I'm trying to practice BeautifulSoup and urlopen by using Basketball-Reference datasets. When I try and get individual player's stats, everything works fine, but then I tried to use the same code for Team's stats and apparently urlopen isn't finding the right table.
The following code is to get the "headers" from the page.
def fetch_years():
#Determine the urls
url = "https://www.basketball-reference.com/leagues/NBA_2000.html?sr&utm_source=direct&utm_medium=Share&utm_campaign=ShareTool#team-stats-per_game::none"
html = urlopen(url)
soup = BeautifulSoup(html)
soup.find_all('tr')
headers = [th.get_text() for th in soup.find_all('tr')[0].find_all('th')]
headers = headers[1:]
print(headers)
I'm trying to get the Team's stats per game data, in a format like:
['Tm', 'G', 'MP', 'FG', ...]
Instead, the header data I'm getting is:
['W', 'L', 'W/L%', ...]
which is the very first table in the 1999-2000 season information about the teams (under the name 'Division Standings').
If you use that same code for a player's data such as this one, you get the result I'm looking for:
Age Tm Lg Pos G GS MP FG ... DRB TRB AST STL BLK TOV PF PTS
0 20 OKC NBA PG 82 65 32.5 5.3 ... 2.7 4.9 5.3 1.3 0.2 3.3 2.3 15.3
1 21 OKC NBA PG 82 82 34.3 5.9 ... 3.1 4.9 8.0 1.3 0.4 3.3 2.5 16.1
2 22 OKC NBA PG 82 82 34.7 7.5 ... 3.1 4.6 8.2 1.9 0.4 3.9 2.5 21.9
3 23 OKC NBA PG 66 66 35.3 8.8 ... 3.1 4.6 5.5 1.7 0.3 3.6 2.2 23.6
4 24 OKC NBA PG 82 82 34.9 8.2 ... 3.9 5.2 7.4 1.8 0.3 3.3 2.3 23.2
The code to webscrape came originally from here.
the sports -reference.com sites are trickier than your standard ones. The tables are rendered after loading the page (with the exception of a few tables on the pages), so you'd need to use Selenium to let it render first, then pull the html source code.
However, the other option is if you look at the html source, you'll see those tables are within the comments. You could use BeautifulSoup to pull out the comments tags, then search through those for the table tags.
This will return a list of dataframes, and the Team Per Game stats are the table in index position 1:
import requests
from bs4 import BeautifulSoup
from bs4 import Comment
import pandas as pd
def fetch_years():
#Determine the urls
url = "https://www.basketball-reference.com/leagues/NBA_2000.html?sr&utm_source=direct&utm_medium=Share&utm_campaign=ShareTool#team-stats-per_game::none"
html = requests.get(url)
soup = BeautifulSoup(html.text)
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
return tables
tables = fetch_years()
Output:
print (tables[1].to_string())
Rk Team G MP FG FGA FG% 3P 3PA 3P% 2P 2PA 2P% FT FTA FT% ORB DRB TRB AST STL BLK TOV PF PTS
0 1.0 Sacramento Kings* 82 241.5 40.0 88.9 0.450 6.5 20.2 0.322 33.4 68.7 0.487 18.5 24.6 0.754 12.9 32.1 45.0 23.8 9.6 4.6 16.2 21.1 105.0
1 2.0 Detroit Pistons* 82 241.8 37.1 80.9 0.459 5.4 14.9 0.359 31.8 66.0 0.481 23.9 30.6 0.781 11.2 30.0 41.2 20.8 8.1 3.3 15.7 24.5 103.5
2 3.0 Dallas Mavericks 82 240.6 39.0 85.9 0.453 6.3 16.2 0.391 32.6 69.8 0.468 17.2 21.4 0.804 11.4 29.8 41.2 22.1 7.2 5.1 13.7 21.6 101.4
3 4.0 Indiana Pacers* 82 240.6 37.2 81.0 0.459 7.1 18.1 0.392 30.0 62.8 0.478 19.9 24.5 0.811 10.3 31.9 42.1 22.6 6.8 5.1 14.1 21.8 101.3
4 5.0 Milwaukee Bucks* 82 242.1 38.7 83.3 0.465 4.8 13.0 0.369 33.9 70.2 0.483 19.0 24.2 0.786 12.4 28.9 41.3 22.6 8.2 4.6 15.0 24.6 101.2
5 6.0 Los Angeles Lakers* 82 241.5 38.3 83.4 0.459 4.2 12.8 0.329 34.1 70.6 0.482 20.1 28.9 0.696 13.6 33.4 47.0 23.4 7.5 6.5 13.9 22.5 100.8
