Pandas sum column for each column pair-CodePudding

I have a dataframe as follows. I'm attempting to sum the values in the Total column, for each date, for each unique pair from columns P_buy and P_sell.

 -------- ---------- ------- --------- -------- ---------- ----------------- 
| Index  |   Date   | Type  | Quantity| P_buy  |  P_sell  |      Total      |
 -------- ---------- ------- --------- -------- ---------- ----------------- 
|      0 | 1/1/2020 |     1 |      10 | 1      | 1        |              10 |
|      1 | 1/1/2020 |     1 |      10 | 2      | 1        |              20 |
|      2 | 1/1/2020 |     2 |      20 | 3      | 1        |              25 |
|      3 | 1/1/2020 |     2 |      20 | 4      | 1        |              20 |
|      4 | 2/1/2020 |     3 |      30 | 1      | 1        |              35 |
|      5 | 2/1/2020 |     3 |      30 | 2      | 1        |              30 |
|      6 | 2/1/2020 |     1 |      40 | 3      | 1        |              45 |
|      7 | 2/1/2020 |     1 |      40 | 4      | 1        |              40 |
|      8 | 3/1/2020 |     2 |      50 | 1      | 1        |              55 |
|      9 | 3/1/2020 |     2 |      50 | 2      | 1        |              53 |
 -------- ---------- ------- --------- -------- ---------- -----------------

My desired output would be as follows: Where for each combination of unique P_buy/P_sell pairs, I'm receiving a sum of the total at each date.

 -------- ---------- ------- --------- 
| P_buy  |  P_sell  |      Total      |
 -------- ---------- ------- --------- 
|      1 |     1    |       100       |
|      2 |     1    |       103       | 
|      3 |     1    |       70        | 
 -------- ---------- ------- ---------

My attempts have been using the groupby function, but I haven't been able to successfully implement.

CodePudding user response：

# use a groupby on the desired columns and sum the total
df.groupby(['P_buy','P_sell'], as_index=False)['Total'].sum()

    P_buy   P_sell  Total
0     1       1     100
1     2       1     103
2     3       1     70
3     4       1     60