[SPARK-19454][PYTHON][SQL] DataFrame.replace improvements

python/pyspark/sql/dataframe.py

@@ -1268,7 +1268,7 @@ def fillna(self, value, subset=None):
             return DataFrame(self._jdf.na().fill(value, self._jseq(subset)), self.sql_ctx)
 
     @since(1.4)
-    def replace(self, to_replace, value, subset=None):
+    def replace(self, to_replace, value=None, subset=None):
         """Returns a new :class:`DataFrame` replacing a value with another value.
         :func:`DataFrame.replace` and :func:`DataFrameNaFunctions.replace` are
         aliases of each other.
@@ -1307,43 +1307,66 @@ def replace(self, to_replace, value, subset=None):
         |null|  null|null|
         +----+------+----+
         """
-        if not isinstance(to_replace, (float, int, long, basestring, list, tuple, dict)):
+        # Helper functions
+        def all_of(types):
+            def all_of_(xs):
+                return all(isinstance(x, types) for x in xs)
+            return all_of_
+
+        all_of_bool = all_of(bool)
+        all_of_str = all_of(basestring)
+        all_of_numeric = all_of((float, int, long))
+
+        # Validate input types
+        valid_types = (bool, float, int, long, basestring, list, tuple)
+        if not isinstance(to_replace, valid_types + (dict, )):
             raise ValueError(
-                "to_replace should be a float, int, long, string, list, tuple, or dict")
+                "to_replace should be a float, int, long, string, list, tuple, or dict. "
+                "Got {0}".format(type(to_replace)))
 
-        if not isinstance(value, (float, int, long, basestring, list, tuple)):
-            raise ValueError("value should be a float, int, long, string, list, or tuple")
+        if (not isinstance(value, valid_types) and
+                not isinstance(to_replace, dict)):
+            raise ValueError("If to_replace is not a dict, value should be "
+                             "a float, int, long, string, list, or tuple. "
+                             "Got {0}".format(type(value)))
+
+        if isinstance(to_replace, (list, tuple)) and isinstance(value, (list, tuple)):
+            if len(to_replace) != len(value):
+                raise ValueError("to_replace and value lists should be of the same length. "
+                                 "Got {0} and {1}".format(len(to_replace), len(value)))
 
-        rep_dict = dict()
+        if not (subset is None or isinstance(subset, (list, tuple, basestring))):
+            raise ValueError("subset should be a list or tuple of column names, "
+                             "column name or None. Got {0}".format(type(subset)))
 
+        # Reshape input arguments if necessary
         if isinstance(to_replace, (float, int, long, basestring)):
             to_replace = [to_replace]
 
-        if isinstance(to_replace, tuple):
-            to_replace = list(to_replace)
+        if isinstance(value, (float, int, long, basestring)):
+            value = [value for _ in range(len(to_replace))]
 
-        if isinstance(value, tuple):
-            value = list(value)
-
-        if isinstance(to_replace, list) and isinstance(value, list):
-            if len(to_replace) != len(value):
-                raise ValueError("to_replace and value lists should be of the same length")
-            rep_dict = dict(zip(to_replace, value))
-        elif isinstance(to_replace, list) and isinstance(value, (float, int, long, basestring)):
-            rep_dict = dict([(tr, value) for tr in to_replace])
-        elif isinstance(to_replace, dict):
+        if isinstance(to_replace, dict):
             rep_dict = to_replace
+            if value is not None:
+                warnings.warn("to_replace is a dict, but value is not None. "
+                              "value will be ignored.")
+        else:
+            rep_dict = dict(zip(to_replace, value))
 
-        if subset is None:
-            return DataFrame(self._jdf.na().replace('*', rep_dict), self.sql_ctx)
-        elif isinstance(subset, basestring):
+        if isinstance(subset, basestring):
             subset = [subset]
 
-        if not isinstance(subset, (list, tuple)):
-            raise ValueError("subset should be a list or tuple of column names")
+        # Check if we won't pass mixed type generics
+        if not any(all_of_type(rep_dict.keys()) and all_of_type(rep_dict.values())
+                   for all_of_type in [all_of_bool, all_of_str, all_of_numeric]):
+            raise ValueError("Mixed type replacements are not supported")
 
