Group the column values in a data frame

Question

I have a Json array with key value pairs like below

[
   [
      {
         "value":"Dimension1",
         "formattedValue":"Dimension1"
      },
      {
         "value":"In",
         "formattedValue":"In"
      },
      {
         "value":"Amount1",
         "formattedValue":"Amount1"
      },
      {
         "value":"100",
         "formattedValue":"100"
      }
   ],
   [
      {
         "value":"Dimension1",
         "formattedValue":"Dimension1"
      },
      {
         "value":"In",
         "formattedValue":"In"
      },
      {
         "value":"Amount2",
         "formattedValue":"Amount2"
      },
      {
         "value":"200",
         "formattedValue":"200"
      }
   ],
   [
      {
         "value":"Dimension1",
         "formattedValue":"Dimension1"
      },
      {
         "value":"Out",
         "formattedValue":"Out"
      },
      {
         "value":"Amount1",
         "formattedValue":"Amount1"
      },
      {
         "value":"30",
         "formattedValue":"30"
      }
   ],
   [
      {
         "value":"Dimension1",
         "formattedValue":"Dimension1"
      },
      {
         "value":"Out",
         "formattedValue":"Out"
      },
      {
         "value":"Amount2",
         "formattedValue":"Amount2"
      },
      {
         "value":"4",
         "formattedValue":"40"
      }
   ]
]

I have flatted this in to a table like below using python

data='<jsonstring>'
data= json.loads(data)
df = pd.DataFrame(data).stack().map(lambda x:x.get('formattedValue')).unstack()
df.columns = ['column0','column1','column2','column3']
df.rename({'column0' : 'Dimension','column1' : 'Type'}, axis=1,inplace=True)
df=df.pivot_table(index=['Dimension','Type'],columns=['column2'],values="column3",aggfunc='sum').reset_index()

Dimension	Type	Amount1	Amount2
Dimension1	In	100	200
Dimension1	Out	30	40

I want the first column value grouped so it only displays one time at the first row. like below. How do I achieve this please?

Dimension	Type	Amount1	Amount2
Dimension1	In	100	200
	Out	30	40

Answer 1

You can either use @AnuragDabas’ solution from the comments, combined with .rename_axis() to remove the index names:

>>> df.set_index(['Dimension', 'Type']).rename_axis([None, None])
                Amount1  Amount2
Dimension1 In       100      200
           Out       30       40

Or you can use .mask() to remove the duplicated rows:

>>> df['Dimension'] = df['Dimension'].mask(df['Dimension'].duplicated(), '')
>>> df
    Dimension Type  Amount1  Amount2
0  Dimension1   In      100      200
1              Out       30       40