使用python beautifulsoup刮取NBA高级统计数据

Question

I want to scrape the NBA advanced stats. To start I just want to be able to scrape the names of the teams and I am having an issue where it is not collecting any of the information. I may be looking for the wrong thing in the find_all function. Any help is appreciated!

 import requests from bs4 import BeautifulSoup url = "https://stats.nba.com/teams/elbow-touch/?sort=ELBOW_TOUCHES&dir=-1" result = requests.get(url) c = result.content soup = Beaut ifulSoup(c,"html.parser") title = soup.title.text print(title) teams = soup.find_all('td',{'class':'team'}) for element in teams: print(element.text) 

Site that I want to scrape:

Answer 1

Another way to do this is to send a get request to the site API and receive a json response. By alter the params you can get different results.

You can look for where the request was sent to by your browser under the chrome developer tool.

import requests

url = "https://stats.nba.com/stats/leaguedashptstats?"

headers = {
    "User-Agent": "Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/72.0.3626.119 Safari/537.36"
}

params = {
    "PerMode": "PerGame",
    "PlayerOrTeam": "Team",
    "PtMeasureType": "ElbowTouch",
    "Season": "2018-19",
    "SeasonType": "Regular Season",
    "StarterBench": "",
    "PlayerPosition": "",
    "PlayerExperience": "",
    "GameScope": "",
    "VsConference": "",
    "VsDivision": "",
    "DateFrom": "",
    "DateTo": "",
    "SeasonSegment": "",
    "Location": "",
    "Outcome": "",
    "LastNGames": "0",
    "Month": "0",
    "OpponentTeamID": "0"
}

r = requests.get(url, params=params, headers=headers)
data = r.json()
results = data['resultSets'][0]['rowSet']

for result in results:
    print(result)

Answer 2

The site is dynamic, thus, you need to use selenium :

from selenium import webdriver
from bs4 import BeautifulSoup as soup 
d = webdriver.Chrome('/path/to/chromedriver')
d.get('https://stats.nba.com/teams/elbow-touch/?sort=ELBOW_TOUCHES&dir=-1')
s = soup(d.page_source, 'html.parser').find('table', {'class':'table'})
headers, [_, *data] = [i.text for i in s.find_all('th')], [[i.text for i in b.find_all('td')] for b in s.find_all('tr')]
final_data = [i for i in data if len(i) > 1]

Now, final_data stores the all team results:

