diff --git a/python/pyspark/mllib/util.py b/python/pyspark/mllib/util.py
index 48867a08dbfa..ed6fd4bca4c5 100644
--- a/python/pyspark/mllib/util.py
+++ b/python/pyspark/mllib/util.py
@@ -140,8 +140,8 @@ def saveAsLibSVMFile(data, dir):
         >>> from pyspark.mllib.regression import LabeledPoint
         >>> from glob import glob
         >>> from pyspark.mllib.util import MLUtils
-        >>> examples = [LabeledPoint(1.1, Vectors.sparse(3, [(0, 1.23), (2, 4.56)])), \
-                        LabeledPoint(0.0, Vectors.dense([1.01, 2.02, 3.03]))]
+        >>> examples = [LabeledPoint(1.1, Vectors.sparse(3, [(0, 1.23), (2, 4.56)])),
+        ...             LabeledPoint(0.0, Vectors.dense([1.01, 2.02, 3.03]))]
         >>> tempFile = NamedTemporaryFile(delete=True)
         >>> tempFile.close()
         >>> MLUtils.saveAsLibSVMFile(sc.parallelize(examples), tempFile.name)
@@ -166,8 +166,8 @@ def loadLabeledPoints(sc, path, minPartitions=None):
         >>> from tempfile import NamedTemporaryFile
         >>> from pyspark.mllib.util import MLUtils
         >>> from pyspark.mllib.regression import LabeledPoint
-        >>> examples = [LabeledPoint(1.1, Vectors.sparse(3, [(0, -1.23), (2, 4.56e-7)])), \
-                        LabeledPoint(0.0, Vectors.dense([1.01, 2.02, 3.03]))]
+        >>> examples = [LabeledPoint(1.1, Vectors.sparse(3, [(0, -1.23), (2, 4.56e-7)])),
+        ...             LabeledPoint(0.0, Vectors.dense([1.01, 2.02, 3.03]))]
         >>> tempFile = NamedTemporaryFile(delete=True)
         >>> tempFile.close()
         >>> sc.parallelize(examples, 1).saveAsTextFile(tempFile.name)
diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 0508235c1c9e..5fb10f86f469 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -754,8 +754,8 @@ def foreachPartition(self, f):
         Applies a function to each partition of this RDD.
 
         >>> def f(iterator):
-        ...      for x in iterator:
-        ...           print(x)
+        ...     for x in iterator:
+        ...          print(x)
         >>> sc.parallelize([1, 2, 3, 4, 5]).foreachPartition(f)
         """
         def func(it):
diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index 0f7d8fba3bd5..0ac481a8a8b5 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -61,7 +61,7 @@ class DataFrame(object):
         people = sqlContext.read.parquet("...")
         department = sqlContext.read.parquet("...")
 
-        people.filter(people.age > 30).join(department, people.deptId == department.id)\
+        people.filter(people.age > 30).join(department, people.deptId == department.id) \\
           .groupBy(department.name, "gender").agg({"salary": "avg", "age": "max"})
 
     .. versionadded:: 1.3
diff --git a/python/pyspark/sql/streaming.py b/python/pyspark/sql/streaming.py
index cbd827950bbb..4e438fd5bee2 100644
--- a/python/pyspark/sql/streaming.py
+++ b/python/pyspark/sql/streaming.py
@@ -315,9 +315,9 @@ def load(self, path=None, format=None, schema=None, **options):
         :param schema: optional :class:`pyspark.sql.types.StructType` for the input schema.
         :param options: all other string options
 
-        >>> json_sdf = spark.readStream.format("json")\
-                                       .schema(sdf_schema)\
-                                       .load(tempfile.mkdtemp())
+        >>> json_sdf = spark.readStream.format("json") \\
+        ...     .schema(sdf_schema) \\
+        ...     .load(tempfile.mkdtemp())
         >>> json_sdf.isStreaming
         True
         >>> json_sdf.schema == sdf_schema