Python基础教程之正则表达式基本语法以及re模块
什么是正则：
正则表达式是可以匹配文本片段的模式。
正则表达式'Python'可以匹配'python'
正则是个很牛逼的东西，python中当然也不会缺少。
所以今天的Python就跟大家一起讨论一下python中的re模块。
re模块包含对正则表达式的支持。
通配符
.表示匹配任何字符：
‘.ython'可以匹配'python'和'fython'
对特殊字符进行转义：
‘python\.org'匹配‘python.org'
字符集
‘[pj]ython'能够匹配'python'和'jython'
反转字符集
‘[^abc]'可以匹配除了abc之外的任何字符
选择符
使用管道符号|
可选项
加上问好就变为了可选项：
r'(http://)?(www.)?python.org‘只能匹配下面几种：
'http://www.python.org'
'http://python.org'
'www.python.org'
'python.org'
重复子模式
*：允许模式重复0次或多次
+：允许模式重复1次或多次
{m, n}允许模式重复m-n次
当然，正则语法规则很多，远不止上面的这些。但是我们只能点到为止了，因为这篇博客的目的是介绍Python中的模块，re模块。
re 模块使 Python 语言拥有全部的正则表达式功能。
compile 函数根据一个模式字符串和可选的标志参数生成一个正则表达式对象。该对象拥有一系列方法用于正则表达式匹配和替换。
re 模块也提供了与这些方法功能完全一致的函数，这些函数使用一个模式字符串做为它们的第一个参数。
re中重要的函数：
compile(pattern[, flags]) 根据包含正则表达式的字符串创建模式对象
search(pattern, string[, flags]) 在字符串中寻找模式
match(pattern, string[, flags]) 在字符串的开始处匹配模式
split(pattern, string[, maxsplit=0]) 根据匹配项分割字符串
findall(pattern, string) 列出字符串中模式的所有匹配项
sub(pat, rep, string[, count=0]) 字符串中所有pat的匹配项用repl替换
escape(string) 将字符串中所有特殊表达式字符转义
下面就进行简单的应用：
使用match
import re
print(re.match('www', 'www.runoob.com').span()) # 在起始位置匹配
print(re.match('com', 'www.runoob.com')) # 不在起始位置匹配使用search
import re
print(re.search('www', 'www.runoob.com').span()) # 在起始位置匹配
print(re.search('com', 'www.runoob.com').span()) # 不在起始位置匹配这时候需要停一下，match和search的区别呢？
看看结果先：
match例子中结果：
(0, 3)
None
search例子中结果：
(0, 3)
(11, 14)
match()函数只检测RE是不是在string的开始位置匹配，search()会扫描整个string查找匹配；
也就是说match()只有在0位置匹配成功的话才有返回，如果不是开始位置匹配成功的话，match()就返回none。
search()会扫描整个字符串并返回第一个成功的匹配。
使用sub
Python 的re模块提供了re.sub用于替换字符串中的匹配项。
#!/usr/bin/python import re phone = "2004-959-559 # This is Phone Number" # Delete Python-style comments num = re.sub(r'#.*$', "", phone) print "Phone Num : ", num # Remove anything other than digits num = re.sub(r'\D', "", phone) print "Phone Num : ", num
结果：
Phone Num : 2004-959-559
Phone Num : 2004959559
最后献上菊花：
^ 匹配字符串的开头
$ 匹配字符串的末尾。
. 匹配任意字符，除了换行符，当re.DOTALL标记被指定时，则可以匹配包括换行符的任意字符。
[...] 用来表示一组字符,单独列出：[amk] 匹配 'a'，'m'或'k'
[^...] 不在[]中的字符：[^abc] 匹配除了a,b,c之外的字符。
re* 匹配0个或多个的表达式。
re+ 匹配1个或多个的表达式。
re? 匹配0个或1个由前面的正则表达式定义的片段，非贪婪方式
re{ n}
re{ n,} 精确匹配n个前面表达式。
re{ n, m} 匹配 n 到 m 次由前面的正则表达式定义的片段，贪婪方式
a| b 匹配a或b
(re) G匹配括号内的表达式，也表示一个组
(?imx) 正则表达式包含三种可选标志：i, m, 或 x 。只影响括号中的区域。
(?-imx) 正则表达式关闭 i, m, 或 x 可选标志。只影响括号中的区域。
(?: re) 类似 (...), 但是不表示一个组
(?imx: re) 在括号中使用i, m, 或 x 可选标志
(?-imx: re) 在括号中不使用i, m, 或 x 可选标志
(?#...) 注释.
(?= re) 前向肯定界定符。如果所含正则表达式，以 ... 表示，在当前位置成功匹配时成功，否则失败。但一旦所含表达式已经尝试，匹配引擎根本没有提高；模式的剩余部分还要尝试界定符的右边。
(?! re) 前向否定界定符。与肯定界定符相反；当所含表达式不能在字符串当前位置匹配时成功
(?> re) 匹配的独立模式，省去回溯。
\w 匹配字母数字
\W 匹配非字母数字
\s 匹配任意空白字符，等价于 [\t\n\r\f].
\S 匹配任意非空字符
\d 匹配任意数字，等价于 [0-9].
