Python re Module

Python'sremodule is a standard library module for handling regular expressions.

Regular expressions (Regular Expression, abbreviated as regex or regexp) are a powerful tool for matching, searching, and manipulating text.

By usingrethe module, you can use regular expressions in Python to process strings.

Why use the re module?

In text processing, we often need to find specific patterns or replace certain characters. For example, validating email addresses, extracting links from web pages, or formatting text. Writing code manually to accomplish these tasks can be very tedious, while regular expressions provide a concise and efficient way to solve these problems.

Basic usage of the re module

1. Import the re module

Before usingrethe module, you first need to import it:

import re

2. Common re module functions

2.1 `re.match()`

re.match()The function is used to match a regular expression from the beginning of a string. If the match succeeds, it returns a match object; otherwise, it returnsNone。

Example

import re

pattern = r"hello"
text = "hello world"

match = re.match(pattern, text)
if match:
print("Match successful:", match.group())
else:
print("Match failed")

Output:

匹配成功: hello

2.2 `re.search()`

re.search()The function is used to search for the first match of a regular expression in a string. Unlikere.match()different,re.search()does not require the match to start from the beginning of the string.

Example

import re

pattern = r"world"
text = "hello world"

match = re.search(pattern, text)
if match:
print("Match successful:", match.group())
else:
print("Match failed")

Output:

匹配成功: world

2.3 `re.findall()`

re.findall()The function is used to find all substrings in a string that match the regular expression, and returns a list.

Example

import re

pattern = r"\d+"
text = "There are 3 apples and 5 oranges."

matches = re.findall(pattern, text)
print("Numbers found:", matches)

Output:

找到的数字: ['3', '5']

2.4 `re.sub()`

re.sub()The function is used to replace the parts of a string that match the regular expression.

Example

import re

pattern = r"apple"
text = "I have an apple."

new_text = re.sub(pattern, "banana", text)
print("Replaced text:", new_text)

Output:

替换后的文本: I have an banana.

Basic syntax of regular expressions

1. Ordinary characters

Ordinary characters (such as letters, digits) match themselves directly.

Example

import re

pattern = r"cat"
text = "The cat is on the mat."

match = re.search(pattern, text)
if match:
print("Match successful:", match.group())

Output:

匹配成功: cat

2. Special characters

There are some special characters in regular expressions that have special meanings. For example:

.: Matches any single character (except newline).
*: Matches the preceding character zero or more times.
+: Matches the preceding character one or more times.
?: Matches the preceding character zero or one time.
\d: Matches any digit character (equivalent to[0-9]）。
\w: Matches any letter, digit, or underscore character (equivalent to[a-zA-Z0-9_]）。

Example

import re

pattern = r"\d+"
text = "The price is 100 dollars."

match = re.search(pattern, text)
if match:
print("Match successful:", match.group())

Output:

匹配成功: 100

3. Character sets

Character sets are used to match any one character from a set. For example,[abc]matchesa、borc。

Example

import re

pattern = r"[aeiou]"
text = "Hello World!"

matches = re.findall(pattern, text)
print("Vowels found:", matches)

Output:

找到的元音字母: ['e', 'o', 'o']

4. Grouping

Grouping allows you to combine multiple characters together and operate on them. For example,(abc)matchesabc。

Example

import re

pattern = r"(ab)+"
text = "ababab"

match = re.search(pattern, text)
if match:
print("Match successful:", match.group())

Output:

匹配成功: ababab

Practical exercises

Exercise 1: Validate email addresses

Write a regular expression to validate the format of an email address.

Example

import re

pattern = r"^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+$"
email = "example@example.com"

if re.match(pattern, email):
print("Valid email address")
else:
print("Invalid email address")

Exercise 2: Extract phone numbers

Write a regular expression to extract phone numbers from text.

Example

import re

pattern = r"\d{3}-\d{3}-\d{4}"
text = "My phone number is 123-456-7890."

match = re.search(pattern, text)
if match:
print("Phone numbers found:", match.group())

1. Core functions

Method	Description	Example
`re.compile(pattern)`	Precompile a regular expression (improves reuse performance)	`pat = re.compile(r'\d+')`
`re.search(pattern, string)`	Search for the first match in a string	`re.search(r'\d+', 'a1b2')`→ matches`'1'`
`re.match(pattern, string)`	Match from the beginning of the string	`re.match(r'\d+', '123a')`→ matches`'123'`
`re.fullmatch(pattern, string)`	The entire string fully matches	`re.fullmatch(r'\d+', '123')`→ matches`'123'`
`re.findall(pattern, string)`	Return a list of all non-overlapping matches	`re.findall(r'\d+', 'a1b22c')` → `['1', '22']`
`re.finditer(pattern, string)`	Return an iterator of all matches (including position information)	`for m in re.finditer(r'\d+', 'a1b2'): print(m.group())`
`re.sub(pattern, repl, string)`	Replace matches	`re.sub(r'\d+', 'X', 'a1b2')` → `'aXbX'`
`re.split(pattern, string)`	Split the string by matches	`re.split(r'\d+', 'a1b2c')` → `['a', 'b', 'c']`
`re.escape()`	Escape special characters	`re.escape("C:\\Users\\test.txt")`
`re.purge()`	Clear cache	`re.purge()`

