CODEWITHSUNDEEP
pythonpandasdatetimetime-seriesmissing-data
Anna Yashina
Anna Yashina

Reputation: 534

Find start and end dates of missing periods in datetime index

I have a huge datetime index which is supposed to have 1 minute frequency. I know that there are periods of missing data. I would like to detect all missing data periods and find start and end dates for each of them. So far I figured out how to find missing timestamps:

fullrange = pd.date_range(start = obs.index.min(), end = obs.index.max(), freq = "1T")
missing_dates = obs.index.difference(fullrange)

Now I don't know how to separate missing_dates into periods and find the start and end dates for them.

The obs.index looks like this:

DatetimeIndex(['2020-05-10 09:08:00', '2020-05-10 09:09:00',
           '2020-05-10 09:10:00', '2020-05-10 09:11:00',
           '2020-05-10 09:12:00', '2020-05-10 09:13:00',
           '2020-05-10 09:14:00', '2020-05-10 09:15:00',
           '2020-05-10 09:16:00', '2020-05-10 12:24:00', # missing data
           ...
           '2020-07-09 12:35:00', '2020-07-09 12:36:00',
           '2020-07-09 12:37:00', '2020-07-09 12:38:00',
           '2020-07-09 12:39:00', '2020-07-09 12:40:00',
           '2020-07-09 12:41:00', '2020-07-09 12:42:00',
           '2020-07-09 12:43:00', '2020-08-09 13:14:00'], # missing data
          dtype='datetime64[ns]', name='timestamp', length=86617)

The expected result is a list of missing data periods, each period is a list with [start, end]:

[['2020-05-10 09:16:00', '2020-05-10 12:24:00'], ['2020-07-09 12:43:00', '2020-08-09 13:14:00']]

Upvotes: 2

Views: 633

Answers (1)

jezrael
jezrael

Reputation: 862591

Use:

rng = pd.date_range('2017-04-03', periods=10, freq='T')
df = pd.DataFrame({'a': range(10)}, index=rng)  

obs = df.iloc[[0,1,2,4,5,7,8,9]]
print (obs)
                     a
2017-04-03 00:00:00  0
2017-04-03 00:01:00  1
2017-04-03 00:02:00  2
2017-04-03 00:04:00  4
2017-04-03 00:05:00  5
2017-04-03 00:07:00  7
2017-04-03 00:08:00  8
2017-04-03 00:09:00  9

First are compared difference of all index values with omitted first value:

a = obs.index[obs.index.to_series().diff().ne(pd.Timedelta(1, 'T'))][1:]

Then get positions of this values by Index.get_indexer:

pos = obs.index.get_indexer(a)

Convert output to strings:

idx = obs.index.astype(str)

And last use zip with indexing previous values by subtract 1 with actual values in list comprehension:

out = [list(x) for x in zip(idx[pos-1], idx[pos])]
print (out)
[['2017-04-03 00:02:00', '2017-04-03 00:04:00'], 
 ['2017-04-03 00:05:00', '2017-04-03 00:07:00']]

Upvotes: 1

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