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. 4.20. Validate Credit Card Numbers

    Problem

    You’re given the job of implementing an order form for a company that accepts payment by credit card. Since the credit card processor charges for each transaction attempt, including failed attempts, you want to use a regular expression to weed out obviously invalid credit card numbers.

    Doing this will also improve the customer’s experience. A regular expression can instantly detect obvious typos as soon as the customer finishes filling in the field on the web form. A round trip to the credit card processor, by contrast, easily takes 10 to 30 seconds.

    Solution

    To keep the implementation simple, this solution is split into two parts. First we strip out spaces and hyphens. Then we validate what remains.

    Strip spaces and hyphens

    Retrieve the credit card number entered by the customer and store it into a variable. Before performing the check for a valid number, perform a search-and-replace to strip out spaces and hyphens. Replace all matches of this regular expression with blank replacement text:

    [-]
    Regex options: None
    Regex flavors: .NET, Java, JavaScript, PCRE, Perl, Python, Ruby

    Recipe 3.14 shows you how to perform this initial replacement.

Validate the number

With spaces and hyphens stripped from the input, the next regular expression checks if the credit card number uses the format of any of the six major credit card companies. It uses named capture to detect which brand of credit card the customer has:

^(?:
(?<visa>4[0-9]{12}(?:[0-9]{3})?) |
(?<mastercard>5[1-5][0-9]{14}) |
(?<discover>6(?:011|5[0-9]{2})[0-9]{12}) |
(?<amex>3[47][0-9]{13}) |
(?<diners>3(?:0[0-5]|[68][0-9])[0-9]{11}) |
(?<jcb>(?:2131|1800|35[0-9]{3})[0-9]{11})
)$
Regex options: Free-spacing
Regex flavors: .NET, Java 7, XRegExp, PCRE 7, Perl 5.10, Ruby 1.9
^(?:
(?P<visa>4[0-9]{12}(?:[0-9]{3})?) |
(?P<mastercard>5[1-5][0-9]{14}) |
(?P<discover>6(?:011|5[0-9]{2})[0-9]{12}) |
(?P<amex>3[47][0-9]{13}) |
(?P<diners>3(?:0[0-5]|[68][0-9])[0-9]{11}) |
(?P<jcb>(?:2131|1800|35[0-9]{3})[0-9]{11})
)$
Regex options: Free-spacing
Regex flavors: PCRE, Python

Java 4 to 6, Perl 5.8 and earlier, and Ruby 1.8 do not support named capture. You can use numbered capture instead. Group 1 will capture Visa cards, group 2 MasterCard, and so on up to group 6 for JCB:

^(?:
(4[0-9]{12}(?:[0-9]{3})?) |          # Visa
(5[1-5][0-9]{14}) |                  # MasterCard
(6(?:011|5[0-9]{2})[0-9]{12}) |      # Discover
(3[47][0-9]{13}) |                   # AMEX
(3(?:0[0-5]|[68][0-9])[0-9]{11}) |   # Diners Club
((?:2131|1800|35[0-9]{3})[0-9]{11})  # JCB
)$
Regex options: Free-spacing
Regex flavors: .NET, Java, XRegExp, PCRE, Perl, Python, Ruby

Standard JavaScript does not support named capture or free-spacing. Removing whitespace and comments, we get:

^(?:(4[0-9]{12}(?:[0-9]{3})?)|(5[1-5][0-9]{14})|↵
(6(?:011|5[0-9]{2})[0-9]{12})|(3[47][0-9]{13})|↵
(3(?:0[0-5]|[68][0-9])[0-9]{11})|((?:2131|1800|35[0-9]{3})[0-9]{11}))$
Regex options: None
Regex flavors: .NET, Java, JavaScript, PCRE, Perl, Python, Ruby

If you don’t need to determine which type the card is, you can remove the six capturing groups that surround the pattern for each card type, as they don’t serve any other purpose.

Follow Recipe 3.6 to add this regular expression to your order form to validate the card number. If you use different processors for different cards, or if you just want to keep some statistics, you can use Recipe 3.9 to check which named or numbered capturing group holds the match. That will tell you which brand of credit card the customer has.

Example web page with JavaScript

<html>
<head>
<title>Credit Card Test</title>
</head>

<body>
<h1>Credit Card Test</h1>

<form>
<p>Please enter your credit card number:</p>

<p><input type="text" size="20" name="cardnumber"
  onkeyup="validatecardnumber(this.value)"></p>

<p id="notice">(no card number entered)</p>
</form>

<script>
function validatecardnumber(cardnumber) {
  // Strip spaces and dashes
  cardnumber = cardnumber.replace(/[ -]/g, '');
  // See if the card is valid
  // The regex will capture the number in one of the capturing groups
  var match = /^(?:(4[0-9]{12}(?:[0-9]{3})?)|(5[1-5][0-9]{14})|↵
(6(?:011|5[0-9]{2})[0-9]{12})|(3[47][0-9]{13})|(3(?:0[0-5]|[68][0-9])↵
[0-9]{11})|((?:2131|1800|35[0-9]{3})[0-9]{11}))$/.exec(cardnumber);
  if (match) {
    // List of card types, in the same order as the regex capturing groups
    var types = ['Visa', 'MasterCard', 'Discover', 'American Express',
                 'Diners Club', 'JCB'];
    // Find the capturing group that matched
    // Skip the zeroth element of the match array (the overall match)
    for (var i = 1; i < match.length; i++) {
      if (match[i]) {
        // Display the card type for that group
        document.getElementById('notice').innerHTML = types[i - 1];
        break;
      }
    }
  } else {
    document.getElementById('notice').innerHTML = '(invalid card number)';
  }
}
</script>
</body>
</html>

Discussion

Strip spaces and hyphens

On an actual credit card, the digits of the embossed card number are usually placed into groups of four. That makes the card number easier for humans to read. Naturally, many people will try to enter the card number in the same way, including the spaces, on order forms.

