使用 BeautifulSoup 和 Python 抓取表格
[英]Scraping Table using BeautifulSoup and Python
问题描述
im trying to scrape the first major table from the following site using BeautifulSoup : https://dailyfantasyrankings.com.au/resources/nba/cheatsheet/moneyball/allproj.php
我正在尝试使用 BeautifulSoup 从以下站点抓取第一个主要表格： https : //dailyfantasyrankings.com.au/resources/nba/cheatsheet/moneyball/allproj.php
Im receiving the error: AttributeError: ResultSet object has no attribute 'find_all'.我收到错误：AttributeError: ResultSet object has no attribute 'find_all'。 You're probably treating a list of items like a single item.您可能将项目列表视为单个项目。 Did you call find_all() when you meant to call find()?当您打算调用 find() 时，您是否调用了 find_all()？
Im sure other parts of my code arent quite right but was hoping someone could assist!我确定我的代码的其他部分不太正确，但希望有人可以提供帮助！
Code:代码：
import requests
from urllib.request import urlopen
from bs4 import BeautifulSoup
import pandas as pd
URL = 'https://dailyfantasyrankings.com.au/resources/nba/cheatsheet/moneyball/allproj.php'
response = requests.get(URL)
html = response.content
soup = BeautifulSoup(response.content, 'html.parser')
table = soup.find('table', attrs={'class': 'xl12520945'})
columns = ['#', 'PLAYER', 'POS', '@', 'TEAM', 'OPP', 'M-UP', 'PACE', 'REST', 'PRICE', 'PROJ', 'VALUE', 'AVE']
df = pd.DataFrame(columns=columns)
trs = table.find_all('tr')
for tr in trs:
tds = tr.find_all('td')
row = [td.text.replace('\n','') for td in tds]
df = df.append(pd.Series(row, index=columns), ignore_index=True)
df.to_csv('dfr_proj.csv', index = False)
3 个解决方案
解决方案1
0 2020-03-01 17:28:41
Here's your answer: Beautiful Soup: 'ResultSet' object has no attribute 'find_all'?这是您的答案： Beautiful Soup：'ResultSet' 对象没有属性 'find_all'？
However, I tried running your script and was running into a different issue.但是，我尝试运行您的脚本并遇到了不同的问题。 The "table" element didn't even exist. “table”元素甚至不存在。
解决方案2
0 已采纳 2020-03-01 18:53:31
The table you're trying to scrape is in an iframe, so it's better to get the iframe source html instead of the page html您尝试抓取的表位于 iframe 中，因此最好获取 iframe 源 html 而不是页面 html
URL = 'https://dailyfantasyrankings.com.au/resources/nba/htm/projections/mballproj.htm'
Get the table with CSS selector instead of class name because there is an issue with how HTML is formatted on the source page:使用 CSS 选择器而不是类名获取表格，因为源页面上的 HTML 格式存在问题：
table = soup.select("#Finish_20945 > table")[0]
Working code.工作代码。 Change are marked with code comments:更改标有代码注释：
import requests
from bs4 import BeautifulSoup
import pandas as pd
#CHANGED LINE BELOW
URL = 'https://dailyfantasyrankings.com.au/resources/nba/htm/projections/mballproj.htm'
response = requests.get(URL)
