How to fill in gaps of duplicate indices in dataframe?

I have a dataframe like as shown below

tdf = pd.DataFrame({'grade': np.random.choice(list('AAAD'),size=(5)),
                   'dash': np.random.choice(list('PPPS'),size=(5)),
                   'dumeel': np.random.choice(list('QWRR'),size=(5)),
                   'dumma': np.random.choice((1234),size=(5)),
                   'target': np.random.choice([0,1],size=(5))

I am trying to create a multi-index dataframe using some of the input columns

So, I tried the below


However, this results in missing/gap for duplicate entries (in red highlight)

enter image description here

How can I avoid that and show my dataframe with all indices (whether it is duplicate or not)

I would like to my output to have all rows with corresponding indices based on original dataframe

>Solution :

It is only display issue:


print (tdf)
             dash  dumma  target
grade dumeel                    
A     W         S    855       1
      R         P    498       1
      R         P    378       0
      W         P    211       0
      W         P     12       0
with pd.option_context("display.multi_sparse", False):
    print (tdf)
             dash  dumma  target
grade dumeel                    
A     W         S    855       1
A     R         P    498       1
A     R         P    378       0
A     W         P    211       0
A     W         P     12       0

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