re — Regular Expressions

Purpose:Searching within and changing text using formal patterns.

Regular expressions are text matching patterns described with a formal syntax. The patterns are interpreted as a set of instructions, which are then executed with a string as input to produce a matching subset or modified version of the original. The term “regular expressions” is frequently shortened to as “regex” or “regexp” in conversation. Expressions can include literal text matching, repetition, pattern-composition, branching, and other sophisticated rules. A large number of parsing problems are easier to solve with a regular expression than by creating a special-purpose lexer and parser.

Regular expressions are typically used in applications that involve a lot of text processing. For example, they are commonly used as search patterns in text editing programs used by developers, including vi, emacs, and modern IDEs. They are also an integral part of Unix command line utilities such as sed, grep, and awk. Many programming languages include support for regular expressions in the language syntax (Perl, Ruby, Awk, and Tcl). Other languages, such as C, C++, and Python, support regular expressions through extension libraries.

There are multiple open source implementations of regular expressions, each sharing a common core syntax but with different extensions or modifications to their advanced features. The syntax used in Python’s re module is based on the syntax used for regular expressions in Perl, with a few Python-specific enhancements.

Note

Although the formal definition of “regular expression” is limited to expressions that describe regular languages, some of the extensions supported by re go beyond describing regular languages. The term “regular expression” is used here in a more general sense to mean any expression that can be evaluated by Python’s re module.

Finding Patterns in Text

The most common use for re is to search for patterns in text. The search() function takes the pattern and text to scan, and returns a Match object when the pattern is found. If the pattern is not found, search() returns None.

Each Match object holds information about the nature of the match, including the original input string, the regular expression used, and the location within the original string where the pattern occurs.

re_simple_match.py
import re

pattern = 'this'
text = 'Does this text match the pattern?'

match = re.search(pattern, text)

s = match.start()
e = match.end()

print('Found "{}"\nin "{}"\nfrom {} to {} ("{}")'.format(
    match.re.pattern, match.string, s, e, text[s:e]))

The start() and end() methods give the indexes into the string showing where the text matched by the pattern occurs.

$ python3 re_simple_match.py

Found "this"
in "Does this text match the pattern?"
from 5 to 9 ("this")

Compiling Expressions

re includes module-level functions for working with regular expressions as text strings, but it is more efficient to compile the expressions a program uses frequently. The compile() function converts an expression string into a RegexObject.

re_simple_compiled.py
import re

# Precompile the patterns
regexes = [
    re.compile(p)
    for p in ['this', 'that']
]
text = 'Does this text match the pattern?'

print('Text: {!r}\n'.format(text))

for regex in regexes:
    print('Seeking "{}" ->'.format(regex.pattern),
          end=' ')

    if regex.search(text):
        print('match!')
    else:
        print('no match')

The module-level functions maintain a cache of compiled expressions, but the size of the cache is limited and using compiled expressions directly avoids the cache lookup overhead. Another advantage of using compiled expressions is that by pre-compiling all of the expressions when the module is loaded, the compilation work is shifted to application start time, instead of a point where the program may be responding to a user action.

$ python3 re_simple_compiled.py

Text: 'Does this text match the pattern?'

Seeking "this" -> match!
Seeking "that" -> no match

Multiple Matches

So far, the example patterns have all used search() to look for single instances of literal text strings. The findall() function returns all of the substrings of the input that match the pattern without overlapping.

re_findall.py
import re

text = 'abbaaabbbbaaaaa'

pattern = 'ab'

for match in re.findall(pattern, text):
    print('Found {!r}'.format(match))

There are two instances of ab in the input string.

$ python3 re_findall.py

Found 'ab'
Found 'ab'

finditer() returns an iterator that produces Match instances instead of the strings returned by findall().

re_finditer.py
import re

text = 'abbaaabbbbaaaaa'

pattern = 'ab'

for match in re.finditer(pattern, text):
    s = match.start()
    e = match.end()
    print('Found {!r} at {:d}:{:d}'.format(
        text[s:e], s, e))

This example finds the same two occurrences of ab, and the Match instance shows where they are in the original input.

$ python3 re_finditer.py

Found 'ab' at 0:2
Found 'ab' at 5:7

Pattern Syntax

Regular expressions support more powerful patterns than simple literal text strings. Patterns can repeat, can be anchored to different logical locations within the input, and can be expressed in compact forms that do not require every literal character be present in the pattern. All of these features are used by combining literal text values with meta-characters that are part of the regular expression pattern syntax implemented by re.

re_test_patterns.py
import re


def test_patterns(text, patterns):
    """Given source text and a list of patterns, look for
    matches for each pattern within the text and print
    them to stdout.
    """
    # Look for each pattern in the text and print the results
    for pattern, desc in patterns:
        print("'{}' ({})\n".format(pattern, desc))
        print("  '{}'".format(text))
        for match in re.finditer(pattern, text):
            s = match.start()
            e = match.end()
            substr = text[s:e]
            n_backslashes = text[:s].count('\\')
            prefix = '.' * (s + n_backslashes)
            print("  {}'{}'".format(prefix, substr))
        print()
    return

if __name__ == '__main__':
    test_patterns('abbaaabbbbaaaaa',
                  [('ab', "'a' followed by 'b'"),
                   ])

The following examples will use test_patterns() to explore how variations in patterns change the way they match the same input text. The output shows the input text and the substring range from each portion of the input that matches the pattern.

$ python3 re_test_patterns.py

'ab' ('a' followed by 'b')

  'abbaaabbbbaaaaa'
  'ab'
  .....'ab'

Repetition

There are five ways to express repetition in a pattern. A pattern followed by the meta-character * is repeated zero or more times (allowing a pattern to repeat zero times means it does not need to appear at all to match). Replace the * with + and the pattern must appear at least once. Using ? means the pattern appears zero or one time. For a specific number of occurrences, use {m} after the pattern, where m is the number of times the pattern should repeat. And finally, to allow a variable but limited number of repetitions, use {m,n} where m is the minimum number of repetitions and n is the maximum. Leaving out n ({m,}) means the value appears at least m times, with no maximum.

re_repetition.py
from re_test_patterns import test_patterns

test_patterns(
    'abbaabbba',
    [('ab*', 'a followed by zero or more b'),
     ('ab+', 'a followed by one or more b'),
     ('ab?', 'a followed by zero or one b'),
     ('ab{3}', 'a followed by three b'),
     ('ab{2,3}', 'a followed by two to three b')],
)

There are more matches for ab* and ab? than ab+.

