Groupby and transform across a group, not within it

I'm trying to get the third column of this dataframe, given the first two columns.

I can't work out what to search for, it's like a within version of groupby('group')['weeklyTotal'].cumsum()??

I know I could pull out those two columns, make them distinct, then do the groupby cumsum, but would much prefer to have this within the same dataframe.

To save a tiny bit of pain, here's an example dataframe:

df = pd.DataFrame({'group':['A','A','A','B','B','B','C','C','C'], 'weeklyTotal':[1,1,1,3,3,3,2,2,2]})

Keep only one row per group with drop_duplicates, compute the cumsum and map the values:

df['CumulativeTotal'] = df['group'].map(df.drop_duplicates(subset='group')
                                          .set_index('group')['weeklyTotal']
                                          .cumsum()
                                       )

Or, using a mask and duplicated:

df['CumulativeTotal'] = (df['weeklyTotal']
                         .mask(df['group'].duplicated(), 0)
                         .cumsum()
                        )

Output:

  group  weeklyTotal  CumulativeTotal
0     A            1                1
1     A            1                1
2     A            1                1
3     B            3                4
4     B            3                4
5     B            3                4
6     C            2                6
7     C            2                6
8     C            2                6

Here is another way:

m = df['group'].ne(df['group'].shift())
m.mul(df['weeklyTotal']).cumsum()

Output:

0    1
1    1
2    1
3    4
4    4
5    4
6    6
7    6
8    6