df.groupby('columns').apply(''.join()), join all the cells to a string - pandas

Question

df.groupby('columns').apply(''.join()), join all the cells to a string.
This is for a junior dataprocessor. In the past, I've tried many ways.
import pandas as pd
data = {'key':['a','b','c','a','b','c','a'], 'profit':
[12,3,4,5,6,7,9],'income':['j','d','d','g','d','t','d']}
df = pd.DataFrame(data)
df = df.set_index(‘key’)
#df2 is expected result
data2 = {'a':['12j5g9d'],'b':['3d6d'],'c':['4d7t']}
df2 = pd.DataFrame(data2)
df2 = df2.set_index(‘key’)

Answer 1

Here's a simple solution, where we first translate the integers to strings and then concatenate profit and income, then finally we concatenate all strings under the same key:
data = {'key':['a','b','c','a','b','c','a'], 'profit':
[12,3,4,5,6,7,9],'income':['j','d','d','g','d','t','d']}
df = pd.DataFrame(data)
df['profit_income'] = df['profit'].apply(str) + df['income']
res = df.groupby('key')['profit_income'].agg(''.join)
print(res)
output:
key
a 12j5g9d
b 3d6d
c 4d7t
Name: profit_income, dtype: object

Answer 2

This question can be solved couple different ways:
First add an extra column by concatenating the profit and income columns.
import pandas as pd
data = {'key':['a','b','c','a','b','c','a'], 'profit':
[12,3,4,5,6,7,9],'income':['j','d','d','g','d','t','d']}
df = pd.DataFrame(data)
df = df.set_index('key')
df['profinc']=df['profit'].astype(str)+df['income']
1) Using sum
df2=df.groupby('key').profinc.sum()
2) Using apply and join
df2=df.groupby('key').profinc.apply(''.join)
Results from both of the above would be the same:
key
a 12j5g9d
b 3d6d
c 4d7t