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8

For the following pandas dataframe

 servo_in_position second_servo_in_position Expected output
0 0 1 0
1 0 1 0
2 1 2 1
3 0 3 0
4 1 4 2
5 1 4 2
6 0 5 0
7 0 5 0
8 1 6 3
9 0 7 0
10 1 8 4
11 0 9 0
12 1 10 5
13 1 10 5
14 1 10 5
15 0 11 0
16 0 11 0
17 0 11 0
18 1 12 6
19 1 12 6
20 0 13 0
21 0 13 0
22 0 13 0

I want to increment the column "Expected output" only if "servo_in_position" changes from 0 to 1. I want also to assume "Expected output" to be 0 (null) if "servo_in_position" equals to 0.

I tried

input_data['second_servo_in_position']=(input_data.servo_in_position.diff()!=0).cumsum()

but it gives output as in "second_servo_in_position" column, which is not what I wanted.

After that I would like to group and calculate mean using:

print("Mean=nn",input_data.groupby('second_servo_in_position').mean())

python pandas

share|improve this question

edited Jul 29 at 14:35

Scott Boston

65.9k77 gold badges3939 silver badges6363 bronze badges

asked Jul 29 at 13:31

TomaszTomasz

8788 bronze badges

add a comment | 

8

For the following pandas dataframe

 servo_in_position second_servo_in_position Expected output
0 0 1 0
1 0 1 0
2 1 2 1
3 0 3 0
4 1 4 2
5 1 4 2
6 0 5 0
7 0 5 0
8 1 6 3
9 0 7 0
10 1 8 4
11 0 9 0
12 1 10 5
13 1 10 5
14 1 10 5
15 0 11 0
16 0 11 0
17 0 11 0
18 1 12 6
19 1 12 6
20 0 13 0
21 0 13 0
22 0 13 0

I want to increment the column "Expected output" only if "servo_in_position" changes from 0 to 1. I want also to assume "Expected output" to be 0 (null) if "servo_in_position" equals to 0.

I tried

input_data['second_servo_in_position']=(input_data.servo_in_position.diff()!=0).cumsum()

but it gives output as in "second_servo_in_position" column, which is not what I wanted.

After that I would like to group and calculate mean using:

print("Mean=nn",input_data.groupby('second_servo_in_position').mean())

python pandas

share|improve this question

edited Jul 29 at 14:35

Scott Boston

65.9k77 gold badges3939 silver badges6363 bronze badges

asked Jul 29 at 13:31

TomaszTomasz

8788 bronze badges

add a comment | 

For the following pandas dataframe

 servo_in_position second_servo_in_position Expected output
0 0 1 0
1 0 1 0
2 1 2 1
3 0 3 0
4 1 4 2
5 1 4 2
6 0 5 0
7 0 5 0
8 1 6 3
9 0 7 0
10 1 8 4
11 0 9 0
12 1 10 5
13 1 10 5
14 1 10 5
15 0 11 0
16 0 11 0
17 0 11 0
18 1 12 6
19 1 12 6
20 0 13 0
21 0 13 0
22 0 13 0

I want to increment the column "Expected output" only if "servo_in_position" changes from 0 to 1. I want also to assume "Expected output" to be 0 (null) if "servo_in_position" equals to 0.

I tried

input_data['second_servo_in_position']=(input_data.servo_in_position.diff()!=0).cumsum()

but it gives output as in "second_servo_in_position" column, which is not what I wanted.

After that I would like to group and calculate mean using:

print("Mean=nn",input_data.groupby('second_servo_in_position').mean())

python pandas

share|improve this question

edited Jul 29 at 14:35

Scott Boston

65.9k77 gold badges3939 silver badges6363 bronze badges

asked Jul 29 at 13:31

TomaszTomasz

8788 bronze badges

 servo_in_position second_servo_in_position Expected output
0 0 1 0
1 0 1 0
2 1 2 1
3 0 3 0
4 1 4 2
5 1 4 2
6 0 5 0
7 0 5 0
8 1 6 3
9 0 7 0
10 1 8 4
11 0 9 0
12 1 10 5
13 1 10 5
14 1 10 5
15 0 11 0
16 0 11 0
17 0 11 0
18 1 12 6
19 1 12 6
20 0 13 0
21 0 13 0
22 0 13 0

input_data['second_servo_in_position']=(input_data.servo_in_position.diff()!=0).cumsum()

print("Mean=nn",input_data.groupby('second_servo_in_position').mean())

