pandas.Series.to_timestamp#
- 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-Write will be enabled by default, which means that all methods with a copy 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