\D 匹配任意非数字
\A 匹配字符串开始
\Z 匹配字符串结束，如果是存在换行，只匹配到换行前的结束字符串。c
\z 匹配字符串结束
\G 匹配最后匹配完成的位置。
\b 匹配一个单词边界，也就是指单词和空格间的位置。例如， 'er\b' 可以匹配"never" 中的 'er'，但不能匹配 "verb" 中的 'er'。
\B 匹配非单词边界。'er\B' 能匹配 "verb" 中的 'er'，但不能匹配 "never" 中的 'er'。
\n, \t, 等. 匹配一个换行符。匹配一个制表符。等
\1...\9 匹配第n个分组的子表达式。
\10 匹配第n个分组的子表达式，如果它经匹配。否则指的是八进制字符码的表达式。
re的正则表达式语法
正则表达式语法表如下：
| 语法 | 意义 | 说明 |
| "." | 任意字符 |
|
| "^" | 字符串开始 | '^hello'匹配'helloworld'而不匹配'aaaahellobbb' |
| "$" | 字符串结尾 | 与上同理 |
|
"*" |
0 个或多个字符（贪婪匹配） |
匹配 |
|
"+" |
1 个或多个字符（贪婪匹配） |
与上同理 |
|
"?" |
0 个或多个字符（贪婪匹配） |
与上同理 |
|
*?,+?,?? |
以上三个取第一个匹配结果（非贪婪匹配） | 匹配 |
Hot AI Tools
Undresser.AI Undress
AI-powered app for creating realistic nude photos
AI Clothes Remover
Online AI tool for removing clothes from photos.
Undress AI Tool
Undress images for free
Clothoff.io
AI clothes remover
AI Hentai Generator
Generate AI Hentai for free.
Hot Article
Hot Tools
Notepad++7.3.1
Easy-to-use and free code editor
SublimeText3 Chinese version
Chinese version, very easy to use
Zend Studio 13.0.1
Powerful PHP integrated development environment
Dreamweaver CS6
Visual web development tools
SublimeText3 Mac version
God-level code editing software (SublimeText3)
Hot Topics
How to Use Python to Find the Zipf Distribution of a Text File
Mar 05, 2025 am 09:58 AM
This tutorial demonstrates how to use Python to process the statistical concept of Zipf's law and demonstrates the efficiency of Python's reading and sorting large text files when processing the law. You may be wondering what the term Zipf distribution means. To understand this term, we first need to define Zipf's law. Don't worry, I'll try to simplify the instructions. Zipf's Law Zipf's law simply means: in a large natural language corpus, the most frequently occurring words appear about twice as frequently as the second frequent words, three times as the third frequent words, four times as the fourth frequent words, and so on. Let's look at an example. If you look at the Brown corpus in American English, you will notice that the most frequent word is "th
How Do I Use Beautiful Soup to Parse HTML?
Mar 10, 2025 pm 06:54 PM
This article explains how to use Beautiful Soup, a Python library, to parse HTML. It details common methods like find(), find_all(), select(), and get_text() for data extraction, handling of diverse HTML structures and errors, and alternatives (Sel
How to Perform Deep Learning with TensorFlow or PyTorch?
Mar 10, 2025 pm 06:52 PM
This article compares TensorFlow and PyTorch for deep learning. It details the steps involved: data preparation, model building, training, evaluation, and deployment. Key differences between the frameworks, particularly regarding computational grap
Mathematical Modules in Python: Statistics
Mar 09, 2025 am 11:40 AM
Python's statistics module provides powerful data statistical analysis capabilities to help us quickly understand the overall characteristics of data, such as biostatistics and business analysis. Instead of looking at data points one by one, just look at statistics such as mean or variance to discover trends and features in the original data that may be ignored, and compare large datasets more easily and effectively. This tutorial will explain how to calculate the mean and measure the degree of dispersion of the dataset. Unless otherwise stated, all functions in this module support the calculation of the mean() function instead of simply summing the average. Floating point numbers can also be used. import random import statistics from fracti
Serialization and Deserialization of Python Objects: Part 1
Mar 08, 2025 am 09:39 AM
Serialization and deserialization of Python objects are key aspects of any non-trivial program. If you save something to a Python file, you do object serialization and deserialization if you read the configuration file, or if you respond to an HTTP request. In a sense, serialization and deserialization are the most boring things in the world. Who cares about all these formats and protocols? You want to persist or stream some Python objects and retrieve them in full at a later time. This is a great way to see the world on a conceptual level. However, on a practical level, the serialization scheme, format or protocol you choose may determine the speed, security, freedom of maintenance status, and other aspects of the program
What are some popular Python libraries and their uses?
Mar 21, 2025 pm 06:46 PM
The article discusses popular Python libraries like NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, Django, Flask, and Requests, detailing their uses in scientific computing, data analysis, visualization, machine learning, web development, and H
How to Create Command-Line Interfaces (CLIs) with Python?
Mar 10, 2025 pm 06:48 PM
This article guides Python developers on building command-line interfaces (CLIs). It details using libraries like typer, click, and argparse, emphasizing input/output handling, and promoting user-friendly design patterns for improved CLI usability.
Scraping Webpages in Python With Beautiful Soup: Search and DOM Modification
Mar 08, 2025 am 10:36 AM
This tutorial builds upon the previous introduction to Beautiful Soup, focusing on DOM manipulation beyond simple tree navigation. We'll explore efficient search methods and techniques for modifying HTML structure. One common DOM search method is ex