$ python3 re_repetition.py

'ab*' (a followed by zero or more b)

  'abbaabbba'
  'abb'
  ...'a'
  ....'abbb'
  ........'a'

'ab+' (a followed by one or more b)

  'abbaabbba'
  'abb'
  ....'abbb'

'ab?' (a followed by zero or one b)

  'abbaabbba'
  'ab'
  ...'a'
  ....'ab'
  ........'a'

'ab{3}' (a followed by three b)

  'abbaabbba'
  ....'abbb'

'ab{2,3}' (a followed by two to three b)

  'abbaabbba'
  'abb'
  ....'abbb'

Normally, when processing a repetition instruction, re will consume as much of the input as possible while matching the pattern. This so-called greedy behavior may result in fewer individual matches, or the matches may include more of the input text than intended. Greediness can be turned off by following the repetition instruction with ?.

re_repetition_non_greedy.py
from re_test_patterns import test_patterns

test_patterns(
    'abbaabbba',
    [('ab*?', 'a followed by zero or more b'),
     ('ab+?', 'a followed by one or more b'),
     ('ab??', 'a followed by zero or one b'),
     ('ab{3}?', 'a followed by three b'),
     ('ab{2,3}?', 'a followed by two to three b')],
)

Disabling greedy consumption of the input for any of the patterns where zero occurrences of b are allowed means the matched substring does not include any b characters.

$ python3 re_repetition_non_greedy.py

'ab*?' (a followed by zero or more b)

  'abbaabbba'
  'a'
  ...'a'
  ....'a'
  ........'a'

'ab+?' (a followed by one or more b)

  'abbaabbba'
  'ab'
  ....'ab'

'ab??' (a followed by zero or one b)

  'abbaabbba'
  'a'
  ...'a'
  ....'a'
  ........'a'

'ab{3}?' (a followed by three b)

  'abbaabbba'
  ....'abbb'

'ab{2,3}?' (a followed by two to three b)

  'abbaabbba'
  'abb'
  ....'abb'

Character Sets

A character set is a group of characters, any one of which can match at that point in the pattern. For example, [ab] would match either a or b.

re_charset.py
from re_test_patterns import test_patterns

test_patterns(
    'abbaabbba',
    [('[ab]', 'either a or b'),
     ('a[ab]+', 'a followed by 1 or more a or b'),
     ('a[ab]+?', 'a followed by 1 or more a or b, not greedy')],
)

The greedy form of the expression (a[ab]+) consumes the entire string because the first letter is a and every subsequent character is either a or b.

$ python3 re_charset.py

'[ab]' (either a or b)

  'abbaabbba'
  'a'
  .'b'
  ..'b'
  ...'a'
  ....'a'
  .....'b'
  ......'b'
  .......'b'
  ........'a'

'a[ab]+' (a followed by 1 or more a or b)

  'abbaabbba'
  'abbaabbba'

'a[ab]+?' (a followed by 1 or more a or b, not greedy)

  'abbaabbba'
  'ab'
  ...'aa'

A character set can also be used to exclude specific characters. The carat (^) means to look for characters not in the set following.

re_charset_exclude.py
from re_test_patterns import test_patterns

test_patterns(
    'This is some text -- with punctuation.',
    [('[^-. ]+', 'sequences without -, ., or space')],
)

This pattern finds all of the substrings that do not contain the characters -, ., or a space.

$ python3 re_charset_exclude.py

'[^-. ]+' (sequences without -, ., or space)

  'This is some text -- with punctuation.'
  'This'
  .....'is'
  ........'some'
  .............'text'
  .....................'with'
  ..........................'punctuation'

As character sets grow larger, typing every character that should (or should not) match becomes tedious. A more compact format using character ranges can be used to define a character set to include all of the contiguous characters between a start and stop point.

re_charset_ranges.py
from re_test_patterns import test_patterns

test_patterns(
    'This is some text -- with punctuation.',
    [('[a-z]+', 'sequences of lowercase letters'),
     ('[A-Z]+', 'sequences of uppercase letters'),
     ('[a-zA-Z]+', 'sequences of lower- or uppercase letters'),
     ('[A-Z][a-z]+', 'one uppercase followed by lowercase')],
)

Here the range a-z includes the lower case ASCII letters, and the range A-Z includes the upper case ASCII letters. The ranges can also be combined into a single character set.

$ python3 re_charset_ranges.py

'[a-z]+' (sequences of lowercase letters)

  'This is some text -- with punctuation.'
  .'his'
  .....'is'
  ........'some'
  .............'text'
  .....................'with'
  ..........................'punctuation'

'[A-Z]+' (sequences of uppercase letters)

  'This is some text -- with punctuation.'
  'T'

'[a-zA-Z]+' (sequences of lower- or uppercase letters)

  'This is some text -- with punctuation.'
  'This'
  .....'is'
  ........'some'
  .............'text'
  .....................'with'
  ..........................'punctuation'

'[A-Z][a-z]+' (one uppercase followed by lowercase)

  'This is some text -- with punctuation.'
  'This'

As a special case of a character set the meta-character dot, or period (.), indicates that the pattern should match any single character in that position.

re_charset_dot.py
from re_test_patterns import test_patterns

test_patterns(
    'abbaabbba',
    [('a.', 'a followed by any one character'),
     ('b.', 'b followed by any one character'),
     ('a.*b', 'a followed by anything, ending in b'),
     ('a.*?b', 'a followed by anything, ending in b')],
)

Combining dot with repetition can result in very long matches, unless the non-greedy form is used.

$ python3 re_charset_dot.py

'a.' (a followed by any one character)

  'abbaabbba'
  'ab'
  ...'aa'

'b.' (b followed by any one character)

  'abbaabbba'
  .'bb'
  .....'bb'
  .......'ba'

'a.*b' (a followed by anything, ending in b)

  'abbaabbba'
  'abbaabbb'

'a.*?b' (a followed by anything, ending in b)

  'abbaabbba'
  'ab'
  ...'aab'

Escape Codes

An even more compact representation uses escape codes for several pre-defined character sets. The escape codes recognized by re are listed in the table below.

Regular Expression Escape Codes
Code Meaning
\d a digit
\D a non-digit
\s whitespace (tab, space, newline, etc.)
\S non-whitespace
\w alphanumeric
\W non-alphanumeric

Note

Escapes are indicated by prefixing the character with a backslash (\). Unfortunately, a backslash must itself be escaped in normal Python strings, and that results in expressions that are difficult to read. Using raw strings, created by prefixing the literal value with r, eliminates this problem and maintains readability.

re_escape_codes.py
from re_test_patterns import test_patterns

test_patterns(
    'A prime #1 example!',
    [(r'\d+', 'sequence of digits'),
     (r'\D+', 'sequence of nondigits'),
     (r'\s+', 'sequence of whitespace'),
     (r'\S+', 'sequence of nonwhitespace'),
     (r'\w+', 'alphanumeric characters'),
     (r'\W+', 'nonalphanumeric')],
)

These sample expressions combine escape codes with repetition to find sequences of like characters in the input string.