6 7.0 Orlando Magic 82 240.9 38.6 85.5 0.452 3.6 10.6 0.338 35.1 74.9 0.468 19.2 26.1 0.735 14.0 31.0 44.9 20.8 9.1 5.7 17.6 24.0 100.1
7 8.0 Houston Rockets 82 241.8 36.6 81.3 0.450 7.1 19.8 0.358 29.5 61.5 0.480 19.2 26.2 0.733 12.3 31.5 43.8 21.6 7.5 5.3 17.4 20.3 99.5
8 9.0 Boston Celtics 82 240.6 37.2 83.9 0.444 5.1 15.4 0.331 32.2 68.5 0.469 19.8 26.5 0.745 13.5 29.5 43.0 21.2 9.7 3.5 15.4 27.1 99.3
9 10.0 Seattle SuperSonics* 82 241.2 37.9 84.7 0.447 6.7 19.6 0.339 31.2 65.1 0.480 16.6 23.9 0.695 12.7 30.3 43.0 22.9 8.0 4.2 14.0 21.7 99.1
10 11.0 Denver Nuggets 82 242.1 37.3 84.3 0.442 5.7 17.0 0.336 31.5 67.2 0.469 18.7 25.8 0.724 13.1 31.6 44.7 23.3 6.8 7.5 15.6 23.9 99.0
11 12.0 Phoenix Suns* 82 241.5 37.7 82.6 0.457 5.6 15.2 0.368 32.1 67.4 0.477 17.9 23.6 0.759 12.5 31.2 43.7 25.6 9.1 5.3 16.7 24.1 98.9
12 13.0 Minnesota Timberwolves* 82 242.7 39.3 84.3 0.467 3.0 8.7 0.346 36.3 75.5 0.481 16.8 21.6 0.780 12.4 30.1 42.5 26.9 7.6 5.4 13.9 23.3 98.5
13 14.0 Charlotte Hornets* 82 241.2 35.8 79.7 0.449 4.1 12.2 0.339 31.7 67.5 0.469 22.7 30.0 0.758 10.8 32.1 42.9 24.7 8.9 5.9 14.7 20.4 98.4
14 15.0 New Jersey Nets 82 241.8 36.3 83.9 0.433 5.8 16.8 0.347 30.5 67.2 0.454 19.5 24.9 0.784 12.7 28.2 40.9 20.6 8.8 4.8 13.6 23.3 98.0
15 16.0 Portland Trail Blazers* 82 241.2 36.8 78.4 0.470 5.0 13.8 0.361 31.9 64.7 0.493 18.8 24.7 0.760 11.8 31.2 43.0 23.5 7.7 4.8 15.2 22.7 97.5
16 17.0 Toronto Raptors* 82 240.9 36.3 83.9 0.433 5.2 14.3 0.363 31.2 69.6 0.447 19.3 25.2 0.765 13.4 29.9 43.3 23.7 8.1 6.6 13.9 24.3 97.2
17 18.0 Cleveland Cavaliers 82 242.1 36.3 82.1 0.442 4.2 11.2 0.373 32.1 70.9 0.453 20.2 26.9 0.750 12.3 30.5 42.8 23.7 8.7 4.4 17.4 27.1 97.0
18 19.0 Washington Wizards 82 241.5 36.7 81.5 0.451 4.1 10.9 0.376 32.6 70.6 0.462 19.1 25.7 0.743 13.0 29.7 42.7 21.6 7.2 4.7 16.1 26.2 96.6
19 20.0 Utah Jazz* 82 240.9 36.1 77.8 0.464 4.0 10.4 0.385 32.1 67.4 0.476 20.3 26.2 0.773 11.4 29.6 41.0 24.9 7.7 5.4 14.9 24.5 96.5
20 21.0 San Antonio Spurs* 82 242.1 36.0 78.0 0.462 4.0 10.8 0.374 32.0 67.2 0.476 20.1 27.0 0.746 11.3 32.5 43.8 22.2 7.5 6.7 15.0 20.9 96.2
21 22.0 Golden State Warriors 82 240.9 36.5 87.1 0.420 4.2 13.0 0.323 32.3 74.0 0.437 18.3 26.2 0.697 15.9 29.7 45.6 22.6 8.9 4.3 15.9 24.9 95.5
22 23.0 Philadelphia 76ers* 82 241.8 36.5 82.6 0.442 2.5 7.8 0.323 34.0 74.8 0.454 19.2 27.1 0.708 14.0 30.1 44.1 22.2 9.6 4.7 15.7 23.6 94.8
23 24.0 Miami Heat* 82 241.8 36.3 78.8 0.460 5.4 14.7 0.371 30.8 64.1 0.481 16.4 22.3 0.736 11.2 31.9 43.2 23.5 7.1 6.4 15.0 23.7 94.4
24 25.0 Atlanta Hawks 82 241.8 36.6 83.0 0.441 3.1 9.9 0.317 33.4 73.1 0.458 18.0 24.2 0.743 14.0 31.3 45.3 18.9 6.1 5.6 15.4 21.0 94.3
25 26.0 Vancouver Grizzlies 82 242.1 35.3 78.5 0.449 4.0 11.0 0.361 31.3 67.6 0.463 19.4 25.1 0.774 12.3 28.3 40.6 20.7 7.4 4.2 16.8 22.9 93.9
26 27.0 New York Knicks* 82 241.8 35.3 77.7 0.455 4.3 11.4 0.375 31.0 66.3 0.468 17.2 22.0 0.781 9.8 30.7 40.5 19.4 6.3 4.3 14.6 24.2 92.1
27 28.0 Los Angeles Clippers 82 240.3 35.1 82.4 0.426 5.2 15.5 0.339 29.9 67.0 0.446 16.6 22.3 0.746 11.6 29.0 40.6 18.0 7.0 6.0 16.2 22.2 92.0
28 29.0 Chicago Bulls 82 241.5 31.3 75.4 0.415 4.1 12.6 0.329 27.1 62.8 0.432 18.1 25.5 0.709 12.6 28.3 40.9 20.1 7.9 4.7 19.0 23.3 84.8
29 NaN League Average 82 241.5 36.8 82.1 0.449 4.8 13.7 0.353 32.0 68.4 0.468 19.0 25.3 0.750 12.4 30.5 42.9 22.3 7.9 5.2 15.5 23.3 97.5