通过熊猫的另一列的总和制作一个分组列

Question

I have this data set:

+===+=======+======+=======+=======+
|   | Group | Cost | Name1 | Name2 |
+===+=======+======+=======+=======+
| 0 | G1    | 1574 | N1A   | N2A   |
+---+-------+------+-------+-------+
| 1 | G2    | 1322 | N1B   | N2B   |
+---+-------+------+-------+-------+
| 2 | G3    | 1188 | N1C   | N2C   |
+---+-------+------+-------+-------+
| 3 | G3    |  942 | N1D   | N2D   |
+---+-------+------+-------+-------+
| 4 | G4    |  838 | N1E   | N2E   |
+---+-------+------+-------+-------+
| 5 | G5    |    5 | N1F   | N2F   |
+---+-------+------+-------+-------+
| 6 | G5    |    4 | N1F   | N2G   |
+---+-------+------+-------+-------+
| 7 | G5    |    3 | N1G   | N2H   |
+---+-------+------+-------+-------+

Now i want to group by "Group" and add a grouped column with the sum of column "Cost" for each group. Dont know how to explain, so here is the expected result:

+===+=======+======+======+=======+=======+
|   | Group | Sum  | Cost | Name1 | Name2 |
+===+=======+======+======+=======+=======+
| 0 | G1    | 1574 | 1574 | N1A   | N2A   |
+---+-------+------+------+-------+-------+
| 1 | G2    | 1322 | 1322 | N1B   | N2B   |
+---+-------+------+------+-------+-------+
| 2 | G3    | 2130 | 1188 | N1C   | N2C   |
|   |       |      +------+-------+-------+
|   |       |      |  942 | N1D   | N2D   |
+---+-------+------+------+-------+-------+
| 3 | G4    |  838 |  838 | N1E   | N2E   |
+---+-------+------+------+-------+-------+
| 4 | G5    |   12 |    5 | N1F   | N2F   |
|   |       |      +------+-------+-------+
|   |       |      |    4 | N1F   | N2G   |
|   |       |      +------+-------+-------+
|   |       |      |    3 | N1G   | N2H   |
+---+-------+------+------+-------+-------+

How can i achieve this with pandas? Is that even possible? Sorry i am new to this stuff

Answer 1

Use GroupBy.transform with sum and then for display your way create MultiIndex by DataFrame.set_index , but 'missing' values in MulitIndex are only not displaing:

df['Sum'] = df.groupby('Group')['Cost'].transform('sum')
df = df.set_index(['Group','Sum','Cost'])

Or:

df1 = (df.assign(Sum = df.groupby('Group')['Cost'].transform('sum'))
         .set_index(['Group','Sum','Cost']))