Create new column in dataframe based on conditions in existing columns

I have the following data:

dict1={"Code":[3,3,3,1,1,2,2,3,3,3],"Num":[10,10,5,5,5,5,10,5,25,25]}

df1=pd.DataFrame(dict1)

which results in:

   Code Num
0   3   10
1   3   10
2   3   5
3   1   5
4   1   5
5   2   5
6   2   10
7   3   5
8   3   25
9   3   25

I want to create a new column (End Balance) which value is based on the existing Code and Num columns.

If Code value is 1 then End Balance is equal to Num

If Code is 2 then End Balance value is the sum of Num values where Code is 2

If Code is 3 then End Balance value is the sum of Num values where Code is 3

I use iterrows and I have the following script:

mylist1=[]
mylist2=[]
for index, row in df1.iterrows():
    if row["Code"]==1:
        end_balance=row["Num"]  
    elif row["Code"]==2:
        mylist1.append(row["Num"])
        end_balance=sum(mylist1) 
    elif row["Code"]==3:
        mylist2.append(row["Num"])
        end_balance=sum(mylist2)
    df1.loc[index,"End_Balance"]=end_balance

which output is

   Code Num End_Balance
0   3   10  10.00
1   3   10  20.00
2   3   5   25.00
3   1   5   5.00
4   1   5   5.00
5   2   5   5.00
6   2   10  15.00
7   3   5   30.00
8   3   25  55.00
9   3   25  80.00

The problem I have with this output is that at the second subset where Code = 3 the End_Balance column starts summation taking into account the first subset where Code is 3. You can see that easily. I want somehow mylist2 in the script to be erased after the first subset of Code=3 and when a new subset with Code = 3 comes the summation in column End_Balance should start over. Expected output is:

   Code Num End_Balance
0   3   10  10.00
1   3   10  20.00
2   3   5   25.00
3   1   5   5.00
4   1   5   5.00
5   2   5   5.00
6   2   10  15.00
7   3   5   5.00
8   3   25  30.00
9   3   25  55.00

May your suggestions follow the same logic - using iterrows. I know that probably with a groupby I can do what I want but here I need a solution with iterrows.