pandas.DataFrame.to_timestamp — pandas 2.2.3 documentation (original) (raw)

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-Writewill be enabled by default, which means that all methods with acopy 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)