对于新手做Python爬虫来说是有点难处的,前期练习的时候可以直接套用模板,这样省时省力还很方便。
使用Python爬取某网站的相关数据,并保存到同目录下Excel。
直接上代码:
import reimport urllib.errorimport urllib.requestimport xlwtfrom bs4 import BeautifulSoupdef main(): baseurl ="http://jshk.com.cn" datelist = getDate(baseurl) savepath=".\\jshk.xls" saveDate(datelist,savepath) # askURL("http://jshk.com.cn/")findlink = re.compile(r'')findimg = re.compile(r',re.S)findtitle = re.compile(r'(.*))findrating = re.compile(r')findjudge = re.compile(r'(\d*)人评价')findinq= re.compile(r'(.*)')def getDate(baseurl): datalist =[] for i in range(0,10): url=baseurl+str(i*25) html=askURL(url) soup = BeautifulSoup(html,"html.parser") for item in soup.find_all('div',class_="item"): data = [] item = str(item) link = re.findall(findlink,item)[0] data.append(link) img=re.findall(findimg,item)[0] data.append(img) title=re.findall(findtitle,item)[0] rating=re.findall(findrating,item)[0] data.append(rating) judge=re.findall(findjudge,item)[0] data.append(judge) inq=re.findall(findinq,item) if len(inq)!=0: inq=inq[0].replace("。","") data.append(inq) else: data.append(" ") print(data) datalist.append(data) print(datalist) return datalistdef askURL(url): head = { "User-Agent":"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/78.0.3904.97 Safari/537.36"} request=urllib.request.Request(url,headers=head) html="" try: response=urllib.request.urlopen(request) html=response.read().decode("utf-8") # print(html) except urllib.error.URLError as e: if hasattr(e,"code"): print(e.code) if hasattr(e,"reason"): print(e.reason) return htmldef saveDate(datalist,savepath): workbook = xlwt.Workbook(encoding='utf-8') worksheet = workbook.add_sheet('电影',cell_overwrite_ok=True) col =("电影详情","图片","影片","评分","评价数","概况") for i in range(0,5): worksheet.write(0,i,col[i]) for i in range(0,250): print("第%d条" %(i+1)) data=datalist[i] for j in range(0,5): worksheet.write(i+1,j,data[j]) workbook.save(savepath)if __name__ == '__main__': main() print("爬取完毕")
直接复制粘贴就行。
若要更改爬取网站,则需要更改URL以及相应的html格式(代码中的“item”)。
来源地址:https://blog.csdn.net/weixin_44617651/article/details/129703015