python pandas groupby：用于分组的删除列

Question

print(my_df)   A    B    C0  1   20  0.11  1   30  0.22  1   40  0.33  2  200  0.74  2  300  0.85  2  400  0.9that I group by column 'A'grouped = my_df.groupby('A')that I transform into a list:grouped.apply(pd.Series.tolist)A1    [[1.0, 20.0, 0.1], [1.0, 30.0, 0.2], [1.0, 40....2    [[2.0, 200.0, 0.7], [2.0, 300.0, 0.8], [2.0, 4...However, I would like to get rid of the now redundant column 'A' values, to haveA1    [[20.0, 0.1], [30.0, 0.2], [40....2    [[200.0, 0.7], [300.0, 0.8], [4...the grouped object contains all three columns:grouped.obj.columnsIndex(['A', 'B', 'C'], dtype='object')but I cannot drop any of them:grouped.drop('A')AttributeError: Cannot access callable attribute 'drop' of 'DataFrameGroupBy' objects, try using the 'apply' methodhow can I use drop with apply?grouped.apply(pd.DataFrame.drop( ??? Thank youEdit:More concretely I am doinggrouped.apply(pd.Series.tolist).tolist()with this I obtain[[[1.0, 20.0, 0.1], [1.0, 30.0, 0.2], [1.0, 40.0, 0.3]], [[2.0, 200.0, 0.7], [2.0, 300.0, 0.8], [2.0, 400.0, 0.9]]]but I would like to have[[[20.0, 0.1], [30.0, 0.2], [40.0, 0.3]], [[200.0, 0.7], [300.0, 0.8], [400.0, 0.9]]]

Answer 1

试试下面的代码，希望对你有帮助：

df.set_index('A',inplace=True)

df.groupby('A').apply(pd.Series.tolist)

输出将是：

A

1       [[20.0, 0.1], [30.0, 0.2], [40.0, 0.3]]

2    [[200.0, 0.7], [300.0, 0.8], [400.0, 0.9]]

dtype: object