Pandas - compute means per category and time

[rama27]

Not clear. Why is the 4th item, which is round 1, (5+1)/2? You have 2 round 1 entries by this time. And the 5th would be (4+2)/2? Still not getting how you arrive at your values.
And agree, groupby or other aggregating functions might work, but not by the formulas I am seeing...

OK, so once again, sorry :) Look at this df:

df = pd.DataFrame(data={ 'name':["a","a","a","b","b","c" ] , 'value':[5,4,3,1,2,1] , 'round':[1,2,3,1,2,1 ]})

In the first round I don`t compute any mean, so 'mean_per_round':[5, , , 1, , 1 ]. In the second round, I compute mean of the value from the second and first round. So 'mean_per_round':[,4.5 , , ,1.5 , ]. Similarly, in the third round I compute the average of the value from the first, second and third round. So 'mean_per_round':[, ,4 , , , ]. I work with unbalanced dataset, so I have no values of "b" in the third round and no values of "c" for the second and third round.
Putting it together, new df will look like this:

new_df = pd.DataFrame(data={ 'name':["a","a","a","b","b","c" ] , 'value':[5,4,3,1,2,1] , 'round':[1,2,3,1,2,1 ] ,  'mean_per_round':[5, 4.5 , 4, 1, 1.5, 1 ]  })

Is it clear now?

I tried groupby, but this gives me something different:

df.groupby(['name', 'round']).mean()