pandas.Series.to_timestamp

Series.to_timestamp(freq=None, how='start', copy=<no_default>)

Cast to DatetimeIndex of Timestamps, at beginning of period.

This can be changed to the end of the period, by specifying how=”e”.

Parameters:

freqstr, default frequency of PeriodIndex

Desired frequency.

how{‘s’, ‘e’, ‘start’, ‘end’}

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

copybool, default False

This keyword is now ignored; changing its value will have no impact on the method.

Deprecated since version 3.0.0: This keyword is ignored and will be removed in pandas 4.0. Since pandas 3.0, this method always returns a new object using a lazy copy mechanism that defers copies until necessary (Copy-on-Write). See the user guide on Copy-on-Write for more details.

Returns:

Series with DatetimeIndex

Series with the PeriodIndex cast to DatetimeIndex.

See also

Series.to_period

Inverse method to cast DatetimeIndex to PeriodIndex.

DataFrame.to_timestamp

Equivalent method for DataFrame.

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: Y-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: YS-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: YE-JAN, dtype: int64