🍁1. 清洗文本

对一些非结构化的文本数据进行基本的清洗

strip
split
replace

python 复制代码

# 创建文本
text_data = ['   Interrobang. By Aishwarya Henriette   ',
             'Parking And goding. by karl fautier',
             '   Today is the night. by jarek prakash    ']

python 复制代码

# 去除文本两端的空格
stripwhitespace = [string.strip() for string in text_data]

python 复制代码

stripwhitespace

css 复制代码

['Interrobang. By Aishwarya Henriette', 'Parking And goding. by karl fautier', 'Today is the night. by jarek prakash']

python 复制代码

# 删除句号
remove_periods = [string.replace('.','') for string in text_data]

python 复制代码

remove_periods

css 复制代码

['   Interrobang By Aishwarya Henriette   ', 'Parking And goding by karl fautier', '   Today is the night by jarek prakash    ']

python 复制代码

# 创建函数
def capitalizer(string):
    return string.upper()

python 复制代码

[capitalizer(string) for string in remove_periods]

css 复制代码

['   INTERROBANG BY AISHWARYA HENRIETTE   ', 'PARKING AND GODING BY KARL FAUTIER', '   TODAY IS THE NIGHT BY JAREK PRAKASH    ']

python 复制代码

# 使用正则表达式
import re

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def replace_letters_with_x(string):
    return re.sub(r'[a-zA-Z]','x',string)

python 复制代码

[replace_letters_with_x(string) for string in remove_periods]

css 复制代码

['   xxxxxxxxxxx xx xxxxxxxxx xxxxxxxxx   ', 'xxxxxxx xxx xxxxxx xx xxxx xxxxxxx', '   xxxxx xx xxx xxxxx xx xxxxx xxxxxxx    ']

🍂2. 解析并清洗HTML

python 复制代码

#使用beautiful soup 对html进行解析

python 复制代码

from bs4 import BeautifulSoup

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# 创建html代码
html = """
        <div class='full_name'><span style='font-weight:bold'>
        Masege Azra"
    
    """

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# 创建soup对象
soup = BeautifulSoup(html, 'lxml')

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soup.find('div')

xml 复制代码

<div class="full_name"><span style="font-weight:bold">
        Masege Azra"
    
    </span></div>

🍃3. 移除标点

python 复制代码

import unicodedata
import sys

python 复制代码

text_data = ['Hi!!!! I. love. This. Song....',
             '10000% Agree!!!! #LoveIT',
             'Right??!!']

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# 创建一个标点符号字典
punctuation = dict.fromkeys(i for i in range(sys.maxunicode) if unicodedata.category(chr(i)).startswith('P'))

python 复制代码

[string.translate(punctuation) for string in text_data]

css 复制代码

['Hi I love This Song', '10000 Agree LoveIT', 'Right']

🌍4. 文本分词

这里介绍一下jieba库

python 复制代码

import jieba

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# 创建文本
string = 'The science of study is the technology of tomorrow'

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seg = jieba.lcut(string)
print(seg)

css 复制代码

['The', ' ', 'science', ' ', 'of', ' ', 'study', ' ', 'is', ' ', 'the', ' ', 'technology', ' ', 'of', ' ', 'tomorrow']

当然，本文只是介绍了在数据清洗中的一些最基本的文本处理方法，后续还会介绍目前NLP的一些主流方法和代码。

Python数据分析从入门到进阶：快速处理文本（含代码）

🍁1. 清洗文本

🍂2. 解析并清洗HTML

🍃3. 移除标点

🌍4. 文本分词