REF: cosmetic differences between DTA/TDA/PA comparison methods

pandas/core/arrays/datetimes.py

@@ -26,12 +26,14 @@
     _INT64_DTYPE,
     _NS_DTYPE,
     is_categorical_dtype,
+    is_datetime64_any_dtype,
     is_datetime64_dtype,
     is_datetime64_ns_dtype,
     is_datetime64tz_dtype,
     is_dtype_equal,
     is_extension_array_dtype,
     is_float_dtype,
+    is_list_like,
     is_object_dtype,
     is_period_dtype,
     is_string_dtype,
@@ -148,17 +150,22 @@ def wrapper(self, other):
                 # string that cannot be parsed to Timestamp
                 return invalid_comparison(self, other, op)
 
-        if isinstance(other, (datetime, np.datetime64)):
-            other = Timestamp(other)
+        if isinstance(other, self._recognized_scalars) or other is NaT:
+            other = self._scalar_type(other)
             self._assert_tzawareness_compat(other)
 
-            result = op(self.asi8, other.value)
+            other_i8 = other.value
+
+            result = op(self.view("i8"), other_i8)
             if isna(other):
                 result.fill(nat_result)
-        elif lib.is_scalar(other) or np.ndim(other) == 0:
+
+        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)
@@ -178,7 +185,7 @@ def wrapper(self, other):
                     )
                 o_mask = isna(other)
 
-            elif not (is_datetime64_dtype(other) or is_datetime64tz_dtype(other)):
+            elif not cls._is_recognized_dtype(other.dtype):
                 # e.g. is_timedelta64_dtype(other)
                 return invalid_comparison(self, other, op)
 
@@ -239,6 +246,8 @@ class DatetimeArray(dtl.DatetimeLikeArrayMixin, dtl.TimelikeOps, dtl.DatelikeOps
 
     _typ = "datetimearray"
     _scalar_type = Timestamp
+    _recognized_scalars = (datetime, np.datetime64)
+    _is_recognized_dtype = is_datetime64_any_dtype
 
     # define my properties & methods for delegation
     _bool_ops = [

pandas/core/arrays/period.py

@@ -75,9 +75,6 @@ def _period_array_cmp(cls, op):
     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)
@@ -90,18 +87,22 @@ def wrapper(self, other):
             other = Period(other, freq=self.freq)
             result = ordinal_op(other.ordinal)
 
-        if isinstance(other, Period):
+        if isinstance(other, self._recognized_scalars) or other is NaT:
+            other = self._scalar_type(other)
             self._check_compatible_with(other)
 
-            result = ordinal_op(other.ordinal)
+            other_i8 = self._unbox_scalar(other)
 
-        elif other is NaT:
-            result = np.empty(len(self.asi8), dtype=bool)
-            result.fill(nat_result)
+            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):
                 # TODO: could use pd.Index to do inference?
@@ -117,7 +118,7 @@ def wrapper(self, other):
                     )
                 o_mask = isna(other)
 
-            elif not is_period_dtype(other):
+            elif not cls._is_recognized_dtype(other.dtype):
                 # e.g. is_timedelta64_dtype(other)
                 return invalid_comparison(self, other, op)
 
@@ -126,7 +127,7 @@ def wrapper(self, other):
 
                 self._check_compatible_with(other)
 
-                result = ordinal_op(other.asi8)
+                result = op(self.view("i8"), other.view("i8"))
                 o_mask = other._isnan
 
             if o_mask.any():
@@ -195,6 +196,8 @@ class PeriodArray(dtl.DatetimeLikeArrayMixin, dtl.DatelikeOps):
     __array_priority__ = 1000
     _typ = "periodarray"  # ABCPeriodArray
     _scalar_type = Period
+    _recognized_scalars = (Period,)
+    _is_recognized_dtype = is_period_dtype
 
     # Names others delegate to us
     _other_ops: List[str] = []

pandas/core/arrays/timedeltas.py

@@ -89,10 +89,13 @@ def wrapper(self, other):
                 # failed to parse as timedelta
                 return invalid_comparison(self, other, op)
 
-        if _is_convertible_to_td(other) or other is NaT:
-            other = Timedelta(other)
+        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.value)
+            result = op(self.view("i8"), other_i8)
             if isna(other):
                 result.fill(nat_result)
 
@@ -116,13 +119,15 @@ def wrapper(self, other):
                     )
                 o_mask = isna(other)
 
-            elif not is_timedelta64_dtype(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
 
@@ -172,6 +177,9 @@ class TimedeltaArray(dtl.DatetimeLikeArrayMixin, dtl.TimelikeOps):
 
     _typ = "timedeltaarray"
     _scalar_type = Timedelta
+    _recognized_scalars = (timedelta, np.timedelta64, Tick)
+    _is_recognized_dtype = is_timedelta64_dtype
+
     __array_priority__ = 1000
     # define my properties & methods for delegation
     _other_ops: List[str] = []