Remove unused methods _nlargest and _nsmallest from icolumn, numerical_column_cpu, and dataframe_cpu (#415)

Summary:
Pull Request resolved: #415

Removes unused private methods in icolumn.py bringing code coverage to 79.2%.

Reviewed By: wenleix

Differential Revision: D37503197

fbshipit-source-id: abbcc601a76100f9568b57a4d8b8f3b924eb1703

Author: andrewaikens87

torcharrow/icolumn.py

@@ -1420,34 +1420,6 @@ def _count(self):
         """Return number of non-NA/null observations pgf the column/frame"""
         return len(self) - self.null_count
 
-    @trace
-    @expression
-    def _nlargest(
-        self,
-        n=5,
-        columns: ty.Optional[ty.List[str]] = None,
-        keep: ty.Literal["last", "first"] = "first",
-    ):
-        """Returns a new data of the *n* largest element."""
-        # keep="all" not supported
-        if columns is not None:
-            raise TypeError(
-                "computing n-largest on non-structured column can't have 'columns' parameter"
-            )
-        return self.sort(ascending=False).head(n)
-
-    @trace
-    @expression
-    def _nsmallest(self, n=5, columns: ty.Optional[ty.List[str]] = None, keep="first"):
-        """Returns a new data of the *n* smallest element."""
-        # keep="all" not supported
-        if columns is not None:
-            raise TypeError(
-                "computing n-smallest on non-structured column can't have 'columns' parameter"
-            )
-
-        return self.sort(ascending=True).head(n)
-
     @trace
     @expression
     def _nunique(self, drop_null=True):

torcharrow/velox_rt/dataframe_cpu.py

@@ -617,29 +617,6 @@ def func(tup):
             res._extend([None] * self.null_count)
         return res._finalize()
 
-    @trace
-    @expression
-    def _nlargest(
-        self,
-        n=5,
-        columns: Optional[List[str]] = None,
-        keep="first",
-    ):
-        """Returns a new dataframe of the *n* largest elements."""
-        # Todo add keep arg
-        return self.sort(by=columns, ascending=False).head(n)
-
-    @trace
-    @expression
-    def _nsmallest(
-        self,
-        n=5,
-        columns: Optional[List[str]] = None,
-        keep="first",
-    ):
-        """Returns a new dataframe of the *n* smallest elements."""
-        return self.sort(by=columns, ascending=True).head(n)
-
     # operators --------------------------------------------------------------
 
     @expression

torcharrow/velox_rt/numerical_column_cpu.py

@@ -193,30 +193,6 @@ def sort(
 
         return ColumnCpuMixin._from_velox(self.device, self.dtype, col, True)
 
-    @trace
-    @expression
-    def _nlargest(
-        self,
-        n=5,
-        columns: Optional[List[str]] = None,
-        keep="first",
-    ):
-        if columns is not None:
-            raise TypeError(
-                "computing n-largest on numerical column can't have 'columns' parameter"
-            )
-        return self.sort(columns=None, ascending=False, na_position=keep).head(n)
-
-    @trace
-    @expression
-    def _nsmallest(self, n=5, columns: Optional[List[str]] = None, keep="first"):
-        if columns is not None:
-            raise TypeError(
-                "computing n-smallest on numerical column can't have 'columns' parameter"
-            )
-
-        return self.sort(columns=None, ascending=True, na_position=keep).head(n)
-
     @trace
     @expression
     def _nunique(self, drop_null=True):