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In the process of data processing, sometimes we need to filter and clean a large amount of data. At this time, using Python's regular expressions can greatly improve the efficiency of data processing. The following will introduce how to use Python regular expressions for big data processing.
First, you need to prepare a data that needs to be processed, such as a data set containing 500,000 Mandarin texts. This data set can be obtained from the Internet or made by yourself.
Before using Python regular expressions, you need to import Python’s built-in re module. This module provides many commonly used regular expression related Functions and methods.
import re
Regular expression is an expression used to match strings. Its syntax is relatively complex, but after mastering the commonly used After the syntax, the efficiency of data processing is greatly improved.
3.1. Expression
The basic syntax of regular expressions is an expression composed of a series of characters and metacharacters. Among them, character represents a character in the matching string, and metacharacter represents a certain type of character.
3.2. Metacharacters
Metacharacters are divided into single character metacharacters and combined character metacharacters.
The single character metacharacter includes:
Combining character metacharacters include:
3.3. Quantifier
Quantifier is used to indicate the number of matching characters. Commonly used quantifiers are as follows:
After introducing the syntax of regular expressions above, we can start using regular expressions for data processing . The following will take a simple example to demonstrate how to use regular expressions for data processing.
4.1. Reading data
First you need to read the data in. Here you can choose to use Python’s built-in open function to read, or you can use the third-party library pandas to read.
# 使用pandas读取数据 import pandas as pd data = pd.read_csv('data.csv', encoding='utf-8')
4.2. Use regular expressions for data cleaning
Suppose you now need to filter the mobile phone numbers in the data and save the filtered data to a new file. In this example, we assume that the mobile phone number is 11 digits.
In the above regular expression syntax, d means to match any number, and {11} means that 11 such numbers need to be matched. So the complete regular expression can be written as:
regexp = r'd{11}'
Then we can use Python's re module to filter and clean the data. First, read the data into memory, and then use regular expressions for matching and extraction.
import re with open('data.csv', encoding='utf-8') as f: lines = f.readlines() # 使用正则表达式进行数据清洗 result = [] regexp = r'd{11}' for line in lines: match_obj = re.search(regexp, line) # 如果匹配成功,则把匹配的内容加入到result if match_obj: result.append(match_obj.group(0)) # 把结果写入到文件中 with open('result.txt', 'w', encoding='utf-8') as f: f.write(' '.join(result))
Through the above code, we successfully used regular expressions to match all mobile phone numbers and saved them in the result.txt file.
In this article, we introduced how to use Python regular expressions for big data processing. Python's built-in re module provides many commonly used regular expression functions and methods. By mastering the syntax of regular expressions, we can quickly and efficiently perform data filtering, cleaning and other operations in big data processing.
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