下面列出Python正则表达式的几种匹配用法:
1.测试正则表达式是否匹配字符串的全部或部分
regex=ur"" #正则表达式 if re.search(regex, subject): do_something() else: do_anotherthing()
2.测试正则表达式是否匹配整个字符串
regex=ur"\Z" #正则表达式末尾以\Z结束 if re.match(regex, subject): do_something() else: do_anotherthing()
3.创建一个匹配对象,然后通过该对象获得匹配细节(Create an object with details about how the regex matches (part of) a string)
regex=ur"" #正则表达式 match = re.search(regex, subject) if match: # match start: match.start() # match end (exclusive): atch.end() # matched text: match.group() do_something() else: do_anotherthing()
4.获取正则表达式所匹配的子串(Get the part of a string matched by the regex)
regex=ur"" #正则表达式 match = re.search(regex, subject) if match: result = match.group() else: result = ""
5. 获取捕获组所匹配的子串(Get the part of a string matched by a capturing group)
regex=ur"" #正则表达式 match = re.search(regex, subject) if match: result = match.group(1) else: result = ""
6. 获取有名组所匹配的子串(Get the part of a string matched by a named group)
regex=ur"" #正则表达式 match = re.search(regex, subject) if match: result = match.group"groupname") else: result = ""
7. 将字符串中所有匹配的子串放入数组中(Get an array of all regex matches in a string)
result = re.findall(regex, subject)
8.遍历所有匹配的子串(Iterate over all matches in a string)
for match in re.finditer(r"<(.*?)\s*.*?/\1>", subject) # match start: match.start() # match end (exclusive): atch.end() # matched text: match.group()
9.通过正则表达式字符串创建一个正则表达式对象(Create an object to use the same regex for many operations)
reobj = re.compile(regex)
10.用法1的正则表达式对象版本(use regex object for if/else branch whether (part of) a string can be matched)
reobj = re.compile(regex) if reobj.search(subject): do_something() else: do_anotherthing()
11.用法2的正则表达式对象版本(use regex object for if/else branch whether a string can be matched entirely)
reobj = re.compile(r"\Z") #正则表达式末尾以\Z 结束 if reobj.match(subject): do_something() else: do_anotherthing()
12.创建一个正则表达式对象,然后通过该对象获得匹配细节(Create an object with details about how the regex object matches (part of) a string)
reobj = re.compile(regex) match = reobj.search(subject) if match: # match start: match.start() # match end (exclusive): atch.end() # matched text: match.group() do_something() else: do_anotherthing()
13.用正则表达式对象获取匹配子串(Use regex object to get the part of a string matched by the regex)
reobj = re.compile(regex) match = reobj.search(subject) if match: result = match.group() else: result = ""
14.用正则表达式对象获取捕获组所匹配的子串(Use regex object to get the part of a string matched by a capturing group)
reobj = re.compile(regex) match = reobj.search(subject) if match: result = match.group(1) else: result = ""
15.用正则表达式对象获取有名组所匹配的子串(Use regex object to get the part of a string matched by a named group)
reobj = re.compile(regex) match = reobj.search(subject) if match: result = match.group("groupname") else: result = ""
16.用正则表达式对象获取所有匹配子串并放入数组(Use regex object to get an array of all regex matches in a string)
reobj = re.compile(regex) result = reobj.findall(subject)
17.通过正则表达式对象遍历所有匹配子串(Use regex object to iterate over all matches in a string)
reobj = re.compile(regex) for match in reobj.finditer(subject): # match start: match.start() # match end (exclusive): match.end() # matched text: match.group()
字符串替换
1.替换所有匹配的子串
#用newstring替换subject中所有与正则表达式regex匹配的子串 result = re.sub(regex, newstring, subject)
2.替换所有匹配的子串(使用正则表达式对象)
reobj = re.compile(regex) result = reobj.sub(newstring, subject)
字符串拆分
1.字符串拆分
result = re.split(regex, subject)
2.字符串拆分(使用正则表示式对象)
reobj = re.compile(regex) result = reobj.split(subject)
以上这篇常见python正则用法的简单实例就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持脚本之家。

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

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

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

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 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

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

In this tutorial you'll learn how to handle error conditions in Python from a whole system point of view. Error handling is a critical aspect of design, and it crosses from the lowest levels (sometimes the hardware) all the way to the end users. If y

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


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

Atom editor mac version download
The most popular open source editor

MantisBT
Mantis is an easy-to-deploy web-based defect tracking tool designed to aid in product defect tracking. It requires PHP, MySQL and a web server. Check out our demo and hosting services.

Dreamweaver Mac version
Visual web development tools

PhpStorm Mac version
The latest (2018.2.1) professional PHP integrated development tool

Safe Exam Browser
Safe Exam Browser is a secure browser environment for taking online exams securely. This software turns any computer into a secure workstation. It controls access to any utility and prevents students from using unauthorized resources.
