modin.pandas.DatetimeIndex.indexer_between_time

DatetimeIndex.indexer_between_time(start_time, end_time, include_start: bool = True, include_end: bool = True) → ndarray[int64][source] (https://github.com/snowflakedb/snowpark-python/blob/v1.26.0/snowpark-python/src/snowflake/snowpark/modin/plugin/extensions/datetime_index.py#L817-L851)

Return index locations of values between particular times of day.

Parameters:

  • start_time (datetime.time, str) – Time passed either as object (datetime.time) or as string in appropriate format (“%H:%M”, “%H%M”, “%I:%M%p”, “%I%M%p”, “%H:%M:%S”, “%H%M%S”, “%I:%M:%S%p”,”%I%M%S%p”).

  • end_time (datetime.time, str) – Time passed either as object (datetime.time) or as string in appropriate format (“%H:%M”, “%H%M”, “%I:%M%p”, “%I%M%p”, “%H:%M:%S”, “%H%M%S”, “%I:%M:%S%p”,”%I%M%S%p”).

  • include_start (bool, default True) –

  • include_end (bool, default True) –

Return type:

np.ndarray[np.intp]

See also

indexer_at_time

Get index locations of values at particular time of day.

DataFrame.between_time

Select values between particular times of day.

Examples

>>> idx = pd.date_range("2023-01-01", periods=4, freq="h")
>>> idx
DatetimeIndex(['2023-01-01 00:00:00', '2023-01-01 01:00:00',
               '2023-01-01 02:00:00', '2023-01-01 03:00:00'],
              dtype='datetime64[ns]', freq=None)
>>> idx.indexer_between_time("00:00", "2:00", include_end=False)  
array([0, 1])
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