pandas.Timestamp.floor¶

Timestamp.floor(freq, ambiguous='raise', nonexistent='raise')¶

Return a new Timestamp floored to this resolution.

Parameters

freqstr

Frequency string indicating the flooring resolution.

ambiguousbool or {‘raise’, ‘NaT’}, default ‘raise’

The behavior is as follows:

bool contains flags to determine if time is dst or not (note that this flag is only applicable for ambiguous fall dst dates).
‘NaT’ will return NaT for an ambiguous time.
‘raise’ will raise an AmbiguousTimeError for an ambiguous time.

nonexistent{‘raise’, ‘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.

Raises

Examples

Create a timestamp object:

>>> ts = pd.Timestamp('2020-03-14T15:32:52.192548651')

A timestamp can be floored using multiple frequency units:

>>> ts.floor(freq='H') # hour
Timestamp('2020-03-14 15:00:00')

>>> ts.floor(freq='T') # minute
Timestamp('2020-03-14 15:32:00')

>>> ts.floor(freq='S') # seconds
Timestamp('2020-03-14 15:32:52')

>>> ts.floor(freq='N') # nanoseconds
Timestamp('2020-03-14 15:32:52.192548651')

freq can also be a multiple of a single unit, like ‘5T’ (i.e. 5 minutes):

>>> ts.floor(freq='5T')
Timestamp('2020-03-14 15:30:00')

or a combination of multiple units, like ‘1H30T’ (i.e. 1 hour and 30 minutes):

>>> ts.floor(freq='1H30T')
Timestamp('2020-03-14 15:00:00')

Analogous for pd.NaT:

>>> pd.NaT.floor()
NaT