Table of Contents for
Regular Expressions Cookbook, 2nd Edition

Version ebook / Retour

Cover image for bash Cookbook, 2nd Edition Regular Expressions Cookbook, 2nd Edition by Steven Levithan Published by O'Reilly Media, Inc., 2012
  1. Cover
  2. Regular Expressions Cookbook
  3. Preface
  4. Caught in the Snarls of Different Versions
  5. Intended Audience
  6. Technology Covered
  7. Organization of This Book
  8. Conventions Used in This Book
  9. Using Code Examples
  10. Safari® Books Online
  11. How to Contact Us
  12. Acknowledgments
  13. 1. Introduction to Regular Expressions
  14. Regular Expressions Defined
  15. Search and Replace with Regular Expressions
  16. Tools for Working with Regular Expressions
  17. 2. Basic Regular Expression Skills
  18. 2.1. Match Literal Text
  19. 2.2. Match Nonprintable Characters
  20. 2.3. Match One of Many Characters
  21. 2.4. Match Any Character
  22. 2.5. Match Something at the Start and/or the End of a Line
  23. 2.6. Match Whole Words
  24. 2.7. Unicode Code Points, Categories, Blocks, and Scripts
  25. 2.8. Match One of Several Alternatives
  26. 2.9. Group and Capture Parts of the Match
  27. 2.10. Match Previously Matched Text Again
  28. 2.11. Capture and Name Parts of the Match
  29. 2.12. Repeat Part of the Regex a Certain Number of Times
  30. 2.13. Choose Minimal or Maximal Repetition
  31. 2.14. Eliminate Needless Backtracking
  32. 2.15. Prevent Runaway Repetition
  33. 2.16. Test for a Match Without Adding It to the Overall Match
  34. 2.17. Match One of Two Alternatives Based on a Condition
  35. 2.18. Add Comments to a Regular Expression
  36. 2.19. Insert Literal Text into the Replacement Text
  37. 2.20. Insert the Regex Match into the Replacement Text
  38. 2.21. Insert Part of the Regex Match into the Replacement Text
  39. 2.22. Insert Match Context into the Replacement Text
  40. 3. Programming with Regular Expressions
  41. Programming Languages and Regex Flavors
  42. 3.1. Literal Regular Expressions in Source Code
  43. 3.2. Import the Regular Expression Library
  44. 3.3. Create Regular Expression Objects
  45. 3.4. Set Regular Expression Options
  46. 3.5. Test If a Match Can Be Found Within a Subject String
  47. 3.6. Test Whether a Regex Matches the Subject String Entirely
  48. 3.7. Retrieve the Matched Text
  49. 3.8. Determine the Position and Length of the Match
  50. 3.9. Retrieve Part of the Matched Text
  51. 3.10. Retrieve a List of All Matches
  52. 3.11. Iterate over All Matches
  53. 3.12. Validate Matches in Procedural Code
  54. 3.13. Find a Match Within Another Match
  55. 3.14. Replace All Matches
  56. 3.15. Replace Matches Reusing Parts of the Match
  57. 3.16. Replace Matches with Replacements Generated in Code
  58. 3.17. Replace All Matches Within the Matches of Another Regex
  59. 3.18. Replace All Matches Between the Matches of Another Regex
  60. 3.19. Split a String
  61. 3.20. Split a String, Keeping the Regex Matches
  62. 3.21. Search Line by Line
  63. Construct a Parser
  64. 4. Validation and Formatting
  65. 4.1. Validate Email Addresses
  66. 4.2. Validate and Format North American Phone Numbers
  67. 4.3. Validate International Phone Numbers
  68. 4.4. Validate Traditional Date Formats
  69. 4.5. Validate Traditional Date Formats, Excluding Invalid Dates
  70. 4.6. Validate Traditional Time Formats
  71. 4.7. Validate ISO 8601 Dates and Times
  72. 4.8. Limit Input to Alphanumeric Characters
  73. 4.9. Limit the Length of Text
