针对以下python代码解释并写活动小结：import requests from bs4 import BeautifulSoup import re #创建获取网页数据的函数geunivText def getnuivText(url): try: r = requests.get(url) r.raise_for_status r.encoding = r.apparent_encoding return r.text except: return '' #创建解析函数并将爬取的目标数据存储 def fillunivList(ulist,html): soup = BeautifulSoup(html,'html.parser') for tr in soup.find('tbody').children: tds = tr.find_all('td') temp = [] for i in [0,1,4,5]: if i != 1: t = tds[i].string temp.append(re.sub('\s*|\n','',t)) else: t = tds[i].find('a') temp.append(t.string) ulist.append(temp) return ulist #创建打印函数 def printunivRank(ulist,num): print('{0:^4}\t{1:^10}\t\t{2:^5}\t{3:^5}'.format('排名','大学名称','总分','办学层次')) for i in range(num): a,b,c,d = ulist[i] print('{0:{4}^4}\t{1:{4}^10}\t{2:{4}^5}\t{3:{4}^5}'.format(a,b,c,d,chr(12288))) def main(): url = 'https://www.shanghairanking.cn/rankings/bcur/202311' html_text = getnuivText(url) unlist = [] fillunivList(unlist,html_text) print(printunivRank(unlist,20)) main()

使用Python的BeautifulSoup库进行网页解析爬虫和数据提取.txt

一个示例，展示如何使用Python的BeautifulSoup库进行网页解析和数据提取。示例代码中，我们定义了一个名为scrape_website的函数。该函数接受一个URL作为参数，发送HTTP GET请求获取网页内容，并使用BeautifulSoup解析网页。我们使用soup.title.string获取网页的标题，并使用soup.find_all('a')找到所有链接元素，然后使用列表推导式提取链接的文本内容。最后，我们返回提取的标题和链接。我们指定要爬取的URL，并调用scrape_website函数进行爬取。如果成功提取到标题和链接，则打印它们。请注意，这只是一个简单的示例，用于演示如何使用BeautifulSoup库进行网页解析和数据提取。你可以根据自己的需求进行自定义和扩展，使用更复杂的选择器、处理更多类型的网页元素等。

以下是一个简单的Python爬虫案例，用来从网页上获取信息这个例子使用了Python的requests库进行HTTP请求和Be

from bs4 import BeautifulSoup def crawl_website(url): # 发起HTTP GET请求 response = requests.get(url) # 检查请求是否成功 if response.status_code == 200: # 使用Beautiful Soup解析HTML soup = ...

针对以下python代码的解释以及实验小结import requests from bs4 import BeautifulSoup import re #创建获取网页数据的函数geunivText def getnuivText(url): try: r = requests.get(url) r.raise_for_status r.encoding = r.apparent_encoding return r.text except: return '' #创建解析函数并将爬取的目标数据存储 def fillunivList(ulist,html): soup = BeautifulSoup(html,'html.parser') for tr in soup.find('tbody').children: tds = tr.find_all('td') temp = [] for i in [0,1,4,5]: if i != 1: t = tds[i].string temp.append(re.sub('\s*|\n','',t)) else: t = tds[i].find('a') temp.append(t.string) ulist.append(temp) return ulist #创建打印函数 def printunivRank(ulist,num): print('{0:^4}\t{1:^10}\t\t{2:^5}\t{3:^5}'.format('排名','大学名称','总分','办学层次')) for i in range(num): a,b,c,d = ulist[i] print('{0:{4}^4}\t{1:{4}^10}\t{2:{4}^5}\t{3:{4}^5}'.format(a,b,c,d,chr(12288))) def main(): url = 'https://www.shanghairanking.cn/rankings/bcur/2021' html_text = getnuivText(url) unlist = [] fillunivList(unlist,html_text) print(printunivRank(unlist,20)) main()

这段 Python 代码使用了 requests、BeautifulSoup 和 re 三个库，爬取了上海交通大学的 2021 年度排名信息。具体实现过程如下： 1. 定义了一个函数 getnuivText，用于获取指定 url 的网页内容，利用 requests 库的 ...

import requests from bs4 import BeautifulSoup import re import docx from docx.oxml.ns import qn

from bs4 import BeautifulSoup import re import docx from docx.oxml.ns import qn def my_function(): # 在这里编写代码请注意，这只是一个示例，您需要根据您的实际需求来编写函数或方法。在您的函数或...

