Regular Expression HOWTO
************************

Author:
   A.M. Kuchling <amk@amk.ca>


Abstract
^^^^^^^^

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


Introduction
============

The "re" module was added in Python 1.5, and provides Perl-style
regular expression patterns.  Earlier versions of Python came with the
"regex" module, which provided Emacs-style patterns.  The "regex"
module was removed completely in Python 2.5.

Regular expressions (called REs, or regexes, or regex patterns) are
essentially a tiny, highly specialized programming language embedded
inside Python and made available through the "re" module. Using this
little language, you specify the rules for the set of possible strings
that you want to match; this set might contain English sentences, or
e-mail addresses, or TeX commands, or anything you like.  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 also
use REs to modify a string or to split it apart in various ways.

Regular expression patterns are compiled into a series of bytecodes
which are then executed by a matching engine written in C.  For
advanced use, it may be necessary 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 a good
understanding of the matching engine’s internals.

The regular expression language is relatively small and restricted, so
not all possible string processing tasks can be done using regular
expressions.  There are also tasks that *can* be done with regular
expressions, but the expressions turn out to be very complicated.  In
these cases, you may be better off writing Python code to do the
processing; while Python code will be slower than an elaborate regular
expression, it will also probably be more understandable.


Simple Patterns
===============

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

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


Matching Characters
-------------------

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

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

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

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

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

Metacharacters are not active inside classes.  For example, "[akm$]"
will match any of the characters "'a'", "'k'", "'m'", or "'$'"; "'$'"
is usually a metacharacter, but inside a character class it’s stripped
of its special nature.

You can match the characters not listed within the class by
*complementing* the set.  This is indicated by including a "'^'" as
the first character of the class. For example, "[^5]" will match any
character except "'5'".  If the caret 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 Python string literals, the backslash can be followed by various
characters to signal various special sequences.  It’s also used to
escape all the metacharacters so you 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 special meaning: "\[" or "\\".

Some of the special sequences beginning with "'\'" represent
predefined sets of characters that are often useful, such as the set
of digits, the set of letters, or the set of anything that isn’t
whitespace.  The following predefined special sequences are a subset
of those available. The equivalent classes are for byte string
patterns. For a complete list of sequences and expanded class
definitions for Unicode string patterns, see the last part of Regular
Expression Syntax.

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

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

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

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

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

"\W"
   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 a newline character, and there’s an alternate mode
("re.DOTALL") where it will match even a newline.  "'.'" is often used
where you want to match “any character”.


Repeating Things
----------------

Being able to match varying sets of characters is the first thing
regular expressions can do that isn’t already possible with the
methods available on strings.  However, if that was the only
additional capability of regexes, they wouldn’t be much of an advance.
Another capability is that you can specify that portions 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 the previous 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.  The RE engine has various
internal limitations stemming from the size of C’s "int" type that
will prevent it from matching over 2 billion "a" characters; you
probably don’t have enough memory to construct a string that large, so
you shouldn’t run into that limit.

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

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

+--------+-------------+-----------------------------------+
| Step   | Matched     | Explanation                       |
|========|=============|===================================|
| 1      | "a"         | The "a" in the RE matches.        |
+--------+-------------+-----------------------------------+
| 2      | "abcbd"     | The engine matches "[bcd]*",      |
|        |             | going as far as it can, which is  |
|        |             | to the end of the string.         |
+--------+-------------+-----------------------------------+
| 3      | *Failure*   | The engine tries to match "b",    |
|        |             | but the current position is at    |
|        |             | the end of the string, so it      |
|        |             | fails.                            |
+--------+-------------+-----------------------------------+
| 4      | "abcb"      | Back up, so that  "[bcd]*"        |
|        |             | matches one less character.       |
+--------+-------------+-----------------------------------+
| 5      | *Failure*   | Try "b" again, but the current    |
|        |             | position is at the last           |
|        |             | character, which is a "'d'".      |
+--------+-------------+-----------------------------------+
| 6      | "abc"       | Back up again, so that "[bcd]*"   |
|        |             | is only matching "bc".            |
+--------+-------------+-----------------------------------+
| 6      | "abcb"      | Try "b" again.  This time the     |
|        |             | character at the current position |
|        |             | is "'b'", so it succeeds.         |
+--------+-------------+-----------------------------------+

The end of the RE has now been reached, and it has matched "abcb".
This demonstrates how the matching engine goes as far as it can at
first, and if no match is found it will then progressively back up and
retry the rest of the RE again and again.  It will back up until it
has tried zero matches for "[bcd]*", and if that subsequently fails,
the engine will conclude that the string doesn’t match the RE at all.

