pandas.PeriodIndex.to_timestamp#

PeriodIndex.to_timestamp(freq=None, how='start')[source]#

Cast to DatetimeArray/Index.

Parameters:

freqstr or DateOffset, optional: Target frequency. The default is ‘D’ for week or longer, ‘s’ otherwise.
how{‘s’, ‘e’, ‘start’, ‘end’}: Whether to use the start or end of the time period being converted.

Returns:

DatetimeArray/Index: Timestamp representation of given Period-like object.

See also

PeriodIndex.day: The days of the period.
PeriodIndex.from_fields: Construct a PeriodIndex from fields (year, month, day, etc.).
PeriodIndex.from_ordinals: Construct a PeriodIndex from ordinals.
PeriodIndex.hour: The hour of the period.
PeriodIndex.minute: The minute of the period.
PeriodIndex.month: The month as January=1, December=12.
PeriodIndex.second: The second of the period.
PeriodIndex.year: The year of the period.

Examples

>>> idx = pd.PeriodIndex(["2023-01", "2023-02", "2023-03"], freq="M")
>>> idx.to_timestamp()
DatetimeIndex(['2023-01-01', '2023-02-01', '2023-03-01'],
dtype='datetime64[ns]', freq='MS')

The frequency will not be inferred if the index contains less than three elements, or if the values of index are not strictly monotonic:

>>> idx = pd.PeriodIndex(["2023-01", "2023-02"], freq="M")
>>> idx.to_timestamp()
DatetimeIndex(['2023-01-01', '2023-02-01'], dtype='datetime64[ns]', freq=None)

>>> idx = pd.PeriodIndex(
...     ["2023-01", "2023-02", "2023-02", "2023-03"], freq="2M"
... )
>>> idx.to_timestamp()
DatetimeIndex(['2023-01-01', '2023-02-01', '2023-02-01', '2023-03-01'],
dtype='datetime64[ns]', freq=None)