Group two(2) or more categorical data by week(7days) in pandas python

This is how my data looks like:

I want to aggregate this by category, Issue and Date(weekly) to get count of record.

Date: Group, the result should be monday to sunday

Count: ADD, if two or more records have same Name and fall in a same week Date time (if falls on same interval 7 days week)

The desired output is given below:

Category	Issue	Date
Bakes	Back Brake failures	11/28/2022
Machines	Oiling of the machines	11/29/2022
Cars	windscreen broken	11/30/2022
Cars	steering wheel is shaking	11/30/2022
Bakes	The tyres are flat	12/01/2022
Machines	Normal wear and tear	12/02/2022
Machines	Normal wear and tear	12/02/2022
Cars	warning lights are on	12/03/2022
Bakes	Back Brake failures	12/04/2022
Machines	Oiling of the machines	12/05/2022
Cars	windscreen broken	12/06/2022
Bakes	excessive emissions	12/06/2022
Bakes	The tyres are flat	12/07/2022
Machines	Normal wear and tear	12/08/2022
Cars	warning lights are on	12/09/2022
Bakes	Brake pads worn	12/10/2022
Machines	Machine is consuming too much oil	12/11/2022
Cars	the tyres are wearing unevenly	12/11/2022

I want to have a result similar to this:

if possible the count should be sorted in descending order

Thanks in advance for your help.

Answer

You can use a crosstab with a weekly period:

pd.crosstab([df['Category'], df['Issue']],
            pd.to_datetime(df['Date']).dt.to_period('W'),
            margins=True, margins_name='Grand Total'
           )

Output:

Date                                           2022-11-28/2022-12-04  2022-12-05/2022-12-11  Grand Total
Category    Issue                                                                                       
Bakes       Back Brake failures                                    2                      0            2
            Brake pads worn                                        0                      1            1
            The tyres are flat                                     1                      1            2
            excessive emissions                                    0                      1            1
Cars        steering wheel is shaking                              1                      0            1
            the tyres are wearing unevenly                         0                      1            1
            warning lights are on                                  1                      1            2
            windscreen broken                                      1                      1            2
Machines    Machine is consuming too much oil                      0                      1            1
            Normal wear and tear                                   2                      1            3
            Oiling of the machines                                 1                      1            2
Grand Total                                                        9                      9           18