How to create a time threshold based column given a time gap?

up vote
1
down vote

favorite

I have a pandas dataframe with several columns, however for visual purposes consider the columns Id and timestamp. As you can see the pandas dataframe is sorted by Id column.

Id                timestamp



11           2018-10-19 13:00:00

11           2018-10-19 13:05:00

11           2018-10-19 13:06:00

11           2018-10-19 13:07:00

11           2018-10-19 13:30:00

11           2018-10-19 13:31:00

11           2018-10-19 13:32:00

11           2018-10-19 13:55:00

11           2018-10-19 13:54:00

11           2018-10-21 20:47:09

11           2018-10-21 20:48:27

11           2018-10-21 20:48:45

11           2018-10-21 20:48:52

12           2018-10-09 20:30:46

12           2018-10-09 20:30:22

12           2018-10-09 20:30:05

12           2018-10-09 20:29:44

12           2018-10-09 20:29:31

13           2018-10-19 18:49:08

13           2018-10-19 18:49:13

13           2018-10-11 18:46:15

14           2018-10-11 10:46:40

14           2018-10-23 10:39:52

How can create create another ID column based on 10 minutes time gaps? That is for every timestamp 10 minutes threshold create a new different `ID_2:

Id                timestamp            ID_2



11           2018-10-19 13:00:00        01   

11           2018-10-19 13:05:00        01

11           2018-10-19 13:06:00        01

11           2018-10-19 13:07:00        01

11           2018-10-19 13:30:00        02

11           2018-10-19 13:31:00        02

11           2018-10-19 13:32:00        02

11           2018-10-19 13:55:00        03

11           2018-10-19 13:54:00        03

11           2018-10-21 20:47:09        04

11           2018-10-21 20:48:27        04

11           2018-10-21 20:48:45        04

11           2018-10-21 20:48:52        04

12           2018-10-09 20:30:46        04

12           2018-10-09 20:30:22        04

12           2018-10-09 20:30:05        04

12           2018-10-09 20:29:44        05

12           2018-10-09 20:29:31        05

13           2018-10-19 18:49:08        06

13           2018-10-19 18:49:13        06

13           2018-10-11 18:46:15        07

14           2018-10-11 10:46:40        07

I tried to detect the time gaps as follows:

df['col_new'] = (df['timestamp'].diff()).dt.seconds > 600

However, I do not understand how to apply a backward fill in order to create the IDs. Therefore, how can I detect time gaps and assign them a new id?

asked Nov 7 at 9:17

anon

1457

add a comment |

up vote
1
down vote

favorite

I have a pandas dataframe with several columns, however for visual purposes consider the columns Id and timestamp. As you can see the pandas dataframe is sorted by Id column.

Id                timestamp



11           2018-10-19 13:00:00

11           2018-10-19 13:05:00

11           2018-10-19 13:06:00

11           2018-10-19 13:07:00

11           2018-10-19 13:30:00

11           2018-10-19 13:31:00

11           2018-10-19 13:32:00

11           2018-10-19 13:55:00

11           2018-10-19 13:54:00

11           2018-10-21 20:47:09

11           2018-10-21 20:48:27

11           2018-10-21 20:48:45

11           2018-10-21 20:48:52

12           2018-10-09 20:30:46

12           2018-10-09 20:30:22

12           2018-10-09 20:30:05

12           2018-10-09 20:29:44

12           2018-10-09 20:29:31

13           2018-10-19 18:49:08

13           2018-10-19 18:49:13

13           2018-10-11 18:46:15

14           2018-10-11 10:46:40

14           2018-10-23 10:39:52

How can create create another ID column based on 10 minutes time gaps? That is for every timestamp 10 minutes threshold create a new different `ID_2:

