Pandas pivot table Nested Sorting

Question

Given this data frame and pivot table:

import pandas as pd
df=pd.DataFrame({'A':['x','y','z','x','y','z'],
                 'B':['one','one','one','two','two','two'],
                 'C':[7,5,3,4,1,6]})
df


    A   B       C
0   x   one     7
1   y   one     5
2   z   one     3
3   x   two     4
4   y   two     1
5   z   two     6

table = pd.pivot_table(df, index=['A', 'B'],aggfunc=np.sum)

table
A  B  
x  one    7
   two    4
y  one    5
   two    1
z  one    3
   two    6
Name: C, dtype: int64

I want to sort the pivot table such that the order of 'A' is z, x, y and the order of 'B' is based on the descendingly-sorted values from data frame column 'C'.

Like this:

A  B  
z  two    6
   one    3
x  one    7
   two    4
y  one    5
   two    1

    Name: C, dtype: int64

Thanks in advance!

Answer 1

I don't believe there is an easy way to accomplish your objective. The following solution first sorts your table is descending order based on the values of column C. It then concatenates each slice based on your desired order.

order = ['z', 'x', 'y']
table = table.reset_index().sort_values('C', ascending=False)
>>> pd.concat([table.loc[table.A == val, :].set_index(['A', 'B']) for val in order])
       C
A B     
z two  6
  one  3
x one  7
  two  4
y one  5
  two  1

Answer 2

Solution

custom_order = ['z', 'x', 'y']
kwargs = dict(axis=0, level=0, drop_level=False)

new_table = pd.concat(
    [table.xs(idx_v, **kwargs).sort_values(ascending=False) for idx_v in custom_order]
)

Alternate one liner

pd.concat([table.xs(i, drop_level=0).sort_values(ascending=0) for i in list('zxy')]

Explanation

custom_order is your desired order. kwargs is a convenient way to improve readability (in my opinion). Key elements to note, axis=0 and level=0 might be important for you if you want to leverage this further. However, those are also the default values and can be left out. drop_level=False is the key argument here and is necessary to keep the idx_v we are taking a xs of such that the pd.concat puts it all together in the way we'd like.

I use a list comprehension in almost the exact same manner as Alexander within the pd.concat call.

Demonstration

print new_table

A  B  
z  two    6
   one    3
x  one    7
   two    4
y  one    5
   two    1
Name: C, dtype: int64

Answer 3

If you can read in column A as categorical data, then it becomes much more straightforward. Setting your categories as list('zxy') and specifying ordered=True uses your custom ordering.

You can read in your data using something similar to:

'A':pd.Categorical(['x','y','z','x','y','z'], list('zxy'), ordered=True)

Alternatively, you can read in the data as you currently are, then use astype to convert A to categorical:

df['A'] = df['A'].astype('category', categories=list('zxy'), ordered=True)

Once A is categorical, you can pivot the same as before, and then sort with:

table = table.sort_values(ascending=False).sortlevel(0, sort_remaining=False)