Pandas dataframe replace column value based on group

I have a dataframe with the below structure,

   master_mac    slave_mac        uuid           rawData               
0  ac233fc01403  ac233f26492b     e2c56db5       NaN                                                         
1  ac233fc01403  ac233f26492b     e2c56db5       NaN                                                         
2  ac233fc01403  ac233f26492b     e2c56db5       NaN                                                          
3  ac233fc01403  ac233f26492b     e2c56db5       ac0228  
4  ac233fc01403  e464eecba5eb     NaN            590080             
5  ac233fc01403  ac233f26492b     e2c56db5       ac0228  
6  ac233fc01403  ac233f26492b     e2c56db5       NaN                                                          
7  ac233fc01403  ac233f26492b     e2c56db5       636800       

• If an "uuid" column is not empty for a group i.e., "master_mac" & "slave_mac", then the respective rows should contain NaN for "rawData" column.

The resultant outcome needs to be,

 master_mac    slave_mac        uuid           rawData               
0  ac233fc01403  ac233f26492b     e2c56db5       NaN                                                         
1  ac233fc01403  ac233f26492b     e2c56db5       NaN                                                         
2  ac233fc01403  ac233f26492b     e2c56db5       NaN                                                          
3  ac233fc01403  ac233f26492b     e2c56db5       NaN  
4  ac233fc01403  e464eecba5eb     NaN            590080             
5  ac233fc01403  ac233f26492b     e2c56db5       NaN  
6  ac233fc01403  ac233f26492b     e2c56db5       NaN                                                          
7  ac233fc01403  ac233f26492b     e2c56db5       NaN

Can anyone help me out in this?

Use:

m = df['uuid'].notna()

If need processes per groups use GroupBy.transform with GroupBy.any for test at least one non NaN per groups:

m = df['uuid'].notna().groupby([df['master_mac'],df['slave_mac']]).transform('any')

df['rawData'] = df['rawData'].mask(m)
print (df)
     master_mac     slave_mac      uuid rawData
0  ac233fc01403  ac233f26492b  e2c56db5     NaN
1  ac233fc01403  ac233f26492b  e2c56db5     NaN
2  ac233fc01403  ac233f26492b  e2c56db5     NaN
3  ac233fc01403  ac233f26492b  e2c56db5     NaN
4  ac233fc01403  e464eecba5eb       NaN  590080
5  ac233fc01403  ac233f26492b  e2c56db5     NaN
6  ac233fc01403  ac233f26492b  e2c56db5     NaN
7  ac233fc01403  ac233f26492b  e2c56db5     NaN

Or:

df.loc[m, 'rawData'] = np.nan