Regular Expressions 101

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An explanation of your regex will be automatically generated as you type.
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

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"^(Q:.*?\n)(?!Q:)([\s\S]+?(?=^Q:|\Z))" test_str = ("Q: What are the service limits associated with Amazon Athena?\n" "Please click here to learn more about service limits.\n" " \n" "Q: What is the underlying technology behind Amazon Athena?\n" "Amazon Athena uses Presto with full standard SQL support and works with a variety of standard data formats, including CSV, JSON, ORC, Avro, and Parquet. Athena can handle complex analysis, including large joins, window functions, and arrays. Because Amazon Athena uses Amazon S3 as the underlying data store, it is highly available and durable with data redundantly stored across multiple facilities and multiple devices in each facility. Learn more about Presto here.\n" " \n" "Q: How does Amazon Athena store table definitions and schema?\n" "Amazon Athena uses a managed Data Catalog to store information and schemas about the databases and tables that you create for your data stored in Amazon S3. In regions where AWS Glue is available, you can upgrade to using the AWS Glue Data Catalog with Amazon Athena. In regions where AWS Glue is not available, Athena uses an internal Catalog.\n" "You can modify the catalog using DDL statements or via the AWS Management Console. Any schemas you define are automatically saved unless you explicitly delete them. Athena uses schema-on-read technology, which means that your table definitions applied to your data in S3 when queries are being executed. There’s no data loading or transformation required. You can delete table definitions and schema without impacting the underlying data stored on Amazon S3.\n") matches = re.finditer(regex, test_str, re.MULTILINE) for matchNum, match in enumerate(matches, start=1): print ("Match {matchNum} was found at {start}-{end}: {match}".format(matchNum = matchNum, start = match.start(), end = match.end(), match = match.group())) for groupNum in range(0, len(match.groups())): groupNum = groupNum + 1 print ("Group {groupNum} found at {start}-{end}: {group}".format(groupNum = groupNum, start = match.start(groupNum), end = match.end(groupNum), group = match.group(groupNum))) # 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