Replace values in dataframe column depending on another column with condition

Multiple steps but works. Find index of rows where y is 255 till you find the next 1. Save the values in idx. Now create new_x using the idx and the other two condition (y == 1 or y == 255). Ffill the rest.

# Index of rows between 255 and 1 in column yidx = df.loc[df['y'].replace(0, np.nan).ffill() == 255, 'y'].index# Create x_new1 and assign value of x where index is idx or y == 1 or y ==255df.loc[idx, 'x_new1'] = df['x']df.loc[(df['y'] == 1) | (df['y'] == 255) , 'x_new1'] = df['x']# ffill rest of the values in x_new1df['x_new1'] = df['x_new1'].ffill()    x       y   z   x_new   x_new10   12.28   1   1   12.28   12.281   11.99   0   1   12.28   12.282   11.50   0   1   12.28   12.283   11.20   0   1   12.28   12.284   11.01   0   1   12.28   12.285   9.74    255 0   9.74    9.746   13.80   0   0   13.80   13.807   15.20   0   0   15.20   15.208   17.80   0   0   17.80   17.809   12.10   1   1   12.10   12.1010  11.90   0   1   12.10   12.1011  11.70   0   1   12.10   12.1012  11.20   0   1   12.10   12.1013  10.30   255 0   10.30   10.30

Try:

# mark the occurrences of 1 and 255df['is_1_255'] = df.y[(df.y==1)|(df.y==255)]df['x_n'] = None# copy the 1's df.loc[df.is_1_255==1,'x_n'] = df.loc[df.is_1_255==1,'x']# fill is_1_255 with markers, #255 means between 255 and 1, 1 means between 1 and 255df['is_1_255'] = df['is_1_255'].ffill()# update the 255 valuesdf.loc[df.is_1_255==255, 'x_n'] = df.loc[df.is_1_255==255,'x']# update the 1 valuesdf['x_n'].ffill(inplace=True)

Output:

+-----+-------+-----+---+-------+----------+-------+| idx |   x   |  y  | z | x_new | is_1_255 |  x_n  |+-----+-------+-----+---+-------+----------+-------+|   0 | 12.28 |   1 | 1 | 12.28 | 1.0      | 12.28 ||   1 | 11.99 |   0 | 1 | 12.28 | 1.0      | 12.28 ||   2 | 11.50 |   0 | 1 | 12.28 | 1.0      | 12.28 ||   3 | 11.20 |   0 | 1 | 12.28 | 1.0      | 12.28 ||   4 | 11.01 |   0 | 1 | 12.28 | 1.0      | 12.28 ||   5 | 9.74  | 255 | 0 | 9.74  | 255.0    | 9.74  ||   6 | 13.80 |   0 | 0 | 13.80 | 255.0    | 13.80 ||   7 | 15.20 |   0 | 0 | 15.20 | 255.0    | 15.20 ||   8 | 17.80 |   0 | 0 | 17.80 | 255.0    | 17.80 ||   9 | 12.10 |   1 | 1 | 12.10 | 1.0      | 12.10 ||  10 | 11.90 |   0 | 1 | 12.10 | 1.0      | 12.10 ||  11 | 11.70 |   0 | 1 | 12.10 | 1.0      | 12.10 ||  12 | 11.20 |   0 | 1 | 12.10 | 1.0      | 12.10 ||  13 | 10.30 | 255 | 0 | 10.30 | 255.0    | 10.30 |+-----+-------+-----+---+-------+----------+-------+

Assuming clean data where 1 and 255 always occur in pairs, we can form groups of 1-255 and groupby to fill in the data.

s = (df.y.eq(1).cumsum() == df.y.eq(255).cumsum()+1)df['xnew'] = df.groupby(s.ne(s.shift()).cumsum().where(s)).x.transform('first').fillna(df.x)        x    y  z   xnew0   12.28    1  1  12.281   11.99    0  1  12.282   11.50    0  1  12.283   11.20    0  1  12.284   11.01    0  1  12.285    9.74  255  0   9.746   13.80    0  0  13.807   15.20    0  0  15.208   17.80    0  0  17.809   12.10    1  1  12.1010  11.90    0  1  12.1011  11.70    0  1  12.1012  11.20    0  1  12.1013  10.30  255  0  10.30

Though for something like this, you should really form a thorough unit test, because this logic can get quite tricky and problematic for incorrect inputs.