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:

CodePudding user response：

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

CodePudding user response：

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

CodePudding user response：

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: