pandas AttributeError：无法访问“DataFrameGroupBy”对象的可调用属性“reset_index”，请尝试使用“apply”方法
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AttributeError: Cannot access callable attribute 'reset_index' of 'DataFrameGroupBy' objects, try using the 'apply' method
提问by Gingerbread
I am very new to pandas and trying to use groupby. I have a df with multiple columns.
我对Pandas很陌生，并试图使用groupby. 我有一个多列的 df。
- I want to groupby a particular column and then sort each group based on a different column.
- I want to groupby
col1and then sort each group bycol5and then doreset_indexto 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
col1分组，然后对每个组进行排序col5，然后执行reset_index以获取数据帧的所有行。 - 我收到以下错误
AttributeError: Cannot access callable attribute 'reset_index' of 'DataFrameGroupBy' objects, try using the 'apply' method。
My input dataframe:
我的输入数据框：
col1 | col2 | col3 | col4 | col5
=================================
A | A1 | A2 | A3 | DATE1
A | B1 | B2 | B3 | DATE2
My code:
我的代码：
df.sort_values(['col5'],ascending=False).groupby('col1').reset_index()
回答by jezrael
For groupbyneed some aggregation function(s), like mean, sum, max:
对于groupby一些需要聚合功能（S），像mean，sum，max：
df.sort_values(['col5'],ascending=False).groupby('col1').mean().reset_index()
Or:
或者：
df.sort_values(['col5'],ascending=False).groupby('col1', as_index=False).mean()
回答by yogitha jaya reddy gari
you can use
您可以使用
grouped = df.sort_values(['col5'],ascending=False).groupby('col1',as_index = False).apply(lambda x: x.reset_index(drop = True))
grouped.reset_index().drop(['level_0','level_1'],axis = 1)
Refer to this stackoverflow link for clear explanation with an example How to reset a DataFrame's indexes for all groups in one step?
请参阅此 stackoverflow 链接以通过示例如何清楚地说明 如何一步重置所有组的 DataFrame 索引？
回答by hakuna_code
You can try the below code, I had a similar issue.
你可以试试下面的代码，我遇到了类似的问题。
grouped=data.groupby(['Colname'])
grouped.apply(lambda _df: _df.sort_values(by=['col_to_be_sorted']))
