我能够从网站上搜集一些数据，但我无法将其分解以显示在表格中。
我使用的代码是：
import pandas as pd
import requests
from bs4 import BeautifulSoup
url = 'https://www.basketball-reference.com/leagues/NBA_2018_games.html'
r = requests.get(url)
soup = BeautifulSoup(r.text, "html.parser")
tablesright = soup.find_all('td', 'right',)
Tables left = soup.find_all('td', 'left')
print (tablesright + tablesleft)
这给了我这样的结果：
====================== RESTART: E:/2017/Python2/box2.py ======================
[<td class="right " data-stat="game_start_time">8:01 pm</td>, <td class="right " data-stat="visitor_pts">99</td>, <td class="right " data- stat="home_pts">102</td>, <td class="right " data-stat="game_start_time">10:30 pm</td>, <td class="right " data-stat="visitor_pts">122</td>, <td class="right " data-stat="home_pts">121</td>, <td class="right " data-stat="game_start_time">7:30 pm</td>, <td class="right " data-stat="visitor_pts">108</td>, <td class="right " data-stat="home_pts">100</td>, <td class="right " data-stat="game_start_time">8:30 pm</td>, <td class="right " data-stat="visitor_pts">117</td>, <td class="right " data-stat="home_pts">111</td>, <td class="right " data-stat="game_start_time">7:00 pm</td>, <td class="right " data-stat="visitor_pts">90</td>, <td class="right " data-stat="home_pts">102</td>, <
和左侧部分：
<td class="left " csk="BOS.201710170CLE" data-stat="visitor_team_name"><a href="/teams/BOS/2018.html">Boston Celtics</a></td>, <td class="left " csk="CLE.201710170CLE" data-stat="home_team_name"><a href="/teams/CLE/2018.html">Cleveland Cavaliers</a></td>, <td class="left " data-stat="game_remarks"></td>, <td class="left " csk="HOU.201710170GSW" data-stat="visitor_team_name"><a href="/teams/HOU/2018.html">Houston Rockets</a></td>, <td class="left " csk="GSW.201710170GSW" data-stat="home_team_name"><a href="/teams/GSW/2018.html">Golden State Warriors</a></td>, <td class="left " data-stat="game_remarks"></td>, <td class="left " csk="MIL.201710180BOS" data-stat="visitor_team_name"><a href="/teams/MIL/2018.html">Milwaukee Bucks</a></td>, <td class="left " csk="BOS.201710180BOS" data-stat="home_team_name"><a href="/teams/BOS/2018.html">Boston Celtics</a></td>, <td class="left " data-stat="game_remarks"></td>, <td class="left " csk="ATL.201710180DAL" data-
好的，现在我无法弄清楚如何打破结果，所以它会有一个像这样的好桌子：
Game start time Home team. Score. Away team. Score
7pm. Boston. 104. Golden state. 103
拔出我的头发试图找出来，
提前谢谢
答案 0 :(得分：1)
您可以尝试在pandas数据框中读取它而不是使用html解析器，然后决定如何操作该数据帧以显示您需要的结果。
示例：
import pandas as pd
url = 'https://www.basketball-reference.com/leagues/NBA_2018_games.html'
dfs = pd.read_html(url, match="Start")
print(dfs[0])
如何在pandas文档中执行此操作的示例以及有关stackoverflow的许多问题。 酱：https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html
答案 1 :(得分：1)
我不知道您是否想要使用pandas进行解决方案，只需使用更高级的attrs关键字和标准Python format来获取格式化表，就可以了解它。
请注意，format中的数字是手动选择的，不会根据实际数据进行调整。
import requests
from bs4 import BeautifulSoup
url = 'https://www.basketball-reference.com/leagues/NBA_2018_games.html'
r = requests.get(url)
soup = BeautifulSoup(r.text, "html.parser")
game_start_times = soup.find_all('td', attrs={"data-stat": "game_start_time", "class": "right"})
visitor_team_names = soup.find_all('td', attrs={"data-stat": "visitor_team_name", "class": "left"})
