xorbits.pandas.Series.to_timestamp#

Series.to_timestamp(freq=None, how: Literal['s', 'e', 'start', 'end'] = 'start', copy: bool | None = None) → Series[source]#

Cast to DatetimeIndex of Timestamps, at beginning of period.

Parameters

  • freq (str, default frequency of PeriodIndex) – Desired frequency.

  • how ({'s', 'e', 'start', 'end'}) – Convention for converting period to timestamp; start of period vs. end.

  • copy (bool, default True) – Whether or not to return a copy.

Return type

Series with DatetimeIndex

Examples

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

The resulting frequency of the Timestamps is YearBegin

>>> s1 = s1.to_timestamp()  
>>> s1  
2023-01-01    1
2024-01-01    2
2025-01-01    3
Freq: AS-JAN, dtype: int64

Using freq which is the offset that the Timestamps will have

>>> s2 = pd.Series([1, 2, 3], index=idx)  
>>> s2 = s2.to_timestamp(freq='M')  
>>> s2  
2023-01-31    1
2024-01-31    2
2025-01-31    3
Freq: A-JAN, dtype: int64

Warning

This method has not been implemented yet. Xorbits will try to execute it with pandas.

This docstring was copied from pandas.core.series.Series.