Нет необходимости использовать Selenium. Таблица находится в комментариях. Быстрее просто вытащить комментарии и получить таблицы. Затем проанализируйте его с помощью Pandas 'read_html(), который использует beautifulsoup под капотом
import pandas as pd
import requests
from bs4 import BeautifulSoup, Comment
url = 'https://www.basketball-reference.com/players/j/jamesle01.html'
response = requests.get(url)
soup = BeautifulSoup(response.content, 'html.parser')
comments = soup.find_all(string=lambda text: isinstance(text, Comment))
tables = ''
for c in comments:
if '<table' in c:
tables += c
soup = BeautifulSoup(tables, 'html.parser')
shootingTable = soup.find('table', {'id':'shooting'})
df = pd.read_html(str(shootingTable))[0]
Вывод:
print (df.to_string())
Unnamed: 0_level_0 Unnamed: 1_level_0 Unnamed: 2_level_0 Unnamed: 3_level_0 Unnamed: 4_level_0 Unnamed: 5_level_0 Unnamed: 6_level_0 Unnamed: 7_level_0 Unnamed: 8_level_0 Unnamed: 9_level_0 Unnamed: 10_level_0 Unnamed: 11_level_0 Unnamed: 12_level_0 Unnamed: 13_level_0 Unnamed: 14_level_0 Unnamed: 15_level_0 Unnamed: 16_level_0 Unnamed: 17_level_0 Unnamed: 18_level_0 Unnamed: 19_level_0 Unnamed: 20_level_0 2-Pt Field Goals 3-Pt Field Goals
Unnamed: 0_level_1 Unnamed: 1_level_1 Unnamed: 2_level_1 Unnamed: 3_level_1 Unnamed: 4_level_1 Unnamed: 5_level_1 Unnamed: 6_level_1 Unnamed: 7_level_1 Unnamed: 8_level_1 % of FGA by Distance % of FGA by Distance % of FGA by Distance % of FGA by Distance % of FGA by Distance % of FGA by Distance FG% by Distance FG% by Distance FG% by Distance FG% by Distance FG% by Distance FG% by Distance Unnamed: 21_level_1 Dunks Unnamed: 24_level_1 Corner Heaves
Season Age Tm Lg Pos G MP FG% Dist. 2P 0-3 3-10 10-16 16-3pt 3P 2P 0-3 3-10 10-16 16-3pt 3P %Ast'd %FGA Md. %Ast'd %3PA 3P% Att. Md.
0 2003-04 19.0 CLE NBA SG 79.0 3122.0 0.417 11.2 0.855 0.315 0.168 0.161 0.211 0.145 0.438 0.604 0.356 0.313 0.352 0.290 0.442 0.064 91.0 0.746 0.286 0.323 3.0 0.0
1 2004-05 20.0 CLE NBA SF 80.0 3388.0 0.472 12.1 0.817 0.311 0.137 0.121 0.248 0.183 0.499 0.725 0.370 0.324 0.373 0.351 0.445 0.076 126.0 0.500 0.185 0.491 0.0 0.0
2 2005-06 21.0 CLE NBA SF 79.0 3361.0 0.480 12.4 0.792 0.353 0.092 0.094 0.253 0.208 0.518 0.689 0.411 0.304 0.398 0.335 0.303 0.065 114.0 0.417 0.185 0.343 5.0 0.0
3 2006-07 22.0 CLE NBA SF 78.0 3190.0 0.476 12.2 0.809 0.333 0.125 0.111 0.241 0.191 0.513 0.725 0.406 0.361 0.346 0.319 0.321 0.068 108.0 0.495 0.145 0.444 5.0 1.0
4 2007-08 23.0 CLE NBA SF 75.0 3027.0 0.484 12.2 0.781 0.373 0.090 0.080 0.238 0.219 0.531 0.706 0.426 0.303 0.372 0.315 0.333 0.069 109.0 0.398 0.095 0.353 2.0 0.0