$ python3 re_escape_codes.py

'\d+' (sequence of digits)

  'A prime #1 example!'
  .........'1'

'\D+' (sequence of nondigits)

  'A prime #1 example!'
  'A prime #'
  ..........' example!'

'\s+' (sequence of whitespace)

  'A prime #1 example!'
  .' '
  .......' '
  ..........' '

'\S+' (sequence of nonwhitespace)

  'A prime #1 example!'
  'A'
  ..'prime'
  ........'#1'
  ...........'example!'

'\w+' (alphanumeric characters)

  'A prime #1 example!'
  'A'
  ..'prime'
  .........'1'
  ...........'example'

'\W+' (nonalphanumeric)

  'A prime #1 example!'
  .' '
  .......' #'
  ..........' '
  ..................'!'

To match the characters that are part of the regular expression syntax, escape the characters in the search pattern.

re_escape_escapes.py
from re_test_patterns import test_patterns

test_patterns(
    r'\d+ \D+ \s+',
    [(r'\\.\+', 'escape code')],
)

The pattern in this example escapes the backslash and plus characters, since as meta-characters both have special meaning in a regular expression.

$ python3 re_escape_escapes.py

'\\.\+' (escape code)

  '\d+ \D+ \s+'
  '\d+'
  .....'\D+'
  ..........'\s+'

Anchoring

In addition to describing the content of a pattern to match, the relative location can be specified in the input text where the pattern should appear by using anchoring instructions. the table below lists valid anchoring codes.

Regular Expression Anchoring Codes
Code Meaning
^ start of string, or line
$ end of string, or line
\A start of string
\Z end of string
\b empty string at the beginning or end of a word
\B empty string not at the beginning or end of a word
re_anchoring.py
from re_test_patterns import test_patterns

test_patterns(
    'This is some text -- with punctuation.',
    [(r'^\w+', 'word at start of string'),
     (r'\A\w+', 'word at start of string'),
     (r'\w+\S*$', 'word near end of string'),
     (r'\w+\S*\Z', 'word near end of string'),
     (r'\w*t\w*', 'word containing t'),
     (r'\bt\w+', 't at start of word'),
     (r'\w+t\b', 't at end of word'),
     (r'\Bt\B', 't, not start or end of word')],
)

The patterns in the example for matching words at the beginning and end of the string are different because the word at the end of the string is followed by punctuation to terminate the sentence. The pattern \w+$ would not match, since . is not considered an alphanumeric character.

$ python3 re_anchoring.py

'^\w+' (word at start of string)

  'This is some text -- with punctuation.'
  'This'

'\A\w+' (word at start of string)

  'This is some text -- with punctuation.'
  'This'

'\w+\S*$' (word near end of string)

  'This is some text -- with punctuation.'
  ..........................'punctuation.'

'\w+\S*\Z' (word near end of string)

  'This is some text -- with punctuation.'
  ..........................'punctuation.'

'\w*t\w*' (word containing t)

  'This is some text -- with punctuation.'
  .............'text'
  .....................'with'
  ..........................'punctuation'

'\bt\w+' (t at start of word)

  'This is some text -- with punctuation.'
  .............'text'

'\w+t\b' (t at end of word)

  'This is some text -- with punctuation.'
  .............'text'

'\Bt\B' (t, not start or end of word)

  'This is some text -- with punctuation.'
  .......................'t'
  ..............................'t'
  .................................'t'

Dissecting Matches with Groups

Searching for pattern matches is the basis of the powerful capabilities provided by regular expressions. Adding groups to a pattern isolates parts of the matching text, expanding those capabilities to create a parser. Groups are defined by enclosing patterns in parentheses.

re_groups.py
from re_test_patterns import test_patterns

test_patterns(
    'abbaaabbbbaaaaa',
    [('a(ab)', 'a followed by literal ab'),
     ('a(a*b*)', 'a followed by 0-n a and 0-n b'),
     ('a(ab)*', 'a followed by 0-n ab'),
     ('a(ab)+', 'a followed by 1-n ab')],
)

Any complete regular expression can be converted to a group and nested within a larger expression. All of the repetition modifiers can be applied to a group as a whole, requiring the entire group pattern to repeat.

$ python3 re_groups.py

'a(ab)' (a followed by literal ab)

  'abbaaabbbbaaaaa'
  ....'aab'

'a(a*b*)' (a followed by 0-n a and 0-n b)

  'abbaaabbbbaaaaa'
  'abb'
  ...'aaabbbb'
  ..........'aaaaa'

'a(ab)*' (a followed by 0-n ab)

  'abbaaabbbbaaaaa'
  'a'
  ...'a'
  ....'aab'
  ..........'a'
  ...........'a'
  ............'a'
  .............'a'
  ..............'a'

'a(ab)+' (a followed by 1-n ab)

  'abbaaabbbbaaaaa'
  ....'aab'

To access the substrings matched by the individual groups within a pattern, use the groups() method of the Match object.

re_groups_match.py
import re

text = 'This is some text -- with punctuation.'

print(text)
print()

patterns = [
    (r'^(\w+)', 'word at start of string'),
    (r'(\w+)\S*$', 'word at end, with optional punctuation'),
    (r'(\bt\w+)\W+(\w+)', 'word starting with t, another word'),
    (r'(\w+t)\b', 'word ending with t'),
]

for pattern, desc in patterns:
    regex = re.compile(pattern)
    match = regex.search(text)
    print("'{}' ({})\n".format(pattern, desc))
    print('  ', match.groups())
    print()

Match.groups() returns a sequence of strings in the order of the groups within the expression that matches the string.

$ python3 re_groups_match.py

This is some text -- with punctuation.

'^(\w+)' (word at start of string)

   ('This',)

'(\w+)\S*$' (word at end, with optional punctuation)

   ('punctuation',)

'(\bt\w+)\W+(\w+)' (word starting with t, another word)

   ('text', 'with')

'(\w+t)\b' (word ending with t)

   ('text',)

Ask for the match of a single group with group(). This is useful when grouping is being used to find parts of the string, but some of the parts matched by groups are not needed in the results.

re_groups_individual.py
import re

text = 'This is some text -- with punctuation.'

print('Input text            :', text)

# word starting with 't' then another word
regex = re.compile(r'(\bt\w+)\W+(\w+)')
print('Pattern               :', regex.pattern)

match = regex.search(text)
print('Entire match          :', match.group(0))
print('Word starting with "t":', match.group(1))
print('Word after "t" word   :', match.group(2))

Group 0 represents the string matched by the entire expression, and sub-groups are numbered starting with 1 in the order their left parenthesis appears in the expression.

