[SPARK-23328][PYTHON] Disallow default value None in na.replace/replace when 'to_replace' is not a dictionary

docs/sql-programming-guide.md

@@ -1929,6 +1929,7 @@ working with timestamps in `pandas_udf`s to get the best performance, see
     - The rules to determine the result type of an arithmetic operation have been updated. In particular, if the precision / scale needed are out of the range of available values, the scale is reduced up to 6, in order to prevent the truncation of the integer part of the decimals. All the arithmetic operations are affected by the change, ie. addition (`+`), subtraction (`-`), multiplication (`*`), division (`/`), remainder (`%`) and positive module (`pmod`).
     - Literal values used in SQL operations are converted to DECIMAL with the exact precision and scale needed by them.
     - The configuration `spark.sql.decimalOperations.allowPrecisionLoss` has been introduced. It defaults to `true`, which means the new behavior described here; if set to `false`, Spark uses previous rules, ie. it doesn't adjust the needed scale to represent the values and it returns NULL if an exact representation of the value is not possible.
+  - In PySpark, `df.replace` does not allow to omit `value` when `to_replace` is not a dictionary. Previously, `value` could be omitted in the other cases and had `None` by default, which is counterintuitive and error prone.
 
 ## Upgrading From Spark SQL 2.1 to 2.2
 

python/pyspark/__init__.py

@@ -54,6 +54,7 @@
 from pyspark.taskcontext import TaskContext
 from pyspark.profiler import Profiler, BasicProfiler
 from pyspark.version import __version__
+from pyspark._globals import _NoValue
 
 
 def since(version):

python/pyspark/_globals.py

@@ -0,0 +1,70 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""
+Module defining global singleton classes.
+
+This module raises a RuntimeError if an attempt to reload it is made. In that
+way the identities of the classes defined here are fixed and will remain so
+even if pyspark itself is reloaded. In particular, a function like the following
+will still work correctly after pyspark is reloaded:
+
+    def foo(arg=pyspark._NoValue):
+        if arg is pyspark._NoValue:
+            ...
+
+See gh-7844 for a discussion of the reload problem that motivated this module.
+
+Note that this approach is taken after from NumPy.
+"""
+
+__ALL__ = ['_NoValue']
+
+
+# Disallow reloading this module so as to preserve the identities of the
+# classes defined here.
+if '_is_loaded' in globals():
+    raise RuntimeError('Reloading pyspark._globals is not allowed')
+_is_loaded = True
+
+
+class _NoValueType(object):
+    """Special keyword value.
+
+    The instance of this class may be used as the default value assigned to a
+    deprecated keyword in order to check if it has been given a user defined
+    value.
+
+    This class was copied from NumPy.
+    """
+    __instance = None
+
+    def __new__(cls):
+        # ensure that only one instance exists
+        if not cls.__instance:
+            cls.__instance = super(_NoValueType, cls).__new__(cls)
+        return cls.__instance
+
+    # needed for python 2 to preserve identity through a pickle
+    def __reduce__(self):
+        return (self.__class__, ())
+
+    def __repr__(self):
+        return "<no value>"
+
+
+_NoValue = _NoValueType()

python/pyspark/sql/dataframe.py

@@ -27,7 +27,7 @@
 
 import warnings
 
-from pyspark import copy_func, since
+from pyspark import copy_func, since, _NoValue
 from pyspark.rdd import RDD, _load_from_socket, ignore_unicode_prefix
 from pyspark.serializers import ArrowSerializer, BatchedSerializer, PickleSerializer, \
     UTF8Deserializer
@@ -1532,7 +1532,7 @@ 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=None, subset=None):
+    def replace(self, to_replace, value=_NoValue, 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.
@@ -1545,8 +1545,8 @@ def replace(self, to_replace, value=None, subset=None):
 
         :param to_replace: bool, int, long, float, string, list or dict.
             Value to be replaced.
-            If the value is a dict, then `value` is ignored and `to_replace` must be a
-            mapping between a value and a replacement.
+            If the value is a dict, then `value` is ignored or can be omitted, and `to_replace`
+            must be a mapping between a value and a replacement.
         :param value: bool, int, long, float, string, list or None.
             The replacement value must be a bool, int, long, float, string or None. If `value` is a
             list, `value` should be of the same length and type as `to_replace`.
@@ -1577,6 +1577,16 @@ def replace(self, to_replace, value=None, subset=None):
         |null|  null|null|
         +----+------+----+
 
+        >>> df4.na.replace({'Alice': None}).show()
+        +----+------+----+
+        | age|height|name|
+        +----+------+----+
+        |  10|    80|null|
+        |   5|  null| Bob|
+        |null|  null| Tom|
+        |null|  null|null|
+        +----+------+----+
+
         >>> df4.na.replace(['Alice', 'Bob'], ['A', 'B'], 'name').show()
         +----+------+----+
         | age|height|name|
@@ -1587,6 +1597,12 @@ def replace(self, to_replace, value=None, subset=None):
         |null|  null|null|
         +----+------+----+
         """
+        if value is _NoValue:
+            if isinstance(to_replace, dict):
+                value = None
+            else:
+                raise TypeError("value argument is required when to_replace is not a dictionary.")
+
         # Helper functions
         def all_of(types):
             """Given a type or tuple of types and a sequence of xs
@@ -2047,7 +2063,7 @@ def fill(self, value, subset=None):
 
     fill.__doc__ = DataFrame.fillna.__doc__
 
-    def replace(self, to_replace, value, subset=None):
+    def replace(self, to_replace, value=_NoValue, subset=None):
         return self.df.replace(to_replace, value, subset)
 
     replace.__doc__ = DataFrame.replace.__doc__

python/pyspark/sql/tests.py

@@ -2243,11 +2243,6 @@ def test_replace(self):
                .replace(False, True).first())
         self.assertTupleEqual(row, (True, True))
 
-        # replace list while value is not given (default to None)
-        row = self.spark.createDataFrame(
-            [(u'Alice', 10, 80.0)], schema).replace(["Alice", "Bob"]).first()
-        self.assertTupleEqual(row, (None, 10, 80.0))
-
         # replace string with None and then drop None rows
         row = self.spark.createDataFrame(
             [(u'Alice', 10, 80.0)], schema).replace(u'Alice', None).dropna()
@@ -2283,6 +2278,12 @@ def test_replace(self):
             self.spark.createDataFrame(
                 [(u'Alice', 10, 80.1)], schema).replace({u"Alice": u"Bob", 10: 20}).first()
 
+        with self.assertRaisesRegexp(
+                TypeError,
+                'value argument is required when to_replace is not a dictionary.'):
+            self.spark.createDataFrame(
+                [(u'Alice', 10, 80.0)], schema).replace(["Alice", "Bob"]).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"))