How to use Python regular expressions for backend development-Python Tutorial-php.cn

In back-end development, data processing and information extraction are very important. Regular expressions are a powerful data processing and information extraction tool that can help us conduct back-end development more efficiently. This article will introduce how to use Python regular expressions for back-end development.

1. Basic knowledge of regular expressions

Regular expressions, also known as regex, are a tool for describing character patterns. It can help us quickly analyze massive text data. Correctly match the required information.

Regular expressions usually consist of characters, operators and metacharacters. Special characters and metacharacters can represent a type of characters or a type of matching rules. The following is a list of common regular expression metacharacters:

##Escape characters . Matches any character except newline characters ^ Matches the beginning of the string $ Matches the end of the string [] Character set [^] Non-character set * Match the preceding character 0 or more times Match the preceding character 1 or more times ? Match the preceding character 0 or 1 times {} Match the preceding character a specified number of times Match the expression to the left or right of () Match expressions in brackets, also represent capture groups

Metacharacter	Matched characters

2. Application of regular expressions in Python

The re module is built into Python, providing With complete regular expression support, data processing and information extraction can be easily performed.

We can use d metacharacters to match numbers, and use to match multiple numbers:

import re text = "John has 2 apples, and Jane has 3 oranges." result = re.findall(r'd+', text) print(result)

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The output result is:

['2', '3']

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We can use [A-Za-z0-9._% -] to match email usernames, use @[A-Za- z0-9.-] .[A-Za-z]{2,} Matches the email domain name:

import re text = "My email address is john@example.com." result = re.findall(r'[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+.[A-Za-z]{2,}', text) print(result)

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The output result is:

['john@example.com']

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We can use ^1href="(.?)" ¹> Match hyperlinks in HTML:

import re html = """ Google， Baidu， Sogou， """ result = re.findall(r']+href="(.*?)"[^>]*>', html) print(result)

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The output result is:

['https://www.google.com', 'https://www.baidu.com', 'https://www.sogou.com']

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3. Regular expression optimization skills

Although regular expressions are very powerful , but when it comes to big data processing and complex matching, the efficiency may become relatively low. Therefore, we need to optimize the way regular expressions are written to achieve faster matching speed.

For example, we can use [A-Za-z0-9] instead of [ A-Z]|[a-z]|[0-9], which can reduce the number of characters in the regular expression and optimize the matching speed.

Greedy mode refers to a pattern that matches as many characters as possible. For example, when matching "hell" in the string "hello world", re.findall(r'he.

l', text) will match "hello worl" because .greedily matches "o wor", this is the result we don't want to see. In order to avoid greedy mode, we can add ? after .and use lazy mode, such as re.findall(r'he.?l', text).

Regular expressions often contain backslashes (). If you do not use raw strings, the backslashes will be interpreted as escaped. character. Therefore, we usually add r before the regular expression to indicate using the original string, such as re.findall(r'<[A-Za-z0-9] >', text).

4. Summary

In back-end development, regular expressions are a very important tool that can help us with data processing and information extraction, and improve development efficiency. This article introduces the basic knowledge of regular expressions and their application in Python, and also provides optimization tips. I hope it will be helpful to readers.

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