share|improve this question

edited Jul 29 at 14:35

Scott Boston

65.9k77 gold badges3939 silver badges6363 bronze badges

asked Jul 29 at 13:31

TomaszTomasz

8788 bronze badges

share|improve this question

edited Jul 29 at 14:35

Scott Boston

65.9k77 gold badges3939 silver badges6363 bronze badges

asked Jul 29 at 13:31

TomaszTomasz

8788 bronze badges

edited Jul 29 at 14:35

Scott Boston

65.9k77 gold badges3939 silver badges6363 bronze badges

edited Jul 29 at 14:35

Scott Boston

65.9k77 gold badges3939 silver badges6363 bronze badges

asked Jul 29 at 13:31

TomaszTomasz

8788 bronze badges

asked Jul 29 at 13:31

TomaszTomasz

8788 bronze badges

5 Answers
5

active

oldest

votes

9

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)

share|improve this answer

edited Aug 6 at 14:47

answered Jul 29 at 13:36

Scott BostonScott Boston

65.9k77 gold badges3939 silver badges6363 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  What to do if servo_in_position in 0th row is equal to 1? With your code E_output in 0th row equals to 0, but should be 1.
  
  – Tomasz
  Aug 6 at 14:04
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @Tomasz Try that updated statement at the bottom. Let me know if that works or not. Thanks.
  
  – Scott Boston
  Aug 6 at 14:17
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @Tomasaz What I did is was to fill that first NaN caused by the diff for the first value, to fill with the first value in 'servo_in_position' hence, it will return the current state of the first value. I think this should work.
  
  – Scott Boston
  Aug 6 at 14:19
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  It worked, thank you! Here is the code: import pandas as pd import numpy as np data = [1,1,1,0,0,0,1,1,1,1,1,0,0,0,0,1,1,1,1,1,1] df = pd.DataFrame(data, columns = ['servo_in_position']) print("Groupping data according to servo positions, please wait...") df['groupped_measurement']=df['servo_in_position'].diff().fillna(df['servo_in_position']).eq(1).cumsum().mask(df['servo_in_position'] == 0, 0) print("Data groupped successfully!") print("Input data:n",df)
  
  – Tomasz
  Aug 6 at 14:29
  
  

  
  
  
  

add a comment | 

8

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

share|improve this answer

answered Jul 29 at 13:36

user3483203user3483203

37.3k88 gold badges3131 silver badges6060 bronze badges

add a comment | 

6

Try np.where:

df['Expected_output'] = np.where(df.servo_in_position.eq(1),
 df.servo_in_position.diff().eq(1).cumsum(),
 0)

share|improve this answer

answered Jul 29 at 13:33

Quang HoangQuang Hoang

15.6k22 gold badges1414 silver badges2525 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  What to do if servo_in_position in 0th row is equal to 1? With your code E_output in 0th row will equal to 0, but should be 1.
  
  – Tomasz
  Aug 6 at 14:06
  

  
  
  
  

add a comment | 

5

That is cumsum and mul

df.servo_in_position.diff().eq(1).cumsum().mul(df.servo_in_position.eq(1),axis=0)

share|improve this answer

answered Jul 29 at 13:55

WeNYoBenWeNYoBen

152k88 gold badges5353 silver badges8484 bronze badges

add a comment | 

3

Fast with Numba

from numba import njit

@njit
def f(u):
 out = np.zeros(len(u), np.int64)
 a = out[0] = u[0]
 for i in range(1, len(u)):
 if u[i] == 1:
 if u[i - 1] == 0:
 a += 1
 out[i] = a
 return out

f(df.servo_in_position.to_numpy())

array([0, 0, 1, 0, 2, 2, 0, 0, 3, 0, 4, 0, 5, 5, 5, 0, 0, 0, 6, 6, 0, 0, 0])

share|improve this answer

answered Jul 29 at 15:21

piRSquaredpiRSquared

175k2626 gold badges191191 silver badges346346 bronze badges

add a comment | 

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9

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)

share|improve this answer

edited Aug 6 at 14:47

answered Jul 29 at 13:36

Scott BostonScott Boston

65.9k77 gold badges3939 silver badges6363 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  What to do if servo_in_position in 0th row is equal to 1? With your code E_output in 0th row equals to 0, but should be 1.
  
  – Tomasz
  Aug 6 at 14:04
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @Tomasz Try that updated statement at the bottom. Let me know if that works or not. Thanks.
  
  – Scott Boston
  Aug 6 at 14:17
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @Tomasaz What I did is was to fill that first NaN caused by the diff for the first value, to fill with the first value in 'servo_in_position' hence, it will return the current state of the first value. I think this should work.
  