$ python3 re_groups_individual.py

Input text            : This is some text -- with punctuation.
Pattern               : (\bt\w+)\W+(\w+)
Entire match          : text -- with
Word starting with "t": text
Word after "t" word   : with

Python extends the basic grouping syntax to add named groups. Using names to refer to groups makes it easier to modify the pattern over time, without having to also modify the code using the match results. To set the name of a group, use the syntax (?P<name>pattern).

re_groups_named.py
import re

text = 'This is some text -- with punctuation.'

print(text)
print()

patterns = [
    r'^(?P<first_word>\w+)',
    r'(?P<last_word>\w+)\S*$',
    r'(?P<t_word>\bt\w+)\W+(?P<other_word>\w+)',
    r'(?P<ends_with_t>\w+t)\b',
]

for pattern in patterns:
    regex = re.compile(pattern)
    match = regex.search(text)
    print("'{}'".format(pattern))
    print('  ', match.groups())
    print('  ', match.groupdict())
    print()

Use groupdict() to retrieve the dictionary mapping group names to substrings from the match. Named patterns are included in the ordered sequence returned by groups(), as well.

$ python3 re_groups_named.py

This is some text -- with punctuation.

'^(?P<first_word>\w+)'
   ('This',)
   {'first_word': 'This'}

'(?P<last_word>\w+)\S*$'
   ('punctuation',)
   {'last_word': 'punctuation'}

'(?P<t_word>\bt\w+)\W+(?P<other_word>\w+)'
   ('text', 'with')
   {'t_word': 'text', 'other_word': 'with'}

'(?P<ends_with_t>\w+t)\b'
   ('text',)
   {'ends_with_t': 'text'}

An updated version of test_patterns() that shows the numbered and named groups matched by a pattern will make the following examples easier to follow.

re_test_patterns_groups.py
import re


def test_patterns(text, patterns):
    """Given source text and a list of patterns, look for
    matches for each pattern within the text and print
    them to stdout.
    """
    # Look for each pattern in the text and print the results
    for pattern, desc in patterns:
        print('{!r} ({})\n'.format(pattern, desc))
        print('  {!r}'.format(text))
        for match in re.finditer(pattern, text):
            s = match.start()
            e = match.end()
            prefix = ' ' * (s)
            print(
                '  {}{!r}{} '.format(prefix,
                                     text[s:e],
                                     ' ' * (len(text) - e)),
                end=' ',
            )
            print(match.groups())
            if match.groupdict():
                print('{}{}'.format(
                    ' ' * (len(text) - s),
                    match.groupdict()),
                )
        print()
    return

Since a group is itself a complete regular expression, groups can be nested within other groups to build even more complicated expressions.

re_groups_nested.py
from re_test_patterns_groups import test_patterns

test_patterns(
    'abbaabbba',
    [(r'a((a*)(b*))', 'a followed by 0-n a and 0-n b')],
)

In this case, the group (a*) matches an empty string, so the return value from groups() includes that empty string as the matched value.

$ python3 re_groups_nested.py

'a((a*)(b*))' (a followed by 0-n a and 0-n b)

  'abbaabbba'
  'abb'        ('bb', '', 'bb')
     'aabbb'   ('abbb', 'a', 'bbb')
          'a'  ('', '', '')

Groups are also useful for specifying alternative patterns. Use the pipe symbol (|) to indicate that one pattern or another should match. Consider the placement of the pipe carefully, though. The first expression in this example matches a sequence of a followed by a sequence consisting entirely of a single letter, a or b. The second pattern matches a followed by a sequence that may include either a or b. The patterns are similar, but the resulting matches are completely different.

re_groups_alternative.py
from re_test_patterns_groups import test_patterns

test_patterns(
    'abbaabbba',
    [(r'a((a+)|(b+))', 'a then seq. of a or seq. of b'),
     (r'a((a|b)+)', 'a then seq. of [ab]')],
)

When an alternative group is not matched, but the entire pattern does match, the return value of groups() includes a None value at the point in the sequence where the alternative group should appear.

$ python3 re_groups_alternative.py

'a((a+)|(b+))' (a then seq. of a or seq. of b)

  'abbaabbba'
  'abb'        ('bb', None, 'bb')
     'aa'      ('a', 'a', None)

'a((a|b)+)' (a then seq. of [ab])

  'abbaabbba'
  'abbaabbba'  ('bbaabbba', 'a')

Defining a group containing a sub-pattern is also useful in cases where the string matching the sub-pattern is not part of what should be extracted from the full text. These groups are called non-capturing. Non-capturing groups can be used to describe repetition patterns or alternatives, without isolating the matching portion of the string in the value returned. To create a non-capturing group, use the syntax (?:pattern).

re_groups_noncapturing.py
from re_test_patterns_groups import test_patterns

test_patterns(
    'abbaabbba',
    [(r'a((a+)|(b+))', 'capturing form'),
     (r'a((?:a+)|(?:b+))', 'noncapturing')],
)

Compare the groups returned for the capturing and non-capturing forms of a pattern that matches the same results.

$ python3 re_groups_noncapturing.py

'a((a+)|(b+))' (capturing form)

  'abbaabbba'
  'abb'        ('bb', None, 'bb')
     'aa'      ('a', 'a', None)

'a((?:a+)|(?:b+))' (noncapturing)

  'abbaabbba'
  'abb'        ('bb',)
     'aa'      ('a',)

Search Options

The way the matching engine processes an expression can be changed using option flags. The flags can be combined using a bit-wise or operation, then passed to compile(), search(), match(), and other functions that accept a pattern for searching.

Case-insensitive Matching

IGNORECASE causes literal characters and character ranges in the pattern to match both upper and lower case characters.

re_flags_ignorecase.py
import re

text = 'This is some text -- with punctuation.'
pattern = r'\bT\w+'
with_case = re.compile(pattern)
without_case = re.compile(pattern, re.IGNORECASE)

print('Text:\n  {!r}'.format(text))
print('Pattern:\n  {}'.format(pattern))
print('Case-sensitive:')
for match in with_case.findall(text):
    print('  {!r}'.format(match))
print('Case-insensitive:')
for match in without_case.findall(text):
    print('  {!r}'.format(match))

Since the pattern includes the literal T, without setting IGNORECASE the only match is the word This. When case is ignored, text also matches.

