pandas.DataFrame.to_timestamp#

DataFrame.to_timestamp(freq=None,how='start',axis=0,copy=None)[source]#

Cast to DatetimeIndex of timestamps, atbeginning of period.

Parameters:

freqstr, default frequency of PeriodIndex

Desired frequency.

how{‘s’, ‘e’, ‘start’, ‘end’}

Convention for converting period to timestamp; start of periodvs. 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

Thecopy 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 andignore thecopy keyword. Thecopy keyword will be removed in afuture version of pandas.

You can already get the future behavior and improvements throughenabling copy on writepd.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   col22023     1      32024     2      4

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

>>>df1=df1.to_timestamp()>>>df1            col1   col22023-01-01     1      32024-01-01     2      4>>>df1.indexDatetimeIndex(['2023-01-01', '2024-01-01'], dtype='datetime64[ns]', freq=None)

Usingfreq which is the offset that the Timestamps will have

>>>df2=pd.DataFrame(data=d,index=idx)>>>df2=df2.to_timestamp(freq='M')>>>df2            col1   col22023-01-31     1      32024-01-31     2      4>>>df2.indexDatetimeIndex(['2023-01-31', '2024-01-31'], dtype='datetime64[ns]', freq=None)