Writing a regular expression that validates a card number, allowing for spaces, hyphens, and whatnot, is much more difficult that writing a regular expression that only allows digits. Thus, unless you want to annoy the customer with retyping the card number without spaces or hyphens, do a quick search-and-replace to strip them out before validating the card number and sending it to the card processor.

The regular expression [-] matches a character that is a space or a hyphen. Replacing all matches of this regular expression with nothing effectively deletes all spaces and hyphens.

Tip

Credit card numbers can consist only of digits. Instead of using [-] to remove only spaces and hyphens, you could use the shorthand character class \D to strip out all nondigits.

Validate the number

Each of the credit card companies uses a different number format. We’ll exploit that difference to allow users to enter a number without specifying a company; the company can be determined from the number. The format for each company is:

Visa

13 or 16 digits, starting with 4.

MasterCard

16 digits, starting with 51 through 55.

Discover

16 digits, starting with 6011 or 65.

American Express

15 digits, starting with 34 or 37.

Diners Club

14 digits, starting with 300 through 305, 36, or 38.

JCB

15 digits, starting with 2131 or 1800, or 16 digits starting with 35.

If you accept only certain brands of credit cards, you can delete the cards that you don’t accept from the regular expression. When deleting JCB, make sure to delete the last remaining | in the regular expression as well. If you end up with || or |) in your regular expression, it will accept the empty string as a valid card number.

For example, to accept only Visa, MasterCard, and AMEX, you can use:

^(?:
4[0-9]{12}(?:[0-9]{3})? |         # Visa
5[1-5][0-9]{14} |                 # MasterCard
3[47][0-9]{13}                    # AMEX
)$
Regex options: Free-spacing
Regex flavors: .NET, Java, XRegExp, PCRE, Perl, Python, Ruby

Alternatively:

^(?:4[0-9]{12}(?:[0-9]{3})?|5[1-5][0-9]{14}|3[47][0-9]{13})$
Regex options: None
Regex flavors: .NET, Java, JavaScript, PCRE, Perl, Python, Ruby

If you’re searching for credit card numbers in a larger body of text, replace the anchors with \b word boundaries.

Incorporating the solution into a web page

The section Example web page with JavaScript shows how you could add these two regular expressions to your order form. The input box for the credit card number has an onkeyup event handler that calls the validatecardnumber() function. This function retrieves the card number from the input box, strips the spaces and hyphens, and then validates it using the regular expression with numbered capturing groups. The result of the validation is displayed by replacing the text in the last paragraph on the page.

If the regular expression fails to match, regexp.exec() returns null, and (invalid card number) is displayed. If the regex does match, regexp.exec() returns an array of strings. The zeroth element holds the overall match. Elements 1 through 6 hold the text matched by the six capturing groups.

Our regular expression has six capturing groups, divided by alternation. This means that exactly one capturing group will participate in the match and hold the card number. The other groups will be empty (either undefined or the empty string, depending on your browser). The function checks the six capturing groups, one by one. When it finds one that is not empty, the card number is recognized and displayed.

Extra Validation with the Luhn Algorithm

There is an extra validation check that you can do on the credit card number before processing the order. The last digit in the credit card number is a checksum calculated according to the Luhn algorithm. Since this algorithm requires basic arithmetic, you cannot implement it with a regular expression.

You can add the Luhn check to the web page example for this recipe by inserting the call luhn(cardnumber); before the “else” line in the validatecardnumber() function. This way, the Luhn check will be done only if the regular expression finds a valid match, and after determining the card brand. However, determining the brand of the credit card is not necessary for the Luhn check. All credit cards use the same method.

In JavaScript, you can code the Luhn function as follows:

function luhn(cardnumber) {
  // Build an array with the digits in the card number
  var digits = cardnumber.split('');
  for (var i = 0; i < digits.length; i++) {
    digits[i] = parseInt(digits[i], 10);
  }  
  // Run the Luhn algorithm on the array
  var sum = 0;
  var alt = false;
  for (i = digits.length - 1; i >= 0; i--) {
    if (alt) {
      digits[i] *= 2;
      if (digits[i] > 9) {
        digits[i] -= 9;
      }
    }
    sum += digits[i];
    alt = !alt;
  }
  // Check the result
  if (sum % 10 == 0) {
    document.getElementById('notice').innerHTML += '; Luhn check passed';
  } else {
    document.getElementById('notice').innerHTML += '; Luhn check failed';
  }
}

This function takes a string with the credit card number as a parameter. The card number should consist only of digits. In our example, validatecardnumber() has already stripped spaces and hyphens and determined the card number to have the right number of digits.

First, we split the string into an array of individual characters. Then we iterate over the array to convert the characters into integers. If we don’t convert them, the sum variable will end up as a string concatenation of the digits, rather than the integer addition of the numbers.

The actual algorithm runs on the array, calculating a checksum. If the sum modulus 10 is zero, then the card number is valid. If not, the number is invalid.

See Also

Techniques used in the regular expressions in this recipe are discussed in Chapter 2. Recipe 2.3 explains character classes. Recipe 2.5 explains anchors. Recipe 2.8 explains alternation. Recipe 2.9 explains grouping. Recipe 2.11 explains named capturing groups. Recipe 2.12 explains repetition.