Pandas Series: dt.ceil() function

Last update on September 15 2022 12:55:08 (UTC/GMT +8 hours)

Series.dt.ceil() function

The dt.ceil() function performs ceil operation on the data to the specified frequency level.

Syntax:

Series.dt.ceil(self, *args, **kwargs)

Parameter:

Name	Description	Type / Default Value	Required / Optional
freq	The frequency level to ceil the index to. Must be a fixed frequency like ‘S’ (second) not ‘ME’ (month end).	'infe'r’, bool-ndarray, ‘NaT’, default ‘raise’	Required
ambiguous	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. ‘NaT’ will return NaT where there are ambiguous times ‘raise’ will raise an AmbiguousTimeError if there are ambiguous times	'infer', bool-ndarray, 'NaT', default 'raise'	Required
nonexistent	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	‘shift_forward’, ‘shift_backward’, ‘NaT’, timedelta, default ‘raise’	Required

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.

Example - DatetimeIndex:

Python-Pandas Code:

import numpy as np
import pandas as pd
rng = pd.date_range('1/2/2019 11:59:00', periods=4, freq='min')
rng

Output:

DatetimeIndex(['2019-01-02 11:59:00', '2019-01-02 12:00:00',
               '2019-01-02 12:01:00', '2019-01-02 12:02:00'],
              dtype='datetime64[ns]', freq='T')

Python-Pandas Code:

import numpy as np
import pandas as pd
rng = pd.date_range('1/2/2019 11:59:00', periods=4, freq='min')
rng.ceil('H')

Output:

DatetimeIndex(['2019-01-02 12:00:00', '2019-01-02 12:00:00',
               '2019-01-02 13:00:00', '2019-01-02 13:00:00'],
              dtype='datetime64[ns]', freq=None)

Example - Series:

Python-Pandas Code:

import numpy as np
import pandas as pd
rng = pd.date_range('1/2/2019 11:59:00', periods=4, freq='min')
pd.Series(rng).dt.ceil("H")

Output:

0   2019-01-02 12:00:00
1   2019-01-02 12:00:00
2   2019-01-02 13:00:00
3   2019-01-02 13:00:00
dtype: datetime64[ns]

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