[DataFrame] Impement sort_values and sort_index

python/ray/dataframe/dataframe.py

@@ -3272,15 +3272,174 @@ def slice_shift(self, periods=1, axis=0):
     def sort_index(self, axis=0, level=None, ascending=True, inplace=False,
                    kind='quicksort', na_position='last', sort_remaining=True,
                    by=None):
-        raise NotImplementedError(
-            "To contribute to Pandas on Ray, please visit "
-            "github.com/ray-project/ray.")
+        """Sort a DataFrame by one of the indices (columns or index).
+
+        Args:
+            axis: The axis to sort over.
+            level: The MultiIndex level to sort over.
+            ascending: Ascending or descending
+            inplace: Whether or not to update this DataFrame inplace.
+            kind: How to perform the sort.
+            na_position: Where to position NA on the sort.
+            sort_remaining: On Multilevel Index sort based on all levels.
+            by: (Deprecated) argument to pass to sort_values.
+
+        Returns:
+            A sorted DataFrame
+        """
+        if level is not None:
+            raise NotImplementedError("Multilevel index not yet implemented.")
+
+        if by is not None:
+            warnings.warn("by argument to sort_index is deprecated, "
+                          "please use .sort_values(by=...)",
+                          FutureWarning, stacklevel=2)
+            if level is not None:
+                raise ValueError("unable to simultaneously sort by and level")
+            return self.sort_values(by, axis=axis, ascending=ascending,
+                                    inplace=inplace)
+
+        axis = pd.DataFrame()._get_axis_number(axis)
+
+        args = (axis, level, ascending, False, kind, na_position,
+                sort_remaining)
+
+        def _sort_helper(df, index, axis, *args):
+            if axis == 0:
+                df.index = index
+            else:
+                df.columns = index
+
+            return df.sort_index(*args)
+
+        if axis == 0:
+            index = (self.index)
+            new_column_parts = _map_partitions(
+                lambda df: _sort_helper(df, index, axis, *args),
+                self._col_partitions)
+
+            new_columns = self.columns
+            new_index = self.index.sort_values()
+            new_row_parts = None
+        else:
+            columns = (self.columns)
+            new_row_parts = _map_partitions(
+                lambda df: _sort_helper(df, columns, axis, *args),
+                self._row_partitions)
+
+            new_columns = self.columns.sort_values()
+            new_index = self.index
+            new_column_parts = None
+
+        if not inplace:
+            return DataFrame(col_partitions=new_column_parts,
+                             row_partitions=new_row_parts,
+                             index=new_index,
+                             columns=new_columns)
+        else:
+            self._update_inplace(row_partitions=new_row_parts,
+                                 col_partitions=new_column_parts,
+                                 columns=new_columns,
+                                 index=new_index)
 
