Groupby to create a list

Question

I am using JupyterLab to print some data in a spreadsheet in a specific way.

I have two different files:

1) 2)

For every original_id == id I want to group by country and list the brands and summing and listing the holding for each brand.

The result I got with my code is this:

FundID  Domicile (brand, AUM)
0   A1      IT (BBB, 10.0), UK (BBB, 7.0),
1   B2      CH (AAA, 12.0),
2   C3      DE (CCC, 5.0),
3   D4      CH (EEE, 9.0), UK (EEE, 11.0),

While, my objective is to get something like this:

The code is

import numpy as np
import pandas as pd

pd.set_option('display.max_columns', 500)

df_fofs = pd.read_excel('SampleDF.xlsx')
df_extract = pd.read_excel('SampleID_ex.xlsx')

df_extract

original_id
0   A1
1   B2
2   C3
3   D4

df_fofs

    brand   country id  holding
0   AAA       UK    A1  2000000
1   AAA       CH    B2  4000000
2   BBB       UK    A1  7000000
3   CCC       DE    C3  5000000
4   BBB       IT    A1  10000000
5   EEE       UK    D4  11000000
6   EEE       CH    D4  3000000
7   EEE       CH    D4  6000000
8   AAA       CH    B2  8000000

fund_ids = list(df_extract['original_id'])

result = {}
for fund in fund_ids:

    temp = []

    df_funds = df_fofs[(df_fofs['id'] == fund )][['country', 'brand', 'holding']]
    domicile_fof = df_fofs[df_fofs['id'] == fund ][['country', 'holding']]

    df_funds = df_funds.groupby(['country', 'brand'])["holding"].sum()
    domicile_fof = domicile_fof.groupby('country')["holding"].sum()

    s = ''

    for i in range(len(df_funds)):
        row = df_funds.reset_index().iloc[i]
        if row['holding'] >= 5000000:
            s += row['country'] + ' (' + str(row['brand']) + ', ' + str(round(((row['holding'])/1000000), 2)) + '), '

    result[fund] = [s]

df_result = pd.DataFrame.from_dict(result, orient = 'index')
df_result.reset_index(inplace = True)
df_result.columns = ['FundID', 'Domicile (brand, AUM)']

df_result

  FundID    Domicile (brand, AUM)
0   A1      IT (BBB, 10.0), UK (BBB, 7.0),
1   B2      CH (AAA, 12.0),
2   C3      DE (CCC, 5.0),
3   D4      CH (EEE, 9.0), UK (EEE, 11.0),

Answer 1

You can combine tables on id, group by id and country to make inner items and then save grouping by id only to create the outer level

def f(x):
    n = x.apply(lambda r: '{} ({})'.format(r['brand'],int(r['holding']/1000000)), axis=1)
    return '{} [{}]'.format(x.iloc[0]['country'],', '.join(n))


df_extract.merge(df_fofs, left_on='original_id', right_on='id') 
   .groupby(['original_id','country']).apply(f) \ 
   .groupby(level=0).apply(', '.join)

original_id
A1    IT [BBB (10)], UK [AAA (2), BBB (7)]
B2                   CH [AAA (4), AAA (8)]
C3                            DE [CCC (5)]
D4    CH [EEE (3), EEE (6)], UK [EEE (11)]
dtype: object