pandas.Series.to_timestamp — pandas 2.2.3 documentation (original) (raw)

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

Cast to DatetimeIndex of Timestamps, at beginning of period.

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 True

Whether or not to return a copy.

Note

The copy 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 and ignore the copy keyword. The copy keyword will be removed in a future version of pandas.

You can already get the future behavior and improvements through enabling copy on write pd.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) 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