Construct non-overlapping datetime record (start, end datetime) dataframe

Question

I need to create a dataframe removing over-lapping start and end datetimes for multiple ids. I will use the start and end datetimes to aggregate values in a high frequency pandas dataframe, so I need to remove those overlapping datetimes in mst_df.

import pandas as pd
 
#Proxy reference dataframe
master = [['site a', '2021-07-08 00:00:00', '2021-07-08 10:56:00'], 
        ['site a', '2021-07-08 06:00:00',   '2021-07-08 12:00:00'], #slightly overlapping
        ['site a', '2021-07-08 17:36:00',   '2021-07-09 11:40:00'],
        ['site a', '2021-07-08 18:00:00',   '2021-07-09 11:40:00'], #overlapping
        ['site a', '2021-07-09 00:00:00',   '2021-07-09 05:40:00'], #overlapping
        ['site b', '2021-07-08 00:00:00',   '2021-07-08 10:24:00'],
        ['site b', '2021-07-08 06:00:00',   '2021-07-08 10:24:00'], #overlapping
        ['site b', '2021-07-08 17:32:00',   '2021-07-09 11:12:00'],
        ['site b', '2021-07-08 18:00:00',   '2021-07-09 11:12:00'], #overlapping
        ['site b', '2021-07-09 00:00:00',   '2021-07-09 13:00:00']] #slightly overlapping

 
mst_df = pd.DataFrame(master, columns = ['id', 'start', 'end'])
mst_df['start'] = pd.to_datetime(mst_df['start'], infer_datetime_format=True)
mst_df['end'] = pd.to_datetime(mst_df['end'], infer_datetime_format=True)

Desired DataFrame:

    id      start               end
    site a  2021-07-08 00:00:00 2021-07-08 12:00:00
    site a  2021-07-08 17:36:00 2021-07-09 11:40:00
    site b  2021-07-08 00:00:00 2021-07-08 10:24:00
    site b  2021-07-08 17:32:00 2021-07-09 13:00:00

Answer 1

I don't know if pandas has special function for this. It has Interval.overlaping() to check if two ranges overlaps (and it works even with datetime) but I don't see function to merge these two ranges so it still needs own code for merging. Fortunately it is easy.

---

Rows are sorted by start so rows are not overlaping when previous_end < next_start and I use it in for-loop.

But first I group by site to work with every site separatelly.

Next I get first row (as previous) and run loop with other rows (as next) and check previous_end < next_start.

If it is True then I can put previous on list of results and get next as previous to work with rest of rows.

If it is False then I create new range from both rows and use it to work with rest of rows.

Finally I add previous to list.

After processing all groups I convert all to DataFrame.

import pandas as pd
 
#Proxy reference dataframe
master = [
    ['site a', '2021-07-08 00:00:00',   '2021-07-08 10:56:00'], 
    ['site a', '2021-07-08 06:00:00',   '2021-07-08 12:00:00'], # slightly overlapping
    ['site a', '2021-07-08 17:36:00',   '2021-07-09 11:40:00'],
    ['site a', '2021-07-08 18:00:00',   '2021-07-09 11:40:00'], # overlapping
    ['site a', '2021-07-09 00:00:00',   '2021-07-09 05:40:00'], # overlapping
    ['site b', '2021-07-08 00:00:00',   '2021-07-08 10:24:00'],
    ['site b', '2021-07-08 06:00:00',   '2021-07-08 10:24:00'], # overlapping
    ['site b', '2021-07-08 17:32:00',   '2021-07-09 11:12:00'],
    ['site b', '2021-07-08 18:00:00',   '2021-07-09 11:12:00'], # overlapping
    ['site b', '2021-07-09 00:00:00',   '2021-07-09 13:00:00']  # slightly overlapping
]

mst_df = pd.DataFrame(master, columns = ['id', 'start', 'end'])

mst_df['start'] = pd.to_datetime(mst_df['start'], infer_datetime_format=True)
mst_df['end']   = pd.to_datetime(mst_df['end'], infer_datetime_format=True)

result = []

for val, group in mst_df.groupby('id'):
    
    # get first
    prev = group.iloc[0]
    
    for idx, item in group[1:].iterrows():
        if prev['end'] < item['start']:
            # not overlapping - put previous to results and use next as previous
            result.append(prev)
            prev = item
        else:
            # overlappig - create on range start, end
            prev['start'] = min(prev['start'], item['start'])
            prev['end']   = max(prev['end'], item['end'])
    
    # add when there is no next item
    result.append(prev)

print(pd.DataFrame(result))

Result:

       id               start                 end
0  site a 2021-07-08 00:00:00 2021-07-08 12:00:00
2  site a 2021-07-08 17:36:00 2021-07-09 11:40:00
5  site b 2021-07-08 00:00:00 2021-07-08 10:24:00
7  site b 2021-07-08 17:32:00 2021-07-09 13:00:00