how do you add values in pandas dataframe column based on group values

I have a data frame like this

print(df)

    Hostname  Slot Port Reserved
    Server1  1    1   0
    Server1  1    2   0
    Server1  2    3   1
    Server2  2    1   1
    Server2  2    2   0
    Server2  2    3   1
    Server3  1    1   0
    Server3  2    2   0
    Server3  3    3   1

I need to sum Reserved column by Hostname and Slot columns.

  Hostname  Slot  Total_Reserved
    Server1  1    1   
    Server2  2    2  
    Server3  1    1  

I tried this to no avail:

new_df = df.groupby([Hostname', 'Slot', 'Reserved']).sum()

>Solution :

groupby.sum is the way to go, but your current command is incorrect.

You need to remove "Reserved" from the groups:

out = (df
       .query('Reserved > 0')
       .groupby(['Hostname', 'Slot'], as_index=False)
       ['Reserved'].sum()
       )

Output:

  Hostname  Slot  Reserved
0  Server1     2         1
1  Server2     2         2
2  Server3     3         1

Alternatively:

out = (df
       .groupby(['Hostname', 'Slot'])
       [['Reserved']].sum()
       .add_prefix('Total_').reset_index()
       )

Output:

  Hostname  Slot  Total_Reserved
0  Server1     1               0
1  Server1     2               1
2  Server2     2               2
3  Server3     1               0
4  Server3     2               0
5  Server3     3               1