Conditional ffill based on another column

Question

I'm trying to conditionally ffill a value until a second column encounters a value and then reset the first column value. Effectively the first column is an 'on' switch until the 'off' switch (second column) encounters a value. I've yet to have a working example using ffill and where.

Example input:

Index   Start   End
0       0       0
1       0       0
2       1       0
3       0       0
4       0       0
5       0       0
6       0       1
7       0       0
8       1       0
9       0       0
10      0       0
11      0       0
12      0       1
13      0       1
14      0       0

Desired output:

Index   Start   End
0       0       0
1       0       0
2       1       0
3       1       0
4       1       0
5       1       0
6       1       1
7       0       0
8       1       0
9       1       0
10      1       0
11      1       0
12      1       1
13      0       1
14      0       0

EDIT:

There are issues when dealing with values set based on another column. The logic is as follows: Start should be zero until R column is below 25, then positive until R column is above 80 and the cycle should repeat. Yet on row 13 Start is inexplicably set 1 despite not matching criteria.

df = pd.DataFrame(np.random.randint(0, 100, size=100), columns=['R'])
df['Start'] = np.where((df.R < 25), 1, 0)
df['End'] = np.where((df.R > 80), 1, 0)
df.loc[df['End'].shift().eq(0), 'Start'] = df['Start'].replace(0, np.nan).ffill().fillna(0).astype(int)

        R   Start  End
0       58  0       0
1       98  0       1
2       91  0       1
3       69  0       0
4       55  0       0
5       57  0       0
6       64  0       0
7       75  0       1
8       78  0       1
9       90  0       1
10      24  1       0
11      89  1       1
12      36  0       0
13      70  **1**   0

Answer 1

Try:

df.loc[df['End'].shift().eq(0), 'Start'] = df['Start'].replace(0, np.nan).ffill().fillna(0).astype(int)

[out]

    Start  End
0       0    0
1       0    0
2       1    0
3       1    0
4       1    0
5       1    0
6       1    1
7       0    0
8       1    0
9       1    0
10      1    0
11      1    0
12      1    1
13      0    1
14      0    0