Skip to main content
`Ctrl`+`K`

# pandas.Series.to_timestamp

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

  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