Id                timestamp            ID_2



11           2018-10-19 13:00:00        01   

11           2018-10-19 13:05:00        01

11           2018-10-19 13:06:00        01

11           2018-10-19 13:07:00        01

11           2018-10-19 13:30:00        02

11           2018-10-19 13:31:00        02

11           2018-10-19 13:32:00        02

11           2018-10-19 13:55:00        03

11           2018-10-19 13:54:00        03

11           2018-10-21 20:47:09        04

11           2018-10-21 20:48:27        04

11           2018-10-21 20:48:45        04

11           2018-10-21 20:48:52        04

12           2018-10-09 20:30:46        04

12           2018-10-09 20:30:22        04

12           2018-10-09 20:30:05        04

12           2018-10-09 20:29:44        05

12           2018-10-09 20:29:31        05

13           2018-10-19 18:49:08        06

13           2018-10-19 18:49:13        06

13           2018-10-11 18:46:15        07

14           2018-10-11 10:46:40        07

I tried to detect the time gaps as follows:

df['col_new'] = (df['timestamp'].diff()).dt.seconds > 600

However, I do not understand how to apply a backward fill in order to create the IDs. Therefore, how can I detect time gaps and assign them a new id?

asked Nov 7 at 9:17

anon

1457

add a comment |

up vote
1
down vote

favorite

I have a pandas dataframe with several columns, however for visual purposes consider the columns Id and timestamp. As you can see the pandas dataframe is sorted by Id column.

Id                timestamp



11           2018-10-19 13:00:00

11           2018-10-19 13:05:00

11           2018-10-19 13:06:00

11           2018-10-19 13:07:00

11           2018-10-19 13:30:00

11           2018-10-19 13:31:00

11           2018-10-19 13:32:00

11           2018-10-19 13:55:00

11           2018-10-19 13:54:00

11           2018-10-21 20:47:09

11           2018-10-21 20:48:27

11           2018-10-21 20:48:45

11           2018-10-21 20:48:52

12           2018-10-09 20:30:46

12           2018-10-09 20:30:22

12           2018-10-09 20:30:05

12           2018-10-09 20:29:44

12           2018-10-09 20:29:31

13           2018-10-19 18:49:08

13           2018-10-19 18:49:13

13           2018-10-11 18:46:15

14           2018-10-11 10:46:40

14           2018-10-23 10:39:52

How can create create another ID column based on 10 minutes time gaps? That is for every timestamp 10 minutes threshold create a new different `ID_2:

Id                timestamp            ID_2



11           2018-10-19 13:00:00        01   

11           2018-10-19 13:05:00        01

11           2018-10-19 13:06:00        01

11           2018-10-19 13:07:00        01

11           2018-10-19 13:30:00        02

11           2018-10-19 13:31:00        02

11           2018-10-19 13:32:00        02

11           2018-10-19 13:55:00        03

11           2018-10-19 13:54:00        03

11           2018-10-21 20:47:09        04

11           2018-10-21 20:48:27        04

11           2018-10-21 20:48:45        04

11           2018-10-21 20:48:52        04

12           2018-10-09 20:30:46        04

12           2018-10-09 20:30:22        04

12           2018-10-09 20:30:05        04

12           2018-10-09 20:29:44        05

12           2018-10-09 20:29:31        05

13           2018-10-19 18:49:08        06

13           2018-10-19 18:49:13        06

13           2018-10-11 18:46:15        07

14           2018-10-11 10:46:40        07

I tried to detect the time gaps as follows:

df['col_new'] = (df['timestamp'].diff()).dt.seconds > 600

However, I do not understand how to apply a backward fill in order to create the IDs. Therefore, how can I detect time gaps and assign them a new id?

asked Nov 7 at 9:17

anon

1457

I have a pandas dataframe with several columns, however for visual purposes consider the columns Id and timestamp. As you can see the pandas dataframe is sorted by Id column.

