Groupby conditional sum of adjacent rows pandas

Question

I have a dataframe, which has been sorted by user and by time

 df = pd.DataFrame({'user' : ['A', 'A', 'A', 'B', 'B', 'B','B'],
              'location' : ['house','house','gym','gym','shop','gym','gym'], 
              'duration':[10,5,5,4,10,4,6]})


   duration location user
0        10    house    A
1         5    house    A
2         5      gym    A
3         4      gym    B
4        10     shop    B
5         4      gym    B
6         6      gym    B

I only want to do the sum() when 'location' fields are the same across adjacent rows for a given user. So it is not just df.groupby(['id','location']).duration.sum(). The desired output will look like the following. In addition, the order is important.

duration location user
      15    house    A
       5      gym    A
       4      gym    B
      10     shop    B
      10      gym    B

Thank you!

Answer 1

Supply sort=False to preserve the ordering between groups like it appeared in the original DF. Then, compute the grouped sum of duration column.

adj_check = (df.location != df.location.shift()).cumsum()
df.groupby(['user', 'location', adj_check], as_index=False, sort=False)['duration'].sum()

---

The only change that needs to be made to what you've tried before is this condition which groups all the similar successive rows into one unique group:

(df.location != df.location.shift()).cumsum()
0    1
1    1
2    2
3    2
4    3
5    4
6    4
Name: location, dtype: int32