不一致的表数据刮取

Question

我正在尝试从此网页的一个表中提取表数据。但是，当我尝试从每个表行提取表数据时，似乎无法从每一行获取数据。我检测到的一种模式是，我无法看到存在图像的行的表数据。有没有其他的方法，我仍然可以刮我想要的数据（职位，家乡，职级等），特别是当涉及到那些图片存在的时候？

# We are unable to get the table data row for individuals that have a picture

rows = soup.find_all('tr')

# This is the data for each table row

for row in rows:
    print(row.text)

我已经能够使用“div”类获得播放器名称，但我不认为我能够将它用于其他列中的数据。

# The name of the player is going to be our first column. So let's make a list of the names for tr in soup.find_all('tr'):

name = soup.find_all('div', {'class':'name'})
                    
# Empty list to put our player name in.
players = []
for person in name:
# This line is used to remove the below text that was also included wth the names
    person = person.text.replace("Video | Scouts Report","")
    players.append(person)
                    
# We see that the length of this list is 100. This means that we extraced the names correctly
len(players)

Answer 1

我认为更简单的方法是直接使用pd.read_html()在pandas的dataframe中读取它，它将立即返回来自URL的所有表的（长度1）列表：

url = r'http://www.espn.com/college-sports/basketball/recruiting/playerrankings/_/view/espnu100/sort/rank/class/2019'
dfs = pd.read_html(url, header=0)
dfs[0].head()
#   RK                                 PLAYER POS  \
#0   1     James WisemanVideo | Scouts Report   C   
#1   2      Cole AnthonyVideo | Scouts Report  PG   
#2   3  Vernon Carey Jr.Video | Scouts Report   C   
#3   4    Isaiah StewartVideo | Scouts Report   C   
#4   5   Anthony EdwardsVideo | Scouts Report  SG   
#
#                                     HOMETOWN      HT   WT  STARS  GRADE  \
#0                 Memphis, TNEast High School   7'0''  230    NaN     97   
#1               Briarwood, NYOak Hill Academy   6'3''  185    NaN     97   
#2  Southwest Ranches, FLNSU University School  6'10''  275    NaN     97   
#3              Rochester, NYLa Lumiere School   6'9''  245    NaN     97   
#4               Atlanta, GAHoly Spirit School   6'4''  205    NaN     97   
#
#                          SCHOOL  
#0                  MemphisSigned  
#1                           List  
#2        DukeCommitted12/06/2018  
#3  WashingtonCommitted01/20/2019  
#4     GeorgiaCommitted02/11/2019 

当然，你将不得不做一些清理，但我认为这会比把所有东西都读成清单效率高得多。