Id                timestamp



11           2018-10-19 13:00:00

11           2018-10-19 13:05:00

11           2018-10-19 13:06:00

11           2018-10-19 13:07:00

11           2018-10-19 13:30:00

11           2018-10-19 13:31:00

11           2018-10-19 13:32:00

11           2018-10-19 13:55:00

11           2018-10-19 13:54:00

11           2018-10-21 20:47:09

11           2018-10-21 20:48:27

11           2018-10-21 20:48:45

11           2018-10-21 20:48:52

12           2018-10-09 20:30:46

12           2018-10-09 20:30:22

12           2018-10-09 20:30:05

12           2018-10-09 20:29:44

12           2018-10-09 20:29:31

13           2018-10-19 18:49:08

13           2018-10-19 18:49:13

13           2018-10-11 18:46:15

14           2018-10-11 10:46:40

14           2018-10-23 10:39:52

How can create create another ID column based on 10 minutes time gaps? That is for every timestamp 10 minutes threshold create a new different `ID_2:

Id                timestamp            ID_2



11           2018-10-19 13:00:00        01   

11           2018-10-19 13:05:00        01

11           2018-10-19 13:06:00        01

11           2018-10-19 13:07:00        01

11           2018-10-19 13:30:00        02

11           2018-10-19 13:31:00        02

11           2018-10-19 13:32:00        02

11           2018-10-19 13:55:00        03

11           2018-10-19 13:54:00        03

11           2018-10-21 20:47:09        04

11           2018-10-21 20:48:27        04

11           2018-10-21 20:48:45        04

11           2018-10-21 20:48:52        04

12           2018-10-09 20:30:46        04

12           2018-10-09 20:30:22        04

12           2018-10-09 20:30:05        04

12           2018-10-09 20:29:44        05

12           2018-10-09 20:29:31        05

13           2018-10-19 18:49:08        06

13           2018-10-19 18:49:13        06

13           2018-10-11 18:46:15        07

14           2018-10-11 10:46:40        07

I tried to detect the time gaps as follows:

df['col_new'] = (df['timestamp'].diff()).dt.seconds > 600

However, I do not understand how to apply a backward fill in order to create the IDs. Therefore, how can I detect time gaps and assign them a new id?

python python-3.x pandas datetime

asked Nov 7 at 9:17

anon

1457

asked Nov 7 at 9:17

anon

1457

asked Nov 7 at 9:17

anon

1457

asked Nov 7 at 9:17

anon

1457

asked Nov 7 at 9:17

anon

1457

add a comment |

1 Answer
1

active

oldest

votes

up vote
3
down vote

accepted

I believe you need floor with factorize, last add zfill:

df['timestamp'] = pd.to_datetime(df['timestamp'])



a = pd.factorize(df['timestamp'].dt.floor('10Min'))[0] + 1

df['col_new'] = pd.Series(a, index=df.index).astype(str).str.zfill(2)



print (df)

    Id           timestamp ID_2 col_new

0   11 2018-10-19 13:00:00   01      01

1   11 2018-10-19 13:05:00   01      01

2   11 2018-10-19 13:06:00   01      01

3   11 2018-10-19 13:07:00   01      01

4   11 2018-10-19 13:30:00   02      02

5   11 2018-10-19 13:31:00   02      02

6   11 2018-10-19 13:32:00   02      02

7   11 2018-10-19 13:55:00   03      03

8   11 2018-10-19 13:54:00   03      03

9   11 2018-10-21 20:47:09   04      04

10  11 2018-10-21 20:48:27   04      04

11  11 2018-10-21 20:48:45   04      04

12  11 2018-10-21 20:48:52   04      04

13  12 2018-10-09 20:30:46   04      05

14  12 2018-10-09 20:30:22   04      05

15  12 2018-10-09 20:30:05   04      05

16  12 2018-10-09 20:29:44   05      06

17  12 2018-10-09 20:29:31   05      06

18  13 2018-10-19 18:49:08   06      07

19  13 2018-10-19 18:49:13   06      07

20  13 2018-10-11 18:46:15   07      08

21  14 2018-10-11 18:46:40   07      08

Detail:

print (df['timestamp'].dt.floor('10Min'))

