Regular expressions (regex) are patterns used to match character combinations in strings. They provide a concise way to search, extract, and manipulate text based on specific rules.

Using the re Module

Python's re module provides regex functionality:

import re

# Match a pattern at string start
match_result = re.match(r'pattern', 'input_string')

# Extract matched substring
if match_result:
    print(match_result.group())

Basic Match Example

import re

result = re.match(r'itcast', 'itcast.cn')
print(result.group())  # Output: itcast

Character Matching Patterns

Pattern Examples

import re

# Dot matches any character
print(re.match(r'.', 'a').group())  # a

# Character set matching
print(re.match(r'[hH]', 'Hello').group())  # H
print(re.match(r'[0-9]', '7Up').group())   # 7

# Digit matching
print(re.match(r'嫦娥\d号', '嫦娥1号发射成功').group())  # 嫦娥1号

Raw Strings

Use raw strings (prefix r) to avoid excessive escaping:

path = r"c:\a\b\c"
print(path)  # c:\a\b\c

# Proper regex escaping
match = re.match(r"c:\\a", path)
print(match.group())  # c:\a

Quantifiers

Quantifier Examples

# Zero or more lowercase letters
print(re.match(r'[A-Z][a-z]*', 'M').group())  # M

# Valid variable names
print(re.match(r'[a-zA-Z_]+\w*', '_name').group())  # _name

# Numbers 0-99
print(re.match(r'[1-9]?\d', '33').group())  # 33

# Password length (8-20 chars)
print(re.match(r'[\w]{8,20}', 'abc123_xyz').group())

Boundary Matching

Boundary Examples

# Exact domain match
match = re.match(r'[\w]{4,20}@163\.com$', 'user@163.com')
print(match.group())

# Word boundary
print(re.search(r'\bver\b', 'ho ver').group())  # ver

Grouping

Grouping Examples

# Match 0-100
print(re.match(r'100|[1-9]?\d$', '100').group())  # 100

# Email provider extraction
match = re.match(r'\w{4,20}@(163|126|qq)\.com', 'test@126.com')
print(match.group(1))  # 126

# Backreference for HTML tags
match = re.match(r'<(\w+)>.*</\1>', '<title>Text</title>')
print(match.group())  # <title>Text</title>

# Named groups
html_match = re.match(
    r"<(?P<tag1>\w+)><(?P<tag2>\w+)>.*</(?P=tag2)></(?P=tag1)>",
    "<div><span>Content</span></div>"
)
print(html_match.group())

Advanced re Methods

search()

Find first occurrence anywhere in string:

match = re.search(r'\d+', 'Views: 9999')
print(match.group())  # 9999

findall()

Find all non-overlapping matches:

numbers = re.findall(r'\d+', 'Python=9999, C=7890, C++=12345')
print(numbers)  # ['9999', '7890', '12345']

sub()

Replace matched patterns:

# Simple replacement
print(re.sub(r'\d+', '998', 'Python=997'))  # Python=998

# Function-based replacement
def increment(match):
    return str(int(match.group()) + 1)

print(re.sub(r'\d+', increment, 'Python=99'))  # Python=100

split()

Split string using pattern:

tokens = re.split(r":|", "info:xiaoZhang 33 shandong")
print(tokens)  # ['info', 'xiaoZhang', '33', 'shandong']

Greedy vs Non-Greedy Matching

Quantifiers are greedy by default. Add ? to make them non-greedy:

# Greedy match
greedy = re.match(r'.+(\d+-\d+-\d+-\d+)', "ID: 234-235-22-423")
print(greedy.group(1))  # '4-235-22-423'

# Non-greedy match
non_greedy = re.match(r'.+?(\d+-\d+-\d+-\d+)', "ID: 234-235-22-423")
print(non_greedy.group(1))  # '234-235-22-423'