def _add_datetimelike_scalar(self, other):
# adding a timedeltaindex to a datetimelike
from pandas.core.arrays import DatetimeArrayMixin
assert other is not NaT
other = Timestamp(other)
if other is NaT:
# In this case we specifically interpret NaT as a datetime, not
# the timedelta interpretation we would get by returning self + NaT
result = self.asi8.view('m8[ms]') + NaT.to_datetime64()
return DatetimeArrayMixin(result)
i8 = self.asi8
result = checked_add_with_arr(i8, other.value, arr_mask=self._isnan)
result = self._maybe_mask_results(result)
return DatetimeArrayMixin(result, tz=other.tz, freq=self.freq)
def _add_datetimelike_scalar(self, other) -> DatetimeArray:
# adding a timedeltaindex to a datetimelike
from pandas.core.arrays import DatetimeArray
assert other is not NaT
other = Timestamp(other)
if other is NaT:
# In this case we specifically interpret NaT as a datetime, not
# the timedelta interpretation we would get by returning self + NaT
result = self.asi8.view("m8[ms]") + NaT.to_datetime64()
return DatetimeArray(result)
i8 = self.asi8
result = checked_add_with_arr(i8, other.value, arr_mask=self._isnan)
result = self._maybe_mask_results(result)
dtype = DatetimeTZDtype(tz=other.tz) if other.tz else DT64NS_DTYPE
return DatetimeArray(result, dtype=dtype, freq=self.freq)
def _add_datetimelike_scalar(self, other):
# adding a timedeltaindex to a datetimelike
from pandas.core.arrays import DatetimeArrayMixin
assert other is not NaT
other = Timestamp(other)
if other is NaT:
# In this case we specifically interpret NaT as a datetime, not
# the timedelta interpretation we would get by returning self + NaT
result = self.asi8.view('m8[ms]') + NaT.to_datetime64()
return DatetimeArrayMixin(result)
i8 = self.asi8
result = checked_add_with_arr(i8, other.value,
arr_mask=self._isnan)
result = self._maybe_mask_results(result)
return DatetimeArrayMixin(result, tz=other.tz)
