Regular expression functions that allow you to better process text

Regular Expression (Regular Expression) is a tool for matching text patterns. Through some specific grammatical rules, you can search and match content that meets the requirements in the text. This search technology is widely used in text processing. , programming, data cleaning and other fields.

In actual text processing, it is often necessary to extract text fragments that comply with specific rules and perform some operations on them, such as replacement, deletion, extraction, etc. These operations can be completed very easily using regular expressions. Whether it is a text editor or a programming language, relevant regular expression functions are built-in for developers to use.

1. Commonly used regular expression functions

1. re.compile(pattern, flags): Compile a regular expression into a regular expression object for subsequent use.
2. re.search(pattern, string, flags): Search for regular expression matches in the string, return the first matching object, or None if there is no match.
3. re.match(pattern, string, flags): Try to match the regular expression from the beginning of the string. If the match is successful, return the first matching object. If there is no match, return None.
4. re.findall(pattern, string, flags): Find all items in the string that match the regular expression and return a list, or an empty list if there is no match.
5. re.sub(pattern, repl, string, count=0, flags=0): Use repl to replace all items in string that match the regular expression pattern. You can limit the number of replacements through the count parameter. If there is no match, the original string is returned.
6. re.split(pattern, string, maxsplit=0, flags=0): Split the string into a list according to the regular expression pattern and return the list. You can limit the number of splits through the maxsplit parameter. If there is no match, the original string is returned.
7. re.finditer(pattern, string, flags=0): Finds all items in the string that match the regular expression pattern and returns an iterator through which the matching object can be accessed in sequence.

2. Practical application cases

1. Extract mobile phone numbers:

In actual business scenarios, we may need to extract from text For mobile phone numbers, you can use regular expressions to match the pattern of mobile phone numbers.

The code is as follows:

import re

text = "我的电话号码是：13888888888，欢迎来电咨询。"

pattern = re.compile(r"1[3456789]d{9}")

res = re.search(pattern, text)

if res:

    print("电话号码：", res.group())

else:

    print("未匹配到电话号码")

The output result is: Phone number: 13888888888.

2. Data cleaning:

When performing data analysis, it may be necessary to remove some useless characters from the data, such as specific punctuation marks, HTML tags, etc. This functionality can be easily achieved using regular expressions.

The code is as follows:

import re

text = "<title>数据分析入门指南</title>"

pattern = re.compile(r"<.+?>")

res = re.sub(pattern, "", text)

print(res)

The output result is: Data Analysis Getting Started Guide.

3. Email format verification:

In user registration, login and other scenarios, it is often necessary to verify whether the email format is correct. This can be achieved using regular expressions. .

The code is as follows:

import re

email = "test@test.com"

pattern = re.compile(r"^w+([-+._]w+)*@w+([-.]w+)*.w+([-.]w+)*$")

res = re.match(pattern, email)

if res:

    print("邮箱格式正确")

else:

    print("邮箱格式错误")

The output result is: the email format is correct.

3. Summary

Although regular expressions are difficult to understand, mastering the relevant functions and grammatical rules can play an important role in text processing, programming, etc. Commonly used regular expression functions include re.compile(), re.search(), re.match(), re.findall(), re.sub(), re.split(), re.finditer(), etc., Functions such as text search, cleaning, format verification, etc. can be easily implemented. In actual use, it is necessary to select appropriate regular expression patterns according to different scenarios to improve processing efficiency and accuracy.

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