  74. 4.10. Limit the Number of Lines in Text
  75. 4.11. Validate Affirmative Responses
  76. 4.12. Validate Social Security Numbers
  77. 4.13. Validate ISBNs
  78. 4.14. Validate ZIP Codes
  79. 4.15. Validate Canadian Postal Codes
  80. 4.16. Validate U.K. Postcodes
  81. 4.17. Find Addresses with Post Office Boxes
  82. 4.18. Reformat Names From “FirstName LastName” to “LastName, FirstName”
  83. 4.19. Validate Password Complexity
  84. 4.20. Validate Credit Card Numbers
  85. 4.21. European VAT Numbers
  86. 5. Words, Lines, and Special Characters
  87. 5.1. Find a Specific Word
  88. 5.2. Find Any of Multiple Words
  89. 5.3. Find Similar Words
  90. 5.4. Find All Except a Specific Word
  91. 5.5. Find Any Word Not Followed by a Specific Word
  92. 5.6. Find Any Word Not Preceded by a Specific Word
  93. 5.7. Find Words Near Each Other
  94. 5.8. Find Repeated Words
  95. 5.9. Remove Duplicate Lines
  96. 5.10. Match Complete Lines That Contain a Word
  97. 5.11. Match Complete Lines That Do Not Contain a Word
  98. 5.12. Trim Leading and Trailing Whitespace
  99. 5.13. Replace Repeated Whitespace with a Single Space
  100. 5.14. Escape Regular Expression Metacharacters
  101. 6. Numbers
  102. 6.1. Integer Numbers
  103. 6.2. Hexadecimal Numbers
  104. 6.3. Binary Numbers
  105. 6.4. Octal Numbers
  106. 6.5. Decimal Numbers
  107. 6.6. Strip Leading Zeros
  108. 6.7. Numbers Within a Certain Range
  109. 6.8. Hexadecimal Numbers Within a Certain Range
  110. 6.9. Integer Numbers with Separators
  111. 6.10. Floating-Point Numbers
  112. 6.11. Numbers with Thousand Separators
  113. 6.12. Add Thousand Separators to Numbers
  114. 6.13. Roman Numerals
  115. 7. Source Code and Log Files
  116. Keywords
  117. Identifiers
  118. Numeric Constants
  119. Operators
  120. Single-Line Comments
  121. Multiline Comments
  122. All Comments
  123. Strings
  124. Strings with Escapes
  125. Regex Literals
  126. Here Documents
  127. Common Log Format
  128. Combined Log Format
  129. Broken Links Reported in Web Logs
  130. 8. URLs, Paths, and Internet Addresses
  131. 8.1. Validating URLs
  132. 8.2. Finding URLs Within Full Text
  133. 8.3. Finding Quoted URLs in Full Text
  134. 8.4. Finding URLs with Parentheses in Full Text
  135. 8.5. Turn URLs into Links
  136. 8.6. Validating URNs
  137. 8.7. Validating Generic URLs
  138. 8.8. Extracting the Scheme from a URL
  139. 8.9. Extracting the User from a URL
  140. 8.10. Extracting the Host from a URL
  141. 8.11. Extracting the Port from a URL
  142. 8.12. Extracting the Path from a URL
  143. 8.13. Extracting the Query from a URL
  144. 8.14. Extracting the Fragment from a URL
  145. 8.15. Validating Domain Names
  146. 8.16. Matching IPv4 Addresses
  147. 8.17. Matching IPv6 Addresses
  148. 8.18. Validate Windows Paths
  149. 8.19. Split Windows Paths into Their Parts
  150. 8.20. Extract the Drive Letter from a Windows Path
  151. 8.21. Extract the Server and Share from a UNC Path
  152. 8.22. Extract the Folder from a Windows Path
  153. 8.23. Extract the Filename from a Windows Path
  154. 8.24. Extract the File Extension from a Windows Path
  155. 8.25. Strip Invalid Characters from Filenames
  156. 9. Markup and Data Formats
  157. Processing Markup and Data Formats with Regular Expressions
  158. 9.1. Find XML-Style Tags
  159. 9.2. Replace Tags with
  160. 9.3. Remove All XML-Style Tags Except and
  161. 9.4. Match XML Names
  162. 9.5. Convert Plain Text to HTML by Adding