soup = BeautifulSoup(response.content, 'html.parser')
#CHANGED LINE BELOW
table = soup.select("#Finish_20945 > table")[0]
columns = ['#', 'PLAYER', 'POS', '@', 'TEAM', 'OPP', 'M-UP', 'PACE', 'REST', 'PRICE', 'PROJ', 'VALUE', 'AVE']
df = pd.DataFrame(columns=columns)
trs = table.find_all('tr')
for tr in trs:
tds = tr.find_all('td')
row = [td.text.replace('\n', '') for td in tds]
print(row)
# df = df.append(pd.Series(row, index=columns), ignore_index=True)
df.to_csv('dfr_proj.csv', index=False)
Output:输出：
['', '', '', '', '', '', '', '', '', '', '', '↓', '↓', '', '\xa0', '\xa0', '\xa0', '\xa0', '\xa0']
['#', 'Player', '\xa0', 'Pos', '@', 'Team', 'Opp', 'M-UP', 'Pace', 'Rest', 'Price', 'Proj', 'Value', 'Ave', 'Last 5 Games']
['1', 'A. Davis', '\xa0', 'PF', 'A', 'LAL', 'NOP', '18', '3.5', 'B2B', '$10,800', '61', '5.6', '51', '70', '52', '61', '41', '37']
['2', 'B. Beal', '\xa0', 'SG', 'A', 'WAS', 'GSW', '5', '0.3', '1', '$9,600', '57', '5.9', '46', '34', '67', '53', '51', '67']
['3', 'L. James', '\xa0', 'SF', 'A', 'LAL', 'NOP', '3', '3.5', 'B2B', '$10,400', '53', '5.1', '51', '51', '50', '55', '\xa0', '45']
['4', 'N. Jokic', '\xa0', 'C', 'H', 'DEN', 'TOR', '19', '0.4', '1', '$10,100', '47', '4.7', '45', '53', '52', '50', '35', '35']
['5', 'P. Siakam', '\xa0', 'SF', 'A', 'TOR', 'DEN', '26', '-3.0', '1', '$8,400', '45', '5.4', '41', '34', '63', '28', '33', '50']
['6', 'K. Lowry', '\xa0', 'PG', 'A', 'TOR', 'DEN', '11', '-3.0', '1', '$7,600', '44', '5.8', '38', '43', '31', '57', '23', '47']
['7', 'A. Wiggins', '\xa0', 'SF', 'H', 'GSW', 'WAS', '11', '3.0', 'B2B', '$7,700', '42', '5.5', '36', '34', '34', '16', '\xa0', '46']
['8', 'B. Ingram', '\xa0', 'SF', 'H', 'NOP', 'LAL', '16', '0.5', '1', '$7,900', '40', '5.1', '40', '\xa0', '26', '32', '47', '47']
['9', 'C. Wood', 'GTD', 'PF', 'A', 'DET', 'SAC', '26', '-2.0', '1', '$7,900', '39', '4.9', '23', '48', '35', '44', '36', '48']
['10', 'D. Fox', 'GTD', 'PG', 'H', 'SAC', 'DET', '3', '-2.4', '1', '$7,500', '38', '5.1', '37', '34', '35', '36', '\xa0', '42']
['11', 'J. Holiday', '\xa0', 'SG', 'H', 'NOP', 'LAL', '24', '0.5', '1', '$8,900', '38', '4.3', '40', '35', '44', '52', '33', '40']
['12', 'D. Rose', '\xa0', 'PG', 'A', 'DET', 'SAC', '18', '-2.0', '1', '$6,000', '36', '6.0', '30', '11', '19', '17', '32', '47']
['13', 'Z. Williamson', '\xa0', 'PF', 'H', 'NOP', 'LAL', '16', '0.5', '1', '$8,300', '36', '4.3', '33', '41', '37', '41', '41', '33']
['14', 'D. Lee', '\xa0', 'SG', 'H', 'GSW', 'WAS', '7', '3.0', 'B2B', '$6,100', '34', '5.7', '24', '26', '36', '32', '25', '41']
['15', 'M. Chriss', '\xa0', 'C', 'H', 'GSW', 'WAS', '21', '3.0', 'B2B', '$6,400', '34', '5.3', '22', '21', '\xa0', '37', '20', '36']
