M

MULTILINE

(^ and $ haven't been explained yet; they'll be introduced in section 4.1.)

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

S

DOTALL

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

X

VERBOSE

This flag allows you to write regular expressions that are more readable by granting you more flexibility in how you can format them. When this flag has been specified, whitespace within the RE string is ignored, except when the whitespace is in a character class or preceded by an unescaped backslash; this lets you organize and indent the RE more clearly. It also enables you to put comments within a RE that will be ignored by the engine; comments are marked by a "#" that's neither in a character class or preceded by an unescaped backslash.

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

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

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

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

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

4 More Pattern Power

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

 
4.1 More Metacharacters

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

Some of the remaining metacharacters to be discussed are zero-width assertions. They don't cause the engine to advance through the string; instead, they consume no characters at all, and simply succeed or fail. For example, \b is an assertion that the current position is located at a word boundary; the position isn't changed by the \b at all. This means that zero-width assertions should never be repeated, because if they match once at a given 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 very low precedence in order to make it work reasonably when you're alternating multi-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 been set, this will only match at the beginning of the string. In MULTILINE mode, 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 a line, the RE to use is ^From.

>>> print re.search('^From', 'From Here to Eternity')
<re.MatchObject instance at 80c1520>
>>> print re.search('^From', 'Reciting From Memory')
None

$

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 re.search('}$', '{block}')
<re.MatchObject instance at 80adfa8>
>>> print re.search('}$', '{block} ')
None
>>> print re.search('}$', '{block}\n')
<re.MatchObject instance at 80adfa8>

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

\A

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

\Z

Matches only at the end of the string.

\b

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

The following example matches "class" only when it's a complete word; it won't match when it's contained inside another word.

>>> p = re.compile(r'\bclass\b')
>>> print p.search('no class at all')
<re.MatchObject instance at 80c8f28>
>>> print p.search('the declassified algorithm')
None
>>> print p.search('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 regular expression sequences. In Python's string literals, "\b" is the backspace character, ASCII value 8. If you're not using raw strings, then Python will convert 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 p.search('no class at all')
None
>>> print p.search('\b' + 'class' + '\b')  
<re.MatchObject instance at 80c3ee0>

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

\B

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

4.2 Grouping

Frequently you need to obtain more information than just whether the RE matched or not. Regular expressions are often used to dissect strings by writing a RE divided 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 ":". This can be handled by writing a regular expression which matches an entire header line, and has one group which matches the header name, and another group which matches the header's value.

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

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

Groups indicated with "(", ")" also capture the starting and ending index of the text that they match; this can be retrieved by passing an argument to group(), start(), end(), and span(). Groups are numbered starting with 0. Group 0 is always present; it's the whole RE, so MatchObject methods all have group 0 as their default argument. Later we'll see how to express groups that don't capture the span of text that they match.

>>> p = re.compile('(a)b')
>>> m = p.match('ab')
>>> m.group()
'ab'
>>> m.group(0)
'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, going from left to right.

>>> p = re.compile('(a(b)c)d')
>>> m = p.match('abcd')
>>> m.group(0)
'abcd'
>>> m.group(1)
'abc'
>>> m.group(2)
'b'

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

  
>>> m.group(2,1,2)
('b', 'abc', 'b')

The groups() method returns a tuple containing the strings for all the subgroups, 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 earlier capturing group must also be found at the current location in the string. For example, \1 will succeed if the exact contents of group 1 can be found at the current position, and fails otherwise. Remember that Python's string literals also use a backslash followed by numbers to allow including arbitrary characters in a string, so be sure to use a raw string when incorporating backreferences in a RE.

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

>>> p = re.compile(r'(\b\w+)\s+\1')
>>> p.search('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 soon find out that they're very useful when performing string substitutions.

4.3 Non-capturing and Named Groups

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

Perl 5 added several additional features to standard regular expressions, and the Python re module supports most of them. It would have been difficult to choose new single-keystroke metacharacters or new special sequences beginning with "\" to represent the new features without making Perl's regular expressions confusingly different from standard REs. If you chose "&" as a new metacharacter, for example, old expressions would be assuming that "&" was a regular character and wouldn't have escaped it by writing \& or [&].

The solution chosen by the Perl developers was to use (?...) as the extension syntax. "?" immediately after a parenthesis was a syntax error because the "?" would have nothing to repeat, so this didn't introduce any compatibility problems. The characters immediately after the "?" indicate what extension is being used, so (?=foo) is one thing (a positive lookahead assertion) and (?:foo) is something else (a non-capturing group containing the subexpression foo).

Python adds an extension syntax to Perl's extension syntax. If the first character after the question mark is a "P", you know that it's an extension that's specific to Python. Currently there are two such extensions: (?P<name>...) defines a named group, and (?P=name) is a backreference to a named group. If future versions of Perl 5 add similar features using a different syntax, the re module will be changed to support the new syntax, while preserving the Python-specific syntax for compatibility's sake.