[['Houston Rockets', '63', '38', '25', '242.0', '367.0', '8.8', '2.4', '3.8', '64.2', '0.4', '0.7', '62.8', '5.5', '-', '3.7', '-', '0.5', '14.0', '0.5', '5.4', '0.3', '-'], ['Milwaukee Bucks', '63', '48', '15', '241.2', '409.5', '9.5', '2.3', '3.6', '62.4', '0.7', '1.0', '73.3', '5.4', '-', '4.3', '-', '0.6', '13.0', '0.5', '5.2', '0.4', '-'], ['New York Knicks', '62', '13', '49', '241.6', '420.4', '9.5', '2.0', '3.4', '56.8', '0.7', '1.0', '69.8', '4.8', '-', '4.7', '-', '0.6', '13.7', '0.5', '5.3', '0.5', '-'], ['Charlotte Hornets', '63', '29', '34', '242.0', '409.7', '9.6', '1.7', '3.5', '50.0', '1.1', '1.5', '71.9', '4.7', '-', '4.6', '-', '0.7', '14.2', '0.4', '4.5', '0.7', '-'], ['Detroit Pistons', '62', '31', '31', '242.8', '437.0', '10.0', '1.6', '3.2', '51.3', '0.9', '1.2', '75.3', '4.4', '-', '5.0', '-', '0.9', '17.6', '0.7', '6.8', '0.6', '-'], ['Washington Wizards', '62', '25', '37', '243.2', '420.2', '10.5', '2.5', '4.3', '58.4', '0.9', '1.2', '76.4', '6.1', '-', '4.6', '-', '0.7', '15.5', '0.6', '5.6', '0.5', '-'], ['Atlanta Hawks', '64', '22', '42', '242.3', '434.9', '11.0', '2.2', '3.7', '58.6', '1.2', '1.5', '77.3', '5.7', '-', '5.3', '-', '0.7', '12.9', '0.7', '6.5', '0.7', '-'], ['Brooklyn Nets', '65', '32', '33', '243.8', '440.3', '11.2', '2.5', '4.4', '58.3', '1.2', '1.7', '70.8', '6.4', '-', '4.6', '-', '0.7', '14.9', '0.9', '7.9', '0.8', '-'], ['San Antonio Spurs', '64', '35', '29', '241.6', '402.3', '11.3', '2.3', '4.1', '55.5', '0.8', '1.0', '85.7', '5.6', '-', '5.8', '-', '1.1', '18.7', '0.5', '4.8', '0.4', '-'], ['Boston Celtics', '64', '38', '26', '241.6', '420.8', '11.5', '2.5', '4.2', '58.4', '0.5', '0.7', '71.7', '5.5', '-', '5.7', '-', '0.9', '15.0', '0.6', '5.6', '0.3', '-'], ['Toronto Raptors', '64', '46', '18', '242.3', '418.0', '11.5', '3.5', '5.9', '59.6', '1.2', '1.5', '78.1', '8.3', '-', '4.1', '-', '0.7', '16.3', '0.4', '3.7', '0.7', '-'], ['Portland Trail Blazers', '63', '39', '24', '241.6', '409.8', '11.8', '2.4', '4.6', '51.9', '1.2', '1.5', '80.2', '6.1', '-', '5.5', '-', '1.0', '18.8', '0.7', '5.7', '0.7', '-'], ['Utah Jazz', '61', '36', '25', '240.8', '435.9', '11.9', '2.0', '3.8', '51.1', '1.4', '2.2', '66.7', '5.4', '-', '5.9', '-', '1.0', '17.1', '0.7', '5.9', '1.0', '-'], ['Minnesota Timberwolves', '63', '29', '34', '241.6', '412.4', '12.0', '2.9', '5.0', '57.3', '1.3', '1.6', '79.8', '7.3', '-', '5.2', '-', '1.0', '19.5', '0.6', '5.2', '0.7', '-'], ['Chicago Bulls', '63', '18', '45', '243.2', '411.3', '12.4', '2.8', '4.8', '57.9', '0.7', '0.9', '77.6', '6.4', '-', '6.3', '-', '0.8', '12.4', '0.6', '4.5', '0.4', '-'], ['LA Clippers', '65', '36', '29', '241.9', '430.4', '12.4', '2.9', '5.1', '56.9', '1.0', '1.5', '69.5', '7.0', '-', '5.4', '-', '0.9', '15.9', '0.7', '5.5', '0.6', '-'], ['Miami Heat', '62', '28', '34', '240.4', '426.1', '12.6', '2.0', '4.0', '50.2', '0.7', '1.3', '56.8', '4.9', '-', '7.0', '-', '1.1', '15.4', '0.4', '3.4', '0.5', '-'], ['New Orleans Pelicans', '65', '29', '36', '240.0', '435.0', '12.6', '3.5', '6.4', '54.8', '1.2', '1.6', '74.5', '8.4', '-', '4.4', '-', '0.9', '20.4', '0.7', '5.2', '0.8', '-'], ['Phoenix Suns', '64', '13', '51', '242.3', '435.8', '12.9', '2.8', '5.0', '56.7', '1.0', '1.3', '73.5', '6.8', '-', '6.2', '-', '0.8', '13.7', '0.6', '4.7', '0.6', '-'], ['Oklahoma City Thunder', '63', '39', '24', '242.0', '364.8', '13.6', '3.2', '5.8', '54.5', '1.0', '1.4', '65.9', '7.5', '-', '5.8', '-', '0.9', '14.7', '0.7', '4.8', '0.6', '-'], ['Dallas Mavericks', '62', '27', '35', '240.8', '435.4', '13.9', '1.8', '3.1', '55.9', '1.2', '1.6', '76.5', '5.0', '-', '8.6', '-', '1.1', '13.1', '0.8', '5.7', '0.7', '-'], ['Golden State Warriors', '63', '44', '19', '241.6', '442.3', '13.9', '2.8', '4.8', '57.0', '1.2', '1.5', '81.7', '6.9', '-', '7.2', '-', '1.6', '21.7', '0.8', '5.8', '0.7', '-'], ['Orlando Magic', '63', '28', '35', '241.2', '405.0', '14.0', '3.2', '5.7', '55.8', '1.1', '1.4', '80.9', '7.7', '-', '6.5', '-', '1.4', '21.8', '0.6', '4.0', '0.7', '-'], ['Los Angeles Lakers', '63', '30', '33', '241.6', '405.9', '14.2', '3.3', '5.7', '57.8', '1.1', '1.6', '67.0', '7.8', '-', '6.3', '-', '1.3', '20.7', '0.9', '6.3', '0.7', '-'], ['Denver Nuggets', '62', '42', '20', '240.8', '435.2', '15.0', '3.1', '5.3', '59.1', '1.1', '1.5', '72.5', '7.5', '-', '7.4', '-', '1.7', '22.3', '1.0', '6.4', '0.7', '-'], ['Indiana Pacers', '64', '41', '23', '240.4', '431.7', '15.3', '4.4', '7.2', '60.6', '1.4', '1.9', '74.2', '10.4', '-', '5.8', '-', '1.2', '20.9', '0.9', '6.0', '0.9', '-'], ['Cleveland Cavaliers', '64', '16', '48', '241.2', '407.3', '16.1', '2.3', '4.5', '51.6', '0.9', '1.1', '80.0', '5.6', '-', '10.0', '-', '1.2', '12.3', '0.5', '3.4', '0.4', '-'], ['Philadelphia 76ers', '63', '40', '23', '242.0', '446.9', '16.6', '2.5', '4.7', '52.7', '1.4', '1.7', '82.6', '6.6', '-', '9.6', '-', '1.8', '18.6', '0.7', '4.3', '0.7', '-'], ['Sacramento Kings', '62', '31', '31', '240.8', '425.2', '16.7', '3.2', '6.3', '50.3', '1.1', '1.6', '65.3', '7.5', '-', '8.0', '-', '1.5', '18.3', '1.0', '6.2', '0.7', '-'], ['Memphis Grizzlies', '65', '25', '40', '241.9', '452.1', '20.5', '3.4', '6.7', '51.3', '1.5', '1.9', '81.1', '8.6', '-', '11.2', '-', '1.6', '14.1', '0.8', '4.1', '0.8', '-']]

