Pandas multi-index divide items by sum of their group

Question

I have a table like below:

I want to change this table so that the values in groupA and groupB are percentages of the totals across each date. For example, the value in groupA for Jim 2023-04-28 should become 13/(44+62). The output table would look like:

I'm unsure how to do this since this is a multi-index table. So far, I have tried manually integrating through all of the dates with a for loop and then storing the sums of the total column in a dictionary, but this seems extremely slow.

Here is the dict of the top data frame (for testing!):

df_a = {'total': {
  ("5:00",
   73482373,
   '2023-04-28',
   'Jim'): 44.0,
  ("5:00",
   73482373,
   '2023-04-28',
   'Amy'): 62.0,
("5:00",
   73482373,
   '2023-04-25',
   'Jim'): 80.0,
  ("5:00",
   73482373,
   '2023-04-25',
   'Amy'): 42.0
},
'groupA': {
  ("5:00",
   73482373,
   '2023-04-28',
   'Jim'): 13.0,
  ("5:00",
   73482373,
   '2023-04-28',
   'Amy'): 15.0,
("5:00",
   73482373,
   '2023-04-25',
   'Jim'): 0.0,
  ("5:00",
   73482373,
   '2023-04-25',
   'Amy'): 12.0    
 },
'groupB': {
  ("5:00",
   73482373,
   '2023-04-28',
   'Jim'): 17.0,
  ("5:00",
   73482373,
   '2023-04-28',
   'Amy'): 21.0,
("5:00",
   73482373,
   '2023-04-25',
   'Jim'): 0.0,
  ("5:00",
   73482373,
   '2023-04-25',
   'Amy'): 12.0}}

Answer 1

You could do it using groupby and transform:

df[['groupA', 'groupB']] = df[['groupA', 'groupB']] / df.groupby(level=[0,1,2])['total'].transform('sum').to_numpy()[:, None]

Output:

>>> df
                              total    groupA    groupB
5:00 73482373 2023-04-28 Jim   44.0  0.122642  0.160377
                         Amy   62.0  0.141509  0.198113
              2023-04-25 Jim   80.0  0.000000  0.000000
                         Amy   42.0  0.098361  0.098361