visitor_ptss = soup.find_all('td', attrs={"data-stat": "visitor_pts", "class": "right"})
home_team_names = soup.find_all('td', attrs={"data-stat": "home_team_name", "class": "left"})
home_pts = soup.find_all('td', attrs={"data-stat": "home_pts", "class": "right"})
for i in range(len(game_start_times)):
print('{:10s} {:28s} {:5s} {:28s} {:5s}'.format(game_start_times[i].text.strip(),
visitor_team_names[i].text.strip(),
visitor_ptss[i].text.strip(),
home_team_names[i].text.strip(),
home_pts[i].text.strip()))
8:01 pm Boston Celtics 99 Cleveland Cavaliers 102
10:30 pm Houston Rockets 122 Golden State Warriors 121
7:30 pm Milwaukee Bucks 108 Boston Celtics 100
8:30 pm Atlanta Hawks 117 Dallas Mavericks 111
答案 2 :(得分：0)
这样可行。调整它以满足您的需求，然后使用熊猫。
import requests
from bs4 import BeautifulSoup
url = 'https://www.basketball-reference.com/leagues/NBA_2018_games.html'
r = requests.get(url)
soup = BeautifulSoup(r.text, "html.parser")
rows = soup.select('#schedule > tbody > tr')
for row in rows:
rights = row.find_all("td", "right")
lefts = row.find_all("td", "left")
print rights[0].text, lefts[0].text, rights[1].text, lefts[1].text, rights[2].text
答案 3 :(得分：0)
对于这样一个简单的结构，我只是删除库并用re（正则表达式）
来完成它首先 findall 以获取所有 tr 标记
然后一个 findall 在每个tr标记内获取所有 td / th 标记
然后一个 sub 来过滤掉字段内的所有标记（主要是标记）
#!/usr/bin/python
import requests
import re
url = 'https://www.basketball-
reference.com/leagues/NBA_2018_games.html'
r = requests.get(url)
content = r.content
data = [
{
k:re.sub('<.+?>','',v) for (k,v) in re.findall('<t[dh].+?data\-stat="(.*?)".*?>(.*?)</t[dh]',tr)
} for tr in re.findall('<tr.+?>(.+?)</tr',content)
]
for game in data:
print "%s" % game['date_game']
for info in game:
print " %s = %s" % (info,game[info])
这提供了一个很好的字典结构（数据），可以很容易地用于显示：
$ ./scores_url.py
Tue, Oct 17, 2017
game_remarks =
box_score_text = Box Score
home_team_name = Cleveland Cavaliers
visitor_team_name = Boston Celtics
game_start_time = 8:01 pm
date_game = Tue, Oct 17, 2017
overtimes =
visitor_pts = 99
home_pts = 102
Tue, Oct 17, 2017
game_remarks =
box_score_text = Box Score
home_team_name = Golden State Warriors
visitor_team_name = Houston Rockets
game_start_time = 10:30 pm
date_game = Tue, Oct 17, 2017
overtimes =
visitor_pts = 122
home_pts = 121
Wed, Oct 18, 2017
game_remarks =
box_score_text = Box Score
home_team_name = Boston Celtics
visitor_team_name = Milwaukee Bucks
game_start_time = 7:30 pm
date_game = Wed, Oct 18, 2017
overtimes =
visitor_pts = 108
home_pts = 100
...
或以你的例子的风格：
cols = [
['game_start_time',15,"Game start time"],
['home_team_name',25,"Home team."],
['home_pts',7,"Score."],
['visitor_team_name',25,"Away team."],
['visitor_pts',7,"Score."]
]
for col in cols:
print ("%%%ds" % col[1]) % col[2],
print
for game in data:
for col in cols:
print ("%%%ds" % col[1]) % game[col[0]],
print
这样的东西：
Game start time Home team. Score. Away team. Score.
8:01 pm Cleveland Cavaliers 102 Boston Celtics 99
10:30 pm Golden State Warriors 121 Houston Rockets 122
7:30 pm Boston Celtics 100 Milwaukee Bucks 108
8:30 pm Dallas Mavericks 111 Atlanta Hawks 117
7:00 pm Detroit Pistons 102 Charlotte Hornets 90
7:00 pm Indiana Pacers 140 Brooklyn Nets 131
8:00 pm Memphis Grizzlies 103 New Orleans Pelicans 91
...