根据 Pandas 数据框中的事件填充当前行中的下一行事件

Question

BrkPressState	VehSpdGS
1	2
1	3
1	2
1	4
0	12
0	13
0	11
1	3
0	15
0	14
0	15
1	12
1	13
0	14

For the above table i am trying to populate the next row value in previous last event, Like the below table

I tried with Shift - 1 but its populating only for the current row , Sample code which i tried.

d['result']=d.loc[d['BrkPressState'] != d['BrkPressState'].shift(-1), 'VehSpdGS'] 

Expected output:

Answer 1

You can use two masks for finding vals and idxs and set values to index for result column.

mask1 = df['BrkPressState'] != df['BrkPressState'].shift()
vals = df.loc[mask1, 'VehSpdGS'][1:].values

mask2 = df['BrkPressState'] != df['BrkPressState'].shift(-1)
idxs = df.loc[mask2, 'VehSpdGS'][:-1].index

df.loc[idxs, 'result'] = vals
print(df)

---

    BrkPressState  VehSpdGS  result
0               1         2     NaN
1               1         3     NaN
2               1         2     NaN
3               1         4    12.0
4               0        12     NaN
5               0        13     NaN
6               0        11     3.0
7               1         3    15.0
8               0        15     NaN
9               0        14     NaN
10              0        15    12.0
11              1        12     NaN
12              1        13    14.0
13              0        14     NaN

Answer 2

Let us do diff to compare the previous and current row in BrkPressState column in order to identify boundaries, then mask and shift the values in VehSpdGS column

m = df['BrkPressState'].diff().ne(0)
df['Results'] = df['VehSpdGS'].mask(~m).shift(-1)

---

    BrkPressState  VehSpdGS  Results
0               1         2      NaN
1               1         3      NaN
2               1         2      NaN
3               1         4     12.0
4               0        12      NaN
5               0        13      NaN
6               0        11      3.0
7               1         3     15.0
8               0        15      NaN
9               0        14      NaN
10              0        15     12.0
11              1        12      NaN
12              1        13     14.0
13              0        14      NaN

Answer 3

You can also do shift(-1) on VehSpdGS and then replace values with NaN if df['BrkPressState'] != df['BrkPressState'].shift(-1)

Code:

df["result"]=df["VehSpdGS"].shift(-1).where(df['BrkPressState'] != df['BrkPressState'].shift(-1),pd.NA)
df

output:

	BrkPressState	VehSpdGS	result
0	1	2	NaN
1	1	3	NaN
2	1	2	NaN
3	1	4	12.0
4	0	12	NaN
5	0	13	NaN
6	0	11	3.0
7	1	3	15.0
8	0	15	NaN
9	0	14	NaN
10	0	15	12.0
11	1	12	NaN
12	1	13	14.0
13	0	14	NaN