Pandas: concatenate dataframes based on multi-level first cases

I'm trying to combine a large framework with a small framework data based on the first occurrence of the criteria of ( month

and num

).

I have pooled viable code (actual / desired output at the bottom), but it looks like it could be much more efficient.

My question is, am I missing a significantly easier way to do this?

Setting:

import pandas as pd

m = [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2]
n = [1,1,1,20,20,300,300,20,20,1,1,1,20,300,20,1,1,1,20,20,300,300,300,20,20,1,1]
df = pd.DataFrame({'month':m, 'num':n, 'x':0})

m2 = [1,1,1,2,2,2]
n2 = [1,20,300,1,20,300]
s2 = [11,222,3333,44,555,6666]
df2 = pd.DataFrame({'month':m2, 'num':n2, 'sum':s2})

      

        
        
        
      

    

Current code:

dfx = pd.DataFrame(df.groupby(['month','num'])['x'].idxmax())
dfx.rename(columns = {'x':'find'}, inplace = True)

df2.set_index(['month','num'], inplace = True)
df2 = pd.merge(df2, dfx, left_index = True, right_index = True)

df = df.merge(df2, left_index = True, right_on = 'find', how = 'left')
df = df.drop(['find','x'], axis = 1).reset_index(drop = True).fillna(0)

      

        
        
        
      

    

Output:

    month  num     sum
0       1    1    11.0
1       1    1     0.0
2       1    1     0.0
3       1   20   222.0
4       1   20     0.0
5       1  300  3333.0
6       1  300     0.0
7       1   20     0.0
8       1   20     0.0
9       1    1     0.0
10      1    1     0.0
11      1    1     0.0
12      1   20     0.0
13      1  300     0.0
14      1   20     0.0
15      1    1     0.0
16      2    1    44.0
17      2    1     0.0
18      2   20   555.0
19      2   20     0.0
20      2  300  6666.0
21      2  300     0.0
22      2  300     0.0
23      2   20     0.0
24      2   20     0.0
25      2    1     0.0
26      2    1     0.0