     def sort_values(self, by, axis=0, ascending=True, inplace=False,
                     kind='quicksort', na_position='last'):
-        raise NotImplementedError(
-            "To contribute to Pandas on Ray, please visit "
-            "github.com/ray-project/ray.")
+        """Sorts by a column/row or list of columns/rows.
+
+        Args:
+            by: A list of labels for the axis to sort over.
+            axis: The axis to sort.
+            ascending: Sort in ascending or descending order.
+            inplace: If true, do the operation inplace.
+            kind: How to sort.
+            na_position: Where to put np.nan values.
+
+        Returns:
+             A sorted DataFrame.
+        """
+
+        axis = pd.DataFrame()._get_axis_number(axis)
+
+        if not is_list_like(by):
+            by = [by]
+
+        if axis == 0:
+            broadcast_value_dict = {str(col): self[col] for col in by}
+            broadcast_values = pd.DataFrame(broadcast_value_dict)
+        else:
+            broadcast_value_list = [to_pandas(self[row::len(self.index)])
+                                    for row in by]
+
+            index_builder = list(zip(broadcast_value_list, by))
+
+            for row, idx in index_builder:
+                row.index = [str(idx)]
+
+            # Put this here to match the by below.
+            by = [str(col) for col in by]
+            broadcast_values = pd.concat([row for row, idx in index_builder])
+
+        # We are converting the by to string here so that we don't have a
+        # collision with the RangeIndex on the inner frame. It is cheap and
+        # gaurantees that we sort by the correct column.
+        by = [str(col) for col in by]
+
+        args = (by, axis, ascending, False, kind, na_position)
+
+        def _sort_helper(df, broadcast_values, axis, *args):
+            """Sorts the data on a partition.
+
+            Args:
+                df: The DataFrame to sort.
+                broadcast_values: The by DataFrame to use for the sort.
+                axis: The axis to sort over.
+                args: The args for the sort.
+
+            Returns:
+                 A new sorted DataFrame.
+            """
+            if axis == 0:
+                broadcast_values.index = df.index
+                names = broadcast_values.columns
+            else:
+                broadcast_values.columns = df.columns
+                names = broadcast_values.index
+
+            return pd.concat([df, broadcast_values], axis=axis ^ 1,
+                             copy=False).sort_values(*args)\
+                .drop(names, axis=axis ^ 1)
+
+        if axis == 0:
+            new_column_partitions = _map_partitions(
+                lambda df: _sort_helper(df, broadcast_values, axis, *args),
+                self._col_partitions)
+
+            new_row_partitions = None
+            new_columns = self.columns
+
+            # This is important because it allows us to get the axis that we
+            # aren't sorting over. We need the order of the columns/rows and
+            # this will provide that in the return value.
+            new_index = broadcast_values.sort_values(*args).index
+        else:
+            new_row_partitions = _map_partitions(
+                lambda df: _sort_helper(df, broadcast_values, axis, *args),
+                self._row_partitions)
+
+            new_column_partitions = None
+            new_columns = broadcast_values.sort_values(*args).columns
+            new_index = self.index
+
+        if inplace:
+            self._update_inplace(row_partitions=new_row_partitions,
+                                 col_partitions=new_column_partitions,
+                                 columns=new_columns,
+                                 index=new_index)
+        else:
+            return DataFrame(row_partitions=new_row_partitions,
+                             col_partitions=new_column_partitions,
+                             columns=new_columns,
+                             index=new_index)
 
     def sortlevel(self, level=0, axis=0, ascending=True, inplace=False,
                   sort_remaining=True):

python/ray/dataframe/test/test_dataframe.py

@@ -927,8 +927,6 @@ def test_append():
 
     pandas_df2 = pd.DataFrame({"col5": [0], "col6": [1]})
 
-    print(ray_df.append(ray_df2))
-
     assert ray_df_equals_pandas(ray_df.append(ray_df2),
                                 pandas_df.append(pandas_df2))
 
@@ -2619,17 +2617,43 @@ def test_slice_shift():
 
 
 def test_sort_index():
-    ray_df = create_test_dataframe()
+    pandas_df = pd.DataFrame(np.random.randint(0, 100, size=(1000, 100)))
+    ray_df = rdf.DataFrame(pandas_df)
 
-    with pytest.raises(NotImplementedError):
-        ray_df.sort_index()
+    pandas_result = pandas_df.sort_index()
+    ray_result = ray_df.sort_index()
+
+    ray_df_equals_pandas(ray_result, pandas_result)
+
+    pandas_result = pandas_df.sort_index(ascending=False)
+    ray_result = ray_df.sort_index(ascending=False)
+
+    ray_df_equals_pandas(ray_result, pandas_result)
 
 
 def test_sort_values():
-    ray_df = create_test_dataframe()
+    pandas_df = pd.DataFrame(np.random.randint(0, 100, size=(1000, 100)))
+    ray_df = rdf.DataFrame(pandas_df)
 
-    with pytest.raises(NotImplementedError):
-        ray_df.sort_values(None)
+    pandas_result = pandas_df.sort_values(by=1)
+    ray_result = ray_df.sort_values(by=1)
+
+    ray_df_equals_pandas(ray_result, pandas_result)
+
+    pandas_result = pandas_df.sort_values(by=1, axis=1)
+    ray_result = ray_df.sort_values(by=1, axis=1)
+
+    ray_df_equals_pandas(ray_result, pandas_result)
+
+    pandas_result = pandas_df.sort_values(by=[1, 3])
+    ray_result = ray_df.sort_values(by=[1, 3])
+
+    ray_df_equals_pandas(ray_result, pandas_result)
+
+    pandas_result = pandas_df.sort_values(by=[1, 67], axis=1)
+    ray_result = ray_df.sort_values(by=[1, 67], axis=1)
+
+    ray_df_equals_pandas(ray_result, pandas_result)
 
 
 def test_sortlevel():