A quick google search and you will find out how to get relevant rows here. For your implementation:
df.groupby("ex")['transaction_fee'].max()
The other thing you should look for is how to get the corresponding indices for the original table. you could find something similar here. For your implementation:
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快速的Google搜索，您将找出如何获得相关行在这里。
对于您的实现：
您应该寻找的另一件事是如何获取原始表的相应索引。您可以找到类似的东西 a>。
为您的实施：
A quick google search and you will find out how to get relevant rows here.
For your implementation:
The other thing you should look for is how to get the corresponding indices for the original table. you could find something similar here.
For your implementation:
通过一个列的所有列值和另一列的最大值获取所有列值。使用
IDXMAX（）和iLoc（）idxmax（）将返回最大值的索引，而不是最大值本身。iLoc（）基于索引，返回DF的选定部分。我们可以使用ID来过滤记录。
For getting all the column values for a group by of one column and max value of another column. Use
idxmax()andiloc()idxmax()will return the index of the maximum value, not the maximum value itself.iloc()Index based, returns the selected section of df.We can use the ids to filter the records.