How to use Python regular expressions for big data processing

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.

1. Preparing data

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.

2. Import re module

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

3. Introduction to regular expression syntax

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:

• .: Matches any character (except newline).
• w: Match any letter, number or underscore.
• d: Match any number.
• s: Matches any whitespace character (including space, tab, newline, etc.).
• W: Matches any non-letter, number or underscore character.
• D: Matches any non-numeric character.
• S: Matches any non-whitespace character.

Combining character metacharacters include:

• []: Matches any character within the square brackets.
• -: represents a hyphen, used to represent a range, such as [0-9] to match any numeric character.
• ^: means non, used to indicate unmatched characters, such as ¹ means matching any non-lowercase alphabetic character.
• |: means or, used to match multiple regular expressions, such as a|b means matching character a or character b.

3.3. Quantifier

Quantifier is used to indicate the number of matching characters. Commonly used quantifiers are as follows:

• *: indicates any character, matches 0 or more.
• : Indicates any character, matching 1 or more.
• ?: Indicates any character, matching 0 or 1.
• {}: Indicates any character and matches the specified number. For example, {3,5} means matching 3 to 5 characters.

4. Use regular expressions for data processing

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.

5. Summary

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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