Pandas pivot table Nested Sorting Part 3 - sorting

Question

Episode 3:
In part 2, we retained the hierarchical nature of the indices while sorting within right-most level. In part 1, we applied a custom sort to the left-most index level while sorting the values within the right-most index.
Now, I'd like to combine both methods.
Given the following data frame and resultant pivot table:
import pandas as pd
df=pd.DataFrame({'A':['a','a','a','a','a','b','b','b','b'],
'B':['x','y','z','x','y','z','x','y','z'],
'C':['a','b','a','b','a','b','a','b','a'],
'D':[7,5,3,4,1,6,5,3,1]})
df
A B C D
0 a x a 7
1 a y b 5
2 a z a 3
3 a x b 4
4 a y a 1
5 b z b 6
6 b x a 5
7 b y b 3
8 b z a 1
table = pd.pivot_table(df, index=['A', 'B','C'],aggfunc='sum')
table
D
A B C
a x a 7
b 4
y a 1
b 5
z a 3
b x a 5
y b 3
z a 1
b 6
I would like to specify a custom order of 'B'.
This seems to work:
df['B']=df['B'].astype('category')
df['B'].cat.set_categories(['z','x','y'],inplace=True)
Next, I'd like for the pivot table to keep the order for 'B' specified above while sorting the values 'D' descendingly within each category of 'B'.
Like this:
D
A B C
z a 3
x a 7
a b 4
y b 5
a 1
z b 6
b a 1
x a 5
y b 3
Thanks in advance!

Answer 1

UPDATE: using pivot_table()
In [79]: df.pivot_table(index=['A','B','C'], aggfunc='sum').reset_index().sort_values(['A','B','D'], ascending=[1,1,0]).set_index(['A','B','C'])
Out[79]:
D
A B C
a x a 7
b 4
y b 5
a 1
z a 3
b x a 5
y b 3
z b 6
a 1
is that what you want?
In [64]: df.sort_values(['A','B','D'], ascending=[1,1,0]).set_index(['A','B','C'])
Out[64]:
D
A B C
a z a 3
x a 7
b 4
y b 5
a 1
b z b 6
a 1
x a 5
y b 3