Removed block management

Author: WillAyd

pandas/core/groupby/generic.py

@@ -979,126 +979,6 @@ def _iterate_slices(self) -> Iterable[Series]:
 
                 yield values
 
-    def _cython_agg_general(
-        self, how: str, alt=None, numeric_only: bool = True, min_count: int = -1
-    ):
-        new_items, new_blocks = self._cython_agg_blocks(
-            how, alt=alt, numeric_only=numeric_only, min_count=min_count
-        )
-        return self._wrap_agged_blocks(new_items, new_blocks)
-
-    def _cython_agg_blocks(
-        self, how: str, alt=None, numeric_only: bool = True, min_count: int = -1
-    ):
-        # TODO: the actual managing of mgr_locs is a PITA
-        # here, it should happen via BlockManager.combine
-
-        data = self._get_data_to_aggregate()
-
-        if numeric_only:
-            data = data.get_numeric_data(copy=False)
-
-        new_blocks = []
-        new_items = []
-        deleted_items = []
-        no_result = object()
-        for block in data.blocks:
-            # Avoid inheriting result from earlier in the loop
-            result = no_result
-            locs = block.mgr_locs.as_array
-            try:
-                result, _ = self.grouper.aggregate(
-                    block.values, how, axis=1, min_count=min_count
-                )
-            except NotImplementedError:
-                # generally if we have numeric_only=False
-                # and non-applicable functions
-                # try to python agg
-
-                if alt is None:
-                    # we cannot perform the operation
-                    # in an alternate way, exclude the block
-                    assert how == "ohlc"
-                    deleted_items.append(locs)
-                    continue
-
-                # call our grouper again with only this block
-                obj = self.obj[data.items[locs]]
-                if obj.shape[1] == 1:
-                    # Avoid call to self.values that can occur in DataFrame
-                    #  reductions; see GH#28949
-                    obj = obj.iloc[:, 0]
-
-                s = get_groupby(obj, self.grouper)
-                try:
-                    result = s.aggregate(lambda x: alt(x, axis=self.axis))
-                except TypeError:
-                    # we may have an exception in trying to aggregate
-                    # continue and exclude the block
-                    deleted_items.append(locs)
-                    continue
-                else:
-                    result = cast(DataFrame, result)
-                    # unwrap DataFrame to get array
-                    assert len(result._data.blocks) == 1
-                    result = result._data.blocks[0].values
-                    if isinstance(result, np.ndarray) and result.ndim == 1:
-                        result = result.reshape(1, -1)
-
-            finally:
-                assert not isinstance(result, DataFrame)
-
-                if result is not no_result:
-                    # see if we can cast the block back to the original dtype
-                    result = maybe_downcast_numeric(result, block.dtype)
-
-                    if block.is_extension and isinstance(result, np.ndarray):
-                        # e.g. block.values was an IntegerArray
-                        # (1, N) case can occur if block.values was Categorical
-                        #  and result is ndarray[object]
-                        assert result.ndim == 1 or result.shape[0] == 1
-                        try:
-                            # Cast back if feasible
-                            result = type(block.values)._from_sequence(
-                                result.ravel(), dtype=block.values.dtype
-                            )
-                        except ValueError:
-                            # reshape to be valid for non-Extension Block
-                            result = result.reshape(1, -1)
-
-                    newb = block.make_block(result)
-
-            new_items.append(locs)
-            new_blocks.append(newb)
-
-        if len(new_blocks) == 0:
-            raise DataError("No numeric types to aggregate")
-
-        # reset the locs in the blocks to correspond to our
-        # current ordering
-        indexer = np.concatenate(new_items)
-        new_items = data.items.take(np.sort(indexer))
-
-        if len(deleted_items):
-
-            # we need to adjust the indexer to account for the
-            # items we have removed
-            # really should be done in internals :<
-
-            deleted = np.concatenate(deleted_items)
-            ai = np.arange(len(data))
-            mask = np.zeros(len(data))
-            mask[deleted] = 1
-            indexer = (ai - mask.cumsum())[indexer]
-
-        offset = 0
-        for b in new_blocks:
-            loc = len(b.mgr_locs)
-            b.mgr_locs = indexer[offset : (offset + loc)]
-            offset += loc
-
-        return new_items, new_blocks
-
     def _aggregate_frame(self, func, *args, **kwargs) -> DataFrame:
         if self.grouper.nkeys != 1:
             raise AssertionError("Number of keys must be 1")