['16', 'O. Anunoby', '\xa0', 'SF', 'A', 'TOR', 'DEN', '27', '-3.0', '1', '$5,000', '33', '6.7', '24', '12', '31', '19', '29', '49']
['17', 'J. Murray', '\xa0', 'PG', 'H', 'DEN', 'TOR', '15', '0.4', '1', '$7,200', '32', '4.4', '33', '49', '35', '29', '39', '17']
['18', 'L. Ball', '\xa0', 'PG', 'H', 'NOP', 'LAL', '30', '0.5', '1', '$6,900', '32', '4.6', '32', '33', '32', '33', '30', '46']
['19', 'E. Paschall', '\xa0', 'PF', 'H', 'GSW', 'WAS', '21', '3.0', 'B2B', '$4,600', '30', '6.5', '23', '21', '20', '20', '30', '34']
['20', 'N. Powell', '\xa0', 'SG', 'A', 'TOR', 'DEN', '10', '-3.0', '1', '$4,500', '28', '6.2', '26', '\xa0', '\xa0', '\xa0', '\xa0', '27']
['21', 'R. Hachimura', '\xa0', 'PF', 'A', 'WAS', 'GSW', '15', '0.3', '1', '$5,100', '28', '5.4', '25', '36', '25', '22', '20', '31']
['22', 'B. Hield', '\xa0', 'SG', 'H', 'SAC', 'DET', '26', '-2.4', '1', '$6,000', '27', '4.4', '32', '39', '12', '32', '24', '24']
['23', 'W. Barton', '\xa0', 'SF', 'H', 'DEN', 'TOR', '11', '0.4', '1', '$5,800', '26', '4.5', '31', '\xa0', '35', '25', '16', '25']
['24', 'H. Giles III', '\xa0', 'PF', 'H', 'SAC', 'DET', '7', '-2.4', '1', '$5,100', '26', '5.1', '15', '15', '33', '33', '29', '30']
['25', 'N. Bjelica', '\xa0', 'PF', 'H', 'SAC', 'DET', '11', '-2.4', '1', '$5,100', '26', '5.1', '27', '26', '24', '10', '31', '31']
['26', 'J. Grant', '\xa0', 'PF', 'H', 'DEN', 'TOR', '23', '0.4', '1', '$4,500', '25', '5.5', '21', '26', '8', '17', '35', '35']
['27', 'S. Napier', '\xa0', 'PG', 'A', 'WAS', 'GSW', '6', '0.3', '1', '$5,000', '24', '4.7', '22', '18', '17', '46', '22', '13']
['28', 'D. Bender', '\xa0', 'PF', 'H', 'GSW', 'WAS', '21', '3.0', 'B2B', '$3,900', '23', '5.9', '12', '\xa0', '20', '8', '13', '39']
['29', 'D. Favors', '\xa0', 'C', 'H', 'NOP', 'LAL', '30', '0.5', '1', '$5,400', '23', '4.3', '27', '21', '27', '24', '18', '37']
['30', 'K. Bazemore', '\xa0', 'SF', 'H', 'SAC', 'DET', '26', '-2.4', '1', '$5,400', '23', '4.2', '19', '25', '47', '28', '18', '26']
['31', 'D. Bertans', '\xa0', 'PF', 'A', 'WAS', 'GSW', '24', '0.3', '1', '$5,100', '23', '4.5', '26', '26', '25', '\xa0', '23', '19']
['32', 'H. Barnes', '\xa0', 'SF', 'H', 'SAC', 'DET', '29', '-2.4', '1', '$5,200', '23', '4.4', '25', '35', '36', '24', '29', '15']
['33', 'T. Bryant', '\xa0', 'C', 'A', 'WAS', 'GSW', '11', '0.3', '1', '$4,100', '23', '5.5', '25', '20', '13', '\xa0', '15', '22']
['34', 'B. Knight', '\xa0', 'PG', 'A', 'DET', 'SAC', '23', '-2.0', '1', '$3,900', '22', '5.8', '11', '\xa0', '1', '22', '25', '28']