$ python3 re_flags_ignorecase.py

Text:
  'This is some text -- with punctuation.'
Pattern:
  \bT\w+
Case-sensitive:
  'This'
Case-insensitive:
  'This'
  'text'

Input with Multiple Lines

There are two flags that effect how searching in multi-line input works: MULTILINE and DOTALL. The MULTILINE flag controls how the pattern matching code processes anchoring instructions for text containing newline characters. When multiline mode is turned on, the anchor rules for ^ and $ apply at the beginning and end of each line, in addition to the entire string.

re_flags_multiline.py
import re

text = 'This is some text -- with punctuation.\nA second line.'
pattern = r'(^\w+)|(\w+\S*$)'
single_line = re.compile(pattern)
multiline = re.compile(pattern, re.MULTILINE)

print('Text:\n  {!r}'.format(text))
print('Pattern:\n  {}'.format(pattern))
print('Single Line :')
for match in single_line.findall(text):
    print('  {!r}'.format(match))
print('Multline    :')
for match in multiline.findall(text):
    print('  {!r}'.format(match))

The pattern in the example matches the first or last word of the input. It matches line. at the end of the string, even though there is no newline.

$ python3 re_flags_multiline.py

Text:
  'This is some text -- with punctuation.\nA second line.'
Pattern:
  (^\w+)|(\w+\S*$)
Single Line :
  ('This', '')
  ('', 'line.')
Multline    :
  ('This', '')
  ('', 'punctuation.')
  ('A', '')
  ('', 'line.')

DOTALL is the other flag related to multiline text. Normally, the dot character (.) matches everything in the input text except a newline character. The flag allows dot to match newlines as well.

re_flags_dotall.py
import re

text = 'This is some text -- with punctuation.\nA second line.'
pattern = r'.+'
no_newlines = re.compile(pattern)
dotall = re.compile(pattern, re.DOTALL)

print('Text:\n  {!r}'.format(text))
print('Pattern:\n  {}'.format(pattern))
print('No newlines :')
for match in no_newlines.findall(text):
    print('  {!r}'.format(match))
print('Dotall      :')
for match in dotall.findall(text):
    print('  {!r}'.format(match))

Without the flag, each line of the input text matches the pattern separately. Adding the flag causes the entire string to be consumed.

$ python3 re_flags_dotall.py

Text:
  'This is some text -- with punctuation.\nA second line.'
Pattern:
  .+
No newlines :
  'This is some text -- with punctuation.'
  'A second line.'
Dotall      :
  'This is some text -- with punctuation.\nA second line.'

Unicode

Under Python 3, str objects use the full Unicode character set, regular expression processing on a str assumes that the pattern and input text are both Unicode. The escape codes described earlier are defined in terms of Unicode by default. Those assumptions mean that the pattern \w+ will match the word “French” and “Français”. To restrict escape codes to the ASCII character set, as was the default in Python 2, use the ASCII flag when compiling the pattern or when calling the module-level functions search() and match().

re_flags_ascii.py
import re

text = u'Français złoty Österreich'
pattern = r'\w+'
ascii_pattern = re.compile(pattern, re.ASCII)
unicode_pattern = re.compile(pattern)

print('Text    :', text)
print('Pattern :', pattern)
print('ASCII   :', list(ascii_pattern.findall(text)))
print('Unicode :', list(unicode_pattern.findall(text)))

The other escape sequences (\W, \b, \B, \d, \D, \s, and \S) are also processed differently for ASCII text. Instead of consulting the Unicode database to find the properties of each character, re assumes the members of the character set identified by the escape sequence based on their ASCII definition.

$ python3 re_flags_ascii.py

Text    : Français złoty Österreich
Pattern : \w+
ASCII   : ['Fran', 'ais', 'z', 'oty', 'sterreich']
Unicode : ['Français', 'złoty', 'Österreich']

Verbose Expression Syntax

The compact format of regular expression syntax can become a hindrance as expressions grow more complicated. As the number of groups in an expression increases, it will be more work to keep track of why each element is needed and how exactly the parts of the expression interact. Using named groups helps mitigate these issues, but a better solution is to use verbose mode expressions, which allow comments and extra whitespace to be embedded in the pattern.

A pattern to validate email addresses will illustrate how verbose mode makes working with regular expressions easier. The first version recognizes addresses that end in one of three top-level domains, .com, .org, and .edu.

re_email_compact.py
import re

address = re.compile('[\w\d.+-]+@([\w\d.]+\.)+(com|org|edu)')

candidates = [
    u'first.last@example.com',
    u'first.last+category@gmail.com',
    u'valid-address@mail.example.com',
    u'not-valid@example.foo',
]

for candidate in candidates:
    match = address.search(candidate)
    print('{:<30}  {}'.format(
        candidate, 'Matches' if match else 'No match')
    )

This expression is already complex. There are several character classes, groups, and repetition expressions.

$ python3 re_email_compact.py

first.last@example.com          Matches
first.last+category@gmail.com   Matches
valid-address@mail.example.com  Matches
not-valid@example.foo           No match

Converting the expression to a more verbose format will make it easier to extend.

re_email_verbose.py
import re

address = re.compile(
    '''
    [\w\d.+-]+       # username
    @
    ([\w\d.]+\.)+    # domain name prefix
    (com|org|edu)    # TODO: support more top-level domains
    ''',
    re.VERBOSE)

candidates = [
    u'first.last@example.com',
    u'first.last+category@gmail.com',
    u'valid-address@mail.example.com',
    u'not-valid@example.foo',
]

for candidate in candidates:
    match = address.search(candidate)
    print('{:<30}  {}'.format(
        candidate, 'Matches' if match else 'No match'),
    )

The expression matches the same inputs, but in this extended format it is easier to read. The comments also help identify different parts of the pattern so that it can be expanded to match more inputs.

$ python3 re_email_verbose.py

first.last@example.com          Matches
first.last+category@gmail.com   Matches
valid-address@mail.example.com  Matches
not-valid@example.foo           No match

This expanded version parses inputs that include a person’s name and email address, as might appear in an email header. The name comes first and stands on its own, and the email address follows surrounded by angle brackets (< and >).

re_email_with_name.py
import re

address = re.compile(
    '''

    # A name is made up of letters, and may include "."
    # for title abbreviations and middle initials.
    ((?P<name>
       ([\w.,]+\s+)*[\w.,]+)
       \s*
       # Email addresses are wrapped in angle
       # brackets: < > but only if a name is
       # found, so keep the start bracket in this
       # group.
       <
    )? # the entire name is optional

    # The address itself: username@domain.tld
    (?P<email>
      [\w\d.+-]+       # username
      @
      ([\w\d.]+\.)+    # domain name prefix
      (com|org|edu)    # limit the allowed top-level domains
    )

    >? # optional closing angle bracket
    ''',
    re.VERBOSE)

candidates = [
    u'first.last@example.com',
    u'first.last+category@gmail.com',
    u'valid-address@mail.example.com',
    u'not-valid@example.foo',
    u'First Last <first.last@example.com>',
    u'No Brackets first.last@example.com',
    u'First Last',
    u'First Middle Last <first.last@example.com>',
    u'First M. Last <first.last@example.com>',
    u'<first.last@example.com>',
]

for candidate in candidates:
    print('Candidate:', candidate)
    match = address.search(candidate)
    if match:
        print('  Name :', match.groupdict()['name'])
        print('  Email:', match.groupdict()['email'])
    else:
        print('  No match')

As with other programming languages, the ability to insert comments into verbose regular expressions helps with their maintainability. This final version includes implementation notes to future maintainers and whitespace to separate the groups from each other and highlight their nesting level.

