PERF: Remove columnarize [upstream] (modin-project#23)

modin/core/storage_formats/base/query_compiler.py

@@ -1065,27 +1065,6 @@ def transpose(self, *args, **kwargs):  # noqa: PR02
             self, *args, **kwargs
         )
 
-    def columnarize(self):
-        """
-        Transpose this QueryCompiler if it has a single row but multiple columns.
-
-        This method should be called for QueryCompilers representing a Series object,
-        i.e. ``self.is_series_like()`` should be True.
-
-        Returns
-        -------
-        BaseQueryCompiler
-            Transposed new QueryCompiler or self.
-        """
-        if self._shape_hint == "column":
-            return self
-
-        if len(self.columns) != 1 or (
-            len(self.index) == 1 and self.index[0] == MODIN_UNNAMED_SERIES_LABEL
-        ):
-            return self.transpose()
-        return self
-
     def is_series_like(self):
         """
         Check whether this QueryCompiler can represent ``modin.pandas.Series`` object.

modin/pandas/base.py

@@ -769,10 +769,10 @@ def all(
                         "level > 0 or level < -1 only valid with MultiIndex"
                     )
                 return self.groupby(level=level, axis=axis, sort=False).all(**kwargs)
-            return self._reduce_dimension(
-                self._query_compiler.all(
+            compiler = self._query_compiler.all(
                     axis=axis, bool_only=bool_only, skipna=skipna, level=level, **kwargs
                 )
+            return self._reduce_dimension(compiler.transpose() if axis == 0 else compiler
             )
         else:
             if bool_only:
@@ -790,7 +790,7 @@ def all(
                         skipna=skipna,
                         level=level,
                         **kwargs,
-                    )
+                    ).transpose()
                 )
             if isinstance(result, BasePandasDataset):
                 return result.all(
@@ -832,10 +832,10 @@ def any(
                         "level > 0 or level < -1 only valid with MultiIndex"
                     )
                 return self.groupby(level=level, axis=axis, sort=False).any(**kwargs)
-            return self._reduce_dimension(
-                self._query_compiler.any(
-                    axis=axis, bool_only=bool_only, skipna=skipna, level=level, **kwargs
-                )
+            compiler = self._query_compiler.any(
+                axis=axis, bool_only=bool_only, skipna=skipna, level=level, **kwargs
+            )            
+            return self._reduce_dimension(compiler.transpose() if axis == 0 else compiler
             )
         else:
             if bool_only:
@@ -851,7 +851,7 @@ def any(
                         skipna=skipna,
                         level=level,
                         **kwargs,
-                    )
+                    ).transpose()
                 )
             if isinstance(result, BasePandasDataset):
                 return result.any(
@@ -1172,11 +1172,10 @@ def count(self, axis=0, level=None, numeric_only=False):  # noqa: PR01, RT01, D2
             if not frame._query_compiler.has_multiindex(axis=axis):
                 raise TypeError("Can only count levels on hierarchical columns.")
             return frame.groupby(level=level, axis=axis, sort=True).count()
-        return frame._reduce_dimension(
-            frame._query_compiler.count(
+        compiler =             frame._query_compiler.count(
                 axis=axis, level=level, numeric_only=numeric_only
             )
-        )
+        return frame._reduce_dimension(compiler.transpose() if axis == 0 else compiler)
 
     def cummax(self, axis=None, skipna=True, *args, **kwargs):  # noqa: PR01, RT01, D200
         """
@@ -1805,16 +1804,14 @@ def kurt(
             if numeric_only is None or numeric_only
             else self
         )
-
-        return self._reduce_dimension(
-            data._query_compiler.kurt(
+        compiler =            data._query_compiler.kurt(
                 axis=axis,
                 skipna=skipna,
                 level=level,
                 numeric_only=numeric_only,
                 **kwargs,
             )
-        )
+        return self._reduce_dimension(compiler.transpose() if axis == 0 else compiler)
 
     kurtosis = kurt
 
@@ -1912,15 +1909,14 @@ def max(
         validate_bool_kwarg(skipna, "skipna", none_allowed=False)
         axis = self._get_axis_number(axis)
         data = self._validate_dtypes_min_max(axis, numeric_only)
-        return data._reduce_dimension(
-            data._query_compiler.max(
-                axis=axis,
-                skipna=skipna,
-                level=level,
-                numeric_only=numeric_only,
-                **kwargs,
-            )
-        )
+        compiler = data._query_compiler.max(
+            axis=axis,
+            skipna=skipna,
+            level=level,
+            numeric_only=numeric_only,
+            **kwargs,
+        )        
+        return data._reduce_dimension(compiler.transpose() if axis == 0 else compiler)
 
