Python Pandas：如何将DataFrame groupby的结果放入具有新列名的数据框中？

Question

I have a data frame with two columns 'id' and 'time'. Need to compute mean times for ids and put result into new data frame with new column name. Input data frame:

        id  time
0    1     1
1    1     1
2    1     1
3    1     1
4    1     2
5    1     2
6    2     1
7    2     1
8    2     2
9    2     2
10   2     2
11   2     2

My code:

import pandas as pd

my_dict = {
    'id':  [1,1,1, 1,1,1, 2,2,2, 2,2,2],
    'time':[1,1,1, 1,2,2, 1,1,2, 2,2,2]
    }

df = pd.DataFrame(my_dict)
x = df.groupby(['id'])['time'].mean()

# x is a pandas.core.series.Series                                                                  
type(x)

y = x.to_frame()
# y is pandas.core.frame.DataFrame                                                                  
type(y)
list(y)

Running this code results in:

In [14]: y                                                                                              
Out[14]:                                                                                                
        time                                                                                            
id                                                                                                      
1   1.333333                                                                                            
2   1.666667                                                                                            

Groupby returns Pandas series 'x' which I then convert to data frame 'y'. How can I change in the output 'y' data frame column name from 'time' to something else, for example 'mean'? Ideally I need output data frame with two columns : 'id' and 'mean'. How to do this?

Update2:

y = x.to_frame('mean').reset_index()

Solves the problem!

Answer 1

You can use agg to pass a name. The key is the name of the column and the value is the alias for the aggregate function. as_index=False is for id column to stay as a column:

df.groupby(['id'], as_index=False)['time'].agg({'mean': 'mean'})
Out: 
   id      mean
0   1  1.333333
1   2  1.666667

Using your Series x , this would also have worked:

x.to_frame('mean').reset_index()
Out: 
   id      mean
0   1  1.333333
1   2  1.666667