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# pandas.DataFrame.to_timestamp

DataFrame.to_timestamp(freq=None, how='start', axis=0, 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.

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

      The axis to convert (the index by default).

    copybool, default True

      If False then underlying input data is not copied.

      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:
    DataFrame

      The DataFrame has a DatetimeIndex.

  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[ns]', 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[ns]', freq=None)