AttributeError:无法访问"DataFrameGroupBy"对象的可调用属性"reset_index"，请尝试使用"apply"方法 [英] AttributeError: Cannot access callable attribute 'reset_index' of 'DataFrameGroupBy' objects, try using the 'apply' method

问题描述

我对熊猫很陌生，尝试使用groupby.我有一个包含多列的df.

I am very new to pandas and trying to use groupby. I have a df with multiple columns.

• 我想对特定列进行分组，然后根据不同的列对每个组进行排序.
• 我想对col1进行分组，然后按col5对每个组进行排序，然后执行reset_index以获得数据帧的所有行.
• 我收到以下错误 AttributeError: Cannot access callable attribute 'reset_index' of 'DataFrameGroupBy' objects, try using the 'apply' method.

• I want to groupby a particular column and then sort each group based on a different column.
• I want to groupby col1 and then sort each group by col5 and then do reset_index to get all rows of the dataframe.
• I get the following error AttributeError: Cannot access callable attribute 'reset_index' of 'DataFrameGroupBy' objects, try using the 'apply' method.

我的输入数据框:

col1 |  col2 | col3 | col4 | col5
=================================
A    |   A1   | A2   | A3   | DATE1
A    |   B1   | B2   | B3   | DATE2

我的代码:

df.sort_values(['col5'],ascending=False).groupby('col1').reset_index()

推荐答案

对于groupby，需要一些聚合函数，例如mean，sum，max:

For groupby need some aggregation function(s), like mean, sum, max:

df.sort_values(['col5'],ascending=False).groupby('col1').mean().reset_index()

或者:

df.sort_values(['col5'],ascending=False).groupby('col1', as_index=False).mean()

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