['35', 'R.\r Hollis-Jefferson', '\xa0', 'PF', 'A', 'TOR', 'DEN', '26', '-3.0', '1', '$4,000', '22', '5.4', '19', '12', '22', '18', '25', '15']
['36', 'I. Smith', '\xa0', 'PG', 'A', 'WAS', 'GSW', '8', '0.3', '1', '$5,000', '22', '4.3', '24', '26', '29', '30', '12', '16']
['37', 'R. Rondo', '\xa0', 'PG', 'A', 'LAL', 'NOP', '4', '3.5', 'B2B', '$4,300', '21', '4.9', '19', '14', '28', '7', '30', '10']
['38', 'A. Len', '\xa0', 'C', 'H', 'SAC', 'DET', '7', '-2.4', '1', '$4,000', '21', '5.2', '20', '\xa0', '16', '11', '26', '24']
['39', 'B. Bogdanovic', '\xa0', 'SG', 'H', 'SAC', 'DET', '25', '-2.4', '1', '$5,100', '20', '4.0', '24', '16', '32', '28', '23', '24']
['40', 'D. Howard', '\xa0', 'C', 'A', 'LAL', 'NOP', '29', '3.5', 'B2B', '$4,400', '20', '4.6', '22', '12', '20', '15', '36', '20']
['41', 'M. Mulder', '\xa0', '-', 'H', 'GSW', 'WAS', '7', '3.0', 'B2B', '-', '20', '-', '-', '\xa0', '\xa0', '\xa0', '7', '21']
['42', 'M. Morris', '\xa0', 'PG', 'H', 'DEN', 'TOR', '15', '0.4', '1', '$4,600', '19', '4.2', '19', '26', '12', '34', '29', '18']
['43', 'J. Henson', '\xa0', 'C', 'A', 'DET', 'SAC', '21', '-2.0', '1', '$4,500', '19', '4.3', '17', '4', '12', '31', '14', '21']
['44', 'N. Melli', '\xa0', 'PF', 'H', 'NOP', 'LAL', '30', '0.5', '1', '$4,100', '19', '4.7', '14', '17', '24', '30', '22', '20']
['45', 'K. Caldwell-Pope', '\xa0', 'SG', 'A', 'LAL', 'NOP', '2', '3.5', 'B2B', '$4,200', '19', '4.5', '17', '19', '16', '22', '20', '16']
['46', 'T. Brown Jr.', '\xa0', 'SF', 'A', 'WAS', 'GSW', '24', '0.3', '1', '$4,400', '19', '4.3', '23', '24', '17', '9', '12', '24']
['47', 'K. Looney', '\xa0', 'C', 'H', 'GSW', 'WAS', '15', '3.0', 'B2B', '$3,500', '19', '5.3', '10', '12', '15', '14', '16', '22']
['48', 'S. Mykhailiuk', '\xa0', 'SG', 'A', 'DET', 'SAC', '23', '-2.0', '1', '$3,500', '18', '5.2', '15', '13', '16', '8', '10', '27']
['49', 'L. Galloway', '\xa0', 'SG', 'A', 'DET', 'SAC', '18', '-2.0', '1', '$4,100', '18', '4.4', '17', '17', '13', '26', '14', '12']
['50', 'J. Toscano-Anderson', '\xa0', 'SF', 'H', 'GSW', 'WAS', '11', '3.0', 'B2B', '$4,300', '18', '4.2', '18', '32', '38', '13', '10', '15']
['51', 'A. Bradley', '\xa0', 'SG', 'A', 'LAL', 'NOP', '3', '3.5', 'B2B', '$4,000', '18', '4.4', '15', '33', '10', '8', '19', '8']
['52', 'P. Millsap', 'GTD', 'PF', 'H', 'DEN', 'TOR', '23', '0.4', '1', '$5,400', '17', '3.2', '25', '27', '11', '41', '17', '8']
['53', 'C. Boucher', '\xa0', 'PF', 'A', 'TOR', 'DEN', '25', '-3.0', '1', '$4,200', '17', '3.9', '16', '4', '21', '37', '20', '8']
['54', 'T. Snell', '\xa0', 'SG', 'A', 'DET', 'SAC', '23', '-2.0', '1', '$3,900', '17', '4.2', '15', '30', '15', '9', '28', '13']