0    2018-10-19 13:00:00

1    2018-10-19 13:00:00

2    2018-10-19 13:00:00

3    2018-10-19 13:00:00

4    2018-10-19 13:30:00

5    2018-10-19 13:30:00

6    2018-10-19 13:30:00

7    2018-10-19 13:50:00

8    2018-10-19 13:50:00

9    2018-10-21 20:40:00

10   2018-10-21 20:40:00

11   2018-10-21 20:40:00

12   2018-10-21 20:40:00

13   2018-10-09 20:30:00

14   2018-10-09 20:30:00

15   2018-10-09 20:30:00

16   2018-10-09 20:20:00

17   2018-10-09 20:20:00

18   2018-10-19 18:40:00

19   2018-10-19 18:40:00

20   2018-10-11 18:40:00

21   2018-10-11 18:40:00

Name: timestamp, dtype: datetime64[ns]

edited Nov 7 at 9:49

answered Nov 7 at 9:36

jezrael

306k20240316

2

wow, this is superbly elegant
– Pankaj Joshi
Nov 7 at 9:48

1

@PankajJoshi - Thank you.
– jezrael
Nov 7 at 9:48

add a comment |

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1 Answer
1

active

oldest

votes

1 Answer
1

active

oldest

votes

up vote
3
down vote

accepted

I believe you need floor with factorize, last add zfill:

df['timestamp'] = pd.to_datetime(df['timestamp'])



a = pd.factorize(df['timestamp'].dt.floor('10Min'))[0] + 1

df['col_new'] = pd.Series(a, index=df.index).astype(str).str.zfill(2)



print (df)

    Id           timestamp ID_2 col_new

0   11 2018-10-19 13:00:00   01      01

1   11 2018-10-19 13:05:00   01      01

2   11 2018-10-19 13:06:00   01      01

3   11 2018-10-19 13:07:00   01      01

4   11 2018-10-19 13:30:00   02      02

5   11 2018-10-19 13:31:00   02      02

6   11 2018-10-19 13:32:00   02      02

7   11 2018-10-19 13:55:00   03      03

8   11 2018-10-19 13:54:00   03      03

9   11 2018-10-21 20:47:09   04      04

10  11 2018-10-21 20:48:27   04      04

11  11 2018-10-21 20:48:45   04      04

12  11 2018-10-21 20:48:52   04      04

13  12 2018-10-09 20:30:46   04      05

14  12 2018-10-09 20:30:22   04      05

15  12 2018-10-09 20:30:05   04      05

16  12 2018-10-09 20:29:44   05      06

17  12 2018-10-09 20:29:31   05      06

18  13 2018-10-19 18:49:08   06      07

19  13 2018-10-19 18:49:13   06      07

20  13 2018-10-11 18:46:15   07      08

21  14 2018-10-11 18:46:40   07      08

Detail:

print (df['timestamp'].dt.floor('10Min'))

0    2018-10-19 13:00:00

1    2018-10-19 13:00:00

2    2018-10-19 13:00:00

3    2018-10-19 13:00:00

4    2018-10-19 13:30:00

5    2018-10-19 13:30:00

6    2018-10-19 13:30:00

7    2018-10-19 13:50:00

8    2018-10-19 13:50:00

9    2018-10-21 20:40:00

10   2018-10-21 20:40:00

11   2018-10-21 20:40:00

12   2018-10-21 20:40:00

13   2018-10-09 20:30:00

14   2018-10-09 20:30:00

15   2018-10-09 20:30:00

16   2018-10-09 20:20:00

17   2018-10-09 20:20:00

18   2018-10-19 18:40:00

19   2018-10-19 18:40:00

20   2018-10-11 18:40:00

21   2018-10-11 18:40:00

Name: timestamp, dtype: datetime64[ns]

edited Nov 7 at 9:49

answered Nov 7 at 9:36

jezrael

306k20240316

2

wow, this is superbly elegant
– Pankaj Joshi
Nov 7 at 9:48

1

@PankajJoshi - Thank you.
– jezrael
Nov 7 at 9:48

add a comment |

up vote
3
down vote

accepted

I believe you need floor with factorize, last add zfill:

df['timestamp'] = pd.to_datetime(df['timestamp'])



a = pd.factorize(df['timestamp'].dt.floor('10Min'))[0] + 1

df['col_new'] = pd.Series(a, index=df.index).astype(str).str.zfill(2)



print (df)

