pandas.DataFrame.to_timestamp#

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