['55', 'K. Kuzma', '\xa0', 'PF', 'A', 'LAL', 'NOP', '18', '3.5', 'B2B', '$4,400', '17', '3.8', '20', '17', '22', '13', '25', '16']
['56', 'A. Caruso', '\xa0', 'PG', 'A', 'LAL', 'NOP', '4', '3.5', 'B2B', '$4,000', '16', '4.1', '14', '18', '9', '32', '14', '14']
['57', 'M. Plumlee', '\xa0', 'C', 'H', 'DEN', 'TOR', '19', '0.4', '1', '$4,200', '16', '3.8', '19', '\xa0', '12', '17', '21', '16']
['58', 'T. Davis', '\xa0', 'SG', 'A', 'TOR', 'DEN', '10', '-3.0', '1', '$4,600', '16', '3.4', '16', '3', '19', '21', '20', '8']
['59', 'J. Hart', '\xa0', 'SG', 'H', 'NOP', 'LAL', '22', '0.5', '1', '$4,900', '15', '3.2', '24', '20', '33', '20', '17', '13']
['60', 'J. Robinson', '\xa0', 'PG', 'A', 'WAS', 'GSW', '19', '0.3', '1', '$3,900', '14', '3.6', '8', '14', '10', '10', '22', '18']
['61', 'G. Harris', '\xa0', 'SG', 'H', 'DEN', 'TOR', '21', '0.4', '1', '$4,300', '14', '3.3', '20', '18', '10', '24', '19', '10']
['62', 'C. Joseph', 'GTD', 'PG', 'H', 'SAC', 'DET', '3', '-2.4', '1', '$3,900', '14', '3.6', '17', '17', '5', '19', '33', '14']
['63', 'S. Doumbouya', '\xa0', 'PF', 'A', 'DET', 'SAC', '23', '-2.0', '1', '$3,500', '14', '4.0', '12', '10', '18', '14', '3', '14']
['64', 'M. Porter Jr.', '\xa0', 'PF', 'H', 'DEN', 'TOR', '23', '0.4', '1', '$3,900', '13', '3.4', '16', '\xa0', '5', '6', '22', '15']
['65', 'J. McGee', '\xa0', 'C', 'A', 'LAL', 'NOP', '29', '3.5', 'B2B', '$3,900', '12', '3.0', '20', '10', '14', '13', '21', '7']
['66', 'T. Maker', 'GTD', 'C', 'A', 'DET', 'SAC', '21', '-2.0', '1', '$4,600', '11', '2.5', '11', '25', '25', '16', '9', '6']
['67', 'I. Mahinmi', '\xa0', 'C', 'A', 'WAS', 'GSW', '11', '0.3', '1', '$4,100', '11', '2.6', '20', '18', '6', '21', '15', '14']
['68', 'M. Morris', '\xa0', 'SF', 'A', 'LAL', 'NOP', '18', '3.5', 'B2B', '$4,200', '10', '2.4', '19', '\xa0', '\xa0', '8', '13', '13']
['69', 'E. Moore', '\xa0', 'SF', 'H', 'NOP', 'LAL', '24', '0.5', '1', '$3,500', '9', '2.7', '16', '15', '10', '6', '3', '19']
['70', 'I. Bonga', '\xa0', 'SF', 'A', 'WAS', 'GSW', '19', '0.3', '1', '$3,500', '9', '2.7', '12', '8', '3', '13', '14', '7']
['71', 'T. Craig', '\xa0', 'SG', 'H', 'DEN', 'TOR', '21', '0.4', '1', '$3,900', '9', '2.3', '12', '20', '7', '6', '\xa0', '12']
['72', 'M. Wagner', '\xa0', 'C', 'A', 'WAS', 'GSW', '11', '0.3', '1', '$4,000', '8', '2.1', '20', '11', '12', '28', '8', '4']
['73', 'P. McCaw', '\xa0', 'SG', 'A', 'TOR', 'DEN', '10', '-3.0', '1', '$3,500', '8', '2.3', '13', '9', '\xa0', '\xa0', '\xa0', '7']
['74', 'M. Thomas', '\xa0', 'SG', 'A', 'TOR', 'DEN', '10', '-3.0', '1', '$3,500', '7', '2.0', '8', '1', '1', '18', '14', '4']
['75', 'J. Hayes', '\xa0', 'C', 'H', 'NOP', 'LAL', '30', '0.5', '1', '$3,500', '3', '0.9', '17', '11', '9', '0', '\xa0', '10']
