我试图从此页面的高级表中抓取名称，per和mp值https://www.basketball-reference.com/teams/WAS/2019.html，但我不知道如何返回和表格中的值。我已经尝试过遵循类似目标的教程，但是却无济于事。这是我当前的代码
from bs4 import BeautifulSoup
import requests
url="https://www.basketball-reference.com/teams/{}/{}.html".format('ATL',2016)
response=requests.get(url)
print(response.text)
soup=BeautifulSoup(response.text,'html.parser')
table=soup.find('table', {'id':'advanced'})
print(table)
但是，尽管尝试了许多不同的操作，但始终不会打印任何内容。这是我尝试从中提取数据的表的一些html
任何帮助，将不胜感激
我在这方面没有很多知识，通常我可以做pd.read_html，它将抓取页面上的所有表格..正如评论所说，可能与页面的格式有关？
但是，如果这是一次性的事情，则可以使用以下代码：
from bs4 import BeautifulSoup as bs
import requests
import pandas as pd
url='https://www.basketball-reference.com/teams/WAS/2019.html'
response=requests.get(url).content
soup = bs(response)
advanced = soup.find('div',{'id':'all_advanced'}).contents[5]
df = pd.read_html(advanced)[0]
输出：
Rk Unnamed: 1 Age G MP PER TS% 3PAr FTr ... OWS DWS WS WS/48 Unnamed: 22 OBPM DBPM BPM VORP
0 1 Bradley Beal 25 82 3028 20.8 0.581 0.370 0.278 ... 5.9 1.7 7.6 0.120 NaN 3.9 -1.1 2.8 3.7
1 2 Tomáš Satoranský 27 80 2164 14.1 0.590 0.306 0.302 ... 3.8 0.9 4.7 0.104 NaN 0.4 -1.0 -0.6 0.8
2 3 Jeff Green 32 77 2097 13.6 0.608 0.466 0.300 ... 2.8 0.8 3.6 0.083 NaN 0.2 -1.4 -1.2 0.4
3 4 Thomas Bryant 21 72 1496 21.0 0.674 0.197 0.273 ... 4.3 1.3 5.6 0.178 NaN 1.2 0.4 1.6 1.3
4 5 Trevor Ariza 33 43 1465 13.0 0.538 0.580 0.238 ... 1.1 0.8 1.9 0.062 NaN 0.6 -0.8 -0.2 0.7
5 6 Otto Porter 25 41 1191 15.0 0.551 0.398 0.145 ... 1.0 1.1 2.1 0.085 NaN -0.2 0.3 0.1 0.6
6 7 John Wall 28 32 1104 18.0 0.527 0.306 0.317 ... 0.5 0.7 1.2 0.051 NaN 1.1 -1.2 -0.2 0.5
7 8 Markieff Morris 29 34 883 12.3 0.543 0.439 0.223 ... 0.4 0.5 0.9 0.051 NaN -1.1 -1.0 -2.0 0.0
8 9 Bobby Portis 23 28 768 15.3 0.530 0.333 0.132 ... 0.2 0.7 0.9 0.058 NaN -1.4 -1.4 -2.8 -0.2
9 10 Kelly Oubre 23 29 755 13.3 0.545 0.433 0.279 ... 0.3 0.5 0.8 0.053 NaN -1.7 -1.9 -3.6 -0.3
10 11 Chasson Randle 25 49 743 9.9 0.555 0.530 0.286 ... 0.4 0.2 0.6 0.041 NaN -1.4 -3.0 -4.4 -0.4
11 12 Troy Brown 19 52 730 11.1 0.487 0.295 0.201 ... 0.2 0.4 0.6 0.039 NaN -2.6 -1.2 -3.7 -0.3
12 13 Austin Rivers 26 29 683 6.8 0.490 0.546 0.237 ... -0.4 0.2 -0.2 -0.014 NaN -3.0 -1.5 -4.6 -0.4
13 14 Jabari Parker 23 25 682 17.0 0.587 0.284 0.267 ... 0.3 0.6 0.9 0.063 NaN -0.8 0.1 -0.7 0.2
14 15 Sam Dekker 24 38 619 13.2 0.514 0.236 0.173 ... 0.4 0.4 0.8 0.059 NaN -1.6 -0.9 -2.5 -0.1
15 16 Ian Mahinmi 32 34 498 12.0 0.531 0.154 0.587 ... 0.5 0.5 1.0 0.092 NaN -2.3 1.3 -1.0 0.1
16 17 Jordan McRae 27 27 333 14.3 0.550 0.269 0.269 ... 0.3 0.2 0.5 0.066 NaN -1.9 -1.8 -3.7 -0.1
17 18 Dwight Howard 33 9 230 17.4 0.638 0.000 0.696 ... 0.4 0.2 0.6 0.124 NaN -2.9 -2.3 -5.2 -0.2
18 19 Wesley Johnson 31 12 157 2.0 0.372 0.650 0.250 ... -0.3 0.0 -0.2 -0.075 NaN -6.3 -1.5 -7.7 -0.2
19 20 Jason Smith 32 12 130 11.7 0.520 0.270 0.324 ... 0.1 0.1 0.2 0.070 NaN -2.9 -0.3 -3.2 0.0
20 21 Devin Robinson 23 7 95 20.6 0.616 0.063 0.438 ... 0.2 0.1 0.3 0.175 NaN -0.2 1.2 1.0 0.1
21 22 Ron Baker 25 4 45 -2.0 0.000 1.000 0.000 ... -0.1 0.0 -0.1 -0.120 NaN -8.8 0.8 -8.1 -0.1
22 23 Gary Payton 26 3 16 36.9 0.688 0.250 0.000 ... 0.1 0.0 0.1 0.358 NaN 9.8 5.2 14.9 0.1
23 24 John Jenkins 27 4 14 20.5 1.500 1.000 0.000 ... 0.1 0.0 0.1 0.202 NaN 7.4 -5.3 2.2 0.0
24 25 Okaro White 26 3 6 -4.5 0.000 1.000 0.000 ... 0.0 0.0 0.0 -0.251 NaN -11.0 -8.2 -19.2 0.0
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