Now that we've looked at the general extension syntax, we can return to the features that simplify working with groups in complex REs. Since groups are numbered from left to right and a complex expression may use many groups, it can become difficult to keep track of the correct numbering, and modifying such a complex RE is annoying. Insert a new group near the beginning, and you change the numbers of everything that follows it.

First, sometimes you'll want to use a group to collect a part of a regular expression, but aren't interested in retrieving the group's contents. You can make this fact explicit by using a non-capturing group: (?:...), where you can put any other regular expression inside the parentheses.

>>> 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 group matched, a non-capturing group behaves exactly the same as a capturing group; you can put anything inside it, repeat it with a repetition metacharacter such as "*", and nest it within other groups (capturing or non-capturing). (?:...) is particularly useful when modifying an existing group, since you can add new groups without changing how all the other groups are numbered. It should be mentioned that there's no performance difference in searching between capturing and non-capturing groups; neither form is any faster than the other.

The second, and more significant, feature is named groups; instead of referring to them by numbers, 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. Except for associating a name with a group, named groups also behave identically to capturing groups. The MatchObject methods that deal with capturing groups all accept either integers, to refer to groups by number, or a string containing the group name. Named groups are still given numbers, so you can retrieve information about a group in two ways:

>>> p = re.compile(r'(?P<word>\b\w+\b)')
>>> m = p.search( '(((( Lots of punctuation )))' )
>>> m.group('word')
'Lots'
>>> m.group(1)
'Lots'

Named groups are handy because they let you use easily-remembered names, instead of having to remember numbers. Here's an example RE from the imaplib module:

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 m.group('zonem'), instead of having to remember to retrieve group 9.

Since the syntax for backreferences, in an expression like (...)\1, refers to the number of the group there's naturally a variant that uses the group name instead of the number. This is also a Python extension: (?P=name) indicates that the contents of the group called name should again be found at the current point. The regular expression for finding doubled words, (\b\w+)\s+\1 can also be written as (?P<word>\b\w+)\s+(?P=word):

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

4.4 Lookahead Assertions

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

(?=...)

Positive lookahead assertion. This succeeds if the contained regular expression, represented here by ..., successfully matches at the current location, and fails otherwise. But, once the contained expression has been tried, the matching engine doesn't advance at all; the rest of the pattern is tried 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 position in the string.

An example will help make this concrete by demonstrating a case where a lookahead is useful. Consider a simple pattern to match a filename and split it apart into a base name 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 a metacharacter; I've put it inside a character class. Also notice the trailing $; this is added to ensure that all the rest of the string must be included in the extension. This regular expression matches "foo.bar" and "autoexec.bat" and "sendmail.cf" and "printers.conf".

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

.*[.][^b].*$

The first attempt above tries to exclude "bat" by requiring that the first character of the extension is not a "b". This is wrong, because the pattern also doesn't match "foo.bar".

.*[.]([^b]..|.[^a].|..[^t])$

The expression gets messier when you try to patch up the first solution by requiring one of the following cases to match: the first character of the extension isn't "b"; the second character isn't "a"; or the third character isn't "t". This accepts "foo.bar" and rejects "autoexec.bat", but it requires a three-letter extension and won't accept a filename with a two-letter extension such as "sendmail.cf". We'll complicate the pattern again in an effort to fix it.

.*[.]([^b].?.?|.[^a]?.?|..?[^t]?)$

In the third attempt, the second and third letters are all made optional in order to allow matching extensions shorter than three characters, such as "sendmail.cf".

The pattern's getting really complicated now, which makes it hard to read and understand. Worse, if the problem changes and you want to exclude both "bat" and "exe" as extensions, the pattern would get even more complicated and confusing.

A negative lookahead cuts through all this:

.*[.](?!bat$).*$

The lookahead means: if the expression bat doesn't match at this point, try the rest of the pattern; if bat$ does match, the whole pattern will fail. The trailing $ is required to ensure that something like "sample.batch", where the extension only starts with "bat", will be allowed.

Excluding another filename extension is now easy; simply add it as an alternative inside the assertion. The following pattern excludes filenames that end in either "bat" or "exe":

.*[.](?!bat$|exe$).*$

5 Modifying Strings

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

Method/Attribute	Purpose
split()	Split the string into a list, splitting it wherever the RE matches
sub()	Find all substrings where the RE matches, and replace them with a different string
subn()	Does the same thing as sub(), but returns the new string and the number of replacements

5.1 Splitting Strings

The split() method of a RegexObject splits a string apart wherever the RE matches, returning a list of the pieces. It's similar to the split() method of strings but provides much more generality in the delimiters that you can split by; split() only supports splitting by whitespace or by a fixed string. As you'd expect, there's a module-level re.split() function, too.

split(

string [, maxsplit = 0])

Split string by the matches of the regular expression. If capturing parentheses are used in the RE, then their contents will also be returned as part of the resulting list. If maxsplit is nonzero, at most maxsplit splits are 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 the remainder of the string is returned as the final element of the list. In the following 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, but also need to know what the delimiter was. If capturing parentheses are used in the RE, then their values are also returned as part of the list. Compare the following 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 first argument, but is otherwise the same.

