python method to extract string using regular expression
Preface
I won’t go into the basic knowledge of regular expressions. If you are interested, you can click here. There are generally two cases of extraction. One is to extract characters at a single position in the text. String, the other is to extract strings with multiple consecutive positions. Log analysis will encounter this situation, and I will talk about the corresponding methods below.
1. String extraction at a single position
In this case we can use the regular expression (.+?) to extract. For example, for a string "a123b", if we want to extract the value 123 between ab, we can use findall with a regular expression, which will return a list containing all the conditions that match the situation.
The code is as follows:
import re str = "a123b" print re.findall(r"a(.+?)b",str)# 输出['123']
1.1 Greedy and non-greedy matching
If we have a string "a123b456b", if we want to match a and the last b All values between instead of the values between a and the first occurrence of b, you can use ? to control regular greedy and non-greedy matching.
The code is as follows:
import re str = "a123b456b" print re.findall(r"a(.+?)b", str) #输出['123']#?控制只匹配0或1个,所以只会输出和最近的b之间的匹配情况 print re.findall(r"a(.+)b", str) #输出['123b456'] print re.findall(r"a(.*)b", str) #输出['123b456']
1.2 Multi-line matching
If you want multi-line matching, you need to add the re.S and re.M flags. Add After taking re.S. Will match newline characters, default. Will not match newline characters.
The code is as follows:
str = "a23b\na34b" re.findall(r"a(\d+)b.+a(\d+)b", str) #输出[] #因为不能处理str中间有\n换行的情况 re.findall(r"a(\d+)b.+a(\d+)b", str, re.S) #s输出[('23', '34')]
After adding re.M, the ^$ mark will match each line. By default, ^ and $ will only match the first line.
The code is as follows:
str = "a23b\na34b" re.findall(r"^a(\d+)b", str) #输出['23'] re.findall(r"^a(\d+)b", str, re.M) #输出['23', '34']
2. Extract strings at multiple consecutive positions
In this case we can use the
(?P<name>…)
regular rule expression to extract. For example, if we have a line of webserver access log:
'192.168.0.1 25/Oct/2012:14:46:34 "GET /api HTTP/1.1" 200 44 "http://abc.com/search" "Mozilla/5.0"'
, and we want to extract all the content in this line of log, we can write multiple
(?P<name>expr)
to extract, and the name can be changed to you For the variable named for the position string, expr can be changed to the regular expression for extracting the position.
The code is as follows:
import re line ='192.168.0.1 25/Oct/2012:14:46:34 "GET /api HTTP/1.1" 200 44 "http://abc.com/search" "Mozilla/5.0"' reg = re.compile('^(?P<remote_ip>[^ ]*) (?P<date>[^ ]*) "(?P<request>[^"]*)" (?P<status>[^ ]*) (?P<size>[^ ]*) "(?P<referrer>[^"]*)" "(?P<user_agent>[^"]*)"') regMatch = reg.match(line) linebits = regMatch.groupdict() print linebits for k, v in linebits.items() : print k+": "+v
The output result is:
status: 200 referrer: request: GET /api HTTP/1.1 user_agent: Mozilla/5.0 date: 25/Oct/2012:14:46:34size: 44 remote_ip: 192.168.0.1
Summary
The above is the entire content of this article. I hope that the content of this article can be of some help to everyone's study or work. If you have any questions, you can leave a message to communicate.
The above is the detailed content of python method to extract string using regular expression. For more information, please follow other related articles on the PHP Chinese website!

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

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

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











Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

Is it enough to learn Python for two hours a day? It depends on your goals and learning methods. 1) Develop a clear learning plan, 2) Select appropriate learning resources and methods, 3) Practice and review and consolidate hands-on practice and review and consolidate, and you can gradually master the basic knowledge and advanced functions of Python during this period.

Python is better than C in development efficiency, but C is higher in execution performance. 1. Python's concise syntax and rich libraries improve development efficiency. 2.C's compilation-type characteristics and hardware control improve execution performance. When making a choice, you need to weigh the development speed and execution efficiency based on project needs.

Python and C each have their own advantages, and the choice should be based on project requirements. 1) Python is suitable for rapid development and data processing due to its concise syntax and dynamic typing. 2)C is suitable for high performance and system programming due to its static typing and manual memory management.

Pythonlistsarepartofthestandardlibrary,whilearraysarenot.Listsarebuilt-in,versatile,andusedforstoringcollections,whereasarraysareprovidedbythearraymoduleandlesscommonlyusedduetolimitedfunctionality.

Python excels in automation, scripting, and task management. 1) Automation: File backup is realized through standard libraries such as os and shutil. 2) Script writing: Use the psutil library to monitor system resources. 3) Task management: Use the schedule library to schedule tasks. Python's ease of use and rich library support makes it the preferred tool in these areas.

Python's applications in scientific computing include data analysis, machine learning, numerical simulation and visualization. 1.Numpy provides efficient multi-dimensional arrays and mathematical functions. 2. SciPy extends Numpy functionality and provides optimization and linear algebra tools. 3. Pandas is used for data processing and analysis. 4.Matplotlib is used to generate various graphs and visual results.

Key applications of Python in web development include the use of Django and Flask frameworks, API development, data analysis and visualization, machine learning and AI, and performance optimization. 1. Django and Flask framework: Django is suitable for rapid development of complex applications, and Flask is suitable for small or highly customized projects. 2. API development: Use Flask or DjangoRESTFramework to build RESTfulAPI. 3. Data analysis and visualization: Use Python to process data and display it through the web interface. 4. Machine Learning and AI: Python is used to build intelligent web applications. 5. Performance optimization: optimized through asynchronous programming, caching and code
