Update column based on other column condition

I need to update vid or maybe create a new column based on the change column

df = [{'vid': 14, 'change': 0}, {'vid': 15, 'change': 1}, {'vid': 16, 'change': 0}, {'vid': 16, 'change': 0}, {'vid': 17, 'change': 0}, {'vid': 17, 'change': 1}, {'vid': 18, 'change': 0}, {'vid': 18, 'change': 0}]

    vid  change
0    14       0
1    15       1
2    16       0
3    16       0
4    17       0
5    17       1
6    18       0
7    18       0

If change == 1 then the next set of vid should be changed to the current and if change == 0 then the next vid should stay the same.

From my example above vid 16 needs to be changed to 15 since 15 has change = 1 however 17 stays since 16 doesn’t have change = 1

Change = 1 will only occur when vid is going to change in the next row

Expected output

    vid  change
0    14       0
1    15       1
2    15       0
3    15       0
4    17       0
5    17       1
6    17       0
7    17       0

Answer

If you don’t mind renumbering all the replaced numbers to be sequential, you can get a pretty clean version of it by subtracting the shifted cumsum of change:

In [59]: df['new_vid'] = df['vid'] - df['change'].shift(fill_value=0).cumsum()

In [60]: df
Out[60]:
   vid  change  new_vid
0   14       0       14
1   15       1       15
2   16       0       15
3   16       0       15
4   17       0       16
5   17       1       16
6   18       0       16
7   18       0       16

Another option with the original numbering is to use map and groupby:

In [81]: df['vid'] = df['vid'] - df['vid'].map(df.groupby("vid")['change'].max().shift(fill_value=0))

In [82]: df
Out[82]:
   vid  change
0   14       0
1   15       1
2   15       0
3   15       0
4   17       0
5   17       1
6   17       0
7   17       0