To get just the teams:

teams = [a for a, *_ in final_data]

Output:

['Houston Rockets', 'Milwaukee Bucks', 'New York Knicks', 'Charlotte Hornets', 'Detroit Pistons', 'Washington Wizards', 'Atlanta Hawks', 'Brooklyn Nets', 'San Antonio Spurs', 'Boston Celtics', 'Toronto Raptors', 'Portland Trail Blazers', 'Utah Jazz', 'Minnesota Timberwolves', 'Chicago Bulls', 'LA Clippers', 'Miami Heat', 'New Orleans Pelicans', 'Phoenix Suns', 'Oklahoma City Thunder', 'Dallas Mavericks', 'Golden State Warriors', 'Orlando Magic', 'Los Angeles Lakers', 'Denver Nuggets', 'Indiana Pacers', 'Cleveland Cavaliers', 'Philadelphia 76ers', 'Sacramento Kings', 'Memphis Grizzlies']

To get specific statistics, it is easiest to create a list of dictionaries by binding the header values to the data lists:

data_attrs = [dict(zip(headers, i)) for i in final_data]
all_touches = [i['Touches'] for i in data_attrs]

Answer 3

A variation on @Ajax1234's answer can get you the whole table loaded into a dataframe:

import pandas as pd

pd.read_html(str(s))

And there's your table.