Python爬虫基础：使用requests和BeautifulSoup

from bs4 import BeautifulSoup def get_html(url): # 发送HTTP GET请求 response = requests.get(url) # 检查响应状态码，200表示请求成功 if response.status_code == 200: # 返回HTML文本 return ...

import requests from bs4 import BeautifulSoup import re import json def getKeywordResult(keyword): url = 'http://www.baidu.com/s?wd='+keyword try: r = requests.get(url, timeout=30) r.raise_for_status() r.encoding = 'utf-8' return r.text except: return "" def parserLinks(html): soup = BeautifulSoup(html, "html.parser") links = [] for div in soup.find_all('div', {'data-tools': re.compile('title')}): data = div.attrs['data-tools'] #获得属性值 d = json.loads(data) #将属性值转换成字典 links.append(d['title']) #将返回链接的题目返回 return links def main(): html = getKeywordResult('Python语言程序设计基础(第2版)') ls = parserLinks(html) count = 1 for i in ls: print("[{:^3}]{}".format(count, i)) count += 1 main()

这段代码的作用是在百度搜索中搜索关键词"Python语言程序设计基础(第2版)"，然后解析搜索结果页面中的链接和标题，并将这些标题列成一个列表进行输出。具体来说，这段代码使用了 requests 库向百度搜索发送了一个 ...

import requests from bs4 import BeautifulSoup # 设置基金代号 fund_code = '400015' # 构造爬取链接 url = f'http://fund.eastmoney.com/{fund_code}.html' # 发送请求 response = requests.get(url) soup = BeautifulSoup(response.text, 'html.parser') # 获取基金净值 net_value = soup.find(class_='dataItem02').find_all('span')[2].text print(f'基金{fund_code}的净值为：{net_value}') 要求：在以上代码的功能上添加一个功能，就是每天固定的时间爬取对应的数据

from bs4 import BeautifulSoup import schedule import time def crawl_net_value(): # 设置基金代号 fund_code = '400015' # 构造爬取链接 url = f'http://fund.eastmoney.com/{fund_code}.html' # 发送...

优化代码：import requests from bs4 import BeautifulSoup import csv # 请求URL url = "https://pvp.qq.com/web201605/herodetail/527.shtml" # 请求头部信息 headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/113.0.0.0 Safari/537.36 Edg/113.0.1774.57"} # 发送请求 response = requests.get(url, headers=headers) # 解析HTML soup = BeautifulSoup(response.content, "html.parser") # 获取所有英雄的链接 hero_links = [] for hero in soup.select(".herolist > li > a"): hero_links.append(hero["href"]) # 爬取每个英雄的属性 heroes = [] for link in hero_links: response = requests.get(link, headers=headers) soup = BeautifulSoup(response.content, "html.parser") # 获取英雄属性 name = soup.select(".cover-name")[0].text survive = soup.select(".")[0].text attack = soup.select(".cover-list-bar data-bar2 fl")[0].text skill = soup.select(".skill")[0].text difficulty = soup.select(".difficulty")[0].text # 保存英雄属性 heroes.append({"name": name, "survive": survive, "attack": attack, "skill": skill, "difficulty": difficulty}) # 将数据写入CSV文件 with open("heroes.csv", "w", newline="", encoding="utf-8-sig") as csvfile: fieldnames = ["name", "survive", "attack", "skill", "difficulty"] writer = csv.DictWriter(csvfile, fieldnames=fieldnames) # 写入表头 writer.writeheader() # 写入数据 for hero in heroes: writer.writerow(hero)

from bs4 import BeautifulSoup from concurrent.futures import ThreadPoolExecutor # 请求头部信息 headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like ...

import requests from bs4 import BeautifulSoup import csv def crawl_news_websites(url): response = requests.get(url)#get url soup = BeautifulSoup(response.text, 'html.parser')#创建 beautifulsoup 对象# 'html.parser' 指定解析器 websites = soup.find_all('a', class_='link')#查找节点 #find_all会将所有满足条件的值取出，组成一个list results = [] for website in websites: site_name = website.text site_url = website['href'] results.append((site_name, site_url)) return results def save_to_csv(results): with open('news_websites.csv', 'w', newline='', encoding='utf-8') as csvfile: writer = csv.writer(csvfile) writer.writerow(['Website Name', 'Website URL']) writer.writerows(results) if name == 'main': url = 'http://www.hao123.com/newswangzhi' news_websites = crawl_news_websites(url)