5 2008-09 24.0 CLE NBA SF 81.0 3054.0 0.489 13.4 0.762 0.333 0.092 0.074 0.263 0.238 0.535 0.728 0.453 0.286 0.388 0.344 0.318 0.083 128.0 0.424 0.107 0.341 7.0 0.0
6 2009-10 25.0 CLE NBA SF 76.0 2966.0 0.503 13.4 0.747 0.342 0.082 0.061 0.262 0.253 0.560 0.745 0.468 0.344 0.398 0.333 0.369 0.080 117.0 0.341 0.111 0.395 4.0 0.0
7 2010-11 26.0 MIA NBA SF 79.0 3063.0 0.510 12.7 0.812 0.284 0.172 0.094 0.262 0.188 0.552 0.744 0.459 0.443 0.445 0.330 0.284 0.068 98.0 0.609 0.211 0.322 5.0 1.0
8 2011-12 27.0 MIA NBA SF 62.0 2326.0 0.531 11.5 0.873 0.329 0.136 0.135 0.272 0.127 0.556 0.758 0.484 0.481 0.384 0.362 0.360 0.092 104.0 0.519 0.121 0.333 0.0 0.0
9 2012-13 28.0 MIA NBA PF 76.0 2877.0 0.565 11.2 0.812 0.373 0.133 0.105 0.202 0.188 0.602 0.776 0.494 0.415 0.447 0.406 0.378 0.109 144.0 0.553 0.161 0.488 0.0 0.0
10 2013-14 29.0 MIA NBA PF 77.0 2902.0 0.567 11.4 0.774 0.399 0.123 0.084 0.168 0.226 0.622 0.796 0.554 0.404 0.366 0.379 0.395 0.101 134.0 0.534 0.144 0.568 0.0 0.0
11 2014-15 30.0 CLE NBA SF 69.0 2493.0 0.488 12.6 0.735 0.332 0.127 0.116 0.159 0.265 0.536 0.722 0.414 0.356 0.377 0.354 0.315 0.072 88.0 0.467 0.115 0.385 1.0 0.0
12 2015-16 31.0 CLE NBA SF 76.0 2709.0 0.520 9.6 0.801 0.459 0.119 0.093 0.129 0.199 0.573 0.722 0.361 0.348 0.404 0.309 0.378 0.083 111.0 0.598 0.117 0.273 0.0 0.0
13 2016-17 32.0 CLE NBA SF 74.0 2794.0 0.548 11.0 0.746 0.432 0.106 0.105 0.102 0.254 0.611 0.780 0.385 0.440 0.307 0.363 0.355 0.112 145.0 0.492 0.102 0.371 0.0 0.0
14 2017-18 33.0 CLE NBA PF 82.0 3026.0 0.542 10.9 0.743 0.423 0.114 0.089 0.118 0.257 0.603 0.766 0.378 0.421 0.371 0.367 0.301 0.080 120.0 0.356 0.084 0.382 1.0 0.0
15 2018-19 34.0 LAL NBA SF 55.0 1937.0 0.510 12.3 0.701 0.380 0.132 0.077 0.112 0.299 0.582 0.755 0.297 0.417 0.447 0.339 0.340 0.081 85.0 0.342 0.073 0.292 0.0 0.0
16 2019-20 35.0 LAL NBA PG 60.0 2094.0 0.498 12.8 0.676 0.366 0.139 0.072 0.099 0.324 0.570 0.728 0.423 0.341 0.359 0.349 0.247 0.066 76.0 0.459 0.055 0.429 1.0 0.0
17 Career NaN NaN NBA NaN 1258.0 48329.0 0.504 12.0 0.781 0.360 0.121 0.098 0.201 0.219 0.549 0.734 0.418 0.368 0.386 0.344 0.347 0.080 1898.0 0.469 0.129 0.387 34.0 2.0
18 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
19 11 seasons NaN CLE NBA NaN 849.0 33130.0 0.492 11.9 0.782 0.363 0.113 0.100 0.206 0.218 0.535 0.721 0.397 0.343 0.375 0.337 0.352 0.077 1257.0 0.456 0.133 0.375 28.0 1.0
20 4 seasons NaN MIA NBA NaN 294.0 11168.0 0.543 11.8 0.816 0.345 0.142 0.103 0.225 0.184 0.582 0.771 0.493 0.439 0.414 0.369 0.353 0.092 480.0 0.556 0.164 0.432 5.0 1.0
21 2 seasons NaN LAL NBA NaN 115.0 4031.0 0.504 12.6 0.688 0.373 0.136 0.074 0.106 0.312 0.576 0.741 0.364 0.379 0.404 0.345 0.293 0.074 161.0 0.406 0.064 0.356 1.0 0.0