Skip to main content

Regex

Regular Expressions (Regex) are powerful tools for pattern matching and text processing in programming.
Author

Benedict Thekkel

1. Basics of Regex Syntax

Common Regex Symbols

Symbol Description Example Matches
. Any single character except newline a.b acb, a1b, a_b
^ Start of string ^hello Matches “hello world”, not “world hello”
$ End of string world$ Matches “hello world”, not “world hello”
* 0 or more repetitions ab* Matches a, ab, abb, abbb
+ 1 or more repetitions ab+ Matches ab, abb, abbb but not a
? 0 or 1 occurrence colou?r Matches color and colour
{n} Exactly n repetitions a{3} Matches aaa
{n,} At least n repetitions a{2,} Matches aa, aaa, aaaa
{n,m} Between n and m repetitions a{2,4} Matches aa, aaa, aaaa
[] Character set [abc] Matches a, b, or c
[^ ] Negated character set [^abc] Matches any character except a, b, c
\d Digit (0-9) \d+ Matches 123, 456
\D Non-digit \D+ Matches abc, hello
\w Word character (a-z, A-Z, 0-9, _) \w+ Matches hello123
\W Non-word character \W+ Matches @#$%^
\s Whitespace \s+ Matches spaces, tabs, newlines
\S Non-whitespace \S+ Matches hello, world
\b Word boundary \bhello\b Matches “hello” in “hello world” but not “helloworld”
\B Non-word boundary \Bhello\B Matches “hello” inside a word

2. Groups & Alternation

Using Parentheses for Grouping

  • () groups patterns together.
  • Example: (hello|hi) matches "hello" or "hi".
import re
pattern = re.compile(r"(hello|hi) world")
print(bool(pattern.search("hi world")))  # True

Capturing Groups

  • Captures parts of a match for later use.
  • Example: Extract date (2024-02-17)
match = re.search(r"(\d{4})-(\d{2})-(\d{2})", "Date: 2024-02-17")
if match:
    print(match.group(1))  # "2024"
    print(match.group(2))  # "02"
    print(match.group(3))  # "17"

Non-Capturing Groups (?:...)

  • Groups without storing matches.
re.search(r"(?:hello|hi) world", "hi world")  # Matches but not stored

Named Groups (?P<name>...)

  • Assign names to capture groups.
match = re.search(r"(?P<year>\d{4})-(?P<month>\d{2})-(?P<day>\d{2})", "2024-02-17")
print(match.group("year"))  # 2024

3. Lookaheads & Lookbehinds

Lookaheads and Lookbehinds match a pattern without consuming characters.

Positive Lookahead (?=...)

  • Ensures something follows a pattern.
  • Example: Match hello only if it’s followed by world
re.search(r"hello(?= world)", "hello world")  # Matches

Negative Lookahead (?!...)

  • Ensures something does not follow.
  • Example: Match hello but not hello world
re.search(r"hello(?! world)", "hello everyone")  # Matches

Positive Lookbehind (?<=...)

  • Ensures something precedes a pattern.
  • Example: Match world only if preceded by hello
re.search(r"(?<=hello )world", "hello world")  # Matches

Negative Lookbehind (?<!...)

  • Ensures something does not precede.
  • Example: Match world only if not preceded by hello
re.search(r"(?<!hello )world", "hi world")  # Matches

4. Regex in Python (re Module)

Finding All Matches (findall)

import re
text = "My number is 123-456-7890 and yours is 987-654-3210."
pattern = r"\d{3}-\d{3}-\d{4}"
matches = re.findall(pattern, text)
print(matches)  # ['123-456-7890', '987-654-3210']

Replacing Text (sub)

text = "I love Java, Java is great!"
print(re.sub(r"Java", "Python", text))  # "I love Python, Python is great!"

Splitting Strings (split)

text = "apple,banana;orange|grape"
print(re.split(r"[,;|]", text))  # ['apple', 'banana', 'orange', 'grape']

5. Practical Use Cases

1️⃣ Validate Email Addresses

email_pattern = r"^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+$"
print(bool(re.match(email_pattern, "user@example.com")))  # True

2️⃣ Validate Phone Numbers

phone_pattern = r"^\+?[1-9]\d{1,14}$"
print(bool(re.match(phone_pattern, "+1234567890")))  # True

3️⃣ Extract Hashtags from Text

text = "Loving the #Python and #AI community!"
hashtags = re.findall(r"#\w+", text)
print(hashtags)  # ['#Python', '#AI']

4️⃣ Validate Password Strength

password_pattern = r"^(?=.*[A-Z])(?=.*\d)(?=.*[@$!%*?&])[A-Za-z\d@$!%*?&]{8,}$"
print(bool(re.match(password_pattern, "StrongP@ss1")))  # True

6. Regex Performance Optimization

  • Use raw strings (r"pattern") to avoid escaping issues.

  • Use compiled regex when reusing patterns:

    pattern = re.compile(r"\d{3}-\d{3}-\d{4}")
pattern.findall("Call 123-456-7890 or 987-654-3210")

  • Avoid backtracking traps by limiting .* usage:

    # Bad (greedy match)
re.search(r"<.*>", "<div>content</div>")  # Matches "<div>content</div>"

# Good (lazy match)
re.search(r"<.*?>", "<div>content</div>")  # Matches "<div>"

Conclusion

🚀 Regex is powerful for text search, validation, and extraction. ✔ Learn the basic symbols (., *, ?, {})
✔ Use groups & lookaheads for flexible patterns
✔ Apply regex for emails, passwords, phone numbers, logs
✔ Optimize with compiled regex and lazy matches

Back to top