为数据透视表中的不同列的行创建小计

Question

I am creating a pivot table with Pandas but got stuck at the subtotals for rows within different columns (under the same Values) for a while, I've done some research on stackoverflow such as Pandas Pivot tables row subtotals and this Subtotals for Pandas pivot table index and column (actually this one is close to but some what also different from my case) but didn't find right hint for my case, so please for this topic, thanks.

Here I will provide a simplified DataFrame (original one is too big to show here, so their values really do not matter, the format is what I am looking for) of my case and the code I wrote to get my pivot table.

import pandas as pd
import numpy as np
df = pd.DataFrame(
{
    'Co':['NN']*6,
    'Op':['A','B']*3,
    'Stk':[1.1,1.2,1.3]*2,
    'Tm':['07-01-2018','08-01-2018','09-01-2018']*2,
    'Qt':[100,200,300]*2
}
)
df

The df output looks like:

Co  Op  Qt  Stk Tm
0   NN  A   100 1.1 07-01-2018
1   NN  B   200 1.2 08-01-2018
2   NN  A   300 1.3 09-01-2018
3   NN  B   100 1.1 07-01-2018
4   NN  A   200 1.2 08-01-2018
5   NN  B   300 1.3 09-01-2018

Then I convert above df to my pivot table by:

df['Qt'] = df['Qt'].apply(pd.to_numeric)
df['Stk']=df['Stk'].apply(pd.to_numeric)
df['Co'] = df['Co'].astype(str)
tb=pd.pivot_table(df,index=["Tm"],columns=["Co","Op","Stk"],aggfunc=np.sum,values=['Qt'], fill_value=0, margins=True, margins_name='All')
tb

The generated pivot table looks like:

            Qt
Co          NN                              All
Op          A              B    
Stk         1.1  1.2  1.3  1.1  1.2  1.3    
        Tm                          
07-01-2018  100  0    0    100  0    0      200
08-01-2018  0    200  0    0    200  0      400
09-01-2018  0    0    300  0    0    300    600
       All  100  200  300  100  200  300    1200

The format which I really expect is:

            Qt
Co          NN                                                All
Op          A              ATotal   B               BTotal
Stk         1.1  1.2  1.3           1.1  1.2  1.3   
        Tm                          
07-01-2018  100  0    0    100      100  0    0     100       200
08-01-2018  0    200  0    200      0    200  0     200       400
09-01-2018  0    0    300  300      0    0    300   300       600
       All  100  200  300  600      100  200  300   600       1200

I was trying to create this exact same format for a while and still can't get the same one (I tried something like creating two separate A and B pivot tables and union them together, but it will mess with the All margins). So help is really needed here. ps I am still new to stackoverflow community, so please pardon if my question is missing some aspects, thank you.

Answer 1

pivot_table doesn't support it, but you can compute it yourself and concatenate it later:

tb.groupby(level='Op', axis=1).sum().add_suffix('Total')

Op          Total  ATotal  BTotal
Tm                               
07-01-2018    200     100     100
08-01-2018    400     200     200
09-01-2018    600     300     300
All          1200     600     600