    and
    Tags

  163. 9.6. Decode XML Entities
  164. 9.7. Find a Specific Attribute in XML-Style Tags
  165. 9.8. Add a cellspacing Attribute to Tags That Do Not Already Include It
  166. 9.9. Remove XML-Style Comments
  167. 9.10. Find Words Within XML-Style Comments
  168. 9.11. Change the Delimiter Used in CSV Files
  169. 9.12. Extract CSV Fields from a Specific Column
  170. 9.13. Match INI Section Headers
  171. 9.14. Match INI Section Blocks
  172. 9.15. Match INI Name-Value Pairs
  173. Index
  174. Index
  175. Index
  176. Index
  177. Index
  178. Index
  179. Index
  180. Index
  181. Index
  182. Index
  183. Index
  184. Index
  185. Index
  186. Index
  187. Index
  188. Index
  189. Index
  190. Index
  191. Index
  192. Index
  193. Index
  194. Index
  195. Index
  196. Index
  197. Index
  198. Index
  199. About the Authors
  200. Colophon
  201. Copyright
  202. 4.18. Reformat Names From “FirstName LastName” to “LastName, FirstName”

    Problem

    You want to convert people’s names from the “FirstName LastName” format to “LastName, FirstName” for use in an alphabetical listing. You additionally want to account for other name parts, so that you can, say convert “FirstName MiddleNames Particles LastName Suffix” to “LastName, FirstName MiddleNames Particles Suffix.”

    Solution

    Unfortunately, it isn’t possible to reliably parse names using a regular expression. Regular expressions are rigid, whereas names are so flexible that even humans get them wrong. Determining the structure of a name or how it should be listed alphabetically often requires taking traditional and national conventions, or even personal preferences, into account. Nevertheless, if you’re willing to make certain assumptions about your data and can handle a moderate level of error, a regular expression can provide a quick solution.

    The following regular expression has intentionally been kept simple, rather than trying to account for edge cases.

    Regular expression

    ^(.+?)([^\s,]+)(,?(?:[JS]r\.?|III?|IV))?$
    Regex options: Case insensitive
    Regex flavors: .NET, Java, JavaScript, PCRE, Perl, Python, Ruby

Replacement

$2,$1$3
Replacement text flavors: .NET, Java, JavaScript, Perl, PHP
\2,\1\3
Replacement text flavors: Python, Ruby

JavaScript example

function formatName(name) {
    return name.replace(/^(.+?) ([^\s,]+)(,? (?:[JS]r\.?|III?|IV))?$/i,
                        "$2, $1$3");
}

Recipe 3.15 has code listings that will help you add this regex search-and-replace to programs written in other languages. Recipe 3.4 shows how to set the “case insensitive” option used here.

Discussion

First, let’s take a look at this regular expression piece by piece. Higher-level comments are provided afterward to help explain which parts of a name are being matched by various segments of the regex. Since the regex is written here in free-spacing mode, the literal space characters have been escaped with backslashes:

^              # Assert position at the beginning of the string.
(              # Capture the enclosed match to backreference 1:
  .+?          #   Match one or more characters, as few times as possible.
)              # End the capturing group.
\              # Match a literal space character.
(              # Capture the enclosed match to backreference 2:
  [^\s,]+      #   Match one or more non-whitespace/comma characters.
)              # End the capturing group.
(              # Capture the enclosed match to backreference 3:
  ,?\          #   Match ", " or " ".
  (?:          #   Group but don't capture:
    [JS]r\.?   #     Match "Jr", "Jr.", "Sr", or "Sr.".
   |           #    Or:
    III?       #     Match "II" or "III".
   |           #    Or:
    IV         #     Match "IV".
  )            #   End the noncapturing group.
)?             # Make the group optional.
$              # Assert position at the end of the string.
Regex options: Case insensitive, free-spacing
Regex flavors: .NET, Java, XRegExp, PCRE, Perl, Python, Ruby

This regular expression makes the following assumptions about the subject data:

  • It contains at least one first name and one last name (other name parts are optional).

  • The first name is listed before the last name (not the norm with some national conventions).

  • If the name contains a suffix, it is one of the values “Jr”, “Jr.”, “Sr”, “Sr.”, “II”, “III”, or “IV”, with an optional preceding comma.