  – Scott Boston
  Aug 6 at 14:19
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  It worked, thank you! Here is the code: import pandas as pd import numpy as np data = [1,1,1,0,0,0,1,1,1,1,1,0,0,0,0,1,1,1,1,1,1] df = pd.DataFrame(data, columns = ['servo_in_position']) print("Groupping data according to servo positions, please wait...") df['groupped_measurement']=df['servo_in_position'].diff().fillna(df['servo_in_position']).eq(1).cumsum().mask(df['servo_in_position'] == 0, 0) print("Data groupped successfully!") print("Input data:n",df)
  
  – Tomasz
  Aug 6 at 14:29
  
  

  
  
  
  

add a comment | 

9

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)

share|improve this answer

edited Aug 6 at 14:47

answered Jul 29 at 13:36

Scott BostonScott Boston

65.9k77 gold badges3939 silver badges6363 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  What to do if servo_in_position in 0th row is equal to 1? With your code E_output in 0th row equals to 0, but should be 1.
  
  – Tomasz
  Aug 6 at 14:04
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @Tomasz Try that updated statement at the bottom. Let me know if that works or not. Thanks.
  
  – Scott Boston
  Aug 6 at 14:17
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @Tomasaz What I did is was to fill that first NaN caused by the diff for the first value, to fill with the first value in 'servo_in_position' hence, it will return the current state of the first value. I think this should work.
  
  – Scott Boston
  Aug 6 at 14:19
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  It worked, thank you! Here is the code: import pandas as pd import numpy as np data = [1,1,1,0,0,0,1,1,1,1,1,0,0,0,0,1,1,1,1,1,1] df = pd.DataFrame(data, columns = ['servo_in_position']) print("Groupping data according to servo positions, please wait...") df['groupped_measurement']=df['servo_in_position'].diff().fillna(df['servo_in_position']).eq(1).cumsum().mask(df['servo_in_position'] == 0, 0) print("Data groupped successfully!") print("Input data:n",df)
  
  – Tomasz
  Aug 6 at 14:29
  
  

  
  
  
  

add a comment | 

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)

share|improve this answer

edited Aug 6 at 14:47

answered Jul 29 at 13:36

Scott BostonScott Boston

65.9k77 gold badges3939 silver badges6363 bronze badges

df['servo_in_position'].diff().fillna(df['servo_in_position']).eq(1).cumsum()
 .mask(df['servo_in_position'] == 0, 0)

 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

df['servo_in_position'].diff().fillna(df['servo_in_position']).eq(1).cumsum()
 .mask(df['servo_in_position'] == 0, 0)

share|improve this answer

edited Aug 6 at 14:47

answered Jul 29 at 13:36

Scott BostonScott Boston

65.9k77 gold badges3939 silver badges6363 bronze badges

answered Jul 29 at 13:36

Scott BostonScott Boston

65.9k77 gold badges3939 silver badges6363 bronze badges

answered Jul 29 at 13:36

Scott BostonScott Boston

65.9k77 gold badges3939 silver badges6363 bronze badges

8

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

share|improve this answer

answered Jul 29 at 13:36

user3483203user3483203

37.3k88 gold badges3131 silver badges6060 bronze badges

add a comment | 

8

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

share|improve this answer

answered Jul 29 at 13:36

user3483203user3483203

37.3k88 gold badges3131 silver badges6060 bronze badges

add a comment | 

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

share|improve this answer

answered Jul 29 at 13:36

user3483203user3483203

37.3k88 gold badges3131 silver badges6060 bronze badges

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

share|improve this answer

answered Jul 29 at 13:36

user3483203user3483203

37.3k88 gold badges3131 silver badges6060 bronze badges

answered Jul 29 at 13:36

user3483203user3483203

37.3k88 gold badges3131 silver badges6060 bronze badges

answered Jul 29 at 13:36

user3483203user3483203

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6

Try np.where:

df['Expected_output'] = np.where(df.servo_in_position.eq(1),
 df.servo_in_position.diff().eq(1).cumsum(),
 0)

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answered Jul 29 at 13:33

Quang HoangQuang Hoang

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• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  What to do if servo_in_position in 0th row is equal to 1? With your code E_output in 0th row will equal to 0, but should be 1.
  
  – Tomasz
  Aug 6 at 14:06
  

  
  
  
  

add a comment | 

6

Try np.where:

df['Expected_output'] = np.where(df.servo_in_position.eq(1),
 df.servo_in_position.diff().eq(1).cumsum(),
 0)

share|improve this answer

answered Jul 29 at 13:33

Quang HoangQuang Hoang

15.6k22 gold badges1414 silver badges2525 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  What to do if servo_in_position in 0th row is equal to 1? With your code E_output in 0th row will equal to 0, but should be 1.
  
