python pandas conditional cumulative sum

Question

Consider my dataframe df

data  data_binary  sum_data
  2       1            1
  5       0            0
  1       1            1
  4       1              


        

Answer 1

I think you can groupby with DataFrameGroupBy.cumsum by Series, where first compare next value by shifted column if not equal (!=) and then create groups by cumsum. Last replace 0 by column data_binary with mask:

print (df.data_binary.ne(df.data_binary.shift()).cumsum())
0    1
1    2
2    3
3    3
4    3
5    4
6    4
7    5
Name: data_binary, dtype: int32

df['sum_data1'] = df.data_binary.groupby(df.data_binary.ne(df.data_binary.shift()).cumsum())
                                .cumsum()
df['sum_data1'] = df['sum_data1'].mask(df.data_binary == 0, 0)
print (df)
   data  data_binary  sum_data  sum_data1
0     2            1         1          1
1     5            0         0          0
2     1            1         1          1
3     4            1         2          2
4     3            1         3          3
5    10            0         0          0
6     7            0         0          0
7     3            1         1          1