['76', 'P. Dozier', '\xa0', 'SG', 'H', 'DEN', 'TOR', '15', '0.4', '1', '$3,500', '3', '0.7', '10', '6', '\xa0', '0', '3', '6']
['77', 'J. McRae', '\xa0', 'SG', 'H', 'DEN', 'TOR', '11', '0.4', '1', '$4,100', '2', '0.6', '20', '6', '\xa0', '0', '\xa0', '3']
['78', 'J. Okafor', '\xa0', 'C', 'H', 'NOP', 'LAL', '30', '0.5', '1', '$3,500', '2', '0.6', '16', '\xa0', '\xa0', '\xa0', '2', '\xa0']
['79', 'F. Jackson', '\xa0', 'PG', 'H', 'NOP', 'LAL', '30', '0.5', '1', '$3,500', '1', '0.2', '9', '7', '0', '\xa0', '\xa0', '4']
解决方案3
0 2020-03-02 09:07:16
Another option is just use Pandas to read in the table (it uses Beautifulsoup under the hood)另一种选择是使用 Pandas 在表中读取（它在引擎盖下使用 Beautifulsoup）
import pandas as pd
URL = 'https://dailyfantasyrankings.com.au/resources/nba/htm/projections/mballproj.htm'
df = pd.read_html(URL)[0]
df.columns = df.iloc[1,:]
df = df.iloc[2:,:]
df.to_csv('dfr_proj.csv', index=False)
Output:输出：
print(df.to_string())
1 # Player NaN Pos @ Team Opp M-UP Pace Rest Price Proj Value Ave Last 5 Games Last 5 Games Last 5 Games Last 5 Games Last 5 Games
2 1 A. Davis GTD PF A LAL NOP 18 3.5 B2B $10,800 59 5.5 51 70 52 61 41 37
3 2 B. Beal NaN SG A WAS GSW 5 0.3 1 $9,600 57 5.9 46 34 67 53 51 67
4 3 L. James NaN SF A LAL NOP 3 3.5 B2B $10,400 53 5.1 51 51 50 55 NaN 45
5 4 N. Jokic NaN C H DEN TOR 19 0.4 1 $10,100 51 5.1 45 53 52 50 35 35
6 5 P. Siakam NaN SF A TOR DEN 26 -3.0 1 $8,400 45 5.4 41 34 63 28 33 50
7 6 K. Lowry NaN PG A TOR DEN 11 -3.0 1 $7,600 45 5.9 38 43 31 57 23 47
8 7 A. Wiggins NaN SF H GSW WAS 11 3.0 B2B $7,700 42 5.5 36 34 34 16 NaN 46
9 8 C. Wood NaN PF A DET SAC 26 -2.0 1 $7,900 42 5.3 23 48 35 44 36 48
10 9 B. Ingram NaN SF H NOP LAL 16 0.5 1 $7,900 40 5.1 40 NaN 26 32 47 47
11 10 D. Fox NaN PG H SAC DET 3 -2.4 1 $7,500 38 5.1 37 34 35 36 NaN 42
12 11 J. Holiday NaN SG H NOP LAL 24 0.5 1 $8,900 38 4.3 40 35 44 52 33 40
13 12 D. Rose NaN PG A DET SAC 18 -2.0 1 $6,000 36 6.0 30 11 19 17 32 47
14 13 Z. Williamson NaN PF H NOP LAL 16 0.5 1 $8,300 36 4.3 33 41 37 41 41 33
15 14 M. Chriss NaN C H GSW WAS 21 3.0 B2B $6,400 34 5.3 22 21 NaN 37 20 36
16 15 O. Anunoby NaN SF A TOR DEN 27 -3.0 1 $5,000 33 6.7 24 12 31 19 29 49
17 16 D. Lee NaN SG H GSW WAS 7 3.0 B2B $6,100 32 5.3 24 26 36 32 25 41
18 17 J. Murray NaN PG H DEN TOR 15 0.4 1 $7,200 32 4.4 33 49 35 29 39 17
19 18 L. Ball NaN PG H NOP LAL 30 0.5 1 $6,900 32 4.6 32 33 32 33 30 46
20 19 N. Powell NaN SG A TOR DEN 10 -3.0 1 $4,500 30 6.7 26 NaN NaN NaN NaN 27
21 20 E. Paschall NaN PF H GSW WAS 21 3.0 B2B $4,600 30 6.5 23 21 20 20 30 34
22 21 J. Grant NaN PF H DEN TOR 23 0.4 1 $4,500 30 6.6 21 26 8 17 35 35
