def wrapper(self, other):
if isinstance(other, str):
try:
other = self._scalar_from_string(other)
except ValueError:
# failed to parse as timedelta
return invalid_comparison(self, other, op)
if isinstance(other, self._recognized_scalars) or other is NaT:
other = self._scalar_type(other)
self._check_compatible_with(other)
other_i8 = self._unbox_scalar(other)
result = op(self.view("i8"), other_i8)
if isna(other):
result.fill(nat_result)
elif not is_list_like(other):
return invalid_comparison(self, other, op)
elif len(other) != len(self):
raise ValueError("Lengths must match")
else:
if isinstance(other, list):
other = np.array(other)
if not isinstance(other, (np.ndarray, cls)):
return invalid_comparison(self, other, op)
if is_object_dtype(other):
with np.errstate(all="ignore"):
result = ops.comp_method_OBJECT_ARRAY(
op, self.astype(object), other
)
o_mask = isna(other)
elif not cls._is_recognized_dtype(other.dtype):
# e.g. other is datetimearray
return invalid_comparison(self, other, op)
else:
other = type(self)._from_sequence(other)
self._check_compatible_with(other)
result = op(self.view("i8"), other.view("i8"))
o_mask = other._isnan
if o_mask.any():
result[o_mask] = nat_result
if self._hasnans:
result[self._isnan] = nat_result
return result
def wrapper(self, other):
ordinal_op = getattr(self.asi8, opname)
if is_list_like(other) and len(other) != len(self):
raise ValueError("Lengths must match")
if isinstance(other, str):
try:
other = self._scalar_from_string(other)
except ValueError:
# string that can't be parsed as Period
return invalid_comparison(self, other, op)
elif isinstance(other, int):
# TODO: sure we want to allow this? we dont for DTA/TDA
# 2 tests rely on this
other = Period(other, freq=self.freq)
result = ordinal_op(other.ordinal)
if isinstance(other, Period):
self._check_compatible_with(other)
result = ordinal_op(other.ordinal)
elif other is NaT:
result = np.empty(len(self.asi8), dtype=bool)
result.fill(nat_result)
elif not is_list_like(other):
return invalid_comparison(self, other, op)
else:
if isinstance(other, list):
# TODO: could use pd.Index to do inference?
other = np.array(other)
if not isinstance(other, (np.ndarray, cls)):
return invalid_comparison(self, other, op)
if is_object_dtype(other):
with np.errstate(all="ignore"):
result = ops.comp_method_OBJECT_ARRAY(
op, self.astype(object), other
)
o_mask = isna(other)
elif not is_period_dtype(other):
# e.g. is_timedelta64_dtype(other)
return invalid_comparison(self, other, op)
else:
assert isinstance(other, cls), type(other)
self._check_compatible_with(other)
result = ordinal_op(other.asi8)
o_mask = other._isnan
if o_mask.any():
result[o_mask] = nat_result
if self._hasnans:
result[self._isnan] = nat_result
return result