Pandas groupby and count across multiple columns

I have data ordered by ID, Year, and then a series of event flags indicating whether a thing did or did not happen for that ID in that year:

ID	Year	x	y	z
1	2015	0	1	0
1	2016	1	1	0
1	2017	0	1	1
2	2015	1	0	1
2	2016	1	1	0
2	2017	0	1	1

I’d like to group by ID and Year and apply a cumulative count to each “event” column, such that I’m left with something like the following

ID	Year	x_total	y_total	z_total
1	2015	0	1	0
1	2016	1	2	0
1	2017	1	3	1
2	2015	1	0	1
2	2016	2	1	1
2	2017	2	2	2

I’ve looked at various options using cumsum and cumcount but I can’t seem to figure this out.

Answer

You can use .groupby() + .cumsum() to get the cumulative count to each “event” column. Then add _total as suffix to the column names by .add_suffix() and then join with the first 2 columns:

df[['ID', 'Year']].join(df.groupby('ID')[['x', 'y', 'z']].cumsum().add_suffix('_total'))

Result:

   ID  Year  x_total  y_total  z_total
0   1  2015        0        1        0
1   1  2016        1        2        0
2   1  2017        1        3        1
3   2  2015        1        0        1
4   2  2016        2        1        1
5   2  2017        2        2        2