find equal time and incrementally add a constant

Question

I have a dataframe df containing some timestamps

df['Date'].values

Out[16]: 
array(['2015-03-25T14:36:39.199994000', '2015-03-25T14:36:39.199994000',
       '2015-03-26T10:05:03.699999000', '2015-04-19T16:01:49.680009000',
       '2015-04-19T16:36:10.040007000', '2015-04-19T16:36:10.040007000',
       '2015-04-19T16:36:10.040007000'], dtype='datetime64[ns]')

As you can see the first and the second timestamps are equal, but also the last 3.

I would like to scan the dataframe and if there are timestamps that are equal, maintain the first and add incrementally 5 seconds to the others that are equal.

The new dataframe should look like

df['Date'].values

Out[16]: 
array(['2015-03-25T14:36:39.199994000', '2015-03-25T14:36:44.199994000',
       '2015-03-26T10:05:03.699999000', '2015-04-19T16:01:49.680009000',
       '2015-04-19T16:36:10.040007000', '2015-04-19T16:36:15.040007000',
       '2015-04-19T16:36:20.040007000'], dtype='datetime64[ns]')

Is there a pythonic way to do so without looping. I was thinking to groupby according to the timestamps, but then I don’t know how to proceed…

Asked By: gabboshow

||

Source

Answer 1

Use groupby cumcount times the timedelta i.e

df = pd.DataFrame({'Date':np.array(['2015-03-25T14:36:39.199994000', '2015-03-25T14:36:39.199994000',
   '2015-03-26T10:05:03.699999000', '2015-04-19T16:01:49.680009000',
   '2015-04-19T16:36:10.040007000', '2015-04-19T16:36:10.040007000',
   '2015-04-19T16:36:10.040007000'], dtype='datetime64[ns]')})

df['Date'] + df.groupby(df['Date']).cumcount()*pd.Timedelta('5 seconds')

Output :

0   2015-03-25 14:36:39.199994
1   2015-03-25 14:36:44.199994
2   2015-03-26 10:05:03.699999
3   2015-04-19 16:01:49.680009
4   2015-04-19 16:36:10.040007
5   2015-04-19 16:36:15.040007
6   2015-04-19 16:36:20.040007
dtype: datetime64[ns]

Answered By: Bharath

find equal time and incrementally add a constant

Question:

Answers: