pandas.TimedeltaIndex.floor#

TimedeltaIndex.floor(freq, ambiguous='raise', nonexistent='raise')[source]#

Perform floor operation on the data to the specified freq.

Parameters:
freqstr or Offset

The frequency level to floor the index to. Must be a fixed frequency like ‘s’ (second) not ‘ME’ (month end). See frequency aliases for a list of possible freq values.

ambiguous‘infer’, bool-ndarray, ‘NaT’, default ‘raise’

Only relevant for DatetimeIndex:

  • ‘infer’ will attempt to infer fall dst-transition hours based on order

  • bool-ndarray where True signifies a DST time, False designates a non-DST time (note that this flag is only applicable for ambiguous times)

  • ‘NaT’ will return NaT where there are ambiguous times

  • ‘raise’ will raise an AmbiguousTimeError if there are ambiguous times.

nonexistent‘shift_forward’, ‘shift_backward’, ‘NaT’, timedelta, default ‘raise’

A nonexistent time does not exist in a particular timezone where clocks moved forward due to DST.

  • ‘shift_forward’ will shift the nonexistent time forward to the closest existing time

  • ‘shift_backward’ will shift the nonexistent time backward to the closest existing time

  • ‘NaT’ will return NaT where there are nonexistent times

  • timedelta objects will shift nonexistent times by the timedelta

  • ‘raise’ will raise an NonExistentTimeError if there are nonexistent times.

Returns:
DatetimeIndex, TimedeltaIndex, or Series

Index of the same type for a DatetimeIndex or TimedeltaIndex, or a Series with the same index for a Series.

Raises:
ValueError if the freq cannot be converted.

See also

DatetimeIndex.floor

Perform floor operation on the data to the specified freq.

DatetimeIndex.snap

Snap time stamps to nearest occurring frequency.

Notes

If the timestamps have a timezone, flooring will take place relative to the local (“wall”) time and re-localized to the same timezone. When flooring near daylight savings time, use nonexistent and ambiguous to control the re-localization behavior.

Examples

DatetimeIndex

>>> rng = pd.date_range('1/1/2018 11:59:00', periods=3, freq='min')
>>> rng
DatetimeIndex(['2018-01-01 11:59:00', '2018-01-01 12:00:00',
               '2018-01-01 12:01:00'],
              dtype='datetime64[ns]', freq='min')
>>> rng.floor('h')
DatetimeIndex(['2018-01-01 11:00:00', '2018-01-01 12:00:00',
               '2018-01-01 12:00:00'],
              dtype='datetime64[ns]', freq=None)

Series

>>> pd.Series(rng).dt.floor("h")
0   2018-01-01 11:00:00
1   2018-01-01 12:00:00
2   2018-01-01 12:00:00
dtype: datetime64[ns]

When rounding near a daylight savings time transition, use ambiguous or nonexistent to control how the timestamp should be re-localized.

>>> rng_tz = pd.DatetimeIndex(["2021-10-31 03:30:00"], tz="Europe/Amsterdam")

>>> rng_tz.floor("2h", ambiguous=False)
DatetimeIndex(['2021-10-31 02:00:00+01:00'],
             dtype='datetime64[ns, Europe/Amsterdam]', freq=None)

>>> rng_tz.floor("2h", ambiguous=True)
DatetimeIndex(['2021-10-31 02:00:00+02:00'],
              dtype='datetime64[ns, Europe/Amsterdam]', freq=None)