$ python3 re_email_with_name.py

Candidate: first.last@example.com
  Name : None
  Email: first.last@example.com
Candidate: first.last+category@gmail.com
  Name : None
  Email: first.last+category@gmail.com
Candidate: valid-address@mail.example.com
  Name : None
  Email: valid-address@mail.example.com
Candidate: not-valid@example.foo
  No match
Candidate: First Last <first.last@example.com>
  Name : First Last
  Email: first.last@example.com
Candidate: No Brackets first.last@example.com
  Name : None
  Email: first.last@example.com
Candidate: First Last
  No match
Candidate: First Middle Last <first.last@example.com>
  Name : First Middle Last
  Email: first.last@example.com
Candidate: First M. Last <first.last@example.com>
  Name : First M. Last
  Email: first.last@example.com
Candidate: <first.last@example.com>
  Name : None
  Email: first.last@example.com

Embedding Flags in Patterns

In situations where flags cannot be added when compiling an expression, such as when a pattern is passed as an argument to a library function that will compile it later, the flags can be embedded inside the expression string itself. For example, to turn case-insensitive matching on, add (?i) to the beginning of the expression.

re_flags_embedded.py
import re

text = 'This is some text -- with punctuation.'
pattern = r'(?i)\bT\w+'
regex = re.compile(pattern)

print('Text      :', text)
print('Pattern   :', pattern)
print('Matches   :', regex.findall(text))

Because the options control the way the entire expression is evaluated or parsed, they should always come at the beginning of the expression.

$ python3 re_flags_embedded.py

Text      : This is some text -- with punctuation.
Pattern   : (?i)\bT\w+
Matches   : ['This', 'text']

The abbreviations for all of the flags are listed in the table below.

Regular Expression Flag Abbreviations
Flag Abbreviation
ASCII a
IGNORECASE i
MULTILINE m
DOTALL s
VERBOSE x

Embedded flags can be combined by placing them within the same group. For example, (?im) turns on case-insensitive matching for multiline strings.

Looking Ahead, or Behind

There are many cases where it is useful to match a part of a pattern only if some other part will also match. For example, in the email parsing expression the angle brackets were each marked as optional. Really, though, the brackets should be paired, and the expression should only match if both are present, or neither are. This modified version of the expression uses a positive look ahead assertion to match the pair. The look ahead assertion syntax is (?=pattern).

re_look_ahead.py
import re

address = re.compile(
    '''
    # A name is made up of letters, and may include "."
    # for title abbreviations and middle initials.
    ((?P<name>
       ([\w.,]+\s+)*[\w.,]+
     )
     \s+
    ) # name is no longer optional

    # LOOKAHEAD
    # Email addresses are wrapped in angle brackets, but only
    # if they are both present or neither is.
    (?= (<.*>$)       # remainder wrapped in angle brackets
        |
        ([^<].*[^>]$) # remainder *not* wrapped in angle brackets
      )

    <? # optional opening angle bracket

    # The address itself: username@domain.tld
    (?P<email>
      [\w\d.+-]+       # username
      @
      ([\w\d.]+\.)+    # domain name prefix
      (com|org|edu)    # limit the allowed top-level domains
    )

    >? # optional closing angle bracket
    ''',
    re.VERBOSE)

candidates = [
    u'First Last <first.last@example.com>',
    u'No Brackets first.last@example.com',
    u'Open Bracket <first.last@example.com',
    u'Close Bracket first.last@example.com>',
]

for candidate in candidates:
    print('Candidate:', candidate)
    match = address.search(candidate)
    if match:
        print('  Name :', match.groupdict()['name'])
        print('  Email:', match.groupdict()['email'])
    else:
        print('  No match')

There are several important changes in this version of the expression. First, the name portion is no longer optional. That means stand-alone addresses do not match, but it also prevents improperly formatted name/address combinations from matching. The positive look ahead rule after the “name” group asserts that the remainder of the string is either wrapped with a pair of angle brackets, or there is not a mismatched bracket; the brackets are either both present or neither is. The look ahead is expressed as a group, but the match for a look ahead group does not consume any of the input text, so the rest of the pattern picks up from the same spot after the look ahead matches.

$ python3 re_look_ahead.py

Candidate: First Last <first.last@example.com>
  Name : First Last
  Email: first.last@example.com
Candidate: No Brackets first.last@example.com
  Name : No Brackets
  Email: first.last@example.com
Candidate: Open Bracket <first.last@example.com
  No match
Candidate: Close Bracket first.last@example.com>
  No match

A negative look ahead assertion ((?!pattern)) says that the pattern does not match the text following the current point. For example, the email recognition pattern could be modified to ignore noreply mailing addresses commonly used by automated systems.

re_negative_look_ahead.py
import re

address = re.compile(
    '''
    ^

    # An address: username@domain.tld

    # Ignore noreply addresses
    (?!noreply@.*$)

    [\w\d.+-]+       # username
    @
    ([\w\d.]+\.)+    # domain name prefix
    (com|org|edu)    # limit the allowed top-level domains

    $
    ''',
    re.VERBOSE)

candidates = [
    u'first.last@example.com',
    u'noreply@example.com',
]

for candidate in candidates:
    print('Candidate:', candidate)
    match = address.search(candidate)
    if match:
        print('  Match:', candidate[match.start():match.end()])
    else:
        print('  No match')

The address starting with noreply does not match the pattern, since the look ahead assertion fails.

