Pandas: flag consecutive values

Similar idea using shift, but writing the result as a single Boolean column:

# Boolean indexers for recession start and stops.
rec_start = (df['signal'] == 1) & (df['signal'].shift(-1) == 1)
rec_end = (df['signal'] == 0) & (df['signal'].shift(-1) == 0)

# Mark the recession start/stops as True/False.
df.loc[rec_start, 'recession'] = True
df.loc[rec_end, 'recession'] = False

# Forward fill the recession column with the last known Boolean.
# Fill any NaN's as False (i.e. locations before the first start/stop).
df['recession'] = df['recession'].ffill().fillna(False)

The resulting output:

    signal recession
0        0     False
1        1     False
2        0     False
3        1      True
4        1      True
5        1      True
6        0     False
7        0     False
8        1      True
9        1      True
10       0      True
11       1      True
12       0     False
13       0     False
14       1     False

The start of a run of 1's satisfies the condition

x_prev = x.shift(1)
x_next = x.shift(-1)
((x_prev != 1) & (x == 1) & (x_next == 1))

That is to say, the value at the start of a run is 1 and the previous value is not 1 and the next value is 1. Similarly, the end of a run satisfies the condition

((x == 1) & (x_next == 0) & (x_next2 == 0))

since the value at the end of a run is 1 and the next two values value are 0. We can find indices where these conditions are true using np.flatnonzero:

import numpy as np
import pandas as pd

x = pd.Series([0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0 , 0 , 1])
x_prev = x.shift(1)
x_next = x.shift(-1)
x_next2 = x.shift(-2)
df = pd.DataFrame(
    dict(start = np.flatnonzero((x_prev != 1) & (x == 1) & (x_next == 1)),
         end = np.flatnonzero((x == 1) & (x_next == 0) & (x_next2 == 0))))
print(df[['start', 'end']])

yields

   start  end
0      3    5
1      8   11

You can use shift:

df = pd.DataFrame([0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0 , 0 , 1], columns=['signal'])
df_prev = df.shift(1)['signal']
df_next = df.shift(-1)['signal']
df_next2 = df.shift(-2)['signal']
df.loc[(df_prev != 1) & (df['signal'] == 1) & (df_next == 1), 'start'] = 1
df.loc[(df['signal'] != 0) & (df_next == 0) & (df_next2 == 0), 'end'] = 1
df.fillna(0, inplace=True)
df = df.astype(int)

    signal  start  end
0        0      0    0
1        1      0    0
2        0      0    0
3        1      1    0
4        1      0    0
5        1      0    1
6        0      0    0
7        0      0    0
8        1      1    0
9        1      0    0
10       0      0    0
11       1      0    1
12       0      0    0
13       0      0    0
14       1      0    0