python str=re.strip

抱歉，您提供的代码有些错误。在Python中，str是字符串类型的关键字，您不能将其用作变量名。如果您想去掉字符串两端的空白字符，可以使用`strip()`方法。请参考以下示例：

s = "   Hello, World!   "
s = s.strip()
print(s)  # 输出: "Hello, World!"

这样，`strip()`方法将去掉字符串`s`两端的空白字符。如果您只想去掉左边（开头）或右边（结尾）的空白字符，可以使用`lstrip()`和`rstrip()`方法。

相关问题

请为以下代码每行代码作注释 import requests import re headers ={ 'User-Agent': 'Mz1lla/5.e (Mindows NT 10.8; win64; x64) Chrome/69.0.3497.100 Safani/537.36'} def baidu(company,page): num = (page - 1) * 18 url = 'https://www.baidu.com/s?tn=news&rtt=4&bsst=1&cl=2&wd'+ company +'&pn='+ str(num) res = requests.get(url, headers =headers).text p_info = '<p class="c-author">(.*?)</p>' info = re.findall(p_info,res, re.S) p_href = '<h3 class="c-title">.*?<a href="(.*?)"' href = re.findall(p_href,res, re.S) p_title = '<h3 class="c-title“>.*?>(.*?)</a>' title = re.findall(p_title,res, re.S) source = [] date = [] for i in range(len(info): title[i] = title[i].strip() title[i] = re.sub('<.*?>,,title[i]) info[i] = re.sub('<.*?>', info[i]) source.append(info[i].split('&nbsp;&nbsp;')[0]) date.append(info[i].split( '&nbsp;&nbsp;')[1]) source[i] = source[i].strip() date[i] =date[i].strip() print(str(i + 1)+'.'+ title[i]+'('+ date[i] +'-'+ source[i] + ')') print(href[i]) companys = ['华能信托', '阿里巴巴', '万科集团', '百度集团', '腾讯'，'京东'] for company in companys: for i in range(20): baidu(company,i+1) print(company+'第'+str(i+1)+'页爬取成功')

# 导入requests和re模块
import requests
import re

# 设置请求头
headers ={ 'User-Agent': 'Mz1lla/5.e (Mindows NT 10.8; win64; x64) Chrome/69.0.3497.100 Safani/537.36'}

# 定义一个函数，用于爬取百度新闻搜索结果
def baidu(company,page):
    # 根据页码计算出相应的条目数
    num = (page - 1) * 18
    # 构造请求URL
    url = 'https://www.baidu.com/s?tn=news&rtt=4&bsst=1&cl=2&wd'+ company +'&pn='+ str(num)
    # 发送GET请求，并获取响应内容
    res = requests.get(url, headers =headers).text
    
    # 提取新闻信息
    p_info = '<p class="c-author">(.*?)</p>'
    info = re.findall(p_info,res, re.S)
    # 提取新闻链接
    p_href = '<h3 class="c-title">.*?<a href="(.*?)"'
    href = re.findall(p_href,res, re.S)
    # 提取新闻标题
    p_title = '<h3 class="c-title“>.*?>(.*?)</a>'
    title = re.findall(p_title,res, re.S)
    
    # 存储新闻来源和发布时间
    source = []
    date = []
    for i in range(len(info)):
        title[i] = title[i].strip() # 去除标题中的空格
        title[i] = re.sub('<.*?>', '',title[i]) # 去除标题中的HTML标签
        info[i] = re.sub('<.*?>', '', info[i]) # 去除信息中的HTML标签
        source.append(info[i].split('&nbsp;&nbsp;')[0]) # 提取新闻来源
        date.append(info[i].split( '&nbsp;&nbsp;')[1]) # 提取新闻发布时间
        source[i] = source[i].strip() # 去除来源中的空格
        date[i] =date[i].strip() # 去除时间中的空格
        
        # 打印新闻标题、发布时间、来源、链接
        print(str(i + 1)+'.'+ title[i]+'('+ date[i] +'-'+ source[i] + ')')
        print(href[i])
        
# 定义一个列表，存储要搜索的公司名称
companys = ['华能信托', '阿里巴巴', '万科集团', '百度集团', '腾讯'，'京东']

# 循环遍历每个公司名称，并爬取前20页的新闻搜索结果
for company in companys:
    for i in range(20):
        baidu(company,i+1)
        print(company+'第'+str(i+1)+'页爬取成功')

如何修改代码，使得输出的每一个词的对应词频和密度分成两列显示 import pandas as pd import re from collections import Counter 读取词典txt文件 with open('词典.txt', 'r', encoding='utf-8') as f: list_dict = [line.strip() for line in f] 读取F列数据 df = pd.read_excel('实验体.xlsx') list_f = df['Answer'].tolist() 统计每行文本中的词频 dict_count_list = [] total_density_list = [] for text in list_f: # 匹配文本中的词列表 text = str(text) words = re.findall('|'.join(list_dict), text) # 统计每个词在该行文本中的出现次数和密度 dict_count = Counter(words) dict_count_list.append(dict_count) dict_count = {} for word in words: count = text.count(word) density = count / len(text) dict_count[word] = {'count': count, 'density': density} dict_count_list.append(dict_count) # 计算每行总词数的密度 total_density = sum([v['density'] for v in dict_count.values()]) total_density_list.append(total_density) 将每行文本的词频统计结果合并为一个DataFrame对象 df_count = pd.DataFrame(dict_count_list) df_count.index = df.index 输出为Excel文件 writer = pd.ExcelWriter('数实验体10.xlsx') df_count.to_excel(writer, sheet_name='Sheet1') writer._save()

你可以尝试修改代码如下：

import pandas as pd
import re
from collections import Counter

# 读取词典txt文件
with open('词典.txt', 'r', encoding='utf-8') as f:
    list_dict = [line.strip() for line in f]

# 读取F列数据
df = pd.read_excel('实验体.xlsx')
list_f = df['Answer'].tolist()

# 统计每行文本中的词频和密度
dict_count_list = []
total_density_list = []
for text in list_f:
    # 匹配文本中的词列表
    text = str(text)
    words = re.findall('|'.join(list_dict), text)
    
    # 统计每个词在该行文本中的出现次数和密度
    dict_count = Counter(words)
    dict_count = {k: {'count': v, 'density': v / len(text)} for k, v in dict_count.items()}
    dict_count_list.append(dict_count)
    
    # 计算每行总词数的密度
    total_density = sum([v['density'] for v in dict_count.values()])
    total_density_list.append(total_density)

# 将每行文本的词频统计结果合并为一个DataFrame对象
df_count = pd.concat([pd.DataFrame.from_dict(d, orient='index') for d in dict_count_list], axis=1)
df_count.index = df.index

# 输出为Excel文件并将每个词的词频和密度分成两列显示
with pd.ExcelWriter('数实验体10.xlsx') as writer:
    df_count.to_excel(writer, sheet_name='Sheet1')
    for col in df_count.columns:
        if col.endswith('count'):
            continue
        count_col = col[:-7] + 'count'
        density_col = col[:-7] + 'density'
        df_count[[count_col, density_col]] = df_count[col].apply(pd.Series)
        df_count = df_count.drop(col, axis=1)
    df_count.to_excel(writer, sheet_name='Sheet1', startrow=0, startcol=len(df_count.columns))

修改后的代码会将每个词的词频和密度分成两列显示，并输出到Excel文件的第二列。