Python Beautiful Soup找不到特定表 [英] Python Beautiful Soup can't find specific table
问题描述
我在抓取Basketball-reference.com时遇到问题.我正在尝试访问每场比赛统计数据团队"表,但似乎无法定位正确的div/表.我正在尝试捕获表格,并使用熊猫将其放入数据框.
I'm having issues with scraping basketball-reference.com. I'm trying to access the "Team Per Game Stats" table but can't seem to target the correct div/table. I'm trying to capture the table and bring it into a dataframe using pandas.
我尝试使用soup.find和soup.find_all查找所有表,但是当我搜索结果时,没有看到我要查找的表的ID.见下文.
I've tried using soup.find and soup.find_all to find a all the tables but when I search the results I do not see the ID of the table I am looking for. See below.
x = soup.find("table", id="team-stats-per_game")
import csv, time, sys, math
import numpy as np
import pandas as pd
import requests
from bs4 import BeautifulSoup
import urllib.request
#NBA season
year = 2019
# URL page we will scraping
url = "https://www.basketball-reference.com/leagues/NBA_2019.html#all_team-stats-base".format(year)
# Basketball reference URL
html = urlopen(url)
soup = BeautifulSoup(html,'lxml')
x = soup.find("table", id="team-stats-per_game")
print(x)
Result:
None
我希望输出列出表元素,特别是将tr和th标签作为目标并将其引入熊猫df.
I expect the output to list the table elements, specifically tr and th tags to target and bring into a pandas df.
推荐答案
表将在之后呈现,因此您需要使用Selenium使其呈现或如上所述.但这不是必需的,因为大多数表都在注释中.您可以使用BeautifulSoup提取注释,然后在那些注释中搜索表格标记.
The tables are rendered after, so you'd need to use Selenium to let it render or as mentioned above. But that isn't necessary as most of the tables are within the comments. You could use BeautifulSoup to pull out the comments, then search through those for the table tags.
import requests
from bs4 import BeautifulSoup
from bs4 import Comment
import pandas as pd
#NBA season
year = 2019
url = 'https://www.basketball-reference.com/leagues/NBA_2019.html#all_team-stats-base'.format(year)
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')
comments = soup.find_all(string=lambda text: isinstance(text, Comment))
tables = []
for each in comments:
if 'table' in each:
try:
tables.append(pd.read_html(each)[0])
except:
continue
这将返回一个数据帧列表,因此只需按索引位置从所需位置拔出所需的表即可
This will return you a list of dataframes, so just pull out the table you want from wherever it is located by its index position:
输出:
print (tables[3])
Rk Team G MP FG ... STL BLK TOV PF PTS
0 1.0 Milwaukee Bucks* 82 19780 3555 ... 615 486 1137 1608 9686
1 2.0 Golden State Warriors* 82 19805 3612 ... 625 525 1169 1757 9650
2 3.0 New Orleans Pelicans 82 19755 3581 ... 610 441 1215 1732 9466
3 4.0 Philadelphia 76ers* 82 19805 3407 ... 606 432 1223 1745 9445
4 5.0 Los Angeles Clippers* 82 19830 3384 ... 561 385 1193 1913 9442
5 6.0 Portland Trail Blazers* 82 19855 3470 ... 546 413 1135 1669 9402
6 7.0 Oklahoma City Thunder* 82 19855 3497 ... 766 425 1145 1839 9387
7 8.0 Toronto Raptors* 82 19880 3460 ... 680 437 1150 1724 9384
8 9.0 Sacramento Kings 82 19730 3541 ... 679 363 1095 1751 9363
9 10.0 Washington Wizards 82 19930 3456 ... 683 379 1154 1701 9350
10 11.0 Houston Rockets* 82 19830 3218 ... 700 405 1094 1803 9341
11 12.0 Atlanta Hawks 82 19855 3392 ... 675 419 1397 1932 9294
12 13.0 Minnesota Timberwolves 82 19830 3413 ... 683 411 1074 1664 9223
13 14.0 Boston Celtics* 82 19780 3451 ... 706 435 1052 1670 9216
14 15.0 Brooklyn Nets* 82 19980 3301 ... 539 339 1236 1763 9204
15 16.0 Los Angeles Lakers 82 19780 3491 ... 618 440 1284 1701 9165
16 17.0 Utah Jazz* 82 19755 3314 ... 663 483 1240 1728 9161
17 18.0 San Antonio Spurs* 82 19805 3468 ... 501 386 992 1487 9156
18 19.0 Charlotte Hornets 82 19830 3297 ... 591 405 1001 1550 9081
19 20.0 Denver Nuggets* 82 19730 3439 ... 634 363 1102 1644 9075
20 21.0 Dallas Mavericks 82 19780 3182 ... 533 351 1167 1650 8927
21 22.0 Indiana Pacers* 82 19705 3390 ... 713 404 1122 1594 8857
22 23.0 Phoenix Suns 82 19880 3289 ... 735 418 1279 1932 8815
23 24.0 Orlando Magic* 82 19780 3316 ... 543 445 1082 1526 8800
24 25.0 Detroit Pistons* 82 19855 3185 ... 569 331 1135 1811 8778
25 26.0 Miami Heat 82 19730 3251 ... 627 448 1208 1712 8668
26 27.0 Chicago Bulls 82 19905 3266 ... 603 351 1159 1663 8605
27 28.0 New York Knicks 82 19780 3134 ... 557 422 1151 1713 8575
28 29.0 Cleveland Cavaliers 82 19755 3189 ... 534 195 1106 1642 8567
29 30.0 Memphis Grizzlies 82 19880 3113 ... 684 448 1147 1801 8490
30 NaN League Average 82 19815 3369 ... 626 406 1155 1714 9119
[31 rows x 25 columns]
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