Sum negative row values with previous rows pandas

Question

I'm having trouble finding a good way to find all negative entries in a column and move them up the column, summing them up with the existing entry (i.e. subtracting the negative entry from the present entry) until all values are positive.

It is important that there are no negative values for the final dataframe & that all previously negative entries = 0. Also, the table is repeating, which means that I need to aggregate the results both based on the ID as well as on the entries (only do summations of entries of the same ID).

Based on an already presented table here:

Present:

ID	Date	Entries
1	2013	100
1	2014	0
1	2015	60
1	2016	-30
1	2017	0
1	2018	50
1	2019	0
1	2020	-20
2	2013	100
2	2014	0
2	2015	60
2	2016	-30
2	2017	0
2	2018	50
2	2019	0
2	2020	-20

Desired:

ID	Date	Entries
1	2013	100
1	2014	0
1	2015	30
1	2016	0
1	2017	0
1	2018	30
1	2019	0
1	2020	0
2	2013	100
2	2014	0
2	2015	30
2	2016	0
2	2017	0
2	2018	30
2	2019	0
2	2020	0

Answer 1

You can try reverse cumsum after creating a group, then mask:

s = df['Entries'].gt(0).cumsum()
u= df['Entries'][::-1].groupby(s).cumsum().mask(df['Entries'].le(0),0)
out = df.assign(New_Entries=u) # you can assign to the original column too.

---

print(out)
    ID  Date  Entries  New_Entries
0    1  2013      100          100
1    1  2014        0            0
2    1  2015       60           30
3    1  2016      -30            0
4    1  2017        0            0
5    1  2018       50           30
6    1  2019        0            0
7    1  2020      -20            0
8    2  2013      100          100
9    2  2014        0            0
10   2  2015       60           30
11   2  2016      -30            0
12   2  2017        0            0
13   2  2018       50           30
14   2  2019        0            0
15   2  2020      -20            0