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Regular Expressions (re Module)

A Regular Expression (RegEx) is a special sequence of characters that helps you match, locate, or replace patterns in text strings.

In Python, the re module provides full support for regular expressions.

1. Common re Functions

Function Description
re.search(pattern, string) Scans through string looking for the first location where pattern matches
re.match(pattern, string) Checks for a match ONLY at the beginning of the string
re.findall(pattern, string) Returns a list of all non-overlapping matches in the string
re.finditer(pattern, string) Returns an iterator yielding Match objects over all matches
re.sub(pattern, repl, string) Replaces occurrences of pattern with repl
re.split(pattern, string) Splits string by occurrences of the pattern

2. Metacharacters and Syntax

Character Description Example Matches
. Any character except newline c.t cat, cot, c9t
^ Starts with ^Hello Strings starting with Hello
$ Ends with world$ Strings ending with world
* Zero or more occurrences ab* a, ab, abbb
+ One or more occurrences ab+ ab, abbb (not a)
? Zero or one occurrence ab? a or ab
{n,m} Between n and m occurrences a{2,4} aa, aaa, aaaa
[] A set of characters [a-z] Any lowercase letter
| Either or cat|dog cat or dog
() Capture and group (ab)+ ab, abab

Special Sequences

Sequence Matches Equivalent To
\d Any decimal digit [0-9]
\D Any non-digit character [^0-9]
\w Any alphanumeric character or underscore [a-zA-Z0-9_]
\W Any non-alphanumeric character [^a-zA-Z0-9_]
\s Any whitespace character (space, tab, newline) [ \t\n\r\f\v]
\S Any non-whitespace character [^ \t\n\r\f\v]

3. Practical Examples

Finding All Email Addresses

Example: Extracting Emails with re.findall()
import re

text = "Contact support@projectpy.dev or sales-team@company.org for assistance."

# Pattern: username@domain.extension
pattern = r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}"

emails = re.findall(pattern, text)
print(emails)
Output
['support@projectpy.dev', 'sales-team@company.org']

Validating Phone Numbers with Capturing Groups

Example: Parsing Groups with re.search()
import re

phone_text = "Call us at 555-123-4567 today."
pattern = r"(\d{3})-(\d{3})-(\d{4})"

match = re.search(pattern, phone_text)
if match:
    print("Full Match: ", match.group(0))
    print("Area Code:  ", match.group(1))
    print("Prefix:     ", match.group(2))
    print("Line Number:", match.group(3))
Output
Full Match: 555-123-4567 Area Code: 555 Prefix: 123 Line Number: 4567

Search and Replace with re.sub()

Example: Masking Sensitive Data
import re

text = "Customer SSN: 123-45-6789 and 987-65-4321."
# Replace first 5 digits with asterisks
masked = re.sub(r"\d{3}-\d{2}-", "***-**-", text)

print(masked)
Output
Customer SSN: ***-**-6789 and ***-**-4321.