Regular Expressions 101

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Detailed match information will be displayed here automatically.
  • All Tokens
  • Common Tokens
  • General Tokens
  • Anchors
  • Meta Sequences
  • Quantifiers
  • Group Constructs
  • Character Classes
  • Flags/Modifiers
  • Substitution
  • A single character of: a, b or c
    [abc]
  • A character except: a, b or c
    [^abc]
  • A character in the range: a-z
    [a-z]
  • A character not in the range: a-z
    [^a-z]
  • A character in the range: a-z or A-Z
    [a-zA-Z]
  • Any single character
    .
  • Alternate - match either a or b
    a|b
  • Any whitespace character
    \s
  • Any non-whitespace character
    \S
  • Any digit
    \d
  • Any non-digit
    \D
  • Any word character
    \w
  • Any non-word character
    \W
  • Non-capturing group
    (?:...)
  • Capturing group
    (...)
  • Zero or one of a
    a?
  • Zero or more of a
    a*
  • One or more of a
    a+
  • Exactly 3 of a
    a{3}
  • 3 or more of a
    a{3,}
  • Between 3 and 6 of a
    a{3,6}
  • Start of string
    ^
  • End of string
    $
  • A word boundary
    \b
  • Non-word boundary
    \B

Regular Expression
No Match

r"
"
gm

Test String

Substitution

Processing...

Code Generator

Language

Generated Code

# coding=utf8 # the above tag defines encoding for this document and is for Python 2.x compatibility import re regex = r"(?<=[\s])(?=[.,;?])" test_str = "The present invention provides an apparatus and a method for classifying and recognizing image patterns using a second-order neural network , thereby achieving high-rate parallel processing while lowering the complexity . The second-order neural network , which is made of adders and multipliers , corrects positional translations generated in a complex-log mapping unit to output the same result for the same object irrespective of the scale and/or rotation of the object . The present invention enables high-rate image pattern classification and recognition based on parallel processing , which is the advantage obtained in neural network models , because consistent neural networks and consistent network structure computation models are applied to all steps from the image input step to the pattern classifying and recognizing step ." subst = "" # You can manually specify the number of replacements by changing the 4th argument result = re.sub(regex, subst, test_str, 0, re.MULTILINE) if result: print (result) # Note: for Python 2.7 compatibility, use ur"" to prefix the regex and u"" to prefix the test string and substitution.

Please keep in mind that these code samples are automatically generated and are not guaranteed to work. If you find any syntax errors, feel free to submit a bug report. For a full regex reference for Python, please visit: https://docs.python.org/3/library/re.html