$ python3 re_negative_look_ahead.py

Candidate: first.last@example.com
  Match: first.last@example.com
Candidate: noreply@example.com
  No match

Instead of looking ahead for noreply in the username portion of the email address, the pattern can also be written using a negative look behind assertion after the username is matched using the syntax (?<!pattern).

re_negative_look_behind.py
import re

address = re.compile(
    '''
    ^

    # An address: username@domain.tld

    [\w\d.+-]+       # username

    # Ignore noreply addresses
    (?<!noreply)

    @
    ([\w\d.]+\.)+    # domain name prefix
    (com|org|edu)    # limit the allowed top-level domains

    $
    ''',
    re.VERBOSE)

candidates = [
    u'first.last@example.com',
    u'noreply@example.com',
]

for candidate in candidates:
    print('Candidate:', candidate)
    match = address.search(candidate)
    if match:
        print('  Match:', candidate[match.start():match.end()])
    else:
        print('  No match')

Looking backwards works a little differently than looking ahead, in that the expression must use a fixed length pattern. Repetitions are allowed, as long as there is a fixed number (no wildcards or ranges).

$ python3 re_negative_look_behind.py

Candidate: first.last@example.com
  Match: first.last@example.com
Candidate: noreply@example.com
  No match

A positive look behind assertion can be used to find text following a pattern using the syntax (?<=pattern). For example, this expression finds Twitter handles.

re_look_behind.py
import re

twitter = re.compile(
    '''
    # A twitter handle: @username
    (?<=@)
    ([\w\d_]+)       # username
    ''',
    re.VERBOSE)

text = '''This text includes two Twitter handles.
One for @ThePSF, and one for the author, @doughellmann.
'''

print(text)
for match in twitter.findall(text):
    print('Handle:', match)

The pattern matches sequences of characters that can make up a Twitter handle, as long as they are preceded by an @.

$ python3 re_look_behind.py

This text includes two Twitter handles.
One for @ThePSF, and one for the author, @doughellmann.

Handle: ThePSF
Handle: doughellmann

Self-referencing Expressions

Matched values can be used in later parts of an expression. For example, the email example can be updated to match only addresses composed of the first and last name of the person by including back-references to those groups. The easiest way to achieve this is by referring to the previously matched group by id number, using \num.

re_refer_to_group.py
import re

address = re.compile(
    r'''

    # The regular name
    (\w+)               # first name
    \s+
    (([\w.]+)\s+)?      # optional middle name or initial
    (\w+)               # last name

    \s+

    <

    # The address: first_name.last_name@domain.tld
    (?P<email>
      \1               # first name
      \.
      \4               # last name
      @
      ([\w\d.]+\.)+    # domain name prefix
      (com|org|edu)    # limit the allowed top-level domains
    )

    >
    ''',
    re.VERBOSE | re.IGNORECASE)

candidates = [
    u'First Last <first.last@example.com>',
    u'Different Name <first.last@example.com>',
    u'First Middle Last <first.last@example.com>',
    u'First M. Last <first.last@example.com>',
]

for candidate in candidates:
    print('Candidate:', candidate)
    match = address.search(candidate)
    if match:
        print('  Match name :', match.group(1), match.group(4))
        print('  Match email:', match.group(5))
    else:
        print('  No match')

Although the syntax is simple, creating back-references by numerical id has a couple of disadvantages. From a practical standpoint, as the expression changes, the groups must be counted again and every reference may need to be updated. The other disadvantage is that only 99 references can be made using the standard back-reference syntax \n, because if the id number is three digits long it will be interpreted as an octal character value instead of a group reference. Of course, if there are more than 99 groups in an expression, there will be more serious maintenance challenges than not being able to refer to all of them.

$ python3 re_refer_to_group.py

Candidate: First Last <first.last@example.com>
  Match name : First Last
  Match email: first.last@example.com
Candidate: Different Name <first.last@example.com>
  No match
Candidate: First Middle Last <first.last@example.com>
  Match name : First Last
  Match email: first.last@example.com
Candidate: First M. Last <first.last@example.com>
  Match name : First Last
  Match email: first.last@example.com

Python’s expression parser includes an extension that uses (?P=name) to refer to the value of a named group matched earlier in the expression.

re_refer_to_named_group.py
import re

address = re.compile(
    '''

    # The regular name
    (?P<first_name>\w+)
    \s+
    (([\w.]+)\s+)?      # optional middle name or initial
    (?P<last_name>\w+)

    \s+

    <

    # The address: first_name.last_name@domain.tld
    (?P<email>
      (?P=first_name)
      \.
      (?P=last_name)
      @
      ([\w\d.]+\.)+    # domain name prefix
      (com|org|edu)    # limit the allowed top-level domains
    )

    >
    ''',
    re.VERBOSE | re.IGNORECASE)

candidates = [
    u'First Last <first.last@example.com>',
    u'Different Name <first.last@example.com>',
    u'First Middle Last <first.last@example.com>',
    u'First M. Last <first.last@example.com>',
]

for candidate in candidates:
    print('Candidate:', candidate)
    match = address.search(candidate)
    if match:
        print('  Match name :', match.groupdict()['first_name'],
              end=' ')
        print(match.groupdict()['last_name'])
        print('  Match email:', match.groupdict()['email'])
    else:
        print('  No match')

The address expression is compiled with the IGNORECASE flag on, since proper names are normally capitalized but email addresses are not.

$ python3 re_refer_to_named_group.py

Candidate: First Last <first.last@example.com>
  Match name : First Last
  Match email: first.last@example.com
Candidate: Different Name <first.last@example.com>
  No match
Candidate: First Middle Last <first.last@example.com>
  Match name : First Last
  Match email: first.last@example.com
Candidate: First M. Last <first.last@example.com>
  Match name : First Last
  Match email: first.last@example.com

The other mechanism for using back-references in expressions chooses a different pattern based on whether or not a previous group matched. The email pattern can be corrected so that the angle brackets are required if a name is present, and not if the email address is by itself. The syntax for testing to see if a group has matched is (?(id)yes-expression|no-expression), where id is the group name or number, yes-expression is the pattern to use if the group has a value and no-expression is the pattern to use otherwise.

re_id.py
import re

address = re.compile(
    '''
    ^

    # A name is made up of letters, and may include "."
    # for title abbreviations and middle initials.
    (?P<name>
       ([\w.]+\s+)*[\w.]+
     )?
    \s*

    # Email addresses are wrapped in angle brackets, but
    # only if a name is found.
    (?(name)
      # remainder wrapped in angle brackets because
      # there is a name
      (?P<brackets>(?=(<.*>$)))
      |
      # remainder does not include angle brackets without name
      (?=([^<].*[^>]$))
     )

    # Only look for a bracket if the look-ahead assertion
    # found both of them.
    (?(brackets)<|\s*)

    # The address itself: username@domain.tld
    (?P<email>
      [\w\d.+-]+       # username
      @
      ([\w\d.]+\.)+    # domain name prefix
      (com|org|edu)    # limit the allowed top-level domains
     )

    # Only look for a bracket if the look-ahead assertion
    # found both of them.
    (?(brackets)>|\s*)

    $
    ''',
    re.VERBOSE)

candidates = [
    u'First Last <first.last@example.com>',
    u'No Brackets first.last@example.com',
    u'Open Bracket <first.last@example.com',
    u'Close Bracket first.last@example.com>',
    u'no.brackets@example.com',
]

for candidate in candidates:
    print('Candidate:', candidate)
    match = address.search(candidate)
    if match:
        print('  Match name :', match.groupdict()['name'])
        print('  Match email:', match.groupdict()['email'])
    else:
        print('  No match')

This version of the email address parser uses two tests. If the name group matches, then the look ahead assertion requires both angle brackets and sets up the brackets group. If name is not matched, the assertion requires the rest of the text not have angle brackets around it. Later, if the brackets group is set, the actual pattern matching code consumes the brackets in the input using literal patterns, otherwise it consumes any blank space.

