pandas.Series.to_timestamp#

Series.to_timestamp(freq=None,how='start',copy=None)[source]#

Cast to DatetimeIndex of Timestamps, atbeginning of period.

Parameters:

freqstr, default frequency of PeriodIndex

Desired frequency.

how{‘s’, ‘e’, ‘start’, ‘end’}

Convention for converting period to timestamp; start of periodvs. end.

copybool, default True

Whether or not to return a copy.

Note

Thecopy keyword will change behavior in pandas 3.0.Copy-on-Writewill be enabled by default, which means that all methods with acopy keyword will use a lazy copy mechanism to defer the copy andignore thecopy keyword. Thecopy keyword will be removed in afuture version of pandas.

You can already get the future behavior and improvements throughenabling copy on writepd.options.mode.copy_on_write=True

Returns:

Series with DatetimeIndex

Examples

>>>idx=pd.PeriodIndex(['2023','2024','2025'],freq='Y')>>>s1=pd.Series([1,2,3],index=idx)>>>s12023    12024    22025    3Freq: Y-DEC, dtype: int64

The resulting frequency of the Timestamps isYearBegin

>>>s1=s1.to_timestamp()>>>s12023-01-01    12024-01-01    22025-01-01    3Freq: YS-JAN, dtype: int64

Usingfreq which is the offset that the Timestamps will have

>>>s2=pd.Series([1,2,3],index=idx)>>>s2=s2.to_timestamp(freq='M')>>>s22023-01-31    12024-01-31    22025-01-31    3Freq: YE-JAN, dtype: int64