REF: use BlockManager.apply for cython_agg_blocks, apply_blockwise (#…

…35900)

pandas/core/groupby/generic.py

@@ -1035,8 +1035,6 @@ def _cython_agg_blocks(
         if numeric_only:
             data = data.get_numeric_data(copy=False)
 
-        agg_blocks: List["Block"] = []
-
         no_result = object()
 
         def cast_agg_result(result, values: ArrayLike, how: str) -> ArrayLike:
@@ -1118,23 +1116,14 @@ def blk_func(bvalues: ArrayLike) -> ArrayLike:
             res_values = cast_agg_result(result, bvalues, how)
             return res_values
 
-        for i, block in enumerate(data.blocks):
-            try:
-                nbs = block.apply(blk_func)
-            except (NotImplementedError, TypeError):
-                # TypeError -> we may have an exception in trying to aggregate
-                #  continue and exclude the block
-                # NotImplementedError -> "ohlc" with wrong dtype
-                pass
-            else:
-                agg_blocks.extend(nbs)
+        # TypeError -> we may have an exception in trying to aggregate
+        #  continue and exclude the block
+        # NotImplementedError -> "ohlc" with wrong dtype
+        new_mgr = data.apply(blk_func, ignore_failures=True)
 
-        if not agg_blocks:
+        if not len(new_mgr):
             raise DataError("No numeric types to aggregate")
 
-        # reset the locs in the blocks to correspond to our
-        # current ordering
-        new_mgr = data._combine(agg_blocks)
         return new_mgr
 
     def _aggregate_frame(self, func, *args, **kwargs) -> DataFrame:

pandas/core/internals/managers.py

@@ -350,14 +350,24 @@ def operate_blockwise(self, other: "BlockManager", array_op) -> "BlockManager":
         """
         return operate_blockwise(self, other, array_op)
 
-    def apply(self: T, f, align_keys=None, **kwargs) -> T:
+    def apply(
+        self: T,
+        f,
+        align_keys: Optional[List[str]] = None,
+        ignore_failures: bool = False,
+        **kwargs,
+    ) -> T:
         """
         Iterate over the blocks, collect and create a new BlockManager.
 
         Parameters
         ----------
         f : str or callable
             Name of the Block method to apply.
+        align_keys: List[str] or None, default None
+        ignore_failures: bool, default False
+        **kwargs
+            Keywords to pass to `f`
 
         Returns
         -------
@@ -387,12 +397,20 @@ def apply(self: T, f, align_keys=None, **kwargs) -> T:
                         # otherwise we have an ndarray
                         kwargs[k] = obj[b.mgr_locs.indexer]
 
-            if callable(f):
-                applied = b.apply(f, **kwargs)
-            else:
-                applied = getattr(b, f)(**kwargs)
+            try:
+                if callable(f):
+                    applied = b.apply(f, **kwargs)
+                else:
+                    applied = getattr(b, f)(**kwargs)
+            except (TypeError, NotImplementedError):
+                if not ignore_failures:
+                    raise
+                continue
             result_blocks = _extend_blocks(applied, result_blocks)
 
+        if ignore_failures:
+            return self._combine(result_blocks)
+
         if len(result_blocks) == 0:
             return self.make_empty(self.axes)
 
@@ -704,7 +722,7 @@ def get_numeric_data(self, copy: bool = False) -> "BlockManager":
         self._consolidate_inplace()
         return self._combine([b for b in self.blocks if b.is_numeric], copy)
 
-    def _combine(self, blocks: List[Block], copy: bool = True) -> "BlockManager":
+    def _combine(self: T, blocks: List[Block], copy: bool = True) -> T:
         """ return a new manager with the blocks """
         if len(blocks) == 0:
             return self.make_empty()

pandas/core/window/rolling.py

@@ -489,8 +489,6 @@ def _apply_blockwise(
         if self._selected_obj.ndim == 1:
             return self._apply_series(homogeneous_func)
 
-        # This isn't quite blockwise, since `blocks` is actually a collection
-        #  of homogenenous DataFrames.
         _, obj = self._create_blocks(self._selected_obj)
         mgr = obj._mgr
 
@@ -500,25 +498,14 @@ def hfunc(bvalues: ArrayLike) -> ArrayLike:
             res_values = homogeneous_func(values)
             return getattr(res_values, "T", res_values)
 
-        skipped: List[int] = []
-        res_blocks: List["Block"] = []
-        for i, blk in enumerate(mgr.blocks):
-            try:
-                nbs = blk.apply(hfunc)
-
-            except (TypeError, NotImplementedError):
-                skipped.append(i)
-                continue
-
-            res_blocks.extend(nbs)
+        new_mgr = mgr.apply(hfunc, ignore_failures=True)
+        out = obj._constructor(new_mgr)
 
-        if not len(res_blocks) and skipped:
+        if out.shape[1] == 0 and obj.shape[1] > 0:
             raise DataError("No numeric types to aggregate")
-        elif not len(res_blocks):
+        elif out.shape[1] == 0:
             return obj.astype("float64")
 
-        new_mgr = mgr._combine(res_blocks)
-        out = obj._constructor(new_mgr)
         self._insert_on_column(out, obj)
         return out