尝试抓取网站时，我遇到了一个特殊情况。我正在通过搜索栏搜索数百个名称，然后抓取表格。但是，有些名称是唯一的，与网站上的名称相比，在我的列表中的拼写有所不同。在这种情况下，我会在网站上手动查找几个名称，但仍然直接将我带到单个页面。其他时候，如果有多个具有相同或相似名字的人（在这种情况下，我想要在nba中打过球的人），则转到名字列表中。我已经考虑了这一点，但是我认为有必要提一下）。我该如何继续进入这些玩家的个人页面，而不必每次都运行脚本并点击错误以查看哪个玩家的拼写稍有不同？再次，即使拼写略有不同或名称列表（在NBA中也需要），数组中的名称也会直接将您带到单个页面。一些示例包括Georgios Papagiannis（在网站上列为George Papagiannis），Ognjen Kuzmic（列为Ognen Kuzmic），Nene（列为Maybyner Nene，但将带您列出姓名-https://basketball.realgm.com/search?q=nene）。这似乎很难，但是我觉得这是可能的。同样，每次将其与下一个播放器一起覆盖时，似乎没有将所有抓取的数据写入到csv中。万分感谢。
我得到的错误： AttributeError: 'NoneType' object has no attribute 'text'
import requests
from bs4 import BeautifulSoup
import pandas as pd
playernames=['Carlos Delfino', 'Nene', 'Yao Ming', 'Marcus Vinicius', 'Raul Neto', 'Timothe Luwawu-Cabarrot']
result = pd.DataFrame()
for name in playernames:
fname=name.split(" ")[0]
lname=name.split(" ")[1]
url="https://basketball.realgm.com/search?q={}+{}".format(fname,lname)
response = requests.get(url)
soup = BeautifulSoup(response.content, 'html.parser')
if soup.find('a',text=name).text==name:
url="https://basketball.realgm.com"+soup.find('a',text=name)['href']
print(url)
response = requests.get(url)
soup = BeautifulSoup(response.text, 'lxml')
try:
table1 = soup.find('h2',text='International Regular Season Stats - Per Game').findNext('table')
table2 = soup.find('h2',text='International Regular Season Stats - Advanced Stats').findNext('table')
df1 = pd.read_html(str(table1))[0]
df2 = pd.read_html(str(table2))[0]
commonCols = list(set(df1.columns) & set(df2.columns))
df = df1.merge(df2, how='left', on=commonCols)
df['Player'] = name
print(df)
except:
print ('No international table for %s.' %name)
df = pd.DataFrame([name], columns=['Player'])
result = result.append(df, sort=False).reset_index(drop=True)
cols = list(result.columns)
cols = [cols[-1]] + cols[:-1]
result = result[cols]
result.to_csv('international players.csv', index=False)
我对名字相似的NBA球员使用循环。您可以在下面的CSS选择器下面找到，以从搜索表中获取NBA球员：
.tablesaw tr:has(a[href*="/nba/teams/"]) a[href*="/player/"]
CSS选择器的含义：按tablesaw类查找表，查找表的tr子项a及其href包含/nba/teams/文本的子项，然后查找a其href包含的项/player/
我添加了“搜索播放器名称”和“真实播放器名称”列，您可以看到如何找到播放器。该列用作第一和第二列insert（请参见代码中的注释）。
import requests
from bs4 import BeautifulSoup
import pandas as pd
from pandas import DataFrame
base_url = 'https://basketball.realgm.com'
player_names = ['Carlos Delfino', 'Nene', 'Yao Ming', 'Marcus Vinicius', 'Raul Neto', 'Timothe Luwawu-Cabarrot']
result = pd.DataFrame()
def def get_player_stats(search_name = None, real_name = None, player_soup = None):
table_per_game = player_soup.find('h2', text='International Regular Season Stats - Per Game')
table_advanced_stats = player_soup.find('h2', text='International Regular Season Stats - Advanced Stats')
if table_per_game and table_advanced_stats:
print('International table for %s.' % search_name)
df1 = pd.read_html(str(table_per_game.findNext('table')))[0]
df2 = pd.read_html(str(table_advanced_stats.findNext('table')))[0]
common_cols = list(set(df1.columns) & set(df2.columns))
df = df1.merge(df2, how='left', on=common_cols)
# insert name columns for the first positions
df.insert(0, 'Search Player Name', search_name)
df.insert(1, 'Real Player Name', real_name)
else:
print('No international table for %s.' % search_name)
df = pd.DataFrame([[search_name, real_name]], columns=['Search Player Name', 'Real Player Name'])
return df
for name in player_names:
url = f'{base_url}/search?q={name.replace(" ", "+")}'
response = requests.get(url)
soup = BeautifulSoup(response.content, 'html.parser')
if url == response.url:
# Get all NBA players
for player in soup.select('.tablesaw tr:has(a[href*="/nba/teams/"]) a[href*="/player/"]'):
response = requests.get(base_url + player['href'])
player_soup = BeautifulSoup(response.content, 'lxml')
player_data = get_player_stats(search_name=player.text, real_name=name, player_soup=player_soup)
result = result.append(player_data, sort=False).reset_index(drop=True)
else:
player_data = get_player_stats(search_name=name, real_name=name, player_soup=soup)
result = result.append(player_data, sort=False).reset_index(drop=True)
result.to_csv('international players.csv', index=False)
# Append to existing file
# result.to_csv('international players.csv', index=False, mode='a')
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