re.match(r"\\", r"\\")
<_sre.SRE_Match object at ...>
>>> re.match("\\\\", r"\\")
<_sre.SRE_Match object at ...>