23 22 R. Hachimura NaN PF A WAS GSW 15 0.3 1 $5,100 28 5.4 25 36 25 22 20 31
24 23 B. Hield NaN SG H SAC DET 26 -2.4 1 $6,000 27 4.4 32 39 12 32 24 24
25 24 W. Barton NaN SF H DEN TOR 11 0.4 1 $5,800 26 4.5 31 NaN 35 25 16 25
26 25 H. Giles III NaN PF H SAC DET 7 -2.4 1 $5,100 26 5.1 15 15 33 33 29 30
27 26 N. Bjelica NaN PF H SAC DET 11 -2.4 1 $5,100 26 5.1 27 26 24 10 31 31
28 27 S. Napier NaN PG A WAS GSW 6 0.3 1 $5,000 24 4.7 22 18 17 46 22 13
29 28 M. Porter Jr. NaN PF H DEN TOR 23 0.4 1 $3,900 23 6.0 16 NaN 5 6 22 15
30 29 D. Bender NaN PF H GSW WAS 21 3.0 B2B $3,900 23 5.9 12 NaN 20 8 13 39
31 30 D. Favors NaN C H NOP LAL 30 0.5 1 $5,400 23 4.3 27 21 27 24 18 37
32 31 K. Bazemore NaN SF H SAC DET 26 -2.4 1 $5,400 23 4.2 19 25 47 28 18 26
33 32 D. Bertans NaN PF A WAS GSW 24 0.3 1 $5,100 23 4.5 26 26 25 NaN 23 19
34 33 H. Barnes NaN SF H SAC DET 29 -2.4 1 $5,200 23 4.4 25 35 36 24 29 15
35 34 T. Bryant NaN C A WAS GSW 11 0.3 1 $4,100 23 5.5 25 20 13 NaN 15 22
36 35 B. Knight NaN PG A DET SAC 23 -2.0 1 $3,900 22 5.8 11 NaN 1 22 25 28
37 36 J. Henson NaN C A DET SAC 21 -2.0 1 $4,500 22 5.0 17 4 12 31 14 21
38 37 R. Hollis-Jeffers NaN PF A TOR DEN 26 -3.0 1 $4,000 22 5.4 19 12 22 18 25 15
39 38 I. Smith NaN PG A WAS GSW 8 0.3 1 $5,000 22 4.3 24 26 29 30 12 16
40 39 A. Len NaN C H SAC DET 7 -2.4 1 $4,000 21 5.2 20 NaN 16 11 26 24
41 40 B. Bogdanovic NaN SG H SAC DET 25 -2.4 1 $5,100 20 4.0 24 16 32 28 23 24
42 41 D. Howard NaN C A LAL NOP 29 3.5 B2B $4,400 20 4.6 22 12 20 15 36 20
43 42 J. Hart NaN SG H NOP LAL 22 0.5 1 $4,900 19 4.0 24 20 33 20 17 13
44 43 M. Morris NaN PG H DEN TOR 15 0.4 1 $4,600 19 4.2 19 26 12 34 29 18
45 44 R. Rondo NaN PG A LAL NOP 4 3.5 B2B $4,300 19 4.5 19 14 28 7 30 10
46 45 T. Brown Jr. NaN SF A WAS GSW 24 0.3 1 $4,400 19 4.3 23 24 17 9 12 24
47 46 T. Davis NaN SG A TOR DEN 10 -3.0 1 $4,600 19 4.1 16 3 19 21 20 8
48 47 C. Boucher NaN PF A TOR DEN 25 -3.0 1 $4,200 19 4.4 16 4 21 37 20 8
49 48 S. Mykhailiuk NaN SG A DET SAC 23 -2.0 1 $3,500 18 5.2 15 13 16 8 10 27
50 49 K. Caldwell-Pop NaN SG A LAL NOP 2 3.5 B2B $4,200 18 4.3 17 19 16 22 20 16
51 50 L. Galloway NaN SG A DET SAC 18 -2.0 1 $4,100 18 4.4 17 17 13 26 14 12
52 51 J. Toscano-And NaN SF H GSW WAS 11 3.0 B2B $4,300 18 4.2 18 32 38 13 10 15
53 52 J. Poole GTD SG H GSW WAS 18 3.0 B2B $4,900 18 3.6 15 27 27 20 25 NaN
54 53 K. Looney NaN C H GSW WAS 15 3.0 B2B $3,500 18 5.1 10 12 15 14 16 22
55 54 A. Bradley NaN SG A LAL NOP 3 3.5 B2B $4,000 18 4.4 15 33 10 8 19 8
56 55 T. Snell NaN SG A DET SAC 23 -2.0 1 $3,900 17 4.2 15 30 15 9 28 13
...
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