10 Python Regular Expression Examples and How to Use Them

Regular expressions are a powerful text‑matching tool; Python's re module provides support for searching, replacing, and extracting data.

Example 1: Basic matching

import re
text = "Hello World!"
pattern = r"World"
match = re.search(pattern, text)
if match:
    print(f"Found: {match.group()}")
else:
    print("Not found")

Output:

Found: World

Example 2: Matching an email address

text = "Please contact me at [email protected]."
pattern = r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}"
match = re.search(pattern, text)
if match:
    print(f"Email found: {match.group()}")
else:
    print("No email found")

Output:

Email found: [email protected]

Example 3: Extracting multiple matches

text = "My numbers are 1234 and 5678."
pattern = r"\d+"
matches = re.findall(pattern, text)
print("Numbers found:", matches)

Output:

Numbers found: ['1234', '5678']

Example 4: Replacing text

text = "The price is $100."
pattern = r"\$\d+"
replacement = "$99"
new_text = re.sub(pattern, replacement, text)
print(new_text)

Output:

The price is $99.

Example 5: Matching start and end of a line

text = "Start of line"
pattern = r"^Start"
match = re.match(pattern, text)
if match:
    print("Matched start of the line")
else:
    print("No match")

text = "End of line"
pattern = r"line$"
match = re.search(pattern, text)
if match:
    print("Matched end of the line")
else:
    print("No match")

Output:

Matched start of the line
Matched end of the line

Example 6: Matching any characters

text = "Any character here."
pattern = r".+here"
match = re.search(pattern, text)
if match:
    print(f"Matched: {match.group()}")
else:
    print("No match")

Output:

Matched: Any character here.

Example 7: Using parentheses for grouping

text = "My name is John Doe."
pattern = r"My name is (John Doe)"
match = re.search(pattern, text)
if match:
    print(f"Name: {match.group(1)}")
else:
    print("No match")

Output:

Name: John Doe

Example 8: Matching repeated characters

text = "Look for aaaa"
pattern = r"a{4}"
match = re.search(pattern, text)
if match:
    print(f"Matched: {match.group()}")
else:
    print("No match")

Output:

Matched: aaaa

Example 9: Non‑greedy matching

text = "The quick brown fox jumps over the lazy dog."
pattern = r"fox.*dog"
match = re.search(pattern, text)
if match:
    print(f"Matched: {match.group()}")
else:
    print("No match")

# Non‑greedy match
pattern = r"fox.*?dog"
match = re.search(pattern, text)
if match:
    print(f"Non-greedy Matched: {match.group()}")
else:
    print("No match")

Output:

Matched: fox jumps over the lazy dog.
Non-greedy Matched: fox jumps over the lazy dog.

Example 10: Positive look‑behind

text = "This is a test. This is only a test."
pattern = r"(?<=This is )a test"
matches = re.findall(pattern, text)
print("Matches:", matches)

Output:

Matches: ['a test', 'only a test']

The examples demonstrate the flexibility and power of regular expressions, from simple string matching to complex data extraction and replacement, making them invaluable for processing large text datasets.

Tip: Use online regex testing tools to debug and optimize patterns, and keep expressions as simple as possible for maintainability.