^RegexR4Strn<3$

Transcript

1. Regular Expressions are for the Strong of Heart ^RegexR4Strn<3$

2. We all know RegExps, amirite?

3. Stephen Kleene invents Regular Expressions in 1956.

4. In 1968, Ken Thompson implements Regular Expressions to match pattern

   in text files. grep, global search for regular expressions and print matching lines

5. So why are they called Regular?

6. ‘Regular’ comes the Regular Sets used by Kleene to describe

   Regular Languages.

7. WAT

8. Some CS background

9. A word is a sequence of symbols. ! The symbols

   we are using is called the alphabet.

10. Given the alphabet {0,1,2,3,4,5,6,7,8,9} ! We could make words like

    0 1 42 1337 9001

11. A language is a subset of all possible words.

12. The language composed of James Bond colleagues codes would have

    these words 007 002 006 0099 ! But not these words 59 078 0935

13. PROBLEM

14. How can we tell if a word belongs to a

    language?

15. Can we answer this question by using a machine with

    finite memory?

16. If we can examine a word symbol by symbol (without

    requiring arbitrary amounts of memory), then we call the language regular.

17. Let’s say we have a language with only the word

    42

18. word.length == 2 && word[0] == 4 && word[1] ==

    2 REGULAR

19. All languages with finite elements are regular, since we can

    just nest if conditions.

20. Let’s look at the language with all prime numbers between

    10 and 99

21. if word[0] == 1 if word[1] == 1 # 11

    return true if word[1] == 3 # 13 return true if word[1] == 7 # 17 return true if word[1] == 9 # 19 return true ! ... ! return false REGULAR

22. Even if all finite sets are regular, not all regular

    sets are finite.

23. i.e. There can be infinite secret agents of the British

    Intelligence 007 0042 00300 009292

24. Formally, a Regular Language can be described by a FSM,

    aka Finite State Machine.

25. start state: if input == 0 then goto state 2

    start state: if input == 1 then fail start state: if input == 2 then fail start state: if input == 3 then fail ... ! state 2: if input == 0 then goto state 3 state 2: if input == 1 then fail state 2: if input == 2 then fail state 2: if input == 3 then fail ... ! state 3: for any input, accept REGULAR

26. Alternatively, we can use a Regular Grammar.

27. S → 0 A ! A → 0 B !

    B → 0 B B → 1 B B → 2 B B → 3 B B → 4 B B → 5 B B → 6 B B → 7 B B → 8 B B → 9 B B → ε REGULAR

28. Or… A Regular Expression.

29. 00[0-9]+ REGULAR

30. wow so short very expressive how possible such regular pliz

    more succint

31. FUNDAMENTALS

32. Every character can be interpreted as a regular character, which

    has a literal meaning, or as a meta-character, which has a special meaning.

33. So which are the metacharacters?

34. WELL, WE DON’T KNOW FOR SURE

35. There are many different flavours of regular expressions, and each

    have their own set of meta-characters. UNIX HATERS

36. The \ character lets us switch from the regular meaning

    to the meta-meaning and back.

37. TRIVIA TIME

38. In grep’s default regexp engine, () and {} are considered

    literal, because Ken Thompson wanted to grep C code.

39. RUBY METACHARACTERS

40. ANY CHARACTER

41. . ANY CHARACTER

42. BOOLEAN OR

43. gray|grey BOOLEAN OR

44. GROUPING

45. gr(a|e)y GROUPING

46. CHARACTER SET

47. gr[ae]y CHARACTER SET

48. gr[^io]y NEGATED CHARACTER SET

49. gr[a-z]y CHARACTER RANGE

50. [\d] => [0-9] DIGITS

51. [\w] => [0-9a-zA-Z_] WORD CHARACTER

52. WHITESPACE [\s] => [ \t\r\n]

53. NEGATED SETS [\D] => [^\d] [\S] => [^\s] [\W] =>

    [^\w]

54. QUANTIFIERS

55. colou?r 0 OR 1

56. yeah* 0 OR MORE

57. foo+bar 1 OR MORE

58. ah{3} BRACES, EXACT

59. oh{3,7} BRACES, RANGE

60. aw{2,} BRACES, OPEN RANGE

61. ANCHORS

62. ^begin BEGINNING OF LINE

63. end$ END OF LINE

64. \bword\b WORD BOUNDARIES

65. WILD

66. .* ANYTHING

67. .*? NON-GREEDY ANYTHING

68. (\w+) \1 BACK-REFERENCE

69. MOAR GROUPS

70. (?:https?|ftp)://(.*) NON-CAPTURING

71. soft(?=ware) POSITIVE LOOK-AHEAD

72. hard(?!ware) NEGATIVE LOOK-AHEAD

73. (?<=tender)love POSITIVE LOOK-BEHIND

74. (?<!tainted)love NEGATIVE LOOK-BEHIND

75. SOME COOL THINGS YOU CAN DO WITH REGEXES

76. ^\s*# Find all comments

77. \s+$ Find all trailing whitespaces

78. (['"]).*?\1 Find all single/double quoted strings in some blurb of

    text

79. ^(?!Hello).* Find all lines not beginning with “Hello”

80. \w+(?<!ay)\b Find all words not ending with ay

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82. None

83. REGEX TIPS

84. Always use anchors

85. Make your regex as specific as possible

86. Build the regex step by step

87. RESOURCES

88. Mastering Regular Expressions

89. regular-expressions.info

90. rubular.com

91. regex101.com

92. regexcrossword.com

93. regex.alf.nu

94. Some people, when confronted with a problem, think “I know,

    I'll use regular expressions.” Now they have two problems.
95. None

96. $