A few more issues to consider:

  • The regular expression cannot identify compound surnames that don’t use hyphens. For example, Sacha Baron Cohen would be replaced with Cohen, Sacha Baron, rather than the correct listing, Baron Cohen, Sacha.

  • It does not keep particles in front of the family name, although this is sometimes called for by convention or personal preference (for example, the correct alphabetical listing of “Charles de Gaulle” is “de Gaulle, Charles” according to the Chicago Manual of Style, 16th Edition, which contradicts Merriam-Webster’s Biographical Dictionary on this particular name).

  • Because of the ^ and $ anchors that bind the match to the beginning and end of the string, no replacement can be made if the entire subject text does not fit the pattern. Hence, if no suitable match is found (for example, if the subject text contains only one name), the name is left unaltered.

As for how the regular expression works, it uses three capturing groups to split up the name. The pieces are then reassembled in the desired order via backreferences in the replacement string. Capturing group 1 uses the maximally flexible .+? pattern to grab the first name along with any number of middle names and surname particles, such as the German “von” or the French, Portuguese, and Spanish “de.” These name parts are handled together because they are listed sequentially in the output. Lumping the first and middle names together also helps avoid errors, because the regular expression cannot distinguish between a compound first name, such as “Mary Lou” or “Norma Jeane,” and a first name plus middle name. Even humans cannot accurately make the distinction just by visual examination.

Capturing group 2 matches the last name using [^\s,]+. Like the dot used in capturing group 1, the flexibility of this character class allows it to match accented characters and any other non-Latin characters. Capturing group 3 matches an optional suffix, such as “Jr.” or “III,” from a predefined list of possible values. The suffix is handled separately from the last name because it should continue to appear at the end of the reformatted name.

Let’s go back for a minute to capturing group 1. Why was the dot within group 1 followed by the lazy +? quantifier, whereas the character class in group 2 was followed by the greedy + quantifier? If group 1 (which handles a variable number of elements and therefore needs to go as far as it can into the name) used a greedy quantifier, capturing group 3 (which attempts to match a suffix) wouldn’t have a shot at participating in the match. The dot from group 1 would match until the end of the string, and since capturing group 3 is optional, the regex engine would only backtrack enough to find a match for group 2 before declaring success. Capturing group 2 can use a greedy quantifier because its more restrictive character class only allows it to match one name.

Table 4-2 shows some examples of how names are formatted using this regular expression and replacement string.

Table 4-2. Formatted names

Input

Output

Robert Downey, Jr.

Downey, Robert, Jr.

John F. Kennedy

Kennedy, John F.

Scarlett O’Hara

O’Hara, Scarlett

Pepé Le Pew

Pew, Pepé Le

J.R.R. Tolkien

Tolkien, J.R.R.

Catherine Zeta-Jones

Zeta-Jones, Catherine

Variations

List surname particles at the beginning of the name

An added segment in the following regular expression allows you to output surname particles from a predefined list in front of the last name. Specifically, this regular expression accounts for the values “de”, “du”, “la”, “le”, “St”, “St.”, “Ste”, “Ste.”, “van”, and “von”. Any number of these values are allowed in sequence (for example, “de la”):

^(.+?)((?:(?:d[eu]|l[ae]|Ste?\.?|v[ao]n))*[^\s,]+)↵
(,?(?:[JS]r\.?|III?|IV))?$
Regex options: Case insensitive
Regex flavors: .NET, Java, JavaScript, PCRE, Perl, Python, Ruby
$2,$1$3
Replacement text flavors: .NET, Java, JavaScript, Perl, PHP
\2,\1\3
Replacement text flavors: Python, Ruby

See Also

Techniques used in the regular expressions and replacement text in this recipe are discussed in Chapter 2. Recipe 2.1 explains which special characters need to be escaped. Recipe 2.3 explains character classes. Recipe 2.4 explains that the dot matches any character. Recipe 2.5 explains anchors. Recipe 2.8 explains alternation. Recipe 2.9 explains grouping. Recipe 2.12 explains repetition. Recipe 2.13 explains how greedy and lazy quantifiers backtrack. Recipe 2.21 explains how to insert text matched by capturing groups into the replacement text.