    Id           timestamp ID_2 col_new

0   11 2018-10-19 13:00:00   01      01

1   11 2018-10-19 13:05:00   01      01

2   11 2018-10-19 13:06:00   01      01

3   11 2018-10-19 13:07:00   01      01

4   11 2018-10-19 13:30:00   02      02

5   11 2018-10-19 13:31:00   02      02

6   11 2018-10-19 13:32:00   02      02

7   11 2018-10-19 13:55:00   03      03

8   11 2018-10-19 13:54:00   03      03

9   11 2018-10-21 20:47:09   04      04

10  11 2018-10-21 20:48:27   04      04

11  11 2018-10-21 20:48:45   04      04

12  11 2018-10-21 20:48:52   04      04

13  12 2018-10-09 20:30:46   04      05

14  12 2018-10-09 20:30:22   04      05

15  12 2018-10-09 20:30:05   04      05

16  12 2018-10-09 20:29:44   05      06

17  12 2018-10-09 20:29:31   05      06

18  13 2018-10-19 18:49:08   06      07

19  13 2018-10-19 18:49:13   06      07

20  13 2018-10-11 18:46:15   07      08

21  14 2018-10-11 18:46:40   07      08

Detail:

print (df['timestamp'].dt.floor('10Min'))

0    2018-10-19 13:00:00

1    2018-10-19 13:00:00

2    2018-10-19 13:00:00

3    2018-10-19 13:00:00

4    2018-10-19 13:30:00

5    2018-10-19 13:30:00

6    2018-10-19 13:30:00

7    2018-10-19 13:50:00

8    2018-10-19 13:50:00

9    2018-10-21 20:40:00

10   2018-10-21 20:40:00

11   2018-10-21 20:40:00

12   2018-10-21 20:40:00

13   2018-10-09 20:30:00

14   2018-10-09 20:30:00

15   2018-10-09 20:30:00

16   2018-10-09 20:20:00

17   2018-10-09 20:20:00

18   2018-10-19 18:40:00

19   2018-10-19 18:40:00

20   2018-10-11 18:40:00

21   2018-10-11 18:40:00

Name: timestamp, dtype: datetime64[ns]

edited Nov 7 at 9:49

answered Nov 7 at 9:36

jezrael

306k20240316

2

wow, this is superbly elegant
– Pankaj Joshi
Nov 7 at 9:48

1

@PankajJoshi - Thank you.
– jezrael
Nov 7 at 9:48

add a comment |

up vote
3
down vote

accepted

I believe you need floor with factorize, last add zfill:

df['timestamp'] = pd.to_datetime(df['timestamp'])



a = pd.factorize(df['timestamp'].dt.floor('10Min'))[0] + 1

df['col_new'] = pd.Series(a, index=df.index).astype(str).str.zfill(2)



print (df)

    Id           timestamp ID_2 col_new

0   11 2018-10-19 13:00:00   01      01

1   11 2018-10-19 13:05:00   01      01

2   11 2018-10-19 13:06:00   01      01

3   11 2018-10-19 13:07:00   01      01

4   11 2018-10-19 13:30:00   02      02

5   11 2018-10-19 13:31:00   02      02

6   11 2018-10-19 13:32:00   02      02

7   11 2018-10-19 13:55:00   03      03

8   11 2018-10-19 13:54:00   03      03

9   11 2018-10-21 20:47:09   04      04

10  11 2018-10-21 20:48:27   04      04

11  11 2018-10-21 20:48:45   04      04

12  11 2018-10-21 20:48:52   04      04

13  12 2018-10-09 20:30:46   04      05

14  12 2018-10-09 20:30:22   04      05

15  12 2018-10-09 20:30:05   04      05

16  12 2018-10-09 20:29:44   05      06

17  12 2018-10-09 20:29:31   05      06

18  13 2018-10-19 18:49:08   06      07

19  13 2018-10-19 18:49:13   06      07

20  13 2018-10-11 18:46:15   07      08

21  14 2018-10-11 18:46:40   07      08

Detail:

print (df['timestamp'].dt.floor('10Min'))

