Calculate difference between current time and next failure in pandas











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I have a dataset where one of the columns contains if there was a failure or not represented by 0 and 1. I need to create a new columns which contains time to next failure in pandas.



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  • So, what have you tried?
    – Andreas
    Nov 22 at 8:33















up vote
1
down vote

favorite












I have a dataset where one of the columns contains if there was a failure or not represented by 0 and 1. I need to create a new columns which contains time to next failure in pandas.



Like this






python pandas







share|improve this question






















  • So, what have you tried?
    – Andreas
    Nov 22 at 8:33













up vote
1
down vote

favorite









up vote
1
down vote

favorite











I have a dataset where one of the columns contains if there was a failure or not represented by 0 and 1. I need to create a new columns which contains time to next failure in pandas.



Like this






python pandas







share|improve this question













I have a dataset where one of the columns contains if there was a failure or not represented by 0 and 1. I need to create a new columns which contains time to next failure in pandas.



Like this






python pandas




python pandas






share|improve this question













share|improve this question











share|improve this question




share|improve this question










asked Nov 22 at 8:32









Hariom Singh

337




337












  • So, what have you tried?
    – Andreas
    Nov 22 at 8:33


















  • So, what have you tried?
    – Andreas
    Nov 22 at 8:33
















So, what have you tried?
– Andreas
Nov 22 at 8:33




So, what have you tried?
– Andreas
Nov 22 at 8:33












1 Answer
1






active

oldest

votes

















up vote
1
down vote



accepted










Use cumsum with swapped values by indexing with [::-1] for groups passed to function cumcount:



df = pd.DataFrame({'failure': [0,0,0,1,0,0,1]})



s = df['failure'].iloc[::-1].cumsum()

df['time to failure'] = s.groupby(s).cumcount()

print (df)

   failure  time to failure

0        0                3

1        0                2

2        0                1

3        1                0

4        0                2

5        0                1

6        1                0





share|improve this answer



















  • 1




    Took me quite a while to understand this, Thankyou.This answers my problem.
    – Hariom Singh
    Nov 22 at 8:53










  • @HariomSingh - You are welcome!
    – jezrael
    Nov 22 at 8:53






  • 1




    @HariomSingh - Btw, small friend advice - dont use pictures with sample data or code in question, link
    – jezrael
    Nov 22 at 8:54






  • 1




    Sure, i will keep that in mind now. Thanks
    – Hariom Singh
    Nov 22 at 9:27











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1 Answer
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active

oldest

votes








1 Answer
1






active

oldest

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active

oldest

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active

oldest

votes








up vote
1
down vote



accepted










Use cumsum with swapped values by indexing with [::-1] for groups passed to function cumcount:



df = pd.DataFrame({'failure': [0,0,0,1,0,0,1]})



s = df['failure'].iloc[::-1].cumsum()

df['time to failure'] = s.groupby(s).cumcount()

print (df)

   failure  time to failure

0        0                3

1        0                2

2        0                1

3        1                0

4        0                2

5        0                1

6        1                0





share|improve this answer



















  • 1




    Took me quite a while to understand this, Thankyou.This answers my problem.
    – Hariom Singh
    Nov 22 at 8:53










  • @HariomSingh - You are welcome!
    – jezrael
    Nov 22 at 8:53






  • 1




    @HariomSingh - Btw, small friend advice - dont use pictures with sample data or code in question, link
    – jezrael
    Nov 22 at 8:54






  • 1




    Sure, i will keep that in mind now. Thanks
    – Hariom Singh
    Nov 22 at 9:27















up vote
1
down vote



accepted










Use cumsum with swapped values by indexing with [::-1] for groups passed to function cumcount:



df = pd.DataFrame({'failure': [0,0,0,1,0,0,1]})



s = df['failure'].iloc[::-1].cumsum()

df['time to failure'] = s.groupby(s).cumcount()

print (df)

   failure  time to failure

0        0                3

1        0                2

2        0                1

3        1                0

4        0                2

5        0                1

6        1                0





share|improve this answer



















  • 1




    Took me quite a while to understand this, Thankyou.This answers my problem.
    – Hariom Singh
    Nov 22 at 8:53










  • @HariomSingh - You are welcome!
    – jezrael
    Nov 22 at 8:53






  • 1




    @HariomSingh - Btw, small friend advice - dont use pictures with sample data or code in question, link
    – jezrael
    Nov 22 at 8:54






  • 1




    Sure, i will keep that in mind now. Thanks
    – Hariom Singh
    Nov 22 at 9:27













up vote
1
down vote



accepted







up vote
1
down vote



accepted






Use cumsum with swapped values by indexing with [::-1] for groups passed to function cumcount:



df = pd.DataFrame({'failure': [0,0,0,1,0,0,1]})



s = df['failure'].iloc[::-1].cumsum()

df['time to failure'] = s.groupby(s).cumcount()

print (df)

   failure  time to failure

0        0                3

1        0                2

2        0                1

3        1                0

4        0                2

5        0                1

6        1                0





share|improve this answer














Use cumsum with swapped values by indexing with [::-1] for groups passed to function cumcount:



df = pd.DataFrame({'failure': [0,0,0,1,0,0,1]})



s = df['failure'].iloc[::-1].cumsum()

df['time to failure'] = s.groupby(s).cumcount()

print (df)

   failure  time to failure

0        0                3

1        0                2

2        0                1

3        1                0

4        0                2

5        0                1

6        1                0






share|improve this answer














share|improve this answer



share|improve this answer








edited Nov 22 at 8:52

























answered Nov 22 at 8:37









jezrael

315k21253331




315k21253331








  • 1




    Took me quite a while to understand this, Thankyou.This answers my problem.
    – Hariom Singh
    Nov 22 at 8:53










  • @HariomSingh - You are welcome!
    – jezrael
    Nov 22 at 8:53






  • 1




    @HariomSingh - Btw, small friend advice - dont use pictures with sample data or code in question, link
    – jezrael
    Nov 22 at 8:54






  • 1




    Sure, i will keep that in mind now. Thanks
    – Hariom Singh
    Nov 22 at 9:27














  • 1




    Took me quite a while to understand this, Thankyou.This answers my problem.
    – Hariom Singh
    Nov 22 at 8:53










  • @HariomSingh - You are welcome!
    – jezrael
    Nov 22 at 8:53






  • 1




    @HariomSingh - Btw, small friend advice - dont use pictures with sample data or code in question, link
    – jezrael
    Nov 22 at 8:54






  • 1




    Sure, i will keep that in mind now. Thanks
    – Hariom Singh
    Nov 22 at 9:27








1




1




Took me quite a while to understand this, Thankyou.This answers my problem.
– Hariom Singh
Nov 22 at 8:53




Took me quite a while to understand this, Thankyou.This answers my problem.
– Hariom Singh
Nov 22 at 8:53












@HariomSingh - You are welcome!
– jezrael
Nov 22 at 8:53




@HariomSingh - You are welcome!
– jezrael
Nov 22 at 8:53




1




1




@HariomSingh - Btw, small friend advice - dont use pictures with sample data or code in question, link
– jezrael
Nov 22 at 8:54




@HariomSingh - Btw, small friend advice - dont use pictures with sample data or code in question, link
– jezrael
Nov 22 at 8:54




1




1




Sure, i will keep that in mind now. Thanks
– Hariom Singh
Nov 22 at 9:27




Sure, i will keep that in mind now. Thanks
– Hariom Singh
Nov 22 at 9:27


















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