-        return DataFrame(
-            self._jdf.na().replace(self._jseq(subset), self._jmap(rep_dict)), self.sql_ctx)
+        if subset is None:
+            return DataFrame(self._jdf.na().replace('*', rep_dict), self.sql_ctx)
+        else:
+            return DataFrame(
+                self._jdf.na().replace(self._jseq(subset), self._jmap(rep_dict)), self.sql_ctx)
 
     @since(2.0)
     def approxQuantile(self, col, probabilities, relativeError):

python/pyspark/sql/tests.py

@@ -1591,6 +1591,67 @@ def test_replace(self):
         self.assertEqual(row.age, 10)
         self.assertEqual(row.height, None)
 
+        # replace with lists
+        row = self.spark.createDataFrame(
+            [(u'Alice', 10, 80.1)], schema).replace([u'Alice'], [u'Ann']).first()
+        self.assertTupleEqual(row, (u'Ann', 10, 80.1))
+
+        # replace with dict
+        row = self.spark.createDataFrame(
+            [(u'Alice', 10, 80.1)], schema).replace({10: 11}).first()
+        self.assertTupleEqual(row, (u'Alice', 11, 80.1))
+
+        # replace with tuples
+        row = self.spark.createDataFrame(
+            [(u'Alice', 10, 80.1)], schema).replace((u'Alice', ), (u'Bob', )).first()
+        self.assertTupleEqual(row, (u'Bob', 10, 80.1))
+
+        # replace multiple columns
+        row = self.spark.createDataFrame(
+            [(u'Alice', 10, 80.0)], schema).replace((10, 80.0), (20, 90)).first()
+        self.assertTupleEqual(row, (u'Alice', 20, 90.0))
+
+        # test for mixed numerics
+        row = self.spark.createDataFrame(
+            [(u'Alice', 10, 80.0)], schema).replace((10, 80), (20, 90.5)).first()
+        self.assertTupleEqual(row, (u'Alice', 20, 90.5))
+
+        row = self.spark.createDataFrame(
+            [(u'Alice', 10, 80.0)], schema).replace({10: 20, 80: 90.5}).first()
+        self.assertTupleEqual(row, (u'Alice', 20, 90.5))
+
+        # replace with boolean
+        row = (self
+               .spark.createDataFrame([(u'Alice', 10, 80.0)], schema)
+               .selectExpr("name = 'Bob'", 'age <= 15')
+               .replace(False, True).first())
+        self.assertTupleEqual(row, (True, True))
+
+        # should fail if subset is not list, tuple or None
+        with self.assertRaises(ValueError):
+            self.spark.createDataFrame(
+                [(u'Alice', 10, 80.1)], schema).replace({10: 11}, subset=1).first()
+
+        # should fail if to_replace and value have different length
+        with self.assertRaises(ValueError):
+            self.spark.createDataFrame(
+                [(u'Alice', 10, 80.1)], schema).replace(["Alice", "Bob"], ["Eve"]).first()
+
+        # should fail if when received unexpected type
+        with self.assertRaises(ValueError):
+            from datetime import datetime
+            self.spark.createDataFrame(
+                [(u'Alice', 10, 80.1)], schema).replace(datetime.now(), datetime.now()).first()
+
+        # should fail if provided mixed type replacements
+        with self.assertRaises(ValueError):
+            self.spark.createDataFrame(
+                [(u'Alice', 10, 80.1)], schema).replace(["Alice", 10], ["Eve", 20]).first()
+
+        with self.assertRaises(ValueError):
+            self.spark.createDataFrame(
+                [(u'Alice', 10, 80.1)], schema).replace({u"Alice": u"Bob", 10: 20}).first()
+
     def test_capture_analysis_exception(self):
         self.assertRaises(AnalysisException, lambda: self.spark.sql("select abc"))
         self.assertRaises(AnalysisException, lambda: self.df.selectExpr("a + b"))