python – Pandas systematically identify missing multi-index categorical values

I have the following dataframe, in the ID column we can have 2 feedbacks, good or bad. I cannot figure out how to systematically identify if a user is missing a feedback and if it is missing, add a new line with the missing feedback in the level 1 and add 0 to all values.

import pandas as pd

df = {'ID': ('Good','Good','Good', 'Bad', 'Bad', 'Bad', 'Good', 'Good', 'Bad'),
      'USERS' : ('A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'C'),
      'DATE_VIEW': ('16/05/2019','16/05/2019', '16/05/2019', '18/03/2020', '18/03/2020', '18/03/2020', '18/03/2020', '18/03/2020', '18/03/2020'),
      'VALUES': (1, 3, 4, 5, 6, 7, 8, 1, 2)

df = pd.DataFrame(df)
df = pd.pivot_table(df, index=('USERS', 'ID'), columns='DATE_VIEW', values='VALUES', aggfunc='sum', fill_value=0)

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this is the expected output:

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