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# pandas.DataFrame.to_timestamp

DataFrame.to_timestamp(freq=None, how='start', axis=0, copy=<no_default>)

  Cast PeriodIndex to DatetimeIndex of timestamps, at beginning of period.

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

  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.

    axis{0 or ‘index’, 1 or ‘columns’}, default 0

      The axis to convert (the index by default).

    copy bool, 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:
    DataFrame with DatetimeIndex

      DataFrame with the PeriodIndex cast to DatetimeIndex.

  See also

  `DataFrame.to_period`

    Inverse method to cast DatetimeIndex to PeriodIndex.

  `Series.to_timestamp`

    Equivalent method for Series.

  Examples

      >>> idx = pd.PeriodIndex(["2023", "2024"], freq="Y")
      >>> d = {"col1": [1, 2], "col2": [3, 4]}
      >>> df1 = pd.DataFrame(data=d, index=idx)
      >>> df1
            col1   col2
      2023     1      3
      2024     2      4

  The resulting timestamps will be at the beginning of the year in this case

      >>> df1 = df1.to_timestamp()
      >>> df1
                  col1   col2
      2023-01-01     1      3
      2024-01-01     2      4
      >>> df1.index
      DatetimeIndex(['2023-01-01', '2024-01-01'], dtype='datetime64[us]', freq=None)

  Using freq which is the offset that the Timestamps will have

      >>> df2 = pd.DataFrame(data=d, index=idx)
      >>> df2 = df2.to_timestamp(freq="M")
      >>> df2
                  col1   col2
      2023-01-31     1      3
      2024-01-31     2      4
      >>> df2.index
      DatetimeIndex(['2023-01-31', '2024-01-31'], dtype='datetime64[us]', freq=None)