How to transform dataframe long to wide with "grouped" columns?

When pivoting the following dataframe from long to wide, I would like to get "groups" of columns and mark them with a prefix or suffix.

The groups of elements can have different sizes, i.e. consist of one, two or more grouped elements/rows, I used pairs of two here to keep the example simple.

import pandas as pd

df = pd.DataFrame(
    [
        {'group': 'group-009297534',  'single_id': 'single-011900051',  'country': 'ESP',  'name': '00000911'},
        {'group': 'group-009297534',  'single_id': 'single-000000821',  'country': 'USA',  'name': '00001054'},
        {'group': 'group-009280053',  'single_id': 'single-000000002',  'country': 'HUN',  'name': '00000496'},
        {'group': 'group-009280053',  'single_id': 'single-000000014',  'country': 'HUN',  'name': '00000795'},
        {'group': 'group-009245039',  'single_id': 'single-000001258',  'country': 'NOR',  'name': '00000527'},
        {'group': 'group-009245039',  'single_id': 'single-000000669',  'country': 'TWN',  'name': '00000535'}
    ]
)

My approach of assigning an index to the elements to be grouped and then using this for the column designation is already going in the right direction, but still deviates from the expected view

df['idx'] = df.groupby('group').cumcount()
df.pivot(index='group', columns='idx')

group	('single_id', 0)	('single_id', 1)	('country', 0)	('country', 1)	('name', 0)	('name', 1)
group-009245039	single-000001258	single-000000669	NOR	TWN	00000527	00000535
group-009280053	single-000000002	single-000000014	HUN	HUN	00000496	00000795
group-009297534	single-011900051	single-000000821	ESP	USA	00000911	00001054

However, the expected solution would look like this:

	group	single_id_1	country_1	name_1	single_id_2	country_2	name_2
0	group-009245039	single-000001258	NOR	00000527	single-000000669	TWN	00000535
1	group-009280053	single-000000002	HUN	00000496	single-000000014	HUN	00000795
2	group-009297534	single-011900051	ESP	00000911	single-000000821	USA	00001054

I'm not sure whether the approach with the multi-index, which would then have to be sorted and merged somehow, is the right one or whether there is a more elegant option.

Solution

Code

out = (df
       .assign(index=df.groupby('group').cumcount().add(1).astype('str'))
       .pivot(index='group', columns='index')
       .pipe(lambda x: x.set_axis(x.columns.map('_'.join), axis=1))
       .sort_index(axis=1, 
                   key=lambda x: x.str.extract(r'.+_(\d+$)')[0].astype('int'))
       .reset_index()
)

out