Applying cumulative mean function to a grouped object

I have a DataFrame `df` where each record represents a soccer game. Teams will appear more than once. I need to compute some sort of a rolling mean for each team scores(well, not exactly the rolling mean to the letter). ``` date home away score_h score_a 166 2013-09-01 Fulham Chelsea 0 0 167 2013-09-03 Arsenal Everton 0 2 164 2013-09-05 Arsenal Swansea 5 1 165 2013-09-06 Fulham Norwich 0 1 163 2013-09-18 Arsenal Swansea 0 0 ``` What I need to calculate, is the mean score for each team (home and away). For brevity, let's just do the home column: ``` grouped = df.groupby('home') grouped = grouped.sort_index(by='date') # rows inside groups must be in asc order ``` This results in: ``` date home away score_h score_a home Arsenal 167 2013-09-03 Arsenal Everton 0 2 164 2013-09-05 Arsenal Swansea 5 1 163 2013-09-18 Arsenal Swansea 0 0 Fulham 166 2013-09-01 Fulham Chelsea 0 0 165 2013-09-06 Fulham Norwich 0 1 ``` Question starts here Now, I need to compute "rolling mean" for teams. Let's do it by hand for the group named `Arsenal`. At the end of this we should wind up with 2 extra columns, let's call them: `rmean_h` and `rmean_a`. First record in the group (`167`) has scores of `0` and `2`. The `rmean` of these is simply `0` and `2` respectively. For second record in the group (`164`), the rmeans will be `(0+5)/2 = 2.5` and `(2+1) / 2 = 1.5`, and for the third record, `(0+5+0)/3 = 1.66` and `(2+1+0)/3 = 1`. Our DataFrame should now looks like this: ``` date home away score_h score_a rmean_h rmean_a home Arsenal 167 2013-09-03 Arsenal Everton 0 2 0 2 164 2013-09-05 Arsenal Swansea 5 1 2.5 1.5 163 2013-09-18 Arsenal Swansea 0 0 1.66 1 Fulham 166 2013-09-01 Fulham Chelsea 0 0 165 2013-09-06 Fulham Norwich 0 1 ``` I want to carry out these calculations for my data, do you have any suggestions please?