floor(self, freq, ambiguous='raise', nonexistent='raise')
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.
The frequency level to floor the index to. Must be a fixed frequency like 'S' (second) not 'ME' (month end). See frequency aliases <timeseries.offset_aliases>
for a list of possible :None:None:`freq` values.
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.
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.
Index of the same type for a DatetimeIndex or TimedeltaIndex, or a Series with the same index for a Series.
Perform floor operation on the data to the specified :None:None:`freq`.
DatetimeIndex
This example is valid syntax, but we were not able to check execution>>> rng = pd.date_range('1/1/2018 11:59:00', periods=3, freq='min')This example is valid syntax, but we were not able to check execution
... rng DatetimeIndex(['2018-01-01 11:59:00', '2018-01-01 12:00:00', '2018-01-01 12:01:00'], dtype='datetime64[ns]', freq='T')
>>> 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
This example is valid syntax, but we were not able to check execution>>> 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")This example is valid syntax, but we were not able to check execution
>>> rng_tz.floor("2H", ambiguous=False) DatetimeIndex(['2021-10-31 02:00:00+01:00'], dtype='datetime64[ns, Europe/Amsterdam]', freq=None)This example is valid syntax, but we were not able to check execution
>>> rng_tz.floor("2H", ambiguous=True) DatetimeIndex(['2021-10-31 02:00:00+02:00'], dtype='datetime64[ns, Europe/Amsterdam]', freq=None)See :
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