  – Tomasz
  Aug 6 at 14:06
  

  
  
  
  

add a comment | 

Try np.where:

df['Expected_output'] = np.where(df.servo_in_position.eq(1),
 df.servo_in_position.diff().eq(1).cumsum(),
 0)

share|improve this answer

answered Jul 29 at 13:33

Quang HoangQuang Hoang

15.6k22 gold badges1414 silver badges2525 bronze badges

df['Expected_output'] = np.where(df.servo_in_position.eq(1),
 df.servo_in_position.diff().eq(1).cumsum(),
 0)

share|improve this answer

answered Jul 29 at 13:33

Quang HoangQuang Hoang

15.6k22 gold badges1414 silver badges2525 bronze badges

answered Jul 29 at 13:33

Quang HoangQuang Hoang

15.6k22 gold badges1414 silver badges2525 bronze badges

answered Jul 29 at 13:33

Quang HoangQuang Hoang

15.6k22 gold badges1414 silver badges2525 bronze badges

5

That is cumsum and mul

df.servo_in_position.diff().eq(1).cumsum().mul(df.servo_in_position.eq(1),axis=0)

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answered Jul 29 at 13:55

WeNYoBenWeNYoBen

152k88 gold badges5353 silver badges8484 bronze badges

add a comment | 

5

That is cumsum and mul

df.servo_in_position.diff().eq(1).cumsum().mul(df.servo_in_position.eq(1),axis=0)

share|improve this answer

answered Jul 29 at 13:55

WeNYoBenWeNYoBen

152k88 gold badges5353 silver badges8484 bronze badges

add a comment | 

That is cumsum and mul

df.servo_in_position.diff().eq(1).cumsum().mul(df.servo_in_position.eq(1),axis=0)

share|improve this answer

answered Jul 29 at 13:55

WeNYoBenWeNYoBen

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df.servo_in_position.diff().eq(1).cumsum().mul(df.servo_in_position.eq(1),axis=0)

share|improve this answer

answered Jul 29 at 13:55

WeNYoBenWeNYoBen

152k88 gold badges5353 silver badges8484 bronze badges

answered Jul 29 at 13:55

WeNYoBenWeNYoBen

152k88 gold badges5353 silver badges8484 bronze badges

answered Jul 29 at 13:55

WeNYoBenWeNYoBen

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3

Fast with Numba

from numba import njit

@njit
def f(u):
 out = np.zeros(len(u), np.int64)
 a = out[0] = u[0]
 for i in range(1, len(u)):
 if u[i] == 1:
 if u[i - 1] == 0:
 a += 1
 out[i] = a
 return out

f(df.servo_in_position.to_numpy())

array([0, 0, 1, 0, 2, 2, 0, 0, 3, 0, 4, 0, 5, 5, 5, 0, 0, 0, 6, 6, 0, 0, 0])

share|improve this answer

answered Jul 29 at 15:21

piRSquaredpiRSquared

175k2626 gold badges191191 silver badges346346 bronze badges

add a comment | 

3

Fast with Numba

from numba import njit

@njit
def f(u):
 out = np.zeros(len(u), np.int64)
 a = out[0] = u[0]
 for i in range(1, len(u)):
 if u[i] == 1:
 if u[i - 1] == 0:
 a += 1
 out[i] = a
 return out

f(df.servo_in_position.to_numpy())

array([0, 0, 1, 0, 2, 2, 0, 0, 3, 0, 4, 0, 5, 5, 5, 0, 0, 0, 6, 6, 0, 0, 0])

share|improve this answer

answered Jul 29 at 15:21

piRSquaredpiRSquared

175k2626 gold badges191191 silver badges346346 bronze badges

add a comment | 

Fast with Numba

from numba import njit

@njit
def f(u):
 out = np.zeros(len(u), np.int64)
 a = out[0] = u[0]
 for i in range(1, len(u)):
 if u[i] == 1:
 if u[i - 1] == 0:
 a += 1
 out[i] = a
 return out

f(df.servo_in_position.to_numpy())

array([0, 0, 1, 0, 2, 2, 0, 0, 3, 0, 4, 0, 5, 5, 5, 0, 0, 0, 6, 6, 0, 0, 0])

share|improve this answer

answered Jul 29 at 15:21

piRSquaredpiRSquared

175k2626 gold badges191191 silver badges346346 bronze badges

from numba import njit

@njit
def f(u):
 out = np.zeros(len(u), np.int64)
 a = out[0] = u[0]
 for i in range(1, len(u)):
 if u[i] == 1:
 if u[i - 1] == 0:
 a += 1
 out[i] = a
 return out

f(df.servo_in_position.to_numpy())

array([0, 0, 1, 0, 2, 2, 0, 0, 3, 0, 4, 0, 5, 5, 5, 0, 0, 0, 6, 6, 0, 0, 0])

share|improve this answer

answered Jul 29 at 15:21

piRSquaredpiRSquared

175k2626 gold badges191191 silver badges346346 bronze badges

answered Jul 29 at 15:21

piRSquaredpiRSquared

175k2626 gold badges191191 silver badges346346 bronze badges

answered Jul 29 at 15:21

piRSquaredpiRSquared

175k2626 gold badges191191 silver badges346346 bronze badges