这段代码是用 Python 实现了一个爬虫程序，用于爬取指定网址中的新闻网站名称和链接，并将结果保存在一个名为 "news_websites.csv" 的文件中。其中，程序使用了 requests 库发送 HTTP 请求，BeautifulSoup 库对 ...

import requests from bs4 import BeautifulSoup def get_data(url, headers): ''' 两个参数 :param url:统一资源定位符,请求网址 :param headers:请求头 :return data:list类型的所有古诗内容 ''' # * Begin # # end ******* # return data

这是一个Python函数，用于从指定的网址中获取古诗的内容。其中，参数url是要请求的网址，参数headers是请求头。函数中使用了requests和BeautifulSoup两个Python库，requests库用于发送HTTP请求，BeautifulSoup库用于...

from fileinput import filename from lib2to3.pgen2 import driver from xml.etree.ElementInclude import include from selenium import webdriver from bs4 import BeautifulSoup import time from selenium.webdriver.common.by import By from bs4 import BeautifulSoup import requests class Downloader(object): def init(self, url): self.url = url self.urlist = [] self.DownloadUrl() # 下载链接 def DownloadUrl(self): driver = webdriver.Chrome() driver.maximize_window() driver.get(url) time.sleep(5) sp = driver.find_elements(By.XPATH, "//video[@class='wbpv-tech']").get_attribute("src") print("开始保存链接……%s" % sp) self.SavePicture(sp) # 保存图片到本地 def SavePicture(self, sp): driver2 = webdriver.Chrome() driver2.maximize_window() time.sleep(5) img = driver2.get(sp) with open("photo.mp4", "wb") as f: f.write(requests.get(sp).content) print("保存完成") if name == "main": url = 'https://s.weibo.com/weibo?q=%23%E5%A4%A9%E5%92%8C%E8%A7%86%E8%A7%92%E4%B8%8B%E7%9A%84%E7%A5%9E%E5%8D%81%E4%B8%89%E6%92%A4%E7%A6%BB%23&Refer=top' Downloader(url)

这段代码是用Python编写的一个下载器，它可以下载指定网页中视频的链接，并将视频保存到本地。它使用了Selenium和BeautifulSoup库来模拟浏览器行为和解析HTML页面，使用requests库来下载文件。在程序中，Downloader...

import requests from bs4 import BeautifulSoup url = 'https://b.guidaye.com/changxiao/9948/540774.html' def getContent(url): response = requests.get（URL）.content.decode（'utf-8') soup = BeautifulSoup(response,'html.parser') content = soup.find('div',class_='mb2').text content = content.replace(' ',' ') content = content.replace(' ',' ') return content print(getContent(url))哪里出问题了

from bs4 import BeautifulSoup url = 'https://b.guidaye.com/changxiao/9948/540774.html' def getContent(url): response = requests.get(url).content.decode('utf-8') soup = BeautifulSoup(response,'...

import os import requests from bs4 import BeautifulSoup def create_image_folder(keyword): desktop_path = os.path.join(os.path.expanduser("~"), "Desktop") folder_path = os.path.join(desktop_path, keyword) os.makedirs(folder_path, exist_ok=True) return folder_path def download_images(keyword, folder_path): url = f"https://image.baidu.com/search/index?tn=baiduimage&word={keyword}" response = requests.get(url) response.raise_for_status() soup = BeautifulSoup(response.text, "html.parser") image_tags = soup.find_all("img", class_="main_img img-hover") for i, image_tag in enumerate(image_tags): image_url = image_tag.get("data-imgurl") if image_url: response = requests.get(image_url) response.raise_for_status() image_path = os.path.join(folder_path, f"{i+1}.jpg") with open(image_path, "wb") as f: f.write(response.content) print(f"下载图片 {i+1}") # 获取用户输入的关键词 keyword = input("请输入关键词：") # 创建文件夹并下载图片 folder_path = create_image_folder(keyword) download_images(keyword, folder_path)

这是一个用于从百度图片搜索下载图片的Python脚本。它使用了os和requests库来创建文件夹和发送HTTP请求，以及...请注意，在运行脚本之前，确保已经安装了所需的库（os、requests和bs4），并且网络连接正常。

from bs4 import BeautifulSoup不要这个

如果你坚持不用BeautifulSoup，可以尝试使用requests获取网页源码，然后使用字符串操作来查找关键字或特定标签。例如： python import requests def get_page_content(keyword): url = f...

