Group By – Total Hours and Hours by Category in Python / Pandas

I need to calculate Total Hours and Hours by Status per Week using Python / Pandas GROUP BY.

Id             Week          Status Hours

1   01/10/2022 - 01/16/2022    On     5
2   01/10/2022 - 01/16/2022    Off    2
3   01/17/2022 - 01/23/2022    Off    6
4   01/17/2022 - 01/23/2022    On     1
5   01/17/2022 - 01/23/2022    On     5
6   01/03/2022 - 01/09/2022    On     10
7   01/10/2022 - 01/16/2022    Off    9
8   01/03/2022 - 01/09/2022    On     3
9   01/24/2022 - 01/30/2022    Off    4
10  01/24/2022 - 01/30/2022    On     7

test_data = {'Id': [1,2,3,4,5,6,7,8,9,10], 
             'Week': ['01/10/2022 - 01/16/2022', '01/10/2022 - 01/16/2022', '01/17/2022 - 01/23/2022', '01/17/2022 - 01/23/2022', '01/17/2022 - 01/23/2022', '01/03/2022 - 01/09/2022', '01/10/2022 - 01/16/2022', '01/03/2022 - 01/09/2022', '01/24/2022 - 01/30/2022', '01/24/2022 - 01/30/2022'], 
             'Status': ['On', 'Off', 'Off', 'On', 'On', 'On', 'Off', 'On', 'Off', 'On'], 
             'Hours': [5,2,6,1,5,10,9,3,4,7]}

test_df = pd.DataFrame(data=test_data)

I can get Total Hours by each Week:

test_df.groupby(by=['Week'], as_index=False).agg({"Hours": "sum"})

But I don’t know how to also group by Status, so it will be 2 additional columns (On Status Hours and Off Status Hours)

If I add Status column just to the groupby part, it creates extra rows (I understand why)

test_df.groupby(by=['Week', 'Status'], as_index=False).agg({"Hours": "sum"})

Output I want:

Week	Total Hours	On Status Hours	Off Status Hours
01/03/2022 – 01/09/2022	13	13	0
01/10/2022 – 01/16/2022	16	5	11
01/17/2022 – 01/23/2022	12	6	6
01/24/2022 – 01/30/2022	11	7	4

Answer

You can use:

(test_df
 .groupby(['Week', 'Status'])['Hours']
 .sum()
 .unstack(1, fill_value=0)
 .add_suffix(' Status Hours')
 .assign(**{'Total Hours': lambda d: d.sum(1)})
 )

Output:

Status                   Off Status Hours  On Status Hours  Total Hours
Week                                                                   
01/03/2022 - 01/09/2022                 0               13           13
01/10/2022 - 01/16/2022                11                5           16
01/17/2022 - 01/23/2022                 6                6           12
01/24/2022 - 01/30/2022                 4                7           11