This article brings you a detailed introduction to regular expressions in python. It has certain reference value. Friends in need can refer to it. I hope it will be helpful to you.
Regular
re = regular experssion
re module enables the Python language to have all the regular expression functions.
The compile function generates a regular expression object based on a pattern string and optional flag parameters. This object has a series of methods for regular expression matching and replacement.
Function: When processing a string, it will check whether the content of the string matches the regular expression you wrote.
If it matches, take out the matching content;If it does not match, ignore it. Matching content;
Writing regular rules
pattern matching regular expression
string string to be matched
Three search methods
1). findall
import re str = 'hello sheen,hello cute.' pattern_1 = r'hello' pattern_2 = r'sheen' print(re.findall(pattern_1,str)) #['hello', 'hello'] print(re.findall(pattern_2,str)) #['sheen']
2).match
match attempts to match from the starting position of the string,
If the starting position is not matched successfully, a None is returned;
If the starting position is matched successfully, an object will be returned;
import re str = 'hello sheen,hello cute.' pattern_1 = r'hello' pattern_2 = r'sheen' print(re.match(pattern_1,str)) #<_sre.sre_match> print(re.match(pattern_1,str).group()) #返回match匹配的字符串内容,hello print(re.match(pattern_2,str)) #None</_sre.sre_match>
3).search
search will scan the entire string and only return the first successfully matched content;
If it can be found, return an object and obtain the corresponding string through the group method;
import re str = 'hello sheen,hello cute.' pattern_1 = r'hello' pattern_2 = r'sheen' print(re.search(pattern_1,str)) #<_sre.sre_match> print(re.search(pattern_1,str).group()) #hello print(re.search(pattern_2,str)) #<_sre.sre_match> print(re.search(pattern_2,str).group()) #sheen</_sre.sre_match></_sre.sre_match>
Special character class
.: Matches any character except \n; [.\n]
\d: digit--(number), matches a numeric character, equivalent to [0-9]
\ D: Matches a non-numeric character, equivalent to [^0-9]
\s: space (generalized space: space, \t, \n, \r), matches any single whitespace character;
\S: Matches any whitespace character except a single one;
\w: Alphanumeric or underscore, [a-zA-Z0-9_]
\W: Except alphanumeric or underscore, [^a-zA- Z0-9_]
import re # . print(re.findall(r'.','sheen\nstar\n')) #['s', 'h', 'e', 'e', 'n', 's', 't', 'a', 'r'] #\d#\D print(re.findall(r'\d','当前声望30')) #['3', '0'] print(re.findall(r'\D','当前声望30')) #['当', '前', '声', '望'] #\s#\S print(re.findall(r'\s', '\n当前\r声望\t为30')) #['\n', '\r', '\t'] print(re.findall(r'\S', '\n当前\r声望\t为30')) #['当', '前', '声', '望', '为', '3', '0'] #\w#\W print(re.findall(r'\w','lucky超可爱!!')) #['l', 'u', 'c', 'k', 'y', '超', '可', '爱'] print(re.findall(r'\W','lucky超可爱!!')) #['!', '!']
Specify the number of occurrences of characters
The number of occurrences of matching characters:
*: represents the previous character appearing 0 times or infinite times; d*, .*
: represents the previous character appearing once or infinite times; d
?: represents the previous character appearing 1 time or 0 times; Assuming that some characters can be omitted, you can also use
when not omitting. The second method:
{m}: The previous character appears m times;
{m, }: The previous character appears at least m times; * == {0,}; ==={1,}
{m,n}: The previous character appears m to n times; ? === {0 ,1}
import re #* 代表前一个字符出现0次或者无限次 print(re.findall(r's*','sheenstar')) #['s', '', '', '', '', 's', '', '', '', ''] print(re.findall(r's*','hello')) #['', '', '', '', '', ''] #+ 代表前一个字符出现一次或者无限次 print(re.findall(r's+','sheenstar')) #['s', 's'] print(re.findall(r's+','hello')) #[] # ? 代表前一个字符出现1次或者0次 print(re.findall(r'188-?', '188 6543')) #['188'] print(re.findall(r'188-?', '188-6543')) #['188-'] print(re.findall(r'188-?', '148-6543')) #[] # 匹配电话号码 pattern = r'\d{3}[\s-]?\d{4}[\s-]?\d{4}' print(re.findall(pattern,'188 0123 4567')) #['188 0123 4567'] print(re.findall(pattern,'188-0123-4567')) #['188-0123-4567'] print(re.findall(pattern,'18801234567')) #['188-0123-4567']
Exercise--Matching IP
You can search for a regular expression generator from the Internet, use the rules written by others, and test it yourself.
import re # | 表示或者 pattern = r'(25[0-5]|2[0-4]\d|[0-1]\d{2}|[1-9]?\d)\.(25[0-5]|2[0-4]\d|[0-1]\d{2}|[1-9]?\d)\.(25[0-5]|2[0-4]\d|[0-1]\d{2}|[1-9]?\d)\.(25[0-5]|2[0-4]\d|[0-1]\d{2}|[1-9]?\d)$' print(re.findall(pattern,'172.25.254.34')) #[('172', '25', '254', '34')] matchObj_1 = re.match(pattern,'172.25.254.34') if matchObj_1: print('匹配项:',matchObj_1.group()) #172.25.254.34 else: print('未找到匹配项') matchObj_2 = re.match(pattern,'172.25.254.343') if matchObj_2: print('匹配项:',matchObj_2.group()) else: print('未找到匹配项')
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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.

Choosing Python or C depends on project requirements: 1) If you need rapid development, data processing and prototype design, choose Python; 2) If you need high performance, low latency and close hardware control, choose C.

By investing 2 hours of Python learning every day, you can effectively improve your programming skills. 1. Learn new knowledge: read documents or watch tutorials. 2. Practice: Write code and complete exercises. 3. Review: Consolidate the content you have learned. 4. Project practice: Apply what you have learned in actual projects. Such a structured learning plan can help you systematically master Python and achieve career goals.

Methods to learn Python efficiently within two hours include: 1. Review the basic knowledge and ensure that you are familiar with Python installation and basic syntax; 2. Understand the core concepts of Python, such as variables, lists, functions, etc.; 3. Master basic and advanced usage by using examples; 4. Learn common errors and debugging techniques; 5. Apply performance optimization and best practices, such as using list comprehensions and following the PEP8 style guide.

Python is suitable for beginners and data science, and C is suitable for system programming and game development. 1. Python is simple and easy to use, suitable for data science and web development. 2.C provides high performance and control, suitable for game development and system programming. The choice should be based on project needs and personal interests.

Python is more suitable for data science and rapid development, while C is more suitable for high performance and system programming. 1. Python syntax is concise and easy to learn, suitable for data processing and scientific computing. 2.C has complex syntax but excellent performance and is often used in game development and system programming.

It is feasible to invest two hours a day to learn Python. 1. Learn new knowledge: Learn new concepts in one hour, such as lists and dictionaries. 2. Practice and exercises: Use one hour to perform programming exercises, such as writing small programs. Through reasonable planning and perseverance, you can master the core concepts of Python in a short time.

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.


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