import requests #引入requests库 from bs4 import BeautifulSoup #引入beautifulsoup4 库 import csv def get_html(url): try: r=requests.get(url) r.raise_for_status() r.encoding=r.apparent_encoding return r.text except: r="fail" return r def getGDP(ulist,html): soup = BeautifulSoup(html, "html.parser") trs=soup.find_all('tr') for tr in trs: list=[] for th in tr: ts=th.string if ts =='\n': continue list.append(ts) ulist.append(list) def saveGDP(ulist,s): file_name = 'GDP'+s+'.csv' with open(file_name, 'a', errors='ignore', newline='') as f: f_csv = csv.writer(f) f_csv.writerows(ulist) def main(): d s='1960' for i in range(64): unifo=[] url='https://www.kylc.com/stats/global/yearly/g_gdp/'+s+'.html' html= get_html (url) getGDP(unifo,html) saveGDP(unifo,s) s1=int(s) s1=s1+1 s=str(s1) if name == "main": main()

这段代码是Python爬虫程序，主要功能是从指定网站获取全球各国家的年度GDP数据，并将数据保存到CSV文件中。具体实现过程如下： 1. 引入requests库和BeautifulSoup库。 2. 定义get_html函数，用于获取指定网址的html...

import requests from bs4 import BeautifulSoup allUniv = [] def getHTMLText(url): try: r = requests.get(url,timeout=30) r.raise_for_status() r.encoding = 'utf-8' return r.text except: return "" def fillUnivList(soup): data = soup.find_all('tr') for tr in data: ltd = tr.find_all('td') if len(ltd) == 0: continue singleUniv = [] for td in ltd: singleUniv.append(td.string) allUniv.append(singleUniv) def printUnivList(num): print("{:^4}{:^10}{:^5}{:^8}{:^10}".format("排名","学校名称","省份","总分","培养规模")) for i in range(num): u=allUniv[i] print("{:^4}{:^10}{:^5}{:^8}{:^10}".format(u[0],u[1],u[2],u[3],u[6])) def main(num): url = 'http://www.zuihaodaxue.cn/zuihaodaxuepaiming2016.html' html = getHTMLText(url) soup = BeautifulSoup(html,"html.parser") fillUnivList(soup) printUnivList(num) main(10)数组超出范围

from bs4 import BeautifulSoup allUniv = [] def getHTMLText(url): try: r = requests.get(url, timeout=30) r.raise_for_status() r.encoding = 'utf-8' return r.text except: return "" def ...

import requests from bs4 import BeautifulSoup url = "https://www.dpm.org.cn/lights/royal/p/1.html" #代码开始 r=requests.get(url) r.encoding='utf-8' soup=BeautifulSoup(r.text,'html.parser') pics=soup.find_all("div",class_="pic") xh=1 for pic in pics: x=pic.find("img") imglj=x.attrs["src"] imgmz=x.attrs["title"].strip() #代码开始 r=requests.get(imglj) cpmc="image//"+imgmz+".jpg" f1=open(cpmc,"bw") f1.write(r.content) f1.close() xh+=1 报错 No such file or directory: 'image//清沈全沈世杰沈世儒合笔婴戏图贴落.jpg'

这段代码报错是因为尝试在不存在的目录下创建文件。可以先在程序中加入一个创建目录的函数，如下所示： python import os def mkdir(path): folder = os.path.exists(path) if not folder: os.makedirs(path...

import requests from bs4 import BeautifulSoup url = 'https://b.guidaye.com/changxiao/9948/' def getCpontent(url): response = requests.get(url).content.decode('gbk',errors='ignore') soup = BeautifulSoup(response,'html.parser') content = soup.find('div',class_='mb2').text return content def getTitleLink(url): html = requests.get(url).content.decode('utf-8') soup = BeautifulSoup(html,'html.parser') chapters = soup.find('div',class_='book-list mb clearfix') chapters = chapters.find_all('a') titleLink = {} for each in chapters: title = each.text link = ('https://b.guidaye.com/'+each.get('href')) titleLink[title] = link return (titleLink) def main(): titleLink = getTitleLink(url) for title,link in titleLink.items(): f = open('天才在左疯子在右.txt','a') print(title) f.write(title) f.write(getCpontent(link)) f.close() main() 哪里出问题了

from bs4 import BeautifulSoup url = 'https://b.guidaye.com/changxiao/9948/' def getContent(url): response = requests.get(url).content.decode('gbk',errors='ignore') soup = BeautifulSoup(response,'...

