Group if difference of datetime index is less than 5 minutes of a pandas series

Question

I want to perform a groupby.first() of a pandas timeseries where the datetime index is almost consecutive, where almost is less than 5 minutes of difference. I have seen a lot of material but never if the datetime is not consecutive like in my example:

ind=['2019-02-28 01:20:00', '2019-02-28 01:21:00','2019-02-28 01:22:00', '2019-02-28 01:23:00',
     '2019-02-28 01:24:00', '2019-02-28 01:25:00','2019-02-28 01:26:00', '2019-02-28 01:27:00',
     '2019-02-28 01:28:00', '2019-02-28 04:05:00','2019-02-28 04:06:00', '2019-02-28 04:07:00',
     '2019-02-28 04:08:00', '2019-02-28 04:09:00','2019-02-28 06:55:00', '2019-02-28 06:56:00',
     '2019-02-28 06:57:00', '2019-02-28 06:58:00','2019-02-28 09:50:00', '2019-02-28 09:51:00',
     '2019-02-28 09:52:00', '2019-02-28 09:53:00','2019-02-28 09:54:00', '2019-02-28 09:55:00',          
     '2019-02-28 09:56:00', '2019-02-28 09:57:00','2019-02-28 09:58:00', '2019-02-28 09:59:00',
     '2019-02-28 10:00:00']

val=[2.11, 2.24, 2.37, 2.42, 2.58, 2.71, 2.76, 3.06, 3.29, 2.04, 2.26,2.55, 2.89, 3.26, 2.2 , 2.54,
     2.85, 3.24, 2.2 , 2.12, 2.11, 2.07,2.1 , 2.16, 2.28, 2.35, 2.44, 2.5 , 2.57]

s = pd.Series(val,index=pd.to_datetime(ind))

My desidered output should be:

Datetime               Value
2019-02-28 01:20:00    2.11
2019-02-28 04:05:00    2.04
2019-02-28 06:55:00    2.20
2019-02-28 09:50:00    2.20

Anyone can help me?

Answer 1

Let us group the dataframe on blocks of consecutive rows where time difference is less than 5min:

df = s.reset_index(name='Value')
b  = df['index'].diff().dt.seconds.gt(300).cumsum()
df = df.groupby(b, as_index=False).first()

Explanations

Reset the index of the given timeseries s then calculate the difference of datetime index compared to previous element and use dt.seconds to get the difference measured in seconds.

>>> df['index'].diff().dt.seconds

0         NaN
1        60.0
2        60.0
3        60.0
4        60.0
5        60.0
6        60.0
7        60.0
8        60.0
9      9420.0
....
25       60.0
26       60.0
27       60.0
28       60.0
Name: index, dtype: float64

Now compare total seconds with 300 to create a boolean mask followed by cumsum to identify blocks of rows where the difference between consecutive datetime values is less than 5 min

>>> df['index'].diff().dt.seconds.gt(300).cumsum()

0     0
1     0
2     0
3     0
4     0
5     0
6     0
7     0
8     0
9     1
...
25    3
26    3
27    3
28    3
Name: index, dtype: int64

Group the dataframe on the above blocks and aggregate using first

>>> df
                index  Value
0 2019-02-28 01:20:00   2.11
1 2019-02-28 04:05:00   2.04
2 2019-02-28 06:55:00   2.20
3 2019-02-28 09:50:00   2.20