Replace values in dataframe column depending on another column with condition

Question

I need to replace values in the dataframe column x. The result should look like x_new. So in detail I have to keep the values in the x column where y is 1 and 255. Between 1 and 255 I must replace the x values with the value where y is 1. The values between 255 and 1 should stay the same. So how can I get the column x_new?

I guess it could work with replace and some condition but I do not know how to combine it. I look forward for any help and hints.

My dataframe looks like e.g.:

x        y    z    x_new
12.28   1    1     12.28
11.99   0    1     12.28
11.50   0    1     12.28
11.20   0    1     12.28
11.01   0    1     12.28
 9.74  255   0      9.74
13.80   0    0     13.80
15.2    0    0     15.2
17.8    0    0     17.8
12.1    1    1     12.1
11.9    0    1     12.1
11.7    0    1     12.1
11.2    0    1     12.1
10.3   255   0     10.3

Answer 1

多个步骤但有效。查找 y 为 255 的行的索引，直到找到下一个 1。将值保存在 idx 中。现在使用 idx 和其他两个条件（y == 1 或 y == 255）创建 new_x。把剩下的填满。

# Index of rows between 255 and 1 in column y
idx = 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 ==255
df.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_new1
df['x_new1'] = df['x_new1'].ffill()


    x       y   z   x_new   x_new1
0   12.28   1   1   12.28   12.28
1   11.99   0   1   12.28   12.28
2   11.50   0   1   12.28   12.28
3   11.20   0   1   12.28   12.28
4   11.01   0   1   12.28   12.28
5   9.74    255 0   9.74    9.74
6   13.80   0   0   13.80   13.80
7   15.20   0   0   15.20   15.20
8   17.80   0   0   17.80   17.80
9   12.10   1   1   12.10   12.10
10  11.90   0   1   12.10   12.10
11  11.70   0   1   12.10   12.10
12  11.20   0   1   12.10   12.10
13  10.30   255 0   10.30   10.30