REF/TYP: use OpsMixin for DataFrame (pandas-dev#37044)

* REF/TYP: use OpsMixin for arithmetic methods

* REF: separate arith_method_FRAME from flex_arith_method_FRAME

* whatsnew

* REF/TYP: use OpsMixin for logical methods (pandas-dev#36964)

* TST: insert 'match' to bare pytest raises in pandas/tests/tools/test_to_datetime.py (pandas-dev#37027)

* TST: insert 'match' to bare pytest raises in pandas/tests/test_flags.py (pandas-dev#37026)

Co-authored-by: Rajat Bishnoi <[email protected]>

* TYP: generic, series, frame (pandas-dev#36989)

* CI: pin pymysql pandas-dev#36465 (pandas-dev#36847)

* CI: unpin sql to verify the bugs pandas-dev#36465

* CI: pin sqlalchemy

* CI: pin pymsql

* CI: pin sqlalchemy

* CI: pin pymysql

* CI: pin pymysql

* CI: add note

* CLN/REF: de-duplicate DatetimeTZBlock.setitem (pandas-dev#37019)

* REF/TYP: define NDFrame numeric methods non-dynamically (pandas-dev#37017)

* CLN: require td64 in TimedeltaBlock (pandas-dev#37018)

* BUG: Raise ValueError instead of bare Exception in sanitize_array (pandas-dev#35769)

* CLN: collected cleanups, warning suppression in tests (pandas-dev#37021)

* REF/TYP: use OpsMixin for DataFrame

* CLN: remove get_op_name

* mypy fixup

* de-privatize

Co-authored-by: krajatcl <[email protected]>
Co-authored-by: Rajat Bishnoi <[email protected]>
Co-authored-by: Fangchen Li <[email protected]>
Co-authored-by: Micah Smith <[email protected]>

pandas/core/frame.py

@@ -124,6 +124,7 @@
     relabel_result,
     transform,
 )
+from pandas.core.arraylike import OpsMixin
 from pandas.core.arrays import Categorical, ExtensionArray
 from pandas.core.arrays.sparse import SparseFrameAccessor
 from pandas.core.construction import extract_array
@@ -336,7 +337,7 @@
 # DataFrame class
 
 
-class DataFrame(NDFrame):
+class DataFrame(NDFrame, OpsMixin):
     """
     Two-dimensional, size-mutable, potentially heterogeneous tabular data.
 
@@ -5838,7 +5839,87 @@ def reorder_levels(self, order, axis=0) -> DataFrame:
         return result
 
     # ----------------------------------------------------------------------
-    # Arithmetic / combination related
+    # Arithmetic Methods
+
+    def _cmp_method(self, other, op):
+        axis = 1  # only relevant for Series other case
+
+        self, other = ops.align_method_FRAME(self, other, axis, flex=False, level=None)
+
+        # See GH#4537 for discussion of scalar op behavior
+        new_data = self._dispatch_frame_op(other, op, axis=axis)
+        return self._construct_result(new_data)
+
+    def _arith_method(self, other, op):
+        if ops.should_reindex_frame_op(self, other, op, 1, 1, None, None):
+            return ops.frame_arith_method_with_reindex(self, other, op)
+
+        axis = 1  # only relevant for Series other case
+
+        self, other = ops.align_method_FRAME(self, other, axis, flex=True, level=None)
+
+        new_data = self._dispatch_frame_op(other, op, axis=axis)
+        return self._construct_result(new_data)
+
+    _logical_method = _arith_method
+
+    def _dispatch_frame_op(self, right, func, axis: Optional[int] = None):
+        """
+        Evaluate the frame operation func(left, right) by evaluating
+        column-by-column, dispatching to the Series implementation.
+
+        Parameters
+        ----------
+        right : scalar, Series, or DataFrame
+        func : arithmetic or comparison operator
+        axis : {None, 0, 1}
+
+        Returns
+        -------
+        DataFrame
+        """
+        # Get the appropriate array-op to apply to each column/block's values.
+        array_op = ops.get_array_op(func)
+
+        right = lib.item_from_zerodim(right)
+        if not is_list_like(right):
+            # i.e. scalar, faster than checking np.ndim(right) == 0
+            bm = self._mgr.apply(array_op, right=right)
+            return type(self)(bm)
+
+        elif isinstance(right, DataFrame):
+            assert self.index.equals(right.index)
+            assert self.columns.equals(right.columns)
+            # TODO: The previous assertion `assert right._indexed_same(self)`
+            #  fails in cases with empty columns reached via
+            #  _frame_arith_method_with_reindex
+
+            bm = self._mgr.operate_blockwise(right._mgr, array_op)
+            return type(self)(bm)
+
+        elif isinstance(right, Series) and axis == 1:
+            # axis=1 means we want to operate row-by-row
+            assert right.index.equals(self.columns)
+
+            right = right._values
+            # maybe_align_as_frame ensures we do not have an ndarray here
+            assert not isinstance(right, np.ndarray)
+
+            arrays = [array_op(l, r) for l, r in zip(self._iter_column_arrays(), right)]
+
+        elif isinstance(right, Series):
+            assert right.index.equals(self.index)  # Handle other cases later
+            right = right._values
+
+            arrays = [array_op(l, right) for l in self._iter_column_arrays()]
+
+        else:
+            # Remaining cases have less-obvious dispatch rules
+            raise NotImplementedError(right)
+
+        return type(self)._from_arrays(
+            arrays, self.columns, self.index, verify_integrity=False
+        )
 
     def _combine_frame(self, other: DataFrame, func, fill_value=None):
         # at this point we have `self._indexed_same(other)`
@@ -5857,7 +5938,7 @@ def _arith_op(left, right):
                 left, right = ops.fill_binop(left, right, fill_value)
                 return func(left, right)
 
-        new_data = ops.dispatch_to_series(self, other, _arith_op)
+        new_data = self._dispatch_frame_op(other, _arith_op)
         return new_data
 
     def _construct_result(self, result) -> DataFrame:
@@ -5879,6 +5960,9 @@ def _construct_result(self, result) -> DataFrame:
         out.index = self.index
         return out
 
+    # ----------------------------------------------------------------------
+    # Combination-Related
+
     @Appender(
         """
 Returns
@@ -7254,7 +7338,7 @@ def diff(self, periods: int = 1, axis: Axis = 0) -> DataFrame:
         bm_axis = self._get_block_manager_axis(axis)
 
         if bm_axis == 0 and periods != 0:
-            return self - self.shift(periods, axis=axis)  # type: ignore[operator]
+            return self - self.shift(periods, axis=axis)
 
         new_data = self._mgr.diff(n=periods, axis=bm_axis)
         return self._constructor(new_data)