import requests import re import os from bs4 import BeautifulSoup from scrapy import Spider from PIL import Image import io def GetBasicInfo(url): res = requests.get(url, headers=headers) res.encoding = 'utf-8' soup = BeautifulSoup(res.text, 'lxml') tmp = soup.find(attrs={'class': 'mhlistbody'}) chapters = tmp.ul.contents chapters.reverse() return chapters def GetRealUrls(mh_info): imgs = [] comic_size = re.findall(r'comic_size:"(.?)"', mh_info)[0] base_url = 'https://mhpic.jumanhua.com/comic/{}.jpg%s.webp' % comic_size num_img = int(re.findall(r'totalimg:(\d+)', mh_info)[0]) pageid = int(re.findall(r'pageid:(\d+)', mh_info)[0]) imgpath = re.findall(r'imgpath:"(.?)"', mh_info)[0] start = 0 while True: idx = imgpath.find('\\', start) if idx == -1: break imgpath = imgpath[:idx] + imgpath[idx+1:] start = idx + 1 for i in range(num_img): realpath = str() for s in imgpath: realpath += chr(ord(s) - pageid % 10) url = base_url.format(realpath + str(i+1)) imgs.append([url, str(i+1)+'.jpg']) return imgs def DownloadChapter(savepath, url): if not os.path.exists(savepath): os.mkdir(savepath) res = requests.get(url, headers=headers) res.encoding = 'utf-8' mh_info = re.findall(r'mh_info={(.*?)}', res.text)[0] img_urls = GetRealUrls(mh_info) for img_url in img_urls: img_content = requests.get(img_url[0]).content filename = os.path.join(savepath, img_url[1]) img = Image.open(io.BytesIO(img_content)) img.save(filename) if name == 'main': url = 'https://www.manhuatai.com/yaoshenji/' headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.3; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/52.0.2743.82 Safari/537.36'} savepath = url.split('/')[-2] Spider(url,savepath)

这段代码是一个简单的Python脚本，用于下载指定漫画网站的漫画图片。它使用了requests库来发送HTTP请求，re库用于正则表达式匹配，os库用于处理文件路径，BeautifulSoup库用于解析HTML，PIL库用于处理图片...

import requests from bs4 import BeautifulSoup import jieba.analyse import jieba.posseg as pseg from snownlp import SnowNLP import matplotlib.pyplot as plt # 设置请求头，模拟浏览器访问 headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'} # 获取网页内容 def get_html(url): resp = requests.get(url, headers=headers) resp.encoding = resp.apparent_encoding html = resp.text return html # 获取新闻列表 def get_news_list(url): html = get_html(url) soup = BeautifulSoup(html, 'html.parser') news_list = soup.find_all('a', class_="news_title") return news_list # 对文本进行情感分析 def sentiment_analysis(text): s = SnowNLP(text) return s.sentiments # 对文本进行关键词提取 def keyword_extraction(text): keywords = jieba.analyse.extract_tags(text, topK=10, withWeight=True, allowPOS=('n', 'vn', 'v')) return keywords # 对新闻进行分析 def analyze_news(url): news_list = get_news_list(url) senti_scores = [] # 情感分数列表 keyword_dict = {} # 关键词词频字典 for news in news_list: title = news.get_text().strip() link = news['href'] content = get_html(link) soup = BeautifulSoup(content, 'html.parser') text = soup.find('div', class_='article').get_text().strip() # 计算情感分数 senti_score = sentiment_analysis(text) senti_scores.append(senti_score) # 提取关键词 keywords = keyword_extraction(text) for keyword in keywords: if keyword[0] in keyword_dict: keyword_dict[keyword[0]] += keyword[1] else: keyword_dict[keyword[0]] = keyword[1] # 绘制情感分数直方图 plt.hist(senti_scores, bins=10, color='skyblue') plt.xlabel('Sentiment Score') plt.ylabel('Number of News') plt.title('Sentiment Analysis') plt.show() # 输出关键词词频排名 keyword_list = sorted(keyword_dict.items(), key=lambda x: x[1], reverse=True) print('Top 10 keywords:') for i in range(10): print('{}. {} - {:.2f}'.format(i+1, keyword_list[i][0], keyword_list[i][1])) if name == 'main': url = 'https://www.sina.com.cn/' analyze_news(url)

这是一段Python代码，用于对新闻进行情感分析和关键词提取。它使用了requests库来获取网页内容，使用BeautifulSoup库来解析HTML文档，使用jieba库来进行中文分词和关键词提取，使用SnowNLP库来进行情感分析，使用...

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