Groupby With Overlapping Intervals Timeseries

I have a time series in python pandas dataframe object and I want to create a group based on index but I want overlapping groups i.e groups are not distinct. The header_sec is the

Solution 1:

Here is a technique:

import numpy as np # if you have not already done this

grouped = df.groupby(df.index)

for name, group in grouped:
    try:
        prev_sec = df.loc[(name - pd.to_timedelta(1, unit='s')), :]
    except KeyError:
        prev_sec = pd.DataFrame(columns=group.columns)
    try:
        next_sec = df.loc[(name + pd.to_timedelta(1, unit='s')), :]
    except KeyError:
        next_sec = pd.DataFrame(columns=group.columns)
    Pn = 2 # replace this with int(len(prev_sec)/2) to get half rows from previous second
    Nn = 2 # replace this with int(len(next_sec)/2) to get half rows from next second
    group = pd.concat([prev_sec.iloc[-Pn:,:], group, next_sec.iloc[:Nn,:]])

    # Replace the below lines with your operations
    print(name, group)