Dispatch Index ops to Series (#27352)

Author: jbrockmendel

pandas/core/indexes/base.py

@@ -70,7 +70,6 @@
 from pandas.core.indexes.frozen import FrozenList
 import pandas.core.missing as missing
 from pandas.core.ops import get_op_result_name, make_invalid_op
-from pandas.core.ops.missing import dispatch_missing
 import pandas.core.sorting as sorting
 from pandas.core.strings import StringMethods
 
@@ -144,27 +143,18 @@ def index_arithmetic_method(self, other):
             out = op(self, other)
             return Index(out, name=self.name)
 
-        other = self._validate_for_numeric_binop(other, op)
-
         # handle time-based others
         if isinstance(other, (ABCDateOffset, np.timedelta64, timedelta)):
             return self._evaluate_with_timedelta_like(other, op)
-        elif isinstance(other, (datetime, np.datetime64)):
-            return self._evaluate_with_datetime_like(other, op)
 
-        values = self.values
-        with np.errstate(all="ignore"):
-            result = op(values, other)
+        other = self._validate_for_numeric_binop(other, op)
 
-        result = dispatch_missing(op, values, other, result)
+        from pandas import Series
 
-        attrs = self._get_attributes_dict()
-        attrs = self._maybe_update_attributes(attrs)
-        if op is divmod:
-            result = (Index(result[0], **attrs), Index(result[1], **attrs))
-        else:
-            result = Index(result, **attrs)
-        return result
+        result = op(Series(self), other)
+        if isinstance(result, tuple):
+            return (Index(result[0]), Index(result[1]))
+        return Index(result)
 
     name = "__{name}__".format(name=op.__name__)
     # TODO: docstring?
@@ -2361,10 +2351,14 @@ def _get_unique_index(self, dropna=False):
     def __add__(self, other):
         if isinstance(other, (ABCSeries, ABCDataFrame)):
             return NotImplemented
-        return Index(np.array(self) + other)
+        from pandas import Series
+
+        return Index(Series(self) + other)
 
     def __radd__(self, other):
-        return Index(other + np.array(self))
+        from pandas import Series
+
+        return Index(other + Series(self))
 
     def __iadd__(self, other):
         # alias for __add__

pandas/core/ops/__init__.py

@@ -38,6 +38,7 @@
 )
 from pandas.core.dtypes.generic import (
     ABCDataFrame,
+    ABCDatetimeArray,
     ABCIndex,
     ABCIndexClass,
     ABCSeries,
@@ -1702,10 +1703,14 @@ def wrapper(left, right):
             #  does inference in the case where `result` has object-dtype.
             return construct_result(left, result, index=left.index, name=res_name)
 
+        elif isinstance(right, (ABCDatetimeArray, pd.DatetimeIndex)):
+            result = op(left._values, right)
+            return construct_result(left, result, index=left.index, name=res_name)
+
         lvalues = left.values
         rvalues = right
-        if isinstance(rvalues, ABCSeries):
-            rvalues = rvalues.values
+        if isinstance(rvalues, (ABCSeries, ABCIndexClass)):
+            rvalues = rvalues._values
 
         with np.errstate(all="ignore"):
             result = na_op(lvalues, rvalues)

pandas/core/ops/missing.py

@@ -148,40 +148,26 @@ def mask_zero_div_zero(x, y, result):
     return result
 
 
-def dispatch_missing(op, left, right, result):
+def dispatch_fill_zeros(op, left, right, result):
     """
-    Fill nulls caused by division by zero, casting to a different dtype
-    if necessary.
+    Call fill_zeros with the appropriate fill value depending on the operation,
+    with special logic for divmod and rdivmod.
 
     Parameters
     ----------
     op : function (operator.add, operator.div, ...)
-    left : object (Index for non-reversed ops)
-    right : object (Index fof reversed ops)
+    left : object (np.ndarray for non-reversed ops)
+    right : object (np.ndarray for reversed ops)
     result : ndarray
 
     Returns
     -------
-    result : ndarray
-    """
-    if op is operator.floordiv:
-        # Note: no need to do this for truediv; in py3 numpy behaves the way
-        #  we want.
-        result = mask_zero_div_zero(left, right, result)
-    elif op is operator.mod:
-        result = fill_zeros(result, left, right, "__mod__", np.nan)
-    elif op is divmod:
-        res0 = mask_zero_div_zero(left, right, result[0])
-        res1 = fill_zeros(result[1], left, right, "__divmod__", np.nan)
-        result = (res0, res1)
-    return result
-
+    result : np.ndarray
 
-# FIXME: de-duplicate with dispatch_missing
-def dispatch_fill_zeros(op, left, right, result):
-    """
-    Call fill_zeros with the appropriate fill value depending on the operation,
-    with special logic for divmod and rdivmod.
+    Notes
+    -----
+    For divmod and rdivmod, the `result` parameter and returned `result`
+    is a 2-tuple of ndarray objects.
     """
     if op is divmod:
         result = (

pandas/tests/arithmetic/test_object.py

@@ -103,18 +103,6 @@ def test_add_extension_scalar(self, other, box, op):
         result = op(arr, other)
         tm.assert_equal(result, expected)
 
-    @pytest.mark.parametrize(
-        "box",
-        [
-            pytest.param(
-                pd.Index,
-                marks=pytest.mark.xfail(reason="Does not mask nulls", raises=TypeError),
-            ),
-            pd.Series,
-            pd.DataFrame,
-        ],
-        ids=lambda x: x.__name__,
-    )
     def test_objarr_add_str(self, box):
         ser = pd.Series(["x", np.nan, "x"])
         expected = pd.Series(["xa", np.nan, "xa"])
@@ -125,18 +113,6 @@ def test_objarr_add_str(self, box):
         result = ser + "a"
         tm.assert_equal(result, expected)
 
-    @pytest.mark.parametrize(
-        "box",
-        [
-            pytest.param(
-                pd.Index,
-                marks=pytest.mark.xfail(reason="Does not mask nulls", raises=TypeError),
-            ),
-            pd.Series,
-            pd.DataFrame,
-        ],
-        ids=lambda x: x.__name__,
-    )
     def test_objarr_radd_str(self, box):
         ser = pd.Series(["x", np.nan, "x"])
         expected = pd.Series(["ax", np.nan, "ax"])

pandas/tests/indexes/test_common.py

@@ -144,6 +144,7 @@ def test_set_name_methods(self, indices):
         assert res is None
         assert indices.name == new_name
         assert indices.names == [new_name]
+        # FIXME: dont leave commented-out
         # with pytest.raises(TypeError, match="list-like"):
         #    # should still fail even if it would be the right length
         #    ind.set_names("a")