[DataFrame] Implement where

python/ray/dataframe/dataframe.py

@@ -4464,9 +4464,105 @@ def remote_func(df):
 
     def where(self, cond, other=np.nan, inplace=False, axis=None, level=None,
               errors='raise', try_cast=False, raise_on_error=None):
-        raise NotImplementedError(
-            "To contribute to Pandas on Ray, please visit "
-            "github.com/ray-project/ray.")
+        """Replaces values not meeting condition with values in other.
+
+        Args:
+            cond: A condition to be met, can be callable, array-like or a
+                DataFrame.
+            other: A value or DataFrame of values to use for setting this.
+            inplace: Whether or not to operate inplace.
+            axis: The axis to apply over. Only valid when a Series is passed
+                as other.
+            level: The MultiLevel index level to apply over.
+            errors: Whether or not to raise errors. Does nothing in Pandas.
+            try_cast: Try to cast the result back to the input type.
+            raise_on_error: Whether to raise invalid datatypes (deprecated).
+
+        Returns:
+            A new DataFrame with the replaced values.
+        """
+
+        inplace = validate_bool_kwarg(inplace, 'inplace')
+
+        if isinstance(other, pd.Series) and axis is None:
+            raise ValueError("Must specify axis=0 or 1")
+
+        if level is not None:
+            raise NotImplementedError("Multilevel Index not yet supported on "
+                                      "Pandas on Ray.")
+
+        axis = pd.DataFrame()._get_axis_number(axis) if axis is not None else 0
+
+        cond = cond(self) if callable(cond) else cond
+
+        if not isinstance(cond, DataFrame):
+            if not hasattr(cond, 'shape'):
+                cond = np.asanyarray(cond)
+            if cond.shape != self.shape:
+                raise ValueError("Array conditional must be same shape as "
+                                 "self")
+            cond = DataFrame(cond, index=self.index, columns=self.columns)
+
+        zipped_partitions = self._copartition(cond, self.index)
+        args = (False, axis, level, errors, try_cast, raise_on_error)
+
+        if isinstance(other, DataFrame):
+            other_zipped = (v for k, v in self._copartition(other,
+                                                            self.index))
+
+            new_partitions = [_where_helper.remote(k, v, next(other_zipped),
+                                                   self.columns, cond.columns,
+                                                   other.columns, *args)
+                              for k, v in zipped_partitions]
+
+        # Series has to be treated specially because we're operating on row
+        # partitions from here on.
+        elif isinstance(other, pd.Series):
+            if axis == 0:
+                # Pandas determines which index to use based on axis.
+                other = other.reindex(self.index)
+                other.index = pd.RangeIndex(len(other))
+
+                # Since we're working on row partitions, we have to partition
+                # the Series based on the partitioning of self (since both
+                # self and cond are co-partitioned by self.
+                other_builder = []
+                for length in self._row_metadata._lengths:
+                    other_builder.append(other[:length])
+                    other = other[length:]
+                    # Resetting the index here ensures that we apply each part
+                    # to the correct row within the partitions.
+                    other.index = pd.RangeIndex(len(other))
+
+                other = (obj for obj in other_builder)
+
+                new_partitions = [_where_helper.remote(k, v, next(other,
+                                                                  pd.Series()),
+                                                       self.columns,
+                                                       cond.columns,
+                                                       None, *args)
+                                  for k, v in zipped_partitions]
+            else:
+                other = other.reindex(self.columns)
+                new_partitions = [_where_helper.remote(k, v, other,
+                                                       self.columns,
+                                                       cond.columns,
+                                                       None, *args)
+                                  for k, v in zipped_partitions]
+
+        else:
+            new_partitions = [_where_helper.remote(k, v, other, self.columns,
+                                                   cond.columns, None, *args)
+                              for k, v in zipped_partitions]
+
+        if inplace:
+            self._update_inplace(row_partitions=new_partitions,
+                                 row_metadata=self._row_metadata,
+                                 col_metadata=self._col_metadata)
+        else:
+            return DataFrame(row_partitions=new_partitions,
+                             row_metadata=self._row_metadata,
+                             col_metadata=self._col_metadata)
 
     def xs(self, key, axis=0, level=None, drop_level=True):
         raise NotImplementedError(
@@ -5093,3 +5189,27 @@ def _merge_columns(left_columns, right_columns, *args):
     return pd.DataFrame(columns=left_columns, index=[0], dtype='uint8').merge(
         pd.DataFrame(columns=right_columns, index=[0], dtype='uint8'),
         *args).columns
+
+
+@ray.remote
+def _where_helper(left, cond, other, left_columns, cond_columns,
+                  other_columns, *args):
+
+    left = pd.concat(ray.get(left.tolist()), axis=1)
+    # We have to reset the index and columns here because we are coming
+    # from blocks and the axes are set according to the blocks. We have
+    # already correctly copartitioned everything, so there's no
+    # correctness problems with doing this.
+    left.reset_index(inplace=True, drop=True)
+    left.columns = left_columns
+
+    cond = pd.concat(ray.get(cond.tolist()), axis=1)
+    cond.reset_index(inplace=True, drop=True)
+    cond.columns = cond_columns
+
+    if isinstance(other, np.ndarray):
+        other = pd.concat(ray.get(other.tolist()), axis=1)
+        other.reset_index(inplace=True, drop=True)
+        other.columns = other_columns
+
+    return left.where(cond, other, *args)

python/ray/dataframe/test/test_dataframe.py

@@ -3053,10 +3053,36 @@ def test_var(ray_df, pandas_df):
 
 
 def test_where():
-    ray_df = create_test_dataframe()
+    pandas_df = pd.DataFrame(np.random.randn(100, 10),
+                             columns=list('abcdefghij'))
+    ray_df = rdf.DataFrame(pandas_df)
 
-    with pytest.raises(NotImplementedError):
-        ray_df.where(None)
+    pandas_cond_df = pandas_df % 5 < 2
+    ray_cond_df = ray_df % 5 < 2
+
+    pandas_result = pandas_df.where(pandas_cond_df, -pandas_df)
+    ray_result = ray_df.where(ray_cond_df, -ray_df)
+
+    assert ray_df_equals_pandas(ray_result, pandas_result)
+
+    other = pandas_df.loc[3]
+
+    pandas_result = pandas_df.where(pandas_cond_df, other, axis=1)
+    ray_result = ray_df.where(ray_cond_df, other, axis=1)
+
+    assert ray_df_equals_pandas(ray_result, pandas_result)
+
+    other = pandas_df['e']
+
+    pandas_result = pandas_df.where(pandas_cond_df, other, axis=0)
+    ray_result = ray_df.where(ray_cond_df, other, axis=0)
+
+    assert ray_df_equals_pandas(ray_result, pandas_result)
+
+    pandas_result = pandas_df.where(pandas_df < 2, True)
+    ray_result = ray_df.where(ray_df < 2, True)
+
+    assert ray_df_equals_pandas(ray_result, pandas_result)
 
 
 def test_xs():

python/ray/dataframe/utils.py

@@ -107,11 +107,7 @@ def to_pandas(df):
     Returns:
         A new pandas DataFrame.
     """
-    if df._row_partitions is not None:
-        pd_df = pd.concat(ray.get(df._row_partitions))
-    else:
-        pd_df = pd.concat(ray.get(df._col_partitions),
-                          axis=1)
+    pd_df = pd.concat(ray.get(df._row_partitions), copy=False)
     pd_df.index = df.index
     pd_df.columns = df.columns
     return pd_df