Incrementing add under condition in pandas
Using cumsum and arithmetic.
u = df['servo_in_position']
(u.eq(1) & u.shift().ne(1)).cumsum() * u
0 0
1 0
2 1
3 0
4 2
5 2
6 0
7 0
8 3
9 0
10 4
11 0
12 5
13 5
14 5
15 0
16 0
17 0
18 6
19 6
20 0
21 0
22 0
Name: servo_in_position, dtype: int64
Try np.where:
df['Expected_output'] = np.where(df.servo_in_position.eq(1),
df.servo_in_position.diff().eq(1).cumsum(),
0)
That is cumsum and mul
df.servo_in_position.diff().eq(1).cumsum().mul(df.servo_in_position.eq(1),axis=0)
Use cumsum and mask:
df['E_output'] = df['servo_in_position'].diff().eq(1).cumsum()\
.mask(df['servo_in_position'] == 0, 0)
df['servo_in_position'].diff().fillna(df['servo_in_position']).eq(1).cumsum()\
.mask(df['servo_in_position'] == 0, 0)
Output:
servo_in_position second_servo_in_position Expected output E_output
0 0 1 0 0
1 0 1 0 0
2 1 2 1 1
3 0 3 0 0
4 1 4 2 2
5 1 4 2 2
6 0 5 0 0
7 0 5 0 0
8 1 6 3 3
9 0 7 0 0
10 1 8 4 4
11 0 9 0 0
12 1 10 5 5
13 1 10 5 5
14 1 10 5 5
15 0 11 0 0
16 0 11 0 0
17 0 11 0 0
18 1 12 6 6
19 1 12 6 6
20 0 13 0 0
21 0 13 0 0
22 0 13 0 0
Update for first position equal to 1.
df['servo_in_position'].diff().fillna(df['servo_in_position']).eq(1).cumsum()\
.mask(df['servo_in_position'] == 0, 0)