xorbits.pandas.DataFrame.to_timestamp#

DataFrame.to_timestamp(freq: Frequency | None = None, how: ToTimestampHow = 'start', axis: Axis = 0, copy: bool | None = None) → DataFrame[source]#

Cast to DatetimeIndex of timestamps, at beginning of period.

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 True) – If False then underlying input data is not copied.

Returns

The DataFrame has a DatetimeIndex.

Return type

DataFrame

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)

Warning

This method has not been implemented yet. Xorbits will try to execute it with pandas.

This docstring was copied from pandas.core.frame.DataFrame.