Data Manipulation - Add Additional index column and sort Index Alphabetically

Question

Following the successful implementation of the index manipulation in my previous question (see link below) where I wanted the columns to be sorted alphanumerically.

I'd like to arrange the data frame with an additional/secondary index - customer category and sort the customer names within each category alphabetically.

I was thinking of creating a dictionary to map each customer name to a specific category and then sort by alphabetically. Not sure if that works or how to implement this.

i'm looking to sort alphabetically first for two_idx and then by name

This is the current code:

df = df.pivot_table(index=['name'], columns=['Duration'],
                                        aggfunc={'sum': np.sum}, fill_value=0)

# Sort Index Values - Duration
c = df_with_col_arg.columns.levels[1]
c = sorted(ns.natsorted(c), key=lambda x: not x.isdigit())

# Reindex Maturity values after Sorting
df_ = df.reindex_axis(pd.MultiIndex.from_product([df.columns.levels[0], c]), axis=1)

map_dict = {
            'Invoice A': 'A1. Retail',
            'Invoice B': 'A1. Retail',
            'Invoice Z': 'A1. Retail',
            'Invoice C': 'C1. Plastics',
            'Invoice F': 'C1. Plastics',
            'Invoice D': 'C2. Electronics',
            'Invoice J': 'C2. Electronics'
            }

# New Column - later to be converted to a secondary index
df['two_idx'] = df.index.to_series().map(map_dict)
df = df.sort_values(['two_idx'], ascending=[False]).set_index(['two_idx', 'name'])

Output of df.columns:

MultiIndex(levels=[[u'sum', u'two_idx'], [u'0', u'1', u'10', u'11', u'2', u'2Y', u'3', u'3Y', u'4', u'4Y', u'5', u'5Y', u'6', u'7', u'8', u'9', u'9Y', u'']],
           labels=[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 1, 4, 6, 8, 10, 2, 3, 5, 7, 9, 11, 16, 17]])

The output I'm looking for is:

Duration                            2          2Y         3         3Y   
two_idx           name                                                                     
A1. Retail        Invoice A      25.50        0.00      0.00       20.00   
A1. Retail        Invoice B      50.00        25.00     -10.50     0.00
C1. Plastics      Invoice C      125.00       0.00      11.20      0.50
C2. Electronics   Invoice D       0.00        15.00      0.00       80.10

[Data Manipulation - Sort Index when values are Alphanumeric

jezrael · Accepted Answer · 2017-11-24 09:44:45Z

I believe you need DataFrame.sort_index:

import natsort as ns

#add parameter values for remove MultiIndex in columns
df = df.pivot_table(index='name', 
                    columns='Duration',
                    values='sum',
                    aggfunc='sum', 
                    fill_value=0)

#https://stackoverflow.com/a/47240142/2901002
c = sorted(ns.natsorted(df.columns), key=lambda x: not x.isdigit())
df = df.reindex(c, axis=1)

map_dict = {
            'Invoice A': 'A1. Retail',
            'Invoice B': 'A1. Retail',
            'Invoice Z': 'A1. Retail',
            'Invoice C': 'C1. Plastics',
            'Invoice F': 'C1. Plastics',
            'Invoice D': 'C2. Electronics',
            'Invoice J': 'C2. Electronics'
            }

#create new level of MultiIndex and assign back
df.index = pd.MultiIndex.from_arrays([df.rename(map_dict).index, 
                                      df.index], names=['name','one'])

#sorting index
df = df.sort_index()
print (df)
                               2     3    2Y    3Y
name            one                               
A1. Retail      Invoice A   25.5   0.0   0.0  20.0
                Invoice B   50.0 -10.5  25.0   0.0
C1. Plastics    Invoice C  125.0  11.2   0.0   0.5
C2. Electronics Invoice D    0.0   0.0  15.0  80.1

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