     def _stat_operation(
         self,
@@ -1986,14 +1982,14 @@ def _stat_operation(
             numeric_only=numeric_only,
             **kwargs,
         )
-        return self._reduce_dimension(result_qc)
+        return self._reduce_dimension(result_qc.transpose() if axis == 0 else result_qc)
 
     def memory_usage(self, index=True, deep=False):  # noqa: PR01, RT01, D200
         """
         Return the memory usage of the `BasePandasDataset`.
         """
         return self._reduce_dimension(
-            self._query_compiler.memory_usage(index=index, deep=deep)
+            self._query_compiler.memory_usage(index=index, deep=deep).transpose()
         )
 
     def min(
@@ -2010,15 +2006,14 @@ def min(
         validate_bool_kwarg(skipna, "skipna", none_allowed=False)
         axis = self._get_axis_number(axis)
         data = self._validate_dtypes_min_max(axis, numeric_only)
-        return data._reduce_dimension(
-            data._query_compiler.min(
+        compiler = data._query_compiler.min(
                 axis=axis,
                 skipna=skipna,
                 level=level,
                 numeric_only=numeric_only,
                 **kwargs,
             )
-        )
+        return data._reduce_dimension(compiler.transpose() if axis == 0 else compiler)    
 
     def mod(
         self, other, axis="columns", level=None, fill_value=None
@@ -2035,10 +2030,11 @@ def mode(self, axis=0, numeric_only=False, dropna=True):  # noqa: PR01, RT01, D2
         Get the mode(s) of each element along the selected axis.
         """
         axis = self._get_axis_number(axis)
-        return self.__constructor__(
-            query_compiler=self._query_compiler.mode(
+        compiler = self._query_compiler.mode(
                 axis=axis, numeric_only=numeric_only, dropna=dropna
             )
+        return self.__constructor__(
+            query_compiler=compiler.transpose() if axis == 0 else compiler
         )
 
     def mul(
@@ -2072,9 +2068,8 @@ def nunique(self, axis=0, dropna=True):  # noqa: PR01, RT01, D200
         Return number of unique elements in the `BasePandasDataset`.
         """
         axis = self._get_axis_number(axis)
-        return self._reduce_dimension(
-            self._query_compiler.nunique(axis=axis, dropna=dropna)
-        )
+        compiler =  self._query_compiler.nunique(axis=axis, dropna=dropna)
+        return self._reduce_dimension(compiler.transpose() if self._get_axis_number(axis) == 0 else compiler)
 
     def pct_change(
         self, periods=1, fill_method="pad", limit=None, freq=None, **kwargs

modin/pandas/dataframe.py

@@ -2055,27 +2055,24 @@ def sum(
             return self.groupby(level=level, axis=axis, sort=False).sum(
                 numeric_only=numeric_only, min_count=min_count
             )
-        if min_count > 1:
-            return data._reduce_dimension(
-                data._query_compiler.sum_min_count(
-                    axis=axis,
-                    skipna=skipna,
-                    level=level,
-                    numeric_only=numeric_only,
-                    min_count=min_count,
-                    **kwargs,
-                )
-            )
-        return data._reduce_dimension(
-            data._query_compiler.sum(
+        sum_result = (data._query_compiler.sum_min_count(
                 axis=axis,
                 skipna=skipna,
                 level=level,
                 numeric_only=numeric_only,
                 min_count=min_count,
                 **kwargs,
-            )
-        )
+            ) if min_count > 1 else data._query_compiler.sum(
+                axis=axis,
+                skipna=skipna,
+                level=level,
+                numeric_only=numeric_only,
+                min_count=min_count,
+                **kwargs,
+            ))
+        if axis == 0:
+            sum_result = sum_result.transpose()
+        return data._reduce_dimension(sum_result)
 
     def to_feather(self, path, **kwargs):  # pragma: no cover # noqa: PR01, RT01, D200
         """

modin/pandas/series.py

@@ -143,7 +143,7 @@ def __init__(
                     )
                 )
             )._query_compiler
-        self._query_compiler = query_compiler.columnarize()
+        self._query_compiler = query_compiler
         if name is not None:
             self.name = name