Python 使用beautifulsoup从span标记中刮取数据
我正在尝试刮网页，在那里我需要解码成一个数据帧的整个表。我用漂亮的汤来做这个。在某些Python 使用beautifulsoup从span标记中刮取数据,python,python-3.x,web-scraping,beautifulsoup,Python,Python 3.x,Web Scraping,Beautifulsoup,我正在尝试刮网页，在那里我需要解码成一个数据帧的整个表。我用漂亮的汤来做这个。在某些td标记中，有span标记没有任何文本。但是这些值显示在网页上的特定span标记中 以下html代码对应于该网页 <td> <span class="nttu">::after</span> <span class="ntbb">::after</span> <span class="n
tdspanhtml<td>
<span class="nttu">::after</span>
<span class="ntbb">::after</span>
<span class="ntyc">::after</span>
<span class="nttu">::after</span>
</td>
误解了您的问题，因为您引用了两个不同的URL。我现在明白你的意思了 是的，奇怪的是，在第二个表中，他们使用CSS填充了一些
import pandas as pd
import requests
from bs4 import BeautifulSoup
import re
http_url = "https://en.tutiempo.net/climate/01-2013/ws-432950.html"
retreived_data = requests.get(http_url).text
soup = BeautifulSoup(retreived_data, "lxml")
hiddenData = str(soup.find_all('style')[1])
hiddenSpan = {}
for group in re.findall(r'span\.(.+?)}',hiddenData):
class_attr = group.split('span.')[-1].split('::')[0]
content = group.split('"')[1]
hiddenSpan[class_attr] = content
climate_table = str(soup.find("table", attrs={"class": "medias mensuales numspan"}))
for k, v in hiddenSpan.items():
climate_table = climate_table.replace('<span class="%s"></span>' %(k), hiddenSpan[k])
df = pd.read_html(climate_table)[0]
页面可能是动态的，您需要从呈现的页面中提取html。除非你分享这个网址，否则没有人能提供更多帮助with@chitown88，我已经添加了该网站的URL，您在第五行中发现了一个问题。谢谢，您最好包含您的代码，否则很难看出问题所在；）@Thanjayas，你只是想拉那张桌子吗？@Isma，我已经添加了代码供你参考，谢谢。我不知道为什么一开始投票被否决了。但现在我明白了。您在问题中使用了两个不同的URL。我在看第一张，那是我提供的桌子。但是所使用的代码和您实际指的是给您带来了问题。@Thanjaya S，我更新了代码来回答您的问题。考虑到两个不同的URL引用，最初并不清楚，但再看一次，我明白你的意思了。
import pandas as pd
import requests
from bs4 import BeautifulSoup
import re
http_url = "https://en.tutiempo.net/climate/01-2013/ws-432950.html"
retreived_data = requests.get(http_url).text
soup = BeautifulSoup(retreived_data, "lxml")
hiddenData = str(soup.find_all('style')[1])
hiddenSpan = {}
for group in re.findall(r'span\.(.+?)}',hiddenData):
class_attr = group.split('span.')[-1].split('::')[0]
content = group.split('"')[1]
hiddenSpan[class_attr] = content
climate_table = str(soup.find("table", attrs={"class": "medias mensuales numspan"}))
for k, v in hiddenSpan.items():
climate_table = climate_table.replace('<span class="%s"></span>' %(k), hiddenSpan[k])
df = pd.read_html(climate_table)[0]
print (df.to_string())
Day T TM Tm SLP H PP VV V VM VG RA SN TS FG
0 1 23.4 30.3 19 - 59 0 6.3 4.3 5.4 - NaN NaN NaN NaN
1 2 22.4 30.3 16.9 - 57 0 6.9 3.3 7.6 - NaN NaN NaN NaN
2 3 24 31.8 16.9 - 51 0 6.9 2.8 5.4 - NaN NaN NaN NaN
3 4 24.2 32 17.4 - 53 0 6 3.3 5.4 - NaN NaN NaN NaN
4 5 23.8 32 18 - 58 0 6.9 3.1 7.6 - NaN NaN NaN NaN
5 6 23.3 31 18.3 - 60 0 6.9 5 9.4 - NaN NaN NaN NaN
6 7 22.8 30.2 17.6 - 55 0 7.7 3.7 7.6 - NaN NaN NaN NaN
7 8 23.1 30.6 17.4 - 46 0 6.9 3.3 5.4 - NaN NaN NaN NaN
8 9 22.9 30.6 17.4 - 51 0 6.9 3.5 3.5 - NaN NaN NaN NaN
9 10 22.3 30 17 - 56 0 6.3 3.3 7.6 - NaN NaN NaN NaN
10 11 22.3 29.4 17 - 53 0 6.9 4.3 7.6 - NaN NaN NaN NaN
11 12 21.8 29.4 15.7 - 54 0 6.9 2.8 3.5 - NaN NaN NaN NaN
12 13 22.3 30.1 15.7 - 43 0 6.9 2.8 5.4 - NaN NaN NaN NaN
13 14 21.8 30.6 14.8 - 41 0 6.9 1.9 5.4 - NaN NaN NaN NaN
14 15 21.6 30.6 14.2 - 43 0 6.9 3.1 7.6 - NaN NaN NaN NaN
15 16 21.1 29.9 15.4 - 55 0 6.9 4.1 7.6 - NaN NaN NaN NaN
16 17 20.4 28.1 15.4 - 59 0 6.9 5 11.1 - NaN NaN NaN NaN
17 18 21.2 28.3 14.5 - 53 0 6.9 3.1 7.6 - NaN NaN NaN NaN
18 19 21.6 29.6 16.4 - 58 0 6.9 2.2 3.5 - NaN NaN NaN NaN
19 20 21.9 29.6 16.6 - 58 0 6.9 2.4 5.4 - NaN NaN NaN NaN
20 21 22.3 29.9 17.5 - 55 0 6.9 3.1 5.4 - NaN NaN NaN NaN
21 22 21.9 29.9 15.1 - 46 0 6.9 4.3 7.6 - NaN NaN NaN NaN
22 23 21.3 29 15.2 - 50 0 6.9 3.3 5.4 - NaN NaN NaN NaN
23 24 21.3 28.8 14.6 - 45 0 6.9 3 5.4 - NaN NaN NaN NaN
24 25 21.6 29.1 15.5 - 47 0 7.7 4.8 7.6 - NaN NaN NaN NaN
25 26 21.8 29.2 14.6 - 41 0 6.9 2.8 3.5 - NaN NaN NaN NaN
26 27 22.3 30.1 15.6 - 40 0 6.9 2.4 5.4 - NaN NaN NaN NaN
27 28 22.4 30.3 16 - 51 0 6.9 2.8 3.5 - NaN NaN NaN NaN
28 29 23 30.3 16.9 - 53 0 6.6 2.8 5.4 - NaN NaN NaN o
29 30 23.1 30 17.8 - 54 0 6.9 5.4 7.6 - NaN NaN NaN NaN
30 31 22.1 29.8 17.3 - 54 0 6.9 5.2 9.4 - NaN NaN NaN NaN
31 Monthly means and totals: Monthly means and totals: Monthly means and totals: Monthly means and totals: Monthly means and totals: Monthly means and totals: Monthly means and totals: Monthly means and totals: Monthly means and totals: Monthly means and totals: Monthly means and totals: Monthly means and totals: Monthly means and totals: Monthly means and totals: Monthly means and totals:
32 NaN 22.3 30 16.4 - 51.6 0 6.9 3.5 6.3 NaN 0 0 0 1