pandas.TimedeltaIndex.floor — pandas 2.2.3 documentation (original) (raw)

TimedeltaIndex.floor(*args, **kwargs)[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). Seefrequency 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.

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 ornonexistent 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)