2. Match object (`Match`) methods/attributes

Method/Attribute	Description	Example
`group()`	Return the entire matched string	`m.group()` → `'abc'`
`group(n)`	Return the content of the nth capture group	`m = re.search(r'(\d)(\d)', '12'); m.group(1)` → `'1'`
`groups()`	Return a tuple of all capture groups	`m.groups()` → `('1', '2')`
`start()`/`end()`	Start/end position of the match	`m.start()` → `0`
`span()`	Return the match range`(start, end)`	`m.span()` → `(0, 2)`

3. Regular expression metacharacters (partial)

Metacharacter	Description	Example match
`.`	Match any character (except newline)	`a.c` → `'abc'`
`\d`	Match a digit	`\d+` → `'123'`
`\D`	Match a non-digit	`\D+` → `'abc'`
`\w`	Match a word character (letter, digit, underscore)	`\w+` → `'Ab_1'`
`\W`	Match a non-word character	`\W+` → `'!@#'`
`\s`	Match a whitespace character (space, tab, etc.)	`\s+` → `' \t'`
`\S`	Match a non-whitespace character	`\S+` → `'abc'`
`[]`	Character set	`[A-Za-z]`→ any letter
`^`	Match the beginning of a string	`^\d+`→ digit at the beginning
`$`	Match the end of a string	`\d+$`→ digit at the end
*``**	Match the preceding character zero or more times	`a*` → `''`, `'aaa'`
`+`	Match the preceding character one or more times	`a+` → `'a'`, `'aaa'`
`?`	Match the preceding character zero or one time	`a?` → `''`, `'a'`
`{m,n}`	Match the preceding character m to n times	`a{2,3}` → `'aa'`, `'aaa'`
`\|`	OR operation	`cat\|dog` → `'cat'`or`'dog'`
`()`	Capture group	`(\d+)`→ extract digits

4. Compilation flags (`flags`parameter)

Flag	Description	Example
`re.IGNORECASE` (`re.I`)	Ignore case	`re.search(r'abc', 'ABC', re.I)`
`re.MULTILINE` (`re.M`)	Multiline mode (affects`^`and`$`）	`re.findall(r'^\d+', '1\n2', re.M)` → `['1', '2']`
`re.DOTALL` (`re.S`)	Let`.`Match all characters including newlines	`re.search(r'a.*b', 'a\nb', re.S)`
`re.ASCII`	Let`\w`, `\W`etc. only match ASCII characters	`re.search(r'\w+', 'こん', re.ASCII)`→ no match
`re.VERBOSE` (`re.X`)	Allow comments and spaces in the regex	`re.compile(r'''\d+ # 匹配数字''', re.X)`

Example

1. Extract email addresses

Example

import re

text = "Contact: admin@example.com, support@test.org"
emails = re.findall(r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b', text)
print(emails) # Output: ['admin@example.com', 'support@test.org']

2. Replace date formats

Example

date_str = "Today is 05-15-2023"
new_str = re.sub(r'(\d{2})-(\d{2})-(\d{4})', r'\3year\1month\2day', date_str)
print(new_str) # Output: "Today is 2023-05-15"

3. Multi-condition matching

Example

pattern = re.compile(r'''
^(?P<username>\w+) # username
:(?P<password>\S+) # password
@(?P<domain>\w+\.\w+) # domain
$''', re.VERBOSE)

m = pattern.match("john:pass123@example.com")
if m:
print(m.groupdict()) # Output: {'username': 'john', 'password': 'pass123', 'domain': 'example.com'}

4. Split complex strings

Example

text = "Apple1Banana2Cherry3Date"
parts = re.split(r'\d+', text)
print(parts) # Output: ['Apple', 'Banana', 'Cherry', 'Date']

Notes

Raw strings: It is recommended to use raw strings for regular expressions (r'\d'), to avoid escape character conflicts.
Greedy matching: Greedy matching by default (e.g..*will match the longest possible), you need to use.*?to implement non-greedy matching.
Performance Optimization: Frequently used regular expressions should prioritize usingre.compile()precompilation.
Backtracking Issues: Complex regular expressions may cause performance issues (such as nested quantifiers(a+)+）。

Other Extensions

Python3Advanced Tutorial

Python re Module

Why use the re module?

Basic usage of the re module

1. Import the re module

2. Common re module functions

2.1 re.match()

Example

2.2 re.search()

Example

2.3 re.findall()

Example

2.4 re.sub()

Example

Basic syntax of regular expressions

1. Ordinary characters

Example

2. Special characters

Example

3. Character sets

Example

4. Grouping

Example

Practical exercises

Exercise 1: Validate email addresses

Example

Exercise 2: Extract phone numbers

Example

1. Core functions

2. Match object (Match) methods/attributes

3. Regular expression metacharacters (partial)

4. Compilation flags (flagsparameter)

Example

Example

Example

Example

Example

Notes

2.1 `re.match()`

2.2 `re.search()`

2.3 `re.findall()`

2.4 `re.sub()`

2. Match object (`Match`) methods/attributes

4. Compilation flags (`flags`parameter)