我想在df 1上左连接df 2，然后按组保留匹配的行，如果没有匹配的组，那么我想保留组的第一行，以获得df 3（所需的结果）。我希望你们能帮助我找到最佳解决方案。
下面是我创建两个 Dataframe 的代码和所需的结果。

import pandas as pd
import numpy as np

market = ['SP', 'SP', 'SP']
underlying = ['TSLA', 'GOOG', 'MSFT']
  
# DF1
df = pd.DataFrame(list(zip(market, underlying)),
               columns=['market', 'underlying'])

market2 = ['SP', 'SP', 'SP', 'SP', 'SP']
underlying2 = [None, 'TSLA', 'GBX', 'GBM', 'GBS']
client2 = [17, 12, 100, 21, 10]

# DF2
df2 = pd.DataFrame(list(zip(market2, underlying2, client2)),
               columns=['market', 'underlying', 'client'])

market3 = ['SP', 'SP', 'SP']
underlying3 = ['TSLA', 'GOOG', 'MSFT']
client3 = [12, 17, 17]

# Desired
df3 = pd.DataFrame(list(zip(market3, underlying3, client3)),
               columns =['market', 'underlying', 'client'])

# This works but feels sub optimal
df3 = pd.merge(df, 
               df2,
               how='left',
               on=['market', 'underlying'])
df3 = pd.merge(df3, 
               df2,
               how='left',
               on=['market'])
df3 = df3.drop_duplicates(['market', 'underlying_x'])
df3['client'] = df3['client_x'].combine_first(df3['client_y'])
df3 = df3.drop(labels=['underlying_y', 'client_x', 'client_y'], axis=1)
df3 = df3.rename(columns={'underlying_x': 'underlying'})

希望你们能帮忙，谢谢！