Group by one columns and find sum and max value for another in pandas

The most (pandas) native way to do this, is to use the .agg() method that allows you to specify the aggregation function you want to apply per column (just like you would do in SQL).

Sample from the documentation:

df.groupby('A').agg({'B': ['min', 'max'], 'C': 'sum'})

You can use groupby/transform to creat the required columns

df[['col1_sum', 'col4_sum']]=df.groupby('id')['col1', 'cl4'].transform('sum')
df[['col2_max', 'col3_max']]=df.groupby('id')['col1', 'cl4'].transform('max')

    Name    id  col1    col2    col3    cl4 col1_sum    col4_sum    col2_max    col3_max
0   PL      252 0       747     3       53  5           101         4   53
1   PL2     252 1       24      2       35  5           101         4   53
2   PL3     252 4       75      24      13  5           101         4   53
3   AD      889 53      24      0       95  76          114         53  95
4   AD2     889 23      2       0       13  76          114         53  95
5   AD3     889 0       24      3       6   76          114         53  95
6   BG      24  12      89      53      66  60          70          43  66
7   BG1     24  43      16      13      0   60          70          43  66
8   BG2     24  5       32      101     4   60          70          43  66