因此,我试图从here中删除标题。整整十年。

years是包含以下内容的列表

/resources/archive/us/2007.html
/resources/archive/us/2008.html
/resources/archive/us/2009.html
/resources/archive/us/2010.html
/resources/archive/us/2011.html
/resources/archive/us/2012.html
/resources/archive/us/2013.html
/resources/archive/us/2014.html
/resources/archive/us/2015.html
/resources/archive/us/2016.html


因此,我的代码在这里执行的操作是:打开每年的页面,收集所有日期链接,然后分别打开每个链接,并获取所有.text,并将每个标题和对应的日期作为一行添加到数据框headlines

headlines = pd.DataFrame(columns=["date", "headline"])

for y in years:
   yurl = "http://www.reuters.com"+str(y)
   response=requests.get(yurl,headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2228.0 Safari/537.36', })
   bs= BeautifulSoup(response.content.decode('ascii', 'ignore'),'lxml')

   days =[]
   links = bs.findAll('h5')
   for mon in links:
      for day in mon.next_sibling.next_sibling:
          days.append(day)

   days = [e for e in days if str(e) not in ('\n')]
   for ind in days:
       hlday = ind['href']
       date = re.findall('(?!\/)[0-9].+(?=\.)', hlday)[0]
       date =  date[4:6] + '-' + date[6:] + '-' + date[:4]
       print(date.split('-')[2])
       yurl = "http://www.reuters.com"+str(hlday)
       response=requests.get(yurl,headers = { 'User-Agent': 'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2228.0 Safari/537.36', })
       if response.status_code == 404 or response.content == b'':
           print('')
       else:
           bs= BeautifulSoup(response.content.decode('ascii', 'ignore'),'lxml')
           lines = bs.findAll('div', {'class':'headlineMed'})
           for h in lines:
               headlines = headlines.append([{"date":date, "headline":h.text}], ignore_index = True)


它需要永远地运行,所以我没有运行for循环,而是只运行了/resources/archive/us/2008.html

已经3个小时了,它仍在运行。

由于我是Python的新手,所以我不明白我做错了什么或如何做得更好。

pandas.append永远占用了它,因为它每次运行都必须读取和写入更大的数据帧吗?

最佳答案

您正在使用此反模式:

headlines = pd.DataFrame()
for for y in years:
    for ind in days:
        headlines = headlines.append(blah)


相反,请执行以下操作:

headlines = []
for for y in years:
    for ind in days:
        headlines.append(pd.DataFrame(blah))

headlines = pd.concat(headlines)


第二个潜在问题是您正在发出3650个Web请求。如果我经营这样的网站,那么我会节流以减慢像您这样的刮板的速度。您可能会发现最好收集一次原始数据,然后将其存储在磁盘上,然后进行第二遍处理。这样,您就不必每次都需要调试程序时花费3650个Web请求。

10-06 15:50