pandas get average of a groupby

Question

I am trying to find the average monthly cost per user_id but i am only able to get average cost per user or monthly cost per user.

Because i group by user and month, there is no way to get the average of the second groupby (month) unless i transform the groupby output to something else.

This is my df:

     df = { 'id' : pd.Series([1,1,1,1,2,2,2,2]),
            'cost' : pd.Series([10,20,30,40,50,60,70,80]),
            'mth': pd.Series([3,3,4,5,3,4,4,5])}

   cost  id  mth
0    10   1    3
1    20   1    3
2    30   1    4
3    40   1    5
4    50   2    3
5    60   2    4
6    70   2    4
7    80   2    5

I can get monthly sum but i want the average of the months for each user_id.

df.groupby(['id','mth'])['cost'].sum()

id  mth
1   3       30
    4       30
    5       40
2   3       50
    4      130
    5       80

i want something like this:

id average_monthly
1 (30+30+40)/3
2 (50+130+80)/3

Answer 1

Resetting the index should work. Try this:

In [19]: df.groupby(['id', 'mth']).sum().reset_index().groupby('id').mean()  
Out[19]: 
    mth       cost
id                
1   4.0  33.333333
2   4.0  86.666667

You can just drop mth if you want. The logic is that after the sum part, you have this:

In [20]: df.groupby(['id', 'mth']).sum()
Out[20]: 
        cost
id mth      
1  3      30
   4      30
   5      40
2  3      50
   4     130
   5      80

Resetting the index at this point will give you unique months.

In [21]: df.groupby(['id', 'mth']).sum().reset_index()
Out[21]: 
   id  mth  cost
0   1    3    30
1   1    4    30
2   1    5    40
3   2    3    50
4   2    4   130
5   2    5    80

It's just a matter of grouping it again, this time using mean instead of sum. This should give you the averages.

Let us know if this helps.