0    2018-10-19 13:00:00

1    2018-10-19 13:00:00

2    2018-10-19 13:00:00

3    2018-10-19 13:00:00

4    2018-10-19 13:30:00

5    2018-10-19 13:30:00

6    2018-10-19 13:30:00

7    2018-10-19 13:50:00

8    2018-10-19 13:50:00

9    2018-10-21 20:40:00

10   2018-10-21 20:40:00

11   2018-10-21 20:40:00

12   2018-10-21 20:40:00

13   2018-10-09 20:30:00

14   2018-10-09 20:30:00

15   2018-10-09 20:30:00

16   2018-10-09 20:20:00

17   2018-10-09 20:20:00

18   2018-10-19 18:40:00

19   2018-10-19 18:40:00

20   2018-10-11 18:40:00

21   2018-10-11 18:40:00

Name: timestamp, dtype: datetime64[ns]

edited Nov 7 at 9:49

answered Nov 7 at 9:36

jezrael

306k20240316

I believe you need floor with factorize, last add zfill:

df['timestamp'] = pd.to_datetime(df['timestamp'])



a = pd.factorize(df['timestamp'].dt.floor('10Min'))[0] + 1

df['col_new'] = pd.Series(a, index=df.index).astype(str).str.zfill(2)



print (df)

    Id           timestamp ID_2 col_new

0   11 2018-10-19 13:00:00   01      01

1   11 2018-10-19 13:05:00   01      01

2   11 2018-10-19 13:06:00   01      01

3   11 2018-10-19 13:07:00   01      01

4   11 2018-10-19 13:30:00   02      02

5   11 2018-10-19 13:31:00   02      02

6   11 2018-10-19 13:32:00   02      02

7   11 2018-10-19 13:55:00   03      03

8   11 2018-10-19 13:54:00   03      03

9   11 2018-10-21 20:47:09   04      04

10  11 2018-10-21 20:48:27   04      04

11  11 2018-10-21 20:48:45   04      04

12  11 2018-10-21 20:48:52   04      04

13  12 2018-10-09 20:30:46   04      05

14  12 2018-10-09 20:30:22   04      05

15  12 2018-10-09 20:30:05   04      05

16  12 2018-10-09 20:29:44   05      06

17  12 2018-10-09 20:29:31   05      06

18  13 2018-10-19 18:49:08   06      07

19  13 2018-10-19 18:49:13   06      07

20  13 2018-10-11 18:46:15   07      08

21  14 2018-10-11 18:46:40   07      08

Detail:

print (df['timestamp'].dt.floor('10Min'))

0    2018-10-19 13:00:00

1    2018-10-19 13:00:00

2    2018-10-19 13:00:00

3    2018-10-19 13:00:00

4    2018-10-19 13:30:00

5    2018-10-19 13:30:00

6    2018-10-19 13:30:00

7    2018-10-19 13:50:00

8    2018-10-19 13:50:00

9    2018-10-21 20:40:00

10   2018-10-21 20:40:00

11   2018-10-21 20:40:00

12   2018-10-21 20:40:00

13   2018-10-09 20:30:00

14   2018-10-09 20:30:00

15   2018-10-09 20:30:00

16   2018-10-09 20:20:00

17   2018-10-09 20:20:00

18   2018-10-19 18:40:00

19   2018-10-19 18:40:00

20   2018-10-11 18:40:00

21   2018-10-11 18:40:00

Name: timestamp, dtype: datetime64[ns]

edited Nov 7 at 9:49

answered Nov 7 at 9:36

jezrael

306k20240316

edited Nov 7 at 9:49

answered Nov 7 at 9:36

jezrael

306k20240316

answered Nov 7 at 9:36

jezrael

306k20240316

answered Nov 7 at 9:36

jezrael

306k20240316

2

wow, this is superbly elegant
– Pankaj Joshi
Nov 7 at 9:48

1

@PankajJoshi - Thank you.
– jezrael
Nov 7 at 9:48

add a comment |

2

wow, this is superbly elegant
– Pankaj Joshi
Nov 7 at 9:48

1

@PankajJoshi - Thank you.
– jezrael
Nov 7 at 9:48

wow, this is superbly elegant
– Pankaj Joshi
Nov 7 at 9:48

@PankajJoshi - Thank you.
– jezrael
Nov 7 at 9:48

add a comment |

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