>>> 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.']

5.2 Search and Replace

Another common task is to find all the matches for a pattern, and replace them with 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-overlapping occurrences of the RE in string by the replacement replacement. If the pattern isn't found, string is returned unchanged.

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

Here's a simple example of using the sub() method. It replaces colour names 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 the new 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 match.

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

If replacement 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 carriage return, and so forth. Unknown escapes such as "\j" are left alone. Backreferences, such as "\6", are replaced with the substring matched by the corresponding group in the RE. This lets you incorporate portions of the original 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 the group named "name", and "\g<number>" uses the corresponding group number. "\g<2>" is therefore equivalent to "\2", but isn't ambiguous in a replacement string 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 following substitutions are all equivalent, but use all three 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. If replacement is a function, the function is called for every non-overlapping occurrence of pattern. On each call, the function is passed a MatchObject argument for the match and can use this information to compute the desired replacement string and return it.

In the following example, the replacement function translates decimals into hexadecimal:

>>> def hexrepl( match ):
...     "Return the hex string for a decimal number"
...     value = int( match.group() )
...     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 as the first argument. The pattern may be a string or a RegexObject; if you need to specify regular expression flags, you must either use a RegexObject as the first parameter, or use embedded modifiers in the pattern, e.g. sub("(?i)b+", "x", "bbbb BBBB") returns 'x x'.

6 Common Problems

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

6.1 Use String Methods

Sometimes using the re module is a mistake. If you're matching a fixed string, or a single character class, and you're not using any re features such as the IGNORECASE flag, then the full power of regular expressions may not be required. Strings have several methods for performing operations with fixed strings and they're usually much faster, because the implementation is a single 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; for example, you might replace "word"with "deed". re.sub() seems like the function to use for this, but consider the replace() method. Note that replace() will also replace "word" inside words, turning "swordfish" into "sdeedfish", but the naïve RE word would have done that, too. (To avoid performing the substitution on parts of words, the pattern would have to be \bword\b, in order to require that "word" have a word boundary on either side. This takes the job beyond replace's abilities.)

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

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

6.2 match() versus search()

The match() function only checks if the RE matches at the beginning of the string while search() will scan forward through the string for a match. It's important to keep this distinction in mind. Remember, match() will only report a successful match which will start at 0; if the match wouldn't start 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 re.search('super', 'superstition').span()
(0, 5)
>>> print re.search('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 re.search() instead. The regular expression compiler does some analysis of REs in order to speed up the process of looking for a match. One such analysis figures out what the first character of a match must be; for example, a pattern starting with Crow must match starting with a "C". The analysis lets the engine quickly scan through the string looking for the starting character, only trying the full match if a "C" is found.

Adding .* defeats this optimization, requiring scanning to the end of the string and then backtracking to find a match for the rest of the RE. Use re.search() instead.

6.3 Greedy versus Non-Greedy

When repeating a regular expression, as in a*, the resulting action is to consume as much of the pattern as possible. This fact often bites you when you're trying to match a pair of balanced delimiters, such as the angle brackets surrounding an HTML tag. The naïve pattern for matching a single HTML tag doesn'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 of the string. There's still more left in the RE, though, and the > can't match at the end of the string, so the regular expression engine has to backtrack character by character until it finds a match for the >. The final 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 qualifiers *?, +?, ??, or {m,n}?, which match as little text as possible. In the above example, the ">" is tried immediately after the first "<" matches, and when 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 special cases that will break the obvious regular expression; by the time you've written a regular expression that handles all of the possible cases, the patterns will be very complicated. Use an HTML or XML parser module for such tasks.)

6.4 Not Using re.VERBOSE

By now you've probably noticed that regular expressions are a very compact notation, but they're not terribly readable. REs of moderate complexity can become 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 regular expression can be helpful, because it allows you to format the regular expression more clearly.

The re.VERBOSE flag has several effects. Whitespace in the regular expression that isn't inside a character class is ignored. This means that an expression 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. In addition, you can also put comments inside a RE; comments extend from a "#" character to the next newline. When used with triple-quoted strings, this enables 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*$")

7 Feedback

Regular expressions are a complicated topic. Did this document help you understand them? Were there parts that were unclear, or Problems you encountered that weren't covered here? If so, please send suggestions for improvements to the author.

The most complete book on regular expressions is almost certainly Jeffrey Friedl'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 a reference for programming in Python. (The first edition covered Python's now-obsolete regex module, which won't help you much.) Consider checking it out from your library.

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Regular Expression HOWTO

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Release 0.05.