按变量索引将一列数据框分组

Question

I have a dataframe which consists of PartialRoutes (which result together in full routes) and a treatment variable and I am trying to reduce the dataframe to the full routes by grouping these together and keeping the treatment variable.

To make this more clear, the df looks like

PartialRoute  Treatment
0             1
1             0
0             0
0             0
1             0
2             0
3             0
0             0
1             1
2             0

where every 0 in 'Partial Route' starts a new group, which means I always want to group all values until a new route starts/ a new 0 in index. So in this example there exists 4 groups

PartialRoute  Treatment
0             1
1             0
-----------------
0             0
-----------------
0             0
1             0
2             0
3             0
-----------------
0             0
1             1
2             0
-----------------

and the result should look like

Route Treatment
0     1
1     0
2     0
3     1

Is there any solution to solve this elegant?

Answer 1

Create groups by comparing by Series.eq with cumulative sum by Series.cumsum and then aggregate per groups, eg by sum or max :

df1 = df.groupby(df['PartialRoute'].eq(0).cumsum())['Treatment'].sum().reset_index()
print (df1)
   PartialRoute  Treatment
0             1          1
1             2          0
2             3          0
3             4          1

Detail :

print (df['PartialRoute'].eq(0).cumsum())
0    1
1    1
2    2
3    3
4    3
5    3
6    3
7    4
8    4
9    4
Name: PartialRoute, dtype: int32

If first value of DataFrame is not 0 get different groups - starting by 0 :

print (df)
   PartialRoute  Treatment
0             1          1
1             1          0
2             0          0
3             0          0
4             1          0
5             2          0
6             3          0
7             0          0
8             1          1
9             2          0

print (df['PartialRoute'].eq(0).cumsum())
0    0
1    0
2    1
3    2
4    2
5    2
6    2
7    3
8    3
9    3
Name: PartialRoute, dtype: int32

df1 = df.groupby(df['PartialRoute'].eq(0).cumsum())['Treatment'].sum().reset_index()
print (df1)
   PartialRoute  Treatment
0             0          1
1             1          0
2             2          0
3             3          1