How to work out percentage of total with groupby for specific columns in a pandas dataframe?

Question

I have the following dataframe:

df = pd.DataFrame( columns = ['Name','Status','Profit','Promotion','Product','Visits']) 
df['Name'] = ['Andy','Andy','Brad','Brad','Cynthia','Cynthia']
df['Status'] =['Old','New','Old','New','Old','New'] 
df['Profit'] = [140,60,110,90,20,100]
df['Promotion'] = [25,30,40,10,22,36]
df['Product'] = [8,6,18,10,7,12]
df['Visits'] = [11,4,7,3,12,5]
df['Month'] = 'Jan'

I would like to work out the percentage of total for the columns 'Profit','Promotion' and 'Product' by 'Name' in order to achieve the following dataframe:

df['Profit'] = [70,30,55,45,17,83]
df['Promotion'] = [45,55,80,20,38,62]
df['Product'] = [57,43,64,36,37,63]
df

I have attempted to group by 'Name','Status' and 'Month' and tried doing something similar to the solution provided here Pandas percentage of total with groupby but can't seem to get my desired output.

Answer 1

Use GroupBy.transform for sum per Name s with divide original columns, multiple by 100 and last round :

cols = ['Profit','Promotion','Product']

print (df.groupby('Name')[cols].transform('sum'))
   Profit  Promotion  Product
0     200         55       14
1     200         55       14
2     200         50       28
3     200         50       28
4     120         58       19
5     120         58       19

df[cols] = df[cols].div(df.groupby('Name')[cols].transform('sum')).mul(100).round()
print (df)
      Name Status  Profit  Promotion  Product  Visits Month
0     Andy    Old    70.0       45.0     57.0      11   Jan
1     Andy    New    30.0       55.0     43.0       4   Jan
2     Brad    Old    55.0       80.0     64.0       7   Jan
3     Brad    New    45.0       20.0     36.0       3   Jan
4  Cynthia    Old    17.0       38.0     37.0      12   Jan
5  Cynthia    New    83.0       62.0     63.0       5   Jan