$ python3 re_id.py

Candidate: First Last <first.last@example.com>
  Match name : First Last
  Match email: first.last@example.com
Candidate: No Brackets first.last@example.com
  No match
Candidate: Open Bracket <first.last@example.com
  No match
Candidate: Close Bracket first.last@example.com>
  No match
Candidate: no.brackets@example.com
  Match name : None
  Match email: no.brackets@example.com

Modifying Strings with Patterns

In addition to searching through text, re also supports modifying text using regular expressions as the search mechanism, and the replacements can reference groups matched in the pattern as part of the substitution text. Use sub() to replace all occurrences of a pattern with another string.

re_sub.py
import re

bold = re.compile(r'\*{2}(.*?)\*{2}')

text = 'Make this **bold**.  This **too**.'

print('Text:', text)
print('Bold:', bold.sub(r'<b>\1</b>', text))

References to the text matched by the pattern can be inserted using the \num syntax used for back-references.

$ python3 re_sub.py

Text: Make this **bold**.  This **too**.
Bold: Make this <b>bold</b>.  This <b>too</b>.

To use named groups in the substitution, use the syntax \g<name>.

re_sub_named_groups.py
import re

bold = re.compile(r'\*{2}(?P<bold_text>.*?)\*{2}')

text = 'Make this **bold**.  This **too**.'

print('Text:', text)
print('Bold:', bold.sub(r'<b>\g<bold_text></b>', text))

The \g<name> syntax also works with numbered references, and using it eliminates any ambiguity between group numbers and surrounding literal digits.

$ python3 re_sub_named_groups.py

Text: Make this **bold**.  This **too**.
Bold: Make this <b>bold</b>.  This <b>too</b>.

Pass a value to count to limit the number of substitutions performed.

re_sub_count.py
import re

bold = re.compile(r'\*{2}(.*?)\*{2}')

text = 'Make this **bold**.  This **too**.'

print('Text:', text)
print('Bold:', bold.sub(r'<b>\1</b>', text, count=1))

Only the first substitution is made because count is 1.

$ python3 re_sub_count.py

Text: Make this **bold**.  This **too**.
Bold: Make this <b>bold</b>.  This **too**.

subn() works just like sub() except that it returns both the modified string and the count of substitutions made.

re_subn.py
import re

bold = re.compile(r'\*{2}(.*?)\*{2}')

text = 'Make this **bold**.  This **too**.'

print('Text:', text)
print('Bold:', bold.subn(r'<b>\1</b>', text))

The search pattern matches twice in the example.

$ python3 re_subn.py

Text: Make this **bold**.  This **too**.
Bold: ('Make this <b>bold</b>.  This <b>too</b>.', 2)

Splitting with Patterns

str.split() is one of the most frequently used methods for breaking apart strings to parse them. It only supports using literal values as separators, though, and sometimes a regular expression is necessary if the input is not consistently formatted. For example, many plain text markup languages define paragraph separators as two or more newline (\n) characters. In this case, str.split() cannot be used because of the “or more” part of the definition.

A strategy for identifying paragraphs using findall() would use a pattern like (.+?)\n{2,}.

re_paragraphs_findall.py
import re

text = '''Paragraph one
on two lines.

Paragraph two.


Paragraph three.'''

for num, para in enumerate(re.findall(r'(.+?)\n{2,}',
                                      text,
                                      flags=re.DOTALL)
                           ):
    print(num, repr(para))
    print()

That pattern fails for paragraphs at the end of the input text, as illustrated by the fact that “Paragraph three.” is not part of the output.

$ python3 re_paragraphs_findall.py

0 'Paragraph one\non two lines.'

1 'Paragraph two.'

Extending the pattern to say that a paragraph ends with two or more newlines or the end of input fixes the problem, but makes the pattern more complicated. Converting to re.split() instead of re.findall() handles the boundary condition automatically and keeps the pattern simpler.

re_split.py
import re

text = '''Paragraph one
on two lines.

Paragraph two.


Paragraph three.'''

print('With findall:')
for num, para in enumerate(re.findall(r'(.+?)(\n{2,}|$)',
                                      text,
                                      flags=re.DOTALL)):
    print(num, repr(para))
    print()

print()
print('With split:')
for num, para in enumerate(re.split(r'\n{2,}', text)):
    print(num, repr(para))
    print()

The pattern argument to split() expresses the markup specification more precisely. Two or more newline characters mark a separator point between paragraphs in the input string.

$ python3 re_split.py

With findall:
0 ('Paragraph one\non two lines.', '\n\n')

1 ('Paragraph two.', '\n\n\n')

2 ('Paragraph three.', '')


With split:
0 'Paragraph one\non two lines.'

1 'Paragraph two.'

2 'Paragraph three.'

Enclosing the expression in parentheses to define a group causes split() to work more like str.partition(), so it returns the separator values as well as the other parts of the string.

re_split_groups.py
import re

text = '''Paragraph one
on two lines.

Paragraph two.


Paragraph three.'''

print('With split:')
for num, para in enumerate(re.split(r'(\n{2,})', text)):
    print(num, repr(para))
    print()

The output now includes each paragraph, as well as the sequence of newlines separating them.

$ python3 re_split_groups.py

With split:
0 'Paragraph one\non two lines.'

1 '\n\n'

2 'Paragraph two.'

3 '\n\n\n'

4 'Paragraph three.'

See also

  • Standard library documentation for re
  • Regular Expression HOWTO – Andrew Kuchling’s introduction to regular expressions for Python developers.
  • Kodos – An interactive regular expression testing tool by Phil Schwartz.
  • pythex – A web-based tool for testing regular expressions created by Gabriel Rodríguez. Inspired by Rubular.
  • Wikipedia: Regular expression – General introduction to regular expression concepts and techniques.
  • locale – Use the locale module to set the language configuration when working with Unicode text.
  • unicodedata – Programmatic access to the Unicode character property database.