Another repeating metacharacter is "+", which matches one or more
times.  Pay careful attention to the difference between "*" and "+";
"*" matches *zero* 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’t match "ct".

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

The most complicated repeated qualifier is "{m,n}", where *m* and *n*
are decimal integers.  This qualifier 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", which has four.

You can omit either *m* or *n*; in that case, a reasonable value is
assumed for the missing value.  Omitting *m* is interpreted as a lower
limit of 0, while omitting *n* results in an upper bound of infinity —
actually, the upper bound is the 2-billion limit mentioned earlier,
but that might as well be infinity.

Readers of a reductionist bent may notice that the three other
qualifiers can all 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 easier to read.


Using Regular Expressions
=========================

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


Compiling Regular Expressions
-----------------------------

Regular expressions are compiled into pattern objects, which have
methods for various operations such as searching for pattern matches
or performing string substitutions.

   >>> import re
   >>> p = re.compile('ab*')
   >>> p  
   <_sre.SRE_Pattern object at 0x...>

"re.compile()" also accepts an optional *flags* argument, used to
enable various special features and syntax variations.  We’ll go over
the available settings 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
strings because regular expressions aren’t part of the core Python
language, and no special syntax was created for expressing them.
(There are applications that don’t need REs at all, so there’s no need
to bloat the language specification by including them.) Instead, the
"re" module is simply a C extension module included with Python, just
like the "socket" or "zlib" modules.

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


The Backslash Plague
--------------------

As stated earlier, regular expressions use the backslash character
("'\'") to indicate special forms or to allow special characters to be
used without invoking their special meaning. This conflicts with
Python’s usage of the same character for the same purpose in string
literals.

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

+---------------------+--------------------------------------------+
| Characters          | Stage                                      |
|=====================|============================================|
| "\section"          | Text string to be matched                  |
+---------------------+--------------------------------------------+
| "\\section"         | Escaped backslash for "re.compile()"       |
+---------------------+--------------------------------------------+
| ""\\\\section""     | Escaped backslashes for a string literal   |
+---------------------+--------------------------------------------+

In short, to match a literal backslash, one has to write "'\\\\'" as
the RE string, because the regular expression must be "\\", and each
backslash must be expressed as "\\" inside a regular Python string
literal.  In REs that feature backslashes repeatedly, this leads to
lots of repeated backslashes and makes 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. Regular expressions will often be written
in Python code using this raw string notation.

+---------------------+--------------------+
| Regular String      | Raw string         |
|=====================|====================|
| ""ab*""             | "r"ab*""           |
+---------------------+--------------------+
| ""\\\\section""     | "r"\\section""     |
+---------------------+--------------------+
| ""\\w+\\s+\\1""     | "r"\w+\s+\1""      |
+---------------------+--------------------+


Performing Matches
------------------

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

+--------------------+-------------------------------------------------+
| Method/Attribute   | Purpose                                         |
|====================|=================================================|
| "match()"          | Determine if the RE matches at the beginning of |
|                    | the string.                                     |
+--------------------+-------------------------------------------------+
| "search()"         | Scan through a string, looking for any location |
|                    | where this RE matches.                          |
+--------------------+-------------------------------------------------+
| "findall()"        | Find all substrings where the RE matches, and   |
|                    | returns them as a list.                         |
+--------------------+-------------------------------------------------+
| "finditer()"       | Find all substrings where the RE matches, and   |
|                    | returns them as an *iterator*.                  |
+--------------------+-------------------------------------------------+

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

You can learn about this by interactively experimenting with the "re"
module.  If you have Tkinter available, you may also want to look at
Tools/scripts/redemo.py, a demonstration program included with the
Python distribution.  It allows you to enter REs and strings, and
displays whether the RE matches or fails. "redemo.py" can be quite
useful when trying to debug a complicated RE.  Phil Schwartz’s Kodos
is also an interactive tool for developing and testing RE patterns.

