How to use the value of another column as a parameter of df.shift-CodePudding

I want to get the value of the ‘shift’ column as an argument to the function shift

I try to

df['gg_shift']=df['gg'].shift(df['shift'])

but it doesn't work

expected result table

gg      bool    shift   gg_shift
0.88    FALSE   0   
0.87    TRUE    0   
0.94    FALSE   1       0.87
0.17    FALSE   2       0.87
0.92    TRUE    0   
0.51    FALSE   1       0.92
0.1     TRUE    0   
0.88    FALSE   1       0.1
0.36    FALSE   2       0.1
0.14    TRUE    0

CodePudding user response：

Because only scalar is possible pass to Series.shift get unique values of shift without 0 (no shift) and assign only match condition:

for x in df.loc[df['shift'].ne(0), 'shift'].unique():
    m = df['shift'].eq(x)
    df.loc[m, 'gg_shift'] = df['gg'].shift(x)
    
print (df)
     gg   bool  shift  gg_shift
0  0.88  False      0       NaN
1  0.87   True      0       NaN
2  0.94  False      1      0.87
3  0.17  False      2      0.87
4  0.92   True      0       NaN
5  0.51  False      1      0.92
6  0.10   True      0       NaN
7  0.88  False      1      0.10
8  0.36  False      2      0.10
9  0.14   True      0       NaN

CodePudding user response：

I might be misinterpreting your logic, but it looks like you want to get the gg values of the previous True for all False.

If this is the case you do not need to use the shift column, simply ffill and mask the data:

df['gg_shift'] = df['gg'].where(df['bool']).ffill().mask(df['bool'])

output:

     gg   bool  shift  gg_shift
0  0.88  False      0       NaN
1  0.87   True      0       NaN
2  0.94  False      1      0.87
3  0.17  False      2      0.87
4  0.92   True      0       NaN
5  0.51  False      1      0.92
6  0.10   True      0       NaN
7  0.88  False      1      0.10
8  0.36  False      2      0.10
9  0.14   True      0       NaN