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I have written some code to calculate the time it takes to complete each milestone in a particular project (by subtracting the previous endtime from the start date in the following row).

jb_id mlstne_id strt_tme end_tme err_tme max
166 1511 2019-01-31 07:14:43 2019-01-31 08:36:07 NaT NaN
166 1515 2019-01-31 08:36:07 2019-01-31 08:38:26 NaT 0.0
166 1518 2019-01-31 12:16:46 NaT 2019-01-31 13:18:40 218.0
166 1520 2019-01-31 13:21:27 2019-01-31 13:30:20 NaT 2.0
166 1524 2019-01-31 13:30:21 2019-01-31 13:34:49 2019-01-31 13:34:49 0.0
167 1530 2019-01-31 13:35:49 2019-01-31 13:36:49 NaT 1.0

Max start returns the largest time delta (between subtracting the previous end time from the current row's start time and subtracting the previous err_time from the current row's start time). So looking at the above example, since 167 is a new jb_id the max time should not be one minute, it should be NaT because this is the start of a new job

testing = time.sort_values(['jb_id','mlstne_id'], ascending=[True,True])
testing['start1'] = (testing['strt_tme'] - testing['end_tme'].shift(1)).apply(lambda x: x.total_seconds()//60)
testing['start2'] = (testing['strt_tme'] - testing['err_tme'].shift(1)).apply(lambda x: x.total_seconds()//60)
testing['max'] = testing[['start1','start2']].max(axis=1)

The problem I am having is that each milestone is tied to a specific project, with a project id column denoting which project the milestone belongs to. When I apply my code to the entire dataframe the first mlstne_id for each new project returns max minutes that is a time delta of the previous jb_id's last project end time and the new jb_id's start time as opposed to returning NaN (because this is a new job).

I suspect the solution may involve using groupby and then finding the timedeltas groupby by the jb_id?

python

share|improve this question

edited Mar 26 at 7:50

Emm

asked Mar 26 at 7:34

EmmEmm

32511 silver badge1111 bronze badges

add a comment | 

0

I have written some code to calculate the time it takes to complete each milestone in a particular project (by subtracting the previous endtime from the start date in the following row).

jb_id mlstne_id strt_tme end_tme err_tme max
166 1511 2019-01-31 07:14:43 2019-01-31 08:36:07 NaT NaN
166 1515 2019-01-31 08:36:07 2019-01-31 08:38:26 NaT 0.0
166 1518 2019-01-31 12:16:46 NaT 2019-01-31 13:18:40 218.0
166 1520 2019-01-31 13:21:27 2019-01-31 13:30:20 NaT 2.0
166 1524 2019-01-31 13:30:21 2019-01-31 13:34:49 2019-01-31 13:34:49 0.0
167 1530 2019-01-31 13:35:49 2019-01-31 13:36:49 NaT 1.0

Max start returns the largest time delta (between subtracting the previous end time from the current row's start time and subtracting the previous err_time from the current row's start time). So looking at the above example, since 167 is a new jb_id the max time should not be one minute, it should be NaT because this is the start of a new job

testing = time.sort_values(['jb_id','mlstne_id'], ascending=[True,True])
testing['start1'] = (testing['strt_tme'] - testing['end_tme'].shift(1)).apply(lambda x: x.total_seconds()//60)
testing['start2'] = (testing['strt_tme'] - testing['err_tme'].shift(1)).apply(lambda x: x.total_seconds()//60)
testing['max'] = testing[['start1','start2']].max(axis=1)

The problem I am having is that each milestone is tied to a specific project, with a project id column denoting which project the milestone belongs to. When I apply my code to the entire dataframe the first mlstne_id for each new project returns max minutes that is a time delta of the previous jb_id's last project end time and the new jb_id's start time as opposed to returning NaN (because this is a new job).

I suspect the solution may involve using groupby and then finding the timedeltas groupby by the jb_id?

python

share|improve this question

edited Mar 26 at 7:50

Emm

asked Mar 26 at 7:34

EmmEmm

32511 silver badge1111 bronze badges

add a comment | 

I have written some code to calculate the time it takes to complete each milestone in a particular project (by subtracting the previous endtime from the start date in the following row).

jb_id mlstne_id strt_tme end_tme err_tme max
166 1511 2019-01-31 07:14:43 2019-01-31 08:36:07 NaT NaN
166 1515 2019-01-31 08:36:07 2019-01-31 08:38:26 NaT 0.0
166 1518 2019-01-31 12:16:46 NaT 2019-01-31 13:18:40 218.0
166 1520 2019-01-31 13:21:27 2019-01-31 13:30:20 NaT 2.0
166 1524 2019-01-31 13:30:21 2019-01-31 13:34:49 2019-01-31 13:34:49 0.0
167 1530 2019-01-31 13:35:49 2019-01-31 13:36:49 NaT 1.0

Max start returns the largest time delta (between subtracting the previous end time from the current row's start time and subtracting the previous err_time from the current row's start time). So looking at the above example, since 167 is a new jb_id the max time should not be one minute, it should be NaT because this is the start of a new job

testing = time.sort_values(['jb_id','mlstne_id'], ascending=[True,True])
testing['start1'] = (testing['strt_tme'] - testing['end_tme'].shift(1)).apply(lambda x: x.total_seconds()//60)
testing['start2'] = (testing['strt_tme'] - testing['err_tme'].shift(1)).apply(lambda x: x.total_seconds()//60)
testing['max'] = testing[['start1','start2']].max(axis=1)

The problem I am having is that each milestone is tied to a specific project, with a project id column denoting which project the milestone belongs to. When I apply my code to the entire dataframe the first mlstne_id for each new project returns max minutes that is a time delta of the previous jb_id's last project end time and the new jb_id's start time as opposed to returning NaN (because this is a new job).

I suspect the solution may involve using groupby and then finding the timedeltas groupby by the jb_id?

python

share|improve this question

edited Mar 26 at 7:50

Emm

asked Mar 26 at 7:34

EmmEmm

32511 silver badge1111 bronze badges

jb_id mlstne_id strt_tme end_tme err_tme max
166 1511 2019-01-31 07:14:43 2019-01-31 08:36:07 NaT NaN
166 1515 2019-01-31 08:36:07 2019-01-31 08:38:26 NaT 0.0
166 1518 2019-01-31 12:16:46 NaT 2019-01-31 13:18:40 218.0
166 1520 2019-01-31 13:21:27 2019-01-31 13:30:20 NaT 2.0
166 1524 2019-01-31 13:30:21 2019-01-31 13:34:49 2019-01-31 13:34:49 0.0
167 1530 2019-01-31 13:35:49 2019-01-31 13:36:49 NaT 1.0

testing = time.sort_values(['jb_id','mlstne_id'], ascending=[True,True])
testing['start1'] = (testing['strt_tme'] - testing['end_tme'].shift(1)).apply(lambda x: x.total_seconds()//60)
testing['start2'] = (testing['strt_tme'] - testing['err_tme'].shift(1)).apply(lambda x: x.total_seconds()//60)
testing['max'] = testing[['start1','start2']].max(axis=1)

share|improve this question

edited Mar 26 at 7:50

Emm

asked Mar 26 at 7:34

EmmEmm

32511 silver badge1111 bronze badges

share|improve this question

edited Mar 26 at 7:50

Emm

asked Mar 26 at 7:34

EmmEmm

32511 silver badge1111 bronze badges

asked Mar 26 at 7:34

EmmEmm

32511 silver badge1111 bronze badges

asked Mar 26 at 7:34

EmmEmm

32511 silver badge1111 bronze badges