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

   Python 2.2.2 (#1, Feb 10 2003, 12:57:01)
   >>> import re
   >>> p = re.compile('[a-z]+')
   >>> p  #doctest: +ELLIPSIS
   <_sre.SRE_Pattern object at 0x...>

Now, you can try matching various strings against the RE "[a-z]+".  An
empty string shouldn’t match at all, since "+" means ‘one or more
repetitions’. "match()" should return "None" in this case, which will
cause the interpreter to print no output.  You can explicitly print
the result of "match()" 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 this case, "match()" will return a match object, so you should
store the result in a variable for later use.

   >>> m = p.match('tempo')
   >>> m  
   <_sre.SRE_Match object at 0x...>

Now you can query the match object for information about the matching
string.  match object instances also have several methods and
attributes; the most important ones are:

+--------------------+----------------------------------------------+
| Method/Attribute   | Purpose                                      |
|====================|==============================================|
| "group()"          | Return the string matched by the RE          |
+--------------------+----------------------------------------------+
| "start()"          | Return the starting position of the match    |
+--------------------+----------------------------------------------+
| "end()"            | Return the ending position of the match      |
+--------------------+----------------------------------------------+
| "span()"           | Return a tuple containing the (start, end)   |
|                    | positions  of the match                      |
+--------------------+----------------------------------------------+

Trying these methods will soon clarify their meaning:

   >>> m.group()
   '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 patterns scans through the string, so  the match may not start at
zero in that case.

   >>> print p.match('::: message')
   None
   >>> m = p.search('::: message'); print m  
   <_sre.SRE_Match object at 0x...>
   >>> m.group()
   'message'
   >>> m.span()
   (4, 11)

In actual programs, the most common style is to store the match object
in a variable, and then check if it was "None".  This usually looks
like:

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

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

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

"findall()" has to create the entire list before it can be returned as
the result.  The "finditer()" method returns a sequence of match
object instances as an *iterator*. [1]

   >>> 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; the
"re" module also provides top-level functions called "match()",
"search()", "findall()", "sub()", and so forth.  These functions take
the same arguments as the corresponding pattern method, with the RE
string added as the first argument, and still return either "None" or
a match 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')  
   <_sre.SRE_Match object at 0x...>

Under the hood, these functions simply create a pattern object for you
and call the appropriate method on it.  They also store the compiled
object in a cache, so future calls using the same RE are faster.

Should you use these module-level functions, or should you get the
pattern and call its methods yourself?  That choice depends on how
frequently the RE will be used, and on your personal coding style.  If
the RE is being used at only one point in the code, then the module
functions are probably more convenient.  If a program contains a lot
of regular expressions, or re-uses the same ones in several locations,
then it might be worthwhile to collect all the definitions in one
place, in a section of code that compiles all the REs ahead of time.
To take an example from the standard library, here’s an extract from
the deprecated "xmllib" module:

   ref = re.compile( ... )
   entityref = re.compile( ... )
   charref = re.compile( ... )
   starttagopen = re.compile( ... )

I generally prefer to work with the compiled object, even for one-time
uses, but few people will be as much of a purist about this as I am.


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 as "IGNORECASE" and a short, one-letter form
such as "I".  (If you’re familiar with Perl’s pattern modifiers, the
one-letter forms use the same letters; 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" sets both the "I" and "M" flags, for
example.

