Grouping daily data by month in python/pandas while firstly grouping by user id

Question

I have the table below in a Pandas dataframe:

date                 user_id  whole_cost  cost1             
02/10/2012 00:00:00        1       1790      12         
07/10/2012 00:00:00        1        364      15         
30/01/2013 00:00:00        1        280      10         
02/02/2013 00:00:00        1        259      24         
05/03/2013 00:00:00        1        201      39         
02/10/2012 00:00:00        3        623       1          
07/12/2012 00:00:00        3         90       0          
30/01/2013 00:00:00        3        312      90         
02/02/2013 00:00:00        5        359      45         
05/03/2013 00:00:00        5        301      34         
02/02/2013 00:00:00        5        359       1          
05/03/2013 00:00:00        5        801      12         
..

The table was extracted from a csv file using the following query :

import pandas as pd

newnames = ['date','user_id', 'whole_cost', 'cost1']
df = pd.read_csv('expenses.csv', names = newnames, index_col = 'date')

I have to analyse the profile of my users and for this purpose:

I would like to group (for each user - they are thousands) queries by month summing the query whole_cost for the entire month e.g. if user_id=1 was has a whole cost of 1790 on 02/10/2012 with cost1 12 and on the 07/10/2012 with whole cost 364, then it should have an entry in the new table of 2154 (as the whole cost) on 31/10/2012 (end of the month end-point representing the month - all dates in the transformed table will be month ends representing the whole month to which they relate).

Answer 1

In 0.14 you'll be able to groupby monthly and another column at the same time:

In [11]: df
Out[11]:
            user_id  whole_cost  cost1
2012-10-02        1        1790     12
2012-10-07        1         364     15
2013-01-30        1         280     10
2013-02-02        1         259     24
2013-03-05        1         201     39
2012-10-02        3         623      1
2012-12-07        3          90      0
2013-01-30        3         312     90
2013-02-02        5         359     45
2013-03-05        5         301     34
2013-02-02        5         359      1
2013-03-05        5         801     12

In [12]: df1 = df.sort_index()  # requires sorted DatetimeIndex

In [13]: df1.groupby([pd.TimeGrouper(freq='M'), 'user_id'])['whole_cost'].sum()
Out[13]:
            user_id
2012-10-31  1          2154
            3           623
2012-12-31  3            90
2013-01-31  1           280
            3           312
2013-02-28  1           259
            5           718
2013-03-31  1           201
            5          1102
Name: whole_cost, dtype: int64

until 0.14 I think you're stuck with doing two groupbys:

In [14]: g = df.groupby('user_id')['whole_cost']

In [15]: g.resample('M', how='sum').dropna()
Out[15]:
user_id
1        2012-10-31    2154
         2013-01-31     280
         2013-02-28     259
         2013-03-31     201
3        2012-10-31     623
         2012-12-31      90
         2013-01-31     312
5        2013-02-28     718
         2013-03-31    1102
dtype: float64

Answer 2

With timegrouper getting deprecated, you can replace it with Grouper to get the same results

df.groupby(['user_id', pd.Grouper(key='date', freq='M')]).agg({'whole_cost':sum})

df.groupby(['user_id', df['date'].dt.dayofweek]).agg({'whole_cost':sum})