Pandas Groupby-如果多行超过另一行的值，则选择一列中值最高的行

Question

This operation groups my DataFrame by two columns, then returns the row with the highest value in ColumnC :

df2 = df.loc[df.groupby(['columnA', 'columnB'], sort=False)['columnC'].idxmax()]

Instead, for all rows where ColumnC > 100 within each group, I would like to take the row with the highest value in ColumnD .

How can I do this?

Edit:

Comment below by @Code Different is basically what I'm looking for, but I don't want to exclude groups where none of the rows have ColumnC > 100 , in these cases I want the row with the highest value in ColumnC , as in the example above.

Answer 1

Usually we split the data by two part , then filter them after the condition

df=sort_values('columnD')

df1 = df[df['columnC'] > 100]].drop_duplicates(['columnA', 'columnB'],keep='last')
df2 = df.drop_duplicates(['columnA', 'columnB'],keep='last')

Yourdf=pd.concat([df1,df2]).drop_duplicates(['columnA', 'columnB'])