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

+-----------------------------------+----------------------------------------------+
| Flag                              | Meaning                                      |
|===================================|==============================================|
| "DOTALL", "S"                     | Make "." match any character, including      |
|                                   | newlines                                     |
+-----------------------------------+----------------------------------------------+
| "IGNORECASE", "I"                 | Do case-insensitive matches                  |
+-----------------------------------+----------------------------------------------+
| "LOCALE", "L"                     | Do a locale-aware match                      |
+-----------------------------------+----------------------------------------------+
| "MULTILINE", "M"                  | Multi-line matching, affecting "^" and "$"   |
+-----------------------------------+----------------------------------------------+
| "VERBOSE", "X"                    | Enable verbose REs, which can be organized   |
|                                   | more cleanly and understandably.             |
+-----------------------------------+----------------------------------------------+
| "UNICODE", "U"                    | Makes several escapes like "\w", "\b", "\s"  |
|                                   | and "\d" dependent on the Unicode character  |
|                                   | database.                                    |
+-----------------------------------+----------------------------------------------+

I
IGNORECASE

   Perform case-insensitive matching; character class and literal
   strings will match letters by ignoring case.  For example, "[A-Z]"
   will match lowercase letters, too, and "Spam" will match "Spam",
   "spam", or "spAM". This lowercasing doesn’t take the current locale
   into account; it will if you also set the "LOCALE" flag.

L
LOCALE

   Make "\w", "\W", "\b", and "\B", dependent on the current locale.

   Locales are a feature of the C library intended to help in writing
   programs that take account of language differences.  For example,
   if you’re processing French text, you’d want to be able to write
   "\w+" to match words, but "\w" only matches the character class
   "[A-Za-z]"; it won’t match "'é'" or "'ç'".  If your system is
   configured properly and a French locale is selected, certain C
   functions will tell the program that "'é'" should also be
   considered a letter. Setting the "LOCALE" flag when compiling a
   regular expression will cause the resulting compiled object to use
   these C functions for "\w"; this is slower, but also enables "\w+"
   to match French words as you’d expect.

M
MULTILINE

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

   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.

U
UNICODE

   Make "\w", "\W", "\b", "\B", "\d", "\D", "\s" and "\S" dependent on
   the Unicode character properties database.

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.  This flag also lets you 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[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 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".


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.


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')  
      <_sre.SRE_Match object at 0x...>
      >>> 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('}$', '{block}')  
      <_sre.SRE_Match object at 0x...>
      >>> print re.search('}$', '{block} ')
      None
      >>> print re.search('}$', '{block}\n')  
      <_sre.SRE_Match object at 0x...>

   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,
   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')  
      <_sre.SRE_Match object at 0x...>
      >>> 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')  
      <_sre.SRE_Match object at 0x...>

   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.


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 "':'", like
this:

   From: author@example.com
   User-Agent: Thunderbird 1.5.0.9 (X11/20061227)
   MIME-Version: 1.0
   To: editor@example.com

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, and 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 match object 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\b')
   >>> 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.


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.  Modifying such a complex RE is annoying, too:
insert a new group near the beginning and you change the numbers of
everything that follows it.

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 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
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 pattern, 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.

A 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.  Named
groups also behave exactly like capturing groups, and additionally
associate a name with a group.  The match object methods that deal
with capturing groups all accept either integers that refer to the
group by number or strings that contain the desired group’s 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.

The syntax for backreferences in an expression such as "(...)\1"
refers to the number of the group.  There’s naturally a variant that
uses the group name instead of the number. This is another Python
extension: "(?P=name)" indicates that the contents of the group called
*name* should again be matched at the current 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')
   >>> p.search('Paris in the the spring').group()
   'the the'


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.

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 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 confusion:

".*[.](?!bat$)[^.]*$"  The negative 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.  The "[^.]*" makes
sure that the pattern works when there are multiple dots in the
filename.

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$|exe$)[^.]*$"


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:

+--------------------+-------------------------------------------------+
| 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   |
+--------------------+-------------------------------------------------+


Splitting Strings
-----------------

The "split()" method of a pattern 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.']


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 match object 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 provided as an object or as
a string; if you need to specify regular expression flags, you must
either use a pattern object as the first parameter, or use embedded
modifiers in the pattern 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 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.


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  naive 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 than
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.


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.


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 naive 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.)


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*$")


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-removed "regex" module, which
won’t help you much.)  Consider checking it out from your library.

-[ Footnotes ]-

[1] Introduced in Python 2.2.2.
